Investigating the Roles of AMP-Activated Protein Kinase and Calcium/Calmodulin-Dependent Protein Kinase Kinase Β in Prostate Cancer

Investigating the roles of AMP-activated protein kinase and calcium/calmodulin-dependent protein kinase kinase β in prostate cancer

Lucy Penfold Beit Fellowship

A thesis submitted to Imperial College London for the degree of Doctor of Philosophy June 2016

Cellular Stress Group Medical Research Council, Clinical Sciences Centre Imperial College London Declarations

I declare that the work presented in this thesis is my own work and information derived from published or unpublished work of others has been acknowledged in the text and in the list of references. This work has not been submitted in any form for another degree or diploma at any university or other institute of tertiary education.

Signed: Date: 15th June 2016

The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the licence terms of this work.

1 Abstract

Prostate cancer cells are characterised by rapid growth, proliferation and migration, which requires rewiring of cellular metabolism including increased lipid and protein synthesis. AMP- activated protein kinase (AMPK) is a conserved master regulator of energy homeostasis and acts to downregulate anabolism and cell growth, whilst upregulating catabolism to maintain cellular ATP levels. Whether these actions inhibit or aid cancer progression is controversial. Intriguingly, an upstream activating kinase of AMPK, calcium/calmodulin-dependent protein kinase kinase β (CaMKKβ) has recently been implicated in prostate cancer progression.

A small-molecule direct activator of AMPK, 991, was used to test the effects of AMPK activation in a panel of prostate cancer cell lines. AMPK activation led to downregulation of cellular proliferation, 2D migration, invasion and lipogenesis, and upregulation of adhesion in all cell lines. However, in PC3 and 22RV1 cells AMPK activation led to an increase in migration down a chemoattractant gradient. This increase in migration was dependent on CaMKKβ and PAK1 activity.

To investigate the role of AMPK and CaMKKβ in vivo the PTEN prostate cancer mouse model was used. AMPKβ1 and CaMKKβ were deleted in this model creating two novel mouse lines. Upon β1-deletion prostate cancer development was increased based on the timing of a switch in protein expression characterised in the PTEN-/- prostate upon disease progression and pathological analysis of tissue sections. In contrast, disease progression was significantly reduced upon CaMKKβ-deletion in the PTEN-/- prostate based on prostate weight, Ki-67 staining and pathological analysis. Disease progression was also inhibited in the PTEN mouse model upon treatment with a pharmacological inhibitor of CaMKKβ, STO609. These data suggest that AMPK and CaMKKβ have different roles in prostate cancer development and progression and likely lie on separate pathways in this disease.

2 Acknowledgements

I would like to sincerely thank my supervisors David Carling and Angela Woods for all their help and guidance over the course of my PhD. I would especially like to thank Ang for all the hands-on assistance she’s given me in the lab over the years. I am grateful to both Ang and Dave for being so approachable, supportive and just generally the best supervisors anyone could ask for! In particular, I have thoroughly enjoyed our scientific discussions that ultimately forwarded the project so greatly.

I would like to say a huge thank you to Phill Muckett for his invaluable help with all the animal work.

It has been a joy to be a member of the Cellular Stress team, who have provided so much support, humour and fun throughout my PhD. I would like to thank Daisy Luff, Lizzie Sandham, Bérengère Snyers and Emma Battell, who did summer placements in the lab and worked on this project.

Additionally, I would like to thank Chad Whilding and Dirk Dormann from the MRC microscopy facility for all their help with image acquisition and analysis. I would also like to thank Peter Faull from the mass spectrometry facility for all his help performing and analysing the mass spectrometry screens.

I would like to thank the Beit trust (Imperial College, London) and the MRC for awarding me funding to complete this PhD and allowing me to gain such valuable scientific training.

Finally, I would like to thank my friends and family for their endless support and encouragement. In particular, I would like to thank my mum and brother for always being there and making me smile. Although my dad died 6 months before starting this PhD, his memory has provided me with a constant source of inspiration.

Abbreviations

ACC Acetyl-Coenzyme A carboxylase Actin β-actin ADT Androgen deprivation therapy AICAR 5-aminoimidazole-4-carboximide ribonucleoside Akt/PKB Protein kinase B AMPK 5’adenosine monophosphate-activated protein kinase AP Anterior prostate AR Androgen receptor ARG Androgen responsive gene ARE Androgen responsive element β1-/-PTEN-/- AMPKβ1fl/fl/Ptenfl/fl /PB-Cre4+ BiP1 Immunoglobulin-binding protein (aka GRP78) BrdU 5-bromo-2’-deoxyuridine CaM Calmodulin CaMK Calcium/Calmodulin-dependent protein kinase CaMKK Calcium/Calmodulin-dependent protein kinase kinase CaMKKβ-/- Camkkβ-/- CaMKKβ-/-PTEN-/- Camkkβ-/-/Ptenfl/fl/PB-Cre4+ CAPS 3-[cyclohexylamino]-1-propanesulfonic acid CRPC Castrate-resistant prostate cancer DAB 3,3′-Diaminobenzidine DHT Dihydrotestosterone DLP Dorsolateral prostate DLVP Dorsolateral ventral prostate DMEM Dulbecco’s modified eagle’s medium DMSO Dimethyl Sulphoxide dn Dominant negative DTT Dithiohtreitol 4EBP1 Eukaryotic translation initiation factor 4E-binding protein 1 EDTA Ethylene diamine tetra-acetic (ethanoic) acid EIF2 Eukaryotic initiation factor 2 et al. et alia FASN Fatty acid synthase FBS Fetal calf serum FM Full media GAPDH Glyceraldehyde 3-phosphate dehydrogenase GEMM Genetically modified mouse model GUSB β-glucuronidase HE Hematoxylin and eosin Hepes 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid HK2 Hexokinase II HKG House-keeping gene HPRT Hypoxanthine Phosphoribosyltransferase HST High Salt Tween IHC Immunohistochemistry IP Immunoprecipitation KD Kinase dead 4

LKB1 Liver kinase B1 LTP Long-term potentiation L-LTP Late long-term potentiation MEFs Mouse embryonic fibroblasts Mib Mibolerone MLC Myosin light chain MLCK Myosin light chain kinase mPIN Mouse prostatic intraepithelial neoplasia mTORC1 Mammalian target of rapamycin complex 1 PAK P21-activated protein kinase PB-Cre4 Probasin-Cre PBS Phosphate-buffered saline PFA Paraformaldehyde PFKFB2 (or PFK2) 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 2 PI3K Phosphatidylinositol-4,5-bisphosphate 3-kinase PIN Prostatic intraepithelial neoplasia PKA Protein kinase A PMSF Phenylmethylsulphonyl fluoride PS Pre-switch PSA Prostate specific antigen PTEN Phosphatase and tensin homolog PTEN-/- Ptenfl/fl/PB-Cre4+ PVDF Polyvinylidene difluoride PYGL Glycogen phosphorylase (liver isoform) qPCR Quantitative polymerase chain reaction RPL Large subunit ribosomal protein RPMI Roswell Park Memorial Institute ROS Reactive oxygen species S Switched SAMS Synthetic peptide, HMRSAMSGLHLVKRR SDS- PAGE Sodium dodecyl sulphate - Polyacrylamide gel electrophoresis SEM Standard error of mean SFM Serum-free media siRNA Small interfering RNA SORD Sorbitol dehydrogenase TAE Tris-acetate buffer TAMs Tumour associated macrophages TAFs Tumour associated fibroblasts TRIS Trishydroxymethylaminomethane UTR Untranslated region VP Ventral prostate WT Wild-type ZMP 5-amino-4-imidazolecarboxyamide ribonucleoside monophosphate

Table of Contents

Declarations ...... 1 Abstract ...... 2 Acknowledgements ...... 3 Abbreviations ...... 4 Table of Contents ...... 6 List of Figures ...... 10 List of Tables ...... 13 List of Appendices ...... 14 1. Introduction ...... 15 1.1 Prostate Cancer ...... 15 1.1.1 Normal prostate composition ...... 16 1.1.2 Stages of prostate cancer development ...... 17 1.1.3 Androgen signalling in prostate cancer ...... 19 1.1.4 Common mutations in prostate cancer ...... 21 1.1.5 Altered metabolism in prostate cancer ...... 24 1.1.6 Tumour microenvironment...... 28 1.2 AMP-activated protein kinase (AMPK) ...... 29 1.2.1 Structure of AMPK ...... 30 1.2.2 Regulation of AMPK activity ...... 32 1.2.3 AMPK and cancer ...... 35 1.2.4 AMPK and prostate cancer ...... 44 1.3 Calcium/calmodulin-dependent protein kinase kinase β (CaMKKβ) ...... 47 1.3.1 CaMKKβ and tissue expression pattern ...... 47 1.3.2 Structure of CaMKKβ ...... 47 1.3.3 Downstream substrates of CaMKKβ ...... 48 1.3.4 Regulation of CaMKKβ ...... 49 1.3.5 Global CaMKKβ knockout mouse ...... 50 1.3.6 CaMKKβ and prostate cancer ...... 51 1.3.7 CaMKKβ and the tumour microenvironment ...... 53

1.4 The PTEN mouse model of prostate cancer ...... 54 1.4.1 Anatomy of the mouse prostate ...... 54 1.4.2 The PTEN mouse model of prostate cancer (Wang et al., 2003) ...... 55 1.5 Project Aims ...... 56 2. Materials and Methods ...... 57 2.1 Materials ...... 57 2.1.1 General Reagents ...... 57 2.1.2 Buffers ...... 58 2.1.3 Antibodies ...... 59 2.1.4 Primers ...... 61 2.1.5 Cells ...... 62 2.1.6 Proteins ...... 62 2.1.7 Compounds ...... 62 2.1.8 Mouse lines ...... 62 2.2 Methods ...... 63 2.2.1 General methods used in all Chapters ...... 63 2.2.2 In vitro studies (Chapter 3) ...... 65 2.2.3 In vivo Studies (Chapters 4 and 5) ...... 69 2.3 Statistical analyses ...... 73 3. Investigation of the effect of pharmacological activation of AMPK in prostate cancer cells ...... 74 3.1 Introduction ...... 74 3.2 Effect of AMPK activation on different phenotypic readouts of cancer cells ...... 75 3.2.1 Characterisation of the AMPK isoforms in prostate cell lines ...... 75 3.2.2 Characterisation of the AMPK upstream kinases in prostate cell lines ...... 76 3.2.3 Dosing studies using 991 in prostate cancer cell lines ...... 77 3.2.4 AMPK activation inhibits cell proliferation ...... 80 3.2.5 AMPK activation inhibits cell migration in 2D scratch assays ...... 84 3.2.6 The effect on AMPK activation on migration in 3D xCELLigence assay ...... 89 3.2.7 AMPK activation inhibits invasion in 22RV1 cells ...... 92 3.2.8 The effect of AMPK activation on cell adhesion ...... 93 3.2.9 AMPK activation inhibits prostate cancer cell lipid synthesis ...... 98 3.3 Investigation of increased migration in response to 991 treatment ...... 99 3.3.1 Increased migration upon 991 treatment is AMPK-dependent ...... 99

3.3.2 AMPK activation leads to increased phosphorylation of PAK1 activation sites in cells ...... 101 3.3.3 Non-specific effect of AMPKα2 antibodies ...... 103 3.3.4 Pharmacological inhibition and knockdown of PAK1 blocks AMPK-dependent migration .. 105 3.3.5 Pharmacological inhibition of CaMKKβ inhibits AMPK-dependent migration...... 110 3.4 Discussion ...... 112 4. Investigation of the effect of AMPKβ1-deletion in the PTEN-/- mouse model of prostate cancer . 122 4.1 AMPKβ1 in human prostate cell lines ...... 122 4.1.1 Investigation of an AMPK-independent role for β1 ...... 122 4.1.2 β1 expression is upregulated in LNCaP cells in response to androgen stimulation ...... 124 4.2 Mouse model generation ...... 125 4.2.1 Knockout-first AMPKβ1 mouse model ...... 126 4.2.2 Phenotyping the global β1 knockout mouse model ...... 128 4.2.3 Breeding strategy ...... 130 4.3 Characterisation of the PTEN-positive mouse prostate ...... 132 4.3.1 AMPK expression in wild-type and AMPKβ1-/- mouse prostate ...... 132 4.3.2 AMPKβ1-deletion and prostate weight ...... 134 4.4 Characterisation of PTEN-/- and AMPKβ1-/-PTEN-/- prostate cancer mouse models ...... 134 4.4.1 Prostate weight ...... 135 4.4.2 Disease pathology ...... 136 4.4.3 Proliferation ...... 138 4.4.3 Molecular characterisation ...... 140 4.5 The ‘switch’: a marker of disease progression ...... 145 4.5.1 Discovery of a switch in global protein expression in the PTEN-/- mouse prostate ...... 145 4.5.2 The ‘switch’: Prostate weight and HE staining ...... 148 4.5.3 Screen for protein biomarkers of the protein switch from prostate hyperplasia to neoplasia ...... 150 4.5.4 The protein switch: validation of biomarkers in immunohistochemistry ...... 158 4.5.5 The switch: changes in gene expression at the RNA level ...... 160 4.5.6 The switch: A putative mechanism ...... 165 4.6 Discussion ...... 167 5. Investigation of the effect of CaMKKβ-deletion in the PTEN-/- mouse model of prostate cancer . 178 5.1 Investigation of regulatory feedback of CaMKKβ activity on AR signalling ...... 178 5.1.1 The effect of CaMKKβ inhibition on AR activity ...... 178 5.2 Mouse model generation and characterisation of the PTEN-positive mouse prostate ...... 181 8

5.2.1 CaMKKβ-/- prostate cancer mouse model generation ...... 181 5.2.2 Characterisation of CaMKKβ in the wild-type mouse prostate ...... 182 5.2.3 Characterisation of AMPK in the CaMKKβ-/- mouse prostate ...... 183 5.2.4 The effect of CaMKKβ-deletion on prostate weight ...... 184 5.3 Characterisation of the CaMKKβ-/-PTEN-/- prostate cancer mouse model ...... 185 5.3.1 Prostate weight ...... 185 5.3.2 Disease pathology ...... 187 5.3.3 Proliferation ...... 189 5.3.4 Differences in stromal morphology between PTEN-/- and CaMKKβ-/-PTEN-/- prostates...... 191 5.3.5 CaMKKβ expression and localisation ...... 197 5.3.6 The switch: CaMKKβ-deletion in the PTEN-/- prostate ...... 198 5.3.7 Molecular characterisation ...... 199 5.4 CaMKKβ and ribosomal subunit expression ...... 203 5.5 In vivo STO609 studies ...... 207 5.5.1 STO609 dosing study in wild-type mice ...... 207 5.5.2 STO609 dosing in PTEN-/- mice ...... 211 5.5.3 HE staining: effect of STO609 in the PTEN-/- prostate ...... 213 5.6 Discussion ...... 215 6. Summary and Future Perspectives ...... 222 References ...... 227 Appendices ...... 246

List of Figures

Figure 1.1 Cellular components of the human prostate gland...... 16 Figure 1.2 Hallmarks of cancer cells...... 17 Figure 1.3 Stage of prostate cancer and the metastatic cascade...... 19 Figure 1.4 Androgen and AR signaling in prostate cells...... 20 Figure 1.5 Mechanisms of maintaining AR signalling in castration-resistant prostate cancer...... 21 Figure 1.6 The phosphoinositide 3-kinase (PI3K)/ phosphatase and tensin homolog (PTEN) signalling pathway...... 23 Figure 1.7 The role of lipids in prostate cancer...... 26 Figure 1.8 Altered metabolism in prostate cancer cells and cell cycle progression...... 27 Figure 1.9 AMPK activation and downstream effects...... 30 Figure 1.10 The domain structure of AMPK subunits...... 32 Figure 1.11 Regulation of AMPK by upstream kinases...... 34 Figure 1.12 Main mechanisms through which AMPK can suppress or promote cancer progression. .... 37 Figure 1.13 Regulation of mTORC1 by AMPK pathways...... 39 Figure 1.14 Domain structure of CaMKKβ...... 48 Figure 1.15 CaMKKβ signalling (adapted from (Racioppi and Means, 2012)...... 50 Figure 1.16 Schematic depictions of human and mouse prostate anatomy...... 55 Figure 3.1 AMPK subunit expression in prostate cell lines...... 75 Figure 3.2 LKB1 and CaMKKβ activity in prostate cell lines...... 76 Figure 3.3 Effect of 991 on AMPK activity in prostate cancer cell lines...... 79 Figure 3.4 Effect of serum in growth medium on proliferation...... 81 Figure 3.5 Effect of 991 on cell proliferation...... 82 Figure 3.6 The effect of 991 on proliferation in WT and AMPKβ1-/- MEFs...... 83 Figure 3.7 Effect of 991 on cell cycle progression...... 84 Figure 3.8 Effect of 991 on migration in 2D scratch assays in prostate cancer cells ...... 86 Figure 3.9 Effect of 991 on migration in 2D scratch assays in WT and AMPK knockout MEFs...... 88 Figure 3.10 DU145 case study: using the xCELLigence system to monitor and calculate migration rate...... 90 Figure 3.11 Effect of 991 on cell migration using the xCELLigence migration assay...... 91 Figure 3.12 Effect of 991 on cancer cell invasion xCELLigence assay...... 93 Figure 3.13 Effect of 991 on cancer cell adhesion xCELLigence assay...... 94 Figure 3.14 Effect of 991 on adhesion using xCELLigence assay in WT and AMPK knockout MEFs...... 95 Figure 3.15 Validation of the effect of 991 on adhesion...... 96 Figure 3.16 Investigation of whether AMPK activity effects MLC2 phosphorylation...... 97 Figure 3.17 Effect of 991 on lipid synthesis (14C- incorporation)...... 98 Figure 3.18 Doxycycline robustly induces AMPKβ1 shRNA and concomitant AMPKβ1 knockdown. .... 100 Figure 3.19 Effect of 991 on AMPK-dependent migration in AMPK WT and AMPKβ1 knockdown PC3 cells...... 101 Figure 3.20 Effect of 991 on PAK1 phosphorylation in PC3 cells...... 103 Figure 3.21 The in-house sheep antibody raised against AMPKα2 non-specifically pulls down the PAK1/GIT/βPIX complex...... 104 10

Figure 3.22 PAK1 inhibitor, PF-3758309, blunts AMPK-dependent migration...... 106 Figure 3.23 Effect of PAK1 inhibitor, PF-3758309, on AMPK activity...... 107 Figure 3.24 Quantification of PAK1 and PAK2 knockdown using siRNA...... 108 Figure 3.25 Effect of PAK1 and PAK2 knockdown on AMPK-dependent migration...... 109 Figure 3.26 Effect of the CaMKKβ inhibitor, STO609, on AMPK-dependent migration...... 110 Figure 3.27 Effect of CaMKKβ inhibitor, STO609, on AMPK activity...... 111 Figure 3.28 Model of AMPK-dependent migration...... 119 Figure 4.1 AMPKβ1 expression in prostate cancer cell lines compared to RWPE1 control cell line. .... 123 Figure 4.2 Expression of AMPK subunits in AMPKβ1-/-β2-/- and AMPKα1-/-α2-/- MEFs...... 124 Figure 4.3 Effect of mibolerone treatment on AMPK subunit expression...... 125 Figure 4.4 Utility of the knockout-first allele...... 126 Figure 4.5 Characterisation of AMPK in wild-type (WT), AMPKβ1+/- and AMPKβ1-/- livers...... 128 Figure 4.6 Body weight and composition in wild-type (WT) and β1-/-mice...... 129 Figure 4.7 Spleen weight, hematocrit and erythrocyte osmotic resistance in wild-type (WT) and ...... 130 Figure 4.8 Breeding strategy used to generate β1-/-PTEN-/- (AMPKβ1fl/fl/ Ptenfl/fl /PB-Cre4+) and PTEN-/- (Pten-/-/PB-Cre4+) prostate cancer mouse models...... 131 Figure 4.9 Characterisation of AMPK in wild-type (WT) and β1-/- prostates...... 132 Figure 4.10 AMPK activity in lobes of the prostate in wild-type (WT) and β1-/- mice...... 133 Figure 4.11 Prostate weight from wild-type (WT) and β1fl/fl/PB-Cre4+ (β1-/-) mice...... 134 Figure 4.12 PTEN-/- and β1-/-PTEN-/- prostate weight ...... 135 Figure 4.13 Pathology of wild-type (WT), PTEN-/- and β1-/-PTEN-/- prostates...... 137 Figure 4.14 Ki-67 staining in PTEN-/- and β1-/-PTEN-/- prostates...... 139 Figure 4.15 Akt and AR signalling in wild-type (WT), PTEN-/- and β1-/-PTEN-/- prostates...... 141 Figure 4.16 Characterisation of AMPK in wild-type (WT), PTEN-/- and β1-/-PTEN-/- prostates...... 143 Figure 4.17 Characterisation of AMPKβ1 expression in the PTEN model of liver cancer...... 145 Figure 4.18 Ponceau stained membranes for wild-type (WT), PTEN-/- and β1-/-PTEN-/- prostate lysate...... 146 Figure 4.19 Androgen receptor, Akt and AMPK expression in wild-type (WT), pre-switch (PS) and switched (S) PTEN-/- prostate tissue...... 147 Figure 4.20 Weight of wild-type (WT), PTEN-/-(PS, pre-switch), PTEN-/-(S, switched) and β1-/-PTEN-/-(S, switched) prostates...... 148 Figure 4.21 Characterisation of the effect of protein switch status on prostate pathology...... 149 Figure 4.22 Differentially expressed proteins in pre-switch (PS) and switched (S) PTEN-/- prostates identified by mass spectrometry...... 152 Figure 4.23 Validation of mass spectrometry results...... 153 Figure 4.24 Western blot showing representative staining of biomarker proteins for the different switch grades defined in Table 4.2...... 155 Figure 4.25 The effect of β1-deletion on PTEN-/- prostate switch progression...... 157 Figure 4.26 Immunohistochemical staining of hexokinase II (HK2) and BiP1 switch biomarkers...... 159 Figure 4.27 House keeper gene (HKG) expression in wild-type (WT), PTEN-/- and β1-/-PTEN-/- prostates measured using qPCR...... 161 Figure 4.28 Protein biomarker gene expression in wild-type (WT), PTEN-/- and β1-/-PTEN-/- prostates using qPCR...... 163

11 Figure 4.29 Androgen receptor (AR) and AR-target gene expression in wild-type (WT), PTEN-/- and β1-/- PTEN-/- prostates using qPCR...... 164 Figure 4.30 Characterisation of p53 in the PTEN-/- and β1-/-PTEN-/- prostate...... 166 Figure 4.31 Model of the role of AMPKβ1 and p53 in prostate cancer development...... 175 Figure 5.1 The effect of STO609 treatment on AR signalling in LNCaP/Luc cells...... 180 Figure 5.2 Breeding strategy used to generate CaMKKβ-/-PTEN-/- (Camkkβ-/-/Ptenfl/fl/PB-Cre4+) and PTEN- /- (Ptenfl/fl/PB-Cre4+) prostate cancer mouse models...... 182 Figure 5.3 Characterisation of CaMKKβ in the different lobes of the wild-type (WT) mouse prostate. 183 Figure 5.4 Characterisation of AMPK in wild-type (WT) and CaMKKβ-/- prostates...... 184 Figure 5.5 Prostate weight from wild-type (WT) and CaMKKβ-/- mice...... 185 Figure 5.6 PTEN-/- and CaMKKβ-/-PTEN-/- prostate weight...... 186 Figure 5.7 Pathology of wild-type (WT), PTEN-/- and CaMKKβ-/-PTEN-/- prostates...... 188 Figure 5.8 Ki-67 staining in PTEN-/- and CaMKKβ-/-PTEN-/- prostates...... 190 Figure 5.9 HE staining of wild-type (WT), CaMKKβ-/-, PTEN-/- and CaMKKβ-/-PTEN-/- prostates...... 193 Figure 5.10 Vimentin staining in PTEN-/- and CaMKKβ-/-PTEN-/- prostate...... 194 Figure 5.11 Plasma cytokine concentrations in wild-type (WT), PTEN-/- and CaMKKβ-/-PTEN-/- models...... 196 Figure 5.12 CaMKKβ staining in wild-type (WT), CaMKKβ-/-, PTEN-/- and CaMKKβ-/-PTEN-/- prostate sections using immunohistochemistry...... 197 Figure 5.13 Hexokinase II expression in PTEN-/- and CaMKKβ-/-PTEN-/- prostates...... 199 Figure 5.14 Characterisation of AMPK in wild-type (WT), CaMKKβ-/-, PTEN-/- and ...... 200 Figure 5.15 Expression of ACC1, AKT1/2, AMPKα1β1γ1, androgen receptor (AR) and IFG1R mRNA levels in wild-type (WT) and CaMKKβ-/- prostates measured using qPCR...... 202 Figure 5.16 Proteins differentially expressed in wild-type and CaMKKβ-/- prostates identified by mass spectrometry...... 205 Figure 5.17 RPL19 and RPL7 expression in wild-type (WT), CaMKKβ-/-, PTEN-/- and CaMKKβ-/-PTEN-/- prostates...... 206 Figure 5.18 In vivo dosing of STO609 using minipump administration in wild-type mice...... 210 Figure 5.19 Effect of STO609 dosing on prostate weight in wild-type (WT) and PTEN-/-mice...... 212 Figure 5.20 Effect of STO609 dosing on prostate pathology in PTEN-/- mice...... 214

List of Tables

Table 1.1 Pharmacological AMPK activators ...... 35 Table 2.1 List of primary antibodies used in this thesis...... 60 Table 2.2 List of primers used for genotyping...... 61 Table 2.3 List of primers used for qPCR (specific to mouse mRNA)...... 62 Table 3.1 Key characteristics of the human prostate cell lines used in this study...... 74 Table 4.1 Description of pathological grading (based on recommendations by (Ittmann et al., 2013) 136 Table 4.2 Characteristic biomarker staining in Western blot analysis of prostate lysate and corresponding switch status classification...... 155

List of Appendices

Appendix 1 Mass spectrometry data analysis……………………………………………………………………………………246

Appendix 2 IPA analysis of wild-type (WT) vs CaMKKβ-/- prostate mass spectrometry screen: A) EIF2 signalling analysis and B) upstream regulator analysis………………………………………………………………………247

1. Introduction

1.1 Prostate Cancer

Prostate cancer is the most commonly diagnosed malignancy among men in industrialised countries and is the second leading cause of cancer-related death (Siegel et al., 2016). Despite its high prevalence, prostate cancer has remained one of the most elusive types of cancer due to a lack of understanding of the basic biology of the human prostate and this disease on molecular and cellular levels (Sharma and Schreiber-Agus, 1999). It is a multistage disease developing over decades; men in their twenties can display pre-neoplastic lesions or prostatic intraepithelial neoplasia (PIN), however clinically detectable prostate cancer generally only presents in men over 50 years of age (Parisotto and Metzger, 2013). In the Western world 1 in 6 men will be diagnosed with prostate cancer during their lifetime (Stangelberger et al., 2008). If diagnosed early, most localised prostate tumours can be treated successfully by surgery; eradicating disease in around 75% of these patients (Siegel et al., 2012). However, after 5 years the remaining patients will develop metastatic disease. Androgen ablation therapies are the mainstay treatment for late-stage prostate cancer and although patients tend to initially respond favourably, most relapse within 1-2 years developing hormone- refractory disease (Isaacs and Isaacs, 2004). Despite the unresponsiveness of this castration- resistant form of prostate cancer to androgen-deprivation therapy, the androgen receptor signalling pathways remain active and are vital to disease progression (Karantanos et al., 2013). Following the emergence of castration-resistant prostate cancer, docetaxel chemotherapy has been shown to have some therapeutic benefit, however the median increase in survival was only found to be 4 months (Petrylak et al., 2004).

Serum levels of the product of an androgen responsive gene, prostate specific antigen (PSA), is used as a biomarker for prostate cancer detection, however this does not predict whether tumours are indolent or clinically aggressive (Prensner et al., 2012). Large scale clinical studies have indicated that systematic use of this biomarker has led to unnecessary transrectal prostatic needle biopsies, over diagnosis and over treatment with severe side effects on patients and increased costs for healthcare systems (Schröder et al., 2012, Siegel et al., 2016).

There is a clear need for improvements in both therapy and diagnosis for prostate cancer. The major challenges in prostate cancer research are a) to elucidate the factors that contribute to disease progression and find new drug targets and b) design reliable screens for distinguishing indolent and aggressive cancers. There are several known risk factors for prostate cancer development including age, family history, diet (specifically fat intake), and ethnicity (most common in African Americans) (Siegel et al., 2016, Sharma and Schreiber-Agus, 1999). The elucidation of the pathogenetic basis of prostate cancer initiation and progression at the molecular level has been greatly facilitated by laboratory and clinical models of the disease. 15

Systems that have emerged as promising over the past three decades include a variety of immortalised prostate cancer cell lines; human xenograft models; genetically engineered mouse models (GEMMs) and the dog (Sharma and Schreiber-Agus, 1999). Other than humans, the dog is the only animal known to develop high grade PIN and prostate adenocarcinomas spontaneously (Waters et al., 1998).

1.1.1 Normal prostate composition

The human prostate is a glandular organ composed of central, peripheral and transitional zones that are not clearly defined (Bhavsar and Verma, 2014). The three zones contain acini located within a fibromuscular stroma. Acini are formed by columnar epithelial cells which secrete prostatic proteins and fluids from their apical surface into the lumen. They are surrounded by basal cells attached to the basement membrane and scattered neuroendocrine cells (Parisotto and Metzger, 2013). Most of the human prostate cancers correspond to acinar adenocarcinoma, while neuroendocrine prostate cancers represent less than 2% of the cases (Parisotto and Metzger, 2013, Grignon, 2004). The cellular composition of the human prostate gland is shown in Figure 1.1.

Figure 1.1 Cellular components of the human prostate gland. Epithelial cells secrete products into the acinar lumen and are surrounded by the basal membrane. Epithelial cells are also interspersed with neuroendocrine cells. The stromal compartment consists of smooth muscle, blood vessels, nerve fibres, fibroblasts, immune cells and extracellular matrix components. (Image adapted from Barron and Rowley, 2012). 16

1.1.2 Stages of prostate cancer development

Drastic phenotypic and biochemical changes occur during the transition of normal cells into invasive cancer cells. Hanahan and Weinberg have outlined the biological capabilities acquired by cancer cells during the multistep development of human tumours and these are shown in Figure 1.2 (Hanahan and Weinberg, 2011). These include sustained proliferative signalling, resistance to cell death, replicative immortality, activating angiogenesis, invasion and metastasis. Recently two new hallmarks have been recognised as being vital for cellular transformation: reprogramming of energy metabolism and evading immune destruction (Hanahan and Weinberg, 2011). Underlying these hallmarks is genome instability and inflammation (Hanahan and Weinberg, 2011). Furthermore, tumours contain ‘normal’ cells that foster the malignant phenotype and create the tumour microenvironment (Hanahan and Weinberg, 2011, Barron and Rowley, 2012).

Figure 1.2 Hallmarks of cancer cells. Capabilities of cancer cells acquired during the development of tumours as described by Hanahan and Weinberg, 2011.

17 Human prostate cancer develops through defined stages (Figure 1.3A). The dogma is that prostate cancer progresses from prostatic intraepithelial neoplasia (PIN), prostate-localised adenocarcinoma, and finally to metastatic disease which results in lethality (Abate-Shen and Shen, 2002). The current consensus suggests that PIN, but not benign prostatic hyperplasia, is a precursor for prostate cancer, although this remains controversial (Häggman et al., 1997, Brawer, 2005). PIN, especially high grade PIN, correlates with prostate cancer with respect to spatial association (as assessed by repeat biopsies) as well as with intrinsic genetic alterations (Häggman et al., 1997).

PIN lesions are formed by cells that proliferate within the prostatic epithelium and disrupt its well defined architecture but without stromal invasion (Brawer, 2005, Ittmann et al., 2013). They are graded according to their degree of atypia. Low-grade PINs describe areas of proliferative epithelial cells with enlarged nuclei, vary in size, inconspicuous nucleoli and an intact basal cells layer (Bostwick and Brawer, 1987). High-grade PINs mainly differ from low- grade PINs by increased crowding of epithelial cells, the presence of prominent nucleoli and a fragmented basal cell layer (Bostwick and Brawer, 1987, Bostwick et al., 2000). Adenocarcinoma is characterised by the stromal invasion of these atypical cells (Brawer, 2005).

Metastasis is the multistep process in which cancer cells spread from the primary site of origin to distal sites, leading to secondary malignant growths. Metastasis is responsible for over 90% of cancer-related deaths (Mehlen and Puisieux, 2006). The stages of metastasis involve the i) local infiltration of tumour cells into the adjacent tissue (detachment), ii) transendothelial migration of cancer cells into vessels (intravasation), iii) survival in the circulatory system, iv) extravasation and v) subsequent proliferation and colonisation of distal sites (Figure 1.3B) (Wirtz et al., 2011, van Zijl et al., 2011). For prostate cancer the primary deposit site for metastatic disease is skeletal bone but metastasis can also occur in lymph nodes and lung tissue (Sturge et al., 2011). Skeletal metastases occur in over 80% of cases of late-stage prostate cancer and confer a 5-year survival rate of 25% and median survival of approximately 40 months (Sturge et al., 2011).

18 Figure 1.3 Stage of prostate cancer and the metastatic cascade. A) The stages of human prostate cancer from PIN (prostate intraepithelial neoplasia) to late-stage metastatic disease. (Image adapted from Abate-Shen and Shen, 2000). B) In the metastatic cascade cancer cells detach from a primary, vascularised tumour, penetrate the surrounding tissue and enter blood vessels (intravasation). Some of these circulating cancer cells will adhere to blood vessel walls and extravasate into the local tissue, forming secondary tumours. (Image adapted from Wirtz et al., 2011).

1.1.3 Androgen signalling in prostate cancer

The androgen receptor (AR) is a ligand-activated transcription factor that plays an important role in the development of the prostate gland as well as in all stages of prostate cancer (Heinlein and Chang, 2004). The discovery of the driving role of AR in prostate cancer initially came from the clinical observation that castration was effective at slowing down the progression of this disease (Huggins, 1942). The AR was then identified as the molecular mediator of this effect (Mulder et al., 1983). The AR is a 110kDa protein with a large N- terminal transactivation domain, a C-terminal ligand-binding domain, a central DNA-binding domain and a hinge region that contributes to regulation of its nuclear localisation and degradation (Cutress et al., 2008, Tan et al., 2015). Testosterone is converted to 19 dihydrotestosterone (DHT) in the cytoplasm where it binds to AR, driving AR dimerisation and translocation into the nucleus. Nuclear AR controls the expression of a large number of genes that regulate growth and survival of prostate cancer cells (Figure 1.4) (Heinlein and Chang, 2004, Tan et al., 2015).

Figure 1.4 Androgen and AR signaling in prostate cells. In the prostate testosterone is converted to dihydrotestosterone (DHT) by 5-α-reductase, here it binds to the androgen receptor (AR) and promotes its dissociation from heat-shock proteins (HSP). The AR can then dimerise and translocate into the nucleus and bind to the androgen response element (ARE) in the promoter region of target genes e.g. prostate-specific antigen (PSA) and TMPRSS2. The AR can then recruit the basal transcription machinery and other co-regulators leading to the transcription of target genes promoting cell growth and survival.

Androgen-deprivation therapy (ADT) is the first line of treatment for advanced prostate cancer and can improve symptoms and reduce PSA levels in around 90% of patients (Karantanos et al., 2013). ADTs include inhibitors of androgen synthesis, e.g. abiraterone, and AR antagonists that prevent androgen binding to AR, e.g. enzalutamide or bicalutamide (Mills, 2014). Over time prostate cancer loses its responsiveness to ADT, leading to the development of castration-resistant prostate cancer (CRPC). Interestingly, in vivo knockdown studies and treatment with second-generation AR antagonists or hormone-synthesis inhibitors have indicated that AR signalling remains active in CRPC (Mills, 2014, Karantanos et al., 2013). Prostate cancer cells rely on aberrant AR signalling, and thus experience intense selection pressures to maintain AR activity despite low levels of circulating androgens. These selection pressures lead to mutations that allow the reestablishment of AR signalling in such conditions. This can occur by a number of mechanisms such as AR amplifications, AR mutations, aberrant

AR co-regulator activities and AR splice-variant expression; as well as AR-independent mechanisms. These themes are summarised in Figure 1.5 and are well reviewed in the literature (Karantanos et al., 2013, Cutress et al., 2008).

Figure 1.5 Mechanisms of maintaining AR signalling in castration-resistant prostate cancer. When the availability of testosterone from the bloodstream becomes limited by androgen deprivation therapies (ADT), prostate cancer cells can maintain AR activity through alternative mechanisms such as AR activation by growth factor and/or cytokine signalling pathways; overexpression of AR as a result of gene amplification; changes in the expression of AR co-regulators and AR splice variants or mutation, which alters responses to hormones, which allow prostate cells to synthesise their own hormones (Mills, 2014).

1.1.4 Common mutations in prostate cancer

Prostate cancer does not comprise a single disease; the spectrum of genetic mutations in primary and advanced prostate cancers is diverse, with a low rate of recurrent lesions and high molecular heterogeneity (Berger et al., 2011, Network, 2015, Robinson et al., 2015). The Cancer Genome Atlas (TCGA) Research Network recently performed a comprehensive analysis of 333 primary prostate cancers and the majority of analysed tumour samples were able to

be categorised into in one of seven defined molecular subtypes (Network, 2015). However, over one quarter of tumour samples could not be assigned due to the failure to identify molecular alterations driving their growth. Other recent genomic studies also describe multiple distinct subtypes of primary and castration-resistant prostate cancer with different driver and passenger genomic alterations (Robinson et al., 2015, Berger et al., 2011).

The phosphoinositide-3-kinase (PI3K) pathway is one of the most commonly altered signalling pathways in human cancer (Keniry and Parsons, 2008, Fresno Vara et al., 2004). This pathway was found to be activated in prostate cancer and affects cell proliferation, survival, and invasion. The phosphatase and tensin homolog (PTEN) gene is among the most frequently mutated tumour suppressor genes in primary and castration-resistant prostate cancer (Network, 2015, Robinson et al., 2015). PTEN acts to dephosphorylate lipid-signalling intermediates, thereby deactivating PI3K-dependent signalling (Figure 1.6). PTEN was found to be deleted or mutated in 17% of primary prostate cancer samples (n=333 samples) (Network, 2015). Furthermore, other aberrations activating PI3K/AKT signalling were defined at lower incidences such as gene amplification/gain-of-function point mutations of the PIK3CA gene, encoding a catalytic subunit of PI3K. Inactivating PTEN lesions are significantly more common in advanced disease and estimated to occur in ~41% of metastatic castration- resistant disease, another 8% of samples contained other alterations activating PI3K/AKT signalling (n=150 samples) (Robinson et al., 2015). This places PTEN inactivation as one of the most common genetic ablations found in primary and metastatic prostate cancer. In addition, dysregulation of PTEN is the lesion most consistently associated with poor prognosis, with a large body of evidence indicating PTEN deletion is associated with advanced localised and/or metastatic disease; higher Gleason grade; and higher risk of progression, recurrence after therapy, and death from disease (Barbieri et al., 2013, Attard et al., 2009, Choucair et al., 2012, McMenamin et al., 1999).

For these reasons the prostate cancer mouse models used in this thesis are based on the genetic deletion of PTEN specifically in mouse prostate epithelial cells (see Section 1.4.2).

Figure 1.6 The phosphoinositide 3-kinase (PI3K)/ phosphatase and tensin homolog (PTEN) signalling pathway. Growth factor activated-receptor tyrosine kinases (RTKs) activate phosphoinositide 3-kinase (PI3K) and this leads to increased phosphatidylinositol-3, 4, 5-triphosphate (PIP3). In turn, PIP3 binds and activates the pleckstrin homology domain-containing proteins e.g. AKT and PDK1. Signalling through these kinases upregulate signalling pathways which favour proliferation, cell growth/protein synthesis and survival. PTEN acts as a critical negative regulator of PI3K signalling by removing the D3 phosphate from PIP3 to produce phosphatidylinositol-4, 5-biphosphate (PIP2). (Image adpated from Keniry and Parsons, 2008).

TP53 was found to be mutated in primary prostate tumours, however similar to PTEN inactivation these lesions were found to be more common in castration-resistant prostate cancers (Robinson et al., 2015). In response to cell stress, the p53 protein acts as transcription factor, activating the transcription of genes involved in cell cycle arrest, DNA repair, and apoptosis and as a result is commonly deleted in human cancers (Olivier et al., 2010).

In addition to genes and pathways that are dysregulated across a spectrum of human cancers such as PTEN and p53, there are also aberrations that are more specific to prostate cancer and this is likely to be due to the unique biology of the prostate. Of the seven subtypes identified in the comprehensive analysis of 333 primary prostate cancers, 4 were characterised by gene fusions involving members of the ETS family of oncogenic transcription factors (ERG, ETV1, ETV4, and FLI1) (Network, 2015). ETS transcription factors regulate genes involved in cell proliferation, differentiation, apoptosis, angiogenesis and inflammation (Sementchenko and Watson, 2000). Rearrangement breakpoints are significantly more likely to occur near AR-bound sites in the genome than predicted by chance (Tomlins et al., 2007). This finding suggests that AR complexes mediate the formation of transcriptional hubs that 23 bring together distant genomic loci and leading to genomic rearrangements. Fusions most commonly occur as a fusion of the TMPRSS2 gene and the transcription factor ERG. The prevalence of ETS rearrangements are as high as 79% in radical prostatectomy and biopsy samples (Tomlins et al., 2009). These findings support the hypothesis of aberrant signalling through the ETS axis as an important factor in prostate tumorigenesis.

The other three subtypes identified in the recent TCGA dataset were defined by mutations in SPOP, FOXA1 and IDH1 genes (Network, 2015). These proteins have roles in protein ubiquitination, regulator of AR-chromatin targeting and oxidative damage mitigation, respectively. Interestingly, gene expression profiles differed based on whether the tumour was driven by a gene-fusion or a mutation, suggesting some drivers may share similar disruptions in the cell to bring about cancer and this may have implication for therapeutic intervention.

As previously discussed, AR signalling is a central axis in the pathogenesis of prostate cancer. AR activity was found to be variable in primary prostate cancers (Network, 2015). Although rare in primary prostate cancers, AR is the most commonly altered gene in castration- resistant prostate cancer, with mutations occurring in nearly two-thirds of cases (Robinson et al., 2015). Aberrations in AR that lead to the reactivation of AR signalling in castration- resistant prostate cancer are shown in Figure 1.5. Alterations in AR signalling take place in metastatic castration-resistant prostate cancer when androgens are limiting although these signalling pathways are still activated in primary treatment-naïve tumours (Heinlein and Chang, 2004, Waltering et al., 2012). In addition to upregulated PI3K/AKT and AR signalling; WNT, MAPK, MYC signalling pathways and DNA repair defects are commonly found to be activated in metastatic castration-prostate cancer and some primary prostate cancers (Yap et al., 2016, Network, 2015).

Recent genomic studies have confirmed that treating prostate cancer is complicated by intra- and inter-patient tumour heterogeneity due to transcriptomic and proteomic diversity. Although specific genetic aberrations are uncommon, different genomic lesions frequently converge on specific cellular functions and pathways. Sequencing technologies will be critical in achieving precision medicine and enabling a better understanding of tumour biology, thereby helping elucidate clinically actionable molecular alterations and pathways. Identification of these will lead to the development of more effective therapeutic strategies to treat prostate cancer.

1.1.5 Altered metabolism in prostate cancer

Metabolic reprogramming is now recognised as a hallmark of cancer (Hanahan and Weinberg, 2011). This metabolic reprogramming supports the increased production of metabolic 24

intermediates for the synthesis of proteins, nucleic acids and lipids, and is a prerequisite for the rapid proliferation of transformed cells and cancer progression. The most prominent metabolic alteration in the majority of cancer is an increase in glucose uptake and the use of aerobic glycolysis, termed the Warburg effect (Vander Heiden et al., 2009). This allows the majority of tumours to be detected by 18F-FDG-PET, however this tends not to be possible with primary prostate cancers.

Normal prostate cells

Normal prostate epithelial cells have a unique intermediary metabolic profile as they function as a source of secreted citrate and zinc required for secretion into the prostatic fluid (Costello and Franklin, 2000). Normal prostate cells perform aerobic glycolysis to provide precursors for the synthesis and secretion of citrate, resulting in an incomplete citric acid cycle and minimal oxidative phosphorylation for energy production. In contrast, during transformation, prostate cancer cells no longer secrete citrate and reactivate the citric acid cycle as an energy source. Citrate is also used as a substrate for de novo fatty acid synthesis (Costello and Franklin, 2000). Since primary prostate cancers do not show increased aerobic glycolysis, they are not efficiently detectable with 18F-FDG-PET (Zadra et al., 2013).

Prostate cancer cells

Prostate cancer cells acquire metabolic alterations that cooperate with “driver” genetic events to allow neoplastic transformation and tumour progression. Prostate cancer cell metabolism and its link to cell cycle progression is shown in Figure 1.8.

Although increased aerobic glycolysis has been found in advanced disease, aberrant increases in de novo lipogenesis which are directly coupled with glucose and glutamine metabolism are detectable at an early stage of prostate cancer development and are associated with tumour progression and poor prognosis (Rossi et al., 2003, Kuhajda et al., 1994, Zadra et al., 2013). Prostate cancer is characterised by the significant overexpression of key enzymes involved in fatty acid and sterol synthesis such as ATP citrate lyase (ACLY), acetyl-CoA carboxylase (ACC), fatty acid synthase (FASN) and 3-hydroxy-3-methyl-glutaryl-CoA (HMG-CoA reductase) (Zadra et al., 2013, Santos and Schulze, 2012). ACC, FASN and HMG-CoA reductase are responsible for the synthesis of malonyl-CoA, the saturated fatty acid palmitate, and mevalonate (the precursor of cholesterol), respectively. Lipogenesis is critical for prostate cancer cell survival, proliferation, migration and invasion as summarised in Figure 1.7. The role of lipogenesis in prostate cancer has been extensively reviewed (Zadra et al., 2013, Suburu and Chen, 2012). Gene expression of key proteins involved in lipogenesis has been shown to be positively regulated by AR (highlighted in red boxes in Figure 1.8) (Massie et al., 2011, Zadra et al., 2013).

Figure 1.7 The role of lipids in prostate cancer. Lipids are important for energy supply and storage, membrane building blocks, signalling molecules, protein post-translation modifications and substrates for steroid synthesis in prostate cancer cells and are therefore required for cancer cell survival, proliferation, migration and invasion. (PI, phosphatidylinositol; PA, phopshatidic acid; DAG, diacylglycerol; PC, phosphocholine; PS, Phosphatidylserine). (Image adpated from Zadra et al., 2013).

Enzymes that function to oxidise fatty acids as an energy source have been shown to be AR- responsive and upregulated in prostate cancer (Zadra et al., 2013). A role for fatty acid oxidation in supplying energy to the prostate cancer cell is supported by the observation that the peroxisomal enzyme, α-methylacyl-CoA racemase (AMACR), which facilitates the transformation of branched chain fatty acids to a form suitable for β-oxidation, is overexpressed in prostate cancer (Kumar-Sinha et al., 2004). AMACR has proven to be a valuable tool in distinguishing normal from cancerous prostate tissue and is now being used in clinical practice (Andrews and Humphrey, 2014).

Another characteristic of prostate cancer cells is the upregulation of protein synthesis due to the hyperactivation of mammalian target of rapamycin (mTOR) (Fresno Vara et al., 2004). Increased lipogenesis and protein synthesis are common features of both primary and advanced prostate cancer and these alterations are induced both by androgen signalling and by the activated PTEN/PI3K/Akt/mTOR pathway. Proteins directly upregulated by AR activity are shown in red boxes in Figure 1.8 (Massie et al., 2011).

Figure 1.8 Altered metabolism in prostate cancer cells and cell cycle progression. Schematic showing key metabolic pathways active in prostate cancer cells and how these direct anabolic biosynthesis and cell cycle progression. Genes directly upregulated by the androgen receptor (AR) are shown in red boxes; green arrows show direct allosteric activation and purple arrows depict anabolic biosynthesis. (Image adapted from Massie et al., 2011).

As discussed, prostate cancer is characterised by significant metabolic alterations, namely increased lipogenesis and protein synthesis. The dependence of prostate cancer on these pathways has made them targets for therapeutic intervention. Small molecule FASN inhibitors, HMG-CoA reductase inhibitors and ACC inhibitors have shown some promising preclinical results both in vitro and in vivo (Flavin et al., 2010, Beckers et al., 2007, Murtola et al., 2008, Zadra et al., 2010). However, the use of these FASN inhibitors as systemic drugs has been hampered by pharmacologic limitations and side effects (Flavin et al., 2011). In addition, despite promising preclinical results, targeting mTOR complex 1 (mTORC1) with rapamycin and its analogues, failed to show clinical efficacy in prostate cancer (Amato et al., 2008) due to the emergence of survival feedback loops (Rodrik-Outmezguine et al., 2011, O'Reilly et al., 2006). Thus, alternative strategies to target these pathways in prostate cancer would offer new hope in treating this prevalent and lethal disease.

1.1.6 Tumour microenvironment

The tumour stromal microenvironment has emerged as a key player in the growth and development of cancer (Hanahan and Weinberg, 2011). A damage response is induced by cancer progression in the prostate stroma and this is referred to as reactive stroma or desmoplasia (Barron and Rowley, 2012). The biology of reactive stromal cells in the tumour microenvironment modulate the progression and severity of cancer, including aspects of angiogenesis and inflammation. Activation of the stromal microenvironment is thought to be critical in adenocarcinoma growth and progression (Barron and Rowley, 2012, Shiao et al., 2016). Reactive stroma initiates during PIN and co-evolves with the cancer. The microenvironment is composed of different cell populations such as endothelial cells, fibroblasts, macrophages, and lymphocytes. The phenotypic diversity of macrophage and reactive fibroblast populations in the tumour microenvironment is only beginning to be elucidated. While normal fibroblasts are instrumental in maintaining tissue homeostasis in the absence of insult, their activated counterpart (myofibroblasts/tumour-associated fibroblasts) promotes tumour progression and this is thought to be via their pro-repair and pro-survival biology that would include new growth and angiogenesis (Barron and Rowley, 2012). Targeting the cancer microenvironment therapeutically is now a key area of research to develop novel therapies to treat prostate cancer.

28 1.2 AMP-activated protein kinase (AMPK)

Cells require energy to carry out cellular processes, such as establishing and maintaining ionic concentrations, transporting molecules across cell membranes and the synthesis of macromolecules, all of which are necessary for cellular and whole-body function. The main unit of energy in the cell is ATP, and the energy generation process involves the hydrolysis of ATP to ADP (and AMP). In order to maintain cellular nucleotide concentrations catabolic processes which produce ATP, such as glycolysis and fatty acid oxidation, must be balanced with anabolic processes which utilise ATP, such as lipid and protein synthesis. Therefore, cellular mechanisms must exist to detect and readdress any changes in the ratio of ADP and AMP to ATP. Almost all diseases involve some measure of dysregulation of cellular energy status.

AMP-activated protein kinase (AMPK) is an evolutionary conserved master regulator of energy homeostasis; sensing changes in nutrient availability at whole-body and cellular level. This kinase is activated in times of energy deficiency by an increase in the ADP/AMP to ATP ratio leading to inhibition of anabolic pathways and stimulation of catabolic pathways (Figure 1.9) (Corton et al., 1994). AMPK activation is mediated by the direct binding of ADP/AMP to the regulatory γ-subunit leading to AMPK phosphorylation and allosteric activation (reviewed in (Carling et al., 2012). AMPK is activated in response to physiological and pathological stimuli, such as exercise, hormone stimulation, energy deprivation, ischemia and hypoxia (Figure 1.9) (reviewed in (Steinberg and Kemp, 2009).

Figure 1.9 AMPK activation and downstream effects. AMPK activation leads to an increase in cellular processes that generate ATP and a decrease in processes that use ATP. Hormones such as adiponectin and leptin; and pharmacological agents such as metformin also activate AMPK likely by altering the ADP/AMP:ATP ratio. (Image adapted from Ruderman and Prentki, 2004).

Cancer cells are characterised by rapid cell growth, proliferation and migration, which requires extensive rewiring of cellular metabolism including increased lipid production and protein synthesis. As discussed, targeting these pathways in prostate cancer holds much therapeutic promise; however attempts so far have been unsuccessful and alternative strategies to target these pathways in prostate cancer are desperately needed. AMPK lies at the heart of energy homeostasis and provides a critical link between normal cellular energy metabolism and oncogenesis. In the last decade there has been growing interest into the roles and regulation of AMPK in cancer and its potential as a therapeutic target in this disease and this is discussed in detail in Sections 1.2.3 and 1.2.4.

1.2.1 Structure of AMPK

AMPK is composed of three subunits, α, β and γ, and in mammalian cells there are two isoforms of the catalytic α subunit (α1, α2), two isoforms of the β subunit (β1, β2) and three isoforms of the γ subunit (γ1, γ2, γ3). Different combinations of these subunits can form AMPK complexes, allowing 12 possible AMPK complexes (Carling et al., 2012). The different 30 AMPK isoforms may play different roles in the cell and/or be localised in different subcellular compartments, for example the α2 subunit has been detected in nucleus as well as in the cytoplasm (Salt et al., 1998). The exact reason for the existence of 12 different AMPK complexes is unknown. The structure of the different AMPK subunits is shown in Figure 1.10.

The α-subunit contains the kinase domain of AMPK. Both α-isoforms have a molecular mass of around 63 kDa. Differential expression patterns have been reported for the two isoforms with α1 being the predominant form expressed in the brain, lungs, kidneys and pancreas, and the α2 subunit being highly expressed in the heart and skeletal muscle (Steinberg and Kemp, 2009). The β-subunits have a molecular weight around 30 kDa in mass, although β1 migrates at about 38 kDa on SDS-PAGE gels. The C-terminal domain of the β subunits binds both the α and γ subunits and the N-terminal region binds glycogen via a glycogen binding domain (Figure 1.10) (Hudson et al., 2003, Polekhina et al., 2003). The presence of this domain in the β-subunit suggests that AMPK activity may be regulated by glycogen (Polekhina et al., 2003).

AMPK γ1 and γ2 show ubiquitous expression in all bodily tissues, whereas γ3 only shows significant expression in glycolytic skeletal muscle (Cheung et al., 2000). The mass of γ1 is 36 kDa, whereas γ2 and γ3 possess large N-terminal domains which result in their molecular weights being 63 kDa and 58 kDa, respectively (Figure 1.10). The γ subunit isoforms contain tandem repeats of a motif that was first identified in CBS (cystathionine-β-synthase) (Bateman, 1997). In AMPK, the CBS domains form Bateman domains that bind adenine nucleotides (Figure 1.10). Several cardiovascular conditions are caused by mutations in the γ2-subunit gene, such as Wolff-Parkinson-White syndrome, some types of hypertrophic cardiomyopathies and glycogen storages diseases of the heart (Steinberg and Kemp, 2009).

Figure 1.10 The domain structure of AMPK subunits. The α-subunits contain the kinase domain, which is phosphorylated and activated by upstream kinases on Thr172. The α-subunits also have a C-terminal domain (α-CTD) required for binding the β and γ subunits and an autoinhibitory domain (AID). The β-subunits contain carbohydrate binding motif (CBM) and β-CTD required for binding of the α- and γ-subunits. The three γ-subunit isoforms have N- terminal domains of variable length, a short region that is required for binding to the β-subunit, and four conserved cystathionine-β-synthase motifs (CBS). The CBS motifs act in pairs to form two Bateman domains required for nucleotide binding. (Image adapted from Gowans and Hardie, 2014).

1.2.2 Regulation of AMPK activity

AMPK activity is tightly regulated. The primary mode of regulation is by phosphorylation of the α-subunit by upstream kinases. The phosphorylation of the key residue threonine-172 (Thr172), in the activation loop of the kinase domain in the α subunit is essential for AMPK activation (Hawley et al., 1996). Recombinant AMPK expressed in E.coli is not phosphorylated on Thr172 and has negligible kinase activity (Neumann et al., 2003). Phosphorylation of this residue therefore acts as an on-off switch for kinase activity. Although other residues within AMPK are phosphorylated, for example Ser485, they do not appear to have any direct effect on AMPK activity (Woods et al., 2003b).

Binding of AMP/ADP to the γ-subunit of AMPK activates the kinase by three mechanisms, all of which are antagonised by ATP: 1) binding of ADP (and AMP) inhibits Thr172 dephosphorylation by phosphatases (Xiao et al., 2011), 2) binding of AMP (and possibly ADP) promotes Thr172 phosphorylation (Hawley et al., 1995, Gowans et al., 2013); and 3) binding of AMP (but not ADP) causes 10-fold allosteric activation (Carling et al., 1989).

Upstream kinases

Liver kinase B1 (LKB1) and calcium/calmodulin-dependent protein kinase kinase β (CaMKKβ) have been shown to act as the primary upstream kinases to AMPK (Figure 1.11) (Hawley et al., 2003, Woods et al., 2003a, Woods et al., 2005). LKB1 is ubiquitously expressed and is responsible for AMPK activation in response to changes in nucleotide levels (Shaw et al., 2005). CaMKKβ phosphorylates AMPK in response to increases in intracellular calcium (Stahmann et al., 2006, Hawley et al., 2005).

The functional LKB1 complex is made up of 3 proteins, the LKB1 kinase, STE20-related adaptor (STRAD) protein and mouse protein 25 (MO25) (Boudeau et al., 2003, Hawley et al., 2003, Boudeau et al., 2004). The binding of STRAD increases LKB1 activity by up to 5-fold and MO25 binding increases the stability of the complex and results in its cytoplasmic localisation (Boudeau et al., 2004). LKB1 seems to be the main AMPK upstream kinase in most metabolic tissues (Carling et al., 2008). Consistently, drastic reductions in AMPK activity have been found in many LKB1 knockout mouse tissues (Shaw et al., 2005, Woods et al., 2011, Sakamoto et al., 2005). LKB1 appears to be constitutively active and its activity is not altered by conditions that increase AMPK activity in cells (Woods et al., 2003a, Carling et al., 2008). Although a number of phosphorylation sites have been within LKB1, mutation of these to alanine residues was found to have no effect on LKB1 activity (Collins et al., 2000).

CaMKKβ is activated by increases in intracellular calcium levels. It was identified as an upstream kinase of AMPK by two groups at the same time (Woods et al., 2005, Hawley et al., 2005). This discovery showed that AMPK had the potential to be involved in calcium-mediated signalling. Subsequently many hormones and cytokines which lead to activation of the G protein coupled receptor pathway, has been shown to mediate their effects at least partially through AMPK. CaMKKβ expression is tissue-specific. CaMKKβ structure, function and regulation are discussed in more detail in Section 1.3.

A third, more controversial upstream kinase candidate is TAK1 (transforming growth factor-β (TGFβ) -activated kinase-1. However the physiological role of this regulation is not clearly defined (Momcilovic et al., 2006).

Figure 1.11 Regulation of AMPK by upstream kinases. AMPK is activated by phosphorylation of Thr172 within the catalytic α-subunit, catalysed by upstream kinases, LKB1 and CaMKKβ. LKB1 forms a heterotrimeric complex with MO25 and STE20 related adaptor protein (STRAD) and this is required for full activity. LKB1 activates AMPK in response to an increase in the ADP/AMP:ATP ratio, whereas CaMKKβ leads to the calcium-dependent activation of AMPK.

Both upstream kinases, LKB1 and CaMKKβ, have been implicated to have roles in various cancers. LKB1 is a bona fide tumour suppressor (Shackelford and Shaw, 2009). Its mutation leads to autosomal dominant inherited Peutz-Jeghers Syndrome predisposing carriers to developing a number of cancers (Hemminki et al., 1998). Mutations in LKB1 have also been reported in sporadic cancers including lung, pancreatic, biliary and skin cancers (Hezel and Bardeesy, 2008, Hemminki et al., 1998, Wingo et al., 2009). It is unclear whether the tumour suppressor activities of LKB1 are mediated solely via AMPK as it has at least 11 other downstream targets (Lizcano et al., 2004, Alessi et al., 2006). Gene expression profiling recently revealed that CaMKKβ is overexpressed in prostate cancer and that this kinase might elicit oncogenic activities in this disease (Massie et al., 2011, Frigo et al., 2011). The current understanding of the role of CaMKKβ in prostate cancer is discussed in Section 1.3.6.

AMPK activation by pharmacological agents

AMPK is activated by a range of pharmacological agents (listed in Table 1.1). The most widely used AMPK activators in scientific research have been metformin and AICAR. Both of these compounds are not direct activators of AMPK and have been shown to have AMPK- independent effects (García-García et al., 2010, Santidrián et al., 2010, Ben Sahra et al., 2011).

Hydrogen peroxide and reactive oxygen species (ROS) also activate AMPK, potentially through disruption of the cells nucleotide balance (Choi et al., 2001).

The AMPK pharmacological inhibitor, Compound C, is used to investigate the effects of downregulating AMPK activity; however these studies are confounded by the AMPK- independent effects associated with this compound (Liu et al., 2014, Bain et al., 2007).

AMPK Activator Mechanism of action Reference

Metformin/phenformin Inhibition of the respiratory chain, (Fryer et al., 2002, Zhou et thereby increasing ADP/AMP:ATP ratio al., 2001) AICAR ZMP (AMP analogue) accumulation (Corton et al., 1995)

A-769662 Direct activator (Sanders et al., 2007) (Abbott compound) MT63-78 Direct activator (Zadra et al., 2014)

991 (used in this study) Direct activator (not currently (Xiao et al., 2013) commercially available), gifted from AstraZeneca.

Table 1.1 Pharmacological AMPK activators This table displays the main pharmacological AMPK activators used in scientific research and their mechanism of action.

1.2.3 AMPK and cancer

AMPK functions as a cellular energy regulator, phosphorylating a plethora of downstream targets and regulating a multitude of pathways. In the last decade there has been growing interest into the role of AMPK in cancer. When pharmacologically activated, AMPK seems to exert pleiotropic effects resulting in suppression of tumourigenesis and tumour progression (Figure 1.12A). This topic has been the focus of some recent reviews (Chuang et al., 2014, Zadra et al., 2015); however an overview of this complex subject is given in this section.

Since activation of AMPK leads to an overall reduction in the flux of anabolic pathways, activation would be expected to act as a tumour suppressor. Indeed, epidemiologic evidence using metformin suggests that AMPK activation does reduce cancer prevalence (Evans et al., 2005). In addition, it is well documented that inactivating mutations at the LKB1 locus results in tumour formation and LKB1 is deleted on many sporadic tumours. However, it is less clear as to whether the tumour suppressor effects of LKB1 are mediated via activation of AMPK or alternative substrates. Despite the majority of work showing that AMPK activation does

negatively impact tumour development and growth, a number of more recent studies have inferred the contrary. In the solid tumour environment, cancers cells are exposed to many metabolic stressors such as hypoxic environments and glucose deprivation. In these scenarios AMPK activation may provide a cancer cells with a survival advantage. Figure 1.12 depicts the main mechanisms through which AMPK activation can affect cancer progression and AMPK substrates that are potentially responsible for mediating the effects.

Figure 1.12 Main mechanisms through which AMPK can suppress or promote cancer progression. A) Studies have shown that AMPK activation can suppress and promote tumour development/progression by activating different downstream pathways in a context-specific manner. B) AMPK regulates a plethora of downstream targets, which can lead to the pro- and anti- tumourigenic effects outlined in A). Arrows indicate direct activation, whereas inhibition is depicted by blunt ended arrows, proteins highlighted in red are inhibited by AMPK activation, whereas those in green are activated by AMPK. (Image adapted from Zadra et al., 2015).

1.2.3.1 AMPK as a tumour suppressor Downregulation of anabolic pathways

Cancer cells have a high energy demand necessary for rapid growth and division. AMPK is activated during low energy conditions, and this leads to a reduction in the flux through anabolic pathways, ultimately reducing cell growth. In this situation, AMPK activation would be predicted to antagonise cancer cell growth. This tumour suppressor activity is predominantly achieved via downregulation of mTOR mitogenic signalling and inhibition of lipogenesis (Figure 1.12B) (Chuang et al., 2014, Zadra et al., 2014).

In the presence of growth factors and when nutrient supply is not limiting, mTORC1 is active and stimulates pathways associated with cell growth, namely protein and lipid synthesis (Fresno Vara et al., 2004). For example, mTORC1 activity results in the phosphorylation and activation of p70 S6 kinase 1 (S6K1) and eukaryotic initiation factor 4E-binding protein 1 (4EBP1) leading to increased protein translation. Unsurprisingly, the mTORC1 pathway is illegitimately activated in many cancers. mTORC1 is activated downstream of the PI3K/Akt signalling pathway. TSC1-TSC2 (tuberous sclerosis 2), a GAP (GTPase-activating protein) complex inhibits the activity of the small GTPase, RHEB (RAS homologue enriched in brain) (Manning and Cantley, 2003, Kwiatkowski and Manning, 2005). Akt and ERK phosphorylate TSC2 at multiple sites, thereby inhibiting the GAP activity of the TSC1-TSC2 complex. This in turn leads to the accumulation of GTP-bound RHEB, which acts as a potent activator of mTORC1. In addition, Akt phosphorylates 40 kDa proline-rich AKT1 substrate (PRAS40), resulting in its dissociation from mTOR, thereby blocking PRAS40-mediated mTORC1 inhibition (Wang et al., 2007). AMPK negatively regulates mTORC1 activity by two mechanisms: a) phosphorylation of TSC2, enhancing its GAP activity which blocks mTORC1 activation by suppressing its upstream regulator, RHEB (Inoki et al., 2003); and b) AMPK phosphorylates the mTORC1-positive regulatory subunit RAPTOR, resulting in 14-3-3 binding and the allosteric inactivation of mTORC1 (Gwinn et al., 2008). The role of AMPK in mTORC1 regulation is shown in Figure 1.13. Ultimately, AMPK activation reduces mTORC1 function, thereby inhibiting cell growth.

Figure 1.13 Regulation of mTORC1 by AMPK pathways. AMPK activity inhibits mammalian target of rapamycin complex 1 (mTORC1) by phosphorylating TSC2 and the mTORC1 component RAPTOR. Phosphorylation of RHEB by AMPK increases its GAP activity, thereby downregulating GTP-RHEB which is a positive regulator of mTOR. Phosphorylation of RAPTOR by AMPK facilitates its binding to 14-3-3 proteins leading to downregulation of mTOR activity. In contrast to AMPK, PI3K–Akt signalling activates mTORC1 (see text).

In addition to mTORC1 inhibition, AMPK downregulates lipogenesis directly via inhibition of the SREBP1 (sterol regulatory element-binding protein 1) transcription factor (Zhou et al., 2001, Li et al., 2011, Zadra et al., 2014). SREBP1 is a master regulator of lipid homeostasis in mammals and acts to upregulate de novo lipogenesis. Inhibition of SREBP1 leads to decreased levels of transcription of a number of genes involved in lipogenesis including ACC, FASN and HMG-CoA reductase which are overexpressed in prostate cancer (Li et al., 2011, Wu et al., 2014, Zadra et al., 2014).

ACC provides another target for AMPK in the regulation of de novo fatty acid synthesis in cancer cells. One of the first substrates identified for AMPK was ACC (Carling et al., 1987). In mammals, there are two isoforms of ACC, ACC1 and ACC2, both are phosphorylated and inactivated by AMPK on the residues Ser79 and Ser218, respectively (Tong, 2005). ACC1 is a 265 kDa protein which catalyses the irreversible carboxylation of acetyl-CoA to produce malonyl-CoA. Malonyl-CoA is a substrate for fatty acid synthesis. Phosphorylation of ACC by AMPK results in its inactivation and decreased fatty acid synthesis. Phosphorylation of ACC at

Ser79 (and Ser218) are now routinely used as indicators of AMPK activity in vivo (Park et al., 2002).

AMPK also plays a role in inhibiting cholesterol synthesis by negatively regulating the HMG- CoA reductase by direct phosphorylation (Corton et al., 1994). Furthermore, both the mitochondrial glycerol-3-phosphate acyl-transferase (GPAT), a rate limiting enzyme in TG synthesis; and hormone-sensitive lipase (HSL), responsible for hydrolysis of triglycerides into free fatty acids are directly inactivated by AMPK (Muoio et al., 1999, Garton et al., 1989, Watt et al., 2006). In summary, AMPK activation acts to downregulate lipogenesis by downregulation of key lipogenic enzymes at the transcriptional level via downregulation of SREBP1 activity and by phosphorylation and inactivation of ACC, HMG-CoA reductase, among others, post-translationally.

Downregulation of Hif1a and Warburg effect

Another facet of mTORC1 activation is the translational upregulation of a number of mRNAs encoding pro-growth proteins, including HIF1α (hypoxia-inducible factor-1α) transcription factor (Shackelford et al., 2009). HIF1α is overexpressed in numerous cancers and exerts oncogenic activities by shifting metabolism toward glycolysis in response to hypoxia. In addition, HIF1α promotes angiogenesis by inducing the gene expression of vascular endothelial growth factor (VEGF) and among others (Weidemann and Johnson, 2008). Thus, as a negative regulator of mTORC1 activity, AMPK also acts to downregulate HIF1α expression. In a recent paper, AMPK depletion was shown to increase mTORC1 activation, HIF1α levels, and aerobic glycolysis in Myc-overexpressing cells (Faubert et al., 2013). However, this point remains controversial as another study argues AMPK activation is required for HIF1α transcriptional activity (Lee et al., 2003). This is further discussed in Section 1.2.3.2.

Activation of p53 and p27

A number of reports show AMPK to be a positive regulator of the tumour suppressor, p53 leading to inhibition of cell proliferation and activation of senescence and/or apoptosis (Jones et al., 2005, Lee et al., 2012, Nieminen et al., 2013). It has been shown that AMPK activation leads to increased p53 phosphorylation at Ser15 in MEFs (Jones et al., 2005). A recent report suggests that increased p53 activation upon AMPK activation is as a result of AMPK- dependent Sirt1 inactivation in hepatocellular carcinoma cells (Lee et al., 2012). AMPK- induced p53 activation could play an integral role in the effect of AMPK activation on cell cycle progression and apoptosis by the transcriptional activation of p53-target genes, such as p21 and Bax (Okoshi et al., 2008, Lee et al., 2012). Convincing evidence that p53 is a direct target for AMPK in vivo has not emerged (Lee et al., 2012). In addition, there are reports of AMPK activation leading to cell cycle arrest in p53-deficient cells indicating this pathway is not necessary for AMPK activation to elicit a brake on proliferation (Zadra et al., 2014). In

addition, AMPK has been shown to directly phosphorylate p27 at Thr198 which was found to inhibit cell proliferation (Liang et al., 2007).

1.2.3.2 AMPK as a tumour promoter

Despite a large body of work indicating that AMPK activation negatively impacts tumour development and growth, a number of more recent studies have inferred the contrary. Under certain conditions (or stages of tumourigenesis) AMPK activation could act as a protective mechanism against metabolic stressors including hypoxic environments and glucose deprivation. In these conditions, AMPK activation has been implicated in aiding cancer cell survival by maintaining cellular energy homeostasis in suboptimal growth conditions by activating processes such as autophagy (Chhipa et al., 2011, Jeon et al., 2012, Laderoute et al., 2006, Liang et al., 2007).

Tolerance to nutrient-deprivation and hypoxic conditions

Pancreatic cancer cells were shown to be resistant to glucose starvation, whereas hepatocellular carcinoma cells were sensitive to these conditions, inducing apoptosis (Kato et al., 2002). Tolerance to glucose deprivation in pancreatic cancer cells was attributed to high levels AMPK expression, as knockdown diminished their ability to withstand glucose starvation. Furthermore, Laderoute et al showed that that AMPK was activated in hypoxic regions of a solid tumour in a mouse xenograft model (Laderoute et al., 2006). They went on to show that AMPK activity was important for tumour growth. This supports the work by Lee et al, which implicates AMPK in the positive regulation of HIF1α (Lee et al., 2003). They showed that AMPK activation was required for HIF1α transcriptional activity in a prostate cancer cell line. Furthermore, they implicate AMPK activity as being necessary to allow cancer cells to metabolically adapt to anaerobic conditions. Therefore, AMPK activation in nutrient-deprived or hypoxic tumour microenvironments could represent a survival advantage for tumour cells to enter a protective quiescent state. Recently, the expression of the AMPKβ1 subunit was shown to be essential to the survival of a number of prostate cancer cell lines, although no mechanism was determined (Ros et al., 2012).

NADPH homeostasis

AMPK activation was shown to confer a survival advantage to cells undergoing glucose deprivation and matrix detachment by maintaining cellular NADPH homeostasis (Jeon et al., 2012). When glucose uptake is decreased, glycolysis and the pentose phosphate pathway are inhibited. The pentose phosphate pathway is the major pathway that generates NADPH. It was shown that activation of LKB1-AMPK pathway under these conditions maintained NADPH production and ROS detoxification. AMPK activation was shown to mediate its effects via ACC 41

inhibition. Inactivation of ACC by AMPK reduced production of malonyl-CoA resulting in a decrease in the rate of fatty acid synthesis. The presence of malonyl-CoA can inhibit carnitine:palmitoyl-CoA transferase (CPT1), which transports activated fatty acids into the mitochondria, thereby decreasing the rate of fatty acid oxidation (Munday, 2002). Thus the actions of AMPK serve to increase fatty acid oxidation via reduction in malonyl-CoA levels, whilst inhibiting fatty acid synthesis. Inhibition of ACC leads to reduced NADPH consumption in fatty acid synthesis and increased NADPH generation by fatty acid oxidation. Knockdown of ACC was found to compensate for AMPK activation and facilitated anchorage-independent growth and solid tumour formation in vivo (Jeon et al., 2012). NADPH maintenance and protection from ROS by ACC inhibition was shown to be a mechanism by which AMPK fosters cancer cell survival during energy stress, anchorage-independent growth and solid tumour formation.

Autophagy

Autophagy is the cellular process by which cells dispose of and recycle malfunctioning proteins and damaged/obsolete organelles. If energy demands become high, due to a lack of nutrients, autophagy is increased to meet the cells requirement for amino acids and other nutrients. AMPK stimulates autophagy through inactivation of mTORC1 and directly through phosphorylation of ULK1 (Egan et al., 2011, Kim et al., 2011b). Autophagy plays an essential role in protecting cells from stressful conditions and allows prolonged survival, thereby generating dormant tumour cells that have the capacity to resume growth when conditions are more favourable (Yang et al., 2011). It was found that co-treatment of mice with an AMPK activator and an autophagy inhibitor increased in vivo efficacy of the AMPK activator in suppressing MDA-MB-231 xenograft growth (Lee et al., 2011). Furthermore, a study showed that loss of AMPK or ULK1 resulted in defective mitophagy and that ULK1 phosphorylation by AMPK was required for mitochondrial homeostasis and cell survival following nutrient deprivation (Egan et al., 2011).

1.2.3.3 AMPK and cell migration In recent years AMPK has been implicated in the regulation of cell migration and invasion. Studies have shown AMPK to be involved in the process of cytoskeletal reorganisation (Brenman, 2007, Miranda et al., 2010). There are a number of studies that support a role for AMPK in cell migration, although whether AMPK activation leads to increased or reduced cell migration is unclear and the mechanisms that regulate these events undefined.

AMPK as negative regulator of cell migration

There are a number of studies that show AMPK to be a negative regulator of cell migration. In the monocyte-like cell line U97 AMPK activators, AICAR and phenformin, were shown to

decrease the random motion of cells and cell migration towards a chemoattractant (Kanellis et al., 2006). Similar effects were observed in both vascular smooth muscle and umbilical vein endothelial cells with AICAR treatment reducing injury induced cell migration (Esfahanian et al., 2012, Peyton et al., 2012, Stone et al., 2013). Additionally, expression of a dominant negative form of AMPK or siRNA-mediated knockdown in a prostate cancer cell line led to increased cell migration (Zhou et al., 2009). Consistently, metformin treatment was seen to reduce migration in glioblastoma cells (Ferla et al., 2012). In a recent study, AMPK activity was shown to inhibit cell migration by phosphorylation of Pdlim5 (PDZ and LIM domain 5), thereby suppressing the Rac1-Arp2/3 signalling pathway and lamellipodia formation (Yan et al., 2015).

AMPK as positive regulator of cell migration

In contrast, a number of studies implicate AMPK activation in stimulating cell migration. For example, one study found AMPK to be a regulator of hypoxia induced angiogenesis, with AMPK suppression leading to reduced migration of human umbilical vein endothelial cells towards VEGF chemoattractant (Nagata et al., 2003). Recently, Frigo et al implicated CaMKKβ acting via AMPK, in promoting migration and invasion of the prostate cancer cell line LNCaP (Frigo et al., 2011). Furthermore, in ovarian cancer cells, lysophosphatidic acid (LPA) induced migration was found to be dependent on AMPK, as decreased LPA induced migration was found upon AMPK knockdown (Kim et al., 2011a). None of these studies looked mechanistically into how AMPK activation could lead to increased cell migration. Of note, AMPK was found to directly phosphorylate the cytoplasmic linker protein CLIP-170, which has a role in modulating microtubule dynamics. Inhibition of AMPK was found to impair microtubule stabilisation and perturbed directional cell migration and all of these phenotypes were rescued by expression of a phosphomimetic CLIP-170 mutant (Nakano et al., 2010). Furthermore, AMPK has recently been implicated as having a role in regulating the phosphorylation of myosin light chain 2 (MLC2), discussed below (Banko et al., 2011). These studies could provide potential mechanisms by which AMPK regulates cell migration.

AMPK and regulation of MLC2 phosphorylation

In a recent study aimed at identifying novel downstream targets of AMPK, several new targets were identified that are involved in cytoskeletal reorganisation (Banko et al., 2011). These included PAK2 (p21-activated kinase 2) and PPP1R12C (PPP1 regulatory subunit 12C). PPP1R12C is a member of the MYPT (myosin phosphatase-targeting) family that interacts with the catalytic subunit to confer substrate specificity and subcellular localisation of the myosin phosphatase complex (Grassie et al., 2011). A potential convergence point for PAK2 and PPP1R12C is at the regulation of MLC2 phosphorylation at Ser19 (Goeckeler et al., 2000, Ito et al., 2004). Previous studies have demonstrated that phosphorylation of MLC2 plays a role in the regulation of cell shape and polarity, cell migration, cell adhesion and cell cycle (Ito et al., 2004, Grassie et al., 2011, Matsumura, 2005). Phosphorylation of MLC2 on Ser19 leads to activation of myosin II and increased contractility (Matsumura, 2005). Furthermore, a study in 43

smooth muscle cells found activation of AMPK to lead to phosphorylation and inhibition myosin light chain kinase (MLCK) leading to attenuation of MLC2 phosphorylation (Horman et al., 2008). How AMPK activation impacts on the dynamics of MLC2 phosphorylation and cell contractility remain to be determined.

1.2.4 AMPK and prostate cancer

From Section 1.2.3, it is clear that the role of AMPK in cancer is far from understood, with AMPK activity seemingly being both tumour suppressive and tumour promoting. This likely depends on a multitude of factors including cancer type, stage and tumour genetics. The situation in prostate cancer is no different and represents one of the cancer types with the most conflicting studies as to whether AMPK activation is pro- or anti-tumorigenic. The following section will examine the current publications addressing the role of AMPK in prostate cancer.

In 2009, Park et al demonstrated that 40% of human prostate cancer specimens examined had elevated phosphorylated ACC, a direct measure of AMPK activity (Park, et al. 2009). This was supported by the observation that downregulation of AMPK in prostate cancer cell lines reduced cell proliferation and AMPK inhibition increased apoptosis in two prostate cancer cell lines tested (LNCaP and 22RV1) (Park et al., 2009). Recently, Tennakoon et al used immunohistochemistry to show that phosphorylated (active) AMPK was upregulated in prostate cancer samples with some evidence this correlated with cancer progression (Tennakoon et al., 2013).

In the androgen-sensitive cell line, LNCaP, it was shown that androgen deprivation coupled with hypoxia boosted AMPK activation to a higher level than that seen in either condition alone (Chhipa et al., 2011). This level of AMPK activation led to the induction of autophagy and improved viability, as when AMPK expression was decreased using siRNA, cell apoptotic index was found to increase. Beclin-1 was identified as the downstream target of AMPK. Hypoxia and androgen deprivation alone were not found to elicit this response. This represents a possible survival mechanism for prostate cancer cells to overcome hypoxic conditions during ADT. Another study implicating AMPK in prostate cancer cell survival was published in 2012 when Ros et al performed an unbiased siRNA screen to identify metabolic proteins required for prostate cancer cell survival (Ros et al., 2012). They found that depletion of the β1 subunit of AMPK increased apoptosis in all the prostate cancer cell lines but not the non-transformed prostate cell line. Interestingly, the other AMPK subunits (α1, α2, β2, γ1, γ2, γ3) were not found to be required for survival. This work suggests an AMPK-independent role for β1 and is further discussed in Chapter 4, Section 4.1.1.

Recently, a number of papers have suggested that AMPK acts downstream of the AR in prostate cancer cell lines via CaMKKβ signalling (Massie et al., 2011, Frigo et al., 2011).

Together they implicate AMPK activity in increased aerobic glycolysis, proliferation, migration and invasion in response to androgens. Massie et al put forward evidence of AMPK activation upon AR stimulation specifically enhancing glycolysis, whilst having no effect on protein synthesis. This increase in glycolysis, in part via AMPK-dependent PFK2 activation (Figure 1.12), was adequate to increase prostate cancer cell anabolic synthesis and proliferation. Of note, these findings were all garnered using the AMPK activator, AICAR, which is known to have AMPK-independent effects (Massie et al., 2011, García-García et al., 2010, Santidrián et al., 2010). In another study, Frigo et al found that AMPK inhibition was sufficient to blunt AR- mediated migration and invasion (Frigo et al., 2011). Further, it was recently shown that AMPK-mediated metabolic changes increased intracellular ATP levels via peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) stimulated mitochondrial biogenesis. AMPK and PGC-1α were shown to be required for prostate cancer cell growth and proliferation in response to androgens, with evidence that PGC-1α is overexpressed in a subset of clinical prostate cancer samples (Tennakoon et al., 2014).

Despite the evidence discussed that AMPK acts downstream of the AR to elicit pro- tumorigenic effects other studies implicate AMPK as being negative regulator of AR signalling. Using AMPK activators, AICAR and metformin, Jurmeister et al found that activation of AMPK inhibited AR transcriptional activity and reduced androgen-dependent expression of known AR target genes, consistent with a tumour suppressive function for AMPK (Jurmeister et al., 2014). Conversely, AMPK knockdown increased AR activity (Jurmeister et al., 2014).They found AMPK activation reduced the nuclear localisation of the AR without affecting protein expression. However, in another study, activation of AMPK by metformin decreased AR protein level through suppression of AR mRNA expression and promotion of AR protein degradation (Shen et al., 2014).

Zhou et al established stable cell lines by introducing a dominant-negative mutant of AMPKα1 subunit or its shRNA into the prostate cancer C4-2 cells (Zhou et al., 2009). Inhibition of AMPK was found to accelerate cell proliferation and promote malignant behaviour such as increased cell migration and anchorage-independent growth. This was associated with a decreased G1 population, downregulation of p53 and p21, and upregulation of S6K, IGF-1 and IGF1R. Conversely, treatment of the C4-2 cells with AICAR was found to cause the opposite effects (Zhou et al., 2009). Consistently, AMPK activation was found to inhibit PC3 prostate cancer cell migration via ACC inhibition and the inhibition of fatty acid synthesis (Scott et al., 2012). The inhibition of fatty acid synthesis was found to reduce invadopodia formation, which was rescued by supplementation with exogenous phospholipids (Scott et al., 2012).

In 2014, Zadra et al published on a novel direct AMPK activator, MT63-78 (Zadra et al., 2014). They showed that direct activation of AMPK inhibited prostate cancer cell growth in both androgen-sensitive and castration-resistant prostate cancer cell lines by inducing mitotic arrest and apoptosis. These effects were argued to be due to a decrease in de novo

lipogenesis upon AMPK activation. In addition, they showed that AMPK activation enhanced the growth inhibitory effect of AR signalling inhibitors e.g. abiraterone (Zadra et al., 2014).

Conclusion

The master regulator, AMPK, is emerging as a potential therapeutic target for prostate cancer treatment, with a large number of studies showing AMPK activation to inhibit cancer growth in culture and xenografts models. However, there is growing controversy in prostate cancer, as to whether AMPK activation is always detrimental to cancer development and progression, with a number of studies implicating AMPK in cancer cell survival. There are conflicting reports as to whether AMPK activation inhibits or promotes prostate cancer cell proliferation, migration and invasion. Previous studies using AMPK activators such as metformin and AICAR, and AMPK inhibitor, Compound C, are confounded by their AMPK-independent effects, thereby preventing researchers from unequivocally testing the efficacy of AMPK activation as an effective drug therapy (García-García et al., 2010, Santidrián et al., 2010, Ben Sahra et al., 2011, Liu et al., 2014, Bain et al., 2007). In addition, the role of AMPK is yet to be tested in vivo using GEMMs. Only through the use of more specific AMPK activators and in vivo mouse models of prostate cancer with modulated AMPK activity can the role of AMPK in this complicated disease begin to be properly understood.

1.3 Calcium/calmodulin-dependent protein kinase kinase β (CaMKKβ)

Calcium is a universal second messenger in all eukaryotic cells, where it regulates many functions through forming a complex with the protein calmodulin (CaM). Upon calcium binding, CaM can bind and activate a wide range of proteins, including the calcium/calmodulin-dependent protein kinase (CaMK) family, which consists of the CaMKI subfamily (α, β, γ, δ), the CaMKII subfamily (α, β, γ, δ), CaMKIV, and the CaMK kinase (CaMKK) subfamily (α, β) (Racioppi, 2013).

There are two CaMKK isoforms encoded by different genes. Human CaMKKβ (also known as CaMKK2) shares 65% protein sequence homology with CaMKKα (also known as CaMKK1); with the greatest heterogeneity seen at N- and C- termini (Anderson et al., 1998). Although both CaMKKβ and CaMKKα can phosphorylate CaMKI and CaMKIV, only CaMKKβ is capable of phosphorylating AMPK (Woods et al., 2005). Despite some common downstream targets CaMKKβ and CaMKKα do have distinct physiological roles, for example CaMKKβ and CaMKKα knockout mice have non-overlapping neurological phenotypes (Peters et al., 2003, Mizuno et al., 2006). In addition, CaMKKα is entirely dependent on the binding of calcium/CaM for its activation; whereas studies using CaMKKβ purified from both bacterial and mammalian sources have shown CaMKKβ to have calcium/CaM-independent activity (Anderson et al., 1998, Tokumitsu et al., 2001, Woods et al., 2005).

1.3.1 CaMKKβ and tissue expression pattern

CaMKI and CaMKII are ubiquitously expressed, while the expression of other members of the CaMK is more restricted. CaMKKβ has a restricted expression pattern. It is expressed in many areas of the brain, including the olfactory bulb, hippocampus, dentate gyrus, amygdala, hypothalamus, and cerebellum (Anderson et al., 1998). In addition to the nervous system, CaMKKβ is present at lower levels in testis, spleen, and lung. In other tissues the evidence for expression remains less clear. Additionally, CaMKKβ is detected in isolated murine embryonic fibroblasts (MEFs). Recently, CaMKKβ has shown to be expressed in prostate cancer tissue (Massie et al., 2011). In immune cells, CaMKKβ is expressed exclusively in cells of the myeloid linage, including bone marrow-derived and peritoneal macrophages (Racioppi et al., 2012). CaMKIV expression is restricted to testis, nervous system and immune system (Means et al., 1997). CaMKKα is highly expressed in the brain, thymus and spleen (Tokumitsu et al., 1995).

1.3.2 Structure of CaMKKβ

CaMKKβ is a 66-68 kDa kinase and consists of a central Ser/Thr-directed kinase domain that is followed by a regulatory domain composed of overlapping autoinhibitory and CaM-binding regions (Figure 1.14) (Anderson et al., 1998). Two major transcripts are generated from the 47

CaMKKβ locus by use of polyadenylation sites present in the last two exons (Hsu et al., 2001). Additionally, CaMKKβ transcripts can be generated by alternative splicing (plus and minus exon 16) and this mechanism produces variants with different roles in neuronal differentiation (Hsu et al., 2001, Cao et al., 2011). PKA and CaMKIV have been shown to be involved in the regulation of alternative splicing (Cao et al., 2011). The role(s) of the different CaMKKβ splice variants remains undetermined.

Figure 1.14 Domain structure of CaMKKβ. CaMKKβ consists of N- and C-terminal domains and a central kinase domain, followed by a regulatory domain with overlapping autoinhibitory and CaM-binding regions. A region of amino acids (residues 129–151) located at the N-terminus has been identified as an important regulatory region, and is phosphorylated by CDK5 (blue), GSK3 (blue) and PKA (orange). Thr482 has been identified as an autophosphorylation site (green). (Image adpated from Racioppi and Means, 2012)

1.3.3 Downstream substrates of CaMKKβ

CaMKKβ phosphorylates CaMKIV and CaMKI on activation loop residues Thr200 and Thr177, respectively. Phosphorylation increases their kinase activities and mutation of the Thr residue abolishes both phosphorylation and activation of CaMKI/CaMKIV by CaMKKβ (Anderson et al., 1998). More recently, CaMKKβ was shown to be an upstream kinase of AMPK, phosphorylating and activating this kinase on Thr172 (Woods et al., 2005, Hawley et al., 2005). Downregulation of CaMKKβ in mammalian LKB1-deficient cells using siRNA was shown to completely abolish AMPK activation (Woods et al., 2005).

1.3.4 Regulation of CaMKKβ

CaMKKβ is regulated at many levels by many signalling pathways. CaMKKβ is autoinhibited by a sequence located immediately C-terminal to their catalytic domain (Figure 1.14) (Tokumitsu et al., 2001). Interestingly, CaMKKβ exhibits autonomous activity even in the absence of calcium/CaM binding. By using truncation mutants of CaMKKβ, a region of 23 amino acids located in the N-terminal region was identified as an important regulatory element of autonomous activity (Figure 1.14) (Tokumitsu et al., 2001). Mass spectrometry revealed three conserved phosphorylation sites, and mutation of these residues led to increased autonomous activity but decreased protein stability (Green et al., 2011b). No change in CaMKKβ calcium/CaM-dependent activity was seen. Phosphorylation of these N-terminal sites and the resulting inhibition of CaMKKβ autonomous activity has been attributed to CDK5 (cyclin-dependent kinase 5) and GSK3 (glycogen synthase kinase 3)( Figure 1.14) (Green et al., 2011b). It has been reported that CaMKKβ has autonomous activity against CaMKI and CaMKIV, substrates that are not regulated by calcium/CaM, such as AMPK, still require calcium/CaM binding to CaMKKβ to be phosphorylated (Green et al., 2011a, Racioppi and Means, 2012). This could explain why two enzymes in a cascade seemingly require the same allosteric activator.

Despite some autonomous activity, CaMKKβ is maximally activated through calcium/CaM binding. CaMKKβ is therefore activated by signalling through Gq-coupled receptors, inositol

1,4,5-trisphosphate (IP3)-mediated release of calcium via activation of the IP3 receptor; calcium entry into cells via plasma membrane ion channels; and Toll-like receptors summarised in Figure 1.15 (reviewed in (Racioppi and Means, 2012). In addition, the cAMP/PKA pathway has been shown to inhibit CaMKKβ activity by direct phosphorylation and this is in part due to disrupted calcium/CaM binding (Wayman et al., 1997). CaMKKβ acts as a signalling hub as it receives and decodes signals transmitted via a plethora of cellular regulatory pathways (Figure 1.15).

Figure 1.15 CaMKKβ signalling (adapted from (Racioppi and Means, 2012). CaMKKβ is activated by signalling through Gq-coupled receptors, IP3-mediated release of calcium, and/or calcium entry into cells by ion-channels such as voltage-dependent calcium channels (VDCCs). Signalling pathways modulating GSK3/CDK5 and PKA activities can also modulate CaMKKβ activity. CaMKKβ activation leads to phosphorylation of downstream substrates CaMKI, CaMKIV and AMPK leading to a plethora of downstream signalling events. (Image adapted from Racioppi and Means, 2012).

1.3.5 Global CaMKKβ knockout mouse

The CaMKKβ-knockout mouse (global deletion of exon 5) used in this study was generated and characterised previously (Peters et al., 2003). In this study, Camkkβ-/- mice were found to have impaired spatial memory formation due to reduced spatial-training induced CREB activation. Furthermore, they were found to have impaired long-term memory for the social transmission of food preferences due to the lack of late long-term potentiation at the hippocampal area CA1 synapses. These phenotypes were found to be male-specific. In contrast, Camkkα-/- mice were found to have impaired contextual fear memory, also male- specific, with no difference found in spatial memory or social transmission of food preferences (Mizuno et al., 2006).

The PhD project of a previous Carling group member, Jiexin Zhao, was to characterise this Camkkβ-/- mouse model for metabolic phenotypes. Global CaMKKβ deletion was found to have no effect on energy metabolism on a chow or high fat diet (Zhao, 2014). Intriguingly, a 50

second Camkkβ-/- mouse model was generated by an independent group and these mice were reported to have a metabolic phenotype not seen in the other model (Anderson et al., 2008). Loss of CaMKKβ, in this second model, was shown to protect mice from high fat diet-induced obesity, insulin resistance and glucose intolerance (Anderson et al., 2008). The reason for these phenotypical differences is currently unclear. However, arguably a phenotype that is not recapitulated in both models is unlikely to be due to CaMKKβ-deletion. A possibility is that expression of another gene has been altered in the model used in the Anderson et al study, and this is responsible for mediating the reported phenotype(s).

A role for CaMKKβ in macrophage activation has also been reported and this is further discussed in Section 1.3.7 (Racioppi et al., 2012).

1.3.6 CaMKKβ and prostate cancer

Recently, a number of papers have implicated CaMKKβ as having an important role in prostate cancer development downstream of androgen signalling. Frigo et al provided the first evidence for the upregulated expression of this kinase in prostate cancer, showing that CaMKKβ was stimulated by treatment with androgens in the androgen-sensitive prostate cancer cell line, LNCaP (Frigo et al., 2011). The authors further characterised an androgen response element in the promoter region of this kinase and showed that removal of the element abolished the androgen-dependent increase in CaMKKβ expression upon androgen stimulation. They found that knockdown using siRNA or pharmacological inhibition of CaMKKβ was sufficient to blunt androgen stimulated migration and invasion of LNCaP cells. The effects of CaMKKβ inhibition were attributed to decreased signalling through AMPK.

To identify a core set of AR-binding sites that regulate gene expression in prostate cancer cells, Massie et al combined genome-wide AR-binding profiles with an analysis of the integrated androgen-stimulated recruitment of the transcriptional machinery (Massie et al., 2011). This study identified CaMKKβ as one of the several AR-regulated genes in prostate cancer, confirming previous findings. They showed that CaMKKβ gene expression is upregulated in human prostate cancer using Oncomine banked datasets, with some evidence given that gene expression positively correlates with disease grade. Using these datasets, they showed that upregulation of CaMKKβ gene expression was unique to prostate cancer and expression was not seen to change in other common malignancies. In addition to gene expression studies, Massie et al evaluated CaMKKβ protein expression using immunohistochemistry. Expression levels were found to be reduced in samples obtained following ADT, supporting the hypothesis of AR regulation of CaMKKβ in vivo. Interestingly, CaMKKβ protein expression was found to be increased in samples from castration-resistant disease stages. These findings are significant as they suggest that CaMKKβ expression is

increased in both hormone-sensitive and castration-resistant prostate cancer. Using the C4-2B xenograft model of CRPC, they found that the CaMKKβ inhibitor, STO609, effectively reduced tumour growth in this model, and this treatment was additive with AR inhibition in castrated mice.

Massie et al found LNCaP cells treated with STO609 or CaMKKβ siRNA had reduced glucose uptake and produced less lactate and citrate, suggesting a reduction in aerobic glycolysis (Massie et al., 2011). In addition, metabolic profiling revealed decreased anabolism from glucose to citrate, ribose and amino acids, together suggesting a metabolic block. CaMKKβ inhibition was found to reduce PFK2 activity, which led to the suggestion that AR-CaMKKβ- AMPK signalling may stimulate glucose uptake and glycolysis. Both siRNA-mediated knockdown and chemical inhibition of CaMKKβ reduced proliferation in LNCaP prostate cancer cells. Thus, Massie et al suggest that CaMKKβ regulates prostate cancer cell growth via its role as a hormone-dependent modulator of anabolic metabolism and propose AMPK to be the relevant downstream target involved. As previously mentioned, the data implicating AMPK in this mechanism is based solely on the use the AMPK activator, AICAR, which is known to have AMPK-independent effects (García-García et al., 2010, Santidrián et al., 2010).

Karacosta et al recently investigated the expression of CaMKKβ during tumour progression in the TRAMP prostate cancer mouse model, CWR22 human xenografts and in clinical prostate cancer samples (Karacosta et al., 2012). They found CaMKKβ expression to be increased in advanced prostate cancer, and found this kinase to be expressed at higher levels in castration- resistant tumour xenografts compared with androgen-sensitive xenografts using immunohistochemistry. In agreement with previous studies, they show that CaMKKβ expression is induced by androgens and required for LNCaP cell growth. In addition to previous studies, they show that the silencing of CaMKKβ in LNCaP cells using siRNA decreased expression of the AR target gene PSA, as well as cyclin D1 and phospho-Rb, two AR- regulated proteins that regulate the cell cycle. Of note, these authors also showed nuclear accumulation of CaMKKβ in response to androgens. Based on these results, they proposed a regulatory loop in which AR feeds forward to induce CaMKKβ expression and CaMKKβ, in turn, feeds back to positively regulate AR activity, AR-dependent cell cycle control, and continued cell proliferation.

The role of CaMKKβ in prostate cancer has most recently been investigated in a study performed by Shima et al (Shima et al., 2012). By performing genome-wide expression analysis on clinical samples, they found a 6-fold increase in CaMKKβ expression in prostate cancer samples compared with normal prostate, further confirming previous reports. Interestingly, these authors also explored CaMKKβ protein expression by tissue microarray and found CaMKKβ to be barely detectable in normal prostate tissue. CaMKKβ was found to accumulate in PIN and prostate cancer lesions, and this accumulation was inversely correlated with prognosis. However, in contrast with previous reports, this study provided evidence for

an inhibitory effect of CaMKKβ on AR-regulated transcriptional activity at the PSA promoter in LNCaP cells. Additional studies are required to delineate the effect of CaMKKβ activity on AR signalling.

1.3.7 CaMKKβ and the tumour microenvironment

Prostate cancer is associated with a chronic inflammatory status as previously discussed, which is in-part sustained by macrophage activation (Barron and Rowley, 2012). This chronic inflammatory state promotes the recruitment of myeloid progenitors, which differentiate into tumour-associated macrophages (TAM) that foster tumour growth. Macrophages act to engulf apoptotic cells and pathogens and produce immune effector molecules. Diverse TAM subsets have been identified on the basis of their ability to blunt the immune response against cancer, support blood vessel formation, and help tumour healing after chemo- and radiotherapy (Racioppi, 2013). TAM infiltration has been found to correlate negatively with prognosis after ADT (Nonomura et al., 2011).

CaMKKβ is selectively expressed in macrophages, and its ablation has been shown to impair their ability to spread, phagocytose bacterial particles and release cytokines/chemokines in response to lipopolysaccharides (Racioppi et al., 2012). Furthermore, in vivo studies showed that genetic ablation of CaMKKβ prevented the accumulation of macrophages and inflammatory cytokine mRNAs in adipose tissue of mice fed a high fat diet. In this study, Camkkβ-/- mice were shown to be more resistant to irritants that lead to systemic inflammation and hepatitis. This was thought to be as a result of the uncoupling of TLR4 signalling from the phosphorylation of protein tyrosine kinase 2 (PYK2) and PYK2 downstream effectors required in the activation of macrophages. Interestingly, PYK2 has also been found to have a role in regulating prostate cancer development (Stanzione et al., 2001).

The involvement of CaMKKβ in signalling controlling macrophage activation is supported by several reports implicating the CaMKKβ downstream targets CaMKI and AMPK in TLR signal cascades (Zhang et al., 2011, Salminen et al., 2011).

Conclusion

CaMKKβ expression is androgen-responsive and it is therefore overexpressed in androgen- sensitive and castration-resistant prostate cancer. A number of recent publications demonstrate that CaMKKβ mediates pro-tumorigenic effects such as increased prostate cancer cell growth, proliferation, migration and invasion in response to androgens. The publications so far link this effect to signalling through AMPK, although the data supporting this is less robust. These studies have relied on prostate cancer cell lines and xenograft models to investigate the role of CaMKKβ in prostate cancer. It will be critical to investigate the role of this kinase in vivo in order to test its efficacy as a therapeutic target in this disease.

This is especially important given the potential role of CaMKKβ in regulating the biology of tumour microenvironment.

1.4 The PTEN mouse model of prostate cancer

Chapters 4 and 5 examine the role of AMPK and CaMKKβ, respectively, in vivo using the PTEN mouse model of prostate cancer. The studies discussed so far have mainly relied on immortalised human prostate cancer cell lines to investigate the role of AMPK and CaMKKβ in prostate cancer. These cell lines are derived from advanced/metastatic tumours, and thus do not allow recapitulation of the various stages of the human disease (Parisotto and Metzger, 2013). Moreover, studies of clonal cell lines in culture do not reproduce their interaction with the various cellular compartments of the prostate, stromal cells, with vascular and lymphatic circulation and immune cells. In addition, xenograft transplantation models based on transformed human cell lines do not mimic the heterogeneity of human tumours and their microenvironment. Furthermore, xenograft models require immunodeficient host animals, which therefore lack crucial modulators of tumorigenesis. These major limitations prompted the development of mouse models of prostate cancer to investigate tumour genetics and tumour- tumour microenvironment interactions.

1.4.1 Anatomy of the mouse prostate

The gross anatomy of the mouse prostate differs from that of the human prostate. While the human prostate is a single lobular structure that surrounds the urethra at the base of the bladder, the rodent prostate is comprised of three distinct lobes (the anterior, dorsolateral and ventral prostate) also arranged around the urethra (Cunha et al., 1987, Parisotto and Metzger, 2013) (Figure 1.16). The prostate of both species are composed of glands and ducts of similar organisation. Despite mice not being prone to develop spontaneous benign or malignant prostate pathologies, GEMMs of prostate cancer have been developed to model the human disease (Parisotto and Metzger, 2013). Despite anatomical distinctions, recent studies have revealed striking similarities in the molecular mechanisms underlying disease progression in these mice and humans (Abate-Shen and Shen, 2002, Parisotto and Metzger, 2013, Wang et al., 2003).

A B

Figure 1.16 Schematic depictions of human and mouse prostate anatomy. (Image adapted from Abate-Shen and Shen, 2002).

1.4.2 The PTEN mouse model of prostate cancer (Wang et al., 2003)

The prostate cancer mouse models generated in this thesis are based on the PTEN prostate cancer mouse model. As previously discussed, PTEN is a major negative regulator of the PI3K/Akt pathway and is one of the most common deletions found in human prostate cancers (see Section 1.1.3, Figure 1.6). Wang et al crossed Ptenfl/fl mice to the ARR2-Probasin-Cre transgenic line, PB-Cre4, in which the Cre recombinase is under the control of an enhanced prostate-specific probasin promoter, to generate mice with prostate-specific homozygous deletion of PTEN. This prostate cancer model was found to recapitulate the disease progression seen in human prostate cancer, with tumour initiation in the form of mouse PIN (mPIN), followed by progression to invasive adenocarcinoma. This model is one of the most widely used prostate cancer GEMMs (Parisotto and Metzger, 2013, Wang et al., 2003). Despite the original paper describing metastatic disease development from 12-29 weeks, this has not been repeated in any other study using this model (Trotman et al., 2003, Backman et al., 2004, Irshad and Abate-Shen, 2013, Wang et al., 2003). The PTEN prostate cancer model is further discussed in Chapter 4.

1.5 Project Aims

Prostate cancer cells rely on androgens for growth and survival (Isaacs and Isaacs 2004). Thus, androgen ablation therapies are the mainstay treatment for late-stage prostate cancer. The majority of patients initially respond favourably, however by within 1-2 years most patients will relapse. Further treatment is palliative. Due to the high prevalence and mortality rates associated with prostate cancer, it is critical to gain a better understanding of the biology of this complicated disease and find novel drug targets to treat it. The aim of this thesis is to address the roles of AMPK and CaMKKβ in prostate cancer to ultimately determine whether either of these kinases could act as novel therapeutic targets for treating this disease.

Aim 1. To examine the effect of AMPK activation on cancer cell proliferation, migration and invasion in a panel of prostate cancer cell lines using a novel direct AMPK activator (991).

The studies published so far give conflicting results as to the effects of AMPK activation on prostate cancer cell proliferation, migration and invasion. This is in part due to the use of non- specific AMPK activators such as metformin and AICAR. The use of a highly selective direct AMPK activator will help address these conflicting results. Work relating to this aim is presented in Chapter 3.

Aim 2. Generation and characterisation of a novel mouse model of prostate cancer lacking AMPKβ1. AMPKβ1 (the major AMPKβ subunit in human and mouse prostate) was genetically deleted in the PTEN prostate cancer model. This model has reduced prostate AMPK activity. Work relating to this aim is presented in Chapter 4.

Aim 3. Generation and characterisation of a novel mouse model of prostate cancer lacking CaMKKβ. CaMKKβ was genetically deleted in the PTEN prostate cancer model. Work relating to this aim is presented in Chapter 5.

Disease progression was assessed in these novel prostate cancer models, using a number of read-outs including prostate weight, pathological analysis of HE (hematoxylin and eosin) stained sections, immunohistological Ki-67 (proliferation marker) staining and Western blot and qPCR analysis of key genes involved in prostate cancer progression.

All pathological and immunohistochemical analyses described in this thesis were performed blinded. Pathological analysis of HE stained prostate sections was performed according to the criteria set out in the Ittmann et al., 2013 study, and is described in Chapter 4. Currently, collaboration with Alexander Nikitin, a Professor of pathology at Cornell University, is in the process of being arranged. Professor Nikitin has experience in the pathological analysis of prostate sections from the PTEN prostate cancer model. Although outside the timeframe of this PhD thesis, grading of HE stained sections will be confirmed by a trained pathologist.

2. Materials and Methods

2.1 Materials

2.1.1 General Reagents

Adenosine triphosphate (ATP), 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES), Tris(hydroxymethyl) aminomethane (Tris), ethylene glycol tetraacetic acid (EGTA), magnesium chloride (MgCl2), sodium fluoride (NaF), sodium pyrophosphate(Na4P2O7), dithiothreitol (DTT), protein A/G-sepharose, sorbitol, β-mercaptoethanol, agarose, glycine, potassium phosphate, hydrogen peroxide, dimethyl sulfoxide (DMSO), bovine serum albumin (BSA), heat-inactivated foetal bovine serum (FBS), Gill’s haematoxylin, normal goat serum (NGS), crystal violet solution, DNase 1 from bovine pancreas, DPX, BrdU, gelatin from cold water fish skin, mixed dNTP (10 mM)and Triton X-100 were obtained from Sigma (Poole, UK). NaOH, sodium lauryl sulfate (SDS), NaCl, Tween 20, glycerol, ethylenediaminetetraacetic acid (EDTA), ethanol, methanol, glycerol, chloroform, KCl, KH2PO4 and NaHCO3were from VWR (West Sussex, UK). Polyvinylidene difluoride (PVDF) membrane, steadylite plus Reporter Gene Assay System and [1-14C] acetic acid sodium salt (NEC084H100/Mc) were from PerkinElmer (Beaconsfield, UK). [γ-32P]ATP (6000 Ci/mmol), and Amersham Hyperfilm MP film, was obtained from GE healthcare (Amersham, UK). SAMS peptide (HMRSAMSGLHLVKRR) was synthesised by Pepceuticals (Leicester, UK) . Bovine Brain Calmodulin was obtained from Calbiochem, (Hertfordshire, UK). RNeasy columns and RNeasy kit from Qiagen (Crawley, UK). BioMix Red mastermix and SensiMix SYBR-HiROX were from Bioline (London,UK). Random primers and Reporter lysis buffer were acquired from Promega (Southampton, UK). G418 was from Clontech (Saint-Germain-en-Laye, France). P81 phosphocellulose paper was obtained from Whatman (Maidstone, UK). RPMI, DMEM, Penicillin/Streptomycin, optiMEM, and trypsin were from Gibco (Paisley, UK). Trypsin neutraliser solution was acquired from Lonza (Slough, UK). Charcoal stripped serum was acquired from First Link (Wolverhampton, UK). Matrigel was ordered from BD Biosciences (Oxford, UK). BioRad Protein assay kit and x20 MOPS running buffer were from BioRad (Hertfordshire, UK). BCA Protein Assay Kits (Peirce) and 10% and 4-12% Nu-PAGE Novex Bis-Tris protein gels were acquired from Thermo Fisher Scientific. TransIT-TKO Transfection reagent (Mirus Bio), xCELLigence E-plates and CIM-plates, Wes Master kits (12-230 kDa, ProteinSimple) were from Cambridge Biosciences, UK. Non-targeting scrambled and targeting siRNAs (Silencer Select siRNA) and SuperScript II reverse transcriptase were obtained from Ambion (Invitrogen). DNA primers were generated by Sigma-Genosys (Havrehill, UK). Skimmed milk powder was from (Oxoid, Thermo Scientific). VECTASTAIN Elite ABC Kit and DAB Substrate Kit were from Vector Laboratories (Peterborough, UK). Triglyceride assay was obtained from Sentinel Diagnostics. Alzet Osmotic Minipumps Model 2004 and 2006 were acquired from Charles Rivers (Harlow, UK). Heparinised microvettes were from Sarstedt (Leicester, UK).

2.1.2 Buffers All chemicals for buffers were obtained from commercial sources and were of at least reagent grade.

SDS-PAGE Buffer – 50 mM Tris-HCl, pH 8.4, 400 mM glycine, 0.1% (w/v) SDS.

Tris/Glycine Transfer Buffer – 25 mM Tris, 192 mM glycine, 20% methanol.

HST – 20 mM Tris, pH 7.4, 500 mM NaCl, 0.5% (v/v) Tween 20

Sample Buffer – 50 mM Tris-HCl, pH 7.4, 2.5% (w/v) SDS, 1% (v/v) β-mercaptoethanol, 10% glycerol, 0.05% (w/v) bromophenol blue.

Homogenisation Buffer – 50 mM Tris, pH 7.4, 50 mM sodium fluoride, 5mM sodium pyrophosphate, 1 mM EDTA, 250 mM sucrose, 1 mM dithiothreitol, 4 μg/ml trypsin inhibitor,0.1 mM phenylmethylsulfonyl fluoride, 1 mM benzamidine.

HBA – 50 mM Hepes pH 7.4, 50 mM sodium fluoride, 5 mM sodium pyrophosphate, 1 mM EDTA, 10% (v/v) glycerol, 1 mM dithiothreitol, 4 μg/ml trypsin inhibitor, 0.1 mM phenylmethylsulfonyl fluoride, 1 mM benzamidine.

Hepes Lysis Buffer (HBA/1% Triton X-100) – 50 mM Hepes pH 7.4, 50 mM sodium fluoride, 5 mM sodium pyrophosphate, 1 mM EDTA, 10% (v/v) glycerol, 1% Triton X-100 (v/v), 1 mM dithiothreitol, 4 μg/ml trypsin inhibitor, 0.1 mM phenylmethylsulfonyl fluoride, 1 mM benzamidine.

HGE – 50 mM Hepes pH 7.4, 1 mM EDTA, 10% (v/v) glycerol and 1% Triton X-100, 1 mM dithiothreitol, 4 μg/ml trypsin inhibitor, 0.1 mM phenylmethylsulfonyl fluoride, 1 mM benzamidine.

Sodium Citrate Buffer – 10 mM sodium citrate, 0.05% Tween 20, pH 6.0.

Phosphate-Buffered Saline (PBS) – 137 mM NaCl, 2.7 mM KCl, 4.3 mM Na2HPO4, 1.4 mM

KH2PO4, pH 7.4.

PBS-Tween (PBT) – PBS, 0.1% Tween20

TAE – 40 mM Tris, 20 mM acetic acid, 1 mM EDTA, pH 8.0.

2.1.3 Antibodies The primary antibodies used in this thesis are outlined in Table 2.1.

Antibody Raised in Dilution Supplier Product no. Application

ACC Mouse 1/1000 Millipore 05-1098 WB pACCSer79 Rabbit 1/1000 CST #3661 WB β-Actin Rabbit/ 1/1000 CST/Sigma #4968/A3853 WB Mouse Akt Rabbit 1/1000 CST #2920 WB pAktSer473 Rabbit 1/1000 CST #3787 WB AMPKα1 Rabbit 1/1000 CST #2795 WB AMPKα1 Sheep 1/200 In-house N/A IP AMPKα1/2 Mouse 1/1000 CST #2793 WB AMPKα2 Rabbit 1/1000 CST #2757 WB AMPKα2 Sheep 1/200 In-house N/A IP pAMPKαThr172 Rabbit 1/1000 CST #2535 WB AMPKβ1 Mouse 1/1000 Abcam Ab58175 WB AMPKβ1/2 Rabbit 1/1000 CST #4150 WB AMPKβ1/2 Rabbit 1/5000 (WB) In-house N/A WB/IP (Sip2) 1/1000 (IP) AMPKβ2 Mouse 1/1000 (WB) In-house N/A WB/IP (8H12) 1/200 (IP) AMPKγ1 Rabbit 1/1000 CST #4187 WB Androgen Rabbit 1/1000 Millipore 06-680 WB receptor (AR) BiP1 Rabbit 1/1000 (WB) CST #3177 WB Brd-U Mouse 1/2000 BD 555627 IF Pharmingen CaMKKβ Mouse 1/1000 (WB) In-house N/A WB/IHC/IP 1/100 (IHC) 1/200 (IP) Cleaved Rabbit 1/1000 CST #9661 WB Caspase 3 Cleaved-PARP Rabbit 1/1000 CST #5625 WB GIT1 Rabbit 1/1000 CST #2919 WB HK2 Rabbit 1/1000 (WB) CST #2867 WB/IHC 1/1000 (IHC) Ki-67 Rabbit 1/250 Abcam Ab16667 IHC LKB1 Rabbit 1/200 In-house N/A IP MLC2 Rabbit 1/1000 CST #3672 WB pMLC2Ser19 Mouse 1/1000 CST #3675 WB 59

p53 Mouse 1/1000 CST #2524 WB p-p53Ser15 Rabbit 1/1000 (WB) CST #9284 WB/IHC 1/200 (IHC) PAK1 Rabbit 1/1000 CST #2602 WB pPAK1Ser199 Rabbit 1/1000 CST #2605 WB pPAK1Thr423 Rabbit 1/1000 CST #2601 WB PAK2 Rabbit 1/1000 CST #2608 WB PFKFB2 Rabbit 1/1000 Abcam Ab70175 WB βPIX Rabbit 1/1000 CST #4515 WB PYGL Rabbit 1/1000 Abcam Ab198268 WB RPL19 Mouse 1/1000 Abcam Ab58328 WB RPL7 Rabbit 1/1000 Abcam Ab72550 WB pS6Ser235 Rabbit 1/1000 CST #2211 WB SORD Rabbit 1/1000 Abcam Ab189248 WB Tubulin Rabbit/ 1/1000 CST/Sigma #2144/T5168 WB Mouse Vimentin Rabbit 1/200 Abcam #5741 IHC Vinculin Mouse 1/1000 Sigma V9131 WB Table 2.1 List of primary antibodies used in this thesis. Application abbreviations: WB, Western blotting; IHC, immunohistochemistry; IP, immunoprecipitation; IF, immunofluorescence.

The secondary antibodies used in this thesis are listed below:

Western Blotting (LI-COR imaging system): IRDye 800CW Goat anti-Rabbit, IRDye 680LT Goat anti-Rabbit, IRDye 800CW Goat anti-Mouse, IRDye 680LT Goat anti-Mouse used at a dilution of 1/20 000. Obtained from LI-COR Biotechnology (Cambridge, UK).

Immunohistochemistry: Biotinylated Goat anti-Rabbit, Biotinylated Goat anti-Mouse used at a dilution of 1/200. Obtained from Vector laboratories LTD (Peterborough, UK).

Immunofluorescence: Goat anti-Mouse, Alexa Fluor® 488 conjugate used at a dilution of 1/500. Obtained from Invitrogen, Thermo Scientific (Loughborough, UK).

2.1.4 Primers DNA primers were ordered from Sigma-Genosys (Havrehill, UK). The primers used for genotyping are shown in Table 2.2, and those used in qPCR are shown in Table 2.3.

Genotyping Sequence 5’-3’ Produce Size(s) (bp) AMPKβ1 (global) F CAG GAA TTC CTC TTT CTC TGG A (x2) WT 608 R CTT CGA TTC TGA CTG AAG AAG AGA (x1) Knockout 843 R’ CAG CCA TAT CAC ATC TGT AGA GGT (x1.5) AMPKβ1 (floxed) F TCT AGC TGG AGT AAG TTC CTC TG WT 219 R GGA GAA GAC AGC CAT GAG TC Floxed 410 CaMKKβ F CAG CAC TCA GCT CCA ATC AA (x1.5) WT 347 F’ GCC ACC TAT TGC CTT GTT TG (x1) Knockout 470 R TAA GCA CAA GCA CTC ATT CC (x1.5) IL-2 F CTA GGC CAC AGA ATT GAA AGA TCT 324 (positive control, R GTA GGT GGA AAT TCT AGC ATC ATC used with PB-Cre4) PB-Cre4 F CTG AAG AAT GGG ACA GGC ATT G 393 R CAT CAC TCG TTG CAT CGA CC PTEN F CAA GCA CTC TGC GAA CTG AG WT 156 R AAG TTT TTG AAG GCA AGA TGC Floxed 328 Table 2.2 List of primers used for genotyping.

qPCR Primers (Mouse) Sequence 5’-3’ Product Size (bp) 18S rRNA F CGG CTA CCA CAT CCA AGG AA 161 R AGC CGC GGT AAT TCC AGC ACC1 F ATG GGC GGA ATG GTC TCT TTC 148 R TGG GGA CCT TGT CTT CAT CAT AKT1 F ATG AAC GAC GTA GCC ATT GTG 116 R TTG TAG CCA ATA AAG GTG CCA T AKT2 F TGG GCC TGA CTC CGA GAA 181 R ATA GCC CGC ATC CAC TCT TC AMPKα1 F CGC CAT GCG CAG ACT CAG TTC 140 R CTT GCC CAC CTT CAC TTT CCC GAA AMPKβ1 F TCC TTG TGT CCC TGC AGA TTC 193 R CTG GAG CCT TGA TCT CTT CG AMPKγ1 F AAA GGA GGA GGA CTG TCA GC 170 R CAG GGT GTG GTG AGG TCT AG Androgen Receptor (AR) F TAT GGG GAC ATG CGT TTG GA 250 R GGA GAC GAC AAG ATG GGC AA β-Actin F CGG GCT GTA TTC CCC TCC AT 210 R GGG CCT CGT CAC CCA CAT AG BiP1 F GTG TGT GAG ACC AGA ACC GT 231 R GAC GCA GGA ATA GGT GGT CC

CaMKKβ F GGA GGA CGA GAA CTG CAC AC 202 R TTC GCT GCC TTG CTT CGT GA GAPDH F AGG TCG GTG TGA ACG GAT TTC 164 R TGT AGA CCA TGT AGT TGA GGT C GUSB F AAC AAC ACA CTG ACC CCT CA 223 R AGT AGG TCA CCA GCC CGA T HPRT F CAG TCC CAG CGT CGT GAT TA 168 R TGG CCT CCC ATC TCC TTC AT HK2 F GGC TAG GAG CTA CCA CAC AC 236 R GTC AAA CAG CTG GGT TCC AC IGF1R F CAT GTG CTG GCA GTA TAA CCC 129 R TCG GGA GGC TTG TTC TCC T PFKFB2 F TGG CCT ATG GTT GCA AAG TG 161 R TGG ACG CCT TAT TGT GTT CG SORD F AAC CAG GAG ATC GGG TTG C 237 R GCA GGC ATA GAT CCC CAC AG Table 2.3 List of primers used for qPCR (specific to mouse mRNA).

2.1.5 Cells RWPE1, LNCaP, DU145, PC3 and 22RV1 cells were attained from ATCC (Teddington, UK). LNCaP/Luc cells were a gift from Charlotte Bevan (Imperial College, London). LNCaP/Luc cells are LNCaP cells stably transfected with linearised pARE.MAR and thus exhibit strong androgen-dependent induction of luciferase activity (Dart et al., 2009). PC3 cells stably transfected with TetOnPLKO plasmid expressing inducible shRNA targeting AMPKβ1 were gifted from Almut Schulze (CRUK) (Ros et al., 2012). Wild-type (WT) and α1-/-α2-/- mouse embryonic fibroblasts (MEFs) were a gift from Benoit Viollet, (Université Paris Descartes). WT and β1-/-β2-/- MEFs were a gift from Biplab Dasgupta (Cincinnati Children's Hospital Medical Center). Primary WT and β1-/- MEFs were generated in-house.

2.1.6 Proteins Recombinant AMPK and CaMKKβ complexes were expressed in E.coli and purified by David Carmena or Huza Zhang.

2.1.7 Compounds A-769662 and 991 (a kind gift from Astrazeneca, UK) were dissolved in DMSO. STO-609 (2g) was synthesised by Enzo Life Sciences (Exeter, UK) and was dissolved in DMSO or 200mM NaOH for cell and mouse treatment, respectively. PF-3758309 (PF-375) (Calbiochem, Hertfordshire, UK) was dissolved in DMSO. Mibolerone (Mib) (PerkinElmer, Buckinghamshire, UK) was dissolved in ethanol.

2.1.8 Mouse lines Mouse lines acquired: AMPKβ1 ‘knockout first’ from KOMP repository described in Chapter 4. Camkkβ-/- mouse model (global CaMKKβ deletion), global deletion of exon 5 generated by 62

(Peters et al., 2003). Ptenfl/fl/PB-Cre4+ (prostate-specific PTEN deletion) generated and characterised in (Wang et al., 2003). Novel mouse lines generated during this project: AMPKβ1fl/fl/Ptenfl/fl /PB-Cre4+(β1-/-PTEN-/-; prostate-specific AMPKβ1 and PTEN deletion), Camkkβ-/-/ Ptenfl/fl /PB-Cre4+ (CaMKKβ-/-PTEN-/-; global CaMKKβ deletion and prostate-specific PTEN deletion).

2.2 Methods

2.2.1 General methods used in all Chapters

2.2.1.1 Western blotting Western blot analysis was performed using either LI-COR or Wes automated systems and is indicated in the figure legend.

LI-COR

50μg-75μg of total protein lysate was boiled in sample buffer and protein resolved on NuPAGE Novex (4-12% or 10%) Bis-Tris Protein Gels by SDS-PAGE (180 V, 1.5 h) using MOPs running buffer (BioRad). Proteins were transferred to PVDF membrane (Immobilon-FL, Millipore) at 100 V for 1.5 h; subsequently membrane were blocked in a HST (high salt buffer) buffer containing 4% (w/v) BSA for a minimum of 1h at room temperature. Antibodies were diluted in 4% (w/v) BSA/HST buffer and incubated with the membrane overnight at 4oC. Membranes were washed in HST and then probed with the appropriate secondary antibody (Alexa Fluor, Invitrogen/LICOR IRye antibodies, see Section 2.1.3) diluted into 4% (w/v) skimmed milk powder (Oxoid)/HST buffer for 1 h at room temperature. Membranes were then washed before visualisation using the Odyssey Imaging System (LI-COR Biosciences). Quantification was performed using Odyssey infrared imaging (LI-COR Image Studio Version 2.0) software.

WES automated system (ProteinSimple)

In some cases, Western blot analysis was performed using a capillary-based automated system (http://www.proteinsimple.com/simon.html). Western blotting was performed using the standard manufacturer’s protocol using a primary antibody dilution of 1/200 (pACCSer79, pAMPKαThr172, AMPKβ1/2, tubulin and actin) or 1/50 (all other antibodies) and the secondary antibodies from ProteinSimple were used neat.

2.2.1.2 Kinase activity assays

AMPK Immunoprecipitation (for activity assays)

AMPK was immunoprecipitated from lysates before assaying for activity in cell and tissue lysates. Lysate of 100μg total protein was added to 10 μl of 50% protein-A sepharose (Sigma), 2 μl of a 1/10 dilution of Sip2 antibody (against AMPKβ) and made up to a volume of 200μl with Hepes lysis buffer or homogenisation buffer for cell and tissue lysate, respectively. Samples were vortexed at 4 °C for 1.5-3 h and then centrifuged at 13000g for 1 minute to pellet the immune complexes. The resulting pellet was washed once with cell lysis buffer and once with HGE. All remaining buffer was removed using a Hamilton pipette before use in an AMPK activity assay.

SAMS AMPK activity assay (Davies et al., 1989)

The activity of AMPK in the immune complexes was measured by the phosphorylation of the synthetic SAMS peptide (HMRSAMSGLVLKRR), by the incorporation of [γ-32P]-ATP (specific radioactivity approximately 200 cpm/pmol). The SAMS peptide is based on a sequence derived from the AMPK phosphorylation site in ACC. Immune complexes were added to 30μl SAMS assay mix (1 mM AMP, 1 mM SAMS peptide [HMRSAMSGLHLVKRR] (in house synthesis, MRC Peptide Synthesis Unit), HGE with 1% (v/v) Triton X-100), 10 μl HGE and 10 μl γ32P-

ATP/MgCl2 (1 mM γ32P-ATP -ATP (PerkinElmer), 25 mM MgCl2, 1 mM ATP), before incubating for 25 minute at 37°C. Immune complexes were then centrifuged at 13000g and 20 μl supernatant spotted onto P81 paper (Whatman). P81 paper was washed in 1% (v/v) orthophosphoric acid. The paper was then air-dried and placed in scintillation tubes and covered with 2 ml of scintillation fluid and placed in a Beckman scintillation counter (LS60000SC), which determined the counts per minute (CPM).

LKB1 and CaMKKβ activity assays

LKB1 and CaMKKβ activity was determined by a two-step assay that measured the in vitro activation of recombinant AMPKα1β1γ1 by immunoprecipitated LKB1 or CaMKKβ (Woods et al., 2003a). 50μg of lysate was incubated with either rabbit LKB1 (Denison et al. 2009) or mouse CaMKKβ antibodies (Table 2.1) for 2 h at 4°C. The resulting immune complexes were incubated with 800 ng bacterially expressed AMPKα1β1γ1 complex (Neumann et al. 2003) and 10 mM HGE, 100 μM ATP, 5 mM MgCl2, 2 mM DTT, with or without 2 mM CaCl2, 2 μM bovine brain calmodulin (Calbiochem) for CaMKKβ or LKB1 activation of AMPK respectively, for 10 min at 37°C. 200 ng of activated AMPK was then added to 30 μl SAMS assay mix (1 mM AMP, 1 mM SAMS peptide [HMRSAMSGLHLVKRR] (in house synthesis, MRC Peptide Synthesis 32 Unit), HGE with 1% (v/v) Triton X-100), 10 μl HGE and 10 μl γ32P-ATP/MgCl2 (1mM [γ P]ATP

(PerkinElmer), 25mM MgCl2, 1mM ATP) incubated for 15 minutes at 30°C, and 20 μl spotted

onto P81 paper. To measure non-specific binding of P81 paper, 200 ng activated AMPK was added to 30 μl SAMS blank mix (1 mM AMP, HGE with 1% (v/v) Triton X-100 in 1:2 ratio with 32 10 μl [γ P]ATP /MgCl2), incubated for 15 minutes at 30°C, and 20 μl spotted onto P81 paper. P81 paper was washed and activity counted the same as in the AMPK activity assay detailed above. Background incorporation into SAMS peptide in the absence of upstream kinase was subtracted from LKB1/CaMKKβ activity for all results.

2.2.2 In vitro studies (Chapter 3)

2.2.2.1 Mammalian Cell culture All prostate cancer cell lines (LNCaP, PC3, DU145 and 22RV1) were cultured in RPMI 1640 medium, GlutaMAX (Gibco-61870) supplemented with 10% foetal bovine Serum (FBS), 100 U/ml penicillin, and 100 μg/ml streptomycin. RWPE1 cells were cultured in keratinocyte serum-free medium (Gibco-17005) supplemented with epidermal growth factor and bovine pituitary extract (KGM), 100 U/ml penicillin, and 100 μg/ml streptomycin. MEFs were maintained in high glucose DMEM (4.5 g/L)(Gibco-41966), supplemented with 10% FBS, 100

U/ml penicillin, and 100 μg/ml streptomycin. All cells were maintained at 37°C and 5% CO2.

PC3 cells stably transfected with TetOnPLKO plasmid expressing inducible shRNA targeting AMPKβ1 (generously supplied by Almut Schulze (CRUK) were selected for at least 48h before being used in experiments by addition of fresh media containing G418 (3 μg/mL). shRNA induction was achieved by supplementing the media with 1μg/ml doxycycline for a minimum of 96 h.

When investigating the effects of androgen on androgen-responsive cell line LNCaP media was replaced with ‘starvation media’ consisting of phenol red-free RPMI supplemented with 5% charcoal-stripped FBS (First Link UK), 100 U/ml penicillin, and 100 μg/ml streptomycin at least 24h before being used in an experiment. LNCaP/Luc (AR-reporter) cells were selected for by supplementation of G418 (500 μg/ml) to the media. Selection was applied every second passage.

2.2.2.2 Primary AMPKβ1 Mouse Embryonic Fibroblast (MEFs) AMPKβ1+/- mice were crossed and MEFs were later harvested from E13.5 embryos. MEF preparations were genotyped and those wild-type and knockout for β1 maintained. MEF preparations were not used for experiments past passage 5.

2.2.2.3 Cell counting and apoptosis assay Cell count, viability and % apoptotic cells was calculated during passaging using the Guava ViaCount assay on the Guava easyCyte Flow Cytometer (Merck, Millipore). The ViaCount assay used a proprietary mixture of two DNA binding dyes to provide sensitive, accurate detection

of viable, apoptotic, and dead cells. This assay was performed according to the manufacturer’s protocol.

2.2.2.4 Cell treatment Cells were treated with pharmacological activators of AMPK including A-769662 compound (a kind gift from AstraZeneca) and 991 compound (a kind gift AstraZeneca, originally developed by Merck Sharp & Dohme Corp. and Metabasis Therapeutics, Inc). Cell lines and mice were treated with the CaMKKβ inhibitor, STO609 (Enzo Life Sciences). PC3 and 22RV1 cells were treated with the PAK1 inhibitor PF-3758309 (Calbiochem). LNCaP cells were treated with the synthetic androgen, Mibolerone (PerkinElmer). Specific conditions and concentrations are indicated in figure legends in the results section. A-769662, 991 and PF-3758309 were dissolved in DMSO; STO609 was dissolved in DMSO for cell line treatments and in 200mM NaOH for in vivo minipump dosing; Mibolerone was dissolved in ethanol. Cells were serum starved at least 4 h before treatment (unless otherwise stated).

2.2.2.5 Small interfering RNA (siRNA) silencing Cells were transfected with either scrambled, PAK1 (validated) and/or PAK2 (validated) Silencer Select siRNA oligonucleotides (Invitrogen) designed to specifically silence the target gene. Cells were transfected at 60% confluency with the indicated siRNA at 25 nM (final concentration) using the TransIT-TKO Transfection reagent (Mirus Bio) according to the manufacturer’s protocol. Cells were transfected 72 h ahead of use in experiments as maximum knockdown of the target protein was achieved at this time.

2.2.2.6 Cell lysis (Fast Lysis) Cells were washed with PBS and lysed into Hepes lysis buffer (50 mM Hepes pH7.4, 50 mM NaF, 5 mM NaPP, 1 mM EDTA, 10% (v/v) glycerol and 1% Triton X-100, 1 mM DTT, 4 μg/ml trypsin inhibitor, 0.1 mM PMSF, 1 mM benzamidine). Fast lysis was done as rapidly as possible to avoid AMPK activation. Cell lysates were centrifuged at 13000g for 15 minutes and the supernatant retained (cell lysate, mainly cytosolic proteins).

2.2.2.7 BioRad Protein Assay Protein concentration in cell lysates was determined using the Bradford assay (Bradford, 1976) with BioRad reagent and bovine serum albumin (BSA) standards (Hertfordshire, UK). This assay was run according to the manufacturer’s protocol. A microplate reader was used to measure absorbance at 595 nM (BioRad, Model 680).

2.2.2.8 BrdU Proliferation Assay Depending on cell line 5000-7000 cells were seeded per well in a 96-well plate and left to adhere for 24 h. Cell media was changed and fresh media added containing 50μM BrdU (Sigma) and drug (DMSO or 991 at indicated concentration). Cells were incubated for 16 h to allow BrdU incorporation. After 16 h cells were washed twice with PBS and then fixed used 66

using 4% (w/v) paraformaldehyde in PBS. Permeabilisation of the membrane was achieved by incubation with 0.2% Triton X-100 in PBS for 10 minutes, cells were then washed with PBS and blocked for 1 h with blocking solution (1% BSA/0.2% Gelatin Fish in PBS). Cells were then incubated for 30 minutes with a 1/2000 dilution of BrdU antibody, 20U/ul DNase1 (Sigma) and 1mM MgCl2 in blocking solution. Cells were washed 3 times with PBS and then incubated with 2ᵒ antibody (Goat anti-Mouse, Alexa Fluor® 488 conjugate) at a 1/500 dilution in blocking solution for 30 minutes. Cells were then washed a further 3 times and counter stained with DAPI (1 μg/ml) for 30 minutes. Image acquisition was achieved on the IN Cell Analyser 1000 Cell Imaging System (GE Healthcare, Life Sciences) using the 20×/0.45 Plan Fluor objective. Image acquisition was automated and 16 frames were taken per well. Images were analysed using the IN Cell 1000 Image Analysis Software.

2.2.2.9 Cell Cycle Analysis by Flow Cytometry Cells were treated with DMSO or 991 for 16 h and then trypsinised and counted using the Guava ViaCount assay. 1x106 cells in 0.5 ml were then added to 4.5 ml of ice cold 70% ethanol and left to fix overnight at 4oC. Cells were then washed twice with PBS and re-dissolved in 300 μl of Propidium Iodide (PI)/RNase Staining Solution (CST) for 30 minutes at room temperature in the dark before being analysed on BD LSR II Flow Cytometer (BD Biosciences).

2.2.2.10 Scratch assay (2D migration) Cells were plated in a 6-well plate and grown until confluent. Using a 1-10 μl pipette tip, a scratch was created in the confluent monolayer of cells. The cells were then washed twice with serum-free media (SFM) and fresh SFM containing the appropriate drug concentration (DMSO or 991 as indicated) was added. Six regions-of-interest were picked per well and images were taken every 30 minutes using an UltraVIEW Live Cell Imaging System (x20 objective) (PerkinElmer). Cells were maintained at 37°C and 5% CO2 for the duration of image acquisition. Quantification of the datasets generated was performed using software developed in collaboration with the CSC MRC microscopy facility. Briefly, the software developed calculated the area covered by cells based on a texture algorithm. As cells migrated to fill the scratch, the textured area increased and this was used as a read-out of cell migration.

2.2.2.11 xCELLigence assays Cells were serum starved overnight before performing xCELLigence assays. Depending on the cell line used 30 000-50 000 cells were plated per chamber. All assays were performed on the xCELLigence RTCA DP (ACEA Biosciences, Inc) and data analysed using the RTCA 2.0 Software. Cells (at 70-90% confluency) were trypsinised and once detached trypsinisation was stopped by addition of trypsin neutraliser solution (TNS, Clonetics) at a ratio of 1:1. SFM was then used to rinse flasks and added to trypsinised cells. Cells were then pelleted using centrifugation and resuspended in 1ml SFM and counted using the Guava ViaCount assay. Cell dilution was then calculated for 30 000-50 000 cells to be present in 50 μl of SFM. 67

Adhesion assay (E-plate, Cambridge BioScience)

Adhesion assays were performed using the single chamber E-plate with media containing 5% serum and DMSO or 991 at the concentration indicated. The rate of adhesion was measured by the increase in impedance (Cell Index) as cells adhered to the gold microelectrode. Adherence was measured every 2 minutes for 4 h after cells seeding.

Migration (3D migration) and Invasion assays (CIM-plate, Cambridge BioScience, UK)

Migration and invasion assays required the CIM-plate, with upper and lower chambers. The lower chamber was filled with 160 μl media containing 10% serum and DMSO or 991 (at 1x concentration indicated), whereas the upper chamber was filled with 50 μl of SFM and DMSO or 991 (at 2x final concentration). The serum gradient was left to equilibrate for 1 h at 37oC,

5% CO2. Cells in 50 μl of SFM were then seeded into the upper chamber, thereby diluting the 991 concentration in the upper chamber to 1x final concentration. Data acquisition was then started and readings were taken every 15 minutes for around 12 h. As cells migrated from upper to the lower chambers and adhered to the gold sensor microelectrode, migration was shown as an increase cell index and gave a direct measure of cell migration. The invasion assay was performed in the same way but the upper chamber was first coated with Matrigel (BD Bioscience) using a 1/40 dilution (1 Matrigel:40 SFM) and left to set at 37oC for 4 h. Readings were taken every 15 minutes for 48 h.

2.2.2.12 Lipid synthesis determination Labelled sodium acetate [1-14C] acetic acid sodium salt (PerkinElmer) 50.5 mCi/mmol was dissolved in 1 ml ethanol (1mCi/ml). Cells were grown until 70% confluent and serum-starved overnight. Unlabelled sodium acetate was added to SFM to final concentration of 2 mM (0.1 ml of 1 M NaAc in 50 ml media) and then spiked with 14C- labelled acetate (3 ul for every ml of media, 3 uCi/ml). To this labelled media DMSO or 991 (at concentration indicated) was added o to cells and incubated for 6 h at 37 C, 5% CO2. Cells were washed 3 times with PBS and all liquid removed, cells were then scraped cells into ice cold ethanol, vortexed and centrifuged to remove cell debris. A duplicate plate was lysed in Hepes lysis buffer for protein determination and normalisation of results. The lipid extracts were then added to scintillation fluid and placed in a Beckman scintillation counter (LS60000SC) and 14C-acetate incorporation determined.

2.2.2.13 Immunoprecipitation in investigation of AMPK and PAK interaction 400 μg of cell lysate was incubated with antibody raised to AMPKα2 (sheep, Table 2.1) and 10 μl of 50% protein-G sepharose (Sigma), volume was made up to 200 μl with lysis buffer. Samples were vortexed at 4 °C for 2 h and then centrifuged at 13000g for 1 minute and the supernatant removed. The resulting pellet was washed 3 times with HGE and remaining buffer was removed using a Hamilton pipette before addition of sample buffer and Western blot analysis. 68

2.2.2.14 AR reporter luciferase assay Media was removed by aspiration and cells were lysed in Reporter lysis buffer (Promega). This was described as slow lysis. Plates were placed at -80oC for a minimum of 2 h. Lysate was then mixed in a OptiPlate (96-well, PerkinElmer) with luciferin substrate and light emission measured using the Steadylite luciferase assay kit (PerkinElmer) using a Topcount luminometer (Packard Instrument Co., Meriden, CT, USA). Readings were normalised to protein concentration.

2.2.3 In vivo Studies (Chapters 4 and 5)

2.2.3.1 Genotyping DNA from ear snip or tail tip biopsies was extracted by boiling at 100oC with 100 mM NaOH for 15 minutes, followed by addition of 1 M Tris pH 8 to adjust the pH to 8. DNA was amplified by polymerase chain reactions (PCR) using Biomix Red (2x BioMix Red mastermix containing Taq DNA polymerase, Bioline) and the appropriate primers (Table 2.2). Following amplification, DNA was analysed by agarose gel electrophoresis at 120 V for 30 min on 2% or 3% (w/v) agarose gel depending on product size. Bands were visualised by ultra-violet light exposure (Gene Genius, Syngene).

2.2.3.2 Hematocrit levels The hematocrit measures the volume of erythrocytes compared to the total blood volume (erythrocytes and plasma). Tail vein blood was collected in heparinised microvettes (Sarstedt) and a sample of heparinised blood taken into a glass capillary and sealed. Samples were then spun in microcentrifuge at 3000g for 20 min at 4°C to separate erythrocyte and plasma fractions. The volume of erythrocytes relative to total blood was then calculated.

2.2.3.3 Erythrocyte osmotic resistance assay Equal numbers of erythrocytes were mixed with NaCl solutions of various concentrations and incubated for 45 minutes at room temperature. After centrifugation at 2000 g for 5 min, the absorbance of the supernatant was determined at 540 nM. The A540 of each sample in water was taken as 100% hemolysis, and readings of the same sample in the solutions of various osmolarities were normalised to this value.

2.2.3.4 Serum cytokine measurements For serum cytokine measurements, cardiac puncture was performed on anaesthetised mice (using isoflurane, Abbott) at harvest and blood transferred into heparinised 1.3 ml microvettes (Sarstedt). Blood was centrifuged to obtain the plasma fraction and frozen at

69 -80oC prior to analysis. Serum levels of the indicated cytokines were determined by the Mouse Biochemistry Laboratories, Cambridge, UK.

2.2.3.5 Osmotic minipump studies STO609 dosing study

Osmotic Minipumps (Alzet model 2004, Charles River, UK) were filled with either 0, 6, 15 and 30 mg/ml STO609 dissolved in 200 mM NaOH. Addition of STO609 lowered the pH of 200 mM NaOH to pH 12.8, making it compatible with Alzet minipump specifications. The pH of all solutions was adjusted to pH 12.8 (with HCl) and minipumps were filled according to manufacturer’s instructions. Minipumps were filled at least 48 h before surgery and submersed in PBS at 37oC. This allowed the pump to begin operating before implantation, and minimised the chance of an occlusion or clot forming in the catheter. C57BL/6J male mice (3 per condition) were ordered from Charles River (Harlow, UK) aged 10 weeks. Mice were anesthetised with isoflurane (Abbott) and the interscapular region shaved, and an osmotic minipump inserted subcutaneously. Osmotic minpumps used for pharmacodynamics were model 2004, with a flow rate of 0.25 µL/hr and 4 week duration.

Investigation of the effect of STO609 on prostate cancer progression in PTEN model

Male Ptenfl/fl/PB-Cre4+ mice and wild-type littermates (bred in-house) and the basic procedure was performed as above in the preliminary dosing study. However, osmotic minipumps were filled with either 30 mg/ml STO609 dissolved in 200 mM NaOH or 200 mM NaOH (pH adjusted to 12.8 with HCl) and Alzet model 2006 (Charles River, UK) was used. These osmotic minipumps had a longer 6 week duration (flow rate of 0.15 µL/hr). Mice were treated from ages 13 weeks to 19 weeks. This timespan was chosen as there is as a large increase in prostate weight in the PTEN-null prostate over this period.

2.2.3.6 STO609 Pharmacodynamic Measurements Minipumps containing varying concentrations of STO609 were inserted into mice and 2 weeks after surgery blood samples (tail vein bleeds) were collected into heparinised microvettes (Sarstedt). Blood was centrifuged to obtain the plasma fraction and frozen at -80oC before analysis. Tissue samples were collected at harvest. Samples were analysed by Donna Smith (Head of PK/Bioanalytics Core Facility, Cancer Research UK Cambridge Institute, UK). The protocol for STO609 detection using mass spectrometry in plasma and tissue samples had been established previously (Massie et al., 2011). Bioanalysis was carried out by protein precipitation of plasma followed by detection using a STO609-specific LC-MS/MS method with an assay range of 1–1000 ng/ml. Tissue samples were homogenised with 4 v/w

50% CH3CN (aq). The assay range for STO609 in prostate homogenate was 4–4000 ng/g.

2.2.3.7 Tissue Lysate Preparation Tissues were snap-frozen in liquid nitrogen immediately following dissection. All tissues were homogenised in homogenisation buffer (50 mM Tris, pH 7.4, 50 mM sodium fluoride, 5mM sodium pyrophosphate, 1 mM EDTA, 250 mM sucrose, 1 mM dithiothreitol, 4μg/ml trypsin inhibitor,0.1 mM phenylmethylsulfonyl fluoride, 1 mM benzamidine) using the TissueLyser II (Qiagen, Manchester, UK). Tissues were homogenised in x10 (v/w) homogenisation buffer at 30 Hz for 2-3 minutes. Adaptor plates were stored for 1 h at -20oC before homogenisation to keep lysate cold. Supernatant was collected following centrifugation at 13000g at 4°C for 15- 20 min.

2.2.3.8 BCA Protein Assay Due to the higher protein concentrations in tissue homogenates compared to cell lysates, protein concentration was determined using the Pierce BCA Protein Assay Kit with a larger linear concentration range (ThermoFisher) compared to the BioRad Bradford assay. This assay used bovine serum albumin (BSA) standards and was run according to the manufacturer’s protocol. A microplate reader was used to measure absorbance at 562 nM (BioRad, Model 680, Hertfordshire, UK)

2.2.3.9 Sample processing and HE staining Upon dissection prostates were placed in 4% paraformaldehyde for 24-48 h at 4oC , then transferred first to PBS (2x 15 minutes), 50% (30 minutes to 1 h) then 70% ethanol. Samples were stored in 70% ethanol before processing. Tissue processing and wax embedding were outsourced to Lorraine Lawrence (Leucocyte Biology Histology Unit, Imperial College, London). HE staining and 4 μM sections were cut for immunohistochemical staining.

2.2.3.10 Immunohistochemistry Tissue sections were rehydrated by 15 minutes in each of the following solutions: Citra-Clear

(Xylene substitute, StatLab) (x2), 100% ethanol (x2), 70% ethanol and dH2O. Antigen retrieval was achieved using the antigen retriever 2100 (Aptum) on the standard 40 minute retriever cycle with sodium citrate buffer (10mM sodium citrate, 0.05% Tween 20, pH 6.0). Sections were washed with PBS and incubated for 20 minutes with 0.3% H2O2 diluted in methanol, washed and then blocked with 10% normal goat serum (Sigma) in PBS. Primary antibodies were diluted in 1% normal goat serum as required (Table 2.1) and incubated with tissue sections overnight at 4oC in a humidified chamber. The following day, sections were washed with PBT and incubated with 1/200 biotinylated goat secondary antibody (Vector Laboratories) diluted in PBS for 1h at room temperature in a humidified chamber. Samples were then washed with PBT and incubated for 30 minutes with avidin-biotin complex (VECTASTAIN Elite ABC Kit (Vector Laboratories)) according to manufacturer’s instructions. Slides were washed with PBS and then stained using the DAB Substrate Kit (Vector Laboratories) according to manufacturer’s instructions. DAB solution was left on sections for

3-10 minutes depending on the primary antibody used. Slides were left in running tap water for 10 minutes and sections counterstained with Gill’s haematoxylin (Sigma). Sections were then dehydrated in the following solutions for 15 minutes each; 70% ethanol, 100% ethanol (x2), and Citra-Clear (Xylene substitute, StatLab) (x2). Slides were then mounted using DPX mountant (Sigma). Images were captured using a Zeiss upright microscope (Axiophot, Zeiss) using either x10, x20 or x40 objectives.

2.2.3.11 qPCR Primer design Specific primers for each target mRNA were designed using www.ncbi.nlm.nih.gov/tools/primer-blast and are shown in Table 2.3. All qPCR primers were designed to cover an exon-exon junction to ensure results were not skewed by genomic DNA contamination. Primer sets were produced by Sigma and resuspended in ddH2O to 100μM. Stock solutions were stored at -20oC and diluted to 5μM for use in qPCR reactions.

2.2.3.12 RNA extraction and reverse transcription Tissue was snap frozen in liquid nitrogen immediately following dissection. 1ml TRIzol reagent (ThermoFisher) was added per 100mg of tissue and homogenised using the Tissue Lyser II (Qiagen) at 30 Hz for 2-3 minutes and left at room temperature for 5 minutes. Chloroform was added (200 μl chloroform per 1 ml TRIzol) used and the cells were inverted for 15 seconds before a further 3 minute incubation. Mixture was then spun at 12000 g for 15 minutes at 4oC to separate the RNA fraction in the upper aqueous phase. Absolute ethanol was added (0.53% v/v) to the upper aqueous phase before adding this to the RNeasy column (RNeasy Kit, Qiagen). The column was then washed with Buffer RW1 and Buffer RPE before elution of the RNA in RNAse free water (according to manufacturer’s protocol). Samples were stored at -80°C before use. The concentration and purity of RNA concentration was determined using the NanoDrop ND-1000 (ThermoFisher Scientific) spectrometer for an absorbance at 260 nm. A minimum absorbance ratio of 1.8 measured at 260/280 nm and 260/230 nm was set for all samples. Synthesis of cDNA was carried out with equal amounts of total RNA (3-4 µg) for each sample using SuperScript II reverse transcriptase (Invitrogen) according to manufacturer’s instructions in the presence of 200ng random primers (Promega) and 500µM mixed dNTP (10 mM mix, Sigma).

2.2.3.13 Quantitative Real-Time PCR (qPCR) To check primer linearity, 5 μl cDNA from each sample were pooled and serially diluted to give a standard curve for each target mRNA primer set. Satisfactory melting curves were obtained for all primers used. To quantify gene expression, cDNA (5 μl of a 1/20 dilution) was added to 10 μl 2x SensiMix SYBR-HiROX (Bioline), 1.6 μl forward and reverse primer mix (5μM) and ddH2O to a total reaction volume of 20 μl. The qPCR plate was analysed using an Opticon thermal cycler (DNA Engine Opticon, BioRad) with Opticon monitor software to generate C(t) values for each reaction. All samples were analysed in duplicate with C(t) values measured in

the exponential phase of the PCR reaction (OpticonMonitor Analysis Software Version 3.1, BioRad), and relative expressions calculated using the 2-ΔΔCt method (Schmittgen 2008).

2.2.3.14 Mass spectrometry Four separate samples per condition were pooled and 65 μg of protein resolved by SDS-PAGE. Protein bands were visualised by staining with Simply Blue Stain (Invitrogen) according to the manufacturer’s protocol. Samples were analysed using mass spectrometry by Peter Faull, CSC MRC proteomics unit. Briefly, each lane of the gel was divided horizontally into four sections based on molecular weight and excised using a scalpel and processed. Proteins were digested using trypsin overnight and applied to a Thermo Scientific LTQ Orbitrap XL hybrid FTMS (Fourier Transform Mass Spectrometer) operating in positive polarity. Raw data files were analysed using MaxQuant software (www.maxquant.org), data were searched against the Uniprot mouse database (up-to-date at the time of analysis).

2.3 Statistical analyses All data in figures presented as mean ± standard error mean (SEM). Differences between datasets were analysed by one-tailed Student’s t tests with statistical significance defined as a p value of 0.05 or less. When appropriate to compare three or more data sets, a one-way analysis of variance (ANOVA) was used, followed by Tukey’s range test to measure significance between means. All statistics were performed using GraphPad Prism 5 software.

3. Investigation of the effect of pharmacological activation of AMPK in prostate cancer cells

3.1 Introduction

In the last decade or so there has been growing interest into the role of AMPK in prostate cancer. Current commercially available AMPK activators such as metformin and AICAR are known to have AMPK-independent effects, calling into question whether the effects seen using these activators are AMPK-dependent (Miller and Birnbaum, 2010, Sullivan et al., 1994). Despite the majority of work showing that AMPK activation negatively impacts tumour development and growth, a number of more recent studies have inferred the contrary (Frigo et al., 2011, Massie et al., 2011, Jeon et al., 2012). Resolving these apparently contradictory results is paramount to gain a better understanding of the disease, and ultimately necessary to develop new and effective therapeutic strategies to prevent the mortalities associated with this prevalent disease. The following chapter of work addresses this by use of a potent and specific AMPK activator, 991 (Xiao et al., 2013). This small molecule activator of AMPK is currently not commercially available. The effects of 991 were characterised in a panel of prostate cancer cell lines using a number of phenotypic readouts: cell proliferation, migration, invasion, adhesion and lipid synthesis. AMPK knockout cells and shRNA studies were used to validate that results seen using 991 were AMPK-dependent.

The cell lines used in this chapter are detailed in Table 3.1. RWPE1 represents a non- malignant prostate epithelial cell line, whereas LNCaP, DU145, PC3, 22RV1 all represent prostate cancer cell lines with varied androgen responsiveness and metastatic potentials. Of note, the DU145 cell line does not express the AMPK upstream kinase, LKB1.

RWPE1 LNCaP DU145 PC3 22RV1 Source Normal Lymph node, Brain, Bone, Derived from a xenograft

prostate metastasis metastasis metastasis propagated in mice after

castration-induced regression and relapse of the parental xenograft (Sramkoski et al., 1999). LKB1 + + - + + CaMKKβ + + + + + p53 + + + - + PTEN + + + - + AR + + - - + Migratory - + ++ ++ +++ potential Invasive - - - + ++ potential Table 3.1 Key characteristics of the human prostate cell lines used in this study. Five different prostate cell lines were used in this study and were purchased from the ATCC. This site provides information on the source and AR (androgen receptor) status of each cell line (as listed).74 p53 and PTEN status were determined in (Skjøth and Issinger, 2006). The status of each cell line for the AMPK upstream kinases, LKB1 and CaMKKβ, (shown in bold) was confirmed experimentally in this study (see Figure 3.2). Migratory and invasive potentials were determined experimentally during the course of this study using scratch/xcelligence migration assays and xCelligence invasion assays.

3.2 Effect of AMPK activation on different phenotypic readouts of cancer cells

3.2.1 Characterisation of the AMPK isoforms in prostate cell lines

There are multiple isoforms of each of the three AMPK subunits (α1, α2, β1, β2, γ1, γ2, γ3); in turn there are 12 possible forms of AMPK complex. These isoforms have tissue specific expression patterns, therefore it was important to first characterise the predominant AMPK isoform(s) expressed in the prostate cell lines. To address this, all prostate cell lines were grown in serum-supplemented media until 90% confluent and cell lysates were then generated for subsequent analysis by Western blotting. Western blots were probed with antibodies raised against AMPKα1, α2, β1 and β2 subunits. Recombinant α1β1γ1 and α2β2γ1 complexes were also run to act as controls to show antibody specificity and sensitivity. Actin was used as a loading control. The commercially available AMPKγ antibodies at the time of this characterisation were not sensitive enough to detect endogenous levels of AMPKγ. AMPKγ1 is ubiquitously expressed and subsequent work showed AMPKγ1 to be expressed in the mouse prostate (Figure 4.16).

The major AMPK subunits expressed in all prostate cells (both normal and cancer) was found to be AMPKα1 and AMPKβ1 (Figure 3.1). A small amount of AMPKβ2 was also detected. Thus the predominant AMPK complexes expressed in prostate cells are α1β1γ, followed by α1β2γ complexes. AMPKα2-containing may be present but were below the detection limit of the antibody.

Figure 3.1 AMPK subunit expression in prostate cell lines. Each cell line was cultured until 90% confluent in the presence of serum, rapidly lysed and subject to Western blot analysis. Western blots were probed with antibodies against AMPKα1, α2, β1, β2 and actin was used as a loading control. Recombinant α1β1γ1 and α2β2γ1 (1 ng) were run to show antibody specificity and sensitivity. Western blots were developed using the LI-COR imaging system. 75

3.2.2 Characterisation of the AMPK upstream kinases in prostate cell lines

Next, the activity of the AMPK upstream kinases was characterised. AMPK has two upstream kinases, LKB1 and CaMKKβ. Together these are responsible for AMPK phosphorylation on Thr172 on the AMPKα subunit leading to AMPK activation. LKB1 is considered the main upstream kinase of AMPK, leading to activation in times of energy stress, in contrast activation by CaMKKβ is dependent on increases in intracellular calcium (see Introduction, Section 1.2.2)(Carling et al., 2008). Due to the growing literature on the roles of these kinases in cancer implicating LKB1 as a tumour suppressor and CaMKKβ as a potential oncogene, it was important to characterise the activity of these kinases in the different cell lines before investigating the effects AMPK activation (Shackelford and Shaw, 2009, Frigo et al., 2011, Massie et al., 2011).

RWPE1, LNCaP, PC3 and 22RV1 cell lines all express both AMPK upstream kinases, whereas DU145 cells only express CaMKKβ and are negative for LKB1 (Table 3.1). LKB1 and CaMKKβ activity was assayed from 50 μg of cell lysate in an AMPK-linked kinase assay. Briefly, LKB1 and CaMKKβ were immunoprecipitated from the cell lysate and the resulting immune complexes incubated with recombinant unphosphorylated AMPK. LKB1 and CaMKKβ- dependent AMPK phosphorylation and concomitant activation was then measured using the SAMS-kinase assay (see Materials and Methods, Section 2.2.1). The results of these assays are shown in Figure 3.2.

Figure 3.2 LKB1 and CaMKKβ activity in prostate cell lines. 50 μg of cell lysate from RWPE1, LNCaP, DU145, PC3 and 22RV1 cell lines was assayed for LKB1 A) and CaMKKβ B) activity in an AMPK-linked kinase assay. Results are presented as AMPK specific activity (nmole/min/mg). Data are means ± SEM (n= 3). * p<0.05; one-way anova relative to RWPE1.

Apart from DU145 cells, which as expected had no LKB1 activity, none of the prostate cancer cell lines were found to have significantly different LKB1 activity relative to RWPE1 cells. Interestingly, however, there was a trend for all the prostate cancer cell lines to have increased CaMKKβ activity, with LNCaP and 22RV1 cell lines having significantly higher CaMKKβ activity. This result is in line with the published literature indicating CaMKKβ gene expression to be upregulated in prostate cancer compared to normal prostate tissue (Frigo et al., 2011, Massie et al., 2011). Intriguingly, the cell lines that had a significant increase in CaMKKβ activity are both positive for AR protein expression. The recent discovery that CaMKKβ expression is androgen dependent might help explain this observation. Previous studies on CaMKKβ overexpression rely heavily on gene expression data; however these data support that gene expression correlates with CaMKKβ activity.

3.2.3 Dosing studies using 991 in prostate cancer cell lines

The direct AMPK activator, 991, is a highly selective (Xiao et al., 2013), making it an invaluable tool to investigate the role of AMPK in prostate cancer and assess the potential therapeutic benefit of AMPK activation.

AMPK was found to be potently activated in a dose-dependent manner by 991 in all cell lines used and the results for DU145, PC3 and 22RV1 cell lines are shown in Figure 3.3A-C (similar results were also achieved for RWPE1 and LNCaP cell lines). AMPK activation by 991 was found to robustly lead to increased phosphorylation of AMPK on Thr172 (pAMPKThr172) and concomitant phosphorylation of ACC at Ser79 (pACCSer79), a well characterised downstream substrate of AMPK (Figure 3.3D, E). AMPK activity assays were used to accurately quantify the fold increase in AMPK activity upon 991 treatment (Figure 3.3A-C). All cell lines were activated 3-5 fold over a concentration range of 5-20 μM 991. In Figures 3.3A-E cells were treated for 16 h as this was the timeframe required for most of the phenotypic assays that were performed. However, it was important to characterise how AMPK activation by 991 varied over time. To do this PC3 cells were treated with varying concentrations of 991 for 3 h, 16 h and 24 h and AMPK activation assessed as before (Figure 3.3F). There was not a large amount of variation in AMPK activation over time, but there was a trend for higher fold AMPK activation at the earlier 3 h time point. These data are reassuring as they show that AMPK can be persistently activated over a reasonable time period for downstream assays to be performed.

991 was found to be at least 10-fold more potent than a commercially available direct AMPK activator, A-769662 (Sanders et al., 2007). A comparison between A-769662 and 991 was performed in PC3 cells over a 16 h time frame. It was found that A-769662 required concentrations of 200 μM to reach similar levels of AMPK activation as 20 μM (Figure 3.3G). Being able to use lower concentrations of 991 to achieve similar AMPK activation significantly reduces the potential for off-target effects. 77

All cell lines were treated for 16 h with 0 and 20 μM 991 (in full and serum-free medium) and cell viability was determined using the Guava easyCyte flow cytometer. In all cell lines tested there was no significant difference in cell viability between treated and non-treated conditions (all >94% cell viability). To confirm this PC3 cell lystates treated with 991 for 3 h and 24 h were subject to Western blot analysis and probed with antibodies against apoptotic markers (increased cleaved-PARP and decreased pro-caspase 3 are indicative of apoptosis). The results shown in Figure 3.3H show that there is no change in cleaved-PARP or pro-caspase 3 upon 991 treatment at either timepoint. This further shows that 991 treatment using this concentration range does not induce apoptosis over this timeframe; an important observation as it means apoptosis is not a contributing factor when discussing results in later sections.

A B C

D E

F G

Figure 3.3 Effect of 991 on AMPK activity in prostate cancer cell lines. A-C) DU145, PC3 and 22RV1 cells were treated with varying concentrations of 991 in serum-free medium for 16 h and rapidly lysed. AMPK activity in immuno-complexes isolated from lysates of cells treated with varying concentrations of 991 was measured using the SAMS-kinase assay. Results were normalised to the DMSO control condition. D-E) Western blot analysis of DU145 and 22RV1 cell lysates grown as described for parts A-C). Western blots were probed with antibodies raised against pACCSer79, pAMPKαThr172 and actin was used as a loading control. Western blots were developed using the LI-COR imaging system. F) PC3 cells were treated with 0, 5, 10 and 20 μM 991 in serum-free medium for 3 h, 16 h and 24 h and rapidly lysed. AMPK activity was then determined using the SAMS-kinase assay. Results were normalised to the DMSO control condition at 3 h. G) PC3 cells were treated with 0, 50, 100, 200 μM A-769662 and 20 μM 991 for comparison for 16 h in serum-free medium and rapidly lysed. AMPK activity was then determined using the SAMS-kinase assay. Results were normalised to the DMSO control condition. H) Cell lysates from 3 h and 24 h as used in part F were subject to Western blot analysis to look at markers of apoptosis. Western blots were probed with antibodies against cleaved-PARP and pro-Caspase 3 (markers of apoptosis), pACCSer79(marker of AMPK activity) and actin as a loading control. Western blots were developed using the LI-COR imaging system.

3.2.4 AMPK activation inhibits cell proliferation

Every cancer cell has undergone a number of mutation events that have propelled it and its progeny into uncontrolled expansion and invasion. One of these is dysregulated cell proliferation, taken together with obligate compensatory suppression of apoptosis needed to support it, provides a platform to support further neoplastic progression. Adroit targeting of these critical events will prove to have potent and specific therapeutic consequences. Given this, the effect of AMPK activation on prostate cancer cell proliferation was investigated in LNCaP, DU145, PC3 and 22RV1 cell lines as these represent a panel with varying propensities for migration and invasion with different driver mutations and androgen responsiveness. This allows the effect of AMPK activation on proliferation in different genetic landscapes to be determined, thereby testing the robustness of AMPK activation as a therapeutic strategy.

Cell proliferation was assessed using the BrdU cell proliferation assay over a 16 h timeframe. Briefly, this assay works on the principle of detecting 5-bromo-2’-deoxyuridine (BrdU) incorporated into cellular DNA during cell proliferation using an anti-BrdU antibody. Cells were cultured in medium containing BrdU and this thymidine analogue was incorporated into the newly synthesized DNA of proliferating cells. Cells were then fixed and stained for BrdU incorporation using immunofluorescence and nuclei counter-stained using DAPI. The percentage of BrdU stained nuclei versus DAPI stained nuclei was used to give an accurate measure of cell proliferation. Images were taken using the IN Cell 1000 confocal microscope, and analysis was automated using Metamorph software (Figure3.4B). Since BrdU and DAPI stains are excited at different wavelengths, nuclei stained in both channels (proliferating) versus those only stained in the DAPI channel (non-proliferating) were easily distinguishable.

Initial preliminary studies were performed to optimise the growth conditions for subsequent analysis. The effect of growing the cell lines in full medium (FM, 10% serum) and serum-free medium (SFM) was determined. Prostate cancer cell lines were maintained in full medium and then switched to full medium or serum-free medium containing BrdU and assayed for proliferation. As expected all cell lines proliferated significantly less in the serum-free condition (Figure 3.4A). However, cell proliferation was still at a reasonable level in serum- free (at least 50% of cells had proliferated with 16 h). Given the fact that all cell lines still proliferated appreciably in serum-free medium, this growth condition was chosen for future experiments. Serum free growth conditions were deemed favourable as they remove the variability associated with the unknown concentrations of various growth factors and metabolites found in serum. In addition, using serum-free conditions was thought to leave a greater range to monitor proliferation over (especially relevant in DU145 cell line) as proliferation could not be monitored if it exceeded 100% using this method.

A Figure 3.4 Effect of serum in growth medium on proliferation. A) BrdU incorporation assay of DU145, PC3, 22RV1 cells over 16 h in media plus (FM, full media) and minus serum (SFM, serum free media). Data acquisition was automated using the IN Cell microscope and 16 frames were taken per condition and analysed as shown in B). Data are means ± SEM (n= 8, performed over 2 separate occasions). * p<0.05; ** p<0.01; *** p<0.005; t-test. B) An example of image analysis using Metamorph software from which A) B was generated. DAPI nuclear stain is present in all cells, whereas BrdU nuclear staining is present only in cells that have gone through cell division. % BrdU incorporation (proliferation) is the total number of nuclei stained in both DAPI and BrdU channels (green) divided by the total number of nuclei in the DAPI channel only (red).

In the previous section maximum AMPK activation was achieved in all cell lines using 20 μM 991 and so this concentration was used to investigate the effect of AMPK activation on proliferation. AMPK activation was found to significantly inhibit cell proliferation in all prostate cancer cell lines (Figure 3.5). AMPK activation was found to lead to a 30- 40% reduction in proliferation over just 16 h. RWPE1 cells did not proliferate significantly without serum and so were excluded from analysis. It follows that non-malignant cells that do not exhibit a proliferative phenotype will be less sensitive to this growth inhibition leading to the selective targeting of cancer cells.

[991] (μM)

Figure 3.5 Effect of 991 on cell proliferation. BrdU-incorporation proliferation assay of LNCaP, DU145, PC3, 22RV1 cells over 16 h in serum-free medium, plus and minus 20 μM 991. Data acquisition was automated using the IN Cell microscope and 16 frames were taken per condition and analysed as outlined in Figure 3.4B. Data are means ± SEM (n= 12, performed over 3 separate occasions). *** p<0.005; t-test.

An issue raised with previous published studies investigating the effect of pharmacological activation of AMPK in cancer is the potential for off-target effects. Therefore, it was important to show that the inhibition of proliferation using 991 is AMPK-dependent. To address this AMPK-deficient cells (mouse embryonic fibroblasts, MEFs) were generated from a global AMPKβ1-/- mouse line. AMPKβ1-/- MEFs were found to have a ~70% reduction in total AMPK activity compared to wild-type (WT) MEFs in basal conditions (Figure 3.6A,B). 991 treatment led to a dose-dependent increase in AMPK activity in WT MEFs; maximally reaching 3-fold of control with 20 μM 991. 991 also led to an increase in AMPK activity in AMPKβ1-/- MEFs and this is due to the remaining AMPKβ2 complexes. Notably, even at the maximum concentration (20 μM) of 991, AMPK activity in the AMPKβ1-/- MEFs did not reach that of WT MEFs. Please note, future studies make use of AMPK knockout MEFs, which were immortalised and kindly received from Benoit Viollet. However, at this stage of the project these cell lines had not been acquired.

Similar to the results generated in the prostate cancer cell lines, treatment of WT MEFs with 991 led to a dose-dependent decrease in proliferation maximally reaching ~35% compared to DMSO control cells (Figure 3.6C). In WT MEFs cell proliferation was found to be significantly deceased with 20 μM 991 treatment, however there was no significant decrease in proliferation upon 991 treatment in AMPKβ1-/- MEFs. These results support that the effect of 991 on proliferation is through AMPK activation. There was a trend for decreased

proliferation in AMPKβ1-/- MEFs upon 991 treatment but this could be explained by the remaining AMPKβ2-complexes which are still activated by 991 (Figure 3.6B). Interestingly, AMPKβ1-/- MEFs were found to have a small but significantly increased rate of proliferation compared to WT cells (Figure 3.6C). These results are consistent with AMPK being a negative regulator of cell proliferation, with activation and depletion of AMPK activity leading to decreased and increased cell proliferation, respectively.

A B

Figure 3.6 The effect of 991 on proliferation in WT and AMPKβ1-/- MEFs. A) WT and AMPKβ1-/- MEFs (3 preparations per genotype) were grown in medium containing 10% serum until 90% confluent, rapidly lysed and subject to Western blot analysis. Western blots were probed with antibodies raised against pAMPKαThr172, AMPKα1, AMPKβ1/2 and actin was used as a loading control. B) MEFs were grown in medium containing 1% serum and treated with 0, 5, 10 and 20 μM 991 for 16 h and rapidly lysed. AMPK activity in immuno-complexes isolated from lysates of cells treated was measured using the SAMS-kinase assay. Results were normalised to the DMSO control condition in WT MEFs. C) BrdU-incorporation proliferation assay using WT and AMPKβ1-/- MEFs treated with 0, 5 and 20 μM 991 cells over 16 h in medium containing 1% serum. Data acquisition was automated using the IN Cell microscope and 16 frames were taken per condition and analysed as outlined in Figure 3.4B. Results were normalised to the DMSO control condition in WT MEFs. Data are means ± SEM (n= 3, separate MEF cultures per genotype, as blotted in part A). * p<0.05; ** p<0.01; t- test. 83

Cell cycle analysis using propidium iodide showed that 16 h treatment with 20 μM 991 induced a significant enrichment in the G1 phase population in both PC3 and DU145 cells (Figure 3.7). This implies that AMPK activation is acting to block the cell cycle in G1 phase. In addition, it would be interesting to investigate whether 991 inhibits proliferation and/or effects cell cycle progression when cells are grown in the presence of serum.

A B [991] (μM)

Figure 3.7 Effect of 991 on cell cycle progression. Cells were treated with 0 and 20 μM 991 in serum-free medium for 16 h, trypsinised and counted. 106 cells were then added to 5 ml ice-cold 70 % ethanol and fixed overnight at 4oC, before being washed in PBS, re-dissolved in propidium iodide/ RNAase staining solution and analysed on the BD LSR II Flow Cytometer. The percent of cells in G1 and G2 phases are shown for PC3 cells (A) and DU145 cells (B). Data are means ± SEM (n= 3). ** p<0.01; t-test. C) shows an example of the raw data from which A) and B) were calculated.

3.2.5 AMPK activation inhibits cell migration in 2D scratch assays

Activation of AMPK was found to inhibit cancer cell proliferation and this would be beneficial in a cancer treatment. A patient rarely succumbs to the primary tumour, but instead from metastases where cancer cells have colonised a distal site. The cellular processes involved in metastasis are extremely complicated and its regulation is not fully understood (see Introduction, Section 1.1.2). The metastatic cascade requires global changes in gene expression to allow local migration, invasion, intravasation, circulation and extravasation of 84

tumour cells and finally angiogenesis and colonisation in the new site. The complexity of the metastatic cascade has led to the development of a plethora of 2D and 3D in vitro assays to model the various steps of metastasis under a more controlled environment. These assays have been invaluable in cancer research not only as tools to delineate the molecular events that underpin metastasis but also to enable drug screens and validation of therapeutic targets.

Cell migration is an integral part of metastasis that is required at virtually every step of the metastatic cascade. Tumour cells migrate either randomly in the presence of a homogeneous concentration of chemokines or directionally when the external cue is provided in the form of a gradient. One way to assay the former is using 2D scratch assays. In this assay, cells are grown to form a confluent monolayer and then scratched typically using a pipette tip to create a ‘wound’. The time it takes for the cells to migrate in and close this wound can be monitored using time-lapse microscopy. DU145, PC3 and 22RV1 migration was assayed using this method. Images were acquired every 15 minutes, typically over a 12 h time period. A drawback of this type of assay is that there is no way to delineate the contribution of cancer cell migration and proliferation to closing the scratch. In an attempt to reduce the confounding effect of proliferation, scratch assays were performed over a relatively short timeframe (12 h) using serum-free medium. Notably, neither RWPE1 nor LNCaP cell lines migrated appreciably during this time period and so were not subject to this analysis.

Datasets were acquired and submitted to the CSC MRC microscopy facility for quantification. In collaboration with the microscope facility a program was developed to analyse the images generated based on a texture recognition algorithm. The area occupied by cells was recognised as high ‘texture’ and the scratch area as low ‘texture’. Using this logic the program was able to generate a boundary around the cells (Figure 3.8D,E). Using this boundary the programme calculated the percentage of the field of view occupied by cells, which increased and the cells migrated to fill the scratch area. A drawback of this method of quantification was that if cell debris occupied the scratch area, the program would wrongly recognise this area as being occupied by cells, thereby miscalculating the cell-filled area. Hence, each dataset was manually verified after analysis to check the quantification was representative. If incorrectly quantitated, the dataset was excluded from further analysis.

AMPK activation using 991 led to a significant decrease in migration in all cell lines studied (Figure 3.8A-C). 22RV1 cell migration was inhibited to the greatest extent by 991 treatment, whereas 991 had the least effect in PC3 cells. The dose-dependency of this migration effect was tested in 22RV1 and PC3 cells, over a concentration range where AMPK is being increasingly activated. 991 treatment over this concentration range was found to lead to a dose-dependent decrease in cell migration (Figure3.8A, B). This is supportive of 991 working through AMPK, but not conclusive. To prove the effect of 991 on migration was AMPK- dependent, AMPKα1-/-α2-/- MEFs were utilised.

A B C

D E

Figure 3.8 Effect of 991 on migration in 2D scratch assays in prostate cancer cells 86 22RV1 (A), PC3 (B) and DU145 (C) cells were grown to form a confluent monolayer and scratched with a pipette tip creating a ‘wound’. Cells were washed and serum-free medium with the appropriate 991 concentration added. Cells were left for 1 h before data acquisition on the Ultraview microscope. Images were acquired every 0.5 h. Quantification of datasets was automated using software based on a texture algorithm. Migration was monitored for 12 h, images quantitated and then percentage migration calculated taking the 12 h timepoint in the 0 μM 991 (DMSO) condition to be 100%. Data are means ± SEM (n=5-8 depending on cell line). ** p<0.01; *** p<0.005; t-test performed on the 12 h timepoint, relative to 0 μM 991 condition. Typical images for 0 h, 6 h and 12 h timepoints for 22RV1 (D) and PC3 (E) cells are shown and the black boundary is that calculated by the software for quantification. The WT and AMPKα1-/-α2-/- MEFs received from Viollet’s group had been immortalised with retroviral transduction of SV40T and each represent a single stable cell line. The WT and AMPKα1-/-α2-/- MEFs were initially characterised. As expected the AMPKα1-/-α2-/- MEFs had no detectable AMPK activity as determined using Western blot analysis of AMPKα (Thr172) and ACC (Ser79) phosphorylation (Figure 3.9A). The WT MEFs showed a dose dependent increase in AMPK activity over the same concentration range as the non-immortalised MEFs made in- house.

Once validated these AMPK WT and knockout cells were used to validate whether the effects of 991 on migration were AMPK-dependent. In WT MEFs 991 treatment was found to lead to a dose-dependent decrease in migration, as seen in the prostate cancer cell lines (Figure 3.9C). This dose-dependent decrease in migration seen in WT MEFs was abolished in AMPK knockout MEFs (Figure 3.9D). There was a small decrease in migration with 991 treatment in AMPK knockout cells and this is due to an off-target effect of the drug. However, the decrease in migration in AMPK knockout cells found not to be dose-dependent and was of much smaller magnitude to that seen upon 991 treatment in WT cells. Therefore, despite this small off-target effect it can be concluded that AMPK activation does lead to decreased cell migration in 2D culture.

An interesting observation made during the course of these experiments was that the AMPK knockout cells tended to close the scratch faster than WT cells (Figure 3.9B). This was not further quantitated as the WT and AMPK knockout MEFs were generated from separate cell lines and likely have genetic differences other than the loss of AMPKα. When generating MEFs in-house, it was possible to negate these differences by generating a number of MEF preparations for each genotype.

A B

C D

Figure 3.9 Effect of 991 on migration in 2D scratch assays in WT and AMPK knockout MEFs. A) AMPK WT and α1-/-α2-/- MEFs were treated for 12 h with 0, 5, 10, 20 μM 991 in 1% serum medium, before being rapidly lysed and subjected to Western blot analysis using the ProteinSimple Wes automated Western blot system. Lysate was probed with antibodies raised against pACCSer79, pAMPKαThr172 and AMPKβ1 was used as a loading control. These WT and α1-/-α2-/- MEFs were grown to form a confluent monolayer and then scratched with a pipette tip to create a ‘wound’. Cells were then washed twice with serum-free medium and medium containing 1% serum with the appropriate 991 concentration was added. Cells were left for 1 h to equilibrate before data acquisition on the Ultraview microscope, where images were acquired every 30 minutes for 12 h; typical images are depicted in (B), where the cell boundary is marked with an orange line. Quantification of these data sets was automated using software based on a texture algorithm. Images were quantitated and then percentage migration calculated taking the 12 h timepoint in the 0 μM 991 condition to be 100%. The results for WT MEFs is shown in C) and for AMPK knockout MEFs shown in D). Data are means ± SEM (n=6 ).** p<0.01; *** p<0.005 (relative to 0 μM 991 condition); ### p<0.005 (relative to 5 μM 991 condition) one-way anova performed on the 12 h timepoint.

3.2.6 The effect on AMPK activation on migration in 3D xCELLigence assay

A major hurdle in studying migration is the lack of techniques that are quantitative, reproducible, and representative of the in vivo scenario. Scratch assays (as performed in the previous section) require repetitive visual evaluation of cells. This assay also fails to model migration in 3D and cannot be adapted to study cancer cell migration down a chemoattractant gradient. Boyden chambers allow 3D migration down a chemoattractant gradient to be interrogated and provide objective data but are labour intensive (as cells must be fixed, stained, and counted) and yield information for just a single time point.

ACEA have produced a cell migration plate (CIM-plate) that contains electronically integrated Boyden chambers that provide, in real-time and without the use of exogenous labels, quantitative data for migration with minimal hands-on time (Limame et al., 2012). These CIM- plates can also be coated with Matrigel to study cell invasion. Cells move from the upper chamber towards chemoattractant in the lower chamber through a membrane containing 8 μm pores and then adhere to gold impedance microelectrodes. The resultant change in impedance signal correlates with the number of cells attached to these electrodes, enabling collection of highly reproducible data on a timescale from minutes to weeks. For these reasons the xCELLigence RTCA DP instrument was purchased from ACEA and used to assess the effect of AMPK activation on prostate cancer cell migration down a chemoattractant gradient. For the purpose of statistical analysis, migration rate was calculated by the gradient of the linear portion of the migration curve (Figure 3.10A). Cell number and serum gradient were optimised for each cell line. Crystal violet staining was used for initial validation of the system and showed cell index was proportional to cells adhered to the microelectrode. This validation is outlined in Figure 3.10B.

Figure 3.10 DU145 case study: using the xCELLigence system to monitor and calculate migration rate. A) Depicts the raw output data produced by the xCELLigence system; cell index versus time. Cell index is a measure of cell impedance and is directly proportional to the number of cells adhered to the gold microelectrode in the lower chamber of the xCELLigence CIM-plate. Using the xCELLigence software it is possible to select a time period over which migration rate can be calculated (as shown). B) Initial validation of the xCELLigence system performed using crystal violet staining. Cells were fixed using 4% PFA and crystal violet solution then used to the stain the cells that had migrated from the upper to the lower chamber of the CIM-plate after treatment with 0 and 10 μM 991 after 16 h. Crystal violet staining was then quantified by lysing the cells in each chamber in 10% acetic acid and measuring absorbance at 595 nm.

Unlike previous assays, the response to AMPK activation by 991 using the xCELLigence migration assay was found to be cell type-specific (Figure 3.11). In DU145 and MEF cells, 991 led to a dose-dependent decease in migration, whereas in PC3 and 22RV1 cells, 991 treatment led to a dose-dependent increase in migration. Interestingly, this response was totally irrespective of the effect of AMPK activation on 2D migration. The reason for this difference in response to 991 in this assay is currently unknown. Although, an interesting observation was that AMPK activation tended to decrease migration in cell lines with lower invasive potential. Those cell lines, such as 22RV1, which are highly invasive, seemed capable of overcoming the inhibitory effects of AMPK activation on migration, conversely utilising it to increase migration rate.

A B C

WT AMPKα1-/-α2-/-

Figure 3.11 Effect of 991 on cell migration using the xCELLigence migration assay. xCELLigence migration assays were performed on DU145 (A), PC3 (B) and 22RV1 (C) cells treated with 0, 5 and 10 μM 991 and migration rate was calculated from the raw impedance output (as outlined in Figure 3.10) and represented as fold change relative to the untreated condition. The typical time period over which migration rate was calculated varied between cell lines and repeats but typically for DU145 and PC3 cells was t= 5-15 h, and for 22RV1 cells was t= 1-7 h, where t=0 corresponds to cell seeding onto the plate. Data are means ± SEM (n= 3-6). * p<0.05, *** p<0.005, t-test. D) AMPK WT and α1-/-α2-/- MEFs were subject to the xCELLigence migration assay and treated with varying concentrations of 991 and data analysed as above. Migration rate is represented as fold change relative to the untreated condition (calculated separately for each genotype). Data are means ± SEM (n= 3). * p<0.05, *** p<0.005, t-test.

AMPKα1-/-α2-/- MEFs could be used to test whether decreased migration in response to 991 treatment was AMPK-dependent. In WT MEFs treatment with 10 μM 991 inhibits cell migration by around 50%. There is a small reduction in migration rate in AMPK knockout MEFs (~15%); however it is nowhere near the magnitude to that seen in WT cells. This small off-target effect of 991 is similar to what was seen in the 2D migration assay. Thus, it can still be confidently concluded that AMPK activation does lead to a decrease in migration rate and 991 is mainly working through AMPK activation.

3.2.7 AMPK activation inhibits invasion in 22RV1 cells

Due to the observation that invasive cells (22RV1 and PC3) had potentially overcome the inhibitory effect of AMPK activation on migration, it was next decided to look at the effect of AMPK activation on invasion. To do this CIM-plates were coated with a thin layer of Matrigel to replicate the extracellular matrix through which cancer cells invade. The effect of AMPK activation on invasion was characterised in the most invasive cell line, 22RV1. Initial studies determined the optimal Matrigel concentration (1:40 dilution; the lowest dilution to give measurable rates of invasion). Invasion was measured over a period of 48 h and invasion rate calculated (t= 15 to t=32). Interestingly, unlike in the xCELLigence migration assays, 991 treatment led to a significant blunting of cell invasion (Figure 3.12). The increase in migration down a chemoattractant gradient in response to 991 did not lead to a corresponding increase in invasion down the same chemoattractant gradient.

Figure 3.12 Effect of 991 on cancer cell invasion xCELLigence assay. A) Cells were seeded in the upper chamber of a Matrigel coated CIM-plate at time 0 h. Invasion was monitored as an increase in cell index as the cells move from the upper to the lower chamber and adhered to the microelectrode. 2 wells were left uncoated to act as a control to monitor cell migration. Cells were treated with 0 and 10 μM 991 and invasion monitored over 48 h. B) Invasion rate was calculated from the raw impedance data (A) from t=14-32 h and represented as fold change relative to the untreated condition. Data are means ± SEM (n= 3).*** p<0.005, t-test.

3.2.8 The effect of AMPK activation on cell adhesion

In addition to cell migration and invasion, cell adhesion is a process that is crucial throughout the metastatic cascade. However, unlike migration and invasion, whether it is beneficial to inhibit or promote cancer cell adhesion depends on context. Initially, the loss of cell-cell adhesion allows malignant tumour cells to dissociate from the primary tumour and changes in cell-matrix interaction enables cancer cells to invade the surrounding stroma. Once in

circulation, adhesion between the cancer cell and surrounding stroma is extremely important for penetration of the endothelium and the basement membrane (extravasion).

A basic measure of cell adhesion was possible using a second type of plate on the xCELLigence instrument. The E-plate or adhesion-plate consists of a single chamber with gold impedance microelectrodes at the bottom. Similar to the migration assay, the attachment of cells to the electrode results in an increase in impedance signal (cell index) correlating with the number of cells attached. This assay is complicated by the fact that there is no way to delineate the processes of cell attachment and cell spreading. Therefore, in this section adhesion is used as a blanket term for cell attachment and spreading. Cell adhesion was easily distinguishable from cell proliferation as it occurs on a much faster timescale (less than 3.5 h, Figure 3.13, A,C). Similar to the migration assays, adhesion is presented as a rate calculated from the linear portion of the adhesion curve.

A B

C D

Figure 3.13 Effect of 991 on cancer cell adhesion xCELLigence assay. DU145 (A, B) and PC3 (C, D) cells were seeded in the E-plate at time 0 h and treated with 0, 10 and 20 μM 991 in medium containing 5% serum and cell index was monitored over 4 h. E-plates consist of a single chamber with the gold microelectrode at the bottom, as cells attach and spread (adhere), there is a corresponding increase in cell index. Raw cell index (impedance) data is shown in A and C. Adhesion rate was calculated from the raw impedance data and represented as fold change relative to the untreated condition. Data are means ± SEM (n= 3).* p<0.05, t-test.

991 treatment led to robust dose-dependent increase in adhesion rate maximally reaching 1.5 fold relative to DMSO control condition for all cell lines (Figure 3.13B,D shows results for DU145 and PC3). This increase in adhesion was also seen in WT MEFs but was abolished in AMPK knockout MEFs, showing it is an AMPK-specific effect of 991 (Figure 3.14).

WT AMPKα1-/-α2-/-

Figure 3.14 Effect of 991 on adhesion using xCELLigence assay in WT and AMPK knockout MEFs. AMPK WT and α1-/-α2-/- MEFs (characterised in Figure 3.9 A) were seeded in the E-plate and treated with 0, 10 and 20 μM 991 and cell index monitored for 4 h. Adhesion rate was calculated from the raw impedance data and represented as fold change relative to the untreated condition. Data are means ± SEM (n= 3).** p<0.01, t-test.

To verify this finding using a separate technique, a set number of DU145 and PC3 cells were seeded in a 96-well plate, in the presence and absence 991 and left to adhere for 1 h. After this time the media was removed, cells were washed with PBS and then fixed before DAPI staining. Cell number per well was then counted using the IN Cell microscope (as used in the BrdU proliferation assay). In agreement with data generated using the xCELLigence assay, the number of cells in 991 treated wells was significantly increased compared to DMSO control wells (Figure 3.15). Neither of these techniques is able to distinguish between the contribution of cell attachment or cell spreading. Although examination of the raw data in Figure 3.13A and C shows that maximum cell index is increased upon 991 treatment and this might imply a contribution from increased cell spreading, since if cells were simply slower at attaching one might imagine the same maximum cell index would be reached after a lag. No difference in cell spread could be seen in fixed cells plus and minus 991. This might have been for technical reasons associated with cell fixation, in conjunction with the subtlety of the effect.

A B

C D

Figure 3.15 Validation of the effect of 991 on adhesion. DU145 (A,C) and PC3 (B,D) cells were passaged and allowed to adhere to a 96-well plate for 1 h before washing, fixation with 4% PFA and DAPI staining. The number of cells adhered were imaged using the IN Cell microscope and counted using Metamorph software and the results for DU145 and PC3 cell shown in A and B, respectively. Representative images from with A and B were quantitated are shown in C and D, respectively. Data are means ± SEM (n= 4). ** p<0.01, *** p<0.005, t-test.

Banko et al, recently reported that AMPK has the potential to co-ordinately regulate both the kinase and a phosphatase involved in MLC2 phosphorylation (see Introduction, Section 1.2.3.3) (Banko et al., 2011). Phosphorylation of MLC2 at Ser19 leads to activation of myosin II and increased contractility (Matsumura, 2005). Changes in MLC2 phosphorylation could explain changes in migration and/or adhesion and was therefore interrogated using Western blotting in DU145 and PC3 cells plus and minus 991 and in AMPK-deficient MEFs (Figure 3.16). However, no significant change in MLC2 Ser19 phosphorylation was observed under any of the conditions tested. Interrogation of MLC2 phosphorylation was not possible in cells grown in serum-free conditions as levels fell below the detection limit of the antibody using the Li- COR imaging system. Furthermore, pMLC2Ser19 levels were often found to be variable and showed poor reproducibility between repeats.

A B

Figure 3.16 Investigation of whether AMPK activity effects MLC2 phosphorylation. A) DU145 and PC3 cells were treated with 0, 10 and 20 μM 991 in medium containing 5% serum for 16 h and rapidly lysed and subject to Western blot analysis. Western blots were probed with antibodies against pACCSer79, pAMPKαThr172 (for interrogation of AMPK activation); pMLC2Ser19 and total MLC2; actin was used as a loading control. Western blots were developed using the LI-COR imaging system. (Media was supplemented with 5% serum due to issues with detection of pMLCSer19 in serum-free conditions). B) Quantification of blots shown in A) to give a ratio of pMLC2Ser19 expression to total MLC2 expression for each condition. Data are means ± SEM (n= 3). C) WT and AMPKβ1-/- MEFs (3 preparations per genotype) were grown in medium containing 5% serum, rapidly lysed and subject to Western blot analysis. Western blots were probed with antibodies raised against pAMPKαThr172, AMPKβ1/2, pMLC2Ser19 and total MLC2; tubulin was used as a loading control. Western blots were developed using the LI-COR imaging system. D) Quantification of blots shown in C) to give a ratio of pMLC2Ser19 expression to total MLC2 expression in AMPK WT and AMPKβ1 MEFs. Data are means ± SEM (n= 3 per genotype).

3.2.9 AMPK activation inhibits prostate cancer cell lipid synthesis

Given the mounting evidence for the role of lipid synthesis in cancer progression including the processes of proliferation, migration and invasion; lipid synthesis was measured using 14C- acetate incorporation over a 6 h time period plus and minus 991 in PC3 cells (Baenke et al., 2013, Suburu and Chen, 2012). AMPK activation was found to significantly down regulate lipid synthesis to less than half of that in DMSO control cells (Figure 3.17).

incorporation) C- C- 14 (Fold control) Lipid synthesis ( synthesis Lipid

Figure 3.17 Effect of 991 on lipid synthesis (14C- incorporation). PC3 cells were treated with 0 and 10 μM 991 and incubated with 14C-acetate for 6 h. After incubation, lipids were extracted in a set volume of ethanol. 14C-acetate incorporation into cellular lipids was quantitated by scintillation counting and normalized to protein content; results are given as fold change relative to the untreated condition. Data are means ± SEM (n= 3).**** p<0.001, t-test.

3.3 Investigation of increased migration in response to 991 treatment

In Section 3.2.6 cancer cell migration down a chemoattractant gradient was investigated using the xCELLigence system. Migration in response to 991 was found to either decrease or increase depending on the cell line used. This result was of interest for 2 reasons a) the differences observed within a cell line in 2D and 3D migrations assays and between cell lines within the 3D assay and b) the therapeutic implications if AMPK activation were to increase cancer cell migration. In response to a) this shows the importance of the assay and cell line used to test these processes and may help to explain some of the apparent contradictory results present in the literature. From Section 3.2 it would appear that AMPK activation would be largely beneficial in stopping prostate cancer progression. However, if AMPK activation were to have the capability to drive migration, it is important to establish and further characterise this for consideration of AMPK activation as a viable therapeutic strategy.

In the following section AMPK-mediated migration was further characterised. Given the xCELLigence assay is performed in the presence of a chemoattractant gradient, it was impossible to recreate these conditions in 2D culture to investigate mechanism. As a compromise, cells were serum starved overnight and then treated in the presence of 5% serum containing medium as this is the midpoint of the 0-10% serum gradient presented in the xCELLigence migration assay.

3.3.1 Increased migration upon 991 treatment is AMPK-dependent

In Section 3.2.6 it was possible to use the AMPKα1-/-α2-/- MEFs to show that decreased migration in response to 991 was AMPK-dependent, however due to the lack of an AMPK knockout cell line in which 991 treatment caused increased migration the AMPK dependency of this response could not be confirmed. However, Almut Schulze’s group generated a PC3 cell line stably transfected with a doxycycline-inducible AMPKβ1 shRNA for another project (Ros et al., 2012). These cells were gratefully received and used as an AMPK-deficient model where 991 treatment led to increased migration.

AMPKβ1 shRNA induction by doxycycline treatment led to a robust knockdown of AMPKβ1 protein in these cells after 72h of treatment (Figure 3.18). AMPKβ1 knockdown led to decreased phosphorylation of AMPKα and ACC as detected by Western blotting; this was found to be the case in both basal and 991-treated conditions (Figure 3.18A). AMPK activity assays showed loss of AMPKβ1 in induced-PC3 cells led to a reduction in AMPK activity of 70% when compared to non-induced cells. AMPK activity in AMPKβ1 knockdown cells after maximal activation by 991 was a similar level to that basally in non-induced cells (Figure3.18B).

A B

DMSO +991 Dox

Figure 3.18 Doxycycline robustly induces AMPKβ1 shRNA and concomitant AMPKβ1 knockdown. A) PC3 cells stably transfected with doxycycline –inducible shRNA were grown in the presence or absence of doxycycline for 72 h before treatment with 0 and 10 μM 991 for 16 h. Cells were rapidly lysed and subject to Western blot analysis and probed with antibodies against pACCSer79, pAMPKαThr172, AMPKβ1/2 and actin was used as a loading control. Western blots were developed using the LI-COR imaging system. B) Immuno-complexes were isolated from lysates (treated as in (A)) using immunoprecipitation with an in-house rabbit antibody raised against total AMPKβ (Sip2) and AMPK activity was measured using the SAMS-kinase assay. Results are shown relative to control condition of non-induced cells. Data are means ± SEM (n= 3).

Using these AMPK-deficient PC3 cells, the AMPK-dependence in 991-mediated migration was tested. It was found that AMPKβ1-knockdown made no difference to basal migration rate (Figure 3.19). However, upon 991 treatment AMPKβ1 knockdown cells migrated significantly slower than non-induced control cells. An increase in migration can still be seen upon 991 treatment in the AMPKβ1-deficient cells and this is likely due to the remaining AMPKβ2- containing complexes, which are still activated by 991 (Figure 3.18B). The migration increase in AMPK-deficient cells was found to be about 70% of that of non-induced PC-3 cells, similar to the decrease in AMPK activity associated with the loss of AMPKβ1. This result implies that the increase in migration upon 991 treatment is AMPK-dependent and not due to off-target effects of 991. In addition, given the results in Figure 3.11D using AMPK knockout MEFs, if 991 were to have off-target effects this would likely lead to a decreased migration rate.

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Figure 3.19 Effect of 991 on AMPK-dependent migration in AMPK WT and AMPKβ1 knockdown PC3 cells. A) PC3 cells stably transfected with doxycycline-inducible shRNA were grown in the presence or absence of doxycycline for 72 h before xCELLigence migration assays were performed in the presence or absence of 10 μM 991. Migration was followed over 16 h and migration rate calculated typically from 2h to 16h and represented as fold change relative to DMSO non-induced cells. Data are means ± SEM (n= 3). ** p<0.01, *** p<0.005 t-test.

3.3.2 AMPK activation leads to increased phosphorylation of PAK1 activation sites in cells

Previous work with in the lab performed by Huza Zhang identified PAK1 as an AMPK substrate (Zhang, 2013). Dr Zhang used immunoprecipitation coupled to SILAC-based quantitative mass spectrometry to identify novel binding partners of AMPK in mammalian HEK293 cells. Antibodies specifically targeting i) AMPKβ1/2 subunits and ii) AMPKα1/2 subunits, were used to immunoprecipitate AMPK from a set amount of cell lysate labelled with ‘heavy’ isotope amino acid. The respective preimmune serum was used to immunoprecipitate from the same amount of lysate containing ‘normal’ isotope amino acid. A protein was considered a true AMPK interactor if enriched by 2-fold or more by immunoprecipitation with AMPKβ1/2 antibody compared to non-targeting preimmune controls. Immunoprecipitations by AMPK- specific antibodies resulted in high levels of enrichment of AMPK subunits and the known AMPK substrate, ACC, acting as a positive control. The screen pulled out P21-activated protein kinase 1 (PAK1), ARF GTPase-activating protein 1 (GIT-1) and Rho guanine nucleotide exchange factor 7 (βPIX). These 3 proteins are known to form a complex in cells (Manser et al., 1998, Turner et al., 1999). PAK1, GIT1 and βPIX were immunoprecipitated by both AMPK α and β antibodies with large enrichment folds. PAK1 and its associated proteins have been

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shown to be involved in the regulation of cytoskeletal arrangement and cell motility (Ong et al., 2011).

AMPK was shown to directly phosphorylate PAK1 in vitro (Zhang, 2013). The major AMPK phosphorylation site was shown to be Ser21 using a S21A mutant of PAK1. Of note, Banko et al had recently identified the equivalent residue in PAK2 (Ser20) as a target for phosphorylation by AMPK in an independent approach (Banko et al. 2011). In order to determine the functional effect of AMPK phosphorylation on PAK1, phospho-antibodies against two PAK1 activation sites were acquired, pPAK1Ser199 and pPAK1Thr423 (Bokoch, 2003). When PAK1 was incubated with ATP alone there was no detectable phosphorylation of either pPAK1Ser199 or pPAK1Thr423, however following the addition of active AMPK, strong signals were observed for both sites, suggesting AMPK phosphorylation leads to activation of PAK1 (Zhang, 2013). AMPK activation of PAK1 was further confirmed by measuring PAK1 activity before and after phosphorylation by AMPK, using the commercial PAK1 substrate, PAKtide (Rennefahrt et al., 2007, Wu and Wang, 2003).

Dr Zhang found AMPK phosphorylated and activated PAK1 in vitro. PAK1 activation has been reported to increase cellular migration and its expression has been shown to be correlative with cancer progression (Radu et al., 2014, Goc et al., 2013, Kumar et al., 2006). For these reasons, a role for PAK1 in AMPK-dependent migration was investigated. First, it was important to show that AMPK activation led to PAK1 activation in cells where AMPK- dependent migration is seen. Figure 3.20 shows that activation of AMPK by 991 leads to increased phospho-PAK at both activation sites (pPAK1Ser199 and pPAK1Thr423) in PC3 cells. Due to the lack of availability of a sensitive and commercially available phospho-PAK1Ser21 antibody, phosphorylation at this site could not be interrogated. Interestingly, in cells phosphorylation at the Ser199 of PAK1 appeared to be most sensitive to AMPK activation (Figure 3.20C).

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A B C

Figure 3.20 Effect of 991 on PAK1 phosphorylation in PC3 cells. PC3 cells were serum starved overnight and then treated for 16 h with 0 and 10 μM 991 in medium containing 5% serum. Cells were rapidly lysed and subjected to Western blot analysis using the ProteinSimple Wes automated Western blot system. Lysate was probed with antibodies raised against pACCSer79and pAMPKαThr172 (A, interrogation of AMPK activation); PAK1Ser423 and PAK1Ser199 (B,C, interrogation of PAK1 activation) and AMPKβ1/2 was used as a loading control.

3.3.3 Non-specific effect of AMPKα2 antibodies

Previously, Dr Zhang had validated the mass spectrometry results in HEK293 cells showing the PAK1/GIT/βPIX complex co-immunoprecipitated with AMPK. To do this a mix of AMPKα1 and AMPKα2 sheep antibodies (generated in-house) were used to immunoprecipitate total AMPK. AMPKα1 and AMPKα2 sheep antibodies were used to immunoprecipitate AMPKα1 and AMPKα2 complexes separately from a set amount of PC3 cell lysate. It was found that the PAK1/GIT/βPIX complex was immunoprecipitated in PC3 cells only with the antibody raised against AMPKα2 (Figure 3.21A). This was even more intriguing given the fact that AMPKα2 complexes account for less than 10% of total AMPK found in PC3 cells.

Next, whether activation of AMPK by 991 could affect the AMPKα2-PAK1/GIT/βPIX interaction was investigated. However, no difference in the amount of PAK1/GIT/βPIX complex immunoprecipitated with AMPKα2 in the presence or absence of 991 was found. To test that the immunoprecipitation of the PAK1/GIT/βPIX complex was AMPKα2 dependent, siRNA against AMPKα2 was used to knockdown AMPKα2 in the PC3 cell line. Worryingly, despite significant knockdown of AMPKα2 compared to scrambled (SCR) control cells, there was no reduction in the amount PAK1, GIT or βPIX immunoprecipitated using the AMPKα2 antibody (Figure 3.21B). As expected significantly less AMPKβ1 was immunoprecipitated from AMPKα2 knockdown cells using the AMPKα2 antibody.

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B C

Figure 3.21 The in-house sheep antibody raised against AMPKα2 non-specifically pulls down the PAK1/GIT/βPIX complex. A) PC3 cells were grown in 5% serum and rapidly lysed. PC3 cell lysate was immunoprecipitated with AMPKα1 and AMPKα2 ‘specific’ antibodies (in-house sheep antibodies) and subject to Western blot analysis. Western blots were probed with antibodies raised to GIT1, βPIX and PAK1; and AMPKβ1/2 to act as a positive control. B) PC3 cells were transiently transfected with either AMPKα2 or a scrambled (SCR) non-specific siRNA for 72 h. Cells were then rapidly lysed and immunoprecipitated with the AMPKα2 sheep antibody and probed with antibodies raised to GIT-1; and AMPKα2 and AMPKβ1/2 to act as a positive control. C) AMPKα2 immuno-complexes were immunoprecipitated using the AMPKα2 sheep antibody in WT and AMPKα1-/-α2-/- MEFs and probed with antibodies GIT-1, β-PIX and PAK1; and AMPKβ1/2 to act as a positive control. All Western blots were developed using the LI-COR imaging system.

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AMPK knockout MEFs were then used to unequivocally test whether the AMPKα2- PAK1/GIT/βPIX interaction was real or a non-specific effect of the antibody. PAK1/GIT/βPIX was immunoprecipitated in WT MEFs, however similar levels were also immunoprecipitated in AMPKα1-/-α2-/- MEFs (Figure 3.21C). Therefore, only one conclusion could be reached; the AMPKα2-specific antibody non-specifically pulled-down the PAK1/GIT/βPIX complex. Other AMPKα2 antibodies were also found to immunoprecipitate the PAK1/GIT/βPIX complex but upon closer inspection they had an overlapping peptide fragment to which they were raised.

In the screen performed by Dr Zhang a protein was considered an AMPK interactor if enriched by 2-fold or more by immunoprecipitation with the AMPKβ1/2 antibody compared to non- targeting preimmune controls. Therefore, the non-specific effect of the AMPKα2 antibody does not affect this result. Furthermore, subsequent work validated that AMPK is capable of PAK1 phosphorylation and activation in vitro and in cells. This finding is supported by Banko’s work identifying the equivalent site in PAK2 as an AMPK target site. The mass spectrometer is a very sensitive instrument, which may explain why it is hard to immunoprecipitate endogenous AMPK and detect endogenous PAK1 using AMPK antibodies (or vice versa). Technical difficulties also hinder this methodology. All the best antibodies for AMPK and PAK1 in immunoprecipitation and Western blotting analysis are raised in rabbit leading to high background from IgG bands, which mask the specific bands of the proteins of interest.

3.3.4 Pharmacological inhibition and knockdown of PAK1 blocks AMPK-dependent migration

To investigate whether PAK1 activity is required for AMPK-mediated migration, the small molecule PAK1-specific inhibitor, PF-375 (PF-3758309), was used (Murray et al., 2010, Ong et al., 2013). Cells treated with this drug were assayed for migration in the presence and absence of 991. The results are shown in Figure 3.22 and indicate that basal migration of PC3 cells was not affected by treatment with this PAK1 inhibitor. Interestingly, however, the increase in migration seen upon AMPK activation with 991 was completely blunted in cells treated with the PAK1 inhibitor, implying that PAK activation is required for AMPK-mediated migration. Similarly, in 22RV1 cells the effect of 991 on migration was blunted by PF-375, although at a 10-fold lower concentration (1 μM). This is consistent with reports in the literature showing 22RV1 requires a lower concentration of PF-375 for PAK1 inhibition (Jiang et al., 2015).

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A B

Figure 3.22 PAK1 inhibitor, PF-3758309, blunts AMPK-dependent migration. xCELLigence migration assays were performed on PC3 cells treated with 0, 991 (10 μM), PF-3758309 (PF-375, 10 μM) or a combination of both 991 (10 μM) and PF-3758309 (10 μM). Migration was followed over 16 h (A) data shown relative to DMSO at t= 16 h which was taken to be 100% migration. Migration rate was calculated from 2h to 16h (B) and represented as fold change relative to DMSO condition. Data are means ± SEM (n= 3). ** p<0.01, t-test. PF-375 is reported to have potential off-target effects inhibiting kinases other than PAK1 in an ATP-competitive manner (Murray et al., 2010). To check that PF-375 was not inhibiting AMPK, AMPK activity was assessed in cells in the absence and presence of PF-375 and 991 by Western blotting in PC3 and 22RV1 cells. Figure 3.23 shows that both AMPKα and ACC phosphorylation remain unchanged upon PF-375 treatment in both basal and activated conditions. This shows that the inhibition of the migration increase in response to 991 is not due to direct inhibition of AMPK. Given the promiscuous nature of small molecules a genetic approach was enlisted to test the PAK1 dependency in AMPK-mediated migration.

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A B

Figure 3.23 Effect of PAK1 inhibitor, PF-3758309, on AMPK activity. PC3 cells were treated with DMSO, 991 (10 μM), PF-3758309 (PF-375, 10 μM) or a combination of both 991 (10 μM) and PF-3758309 (10 μM). Cells were rapidly lysed and subjected to Western blot analysis using the ProteinSimple Wes automated Western blot system (A). Lysate was probed with antibodies raised against pACCSer79, pAMPKαThr172 ; and AMPKβ1/2 was used as a loading control. B) 22RV1 cells were treated as described in A). Lysates were subject to Western blot analysis and probed with the same antibodies as in A). Western blots developed using the LI-COR imaging system.

Commercially available siRNAs against PAK1 and PAK2 were purchased. PAK2 was bought in addition to PAK1 given the recent finding that AMPK can phosphorylate PAK2 and since PF- 375 has been shown to inhibit other Group 1 PAK isoforms (although with lower potencies) (Murray et al., 2010). PC3 cells express both PAK1 and PAK2 isoforms. PAK knockdown was optimised and the results are shown in Figure 3.24. After optimisation, PAK1 and PAK2 protein was knocked down to 80% of that in scrambled (SCR) control cells (Figure 3.24).

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A B

Figure 3.24 Quantification of PAK1 and PAK2 knockdown using siRNA. PC3 cells were transiently transfected with either scrambled (SCR) non-specific, PAK1, PAK2 or a combination of both PAK1 and PAK2 siRNAs for 72 h. Cells were rapidly lysed and subjected to Western blot analysis using the ProteinSimple Wes automated Western blot system. Lysate was probed with antibodies raised against total PAK1 (A) and total PAK2 (B); tubulin was used as a loading control. Quantification of A and B is shown in C). PAK expression was normalised to tubulin and then expressed relative to PAK1 or PAK2 expression in SCR control cells. Data are means ± SEM (n= 3). *** p<0.005 t-test

PAK1 and PAK2 were knocked down in PC3 cells and then these were subject to the xCELLigence migration assay and the result is shown in Figure 3.25. PAK1 knockdown alone did not affect basal PC3 migration; however PAK2 knockdown alone was found to significantly reduce cell migration rate. PAK1 and PAK2 knockdown in combination led to a further decrease in basal migration, likely due to the lack of compensation between isoforms when both were knocked down. Interestingly, knockdown of either PAK isoform was found to inhibit the migration increase seen in response to 991 in the SCR control cells (Figure 3.25). This result suggests that both PAK1 and PAK2 are required for AMPK-mediated migration. However, given that PAK2 knockdown affected basal migration rate, these cell were migrating ‘differently’ compared to scrambled control cells, which consequently confounds any effects then seen in the presence of 991. PF-375 did not affect basal migration rate but blunted AMPK-mediated migration mimicking the results seen upon PAK1 knockdown. Given that PF- 375 also has a much lower Ki for PAK1 it is probable that the drug would be working through PAK1 rather than PAK2.

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Figure 3.25 Effect of PAK1 and PAK2 knockdown on AMPK-dependent migration. PC3 cells were transiently transfected with either scrambled (SCR), PAK1, PAK2 or both PAK1 and PAK2 siRNAs for 72 h. Cells were then passaged and subject to xCELLigence migration assays either minus or plus 10 μM 991. Migration rate from 6 h to 16 h was calculated and represented as fold change relative to DMSO SCR control condition. Data are means ± SEM (n= 4). * p<0.05, *** p<0.005, t-test.

These knockdown experiments were technically challenging. Transfection seemed to reduce migration rate and the magnitude of the increase in migration in response to 991 was reduced after transfection compared to untreated cells. A limiting number of cells had to be used for each experiment to save on expensive reagents and this created a large amount of variation within experiments. Differences in knockdown efficiency also potentially introduced variation between repeats. Ideally, cells stably depleted of PAK1 and PAK2 would have been used, but these would have been costly and time consuming to produce. However, despite this there were still significant increases in scrambled control cells upon 991 treatment, which was lost upon PAK depletion.

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3.3.5 Pharmacological inhibition of CaMKKβ inhibits AMPK-dependent migration

In most scenarios LKB1 is thought to be responsible for AMPK activation (Carling et al., 2008). However, given the fact that CaMKKβ has been implicated as a potential oncogene in prostate cancer and AMPK-mediated migration would be deemed oncogenic, it was decided to investigate whether this process was dependent on a specific upstream kinase. In addition, a previous study reported that CaMKKβ is involved in PAK activation in neurons, although in this scenario the authors argue activation is through the CaMKKβ substrate, CaMKI and not AMPK (Racioppi and Means, 2012).

To initially investigate whether CaMKKβ is required for AMPK-mediated migration the CaMKKβ specific inhibitor, STO609, was used (Tokumitsu et al., 2003, Kukimoto-Niino et al., 2011). It was found that STO609 treatment blunts AMPK-dependent migration in PC3 cells (Figure 3.26). STO609 was also found to have this effect in 22RV1 cells. This would be consistent with CaMKKβ being upstream of AMPK, mediating this migration mechanism.

A B

Figure 3.26 Effect of the CaMKKβ inhibitor, STO609, on AMPK-dependent migration. xCELLigence migration assays were performed on PC3 cells treated with 0, 991 (10 μM), STO609 (5 μM) or a combination of both 991 (10 μM) and STO609 (10 μM). Migration was followed over 18 h (A) data shown relative to DMSO at t= 18 h which was taken to be 100% migration. Migration rate calculated from 6 h to 18 h was calculated (B) and represented as fold change relative to DMSO. Data are means ± SEM (n= 2). *** p<0.005, t-test.

The effect of inhibiting CaMKKβ on AMPK activity was investigated in PC3 cells. CaMKKβ inhibition using STO609 was found to decrease AMPK activity by about 20% in basal and activating conditions (3.27A,B). It is interesting that despite this modest decrease in AMPK activity upon CaMKKβ inhibition, AMPK-mediated migration was significantly inhibited. 110

Preliminary results suggest that STO609 treatment lead to a decrease in PAK1 phosphorylation at Ser199, presumably indirectly by loss of AMPK phosphorylation through CaMKKβ inhibition (Figure 3.27C).

A B

Figure 3.27 Effect of CaMKKβ inhibitor, STO609, on AMPK activity. PC3 cells were treated with DMSO, 991 (10 μM), STO609 (5 μM) or a combination of both 991 (10 μM) and STO609 (5 μM). Cells were rapidly lysed and subjected to Western blot analysis using the ProteinSimple Wes automated Western blot system (A). Lysate was probed with antibodies raised against pACCSer79, pAMPKαThr172 , PAK1Ser199 and AMPKβ1/2 was used as a loading control. B) Immuno- complexes were isolated from lysates (treated as in (A)) using immunoprecipitation with an in-house antibody raised against total AMPKβ (Sip2) and AMPK activity was measured using the SAMS-kinase assay. Results are shown relative to DMSO control. Data are means ± SEM (n= 4).C) shows the quantification of A) for pPAK1Ser199 expression normalised to AMPKβ expression (n=2).

Like most small molecule inhibitors, STO609 has been shown to have off-target effects (Bain et al., 2007). Therefore, to verify the requirement of CaMKKβ in this migration mechanism it is important to genetically knockdown this kinase. Given the technical difficulties associated with using siRNA to transiently knockdown PAK proteins it was decided not to use siRNA to knockdown CaMKKβ. Other members of the lab have recently received CRISPR constructs to create cell lines deleted for CaMKKβ using the CRISPR/Cas9 system. This has been achieved in HEK293T cells, acting as proof of principle. Future work for this project includes deleting

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CaMKKβ in PC3 cells (and a number of other prostate cancer cell lines). Unfortunately, these cell lines have not been generated within the timeframe of this PhD. However, once these CaMKKβ-/- PC3 cells have been generated, they will be used to test the role of CaMKKβ in AMPK-mediated migration to further validate the inhibitor studies. If consistent, then these data place CaMKKβ upstream of AMPK in AMPK-PAK mediated migration (Figure 3.28). This would be supportive of the reports of the pro-cancerous roles described for CaMKKβ, AMPK and PAK1 proteins.

3.4 Discussion

The master regulator, AMPK, is emerging as a potential therapeutic target for cancer treatment. The majority of studies infer AMPK activation would be of therapeutic benefit inhibiting prostate cancer cell growth in culture and xenograft models (see Introduction, Section 1.2.3) (Huang et al., 2008, Xiang et al., 2004). However, there is growing controversy especially in prostate cancer, as to whether AMPK activation is always detrimental to cancer progression with some recent studies arguing that inhibition of AMPK leads to decreases in prostate cell migration, invasion and growth (see Introduction, Section 1.2.4) (Frigo et al., 2011, Massie et al., 2011). Previous studies make use of indirect AMPK activators such as metformin and AICAR, which are known to have AMPK-independent effects (Ben Sahra et al., 2008, Ben Sahra et al., 2011, García-García et al., 2010, Moreno et al., 2008, Santidrián et al., 2010, Liu et al., 2014, Bain et al., 2007). To circumvent these issues the highly selective direct AMPK activator, 991, was used to investigate the effect of AMPK activation on prostate cancer cell proliferation, migration, invasion and adhesion. AMPK knockout MEFs were used to test that responses to the drug were AMPK-dependent. Use of 991 on prostate cancer cells in vitro is an invaluable tool; however this tells us little about the role of AMPK in prostate cancer cells in a heterogeneous cellular population contending with the selection pressures of the suboptimal tumour microenvironment in vivo. Hence, this cell based approach was complemented with the generation of genetic mouse models of prostate cancer discussed later in later chapters.

A panel of prostate cancer cell lines was used to test whether AMPK activation is necessary and sufficient for the suppression of prostate cancer cell growth. The panel included cell lines with a range of driver mutations and AR responsiveness; with different migratory and invasive capacities. 991 led to potent AMPK activation in a dose-dependent manner in all cell lines used. In response to 991, all cell lines displayed AMPK-dependent decreases in cellular proliferation, 2D migration, invasion, lipid synthesis and increases in cellular adhesion (attachment and spreading). Interestingly, migration down a chemoattractant gradient (xCELLigence migration assay) was found to be increased or decreased in a cell line-specific manner. During the course of this study a paper was published by Zadra et al, characterising 112

another novel AMPK direct activator, MT63-78. MT63-78 achieved similar levels of AMPK activation over a similar concentration range as 991. In this study, a panel of prostate cancer cell lines was used (Zadra et al., 2014). The authors comprehensively characterised the effect of MT63-78 on cell growth, however they did not investigate the effect of their new compound on cellular migration, invasion or adhesion.

A criticism of the work presented in this chapter is that only one AMPK activator was used to investigate the effect of AMPK activation in the different prostate cancer cell lines. Although 991 is a highly selective AMPK activator, off-target effects of this compound were observed in scratch and xCELLigence migration assays in MEF cells. The use of other AMPK activators such as AICAR (indirect activator) or A-769662 (direct activator) in these assays would arguably allow the effect of AMPK activation to be more robustly tested. These compounds activate AMPK through different mechanisms and would therefore likely have different off-target effects. If the same responses were observed using different AMPK activators this would add confidence that the effects seen were due to AMPK activation and not off-target effects of the compound used.

AMPK knockout MEFs were used, where appropriate, to investigate the AMPK-dependence of responses seen in the different assays. However, a better approach would have been to knockdown AMPK activity in the cell lines of interest rather than moving into a different cell type. AMPKβ1 was knocked down in the PC3 cell line when investigating the increase in migration in response to 991 treatment, however even in this case it would have been cleaner to generate PC3 cells lacking AMPK activity. This could have been achieved using siRNA against both α- or β- subunits, however given the recent success of the CRISPR-Cas9 technology stable AMPK knockout prostate cancer cell lines can now be generated. Generally, the CRISPR system allows a greater degree of knockdown compared to siRNA technologies. In addition, the stable AMPK knockout prostate cancer cells would represent a permanent resource that could be used in future experiments. As proof-of-principle the lab have recently generated AMPKβ1-/-β2-/- HEK293T cells. Future work includes the generation of AMPK- knockout prostate cancer cell lines to unequivocally test the AMPK-dependence of the responses seen upon 991 treatment in the different assays.

Another approach to test whether the effects of 991 are dependent on AMPK binding would be knockin mutagenesis to abolish 991 binding to AMPK using CRISPR-Cas9 technology. The advantage of this approach is that the AMPK complex would still be present in cells, with AMPK activation in response to 991 being specifically abolished. This is a cleaner approach as deletion of the AMPK complex may have other effects. However, first it would be necessary to identify key residues required for 991 binding. This is made possible by the crystal structure that was recently solved for AMPK bound to 991 (Xiao et al., 2013). This avenue may be also investigated in future work.

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AMPK activation and decreased cell proliferation

AMPK activation was found to inhibit prostate cancer cell proliferation and this was independent of cell line used. Flow cytometry results suggest this may be through G1 cell cycle arrest. Several reports show that AMPK activation leads to cell cycle arrest at G1-phase and that this response may have evolved to limit proliferation in suboptimal conditions, such as low nutrient supply (reviewed in (Carling et al., 2012). This cell cycle arrest is thought to be via phosphorylation of the tumour suppressor proteins p53 and p27 (Jones et al., 2005, Liang et al., 2007). Evidence for AMPK directly phosphorylating these proteins is still lacking. Since PC3 cells are p53-negative but are still sensitive to AMPK-dependent cell cycle arrest, alternative mechanisms may be sought. AMPK antagonises mitogenic pathways, thereby down-regulating anabolic pathways required for cell growth and proliferation and this is likely to indirectly cause G1 cell cycle arrest, and so preventing cells from entering S-phase (Carling et al., 2012). This is consistent with the finding that AMPK activation significantly inhibits lipid synthesis.

Zadra et al using their novel AMPK activator, MT 63-78, showed that AMPK activation leads to decreased cell proliferation (Zadra et al., 2014). Interestingly, they looked at the effect of the drug over much longer timescales (from 24 h to 72 h). After 48 h at the highest concentration of MT63-78 they saw induction of apoptosis. No induction of apoptosis was seen after 16 h treatment of 991, however this may have been seen if cells were treated with 991 for a longer period. Zadra et al claim that AMPK activation causes growth arrest at the G2 phase of the cell cycle. This is contrary to the findings of this study which suggest AMPK activation leads to G1-phase arrest. However, in their publication cell cycle analysis was performed on cells treated with MT63-78 for 48 h in media containing 10% serum, whereas in this study cells were treated for half this time in serum-free media; this may explain the difference in result. Zadra et al conclude that MT63-78 induces mitotic arrest by AMPK‐ mediated inhibition of de novo fatty-acid synthesis (metabolic role) and due to mitotic spindle assembly/chromosome segregation abnormalities (non‐metabolic role). The latter non- metabolic role is supported by recent papers identifying several new AMPK substrates involved in mitotic division (Banko et al., 2011) and another showing a fine-tuned biphasic activation of AMPK is required for proper mitotic progression (Thaiparambil et al., 2012, Vazquez-Martin et al., 2009).

Zadra et al found that AMPK activation led to a sharp blunting of lipogenesis in the prostate cancer cell line, LNCaP and this was shown to also be true in PC3 cells in this study. Taken together, these data demonstrate that AMPK activation inhibits lipid synthesis in androgen- sensitive and castration resistant cell lines. AMPK activation leads to phosphorylation and inhibition of ACC1, the committed step of fatty acid synthesis. It has been shown that 991 treatment leads to increased ACC phosphorylation in all prostate cancer cell lines and this is probably a key event in the blunting of lipogenesis seen upon AMPK activation. In this study

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lipogenesis was measured over a short time period of 6 h post-AMPK activation, in contrast Zadra et al looked at lipogenesis 24 h, 48 h and 72 h post-AMPK activation. They show that on a longer time scale persistent AMPK activation leads to a significant reduction in the level of the mature form of SREBP1; a protein responsible for transcription of ACC and FASN, which were seen to be reduced at later time points. Since 991 treatment was restricted to time periods of less than 24 h in this study, it is unlikely that changes in gene expression would contribute to responses seen in the short-term. Zadra et al further show that supplementation with palmitate and/or mevalonate (the products of de novo fatty acid synthesis) can partially rescue the growth arrest caused by AMPK activation. Thereby, solidifying that inhibition of lipogenesis by AMPK activation does negatively regulate prostate cancer cell growth. It is likely that de novo lipogenesis is important for prostate cancer cells at all stages of the cell cycle, therefore different growth conditions, namely whether exogenous lipids are present in the medium (e.g. plus or minus serum), will influence the type cell cycle arrest incurred. However, it is also likely given the plethora of processes regulated by AMPK that the growth arrest caused by AMPK activation is multifactorial with nature tending to take a belt and braces approach to cellular regulation.

AMPK activation and decreased cell migration and invasion

The effectors downstream of AMPK involved in the control of cell migration and invasion are not well understood (Carling et al., 2012).

In scratch assays, migration was seen to decrease in all prostate cancer cell lines upon AMPK activation and this was also true for DU145 and MEF cells in the xCELLigence migration assay. In addition, 22RV1 invasion was found to be severely blunted by 991 treatment. The finding by Banko et al, that AMPK has the potential to co-ordinately regulate both the kinase and a phosphatase involved in MLC2 phosphorylation was interesting as MLC2 plays a role in the regulation of many diverse functions including cell migration, invasion and adhesion (Ito et al., 2004). However, no change in phosphorylation of MLC2 was detected upon 991 treatment or upon AMPKβ1 deletion in MEFs using Western blotting. However, it should be noted that detection of MLC2 phosphorylation was often not possible as levels fell below the sensitivity limit of the antibody and this was especially true if cells had been grown in serum-free growth conditions. Furthermore, phospho-MLC2 levels were very variable and showed poor reproducibility between repeats.

A recent study showed that augmented AMPK activity inhibits cell migration in C2C12 cells and identified PDZ and LIM domain 5 (Pdlim5) as a novel AMPK substrate, showing that it plays a critical role in the inhibition of cell migration using 2D migration assays (Yan et al., 2015). These authors argue that phosphorylation of this scaffold protein on Ser177 by AMPK mediates inhibition of cell migration by suppressing the Rac1-Arp2/3 signalling pathway. The study claims that phosphorylation of Pdlim5 alters actin architectures and focal adhesion formation. Furthermore, loss of directional cell migration and cell polarity was reported upon 115

AMPK inhibition due to a loss of CLIP-170 phosphorylation (Nakano et al., 2010). In this study, AMPK inhibition was also found to affect focal adhesion dynamics. Given the recent flurry of evidence implicating AMPK as regulating migration and focal adhesion formation, it will be an important avenue to visit in future work (Nakano et al., 2010, Jiang et al., 2015, Yan et al., 2015). Of note, preliminary work in collaboration with Buzz Baum, UCL, using RPE-1 cells (epithelial retina cell line) stably transfected with the focal adhesion protein, GFP-tagged zyxin, showed no overt differences in focal adhesion number or turnover upon 991 treatment (data not shown). This work was performed monitoring random migration of cells on a 2D tissue culture plate using confocal microscopy. This work was very preliminary and datasets were not quantitated.

Intriguingly, a number of studies have highlighted the importance of de novo fatty acid synthesis and lipogenesis in prostate cancer migration and invasion (Scott et al., 2012, Yue et al., 2014). Prostate cancer is characterised by alterations in the PI3K/Akt/mTOR pathway and by a unique lipogenic reprogramming, active both at tumour initiation and in advanced stages of the disease (Suburu and Chen, 2012). Activation of de novo lipogenesis has recently been recognised as not only as an adaptation to fulfil the metabolic requirements of highly proliferating cancer cells, but as a hallmark of tumourigenesis, tumour progression and development of androgen‐resistance (Ettinger et al., 2004, Santos and Schulze, 2012).

In a recent paper inhibition of ACC1, both pharmacologically and by AMPK activation reduced invadopodia formation and decreased PC3 ability to degrade gelatin (Scott et al., 2012). The addition of exogenous fatty acids restored invadopodia and gelatin degradation to cells with decreased ACC1 activity. In another recent paper, migration and prostate cancer aggressiveness was decreased by depletion of cholesteryl ester storage in prostate cancer cell lipid droplets (Yue et al., 2014). Such accumulation was found to arise from significantly enhanced uptake of exogenous lipoproteins. Cholesteryl ester accumulation was dependent on the PI3K/AKT/mTOR/SREBP pathway. Knockdown of SREBP1 and SREBP2 reduced cholesteryl ester accumulation. AMPK directly phosphorylates and inhibits ACC, SREBP1 and SREBP2 (Zhou et al., 2001). Conceivably, these events may be important in the downregulation of migration/invasion upon AMPK activation.

AMPK activation and increased cell adhesion

AMPK activation by 991 was found to increase cellular adhesion in a dose-dependent manner in all cell lines. Curiously, NUAK1, an AMPK-related kinase has been found to have a role in cell detachment (Zagórska et al., 2010). Inhibition of the LKB1-NUAK1 pathway impaired cell detachment. Cell detachment was seen to induce phosphorylation of MYPT1 by NUAK1, resulting in 14-3-3 binding to MYPT1 and enhanced phosphorylation of MLC2, in an AMPK- independent manner (Zagórska et al., 2010). This is the opposite of observations made in this chapter with AMPK activation leading to increased cell attachment (and/or spreading); in addition no change in MLC2 phosphorylation was detected upon 991 treatment. These data 116

argue for a different mechanism leading to this observation. However, MLC2 phosphorylation was examined after cell attachment and spreading in standard culturing conditions. It is hard to accurately recapitulate the attachment process to get representative samples to interrogate the mechanism behind any response seen using the xCELLigence instrument. Pathways that are activated as cells attach and spread are unlikely to be active or at least will not be fulfilling the same role once the cells have adhered in 2D culture.

Interestingly, the recent paper identifying Pdlim5 as an AMPK substrate, shows that AMPK activation and phosphorylation of Pdlim5 affects cell spreading (using a phosphomimetic Pdlim5 mutant). This could be a contributing factor to the increase in cell impedance seen upon AMPK activation using 991 in the xCELLigence adhesion assay (Yan et al., 2015).

AMPK activation and increased cell migration: implication of the CaMKKβ/AMPK/PAK1 signalling axis

Intriguingly, it was found that PC3 and 22RV1 cell migration was increased in response to 991 using the xCELLigence assay of migration. These cell lines are both castration-resistant cell models of prostate cancer with invasive capacities. They differ in their expression of tumour suppressors and androgen receptor proteins, PC3 cells are PTEN and p53 negative, while 22RV1 cell are androgen receptor positive. Despite these key differences both cell line have overcome the inhibitory effects of AMPK on migration and in contrast, utilise AMPK activation to increase their migration rate. Studies showing that AMPK activation can increase migration rate are in the minority, however those that do tend to utilise transwell assays of migration (Frigo et al., 2011, Nagata et al., 2003). However, one study does show AMPK-dependent migration in response to LPS in ovarian cells using a 2D scratch assay (Kim et al., 2011a).

PAK1 is a regulator of cytoskeletal dynamics and cellular motility and has a recognised role in promoting migration of cells (Eswaran et al., 2012, Whale et al., 2011). In recent years, AMPK has also been implicated in as a controller of these processes (see Chapter 1, Section 1.2.3.3). Previous work by Dr Zhang, found that AMPK formed a complex and directly phosphorylated PAK1 in vitro on Ser21. In addition, Banko et al performed a genetic screen to identify novel downstream targets of AMPK. They found that AMPK directly phosphorylated PAK2 on the analogous site, Ser20. Using PF-375, a pharmacological inhibitor of PAK1 in PC3 and 22RV1 cells and siRNA-mediated PAK1 knockdown in PC3 cells, PAK1 activation was shown to be necessary for AMPK-mediated cell migration.

Although no papers yet directly link AMPK and PAK1 in cell migration, there have been hints that the Rac1/PAK pathways may have a role in this process. Bae et al implicated AMPK activation as partially mediating increased migration and phagocytosis in macrophages via activation of Rac1 and PAK1/2 (Bae et al., 2011). In the same year, Kim et al found that AMPK activation mediated LPA induced migration of ovarian cancer cells. In addition to AMPK, LPA treatment also resulted in the activation of the GTPases RhoA and Rac1. However, knockdown

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of AMPKα1 by siRNA only attenuated LPA induced RhoA activation and not that of Rac1 (Kim et al., 2011a). Finally, Akt was also found to phosphorylate PAK1 on Ser21 leading to activation of PAK1. In this paper, the authors report that this phosphorylation event leads to a reduction in the association of PAK1 with the adaptor protein Nck (Zhou et al., 2003). The same study found that Akt phosphorylation caused release of PAK1 from focal adhesions and these effects led to stimulation of this pathway and caused increased transwell migration of Rat1 cells (Zhou et al., 2003). Since AMPK also phosphorylates PAK1 on Ser21, it is likely that PAK1/Nck binding is also affected leading to the increased migration of cancer cells observed (although this hypothesis was never tested). Of note, this paper uses a transwell migration assay down a chemoattractant, the same principle as the xCELLigence CIM-plate migration assay.

In this current study inhibition of CaMKKβ by the pharmacological inhibitor STO609 was found to inhibit AMPK-mediated cell migration in response to 991, despite a modest effect on total AMPK activity under these conditions. This finding provides further support for the role of CaMKKβ as a potential oncogene in prostate cancer and in turn means AMPK activation in certain scenarios could have oncogenic capacities. Interestingly, studies that looked at the upstream signalling pathways where AMPK activation was found to increase cellular migration, found CaMKKβ to be responsible for AMPK activation (Frigo et al., 2011, Kim et al., 2011a). In fact, Frigo et al set out looking at the effect of CaMKKβ (as an androgen-responsive gene) on prostate cancer cell migration in the androgen-responsive cell line, LNCaP. They found that plus and minus androgens, loss of CaMKKβ or AMPKα1 by siRNA-mediated knockdown inhibited cell migration in a transwell migration assay. In addition, a previous study reported that CaMKKβ is involved in PAK activation in neurons, although in this scenario the authors argue activation to be through the CaMKKβ substrate, CaMKI and not AMPK (Racioppi and Means, 2012). A schematic to depict the model of AMPK-mediated migration is shown in Figure 3.28.

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Figure 3.28 Model of AMPK-dependent migration. 991 treatment leads to increased phosphorylation and activation of AMPK mediated through upstream kinase, CaMKKβ. AMPK activation leads to increased PAK1 phosphorylation (probably at Ser21) and concomitant PAK1 activation leading to increased cancer cell migration.

Initial studies using highly sensitive mass spectrometry identified the PAK/GIT/βPIX complex as co-immunoprecipitating with AMPK; however this could not be shown using co- immunoprecipitation in combination with Western blotting on endogenous levels of AMPK and PAK1. This does not mean that the AMPK-PAK/GIT/βPIX interaction does not occur in cells, but probably the pool of AMPK bound to PAK1 is small and therefore technically difficult to detect. More sensitive antibodies against PAK1 and AMPK capable of immunoprecipitation and Western blotting (and raised in different species) would be required to follow this up. Being able to monitor the interaction between PAK1 and AMPK would be very powerful as it would allow interrogation of the regulation of this interaction.

Due to the lack of a sensitive antibody, interrogation of PAK1 phosphorylation on Ser21 in this study was not possible. However, previous work showed that phosphorylation at this site led to concomitant increases in phosphorylation at the PAK1 activation sites (Ser199 and Thr423) as a result these were used as a readout of PAK1 activity in response to AMPK activation. The Ser199 site was found to be particularly sensitive to AMPK activation.

It would be very useful to validate the PAK1/ AMPK interaction and investigate how complex formation is regulated and whether CaMKKβ activity can affect this. In addition, it would be interesting to investigate whether AMPK binding to PAK1 affects its auto-inhibition. The contribution of GIT1 and βPIX in this pathway also warrant further investigation, as they were found to form a signalling complex with PAK1 and AMPK. Due to the acknowledged off-target effects of the CaMKKβ inhibitor, STO609, further validation of the role of this CaMKKβ in 119

AMPK-mediated migration is required. This will be followed up by the production CaMKKβ- null CRISPR PC3 and 22RV1 cell lines.

Conclusions and comments

Mirroring the sea of literature exploring the possibility of AMPK activation as a therapeutic strategy in prostate cancer, this section of work supports the idea that on the whole, AMPK activation would be an effective therapeutic strategy: inhibiting cell proliferation, migration and invasion. These effects were seen universally in androgen-sensitive or castration resistant cell models, regardless of the differential expression and activation of different tumour suppressor gene and oncogenes. The exact mechanism of how AMPK leads to increased attachment/spreading requires further characterisation. However, if the increase in adhesion acts to prevent malignant cells from disseminating into the circulation then this too would be of therapeutic benefit.

The exact mechanisms of the discussed antitumorigenic capacities of AMPK activation remain unknown, although I would suggest a dominant component is AMPK‐mediated repression of lipogenesis. As shown in this section of work, as well as a flurry of other studies, AMPK activation significantly inhibits de novo lipogenesis, an anabolic pathway significantly upregulated in prostate cancer and one to which malignant cells become addicted. Lipogenesis is important in a plethora of cellular processes, including membrane biogenesis, signal transduction, intracellular trafficking, and lipid‐based protein modification, all of which are hijacked by cancer cells for uncontrolled proliferation, migration and invasion (Baenke et al., 2013).

It cannot be ignored that AMPK activation did also cause increased migration in the transwell migration assay in certain cell lines. Therapeutically, this facet of AMPK activation could be devastating leading to increased metastasis and disease progression. Given that the oncogenic effects of AMPK activation were abolished by CaMKKβ and PAK1 inhibition a logical therapeutic strategy would be to activate AMPK, whilst inhibiting these proteins. Given the mounting literature supporting the idea that CaMKKβ is a potential oncogene in prostate cancer and that the previous work within the lab has shown that the global knockout mouse model has no overt phenotype, this is an obvious target in prostate cancer. Thus, I would favour a combination of AMPK activation and CaMKKβ inhibition as a therapeutic strategy. To further test this therapeutic regime the roles of AMPK and CaMKKβ in prostate cancer were further investigated in vivo in Chapters 4 and 5, respectively.

Of note, differences in response to 991 between migration assays and between cell lines within an assay highlight the importance of assay and cell line choice when testing potential pharmacological agents. Differences in cancer cell line, growth condition and assay, in combination with the use of non-specific drugs likely account for the seeming contradictory

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results in the literature. Nonetheless, it is important to use a range of assays and cell lines to try and best represent the infinite different conditions faced in the complex in vivo milieu.

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4. Investigation of the effect of AMPKβ1-deletion in the PTEN-/- mouse model of prostate cancer

4.1 AMPKβ1 in human prostate cell lines

Recent to the start of this project, a study was reported on an unbiased siRNA screen to identify metabolic proteins required for prostate cancer cell survival (Ros et al., 2012). In this study, it was found that the β1-subunit of AMPK was required for survival of the prostate cancer cell lines (LNCaP, DU145, PC3) but not the non-transformed cell line, RWPE1. The other AMPK subunits (α1, α2, β2, γ1, γ2, γ3) were not required for cell survival; suggesting β1 could have an AMPK-independent role or AMPKβ1-containing complexes have an essential role in prostate cancer cell survival.

The hypothesis of an AMPK-independent role for the β1-subunit was investigated in the cell lines detailed in the previous chapter (Table 3.1).

4.1.1 Investigation of an AMPK-independent role for β1

To follow up the study by Ros et al paper, and investigate the possibility of and an AMPK- independent role for the β1-subunit, β1 expression was quantified and normalised with respect to actin or the AMPKα-subunit in the different prostate cancer cell lines and compared to the RWPE1 control prostate cell line. For β1 to have an AMPK-independent role, the expectation might be that the β1-subunit would be upregulated relative to α-subunit expression in the cancer cell lines compared to the control cell line.

Ros et al performed their siRNA screen in 2 different conditions, using full or lipid-depleted medium. Notably, RWPE1 control cells were cultured in a different medium to the cancer cell lines (keratinocyte-growth medium). The requirement of β1 was seen in the cancer cell lines under both growth conditions; with β1-knockdown leading to the induction of apoptosis, which was not seen in the control cell line. Since the effect of β1-depletion on cell survival was seen in both growth conditions, for follow-up studies prostate cancer cell lines were cultured in full medium and subsequently rapidly lysed for subsequent analysis of β1 expression (Figure 4.1).

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A B

Figure 4.1 AMPKβ1 expression in prostate cancer cell lines compared to RWPE1 control cell line. AMPKβ1 expression normalised to A) actin and B) AMPKα1 expression in RWPE1, LNCaP, DU145 and PC3 cell lines. These data are represented as fold change relative to RWPE1 control cell line. Data are means ± SEM (n= 3). ** p<0.01; one-way anova. A representative Western blot from which A) and B) were quantified is shown in Figure 3.1 on page 75. C) AMPK activity in immuno-complexes isolated from lysates subject to fast lysis was measured using the SAMS-kinase assay. Results are expressed as fold change relative to RWPE1 cell line. Data are means ± SEM (n= 3). **p<0.01; one-way anova.

As shown in Figure 4.1A, β1 expression (normalised to actin) was found to be significantly upregulated in LNCaP cells compared to the RWPE1 control cell line and this was consistent with increased AMPK activity (Figure 4.1D). However, when normalised to α1 expression (Figure 4.1B), none of the prostate cancer cell lines were found to have significantly different expression of β1 compared to RWPE1 cells.

β1-protein expression not being upregulated relative to the alpha catalytic subunit in any of the cancer cell lines used in the Ros et al paper does not provide support for an AMPK- independent for β1. β1 expression was found to be tightly correlated with α1 expression and AMPK activity. However, an observation pertaining to β1 having an AMPK-independent role 123

was made when characterising different AMPK knockout MEFs. β1 was found to be stable in the absence of the alpha catalytic subunits; however α-subunits were not stable in the absence of β-subunits (Figure 4.2).

Figure 4.2 Expression of AMPK subunits in AMPKβ1-/-β2-/- and AMPKα1-/-α2-/- MEFs. Wild-type (WT), AMPKβ1-/-β2-/- and AMPKα1-/-α2-/- MEF lysate was subject to Western blot analysis and probed with antibodies raised to AMPKα1/2, AMPKβ1/2 and γ1. Actin was used as a loading control. Western blots were developed using the LI-COR imaging system.

Another explanation for the results reported by Ros et al is that AMPKβ1 complexes have an essential role, which cannot be compensated for by AMPKβ2 complexes. However, AMPKα1 isoforms represent at least 90% of total AMPK in the cancer cell lines and no effect was seen upon AMPKα1 knockdown, arguing against this hypothesis. In addition, total AMPK expression and activity was only significantly upregulated in LNCaP relative to RWPE1 cells. Dependency on β1 for cell survival was found in all prostate cancer cell lines; this argues against an increase in cancer cell AMPK activity being required for cancer cell survival.

There are a number of obvious caveats to the experimental approach taken by Ros et al, namely the use of only one control ‘non-transformed’ cell line and culturing this cell line in different media to the cancer cell lines. Additionally, treating any immortalised clonal cell line as a non-transformed control is misleading. To address the role of AMPK in prostate cancer in vivo and further the Ros et al study, a β1-null mouse model of prostate cancer was generated. Further validation, for the choice in mouse model is provided in the following section (Section 4.1.2).

4.1.2 β1 expression is upregulated in LNCaP cells in response to androgen stimulation

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LNCaP cells express the androgen receptor (AR) and are an androgen-sensitive cell line. To investigate AMPK expression in response to AR signalling, cells were grown in the presence and absence of the synthetic androgen, mibolerone and AMPK expression assessed using Western blotting. LNCaP cells were cultured in charcoal-stripped serum. Charcoal-stripped serum removes androgens and other hormones, which would otherwise interfere with AR signalling. Note: in previous figures LNCaP cells were grown using non-serum-stripped medium.

LNCaP cells treated with mibolerone showed the expected induction of AR expression (Figure 4.3). Interestingly, β1 and α subunit expression were also found to be increased after 48 h mibolerone treatment. In contrast, β2 expression was not affected by androgen stimulation. These data suggest that AMPK expression is androgen responsive, specifically AMPKβ1 complexes. The significance of androgen-dependent AMPKβ1 expression will be further characterised using the β1-null prostate cancer mouse model.

A B

-Mib -Mib +Mib +Mib

AR 1.08 0.92 4.36 3.98

AMPKα1/2 1.14 0.86 1.61 1.53

AMPKβ1 1.05 0.95 1.82 1.83

AMPKβ2 1.25 0.75 0.83 1.08

Figure 4.3 Effect of mibolerone treatment on AMPK subunit expression. A) LNCaP cells were cultured in phenol red-free RPMI medium containing 5% charcoal-stripped serum. Cells were treated minus and plus 1 nM mibolerone for 48 h and slowly lysed. These lysates were then subject to Western blot analysis, and probed with antibodies against the androgen receptor (AR), AMPKα1, β1 (mouse antibody) and β2 (mouse antibody); RPL7 is used as a loading control. B) Quantification of AR, α1, β1 and β2 bands from the Western blot presented in A), results were normalised to RPL7, before normalisation to the untreated condition.

4.2 Mouse model generation

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4.2.1 Knockout-first AMPKβ1 mouse model

A previously uncharacterised AMPKβ1 ‘knockout-first’ mouse model was obtained from the KOMP (knockout mouse project) repository. Knockout-first conditional allele targeting is a powerful approach that allows both lacZ reporter-tagging and conditional mutation of a given gene of interest (Skarnes et al., 2011). Conditional alleles permit the analysis of gene function in a tissue-specific and/or temporal manner. In contrast to standard conditional designs, the starting allele generates a null allele through splicing to a lacZ trapping element contained in the targeting cassette. The knockout-first allele can be modified in crosses to transgenic Flp and Cre mice (Figure 4.4). Conditional alleles are generated by removal of the gene-trap cassette by Flp recombinase, the mutation is reverted to wild-type, leaving loxP sites on either side of the critical exon. In this model the critical exon is exon 2 of the AMPKβ1 gene. Subsequent crosses with Cre mice leads to deletion of exon 2 to induce a frameshift mutation and trigger nonsense-mediated decay of the mutant transcript.

Figure 4.4 Utility of the knockout-first allele. A) The ‘knockout-first’ allele contains an internal ribosome entry site upstream of a lacZ reporter gene and a floxed neo cassette which disrupt gene function. Flp converts the ‘knockout-first’ allele to a conditional allele (B), thereby restoring gene activity. In contrast, Cre action on the A allele deletes the floxed neo cassette and exon to generate a lacZ-tagged allele (C). Alternatively, Cre action on the B allele leads to the deletion the floxed exon generating a frameshift mutation (D), and nonsense mediated decay of the transcript. Generation of D using a tissue-specific Cre allows conditional deletion of the gene-of-interest. (Skarnes et al., 2011).

This mouse model was used in crosses with the previously characterised PTEN mouse model of prostate cancer (Ptenfl/fl/PB-Cre4+) (Wang et al., 2003, Martin et al., 2011, Francis et al., 126

2013, Wang et al., 2013). The PTEN prostate cancer model is generated by crossing Ptenfl/fl mice with the ARR2-Probasin-Cre transgenic line, PB-Cre4, in which the Cre recombinase is under the control of an enhanced prostate-specific probasin promoter. These crosses lead to the generation of mice with homozygous deletion of PTEN in prostate epithelial cells. The mice ultimately go on to development of adenocarcinoma in the prostate (see Chapter 1, Section 1.4.2). By crossing this prostate cancer model with the β1-deletion line, a novel transgenic mouse model for studying the role of AMPK in prostate cancer was generated.

Global and tissue-specific 1-deletion can be achieved using the knockout-first 1 mouse line; therefore two possible 1-knockout mouse models of prostate cancer could be generated: β β Model 1: AMPKβ1-/-/Ptenβ fl/fl/PB-Cre4+ (A, Figure 4.4) Global β1-deletion and prostate-specific PTEN deletion

Model 2: AMPKβ1fl/fl/Ptenfl/fl/PB-Cre4+ (D, Figure 4.4) Prostate-specific β1 and prostate-specific PTEN deletion

The global β1 model (model 1) has the advantage of requiring less rounds of breeding, as generation of the conditional β1 model (model 2) requires additional crosses to remove the gene-trap cassette. An advantage of using model 2 is that β1-deletion is restricted to the prostate, thus any affect due to global β1-deletion would be alleviated. The use of the conditional model was deemed necessary only if global β1 knockout mice were found to have an overt phenotype that could affect prostate cancer development. The breeding programme to produce the novel transgenic mouse lines used in this study was challenging (Figure 4.8) and so any opportunity to limit the number of crosses and therefore save time and money was important. To this end, the global β1-knockout mice were phenotyped to decide whether model 1 was a viable option.

Initially, it was shown that the β1 subunit was deleted in the homozygous knockout mouse using liver lysates (Figure 4.5). In liver lysates, β1 knockout was found to reduce total AMPK activity by 95% AMPK and lead to a 95% reduction in AMPKα subunit protein expression. AMPK activity was decreased in different tissues by varying amounts depending on the level of β1 expression in the wild-type tissue (data not shown).

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Figure 4.5 Characterisation of AMPK in wild-type (WT), AMPKβ1+/- and AMPKβ1-/- livers. A) Livers were obtained from mice at 12 weeks of age, homogenised and subject to Western blot analysis. Western blots were probed with antibodies against AMPKα1/2, AMPKβ1 and tubulin was used as a loading control. Western blot was developed using the LI-COR imaging system. B) AMPK activity from 50 μg of the liver lysates used in A) were assayed using the SAMS kinase assay. These data are represented as fold change relative to wild-type (WT) mice. Data are means ± SEM (n= 3 per genotype). *p<0.05; t-test.

4.2.2 Phenotyping the global β1 knockout mouse model

Dzamko et al, previously generated an AMPKβ1 knockout mouse using a different construct but on the same mouse background and reported no significant differences in metabolic rate, physical activity, adipose tissue lipolysis, and lipogenesis between β1 knockout mice and WT littermates on a chow diet (Dzamko et al., 2010). However, they did find β1 knockout mice to have reduced food intake and therefore reduced body mass. Reduced body mass was confirmed in the knockout-first β1-/- model and correspondingly β1-/- mice were found to have reduced body fat mass and increased lean mass using ecoMRI (Figure 4.6).

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A B C

Figure 4.6 Body weight and composition in wild-type (WT) and β1-/-mice. A) Body weight of wild-type and β1-/- mice age 20-23 weeks. Data are means ± SEM (n= 5 per genotype). **p<0.01; t-test. B) Fat mass and C) lean mass were measured in the same mice as used in A) by ecoMRI and normalised to body weight. Data are means ± SEM (n= 5 per genotype). **p<0.01; t- test.

Upon further characterisation of the global β1 knockout mouse model, it was found that β1-/- mice had a more severe phenotype, not reported by Dzamko et al. β1-deletion was found to lead to splenomegaly and hemolytic anaemia. Spleens from β1 knockout mice were found to be double the size of their wild-type counterparts (Figure 4.7A,B). AMPKβ1 knockout mice were found to have on average 6% lower hematocrit levels compared to wild-type, indicative of a low red blood cell count and anaemia (Figure 4.7C). Global AMPKα1 and AMPKγ1 knockout mouse lines have been generated previously by other groups and both mouse lines were found to have this phenotype (Foretz et al., 2011, Föller et al., 2009). Foretz et al showed that γ1-knockout mice displayed splenomegaly and iron accumulation due to compensatory splenic erythropoiesis and erythrophagocytosis. They showed γ1-deficient erythrocytes were highly resistant to osmotic haemolysis and demonstrated that the γ1 subunit is required for the maintenance of erythrocyte membrane elasticity. β1-deficient erythrocytes were also found to be resistant to osmotic hemolysis (Figure 4.7D). β1-knockout mice phenocopy γ1-knockout mice for this systemic phenotype, which is perhaps unsurprising given that α1β1γ1 is the only AMPK complex expressed in erythrocytes (Foretz et al., 2011).

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A B

C D

Figure 4.7 Spleen weight, hematocrit and erythrocyte osmotic resistance in wild-type (WT) and β1-/- mice. A) An image of spleens dissected from WT and β1-/- mice aged 17 weeks. B) Spleen weight normalised to body weight from WT and β1-/- mice aged 17-20 weeks. C) Hematocrit (volume percentage of erythrocytes in blood) for mice used in B). D) Erythrocyte osmotic resistance as determined by -/- percentage hemolysis for WT and β1 mice used in parts B) and C). The A540 of each sample in water was taken as 100% hemolysis, and readings of the same sample in solutions of various osmolarities were normalised to this value. Data are means ± SEM (n= 8 per genotype). **p<0.001,***p<0.005; t- test.

4.2.3 Breeding strategy

Due to the more severe systemic phenotype discussed in the previous section, the conditional β1 model of prostate cancer (model 2) was generated, despite the more complicated breeding programme. The final crosses used to generate the novel AMPKβ1fl/fl/Ptenfl/fl/PB- Cre4+ mouse line and the Ptenfl/fl/PB-Cre4+ control line are shown in Figure 4.8. Mice with loss of AMPKβ1 and PTEN in the prostate epithelium (AMPKβ1fl/fl/Ptenfl/fl/PB-Cre4+) will hereafter be referred to as β1-/-PTEN-/-, and Ptenfl/fl/PB-Cre4+ control mice will be referred to as PTEN-/-.

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Figure 4.8 Breeding strategy used to generate β1-/-PTEN-/- (AMPKβ1fl/fl/ Ptenfl/fl /PB-Cre4+) and PTEN-/- (Pten-/-/PB-Cre4+) prostate cancer mouse models.

The generation of the novel transgenic mouse models used in this study was not trivial and required a complicated breeding programme with a number of rounds of different crosses. It took 2 years before significant numbers of animals with the desired genotypes were produced. A number of factors contributed to the length of the breeding programme, not least that the model required homozygous deletion of two genes dependent on Cre- recombinase expression. In addition, the probasin Cre used in this study could not be transmitted along the maternal line. This is due to low level Cre expression in oocytes, which could potentially produce offspring harbouring global deletions of floxed genes (Wu et al., 2001). Furthermore, PTEN-/- males could not be used for breeding due to infertility associated with disease progression. Stud males therefore had to be heterozygous for the floxed PTEN allele, which lowered the proportion of offspring with the correct genotype. As indicated in Figure 4.8, the number of experimental animals produced per cross was low. Cross 1, produces prostate-specific PTEN-knockout mice both wild-type and knockout for β1, but to increase the numbers of control and test animals produced, crosses 2 and 3, respectively were used to supplement the pool.

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4.3 Characterisation of the PTEN-positive mouse prostate

4.3.1 AMPK expression in wild-type and AMPKβ1-/- mouse prostate

Before moving into the different cancer models, characterisation of the AMPK pathway was carried out in wild-type and β1-knockout prostates in PTEN-positive mice. Animals were sacrificed at 20 weeks of age and the prostate dissected and snap frozen. Protein was extracted from the total prostate and subject to Western blot analysis and AMPK activity assays (Figure 4.9). These preliminary studies were performed using the already established global β1-/- mice (the aforementioned phenotypes did not affect normal prostate development).

A B

Figure 4.9 Characterisation of AMPK in wild-type (WT) and β1-/- prostates. A) Prostates were taken from wild-type (WT) and β1-/- mice aged 17-20 weeks, homogenised and subject to Western blot analysis. Western blots were probed with antibodies raised against pACCSer79, ACC, AMPKα1 and AMPKβ1/2; tubulin was used as a loading control. Western blot was developed using the LI-COR imaging system B) AMPK activity was measured in prostate lysate used in A) by SAMS kinase assay. Data are presented as fold relative to WT. Data are means ± SEM (n= 4 per genotype). ***p<0.005; t-test

The predominant AMPK α and β subunits expressed in the mouse prostate were found to be α1 and β1 (Figure 4.9A). β2 expression was detected, whereas α2 expression was below the detection limit of the antibody. AMPK isoform expression in the mouse prostate mirrors that in the human prostate cell lines (see Chapter 3, Section 3.2.1). In the β1-knockout prostate, total AMPK activity and AMPKα1 expression was found to be reduced by 70% compared to wild-type mice (Figure 4.9B). The remaining 30% of AMPK activity is attributable to the remaining β2-complexes.

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Despite the large reduction in total AMPK activity, no difference in phosphorylation of ACC at Ser79 was detected by Western blotting (Figure 4.9A). This is likely due to a dissection artefact. AMPK is activated upon dissection as the ratio of ADP/AMP to ATP becomes increased. ACC phosphorylation is quickly saturated by AMPK activation as it represents a very sensitive downstream substrate. To overcome this artefact of dissection, it is necessary to generate primary cells and rapidly lyse them. This itself is technically challenging and is still not representative of the in vivo situation. In summary, the level of ACC and AMPK phosphorylation in dissected tissue is generally reflective of total protein expression rather than basal activation state in vivo.

The mouse prostate is lobular (see Chapter 1, Section 1.4.1), consisting of 3 distinct lobes: anterior prostate (AP), dorsolateral prostate (DLP) and ventral prostate (VP). Gene expression profiling has shown that the different lobes have similar gene expression patterns to the different functional zones of the human prostate (Berquin et al., 2005). AMPK expression and activity was thus assessed in each lobe of the prostate (Figure 4.10).

A B

Figure 4.10 AMPK activity in lobes of the prostate in wild-type (WT) and β1-/- mice. A) Prostate lobes were taken from wild-type (WT) and β1-/- mice aged 20-23 weeks, homogenised and subject to Western blot analysis using the ProteinSimple Wes automated Western blot system. Western blots were probed with antibodies raised against pAMPKαThr172 and AMPKβ1/2. B) The ProteinSimple software allowed accurate quantification of pAMPKαThr172 signal. pAMPKαThr172 signal (peak area) is normalised to lysate protein concentration and is shown in A). Data are means ± SEM (n= 4, per genotype). *<0.05, **<0.01, ***p<0.005; t-test B) A representative virtual Western blot from which A) was calculated.

The ventral lobe was found to have significantly increased AMPK expression compared to the other lobes in the wild-type prostate. In all prostate lobes, AMPKβ1 was found to be the major β-isoform, with β1-deletion decreasing total AMPK activity (as judged by pAMPKThr172 immunostaining) by 70% in anterior and dorsolateral lobes and 85% in the ventral lobe. AMPKβ2 expression was not found to be significantly altered upon β1 deletion.

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4.3.2 AMPKβ1-deletion and prostate weight

Wild-type and prostate-specific β1 deletion mice were sacrificed at 12 and 17 weeks and prostate weight recorded and normalised to body weight (Figure 4.11). A small increase in prostate weight was seen at the 17 week timepoint, compared to 12 timepoint. However, at neither timepoint was a difference between wild-type and 1-null prostate weight seen.

Figure 4.11 Prostate weight from wild-type (WT) and β1fl/fl/PB-Cre4+ (β1-/-) mice. Wet weights were taken for prostates from wild-type (WT) and β1fl/fl/PB-Cre4+ (β1-/-) mice aged 12 and 17 weeks and normalised to body weight. Data are means ± SEM (n=6-10 per genotype). *<0.05; t-test

4.4 Characterisation of PTEN-/- and AMPKβ1-/-PTEN-/- prostate cancer mouse models

The PI3K/Akt pathway is a major signalling pathway that is activated in many human malignancies (see Introduction, Section 1.1.4) (Fresno Vara et al., 2004). Activation of the PI3K/Akt pathway by a variety of mechanisms has been found in almost all late-stage prostate cancers (Taylor et al., 2010). PTEN deletions and/or mutations are among the most common genetic alteration in human prostate cancer. The major function of the tumour suppressor, PTEN, relies on its phosphatase activity and consequent antagonism of the PI3K/Akt pathway. The PTEN mouse model of prostate cancer accurately recapitulates the different stages seen in the human disease (progression from PIN to adenocarcinoma) albeit on a more rapid time scale making it an excellent model for the human disease (see Chapter 1, Section 1.1.2 and 1.4.2). The original paper characterising this model reports the occurrence of metastatic disease in the lymph nodes and lungs between 12-29 weeks of age. This observation was not reproduced in other initial reports using the PTEN-/- model and there have been no further 134

reports of PTEN loss alone in prostate epithelial cells leading to metastatic disease (Trotman et al., 2003, Backman et al., 2004, Irshad and Abate-Shen, 2013). Indeed, in this study, PTEN-/- tumours were not found to metastasise.

4.4.1 Prostate weight

PTEN-/- and β1-/-PTEN-/- mice were sacrificed at 12, 17 and 26 weeks and prostate wet weight was recorded and normalised to body weight (Figure 4.12A). At each timepoint there was a significant increase in prostate size correlating with disease progression; however at no timepoint was there a significant difference between PTEN-/- and β1-/-PTEN-/- prostate weight. By 26 weeks of age the anterior lobe of the PTEN-/- prostate was found to become fluid-filled and cystic (Figure 4.12B) and this led to high variability in prostate weight at this timepoint.

Figure 4.12 PTEN-/- and β1-/-PTEN-/- prostate weight A) PTEN-/- and β1-/-PTEN-/- prostate wet weight was recorded at harvest for mice aged 12, 17 and 26 weeks and normalised to body weight. Data are means ± SEM (for each genotype n=17-21 at 12 weeks; n=12-15 at 17 weeks; n=14-17 at 26 weeks). ***p<0.005. B) Pictures of prostates taken at harvest in 26 week old mice, a typical example of each genotype relative to wild-type (WT) prostate shown. 135

4.4.2 Disease pathology

Prostate weight is a blunt measure of disease progression and provides no insight into disease grade or pathology. To further assess disease progression, prostate tissue was taken from 26- week old PTEN-/- and β1-/-PTEN-/- mice, fixed in 4% paraformaldehyde (PFA) and stained with haematoxylin and eosin (HE) for histopathology.

At harvest the mouse prostate was divided into anterior prostate (AP) and dorsolateral/ventral prostate (DLVP) lobes. The DLVP was not further divided into dorsolateral and ventral lobes as with disease progression these two lobes become fused and difficult to define. A similar dissection protocol was adopted by other groups using this model (Francis et al., 2013). As previously reported, disease progression was slightly accelerated in the DVLP lobe (Wang et al., 2003). This was found to be consistent with the efficiency of Cre-mediated PTEN deletion. Nonetheless, a strong correlation in disease progression between the different lobes within a prostate from the same mouse was observed. A description of how disease progression was assessed and lesions graded is shown in Table 4.1. In Figure 4.13, representative images are shown for the highest grade lesions present in the anterior lobe of the prostate from each tissue section for 3 mice of each genotype. As previously mentioned, upon disease progression the AP lobes of the mouse prostate became cystic, large cysts led to visible holes in HE stained sections and are marked with an asterisk (*).

Grade Description (Ittmann et al., 2013)

Hyperplasia Non-neoplastic increase in epithelial tissue compared with age-matched wild-type control mice mPIN mPIN is the neoplastic proliferation of epithelial cells within pre-existing glandular spaces but without frank invasion. mPIN1: 1 to 2 layers of cells and mild nuclear atypia. mPIN2: increased nuclear atypia and contain 2 or more layers of cells often in papillary, tufting, or cribriform arrangements. mPIN3: obvious nuclear atypia and fill or almost fill the duct lumens in papillary or cribriform patterns. mPIN4: similar to mPIN3 but have even more severe atypia, fill the ductal lumens and may bulge into the surrounding stroma but without clear invasion. Adenocarcinoma Invasive carcinoma recognised by an infiltrative and destructive growth pattern of atypical cells with at least focal glandular differentiation. In mice, this is usually associated with stromal desmoplasia, which is characterized by stroma with abundant fibrosis. The fibrotic stroma usually has increased numbers of spindle-shaped cells as well as chronic inflammatory cells. (Stromal desmoplasia can also be seen in mPIN lesions). Table 4.1 Description of pathological grading (based on recommendations by (Ittmann et al., 2013)

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Figure 4.13 Pathology of wild-type (WT), PTEN-/- and β1-/-PTEN-/- prostates. 137 HE staining of anterior lobe (AP) tissue sections of wild-type (WT) (top panel), PTEN-/- (middle panel) and β1-/-PTEN-/- (bottom panel) prostates. Highest grade lesions shown for each tissue section and indicated in white in top right of image. mPIN lesions are indicated by a white arrowhead and adenocarcinoma lesions with a black arrowhead. Prostate cysts are marked with an asterisk (*). (n=5 examples per genotype, 3 examples shown). A scale bar is shown in the bottom right of the figure.

By 26-weeks AP and DLVP lobes from both PTEN-/- and β1-/- PTEN-/- prostates had the potential to develop invasive adenocarcinoma (Figure 4.13). However, disease progression was found to be accelerated in the β1-/-PTEN-/- prostate. By 26-weeks of age all β1-/-PTEN-/- mice were found to have developed adenocarcinoma in the AP lobe, compared to 80 % of PTEN-/- mice (n=5). In addition, the adenocarcinomas found in the β1-/-PTEN-/- mice were more developed compared to the PTEN-/- control mice, with greater areas of infiltrative and destructive growth by atypical cells. Adenocarcinomas were also associated with a greater degree of stromal desmoplasia and increased numbers of spindle shaped cells in β1-/-PTEN-/- animals indicative of worse disease.

In this thesis, all pathological and immunohistochemical analyses were performed blinded. As discussed in Chapter 1, we are in the process of setting up a collaboration with Alexander Nikitin, a Professor of pathology at Cornell University. Professor Nikitin has experience in the pathological analysis of the mouse prostate tissue and has worked with the PTEN prostate cancer model, previously. Although not possible within the timescale of this PhD, HE stained prostate tissue sections from the different models generated in this thesis, will be sent to him for confirmation of disease grading.

4.4.3 Proliferation

To investigate the effect of β1-deletion on cellular proliferation in the PTEN-deleted prostate, tissue sections were stained with Ki-67, a nuclear proliferation marker. This was done using immunohistochemistry (IHC) in AP and DLVP lobes from mice aged 26 weeks. To quantify staining, 10 regions of interest were chosen at random within a section, and the percentage of Ki-67 positive cells calculated. This was performed for both lobes of 5 mice per genotype (Figure 4.14). Quantification was automated by software written in collaboration with Chad Whilding, MRC Microscope Facility. The software recognised Ki-67 positive nuclei (DAB stained, brown) and Ki-67 negative nuclei (haematoxylin stained, purple) and subsequently quantitated the area (number of pixels) occupied by each stain in a given image. The software calculated:

%Ki-67 positive cells = (no. pixels Ki-67 stained / (no.pixels Ki-67 stained + no.pixels haematoxylin stained)) x 100

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A p=0.148 p=0.163

Figure 4.14 Ki-67 staining in PTEN-/- and β1-/-PTEN-/- prostates. A) Percentage of Ki-67-positive cells (proliferation marker) in anterior prostate (AP) lobe and dorsolateral ventral prostate (DLVP) of PTEN-/- and β1-/-PTEN-/- mice aged 26 weeks. Data are means ± SEM (n= 5 mice per genotype; on average 4000-5000 cells per tissue section across 10 regions of interest). B) Examples of the types of image quantified in (A) (n=3 examples per lobe per genotype). A scale bar is shown in the bottom right of the figure. 139

No significant difference in Ki-67 staining was found between genotypes or the different lobes of the mouse prostate. However, there was a trend for both AP and DLVP lobes of the β1-/-PTEN-/- prostate to have increased Ki-67 staining compared to the PTEN-/- (Figure 4.14A). Ki-67 staining was found to be highly variable within a tissue section. Different regions of interest within the same section typically ranged from 5% to 30% Ki-67-positive cells. The average percentage of Ki-67 positive cells per mouse lobe could thus be skewed by regions of interest displaying extremes in staining of this proliferation marker. This high degree of variability can be seen in the images shown in Figure 4.14B. There was a tendency for lobes from β1-/-PTEN-/- prostates to contain more regions displaying high Ki-67 staining.

4.4.3 Molecular characterisation

IHC using fixed tissues has the advantage of enabling visualisation of the distribution and localisation of specific proteins-of-interest in the proper tissue context. This is particularly helpful in cancerous tissues, which by their nature contain heterogeneous populations of cells as demonstrated by the Ki-67 staining performed in the last section. However, the major disadvantage with this technique is that, unlike Western blotting where staining can be checked against a molecular weight ladder, in IHC it is impossible to show staining corresponds with the protein-of-interest (unless knockout tissue is available). In addition, intensity of IHC staining is rarely quantitative. Prostate tissue was therefore also snap-frozen for subsequent Western blot analysis and molecular characterisation.

The results presented in the last section suggest that β1-deletion in the PTEN-/- model leads to increased disease progression. Akt and AR signalling were initially characterised in the prostates of 12 week old mice. Figure 4.15 shows the results generated for the DLVP lobe of the prostate. The 12 week timepoint was chosen for further characterisation as it represents an early timepoint critical for exploring initial molecular changes that lead to cancer development. This early timepoint has the potential to highlight early differences between the β1-/-PTEN-/- and the PTEN-/- prostate, which lead to the differences in disease progression seen at 26-weeks. In addition, at this early timepoint, the PTEN-null prostate is yet to develop discrete adenocarcinomas, instead both lobes display more global changes in epithelia, presenting with hyperplasia and mPIN1-2. It follows that protein expression across the tissue is more homogenous, meaning the averaging associated with Western blot analysis is more appropriate.

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C D

p=0.094

Figure 4.15 Akt and AR signalling in wild-type (WT), PTEN-/- and β1-/-PTEN-/- prostates. A) Dorsolateral ventral prostate (DLVP) lobes from wild-type (WT), PTEN-/- and β1-/-PTEN-/- mice aged 12 weeks were dissected, homogenised and subject to Western blot analysis. Western blots were probed with antibodies raised against A) pAktSer473, total Akt, pS6Ser235 (Akt signalling) and B) androgen receptor (AR), CaMKKβ (AR signalling); actin was used as a loading control. Western blots were imaged using the LI-COR system. Quantification of C) pAktSer473 normalised to Akt expression and D) CaMKKβ normalised to actin expression from Western blots shown in parts A) and B), respectively. Data are means ± SEM ( n= 4 per genotype for DLVP lobe).

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Upon PTEN deletion, there is significant upregulation of Akt phosphorylation in both lobes of both genotypes (shown for DLVP in Figure 4.15A). Phosphorylation of Ser473 activates Akt and therefore acts as a read-out of Akt activity (Sarbassov et al., 2005). Consistently, S6 phosphorylation is also upregulated upon PTEN-deletion due Akt-dependent activation of mTORC1 and ribosomal protein S6 kinase, thereby promoting protein synthesis and cellular proliferation (Fresno Vara et al., 2004). No difference in Akt or S6 phosphorylation was seen between β1-/-PTEN-/- and the PTEN-/- prostates. In addition, to Akt signalling, AR protein expression was found to be significantly increased upon PTEN-deletion (Figure 4.15B). CaMKKβ is an androgen-responsive gene (Massie et al., 2011, Frigo et al., 2011) and its overexpression provides further confirmation of upregulated androgen-signalling in the PTEN-null prostate. These data are consistent with AR being an important driver of prostate cancer development (Heinlein and Chang, 2004). The exact mechanism of how PTEN deletion causes upregulation of AR signalling is currently unknown (Wang et al., 2003). Since these signalling pathways have been shown to drive human prostate cancers, their upregulation in this model adds confidence in its use to model the human disease. Although, no differences in Akt phosphorylation was seen between PTEN-/- and β1-/-PTEN-/- prostates (Figure 4.15C), there was a trend for increased CaMKKβ expression upon β1 deletion (Figure 4.15D).

Following confirmation that PTEN-deletion led to upregulation of PI3K/Akt and AR signalling, the PTEN-null prostates were characterised with respect to the AMPK pathway (Figure 4.16).

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A B

Figure 4.16 Characterisation of AMPK in wild-type (WT), PTEN-/- and β1-/-PTEN-/- prostates. Anterior prostate (AP) lobes from wild-type (WT), PTEN-/- and β1-/-PTEN-/- mice aged 12 weeks were dissected, homogenised and subject to Western blot analysis. Western blots were probed with antibodies raised against the pACCSer79, ACC, pAMPKThr172, α1/2, β1/2 and γ1; actin was used as a loading control. Western blots were imaged using the LI-COR system. B) Similar analysis outlined in A) was performed in the dorsolateral ventral (DLVP) lobe of the prostate; however pAMPKThr172 and γ1 expression was not measured. Quantification of C) β1 and D) α1/2 expression normalised to actin expression for AP and DLVP lobes as indicated. Data are means ± SEM (n=6 mice per genotype for AP lobe; n= 4 per genotype for DLVP lobe).

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Upon PTEN-deletion, ACC protein expression was found to be upregulated in the mouse prostate (Figure 4.15A,B); again this protein is commonly overexpressed in human prostate cancers (Racioppi and Means, 2012, Wu et al., 2014). However, ACC expression was not affected by prostate β1-status.

In addition to these well documented changes in gene expression upon prostate cancer development, AMPKα1, β1 and γ1 expression were found to be robustly upregulated upon PTEN-deletion. AMPKβ2 expression was not significantly changed upon PTEN-deletion. This result is consistent with previous data generated in the human LNCaP cell line (Figure 4.3), where incubation with a synthetic androgen led to upregulation of AMPKβ1-complexes. AMPKβ1 was found to be upregulated between 2.5- and 4- fold depending on prostatic lobe (quantitation shown in Figure 4.15C). Notably, there is still β1 expressed in the β1-/-PTEN-/- prostate. This is on account of β1 only being deleted in the prostate epithelial cells and it is therefore still expressed in other cell types of mouse prostate tissue. Total AMPK was found to be reduced by 67% and 75% in β1-/-PTEN-/- AP and DLVP lobes, respectively, compared PTEN-/- (based on α1/2 expression, Figure 4.16 D). This is very similar to the reduction seen upon β1-deletion in prostates from mice wild-type for PTEN.

To further show that overexpression of AMPKβ1 complexes in the PTEN-/- prostate was an androgen driven process, expression of AMPKβ subunits was interrogated in another Pten-/- driven cancer model. To do this the PTEN hepatocellular carcinoma model was chosen, where albumin Cre is used to specifically delete PTEN in hepatocytes (Watanabe et al., 2007). In this model PTEN deficiency induces hepatic steatosis, fibrosis and tumour development by 40 weeks of age. This is thought to be an androgen independent process, consistently AR and CaMKKβ expression are undetectable in this cancer model (data not shown). Tissue was taken from wild-type and PTEN-/- livers at 10, 36 and 47 weeks of age. Similar to the PTEN prostate cancer model, at each timepoint Akt phosphorylation was significantly increased in PTEN-/- tissue relative to wild-type tissue (Figure 4.17). However, unlike the prostate cancer model, no difference in AMPKβ1 expression was found upon PTEN-deletion. This is consistent with β1 upregulation being androgen-dependent.

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Figure 4.17 Characterisation of AMPKβ1 expression in the PTEN model of liver cancer. Livers were dissected from wild-type (WT) and PTEN-/- (Ptenfl/fl/Alb-Cre) mice aged 10, 36 and 47 weeks and homogenised and subject to Western blot analysis. Western blots were probed with antibodies raised against pAktSer473, Akt, and β1/2; actin was used as a loading control. Western blots were imaged using the LI-COR system.

4.5 The ‘switch’: a marker of disease progression

4.5.1 Discovery of a switch in global protein expression in the PTEN-/- mouse prostate

Upon initial characterisation of the prostatic lobes a switch in global protein expression was found to be occurring in the PTEN-null prostates during disease progression. This ‘switch’ was initially discovered during ponceau staining membranes to visualise the protein in prostate lysate after separation by SDS-PAGE (Figure 4.18; lanes marked green and red correspond to samples pre-switch and switched samples, respectively). Notably, upon the switch there was loss of a band at around 80 kDa and intensification of a band around 65 kDa.

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A B

Figure 4.18 Ponceau stained membranes for wild-type (WT), PTEN-/- and β1-/-PTEN-/- prostate lysate. A) Anterior prostate (AP) lobes from wild-type (WT), PTEN-/- and β1-/-PTEN-/- mice aged 12 weeks were dissected, homogenised, separated by SDS-PAGE and transferred to PDFV membrane. This membrane was stained with ponceau protein stain. Protein standards are shown in lane 1 (n= 2 WT, n=3 PTEN-/-, n=3 β1-/-PTEN-/-). Lanes are marked with green (pre-switch tissue) or red (switched tissue). B) Same as for A) but in the dorsolateral ventral (DLVP) lobe of the prostate (n= 2 WT, n=4 PTEN-/-, n=4 β1-/-PTEN-/-).

By 12 weeks, all DLVP lobes were found to have gone through the switch compared to less than 50% of AP lobes from PTEN-/- mice. In contrast, at 12 weeks initial observations showed all β1-/-PTEN-/- AP lobes had progressed through the switch. Preliminary studies in the AP lobe showed that the switch in PTEN-/- prostates correlated with increased AR signalling (increased AR and CaMKKβ expression) and increased Akt phosphorylation (Figure 4.19). AMPKβ1-complex overexpression was found to occur prior to switch progression and was not altered by sample switch status (Figure 4.25C). Since switch status corresponded with molecular markers known to drive disease progression (Akt and AR signalling) and appeared to be affected by β1-deletion, this change in prostate expression profile was further characterised.

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Figure 4.19 Androgen receptor, Akt and AMPK expression in wild-type (WT), pre-switch (PS) and switched (S) PTEN-/- prostate tissue. Anterior prostate (AP) lobes from wild-type (WT) PTEN-/- mice aged 12 weeks were dissected, homogenised, separated by SDS-PAGE and subject to Western blot analysis. A) The ProteinSimple Wes system was used to probe samples for pAktSer473 and Akt expression; actin was used as a loading control. B) Samples were also probed with antibodies raised against the androgen receptor (AR), and CaMKKβ; actin is used as a loading control using the LI-COR imaging system. C) Ponceau staining of the PDVF membrane used for Western blot analysis in B).

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4.5.2 The ‘switch’: Prostate weight and HE staining

Since a correlation was found between the protein switch and disease progression, whether prostate weight was affected by this event was investigated. Prostate weight was recorded at harvest and normalised to body weight and grouped according genotype and protein switch status and plotted in Figure 4.20.

Figure 4.20 Weight of wild-type (WT), PTEN-/-(PS, pre-switch), PTEN-/-(S, switched) and β1-/-PTEN-/- (S, switched) prostates. Prostate wet weight was recorded at harvest for mice aged 12 weeks and normalised to body weight. Data are grouped based on genotype and switch status of anterior lobe based on ponceau staining and descriptions outlined in Table 4.2. Data are means ± SEM (n=4-7, per genotype).

No difference in prostate weight was found based on the switch status of the AP lobe of the prostate. The AP lobes account for two-thirds of total prostate weight, therefore a significant change in AP weight would be expected to correlate with a change in prostate weight. This highlights that tumour weight is a poor measure of disease progression.

To investigate how this switch in protein expression correlates with prostate pathology, both AP lobes were taken from the same mouse and one snap frozen, while the other was fixed in 4% PFA. The frozen AP lobe allowed assignment of switch status, while the fixed lobe was HE stained for histopathological analysis. Based on HE staining there a clear difference in tissue morphology between pre-switch and switched groups was found (Figure 4.21).

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80 μm

Figure 4.21 Characterisation of the effect of protein switch status on prostate pathology. HE staining of anterior lobe (AP) tissue sections of wild-type (WT), PTEN-/- and β1-/-PTEN-/- prostates and grouped according to switch status (indicated bottom right of image; PS (pre-switch), S (switched). Switch status was defined based on ponceau staining and descriptions outlined in Table 4.2 of corresponding AP lobe from the same mouse. Highest grade lesions shown for each tissue section and indicated in bottom right left of image as defined by Table 4.1. Reactive stroma is indicated by ‘#’. (n=4, 2 examples shown for WT, PS and S tissues). A scale bar is shown in the bottom right of the figure.

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In Figure 4.21, it is possible to follow the progression from normal prostate epithelium (top wild-type panel) to mPIN1-2 (bottom switch panel). The middle panel displays pre-switch PTEN-/- tissue, here the epithelium displays hyperplasia but no atypical nuclei. Furthermore, switched epithelium is associated with reactive stroma or stromal desmoplasia (indicated by ‘#’ in Figure 4.21) and this is indicative of mPIN and disease progression (Table 4.1).

Despite no difference in weight between pre-switch and switched prostates a clear difference in tissue morphology was seen. Consistent with molecular markers of disease progression, histology showed switched tissues to have progressed to epithelial neoplasia, while pre-switch tissues were graded as benign hyperplasia.

4.5.3 Screen for protein biomarkers of the protein switch from prostate hyperplasia to neoplasia

Protein biomarkers of the switch were screened for using protein lysate from WT, PTEN-/- (PS), and PTEN-/- (S) AP lobes. Separate lysate from four mice per condition were pooled and separated by SDS-PAGE and the gel submitted to the MRC mass spectrometry facility. This allowed the identification of proteins signature for each condition and the identification of proteins that had significantly altered expression in switched tissues compared to pre- switch and wild-type tissue. Although the results did not provide comprehensive coverage of the proteome, a number of interesting hits were found. The top 30 hits for proteins down and upregulated upon the protein switch are shown in Figure 4.22A and B, respectively. The total number of proteins considered to be differentially expressed between pre-switch and switched samples was 433 (log2 values greater than 1.0 and less than -1.0; see Appendix 1 for a full explanation of how this was defined).

Pathway analysis was performed on the results generated from the mass spectrometry screen, despite low coverage of the proteome characteristic of mass spectrometry experiments. To do this protein accession number and differential protein expression data

(log2(S/PS)) were entered into IPA (Ingenuity Pathway Analysis). An overview of this analysis is shown in Figure 4.22C. Unsurprisingly, cancer was the top hit for diseases associated with the changes in protein expression seen. The other pathways highlighted to be affected by the protein switch have all be implicated in cancer progression and are consistent with previous data indicating this protein switch is correlative with this process. To properly interrogate this system using pathway analysis increased coverage of the proteome is required along with a larger number of samples per condition.

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A Downregulated upon switch

B Upregulated upon switch

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C i

iii

Figure 4.22 Differentially expressed proteins in pre-switch (PS) and switched (S) PTEN-/- prostates identified by mass spectrometry. A mixture of 4 anterior lobe (AP) lysates (mice aged 12 weeks) were combined based on switch status, the same was done for wild-type AP lobe lysate. Switch status was defined based on ponceau staining and descriptions outlined in Table 4.2. The mixed wild-type (WT), pre-switch (PS) and switched (S) lysate was separated by SDS-PAGE and submitted to the mass spectrometry facility to identify proteins differentially expressed in the 3 lysates. Data presented are the top 30 hits for proteins downregulated A) and upregulated B) upon the switch compared to PS and WT tissue. The level to which these proteins are differentially expressed is expressed as log2(S/PS) and indicated by yellow (low expression) or blue (high expression) in PS or S lysate columns. The rows highlighted represent proteins that were further validated. An explanation of how the lists in A and B were generated is offered in Appendix 1. C) Ingenuity Pathway Analysis (IPA) overview performed on the

entire mass spectrometry dataset (i.e. not refined based on log2 value). Protein accession number and protein expression between conditions (log2(S/PS)) were input into IPA to generate the overview shown. i) The most significant molecular and cellular function classifications, the respective number of molecules and their corresponding range of p-values are shown. ii) Canonical pathway analysis shows those that are the most significant to the input dataset. The differentially expressed genes from the dataset that map to a pathway are divided by the total number of genes mapped to the canonical pathway to give a ration and associated p-values shown. iii) Top associated networks are listed for the input datasets demonstrating the processes that are likely affected by the expression changes; a score of 40 and above is considered to be a tight association. 152

A number of proteins were chosen for subsequent validation of the mass spectrometry results and furthermore to act as biomarkers of this protein switch (Figure 4.22, highlighted in blue). A combination of both up- and down-regulated protein hits were chosen for to ensure against artefacts based on differences in sample protein concentration and/or processing. The 5 proteins further characterised were:

- Hexokinase II (HK2), glycogen phosphorylase (PYGL), phosphofructokinase (PFKFB2) (upregulated)

- Sorbital dehydrogenase (SORD), BiP1 (downregulated)

These 5 proteins were chosen as they represent some of the top hits revealed by the screen, with the largest magnitude of change in protein expression between conditions. Furthermore, the majority had been implicated as having a role in prostate cancer development in the literature (see Discussion, Section 4.6). In addition, validated reagents were commercially available against these proteins.

Antibodies raised against the 5 proteins were ordered and used for Western blot analysis of pre-switch (PS) and switched (S) samples to validate the mass spectrometry results (Figure 4.23).

Figure 4.23 Validation of mass spectrometry results. The anterior lobe of the PTEN-/- mouse prostate was harvested at 12 weeks and grouped based on switch status based on ponceau staining pattern. AP lysate from pre-switch (PS) and switched (S) tissue was subject to Western blot analysis. Western blots were probed with the highlighted switch biomarkers from Figure 4.22A,B and indicated in this Figure. Actin was used as a loading control. Blots were imaged using the LI-COR imaging system.

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The antibodies gave the expected staining pattern in Western blot analysis of pre-switch and switched prostate tissue, thereby validating the mass spectrometry results (Figure 4.23). To further characterise how the expression of these 5 proteins altered during the course of the protein switch, AP lobes from PTEN-null prostate were homogenised and subject to Western blot analysis and probed with the different validated antibodies. This revealed intermediate stages along the course of progression from pre-switch to switched states. A grading system was worked up based on the protein expression of all 5 biomarkers and is described in Table 4.2, with examples of corresponding staining patterns shown in Figure 4.24. The mobility of HK2 and PYGL on SDS-PAGE was found to be higher in certain intermediate grade pre-switch lysate and this is likely due to differences in post- translational modification status as both proteins are regulated at this level (Roberts et al., 2013, Mathupala et al., 2006, Zois and Harris, 2016). This difference in mobility shift is most evident in Figure 4.25C.

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Figure 4.24 Western blot showing representative staining of biomarker proteins for the different switch grades defined in Table 4.2. The anterior lobe of the PTEN-/- mouse prostate was harvested at 12 weeks. The corresponding protein lysate was then subject to Western blot analysis and probed with the highlighted switch biomarkers from Figure 4.21 and indicated in this Figure. Actin was used as a loading control. The grade 1, 2i-ii, 3 correspond to lystate switch status and are defined in Table 4.2.

Classification Protein expression (Based on Western blotting)

1 (PS) - No hexokinase II and glycogen phosphorylase -Low phosphofructokinase 2 -High sorbitol dehydrogenase and/or BiP1 2i -Low hexokinase II, glycogen phosphorylase and/or phosphofructokinase 2 - Some sorbitol dehydrogenase & BiP1

2ii -Low hexokinase II and/or phosphofructokinase 2 (but increased relative to 2i) -Low sorbitol dehydrogenase and/or BiP1

3 (S) -High hexokinase II , glycogen phosphorylase and phosphofructokinase 2 -No sorbitol dehydrogenase and BiP1 Table 4.2 Characteristic biomarker staining in Western blot analysis of prostate lysate and corresponding switch status classification.

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Upon establishment of a robust grading system, the effect of β1-deletion on switch progression in the PTEN-/- prostate was investigated. PTEN-/- and β1-/-PTEN-/- prostates were harvested aged 8, 10, 12 and 17 weeks and AP samples graded according to Figure 4.24 and Table 4.2. The effect of genotype on switch-free survival (AP below classification 3) is shown in Figure 4.25A.

PTEN-/- β1-/-PTEN-/-

Grade 1 (PS) 2 3 (S) 1 (PS) 2 3 (S) (%) (%) (%) (%) (%) (%) Age N numbers (Weeks) 8 100 0 0 100 0 0 3,3 10 54 33 13 36 9 55 15, 11 12 40 20 40 10 0 90 10, 10 17 0 0 100 0.0 0 100 6, 6

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C Example of classification (mice aged 10 weeks)

Figure 4.25 The effect of β1-deletion on PTEN-/- prostate switch progression. Mice with PTEN-/- and β1-/-PTEN-/- prostates were harvested at 8, 10, 12 and 17 weeks and their anterior prostates homogenised and subject to Western blot analysis and switch status defined based on the classification system worked up in Figure 4.24 and Table 4.2. A) Switch-free survival is plotted against age for each genotype. Switch-free survival is defined as mice with prostates classed below a 3 classification (i.e. 1, 2i and 2ii). B) Table showing the percentage of mice per genotype for each classification. N numbers are displayed in the right-hand column. C) A representative Western blot for the 10 week timepoint showing biomarker staining and corresponding classification.

The deletion of β1 was found to bring the protein switch on earlier in the PTEN-/- mouse prostate (Figure 4.25). Half of β1-/-PTEN-/- prostates have progressed through the protein switch by 10 weeks, whereas in the PTEN-/- prostate this occurs at 13 weeks. Nonetheless, by 17 weeks all mice PTEN-null mice were found to have undergone the switch. This 3-week lag in progression to neoplastic disease is relatively subtle; however this model is a very aggressive model of prostate cancer and in the human prostate this could be a critical tumour suppressive mechanism. In addition, the remaining AMPKβ2-activity may reduce the magnitude of any effect seen due to loss of AMPK activity.

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An observation made during the course of this study was that prostate lobes from β1-/-PTEN-/- mice were rarely caught in the intermediate switch state (classification 2). Together, these data suggest that AMPK activity has a role in slowing disease progression and deletion of β1 lifts this brake. Consistent with this hypothesis, AMPK upregulation in PTEN-/- prostates occurs in pre-switch tissues before switch progression (Figure 4.25C).

4.5.4 The protein switch: validation of biomarkers in immunohistochemistry

Upon progression through the protein switch there are changes and outgrowth in both prostate epithelial and stromal cells. It is important to investigate which from cell type(s) these changes in protein expression are originating. A way to get information on where a protein is being expressed within a tissue is using IHC. As discussed previously, it is often difficult to prove IHC staining as specific to the protein-of-interest, to best alleviate this all antibodies were first validated in Western blotting (Figure 4.23). In addition, the differential expression of the biomarkers between pre-switch and switched states gives the ideal positive and negative controls for each antibody. Wild-type and PTEN-/- AP lobes from 17 week old mice were used as pre-switch and switched tissues, respectively. All antibodies were tested in IHC, however only those raised against HK2 and BiP1 were found to give specific staining (Figure 4.26). The antibody raised against SORD failed to give any staining, whereas PFKFB2 and PYGL antibodies were found to stain pre-switch and switched sections with equal intensity.

As shown in Figure 4.26, both HK2 and BiP1 are expressed specifically in prostate epithelial cells of switched and non-switched mouse prostate tissue, respectively. Neither protein gave significant staining in the stromal cells of the prostate. BiP1 was shown to be expressed in the cytoplasmic and occluded from the nucleus. The majority of HK2 staining was also found in the nucleus. Despite the outgrowth of the stromal cell population, changes in protein switch biomarker expression were found to be emanating in the prostate epithelial cells themselves.

Interestingly, some cells were still found to express BiP1 in the switched prostate. This staining was limited to discrete patches located around holes in the mass of outgrown epithelial cells (Figure 4.26B). These cells are likely experiencing oncogenic stress. These switched tissues are taken from older PTEN-/- mice (aged 17 weeks) and this is the timepoint that the AP lobe starts to develop cysts. Cleaved-caspase 3 staining (a marker of apoptosis) gave a similar staining pattern to BiP1 in the 17-week old PTEN-/- prostate (data not shown) (see Discussion, Section 4.6).

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80 μm

Figure 4.26 Immunohistochemical staining of hexokinase II (HK2) and BiP1 switch biomarkers. Anterior prostate (AP) lobes were taken from wild-type (pre-switch, PS) and PTEN-/- (switched, S) aged 17 weeks and fixed overnight in 4% PFA. Tissue sections were then stained with 159 antibodies raised to either A) hexokinase II (HK2), or B) BiP1. Tissue staining was achieved using

DAB and sections were counterstained with haematoxylin. A scale bar is shown in the bottom right of the figure.

4.5.5 The switch: changes in gene expression at the RNA level

The next question to be addressed was whether these changes in protein expression seen upon the switch were associated with changes in gene expression. AP lobes from WT, PTEN-/- and β1-/-PTEN-/- prostates were divided in half for subsequent protein and RNA extraction. The protein lysate was used for Western blot analysis and switch classification, and the corresponding RNA used in qPCR to look at gene expression. Gene expression of 4 of the switch biomarkers was interrogated (HK2, PFKFB2, SORD and BiP1).

In gene expression studies, it is important to find a house-keeping gene (HKG), for which expression does not change between conditions. The gene expression of target genes can then normalised against this HKG to account for differences in input cDNA and reaction efficiency. An issue encountered during these gene expression studies was finding an appropriate HKG with constant expression between wild-type and PTEN-/- conditions. Three standard HKGs (βactin, GUSB and GAPDH) were tested and all found to be significantly increased upon PTEN-deletion (Figure 4.27A). Increased HKG expression was also found to correlate with switch progression; switched tissues were found to have higher amounts of all HKG RNA, compared to pre-switch tissues. Upon further investigation, it was found that gene expression of all the tested HKGs had the potential to be androgen-regulated and/or increased upon prostate cancer progression (Mori et al., 2008, Lund et al., 1991, Matsumoto et al., 1992, Ohl et al., 2005, Altintas et al., 2013). This likely explains the increased gene expression observed upon switch progression. Expression of 18S ribosomal RNA (18S rRNA) was found not to be affected by PTEN-deletion and disease progression (Figure 4.27B) and this was therefore used as a HKG for subsequent gene expression analysis.

In Figures 4.27 to 4.29, individual samples are displayed separately and grouped according to genotype (n=6, per genotype). Samples were not averaged based on genotype due to the variability in gene expression associated with switch status. Switch status of PTEN-null samples is indicated in each figure by HK2 protein expression as determined by Western blot analysis. Of note, despite increased actin mRNA levels in switched samples there is no corresponding difference in actin expression at the protein level (Figure 4.27A). This indicates regulation of protein expression at the level of translation or protein degradation.

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Figure 4.27 House keeper gene (HKG) expression in wild-type (WT), PTEN-/- and β1-/-PTEN-/- prostates measured using qPCR. A) Anterior prostate (AP) lobes from wild-type (WT), PTEN-/- and β1-/-PTEN-/- mice aged 12 weeks were dissected and halved for subsequent RNA and protein extraction. Actin, GUSB and GAPDH gene expression was assessed with qPCR using primers specific for each gene. The results of each mouse are plotted separately and grouped according to genotype. Results were normalised to the amount of RNA extracted from each sample. Gene expression data are represented as fold wild-type (WT). Western blot analysis from the protein lysate for PTEN-/- and β1-/-PTEN-/- mouse prostates is shown underneath the corresponding gene expression data. Western blots were probed with antibodies raised again hexokinase II (switch biomarker), AMPKβ1/2 (proof of genotype) and actin is shown as a loading control. B) Expression of the 18S ribosomal RNA (rRNA) for samples used in A).

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Next, gene expression of HK2, PFKFB2, SORD and BiP1 was investigated using qPCR (Figure 4.28). Gene expression of HK2 and PFKFB2 was found to increase at the transcriptional level and correlated with switch progression at the protein level (Figure 4.28A,B). A threshold separating wild-type and pre-switch samples from switched tissues for these genes was apparent (horizontal line drawn in Figure 4.28A,B). Despite significant downregulation of SORD and BiP1 at the protein level upon switch progression, this was not recapitulated at the mRNA level (Figure 4.28C,D). Instead, there was a trend for switched samples to have increased BiP1 mRNA levels compared to pre-switch samples, while SORD mRNA levels remained unchanged between conditions. HK2 gene expression was the most robust marker of the switch as it is very lowly expressed in pre-switch prostate tissue.

Sample PTEN-/- 1, was classed as switch 2 grade (weak HK2 staining in Western blot analysis) (Figure 4.28). This sample robustly grouped with the pre-switch samples at the gene expression level despite the weak HK2 signal at the protein level. This indicates that multiple mechanisms likely work together to increase expression at the protein level. This is supported by the idea that HK2 is upregulated at the protein level by PTEN-deletion but other mechanisms can then act to significantly upregulate at the mRNA level (Wang et al., 2014). Additionally, the changes in SORD and BiP1 protein expression were not seen at the mRNA level, lending further support that a combination of factors are required to bring about the complex changes in protein expression seen upon the switch.

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A B

C D

Figure 4.28 Protein biomarker gene expression in wild-type (WT), PTEN-/- and β1-/-PTEN-/- prostates using qPCR. Anterior prostate (AP) lobes from wild-type (WT), PTEN-/- and β1-/-PTEN-/- mice aged 12 weeks were dissected and halved for subsequent RNA and protein extraction. A) Hexokinase II (HK2), B) PFKFB2, C) SORD and D) BiP1 gene expression was assessed with qPCR using primers specific for each gene and normalised to 18S rRNA expression. The results of each mouse are plotted separately and grouped according to genotype. Gene expression data are represented as fold wild-type (WT). A line representing the threshold separating WT and pre-switch samples from switched samples has been added to each graph where applicable. Western blot analysis from the protein lysate for PTEN-/- and β1-/-PTEN-/- mouse prostates is shown as a point of reference (same as in Figure 4.27). Western blots were probed with antibodies raised again hexokinase II (switch biomarker), and actin is shown as a loading control.

Given the relationship between androgen signalling and prostate cancer progression, gene expression of the androgen receptor and the bona fide androgen-responsive gene (ARG), CaMKKβ, was investigated. Furthermore, in light of the results presented in Figure 4.27 and

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the literature supporting the androgen responsiveness of actin and GUSB at the gene expression level, normalised gene expression for these genes was determined. To this end the relationship between the switch and androgen-signalling was investigated (Figure 4.29), furthering the data presented in Figure 4.19B (AR and CaMKKβ protein expression and protein switch status).

A B

C D

Figure 4.29 Androgen receptor (AR) and AR-target gene expression in wild-type (WT), PTEN-/- and β1-/-PTEN-/- prostates using qPCR. A nterior prostate (AP) lobes from wild-type (WT), PTEN-/- and β1-/-PTEN-/- mice aged 12 weeks were dissected and halved for subsequent RNA and protein extraction. A) Androgen receptor (AR), B) CaMKKβ, C) actin and D) GUSB gene expression was assessed with qPCR using primers specific for each gene and normalised to 18S rRNA expression. The results of each mouse are plotted separately and grouped according to genotype. Gene expression data are represented as fold wild-type (WT). A line representing the threshold separating WT and pre-switch samples from switched samples has been added164 to each graph where a difference where applicable. Western blot analysis from the protein lysate for PTEN-/- and -/- -/- β1 PTEN mouse prostates is shown as a point of reference (same as in Figure 4.27).

AR, CaMKKβ, β-actin and GUSB gene expression was found to be increased in switched samples relative to pre-switch samples (Figure 4.29). These results indicate that androgen signalling is increased in tissues that have undergone the protein switch and this is consistent with results presented in Figure 4.19B. Similar to switch biomarker HK2 and PFKFB2 gene expression (Figure 4.28), AR and ARG gene expression could be split into 2 groups correlating with switch status (threshold indicated in Figure 4.29). Sample grouping for AR and ARG gene expression was identical to that found when interrogating switch biomarker gene expression, apart from the sample graded as switch grade 2 (PTEN-/-, sample 1). When investigating biomarker gene expression this sample grouped with pre- switch samples, however when measuring gene expression indicative of AR signalling, this sample consistently was found to group with switched samples. This result suggests that changes in AR signalling may precede those that occur upon the switch.

4.5.6 The switch: A putative mechanism

In a recent paper, Wang et al reported that HK2 was selectively upregulated by the combined loss of PTEN and p53 in prostate cancer cells. PTEN-deletion was found to increase HK2 mRNA translation and p53 loss enhanced HK2 mRNA stability (Wang et al., 2014). In addition, Chen et al have shown PTEN inactivation induces growth arrest through the p53-dependent cellular senescence pathway both in vitro and in vivo and that this mechanism is key in slowing down tumour development (Chen et al., 2005). Furthermore, AMPK has been implicated in regulating p53 activity (see Introduction, Section 1.2.3.1) (Jones et al., 2005, Lee et al., 2012, Nieminen et al., 2013). Since the prostate cancer model used in this study is PTEN-null and the mounting evidence of the key tumour suppressive function of p53 in this process, the potential role of p53 in the switch was investigated.

Total p53 protein expression was interrogated in wild-type, PTEN-/- and β1-/-PTEN-/- prostates using Western blotting. p53 protein was found to be stabilised upon PTEN- deletion (Figure 4.30A); this was previously reported by Chen et al in PTEN-/- anterior prostates in mice aged 11 weeks (Chen et al., 2005). p53 was not found to be stabilised in the β1-/-PTEN-/- prostate, with significantly decreased expression compared to the PTEN-/- prostate (Figure 4.30A). This implies AMPK activity is important in p53 stabilisation upon PTEN-deletion.

It has been reported that under low glucose conditions, AMPK induces phosphorylation of p53 at Ser15, mediating AMPK-dependent cell cycle arrest (Jones et al., 2005). In addition, a group recently found Myc activation induced stabilising phosphorylation of p53 at Ser15 and showed that this phosphorylation was dependent on AMPK activation (Nieminen et al., 2013). A commercially available antibody recognising phospho-Ser15 was used to

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investigate whether AMPK activation in the PTEN-/- prostate led to increased phosphorylation of p53 at this site. This antibody was found not to be sensitive enough to detect this molecular mark in crude lysate in Western blot analysis. However, this antibody proved to be a valuable reagent when used in IHC (Figure 4.30B).

Figure 4.30 Characterisation of p53 in the PTEN-/- and β1-/-PTEN-/- prostate. A) The anterior lobe (AP) was taken from wild-type (WT), PTEN-/- and β1-/-PTEN-/- prostates from mice aged 12 weeks, homogenised and subject to Western blot analysis. Western blots were probed with antibodies raised against total p53, AMPKα1/2 and actin was used as a loading control. B) p-p53Ser15 immunohistological staining of AP tissue sections of WT, PTEN-/- and β1-/-PTEN-/- prostates aged 12 weeks. Tissue staining was achieved using DAB and counterstained using haematoxylin. A scale bar is shown in the bottom right of the figure. 166

Wild-type and β1-/-PTEN-/- prostates did not show significant staining when probed with the phospho-p53Ser15 antibody; however staining was achieved in the PTEN-/- prostate (Figure 4.30B). This is consistent with the total p53 expression Western blot data. IHC using the antibody raised against total p53 was unsuccessful, potentially due to the issues of using mouse antibodies on mouse tissues. Expression of phosphorylated p53 was found to be localised to patches of prostate epithelial cells. These areas may correspond to those cells experiencing oncogenic stress, leading to AMPK activation and p53 stabilisation. The fact that phospho-p53Ser15 is highly expressed in discrete areas may explain why this antibody is not as sensitive when used for Western blotting.

In summary, these data are supportive of a mechanism whereby AMPKβ1-complexes induce p53 phosphorylation on Ser15 and subsequent p53 stabilisation. In light of recent reports, this AMPK-dependent activation of p53 may be enough to prevent neoplastic growth upon PTEN-deletion in the mouse prostate.

4.6 Discussion

In this chapter the role of AMPKβ1 was investigated in an in vivo model of prostate cancer. This murine prostate cancer model relies on the homozygous deletion of the tumour suppressor, PTEN, specifically in prostate epithelial cells. This model was previously characterised by Wang et al and recapitulates the disease progression seen in humans from initiation of prostate cancer with mPIN, followed by progression to invasive adenocarcinoma (Wang et al., 2003).

Initial studies focused on the characterisation of the global β1-knockout mouse to inform the decision on the appropriate breeding strategy: global or conditional deletion of AMPKβ1. These studies revealed that global β1-/- mice had splenomegaly and hemolytic anaemia and it was therefore decided to generate a PTEN prostate cancer model with conditional homozygous deletion of β1. Similar to the human prostate cell lines, the major AMPK form expressed in all lobes of the mouse prostate was α1β1γ1.

Studies in the androgen-sensitive cell line, LNCaP, revealed that after stimulation of androgen-signalling using the synthetic androgen, mibolerone, AMPKβ1-complex expression was upregulated. AMPKβ2 expression was not affected by this treatment. In the AMPKβ1/2- knockout MEFs, it was found that AMPKβ expression was critical for AMPK complex detection. It follows that induction of the β1 subunit alone could be sufficient for upregulation of the AMPK complex. Consistent with the in vitro studies, in the prostate cancer mouse model upon PTEN-deletion, protein expression of AMPKβ1, α1 and γ1 was found to be significantly upregulated relative to wild-type tissue. This upregulation was abolished upon β1-deletion. Interestingly, β1 expression in the PTEN hepatocellular 167

carcinoma model was unaltered upon PTEN-deletion. This implies PTEN-deletion alone is insufficient to increase AMPK expression. This finding, along with in vitro studies, is consistent with AMPKβ1-complex upregulation being an androgen driven process in vivo.

Two studies have reported AMPK activation in prostate cancer in clinical samples using immunohistochemistry (IHC) (Park et al., 2009, Tennakoon et al., 2014). Park et al used ACC phosphorylation as a measure of AMPK activity, whereas Tennakoon et al stained for AMPKα phosphorylation at Thr172. Increased phospho-ACC staining was found in 40% of malignant prostate tissue relative to benign tissue (Park et al., 2009). An issue with this approach is that ACC expression is upregulated in prostate cancer; therefore it is unclear whether this increased staining is as a result of AMPK activation or ACC overexpression. In addition, AMPK is activated upon dissection therefore tissue dissection and processing will influence results and this is true for any group attempting to look at AMPK activation in dissected tissue. Staining for AMPKα phosphorylation was also found to be stronger in cancer samples relative to benign tissue (Tennakoon et al., 2014). Higher staining correlated with biochemical (PSA) recurrence following radical prostatectomy, however no correlation was found with pathological variables such as Gleason score or disease stage at the time of surgery (Tennakoon et al., 2014). The phospho-AMPKThr172 antibody used in this study was purchased and tested in IHC in PTEN-/- and β1-/-PTEN-/- mouse prostate sections. A range of antibody concentrations and antigen retrieval methods were tested including those used in the original paper and under no condition was this antibody found to produce specific staining. Nonetheless, upregulation of AMPK in primary human prostate cancers is consistent with the data presented in this chapter.

Previous papers investigating the role of AMPK in prostate cancer have focused on AMPK activity and have failed to interrogate the expression of the different AMPK subunits (Park et al., 2009, Tennakoon et al., 2014, Massie et al., 2011, Ros et al., 2012). This is the first report of AMPKβ1-complex expression being upregulated in response to androgen signalling. A number of reports have shown AMPK activity to be upregulated in response androgens (Jurmeister et al., 2014, Massie et al., 2011, Shen et al., 2014). In these reports, increased AMPK activity has been hypothesised to be down to increased CaMKKβ expression (Massie et al., 2011). Whether AMPK activation in response to androgens is oncogenic or tumour suppressive is subject to debate. Jurmeister et al argue that AMPK activation in response to androgens leads to down regulation of AR transcriptional activity, while Massie et al argue it is required for cancer cell growth as a modulator of anabolic metabolism (Jurmeister et al., 2014, Massie et al., 2011, Shen et al., 2014). The novel β1- deletion PTEN prostate cancer model (total AMPK activity reduced by 70%) generated in this study gave the unique opportunity to investigate the effect of downregulating AMPK activity on prostate cancer development in vivo.

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By 26-weeks of age, despite no difference in prostate weight, the β1-/-PTEN-/- prostate was found to have worse disease compared to PTEN-/- prostates. The adenocarcinoma lesions in β1-/-PTEN-/- prostates were found to be more frequent, with larger areas of cancer cell invasion into the surrounding stroma. Although, no significant difference in cellular proliferation (as judged by Ki-67 staining) was seen, there was a trend for β1-/-PTEN-/- prostate sections to have increased Ki-67 staining compared to PTEN-/-. These studies were challenged by the variation of Ki-67 staining within a tissue section. This trend is consistent with the cell-based studies in the previous chapter, where AMPK was found to be a negative regulator of cellular proliferation. Despite initial reports that prostate specific PTEN-deletion was sufficient to give metastatic disease, this has not be repeated in any subsequent study (Trotman et al., 2003, Backman et al., 2004, Irshad and Abate-Shen, 2013). Therefore, metastatic disease and the effect of β1-deletion on the metastatic cascade could not be investigated using this model.

The initial molecular changes leading to prostate cancer development were investigated by characterisation of the PTEN-null prostate at the 12 week timepoint. As previously reported, upon PTEN-deletion Akt phosphorylation on Ser473 and AR expression were significantly increased (Wang et al., 2003). Consistent with an upregulation of AR signalling, an increase in the androgen-responsive gene, CaMKKβ, at the protein level was seen. In addition, AR gene expression was also shown to be increased at the transcriptional level, along with CaMKKβ. The original paper characterising the PTEN model and another subsequent publication have demonstrated that the adenocarcinomas resulting from PTEN-deletion are androgen-responsive and show regression in response to androgen withdrawal (Wang et al., 2003, Zhang et al., 2009).

A switch in protein expression was found to occur in the PTEN-null prostate (between 8 and 17 weeks). This protein switch was found to correspond to the switch from benign hyperplasia to neoplasia in the PTEN-null prostate. This switch in protein expression was found to occur in both lobes, although by 12 weeks 100% of PTEN-null DLVP lobes had progressed through it, compared to 50% of the corresponding AP lobes. This result is perhaps unsurprising given that in the original paper, hyperplasia was first observed in the dorsolateral and ventral lobes and subsequently involved the anterior lobes (Wang et al., 2003). This was found to be consistent with the efficiency of Cre-mediated PTEN deletion. Importantly, β1-deletion in the PTEN-null prostate was found to bring the switch on earlier. Akt activation and AR-signalling were found to be upregulated in switched tissues. Interestingly, upregulation of AMPK expression upon PTEN-deletion was found to occur before the switch in the hyperplasic epithelial cells and was not further increased upon switch progression.

There are a number of possible explanations for the observed switch, namely A) the epithelial cells of the PTEN-/- independently switch and converge their protein expression

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profiles or B) an amplification of rare cell types in the PTEN-/- model. It is unlikely that all epithelial cells of the PTEN-/- prostate independently make the switch in protein expression, it is arguably more likely that progenitor cell(s) make the switch and then expands, outcompeting neighbouring cells thereby becoming the dominant cell population. Competitive cell interactions is a recurring theme in tumour biology (Wagstaff et al., 2013). The changes seen upon the switch are thought to provide a survival advantage supporting uncontrolled cell proliferation potentially allowing outgrowth of this cellular population. Since the same changes in protein expression were observed in all PTEN-null prostates the selection pressure to bring about these changes must be incredibly strong. A way to distinguish between A and B would be to perform immunohistochemical staining of the switch biomarkers on a number of PTEN-/- prostates at different time points, to examine the localisation of the cells expressing the different markers. For example, if staining of a protein upregulated in the switch (e.g. HKII) were observed in few discrete patches this would be supportive of model B, whereas if staining was more diffuse and widespread across the section this would be supportive of model A. By staining sections from different timepoints information would be garnered about the localisation of the cells making the switch over time and this may be indicative of a mechanism of how the switch arises.

A mass spectrometry approach was adopted to look for differentially expressed proteins in pre-switch and switched prostates marking the transition from hyperplasia to mPIN. Despite low coverage of the proteome, over 500 proteins were found to be differentially expressed. HK2, PYGL and PFKFB2 were found to be upregulated upon the switch, whereas BiP1 and SORD were down regulated. These proteins were chosen to act as biomarkers, since they represented a selection up- and down-regulated proteins upon disease progression and all had previously been implicated in prostate cancer development (Wang et al., 2014, Schnier et al., 2005, Ros and Schulze, 2012, Massie et al., 2011, Racek et al., 2008, Fu et al., 2008, Szabó et al., 2010). Although these 5 proteins were chosen for further characterisation, a number of other hits had also been implicated in prostate cancer. For example, hormone- sensitive lipase, monoglyceride lipase (lipid metabolism), glycogen synthase (glycogen metabolism) and ROCK1 (cell invasion and migration) were also significantly upregulated in neoplastic tissue (Kroiss et al., 2015, Nomura et al., 2011, Favaro et al., 2012).

Hexokinases catalyse the first committed step in glucose metabolism, namely the ATP- dependent phosphorylation of glucose to yield glucose-6-phosphate. One of the earliest adaptations observed during tumorigenesis has been a 'switch-over' to the expression of high-affinity isoforms of hexokinase HK2 and to a lesser extent HK1 (Mathupala et al., 2006). By catalysing the phosphorylation of glucose, HK2 promotes and sustains a concentration gradient facilitating glucose entry into cells and the initiation of all major pathways of glucose utilisation. HK2 has also been shown to associate with mitochondria and been implicated to have a role in cancer cell survival (Mathupala et al., 2006). Patra et al reported that HK2 is required for tumour initiation and maintenance, and furthermore

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showed that HK2 deletion was therapeutic in mouse models of cancer (Patra et al., 2013). Shortly after, HK2 upregulation was reported in prostate cancer cells, specifically those harbouring PTEN and p53 inactivation (Wang et al., 2014). Furthermore, they used genetic studies to demonstrate that HK2-mediated aerobic glycolysis was required for tumour growth in xenograft mouse models of prostate cancer (Wang et al., 2014). Interestingly, HK2 expression was a top hit from the mass spectrometry screen and found to be a robust biomarker of the switch to neoplastic growth in the PTEN-null prostate.

In addition, AR-signalling has been implicated in upregulation of HK2 and PFKFB2 protein expression (Massie et al., 2011). It was found that androgen stimulation of LNCaP (PTEN- negative) cells stimulated aerobic glycolysis, in part to the upregulation of these key proteins. They implicate AR-CaMKKβ-AMPK axis in promoting aerobic glycolysis by AMPK- dependent phosphorylation of PFK2. The evidence linking these observations to AMPK activity was lacking; and based on the use the non-specific AMPK activator, AICAR (Santidrián et al., 2010, García-García et al., 2010). It would be interesting to look at PFK2 phosphorylation and aerobic glycolysis in PTEN-/- and β1-/-PTEN-/- prostates to investigate the role of AMPK in this process in vivo.

The role of androgen signalling in upregulating HK2 and PFKFB2 expression in LNCaP cells was further shown by Moon et al (Moon et al., 2011). Here, the authors show androgen- dependent activation of CREB via PKA leading to HK2 transcriptional activation. The upregulation of PFKFB2 expression was shown to be mediated by the direct binding of ligand-activated AR to the PFKFB2 promoter, with activated PI3K/Akt signalling leading to the constitutive activation of PFK2 activity of PFKFB2. Glucose uptake and lipogenesis were found to be blocked upon PFKFB2 knockdown or pharmacological inhibition of PFK2 activity. The authors show that the upregulation of these two proteins is required for increased of de novo lipid synthesis by androgens and disease progression (Moon et al., 2011).

Literature on the role of PYGL (glycogen phosphorylase, liver form) in prostate cancer is more limited. Glycogen phosphorylase catalyses the rate-limiting step in glycogenolysis by releasing glucose-1-phosphate from the terminal α-1,4-glycosidic bond. Glycogen metabolism has been shown to be upregulated in tumours in vivo and in cancer cells in vitro in response to hypoxia (Favaro et al., 2012). In 3 different cancer cell lines, hypoxia was shown to induce glycogen accumulation followed by a decline in glycogen content. In parallel, glycogen synthase showed rapid induction, followed by a later increase of PYGL. PYGL depletion and consequent glycogen accumulation led to increased reactive oxygen species levels that contributed to a p53-dependent induction of senescence and markedly impaired tumorigenesis in vivo (Favaro et al., 2012). Furthermore, metabolic analyses indicated that glycogen degradation by PYGL was important for the function of the pentose phosphate pathway (Favaro et al., 2012). Upon disease progression and dysregulated cell division, cells experience oncogenic stresses such as hypoxia and this could contribute to

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induced PYGL expression in conjunction with PI3K/AR signalling. Interestingly, glycogen synthase was also a hit from the mass spectrometry screen and was significantly upregulated upon the switch.

BiP1 and SORD were found to be significantly downregulated upon the switch from benign hyperplasia to epithelial neoplasia. These proteins are secreted by the normal prostate epithelium. The term ‘prostasome’ is used to classify the extracellular vesicles released into prostatic fluid by prostate epithelial cells. Prostate tumour cells are known to produce atypical prostasomes and these have been suggested to support prostate cancer development and spread (Aalberts et al., 2014). Prostasomes can be isolated from seminal fluid, urine and blood and therefore changes in these vesicles represent promising non- invasive biomarkers. Prostasomes have been shown to contain SORD, a key enzyme involved in the polyol pathway, which oxidises sorbitol to fructose (Aalberts et al., 2014). Interestingly, aldose reductase, another enzyme in the polyol pathway and contained in prostasomes, was identified by mass spectrometry to be significantly downregulated upon the switch. Fructose is a major energy source for spermatozoa, thus these enzymes may play an important role in sperm cell motility.

Bip1 and HSP70 are heat-shock proteins and have also been shown to be secreted by the normal prostate epithelium (Sahlén et al., 2010). Both of these proteins were significantly downregulated upon the switch. Bip1 is a marker for ER stress and is involved in many cellular processes, including translocating the newly synthesized polypeptides across the ER membrane, facilitating the folding and assembly of proteins, targeting misfolded proteins for ER-associated degradation, regulating calcium homeostasis, and serving as an ER stress sensor with anti-apoptotic properties (Lee, 2005). The exact function(s) of heat-shock proteins in prostatic fluid is unknown.

Recent studies have found that BiP1 has a role in prostate cancer development (Fu et al., 2008). Fu et al, found that homozygous deletion of Bip1 in the PTEN mouse model of prostate cancer supressed prostate tumourigenesis (Fu et al., 2008). Interestingly, the authors never compared BiP1 expression in WT and PTEN-/- mice. A number of studies support a role for BiP1 in cancer cell survival (Li and Lee, 2006). Overall, expression of BiP1 in the prostate is significantly downregulated upon the switch and this is likely because it is no longer secreted in prostasomes. However, BiP1 immunohistochemical staining did reveal discrete regions within the neoplastic prostate (from 17-week old mice) that were positive for BiP1 expression. The staining pattern of BiP1 was similar to that seen using the apoptotic marker, cleaved-caspase-3 in these sections. Stress-induced BiP1 expression in this situation could potentially alleviate oncogenic stresses, thereby aiding cancer cell survival (Yadav et al., 2014).

In this chapter, global changes in protein expression were found to occur upon the progression from benign hyperplasia to mPIN. Although many of the proteins shown to be 172

differentially expressed have been previously implicated in prostate cancer development and progression, this is the first study to show co-ordinated global changes in protein expression in the PTEN-/- model. Referring back to Figures 4.24 and 4.25, HK2, PYGL and PFK2 expression was shown to increase and BiP2 and SORD expression downregulated in a closely regulated reciprocal fashion. The tight reciprocal expression patterns of the protein biomarkers suggest genes expression changes are co-ordinately regulated as a result of combined AR and PI3K/Akt pathway activation. Gene expression studies were suggestive that upregulated AR-signalling was a prerequisite for switch progression. Furthermore, these gene expression studies revealed that the changes in protein expression seen to occur upon the switch are regulated at multiple levels, and do not simply reflect changes in mRNA content. For example, SORD and BiP1 mRNA was not found to be downregulated in switched samples. In addition, despite increased levels of actin mRNA in switched samples, actin protein levels remained unaltered between conditions. This is perhaps unsurprising given that only 40% of the variation in protein concentration can be explained by knowing mRNA abundances (Vogel and Marcotte, 2012). The role of translational control in the regulation of protein level is discussed in Chapter 5.

Cellular senescence has been shown to oppose neoplastic transformation triggered by activation of oncogenic pathways (Chen et al., 2005, Lowe et al., 2004). PTEN and p53 are among the most commonly inactivated or mutated genes in human cancer including prostate cancer (Markert et al., 2011). It has been shown that PTEN inactivation induces growth arrest through the p53-dependent cellular senescence pathway both in vitro and in vivo (Chen et al., 2005). Combined inactivation of PTEN and p53 in the mouse prostate was found to lead to invasive adenocarcinoma, which was lethal by 7 months of age (Chen et al., 2005). Furthermore, evidence was provided that this mechanism of cellular senescence was relevant in halting progression in specimens from early-stage human prostate cancer (Chen et al., 2005). In addition, HK2 expression was robustly switched on upon PTEN and p53 inactivation (Wang et al., 2014). Others studies have shown that HK2 and PFK2 expression is upregulated in response to increased androgen signalling (Massie et al., 2011, Moon et al., 2011). p53 has been shown to be a negative regulator of androgen signalling (Alimirah et al., 2007). Separately, AMPK has also been shown to be a negative regulator of androgen signalling (Jurmeister et al., 2014, Shen et al., 2014).

PTEN-null prostates lacking AMPKβ1 were found to go through the switch and presented with mPIN earlier than those wild-type for β1. Additionally, β1-/-PTEN-/- prostates were found to be less likely to fall in to the intermediary switch classification 2, compared to PTEN-/- prostates (Figure 4.25). Taken together, these data support a role for AMPKβ1- complexes in delaying progression to mPIN and ultimately adenocarcinoma.

In response to metabolic and oncogenic stresses, AMPK activation has been shown to lead to p53 stabilisation and activation by phosphorylation of serine 15 (Jones et al., 2005,

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Nieminen et al., 2013). AMPK-dependent p53 activation has been shown to lead to cellular senescence and/or apoptosis depending on cell type and type of stress (Jones et al., 2005, He et al., 2014, Nieminen et al., 2013, Okoshi et al., 2008). In this chapter of work, p53 was found to be stabilised upon PTEN-deletion, as previously reported (Chen et al., 2005). However, this stabilisation was found to be lost in PTEN-null prostates lacking AMPKβ1. Furthermore, immunohistological staining of phospho-p53Ser15 showed significant staining in PTEN-/- prostate tissue, which was absent in wild-type and β1-/-PTEN-/- prostates. These data, along with the published studies discussed, support a mechanism whereby AMPKβ1- complex upregulation and activation induces p53 stabilisation. In turn, p53 activation prevents the switch from benign hyperplasia to neoplasia. A proposed mechanism is outlined in Figure 4.31.

In the following chapter, the CaMKKβ-/-PTEN-/- prostate is characterised. In this model, preliminary data suggests there is no difference in switch progression compared to the PTEN-/- prostate (see Chapter 5, Section 5.3.6). Furthermore, in the literature when the upstream kinase was interrogated in AMPK-dependent p53 stabilisation, LKB1 was implicated (Jones et al., 2005). Together, this places LKB1 as the major upstream kinase responsible for AMPK activation leading to p53 stabilisation. This is consistent with LKB1 being a bona fide tumour suppressor (Carling et al., 2012).

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Figure 4.31 Model of the role of AMPKβ1 and p53 in prostate cancer development. A combination of upregulated PI3K/Akt and androgen signalling, together with the associated oncogenic stresses leads disease progression from benign prostate hyperplasia to mPIN and adenocarcinoma. AMPKβ1-complexes are upregulated an early stage of disease progression in the hyperplasic prostate. AMPK activation (likely by LKB1 due to the stresses associated with dysregulated growth) leads to upregulation of p53 phosphorylation at serine 15. This leads to p53 stabilisation and activation which inhibits the switch to neoplastic growth and adenocarcinoma formation. However, this brake mechanism is eventually overcome in the PTEN-/- prostate and by 17 weeks all PTEN-null (β1-null and β1-wild-type) have progressed to mPIN.

Additionally, AMPK activity could delay the switch by other mechanisms, such as antagonism of the PI3K/Akt pathway and/or antagonism of AR signalling. AMPK has a well characterised role in antagonising Akt signalling by direct phosphorylate mTOR, RAPTOR and TSC2 (see Chapter 1, Section 1.2.3.1) (Carling et al., 2012). However, no difference in Akt or S6 phosphorylation was found between PTEN-/- and β1-/-PTEN-/- prostates (Figure 4.15). In addition, activation of AMPK has been shown to inhibit the AR transcriptional activity (Jurmeister et al., 2014, Shen et al., 2014). Jurmeister et al, argue this occurs in the absence of a change in AR protein expression and leads to significant downregulation of a number of AR target genes (Jurmeister et al., 2014). Furthermore, AMPK-knockdown was found to increase AR activity. In this chapter, a trend was found for increased CaMKKβ protein expression in β1-/-PTEN prostates which is consistent with increased AR signalling. However, 175

further work is required to look at androgen signalling in this model. More samples for each genotype (either pre-switch or switched) would need to be processed and the expression of a greater number of AR-target genes characterised.

Conclusions and Perspectives

In this chapter of work the effect of AMPKβ1 on prostate cancer development was assessed in the PTEN-null mouse model. A global switch in protein expression has been defined upon progression from benign hyperplasia to mPIN, a precursor to invasive adenocarcinoma. A number of proteins were shown to be robustly up or downregulated in the development of neoplastic lesions. Interestingly, these changes appear to be co-ordinately regulated. The switch correlates with increased PI3K/Akt and AR signalling as a consequence of PTEN- deletion. An important paper showed the role of the tumour suppressor, p53, in activating cellular senescence to prevent prostate oncogenesis (Chen et al., 2005). Here, the role of AMPKβ1 in p53 activation was demonstrated and shown to in inhibit prostate cancer development in vivo. AMPKβ1 complex expression was found to be androgen-dependent, and AMPK overexpression was found to occur in the PTEN-/- hyperplasic prostate (before progression to mPIN). In β1-/-PTEN-/- prostates, the switch to mPIN was found to occur on average 3-weeks ahead of PTEN-/- prostates expressing β1. Although 3-weeks could be argued to be a subtle effect, this is an aggressive model of prostate cancer and this mechanism in the human disease could potentially prevent prostate tumourigenesis.

An important question raised from this work is how the PTEN-/- prostate cells do eventually overcome this tumour suppressive mechanism and make the switch to neoplastic growth by 17-weeks. Preliminary data shows a correlation between increased AR and PI3K/Akt signalling and the switch. Oncogenic stresses such as hypoxia may also be a triggering factor.

The effect of β1-deletion on prostate cancer development is subtle compared to the double p53/PTEN-null mouse (Chen et al., 2005). In part, this could be due to the remaining AMPK activity found in AMPKβ2-complexes. In addition, AMPK-independent mechanisms may lead to p53 activation and p53 may also have AMPK-independent roles at later stages of tumourigenesis.

In this chapter, the data generated looking at the role of AMPKβ1 in vivo supports the idea that AMPK is acting as a tumour suppressor in prostate cancer development. A conclusion supported by work done in the previous chapter looking at the effects of AMPK activation in a panel of prostate cancer cell lines. AMPKβ1-complexes and AMPK activation have been suggested to have oncogenic capabilities in certain in vitro settings (Ros et al., 2012, Massie et al., 2011, Frigo et al., 2011), however based on the effect of β1-deletion in the PTEN-null prostate cancer mouse model, AMPK activity appears to retard disease progression in vivo.

Future studies involve acquiring p53/PTEN-null prostate tissue to look at the biomarkers of the switch identified in this study in this model and further characterise the role of p53 in 176

this process. It would also be interesting to look at AMPK signalling in this model. In addition, tissue from this model can be used as a true negative control to further validate the phospho-p53Ser15 antibody in IHC.

Currently, we are generating a novel transgenic PTEN-null model of prostate cancer, expressing a constitutively active mutant of AMPK. This genetic model utilises our recently (unpublished) mouse model which expresses a mutant form of γ1 that leads to constitutive activation of AMPK. The ROSA26 floxed STOP cassette has been used to allow the transgene to be expressed in a tissue-specific manner. These mice will be critical for looking at AMPK activation as a therapeutic strategy in prostate cancer. Dosing mice with the AMPK activator, 991, was attempted using osmotic minipumps. However, due to limited solubility, it was not possible to achieve high enough plasma concentrations to allow significant accumulation of the drug in the mouse prostate (data not shown).

In addition to investigating the role of AMPK in prostate cancer, a critical switch in protein expression was defined upon the switch to neoplastic growth in prostate cancer development. It will be crucial to investigate the relevance of this switch in human prostate cancer samples. It is important to determine how expression of these proteins varies with prostate cancer grade, in response to treatment e.g. anti-androgens and in castration- resistant disease. If a correlation was found, a set of these proteins could act as biomarkers to aid more accurate disease staging, prognosis and treatment strategies. The limited predictive value of PSA as a diagnostic marker for prostate cancer has led to an intense search for novel biomarkers in blood or urine with better sensitivity and specificity (Prensner et al., 2012). The finding that components of secreted prostasomes were downregulated upon progression to prostate neoplasia, provides an opportunity for screening these extracellular vesicles isolated from blood or urine in prostate cancer diagnosis (Zijlstra and Stoorvogel, 2016).

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5. Investigation of the effect of CaMKKβ-deletion in the PTEN-/- mouse model of prostate cancer

A number of recent reports have provided convincing evidence that the CaMKKβ gene is androgen responsive in human prostate cancer cell lines (Massie et al., 2011, Frigo et al., 2011, Karacosta et al., 2012). An androgen response element was identified in the promoter region of the CAMKKβ gene and removal of this element abolished the androgen-dependent increase in CaMKKβ expression (Frigo et al., 2011). Using immunohistochemistry, CaMKKβ expression was shown to be increased in prostate cancer samples and reduced in samples obtained following neoadjuvant hormone therapy, supporting the hypothesis of androgen receptor (AR) regulation of CaMKKβ expression in vivo. Interestingly, CaMKKβ protein expression was found to be increased again in samples from castration-resistant disease stages (Massie et al., 2011). These findings are significant as they suggest that CaMKKβ expression is increased in both hormone-sensitive and castration-resistant prostate cancer. For further discussion on the role of CaMKKβ in prostate cancer, see Chapter 1 (Section 1.3.6). No study to-date has interrogated the role of CaMKKβ in prostate cancer in vivo; this key question is addressed in the following chapter of work by the generation and characterisation of a novel transgenic mouse model deficient for this kinase.

5.1 Investigation of regulatory feedback of CaMKKβ activity on AR signalling

The majority of publications have focused on the effect of AR signalling on CaMKKβ expression (Frigo et al., 2011, Massie et al., 2011). In these studies the effects of CaMKKβ inhibition were attributed to decreased signalling through AMPK (Massie et al., 2011, Frigo et al., 2011). However, one study implicates CaMKKβ activity in AR activation, suggesting a feedforward-loop between these two proteins (Karacosta et al., 2012). They found CaMKKβ siRNA-mediated knockdown reduced expression of the AR target gene prostate-specific antigen (PSA) at the RNA and protein level in LNCaP cells. In contrast with this study, Shima et al provide evidence for an inhibitory effect of CaMKKβ on AR-regulated transcriptional activity at the PSA promoter in LNCaP cells (Shima et al., 2012). These studies are further discussed in Chapter 1, Section 1.3.6. The reason for these contradictory results is unclear.

5.1.1 The effect of CaMKKβ inhibition on AR activity

To investigate the role of CaMKKβ in regulating the AR, an LNCaP cell line stably transfected with an AR reporter luciferase construct (LNCaP/Luc cells), generously provided by Charlotte Bevan (Imperial College, London) was used. In LNCaP/Luc cells, the androgen responsive element (ARE) sequence from SC1.2 is placed upstream of the luciferase reporter gene. Generation and characterisation of these cells has been previously outlined (Dart et al., 178

2009). Briefly, the introduction of the SC1.2 ARE sequence conferred hormone inducibility to the luciferase reporter gene, resulting in two to threefold induction of luciferase activity upon androgen stimulation (Dart et al., 2009). Other AREs were tested, however the SC1.2 ARE showed the greatest degree of hormone induction and specificity for the AR.

LNCaP/Luc cells were grown in ‘starvation media’ consisting of phenol red-free RPMI supplemented with 5% charcoal-stripped serum. Cells were grown in the presence or absence of the synthetic androgen, mibolerone and the CaMKKβ inhibitor, STO609. Cells were treated for 24h and lysed using reporter lysis buffer. Luciferase activity was then determined using the luciferase assay as detailed in Chapter 2 (Section 2.2.2.14). Luciferase activity was normalised to sample protein concentration giving a measure of AR activity. The 24 h timepoint was chosen as longer treatments with STO609 were found to lead to a reduction in cell growth; this would likely have knock-on effects on AR signalling and additionally lead to issues with data normalisation.

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Figure 5.1 The effect of STO609 treatment on AR signalling in LNCaP/Luc cells. A) Cells were grown in ‘starvation media’ and treated with STO609 in the presence and absence of the synthetic androgen, mibolerone (Mib) for 24 h. Cells were then lysed and subject to a luciferase assay. Results from the luciferase assay were normalised to sample protein concentration and are presented as fold control. The control condition refers to cells grown in the absence of mibolerone and STO609. Data are means ± SEM (n=12, performed over 2 separate occasions). **p<0.01, ***p<0.005, t-test. B) Samples derived as outlined in A) were subject to Western blot analysis. Western blots were probed with antibodies against AR and CaMKKβ; actin was used as a loading control. Western blots were developed using the LI-COR imaging system. C) Quantification of Western blot shown in B) for AR expression, which was normalised to actin expression and represented as fold control. Data are means ± SEM (n=2, therefore no statistical tests were performed). D) Quantification of Western blot shown in B) for CaMKKβ expression, normalised to actin expression and represented as fold control. Data are means ± SEM (n=3). *p<0.05, ***p<0.005.

Upon androgen stimulation, luciferase activity was robustly increased to around 3-fold that of control cells (Figure 5.1A), consistent with published data (Dart et al., 2009). CaMKKβ inhibition using STO609 led to a 20% decrease in AR activity in mibolerone treated cells. CaMKKβ inhibition had no effect on AR activity in unstimulated cells. These samples were then subject to Western blot analysis to interrogate AR and CaMKKβ (AR responsive gene) expression. After 24h of mibolerone treatment in the absence of STO609, AR and CaMKKβ expression was robustly upregulated (Figure 5.1B). Interestingly, in contrast to AR protein expression, CaMKKβ protein expression was significantly downregulated upon STO609

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treatment. These data support the results obtained from the luciferase assay which showed a downregulation of AR activity upon STO609 treatment. These results support a role for CaMKKβ in regulating AR activity positively as suggested by the study performed by Karacosta et al. Furthermore, similar to the results presented in Figure 5.1, Karacosta et al reported that upon CaMKKβ knockdown PSA expression was decreased with no change in AR protein expression (Karacosta et al., 2012).

Notably, CaMKKβ expression was more sensitive to STO609 treatment than the luciferase reporter gene. There could be a number of reasons for this, such as differences in promoter ARE sequence and/or differences in mRNA translation efficiency.

5.2 Mouse model generation and characterisation of the PTEN-positive mouse prostate

CaMKKβ knockout mice with global deletion of exon 5 were generated (Peters et al., 2003). Jiexin Zhao in the Carling lab characterised the global CaMKKβ knockout mouse and found no obvious overt phenotypes (Zhao, 2014). However, CaMKKβ deletion was shown to lead to a subtle neuronal phenotype associated with hippocampus-dependent long‐term memory function using this mouse model (Peters et al., 2003). The Camkkβ-/- mouse is further discussed in Chapter1, Section 1.3.5.

5.2.1 CaMKKβ-/- prostate cancer mouse model generation

To investigate whether CaMKKβ inhibition is a viable therapeutic strategy for treating prostate cancer, global CaMKKβ knockout (Camkkβ-/-) mice were crossed with the Ptenfl/fl/PB-Cre4+ line to generate a novel prostate cancer model deficient for CaMKKβ: Camkkβ-/-/Ptenfl/fl/PB-Cre4+ (Figure 5.2). Hereafter, Camkkβ-/-/Ptenfl/fl/PB-Cre4+ mice will be referred to as CaMKKβ-/-PTEN-/-, likewise Camkkβ-/- mice will be referred to as CaMKKβ-/-. Unlike the conditional AMPKβ1-deletion model used in the previous chapter, this CaMKKβ- deletion model is globally deleted for CaMKKβ. This model choice was supported by the lack of an overt phenotype in the CaMKKβ-null mouse model. In addition, this model is relevant to investigate the effect of a CaMKKβ inhibitor in the treatment of prostate cancer, as any drug developed would act globally as it is not currently possible to specifically target the prostate.

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Figure 5.2 Breeding strategy used to generate CaMKKβ-/-PTEN-/- (Camkkβ-/-/Ptenfl/fl/PB-Cre4+) and PTEN-/- (Ptenfl/fl/PB-Cre4+) prostate cancer mouse models.

5.2.2 Characterisation of CaMKKβ in the wild-type mouse prostate

CaMKKβ protein expression was initially characterised in the different lobes of the prostate from wild-type mice. This was determined using the CaMKKβ activity assay, a two-step assay

that measured the in vitro activation of recombinant AMPKα1β1γ1 by immunoprecipitated CaMKKβ. Expression of CaMKKβ in wild-type prostate tissue is low and therefore difficult to detect by Western blot, and the CaMKKβ assay provides a more sensitive and quantitative alternative for measuring CaMKKβ activity. Prostate lobes were individually dissected from wild-type and CaMKKβ-/- mice for subsequent homogenisation. CaMKKβ was immunoprecipitated from 50 μg of lysate and the resulting immune-complexes were incubated with bacterially expressed AMPK in the presence of calcium/calmodulin. AMPK activity was then assayed in a SAMS kinase assay. Brain was used as a positive control as it has high CaMKKβ expression. The results are shown in Figure 5.3.

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Figure 5.3 Characterisation of CaMKKβ in the different lobes of the wild-type (WT) mouse prostate. Anterior prostate (AP), dorsolateral prostate (DLP) and ventral prostate (VP) lobes were dissected from wild-type (WT) and CaMKKβ-/- mice aged 17-20 weeks and homogenised. CaMKKβ was immunoprecipitated from 50 μg of lysate and the resulting immune complexes were incubated with bacterially expressed AMPK in the presence of calcium/calmodulin. AMPK activity was then assayed in a SAMS kinase assay. Results are presented as AMPK specific activity (nmole/min/mg). Wild-type brain was used as a positive control as it has high CaMKKβ expression. Data are means ± SEM (n= 4 per genotype). *p<0.05, **p<0.01; one-way anova for prostatic lobe results.

The CaMKKβ activity assay was shown to be highly specific, with no significant CaMKKβ activity detectable in knockout tissue and high activity in the wild-type brain (Figure 5.3). CaMKKβ activity was found to be significantly higher in dorsal lateral (DLP) and ventral prostate (VP) compared to the anterior prostate (AP) (Figure 5.3).

5.2.3 Characterisation of AMPK in the CaMKKβ-/- mouse prostate

CaMKKβ is one of two upstream kinases responsible for phosphorylation of AMPK on Thr172, leading to AMPK activation. Therefore AMPK activity and expression was characterised in the different lobes of the CaMKKβ-/- prostate, using the SAMS kinase activity assay and Western blotting.

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Figure 5.4 Characterisation of AMPK in wild-type (WT) and CaMKKβ-/- prostates. A) Anterior prostate (AP), dorsolateral prostate (DLP) and ventral prostate (VP) lobes were taken from wild-type (WT) and CaMKKβ-/- mice aged 17-20 weeks, homogenised and subject to an SAMS kinase assay. Results are presented as AMPK specific activity (pmoles/min/mg). Data are means ± SEM (n= 4 per genotype). *<0.05, ***p<0.005, t-test. B) The samples generated in A) were subject to Western blot analysis and probed with antibodies raised against pAMPKThr172, AMPKβ1 and AMPKγ1; actin was used as a loading control. Western blot was developed using the LI-COR imaging system.

Consistent with results presented in the previous chapter (Figure 4.10), AMPK activity was found to be significantly increased in the ventral lobe (VP) of the mouse prostate, compared to anterior (AP) and dorsolateral lobes (DLP) (Figure 5.4A). Interestingly, AMPK activity was found to follow a similar pattern in the different lobes of the mouse prostate to CaMKKβ activity (highest in VP lobe and lowest in AP lobe). There was a trend for all CaMKKβ-/- lobes to have decreased AMPK activity and this reached significance in the VP lobe. The AMPK activity assays results were consistent with the phospho-AMPKαThr172 staining in the Western blot analysis shown in Figure 5.4B. As discussed previously, AMPK is activated upon dissection due to imbalances in cellular nucleotide, thus AMPK activity measured in dissected tissues is normally found to be reflective of overall AMPK expression. Concordantly, the decreased AMPK activity seen in CaMKKβ-/- tissue was found to correspond to reduced AMPK expression in the knockout prostate. Decreased AMPK complex expression in the CaMKKβ-/- prostate was particularly obvious in the VP lobe (Figure 5.4B).

5.2.4 The effect of CaMKKβ-deletion on prostate weight

Wild-type and global CaMKKβ-deletion mice were sacrificed at 12 and 17 weeks and prostate weight recorded and normalised to body weight (Figure 5.5). As found in the last chapter, a small increase in prostate weight was seen in wild-type (WT) mice from 12 to 17

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weeks of age. However, in CaMKKβ-/- mice there is no significant increase in prostate weight and at 17 weeks CaMKKβ-/- prostates weighed significantly less than wild-type prostates. These data support a role for CaMKKβ in prostate growth.

Figure 5.5 Prostate weight from wild-type (WT) and CaMKKβ-/- mice. Wet weights were taken for prostates from wild-type (WT) and CaMKKβ-/- mice aged 12 and 17 weeks and normalised to body weight. Data are means ± SEM (At 12 weeks n= 5-10 per genotype, at 17 weeks 14-21 per genotype). ***<0.005; t-test.

5.3 Characterisation of the CaMKKβ-/-PTEN-/- prostate cancer mouse model

Similar to generation of the AMPKβ1-/-PTEN-/- prostate cancer mouse model, the generation of the CaMKKβ-/-PTEN-/- prostate cancer mouse was not trivial and took 2 years before significant numbers of animals with the desired genotypes were produced. The reasons for this are discussed in the previous chapter (Section 4.2.3). Once this CaMKKβ-/-PTEN-/- model was established, prostate cancer development was characterised and compared to PTEN-/- control animals.

5.3.1 Prostate weight

PTEN-/- and CaMKKβ-/-PTEN-/- mice were sacrificed at 12, 17 and 26 weeks and prostate wet weight was recorded and normalised to body weight (Figure 5.6). Note, the PTEN-/- control

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mice used in this figure are the same as those used in the previous chapter characterising the AMPKβ1-/-PTEN-/- model. For results and discussion on the initial characterisation of the PTEN-/- model please refer to the previous chapter.

B C

Figure 5.6 PTEN-/- and CaMKKβ-/-PTEN-/- prostate weight. A) PTEN-/- and CaMKKβ-/-PTEN-/- prostate wet weight was recorded at harvest for mice aged 12, 17 and 26 weeks and normalised to body weight. Data are means ± SEM (for each genotype n=5-21 at 12 weeks; n=9-15 at 17 weeks; n=6-17 at 26 weeks, PTEN-/- controls are the same as used in Figure 4.12). **p<0.01, ***p<0.005, t-test performed for PTEN-/- and CaMKKβ-/-PTEN-/- prostate weight at each timepoint. Representative images were taken at harvest from B) 17 and C) 26 week old mice of the indicated genotypes.

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CaMKKβ-deletion led to a significant reduction in the PTEN-null prostate at all timepoints examined (Figure 5.6). At 26 weeks of age CaMKKβ-/-PTEN-/- prostates weighed 40% less compared to PTEN-deletion alone. Representative images showing the difference in prostate size between PTEN-/- and CaMKKβ-/-PTEN-/- prostates are shown in Figure 5.6B. These data support the hypothesis that CaMKKβ is an oncogenic driver of prostate cancer.

5.3.2 Disease pathology

To further characterise the effect of CaMKKβ-deletion on prostate cancer development, prostate tissue from 26-week old mice was fixed, sectioned and stained with haematoxylin and eosin (HE) to investigate disease pathology. Upon dissection the mouse prostate was divided into anterior (AP) and dorsolateral/ventral (DLVP) and graded based on the criteria outlined in the previous chapter (Table 4.1). The results shown in Figure 5.7 are examples of the highest grade lesions found in the DLVP lobe from PTEN-/- and CaMKKβ-/-PTEN-/- prostates (three mice per genotype). Of note, DLVP lobes were found to develop adenocarcinoma before AP lobes and this is consistent with the efficiency of Cre-mediated PTEN-deletion (Wang et al., 2003).

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Figure 5.7 Pathology of wild-type (WT), PTEN-/- and CaMKKβ-/-PTEN-/- prostates. 188 HE staining of dorsolateral ventral prostate (DLVP) tissue sections of wild-type (WT) (top panel), PTEN-/- (middle panel) and CaMKKβ-/-PTEN-/- (bottom panel) prostates. Representative images of highest grade lesions in each tissue section are shown and associated grade is indicated in the top right of image. Images were graded by the criteria outline in Table 4.1. mPIN lesions are indicated by a white arrowhead and adenocarcinoma lesions with a black arrowhead. (n=3 examples per genotype). Reactive stroma is indicated by ‘#’. A scale bar is shown in the bottom right of the figure. CaMKKβ-/-PTEN-/- prostates were found to have slower disease progression to adenocarcinoma compared to PTEN-/- prostates (Figure 5.7). Invasive carcinoma is recognised by an infiltrative and destructive pattern of growth by atypical cells into the surrounding basement membrane and stroma (Ittmann et al., 2013). In invasive carcinomas the neoplastic cells often lack a continuous basement membrane and this is clear in the images labelled as adenocarcinoma and indicated by black arrowheads. Lesions graded as adenocarcinoma occurred with lower frequency in CaMKKβ-/-PTEN-/- prostate compared to the PTEN-/- prostate. Lesions classed as mPIN3-4 (indicated by white arrowheads) were present in both models. mPIN3-4 was characterised based on the presence of atypical nuclei and epithelial cell outgrowth to fill the duct lumen, however unlike in adenocarcinoma, gland ducts were generally still a regular shape showing no signs of epithelial cell invasion into the basement membrane/ surrounding stroma (Figure 5.7). Although the number of samples available for pathological analysis of the CaMKKβ-/-PTEN-/- prostate was low (due to a complicated breeding regime), reduced disease progression was consistently seen across the different timepoints examined when compared to age-matched PTEN-/- sections (Figure 5.7 for 26 week timepoint; and Figure 5.9 for 17 and 12 week timepoints).

5.3.3 Proliferation

CaMKKβ-null PTEN-/- prostates were found to have reduced mass compared to PTEN-/- prostates. To investigate whether this was associated with differences in cellular proliferation AP and DLVP tissue sections from both genotypes were stained with the proliferation marker, Ki-67 and counterstained with haematoxylin. This was performed on tissues from mice aged 26 weeks and staining was quantified as outlined in Chapter 4.

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Figure 5.8 Ki-67 staining in PTEN-/- and CaMKKβ-/-PTEN-/- prostates. A) Percentage of Ki-67-positive cells (proliferation marker) in anterior prostate (AP) lobe and dorsolateral ventral prostate (DLVP) of PTEN-/- and CaMKKβ-/-PTEN-/- mice aged 26 weeks. Data are means ± SEM (n= 4 mice per genotype; on average 4000-5000 cells per tissue section across 10 regions of interest). B) Representative images showing Ki-67 staining in PTEN-/- and CaMKKβ-/-PTEN-/- prostates. (n=3 examples per lobe per genotype). A scale bar is shown in the bottom right of the figure.

CaMKKβ-/-PTEN-/- mice were found to have significantly reduced Ki-67 staining compared to PTEN-/- mice in both lobes of the prostate at 26-weeks (Figure 5.8). Based on Ki-67 staining, CaMKKβ-deletion reduced prostate epithelial cell proliferation by between 30-45% 190

depending on lobe. Reduced proliferation likely contributes to the reduced weight seen upon CaMKKβ-deletion in the PTEN-/- prostate. Of note, Ki-67 staining was found mainly in the prostate epithelial cells, despite the outgrowth of the stromal cell fraction in prostate cancer progression. Furthermore, staining of this proliferation marker was found to be more abundant in the epithelial cell layers adjacent to the stroma/ basement membrane, presumably as these cells are experiencing more optimal conditions conducive with cell proliferation.

5.3.4 Differences in stromal morphology between PTEN-/- and CaMKKβ-/-PTEN-/- prostates.

An observation made during the pathological analysis presented in Section 5.3.2 was that PTEN-/- and CaMKKβ-/-PTEN-/- prostates appeared to have differences in the stromal tissue morphology surrounding the prostate ducts (Figure 5.7, indicated by #). To investigate whether the differences seen in stromal morphology between PTEN-/- and CaMKKβ-/-PTEN-/- prostates at 26 weeks of age, were present at earlier timepoints in prostate cancer development, AP lobes from mice aged 17 and 12 weeks were fixed and HE stained (Figure 5.9A,B). At both timepoints CaMKKβ-/-PTEN-/- prostate ducts were found to be separated by larger regions of stromal cells compared to PTEN-/-prostates. The stromal cells also had a more compact and ordered appearance. Furthermore, the prostate ducts themselves appeared to have a reduced area. These differences were not observed in the in CaMKKβ- null PTEN-positive prostate (Figure 5.9A,B). Interestingly, these differences in stromal morphology were only present in CaMKKβ-/-PTEN-/- prostates that had gone through the switch to neoplastic growth (defined in the previous chapter) and were positive for hexokinase II expression. Thus, the PTEN-/- prostates used for comparison in Figure 5.9B are all switched samples.

By 17 weeks of age the PTEN-/- anterior prostatic lobe starts developing fluid-filled cysts, which significantly increase in size by 26 weeks of age. Cysts are characterised by regression of epithelial cells and large holes in HE stained sections, in Figure 5.9A, cystic regions are marked with an asterisk (*). Consistent with CaMKKβ-/-PTEN-/- having a slowed rate of disease progression, these mice were found to have fewer and smaller cysts as judged by HE staining at the 17 week timepoint (Figure 5.9A). This was also true at the 26 week timepoint (data not shown). Furthermore, at the 12 week timepoint CaMKKβ-/-PTEN-/- prostates were found to have less epithelial cell outgrowth filling the duct lumen compared to PTEN-/- prostates (Figure 5.9B). No overt differences between wild-type and CaMKKβ-/- prostate morphology was found on examination of HE stained sections.

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A) AP lobes from 17 week old mice

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B) AP lobes from 12 week old mice

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Figure 5.9 HE staining of wild-type (WT), CaMKKβ-/-, PTEN-/- and CaMKKβ-/-PTEN-/- prostates. Representative images from HE stained sections from anterior prostate (AP) tissue from wild-type (WT), CaMKKβ-/- , PTEN-/- (switched) and CaMKKβ-/-PTEN-/- animals (from top to bottom panel). A) 17 week old animals (n=3 examples per genotype) and B) 12 week old animals (n=2 examples per genotype). Reactive stroma is indicated by ‘#’ and in A). A scale bar is shown in the bottom right of each figure. 193

5.3.4.1 Vimentin staining

Activation of the stromal microenvironment is thought to be a critical step in adenocarcinoma growth and progression (discussed in Chapter 1, Sections 1.1.6 and 1.3.7) (Tuxhorn et al., 2002). Prostate cancers have been shown to induce a stromal reaction (desmoplasia) as part of carcinoma progression. The specific mechanisms leading to reactive stroma are not known, and the extent to which stroma regulates tumorigenesis is also not well understood (Tuxhorn et al., 2002). Tumour-associated fibroblasts (TAFs) and myofibroblasts compose the reactive stroma in prostate cancer (Barron and Rowley, 2012). While normal fibroblasts maintain tissue homeostasis, their activated counterparts (myofibroblasts/TAFs) promote tumour progression through their repair and pro-survival biology leading to new growth and angiogenesis (Barron and Rowley, 2012). Vimentin is a type III intermediate filament protein and is a commonly used marker of reactive stroma as it is highly expressed by myofibroblasts/TAFs and lowly expressed in normal prostate stroma (Tuxhorn et al., 2002). To investigate if the stroma in PTEN-/- and CaMKKβ-/-PTEN-/- prostates is canonical reactive stroma, tissue sections from 12 week old PTEN-/- and CaMKKβ-/-PTEN-/- mice were stained with vimentin using immunohistochemistry (Figure 5.10).

Figure 5.10 Vimentin staining in PTEN-/- and CaMKKβ-/-PTEN-/- prostate. Anterior prostate (AP) lobes were taken from PTEN-/- and CaMKKβ-/-PTEN-/- mice aged 12 weeks and fixed overnight in 4% PFA. Tissue sections were then stained an antibody raised to 194 vimentin using immunohistochemistry. Tissue staining was achieved using DAB and sections were counterstained with haematoxylin. A scale bar is shown in the bottom right of the figure.

Vimentin was found to stain the fibroblasts and blood vessel walls in both PTEN-/- and CaMKKβ-/-PTEN-/- sections. CaMKKβ-/-PTEN-/- sections were found to have increased vimentin staining due to the increased cell density in stromal regions. This shows that the outgrowth of stroma found in prostate cancer models is likely reactive stroma. Studies in the literature support the idea that reactive stroma develops alongside, and is a driver of, disease progression. Interestingly, in this scenario we see an increased vimentin-positive stromal cell population associated with slowed disease progression (Tuxhorn et al., 2002, Barron and Rowley, 2012). Understanding the biology of the different stromal tissues and tumour microenvironments will be important in understanding its regulation of disease progression.

5.3.4.1 Serum cytokine levels

The prostate cancer microenvironment is associated with a chronic inflammatory state which can promote tumour growth, and this is in part sustained by macrophage activation in the absence of detectable microorganisms (Balkwill and Mantovani, 2012). In the immune system, CaMKKβ is selectively expressed in macrophages, and its ablation has been shown to impair the ability of macrophages to spread, phagocytose bacterial particles and release cytokines and chemokines (Racioppi et al., 2012). A number of cytokines and the interaction between immune and stromal cells have been identified as key regulatory factors in the development of reactive stroma (Barron and Rowley, 2012, Landskron et al., 2014).

IL-6 has been shown to stimulate the growth of tumour prostate cells, and serum levels of IL-6 and soluble IL6R have been shown to be correlative with prostate cancer progression (Azevedo et al., 2011). In addition to IL-6, serum levels of other pro-inflammatory cytokines have been shown to be elevated in prostate cancer and implicated in disease progression, such as TNF-α and IL-10. The CaMKKβ-null model of prostate cancer used in this study is a global deletion model. Given the differences in the stroma morphology in the PTEN-/- and CaMKKβ-/-PTEN-/- prostate, the requirement of CaMKKβ in the macrophage inflammatory responses and the role of chronic inflammation in prostate cancer progression, a selection of cytokine serum concentrations were measured. The concentrations of IL-6, TNF-α, IL-10, KC/GRO, IFNγ and IL-2 were determined as these have previously been implicated in having roles in cancer development and progression (Landskron et al., 2014). Differences in serum cytokine levels could help to explain differences in stromal biology and disease progression between genotypes.

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Figure 5.11 Plasma cytokine concentrations in wild-type (WT), PTEN-/- and CaMKKβ-/-PTEN-/- models. Blood samples were collected by cardiac puncture at harvest from wild-type (WT), PTEN-/- and CaMKKβ-/-PTEN-/- mice aged 26 weeks. Total blood was then centrifuged and the plasma fraction collected. Serum levels of IL-6, TNF-α, IL-10, KC/GRO, IFNγ and IL-2 were determined by the Mouse Biochemistry Laboratories, Cambridge, UK. Data are means ± SEM (n=4 mice for WT; n= 6 for PTEN-/- and CaMKKβ-/-PTEN-/- mice). *p<0.05; t-test.

There was a trend for PTEN-null mice to have increased serum inflammatory cytokine levels compared to wild-type mice and this was significant for IL-6, TNF-α and IL-10 (Figure 5.11). However, there were no significant differences in serum cytokine concentrations for any of the cytokines measured between PTEN-/- and CaMKKβ-/-PTEN-/- mice at 26 weeks.

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5.3.5 CaMKKβ expression and localisation

To garner information about where CaMKKβ is expressed in the mouse prostate, anterior lobe tissue sections were stained using the in-house CaMKKβ antibody by immunohistochemistry. The specificity of this antibody was validated using CaMKKβ knockout tissue from the CaMKKβ-/- models.

Figure 5.12 CaMKKβ staining in wild-type (WT), CaMKKβ-/-, PTEN-/- and CaMKKβ-/-PTEN-/- prostate sections using immunohistochemistry. Anterior prostate (AP) lobes were taken from wild-type (WT), CaMKKβ-/-, PTEN-/- and CaMKKβ-/-PTEN-/- mice aged 12 weeks and fixed overnight in 4% PFA. Tissue sections were then stained the in-house CaMKKβ antibody using immunohistochemistry. Tissue staining was achieved using DAB and sections were counterstained with haematoxylin. A scale bar is shown in the bottom right of the figure. 197

After initial rounds of optimisation, the CaMKKβ antibody was found to be specific and produced staining in the PTEN-/- prostate but not the CaMKKβ-/-PTEN-/- prostate (Figure 5.12). Wild-type prostate was found to be negative for CaMKKβ staining and this is consistent with the low expression of this kinase in wild-type tissue. CaMKKβ staining was found to be localised mainly to the prostate epithelial cells of the PTEN-/- prostate, with a greater number of CaMKKβ-positive cells being localised to the basement membrane/stroma. A similar staining pattern was found for the proliferation marker, Ki-67 (Figure 5.8B). This may indicate that CaMKKβ expression has a role in dividing cells. This staining was performed in animals aged 12 weeks.

In future studies, it will be important to look at CaMKKβ staining in more advanced disease to investigate whether CaMKKβ expression and/or localisation changes with prostate cancer progression in the PTEN-/- model. One study has presented data that a nuclear pool of CaMKKβ becomes evident in more advanced prostate cancer, although there is no evidence of nuclear localisation of CaMKKβ at 12 weeks, this may only become apparent in higher grade lesions (Karacosta et al., 2012).

5.3.6 The switch: CaMKKβ-deletion in the PTEN-/- prostate

In the previous chapter (Section 4.5), a switch in protein expression was observed that appeared to correspond with the switch of epithelial cells from hyperplasic to neoplastic growth. Whether CaMKKβ-deletion in the PTEN-/- prostate had an effect on switch progression was investigated. This was done in the anterior lobe of 12 week old animals using hexokinase II staining as a biomarker of the switch (Figure 5.13). In the last chapter, it was found that by 12 weeks of age 50% of PTEN-/- AP lobes had gone through the switch, compared to nearly all of AMPKβ1-/-PTEN-/- lobes. However, the preliminary data presented in Figure 5.13 implies that CaMKKβ-deletion in the PTEN-/- prostate does not have a significant effect on switch progression (Figure 5.13). However, more n numbers are required to conclude this for certain. If this withholds, it may imply that CaMKKβ elicits its oncogenic effects after the development of prostate neoplasia.

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Figure 5.13 Hexokinase II expression in PTEN-/- and CaMKKβ-/-PTEN-/- prostates. Anterior prostate lobes were dissected from PTEN-/- and CaMKKβ-/-PTEN-/- prostates at 12 weeks and homogenised. Protein lysate was then subject to Western blot analysis. Western blots were probed with antibodies raised against hexokinase II (as a marker of the switch) and actin was used as a loading control. Western blots were developed using the LI-COR imaging system.

5.3.7 Molecular characterisation

The CaMKKβ-/-PTEN-/- prostate was characterised by Western blot analysis at the 17 week timepoint and this timepoint was chosen for a number of reasons. Firstly, at 12 weeks the switch (identified in the previous chapter) introduced large variability in protein expression within a genotype, making it different to critically evaluate whether differences in protein expression occurs between genotypes. This variability was found to be absent at later timepoints. Secondly, there is a significant difference in prostate weight between wild-type and CaMKKβ-/- prostates at the 17 week timepoint, thus the wild-type and CaMKKβ-/- prostate were characterised at this timepoint in parallel.

5.3.7.1 Western blot analysis

Anterior lobes from wild-type (WT), CaMKKβ-/-, PTEN-/- and CaMKKβ-/-PTEN-/- prostates were dissected from 17-week old mice, homogenised and subject to Western blot analysis. Western blots were probed with antibodies raised against phospho-AktSer473, Akt and the androgen receptor (AR) in order to interrogate Akt and AR signalling, respectively (Figure 5.14, upper panel). In addition, blots were probed with phospho-ACCSer79, ACC, phospho-AMPKαThr172 and AMPKβ1/2 to characterise AMPK signalling (5.14, lower panel).

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Figure 5.14 Characterisation of AMPK in wild-type (WT), CaMKKβ-/-, PTEN-/- and CaMKKβ-/-PTEN-/- prostates. A) Anterior prostate (AP) lobes from wild-type (WT), CaMKKβ-/-, PTEN-/- and CaMKKβ-/-PTEN-/- mice aged 17 weeks were dissected, homogenised and subject to Western blot analysis. Western blots were probed with antibodies raised against pAktSer473, Akt and AR (top panel) and pACCSer79, ACC, pAMPKαThr172, and β1/2 (bottom panel); actin was used as a loading control. Western blots were Ser473 imaged using the LI-COR system. Quantification of the expression of B) pAkt / Akt; C) Akt/actin;200 D) ACC/actin; E) pAMPKThr172/actin and F) AMPKβ1/actin was performed based in the blots presented in A). Data are means ± SEM (n=3 mice per genotype). *p<0.05, **p<0.01, ***p<0.005; t-test.

Consistent with the results presented in Figure 5.4, AMPK complex expression was found to be significantly downregulated in the CaMKKβ-/- and CaMKKβ-/-PTEN-/- prostate compared to their respective CaMKKβ-positive controls (Figure 5.14A, E-F). In addition, Akt and ACC protein levels were found to be downregulated in CaMKKβ-/- prostates (Figure 5.14A, C-D), however no difference in Akt phosphorylation was found upon CaMKKβ-deletion (Figure 5.14A, B). AR expression was found to be upregulated upon PTEN-deletion, however this upregulation varied between samples. Overall, AR expression appeared to be downregulated in the CaMKKβ-/-PTEN-/- prostate compared to the PTEN-/- prostate. AR expression was not quantified due to the appearance of a doublet band in Western blot analysis and the variability in expression levels.

Akt, ACC and AMPK expression is upregulated upon PTEN-deletion and Akt and ACC have an established role in prostate cancer development and progression (Fresno Vara et al., 2004, Scott et al., 2012, Massie et al., 2011). Downregulation of these oncogenic proteins in the CaMKKβ-/- prostate may be key to the inhibition of cancer development in the PTEN prostate cancer model.

5.3.7.2 Gene expression qPCR analysis

To investigate whether these changes in protein expression were occurring at the level of transcription, gene expression of ACC1, AK,T1, AKT2 AMPKα1β1γ1 and AR were measured using qPCR in wild-type (WT) and CaMKKβ-/- prostates from 17 week old mice. In addition, the expression of the AR regulated gene, IFG1R, was determined. These results were normalised to the expression of the house keeper gene, HPRT. This analysis was performed in both anterior (AP) and dorsolateral ventral prostate (DLVP) lobes.

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Figure 5.15 Expression of ACC1, AKT1/2, AMPKα1β1γ1, androgen receptor (AR) and IFG1R mRNA levels in wild-type (WT) and CaMKKβ-/- prostates measured using qPCR. Anterior prostate (AP) lobes and dorsolateral ventral prostate (DLVP) were dissected from wild-type (WT) and CaMKKβ-/- mice aged 17 weeks for subsequent RNA extraction and gene expression analysis by qPCR. ACC1, AKT1, AKT2, AMPKα1, AMPKβ1, AMPKγ1, androgen receptor (AR) and IFG1R gene expression was normalised to expression of the house keeping gene, HPRT (hypoxanthine- guanine phosphoribosyltransferase) (A-H). Gene expression data are represented as fold wild-type (WT) for each lobe. Data are means ± SEM (n=6 mice per genotype).

Despite a significant downregulation of ACC, AMPK subunits and Akt at the protein level in the CaMKKβ-/- prostate compared to the wild-type prostate, no difference in ACC1, AKT1, AKT2, AMPKα1, AMPKβ1, AMPKγ1 mRNA levels was found in either AP or DLVP lobe between genotypes (Figure 5.15A-F). Similarly, no difference in AR or IFG1R gene expression was found (Figure 5.15G-H). Notably, AR signalling is low in the PTEN-positive prostate (characterised in Chapter 4, Figure 4.29) and therefore any differences in AR signalling upon CaMKKβ-deletion may only become apparent in the PTEN-/- prostate.

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5.4 CaMKKβ and ribosomal subunit expression

In the previous chapter, a mass spectrometry screen was performed to identify biomarkers of the protein switch. In addition to the samples detailed in the last chapter, anterior lobe lysate from 4 CaMKKβ-/- mice was pooled and submitted for analysis alongside the other samples to screen for proteins differentially expressed in the wild-type (WT) and CaMKKβ-/- prostate. Around 200 proteins were found to be differentially regulated (log2 score >|0.8|) between WT and CaMKKβ-/- prostates. The top 35 hits downregulated and upregulated in the CaMKKβ-/- prostate relative to the wild-type prostate are shown in Figure 5.16 A and B, respectively. The total number of proteins considered to be differentially expressed -/- between wild-type and CaMKKβ samples was 111 (log2 values greater than 1.0 and less than -1.0; see Appendix 1 for a full explanation of how this was defined).

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-/- Protein Accession no WT CaMKKβ log2 (CaMKKβ/WT) 1 NAD(P)H dehydrogenase [quinone] 1 Q64669 1 3.011E-08 < -10 2 Bifunctional 3-phosphoadenosine 5-phosphosulfate synthase 2 O88428 1 3.509E-08 < -10 3 Aldehyde dehydrogenase O35945 1 5.923E-08 < -10 4 Coatomer subunit gamma-2 Q9QXK3 1 9.653E-08 < -10 5 Eukaryotic initiation factor 4A-III Q91VC3 1 1.103E-07 < -10 6 Splicing factor 3B subunit 1 Q99NB9 1 1.952E-07 < -10 7 Calcium-binding protein 39-like Q9DB16 1 4.181E-07 < -10 8 Prostatic spermine-binding protein P15501 1 0.0661731 -3.92 9 Carbonyl reductase [NADPH] 2 P08074 1 0.1135953 -3.14 10 Reticulocalbin-2 Q8BP92 1 0.1445889 -2.79 11 Ras GTPase-activating-like protein Q3UQ44 1 0.2219233 -2.17 12 60S ribosomal protein L19 P84099 1 0.2281858 -2.13 13 Transcription intermediary factor 1-beta Q62318 1 0.2472505 -2.02 14 60S ribosomal protein L15 Q9CZM2 1 0.2542459 -1.98 15 60S ribosomal protein L7 P14148 1 0.3146884 -1.67 16 Heterogeneous nuclear ribonucleoprotein L Q8R081 1 0.31862 -1.65 17 60S ribosomal protein L26 P61255 1 0.3211376 -1.64 18 Coatomer subunit gamma-1 Q9QZE5 1 0.3402174 -1.56 19 60S ribosomal protein L28 P41105 1 0.3413202 -1.55 20 60S ribosomal protein L23 P62830 1 0.3509869 -1.51 21 60S ribosomal protein L3 P27659 1 0.3562673 -1.49 22 Alpha-actinin-4 P57780 1 0.3568277 -1.49 23 60S ribosomal protein L13 P47963 1 0.362157 -1.47 24 60S ribosomal protein L5 P47962 1 0.3801436 -1.40 25 Adenylyl cyclase-associated protein 1 P40124 1 0.38603 -1.37 26 60S ribosomal protein L4 Q9D8E6 1 0.3989203 -1.33 27 60S ribosomal protein L8 P62918 1 0.404357 -1.31 28 Vacuolar protein sorting-associated protein 35 Q9EQH3 1 0.4130305 -1.28 29 Keratin, type II cytoskeletal 8 P11679 1 0.4204228 -1.25 30 Isocitrate dehydrogenase [NADP], mitochondrial P54071 1 0.4259434 -1.23 31 Ras-related protein Rab-18 P35293 1 0.4269913 -1.23 32 60S ribosomal protein L32 P62911 1 0.4278864 -1.22 33 60S acidic ribosomal protein P1 P47955 1 0.431719 -1.21 34 60S ribosomal protein L9 P51410 1 0.4408785 -1.18 35 60S ribosomal protein L27a P14115 1 0.4461295 -1.16

-/- Protein Accession no WT CaMKKβ log2 (CaMKKβ/WT) 1 Major urinary protein 3 P04939 2.575E-09 1 > 10 2 Poly(rC)-binding protein 2 Q61990 3.944E-09 1 > 10 3 Hemoglobin subunit beta-2 P02089 5.699E-09 1 > 10 4 Cullin-associated NEDD8-dissociated protein 2 Q6ZQ73 9.304E-09 1 > 10 5 Kynurenine--oxoglutarate transaminase 1 Q8BTY1 9.708E-09 1 > 10 6 Tubulin alpha-1C chain P68373 1.02E-08 1 > 10 7 Alpha-1-antitrypsin 1-4 Q00897 1.552E-08 1 > 10 8 Fructose-bisphosphate aldolase P05063 1.692E-08 1 > 10 9 Bifunctional purine biosynthesis protein Q9CWJ9 1.907E-08 1 > 10 10 Ras-related protein Rab-1B Q9D1G1 2.135E-08 1 > 10 11 Actin, gamma-enteric smooth muscle P63268 2.756E-08 1 > 10 12 Ectonucleoside triphosphate diphosphohydrolase 2 O55026 3.08E-08 1 > 10 13 Mitochondrial peptide methionine sulfoxide reductase Q9D6Y7 3.845E-08 1 > 10 14 Tubulin beta-2A chain Q7TMM9 4.111E-08 1 > 10 15 116 kDa U5 small nuclear ribonucleoprotein component O08810 7.675E-08 1 > 10 16 Cytosolic non-specific dipeptidase Q9D1A2 8.926E-08 1 > 10 17 Major urinary protein 2 P11589 0.1606398 1 2.64 18 Major urinary proteins 11 and 8 (Fragment) P04938 0.2494473 1 2.00 19 Tubulin alpha-4A chain P68368 0.2954735 1 1.76 20 Major urinary protein 20 Q5FW60 0.3196711 1 1.65 21 Twinfilin-1 Q91YR1 0.3828721 1 1.39 22 Myosin-11 O08638 0.3887208 1 1.36 23 THO complex subunit 4 O08583 0.3899343 1 1.36 24 Seminal vesicle secretory protein 4 P18419 0.413517 1 1.27 25 Phosphatidylinositol transfer protein alpha isoform P53810 0.4258764 1 1.23 26 Protein S100-A6 P14069 0.4335359 1 1.21 27 Seminal vesicle secretory protein 5 P30933 0.4368023 1 1.19 28 Histone H2A type 1-H Q8CGP6 0.4460683 1 1.16 29 Beta-2-microglobulin P01887 0.4604178 1 1.12 30 Seminal vesicle secretory protein 6 Q64356 0.4859919 1 1.04 31 Myosin light chain kinase Q6PDN3 0.4994686 1 1.00 32 Tubulin polymerization-promoting protein family member 3 Q9CRB6 0.5020761 1 0.99 33 Tropomyosin alpha-1 chain P58771 0.5041345 1 0.99 34 Cystatin-C P21460 0.5178427 1 0.95 35 Sulfhydryl oxidase 1 Q8BND5 0.5181516 1 0.95 Figure 5.16 Proteins differentially expressed in wild-type and CaMKKβ-/- prostates identified by mass spectrometry. Anterior lobe (AP) lysate from 4 wild-type (WT) and 4 CaMKKβ-/- mice aged 12 weeks was combined based on genotype. WT and CaMKKβ-/- pooled samples were separated by SDS-PAGE and submitted to the mass spectrometry facility to identify proteins differentially expressed between genotypes. Data presented are the top hits for proteins downregulated A) and upregulated B) upon CaMKKβ- deletion compared WT tissue. The level to which these proteins are differentially expressed is expressed as log2(CaMKKβ-/-/WT) and indicated by yellow (low expression) to blue (high expression) in WT and CaMKKβ-/- columns. 60S ribosomal proteins and translation initiation factors are highlighted in tan. An explanation of how the lists in A and B were generated is offered in Appendix 1.

The mass spectrometry screen revealed downregulation of a number of 60S ribosomal proteins and translation initiation factors in the CaMKKβ-/- prostate (Figure 5.16A, highlighted in tan). In fact, half of the top hits downregulated in the CaMKKβ-/- prostate, compared to wild-type prostate, have roles in protein translation (Bhat et al., 2015, Jackson et al., 2010). The mass spectrometry results were analysed by IPA (Ingenuity Pathway Analysis). This powerful programme predicted EIF2 signalling to be significantly inhibited (activation z-score -5.692, p-value 9.57x10-62) in CaMKKβ-/- prostates compared to wild-type prostates. For a full list of proteins and their relative expression levels leading to this prediction see Appendix 2A. EIF2 signalling has been implicated as being upregulated in human prostate cancers (Overcash et al., 2013). In addition, MYC and MYCN signalling were predicted to be significantly downregulated in the CaMKKβ-/- prostate (see Appendix 2B for full list of transcriptional regulator networks and activation status). MYC is well characterised as an oncogene, driving prostate tumourigenesis as well as most other cancers (Dang, 2012, Koh et al., 2010). These predictions were predominantly based on the downregulation of the translation apparatus highlighted in Figure 5.16A.

To validate the mass spectrometry results Western blotting using antibodies against RPL19 and RPL7 was carried out (Figure 5.17). These proteins were shown to be significantly downregulated in the CaMKKβ knockout prostate. RPL19 was chosen as it has been shown to be a prognostic marker of human prostate cancer (Bee et al., 2006). Differential display analysis of gene expression profiles between benign and malignant human prostate cell lines highlighted RPL19 as being overexpressed in the malignant cells (Bee et al., 2006). In addition, using a range of human cell lines and tissues RPL19 mRNA was found to between 5-8 fold higher in malignant cell lines and tissues, compared with their benign counterparts (Bee et al., 2006). RPL7 has been shown to have a role in the regulation of proliferation and apoptosis in colorectal cancer (Lai and Xu, 2007). In addition, anterior lobe blots were probed with an antibody specific for myosin light chain kinase (MLCK) (Figure 5.17A) as this was shown to be upregulated in the CaMKKβ-/- prostate by mass spectrometry (Figure 5.16). RPL19 and RPL7 expression was further characterised in the PTEN-/- and CaMKKβ-/-PTEN-/- AP and DLVP lobe by Western blot analysis (Figure 5.17).

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C D

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p=0.061

Figure 5.17 RPL19 and RPL7 expression in wild-type (WT), CaMKKβ-/-, PTEN-/- and CaMKKβ-/-PTEN-/- prostates. A) Anterior prostate (AP) and B) dorsolateral ventral prostate (DLVP) lobes from wild-type (WT), CaMKKβ-/-, PTEN-/- and CaMKKβ-/-PTEN-/- mice aged 12 weeks were dissected, homogenised and subject to Western blot analysis. Western blots were probed with antibodies raised against RPL19 and RPL7; actin was used as a loading control. Western blots were imaged using the LI-COR system. In addition, AP lobe Western blots were probed with an antibody raised to MLCK A). Quantification of the expression of RPL19 and206 RPL7 normalised to actin in AP and DLVP lobes are presented C), D) and E), F), respectively. Data are means ± SEM (n=3 mice per genotype, with the exception of PTEN-/- and CaMKKβ-/-PTEN-/- DLVP where n=4 per genotype). *p<0.05, **p<0.01, ***p<0.005; t-test. Expression of RPL19 and RPL7 was found to be significantly increased upon PTEN-deletion in both lobes of the mouse prostate (Figure 5.17). There was a robust trend, which often reached significance, for these ribosomal proteins to be downregulated in the CaMKKβ-/- prostate on both PTEN+/+ and PTEN-/- backgrounds (Figure 5.17C-F). Further validating the mass spectrometry results, MLCK expression was found to be upregulated in the CaMKKβ-/- prostate; however this differential expression was lost upon PTEN-deletion (Figure 5.17A).

The data presented in Figure 5.17 validate the results generated by the mass spectrometry screen (Figure 5.16). Further to the mass spectrometry screen, it was shown that these changes in ribosomal protein expression are relevant in the PTEN-/- prostate and in the dorsolateral lobe of the prostate. Downregulation of these ribosomal proteins could potentially lead to downregulation of protein translation in the CaMKKβ-/- prostate. Downregulation of translation could explain the decreased expression of a number of proteins in the CaMKKβ-/- prostate at 17 weeks. Furthermore, regulation at the translational level would be consistent the gene expression data presented in Figure 5.15. Furthermore, downregulation of translation is known to limit cellular proliferation and organ size (Montagne et al., 1999).

5.5 In vivo STO609 studies

Genetic deletion of CaMKKβ was found to inhibit prostate cancer development in the PTEN prostate cancer mouse model. In this model, CaMKKβ is globally deleted before PTEN- deletion and therefore prior to development of any pre-cancerous/cancerous lesions. To investigate whether CaMKKβ inhibition could be used as a therapeutic intervention in disease progression, PTEN-/- mice were dosed with the CaMKKβ inhibitor, STO609 (Tokumitsu et al., 2002). This compound has previously been used for in vivo dosing in mouse xenograft models of prostate cancer and no signs of compound toxicity were reported during 5 weeks of administration (Massie et al., 2011).

5.5.1 STO609 dosing study in wild-type mice

In a previous study, the plasma concentrations of STO609 were determined in mice following intraperitoneal (IP) and intravenous (IV) injection and pharmacokinetics established (Figure 5.18B) (Massie et al., 2011). Massie et al investigated CaMKKβ inhibition in tumour formation using the C4-2B xenograft model of castrate-resistant prostate cancer (Massie et al., 2011, Snoek et al., 2009). Investigation of the effect of CaMKKβ inhibition using a genetically engineered mouse model (GEMM) has a number of advantages over the use of xenograft models, namely the mice used for xenograft studies are immunocompetent, therefore the tumour microenvironment is more realistically modelled in GEMMs. Furthermore, the use of GEMMs allow the stages of tumour progression to be

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studied over time and therapeutic approaches to be explored at these different stages of tumour development (Richmond and Su, 2008).

The dosing regimen used in the xenograft study was 3 IP injections a week, for 5 weeks (Massie et al., 2011). This was a labour intensive regime and led to peaks and troughs in STO609 plasma concentrations as the drug was cleared after injection. To alleviate both these issues, minipump administration of STO609 was investigated. Minipump dosing has the advantage of ensuring continuous delivery and constant compound plasma levels, thereby maximising therapeutic efficiency whilst saving time and reducing the stress of the animal caused by repetitive injection schedules.

Alzet osmotic minipumps were the minipumps chosen for this study. These minipumps are available with a variety of durations and flowrates. While the pumping rate of each model is fixed, the dose of compound delivered can be adjusted by varying the concentration of agent used to fill the pump. However, there is a trade-off between flow rate and duration; the minipumps capable of dosing for the longest duration (6 weeks) have the lowest flowrate (0.15 μl/h). Ideally, for this study a minipump with high flow rate and long duration was required. As a compromise minipump model 2004 was selected, with 4 week duration and 0.25 μl/h flowrate. Wild-type male (C57BL/6J) mice were used for initial dosing studies and underwent minipump implantation surgery aged 13 weeks. This age range (13-17 weeks) was chosen, as over this period there is a significant and measurable increase in prostate weight and disease progression in PTEN-/- mice.

The maximum concentration of STO609 achieved was 30 mg/ml dissolved in 200 mM NaOH. Minipumps were filled with 0, 6, 15, 30 mg/ml STO609 to gain an idea of the dose- dependence and variability associated with minipump compound administration. Dissolution of STO609 in 200 mM NaOH to a concentration of 30 mg/ml reduced the pH of the NaOH to pH 12.7, thereby making it compatible with the specifications of the minipump (pH<14). The control minipumps (0 mg/ml) were filled with 200 mM, adjusted to pH12.7 using HCl. Solutions of 0 mg/ml and 30 mg/ml STO609 were then used to generate the intermediate STO609 concentrations.

To determine the steady state plasma concentration of STO609 for the different minipump concentrations, a tail-vein bleed was taken at 2 weeks post-surgery. STO609 levels were measured using LC-MS/MS and quantified relative to standard curve measurements (Figure 5.18A). STO609 measurements were performed by Donna-Michelle Smith (PK/Bioanalytics Core Facility, CRUK Cambridge Institute). Donna Smith also performed the pharmacokinetic measurements in the Massie et al paper discussed earlier and these are shown for reference in Figure 5.18B (Massie et al., 2011). At harvest prostate and liver tissue were taken from mice receiving the highest STO609 doses (15 mg/ml and 30 mg/ml) and tissue STO609 accumulation over the 4 weeks dosing period determined (Figure 5.18C).

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Figure 5.18 In vivo dosing of STO609 using minipump administration in wild-type mice. Alzet minipumps (model 2004) containing 0, 6, 15, 30 mg/ml STO609 in NaOH were implanted in wild-type mice aged 13 weeks. A) A tail bleed was taken at 2 weeks post-minipump surgery, bloods were centrifuged and the STO609 concentration of plasma fraction determined. Data are means ± SEM, (n=3 per condition). B) Published pharmacokinetic measurement of plasma concentrations of STO609 in mice following IP or IV injection (Massie et al., 2011). In this study, 10 μmol/kg STO609 or vehicle control (DMSO) was injected. C) Mice were harvested at 17 weeks after 4 week treatment duration. STO609 tissue accumulation was measured in prostate and liver tissue from mice receiving 15 mg/ml and 30 mg/ml STO609 doses. Data are means ± SEM (n=6 for prostate tissue; n=3 for liver tissue). **p<0.01, t-test. STO609 levels were measured using LC-MS/MS and quantified relative to standard curve measurements.

The initial dosing study showed minipump delivery of STO609 to be reliable and efficient. There was clear dose-dependency between STO609 plasma concentration and the STO609 concentration used to fill the pump (Figure 5.18). The STO609 plasma concentration became saturated in animals with minipumps containing STO609 at a concentration of 15mg/ml and above (Figure 5.18A). The maximum steady-state plasma concentration achieved using minipump administration was 1000 nM (Figure 5.18A) and this was the same maximum achieved after IP or IV injection of STO609 in the Massie et al study (Figure 5.18B). Using minipumps this maximum STO609 concentration was maintained throughout dosing, whereas when administered via injection plasma concentrations had significantly dropped after 1 h of dosing. No signs of compound toxicity were observed during this minipump dosing study.

At 4 weeks post-surgery, mice were harvested and prostate and liver tissue from mice dosed with 15 mg/ml and 30 mg/ml STO609 was snap frozen and STO609 accumulation measured (Figure 5.18C). STO609 was found to accumulate to measurable levels in both liver and prostate tissue. Accumulation of STO609 in the liver was significantly higher than in the prostate, as this organ is highly vascularised. STO609 was found to accumulate in the wild- type prostate to just under 50 ng/g tissue. During disease progression the PTEN-/- prostate

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becomes more vascularised compared to wild-type tissue, therefore STO609 delivery and accumulation may be increased upon disease progression. No difference in prostate or liver weight was observed between STO609 dosed and vehicle control mice.

5.5.2 STO609 dosing in PTEN-/- mice

In the previous section, STO609 plasma concentrations became saturated in mice with minipumps filled with concentrations of 15 mg/ml STO609 and above. However, STO609 concentrations of 30 mg/ml could be reached when dissolved in 200 mM NaOH. In the last study, a minipump of 4 week duration was used as a compromise between flowrate and duration. Ideally, dosing would be continued for as long as possible, thus for the PTEN-/- dosing study a minipump of 6 week duration (flow rate of 0.15 μl/h) was opted for and filled with 30 mg/ml STO609. This should allow saturating STO609 plasma concentrations to be reached for a 6 week duration. Minipumps filled with 0 mg/ml and 30 mg/ml STO609 were implanted into wild-type and PTEN-/- mice at 13 weeks of age and harvested at 19 weeks of age. At harvest, prostate weight was determined and normalised to body weight (Figure 5.19). Wild-type and PTEN-/- mice were treated with STO609 as CaMKKβ-deletion was found to have effects in the wild-type prostate as well as the PTEN-/- prostate (Section 5.3).

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Figure 5.19 Effect of STO609 dosing on prostate weight in wild-type (WT) and PTEN-/-mice. Alzet minipumps (model 2006) containing vehicle (NaOH) or 30 mg/ml STO609 were implanted in wild-type and PTEN-/- mice aged 13 weeks. Mice were harvested at 19 weeks after 6 week treatment duration. A) Prostate wet weight was recorded at harvest and normalised to body weight. Data are means ± SEM (n=6-7 for WT mice and n= 7-8 for PTEN-/- mice for each condition, performed over 2 independent rounds of surgery). **p<0.01, ***p<0.005. B) Representative images taken at harvest of PTEN-/- treated with vehicle or STO609. C) Anterior prostate (AP) lobes from mice treated with vehicle or STO609 taken after overnight fixation in 4% PFA.

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STO609 administration using minipumps over a 6 week period significantly reduced the growth of the PTEN-/- prostate, leading to a reduction of over 30% in prostate weight (Figure 5.19). No difference in prostate weight was found between STO609- and vehicle-dosed wild- type mice. Again, no toxicity was associated with STO609 administration over this 6 week period. These results support the hypothesis that CaMKKβ inhibition can be used as a therapeutic intervention to slow the progression of prostate cancer.

5.5.3 HE staining: effect of STO609 in the PTEN-/- prostate

At harvest AP lobes from PTEN-/- mice dosed with STO609 and vehicle controls were fixed for HE staining and pathological analysis. Images from these HE stained sections are presented in Figure 5.20.

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Figure 5.20 Effect of STO609 dosing on prostate pathology in PTEN-/- mice. Alzet minipumps (model 2006) containing NaOH or 30 mg/ml STO609 were implanted in PTEN-/- mice aged 13 weeks. Mice were harvested at 19 weeks after 6 week treatment duration and anterior prostate (AP) lobes fixed in 4% PFA for subsequent sectioning and HE staining. Representative images of the highest grade lesions in each tissue section are shown and associated grade is indicated in white in bottom right of image. mPIN lesions are indicated by a white arrowhead and 214 adenocarcinoma lesions with a black arrowhead. Cystic regions are marked with an asterisk (*). (n=3 per each condition, two images per section).

Consistent with reduced prostate weight, STO609 treated PTEN-/- prostates were found to have reduced incidence of adenocarcinoma as determined from HE stained sections (Figure 5.20). By 19 weeks anterior lobe of age the PTEN-/- prostate becomes cystic. The PTEN-/- prostates from STO609 treated mice were found to have fewer and smaller cysts (Figure 5.20, cysts indicated by asterisks (*)).

Previous studies have shown that STO609 inhibits other protein kinases in addition to CaMKKβ (Bain et al., 2007). To investigate whether the effects of STO609 on prostate cancer development are due to inhibition of CaMKKβ, it will be important to find biomarkers indicative of CaMKKβ downregulation in the PTEN-/- prostate. However, to unequivocally test the requirement of CaMKKβ for the anti-tumorigenic effects of STO609, it will be important to dose CaMKKβ-/-PTEN-/- mice with this compound. An issue with this methodology is that the CaMKKβ-/-PTEN-/- prostate is already different to the PTEN-/- prostate and this would confound any comparison made between genotypes after STO609 treatment.

5.6 Discussion

Previous studies have shown that CaMKKβ gene expression is androgen responsive and is upregulated in human prostate cancer, with some evidence given that gene expression positively correlates with disease grade (Massie et al., 2011, Frigo et al., 2011). Two papers place CaMKKβ upstream of the AR (Karacosta et al., 2012, Shima et al., 2012). One suggests a positive feedback loop in which CaMKKβ is induced by the AR to maintain AR activity (Karacosta et al., 2012), while the other suggests CaMKKβ as a negative regulator of AR activity (Shima et al., 2012). To address these conflicting results, the LNCaP/Luc cell line stably transfected with an AR reporter construct driven by a different and more specific ARE (SC1.2 gene ARE) was used (Dart et al., 2009). These cells were stimulated using mibolerone in the presence or absence of the CaMKKβ inhibitor, STO609. Consistent with the results presented by Karacosta et al, STO609 treatment led to a significant reduction in luciferase activity and CaMKKβ protein expression in stimulated cells. This approach complemented that taken by Karacosta et al, where siRNA-mediated knockdown of CaMKKβ was performed prior to stimulation using dihydrotestosterone (DHT) (Karacosta et al., 2012). PSA expression at both mRNA and protein levels was found to be reduced in CaMKKβ knockdown cells. This was further supported in LNCaP cells using a transiently transfected AR reporter, in which luciferase expression was driven by tandem AREs from the prostate-specific probasin gene promoter (Karacosta et al., 2012).

No change in AR expression was found in this study or the Karacosta et al study upon reduction of CaMKKβ activity (Karacosta et al., 2012). This suggests CaMKKβ may affect the

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post-translational modification status of the AR, which could affect AR activity and/or subcellular localisation. Karacosta et al provide some evidence that CaMKKβ may become nuclear and/or perinuclear in clinical samples of advanced prostate cancer using immunohistochemistry. They show that DHT can induce translocation of a small pool of CaMKKβ to the nucleus in LNCaP cells (Karacosta et al., 2012). No evidence of nuclear CaMKKβ staining was found in the PTEN-/- mouse prostate at 12 weeks (Figure 5.12). It will be important to look at CaMKKβ localisation using immunohistochemistry at later stages of disease development in the PTEN-/- prostate in future studies.

Using cell-based models, inhibition of CaMKKβ was shown to block androgen stimulated growth, migration and invasion in vitro (Massie et al., 2011, Frigo et al., 2011). In these studies the effects of CaMKKβ inhibition were attributed to decreased signalling through AMPK. In addition to AMPK, CaMKKβ is upstream of two other related kinases, CaMKI and CaMKIV (Carling et al., 2012). These previous studies addressing the role of the CaMKKβ pathway in prostate cancer were limited to the use of cell line models of prostate cancer (Massie et al., 2011, Frigo et al., 2011, Karacosta et al., 2012). To address the role of CaMKKβ in prostate cancer in vivo, CaMKKβ was deleted in the PTEN prostate cancer mouse model. CaMKKβ expression was shown to be significantly increased in prostate tissue lacking PTEN, mirroring the changes seen in human prostate cancer tissue and supporting the use of the PTEN model for investigating the role of this kinase in prostate cancer.

Deletion of CaMKKβ in the PTEN prostate cancer model was found to significantly slow disease progression based on prostate weight, Ki-67 staining and disease pathology performed on HE stained sections. Consistent with this finding, dosing PTEN-/- mice with the CaMKKβ inhibitor, STO609, led to a significant decrease in weight in PTEN-/- mice and appeared to slow disease progression. These data are consistent with previous studies supporting a role for CaMKKβ in driving prostate cancer development and progression (Massie et al., 2011, Frigo et al., 2011, Karacosta et al., 2012). Furthermore, a significant difference in prostate weight between wild-type and CaMKKβ-/- in the PTEN+/+ prostate was found at 17 weeks, supporting a role for CaMKKβ in wild-type prostate growth.

The CaMKKβ-/-PTEN-/- prostate was found to have different stromal morphology and increased vimentin-staining compared to the PTEN-/- prostate. In contrast, no differences in stromal morphology were seen between wild-type and CaMKKβ-/- prostates. This suggests that CaMKKβ signalling could be a regulator in shaping the microenvironment surrounding prostate tumours. In the immune system, CaMKKβ is selectively expressed in macrophages (Racioppi, 2013). CaMKKβ deletion has been reported to impair the ability of macrophages to release cytokines and chemokines in response to lipopolysaccharides, prevent the accumulation of macrophages and inflammatory cytokine mRNAs in adipose tissue of mice on a high fat diet and provide resistance to irritants that lead to systematic inflammation and hepatitis (Racioppi et al., 2012). Although no differences were found in serum cytokine

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levels between PTEN-/- and CaMKKβ-/-PTEN-/- models, more localised changes in immune response in the tumour microenvironment may regulate prostate cancer development in these models.

CaMKKβ is expressed in tumour associated macrophages (TAMs), where it has been shown to regulate metabolic responses and cytokine release in response to pattern recognition receptor/integrin stimulation (Racioppi and Means, 2012, Racioppi et al., 2012). TAM subsets have been implicated in blunting the immune response against cancer, supporting blood vessel formation and aiding tumour healing after chemo and radiotherapy (Pollard, 2009, Kozin et al., 2010). The connection between inflammation and prostate cancer was also highlighted when tissue arrays containing normal and cancerous prostate tissue sections revealed that almost 100% of prostate tumour samples exhibited macrophage infiltration and stromal interactions with macrophages and this was greatly reduced in non- cancerous tissue (Zhu et al., 2006). In vitro investigations found that the integrin-mediated adhesion of macrophages to cancer cells promoted the release of IL-1, leading to the conversion of selective AR modulators from inhibitors of AR-induced gene expression to activators of AR-regulated gene expression (Zhu et al., 2006). TAM infiltration was also found to correlate negatively with prognosis after ADT (Nonomura et al., 2011). These studies illustrate the importance of the interaction between the immune system/stroma with cancer cells in regulating tumour progression. Given the role of CaMKKβ in macrophage activation, in future studies it will be critical to define the different TAM subsets associated with PTEN-/- and CaMKKβ-/-PTEN-/- tumours, and further characterise the different tumour microenvironments.

CaMKKβ knockout prostates were found to have significantly reduced protein expression of key proteins shown to be upregulated in the PTEN-/- prostate, e.g. Akt, ACC and AMPK. Changes in protein expression occurred in the absence of any measurable changes in mRNA expression. A mass spectrometry screen of wild-type and CaMKKβ-/- prostates from mice aged 12 weeks revealed significant downregulation of a number of 60S ribosomal proteins and the eukaryotic initiation factor 4A3 (eIF4A3). IPA analysis predicted the CaMKKβ-null prostate to have significantly reduced EIF2 signalling and MYC downregulation compared to wild-type prostates, based primarily on the downregulation of these ribosomal proteins. The mass spectrometry results were validated in both AP and DLVP lopes of the mouse prostate using antibodies raised against RPL19 and RPL7. It was further shown that expression of these ribosomal proteins was downregulated in the CaMKKβ-/-PTEN-/- prostate compared to PTEN-/- prostates.

Downregulation of ribosomal proteins and other proteins regulating translation could explain the downregulation of certain proteins in the CaMKKβ-null prostate. Furthermore, a downregulation of translation could explain the size difference between wild-type and CaMKKβ-/- prostates on a PTEN-positive background and the difference in disease

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progression in CaMKKβ-/-PTEN-/- and PTEN-/- prostates. For example, the MYC transcription factor is a key regulator of growth, it induces several genes involved in ribosome biogenesis and protein synthesis which are required for growth and proliferation (Tumaneng et al., 2012). Unsurprisingly as a result, MYC signalling is upregulated in nearly all cancers (Dang, 2012). Aberrant translation is a widespread characteristic of tumour cells, therefore therapeutic agents that target components of the protein synthesis machinery are currently being explored and hold promise as anticancer agents (Bhat et al., 2015).

A number of studies link CaMKK signalling to the regulation of translation in neurons (Wayman et al., 2008). CaMKKβ is highly expressed in neurons and characterisation of the CaMKKβ global knockout mouse identified that these null mutants had impaired spatial and long-term memory formation, lacking late long-term potentiation (L-LTP) at the hippocampal CA1 synapse (Peters et al., 2003). A growing body of evidence now supports the crucial role of mRNA translation and upregulation of protein synthesis in L-LTP (reviewed in (Kelleher et al., 2004b). Treatment of hippocampal slices with STO609 or the translational inhibitor, anisomycin, were found to disrupt L-LTP (Schmitt et al., 2005, Kelleher et al., 2004a). Furthermore, hippocampal neurons expressing a dominant-negative form of MEK1 (dnMEK1) was found to lead to deficits in L-LTP and this was shown to be via inhibition of protein translation and not transcription (Kelleher et al., 2004a). There is evidence that this signalling may be mediated by the CaMKK crosstalk with MEK/ERK, since treatment with STO609 blocks their phosphorylation in response to LTP induction and this was shown to be due to decreased signalling through CaMKI (Schmitt et al., 2005). In addition, CaMKI has been shown to upregulate neuronal mRNA translation via phosphorylation of eIF4GII (Srivastava et al., 2012). Interestingly, hippocampal neurons expressing dominant-negative CaMKIV, were shown to have a similar L-LTP phenotype to those expressing dnMEK1 (Kang et al., 2001, Kelleher et al., 2004a). These studies implicate CaMKK signalling in the upregulation of translation and protein synthesis in neurons and it follows that CaMKKβ signalling could have a similar role in the mouse prostate. These studies implicate increased regulatory phosphorylation of several proteins that enhance cap-dependent mRNA initiation as being responsible for enhanced translation, such eIF4E and 4EBP1 (Kelleher et al., 2004a, Srivastava et al., 2012, Schmitt et al., 2005). However, none of these papers have looked at expression of ribosomal subunits.

In the literature examining the role of CaMKK signalling in neurons, there is a tendency to group the effects of CaMKKβ and CaMKKα isoforms. However, with respect to the prostate, unlike CaMKKβ, CaMKKα expression has not been reported to be androgen responsive or upregulated in prostate cancer and this may indicate that CaMKKβ is the major source of CaMKK signalling in the prostate (Massie et al., 2011, Frigo et al., 2011).

The malignant phenotype is largely the consequence of dysregulated gene expression, depending upon not simply a global increase in protein synthesis but an altered

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translational landscape in which pro-oncogenic mRNAs are translationally upregulated (Raza et al., 2015, Holland et al., 2004). The translation initiation factor, eIF4E, is required for cap- dependent translation of all nuclear-encoded mRNAs, changes in its levels disproportionally affect the translation of a subset of mRNAs encoding proliferation, survival and tumour- promoting proteins (Bhat et al., 2015). However, changes in eIF4E levels have only a small effect on the expression of house-keeping gene (HKG) mRNAs, such as β-actin (De Benedetti and Graff, 2004). Unlike HKG mRNAs that are mostly characterised by short and low complexity 5′ untranslated regions (UTRs), most eIF4E-sensitive pro-oncogenic mRNAs have long and highly structured 5′UTRs and require high levels of eIF4A for efficient translation (Raza et al., 2015, Wolfe et al., 2014, Rubio et al., 2014). eIF4A, an ATP-dependent DEAD box RNA helicase, is loaded onto mRNA templates as a subunit of eIF4F and gives 5′-3′ directionality (Bhat et al., 2015). As such, there is a developing focus on targeting eIF4A as a cancer therapy.

In this chapter, a mass spectrometry screen revealed that along with the downregulation of a number of 60S ribosomal subunits, eIF4A3 expression was absent in the CaMKKβ-deleted prostate. This result requires validation by Western blot analysis but could explain the downregulation of only a subset of proteins, such as Akt and ACC with known roles in oncogenesis (Fresno Vara et al., 2004, Massie et al., 2011). In addition, eIF4A3 has also been shown to have a specific role in mRNA localisation and nonsense mediated mRNA decay (Palacios et al., 2004). The exact mechanism by which CaMKKβ-deletion leads to downregulation of eIF4A3 and the 60S ribosomal proteins is currently unknown.

Further work evaluating the role of CaMKKβ in translation and AR signalling in the CaMKKβ-/- prostate cancer mouse model is required. In particular, it will be important to look at the effect of CaMKKβ depletion on translation in human prostate cancer cell lines. In addition to pharmacological inhibition, future work will include the generation of a panel of CaMKKβ-/- human prostate cancer cell lines using the CRISPR/Cas9 system. This panel will include androgen-sensitive and androgen-insensitive cell lines, to assess the role of CaMKKβ in androgen-sensitive and castration-resistant disease. In these cells, global translation can be measured and expression of eIF4A3 and different 60S ribosomal subunits quantified. In addition, AR signalling can be characterised. Furthermore, it will be important to further explore the effect of CaMKKβ-deletion on AR signalling in the PTEN-/- prostate. This will include looking at the gene expression of a panel of androgen responsive genes and AR immunohistochemistry to investigate AR expression and subcellular localisation.

Since the prostate cancer model generated in this chapter was a global deletion for CaMKKβ, it is impossible to disentangle primary effects of CaMKKβ-deletion in the prostate epithelial cells from possible secondary effects of deletion of this kinase in other cell types on disease development. In collaboration with Ian Mills, Belfast University, primary epithelial cells will be generated from PTEN-/- and CaMKKβ-/-PTEN-/- prostates and will help

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address this issue. Furthermore AR signalling and translation rate can be characterised in these cells.

Conclusions and future perspectives

CaMKKβ is only highly expressed in a few cell types outside the brain and testes. In the non- cancerous prostate, this kinase is expressed at low levels but is significantly upregulated in prostate cancer cells due to its androgen responsiveness. CaMKKβ is also expressed in macrophages, including TAMs which have been shown to facilitate cancer progression, but not in natural killer or T-cells which orchestrate an effective immune response against cancer (Racioppi et al., 2012, Racioppi and Means, 2012, Racioppi, 2013). In this study, both genetic deletion and pharmacological inhibition of CaMKKβ significantly slowed prostate cancer progression in the PTEN-/- prostate. This is consistent with published in vitro studies, which showed inhibition of CaMKKβ blocked androgen stimulated growth, migration and invasion (Massie et al., 2011, Frigo et al., 2011). Delineating the contribution of CaMKKβ deletion in the prostate epithelial cells themselves and other cell types such as macrophages to the inhibition of prostate cancer development will be an interesting aspect of future work. However, the global deletion model mimics pharmacological intervention as a CaMKKβ inhibitor will act globally.

Previous studies have implicated the CaMKKβ-AMPK axis in mediating the oncogenic effects of CaMKKβ upregulation (Massie et al., 2011, Frigo et al., 2011). In the previous chapter, the effect of AMPKβ1 deletion in the PTEN model was characterised. Deletion of AMPKβ1 led to a 70% reduction in total AMPK activity in mouse prostate tissue. In contrast to the CaMKKβ deletion model, loss of AMPKβ1 was found to lead to worse disease progression, with more rapid onset of neoplastic growth and adenocarcinoma development. However, it is difficult to directly compare these two models as the AMPKβ1-/-PTEN-/- model has AMPKβ1 deleted specifically in the prostate, whereas the CaMKKβ-/-PTEN-/- model is a global deletion for CaMKKβ-/-. It is therefore not possible to comment on whether the pro-tumorigenic effects of CaMKKβ are through the CaMKKβ-AMPK axis.

Although CaMKKβ was identified many years ago, elucidation of its role in physiopathology are still in their infancy (Racioppi and Means, 2012). During the course of this project it became clear that there is a lack of literature on the non-cancerous prostate. An understanding of the pathways and regulation of the normal prostate will be key in tackling associated pathologies. Further studies exploring the role of CaMKKβ in the normal prostate, as well as the transformed prostate, will be paramount to understanding the role of this kinase in prostate cancer. This study, as well as the previously published studies discussed, strongly support that inhibition of CaMKKβ would be an effective therapeutic strategy for treating prostate cancer (Frigo et al., 2011, Massie et al., 2011, Karacosta et al., 2012).

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CaMKKβ represents an excellent druggable target as mice globally deleted for CaMKKβ have no obvious overt phenotype. As discussed, CaMKKβ deletion was found to lead a subtle neuronal phenotype associated with hippocampus-dependent long‐term memory function (Peters et al., 2003). However, the lack of an overt phenotype suggests that long‐term pharmacological inhibition of CaMKKβ is unlikely to lead to significant deleterious side effects, especially since it is relatively easy to prevent drugs crossing the blood‐brain barrier. In addition, in vivo dosing of mice with the CaMKKβ inhibitor, STO609, was not found to be associated with any signs of toxicity, despite the maintenance of high plasma concentrations for 6 weeks. More selective and potent inhibitors, compared to STO609, are required as STO609 is not a specific CaMKKβ inhibitor (Bain et al., 2007). Of note, the crystal structure of CaMKKβ bound to STO609 has been published previously, which should facilitate this work (Tokumitsu et al., 2002). CaMKKβ protein expression was found to be increased in samples from castration-resistant disease, indicating CaMKKβ expression is increased and likely has a role in hormone-sensitive and castration-resistant prostate cancers (Massie et al., 2011).

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6. Summary and Future Perspectives

Due to the high prevalence and mortality rates associated with this prostate cancer, it is critical to find novel drug targets and develop new small molecules to treat this disease. In this study the roles of AMPK and CaMKKβ were investigated in prostate cancer and their potential as novel drug targets explored. Despite CaMKKβ being an activating upstream kinase of AMPK, results from in vitro and in vivo studies suggest that these two kinases may have different effects on prostate cancer development and progression.

In Chapter 3, a panel of prostate cancer cell lines was used to test whether AMPK activation is necessary and sufficient for the suppression of a number prostate cancer cell oncogenic capabilities. Using the highly selective direct AMPK activator, 991, it was shown that AMPK activation led to decreased cellular proliferation, 2D migration, invasion, lipid synthesis and increased cellular adhesion in all cells investigated. During the course of this study a paper was published characterising another novel AMPK direct activator, MT63-78 (Zadra et al., 2014). In this study, they also showed that AMPK activation was a powerful inhibitor of prostate cancer cell growth due to a blockage on de novo lipogenesis. The effect of AMPK activation on migration down a chemoattractant gradient was found to be cell-type specific, with PC3 and 22RV1 cell migration being increased upon AMPK activation. This increase in cell migration was found to be dependent upon CaMKKβ and PAK1 activity. Further validation of the role of CaMKKβ in this process will come from work performed in CaMKKβ-/- cell lines generated using the CRISPR/Cas9 system.

The role of AMPK in prostate cancer was further investigated in vivo using the murine PTEN prostate cancer model (PTEN-/-). AMPK expression was found to be increased in the PTEN- null prostate and in LNCaP cells upon androgen stimulation. These findings are consistent with AMPK expression being induced by androgens. Furthermore, β1 expression was unaltered in a liver-specific PTEN-deletion model implying PTEN-deletion alone is insufficient to lead to AMPK upregulation.

Two novel prostate cancer mouse models were generated during the course of this study. In the first model, AMPKβ1 was specifically deleted in the mouse epithelial cells in the PTEN-/- model. By 26-weeks of age, despite no difference in prostate weight, the β1/PTEN-null prostate was found to have more advanced disease compared to PTEN-/- prostates based on pathological analysis of HE-stained prostate sections. There was also a trend for the β1/PTEN-null prostate to have increased Ki-67 staining compared to PTEN-null prostates. This study was challenged by the variability of Ki-67 staining within a tissue section. In addition, a switch in global protein expression was characterised upon disease progression in PTEN-/- mice, and this was brought on significantly earlier in the β1-/-PTEN-/- mouse prostate. This switch in protein expression appeared to correlate with the switch from

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prostate hyperplasia to neoplasia. AMPK is hypothesised to inhibit disease progression by stabilisation and activation of the tumour suppressor, p53.

In future studies, it will be critical to investigate the relevance of this switch in human prostate cancer samples. It is important to determine whether the expression of the switch biomarker proteins vary with prostate cancer grade and in response to treatment e.g. antiandrogens and in castration-resistant disease. If a correlation was found, these proteins could act as biomarkers to aid more accurate disease staging, prognosis and treatment strategies.

Previous studies showed CaMKKβ gene expression to be androgen responsive and upregulated in human prostate cancer (Massie et al., 2011, Frigo et al., 2011). A study by Karacosta et al placed CaMKKβ upstream of the AR suggesting a positive feedback loop in which CaMKKβ is induced by the AR and is required to maintain AR signalling (Karacosta et al., 2012). In agreement with this study, treatment with the CaMKKβ inhibitor, STO609, was found to lead to a significant reduction in androgen-dependent luciferase activity and CaMKKβ (AR-responsive gene) expression in androgen-stimulated LNCaP cells. These results support the idea of a positive feedback cycle between AR and CaMKKβ signalling. Since no changes in AR expression was revealed in either study, it will be important to investigate the effect of CaMKKβ signalling on the phosphorylation status and nuclear localisation of AR in future studies.

In a second novel prostate cancer mouse model generated during the course of this project, the role of CaMKKβ was investigated. Deletion of CaMKKβ in the PTEN prostate cancer model was found to significantly slow disease progression based on prostate weight, Ki-67 staining and disease pathology in HE-stained sections. STO609 dosing studies in the PTEN model lend further support to the use of CaMKKβ inhibitors as a therapeutic strategy in prostate cancer. These data are consistent with previous studies supporting an oncogenic role for CaMKKβ in driving prostate cancer development and progression (Massie et al., 2011, Frigo et al., 2011, Karacosta et al., 2012). The exact mechanism(s) by which CaMKKβ leads to prostate cancer progression will be the subject of future studies.

CaMKKβ knockout prostates were found to have significantly reduced protein expression of a number of key proteins upregulated in the PTEN-null prostate. A mass spectrometry screen revealed significant downregulation of a number of 60S ribosomal proteins and the eukaryotic initiation factor 4A3 in the CaMKKβ-null prostate. Future work will involve investigating whether these changes could potentially provide a mechanism behind the reduction in cancer growth in the CaMKKβ-/-PTEN-/- model compared to the PTEN-/- model; and upon STO609 dosing in the PTEN-/- model. This will involve looking at the rate of translation the prostate cancer cell lines upon STO609 treatment and in CaMKKβ-/- prostate cancer cell lines (generated using the CRISPR/Cas9 system). It will also be interesting to

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investigate whether CaMKKβ activity favours the translation of a subset of potentially oncogenic proteins.

Due to time limitations and the complicated breeding programmes involved in generating the mouse models, the AR-CaMKKβ signalling axis was not investigated in the CaMKKβ-/-PTEN-/- model. This will be addressed in future work, specifically looking at AR and CaMKKβ expression and localisation in prostate tissue sections at different stages of disease development. Furthermore, qPCR will be performed on AR-responsive genes to look at AR activity in this model compared to PTEN-/- control animals.

The CaMKKβ/PTEN-null prostate was found to have a different stromal morphology compared to the PTEN-null prostate based on HE-stained tissue sections. This suggests that CaMKKβ signalling could be a regulator in shaping the tumour microenvironment (Racioppi, 2013). Given the nature of the mouse model, it is challenging to disentangle primary effects of CaMKKβ-deletion in prostate epithelial cells from secondary effects of deletion of this kinase in other cell types. Isolation of primary prostate epithelial cells will be generated from the different models will help address this issue. A number of phenotypic assays can be performed on these cell lines including proliferation, migration and invasion assays (as discussed in Chapter 3). Furthermore, AR signalling and translation can be investigated in these cells.

Despite an initial report that prostate specific PTEN-deletion in mice was sufficient to give metastatic disease, this has not be repeated in any subsequent study (Trotman et al., 2003, Backman et al., 2004, Irshad and Abate-Shen, 2013). Therefore, the effect of AMPK and CaMKKβ on metastatic disease could not be investigated using this model. This will be important in the validation of these kinases as therapeutic targets in prostate cancer in future studies.

Previous studies have implicated the CaMKKβ-AMPK axis in mediating the oncogenic effects of CaMKKβ upregulation (Massie et al., 2011, Frigo et al., 2011). This study supports the idea of AMPK as acting as a tumour suppressor in prostate cancer and CaMKKβ as an oncogene. The role of AMPK in mediating the oncogenic effects of CaMKKβ was not directly tested. Ros et al argued that AMPKβ1 was essential for prostate cancer cell survival (Ros et al., 2012). The findings of this study show that AMPKβ1 is not essential for prostate cancer cell survival in the in vivo PTEN mouse model of prostate cancer.

Currently, we are generating another novel transgenic PTEN-null model of prostate cancer, expressing a constitutively active mutant of AMPK. These mice will be critical for looking at AMPK activation as a therapeutic strategy in the treatment of prostate cancer. Furthermore, dosing these mice with STO609 will provide a means of investigating whether AMPK activation and concurrent CaMKKβ inhibition could provide increased therapeutic efficacy. In vitro studies utilising the direct AMPK activator, 991, in the panel of CaMKKβ-/- CRISPR

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human prostate cancer cell lines will also allow further interrogation of the effectiveness of this dual therapy.

During this study the role of AMPK and CaMKKβ in prostate cancer was investigated in vivo using GEMMs, previous work has relied largely on prostate cancer cell lines and xenograft models using these cell lines, which do not recapitulate the stages of disease development and tumour-microenvironment interactions. This work and ongoing studies will further aid our understanding of the biology of the prostate and prostate cancer, ultimately helping to elucidate novel drug targets in treating this lethal disease.

Despite the advantages of the use of GEMMs in prostate cancer research, there are also associated limitations. One clear limitation is the modelling of a human disease in a mouse system. As described in Section 1.4.1, the anatomy of the human and mouse prostate are different, thus conclusions made from mouse models may not apply to the human disease. Unlike humans, mice do not sporadically get prostate cancer. Thus, another limitation of is that prostate tumourigenesis in the PTEN mouse model is initiated by homozygous deletion of Pten at an early timepoint (by 2 weeks of age). In humans prostate cancer is a disease of later life and is rare in men under 50 years of age. Deletion of PTEN at this early timepoint could affect prostate development, which is unrepresentative of the human disease. Furthermore, from recent genomic studies it has been shown that human prostate cancers are unlikely to be initiated from PTEN deletion (see Section 1.1.4). PTEN is inactivated in 17% of primary prostate cancers but is more commonly inactivated in late-stage castration- resistant prostate cancers (41%) (Network, 2015, Robinson et al., 2015). There is great genetic heterogeneity in human prostate cancer tumours and this is not modelled in GEMMs as are derived from the same genetic aberration. These limitations highlight the importance of validating any findings made in mouse models in human cell models and clinically relevant samples at the earliest opportunity.

Recently, there have been huge advances in prostate cancer model systems such as the use of organoid culture, tumour explant and patient-derived xenograft (PDX) models. It is now possible to grow organoids from human tumour biopsies or circulating tumour cells (Gao et al., 2014, Karthaus et al., 2014). Organoids have been shown to recapitulate the molecular diversity of prostate cancer subtypes including TMPRSS2-ERG fusion and SPOP mutations (see Section 1.1.4) (Gao et al., 2014). Furthermore, they can retain histological and molecular features of the patient specimen making them a useful, dynamic and manipulatable in vitro tool. Other approaches that are gaining popularity include the ex vivo culturing of primary human prostate cancer tissue and the development of patient-derived xenograft (PDX) models. Similarly, explants and PDXs models have been shown to retain the histopathology and global gene expression of donor tumours (Centenera et al., 2013, Lin et al., 2014). These systems have great utility in the investigation of AMPK and CaMKKβ as therapeutic targets in prostate cancer. Organoid and explant models are amenable to

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treatment with different pharmacological agents (e.g. 991, STO609), and/or generating AMPK or CaMKKβ knockout models using siRNA or CRISPR/Cas9 technologies (Matano et al., 2015, Sánchez-Rivera and Jacks, 2015). The effects of such manipulations can then be assessed by looking at readouts such as growth, proliferation, apoptotic index and gene expression profiles. In the PDX models, mice can be treated with pharmacological agents such as STO609 and similar parameters can be measured. For example, high grafted tumour growth rates have been correlated with poor clinical outcome (Lin et al., 2014). Importantly, these models recapitulate the structural and genomic heterogeneity of human prostate cancers in a laboratory setting, making them an important addition to current cell based and GEMMs. However, a major criterion for implementing these techniques is access to fresh human tissue samples which requires collaborations with surgeons and pathologists. Although PDXs developed from patient tumour samples will ultimately generate transplantable tumour lines (Lin et al., 2014). Once generated, these model systems represent valuable tools for evaluating AMPK and CaMKKβ as therapeutic targets in prostate cancer and validating findings from GEMMs in clinically-relevant samples.

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References

AALBERTS, M., STOUT, T. A. & STOORVOGEL, W. 2014. Prostasomes: extracellular vesicles from the prostate. Reproduction, 147, R1-14. ABATE-SHEN, C. & SHEN, M. M. 2000. Molecular genetics of prostate cancer. Genes Dev, 14, 2410- 34. ABATE-SHEN, C. & SHEN, M. M. 2002. Mouse models of prostate carcinogenesis. Trends Genet, 18, S1-5. ALESSI, D. R., SAKAMOTO, K. & BAYASCAS, J. R. 2006. LKB1-dependent signaling pathways. Annu Rev Biochem, 75, 137-63. ALIMIRAH, F., PANCHANATHAN, R., CHEN, J., ZHANG, X., HO, S. M. & CHOUBEY, D. 2007. Expression of androgen receptor is negatively regulated by p53. Neoplasia, 9, 1152-9. ALTINTAS, D. M., ALLIOLI, N., DECAUSSIN, M., DE BERNARD, S., RUFFION, A., SAMARUT, J. & VLAEMINCK-GUILLEM, V. 2013. Differentially expressed androgen-regulated genes in androgen-sensitive tissues reveal potential biomarkers of early prostate cancer. PLoS One, 8, e66278. AMATO, R. J., JAC, J., MOHAMMAD, T. & SAXENA, S. 2008. Pilot study of rapamycin in patients with hormone-refractory prostate cancer. Clin Genitourin Cancer, 6, 97-102. ANDERSON, K. A., MEANS, R. L., HUANG, Q. H., KEMP, B. E., GOLDSTEIN, E. G., SELBERT, M. A., EDELMAN, A. M., FREMEAU, R. T. & MEANS, A. R. 1998. Components of a calmodulin- dependent protein kinase cascade. Molecular cloning, functional characterization and cellular localization of Ca2+/calmodulin-dependent protein kinase kinase beta. J Biol Chem, 273, 31880-9. ANDERSON, K. A., RIBAR, T. J., LIN, F., NOELDNER, P. K., GREEN, M. F., MUEHLBAUER, M. J., WITTERS, L. A., KEMP, B. E. & MEANS, A. R. 2008. Hypothalamic CaMKK2 contributes to the regulation of energy balance. Cell Metab, 7, 377-88. ANDREWS, C. & HUMPHREY, P. A. 2014. Utility of ERG versus AMACR expression in diagnosis of minimal adenocarcinoma of the prostate in needle biopsy tissue. Am J Surg Pathol, 38, 1007- 12. ATTARD, G., SWENNENHUIS, J. F., OLMOS, D., REID, A. H., VICKERS, E., A'HERN, R., LEVINK, R., COUMANS, F., MOREIRA, J., RIISNAES, R., OOMMEN, N. B., HAWCHE, G., JAMESON, C., THOMPSON, E., SIPKEMA, R., CARDEN, C. P., PARKER, C., DEARNALEY, D., KAYE, S. B., COOPER, C. S., MOLINA, A., COX, M. E., TERSTAPPEN, L. W. & DE BONO, J. S. 2009. Characterization of ERG, AR and PTEN gene status in circulating tumor cells from patients with castration-resistant prostate cancer. Cancer Res, 69, 2912-8. AZEVEDO, A., CUNHA, V., TEIXEIRA, A. L. & MEDEIROS, R. 2011. IL-6/IL-6R as a potential key signaling pathway in prostate cancer development. World J Clin Oncol, 2, 384-96. BACKMAN, S. A., GHAZARIAN, D., SO, K., SANCHEZ, O., WAGNER, K. U., HENNIGHAUSEN, L., SUZUKI, A., TSAO, M. S., CHAPMAN, W. B., STAMBOLIC, V. & MAK, T. W. 2004. Early onset of neoplasia in the prostate and skin of mice with tissue-specific deletion of Pten. Proc Natl Acad Sci U S A, 101, 1725-30. BAE, H. B., ZMIJEWSKI, J. W., DESHANE, J. S., TADIE, J. M., CHAPLIN, D. D., TAKASHIMA, S. & ABRAHAM, E. 2011. AMP-activated protein kinase enhances the phagocytic ability of macrophages and neutrophils. FASEB J, 25, 4358-68. BAENKE, F., PECK, B., MIESS, H. & SCHULZE, A. 2013. Hooked on fat: the role of lipid synthesis in cancer metabolism and tumour development. Dis Model Mech, 6, 1353-63. BAIN, J., PLATER, L., ELLIOTT, M., SHPIRO, N., HASTIE, C. J., MCLAUCHLAN, H., KLEVERNIC, I., ARTHUR, J. S., ALESSI, D. R. & COHEN, P. 2007. The selectivity of protein kinase inhibitors: a further update. Biochem J, 408, 297-315.

227

BALKWILL, F. R. & MANTOVANI, A. 2012. Cancer-related inflammation: common themes and therapeutic opportunities. Semin Cancer Biol, 22, 33-40. BANKO, M. R., ALLEN, J. J., SCHAFFER, B. E., WILKER, E. W., TSOU, P., WHITE, J. L., VILLÉN, J., WANG, B., KIM, S. R., SAKAMOTO, K., GYGI, S. P., CANTLEY, L. C., YAFFE, M. B., SHOKAT, K. M. & BRUNET, A. 2011. Chemical genetic screen for AMPKα2 substrates uncovers a network of proteins involved in mitosis. Mol Cell, 44, 878-92. BARBIERI, C. E., BANGMA, C. H., BJARTELL, A., CATTO, J. W., CULIG, Z., GRÖNBERG, H., LUO, J., VISAKORPI, T. & RUBIN, M. A. 2013. The mutational landscape of prostate cancer. Eur Urol, 64, 567-76. BARRON, D. A. & ROWLEY, D. R. 2012. The reactive stroma microenvironment and prostate cancer progression. Endocr Relat Cancer, 19, R187-204. BATEMAN, A. 1997. The structure of a domain common to archaebacteria and the homocystinuria disease protein. Trends Biochem Sci, 22, 12-3. BECKERS, A., ORGANE, S., TIMMERMANS, L., SCHEYS, K., PEETERS, A., BRUSSELMANS, K., VERHOEVEN, G. & SWINNEN, J. V. 2007. Chemical inhibition of acetyl-CoA carboxylase induces growth arrest and cytotoxicity selectively in cancer cells. Cancer Res, 67, 8180-7. BEE, A., KE, Y., FOROOTAN, S., LIN, K., BEESLEY, C., FORREST, S. E. & FOSTER, C. S. 2006. Ribosomal protein l19 is a prognostic marker for human prostate cancer. Clin Cancer Res, 12, 2061-5. BEN SAHRA, I., LAURENT, K., LOUBAT, A., GIORGETTI-PERALDI, S., COLOSETTI, P., AUBERGER, P., TANTI, J. F., LE MARCHAND-BRUSTEL, Y. & BOST, F. 2008. The antidiabetic drug metformin exerts an antitumoral effect in vitro and in vivo through a decrease of cyclin D1 level. Oncogene, 27, 3576-86. BEN SAHRA, I., REGAZZETTI, C., ROBERT, G., LAURENT, K., LE MARCHAND-BRUSTEL, Y., AUBERGER, P., TANTI, J. F., GIORGETTI-PERALDI, S. & BOST, F. 2011. Metformin, independent of AMPK, induces mTOR inhibition and cell-cycle arrest through REDD1. Cancer Res, 71, 4366-72. BERGER, M. F., LAWRENCE, M. S., DEMICHELIS, F., DRIER, Y., CIBULSKIS, K., SIVACHENKO, A. Y., SBONER, A., ESGUEVA, R., PFLUEGER, D., SOUGNEZ, C., ONOFRIO, R., CARTER, S. L., PARK, K., HABEGGER, L., AMBROGIO, L., FENNELL, T., PARKIN, M., SAKSENA, G., VOET, D., RAMOS, A. H., PUGH, T. J., WILKINSON, J., FISHER, S., WINCKLER, W., MAHAN, S., ARDLIE, K., BALDWIN, J., SIMONS, J. W., KITABAYASHI, N., MACDONALD, T. Y., KANTOFF, P. W., CHIN, L., GABRIEL, S. B., GERSTEIN, M. B., GOLUB, T. R., MEYERSON, M., TEWARI, A., LANDER, E. S., GETZ, G., RUBIN, M. A. & GARRAWAY, L. A. 2011. The genomic complexity of primary human prostate cancer. Nature, 470, 214-20. BERQUIN, I. M., MIN, Y., WU, R., WU, H. & CHEN, Y. Q. 2005. Expression signature of the mouse prostate. J Biol Chem, 280, 36442-51. BHAT, M., ROBICHAUD, N., HULEA, L., SONENBERG, N., PELLETIER, J. & TOPISIROVIC, I. 2015. Targeting the translation machinery in cancer. Nat Rev Drug Discov, 14, 261-78. BHAVSAR, A. & VERMA, S. 2014. Anatomic imaging of the prostate. Biomed Res Int, 2014, 728539. BOKOCH, G. M. 2003. Biology of the p21-activated kinases. Annu Rev Biochem, 72, 743-81. BOSTWICK, D. G. & BRAWER, M. K. 1987. Prostatic intra-epithelial neoplasia and early invasion in prostate cancer. Cancer, 59, 788-94. BOSTWICK, D. G., NORLÉN, B. J. & DENIS, L. 2000. Prostatic intraepithelial neoplasia: the preinvasive stage of prostate cancer. Overview of the prostate committee report. Scand J Urol Nephrol Suppl, 1-2. BOUDEAU, J., BAAS, A. F., DEAK, M., MORRICE, N. A., KIELOCH, A., SCHUTKOWSKI, M., PRESCOTT, A. R., CLEVERS, H. C. & ALESSI, D. R. 2003. MO25alpha/beta interact with STRADalpha/beta enhancing their ability to bind, activate and localize LKB1 in the cytoplasm. EMBO J, 22, 5102-14.

228

BOUDEAU, J., SCOTT, J. W., RESTA, N., DEAK, M., KIELOCH, A., KOMANDER, D., HARDIE, D. G., PRESCOTT, A. R., VAN AALTEN, D. M. & ALESSI, D. R. 2004. Analysis of the LKB1-STRAD-MO25 complex. J Cell Sci, 117, 6365-75. BRADFORD, M. M. 1976. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal Biochem, 72, 248-54. BRAWER, M. K. 2005. Prostatic intraepithelial neoplasia: an overview. Rev Urol, 7 Suppl 3, S11-8. BRENMAN, J. E. 2007. AMPK/LKB1 signaling in epithelial cell polarity and cell division. Cell Cycle, 6, 2755-9. CAO, W., SOHAIL, M., LIU, G., KOUMBADINGA, G. A., LOBO, V. G. & XIE, J. 2011. Differential effects of PKA-controlled CaMKK2 variants on neuronal differentiation. RNA Biol, 8, 1061-72. CARLING, D., CLARKE, P. R., ZAMMIT, V. A. & HARDIE, D. G. 1989. Purification and characterization of the AMP-activated protein kinase. Copurification of acetyl-CoA carboxylase kinase and 3- hydroxy-3-methylglutaryl-CoA reductase kinase activities. Eur J Biochem, 186, 129-36. CARLING, D., SANDERS, M. J. & WOODS, A. 2008. The regulation of AMP-activated protein kinase by upstream kinases. Int J Obes (Lond), 32 Suppl 4, S55-9. CARLING, D., THORNTON, C., WOODS, A. & SANDERS, M. J. 2012. AMP-activated protein kinase: new regulation, new roles? Biochem J, 445, 11-27. CARLING, D., ZAMMIT, V. A. & HARDIE, D. G. 1987. A common bicyclic protein kinase cascade inactivates the regulatory enzymes of fatty acid and cholesterol biosynthesis. FEBS Lett, 223, 217-22. CENTENERA, M. M., RAJ, G. V., KNUDSEN, K. E., TILLEY, W. D. & BUTLER, L. M. 2013. Ex vivo culture of human prostate tissue and drug development. Nat Rev Urol, 10, 483-7. CHEN, Z., TROTMAN, L. C., SHAFFER, D., LIN, H. K., DOTAN, Z. A., NIKI, M., KOUTCHER, J. A., SCHER, H. I., LUDWIG, T., GERALD, W., CORDON-CARDO, C. & PANDOLFI, P. P. 2005. Crucial role of p53- dependent cellular senescence in suppression of Pten-deficient tumorigenesis. Nature, 436, 725-30. CHEUNG, P. C., SALT, I. P., DAVIES, S. P., HARDIE, D. G. & CARLING, D. 2000. Characterization of AMP- activated protein kinase gamma-subunit isoforms and their role in AMP binding. Biochem J, 346 Pt 3, 659-69. CHHIPA, R. R., WU, Y. & IP, C. 2011. AMPK-mediated autophagy is a survival mechanism in androgen- dependent prostate cancer cells subjected to androgen deprivation and hypoxia. Cell Signal, 23, 1466-72. CHOI, S. L., KIM, S. J., LEE, K. T., KIM, J., MU, J., BIRNBAUM, M. J., SOO KIM, S. & HA, J. 2001. The regulation of AMP-activated protein kinase by H(2)O(2). Biochem Biophys Res Commun, 287, 92-7. CHOUCAIR, K., EJDELMAN, J., BRIMO, F., APRIKIAN, A., CHEVALIER, S. & LAPOINTE, J. 2012. PTEN genomic deletion predicts prostate cancer recurrence and is associated with low AR expression and transcriptional activity. BMC Cancer, 12, 543. CHUANG, H. C., CHOU, C. C., KULP, S. K. & CHEN, C. S. 2014. AMPK as a potential anticancer target - friend or foe? Curr Pharm Des, 20, 2607-18. COLLINS, S. P., REOMA, J. L., GAMM, D. M. & UHLER, M. D. 2000. LKB1, a novel serine/threonine protein kinase and potential tumour suppressor, is phosphorylated by cAMP-dependent protein kinase (PKA) and prenylated in vivo. Biochem J, 345 Pt 3, 673-80. CORTON, J. M., GILLESPIE, J. G. & HARDIE, D. G. 1994. Role of the AMP-activated protein kinase in the cellular stress response. Curr Biol, 4, 315-24. CORTON, J. M., GILLESPIE, J. G., HAWLEY, S. A. & HARDIE, D. G. 1995. 5-aminoimidazole-4- carboxamide ribonucleoside. A specific method for activating AMP-activated protein kinase in intact cells? Eur J Biochem, 229, 558-65.

229

COSTELLO, L. C. & FRANKLIN, R. B. 2000. The intermediary metabolism of the prostate: a key to understanding the pathogenesis and progression of prostate malignancy. Oncology, 59, 269- 82. CUNHA, G. R., DONJACOUR, A. A., COOKE, P. S., MEE, S., BIGSBY, R. M., HIGGINS, S. J. & SUGIMURA, Y. 1987. The endocrinology and developmental biology of the prostate. Endocr Rev, 8, 338- 62. CUTRESS, M. L., WHITAKER, H. C., MILLS, I. G., STEWART, M. & NEAL, D. E. 2008. Structural basis for the nuclear import of the human androgen receptor. J Cell Sci, 121, 957-68. DANG, C. V. 2012. MYC on the path to cancer. Cell, 149, 22-35. DART, D. A., SPENCER-DENE, B., GAMBLE, S. C., WAXMAN, J. & BEVAN, C. L. 2009. Manipulating prohibitin levels provides evidence for an in vivo role in androgen regulation of prostate tumours. Endocr Relat Cancer, 16, 1157-69. DAVIES, S. P., CARLING, D. & HARDIE, D. G. 1989. Tissue distribution of the AMP-activated protein kinase, and lack of activation by cyclic-AMP-dependent protein kinase, studied using a specific and sensitive peptide assay. Eur J Biochem, 186, 123-8. DE BENEDETTI, A. & GRAFF, J. R. 2004. eIF-4E expression and its role in malignancies and metastases. Oncogene, 23, 3189-99. DZAMKO, N., VAN DENDEREN, B. J., HEVENER, A. L., JØRGENSEN, S. B., HONEYMAN, J., GALIC, S., CHEN, Z. P., WATT, M. J., CAMPBELL, D. J., STEINBERG, G. R. & KEMP, B. E. 2010. AMPK beta1 deletion reduces appetite, preventing obesity and hepatic insulin resistance. J Biol Chem, 285, 115-22. EGAN, D. F., SHACKELFORD, D. B., MIHAYLOVA, M. M., GELINO, S., KOHNZ, R. A., MAIR, W., VASQUEZ, D. S., JOSHI, A., GWINN, D. M., TAYLOR, R., ASARA, J. M., FITZPATRICK, J., DILLIN, A., VIOLLET, B., KUNDU, M., HANSEN, M. & SHAW, R. J. 2011. Phosphorylation of ULK1 (hATG1) by AMP-activated protein kinase connects energy sensing to mitophagy. Science, 331, 456-61. ESFAHANIAN, N., SHAKIBA, Y., NIKBIN, B., SORAYA, H., MALEKI-DIZAJI, N., GHAZI-KHANSARI, M. & GARJANI, A. 2012. Effect of metformin on the proliferation, migration, and MMP-2 and -9 expression of human umbilical vein endothelial cells. Mol Med Rep, 5, 1068-74. ESWARAN, J., LI, D. Q., SHAH, A. & KUMAR, R. 2012. Molecular pathways: targeting p21-activated kinase 1 signaling in cancer--opportunities, challenges, and limitations. Clin Cancer Res, 18, 3743-9. ETTINGER, S. L., SOBEL, R., WHITMORE, T. G., AKBARI, M., BRADLEY, D. R., GLEAVE, M. E. & NELSON, C. C. 2004. Dysregulation of sterol response element-binding proteins and downstream effectors in prostate cancer during progression to androgen independence. Cancer Res, 64, 2212-21. EVANS, J. M., DONNELLY, L. A., EMSLIE-SMITH, A. M., ALESSI, D. R. & MORRIS, A. D. 2005. Metformin and reduced risk of cancer in diabetic patients. BMJ, 330, 1304-5. FAUBERT, B., BOILY, G., IZREIG, S., GRISS, T., SAMBORSKA, B., DONG, Z., DUPUY, F., CHAMBERS, C., FUERTH, B. J., VIOLLET, B., MAMER, O. A., AVIZONIS, D., DEBERARDINIS, R. J., SIEGEL, P. M. & JONES, R. G. 2013. AMPK is a negative regulator of the Warburg effect and suppresses tumor growth in vivo. Cell Metab, 17, 113-24. FAVARO, E., BENSAAD, K., CHONG, M. G., TENNANT, D. A., FERGUSON, D. J., SNELL, C., STEERS, G., TURLEY, H., LI, J. L., GÜNTHER, U. L., BUFFA, F. M., MCINTYRE, A. & HARRIS, A. L. 2012. Glucose utilization via glycogen phosphorylase sustains proliferation and prevents premature senescence in cancer cells. Cell Metab, 16, 751-64. FERLA, R., HASPINGER, E. & SURMACZ, E. 2012. Metformin inhibits leptin-induced growth and migration of glioblastoma cells. Oncol Lett, 4, 1077-1081. FLAVIN, R., PELUSO, S., NGUYEN, P. L. & LODA, M. 2010. Fatty acid synthase as a potential therapeutic target in cancer. Future Oncol, 6, 551-62.

230

FLAVIN, R., ZADRA, G. & LODA, M. 2011. Metabolic alterations and targeted therapies in prostate cancer. J Pathol, 223, 283-94. FORETZ, M., HÉBRARD, S., GUIHARD, S., LECLERC, J., DO CRUZEIRO, M., HAMARD, G., NIEDERGANG, F., GAUDRY, M. & VIOLLET, B. 2011. The AMPKγ1 subunit plays an essential role in erythrocyte membrane elasticity, and its genetic inactivation induces splenomegaly and anemia. FASEB J, 25, 337-47. FRANCIS, J. C., THOMSEN, M. K., TAKETO, M. M. & SWAIN, A. 2013. β-catenin is required for prostate development and cooperates with Pten loss to drive invasive carcinoma. PLoS Genet, 9, e1003180. FRESNO VARA, J. A., CASADO, E., DE CASTRO, J., CEJAS, P., BELDA-INIESTA, C. & GONZÁLEZ-BARÓN, M. 2004. PI3K/Akt signalling pathway and cancer. Cancer Treat Rev, 30, 193-204. FRIGO, D. E., HOWE, M. K., WITTMANN, B. M., BRUNNER, A. M., CUSHMAN, I., WANG, Q., BROWN, M., MEANS, A. R. & MCDONNELL, D. P. 2011. CaM kinase kinase beta-mediated activation of the growth regulatory kinase AMPK is required for androgen-dependent migration of prostate cancer cells. Cancer Res, 71, 528-37. FRYER, L. G., PARBU-PATEL, A. & CARLING, D. 2002. The Anti-diabetic drugs rosiglitazone and metformin stimulate AMP-activated protein kinase through distinct signaling pathways. J Biol Chem, 277, 25226-32. FU, Y., WEY, S., WANG, M., YE, R., LIAO, C. P., ROY-BURMAN, P. & LEE, A. S. 2008. Pten null prostate tumorigenesis and AKT activation are blocked by targeted knockout of ER chaperone GRP78/BiP in prostate epithelium. Proc Natl Acad Sci U S A, 105, 19444-9. FÖLLER, M., SOPJANI, M., KOKA, S., GU, S., MAHMUD, H., WANG, K., FLORIDE, E., SCHLEICHER, E., SCHULZ, E., MÜNZEL, T. & LANG, F. 2009. Regulation of erythrocyte survival by AMP- activated protein kinase. FASEB J, 23, 1072-80. GAO, D., VELA, I., SBONER, A., IAQUINTA, P. J., KARTHAUS, W. R., GOPALAN, A., DOWLING, C., WANJALA, J. N., UNDVALL, E. A., ARORA, V. K., WONGVIPAT, J., KOSSAI, M., RAMAZANOGLU, S., BARBOZA, L. P., DI, W., CAO, Z., ZHANG, Q. F., SIROTA, I., RAN, L., MACDONALD, T. Y., BELTRAN, H., MOSQUERA, J. M., TOUIJER, K. A., SCARDINO, P. T., LAUDONE, V. P., CURTIS, K. R., RATHKOPF, D. E., MORRIS, M. J., DANILA, D. C., SLOVIN, S. F., SOLOMON, S. B., EASTHAM, J. A., CHI, P., CARVER, B., RUBIN, M. A., SCHER, H. I., CLEVERS, H., SAWYERS, C. L. & CHEN, Y. 2014. Organoid cultures derived from patients with advanced prostate cancer. Cell, 159, 176-87. GARCÍA-GARCÍA, C., FUMAROLA, C., NAVARATNAM, N., CARLING, D. & LÓPEZ-RIVAS, A. 2010. AMPK- independent down-regulation of cFLIP and sensitization to TRAIL-induced apoptosis by AMPK activators. Biochem Pharmacol, 79, 853-63. GARTON, A. J., CAMPBELL, D. G., CARLING, D., HARDIE, D. G., COLBRAN, R. J. & YEAMAN, S. J. 1989. Phosphorylation of bovine hormone-sensitive lipase by the AMP-activated protein kinase. A possible antilipolytic mechanism. Eur J Biochem, 179, 249-54. GOC, A., AL-AZAYZIH, A., ABDALLA, M., AL-HUSEIN, B., KAVURI, S., LEE, J., MOSES, K. & SOMANATH, P. R. 2013. P21 activated kinase-1 (Pak1) promotes prostate tumor growth and microinvasion via inhibition of transforming growth factor β expression and enhanced matrix metalloproteinase 9 secretion. J Biol Chem, 288, 3025-35. GOECKELER, Z. M., MASARACCHIA, R. A., ZENG, Q., CHEW, T. L., GALLAGHER, P. & WYSOLMERSKI, R. B. 2000. Phosphorylation of myosin light chain kinase by p21-activated kinase PAK2. J Biol Chem, 275, 18366-74. GOWANS, G. J. & HARDIE, D. G. 2014. AMPK: a cellular energy sensor primarily regulated by AMP. Biochem Soc Trans, 42, 71-5. GOWANS, G. J., HAWLEY, S. A., ROSS, F. A. & HARDIE, D. G. 2013. AMP is a true physiological regulator of AMP-activated protein kinase by both allosteric activation and enhancing net phosphorylation. Cell Metab, 18, 556-66.

231

GRASSIE, M. E., MOFFAT, L. D., WALSH, M. P. & MACDONALD, J. A. 2011. The myosin phosphatase targeting protein (MYPT) family: a regulated mechanism for achieving substrate specificity of the catalytic subunit of protein phosphatase type 1δ. Arch Biochem Biophys, 510, 147-59. GREEN, M. F., ANDERSON, K. A. & MEANS, A. R. 2011a. Characterization of the CaMKKβ-AMPK signaling complex. Cell Signal, 23, 2005-12. GREEN, M. F., SCOTT, J. W., STEEL, R., OAKHILL, J. S., KEMP, B. E. & MEANS, A. R. 2011b. Ca2+/Calmodulin-dependent protein kinase kinase beta is regulated by multisite phosphorylation. J Biol Chem, 286, 28066-79. GRIGNON, D. J. 2004. Unusual subtypes of prostate cancer. Mod Pathol, 17, 316-27. GWINN, D. M., SHACKELFORD, D. B., EGAN, D. F., MIHAYLOVA, M. M., MERY, A., VASQUEZ, D. S., TURK, B. E. & SHAW, R. J. 2008. AMPK phosphorylation of raptor mediates a metabolic checkpoint. Mol Cell, 30, 214-26. HANAHAN, D. & WEINBERG, R. A. 2011. Hallmarks of cancer: the next generation. Cell, 144, 646-74. HAWLEY, S. A., BOUDEAU, J., REID, J. L., MUSTARD, K. J., UDD, L., MÄKELÄ, T. P., ALESSI, D. R. & HARDIE, D. G. 2003. Complexes between the LKB1 tumor suppressor, STRAD alpha/beta and MO25 alpha/beta are upstream kinases in the AMP-activated protein kinase cascade. J Biol, 2, 28. HAWLEY, S. A., DAVISON, M., WOODS, A., DAVIES, S. P., BERI, R. K., CARLING, D. & HARDIE, D. G. 1996. Characterization of the AMP-activated protein kinase kinase from rat liver and identification of threonine 172 as the major site at which it phosphorylates AMP-activated protein kinase. J Biol Chem, 271, 27879-87. HAWLEY, S. A., PAN, D. A., MUSTARD, K. J., ROSS, L., BAIN, J., EDELMAN, A. M., FRENGUELLI, B. G. & HARDIE, D. G. 2005. Calmodulin-dependent protein kinase kinase-beta is an alternative upstream kinase for AMP-activated protein kinase. Cell Metab, 2, 9-19. HAWLEY, S. A., SELBERT, M. A., GOLDSTEIN, E. G., EDELMAN, A. M., CARLING, D. & HARDIE, D. G. 1995. 5'-AMP activates the AMP-activated protein kinase cascade, and Ca2+/calmodulin activates the calmodulin-dependent protein kinase I cascade, via three independent mechanisms. J Biol Chem, 270, 27186-91. HE, G., ZHANG, Y. W., LEE, J. H., ZENG, S. X., WANG, Y. V., LUO, Z., DONG, X. C., VIOLLET, B., WAHL, G. M. & LU, H. 2014. AMP-activated protein kinase induces p53 by phosphorylating MDMX and inhibiting its activity. Mol Cell Biol, 34, 148-57. HEINLEIN, C. A. & CHANG, C. 2004. Androgen receptor in prostate cancer. Endocr Rev, 25, 276-308. HEMMINKI, A., MARKIE, D., TOMLINSON, I., AVIZIENYTE, E., ROTH, S., LOUKOLA, A., BIGNELL, G., WARREN, W., AMINOFF, M., HÖGLUND, P., JÄRVINEN, H., KRISTO, P., PELIN, K., RIDANPÄÄ, M., SALOVAARA, R., TORO, T., BODMER, W., OLSCHWANG, S., OLSEN, A. S., STRATTON, M. R., DE LA CHAPELLE, A. & AALTONEN, L. A. 1998. A serine/threonine kinase gene defective in Peutz-Jeghers syndrome. Nature, 391, 184-7. HEZEL, A. F. & BARDEESY, N. 2008. LKB1; linking cell structure and tumor suppression. Oncogene, 27, 6908-19. HOLLAND, E. C., SONENBERG, N., PANDOLFI, P. P. & THOMAS, G. 2004. Signaling control of mRNA translation in cancer pathogenesis. Oncogene, 23, 3138-44. HORMAN, S., MOREL, N., VERTOMMEN, D., HUSSAIN, N., NEUMANN, D., BEAULOYE, C., EL NAJJAR, N., FORCET, C., VIOLLET, B., WALSH, M. P., HUE, L. & RIDER, M. H. 2008. AMP-activated protein kinase phosphorylates and desensitizes smooth muscle myosin light chain kinase. J Biol Chem, 283, 18505-12. HSU, L. S., CHEN, G. D., LEE, L. S., CHI, C. W., CHENG, J. F. & CHEN, J. Y. 2001. Human Ca2+/calmodulin-dependent protein kinase kinase beta gene encodes multiple isoforms that display distinct kinase activity. J Biol Chem, 276, 31113-23.

232

HUANG, X., WULLSCHLEGER, S., SHPIRO, N., MCGUIRE, V. A., SAKAMOTO, K., WOODS, Y. L., MCBURNIE, W., FLEMING, S. & ALESSI, D. R. 2008. Important role of the LKB1-AMPK pathway in suppressing tumorigenesis in PTEN-deficient mice. Biochem J, 412, 211-21. HUDSON, E. R., PAN, D. A., JAMES, J., LUCOCQ, J. M., HAWLEY, S. A., GREEN, K. A., BABA, O., TERASHIMA, T. & HARDIE, D. G. 2003. A novel domain in AMP-activated protein kinase causes glycogen storage bodies similar to those seen in hereditary cardiac arrhythmias. Curr Biol, 13, 861-6. HUGGINS, C. 1942. EFFECT OF ORCHIECTOMY AND IRRADIATION ON CANCER OF THE PROSTATE. Ann Surg, 115, 1192-200. HÄGGMAN, M. J., MACOSKA, J. A., WOJNO, K. J. & OESTERLING, J. E. 1997. The relationship between prostatic intraepithelial neoplasia and prostate cancer: critical issues. J Urol, 158, 12-22. INOKI, K., LI, Y., XU, T. & GUAN, K. L. 2003. Rheb GTPase is a direct target of TSC2 GAP activity and regulates mTOR signaling. Genes Dev, 17, 1829-34. IRSHAD, S. & ABATE-SHEN, C. 2013. Modeling prostate cancer in mice: something old, something new, something premalignant, something metastatic. Cancer Metastasis Rev, 32, 109-22. ISAACS, J. T. & ISAACS, W. B. 2004. Androgen receptor outwits prostate cancer drugs. Nat Med, 10, 26-7. ITO, M., NAKANO, T., ERDODI, F. & HARTSHORNE, D. J. 2004. Myosin phosphatase: structure, regulation and function. Mol Cell Biochem, 259, 197-209. ITTMANN, M., HUANG, J., RADAELLI, E., MARTIN, P., SIGNORETTI, S., SULLIVAN, R., SIMONS, B. W., WARD, J. M., ROBINSON, B. D., CHU, G. C., LODA, M., THOMAS, G., BOROWSKY, A. & CARDIFF, R. D. 2013. Animal models of human prostate cancer: the consensus report of the New York meeting of the Mouse Models of Human Cancers Consortium Prostate Pathology Committee. Cancer Res, 73, 2718-36. JACKSON, R. J., HELLEN, C. U. & PESTOVA, T. V. 2010. The mechanism of eukaryotic translation initiation and principles of its regulation. Nat Rev Mol Cell Biol, 11, 113-27. JEON, S. M., CHANDEL, N. S. & HAY, N. 2012. AMPK regulates NADPH homeostasis to promote tumour cell survival during energy stress. Nature, 485, 661-5. JIANG, N., HJORTH-JENSEN, K., HEKMAT, O., IGLESIAS-GATO, D., KRUSE, T., WANG, C., WEI, W., KE, B., YAN, B., NIU, Y., OLSEN, J. V. & FLORES-MORALES, A. 2015. In vivo quantitative phosphoproteomic profiling identifies novel regulators of castration-resistant prostate cancer growth. Oncogene, 34, 2764-76. JONES, R. G., PLAS, D. R., KUBEK, S., BUZZAI, M., MU, J., XU, Y., BIRNBAUM, M. J. & THOMPSON, C. B. 2005. AMP-activated protein kinase induces a p53-dependent metabolic checkpoint. Mol Cell, 18, 283-93. JURMEISTER, S., RAMOS-MONTOYA, A., NEAL, D. E. & FRYER, L. G. 2014. Transcriptomic analysis reveals inhibition of androgen receptor activity by AMPK in prostate cancer cells. Oncotarget, 5, 3785-99. KANELLIS, J., KANDANE, R. K., ETEMADMOGHADAM, D., FRASER, S. A., MOUNT, P. F., LEVIDIOTIS, V., KEMP, B. E. & POWER, D. A. 2006. Activators of the energy sensing kinase AMPK inhibit random cell movement and chemotaxis in U937 cells. Immunol Cell Biol, 84, 6-12. KANG, H., SUN, L. D., ATKINS, C. M., SODERLING, T. R., WILSON, M. A. & TONEGAWA, S. 2001. An important role of neural activity-dependent CaMKIV signaling in the consolidation of long- term memory. Cell, 106, 771-83. KARACOSTA, L. G., FOSTER, B. A., AZABDAFTARI, G., FELICIANO, D. M. & EDELMAN, A. M. 2012. A regulatory feedback loop between Ca2+/calmodulin-dependent protein kinase kinase 2 (CaMKK2) and the androgen receptor in prostate cancer progression. J Biol Chem, 287, 24832-43.

233

KARANTANOS, T., CORN, P. G. & THOMPSON, T. C. 2013. Prostate cancer progression after androgen deprivation therapy: mechanisms of castrate resistance and novel therapeutic approaches. Oncogene, 32, 5501-11. KARTHAUS, W. R., IAQUINTA, P. J., DROST, J., GRACANIN, A., VAN BOXTEL, R., WONGVIPAT, J., DOWLING, C. M., GAO, D., BEGTHEL, H., SACHS, N., VRIES, R. G., CUPPEN, E., CHEN, Y., SAWYERS, C. L. & CLEVERS, H. C. 2014. Identification of multipotent luminal progenitor cells in human prostate organoid cultures. Cell, 159, 163-75. KATO, K., OGURA, T., KISHIMOTO, A., MINEGISHI, Y., NAKAJIMA, N., MIYAZAKI, M. & ESUMI, H. 2002. Critical roles of AMP-activated protein kinase in constitutive tolerance of cancer cells to nutrient deprivation and tumor formation. Oncogene, 21, 6082-90. KELLEHER, R. J., GOVINDARAJAN, A., JUNG, H. Y., KANG, H. & TONEGAWA, S. 2004a. Translational control by MAPK signaling in long-term synaptic plasticity and memory. Cell, 116, 467-79. KELLEHER, R. J., GOVINDARAJAN, A. & TONEGAWA, S. 2004b. Translational regulatory mechanisms in persistent forms of synaptic plasticity. Neuron, 44, 59-73. KENIRY, M. & PARSONS, R. 2008. The role of PTEN signaling perturbations in cancer and in targeted therapy. Oncogene, 27, 5477-85. KIM, E. K., PARK, J. M., LIM, S., CHOI, J. W., KIM, H. S., SEOK, H., SEO, J. K., OH, K., LEE, D. S., KIM, K. T., RYU, S. H. & SUH, P. G. 2011a. Activation of AMP-activated protein kinase is essential for lysophosphatidic acid-induced cell migration in ovarian cancer cells. J Biol Chem, 286, 24036- 45. KIM, J., KUNDU, M., VIOLLET, B. & GUAN, K. L. 2011b. AMPK and mTOR regulate autophagy through direct phosphorylation of Ulk1. Nat Cell Biol, 13, 132-41. KOH, C. M., BIEBERICH, C. J., DANG, C. V., NELSON, W. G., YEGNASUBRAMANIAN, S. & DE MARZO, A. M. 2010. MYC and Prostate Cancer. Genes Cancer, 1, 617-28. KOZIN, S. V., KAMOUN, W. S., HUANG, Y., DAWSON, M. R., JAIN, R. K. & DUDA, D. G. 2010. Recruitment of myeloid but not endothelial precursor cells facilitates tumor regrowth after local irradiation. Cancer Res, 70, 5679-85. KROISS, A., VINCENT, S., DECAUSSIN-PETRUCCI, M., MEUGNIER, E., VIALLET, J., RUFFION, A., CHALMEL, F., SAMARUT, J. & ALLIOLI, N. 2015. Androgen-regulated microRNA-135a decreases prostate cancer cell migration and invasion through downregulating ROCK1 and ROCK2. Oncogene, 34, 2846-55. KUHAJDA, F. P., JENNER, K., WOOD, F. D., HENNIGAR, R. A., JACOBS, L. B., DICK, J. D. & PASTERNACK, G. R. 1994. Fatty acid synthesis: a potential selective target for antineoplastic therapy. Proc Natl Acad Sci U S A, 91, 6379-83. KUKIMOTO-NIINO, M., YOSHIKAWA, S., TAKAGI, T., OHSAWA, N., TOMABECHI, Y., TERADA, T., SHIROUZU, M., SUZUKI, A., LEE, S., YAMAUCHI, T., OKADA-IWABU, M., IWABU, M., KADOWAKI, T., MINOKOSHI, Y. & YOKOYAMA, S. 2011. Crystal structure of the Ca²⁺/calmodulin-dependent protein kinase kinase in complex with the inhibitor STO-609. J Biol Chem, 286, 22570-9. KUMAR, R., GURURAJ, A. E. & BARNES, C. J. 2006. p21-activated kinases in cancer. Nat Rev Cancer, 6, 459-71. KUMAR-SINHA, C., SHAH, R. B., LAXMAN, B., TOMLINS, S. A., HARWOOD, J., SCHMITZ, W., CONZELMANN, E., SANDA, M. G., WEI, J. T., RUBIN, M. A. & CHINNAIYAN, A. M. 2004. Elevated alpha-methylacyl-CoA racemase enzymatic activity in prostate cancer. Am J Pathol, 164, 787-93. KWIATKOWSKI, D. J. & MANNING, B. D. 2005. Tuberous sclerosis: a GAP at the crossroads of multiple signaling pathways. Hum Mol Genet, 14 Spec No. 2, R251-8. LADEROUTE, K. R., AMIN, K., CALAOAGAN, J. M., KNAPP, M., LE, T., ORDUNA, J., FORETZ, M. & VIOLLET, B. 2006. 5'-AMP-activated protein kinase (AMPK) is induced by low-oxygen and

234

glucose deprivation conditions found in solid-tumor microenvironments. Mol Cell Biol, 26, 5336-47. LAI, M. D. & XU, J. 2007. Ribosomal proteins and colorectal cancer. Curr Genomics, 8, 43-9. LANDSKRON, G., DE LA FUENTE, M., THUWAJIT, P., THUWAJIT, C. & HERMOSO, M. A. 2014. Chronic inflammation and cytokines in the tumor microenvironment. J Immunol Res, 2014, 149185. LEE, A. S. 2005. The ER chaperone and signaling regulator GRP78/BiP as a monitor of endoplasmic reticulum stress. Methods, 35, 373-81. LEE, C. W., WONG, L. L., TSE, E. Y., LIU, H. F., LEONG, V. Y., LEE, J. M., HARDIE, D. G., NG, I. O. & CHING, Y. P. 2012. AMPK promotes p53 acetylation via phosphorylation and inactivation of SIRT1 in liver cancer cells. Cancer Res, 72, 4394-404. LEE, K. H., HSU, E. C., GUH, J. H., YANG, H. C., WANG, D., KULP, S. K., SHAPIRO, C. L. & CHEN, C. S. 2011. Targeting energy metabolic and oncogenic signaling pathways in triple-negative breast cancer by a novel adenosine monophosphate-activated protein kinase (AMPK) activator. J Biol Chem, 286, 39247-58. LEE, M., HWANG, J. T., LEE, H. J., JUNG, S. N., KANG, I., CHI, S. G., KIM, S. S. & HA, J. 2003. AMP- activated protein kinase activity is critical for hypoxia-inducible factor-1 transcriptional activity and its target gene expression under hypoxic conditions in DU145 cells. J Biol Chem, 278, 39653-61. LI, J. & LEE, A. S. 2006. Stress induction of GRP78/BiP and its role in cancer. Curr Mol Med, 6, 45-54. LI, Y., XU, S., MIHAYLOVA, M. M., ZHENG, B., HOU, X., JIANG, B., PARK, O., LUO, Z., LEFAI, E., SHYY, J. Y., GAO, B., WIERZBICKI, M., VERBEUREN, T. J., SHAW, R. J., COHEN, R. A. & ZANG, M. 2011. AMPK phosphorylates and inhibits SREBP activity to attenuate hepatic steatosis and atherosclerosis in diet-induced insulin-resistant mice. Cell Metab, 13, 376-88. LIANG, J., SHAO, S. H., XU, Z. X., HENNESSY, B., DING, Z., LARREA, M., KONDO, S., DUMONT, D. J., GUTTERMAN, J. U., WALKER, C. L., SLINGERLAND, J. M. & MILLS, G. B. 2007. The energy sensing LKB1-AMPK pathway regulates p27(kip1) phosphorylation mediating the decision to enter autophagy or apoptosis. Nat Cell Biol, 9, 218-24. LIMAME, R., WOUTERS, A., PAUWELS, B., FRANSEN, E., PEETERS, M., LARDON, F., DE WEVER, O. & PAUWELS, P. 2012. Comparative analysis of dynamic cell viability, migration and invasion assessments by novel real-time technology and classic endpoint assays. PLoS One, 7, e46536. LIN, D., WYATT, A. W., XUE, H., WANG, Y., DONG, X., HAEGERT, A., WU, R., BRAHMBHATT, S., MO, F., JONG, L., BELL, R. H., ANDERSON, S., HURTADO-COLL, A., FAZLI, L., SHARMA, M., BELTRAN, H., RUBIN, M., COX, M., GOUT, P. W., MORRIS, J., GOLDENBERG, L., VOLIK, S. V., GLEAVE, M. E. & COLLINS, C. C. 2014. High fidelity patient-derived xenografts for accelerating prostate cancer discovery and drug development. Cancer Res, 74, 1272-83. LIU, X., CHHIPA, R. R., NAKANO, I. & DASGUPTA, B. 2014. The AMPK inhibitor compound C is a potent AMPK-independent antiglioma agent. Mol Cancer Ther, 13, 596-605. LIZCANO, J. M., GÖRANSSON, O., TOTH, R., DEAK, M., MORRICE, N. A., BOUDEAU, J., HAWLEY, S. A., UDD, L., MÄKELÄ, T. P., HARDIE, D. G. & ALESSI, D. R. 2004. LKB1 is a master kinase that activates 13 kinases of the AMPK subfamily, including MARK/PAR-1. EMBO J, 23, 833-43. LOWE, S. W., CEPERO, E. & EVAN, G. 2004. Intrinsic tumour suppression. Nature, 432, 307-15. LUND, S. D., GALLAGHER, P. M., WANG, B., PORTER, S. C. & GANSCHOW, R. E. 1991. Androgen responsiveness of the murine beta-glucuronidase gene is associated with nuclease hypersensitivity, protein binding, and haplotype-specific sequence diversity within intron 9. Mol Cell Biol, 11, 5426-34. MANNING, B. D. & CANTLEY, L. C. 2003. Rheb fills a GAP between TSC and TOR. Trends Biochem Sci, 28, 573-6. MANSER, E., LOO, T. H., KOH, C. G., ZHAO, Z. S., CHEN, X. Q., TAN, L., TAN, I., LEUNG, T. & LIM, L. 1998. PAK kinases are directly coupled to the PIX family of nucleotide exchange factors. Mol Cell, 1, 183-92.

235

MARKERT, E. K., MIZUNO, H., VAZQUEZ, A. & LEVINE, A. J. 2011. Molecular classification of prostate cancer using curated expression signatures. Proc Natl Acad Sci U S A, 108, 21276-81. MARTIN, P., LIU, Y. N., PIERCE, R., ABOU-KHEIR, W., CASEY, O., SENG, V., CAMACHO, D., SIMPSON, R. M. & KELLY, K. 2011. Prostate epithelial Pten/TP53 loss leads to transformation of multipotential progenitors and epithelial to mesenchymal transition. Am J Pathol, 179, 422- 35. MASSIE, C. E., LYNCH, A., RAMOS-MONTOYA, A., BOREN, J., STARK, R., FAZLI, L., WARREN, A., SCOTT, H., MADHU, B., SHARMA, N., BON, H., ZECCHINI, V., SMITH, D. M., DENICOLA, G. M., MATHEWS, N., OSBORNE, M., HADFIELD, J., MACARTHUR, S., ADRYAN, B., LYONS, S. K., BRINDLE, K. M., GRIFFITHS, J., GLEAVE, M. E., RENNIE, P. S., NEAL, D. E. & MILLS, I. G. 2011. The androgen receptor fuels prostate cancer by regulating central metabolism and biosynthesis. EMBO J, 30, 2719-33. MATANO, M., DATE, S., SHIMOKAWA, M., TAKANO, A., FUJII, M., OHTA, Y., WATANABE, T., KANAI, T. & SATO, T. 2015. Modeling colorectal cancer using CRISPR-Cas9-mediated engineering of human intestinal organoids. Nat Med, 21, 256-62. MATHUPALA, S. P., KO, Y. H. & PEDERSEN, P. L. 2006. Hexokinase II: cancer's double-edged sword acting as both facilitator and gatekeeper of malignancy when bound to mitochondria. Oncogene, 25, 4777-86. MATSUMOTO, A., ARAI, Y., URANO, A. & HYODO, S. 1992. Effect of androgen on the expression of gap junction and beta-actin mRNAs in adult rat motoneurons. Neurosci Res, 14, 133-44. MATSUMURA, F. 2005. Regulation of myosin II during cytokinesis in higher eukaryotes. Trends Cell Biol, 15, 371-7. MCMENAMIN, M. E., SOUNG, P., PERERA, S., KAPLAN, I., LODA, M. & SELLERS, W. R. 1999. Loss of PTEN expression in paraffin-embedded primary prostate cancer correlates with high Gleason score and advanced stage. Cancer Res, 59, 4291-6. MEANS, A. R., RIBAR, T. J., KANE, C. D., HOOK, S. S. & ANDERSON, K. A. 1997. Regulation and properties of the rat Ca2+/calmodulin-dependent protein kinase IV gene and its protein products. Recent Prog Horm Res, 52, 389-406; discussion 406-7. MEHLEN, P. & PUISIEUX, A. 2006. Metastasis: a question of life or death. Nat Rev Cancer, 6, 449-58. MILLER, R. A. & BIRNBAUM, M. J. 2010. An energetic tale of AMPK-independent effects of metformin. J Clin Invest, 120, 2267-70. MILLS, I. G. 2014. Maintaining and reprogramming genomic androgen receptor activity in prostate cancer. Nat Rev Cancer, 14, 187-98. MIRANDA, L., CARPENTIER, S., PLATEK, A., HUSSAIN, N., GUEUNING, M. A., VERTOMMEN, D., OZKAN, Y., SID, B., HUE, L., COURTOY, P. J., RIDER, M. H. & HORMAN, S. 2010. AMP-activated protein kinase induces actin cytoskeleton reorganization in epithelial cells. Biochem Biophys Res Commun, 396, 656-61. MIZUNO, K., RIS, L., SÁNCHEZ-CAPELO, A., GODAUX, E. & GIESE, K. P. 2006. Ca2+/calmodulin kinase kinase alpha is dispensable for brain development but is required for distinct memories in male, though not in female, mice. Mol Cell Biol, 26, 9094-104. MOMCILOVIC, M., HONG, S. P. & CARLSON, M. 2006. Mammalian TAK1 activates Snf1 protein kinase in yeast and phosphorylates AMP-activated protein kinase in vitro. J Biol Chem, 281, 25336- 43. MONTAGNE, J., STEWART, M. J., STOCKER, H., HAFEN, E., KOZMA, S. C. & THOMAS, G. 1999. Drosophila S6 kinase: a regulator of cell size. Science, 285, 2126-9. MOON, J. S., JIN, W. J., KWAK, J. H., KIM, H. J., YUN, M. J., KIM, J. W., PARK, S. W. & KIM, K. S. 2011. Androgen stimulates glycolysis for de novo lipid synthesis by increasing the activities of hexokinase 2 and 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 2 in prostate cancer cells. Biochem J, 433, 225-33.

236

MORENO, D., KNECHT, E., VIOLLET, B. & SANZ, P. 2008. A769662, a novel activator of AMP-activated protein kinase, inhibits non-proteolytic components of the 26S proteasome by an AMPK- independent mechanism. FEBS Lett, 582, 2650-4. MORI, R., WANG, Q., DANENBERG, K. D., PINSKI, J. K. & DANENBERG, P. V. 2008. Both beta-actin and GAPDH are useful reference genes for normalization of quantitative RT-PCR in human FFPE tissue samples of prostate cancer. Prostate, 68, 1555-60. MULDER, E., VRIJ, A. A. & BRINKMANN, A. O. 1983. DNA and ribonucleotide binding characteristics of two forms of the androgen receptor from rat prostates. Biochem Biophys Res Commun, 114, 1147-53. MUNDAY, M. R. 2002. Regulation of mammalian acetyl-CoA carboxylase. Biochem Soc Trans, 30, 1059-64. MUOIO, D. M., SEEFELD, K., WITTERS, L. A. & COLEMAN, R. A. 1999. AMP-activated kinase reciprocally regulates triacylglycerol synthesis and fatty acid oxidation in liver and muscle: evidence that sn-glycerol-3-phosphate acyltransferase is a novel target. Biochem J, 338 ( Pt 3), 783-91. MURRAY, B. W., GUO, C., PIRAINO, J., WESTWICK, J. K., ZHANG, C., LAMERDIN, J., DAGOSTINO, E., KNIGHTON, D., LOI, C. M., ZAGER, M., KRAYNOV, E., POPOFF, I., CHRISTENSEN, J. G., MARTINEZ, R., KEPHART, S. E., MARAKOVITS, J., KARLICEK, S., BERGQVIST, S. & SMEAL, T. 2010. Small-molecule p21-activated kinase inhibitor PF-3758309 is a potent inhibitor of oncogenic signaling and tumor growth. Proc Natl Acad Sci U S A, 107, 9446-51. MURTOLA, T. J., VISAKORPI, T., LAHTELA, J., SYVÄLÄ, H. & TAMMELA, T. L. J. 2008. Statins and prostate cancer prevention: where we are now, and future directions. Nat Clin Pract Urol, 5, 376-87. NAGATA, D., MOGI, M. & WALSH, K. 2003. AMP-activated protein kinase (AMPK) signaling in endothelial cells is essential for angiogenesis in response to hypoxic stress. J Biol Chem, 278, 31000-6. NAKANO, A., KATO, H., WATANABE, T., MIN, K. D., YAMAZAKI, S., ASANO, Y., SEGUCHI, O., HIGO, S., SHINTANI, Y., ASANUMA, H., ASAKURA, M., MINAMINO, T., KAIBUCHI, K., MOCHIZUKI, N., KITAKAZE, M. & TAKASHIMA, S. 2010. AMPK controls the speed of microtubule polymerization and directional cell migration through CLIP-170 phosphorylation. Nat Cell Biol, 12, 583-90. NETWORK, C. G. A. R. 2015. The Molecular Taxonomy of Primary Prostate Cancer. Cell, 163, 1011-25. NEUMANN, D., WOODS, A., CARLING, D., WALLIMANN, T. & SCHLATTNER, U. 2003. Mammalian AMP-activated protein kinase: functional, heterotrimeric complexes by co-expression of subunits in Escherichia coli. Protein Expr Purif, 30, 230-7. NIEMINEN, A. I., ESKELINEN, V. M., HAIKALA, H. M., TERVONEN, T. A., YAN, Y., PARTANEN, J. I. & KLEFSTRÖM, J. 2013. Myc-induced AMPK-phospho p53 pathway activates Bak to sensitize mitochondrial apoptosis. Proc Natl Acad Sci U S A, 110, E1839-48. NOMURA, D. K., LOMBARDI, D. P., CHANG, J. W., NIESSEN, S., WARD, A. M., LONG, J. Z., HOOVER, H. H. & CRAVATT, B. F. 2011. Monoacylglycerol lipase exerts dual control over endocannabinoid and fatty acid pathways to support prostate cancer. Chem Biol, 18, 846-56. NONOMURA, N., TAKAYAMA, H., NAKAYAMA, M., NAKAI, Y., KAWASHIMA, A., MUKAI, M., NAGAHARA, A., AOZASA, K. & TSUJIMURA, A. 2011. Infiltration of tumour-associated macrophages in prostate biopsy specimens is predictive of disease progression after hormonal therapy for prostate cancer. BJU Int, 107, 1918-22. O'REILLY, K. E., ROJO, F., SHE, Q. B., SOLIT, D., MILLS, G. B., SMITH, D., LANE, H., HOFMANN, F., HICKLIN, D. J., LUDWIG, D. L., BASELGA, J. & ROSEN, N. 2006. mTOR inhibition induces upstream receptor tyrosine kinase signaling and activates Akt. Cancer Res, 66, 1500-8. OHL, F., JUNG, M., XU, C., STEPHAN, C., RABIEN, A., BURKHARDT, M., NITSCHE, A., KRISTIANSEN, G., LOENING, S. A., RADONIĆ, A. & JUNG, K. 2005. Gene expression studies in prostate cancer

237

tissue: which reference gene should be selected for normalization? J Mol Med (Berl), 83, 1014-24. OKOSHI, R., OZAKI, T., YAMAMOTO, H., ANDO, K., KOIDA, N., ONO, S., KODA, T., KAMIJO, T., NAKAGAWARA, A. & KIZAKI, H. 2008. Activation of AMP-activated protein kinase induces p53-dependent apoptotic cell death in response to energetic stress. J Biol Chem, 283, 3979- 87. OLIVIER, M., HOLLSTEIN, M. & HAINAUT, P. 2010. TP53 mutations in human cancers: origins, consequences, and clinical use. Cold Spring Harb Perspect Biol, 2, a001008. ONG, C. C., JUBB, A. M., JAKUBIAK, D., ZHOU, W., RUDOLPH, J., HAVERTY, P. M., KOWANETZ, M., YAN, Y., TREMAYNE, J., LISLE, R., HARRIS, A. L., FRIEDMAN, L. S., BELVIN, M., MIDDLETON, M. R., BLACKWOOD, E. M., KOEPPEN, H. & HOEFLICH, K. P. 2013. P21-activated kinase 1 (PAK1) as a therapeutic target in BRAF wild-type melanoma. J Natl Cancer Inst, 105, 606-7. ONG, C. C., JUBB, A. M., ZHOU, W., HAVERTY, P. M., HARRIS, A. L., BELVIN, M., FRIEDMAN, L. S., KOEPPEN, H. & HOEFLICH, K. P. 2011. p21-activated kinase 1: PAK'ed with potential. Oncotarget, 2, 491-6. OVERCASH, R. F., CHAPPELL, V. A., GREEN, T., GEYER, C. B., ASCH, A. S. & RUIZ-ECHEVARRÍA, M. J. 2013. Androgen signaling promotes translation of TMEFF2 in prostate cancer cells via phosphorylation of the α subunit of the translation initiation factor 2. PLoS One, 8, e55257. PALACIOS, I. M., GATFIELD, D., ST JOHNSTON, D. & IZAURRALDE, E. 2004. An eIF4AIII-containing complex required for mRNA localization and nonsense-mediated mRNA decay. Nature, 427, 753-7. PARISOTTO, M. & METZGER, D. 2013. Genetically engineered mouse models of prostate cancer. Mol Oncol, 7, 190-205. PARK, H. U., SUY, S., DANNER, M., DAILEY, V., ZHANG, Y., LI, H., HYDUKE, D. R., COLLINS, B. T., GAGNON, G., KALLAKURY, B., KUMAR, D., BROWN, M. L., FORNACE, A., DRITSCHILO, A. & COLLINS, S. P. 2009. AMP-activated protein kinase promotes human prostate cancer cell growth and survival. Mol Cancer Ther, 8, 733-41. PARK, S. H., GAMMON, S. R., KNIPPERS, J. D., PAULSEN, S. R., RUBINK, D. S. & WINDER, W. W. 2002. Phosphorylation-activity relationships of AMPK and acetyl-CoA carboxylase in muscle. J Appl Physiol (1985), 92, 2475-82. PATRA, K. C., WANG, Q., BHASKAR, P. T., MILLER, L., WANG, Z., WHEATON, W., CHANDEL, N., LAAKSO, M., MULLER, W. J., ALLEN, E. L., JHA, A. K., SMOLEN, G. A., CLASQUIN, M. F., ROBEY, R. B. & HAY, N. 2013. Hexokinase 2 is required for tumor initiation and maintenance and its systemic deletion is therapeutic in mouse models of cancer. Cancer Cell, 24, 213-28. PETERS, M., MIZUNO, K., RIS, L., ANGELO, M., GODAUX, E. & GIESE, K. P. 2003. Loss of Ca2+/calmodulin kinase kinase beta affects the formation of some, but not all, types of hippocampus-dependent long-term memory. J Neurosci, 23, 9752-60. PETRYLAK, D. P., TANGEN, C. M., HUSSAIN, M. H., LARA, P. N., JONES, J. A., TAPLIN, M. E., BURCH, P. A., BERRY, D., MOINPOUR, C., KOHLI, M., BENSON, M. C., SMALL, E. J., RAGHAVAN, D. & CRAWFORD, E. D. 2004. Docetaxel and estramustine compared with mitoxantrone and prednisone for advanced refractory prostate cancer. N Engl J Med, 351, 1513-20. PEYTON, K. J., LIU, X. M., YU, Y., YATES, B. & DURANTE, W. 2012. Activation of AMP-activated protein kinase inhibits the proliferation of human endothelial cells. J Pharmacol Exp Ther, 342, 827- 34. POLEKHINA, G., GUPTA, A., MICHELL, B. J., VAN DENDEREN, B., MURTHY, S., FEIL, S. C., JENNINGS, I. G., CAMPBELL, D. J., WITTERS, L. A., PARKER, M. W., KEMP, B. E. & STAPLETON, D. 2003. AMPK beta subunit targets metabolic stress sensing to glycogen. Curr Biol, 13, 867-71. POLLARD, J. W. 2009. Trophic macrophages in development and disease. Nat Rev Immunol, 9, 259- 70.

238

PRENSNER, J. R., RUBIN, M. A., WEI, J. T. & CHINNAIYAN, A. M. 2012. Beyond PSA: the next generation of prostate cancer biomarkers. Sci Transl Med, 4, 127rv3. RACEK, T., BUHLMANN, S., RÜST, F., KNOLL, S., ALLA, V. & PÜTZER, B. M. 2008. Transcriptional repression of the prosurvival endoplasmic reticulum chaperone GRP78/BIP by E2F1. J Biol Chem, 283, 34305-14. RACIOPPI, L. 2013. CaMKK2: a novel target for shaping the androgen-regulated tumor ecosystem. Trends Mol Med, 19, 83-8. RACIOPPI, L. & MEANS, A. R. 2012. Calcium/calmodulin-dependent protein kinase kinase 2: roles in signaling and pathophysiology. J Biol Chem, 287, 31658-65. RACIOPPI, L., NOELDNER, P. K., LIN, F., ARVAI, S. & MEANS, A. R. 2012. Calcium/calmodulin- dependent protein kinase kinase 2 regulates macrophage-mediated inflammatory responses. J Biol Chem, 287, 11579-91. RADU, M., SEMENOVA, G., KOSOFF, R. & CHERNOFF, J. 2014. PAK signalling during the development and progression of cancer. Nat Rev Cancer, 14, 13-25. RAZA, F., WALDRON, J. A. & QUESNE, J. L. 2015. Translational dysregulation in cancer: eIF4A isoforms and sequence determinants of eIF4A dependence. Biochem Soc Trans, 43, 1227-33. RENNEFAHRT, U. E., DEACON, S. W., PARKER, S. A., DEVARAJAN, K., BEESER, A., CHERNOFF, J., KNAPP, S., TURK, B. E. & PETERSON, J. R. 2007. Specificity profiling of Pak kinases allows identification of novel phosphorylation sites. J Biol Chem, 282, 15667-78. RICHMOND, A. & SU, Y. 2008. Mouse xenograft models vs GEM models for human cancer therapeutics. Dis Model Mech, 1, 78-82. ROBERTS, D. J., TAN-SAH, V. P., SMITH, J. M. & MIYAMOTO, S. 2013. Akt phosphorylates HK-II at Thr- 473 and increases mitochondrial HK-II association to protect cardiomyocytes. J Biol Chem, 288, 23798-806. ROBINSON, D., VAN ALLEN, E. M., WU, Y. M., SCHULTZ, N., LONIGRO, R. J., MOSQUERA, J. M., MONTGOMERY, B., TAPLIN, M. E., PRITCHARD, C. C., ATTARD, G., BELTRAN, H., ABIDA, W., BRADLEY, R. K., VINSON, J., CAO, X., VATS, P., KUNJU, L. P., HUSSAIN, M., FENG, F. Y., TOMLINS, S. A., COONEY, K. A., SMITH, D. C., BRENNAN, C., SIDDIQUI, J., MEHRA, R., CHEN, Y., RATHKOPF, D. E., MORRIS, M. J., SOLOMON, S. B., DURACK, J. C., REUTER, V. E., GOPALAN, A., GAO, J., LODA, M., LIS, R. T., BOWDEN, M., BALK, S. P., GAVIOLA, G., SOUGNEZ, C., GUPTA, M., YU, E. Y., MOSTAGHEL, E. A., CHENG, H. H., MULCAHY, H., TRUE, L. D., PLYMATE, S. R., DVINGE, H., FERRALDESCHI, R., FLOHR, P., MIRANDA, S., ZAFEIRIOU, Z., TUNARIU, N., MATEO, J., PEREZ-LOPEZ, R., DEMICHELIS, F., ROBINSON, B. D., SCHIFFMAN, M., NANUS, D. M., TAGAWA, S. T., SIGARAS, A., ENG, K. W., ELEMENTO, O., SBONER, A., HEATH, E. I., SCHER, H. I., PIENTA, K. J., KANTOFF, P., DE BONO, J. S., RUBIN, M. A., NELSON, P. S., GARRAWAY, L. A., SAWYERS, C. L. & CHINNAIYAN, A. M. 2015. Integrative clinical genomics of advanced prostate cancer. Cell, 161, 1215-28. RODRIK-OUTMEZGUINE, V. S., CHANDARLAPATY, S., PAGANO, N. C., POULIKAKOS, P. I., SCALTRITI, M., MOSKATEL, E., BASELGA, J., GUICHARD, S. & ROSEN, N. 2011. mTOR kinase inhibition causes feedback-dependent biphasic regulation of AKT signaling. Cancer Discov, 1, 248-59. ROS, S., SANTOS, C. R., MOCO, S., BAENKE, F., KELLY, G., HOWELL, M., ZAMBONI, N. & SCHULZE, A. 2012. Functional metabolic screen identifies 6-phosphofructo-2-kinase/fructose-2,6- biphosphatase 4 as an important regulator of prostate cancer cell survival. Cancer Discov, 2, 328-43. ROS, S. & SCHULZE, A. 2012. Linking glycogen and senescence in cancer cells. Cell Metab, 16, 687-8. ROSSI, S., GRANER, E., FEBBO, P., WEINSTEIN, L., BHATTACHARYA, N., ONODY, T., BUBLEY, G., BALK, S. & LODA, M. 2003. Fatty acid synthase expression defines distinct molecular signatures in prostate cancer. Mol Cancer Res, 1, 707-15.

239

RUBIO, C. A., WEISBURD, B., HOLDERFIELD, M., ARIAS, C., FANG, E., DERISI, J. L. & FANIDI, A. 2014. Transcriptome-wide characterization of the eIF4A signature highlights plasticity in translation regulation. Genome Biol, 15, 476. RUDERMAN, N. & PRENTKI, M. 2004. AMP kinase and malonyl-CoA: targets for therapy of the metabolic syndrome. Nat Rev Drug Discov, 3, 340-51. SAHLÉN, G., NILSSON, O., LARSSON, A., CARLSSON, L., NORLÉN, B. J. & RONQUIST, G. 2010. Secretions from seminal vesicles lack characteristic markers for prostasomes. Ups J Med Sci, 115, 107-12. SAKAMOTO, K., MCCARTHY, A., SMITH, D., GREEN, K. A., GRAHAME HARDIE, D., ASHWORTH, A. & ALESSI, D. R. 2005. Deficiency of LKB1 in skeletal muscle prevents AMPK activation and glucose uptake during contraction. EMBO J, 24, 1810-20. SALMINEN, A., HYTTINEN, J. M. & KAARNIRANTA, K. 2011. AMP-activated protein kinase inhibits NF- κB signaling and inflammation: impact on healthspan and lifespan. J Mol Med (Berl), 89, 667- 76. SALT, I., CELLER, J. W., HAWLEY, S. A., PRESCOTT, A., WOODS, A., CARLING, D. & HARDIE, D. G. 1998. AMP-activated protein kinase: greater AMP dependence, and preferential nuclear localization, of complexes containing the alpha2 isoform. Biochem J, 334 ( Pt 1), 177-87. SANDERS, M. J., ALI, Z. S., HEGARTY, B. D., HEATH, R., SNOWDEN, M. A. & CARLING, D. 2007. Defining the mechanism of activation of AMP-activated protein kinase by the small molecule A- 769662, a member of the thienopyridone family. J Biol Chem, 282, 32539-48. SANTIDRIÁN, A. F., GONZÁLEZ-GIRONÈS, D. M., IGLESIAS-SERRET, D., COLL-MULET, L., COSIALLS, A. M., DE FRIAS, M., CAMPÀS, C., GONZÁLEZ-BARCA, E., ALONSO, E., LABI, V., VIOLLET, B., BENITO, A., PONS, G., VILLUNGER, A. & GIL, J. 2010. AICAR induces apoptosis independently of AMPK and p53 through up-regulation of the BH3-only proteins BIM and NOXA in chronic lymphocytic leukemia cells. Blood, 116, 3023-32. SANTOS, C. R. & SCHULZE, A. 2012. Lipid metabolism in cancer. FEBS J, 279, 2610-23. SARBASSOV, D. D., GUERTIN, D. A., ALI, S. M. & SABATINI, D. M. 2005. Phosphorylation and regulation of Akt/PKB by the rictor-mTOR complex. Science, 307, 1098-101. SCHMITT, J. M., GUIRE, E. S., SANEYOSHI, T. & SODERLING, T. R. 2005. Calmodulin-dependent kinase kinase/calmodulin kinase I activity gates extracellular-regulated kinase-dependent long-term potentiation. J Neurosci, 25, 1281-90. SCHNIER, J. B., NISHI, K., GUMERLOCK, P. H., GORIN, F. A. & BRADBURY, E. M. 2005. Glycogen synthesis correlates with androgen-dependent growth arrest in prostate cancer. BMC Urol, 5, 6. SCHRÖDER, F. H., HUGOSSON, J., ROOBOL, M. J., TAMMELA, T. L., CIATTO, S., NELEN, V., KWIATKOWSKI, M., LUJAN, M., LILJA, H., ZAPPA, M., DENIS, L. J., RECKER, F., PÁEZ, A., MÄÄTTÄNEN, L., BANGMA, C. H., AUS, G., CARLSSON, S., VILLERS, A., REBILLARD, X., VAN DER KWAST, T., KUJALA, P. M., BLIJENBERG, B. G., STENMAN, U. H., HUBER, A., TAARI, K., HAKAMA, M., MOSS, S. M., DE KONING, H. J., AUVINEN, A. & INVESTIGATORS, E. 2012. Prostate-cancer mortality at 11 years of follow-up. N Engl J Med, 366, 981-90. SCOTT, K. E., WHEELER, F. B., DAVIS, A. L., THOMAS, M. J., NTAMBI, J. M., SEALS, D. F. & KRIDEL, S. J. 2012. Metabolic regulation of invadopodia and invasion by acetyl-CoA carboxylase 1 and de novo lipogenesis. PLoS One, 7, e29761. SEMENTCHENKO, V. I. & WATSON, D. K. 2000. Ets target genes: past, present and future. Oncogene, 19, 6533-48. SHACKELFORD, D. B. & SHAW, R. J. 2009. The LKB1-AMPK pathway: metabolism and growth control in tumour suppression. Nat Rev Cancer, 9, 563-75. SHACKELFORD, D. B., VASQUEZ, D. S., CORBEIL, J., WU, S., LEBLANC, M., WU, C. L., VERA, D. R. & SHAW, R. J. 2009. mTOR and HIF-1alpha-mediated tumor metabolism in an LKB1 mouse model of Peutz-Jeghers syndrome. Proc Natl Acad Sci U S A, 106, 11137-42.

240

SHARMA, P. & SCHREIBER-AGUS, N. 1999. Mouse models of prostate cancer. Oncogene, 18, 5349-55. SHAW, R. J., LAMIA, K. A., VASQUEZ, D., KOO, S. H., BARDEESY, N., DEPINHO, R. A., MONTMINY, M. & CANTLEY, L. C. 2005. The kinase LKB1 mediates glucose homeostasis in liver and therapeutic effects of metformin. Science, 310, 1642-6. SHEN, M., ZHANG, Z., RATNAM, M. & DOU, Q. P. 2014. The interplay of AMP-activated protein kinase and androgen receptor in prostate cancer cells. J Cell Physiol, 229, 688-95. SHIAO, S. L., CHU, G. C. & CHUNG, L. W. 2016. Regulation of prostate cancer progression by the tumor microenvironment. Cancer Lett. SHIMA, T., MIZOKAMI, A., MIYAGI, T., KAWAI, K., IZUMI, K., KUMAKI, M., OFUDE, M., ZHANG, J., KELLER, E. T. & NAMIKI, M. 2012. Down-regulation of calcium/calmodulin-dependent protein kinase kinase 2 by androgen deprivation induces castration-resistant prostate cancer. Prostate, 72, 1789-801. SIEGEL, R., DESANTIS, C., VIRGO, K., STEIN, K., MARIOTTO, A., SMITH, T., COOPER, D., GANSLER, T., LERRO, C., FEDEWA, S., LIN, C., LEACH, C., CANNADY, R. S., CHO, H., SCOPPA, S., HACHEY, M., KIRCH, R., JEMAL, A. & WARD, E. 2012. Cancer treatment and survivorship statistics, 2012. CA Cancer J Clin, 62, 220-41. SIEGEL, R. L., MILLER, K. D. & JEMAL, A. 2016. Cancer statistics, 2016. CA Cancer J Clin, 66, 7-30. SKARNES, W. C., ROSEN, B., WEST, A. P., KOUTSOURAKIS, M., BUSHELL, W., IYER, V., MUJICA, A. O., THOMAS, M., HARROW, J., COX, T., JACKSON, D., SEVERIN, J., BIGGS, P., FU, J., NEFEDOV, M., DE JONG, P. J., STEWART, A. F. & BRADLEY, A. 2011. A conditional knockout resource for the genome-wide study of mouse gene function. Nature, 474, 337-42. SKJØTH, I. H. & ISSINGER, O. G. 2006. Profiling of signaling molecules in four different human prostate carcinoma cell lines before and after induction of apoptosis. Int J Oncol, 28, 217-29. SNOEK, R., CHENG, H., MARGIOTTI, K., WAFA, L. A., WONG, C. A., WONG, E. C., FAZLI, L., NELSON, C. C., GLEAVE, M. E. & RENNIE, P. S. 2009. In vivo knockdown of the androgen receptor results in growth inhibition and regression of well-established, castration-resistant prostate tumors. Clin Cancer Res, 15, 39-47. SRAMKOSKI, R. M., PRETLOW, T. G., GIACONIA, J. M., PRETLOW, T. P., SCHWARTZ, S., SY, M. S., MARENGO, S. R., RHIM, J. S., ZHANG, D. & JACOBBERGER, J. W. 1999. A new human prostate carcinoma cell line, 22Rv1. In Vitro Cell Dev Biol Anim, 35, 403-9. SRIVASTAVA, T., FORTIN, D. A., NYGAARD, S., KAECH, S., SONENBERG, N., EDELMAN, A. M. & SODERLING, T. R. 2012. Regulation of neuronal mRNA translation by CaM-kinase I phosphorylation of eIF4GII. J Neurosci, 32, 5620-30. STAHMANN, N., WOODS, A., CARLING, D. & HELLER, R. 2006. Thrombin activates AMP-activated protein kinase in endothelial cells via a pathway involving Ca2+/calmodulin-dependent protein kinase kinase beta. Mol Cell Biol, 26, 5933-45. STANGELBERGER, A., WALDERT, M. & DJAVAN, B. 2008. Prostate cancer in elderly men. Rev Urol, 10, 111-9. STANZIONE, R., PICASCIA, A., CHIEFFI, P., IMBIMBO, C., PALMIERI, A., MIRONE, V., STAIBANO, S., FRANCO, R., DE ROSA, G., SCHLESSINGER, J. & TRAMONTANO, D. 2001. Variations of proline- rich kinase Pyk2 expression correlate with prostate cancer progression. Lab Invest, 81, 51-9. STEINBERG, G. R. & KEMP, B. E. 2009. AMPK in Health and Disease. Physiol Rev, 89, 1025-78. STONE, J. D., NARINE, A., SHAVER, P. R., FOX, J. C., VUNCANNON, J. R. & TULIS, D. A. 2013. AMP- activated protein kinase inhibits vascular smooth muscle cell proliferation and migration and vascular remodeling following injury. Am J Physiol Heart Circ Physiol, 304, H369-81. STURGE, J., CALEY, M. P. & WAXMAN, J. 2011. Bone metastasis in prostate cancer: emerging therapeutic strategies. Nat Rev Clin Oncol, 8, 357-68. SUBURU, J. & CHEN, Y. Q. 2012. Lipids and prostate cancer. Prostaglandins Other Lipid Mediat, 98, 1- 10.

241

SULLIVAN, J. E., BROCKLEHURST, K. J., MARLEY, A. E., CAREY, F., CARLING, D. & BERI, R. K. 1994. Inhibition of lipolysis and lipogenesis in isolated rat adipocytes with AICAR, a cell-permeable activator of AMP-activated protein kinase. FEBS Lett, 353, 33-6. SZABÓ, Z., HÄMÄLÄINEN, J., LOIKKANEN, I., MOILANEN, A. M., HIRVIKOSKI, P., VÄISÄNEN, T., PAAVONEN, T. K. & VAARALA, M. H. 2010. Sorbitol dehydrogenase expression is regulated by androgens in the human prostate. Oncol Rep, 23, 1233-9. SÁNCHEZ-RIVERA, F. J. & JACKS, T. 2015. Applications of the CRISPR-Cas9 system in cancer biology. Nat Rev Cancer, 15, 387-95. TAN, M. H., LI, J., XU, H. E., MELCHER, K. & YONG, E. L. 2015. Androgen receptor: structure, role in prostate cancer and drug discovery. Acta Pharmacol Sin, 36, 3-23. TAYLOR, B. S., SCHULTZ, N., HIERONYMUS, H., GOPALAN, A., XIAO, Y., CARVER, B. S., ARORA, V. K., KAUSHIK, P., CERAMI, E., REVA, B., ANTIPIN, Y., MITSIADES, N., LANDERS, T., DOLGALEV, I., MAJOR, J. E., WILSON, M., SOCCI, N. D., LASH, A. E., HEGUY, A., EASTHAM, J. A., SCHER, H. I., REUTER, V. E., SCARDINO, P. T., SANDER, C., SAWYERS, C. L. & GERALD, W. L. 2010. Integrative genomic profiling of human prostate cancer. Cancer Cell, 18, 11-22. TENNAKOON, J. B., SHI, Y., HAN, J. J., TSOUKO, E., WHITE, M. A., BURNS, A. R., ZHANG, A., XIA, X., ILKAYEVA, O. R., XIN, L., ITTMANN, M. M., RICK, F. G., SCHALLY, A. V. & FRIGO, D. E. 2014. Androgens regulate prostate cancer cell growth via an AMPK-PGC-1α-mediated metabolic switch. Oncogene, 33, 5251-61. THAIPARAMBIL, J. T., EGGERS, C. M. & MARCUS, A. I. 2012. AMPK regulates mitotic spindle orientation through phosphorylation of myosin regulatory light chain. Mol Cell Biol, 32, 3203-17. TOKUMITSU, H., ENSLEN, H. & SODERLING, T. R. 1995. Characterization of a Ca2+/calmodulin- dependent protein kinase cascade. Molecular cloning and expression of calcium/calmodulin- dependent protein kinase kinase. J Biol Chem, 270, 19320-4. TOKUMITSU, H., INUZUKA, H., ISHIKAWA, Y., IKEDA, M., SAJI, I. & KOBAYASHI, R. 2002. STO-609, a Specific Inhibitor of the Ca2+/Calmodulin-dependent Protein Kinase Kinase. Journal of Biological Chemistry, 277, 15813-15818. TOKUMITSU, H., INUZUKA, H., ISHIKAWA, Y. & KOBAYASHI, R. 2003. A single amino acid difference between alpha and beta Ca2+/calmodulin-dependent protein kinase kinase dictates sensitivity to the specific inhibitor, STO-609. J Biol Chem, 278, 10908-13. TOKUMITSU, H., IWABU, M., ISHIKAWA, Y. & KOBAYASHI, R. 2001. Differential regulatory mechanism of Ca2+/calmodulin-dependent protein kinase kinase isoforms. Biochemistry, 40, 13925-32. TOMLINS, S. A., BJARTELL, A., CHINNAIYAN, A. M., JENSTER, G., NAM, R. K., RUBIN, M. A. & SCHALKEN, J. A. 2009. ETS gene fusions in prostate cancer: from discovery to daily clinical practice. Eur Urol, 56, 275-86. TOMLINS, S. A., LAXMAN, B., DHANASEKARAN, S. M., HELGESON, B. E., CAO, X., MORRIS, D. S., MENON, A., JING, X., CAO, Q., HAN, B., YU, J., WANG, L., MONTIE, J. E., RUBIN, M. A., PIENTA, K. J., ROULSTON, D., SHAH, R. B., VARAMBALLY, S., MEHRA, R. & CHINNAIYAN, A. M. 2007. Distinct classes of chromosomal rearrangements create oncogenic ETS gene fusions in prostate cancer. Nature, 448, 595-9. TONG, L. 2005. Acetyl-coenzyme A carboxylase: crucial metabolic enzyme and attractive target for drug discovery. Cell Mol Life Sci, 62, 1784-803. TROTMAN, L. C., NIKI, M., DOTAN, Z. A., KOUTCHER, J. A., DI CRISTOFANO, A., XIAO, A., KHOO, A. S., ROY-BURMAN, P., GREENBERG, N. M., VAN DYKE, T., CORDON-CARDO, C. & PANDOLFI, P. P. 2003. Pten dose dictates cancer progression in the prostate. PLoS Biol, 1, E59. TUMANENG, K., RUSSELL, R. C. & GUAN, K. L. 2012. Organ size control by Hippo and TOR pathways. Curr Biol, 22, R368-79. TURNER, C. E., BROWN, M. C., PERROTTA, J. A., RIEDY, M. C., NIKOLOPOULOS, S. N., MCDONALD, A. R., BAGRODIA, S., THOMAS, S. & LEVENTHAL, P. S. 1999. Paxillin LD4 motif binds PAK and PIX

242

through a novel 95-kD ankyrin repeat, ARF-GAP protein: A role in cytoskeletal remodeling. J Cell Biol, 145, 851-63. TUXHORN, J. A., AYALA, G. E., SMITH, M. J., SMITH, V. C., DANG, T. D. & ROWLEY, D. R. 2002. Reactive stroma in human prostate cancer: induction of myofibroblast phenotype and extracellular matrix remodeling. Clin Cancer Res, 8, 2912-23. VAN ZIJL, F., KRUPITZA, G. & MIKULITS, W. 2011. Initial steps of metastasis: cell invasion and endothelial transmigration. Mutat Res, 728, 23-34. VANDER HEIDEN, M. G., CANTLEY, L. C. & THOMPSON, C. B. 2009. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science, 324, 1029-33. VAZQUEZ-MARTIN, A., OLIVERAS-FERRAROS, C., LOPEZ-BONET, E. & MENENDEZ, J. A. 2009. AMPK: Evidence for an energy-sensing cytokinetic tumor suppressor. Cell Cycle, 8, 3679-83. VOGEL, C. & MARCOTTE, E. M. 2012. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet, 13, 227-32. WAGSTAFF, L., KOLAHGAR, G. & PIDDINI, E. 2013. Competitive cell interactions in cancer: a cellular tug of war. Trends Cell Biol, 23, 160-7. WALTERING, K. K., URBANUCCI, A. & VISAKORPI, T. 2012. Androgen receptor (AR) aberrations in castration-resistant prostate cancer. Mol Cell Endocrinol, 360, 38-43. WANG, G., LUNARDI, A., ZHANG, J., CHEN, Z., ALA, U., WEBSTER, K. A., TAY, Y., GONZALEZ- BILLALABEITIA, E., EGIA, A., SHAFFER, D. R., CARVER, B., LIU, X. S., TAULLI, R., KUO, W. P., NARDELLA, C., SIGNORETTI, S., CORDON-CARDO, C., GERALD, W. L. & PANDOLFI, P. P. 2013. Zbtb7a suppresses prostate cancer through repression of a Sox9-dependent pathway for cellular senescence bypass and tumor invasion. Nat Genet, 45, 739-46. WANG, L., HARRIS, T. E., ROTH, R. A. & LAWRENCE, J. C. 2007. PRAS40 regulates mTORC1 kinase activity by functioning as a direct inhibitor of substrate binding. J Biol Chem, 282, 20036-44. WANG, L., XIONG, H., WU, F., ZHANG, Y., WANG, J., ZHAO, L., GUO, X., CHANG, L. J., YOU, M. J., KOOCHEKPOUR, S., SALEEM, M., HUANG, H., LU, J. & DENG, Y. 2014. Hexokinase 2-mediated Warburg effect is required for PTEN- and p53-deficiency-driven prostate cancer growth. Cell Rep, 8, 1461-74. WANG, S., GAO, J., LEI, Q., ROZENGURT, N., PRITCHARD, C., JIAO, J., THOMAS, G. V., LI, G., ROY- BURMAN, P., NELSON, P. S., LIU, X. & WU, H. 2003. Prostate-specific deletion of the murine Pten tumor suppressor gene leads to metastatic prostate cancer. Cancer Cell, 4, 209-21. WATANABE, S., HORIE, Y., KATAOKA, E., SATO, W., DOHMEN, T., OHSHIMA, S., GOTO, T. & SUZUKI, A. 2007. Non-alcoholic steatohepatitis and hepatocellular carcinoma: lessons from hepatocyte- specific phosphatase and tensin homolog (PTEN)-deficient mice. J Gastroenterol Hepatol, 22 Suppl 1, S96-S100. WATERS, D. J., SAKR, W. A., HAYDEN, D. W., LANG, C. M., MCKINNEY, L., MURPHY, G. P., RADINSKY, R., RAMONER, R., RICHARDSON, R. C. & TINDALL, D. J. 1998. Workgroup 4: spontaneous prostate carcinoma in dogs and nonhuman primates. Prostate, 36, 64-7. WATT, M. J., HOLMES, A. G., PINNAMANENI, S. K., GARNHAM, A. P., STEINBERG, G. R., KEMP, B. E. & FEBBRAIO, M. A. 2006. Regulation of HSL serine phosphorylation in skeletal muscle and adipose tissue. Am J Physiol Endocrinol Metab, 290, E500-8. WAYMAN, G. A., LEE, Y. S., TOKUMITSU, H., SILVA, A. J., SILVA, A. & SODERLING, T. R. 2008. Calmodulin-kinases: modulators of neuronal development and plasticity. Neuron, 59, 914- 31. WAYMAN, G. A., TOKUMITSU, H. & SODERLING, T. R. 1997. Inhibitory cross-talk by cAMP kinase on the calmodulin-dependent protein kinase cascade. J Biol Chem, 272, 16073-6. WEIDEMANN, A. & JOHNSON, R. S. 2008. Biology of HIF-1alpha. Cell Death Differ, 15, 621-7. WHALE, A., HASHIM, F. N., FRAM, S., JONES, G. E. & WELLS, C. M. 2011. Signalling to cancer cell invasion through PAK family kinases. Front Biosci (Landmark Ed), 16, 849-64.

243

WINGO, S. N., GALLARDO, T. D., AKBAY, E. A., LIANG, M. C., CONTRERAS, C. M., BOREN, T., SHIMAMURA, T., MILLER, D. S., SHARPLESS, N. E., BARDEESY, N., KWIATKOWSKI, D. J., SCHORGE, J. O., WONG, K. K. & CASTRILLON, D. H. 2009. Somatic LKB1 mutations promote cervical cancer progression. PLoS One, 4, e5137. WIRTZ, D., KONSTANTOPOULOS, K. & SEARSON, P. C. 2011. The physics of cancer: the role of physical interactions and mechanical forces in metastasis. Nat Rev Cancer, 11, 512-22. WOLFE, A. L., SINGH, K., ZHONG, Y., DREWE, P., RAJASEKHAR, V. K., SANGHVI, V. R., MAVRAKIS, K. J., JIANG, M., RODERICK, J. E., VAN DER MEULEN, J., SCHATZ, J. H., RODRIGO, C. M., ZHAO, C., RONDOU, P., DE STANCHINA, E., TERUYA-FELDSTEIN, J., KELLIHER, M. A., SPELEMAN, F., PORCO, J. A., PELLETIER, J., RÄTSCH, G. & WENDEL, H. G. 2014. RNA G-quadruplexes cause eIF4A-dependent oncogene translation in cancer. Nature, 513, 65-70. WOODS, A., DICKERSON, K., HEATH, R., HONG, S. P., MOMCILOVIC, M., JOHNSTONE, S. R., CARLSON, M. & CARLING, D. 2005. Ca2+/calmodulin-dependent protein kinase kinase-beta acts upstream of AMP-activated protein kinase in mammalian cells. Cell Metab, 2, 21-33. WOODS, A., HESLEGRAVE, A. J., MUCKETT, P. J., LEVENE, A. P., CLEMENTS, M., MOBBERLEY, M., RYDER, T. A., ABU-HAYYEH, S., WILLIAMSON, C., GOLDIN, R. D., ASHWORTH, A., WITHERS, D. J. & CARLING, D. 2011. LKB1 is required for hepatic bile acid transport and canalicular membrane integrity in mice. Biochem J, 434, 49-60. WOODS, A., JOHNSTONE, S. R., DICKERSON, K., LEIPER, F. C., FRYER, L. G., NEUMANN, D., SCHLATTNER, U., WALLIMANN, T., CARLSON, M. & CARLING, D. 2003a. LKB1 is the upstream kinase in the AMP-activated protein kinase cascade. Curr Biol, 13, 2004-8. WOODS, A., VERTOMMEN, D., NEUMANN, D., TURK, R., BAYLISS, J., SCHLATTNER, U., WALLIMANN, T., CARLING, D. & RIDER, M. H. 2003b. Identification of phosphorylation sites in AMP- activated protein kinase (AMPK) for upstream AMPK kinases and study of their roles by site- directed mutagenesis. J Biol Chem, 278, 28434-42. WU, H. & WANG, Z. X. 2003. The mechanism of p21-activated kinase 2 autoactivation. J Biol Chem, 278, 41768-78. WU, X., DANIELS, G., LEE, P. & MONACO, M. E. 2014. Lipid metabolism in prostate cancer. Am J Clin Exp Urol, 2, 111-20. WU, X., WU, J., HUANG, J., POWELL, W. C., ZHANG, J., MATUSIK, R. J., SANGIORGI, F. O., MAXSON, R. E., SUCOV, H. M. & ROY-BURMAN, P. 2001. Generation of a prostate epithelial cell-specific Cre transgenic mouse model for tissue-specific gene ablation. Mech Dev, 101, 61-9. XIANG, X., SAHA, A. K., WEN, R., RUDERMAN, N. B. & LUO, Z. 2004. AMP-activated protein kinase activators can inhibit the growth of prostate cancer cells by multiple mechanisms. Biochem Biophys Res Commun, 321, 161-7. XIAO, B., SANDERS, M. J., CARMENA, D., BRIGHT, N. J., HAIRE, L. F., UNDERWOOD, E., PATEL, B. R., HEATH, R. B., WALKER, P. A., HALLEN, S., GIORDANETTO, F., MARTIN, S. R., CARLING, D. & GAMBLIN, S. J. 2013. Structural basis of AMPK regulation by small molecule activators. Nat Commun, 4, 3017. XIAO, B., SANDERS, M. J., UNDERWOOD, E., HEATH, R., MAYER, F. V., CARMENA, D., JING, C., WALKER, P. A., ECCLESTON, J. F., HAIRE, L. F., SAIU, P., HOWELL, S. A., AASLAND, R., MARTIN, S. R., CARLING, D. & GAMBLIN, S. J. 2011. Structure of mammalian AMPK and its regulation by ADP. Nature, 472, 230-3. YADAV, R. K., CHAE, S. W., KIM, H. R. & CHAE, H. J. 2014. Endoplasmic reticulum stress and cancer. J Cancer Prev, 19, 75-88. YAN, Y., TSUKAMOTO, O., NAKANO, A., KATO, H., KIOKA, H., ITO, N., HIGO, S., YAMAZAKI, S., SHINTANI, Y., MATSUOKA, K., LIAO, Y., ASANUMA, H., ASAKURA, M., TAKAFUJI, K., MINAMINO, T., ASANO, Y., KITAKAZE, M. & TAKASHIMA, S. 2015. Augmented AMPK activity inhibits cell migration by phosphorylating the novel substrate Pdlim5. Nat Commun, 6, 6137.

244

YANG, Z. J., CHEE, C. E., HUANG, S. & SINICROPE, F. A. 2011. The role of autophagy in cancer: therapeutic implications. Mol Cancer Ther, 10, 1533-41. YAP, T. A., SMITH, A. D., FERRALDESCHI, R., AL-LAZIKANI, B., WORKMAN, P. & DE BONO, J. S. 2016. Drug discovery in advanced prostate cancer: translating biology into therapy. Nat Rev Drug Discov. YUE, S., LI, J., LEE, S. Y., LEE, H. J., SHAO, T., SONG, B., CHENG, L., MASTERSON, T. A., LIU, X., RATLIFF, T. L. & CHENG, J. X. 2014. Cholesteryl Ester Accumulation Induced by PTEN Loss and PI3K/AKT Activation Underlies Human Prostate Cancer Aggressiveness. Cell Metab, 19, 393- 406. ZADRA, G., BATISTA, J. L. & LODA, M. 2015. Dissecting the Dual Role of AMPK in Cancer: From Experimental to Human Studies. Mol Cancer Res, 13, 1059-72. ZADRA, G., PHOTOPOULOS, C. & LODA, M. 2013. The fat side of prostate cancer. Biochim Biophys Acta, 1831, 1518-32. ZADRA, G., PHOTOPOULOS, C., TYEKUCHEVA, S., HEIDARI, P., WENG, Q. P., FEDELE, G., LIU, H., SCAGLIA, N., PRIOLO, C., SICINSKA, E., MAHMOOD, U., SIGNORETTI, S., BIRNBERG, N. & LODA, M. 2014. A novel direct activator of AMPK inhibits prostate cancer growth by blocking lipogenesis. EMBO Mol Med. ZADRA, G., PRIOLO, C., PATNAIK, A. & LODA, M. 2010. New strategies in prostate cancer: targeting lipogenic pathways and the energy sensor AMPK. Clin Cancer Res, 16, 3322-8. ZAGÓRSKA, A., DEAK, M., CAMPBELL, D. G., BANERJEE, S., HIRANO, M., AIZAWA, S., PRESCOTT, A. R. & ALESSI, D. R. 2010. New roles for the LKB1-NUAK pathway in controlling myosin phosphatase complexes and cell adhesion. Sci Signal, 3, ra25. ZHANG, S. H. 2013. Investigation into AMPK phosphatases and identification and characterisation of the interaction between AMPK and PAK1. Unpublished PhD thesis, Imperial College, London. ZHANG, W., ZHU, J., EFFERSON, C. L., WARE, C., TAMMAM, J., ANGAGAW, M., LASKEY, J., BETTANO, K. A., KASIBHATLA, S., REILLY, J. F., SUR, C. & MAJUMDER, P. K. 2009. Inhibition of tumor growth progression by antiandrogens and mTOR inhibitor in a Pten-deficient mouse model of prostate cancer. Cancer Res, 69, 7466-72. ZHANG, X., GUO, L., COLLAGE, R. D., STRIPAY, J. L., TSUNG, A., LEE, J. S. & ROSENGART, M. R. 2011. Calcium/calmodulin-dependent protein kinase (CaMK) Ialpha mediates the macrophage inflammatory response to sepsis. J Leukoc Biol, 90, 249-61. ZHAO, J. 2014. Investigating the CaMKKβ signalling pathway and its role in the regulation of blood pressure. PhD thesis, Imperial College, London. ZHOU, G., MYERS, R., LI, Y., CHEN, Y., SHEN, X., FENYK-MELODY, J., WU, M., VENTRE, J., DOEBBER, T., FUJII, N., MUSI, N., HIRSHMAN, M. F., GOODYEAR, L. J. & MOLLER, D. E. 2001. Role of AMP- activated protein kinase in mechanism of metformin action. J Clin Invest, 108, 1167-74. ZHOU, G. L., ZHUO, Y., KING, C. C., FRYER, B. H., BOKOCH, G. M. & FIELD, J. 2003. Akt phosphorylation of serine 21 on Pak1 modulates Nck binding and cell migration. Mol Cell Biol, 23, 8058-69. ZHOU, J., HUANG, W., TAO, R., IBARAGI, S., LAN, F., IDO, Y., WU, X., ALEKSEYEV, Y. O., LENBURG, M. E., HU, G. F. & LUO, Z. 2009. Inactivation of AMPK alters gene expression and promotes growth of prostate cancer cells. Oncogene, 28, 1993-2002. ZHU, P., BAEK, S. H., BOURK, E. M., OHGI, K. A., GARCIA-BASSETS, I., SANJO, H., AKIRA, S., KOTOL, P. F., GLASS, C. K., ROSENFELD, M. G. & ROSE, D. W. 2006. Macrophage/cancer cell interactions mediate hormone resistance by a nuclear receptor derepression pathway. Cell, 124, 615-29. ZIJLSTRA, C. & STOORVOGEL, W. 2016. Prostasomes as a source of diagnostic biomarkers for prostate cancer. J Clin Invest, 126, 1144-51. ZOIS, C. E. & HARRIS, A. L. 2016. Glycogen metabolism has a key role in the cancer microenvironment and provides new targets for cancer therapy. J Mol Med (Berl), 94, 137-54.

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Appendices

Appendix 1 Mass spectrometry data analysis

Raw data were searched using Maxquant software v1.5.3.8 and the resulting proteinGroups.txt file was used for data reporting. Maxquant’s label free quantification (MaxLFQ)1 algorithm was utilised and the LFQ intensity values were reported.

These LFQ intensity values were then converted to the heat map (yellow to blue; low to high intensity) by dividing both LFQ values by the maximum LFQ value for each individual protein. The heat map therefore provides an indication of the fold change relative to the maximum condition.

Finally, the log2 value of the LFQ intensity values were calculated and to show the fold change relative to the pre-switch (Figure 4.22) or WT (Figure 5.16) condition.

As the samples were pooled, limited statistics could be performed therefore the following stringent criteria were adhered to:

1. A 1% false discovery rate (FDR) was applied at the protein and peptide level; 2. Only proteins with >2 razor + unique peptides were considered for identification and relative quantification; 3. Search engine (“Andromeda”) scores of >=10 were considered for identification and relative quantification;

4. Log2 values greater than 1.0 and less than -1.0 (2 fold upregulated and 2 fold downregulated respectively; were considered for identification and relative quantification. 5. Only the mouse Swissprot database (denoted “>sp…” in column 7) was searched as this contains, “…records with information extracted from literature and curator-evaluated computational analysis.”

References 1. “Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ.” Jurgen Cox, Marco Y. Hein, Christian A. Luber, Igor Paron, Nagarjuna Nagaraj, and Matthias Mann. Molecular & Cellular Proteomics 13, 2513–2526, 2014.

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Appendix 2 IPA analysis of wild-type (WT) vs CaMKKβ-/- prostate mass spectrometry screen: A) EIF2 signalling analysis and B) upstream regulator analysis. Pathway analysis predictions generated using IPA (Ingenuity Pathway Analysis). Protein accession number -/- and protein expression between conditions (log2(CaMKKβ /WT)) generated by mass spectrometry was imported and analysed using IPA software. For upstream regulator analysis an activation z-score of magnitude 2 or above is classed as significant.

247

Appendix 2A) EIF2 signalling analysis Symbol Entrez Gene Name UniProt/Swiss-Prot Accession Exp Log Ratio (CaMKKβ/WT) Expected Location Type(s) EIF5 eukaryotic translation initiation factor 5 P59325 -0.150 Up Cytoplasm translation regulator EIF2A eukaryotic translation initiation factor 2A Q8BJW6 -0.323 Cytoplasm translation regulator EIF2B2 eukaryotic translation initiation factor 2B subunit beta Q99LD9 -0.556 Up Cytoplasm other EIF2S1 eukaryotic translation initiation factor 2 subunit alpha Q6ZWX6 -0.266 Cytoplasm translation regulator EIF2S2 eukaryotic translation initiation factor 2 subunit beta Q99L45 -0.535 Cytoplasm translation regulator EIF2S3 eukaryotic translation initiation factor 2 subunit gamma Q9Z0N1 0.157 Cytoplasm translation regulator EIF3A eukaryotic translation initiation factor 3 subunit A P23116 -0.293 Cytoplasm other EIF3B eukaryotic translation initiation factor 3 subunit B Q8JZQ9 -0.110 Cytoplasm translation regulator EIF3G eukaryotic translation initiation factor 3 subunit G Q9Z1D1 -0.282 Cytoplasm other EIF3H eukaryotic translation initiation factor 3 subunit H Q91WK2 -0.558 Cytoplasm other EIF3I eukaryotic translation initiation factor 3 subunit I Q9QZD9 0.419 Cytoplasm translation regulator EIF3J eukaryotic translation initiation factor 3 subunit J Q3UGC7 -0.605 Cytoplasm translation regulator EIF3M eukaryotic translation initiation factor 3 subunit M Q99JX4 -0.245 Cytoplasm other EIF4A1 eukaryotic translation initiation factor 4A1 P60843 -0.833 Cytoplasm translation regulator EIF4A2 eukaryotic translation initiation factor 4A2 P10630 -0.177 Cytoplasm translation regulator MAPK1 mitogen-activated protein kinase 1 P63085 0.470 Up Cytoplasm kinase MAPK3 mitogen-activated protein kinase 3 Q63844 0.511 Up Cytoplasm kinase PABPC1 poly(A) binding protein, cytoplasmic 1 P29341 0.310 Cytoplasm translation regulator PPP1CA protein phosphatase 1, catalytic subunit, alpha isozyme P62137 0.110 Cytoplasm phosphatase PPP1CB protein phosphatase 1, catalytic subunit, beta isozyme P62141 0.294 Cytoplasm phosphatase RPL3 ribosomal protein L3 P27659 -1.489 Up Nucleus other RPL4 ribosomal protein L4 Q9D8E6 -1.326 Up Cytoplasm enzyme RPL5 ribosomal protein L5 P47962 -1.395 Up Cytoplasm other RPL6 ribosomal protein L6 P47911 -1.094 Up Nucleus other RPL7 ribosomal protein L7 P14148 -1.668 Up Nucleus transcription regulator RPL8 ribosomal protein L8 P62918 -1.306 Up Other other RPL9 ribosomal protein L9 P51410 -1.182 Up Nucleus other RPL10 ribosomal protein L10 Q6ZWV3 -0.474 Up Cytoplasm other RPL11 ribosomal protein L11 Q9CXW4 -0.667 Up Cytoplasm other RPL12 ribosomal protein L12 P35979 -0.907 Up Nucleus other RPL13 ribosomal protein L13 P47963 -1.465 Up Nucleus other RPL14 ribosomal protein L14 Q9CR57 -0.985 Up Cytoplasm other RPL15 ribosomal protein L15 Q9CZM2 -1.976 Up Cytoplasm other RPL17 ribosomal protein L17 Q9CPR4 -0.777 Up Cytoplasm other RPL18 ribosomal protein L18 P35980 -1.889 Up Cytoplasm other RPL19 ribosomal protein L19 P84099 -2.132 Up Cytoplasm other RPL21 ribosomal protein L21 O09167 -1.604 Up Cytoplasm other RPL22 ribosomal protein L22 P67984 -0.596 Up Nucleus other RPL23 ribosomal protein L23 P62830 -1.511 Up Cytoplasm other RPL24 ribosomal protein L24 Q8BP67 -0.486 Up Cytoplasm other RPL26 ribosomal protein L26 P61255 -1.639 Up Cytoplasm other RPL27 ribosomal protein L27 P61358 -0.742 Up Cytoplasm other RPL28 ribosomal protein L28 P41105 -1.551 Up Cytoplasm other RPL30 ribosomal protein L30 P62889 -1.134 Up Cytoplasm other RPL31 ribosomal protein L31 P62900 -0.995 Up Cytoplasm other RPL35 ribosomal protein L35 Q6ZWV7 -0.790 Up Cytoplasm other RPL10A ribosomal protein L10a P53026 -0.618 Up Nucleus other RPL18A ribosomal protein L18a P62717 -0.969 Up Cytoplasm other RPL27A ribosomal protein L27a P14115 -1.164 Up Cytoplasm other Rpl36a ribosomal protein L36A P83882 -1.114 Up Other other RPL37A ribosomal protein L37a P61514 -1.189 Up Cytoplasm other RPL7A ribosomal protein L7a P12970 -1.054 Up Cytoplasm other RPLP0 ribosomal protein, large, P0 P14869 -1.027 Up Cytoplasm other RPLP2 ribosomal protein, large, P2 P99027 -1.953 Up Cytoplasm other RPS2 ribosomal protein S2 P25444 -0.233 Cytoplasm other RPS3 ribosomal protein S3 P62908 -0.379 Cytoplasm enzyme RPS5 ribosomal protein S5 P97461 -0.556 Cytoplasm other RPS6 ribosomal protein S6 P62754 -0.108 Up Cytoplasm other RPS7 ribosomal protein S7 P62082 -0.414 Cytoplasm other RPS8 ribosomal protein S8 P62242 -0.342 Cytoplasm other RPS9 ribosomal protein S9 Q6ZWN5 -0.361 Cytoplasm translation regulator RPS10 ribosomal protein S10 P63325 -0.457 Cytoplasm other RPS11 ribosomal protein S11 P62281 -0.232 Cytoplasm other RPS12 ribosomal protein S12 P63323 -0.267 Cytoplasm other RPS13 ribosomal protein S13 P62301 -0.004 Cytoplasm other RPS14 ribosomal protein S14 P62264 -0.007 Cytoplasm translation regulator RPS15 ribosomal protein S15 P62843 -0.679 Cytoplasm other RPS16 ribosomal protein S16 P14131 -0.296 Cytoplasm other RPS17 ribosomal protein S17 P63276 -0.457 Cytoplasm other RPS18 ribosomal protein S18 P62270 -0.215 Cytoplasm other RPS19 ribosomal protein S19 Q9CZX8 0.015 Cytoplasm other RPS20 ribosomal protein S20 P60867 -0.380 Cytoplasm other RPS23 ribosomal protein S23 P62267 -0.762 Cytoplasm translation regulator RPS24 ribosomal protein S24 P62849 -0.143 Cytoplasm other RPS25 ribosomal protein S25 P62852 -0.243 Cytoplasm other RPS26 ribosomal protein S26 P62855 -0.161 Cytoplasm other RPS15A ribosomal protein S15a P62245 -0.632 Cytoplasm other RPS27A ribosomal protein S27a P62983 -0.300 Cytoplasm other RPS4Y1 ribosomal protein S4, Y-linked 1 P62702 -0.062 Cytoplasm other RPSA ribosomal protein SA P14206 -0.178 Cytoplasm translation regulator RRAS related RAS viral (r-ras) oncogene homolog P10833 -0.524 Up Cytoplasm enzyme © 2000-2016 QIAGEN. All rights reserved. Appendix 2B) Predicted Upstream Regulator Exp Log Activation Analysis Ratio Molecule Type State Activation z-score p-value of overlap Target molecules in dataset

ABCE1,ACAT1,ACTA1,ACTB,ACTN1,ACTN4,ADK,AHCY,AIMP2,AK2,ALB,ALCAM,ALDH18A1,ALDOA,ANXA4,ANXA5,ANXA6,APEX 1,ASNS,BCKDHB,CAPN2,CAPNS1,CAPZB,CCT3,CD9,Cdc42,CLIC4,CNBP,Crip2,CSTB,CTSB,CTSD,CYCS,EEF2,EIF2S1,EIF2S2,EI F3G,EIF4A1,ELAVL1,ENO1,EPCAM,FABP4,FABP5,FLNA,FTH1,G6PD,GAA,GAPDH,GART,GDI1,GDI2,GFPT1,GLUD1,GLUL,GOT1, GOT2,GSR,Hnrnpa1,HNRNPA2B1,HNRNPAB,HNRNPD,HNRNPH1,HNRNPU,HSP90AA1,Hspa1b,HSPA9,HSPB1,HSPD1,HSPE1,HS PH1,IDH1,IDH2,IFI35,IQGAP2,ITGB1,LDHA,LDHB,LGALS1,LIMA1,LIMS1,LUM,MAN2A1,MAP4,MAPK3,MAT2A,ME2,MIF,MMP7,MSN, MTHFD1,MYL9,MYO1C,NARS,NCL,NME1,NME2,NPM1,NUCB1,NUDC,OAT,PAICS,PDHA1,Pdlim3,PFAS,PFKL,PGAM1,PGK1,PHB2, PKM,PPIA,PPID,PRDX2,PRDX3,PRDX4,PREP,PRKACB,PRMT1,PSAT1,PTBP1,RARS,RBP1,REN,RHOA,RPL10,RPL13,RPL19,RPL2 1,RPL22,RPL23,RPL26,RPL27,RPL3,RPL30,Rpl32,RPL35,RPL5,RPL6,RPL7,RPL7A,RPL9,RPS12,RPS15A,RPS16,RPS18,RPS19,R PS20,RPS23,RPS6,RPS7,RPS9,S100A10,S100A6,SAE1,SARDH,SCPEP1,SERPINA1,SERPINE2,SHMT1,SLC25A5,SMS,SOD2,SQR MYC transcription regulator Inhibited -2.375 3.54E-69 DL,SRM,SRSF1,SRSF2,SUCLA2,SUMO2,TAGLN2,TES,TF,THOP1,TKT,TPI1,Tpm1,TXN,TXNRD1,VARS,VIM,YBX1 ACTB,ACTN4,ALDH1A1,ALDOA,APEX1,ARPC1B,B2M,CKAP4,DPYSL3,EEF1A1,EEF1D,EEF1G,EEF2,EIF4A1,EIF5A,GAPDH,HSP90 AB1,HSPD1,ITGB1,LDHA,LGALS1,MAP4,MYL12A,NCL,NME1,NME2,NPM1,NUCB1,PDIA4,PHGDH,PSMA7,PSMB6,PSMB7,RPL10,R PL11,RPL12,RPL13,RPL17,RPL18,RPL18A,RPL19,RPL21,RPL22,RPL23,RPL24,RPL26,RPL27,RPL27A,RPL28,RPL3,RPL30,RPL31 ,RPL35,RPL37A,RPL4,RPL5,RPL6,RPL7,RPL8,RPL9,RPLP0,RPLP2,RPS12,RPS13,RPS15,RPS16,RPS17,RPS19,RPS2,RPS20,RPS 23,RPS24,RPS25,RPS26,RPS3,RPS5,RPS6,RPS7,RPS8,RPS9,S100A10,SORD,TAGLN,TPI1,TUBA1B,TUBB,TUFM,UBE2V2,UCHL1, MYCN transcription regulator Inhibited -4.036 1.65E-64 VIM,ZYX

ACAA2,ACAT1,ACE,ACLY,ACO2,ACTA2,ACTB,ACTN1,ACTN4,ADH5,AHCY,AK1,AKR1B1,ALB,ALDH18A1,ALDH4A1,ALDH9A1,ANXA1, ANXA2,ANXA3,ANXA4,ANXA6,APEX1,APOA1,ARPC1B,ASL,ASNS,ATP5C1,BCAP31,CALU,CAMK2D,CAP1,CAPNS1,CARS,CASP6,CA T,Cdc42,Ces1b/Ces1c,CKB,CKM,CLIC4,CLPP,CNN1,COMT,COPB1,CP,Crip2,CSRP1,CSTB,CTSB,CTSD,CTSH,CYB5A,DLD,DSTN,EG F,ETFA,EZR,FABP3,FABP4,FDPS,FHL1,FKBP1A,FKBP3,FKBP4,G6PD,GAPDH,GART,GLB1,GLUL,GPD1L,GPX1,GSN,GSR,GSTM1,G STM5,GSTP1,HADH,HADHA,HADHB,HDLBP,HNRNPA2B1,HSP90AA1,HSP90AB1,HSPA1A/HSPA1B,HSPA8,HSPB1,HSPD1,HSPH1,I DH1,IDH2,IFI30,IFI35,KPNB1,KRT8,LASP1,LDHA,LIMA1,LMAN2,LPP,LYPLA1,MAN2A1,MAP4,MAPK1,MAPK3,MDH2,ME1,ME2,MPI,MT- CO2,MVP,MYL9,MYO1C,NAMPT,NARS,NME1,NPM1,NUDT5,OAT,P4HB,PARK7,PARVA,PAWR,Pbsn,PCCA,Pcmt1,PDIA6,PGM3,PHG DH,PPP1CA,PPT1,PRDX2,PRDX3,PRDX6,PRKAR2A,PRPSAP1,PSMA1,PSMD1,PSMD2,PSMD3,PTPN6,PURA,PYCARD,RBM3,RNAS E4,RPN1,RPS25,RPSA,S100A4,SCPEP1,SEC23B,SERPINA3,SERPINB5,SERPINB6,SERPINC1,SERPINE2,SFPQ,SOD1,SOD2,SQRD TP53 transcription regulator 0.847 3.24E-47 L,SRSF3,ST13,STIP1,SUCLG1,TAGLN2,THOP1,Tpm1,Tpm4,TPP1,TRIM28,TUBB,UBA1,VAPA,VCL,VIM,XPNPEP1,YWHAH,ZYX

AKR1A1,AKR1B10,AKR7A2,ALDOA,ANG,ARF1,CAT,CBR1,CCT3,CCT7,CLPP,CTSD,DNAJB11,DNAJC3,DSTN,EIF2S1,EIF3G,ERP29,E SD,FTH1,FTL,G6PD,GNB2L1,GPX1,GSR,GSS,GSTM1,GSTM5,GSTO1,GSTP1,Gstt1,HMBS,HSP90AA1,HSP90AB1,HSP90B1,HSPA9,I DE,INMT,LMNA,ME1,NARS,NUCB2,OAT,PCBP1,PDIA3,PDIA4,PDIA6,PGD,PPIB,PRDX1,PREP,PSAT1,PSMA1,PSMA4,PSMA5,PSMA6, PSMA7,PSMB1,PSMB2,PSMB3,PSMB4,PSMB5,PSMB6,PSMC1,PSMC3,PSMD1,PSMD11,PSMD13,PSMD3,PSMD7,RAN,RARS,RPL18 NFE2L2 transcription regulator 1.861 6.54E-40 ,RPLP0,RPS16,SEC23A,SERPINA3,SOD1,SOD2,STIP1,TPI1,Tpm1,TTR,TXN,TXNRD1,VCP AASS,ABCE1,ACAA2,ACAD8,ACAT1,ACLY,ACO2,ACOT13,ACTA2,ACTN1,ACTR1A,ACTR3,ADSS,AGA,AHSG,AIMP1,AK2,AKR1B1,AL DH1A1,ALDH2,AMT,ANG,ANXA5,APIP,APOA1,ARFIP2,ARG2,AS3MT,ASAH1,ATP6V1F,BLVRB,BPHL,C11orf54,C1orf123,C1S,C21orf3 3/LOC102724023,C3,CAMK2D,CAP1,CAPZA2,CBR3,CCT8,CFL2,CNBP,COPB1,COPB2,COPZ1,CRKL,CRYZ,CS,CSNK2A1,CTSA,CTS B,CTSZ,CUTC,DBT,DDOST,DHRS4,DLST,DUSP3,EIF5,EPCAM,ERO1A,FARSB,FBP1,FDPS,FH,FHL1,FKBP3,FLNA,G6PD,GAPDH,GM DS,GOT1,GPX1,GRHPR,GSN,GSS,GSTO1,GSTZ1,GUSB,HADHA,HADHB,HIST1H2AH,HNRNPA0,HNRNPC,HPX,HSD17B4,HSP90B1, HSPA4L,HSPA5,HSPE1,HSPH1,IFI30,ISOC1,ISOC2,KARS,KPNB1,KRT8,LIMS1,LTA4H,MAT2A,MCEE,MDH1,MDH2,MTHFD1,MUT,MY L6,NAGA,NAMPT,NAPA,NME1,NUCB1,NUDT11,NUDT2,NUDT5,OGDH,PCK2,PEF1,PEPD,PGM1,PGM3,PHB2,PHPT1,PKM,PNLIPRP1 ,PNP,PPME1,PPP1CA,PRDX5,PRMT1,PRPS1,PSAT1,PSMA1,PSMA2,PSMA3,PSMA5,PSMB1,PSMB5,PSMB7,PSMC4,PSMD1,PSMD7 ,PTGES3,RAB18,RAB2A,RAB7A,RNASE4,RPL10,RPL12,RPL18,RPL18A,RPL31,RPRD1B,RPS18,RPS20,RPS25,RPS27A,RPS6,RSU1 ,SAMHD1,SEC23A,SEC31A,SERPINA1,SERPINA3,SERPINB8,SLC25A5,SNRPD3,SQRDL,SRSF1,SRSF2,ST13,STARD10,STRAP,SUC LA2,SUCLG1,SUGT1,TAGLN2,TARS,TF,TTC38,TTR,TXNDC12,TXNL1,TXNRD1,UBE2N,UCHL1,USP5,VDAC1,VPS29,WDR77,YBX1,Y HNF4A transcription regulator 0.388 3.20E-33 WHAB APOA1,ARCN1,CALR,CAT,COPB1,COPB2,COPE,COPG1,COPZ1,DDOST,DNAJB11,DNAJC10,DNAJC3,ERP29,ERP44,FKBP2,GOLP H3,GOPC,HSP90B1,HSPA5,HYOU1,LMAN2,NUCB2,PDIA3,PDIA4,PDIA6,PPIB,RPN1,Rrbp1,S100A6,SDF2L1,SEC22B,SEC23A,SEC23 XBP1 transcription regulator -1.136 2.38E-18 B,SEC31A,SERPINA1,SOD1,SSR4,TTR,TXN,TXNDC5

ACADM,ACAT2,ACOT2,ACTA1,ACTA2,ALDOA,Anp32a,APOA1,ATP5A1,ATP5B,ATP5O,B2M,BCAP31,C1R,CBR1,CD9,CKM,COX4I1,CT SD,CYCS,DLST,DNAJC3,ECI1,EEF1A1,EEF2,FDPS,FHL1,FKBP4,GAPDH,GLO1,GLUL,Gm21596/Hmgb1,GSN,GSR,GSS,HBA1/HBA2, HSP90AB1,Hspa1b,HSPA5,HSPA8,HSPA9,HSPD1,HSPE1,ITGB1,KRT1,LDHA,LDHB,MAN1A1,MDH2,MFAP4,Mup1 (includes others),PC,PDXK,PRKACB,PSMA5,PSMB4,PSMD3,PTBP1,PURB,SDHA,SEC22B,SERPINA1,SERPINA3,SFPQ,SGTA,SLMAP,SOD1,S HTT transcription regulator -0.396 4.26E-18 OD2,SPTAN1,SRM,SRSF2,TAGLN,TPI1,TUBA4A,TUBB,UBE2N,UCHL1,VAPA,YBX1,YWHAG

ACTR1A,ACTR3,ADK,CA2,CALR,CASP6,CCT2,CCT4,CD9,Ces1b/Ces1c,CKB,CLIC1,COX4I1,CRKL,CTSB,CYB5A,DDAH2,DDX1,EIF3I, FHL1,FKBP3,HADHA,HNRNPD,HNRNPK,HSP90B1,Hspa1b,HSPA8,HSPD1,HSPE1,INMT,KRT1,LTA4H,MDH1,MTHFD1,NCL,NUDC,PA E2F1 transcription regulator 0.192 4.01E-14 2G4,PAWR,PRPSAP1,PSAT1,PSMD2,RAB1A,RAN,RPS16,SOD2,SRSF1,SRSF2,TRIM28,TXNL1,TXNRD1,VCP,VIM,YARS,YWHAE B2M,CCT2,CCT3,CCT4,CCT5,CCT6A,CCT7,CCT8,DNAJA1,EIF4A2,FKBP4,HSP90AA1,HSP90AB1,HSPA1A/HSPA1B,Hspa1b,HSPA4,H HSF1 transcription regulator Activated 2.340 4.49E-13 SPA4L,HSPA8,HSPB1,HSPD1,HSPE1,HSPH1,MAT2A,PGK1,RPL22,SPR,SPTAN1,ST13,STIP1,TCP1,TRA2B,TTR HSF2 transcription regulator 1.223 2.32E-12 CCT2,CCT3,CCT4,CCT5,CCT6A,CCT7,CCT8,HSPA1A/HSPA1B,Hspa1b,HSPA4,HSPB1,HSPH1,TCP1,TXN ACADL,ACADM,ACAT1,AK1,ATP5A1,ATP5B,ATP5O,BCAT2,C3,CAT,COX4I1,CS,CYCS,FABP3,FABP4,GOT2,GPX1,HMGCL,IDH3A,IMP PPARGC1A transcription regulator Activated 3.057 1.51E-11 A1,LDHA,MDH2,ME1,MT-CO2,OXCT1,PCK2,PDHA1,PGAM1,PRDX3,PRDX5,SDHA,SOD1,SOD2

ANXA4,ARF1,ASL,CA2,CALU,CAMK2D,CAPN2,CAT,CTSB,CTSH,CYCS,ENO1,EZR,FABP4,FTH1,GSR,GSTP1,HBA1/HBA2,Hbb- b1,HSD17B10,HSP90B1,HSPA5,ITGB1,KIF5B,KRT8,MMP7,PKIB,PRDX1,PSMA2,PSMA7,PSMB3,RBP1,RNH1,RPL9,RPLP0,RPS18,RP FOS transcription regulator 0.366 1.17E-10 S24,RPS6,RPS7,RPS9,S100A10,SEC23B,SELENBP1,SERPINE2,SUMO2,TBCA,TCEA1,Tpm1,Tpm4,TPP1,TUBB,TXN,VIM,YWHAZ TFAM transcription regulator 1.897 9.68E-10 ACADL,ACADM,ACADS,ECHS1,FABP3,FABP4,FABP5,HK1,PGAM1,PGK1 ACADL,ACADM,ACADS,AKR1A1,AKR7A2,ANXA4,APOA1,CACYBP,CAT,CFL1,FTL,HADHB,HSPB1,MAPK1,NSFL1C,PRDX1,RSU1,STI PML transcription regulator 0.227 1.45E-09 P1,SUMO2,TXN,TXNRD1,YWHAG NFE2L1 transcription regulator 0.905 1.19E-08 GSTP1,PSMA6,PSMA7,PSMB1,PSMB2,PSMB5,PSMB6,PSMB7,PSMC2,PSMC3,PSMD11 AHCY,ANXA11,APRT,ASNS,CIRBP,CKM,CLIC1,CLIC4,CMPK1,ETFA,EZR,FDPS,GSR,HDLBP,HNRNPL,HSP90B1,MYL6,NME1,NUCB1 WT1 transcription regulator -1.871 2.85E-08 ,PCK2,PDIA4,PPIB,Ptma (includes others),RPL19,SEC13,TARS,WARS,YARS,YBX1 ACTA1,ACTB,AIMP2,ALDH1A1,CA3,CFD,EIF3A,FKBP3,Gstt1,HDLBP,HMBS,HNRNPL,HSP90AA1,IDH2,INMT,ME1,MSN,MYLK,P4HB,P HOXA10 transcription regulator 0.293 3.09E-08 HGDH,PIGR,RBP1,RNASE4,RPL37A,YWHAG ACAT2,ADH1C,AHSG,AIMP1,AK2,Akr1c12/Akr1c13,ALB,ANXA4,C1S,CLIC5,CTSZ,CYB5A,CYB5B,FBP1,FGL1,FH,GARS,GDI2,GLUL,G MDS,GNB2L1,GOT1,GRHPR,HPX,HSPA5,MTHFD1,NAPA,OGDH,PFKL,PGK1,PIGR,PSMA5,RNASE4,SERPINA1,SRI,TARS,TMED10,T HNF1A transcription regulator 1.261 8.34E-08 PI1,TTR,TXNDC12,UQCRC2,UROD

AASS,AMT,APIP,C1S,CCT8,COPB2,COX4I1,DHRS4,EIF4A1,FABP5,GSS,HNRNPA2B1,HSD17B4,HSPA1A/HSPA1B,HSPH1,MDH1,NU ONECUT1 transcription regulator 1.38E-07 DT2,OAT,PFKL,PHB2,PSMA1,PSMB1,SAMHD1,SERPINA1,SERPINB8,SH3BGRL,SPINK1,TTR,TXNDC12,VCP,VWA5A ACTA2,ACTB,ACTN4,ACTR3,ADSS,ALCAM,ALDH1A1,ANXA1,ARHGDIA,BCAP31,BTF3,CA3,CAPNS1,CAPZB,CFD,CTSZ,CYB5A,DES, EHD1,EIF5A,EPCAM,FABP4,GLUL,HDGF,HNRNPA0,HNRNPUL1,HSPE1,IGHM,IMPDH2,ITGB1,KRT1,LAP3,LMNA,MAP4,ME1,MMP7,M up1 (includes others),MYLK,OAT,OGN,Pbsn,PCCA,PRKCSH,RAB18,RCN1,S100A4,SERPINA1,SERPINA3,SERPINE2,SMS,SRSF1,STAT5A,SYNM,U CTNNB1 transcription regulator 1.265 1.96E-07 BA1,VIM AARS,ACADS,ACLY,ADH1C,CAPZB,CFD,CSAD,CYB5A,DARS,ETHE1,FABP4,FABP5,FDPS,G6PD,GSR,HSPA1A/HSPA1B,HSPA5,IDH SREBF1 transcription regulator 1.305 2.28E-07 1,IGHM,OAT,PCK2,RPLP0,RPS24,SUCLG1,TF ABRACL,ACTA1,ACTA2,ACTN4,AGR2,ALB,ALDH2,APOA1,ASNS,CKM,CP,CTSB,CTSH,DES,FABP4,FBP1,GAPDH,GCAT,GPX1,GSTO 1,GSTP1,IFI30,ITGB1,LGALS1,LMNA,LUM,MAPK1,MMP7,MYH11,MYLK,NPC2,OXCT1,PARVA,Pbsn,Pdlim3,PGM2L1,RBP1,S100A6,SE SMARCA4 transcription regulator Activated 3.013 2.99E-07 RPINB5,SERPINB8,SERPINE2,SPINK1,STARD10,TAGLN,TCAF2,TTR,TUBB,TWF1,TXNRD1

MKL1 transcription regulator 1.714 5.99E-07 ACTA1,ACTA2,ACTB,ARPC1B,ARPC4,CNN1,COMT,FABP3,FLNA,GSTM5,ITGB1,MYH11,MYL9,MYLK,RAC1,TAGLN,Tpm4,TST,VCL ACTA1,ACTA2,ACTB,CAP1,CKM,CNN1,CSRP1,DES,DSTN,FHL1,FLNA,GLRX3,GSN,GSTM5,IGHM,ITGB1,LMOD1,MYH11,MYL9,MYLK SRF transcription regulator Activated 3.671 6.57E-07 ,NPM1,PGM1,PPIA,PTGR1,RNH1,SND1,TAGLN,Tpm1,Tpm4,TST,TUBB4B,VCL,ZYX Esrra transcription regulator 0.093 7.48E-07 ACADL,ACADM,ACO1,ALDOA,ATP5B,ATP5O,CYCS,ENO1,FBP1,IDH3A,PCK2,PFKL,PGM1,PPA1,PYGM ARNT transcription regulator 0.366 2.20E-06 ALDH7A1,ALDOA,ATP5O,CTSD,ENO1,ERO1A,G6PD,GAPDH,IGHM,LDHA,MYL6,MYO1C,PCK2,PGK1,TPI1,TUBA4A,VIM SRSF2 -0.951 transcription regulator 4.22E-06 HNRNPH1,HNRNPL,PTBP1,PYGM,SRSF1,SRSF2,SRSF7,VIM

ATF4 transcription regulator Activated 3.773 5.62E-06 AARS,APEX1,ASNS,CALR,EIF2S2,ERO1A,GARS,HSP90B1,HSPA5,IARS,LARS,MARS,NARS,PCK2,PHGDH,PSAT1,SARS,WARS

ACTA2,ADH1C,AKR1B10,Akr1b7,ALB,ALDH1A1,ASNS,C3,CFD,CIRBP,CLPP,CP,CTSC,CYB5A,DHX9,FABP4,FHL1,FTL,Hnrnpa3,HPX, CEBPB transcription regulator 0.435 5.93E-06 HSD17B4,HSP90AA1,HSPA8,HSPD1,HSPE1,IGKC,INMT,PPP1R14A,PSMA3,PSMA6,SERPINA1,SQRDL,TF,VIM,ZYX VHL transcription regulator 1.233 7.02E-06 ALDH1A1,CFL1,CLTC,CNN1,EIF5,FTH1,FTL,GLO1,GPX1,HNRNPA2B1,LMNA,MIF,RAP1B,RPS6,SOD2,TAGLN,VIM

ACAT2,ANXA1,ANXA2,APEX1,ASNS,CA3,CAPN2,CAPNS1,COMT,Crip2,CYCS,DARS,EEF2,EIF4A1,EZR,FABP4,FTH1,GOT1,GSTP1,H JUN transcription regulator 1.957 7.03E-06 NRNPA2B1,HNRNPU,ITGB1,LMNA,MAPK3,MAT2A,MMP7,PARVA,PKIB,PRDX1,PTPN6,S100A10,SOD2,SRSF2,TXN,VIM,YWHAG ANXA1,ASNS,CST3,CTSA,EIF2S1,FHL1,FKBP1A,G6PD,GLB1,GPX4,GSTM5,HARS,HPX,NPC2,PHGDH,PPIB,PPP1R7,RAB11B,SHMT PAX3 transcription regulator 9.12E-06 1,SRSF3,YARS,YWHAE MECP2 transcription regulator 1.08E-05 ACTB,ANXA1,BCAT2,CYB5A,DPYSL2,GSTO1,GSTP1,HBA1/HBA2,HSPA5,IGHM,IVD,LMNA,S100A4,VIM,YWHAB,YWHAZ ATF6 transcription regulator Activated 2.333 1.45E-05 CALR,DNAJB11,DNAJC3,FDPS,HSP90B1,HSPA5,MANF,NUCB2,PDIA4,PRDX2,RAN PPARGC1B transcription regulator Activated 2.373 1.55E-05 ACADL,ACADM,ATP5B,C3,COX4I1,CS,CYCS,ENO1,ERO1A,FDPS

PDX1 transcription regulator -1.500 1.64E-05 ACTB,AKR1B1,AKR7A2,ALB,ANXA2,CAT,CKB,CRELD2,FH,GOT1,GUSB,HK1,IDH1,KRT8,LTA4H,MAN1A1,MDH1,PC,PHGDH,SDHA ACO1,ACTA2,ALDOA,ARPC2,ASNS,CARS,CTSD,CYB5A,EIF5A,ENO1,ERO1A,FHL1,GAPDH,HIST2H2AC,HMGCL,HSPA4,HSPA5,HSP HIF1A transcription regulator 0.660 2.27E-05 B1,LDHA,LGALS1,MANF,MIF,NPM1,NUDCD2,PDHA1,PFKL,PGK1,PKM,PPIA,SOD2,TPI1,VIM HDAC5 transcription regulator -1.342 3.78E-05 ACADL,ACADM,ACTA1,ACTA2,ATP5B,CKM,PFKL,PYGM,RAD23B,TAGLN MYOCD transcription regulator Activated 2.820 5.13E-05 ACTA1,ACTA2,CNN1,DES,EIF2A,LMOD1,LPP,MYH11,MYLK,S100A4,SERPINB5,TAGLN LPXN transcription regulator 5.85E-05 ACTA2,MYH11,TAGLN SNAI1 transcription regulator 8.63E-05 ACTN1,ACTR1A,ACTR2,ACTR3,CFL1,CFL2,FLNA,GSN,ITGB1,LASP1,LIMA1,PEBP1,TPM3

AK2,APOA1,C21orf33/LOC102724023,C3,CAPZA2,CP,CTSB,CTSZ,ERO1A,Gm21596/Hmgb1,GSTM5,Gstt1,Hnrnpa1,HSPA5,HSPA8,IT NFKBIA transcription regulator 0.628 8.95E-05 GB1,KRT8,LIMA1,NID1,OGN,PARK7,PDHA1,RAC1,RBP1,RPL27A,RPL8,RPS18,RPS2,RPSA,SERPINE2,SOD1,SOD2,UBA1,YWHAZ ZFHX3 transcription regulator 1.20E-04 ALB,APOA1,GSTP1,MIF,PYGM,Rplp1 (includes others),RPLP2,RPS12,RPS14,SERPINA1,SSB,TUBA1B KDM5A transcription regulator -1.807 1.63E-04 ACO2,ATP5A1,ATP6V1F,COX4I1,DLST,GPT2,ITGB1,MDH1,NIT1,NME1,PARK7,PGD,SOD2,Tpm1,VDAC1

FOXA2 transcription regulator 0.311 2.22E-04 ACTA2,AGR2,ALB,APOA1,C3,CNN1,EPCAM,FABP5,GSTM3,HADH,MYH11,MYL9,PKM,PNLIPRP1,SERPINA1,TAGLN,TF,TTR DRAP1 transcription regulator 2.50E-04 GAPDH,ILF2,RPL7A,RPLP0,RRAS,SOD1 KLF15 transcription regulator 1.223 2.57E-04 ACADL,ACADM,ACADS,ACAT1,FABP3,FABP5,HADHA,HADHB ABHD14B,ANXA5,ANXA6,APRT,ARPC4,CAMK2D,CAPNS1,CCT3,CIRBP,CTSC,DRG1,EIF5A,EPRS,GSTP1,HNRNPF,HNRNPH1,IDH1, KLF3 transcription regulator 0.962 3.04E-04 MIF,MSN,PRKACA,PSMB2,PSMC5,PSMD7,RAB7A,TES,TPM3,VIM SUPT16H transcription regulator 3.18E-04 ACTA1,HNRNPK,HSPA1A/HSPA1B,KRT8,LYPLA1,MSN ACLY,ACTA2,ACTB,ATP5B,ELAVL1,FTH1,GAPDH,GART,MTAP,MTPN,NUDT2,OAT,PDIA4,PGM3,PPP1R7,PSMB7,PSMC6,PTPN6,S10 NFYB transcription regulator 3.26E-04 0A4,SEPT7,SERBP1,TXNDC12,VIM IRF4 transcription regulator 0.000 5.36E-04 B2M,EZR,HSPA8,ITGB1,MANF,MAPRE1,PPIB,PSMA1,PSMA2,RAB7A,RAC1,RHOA,RPL6,YBX1,YWHAH FOXA1 transcription regulator 0.063 6.32E-04 ACTA2,AGR2,ALDH6A1,ANXA1,CNN1,FKBP4,HK1,MYH11,MYL9,Pbsn,SOD1,TAGLN,TF,TTR ABCE1,ACTA1,ACTA2,ALCAM,BTF3,CALR,FDPS,Hnrnpa1,HNRNPC,HSPA5,KIF5B,MAT2A,MYH11,PAWR,RPL30,SFPQ,SRSF1,SYNC YY1 transcription regulator 7.66E-04 RIP,TAGLN,Tpm1,YWHAE MAFK transcription regulator 8.34E-04 GSTP1,Hbb-b1,HMBS,TXN,TXNRD1 NEUROG1 transcription regulator -0.333 8.80E-04 C1S,C3,CFH,FABP3,LCP1,MFAP4,RHOA,S100A4,SQRDL SF1 transcription regulator 1.07E-03 ACTA2,CNN1,MYL6 EPAS1 transcription regulator Activated 2.958 1.24E-03 ARG2,CAT,CKB,CKM,EIF5A,ENO1,FHL1,GPX1,HSPA4,HSPA5,LDHA,MANF,MIF,PKIB,SOD1,SOD2,UGP2 MTPN -0.310 transcription regulator 1.576 1.27E-03 AHSG,ANXA1,CP,H6PD,MIF,MTPN,S100A10,S100A6,SLMAP,TAGLN DDIT3 transcription regulator Activated 2.200 1.37E-03 CLPP,ERO1A,HSPA5,HSPD1,HSPE1,SARS,STAT5A,WARS ARID5B transcription regulator 1.51E-03 ACTA2,TAGLN ZNF746 transcription regulator 1.82E-03 COX4I1,PDHA1,SDHA MYCBP transcription regulator 1.82E-03 EIF2A,LDHA,NCL Akr1c12/Akr1c13,CAPN2,DDAH2,FHL1,GCAT,Hbb- SOX4 transcription regulator 0.895 1.93E-03 b1,IFI30,IGKC,LOC102637129/S100a11,PNP,RNASE4,SERPINE2,TES,Tpm4,UCHL1,VIM HNF1B transcription regulator -0.788 2.01E-03 ACTN1,ALB,ANXA4,CA2,GDI2,LUM,MMP7,RNASE4,S100A4,S100A6,SERPINA1,TTR CARM1 transcription regulator 2.43E-03 C3,FTH1,FTL,HSP90AA1,KRT1,SPINK1 FOXO3 transcription regulator -0.350 2.67E-03 APEX1,CAT,GLUL,GPX1,GSTM5,IARS,IMPDH2,LARS,NAMPT,PRDX3,PRDX5,SOD1,SOD2,VIM,YBX1 MXI1 transcription regulator Inhibited -2.000 2.83E-03 APEX1,IARS,IMPDH2,LARS MAFB transcription regulator -0.663 3.22E-03 ACTB,ACTN1,AKR1B10,ARHGDIA,CAP1,TAGLN ACLY,ADH1C,AKR1B1,AKR1B10,ALB,ANXA1,ARG2,ASL,ASNS,C3,CA2,CFD,CKAP4,COX4I1,CYCS,FABP4,FHL1,GAPDH,GSTP1,HSP CEBPA transcription regulator 1.121 3.38E-03 A5,LGALS1,Mup1 (includes others),PGD,PPP1R14A,SERPINB1,SOD1,SOD2,VCL FHL2 transcription regulator 0.246 3.47E-03 ACTA2,CNN1,CTSD,Pbsn,TAGLN GATA6 transcription regulator 0.564 3.52E-03 ACO1,ACTA2,C1R,CRYL1,DLD,DLST,FH,HBA1/HBA2,LDHB,MYH11,MYLK,PGK1,PPP1R14A,SUCLA2,TAGLN TFAP2A transcription regulator 0.375 3.54E-03 ALCAM,ANXA1,GLO1,PRDX1,RPL6,RPL7,RPLP0,RPS5,SOD2,TPM3 NFYC transcription regulator 3.69E-03 ALB,FTH1,MTPN,PGM3,PSMC6,TXNDC12 SMARCA2 transcription regulator Activated 2.433 4.24E-03 ACTA1,CP,DES,MYLK,NME1,Pbsn,TAGLN HOXB8 transcription regulator 4.41E-03 MYLK,TAGLN Yap1 transcription regulator 4.41E-03 MYH11,TAGLN TEAD1 transcription regulator 4.48E-03 ACTA1,ACTA2,CS,DES NFYA transcription regulator 1.432 4.50E-03 ACLY,ALB,CALR,CAT,FTH1,HSPA5,PGK1,PKM,THOP1 ACAA1,ACADL,CAP1,CSRP1,EPCAM,FBP1,FH,FKBP1A,FTH1,GLUL,GSN,GSS,IDH1,KIF5B,MAN1A1,ME1,NPC2,PPP1R14A,RHOA,R TCF7L2 transcription regulator 1.000 4.69E-03 NASE4,SRSF1,TWF1 MKL2 transcription regulator 0.707 4.88E-03 ACTA1,ACTB,FLNA,GSTM5,ITGB1,TAGLN,Tpm4,TST,VCL CREB3L2 transcription regulator 5.72E-03 HSPA5,SEC23A,SEC23B ZNF143 transcription regulator 5.72E-03 AKR1A1,TALDO1,TCP1 ACTA2,ADH5,Akr1b7,APOA1,ASNS,ATP5B,CAT,CIRBP,CKB,CSNK2A1,CTSD,EZR,FLNA,GPX4,GSS,HADHA,HADHB,HINT1,Hspa1b,H SP1 transcription regulator 0.340 5.87E-03 SPA5,MAT2A,MYH11,MYLK,PGK1,PKM,RRAS,SERPINE2,SLC25A5,SOD1,SOD2,TAGLN,TXNRD1,VIM

NOTCH1 transcription regulator 0.484 6.48E-03 ACTA2,C21orf33/LOC102724023,CFD,CKM,DLD,ECHS1,GAA,GLUD1,HBA1/HBA2,ITGB1,KRT8,MYLK,OAT,PDIA3,REN,SDHA,TAGLN KAT5 transcription regulator 1.387 7.74E-03 ALB,APEX1,APOA1,HSPD1,NME2,OGN,PSMD6,SOD2,SRSF2,TTR EPC1 transcription regulator 8.59E-03 ACTA1,CKM NRIP1 transcription regulator 0.378 9.20E-03 ACAA2,ACO2,APOA1,HADHB,IDH3A,LDHB,SUOX NFE2 transcription regulator 9.47E-03 CAT,Hbb-b1,HMBS,TXN H2AFX transcription regulator 9.47E-03 ACTB,MYL6,RPL10A,RPL13 NFIX transcription regulator 9.47E-03 CKM,FABP4,SERPINA3,SLC25A5 FOXF2 transcription regulator 9.89E-03 ACTA2,MYH11,TAGLN HOXD10 transcription regulator -0.174 1.00E-02 ERP44,EZR,LOC102637129/S100a11,NME1,NUDT5,REN,RPSA NRF1 transcription regulator Activated 2.000 1.03E-02 COX4I1,CYCS,FTH1,IDE,SDHA,VDAC1 ACO2,ACTB,ATP5A1,ATP6V1F,CASP6,CHAF1A,CKM,COX4I1,DLST,EZR,FABP4,GMPS,GPT2,MAPK1,MAPK3,MDH1,NIT1,NME1,PAR RB1 transcription regulator 1.867 1.10E-02 K7,PGD,SOD2,Tpm1,VDAC1,YWHAE HMGA1 transcription regulator 1.155 1.11E-02 ACTA2,B2M,CTSC,CTSH,GSN,Hbb-b1,PYCR2,RAN,RPL7A,TAGLN,TRIM28,ZYX TFEB transcription regulator -0.845 1.22E-02 CTSA,CTSB,CTSD,SCPEP1,TPP1 SATB1 transcription regulator 0.691 1.24E-02 ACTN1,ARF5,CTSD,GPT2,HSP90AA1,HSPA8,ITGB1,PSMD2,PTGES3,RPLP0,TUBA4A,VIM,WARS POU2AF1 transcription regulator 1.25E-02 Akr1c12/Akr1c13,IDH2,IGHM,LGALS1,RBP1,S100A10 NCOA3 transcription regulator 1.985 1.29E-02 LDHA,MAPK1,MYH11,Pbsn,PGK1,PSMA2,TAGLN BRCA1 transcription regulator 0.599 1.30E-02 ACTB,AK1,BCKDHB,CTSD,DUSP3,ELAVL1,EZR,HNRNPD,HSPB1,Pbsn,SERPINE2,SFPQ,STAT5A MYOD1 transcription regulator Activated 2.151 1.35E-02 ACTA1,ACTA2,ALDOA,CKM,DES,HADH,HSPA4L,Pdlim3,PNP,PRKACA,PSMB4,RPL7A,RRAS,TKT GATA1 transcription regulator -0.845 1.38E-02 ACTN1,CA2,FHL1,GART,HBA1/HBA2,Hbb-b1,HNRNPAB,HSPA9,PTPN6,RPL22,RPL27A,RPS11,SLMAP,SRM,Tpm1 HEXIM1 transcription regulator 1.49E-02 ANXA1,HNRNPH1,PTBP1,VIM ARNT2 transcription regulator -0.258 1.50E-02 AKR1B1,ANG,CFL2,EGF,EIF5A,FKBP3,GLUL,LCP1,MTPN,NUDC,PGK1,PKIB,SELENBP1,SRSF1,UBA1 APBB1 transcription regulator 1.54E-02 ACTA2,TAGLN,Tpm1 GTF2I transcription regulator 1.54E-02 CAT,HSPA5,PDIA4 SREBF2 transcription regulator -0.114 1.75E-02 ACAA2,ACLY,CYB5A,FABP5,FDPS,G6PD,IDH1 HOXA9 transcription regulator 1.78E-02 ALDH1A1,ARF1,CLTC,GLRX3,HNRNPU,MAN2A1,RAP1B,RDX,RPL27A,RPL37A,ST13,VIM,YWHAG MAX transcription regulator 1.86E-02 APEX1,CYCS,FTH1,MTHFD1,NCL,NPM1,SCPEP1,YBX1 KMT2D transcription regulator 1.976 1.86E-02 ACADM,ASL,CKAP4,CTSD,FABP3,FAH,FHL1,GALK1,GUSB,PTGR1,PTGR2,SORD,Tpm4 POU2F1 transcription regulator 1.86E-02 APEX1,GSTM1,GSTM3,GSTM5,Gstt1,IDH1,IGKC,PRDX2,PURB,SOD1 PLAGL1 transcription regulator 1.88E-02 FABP4,LDHA,SORD MYB transcription regulator 1.98E-02 CASP6,COPA,GSTM1,HSPA5,HSPA8,IQGAP1,MAT2A,VIM SOX15 transcription regulator 2.04E-02 CKM,PYCR2 ERG transcription regulator -0.832 2.04E-02 ACTN1,DSTN,FHL1,PARVA,PTPN6,RAB2A,RAB7A,RHOA,RPL22,RPL27A,RPS11,RRAS,RSU1,Tpm1 MYBL2 transcription regulator 2.06E-02 CASP6,COPA,HSPA8,IQGAP1,ITGB1 JUNB transcription regulator -1.067 2.17E-02 ACAT2,ACLY,C3,COMT,DARS,EEF2,FTH1,MYL12A,SPTAN1,YWHAG MAFG transcription regulator 2.24E-02 GSTP1,HMBS,TXNRD1 KLF11 transcription regulator -0.651 2.27E-02 CAT,FABP3,HADHB,IDH3A,SOD2 ACLY,ATP5B,CA2,CASP6,CAT,CS,CYCS,FABP4,FDPS,GPD1,GRHPR,HSPA5,ME1,NAMPT,PCK2,PDHB,PSMB6,RAB7A,SERPINB5,S FOXO1 transcription regulator 0.090 2.40E-02 OD2,TKT HEY2 transcription regulator -1.939 2.48E-02 ACTA1,ACTA2,MYH11,TAGLN COPS5 transcription regulator 2.65E-02 AIMP1,MARS,RARS BTG2 transcription regulator 2.65E-02 CAT,SOD1,SOD2 MBD2 transcription regulator 2.66E-02 ACTB,BCAT2,G6PD,GSTP1,IGHM,RNASE1 TFAP2C transcription regulator -1.184 2.86E-02 ALCAM,FKBP4,GPX1,HK1,KRT8,SOD2 SOX11 transcription regulator -0.570 2.99E-02 ADSS,AS3MT,CASP6,CD9,HSPD1,ITGB1,SEPT2 SP3 transcription regulator 0.730 3.08E-02 ACTA2,ADH5,ASNS,HSPA5,MAT2A,MYH11,MYLK,PGK1,PKM,RRAS,SOD2,TAGLN,TXNRD1,VIM BARX2 transcription regulator 3.09E-02 ACTA2,FLNA,HSPA9 CREB3L1 transcription regulator 3.09E-02 AGR2,HSP90B1,HSPA5 SIRT1 transcription regulator -0.284 3.12E-02 ACADM,ACTB,CAT,DDAH2,EIF2S2,ERO1A,FABP4,IDH2,MYL6,PC,PCK2,RPL10A,RPL13,SOD2,UQCRC2 MED30 transcription regulator 3.56E-02 CS,SDHA,SOD2 HOXA13 transcription regulator 3.56E-02 ALDH1A1,ENO1,VAPA PKNOX1 transcription regulator 3.56E-02 PGK1,PTPN6,REN BACH1 transcription regulator 3.56E-02 DNAJC7,FTH1,ME1 MED14 transcription regulator 3.61E-02 FABP4,Pbsn TEAD3 transcription regulator 3.61E-02 ACTA1,DES SIM1 transcription regulator 0.000 3.78E-02 AKR1B1,ANG,CFL2,EGF,EIF5A,FKBP3,GLUL,LCP1,MTPN,NUDC,PKIB,SELENBP1,SRSF1,UBA1 ASB9 transcription regulator 3.89E-02 CKB CERS5 transcription regulator 3.89E-02 ASAH1 CERS3 transcription regulator 3.89E-02 ASAH1 CERS4 transcription regulator 3.89E-02 ASAH1 MEF2B transcription regulator 3.89E-02 CKM REXO4 transcription regulator 3.89E-02 CRYZ HOXA4 transcription regulator 3.89E-02 ITGB1 THOC1 transcription regulator 3.89E-02 CALR EID1 transcription regulator 3.89E-02 CKM HOXB6 transcription regulator 3.89E-02 REN HEY1 transcription regulator 4.07E-02 ACTA2,MYH11,OAT PIAS1 transcription regulator 1.970 4.17E-02 ACTA2,MYH11,Pbsn,TAGLN TRIM28 -2.016 transcription regulator 4.40E-02 ALAD,Hbb-b1,HMBS,S100A4,UROD AFF4 transcription regulator 4.53E-02 HNRNPH1,PTBP1 TEF transcription regulator 4.53E-02 MYLK,PDXK RFX2 transcription regulator 4.53E-02 LMNA,RPL30 TBP transcription regulator 4.55E-02 ACTB,ASNS,GAPDH,MANF,RPL31,RPS10 ACTA2,AHSG,ALDH1A1,APEX1,ARG2,CAT,CD9,CLIC5,CTSB,HNRNPD,IFI30,IFI35,ITGB1,MMP7,NAMPT,PDIA4,PFKL,PSMB5,PTPN6, STAT3 transcription regulator 1.259 4.64E-02 REN,SERPINA1,SERPINA3,SERPINB1,SERPINE2,SOD2,TAGLN,VIM,WARS SMAD3 transcription regulator 0.823 4.75E-02 ACTA2,ALB,APOA1,C3,CKM,DES,ITGB1,LUM,RAC1,TAGLN,TF,TPM3,VIM,ZYX ATF3 transcription regulator -0.156 4.84E-02 ASNS,FBP1,GSN,HSPB1,SERPINB5,SRI NANOG transcription regulator Activated 2.121 7.34E-02 HNRNPH1,HSPA1A/HSPA1B,LGALS1,S100A6,SEC22B,SET,SRRT,YWHAE STAT4 transcription regulator 1.849 8.63E-02 ACADL,ADSS,C3,COPG1,DDAH2,ERO1A,Hspa1b,PRDX6,S100A4,SELENBP1,SERPINB1,SERPINB5,VDAC1 ACADL,ACADM,ACTA1,ANXA6,B2M,CA2,CAMK2D,CKB,CKM,COMT,FABP4,FTH1,H6PD,HSP90AB1,LDHA,LMNA,MMP7,MYH11,Pbsn, EP300 transcription regulator Activated 2.782 8.68E-02 RBP1,REN,SOD2 ATF2 transcription regulator Activated 2.216 1.04E-01 ACTB,ASNS,MAT2A,MYLK,SERPINB5,SOD2 CBX5 transcription regulator -1.890 1.71E-01 AGR2,CKM,EIF1AY,MMP7,SELENBP1,SERPINA3,STAT5A MED1 transcription regulator Activated 2.619 2.08E-01 ACAA1,CFD,CHAF1A,FABP4,GSTP1,HSD17B4,NCL,Pbsn ID1 transcription regulator -1.969 2.12E-01 ALDH1A1,FABP4,TF,VIM SMARCB1 transcription regulator 1.914 3.18E-01 ACAT1,ACTR2,FABP4,FBP1,GSN,Mup1 (includes others),PC,RAB14