The role of MDM2 and CDK4 in well differentiated liposarcoma

Dr Rachel Katherine Conyers

Submitted in total fulfillment of the requirements of the degree of Doctor of Philosophy

April 2015 Department of Pathology The University of Melbourne

i Abstract

Transformation of normal cells to cancer cells is tightly linked to fundamental changes in cell cycle regulation. In addition, oncogenes can aberrantly enhance cell proliferation. Two genes; Cyclin dependent kinase-4 (CDK4) and Murine double minute 2 (MDM2) are amplified and overexpressed in over 90% of well differentiated liposarcomas. Their role in cell cycle control, and regulation of tumour suppressor p53 respectively, strongly suggesting that deregulation of these genes confers some selective advantage to this tumour. To elucidate the role of these genes in the development and progression of liposarcoma I have used transgenic mouse models and in vitro assays. Given the recent development of novel CDK4 inhibitors, I have tested several CDK4 inhibitors (sc-203873, sc-203874, NPCD, PD 0332991) on liposarcoma cell lines (449B, T1000, 778, GOT3) to determine sensitivity to inhibition, cell cycle arrest and downstream effects of inhibition. PD033991 was found to be the most selective and sensitive CDK4 inhibitor and, as such, was used in a siRNA screen of the genome to identify co-modifiers of CDK4 inhibition. A total of 13 genes were identified that produced a resistance phenotype in the context of CDK4 inhibition. Two of these genes; Arrestin, beta 2 (ARRB2) and Dysferlin (DYSF) demonstrated a reproducible resistance phenotype in a series of functional validation studies.

ii Declaration

This is to certify that: i the thesis comprises only my original work towards the PhD except where indicated in the Preface, ii due acknowledgement has been made in the text to all other material used, iii the thesis is fewer than 100 000 words in length, exclusive of tables, maps, bibliographies and appendices.

______

Dr Rachel Katherine Conyers Date

iii Preface

This is to confirm that all work carried out in this PhD candidature was performed during the time period of the PhD. No work was carried out prior to PhD candidature enrolment. No work has been submitted for other qualifications. The work carried out in this project was all performed by the PhD candidate except for the following:

1. Generation of original MDM2 and CDK4 transgenic mice performed by Ozgene Pty Ltd.

2. Bioinformatics analysis of silencing RNA screen of genome performed by Dr. Kate Gould as part of the Victorian Centre for Functional Genomics (VCFG) at Peter MacCallum Cancer Centre.

Editorial assistance was provided by the supervisors of this PhD; Associate Professor David Thomas and Dr. Maya Kansara. Further editorial support was provided by Associate Professor Paul Ekert, Professor Richard Sullivan, Dr. Seong Khaw and Ms Judith Gregory.

iv Acknowledgements

When I started my PhD, my supervisor, Associate Professor David Thomas, told me thatlike acompleting marathon, aand PhD personally, was like running I think ita marathon.should have He come wasnt with wrong. a government It was much health warning! Having said this, completing my PhD has proven to be one of the most satisfying and fulfilling aspects of my medical career thus far. So the old adage, no pain, no gain, definitely holds true.

IdCouncil like to for acknowledge my PhD scholarship. the support I am of grateful the National to both Health my supervisors, and Medical Associate Research Professor David Thomas and Dr Maya Kansara, whose vision created this project, for guiding me through the world of science and teaching me a skill set that I otherwise was not privy too.

There are a number of special souls who helped me at various times throughout this journey. Associate Professor Paul Ekert, Dr Seong Khaw deserve special mentions for their help and inspiration at times when I needed it most. To Professor Richard Sullivan and Ms Judith Gregory for their help with the editing process. To my best friends Dr Romi Anaf and Dr Elise Harrison,

who must have heard the phrase my PhDour many, at least many, a million years times of friendship. by now, thankTo my you sisters for yourSara unwaveringand Katherine support for always and for enriching my life and being a source of fun and frivolity. To my partner Peter for being a wonderful partner and friend.

Finally, I dedicate this PhD to my parents Mr Michael Conyers and Mrs Wendy Conyers. I was lucky enough to have parents who taught me to believe in myself and to strive for whatever I desired in life. My parents taught me that hard work never hurt anybody. They instilled in me both dedication and determination. They are wonderful human beings. Mum and Dad, I love you.

v

Figures and Tables

Chapter 1: Background

Figure 1 MDM2 Structure 6 Figure 2 TP 53 Structure 8 Figure 3 Schema of p53 downstream effects 9 Figure 4 Function of CDK4 13

Table 1 Published CDK4/6 inhibitors 25 Table 2 MDM2 inhibitors in clinical trials 27

Chapter 2: Materials and Methods

Figure 1 Schematic representation of MDM2 transgene 33 Figure 2 PCR confirming transgene in embryonic stem cells 34 Figure 3 PCR confirming transgene in mice chimera 35 Figure 4 Primer design for mouse studies 37 Figure 5 Experimental design siRNA screen 59 Figure 6 Control setup for high through-put screen 62 Figure 7 Pipeline for determining siRNA screen hits 63

Table 1 DNA lysis mixture 36 Table 2 Primer sequences for mouse studies 37 Table 3 PCR master mix 38 Table 4 Lenti- 40 Table 5 Lenti- n 40 Table 6 PrimerX™ sequences HT Master for Mix RT-PCR Specifications mouse studies 47 Table 7 PrimaryX™ antibodies HT PEI transfectio for Western Blot 51 Table 8 SiRNA smartpool complexes 52 Table 9 Vectors used for MEFS transfections 53 Table 10 NanoJuice Transfection Master Mix 53 Table 11 Antibiotics used in Bacterial Culture 55 Table 12 Master mix for PEI Transfection 55 Table 13 Lenti- 55 Table 14 shRNA vectors 57 Table 15 CDK4 X™inhibitors HT PEI usedtransfection in experiments 58 Table 16 Quality control metrics 64

Chapter 3: Mouse Studies

Figure 1 Primer design for Mdm2fl/+ mouse studies 71

vi Figure 2 Genotyping results for mouse studies 71 Figure 3 PCR analysis for Cre-mediated recombination 74 Figure 4 Transfection of Cre-ERT/MDM2fl/+ MEFS. 77 Figure 5 Western blot analysis for MDM2 in Cre-ERT/MDM2fl/+ MEFS 79 Figure 6 Characterisation of the conditional Cre-ERT2/MDM2fl/+ mice 82 Figure 7 GFP detection via immunofluorescence 83 Figure 8 Flow cytometry analyses of reporter GFP expression 85 Figure 9 RT-PCR analysis of Cre-ERT2/MDM2fl/+ mice 87 Figure 10 Western blot analysis for MDM2 in MDM2 mice 88 Figure 11 Stable MEF transfections with CDK4 and MDM2 91

Table 1 Summary of MEF and MDM2fl/+ mouse studies 89

Chapter 4: CDK4 and CDK4 inhibitors in Well differentiated liposarcoma

Figure 1 The role of CDK4 in cell cycle progression 96 Figure 2 MTS assay using SC-203874 and SC-203873 on 449B cell line 102 Figure 3 MTS assay using SC-203874 and SC-203873 on 778 cell line 103 Figure 4 CFA using SC-203874 and SC-203873 on 449B cell line 105 Figure 5 MTS assay using NPCD and variety of CDK4 amplified cell lines 107 Figure 6 CFA using NPCD on 449B cell line 108 Figure 7 Western blot analysis of NPCD treated 449B cell line 109 Figure 8 Apoptotic response following NPCD treatment to 449B cell line 111 Figure 9 Western blot of RB1 knockdown in 449B cell line 113 Figure 10 CFA and MTS assay of 449B shRB1 cells treated with NPCD 114 Figure 11 MTS assay of PD03322991 treated melanoma cell lines 115 Figure 12 Western blot of RB1 knockdown in CDK4 amplified cell lines 117 Figure 13 MTS assay performed upon RB1 knockdown cell lines 117 Figure 14 MTS and CFA using PD0332991 on 449B cell lines 119 Figure 15 High dose PD0332991 on 449BshRB1 cell lines 120 Figure 16 Cell cycle profile of PD0332991 treated 449B cell line 121 Figure 17 Western blot analysis of PD0332991 treated 449B cell line 122

Table 1 MTS assay results 101

Chapter 5: Genome wide siRNA screen of the genome to identify co-modifiers of CDK4 inhibition in Well differentiated liposarcoma

Figure 1 Optimisation workup for siRNA screen 134 Figure 2 Pipeline for siRNA screen 136 Figure 3 Optimisation and experimental design 138 Figure 4 Optimisation of transfection based on RB1 silencing 142 Figure 5 CTG signal stability over extended timeframe 143 Figure 6 Optimisation of transfection controls 146 Figure 7 Quality control measures for screen (Box plots) 150

vii Figure 8 Well scatter plots of optimisation plates 152 Figure 9 Repeat optimisation experiments: effects on quality control 153 Figure 10 Well scatter plots of repeat optimisation plates 154 Figure 11 Pipeline for determining screen resistance hits 156 Figure 12 Well scatter plots of primary screen 158 Figure 13 Quality control metrics of secondary screen 162 Figure 14 Suppression of RNA expression of siRNA targets 169 Figure 15 Tertiary screen results across CDK4 amplified cell lines 170 Figure 16 Validation studies of final screen hits 173

Table 1 Transfection reagents and efficiency 139 Table 2 Transfection reagent conditions using PLK1 and siGLO 140 Table 3 Efficiency of gene silencing by RT-PCR 141 Table 4 Quality control measures used in screen 148 Table 5 Primary screen quality control metrics 157 Table 6 Validated resistance hits for secondary screen 161 Table 7 GSEA analysis of secondary screen siRNA targets 165 Table 8 GeneGo analysis of secondary screen siRNA targets 166 Table 9 Gene list for tertiary screen 167

Appendix

Appendix 1 Secondary screen siRNA targets 211 Appendix 2 Validated potent inhibitory duplexes from secondary screen 220 Appendix 3 Negative control optimization 221 Appendix 4 Control plate setup for siRNA screen 222

viii

Abbreviations

ACCD Acid central core domain ADR Adriamycin ALK Anaplastic lymphoma kinase ARF Tumour suppressor ARF ARMCX6 Armadillo repeat containing X-linked 6 ARRB2 Arrestin, beta 2 BB Bromophenol Blue Beta mercaptoethanol BP Base pair BSAME Bovine serum albumin CAPZB Capping protein (actin filament) muscle Z-line, beta CDK Cyclin dependent kinase CDKi Cyclin dependent kinase inhibitor CDK1 Cyclin dependent kinase 1 CDK2 Cyclin dependent kinase 2 CDK3 Cyclin dependent kinase 3 CDK4 Cyclin dependent kinase 4 CDK6 Cyclin dependent kinase 6 CDK7 Cyclin dependent kinase 7 CFA Colony forming assay CHOP C/EBP homologous protein CMV Cytomegalovirus CPSF1 Cleavage and polyadenylate specific factor 1 CT C- terminus CTG Cell titer glo CV Coefficient of variations DF1 DharmaFECT 1 reagent DF2 DharmaFECT 2 reagent DF3 DharmaFECT 3 reagent DC Detergent compatible DDLPS De-differentiated liposarcoma DMEM DNA Deoxyribonucleic acid DNTP Dulbeccos deoxynucleoside modified triphosphates eagles medium DYSF Dysferlin EDTA ethylenediaminetetraacetic acid EGFP Enhanced green fluorescent protein ES Embryonic stem cells FAT4 Photocadherin FAT4 FBS Fetal bovine serum

ix FCM Fold change to mock GAPDH Glyeraldehyde 3-phosphate dehydrogenase GLI-1 GLI family zinc finger 1 GMFB Glia maturation factor, beta GRIK2 Glutamate receptor, ionotropic kainite 2 GSEA Gene set enrichment analysis HIF-1 Hypoxia inducible factor 1 HIST1H2BC Histone H2B type 2BC HMGA2 High mobility group A IAPS Inhibitors of apoptosis proteins IHOP Information hyperlinked over proteins IL-6 Interleukin 6 KIBA Kinase inhibitory bioactivity LATS2 Large tumour suppressor kinase 2 LS Liposarcoma MCM7 Minichromosome maintenance complex 7 MDR Multi-drug resistance MEF Mesenchymal embryonic fibroblasts MFH Malignant fibrous histiocytoma MGB Modified gitschier buffer MLPS Myxoid liposarcoma MDM1 Murine double minute 1 MDM2 Murine double minute 2 MDMX Protein MDM4 MEN1 Multiple endocrine neoplasia type 1 MYOF Myoferlin NCBI National center for biotechnology information NCOA6 Nuclear receptor coactivator 6 NPCD Naphtho[2,1- -c] carbazole-5, 7 (6H,12H)-dione NSCLC Non small cell lung cancer NT N- terminus ] pyrrolo [, NTSP1 Non-targeting siRNA pool 1 NTSP2 Non-targeting siRNA pool 2 4-OHT 4-hydroxytestostorone OCT Optimal cutting temperature OS1 Osteosarcoma amplified 1 OS4 Osteosarcoma amplified 4 OS9 Osteosarcoma amplified 9 OTOF Otoferlin PBS Phosphate buffered saline PARP poly (ADP-ribose) polymerase PCNA Proliferating cell nuclear antigen PCR Polymerase chain reaction Protein kinase C alpha PEI Polyethyleneimine PI3KPKC Phosphoinositide-3 kinase

x PLK Polo-like kinase PLPS Pleomorphic liposarcoma PI Propidium iodide P-TEFb Positive transcription elongation factor b QC Quality control QU Queensland university pRB Retinoblastoma protein RC Round cell RNA Ribonucleic acid RNAi Ribonucleic acid interference RISC RNA induced silencing complex RPMI Rosewall park memorial institute R26R Rosa 26 reporter RT Reverse transcriptase SAS Sarcoma amplified sequence SCLY Selenocystein Lyase SDS Sodium dodecyl sulphate SEM Standard error of the mean shRNA Short hairpin RNA siRNA Small interfering RNA SKP2 S-phase kinase associated protein 2 SNRPA U1 small nuclear ribonucleoprotein A SSMD Strictly standardized mean difference STS Soft tissue sarcoma TBS Tris buffered saline TET Tetracycline TG Transgene TGF- Transforming growth factor beta TP53 Tumour protein 53 TSPAN31 Tetraspanin 31 UTR Untranslated region UV Ultraviolet VCFG Victorian cancer functional genomics WDLPS Well-differentiated liposarcoma WHO World health organization WT Wild type YEATS4 Yeats domain containing 4 YY1 Yin yang 1

xi

Table of Contents Page Title Page i Abstract ii Declaration iii Preface iv Acknowledgements v Figures and Tables vi Abbreviations ix 1 CHAPTER ONE: BACKGROUND AND RATIONALE 1 1.1 Incidence and prevalence of WDLPS 1 1.2 Histological Subtype 2 1.3 Clinical Presentation 3 1.4 Prognosis and Therapeutic Options 3 1.5 Molecular Genetics of WDLPS 4 1.5.1 MDM2 5 1.5.2 CDK4 12 1.5.3 TSPAN31 20 1.5.5 HMGA2 20 1.5.6 OS1, OS4, OS9 22 1.5.7 YEATS4 22 1.6 Clinical Implications of CDK4 and MDM2 23 1.6.1 The role of CDK4 in human disease 23 1.6.2 CDK4 inhibitor development 24 1.6.3 The role of MDM2 in human disease 25 1.6.4 MDM2 inhibitor development 26 1.7 Gaps in the literature and key research questions 29

xii 2 CHAPTER TWO: MATERIALS AND METHODS 31 2.1 Materials 31 2.1.1 Reagents and Chemicals 31 2.1.2 Molecular Biology Materials 32 2.2 Generation of Mice 33

2.2.1 MDM2fl/fl transgenic mice 33 2.2.2 ERT-Cre/ MDM2fl/fl mice 35 2.3 Genotyping 36 2.3.1 DNA isolation for genotyping 36 2.3.2 Primer development for genotyping 36 2.3.3 PCR for genotyping 38 2.4 Mesenchymal Embryonic Fibroblasts (MEFS) 39 2.4.1 Preparation of MEFS 39 2.4.2 Cre containing viral plasmids to induce MEFS 39 2.4.3 Lentiviral production of cre containing viral constructs 40 2.4.4 Transduction of MEFS with Lentiviral constructs 41 2.4.5 Transduction of MEFS with Adenoviral constructs 41 2.5 Cell lines and tissues 42 2.5.1 Cell lines 42 2.5.2 Tamoxifen administration and preparation of tissues 43 2.5.3 Embedding tissues from mouse studies 43 2.6 Cell Culture 43 2.6.1 Maintenance of cells 43 2.6.2 MTS proliferation assays 44 2.6.3 Cell Cycle analysis 44 2.6.4 Colony forming assays 45 2.6.5 Apoptosis assay 45 2.7 RNA Isolation and RT-PCR 46

xiii 2.7.1 RNA isolation from cells or tissue 46 2.7.2 cDNA preparation 47 2.7.3 Primers for RT-PCR 47 2.7.4 Quantitative RT-PCR 48 2.8 DNA Isolation and electrophoresis 49 2.8.1 DNA isolation from cells and tissues 49 2.8.2 Primers for PCR 49 2.8.3 PCR protocol 49 2.9 Western Blots 50 2.9.1 RIPA lysis protocol 50 2.9.2 Protein quantification protocol 50 2.9.3 SDS Page separating and stacking gels 51 2.9.4 Immunoblotting 51 2.9.5 Antibodies for Western blots 52 2.10 Transfection experiments 53 2.10.1 Transient transfections Small interfering RNA 53

2.10.2 Stable transfections – 55 2.11 CDK4 inhibitors 59 2.12 Primary siRNA screen 60 2.12.1 Optimisation studies 63 2.12.2 Statistical identification of screen hits 65 2.12.3 Quality control metrics 65 2.13 Secondary and tertiary screens 65 2.14 Validation studies 64

3 CHAPTER THREE: CHARACTERISATION OF MDM2 TRANSGENIC MICE 3.1 Introduction 67 3.1.1 MDM2 67

xiv 3.1.2 Mouse studies investigating role of Mdm2 67 3.1.3 Aims 70 3.2 Results 71 3.2.1 Generation of mice 71 3.2.2 Genotyping and maintenance of mice 71 3.2.3 Investigation of MDM2fl/fl mice in in vitro 73 3.2.3.1 Induction of gene using cre containing constructs 73 3.2.3.2 PCR analysis in vitro 74 3.2.3.3 Transfection and Transduction of Cre-ERT/MDM2 MEFS 76 3.2.3.4 Western blot analysis of Cre treated MEFS 79 3.2.4 Investigation of MDM2fl/fl / Cre-ERT mice in vivo 81 3.2.4.1 PCR analysis 81 3.2.4.2 GFP analysis via microscopy 84 3.2.4.3 GFP analysis via flow cytometry 85 3.2.4.4 RT-PCR analysis 86 3.2.4.5 Western blot analysis 89 3.2.5 Overexpression of MDM2 and CDK4 in vitro 92 3.3 Discussion 94

4 CHAPTER FOUR: CDK4 AND CDK4 INHIBITORS IN WDLPS 98 4.1 Introduction 98 4.1.1 CDK4 background 98 4.1.2 CDK4 structure and function 98 4.1.3 The role of CDK4 in cancer 99 4.1.4 Small molecule CDK4 inhibitors 100 4.1.4.1 Indolocarbozoles (SC-203873/NPCD) 100 4.1.4.2 Triaminopyrimidines (SC-203874) 100 4.1.4.3 Palbociclib (PD0332991) 102

xv 4.2 Aim 102 4.3 Results 102 4.3.1 MTS assays of SC 203873/203874 103 4.3.2 Colony forming assays of SC203873/203874 106 4.3.3 MTS assay of NPCD 108 4.3.4 Colony forming assays of NPCD 109 4.3.5 Downstream effects of NPCD 111 4.3.6 Apoptosis Analysis of NPCD 112 4.3.7 Off target effects of NPCD 114 4.3.8 PD0332991 effects on RB null WDLPS cell lines 117 4.3.9 Colony forming assay of PD0332991 120 4.3.10 Cell Cycle Analysis of PD0332991 122 4.3.11 Downstream effects of PD0332991 124 4.4 Discussion 126 5 CHAPTER FIVE: GENOME WIDE RNAi SCREEN TO IDENTIFY CO-MODIFIERS OF CDK4 INHIBITION IN WDLPS CELL LINES 132 5.1 Introduction 132 5.1.1 Background to SiRNA screens 132 5.1.2 Aims 135 5.2 Results 135 5.2.1 Optimisation steps and high throughput algorithm 136 5.2.2 High throughput screening pipeline 138 5.2.3 Screen optimisation 139 5.2.4 Statistical identification of screen hits 157 5.2.5 High throughput primary screen 159 5.2.6 Secondary Screen, Ontology and literature analysis 165 5.2.7 Tertiary Screen 170 5.2.8 Validation studies of tertiary screen 173

xvi 5.3 Discussion 176 6 SUMMARY, FUTURE DIRECTIONS AND CONCLUSIONS 183 6.1 Summary 179 6.2 Future directions 185 6.2.1 Characterisation of MDM2 and CDK4 transgenic mice 185 6.2.2 Implications of CDK4 inhibitors in WDLPS 186 6.2.3 Genetic determinants of resistance to CDK4 inhibition 188 6.4 Concluding remarks 190 7 REFERENCES 191 8 APPENDICES 214

xvii 1 CHAPTER ONE: BACKGROUND

1.1 Epidemiology

Liposarcoma (LS) is a malignant neoplasm of adipose tissue. It is the most common soft tissue sarcoma (STS) in adult life, accounting for 20% of the total 13,000 new diagnoses of soft tissue and bone sarcoma per annum in the United States(Mack 1995; Dalal, Antonescu et al. 2008). The annual global incidence of LS is 2.5 cases per million population(Antonescu 2006). LS carries significant morbidity and mortality, which varies according to the site of disease, tumour volume and histopathology subtype(Fletcher C.D.M 2001; Dalal, Antonescu et al. 2008; Gutierrez, Snyder et al. 2011). The 5 year overall survival rate for all LS subtypes is between 60 -77% (2013) . With an age standardised incidence rate between 2 and 6 cases per million in high income countries (Dennis N 2012), LS is a rare cancer but one with a high mortality rate.

1.2 Histological Subtype

Histological subtype classification has produced much debate in the last twenty years(Dalal, Antonescu et al. 2008) due to the interaction between genetics and pathology(Dei Tos 2000). Despite this, the current World Health Organisation (WHO) classification remains unchanged from the original classification by Enzinger and Winslow in 1960(Dalal, Antonescu et al. 2008). Morphologically, liposarcomas are divided into 3 main subtypes: Well- differentiated/de-differentiated liposarcoma (WD/DDLPS), myxoid/round cell liposarcoma (MLPS) and pleomorphic liposarcoma (PLPS)(Dalal, Antonescu et al. 2008). Diverse morphology is reflected in variable tumour biology that gives rise to LS that range from low to high in their metastatic potential.

WDLPS comprise 40 - 45% of all LS diagnosis (Dalal, Antonescu et al. 2008). The WHO divides WDLPS into three main subtypes: adipocytic, sclerosing and inflammatory. The adipocytic subtype is lipoma-like, and consists of mature adipocytes which show variation in cell size, focal nuclear atypia and hyperchromasia (Dalal, Antonescu et al. 2008). Significant variation in cell size helps to discern this lesion from benign lipoma (Fletcher C.D.M 2001). The sclerosing subtype shows distinctive scattered, pleiomorphic stromal cells with rare multivacuolated lipoblasts in a fibrillary collagenous background (Dalal, Antonescu et al.

1 2008). The sclerosing subtype arises predominantly from paratesticular or retroperitoneal regions(Fletcher C.D.M 2001) . The inflammatory subtype shows varied phenotypic lymphoplasmacytic infiltrate, with a B cell predominance. There is little in the literature describing this rare subtype (Dei Tos A.P. 2002; Dalal, Antonescu et al. 2008). Interestingly, there are cases where T-cells dominate the inflammatory infiltrate and, therefore, ruling out haematological malignancy is often necessary (Argani, Facchetti et al. 1997; Kraus, Guillou et al. 1997).

DDLPS corresponds with progression from low-grade to high-grade non-lipogenic morphology within a WDLPS. The non-lipogenic component can be either high-grade, or low- grade representing fibromatosis or low myxofibrosarcoma(McCormick, Mentzel et al. 1994; Fletcher C.D.M 2001; Dei Tos A.P. 2002; Dalal, Kattan et al. 2006). The distinction of de- differentiation is not reserved purely for high-grade transformation. Low-grade transformation can occur with the presence of fibroblastic spindle cells with mild nuclear atypia. The cellularity component of low-grade de-differentiation is neither consistent with WDLPS or high-grade de-differentiation (Fletcher C.D.M 2001). In general, the de- differentiated component tends to be non-lipogenic.

MLPS is the second most common LS subtype, and is characterised by the presence of ovoid or spindle cells in a myxoid stroma with signet ring lipoblasts and a distinguishing chicken wire pattern vasculature (Graadt van Roggen, Hogendoorn et al. 1999). De-differentiation, recognised as areas of increased cellularity, is referred to as round cell (RC) and confers a poor prognosis (Brown and Fletcher 2000; Fletcher C.D 2000). The RC de-differentiation regions often consist of sheets of primitive round cells, with obvious nucleoli and stark absence of myxoid stroma (Singer, Millis et al. 1997). It is thought that MLPS and RC differentiation are part of a spectrum of a single disease type (Weiss 1994).

PLPS is an aggressive but rare subtype. It presents as either high-grade pleomorphic sarcoma with scattered multivacuolated lipoblasts, or cellular pleomorphic sarcoma with sheets of pleomorphic monovacuolated lipoblasts (Downes, Goldblum et al. 2001) Histology is similar to malignant fibrous histiocytoma (MFH) with the addition of lipoblasts (Conyers, Young et al. 2011). The spectrum of histology is varied with some tumours exhibiting extensive sheets of pleomorphic lipoblasts, and others revealing only scattered,

2 scant pleomorphic lipoblast clusters(Fletcher C.D.M 2001). Occasionally an inflammatory infiltrate may be present.

This thesis is focused on WDLPS/DDLPS types of LS and, as such, the remainder of the background is devoted to these.

1.3 Clinical Presentation

Liposarcomas arise de novo (Dalal, Antonescu et al. 2008), with no identified pre-disposing factors. LS can originate in soft tissue anywhere in the body but is commonest in the retroperitoneum and thigh (Dalal, Antonescu et al. 2008). The site of disease distribution depends upon the histopathology, and correlates with survival which is largely dependent upon local surgical control(Dei Tos A.P. 2002; Dalal, Antonescu et al. 2008).

WDLPS presents as a painless enlarging mass, occurring commonly in patients aged 50 to 60 years with a male sex bias (1:1.2). Sites of distribution of WDLPS include soft tissue of the extremities, retroperitoneum, paratesticular region and mediastinum (Fletcher C.D.M 2001; Dei Tos A.P. 2002; Dalal, Antonescu et al. 2008).

Like WDLPS, DDLPS often present as a painless enlarging mass, with recent growth of a long- standing WDLPS lesion. De-differentiation occurs in 10% of WDLPS, most commonly in WDLPS of the retroperitoneum (Fletcher C.D.M 2001; McCormick, Mentzel et al. 1994) (Dalal, Kattan et al. 2006). Unlike WDLPS, DDLPS presents equally in both sexes. The most prevalent site of presentation is the retroperitoneum, which is three times more common than presentation within the extremities(Fletcher C.D 2000). DDLPS is more aggressive than WDLPS, but interestingly, recurrence can occur involving only the WDLPS tumour component.

1.4 Prognosis and Therapeutic Options

Prognosis is dependent on histological subtype, site, volume of disease, and nodal status. WDLPS and MLPS are classified as low-grade neoplasms with low metastatic potential and a 5 year overall survival rate of 85%(Lewis, Leung et al. 1998). In contrast, high-grade lesions such as PLPS or DDLPS have a poorer prognosis. Approximately 10-20% of primary DDLPS

3 tumours metastasise with a 5 year overall survival rate of <50% (Antonescu, Tschernyavsky et al. 2001; Dalal, Kattan et al. 2006). PLPS is, again, a more aggressive subtype with inferior 5-year overall survival of 30%(Gebhard, Coindre et al. 2002), and a metastatic rate of 30%(Antonescu, Tschernyavsky et al. 2001).

Current protocols for the treatment of liposarcomas are multimodal; surgery and radiotherapy are used for local control, whilst chemotherapy is reserved for systemic disease. The gold standard regimens include the alkylating agents such as ifosfamide together with doxorubicin, an anthracyline(Papai 2014). Chemotherapy and radiotherapy sensitivity varies according to subtype but, in general, MLPS respond to both modalities; whereas other subtypes, particularly WDLPS/DDLPS, show much more chemo and radioresistance. This is reflected in the poor survival outcomes with limited therapeutic options for aggressive local, or metastatic disease. The site of primary disease i.e. retroperitoneum, also makes local control difficult due to poor penetration of chemotherapy into this protected anatomical site.

The combination of difficult local control and limited sensitivity to chemotherapy and radiotherapy underpins the poor survival outcomes. The high mortality rate heralds the need to identify true genetic modifiers that can be targeted with novel molecular agents.

1.5 Molecular Genetics of WDLPS

WDLPS is characterised by a region of amplification located within supernumerary or giant chromosomes in the 12q13-15 region(Fletcher C.D.M 2001). Several genes lie within 12q13- 15 and have been linked to LS including known cancer genes including Cyclin Dependent Kinase 4 (CDK4) and Murine Double Minute 2 (MDM2 ) (12q14-15), as well as other genes such as Tetraspanin 31 (TSPAN 31) (12q13.3), Yeats domain containing 4 (YEATS 4), Osteosarcoma Amplified 9 (OS9), Osteosarcoma Amplified 1 (OS1), Osteosarcoma Amplified 4 (OS4), High mobility group A gene (HMGA2), C/EBP Homologous Protein (CHOP) and GLI family zinc finger 1( GLI-1) (reviewed in (Weiss 1994; Conyers, Young et al. 2011). DDLPS fall into the same genetic and biological group as WDLPS showing consistent amplification of the 12q13-21 region.(Dalal, Antonescu et al. 2008). Of the research, the data strongly supports a role for MDM2, CDK4, HMGA2 and TSPAN31 in WD/DDLPS.

4 1.5.1 MDM2

Mdm2 was first discovered in 1987 from studies of paired chromatin bodies within BALB/C mouse 3T3 cell lines(Cahilly-Snyder, Yang-Feng et al. 1987). Double minutes are small, acentromeric extrachromosomal nuclear bodies(Iwakuma and Lozano 2003), representing amplified segments of DNA. The study of the 3T3 cell lines revealed two, previously unclassified, double minutes that were thought to represent either growth factors or oncogenes. The two novel double minutes were called murine double minute 1 (Mdm1) and murine double minute 2 (Mdm2). The later discovered human monologue of this protein is sometimes called HDM2.

The oncogenic potential of Mdm2 amplification was studied by introducing the cloned DNA sequence of Mdm2 into non-transformed cell lines (Fakharzadeh, Trusko et al. 1991). Each gene was cloned into cosmid PCV001(Choo, Filby et al. 1986) and transfected into non transformed mouse NIH3T3 cells and Rat2 cells. Following transfection, appropriate selection was applied (Fakharzadeh, Trusko et al. 1991). The cell lines were next introduced into nude mice, following selection, by subcutaneous injection. Mdm2 amplification through transfected NIH3T3 was closely aligned with tumorigenesis with 100% of mice developing tumours within 5-11 weeks. Similarly, the Rat2 transfectants (R/Mdm2) were found to be tumorigenic with combined results of 16 tumours in 16 mice. This study correlated enhanced expression of Mdm2 with oncogenic potential. Leading on from this discovery, it was later found that Mdm2 bound to, and inhibited, p-53 transactivation, predisposing cells to transformation (Momand, Zambetti et al. 1992).

1.5.1.1 Structure and function

MDM2 encodes a 491 amino acid protein with a weight of 56 kDa. It consists of several conserved domains including a zinc finger, C-terminal really interesting new gene (RING) domain, central acidic domain, and N-terminal interaction domain (Figure 1)(Montes de Oca Luna, Wagner et al. 1995). The N-terminal domain binds the helix hydrophobic pocket of the N-terminal transactivation domain of p53(Chen, Marechal et al. 1993; Chen, Lin et al. 1995; Roth, Dobbelstein et al. 1998). The binding of MDM2 to p53 within this domain results in the blockade of p53 transcription(Chen, Marechal et al. 1993; Stommel and Wahl 2005) by direct interaction with the transcriptional apparatus(Chen, Marechal et al. 1993; Oliner,

5 Pietenpol et al. 1993) The RING and zinc finger domain together form the c-terminus(Roth, Dobbelstein et al. 1998), with the RING domain necessary for ubiquitin ligase activity and subsequent degradation of p53(Haupt, Maya et al. 1997; Maki 1999).

Figure 1: MDM2 primary structure. Functional domains include p53-binding domain, Nuclear Localisation Signal (NLS), Acidic domain, Zinc Finger and C-terminal really interesting new gene (RING) finger.

The crystal structure of MDM2 was first reported in 1996(Nakayama, Toguchida et al. 1995). This led to the finding that the 109 residue amino-terminal of MDM2 binds to the 11-residue transactivation domain of p53(Pedeutour, Maire et al. 2012). The binding process between MDM2 and p53 is dependent upon amino acids Phe 19, Trp 23 and Leu 26 binding with the hydrophobic cleft within MDM2(Bustin 2001)

MDM2 acts as an ubiquitin ligase that targets p53 for proteasomal degradation, which occurs through the action of both cytoplasmic and nuclear proteasomes(Ashar, Fejzo et al. 1995; Yie, Liang et al. 1997; Noro, Licheri et al. 2003). In addition, MDM2 controls its own auto- degradation, that is particularly important in times of stress when p53 needs to be functional, and not inhibited by MDM2 (Cleynen and Van de Ven 2008).

MDM2 is mainly expressed in the nucleus, although a number of studies have reported its expression in the cytoplasm. Cytoplasmic expression may relate to the nuclear localization domain in MDM2 which enhances p53 translocation from the nucleus to the cytoplasm(Ohtsubo, Shiokawa et al. 2009). The p53 proteasomal degradation occurs in the

6 cytoplasm, and is MDM2 mediated, suggestive of the importance of MDM2 to localize to the cytoplasm.

1.5.1.2 Tumour protein 53 (TP53) and the interaction with MDM2

The p53 protein was first described as a transformation-related protein in chemically induced sarcoma and other cancers in 1979(DeLeo, Jay et al. 1979). Initially, TP53 was thought to possess weak oncogenic activity, due to an initial misconception of its mutant profile as wild type (WT). A decade later, the actual WT sequence of TP53 was discovered, and the significance of mutations in human cancers started to be unveiled(Finlay, Hinds et al. 1989). Subsequent to this, TP53 knockout mouse models in the 1990s provided indisputable evidence of the potent tumour suppressor role of WT P53(Donehower, Harvey et al. 1992). Following this, TP53 has become the most common site of known genetic alterations in human cancer (Robles and Harris 2010). TP53 is mapped to the short arm of chromosome 17 (17p13.1). The gene is 20kb and contains 11 exons and 10 introns. The gene encodes p53, a 53 kDa protein comprised of 393 amino acids. The protein is composed of three main domains including the N- terminus (NT), Acid central core domain (ACCD) and C-terminus (CT) (Figure 3).

Although 50% of cancers have acquired inactivating mutations or deletions of TP53(Petitjean, Mathe et al. 2007), the incidence of TP53 mutations in STS are much lower with < 12% of LS possessing a somatic TP53 mutation(Taubert, Meye et al. 1998). Despite the majority of LS maintaining WT status of TP53, the proteins function is often compromised through other mechanisms. One of the main cellular inhibitors, MDM2, which is amplified and overexpressed in 100% of cases of WDLPS/DDLPS, is the main modulator of the TP53 response in this tumour (Figure 2).

The protein p53 belongs to a specific protein family that includes p63 and p73, which shares a common ancestral gene(Levine and Oren 2009) .The proteins are both structurally and functionally related. P53, together with its homologs p63 and p73, plays key roles in cell cycle control, the induction of apoptosis, and senescence. Furthermore, p53 behaves as a transcription factor attributable to its functional transactivation domains(Fields and Jang 1990) that bind DNA sequences causing transactivation of many other genes. The targets of p53 are diverse, a large component of which are the DNA repair enzymes, or genes controlling

7 cell cycle progression(Menendez, Inga et al. 2009). In addition, p53 also functions to cause cell death via cellular signaling pathways that are transcription independent. The various functions of the protein p53, encoded for by TP53, are summarised in Figure 3.

Figure 2: Schematic representation of p53 structure. TP53 contains a total of 393 amino acids with three distinct domains. The N-Terminal (1-94) contains an amino acid terminal domain (1-42) which is responsible for transactivation activity. The N- Terminal also contains a proline rich region (62-94) that is responsible for maintaining p53 stability, and resistance to degradation. The central core, or acid central core domain (102-292), is the sequence specific DNA binding domain. This region contains most of the identified mutation hot spots for the protein. Finally, the C-terminus contains both a tetramisation domain (324-355) and also a regulatory domain (363-393). p53 is unique in that it has a second DNA binding domain within the protein. Modified from Bai et al (Ling Bai 2006).

8

Figure 3: Simplified schema of p53 downstream effects. p53 downstream effects are aided by its ability to transactivate, and also repress, a variety of other gene functions. The degree of stress placed upon the cell determines the cellular response produced by p53, and its target genes. Cells undergoing extreme stress will result in p53 producing senescence, via direct gene targeting, or apoptosis via protein-protein interaction. Figure adapted from Levine et al. (Levine and Oren 2009).

9 1.5.1.3 Regulation of MDM2

MDM2 is highly regulated by a number of proteins. ARF, or p14ARF, is a tumour suppressor that binds to the MDM2 protein and inhibits its ubiquitin ligase activity(Cleynen and Van de Ven 2008; Conyers, Young et al. 2011). This results in increased levels of p53(Cleynen and Van de Ven 2008). When cells are stressed, levels of ARF and subsequently p53 rise to allow growth arrest and apoptosis(Wolffe 1994). The exact mechanism by which ARF inhibits MDM2 remains unclear and controversial(Zhou, Benson et al. 1995; Fedele, Berlingieri et al. 1998).

Other modifiers of MDM2 include ribosomal proteins L5, L11 and L23 that inhibit MDM2 directly, in times of ribosomal stress, in an effort to increase p53 levels(Smas and Sul 1995; Ayoubi, Jansen et al. 1999). Ataxia Telangiectasia Mutated gene (ATM) and Abelson Leukaemia oncogene c-ABL inhibit MDM2 via the phosphorylation of Ser 395 and Tyr 394(Ashar, Fejzo et al. 1995). A reduction in ubiquitination, via MDM2 inhibition, is seen with CBP/p300 mediated acetylation(Xiang, Benson et al. 1990).

Protein MDM4 (MDMX) can also help to modulate MDM2 activity. MDMX stabilizes MDM2 and p53, which results in increased E3-ligase activity of MDM2(Zhou, Benson et al. 1995). Additional MDM2 modifiers include transcription factor Yin Yang 1 (YY1)(Berner, Meza- Zepeda et al. 1997) and the phosphorylation of Ser 166 and Ser 186 , both promoting the interaction MDM2/p53 (Abe, Watanabe et al. 2003).

1.5.1.4 Pre-clinical studies investigating the role of MDM2

A number of in vivo experiments have proven the importance of the p53/MDM2 interaction in maintaining cell homeostasis. Mouse models that lack functional Mdm2 are lethal during embryogenesis; this is in contrast to p53 null mice that undergo normal development. The developmental defects observed are secondary to unregulated p53 activity as elimination of p53 activity rescues the embryonic lethality phenotype (Jones, Roe et al. 1995; Montes de Oca Luna, Wagner et al. 1995). In a central nervous system specific model, loss of Mdm2 activity resulted in hydranencephaly, and subsequent death during embryonic development. This model involved crossing Mdm2 conditional allele mice with neuronal specific Nes-Cre

10 transgenic mice(Xiong, Van Pelt et al. 2006). The lethal phenotypes observed with loss of the Mdm2 were again observed to be p53 dependent with lethality rescued by the introduction of p53 loss.

A Mdm2-hypomorphic allele model has also been created (Mdm2puro). Mice that were heterozygous for an Mdm2-null allele and a Mdm2-hypomorphic allele were, in this case, viable(Mendrysa, McElwee et al. 2003). This finding indicated that the hypomorphic allele continued to maintain some functional regulation of the wild-type Mdm2 axis. Despite being viable, these mice were underweight and had some deficits in hematopoiesis. A number of conditional Mdm2 and Mdmx conditional knockout mouse models have also been generated(Steinman and Jones 2002; Grier, Xiong et al. 2006). The advantage of these mice is that the knockout of the proteins can be introduced at any time point, and thus avoid the embryonic lethal phenotype of the Mdm2 null models. These models demonstrated again the importance of Mdm2 to neuronal development(Xiong, Van Pelt et al. 2006). In addition, these mice models showed tissue specific consequences of a Mdm2 knockout. Deletion of Mdm2 in mouse epithelial cells induced p53 dependent senescence, whereas the same knockout in red blood cells, cardiomyocytes, intestinal and embryonic epithelial cells produced a p53 dependent apoptosis(Grier, Xiong et al. 2006). The observed effects were dependent not only on tissue specificity, but also on the timing of Mdm2 deletion.

A number of transgenic Mdm2 mouse models have been created to explore the effect of overexpression or amplification of the gene, and protein. The mice have been difficult to develop, and there are not many examples currently in the literature. The first development of such a model introduced Mdm2 within a cosmid into embryonic stem cells (ES)(Brown, Thomas et al. 1998). The ES that were found to have large numbers of Mdm2 were used to produce chimeric mice. The resultant transgenic Mdm2 mice had Mdm2 levels five times that of their WT counterparts, and were predisposed to tumorigenesis. Further to this, a transgenic Mdm2 mouse that produces overexpression of Mdm2 with splice variants, similar to those found in human sarcomas, also demonstrated predisposition to tumorigenesis(Steinman, Burstein et al. 2004). The splice variant described produces the B- isoform which is the mostly commonly described isoform in human cancers. The produced protein lacks the p53 binding region present in full length MDM2 (Steinman, Burstein et al.

11 2004). The role Mdm2 plays in DNA repair and response to cellular damage has also more recently been demonstrated in transgenic Mdm2 mouse models(Bouska and Eischen 2009).

Collectively these models demonstrate the important role Mdm2 plays in tumorigenesis, and the maintenance of cellular homeostasis together with p53.

1.5.2 CDK4

CDK4 was first identified in 1987 by Hanks and colleagues (Hanks 1987) through a homology probe of a HeLa cDNA library. Mixed oligonucleotide probes were used to screen the cDNA library for conserved clones, characteristic of the protein serine kinase family. A total of eighty thousand clones were screened, of which four were further characterised by partial DNA sequencing. CDK4 was identified as one of these four clones.

1.5.2.1 Structure and Function

CDK4 is a member of the cyclin dependent kinases; a conserved family of proline-directed serine/threonine kinases that regulates cell cycle progression. CDK4 is a compact gene on the long arm of chromosome 12. The gene encodes a 33-kD protein that plays an important role in the cell cycle, particularly the G1-S transition(Battista, Fidanza et al. 1999).

CDK4 complexes with the D-type cyclins, particularly Cyclin D1 encoded by CCND1. The CDK4-cyclin D complex phosphorylates the retinoblastoma protein (pRB), resulting in release and activation of E2F target genes, including E-type cyclins(Caldas, Hahn et al. 1994). The interaction between the complex and pRB enables the progression through the G1 phase of the cell cycle(Ashar, Fejzo et al. 1995; Zaidi, Okada et al. 2006).

The effect that the CDK cyclin complex has on the cell cycle has been mostly elucidated using a mammalian model system– . This has found that the CDK-cyclin complex controls cell cycle progression, whilst at the same time inactivating Rb protein family members p107 and p130 (Fedele, Battista et al. 2002). This model, however, has been challenged by genetic studies in mice using knockdown of CDK loci in the germline. Santamaria et al.(Fusco and Fedele 2007) showed that CDK1 was the only kinase required for cell cycle progression. but Santamarias his theory modeldid not does challenge suggest the that proposed not all interactionsCDKs are required of CDK4, for or cell the cycle mechanism progression of action upon pRb.

12 The mammalian model for CDK4 interaction is summarised in Figure 4. The Cyclin dependent kinases (CDK) are inhibited by cyclin dependent kinase inhibitors (CDKi) which is discussed further in section 1.5.2.4.

Figure 4: Function of Cyclin dependent kinase 4 (CDK4) CDK4 binds with cyclin D to form active complexes. The formed complexes then phosphorylate pRb,

dissociating pRb from the pRb-E2F complex. Released E2F then binds to DNA resulting in upregulation and transcription of genes required for S phase

progression. This figure was adapted from Conyers et al (Conyers, Young et al. 2011).

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1.5.2.2 Cell Cycle: The role of the RB1-CyclinD-CDK4 axis

The cell cycle is a series of highly orchestrated events which lead to the duplication and division that results in two daughter cells from one. The cell cycle is divided into Gap 0 (G0) resting or quiescent cells, Gap1 (G1), synthesis (S), Gap 2 (G2) and mitosis (M). Progression through the cell cycle is regulated by cyclins, CDK, CDKi (p15, p16, p18, p19, p21, p27, CDKN1A), pRb, E2F and p53(Fletcher C.D.M 2001). The first cyclins to be induced as G0 cells move into G1 are the D-type cyclins. The D-type cyclins form a complex with CDK4, then CDK6, which then results in phosphorylation of pRb-p105-E2F(Fletcher C.D.M 2001 ; McCormick, Mentzel et al. 1994; Conyers, Young et al. 2011). Phosphorylation of pRb inactivates its growth suppression function and releases E2F. E2F then activates a number of genes in cell cycle progression and DNA synthesis including Cyclin E, Cyclin A, Cyclin dependent kinase 1(CDK1), B-myb, dihydrofolate reductase, thymidine kinase and DNA polymerase alpha(Weiss and Rao 1992). The production and activation of these proteins allow for progression of the cell through the cell cycle.

In the S phase, complexes form between both Cyclin E and Cyclin dependent kinase 2 (CDK2) and Cyclin A and CDK2, which again phosphorylate pRb leading to release of E2F and the onset of DNA synthesis(Fletcher C.D.M 2001). The binding of CDK2 to Cyclin A is required for cell progress from the G1 to S, and M phases. Cyclins A and E are involved in feedback loops for E2F. Cyclin E upregulates E2F, whilst Cyclin A negatively regulates E2F(Stewart, Schwartz et al. 1994; Conyers, Young et al. 2011).

For the cell to move from the G2 to M phase involves a G2/M entry checkpoint, allowing for potential DNA damage to be recognised and repaired. CDK1, Cyclin A, B1 and B2 must be present for the cell to enter M phase and mitosis(Fletcher C.D.M 2001). During the M phase, CDK1 forms complexes with these cyclins allowing for phosphorylation of the mitotic spindle and cytoskeletal proteins, which is vital(Miettinen and Enzinger 1999).

Arrest can occur at any of the G1/S or G2/M checkpoints. G1 arrest is controlled by p53, which causes an increase in p21CIP1, inhibition of Cyclin/CDK2 and Cyclin/CDK4 complexes and hypophosphorylation of pRB(Fletcher C.D.M 2001; Miettinen and Enzinger 1999). The G2/M checkpoint is mediated by p53 along with other p53 independent mechanisms. Two homologs of p53, p63 and p73, play a role in cell cycle arrest. The homolog p73 accumulates

14 following DNA damage and is phosphorylated. This is a c-Abl tyrosine kinase dependent process, that together with p53 results in apoptosis(Conyers, Young et al. 2011). The high degree of homology between p53, p73 and p63 suggests a similarity in the role they play on arrest and apoptosis; however, the distinct mechanism requires further investigation(Fletcher C.D.M 2001)

1.5.2.3 The role of CDKi in the cell cycle

The INK4 pathway

Two protein families of CDKi INK and CIP/KIP mediate inhibition of the cell cycle. The INK4 family includes p15INK4b, p16INK4a, p18INK4c, P19INK4d which are all structurally similar, comprising 3.5 and 5 ankyrin type repeats(Ruas and Peters 1998). These proteins inhibit both CDK4 and Cyclin dependent kinase 6 (CDK6) forming cyclin complexes, thus resulting in a G1 arrest. Interestingly, the INK4 families maintain exclusivity to interaction with CDK4 and CDK6, with no direct interaction with other CDK (Vogt PK 1998). P19INK4d forms a complex with MDM2, inhibiting the p53-MDM2 interaction. The result of this is an increase in p53 activity, upregulation of p21CIP1 and either apoptosis or G1 arrest.

P16INK4a plays an integral role in the regulation of CDK, having been first recognised as involved with CDK4 within virally transformed cells(Xiong, Zhang et al. 1993). The encoding gene for p16INK4a (CDKN2A) was identified via yeast two-hybrid screening(Serrano, Hannon et al. 1993). P16INK4a is identified as a tumour suppressor, often found to be deleted, mutated or hypermethylated in a variety of tumours(Caldas, Hahn et al. 1994; Hussussian, Struewing et al. 1994; Kamb, Gruis et al. 1994; Mori, Miura et al. 1994; Nobori, Miura et al. 1994; Okamoto, Demetrick et al. 1994; Koh, Enders et al. 1995). Loss or inactivity of CDKN2A, comprising both INK4a and ARF transcripts, is one of the most frequent events in human cancer development(N 2008; Research 2011; Cerami E 2012; N 2012; Gao J 2013; N 2013). Regulation of pRB phosphorylation is controlled, in part, by p16INK4a. The p16INK4a protein binds CDK4 and CDK6, and exerts its effects upon these and the bound cyclin-D/CDK4 complex, by inhibiting the phosphorylation of pRb(Serrano, Hannon et al. 1993). Cells lacking pRb have increased p16INK4a levels suggesting that transcription of p16INK4a is suppressed by pRb. Furthermore, cells lacking TP53 have increased levels of p16INK4a mRNA suggesting a negative feedback loop interaction between both TP53 and pRb(Kamb, Gruis et al. 1994; Li, Nichols et al. 1994).

15

Closely aligned to p16INK4A function is p15INK4b(Hannon and Beach 1994), as it also demonstrates sensitive and specific bound inhibition of both CDK4/CDK6. P15INK4b (CDKN2B) is located adjacent to p16INK4a (CDKN2A) on chromosome 9p21(Hannon and Beach 1994; Kamb, Gruis et al. 1994). Although p15INK4b function is rarely altered in cancers, it is still flagged as a tumour suppressor in the literature (Ruas and Peters 1998) . Transforming growth factor beta (TGF- INK4b, causing it to bind to CDK4/CDK6 and release produces p27 upregulation that results and in cell expression cycle arrest(Moller of p15 2000). This demonstrates how the INK4a/b proteins have the ability to regulate multiple CDKs through the knock-on effects of protein displacement.

There is some suggestion that p18INK4c plays a role in tumour suppression. Mice deficient in Ink4c have been shown to develop pituitary adenomas(Franklin, Godfrey et al. 1998). Further crossbreeding with p27 -/- mice developed tumours with a phenotype consistent with multiple endocrine neoplasia type 1 (MEN1) syndrome(Franklin, Godfrey et al. 2000). The mice that were p53 -/- crossed with p18INK4c-/- were shown to be predisposed to both medulloblastoma and sarcoma development (Zindy, Nilsson et al. 2003; Uziel, Zindy et al. 2005). In humans, p18Ink4c has been inactivated via mutations, loss or hypermethylation in multiple tumour subtypes(Bartkova, Thullberg et al. 2000; Kulkarni, Daggett et al. 2002; Morishita, Masaki et al. 2004; Sanchez-Aguilera, Delgado et al. 2004; Uziel, Zindy et al. 2005; Solomon, Kim et al. 2008; Kirsch, Morz et al. 2009). Epigenetic silencing of the CDKN2C promoter through methylation is reported in both Hodgkin lymphoma and medulloblastoma(Sanchez-Aguilera, Delgado et al. 2004; Uziel, Zindy et al. 2005). Concerning the cell cycle, loss of p18Ink4c correlates with reduction of total levels of E2F1(Gagrica, Brookes et al. 2012)

P19INK4d was initially isolated via the yeast two hybrid system(Hirai, Roussel et al. 1995). Studies have shown that p19INK4d, like the other INK4 inhibitors, inhibit CDK4 and CDK6 specifically(Chan, Zhang et al. 1995; Hirai, Roussel et al. 1995), and overexpression halts the progression of cells through the S phase of the cell cycle(Hirai, Roussel et al. 1995). In addition, p19INK4d interacts with p53 by direct regulation with MDM2(Pomerantz, Schreiber- Agus et al. 1998).

16

Ciprofloxacin/Kinase protein (Cip/Kip) pathway

The Cip/Kip family of proteins are involved primarily in cell cycle inhibition and include p21CIP1, p27KIP1 and p57KIP2(Tateishi, Matsumoto et al. 2012). These proteins promote the formation of the CDK4/D-type cyclin complex and target CDK2, CDK4 and CDK6(LaBaer,

Garrett et al. 1997). Structurally, all the proteins have a conserved NH2 domain that is imperative for binding to the CDK-cyclin complexes which lead to inhibition (Nakayama and Nakayama 1998; Sherr and Roberts 1999). Beyond cell cycle control, the Cip/Kip proteins also influence cell migration and cytoskeletal organization (Besson, Assoian et al. 2004).

The first identified Cip/Kip family protein was p21CIP1, which is encoded by CDKN1A gene located on chromosome 6 (6p21.2)(Harper, Adami et al. 1993). p21CIP1 is also known as CIP1, WAF, SD11, CAP20, PIC1 and mda-6(Vogt PK 1998). The p21CIP1 protein works in conjunction with p53, and is an important contributor to G1 cell cycle arrest following DNA damage(Dulic, Kaufmann et al. 1994; Deng, Zhang et al. 1995; Waldman, Kinzler et al. 1995). More specifically, p21CIP1 is transcriptionally activated through a specific p53 binding site. Transcriptionally activated, p21CIP1 then inhibits the formation of cyclin/CDK complexes and abrogates pRB phosphorylation resulting in a G1 arrest (Rousseau, Cannella et al. 1999). The protein further contributes to cell cycle arrest by targeting pRB for proteasomal degradation (Broude, Swift et al. 2007) and prevents proliferating cell nuclear antigen (PCNA) dependent DNA replication by binding directly to PCNA(Johnson and Walker 1999; Mirzayans, Andrais et al. 2012). A G2 arrest can also be brought about by p21CIP1 as it can initiate degradation of cyclin B1 when DNA damage is present and result in activation of the G2/M checkpoint (Bunz, Dutriaux et al. 1998; Gillis, Leidal et al. 2009) Finally, the protein plays a role in gene expression, senescence and apoptosis (Chang, Watanabe et al. 2000; Roninson, Broude et al. 2001; Lohr, Moritz et al. 2003).

The second protein of the Cip/Kip family, p27KIP1 , inhibits the CDK2-cyclin E complex and provides regulation of the cell cycle at the G1-S transition(Fearon 2011; Itamochi, Yoshida et al. 2011). p27KIP1 was discovered in cells arrested by the

CIP1 KIP1 (Sherr and Roberts 1999). Unlike p21 , p27 inhibitstransforming proliferation growth in factor normal TGF and KIP1 genetically damaged cells(Sherr and Roberts 1999). The efficiency with which p27 produces a G1 arrest is affected by its cellular expression level (Itamochi, Yoshida et al. 2011).

17 Cells that are not dividing have high levels of p27KIP1, however, these levels decline as the cell proceeds through the G1 to S transition because of increased ubiquitin mediated degradation (Pagano, Tam et al. 1995). Ubiquitin mediated degradation requires an F-box protein known as s-phase kinase-associated protein 2 (SKP2), which regulates all Cip/Kip cell cycle inhibitors (Starostina and Kipreos 2012). Control of the G1-S checkpoint is dysregulated in cancer subtypes, with low levels of p27KIP1 making it less likely that cells will remain quiescent, and more likely that they will proceed through the cell cycle with uncontrolled proliferation (Psyrri, Bamias et al. 2005; Rosen, Yang et al. 2005; Chu, Hengst et al. 2008). p27KIP1-/- mice develop marked specific organomegaly of the thymus, pituitary and gonads, and are predisposed to pituitary tumour development(Fero, Rivkin et al. 1996; Kiyokawa, Kineman et al. 1996; Nakayama, Ishida et al. 1996).

The p57KIP2, encoded by CDKN1C localised at 11p15.5, consists of 4 exons and 3 introns(Tokino, Urano et al. 1996), and is a 316 amino acid protein. Adjacent to the transcriptional start site the gene is highly populated with CpG islands suggestive of strong epigenetic control (Matsuoka, Thompson et al. 1996; Soejima and Higashimoto 2013). In addition to the N-terminal region shared with p21CIP1 and p27KIP1, the protein has two further domains; the QT and PAPA domain. p57KIP2 inhibits all cyclin-CDK complexes preventing phosphorylation of pRB. Much like p21CIP1, over expression of the p57KIP1 protein results in cell quiescence in G1, and a decrease in protein expression is seen as cells move from G1 to the S phase of the cell cycle mediated by ubiquitin degradation(Cerqueira, Martin et al. 2014), (Leibovitch, Kannengiesser et al. 2003; Kim, Nakamoto et al. 2008). The protein can interact directly with transcription factors, including MyoD, Mash1, NeuroD and Nex/Math2(Joseph, Wallen-Mackenzie et al. 2003). Along with cell quiescence, apoptosis and senescence are reported to be potentiated by p57KIP2 (Samuelsson, Pazirandeh et al. 2002; Vlachos, Nyman et al. 2007). The pro-apoptotic role of p57KIP2 appears to be cell line specific, and not generalizable across all cancer subtypes (Chang, Kim et al. 2003; Pateras, Apostolopoulou et al. 2006). p57KIP2-/- mice develop multiple congenital malformations including defective bone formation and cleft palates, both which contribute to early respiratory failure, and death(Yan, Frisen et al. 1997; Zhang, Liegeois et al. 1997; Takahashi, Nakayama et al. 2000). Combinations of Cip/Kip inhibition in p27KIP1-/- / p57KIP2-/- mice develop the most severe embryological defects and the defect is lethal in utero(Zhang, Wong et al. 1998) In humans, reduction in p57KIP2 leading to uncontrolled proliferation has been reported in hepatocellular(Bonilla, Orlow et al. 1998; Schwienbacher, Gramantieri et al. 2000),

18 uroethelial(Bozdogan, Atasoy et al. 2008), pancreatic(Ito, Takeda et al. 2001; Sato, Matsubayashi et al. 2005), colorectal(Noura, Yamamoto et al. 2001), poorly differentiated and undifferentiated thyroid(Ito, Yoshida et al. 2002), oral squamous cell(Fan, Chen et al. 2006), and ovarian carcinoma(Kitzmann and Fernandez 2001; Khouja, Baekelandt et al. 2007). In some tumour groups reduction in p57KIP2 correlated with poorer survival rates(Kitzmann and Fernandez 2001; Fan, Chen et al. 2006; Khouja, Baekelandt et al. 2007).

1.5.2.4 Pre-clinical studies investigating the role of CDK4

The role of CDKs and CDKi has been studied both in vitro and in vivo. Using conditional knockout mice, Cdk1 was found to be essential for cell proliferation and early embryonic development as loss of Cdk1 resulted in death at the blastocyst stage, as previously mentioned (Diril, Ratnacaram et al. 2012). In contrast, knockout mice involving either Cdk2, cyclin dependent kinase 3 (Cdk3), Cdk4 or Cdk6, were not embryonically lethal. These findings indicated that development was only dependent upon the functionality of Cdk1. Double Cdk knockout mice also produced lethality including, Cdk2-/-/Cdk4-/- and Cdk4-/-/Cdk6-/-, but not Cdk2-/-/Cdk6-/- (Kozar, Ciemerych et al. 2004; Barriere, Santamaria et al. 2007).

Despite Cdk4 null mice producing viable progeny, they do develop a range of clinical pathologies. Cdk4-/-mice develop with deficiencies in both the size and number of pancreatic (Rane,

Dubus islet cells et al. resulting 1999). Both in a diabeticfemale and phenotype male Cdk4 polyuria, null mice polydipsia are infertile, and and hypoglycaemia in females this may be secondary to pituitary hypoplasia (Moons, Jirawatnotai et al. 2002; Mettus and Rane 2003; Jirawatnotai, Aziyu et al. 2004). The, Cdk4 null mice have been shown to also have neurological deficits, inefficient thymocyte maturation and impaired adipocyte differentiation(Rane, Dubus et al. 1999; Abella, Dubus et al. 2005). MEFS derived from Cdk4 null mice demonstrate a delay in entering the S phase of the cell cycle, indicating the important role Cdk4 plays in facilitating the G1-S transition(Rane, Dubus et al. 1999; Rane, Cosenza et al. 2002).

The role of Cdk4 in the development and progression of cancer has been studied in a number of knockout, knockin and transgenic mouse models. Breast tumorigenesis mouse models have shown that both cyclin D1 and CDK4 are not required for Wnt or Myc induced tumours. However, mice with ectopically expressed Neu required the presence of either CDK4 or cyclin

19 D1 in order to efficiently induce tumorigenesis (Reddy, Mettus et al. 2005). This finding suggests that the CDK4/Cyclin D1 complex is necessary to induce tumorigenesis in mice with ectopic Neu expression. The findings infer that the Wnt and Myc oncogenes operate independently, and possibly downstream, of the CDK4/Cyclin D complex(Reddy, Mettus et al. 2005). The results of these mouse studies suggest that an active CDK4/Cyclin D1 complex is necessary for tumorigenesis, but this is critically dependent upon additional oncogene presence(Ortega, Malumbres et al. 2002).

The role of CDK4 in tumorigenesis has also been studied by generating mice with a point mutation (Arginine to Cysteine 24) in the p16INK4a binding site of Cdk4 (Cdk4R24C ). This point mutation renders p16INK4a unable to bind, and thus inhibit, CDK4 activity. MEFS derived from Cdk4R24C mice fail to senesce and have a predisposition to transform. The Cdk4R24C mice also rapidly develop a variety of primary and metastatic tumours (Sotillo, Dubus et al. 2001; Rane, Cosenza et al. 2002; Tormo, Ferrer et al. 2006) further validating the oncogenic potential of Cdk4. Manipulation of other inhibitors of CDK4 provides insight into its oncogenic role as discussed in 1.5.5.2.

1.5.3 TSPAN31

TSPAN31 is a member of the tetraspanin family or transmembrane superfamily, also referred to as the sarcoma amplified sequence (SAS). The gene encodes proteins that are fundamental to cell growth, motility and development. TSPAN31 was originally identified as an amplified sequence in MFH (Dei Tos, Doglioni et al. 2000), and has since been identified in a range of other sarcoma subtypes. It is implicated in the de-differentiating process for WDLPS, although its precise role is not well understood(Conyers, Young et al. 2011).

1.5.4 HMGA2

HMGA2 is part of the high mobility group of proteins broadly sub-classified into HMGA, HMGB and HMGN, previously known as HMG1/Y, HMG1/2 and HMG14/17(Bustin 2001). The HMGA subgroup includes HMGA1a, HMGA1c, HMA2(Ashar, Fejzo et al. 1995). The proteins each have their own signature motif. The HMGA proteins have a conserved binding peptide motif (AT hook). The AT hook consists of 9 amino acids containing an invariant repeat Arg- Gly-Arg-Pro (R-G-R-P)(Cleynen and Van de Ven 2008). It is the AT hook that binds the minor groove of many AT rich promoters and DNA regulatory elements, and is responsible for the

20 changesregions (Grosschedl, to chromatin Giese substrates et al. and 1994 formation; Reeves of and enhanceosomes Beckerbauer 2001) on gene allowing promotor for subsequent control of transcription. In this way HMG proteins bind to either DNA or chromatin independent of sequence(Cleynen and Van de Ven 2008), and influence transcription without being direct transcriptional activators(Cleynen and Van de Ven 2008). The HMGA2 protein also contains an acidic carboxyterminal tail that is thought to be important for protein to protein interaction(Ashar, Fejzo et al. 1995), although its function is still poorly understood(Yie, Liang et al. 1997; Noro, Licheri et al. 2003). There are a variety of mechanisms by which HMGA2 can induce structural changes within a variety of substrates, thereby positively and negatively regulating gene expression (Cleynen and Van de Ven 2008). There is also growing evidence for the role HGMA2 plays in growth regulation, cellular proliferation and differentiation(Cleynen and Van de Ven 2008).

HMGA2 overexpression is linked to a range of solid tumours including sarcoma(Berner, Meza- Zepeda et al. 1997), pancreatic tumours(Abe, Watanabe et al. 2003), breast (Rogalla, Drechsler et al. 1997), non small cell lung(Meyer, Loeschke et al. 2007) and oral squamous cell carcinomas(Miyazawa, Mitoro et al. 2004). Rearrangements in the HMGA genes are found in a range of benign mesenchymal tumours including pulmonary chondroid hamartomas, uterine leiomyomas, angiomyoxomas and lipomas(Kazmierczak, Wanschura et al. 1995; Schoenmakers, Wanschura et al. 1995; Nucci, Weremowicz et al. 2001). The redundancy of the HMGA2 gene in the development of these solid tumours is thought to relate to chromosome 12 rearrangement, break points, deletions or translocation. Recent studies havetranslocation introduced may theplay concept an important that deletionrole in tumorigenesis. of the 3 untranslated The region UTR duringLet- 7 reputed binding sites. Let-7 expression is inversely proportional3UTR to HMGA2 contains, heralding multiple it as a negative regulator of HMGA2. Two studies have recently made the link between aberrant let-7 miRNA expression and tumorigenesis(Lee and Dutta 2007; Mayr, Hemann et al. 2007). Thus HMGA2 has been found to have an important role in cellular growth, development proliferation and subsequent tumorigenesis in vitro and in vivo. The chromosomal rearrangements that occur in WDLPS/DDLPS support its role as a proto-oncogene in pathogenesis of this tumour(Fusco and Fedele 2007).

21

1.5.5 OS1, OS4, OS9

OS1, OS4 and OS9 are frequently amplified within the 12q13-q15 amplicon and encode proteins that are highly expressed in human sarcomas(Su, Hutter et al. 1996). OS9 binds to hypoxia-inducible factor (HIF-1), a gene which normally plays a role in hypoxic response and angiogenesis(Kraus, Guillou et al. 1997).

1.5.6 YEATS4

YEATS4 (GAS41, YAF9, NuB1-1, 49030573H17Rik, B230215MIORik) was initially cloned and sequenced from glioblastoma following microdissection mediated cDNA capture in a homogenously stained region(Fischer, Heckel et al. 1997). Following sequencing it was localised to the 12q13-15 chromosome region. YEATS 4 is a member of a protein family characterised by the presence of an N-terminal YEATS domain. Homology searches have revealed homology with transcription factor family members AF-9 and ENL proteins(Schulze, Wang et al. 2009). YEATS4 is also thought to have a role in transcription regulation because of the homology with AF-9 and ENL, as well as the f within the protein, which were previously described as activationinding domains of helical of transcription structures factors in eukaryotes. (Fischer, Heckel et al. 1997).

YEATS4 is involved with chromatin modification along with transcriptional regulation through its incorporatation into multi-subunit histone acetyltransferase complexes(Doyon, Selleck et al. 2004; Cai, Jin et al. 2005). These complexes are integral to the formation of nucleosomes, alteration of chromatin structure and transcriptional regulation. Further to its role in transcriptional regulation, YEATS4 has been found to have a number of binding partners identified through yeast 2 hybrid screens including MYC, MYCN, TACC1, TACC2, NuMa, AF10, PFDN1, K1AA1009(Harborth, Weber et al. 2000; Lauffart, Howell et al. 2002; Piccinni, Chelstowska et al. 2011). More specifically in WDLPS, YEATS4 encodes a putative transcription factor, GAS41, that represses the p53 tumor suppressor network(Park and Roeder 2006). In a recent integrative analysis of DNA sequence, copy number and mRNA expression in 207 STS described by Barretina et al.; YEATS4 was frequently amplified together with MDM2. Transcriptional upregulation of YEATS4 was demonstrated in those tumors with gene amplification compared to those copy-neutral for the locus in DDLPS(Barretina, Taylor et al. 2010). It has been suggested that MDM2 and YEATS4 work

22 cooperatively to repress the p53 network and allow for tumorigenesis to occur(Muller, Paulsen et al. 2007).

YEATS4 has been shown to be amplified and overexpressed in a range of malignancies including non-small cell lung cancer (NSCLC)(Pikor, Lockwood et al. 2013), STS(Barretina, Taylor et al. 2010), central nervous system (Park, Smith et al. 2011) and breast cancer(Lauffart, Gangisetty et al. 2003)

1.6 Clinical Implications of CDK4 and MDM2

1.6.1 The role of CDK4 in human disease

Deregulated cell cycle control is a common occurrence in oncogenesis. Overexpression of CDK4 is present in many cancer types including lymphomas(Amin, McDonnell et al. 2003), sarcoma (Binh, Sastre-Garau et al. 2005), breast carcinoma, melanoma(Arnold and Papanikolaou 2005) squamous cell carcinoma and leukaemia (Ortega, Malumbres et al. 2002). CDK4 is amplified in combination with MDM2 in breast carcinoma, glioma and sarcoma (Tap, Eilber et al. 2011) (Santarius, Shipley et al. 2010). In WDLPS, CDK4 is overexpressed in 90% of cases. Activation of CDK4 can also result from functional inactivity of CDKi CIP/KIP or INK4 proteins, or activating mutations of positive regulators (i.e. Cyclin D)(Baker SJ 2013). The loss of inhibitory protein function can result from gene deletion, point mutations or promoter methylation(Sheppard KE 2013), and renders CDK4 constitutively active.

Absence of p16INK4a results in cancer development in both mice and humans(Matheu, Maraver et al. 2008; Lapak K 2014). Ordinarily following DNA damage, upregulation of p16INK4a enables cells to enter senescence. Therefore, inactivation of p16INK4a allows cells to bypass senescence, whilst progressing through the cell cycle through sustained CDK4 activity(Lapak K 2014). Furthermore, several tumour types (i.e. glioblastoma multiforme, squamous cell carcinoma, melanoma, urothelial carcinoma) demonstrate multiple alterations in CDK4/6

INK4A cyclin D-INK4-pRB pathway(Lapak K 2014). Functional inactivation of p16 infrequently– occurs in combination with RB1 mutations at the genomic level, supporting the critical role the CDK4/6-INK4a axis in tumorigenesis. The important function p16Ink4a plays in

23 gatekeeping uncontrolled proliferation has led to development of small molecular inhibitors targeting CDK4/6 (Shapiro 2006).

1.6.2 CDK4 inhibitors development

trials were pan-CDK inhibitors including flavopiridol(Senderowicz,The first CDKis to enter Headlee clinical et al. 1998), R-roscovitine(Le Tourneau, Faivre et al. 2010). Flavopiridol and r-roscovitine inhibit CDK1, CDK2, CDK4, CDK6, Cyclin dependent kinase 7 (CDK7) and CDK1, CDK2, CDK7, CDK9 respectively (De Azevedo, Leclerc et al. 1997). These early compounds lacked specificity for CDK4. Flavopiridol most effectively inhibits CDK1, CDK2, and CDK4. Both flavopiridol and r-roscovitine combine with the cyclin involved (i.e. CDK2) to target the ATP binding site(De Azevedo W.F 1996). More recently flavopiridol has also been shown to inhibit CDK9/ positive transcription elongation factor b (P-TEFb) and RNA polymerase II transcription(Chao SH 2001). UCN-01 is a small molecule derivative of the serine/threonine kinase inhibitor staurosporine. The inhibitory effects of this molecule upon the cell cycle are poorly understood, and thought to involve transcriptional activation of p21(Facchinetti M.M 2004).

Pre-clinical studies revealed that early pan-inhibitors were capable of causing a potent G1- G2 arrest in a range of tumour cell lines(Sedlacek 2001; Shapiro 2004; Shapiro 2006). Unfortunately in the clinical setting these pan inhibitors were found to have substantial toxicity and were associated with poor clinical outcomes(Benson, White et al. 2007; Lapenna and Giordano 2009). Dose limiting toxicities included grade III/IV fatigue, neutropenia, hypotension, hypoalbuminaemia and diarrhea (Tan AR 2002; Fracasso PM 2011).

Next generation CDK4 inhibitors were then developed with better sensitivity and specificity to avoid these off-target associated toxicities. The range of CDK4/6 selective compounds included compounds belonging to drug classes derived from pyrimidines, diarylureas, thioacridones, benzothiadiazines, indolocarbazoles, and pyrido[2,3-d]pyrimidines. The range of clinical CDK4/6 inhibitors has been reviewed by Lee et al.(Lee and Sicinski 2006), and are summarised in Table 1. Some of the more recent CDK4 inhibitors have been developed and entered Phase 1 clinical trials including BAY1000394(Siemeister 2010), Palbociclib (PD0332991)(Fry, Harvey et al. 2004), R547(DePinto, Chu et al. 2006), RGB- 2886638(Cirstea 2008) and ZK304709(Siemeister, Luecking et al. 2006). Third generation CDK4/6 inhibitors in development include P276-00, LY2835219 and LEE011(Baker and

24 Reddy 2012). The CDK4 inhibitors that are currently being introduced into Phase 1 trials are either single agents or in combination with other therapeutics including proteasome inhibitors or chemotherapeutics. The range of CDK4 specific inhibitors used in the studies in this thesis are summarised in Results Chapter 2.

Table 1: Cyclin D- CDK4/6 kinase inhibitors published in the literature. Table adapted from Lee et al. (Lee and Sicinski 2006).

1.6.3 Role of MDM2 in human disease

MDM2 is classed as an oncogene due to its propensity to induce malignant change in a variety of tissue types (Zhang, Zeng et al. 2014). MDM2 amplification has been found in a number of human cancers including sarcoma(Zhang, Ding et al. 2014), leukaemia (Ou 2015), melanoma

25 (Vilgelm, Pawlikowski et al. 2015), breast carcinoma (Gansmo, Knappskog et al. 2014) and high-grade brain tumours(Reifenberger, Liu et al. 1993).

Amplification is one of many ways MDM2 can be overexpressed in human malignancy. Although less common, altered transcription (Bond, Hu et al. 2004), or translation (Hoffman, Bublik et al. 2014) can also result in overexpression of the MDM2. The result of MDM2 overexpression is maximal inhibition and inactivation of tumour suppressor p53, leading to cellular transformation and proliferation. Beyond the interaction with p53, MDM2 also interacts with tumour suppressor pRB influencing the cell cycle and proliferation. Furthermore, MDM2 up regulates E2F1 and anti-apoptotic protein XIAP, predisposing cells to ongoing proliferation and escape from apoptosis(Huart, MacLaine et al. 2009).

Therefore, the overexpression of the oncogene MDM2 creates a growth advantage to the cell, via a number of mechanisms. The interest in targeting MDM2, given its common overexpression in human malignancy, has resulted in the development of a range of MDM2 inhibitors with pre-clinical and now clinical studies in current use.

1.6.4 MDM2 inhibitors in development

The functional role of MDM2 inhibitors is to reactivate p53, thus allowing cell death to occur in response to stress whilst also maintaining p53 tumour suppressor function(Zhao, Aguilar et al. 2014). Sensitivity to MDM2 antagonists is predicted by MDM2 amplification and functioning of WT TP53(Meyer, Loeschke et al. 2007). To date there are seven small molecule inhibitors of MDM2 that have advanced into clinical trials. These are described in Table 2 and detailed below.

26

Table 2: MDM2 Inhibitors in Phase 1 Clinical Trials for Human Malignancy. Table adapted from Zhao et al.(Zhao, Aguilar et al. 2014)

27

First generation MDM2 inhibitors work by inhibiting the p53/MDM2 interaction. Nutlin-3a was the most promising first generation MDM2 inhibitor. This inhibitor triggered wild-type p53 activation and dependent apoptosis in cancer cell lines(Miyazawa, Mitoro et al. 2004) and cell cycle arrest in normal proliferating cells(Vassilev 2004) .

In the footsteps of Nutlins a new class variation of MDM2 inhibitors was designed by Shangary et al. spirooxindoles targeting the MDM2-p53 complex(Kazmierczak,

Wanschura et al. 1995).– Spiro-oxindoles– bind with higher affinity than Nutlin, but induce the same growth inhibition of cell lines that have functional downstream pathways and express wild-type p53(Schoenmakers, Wanschura et al. 1995). The most impressive compound in spiro-oxindole class is MI-219, both for its potency and superior pharmacokinetic profile compared to Nutlin-3a(Nucci, Weremowicz et al. 2001; Lee and Dutta 2007). Various studies, using both Nutlin-3a and MI-219, have shown p53 and p21 dependent cell cycle arrest in normal cells, with p53 dependent apoptosis of tumour cells (Battista, Fidanza et al. 1999; Arlotta, Tai et al. 2000; Zaidi, Okada et al. 2006; Mayr, Hemann et al. 2007). Of specific clinical significance is the fact that both Nutlin-3a and MI-219 do not cause visible toxicity to normal tissues(Arlotta, Tai et al. 2000; Zaidi, Okada et al. 2006).

Combining the structures of MI-219 and Nutlin-3a, Hoffman-La Roche designed a pyrrolidine containing MDM2 inhibitor compound RG7388(Ding, Zhang et al. 2013). Whilst initial pharmacokinetic studies showed that RG7388 had poor bioavailability, further modifications improved bioavailability, with the addition of a dihydroxybutyl side chain, to create a the new compound, RO5503781 with a IC50 of 6nM, which has since entered Phase I clinical trials. This compound, in contrast to RG7388, has a good pharmacokinetic profile with potent inhibitory activity in wild-type p53 cell lines.

AMG-232 commenced Phase 1 clinical trial investigation in 2012, and forms the first of a new range of piperidin-2-one containing MDM2 inhibitors(Rew, Sun et al. 2012). To date the crystal structure for this compound has not been elucidated; however, it binds to MDM2 with

IC50 of 0.6 nM. This compound inhibits cell proliferation in both cancer cell lines and human tumour xenograft models and is orally administered.

28 Newer class MDM2 inhibitors have been developed over the last 5 years. The newest spirooxindole is SAR405838 (MI-77301) (Ding, Zhang et al. 2013). SAR405838 binds to

MDM2 with an exceedingly low IC50 of 0.88nM. This inhibitor induces refolding of the MDM2 N-terminal resulting in its high affinity (Wang, Sun et al. 2014). This molecular inhibitor is able to activate wild-type p53 in cancer cell lines and in xenograft tumours in both solid and haematological malignancies, resulting in apoptosis or cell cycle arrest. Currently, SAR405838 is in Phase 1 trials in solid and haematological human malignancies both as a single agent and in combination with the MEK1/2 inhibitor pimasertib (Wang, Sun et al. 2014). Other new compounds only just entering clinical trials include MK-8242 (Perez- Moreno, Brambilla et al. 2012), CGM097 (Parks, LaFrance et al. 2006) and DS-3032b (Grasberger, Lu et al. 2005). The pre-clinical data for these compounds is not currently publically available.

1.7 Gaps in the literature and key research questions

The slow progress in improving survival in WDLPS is hampered by a lack of relevant experimental models. The paucity of experimental models places a limitation on comprehensive molecular investigation(Peng, Zhang et al. 2011) thus slowing the application and understanding of relevant novel targets. In order to bridge this gap we planned to establish and characterize a transgenic mouse model, thus facilitating the roles of MDM2 and CDK4 to be further understood.

Novel therapeutics targeting both MDM2 and CDK4 are being developed and rapidly moving into Phase I/II clinical trials across a range of cancer subtypes. In WDLPS, CDK4 inhibition, with PD0332991, has shown a progression free survival of 66% at 12 weeks with dose limiting toxicities of thrombocytopenia, neutropenia and anaemia (Dickson, Tap et al. 2013). Ray-Coquard et al. recently reported the first clinical trial of an MDM2 antagonist in patients with WD/DDLPS (Ray-Coquard, Blay et al. 2012). The results, using RG7112 as a neoadjuvant agent, were disappointing as only one patient had a partial response via RECIST criteria whilst the majority of patients were developing nausea, vomiting and fatigue. The poor reported response to CDK4 and MDM2 inhibitors raises the question of molecular resistance. Mechanisms of resistance to these inhibitors are not currently understood for either the novel MDM2 or CDK4 inhibitors. New state of the art technology using RNA interference is now starting to address these theoretical questions in high through-put screening.

29

With these gaps in the literature identified, this PhD was designed to further explore the role of MDM2 and CDK4 in WDLPS. The hypothesis is that MDM2 and CDK4 play crucial roles in the development of tumorigenesis in WDLPS.

More specifically, the aims of the PhD include:

Aim 1: Does over expression of MDM2 in vitro and in vivo produce biochemical and functional effects that support its role in WDLPS tumorigenesis? Aim 2: Do currently available CDK4 inhibitors demonstrate sensitivity and specificity to pathway inhibition across a range of WDLPS cell lines? Aim 3: Can a siRNA screen of the genome using WDLPS cell lines treated with a sensitive and specific CDK4 inhibitor PD0332991, help to identify genetic modifiers of resistance in WDLPS?

30 CHAPTER 2: MATERIALS AND METHODS

This chapter outlines the materials and methods used throughout the various experiments in this PhD.

2.1 MATERIALS 2.1.1 Reagents and Chemicals

31 2.1.2 Molecular Biology Materials

32 2.2 GENERATION OF MICE

2.2.1 MDM2 Cre- recombinase (Cre) inducible knock-in LoxP-Stop-LoxP transgenic mice

MDM2 transgenic mice were generated on a C57/Bl6 background by Ozgene(Ozgene 2012), and named Tuco by the company. Ozgene is an Australian company that provides contract research services for the generation of genetically modified mice to the global research community. For generation of these mice we provided a MDM2 in a pCMV vector, which was cloned into targeting vector 1050_ROSA_T-B000015E_GO4 and sequenced. The targeting vector contained an Ubiquitin-C promoter (Ubic) followed by LoxP (locus of X-over P1) site on bacteriophage P1, a STOP and another LoxP, followed by the MDM2 gene, then IRES (Internal ribosome entry site). This allows translation initiation in the middle of mRNA sequence followed by an internal reporter EGFP gene (Enhanced Green Fluorescent Protein). The generated Cre-recombinase inducible knock in LoxP-Stop-LoxP MDM2fl transgene was inserted at the Rosa 26 locus.

Figure 1.0: Schematic representation of transgene inserted at Rosa 26 Locus. MDM2 gene is flanked by Ubic promoter, LoxP-Stop-LoxP, IRES and eGFP reporter.

The targeting vector carrying the Cre inducible transgene MDM2 was introduced into embryonic stem cells (ES) using electroporation. Verification of insertion of the transgene was carried by Deoxyribonucleic acid (DNA) purification and digestion with restriction endonuclease enzyme EcoRV. The digest was probed for the transgene. This confirmed three embryonic stem cells clones were correctly targeted and suitable for injection (1A12, 1A6, 1C6).

33

Figure 2 : PCR confirming correctly targeted embryonic stem cell (1A12, 1A6, 1C6) containing transgene. The vector carrying the Cre inducible transgene MDM2 was introduced into ES using electroporation. The transgene was confirmed present by DNA purification and digestion with restriction enzyme EcoRV and PCR performed to confirm the transgene insert. Transgene was confirmed present in 1A12, 1C6 and 1A6. These investigations were performed by Ozgene prior to transfer of the mice to the Peter MacCallum Cancer Centre facility.

After the injection of identified embryonic stem cell clones containing transgene into blastocysts, Ozgene generated and bred transgenic embryonic stem cell chimeras. Genotyping of chimeras helped to confirm the presence of the transgene within progeny.

34

Figure 3: PCR confirming presence of transgene within chimera progeny within three locations (named Tuco, Transgene and Neo). Transgenic embryonic stem cells were generated from blastocysts containing the transgene. PCR was performed to confirm the presence of the transgene.

MDM2fl/fl transgenic mice were crossed with each other to generate heterozygous and homozygous strains. Mouse husbandry and experimental procedures followed standard protocols within Peter MacCallum Cancer Centre usage guidelines.

2.2.2 Generation of ERT-Cre/ Mdm2fi/fl transgenic mice Cre-ERT mice (C57 Bl/6) were provided by the Humbert laboratory (Peter MacCallum Cancer Centre, Melbourne, AUS (Indra, Li et al. 2000). Subsequent strain maintenance and crosses utilised MDM2fl/fl mice to generate Cre-ERT / MDM2fl strains.

35 2.3 GENOTYPING

2.3.1 DNA isolation for genotyping Mouse tails (5mm of the tips) were collected in microfuge tubes by animal technicians in the Peter MacCallum Cancer Centre animal facility. Tails were suspended in 200 lysis mixture (see Table 1) and incubated overnight at 55 °C to digest. The digestsμl were of the then incubated at 95°C for 15 minutes. The mixture was vortexed and then centrifuged at 13,000 rpm for 5 minutes. The supernatant was placed in a fresh microfuge tube and stored at -4 °C.

Lysis Mixture Μl 10 X Modified Gitschier Buffer (MGB) 20 10% Triton X 10 2 Proteinase k 4 Waterβ mercaptoethanol 164 μg/ml Table 1 DNA lysis mixture for mouse genotyping. Genotyping of mice required digestion of tails in lysis, for extraction of DNA. The mixture specifics are summarised.

2.3.2 Primer development for Genotyping

Forward and reverse oligonucleotide primers for both genotyping and excision of the LoxP- stop-LoxP site were designed to target the transgene sequence provided by Ozgene (Figure 4). The details of the primers are summarised in Table 2.

36

Tuco1050Tg STOPMdm2F Tuco1050 Tg STOPMdm2-R Transgene present

Tuco1050 UbiC..Mdm2-F1 Tuco1050 UbiC..Mdm2-R1

Figure 4: Primer design for mouse studies. Primers were designed to identify if the transgene was present for genotyping of wild-type, heterozygous and homozygous mice. Two other primer sets were created to identify if the Loxp-Stop-LoxP site had been excised.

Table 2: Primers utilised in mouse studies. Primers were designed to identify if the transgene was present for genotyping of wild-type, heterozygous and homozygous mice. Two other primer sets were created to ascertain if the Loxp-Stop-LoxP site had been excised, and also if Cre was present. TG: transgene; WT: wild type

37 2.3.3 Polymerase Chain Reaction (PCR) for genotyping

PCR was conducted in a final volume of 25 . The final volume contained 1 l DNA digest

each of the forward and μl μ andmix (Promega,. μl of Madison, USA) (Table reverse3). primer together with μl of the PCR master

GoTaq® DNA polymerase master mix 5 X green GoTaq® reaction buffer 5 (Promega) μl Magnesium Chloride 2 DNTP 0.5 GoTaq® DNA polymerase enzyme 0.18 Water 15.3 Total 23 . Table 3: Specifications of PCR Master mix. PCR was performed to confirm the genotype of the mice. The GoTaq® DNA polymerase master mix specifications are summarised.

PCR was carried out using a PTC-100 thermal cycler (MJ Research, Alameda CA). Thermal cycling conditions included a 2 minute denaturation step at 95°C followed by 34 cycles at 95 °C for 2 minutes, 54 °C for 30 seconds, 72°C for 45 seconds, and lastly this was followed by 5 minutes at 72°C.

The PCR products were analysed

by % agarose gel-tetra electrophoresis acetic acid) in with TAE ethidium buffer mΜ Tris,bromide mΜ to facilitate acetic acid, staining. and AmΜ low ethylenediamine molecular weight DNA ladder was used as a molecular weight size marker for genotyping (New England biolabs, Beverly, MA) with a 1-kb base plus ladder (Life technologies, Australia) for excision PCR. PCR product bands were visualised under ultra-violet light and photographed prior to interpretation.

38

2.4 Mesenchymal Embryonic Fibroblasts (MEFS)

2.4.1 Preparation of MEFS

MEFS were prepared from pregnant MDM2 homozygous mice. Females of 14 days gestation were euthanised as previously described in the literature using isoflurane (1-Chloro-2,2,2- trifluoroethyl-difluoromethyl ether) (Lawrance, Lucas et al. 2009). Following this, the uterus was removed and placed in a 10cm plate on ice containing 5ml sterile 2% FBS in PBS (v/v). In a tissue culture hood the uterine decidua was cut away and each embryo along with its yolk sac was transferred into a fresh 12 well plate on ice containing 2ml sterile 2% FBS in PBS. The yolk sac was carefully removed and the embryo examined to determine that the viability and morphology was as expected. The embryo was placed in a 6 cm dish on ice containing 4ml 2% FBS in PBS and the head was removed from the body with watchmaker forceps. The head was saved for later genotyping. The embryo innards were removed, ensuring that the majority of non-fibroblastic tissues were removed. The embryo bodies were then returned to a fresh 12-well plate on ice containing 2ml sterile 2% FBS in PBS. The body was then placed in a 6 cm dish containing 2ml trypsin-EDTA and cut into small pieces with fine scissors. An 18 gage sterile needle and 5ml syringe was used to mince the tissue further. The tissue was incubated at 37°C for at least 30 minutes with agitation every 10 minutes. After 30 minutes of incubation, 5 ml MEF culture media containing DMEM, 10% FBS and 1% penicillin, streptomycin and glutamine (PSG) (Gibco, Life Technologies) was added and pipetted up and down to disperse the cells. The entire volume was then transferred into a 10cm dish and allowed to incubate overnight at 37°c. After 24 hours of incubation the spent media was aspirated and cells washed with PBS before fresh media was applied. The MEFS were then allowed to reach confluency and passaged using TrypLE (Gibco, Life Technologies) to detach them from the plate media.

2.4.2 Cre-containing viral plasmids to transduce MEFS

Two plasmids were used to generate lentiviral supernatants using PEI. Lenti-LUCOS was kindly donated by Tyler Jacks (Massachusetts Institute of Technology, USA)(DuPage, Dooley et al. 2009). Lenti-Cre-GFP, was kindly donated by Andreas Strasser (Walter and Eliza Hall Institute, Australia).

39 2.4.3 Lentivirus Production of Cre-containing viral constructs

To produce recombinant lentivirus for target cell infection, vectors of interest were co- transfected with Lenti-XTMHT Packaging mix (Clontech) into HEK 293 T cells using PEI transfection protocol as described by Reed et al(Reed, Staley et al. 2006). HEK 293T cells were seeded on Day 1 in the afternoon at either 12 X 106 cells per T175 flask in 25 ml, or 5.0 X 106 cells in 12 mls DMEM + 10% TET-free FBS. On day 2 co-transfection was carried out in 1.5 ml microfuge tubes according to Table 4.

Flask T 75 T175 Lenti-XTM HT packaging mix

DMEM (No supplements) μl μl TablePG)PZ 4:construct Lenti-X™ / poolHT Master μg/ Mixμl Specifications μl μl μl μl Microfuges were then vortexed and PEI added at 4.5

μg of DNA according to Table Flask T 75 T175 PEI

Table 5: Lenti-X™ HT PEI transfection . μl . μl

Microfuge tubes were vortexed and incubated at room temperature for 10 minutes. Subsequent to incubation and a final vortex, the mixture was added to cells in either T75 or T175 flasks, swirling to distribute. Cells were incubated overnight at 37°C. On day 3, media was aspirated from cells and then replaced with appropriate amount of TET free 10% DMEM. On day

® glass fibre pre-filter the (minisartsupernatants, Sartorius were removed AG, Germany) and filtered and stored through at -80°ca . until μm polyamiderequired for cell transduction.

2.4.4 Transduction of MEFS with Lentiviral-Cre constructs

MEFS were plated 24 hours prior to transduction at 1 X 105 cells per well in 6 well format. Viral supernatants (Empty Vector, Lenti-LUCOS and Lenti-Cre-GFP) were added (volume 2ml) either neat or at 1:2 dilution to seeded cells, in triplicate. Cells were cultured for 48

40 hours and then fresh viral supernatants added for another 48 hours. Transduced cells were 2 days then maintained in puromycin at to selectselected for using stably puromycin transduced μg/ml cells. Cre for induced excision of floxed sites was determinedμg/ml using various techniques including PCR, RT-PCR and Western blot analysis.

2.4.5 Transduction of MEFS with Adenoviral-Cre

Adenoviral Cre was obtained from Dr Kathryn Kinross, Molecular Oncology laboratory, Peter MacCallum Cancer Centre. This Adenoviral Cre was originally sourced from the Molecular Oncology laboratory by the Baker Institute, Melbourne. The virus had been previously analysed to determine viral DNA present by measuring OD260 post viral lysis (2.44 X 109 optic ). The Molecular Oncology laboratory had previously had success usingal this particle virus units/μl to induce Cre-excision at a concentration of 1: 20 000 dilution.

MEFS were plated 24 hours prior to transduction at 1 X 105 per well in 6 well format. Adenoviral-Cre was added to media at concentrations of 1: 5000, 1: 10, 000 and 1: 20, 000. After 48 hours, media was changed, and more adenoviral-Cre containing media added. After 120 hours assessment was made, using varying techniques, to determine if Cre-induced excision of LoxP-Stop-LoxP site had occurred.

41 2.5 CELL LINES AND TISSUES 2.5.1 Cell lines

449B, 778, T1000, SW872, HT01080 and GOT 3 were cultured in RPMI-1640; 10% (v/v) Fetal bovine serum (FBS); GlutaMAX (2mM) and penicillin/streptomycin (PSG) at 37°c in a humidified atmosphere containing 5% carbon dioxide (CO2). MALME-μg/ml3M and MeWO cell lines were cultured in DMEM; heat inactivated 10% (v/v) FBS ; GlutaMAX and PSG (Gibco, Life Technologies) at 37° in a humidified atmosphere containing 5% CO2.

42 2.5.2 Tamoxifen administration to mice and preparation of tissues

ERT-Cre/MDM2 mice were verified by genotyping, and a cohort of 3 heterozygote female and 3 heterozygous male mice were fed tamoxifen for 2 weeks. To prepare tamoxifen feed, 100mg of tamoxifen was vortexed with 3 mls of 100% ethanol until the powder dissolved (Kiermayer, Conrad et al. 2007). Irradiated mouse feed 100g was placed in a beaker. Sucrose 10g was dissolved in mouse drinking water and mixed well with a fork to achieve a solid pastry. Another 10 ml peanut oil was added to the tamoxifen-ethanol mixture and shaken vigorously before being poured directly onto wet food powder and mixed well. Food was then stored in 3 cm plates in the fridge. Mice received 5-6 g of this tamoxifen feed per day for one week and then normal feed for another week.

On day 14, post the commencement of tamoxifen feed, mice were euthanised as previously described (Lawrance, Lucas et al. 2009). Mice were then dissected to remove tissue from key body organs (muscle, fat, stomach, bone, brain, spleen, kidney, liver and blood). Four samples of each organ were taken for preparation of DNA, ribonucleic acid (RNA), protein and embedding in OTC. Samples were placed immediately on dry ice, and then stored at - 80°C until ready for use.

2.5.3 Embedding Tissue from Mouse Studies

Tissues prepared from the Cre-ERT/MDM2fl mice were placed in pre-labeled Tissue Tek wells with optimal cutting temperature (OCT) embedding media for frozen tissue specimens. All tissues per mouse were placed within one tissue tek well and covered with OCT. OCT was frozen by placing the wells on dry ice. OCT samples were stored in -20°C freezer until ready to be reviewed.

2.6 CELL CULTURE

2.6.1 Maintenance of Cells

Cell lines were grown in a humidified incubator at 37°C containing 5% CO2 in 75 cm2 flasks. Adherent cells removed from plates and flasks by aspirating off the media, washing once in

43 PBS and incubating cells in Trypsin-EDTA at 37°C until all cells lifted from the flask. The cells were then placed in a tube and a small amount of media containing FBS was added to inactivate the trypsin. Cells were pelleted via centrifugation at 1,500 rpm for 5 minutes at room temperature in a centrifuge (Eppendorph Centrifuge 5810 R). The supernatant was then discarded. The cells were resuspended in media (containing FBS) before the appropriate volume of cells was pipetted into a fresh flask containing media.

2.6.2 MTS Proliferation Assays

Cell proliferation was measured using the CellTitre 96® kit (Promega) as outlined by the manufacturer. This kit uses MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3- carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium). These assays measure cellular metabolic activity through NAD(P)H-dependent cellular oxidoreductase enzymes. Briefly, 2000 cells/well were seeded in triplicate in a 96 well plate and cultured in RPMI media supplemented with 10% FBS and PSG. Appropriate CDK4 inhibitor or vehicle alone was applied 24 hours after cells were seeded. Drug and media changes were carried out every 72 hours. All assays were performed in triplicate.

2.6.3 Cell Cycle Analysis

Cell cycle analysis was assessed using a PI assay to specifically analyse DNA content per cell. Cell lines were plated initially at 1 x 105 cells per well in 6 well format. Cells were treated, from 24 hours, with or without CDK4 inhibitor. Cells were collected at different timepoints using trypsin, as previously described (24, 48, 72 hrs). Between 105- 106 cells were placed in 5ml FACs tubes (polystyrene round bottom tube (3520, Becton Dixon)). Cells were pelleted via centrifugation at 1,500 rpm for 5 minutes at room temperature (Eppendorph Centrifuge 5810 R). The supernatant was then discarded and cells resuspended in dropwise in order to permeabilise the cell plasma membranes. The resuspended cells were then stored at 4°C until all timepoints had been processed. Once all timepoints had been processed, cells were pelleted via centrifugation at 1,500 rpm for 5 minutes. A master mix was 10 mls of PBS. The pelletsmade were containing resuspended μl in P) 2mlmg/ml, of the master μl RNAsemixture mg/ml and incubated and for at least 30 minutes in the dark. Analysis of the cell cycle was then assessed by flow cytometry on the

44 cells. Measurements were made using red fluorescence and side-scatter, collecting at least 10,000 events per sample. Residual debris was gated out. Peaks representing apoptosis, G1, G2-M and S phase were analysed according to standard methods (ref).

2.7.4 Colony Forming Assays (CFA)

LS cell lines were plated at 1 x 103 cells in 6 well dishes. Cells were allowed to attach overnight and then were exposed to CDK4 inhibitors and incubated at 37°C. Seventy two hours after drug treatment a media change was performed. Between 7 10 days later, depending on the control well confluency, the cells were fixed and stained– with crystal violet. Uniform colonies were counted using MetaMorph® premium microscopy automation and image analysis software (Molecular Devices, CA, USA). The surviving fraction was calculated as a fraction of the untreated sample and plotted using Prism 5 for Mac OSX.

2.6.5 Apoptosis assay using Annexin-V and PI

Cells were seeded at 1 X 105 cells per well in 6 well format. CDK4 inhibitors were applied 24 hours after seeding, to allow time for cells to adhere to the flask. The inhibitor was applied for 48 hours. Media was removed and saved in 5 ml culture tubes to retain cells that had undergone apoptosis. Adherent cells were collected and added to the media, cells were washed twice with ice-cold P

TM Annexin V, BD Pharminogen BS). andThe then100 resuspended in μl X Binding Buffer PE TM μl of solution) and 5 was then-AAD transferred was added toto athe ml samples. culture Cellstube. wereμl of gently PE Annexin vortexed V BD and Pharminogen incubated for 20 minutesμl of at room temperature in the dark. An additional 400 analysed by flow cytometry straightμl of binding away. buffer was added to each tube and cells were

Controls were used to set up for compensation and quadrants on flow cytometer. During apoptosis there is usually an increase in SSC with a reduction in FSC. The controls included unstained cells, cells stained with PE Annexin V alone and cells stained with 7- AAD alone. The untreated population was used to define the baseline level of apoptotic and dead cells To isolate the apoptotic cell population side scatter (SSC) was plotted (y axis) against

45 forward scatter (FSC) on the x axis. The percentage of cells that underwent apoptosis was then calculated by subtracting the total amount of apoptotic cells, within the sample, from the controls. This method is described in the BD FACSuite Software Research Assays Guide (BD FACSuite, 2010). Histograms were analysed via ModFit LTTM (Topsham, ME, USA) to calculate total apoptotic cells.

2.7 RNA ISOLATION AND RT- PCR

2.7.1 RNA Isolation from Cells or Tissue

For isolation of RNA from cells, the media was aspirated from 6 well plates, and cells were washed twice with PBS. Cells were then lysed, with the addition of 1 ml of Trizol ® Reagent for 2-3 min with gentle agitation, RNA was isolated as follows and samples were stored at 20°C. For RNA isolation from animal tissue, tissue samples <100 mg were homogenised in 1 ml of Trizol® for 3 minutes before being left to incubate at room temperature for 2-3 minutes. The Trizol was removed from the plates or from the homogenised samples and placed into a 2ml tube. Chloroform 0.2ml/1ml Trizol was added and the samples shaken vigorously by hand for 15 seconds. Samples were incubated at room temperature for 3 minutes. Lysates were then centrifuged at 12,000 x g for 15 minutes at 4°C. The aqueous clear phase was then transferred to a fresh tube and the organic phase discarded. RNA was precipitated by addition of 0.5ml isopropyl alcohol /1ml of Trizol initially used and the samples left to incubate for 10 minutes at room temperature. Samples were then centrifuged at 12, 000 g for 10 minutes at 4°C. Supernatant was removed and the RNA pellet was washed with 1ml of 75% ETOH per 1 ml of Trizol® reagent which was used for the initial homogenisation. Sample was then mixed by vortex and centrifuged at 7,500 g for 5 minutes at 4°C. Ethanol was then removed and the RNA pellet air-dried for 5- 10 minutes. Pellets were re-suspended in RNase-free water. Amount of RNA was quantified by measuring the absorbance at 260 nm and 280 nm using NanoDrop spectrophometer (Thermo Scientific, Wilmington, DE) and products were stored at -20°C until use.

46 2.7.2 cDNA preparation

tubes and volume made up to 32 water. A

A total of μg of RNA-250ng was Oligo added DTs to were PCR added and the RNA was denaturedμl withat 70 °C for 5 furtherminutes. μl Tubes of were then cooled quickly on ice for 5 minutes. A master mix consisting of 10 - -MLV reverse transcriptase (RT) per tube was added

μland M incubatedMLV TR, for μl 10dNTPs, minutes and at μlroom M temperature. The reaction was incubated at 42°C for 50 minutes and inactivated for 15 minutes at 70°C.

2.7.3 Primers for RT-PCR

Primers were designed for various experiments using gene reference sequences accessed through information hyperlinked over proteins (IHOP) to the National Center for Biotechnology Information (NCBI) utilizing the Primer3 softw - and annealing temperatures are as shown in Table 5. are. The sequences

47

Table 6: Primer sequences used in RT-PCR experiments. Primers were designed using Primer3 software - species are given for each construct. . The annealing temperature, sequence and

2.7.4 Quantitative RT-PCR

Quantitative PCR was carried out using 0.2 ul of mix

mix (MountaincDNA .vi μl mixed with a PCR master of forwardcontaining and μlreverse SyBr Greenprimers. PCR cDNA master reactions were set upew, in CA,triplicate USA, .per μloligonucleotide. water, .μl Real time PCR was run on Applied Biosystems SDS 7900HT Fast Real time PCR System version 2.3 (Life Technologies, Melbourne Australia). The protocol for PCR amplification included 50°C for two minutes, 95°C for 10 minutes, 95°C for 15 seconds, 60°C for 1 minute. This sequence was repeated 40 times. The samples were next incubated for 15 seconds at 95°C, then 15 seconds at 60°C. The results were analysed using SDS version 2.3 software and plotted as using Prism 5 for Mac OSX.

48

2.8 DNA ISOLATION AND ELECTROPHORESIS

2.8.1 DNA isolation from cells and tissues

DNA from the cell lines or mouse tissue was prepared using DNeasy blood and tissue kit as described by the manufacturers (Qiagen, Venlo, The Netherlands). The cells were collected and centrifuged for 5 minutes at 300 x g. The cell pellet, or animal tissue, was resuspended nd 20 was added to digest protein. A totalin PBS of to 200 a final volume of μl, a μl of proteinase K sample wasμl incubated of Buffer at AL 56°C was foradded 10 minutes. to the sample The tube and wasvortexed centrifuged for seconds.to remove The drops from the inside of the lid and (96-100%) added and the sample was mixed by vortexing for 15 seconds. The mixture μl of ETO( was transferred to a QIAamp mini spin column and centrifuged at 6000 x g (8000 rpm) for 1 minute. The column was placed in a new be centrifuged at 6000 x g collectionfor 1 minute. tube The and collection μl of tube Buffer was AW again was replaced. added, and An additionalthe tu 500 was added and the tube was centrifuged at full speed (20, 000 X g; 14,000μl ofrpm) Buffer for 3AW minutes. The column was placed in a fresh 1.5 ml microfuge was added. The tube was incubated at room temperature (15tube 25°C and ) for 1 μl minute of Buffer and AE centrifuged at 6000 X g (8000 rpm) to elute the DNA. The DNA– concentration was then determined by ultraviolet (UV) spectrophotometry at 260 nm using NanoDrop spectrophometer (Thermo Scientific, Wilmington, DE). The DNA was stored at -4°C.

2.8.2 Primers for PCR

Primers for excision of the Lox-P-Stop-Lox-P site are described in section 2.3.2.

2.9.2 PCR Protocol

Protocol for genotyping is described in section 2.3.3.

49 2.9 WESTERN BLOTTING

2.9.1 RIPA Lysis protocol

Cells were collected from 6-well plates as described in 2.7.1. The cell pellet was then washed in 1 ml of ice-cold PBS and spun for 5 minutes at 5000 rpm. The supernatant was removed1M NaF and and 1: the 1000 cells of were 0.1 M resuspended Sodium orthovanadate. in μl of TheR)PA cells lysis were buffer allowed containing to lyse : for 10 of min on ice with gentle pipetting. The lysed cells were spun at 10,000 rpm for 10 minutes at 4°C and the solubilised protein pipetted carefully into a fresh centrifuge tube and stored upon ice. A total of 5µl of each sample was pipetted in duplicate into a 96 well dish for protein estimation. Accordingly, the appropriate amount of sodium dodecyl suphate (SDS) sample buffer (50mM Tris-HCl pH 6.8, 2% SDS, 10% glycerol, 12.5mM ethylenediaminetetraacetic acid (EDTA) 1% , 0.02% BB was added to create a final concentration of 1.5 mg/ml of protein. Prior toβME storage, samples were boiled at 95 °C for 10 minutes, then stored at 4 °C.

2.9.2 Protein Quantitation Protocol

Protein concentration was determined using the DC Protein assay kit (Biorad Laboratories, CA, US). The protein standards were prepared with serial dilution of BSA in PBS at concentrations of 0, 0.0625, 0.125, 0.25, 0.5, 1, 2, 4 and 8 mg/ml. In a 95 well plate 5 µl standards or samples were pipetted in duplicate. To each well was added 25 and mixed, followed by the addition 200 plate was gentlyμl agitated of Reagent to A mix the reagents and allowed to incubateμl at of room Reagent temperature B. The for 15 minutes. Absorbance was read using a Versa Max tunable microplate reader (Molecular Devices, US) at 750nm. Data was analysed using SoftMax® Pro software according to the Lowry protocol for protein estimation (Molecular Devices, US). Following protein quantitation, appropriate the final concentration of protein up amountsto 1.5 mg/ml. of SDS, βME and BB could be added to bring

50 2.9.3 SDS-PAGE separating and stacking gels

SDS-polyacrylamide separating (7-12 %) and stacking (3 %) gels were prepared and allowed to polymerise before running. Protein samples were mixed with sample buffer and heated to 95-100 °C for 3-5 min. Equal amounts of protein (20-40 ug were resolved on the SDS-PAGE gel. Samples were run denatured (in the presence of SDS) and reduced (in the . For mini gels 10-20 µg protein and 5-10 µl of standard were run per lane atpresence 150V/400 of βME mA for 45 min using a BioRad minigel apparatus.

2.9.4 Immunoblotting For immunoblotting, SDS-PAGE gels were equilibrated in transfer buffer for 15 min and proteins transferred onto Immobulin-P membrane using a BioRad Semi Dry Transfer cell at 10-15V for 45 min. To block non-specific binding, membranes were blocked with 5% nonfat dry milk in Tris-buffered saline/Tween 20 (TBS-Tween). Membranes were probed at room temperature with appropriate concentration of primary antibodies (Table 5) for 2 hours or overnight at 4°C. After being washed (6 X 5 minutes, TBS-Tween) the membranes were incubated for 1 hour with appropriate secondary antibody at a dilution of 1: 10,000. Membranes were washed (2 X 10 minutes, TBS-Tween) and incubated for 1 minute with ECl-enhanced chemiluminescent (Pierce, Thermo Scientific), and exposed to Kodak Omat film. Dilutions were chosen from recommendations provided by the antibody companies.

51 2.9.5 Antibodies for Western Blotting

Table 7: Primary Antibodies for Western Blotting. The species and source of the antibody is listed, together with the working dilution used in the experiments.

2.10 TRANSFECTION EXPERIMENTS

2.10.1 Transient Transfections : Small interfering RNA (SiRNA)

The siRNA reagents used were Dharmacon Human siGENOME® SMARTpool® or onTARGET® Smartpool. LS cell lines were cultured in their appropriate media and seeded at 1.1 X 105 cells per well, in 6 well format 24 hours prior to transfection. The cells were incubated at 37°C, 5% (v/v) CO2. DharmaFECT 2 transfection reagent (Thermo Scientific, US) 3 were combined and incubated at room temperatureμl per well for 5and minutes. Optimem, A master μl/well, mix of chosen siRNA smartpool (Table 7) was made up well) in combination with optime This fromensured μM a finalstock siRNA μl concentration per of 40 nM. SiRNA / Optimemm and μl Lipid/Optimem per well. were combined and vortexed prior to being added, dropwise, to wells. Cell lines were analysed 48 hours after transfection to quantify transcript levels, as described in 2.8.

52

Table 8: SiRNA smartpool complexes used in SiRNA transfection experiments. SiRNA reagents are listed, together with the relevant gene target, Entrez gene ID, Dharmacon cataglogue number and species targeted.

2.10.2 Stable Transfections

2.10.2.1 MEF Transfections

Vectors containing pcDNA3, CDK4, MDM2 and pBabe were obtained from the Genetics and Genomics Laboratory, Peter MacCallum Cancer Centre (Melbourne AUS). All vectors contained a cytomegalovirus (CMV) backbone and had a range of selection cassettes (Table 8).

53

Vector Promoter Selection MDM2 CMV Neomycin CDK4 CMV Puromycin PBabe CMV Puromycin PCDNA3 CMV Neomycin

Table 9: Vectors used for MEFS transfections. The vectors used in the MEF transfections are outlined, together with promoter and selection antibiotic required.

Vectors were stably transfected into MDM2 MEFS according to the NanoJuice Transfection kit Protocol (Novagen®, Emd-biosciences, US). The day prior to transfection, cells were plated at 1 X 105 cells in a 6 well plate in MEF growth media. Cells were incubated overnight at 37°C (5% CO2). The cells were plated at a seeding density to enable 50% confluency at the time of transfection. Each transfection condition was set up in triplicate. A master mix was made up for each condition required (Table 9).

Table 10: NanoJuice Transfection Master Mix. Nanojuice master mix specifications required for transfection MEF experiments.

Serum free media was added to NanoJuice core transfection reagent followed by NanoJuice Transfection booster and incubated at room temperature for 5 minutes. The per well) was then added and mixed by gentle pipetting. The transfection mixtureDNA .μgwas then incubated at room temperature for 15 minutes. 100µl of the master mix was added, dropwise, to each well . The transfection mixture was distributed by rocking the plate.

54 After 72 hours the media was removed and the cells were washed with PBS. Cells were then selected with puromycin (2mg/ml) for 48 hours. After puromycin selection the media A puromycin resistant wascassette removed, was present and neomycin in CDK4 was and addedPBABE μg/ml vectors, whilst to the a neomycin cells. resistant cassette was present in MDM2 and PCDNA3 vectors.

2.10.2.2 Stable short hairpin RNA (shRNA) transfections

Short-hairpin RNA plasmids were purchased from the Victorian Cancer Functional Genomics Laboratory (VCFG) or Queensland University (QU). These were supplied in glycerol in a 96 well plate. Initially, a small sample was taken using a 200 tip and incubated in a starter culture of 5 ml LB medium containing the appropriateμl sterile antibiotic (Table 10). The LB broth was incubated overnight at 37°C with vigorous shaking (300 rpm). The bacterial cells were next harvested by centrifugation at 600 x g for 15 minutes at 4°C. Plasmids were isolated using the (Qiagen® Plasmid Purification Minikit, USA). Briefly, the bacterial pellet was resuspended in 0.3ml of Buffer P1 and 0.3ml of Buffer P2 was added and mixed thoroughly by inverting the tube 4-6 times. Samples were then allowed to incubate at room temperature for 5 minutes then 0.3 ml of Buffer P3 was added and mixed immediately before allowing the mixture to incubate on ice for 5 minutes. The samples were then centrifuged at maximum speed in a microcentrifuge for 10 minutes. The supernatant was removed and applied to Qiagen Tip 20 following equilibration with 1 ml Buffer QBT. The supernatant was allowed to drip through QIAGEN-tip 20 by gravity flow. Qiagen-tip 20 was then washed twice with 2 ml of Buffer QC and DNA was eluted using 0.8 ml Buffer QF. DNA was precipitated by adding 0.7 ml of isopropanol. Samples were vortexed and then 000 rpm for 30 minutes in a microfuge. The supernatant was removed centrifugedand the DNA at pellet , washed with 70% ethanol. Samples were spun again at 10,000 rpm for 10 minutes. The supernatant was removed and the pellet allowed to air-dry for 10 minutes and then resuspended in TE Buffer. DNA concentration was then determined by UV spectrophotometry at 260 nm using NanoDrop spectrophometer (Thermo Scientific, Wilmington, DE). Samples were stored at -20°C prior to use.

55

Antibiotic Concentration in LB Broth Ampicillin Carbinacillin Zeocin 25μg/ml Table 11 : Antibiotics added to LB broth for Bacterialμg/ml Culture. ShRNA were supplied in μg/ml glycerol and grown in 5 ml LB broth containing ampicillin, carbinacillin and zeocin at concentrations outlined.

To produce recombinant lentivirus for target cell infection, vectors of interest were co- transfected with Lenti-XTMHT Packaging mix (Clontech) into HEK 293 T cells, in order to assemble the vector. Transfection followed a PEI transfection protocol as first described by Reed et al(Reed, Staley et al. 2006). HEK 293T cells were seeded on the afternoon of Day 1 at either 5 X 106 cells per T75 flask in 12 ml DMEM + 10% Tet-free FBS. On day 2 co- transfection was performed. A master mix was made in microfuge tubes (Table 11).

Transfection Ingredient T75 T175 Lenti-XTM HT packaging mix PGIPZ construct / pool μl μl DMEM (no supplements) μl μl μg/ μl μl μl Table 12: Master mix for PEI Transfection. Master mix required for co-transfection of HEK293T cells with relevant vectors of interest.

Twenty eight vectors were co-transfected using the Lenti-XTM packaging approach (Table 13. Each individual master mix was vortexed DNA according to the Table 12. and PE) added at . μg of

T75 T175 PEI Table 13: Lenti-X™ HT PEI transfection. Master mix required for co-transfection of HEK . μl . μl 293T cells with relevant vectors of interest.

Microcentrifuge tubes were again vortexed and incubated at room temperature for 10 minutes. Following incubation, the mixture was added to cells in either T75 or T175 flasks, swirling to distribute. Cells were incubated overnight at 37°C. On day 3, spent media was aspirated from cells and then replaced with appropriate amount of TET free 10% DMEM. On

56 yamide glass fibre

® daypre-filter the (Minisartsupernatants, Sartorius were removed AG, Germany) and filtered and stored through at -80°Ca . until μm pol required for cell transduction.

LS cell lines were plated 24 hours prior to transduction at 0.1 X 106 cells per well in 12 well format. Viral supernatants were added (1ml) neat to seeded cells, in duplicate. Supernatant was left for 48 hours and then media changed, with viral supernatant added for another 48 hours. As all shRNA contained a pGiPZ backbone, transduction efficiency was checked by identifying GFP fluoresecent cells via fluorescent microscopy. Transduced cells were selected using puromycin μg/ml for a total of-transduced hours, and cells. then kept in puromycin μg/ml for a total of week to eliminate non

57

Table 14: GIPZ Lentiviral ShRNA. The range of vectors used in shRNA experiments are outlined. All vectors were supplied by Dharmacon. The vector targets are listed together with their Entrez Gene identification and Dharmacon oligo identification.

58 2.11 CDK4 INHIBITORS

A range of CDK4 inhibitors were used, as summarised in Table 14.

Table 15: CDK4 inhibitors. Summary of the various CDK4 inhibitors used in these studies.

2.12 PRIMARY siRNA SCREEN

2.12.1 Optimisation studies

Prior to high-throughput screening, conditions were optimised for cell number, transfection reagent and concentration of CDK4 inhibitor. The overall protocol for experiment screening is shown in figure 5.

59

Figure 5 Experimental design. The experimental design was carried out in 384 well plates. Each experiment contained drug and non-drug plates in duplicate. Cells were reverse transfected with siRNA on day 1 and a media change was performed on day 2 with PD0332991 application in the drug plates. PD0332991 was changed again at day 4 and then experiment readout was performed at day 6. Cell Titer Glo was the cell viability readout assay chosen.

2.12.1.1 Optimisation of cell density

A cell seeding experiment was performed to determine the optimal number of seeded cells in order to achieve 85% confluency by day 6-post transfection. Cells were seeded and reverse mock transfected at either 500 or 700 cells per well, in a 384 well plate, in duplicate, with or without PD0332991. Drug and media were applied on day 2 and day 4 as per Figure 4. The drug dose used was 1500nM of PD0332991. A total of 24 replicates were used to assess cell density for this experiment.

2.12.1.2 Optimisation of siRNA delivery system

Transfection efficiency was assessed using lipid reagents from Dharmacon: DharmaFECT 1 (DF1), DharmaFECT 2 (DF2) and DharmaFECT 3 (DF3). 449B cells were seeded at 600 cells per well in 384 well format and reverse transfected with siGlo red fluorescent siRNA (scrambled non-targeting) and DF1, DF2, DF3. A range of transfection reagent concentrations were used (0- l). Transfection efficiency was assessed at 24 hours by counting the total number of siGLO.μ red fluorescent cells divided by the total number of nuclei per field. This was established visually, and not by quantitative imaging. The control

60 well was mock transfected without lipid to provide baseline number cells/well. An appropriate lipid concentration was chosen, based upon the results of these studies.

2.12.1.3 Optimisation of siRNA concentration

Determining the optimal siRNA concentration was determined by quantifying efficacy of knockdown by RT-PCR at 24 and 48 hours. In addition, the efficacy of knockdown was determined by protein expression of the gene target at 72 hours by Western Blot. Previous laboratory experience within the Victorian Centre for Functional Genomics (VCFG) suggested an siRNA concentration of either 20nM or 40nM should be optimal for gene target knockdown. The 449B cell line was seeded at 600 cells per well and reverse transfected with siRNA at either 20nM or 40nM. A 384 well plate was prepared using knockdown of Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as a control and Dharmacon siRNA targeting RB1 to assess knockdown efficiency. Expression was assessed at 24 and 48 hours post siRNA transfection using the RT-PCR techniques outlined in 2.8.

In a separate experiment 449B cells were seeded at 1.1 X 105 cells per well in 6 well format. siRNA targeting RB1 was transfected using a siRNA concentration of 40nM. A Mock transfected plate was used as the control. Protein was collected at 72 hours and Western Blot was performed according to 2.10.

2.12.1.4 Optimisation of Cell Titer Glo (CTG) assay

Cell viability was the CTG luminescent assay (Promega , Wisconsin, USA). This is a luminescent assay that quantifies intracellular ATP after cell lysis, through generation of a luminescent signal. In this CTG optimisation assay 449B cells were seeded at 600 cells per well and reverse transfected with lipid only in 384 well plates. PD0332991 was applied across a range of doses (20, 100, 200, 500, 1000, 1500nM) in duplicates of 12 in the 384 well plate. Plates were prepared in duplicate. CTG readouts were taken on day 6 of the assay at either a 30 minute or 2 hour timepoint post CTG application. Before the readout assay was performed, CTG was initially thawed and left at room temperature for 2 hours prior to the experiment. CTG reagent was applied to cells in media at a 1:3 dilution. The plates were put on a shaker for 2 minutes to distribute the reagent thoroughly. Plates were batched for

61 screening at either the 30 minute or 2 hour timepoint. Luminescence was detected using the Synergy H4 high-throughput multimode microplate reader (Biotek).

2.12.1.5 Optimisation of control plates

Initially, control plates were set-up for each week of experiments. Controls for the experiment consisted of mock transfections (DharmaFECT alone), and transfection with siGENOME SmartPOOLS from Dharmacon RNAi Technologies (Thermo Fischer Scientific) for CDK4, RB1, and Polo-like kinase 1 (PLK1). A total of 120 control and set-up as per Figure 6. Control plates were labelledul of andμM stored siRNA at was -80°C added until per ready for use. On the day of transfection the control plate was allowed to defrost at room temperature for 15 minutes, and spun at 500 rpm before use. Optimem and lipid were then prepared for the 3 assay plates (1A/2A/3A). This required 70 ml Opti-MEM® (Life

Technologies,Scientific, US). Victoria,Opti- Australia and μl DharmaFECT transfection reagent Thermo with HEPES and sodiumMEM® bicarbonate is a modification and supplements. of Eagles Minimum Opti-MEM® Essential and Dharmaffect Media buffered were allowed to incubate in a 70 ml sterile specimen container (Grale Scientific, Victoria, Australia). Optim-MEM®/DharmaFECT were then dispensed into the assay plates using BioTeK 406 (BioTek, Winoski VT). The siRNA library of 18 120 siGENOME complexes was supplied from Dharmacon RNAi Technologies (Thermo Fischer Scientific). The library plates were in 384 well format. Each well contained a SMARTpool of 4 siRNA targeting different sequences of the target transcript. The siRNA library plates were hydrated and diluted to 1nmol using 1 X siRNA buffer (Dharmacon). This was performed by the VCFG using the Sciclone ALH3000 (Caliper Life Sciences, Hopkinton, MA). The pre-prepared Opti- MEM/DharmaFECT would be mixed with the appropriate amount of siRNA from the library plates, allowing it to complex for 20 minutes. Cells were then reverse transfected at a final concentration of 40nmol/L with the SMARTpool library using the Sciclone ALH3000 (Caliper Life Sciences, Hopkinton, MA). The specifics of transfection conditions are included in Table 14. Each library plate screened was transfected in duplicate with 2 independent biological replicates with, and without drug. The CDK4 inhibitor PD0332991 (Fry, Harvey et al. 2004) was added to plates C and D, 24 hours post transfection, with plates A and B undergoing a media change only. Drug and media were changed at 96 hours. The assay was

62 optimised to reach confluency at 144 hours (6 days) and the read-out assay, for viability, was then performed.

Figure 6: Control Set-up for High-Throughput screen. Control plate set-up for screen including positive (RB, PLK, CDK4) and negative (Mock, Risk Free). SiBuffer and empty control wells were used to control for evaporation and CTG bleeding.

2.12.2 Statistical Identification of Screen hits

The statistical identification of hits was defined for both resistance and lethal phenotypes. The pipeline for the screen is shown in Figure 7. To compare the data collected across drug exposed and non-drug plates, normalisation of all samples to the average of the Mock controls on each plate, averaged for the A and B plate, was performed. Following this,

63 results were robust z score normalised (Brough R 2011). A positive Z-score represents gain in cell viability, whilst a negative Z score represents loss of cell viability. The gain in viability required to represent a is further defined below

resistance hit

Figure 7: Pipeline for determining screen hits Non-drug plates were first analysed. Those siRNA with fold change to mock (FCM -offs were defined by the positive control for lethality <. PLK1. were definedsiRNA that as lethal. were determined The cut viable in the non-drug plates, with FCM 0.2-1.0 were further analysed in PD0332991 exposed plates. Resistance hits were defined as those siRNA that were viable in non-drug plates, and thus 2.49 / FCM . The cut-off for z scores and FCM were determined by elicitedRB1, the a positive z score control for resistance . phenotype. Potent inhibition hits were those siRNA targets that produced a viable phenotype in the non-drug plates, but a strong inhibitory phenotype in the drug exposed plates. Inhibition was defined as a FCM

..

64 2.12.3 Quality Control Metrics

Screening data was assessed weekly for robustness and reproducibility via multi- parametric quality control analysis. The quality control measures produced each week are summarised in Table 15.

Table 16: Quality Control Metrics. The range of quality control metrics used within primary, secondary and tertiary screens.

2.13 SECONDARY AND TERTIARY SCREENS

The top 400 hits from the primary screen were put through a secondary screen process. The secondary screen is a deconvolution step whereby the four duplexes from each of the siRNA SMARTpools are individually screened. The duplexes were cherry-picked and screened at a concentration of 25 nM. The duplexes were aliquoted in 384 well assay plates in a precise all 4 duplexes on the same plate and empty columns left for

randomcontrols as arrangement outlined in sectionwith 2.13.1.4. There were 1600 individual duplexes in the secondary screen. The cells were reverse transfected at 600 cells per well with drug and media change at day 2 and day 4 as per the primary screen. The drug concentration of PD0332991 was again 1500nM. CTG readout was performed at day 6.

The tertiary screen involved screening the verified duplex hits from the secondary screen in alternate CDK4 amplified cell lines 778 and T1000. The experimental design was identical to the primary screen.

65 2.14 VALIDATION STUDIES OF SCREEN

Functional validation studies on hits identified through the secondary and tertiary screen were carried out. CFA were first initiated using siRNA targeting the genes of interest. 449B cells were seeded at 1.1 X 105 cells per well, in 6 well format, and reverse transfected, in duplicate, with siRNA targeting RB1, ARRB2, CPSF1, DYSF, LATS2, NCOA6, SCLY and SNRPA. A mock transfection was used as the control. PD0332991 was applied after 24 hours and changed every 72 hours. The colony assays were allowed to grow out until 7 days and quantified as described in 2.7.4.

Following this, more short-hairpin plasmids targeting ARRB2, LATS2 and DYSF were purchased from Dharmacon and stable 449B cell lines generated using the approach described in 2.10.2.

66 3.0 CHAPTER 3: CHARACTERISATION OF MDM2 TRANSGENIC MICE

3.1 INTRODUCTION

3.1.1 MDM2

MDM2, also known as E3 ubiquitin ligase, was first identified as an amplified region found on the double minute chromosomes in a transformed murine BALB/C cell line (Cahilly- Snyder, Yang-Feng et al. 1987). In humans, MDM2 is located at chromosome 12q13-q14 and encodes a 90KDa protein. In mice, Mdm2 is located at chromosome 12q15. MDM2 is described as an oncogene due to its negative regulation of the tumour suppressor p53. The MDM2 protein was first shown to bind to p53 in rat cells transfected with the p53 gene (Momand, 1992). There are several mechanisms by which MDM2 can modify p53 activity. MDM2 can form a tight complex with p53 which inhibits its transcriptional activity. MDM2, as an E3 ubiquitin ligase, can also target p53 for proteasomal degradation (Eischen and Lozano 2009), and NIH-3T3 cells that overexpress MDM2, have been found to undergo transformation(Fakharzadeh, Trusko et al. 1991). This demonstrates how MDM2 can enhance the tumorigenicity of cells. There is a growing body of evidence that MDM2 plays a role in cell transformation that is independent of its regulation of p53 (Brekman, Singh et al. 2011). In regard to the cell cycle, MDM2 has been shown to interact directly with several proteins including RB1, SMAD and MDM2 binding protein (MTBP)(Bouska and Eischen 2009; De Clercq, Gembarska et al. 2010). MDM2 is overexpressed in 10% of all cancers(De Clercq, Gembarska et al. 2010) including, high-grade central nervous system tumours(Reifenberger, Liu et al. 1993), sarcomas(Oliner, Kinzler et al. 1992) and lymphomas(Watanabe, Hotta et al. 1994). Overexpression of MDM2 can result from gene amplification, increased transcription, increased or translation, depending on the cancer subtype (Capoulade, Bressac-de Paillerets et al. 1998; Momand, Jung et al. 1998).

3.1.2 Mouse studies investigating the role of MDM2

There are many reported mouse models that investigate the functional role of Mdm2 and p53. To date, a range of Mdm2 knockout, knockin, and transgenic mouse models have been generated (reviewed (Xiong 2013) ) (Jones, Roe et al. 1995; Montes de Oca Luna, Wagner et

67 al. 1995). One of the first models described was an Mdm2 null allele mouse. The null allele ensured that two-thirds of the protein coding sequence was deleted(Montes de Oca Luna, Wagner et al. 1995). Mice that were heterozygous for the Mdm2 deletion were phenotypically normal and fertile. However, mice with a homozygous deletion of Mdm2, died, usually by day 5 of the embryonal period. Mice that were heterozygous for the null allele were then crossed with p53 null mice. The embryonic lethality phenotype, observed in the Mdm2 null mice, could be rescued when p53 was inactivated(Montes de Oca Luna, Wagner et al. 1995). These findings highlight the critical role of Mdm2 and p53 play during development, implying that embryonic lethality, induced by the absence of Mdm2, is secondary to the inability to down-regulate p53.

The effect of a reduction in Mdm2 has been further investigated by the generation of mice carrying a hypomorphic allele of Mdm2(Mendrysa, McElwee et al. 2003). The Mdm2puro allele results in a reduction in expression of RNA and protein levels of Mdm2, due to the insertion of a puromycin resistant cassette, at intron 6, of the Mdm2 locus. Mice heterozygous for Mdm2puro+/- were shown to express 30% of the total amount of Mdm2, compared to that expressed in wild type mice. The phenotype of Mdm2puro+/- mice was low body weight, decreased thymus size, decreased total lymphoid cell populations, increased apoptosis in lymphocytes and increased radio sensitivity, compared to wild type mice. The generated Mdm2puro mice did not exhibit the expected patterns of Mendelian inheritance, with the percentage of generated Mdm2puro mice being < 25%. This finding is indicates that some of the mice must have been dying in utero(Mendrysa, McElwee et al. 2003). Previously, Liu et al. had reported on mice containing a p53 hypomorphic allele (p53515C/515C)(Liu, Parant et al. 2004) which has been shown to elicit a partial cell cycle checkpoint, through the transactivation of p21. When p53515C/515C mice were crossed onto Mdm2puro-/- mice, the generated progeny regained the expected Mendelian ratio inheritance pattern, and were found to be morphologically normal at birth. However, the p53515C/515C / Mdm2puro-/- mice then died two weeks post birth, due to severe impairment of hematopoietic stem cell progenitors and cerebellar defects. This mouse model demonstrates that, despite salvaging the embryonic lethality induced by a null Mdm2 allele, there are substantial consequences, related to the addition of the hypomorphic p53 allele. The dire consequences, attributed to the reduced function of p53, have been further explored by Ringshausen et al (Ringshausen, O'Shea et al. 2006). Ringshausen et al. crossed a knockin p53 mouse p53KI/KI,

68 inducible by 4-hydroxytestostorone (4-OHT) (Christophorou, Martin-Zanca et al. 2005) with Mdm2 null mice. The generated cross demonstrated expected Mendelian inheritance, with mice being morphologically normal(Christophorou, Martin-Zanca et al. 2005). Restoration of p53 expression by administration of the 4-OHT ligand during adulthood, resulted in death, secondary to the inhibition of cell proliferation (Ringshausen, O'Shea et al. 2006). This study reveals the delicate balance between p53 and Mdm2, proving that the introduction of p53 function, although permissive of survival in embryogenesis, results in lethality in the post-natal period.

The role of Mdm2 had been further investigated through the development of mice overexpressing this full length gene(Jones, Hancock et al. 1998). Mice were generated by electroporation of the entire Mdm2 gene into embryonic stem cells. Subsequently, the increased copy number of Mdm2 was confirmed in the generated clones. Germ line chimeras were generated, with these mice being crossed with C57BL/6 mice, creating transgenic Mdm2Tg mice. Northern blot analysis confirmed that Mdm2Tg mice contained a four-fold increase in Mdm2, on average, in a variety of tissues, compared to wild type mice. The development of tumours in heterozygous and homozygous transgenic mice were compared with p53-null and wild type mice. Mdm2Tg mice had a higher rate of sarcomas and lymphomas, than wild type mice. Mdm2Tg mice also developed tumours at a faster rate than wild type mice and at a slower rate than p53-null mice. Mdm2Tg homozygous mice developed tumours faster than heterozygous mice. Approximately 50%, of p53-null cohorts developed tumours by 20 weeks, compared with 84 weeks (Mdm2Tg heterozygous) and 61 weeks (Mdm2Tg homozygous). This confirms that the rate of tumorigenesis correlates closely with the level of Mdm2 expression.

Mdm2 has also been shown to play a role in genomic instability, that may contribute to tumorigenesis. Genomic instability is characterised by multiple changes in chromosome structure and number including breaks, deletions, translocation, aneuploidy and polyploidy (Negrini, Gorgoulis et al. 2010). To evaluate genomic instability prior to tumour development, Mdm2 transgenic mice were backcrossed over 10 generations to generate congenic C57/BL6 mice. Genomic instability was quantified by the analysis of ~ 50 metaphases. Each metaphase was examined to quantify the number of chromosome and chromatid breaks, chromosome fusions and metaphases with an abnormal number of

69 chromosomes. This mouse model demonstrated that the overexpression of Mdm2 correlated with increased genomic stability, in comparison to wild type mice (Lushnikova, Bouska et al. 2011). The chromosomal changes identified in the Mdm2 transgenic mice were both more frequent and complex, than the changes observed in wild type mice. Mdm2 transgenic mice were also mated onto Eμ-myc transgenics, with generation of single/double transgenics and wild type mice (Jones, Hancock et al. 1998) (Wang, Lushnikova et al. 2008) (Adams and Cory 1985; Adams, Harris et al. 1985). The Mdm2 mice had both a growth and a survival advantage. Lymphocytes, namely B cell derived from Mdm2 mice, exhibited greater genomic instability than B cells isolated from wild type mice. As previously discussed, these mice were prone to develop lymphoma, likely secondary to the regulatory control of Mdm2 on TP53(Jones, Hancock et al. 1998).

Targeted expression of Mdm2 was further explored, using the Mdm2 minigene (Montes de Oca Luna, Wagner et al. 1995) and the ovine milk promote - . This system allowed for the investigation of Mdm2 overexpressionr lactoglobulinduring mammary LG gland development in mice. Targeted overexpression of Mdm2 in epithelial cells resulted in polyploidy, with a greater propensity for the cells to develop into mammary tumours (Lundgren, Montes de Oca Luna et al. 1997). Nevertheless, not all mouse models support the role of Mdm2 as an oncogene. Mdm2-/-/p53-/- mice have been shown to have shorter latency periods for tumour development than Mdm2+/-/p53-/- mice(McDonnell, Montes de Oca Luna et al. 1999). This demonstrates that, although the majority of mouse studies support the role of Mdm2 as an oncogene, there is some variance amongst the experimental theories as to how profound this oncogenic role is.

3.1.3 Aim 1: To examine the biochemical and functional effects of MDM2 overexpression in vitro.

As MDM2 is amplified in 100% of WDLPS, we wanted to determine what role this gene had on tumorigenesis in the adult mouse. When this study was initiated, there were no transgenic mouse models containing MDM2 that were published. We commissioned the generation of conditional Cre LoxP-STOP-LoxP inducible MDM2 mice, with a GFP reporter, from OzGene. In this chapter, characterization of the MDM2 floxed mouse is described.

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3.2 RESULTS

3.2.1 Generation of Mice

MDM2 transgenic mice were generated on a C57/BL6 background by Ozgene. The Peter MacCallum Cancer Centre Genomics and Genetics laboratory supplied MDM2 in a pCMV vector, that was then cloned into the Ozgene vector (pBluescript SK(+) vector DNA, phagemid excised from lambda ZAP), then sequenced. The generated Cre recombinase inducible knockin LoxP-Stop-LoxP MDM2fl/+ transgene was inserted at the Rosa 26 locus.

Cre-ERT2 mice express strong, tamoxifen-inducible, Cre recombinase activity in all tissues following the exposure of the mice to tamoxifen (Indra, Warot et al. 1999). These mice were obtained from the Humbert laboratory (Peter MacCallum Cancer Centre, Melbourne, AUS). Strains were maintained by heterozygous crosses of Mdm2fl/+ to generate all possible combinations. Crosses were also carried out with Mdm2fl/fl and Cre-ERT2+/- mice. Cre-ERT2 mice were crossed and genotyped as described in the methods. These Cre-ERT2 mice were viable and fertile. Tamoxifen feed was prepared as detailed in the methods. All procedures using animals were reviewed and approved by the Peter MacCallum Cancer Centre Animal Ethics Experimentation Committee (AEEC).

3.2.2 Genotyping and maintenance of mice

The Mdm2fl/fl and wild type alleles were distinguished by specifically designed primers (Figure 1). The Mdm2fl/+

and wild type allele were detected with thatprimers yield ’ a 494-bp fl ACGTTTCCGACTTGAGTTGC’(Mdm2 ) and 285bp (wild type) and PCR ’CATCTCCCTCAGCTCACACA’ product respectively (Figure 2). The Cre-ERT2 allele - ds a 231-bp PCR product

(Figurewas detected 3). with ’GCACGTTCACCGGCATCAAC ’ which yiel

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Figure 1: Primer design for Mdm2fl/+ mouse studies. Primers were designed to genotype the MDM2 mice to flank the LoxP-Stop-Lox P site. The presence of the floxed allele generated a 494 bp PCR product while the wild-type allele generated a 285 bp PCR product.

Figure 2: Genotyping of Mdm2 transgenic mice. Representative example of genotyping results showing the presence of the floxed allele 494 bp in mice 1,2,4 and 5. Mice 1,2 and 4, were heterozygous, as the wild type allele, 294 bp, was also present. Mouse 3 was wild type. Mouse 5 was homozygous for the allele. The molecular weight ladder was New England BioLabs.

72 Approximately 350 mice were generated during the project. As expected, Mendelian inheritance patterns were observed, as mice were essentially wild type, until the Lox-stop- Lox locus was excised. Given that amplified MDM2 had previously been shown to be lethal during embryogenesis, the inheritance pattern confirmed that the lox-P-STOP-lox-P cassettes were intact.

3.2.3 Investigation of MDM2fl/fl mice in vitro

The first studies using these mice were performed using MEFS. Female mice of 14 days gestation were euthanised, and the MEFS were prepared as outlined in the methods. A total of 12 MEF samples were prepared and passaged over a period of two weeks. Removed heads of embryos were genotyped for the presence of the MDM2 transgene. Once passaged, the MEFS were frozen down and stored at -80°C, until ready for use. Only MEFS found to be homozygous for the MDM2 transgene were used for the Cre recombinase viral studies.

3.2.3.1 Viral Induction of gene using Cre-containing viral constructs

Cre recombinase expressing lentiviral supernatants were produced. Several different viral constructs were used, including Lenti-LUCOS, that was kindly donated by Tyler Jacks (Massachusetts Institute of Technology, USA)(DuPage, Dooley et al. 2009) and Lenti-Cre- GFP, that was kindly donated by Andreas Strasser (Walter and Eliza Hall Institute, Australia).

To produce lentivirus, HEK 293T cells were seeded on Day 1 in the afternoon, at either 12 x 106 cells per T175 flask in 25 ml, or 5 x 106 cells in 12 mls DMEM + 10% tetracycline-free FBS. HEK 293T cells were then transfected with viral constructs and Lenti-XTMHT Packaging mix (Clontech) cells using PEI transfection protocol, first described by Reed et al.(Reed, Staley et al. 2006), and detailed in the methods. MEFS were plated 24 hours prior to transduction, at 1 x 105 cells per well, in a 6 well format. A total of 2ml of Lentiviral supernatant (Empty Vector, Lenti-LUCOS and Lenti-Cre-GFP) was added, either neat or at 1:2 dilution, to seeded cells, in triplicate. Adenoviral Cre recombinase supernatants were obtained from Dr. Kathryn Kinross, Molecular Oncology Laboratory (Peter MacCallum Cancer Centre). The adenoviral cre supernatants had previously been quantified with a viral

73 DNA quantity of 2.44 x 109 optical particle units. The adenoviral supernatant was applied at 1:20,000, 1: 10,000 and 1: 5000 dilutions. Cells were transduced with supernatant for 48 hours, the media was then changed, further supernatant was added, for another 48 hours at 37°C. Transduced cells were selected using puromycin ( were μg/mlselect for for transduced a total of 8 cells. hours. Cells Expressionkept in of puromycin the transgene μg/ml was under for a thetotal control of week of the to Ubic promoter to ensure robust expression of MDM2, post Cre recombinase induced excision of the LoxP-STOP-LoxP site. At n IRES-enhanced green fluorescent protein (eGFP) was theengineered, ’ end of to the allow transgene eGFP tagging a of the transgenic cells and evaluation of expression levels when present. Following transduction with viral supernatant, various techniques were used to determine if excision of the LoxP-Stop-LoxP site had occurred. These techniques included: PCR to determine excision of the floxed allele; RT-PCR for expression of MDM2, eGFP and Cre; Western blotting for protein expression and fluorescent microscopy, to examine for the presence of the eGFP reporter.

3.2.3.2 PCR analysis to determine conditional deletion of floxed Mdm2 allele

Upon exposure of the MEFS to viral Cre recombinase, the cells were analysed via PCR for evidence of recombination of the floxed allele. DNA was prepared using the QIAGEN DNeasy® kit. DNA concentration and quality was determined by UV spectrophotometry at 260 nm using the NanoDrop spectrophometer (Thermo Scientific, Wilmington, DE) prior to PCR analysis. The DNA was stored at -4°C until ready for use. PCR primers were designed to detect excision of the LoxP-STOP-LoxP site (Figure 3A/B). The excised allele was detected CACCCGTTCTGTTGGCTTAT , that yield witha 611bp primers PCR product’ if Cre recombinase had’ inducedand ’GCATATAACTTCGTATAGC’ recombination. If the LoxP-STOP- LoxP site was not excised, the PCR product measured 398 bp. Each experiment was repeated 3 times in order to determine the status of the conditional allele. Despite multiple attempts, with exposure to a variety of lentiviral and adenoviral Cre recombinase, the repeat PCR analysis showed failed attempts at LoxP-STOP-LoxP excision (Figure 3C).

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Figure 3. PCR analysis for Cre-mediated recombination (A) Excision primers were designed across the Ubic promoter and LoxP-STOP-LoxP site (arrows) (B) Excision primers yield a 611 base pair (bp) product if conditional excision of LoxP-STOP-LoxP site has occurred, or 398 bp product, if LoxP-STOP-LoxP site remains unexcised. (C) MDM2fl/fl MEFS treated with variety of Cre recombinase supernatants reveals inadequate recombination of the floxed allele, with 398 bp product present in all lentiviral treated cells.

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3.2.3.3 Transfection and transduction of Cre-ERT/MDM2fl/+ MEFS

The IRES-eGFP reporter was engineered into the construct to allow for easier identification of the cells that had undergone Cre-mediated recombination of the floxed MDM2 allele. MEFs transduced by FPCreER-UL2tG vector (Lentiviral LUCOS) or Adenoviral Cre did not show any expression of eGFP by fluorescent microscopy. Transduction with a Lentiviral- Cre-GFP showed that the virus was entering the cells.

HEK 293T cells transfected with both vectors demonstrated a transfection efficiency of 95% (Figure 4A). Lentiviral-Cre-GFP found a MEF transduction efficiency of 50% (Figure 4B). The empty vector for these experiments was pGIPZ, with an EGF cassette, which demonstrated that GFP was able to be reported. These findings showed that despite an adequate transfection and transduction efficiency, Cre-mediated recombination was not occurring.

It was necessary to determine that, along with efficient transduction, the viral supernatants had functional Cre recombinase activity. In order to assess Cre recombinase activity of lentiviral LUCOS and lentiviral-Cre-GFP supernatants, MEFS from Rosa 26 reporter (R26R) mice were obtained from the Phillips Laboratory (Peter MacCallum Cancer Centre). The R26R reporter transgene is highly sensitive to Cre-mediated activation. Low levels of Cre recombinase activity should - galactosidase expression that can be detected by X-

5 Gal staining across all tissues(Sorianoresult in 1999). R26R MEFS were plated at 1 x 10 cells per well and the viral supernatants were applied as described in the methods. Following 48 hours of supernatant exposure, cells that had undergone Cre-mediated excision were identified, using X-Gal histochemistry, to detect -galactosidase activity. X-gal positive cells, correlating with functional Cre recombinase, were present in R26R MEFS exposed to lentiviral LUCOS and Lentiviral-Cre-GFP supernatants (Figure 4C).

The results suggest that despite adequate viral transduction and functional Cre recombinase activity, recombination of the floxed allele was not occurring. The presence of

76 GFP in the empty vector and lentiviral Cre-GFP, made it difficult to assess whether intrinsic GFP was being upregulated.

Lentiviral LUCOS treated MEFS, with known functional Cre recombinase activity, also failed to show GFP expression. This shows that recombination of the floxed allele was not occurring, or potentially, that GFP was not a reliable marker for excision of the floxed allele. Similarly, adenoviral Cre treated MEFS did not demonstrate GFP expression or recombination of the floxed allele. Because eGFP was incorporated in two of the viral backbones, it was necessary to investigate recombination of the floxed allele further without relying on reporter expression. Therefore, the next step was to analyze the MEFS, post transduction, for MDM2 expression via Western Blot.

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Figure 4: Transfection and transduction of Cre-ERT/MDM2fl/+ MEFS. (A) Transfection of HEK 293T cells with empty vector and Lentiviral-Cre-GFP vectors confirms transfection efficiency of 95% using the PEI transfection protocol. (B) Transduction of heterozygote MEFS confirms transduction efficiency of 50% using Lentiviral-Cre-GFP and LUCOS supernatants. (C) Transfection of R26R Cre-deleter MEFS shows positive X- -galactosidase expression and functional Cre recombinase activity in both Lentiviral-Cre-GFP and LUCOS viral supernatants. Gal stains, confirming

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3.2.3.4 Western Blot analysis for MDM2 in viral treated MEFS

Western blot analysis was performed, following MEF transduction, with lentiviral and adenoviral Cre recombinase supernatant, to determine if the expression of MDM2 was increased. The MEFS were plated at 1 X 105 cells per well and viral supernatants were applied as described in the methods. Following selection with puromycin for 5 days, cells were trypsinised using Trypsin-EDTA. Protein was next prepared as per the RIPA lysis protocol. The control for MDM2 expression was protein prepared from 449B WDLPS cell lines. 449B cells are known to overexpress MDM2 (Pedeutour, Suijkerbuijk et al. 1994). Cell lysates containing 25-30 -PAGE and transferred onto nitrocellulose membranes.μg Proteins of protein were were then separated probed bywith SDS an monoclonal antibody against human MDM2 and control -tubulin. Membranes were incubated with ECL-enhanced chemiluminscent prior to exposure and development onto film.

Western blot analyses revealed that, whilst MDM2 was present in the 449B control cell line, MDM2 was not detectable within MEFS that had been exposed to a variety of viral Cre recombinase supernatants (Figure 5). The antibody was specific to human MDM2. This finding shows that, despite adequate viral transduction and a variety of efficient Cre recombinase, excision of the LoxP-STOP-LoxP site and subsequent overexpression of MDM2 was not demonstrated. This result may have been secondary to inadequate recombination, or because of rapid proteasomal degradation of the protein product that has been previously described (De Clercq, Gembarska et al. 2010).

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Figure 5: Western Blot analyses for MDM2 expression in Cre-ERT2/MDM2fl/+MEFS. Endogenous MDM2 is detected by Western blot analysis in extracts from tranduced Cre-ERT2/MDM2fl/+ MEFS. Protein extracted from the 449B cell line was used as the control. No expression of MDM2 was demonstrated in any of the transduced Cre- ERT2/MDM2fl/+ MEFS exposed to Lentiviral-Cre, Lenti LUCOS or Adenoviral-Cre.

80 3.2.4 Generation of MDM2fl/fl crosses onto Cre-ERT2 mice conditional transgenic mice

Whilst the in vitro MEF experiments were conducted, MDM2fl/fl mice were crossed with mice hemizygous for the Cre-ERT2 transgene. Once bred and genotyped, MDM2fl/+CreERT2+ mice were exposed to tamoxifen via their food. Six mice, three females and three males, were fed tamoxifen for one week to induce Cre recombinase activity. Two weeks post feed, mice were euthanised and their tissues were harvested from multiple organs for preparation of DNA, RNA and protein. A small sample of all tissue was embedded in OTC for examining the immunofluorescence of tissues. Samples were analyzed through a variety of techniques as follows, to examine Cre-mediated recombination of the floxed allele.

3.2.4.1 PCR analysis of genomic DNA from conditional Cre-ERT2/ MDM2 mice

Genomic DNA was prepared from mouse tails as detailed in the methods. A Cre-ERT2 mouse, without the conditional MDM2 allele, was used as the control for these studies. PCR analysis was performed using the primers described in Figure 5. Mice #130, 139, 153 and 154 all showed the presence of the floxed, non recombined allele (398bp), pre and post, tamoxifen exposure (Figure 6). This was expected, as the mice were heterozygous for the MDM2fl/+ allele. Cre recombinase was detected in all mice including the Cre-ERT2 control (231 bp), except mouse #137, which failed to work for any of the primer sets. Efficient Cre- mediated recombination was detected in transgenic mice #130, 139, 153 and 154 post tamoxifen feed (611bp). This result was encouraging as it inferred that Cre-mediated excision was occurring in the mouse model because of the strong tamoxifen induced Cre recombinase activity.

To determine if conditional Cre-mediated excision was occurring in all tissues, mice exposed to tamoxifen were euthanised at day fourteen, and tissue samples were collected for genomic DNA analysis. Tissue samples included bone, brain, fat, kidney, liver, muscle, spleen and stomach. Only one mouse out of six (#154) demonstrated Cre-mediated excision within a tissue sample. PCR analysis of genomic DNA isolated from mesenchymal tissue (bone, fat, muscle) of the mouse #154, along with an untreated Cre-ERT2/MDM2fl/+ mouse, is shown in Figure 6B. Presence of the floxed non recombined allele (398 bp) was present within all samples. Efficient Cre-mediated recombination was only detected in the sample isolated from bone, in the tamoxifen treated Cre-ERT/MDM2fl/+ mouse (611bp). All genomic

81 samples were of good quality and actin (335bp) could be detected in all of the samples. However, repeated studies on tissues from different organs, did not confirm these findings. The repeat studies failed to show robust or reproducible results. Although Cre-mediated excision occurred in some mice, it appeared to be sample specific, and poorly reproducible. Overall, the analyses were inconclusive as evidence for recombination.

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Figure 6: Characterisation of the conditional Cre-ERT2/MDM2fl/+ mice. (A) PCR analysis of genomic DNA extracted from mouse tails from Cre ERT2/ MDM2fl/+, and Cre- ERT2 control mice. Samples 1-6 were taken pre-tamoxifen exposure. Samples 6-12 were taken 14 days post-tamoxifen exposure. Mice #130, 139, 153 and 154 show the presence of the floxed non-recombined allele (398 bp). Cre is present in all mice including the Cre- ERT2 control, except mouse 137 (231hp). Efficient Cre-mediated recombination was detected in transgenic mice #130, 139, 153, and 154 following the tamoxifen feed (611bp). (B) PCR analysis of genomic DNA isolated from tissues from Cre ERT2/ MDM2fl/+ tamoxifen treated mouse (#154) and Cre-ERT2/MDM2fl/+ untreated control. M; muscle: B; bone: F; fat. Samples showed the presence of the floxed non recombined allele (398bp) and actin (335bp). Efficient Cre-mediated recombination was only detected in the treated mouse in the sample isolated from bone (611bp). 83 3.2.4.2 Determination of GFP expression, via microscopy, in tissue from conditional Cre-ERT2/ MDM2 mice

Tissues samples from Cre-ERT2/MDM2fl/+ mice, exposed to tamoxifen, were embedded in OTC compound and 4 µm sections cut by cryotome, to analyze the presence of GFP. An IL-6 mouse, without the MDM2 transgene, was used as a control for this experiment. Fluorescence was quantified using a Leica LED 4000 B fluorescent microscope at 10X magnification. Unfortunately the control and tamoxifen treated Cre-ERT2/MDM2fl/+ mice, demonstrated identical GFP expression (Figure 7). Autofluorescence is common in mice tissues, especially in collagen and elastin rich sources within the skin(Tam, Upadhyay et al. 2007). Unfortunately, due to the autofluorescence of tissues within both control and tamoxifen fed Cre-ERT2/ MDM2fl/+ mice, the presence of the GFP reporter, the surrogate marker of MDM2 overexpression could not be determined..

Figure 7: Detecting GFP within tissue samples from conditional Cre-ERT/ MDM2 mice . Green fluorescence is detected in the skin of both control and tamoxifen exposed Cre- ERT/MDM2fl/+mice. The autofluorescence of murine tissues make it impossible to determine if the IRES-GFP reporter has been transcribed.

84 3.2.4.3 Determination of GFP expression in tissue using flow cytometry from conditional Cre-ERT2/ MDM2 mice

Given that examination of embedded tissue was unable to decipher between autofluorescence and the presence of the IRES-GFP reporter, an alternative method of reporter GFP analysis was desirable. Quantifying GFP, via flow cytometry, is a convenient and reliable way of determining expression levels. Blood was extracted from the Cre- ERT/MDM2fl/+ via eye bleeds, prior to, and post, tamoxifen exposure, and prior to mice being euthanised on day fourteen. Cells were prepared for flow cytometry and GFP sorting as detailed in the methods. Figure 8 shows the histogram of the fluorescence data from the control and the tamoxifen exposed Cre-ERT/MDM2fl/+ mice. A total of 10,000 events were collected using the flow cytometer. There was no increase in reporter GFP expression in the tamoxifen-exposed mouse, again pointing towards inadequate Cre-mediated recombination of the floxed allele, or with the utility of the GFP reporter as a measure of recombination.

85 Figure 8: Flow cytometry analyses of reporter GFP expression. The histograms were generated from control Cre-ERT2/MDM2fl/+ and tamoxifen exposed Cre- ERT2/MDM2fl/+ (#154), following eGFP sorting from blood samples. The tamoxifen exposed mouse showed no evidence of overexpression of GFP, and thus inadequate recombination of the floxed allele.

3.2.4.4 RT-PCR for overexpression of Mdm2, MDM2, Cre and GFP in tissue from conditional Cre-ERT2/ MDM2 mice

To quantitate an increase in transcript level of MDM2, GFP and Cre in the tissues from conditional Cre- ERT2/MDM2fl/+ mice, quantitative RT-PCR was performed both pre and post-tamoxifen exposure. Expression of mouse Mdm2 was included as a control. RNA was prepared from mesenchymal tissue in four tamoxifen treated Cre- ERT2/MDM2fl/+ mice, as detailed in the methods. A control interleukin 6 (IL-6) mouse was used for this study to convey absence of the floxed allele. RT-PCR studies confirmed a 50 80 fold increase in

fl/+ MDM2 transcript level between Cre-ERT2/MDM2 mice and controls– (Figure 9A). This increase in MDM2 transcript levels was not seen in all mice, but was demonstrated in #154 and 130. In addition, the overexpression of MDM2 was not uniform across all sampled

86 tissues, being only demonstrated in adipose samples. Interestingly, the mice that overexpressed MDM2 (#130,154) had previously shown recombination of the floxed allele via PCR. Similar trends were demonstrated in both eGFP (Figure 9B) and Cre (Figure 9C) transcript expression. However, like MDM2, the expression of both eGFP and Cre seemed to be sample specific with overexpression demonstrated in fat samples of #130 and 154 mice. Mouse Mdm2 was also overexpressed in #130 and 154 in comparison to the IL-6 control (Figure 9D). This may reflect cross reactivity of the primers, or indicate something within the two samples that was interpreted as overexpression across the primer set. Although these results were encouraging for the conditional upregulation of MDM2, Cre and eGFP, at the transcript level, the results were not robust enough to confirm this within all tissue types and were not reproducible.

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Figure 9: RT-PCR analysis for evidence of excision of the floxed MDM2 allele. RT-PCR analysis of cDNA isolated from Cre-ERT2/MDM2fl/+ tamoxifen exposed mice and control (IL-6). There is a 50-80 fold overexpression of MDM2 and GFP in fat samples from #130 and 154 in comparison to wild type (WT) (Figure A and B). Overexpression of Cre, in the order of 20-250 fold, in comparison to WT in mice #130, #153 and #154 (Figure C). These findings are sample specific and not uniform across all mesenchymal tissue types. Mdm2 is overexpressed in fat samples from #130 and 154 (Figure D).

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3.2.4.5 Western Blot analyses for MDM2 overexpression in tissue from conditional Cre- ERT2/ MDM2 mice

Western blot analyses were performed on protein derived from mesenchymal tissue, and from tamoxifen exposed Cre-ERT/MDM2fl/- mice. Protein was prepared from mouse tissue as per the RIPA lysis protocol described in the methods. Cell lysates containing 25- protein were separated by SDS-PAGE and transferred onto nitrocellulose membranes.μg of Proteins were then probed with an antibody -tubulin. 449B cell line lysates were used as the experimental controlsfor. Westernhuman MDM blot analyses and revealed that MDM2 was present in the 449B control and #130 bone sample (Figure 10). The #130 mouse had also been shown via RT-PCR to demonstrate increased MDM2 at the transcriptional level in fat. MDM2 was not overexpressed in any of the remaining tamoxifen exposed mice.

After 12 months of work, trying to characterise the MDM2fl/fl mice, few positive results had been forthcoming. The results are summarised in Table 1. The data failed to show convincing evidence of recombination of the floxed allele, or over-expression of MDM2, for reasons that are not fully understood.

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Figure 10: Western Blot analysis for overexpression of MDM2 post Cre mediated recombination of the floxed allele. Mesenchymal tissue was harvested from tamoxifen exposed mice (#153, 130, 154). The 449B WDLPS line was used as the control. Efficient Cre-mediated recombination and overexpression of MDM2 was only present in one tamoxifen exposed mouse (#130). The overexpression was limited to the sample from bone.

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Table 1: Summary of in vitro and in vivo studies for transgenic Cre- ERT/MDM2fl mice. This table forms a summary of the findings from all the experiments performed in vitro on Cre-ERT2/MDM2fl MEFS and, in vivo, on tissues prepared from Cre-ERT2/MDM2fl mice. ** Contains eGFP backbone NP; not performed

91 3.2.5 Over-expression of MDM2 and CDK4 in vitro

In a separate set of experiments, the consequences of overexpression of MDM2 and CDK4 in vitro was investigated via a different approach, given the failure of the conditional Cre- ERT1/MDM2fl+ MEF studies. Vectors containing pcDNA3, CDK4, MDM2 and pBabe were obtained from the Genetics and Genomics laboratory at Peter MacCallum Cancer Centre. All vectors contained a cytomegalovirus backbone and either a Neomycin or Puromycin resistance cassette as outlined in the methods. The experimental design included introduction of a vector containing puromycin and a vector containing neomycin resistance, per well. The experiment aimed to determine the effect of overexpression, individually of MDM2 and CDK4 in wild type C57/BL6 MEFS. In addition, to determine the effect of overexpression of each gene, the combined effect of the overexpression of both genes was also examined. The MEFS were then exposed to -galactosidase for evidence of senescence.

The aim was to determine if overexpression of either CDK4 or MDM2 was permissive for WDLPS development.

Vectors were stably transfected into MDM2fl/fl MEFS according to the NanoJuice Transfection kit protocol as described in the methods. Seventy-two hours post-transfection, cells were selected with puromycin (2mg/ml) for 48 hours, followed by neomycin a -galactosidase

μg/mlkit and quantified, for a further as the percentage days. Cells of were senescent then fixed cells and within stained one magnificationusing field. The control transfection, consisting of vectors pBABE and pCDNA3, showed 10% of cells to be positive to - galactosidase staining (Figure 11A). The overexpression of CDK4 resulted in a

-galactosidase stain in 80% of cells (Figure 11B). This finding implies that positiveoverexpression of the CDK4 oncogene results in senescence. In contrast, MDM2 -galactosidase staining in 20% of cells (Figure

11overexpressionC). The expression only resulted of both MDM2in positive and CDK4 -galactosidase staining in 20% of cells (Figure 11D). These resultstogether suggest resulted that whilstin positive CDK4 overexpression resulted in senescence, the overexpression of MDM2 abrogated the CDK4- induced senescence response. Thus, the overexpression of MDM2 is permissive for the tumorigenic effects of over-expressed CDK4. This implies that the order of amplification may be important in tumor progression, such that MDM2 amplification precedes (or co- occurs with) amplification of CDK4, but not the other way around.

92

Figure 11: Stable MEF transfections with CDK4 and MDM2. Stable transfection of MDM2fl/fl MEFS with a variety of cytomegalovirus backbone vectors, with dual -galactosidase staining for senescence. The bar antibioticgraph shows selection that the followed co-transfection by with PCDNA3 and PBABE, the control for the experiment, has - galactosidase staining in 10% of cells per magnified field. The

CDK4 transfection, with concomitant PBABE, - galactosidase staining in 80% of cells. In contrast, MDM2 and PCDNA3 revealsco-transfection, - galactosidase staining in only 20% of cells. The combined transfectionreveals of CDK4 and MDM2 reveals 20% of cells to be -galactosidase staining. These results imply that, unlike CDK4, thepositive overexpression for of MDM2 is permissive for liposarcoma development.

93 3.3 DISCUSSION

The main objective of this chapter was to both, genotypically and phenotypically, characterise the MDM2fl/fl mice, and use this characterization to explore the oncogenic properties of MDM2, in vitro and in vivo. MDM2 and CDK4 floxed mice were generated by Ozgene. When this project was initiated, the MDM2 floxed mice had been moved into the Peter MacCallum Cancer Centre mouse facility. The CDK4 floxed mice were still being generated due to a difficulty with the genotype in the hands of Ozgene. The initial project planned to characterise each mouse, then conduct triple crosses between MDM2fl/fl, CDK4fl/fl and fat-specific Cre recombinase mice. This complex triple cross would help to determine the role of MDM2 and CDK4 in the development of WDLPS.

MDM2 floxed mice were engineered with a Ubic promoter, followed by the floxed MDM2 allele and an IRES-GFP reporter. The generated mice were fertile and exhibited Mendelian inheritance patterns. The role of MDM2 was first studied in vitro. Lentiviral and adenoviral vectors encoding Cre recombinase were obtained from a variety of sources, and supernatants were then generated. The Cre recombinase activity of viral supernatants was confirmed using MEFS from R26R mice. Following the generation and genotyping of MDM2fl/fl MEFS, the viral supernatants were applied in order to induce Cre-mediated recombination of the floxed allele. The transduction efficiency was confirmed at 50%. The accumulation of MDM2 was hoped to be detected via the IRES-GFP reporter. Two of the generated supernatants contained a GIPZ backbone, making the direct interpretation of GFP expression impossible. However, the LUCOS supernatant, which had confirmed Cre recombinase activity and did not have a GIPZ background, also failed to show evidence of the IRES-GFP reporter by fluorescent microscopy, despite GFP being a widely used, reliable reporter gene(Chiocchetti, Tolosano et al. 1997). Along with being stable, it is non-toxic to cells and does not alter the localization of proteins. It is widely used to study the changes in gene expression in living tissues, when used as a gene reporter. The validity of GFP in transgenic mice and MEFS is well documented (Cubitt, Heim et al. 1995; Cruz, Chapman et al. 1996; Htun, Barsony et al. 1996). Therefore, the likely explanation of the lack of GFP expression is unrelated to the efficiency and sensitivity of GFP. Lack of GFP expression most likely relates to either the upstream transgenic insert, or is particular to the MEFS, as discussed below. In addition to a lack of GFP expression, Cre recombinase exposed MEFS

94 did not demonstrate overexpression of MDM2 protein. This again demonstrates either a problem with the transgenic construct, or difficulty with Cre recombinase delivery to the MEFS.

The lack of evidence supporting the recombination of the floxed allele may relate to either the cell type, or the viral construct in use. It is recognised that transduction, with adenoviral and retroviral constructs, requires cells to be dividing in order for adequate transduction to occur(Tashiro 2011; Antoniou, Skipper et al. 2013). However, in our model, even lentiviral supernatants, which are known to effectively and efficiently transduce non-dividing cells, failed to demonstrate evidence of recombination of the floxed allele. In order for adequately transduced cells to initiate Cre recombinase activity, cells must be dividing. MEFS have been reported to have inefficient mitotic activity, that can occur at any stage of passage, but most likely during early passages(Hogan B 1994). If cells must be dividing in order for Cre recombinase to be effective, there is the possibility that the MEFS used in our experiments may not have yielded enough mitotic activity (Bishof 2000). In contrast, many researchers use MEFS to validate Cre-mediated recombination from transgenic mice, without evidence of problems occurring. The findings from the MEF studies were inconclusive and failed to provide evidence that Cre-mediated recombination was occurring, or resulting in, over- expression of either MDM2 or the GFP reporter.

The Cre-ERT2/MDM2fl mouse studies were also inconclusive. The GFP reporter proved problematic, with strong autofluorescence detected in the tissues of tamoxifen exposed mice, a problem that has previously been described in mouse models (Tam, Upadhyay et al. 2007). There are reports of overcoming autofluorescence by using filters that distinguish between the GFP fluorescent, and the non-specific signal(Brand 1995; Tam, Upadhyay et al. 2007). In retrospect, we could have employed these filters in our studies. The PCR studies were initially promising as they reflected excision of the floxed allele in the tails of tamoxifen exposed mice. However, detailed tissue analysis from these mice failed to reveal robust and reproducible recombination of the floxed allele. With a similar trend, RT-PCR failed to show convincing overexpression of MDM2, GFP or Cre, in the tamoxifen exposed mice. Elevation of transcript levels was evident in two mice and appeared to be sample specific. The elevation of transcript level was also present in mouse Mdm2, which should not be overexpressed with Cre recombination. Thus, RT-PCR findings were inconclusive and

95 again, not reproducible. Western blot failed to show overexpression of MDM2 at a protein level. Overall, the findings arising from the Cre-ERT2/MDM2fl mouse studies were inconclusive. They failed to provide evidence that Cre-mediated recombination was occurring in a recurrent and reproducible manner. In addition, overexpression of either MDM2 or the GFP reporter, was not found consistently. This may reflect inability of the Cre- ER to move reliably and reproducibly into the nucleus in mouse tissue. Furthermore, it may be that the levels of Tamoxifen were not high enough with oral dosing and needed to be further increased. This was not further evaluated within this thesis.

Two years after the commencement of these studies, De Clercq et al.(De Clercq, Gembarska et al. 2010) published their results, using a conditional MDM2 transgenic mice model. This study used a conditional MDM2 transgenic, with the target vector inserted at the Rosa26 locus that has been described previously(Nyabi, Naessens et al. 2009). The mice contained a pCAGG promoter -Geo-STOP cassette(De Clercq, Gembarska et al.

2010). The cassettefollowed was electively by a floxed excised, using Cre recombinase, resulting in downstream transcription of MDM2-IRES-GFP -Geo-STOP cassette, and expression of GFP in tissues, the authors. Despite were verifying unable excision to demonstrate of the the overexpression of MDM2. In vitro studies, using MEFS generated from the transgenic mice, only exhibited MDM2 in the presence of a proteasome inhibitor(De Clercq, Gembarska et al. 2010). The authors concluded that the MDM2 transgenic protein was extremely unstable and likely it rapidly underwent proteasome dependent degradation, in vivo. These findings could explain some of our studies findings. However, unlike the data from the De Clercq research, in addition to failing to observe the overexpression of MDM2, we also failed to observe robust and reproducible Cre-mediated recombination of the floxed allele.

The most likely explanation for the multiplicity of findings in our and other studies of Cre- ERT2/MDM2fl/+ is that the transgenic insert was itself non-functional. The findings do not suggest that the transgene insert was leaking, as MDM2 has previously been shown to be embryonically lethal(Montes de Oca Luna, Wagner et al. 1995). Mice in these studies demonstrate Mendelian inheritance patterns which does not support MDM2 cassette leakage. The transgenic mice were established using the embryonic stem cell method, with evidence of transgene insert prior to blastocyst injection. Chimeric mice were generated and the transgene was sequenced to confirm insertion. The sequenced transgene was

96 identical to that supplied by Ozgene. Therefore, failure of the transgene to overexpress MDM2 may relate to inefficient transcription, driven by the Ubic promoter. Transcriptional interference when using Ubic promoter systems has been previously documented(Nie, Das Thakur et al. 2010) and may account for the difficulty in demonstrating downstream upregulation of both MDM2 and GFP. Promoter strength closely correlates with the potency of downstream gene overexpression, and it can also relate to the cell type in use. As an example, the CMV promoter results in efficient and robust expression in cell lines derived from epithelial or fibroblast origins, but is not as potent in hematopoietic cells (An, Wersto et al. 2000). Interestingly, the successful MDM2 murine models to date have not utilised a Ubic promoter. The most successful conditional transgenic MDM2 model used a pCAGG promoter and these authors were able to demonstrate overexpression of the gene, at both the transcript and protein level(De Clercq, Gembarska et al. 2010). It may be that the inadequacies of the transgenic mouse experiments in these studies relate to the chosen promoter. As an alternate hypothesis it is possible that there is a selection bias against recombination whereby cells where an inefficient recombination process has occurred, undergo rapid depletion. It is also conceivable that the construct has a defect, beyond promoter selection and transgene sequence, which we have been unable to adequately delineate.

Initially, the plan for the project was to investigate both MDM2fl/+ and CDK4fll/+ transgenic mice. However, due to the difficulties arising from the study of the MDM2 transgenic mouse, it was decided after 14 months to change the focus of the studies. Further mouse studies were abandoned in order to investigate CDK4 inhibitors, and ultimately perform an siRNA screen of the genome, investigating mechanisms of resistance in WDLPS.

97 CHAPTER FOUR: CDK4 AND CDK4 INHIBITORS IN WELL DIFFERENTIATED LIPOSARCOMA

4.1 INTRODUCTION

4.1.1 Cyclin-dependent kinase-4 (CDK4)

CDK4 belongs to a conserved family of proline-directed serine/threonine kinases that regulate cell cycle progression. Human cell division is regulated at two boundaries; G1 to S and G2 to M. The expression of CDKs and subsequent phosphorylation of substrates allows controlled progression through the cell cycle. CDK4 is located on chromosome 12q14 and was first cloned in 1987 (Hanks 1987) . Encoding a 33.4 KDa protein (Zuo et al 1996) it forms an active kinase when bound with G1 phase Cyclin D (CCND1, CCND2 or CCND3) (Ortega, Malumbres et al. 2002). CDK4 activity is inhibited or enhanced depending on the availability of cyclins and CDKi; p16INK4A (CDKN2A), p21CIP (CDKN1A), p27kip (CDKN1B), p18ink4c (CDKN2C) (Besson, Dowdy et al. 2008).

4.1.2 Structure and Function of CDK4 The major function of CDK4/CDK6 is to allow progression through the G1-S phase transition. CDK4 complexes with cyclin D1 then phosphorylates the C-terminus of the retinoblastoma tumor suppressor protein (pRb), resulting in release, and activation of E2F target genes, including E-type cyclins. The Figure 1: Role of Cyclin- Dependent Kinase 4 (CDK4) in cell cycle progression. CDK4 binds with cyclin D to form active complexes. The CDK4-Cyclin D complex then phosphorylates pRb and releases it from the pRb-E2F complex. E2F then binds DNA to up-regulate the transcription of genes required to enable progression to the S phase of the cell cycle. Figure modified from Piepkorn et al. (Piepkorn 2000).

98 interaction between the CDK4-Cyclin D complex and pRb enables the progression through the G1 phase of the cell cycle (Harbour, Luo et al. 1999; Italiano, Bianchini et al. 2009) (Figure 1). D-type cyclins must stoichiometrically overcome the inhibitory threshold of the INK4 proteins in order to activate CDK4/6, thus permitting cell cycle progression (Paternot, Bockstaele et al. 2010).

4.1.3 The role of CDK4 in cancer

Deregulated cell cycle control and proliferative abnormalities frequently manifest in oncogenesis. Overexpression of CDK4 is a common event in many cancer types and occurs in ~90% of WDLPS. Gene amplification and overexpression of CDK4 has been linked to a range of tumours including lymphomas (Amin, McDonnell et al. 2003), sarcoma (Binh, Sastre-Garau et al. 2005), breast carcinoma, melanoma (Arnold and Papanikolaou 2005) squamous cell carcinoma and leukaemia (Ortega, Malumbres et al. 2002). CDK4 is amplified in combination with MDM2 in breast carcinoma, glioma and sarcoma (Santarius, Shipley et al. 2010; Tap, Eilber et al. 2011). Aberrant activation of CDK4 can also result from functional inactivity of the CDKi; CIP/KIP or INK4 proteins, or activating mutations of positive regulators (e.g. Cyclin D) (Baker SJ 2013). Loss of inhibitory protein function (e.g. p16 INK4a) can result from gene deletion, point mutations or promoter methylation (Sheppard KE 2013) and renders CDK4 constitutively active.

Absence of p16INK4a results in cancer development in both mice and humans (Matheu, Maraver et al. 2008; Lapak K 2014). Loss or inactivity of CDN2A, comprising both INK4a and ARF transcripts, is one of the most frequent events in human cancer development (N 2008; Research 2011; Cerami E 2012; N 2012; Gao J 2013; N 2013). Ordinarily, DNA damage upregulation of p16INK4a enables cells to enter senescence. Therefore, inactivation of p16INK4a allows cells to bypass senescence, whilst progressing through the cell cycle through sustained CDK4 activity(Lapak K 2014). Furthermore, several tumour types (i.e. glioblastoma multiforme, squamous cell carcinoma, melanoma, urothelial carcinoma) demonstrate multiple alterations in the CDK4/6 Cyclin-D-INK4-RB pathway (Lapak K 2014). Functional

INK4A inactivation of p16 infrequently– occurs in combination with RB1 mutations, supporting the critical role played by the CDK4/6-INK4a axis in tumorigenesis. This has led to development of small molecular inhibitors targeting CDK4/6 (Shapiro 2006).

99 4.1.4 Small molecule Cyclin Dependent Kinase Inhibitors (CDKi)

The first CDKi s to enter clinical trials included flavopiridol (Senderowicz, Headlee et al.

1998), R-roscoviti’ ne (Le Tourneau, Faivre et al. 2010) and UCN-01 (Akinaga, Sugiyama et al. 2000). Flavopiridol and r-roscovitine are both pan-CDK inhibitors (De Azevedo, Leclerc et al. 1997). However, these early compounds lacked specificity for CDK4. Flavopiridol is the most effective inhibitor of CDK1, CDK2, and CDK4. Both flavopiridol and r-roscovitine combine with the cyclin involved (i.e. CDK2) in the ATP binding site (De Azevedo W.F 1996). More recently flavopiridol has been shown to inhibit CDK9/ positive transcription elongation factor b (P- TEFb) and RNA polymerase II transcription (Chao SH 2001). UCN-01 is a small molecule derivative of the serine/threonine kinase inhibitor staurosporine. UCN-01 inhibitory effects upon the cell cycle are poorly understood, and thought to involve transcriptional activation of p21 (Facchinetti M.M 2004).

Pre-clinical studies revealed that the early pan-inhibitors were capable of causing a potent G1- G2 arrest in a range of tumour cell lines (Sedlacek 2001; Shapiro 2004; Shapiro 2006). However, in the clinical setting these pan inhibitors were found to have high toxicity profiles and were associated with poor clinical outcomes (Benson, White et al. 2007; Lapenna and Giordano 2009). Dose limiting toxicities included fatigue, neutropenia, hypotension, hypoalbuminaemia and diarrhea (Tan AR 2002; Fracasso PM 2011).

Recently, more specific CDK4 inhibitors have been developed and entered clinical trials including BAY1000394 (Siemeister 2010), Palbociclib (PD0332991) (Fry, Harvey et al. 2004), R547 (DePinto, Chu et al. 2006), RGB-2886638 (Cirstea 2008) and ZK304709 (Siemeister, Luecking et al. 2006). Third generation CDK4/6 inhibitors include P276-00, LY2835219 and LEE011. Four second-generation CDK4/6 inhibitors were investigated during this study and are outlined as follows.

4.1.4.1 Indolocarbazoles (SC-203873 and NPCD)

To identify drugs targeting CDK4, Zhu et al (Zhu, Conner et al. 2003) synthesized and developed a range of novel indolocarbazole compounds that were cell permeable and showed potent and selective ATP-competitive inhibition of CDK4/D1. Their most potent compound,

SC-203873, had an IC50 inhibition of kinase activity, the indolocarbazole was ableof to. stop μM. proliferation )n addition of to colorectal (HCT-116) and NSCLC cell lines

100 (NCI-460). These cells also exhibited a G1 arrest in a dose-dependent manner following treatment with SC-203873. In vivo studies using SC-203873 to treat the human colon carcinoma cell line in a xenograft mouse model, showed some inhibition of tumour growth (Zhu, Conner et al. 2003).

Another indolocarbazole-derived inhibitor; Naphtho[2,1- -c] carbazole-5, 7

(6H,12H)-dione (NPCD) was first described by Sun et al. (Sun,] pyrrolo Li et al. [, 2011). To investigate the efficacy of NPCD, breast cancer cell lines (MCF7, MB 231, MCF 15, T47D, GI 101Ap) were treated and showed inhibition of cell proliferation, apoptosis, and reduced colony formation in the presence of NPCD. The concentration of NPCD required to induce apoptosis ranged from 3 and was dependent on the type of cell line used. The efficacy of NPCD in these studies –was μM independent of CDK4 expression as these breast cancer cell lines were not defined by high levels of CDK4. CDK4 inhibition was demonstrated by a potent G1-S arrest and phosphorylation of pRb. To date NPCD has not been used in in vivo studies.

4.1.4.2 Triaminopyrimidines (SC-203874)

SC-203874 is a triaminopyrimidine derivative first described by Soni et al. (Soni, O'Reilly et al. 2001). This inhibitor was identified following a high throughput screen monitoring the phosphorylation of pRb by human recombinant CDK4/cyclin D1 in comparison to a novel library of molecular inhibitors (Wu, Yarwood et al. 2000). SC-203874 was tested against a range of CDKs and prototypic tyrosine kinases within a kinase assay. The studies showed that

SC-203874 was specific for CDK4 inhibition (IC50 higher concentrations (IC50 . μM with CDKCDKs inhibition exhibited at slightlyan IC50 greater than (Wu, Yarwood. μM. et Allal. 2000)other .protein The effect kinases on the and cell cycle following application ofμM SC-203874 was tested on the human osteosarcoma cell line (U2OS) and normal human fibroblast cell line (MRC-5). Both cell lines had functional pRb, with U2OS being p16 negative and MRC-5, p16 positive. Flow cytometry analyses found that SC-203874 induced a potent G1 arrest in both cell lines and Western blotting showed decreased phosphorylation of pRb780/795 in a dose dependent. The authors found that SC-203874 could induce either senescence or apoptosis depending on the concentration of the inhibitor, with higher concentrations trending towards apoptosis (Wu, Yarwood et al. 2000). The sensitivity to the inhibitor was dependent upon RB1 status. In vivo studies using mice implanted with human

101 HCT116 cells showed that SC-203874 reduced tumour volume and slowed tumour growth rates by approximately 17%.

4.1.4.3 Palbociclib (PD0332991)

Palbociclib (PD0332991) a pyrido[2,3-d]pyrimidine derivative is a potent oral inhibitor of both CDK4 and CDK6 and has been shown to prevent downstream phosphorylation of pRb (Fry, Harvey et al. 2004). Palbociclib (PD0332991), the most extensively evaluated selective CDK4/6 inhibitor to date, was first tested against a series of 36 protein kinases and found to be potently specific for CDK4/CDK6 with a low IC50 in both breast cancer and colorectal cancer cell lines (Fry, Harvey et al. 2004). In vitro studies using PD0332991 showed convincing evidence of decreased phosphorylation of pRb and G1 arrest, with inhibition of 18F-fluoro-L-thymidine (Fry, Harvey et al. 2004; Leonard, LaCasce et al. 2012). The effect of PD0332991 has been studied using a number of different tumour cell lines including colorectal (Heijink, Fehrmann et al. 2011), breast (Dean, McClendon et al. 2012), pancreatic ductal carcinoma (Liu and Korc 2012), lymphoma (Leonard, LaCasce et al. 2012), rhabdoid (Katsumi, Iehara et al. 2011), and ovarian cancer cell lines (Konecny, Winterhoff et al. 2011). The sensitivity of cell lines to PD0332991 was related to both functional pRb and p16 status of the cell, with mutations or low copy number of p16 correlating with increased sensitivity to the inhibitor (Fry, Harvey et al. 2004; Katsumi, Iehara et al. 2011; Konecny, Winterhoff et al. 2011; Cen, Carlson et al. 2012).

4.2 AIM The aim of this chapter was to investigate the efficacy of a range of CDK4 inhibitors in WDLPS and to identify drugs for further study.

4.3 RESULTS The ability of small molecular CDK4 inhibitors to effect cell proliferation was investigated using RB-proficient WDLPS cell lines; 449B and 778. Control cell lines included SW-872 (established from a primary fibrosarcoma, histopathology of a undifferentiated liposarcoma) and HT-1080 (established from a primary fibrosarcoma). Neither of the control cell lines overexpress CDK4. The CDK4 inhibitors were sourced from various distributers; SC-203873, SC-203874 (Santa Cruz), Palbociclib (Selleckchem) and NPCD from the Hormel Institute (Minnesota, MA, USA) (Sun et al).

102

4.3.1 SC-203873 and SC-203874 suppress proliferation of 449B and 778 WDLPS lines using MTS assay.

The effect of the CDK4 inhibitors SC-203873 and SC-203874 on cell proliferation was measured using an MTS assay. Dose response curves are shown in Figure 2 and 3. Both inhibitors suppressed cell proliferation at concentrations above 50nM for SC-203873 (range:

5nM- M) and for SC-203874 (range: 50nM- . Comparisons of IC50 between test

(449B,μ 778) andμM control cell lines (SW-872, HT1080)μM following treatment with SC-203873 or SC-203874 are shown in Table 1. In 449B and SW-872 cell lines, SC-203873 was able to induce complete proliferation arrest at 800nM with an IC50 of 137 nM in 449B cells compared with 349 nM in the SW-872 control cell line (Figure 2A). In the 778 cell line, SC-203873 was able to induce complete proliferation arrest at 800nM with an IC50 of 136 nM in 778 cells compared with 229nM in the HT1080 control cell line (Figure 3A). SC-203874 induced a proliferation arrest at a higher IC50, in the micromolar range, as is previously published (Soni,

O'Reilly et al. 2001). The observed IC50 2B). The 778 cell line was more sensitive to inhibitionwas with . SC-203874 μM in the with B an cell IC50 line of 1.Figure 3B). Unexpectedly, the SC-203873 inhibitor did not demonstrate a statistically significantμM Figure difference in IC50 between the CDK4 amplified 778 cell line (136nM) and control HT 1080 cell line (229nM). However, a statistically significant difference in IC50 was demonstrated when using SC-203874 on 778 and HT 1080 cell lines (IC50 respectively, p 0.04).

SW-872 also did not demonstrate IC50 results significantly. μM different and . fromμM the CDK4 amplified cell lines. SW 872 cell line showed an IC50 -203874) and 349 nanomolar (SC-

203873). The collective results using bothof inhibitors . μM SCacross all cell lines are shown in Table 1. Only the 778 cells showed a statistically stronger response to the CDK4 inhibitor SC-203874 in comparison to control cell line HT1080.

Drug Cell line IC50 Cell line IC50 P-value (Students t-test) SC-203873 449B 137 nM SW-872 349 nM P=0.66 SC-203873 778 136 nM HT1080 229 nM P= 0.89 SC-203874 P=0.35

SC-203874 B . μM SW . μM P = 0.04* . μM (T . μM

103

Table 1: Comparison of MTS assay results across CDK4 amplified (778. 449B) and non-amplified (SW 872, HT 1080) cell lines. *P = statistically significant result.

Figure 2: MTS assay showing the effects of SC-203874 and SC-203873 on WDLPS and control lines (A/B). Cells were plated in 96 well format at 2000 cells/well and treated with inhibitors ranging from 50nM- 20 M for SC-203874 and 5nM- 5 for SC-203873. Cells were cultured in the presence of drug forμ 3 days and plates read at 490μM nm on day 3. These results are representative of three independent experiments. A) 449B and SW-872 cells treated with SC- 203873; B) 449B and SW 872 cells treated with SC- 203874. Error bars indicate standard error deviation measured as replicate measurements within the same experiment.

104

Figure 3: MTS assay showing the effects of SC-203874 and SC-203873 on WDLPS (778) and control line (HT 1080) (A/B). Cells were plated in 96 well format at 2000 cells/well and treated with inhibitors ranging from (50nM- 20 M for SC-203874) and (5nM- 5 for

SC-203873). Cells were cultured in the presence of drugμ for 3 days plates, read at 490μM nm on day 3. These results are representative of three independent experiments. A) 778 and HT 1080 cell lines treated with SC-203873; B) 778 and HT1080 cell lines treated with SC- 203874. Error bars indicate standard deviation generated across independent experiments.

105 4.3.2 SC-203873 and SC-203874 suppress proliferation of 449B and 778 WDLPS lines using CFA

CFA were performed to assess the growth inhibitory effect of the CDK4 inhibitors on both 449B and SW-872 cells. Cells were seeded at 1000 cells/well and treated with SC-203873 (0- 800nM) and SC-203874 (1-8 . The CFA was performed over 10 days with drug and media changed every 72 hours. UponμΜ completion of the assay the cells were stained with crystal violet and colony formation quantitated using the program MetamorphTM as described in the

Methods 2.7.4. Cell lines treated with SC-203873 underwent a proliferation arrest with an IC50 of 32 nM in the 449B cell line. Compared to the MTS assay where control cells were also susceptible to the inhibitory effects of SC-203873, in the CFA assay the SW-872 cell line showed some resistance to the inhibitory effect of SC-203873, with an IC50 of 200 nM (Figure 4A). This resistance to SC-203873 in the SW-872 cell line was not significant (P= 0.06, two- tailed t test). In contrast, SC-203874 did not demonstrate a difference in inhibitory effect between amplified and non-amplified cell lines in the CFA, with an IC50 and (Figure 4B) (P =0.57, two-tailed t test).of . μΜ in the B, . μΜ in the SW cell line

106

Figure 4: CFA assay showing the effects of SC-203874 and SC-203873 on WDLPS (449B) and control line (SW-872) (A/B). Cells were plated in 6 well format at 1000 cells/well and treated with inhibitors SC-203873 and SC-203874. The assay was performed over 10 days with drug and media changed every 72 hours.

After 10 days cells were stained with crystal violet and colonies were quantitated using Metamorph software. These results are representative of three independent experiments. A) 449™ and SW-872 cell lines treated with SC-203873 reveal an IC50 32 nM (449B) and 200 nM (SW-872). B) 449B and SW-872 cell lines treated with SC-

203874 reveal IC50 2.3 M (449B) and 3.2 M (SW-872). Error bars indicate standard deviation generated across independent experiments. μ μ

107 4.3.3 Anti-proliferative effects of NPCD in MTS assays

Although SC-203873 showed some specificity to CDK4 overexpression in the CFA using the 449B cells, compared to the control SW-872 cell line, the limited effect on specificity in the MTS assay led to the investigation of other potential CDK4 inhibitors. The next CDK4 inhibitor studied was NPCD. The published IC50 for this inhibitor in breast cancer lines was 3- .

Unfortunately the CDK4 status of these cell lines was not specified by the authors (Sun μΜ et al. 2011). In investigating the effect of NPCD on growth suppression in WDLPS cell lines, a range of cell lines with amplified CDK4 (449B, 778, T1000, GOT3) were exposed to escalating doses of NPCD. Two control cell lines were used in these experiments: the SAOS2 osteosarcoma cell line, together with 5807A, a melanoma cell line. SAOS2 osteosarcoma cell line is RB null, theoretically making it insensitive to CDK4 inhibition. Similarly, 5807A has been previously shown to be resistant to CDK4 inhibition. The MTS assay results showed that the CDK4 amplified cell lines were sensitive to NPCD with IC50 ranging from 3.1

5807A cell lines both showed resistance to the inhibitor with IC50 – .μM. The SAOS and (Figure 5A). This result was found to be statistically significant (P of<0.0002 . μM ANOVA). and . μM

108

Figure 5: NPCD showed specificity to CDK4 amplified cell lines in MTS assays. Dose response curve showing results of NPCD upon CDK4 amplified liposarcoma cell lines (449B, 778, T1000, GOT 3) and non CDK4 amplified cell lines (SAOS2, 5807A). These results are representative of three independent experiments. Statistical analysis was performed using a one way ANOVA test between CDK4 amplified and A5807 amplified lines (P<0.0002). Error bars indicate standard deviation generated across independent experiments.

4.3.4 NPCD produces marked inhibition in colony formation assays using 449B and SAOS2 cell lines

CFA were then performed to determine if NPCD altered cell growth, as described in the Methods 2.7.4. The 449B cells were treated with NPCD (0- for 10 days, with the SAOS2

cell line as the control for this experiment. The results show a μM mark ed inhibitory growth effect on the 449B cell line, with only partial inhibition of colony formation in the RB1 deficient

SAOS2 cell line. Analysis of the results revealed an IC50 of 2.35 ensitive 449B cell

line, and 7.62 itive SAOS2 cell line (Figure μM6). Thefor the degree s of inhibition was more pronouncedμM for in the MTSless sens assay (4.3.3), with students t-test revealing the CFA result not being statistically significant (P=0.69).

109

Figure 6: NPCD suppresses colony forming ability 449B cells compared to control

SAOS2 cells. Results of the clonogenic assay plotted to show estimated IC50 per cell line following Metamorph analysis. 449B reveals an IC50 of 2.35 M, whereas the SAOS2 cell line shows more resistant™ phenotype with IC50 of 7.62 M. Twoμ tailed t-test P= 0.69. These results are representative of three independent experimentsμ . Error bars indicate standard deviation generated across independent experiments.

110 4.3.5 Downstream effects of NCPD treatment using Western Blot Analysis

As NPCD had been shown to have a degree of sensitivity for the CDK4 amplified cell lines, it was important to further examine what effect NPCD had on the downstream targets of the CDK4 pathway. To examine the downstream targets of CDK4 inhibitors, 449B cells were treated with varying concentrations of NPCD for 72 hours and Western blots carried out to determine both the level andμM, phosphorylation μM, μM of RB1. The protein levels of pRb stayed relatively constant following treatment; however, phosphorylation state of pRb (at residues 780 and 811) decreased in a dose-dependent manner. D1-CDK4 are known to phosphorylate pRb protein at ser780, ser807 and ser811(Graftstrom RH 1999). On-target inhibition of the CDK4 pathway is demonstrated through the progressive lack of phosphorylation of pRb at these sites (Figure 7).

Figure 7: Western blot analysis following treatment of 449B WDLPS cell line with NPCD. 449B cells were treated with NPCD at 0, 4, and 8 . Total RB1

expression and phosphorylation of Rb protein at Ser 811 and μM Ser 780 after treatment with NPCD at 0, 4 or 8 - actin as loading control.

μM with 111 4.3.6 NPCD treatment produces apoptosis in 449B cell line

To determine if NPCD was inducing apoptosis rather than cell cycle arrest, 449B cells were treated as described above in 4.3.5 and stained with Annexin V and Propidium iodide. The overall percentage of apoptotic (Annexin V positive staining) or necrotic cells (Annexin V and propidium iodide positive staining) were quantified by flow cytometry analysis. NPCD induced apoptosis in 35-37% and necrosis in 17-20% of the 449B cell population (Figure 8 A, B, C and D). This was validated in three separate experiments. Previous studies suggest that CDK4/6 inhibitors are cytostatic rather than cytocidal, particularly in combination with other chemotherapy agents (A.K 2012) Beyond direct cell cycle inhibition, CDK4 inhibitors have been shown to inhibit downstream transcription factors resulting in apoptosis(HC Thomas 2007). The results validate this finding with one third of the total cell population undergoing apoptosis (Figure 8D).

112

Figure 8: Inhibitory effects of NPCD on 449B cells, is in part due to induction of apoptosis. 449B cells were treated with NPCD for 48 hours and stained with Annexin V following by FACs analysis, as shown in (A) no drug, (B) 4 M and (C) 8 M. The percentage of cells alive, necrotic or apoptotic are summarised in (D) μWestern blot μanalysis for cleaved PARP by product (85 KDa) compared with -actin control (E).

113 The induction of apoptosis by NPCD was also assessed by poly (ADP-ribose) polymerase (PARP) cleavage using Western blotting. During apoptosis, in response to cell damage, PARP, a 116 kDa nuclear protein that normally functions in DNA detection and repair, is cleaved by caspase 3 resulting in two fragments, a p85, and a p25 fragment. 449B cells were treated with increasing doses of NPCD, which corresponded to an increased abundance of cleaved PARP product (p85). This suggests NPCD mediates its effects through both cell cycle arrest and apoptosis (Figure 8E).

4.3.7 Assessment of the off-target effects of NPCD

To determine if the effects of NPCD were dependent on the presence of functional pRb, 449B cells with stably knocked down RB1 were generated. RB1 was knocked-down in 449B cells using short hairpin microRNA (shRNAmir). A non-silencing empty vector was used as a control (EV). 449B cells were transduced and pooled before selection with 1 µg/ml of puromycin. Western blotting was used to verify gene knockdown, and stable cell lines were used for all further experiments (Figure 9). 449BshEV and 449BshRB1 cells were treated with NPCD and both MTS and CFA were performed. The CFA failed to show any difference in the activity of NPCD on 449BshRB1 cells compared to the control 449BshEV cells with IC50 1.36 respectively (P=0.96) (Figure 10A). The MTS assay showed that

449BμΜshEVand had . a slightly μΜ higher IC50 (12.3 the 449BshRB1 line (5.7 M), although the results were not statistically significantμΜ compared (P=0.74) to (Figure 10B). It has beenμ previously reported that RB1 null cell lines are resistant to inhibition with CDK4 inhibitors in the low nanomolar range (Fry, Harvey et al. 2004). The results from these studies suggest that NPCD used at micromolar concentrations was anti-proliferative but these effects were not solely attributable to CDK4 inhibition and may be due to off target effects. Because of these findings, and the high concentrations at which NPCD was active, all further experiments were carried out using PD0332991.

114

Figure 9: Western Blot verifying knockdown of

RB1 in 449B shRB1 cell line compared with control

449B and 449B empty vector cell lines.

115

Figure 10: Activity of NPCD was not dependent on functional RB1. 449BshEV and 449BshRB1 cells were treated with a range of concentrations of NPCD (0-20 (A)

Colony forming (P=0.96) and (B) MTS assays (P=0.74) carried out. Neither experimentμΜ and demonstrated a statistically significant difference between RB1 knockout cells and control cells using a two tailed t-test. These results are representative of three different experiments. Error bars indicate standard deviation generated across independent experiments.

116

4.3.8 PD0332991 suppresses WDLPS cell growth in an RB1 dependent manner

The next CDK4 inhibitor investigated was PD0332991 (Fry, Harvey et al. 2004). Previous studies had reported two melanoma cell lines with differential responses to PD0332991 as follows; (1) MALME-3M, which has wildtype p53 and a frame deletion in p16 (M1-157) and is sensitive to PD0332991 with a reported IC50 of 7-11 nM and (2) MeWO which has a loss of p16

(R80 STOP) and mutated p53 and is resistant to PD0332991 with a reported IC50 of 100-200 nM (Young, Waldeck et al. 2014). We investigated both these cell lines and responses to PD0332991 in our laboratory using an MTS assay with a drug concentration range from 0-10

confirmed previous observations with MALME-3M revealing an IC50 of 16 nM

μM.and TheMeWO results an IC 50 of 185 nM (P<0.005, paired t-test) (Figure 11).

Figure 11: MTS assay of sensitive and resistant melanoma cell lines treated with PD 0332991. MALME-3M and MeWo cell lines were treated with PD0332991 (0-10 )

and proliferation assessed using the MTS assay. MeWo showed sensitivity with IC50 16μM nM

compared with MALME-3M with IC50 185 nM. The experimental results were statistically significant (p<0.005 paired t-test). These results are representative of three independent experiments. Error bars indicate standard deviation generated across independent experiments.

117 After testing PD0332991 on control MALME-3M and MeWo cell lines, this inhibitor was tested on previously generated 449BshEV and 499BshRB1 449B cell lines. Two additional CDK4 amplified WDLPS lines 778 and T1000 with corresponding EV and RB1 knockdown lines (778shEV and 778shRB1and T1000shEV and T1000shRB1) were also generated so that inhibitory effects of PD0332991 could be further explored.

As CDK4 is amplified in 449B, 778 and T1000 cell lines, knockdown of RB1 should induce a resistance phenotype. After viral transduction of shRNAs EV and RB1 and selection, RB1 knockdown was confirmed using Western Blotting in both the 778 and T1000 cell lines (Figure 12). A proliferation assay was conducted where cells were treated with PD0332991 at a range of concentrations (0 - and an MTS assay carried out. All control shEV transduced WDLPS lines with expressionμM and functional activity of pRb were sensitive to PD0332991. The highest sensitivity to PD0332991 was observed in 449B and 778 cells with

IC50 of 28 and 71nM, respectively (Figure 13A/B). The T1000 cell line was not as sensitive to

PD0332991 with an IC50 of 632 nM (Figure 13C). All WDLPS cell demonstrated that the knockdown of RB1 induced robust and reproducible resistance phenotypes in the short-term MTS assays.

Figure 12: Western Blot verifying knockdown of RB1 in 778shRB1 and T 1000shRB1 cell lines compared with empty vector cell lines (778shEV/T1000shEV)

118 Figure 13: WDLPS cell lines are sensitive to PD0332991 in an RB1 dependent manner. WDLPS cell lines (A) 449BshEV and 449BshRB1 (B) 778shEV and 778shRB1 and (C) T1000shEV and T1000 shRB1 were treated for 72 hours with PD0332991 at concentrations ranging from (0-10μM). Cell proliferation measured by MTS assay. The 449BshEV cell line was the most sensitive to PD0332991 with IC50 = 28 nM. All shRB1 WDLPS lines demonstrated a resistance phenotype (two tailed t-test P <0.05). These results are representative of three independent experiments. Error bars indicate standard deviation generated across independent experiments.

119 4.3.9 PD032291 suppresses colony formation ability of 449B cell line

Colony forming assays were conducted to test the efficacy of PD0332991 (0 - 400 nM) on 449B cells as described in the Methods 2.7.4. An example of the CFA is shown in Figure 14A.

The IC50 generated in the CFA was 57 nM, consistent with, but not identical to, the previous MTS assay (Figure 14B). PD0332991 treatment at higher concentrations produced a cytostatic, rather than cytocidal effect as it failed to abolish growth of all colonies from the plates (Figure 15 A/B), as previously reported (McClendon, Dean et al. 2012). At higher concentrations, PD0332991 does abolish more than 90% of cells on the plate. However, this cannot be directly attributable to CDK4/6 inhibition, as off-target effects with inhibition across a range of protein kinases, has been shown at concentrations greater than 4 (Fry,

Harvey et al. 2004). μΜ

120

Figure 14: PD0332991 reduces colony forming ability of 449B cells. (A) Representative image of a colony formation assay of 449BshEV cells treated with PD0332991 at varying concentration (0-100nM). (B) Dose response curve of quantified colonies at different concentrations of PD0332991. The graph demonstrates an IC50 of 57 nM, and confirms sensitivity of 449B cell line to growth inhibition in response to PD0332991. These results are representative of three independent experiments. Error bars indicate standard deviation generated across independent experiments.

121 Figure 15: PD0332991 suppresses colony formation at high doses. (A) Colony forming assay showing inhibition of 449B cell growth at 500 and 1500 nM. (B) Bar graph following Metamorph analysis

showing reduction in ™colony formation with increasing concentration of PD0332991. Error bars indicate standard deviation generated across independent experiments.

4.3.10 PD0332991 induces a G1 arrest in 449B cells

Previous studies in colorectal cancer cell lines have shown that PD0332991 induces a G1 cell cycle arrest (Fry, Harvey et al. 2004). We investigated whether PD0332991 induces cell cycle arrest in 449B cells. Cells were treated with the drug, and cell cycle analysis was carried out at 24, 48 and 72 hours using propidium iodide and fluorescence-activated cell sorting (FACS) as outlined in the Methods 2.6.3. We found potent G1 arrest at 24 and 48 hours after treatment with 500 nM PD0332991 (Figure 16 B) versus control (Figure 16 A). Although the cells were still arrested at 72 hours, the percentage of cells undergoing a G1 arrest was 8% less than at the two earlier time points (Figure 16 C). This finding justified changing the drug at 72 hours for all further assays. The findings verify that through CDK4 inhibition, cell cycle arrest is mediated via a potent and immediate Go/G1 arrest.

122

Figure 16: PD0332991 induces a G1 arrest in 449B cells. Cell cycle profile of (A) 449B control untreated cells (B) 449B cells treated with PD0332991 for 24 hrs (C) Summary of data showing percentage of cells in each phase of the cell cycle at 24, 48, 72 hrs (control and PD033991 treated).

4.3.11 Therapeutically active doses of PD0332991 result in down regulation of proteins consistent with inhibition of the CDK4 pathway.

As previously described in the literature, application of PD0332991 should result in down- regulation of proteins consistent with inhibition of the CDK4/6 pathway(Finn RS 2009). To investigate the biochemical effects of PD0332991 on WDLPS cell lines, 449B cells were seeded in 6 well format and treated for 24, 48 and 72 hours with PD0332991. Protein was collected at the conclusion of each time point, in both treated and untreated cells. Western blots were performed probing for proteins integral to the CDK4/CDK6 pathway including RB795, p21KIP1, p21CIP1, p57KIP2, MCM7 and PARP. Similar to colorectal cell lines treated with PD0332991 (Fry, Harvey et al. 2004), phosphorylation of pRb at Ser 780 was reduced in the 449B cell line treated with PD0332991 at all the time points examined (Figure 17). This demonstrates specific inhibition of the CDK4/6 complex at these concentrations

123 .

Figure 17: Treatment of 449B cells with PD0332991 results in up regulation of proteins consistent with inhibition of the CDK4 pathway, cell cycle arrest and apoptosis. Western Blot analysis of 449B cell lines treated with or without PD0332991 (500 nM) for 24, 48 and 72 hours. Expression of CDK4 inhibitory proteins (p21KIP1, p21CIP1, p57KIP2) and E2F transcription targets (MCM7) were analysed. Total PARP expression correlated with apoptosis induced by PD0332991. Beta-ACTIN was used as a control for loading.

124

Investigation of the CDKi; p21CIP1, p27KIP1 and p57KIP2 was carried out to determine what downstream molecular changes occurred following treatment with PD0332991. Increased p21CIP1 and p27KIP1protein expression was observed in 449B cells treated with PD0332991. The expression of p57KIP2 was also slightly up-regulated at 48 hours and 72 hours. Induction of p57KIP2 is known to be associated with cell cycle arrest in the G1 phase (Lee, Reynisdottir et al. 1995; Matsuoka, Edwards et al. 1995), which would be expected with application of PD0332991. 449B cells were also assessed for evidence of apoptosis. During apoptosis full length PARP 116 KDa is cleaved by caspase 3 into an 85 KDa fragment. Treatment of 449B cells with PD0332991 showed a decrease in expression of total full length PARP suggestive of apoptosis occurring in response to drug.

Phosphorylation of pRb by CDK4/Cyclin D1 results in the up-regulation of the genes under the transcriptional control of E2F and leads to progression through the cell cycle. Thus, hypophosphorylation of pRb should result in suppression of the genes and proteins under E2F transcriptional control. MCM7, a transcriptional target of E2F (Arata, Fujita et al. 2000), was investigated to determine the downstream effects of PD0332991 inhibition. The results show that treatment of 449B cell line with PD0332991 down regulates protein expression of MCM7. These results, together with hypophosphorylation of pRb in the presence of the inhibitor, suggest that the CDK4 pathway is being specifically targeted by PD0332991. PD0332991 was also shown to induce PARP cleavage showing reduced PARP expression (116KDa) correlating with the induction of apoptosis.

125

4.4 Discussion

That CDK4 is not required for development but involved in tumorigenesis suggests that targeting this kinase is of potential clinical benefit in the treatment of cancer (Lee and Sicinski 2006). The first generation CDKi were pan-inhibitors and were relatively non-specific and marred by many toxic side-effects (Musgrove, Caldon et al. 2011). Recent development of more selective and specific inhibitors, particularly those inhibiting CDK4/6, has renewed the interest in these inhibitors for systemic therapy (Malumbres and Barbacid 2001).

Given that 90% of WDLPS tumours have CDK4 amplification, this tumour provides a unique opportunity to study the biological effects of CDK4 inhibition. Using four new CDK4 inhibitors (SC-203873, SC-203874, NPCD and PD0332991) it was possible to study the effect of CDK4 inhibition across a range of WDLPS cell lines. The overall aim was to find a sensitive and specific CDK4 inhibitor to use in an siRNA screen of the genome, discussed in Chapter 5. The results were encouraging and showed that all the CDK4 inhibitors studied could produce a potent anti-proliferative affect.

The Merck inhibitor, CINK4, was identified by screening for compounds that could inhibit

CDK4 kinase in vitro and was shown to induce growth arrest with IC50 50

) (Soni, O'Reilly et al. 2001). The studies on liposarcoma cell.μM line CDKs, 449B andand )C778, .μMshowed CDK similar results with an IC50 of . Zhu et al. (Zhu, Conner et al. 2003) investigated the anti-proliferative.μM and .μM potential respectively of six novel substituted indolocarbazoles in vitro. The inhibitor, initially referred to as 4d (SC-203873), was able to exhibit potent anti-proliferative effects with an IC50 0.0042- similar to the effect of SC-203873 on 449B cell line in colony.μM. forming assays These (IC findings50 0.0032 were . Sun et al. studied the novel indolocarbazole inhibitor NPCD on breast cancer cell lines (MCF7,μM MB231, MCF15, T47D, G1101Ap) (Sun, Li et al. 2011). The anti-proliferative effects of NPCD were studied using MTT and colony forming assays. Similar to the studies on liposarcoma

CDK4 amplified cell lines (IC50 3.1-5.2 an IC50 ranging from 1-

nistered as μM,a single the dose.authors Sun demonstrated et al. concluded that NPCD could μMcause when a long-lasting NPCD was growth admi arrest. PD0332991, first studied by Fry et al. (Fry, Harvey et al.

2004) was verified to be a highly potent and specific CDK4 inhibitor with a low IC50 (0.011

). These findings were reflected in the WDLPS studies showing an IC50 28 nM (449B) and

μM 126 71 nM (778). Fry et al. showed that the anti-proliferative action required cells to have functional RB1 in order to be sensitive to the drug (Fry, Harvey et al. 2004). The requirement for functional RB1 was verified in WDLPS cell lines following lentiviral short-hairpins of RB1 transfection across a range of CDK4 amplified WDLPS cell lines (449B, 778, T1000). Cell lines in which RB1 had been knocked down showed resistance to PD0332991 compared to the same lines containing a short-hairpin empty vector.

Up-regulation of CDKi p21CIP1, p27KIP1 and p57KIP2 following treatment with PD0332991 is likely a result of cell cycle arrest as shown in Figure 17. The results of the studies suggest that CDK4 inhibitors have both cytostatic activity and cytocidal activity depending on the concentrations used. We found evidence for apoptosis in the experiments using NPCD and PD0332991. In 449B cell line, Annexin V staining following treatment with NPCD (4 and

M) showed an increasing degree of apoptosis compared with control cells. FurtherμM support forμ the induction of apoptosis was demonstrated biochemically by Western blot. Application of NPCD on 449B cells resulted in increased fragmented PARP signal, whilst PD0332991 application resulted in a decreased total PARP signal. PARP is required for apoptosis, and is a reliable indicator of this process occurring. Evidence of cleaved PARP ) and reduction of total PARP (PD0332991 500 nM) further confirms the abilityNPCD of bothμM inhibitors to induce apoptosis. Apoptosis occurred at doses where there was also hypophosphorylation of Rb780 or Rb795 suggesting that apoptosis was occurring at times when the inhibitors were acting upon CDK4. Apoptosis has previously been reported to occur with a range of CDK inhibitors, usually in a dose dependent manner (Payton, Chung et al. 2006). The mechanism of apoptosis, however, remains unclear. Some of the literature suggests that, at higher doses (i.e., micromolar concentrations), CDK inhibitors inhibit CDK9, which in turn results in suppression of MCL-1(Gojo, Zhang et al. 2002; MacCallum, Melville et al. 2005). MCL-1 is a member of the BCL-2 family of genes and inhibits apoptosis, whilst the alternatively spliced, shorter gene product (MCL-2), enhances apoptosis. Suppression of MCL-1, via CDK9 inhibition may, therefore, free the cell to undergo apoptosis (Wang, Hampson et al. 2012). Inhibition of CDK9 has not previously been reported for SC-203873, SC-203874, NPCD or PD0332991, but has been shown to occur with other pan-CDK inhibitors (Lam, Pickeral et al. 2001). In order to further determine the mechanism of apoptosis utilizing the more specific CDK4 inhibitors, it will be important to consider surrogate markers of CDK9 suppression including cyclin-D1, MCL-1 and the induction of p53 (Shapiro 2006).

127

The anti-proliferative effects demonstrated by the inhibitors may also be attributable to decreased cell division. All inhibitors utilised in these studies have been previously shown to induce a potent G1-S arrest across a range of malignant cell lines (Soni, O'Reilly et al. 2001; Zhu, Conner et al. 2003; Fry, Harvey et al. 2004; Sun, Li et al. 2011). In the WDLPS cell line studies we confirmed that a potent G1-S arrest was possible with the most sensitive and specific CDK4 inhibitor, PD0332991. Application of the inhibitor resulted in decreased cell division with a G1-S arrest when administered to 449B cells for 72 hours. Therefore, the anti- proliferative effects of the CDK4 inhibitors I studied, appear to be a combination of cytocidal and cytostatic effects.

The biochemical changes seen with application of the CDK4 inhibitors are largely generalizable. Previously, all utilised inhibitors have been shown to reduce phosphorylation of pRb across a range of phosphorylation sites (RB795, RB780, RB811) (Zhu, Conner et al. 2003; Fry, Harvey et al. 2004; Sun, Li et al. 2011). In the WDLPS studies we found that application of NPCD resulted in a reduction in pRb phosphorylation at RB780 and RB811 residues. The PD0332991 inhibitor resulted in a similar reduction in the phosphorylation of pRb, although at a different phosphorylation site (RB795). Following confirmation of the reduction in phosphorylation of pRb, it was important to confirm downstream target effects as a result of CDK4 inhibition. It is well reported that phosphorylation of pRb results in the release of E2F and the transcription of a number of genes involved in the G1-GS phase transition. Minichromosome maintenance complex component 7 (MCM7) is reported to be an E2F related protein that should be reduced in the setting of CDK4 inhibition (Gladden and Diehl 2003). The WDLPS studies revealed a reduction in MCM7 in the presence of the CDK4 inhibitor PD0332991. This finding provides proof of the downstream target effect of CDK4 inhibition.

Another interesting biochemical finding from the studies involved the natural CDK4 inhibitors from the CIP/KIP pathways. These CDKi all form complexes with a range of CDKs and inhibit their progression through the cell cycle at both the G1/G2 and G2/M phase checkpoints (Harper, Adami et al. 1993; Toyoshima and Hunter 1994). Although initially described as inhibitors of transition through the cell cycle, their role is far more versatile than originally proposed, with some studies showing that p21CIP1 and p27KIP1 can positively regulate the

128 Cyclin D-CDK4 complexes promoting cell cycle progression (Zhang, Hannon et al. 1994; LaBaer, Garrett et al. 1997; Cheng, Olivier et al. 1999; Alt, Gladden et al. 2002; Grimmler, Wang et al. 2007; Blain 2008). The WDLPS studies showed that in the presence of the CDK4 inhibitor PD0332991, there was an increase in p21CIP1, p27KIP1 p57KIP2.

The expression of p27KIP1 is closely related to Cyclin D- CDK4 complex formation. In vitro studies using MEFS from p21-/- and p27-/- mice showed a reduction in the number of formed Cyclin D-CDK4 complexes in comparison to wild-type controls. This reduction in Cyclin D- CDK4 complex number was subsequently restored with p27KIP1 re-expression (Iakoucheva, Radivojac et al. 2004). The importance of p27KIP1 in complex formation relates not only to complex assembly but also to complex stabilization (LaBaer, Garrett et al. 1997). Beyond complex formation, p27KIP1 has been shown to be versatile in its ability to be a Cyclin D- CDK4 bound inhibitor, or a non-inhibitor, dependent upon the growth phase of the involved cell (Blain 2008; James, Ray et al. 2008). In proliferating cells p27KIP1 is tyrosine Y phosphorylated, resulting in it binding to the Cyclin D-CDK4 complex in a non-inhibitory manner. Dephosphorylation of p27KIP1 then converts p27KIP1 into its kinase inhibitory mode(Blain 2008). Conversely, phosphorylation of p27 at Tyr88 results in the relinquishing of the inhibitory state. This illustrates the plasticity and variable functions attributable to the protein. Adding to this complexity, the activity of CDK4 is believed to relate to the stoichiometry between p21CIP1 and Cyclin D-CDK4 complexes (Zhang, Hannon et al. 1994; LaBaer, Garrett et al. 1997).

Some studies have suggested that in the presence of CDK4 inhibition, the p27KIP1 degrading protein is inhibited, resulting in p27KIP1 stabilization (Tomoda, Kubota et al. 1999), as demonstrated in our studies. In contrast, one of the published studies did demonstrate that the use of a CDK4 inhibitor can reduce the amount of p21CIP1 and p27KIP1 proteins without compromising the anti-proliferative effects of the utilised inhibitor (Sun, Li et al. 2011). Sun et al, whilst studying the biochemical effects of NPCD in breast cancer cell lines (MCF7, MCF 15, MB231), showed a significant reduction in p27KIP1 with the application of NPCD at 4 and 8 M at a 72 hour time point. A concomitant reduction in p21CIP1 was also seen in two of the

KIP1 CIP1 threeμ breast cancer cell lines. The authors postulated that p27 and p21 may have been direct cellular targets of NPCD within the micromolar dose range(Sun, Li et al. 2011). The authors also questioned if the reduction in p27KIP1 and p21CIP1 assisted the cell in undergoing

129 apoptosis once the Cyclin D- CDK4 complex was rendered inactive. Both proteins play a role in the formation of the Cyclin D-CDK4 complex and aid localization of the complex to the cell nucleus, which directly helps in transition of the cell through the cell cycle. The reduction in p21CIP1 and p27KIP1 would reduce the efficiency of complex formation, or indeed nucleus localization, which is thought to influence apoptosis (Cheng, Olivier et al. 1999; Murakami, Horihata et al. 2009; Sun, Li et al. 2011).

Our results and the published literature support a very diverse role for CIP/KIP in facilitating, and at times inhibiting, the CDK complex formation. The response of the CIP/KIP proteins also appears to be cell line specific. The up-regulation of CDKi p21CIP1, p27KIP1 and p57KIP2 following treatment with PD0332991 is likely to be a result of cell cycle arrest.

The main purpose of screening CDK4 inhibitors in WDLPS cell lines was to obtain a sensitive and specific inhibitor for use in the siRNA screen of the genome, in order to interrogate genes involved in generation of resistance to CDK4 inhibitors. When picking a sensitive and specific inhibitor, it was important that a resistance phenotype was seen in the control cell line. Using the SAOS2 and HT1080 cell lines and the SC-203873/SC-203874 inhibitors it was not possible to elucidate a resistance phenotype. The two control cell lines, SAOS and HT1080, were not CDK4 amplified and initially it was thought that this difference would elicit a resistance response. However, none of proliferation assays, either clonogenic or MTS, showed a difference between the CDK4 amplified and non-amplified cell lines using these inhibitors. Following this, lentiviral short-hairpin RNAs for RB1 were transduced into 449B cell lines. This hairpin was generated to create the ideal control cell line for the CDK4 pathway. Using the NPCD inhibitor a resistance phenotype was again unable to be generated between RB null and empty vector variants of the 449B cell line. This finding raised the possibility that the high micromolar doses of NPCD were having a pan-inhibitory effect. The IC50 for NPCD was far higher than the previously reported IC50 required for in vitro CDK4 inhibition(Sanchez- Martinez, Shih et al. 2003; Zhu, Conner et al. 2003; Zhu, Conner et al. 2003). The inability to discern between RB null and empty vector hairpin variants of the CDK4 amplified cell line could be explained by the inhibitor having a range of off-target effects involving other CDKs.

Given these findings, it was important to choose an inhibitor with low reported IC50 in other cell lines. PD0332991 had been previously published to have extremely low IC50 across a number of tumour cell lines(Fry, Harvey et al. 2004). In the WDLPS cell lines, PD0332991 generated a potent antiproliferative effect with low nanomolar-range IC50 . In addition, cell

130 lines with RB1 knocked down showed a robust and reproducible resistance phenotype (T1000, 449B, 778).

In conclusion, my combined studies have investigated the effects of a range of CDK4 inhibitors upon WDLPS cell lines. All inhibitors showed a potent anti-proliferative effect, with the expected decreased phosphorylation of the pRb and potent G1-S phase arrest. Concerns regarding off-target effects arose in the inhibitors with higher concentration IC50. These inhibitors were unable to deliver a resistant phenotype in cell lines with RB1 knocked down. The most sensitive and specific inhibitor was PD0332991. This inhibitor was able to deliver a potent anti-proliferative effect at nanomolar concentrations, with a sustainable G1-G2 cell cycle phase arrest. In addition, CDK4 amplified RB null cell lines were able to elicit a resistant phenotype when treated with PD0332991. Following these experiments, it was decided that PD0332991 would be a suitable inhibitor to use on WDLPS cell lines in the genome-wide siRNA screen.

131 CHAPTER 5: GENOME-WIDE RNAi SCREEN TO IDENTIFY CO-MODIFIERS OF CDK4 INHIBITION ON WELL-DIFFERENTIATED LIPOSARCOMA CELL LINES

5.1 INTRODUCTION

5.1.1 Background

The identification of novel mediators of cellular resistance to CDK4 inhibitors is a powerful approach to identify molecular mechanisms of drug resistance that may be selected by treatment with CDK4 inhibitors. In Chapter 4, I identified a CDK4 inhibitor, PD0332991, that suppressed colony formation and proliferation of the liposarcoma cell line 449B at IC50 28 nM. To further investigate this inhibitor and mechanisms of resistance to this inhibitor, a screen of siRNAs that could promote colony formation in the presence of PD0332991 was performed. To date no functional siRNA screens investigating CDK4 inhibitors have been published.

Molecularly targeted therapies and problems with drug resistance.

Despite the promise of targeted therapies in cancer, most eventually develop resistance. Resistance can be intrinsic or acquired. Intrinsic resistance describes tumours that are resistant to therapy without prior exposure. Acquired resistance evolves in tumour cells that were initially sensitive to the exposed drug(Holohan, Van Schaeybroeck et al. 2013). Resistance may result from genetic alterations in drug targets, ineffective induction of cell death, oncogenic bypass and pathway redundancy. These genetic alterations may be acquired during drug exposure, or may exist in a subset of cells (a clone) that is positively selected following drug exposure.

Alterations to a drugs target is a common mechanism of acquired resistance. This alteration in drug targets can result from mutations or changes in the level of target expression. Anaplastic Lymphoma Kinase (ALK) chromosomal rearrangements or overexpression mutations are reported in ALK positive anaplastic large cell lymphoma, non-small cell lung cancer and neuroblastoma. Excellent response rates are seen with exposure to the ALK tyrosine kinase inhibitor, crizotinib. However, resistance to the drug is later acquired with mutations commonly identified in the ALK tyrosine kinase domain(Shaw, Yeap et al. 2011). Similar target mutations in the kinase domain are seen with other tyrosine kinase inhibitors (Gorre, Mohammed et al. 2001), and illustrate important mechanisms by which resistance is acquired in novel therapeutics.

132 Dysregulation of apoptosis, and cancer cells predilection for anti-apoptotic proteins is another important mechanism of drug resistance(Holohan, Van Schaeybroeck et al. 2013). Anti-apoptotic proteins include proteins of the BCL-2 family, caspase 8 inhibitor FLIP and inhibitors of apoptosis proteins (IAPS). Cancer cells achieve dysregulation of apoptosis through activating mutations, chromosomal translocation and overexpression of the genes encoding these proteins (Chen, Dai et al. 2007).

Lastly, oncogenic bypass is another important mechanism of acquired drug resistance. In this, the drug target operates effectively; however, alternative kinases are activated and override the drug target through various biological feedback loops, i.e. overexpression of ERRBB3 and the PI3K-AKT pathway in response to Epidermal growth factor receptor (EGFR) inhibitors(Sergina, Rausch et al. 2007).

Functional genetic approaches to identifying mechanisms of drug resistance.

Experimentally, RNAi technology creates the ability to investigate the genetic basis for phenotypes in cultured cells, and has multiple therapeutic implications. In cancer, RNAi technology lends itself as a tool to identify genetic drivers, and the promise of a therapeutic target. Commonly, RNAi technology is used to find genes, or pathways, involved in synthetic lethality or resistance phenotypes(Fennell, Xiang et al. 2014). These screens are useful not only for finding novel cancer targets, but also drug resistance pathways and delineating side- effects of targeted therapeutics (Fatemian, Othman et al. 2014).

RNAi screens have identified pathways or modifiers of targeted therapeutics. Park et al identified oncogenic mutants of the PI3K pathway as a modulator of resistance to trastuzumab treatment in breast cancer cell lines(Park and Davidson 2007). Again in breast cancer cell lines, Iorns et al demonstrated that CDK10 silencing was an important modifier of sensitivity to tamoxifen therapy(Iorns, Turner et al. 2008). RNAi drug modifier screens in myeloid malignancy recently identified BCL-2 family members as potent sensitising agents to 5-azacytadine, a mainstay chemotherapeutic for the elderly with this malignancy (Bogenberger, Kornblau et al. 2014). RNAi inactivation of RASSF2 in K-RAS lung cancer cell lines resulted in a more aggressive lung cancer cell phenotype, as well as resistance to chemotherapeutics taxol and cisplatin(Clark, Freeman et al. 2012). These examples demonstrate the strength of RNAi technology as a tool to help identify and understand molecular modifiers of resistance to both classical chemotherapeutics and the new, molecularly targeted agents in a wide variety of cancers.

133

Functional screen of genetic modifiers of the CDK inhibitor PD0332991 in WDLPS cells.

The p16-cyclin D-CDK4/6 retinoblastoma protein pathway (CDK4 pathway) is dysregulated in 90% of WDLPS and an obvious therapeutic target for this disease. Activation of the CDK4 pathway leads to G1-S transition (see chapter four). Preclinical studies have shown that the molecular status of P16 is an important determinant of CDK4 activity, with increased CDK4 activity correlating with P16 deletion(Cen, Carlson et al. 2012). In chapter 4, I showed that PD0332991 induced cell cycle arrest and apoptosis of 449B cells. PD0332991 reduced colony-forming ability of cells in a dose-dependent manner, but at doses (greater than 1500 nM), 5 % of colonies still remained viable. I also demonstrated that there was a concomitant reduction in pRb and pRb phosphorylation, consistent with inhibition of the CDK4 pathway.

The molecular mechanism by which PD0332991 exerts its anti-tumour effect and genetic mechanisms that then confer resistance to this drug are not fully understood. To date a functional siRNA screen has not been conducted using PD0332991. In this study I have used an RNAi siRNA screen of 18120 genes to investigate the effect of PD0332991 on WDLPS cell lines in an attempt to identify genetic modifiers of drug resistance in WDLPS.

Using RNAi technology to identify genetic modifiers of resistance in a genome wide screen

In this study, we used the Dharmacon siRNA SMARTpool library of 18120 genes. The siRNA are designed to closely resemble and function as endogenous 21-nt siRNAs (Echeverri and Perrimon 2006). To maximise the potency of target mRNA cleavage, and hence gene silencing, Dharmacon employs a SMARTpool strategy, using 4 siRNAs targeting the one gene. Using multiple siRNAs to target individual genes enhances the probabilities of producing > 70% reduction in expression of the target gene to >95%. This level of robust silencing is critical to the selection of true gene targets that promote colony formation from the background colony formation rate in the presence of the CDK4 inhibitor.

134 5.1.2 Aims

The experiments in this chapter aim to identify genetic modifiers of resistance on WDLPS cell lines treated with CDK4 inhibitor PD0332991

5.2 RESULTS

5.2.1 Optimisation Steps & High throughput screen Algorithm

The quality of an RNAi screen depends heavily on detecting the desired phenotype repeatedly and quantitatively in high-throughput format, in this case using a multiwell, 384 well plate reader. Before primary screening, a number of optimisation experiments were performed (Figure 1).

Two components of the screen optimisation warrant additional explanation; choosing positive controls, and an appropriate readout assay. The positive controls are used to demonstrate the phenotype of interest. In this screen we were interested in hits that induced a resistance phenotype in the presence of a CDK4 inhibitor. Initially, we were also interested in hits that, in the presence of drugs, induced a synthetically lethal phenotype. The lethality phenotype is easy to produce using PLK-1. Effective loss of PLK-1 induces apoptosis pathways and inhibits growth (Liu X 2003).

RB1 knockdown was chosen as the biological control for the resistance phenotype. RB1 is an intrinsic component of the CDK4 pathway. CDK4 and CDK6 associate with Cyclin D to form active complexes that interact with, and phosphorylate pRB. Phosphorylation results in transcription of E2F regulated genes and progression through the cell cycle (McArthur G 2013). As a tumour suppressor, RB1 normally binds and inhibits transcription factors from the E2F family, thus preventing replication of damaged DNA. It is postulated that somatic inactivation of RB1 promotes tumourigenesis by the induction of uncontrolled proliferation. Beyond cell cycle control, research has shown that genes regulated by the RB/E2F axis play crucial roles in the cellular process including mitosis, apoptosis, differentiation, and DNA repair (Ishida S 2001) (Muller H 2001). Dysregulation of many of these cellular processes can result in genomic instability, that may also contribute to the tumorigenic model produced by RB silencing (Gray-Schopfer VC 2006).

135

Figure 1: Optimisation work up for high through-put screen. Initially an appropriate cell density and siRNA delivery system must be chosen. The efficacy of gene silencing is highly dependent upon the delivery of siRNA to cell lines. Delivery systems have to be chosen based upon transfection efficiency and degree of toxicity. Controls are next identified.

Positive controls must biologically represent the phenotype of interest. Negative controls are non-targeting siRNA that control for siRNA specific side effects. A read out assay is next chosen together with quality control metrics to ensure screen reproducibility and accuracy.

Customised statistical analysis is performed to identify gene hits.

136 The read-out assay must be aligned with the biological question being asked as summarised in Sharma et al (Sharma S 2009). In this screen we wished to assess cell viability by quantifying intracellular ATP . The CTG luminescent assay (Promega) quantifies ATP as a correlate to metabolic activity of cells. CTG reagent is added directly to the cells in media, resulting in cell lysis and the production of a luminescent signal proportional to ATP production. The CTG assay uses a thermostable luciferase, based on the gene from the firefly Photuris Pennsylvanica (LicPpe2), and generates a stable glow signal that is reportedly stable for 5 hours.

Finally, defining screen hits is the last step prior to the high throughput screen. Statistical analysis should begin during the optimisation experiments. The chosen controls define the baseline and the standard deviation values for the entire assay. There are many metrics that are used to identify hits including fold-change to Mock, mean difference, percent inhibition, and z-scores. Some methods are sensitive to outlier information, but this can be improved by using the robust z-score method (as I have used), strictly standardised mean difference (SSMD), and B-score method(Zhang XHD 2006). The SSMD is only useful if screening is in triplicate(Birmingham, Selfors et al. 2009). Using a selected metric, and guided by positive and negative controls for the screen, cut-offs are derived that i phenotype. This statistical algorithm is then always applied throughdentify each a hit of the for subsequentthe chosen screening steps.

5.2.2 High throughput screening Pipeline

Once the optimisation is completed, high throughput screening can begin. Genome-wide screens involve multi in order to minimise false negative or positives that can occurple withpasses broad- or screening steps referred to as primary, secondary and tertiary screens.scale Thecoverage. algorithm These for passes the screen are oftenused in this PhD is presented in Figure 2. The primary screen involves screening all genes within the Dharmacon SMARTpool library (18120 genes). Following this, a statistical analysis is applied to identify . This statistical application curates 18120 genes to approximately 400-500 genes. The

hitssecondary screen deconvolutes each of the 400-500 SMARTpool hits. Each duplex is deconvoluted to check .

validity as a hit. The same statistical approach is applied

137

Figure 2: Pipeline for High Throughput Screen. High throughput screening technology was used to systematically assess the genome for a given phenotype. In the primary screen, cell lines are grown and reverse transfected with SMARTpool siRNA and chosen lipid reagent. Each primary library screen plate is transfected in quadruplicate with A/B plates not receiving drug doses, and C/D plates receiving drug doses (18120 genes). Following the appropriate assay length, an appropriate read-out method can be used (i.e. cell viability). Hits for the screen are identified following normalisation of the data, and according to defined z-scores chosen during optimisation stages. Validation of hits from the primary screen is then carried out in a secondary screen (400-500 genes), whereby Dharmacon SMARTpool hits are deconvoluted to individual siRNA. Gene ontology analysis is applied to the hits revealed in the secondary screen. A tertiary screen is then performed, usually upon an alternative cell line (10-20 genes). Finally, functional validation is performed on the remaining hits (5-10 genes).

138 I shouldn order produce for an siRNA the desired to be considered phenotype. on High target, confidence of 4 duplexes mediumare those confidencewith 3 of 4 or 4 of 4 duplexes eliciting the phenotype. The secondary screen analysis identifies 50-60 hits. This list is further curated after gene ontology analysis, to yield a final target of 10-15 genes prior to a tertiary screen being performed. In this PhD, the tertiary screen was performed upon other liposarcoma cell lines known to have CDK4 amplification. Finally, functional analysis can be performed on hits arising from the tertiary screen. These analyses refine the gene list and should complement the findings found using the primary, secondary and tertiary screens.

5.2.3 Screen Optimisation results

From the CDK4 inhibitor studies described in chapter 4, we had already selected the cell line (449B) and CDK4 inhibitor (PD0332991) for use in the high-throughput screen. The Dharmacon SMARTpool library was used to screen the genome. This library consist of four individual siRNA targeting a distinct region of targeted gene mRNA. Prior to launching into the primary screen, a number of conditions needed to be optimised exhaustively. These conditions are outlined below.

Optimisation of Cell density

A cell seeding experiment was performed to determine the optimal number of seeded cells required to achieve 85% confluency by day 6-post transfection. A six day assay was chosen to allow for two separate applications of the PD0332991 drug prior to assay readout. In addition, as the planned phenotype of interest was resistance, the final confluency had to allow range for resistance to be depicted. The planned experimental design is summarised in Figure 3A.

Cells were seeded and Mock transfected at either 500 or 700 cells per well, in a 384 well plate, in duplicate, with or without PD0332991. The drug dose used was 1500nM of PD0332991 in 24 replicates as described in the Methods. The average confluency was calculated across all 24 replicates. Seeding 500 cells per well was ineffective, yielding only 30% confluency at 6 days. This low yield was probably secondary to toxicity at the time of seeding. Conversely, seeding 700 cells produced >90% confluency at day 6. A repeat experiment using 600 cells per well produced ~ 85% confluency in the non-treated wells, and approximately 15% confluency at the time of seeding (Figure 3B). This result was reproducible over a number of

139 experiments (n=3). Based on these results a seeding density of 600 was chosen for future assay development.

Figure 3: Optimisation of cell density and experimental design (A) The experimental design was carried out in 384 well plates. Each experiment contained drug and no-drug plates in duplicate. Cells were reverse transfected with siRNA on day 1 and a media change was performed on day 2 with PD0332991 application, in the drug plates. PD0332991 was changed again at day 4 and then experiment readout was performed at day 6 (144 hours). The cell viability assay chosen was Cell Titer Glo. Cell density experiments showed confluency of wells at 6 days, post Mock transfection, in plates not treated with PD0332991. The chosen initial cell densities were 500 (B), 600 (C) or 700 (D) cells.

140 Optimisation of siRNA delivery system

Optimising lipid reagent was necessary to balance transfection efficiency and toxicity specific to the cell line. Transfection efficiency was assessed using lipid reagents from Dharmacon: DharmaFECT 1 (DF1), DharmaFECT 2 (DF2) and DharmaFECT 3 (DF3). 449B cells were reverse transfected with siGlo red fluorescent siRNA (scrambled non-targeting) and DF1, DF2, DF3 as described (Methods 2.12.1.2). We assessed transfection reagent efficiency across a range of volumes of lipid reagent volumes (0-0.06 l). Transfection efficiency was assessed by counting the total number of siGLO red fluorescentμ cells divided by the total number of nuclei per field. This was established visually, and not by quantitative imaging. DF1 and DF3 both produced significant toxicity to the cells and limited transfection efficiency (Table 1) as assessed at 48 hours. The DF2 (0.04 l) reagent produced efficient transfection (~96%) and limited toxicity (2%) and outperformedμ in other experiment conditions.

Table 1. Transfection reagent conditions and efficiency using 384 well plate format

Based on the finding that DF2 at a concentration of 0.04 ul per well was an effective transfection reagent, we proceeded to confirm transfection efficiency using PLK1, where silencing results in cell death secondary to apoptosis as previously described (Medema, 2006, Cell cycle, 853). Effective knockdown of PLK1 should cause death of 449B cell lines in the assays, as previously reported(Uqras S 2011). Using siRNA siGLO and siPLK in a repeat experiment, DF-2 produced 100% cell death using the siRNA targeting PLK1 at a concentration of 40nM siRNA and 0.04 l per well (Table 2). Therefore, DF2 at a concentration of 0.04 ul/well was used for allμ subsequent experiments.

141

Table 2: Transfection reagent conditions using siGLO transfection and PLK1 in 384 well format. Cells were seeded at 600 cells per well and reverse transfected with either SiGLO or PLK1(40 nM) at 0.02-0.06 l per well in 384 well format. Transfection efficiency and cell death was assessed at 48 hours.μ

Optimising siRNA concentration

Next it was necessary to determine the optimal siRNA concentration that would ensure adequate reduction in expression, as quantified by RT-PCR at 24 and 48 hours, and protein knockdown at 72 hours . Previous laboratory experience within the Victorian Centre for Functional Genomics (VCFG) had used Dharmacon siRNA products at a concentration of 40nM with excellent protein knockdown at 72 hours. In this experiment 20nM and 40nM were the chosen concentration of the siRNAs. A 384 well plate was prepared using knockdown of Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as a control and Dharmacon siRNA targeting RB1 to assess knockdown efficiency as outlined in the Methods (section 2.12.1.3). Expression was assessed at 24 and 48 hours post siRNA transfection. RB1 expression was 60% that of wild type control at 24 hours and 2% at 48 hours using the 40nM concentration of siRNA (Figure 4A). Interestingly, 20nM also showed 60% expression of RB1 relative to wild-type at 24 hours, and 30% expression at 48 hours (Figure 4B). This experiment was repeated three times using Dharmacon SMARTpool siRNA targeting RB1 and also CDK4. The results (Table 3) proove good knockdown of both CDK4 and RB1, when using the 40nM siRNA concentration, which was chosen for all future screening experiments. A knockdown efficiency of 70% at 48 hours was the cut-off for minimum level of knockdown efficiency.

142

Table 3: Efficiency of gene silencing in 449B cell lines as measured by RT-PCR 449B cells were reverse transfected with SMARTpool siRNA targets (CDK and RB1) and the level of gene expression was measured by RT-PCR at 24 hour and 48hrs. Expression is measured relative to wild-type control in 449B cell lines. The reported expression is the remaining gene expression after knockdown. These expression levels are reflective of an average expression across three separate experiments. Dharmacon SMARTpool siRNA products were used together with DharmaFECT 2 (DF2) lipid reagent.

143

It was necessary to next confirm the reduction in protein expression secondary to the knockdown of siRNA targets in 449B cells. The experiment was converted to 6 well format to allow adequate protein to be collected. Cells were lysed for Western Blot analysis 72 hours after transfection. Protein levels of RB1 were probed for in 449B cell lines (Mock transfected) and 449B transfected with siRNA RB1. By 72 hours, RB1 protein was not detectable in the presence of siRNA (Figure 4C).

Figure 4: Optimisation of transfection conditions based on RB1 silencing by RB1 targeting siRNA. 449B cells were reverse transfected in a 384 well plate with 20nM or 40nM final concentration of siRNA targeting RB1 using DF2 transfection reagent. RT-PCR was performed at 24 and 48 hours to quantify expression of RB1 knockdown cells versus wild- type (control). RB1 mRNA levels were markedly reduced (98%) at 48 hours using 40nM siRNA (A). RB1 expression was also reduced using 20nM final concentration siRNA, however, not to the same potency (70%) (B). Western Blot reveals complete knockdown of RB1 compared with control at 72 hours post reverse transfection (C). Error bars show standard error mean (SEM) and were generated from three independent experiments.

144 Optimisation of Cell Titer Glo viability assay

As previously outlined in 5.1, the CTG luminescent assay (Promega) was selected as the readout assay for these studies. Temperature can be an important confounder of the luminescence reaction and so CTG had to be thawed and left at room temperature for 2 hours prior to any experiments. After CTG reagent was applied to cells in media at a 1:3 dilution, plates were put on a shaker for 2 minutes to distribute the reagent. The measurement of luminescence was performed on the Synergy H4 high- throughput multimode microplate reader (Biotek).

This screen requires cell viability analysis of 24 plates at a time, requiring approximately one hour to screen all plates. It was thus necessary to confirm stability of the assay over this time period. Cells from the 449B cell line were seeded at 600 cells per well and reverse transfected with lipid only in 384 well plates. PD0332991 was applied across a range of doses (20-1500nM) as per the Methods (section 2.12.1.4). CTG readouts were taken on day 6 of the assay at different timepoints (30 minutes, 2 hours) post CTG application. The results demonstrated a 1.3% increase in ATP readout at the later timepoint in the 449B cell line treated with PD0332991 at a concentration of 1500nM (Figure 5). Similarly, in untreated 449B cells, a variance of 0.8% variance in ATP readout was shown at the later timepoint. This degree of variability, however, across a 2 hour period of screening, was deemed acceptable for proceeding with the assay.

145

Figure 5: Signal stability of Cell Titre Glo Assay across 2 hour timeframe. 449B cells were reverse transfected with lipid only at a cell density of 600 cells/well. PD0332991 was applied 24 hours later (0-1500nM). CTG assay was performed at 6 days post drug application. Luminescent readout was performed upon Synergy H4 high-throughput multimode microplate reader (Biotek) either 30 minutes or two hours post application of reagent. Error bars show SEM and were generated from three independent experiments.

Optimisation of Transfection Controls

In high-throughput screens, - cts and are a direct

reflection of the screen reproducibilitnegative y.controls It is essential are used to testto convey a range off of non-targetingtarget effe siRNA to ensure that the scrambled gene, or, in the case of Dharmacon products, sequences that do not have any homology with genes, do not, by accident, include a complementary gene sequence that impacts upon the biological pathway of interest, in this case, the CDK4 pathway. This is only evaluable phenotypically, in the absence of using RNA sequencing data. Two non-targeting scrambled siRNA products were assessed as negative controls from the siGENOME research range; Non- targeting siRNA pool #1 (NTSP1) and non-targeting siRNA pool #2 (NTSP2).

449B cells were reverse transfected with NTSP1, NTSP2 or Mock transfection according to the now defined screening practices outlined previously (Figure 5). The results showed that NTSP1 had considerable toxic effects with a 20 % reduction in cell viability, compared to Mock transfection in the absence of drug (Appendix 3). Across the range of PD0332991 concentrations, NTSP1 continued to demonstrate toxicity, with ATP readout 60-75% that of the Mock transfected cell. In the absence of drug, NTSP2 performed better with only a 1% difference in ATP readout compared to Mock. At the

146 concentration of PD0332991 chosen for the screen (1500nM), NTSP2 was found to be comparable to Mock transfected cells with ATP readout only 2-3% less, relative to Mock (Appendix 3). Therefore, NTSP2 control was chosen as the best negative control, together with Mock transfection, for the siRNA screen, given the limited off target effects.

Positive biological controls for the screen needed to be able to convey the biological phenotype of interest (i.e. lethality or resistance). As such, we used the SiGENOME siRNA reagent targeting PLK1 to assess the efficiency of cell death. As a resistance phenotype was the main outcome of the siRNA screen, we identified RB1 as an appropriate biological target to confer resistance as outlined in 5.1.

449B cells were seeded and reverse transfected using the Dharmacon siRNA SMARTpool targeting RB1, NTSP2, and Mock according to the screening protocol. The silencing of PLK1 via siRNA reduced cell viability by 98% in both the PD0332991 treated and non-treated 449B cells (Figure 6A). The silencing of RB1 via siRNA successfully induced a resistance phenotype. In 449B cells that were not exposed to PD0332991, the silencing of RB1 resulted in a 25% increase in cell viability compared to negative controls (Figure 6B). Similarly, in PD0332991 treated cells RB1 knockdown conferred resistance with an increase in cell viability ranging from 15-40% in comparison to negative controls.

CDK4 was picked as another biologically relevant control for the screen. Although CDK4 knockdown was not useful for conferring either a resistant or lethal phenotype, it was chosen as a quality control measure because the degree of cell viability in the presence and absence of PD0332991 should be uniform throughout the screen.

147

148

Figure 6: Optimisation of Transfection Controls. (A) 449B cells were reverse transfected with either Mock, NTSP2 or PLK-1. The results show normalised data of the ATP response

following 6 days, in the presence or absence of PD0332991(D). PD0332991 reduced the

overall cell viability by 30%. SiRNA targeting PLK induced cell death in both the treated

and untreated 449B cells with 98% efficacy. (B) Repeat experiment using siRNA targeting

RB1. The siRNA targeting RB1 induced impressive resistance of the 449B cell line with 30-

70% increase in ATP response as shown in (C). Error bars show SEM and were generated

from three independent experiments.

Optimisation of Quality control measures

Quality control (QC) is essential to validate screen results, and determine screen plates that require re-screening. Sources of variability include, but are not limited to, technical issues, cell health, contamination and incorrect drug application.

QC metrics include the measurement of the variability between replicates via the percentage coefficient of variations (%CV). In robust cell-based assays the ideal %CV is <10%(Birmingham A 2009). controlsThe dynamic Z prime range factor or signalis used window for most in thehigh assay throughput and includes screens standard and gives deviations( a measureZhang of the JH 1999; Nebane NM 2013), In this way, the Z the screen quality. The Z factor compares the mean value of the maximum prime positive factor control reflects to the mean value of the minimum prime negative control. The Z approaching 1.0 if the standard deviations are

prime factor will have a value prime factor values > 0.3 indicate a significant low,assay and result if the when dynamic used range in high of the throughput assay is broad. siRNA Z screening, as compound screening results in excellent Z

prime factors. - ( 3SD positive controls + 3 SD negative controls)

Z prime factor = Mean positive controls mean negative controls – The QC measures included in this screen are summarised in Table 4. These included box plots for the controls to reflect the spread of data and concordance between plates. Replicate correlation plots, well scatter plots and Q-Q plots were also performed. The replicate correlation plots allow for the

149 reproducibility of the data to be quickly ascertained, whilst the well scatter plot indicate drift, or any other inconsistency both within and between replicate plates and across the entire data set. Quantile (Q-Q) plots are a probability plot that compares two probability distributions. The plot provides a graphical distribution of the data. These methods allow for screening plates with variability and outliers to be quickly identified and repeated as required. Defining outliers is important as they can distort final results (Zhang JH 1999).

Reportable siRNA specifics Reason Average +/- SD, % CV PLK1 To look at spread of data and (health reports) P16 compare to expected values normalised to Mock control RB generated during assay values per plate CDK4 optimisation. Mock prime factor PLK1 To determine statistical

Z P16 significance of assay controls RB to ascertain CDK4  resistance Mock  lethality Boxplots PLK1 distribution of all Data spread samples are also included in box plots P16 RB CDK4 Mock Replicate correlation plot All data Enable reproducibility to be visualised Well scatter plot For all plates using raw CTG Identify Drift or unusually values and Mock normalised high/low plate values per plate values Q-Q plot For all plates Assess data for normal distribution

Table 4: Quality control (QC) measures employed within high throughput screen. Various quality control measures allow for outliers to be detected that may distort the overall hits identified. Examples of some of the QC measures can be found in Figure 7/8.

150 Control Plate Optimisation

Prior to beginning the entire high throughput screen, the optimised siRNA conditions were tested in a small-scale siRNA screen, to which QC metrics could be applied. It was necessary to construct a control plate for the screen using the positive and negative siRNA controls outlined in section 5.2, in order to carry out a small scale screen. Small-scale screening allows for the reproducibility of the controls to be assessed and the dynamic range of the assay to be ascertained. The optimisation plate design is laid out in Appendix 4. An additional negative control was also used, RNA induced silencing complex (RISC) free, for the control optimisation plate. This is a stable RNAi control that does not engage RISC, and is designed not to target known human or mouse genes. It was used as a comparator to Mock transfection.

A total of 3 optimisation plates were prepared in duplicate with and without the application of PD0332991 at 1500nM. Drug and media changes and CTG readout were performed according to the normal screening protocol. QC metrics were applied across all controls. Representative box-plots of the controls used in the optimisation assay are presented in Figure 7. The well scatter plots across the three optimisation plates are shown in Figure 8. The well scatter plots visually depict drift and variance between plates.

The box plots (Figure 7) show that both in the presence and absence of PD0332991, the siRNA targeting RB1 induced a resistance phenotype with cell viability 15-30% greater than Mock transfected cells. The siRNA targeting CDK4 reduced cell viability by 50-70%. The siRNA targeting PLK1 consistently induced lethality with cell viability reduced by over 80% in the non-drug plates and 60% in the drug plates. This protection against PLK1 induced lethality in the presence of drug had not been seen previously in the siRNA workup (Figure 6). The protective effect against lethality induced by PLK1 was investigated again in a repeat optimisation study. In the repeat optimisation, two control plates where prepared in duplicates with drug, media and CTG readout according to the normal screening protocol. These studies confirmed that in the presence of PD0332991 the cell viability was reduced by around 60%, whilst in the absence of PD0332991 the cell viability was reduced by approximately 90% (Figure 9).

151

Figure 7: Quality control measures for siRNA screen. Boxplot showing data spread for positive and negative controls either with PD0332991 application (A) or without

(B).

152 It has been found previously in melanoma cell lines that CDK4 mutations are associated with differentially sensitivity to PLK1 knockdown (Jalili, Moser et al. 2011). One likely explanation is that manipulation of the CDK4/RB1 axis using PD0332991 confers resistance to PLK1 knockdown. We did not pursue further explanation for this experimental finding.

The optimisation experiments were then repeated with each replication demonstrating a marked improvement as seen in the degree of drift and variance between plates. This demonstrates that with increasing practice, proficiency and variance could be improved (Figure 10). The dynamic range for the resistance controls was more obvious in the drug treated plates, where a growth advantage was easily detected.

153

Figure 8: Well scatter plots of the cell viability across optimisation plates as measured by Cell Titre Glo. Cells were reverse transfected with range of positive and negative controls and CTG readout performed at day 6. Three 384 plates were transfected, in duplicate, both with and without exposure to PD0332991. Well scatter plots show ATP result, per well, after normalisation to Mock. (A) Plates exposed to PD0332991 showing good concordance of positive and negative controls. (B) Plates not exposed to drug, show some baseline variability of the data, requiring a repeat optimisation experiment. 154 Figure 9: Repeat optimisation experiment verifying efficacy of positive and negative controls. The efficacy of PLK1 knockdown was reassessed in a repeat optimisation experiment. 449B cells were again reverse transfected in 384 well format according to the control plate specified earlier. CTG readout was performed at Day 6. In the presence of PD0332991 the cell viability was reduced by approximately 60%, and in the absence of PD0332991 the cell viability was reduced by approximately 90%.

155

Figure 10: Well scatter plots of the cell viability across repeat optimisation. Cells were reverse transfected with range of controls and CTG readout performed at day 6. Well scatter plots are normalised to Mock. (A) Plates exposed to PD0332991 showing good concordance of positive and negative controls. (B) Plates not exposed to drug. This well scatter plot shows improvement in baseline variability seen previously.

156 5.2.4 Statistical Identification of screen hits

The statistical identification of hits was next defined for both resistance and lethal phenotypes. The pipeline for the screen is shown in Figure 11. To compare the data collected across drug exposed and non-drug plates, normalisation of all samples to the average of the Mock controls on each plate, averaged for the A and B plate, was performed. Subsequent to this the results were robust z-score normalised (Brough R 2011). Z-scores are a key measurement used to reflect the number of standard deviations from the mean. They are used to reflect the strength of the target siRNA. A positive z-score represents gain in cell viability, whilst a negative z-score represents loss of cell viability. The gain in viability required to represent a resistance

hit is defined below. To begin the pipeline, the siRNA plates without drug were first analysed. The cut-off for z-score and fold-change to Mock (FCM), that defined a hit, were based upon the positive controls for the screen (RB1 and PLK1). SiRNA wells with a FCM of <0.2 or z-score <-1.3 were defined as hits and removed from the screen. These cut-off for FCM and z-score were derived fromlethal the po

sitive lethality control PLK, which is deemed to be the lethality phenotype for the screen. Those siRNA that were found to be viable in the non-drug plates (FCM 0.2-1.0) were analysed in the drug-plates. Any hits with a FCM >1.0 in the non-drug plates were not further analysed as it was only considered necessary to include those hits that induced a resistance phenotype in the presence of drug. The siRNA that in the presence of PD0332991 induced a resistance phenotype were phenotype was defined as an averaged z-score of > 2.49 or FCM

1.25. termedThis cut- hits.off z-score The resistance and FCM were derived from results for the positive resistance control siRNA (RB1).

Finally, a subset of siRNA, found to be viable in the drug free plates (FCM 0.2-1.0), induced an inhibitory, and thus synthetically lethal, phenotype in the presence of PD0332991. The cut-off for potent inhibition of growth was a z- -0.8 in the drug treated plates. These hits were not further analysed in thisFCM high-throughput of . or score screen as we were primarily interested in resistance hits.

157

Figure 11: Pipeline for determining screen resistance hits. Non-drug plates were first analysed (1). Those siRNA with fold change to mock (FCM cut-offs were defined by the positive control for lethality PLK1. <. siRNAwere defined that were as lethal.determine Thed viable in the non-drug plates with FCM 0.2-1.0 were further analysed in PD0332991 exposed plates (2). Resistance hits were defined as those siRNA that were viable in non-drug plates, and thus elicited a z-score 2.49 / FCM 1.25. The cut-off for z-scores and FCM were determined by RB1 the positive control for resistance phenotype. Potent inhibition hits were those siRNA that showed a viable phenotype in non-drug plates, but a strong inhibitory phenotype in the drug exposed plates. Inhibition was defined as a FCM 0.2.

158 5.2.5 High throughput Primary screen results

Primary Screen Quality Control Metrics

Once the optimisation studies were completed and the algorithm for identifying hits determined, the primary screen of the genome was performed. Quality control metrics were applied and hits determined. The z prime factor for the primary screen was > 0 with average of 0.31 for drug exposed and 0.43 for non-drug plates indicating a good quality screening assay. The variability between control variants was calculated according to the percentage coefficient of variation (%CV). The %CV for control variants was shown to be 12-15% using both positive (RB1, PLK1) and negative (Mock) controls as shown in Table 5. The variability between replicates of individual siRNA conditions was calculated as an average %CV 9.4, across the entire screen indicating excellent screening conditions.

Scatter plots were generated in both drug and non-drug plates to delineate the drift and variance amongst plates for the entire screen (Figure 12). Overall, the screen variance and drift was minimal throughout the controls. This finding was supported by the %CV results. In addition, there was a discordancy flag built into the screen to alert if there was >25% discordancy between A/B or C/D plate replicates. If this alert was triggered during the screen then the replicate plates were repeated. This occurred twice during the screening process, and the experiment was repeated for these plates (plates 12034, 12009).

Table 5: Primary screen quality control metrics. % CV across positive and negative controls for the primary screen in drug treated and non-drug treated plates.

159

Figure 12: Primary Screen quality control metrics. Scatter well plots showing all CTG replicates averaged to Mock across the entire Primary screen in non-drug plates (A) and drug exposed plates (B).

160 Primary Screen Results

After the primary screen of 18120 genes, a total of 168 siRNA resistance hits were identified. In addition there were 3264 hits, that in the presence of drug evoked a synthetically lethal phenotype. There were 994 siRNA hits that induced lethality both in the presence and absence of drug. Furthermore, there were 51 siRNA hits that induced lethality in the absence of drug, but resistance in the presence of drug. From these results a total of 400 siRNA hits were chosen to comprise the secondary screen, which was the maximum capacity for the secondary screening process. These 400 siRNA included 168 siRNA that induced a resistance phenotype in the presence of PD0332991, 51 siRNA that were lethal in the absence of drug, but produced growth in the presence of drug, 38 siRNA that induced a synthetic lethality phenotype in the presence of drug, 100 siRNA that were lethal in both the presence and absence of drug, and 43 siRNA whose activity had previously been described in liposarcoma (i.e., CDK4, YEATS, LATS2). The siRNA hits selected for the secondary screen are listed in Appendix 1.

Secondary Screen Setup and Quality Control

The secondary screen is a deconvolution step whereby the four duplexes from each of the siRNA SMARTpools are individually screened. The duplexes were cherry-picked and screened at 25nM. They were aliquoted in advance in 3 all

4 duplexes on the same plate and empty84 well columnsassay plates left in for a controlsprecise random as described arrangement in the Methods with (section 2.13). There were 400 identified the screen, and therefore, 1600 individual duplexes in the secondary screen. The qualitysiRNA hits control for metrics, like the primary screen, showed limited variance amongst the controls. The %CV data is shown together with scatter well plots in Figure 13.

Secondary Screen Results

The deconvolution step from the secondary screen confirmed that siRNA targeting 54 of 168 genes induced a resistance phenotype in the presence of drug in 2 duplexes, indicating, with moderate confidence, that the reduced expression of the target gene caused the resistance phenotype. The potent inhibition phenotype was validated in 10 genes in of the screen was to study resistance in the presence of CDK4 duplexes. inhibition, (owever, the potent as the inhibitprimaryion focus hits

161 were not studied further (Appendix 2). The 54 genes validated in the secondary screen are summarised in Table 6.

162 Gene Gene Name Entrez Gene ID ACTG1 Actin, gamma 1 71 ARRB2 Arrestin, beta 2 409 CAPZB Capping protein (actin filament) muscle Z-line, beta 832 CDKN2A Cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) 1029 GIPR Gastric inhibitory polypeptide receptor 2696 GMFB Glia maturation factor, beta 2764 GRIK2 Glutamate receptor, ionotropic kainate 2 2898 ITIH3 Inter alpha trypsin inhibitor heavy chain 3 3699 KIF5A Kineisin heavy chain isoform 5A 3798 PLAGL2 Zinc finger protein PLAGL2 5326 VPS52 Vacuolar protein sorting-associated protein 52 homolog 6293 CCL4 Chemokine (C-C motif) ligand 4 6351 SLC19A1 Solute carrier family 19 (folate transporter), member 1 6573 SNRPA U1 small nuclear ribonucleoprotein A 6626 SON Conserved DNA binding protein SON 6651 WNT9A WNT signal pathway 9 7483 DYSF Dysferlin 8291 HIST1H2BC Histone H2B type 2BC 8347 HIST1H3B Histone H3B 8358 SLC25A14 Solute carrier family 14 9016 PPIP5K1 Diphosphoinositol pentakisphosphate 1 9677 COQ7 Coenzyme Q7 homolog 10229 LILRB2 Leukocyte immunoglobulin type receptor subfamily B 10288 PDLIM5 PDZ and LIM protein 5 10611 DOLK Dolichol kinase 22845 RSL1D1 Ribosomal L1 domain containing 1 26156 NXT1 Nuclear transport factor 2-like export factor 1 29107 CPSF1 Cleavage and polyadenylate specific factor 1 29894 SCLY Selenocystein Lyase 51540 SIAE Sialic acid acetylesterase 54414 ARMCX6 Armadillo repeat containing X-linked 6 54470 LGI2 Leucine rich repeat LG1 family member 2 55203 C4orf43 Chromosome 4 open reading fram 43 55319 TUBB7P Tubulin Beta 7 pseudogene 56604 FIGNL1 Fidgetin like 1 63979 AIDA Axin interactor dorsalization associated gene 64853 THOC6 THO complex 6 homolog 79228 FAT4 FAT tumour suppressor homolog 4 79633 NBPF3 Neuroblastoma breakpoint family member 3 84224 C12orf65 Chromosome 12 open reading frame 65 91574 DBX1 Developing brain homeobox protein 1 120237 ZFP3 Zinc finger protein 3 124961 BEST3 Bestrophin 3 144453 PPIAL4A Peptidylpropyl Isomerase A 164022 PSORS1C1 Psoriasis susceptibility 1 candidate 1 170679 C3orf43 Chromosome 3 open reading frame 43 255798 C17orf47 Chromose 17 open reading frame 47 284083 FAM83H Family with sequence similarity 83, member H 286077 CRB2 Crumbs family member 2 286204 FAM133A Family with sequence similarity 133, member A 286499 HEATR8 HEAT repeat containing 8 374977

Table 6: Deconvoluted and validated resistance hits from secondary screen. The four duplexes from each of the siRNA SMARTpools were individually screened as part of a secondary screen to validate identified hits. The validated hits are outlined above, they represent siRNA where a resistance phenotype was found in the presence of drug in 2

duplexes. The duplexes are listed in rank order of enrichment within the screen.

163

Figure 13: Secondary Screen quality control metrics. To assess the quality and performance of the secondary screen, siRNA replicates were put through a number of multi-parametric quality control analysis. The scatter well plots showing all CTG replicates averaged to Mock across the entire secondary screen are shown in non-drug plates (A) and drug exposed plates (B). The %CV for positive and negative controls, in drug exposed and non-drug plates is shown in (C).

164 5.2.6 Secondary Screen: Gene Ontology Analysis & Literature Review

After the secondary screen confirmed 54 genes that conferred a resistance phenotype in the presence of drug, a Gene Ontology Analysis was performed, together with a literature review to identify targets, genes and pathways, to be further investigated in a tertiary screen. The gene set enrichment analysis (GSEA) was carried out using the Broad institute algorithm as published by Subramanian et al(Subramanian, Tamayo et al. 2005). The advantage of the GSEA analysis is that it provides transcriptomic signatures within datasets using experimentally derived gene families rather than pre-defined gene families. In addition, the GSEA algorithm uses Kolgomirov- Smirnov statistics to rank genes between phenotypes and calculates the cumulative enrichment of a gene set towards a particular phenotype. As the GSEA ranks the entire data set, it does not have pre-defined statistical cut-offs and therefore is theoretically a robust analysis (reviewed in (Maciejewski 2014)

The GSEA analyses delineated that of the 54 genes, many were involved either directly, or indirectly with the MYC pathway. MYC is located on chromosome 8q and is a transcription factor involved in regulating expression of over 15% of genes within the genome, and may also have histone acetylation functions (Cotterman R 2008). Of the 54 genes analysed from our data, 9 have been described as having direct interactions with MYC (in transcriptional regulatory complexes?); NXT1, SNPA, CPSF1, ARRB2, VPS52, HIST1H2BC, SCLY, SLC19A1, ACTG1. In addition, 7 genes within this set were identified as Benporath_MYC_MAX_Targets. This gene set identifies transcriptional targets of MYC (Gene ID 4609) and MAX (Gene ID 4149) originally identified by ChIP in a Burki lymphoma cell line (Ben-Porath 2008). The

GSEA revealed that NXT1, SNPA, CPSF1, ARRB2,tts VPS52, HIST1H2BC and SCLY were included in the Benporath MYC_MAX_Targets. Finally, 7 genes were identified as encoding constituent parts of the nucleus, mitochondria, vacuoles, vesicles, ribosomes and the cytoskeleton. The genes identified within this subgroup included NXT1, SNRPA, CPSF1, CDKN2A, NCOA6, KIF5A and LILRB2. The entire analysis results are shown in Table 7.

At present, there are no preferred methods, or gold standards, for the ontological or pathway analyses of gene lists identified in a screening experiment such as ours and for that reason, using multiple different analysis is the best approach. GeneGo (MetaCore, Thomson Reuters) enrichment analysis was used as a second gene ontology analysis tool. This analysis is classified as a modular enrichment analysis, and will identify genes within the same pathway, with protein-protein interactions or related gene expression. The top three most significant 165 GeneGo Pathway Maps were 1) cytoskeleton remodelling_neurofilaments 2) clathrin coated vesicle formation and 3) cytoskeletal remodelling; regulation of the actin cytoskeleton by Rho monomeric G protein family (Table 8). The GeneGo pathway maps identified four genes that were prominent within more than one pathway including ACTG1, TUBB7P, KIF5A and ARRB2. The only overlapping gene between the two ontology analysis was ARRB2.

In conjunction with analysis of the 52 screen hits using GeneGo and GSEA, a literature review was conducted to determine which of the identified candidates had previously been described in neoplastic processes. The most compelling literature surrounding soft tissue sarcoma was found for the dysferlin gene (DYSF). DYSF codes for the dystrophy associated fer-1 like protein which plays an important role in skeletal muscle repair(Fuson K 2014). Dysferlin and dystrophin are both muscular dystrophy genes that act as tumour suppressors in mouse models of muscular dystrophy. Dysferlin deficient A/J mice, that were described in a spontaneous model of Duchennes muscular and limb-girdle dystrophy type 2B, were found to develop sarcomas with variable penetrance and latency(Hosur V 2012). The dysferlin gene proved interesting to validate given the previously described links with soft-tissue sarcomas.

Combining the results of the GeneGo, GSEA and literature analysis, 13 siRNA targets were named for the tertiary screen. The MYC targets identified via the GSEA together with the cytoskeletal remodeling proteins were selected as priorities. In addition, several siRNA targets within the literature screen had previously been described in soft-tissue tumours and tumorigenesis, and were subsequently also chosen targets for the tertiary screen, including DYSF, FAT4 and LATS2. The final 13 siRNA targets for the tertiary screen are summarised in Table 9.

166 GENE GENE SET Myc Bound Myc Max V$MEF2_03 Kinase Intracellular Organelle Part [1197] (Dang) targets inhibitory Organelle (Benporath) activity [1192] NXT1 X X X X SNRPA X X X X CPSF1 X X X X ARRB2 X X VPS52 X X HIST1H2BC X X SCLY X X SLC19A1 X ACTG1 X CDKN2A X X PDLIM5 X PLAGL2 X ARMCX6 X THOC6 X GMFB X CAPZB X X SON X

Table 7: Enrichment analysis of secondary screen siRNA targets by GSEA Broad Institute. The most significant pathways identified included genes coding for proteins whose promoters are bound by MYC (p = 1.35 X 10-5) and c-MYC or MAX targets (p=7.39 X 10-5).

167

Table 8: Enrichment analysis of secondary screen siRNA targets by GeneGo MetaCore analysis. The top three most significant GeneGo Pathway Maps were 1)Cytoskeletal remodelling Neurofilaments, 2) wtCTFR and delta 508 traffic/ Clathrin coated vesicle formation and 3) Cytoskeletal remodelling : regulation of Actin cytoskeleton by Rho GTPases. Within the disease folders, representing over 500 human diseases annotated by GeneGo, these genes were mainly related to carotid body and ureteral tumours and dystonia (data not shown).

168 Gene Description Entrez Gene ID ARMCX6 Armadillo repeat containing X-linked 6 54420 CPSF1 Cleavage and polyadenylate specific factor 1 29894 SCLY Selenocystein Lyase 511540 ARRB2 Arrestin, beta 2 409 GMFB Glia maturation factor, beta 2764 CAPZB Capping protein (actin filament) muscle Z-line, beta 832 GRIK2 Glutamate receptor, ionotropic kainate 2 2764 HIST1H2BC Histone H2B type 2BC 8347 SNRPA U1 small nuclear ribonucleoprotein A 6226 LATS2 Large tumour suppressor kinase 2 26524 DYSF Dysferlin 8291 FAT4 Photocadherin Fat 4 79633 NCOA6 Nuclear receptor coactivator 6 23054

Table 9: Gene List for Tertiary Screen. The curated list represents the top 13 gene targets conferring a resistance phenotype in the presence of PD0332991.

169

5.2.7 Tertiary Screen results

The tertiary screen is used to identify the best candidate(s) from the deconvolution and ontology analysis as a supplementary triage to the data . This screen focused on the 13 genes summarised in Table 9. Dharmacon siRNA targeting the tertiary screen candidates were ordered, along with real-time PCR primers, for each identified gene. It was important to first complete successful knockdown of the candidate genes in all CDK4 amplified cell lines (449B, 778, T1000). Cells were seeded at a cell density of 1.1 X105 cells per well in 6 well format, and siRNA reverse transfected as outlined in the Methods (section 2.13). Mock transfected wells were used as controls for the experiment. The siRNA were transfected in triplicate in all three cell lines, and at 48 hours RNA was collected for RT-PCR. The majority of the siRNA targets revealed a 80-90% reduction in mRNA expression (Figure 14). We were unable to design primers to screen the CAPZB siRNA target, so this was eliminated from the tertiary screen. GMFB1 and GRIK2 both showed less efficient knockdown at 48 hours, with knockdown efficiency between 50-80%. The T1000 cell line had a slightly lower reduction in expression of all candidates, than either the 449B or 778 cell lines.

Tertiary Screen Setup, Quality Control and Results

Once adequate transcript suppression was confirmed, a tertiary screen was performed on the T1000, 778 and 449B cell lines, using the same assay conditions as described previously. The % CV is shown in Figure 15, and reflects a similar and acceptable degree of variance as the primary and secondary assays. The identified tertiary screen candidates conferred a resistance phenotype if the FCM was greater than, or equal to, the siRNA RB1 control ( 1.2).

The results from the screen are shown in Figure 15B. The siRNA targets that displayed a resistance phenotype in the presence of PD0332991 in all CDK4 amplified lines were ARRB2, DYSF, LATS2. In both 778 and 449B cell lines, SCLY and FAT4 knockdown produced a resistance phenotype, but this was not demonstrated in the T1000 cell line. NCOA6, ARMCX6 , and GMFB were found to confer resistance only in the 449B cell line.

170

Figure 14: Suppression of RNA expression of siRNA targets in Tertiary Screen. RT-PCR results of siRNA targets in 449B (A), 778 (B) and T1000 (C) cell lines. RNA transcript levels are expressed as a ratio to wild-type in each cell line. The error bars represent SEM across three experiments. % Control = relative expression of gene knock down compared to control (2(-Delta Delta C(T) method (Livak and Schmittgen 2001).

171

Figure 15: Tertiary Screen results across three CDK4 amplified cell lines. Quality metrics performed on tertiary screen shows %CV for positive and negative controls, in drug exposed and non-drug plates is shown (A). Three individual CDK4 amplified cell lines (449B, 778, T1000) were reverse transfected with the 13 siRNA chosen for the tertiary secondary screen. Viability results are shown as a fold change to mock (FCM)(B). Hits were considered to confer resistance if the FCM was 1.2, synonymous with RB1 positive control (demarcation line in data). Error bars represent SEM and reflect three independent experiments.

172 5.2.8 Validation studies tertiary screen hits

The tertiary screen identified three siRNA targets (ARRB2, DYSF, LATS2) that conferred resistance across all CDK4 amplified cell lines (449B, T1000, 778). In order to validate these hits, CFA were performed. 449B cells were seeded at 1.1 X 105 cells per well, in 6 well format, and reverse transfected, in duplicate, with siRNA targeting RB1, ARRB2, CPSF1, DYSF, LATS2, NCOA6, SCLY and SNRPA. A mock transfection was used as the control. PD0332991 was applied after 24 hours and changed every 72 hours. The colony assays were allowed to grow out until 7 days and quantified as previously described. The results are shown in Figure 16A.

The experiment demonstrated that ARRB2 and DYSF both produced a resistance phenotype in the presence of PD0332991. The growth advantage produced by knockdown of ARRB2 was 30% more than Mock transfection, and 8% more than RB1 knockdown. DYSF produced the most convincing growth advantage with colony formation 60% more than Mock transfection and 40% more than RB1 knockdown. LATS2 growth advantage was not as convincing as that seen in the CTG assay with colony formation of only 4% more than Mock transfection. The resistance phenotype was not seen in the CFA for CPSF1, NCOA6, SCLY or SNRPA.

In order to verify the siRNA results, and to create a more stable construct with ongoing reduced expression of the target genes, short-hairpin plasmids targeting ARRB2, LATS2 and DYSF were purchased from Dharmacon. We selected 2 hairpins per gene, where available. Plasmids were grown and isolated using Qiagen ® Plasmid purification minikit and then transfected, individually, into HEK293T cells using Lenti-XTMHT Packaging mix (Clontech) as outlined in the Methods (section 2.4.3). The 449B cell line was then transduced with the short hairpin containing lentiviral supernatants for 48 hours, with repeat supernatant application for 48 hours. The cell lines were cultured in the presence of puromycin to ensure selection of the 449B cell line containing the short hairpin knockdown. Once the various knockdown cell lines were established, RT-PCR was performed to verify reduction in gene expression for the genes of interest. Sh449 (ARRB2, LATS2, DYSF) were seeded at 1.1 X 105 per well in 6 well format. Cells were allowed to grow for 48 hours and then removed and processed for RNA. RT-PCR was performed as described previously (Methods section 2.7.4). The RT-PCR verified that there was 99% knockdown of ARRB2 compared to empty vector control, 97% knockdown of DYSF sh14 and 95% knockdown of DYSF sh17. The short-hairpin for LATS2 showed 64% knockdown efficiency (Figure 16B).

173

Following confirmation of gene knockdown for the various 449B hairpin cell lines (Sh449BLATS, sh449BARRB2, sh449BDYSF) the colony forming assay was repeated. The sh449BLATS2 hairpin was not used given the limited target knockdown demonstrated on RT- PCR, and CFA findings where LATS2 failed to demonstrate a resistance phenotype. The CFA may be a direct reflection of poor siRNA knockdown. Cells were again seeded at 1.1 x 105 cells per well, in triplicate, as per the previous studies. Drug and media were applied and changed every 72 hours for 7 days. An empty vector 449B cell line was used as the control for this experiment. MetaMorph analysis was performed, after staining with crystal violet and representative photographs™ of the colonies were taken (Figure 16C). The results show that dysferlin conferred the most profound growth advantage (*p = 0.05, paired t-test). ARRB2 knockdown did result in some growth advantage compared to empty vector control, although the effect was not as profound as with the dysferlin knockdown. Dysferlin produced a growth advantage in both the presence and absence of PD0332991.

174

Figure 16: Validation Studies of final screen hits. siRNA targets conferring resistance in the tertiary screen were reverse transfected into 449B cells seeded at a density of 1.1 X105 cells per well in 6 well format. Wells were treated with or without drug with a drug/media change every 72 hours. Cells were allowed to grow out until 7 days and then fixed and stained with crystal violet. Colony numbers were assessed via MetaMorph Bar graph demonstrates colony numbers relative to Mock transfected cells in drug™ analysis. treated wells (A). Short-hairpin containing lentiviral supernatants were constructed and stably transfected into 449B cells for a range of targets (shARRB2, shLATS2, shDYSF). Gene expression of the shRNA target was assessed via RT-PCR and compared to 449B empty vector controls (B). CFA using 449B cells containing hairpin targets with empty vector, shARRB2, shRB1 and shDYSF (C). Error bars represent SEM from three independent variables within the one experiment.

175 5.3 DISCUSSION

Development of resistance to drugs is an important complication of therapy(Holohan, Van Schaeybroeck et al. 2013). Documented mechanism of resistance include alterations in drug targets including mutations and changes in overall copy number, resistance to apoptosis, and pathway redundancy. In melanoma cell lines resistant to B-RAF inhibition with vemurafinib, the addition of CDK4/6 inhibitor could restore sensitivity of the cells to inhibition (Yadav, Burke et al. 2014). The mechanistic process restoring sensitivity was induction of apoptosis. Similarly, in a combinatorial drug screen of different phosphoinositide-3-kinase (PI3K) cancers resistant to PI3K inhibition, the addition of a CDK4/6 inhibitor produced synergistic cell death (Vora, Juric et al. 2014). Uncontrolled expression of pRb in PI3K resistance tumours was thought to explain the increased sensitivity seen with CDK4/6 inhibition. The synergistic benefit of CDK4/6 inhibition has led to two Phase 1 trials one investigating CDK4 inhibitor LEE011 with the MEK inhibitor MEK 162 in NRAS- mutant melanoma (NCT01781572) and LEE011 with the BRAF inhibitor LGX818 in BRAF-mutant melanoma (NCT01777776). However, there is limited investigation into acquired resistance to CDK4 inhibition.

Growth arrest in response to CDK4 inhibition can be induced in tumours with ubiquitous CDK4 amplification, as is the case with WDLPS. In malignant melanoma, the dysregulation of the p16-cyclin D-CDK4/6-retinoblastoma protein pathway suggests that this tumour may also be susceptible to CDK4 inhibition(Sheppard and McArthur 2013). Increased sensitivity to CDK4 inhibition is also seen in Cyclin-D amplified tumours such as Mantle Cell Lymphoma(Leonard, LaCasce et al. 2012). Cancers with a loss of p16, the biological inhibitor of CDK4, have increased susceptibility to CDK4 inhibition, for example, melanoma, lung cancer and ovarian tumours(Konecny, Winterhoff et al. 2011; Gopalan PK 2013; Young, Waldeck et al. 2014). In contrast, some cell lines are resistant to CDK4 inhibition, particularly those that lack functional pRb. For the CDK4 inhibitors to be effective, they are dependent upon functional downstream pRb, hence the lack of response in these tumour types.

In this study we aimed to identify modifiers of resistance to CDK4 inhibition in a variety of WDLPS cell lines through a genome-wide RNAi screen. Our primary screen revealed 168 siRNA that, with gene knockdown, induced a resistance phenotype. The secondary screen confirmed 54 of the 168 siRNA produced a resistance phenotype. Prior to carrying out a tertiary screen, the gene list was curated further with gene ontology analysis using the GSEA from the Broad Institute(Subramanian, Tamayo et al. 2005) A total of 9 of the 54 genes were

176 identified as involved directly, or indirectly, with the myc pathway. In the tertiary screen, dysferlin gene knockdown, both with and without CDK4 inhibitor, produced the most impressive resistance phenotype.

It is important to note that in Chapter 4 we identified that 5% of cells survived inhibition with PD0332991 using CFA. This raises the possibility that overall this PhD potentially isolates two resistance populations. The first resistance population, identified as the 5% of cells remaining following CFA, possibly contain a mutational genomic or epigenomic background predisposing them to CDK4 inhibition resistance. The second population of resistance is identified by the population of cells that are sensitive to CDK4 inhibition, but with a second genetic hit, become resistant to CDK4 inhibition. The siRNA screen was setup to identify the population of cells sensitive to CDK4 inhibition, that then develop resistance in the context of gene silencing.

The ferlin family of proteins have only recently been implicated in tumorigenesis(Leung, Yu et al. 2013). These proteins are closely involved in active membrane repair, and include dysferlin (DYSF), myoferlin (MYOF) and otoferlin (OTOF)(Britton, Freeman et al. 2000; Davis, Delmonte et al. 2000; Roux, Safieddine et al. 2006). Initially, the expression of DYSF and MYOF was thought to be limited to muscle cells; however, there is a growing body of evidence that proves its expression in many other cell types and tissues(Karsan, Blonder et al. 2005; Lang, Markham et al. 2009). Vascular endothelial cells express high levels of DYSF and MYOF, which are thought to play a role in endocytosis, proliferation and adhesion(Bernatchez, Acevedo et al. 2007). In muscle fibres, both DYSF and MYOF play a role in membrane repair. In the absence of DYSF or MYOF, cardiomyopathies are known to develop(Davis, Delmonte et al. 2000), and systemic muscular dystrophies are a well known sequelae (Bansal and Campbell 2004). Therefore, the ferlin family of proteins is essential for the remodelling and ongoing maintenance of muscle and non-muscle cells.

In 1998, DYSF was identified as the genetic cause of recessive limb girdle muscular dystrophy type 2B(Bashir, Britton et al. 1998), as it was required for calcium dependent resealing of myofibres. DYSF contains seven C2 domains(Evesson, Peat et al. 2010), which are calcium binding motifs responsible for the calcium sensing, lipid binding, and calcium-activated vesicle fusion(Shin, Xu et al. 2009). Given the important role that DYSF plays in calcium- activated vesicle formation, it was proposed that it functions as a specialised calcium trigger

177 for the vesicle formation of sarcolemma membrane repair(McNeil and Khakee 1992). Skeletal muscle injury is incredibly common as it is constantly under stress, and strain, and thus repair is an important process to ensure ongoing normal function of the cells(Han, Rader et al. 2011).

The precise role DYSF plays in tumorigenesis is not well understood or published. In the absence of DYSF, it is possible that the crucial membrane repair function provided by DYSF is compromised, thus allowing damaged cells to continue proliferating, lending themselves to transformation. In mouse models of muscular dystrophy, DYSF and dystrophin (DYS) are thought to act as tumour suppressors(Hosur, Kavirayani et al. 2012). DYS deficient and DYSF deficient mice all develop sarcomas at different rates(Fernandez, Serinagaoglu et al. 2010; Schmidt, Uddin et al. 2011). Double mutant mice have higher incidence rates (47%) than DYS null (39%) or DYSF null (23%) mice (Schmidt, Uddin et al. 2011). The sarcomas described by Schmidt et al in these knockout mice included rhabdomyosarcoma, fibrosarcoma and liposarcoma. Therefore, much like RB1, dysferlin most likely behaves as a tumour suppressor, exerting its effects through membrane repair. Demonstrated by our experiments, a growth advantage was seen both in the presence and absence of drug with DYSF knockdown. It is possible that CDK4 inhibition further potentiates this by direct interaction within the Rb- MyoD-p300-HDAC pathway(Bakay, Wang et al. 2006). The Myo-D Rb pathway is central to the process of skeletal muscle differentiation, with high levels of Myo-D– resulting in cell cycle arrest via the RB protein. This process is enabled by the CREB/p300 nuclear transduction pathway(Magenta, Cenciarelli et al. 2003). It is plausible that in the absence of DYSF, there is down regulation of myogenin and MyoD(de Luna, Gallardo et al. 2006) leading to less activity of RB. As such, CDK4 inhibition would further inhibit the activity of RB, leading to a proliferative advantage.

ARRB2 knockdown was the second hit that induced a resistance phenotype in the validation screen. ARRB2 encodes the beta arrestin 2 protein(Attramadal, Arriza et al. 1992). These proteins are thought to participate in agonist mediated desensitisation of G protein coupled receptors. Most recently it has been described as a mediator of signal transduction in the Wnt/ catenin pathway(Bonnans, Flaceliere et al. 2012). Interestingly, recent findings by

Seitz et al suggest that ARRB2 knockdown blocks frizzled 7 induced protein kinase C alpha (PKC ). PKC (Lonne,

Cornmark et al. expression2010), promotion has been of tumorigenesis linked to poor (Haughian prognosis and Bradford in breast 2009) cancer and drug

178 resistance with recent studies implicating it as a new therapeutic target (El-Gamal, Williams et al. 2014; Hung, Chen et al. 2014). One may have predicted that knockdown of ARRB2 shouldplausible reduce that thePKC knockdown expression of and ARRB2 thus not causes induce upregulation a resistance of phenotype. other components (owever, of it the is , thereby inducing a resistant phenotype.

PKC pathway My screen also identified a number of hits that were involved in the MYC pathway. The MYC family of proteins (c-MYC, N-MYC,L-MYC, S-MYC, B-MYC) are major transcriptional regulators, and induced as a primary response to most signal transduction pathways altered in human malignancy(Nilsson and Cleveland 2003; Ruggero and Pandolfi 2003; Tonini and Romani 2003). MYC expression is necessary for cells to progress into the S phase of the cell cycle, as it induces DNA replication(Dominguez-Sola, Ying et al. 2007) and has a potent proliferative effect on cells(de Alboran, O'Hagan et al. 2001). MYC maintains tumorigenesis by influencing programs involved in DNA replication, death, self renewal, survival, and the microenvironment (Li, Casey et al. 2014). Furthermore, some tumour types appear to be Myc oncogene addicted. Oncogene addiction supports a model whereby inactivation of the oncogene, depending on cell type, initiates proliferative arrest, apoptosis, differentiation and modulation of the microenvironment via the immune system contributing to cellular senescence(Casey, Li et al. 2014) (Felsher 2008).

Opposing its role in tumourigenesis, MYC has also been found to play a critical role in inducing apoptosis(Askew, Ashmun et al. 1991). The specific MYC targets that trigger apoptotic responses are extremely difficult to identify, with over 650 targets identified to date(Coller, Grandori et al. 2000; Schuldiner and Benvenisty 2001; Fernandez, Frank et al. 2003). MYC can both induce and sensitise cells to an apoptotic response with rapid turning on of apoptosis dependent upon the level of c-MYC expression(Evan, Wyllie et al. 1992; Meyer, Kim et al. 2006). Thus, apoptosis for MYC may relate to expression models. The current literature suggests that many cells, when induced to over-express MYC, will activate their intrinsic cell death pathway. This may be p53 dependent (Nieminen, Eskelinen et al. 2013; Spender, Carter et al. 2013). During this process a small number of cells are rapidly selected that can tolerate MYC over-expression. These cells are capable of rapid proliferation. Therefore, MYC expression also rapidly selects highly malignant clones.

179 Given the complex and delicate balance between the level of MYC expression and the creation of a pro-apoptotic versus tumorigenic response, it is possible to speculate on links between the siRNA targets identified for the tertiary screen and CDK4 inhibition. It may be that the level of MYC expression was altered by the presence of the CDK4 inhibitor and the silencing of gene targets. As previously described, if the level of MYC expression is lower, cells are less likely to undergo an apoptotic response. Conversely, MYC expression may rapidly select for highly malignant clones as described above. Therefore, a resistance phenotype may have been borne out of subtle changes in the level of MYC expression through complex feedback loops. In addition, higher levels of E2F-1 favour an apoptotic state (Qin, Livingston et al. 1994; Kowalik, DeGregori et al. 1995; Holmberg, Helin et al. 1998; Pierce, Fisher et al. 1998). CDK4 inhibition leads to the down regulation of E2F and its various transcription factors. It is known that the ability of MYC to induce apoptosis is dependent upon E2F-1(Leone, Sears et al. 2001). Therefore, it is possible that with alteration of E2F-1 levels, secondary to CDK4 expression, and alteration of MYC pathway genes identified within the tertiary screen, the ability of MYC, and indeed E2F-1 to induce apoptosis was abrogated and thus a resistance phenotype produced.

In conclusion, a high-throughput RNAi screen of the genome provided an ideal platform to study resistance to the CDK4 inhibitor, PD0332991, in WDLPS cell lines. The process of primary, secondary and tertiary screens, together with stringent bioinformatics analysis, were analysed in the validation ensuredstudies, both that ARRB2true hits and were DYSF identified. warrant additional Of the genes investigation, that together with extra study of the involvement of the MYC pathway in promoting resistance in WDLPS. Further directions for these findings are discussed in chapter six.

180 CHAPTER 6: SUMMARY, FUTURE DIRECTIONS AND CONCLUSION

6.1 SUMMARY

Liposarcoma is the most common soft tissue sarcoma in adult life and it carries significant morbidity and mortality. The rare nature of this cancer, coupled with the relatively modest research that has been undertaken on the various subtypes, means that there are still gaps in our understanding of the underlying changes to genes and signaling pathways driving this tumour. CDK4 is amplified and overexpressed in 90% of WDLPS, bestowing a proliferative advantage on this cancer. Likewise, MDM2 is amplified and overexpressed in 100% of WDLPS. MDM2 regulates TP53 and plays a critical role in allowing p53 responses to DNA damage to be mitigated. The frequency of deregulation of CDK4 and MDM2 together, confers a selective advantage to WDLPS that warrants further exploration.

The research for this thesis initially focused on CDK4 and MDM2 because of the frequency of overexpression in WDLPS. We hypothesised that aberrant expression of MDM2 and CDK4 played driver roles in WDLPS development. We commissioned the generation of conditional knock in MDM2 mice (MDM2fl/fl) and carried out studies to geno- and phenotypically characterise these mice, using in vitro and in vivo assays. In order to investigate the function of CDK4 in WDLPS, a range of CDK4 molecular inhibitors were assessed in liposarcoma cell lines. These experiments helped study the sensitivity and specificity of CDK4 inhibition in WDLPS. The inhibitor found to produce the most potent growth arrest, PD0332991, was then used in a silencing RNA screen of the genome, in an effort to define genetic targets that conferred resistance to CDK4 inhibition. The following is a summary of the work undertaken and the major findings in this thesis.

Aim 1. To examine the biochemical and functional effects of MDM2 overexpression in vitro and in vivo.

The development of transgenic MDM2fl/fl mice was undertaken to explore the tumourigenic properties of MDM2 in WDLPS in an animal model. These mice were generated for this project and had not previously been reported in the literature. Overall the characterisation of the MDM2 mice in vivo and in vitro was limited, despite months of intensive work, as we could not reproducibly or reliably demonstrate recombination of the floxed allele, using multiple strategies. In vitro, MEFS from MDM2fl/fl mice were exposed to a range of lentiviral and

182 adenoviral vectors encoding Cre recombinase. The activity of Cre recombinase was confirmed for all viral supernatants using R26R MEFS. Transduction efficiency was also confirmed; however, demonstration of the IRES-GFP reporter was confounded because most of the viral supernatants contained a GIPZ backbone that produced a fluorescent signal. The LUCOS viral supernatant did not have a GIPZ backbone and, disappointingly, did not demonstrate increased IRES-GFP expression, suggestive of recombination of the floxed allele. We were unable to demonstrate MDM2, via Western blot, following exposure to all the varieties of viral supernatants. It is likely that the MEF experiments failed secondarily to the construct not working, as Cre recombinase activity and transduction efficiency were confirmed.

At the same time as the in vitro work was being performed, MDM2fl/fl mice were crossed onto ERT-Cre mice thus generating a hemizygous MDM2fl/ ERT-Cre strain which, once tamoxifen fed, would have strong inducible Cre recombinase activity. Theoretically, the Cre recombinase activity should have allowed for efficient recombination of the floxed allele. Despite rigorous and multiple experiments, we were unable to reliably and repeatedly provide evidence of recombination of the floxed allele. Tissues from tamoxifen fed mice were analyzed by PCR, RT-PCR, and Western blot, for evidence of recombination of the allele at the DNA, RNA and protein level. In addition, analysis of tissues for GFP expression was performed by flow cytometry and immunofluorescence. Autofluorescence of tissues, well reported in mice, hindered the accurate interpretation of immunofluorescence and GFP expression. Via an alternate method, flow cytometry analysis failed to reproducibly and reliably demonstrate an increase in GFP expression. Although the initial PCR studies from the tails of tamoxifen exposed mice were promising, tissue specific analysis failed to demonstrate recombination of the floxed allele. RT-PCR and Western blot analysis, similarly, failed to demonstrate convincing overexpression of MDM2, GFP or Cre in the tamoxifen-exposed mice. After 12 months it was clear that the MDM2fl/fl mice generated by Ozgene could not be used to determine the consequence of MDM2 overexpression in vivo or in vitro, due to the failure to achieve recombination of the floxed allele. The possible causes of failure of the model are discussed further in 6.2.1. Accordingly, we decided to shift the focus of the PhD to another highly amplified and overexpressed gene in WDLPS; CDK4.

183 Aim 2. To investigate the efficacy of a range of CDK4 inhibitors in WDLPS and identify an inhibitor for use in the siRNA screen

Our studies of the CDK4 inhibitors SC-203874, SC-203983, NPCD and PD0332991 all induced a proliferative arrest in liposarcoma cell lines at different IC50. CDK4 inhibitors had both cytostatic and cytocidal effects with evidence of apoptosis confirmed via Western Blot and flow cytometry Annexin V studies using both PDO332991 and NPCD on 449B cell lines. CDK4 inhibition, measured through the surrogate reduction in phosphorylation of pRb, was confirmed at the same time as the apoptotic responses. The mechanism of the apoptotic response is not fully understood. The growth arrest, or cystostatic effect of CDK4 inhibition, had been previously published for all the utilised inhibitors. In our studies, PD0332991 was found to exert the most potent G1-S arrest.

Biochemically both NPCD and PD0332991 were found to cause reduction in the phosphorylation state of pRb at a variety of phosphorylation sites. In addition, reduction in E2F transcribed proteins (i.e. MCM7) helped confirm the downstream effects of the inhibitors. Lastly, the CIP/KIP family of inhibitors (p21CIP1, p27KIP1 and p57KIP2) were found to be up-regulated in the presence of PD0332991, which was likely to reflect cell cycle arrest. SC-203874, SC-203873 and NPCD failed to produce a resistance phenotype with RB knockdown in the 449B cell line, raising the possibility of off-target effects, which has important therapeutic implications. PD0332991 produced a resistance phenotype with RB knockdown and was also found to be the most sensitive inhibitor with an IC50 in the nanomolar range. Because of its sensitivity and specificity, PD0332991 was chosen as the inhibitor for the siRNA screen of the genome.

184 Aim 3. To identify genetic modifiers of resistance on WDLPS treated with CDK4 inhibitor PD0332991

The functional siRNA whole genome screen was designed and performed to identify genetic modifiers of CDK4 drug resistance in WDLPS cell lines, using the Dharmacon SMARTpool library. The screen was first optimised for cell density, lipid delivery system, positive and negative controls, readout assay, quality control metrics and finally, statistical pipeline for identifying hits. A primary screen was performed on 18120 genes, followed by a secondary validation screen of approximately 400 genes and a tertiary screen of 13 genes. Following ontology analysis, many of the genes identified to modify resistance to PD0332991, including NXT1, SNRPA, CPSF1, CDKN2A, NCOA6, KIF5A and LILRB2, were found to be MYC targets. Of these 13 genes, three were functionally validated and two were found to reproducibly produce a resistance phenotype (ARRB2 and DYSF).

6.2 FUTURE DIRECTIONS

6.2.1 Characterization of MDM2 and CDK4 transgenic mice

When commencing this PhD there were no transgenic inducible MDM2 and CDK4 mouse models reported in the literature. As such, we had hoped that our transgenic mouse model would be a first, and provide further insight into the role both MDM2 and CDK4 play in WDLPS. As a result of the transgenic MDM2 mouse model failing to show convincing recombination of the floxed allele, we were unable to achieve this. However, in 2010, De Clercq and colleagues published results using a conditional MDM2 transgenic mice model(De Clercq, Gembarska et al. 2010). Unlike our transgenic mice, the MDM2 transgenic contained a -Geo-STOP cassette(De Clercq, Gembarska et al.

2010pCAGG). The promotor cassette followedwas electively by a floxedexcised β using Cre recombinase, resulting in downstream transcription of MDM2-IRES-GFP. The authors verified excision -Geo-STOP cassette, and expression of GFP in tissues. They were able to show that MDM2of the quickly β degraded in the absence of a proteasomal inhibitor. The authors concluded that there are complex, highly regulated systems in place, mainly involving the ubiquitin-proteasome system, that ensure that cells do not accumulate MDM2. They also found that, in order for tumourigenesis to occur, overexpression of MDM2, together with mechanisms that impede proteasome degradation, must occur. This finding could be incorporated into further transgenic mouse

185 models involving MDM2. In our model, failure of the transgene to overexpress MDM2 may relate to inefficient transcription driven by the Ubic promotor. The studies by De Clercq et al. (De Clercq, Gembarska et al. 2010) showed efficient recombination of the allele using a pCAGG promotor. It is also possible that transcriptional interference could have occurred using the Ubic promoter system, as has been previously documented(Nie, Das Thakur et al. 2010).

There continues to be an absence of CDK4 transgenic mouse models described in the literature. The previously discussed mutant activated CDK4R24C mouse is often used as a model of constitutive CDK4 expression. These CDK4R24C mouse models have most recently been utilised to study melanoma (Coleman, Chagani et al. 2015) and leukaemia(Rodriguez- Diez, Quereda et al. 2014). Although the use of CDK4(Luke, D'Adamo et al. 2012; Zhang, Sicinska et al. 2014) and MDM2 inhibitors (reviewed in (Lv, Sun et al. 2015) in WDLPS is regularly reported, a combined CDK4 and MDM2 transgenic mouse model has not, to date, been successfully generated.

In continuing to explore the role of MDM2 and CDK4 in vivo, it would be necessary to re- engineer the transgenic mice. In the first instance, it would be advisable to engineer a mouse with the pCAGG promotor, as it has previously been shown to be successful by De Clercq. In addition, it would be worth pursuing an approach that also inhibited proteasomal degradation in order to prove overexpression of MDM2. We still have a poor understanding of the individual roles CDK4 and MDM2 play alone, or in combination, in WDLPS tumourigenesis. With the advent of novel inhibitors targeting both of these genes, there is still an interest in pursuing a model with inducible MDM2 and CDK4, to further understand the disease biology.

6.2.2 Implications of molecular inhibitors of CDK4 in WDLPS

CDK4 inhibitors have been widely used across a range of malignancies. The first pan- CDK inhibitors were not specific for CDK4, targeting multiple CDKs, and their use, in clinical trials, was substantially limited by severe dose-limiting toxicities (reviewed in (Shapiro 2006; Musgrove, Caldon et al. 2011)). Second and third generation CDK4/6 inhibitors based on chemical backbones that utilised oxindoles, thioacrindones, indolocarbazoles, benzothiadiazines and triaminopyrimidines structures (Lee and Sicinski 2006) promised

186 greater sensitivity and specificity when first introduced.

My studies found that CDK4 inhibitors, used in the micromolar range, failed to illuminate a difference in IC50 between RB deficient (biologically insensitive) and RB proficient (biologically sensitive) cell lines. These findings imply off-target effects of CDK4 inhibitors within the micromolar range, which may be a therapeutic issue. One of the first steps in understanding this further would be to identify and quantify off-target kinase interactions. Kinase inhibitor selectivity has been studied previously. One of the first studies to examine the interaction between kinase inhibitors (n= 38) against a panel of 317 kinases was published by Karaman et al. (Karaman, Herrgard et al. 2008). Using an in vitro competition bindingdissociative assay, constant each (K kinased) was inhibitor determined. was The screened results againstprovided the a comprehensive panel at μM overview and a of kinase inhibitor selectivity and interaction maps. Of the kinase inhibitors used in these studies, four had pre-determined sensitivity to CDKs (BMS-387032 (CDK2), flavoperidol (pan-CDKi), JNJ-7706261 (CDK1, CDK2), riscovitine (CDK1, CDK2, CDK5) (Karaman, Herrgard et al. 2008). Although a CDK4/6 inhibitor was not reported, it was clear that CDK inhibitors with moderate selectivity (e.g BMS-387032) had multiple interactions with other protein kinases across a range of pathways (supplementary data 1 (Karaman, Herrgard et al. 2008). The authors also quantified the frequency of off-target interactions by examining the ratio between Kd(off target)/Kd(primary target). Some inhibitors (i.e. sorafenib and dasatinib) bound 10% of the off-target kinases tested with affinities within tenfold of their primary target, raising the likelihood of off-target effects at therapeutic doses for these compounds.

Traditional kinase inhibitor analysis has been centred on measuring the capability of molecules to inhibit phosphorylation and binding affinity. Using the CheEMBL kinase SARfari, a kinase-dedicated public resource, the phosphorylation activity and binding affinity of CDK4 inhibitors is well documented(Gaulton, Bellis et al. 2012). However, what has not been well studied is the selectivity of CDK4 inhibitors across the entire kinome.

High through-put screens have enabled a wealth of kinase inhibition profiling data to be performed (Posy, Hermsmeier et al. 2011) but what remains difficult is the ability to collate this data, often from different sources, into meaningful molecular inhibitor selectivity

187 information across the kinome. Tang et al recently reported upon kinase inhibitory bioactivity (KIBA), a computational approach that combines a number of different kinase datasets including the Davis, Metz, CheEMBL, STITCH and Anasstassiadis(Tang, Szwajda et al. 2014) and provides comprehensive assessment of bioactivity of over 50000 compounds upon 467 kinases. This KIBA database is soon to be made publicly available, and will significantly help navigate and co-ordinate the data from high throughput screens.

Interestingly, the KIBA bioactivity assessment found that 1000nM should be considered to be a maximum inhibitory threshold for true positive on-target interactions for a number of kinase inhibitors, supportive of the findings from my research in this thesis. More recently Lu and colleagues produced results that suggested potential off-target effects using CDK4 inhibitors with IC50 in the nanomolar range (Lu, Wu et al. 2014). The suppression of CDK4, using PD0332991, produced a potent and unexpected tumourigenic effect upon MYC expressing B cells. This finding highlights the need to precisely define molecular target interactions.

Building upon the findings of the research presented in this thesis it would be necessary to investigate and validate the selectivity of the CDK4 inhibitors both in the nanomolar and micromolar range. As the KIBA database becomes publicly available, it could be used to examine the integrated bioactivity data for CDK4 inhibitors. This data could then be validated using PD0332991, SC-203874, SC-203873 and NPCD. Increasing the knowledge base surrounding CDK4 inhibitor selectivity will help inform interaction patterns across the kinome. This, in turn, will help interpret the activity of these inhibitors observed in Phase I/II clinical trials.

6.2.3 Genetic determinants of resistance to CDK4 inhibition in WDLPS

Two novel genetic targets (ARRB2, DYSF) were shown to produce resistance in the presence of CDK4 inhibition with PD0332991 in WDLPS cell lines. Both of these targets warrant further exploration.

The literature surrounding ARRB2 and resistance to therapeutic measures in cancer, either cytotoxic or molecular, is limited. One study has implicated ARRB2 in high-grade glioma multidrug resistance (MDR) (Chen, Xu et al. 2015). Here a MDR glioma cell line (SGH-

188 44/ADR) was generated following impulse adriamycin (ADR) treatment. DNA methylation was analysed using a methylation DNA immunoprecipitation microarray chip (MeDIP-Chip). Ontology analysis of the MeDIP-Chip data implicated ARRB2, as one of seven genes, whose methylation status was crucial to the development of MDR. Methylation is another mechanism of gene silencing, and thus, mirrors the findings presented in this thesis, whereby silencing of the ARRB2 gene produced resistance. More recently the necessity of ARRB2 in colorectal tumou -catenin pathway, was published

(Bonnans, Flaceliererigenesis, et al. 2012). mediated Contrary via the to the Wnt/β findings from my PhD, these studies, comparing tumourigenesis in ARRB2-/- and ARRB2-Wild type mice, found that the knockdown of ARRB2 decreased the incidence and proliferative rate of tumours. This finding demonstrates that resistance mediated by silencing the ARRB2 expression may be cell line and pathway specific, which would account for the discordant findings.

The second identified target inducing resistance in the context of CDK4 inhibition in WDLPS cell lines was DYSF. Knockdown of this gene in the siRNA screen also increased the background proliferative rate in WDLPS cell lines. This genetic target is a muscular dystrophy gene. The involvement of DYSF in tumourigenesis has previously been proposed, as silencing of the gene (Dysf-/-) resulted in the increased development of mixed sarcomas in AJ mice (Sher, Cox et al. 2011). Furthermore, double mutant mice incorporating both DYSF and Duchene muscular dystrophy (DMD) gene knockdown (DYSF-/-/DMD-/-) were also found to develop sarcomas, particularly rhabdomyosarcoma, with increasing frequency and decreased latency compared to their wild type controls(Hosur, Kavirayani et al. 2012). It has previously been hypothesised that the loss of DYSF increases genomic instability (Schmidt, Uddin et al. 2011), thus leading to tumour formation. Schmidt et al recently analyzed tumours from DMD-/- or Dysf-/- mice via an array comparative genomic hybridisation screen. The studies found that mutations involving CDKN2A, NF1 and TP53, with gains of chromosome 8 where common within tumours. Interestingly, these genomic findings were present in normal muscle of Dysf-/- mice before tumour development, suggesting that the knockdown predisposes to genomic instability. The published data supports the functional studies from my thesis, whereby the knockdown of DYSF not only induced a resistance phenotype in the context of CDK4 inhibition, but also produced a proliferative advantage to WDLPS cells, which may be born out of genomic instability.

189 An extensive functional validation of DYSF and ARRB2 induced resistance in WDLPS was unable to be achieved during the time of this thesis. The first step in developing this model further would be to create tumour xenograft models in Balb/c nude mice. A range of WDLPS cell lines containing short hairpin targets of DYSF (shDYSF) and shARRB2 (shARRB2) could be developed using the same techniques used to generate the 449BshDYSF and 449BshARRB2 cell lines described in this PhD. Cell lines with shDYSF and shARRB2 knockdown, and their corresponding empty vector control could be suspended and inoculated subcutaneously into nude mice at 5-6 weeks of age, using previously published techniques(Zhang, Zhang et al. 2015). Mice could then be administered oral PD0332991 by daily gavage at 150mg/kg as previously published (Cen, Carlson et al. 2012). Treatment of the various xenografts with PD0332991 could then be assessed for effect on proliferation, and the development of resistance. Using this approach, the findings from the siRNA screen of the genome, and the two genetic targets producing resistance to CDK4 inhibition, could be extensively validated.

6.4 CONCLUDING REMARKS

This study aimed to define the role of MDM2 and CDK4 in the development of WDLPS. Although extensively studied, the transgenic mouse model did not perform adequately and was abandoned. The CDK4 inhibitor studies comprehensively demonstrated the anti- proliferative effects of these novel second generation CDK4 inhibitors. The studies verified on-target effects of the inhibitors, and raised the compelling question surrounding off-target effects in inhibitors used at micromolar concentrations. The recent finding of CDK4 inhibitors producing tumourigenicity in haematopoeitic cells, and known off-target effects of many kinase inhibitors (Karaman, Herrgard et al. 2008), raises the need for further investigation into off-target effects of these molecular inhibitors. Finally, the siRNA screen of the genome provided a robust analysis of genetic targets, that in the presence of the CDK4 inhibitor PD0332991, produced a resistance phenotype. Many of the gene targets verified in the secondary screen were found, by gene ontology analysis, to involve MYC targets. Two of the gene targets, ARRB2 and DYSF were functionally validated, and warrant further investigation. The role these two genes play in producing resistance remains unpublished and provides a platform for a better understanding of resistance, when using CDK4 inhibition, as a molecular target in WDLPS.

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213 Appendix 1 – Secondary Screen SiRNA targets

Entrez Gene Gene Identification Symbol Gene description 26 ABP1 amiloride binding protein 1 (amine oxidase (copper-containing)) 71 ACTG1 actin, gamma 1 105 ADARB2 adenosine deaminase, RNA-specific, B2 161 AP2A2 adaptor-related protein complex 2, alpha 2 subunit 238 ALK anaplastic lymphoma receptor tyrosine kinase 249 ALPL alkaline phosphatase, liver/bone/kidney 319 APOF apolipoprotein F 323 APBB2 amyloid beta (A4) precursor protein-binding, family B, member 2 336 APOA2 apolipoprotein A-II 409 ARRB2 arrestin, beta 2 467 ATF3 activating transcription factor 3 487 ATP2A1 ATPase, Ca++ transporting, cardiac muscle, fast twitch 1 582 BBS1 Bardet-Biedl syndrome 1 778 CACNA1F calcium channel, voltage-dependent, L type, alpha 1F subunit 785 CACNB4 calcium channel, voltage-dependent, beta 4 subunit 824 CAPN2 calpain 2, (m/II) large subunit 832 CAPZB capping protein (actin filament) muscle Z-line, beta 908 CCT6A chaperonin containing TCP1, subunit 6A (zeta 1) 991 CDC20 cell division cycle 20 homolog (S. cerevisiae) 1014 CDH16 cadherin 16, KSP-cadherin 1019 CDK4 cyclin-dependent kinase 4 1029 CDKN2A cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) 1130 LYST lysosomal trafficking regulator 1146 CHRNG cholinergic receptor, nicotinic, gamma 1161 ERCC8 excision repair cross-complementing rodent repair deficiency 1262 CNGA4 cyclic nucleotide gated channel alpha 4 1297 COL9A1 collagen, type IX, alpha 1 1302 COL11A2 collagen, type XI, alpha 2 1317 SLC31A1 solute carrier family 31 (copper transporters), member 1 1318 SLC31A2 solute carrier family 31 (copper transporters), member 2 1373 CPS1 carbamoyl-phosphate synthase 1, mitochondrial 1453 CSNK1D casein kinase 1, delta 1454 CSNK1E casein kinase 1, epsilon 1846 DUSP4 dual specificity phosphatase 4 1917 EEF1A2 eukaryotic translation elongation factor 1 alpha 2 2018 EMX2 empty spiracles homeobox 2 2140 EYA3 eyes absent homolog 3 (Drosophila) 2168 FABP1 fatty acid binding protein 1, liver 2195 FAT1 FAT tumor suppressor homolog 1 (Drosophila) 2196 FAT2 FAT tumor suppressor homolog 2 (Drosophila) 2309 FOXO3 forkhead box O3 2517 FUCA1 fucosidase, alpha-L- 1, tissue 2561 GABRB2 gamma-aminobutyric acid (GABA) A receptor, beta 2 2696 GIPR gastric inhibitory polypeptide receptor 2764 GMFB glia maturation factor, beta

2773 GNAI3 guanine nucleotide binding protein (G protein), alpha inhibiting activity polypeptide 3 214 Entrez Gene Gene Identification Symbol Gene description 2898 GRIK2 glutamate receptor, ionotropic, kainate 2 2922 GRP gastrin-releasing peptide 3014 H2AFX H2A histone family, member X 3135 HLA-G major histocompatibility complex, class I, G 3151 HMGN2 high mobility group nucleosomal binding domain 2 3183 HNRNPC heterogeneous nuclear ribonucleoprotein C (C1/C2) 3234 HOXD8 homeobox D8 3550 IK IK cytokine, down-regulator of HLA II 3596 IL13 interleukin 13 3640 INSL3 insulin-like 3 (Leydig cell) 3699 ITIH3 inter-alpha-trypsin inhibitor heavy chain 3 3725 JUN jun proto-oncogene 3798 KIF5A kinesin family member 5A 3996 LLGL1 lethal giant larvae homolog 1 (Drosophila) 4017 LOXL2 lysyl oxidase-like 2 4090 SMAD5 SMAD family member 5 4105 MAGEA6 melanoma antigen family A, 6 4107 MAGEA8 melanoma antigen family A, 8 4159 MC3R melanocortin 3 receptor 4162 MCAM melanoma cell adhesion molecule 4166 CHST6 carbohydrate (N-acetylglucosamine 6-O) sulfotransferase 6 4193 MDM2 Mdm2 p53 binding protein homolog (mouse) 4486 MST1R macrophage stimulating 1 receptor (c-met-related tyrosine kinase) 4605 MYBL2 v-myb myeloblastosis viral oncogene homolog (avian)-like 2 4622 MYH4 myosin, heavy chain 4, skeletal muscle 4635 MYL4 myosin, light chain 4, alkali; atrial, embryonic 4771 NF2 neurofibromin 2 (merlin) 4778 NFE2 nuclear factor (erythroid-derived 2), 45kDa 4793 NFKBIB nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, beta 4828 NMB neuromedin B 4848 CNOT2 CCR4-NOT transcription complex, subunit 2 5134 PDCD2 programmed cell death 2 5295 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 5326 PLAGL2 pleiomorphic adenoma gene-like 2 5436 POLR2G polymerase (RNA) II (DNA directed) polypeptide G 5438 POLR2I polymerase (RNA) II (DNA directed) polypeptide I, 14.5kDa 5514 PPP1R10 protein phosphatase 1, regulatory subunit 10 5554 PRH1 proline-rich protein HaeIII subfamily 1 5564 PRKAB1 protein kinase, AMP-activated, beta 1 non-catalytic subunit 5567 PRKACB protein kinase, cAMP-dependent, catalytic, beta 5570 PKIB protein kinase (cAMP-dependent, catalytic) inhibitor beta 5687 PSMA6 proteasome (prosome, macropain) subunit, alpha type, 6 5707 PSMD1 proteasome (prosome, macropain) 26S subunit, non-ATPase, 1 5709 PSMD3 proteasome (prosome, macropain) 26S subunit, non-ATPase, 3 5714 PSMD8 proteasome (prosome, macropain) 26S subunit, non-ATPase, 8 5717 PSMD11 proteasome (prosome, macropain) 26S subunit, non-ATPase, 11 5719 PSMD13 proteasome (prosome, macropain) 26S subunit, non-ATPase, 13 5730 PTGDS prostaglandin D2 synthase 21kDa (brain) 5756 TWF1 twinfilin, actin-binding protein, homolog 1 (Drosophila) 215 Entrez Gene Gene Identification Symbol Gene description 5784 PTPN14 protein tyrosine phosphatase, non-receptor type 14 5911 RAP2A RAP2A, member of RAS oncogene family 5912 RAP2B RAP2B, member of RAS oncogene family 5925 RB1 retinoblastoma 1 5978 REST RE1-silencing transcription factor 6208 RPS14 ribosomal protein S14 6293 VPS52 vacuolar protein sorting 52 homolog (S. cerevisiae) 6302 TSPAN31 tetraspanin 31 6351 CCL4 chemokine (C-C motif) ligand 4 6388 SDF2 stromal cell-derived factor 2 6494 SIPA1 signal-induced proliferation-associated 1 6497 SKI v-ski sarcoma viral oncogene homolog (avian) 6567 SLC16A2 solute carrier family 16, member 2 (monocarboxylic acid transporter 8) 6573 SLC19A1 solute carrier family 19 (folate transporter), member 1 6626 SNRPA small nuclear ribonucleoprotein polypeptide A 6633 SNRPD2 small nuclear ribonucleoprotein D2 polypeptide 16.5kDa 6634 SNRPD3 small nuclear ribonucleoprotein D3 polypeptide 18kDa 6636 SNRPF small nuclear ribonucleoprotein polypeptide F 6651 SON SON DNA binding protein 6725 SRMS src-related kinase lacking C-terminal regulatory tyrosine and N-terminal myristylation sites 6788 STK3 serine/threonine kinase 3 6789 STK4 serine/threonine kinase 4 6866 TAC3 tachykinin 3 6871 TADA2A transcriptional adaptor 2A 6929 TCF3 transcription factor 3 (E2A immunoglobulin enhancer binding factors E12/E47) 6936 C2orf3 chromosome 2 open reading frame 3 7003 TEAD1 TEA domain family member 1 (SV40 transcriptional enhancer factor) 7004 TEAD4 TEA domain family member 4 7005 TEAD3 TEA domain family member 3 7011 TEP1 telomerase-associated protein 1 7018 TF transferrin 7019 TFAM transcription factor A, mitochondrial 7100 TLR5 toll-like receptor 5 7157 TP53 tumor protein p53 7277 TUBA4A tubulin, alpha 4a 7307 U2AF1 U2 small nuclear RNA auxiliary factor 1 7314 UBB ubiquitin B 7316 UBC ubiquitin C 7415 VCP valosin containing protein 7421 VDR vitamin D (1,25- dihydroxyvitamin D3) receptor 7433 VIPR1 vasoactive intestinal peptide receptor 1 7483 WNT9A wingless-type MMTV integration site family, member 9A 7691 ZNF132 zinc finger protein 132 7768 ZNF225 zinc finger protein 225 8082 SSPN sarcospan (Kras oncogene-associated gene) 8089 YEATS4 YEATS domain containing 4 8091 HMGA2 high mobility group AT-hook 2 8291 DYSF dysferlin, limb girdle muscular dystrophy 2B (autosomal recessive) 8310 ACOX3 acyl-CoA oxidase 3, pristanoyl 216 Entrez Gene Gene Identification Symbol Gene description 8347 HIST1H2BC histone cluster 1, H2bc 8351 HIST1H3D histone cluster 1, H3d 8358 HIST1H3B histone cluster 1, H3b 8444 DYRK3 dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 3 8463 TEAD2 TEA domain family member 2 8557 TCAP titin-cap (telethonin) 8602 NOP14 NOP14 nucleolar protein homolog (yeast) 8631 SKAP1 src kinase associated phosphoprotein 1 8642 DCHS1 dachsous 1 (Drosophila) 8710 SERPINB7 serpin peptidase inhibitor, clade B (ovalbumin), member 7 8775 NAPA N-ethylmaleimide-sensitive factor attachment protein, alpha 8796 SCEL sciellin 8837 CFLAR CASP8 and FADD-like apoptosis regulator 8943 AP3D1 adaptor-related protein complex 3, delta 1 subunit 8994 LIMD1 LIM domains containing 1 9001 HAP1 huntingtin-associated protein 1 9016 SLC25A14 solute carrier family 25 (mitochondrial carrier, brain), member 14 9093 DNAJA3 DnaJ (Hsp40) homolog, subfamily A, member 3 9095 TBX19 T-box 19 9099 USP2 ubiquitin specific peptidase 2 9113 LATS1 LATS, large tumor suppressor, homolog 1 (Drosophila) 9132 KCNQ4 potassium voltage-gated channel, KQT-like subfamily, member 4 9213 XPR1 xenotropic and polytropic retrovirus receptor 1 9267 CYTH1 cytohesin 1 9276 COPB2 coatomer protein complex, subunit beta 2 (beta prime) 9344 TAOK2 TAO kinase 2 9641 IKBKE inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase epsilon 9677 PPIP5K1 diphosphoinositol pentakisphosphate kinase 1 9842 PLEKHM1 pleckstrin homology domain containing, family M (with RUN domain) member 1 9852 EPM2AIP1 EPM2A (laforin) interacting protein 1 9861 PSMD6 proteasome (prosome, macropain) 26S subunit, non-ATPase, 6 9878 TOX4 TOX high mobility group box family member 4 9961 MVP major vault protein 10045 SH2D3A SH2 domain containing 3A 10126 DNAL4 dynein, axonemal, light chain 4 10135 NAMPT nicotinamide phosphoribosyltransferase 10184 LHFPL2 lipoma HMGIC fusion partner-like 2 10229 COQ7 coenzyme Q7 homolog, ubiquinone (yeast) 10246 SLC17A2 solute carrier family 17 (sodium phosphate), member 2 10288 LILRB2 leukocyte immunoglobulin-like receptor, subfamily B (with TM and ITIM domains), member 2 10318 TNIP1 TNFAIP3 interacting protein 1 10413 YAP1 Yes-associated protein 1 10536 LEPREL2 leprecan-like 2 10569 SLU7 SLU7 splicing factor homolog (S. cerevisiae) 10575 CCT4 chaperonin containing TCP1, subunit 4 (delta) 10611 PDLIM5 PDZ and LIM domain 5 10653 SPINT2 serine peptidase inhibitor, Kunitz type, 2 10746 MAP3K2 mitogen-activated protein kinase kinase 2 10809 STARD10 StAR-related lipid transfer (START) domain containing 10 217 Entrez Gene Gene Identification Symbol Gene description 10813 UTP14A UTP14, U3 small nucleolar ribonucleoprotein, homolog A (yeast) 10818 FRS2 fibroblast growth factor receptor substrate 2 10956 OS9 osteosarcoma amplified 9, endoplasmic reticulum lectin 10994 ILVBL ilvB (bacterial acetolactate synthase)-like 11073 TOPBP1 topoisomerase (DNA) II binding protein 1 11269 DDX19B DEAD (Asp-Glu-Ala-As) box polypeptide 19B 11321 GPN1 GPN-loop GTPase 1 22845 DOLK dolichol kinase 22933 SIRT2 sirtuin 2 22937 SCAP SREBF chaperone 23016 EXOSC7 exosome component 7 23054 NCOA6 nuclear receptor coactivator 6 23361 ZNF629 zinc finger protein 629 23418 CRB1 crumbs homolog 1 (Drosophila) 23451 SF3B1 splicing factor 3b, subunit 1, 155kDa 23491 CES3 carboxylesterase 3 23499 MACF1 microtubule-actin crosslinking factor 1 23503 ZFYVE26 zinc finger, FYVE domain containing 26 23513 SCRIB scribbled homolog (Drosophila) 23553 HYAL4 hyaluronoglucosaminidase 4 23590 PDSS1 prenyl (decaprenyl) diphosphate synthase, subunit 1 23594 ORC6 origin recognition complex, subunit 6 23659 PLA2G15 phospholipase A2, group XV 23784 POTEH POTE ankyrin domain family, member H 23786 BCL2L13 BCL2-like 13 (apoptosis facilitator) 25827 FBXL2 F-box and leucine-rich repeat protein 2 25937 WWTR1 WW domain containing transcription regulator 1 26156 RSL1D1 ribosomal L1 domain containing 1 26224 FBXL3 F-box and leucine-rich repeat protein 3 26524 LATS2 LATS, large tumor suppressor, homolog 2 (Drosophila) 26995 TRUB2 TruB pseudouridine (psi) synthase homolog 2 (E. coli) 27065 D4S234E DNA segment on chromosome 4 (unique) 234 expressed sequence 27180 SIGLEC9 sialic acid binding Ig-like lectin 9 27243 CHMP2A charged multivesicular body protein 2A 27254 CSDC2 cold shock domain containing C2, RNA binding 28987 NOB1 NIN1/RPN12 binding protein 1 homolog (S. cerevisiae) 29078 NDUFAF4 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, assembly factor 4 29080 CCDC59 coiled-coil domain containing 59 29107 NXT1 NTF2-like export factor 1 29767 TMOD2 tropomodulin 2 (neuronal) 29894 CPSF1 cleavage and polyadenylation specific factor 1, 160kDa 30848 CTAG2 cancer/testis antigen 2 49860 CRNN cornulin 50835 TAS2R9 taste receptor, type 2, member 9 51129 ANGPTL4 angiopoietin-like 4 51314 TXNDC3 thioredoxin domain containing 3 (spermatozoa) 51347 TAOK3 TAO kinase 3 51378 ANGPT4 angiopoietin 4 51421 AMOTL2 angiomotin like 2 218 Entrez Gene Gene Identification Symbol Gene description 51433 ANAPC5 anaphase promoting complex subunit 5 51527 C14orf129 chromosome 14 open reading frame 129 51540 SCLY selenocysteine lyase 51603 METTL13 methyltransferase like 13 51619 UBE2D4 ubiquitin-conjugating enzyme E2D 4 (putative) 51765 MST4 serine/threonine protein kinase MST4 51776 ZAK sterile alpha motif and leucine zipper containing kinase AZK 53831 GPR84 G protein-coupled receptor 84 54414 SIAE sialic acid acetylesterase 54470 ARMCX6 armadillo repeat containing, X-linked 6 54543 TOMM7 translocase of outer mitochondrial membrane 7 homolog (yeast) 54626 HES2 hairy and enhancer of split 2 (Drosophila) 54820 NDE1 nudE nuclear distribution gene E homolog 1 (A. nidulans) 54979 HRASLS2 HRAS-like suppressor 2 54981 C9orf95 chromosome 9 open reading frame 95 55033 FKBP14 FK506 binding protein 14, 22 kDa 55154 MSTO1 misato homolog 1 (Drosophila) 55190 NUDT11 nudix (nucleoside diphosphate linked moiety X)-type motif 11 55203 LGI2 leucine-rich repeat LGI family, member 2 55246 CCDC25 coiled-coil domain containing 25 55259 CASC1 cancer susceptibility candidate 1 55308 DDX19A DEAD (Asp-Glu-Ala-As) box polypeptide 19A 55319 C4orf43 chromosome 4 open reading frame 43 55421 C17orf85 chromosome 17 open reading frame 85 55544 RBM38 RNA binding motif protein 38 55720 TSR1 TSR1, 20S rRNA accumulation, homolog (S. cerevisiae) 55832 CAND1 cullin-associated and neddylation-dissociated 1 55835 CENPJ centromere protein J 55837 EAPP E2F-associated phosphoprotein 55851 PSENEN presenilin enhancer 2 homolog (C. elegans) 55973 BCAP29 B-cell receptor-associated protein 29 56126 PCDHB10 protocadherin beta 10 56604 TUBB7P tubulin, beta 7, pseudogene 56605 ERO1LB ERO1-like beta (S. cerevisiae) 56892 C8orf4 chromosome 8 open reading frame 4 56917 MEIS3 Meis homeobox 3 56954 NIT2 nitrilase family, member 2 57122 NUP107 nucleoporin 107kDa 57187 THOC2 THO complex 2 57410 SCYL1 SCY1-like 1 (S. cerevisiae) 57551 TAOK1 TAO kinase 1 57661 PHRF1 PHD and ring finger domains 1 57703 CWC22 CWC22 spliceosome-associated protein homolog (S. cerevisiae) 57727 NCOA5 nuclear receptor coactivator 5 57819 LSM2 LSM2 homolog, U6 small nuclear RNA associated (S. cerevisiae) 58499 ZNF462 zinc finger protein 462 59342 SCPEP1 serine carboxypeptidase 1 60506 NYX nyctalopin 60673 C12orf44 chromosome 12 open reading frame 44 219 Entrez Gene Gene Identification Symbol Gene description 63979 FIGNL1 fidgetin-like 1 64426 SUDS3 suppressor of defective silencing 3 homolog (S. cerevisiae) 64746 ACBD3 acyl-CoA binding domain containing 3 64853 AIDA axin interactor, dorsalization associated 64859 OBFC2A oligonucleotide/oligosaccharide-binding fold containing 2A 65260 SELRC1 Sel1 repeat containing 1 65990 FAM173A family with sequence similarity 173, member A 79228 THOC6 THO complex 6 homolog (Drosophila) 79412 KREMEN2 kringle containing transmembrane protein 2 79624 C6orf211 chromosome 6 open reading frame 211 79633 FAT4 FAT tumor suppressor homolog 4 (Drosophila) 79635 CCDC121 coiled-coil domain containing 121 79677 SMC6 structural maintenance of chromosomes 6 79738 BBS10 Bardet-Biedl syndrome 10 79924 ADM2 adrenomedullin 2 79927 GRRP1 glycine/arginine rich protein 1 79979 TRMT2B TRM2 tRNA methyltransferase 2 homolog B (S. cerevisiae) 80045 GPR157 G protein-coupled receptor 157 80235 PIGZ phosphatidylinositol glycan anchor biosynthesis, class Z 80273 GRPEL1 GrpE-like 1, mitochondrial (E. coli) 80774 LIMD2 LIM domain containing 2 81542 TMX1 thioredoxin-related transmembrane protein 1 81671 VMP1 vacuole membrane protein 1 81930 KIF18A kinesin family member 18A 83872 HMCN1 hemicentin 1 84224 NBPF3 neuroblastoma breakpoint family, member 3 84273 NOA1 nitric oxide associated 1 84330 ZNF414 zinc finger protein 414 84560 MT4 metallothionein 4 84836 ABHD14B abhydrolase domain containing 14B 89848 FCHSD1 FCH and double SH3 domains 1 91526 ANKRD44 ankyrin repeat domain 44 91574 C12orf65 chromosome 12 open reading frame 65 92259 MRPS36 mitochondrial ribosomal protein S36 92342 METTL18 methyltransferase like 18 92359 CRB3 crumbs homolog 3 (Drosophila) 93986 FOXP2 forkhead box P2 112770 C1orf85 chromosome 1 open reading frame 85 112950 MED8 mediator complex subunit 8 114785 MBD6 methyl-CpG binding domain protein 6 116511 MAS1L MAS1 oncogene-like 118924 FRA10AC1 fragile site, folic acid type, rare, fra(10)(q23.3) or fra(10)(q24.2) candidate 1 120071 GYLTL1B glycosyltransferase-like 1B 120237 DBX1 developing brain homeobox 1 123263 MTFMT mitochondrial methionyl-tRNA formyltransferase 124056 NOXO1 NADPH oxidase organiser 1 124961 ZFP3 zinc finger protein 3 homolog (mouse) 125893 ZNF816 zinc finger protein 816 126374 WTIP Wilms tumor 1 interacting protein 220 Entrez Gene Gene Identification Symbol Gene description 126969 SLC44A3 solute carrier family 44, member 3 127068 OR2T34 olfactory receptor, family 2, subfamily T, member 34 128866 CHMP4B charged multivesicular body protein 4B 137868 SGCZ sarcoglycan, zeta 140625 ACTRT2 actin-related protein T2 140893 C20orf151 chromosome 20 open reading frame 151 144453 BEST3 bestrophin 3 146850 PIK3R6 phosphoinositide-3-kinase, regulatory subunit 6 149233 IL23R interleukin 23 receptor 150082 LCA5L Leber congenital amaurosis 5-like 150290 DUSP18 dual specificity phosphatase 18 157855 KCNU1 potassium channel, subfamily U, member 1 162998 OR7D2 olfactory receptor, family 7, subfamily D, member 2 164022 PPIAL4A peptidylprolyl isomerase A (cyclophilin A)-like 4A 170679 PSORS1C1 psoriasis susceptibility 1 candidate 1 197370 NSMCE1 non-SMC element 1 homolog (S. cerevisiae) 204851 HIPK1 homeodomain interacting protein kinase 1 221395 GPR116 G protein-coupled receptor 116 222255 ATXN7L1 ataxin 7-like 1 245806 VGLL2 vestigial like 2 (Drosophila) 253017 TECRL trans-2,3-enoyl-CoA reductase-like 253175 CDY1B chromodomain protein, Y-linked, 1B 255798 C3orf43 chromosome 3 open reading frame 43 283284 IGSF22 immunoglobulin superfamily, member 22 283455 KSR2 kinase suppressor of ras 2 284083 C17orf47 chromosome 17 open reading frame 47 284129 SLC26A11 solute carrier family 26, member 11 285025 CCDC141 coiled-coil domain containing 141 286077 FAM83H family with sequence similarity 83, member H 286133 SCARA5 scavenger receptor class A, member 5 (putative) 286204 CRB2 crumbs homolog 2 (Drosophila) 286499 FAM133A family with sequence similarity 133, member A 286530 P2RY8 purinergic receptor P2Y, G-protein coupled, 8 348995 NUP43 nucleoporin 43kDa 353132 LCE1B late cornified envelope 1B 353133 LCE1C late cornified envelope 1C 374977 HEATR8 HEAT repeat containing 8 388228 SBK1 SH3-binding domain kinase 1 388335 TMEM220 transmembrane protein 220 388372 CCL4L2 chemokine (C-C motif) ligand 4-like 2 388730 TMEM81 transmembrane protein 81 390063 OR51I1 olfactory receptor, family 51, subfamily I, member 1 391107 OR10K2 olfactory receptor, family 10, subfamily K, member 2 400555 C16orf85 chromosome 16 open reading frame 85 400591 C17orf102 chromosome 17 open reading frame 102 441108 C5orf56 chromosome 5 open reading frame 56 474383 F8A2 coagulation factor VIII-associated 2 503542 SPRN shadow of prion protein homolog (zebrafish) 613209 DEFB135 defensin, beta 135 221 Entrez Gene Gene Identification Symbol Gene description 613212 CTXN3 cortexin 3 646600 C3orf65 chromosome 3 open reading frame 65 728635 DHRS4L1 dehydrogenase/reductase (SDR family) member 4 like 1 728642 CDK11A cyclin-dependent kinase 11A

222 Appendix 2 – Validated potent inhibitory duplexes from secondary screen

Secondary Screen validated potent inhibitory hits. The secondary screen validated siRNA using duplexes. Following the screen hit algorithm the above list of genes were validated in inhibition in≥ the 1 duplex. secondary A total screen. of 14 genes were validated as demonstrating potent

223 Appendix 3 – Negative Control Optimisation

Negative control transfection optimisation. 449B cells were reverse transfected with siGENOME non- targeting siRNA SMART pool #1 (NTSP1), non-targeting siRNA SMARTpool #2 (NTSP2), or Mock transfected using 40nM of siRNA product and 0.04ul of DF2. PD0332991 was applied at varying concentrations (0-1500nM) 24 hour hours after transfection, and changed every 72 hours. Cell Titer Glo was used to quantify ATP at Day 6. ATP levels were compared between NTSP1, NTSP2 and Mock transfected 449B cells (A). Levels of ATP were compared between no-targeting siRNA (NTSP1/NTSP2) and Mock transfected cells (control)(B). Values were calculated from averaged experimental data across three separate experiments. NTSP1 was found to have more off-target effects with 80% ATP response relative to Mock transfected cells, even in the absence of PD0332991. NTSP2 demonstrated less toxic off target effects with results similar to Mock transfected 449B cells. Error bars represent SEM and were generated across three individual experiments.

224

Appendix 4: Control Optimisation plate setup

Control plate setup for screen. The control plate for screen included positive (RB, PLK, CDK4) and negative (Mock, Risc Free) controls. SiBuffer and empty control wells were used to control for evaporation and CTG bleeding. Evaporation results in a change in concentration of reagents in the circumferential wells in a 384 well plate and can result in an increase in CV values and z-scores. For this reason the outer wells in column 1 contain siBuffer only and column 24 is completely empty.

225

Minerva Access is the Institutional Repository of The University of Melbourne

Author/s: CONYERS, RACHEL

Title: The role of MDM2 and CDK4 in well differentiated liposarcoma

Date: 2015

Persistent Link: http://hdl.handle.net/11343/55576

File Description: The role of MDM2 and CDK4 in well differentiated liposarcoma