Employing pancreatic tumour γ-glutamyl transferase for therapeutic delivery

Emma E Ramsay

A thesis submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

Prince of Wales Clinical School

Lowy Cancer Research Centre

Faculty of Medicine

University of New South Wales

March 2014

List of publications

Ramsay, E. E., Hogg, P. J., & Dilda, P. J. (2011). Mitochondrial metabolism inhibitors for cancer therapy. Pharmaceutical Research, 28, 2731–2744.

Ramsay, E.E., Decollogne, S., Joshi, S., Corti, A., Apte, M., Pompella, A., Hogg, P.J. & Dilda, P.J. (2014). Employing pancreatic tumour γ-glutamyltransferase for therapeutic delivery. Molecular Pharmaceutics, (in press).

Ramsay, E.E., Hogg, P.J. & Dilda, P.J. Glutathione-S-conjugates as potential prodrugs to target γGT-expressing, drug-resistant tumours. Frontiers in Pharmacology, (in preparation). List of published abstracts

Decollogne, S., Ramsay, E. E., Joshi, S., Corti, A., Pompella, A., Apte, M., Hogg, P. J., Dilda, P. J. (2012). Both pancreatic cancer and pancreatic stellate cells express high levels of gamma- glutamyl transferase that may be employed to deliver a metabolism inhibitor to the tumour mass. EACR 22nd Biennial Meeting, Barcelona, European Journal of Cancer, 48(s5), 251, abstract 1041. List of presentations

Dilda, P. J., Decollogne, S., Ramsay, E. E., Park, D., & Hogg, P. J. (2010). A tumour marker for selection of patients that should better respond to treatment with the Australian anti-cancer drug, GSAO. In The Lowy Symposium. Sydney, Australia.

Ramsay, E. E., Decollogne, S., Joshi, S., Hogg, P. J., & Dilda, P. J. (2011). Identification of a tumour enzyme responsible for the activation of the metabolism inhibitor, GSAO. In 23rd Lorne Cancer Conference. Lorne, Australia.

Decollogne, S., Ramsay, E. E., Joshi, S., Hogg, P. J., & Dilda, P. J. (2011). Tumour cell expression of γ-glutamyl transpeptidase positively correlates with the anti-tumour efficacy of the metabolism inhibitor, GSAO. In 23rd Lorne Cancer Conference. Lorne, Australia.

Ramsay, E. E., Decollogne, S., Joshi, S., Corti, A., Pompella, A., Apte, M., Hogg, P.J. & Dilda, P. J. (2012). Pancreatic tumor stromal cells express high levels of γ-glutamyl transferase that may

iii be employed to deliver a metabolism inhibitor to the tumor mass. In 103rd Annual Meeting of the American Association for Cancer Research. Chicago, Illinois.

Ramsay, E. E., Decollogne, S., Joshi, S., Corti, A., Pompella, A., Apte, M., Hogg, P.J. & Dilda, P. J. (2012). Tumour expression of γ-glutamyl transferase may be utilised to predict patient response to γ-glutamyl prodrugs. In ASMR 20th New South Wales Scientific Meeting. Sydney, Australia.

Ramsay, E. E., Decollogne, S., Joshi, S., Corti, A., Pompella, A., Apte, M., Hogg, P.J. & Dilda, P. J. (2012). Pancreatic tumour gamma-glutamyl transferase expression to predict patient response to the metabolism inhibitor GSAO. In Sydney Cancer Conference. Sydney, Australia. (Oral presentation). List of scholarships and awards

(2013) European Association for Cancer Research Travel Fellowship Award (2013) Australian Society for Medical Research Research Award (International) (2012-2013) Pfizer Oncology Research Unit Scholarship (2010-2014) NHMRC Biomedical Scholarship (2010-2012) The Cancer Institute of NSW Research Scholar Award (2012) Translational Cancer Research Network Top-Up Scholarship (2011-2012) The Commercialisation Training Scheme Scholarship (2011) Lorne Cancer Bursary

iv Acknowledgments

I would like to thank and acknowledge the contributions of Doctor Stephanie Decollogne from the Lowy Cancer Research Centre, Sydney, who performed the in vivo experiments described in Chapter 7 with my assistance. Thank you for your time and patience in showing me these skills. I would also like to thank Mrs Swapna Joshi for her assistance with the immunohistochemistry presented in Chapter 2 and 7.

I would like to thank and acknowledge the work of the Pancreatic Research Group, University of New South Wales, in particular Professor Minoti Apte and Ms Eva Fiala-Beer for the primary human pancreatic stellate cells.

I wish to acknowledge Mrs Rabeya Akter from the Mark Wainwright Analytical Centre at the University of New South Wales for elemental analysis. I would like to acknowledge the work of the Infection and Immunity Research Group of the University of New South Wales, in particular Professor Andrew Lloyd, Mrs Koko Bu and Doctor Hoai Nguyen for provision of the plasma samples used in Chapter 6. I would like to acknowledge Doctor Amber Johns of the New South Wales Pancreatic Cancer Network for providing human pancreatic sections in Chapter 2.

I would like to acknowledge those who funded me through my PhD: the National Health and Medical Research Council, the Cancer Institute of New South Wales, and the Translational Cancer Research Network.

I would like to thank my supervisors, Professor Philip Hogg and Doctor Pierre Dilda. Their support and encouragement made this experience much easier than I had ever imagined. They have generously allowed me to experience so much of research life; giving me opportunity for an internship, collaborations, conferences and courses, all of which have given me many insights into the facets of biomedical research. In particular, I’d like to thank them for the opportunity to travel to Sweden and the USA to experience research life in different cultures and contexts.

I would like to thank the Australian Society for Medical Research and the European Association for Cancer Research for the opportunity to travel to Sweden for collaboration with Professor Matthias Löhr and Doctor Rainer Heuchel at the Karolinska Institutet. Thank you, Matthias and Rainer, for accepting me into your lab and giving me time and guidance throughout my stay. I would also like to thank Pfizer Inc. and the Cancer Institute of New South Wales for the opportunity to undertake a six month internship with Pfizer under the supervision of Doctor

v Julie (Xie) Zhi. Both trips were enriching experiences, and I am very thankful to have had these opportunities.

Thank you to everyone who contributed in their own way to this thesis: family, friends and colleagues. Your encouragement and support throughout have been a huge blessing.

Finally, to my Dad, thank you for all your love and support and thank you for always believing in me, no one could ask for a better father.

vi Abbreviations

5FU 5-fluorouracil

ABBA L-2-amino-4-boronobutanoic acid

ADEPT Antibody-directed enzyme prodrug therapy

ADP Adenine dinuceleotide phosphate

ALK Anaplastic lymphoma kinase

ANT Adenine nucleotide translocase

APC Adenomatous polyposis coli

As Arsenic

ATP Adenine trinucleotide phosphate

BAE Bovine aortic endothelial

bFGF Basic fibroblast growth factor

BMI Body mass index

BxPC-3/vector BxPC-3 transfected with empty vector

BxPC-3/γGT BxPC-3 transfected with the human γGT gene

C Control

CAO 4-(N-(S-cysteinylacetyl)amino)phenylarsonous acid

Cdkn2A Cyclin-dependent kinase inhibitor 2A

DMEM Dulbecco’s modified eagle’s media

DNA Deoxyribonucleic acid

DOPA Dihydroxyphenylalanine

DPC Deleted in pancreatic cancer

vii EBM-2 Endothelium basal medium -2

ECM Extracellular matrix

EDTA Ethylenediaminetetraacetic acid

EGFR Epidermal growth factor receptor

FAP Fibroblast activation protein

FGF Fibroblast growth factor

FOLFIRINOX Folinic acid, 5FU, irinotecan and oxaliplatin

GCAO 4-(N-(S-cysteinylglycylacetyl)amino)phenylarsonous acid

GD Growth delay

GDEPT Gene-directed enzyme prodrug therapy

GFP Green fluorescent protein

GSAO 4-(N-(S-glutathionylacetyl)amino) phenylarsonous acid

GSNO S-nitrosoglutathione

GST Glutathione transferase hEGF Human epidermal growth factor hENT1 Human equilibrative nucleoside transporter 1

HER2 Human epidermal growth factor receptor-2 hFGF-B Human fibroblast growth factor

HMEC Human dermal microvascular endothelial cells

HNE 4-hydroxynoneal

HPLC High performance liquid chromatography

HUVEC Human umbilical vein endothelial cells

IGF-1 Insulin-like growth factor-1

viii IMDM Iscove's modified dulbecco's media

KRAS Kirsten rat sarcoma viral oncogene homologue

LTC4 Leukotriene C4

MAPK Mitogen-activated protein kinase

MMP Matrix metalloproteinase

MPTP Mitochondrial permeability transition pore

MRP Multi-drug resistance proteins

MTT 3-(4,5-dimethylthiazol-2-yl)- 2,5-diphenyltetrazolium bromide

N/A Not applicable

NHGs N-hydroxyguanidines

NIEHS National Institute of Environmental Health Sciences

NP No processing

N-PSC Normal human pancreatic stellate cells

NSCLC Non-small cell lung cancer

P Processing

PAO Phenylarsonous acid

PCNA Proliferating cell nuclear antigen

PDAC Pancreatic ductal adenocarcinoma

PDGE Prostaglandin E

PDGF Platelet-derived growth factor

PI Propidium iodide

PI3K Phosphatidylinositol 3-kinase

PSA Prostate specific antigen

ix PSC Pancreatic stellate cells

PSMA Prostate specific membrane antigen

R3-IGF-1 Recombinant insulin-like growth factor

RPMI Roswell park memorial institute

SD Standard deviation

SDF-1 Stromal cell-derived factor 1

SE Standard error

SERCA Sarcoendoplasmic reticulum calcium transport ATPase

T Treated

TA-PSC Tumour associated human pancreatic stellate cell

TGDi Tumour growth delay index

TGF-h1 Transforming growth factor h1

TGFβ Transforming growth factor β

TVQT Tumour volume quadrupling time uPA Urokinase-type plasminogen activator

VDEPT Virus-directed enzyme prodrug therapy

VEGF Vascular endothelial growth factor

α-SMA Alpha-smooth muscle actin

γGT γ-glutamyl transferase

x Table of contents

Declarations ...... ii

List of publications ...... iii

List of published abstracts ...... iii

List of presentations ...... iii

List of scholarships and awards ...... iv

Acknowledgments ...... v

Abbreviations ...... vii

Table of contents ...... xi

List of figures ...... xv

List of tables ...... xvi

Abstract ...... 17

Chapter 1. Introduction ...... 19

1.1 Pancreatic cancer ...... 19

1.2 Pancreatic ductal adenocarcinoma ...... 21

1.2.1 Tumour microenvironment ...... 23

1.2.1.1 The desmoplastic reaction of pancreatic ductal adenocarcinoma ...... 24

1.2.1.1.1 Stellate cells ...... 25

1.2.1.2 Angiogenesis in pancreatic cancer ...... 29

1.3 Pancreatic cancer therapy ...... 31

1.3.1 Targeted therapy ...... 32

1.3.2 Prodrugs ...... 33

1.3.3 Targeted prodrugs...... 36

1.4 Biomarkers in cancer...... 41

1.4.1 Predictive markers in cancer therapy ...... 42

1.5 γ-glutamyl transferase ...... 44

1.5.1 γ-glutamyl transferase in cancer ...... 46

xi 1.5.2 γ-glutamyl transferase and cancer therapy ...... 47

1.6 Glutathionylated prodrugs ...... 49

1.7 GSAO ...... 50

1.8 Hypothesis ...... 53

1.8.1 Significance ...... 53

1.8.2 Specific aims ...... 54

Chapter 2. Expression and activity of γGT in pancreatic ductal adenocarcinoma ...... 56

2.1 Introduction ...... 56

2.2 Methods ...... 56

2.2.1 γGT expression in pancreatic ductal adenocarcinoma ...... 56

2.2.2 γGT activity in pancreatic ductal adenocarcinoma ...... 57

2.3 Results ...... 57

2.3.1 γGT expression in pancreatic ductal adenocarcinoma ...... 57

2.3.2 γGT activity in pancreatic ductal adenocarcinoma ...... 61

2.4 Conclusions and discussion ...... 63

Chapter 3. γGT activation of the glutathione-S-conjugate, GSAO ...... 65

3.1 Introduction ...... 65

3.2 Methods ...... 66

3.2.1 GSAO ...... 66

3.2.2 High performance liquid chromatography ...... 66

3.2.3 Drug accumulation ...... 67

3.2.4 Proliferation assay ...... 67

3.3 Results ...... 67

3.3.1 High performance liquid chromatography ...... 67

3.3.2 Drug accumulation ...... 69

3.3.3 Inhibition of proliferation ...... 70

3.4 Conclusions and discussion ...... 72

Chapter 4. Paracrine activation and action of GSAO ...... 74

xii 4.1 Introduction ...... 74

4.2 Methods ...... 75

4.2.1 Conditioned media ...... 75

4.2.2 Transwell assays ...... 75

4.3 Results ...... 76

4.3.1 Conditioned media ...... 76

4.3.2 Transwell assays ...... 79

4.4 Conclusions and discussion ...... 81

Chapter 5. Juxtacrine activation and action of GSAO ...... 83

5.1 Introduction ...... 83

5.2 Methods ...... 83

5.3 Results ...... 84

5.4 Conclusions and discussion ...... 88

Chapter 6. Evaluation of potential off-target activation of GSAO in the blood ...... 91

6.1 Introduction ...... 91

6.2 Methods ...... 92

6.3 Results ...... 92

6.4 Conclusions and discussion ...... 99

Chapter 7. Tumour γGT status predicts GSAO efficacy ...... 102

7.1 Introduction ...... 102

7.2 Methods ...... 102

7.3 Results ...... 103

7.3.1 Genetically engineered model ...... 103

7.3.2 Natural γGT expression model ...... 108

7.4 Conclusions and discussion ...... 112

Chapter 8. Discussion and conclusion ...... 115

8.1 Discussion ...... 115

8.1.1 GSAO as a targeted prodrug ...... 117

xiii 8.1.2 γGT as a predictive marker ...... 118

8.2 Proposed model ...... 119

8.3 Future directions ...... 120

8.3.1 The predictive marker γGT ...... 120

8.3.2 Application in other cancers ...... 120

8.3.3 Glutathione-S-conjugates ...... 121

8.3.3.1 Metabolism of glutathione-S-conjugates ...... 121

8.3.3.2 Potential glutathione-S-conjugates ...... 123

8.3.3.2.1 Glutathione-S-cisplatin ...... 124

8.3.3.2.2 Glutathione-S-dichlorovinyl ...... 124

8.3.3.2.3 Glutathione-S-hydroxynonenal ...... 125

8.4 Conclusion ...... 125

Appendix ...... 128

A1. Cells utilised ...... 128

A2. Approvals ...... 130

Chapter 9. References ...... 131

xiv List of figures

Figure 1.1 The types of pancreatic cancer and their relative frequencies ...... 21 Figure 1.2 The interaction of pancreatic stellate cells with other components of the tumour. 29 Figure 1.3 A. Five-year survival rates of pancreatic cancer by stage at diagnosis. B. Stage at diagnosis for pancreatic cancer...... 32 Figure 1.4 Different classes of prodrugs...... 35 Figure 1.5 Time till effective treatment administered...... 44 Figure 1.6 The structure of GSAO...... 51 Figure 1.7 Metabolism of the gutathionylated prodrug GSAO...... 52 Figure 2.1 γGT expression in PDAC...... 59 Figure 2.2 Immunostaining of γGT and α-SMA in mouse BxPC-3 xenografts...... 61 Figure 2.3 γGT activity in PDAC...... 62 Figure 3.1 Structure of GSAO and the products of γGT and peptidase hydrolysis...... 66 Figure 3.2 Pancreatic tumour and stellate cell γGT activates GSAO to GCAO...... 68 Figure 3.3 The membrane permeable metabolite of GSAO, GCAO, accumulates rapidly in high γGT activity cells...... 69 Figure 3.4 The anti-proliferative activity of GSAO correlates with the γGT expression of cancer cells...... 71 Figure 4.1 GSAO conditioned by TA-PSC inhibits tumour and endothelial cell proliferation. .... 77 Figure 4.2 GSAO conditioned by γGT-expressing cancer cells inhibits endothelial cell proliferation...... 78 Figure 4.3 The response of endothelial cells to GSAO increases with γGT activity of cells in the upper well...... 80 Figure 5.1 GSAO and γGT-expressing cells inhibit proliferation of co-cultured endothelial cells...... 84 Figure 5.2 High γGT tumour cells mediate GSAO-induced death of co-cultured endothelial cells...... 86 Figure 5.3 Increasing the number of tumour cells increases the response of co-cultured endothelial cells to GSAO...... 88 Figure 6.1 The viability of MIA PaCa-2 cells treated with plasma-conditioned GSAO...... 94 Figure 6.2 Rate of GSAO activation by plasma γGT...... 96 Figure 6.3 The percentage of GSAO activated within its half-life by plasma with varying γGT levels...... 97

xv Figure 6.4 Derivation of the relationship between the number of passes of GSAO through the pancreas until 50% activated, and the plasma concentration of γGT ([γGT])...... 98 Figure 6.5 The number of times GSAO passes through the pancreatic blood supply before 50% activation, as a function of human plasma γGT concentration...... 99 Figure 7.1 Pancreatic tumour cell γGT activity positively correlates with GSAO-mediated inhibition of tumour angiogenesis and tumour growth in mice...... 104 Figure 7.2 γGT expression of the tumours reflects the γGT activity of the cells implanted. .... 105 Figure 7.3 Tumour vascularity and proliferation following GSAO treatment...... 107 Figure 7.4 Pancreatic tumour cell γGT activity positively correlates with GSAO-mediated inhibition of tumour angiogenesis and tumour growth in mice...... 109 Figure 7.5 γGT expression of the tumours reflects the γGT activity of the cells implanted. .... 110 Figure 7.6 Tumour vascularity and proliferation following GSAO treatment...... 111 Figure 8.1 Model of GSAO activation in pancreatic ductal adenocarcinoma...... 119 Figure 8.2 Metabolism of xenobiotics by the mercapturic acid pathway and the alternative product produced by cysteine S-conjugate β-lyase activity...... 123

List of tables

Table 1.1 Signalling pathways that are mutated in PDAC and their relative frequency ...... 23 Table 1.2 A comparison of the characteristics of pancreatic stellate cells and fibroblasts...... 26 Table 1.3 A comparison of the characteristics of quiescent and activated pancreatic stellate cells...... 27 Table 1.4 Reasons to develop prodrugs...... 34 Table 1.5 Overexpressed enzymes in tumours ...... 39 Table 6.1 Patient characteristics of plasma samples...... 93 Table A1.1 Cells used in the thesis and the conditions they were cultured under...... 128

xvi Abstract

γ-glutamyltransferase (γGT) is a cell surface enzyme that catalyses hydrolysis of the bond linking the glutamate and cysteine residues of glutathione and glutathione-S-conjugates. I have observed that human pancreatic tumour cells and tumour-associated stellate cells express high levels of this enzyme when compared to normal pancreatic epithelial and stellate cells. Detection of the protein in tumour sections correlated with γGT activity on the surface of the cultured tumour and stellate cells. I tested whether the tumour γGT could be employed to deliver a therapeutic to the tumour endothelial cells. GSAO is a glutathione-S-conjugate of a trivalent arsenical that is activated to enter endothelial cells by γGT cleavage of the γ-glutamyl residue. The arsenical moiety triggers proliferation arrest and death of the endothelial cells by targeting the mitochondria. Human pancreatic tumour and stellate cell γGT activated GSAO in culture and γGT activity positively correlated with GSAO-mediated proliferation arrest of endothelial cells in transwell and co-culture systems. A soluble form of γGT is found in blood and I measured the rate of activation of GSAO by this enzyme. I calculated that systemically administered GSAO would circulate through the pancreatic blood supply several times before appreciable activation by normal blood levels of γGT. In support of this finding, tumour γGT activity positively correlated with GSAO-mediated inhibition of pancreatic tumour angiogenesis and tumour growth in mice. My findings indicate that pancreatic tumour γGT can be used to deliver a therapeutic to the tumour.

17

18 Chapter 1. Introduction

Cancer is the second highest cause of death in Australia, representing almost 30% of all deaths (Australian Bureau of Statistics, 2011). Cancer is a disease in which there is uncontrolled multiplication and spread within the body of abnormal forms of the body’s own cells (Rang, Dale et al 2003). As genetic mutations accumulate in cells, they begin to exhibit abnormal control of cellular processes. Tumours are considered to develop from cells exhibiting the ‘hallmarks of cancer’ – self-sufficiency in growth signals, insensitivity to growth-inhibitory signals, evasion of apoptosis, limitless replicative ability, sustained angiogenesis, deregulated cellular energetics, avoidance of immune destruction, tissue evasion and metastasis (Hanahan and Weinberg, 2000, Hanahan and Weinberg, 2011). When the tumour develops the ability to metastasise it is considered malignant. Spreading uncontrollably throughout the body, it is extremely difficult to treat. As such, malignant tumours account for the majority of cancer related deaths (Coghlin and Murray, 2010, Australian Bureau of Statistics, 2011).

There are many types of cancer, classified according to the site where the tumour was initiated. Surgery and therapy can drastically increase the survival rate of some, while others remain largely untreatable. Breast cancer rates have decreased by 30% between 1994 and 2010, decreasing from 30.8 deaths per 100,000 to 21.6 deaths per 100,000 (Australian Institute of Health and Welfare, 2012a). In contrast, there has been little change in pancreatic cancer survival and mortality. The mortality rate has remained stable; in 1982 the rate was 9.8 deaths per 100,000; in 2010 the rate increased slightly to 9.9 deaths per 100,000 (Australian Institute of Health and Welfare, 2012b, Australian Institute of Health and Welfare, 2012a). A greater understanding of the mechanisms underlying pancreatic cancer and an increased range of treatment options would help improve the outcome for patients.

1.1 Pancreatic cancer

Pancreatic cancer represents 2.2% of the total cancer incidence per year, yet it represents 5.6% of all cancer related deaths per year (Australian Institute of Health and Welfare, 2012a). This represents a survival rate of 4.6% five years post diagnosis (Australian Institute of Health and Welfare, 2008). The difficulty in diagnosing pancreatic cancer is exemplified by the low survival rate, with a third of patients being diagnosed at a late stage (Hidalgo, 2010). This is a result of the retroperitoneal position, the lack of specific symptoms, and the limited sensitivity

19 of diagnostic methods (Erkan et al., 2012a). Such late diagnoses limit the treatment options available.

Pancreatic cancer is classified into subtypes according to histology. Cancer can develop in the exocrine, endocrine or stromal portions of the pancreas (Basturk et al., 2010). The majority of pancreatic cancers arise in the exocrine portion of the pancreas. The most common type is pancreatic ductal adenocarcinoma (PDAC), arising in the epithelial cells of the pancreatic ducts (Fesinmeyer et al., 2005). PDAC accounts for approximately 90% of all pancreatic cancers (Cowgill and Muscarella, 2003). The second most common type of pancreatic cancer is mucinous tumours, also an exocrine tumour and also developing in the pancreatic ductal epithelium, accounting for less than 10% of all pancreatic cancers (Fesinmeyer et al., 2005). Tumours of the endocrine portion of the pancreas account for 2 to 4% of all pancreatic cancer (Li et al., 2010, Fesinmeyer et al., 2005). Due to the large portion of pancreatic cancers being PDAC, many references to pancreatic cancer refer to PDAC. For example, many of the characteristics of pancreatic cancer, such as late diagnosis of pancreatic cancer, are applicable to PDAC. In comparison, endocrine tumours are often diagnosed at an earlier stage as the symptoms of these tumours are more noticeable due to the increased production of hormones (Basturk et al., 2010).

20 Pancreatic cancer

Exocrine Endocrine >95% <5%

Ductal Functional adeno- Non- Others <10% (hormone carcinoma functional producing) >85%

Intraductal Acinar cell papillary Glucagon- Gastrinoma carcinoma mucinous oma neoplasm

Mucinous Serous Somatostatin cystaden- cystadeno- Insulinoma -oma ocarcinoma carcinoma

Solid-pseudo Pancreato- papillary VIPoma Others blastoma carcinoma

Figure 1.1 The types of pancreatic cancer and their relative frequencies

(Fesinmeyer et al., 2005, Neoptolemos et al., 2010).

1.2 Pancreatic ductal adenocarcinoma

The most common symptom of all pancreatic cancers is obstructive cholestasis. This reflects the location of the majority (more than two thirds) of PDAC in the head of the pancreas (Hidalgo, 2010, Porta et al., 2012, Muniraj et al., 2013), where it is capable of blocking the bile duct. This symptom generally allows for slightly earlier presentation of these cases (Porta et al., 2012). PDAC tumours of the body and tail lack the physical obstruction of the bile duct. They present at a later stage with less specific and less obvious symptoms. Symptoms include asthenia, anorexia and weight loss.

Surgical resection is the only curative action, yet only 10 to 20% of PDAC cases are resectable at diagnosis (Rückert et al., 2010, Hidalgo, 2010, Habisch et al., 2010, Muniraj et al., 2013). The

21 remaining 80 to 90% of PDAC patients have locally advanced or metastatic disease (Jaffee et al., 2002). Palliative treatment, consisting of a combination of radiation and chemotherapy, is provided where surgery is not an option (Hidalgo, 2010). For over a decade, gemcitabine, administered alone, has remained the first choice for chemotherapy (Hidalgo, 2010, Long et al., 2011). However, gemcitabine provides only a minor benefit, increasing survival by 5 weeks compared to the previous standard of care, 5-fluorouracil (5FU) (Burris III et al., 1997). PDAC is characterised by primary chemoresistance, responding poorly, if at all, to the majority of drugs tested (Hagmann et al., 2011). This, when combined with late diagnosis, contributes to the poor survival rates of PDAC patients.

PDAC is an extremely complex disease. A number of factors make it significantly difficult to treat (Oberstein and Olive, 2013). Firstly, the average age at diagnosis is 71 years. This fact implies that the patient population are more likely to be in poor health and more likely to develop complications during treatment. PDAC also metastasises at an early stage of the disease, diminishing the effectiveness of surgery, or making it an unviable option. There are a significant number of oncogenic alterations in PDAC, found at high frequency. Further, there is genetic instability that enables PDAC to quickly acquire resistance to therapy and it is characterised by a rich desmoplastic stroma with poor perfusion, impacting drug delivery and creating a hypoxic environment. The tumour microenvironment is immunosuppressive, preventing the local immune cells from attacking the tumour. These characteristics contribute to the difficulties in treating PDAC.

Variability across cancer patient populations is due to spontaneity in the development of genetic mutations. Of all cancers, PDAC has some of the highest rates of common genetic mutations. Over 90% of PDAC have activating mutations in Kirsten rat sarcoma viral oncogene homologue (KRAS) and over 90% have inactivating mutations in the Cyclin-dependent kinase inhibitor 2A (cdkn2a) locus. Between 75 to 90% have inactivating mutations in p53 and more than half have mutations in the deleted in pancreatic cancer (DPC) 4 gene (Oberstein and Olive, 2013). Yet despite these high penetrance genetic mutations, PDAC is characterised by clinical and pathological heterogeneity (Oberstein and Olive, 2013). Recently, the variation between PDAC cases has been highlighted, with additional mutated genes being identified in chromatin modelling, DNA repair mechanisms and axon guidance (Biankin et al., 2012). In a study of 99 samples, over one thousand genes were found to be mutated in at least 1 of the 99 samples (Biankin et al., 2012). This is further supported in an earlier and smaller study performed by Jones et al. (2008). Together, these observations suggest that within PDAC there

22 are a number of subtypes. With such variation in a fast-paced disease it is important to ensure the right treatment is chosen immediately.

Table 1.1 Signalling pathways that are mutated in PDAC and their relative frequency (Partensky, 2013, Biankin et al., 2012, Jones et al., 2008, Muniraj et al., 2013, Wolfgang et al., 2013).

Signalling pathway Percentage mutated

KRAS 90

p16 85–95

p53 24–75

SMAD4 16–55

Hedgehog >70

1.2.1 Tumour microenvironment A key characteristic of cancer is the involvement of the tumour microenvironment, consisting of a variety of cell types, extracellular proteins and signalling molecules. Within the tumour margins, alongside tumour cells, are fibroblasts, myofibroblasts, stellate cells, inflammatory cells, immune cells, and blood and lymphatic vessels. Combined, these components are referred to as the tumour stroma (Li et al., 2007, Erkan et al., 2012a). These components support the malignant cells, providing the ‘good soil’ of Paget’s 1889 hypothesis that metastases are predisposed to develop at sites that provide a fertile environment for the malignant cells to grow. However, it is not just in metastasis that the tumour microenvironment supports the malignant cells. Within the margin of primary tumours the support players of the tumour microenvironment also exist. Each of the participating players contributes in a myriad of ways to tumour growth. The most obvious and well researched example is the role of endothelial cells in forming the vessels for blood to be supplied to the tumour. The stroma also plays an important role in metastasis and in the characteristic chemoresistance of PDAC.

23 1.2.1.1 The desmoplastic reaction of pancreatic ductal adenocarcinoma Despite the large variability across pancreatic cancer, within PDAC an extensive desmoplastic reaction is consistently present. This comprises of a dense stroma, encompassing greater than 80% of the tumour volume (Neesse et al., 2011, Erkan et al., 2012b, Luo et al., 2012, Erkan, 2013b). The desmoplastic reaction results in increased fibrosis (Yen et al., 2002).

There are two contrasting positions on the role of desmoplasia in PDAC. One side favours the stroma and desmoplasia working against the development and growth of the tumour, whilst the other suggests the stroma and desmoplasia work in conjunction with the cancer cells to promote the growth and spread of the tumour.

The first perspective suggests that the desmoplasia is initiated in order to encapsulate the tumour and block its growth, quarantining it from normal tissue. The presence of this fortification is clearly demonstrated in the inability to deliver chemotherapies to the PDAC mass. There are numerous studies outlining the need for the right stromal environment for tumourigenesis. This is most clearly demonstrated in individuals with familial polyposis coli. Whilst all cells harbour a mutation in the adenomatous polyposis coli (APC) gene, cancer predominantly develops in the colon, where the stromal environment favours cancer development. In other organs the stromal environment constrains cancer development (Erkan, 2013a). Experimentally, rouse sarcoma virus, a highly tumourigenic virus in chickens, when injected into embryo chicken wings, did not result in tumours. When these wings were resected and the cells isolated and grown in vitro, the cells became malignant (Dolberg and Bissell, 1984).

In contrast, the second perspective is supported by a large and growing body of work supporting the cooperation of cancer cells and the stroma. The extensive matrix of PDAC can store growth factors for the progression of the cancer and has been shown to suppress the immune system (Hartel et al., 2004). Through distortion of the normal parenchyma architecture the dense volume of the stroma inhibits oxygen diffusion. This, combined with the characteristic poor vascularisation of PDAC, contributes to poor drug penetration and chemoresistance, providing a growth benefit for the cancer (Erkan et al., 2012b). The major player of the PDAC desmoplastic reaction, the stellate cell, has been shown to play a role in the growth, chemoresistance and metastatic spread of the tumour. The interplay between the pancreatic stellate cells and the pancreatic cancer cells will be explored in more depth in Section 1.2.1.1.1.

24 As Erkan suggests, it is possible that the role of the desmoplasia in pancreatic cancer is a combination of the above arguments; one argument does not preclude the other (2013b). The stromal reaction may occur initially to isolate the genetically abnormal cells, and then, through a process of selection, the fittest of these cells co-opt the stroma for the promotion of tumour growth.

Erkan et al. propose an activated stroma index to correlate the extent of desmoplasia in pancreatic cancer with prognosis (2012b). This index incorporates the amount of collagen deposition and the extent of α-smooth muscle actin (α-SMA) expression in the tumour. It comprises of the ratio of α-SMA stained area to aniline stained collagen deposition area in consecutive sections. Collagen deposition is an independent positive prognostic marker. α-SMA is a marker of activated stellate cells, the main producers of desmoplasia, however, its expression is not linearly correlated with the amount of extracellular matrix (ECM) deposition, reflecting the role stellate cells play in both the production and degradation of ECM. In this study, the higher the ratio of α-SMA stained area to aniline stained collagen deposition area, the poorer the prognosis for patients. As this ratio reversed, the prognosis improved.

1.2.1.1.1 Stellate cells Pancreatic stellate cells (PSC) were first described in 1982 by Watari et al. Cells exhibiting a blue-green fluorescence, characteristic of vitamin A, were observed in the periacinar region of the pancreas of mice loaded with vitamin A. These cells were re-examined in 1998 as the corresponding cells of the liver were proving to be responsible for matrix production in liver injury (Apte et al., 1998, Bachem et al., 1998). PSC are a star shaped cell located adjacent to the basolateral aspects of pancreatic acinar cells (Apte et al., 1998). They represent 4 to 7% of the total cell mass of the pancreas (Apte et al., 1998, Bachem et al., 1998). In their quiescent state within the healthy pancreas, PSC exhibit abundant vitamin A-containing lipid droplets. These droplets enabled the original identification of the cells. In culture, quiescent PSC become activated after a period of 48 h, losing their vitamin A-storing lipid droplets, gaining a myofibroblast morphology and changing protein expression (Apte et al., 2012). Whilst the role of these cells in a healthy pancreas is largely undetermined, there is a large and growing volume of work regarding the role of the activated PSC in pancreatic injury and disease. In their quiescent state it has been proposed that PSC function as:

1. progenitor cells; 2. immune cells; or

25 3. intermediary cells in cholecystokinin-induced pancreatic digestive enzyme secretion (Apte et al., 2012, Demir et al., 2012).

Upon activation, PSC produce and release excessive amounts of ECM proteins contributing to the fibrosis of pancreatitis and the desmoplasia of pancreatic cancer. Activated PSC are also characterised by increased cell proliferation, α-SMA expression, cell migration, cytokine release, ECM production and release, and contractility. See Table 1.2 and Table 1.3 for a comparison between stellate cells and fibroblasts, and quiescent and activated stellate cells, respectively.

Table 1.2 A comparison of the characteristics of pancreatic stellate cells and fibroblasts. Reviewed in Apte et al., 2012.

Characteristic Pancreatic stellate cells Fibroblasts

Vimentin expression Yes No

Desmin expression Yes, variable No

Nestin expression Yes, variable No

Glial fibrillar acidic protein expression Yes No

Nerve growth factor expression Yes No

Neural cell adhesion molecule expression Yes No

26 Table 1.3 A comparison of the characteristics of quiescent and activated pancreatic stellate cells. As reviewed in Apte et al., 2012.

Characteristic Quiescent pancreatic stellate cells Activated pancreatic stellate cells

Localisation Periacinar and interlobular Interlobular in fibrotic areas adjacent to carcinoma cells

ECM expression Low capacity High capacity

Migration Low capacity High motility and contractility

Proliferative ability Low mitotic index High rate of proliferation

Signalling Limited Increased release of growth factors and cytokines, neurotrophic factors and transmitters, express various receptors

Vitamin A- Yes No containing droplets

α-smooth muscle No Yes actin expression

Activation of PSC is mediated by a number of different factors including: alcohol, endotoxin, growth factors and cytokines, oxidant stress, hypoxia, and increased pancreatic pressure (Apte et al., 2012, Rebours et al., 2013, Watanabe et al., 2004, Asaumi et al., 2007). Cancer cells can also activate quiescent PSC. This has been demonstrated with media conditioned by pancreatic cancer cells (Apte et al., 2004, Yoshida et al., 2004). Multiple studies have demonstrated cytokines that are capable of PSC activation, including, but not limited to: transforming growth factor β (TGFβ), transforming growth factor h1 (TGF-h1), platelet-derived growth factor (PDGF), vascular endothelial growth factor (VEGF), prostaglandin E (PDGE) and basic fibroblast growth factor (bFGF) (Apte et al., 2004). Whilst pancreatic cancer cells are a commonly studied source of these cytokines, other cells within the tumour microenvironment, such as macrophages, endothelial cells and platelets, are also sources. PDGE2 has been shown to

27 increase the proliferation, migration and ECM production of PSC (Charo et al., 2013); TGFβ and fibroblast growth factor are likely responsible for the increase in ECM synthesis, whilst PDGF promotes proliferation (Bachem et al., 2005). Once activated, PSC no longer display the vitamin A-containing lipid droplets that originally brought them to attention. In contrast, the α-SMA expression increases and is one of the earliest signs of PSC activation (Apte et al., 2004). They also display increased proliferation, migration and production of ECM, contributing to the desmoplasia of PDAC (Apte et al., 2013). Neese et al. speculate that sustained activation of PSC promotes fibrogenesis, and ultimately may create the highly desmoplastic, hypovascular and hypoxic environment characteristic of PDAC (2011).

As signalling from cancer cells increases the proliferation and migration of PSC, so does PSC signalling increase proliferation and migration, and inhibit apoptosis of cancer cells (Vonlaufen et al., 2008b, Fujita et al., 2009, Jiang et al., 2008, Farrow et al., 2009). PSC release a number of factors that support cancer growth and progression, including insulin-like growth factor-1 (IGF-1), bFGF, PDGF and stromal cell-derived factor 1 (SDF-1) (Vonlaufen et al., 2008b). In both subcutaneous and orthotopic in vivo models, the combination of pancreatic cancer cells and PSC have grown at a faster growth rate than cancer cells alone (Lange et al., 2011, Xu et al., 2010, Hwang et al., 2008, Vonlaufen et al., 2008a, Schneiderhan et al., 2007). An increased number of metastases were also observed. This aligns with the data of Erkan et al., where the extent of PSC activation, as determined by the activated stroma index, reflects the aggressiveness and resistance to therapy in clinical data (2012b). PSC may also play a role in the characteristic chemoresistance of PDAC beyond their role in desmoplasia. PSC-conditioned media can protect cancer cells from gemcitabine in culture (Hwang et al., 2008).

Activated PSC also have an influence on the other components of the stroma, including endothelial cells and macrophages. They release ECM proteins and matrix metalloproteinases (MMPs) supporting the desmoplasia of PDAC (Apte et al., 2012, Apte et al., 2004), and produce and secrete pro-angiogenic factors (Masamune et al., 2008). Additionally, it has been proposed that PSC promote the peri-neural invasion (the invasion of nerves by the tumour) (Zhou et al., 2012), which allows for metastatic spread. Peri-neural invasion is a poor prognostic marker and pathological feature of PDAC (Liebig et al., 2009).

28 Neural cells Endothelial cells

Angiogenesis (in vitro)

Remodelling Stellate Immunosuppression cells

Immune cells

Growth Extracellular matrix Proliferation Metastasis Migration Evasion of apoptosis

Cancer cells

Figure 1.2 The interaction of pancreatic stellate cells with other components of the tumour. Adapted from Luo et al., 2012, Apte and Wilson, 2012, Pietras and Ostman, 2010, Apte et al., 2013, Lunardi et al., 2014.

1.2.1.2 Angiogenesis in pancreatic cancer Angiogenesis is the process by which new capillaries are formed from existing quiescent vascular endothelial cells (Pralhad et al., 2003, Bergers and Benjamin, 2003). In healthy adults angiogenesis is rare, occurring during wound healing, menstruation and pregnancy. A number of pathologies induce angiogenesis, including rheumatoid arthritis, macular degeneration, atherosclerosis and cancer (Folkman, 2007). Since Folkman’s seminal paper in 1971 the possibility of halting tumour growth, by inhibiting the supply of nutrients, has been extensively explored as a possible target for anti-cancer therapy.

Without angiogenesis, tumours reach a critical size at which further growth is inhibited. This is a result of insufficient nutrients reaching the centre of the mass, limited by the ability of the nutrients to diffuse from the blood stream (Folkman, 1971, Carmeliet and Jain, 2000, Pralhad et al., 2003). Without vasculature, tumours are limited in size to 1 mm3 (Folkman, 1971, Bergers and Benjamin, 2003, Park and Dilda, 2010). Angiogenesis is an important step in the development of malignancy, enabling further growth and the ability of the tumour to metastasise (Carmeliet and Jain, 2000, Bisacchi et al., 2003, Jekunen and Kairemo, 2003).

29 Tumours induce angiogenesis by secreting various angiogenic factors. These induce the degradation of the basement membrane, detachment of the pericytes, and the proliferation and migration of the local endothelial cells, which then come together to form the lumen of an intermediate vessel. In the final stages, a new basement membrane is assembled and the immature vessel remodelled and stabilised, with completion of the vessel structure (Park and Dilda, 2010). However, in cancer, angiogenesis is unresolved; the angiogenic cascade is persistent and incomplete. Consequently the vessels are disorganised and tortuous (Chung et al., 2010). Tumours are characterised by leaky vessels that contribute to the high interstitial pressure found within tumours. This results in limited delivery of drugs to, and accumulation of drugs in, tumour cells (Nagy et al., 2009).

The first angiogenesis inhibitor was approved for clinical use in 2004. Bevacizumab (Avastin®) is a VEGF antibody. Since then bevacizumab has been joined by sorafenib (Nexamar®), sunitab (Sutent®), everolimus (Afinitor®), axitinib (Inlyta®) and pazopanib (Votrient®). Whilst angiogenesis inhibitors were first considered to have minimal risk of side effects, those that target VEGF have been shown to impair the normal functions of VEGF, resulting in bleeding, disturbed wound healing, thrombotic events, hypertension, and a variety of other effects (Verheul and Pinedo, 2007, La Thangue and Kerr, 2011). It has become apparent that those signalling pathways involved in angiogenesis also play a role in the homeostasis of the vascular system.

It must be noted that not all tumours exhibit a vascular dense state with extensive angiogenesis; instead being characterised by minimal vascular density and extensive regions of hypoxia. For example, pancreatic cancer is characterised by both low vascularity and extensive fibrotic tissue (Sofuni et al., 2005, Hosoki, 1983). In contrast, tumour models of PDAC when designed to best reflect human PDAC are reasonably vascularised. The data produced from these models concurs with in vitro studies demonstrating the pro-angiogenic property of all cells in the tumour microenvironment (tumour, stellate and inflammatory) (Erkan et al., 2012a). Clinical trials combining bevacizumab or axitinib with gemcitabine based regimens show minimal extension to overall survival in PDAC (Heinemann et al., 2012). In mouse models that have the stromal component included, there is evidence that angiogenesis inhibitors can improve the vascularity by removing leaky vessels, and thereby improving the delivery of drugs that target the tumour cells. For example, in a genetically engineered mouse model of pancreatic cancer, characterised by poor perfusion and vascularity, and resistant to gemcitabine treatment, the combination of gemcitabine with IPI-926, an inhibitor of smoothened from the hedgehog signalling pathway, showed promise of increasing

30 intratumoural vascular density and improving delivery of gemcitabine, leading to a temporary delay in disease progression (Olive et al., 2009). With such promising results in mice, the combination moved to clinical trials and progressed to a phase II trial. However, the study was concluded early due to increased mortality in the treatment arm (ClinicalTrials.gov, Tamburrino et al., 2013).

1.3 Pancreatic cancer therapy

Pancreatic cancer is renowned for its poor survival rate. Whilst surgery is the treatment of choice, as it provides the best chance of a cure, very few patients are diagnosed earlier enough to permit this course of action (less than 10%). As a result, the majority of PDAC patients receive treatment with chemotherapy. Gemcitabine (Gemzar®) is a chemotherapeutic that has been the standard of care for patients with un-resectable pancreatic cancer for over a decade. Whilst gemcitabine shows only a minor increase in survival, it showed a significant improvement in 1-year survival, clinical benefit and response rate compared with 5FU (Burris III et al., 1997, Muniraj et al., 2013). Until recently, FOLFIRINOX (folinic acid, 5FU, irinotecan and oxaliplatin) was the only combination shown to improve the survival of patients with metastatic pancreatic cancer. It extended survival to 11.1 months compared with gemcitabine providing 6.8 months (Conroy et al., 2011). In 2013, the combination of gemcitabine and nab- paclitaxel was also shown to improve survival for metastatic pancreatic cancer compared to gemcitabine alone (8.5 months versus 6.7 months) (Von Hoff et al., 2013). With such short survival periods even with treatment, it is essential that the best therapy be chosen at diagnosis.

31 A 100 B 11% 9% 90 Localised 80

Regional 27%

70 Distant 60 Unknown 53% 50

40 year survival rate, % rate, survival year

- 30 24.1 5 20 9 10 2 4.1 0 Localised Regional Distant Unknown Stage at diagnosis

Figure 1.3 A. Five-year survival rates of pancreatic cancer by stage at diagnosis. B. Stage at diagnosis for pancreatic cancer. Data adapted from the SEER Cancer Statistics Factsheets: Pancreatic Cancer, National Cancer Institute.

1.3.1 Targeted therapy Chemotherapies target proliferating cells indiscriminately. As a result they have been traditionally plagued by side effects, since healthy, proliferating cells, including hair follicles, gut epithelia, bone marrow and lymphatic cells also respond to this type of drug. As a consequence, the dose is limited by the toxicity at these off-site targets. As a result, chemotherapies are not suitable as long term treatments (Mahato et al., 2011). Targeted drugs provide a method to selectively affect cancer cells without impacting normal proliferating cells. This improves both the selectivity and the efficacy of the therapy.

Targeted therapies have the potential to completely cure cancer, turn others into chronic disease or increase progression-free survival (Chong and Jänne, 2013). For example, retinoic acid in combination with chemotherapy in acute promyelocytic leukaemia cured patients (Tallman et al., 1997), whilst imatinib in chronic myeloid leukaemia transforms the cancer into a chronic disease (Kantarjian et al., 2002). Erlotinib (Tarceva®) represents one of the more common types of targeted therapies, increasing the period of progression-free survival. It is an epidermal growth factor receptor tyrosine kinase inhibitor. Endothelial growth factor receptor

32 (EGFR) signalling leads to cell proliferation, evasion of apoptosis, angiogenesis and metastasis (Chong and Jänne, 2013). A number of targeted therapies have been tested in pancreatic cancer. When combined with gemcitabine, erlotinib shows a modest improvement in median survival, compared to gemcitabine alone (Muniraj et al., 2013). However, erlotinib is plagued by both innate and acquired resistance (Chong and Jänne, 2013).

1.3.2 Prodrugs Prodrugs are inactive precursor compounds when they are administered. They become activated through chemical or enzymatic transformation within the body. Similar to the design of analogues, prodrugs are designed to improve the pharmaceutical, pharmacokinetic, pharmacodynamic or economic properties of a drug in order to increase their usefulness or to alter or decrease their toxicity (Table 1.4). More specifically, a prodrug should have favourable administration, distribution, metabolism, excretion and toxicity, be chemically stable in its dosage form, and transform the active molecule at an appropriate rate and site (Huttunen and Rautio, 2011, Ettmayer et al., 2004).

33 Table 1.4 Reasons to develop prodrugs. Adapted from Huttunen and Rautio, 2011.

Characteristic Unfavourable property

Pharmaceutical Poor chemical stability

Poor aqueous solubility

Offensive taste or odour

Irritation or pain

Pharmacodynamics Inadequate site specificity

Toxicity

Pharmacokinetics Low oral absorption/ systemic exposure/ bioavailability

Marked pre-systemic metabolism

Short duration of action

Unfavourable distribution in the body

Economic Expiry of patent

There are generally considered four classes of prodrugs (Huttunen and Rautio, 2011, Lin et al., 2012):

1. Carrier-linked prodrugs consist of a promoiety attached to the active drug. They require cleavage of the promoiety for a therapeutic effect. 2. Bio-precursors require metabolism to form the active drug and do not contain a promoiety (Lin et al., 2012). 3. Polymer drug conjugates consist of a polymer attached to the active drug. Depending on the polymer attached, this can increase the half-life of the drug, modify the bio- distribution profile, increase the water solubility or increase oral bioavailability (Mahato et al., 2011). 4. Drug-antibody conjugates use an antibody promoiety to target the drug to sites that express the respective antigen. Upon binding to their cancer-cell specific antigen, most drug-antibody conjugates are internalised by endocytosis (Bildstein et al., 2011). 34 A B

C D

n

Locating moieties

Antibody Active drug Targeting group Polymer backbone Carrier Receptor/transporter Side chain Plasma membrane Side chain

Figure 1.4 Different classes of prodrugs. Locating moieties are red, the active moiety is blue. (A) Carrier-linked prodrug. (B) Bio-precursors. (C) Polymer-drug conjugates. (D) Antibody-drug conjugates.

35

The most common prodrug in cancer is capecitabine. It is a prodrug of the antimetabolite 5FU. Capecitabine is an example of a bio-precursor prodrug, requiring three steps for activation (Rooseboom et al., 2004). Due to the expression pattern of the activating enzymes, both the side effects of the active compound are minimised and the action at the tumour site is increased. The first step of activation occurs in the liver. As a result, no active compound is found in the gastrointestinal tract, eliminating the gastrointestinal toxicity of 5FU. The final step is catalysed by thymidine phosphorylase (Miwa et al., 1998). This enzyme is found at 3 to 10-fold higher concentrations in cancer cells than normal cells (Aprile et al., 2009, Rooseboom et al., 2004). The increased expression of thymidine phosphorylase within cancer cells provides a unique targeting mechanism by which the precursor is preferentially activated in the tumour. This potential to use the activating mechanism of a prodrug to better target a drug to the site of the tumour has been a growing field of research.

1.3.3 Targeted prodrugs Targeting cancer therapies directly to the tumour, minimising side effects, and maximising anti-tumour activity, is achievable with the development of tumour activated prodrugs. A number of mechanisms have been explored in the development of tumour directed prodrugs; the common feature being that they utilise a moiety that directs the prodrug to the tumour site, which is linked to the active compound. The linker is generally an enzyme cleavable sequence that allows for the release of the active drug. There are three essential characteristics that an ideal targeted prodrug should possess:

1. The prodrug will be precisely transported to the site of action, reducing the incidence of side effects; 2. It will be selectively and quantitatively transformed into the active drug, with insignificant transformation into any other compounds; and 3. It will be retained in the target tissue to produce the desired therapeutic effect (Huttunen and Rautio, 2011, Stella et al., 1985).

The mechanism of a targeted prodrug can be classified as passive or active (Kratz et al., 2008). Passive targeting uses the biochemical and physiological differences of tumours to accumulate activated prodrugs. For example, PR-104 is a phosphate ester dinitrobenzamide mustard activated by hypoxia to form cytotoxic nitrogen mustards (Foehrenbacher et al., 2013). Active

36 targeting relies on the interaction of a carrier-linked prodrug with a tumour-associated cell surface marker. Active targeted prodrugs can be further classified by the specific mechanism of activation. Mahato et al. recognises five features that can be employed to improve targeting of therapies to cancer cells (2011):

1. Targeting-ligand conjugate prodrugs make use of the antigens, receptors or transporters that are overexpressed at the target site. Antibodies, due to their high affinity for their respective antigen make attractive targeting moieties for targeted prodrugs. Unfortunately, their size can limit their pharmacodynamic properties. Gemtuzumab ozogamicin (Mylotarg®) was originally approved on the U.S. market in May 2000 under the U.S. Food and Drug Administration’s accelerated approval program for use in acute myeloid leukaemia (Kratz et al., 2008). It consists of a CD33 antibody attached to the cytotoxic calicheamicin. However, in 2010, following additional clinical trials showing no clinical improvement and a greater number of deaths in the Mylotarg group, it was withdrawn from the market (Huttunen and Rautio, 2011);

2. Membrane transporter-associated prodrugs make use of the recognition of the prodrug by a transporter and the subsequent uptake of the compound. Whilst not a prodrug, gemcitabine is a substrate of the human equilibrative nucleoside transporter (hENT1). Tumours with high expression of these transporters are more responsive to gemcitabine than those with low expression (Oberstein and Olive, 2013);

3. Polymeric prodrugs increase the exposure of the active moiety to tumour cells, via a passive affect involving the enhanced permeation and retention effect (Mahato et al., 2011);

4. Enzyme cleavable prodrugs are activated by enzymes either uniquely expressed or with increased expression at the tumour; and

5. Enzyme activated prodrugs encompasses two strategies: antibody-directed enzyme prodrug therapy (ADEPT) and gene- or virus-directed enzyme prodrug therapy (GDEPT or VDEPT respectively). The first strategy, ADEPT, delivers an antibody-enzyme conjugate to the tumour which then enables activation of the prodrug. The second strategy, GDEPT or VDEPT, delivers the gene for an enzyme to the tumour, which once expressed can then activate the prodrug at the site of the

37 tumour. This could potentially increase the specificity of targeting as two different targeting mechanisms could be utilised. However, this method would presumably have difficulties in gaining regulatory approval, exemplified by polymer drugs.

A number of enzymes are overexpressed in tumours. This provides a clear mechanism to deliver a prodrug to be activated at the tumour. Capecitabine, as discussed above, targets tumours with 5FU by multiple enzymatic steps. The most obvious risk with an enzyme activated prodrug is the expression of the enzyme at other sites in the body. If there is significantly greater expression of the activating enzyme at another site, then there is potential for the drug to accumulate and impart toxicity at this site before inducing a therapeutic effect. However, this is dependent on the individual characteristics of each prodrug and its mechanism of action. It will require the unique combination of the presence of the uptake mechanism, the presence of the final target, and minimisation of mechanisms of resistance. Listed in Table 1.5 are a number of enzymes that are overexpressed in cancer. More attention is given to enzymes that relate to PDAC.

38 Table 1.5 Overexpressed enzymes in tumours (Kratz et al., 2008, Lin et al., 2012, Rooseboom et al., 2004).

Enzyme Function Tumour types with References overexpression Cathepsin B, H and L Lysosomal Breast, glioma, lung, (Thomssen et al., degradation of prostate and colorectal 1995, Demchik et al., proteins 1999, Gondi and Rao, 2013, Kratz et al., 2008) Cathepsin D Degradation of ECM Breast, melanoma, (Rochefort et al., ovary 2000) Urokinase-type Activation of Various, including (Lunardi et al., 2014, plasminogen plasmin formation, breast, colorectal, Ulisse et al., 2009, activator (uPA) angiogenesis, bladder Dass et al., 2008) invasion Prostate specific Liquefaction of Prostate (Kratz et al., 2008) antigen (PSA) semen Matrix Degradation of ECM Melanoma, breast (Hofmann et al., metalloproteinases and collagens 1999, Köhrmann et (MMP2, MMP9) al., 2009) β-glucuronidase Hydrolysis of Pancreatic, lung (Sperker et al., 2000) glucuronide moieties from proteins Thymidine Pyramidine Various, including (Aprile et al., 2009, phosphorylase metabolism colorectal, Rooseboom et al., gastrointestinal, breast 2004) and ovarian Prostate specific Metallopeptidase Prostate (Zhao et al., 2012, membrane antigen Silver et al., 1997, (PSMA) Wright et al., 1995, Rajasekaran et al., 2005) Fibroblast activation ECM remodelling, Desmoplastic tumours- (Brennen et al., protein (FAP) increases PSC breast, colon, 2012a, Lunardi et al., motility pancreatic 2014)

γ-glutamyl Removal of a γ- Various tumours- (Gerber and Thung, transferase (γGT) glutamyl moiety, including cancer of the 1980, Fujisawa et al., particularly from ovary, liver, lung, 1976, Dempo et al., glutathione. breast and pancreatic 1981, Bard et al., and in melanoma and 1986, Corti et al., leukaemia 2010, Mareš et al., 2012, Hanigan et al., 1999b)

39 Desmoplastic tumours such as the tumours of the breast and colon have significant numbers of carcinoma-associated fibroblasts within the stroma. A key characteristic of these cells is the expression of fibroblast activation protein (FAP) on their surface (Brennen et al., 2012a). It has been shown to be expressed by the stroma of more than 90% of epithelial cancers. FAP is also expressed during wound healing, embryogenesis and in areas of chronic inflammation and fibrosis. FAP is a dipeptidase, cleaving the bond between proline and any amino acid. Thapsigargin is a highly cytotoxic sarcoendoplasmic reticulum calcium transport ATPase (SERCA) pump inhibitor (Amorim et al., 2013). It has been linked to a suitable peptide to study the potential of FAP for targeting cytotoxic drugs to the tumour site by enzyme activation. Brennen et al. demonstrated preferential killing of the stromal cells compared with malignant epithelial cells (2012b). This effect translated to inhibition of tumour growth in vivo. The use of FAP as the activating mechanism for tumour directed prodrugs has also been studied with mellitin, a cytolytic bee toxin. Utilising the protoxin form secreted by bees, LeBeau et al. designed a mellitin prodrug that is activated by FAP (2009). Again, selectivity for FAP expressing cells and in vivo efficacy was demonstrated.

Urano et al. have proposed using the activating mechanism of prodrugs to probe the margins of tumours during surgery, endoscopic biopsy or resection procedures (2011). The group has conjugated the dye, hydroxymethyl rhodamine green through a gamma bond to a glutamyl group. They show that the γ-glutamyl conjugate can be sprayed within the peritoneum of athymic mice having ovarian cancer xenografts, and that the cleavage by membrane bound γ- glutamyl transferase releases the dye, allowing uptake and accumulation within the cells. The fluorescence of the dye then enabled removal of 1 mm implants. This mechanism makes use of the increased γ-glutamyl transferase expression found in many types of cancer (Hanigan et al., 1999b), to be further discussed in Section 1.5.1.

Targeted prodrugs could be potentially used in pancreatic cancer to deliver a therapy directly to the tumour site. The combination of the ability to be targeted to, and then activated at a particular site will limit the action of the compound to the tumour. By minimising side effects, the active concentration at the target can be maximised thus increasing the response of the tumour.

40 1.4 Biomarkers in cancer

Cancer is a highly heterogeneous disease, with variation between patients and within tumours. The National Cancer Institute defines a biomarker as “a biological molecule found in blood, other body fluids, or tissues that is a sign of a normal or abnormal process, or of a condition or disease” (2013a). The use of biomarkers can aid clinical decisions at many points in the course of the disease, including (Henry and Hayes, 2012):

 Estimating the risk of developing cancer;

 Screening for disease;

 Differential diagnosis ;

 Determining prognosis of disease;

 Predicting response to therapy;

 Monitoring for disease recurrence; and

 Monitoring for response or progression in metastatic disease.

The use of biomarkers provides a number of benefits, including: reduced unnecessary treatment; reduced quantity of adverse events; reduced drug attrition rates; improved therapeutic benefit; and better control of medical costs (La Thangue and Kerr, 2011). However, many biomarkers lack the specificity and sensitivity to prove them useful (Chatterjee and Zetter, 2005).

Biomarkers can be defined as either diagnostic or predictive. Diagnostic markers help determine whether a patient has cancer and determine the stage of the disease (e.g. recurrence). A predictive marker helps clinicians make decisions regarding the choice and dose of therapy.

A diagnostic test should have sufficient accuracy to avoid over-diagnosis and over-treatment, whilst being cost-effective and suitable for use across the population (Brennen et al., 2012b). A blood test for prostate specific antigen (PSA) can be used to test for prostate cancer; however, more recently the usefulness of PSA alone has been questioned, as a significant proportion of metastatic prostate carcinoma cases are negative (Bernacki et al., 2013).

41 Prognostic markers, a category of diagnostic markers, are baseline (pre-treatment) measurements that provide information about the patient’s probable long-term outcome (Simon, 2010). These markers indicate the course of the disease without intervention (Sawyers, 2008).

1.4.1 Predictive markers in cancer therapy Predictive markers are measurements that indicate whether the patient is likely (or unlikely) to benefit from a specific drug or regimen. They generally indicate the presence of a drug’s target, or the activator of a prodrug. For example, amplification of the human epithelial growth factor receptor 2 gene (HER2) predicts patient response to trastuzumab (Gennari et al., 2008, Hayes et al., 2007).

A further capability of some predictive markers is the ability to guide dosing. Pharmacodynamic biomarkers measure the effect of a drug on the disease. The expression of this type of marker helps to guide clinicians as to the dose of the indicated drug the patient should be given. For example, colorectal cancer patients with expression of UGT1A1*28 metabolise irinotecan at a slower rate than the rest of the population. Identifying these patients allows doctors to prescribe a lower dose, and hence prevent drug accumulation and reduce toxic side effects (Iyer et al., 2002).

The traditional approach to identify potential cancer therapies has been to identify a drug that helps the greatest number of patients. In doing this, the potential of therapies that have a significant impact on a small subset is lost within the larger sample population. The current trend to target particular abnormalities in cancer allows for the development of predictive markers and companion tests to identify which patients have a particular tumour characteristic. From this stems the ability to identify the patient population that will (or will not) respond to the drug. Developing new therapies with the target population in mind will facilitate the development of drugs that will induce a greater tumour response for individual patients. For this to occur, a marker that predicts patient response to the drug is essential.

For a marker to be used in the clinic it must have proven validity and utility for its proposed purpose. Simon suggests three phases for the development of predictive markers (2010):

1. Analytical validity shown in pre-clinical research; 2. Clinical validity where the test correlates with a clinical outcome in phase I and II trials; and

42 3. Clinical utility where phase II trials show improved outcome for patients with the indicated marker (Henry and Hayes, 2012).

A very small number of predictive markers exist in clinical use, the most notable being the amplification of HER2. Amplification of this gene is used to determine which patients will respond to trastuzumab. HER2 is a member of the Her/ ErbB2/Neu family of transmembrane receptors. When activated, this family phosphorylates signalling molecules. In particular, HER2 activates the phosphatidylinositol 3-kinase (PI3K)-AKT and the mitogen-activated protein kinase (MAPK) pathways (Dean-Colomb and Esteva, 2008, Zheng et al., 2004, Kurokawa et al., 2000). Downstream effects include downregulation of cyclin D1 and decreased transcription of p27, resulting in increased cell proliferation and survival (Dean-Colomb and Esteva, 2008). Consequently, tumours that overexpress HER2 are poorly differentiated, with increased proliferation and invasion, and frequent metastasis. Accordingly, HER2 overexpression has been associated with more aggressive tumours and a poorer prognosis (Cooke et al., 2001, Ménard et al., 2004).

Many targeted therapies come with their own predictive marker; although not all targets will make suitable biomarkers. Colorectal patients with KRAS mutations have a poor response to EGFR antibodies (Allegra et al., 2009, Karapetis et al., 2008, Amado et al., 2008). In this case KRAS, not the target, EGFR, is the predictive marker. More recently the anaplastic lymphoma kinase (ALK) and c-Met inhibitor, crizotinib has been approved for use in locally advanced or metastatic non-small cell lung cancer (NSCLC) with a mutation in ALK. In a study of ALK-positive metastatic NSCLC, median progression free survival was 3 months for chemotherapy with premedtrexed and docetaxel, and 7.7 months for patients receiving crizotinib (U.S. Food and Drug Administration, Gridelli et al., 2014). Having a predictive marker to determine the potential response of a patient to a therapy will have a significant effect on patient treatment. Whilst taking the time to test for predictive markers may slightly delay treatment, it will ensure that a therapy that will elicit a therapeutic response will be administered as soon as possible. In such a fast-paced disease as pancreatic cancer, ensuring a patient receives the best therapeutic choice first will give the patient the best chance of response.

43 Diagnosis Emprical Individualised medicine medicine

Biomarker testing

Drug A Drug A Drug B Drug C

If Drug A doesn’t work Effective Effective treatment Drug B response administered

Effective response Time

Figure 1.5 Time till effective treatment administered. Adapted from (Research Advocacy Network, 2010).

1.5 γ-glutamyl transferase

γ-glutamyl transferase (γGT, EC2.3.2.2) catalyses the transpeptidation and hydrolysis of the γ- glutamyl group of glutathione and related compounds to an acceptor molecule, including water, amino acids and peptides. It is found on the plasma membrane, facing extracellularly, playing an essential role in the maintenance of intracellular cysteine (Moriarty-Craige and Jones, 2004). Considering glutathione’s important role as a cellular antioxidant, γGT has traditionally been considered a component of the cell’s oxidative stress defences. However, the range of γGT substrates has expanded considerably, now including a range of glutathione conjugates, leukotriene C4 (LTC4), S-nitroso-glutathione and glutathione adducts of xenobiotics, suggesting diverse roles for γGT.

γGT expression varies considerably across the body. High expression of γGT is commonly found on cells involved in transport, on the luminal surface of secretory and absorptive cells. Its highest expression is on the luminal surface of the proximal tubules in the kidney, whilst the bile ducts, bile canaliculi and endothelial cells of the nervous system capillaries also have high expression (Hanigan and Frierson Jr, 1996, Shiozawa et al., 1989).

44 γGT deficiency is reported to result in increased glutathione levels in plasma and urine, yet intracellular concentrations remain normal. Animal models of γGT deficiency indicate it also results in reduced growth, infertility and a decreased lifespan (Lieberman et al., 1996, Harding et al., 1997). Only seven cases of γGT deficiency have been described in humans (Ristoff and Larsson, 2007, Heisterkamp et al., 2008). In addition to glutathionaemia and glutathionurea, five of those cases exhibited central nervous system involvement in the form of mild retardation, although the direct link between the two has yet to be made. Further, in the three cases tested, leukotriene D4 was also deficient, in agreement with the role γGT plays in its production from LTC4 (Ristoff and Larsson, 2007).

γGT is an evolutionary conserved enzyme. It is expressed in numerous organisms sharing greater than 25% sequence identity. Whilst numerous genes have been shown to be transcribed with γGT homology, only two have been shown to be functional: γGT1 and γGT5 (Heisterkamp et al., 2008, Wickham et al., 2011). γGT1 transcribes the typical γGT protein, whilst γGT5 transcribes what has traditionally been considered a γ-glutamyl leukotrienase. Wickham et al. have recently shown γGT5 will also act as a glutathionase, however, γGT1 has stronger γ-glutamyl leukotrienase activity than γGT5 (2011).

Once the γGT gene is translated, it undergoes autocatalytic cleavage into a large and small subunit. The subunits come together as a heterodimer. In humans the large subunit is 45 kDa and the small subunit is 22 kDa. Human γGT requires glycosylation to produce the mature enzyme. The light chain provides the catalytic activity, whilst the heavy chain anchors γGT in the plasma membrane (Zhang and Forman, 2009). Without the presence of the heavy chain, the light chain has very little enzymatic activity (Heisterkamp et al., 2008).

γGT can also be found in the blood, where it forms one of a panel of tests for liver function. The majority of plasma γGT is derived from the liver and can be found associated with carriers such as albumin. Serum γGT has been linked with increased morbidity and a number of diseases besides the conventional hepatobilliary dysfunction, including cardiovascular disease (Emdin et al., 2005), obesity (Suh et al., 2013), diabetes (Lee et al., 2013), non-alcoholic fatty liver disease (Franzini et al., 2012), myotonic dystrophy (Franzini et al., 2010) and cancer (Strasak et al., 2010, Polterauer et al., 2011). γGT plasma levels in a number of cancers have been associated with severity of disease and prognosis (Yin et al., 2013, Grimm et al., 2013, He et al., 2013). For example, in intrahepatic cholangiocarcinoma elevated serum levels of γGT predict aggressive tumour behaviour (vascular invasion, lymph node involvement and

45 incomplete tumour encapsulation) and unfavourable prognosis (survival and recurrence) (Yin et al., 2013).

1.5.1 γ-glutamyl transferase in cancer γGT expression has been shown to be increased in numerous cancers. Increased levels have been observed in cancer of the ovary, liver, lung and breast, and in melanoma and leukaemia (Gerber and Thung, 1980, Fujisawa et al., 1976, Dempo et al., 1981, Bard et al., 1986, Corti et al., 2010, Mareš et al., 2012). In many cases, the γGT levels are higher in the corresponding primary tumour (Maellaro et al., 2000). An extensive study by Hanigan et al. scored the expression of γGT in a variety of tumours (1999b). Carcinomas in particular express γGT, with carcinomas of the kidney, liver and prostate showing strong expression. Furthermore, some carcinomas of the breast, ovarian, uterine and pancreas were shown to express γGT. In particular, of ten samples tested, nine pancreatic adenocarcinomas expressed γGT. Non- epithelial malignancies and sarcomas rarely expressed γGT.

Further differences in the normal expression of γGT have been observed in cancer cells. When tumours develop at sites with normal expression of γGT, the formation of ducts and glands is often incomplete. This results in a lack of polarity of the cells and consequently γGT is expressed on all surfaces (Hanigan et al., 1999b). In HepG2 cells, γGT has more glycosylation than normal cells from the liver and kidney (West and Hanigan, 2010).

γGT has been considered an early marker of neoplastic transformation. Many early studies have demonstrated in in vivo models the appearance of γGT expression in areas previously negative following exposure to carcinogens (Pompella et al., 2006). The mechanism of this is still unclear. A genome-wide analysis of pancreatic cancer implicated γGT1 as playing a role in carcinogenesis (Diergaarde et al., 2010). The proto-oncogene KRAS has been implicated in the upregulation of γGT. Recently, Moon et al. have demonstrated that KRAS transformed prostate epithelial cells are more resistant to hydrogen peroxide induced free-radicals than non- transformed cells (2012). They observed an upregulation of γGT2 in the KRAS cells and confirmed its involvement in resistance to hydrogen peroxide treatment though γGT2 siRNA. This has also been observed in colon carcinoma cells where radiation induced γGT activity through a Ras mediated pathway (Pankiv et al., 2006).

The early appearance of γGT in neoplasms suggests the potential for γGT to play a role in tumour progression. γGT has been shown to give cells a growth advantage in vitro and in vivo. High expression of γGT provides cells with greater quantities of cysteine through the breakdown of extracellular glutathione (Hochwald et al., 1996, Gerber and Thung, 1980,

46 Hanigan, 1995). This explains the difference in growth rates of cells when moved from the in vitro setting to mice, where extracellular glutathione and cysteine is limited in vivo. In clones of melanoma cells, the extent of γGT expression was shown to be proportional to the invasive ability of the clone (Supino et al., 1992).

The role γGT plays in redox is likely to depend on the context. Traditionally, it has been thought to play a role in defence mechanisms against oxidative stress because of its necessary role in maintaining glutathione levels intracellularly. However, there is a growing body of evidence of a pro-oxidant role.

γGT expression is induced by many substances, especially those that generate reactive oxygen species and or perturb redox homeostasis. γGT expression was observed to increase in rat lung epithelial cells after quinine-induced oxidative stress (Kugelman et al., 1994). Persistent oxidative stress is a key feature of tumours (Liou and Storz, 2010, Toyokuni, 1995). Numerous papers have shown a direct link between oxidative stress and γGT upregulation (Borud et al., 2000, Roomi et al., 2006, Kugelman et al., 1994, Knickelbein et al., 1996, Liu et al., 1998). High expression of γGT allows cancer cells to better maintain their intracellular glutathione levels, thus enabling them to better respond to oxidative stress. Again, melanoma cells transfected with γGT are better able to respond to hydrogen peroxide or ascorbic acid induced oxidative stress than those with basal γGT expression (Giommarelli et al., 2008).,

In contrast, it has been demonstrated both biochemically and in cells that γGT can have pro- oxidant affects. As early as 1993 it has been shown that in the presence of iron ions (Fe2+), purified γGT can cause lipid peroxidation. This was attributed to the cysteinyl-glycine product of the reaction (Stark et al., 1993). This affect is not limited to iron; copper ions (Cu2+) have also been demonstrated to play a role (Glass and Stark, 1997, Stark and Glass, 1997). The pro- oxidant effect has been further demonstrated in liver cell lines (Paolicchi et al., 1997). γGT has been linked to reactive oxygen species production in cells (Drozdz et al., 1998), including hydrogen peroxide (Del Bello et al., 1999). In U937 cells, the low levels of hydrogen peroxide produced as a by-product of γGT activity was shown to maintain proliferation and prevent against apoptosis (Del Bello et al., 1999). This has also been demonstrated in A2780 (Paolicchi et al., 2002). Paolicchi et al. further suggest that the pro-oxidant effects of γGT play a signalling role through NF-κB (2002).

1.5.2 γ-glutamyl transferase and cancer therapy Beyond its differential expression in cancer, γGT is considered to be part of a resistance phenotype (Pompella et al., 2006). A large reason for this, is the role that γGT plays in

47 maintaining glutathione levels within the cell. Glutathione plays an important part in the detoxification of xenobiotics by binding to a range of agents. This allows for glutathione mediated expulsion of these compounds from the cell.

The relationship between γGT and chemotherapy resistance is demonstrated by a number of experiments showing that transfection with γGT both in vitro and in vivo leads to resistance to members of the platinum drug family, in particular cisplatin (Franzini et al., 2006, Hanigan et al., 1999c, Daubeuf et al., 2002). Further evidence of the relationship comes from biopsies of an ovarian adenocarcinoma patient before and after the onset of drug resistance (cisplatin, chlorambucil and 5FU). Cells grown from biopsies taken before and after treatment, showed a 6.5-fold increase in γGT activity following treatment (Lewis et al., 1988). However, Schäfer et al. found no direct link between γGT and resistance, but a growth advantage for γGT overexpressing cells was evident (2001).

Another proposed mechanism for platinum drug resistance is the formation of adducts between the platinum drug and the cysteinyl-glycine product of γGT. These complexes have very poor transport across the cell membrane; as a result the platinum drug rarely reaches its target (DNA). These adducts have been described in the extracellular media of γGT overexpressing cells and the plasma of patients treated with oxaliplatin (Daubeuf et al., 2002, Daubeuf et al., 2003, Corti et al., 2010, Paolicchi et al., 2003, Jerremalm et al., 2006).

Platinum drugs have a well-known nephrotoxicity. There is evidence linking γGT expression on the luminal surface of the proximal tubules to cisplatin nephrotoxicity. It was proposed that the action of γGT and extracellular dipeptidases could metabolise the cisplatin-glutathione adducts forming cysteine-cisplatin. This compound is then responsible for the nephrotoxicity. However, whilst evidence exists for the involvement of γGT, inhibition of renal dipeptidase did not reduce cisplatin toxicity (Corti et al., 2010). Further, the cysteinyl-glycyl platinum adducts described above had less effect on proliferation of HK-2 cells (immortalised cells of the proximal tubules) than the parent compound (Paolicchi et al., 2003). It is potentially the glutathione content of the plasma, and thus indirectly γGT, that plays a role in determining cisplatin nephrotoxicity (Pompella et al., 2006).

γGT has also been implicated in resistance to radiation therapy. In lymphoid cells, γGT plays a role in maintaining the intracellular glutathione levels that are essential for protection against radiation (Jensen and Meister, 1983). Inhibition of γGT in melanoma cells significantly increased the radiosensitivity on a high γGT variant (Prezioso et al., 1994b). In CC531, a colon cancer cell line, γGT was upregulated in a time and dose-dependent manner to irradiation. This

48 increase in γGT activity was attributed to de novo synthesis of the mRNA. It was further demonstrated that signalling through the Ras pathway was responsible (Pankiv et al., 2006).

γGT has been explored as a potential target in cancer. The benefits of inhibiting γGT stem from the reduction of intracellular glutathione levels and the suppression of the conjugation of drugs to glutathione. Subsequently, export of glutathione-chemotherapy conjugates is reduced. Acivicin (also known as AT-125) is the most widely known γGT inhibitory compound. Acivicin has been evaluated in phase I and II trials in NSCLC and colorectal cancer; however, the side effects proved too toxic to allow clinical use (Maroun et al., 1984, Maroun et al., 1986, Maroun et al., 1990, Bonomi et al., 1994, Adolphson et al., 1986).

1.6 Glutathionylated prodrugs

The expression pattern of γGT in cancer presents the possibility to utilise the enzyme to activate a prodrug at the site of the tumour (Castellano and Merlino, 2012, Corti et al., 2010). This in turn would allow γGT to be used as a marker to predict patient response to such a compound. Hanigan first proposed this in a patent in 1998. Antibodies, specific for γGT, were proposed to identify tumours with high levels of γGT. Once identified these tumours should then be treated with a γ-glutamyl prodrug. The development of potential compounds was left unexplored and the patent has since lapsed. Prior to this, γ-glutamyl prodrugs were explored in other conditions, most extensively in diseases of the kidney. An anti-nociceptive prodrug, γ- glutamyl-dermorphin, was explored by comparing the pain threshold of mice (Misicka et al., 1996). More extensively researched is the γ-glutamyl conjugate of L-DOPA (dihydroxyphenylalanine). This prodrug provides the precursor of dopamine, DOPA, on activation. Whilst more commonly thought of as a treatment for Parkinson’s disease, dopamine delivered to the kidney was explored as a renal vasodilator where γGT expression is high (Worth et al., 1985, Sadiq et al., 2000, Wilk et al., 1978). Clinical testing of the prodrug determined that despite reasonable kidney specificity it had low bioavailability (Lee, 1990). A number of other compounds were conjugated to a glutamyl group and tested; however none reached the market (Huttunen and Rautio, 2011).

Alternatively, glutathione can be used in place of glutamate to create a precursor activated by γGT. This is demonstrated naturally by S-nitrosoglutathione (GSNO), a glutathione molecule attached to nitric oxide. GSNO can be cleaved by γGT (Hogg et al., 1997, Angeli et al., 2009, Bramanti et al., 2009), releasing nitric oxide in vitro in accordance with γGT expression

49 patterns. More recently, glutamyl-protected N-hydroxyguanidines (NHGs) have been developed to explore the ability to deliver nitric oxide to the kidney (Zhang et al., 2013a). The NHGs include Nw-hydroxy-L-arginine, which is an intermediate in the nitric oxide synthase synthesis of nitric oxide (Zhang et al., 2013b). Whilst promising results were seen, there was a propensity for the potential compounds to cyclise (Zhang et al., 2013b, Zhang et al., 2013a). This suggests that conjugation to glutathione may be more stable. 4-(N-(S- glutathionylacetyl)amino) phenylarsonous acid (GSAO) is a glutathione conjugate with demonstrated anti-cancer ability (Don et al., 2003) and it has been shown to be activated by purified γGT enzyme (Dilda et al., 2008).

1.7 GSAO

4-(N-(S-glutathionylacetyl)amino) phenylarsonous acid (GSAO) is a prospective cancer drug and has just completed a phase I dose escalation study in patients with solid tumours refractory to standard therapy (Horsley et al., 2013). Treatment was very well tolerated. Of 34 patients, 20 were evaluated for response (having received two or more cycles of GSAO). Whilst no patient exhibited an objective response, eight had stable disease, with one patient having stable disease for 18 weeks.

GSAO consists of a phenylarsonous acid (PAO) moiety attached by an N-acetyl linker to the cysteine thiol of reduced glutathione. The PAO group is the active moiety, imparting to GSAO its activity by crosslinking closely spaced protein thiols and forming a high affinity ring structure between its arsenic and the thiols (Park et al., 2012). The trivalent arsenical of the PAO moiety targets adenine nucleotide translocase (ANT) on the inner membrane of mitochondria in angiogenic endothelial cells (Don et al., 2003). This results in loss of mitochondrial membrane integrity and eventual loss of cell viability, which consequently starves the tumour of the nutrients required to support its expanding growth. The glutathione moiety contributes to the transport of GSAO in and out of the cell (Dilda et al., 2005b, Dilda et al., 2008).

50 Glutathione- Linker Phenylarsonous acid- targeting moiety active moiety - CO2

NH O S NH HN H O O

As OH + H N HO 3 - H CO2 Figure 1.6 The structure of GSAO. Phenylarsonous acid is the active moiety, glutathione acts as a targeting moiety of the prodrug.

GSAO acts by inducing death of proliferating endothelial cells, resulting in the inhibition of angiogenesis. Upon reaching the cell surface the γ-glutamyl group is cleaved by γGT (Dilda et al., 2008), generating the dipeptide form, 4-(N-(S-cysteinylglycylacetyl)amino)phenylarsonous acid (GCAO). This form is then able to enter the cell by an organic anion transporter. Within the cell further processing by dipeptidases likely occurs, resulting in the formation of the single peptide form of GSAO, 4-(N-(S-cysteinylacetyl)amino)phenylarsonous acid (CAO) (Dilda et al., 2008).

Within the cell, CAO localises to the mitochondria, inducing calcium-dependent opening of the mitochondrial permeability transition pore (MPTP) (Don et al., 2003). The PAO group of CAO crosslinks closely spaced thiols of proteins (McStay et al., 2002, Halestrap et al., 2002). CAO has been confirmed to crosslink the cysteine residues 57 and 257 of ANT (Park et al., 2012). ANT is the most abundant protein found on the inner mitochondrial membrane and it is responsible for the exchange of matrix ATP for cytosolic ADP across the inner mitochondrial membrane. Disruption of its function has been shown to have major impacts on mitochondrial integrity and cell survival (Don et al., 2003). When PAO binds to ANT there is inhibition of ADP binding and an increase in cyclophilin D binding, both of which enhance the chance of a conformational change of ANT (Halestrap et al., 2002). This conformational change in ANT results in the opening of the MPTP, which allows the equilibration of small solutes and the release of pro-apoptotic proteins from the intermembrane space (McStay et al., 2002). The

51 equilibration that occurs leads to a collapse of the proton-motive force across the membrane and a colloid osmotic pressure that causes massive swelling of the mitochondria and its bursting (McStay et al., 2002). When the MPTP is opened for a short period of time it allows the exit of pro-apoptotic proteins from the intermembrane space, causing cell death by apoptosis. When open for a long period, the loss of ATP leads to major ionic disturbances, resulting in cell swelling and rupture (McStay et al., 2002). Since CAO localises to the mitochondria, triggers mitochondrial swelling and membrane depolarisation, and is able to bind to ANT, it is thought that CAO has the same action as PAO (Don et al., 2003, Park et al., 2012).

Figure 1.7 Metabolism of the gutathionylated prodrug GSAO. GSAO is cleaved of its γ-glutamyl group by γGT to yield GCAO. Further processing is likely to occur by peptidases producing a cysteine conjugate of PAO, CAO.

There are a number of factors that affect the site of action of GSAO: expression of the activating enzyme, γGT; intracellular calcium and glutathione levels; and the expression of the multi-drug resistance association proteins, MRP1 and MRP2. The combination of high intracellular glutathione levels and frequently high expression of MRP1 and 2 in tumour cells accounts for tumour cells’ relative resistance to GSAO. The resistance of tumour cells relative to proliferating endothelial cells is demonstrated by IC50 values being up to 30-fold higher in tumour cells (Dilda et al., 2005b). The selectivity of GSAO for endothelial cells versus tumour cells is accounted for by differences in MRP1 and 2 activity and cellular glutathione levels (Dilda et al., 2005b). High calcium levels in the mitochondria encourage calcium-dependent binding of cyclophilin D to ANT, inducing opening of the mitochondrial permeability transition pore. Don et al. showed that the strong selectivity of GSAO for proliferating endothelial cells is a consequence of the higher mitochondrial calcium levels in proliferating cells (2003).

52 1.8 Hypothesis

The goal of modern day cancer research is to develop therapies that specifically target the molecular differences between malignant and normal cells. A targeted therapy can be achieved by specifically targeting a molecule that is involved in the growth and progression of the tumour or by utilising a prodrug that is activated at the selected site. Targeted therapy has arisen from the need to reduce the side effects of traditional, broad spectrum chemotherapies due to their lack of specificity between rapidly proliferating cells of the gut, hair follicles and blood cells, and proliferating malignant cells. However, these targets are not always differentially expressed in every patient; leading to results from clinical trials where the positive benefits from a subset of patients is obscured by the minimal effect in the larger population. In order to gain the benefits of targeted therapy, identification of the relevant subset of patients who will respond to the treatment is necessary. This requires the development of predictive markers and companion tests for these markers alongside the development of the targeted therapy.

As discussed in Section 1.5.1, a variety of tumours differentially express γGT: tumours of the pancreas, colon, liver and ovary, while soft tissue tumours tend not to express this enzyme (Hanigan and Frierson Jr, 1996, Hanigan, 1998b). The expression pattern of γGT suggests the potential to utilise it to activate prodrugs and create high concentrations of the active compound at the site of the tumour. GSAO is a γGT-activated prodrug, chosen because of the availability of data regarding its anti-cancer effects. The role of γGT expression in the mechanism of GSAO has yet to be studied. It is proposed that the expression of γGT at the site of the tumour will determine the extent of GSAO action in the tumour. Metabolism of GSAO by tumour cell γGT should produce high local concentrations of GCAO that will then block tumour angiogenesis. If this is true, then the expression of tumour γGT can be used to deliver glutathione-S-conjugates to the tumour, with response predicted by tumour γGT expression.

1.8.1 Significance The potential of γGT activated prodrugs has been explored in renal disease. However, the utility of γGT for delivering a prodrug to a tumour has not been explored. Expression of γGT is high in a number of tumours. There is the potential to exploit this characteristic and deliver a compound selectively to a tumour site. The expression of γGT at the tumour will predict the extent of action of the drug. There is a need for new therapeutics in PDAC to overcome the chemoresistance of this cancer type. This thesis explores the potential of tumour γGT expression to deliver a prodrug to the PDAC tumour. Proof of this concept will be the first step

53 in the creation of a new class of targeting therapeutics. They will be designed for activation by tumour γGT and could potentially be applied to other types of cancer.

1.8.2 Specific aims 1. Determine γGT status in pancreatic ductal adenocarcinoma.

2. Determine in vitro if a correlation exists between cell expression of γGT and GSAO activation and subsequent sensitivity of cells to GSAO.

3. Determine in vitro if tumour cells expressing high levels of γGT can transform GSAO into its active form and then induce proliferation arrest of adjacent endothelial cells.

4. Determine if plasma γGT will play a significant role in the activation of GSAO.

5. Determine in a murine pancreatic tumour model if the anti-tumour efficacy of GSAO correlates with tumour expression of γGT.

54

55 Chapter 2. Expression and activity of γGT in pancreatic ductal adenocarcinoma

2.1 Introduction

The role of γGT has been explored from many perspectives. It is considered to play a role in both tumourigenesis and in resistance to chemotherapy. It represents a risk factor (γGT in the plasma and at the tumour) and a predictor of poor prognosis in patients.

In a study of numerous cancer types, γGT was shown to be expressed in nine of ten PDAC samples, with three showing high expression (Hanigan et al., 1999b). Typically, γGT is found expressed in the acinar cells and on the apical side of the ductal epithelium (Hanigan and Frierson Jr, 1996). However, in poorly differentiated tumours this location specificity can be diminished (Hanigan et al., 1999b). γGT has been shown to be alternatively glycosylated in cancer. HepG2 cells have greater glycosylation than cells of the liver or kidney (West and Hanigan, 2010). Four of five pancreatic cancer γGT showed a slower electrophoretic mobility than γGT from normal pancreatic tissue (Ohta et al., 1990). This was attributed to lower sialic acid content in the cancer sourced samples.

In order to test a glutathione-S-conjugate as a prodrug for anti-cancer therapy in PDAC it was essential to confirm the expression and activity of γGT in PDAC, the chosen model system.

2.2 Methods

2.2.1 γGT expression in pancreatic ductal adenocarcinoma Human pancreatic adenocarcinoma and normal pancreas sections were provided by Dr Amber Johns of the New South Wales Pancreatic Cancer Network. Sections (5 μm) of formaldehyde- fixed paraffin embedded tumours were immunostained for γGT or α-SMA using rabbit anti-γGT (1:1000; Professor Alfonso Pompella, University of Pisa) or rabbit anti-α-SMA (1:200; Sigma) and goat anti-rabbit-biotinylated secondary antibody (1:300, Dako). Human pancreatic BxPC-3 56 tumour cells were transfected with the empty pcDNA3 vector (Life Technologies) or with the same vector containing the human γGT gene using FuGENE® 6 (Promega). Stable transfected cells were selected using 0.1 mg/mL geneticin. Female BALB/c nude mice, six to eight weeks old, were obtained from the Animal Resources Centre (Perth, WA, Australia). This study was approved by the Animal Care and Ethics Committee of the University of New South Wales. Wild-type and BxPC-3/γGT tumour cells were injected subcutaneously in the proximal midline. Tumours were excised after 28 days of treatment and 5 μm sections of formaldehyde-fixed, paraffin embedded tissue were immunostained for γGT and α-SMA. All slides were counterstained with haematoxylin.

2.2.2 γGT activity in pancreatic ductal adenocarcinoma γGT activity was measured using γ-glutamyl-p-nitroanilide as a substrate and glycylglycine as an acceptor (Huseby and Stromme, 1974). Cells were washed with phosphate buffered saline before incubation with γ-glutamyl-p-nitroanilide (3.5 mM) in 15 mM tris HCl, pH 7.4 buffer containing 40 mM glycylglycine. Cells were incubated at 37°C, and shaken at 1050 rpm on a Thermomixer Comfort (Eppendorf, Germany). When sufficient formation of p-nitroanilide was produced (yellow colour) a 300 μL aliquot was removed and the cells were spun down. A portion of the supernatant (200 μL) was transferred to a clear flat-bottomed 96-well plate. Absorbance was measured at 405 nm using a Molecular Devices M2 spectrophotometer (Palo Alto, California). One unit of γGT activity is defined as μmoles of substrate transformed per mL per min (Franzini et al., 2009b). Activity is expressed as mU per million cells.

Tumour lysates were prepared by crushing snap-frozen tumour samples with a pestle in lysis buffer (50 mM tris HCl, 1 mM EDTA, 0.27 M sucrose, 0.25% triton X-100, 1X protease cocktail inhibitor (Roche), pH 7.5). Once homogenous, the solution was spun briefly to remove debris. The protein concentration of the supernatant was determined by BCA assay (Thermo Fisher Scientific, Scoresby, VIC, Australia). The supernatant was diluted in a solution of γ-glutamyl-p- nitroanilide (3.5 mM) in 15 mM tris HCl, pH 7.4 buffer containing 40 mM glycylglycine and γGT activity determined as described above. Activity is expressed as mU per mg of protein.

2.3 Results

2.3.1 γGT expression in pancreatic ductal adenocarcinoma PDAC has been reported to express elevated levels of γGT compared to normal pancreas (Hanigan et al., 1999b). This was confirmed, and the cell types in the tumour that express γGT

57 examined. Human PDAC express γGT, as indicated by the brown staining in Figure 2.1. On closer examination, γGT could be seen to be expressed by both cancer cells and the associated stellate cells. PDAC has a substantial desmoplastic reaction in which the PSC play a significant role. These cells can be identified by their expression of α-SMA (Figure 2.1B, right panel).

58 PDAC Normal pancreas A

γGT α-SMA B

x100 x100

x400 x400 Figure 2.1 γGT expression in PDAC. (A) Human PDAC tumour (left panel) and normal pancreas (right panel) sections immunostained for γGT (brown). γGT expression is highlighted by arrows. (B) Human PDAC serial tumour sections immunostained for γGT (brown, top left panel) and α-SMA (brown, top right panel). Higher magnification is shown in the bottom panels. The arrows highlight γGT expression in the stellate cells.

59 Mouse xenografts of the human PDAC cell line, BxPC-3, also express γGT (Figure 2.2). A xenograft established with γGT transfected BxPC-3 cells (bottom panel) showed significantly higher expression of γGT than the wild type BxPC-3 tumour (top panel). The significantly increased staining of the γGT transfected tumour demonstrates the specificity of the assay. γGT expression was not limited to the BxPC-3/γGT cancer cells, but also identified in murine stromal cells of both tumour models, identified by α-SMA staining.

60 A

x400 x400 x400 Negative control γGT α-SMA

B

x400 x400 x400 Negative control γGT α-SMA

Figure 2.2 Immunostaining of γGT and α-SMA in mouse BxPC-3 xenografts. Serial sections of wild-type human pancreatic BxPC-3 (A) and γGT-transfected BxPC-3 (B) tumours grown in immunodeficient mice. Sections were immunostained for γGT (brown; centre panel) or α-SMA (brown; right panel). γGT expression in murine stromal cells is highlighted by arrows.

To confirm the veracity of this analysis and the subcellular localisation of the γGT, pancreatic tumour and stellate cells were cultured and cell surface γGT activity was measured.

2.3.2 γGT activity in pancreatic ductal adenocarcinoma γGT activity of four human PDAC cell lines was measured. Enzyme activity ranged from 0.07 ± 0.01 mU/106 cells (BxPC-3) to 8.03 ± 0.39 mU/106 cells (MIA PaCa-2). The γGT activity of primary human stellate cells isolated away from the tumour mass (N-PSC), or associated with

61 the tumour mass (TA-PSC) was compared. TA-PSC had approximately 10-fold higher γGT on the surface than N-PSC (Figure 2.3B). Tumour homogenates of BxPC-3 and MIA PaCa-2 tumours were tested to confirm the cells maintained both γGT expression and activity after injection into immunocompromised animals. The relative activity of the tumour homogenates reflected the activity of the cells in culture (compare Figure 2.3A and C).

A 8 B

20

6

cells)

cells)

15

6 6

4 10

2

5

GT activity (mU/10 activityGT

GT activity (mU/10 activityGT

γ γ 0 0 BxPC-3 PANC-1 AsPc-1 MIA #96 #66 #77 #78 #11 #37 #48 PaCa-2 N-PSC TA-PSC C 12

10

8

6

4

2

GT activity (mU/mg protein) protein) (mU/mg activityGT γ 0 BxPC-3 MIA PaCa-2

Figure 2.3 γGT activity in PDAC. (A) Cell surface γGT activity of four PDAC cell lines. Results are mean ± SD of at least three experiments performed in triplicate. (B) Cell surface γGT activity of four normal (N-PSC) and three tumour associated (TA- PSC) stellate cells. Results are mean ± SD of at least two experiments performed in triplicate. (C) γGT activity of wild-type BxPC-3 and MIA PaCa-2 tumour homogenates. Results are mean ± standard deviation (SD) of two experiments performed in triplicate.

62 2.4 Conclusions and discussion

γGT expression was confirmed in PDAC. Human tumour samples demonstrate the expression of γGT in pancreatic cancer. Closer examination of both PDAC sections and mice xenografts demonstrated that the tumour associated stellate cells (identified by α-SMA expression) were also capable of expressing γGT. In mice xenografts of the human PDAC cell line BxPC-3, the validity of the γGT antibody used was demonstrated. Much stronger staining was revealed in tumours established from the γGT-transfected BxPC-3 compared to parental cells. γGT expression was then confirmed by the detection of its activity in PDAC cell lines and TA-PSC. Given the nature of the assay, the activity was confirmed to be on the extracellular surface of the cells. This agrees with prior knowledge of γGT localisation. Intriguingly, an almost 10-fold increase in γGT activity of TA-PSC compared to N-PSC, isolated from normal pancreatic tissue, was observed. PSC play a significant role in the desmoplasia that characterises PDAC. The desmoplasia forms greater than 80% of the total volume of PDAC (Neesse et al., 2011, Erkan et al., 2012b, Luo et al., 2012). Whilst the TA-PSC cells have a significantly higher γGT activity than MIA PaCa-2 cells (Figure 2.3A and B), in contrast, the BxPC-3/γGT have a significantly higher γGT staining than the murine stromal cells highlighted by arrows in Figure 2.2B. The different species and the different conditions of the disease (subcutaneous model and intrapancreatic tumour) might explain the observed differences in γGT activity.

The expression of γGT in PDAC by both cancer cells and TA-PSC offers the possibility to make use of γGT to activate a prodrug. This combined source of γGT would increase the production of an active metabolite within the tumour margins. It is interesting to observe that in a BxPc-3 tumour (basal/low γGT expression), invading murine stromal cells express detectable γGT (Figure 2.2A). This expression could potentially participate in partial GSAO activation in a xenograft model. This hypothesis will be tested using GSAO (4-(N-(S-glutathionylacetyl)amino) phenylarsonous acid). GSAO is a glutathione-S-conjugate of the trivalent arsenical, PAO. For entry of the active moiety into cells, GSAO requires cleavage by γGT, cleaving the γ-glutamyl moiety to produce a dipeptide form, GCAO (4-(N-(S- cysteinylglycylacetyl)amino)phenylarsonous acid) (Dilda et al., 2008). GSAO has demonstrated anti-tumour activity, recently completing a phase I trial in patients who are refractory to therapy (Horsley et al., 2013). The arsenical moiety of GCAO targets mitochondrial ANT, inducing proliferation arrest and death of angiogenic endothelial cells (Don et al., 2003).

63

64 Chapter 3. γGT activation of the glutathione-S-conjugate, GSAO

3.1 Introduction

The presence of γGT within the tumour margins of PDAC suggests the possibility to utilise its action to deliver a prodrug to the tumour. This, by virtue of the enzyme’s location, will then target the tumour with greater specificity. GSAO is a glutathione-S-conjugate that requires removal of the glutamyl residue to impart its mechanism of action (Dilda et al., 2008). GSAO was first described in 2000 to detect closely spaced protein thiols (Donoghue et al.). It consists of glutathione attached to PAO by an N-acetyl linker to form a membrane impermeable compound. It was later described to inhibit angiogenesis and inhibit tumour growth (Don et al., 2003), and upon closer examination discovered to require cleavage of the glutamyl residue in order to enter the cell (Dilda et al., 2008). The GSAO metabolite formed through γGT action, GCAO, enters the cell in spite of γGT inhibition (Dilda et al., 2008). GSAO has a well described mechanism of action: binding to ANT, induction of mitochondrial swelling, a preference for proliferating endothelial cells, inhibition in the chorioallantoic membrane assay and of tumour angiogenesis and inhibition of tumour growth (Don et al., 2003, Dilda et al., 2005b, Park et al., 2012). The presence of an arsenic atom within the structure of GSAO allows for simple tracking by inductively coupled plasma spectrometry. GSAO has recently completed a phase I clinical trial (Horsley et al., 2013). Treatment was well tolerated.

More specifically, GSAO has been shown to induce mitochondrial swelling. This is mediated through the binding of GSAO and its active metabolites to ANT, found on the inner mitochondrial membrane (Don et al., 2003). The PAO moiety of these compounds crosslinks cysteine residues 57 and 257 of ANT (Park et al., 2012). The consequence of PAO binding to ANT is inhibition of ADP binding and an increase in cyclophilin D binding, both of which enhance the chance of a conformational change of ANT (Halestrap et al., 2002), resulting in opening of the MPTP and subsequent equilibration of small solutes across the membrane and mitochondrial swelling. The end result is cell death.

65 For the active moiety of GSAO to reach the mitochondria it has been demonstrated that the action of γGT is essential. GSAO has been demonstrated to be a substrate of purified γGT, and the metabolite of this reaction, GCAO, was then observed to enter and accumulate in cells at a faster rate than the parent compound (Dilda et al., 2008). GCAO was also able to affect the MPTP, inducing mitochondrial swelling (Dilda et al., 2008). Further, metabolism of GCAO by purified aminopeptidase N produced CAO; this compound also induced mitochondrial permeability transition and mitochondrial swelling (Dilda et al., 2008). The reactions of GSAO metabolism/activation are outlined in Figure 3.1. This chapter will explore the potential of the precursor, GSAO, to be metabolised by cell surface γGT and the relationship between cellular γGT activity and the accumulation and anti-proliferative action of GSAO.

Figure 3.1 Structure of GSAO and the products of γGT and peptidase hydrolysis.

3.2 Methods

3.2.1 GSAO GSAO (4-(N-(S-glutathionylacetyl)amino)phenylarsonous acid) was produced and the concentration determined by titration with dimercaptopropanol and measurement of unbound compound with 5,5’-dithiobis(2-nitrobenzoic acid) (Sigma, St Louis, Missouri) as previously described (Donoghue et al., 2000).

3.2.2 High performance liquid chromatography Cells expressing high or low levels of γGT were treated with 100 μM GSAO dissolved in 15 mM tris HCl, pH 7.4 buffer containing 40 mM glycylglycine. When indicated, 10 μM of the γGT competitive inhibitor, L-2-amino-4-boronobutanoic acid (ABBA) was used. ABBA was provided by Doctor R. E. London (NIEHS, National Institutes of Health, Research Triangle Park, NC) (Antczak et al., 2001). The culture supernatants were clarified using a 3K Amicon Ultra-0.5 mL centrifugal filter (Millipore, Billerica, MA) and samples were resolved by high performance

66 liquid chromatography (HPLC; Thermo Fisher Scientific, Scoresby, VIC, Australia) on a Zorbax Eclipse XDB-C18 column (4.6 mm × 150 mm, 5 μm; Agilent Technologies, Mulgrave, VIC, Australia) using a mobile phase of acetonitrile-water (25:75 v/v), a flow rate of 0.5 mL/min, and detection by absorbance at 256 nm.

3.2.3 Drug accumulation Cells were seeded at a density of 7.5 x 105 cells per well in 6-well plates. Twenty four hours later, the cells were treated with 100 µM GSAO for 4 h at 37°C, 5% CO2. When used, the cells were pre-treated for 30 min at 37°C, 5% CO2 with 10 µM ABBA before addition of GSAO. After 4 h incubation with GSAO, the culture medium was removed, the cells washed twice with ice- cold phosphate-buffered saline, then lysed with 1 mL of 70% w/w nitric acid. Lysates were diluted 50-fold and analysed for arsenic atoms using an Elan 6100 Inductively Coupled Plasma Spectrometer (PerkinElmer Sciex Instruments). Results were analysed by a two-sided t-test to evaluate the significance, p values less than 0.05 were considered statistically significant.

3.2.4 Proliferation assay BxPC-3, AsPC-1, PANC-1 and MIA PaCa-2 were seeded in 96-well plates at a density of 4,000 cells per well in 0.1 mL of culture medium. After 72 h contact with GSAO, the MTT assay (3-(4,5-dimethylthiazol-2-yl)- 2,5-diphenyltetrazolium bromide; Sigma) was used to determine the number of viable cells in a 96-well plate according to the manufacturer’s protocol. Results are expressed as percentage of cell proliferation in untreated controls.

3.3 Results

3.3.1 High performance liquid chromatography Cell surface γGT conversion of GSAO to GCAO was measured by HPLC. Following incubation of GSAO with pancreatic tumour or stellate cells the conditioned media was analysed for GSAO and metabolites thereof. The metabolite of GSAO, GCAO was detected, and a metabolite of GCAO, CAO was also detected (Figure 3.2). The structures of GCAO and CAO were confirmed by electrospray mass ionisation-mass spectrometry. GCAO molecular ions resolved at

+ + + 420.0 m/z [GCAO+H] , 402.0 m/z [GCAO+H-H2O] and 383.9 m/z [GCAO+H-2H2O] , while the + + CAO ions resolved at 362.9 m/z [CAO+H] and 344.9 m/z [CAO+H-H2O] (Dilda et al., 2008).

Hydrolysis of GSAO correlated with γGT activity. Wild type BxPC-3 cells (γGT of 0.07 ± 0.01 mU/106 cells) activated minimal GSAO, whilst γGT-transfected BxPC-3 (γGT of 40.52 ± 6.55 mU/106 cells) and MIA PaCa-2 (γGT of 8.03 ± 0.39 mU/106 cells) converted the majority of

67 the GSAO in 3 h and 16 h respectively (Figure 3.2A). TA-PSC also converted GSAO, producing a significant amount of CAO, the metabolite of GCAO (Figure 3.2B). The positive control for GSAO transformation was obtained using purified porcine kidney γGT (Sigma) and the negative control was performed in the absence of cells, or cells in the presence of the γGT inhibitor, ABBA.

A GSAO GCAO B GSAO CAO CAO

no cells + γGT no cells

BxPC-3/vector BxPC-3 TA-PSC

TA-PSC BxPC-3/γGT MIA PaCa-2 + ABBA

BxPC-3/γGT MIA PaCa-2 + ABBA + ABBA

Figure 3.2 Pancreatic tumour and stellate cell γGT activates GSAO to GCAO. (A) C18 reverse phase HPLC analysis of GSAO conversion to GCAO and CAO at the surface of pancreatic adenocarcinoma cell lines. The HPLC profiles in the absence of cells or cells in the presence of 10 μM of the γGT inhibitor ABBA serve as the negative controls. The profiles in the presence of purified γGT are the positive controls. Profiles are representative of at least two separate experiments. (B) HPLC analysis of GSAO conversion to CAO at the surface of representative TA-PSC (preparation #48). The negative control is the HPLC profile in the presence of 10 µM of ABBA. HPLC profiles are representative of at least two separate experiments.

68 3.3.2 Drug accumulation Cells with high γGT activity have a greater ability to metabolise GSAO. As a consequence of the high local concentrations of GCAO, the metabolite should accumulate more readily in these cells. Tumour cells with high γGT activity accumulated significantly greater amounts of GCAO, as reflected by increased arsenic (As) content in the cells (Figure 3.3). The ability for the cells to accumulate GCAO was abolished by the presence of the γGT inhibitor ABBA (Figure 3.3B).

A BxPC-3 B MIA PaCa-2

*** BxPC-3/vector *** BxPC-3/γGT

0.6 C11 0.6 * C7

cells)

cells)

6 6

0.4 0.4

###

0.2 0.2 ### #

As accumulation (ng/10 accumulation As As accumulation (ng/10 accumulation As 0 0 GSAO GSAO + ABBA GSAO GSAO + ABBA Figure 3.3 The membrane permeable metabolite of GSAO, GCAO, accumulates rapidly in high γGT activity cells. As GSAO is membrane impermeable, the level of arsenic in the cell is representative of the accumulation of the metabolite of GSAO (GCAO). Arsenic (As) accumulation reflects GCAO accumulation in cells expressing low (A) or high (B) levels of γGT. BxPC-3/vector and clone C11 are BxPC-3 (pancreas) and c21 (melanoma) mock-transfected cells, expressing low levels of γGT. BxPC-3/γGT and clone C7 are BxPC-3 and c21 cells transfected with the human γGT gene. Following pre-treatment or not with 10 µM ABBA for 30 min,

the cells were incubated with 100 µM GSAO for 4 h at 37°C, 5% CO2. Cellular accumulation of arsenic was determined by inductively coupled plasma spectrometry. Values are mean ± SD of triplicate determinations. Results are representative of two experiments. MIA PaCa-2, BxPC-3/γGT and C7 significantly accumulated more GCAO than BxPC-3, BxPC-3/vector and C11, respectively. ***, p< 0.001; *, p< 0.05. ABBA pre-treatment significantly inhibited GCAO accumulation. ###, p< 0.001; #, p< 0.05.

69 3.3.3 Inhibition of proliferation Increased accumulation of GCAO in turn reflected an increased response to GSAO. In four pancreatic tumour cell lines, with varying γGT activities (Figure 3.4E), response to GSAO was observed. BxPC-3 cells have the lowest γGT activity of the four cells, and in agreement with both minimal activation and accumulation of GSAO have the highest IC50 (170.5 ± 7.7 μM; Figure 3.4A). In contrast MIA PaCa-2 cells with the highest γGT activity of the four cell lines, has a noticeably lower IC50 (13.5 ± 0.0 μM; Figure 3.4D). In all four cells lines, the response to GSAO could be minimised, if not abrogated, by the γGT inhibitor ABBA. A correlation was observed between the cells’ respective γGT activities and the response of the different cells lines to

GSAO (measured by IC50, Figure 3.4E). It is of interest to note that the KRAS status of the cells does not appear to influence the activity of GSAO. KRAS mutations occur in more than 90% of PDAC cases, driving uncontrolled proliferation and enhancing survival of cancers cells though the activation of its downstream signalling pathways (Di Magliano and Logsdon, 2013). There is evidence of KRAS activation increasing expression of the GGT2 gene (Moon et al., 2012). The three KRAS mutated cell lines (MIA PaCa-2G12C, PANC-1G12D, AsPC-1G12D (Deer et al., 2010)) have greater γGT activity than the wild type BxPC-3 cells (Figure 2.4E). GSAO was 4 to 13-fold more potent at inhibiting the proliferation of the mutated KRAS cell lines than the wild type cell line. Without testing of a wider range of KRAS wild-type and mutant cell lines it is premature to reach a conclusion regarding GSAO sensitivity and KRAS status.

70 120 GSAO 120

A B GSAO

GSAO+ ABBA GSAO+ ABBA 100 100 80 80 60 60 40 40

20 20

Cell growth (% control) (% growth Cell Cell growth (% control) (% growth Cell 0 0 0 250 500 750 1000 0 50 100 150 200 GSAO (µM) GSAO (µM)

120 120

C D

100 100 80 80 60 60 40 40

20 GSAO 20 GSAO Cell growth (% control) (% growth Cell Cell growth (% control) (% growth Cell GSAO+ ABBA GSAO+ ABBA 0 0 0 50 100 150 200 0 10 20 30 GSAO (µM) GSAO (µM)

GSAO IC 9 E GSAO IC5050 8 150 γGT Activity

7

cells)

6 6 (µM)

100 5 50 4

GSAO IC GSAO 3 50

2 (mU/10 activityGT γ 1

0 0 BxPC-3 PANC-1 AsPC-1 MIA PaCa-2

Figure 3.4 The anti-proliferative activity of GSAO correlates with the γGT

expression of cancer cells. IC50 values for GSAO-induced proliferation arrest of four pancreatic cancer cell lines after 72 h of contact with the drug. Ranges of GSAO concentrations, in the presence or absence of 10 µM ABBA, were employed to study the anti-proliferative activity of GSAO on BxPC-3 (A), PANC-1 (B), AsPC-1

71 (C) and MIA PaCa-2 (D) after 72 h. Cell proliferation was determined using MTT and the results were expressed as the percentage of untreated control. Values are mean ± SD of triplicate determinations. Results are representative of at least two experiments. (E) Comparison of the γGT activity of the four pancreatic cancer cell

lines and the respective response to GSAO (measured by IC50). Values are mean ± SD of triplicate determinations.

3.4 Conclusions and discussion

Cells expressing γGT can cleave the glutamyl group from the glutathione-S-conjugate GSAO. This enables the metabolite to enter and accumulate in cells and impart the documented anti- proliferative effect of GSAO. This chapter extends earlier work studying the metabolism of GSAO. The dipeptide metabolite GCAO was previously revealed following the metabolism of GSAO by purified γGT (Dilda et al., 2008). It was demonstrated in a transfected pair of the c21 melanoma cell line that high γGT activity increased the response of the cells to GSAO. This chapter builds on this work, demonstrating metabolism of GSAO by cellular γGT, which then enables the accumulation of the active moiety within the cell as demonstrated by increased arsenic presence within cells with high γGT. In cells with greater accumulation of the active moiety the response to GSAO was greater than those with low accumulation. For example, MIA PaCa-2 cells exhibit a γGT activity 110-times higher than BxPC-3 cells and accumulate GSAO metabolites approximately fourfold faster, and are subsequently greater than 12-times more sensitive to GSAO (according to IC50 values). The usage of the γGT specific inhibitor ABBA, further demonstrated that the γGT activity is the limiting step in GSAO activation, controlling both cell accumulation and anti-proliferative activity. Overall, these data demonstrate that the extent of γGT activity at a location will determine the extent of GSAO action.

It has been previously established that GSAO is selective for proliferating endothelial cells (Don et al., 2003, Dilda et al., 2005b). However, endothelial cells have minimal γGT activity (similar activity to BxPC-3, data not shown). It is likely that tumour and stellate cell γGT will activate GSAO, enabling its metabolites to inhibit endothelial cells within the tumour. The next chapter will investigate the activation of GSAO by pancreatic tumour and stellate cells and the ability of the metabolite to exert its anti-proliferative effect on the preferred target of GSAO, the proliferating endothelial cell.

72

73 Chapter 4. Paracrine activation and action of GSAO

4.1 Introduction

Within the margins of a tumour, endothelial cells, tumour cells and stellate cells exist in close proximity. The different cell types communicate with one another, transmitting signals that support the growth of the tumour. GSAO preferentially targets proliferating endothelial cells over tumour cells, inhibiting angiogenesis. It has been shown to reduce blood vessel density in BxPC-3 and HT1080 tumours in SCID mice and in Lewis Lung carcinoma tumours in C57B16/J mice (Don et al., 2003). Whilst in all tumours, minimal effect was seen in the proliferation of tumour cells as measured by proliferating cell nuclear antigen (PCNA). This is also observed in vitro. Endothelial cells have IC50 values for proliferation inhibition up to 30-fold lower than those measured with cancer cell lines (Don et al., 2003, Dilda et al., 2005b). This has been attributed to low expression of MRP1 and 2 by endothelial cells. Cells overexpressing MRP1 or 2 are significantly more resistant to GSAO than wild type or MRP 3, 4 or 5 overexpressing cells (Dilda et al., 2005b). It is likely that MRP1 and 2 pump GSAO from the cell in conjunction with glutathione. Limiting de novo synthesis of glutathione with buthionine sulfoximine, an inhibitor of γ-glutamylcysteine synthase, significantly reduces intracellular glutathione content and cells become significantly more susceptible to GSAO (Dilda et al., 2005b). The combination of high expression of MRP1 and 2 and high glutathione levels within many tumour cells combine to ensure tumour cell resistance to GSAO (Dilda et al., 2005b). The metabolites of GSAO, GCAO and CAO have also been demonstrated to be exported from the cell by MRP1 and 2 (Dilda et al., 2008).

This chapter examines the paracrine action of GSAO. Within the tumour GSAO will commonly be activated by tumour cells distant from the angiogenic endothelial cells. This chapter simulates this distance in vitro using two models: conditioned media and transwell plates. This will determine the ability of GSAO to be activated by tumour cells and induce its action on a distant endothelial cell.

74 4.2 Methods

4.2.1 Conditioned media Tumour associated human pancreatic stellate cells (TA-PSC) were incubated with 100 μM GSAO in the absence or presence of 10 μM ABBA. After 6, 8 or 24 h incubation, the medium was transferred to 96-well plates containing MIA PaCa-2 (4,000 cells per well), BxPC-3 (4,000 cells per well) or human umbilical vein endothelial cells (HUVEC; 8,000 cells per well), in the presence of 10 μM ABBA to prevent further processing of GSAO. The MIA PaCa-2, BxPC-3 and HUVEC were incubated for 24, 48 or 72 h respectively, before viability was determined using the MTT assay. Results were analysed by a Mann-Whitney test to evaluate the significance of the results within groups. Statistical significance was considered if the p value was less than 0.05.

Melanoma cells expressing high (C7) or basal (C11) γGT levels were incubated with various concentrations of GSAO for 3 h in a 96-well plate at a density of 25,000 cells per well. After 3 h incubation the medium was transferred to 96-well plates containing endothelial cells (murine bEnd.3, bovine aortic endothelial (BAE) cells, human dermal microvascular endothelial cells (HMEC-1) or HUVEC at a density of 3,000, 4,000, 6,000 and 8,000 cells per well, respectively) in the presence of 10 μM ABBA to prevent further processing of GSAO. Twenty four hours later, the viability of the endothelial cells was determined using the MTT assay. Similarly, various concentrations of GSAO were conditioned by BxPC-3 or MIA PaCa-2; or by BxPC-3/vector (mock-transfected) or BxPC-3/γGT (γGT-transfected), for 24 or 5 h respectively, in 96-well plates at a density of 25,000 cells per well. Conditioned media was transferred to 96-well plates containing HUVEC (8,000 cells per well) and incubated for 48 or 24 h for the natural γGT pair or the transfected γGT pair respectively.

4.2.2 Transwell assays Murine bEnd.3 endothelial cells were seeded in the bottom chambers of 24-well transwell plates (Corning, HTS Transwell-24, pore size 0.4 μm, polycarbonate membrane) at a density of 10,000 cells per well. Pancreatic tumour or stellate cells were seeded in the upper chamber at a density of 50,000 cells per well. The cells were allowed to attach overnight at 37°C, 5% CO2 before the two chambers were assembled. Both chambers were incubated without or with GSAO for 48 h before changes in the viability of the endothelial cells in the bottom were determined by the WST-1 assay (Roche Diagnostics).

75 4.3 Results

4.3.1 Conditioned media In a conditioned media experiment, GSAO activated by TA-PSC significantly interfered with the proliferation of pancreatic tumour cells (MIA PaCa-2 and BxPC-3) and primary endothelial cells (Figure 4.1). This was further demonstrated on various types of endothelial cells with media conditioned by tumour cell lines expressing different levels of γGT activity. Melanoma cells with basal or high γGT (previously described by Dilda et al., 2008) processed GSAO before it was transferred onto mBenD.3 (murine), BAE cells (bovine), HMEC-1 (human) or HUVEC (human primary) cells (Figure 4.2A-D). On all endothelial cells the media conditioned by high γGT-expressing melanoma cells (C7) had a significantly greater effect on cell proliferation than the mock-transfected (C11). Pancreatic tumour cells were also able to process GSAO and increase endothelial cell response. Two pairs of pancreatic tumour cell lines with different γGT activities were used: a pair naturally expressing low and high γGT activity (BxPC-3 and MIA PaCa-2); and mock-transfected and γGT-transfected BxPC-3 cells. Following conditioning by the high γGT-expressing cells, the endothelial cells had a greater response to GSAO than following conditioning by the basal γGT-expressing cells (Figure 4.2E, F).

76

100

80 *** 60 *** 40

20

Cell growth (% of control) of (% growth Cell ** 0 NP P NP P NP P MIA PaCa-2 BxPC-3 HUVEC Figure 4.1 GSAO conditioned by TA-PSC inhibits tumour and endothelial cell proliferation. GSAO was conditioned by TA-PSC#48 in the presence (no processing, NP) or absence (processing, P) of 10 µM ABBA. Conditioned medium was then transferred onto MIA PaCa-2, BxPC-3 and HUVEC cultures containing 10 µM ABBA to avoid further drug processing. Cell growth was determined after 72 h using MTT, and the results were expressed as the percentage of untreated control. Values are mean ± SD of at least two experiments performed in triplicate. **, p<0.01; ***, p< 0.001 versus non-processing conditions (NP).

77

A GSAO on C11 B 120 GSAO on C11

120 GSAO on C7 GSAO on C7 100 100 80 80 60 60 40 40

20 20 Cell growth (% of control) of (% growth Cell Cell growth (% of control) of (% growth Cell 0 0 0 50 100 150 200 0 50 100 150 200 GSAO (µM) GSAO (µM)

C GSAO on c11 D GSAO on C11 100 120 GSAO on C7 GSAO on C7 80 100 80 60 60 40 40

20 20 Cell growth (% of control) of (% growth Cell Cell growth (% of control) of (% growth Cell 0 0 0 50 100 150 200 0 50 100 150 200 250 300 GSAO (µM) GSAO (µM) GSAO on BxPC-3

GSAO on BxPC-3/vector

E GSAO on MIA PaCa-2 F 120 120 GSAO on BxPC-3/GGTBxPC-3/γGT 100 100 80 80 60 60 40 40

20 20 Cell growth (% of control) of (% growth Cell Cell growth (% of control) of (% growth Cell 0 0 0 25 50 75 100 125 0 25 50 75 100 125 GSAO (µM) GSAO (µM)

Figure 4.2 GSAO conditioned by γGT-expressing cancer cells inhibits endothelial cell proliferation. GSAO processed for 3 h by C7 melanoma cells (γGT-transfected, ●) induces a stronger inhibition of endothelial cell proliferation than GSAO in contact with C11 melanoma cells (mock-transfected, ○). In this study, murine (A, bEnd.3), bovine (B, BAE) and human (C, HMEC-1) endothelial cell lines were employed. Similar results were obtained with primary human umbilical vein endothelial cells (HUVEC) when GSAO was conditioned by C7 and C11 (D); by

78 MIA PaCa-2 and BxPC-3 (E); or by BxPC-3/γGT (γGT-transfected BxPC-3 cells) and BxPC-3/vector (mock-transfected BxPC-3 cells) (F). Endothelial cell growth was determined after 72 h using MTT, and the results were expressed as the percentage of untreated control. Values are mean ± SD of triplicate determinations. Results are representative of at least two experiments.

4.3.2 Transwell assays In the transwell assay, endothelial cells and tumour or stellate cells were separated by a porous polycarbonate membrane. The membrane allowed for circulation of GSAO and its metabolites but prevented cell migration (0.5 μm diameter pores). GSAO and GSAO metabolite-induced proliferation arrest of the endothelial cells was determined. The extent of proliferation arrest was plotted as a function of the pancreatic cell γGT activity. A range of pancreatic tumour cell lines were employed: wild type and γGT-transfected; and both normal and tumour associated stellate cells. There was a strong correlation between inhibition of endothelial cell proliferation and surface γGT activity of the pancreatic cells (Figure 4.3).

79 100

BxPC-3/γGT

75 MIA PaCa-2

50 TA-PSC

AsPC-1

BxPC-3 Inhibition of endothelial cell proliferation (%) proliferation cell endothelial of Inhibition 25 PANC-1

N-PSC

0 0 10 20 30 40 γGT activity (mU/106 cells)

Figure 4.3 The response of endothelial cells to GSAO increases with γGT activity of cells in the upper well. GSAO conditioned by γGT-expressing cancer cells inhibits endothelial cell proliferation. Tumour cells were seeded in the upper chambers of transwell plates and endothelial (bEnd.3) cells in the lower chamber. Once assembled, both chambers were incubated for 48 h with GSAO, and endothelial cell viability determined. Results are expressed as the percentage of inhibition of endothelial proliferation as a function of the cell-surface γGT activity. Pancreatic adenocarcinoma cells are represented by circles and normal and tumour- associated pancreatic stellate cells by squares. The BxPC-3 pancreatic tumour cells transfected with γGT are represented by a triangle. Values are mean ± SD of at least two experiments performed in triplicate.

80 4.4 Conclusions and discussion

The data presented here, indicates that GSAO activated by pancreatic tumour and stellate cells has the ability to inhibit endothelial cell proliferation from a distance. The extent of γGT activity predicts the extent of action of GSAO in both the conditioned media and transwell models. GSAO conditioned by TA-PSC was able to inhibit both endothelial and tumour cells. Cancer cell conditioned GSAO was capable of inhibiting a variety of endothelial cells: bovine; murine; and human cell lines; and primary human endothelial cells. In the transwell model, the activity of a range of pancreatic tumour and stellate cells in the upper well correlated with the degree of endothelial cell inhibition in the bottom well. Both models demonstrate that GSAO, when activated by γGT-expressing tumour or stellate cells, is capable of inhibiting distant endothelial cells. The magnitude of this effect is determined by the γGT activity of the cells. It demonstrates that paracrine signalling, between the stellate or tumours cells and the tumour or endothelial cells, does not prevent the action of the activated GSAO.

The conditioned media and transwell models demonstrate the paracrine action of GSAO. This suggests that within a tumour, GSAO activated by tumour cells located distant from endothelial cells will exert an action on angiogenesis. However, these studies do not account for juxtacrine cell interactions. In order to determine if the interactions between adjacent endothelial cells and GSAO-activating tumour cells can modulate the effectiveness of endothelial cell inhibition by GSAO, co-culture studies comparing the ability of GSAO to inhibit endothelial cell proliferation in direct contact with the cells activating the drug were used.

81

82 Chapter 5. Juxtacrine activation and action of GSAO

5.1 Introduction

GSAO activated by tumour cells distant from endothelial cells is capable of exerting an inhibitory action on endothelial cell proliferation. However, endothelial cells and tumour cells are also found in contact within the tumour. Juxtacrine interactions between the tumour and endothelial cells can affect the phenotype of each other. These interactions have the potential to increase the resistance of the cells or increase the sensitivity of the cells to drugs. The last chapter examined the paracrine action of GSAO. This chapter reduces the distance between the different cells types, growing the cells in co-culture. These conditions simulate the areas of the tumour where tumour and endothelial cells directly interact. In the transwell plates, the cells were separated by approximately 1.36 mm (Corning Inc., 2013). Given the anti-angiogenic action of GSAO and the promotion of angiogenesis within a tumour, it is necessary to confirm that juxtacrine interactions between tumour and endothelial cells do not prevent the action of GSAO.

5.2 Methods

HMEC-1 cells were transfected with the green fluorescent protein (GFP), in a pcDNA3 vector (Life Technologies) using FuGENE® 6 (Promega). A pool of bright GFP, stably transfected cells were selected using 0.3 mg/mL geneticin and enriched by flow sorting. HMEC-1-GFP cells were mixed with pancreatic tumour cells expressing different levels of γGT at a ratio of 1:5 and allowed to attach overnight. The cultures were incubated without or with 10 μM GSAO for 72 h or 50 μM GSAO for 48 h in the absence or presence of 10 μM ABBA. HMEC-1-GFP cells were also cultured with increasing numbers of BxPC-3/γGT cells in the ratios 1:10, 1:20, 1:50, 1:100 and 1:200 and allowed to attach overnight. The cultures were incubated without or with 200 μM GSAO for 1 h before the addition of 10 μM ABBA and incubated for a further 24 h. The cells were stained with propidium iodide and analysed using flow cytometry. The number of viable GFP positive (endothelial) and GFP negative (tumour) cells was expressed as a

83 percentage of untreated control. Where relevant, results were analysed by Mann-Whitney test to evaluate the significance of the results within groups. Statistical significance was considered if the p value was less than 0.05.

5.3 Results

GFP-transfected HMEC-1 endothelial cells were co-cultured with pancreatic tumour cells expressing various γGT activities in a ratio of 1:5. The γGT activity of the different tumour cells ranges from 0.07 ± 0.01 to 40.5 ± 6.6 mU/106 cells. Following incubation with GSAO, cells were stained with propidium iodide and counted by flow cytometry. Viable cells were defined by no propidium iodide staining. To observe changes in the rate of proliferation of the endothelial cells as a result of GSAO treatment, the percentage change in endothelial cell number (GFP positive cells) was compared to untreated control (100%). The degree of inhibition of endothelial cell proliferation was reduced to a greater extent in co-culture with high γGT- activity cells (Figure 5.1). A significant, but not as large, inhibition in endothelial cell proliferation was observed when co-cultured with the basal γGT expressing cells. The addition of the γGT inhibitor ABBA prevented inhibition of endothelial cell proliferation (data not shown). Endothelial cell death was observed by increasing the concentration of GSAO used and observing the loss in GFP expressing cells. The loss of GFP expression indicating cell death (Strebel et al., 2001, Steff et al., 2001). The increase in staining with propidium iodide corroborates the death of the endothelial cells (Figure 5.2).

A 120 ** B 120

** 100 100

80 80

60 60

40 40

20 20

Endothelial Endothelial growth cell (%) Endothelial Endothelial growth cell (%) 0 0 Nil GSAO Nil GSAO Nil GSAO Nil GSAO BxPC-3 MIA PaCa-2 BxPC-3/vector BxPC-3/γGT

Figure 5.1 GSAO and γGT-expressing cells inhibit proliferation of co-cultured endothelial cells. The impact of γGT-expressing cells, MIA PaCa-2 (A) or

84 BxPC-3/γGT (B), on co-cultured endothelial cell proliferation in the presence or absence of GSAO (10 µM) was determined by flow cytometry. BxPC-3 and BxPC-3/vector cells were used as low γGT-expressing counterparts. Values are mean ± SD of at least two experiments performed in duplicates. **, p< 0.01.

85 A HMEC-1-GFP

+ BxPC-3/vector + BxPC-3/γGT

PI no GSAO no

PI + + GSAO

GFP GFP

B HMEC-1-GFP

+ BxPC-3 + MIA PaCa-2

PI no GSAO no

PI + + GSAO

GFP GFP Figure 5.2 High γGT tumour cells mediate GSAO-induced death of co-cultured endothelial cells. GFP-transfected endothelial cells (HMEC-1) were cultured with BxPC-3 pancreatic tumour cells expressing low (BxPC-3/vector) or high (BxPC-3/γGT) levels of γGT (A), or pancreatic tumour cells expressing low (BxPC-3)

86 or high (MIA PaCa-2) levels of γGT (B) in the absence or presence of 50 µM GSAO for 48 h. Viable endothelial cells were measured by flow cytometry following staining with propidium iodide (PI). The arrows indicate the healthy GFP-positive endothelial cells and the circles show the dead/dying endothelial cells staining for propidium iodide and with intermediate GFP expression. Figures are representative of two experiments performed in triplicate.

The ratio of endothelial cells to tumour cells within a tumour varies. To determine whether GSAO could exert an action on endothelial cells in tumours with different endothelial cell densities, the effect of the ratio of tumour cells to endothelial cells was studied. In a fibrosarcoma tumour, endothelial cells represent 4 to 7% of the total population of cells (Modzelewski et al., 1994). This value corresponds with the 7.4 ± 3.5% endothelial cells in murine Colon-26 tumours as determined by Okaji et al. (2004). In contrast, PDAC is considered a hypovascular tumour (Izuishi et al., 2000, Olive et al., 2009, Provenzano et al., 2012, Neesse et al., 2011). The ratio of endothelial cells to tumour cells tested ranged from 1:10 down to 1:200. The co-culture of HEMC-1-GFP cells and γGT-transfected BxPC-3 was incubated with GSAO for 60 min. To prevent complete processing of GSAO under all conditions, ABBA was added. Following a further 24 h of culture the viability of the two cell types was measured by flow cytometry. As the number of tumour cells to endothelial cells increased, there was an increasing response of the endothelial cells to GSAO. There was little change in tumour cell viability (Figure 5.3).

87 BxPC-3/γGT HMEC-1-GFP

100

75

50

Cell viability (%) viability Cell 25

0 10/1 20/1 50/1 100/1 200/1 Tumour/endothelial cell ratio

Figure 5.3 Increasing the number of tumour cells increases the response of co- cultured endothelial cells to GSAO. GFP-transfected endothelial cells (HMEC-1) were cultured with increasing numbers of BxPC-3/γGT pancreatic tumour cells and 200 µM GSAO for 60 min. ABBA was added to block activation of GSAO and the cells cultured for a further 24 h. Viable tumour and endothelial cells were measured by flow cytometry following staining with propidium iodide. The number of GFP-positive (endothelial) and GFP-negative (tumour) cells were expressed as a percentage of untreated control. Values are mean ± SD of at least two experiments performed in duplicate.

5.4 Conclusions and discussion

This chapter demonstrates that when tumour and endothelial cells are grown side by side the interactions between the cell types do not impede GSAO action. The extent of action of GSAO was governed by the degree of γGT activity present, determined by either the cell type or the number of γGT-expressing cells. This was demonstrated with two pairs of tumour cell lines and by increasing the portion of tumour cells. Co-culture with the high γGT-activity tumour cells induced significantly greater loss in viability of the endothelial cells than co-culture with low γGT-activity tumour cells. Increasing the γGT activity, by increasing the number of tumour cells relative to endothelial cells, also increased the response of the endothelial cells. However, there was no or limited effect on the tumour cells. This contrasts with data presented in Chapter 4, where TA-PSC conditioned media was able to inhibit tumour cell growth (Figure 4.1). This is likely to be due to differences in experimental design (period of activation of GSAO and period of incubation with the active metabolite).

88 This chapter and the last demonstrate the ability of tumour cells located distant, and those located next to the endothelial cells, to activate GSAO and inhibit endothelial cell proliferation. The distance does not influence the ability of GSAO to inhibit endothelial cell proliferation. This suggests that within a tumour, both tumour cells located distant to, and those adjacent to the angiogenic blood vessels will inhibit angiogenesis. Before testing whether γGT status can predict response to GSAO in a more complex system, the potential for off-site activation of GSAO needs to be explored. The amount of the prodrug that is delivered to the tumour could be limited by the action of blood γGT. This could potentially result in off-target activation of the drug, reducing the selectivity for γGT-expressing tumours.

89

90 Chapter 6. Evaluation of potential off- target activation of GSAO in the blood

6.1 Introduction

The enzyme, γGT, is found in the blood where it forms one of a panel of enzymes tested to determine liver function. Its presence in the blood presents a potential limitation to the use of γGT tumour expression to deliver a glutathione-S-conjugate to the tumour site. Blood γGT has the potential to activate GSAO, delivering active GSAO to the entire body. Normal blood γGT levels range between 5 and 80 U/L (Schiele et al., 1977). Levels increase throughout life, males having higher γGT levels than females (Schiele et al., 1977). γGT levels also positively correlate with alcohol consumption; smoking; heart rate; systolic and diastolic blood pressure; obesity indexes, such as waist circumference and body mass index (BMI); and with serum level of glucose, triglycerides, total and LDL cholesterol and uric acid (Franzini et al., 2013). γGT found in the blood is primarily sourced from the liver; however, there is evidence that γGT could be released into the blood stream from tumours (Franzini et al., 2009a). In ovarian cancer no relationship was found between tumour and serum γGT levels (Paolicchi et al., 1996). The work of Franzini et al. has demonstrated that γGT exists in four forms in the blood (2008). The molecular weights of these fractions suggest three are complexes between the enzyme and lipoproteins: very low-density lipoprotein; low-density lipoprotein; and high-density lipoprotein. The fourth form being the free enzyme.

Plasma γGT can be increased in pancreatic cancer (Murr et al., 1994, Muniraj et al., 2013). This is a consequence of the physical manifestation of the tumour. Tumours of the head of the pancreas commonly cause obstructive cholestasis (Hidalgo, 2010), physically blocking the flow of bile from the liver to the duodenum. Increased plasma γGT levels are a consequence of this obstruction, indicating patients with more progressed cancers and a longer duration of obstruction (Engelken et al., 2003). Hence, it is particularly important to consider the impact plasma γGT may have on the potential to deliver a glutathione-S-conjugate to a high γGT expressing tumour.

91 The presence of γGT in the blood presents the possibility of GSAO being activated by plasma γGT. The extent of this activation has implications on the targeted aspect of GSAO’s prodrug characteristic. This chapter attempts to clarify the role blood γGT may play in the activation of GSAO. Firstly, determining whether blood γGT is able to cleave GSAO, and secondly, to what extent blood γGT is able to activate GSAO and the implications this may have on the delivery of GSAO and its active metabolites to the tumour and other organs of the body.

6.2 Methods

Human plasma samples were kindly provided by Professor Andrew Lloyd, Faculty of Medicine, University of New South Wales. Samples were selected for a range of γGT activities (10 to 223 U/L). MIA PaCa-2 cells were seeded at 4,000 cells per well in 96-well plates and allowed to attach overnight. GSAO was mixed in the plasma samples (600 μM) and incubated for 2.5 h at 37°C on a Comfort Thermomixer (Eppendorf, Germany) at 1050 rpm in the absence or presence of ABBA (20 µM, no activation) or purified γGT (16 U/mL, complete activation). MIA PaCa-2 cells were pre-incubated with 10 μM ABBA for 30 min in order to inhibit any further processing of GSAO, before addition of the plasma samples. The inhibition of MIA PaCa-2 cell proliferation as a consequence of GSAO activation in human plasma was measured by the MTT assay after 72 h.

6.3 Results

Plasma samples came from patients with non-alcoholic steatohepatitis. Sample data is summarised in Table 6.1. Patient age ranged from 29 to 67 years. Samples were selected for a range of γGT activities, ranging from 10 to 223 U/L. No relationship was observed in this dataset between blood γGT activity and body mass index (BMI), although the data set is limited in size.

92 Table 6.1 Patient characteristics of plasma samples.

Sample Plasma γGT Gender Age Height Weight Body mass number activity pre- pre- index (U/L) operation operation (kg/m2) (cm) (kg) 1 10 F 35 168 117 41.5

2 11 F 28 163 95 35.8

3 46 M 29 182 134 40.5

4 49 M 37 - - N/A

5 67 F 54 165 110 40.4

6 84 M 32 180 86 26.5

7 95 F 38 168 101.3 35.9

8 110 M 53 167.6 104 37

9 223 M 67 - 81 N/A

GSAO, pre-incubated in plasma for 2.5 h, was used to treat MIA PaCa-2 cells. Change in MIA PaCa-2 viability indicated GSAO activation by γGT in the plasma. No significant activation of GSAO was observed until the plasma concentration of γGT was 67 U/L. When the sample γGT activities exceed 67 U/L, ABBA no longer prevented activation of GSAO, and a decrease in viability of the MIA PaCa-2 cells was observed (in particular Samples 6, 8, and 9). This is likely due to the high concentrations of γGT overwhelming the ABBA. Using higher concentrations of ABBA would have had an impact on cell growth. It is possible another enzyme exists in the plasma that has the capability to activate GSAO; however, there is no documented knowledge of potential enzymes. A correlation was observed between plasma γGT activity and the ability of γGT to inhibit MIA PaCa-2 cells.

93 S a m p le 1 S a m p le 2

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Figure 6.1 The viability of MIA PaCa-2 cells treated with plasma-conditioned GSAO. GSAO was incubated for 2.5 h with plasma from patients with a range of γGT activities (see Table 6.1 for activities). A range of concentrations of plasma, diluted in media, was used to treat MIA PaCa-2 cells grown in 96-well plates (4,000 cells per well). The negative control consisted of plasma incubated with GSAO and ABBA and is represented by squares, the positive control consisted of plasma incubated with γGT and GSAO and is represented by triangles. The test plasma was incubated with GSAO alone and is represented by circles. The volume of plasma used for the maximum concentration of total GSAO and metabolites, was used for the untreated control (without GSAO). Cells were incubated for 72 h at 5% CO2, 37°C, before viability was determined by the MTT assay. Graphs are

94 representative of two experiments completed in duplicate. Data points represent mean ± SD.

The concentration of GSAO used in the above experiment applies to a concentration much greater than the highest dose reached in the phase I clinical trial (44 mg/m2/day), and to a resident time in plasma 15 times the half-life of GSAO (Horsley et al., 2013). As such, the data obtained under these conditions was used to determine the percentage of GSAO activated per minute. The percentage of GSAO activated was determined through the viability curves of MIA PaCa-2 cells treated with plasma containing GSAO (see Figure 6.1). No activation was assumed to be 100% viability at all concentrations. The area between this line and the viability of cells treated with plasma incubated with GSAO and γGT was considered to be 100% activation. The difference in area between 100% viability and the response of MIA PaCa-2 cells to GSAO conditioned by plasma was expressed as a percentage of 100% activation. This percentage was graphed as a function of the γGT activity of the plasma sample. From this data it was determined that 0.0035% of GSAO was activated per minute of exposure for each unit of plasma γGT activity (linear regression, Figure 6.2). For a patient with 200 U/L of γGT in their blood, just 7.35% of the GSAO will be activated by the time 50% of the GSAO is excreted (10.5 min (Horsley et al., 2013)). This allows extrapolation of the percentage activated within the half-life for a range of plasma γGT levels (Figure 6.3). Note that the linearity of the relationship between γGT levels and portion of activated GSAO is only relevant when the concentration of GSAO is in excess of the enzyme. It is anticipated that the relationship between the two variables is no longer linear at a γGT level greater than tested here.

95

0.7

0.5

0.3 GSAO activation (%/min) activation GSAO

0.1

0 50 100 150 200 -0.1 γGT activity (U/L)

Figure 6.2 Rate of GSAO activation by plasma γGT. Results are expressed as a percentage of complete activation observed in the presence of additional γGT (purified). The specific activity of plasma γGT is 0.0035% of GSAO activated.min-1.U-1 (R2 = 0.64). Values are mean ± SD of two experiments performed in duplicates.

96

100

80

60 life, 10.5 min (%) min 10.5life,

- 40 half

Percentage of GSAO activated in its in activated GSAO of Percentage 20

0 0 100 200 300 400 500 γGT activity (U/L)

Figure 6.3 The percentage of GSAO activated within its half-life by plasma with varying γGT levels. This gives an indication of how fast a patient’s plasma γGT will activate GSAO. Determined by linear regression performed on Figure 6.2 and the published half-life of GSAO, 10.5 min.

Using the data from Figure 6.2, and assuming average blood volume and rate of blood flow through the pancreas, it was possible to determine how many passes the prodrug will have via the tumour before it will lose its targeting ability. Assuming a total blood volume of 5 L and a rate of blood flow through a healthy, 100 g pancreas as 130.4 mL/min (d'Assignies et al., 2009, Kin et al., 2006, Schaefer, 1926), the number of times the total blood volume will pass through the pancreas before GSAO is 50% activated can be determined by the following equation:

Number of passes through a healthy pancreas until GSAO is 50% activated = 372.99 / [γGT]

The derivation of the equation can be found in Figure 6.4. This relationship is demonstrated in Figure 6.5. In patients with normal plasma γGT levels (5 to 85 U/L), GSAO will pass through the pancreas 74.5 to 4.4 times before 50% activation.

97 From experimental data:

Percent GSAO activated in 1 min = 0.0035 x [γGT]

Therefore, time till GSAO is 50% activated

= 50 / percent GSAO activated in 1 min

= 50 / (0.0035 x [γGT])

Given:

- Blood flow through normal pancreas (100g) = 130.4 mL/min

- Average blood volume is 5000 mL

Then, time for total blood through pancreas = volume / flow rate

= 5000 / 130.4

= 38.3 min

Therefore, the number of passes through the pancreas of the total blood flow until GSAO is 50% activated

= Time till 50% activated / 38.3 min

= (50 / (0.0035 x [γGT])) / 38.3 min

= 372.99 / [γGT]

Figure 6.4 Derivation of the relationship between the number of passes of GSAO through the pancreas until 50% activated, and the plasma concentration of γGT ([γGT]).

98

300

200

100 Passes in pancreas before 50% activation 50%before pancreas in Passes

0 0 25 50 75 100 γGT activity (U/L)

Figure 6.5 The number of times GSAO passes through the pancreatic blood supply before 50% activation, as a function of human plasma γGT concentration. This assumes blood flow through a normal 100 g pancreas of 130.4 mL/min and a total blood volume of 5 L.

6.4 Conclusions and discussion

These results suggest that plasma γGT will not normally impact the targeting of GSAO to high γGT-expressing pancreatic tumours. The data demonstrates that the forms of γGT found in blood are capable of activating GSAO. GSAO incubated in plasma was capable of inhibiting MIA PaCa-2 proliferation. However, the extent of activation in the period of the half-life of GSAO (10.5 min) was just 7.5% at the highest γGT level tested. This suggests that a significant portion of the administered GSAO would then be activated at the site of the tumour, delivering high concentrations of the active compound to the malignant site.

Prodrug design can also improve the stability of a drug in blood. Conjugation of glutathione to PAO significantly reduces its toxicity by reducing its lipophilicity. GSAO has greater stability in solution than CAO, its active metabolite. The inherent aqueous instability of CAO led to the development of the mimetic, PENAO (4-(N-(S- penicillaminylacetyl)amino) phenylarsonous acid). PENAO is a cysteine mimetic of CAO, utilising a penicillamine group in place of the

99 cysteine. Whether conjugation of a promoiety increases the stability of a particular drug in blood will be determined by the relative stability of the chosen active compound.

This chapter demonstrates minimal activation of GSAO by plasma, indicating that a significant portion of the precursor will be delivered to the tumour. To determine the ability of tumour γGT to activate the prodrug and the potential to predict response of the tumour to the active drug, the anti-tumour efficacy of GSAO in mice bearing pancreatic tumours expressing different levels of γGT was compared.

100

101 Chapter 7. Tumour γGT status predicts GSAO efficacy

7.1 Introduction

The previous chapters have investigated the relationship between tumour cell γGT activity and the extent of endothelial cell inhibition in vitro, revealing the ability that the γGT activity of the environment can predict the extent of endothelial cell inhibition. This is highlighted strongly by the ratio co-culture model and the transwell models. In each model a trend was observed where increased γGT activity increased the response of endothelial cells to GSAO. Thus the importance of tumour γGT status was studied in BalB/C nude subcutaneous models, where tumours of different γGT activity were compared for differential GSAO efficacy. These studies will demonstrate the application of this strategy to a whole organism model.

This chapter deals with two models. Each model utilises mice with pancreatic subcutaneous tumours with two different γGT activities. The first model accentuates the difference in γGT activity by using a γGT-transfected BxPC-3 cell line. The second model employs natural high and basal γGT-expressing, MIA PaCa-2 and BxPC-3 cells, respectively. Following tumour implantation, mice were treated with or without GSAO. The rate of tumour growth was measured twice a week throughout treatment. The growth of the tumours was analysed to compare growth delay. At the end of experiments, animals were culled and the tumours were collected for immunohistochemistry studies of CD31, an angiogenic marker, and PCNA, a proliferation marker. Tumours samples were also used to confirm in vitro γGT activities.

7.2 Methods

Pancreatic tumour cells were injected subcutaneously in the proximal midline. Mice bearing tumours approximately 50 mm3, were randomized into groups of four to six mice, and micro osmotic pumps (Alzet model 1004, Cupertino, CA) were implanted under the skin on the flank. Mice were treated with vehicle, 0.5 or 1 mg/kg/day GSAO for 28 days. Twice a week mice were weighed and tumour size measured with callipers. Tumour volume was calculated using the

102 relationship length × height × width × 0.523. At the completion of treatment, the mice were euthanized and tumours excised, weighed and processed for immunohistochemistry.

Tumour growth curves were obtained by plotting the mean tumour volumes against time for each experimental group. The tumour growth curves were compared using repeated measures two-way analysis of variance (ANOVA) using GraphPad Prism 6 (Tseng et al., 2010). Tumour doubling and tumour volume quadrupling time (TVQT) was estimated using interpolation from the best fit from a nonlinear regression curve fitting an exponential growth curve using GraphPad Prism 6 (Bissery et al., 1991). Growth delay (GD) was defined as the difference between the TVQT of the treated group (T) compared with that of the control group (C): GD = TVQT(T)-TVQT(C). The tumour growth delay index (TGDi) is calculated as the median growth delay in the treated group divided by the median growth delay in the control group (Begg, 1987). Sections of formaldehyde-fixed, paraffin embedded tissue (5 μm) were immunostained for γGT and counter-stained with haematoxylin. Tumour γGT activity was determined as described in Section 2.2.2. Sections (5 μm) of fresh frozen tumours were prepared and immunostained for CD31 and PCNA, counter-stained with haematoxylin and analysed as previously described (Dilda et al., 2009). Statistical analysis for significance between vehicle and treated group was performed using Newman-Keuls test.

7.3 Results

7.3.1 Genetically engineered model Genetically engineered BxPC-3 tumours expressing different γGT activities were established subcutaneously in the proximal mid-line of immunodeficient mice. One tumour was established with the empty vector, whilst the other was established with cells transfected with the human γGT gene. Established tumours were treated with 0.5 mg/kg/day GSAO by continuous systemic infusion. The anti-tumour efficacy of GSAO positively correlated with tumour γGT activity (Figure 7.1). GSAO treatment had no significant effect on the growth of tumours expressing low levels of γGT (BxPC-3/vector). For tumours expressing high γGT levels (BxPC-3/γGT), whilst not reaching the limit of statistical significance according to the tumour growth delay index value (TGDi = 1.45, the limit being 1.5), the growth delay observed over two tumour volume doubling times was 9.6 days. The rate of BxPC-3/γGT tumour growth inhibition was 47% (T/C: 53%) at day 28. Results obtained with the genetically engineered model provided interesting results that tended to confirm the role of γGT in GSAO anti-tumour activity. The strong γGT over expression of the transfected cells might have, in the context of a

103 tumour, limited the impact of GSAO activation. With unlimited plasma glutathione supply, BxPC-3/γGT cells could have had a growth advantage (Schäfer et al., 2001) and improved detoxification/buffering capacities (Pompella et al., 2006), which to some extent may have counteracted, the effects of local drug activation. As a consequence, it was decided to validate these observations with a more relevant model employing tumour cells expressing natural levels of γGT. At the end of treatment the tumours were tested for γGT activity (Figure 7.2). The activity of the tumours correlated with the γGT activity of the cells used to establish the tumours when cultured in vitro.

A B BxPC-3 γGT 500 BxPC-3/vector

(vehicle) 300 (vehicle)

) )

3 3 400 BxPC-3/vector BxPC-3 γGT (GSAO) (GSAO) 300 200

200 100

100

Tumour volume (mm volume Tumour (mm volume Tumour 0 0 0 7 14 21 28 0 7 14 21 28 Treatment (days) Treatment (days)

Figure 7.1 Pancreatic tumour cell γGT activity positively correlates with GSAO- mediated inhibition of tumour angiogenesis and tumour growth in mice. Human pancreatic BxPC-3 tumours were established subcutaneously in the proximal midline of female BalB/C nude mice with BxPC-3/vector (A) or BxPC-3/γGT cells (B). Mice bearing approximately 50 mm3 tumours were randomized in two groups (n = 4-6) and implanted with subcutaneous 28 day Alzet micro-osmotic pumps in the flank that delivered vehicle, 0.5 mg/kg/day GSAO. The data points are mean ± standard error (SE) of the tumour volumes.

104 A

100

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40 GT activity (mU/mg protein) protein) (mU/mg activityGT γ 20

0 BxPC-3/ BxPC-3/ BxPC-3/ BxPC-3/ vector vector + GSAO γGT γGT +GSAO

B C

X100 X100

Figure 7.2 γGT expression of the tumours reflects the γGT activity of the cells implanted. γGT activity of BxPC-3/vector and BxPC-3/γGT tumours following 28 days of treatment with vehicle or GSAO (A). Results are mean ± SD. γGT staining (brown) of the BxPC-3/vector tumour (B) and of the BxPC-3/γGT tumour (C).

105 At the end of treatment, tumours were analysed for tumour vascularity and tumour cell proliferation. The anti-tumour efficacy of GSAO also positively correlated with tumour γGT activity (Figure 7.3A). Tumour vascularity was inhibited in the high γGT tumours where GSAO had an effect on tumour growth, this reduction was significantly more pronounced than the reduction in BxPC-3/vector tumours (BxPC-3/vector versus BxPC-3/γGT, p<0.05). Representative slides of the BxPC-3/γGT tumours show the decrease in CD31 staining as a result of GSAO treatment. (Figure 7.3B). The reduction in CD31 staining observed in the BxPC-3 xenografts is likely a result of partial activation of GSAO by the murine stellate cell γGT, highlighted in Figure 2.2. Tumour cell proliferation was also tested, but minimal effect was observed, as reported previously (Figure 7.3C).

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Figure 7.3 Tumour vascularity and proliferation following GSAO treatment.

107 Tumours were excised at day 28, fixed, sectioned, and analysed. (A) Tumours were analysed for vascularity (CD31 hotspots). (B) CD31 staining (brown) of representative BxPC-3/γGT tumours treated with vehicle (left panel) or GSAO 0.5 mg/kg/day (right panel). (C) Tumours were analysed for proliferation (PCNA). *, p< 0.05; **, p< 0.01; ***, p< 0.001.

7.3.2 Natural γGT expression model Initial observations made with a genetically engineered model were confirmed in a second model employing a pair of naturally γGT-expressing cells. BxPC-3 and MIA PaCa-2 cells were used to establish subcutaneous tumours. In this experiment, tumours were treated with 1 mg/kg/day of GSAO by continuous systemic administration. GSAO was able to significantly inhibit (**, p< 0.01) the growth of tumours with high γGT activity (MIA PaCa-2, TGDi = 3.34), whilst having no significant effect on basal γGT-expressing tumours (BxPC-3, TGDi = 0.85, Figure 7.4). The growth delay observed over two tumour volume doubling times was 46.2 days. The rate of MIA PaCa-2 tumour growth was inhibited by 80% (T/C: 20%) at day 27.

Tumours were excised at the end of treatment and γGT activity determined (Figure 7.5). The γGT activity measured in tumour homogenates and the γGT expression detected by immunohistochemistry on tumour sections correlated with the activity observed by the tumour cells in vitro. Tumour vascularity and tumour cell proliferation was also examined. As observed earlier, inhibition of tumour vascularity by GSAO was significantly more pronounced in tumours expressing high γGT activity (MIA PaCa-2) than in tumours with low γGT activity (BxPC-3 versus MIA PaCa-2, p< 0.01). Representative slides of the MIA PaCa-2 tumours show the decrease in CD31 staining as a result of GSAO treatment (Figure 7.6B). Again, this is likely due to partial activation of GSAO by the invading murine stellate cells (see Figure 2.2). There was little effect on tumour cell proliferation (Figure 7.6), as reported previously (Don et al., 2003).

108 A 500 BxPC-3 (vehicle) B 700 MIA PaCa-2 (vehicle)

BxPC-3 (GSAO) MIA PaCa-2 (GSAO)

600

)

) 3 3 400 500 ** 300 400

300 200

200 Tumor volume (mm volume Tumor Tumor volume (mm volume Tumor 100 100

0 0 0 7 14 21 28 0 7 14 21 28 Treatment (days) Treatment (days)

Figure 7.4 Pancreatic tumour cell γGT activity positively correlates with GSAO- mediated inhibition of tumour angiogenesis and tumour growth in mice. Human pancreatic tumours were established subcutaneously in the proximal midline of female BalB/C nude mice with BxPC-3 (A) or MIA PaCa-2 (B) cells. Mice bearing approximately 50 mm3 tumours were randomized in two groups (n = 4-6) and implanted with subcutaneous, 28 day Alzet micro-osmotic pumps in the flank that delivered vehicle, 1 mg/kg/day GSAO. The data points are mean ± SE of the tumour volumes. **, p< 0.01.

109 A

12

10

8

6

4

GT activity (mU/mg protein) protein) (mU/mg activityGT γ 2

0 BxPC-3 BxPC-3 + GSAO MIA PaCa-2 MIA PaCa-2 + GSAO

B C

X100 X100 Figure 7.5 γGT expression of the tumours reflects the γGT activity of the cells implanted. (A) Activity of the enzyme γGT of BxPC-3 and MIA PaCa-2 tumours following 28 days of treatment with vehicle or GSAO. Results are mean ± SD. Staining for γGT (brown) of BxPC-3 (B) and MIA PaCa-2 (C) tumours.

110 A 2 0 0

1 5 0

s

t o

p *

s t

o 1 0 0

h

1 * * *

3

D C 5 0

0 * * B x P C -3 B x P C -3 M IA P a C a -2 M IA P a C a -2 (v e h icle ) (G S A O ) (v e h icle ) (G S A O )

B

C 1 0 0 *

) s

l 8 0

l

e

c

e v

i 6 0

t

i

s

o

p

% 4 0

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C 2 0 P

0 B x P C -3 B x P C -3 M IA P a C a -2 M IA P a C a -2 (v e h ic le ) (G S A O ) (v e h ic le ) (G S A O )

Figure 7.6 Tumour vascularity and proliferation following GSAO treatment. Tumours were excised at day 28, fixed, sectioned, and analysed. (A) Tumours were analysed for vascularity (CD31 hotspots). (B) CD31 staining (brown) of representative MIA PaCa-2 tumours treated with vehicle (left panel) or GSAO

111 1 mg/kg/day (right panel). (C) Tumours were analysed for proliferation (PCNA). *, p< 0.05; **, p< 0.01; ***, p< 0.001.

7.4 Conclusions and discussion

This data shows the potential to target γGT expressing tumours with a γGT-activated prodrug. Two human pancreatic tumour models in immunocompromised mice were used to demonstrate the increased efficacy of GSAO for high γGT expressing tumours. The first model compared GSAO activity on tumours established from mock transfected and γGT-transfected BxPC-3 pancreatic tumour cells. The second model was performed with two tumour cells, expressing naturally high (MIA PaCa-2) or low (BxPC-3) levels of γGT. The modest activity of GSAO on tumours established from γGT-transfected BxPC-3 cells was attributed to the nature of the model. According to γGT activity measurements (Figure 4.3), γGT-transfected BxPC-3 cells express approximately 500-fold more of the enzyme than normal (or mock transfected) BxPC-3 cells. It is believed that in the context of a tumour with unlimited plasma glutathione supply, the massive γGT activity of the transfected cells could have, to some extent, protected the cells against activated GSAO. In these cells, γGT expression has probably been responsible for an elevation of cytosolic glutathione concentration (Moriarty-Craige and Jones, 2004) which is an important factor in resistance towards GSAO activity (Dilda et al., 2005b) and is a cofactor for detoxification through MRP1 and MRP2 (Dilda et al., 2008). In both models, GSAO treatment induced growth delay for tumours established by cells with elevated γGT. The tumour growth inhibition observed is thought to be the result of nutrient/oxygen deprivation due to angiogenesis inhibition as indicated by reduced CD31 hotspots. The PCNA data (Figure 7.3C and 7.6C) confirms that the drug has no direct effect on cancer cell proliferation. The response of endothelial cells and lack of response of tumours cells, agrees with previously published data and the response seen in the co-culture ratio study (Figure 5.3). Whilst BxPC-3 responded significantly to GSAO in Figure 4.1, this was not observed in the BxPC-3 xenograft. This is likely the result of the inter-species variation in γGT activity/expression observed in murine and human stellate cells, as discussed in Chapter 2. This data further demonstrates the ability of GSAO and its metabolites to overcome the interplay between the different cell types within the tumour.

Comparing the efficacy of GSAO for differentially γGT expressing tumours in mice, enabled testing of the specific tumour response without being obscured by the activation of GSAO by γGT found in the blood. Mouse plasma contains very little γGT, yet human plasma contains 112 more than 50-fold γGT than mouse plasma (humans: 16.1 U/L, mice: 0.3 U/L (Fierabracci et al., 2012)). The initial results from the clinical trial of GSAO do not address this issue.

This data, taken together with the minimal activation observed in plasma in the previous chapter, suggests that γGT activity at the site of the tumour could be used to predict patient response to GSAO. This has the potential to allow γGT expression to be used as a predictive marker for GSAO and other glutathione-S-conjugates; however, this needs to be tested in the clinical setting.

113

114 Chapter 8. Discussion and conclusion

8.1 Discussion

Pancreatic ductal adenocarcinoma (PDAC) has been shown to consist of multiple cell types in close relationship that perpetuate the growth of the tumour. Tumour associated stellate cells play a significant role in PDAC. These cells are attributed with a major role in the desmoplastic reaction of the cancer (Xu et al., 2010, Erkan et al., 2012a). In in vivo models, tumour growth is enhanced in the presence of stellate cells (Bachem et al., 2005, Vonlaufen et al., 2008a, Xu et al., 2010). The enzyme γGT has been shown to be highly expressed in a number of cancers, and it was confirmed that pancreatic tumours also express γGT (Figure 2.1). This expression was observed on tumour cells, and for the first time, on TA-PSC. The activity of γGT was significantly higher in TA-PSC than N-PSC. Due to the significant role of TA-PSC in PDAC, targets within the stroma are being investigated in an effort to overcome the limited therapeutic options available (Olive et al., 2009, Provenzano et al., 2012, Apte et al., 2013, Lunardi et al., 2014). One possibility is to utilise the altered phenotype of the TA-PSC to localise drug action. The high expression of γGT on TA-PSC could be exploited to deliver a γGT-activated prodrug to the tumour.

The expression of γGT by two major participants in PDAC should increase the efficacy of γGT- activated compounds, due to the greater potential for high local concentrations of the active compound. The glutathione-S-conjugate GSAO was used to explore this hypothesis. GSAO has previously been shown to be metabolised by purified γGT (Dilda et al., 2008). It was confirmed that cell surface γGT can also metabolise GSAO. Once activated, GSAO then accumulates within the cell and exerts an anti-proliferative action. The degree of accumulation and of inhibition of proliferation, relates to the γGT activity of the cell. This is in accordance with previous studies where the metabolites, GCAO and CAO, were shown to accumulate 9.5 and 8.25 times faster than GSAO, respectively (Dilda et al., 2008). The addition of the γGT inhibitor, ABBA, in all these studies, significantly inhibited GSAO metabolism, accumulation and action. This firmly establishes γGT as the principle factor in determining GSAO action.

The anti-tumour effect of GSAO is mediated by an anti-angiogenic action, with a primary target of proliferating endothelial cells. Yet γGT activity is principally located on the surface of stellate and tumour cells. Cooperation exists between the different types of cells in cancer. Both

115 stellate cells and cancer cells can induce angiogenesis. Stellate cells increase the angiogenic properties of endothelial cells in vitro, increasing proliferation and tube formation (Xu et al., 2010, Masamune et al., 2008, Erkan et al., 2009). Hence it was necessary to confirm that the anti-proliferative action of GSAO can overcome both the distance and the cooperation between the cells. Firstly, it was shown that GSAO, activated by tumour or stellate cells, is able to inhibit endothelial cell proliferation. This further supports the essential requirement for γGT in the mechanism of action of GSAO, as this was the factor determining its action. The dependence of the relationship between γGT activity and inhibition of endothelial cells was validated through transwell assays. In this assay, a strong relationship was observed between the γGT activity of the tumour or stellate cells, and the ability of GSAO to inhibit the proliferation of endothelial cells. The co-culture studies suggest that adjacent cell interactions do not inhibit the action of the drug; endothelial cell growth was inhibited by GSAO treatment. This inhibition again related to the extent of the γGT activity of the surrounding tumour cells. This was validated in two ways: by changing the γGT activity of the co-cultured tumour cells; and by increasing the number of tumour cells. These two chapters demonstrate that the activation of GSAO by cellular γGT is essential for its action on endothelial cells, and that when activated at a distant or adjacent location, GSAO is still capable of inhibiting endothelial cell proliferation. This suggests that tumour cells, both distant and adjacent to the proliferating endothelial cells of a tumour, will contribute to the inhibition of angiogenesis by GSAO.

A potential confounding factor for the targeting of glutathione-S-conjugates to γGT expressing tumours is the presence of γGT in the blood, as it could potentially negate the ability of tumour γGT to create high local concentrations of GCAO. In particular, the high plasma γGT activity commonly observed in pancreatic cancer as a consequence of physical obstruction of the bile duct (Murr et al., 1994, Muniraj et al., 2013, Hidalgo, 2010, Engelken et al., 2003). To address this, GSAO stability was tested in human plasma from patients with γGT activities ranging from normal to 223 U/L. Plasma γGT was capable of activating GSAO. However, the extent of activation is minimal when considering the half-life of GSAO. For patients with γGT in the normal range, 0.18 to 2.94% of GSAO will be activated in the time it takes for 50% of GSAO to be excreted. In these patients, this rate of activation will allow GSAO to pass through the pancreas 4 to 74 times before it is 50% activated. Within the range of γGT levels tested, less than 10% of GSAO is activated within its half-life. It is unlikely that systemically administered GSAO will be appreciably activated by normal plasma levels of γGT until it has circulated through the pancreatic blood supply several times.

116 Establishing that GSAO is activated in culture by tumour cells with high γGT activity and subsequently inhibits endothelial cells, and that plasma γGT will minimally activate GSAO, allows us to test the difference in response to GSAO of tumours expressing varying γGT in a whole animal. In a mouse model of human pancreatic xenografts, GSAO showed greater efficacy for tumours expressing high γGT. This was observed as delayed tumour growth and inhibition of angiogenesis. This demonstrates the prodrug capabilities of GSAO and the predictive ability of γGT. The suitability of GSAO as a prodrug and γGT as a predictive marker will be discussed.

8.1.1 GSAO as a targeted prodrug

GSAO is a classical carrier-linked prodrug. It is a glutathione-S-conjugate of N-acetyl phenylarsonous acid. As listed in Table 1.4, prodrugs allow modification of unfavourable characteristics of drugs. The conjugation of PAO to glutathione reduces its toxic effects by reducing membrane permeability (Donoghue et al., 2000). PAO is classified as a toxic compound, in extreme cases causing death. GSAO has been repeatedly shown to be much safer than the documented effects of PAO, in both mice (Don et al., 2003, Dilda et al., 2005a, Dilda et al., 2009) and humans (Horsley et al., 2013). Whilst reducing the toxicity of PAO, the glutathione moiety also improves the site-selective ability of the compound. The glutathione moiety is recognised by γGT, and cleavage of the glutamyl group allows for entry of the active metabolite into the cell. Sites with high γGT activity, activate more GSAO and are subsequently more susceptible to the action of the active metabolite. The choice to use glutathione as the pro-moiety in GSAO minimises the risk of harmful degradation by-products. GSAO activation releases glutamate and glycine, both are found naturally in the body.

Huttunen and Rautio describe three features that a site-selective prodrug should minimally have (2011). Firstly, it should be accurately transported to the desired site of action. Secondly, it should be selectively and quantitatively transformed to the active drug. Finally, it should be retained in the target tissue to produce the therapeutic effect in the desired target. All data to date suggests GSAO is precisely transported to the site of action and there selectively and quantitatively transformed to the active drug. The kidney has the highest expression of γGT and is the most probable place for off-site action. There is minimal evidence of GSAO nephrotoxicity; however, it would be of value to compare the activity of kidney γGT with PDAC γGT. Kidney γGT expression could have a similar confounding effect as blood γGT, activating the prodrug before it reaches the target. Plasma γGT was considered because of the relationship between pancreatic cancer and high γGT levels in the blood. In the recently

117 completed phase I trial, the dose limiting toxicities observed were not specifically related to the kidney. The dose limiting toxicities observed were: reversible grade 4 derangement of liver function tests; supra-ventricular tachycardia; and reversible grade 3 encephalopathy (Horsley et al., 2013). In previous pharmacokinetic studies in mice, bolus GSAO doses, higher than the maximum tolerated dose, caused reversible damage to the kidney (essentially in the distal tubules). The rapid clearance (and possible activation) through the kidney was taken into account to determine the route of administration of GSAO in animal tumour models. Continuous infusion of the drug using subcutaneously implanted osmotic pumps, showed success in terms of anti-tumour activity as it maintains a constant pressure on the tumour (this study, (Dilda et al., 2009, Don et al., 2003)). This demonstrates that systemically administered GSAO can reach its target and exert its anti-tumour activity before being degraded by kidney or other sources of γGT. It suggests that the γGT activity in PDAC provides better site-selective targeting than kidney γGT. The final essential characteristic of retention in the target tissue and elicitation of a therapeutic effect was demonstrated in the previous chapter. Mice with high γGT tumours exhibited a greater inhibition in tumour growth than mice with basal γGT tumours. This establishes that the γGT expression is guiding the GSAO to the tumour, and the drug is activated to such an extent as to elicit a therapeutic response. GSAO exhibits many of the desired characteristics of prodrugs, in particular improving the safety profile and targeting of the active compound.

8.1.2 γGT as a predictive marker The preclinical data presented here makes a strong case for the predictive value of γGT in indicating GSAO response. There is a strong association between tumour cell γGT expression and the anti-proliferative response of these cells to GSAO. This is further evidenced in the relationship between γGT activity of tumour and stellate cells and the response of endothelial cells to GSAO treatment in transwell and co-culture experiments. This was further demonstrated by an increased efficacy in xenografts with high γGT expression compared to basal γGT-expressing tumours. Initial steps in demonstrating the predictive value of HER2 expression in determining trastuzumab response in breast cancer consisted of both cell and animal models. Lewis et al. demonstrated a relationship between relative HER2 expression and the response of a range of cells to an anti-HER2 antibody (1993), as demonstrated here for γGT and GSAO in chapters 4, 5 and 6. In vivo, it was demonstrated that an anti-HER2 antibody can inhibit tumour growth in HER2 expressing tumours (Harwerth et al., 1993, Drebin et al., 1986, Stancovski et al., 1991). Tumours overexpressing HER2 were inhibited by an anti-HER2 antibody, in contrast, tumours with basal HER2 expression were not (Drebin et al., 1986).

118 These studies were followed by clinical trials designed to test both trastuzumab efficacy and HER2 as a predictive marker (Baselga, 2001, Baselga et al., 1996, Pegram et al., 1998). The next step in demonstrating the value of γGT as a predictive marker is in clinical trials. This is further discussed in Section 8.3.1.

8.2 Proposed model

The data presented here suggests the following model. GSAO is administered systemically, where following minimal activation by plasma γGT, the prodrug reaches the PDAC mass. High expression of γGT on the cancer and stellate cells within the tumour locally activates the prodrug GSAO, creating high local concentrations of the active metabolite. The active metabolite can then enter proliferating endothelial cells (Don et al., 2003, Dilda et al., 2005b) and enter the mitochondria where the arsenical moiety of GSAO binds to ANT and cell death is induced (Don et al., 2003, Park et al., 2012). Due to basal expression of γGT at other sites, GSAO activation is confined to the tumour site, minimising off target side effects. This model for local activation of a γGT-activated prodrug can be applied to other forms of cancer with high γGT expression, and can be used to deliver other γGT-activated compounds to these tumours (see Sections 8.3.2 and 8.3.3).

Figure 8.1 Model of GSAO activation in pancreatic ductal adenocarcinoma.

119 8.3 Future directions

8.3.1 The predictive marker γGT

In this thesis, high activity of γGT successfully predicts response to GSAO. These studies are limited to the laboratory. To truly be considered a marker of GSAO efficacy, the relationship needs to be investigated in the clinical setting. When GSAO enters phase II trials it is recommended that the utility of γGT as its predictive marker also be examined. Developing the predictive marker alongside the drug holds greater value than developing them separately (Yap et al., 2010). Initial steps would entail the development of a reproducible and robust test of γGT levels. This could be measured by activity or expression. Testing for activity would be the truest representation of the ability of the tumour to activate GSAO. Given the expression of γGT on stellate cells, it is important to observe activity in the whole tumour, accounting for both cancer and stromal cell γGT. Standard techniques used for measuring plasma γGT levels could be adapted. Following early phase clinical trials, tumour γGT expression can be correlated with a clinical end point or characteristic. This could be achieved with a retrospective study. Simon et al. describes a number of trial designs that could be utilised in future clinical trials in order to demonstrate the utility of a predictive marker (2010). Testing GSAO in patients without regard for γGT expression could reduce the size of the benefit observed. The frequency of high γGT tumours in the sample population will determine the benefit of GSAO observed. Selecting patients who are most likely to respond to GSAO will better validate the clinical value of the targeted compound. The ultimate goal of this validation is to demonstrate that testing for tumour γGT to determine the benefit of GSAO treatment improves patient outcome.

8.3.2 Application in other cancers

Pancreatic cancer demonstrates the capability of tumour-located γGT to activate a glutathione-S-conjugate. As discussed, many tumours differentially express γGT, including lung, breast, ovarian and brain cancers. For example in the brain, γGT is a frequent feature of higher-grade human astrocytic gliomas but not of normal brain tissue. In grade III astrocytoma, 85% were strongly positive for γGT, and in grade IV astrocytoma 65% of cases were strongly positive (Schäfer et al., 2001).

The large study by Hanigan et al. showed that both ovarian and lung carcinomas are typically positive for γGT, whilst the normal epithelium is not (1999b). In epithelial ovarian cancer, 33 of 45 patients were γGT positive prior to treatment (Hanigan et al., 1994). This concurs with a

120 higher mean γGT activity in malignant than benign ovarian tumours (Paolicchi et al., 1996). Germ cell tumours of the ovary can also be positive for γGT expression (Hanigan et al., 1999a). Ovarian cancer presents an interesting target for γGT-activated compounds as there are multiple studies exploring γGT activity following cisplatin treatment. Cisplatin is still used today in the treatment of ovarian cancer, usually when tumours no longer respond to more modern treatment options. In ovarian cancer A2780 cells treated intermittently with cisplatin until resistant, γGT mRNA expression increased 15 to 40-fold (Godwin et al., 1992). This was also observed in cell lines derived from a patient before and after the onset of resistance following treatment with cisplatin, chlorambucil and 5FU (Lewis et al., 1988). With both high γGT expression prior to treatment, and increased expression following resistance to treatment, it is worth investigating the potential to utilise ovarian tumour γGT expression to deliver a glutathione-S-conjugate.

8.3.3 Glutathione-S-conjugates

This research illustrates the possibility of targeting a glutathione-S-conjugate to a tumour with high expression of γGT. Compounds activated by γGT were extensively studied throughout the 1970s and 1980s. They were considered as a possible mechanism to target compounds to the kidney. Despite this research, no renal specific prodrugs reached the market (Huttunen and Rautio, 2011). Beyond targeting the kidney, minimal research utilising γGT expression for targeting other sites has been published. The extent of testing of glutathione-S-conjugates in cancer is limited. Previous studies with GSAO did not explore the γGT selectivity of the compound beyond basic viability assays (Dilda et al., 2008). Glutathione conjugates of sulofenur were tested against a solitary cell line (Guan et al., 2002). Both γ-L-glutaminyl-4- hydroxybenzene and γ-L-glutaminyl-4-iodobenzene have been demonstrated to be activated by tyrosinase (Prezioso et al., 1993) and γGT (Prezioso et al., 1994a). Both compounds were shown to have greater cytotoxicity in melanoma cell lines when γGT activity was not inhibited (Prezioso et al., 1994a). In past research utilising γGT for activation of a prodrug, a glutamyl group was more commonly conjugated to than glutathione. However, glutathione conjugates may prove to be more easily designed than γ-glutamyl prodrugs, which hold some difficulty in preparation (Zhang et al., 2013b, Zhang et al., 2013a).

8.3.3.1 Metabolism of glutathione-S-conjugates

The majority of glutathione-S-conjugates are metabolised by the mercapturic acid pathway. The mercapturic acid pathway is a common pathway in the detoxification of xenobiotics. The

121 xenobiotic is conjugated to glutathione by either spontaneous reaction or enzymatically by glutathione transferases (GST). This compound then exits the cell where the extracellularly facing γGT can remove the glutamyl group. Further action by dipeptidases leaves the cysteine- S-conjugate of the xenobiotic, having removed the glycine group. This compound then re- enters the cell, where N-acetyl transferases create the mercapturic acid of the xenobiotic, a generally more polar and water soluble compound. At this point the compound is generally non-toxic and excreted from the body through bile or urine. Alternatively, instead of acetylation, some compounds can be converted by cysteine S-conjugate β lyase. This produces an unstable and highly reactive thiol. This is one possible explanation for the nephrotoxicity of cisplatin as cysteine S-conjugate β lyase has high expression in the kidney (Zhang and Hanigan, 2003, Townsend and Hanigan, 2002). Note that there are alternative mechanisms proposed for cisplatin toxicity (Wainford et al., 2008, Pompella et al., 2006). Cysteine S-conjugate β-lyases are widely distributed throughout the body, and yet the kidney is most susceptible to damage by cysteine-S-conjugates (Cooper and Pinto, 2006). This suggests that more than β-lyase action determines the nephrotoxicity of cysteine-S-conjugates. The presence of both γGT and cysteine S-conjugate β-lyase at a tumour could potentially deliver an unstable and highly reactive thiol. The factor that determines the toxicity of cysteine-S-conjugates at the kidney, could determine whether this is a viable mechanism to exploit. It is likely that the combination of expression, and the unique characteristics of the active drug, will determine the sites at which the compound is active.

122 Glu―Cys―Gly + xenobiotic

SH glutathione transferase

Glu―Cys―Gly

S―xenobiotic water γ-glutamyl transferase glutamate

Cys―Gly

S―xenobiotic

water

dipeptidase glycine Cys

S―xenobiotic Acetyl coA

N-acetyl transferase cysteine-s-conjugate β-lyase

— N-acetyl―Cys CoA S―xenobiotic + pyruvate + ammonia

S―xenobiotic

Figure 8.2 Metabolism of xenobiotics by the mercapturic acid pathway and the alternative product produced by cysteine S-conjugate β-lyase activity.

8.3.3.2 Potential glutathione-S-conjugates

Activation of the glutathione-S-conjugate GSAO consists of the two middle steps of the mercapturic pathway. It is the high expression of γGT in PDAC that will target the active moiety to the tumour, potentially before nephrotoxicity occurs as has been observed with GSAO. This pathway could be utilised to target the tumour with a variety of anti-cancer agents. Conjugation of glutathione to cisplatin, dichlorovinyl and 4-hydroxynoneal are compounds that could be explored for preferential targeting of high γGT activity tumours.

123 8.3.3.2.1 Glutathione-S-cisplatin For many years cisplatin was a mainstay of cancer treatment. Cisplatin crosslinks with DNA. The ensuing inability to repair the DNA induces apoptosis. Cisplatin resistance has been associated with increased expression of γGT in a number of models. Upregulation of γGT has been shown in response to cisplatin treatment in astrocytic glioma cells (Mares et al., 2005, Mareš et al., 2003), in colon carcinoma cell lines (Borud et al., 2000), a human squamous carcinoma cell line in vitro (Bier et al., 1990) and in vivo (Bier et al., 1988), and in a prostate cancer cell line in vivo (Hanigan et al., 1999c). The kidney expression of γGT has also been associated with the characteristic nephrotoxicity of cisplatin. Inhibition of γGT or knock out studies of γGT in animals prevents the nephrotoxicity of cisplatin (Hanigan et al., 2001, Townsend and Hanigan, 2002). However, whether this is a result of loss of γGT activity or a consequence of increased glutathione concentrations in the kidneys is not clear (Pompella et al., 2006). The glutathione conjugate of cisplatin has been shown to have reduced toxicity against HK-2 cells (human proximal convoluted tubule cells), compared to cisplatin (Paolicchi et al., 2003). Exploring the glutathione conjugate of cisplatin in a cancer model in vivo would increase the understanding of cisplatin nephrotoxicity. In a high γGT tumour, it is possible glutathione-S-cisplatin would target the cisplatin to the tumour before it reaches toxic doses in the kidney. By making use of the γGT associated with resistance to cisplatin to deliver a cisplatin prodrug to the tumour, it may be possible to inhibit the resistant mechanism and reactivate the sensitivity of the cell to cisplatin.

8.3.3.2.2 Glutathione-S-dichlorovinyl Trichloroethylene is a widespread organic solvent, commonly used in metal degreasing (Rusyn et al., 2014, Chiu et al., 2009). Two metabolic pathways have been characterised in the metabolism of trichloroethylene: cytochrome P450-dependent oxidation and glutathione conjugation. Trichloroethylene is metabolised to form N-acetyl-S-(1,2-dichlorvinyl)-L-cysteine and S-(1,2-dichlorovinyl)-L-cysteine through the pathways outlined in Figure 8.2. Glutathione- S-dichlorovinyl (S-(1,2-dichlorovinyl)glutathione) is the first step in the glutathione metabolism pathway. Conjugation to glutathione occurs at a several-fold slower rate than cytochrome P450 oxidation (Lash et al., 2000). The glutathione conjugate should bypass toxicities associated with cytochrome P450 metabolism. In humans, the cysteine-S-conjugate can be converted by cysteine S-conjugate β-lyases to form a reactive thiol, S-(1,2-dichlorovinyl)thiol. This metabolite forms covalent adducts with cellular nucleophiles, including proteins. In numerous species (mouse, rat, guinea pig, rabbit, cat and dog (Terracini and Parker, 1965, Elfarra et al., 1986)) and in isolated kidney cells (Lash et al., 1986, Chen et al., 1990, Cummings

124 and Lash, 2000), the downstream metabolites of glutathione-S-dichlorovinyl induce toxicity (in particular S-(1,2-dichlorovinyl)glutathione). It has also been shown to induce neurotoxicity in rats (Patel et al., 1993, Spencer and Schaumburg, 1985). Glutathione-S-dichlorovinyl should be tested in PDAC. GSAO has proven to act at the tumour site in preference to the kidneys. This suggests that it may be possible to deliver glutathione-S-dichlorovinyl to PDAC without inducing toxicity in the kidney or the brain.

8.3.3.2.3 Glutathione-S-hydroxynonenal The lipid peroxidation product 4-hydroxynoneal (HNE) is excreted from the body as a mercapturic acid. HNE modifies 1 to 8% of proteins in the cell by forming adducts through cysteine, histidine and lysine residues; this impairs the function of the protein (Poli et al., 2008). The observed effects of HNE differ with concentration. At low concentrations HNE promotes proliferation, whilst at high concentrations HNE induces cell death (Kumar et al., 2011). The glutathione-S-conjugate of HNE causes a loss in cell viability in γGT-expressing v79 Chinese hamster lung fibroblast cells (Enoiu et al., 2002). This effect was attributed to the cysteinyl-glycine conjugate of HNE, as both basal and high expressers of γGT responded to this conjugate (Enoiu et al., 2002). Additionally, in a number of cancer cell lines, HNE was able to inhibit cell growth and induce apoptosis, using a repeated dose of 1μM to simulate physiological conditions (Cerbone et al., 2007, Pettazzoni et al., 2011, Calonghi et al., 2002). Delivery of a glutathione-S-conjugate of HNE to a γGT expressing tumour could exploit this natural response to HNE.

8.4 Conclusion

This thesis describes the expression of γGT in pancreatic ductal adenocarcinoma on both tumour cells and associated stellate cells. The γGT of these cells is capable of activating a glutathione-S-conjugate prodrug. In vitro, the expression of γGT predicted the response of cells to GSAO. This was similarly observed in vivo, where high γGT expressing PDAC tumours responded to GSAO with greater efficacy and significant inhibition of angiogenesis. This work demonstrates that tumour γGT can be utilised to deliver a γGT-activated prodrug to the tumour mass. This is of particular interest in PDAC where there is a need for more therapeutic options. The high expression of γGT on PSC is particularly noteworthy, given the important role they play in PDAC. The high expression of γGT in PDAC creates further possibility in designing alternative γGT-activated prodrugs, with the potential to develop alongside such compounds a marker to predict patient benefit. Further investigation of γGT expression in other cancers 125 could enable the application of this delivery strategy to other cancers.

126

127 Appendix

A1. Cells utilised

Unless otherwise mentioned, all cells were from ATCC (Manassas, VA) and all culture media, serum, antibiotics and supplements were from Life Technologies (Mulgrave, Victoria,

Australia). All cells were cultured in a humidified incubator at 37°C, 5% CO2. All cultures contained 20 units/mL penicillin and streptomycin.

Normal (N-PSC) and tumour-associated (TA-PSC) human pancreatic stellate cells were isolated from resected tissue from patients with benign conditions of the pancreas and PDAC as described (Xu et al., 2010, Vonlaufen et al., 2010). Purity of pancreatic stellate cell isolates was confirmed by positive staining of cells for the selective marker, glial fibrillary acidic protein, and negative staining for the cancer cell marker, cytokeratin.

Table A1.1 Cells used in the thesis and the conditions they were cultured under.

Cells Source Media (source) Supplements (source) BxPC-3 (CRL- ATCC (Bethesda, RPMI 10% v/v foetal bovine serum 1687) MD) 2 mM glutamine BxPC-3 - RPMI 10% v/v foetal bovine serum transfected 2 mM glutamine, 0.1 mg/mL gentamicin MIA PaCa-2 (CRL- ATCC (Bethesda, DMEM 10% v/v foetal bovine serum 1420) MD) 2 mM L-glutamine 2.5% v/v horse serum Pancreatic Professor M. IMDM 10% v/v foetal bovine serum stellate cells Apte, University 2 mM glutamine of NSW, Australia Melanoma Professor A. RPMI 10% v/v foetal bovine serum transfectants Pompella, 2 mM glutamine University of 0.5 mg/mL geneticin Pisa, Italy

128 AsPC-1 (CRL-1682 ATCC (Bethesda, RPMI 10% v/v foetal bovine serum MD) 2 mM L-glutamine PANC-1 (CRL- ATCC (Bethesda, DMEM 10% v/v foetal bovine serum 1469) MD) 2 mM L-glutamine bEnd.3 Doctor L. DMEM 10% v/v foetal bovine serum Lourenco-Dias, 2 mM L-glutamine University of NSW, Australia BAE cells Cell Applications DMEM 10% v/v foetal bovine serum (San Diego, CA) 2 mM L-glutamine HMEC-1 Doctor L. MCDB 131 10% v/v foetal bovine serum Lourenco-Dias, 5 U/mL penicillin University of 5 U/mL streptomycin NSW, Australia 2 mM L-glutamine 10 ng/mL recombinant human epidermal growth factor 1 μg/mL hydrocortisone (Sigma) HMEC-1-GFP - MCDB 131 10% v/v foetal bovine serum 5 U/mL penicillin 5 U/mL streptomycin 2 mM L-glutamine 10 ng/mL recombinant human epidermal growth factor 1 μg/mL hydrocortisone 0.3 mg/mL geneticin HUVEC Cell Applications EBM-2 (Lonza) 10% foetal bovine serum (San Diego, CA) EGM bullet kit (Lonza), containing: hEGF, hydrocortisone, GA-1000 (gentamicin, amphotericin-B), VEGF, hFGF-B, R3-IGF-1, ascorbic acid and heparin

129 A2. Approvals

Pancreatic tumour sections from the New South Wales Pancreatic Network, were used with approval from the UNSW Human Research Ethics Advisory Panel; reference number: 10003.

All experiments involving genetically modified organisms were authorised by the University of New South Wales Institutional Biosafety Committee; reference number: Exempt 12/15,

Plasma samples from the Infection and Immunity Research Group, School of Medical Sciences, University of New South Wales, were used with approval from the University of New South Wales Human Research Ethics Advisory Panel, under an amendment; reference number: 06/27.

All animal experiments were conducted with approval from the Animal Care and Ethics Committee, University of New South Wales; reference number: 10/12B.

130 Chapter 9. References

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