Epigenetic Biomarkers for Clinical Outcomes and Mechanisms Driving Emergence of Drug Resistance in Ovarian Cancer

Epigenetic biomarkers for clinical outcomes and mechanisms driving emergence of drug resistance in ovarian cancer

For the degree of PhD

Alun Passey

Imperial College London

Department of Surgery and Cancer

Declaration of Originality

It is my declaration that all work presented within this thesis is my own, with the exception of where otherwise stated and acknowledged. MRes student Georgia Spain contributed the cisplatin dose response experiments, flow cytometry for CSC markers (primarily ALDH1A1 activity) and tumoursphere formation assays in cell lines: A2780, MCP3, MCP6, MCP9 and A2780/CP70, and generated the figure panels representing these experiments in Figure 5.1. Darren Patten conducted all lab work related to ChIP-seq following chromatin collection as described in methods. All previously published and conducted work has been properly referenced during the preparation of this manuscript.

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

Abstract

Epithelial ovarian cancer (EOC) has a poor prognosis, with a 5 year survival rate of ~40%. A major therapeutic barrier is disease chemoresistant to the standard therapeutic regimen of taxol and platinum. Efficacious therapies in chemoresistant disease, a fuller understanding of the molecular mechanisms driving tumour evolution, and drugs to prevent emergence of chemoresistance are required. Bevacizumab, a VEGF inhibitor approved for second line treatment of recurrent, chemoresistant EOC, requires stratification biomarkers to identify patients who will benefit. We investigated VEGF gene promoter methylation in EOC from ICON7 clinical trial patients. Patients with low VEGF-B methylation showed better overall survival (OS) when treated with standard chemotherapy (HR=2.07(1.23, 3.49), P=0.006) but not bevacizumab (HR=1.77(0.99, 3.15), P=0.05), potentially identifying a patient sub-group which should not be treated with bevacizumab. An association between VEGF-C methylation and progression-free survival (PFS) in ICON7 patients (HR=0.66(0.52, 0.84), P=0.0006) was confounded by stage. ICGC patient samples collected at relapse showed elevated VEGF-C compared to primary presentation. In a CRISPR-generated VEGF-C null EOC model we demonstrated an autocrine mechanism by which VEGF-C deregulation and over-expression may drive EOC stage and metastasis by suppressing anoikis-induced apoptosis and promoting growth in non-adherent conditions. Further, we investigated the role of epigenetics and heterogeneity in acquisition of resistance to platinum-based therapies. Epigenetically defined sub-populations which show a reversibly resistant "drug tolerant" phenotype have been demonstrated to exist in chemosensitive tumour populations of many cancers. We isolated reversibly cisplatin tolerant populations (CTPs) from a cisplatin sensitive EOC cell line A2780, which were re-sensitised by EZH2 and HDAC inhibitors. Epigenomic profiling of these populations revealed heavily H3K27me3-modified chromatin in CTPs, preferentially in non-coding regions of the genome, which we hypothesise as a novel mechanism of tolerance for cisplatin as an initial stage for acquisition of stable resistance.

Acknowledgements

During the course of my PhD I have acquired a plethora of contributions, kind help, friendships and pastoral support to which I must devote my work as well as express my abundance of gratitude. Firstly, I must acknowledge the unerring support of Professor Bob Brown, to whom I am indebted for his unwavering direction, willingness to provide the infrastructure for my ideas and lay the pillars for my research development as well as his consistent availability. My initial recruitment to the lab was facilitated by Charlotte Wilhelm-Benartzi, who supported my initial computational development and instalment into the lab as a masters student, and has continued to have a profound effect upon my life. Additionally, without the agreement of Dr Sadaf Ghaem-Maghami to provide supervision I may not have undertaken my PhD in this wonderful group. Without the funding provided by the MRC, I could not have undertaken this research or have benefited from my last 4.5 years of academic education.

The ensuing thesis would be incomplete without the collaborative additions of Dr Luca Magnani and Dr Darren Patten who performed the ChIP-seq for this study, and Professor Charlie Gourley for sending us the microarray expression data we asked for and of course ICON7 for the patient samples and clinical data.

I am absolutely indebted to additional number of people without whom my professional development would not have been possible. A multitude of challenging and enlightening conversations with Dr Erick Loomis opened my eyes to the delights of next generation sequencing and his technical advice and adept teaching of molecular cloning made the CRISPR work in this thesis possible and facilitated the completion of ATAC-seq experiments. In addition, his stream of career advice broadened my horizons substantially. Scientifically rigorous and challenging conversations are often an undervalued aspect of the work and are essential for intellectual development. The second person I owe a great debt of gratitude to is Dr Nair Bonito who has consistently throughout the last 3.5 years challenged my scientific thinking at every possible juncture, provided idiotproof protocols, teaching and guidance with a huge number of cellular and molecular assays: western blots, qRT-PCR, flow cytometry, and transfections. She has also unquestioningly let me steal her buffers and reagents, shown undying interest in my personal life and has all told been the best “work wife” a person could possibly ask for, working without her will feel incomplete. The bioinformatics chapter of this thesis would not exist without the coding scripts and computational help and expertise of Emma Bell who is a great source of knowledge in NGS processing and all things ChIP-seq, and John Gallon who has an uncanny talent for scripting and with whom I have shared conversations about what on earth to do with our data and coffee purchasing responsibilities – a resource necessary for the completion of a PhD. Nahal Masrour

4 taught me how to do the pyrosequencing that was necessary for the first chapter of this thesis as well as much of the preliminary work. Teaching John Gallon and Georgia Spain who were my masters students was one of the most challenging and fulfilling experiences of my time in the Epigenetics Group, to both of them I am thankful, and I owe Georgia gratitude for her contribution to the work shown in this thesis. For caring and positive discussion throughout the 3.5 years here I would like to thank Kirsty Flower and Ed Curry and for stimulating company and adventures in New Orleans thank you to Kayleigh Davis, with whom my PhD experience has been shared. For coffee trips, chats and fulfilling lunchtime conversation I additionally would like to thank the rest of the Epigenetics Group past and present as well as Paula Cunnea.

I have had the pleasure of developing strong, deep friendships with several members of our laboratory community which I greatly hope will be of longevity, it is rare to have find such a wonderful concentration of people in a workplace, with whom such a relationship can be shared. So for making my time in London to this point truly meaningful and adding great richness to my life by hanging out with me outside of work, I would like to thank Nair Bonito, Ian Green, John Gallon, Angela Wilson, Erick Loomis, Kayleigh Davis, Adam Beech, Ed Curry, Paula Cunnea and Kirsty Flower, I am extremely grateful to Kirsty – a wonderful boulder of empathy and kindness who has helped me a through the darker times of my London life, and all of these friends have been rays of sunshine to guide me to the lightest.

My family have been ever supportive of my academic career: my Mum, Dad and Nan have never erred in their support and encouragement, and my sister with whom for the first time I have had the great pleasure of sharing a city during my studies, has never failed to be my stead-fast rock, pillar of wisdom, and continuous source of kindness and care (as well as occasional but necessary micro-management).

Finally, for bringing a lightness of step, a joy that has carried me through the final year and writing of this work and reminders to keep chipping away, as well as a seemingly endless supply of belief in me, I must thank Aude Wilhelm, without whom life would be a much drearier and less adventurous place.

List of figures

Figure 1.1: Clonal, CSC and drug tolerant persister models of emerging drug resistance ...... 27 Figure 1.2: Epigenetic modifications and their effect on chromatin structure and transcription and drugs to target them ...... 32 Figure 3.1: Flow of ICON7 patient samples through study quality control and pyrosequencing ...... 75 Figure 3.2: Loci of VEGF gene CpG sites of assayed by bisulfite pyrosequencing in ICON7 EOC patients ...... 78 Figure 3.3: Bisulfite pyrosequencing of VEGF gene promoter regions in ICON7 EOC patients ...... 79 Figure 3.3: Flow of analysis of statistical analysis for investigation of associations between VEGF methylation and clinical endpoints PFs and OS in EOC and HGSOC ICON7 patients...... 81 Figure 3.4: VEGF-B methylation associates with OS in HSGOC patients treated with standard chemotherapy ...... 85 Figure 3.5: Loss of VEGF-C methylation associates poor PFS and advanced stage in ICON7 EOC patients...... 88 Figure 3.6: Low VEGF-A methylation shows a trend for being prognostically beneficial for ICON7 EOC patients treated with standard chemotherapy only ...... 91 Figure 4.1: VEGF-C is up-regulated in patient samples at chemoresistant relapse compared to primary presentation ...... 96 Figure 4.2: VEGF-C is over-expressed in chemoresistant cell line PEO4 and VEGF-C expression is associated with promoter de-methylation ...... 98 Figure 4.3: Genome editing using RNA-guided Fok1 nucleases (RFNs)...... 100 Figure 4.4: Genomic editing of the VEGF-C locus in the PEO4 cell line using RNA-guided Fok1 nucleases ...... 101 Figure 4.5: VEGF-C is not necessary for resistance to cisplatin in PEO4 EOC cells ...... 102 Figure 4.6: VEGF-C does not drive invasion or migration in PEO4 EOC ...... 104 Figure 4.7: VEGF-C expression drives proliferation and tumoursphere formation ...... 106 Figure 4.8: VEGF-C does not promote cancer stem cell marker expression ...... 107 Figure 4.9: VEGF-C expression inhibits anoikis-induced apoptosis...... 109 Figure 5.2: Dose selection for isolating surviving A2780 populations ...... 122 Figure 5.3: Flow cytometry analysis for quiescent CSC populations after 8 days of cisplatin treatment ...... 122 Figure 5.4: Low dose cisplatin treatment of A2780 followed by colony formation selects for a epigenetically defined, transient, cisplatin tolerant population ...... 125 Figure 5.5: A hypothesised model of cisplatin tolerance in A2780 cells ...... 126 Figure 6.1: Experimental design of experiment assessing transcriptional and epigenomic changes in populations of A2780 CTPs ...... 134 Figure 6.2: FastQC analysis of RNA-seq library quality ...... 137 Figure 6.3: FastQC analysis of RNA-seq library quality ...... 138 Figure 6.4: RNA-seq sample quality control and identification of differentially expressed genes in A2780 parental and CTP populations ...... 143 Figure 6.5: ATAC library fragment size distribution H3K27me3, H3K27ac and ATAC peak distribution in positive and negative control genesFigure 6.4: RNA-seq sample quality control and identification of differentially expressed genes in A2780 parental and CTP populations ...... 143 Figure 6.6: H3K27 trimethylation peak distribution and expression of PRC2 components ...... 145 Figure 6.7: H3K27 acetylation and ATAC-seq peak distribution...... 150 Figure 6.8: Association between H3K27me3/ac and differential gene expression in A2780 CTPs .... 153 Figure 6.9: Gene ontology analysis of differential gene expression in A2780 CTPs ...... 155

Figure 6.10: Hypothetical model for H3K27me3 distribution and chromatin structure in CTP cells and the conferring of tolerance to cisplatin treatment for the CTP genome ...... 157 Figure 6.11: Hypothetical interactome for maintenance of a drug tolerant phenotype by genes differentially expressed in A2780 CTPs ...... 159

List of tables

Table 2.1: General reagents ...... 47 Table 2.2: Primer sequences ...... 50 Table 2.3: Cell line information ...... 52 Table 2.4: qRT-PCR primer sequences ...... 53 Table 2.5: Antibodies used for Western blotting ...... 55 Table 2.6: chemical inhibitors used for inhibition of epigenetic modifying enzymes ...... 56 Table 2.8: sgRNA oligonucleotide sequences...... 62 Table 2.9: Sanger sequencing primer sequences ...... 62 Table 2.10: ChIP-seq antibodies and qRT-PCR primer sequences used for positive/negative control . 65 Table 3.1: ICON 7 patient demograhics ...... 74 Table 3.2: Power to detect clinically significant hazard ratios ...... 75 Table 3.3: Pyrosequencing assay and intra-patient variability ...... 80 Table 3.4: Cox proportional hazards models for association of VEGF promoter methylation with overall and progression free survival (OS/PFS) in ICON7 Epithelial (EOC) and high grade serous (HGS)...... 82 Table 3.5: Association test P values for clinical covariates with relevant outcomes and exposures identified via univariate modelling ...... 84 Table 6.1: Read counts for experimental replicates in RNA-seq experiments ...... 139 Table 6.2: Read and MACS2 called peak counts for experimental replicates in ChIP-seq experiments ...... 139 Table 6.3: Read and MACS2 called peak counts for experimental replicates in ATAC-seq experiments ...... 140 Table 6.4: Fisher’s Exact test statistics for peak enrichment or depletion relative to total peak counts in A278- CTPs versus A2780 parental cells for MACS2-called H3K27me3, H3K27ac and ATAC peaks...... 150

List of abbreviations

2’-deoxy-5-azacytidine decitabine 5-azacytidine 5-Aza 5-methylcytosine 5mC 7-aminoactinomycin D 7-AAD Activated Leukocyte Cell Adhesion Molecule ALCAM Adenosine triphosphate ATP Adenosylmethionine Decarboxylase 1 AMD1 Aldehyde dehydrogenase ALDH Allophycocyanin APC Aminomethyltransferase AMT Ammonium persulfate APS Ancient retroviral element ARVE Angiopoetin-1 Ang1 Apoptosis Associated Tyrosine Kinase AATK Assay for Transposase-Accessible Chromatin sequencing ATAC-seq Ataxia Talangectasia mutated ATM Ataxia Telangiectasia And Rad3-Related Protein ATR ATP binding cassette ABC AT-Rich Interaction Domain 1A ARID1A Australian Ovarian Cancer Study AOCS Bcl associated X Bax Body mass index BMI Bone morphogenetic protein 7 BMP-7 Bovine serum albumin BSA BRCA1 Interacting Protein C-Terminal Helicase 1 BRIP1 BRCA1-associated RING domain protein 1 BARD1 Breast cancer associated 1 BRCA1 Breast cancer associated 2 BRCA2 Bromodeoxyuridine BrdU Bromodomain containing 4 BRD4 Cancer Antigen 125 CA 125 Cancer stem cell CSC CCAAT Enhancer binding protein CEBP Cell free DNA cfDNA Checkpoint kinase 1 Chk1 Checkpoint kinase 2 CHEK2 Chromatin immune precipitation sequencing ChIP-seq Chronic myoproliferative leukemia CML Cis-diammine-dichloro-platinum cisplatin Cisplatin tolerant proliferator CTP c-Jun N-terminal kinase JNK

CCOC/CC- Clear cell ovarian cancer EOC Clustered regularly interspaced short palindromic repeats CRISPR Colorectal cancer CRC Contactin 2 CNTN2 Counts per million mapped reads CPM CpG island CGI CpG island methylator phenotype CIMP Cyclooxygenase-2 COX2 Cytosine-guanine CpG Deoxycytidine Kinase DCK Deoxyribonucleic acid DNA Dimethyl sulfoxide DMSO Dithiothreitol DTT DNA methyl transferase DNMT Double strand break DSB Double strand break repair DSBR Drug tolerant expanded persister DTEP Drug tolerant persister DTP Dynamic Contrast-Enhanced Magnetic Resonance Imaging DCE-MRI EH Domain Containing 1 EHD1 Embryonic stem cell ESC Encyclopaedia of DNA elements ENCODE Endothelin-1 ET1 Enhanced chemiluminescence ECL Enhancer of zeste homolog 2 EZH2 Epithelial ovarian cancer EOC Ephrin A5 EFNA5 Epidermal growth factor receptor EGFR Epithelial Cell Adhesion Molecule EPCAM Epithelial to mesenchymal transition EMT Ethylenediaminetetraacetic acid EDTA Excision repair 1 ERCC1 Extracellular signal-regulated kinase ERK False discovery rate FDR Fas ligand FasL Fluorescein isothiocyanate FITC Fluorescence associated cell sorting FACS Fluorescent ubiquitination-based cell cycle imaging FUCCI Foetal calf serum FCS Food and drug administration FDA Forkhead box O1 FOXO1 Formalin fixed paraffin embedded FFPE Forward scatter area FSC-A Forward scatter height FSC-H Forward scatter width FSC-W

Fragments per kilobase per million mapped reads FPKM General transfer format GTF Glioblastoma multiforme GBM Glioblastoma stem cell GSC Glutathione GSH Glyceraldehyde-3-Phosphate Dehydrogenase GAPDH Green fluorescent protein GFP Growth Factor Independent 1 Transcriptional Repressor GFI1 Gynaecologic Cancer Intergroup GCIG Gynaecologic oncology group GOG Haematopoetic precursor cell HPC Haematopoetic stem cell HSC Hazard ratio HR High mobility group protein 1 HMG1 High mobility group protein 2 HMG2 Histone 3 lysine 4 trimethylation H3K4me3 Histone 3 lysine residue 27 acetylation H3K27ac histone 3 lysine residue 27 trimethylation H3K27me3 Histone acetyl transferase HAT Histone deacetylase HDAC HNF1 homeobox B HNF1B Homeobox HOX hormonal replacement therapy HRT Hydrochloric acid HCl Inhibitory concentration 50 IC50 Insulin-like growth factor receptor 1 IGFR1 International Cancer Genome Consortium ICGC International Federation of Gynaecology and Obstetrics FIGO Kyoto encyclopaedia of genes and genomes KEGG Leukemia initiating cell LIC Lipopolysaccharide induced TNF factor LITAF LON Peptidase N-Terminal Domain And Ring Finger 2 LONRF2 Long interspersed nuclear element LINE Long non-coding RNA lncRNA Long tandem repeat LTR Low grade serous ovarian cancer LGS/LGSOC Lysine demethylase KDM Malignant peripheral nerve sheath tumour MPNST Median fluorescent intensity MFI Medical Research Council MRC Meiotic Recombination 11 Homolog A MRE11A Micro RNA miRNA Mismatch repair MMR Mitogen activated protein kinase MAPK Mitotic arrest deficient like 1 MAD2L1 Model Based Analysis for ChIP-seq data 2 MACS2

Multi drug resistance protein 1 MDR1 MutL homolog 1 MLH1 MutS homolog 2 MSH2 MutS homolog 6 MSH6 Myogenic differentiation 1 MYOD1 N,N-diethylaminobenzaldehyde DEAB Neural Proliferation, Differentiation And Control 1 NPDC1 Neurofibromatosis 1 NF1 Neuropilin NRP Non small cell lung carcinoma NSCLC nucleotide excision repair NER O6-methylguanine-DNA methyltransferase MGMT Objective response rate ORR Overall survival OS Pancreatic ductal adenocarcinoma PDAC Partner and localizer of BRCA2 PALB2 Peptidylprolyl Isomerase A PP1A Peridinin Chlorophyll Protein Complex Per-CP Phenylmethylsulfonyl fluoride PMSF Phosphatase And Tensin Homolog PTEN Phosphate buffered saline PBS Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha PIK3CA Phycoerythrin PE Poly(ADP-Ribose) polymerase PARP Polycomb repressive complex 2 PRC2 Polymerase chain reaction PCR Progression free survival PFS Protocadherin 10 PCDH10 Radioimmunoprecipitation assay RIPA Ras association domain family 1 isoform A RASSF1A Real time quantitative PCR qRT-PCR Response Evaluation Criteria in Solid Tumours RECIST Retinoblastoma Rb Ribonucleic acid RNA RNA guided Fok1 nuclease RFN Roswell park memorial institute RPMI Scottish Gynaecological Clinical Trial Group SGCTG Sodium valproate Na3VO4 SDS-polyacrylamide gel electrophoresis SDS-PAGE sgRNA small guide RNA Short hairpin RNA shRNA Short Stature Homeobox 2 SHOX2 Side scatter area SSC-A Single nucleotide polymorphism SNP

Sodium dodecyl sulfate SDS Sodium fluoride NaF Solute Carrier Family 16 Member 5 SLC16A5 Sprouty RTK signalling agonist 4 SPRY4 subcutaneous fat area SFA Suberanilohydroxamic acid SAHA T-cell acute lymphoblastic leukaemia T-ALL Tetramethylethylenediamine TEMED The Cancer Genome Atlas TCGA

The National Institute for Health and Care Excellence NICE Transcription start site TSS Transducin like enhancer of split 3 TLE3 Tris buffered saline TBS Tris buffered saline with Tween TBS-T Tumour growth factor β TGF-β Tumour necrosis factor TNF Tumour suppressor gene TSG Tyrosine kinase inhibitor TKI Ultra low adherence ULA University of California Santa Cruz UCSC Vascular endothelial growth factor VEGF Vascular endothelial growth factor receptor VEGFR WD Repeat Domain 19 WDR19 Widely interspaced zinc finger motifs WIZ

Contents Declaration of Originality ...... 2 Copyright Declaration ...... 2 Abstract ...... 3 Acknowledgements ...... 4 List of tables ...... 5 List of figures ...... 6 List of abbreviations ...... 7 Chapter 1: General Introduction ...... 18 1.1 Ovarian cancer aetiology ...... 19 1.2 Epidemiology ...... 19 1.2.1 Genetic risk associated with ovarian cancer ...... 19 1.2.2 Environmental factors associating with ovarian cancer ...... 20 1.3 Ovarian cancer histological sub-types and molecular pathology ...... 20 1.4 Ovarian cancer treatment ...... 22 1.4.1 Current treatment of ovarian cancer ...... 22 1.4.2 Chemoresistance ...... 22 1.4.3 Mechanism of action of platinum based therapies ...... 22 1.4.4 Mechanisms of platinum resistance in cancer ...... 23 1.4.5 Mechanisms of platinum resistance in ovarian cancer ...... 24 1.4.6 Cancer stem cells and drug tolerant persisters as the unit of selection for platinum resistance in ovarian cancer ...... 25 1.5 Targeted therapies for the management of ovarian cancer and the necessities for biomarkers to stratify for patient outcomes ...... 27 1.6 The role of VEGF family genes in ovarian cancer ...... 28 1.7 Epigenetic aberrances in ovarian cancer ...... 30 1.7.1 DNA Methylation ...... 30 1.7.2 DNA Methylation as a driver of ovarian cancer ...... 33 1.7.3 DNA methylation and chemoresistance in ovarian cancer ...... 35 1.7.4 DNA methylation as a therapeutic target ...... 37 1.7.5 DNA methylation as a biomarker for patient stratification ...... 38 1.7.6 Histone modifications and regulation of transcription ...... 38 1.7.7 Aberrant EZH2 activity and H3K27 trimethylation as a driver of ovarian cancer malignancy ...... 39 1.7.8 EZH2 as a driver for platinum resistance in ovarian cancer ...... 40 1.7.9 Aberrant bivalent histone marked chromatin as an epigenetic driver of ovarian cancer malignancy and chemoresistance ...... 40

1.7.10 Targeting EZH2 as a therapeutic option for ovarian cancer ...... 41 1.7.11 Aberrant histone acetylation and HDAC activity as a driver for ovarian cancer malignancy and platinum resistance ...... 42 1.7.12 Targeting HDACs as a therapeutic intervention for ovarian cancer ...... 43 1.8 Hypothesis and Aims ...... 44 1.8.1 Prognostic value of VEGF family methylation in EOC patients from the ICON7 clinical trial and follow up in functional cell line studies ...... 44 1.8.2 Isolation and characterisation of a cisplatin tolerant population from a chemosensitive ovarian cancer cell line ...... 45 Chapter 2: Materials and Methods ...... 46 2.0 General Reagents ...... 47 2.1 Ovarian cancer patient samples, clinical data and analysis ...... 48 2.1.1 ICON7 clinical trial VEGF biomarker study ...... 48 2.2 Cell based assays ...... 51 2.2.1 Cell lines ...... 51 2.2.2 Cell culture ...... 51 2.2.3 RNA extraction and real time quantitative PCR (qRT-PCR) ...... 52 2.2.4 Western blotting ...... 53 2.2.5 Cellular response to drug treatments ...... 55 2.2.6 Cell growth assays ...... 56 2.2.7 Flow cytometry ...... 56 2.2.8 Migration assays ...... 58 2.2.9 Invasion assays ...... 59 2.2.10 Assessment of apoptosis in adherent and non-adherent conditions ...... 59 2.2.11 Clonogenic assays and drug treatments for isolating cisplatin tolerant populations of A2780 ...... 59 2.2.12 Statistics ...... 60 2.3 Generation of VEGF-C null cell lines via CRISPR-RFN ...... 60 2.3.1 CRISPR plasmids ...... 60 2.3.2 Sub-cloning gRNA sequences into the pSQT1313 vector ...... 60 2.3.3 Cloning and pDNA isolation for transfection ...... 63 2.3.4 Transfections and single cell cloning ...... 63 2.4 Sequencing analysis of cisplatin tolerant and parental A2780 populations ...... 63 2.4.1 Preparation of genomic libraries and sequencing ...... 63 2.4.2 Bioinformatics analysis ...... 66 Chapter 3: Investigation of prognostic association between VEGF gene methylation and clinical outcomes in the ICON7 clinical trial ...... 68

3.1 Introduction...... 69 3.2 Background...... 69 3.2.1 The ICON7 clinical trial ...... 69 3.2.2 Bevacizumab in phase III clinical trials ...... 70 3.2.3 The current clinical role of bevacizumab in ovarian cancer treatment ...... 71 3.2.4 Biomarkers for bevacizumab response ...... 71 3.2.5 VEGF methylation as a prognostic biomarker for HGSOC patient outcomes ...... 72 3.3 Results ...... 73 3.3.1 ICON7 patient DNA and demographics ...... 73 3.3.3 Power analysis ...... 75 3.3.4 Bisulfite pyrosequencing of VEGF genes in ICON7 EOC patients ...... 76 3.3.5 Univariate analysis to investigate associations between VEGF promoter methylation and ICON7 patient outcomes ...... 80 3.3.6 Multivariable analysis to investigate independent associations between VEGF-B methylation with patient outcomes in the ICON7 trial ...... 83 3.3.7 Multivariable analysis to investigate independent associations between VEGF-C methylation with patient outcomes in the ICON7 trial ...... 87 3.3.8 VEGF-A methylation shows a trend for prognostic benefit in ICON7 EOC patients treated with standard chemotherapy, not bevacizumab...... 89 3.4 Discussion ...... 92 3.5 Summary ...... 93 Chapter 4: Investigation of VEGF-B and VEGF-C methylation and expression in OC patients at relapse and the autocrine role of VEGF-C in promoting tumour progression ...... 94 4.1 Introduction...... 95 4.2 Results ...... 95 4.2.1 Analysis of VEGF-C expression and methylation in patients at primary presentation and relapse ...... 95 4.2.2 Investigation of VEGF-C expression and methylation in paired chemosensitive primary and chemoresistant relapse EOC cell lines...... 97 ...... 98 4.2.3 Generation of VEGF-C KNOCK-OUT PEO4 lines using CRISPR ...... 99 4.2.4 Investigation of the role of VEGF-C in driving cisplatin resistance in the PEO4 line ...... 102 4.2.5 Investigation of the role of VEGF-C in driving invasion and migration in the PEO4 line .... 102 4.2.6 Investigation of the role of VEGF-C in driving cell growth and tumour initiation in the PEO4 line ...... 105 4.2.7 VEGF-C may negatively regulate expression of CD44 and ALDH1A3 ...... 105 4.2.7 Evaluation of role of VEGF-C in anoikis-induced apoptosis and cell death in the PEO4 line ...... 108

4.3 Discussion ...... 110 4.4 Summary ...... 114 Chapter 5: Isolation of a cisplatin tolerant population from a cisplatin sensitive ovarian cancer cell line ...... 115 5.1 Introduction...... 116 5.2 Background...... 116 5.2.1 Drug tolerant populations in chemosensitive cancer cell lines ...... 116 5.3 Results ...... 118 5.3.1 Investigation of association between response to cisplatin and CSC-like sub-populations in A2780 and in vitro derived resistant cell lines ...... 118 5.3.2 Identification of an A2780-derived cisplatin tolerant sub-population ...... 121 5.3.4 Investigation of epigenetic sustainment of an A2780-derived cisplatin tolerant population ...... 121 5.4 Discussion ...... 126 5.5 Summary ...... 130 Chapter 6: Analysis of the transcriptome and epigenome of a cisplatin tolerant population derived from a cisplatin sensitive ovarian cancer cell line ...... 131 6.1 Introduction...... 132 6.2 Results ...... 132 6.2.1 Experimental approach for transcriptomic and epigenomic profiling of cisplatin tolerant A2780 cells ...... 132 6.2.2 Quality control of sequencing libraries and summary of FastQC reports ...... 135 6.2.3 Differential gene expression analysis in A2780 lines ...... 141 6.2.3 Peak calling in A2780 ChIP-seq and ATAC-seq datasets...... 143 6.2.4 Analysis of the H3K27me3 landscape in A2780 CTPs ...... 146 6.2.5 Analysis of the H3K27ac and accessible chromatin landscape in A2780 CTPs ...... 149 6.2.6 Analysis of the regulation of the A2780 CTP transcriptome by the epigenome ...... 151 6.2.7 Analysis of the transcriptomic profile of A2780 CTP cells ...... 154 6.3 Discussion ...... 154 6.4 Conclusion ...... 160 Chapter 7: General Discussion ...... 161 7.1 Summary of findings ...... 162 7.2 Limitations ...... 165 7.2.1 Limitations of ICON7 study ...... 165 7.2.2 Limitations of study with VEGF-C in ICGC patient cohort ...... 166 7.2.3 Limitations of study with VEGF-C knock-out in an ovarian cancer cell line ...... 167 7.2.4 Limitations of drug tolerance in chemosensitive ovarian cancer cell lines study ...... 168

7.3 Future Perspectives ...... 169 7.3.1 Elucidation of the role of VEGF-B in promoting EOC patient survival outcomes, interaction with bevacizumab and validation as a potential biomarker for exclusion of patients from bevacizumab therapy ...... 169 7.3.2 Further elucidation of the role of VEGF-C in platinum resistance, tumoursphere initiation, metastasis and cancer stem cell phenotype ...... 170 7.3.2 Further characterisation of the CTP phenotype and its role in the acquisition of platinum resistance in ovarian cancer ...... 171 7.4 Summary of findings...... 174 8 References ...... 175

Chapter 1: General Introduction

1.1 Ovarian cancer aetiology

Epithelial ovarian cancer (EOC) is the most lethal gynaecological malignancy. 7,378 new cases were reported in the UK in 2014, with 4,128 deaths attributable to ovarian cancer that year. This reflects a 10 year survival rate in UK of 35%, which has remained stable, with little significant improvement since the early 1990s (1). Globally 225,500 women were diagnosed with ovarian cancer, with 140,200 deaths attributed to the disease in 2008 (2). Ovarian cancer symptoms often present as abdominal pain and bloating for an average of 3-4 months pre-diagnosis (3), which are misdiagnosed as irritable bowel syndrome (4) and patients often misinterpret symptoms as signs of ageing, menopause or previous pregnancies (5). As a result a substantial proportion of cases (>70%) are diagnosed as late stage disease (6), International Federation of Gynaecology and Obstetrics (FIGO) stages III and IV after metastatic spread to the pelvis or surrounding abdomen. Early stage ovarian cancer (stage I/II) show 5 year survival rates in excess of 70%, whereas prognosis for late stage disease is 27% for stage III and 16% for stage IV surviving for 5 years or more (7). Reducing mortality for ovarian cancer via early screening strategies was attempted in over 200,000 OC patients (8), comparing the blood based diagnostic and prognostic biomarker Cancer Antigen 125 (CA 125) (9) combined with transvaginal ultrasound to no screening. No significant difference in mortality was observed with the exception of analysis performed after removal of prevalent cases where mortality was significantly reduced by 20% indicating the potential of multi-modal screening for EOC early detection, though further validation must occur before translation of this screening can be made to a clinical setting. The diagnostic sensitivity of CA 125 is reasonably high (75-90%) for advanced stage EOC, although limited for detection of low stage EOC (50-60%), indicating the requirement for more accurate screening methods for early detection in this disease (10),

1.2 Epidemiology

1.2.1 Genetic risk associated with ovarian cancer

A number of familial and environmental exposures have been linked to risk for development of ovarian cancer. Genetic inheritance of a germline BRCA1 or BRCA2 mutation occurs in 17% of ovarian cancer patients, and accounts for the majority of inherited disease (11). Inherited mutations in the BRCA mediated homologous recombination repair also occur in the patients such as RAD51C, RAD51D, BRIP1, BARD1 and PALB2, as well as mutations in other DNA damage repair pathways showing association with ovarian cancer risk: CHEK2, MRE11A, RAD50, ATM and TP53. There is an association between occurrence of Lynch syndrome, a genetic condition caused by mutations in the mismatch repair (MMR) DNA damage repair pathway, primarily MLH1 and MSH2, but also MSH6, PMS2 and EPCAM (ovarian cancer familial risk association with DNA repair pathway mutations, (reviewed by

Norquist et al,. (12)). Lynch syndrome is associated with familial development of colorectal cancer, often early under the age of 50. Lynch syndrome also generates pre-disposition to other types of cancer including ovarian (13). MMR pathway mutations are more often associated with the development of clear cell and endometrioid sub-types of OC and patients presenting with Lynch syndrome show an average age of presentation 20 years earlier than the average age of presentation for ovarian cancer which is 68 (13).

1.2.2 Environmental factors associating with ovarian cancer

Environmental factors associated with the development of ovarian cancer are use of oral contraceptives and hormonal replacement therapy (HRT), dietary intake and BMI, medication, mental health and reproductive history. Use of oral contraception is associated with a reduced risk of ovarian cancer, with the apparent exception of the mucinous sub-type (14), whereas use of HRT increases the risk of ovarian cancer for post-menopausal women by 10-22% (15). A tentative association has been identified between lactose intake and lower risk of ovarian cancer (16), as have flavonoids and black tea (17). Conflicting results have been shown for the link between obesity and OC, with one study indicating that obesity and weight gain are risk factors for certain OC sub-types (18). Perhaps more importantly, a meta-analysis showed that regular physical exercise and activity is protective from OC, with sedentary women showing an increase likelihood of up to 60% of acquiring the disease compared to active women (19). Aspirin use appears to associate with reduced risk of ovarian cancer (20). Severe persistent depression is associated with increased occurrence (21). Surgical removal of the uterus, fallopian tubes and ovaries are associated with a reduced risk of ovarian cancer, surgical intervention is highly effective at preventing occurrence of ovarian cancer in women with a germline BRCA1 or BRCA2 mutation (22).

1.3 Ovarian cancer histological sub-types and molecular pathology

EOC is a heterogeneous disease consisting of several histopathological sub-types, which seem to have distinct molecular drivers and are treated as different diseases. It was originally assumed that all EOC originated from the ovary epithelium, however gene expression profiling of histological sub-types and local tissues indicated that different EOC sub-types are likely to emerge from different tissues of origin. Serous EOC comprises 70% of EOCs (23) and is likely to originate from the epithelium of the fallopian tube (24), and are sub-divided in two histological sub-types: high grade serous (HGS) and low grade serous (LGS). Endometrioid OC and clear cell (CC) OC (11% and 12% of EOC respectively (23)) share features with endometrioid carcinomas and endometriosis and are likely from the endometrium or endometriosis (24). Mucinous OC is the rarest sub-type, comprising 4% of EOC (23) and its tissue of origin is likely to be endocervix or intestinal mucosa (24). These sub-types have different pathologies

20 and prognosis and likely different molecular drivers. Of all EOC sub-types HGSOC is the most lethal, contributing to ~70% of OC-related mortality, 80% of cases are stage III/IV tumours (25) and the vast majority relapse within 2 years of surgical resection and become resistant to taxane/platinum based therapies (26). TP53 inactivating mutations were identified at a prevalence of 96.7% in a cohort of 123 HGSOC patients (27). Genome-wide analysis of the cancer genome atlas (TCGA) EOC cohorts identified that high grade serous ovarian cancer (HGSOC) tumours harbour p53 inactivating mutations in >96% of cases (28). The high frequency of somatic p53 mutation is likely to contribute to the high level of genomic instability endemic to HGSOC which leads to frequent chromosomal aberrations, despite the low frequency of focal point mutations (29). Other contributing factors are likely to be the frequency of mutations in homologous recombination repair pathway genes: HGSOC patients frequently harbour RAD51C germline mutations and BRCA1 and BRCA2 germline or somatic mutations (30) and loss of the TSG PTEN is a common driver mutation in HGSOC (31). LGSOC on the other hand does not demonstrate chromosomal instability or frequent mutations in TSGs necessary for homologous recombination repair, instead it shows frequent mutation in RAS pathway genes: KRAS, BRAF, NRAS, ERBB2, and PTEN (32, 33); which are likely to drive the phenotype (34). Survival rates are reported as much higher for LGSOC than HGSOC, with this sub-type showing limited invasive potential at presentation and appearing to originate from benign tumours or adenofibromas in contrast to HGSOC which presents as highly invasive, advanced disease, with no clear disease origin, leading to its classification as a de novo cancer (35). Clear cell carcinoma seems to have different driver mutations depending on whether it is familial, in which case it shows high levels of microsatellite instability and germline mutations in mismatch repair (MMR) machinery genes: MLH1, MSH, MSH6 and PSM2, whereas somatic mutations occur in ARID1A, PTEN, and PIK3CA in sporadic cases (32, 33). Endometrioid EOC similarly to familial CC-EOC is associated with Lynch syndrome and frequently bears mutations in MMR pathway components in familial cases whereas sporadic cases commonly have mutations in ARID1A, PTEN, and PIK3CA, though these mutations are frequent only in high grade endometrioid OC (32, 33). Few potential drivers have been identified for mucinous EOC, however KRAS and ERBB2 are the most commonly mutated genes indicating potential molecular reliance on the MAPK pathway and similarity in molecular pathology to LGSOC (32, 33). In 2008, a study published by Tothill et al., (36) investigating genome wide expression profiles of serous and endometrioid gynaecological tumours identified six novel molecular sub-classes that clustered tumours into low grade endometrioid and low malignant potential serous tumours versus subtypes with poor prognosis showing 1) a highly mesenchymal signature with low expression of differentiation markers, 2) high expression of stromal cell content, 3) groups with higher indications of T cell infiltration and lower stromal content with better survival outcomes than the previous two subtypes.

1.4 Ovarian cancer treatment

1.4.1 Current treatment of ovarian cancer

Whilst there has been no tangible improvement in survival rates for ovarian cancer patients over the last decade, there has been a small increase in 5 year survival rates over the last 30 years from 37% to 45%. This is largely due to the implementation of cytoreductive surgery and combination chemotherapy in the treatment of the disease at primary presentation. Standard cytoreductive surgery involves abdominal hysterectomy, bilateral salpingoophrectomy, and pelvic and paraaortic lymphadenectomy. Debulking surgery makes a massive impact on survival with more radical debulking strategies such as retroperitoneal lymphadenectomy (lymph node secondary site tumours tend to respond poorly to chemotherapy), cholecystectomy, omentectomy and splenectomy as well as stripping the diaphragm. Studies investigating initial tumour size, surgical success and patient outcomes indicate that effectiveness of total cytoreduction in debulking surgery for advanced disease has a very significant effect on patient survival, irrespective of initial tumour burden (37). Chemotherapy is usually adjuvant after primary debulking surgery with a combination of carboplatin and paclitaxel (38) administered for 6 cycles, as an extension to 8 or 12 cycles does not show the benefit of extended survival (39, 40). Surgery can also be performed during the course of treatment as interval debulking or to debulk relapse patients’ secondary tumours, though this is rare. Adjuvant chemotherapy is recommended by NICE for patients following cytoreductive surgery with stage 1C disease or above. Modifications to therapy have shown advantages to survival, notably adjuvant intraperitoneal administration of cisplatin and paclitaxel for optimally debulked patients (41) and dose- dense paclitaxel administration every week instead of every 3 weeks (42, 43).

1.4.2 Chemoresistance

Whilst the number of patients who initially respond to chemotherapy are high at around 80%, most patients (around 70%) subsequently relapse, the majority of recurrent tumours will either have developed resistance to therapy or develop resistance after subsequent rounds of treatment. Additionally, around 20% of EOC will be initially refractive to chemotherapy, indicating resistance of the primary tumour to chemotherapy, and will recur within 6 months of the last cycle of chemotherapy (44). The median survival time after first relapse is around 2 years (38). Acquisition of chemoresistance is a major barrier to effective treatment of ovarian cancer.

1.4.3 Mechanism of action of platinum based therapies

The introduction of cis-diammine-dichloro-platinum (cisplatin) to the clinic changed outcomes of several solid tumour types: ovarian cancer, head and neck cancer and testicular cancer. Overall

22 cisplatin exerts a cytotoxic effect on the cancer cell which is primarily mediated by the induction of DNA damage, though other mechanisms of action have been suggested. Effects of cisplatin therapy on the cell phenotype are suspension of DNA synthesis, suppression of RNA transcription, cell cycle checkpoint inhibition and induction of apoptosis. Cisplatin becomes activated in its mono-aquated form where it covalently binds purine N7 sites generating inter and intra strand A-G, G-G and A-A crosslinks in the DNA heteroduplex (45). Cisplatin-DNA adducts seem to be recognised by a plethora of DNA damage recognition proteins which accumulate at the sites of damage and initiate DNA damage signalling pathways – a major downstream consequence of which is apoptosis. The multitude of DNA damage signalling pathways which regulate cisplatin-dependent apoptosis are reviewed by Siddik et al., (46). The major DNA damage recognition complexes appear to be mismatch repair (MMR) complex, recruited by the DNA damage recognition proteins hMSH2 and hMutSa and high mobility group proteins HMG1 and HMG2. The major apoptotic pathways involved appear to be ATR/Chk1 induced p53 activation which activates extrinsic Fas/FasL programmed cell death as well as the intrinsic Bax dependent caspase induction. Activation of p53 seems to be dependent on recruitment by HMG1/2, ATR/Chk1 signalling as well as MMR pathway activation, and its downstream effects also include activation of cell cycle arrest and DNA repair. Additionally apoptosis is induced by the TSG p73 activated by the c-Abl tyrosine kinase, this pathway seems to be particularly dependent on MMR signalling. The mitogen activated protein kinase (MAPK) signalling pathway appears to be necessary for apoptosis as well, particularly via the extracellular signal-regulated kinases (ERK), c-Jun N-terminal kinases (JNKs) and p38 kinases. Additionally ATR/Chk1 induced p53 and the PI3K/Akt pathway are involved in cell cycle arrest, likely by components of the nucleotide excision repair (NER) pathway, the pathway involved in attempting DNA repair of cisplatin induced DNA lesions, for which cell cycle arrest is necessary. 1.4.4 Mechanisms of platinum resistance in cancer

The eventual regrowth of tumours which are resistant to cytotoxic therapies is largely believed to be due to heterogeneity in the primary tumour, i.e. the existence of an underlying population pre- disposed to be less susceptible to cell killing by platinum which is selected for during the course of cytotoxic therapy and is responsible for repopulation of the tumour niche (47). It was originally postulated that the underlying resistant population is defined by newly acquired mutations permissible to survival of platinum therapy (48). The acquisition of mutations conferring resistance is postulated to be due to the high level of genomic instability inherent to ovarian cancer, likely to be in part due to high frequency of p53 mutations (49). Mechanisms conferring resistance to cytotoxic compounds such as platinum in cancer have been identified largely in drug efflux and exclusion systems preventing accumulation of intra-cellular platinum, impaired apoptotic induction resulting from DNA damage via

23 down-regulation of pro-apoptotic pathways and up-regulated survival/anti-apoptotic pathways, and increased efficiency of DNA repair (47). ABC transporters have widely been identified as up-regulated in cancer, the most well-known of which is ABCB1, which encodes multi drug resistance protein 1 (MDR1), or p-glycoprotein, responsible for prevention of accumulation of lipophilic compounds intracellularly and implicated with the emergence of tumour multi-drug resistance (50), though aberrations of this particular gene are more likely to confer resistance to paclitaxel than cisplatin as this receptor is not responsible to export of platinum based compounds. Tumour cell lines show reduced effects of cytotoxic therapy when rates of repair are enhanced (46), though an increased rate of repair has an estimated maximal contribution of 1.5-2-fold increase in resistance because certain adducts are refractory to repair (51). The primary DNA repair pathway responsible for removing and repairing DNA adducts is the nucleotide excision repair pathway (NER), when cells lose the ability to carry out transcription coupled NER, they are exquisitely sensitised to platinum (52). Expression of DNA recognition and repair proteins involved in the NER pathway, notably topoisomerase II, BRCA1, XPA and ERCC1 leads to up-regulated NER, contributing to enhanced resistance to platinum (46). Additionally mutation or down-regulation of DNA damage recognition pathway components, commonly belonging to the MMR pathway, which recognises DNA-platinum adducts and induce signalling cascades culminating in cell cycle arrest and apoptosis commonly contribute to reduced sensitivity to cisplatin (46). Additionally, signalling mediators and effector proteins involved in inducing apoptosis are commonly down-regulated and mutated in cancers, p53 is the most frequently inactivated gene in cancer and inactivation of p53 and/or its components which effect apoptosis e.g. the TSGs p14, p21 and Bax regularly contribute to cisplatin resistance in cancer, as well as up-regulation of pro-survival pathways which oppose apoptosis for example the membrane receptor HER2 and its downstream effectors in PI3K/Akt pathway as well as inhibitors of apoptosis such as Bcl-2 family proteins and survivin (46). It is believed that bypassing cell cycle checkpoint controls in part regulated by MMR and p53 will permit cells to initiate post-replication repair. One additional mechanism which appears to contribute to platinum resistance is the accumulation of intra-cellular thiol containing molecules such as nucleophilic GSH and the cysteine-rich metallothionein which lead to the inactivation of platinum-based compounds before they can generate adducts (46). 1.4.5 Mechanisms of platinum resistance in ovarian cancer

A recent genome wide mutational study in a large cohort of HGS ovarian cancer patients (28), the sub- type in which recurrent chemoresistant disease is most common, indicated that p53 is the most frequently occurring genomic aberration in HGSOC, other frequently occurring genomic abberancies occurred in kras, myc, PI3K signalling pathway, CCNE1 and BRCA1 genes as well as genome wide copy number aberrations.. As inactivating p53 mutations are almost ubiquitous in HGSOC they are likely to

24 be the single molecular driver of the sub-type but are insufficient to drive the development of chemoresistance. It is likely that p53 mutations define the chromosomal instability endemic to HGSOC. Several genomic aberrations involving chromosomal rearrangements were identified as occurring in HGSOC in which resistance was acquired during chemotherapy or in primary tumours refractive to chemotherapy, however none of these occurred at high frequency indicating that drivers of chemoresistance are likely to be highly heterogeneous between tumours even within one histological sub type of ovarian cancer. Gene breakage caused aberrations in NF1 and Rb, this finding is particularly interesting in the case of Rb which regulates E2F transcription factor activity and expression of cell cycle control genes. Other cell cycle control genes are likely involved in the development of platinum resistance, notably cyclin E (CCNE1) which when amplified in BRCA1 wild type tumours promotes cell cycle progression in the presence of platinum (53). Additionally, reversion mutations activating BRCA1 after inactivation in primary samples were observed in 12 patients, and reversion mutations in BRCA1 in one patient co-occurred with acquired chemoresistance in this study indicating the necessity of double strand break repair (DSBR) to occur for HGSOC cells to repair platinum adducts. 8% of recurrent ovarian cancer samples showed up-regulation of the MDR1 encoding gene ABCB1 due to chromosomal fusion events. Prevention of intra-cellular accumulation of cisplatin appears to be a major mechanism of drug resistance in ovarian cancer as ABC transporters identified capable of excluding platinum based compounds from ovarian cancer cells or associating with emergence of platinum resistance or poor prognosis in clinical ovarian cancer cohorts are ABCA1 (54), ABCA3 (55), ABCB3 (48, 56), ABCB4 (55), ABCB6 (55), ABCB10 (57), ABCC1 (56, 58–63), ABCC2 (48, 56, 64–66), ABCC4 (58, 67). In addition to ABC transporter proteins the copper transporter Ctr1 has been identified as responsible for efflux of platinum in chemoresistant ovarian cancer cells (68).

1.4.6 Cancer stem cells and drug tolerant persisters as the unit of selection for platinum resistance in ovarian cancer

More recently it was postulated that the unit of population heterogeneity under selection by platinum rather than a single mutation in a resistance conferring pathway is a cell state, commonly referred to as a cancer stem cell (CSC), believed to be responsible for initiation of primary and metastatic site tumours as well as retaining innate resistance to multiple therapies. It has been postulated that ABC transporter genes play a role in reducing intracellular concentrations of drugs in these cells, in part contributing to their multi-drug resistance phenotype. ABCB1 and ABCG2 have been shown to be over- expressed in ovarian cancer stem cells (69, 70). Efficiency of DNA repair and plasticity of cell cycle stage are additional mechanisms proposed to confer the chemoresistant phenotype to CSCs (71). A sub- population of stem-like cells has previously been linked to the acquisition of drug resistance in ovarian cancer (72–79). Tumour enrichment for CSC markers and stem cell associated signalling pathways Wnt,

Hedgehog and Notch, is associated with development of clinical chemoresistance in ovarian cancer (78) and ALDH1A1/CD133 expressing cancer stem cell populations are associated with poor patient prognosis (80). Ovarian CSCs have been proposed as a relevant target for combatting drug resistant ovarian cancer (72, 73). Sub-populations of cells expressing stem cell markers, showing tumour initiating activity and with enhanced resistance to therapeutics exist in an array of ovarian cancer cell lines (81) and a side population of tumoursphere forming cells with enhanced drug efflux efficiency were identified in patients following platinum-based chemotherapy, indicating the selection of such a heterogeneous population by therapy (70). Side populations isolated from primary EOC patient samples have been shown to be less sensitive to cisplatin than the bulk population (69) as to be more highly tumourigenic in mouse models providing strong evidence that sub-populations of cells responsible for tumour initiation are the same population selected by chemotherapy and conferring chemoresistance. So far in ovarian cancer and most other cancers studies identifying a role for CSCs in response to therapy have been largely correlative with the exception of cancers such as chronic myoproliferative leukemia (CML) in which in vivo and ex vivo studies with patients samples as well as in vivo studies have shown that CML-CSCs are innately less responsive to TKIs and appear to be resensitised to TKI therapy by inhibition of chromatin modifying enzymes EZH2 and HDACs. Several in vitro studies have expanded on the functional role of the CSC in drug resistance by elucidating the existence of drug tolerant persister (DTP) populations surviving EGFR inhibition as well as treatment with cytotoxic therapy including cisplatin in NSCLC cell lines (82), TKI inhibition glioblastoma (83), B- Raf inhibition, cisplatin, docetaxel and fluorouracil treatment in melanoma (84), and Notch inhibition in T-ALL (85). These cells appear to display multi-drug tolerance (84, 86), express CSC markers (82, 83, 86) and largely exist in a slow cycling, quiescent state, which appears to block cell killing. A recent in vivo model of gastric cancer used fluorescent ubiquitination-based cell cycle imaging (FUCCI) system to demonstrate that in the 3-dimensional architecture of the tumour the cells resistant to cytotoxic therapies exist within the hypoxic core of the tumour in G0/G1 arrest, whilst the cells killed exist in an S/G2 proliferative state at the tumour periphery. After treatment a shift in cell cycle dynamics to the periphery of the tumour permits repopulation and return to a proliferative state (87). In DTP populations derived from cell lines from several cancer types including NSCLC, CRC and breast cancer ALDH activity, which is high in stem cells and CSC populations from cancers including ovarian cancer as described earlier, was shown to be essential for cell survival of TKI inhibition, potentially due to its role in reducing DNA damaging aldehydes and reactive oxygen species (ROS) which induced apoptosis (82). The drug tolerant persistent cell state in glioblastoma has been identified as being induced by therapy in isolated glioblastoma stem cells (GSCs), due to a shift in chromatin dynamics (83), indicating that the drug tolerant, slow cycling state is induced by therapy and an epigenomic shift. Clinical evidence supports this state as periods of “drug holiday” in the course of drug administration have

Figure 1.1: Clonal, CSC and drug tolerant persister models of emerging drug resistance. Models of drug resistance emerging from clonal selection and innately drug resistant CSCs are adapted from Agarwal and Kaye 2003 (297). The schematic describing the model of emerging drug resistance incorporates theories of drug tolerant persisters from Sharma et al., 2010 (86), in which the existence of drug tolerant persisters (DTPs), expansion of DTPs under drug selection and reversion to sensitivity under absence of drug selection are described, mechanisms of emergence of stable, non-reversible resistance are based on work by Hata et al,. 2016 (251), in which emergence of stable resistance conferring mutations are observed in long term culture of DTPs and incorporation of the CSC model in which some CSCs switch to a slow cycling DTP cell state on exposure to drug is based on DTP isolation from GSCs by Liao et al., 2016 (83). been observed to extend the time during which a patient’s tumour remains sensitive to therapy (88– 90). Current theories describing the evolution of drug resistance are summarised in Figure 1.1. 1.5 Targeted therapies for the management of ovarian cancer and the necessities for biomarkers to stratify for patient outcomes

Due to the severity of the issue presented by resistance to cytotoxic therapies to effective ovarian cancer patient treatment, there has been a movement towards development of effective targeted therapies. There are however only two targeted therapies currently in clinic use for the treatment of ovarian cancer. One drug in clinical use is the Poly(ADP-Ribose) polymerase (PARP) inhibitor olaparib (Lynparza, Astra Zeneca), which after promising clinical trials (91–95) was approved in 2014 by the European Medicines Association for treatment of high grade serous ovarian cancer patients with germline or sporadic BRCA1 or BRCA2 mutations at relapse with platinum sensitive tumours (96). This

27 means that for the first time a drug is in stratified clinical use for ovarian cancer therapy based on BRCA-mutation status as a biomarker. Four clinical trials have tested the efficacy of bevacizumab (Avastin, Roche), a monoclonal antibody with specificity for vascular endothelial growth factor (VEGF) in the treatment of EOC. The ICON7 phase III trial (97) and GOG protocol 218 (98) both tested bevacizumab in EOC as first line therapies in combination with platinum and paclitaxel and both showed an extension of time to progression, with hazard ratios for progression with bevacizumab respectively: HR=0.717 (0.63, 0.82), P<0.001 and HR=0.87 (0.77, 0.99), P=0.04. Two additional clinical trials tested the efficacy of bevacizumab in a recurrent setting with the OCEANS trial (99) treating patients with bevacizumab in a chemosensitive recurrent setting in combination with carboplatin and gemcitabine, showing a delay in progression (HR=0.48, (0.39,0.61), P=0001) and the AURELIA trial (100) examining the efficacy of bevacizumab in treating recurrent ovarian cancer refractory to platinum, which also identified delayed progression of the disease with bevacizumab HR=0.48 (0.38 to 0.60), P=0.001. Whilst each of these trials identified improvements in EOC patient PFS, no trial identified relevant extensions in patient survival, with the exception of the ICON7 trial which identified improved OS when patients were stratified into a high risk sub-group with residual disease >1cm and with stage IIIC or IV disease: HR=0.64, (0.48, 0.85), P=0.002. Bevacizumab has been approved for use as a maintenance therapy to treat recurrent, chemoresistant EOC. Predicting which patients are likely to respond to therapies, and the underlying mechanisms associated with response, is essential. Clinical features of the disease at presentation do not permit accurate prediction of prognosis. Clinically useful approaches for stratifying ovarian cancer treatment are essential in order to provide more personalised healthcare and treatment strategies for this disease by identifying cancer early in the progression of the disease, providing prognostic information, identifying patients refractory to therapy and those likely to respond to targeted therapies.

1.6 The role of VEGF family genes in ovarian cancer

VEGF has an extremely well characterised role in promoting angiogenesis in all solid tumours, which is a hallmark of cancer (101). VEGF-A binds vascular endothelial growth factor receptors 1 and 2 (VEGFR- 1/2) as well as co-receptor neuropilin 1 (NRP-1) and transcription principally appears to be regulated by SP1 and SP3 factors at the proximal promoter region, though this likely occurs in concert with many other factors such as HIF1a, STAT-3 and AP-2, as many consensus binding sequences occur at the promoter (102), and the occurence of hypoxic stress appears to be a key factor in inducing VEGF-A expression. The transcriptional regulation of other VEGF family members is less well researched. VEGF- A is expressed in several isoforms, the majority of research has categorised VEGF-A165. The gene has an 8 exon structure and many additional alternatively spliced forms of VEGF-A have been discovered: VEGF-A121 with an exon 5/8 splicing junction, VEGF-A111 with an exon 4/8 splicing junction,

VEGF165/148 which contain exon 7, VEGF-A145 which contains exon 6, VEGF183/189/206 which contain exons 6 and 7 and VEGF-A165b/121b/183b/189b/145b otherwise known as the VEGF-Axxxb isoforms. The VEGF-A165 and 121 are the most abundant isoforms. VEGF-Axxx isoforms are generally shown to be pro-angiogenic, whereas VEGF-Axxxb isoforms seem to antagonise angiogenesis. These isoform categories define inclusion of sub-exon alternatives of exon 8, which all VEGF-A isoforms contain (103). VEGF-A165 and VEGF-165b differ by 6 carboxy terminal amino acids and have been shown to function similarly but with different downstream intra-cellular signalling strengths, VEGF- A165b activates VEGF-R2 only partially and activates weak ERK1 phosphorylation and signalling, and unlike VEGF-A165:VEGF-R2 interaction VEGF-R165b does not induce phospholipase C signalling (VEGF- A function reviewed (104)) Tumour cells often over-express VEGF which when secreted extra-cellularly binds VEGFR-1 and VEGFR-2 expressed by vascular endothelial cells (VECs), and promote cell proliferation, migration and secretion of matrix metalloproteinases to degrade the basement membrane to promote neovascularisation around the tumour, and an increase in vascular permeability. This supports the provision of nutrients and prevention necrotic death of the tumour. Angiogenesis also provides a route for aggressive, invasive tumour cells to extravasate and metastasise at secondary sites (105). Comparison of the roles of VEGF-A165 versus VEGF-165b indicate that VEGF- A165 is the major factor in pro-tumour angiogenesis (104). Additionally, tumour cells often express cognate receptors to VEGF on their cell surface, providing an autocrine mechanism driving similar phenotypes in cancer cells observed in activated vascular endothelial cells: enhanced proliferation, inhibition of apoptosis, secretion of matrix metalloproteinases, migration and invasion. VEGF is highly expressed in ovarian cancer and is likely to promote tumour malignancy via autocrine signalling as ovarian cancer tumour cells commonly express VEGFR-2 (106). Under normal physiological conditions the biological role VEGF-B is not completely understood, although evidence seems to point toward an antagonistic role in homeostatic regulation of the vasculature (reviewed (107)). VEGF-B interacts with both VEGFR-1 and NRP-1, via which it seems capable of promoting the survival of VECs by inhibiting the expression of apoptotic genes such as Casp 3 and Bcl2, and enhancing expression of pro-survival genes such as Myc or Cox2. This effect is particularly pronounced for VECs under oxidative stress or serum starvation. Via enhanced VEC survival VEGF-B seems to promote neovascularisation in pathogenic conditions such as myocardial or ocular ischaemia or neurodegenerative conditions. On the other hand, VEGF-B does not seem capable of promoting VEC proliferation or increase vascular permeability in the way that VEGF-A does. Under normal conditions VEGF-B has been shown to actively suppress VEC migration. In an in vivo model of neuro-endocrine pancreatic cancer VEGF-B over- expression was shown to inhibit tumour growth (108). There are two alternatively spliced isoforms of VEGF-B: VEGFB167 and VEGFB186. Alternative splicing of VEGF-B186 includes a heparin sulfate domain which is captured by heparin sulfate proteoglycans expressed extra-cellularly on the cell surface,

29 leading to more diffuse paracrine signalling via the VEGF-B167 isoform. VEGF-C, which has only one expressed isoform, functions via binding cognate receptors VEGFR-2 and VEGFR-3. It is often over- expressed and secreted by tumour cells and has an extremely well characterised role in cancer, promoting lymphangiogenesis (109) in solid tumours, the recruitment of lymphatic endothelial cells, induced by VEGF-R3 axis signalling provides a metastatic route to the local lymphatic nodes in which secondary tumours can seed and disseminate to more distal metastatic sites (110). VEGF-C is also a potent agonist of vascular endothelial cell activation and angiogenesis (111), via interaction with VEGFR-2. In ovarian cancer VEGF-C serum levels and tumour mRNA expression are associated in multiple patient datasets with more advanced stage, metastasis, and poorer survival outcomes (112, 113) and promotes tumour growth and metastasis, likely via paracrine stimulation of angiogenesis and lymphangiogenesis (114). VEGF-C is also becoming better characterised as playing an autocrine role in solid tumours, promoting growth, migration, invasion, chemoresistance and cancers stem cell phenotypes (115–125), in breast oesophageal, prostate and gastric cancer, and AML. This occurs via interaction with its cognate receptors VEGFR-2 (106) and VEGFR-3 (123) on the surface of expressing tumour cells. VEGF-C expression has been linked to more aggressive, invasive ovarian carcinomas (126) and promotes invasion and migration via autocrine signalling in cell line and in vivo models (114). Methylation at the promoters of two VEGF genes have been shown to associate with patient progression in two independent cohorts of HGSOC, high VEGF-B methylation associated with shorter time to progression (HR=1.9(1.12, 3.42), P=0.018 in TCGA and HR=1.12 (0.99, 1.28), P=0.04 in the Scottish Gynaecological Clinical Trial Group (SGCTG) cohorts)) and high VEGF-C methylation is associated with improved HGSOC PFS (HR=0.92 (0.86,0.99), P=0.021 in TCGA and HR=0.99 (0.98, 1.0), P=0.03 in the SGCTG cohorts). The reason for the disparity in direction of association between VEGF promoter methylation and clinical outcome has not been concretely demonstrated, Dai et al., suggest the reason is that VEGF-B methylation associates with increased expression as the molecular data in TCGA patients suggested.

1.7 Epigenetic aberrances in ovarian cancer

1.7.1 DNA Methylation

DNA methylation describes the catalysis of the addition of a methyl group to the fifth carbon of the cytosine base in a cytosine-guanine (CpG) dinucleotide context to generate 5-methylcytosine (5mC), was identified as an epigenetic modification with consequences for transcription. Distribution of CpG dinucleotides in the genome is bimodal, with discrete regions of CpG sites comprising regions known as CpG islands (CGIs) whereas throughout the rest of the genome CpG dinucleotides are depleted to the extent that overall the CpG dinucleotide occurs at 20% of the expected frequency genome-wide.

60-70% of gene transcription start sites (TSS) overlap with CGIs (127) at the promoters of genes over- represented for house-keeping and development function. The majority of research investigating the relationship between CpG methylation and gene expression focuses on methylation around TSS

Figure 1.2: Epigenetic modifications and their effect on chromatin structure and transcription and drugs to target them. Taken from Mack et al., 2015 (298), summarising the interplay between the epigenetic marks of DNA methylation at the gene promoter of CpG islands and various histone modifications around the gene promoter in regulating euchromatin versus heterochromatin formation and regulation of gene expression versus silencing. The role of chromatin modifying enzymes in the regulation of these epigenetic marks is shown: DNA methyl transferases (DNMTs) catalyse addition of repressive methyl groups to CpGs, lysine demethylases such as KDM6A remove permissive H3K4 trimethyl groups and the permissive mark H3K27ac is removed by histone deacetylases (HDACs). EZH2 catalyses H3K27 trimethylation which results in the formation of repressive heterochromatin. The mechanism of drugs which target chromatin modifying enzymes are also shown: EZH2 inhibition via GSK343, HDAC are targeted via small molecule inhibitors such as Vorinostat and BRD4 which promotes transcription upon recruitment to highly acetylated regions is druggable via BET inhibitors such as JQ1.

32 associated CGIs and its effect on the expression of the gene. Promoter-CGIs tend to be universally unmethylated irrelevant of tissue specificity in differentiated tissues (128), whilst promoter-CGIs are more heavily methylated in embryonic stem cells (ESCs) . DNA methylation is depleted in the pre- implantation embryo genome and re-established by enzymes early in embryonic development by enzymes referred to as de novo DNA methyl transferases (DNMT), known as DNMT3a and DMNT3b (129). DNA methylation is propagated through mitotic cell divisions via a maintenance enzyme DNMT1 which recognises hemi-methylated strands during replication and restores methylation to CpG sites on both DNA strands, likely synergistically with other DNMTs (130). Removal of CpG methyl groups largely seem to be catalysed by the TET family of methyl cytosine dioxygenase enzmes which via coupling of multiple oxidation steps - between 5-methylcytosine, 5-hydroxymethylcytosine, 5- formylcytosine and 5-carboxylcytosine - with base excision repair, in which 5-formylcytosine or 5- carobxycytosine are removed via thymine DNA glycosylase. This constitutes an active mechanism by which 5mC can be removed (131). Methylation of CGIs correlates with gene expression, with heavily methylated CGIs associated with gene silencing. It has been demonstrated that CpG methylation at CGIs controls gene expression and effects gene transcriptional silencing (132, 133). Given the almost ubiquitous nature of unmethylated promoter-CGIs in differentiated tissues, it has been postulated that methylation within the regions surrounding CGIs at the TSSs of genes, known as CGI “shores” and “shelves” within 4kb of the CGI and with elevated CpG density over the rest of the genome, may often have more control over modulating gene expression than methylation at the promoter-CGI. The most heavily methylated regions of the genome are repeat elements likely serving the function of silencing transposon sequences and ancient retroviral elements. In contrast to CpG dinucleotides within CGIs, CpGs within the rest of the genome in intronic, exonic and intergenic regions tend to show heterogeneity for the methylated/unmethylated ratio within a population of cells (128). In addition to methylation at the promoters of genes having consequences for transcription changes in methylation at CpGs within gene bodies show correlation with increased rates of transcription. Functional regions of the mouse methylome have been identified, bearing residual levels of low methylation (~30%) which correlate with expression of distal genes and overlap with chromatin marks identifying enhancer sites, functional regions of the genome responsible for controlling transcriptional programmes during development and regulating cell differentiation, indicating a role for methylation in regulating gene expression programmes via distal as well as proximal control elements (134). DNA methylation and its interplay which other epigenetic marks is summarised in Figure 1.2.

1.7.2 DNA Methylation as a driver of ovarian cancer

Occurrence of epimutation at CpG islands has been proposed as a unit of drug resistance conferring selection which occurs at a higher frequency than single mutations and has the potential to influence

33 the transcription of more genes (135). DNA methylation is now understood to be widely deregulated in cancer contributing substantially to carcinogenesis and progression of cancer (136). Initially global hypomethylation of cancer genomes was observed, EOC is no exception with no differences in patterns of global hypomethylation observed between histological sub-types, despite consideration of these sub-types as different diseases, supporting the observation that genomic hypomethylation is a ubiquitous feature of the cancer genome (137). Another DNA methylation event common to all histological sub-types of EOC is the hypomethylation of repeat sequences such as LINE/Alu elements and satellite DNA, hypomethylation of these elements in EOC is associated with more advanced stage and grade as well as poorer survival outcomes (138–142). Pathogenic transcriptional silencing of important tumour suppressor genes such as p16INK4A is induced by promoter-CGI hypermethylation in cancer (143). DNA hypermethylation occurs within CGIs at the promoters of tumour suppressor genes and genes involved in most major pathways related to the development and progression of cancer: DNA repair, cell adhesion, redox pathways, cell cycle progression and control of cell lineage and differentiation in ovarian cancer (144). Interestingly different histological sub-types of EOC show different pathogenic patterns of aberrant methylation, highlighting different epigenetic risk markers and drivers. A feature of HGSOC is the deregulation of homologous recombination repair pathways, and the most well characterised gene deregulated by aberrant methylation in ovarian cancer are the tumour suppressor genes BRCA1 and BRCA2 (30, 145, 146), transcriptional silencing of these gene by promoter hypermethylation likely contributes to the high level of genomic instability endemic to HGSOC in cases in which hypermethylation occurs, despite the low frequency of focal mutational events in this sub-type (147). Two studies using a whole genome approach in order to identify genes commonly hypermethylated at the promoter in HGSOC tumours compared to normal fallopian tube identified only 5 genes: ALDH1A3, AMT, LONRF2, NPDC1, and SLC16A5 hypermethylated in tumours in both studies (28, 29). Likewise, studies attempting to identify methylation signatures of HGSOC as well as primary compared to recurrent tumours show very little concurrence between studies indicating that overall HGSOC tumour development may not rely on frequent hypermethylation of key genes (148), supported by the observation that only 1% of promoter-CGIs are hypermethylated in HGSOC (149). One exception to this may be HOX family genes, an array of HOX genes commonly show dysregulation of expression in HGSOC (150) and DNA methylation may be a contributing factor to deregulation. HOXA9 has been observed as hypermethylated in two independent studies (151, 152), with one study identifying 95% of HGSOC patient samples showing hypermethylation of HOXA9 (152). HOX genes are well established as developmental, lineage defining genes expressed in differentiated tissues, HOXA9 has been characterised as expressed in fallopian tube epithelium only, as this is likely to be the tissue of origin for HGSOC, it is tempting to infer a role for HOXA9 hypermethylation in malignant transformation of fallopian tube and ovarian cancer tumourigenesis. Interestingly HOXA9

34 hypermethylation has also been reported in low grade serous (153), endometrioid (154) and clear cell (155) OC. One study suggested that LGSOC has frequent mutations in the promoter-CGIs of MAPK4, HOXA9, AATK, WNT5A, and GFI1 (153), however further studies are needed to confirm the role of hypermethylation in driving this OC histo-type. In an analysis of EOC Mayo Clinic patients CC carcinoma showed a propensity for to display a CpG island methylator phenotype (CIMP) (149), with hypermethylation occurring at 71% of promoter-CGIs. Sen et al., identified that this phenotype is likely driven by over-expression of the HNF1 homeobox B (HNF1B) gene, which is common in this sub-type. Conversely, Shen et al., also demonstrated that SNPs associated with the serous sub-type of EOC in TCGA patients were identified in or near polycomb repressive complex 2 (PRC2) binding regions of the HNF1B gene and associated with promoter-CGI hypermethylation, indicating that molecular pathologies of EOC histological sub-types may be defined by epigenetic modification driven by SNPs in genes such as HNF1B (156). Other studies have failed to show convincing evidence of CGI methylation at specific gene promoters as a driver for CC-EOC development (148). Highly methylated TSGs seem to be a particular feature of low grade, low stage CC-EOC, with some high grade/high stage tumours escaping the CIMP phenotype and losing genome-wide promoter methylation (32, 157). This is also a characteristic of the methylome of endometrioid OC, which tends to show a CIMP-like abundance of promoter hypermethylation in low grade tumours, Cicek et al., identified hypermethylation at 21% of promoter-CGIs in low grade endometrioid tumours, with a reduction to 16% of genes in high grade tumours (149). It has been postulated that TSG silencing due to the CIMP phenotype is essential in CC and endometrioid EOCs to drive tumourigenesis, but that the silenced phenotype is not required for progression to a more advanced disease, and may even be selected against (148). The occurrence of mucinous tumours is too rare to convincingly identify specific epigenetic drivers. It has been postulated that propensity for acquisition of CGI methylation in ovarian cancer may be due to a genetic polymorphism in the DNA methyltransferase 3b6 (DNMT3b6) gene (158).

1.7.3 DNA methylation and chemoresistance in ovarian cancer

In addition to selection for mutationally acquired defects conferring resistance to platinum therapies, intrinsic and de novo defects are defined by alterations in DNA methylation, impairing expression of genes which are required to maintain sensitivity to platinum. Targeted studies have identified several genes for which shifts in methylation are associated with chemoresistance, however many of these studies suffer from a lack of reproducibility and low sample numbers (159). There are two recurring candidate genes where promoter-CGI hypermethylation is likely to drive platinum resistance. Silencing of human mutL homolog 1 (hMLH1) by promoter-CGI hypermethylation occurred in 90% of in vitro derived chemoresistant EOC cell lines derived from a chemosensitive precursor which expresses unmethylated MLH1 (160). As a consequence of MLH1 silencing these chemoresistant populations

35 were MMR deficient and suffered impaired platinum–dependent cell cycle arrest and apoptosis (161) and when re-expression of MLH1 was induced by 2’-Deoxy-5-azacytidine the resistant populations were resensitised in in vivo models (162). In addition the acquisition of MLH1 methylation over the course of platinum based chemotherapy has been observed in OEC patients and associated with clinical resistance to platinum based therapies and poorer patient prognosis (161, 163, 164). Whilst the function of the MMR pathway is to ensure the integrity of the genome in the presence of DNA heteroduplex base mismatches via homologous recombination repair, the MMR machinery in the presence of platinum induced DNA damage appears to be to ensure G2 cell cycle arrest via ATR/Chk1 and p73/c-Abl signalling (165) or programmed cell death, likely via p53-dependent as well as p73- dependent pro-apoptotic signalling involving c-Abl (166). The loss of MMR signalling due to the hypermethylation and silencing of MLH1 in EOC is considered to be a major mechanism of resistance to platinum based therapy due to avoidance of cell cycle checkpoint induction and/or apoptosis in ovarian tumour cells. Additional TSGs for which promoter-CGI hypermethylation and silencing has been observed concurrently with platinum response are BRCA1 and BRCA2. Tumourigenesis of 11% of serous tumours are likely to be driven by hypermethylated at the BRCA1 promoter (167). BRCA1 and BRCA2 hypermethylation or mutational inactivation in ovarian cancer tumours is strongly associated with sensitivity to platinum therapy (168), because BRCA1 and BRCA2 are involved in RAD51 assembly essential for DSBR via non-homologous end joining (169, 170), double strand breaks (DSBs) are likely to occur due to replication fork crashes induced by inter-strand cross-links (171), an ability of the tumour cell to repair these lesions is likely to result in lack of cell viability upon treatment with cisplatin. Revertant OC tumours with BRCA gain-of-function due to secondary mutation of BRCA1 or BRCA2 (172, 173) or re-expression due to loss of methylation (28) is associated with acquisition of resistance to chemotherapy. In addition to selection for survival of tumour cells with focal methylation changes at the promoters of key genes driving resistance, whether hypomethylation of genes protective of cisplatin damage or induction of apoptosis of hypermethylation of genes conferring sensitivity and pro- apoptotic signals, an alternative hypothesis has been postulated for the selection of cells with DNA methylation changes conferring resistance to platinum. MMR (174) and DSBR (175–177) machinery in the presence of oxidative damage can recruit DNMT1 to the site of damage causing de novo aberrant methylation. It was suggested by Flanagan et al., that as cisplatin-DNA adducts recruit MMR and DSBR machinery, that they are capable of recruiting DNMT1 to the site of the adduct and inducing aberrant methylation at the site of repair. If methylation at this site affects expression of key genes conferring resistance to cisplatin, these cells would be selected from the population for repopulation of the tumour niche over the course of treatment, with cisplatin treatment generating a de novo resistant population rather than selecting for a pre-existing one. Flanagan et al., observed that where genome hypermethylation occurred in DNA extracted from OC patients’ bloods at relapse after platinum based

36 therapy compared to blood DNA at primary presentation, patients were more likely to experience a poor survival outcome (178). Additionally the authors showed that global methylation increased after treatment with low doses of cisplatin and clonal selection of chemosensitive cell lines, with a significant enrichment for methylation in MMR proficient versus MMR deficient cell backgrounds, supporting the hypothesis that aberrant methylation is induced by cisplatin induced DNA damage and recruitment of DNMT1 at least in a partially MMR-dependent manner. However it is difficult to be certain whether the cell populations cloned after cisplatin treatment were highly methylated because of cisplatin- dependent induction of methylation rather than selection by treatment of a pre-existing cell population with a more methylated genome, which confers tolerance to cisplatin.

1.7.4 DNA methylation as a therapeutic target

Due to the number of EOC cases which show hypermethylation of key TSGs, treatment with demethylating agents became the first serious candidates for epigenetic therapies. The two primary demethylating agents are 5-azacytidine (5-Aza), and 2’-deoxy-5-azacytidine (decitabine) for which attempt have been made to incorporate DNA demethylating agents into cancer patient therapeutic regimens. Both are nucleoside analogues, which are incorporated into the DNA during replication. Upon DNMT1 binding during re-establishment of double stranded methylation the nucleoside analogues bind covalently to the DNMT1 active site competitively inhibiting the enzymes from catalysing methylation elsewhere (179). Nucleoside analogues have shown efficacy for and are in clinical use for treatment of all five sub-types of myelodysplastic disease (MDS) following Food and Drug Administration (FDA) approval for azacytidine in 2004 and decitabine in 2006. Given the promising indications from treating chemoresistant human ovarian xenograft tumours with a combination of azacytidine and carboplatin (162), a phase II study was implemented in platinum resistant OC patients (180) treating with low dose decitabine before carboplatin. Results from 17 patients were reported as promising with an objective response rate (ORR) of 35% achieved and an average PFS of >10 months. Demethylation of MLH1, RASSF1A, HOXA10, and HOXA11 were also reported positively associating with longer patient PFS. A different phase II trial tested the efficacy of decitabine in sensitising partially platinum-resistant OC tumours, i.e. where recurrence occurred 6-12 months within the final chemotherapy dose, to carboplatin by azacytidine (181). Unfortunately the patient arms treated with azacytidine showed poorer response rates than the patients treated with carboplatin alone, and so the study was closed early. Currently a phase II trial (identifier NCT02901899, unpublished) is in the recruitment phase to evaluate ORR in platinum resistant, recurrent ovarian cancer patients treatment with DNMT inhibitor Guadecitabine and the immune checkpoint inhibitor Pembrolizumab.

1.7.5 DNA methylation as a biomarker for patient stratification

DNA methylation levels are stable and detectable in blood plasma (182), providing potential for non- invasive screening. Cell free DNA (cfDNA) is released into the blood by solid tumours and due to the stability of DNA methylation it is considered that tumour-specific methylation can be detected via blood based analytical methods as a biomarker for detection, prognosis and prediction of response to therapies. The only DNA methylation biomarker in current clinical use is hypermethylation of the O6- methylguanine-DNA methyltransferase (MGMT) gene in glioblastoma as a predictive marker for response to alkylating drugs such as Temozolomide (183). A number of blood based DNA methylation biomarkers seem promising for early detection of disease (184), for example SHOX2 methylation has shown an 80% sensitivity in the detection of small cell lung carcinoma (185). Unfortunately due to issues with sensitivity, specificity, cut-offs, cellular heterogeneity and costing blood based biomarkers have yet to make an impact in a clinical setting (184).

1.7.6 Histone modifications and regulation of transcription

Accessibility of the genome to transcription factors is controlled by the covalent addition largely of acetyl and methyl groups to the tails of histones. The histone octamer forms the basis of the nucleosome structure, in which 180bp of DNA heteroduplex are wrapped around the histone octamer core. Nucleosomal packaging and positioning forms the basis of higher order 3-dimensional structure of DNA as well as altering accessibility of transcribable regions of the genome, particularly around the promoter regions of protein coding genes, to transcription factors and RNA Pol II, making these regions permissible for transcription and expression. Transcription of the genome is kept under tight control by combinations of histone modifications in distinct and discrete functional regions, otherwise known as the “histone code”. Two major histone marks involved in regulation of transcription are trimethylation of histone 3 lysine residue 27 (H3K27me3) and acetylation of histone 3 lysine residue 27 (H3K27ac). H3K27me3 and H3K27ac tend to exist mutually exclusively and are enriched at the promoters of genes. H3K27me3 around the gene TSS is associated with and likely to mediate transcriptional silencing of gene expression due to loss of accessibility of the promoter, and H3K27ac is associated with and likely to mediate chromatin accessibility at gene promoters and transcriptional activity. H3K27ac is also a primary histone mark of active enhancer sequences, functional genomic elements occurring in introns and intergenic regions, which regulate active transcription of nearby genes by looping and interacting with promoters and facilitating recruitment of transcription initiation complexes. The addition of acetyl groups to H3K27 residues as well as others are catalysed by histone acetyl transferases (HATs) and their removal is catalysed by histone deacetylases (HDACs) which are commonly deregulated in cancers (186). H3K27me3 deposition is regulated by the enhancer of zeste

38 homolog 2 (EZH2), also commonly deregulated in cancers (187), the catalytic subunit of the polycomb repressive 2 complex (PRC2), which is responsible for maintaining transcriptional silencing of key differentiation genes in embryonic stem cells ESCs. Trimethylation of histone 3 lysine residue 4 (H3K4me3) is also associated with transcriptional activity at the promoters of transcriptionally active genes. In ESCs the active H3K4me3 mark and repressive H3K27me3 mark persist at certain promoters which are considered bivalently marked and although maintained as transcriptionally active at a low level whilst being poised for highly active expression or silencing during the process of development and cellular differentiation (188). The role of histone modifications in regulating the epigenome is included in Figure 1.2

1.7.7 Aberrant EZH2 activity and H3K27 trimethylation as a driver of ovarian cancer malignancy

Over-expression of EZH2 has been linked to poorer patient prognosis, both faster progression and shorter survival times, in many types of cancer, including but not limited to breast (189), prostate (190), non small cell lung carcinoma (191) and melanoma (192), as well as ovarian cancer (193). EZH2 expression has been identified as associating with enhanced tumour metastasis as well as driving tumour cell phenotypes of enhanced proliferation, migration and invasion in melanoma (192), hepatocellular carcinoma (194), endometrial carcinoma (195), NSCLC (191, 196), and ovarian cancer (193). The cell signalling effects of EZH2 deregulation appear to be pleiotropic and drive malignant cancer phenotypes via different pathways in different cancers with EZH2 silencing key TSGs in melanoma: DCK, AMD1 and WDR19 (192), EZH2 appears to drive malignancy in endometrial cell carcinoma via deregulation of the Wnt pathway (195), silencing of the long non-coding RNA (lncRNA) SPRY4 in NSCLC linked with growth, metastasis and poor patient outcomes (196), whilst deregulation of VEGF axis signalling due to epigenetic silencing is linked to similar phenotypes in the same cancer (191). In ovarian cancer phenotypes associated with tumour growth and metastasis are linked to the deregulation of TGF-β1 signalling (193). The multitude of tumourigenic pathways associated with over- expression of EZH2 support programmatic genome wide transcriptional silencing in cancer by this gene. EZH2 has a well-established function in ESCs suppressing cell differentiation and promoting limitless growth and pluripotency. It is fitting therefore that the most commonly reported associated between EZH2 expression and malignant cancer cells is in CSCs. EZH2 is over-expressed in CSCs derived from both patient and cancer cell lines in a number of cancers including breast cancer, pancreatic cancer, skin cancer, melanoma, leukemia, colorectal cancer, hepatocarcinoma, glioblastoma multiforme (GBM), pancreatic ductal adenocarcinoma (PDAC) and ovarian cancer (summarised by Wen et al., (187)). In these studies CSCs are defined as tumour cell sub-populations expressing cancer stem cell markers associated with the tumour type of interest, the ability to form spheroids in non-adherent culture, or side populations defined by Hoechst dye efflux. EZH2 is over-expressed in ovarian cancer

CSCs defined by ABC-transporter activity and ability to grow in anchorage-independent conditions. Loss of EZH2 resulted in loss of side population and spheroid formation in EOC cancer cell line populations indicating that EZH2 is necessary for the maintenance of a CSC sub-population in EOC (70). EZH2 mediated targeted silencing of miRNA-98 has been linked to deregulation of the oncogene c- myc, well characterised as driving proliferation in cancer, and suppressing p21-dependent cell cycle arrest as well as deregulating Rb-E2F signalling in EOC CSCs, defined by CD44+/CD117+ (197). In certain studies, notably chronic myeloid leukemia CML, both EZH2 expression and the associated H3K27me3 landscape have been identified as essential for maintaining a cancer precursor population: leukemia initiating cells (LICs) in CML (198, 199), with EZH2 driving aberrant differentiation of haematopoetic stem cells (HSCs) into LICs, instead of haematopoetic precursor cells (HPCs), in a manner associated with enrichment for promoter H3K27me3 gain and promoter-CGI hypermethylation (198). CSCs have been implicated as the major driving cellular compartment in tumour initiation and metastasis and many cancers, the data identifying EZH2 and PRC2-dependent epigenomic remodelling in defining this cellular phenotype as a promising therapeutic target.

1.7.8 EZH2 as a driver for platinum resistance in ovarian cancer

Side populations over-expressing EZH2 were identified as enriched post chemotherapy in EOC patient samples (200). CSCs have been implicated as a driver for platinum resistance and tumour repopulation following chemotherapy in ovarian cancer (73), and so if EZH2 is a major contributor to maintenance of an EOC CSC population it is likely to be a major driver of drug resistance. EZH2 has been implicated in development of chemoresistance in other cancers, notably LICs in CML are exquisitely sensitive to EZH2 inhibition indicating that up front combination therapy with Imatinib or other TKIs may be preventative of TKI resistant recurrent disease. Conversely, EZH2 has been identified as down- regulated in GBM stem cells (GSCs), which persist when treated with TKI, switching to an inactive cell cycle state associated with up-regulated KDM6A/B, upon which the cells are dependent (83), indicating that EZH2 may regulate stemness and drug resistance differently dependent on cell and cancer type. 1.7.9 Aberrant bivalent histone marked chromatin as an epigenetic driver of ovarian cancer malignancy and chemoresistance

A study by Chapman-Rothe et al., identified co-occurrence of H3K4me3 and H3K27me3 marks at gene promoters showing silencing in a serous EOC tumour (201). They therefore hypothesised that bivalent chromatin marking occurs at promoters of certain gene sets in EOC, poising them for expression. This study identified silenced gene sets in this tumour enriched for PI3K and TGFβ pathways. Over- representation for transcriptional silencing of these gene sets was identified in malignant HGSOC compared to normal fallopian tube epithelium, isolated CSC populations compared to bulk tumour cell

40 populations and chemoresistant cell lines compared to their drug sensitive matched populations. The authors hypothesised that programmatic regulation of transcription and silencing of genes bivalently marked by H3K4me3 and H3K27me3 contributes to programming of tumour initiating cells, more malignant tumour phenotypes and evolution of chemoresistant tumour cells in EOC. Brown et al., expand upon this hypothesis, postulating that the maintenance of low level gene expression at key genes drives the persistence of CSC populations which have an innate propensity to tolerate cytotoxic therapy. The emergence of a chemoresistant or chemosensitive tumour from the tumour sustaining population in this model depends on retention of the bivalent epigenome state in the case of chemosensitive recurrence, versus emergence of a drug resistant tumour which would depend on the loss of H3K4me3 at key promoters, resulting in the silencing of key “drug sensitivity casettes” or pro- apoptotic genes, and/or the loss of H3K27me3 at the promoters of genes promoting resistance to therapy, or “drug resistance casettes” (202). Unfortunately the possibility of heterogeneity confounding this analysis with H3K27me3 and H3K4me3 enriched at the promoters of different cells in the tumour population cannot be discounted, however selection tumour cells with enrichment for H3K27me3 silenced genesets in more advanced, metastatic and chemoresistant cancers is still a distinct possibility based on this study. This hypothesis is supported by the over-expression of EZH2 in EOC CSC-like side populations (200). EZH2 over-expression and H3K27me3 enrichment and silencing of gene expression has been observed in many cancers including EOC, and is associated with malignancy and particularly with drug resistance. Moreover epigenetic silencing events have regularly been associated with CSC activity, malignancy and drug resistance in cancer. As described above this correlates with the common occurrence of DNA hypermethylation events occurring at key TSGs and its association with EOC development, progression and chemoresistance. Depletion of global acetylation has been observed alongside the reliance on histone deacetylase (HDAC) expression for malignant phenotypes in many cancers (186), which is associated with transcriptional silencing as is methylation of H3K4me1 via histone methyltransferases which have been identified as required for the development of certain drug resistant phenotypes in a variety of cancers (83, 84, 86). Overall the evidence for epigenomic reprogramming events enforcing gene silencing driving the emergence of more malignant cancer phenotypes and resistance to cytotoxic and targeted therapies is fairly over- whelming.

1.7.10 Targeting EZH2 as a therapeutic option for ovarian cancer

A large number of enzymatic inhibitors, and some non-enzymatic inhibitors have been developed to inhibit the action of EZH2 (summarised by Wen et al., (187), included in Figure 1.2). EZH2 inhibition has been shown to resensitise CML LICs to tyrosine kinase inhibition (198) and NSCLC in vivo and in vitro models mutant in either BRG1 or EGFR, which accumulate EZH2, and are refractory to treatment

41 with Topoisomerase II inhibitors: etoposides, are sensitised to etoposide therapy by EZH2 inhibition. Unfortunately EZH2 inhibition has in some cases shown amplification of cancer cell growth due to increased Ras signalling (203), the correct combination of therapeutics must be identified for translational to the clinical setting, for example in malignant peripheral nerve sheath tumours (MPNSTs) Ras pathway activation by EZH2 inhibition sensitises the tumour to BRD4 inhibition. Dual EZH2 and EHMT2 inhibition in breast cancer cells shows loss of H3K27me3 and H3K9me3 remodelling, both associated with transcriptional silencing, and enhanced growth inhibition (204).

1.7.11 Aberrant histone acetylation and HDAC activity as a driver for ovarian cancer malignancy and platinum resistance

Deregulation of HDAC expression and resultant histone hypoactelyation has been observed in a wide range of cancers, with type I HDACs alone (HDACs 1, 2, 3 and 8) being over-expressed in tumour tissues derived from gastric, oesophageal, colorectal, prostate, breast, ovarian, lung and pancreatic cancers (205), representing >75% of cancer types. Inhibition of HDACs in a range of cancers has shown anti- tumourigenic effects. Over-expression of Class I HDACs 1, 2 and 3 was shown to associate with poorer survival in a cohort of patients with tumours of the endometrioid sub-type of EOC (206). HDACs 1, 2 and 3 have been shown to promote malignant phenotypes of ovarian cancer (207–209). Expression of class I HDACs 1, 2 and 3 were investigated in a cohort of 115 EOC patients, although 23 of these patients were benign, the EOC cohort was representative of serous, mucinous, clear cell and endometrioid EOC sub-types. Expression of HDACs 1 and 2 was shown to associate with Ki67 expression, indicating a role in promoting tumour cell proliferation, confirmed with in vitro studies (208) which confirmed a role for HDAC 1 in promoting growth and survival of an ovarian cancer cell line, via re-expression of cyclin A. This study additionally outlined a role for HDAC 3 in promoting EOC cell migration by deregulating the expression of E-cadherin. Over-expression of HDAC 1 was validated in an independent EOC cohort (207). The same study identified for the first time that inhibition of class I HDACs via chemical inhibition inhibited OC growth, recapitulating these results with selective knock-down of class I HDACs, identifying HDAC 3 as the most interesting putative target HDAC gene (HDAC inhibition as a target for altered epigenomic regulation included in Figure 1.2). Most notably, cancer cells seem to have some dependency on HDAC activity to maintain viability and avoid cell death as inhibition of HDACs have resulted in increased cellular apoptosis, this is likely mediated by HDAC-dependent suppression of pro- apoptotic signalling pathways in tumours, review by Zagni et al., (210) indicates that the key mechanisms by which HDACs are likely to drive proliferation and survival in cancer cells are via silencing of key TSGs such as p21 and p27. Inhibition of HDACs has been shown to lead to re-expression and accumulation of p21 and p27, which promote cell death, and lead to increased caspase activity, likely to occur in part due the loss of expression of Bcl-2 family anti-apoptotic proteins and re-expression of

42 pro-apoptotic proteins such as Bax and Bak upon HDAC inhibition. HDACs have been implicated in maintaining a tumour sustaining CSC population in certain cancers, a cellular sub-population also implicated in driving evolution of drug resistant cancers (described above), for example HDAC inhibition has showed synergistic killing in combination with Imatinib of chronic myelogenous leukemia (CML) CSCs which are otherwise resistant to tyrosine kinase inhibitors (211). In two EOC cell lines over- expression of HDAC1, HDAC2, HDAC3 and HDAC4 resulted in an increased IC50 dose response to cisplatin, indicating that HDACs can drive platinum resistance in EOC tumours (209). It was observed that reduction of cytotoxicity was dependent on the combination of the HDAC being over-expressed and the cell line being treated, indicating that different HDACs may drive EOC cell survival and platinum resistance in different EOC tumours, depending on factors such histological sub-type or genetic background. Additionally this study showed that surviving, cisplatin treated EOC cell line populations had higher HDAC expression, perhaps indicating selection of an underlying CSC-like sub-population with intrinsically higher HDAC expression and resistance to therapy.

1.7.12 Targeting HDACs as a therapeutic intervention for ovarian cancer

The HDAC enzymes belong to four classes of protein. HDAC classes I, II and IV function via a catalytic zinc ion in the active site. Most HDAC inhibitors are designed to prevent activity by inhibiting the catalytic activity of the active site zinc ion, and therefore show pan activity across three classes of HDAC: I (HDAC1, HDAC2, HDAC3 and HDAC8), II (HDAC4, HDAC5, HDAC6, HDAC7 and HDAC9a/b) and IV (HDAC11). Hydroxamic acids are one such type of drug developed as small molecule inhibitors for the inhibition of HDACs, and are widely entering clinical use for the treatment of cancers (210). Vorinostat was the first developed, and approved by the FDA for the treatment of cutaneous T cell lymphoma in 2006, belinostat has been approved for the treatment of peripheral T cell lymphomas and pabinostat is in clinical use for the treatment of multiple myeloma. HDAC inhibitors are in clinical trials for a wide range of cancers, interestingly often in combination with other drugs as HDAC inhibitors are showing sensitisation to other drugs in some cases. HDAC inhibitors have been trialled in EOC patients in two phase II clinical trials as single agent therapies testing Vorinostat in the treatment of recurrent EOC (212), and Belinostat in the treatment of platinum resistant EOC (213). HDAC inhibitors were not taken forward to phase III trials as in the former study 25 of 27 patients died within 6 months of therapy and in the latter study the median survival was only 2.3 months in EOC patients. HDAC inhibitors were therefore also not taken forward as a combination therapy, unfortunately the only phase II study testing the combination efficacy of HDAC inhibition in treating recurrent and platinum resistant EOC had to terminate early due to lack of efficacy(212). Of 27 patients evaluated only one showed complete response and one showed partial response. It was concluded that Belinostat does not resensitise platinum resistant EOC tumours to carboplatin. It may lend

43 credence to the hypothesis that HDAC activity supports the survival of EOC CSC-like sub-populations which accrue redundant mutations and epigenetic aberrations during the course of therapy, meaning that after a 12 cycle course of platinum therapy HDAC inhibition is no longer sufficient to resensitise robustly platinum resistant tumours. This may be an argument for treating patients with HDAC inhibitors before carboplatin at primary presentation to prevent to the occurrence of resistance. EOC patients treated with the up front combination of vorinostat with the cytotoxic therapies carboplatin and paclitaxel showed high toxicity, notable severe gastrointestinal effects (214) and the combination of vorinostat, carboplatin and gemcitabine in the recurrent chemosensitive setting for EOC patients resulted in high levels of haematolocial toxicity in a phase I study (215) although the combination appeared to show efficacy according to RECIST criteria. Finally a combination of Decitabine and valproic acid were used prior to platinum based therapy in a phase I study of EOC patients with advanced disease who were resistant or refractory to standard first line chemotherapy (216) in an attempt to synergise HDAC inhibition and genome demethylation to reverse the epigenomic silencing associated with drug resistant EOC. This study showed some evidence of anti-tumour activity.

1.8 Hypothesis and Aims

1.8.1 Prognostic value of VEGF family methylation in EOC patients from the ICON7 clinical trial and follow up in functional cell line studies

From chapter 3, this thesis starts from the basis that VEGF-B and VEGF-C CGI methylation have been identified as prognostic for HGSOC patient outcomes previously in two independent patient cohorts (217). In the ICON7 clinical trial a subset of high risk patients, based on stratification for stage and suboptimal surgical debulking, was identified which showed an OS advantage for receiving bevacizumab in addition to carboplatin and paclitaxel. We hypothesised therefore that changes in methylation at the promoters of VEGF genes in EOC or HGSOC patients within the cohort are likely to affect gene expression and potentially affect the interplay between the effect of bevacizumab upon angiogenesis and the behaviour of the tumour in recruiting a blood supply or self-sufficiency of growth. Where these biological effects are relevant, patient methylation of VEGF genes may show differential associations with patient survival in ICON7 trial arms. We also hypothesised that as VEGF-B and VEGF- C affect patient outcomes in HGSOC patients treated with carboplatin and paclitaxel that they may have an impact on the development of chemoresistance, VEGF-B methylation was previously shown to association with overall response rate of therapy.

1.8.1.1 Aims

1 Perform bisulfite pyrosequencing of ICON7 EOC patient DNA and use survival analysis to identify prognostic associations of methylation at VEGF loci (and expression where

available) in ICON7 patients from opposing trial arms to identify their value as prognostic markers for progression and survival outcomes when treating with bevacizumab. 2 For VEGF genes where methylation associated with clinical outcomes in ICON7 patients identify trends in methylation and expression in HGSOC patients at primary presentation and chemoresistant relapse to identify potential interactions between deregulation and platinum resistance in the ICGC cohort 3 Identify expression and methylation patterns of VEGF loci of interest in appropriate cell lines and generate functional in vitro models via CRISPR to follow up on hypotheses generated from aims 1 and 3 on the roles of these VEGFs in promoting EOC/HGSOC malignancy.

1.8.2 Isolation and characterisation of a cisplatin tolerant population from a chemosensitive ovarian cancer cell line

Based on the studies isolating slow cycling, epigenetically defined drug tolerant populations of cells from chemosensitive tumour populations from cancers, including NSCLC (86); breast, pancreatic and CRC (82); glioblastoma (83); T-ALL (85) and melanoma (84), we hypothesised that similar sub- populations may exist within ovarian cancer tumours, the aim from chapter 5 was to identify their presence within an exquisitely chemosensitive OC cell line and identify any epigenomic mechanisms driving their survival.

1 Identify cancer stem cell populations within the A2780 cell line and chemoresistant lines derived in vitro as evidence of CSC sub-populations correlating with drug platinum resistance 2 Identify the existence of a sub-population of A2780 cells showing tolerance to cisplatin and slow cycling, CSC enrichment if relevant 3 Identify any chromatin modifying enzymes upon which survival of any cisplatin tolerant population of cisplatin is dependent and profile the isolated cell population to identify potential genome wide changes in epigenome regulation and transcription.

Chapter 2: Materials and Methods

2.0 General Reagents

Table 2.1: General reagents

Experimental use Reagent Supplier Phosphate buffered saline Imperial College laboratories Tris buffered saline Imperial College laboratories Methanol analytic reagent grade Thermo Fisher Scientific RNase free DEPC treated water Ambion Distilled water Imperial College laboratories General Bovine serum albumin Santa Cruz Sodium dodecyl sulfate (SDS) Sigma Aldrich NaCl Thermo Fisher Scientific Glycine Sigma Aldrich Sodium azide Sigma Aldrich Ethanol absolute analytic regent grade Thermo Fisher Scientific RPMI-1640 medium Sigma Aldrich Foetal calf serum First Link UK EDTA Sigma Aldrich Trypan blue 0.4% Gibco Cell culture Dimethyl sulfoxide Thermo Fisher Scientific Penicillin Streptomycin (5000 units/ml Gibco Pen & 5000µg/ml Strep) Trypsin 10x Sigma Aldrich L-Glutamine 200mM Gibco Cell line Geneticin 50mg/ml Gibco transfections FuGENE HD Transfection Reagent Promega One Shot® TOP10 chemically Thermo Fisher Scientific competent E. coli Bacterial cloning Ampicillin solution 100mg/ml Sigma Aldrich Ampicillin+ LB agar plates Imperial College laboratories LB broth Imperial College laboratories Tris buffered saline with Tween 20 10x Imperial College laboratories iBlotTM Transfer stacks, PVDF, regular Thermo Fisher Scientific size Original Dried Skimmed Milk Powder Marvel Ammonium persulfate (APS) Sigma Aldrich N,N,N’,N’‐tetramethylethylene Western blotting Sigma Aldrich diamene (TEMED) Bromophenol blue Sigma Aldrich Ultrapure protogel 30% (w/v) National Diagnostics acrylamide, 0.8 % Bis-acrylamide Dithiothreitol Sigma Aldrich PageRuler Prestained Protein Ladder Thermo Fisher Scientific Pyrosequencing Agarose Thermo Fisher Scientific

Binding buffer Qiagen Annealling buffer Qiagen Denaturation buffer Qiagen Enzyme (DNA Pol, ATP sulfurylase, Qiagen Luciferase, Apyrase) Substrate (Adenosine Qiagen 5'phosphosulfate, luciferin) dATP, dCTP, dGTP, dTTP Qiagen Cell based assays Cyrstal violet Sigma Aldrich ChIP Formaldehyde solution 16% Sigma Aldrich

2.1 Ovarian cancer patient samples, clinical data and analysis

2.1.1 ICON7 clinical trial VEGF biomarker study

2.1.1.1 ICON7 clinical trial patient DNA and clinical data

We received EOC patient DNA samples from the IOCN7 clinical trial, extracted from formalin fixed paraffin embedded (FFPE) tumour samples. The ICON7 phase III clinical trial (97) was conducted in EOC patients at primary presentation treating with either an adjuvant chemotherapy regimen of 5-6 cycles carboplatin and paclitaxel at 3 week intervals or with the same regimen plus Bevacizumab for 5-6 cycles plus 12 additional cycles or until disease progression. Enrolment criteria were histologically confirmed high-risk early International Federation of stage Gynaecology and Obstetrics (FIGO) stage I or IIA, disease, either clear call or grade 3 tumours (restricted to 10% of the study cohort), or advanced (FIGO stage IIB-IV) EOC, primary peritoneal cancer or carcinoma of the fallopian tube. DNA samples provided were selected for primary EOC tumour of origin only. Randomisation criteria for trial arms were 1: Gynaecologic Cancer Intergroup (GCIG) study, 2: FIGO stage and residual disease (stage I-III with ≤1cm residual disease, stage I-III with ≥1cm residual disease and inoperable stage III/stage IV disease), and 3: interval period between surgery and initiation of chemotherapy, ≤ 4 weeks versus ≥ 4 weeks. Written and informed consent was gathered for all patients in the study. Clinical data for ICON7 patients was obtained for FIGO stage, grade, histological type, surgical debulking status: optimal ≤1cm residual disease or absence of macroscopic disease and suboptimal ≥1cm residual disease, progression free and overall survival (PFS/OS) endpoint data: survival status, progression status, time to progression or censorship and time to death or censorship, as well as Response Evaluation Criteria in Solid Tumours (RECIST) response to treatment: partial response, complete response, stable disease, progressive disease.

2.1.1.2 Bisulfite pyrosequencing of patient DNA

Bisulfite conversion: 500ng of patient DNA was bisulfite converted using the EZ-DNA Methylation Kit D5004 (Zymo Research Corporation). In brief DNA was denatured enzymatically followed by treatment with sodium bisulfite at low pH to induce deamination followed by desulphonation at high pH. PCR: Forward and reverse primers for PCR amplification of regions of interest of DNA and sequencing primers for pyrosequencing within those regions were designed and tested by Nahal Masrour using Pyromark Assay Design 2.0 software with gene sequences from the University of California Santa Cruz (UCSC) genome browser. Primer sequences are shown in Table 1. PCR was conducted using the FastStart Taq DNA Polymerase kit (Roche), concentrations of forward and reverse primers were optimised via electrophoresis and initial pyrosequencing assays in duplicate samples. All genes were amplified with the following protocol: initial denaturation 6 minutes at 950C, denaturation 30 seconds at 950C, annealing 30 seconds (temperatures shown in Table 2.1), extension 30 seconds at 720C and final extension 5 minutes at 720C. Denaturation, annealing and extension were repeated for 40 cycles. Annealing temperatures for each primer (Table 2.1) were optimised via gradient PCR. Products were run on a 2% agarose gel to ensure amplification prior to pyrosequencing. Pyrosequencing: Pyrosequencing was conducted using the Pyromark Q96 MD (Qiagen) platform. PCR generates a biotinylated product via introduction of a biotinylated primer. Streptavidin-coated sepharose beads (GE Healthcare) are used to capture the biotinylated product and DNA is washed and denatured at high pH. Sequencing primer is annealed and DNA is sequenced via synthesis, pyrophosphate released from nucleotide incorporation is converted to ATP by sulfurylase and luciferase catalyses luciferin conversion generating light recorded as incorporation. The ratio of cytosine to thymine incorporated at a CpG site is calculated by the Pyro Q-CpG software as the methylated % of cytosine. PCR product volume was optimised for each assay using the duplicate ICON7 samples. Data not meeting quality control requirements was excluded from further analysis defined by inbuilt sequencing quality control criteria in the Pyro Q-CpG software.

Table 2.2: Primer sequences

Primer sequences Annealing temperature VEGF-A Forward: GTGGAGTTGGGGGTTAGTAT 56oC Reverse: TCAATAAATATCAAATTCCAACA* Sequencing: TGGAGTTGGGGGTTAGTATTA VEGF-B Forward: GGGGTTTTTTAGGTGTTTGTAGGATT 56oC Reverse: CCTAACCCAAAACCCAACAAATACTCTAAT * Sequencing: GATTTGTATGTAGTTTTTAAATATG

VEGF-C Forward: GGTTTTATAGGGTTTTTTGATATAGTGAT* 57oC Reverse: ACCACTCCCCAATTCTTATCCTCC Sequencing: TCTTATCCTCCCTCC

*Biotinylated primer.

2.1.2 VEGF expression and methylation in ICGC paired primary and relapse patient samples

2.1.2.1 ICGC cohort patient and biological data

Biological data for 15 EOC patients was downloaded from the publically available International Cancer Genome Consortium (ICGC) (40), from the Ovarian Cancer-AU project https://dcc.icgc.org/projects/details?filters=%7B%22project%22:%7B%22tumourType%22:%7B%22is %22:%5B%22Ovarian%20cancer%22%5D%7D%7D%7D&projects=%7B%22from%22:1%7D. Biological data was obtained for 15 paired HGSOC patients which had been sampled from the tumour site at primary presentation and from ascites at relapse. Patients were selected based on chemoresistance at relapse with relapse occurring within 6 months of the last chemotherapy cycle. All patients were treated with platinum based therapeutic regimens. Methylation data obtained via Illumina 450K Beadchip array were included by integrating methylation studies: a 450K array on 3 paired patients from the Australian Ovarian Cancer Study (AOCS) with 12 ICGC paired samples, methylation data was obtained as β values, derived by calculating M(M+U), where M represents the signal for methylated beads and U the signal for unmethylated beads for targeted CpG sites. Two primary samples had methylation data available for matched controls derived from ascites at primary presentation. Methylation data was used from the VEGF-B and VEGF-C probes measuring methylation at CpG sites identified previously as associating with EOC patient outcomes (217). Expression data derived via RNA- seq was available for the 12 ICGC patients at primary presentation and relapse, expression values were

50 used for the VEGF-B and VEGF-C genes. RNA-seq expression values were available as log2(read counts per million reads mapped (CPM)). Appropriate ethical approval had been obtained for all patients.

2.1.2.2 Statistics

Methylation β values and RNA-seq log2(CPM) from paired primary/relapse HGSOC patient samples obtained from ICGC patients were compared using a Wilcoxon Paired Samples Rank Sum Test to generate p values.

2.2 Cell based assays

2.2.1 Cell lines

The histological sub-typing, tissue of origin, p53 mutation status and response to cisplatin are summarised for all of the paired EOC cell lines used in this study (Table 2.3). The PEO1/PEO4, PEO14/PEO23 and PEA1/PEA2 lines are derived from the same patients both at primary presentation and relapse after chemotherapy, either from peritoneal ascites or plural effusion (218). A2780 was derived from a chemonaïve patient with ovarian adenocarcinoma at primary presentation and A2780/CP70 was derived from A2780 via intermittent treatments with increasing doses of cisplatin up to 70μM (41). A2780/MCP3-9 cell lines were derived from the A2780 line via 7 intermittent treatments with increasing cisplatin doses up to 15μM (42). Cell lines were obtained from the Epigenetics Unit liquid nitrogen store and previously authenticated by Jane Borley via STR profiling performed by Genetica DNA Laboratories USA. Cell lines were maintained as mycoplasma free by bimonthly testing using the MycoAlert™ Mycoplasma Detection Kit. 2.2.2 Cell culture

Ovarian cancer cell lines PEO1, PEO4, A2780 and A2780/CP70 cells were cultured in RPMI-1640 (Sigma- Aldrich), supplemented with 10% foetal bovine serum and 2mM L-glutamine, and incubated at 37oC in at 5% CO2. Cells were passaged using 2.5mg/ml, 0.09% EDTA in NaCl at 37oC. Cells were passaged using 5% 2.5mg/ml Trypsin in 0.09% EDTA in NaCl. Cells were frozen in a medium of 1% DMSO in FCS at around 3x106 cells in 1ml initially freezing gradually in isopropanol to -80oC before transferring to liquid nitrogen long term storage. Cells were recovered from long term liquid nitrogen storage by storing at -80oC for 15 minutes before thawing at 37oC and plating with media made as described above.

Table 2.3: Cell line information

Cisplatin Cell line pair Tissue of origin Tumour of origin P53 status response

Untreated primary Sensitive A2780 Wild type tumour Endometrioid adenocarcinoma In vitro derived from Resistant A2780/Cp70 Mutant A2780

Primary untreated Sensitive PEO1 peritoneal ascites Poorly differentiated serous Mutant adenocarcinoma Relapse ascites post- Resistant PEO4 chemo

Primary untreated Sensitive PEO14 peritoneal ascites Well differentiated serous Mutant adenocarcinoma Relapse ascites post- Resistant PEO23 chemo

Primary untreated Sensitive PEA1 pleural effusion Poorly differentiated Mutant adenocarcinoma Relapse ascites post- Resistant PEA2 chemo

2.2.3 RNA extraction and real time quantitative PCR (qRT-PCR)

Cells were plated for RNA analysis at 5x105 cells per well in a 6 well plate for 24 hours. Cell lysis and RNA extraction was performed using the Qiagen RNeasy© Mini Kit. In brief RLT buffer supplemented with β-mercaptoethanol was used to lyse cells which were spun through a QiaShredder spin-column. Ethanol was added to the flow through which was spun through an RNeasy spin column and washed with RW1 and RPE buffers. Elution was performed using RNase-free water. RNA was quantitated using the Nanodrop ND-1000 spectrophotometer and reverse transcription was performed using the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems). qPCR was performed using 10μl Fast

SYBR© Green Master Mix (Applied Biosystems) and 300nM RT-PCR primers using an ABI PRISM 7900HT. Primers were designed using Primer3 software (http://simgene.com/Primer3). RT-PCR primers were optimised by testing serially diluted (1/4) cDNA inputs and standard curve and dissociation curve analysis using the SDS 2.4 (Applied Biosystems) software. All qPCR reactions were run using a programme of 20 seconds activation at 95oC, 40 cycles of 1 second denaturation 95oC, 20 seconds annealing/elongation at 60oC with a final elongation step of 65oC for 15 seconds. The additional dissociation step used to show the melting curve was 15 seconds at 95oC followed by 15 seconds at 60oC. Primers were selected where only one peak was observed in the dissociation curve and slope gradient calculated from the standard curve was between -3.1 and -3.6, with an R of >0.98. RT-PCR primer sequences used are shown in Table 2.4. Target gene expression was normalised to two house-keeping genes β-actin and PP1A and expression level displayed as normalised relative quantity. RNA for cell lines PEO14, PEO23, PEA1 and PEA2 was obtained from Jane Borley.

Table 2.4: qRT-PCR primer sequences

Locus Primer sequences VEGF-B Forward: CACCAAGTCCGGATGCAG Reverse: TGGCTGTGTTCTTCCAGGG VEGF-C Forward: GTCCGGACTCGACCTCTC Reverse: AGACCGTAACTGCTCCTCCA Β-actin Forward: ACCGAGCGCGGCTACAG Reverse: CTTAATGTCACGCACGATTTCC PPIA Forward: CCTGGTGGTGCATGCCTAGT Reverse: CTCACTCTAGGCTCAAGCAATCC

2.2.4 Western blotting

Protein extraction: Cells were plated at 1x106 cells per well of a 6 well plate overnight, washed twice with ice cold PBS and lysed with radioimmunoprecipitation assay (RIPA) buffer made from 50mM Tris pH 8, 150mM NaCl, 0.5% SDS (0.1%), 1% Triton, 0.5% sodium-deoxycholate, 2mM DTT, 10% glycerol,

50mM NaF (Sigma Aldrich), 0.1mM Na3VO4 (Sigma Aldrich), 100µg/ml aprotinin (Calbiochem), 10µg/ml leupeptin (Calbiochem) and 1mM PMSF (Sigma Aldrich). Cell lysates were incubated on ice for 30 minutes before being centrifuged at maximum RPM for 15 minutes at 4oC. The protein concentration was measured using the Bio-Rad Protein Assay according to manufacturer’s instructions using 1µg/ml bovine serum albumin (BSA) as a standard. After quantification protein extracts were suspended in Laemmli buffer made from 0.25M Tris-HCl, p6.8, 8% SDS, 40% glycerol, 0.042% bromophenol blue (Sigma Aldrich), 10% β-mercaptoethanol (Sigma Aldrich). Lysates in Laemmli buffer were frozen at -20 oC for future use.

SDS-polyacrylamide gel electrophoresis (SDS-PAGE) and western blotting: Before SDS-PAGE separation, protein extracts were denatured for 5 minutes at 99oC and 20 µg of protein was loaded on a 5% acrylamide stacking gel made from 0.125M pH6.8 Tris, 0.1% SDS, 0.075% APS and 0.083% TEMED. A 10% resolving gel was used for all proteins in this study, made from 10% acrylamide, 0.375M pH8.8 Tris, 0.1% SDS, 0.06% APS, 0.07% TEMED. SDS-PAGE separation was run at 160V using a Tris-glycine buffer (2.5mM Tris, 0.2m glycine, and 0.1% SDS). The iBlot (ThermoFisher Scientific) was used for 5 minute dry transfer of SDS-PAGE separated protein to PVDF membrane. After transfer membranes were blocked for 1 hour at room temperature to reduce non-specific antibody binding, using 5% BSA in TBST/Tween (0.01M pH7.4 Tris, 75mM NaCl, 1.25mM pH8 EDTA, 0.1% Tween 20 and 0.02% sodium azide). Primary antibodies were diluted at the concentrations and for the times and temperatures indicated (Table 2.4) in BSA-based blocking solution prepared as described above. Membranes were washed in TBST for 5x5 minutes. Secondary antibody incubations were performed for 1 hour at room temperature using the horse radish peroxidase (HRP)-conjugated antibodies shown (Table 2.5) diluted at 0.5µl/ml in 5% milk in TBST, after which membranes were washed again in TBST for 5x5 minutes. Membranes were then incubated in peroxidase substrate containing PierceTM enhanced chemiluminescence (ECL) Western Blotting Substrate (Thermo Fisher Scientific) reagent according to manufacturer’s instructions for 1 minute. Chemiluminescence images were generated using the ImageQuant LAS 4000 (GE Healthcare Life Sciences). Membranes incubated with primary antibody detecting VEGF-C were re-probed for β-actin. Image Studio Lite (LI-COR Biosciences) software was used to generate western blot images.

Table 2.5: Antibodies used for Western blotting

Dilution (incubation Antibody target (clone) Manufacturer Secondary antibody conditions) 5µl/ml (overnight, Mouse anti-rabbit monoclonal VEGF-C (H-22) Santa-Cruz Biotechnology 4oC) MR12/53 (Agilent Technologies) 0.1µl/ml (20 mins, Rabbit anti-mouse polyclonal β-actin Sigma-Aldrich room temp) (Agilent Technologies)

2.2.5 Cellular response to drug treatments

2.2.5.1 Cisplatin dose response assays

Cells trypsinised and counted using the Invitrogen Countess cell counter using trypan blue to assess cell viability and seeded at 10,000 cells per well of a 96 well plate and allowed to seed for 5-6 hours and cultured in 100μl of media at each stage of the procedure. The cells were then treated with a serial dilution of cisplatin in RPMI-1640 based media for 24 hours after which the cells were washed once with dPBS and media was replaced for 2-4 days (dependent on cell line – A2780 lines were given a recovery period 4 days, PEO lines were given a recovery period of 2 days) and assayed before the untreated wells could reach confluence. Cells were washed once with dPBS before cell viability was assessed via incubation for 1 hour with MTS and PES solution: CellTiter 96® AQueous One Solution Cell Proliferation Assay (Promega) according to manufacturer’s instructions, and read at 495nm in a SunriseTM Absorbance Microplate Reader (Tecan) spectrophotometer. Viability was calculated relative to an untreated control and dose response values were log transformed and IC50s were interpolated from a sigmoidal best fit curve using GraphPad Prism 7 software.

2.2.5.2 Epigenetic enzyme inhibition and effect on cisplatin dose response

Cells were seeded in 96 well plates as for cisplatin dose response assay, and treated for 24 hours with inhibitors (listed in Table 2.6), DMSO vehicle controls were included at the concentration of DMSO equivalent to the highest drug dose. Cells were washed with dPBS and cisplatin treatments, viability assessments and measurements and IC50s were calculated as in cisplatin dose response treatments (above: 2.2.5.1).

Table 2.6: chemical inhibitors used for inhibition of epigenetic modifying enzymes

Target Inhibitor Manufacturer EZH2 GSK343 Vorinostat (suberoylanilide HDACs class I and II Sigma-Aldrich hydroxamic acid (SAHA) BRD4 JQ1

2.2.6 Cell growth assays

2.2.6.1 Adherent growth assay

Cells were plated 5,000 per well in a 96 well plate. A seeding control fixed and measured 6 hours after seeding was used as the 0 hour timepoint and cells were quantitated every 24-48 hours to assess growth. Number of cells present per well were assessed via 1 hour fixation and crystal violet staining

(0.5% crystal violet and 25% methanol in dH2O) after 3x washes in ice cold PBS. After fixation the cells are gently washed using tap water before being left to dry overnight. The following day the cells are incubated in 10% acetic acid for 30 minutes and measured for absorbance at 595nm using a SunriseTM Absorbance Microplate Reader (Tecan). Growth is calculated relative to the 0 hour control.

2.2.6.2 Tumoursphere formation efficiency (non-adherent growth) assay

Tumoursphere formation efficiency was evaluated by adapting a previously published method for estimating the formation of mammospheres (219). In brief: cells are trypsinised and centrifuged for 2 min at 580g and resuspended in ice cold PBS before disaggregation into a ubiquitously single cell solution using a 25G needle and syringe, cells are then counted and plated at an optimised cell concentration to generate 50-100 tumourspheres per well of a 6 well non-adherent plate. Tumourspheres over 50µM in diameter were counted at 40x magnification using an Eyepiece micrometer EWF10x/22mm (VWR) after 4 replication cycles (3 days for A2780 and derived cell lines and 6 days for PEO1/PEO4 cell lines) and tumoursphere formation efficiency was calculated as TFE=no. spheroids/no. cells seeded*100.

2.2.7 Flow cytometry

Flow cytometry analysis was performed using a FACS Canto II (BD Biosciences) according to manufacturer’s instructions. Cell populations were selected via gating forward scatter area (FSC-A) and side scatter area (SSC-A) and apoptotic particles and multiplicate cells were excluded from analysis via forward scatter height (FSC-H) and forward scatter width (FSC-W) using untreated and unstained cell populations suspended in PBS.

2.2.7.1 ALDEFLUOR® enzymatic assay for cancer stem cell detection

The ALDEFLUOR® assay was used to assess cell populations expressing high levels of ALDH enzyme. Cells were trypsinised and counted as described above before washing once in dPBS before resuspending at 1x106 cells in 1ml of ALDEFLUOR® assay buffer. 2.5μl of ALDEFLUOR® substrate was added per 5x105 cells and 1μl (optimised via titration) inhibitor N,N-diethylaminobenzaldehyde (DEAB) was added as a negative control. Samples were incubated for 30 minutes at 37oC before spinning down at 4oC and resuspending in assay buffer. Fluorescence intensity was measured in the FITC channel. Analysis of ALDH expressing populations involved setting a gate above the peak indicating the majority of the negative cells as well as above 100% of the DEAB treated population to identify highly expressing ALDH populations.

2.2.7.2 Antibody based detection of extra-cellular cancer stem cell marker detection

The expression of extra-cellularly expressed proteins was assessed using primary antibodies conjugated to Per-CP, FITC or APC antibodies (listed with concentrations used in Table 2.7). Cells were seeded 5x105 per well of a 6 well plate overnight, then trypsinised and counted the following day. 1x105 cells were centrifuged at 300g in the wells of a v-bottom plate before being washed in ice cold PBS 3 times. Cells were resuspended and incubated with antibody diluted in flow cytometry staining buffer (0.5% BSA and 5mM EDTA in PBS) for 1 hour at 4oC before being centrifuged at 300g again and washed another 3 times with PBS. The cells were resuspended in flow cytometry staining buffer before being analysed the same day using the FACS Canto.

Table 2.7: Antibodies used to assess ovarian cancer stem cell marker expression via flow cytometry

Antibody (clone) Fluophore Manufacturer Concentration CD44 (BJ176) FITC BioLegend 5µl/ml CD133/2 (293C3) APC Miltenyi Biotech 5µl/ml CD117 (104D2) PE BioLegend 5µl/ml Mouse IgG1, κ FITC BD Biosciences 5µl/ml

2.2.7.3 BrdU pulse labelling and 7-AAD staining for cell cycle analysis

Cells were labelled with BrdU and 7-AAD (BD Pharmingen® Flow Staining Kit) according to manufacturer’s instructions in order to assess cell cycle dynamics. Cells were treated with 10μM BrdU for 50 minutes in culture. Cells were then trypsinised and counted as described above before washing twice in dPBS. 2 million cells per sample were fixed in BD Cytofix/Cytoperm buffer for 30 minutes at

o 4 C before washing the cells in BD Perm/Wash Buffer (10x buffer diluted in dH2O) and then washed with staining buffer (3% BSA and 2mM EDTA in dPBS) and freezing at -80oC in 10% DMSO in FCS. Cells were all thawed on the same day for flow cytometry analysis. Cells were refixed in BD Cytofix/Cytoperm buffer for 5 minutes at room temperature before washing in BD Perm/Wash Buffer and treating with 300μg/ml DNase for 1 hour at 37oC. Cells were then washed in BD Perm/Wash Buffer and incubated with 1/50 diluted anti-BrdU-FITC conjugated antibody. After washing in BD Perm/Wash Buffer cells were resuspended in 20μl 7-AAD solution and diluted in staining buffer. Flow cytometry with the FACS Canto was used to acquire the cells in the FITC channel to measure BrdU and the PerCP channel measure 7-AAD. Unlabelled cells (anti-BrdU antibody) were used as a negative control to gate for BrdU incorporating cells and fluorescence in the 7-AAD channel was adjusted to observe 2N and 4N DNA content. In multiplexed FITC/PerCP analysis 2N cells within the FITC negative gate were classified G0/G1, 4N cells in the FITC negative gate were classified G2/M and FITC positive cells were classified as S phase. Cells were gated for viability as described above. As emission spectra from FITC and PerCP channels are non-overlapping, compensation was unnecessary.

2.2.7.4 Analysis

Analysis of flow cytometric count and intensity data was performed using FlowJo v7.0 (BD Biosciences) software.

2.2.8 Migration assays

Cells were plated 1x106 per well of a 6 well plate and cultured until confluent before being serum starved for 24 hours in media containing only 0.1% FCS before scratch assays were used to assess cell

58 motility: scratches are generated using the tip of a 200µl pipette tip and imaged using a brightfield microscope. After 72 hours in 0.1% FCS the scratches are again imaged in the same scratch locations. Images were imported into ImageQuant TL software (GE Healthcare Life Sciences) and total and delta changes in area between the cells was measured with migration index calculated as change in cell-free area relative to control.

2.2.9 Invasion assays

The invasive capacity of cells was assessed using a published method for assessing the invasion of cells from a 3D spheroid structure into surrounding matrix. In brief: spheroids were grown from 10,000 cells per well in a 96 well ultra low adherence (ULA) plate for 4 days. 50µl of 100µl volume per well was replaced with Matrigel Matrix (Corning). Spheroids were imaged the same day via Brightfield microscopy after matrigel had polymerised and after 72 hours. Spheroid areas were calculated using ImageQuant TL software (GE Healthcare Life Sciences) and normalised to the 0 hour control.

2.2.10 Assessment of apoptosis in adherent and non-adherent conditions

Cells were plated 1x104 per well of both adherent and low adherence 96 well plates, incubated overnight and tested for caspase 3/7 activity using the Caspase-Glo 3/7 Assay (Promega) according to manufacturer’s instructions, adding 100 µl Caspase-Glo 3/7 Assay substrate to each well and incubating for an hour in the dark at room temperature before reading luminescence using a PHERAstar luminescence microplate reader (BMG LABTECH).

2.2.11 Clonogenic assays and drug treatments for isolating cisplatin tolerant populations of A2780

2.2.11.1 Colony formation assays to optimise cisplatin concentrations

A2780 cells were seeded at 5x106 cells in a 15cm dish for 5-6 hours before being treated with cisplatin at indicated concentrations in 20ml of media. Colony forming assays were performed by treating cells for 48 hours at indicated concentrations followed by 12 days recovery in non-selective media. Cells were washed 2x in ice cold PBS before staining for 30 minutes using 0.5% crystal violet dissolved in 25% methanol. The plates were washed gently with tap water and left to dry overnight. Quantitation of colony formation was conducted via field counting of >10 colonies per plate at 10x magnification using a brightfield microscope and normalising to an untreated control plate seeded at 100x lower density.

2.2.11.2 Cisplatin treatment and colony formation to isolate cisplatin tolerant A2780 populations

A2780 cells were seeded for 6 hours at a density of 5x106 cells in 15cm cell culture dishes treated with 2µM cisplatin in 20ml media. Media and cisplatin was replaced every 48 hours if treatments exceeded 2 days. After treatment cells were cultured in non-selective media for 12 days, when they were harvested and tested for proliferation and viability via cisplatin dose response analysis (see above, 2.2.5.1).

2.2.12 Statistics

When comparing two group means two-sided student T-tests were used to generate p values. Data visualisation for cell based assays and statistics were generated using GraphPad Prism 7.

2.3 Generation of VEGF-C null cell lines via CRISPR-RFN

2.3.1 CRISPR plasmids

Optimised plasmids expressing FokI-dCas9 and Csy4 (pSQT1601) and Csy4-flanked gRNA cloning vector (pSQT1313) plasmids (43) were received from Addgene as well as pCDNA3 containing GFP and neomycin resistance cassettes as bacterial stabs. Bacteria were streaked onto ampicillin selective plates and grown overnight at 37oC. 3ml day cultures were grown from picked colonies in ampicillin selective LB broth at 37oC and bacterial pellets were generated by centrifugation for 2 min at 6000G. pDNA was extracted using a QIAprep Spin Miniprep Kit (Qiagen) according to manufacturer’s instructions. pSQT1313 and pSQT1601 were confirmed as the correct plasmids by restriction digestion with XhoI and BamHI-HF, and KpnI respectively. Restriction digests were performed in 10µl reactions with 1x CutSmart buffer, 200µg DNA and 0.1µl restriction enzyme at 37oC for 30-60 mionutes, before separating on a 1% agarose gel. All restriction enzymes and CutSmart buffer were obtained from New England Biolabs. Glycerol stocks were made from 1ml overnight culture and 1ml 25% glycerol and stored at -80oC. Plasmid DNA sequences were viewed and manipulated via the ApE version 1.17 software. 2.3.2 Sub-cloning gRNA sequences into the pSQT1313 vector

Dual targeting gRNAs were designed using ZiFiT publically available software (44) by entering VEGF-C exonic sequences downloaded from the UCSC Genome Browser, and oligos for sub-cloning (Table 2.8) were ordered from Invitrogen. Left and right oligoduplex sequences containing sgRNA coding sequences were sub-cloned into 5µg of pSQT1313 digested at 55oC overnight by BamBI (New England Biosciences). The restriction enzyme was inactivated by heating to 80oC for 20 minutes before the pSQT1313 backbone was purified by Ampure XP beads according to manufacturer’s instructions and confirmed by KpnI digestion and fragment separation on a 1% agarose gel. Oligoduplexes were

60 generated by annealing oligonucleotide pairs at 10µM each in a 1x solution of 10x Oligoduplex

o Annealing Buffer (100mM Tris-HCl, 500mM NaCl and 10mM EDTA in dH2O) at 95 C for 5 minutes followed by temperature de-escalation to 25oC by 1oC/30 seconds. Oligoduplex sequences containing sgRNA sequences are shown in Table 2.7. Left, middle and right Oligoduplex sequences were ligated into the pSQT1313 backbone in a 10µl reaction at 0.002µM each, with 2ng/µl pSQT1313 backbone, 1x T4 Ligase Buffer, 0.5µl Polynucleotide Kinase and 0.5µl DNA Kinase (all obtained from New England Biosciences). The ligation reaction was incubated at 16°C for 30 minutes, then at 4°C overnight. The following day, 5µl of the ligation reaction was transformed into 50 µl of One Shot® TOP10 chemically competent E. coli by incubating on ice for 5 minutes. Transformed chemically competent cells were plated on LB Agar plates containing ampicillin. 5 colonies were picked per sgRNA construct made and grown in 3ml overnight cultures in LB medium supplemented with 50 µg/ml ampicillin before being centrifuged and pDNA extracted via miniprep as previously described. Plasmids were validated via Sanger sequencing performed by Beckman Coulter (primer sequences obtained from Invitrogen shown in Table 2.9).

Table 2.8: sgRNA oligonucleotide sequences

VEGF-C sgRNA Left oligo Right oligo Middle oligo Target

Target seq: CGGGCGCCTCGCGAGGACCC Target seq: CGAGTCCGGACTCGACCTCT Oligo1: 1: Exon GCAGCGGGCGCCTCGCGAGGACCCGTTTTAG Oligo1: 1 GGCAGCGAGTCCGGACTCGACCTCT Oligo2: AGCTCTAAAACGGGTCCTCGCGAGGCGCCCG Oligo2: AAACAGAGGTCGAGTCCGGACTCGC Oligo1: Target seq: /5Phos/AGCTAGAAATAGCAAGTTA Target seq: TCGAAGGCGGCGGCGGCGGC TCGGACGCGGAGCCCGACGC AAATAAGGCTAGTCCGTTATCAACTT GAAAAAGTGGCACCGAGTCGGTGC Oligo1: GTTCACTGCCGTATA 2: Exon Oligo1: GCAGTCGAAGGCGGCGGCGGCGGCGTTTTAG GGCAGTCGGACGCGGAGCCCGACGC 1 Oligo2: Oligo2: /5Phos/TGCCTATACGGCAGTGAAC Oligo2: AGCTCTAAAACGCCGCCGCCGCCGCCTTCGA GCACCGACTCGGTGCCACTTTTTCAA AAACGCGTCGGGCTCCGCGTCCGAC GTTGATAACGGACTAGCCTTATTTTA ACTTGCTATTTCT Target seq: Target seq: CACACATGGAGGTTTAAAGA GGGGTTGCTGCAATAGTGAG

Oligo1: 3: Exon Oligo1: GCAGCACACATGGAGGTTTAAAGAGTTTTAG 3 GGCAGGGGGTTGCTGCAATAGTGAG Oligo2: Oligo2: AGCTCTAAAACTCTTTAAACCTCCATGTGTG AAACCTCACTATTGCAGCAACCCCC

Table 2.9: Sanger sequencing primer sequences

Plasmid Primer Sequence pSQT1313 oSQT379 AGGGTTATTGTCTCATGAGCGG pSQT1601 pCAG-F GCAACGTGCTGGTTATTGTG pcDNA3-eGFP-LIC CMV-F CGCAAATGGGCGGTAGGCGTG

Table 2.9: Sanger sequencing primer sequences

Plasmid Primer Sequence pSQT1313 oSQT379 AGGGTTATTGTCTCATGAGCGG pSQT1601 pCAG-F GCAACGTGCTGGTTATTGTG pcDNA3-eGFP-LIC CMV-F CGCAAATGGGCGGTAGGCGTG

Table 2.9: Sanger sequencing primer sequences

Plasmid Primer Sequence pSQT1313 oSQT37962 AGGGTTATTGTCTCATGAGCGG pSQT1601 pCAG-F GCAACGTGCTGGTTATTGTG pcDNA3-eGFP-LIC CMV-F CGCAAATGGGCGGTAGGCGTG

2.3.3 Cloning and pDNA isolation for transfection

Plasmid DNA for use in cell line transfections was extracted via a Plasmid Plus Midi Kit (Qiagen) according to the manufacturer protocol after inoculating an overnight starter culture in 5ml. In brief bacterial pellets are lysed via alkaline lysis and clarified and filtered with Qiagen QIAfilter cartriges before DNA immobilisation on Qiagen Plasmid Plus spin columns via a QIAvac 24 vacuum manifold, then washed and eluted. pDNA was stored at -20oC for transfections.

2.3.4 Transfections and single cell cloning

Cells were seeded in 6 well plates at a density of 5x104 cells per well over night for transfection. Cells were transfected with combinations of 250ng recombinant pSQT1313, 750ng pSQT1601 and 500ng or 1µg pcDNA3-GFP-LIC. Transfections were performed using FuGENE transfection reagent (Promega) according to manufacturer’s protocol. Cells were transfected for 24 hours before treating with 0.5mg/ml G418 sulfate (Geneticin, Gibco) for 72 hours to select for transfected cells. Cells were allowed to recover and were passaged and tested for loss of VEGF-C expression via western blot. Populations showing visible reduction of VEGF-C expression were assumed to have cells present with VEGF-C KO and so were taken forward to single cell cloning. Cells were trypsinised, syringed with a 25G needle to ensure single cell suspension and plated in 10cm culture dishes at a low density ~1000 cells/dish. After 14 days colonies were selected by using 150µl glass cloning cylinders (Sigma Aldrich) according to directions from the manufacturer to trypsinise colonies and replate in a 96 well plate. Populations which survived cloning were gradually expanded by passaging through 96 well plate wells, to 24 well plates, to 6 well plates and eventually to T75 flasks at which point the populations were retested for VEGF-C expression to identify clonal VEGF-C null populations.

2.4 Sequencing analysis of cisplatin tolerant and parental A2780 populations

2.4.1 Preparation of genomic libraries and sequencing

2.4.1.1 RNA-seq

Cells were trypsinised and reseeded into 6 well culture plates at a density of 5x106 per well. Cells were lysed and RNA extracted using an RNeasy RNA extraction kit (Qiagen) as described above (2.2.3) and

63 the concentration was determined using a Nanodrop 1000 (ThermoFisher Scientific). mRNA-seq quality control library prep and sequencing was performed by the Imperial College London Biomedical Research Centre (BRC) Genomics Facility. RNA quality was assessed using a Bioanalyser (Agilent Genomics) and quantity determined via Qubit (Thermo Fisher Scientific). PolyA primed, stranded libraries were generated using a TruSeq Stranded mRNA Library Prep Kit (Illumina) according to the manufacturer’s protocol and quality control was performed via RT-qPCR and samples were pooled, pools were sequenced via HiSeq 2500 v3 (Illumina) on 3 lanes at 2x100bp.

2.4.1.2 ChIP-seq

ChIP-seq was performed based on a published protocol by Shmidt et al., (183, see for detailed protocol/reagents/buffer recipes). In brief: cells were seeded overnight at 5-10x106 cells per 15cm dish, fixed in 1% paraformaldehyde (PFA) the following day at 37oC for 30 minutes before inactivating in 1M Glycine. Cells were scraped, collected, spun down and stored at -80oC. The following ChIP-seq protocol was performed by Luca Magnani and Darren Patten: cells were lysed and pelleted, DNA was sonicated in Biorupter® Pico tubes (Diagenode) using 10 cycles of 30 seconds. Triton-X100 was used for solubilisation. The samples were then incubated with Dynabead/ChIP antibody)/BSA in PBS solution (bead A used for rabbit, bead G used for mouse) for 6 hours before the beads were magnetically isolated overnight suspended in lysis and Triton solution, none immunoprecipitated sample is retained as input control. Antibodies were chosen from a list of recommended ChIP antibodies by ENCODE, see Table 2.10 for details. DNA fragment sizes were checked by de-crosslinking, phenol-chloroform extraction and gel electrophoresis. Chromatin proteins were solubilised in RIPA buffer, then TE buffer and de-cross-linked overnight at 65oC. The following day RNA and proteins in the sample were degraded via RNase and proteinase treatment, DNA was extracted via phenol chloroform extraction and precipitated in NaCl. DNA in each sample was quantified via a Qubit Fluorometer (ThermoFisher Scientific). Control qRT-PCR (see Table 2.9 for primer sequences) was performed at the MYOD1 and GAPDH loci as they are silenced and highly expressed respectively in the A2780 cell line. ChIPs-seq and input controls were prepared for sequencing by ligation of Illumina barcoded adaptors and sequencing via Hi-Seq 2500 v3 (Illumina) on 3 lanes at 1x100bp by the Medical Research Council (MRC) London Institute of Medical Research Genomics Facility, Imperial College London.

Table 2.10: ChIP-seq antibodies and qRT-PCR primer sequences used for positive/negative control

ChIP target Antibody (manufacturer) Control qRT-PCR primer sequences H3K27me3 Anti-Histone H3 (di methyl K27, tri MYOD1 methyl K27) antibody, ab6147 Forward: CACTTCAACTCTCGGGGTCT (Abcam) Reverse: CCGAAAGAGGCTGAGAGACA H3K27ac Anti-Histone H3 (acetyl K27) GAPDH antibody - ChIP Grade, ab4729 Forward: TCCCATCCCACACTCACAAA (Abcam) Reverse: TATTGAGGGCAGGGTGAGTC

2.4.1.3 ATAC-seq

Paired-end ATAC-seq libraries were generated according to a published protocol (221), which can be referred to for detailed methods. In brief: cells were plated in 6 well plates overnight. The following day 50,000 cells were trypsinised, counted and washed in dPBS, nuclei were isolated by centrifugation for 5 minutes at 500G at 4˚C, washed again in ice cold dPBS and resuspended in ice cold lysis buffer

(10mM pH 7.4 Tris-HCl, 10mM NaCl, 3mM MgCl2 and 0.1% (v/v) Igepal CA-630 (Sigma Aldrich)) before centrifugation for 10 minutes at 4˚C. Transposition of DNA was catalysed by resuspension of the lysate in transposase reaction mix: 25µl Tagmentation DNA buffer, 2.5µl Tagmentation DNA Enzyme 1 and

22.5 µl nuclease free H2O (Nextera DNA Library Prep Kit (Illumina)) and incubation for 37˚C for 30 minutes, which also tagments DNA fragments with Illumina adaptor sequences. The sample was then purified using a MinElute PCR Purification kit (Qiagen) according to manufacturer’s protocol. Following purification the DNA fragments were amplified using the NEBNext PCR master mix (Illumina) and custom Nextera PCR primers which amplify from the Illumina adaptor sequences and were used to tagment sample replicate DNA with different Illumina multiplex barcode sequences (full primer and barcode list (222). To avoid PCR band GC bias in amplification the optimum number of additional PCR cycles were calculated by RT-qPCR using universal primers for the tagmented adapters, iQ SYBR Green Supermix (BioRad) and an aliquot of DNA removed from the PCR mix after 6 rounds of amplification by calculating the number of cycles representing a third of the maximum amplification in the linear phase. After completion of amplification the DNA was purified as earlier in the protocol and library quality was assessed using the 2200 TapeStation (Agilent) according to manufacturer’s protocol, examining 180bp periodicity in the fragment size distribution. The samples were pooled with 3 samples from a different study and submitted to the Imperial College London BRC Genomics facility where DNA quality was assessed using a Bioanalyser (Agilent Genomics) and quantity determined via Qubit (Thermo Fisher Scientific. Pools were sequenced via HiSeq 2500 v3 (Illumina) on 1 lane at 2x100bp.

2.4.2 Bioinformatics analysis

All library processing, sequencing quality assessment, filtering and genomic alignments were performed in the Unix Shell environment. Sequencing quality was assessed using the FastQC version 0.11.2 package (Babraham Bioinformatics). All libraries were aligned to the UCSC hg19 genome assembly. Data visualisation and statistical testing, unless otherwise mentioned, was performed in the R version 3.1.2 programming environment.

2.4.2.1 RNA-seq library processing and differential gene expression analysis

Read quality filtering and adaptor trimming was performed using Trimmomatic version 0.32 software. Read alignment to the hg19 genome were performed via TopHat 2.1.0 which aligns reads via the Bowtie genome alignment software whilst identifying splice junctions to identify exon boundaries and facilitate transcriptome alignment. The Cufflinks suite of tools, downloaded for availability from the Unix Shell command line was used for transcriptome assembly and quantification of transcript abundance in each sample. The Cufflinks tool was used to assemble a GTF file with genomic coordinates and estimates of exon and transcript abundance, these files were used for transcriptome assembly merging via Cuffmerge to maximise transcriptome assembly confidence and exclude artefact transcribed fragments. Cuffnorm was used to calculate transcript abundances per replicate sample as fragments per kilobase of mapped reads (FPKM) and Cuffdiff was used to calculate log2 fold change and significance values between groups of replicates for transcripts associated with the same TSS. Significance q values are based on FDR correction of p values according to a negative binomial distribution. Genes were considered differentially expressed to a significant level where q<0.05, unless the average FPKM for neither sample exceeded 1, in which case the transcript was excluded from the analysis. Gene Ontology analysis was performed via online bioinformatics software Panther version 11 (223). Gene set enrichment analysis was performed by calculating F statistics for all samples and using the Limma gene set test function to identify enriched sets of genes grouped by KEGG pathway (KEGG pathway gene list downloaded from the KEGG GENE Database) based on ranked F statistics.

2.4.2.2 ChIP-seq and ATAC-seq library processing and analysis of peak distributions

Libraries of ChIP-seq and ATAC-seq were demultiplexed by adaptor barcode sequences previous to being received. Cutadapt 1.13 software was used to quality filter and trim adaptor sequences from library reads. Bowtie 2 version 2.2.3 was used for single end genome alignment in the case of the ChIP- seq library reads and paired end alignment to the hg19 genome in the case of ATAC-seq libraries. The BEDtools suite (Quinlan laboratory, University of Utah) was used to exclude read alignments to repetitive regions blacklisted for ChIP-seq and alignments to the mitochondrial genome for ATAC-seq, files containing blacklisted regions for filtering were obtained from the ENCODE site (224). The Picard-

66 tools suite (Broad Institute) was used to calculate fragment insert sizes in ATAC-seq libraries. The Model Based Analysis for ChIP-seq data 2 (MACS2) version 2.2.1 was used to call enrichment or depletion of histone marked areas or regions of chromatin accessibility via broadpeak calling across the genome for merged replicate libraries (according to MACS2 recommendations) normalising to input and sequencing depth in the case of ChIP-seq libraries and sequencing depth alone in the case of ATAC-seq libraries use an FDR corrected q value cut off of 0.1. Annotation of genomic regions was performed using Homer. Enrichment tests for peaks in genomic regions between samples and for differential gene expression with associated histone mark peak loss or gain were performed using Fisher’s Exact tests.

Chapter 3: Investigation of prognostic association between VEGF gene methylation and clinical outcomes in the ICON7 clinical trial

3.1 Introduction

The purpose of the work presented in this Chapter were to investigate possible associations between VEGF gene methylation and patient clinical outcomes of progression free survival (PFS) and overall survival (OS) in the ICON7 phase III clinical trial of EOC patients. Our primary aim was to investigate whether there were prognostic groups that show differential survival outcomes between the standard chemotherapy and bevacizumab arms of the trial, therefore indicating potential methylation biomarkers which could be clinically useful for stratifying EOC patient therapy into groups which show a survival advantage from treatment with the drug.

Aims:

1. To evaluate prognostic significance of VEGF methylation and expression (where available) in EOC treated with bevacizumab, carboplatin and paclitaxel, and to identify sub-groups showing differential prognostic outcomes when treated with bevacizumab, using OS and PFS and clinical endpoints

2. Perform multivariable analysis to account for known clinical features with prognostic outcomes, and reduce the chance of confounding in discoveries of prognostic associations with methylation.

3.2 Background

3.2.1 The ICON7 clinical trial

The Gynaecologic Cancer InterGroup (GCIG) International Collaboration on Ovarian Neoplasms (ICON7) ICON7 phase III clinical trial (97) examined the efficacy of the anti-angiogenesis drug bevacizumab (Avastin®, Genentech; South San Francisco, CA, USA), a monoclonal antibody with binding affinity for VEGF-A, as a first line therapy for ovarian cancer patients when added to standard chemotherapy (carboplatin and paclitaxel) compared to standard chemotherapy alone. The trial was conducted in 1528 women with EOC with high risk (grade 3 or clear cell carcinoma) stage I-IIA low grade disease or grade IIB-IV advanced EOC. The patients were randomised into a standard chemotherapy (carboplatin and paclitaxel) arm and an arm treated with both standard chemotherapy and bevacizumab. Primary endpoints of the trial were to identify outcomes of PFS and interim OS these involved an initial analysis and an updated analysis after median follow up of 28 months. Secondary outcomes analysed were biologic progression free interval, overall response, toxicity and quality of life. At second follow-up the trial demonstrated that the drug had a small, significant beneficial effect on patient PFS (HR=0.87 (0.77,0.99), P=0.04), with patients treated with bevacizumab showing a

69 median PFS time of 19.8, while patients surviving without progression in the standard therapy arm showed a median of 17.4 months. However, no beneficial effect on OS was observed (HR=0.85 (0.69,1.04), P=0.11). A beneficial outcome for patient OS was identified in a subset of patients considered to be at high risk of progression, with high stage EOC (FIGO stage IIIC or IV) and >1cm residual disease (HR=0.64, (0.48, 0.85), P=0.002), with patients in the bevacizumab treated group showing a median survival time of 36.6 months versus 28.8 months in the standard therapy arm.

3.2.2 Bevacizumab in phase III clinical trials

Bevacizumab has shown efficacy as a first line therapy agent in several cancers including metastatic CRC (225, 226). As described above the ICON7 phase III trial tested bevacizumab in EOC as a first line therapy. Differences in both clinical endpoints of PFS and OS were only detectable between the trial arms when patients were stratified into a “high risk” group. Three additional phase III trials tested bevacizumab in ovarian cancer. GOG protocol 218 (98) was a placebo controlled trial conducted in EOC patients with stage III and IV disease which also treated patients with bevacizumab, in combination with carboplatin and paclitaxel, immediately post-surgery as a first line therapeutic. Two bevacizumab arms were included in the trial, with one arm including bevacizumab with standard chemotherapy until cycle 6, and the other including bevacizumab with chemotherapy until cycle 22. The bevacizumab “throughout” arm showed a small increase in PFS over the other two arms (maintenance bevacizumab arm = 14.1 months, bevacizumab initiation = 11.2 months and chemotherapy only = 10.3 months, HR for bev at initiation versus chemo only = 0.91 (0.8, 1.04), P = 0.16 and HR for bev as maintenance versus chemo only = 0.717 (0.63, 0.82), P<0.001), providing some evidence for continued use of bevacizumab as a maintenance therapy delaying progression. No difference in median OS was observed between patients treated with bevacizumab throughout (39.7 months), bevacizumab at initiation (38.7 months) or chemotherapy only (39.3 months). Stratified analysis within GOG protocol 218 identified an increase of around 8 months for overall survival in stage IV patients alone. The OCEANS (99) and AURELIA (100) placebo controlled trials tested efficacy of bevacizumab for treatment of recurrent EOC. In the AURELIA trial patients were treated with bevacizumab as a second line therapy where progression had occurred within 6 months of completion of first line platinum based therapy, these patients were therefore classified as having platinum resistant OC. Patients treated with bevacizumab in combination with another second line chemotherapeutic agent showed an almost doubled length of median PFS in comparison to single agent chemotherapy (6.7 and 3.4 months respectively) , as well as a higher objective response rate (ORR) (27.3% versus 11.8%, P=0.001). However, no significant difference in median OS was identified between trial arms (HR=0.85 (0.66, 1.08), P<0.174). The OCEANS trial tested bevacizumab in a platinum sensitive recurrent context (recurrence >12 months post final

70 chemotherapy cycle), in combination carboplatin and gemcitabine. Similarly to the AURELIA trial a significantly increased median PFS was observed in the bevacizumab treated arm (12.4 versus 8.4 months, HR=0.48, (0.39,0.61), P=0001) as well as ORR (78.5% versus 57.4%, P<0.0001) but no significant difference in OS between arms is reported, with a median OS of 35.2 months in the bevacizumab arm and 33.3 months in the carboplatin/gemcitabine arm at second interim analysis. The above trials show strong evidence for bevacizumab in delaying OC progression but not in prolonging survival, therefore supporting its use as a maintenance therapy. The ICON7 trial identified a “high-risk” group which showed evidence of improved overall survival indicating that stratification of OC patients may be possible for effective anti-angiogenesis therapy in groups of responsive patients who may show a survival benefit. Identification of biomarkers which predict a beneficial response to Bevacizumab has been postulated (227), and would be beneficial to permit further stratification of EOC patients for bevacizumab therapy.

3.2.3 The current clinical role of bevacizumab in ovarian cancer treatment

Bevacizumab is the first molecularly targeted therapy in use for ovarian cancer treatment. In light of the phase III AURELIA trial described above the FDA approved the drug as a second line maintenance therapy in cases of recurrent ovarian cancer.

3.2.4 Biomarkers for bevacizumab response

Initial studies identified no correlation between abundance of circulating VEGF and efficacy of Bevacizumab in patients in four phase III clinical trials (228), although one study investigating circulating plasma proteins identified that CD31-MVD and TSP1-IA levels appeared to associate with patient OS and PFS respectively, though only when separated by standard deviation, making drawing meaningful cut-offs for clinical practice difficult (229). Zhou et al., modelled all angiogenesis-related circulating markers to attempt to identify correlations with patient response to bevacizumab. Plasma concentrations of angiopoetin-1 (Ang1) and the Ang1 receptor Tie2 were identified as highly correlated with tumour burden, likely to be indicative of pro-tumourigenic vascular activity (inferred as the correlation was only detected where patients were treated with bevacizumab), and Tie2 in combination with Ca125 EOC tumour progression in patients treated with bevacizumab predicted progression in 74.1% of patients versus 47.3% detected by Ca125 levels alone. Tie2 provides a promising potential blood-based biomarker for monitoring of EOC patient response to bevacizumab over the course of therapy including potential early prediction of patients not responding (230). In a phase II trial of recurrent/persistent EOC/primary peritoneal cancer (PPC) Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) demonstrated an association between increased tumoural

71 blood flow rate and progression free survival to 6 months (231). It was postulated that this was due to improved vascular organisation and more efficacious drug delivery to the tumour. However the study suffered from low sample numbers, and had a large volume of missing data, it is also unclear how practical and cost-effective MRI would be to monitor patient intra-tumour blood flow. Additionally the predictive value of this biomarker is not assessed for progression, only association. Associations between miR-378 expression as well as expression of miR-378 target genes ALCAM and EHD1 were shown to be associated with PFS independently of other clinical factors in a sub-set of EOC patients treated with bevacizumab available from TCGA (232). Again, this study whilst showing interesting associations requires evidence of predictive value for progression. A strong independent association was identified between body mass index (BMI) and shorter time to progression in bevacizumab treated EOC patients (not in patients prescribed chemotherapy alone), and subcutaneous fat area (SFA) associated negatively with OS in these patients, leading the authors to infer that adiposity reduces the chances of response to the drug and that excluding patients with high adiposity may be a useful method of stratification for anti-angiogenesis therapy. However, BMI was not shown to associate with differential OS and SFA was not shown to associate with differential PFS, importantly the measure of visceral fat associated with neither clinical outcome. Therefore further validation of these findings is required, ideally in a larger patient cohort, as only 25 samples per treatment group were available. All of the above described studies require validation in independent cohorts. Ideally, biomarkers would identify whether patient sub-groups exist which will show the benefit of extended survival from bevacizumab, measurable by prediction of increased OS, in order to demonstrate whether bevacizumab has any value beyond maintenance and delayed progression. 3.2.5 VEGF methylation as a prognostic biomarker for HGSOC patient outcomes

Published work identified differential methylation in the promoters of angiogenesis related genes VEGF-A, VEGF-B and VEGF-C as associated with PFS in HGSOC patients treated with carboplatin and paclitaxel (233), with VEGF-B and VEGF-C validating in independent patient cohorts, indicating that epigenetic deregulation of these genes may be related to tumourigenesis and influence patient relapse. Hypomethylation at the VEGF-B promoter association with improved PFS in TCGA cohort (HR=1.9(1.12, 3.42), P=0.018) and in the Scottish Gynaecological Clinical Trial Group (SGCTG) cohort (HR=1.12 (0.99, 1.28), P=0.04). Conversely, hypomethylation at the VEGF-C promoter associated with poor PFS in the TCGA cohort (HR=0.92 (0.86,0.99), P=0.021) and in the SGCTG cohort (HR=0.99 (0.98, 1.0), P=0.03). VEGF-B methylation also associated with ORR according to RECIST criteria (HR for partial or complete response versus stable or progressive disease = 2.57 (1.22, 5.43), 0.013) in the SGCTG patients as well as showing a borderline association in the TCGA cohort (HR=2.13, (0.97,4.68), P=0.056). We hypothesised that as differential promoter methylation of these genes is prognostic for

HGSOC patient outcomes, methylation at one or more of these loci may allow identification of a patient sub-group shows differential response to bevacizumab, which may potentially be used for a stratified approach to EOC patient therapy.

3.3 Results 3.3.1 ICON7 patient DNA and demographics

454 ICON7 EOC patient DNA samples were received, DNA had been extracted from formalin fixed, paraffin embedded (FFPE) tumour tissue samples. Quality control of the DNA was conducted via DNA quantification and GAPDH multiplex PCR. Samples were considered to have failed QC and excluded based on a combination of the following: low or negligible DNA concentration, DNA fragmentation in the GAPDH region and pyrosequencing assay failure across multiple assays. 11 patient samples were excluded according to these criteria. After accounting for duplicate patient samples we had 399 high quality unique EOC patient samples (200 from the standard chemotherapy arm and 199 from the bevacizumab arm). The flow of ICON7 sample numbers through the study are shown in Figure 3.1 and the demographics for these patients are shown in Table 3.1. Median time of PFS was very similar in both arms 525 days in the bevacizumab arm with 71% of patients experiencing a progression event and 547 days in the standard chemotherapy arm with 80% experiencing a progression event. PFS in this subset of patients is not fully representative of this endpoint in the full ICON7 cohort, as patients given standard chemotherapy had a median PFS time of 17.4 months (531 days), whilst the bevacizumab arm was 19.8 months (603.9). Median OS time differs between the 399 patients belonging to the standard chemotherapy and bevacizumab arm (1396 and 1550 respectively), with 58% of patients in the bevacizumab arm surviving to final follow up and 49% of patients treated with standard chemotherapy surviving to final follow up. This is representative of the findings in the larger cohort which identified 200 deaths in the standard chemotherapy group and 178 deaths in the bevacizumab group, although OS events as a proportion of the patient number are much higher in the subset used for analysis (42%) than the full ICON7 cohort (24%). This may indicate a bias in the way the subjects were selected for the study. One contributing factor is the selection of a slightly higher proportion of high grade serous tumours (72%) in the sub-group as opposed to 69% of serous tumours total in the original cohort, HGSOC tends to have a worse prognosis. Other clinical characteristics: grade, FIGO stage, age and residual disease, show similar numbers in each trial arm in the 399 patient subset, representing the matching in the full cohort.

Table 3.1: ICON 7 patient demograhics

Demographic characteristics Total ICON7 subset (N=399) Arm A (N=199) Arm B (N=200) ICON7 Arm A (N=764) ICON7 Arm B (N=764) Progression free survival, N (%) Median (days) 553 525 547 531 603.9 Progressed 300 (75) 141 (71) 159 (80) 464 (61) 470 (62) Overall survival, N (%) Median (days) 1489 1550 1396 Not reported * Dead 185 (46) 84 (42) 101 (51) 178 (23) 200 (26) Age, mean (SD) Mean (SD) 57 (9.93) 57.32 (10.4) 56.69 (9.44) 57 57 Grade, N (%) 1 19 (5) 11 (6) 8 (4) 56 (7) 41 (5) 2 65 (16) 34 (17) 31 (16) 142 (19) 175 (23) 3 312 (78) 152 (76) 160 (80) 556 (74) 538 (71) Histological subtype, N (%) Clear Cell 45 (11) 23 (12) 22 (11) 60 (8) 67 (9) Endometrioid 17 (4) 9 (5) 8 (4) 57 (7) 60 (8) High Grade Serous 289 (72) 145 (73) 144 (72) 529 (69) 525 (69) Low Grade Serous 19 (5) 10 (5) 9 (5) Mucinous 3 (1) 2 (1) 1 (1) 15 (2) 19 (2) Unknown 26 (7) 10 (5) 16 (8) 103 (13) 93 (12) Stage, N(%) I 33 (8) 17 (9) 16 (8) 140 (19) 137 (18) II 48 (12) 20 (10) 28 (14) IIIA/B 55 (14) 29 (15) 26 (13) 76 (10) 67 (9) IIIC 223 (56) 115 (58) 108 (54) 432 (57) 438 (57) IV 40 (10) 18 (9) 22 (11) 97 (12) 104 (13) Residual disease, N (%) <1cm 267 (67) 128 (64) 139 (70) 195 (26) 192 (26) >1cm 127 (32) 67 (34) 60 (30) 552 (74) 559 (74)

Arms: A=standard chemotherapy (carboplatin+paclitaxel), B=standard chemotherapy+bevacizumab *Median survival for ICON7 trial arms is not reported in the publication as “not yet reached”

Figure 3.1: Flow of ICON7 patient samples through study quality control and pyrosequencing

3.3.3 Power analysis

A prospective power analysis to detect clinically relevant hazard ratios (>2) for PFS and OS with the ICON7 patient cohort to be analysed of 399 patients was assessed (Table 3.2). A priori power calculations were made using the equation provided by the ‘powerSurv Epi’ R package to identify the likelihood of β (Type II error) using known ratios of patients experiencing PFS and OS events in the 399 patient trial sub-cohort as a whole and in standard chemotherapy versus bevacizumab, with a predicted α (Type 1 error) of 0.05. The calculations define a β>0.8 to detect clinically relevant hazard ratios where HR>2 in the entire cohort, indicating a high power for avoiding false negatives and a β=0.8 to detect an HR>2.2 within individual trial arms, this indicates that we have a greater than 20% chance of a false negative occurring for HR<2.2, which is not considered optimally powered, and results for studies within study arms should be interpreted with caution.

Table 3.2: Power to detect clinically significant hazard ratios

3.3.4 Bisulfite pyrosequencing of VEGF genes in ICON7 EOC patients

Bisulfite pyrosequencing primers and assays were designed to quantify methylation of the CpG dinucleotides surrounding the promoter associated CpG sites for VEGF-A, VEGF-B and VEGF-C (Figure 3.2A) that were originally identified as prognostic for PFS by Illumina 27K Beadchip array in the Scottish Gynaecological Clinical Trials Cohort (217). For the VEGF-A and VEGF-C methylation assays, methylation was quantified at the CpG sites previously published. The VEGF-A CpG site lies towards the 5’ end of the CpG island near the TSS and the VEGF-C site at the 3’ end of the CpG island at the boundary between island and shore. For quantification of VEGF-B promoter methylation reliable pyrosequencing assays could not be designed to assay the CpG site published by Wei et al., therefore the primers and assays were designed around the nearest Illumina 450K Beadchip site within the VEGF- B CpG promoter region for which pyrosequencing primers could be designed. The region assayed is ~350bp from the published 27K site and located on the 3’ shore of the CpG island. Samples passing QC but failing on a specific pyrosequencing assay were repeated and excluded on second failure. Total numbers of unique EOC patient samples for which methylation was quantitated for VEGF-A, VEGF-B and VEGF-C were 378, 387 and 381 respectively (Figure 3.1). Functionality of pyrosequencing assays was assayed by measuring methylation of methylation standards generated by combinations of 0% whole genome amplified DNA and in vitro methylated DNA to show that a measurable difference occurs across the full potential range of DNA methylation (Figure 3.3A). Methylation was measured in normal female DNA, methylation was low at just over 0% for VEGF-A and VEGF-B as is expected for CpG island methylation, while VEGF-C methylation was fairly high at 40%. However CpG sites in the assay are located in the CpG shore, methylation of CpG shores tend to show more variability in methylation than the CpG islands and the VEGF-C assay does show sensitivity to detect low methylation (Figure 3.3A), reliably estimating methylation in the 0% control, although it also shows over-estimation of methylation, identifying the 25% control as ~50% methylated. The VEGF-B assay also tends towards over-estimation of methylation and the VEGF-A assay tended to under-estimate methylation but both showed a detectable difference in methylation at all ranges. Assay variability was estimated by repeat pyrosequencing in >150 samples for each assay, assay variability was <2% for all 3 VEGF assays. Bisulfite conversion controls are built into the assay sequences where possible, which estimate the presence of the base T read in the sequence for a C not followed by a G which are ubiquitously unmethylated and therefore converted to T during bisulfite treatment (Figure 3.2D). Bisulfite conversion controls were only included for the VEGF-A and VEGF-C assays, amplification of bisuflite converted DNA was performed using converted sequence-specific primers from which agarose gel images were also used to confirm conversion (Figure 3.2B). Intra-sample variability was measured by pyrosequencing in the duplicate patient DNA samples available (Table 3.3), which

76 provides data on analytical variability inclusive of variability induced by the DNA extraction process. The VEGF-B and VEGF-C pyrosequencing assays were repeated in 40 duplicate DNA samples available from the same patients, in both cases mean intra-sample variability was <2%, and in 29 patients for the VEGF-A assay which showed a mean discrepancy of 2.07%. The range of methylation (Figure 3.2E) for VEGF-A and VEGF-B was fairly low from 0 to around 10% across all patients. A possible explanation for the low range of methylation at these promoters is cellular heterogeneity, in most normal tissues promoter CGI regions tend to be unmethylated, it is possible that these samples also contain DNA from tissues constituting surrounding stroma which would likely be unmethylated at the promoter. Therefore changes in tumour tissue methylation observed may be diluted but may be of biological importance. As we were unable to attain cellularity measures for the tumour samples analysed it was not possible to attempt to investigate the level of tissue heterogeneity in these samples or to correlate this with methylation. The range of methylation detected at the VEGF-C promoter was much higher with 95% of patients ranging from around 13% to 65%, which may in part be due to the assay’s over- estimation of methylation.

Figure 3.2: Loci of VEGF gene CpG sites of assayed by bisulfite pyrosequencing in ICON7 EOC patients. Diagrammatical representation of the genomic location of CpG sites for which methylation is quantified at the VEGFA, VEGFB and VEGFC promoters are shown in relation to the promoter TSS and CpG island as well as loci of Illumina 27K Beadchip Array CpGs identified as prognostic by Dai et al., (19).

Figure 3.3: Bisulfite pyrosequencing of VEGF gene promoter regions in ICON7 EOC patients. A) Methylation estimates of control DNA standards for each assay and plotted polynomial lines of best fit as well as estimation of methylation in normal female DNA. B) Representative images of 8 patient DNA samples amplified by bislufite conversion specific PCR primers. C) Violin plot showing distribution and density of methylation at described CpG sites quantified by bisulfite pyrosequencing at VEGF-A, VEGF-B and VEGF-C promoters in ICON7 patients. D) Representative pyrograms for pyrosequenced patient samples showing the sequence of the sequence primer and sequencing assays including passed reference peaks (blue diamonds) as sequence controls and the bisulfite conversion control (yellow) for the VEGF-A and VEGF-B assays.

Table 3.3: Pyrosequencing assay and intra-patient variability

3.3.5 Univariate analysis to investigate associations between VEGF promoter methylation and ICON7 patient outcomes

The analytical strategy taken for statistical analysis of associations between VEGF promoter methylation and the clinical endpoints PFS and OS in the ICON7 cohort is displayed in a flow diagram (Figure 3.3). The analytical methods and reporting of findings in this study adhere to standards laid out for reporting recommendations for tumour marker prognostic studies (REMARK) (234). Initially we investigated associations between methylation and survival in ICON7 patients via univariate Cox proportional hazards modelling, treating methylation as a continuous variable (Table 3.4). In the full cohort of EOC patients VEGF-A showed no association with either PFS (P=0.17) or OS (P=0.6). VEGF-B methylation also showed no association with EOC patient PFS or OS when analysed across the entire cohort (P=0.61, P=0.58 respectively). VEGF-C methylation showed a highly significant association with PFS in the 399 EOC patients (HR/1 SD=0.85(0.74-0.95), P=0.004), with low VEGF-C methylation associating with an increased non-proportional hazard of patients experiencing progression. However, VEGF-C did not show a significant association with patient OS (P=0.19).

Previous associations between VEGF-B and VEGF-C methylation and patient PFS outcomes were confirmed in the HGSOC sub-type (217). VEGF-B in particular showed a very strong association with PFS. We hypothesised that if associations existing between methylation at these loci and survival

Figure 3.3: Flow of analysis of statistical analysis for investigation of associations between VEGF methylation and clinical endpoints PFs and OS in EOC and HGSOC ICON7 patients.

outcomes are specific to the high grade serous sub-type of ovarian cancer, significant associations may be being masked by including other non-serous EOC sub-types in the analysis. The univariate Cox proportional hazards regression models were therefore refined to the 289 HGS patients in the cohort. VEGF-A methylation still showed no association with PFS (P=0.6) or OS (P=0.47). When the analysis was refined to the HGSOC patients within the cohort an association passing the threshold for significance was identified between VEGF-B methylation and OS (HR/1 SD = 1.17(1.002-1.37), P=0.047), although no statistically significant association with PFS (P=0.13). Again, no significant association was identified between VEGF-C and OS in HGSOC patients (P=0.5). Unlike in all EOC patients only a

81 borderline significant association was detected between VEGF-C methylation and PFS in HGS patients (HR/1 SD=0.89(0.78-1.01), P=0.08), however the effect size is similar to that observed in the whole EOC cohort (HR/1 SD=0.85), indicating a loss of power to detect a significant association in the HGS patients perhaps due to small sample size. In order to adjust for multiple comparisons we considered that according to the Bonferroni corrected p-value for 12 comparisons (P=0.004), which is the most stringent adjustment for multiple hypotheses the association between VEGF-B methylation and OS is in HGSOC patients is no longer significant, whilst the association between VEGF-C methylation and PFS in EOC patients remains significant.

Table 3.4: Cox proportional hazards models for association of VEGF promoter methylation with overall and progression free survival (OS/PFS) in ICON7 Epithelial (EOC) and high grade serous (HGS).

+ Analysis performed in HGS patients only

3.3.6 Multivariable analysis to investigate independent associations between VEGF-B methylation with patient outcomes in the ICON7 trial

As VEGF-B showed statistically significant association with OS in HGSOC patients in the continuous univariate Cox regression analysis (Table 3.4), the cohort was dichotomised into low and high methylation groups for VEGF-B, with the cohort split at the top quartile for methylation. As we had identified a significant association in continuous analysis we split at the top quartile as there was evidence of bimodality in the distribution of data (Figure 3.4A). It could also be argues that this top quartile of methylation is more likely to have an effect on gene expression. Again we demonstrated a significant association between VEGF-B methylation and patient OS (HR = 1.48(1.03-2.12), P = 0.03) (Figure 3.4B), with HGSOC patients with low methylation showing improved OS.

We conducted association testing for the construction of multivariable models by investigating whether clinical covariates of patient age, residual disease, FIGO stage or tumour grade associated independently with OS or VEGF-B methylation (Table 3.5). Whilst clinical covariates age, stage and residual disease showed significant association with OS they showed no association with VEGF-B methylation in univariate analysis so adjusting for these covariates in multivariable analysis was not necessary and they were not included in a multivariable model as they do not qualify as potential confounding variables.

In order to investigate the associative relationship between VEGF-B methylation and OS in HGSOC patients within different treatment contexts, survival analysis was performed with patients split by trial arm (standard therapy: carboplatin/paclitaxel, versus standard therapy plus bevacizumab) as well as low and high VEGF-B methylation, again with patients split at the top quartile (Figure 3.4A-B). We observed that patients treated with carboplatin and paclitaxel only and with low VEGF-B methylation appeared to have better OS than patients with high VEGF-B methylation in the same trial arm and patients treated with carboplatin, paclitaxel and bevacizumab with either low or high VEGF-B methylation (multivariable logrank P=0.01). This was confirmed by Kaplan Meier plots and Cox regression analysis between the four groups (Figure 3.4C-D). A significant association was detected between OS and VEGF-B methylation in the HGS patients in the carboplatin/paclitaxel treated arm of the trial (HR=2.07(1.23-3.49), P=0.006) with a dramatically increased hazard ratio than that detected in all HGS patients irrespective of trial arm. There was no difference in OS between the VEGF-B low and VEGF-B high patients in the trial arm treated with bevacizumab (P=0.41). Additionally, there was a borderline significant difference in patient survival between the VEGF-B low patients, considered to be the prognostically beneficial methylation group, which were treated with carboplatin/paclitaxel alone and bevacizumab (HR=1.77(0.99-3.18), P=0.056). These data indicate that as previously

83 identified there is a strong association between VEGF-B methylation and HGSOC patient survival with low VEGF-B methylation being prognostically beneficial.

Table 3.5: Association test P values for clinical covariates with relevant outcomes and exposures identified via univariate modelling

Figure 3.4: VEGF-B methylation associates with OS in HSGOC patients treated with standard chemotherapy.

A) Distribution of VEGF-B promoter methylation in ICON7 HGSOC patients, abline showing top quartile cut- off used for dichotomised analysis. B) OS plotted for all HGS patients separated for VEGF-B methylation, Cox proportional hazards regression HR and 95% CI shown. C) OS (left) and PFS (right) plotted for patients separated by trial arm and VEGF-B methylation. D) OS plotted for patients in the 4 groups from (C) split by VEGF-B methylation in the standard chemotherapy arm (left) and bevacizumab arm (middle) and split by trial arm in the VEGF-B low group (right). HRs and 95% CIs shown, Cox model HR, CI and P value shown for expression for patients split by methylation in the carboplatin/paclitaxel only and bevacizumab treated arms.

However, in the ICON7 patients the association was masked by bevacizumab treated patients who do not show survival benefits of lower VEGF-B methylation. VEGF-B expression (obtained from Charlie Gourley, Cancer Research UK, Edinburgh) showed the inverse relationship with trial arm and patient outcome with HGSOC to VEGF-B methylation. Patients with high VEGF-B expression experienced better OS in the carboplatin/paclitaxel arm than patient with low VEGF-B expression (HR=0.65(0.44-0.98), P=0.039) but patients in the Bevacizumab treated arm of the trial showed no difference in OS when split by low and high VEGF-B expression (P=0.97). These data indicate that low VEGF-B methylation and high VEGF-B expression are beneficial for HGSOC patient survival when patients are treated with standard chemotherapy alone, but not bevacizumab. Importantly no significant association was detected between VEGF-B and the trial arm (p=0.21, Table 3.5) – as bias generated by such an association may affect the interpretability of these findings.

Since the association between VEGF-B and OS had been partially masked by including the bevacizumab arm patients in the analysis, it seemed possible that there may also be an association between VEGF- B methylation and PFS in the standard chemotherapy arm which was being masked. We therefore split the patients by trial arm and VEGF-B methylation for PFS as well (Figure 3.4D). Overall a significant difference between the four patient groups was not detected (multivariable logrank P=0.1), but there seemed to be a trend for HGSOC patients with low VEGF-B methylation in the standard chemotherapy arm to show better PFS than both patients in the same arm with high methylation and patients in the bevacizumab arm with low VEGF-B methylation. This trend indicates that treatment of VEGF-B low patients with bevacizumab may have a detrimental effect on patient OS.

3.3.7 Multivariable analysis to investigate independent associations between VEGF-C methylation with patient outcomes in the ICON7 trial

VEGF-C methylation showed strong association with PFS in ICON7 EOC patients identified via continuous univariate Cox proportional hazards regression analysis (Table 3.1). We continued analysing association with PFS in EOC patients by dichotomising patients at the median (Figure 3.5A) into VEGF-C low and VEGF-C high groups. Cox proportional hazards regression revealed that patients with high methylation showed significantly better PFS (HR=0.66(0.52-0.84), P=0.0006) (Figure 3.5B). Additionally, VEGF-C methylation showed a borderline significant association with OS (P=0.09, Figure 3.5C). A difference in EOC patient survival in the VEGF-C low versus VEGF-C high groups only becomes apparent after ~1000 days, whilst at ~2200 days almost 60% of VEGF-C high patients survived in comparison to less than 40% in the VEGF-C low group. These data indicate that low VEGF-C promoter methylation may associate with poorer EOC patient survival as well as progression.

Clinical factors were tested for association with VEGF-C methylation and patient PFS, for multivariable model construction (Table 3.5), patient age and FIGO stage showed association with both exposure and outcome, and were included in multivariable models (Table 3.6). When adjusted for stage and age association between VEGF-C methylation and patient PFS was lost (P=0.57), though the association was shown to be independent of patient age alone (P=0.002), the association was not independent of FIGO stage (P=0.4), indicating that the stage is the explanatory variable for the association between VEGF-C and patient PFS. VEGF-C therefore associates significantly with stage (P=0.006), with higher FIGO tumour stages showing progressively lower methylation (Figure 3.5D). This high-lights a correlation between loss of VEGF-C methylation and advanced tumour stage indicating a potential role for loss of VEGF-C methylation in driving more advanced tumour stage.

As a relationship was identified between VEGF-B methylation and expression and OS in the standard chemotherapy arm of the trial only, we investigated a potential arm-specific association between VEGF-C methylation and PFS (Figure 3.5E-H). Low VEGF-C methylation remained prognostic for poor PFS in both patients treated with standard chemotherapy (logrank P=0.04) and bevacizumab (logrank P=0.004), and no significant difference in PFS was detected in survival between trial arms where VEGF- C patients alone were selected (P=0.37), or where VEGF-C high patients were selected (P=0.72). This indicates that the association between VEGF-C methylation and PFS is independent of trial arm. No significant association was detected between VEGF-C methylation and trial arm (p=0.6, Table 3.5)

Figure 3.5: Loss of VEGF-C methylation associates poor PFS and advanced stage in ICON7 EOC patients. A) Distribution of VEGF-C promoter methylation in ICON7 EOC patients, abline showing median cut-off used for dichotomised analysis. PFS (B) and OS (C) plotted in EOC patients split by VEGF-C methylation. Cox proportional hazards model HR and 95% CI as well as multivariable P after adjustment for stage as a co-variate shown. D) VEGF-C methylation at EOC stages 1 (I) to 4 (IV) and Kruskal Wallis derived P shown. KM plots showing EOC patients split by methylation in trial arms given standard chemotherapy (E) and Bevacizumab (F), VEGF-C low patients split by trial arm (G), and VEGF-C high patients split by trial arm (H). Cox proportional hazards model HR and 95% CI shown.

3.3.8 VEGF-A methylation shows a trend for prognostic benefit in ICON7 EOC patients treated with standard chemotherapy, not bevacizumab.

Table 3.6: multivariable Cox proportional hazards modelling for VEGF-C methylation and PFS

In univariate analysis treating ICON7 patients homogenously in terms of trial arm VEGF-A methylation showed no association with PFS (P=0.17) or OS (P=0.31) in EOC patients or HGS patients (PFS P=0.6, OS P=0.47). However, as described above the true strength of association between VEGF-B and OS in HGSOC patients was being masked by the lack of association between these variables in the bevacizumab treated arm of the trial. We investigated whether this was also the case for VEGF-A methylation and clinical outcome in EOC patients (Figure 3.6). We discovered a trend between VEGF- A methylation and PFS (P=0.097) and OS (P=0.08) in EOC patients treated with standard chemotherapy only, with patients in this arm with low VEGF-A methylation showing poorer PFS and OS. Conversely, EOC patients in the trial arm treated with standard chemotherapy and bevacizumab showed no difference in PFS or OS (respectively P=0.45 and P=0.82). As expected therefore there was a borderline significant difference in PFS was observed between the patients in the prognostically beneficial VEGF- A high group between the standard chemotherapy arm and the bevacizumab arm (P=0.12), with a marked difference in progression emerging after 500 days. A significant difference in OS was observed between arms in the VEGF-A high group for OS (P=0.03). Despite many of the statistical outcomes showing borderline significance, there is a very apparent trend similar, though opposite in direction, to that of VEGF-B methylation for EOC patients to show benefit high VEGF-A methylation in the trial arm treated only with standard chemotherapy and to lose the benefit of high methylation when treated with bevacizumab. A Wilcox test reveals a significance of p=0.41 for association between VEGFA and trial arms indicated that this trend is unlikely to be a result of bias induced by difference in VEGFA methylation between the two arms.

Lastly, correlation in methylation at different loci was investigated to identify whether this was likely to confound any analytical findings or whether effects of methylation at different loci upon clinical outcomes was likely to be inter-related, this was investigated via linear regression. Correlation between VEGF-B and VEGF-C methylation was not detected as significant and a very small correlation coefficient was identified (R2=0.007, p=0.055), as methylation associated with different clinical outcomes in different patient histological groups an inter-related effect seems unlikely. VEGF-A correlated significantly with both VEGF-B (R2=0.009, p=0.03) and VEGF-C ((R2=0.009, p=0.04), however the correlation coefficients are extremely small, therefore interpreting any meaning from correlation between methylation at these loci and associations with clinical end-points is very difficult.

Figure 3.6: Low VEGF-A methylation shows a trend for being prognostically beneficial for ICON7 EOC patients treated with standard chemotherapy only. ICON7 EOC patients were split by trial arm and VEGF-A methylation dichotomised at the median. PFS (A) and OS (B) plotted for patients split by VEGF-A methylation in the standard chemotherapy arm (left) and bevacizumab arm (middle) and split by trial arm in the VEGF-A high group (right). Cox proportional hazards model HR and 95% CI for OS shown for patient groups with significant/borderline significant P.

3.4 Discussion VEGF-B methylation was identified in this study as prognostic for HGSOC patient survival, aligning with published data identifying an association between loss of VEGF-B methylation and improved PFS (217). In this study however no statistically significant association was observed between VEGF-B methylation and PFS, however an association was identified with OS. Patients in this cohort treated with bevacizumab in addition to standard chemotherapy did not benefit from the low VEGF-B methylation which associated with a survival advantage in patients given standard chemotherapy. We observed the converse association between VEGF-B expression and patient OS, patients with high VEGF-B expression showed better OS unless treated with bevacizumab. These findings indicate that low VEGF- B promoter methylation and the resulting increase in expression is beneficial for HGSOC patient survival and may exert a protective, anti-tumourigenic effect. The mechanism by which VEGF-B confers a beneficial effect in HGSOC is unclear, studies aiming to identify the role of VEGF-B in angiogenesis show differing results however it is possible that VEGF-B has a role in preventing malignant neoplastic vascular growth (107) and over-expression of VEGF-B has been shown to suppress tumour growth in a mouse model of neuro-endocrine pancreatic cancer (108).

Our findings indicated that introduction of bevacizumab to patients with low VEGF-B methylation is counteracting the beneficial effect of low methylation and increased VEGF-B expression. We speculate this may be due to off target activity of bevacizumab: sequestration of VEGF-B may inhibit the protein from exerting its anti-tumorigenic effect. Papadopoulos et al., investigate off-target binding activity of angiogenesis inhibiting drugs including bevacizumab (235) and imply that bevacizumab does not show off target binding to VEGF-B but do not explicitly show data on the interaction between bevacizumab and the VEGF-B protein. We identified a similar trend towards association between VEGF-A promoter methylation and both PFS and OS, with high VEGF-A methylation being associated with better OS and PFS in the standard chemotherapy arm, but with both methylation groups showing equally poor outcomes in the bevacizumab arm. These associations are mostly borderline significant, however, with only one of four tests reaching statistical significance, these findings would require independent validation to substantiate them and do not provide strong evidence of a sub-group of patients in which time of survival or progression differs dependent on bevacizumab treatment. VEGF-A promoter methylation has been shown to associate with decreased expression of the gene in HGSOC (217) and VEGF-A expression is strongly associated with poor prognosis in ovarian cancer (236) due to enhanced angiogenesis (237), and autocrine signalling generating more stem cell like tumour phenotypes (106, 238, 239), so it seems unsurprising that hypermethylation at the VEGF-A promoter would be associated with reduced tumour progression and increased survival. However, the trend indicates that the beneficial effect of VEGF-A hypermethylation, much like the beneficial effect of VEGF-B

92 hypomethylation, is lost when bevacizumab is introduced to the patient. It is difficult to speculate as to why this may be as there is potentially less VEGF-A being secreted by the tumour cells there is less VEGF-A protein for bevacizumab to interact with, this finding might indicate that bevacizumab may therefore be free to interact non-specifically with beneficial secreted proteins which would otherwise be tumour suppressive.

This study showed a strong association between VEGF-C methylation and EOC patient PFS as well as a borderline significant association with OS. This confirms previously identified prognostic associations between VEGF-C hypermethylation and improved patient PFS (217). The association between VEGF-C methylation was confounded by tumour stage – indicating a potential relationship between VEGF-C methylation and tumour stage. VEGF-C has a well characterised role in promoting angiogenesis (111) and lymphangiogenesis (109), two primary routes of tumour metastasis and VEGF-C expression has been linked to more aggressive, invasive ovarian carcinomas (126) due to both paracrine and autocrine signalling cascades. The increase in expression of secreted VEGF-C due to the epigenetic deregulation associated with promoter hypomethylation is a potential paracrine driver of advanced tumour stage. The mechanistic basis of the association between VEGF-C and stage will be explored in Chapter X.

3.5 Summary

VEGF-B methylation was identified as a prognostic marker for ICON7 HGSOC patient survival in patients treated with standard chemotherapy alone with low VEGF-B methylation and increased expression beneficial for patient survival unless treated with bevacizumab. These data supported previous findings associating lower VEGF-B promoter methylation with better clinical outcome in HGSOC patients treated with carboplatin and paclitaxel and also indicated that treatment of HGSOC patients with low VEGF-B methylation with bevacizumab may interfere with beneficial survival outcomes. We also showed that high expression of VEGF-B associated with longer survival in carboplatin and paclitaxel treated patients only. No DNA samples were available from similar datasets comparing bevacizumab with standard chemotherapy treatment for validation of this prognostic association. VEGF-C associated with EOC patient PFS, though not independently from stage – highlighting VEGF-C hypomethylation as a potential driver of tumour stage and patient progression.

Chapter 4: Investigation of VEGF-B and VEGF-C methylation and expression in OC patients at relapse and the autocrine role of VEGF-C in promoting tumour progression

4.1 Introduction

Given the association between methylation and expression of VEGF-B with patient overall survival and VEGF-C methylation with patient progression dependently with tumour stage, as described in the previous chapter, we investigated methylation and expression of these genes in patients at chemoresistant relapse. Any associations identified between expression/methylation and relapse we would follow up by generating cell line models with VEGF-B and VEGF-C knocked-out to identify autocrine mechanisms regulated by expression of these genes. This aims of this section of the project were:

1. Identify associations between VEGF-B/C promoter methylation/expression and chemoresistant relapse in primary HGSOC samples

Upon identification of associations described above the follow up aims of this work were:

2. Identify primary and relapse OC cell line models with similar methylation and expression patterns to patient primary and relapse samples 3. Generate clonal CRISPR knock-out lines for downstream functional work 4. Investigate potential autocrine role of VEGF of interest in chemoresistance and cellular phenotypes linked to relapse: invasion, migration, growth and tumour initiation

4.2 Results

4.2.1 Analysis of VEGF-C expression and methylation in patients at primary presentation and relapse

We examined VEGF-B and VEGF-C gene expression and promoter methylation in serous cystadenocarcinoma samples derived from 11 patient tumours at primary presentation, and matched ascites samples obtained after relapse within 6 months, indicating clinical resistance. The RNA-seq and Illumina 450K BeadChip Array data is publically available from the International Cancer Genome Consortium (ICGC) (28). VEGF-B showed expression and extremely low promoter methylation in both primary and relapse samples (Figure 4.1A-B) but no significant difference between either. Significantly higher VEGF-C expression was identified in the samples at relapse than at primary presentation (P<0.01, Figure 4.1C-E), this coincided with a small but significant loss of methylation (P<0.05, Figure 4.1D-E) at the VEGF-C promoter. These data indicate a potential association as observed previously (217) between loss of VEGF-C methylation, increased VEGF-C expression and more progressive disease

Figure 4.1: VEGF-C is up-regulated in patient samples at chemoresistant relapse compared to primary presentation. Expression of VEGF-B (A) and VEGF-C (C) are represented as log2 counts per million reads mapped (log2CPM) and promoter methylation (right) calculated as average β value of CpG sites across the promoter of VEGF-B (B) and VEGF-C (D), as well as β value calculated for the CpG site previously identified as associating with HGSOC prognosis (217) and assayed via targeting pyrosequencing in the previous chapter (E, in paired patient samples taken from chemosensitive primary tumour and ascites at chemoresistant relapse. Average methylation across the promoter is shown for VEGF-C in matched tumour and ascites samples from two patients.

in HGSOC patients. These data also high-lighted a possible relationship between up-regulation of VEGF-

C expression and clinical resistance to platinum based therapy. It is possible that the observed Figure 4.2: VEGF-C is over-expressed in chemoresistant cell line PEO4 and VEGF-C expression is associated with promoter de-methylationFigure 4.1: VEGF-C is up-regulated in patient samples at chemoresistant relapse compared to primary presentation96 . Expression of VEGF-B (A) and VEGF-C (C) are represented as log2 counts per million reads mapped (log2CPM) and promoter methylation (right) calculated as average β value of CpG sites across the promoter of VEGF-B (B) and VEGF-C (D), as well as β value calculated for the CpG site previously identified as associating with HGSOC prognosis (217) and assayed via targeting pyrosequencing in the previous chapter (E, in paired patient samples taken from chemosensitive primary

difference in VEGF-C methylation and expression between the matched samples is due to the tissue of origin Unfortunately there was no expression data available for both tumour and ascites at either primary presentation or relapse and methylation data was available only for two patients in matched tumour and ascites and tumour samples at presentation which is inadequate to provide a control measurement.

4.2.2 Investigation of VEGF-C expression and methylation in paired chemosensitive primary and chemoresistant relapse EOC cell lines

Given the over-expression observed in tumour pairs at chemoresistant relapse compared to primary presentation, it seemed likely that VEGF-C may be a driver of both tumour progression and development of resistance to platinum-based therapies. We examined VEGF-C expression via RT-qPCR in four EOC cell line pairs, three of which were derived from the same patient at primary presentation and relapse (PEO1/PEO4, PEO14/PEO23 and PEA1/PEA2) and one pair derived at primary presentation and treated with successively increased concentrations of chemotherapy to derive an extremely chemoresistant isogenic model line in vitro (A2780/CP70) (Figure 4.2A). Though no significant change in VEGF-C expression was observed in three of the four pairs, expression of VEGF-C showed significant fold increase in the chemoresistant relapse-derived EOC cell line PEO4 compared to its chemosensitive primary derived pair. This was confirmed via western blot (Figure 4.2C) and RT-qPCR using a different house-keeping gene for relative quantification (Figure 4.2D). Next, promoter methylation was analysed in the PEO1 and PEO4 cell lines at the Illumina 27K CpG site where methylation was quantified in the ICON7 cohort (Figure 4.2C). Inversely to VEGF-C expression, methylation was lower in the PEO4 line than PEO1, indicating a potential association between VEGF-C methylation and expression between relapse and primary tumour cells, indicating that loss of methylation at the promoter may promote higher levels of expression. PEO1 cells were treated with 5-azadeoxycytidine (Decitabine) to investigate the effect on VEGF-C expression (Figure 4.2E). Decitabine treatment of PEO1

Figure 4.2: VEGF-C is over-expressed in chemoresistant cell line PEO4 and VEGF-C expression is associated with promoter de-methylation. A) Expression of VEGF-C, measured by RT-qPCR in 4 pairs chemosensitive/resistant ovarian cancer cell line pairs. B) Dose response curves for PEO1 and PEO4 cell proliferation after 24 hours cisplatin treatment. Expression of VEGF-C in PEO1 and PEO4 cell lines shown via western blot (C) and RT-qPCR relative to β-actin (D). E) Methylation at the VEGF-C promoter in the PEO1 and PEO4 lines detected by bisulfite pyrosequencing at the same locus used in ICON7 patient samples. F) Expression (left)of VEGF-C relative to β-actin and VEGF-C methylation (right) of PEO1 cells after 6 days Decitabine treatment, plotted relative to DMSO control. Significance of p-values denoted by *<0.05, **<0.01, ***<0.001, ****<0.0001.

Figure 4.3: Genome editing using RNA-guided Fok1 nucleases (RFNs).Figure 4.2: VEGF-C is over- expressed in chemoresistant cell line PEO4 and VEGF-C expression is associated with promoter demethylation. A) Expression of VEGF-C, measured by98 RT -qPCR in 4 pairs chemosensitive/resistant ovarian cancer cell line pairs. B) Dose response curves for PEO1 and PEO4 cell proliferation after 24 hours cisplatin treatment. Expression of VEGF-C in PEO1 and PEO4 cell lines shown via western blot (C) and RT-qPCR relative to β-actin (D). E) Methylation at the VEGF-C promoter in the PEO1 and PEO4 lines detected by bisulfite pyrosequencing at the same locus used in ICON7 patient samples. F) Expression (left)of VEGF-C relative to β- induced dramatic re-expression of VEGF-C indicating a causative correlation between VEGF-C methylation and expression at this locus. Notably DNA methylation at higher concentrations of Decitabine (1/2µM) showed increased methylation equivalent to the DMSO control. This is likely due to the well established DNA damaging and cytotoxic effect of Dectabine at these concentrations. The higher DNA methylation may be concomitant with a more heterochromatic apoptotic genome in some cells. There is an increase of VEGF-C expression at these Decitabine concentrations despite the regaining of methylation, this may be an effect of bulk measurements of a population heterogenous for apoptosis, surviving cells with demethylated VEGF-C may have a much higher level of expression.

4.2.3 Generation of VEGF-C knock-out PEO4 lines using CRISPR

In order to investigate the potential autocrine role of VEGF-C expression in driving cellular phenotypes we generated clonal VEGF-C knock-out PEO4 lines via RNA-guided Fok1 nuclease CRISPR (240) (Figure 4.33/4.4). We opted for this method as targeting DNA using sgRNA-guided Fok1 endonucleases, which perform cleavage only as dimers is a much more specific CRISPR methodology than using a single RNA- guided dCas9. This results in reduced off-target cleavage events, as demonstrated by Tsai et al., in their methods article. As we adapted a published CRISPR method the methodology used to generate the knock-out lines is described here. As described by authors of the RFN-CRISPR method we used the online bioinformatics tool Zifit to identify Fok1 dimer target sites within sequences of interest, and design sgRNA sequences for sub-cloning into the sgRNA vector (pSQT1313). sgRNA sequences were chosen based on optimal 16bp spacing within VEGF-C exon target sequences. Three RFN targeting pairs were designed and sub-cloned into pSQT1313, two targeting sites within the first exon of VEGF-C and one targeting a locus within the third exon. Sequences were derived from early VEGF-C exons with the rationale that mutation at these sites is more likely to result in a premature stop codon during transcription and induce nonsense-mediated decay. Successful ligation of sgRNA sequences into the sgRNA plasmid was confirmed via Sanger sequencing (Figure 4.3B). Neither the Fok1 (pSQT1601) nor sgRNA plasmids contain selectable marker cassettes, therefore we devised a transfection protocol which involved transfecting a third plasmid (pcDNA3) with a selectable neomycin marker. PEO4 cells were transfected with previously established ratios of pSQT1313: pSQT1601 (250ng: 750ng) and with two different amounts of co-transfected pcDNA3: 500ng and 1000ng. We then selected for transfected cells with 72 hours of G418 treatment. After treatment we allowed the cells to recover and confirmed a pooled VEGF-C knock-out effect via western blot (Figure 4.4C). We then conducted single cell cloning, grew out clonal lines and western blotted to confirm a clonal VEGF-C knock-out PEO4 line. We selected three clonal knock-out lines to take forward for phenotypic assays (Figure 4.4D).

Figure 4.3: Genome editing using RNA-guided Fok1 nucleases (RFNs). Diagrammatic representations of A) multiplex gRNA expression plasmid pSQT1313 (above) and human codon optimized Csy4 and FokI-dCas9 expression plasmid pSQT1601 (below) used for RFN based genome editing, B) the molecular basis of RFN-based genome editing, showing Csy4-mediated cleavage of the multiplexed gRNA transcript expressed by pSQT1313 and association of the dCas9-Fok1 fusion protein with the gRNAs and Fok1-dimer induced cleavage of a 16bp spacer target site in a genomic locus of interest.

Figure 4.4: Genomic editing of the VEGF-C locus in the PEO4 cell line using RNA-guided Fok1 nucleases. A) Genomic sequences of target loci within the VEGF-C gene designed using the Zifit tool. Showing left (orange) and right (purple) sgRNA binding sites and 5’-NGG-3’ PAM sites (red). B) Workflow for generating clonal VEGF-C knock-out lines using CRISPR-RFN after plasmid cloning. C) Western blot performed after G418 selection to identify pooled VEGF-C knock-out lines. D) Western blot performed after single cell cloning to confirm clonal VEGF-C knock-out lines.

Figure 4.5: VEGF-C is not necessary for resistance to cisplatin in PEO4 EOC cells. A) Dose response curves and extrapolated IC50s (B) for 24 hours cisplatin treatment of and cell survival in PEO1, PEO4 and three VEGF-C KNOCK-OUT PEO4 lines. Significance of p-values denoted by * P<0.05, ** P<0.01, *** P<0.001, **** P<0.0001. N=3.

Figure 4.5: VEGF-C is not necessary for resistance to cisplatin in PEO4 EOC cells. A) Dose response curves and extrapolated IC50s (B) for 24101 hours cisplatin treatment of and cell survival in PEO1, PEO4 and three VEGF-C KNOCK-OUT PEO4 lines. Significance of p-values denoted by * P<0.05, ** P<0.01, *** P<0.001, **** P<0.0001. N=3.

4.2.4 Investigation of the role of VEGF-C in driving cisplatin resistance in the PEO4 line

We initially investigated the effect of attenuated VEGF-C expression upon response to cisplatin treatment in the chemoresistant PEO4 cell line (Figure 4.5) by treating the PEO1, PEO4 WT and PEO4 VEGF-C null lines with a dose range of cisplatin for 24 hours and allowing the cells recover over a period of 48 hours. The loss of VEGF-C expression had no effect on PEO4 dose response to cisplatin treatment in comparison to the wild type line (Figure 4.5A). A slight increase in IC50 was observed in the VEGF-C null PEO4 lines when compared to the WT line (Figure 4.5B), though small, they were statistically significant. However, since one of the VEGF-C null PEO4 lines showed no difference in IC50 to PEO4 is seems unlikely that this is a biologically meaningful statistic as the variations are small. These data indicate that VEGF-C expression is not necessary to maintain the enhanced resistance of the PEO4 line to cisplatin.

4.2.5 Investigation of the role of VEGF-C in driving invasion and migration in the PEO4 line Figure 4.5: VEGF-C is not necessary for resistance to cisplatin in PEO4 EOC cells. A) Dose Previously,response ancurves autocrine and extrapolated role has been IC50s identified (B) for 24 hoursfor VEGF cisplatin-C in promotingtreatment of invasion and cell andsurvival migratory in cell PEO1, PEO4 and three VEGF-C KNOCK-OUT PEO4 lines. Significance of p-values denoted by * P<0.05, phenotypes** P<0.01, in*** a P<0.001,lymphangiogenesis **** P<0.0001. independent N=3. manner (114). Therefore, we investigated whether invasive and migratory potential was impaired in VEGF-C null PEO4 lines (Figure 4.6). Initially we investigated invasive potential via 3D spheroid MatrigelTM invasion assays in the VEGF-C pooled null linesFigure (Figure 4.5: 4.6A). VEGF Although-C is not thenecessary PEO4 forlines resistance appeared to to cisplatin show greaterin PEO4 spheroid EOC cells growth. A) Dose than PEO1, response curves and extrapolated IC50s (B) for 24 hours cisplatin treatment of and cell survival in there was no obvious leading invasive “edge” to the tumour spheroid and the differences in spheroid PEO1, PEO4 and three VEGF-C KNOCK-OUT PEO4 lines. Significance of p-values denoted by * P<0.05, growth** P<0.01, were ***not P<0.001,statistically **** significant P<0.0001. dueN=3. to large variance in the data. Additionally based on growth analysis (Figure 4.6, data described below) we know that PEO4 cells proliferate significantly faster than

102 Figure 4.5: VEGF-C is not necessary for resistance to cisplatin in PEO4 EOC cells. A) Dose response curves and extrapolated IC50s (B) for 24 hours cisplatin treatment of and cell survival in PEO1, PEO4 and three VEGF-C KNOCK-OUT PEO4 lines. Significance of p-values denoted by * P<0.05, ** P<0.01, *** P<0.001, **** P<0.0001. N=3.

PEO1 adherent culture and in 3D spheroid culture. We therefore surmised that the faster increase in PEO4 spheroid size, if real, was likely to be due to the greater proliferative capacity of this line. However, since no statistically significant differences in growth were observed in spheroid expansion and invasive potential between PEO1 and PEO4, or between the parental PEO4 line and the pooled VEGF-C knock-out line, and the apparent absence of leading edge or invasive potential of the lines we decided not to pursue investigation of the relationship between VEGF-C expression and invasiveness in the clonal VEGF-C knock-out lines. Cell migration was assessed via scratch assay in PEO1, PEO4 and the three clonal VEGF-C knock-out PEO4 lines (Figure 4.6B). While the data indicates a significantly greater migratory distance PEO1 than PEO4 over 72 hours in culture, the distance migrated is barely visible. The PEO4 VEGF-C knock-out lines appeared to show greater migratory potential than PEO4, with one showing a significantly increased migratory potential. However, distances of migration were minimal.

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Figure 4.6: VEGF-C does not drive invasion or migration in PEO4 EOC cells. A) Spheroid invasion assays TM in Matrigel over 48 hours for PEO1, PEO4 and a pooled VEGF-C null PEO4 line. B) Scratch migration assays recorded over 72 hours for PEO1, PEO4 and three clonal VEGF-C null PEO4 lines. Representative microscope images (left) and summary of data for N=3 replicates (right) shown for both assays. Levels of significance determined via students T-test, significant P values represented as * P<0.05, **, P<0.01.

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4.2.6 Investigation of the role of VEGF-C in driving cell growth and tumour initiation in the PEO4 line

Growth in adherent culture and non-adherent culture was measured for PEO1, PEO4 and the VEGF-C knock-out PEO4 clones (Figure 4.7). The PEO1 and VEGF-C knock-out PEO4 lines showed a significantly lower rate of growth than PEO4 in 2D adherent culture as assessed by growth curve (Figure 4.7A). We also tested the ability of these lines to form spheroids in 3D non-adherent culture via tumoursphere formation assays (219) (Figure 4.7B). The relapse derived PEO4 line showed expansive formation of spheroids in comparison to the parental PEO1 line which had a very low tumoursphere formation efficiency. The VEGF-C knock-out PEO4 lines showed an impaired ability to form 3D spheroids in non- adherent culture with two reaching statistical significance (P<0.05). These data indicate an oncogenic autocrine role for VEGF-C in promoting proliferation of the EOC cell line PEO4 as well as promoting formation of spheroids in non-adherent culture. The initiation of tumoursphere formation in vitro indicates an enhanced resistance to anoikis-induced cell death as well as an ability to proliferate and form a 3D structure in absence of cellular contact or adherence to extra-cellular matrix.

4.2.7 VEGF-C may negatively regulate expression of CD44 and ALDH1A3

Given the lower tumoursphere formation capacity of the low VEGF-C expressing cell line PEO1 and attenuated spheroid formation of the VEGF-C knock-out PEO4 lines, the data seemed to indicate that VEGF-C may be in some way act as an autocrine regulator of cancer stem cell activity in these lines. We hypothesised therefore that cancer stem cell gene expression would be lower in the VEGF-C low/null lines. We assessed whether the PEO1 and PEO4 lines expressed detectable activity of common cancer stem cell related genes in ovarian cancer: CD117, CD44, CD133 and ALDH1A1. We could not identify detectable expression of CD117 or CD133 in these lines (data not shown). CD44 and ALDH activity were detectable in these lines, however, unexpectedly CD44 expression and ALDH activity were both higher in the PEO1 and VEGF-C knock-out PEO4 lines (Figure 4.8A-D). ALDH activity as detected via the ALDEFLUORTM assay, indicated that an ALDH+ population is significantly higher in the parental PEO1 than the relapse line (P<0.001), with the ALDH+ population more than 4x higher in the PEO1 line. All three clonal VEGF-C knock-out lines show a significantly (P<0.05) enriched ALDH+ population, around 2x higher than the PEO4 wild type line indicating that ALDH+ activity and content may be regulated by VEGF-C expression. CD44 was also highly expressed in PEO1 compared to PEO4 with a median fluorescent intensity (MFI) almost 10x higher than PEO4. The PEO4 VEGF-C knock-out lines all showed enhanced expression of CD44 with MFIs ~7-10x higher. The differences between PEO4 and

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Figure 4.7: VEGF-C expression drives proliferation and tumoursphere formation. Cell growth determined by cell density at day 4 in adherent culture, normalised to cell density at day 0 (A) and tumoursphere formation in non-adherent culture over 6 days (A) for PEO1, PEO4 and PEO4 VEGF-C null lines. Significance of p-values denoted by * P<0.05, ** P<0.01, *** P<0.001, **** P<0.0001.

Figure 4.7: VEGF-C expression drives proliferation and tumoursphere formation. Cell growth determined by cell density at day 4 in adherent culture, normalised to cell density at day 0 (A) and tumoursphere formation in non-adherent culture over 6 days (A) for PEO1, PEO4 and PEO4 VEGF-C KNOCK-OUT lines. Significance of p-values denoted by * P<0.05, ** P<0.01, *** P<0.001, **** P<0.0001.

Figure 4.8: VEGF-C Figure 4.7: VEGF-C expression drives proliferation and tumoursphere formation. Cell growth determined by cell density at day 4 in adherent culture, normalised to cell density at day 0 (A) and tumoursphere formation in non-adherent culture over 6 days (A) for PEO1, PEO4 and PEO4 VEGF-C null lines. Significance of p-values denoted by * P<0.05, ** P<0.01, *** P<0.001, **** P<0.0001.

Figure 4.7: VEGF-C expression drives proliferation and tumoursphere formation. Cell growth determined by cell density at day 4 in adherent culture, normalised to cell density at day 0 (A) and tumoursphere formation in non-adherent106 culture over 6 days (A) for PEO1, PEO4 and PEO4 VEGF-C KNOCK-OUT lines. Significance of p-values denoted by * P<0.05, ** P<0.01, *** P<0.001, **** P<0.0001.

Figure 4.8: VEGF-C may negatively regulate CD44 and ALDH1A3 expression. A) TM Representative flow cytometry images showing ALDH activity detected by ALDEFLUOR intensity and CD44 expression by FITC instensity in PEO1, PEO4 and PEO4 VEGF-C null cell lines. Barplots showing ALDH-high content (C) gated by the negative DEAB treated control (A), representing N=3 replicates and CD44 expression (D) via mean fluorescent intensity (MFI) gated from the negative IgG isotype control (B), representing N=2 replicates. Statistical significance evaluated via Wilcoxon rank sum tests. Significance of p-values denoted by * P<0.05, ** P<0.01, *** P<0.001, **** P<0.0001.

Figure 4.8: VEGF-C does not promote cancer stem cell marker expression. A) Representative TM flow cytometry images showing ALDH activity detected by ALDEFLUOR intensity and CD44 expression by FITC instensity in PEO1, PEO4 and107 PEO4 VEGF-C KNOCK-OUT cell lines. Barplots showing ALDH-high content (C) gated by the negative DEAB treated control (A), representing N=3 replicates and CD44 expression (D) via mean fluorescent intensity (MFI) gated from the negative IgG isotype control (B), representing N=2 replicates. Statistical significance evaluated via Wilcoxon rank sum tests. Significance of p-values denoted by * P<0.05, ** P<0.01, *** P<0.001, **** P<0.0001. the low VEGF-C expressing line PEO1 and the VEGF-C knock-out lines show a convincing trend similar to ALDH activity however values of statistical significance could not be shown as these show the average of two experimental replicates. These data indicated that VEGF-C expression may negatively regulate CSC marker CD44 expression and ALDH activity. RNA-seq data was available for the paired cell lines PEO1 and PEO4 (John Gallon, pers comm) and so we decided to plot fragments per kilobase of reads mapped (FPKM) expression values for CD44 and ALDH1 isotypes (Figure 4.8E) as a control for the experiment in these lines. The ALDEFLUORTM assay is known to detect activity from other ALDH family members, in this case the RNA-seq data indicated that ALDH1A1 is not expressed in PEO1 or PEO4, however, ALDH1A3 was expressed in both lines with PEO1 showing significantly higher expression of this gene than PEO4 (P<0.0001). It seems likely that the enriched ALDH activity detected in PEO1 was due to expression of the ALDH1A3 gene rather than the OC cancer stem cell gene ALDH1A1. CD44 did show higher expression in PEO1 than PEO4 (P<0.0001), indicating that the flow cytometry data showing higher expression of CD44 in the PEO1 and VEGF-C knock-out PEO4 lines than in PEO4 may be a real trend and that VEGF-C may negatively regulate expression of both CD44 and ALDH1A3 genes in the PEO4 cell line.

4.2.7 Evaluation of role of VEGF-C in anoikis-induced apoptosis and cell death in the PEO4 line

Anoikis is characterised by cell death in the absence of adherence to extra-cellular matrix (ECM), and is considered an emerging hallmark of cancer, necessary for metastatic spread (reviewed (241)). In brief, disengagement of integrins from the ECM results in activation of Fas receptors and caspases (the extrinsic pathway) and perturbation of the mitochondria and cytochrome c release (the intrinsic pathway). Given the observed effect of loss of VEGF-C expression on the ability of PEO4 cells to grow in adherent culture and form 3D tumourspheres: caspase 3/7 gloTM assay were used to identify the rate of apoptosis in these lines. These experiments confirmed that apoptosis was lower in PEO4 cells than PEO1 or the PEO4 VEGF-C knock-out lines in both adherent and non-adherent culture (Figure 4.9A), indicating the role of VEGF-C in suppressing apoptosis. Additionally the apoptosis induced by having the cells in non-adherent culture was negligible in the PEO4 line however a large relative induction of apoptosis was observed in the PEO1 and VEGF-C knock-out PEO4 lines (Figure 4.9A-B). These data indicate the role of VEGF-C in inhibiting apoptosis in PEO4 EOC cells and most notably inhibiting anoikis induced apoptosis – thus enhancing the ability of the cells to form spheroids and proliferate in non-adherent culture.

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Figure 4.9: VEGF-C expression inhibits anoikis-induced apoptosis. A) Apoptosis measure by caspase -3 and -7 activity after 24 hours in adherent and non-adherent culture for PEO1, PEO4 and VEGF-C knock-out PEO4 cell lines. B) Δ caspase 3/7 activity in adherent versus non-adherent culture, plotted relative to Δ caspase 3/7 activity for PEO4. Significance evaluated via Wilcoxon rank sum tests. Significance of p-values denoted by *P <0.05, ** P<0.01, *** P<0.001, **** P<0.0001.

Figure 4.9: VEGF-C expression inhibits anoikis-induced apoptosis. A) Apoptosis measure by caspase -3 and -7 activity after 24 hours in adherent and non-adherent culture for PEO1, PEO4 and VEGF-C knock-out PEO4 cell lines. B) Δ caspase 3/7 activity in adherent versus nopn-adherent culture, plotted relative to Δ caspase 3/7 activity for PEO4. Significance evaluated via Wilcoxon rank sum tests. Significance of p-values denoted by *P <0.05, ** P<0.01, *** P<0.001, **** P<0.0001.

Figure 4.9: VEGF-C expression inhibits anoikis-induced apoptosis. A) Apoptosis measure by caspase -3 and -7 activity after 24 hours in adherent109 and non-adherent culture for PEO1, PEO4 and VEGF-C knock-out PEO4 cell lines. B) Δ caspase 3/7 activity in adherent versus nopn-adherent culture, plotted relative to Δ caspase 3/7 activity for PEO4. Significance evaluated via Wilcoxon rank sum tests. Significance of p-values denoted by *P <0.05, ** P<0.01, *** P<0.001, **** P<0.0001.

4.3 Discussion

We identified an increase of VEGF-C expression in serous EOC patients at chemoresistant relapse compared to primary presentation with a potential correlation between methylation and expression providing a link between deregulation of VEGF-C expression and both tumour stage and relapse. Higher VEGF-C expression and loss of promoter methylation was also identified in a patient derived chemoresistant serous EOC cell line compared to its chemosensitive parental line derived at primary presentation. This adds to data from this study (described in the previous chapter) identifying an association between loss of VEGF-C methylation and increased FIGO stage, as well as non- independently associating with poor patient PFS. This is supported by published evidence of association between low VEGF-C promoter methylation and poor HGSOC patient survival (217). VEGF- C serum expression level has been identified as associating with patient FIGO stage, lymph node metastasis and resectability as well as survival (112) and high intra-tumour VEGF-C mRNA has been linked to poor OS in ovarian cancer patients (113). VEGF-C has a well characterised role in promoting angiogenesis (111) and lymphangiogenesis (109), two primary routes of tumour metastasis. VEGF-C expression has been linked to more aggressive, invasive ovarian carcinomas (126). We hypothesise that the increase in expression of secreted VEGF-C due to epigenetic deregulation and promoter hypomethylation is a potential paracrine driver of advanced tumour stage and poor PFS. Additionally the observation that VEGF-C was more highly expressed in chemoresistant ascites samples at relapse than at primary presentation in tumours indicated that VEGF-C expression may have some impact on the development of resistance to cytotoxic agents in HGSOC, and contribute further to tumour relapse via this mechanism. One limitation of these data was the absence of a control group for VEGF-C expression data and the statistically untestable methylation control data which had tumour and ascites samples from only two patients at primary presentation. Showing these data for ascites at chemosensitive relapse would have provided an appropriate negative control for gain of VEGF-C expression in chemoresistant relapse ascites and allowed inference to be made about whether increase of VEGF-C expression is concurrent with relapse only or chemoresistance.

Although we did not identify a trend for over-expression of VEGF-C in all of the chemoresistant paired OC cell lines we tested we did observe that VEGF-C promoter methylation was significantly higher in the chemoresistant EOC PEO4 line than PEO1 cells, and that expression was around 40-200 fold higher, indicating that methylation may regulate expression of this gene, and supporting the hypothesis that in some EOC relapse tumours VEGF-C expression increased as we observed in the clinical samples. Treatment of PEO1 cells with 5-azadeoxycytidine resulted in dramatic re-expression of VEGF-C confirming a causal relationship between methylation and expression at the VEGF-C locus in this line.

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We hypothesised that based on seeing higher expression of VEGF-C in chemoresistant relapse ascites samples than matched primary tumour and higher VEGF-C expression in the chemoresistant relapse cell line PEO4 than its matched primary line PEO1, that VEGF-C may have a role in promoting chemoresistance as well as malignant phenotypes contributing to advanced tumour stage and disease progression.

In order to investigate the potential role of VEGF-C in promoting the chemoresistant phenotype as well as other malignant phenotypes in PEO4 we generated clonal VEGF-C knock-out PEO4 lines via RNA- guided Fok1 nuclease CRISPR (240), an established methodology for generating targeted double-strand breaks with decreased off-target specificity for which we generated a unique transfection and selection protocol.

We initially investigated the effect of the VEGF-C knock-out upon response to cisplatin treatment in PEO4 and identified no reduction in IC50 for cisplatin, indicating that VEGF-C expression is not necessary for the attenuation of apoptotic cell death induced by cisplatin in PEO4. It is possible that increased VEGF-C expression is sufficient to increase resistance to cisplatin-induced cell death, in order to investigate we would ideally need gain of function data showing the effect of VEGF-C over- expression on cisplatin response in the chemosensitive PEO1 cell line. We did initiate experiments of in this direction by attempting to over-express VEGF-C in PEO1 using a CRISPR based activation system targeting the VEGF-C promoter with a VP64 transcriptional activator (Sigma-Aldrich). However we were unsuccessful in isolating transfected cells over-expressing VEGF-C (data not shown). Lim et al., claim that inhibition of VEGFR3, the primary receptor for VEGF-C, sensitises the PEO4 line to cisplatin, however as VEGFR3 inhibition exerts a growth-inhibitory effect on PEO4, it is unclear whether the reduced cell count with combined Maz51 and cisplatin treatment observed in this study is an additive effect of VEGFR3-dependent growth inhibition and cisplatin induced apoptosis or whether VEGFR3 is acting in synergy to cisplatin as a sensitising agent. Therefore it is difficult to determine from this study whether we should expect inactivation of the primary VEGF-C receptor in the PEO4 line to be sensitising the line to cisplatin. No clear published link has been demonstrated to date between VEGF- C signalling and development of chemoresistance in ovarian cancer however a number of studies have identified a role for VEGF-C/VEGFR-2/3 interaction in promoting chemoresistance in other cancers. In cell line and mouse models of acute myeloid leukaemia (AML) VEGF-C was shown to exert a protective effect for cell survival when treated with Etoposide and Cytarabine, dependent on the Endothelin-1 (ET1)/cyclooxygenase-2(COX2) axis (116) in part by increasing Bcl2 expression. Bcl2-dependent chemoresistance induced by VEGF-C/VEGFR-3 signalling has been observed independently in leukaemia (117). RhoGDI2 was shown to regulate expression of VEGF-C in gastric cancer – promoting VEGF-C mediated metastatic and Bcl2-depedant cisplatin resistant phenotypes (115), and miRNA-101

111 expression in gastric cancer cells seems to re-activate apoptotic pathways by silencing VEGF-C expression (119). Cisplatin resistance has been linked to VEGF-C expression in bladder cancer via association in a clinical cohort and inhibition of VEGF-C has inhibited proliferation of cisplatin treated bladder cancer cells (242), the authors claim this is due to maspin re-expression, which was not confirmed casually and maspin generally seems to have been debunked as a cancer tumour suppressor gene (243). VEGF-C-dependent etoposide and doxorubicin resistance has been demonstrated in breast cancer models (125).

The role of VEGF in promoting tumour growth and the cell-autonomous phenotypes of increased migratory capacity and invasiveness, promoting metastasis in solid tumours including ovarian cancer via autocrine signalling have been well established (239, 244, 245). VEGF-C expression and interaction with VEGFR-3 was first shown to increase the migratory and invasive potential of lung carcinoma cells via Src/p38 MAPK-mediated C/EBP signalling and CNTN2 activation (123) and autocrine interaction between VEGF-C and cognate receptors has since been shown to promote cancer phenotypes of migration and invasion in breast cancer (124), gallbladder cancer (118) and oesophageal cancer (120), and was more recently shown to promote a more invasive phenotype in an ovarian cancer cell line (114). We therefore investigated whether the loss of VEGF-C expression in the PEO4 line affected invasive or migratory potential, however the lines had a very low migratory and invasive index, thus appearing to be a more epithelial like cell line. Given the extremely high level of expression of VEGF-C in the PEO4 line it may be the case that VEGF-C expression alone is insufficient to induce a transition of tumour cells into a more migratory and invasive mesenchymal-like state.

VEGF-C signalling has been shown to promote tumour cell proliferation in gallbladder cancer (118), oesophageal cancer (120), pancreatic cancer (246), lung and colon cancer in vivo (247) and leukemia (117) so we observed whether the VEGF-C null PEO4 cells showed any attenuation of growth. The PEO1 and VEGF-C null PEO4 lines showed a significantly lower rate of growth in adherent culture than the parental PEO4 line. Additionally, PEO1 and VEGF-C knock-out PEO4 lines showed an impaired ability to form 3D spheroids in non-adherent tumoursphere formation assays (219), indicating a VEGF-C- dependent ability to proliferate in anchorage independent conditions as well as resist anoikis. We confirmed the latter hypothesis showing an increased rate of anoikis-induced apoptosis in the PEO1 and VEGF-C null PEO4 lines. The VEGF-C-dependent promotion of growth, tumoursphere initiation and inhibition of anoikis-induced cell death is strongly suggestive of an enhanced cancer stem cell phenotype in PEO4 cells expressing VEGF-C. Published evidence suggests that VEGF-C promotes a malignant cancer stem cell phenotype in other cancers, particularly breast cancer in which VEGF-C expression is enriched in endocrine breast cancer CD44+/CD24- CSC sub-populations responsible for tumour initiation (122), and inhibition of VEGF-C in triple negative breast cancer tumour initiating cells

112 attenuated tumour initiation in vivo (125), as well as reducing ALDH-high sub-populations and multi- drug resistance. VEGF-C down-regulation lead to attenuated tumour initiation, growth and metastasis in in vivo models of lung and colorectal cancer populations (247). Given the apparent loss of functional cancer stem cell activity in our VEGF-C null EOC model and the link between functional CSC activity and marker expression in other cancers, it was surprising not to see a relationship between VEGF-C and expression of CSC genes in the PEO1/4 lines. We identified activity of ALDH and CD44 expression in these in these lines however unexpectedly CD44 and ALDH activity was higher in the PEO1 and VEGF- C null PEO4 lines than EPO4. We identified via RNA-seq expression data from these cells that ALDH activity was likely due to transcripts from the ALDH1A3 gene, which has not been implicated as a regulator of ovarian CSC activity. CD44 does appear to be more highly expressed in PEO1 than PEO4, and though a statistically significant relationship could not be established there was a trend for lower CD44 expression in VEGF-C null PEO4 cells indicating that VEGF-C expression may negatively regulate CD44 expression. However given the functional data on the much higher tumoursphere initiation capacity of PEO4 than PEO1 and published evidence that PEO4 has a larger side-population than PEO1 (70), it is likely that in these lines CD44 expression is independent of CSC content or activity.

The primary caveat to the in vitro data gathered in this study is the absence of expression data of cognate receptors for the PEO1 and PEO4 lines and the absence of experiments inhibiting function of VEGF-C cognate receptors: VEGFR-2/3 and NRP-1/2 in the PEO4 line whilst investigating effect on tumoursphere formation or apoptosis in non-adherent culture. Without these experiments we cannot state for certain that attenuation of growth, tumoursphere initiation or increased apoptosis in non- adherent culture were due to the loss of signalling via a VEGF-C/receptor axis. The evidence for VEGF- C signalling in these cancer phenotypes for this line are very strong however. We also are uncertain of the intracellular signalling pathways altered by loss of VEGF-C which resulted in the reduction of the malignant phenotypes we observed.

There is a significant body of literature in a multitude of cancers implicating VEGF-C expression with metastasis and progression conducted via angiogenesis and lymphangiogenesis as well as autocrine promotion of growth, invasion, migration and tumour initiation as well as resistance to cytotoxic therapies, many of these have been identified in ovarian cancer. This research is the first to identify a role for VEGF-C in tumoursphere initiation and a role for VEGF-C in preventing anoikis-induced apoptosis, thus promoting cell survival and the ability to proliferate in non-adherent conditions. We propose a mechanism whereby hypomethylation at the VEGF-C promoter in some EOC tumours results in amplification in expression of the gene supporting the ability of the tumour cell to avoid anoikis- induced cell death and survive in non-adherent culture, permitting metastasis via the lymph and vascular systems and proliferation at metastatic sites.

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4.4 Summary

In the previous chapter we validated loss of methylation at the promoter of VEGF-B as associating with increased patient survival time, and loss of methylation at the VEGF-C promoter associated with faster disease progression in an EOC cohort. We identified an increase of VEGF-C expression in HGSOC patients at platinum resistant relapse, concomitant with a small loss of methylation at the promoter, as well as a large fold increase in VEGF-C expression in a chemoresistant EOC relapse derived cell line PEO4 compared to its primary paired line PEO1, which we demonstrated was regulated by methylation state at the promoter, indicating that loss of VEGF-C promoter methylation and consequential deregulation of expression has a role in driving EOC patient relapse and progression. We generated clonal VEGF-C CRISPR knock-out PEO4 lines and demonstrated an autocrine role for VEGF-C expression in promoting cell growth and tumoursphere formation as well as inhibiting apoptosis in both adherent and non-adherent conditions. Most interestingly we observed a higher levels of apoptosis in the PEO1 and VEGF-C null PEO4 cells cultured in non-adherent than adherent culture, a difference not observed in the PEO4 line, consistent with the induction of apoptosis by anoikis. We hypothesise therefore that VEGF-C suppresses anoikis-induced cell death in EOC cells aided their ability to survive in the absence of cell contacts, e.g. in blood, metastasise and seed secondary site tumours, thus promoting tumour progression and limiting patient survival.

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Chapter 5: Isolation of a cisplatin tolerant population from a cisplatin sensitive ovarian cancer cell line

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5.1 Introduction

This work was based on previous studies identifying epigenetically defined, reversibly drug tolerant populations in chemosensitive cancer cell lines (83–86). This work was undertaken to address the hypothesis that in an exquisitely chemosensitive ovarian cancer cell line an epigenetically maintained sub-population of cells, tolerant to cisplatin are able to survive drug treatment. The primary objectives of this work were as follows:

1. Identify evidence of an association between response to cisplatin and a cancer stem cell-like cellular sub-population in a chemosensitive ovarian cancer cell line (A2780) and in vitro derived chemoresistant lines 2. Identify whether a drug tolerant population could be isolated from the chemosensitive A2780 cell line, including reversibility of the resistant phenotype as a criterion for tolerance

The follow up objectives of the work given the successful identification and isolation of a cisplatin tolerant A2780 population were as follows:

3. Identify whether the drug tolerant population was epigenetically defined by treating with inhibitors of chromatin regulators and identifying whether reversal of the tolerant phenotype occurred.

5.2 Background

5.2.1 Drug tolerant populations in chemosensitive cancer cell lines

Models of drug resistance in tumour evolution have involved selection over treatment time for clonal expansion of tumour cells with single selectable mutations or epimutations conferring resistance to therapeutic agents, for which there is a wide range of published evidence (248). Mutational models of tumour evolution however often do not account for the rapid emergence of multi-drug resistance on the basis that very few mutational changes in single genes are unlikely to confer resistance to a multitude of drugs with different mechanisms of action. A drug tolerant persistent (DTP) sub- population was described by Sharma et al., in a drug sensitive non-small cell lung carcinoma (NSCLC) cell line (86). The DTP population was isolated via treatment with extremely high IC90 doses of the tyrosine kinase inhibitor (TKI) Erlotinib, long term resistance to which is commonly conferred by activating EGFR mutations, most commonly EGFRT790M. These DTP populations remained in a state of quiescence throughout drug treatment and upon removal of selection yielded a re-established Erlotinib sensitive proliferative population, indicating plasticity of the drug tolerant state. After

116 extended selection the emergence of a drug tolerant expanded persistent (DTEP) population was observed which showed massively increased resistance to TKIs, however after successive passages in non-selective drug-free conditions reverted back to the drug sensitive state of the original cell population, indicating plasticity in the response of the cell population to chemotherapeutic drugs. Additionally the DTEP population showed multi-drug resistance to a variety of chemotherapeutic agents including cisplatin. Epigenetic maintenance of the DTP population and growth after drug treatment was shown to be dependent on expression of H3K4 histone demethylase KDM5A as well as showing inhibition by histone deacetylase (HDAC) inhibitors. Additionally these tolerant populations were shown to be enriched for expression of cancer stem cell markers. Attenuated response to drug treatment in these populations was shown to be dependent on expression of the stem cell related gene ALDH1A1 (82) – a metabolic enzyme responsible for detoxifying reactive oxygen species, and over-expressed in stem cells (249). These DTP cells demonstrated an innate ability to tolerate drug in part due to their ability to arrest cell division when treated with drug (250) in addition to up-regulation of ALDH genes (82). Plasticity is observed in the phenotypes DTP progeny – as bulk populations derived from DTPs appear to be chemosensitive. Some of these cells attain the ability to proliferate in drug, which seems to be possible due to up-regulation the IGFR1 signalling pathway. Long term treatment of DTEP populations appears to eventually select for activating EGFR, permitting the establishment of a stably Erlotinib resistant population (251).

Similar drug tolerant or drug tolerant-like sub-populations have been identified in chemosensitive tumour cell populations of other cancers. An epigenetically defined drug tolerant reversible sub- population has been described in T-cell acute lymphoblastic leukaemia (T-ALL), showing NOTCH1 independent growth and tolerance to γ-secretase inhibitors dependent on bromodomain-containing protein 4 (BRD4) recruitment to oncogenic regulatory elements (85). A proposed mechanism by which CSCs are able to survive anti-proliferative treatment is their ability to enter a quiescent or dormant non-proliferative state (71). This is described in chronic myoproliferative leukemia (CML) in which CML- CSCs survive Imatinib or Nilotinib therapy by existing in a quiescent non-cycling state. Induced drug tolerant melanoma cells (IDTCs) were isolated using BRAF inhibitor treatment (84) which resembled a slow cycling dormant melanoma population dependent on chromatin modifier JARID1B (252). A recent study identified an epigenetic mechanism permitting Glioblastoma stem cells (GSCs) to reversibly transition into a slow-cycling, drug tolerant, persister-like state, dependent on Notch signalling, in response to dasatinib treatment, by high-jacking a developmental epigenomic programme dependent on H3K27 demethylases KDM6A/B (83). An interesting in vivo model of gastric cancer used fluorescent ubiquitination-based cell cycle imaging (FUCCI) to demonstrate that in the 3-dimensional architecture of the tumour the cells resistant to cytotoxic therapies exist within the hypoxic core of the tumour in

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G0/G1 arrest, whilst the cells killed exist in an S/G2 proliferative state at the tumour periphery. After treatment a shift in cell cycle dynamics to the periphery of the tumour permits repopulation and return to a proliferative state (87).

We hypothesised that a sub-population of drug tolerant cells may reside within a chemosensitive ovarian cancer cell line, for which drug induced quiescence which may permit survival of the cytotoxic effects of cisplatin which targets proliferative cells by inducing DNA adducts and replication fork crashes in S phase.

5.3 Results

5.3.1 Investigation of association between response to cisplatin and CSC-like sub-populations in A2780 and in vitro derived resistant cell lines

In order to identify a model system appropriate for investigating a potential drug tolerant ovarian cancer sub-population we decided to first design experiments to identify whether a CSC-like sub- population existed within the cisplatin sensitive ovarian cancer cell line A2780 and chemoresistant lines MCP3, MCP6, MCP9 and A2780/CP70, derived from A2780 in vitro over successive cisplatin treatments (253, 254), described more extensively in Methods 2.2.1. We chose A2780 and its in vitro chemoresistant derivatives for this experiment as it provides a more clonally homogenous background than selection of lines derived from a patient pre and post-chemotherapy and therefore provides a better model for investigating whether CSC population size correlates with cisplatin response. We also wanted to identify whether in these cell lines any identifiable sub-population or cancer stem cell (CSC) correlated in population size with chemoresistance. Cell culture, dose response, flow cytometry and tumoursphere formation assays

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Figure 5.1: The relationship between platinum response and CSC content in A2870 and its cisplatin resistant in vitro derived lines. A) Dose response curves for A2780 and its derived cisplatin resistant lines (above) and table showing IC50s of these lines and fold change compared to A2780 parental line (below). B) Tumoursphere formation assay frequencies for A2780 lines, calculated as TFE=no. cells plated x no spheres formed, spheroid classification >50nm. C) Flow cytometry plots showing ALDH1 activity in A2780 and its derived cisplatin resistant lines plotting ALDH activity (determined via FITC intensity) by SSC (above) and frequency (below). Gating for ALDH+ (SSC and frequency plots) determined by gating above 99% of the negative control and for ALDH-high (SSC plots only) by gating above 100% of the negative control. D)

ALDH1A1 expression in A2780 and CP70 derived from gene expression microarray (n=3), log2 fold change shown. E) Scatterplot showing cisplatin IC50 against ALDH-high content of A2780 lines. Rho coefficient and P derived from Spearman’s rank correlation shown. Significant P values represented as *** P<0.001.

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were performed in collaboration with MRes student Georgia Spain. Initially we tested the IC50s of the five cell lines (Figure 5.1A) identifying A2780 as the most sensitive to cisplatin with an IC50 of 2.2µM and A2780/CP70 was identified as the most resistant to cisplatin with an IC50 of 39.9µM, with over an 18 fold change in ability to survive cisplatin. The MCP lines showed a lower, intermediate range of IC50s between 2.5 and 8µM.

In order to identify CSC sub-populations in the five lines we first performed flow cytometry to detect ALDH intensity (Figure 5.1C) as ALDH1A1 has been identified as a marker of ovarian tumour CSCs (80). We identified an extremely low ALDHhigh content in the sensitive A2780 cell line (0.12%). The MCP3 line which showed an IC50 for cisplatin of 6.3µM also showed a very low ALDHhigh population of 0.07%. The MCP6 line had an IC50 similarly low to A2780 (2.5µM) and a similarly low ALDHhigh population of 0.09%. The MCP9 line which had an IC50 of 8µM showed more than a 10 fold increase in ALDHhigh content, and the A2780/CP70 line showed massively elevated ALDH activity, with a sub-population of 33.9%, as well as a hugely amplified fold resistance to cisplatin. Spearman’s ranking was used to investigate whether a correlation existed between ALDHhigh content and cisplatin IC50 for the A2780 lines (Figure 5.1E). We used Spearman’s correlation as it does not require the assumption of normally distributed variables and as a ranking test is not easily biased by extreme values such as the IC50 and the ALDHhigh content of the A2780/CP70 line. Although a large Rho coefficient was identified (0.6), the correlation was not significant (P=0.35). In order to corroborate the difference in ALDH activity between A2780 and A2780/CP70 given the absence of biological repeats and the known cross-reactivity of the ALDEFLUORTM assay with other ALDH1 species we examined ALDH1A1 expression in expression microarray data available for the two cell lines (Figure 5.1D). A2780/CP70 showed an expression almost 6x higher than A2780 confirming the observation of enhanced ALDH activity in the CP70 line.

In addition to expression of a CSC marker we utilised the tumoursphere formation assay (219) as a functional in vitro assay for CSC activity (Figure 5.1B). We identified a tumoursphere formation efficiency of around 2% in the A2780 cell line. The MCP lines did not show a statistically significant difference in TFE, however the highly cisplatin resistant cell line A2780/CP70 showed a TFE of around 11%, representing over a 4 fold increase in the ability of this line to form spheroids in non-adherent culture to a significant statistical level (p<0.001). These data indicate a large increase in CSC content in the A2780/CP70 line based on ALDH activity and ability to resistant anoikis and form tumourspheres, this indicates that cisplatin response in this cell line model may be related to CSC content.

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5.3.2 Identification of an A2780-derived cisplatin tolerant sub-population

The chemosensitive A2780 cell line was treated with a range of molarities of cisplatin for a period of 2 days before allowing colonies to form for 2 weeks in order to identify appropriate doses that sub- populations of cells would be able to survive (Figure 5.2) and grow after removal of the drug. We found that after 2 days at 2µM of cisplatin 0.08% of cells retained the ability to survive and grow to form colonies, we therefore selected this dose to continue experiments attempting to isolate a cisplatin tolerant population.

Having identified a potential relationship between the size of CSC population and chemoresistance in the cisplatin sensitive A2780 line and its in vitro derived chemoresistant lines, as we described earlier, we hypothesised that sustained treatment of A2780 at 2µM may select out an ALDHhigh CSC population. Based on the original study identifying DTP cell populations (86) which identified a quiescent population able to resist drug we decided to accompany flow cytometry to identify CSC populations with cell cycle analysis (Figure 5.3). After 8 days in 2µM of cisplatin A2780 cells did not show an increased size of ALDHhigh population compared to untreated cells (Figure 5.3A). We also expected to see an increase in cells at G1 phase as other cells entered apoptosis indicating a population of cells in G0 phase, however we did not observe this at day 8, instead we observed an increase in the proportion of cells in G2 arrest, concurrent with a depletion of cells in S phase as expected, indicating a p53- dependent G2 blockade, probably before cells enter apoptosis. This is consistent with the original study which indicated that PC9 NSCLC cells needed to be cultured in cisplatin for 30 days before a pure population of quiescent cells were identified. We also observed what appeared to be cells with 4N DNA content after the 8 days of cisplatin treatment, which we hypothesise were multi-nucleated giant cells (255), which have been shown to be related with chemoresistance in other cancer cell lines.

5.3.4 Investigation of epigenetic sustainment of an A2780-derived cisplatin tolerant population

We hypothesised that in the experiment described earlier where cells were selected for ability to survive short-term cisplatin treatment and permitted to grow into colonies after (Figure 5.4), that if we were selecting for a proliferative cisplatin tolerant population we would see a difference in IC50 for cisplatin in the colonies selected. In order to identify whether there was any change in the IC50 of these cells for cisplatin we treated 5x106 cells with 2µM of cisplatin for 2, 4, 6 and 8 days, before removing the drug and leaving the cells in non-selective media for 12 days and then testing the IC50 of the cells in the colonies which had formed (Figure 5.1A). After 2 days treatment the colonies

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Figure 5.2: Dose selection for isolating surviving A2780 populations. Representative images of clonogenic 6 assays showing colony formation of 5x10 plated A2780 cells after 48 hours of cisplatin treatment at escalating doses (left) and colony formation calculated relative to the untreated plates (n=3) (right).

Figure 5.3: Flow cytometry analysis for quiescent CSC populations after 8 days of cisplatin treatment. A) Representative selection of single non-apoptotic A2780 cells for cell cycle and ALDH1 activity analysis via FSC-H/FSC-W. B) Cell cycle analysis using BrdU pulse labelling to identify actively cycling cells and 7-AAD staining for DNA content. C) ALDH activity measured via FTIC intensity to detect enzymatic conversion of BODIPY-aminoacetaldehyde. Negative DEAB treated controls shown (left). FITC plotted against SSC-A.

showed no difference in IC50 for cisplatin from the untreated A2780 cells. However the 4 and 6 day treated cells, after colony formation, both showed around a 3 fold, statistically significant (p<0.05) increase in IC50 over the untreated population, indicating that 4 days treatment in 2µM of cisplatin is

122 sufficient to select for a cisplatin tolerant population that is better able to survive and proliferate after treatment with cisplatin. In order to identify whether the “tolerant” population selected was permanently or transiently resistant, we maintained a tolerant population in non-selective culture for 3 months, after which the IC50 was very comparable to the untreated A2780 cell line indicating that we had selected a transiently cisplatin tolerant population with this method.

Previous studies reported that DTPs or “slow cycling” populations are epigenetically maintained and that epigenetic plasticity is explanatory for the transiently tolerant state, describing a model where drug treatment can select for a modifiable chromatin state. In order to begin investigating whether the cisplatin tolerant A2780 population (CTP) we had derived was epigenetically defined we exposed the drug treated and expanded, colonies selected with 2µM of cisplatin for 4 days, to chemical inhibitors targeting chromatin modifying enzymes. We treated the CTPs with the EZH2 inhibitor GSK343, the pan HDAC class I and II inhibitor SAHA and the BRD4/BET inhibitor JQ1 (Figure 5.4C-D). We observed sensitisation of the CTP populations by all three of these inhibitors in comparison to the DMSO control to a statistically significant level (p<0.001). In the case of EZH2 and BRD4 inhibition we see a sensitisation of the CTP populations to IC50s equivalent to that of the untreated A2780 population, in the case of HDAC inhibition we see a sensitisation of the CTP population to an IC50 of less than 0.5µM, vastly more sensitive to cisplatin than the chemonaïve A2780. We also see a further sensitisation of the chemonaïve A2780 line when treated with GSK343, SAHA and JQ1. EZH2 and BRD4 inhibition in the untreated A2780 populations reduces the IC50 for cisplatin to less than a third that of the DMSO treated population, and SAHA reduces the IC50 of chemonaive A2780s to a similar level as SAHA-treated CTPs. This may be explained by the presence of a pre-existing, underlying cisplatin tolerant population within the A2780 line. Inhibition of this state with these inhibitors may cause reversion of this drug tolerant state in the sub-population showing a reduction in IC50 of the bulk population. The dramatic effect of SAHA indicates that the CTP cells are particularly reliant on the maintenance of the chromatin state by HDACs. We tested the viability of untreated A2780 and A2780- CTPs after treatment with these agents only, in the absence of cisplatin, to ensure that increased cytotoxicity when treated with both inhibitors and cisplatin is caused by increased sensitivity to cisplatin after alteration of the chromatin state, not by inhibitor toxicity (Figure 5.4D). The A2780 populations retained their viability when treated with GSK343 and SAHA, indicating that these agents do sensitise A2780 to cisplatin, and are not cytotoxic. However less than 50% viability was observed in the CTP population treated with JQ1, therefore it cannot be concluded that JQ1 at this dose in combination with cisplatin is inducing cytotoxicity by sensitising the CTPs to cisplatin and we cannot draw the conclusion that bromodomain/BET containing transcription factors are necessary for maintaining a chromatin state which supports a cisplatin tolerant phenotype. However the evidence

123 supports the presence of a pre-existing cisplatin tolerant population within the A2780 cell line which after short term cisplatin treatment can proliferate and maintain a population with an enhanced ability to resist cisplatin, though only transiently. Finally, based on the observed difference in ALDH activity in A2780 and the MCP9 and CP70 lines, described earlier, we wanted to investigate whether the A2780-CTP population showed evidence of section for and ALDHhigh CSC population (Figure 5.4E). However when performing flow cytometry in the A2780 and A2780-CTP populations we observed very similar proportions of ALDHhigh populations, providing no evidence of CSC selection in the process of CTP isolation.

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Figure 5.4: Low dose cisplatin treatment of A2780 followed by colony formation selects for a epigenetically defined, transient, cisplatin tolerant population. A) Treatment regimen used for selection of cisplatin tolerant populations using a range of treatment times from 2 to 6 days with 2µM cisplatin followed by colony formation in non selective media and dose response selection with cisplatin. B) A2780 IC50s for cisplatin following selection at 2µM for the indicated time and colony formation. CTPs isolated were grown in culture for 3 months and retested for cisplatin IC50. C) A2780 CTPs isolated by 4 days cisplatin treatment were treated with inhibitors of EZH2 (GSK343), HDACs (SAHA) and BET (JQ1) for 24 hours at the indicated concentrations. D) Survival of parental A2780s and A2780 CTPs following 24 hours inhibitor treatments compared to survival with 24 hours DMSO treatment indicating cytotoxicity of inhibitors. E) ALDH TM content of A2780 parental an CTP cells derived from ALDEFLUOR staining and FITC intensity via flow cytometry. N=3 for all experiments. Significant P values represented as * P<0.05, ** P<0.01, *** P<0.001.

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5.4 Discussion

We tested for the presence of a CSC-like sub-population within A2780 and its in vitro derived cisplatin resistant lines with the hypothesis that a larger CSC population may be selected for by successive rounds of cisplatin treatment and confer resistance to these lines. In the most cisplatin resistant derived line A2780/CP70 we observed a significantly larger population of cells able to form spheroids in non-adherent culture, indicative of a higher CSC side-population with tumour initiating potential (219). Additionally we showed that an ALDH-high population was enormously enriched in the CP70 line and enriched 10 fold in the MCP9 line which showed the second highest IC50. It was tempting to infer that the ability of the cell population to survive and proliferate following cisplatin treatment reflects and is perhaps determined by ALDH-high cell content particularly given previous research showing enrichment for tumour cell populations with high ALDH1A1 activity in relapse compared to primary presentation in clinical samples (78) and indications that the A2780/CP70 drug resistant state is reversed by ALDH1A1 inhibition (256). However in the CTP populations isolated in

Figure 5.5: A hypothesised model of cisplatin tolerance in A2780 cells.

this study we did identify increased ALDH1A1 indicating a different mechanism of tolerance for these populations.

We were able to isolate a population of cells from the A2780 cell line via treatment with 2µM cisplatin for >4days and allowing the cells to recover and proliferate. The resulting proliferative population showed an IC50 ~3 fold higher than the parental population, these populations were termed cisplatin tolerant proliferative (CTP) populations. After growth in non-selective media the A2780 CTP population showed a revertant phenotype, with a cisplatin IC50 statistically unidentifiable from the IC50 of the parental population. We aimed to identify whether the CTP populations were sustained by epigenetic reprogramming, which we investigated by treating the populations with small molecule inhibitors of BRD/BET, EZH2 and HDACs. The CTP populations were sensitised to cisplatin via treatment with the HDAC and EZH2 inhibitors indicating that cisplatin tolerance is regulated by epigenetically defined

126 suppressive transcriptional programmes regulated by EZH2 and HDACs. Our hypothesis for CTP population derivation is represented in Figure 5.5, with an epigenetically defined sub-population, likely to show altered activity of EZH2 at key target genes, selected via treatment with 2µM cisplatin for >4days. This epigenetically plastic state is reversed by treatment with HDAC inhibitors or EZH2 inhibitors releasing the population from its suppressive, cisplatin tolerant state. Over time the reversal of the epigenetically tolerant in the bulk population is observed.

There are several caveats and limitations to this study and the described hypothesis. Firstly the hypothesis cannot rest on the assumption that a pure CTP population has been derived via 4 days cisplatin treatment. The initial study identifying DTP populations in PC9 NSCLC cells (257) used single cell cloning to demonstrate that selection of pre-existing mutations advantageous to survival of drug treatment were not being selected from a potentially genetically heterogenous population. After derivation of a drug tolerant expanded (DTEP) populations cells were cloned and allowed to revert to drug sensitivity before re-treatment and expansion to demonstrate that the sensitive and tolerant phenotypes were not confounded by genetic heterogeneity. In this study the resensitisation of the CTP populations by HDAC and EZH2 inhibitors is strong evidence for the absence of genetic selection and supports the hypothesis that drug tolerance arises due to an epigenetically and phenotypically plastic state however we cannot make the assertion that we are selecting for expansion of a pure drug tolerant population and it is unclear whether phenotypic reversion of the CTP IC50 after non-selective culture has occurred due to epigenetic reprogramming of the tolerant population, or whether drug treatment has selected for expansion of an enhanced ratio of epigenetically defined CTPs versus normal chemosensitive cells. It is possible that if the chemosensitive population have a growth advantage over a population with enhanced resistance that over time in non-selective media the ratio of resistant versus sensitive cells would have normalised to the ratio present in the parental population. Previous studies of cellular sub-populations of drug tolerant cells have identified slow cycling cells (83, 252), lending credence to the possibility of a slower replicating sub-population. In other words it is not possible from this data to rule out the epigenetic heterogeneity as a confounding factor, and dismiss the possibility that we are selecting for clonal expansion of a more resistant population, with a non-plastic, though epigenomically different profile. In order to rule out selection of heterogenous populations and confirm our hypothetical model we would need to conduct single cell cloning, either from the parental population before CTP selection, or from individual colonies grown after drug treatment. It is also unclear why a population of CTP A2780 cells is not selected after 2 days cisplatin treatment, but only after 4 days treatment or more, it is tempting to suggest that only after a certain period of culture in cisplatin the epigenomically altered state is inherited in subsequent cell divisions. It is possible that asymmetric cell division, much as in sustaining tissue specific stem cell

127 pluripotency, maintains the presence of a drug tolerant population in chemosensitive cell lines. Again, this is speculative and cloning would be required to confirm that 2 days selection is simply insufficient to permit selection of a sufficiently expanded, epigenetically defined, resistant population to observe a difference in the population’s IC50. Even with these described caveats the expansion of an epigenetically defined population with an increased IC50 for cisplatin from a chemosensitive ovarian cancer cell line with only 4 days of drug treatment at a relatively low concentration is interesting and clinically significant finding which could support the further development of epigenetic inhibitors, particularly EZH2 for clinical development for ovarian cancer treatment particularly at primary presentation, to prevent selection of more resistant populations through successive rounds of chemotherapy. As postulated by Sharma et al., (86), this is accumulating evidence that in the administration of therapy to patients at primary presentation drug holidays may reduce the incidence of developing resistance to standard chemotherapies as well as targeted agents in a number of cancers (88–90, 258, 259).

The sustainment of a drug tolerant state by changes in chromatin structure and expression of epigenetic modifying genes has been observed in every study identifying drug tolerant or slow-cycling sub-populations in chemosensitive tumour cell populations to date (83, 85, 86, 252). In particular there is a trend for dependency of drug tolerance on histone demethylase expression. NSCLC DTPs showed dependence on H3K4 demethylase KDM5A expression (86), slow cycling, dormant melanoma populations showed dependence on H3K4 demethylase JARID1B (KDM5B) (252) and GSCs showed dependence of the drug tolerant quiescent state on H3K27 demethylases KDM6A/B (83). The chromatin modifying factor identified as essential to a drug tolerant state in T-ALL, BRD4, is conversely associated with a transcriptionally active state, and drives expression of BCL2 and MYC in T-ALL persister populations (85). The epigenomic programmes which seem to regulate the tolerant state appears to differ between cancer type and study – while there is a similarity between maintenance of the state by H3K4 demethylases in NSCLC (86) and melanoma (252), indicating a suppressive epigenomic programme, whereas in T-ALL and glioblastoma the state is maintained by BRD4 and H3K27 demethylases KDM6A/B (83) respectively indicating dependence an transcriptionally activating epigenomic programme for these drug tolerant cells. Particularly in the case of GSCs we see down- regulation of EZH2 in concert with KDM6A/B up-regulation. In the case of A2780 derived CTPs we see a dependence of tolerance on EZH2 and HDAC activity indicating a transcriptionally suppressed state suggesting that the epigenomic programming in CTP ovarian cancer cells may have more in common with the programmes observed in NSCLC and melanoma.

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The data shown does not support the initial hypothesis that extended cisplatin treatment would select for a drug tolerant subset of cells which is able to exist in a quiescent cell cycle suspension as observed in a chemosensitive colon cancer cell line (257). G1 phase enrichment was not demonstrated over a treatment course of 8 days, 2N cells appeared to be diminished. S phase appeared to be mostly depleted after only 48 hours treatment indicating an inability of these cells to maintain a proliferative state whilst in cisplatin. Whilst some cells appear within the S phase gate the pattern is not consistent with S phase in untreated cells and may represent a population of cells actively incorporating BrdU in DNA damage repair. Accumulation of 4N cells was observed over the treatment course, indicating G2 phase and mitotic arrest consistent with the previously observed effects of cisplatin on cell cycle in A2780 cells (260). It is possible that accumulation of cells in G2 arrest are obscuring evidence of a quiescent population and that after extended drug treatment past 8 days, apoptosis of these cells will reveal a surviving, quiescent population. Previous isolation of a cisplatin tolerant NSCLC population used a treatment length of 30 days (86). In order to further elucidate whether there are a population of cells able to survive cisplatin treatment by remaining in a quiescent or slow cycling state and re- enter cell cycle after removal of the drug to form the tolerant colonies we observed we would need repeat cell cycle analysis after extended periods of drug treatment. Interestingly the accumulation of cells with >4N DNA content was observed in our cell cycle experiments over 8 days of cisplatin treatment. The accumulation of multinucleated polypoid giant cells which continue to replicate their DNA and can be observed with 8N and 16N DNA content have been identified in ovarian tumour populations after cisplatin treatment and are implicated in cisplatin resistance and growth of tumour cells after treatment (255).

Although we showed indications of an enriched CSC population in chemoresistant cell lines derived in vitro from A2780, in particular A2780/CP70, there was no evidence of the CTP population isolated showing an enrichment for ALDH1A1 activity. ALDH1A1 expression was been previously identified as essential to the drug tolerant state in the NSCLC line PC9 DTPs due to its role in maintaining low ROS levels (82), however according to previous findings which identified a loss of CSC marker enrichment once the quiescent DTP population was expanded into a proliferative DTEP population (86) it is perhaps not surprising that we observed an absence of enrichment in our expanded ovarian cancer CTP population. It would be interesting to investigate whether we observe enrichment of ALDH1A1 expression/activity in a population selected out over extended treatment time as described above.

Two studies have identified multi-drug tolerance as a feature of drug tolerant persistent populations. Erlotinib isolated NSCLC DTEPs showed enhanced survival after cisplatin treatment (86) and BRAF inhibitor-induced IDTCs showed attenuated cell death upon treatment with MEK inhibitors and cisplatin (84). It would be highly relevant to investigate whether the CTPs isolated from the parental

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A2780 line additionally showed attenuation of cytotoxicity in response to paclitaxel treatment, which is the other chemotherapeutic commonly used as first lie therapy for treatment of ovarian cancer. A model identifying an epigenetically defined persister population tolerant to both cisplatin and paclitaxel and reversible by HDAC or EZH2 inhibitor treatment would be highly clinically relevant and worth consideration for application of therapies in pre-clinical models.

5.5 Summary We identified a reversible, proliferative cisplatin tolerant population, termed “CTPs”, which were sensitised by EZH2 and HDAC inhibition. This study bears the caveat of not having ruled out clonal selection as a contributory factor to selection of drug tolerance and reversibility of the phenotype, however the study provides evidence of epigenetic heterogeneity contributing to initial acquisition of drug resistance, which may be clinically important for first line treatment of ovarian cancer and prevention of the emergence of resistance to cytotoxic therapies. We attempt to elucidate how HDAC and EZH2 activity maintain a cisplatin tolerant proliferative state in A2780s via transcriptomic and epigenomic profiling of these cells in the next chapter, in order to begin to highlight how this cellular phenotype may be targeted to prevent emergence of drug resistance in a clinical setting.

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Chapter 6: Analysis of the transcriptome and epigenome of a cisplatin tolerant population derived from a cisplatin sensitive ovarian cancer cell line

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6.1 Introduction

The previous chapter showed derivation of a cisplatin tolerant proliferative (CTP) population from the chemosensitive ovarian cancer cell line A2780 and the observation that these populations were resensitised to cisplatin by EZH2 and pan-HDAC inhibition indicating a potential role of a more suppressive chromatin landscape in defining increased tolerance to cytotoxic therapy in these CTPs. Therefore profiling of gene expression and the epigenomic landscape was undertaken in an attempt to identify causative associations between changes in the epigenome and gene expression in CTPs versus chemonaïve parental A2780 populations. The aims of this work were as follows:

1. Profiling of the CTP transcriptome to identify key transcriptional programmes and/or drivers of cisplatin tolerance 2. Genome wide profiling of the histone modifications H3K27me3 and H3K27ac via ChIP-seq to identify the potential role of EZH2 and HDAC maintenance of the epigenome in CTPs and potential associations with any transcriptional programmes related to cisplatin tolerance. 3. Genome wide profiling of chromatin accessibility via ATAC-seq to identify global remodelling in the CTP epigenome associated with H3K27me3 and/or H3K27ac changes.

6.2 Results

6.2.1 Experimental approach for transcriptomic and epigenomic profiling of cisplatin tolerant A2780 cells

The experimental next generation sequencing approach to characterise transcription and epigenomic remodelling in A2780 CTPs (Figure 6.1) involved RNA-seq in cisplatin sensitive A2780 cells both untreated and derived from 2 days cisplatin treatment at 2µM and A2780 CTPs derived after 4 days and 6 days cisplatin treatment at 2µM. we profiled four populations of cells, two with a chemosensitive phenotype, with one treated with cisplatin and two CTP populations derived with cisplatin treatment in order to maximise the probability of identifying changes in gene expression that are key to maintaining the drug tolerant phenotype. We hypothesised that the emergence of the drug tolerant phenotype was dependent on epigenomic remodelling dependent on EZH2 and HDACs, based on the observations in the previous chapter that the maintenance of the drug tolerant state was reversed by treatment with EZH2 and HDAC inhibitors. We hypothesised that EZH2 mediated deposition of trimethyl groups at H3K27, concurrent with HDAC-dependent loss of acetylation at H3K27 at the same genomic loci would result in silencing of genes necessary for maintaining a drug sensitive phenotype, therefore we conducted ChIP-seq for H3K27me3 and H3K27ac histone modifications in the A2780 parental and CTP populations to address this hypothesis. We also conducted Assay for Transposase Accessible Chromatin sequencing (ATAC-seq), a methodology used to identify regions of open and

132 closed chromatin (221), in order to attempt to further elucidate changes in the chromatin landscape which may be affecting gene expression, and ensuring maintenance of the drug tolerant phenotype.

RNA was extracted from cisplatin sensitive and tolerant A2780 cells sample groups in technical triplicate and poly(A)-primed mRNA-seq libraries were generated and sequenced by the ICL BRC Genomic Facility. RNA-seq libraries were stranded to permit identification of transcripts from genome strand orientation. Paired-end, 100bp sequencing was conducted to allow for fragment quantification and increased mappability at a depth of ~100 million reads per sample. ChIP-seq was conducted using chromatin collected in technical duplicate, as per ENCODE recommended guidelines for ChIP-seq (261), from untreated A2780 cells and A2780 CTPs derived after 4 days of cisplatin treatment. Immunoprecipitation was performed using antibodies for the H3K27me3 and H3K27ac post- translational histone modifications and input controls were collected from sensitive and tolerant A2780 chromatin samples to control for off-target antibody specificity (ChIP experiments were done in collaboration with Darren Patten, Div Cancer, ICL). Single end 50bp sequencing was conducted for ChIP-seq libraries at a depth of ~50 million reads per sample as per ENCODE guidelines (261) by the MRC CSC, Centre for Genomic Research. One A2780 CTP, H3K27me3 pull-down library, did not pass initial RT-PCR based quality control and so was excluded before sequencing. ATAC-seq libraries were also generated in duplicate from untreated A2780 and 4 day cisplatin treatment derived CTPs. ATAC- seq relies on the activity of Tn5 resolvase to fragment DNA and transpose adaptor sequences in regions of open chromatin between nucleosome complexes, permitting high throughput sequencing of DNA in regions of accessible chromatin (262). Paired end, 100bp sequencing was conducted by the ICL BCR Genomics Facility at a depth of ~50 million reads per sample as per the original ATAC-seq protocol advising a sequencing depth of 100 million reads total across experimental replicates.

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Figure 6.1: Experimental design of experiment assessing transcriptional and epigenomic changes in populations of A2780 CTPs. Showing the sampling strategy for RNA-seq, ChIP-seq and ATAC-seq as well as key library prep steps, sequencing strategy and bioinformatics processing pipelines used for filtering, QC, genome alignment and analysis.

134 Table 6.1: Read counts for experimental replicates in RNA-seq experimentsFigure 6.1: Experimental design of experiment assessing transcriptional and epigenomic changes in populations of A2780 CTPs. Showing the sampling strategy for RNA-seq, ChIP-seq and ATAC-seq as well as key library prep steps, sequencing strategy and bioinformatics processing pipelines used for filtering, QC, genome alignment and analysis. 6.2.2 Quality control of sequencing libraries and summary of FastQC reports

FastQC was performed in all libraries to estimate sequencing accuracy and library quality (Figure 6.2). All libraries show an average Phred quality score above 30, indicating an average base calling accuracy of more than 99.9%. The ChIP-seq libraries shown were filtered for Phred quality post-sequencing, reads with an average Phred score below 20 were excluded. Read trimming was performed in all libraries based on Phred score. For the ATAC-seq libraries the read ends were trimmed to a minimum average Phred score of 20. In all libraries reads below a length of 20bp were excluded. As a consequence of the quality filtering and trimming pipeline all libraries. Numbers of reads sequenced, uniquely aligned and retained after filtering are shown for RNA-seq, ChIP-seq and ATAC-seq libraries in Tables 6.1, 6.2 and 6.3 respectively.

6.2.2.1 RNA seq QC

Post sequencing reads in the RNA-seq libraries were trimmed and adaptors removed, using Trimmomatic software, at leading and trailing read ends where the average Phred score was below 20 or N bases has been called. Additionally a sliding window trimming method was used for the exclude regions of reads where the average Phred score dropped below 20 over 4 bases. FastQC analysis (Figure 6.2 and 6.3) revealed that the overall Phred score per single base pair within these libraries was above 30, indicating an average base calling accuracy of more than 99.9%. The per sequence Phred score (Figure 6.2A) was high for these libraries with the majority of reads above an average Phred score of 20. Sequencing quality across individual tiles of the flow cell (Figure 6.2B) was high, per base GC content (Figure 6.2E) was normally distributed, and reads showed no adaptor contamination (Figure 6.3C) and no N base calls (Figure 6.2F). Sequence length distribution (Figure 6.3A) was largely maintained at >95bp despite trimming. FastQC flagged per base sequence content as abnormal (Figure 6.2D), which is expected due to poly(A) isolation of transcripts and therefore over-representation of poly(T) tracts in reads. Sequence duplication levels were high (Figure 6.3B), which is normal in RNA- seq libraries due to over-sequencing of highly abundant transcripts present due to high levels of expression of certain genes. Failure of QC for positional kmer enrichment also occurred (Figure 6.3D), likely at the start of the read due to Illumina sequencing bias, and mid-read kmer amplification may again be due to the prevalence of fragments from highly expressed genes. We aimed to sequence the RNA-seq libraries at a depth of 100 million reads per sample, numbers of uniquely mapping reads counted after alignment to the human reference genome (Table 1) were between 77,795,844 and 120,077,780 reads.

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6.2.2.2 ChIP-seq QC

ChIP-seq libraries shown were filtered for Phred quality post-sequencing, reads with an average Phred score below 20 were excluded, according to FastQC (data not shown) all libraries showed an average per base Phred score above 30, indicating the retention of reads with only high base calling quality, likewise average per fragment Phred score was above 30 for the majority of samples. Per tile base call confidences were unevenly distributed with some tiles showing poorer than average per base Phred scores, indicating some bias in the sequencing. Per base sequence content showed no unexpected base distribution and fragments were devoid of N base calls. Fragment lengths were largely 50bp and fragments showed no adaptor contamination due to previous trimming. GC content was unusually distributed, with a skew towards an enrichment for GC content, this a common phenomenon for ChIP- seq libraries during fragment selection (263), and likely to occur due PCR bias (264). Reads also showed enrichment for kmer sequences at the leading and trailing ends, this enrichment along with GC content may in part be due to high mappability of ChIP-seq fragments to highly repetitive regions, which was corrected for by excluding reads mapping to genomic blacklisted sites. We aimed to sequence ChIP- seq libraries at a depth of 50 million reads per sample, for H3K27ac pull-down libraries, for parental A2780s sequencing depth was between ~25-32 million reads for H3K27ac pull-down libraries, ~25-27 million reads for H3K27me3 pull-down libraries and ~18 million reads for input. On the other hand, A2780 CTP libraries had a sequencing depth of ~46-48 million reads for H3K27ac pull-down libraries, ~35 million reads for the H3K27me3 pull-down library and ~21 million reads for the input control. These sequencing depths may indicate that the A2780 parental samples may have been under- sequenced in comparison to the CTP libraries. Blacklisting for repetitive genomic regions affected sequencing depth very little, resulting in exclusion of only ~200,000 reads per sample. Sequence duplication levels were low indicating high library complexity, confirmed by the loss of very few reads from these libraries after deduplication, only ~100,000-500,000 reads per sample.

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Figure 6.2: FastQC analysis of RNA-seq library quality. A) Per Base Sequence Quality shows average Phred score quality (base call confidence) per read base pair, across the reads in a sequencing lane. B) Per Tile Sequence Quality reflects average Phred score quality across tiles of the same position of the tiling array. C) Per Sequence Quality Scores show the distribution of mean sequence quality Phred scores across all reads of a library. D) Per Base Sequence Content shows averaged nucleotide content per base pair of reads in a library. E) Per Sequence GC Content shows distribution of % content of GC nucleotides within library reads. F) Per Base N Content shows average N called bases (unidentified bases) per base pair of library reads.

Table 6.2: Read and MACS2 called peak counts for experimental replicates in ChIP-seq experimentsFigure 6.2: FastQC analysis of RNA-seq library quality. A) Per Base Sequence Quality shows average Phred score quality (base call confidence) per read base pair, across the reads in a sequencing lane. B) Per Tile Sequence Quality reflects average Phred score quality across tiles of the same position of the tiling array. C) Per Sequence Quality Scores show the distribution of mean sequence quality Phred scores across all reads of a library. D) Per Base Sequence Content shows averaged nucleotide content per base pair of reads in a library. E) Per Sequence GC Content shows distribution of % content of GC nucleotides within 137 library reads. F) Per Base N Content shows average N called bases (unidentified bases) per base pair of library reads.

Table 6.2: Read and MACS2 called peak counts for experimental replicates in ChIP-seq experiments Figure 6.3: FastQC analysis of RNA-seq library quality. A) Sequence Length Distribution shows distribution of library read lengths. B) Sequence Duplication levels shows distribution of read duplication. C) Adapter content shows average frequency of Illumina adaptor mapping throughout library reads. D) Kmer content shows average kmer sequence content through library read sequences.

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Table 6.1: Read counts for experimental replicates in RNA-seq experiments

Table 6.2: Read and MACS2 called peak counts for experimental replicates in ChIP-seq experiments

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Table 6.3: Read and MACS2 called peak counts for experimental replicates in ATAC-seq experiments

6.2.2.3 ATAC-seq QC

For the ATAC-seq libraries the read ends were trimmed to a minimum average Phred score of 20. In all libraries reads below a length of 25bp were excluded. Average per base quality scores reported by FastQC (data not shown) were high, again above a Phred score of 30, and most sequences showed an average Phred score above 30. Unfortunately the per tile base quality measure failed to meet the quality control threshold, indicating that there was a large amount of bases in poor quality base calls localised to tiles of the array. Fragments showed no adaptor contamination or N content. Sequence content was not distributed as expected through the reads, for the first 12 bases at the leading edge all bases showed biased distribution, notably an enrichment for G and a depletion for T at the very start and an enrichment for A and C further into the read. GC distribution showed enrichment for GC content, this may be due to increased mappability to regions of open chromatin near the gene TSS and promoters, where CpG islands with high GC content are likely to be situated. This can also be a sign of PCR bias (264), which is likely to have occurred, indicated by the high amount of sequence duplication, and indicates over-amplification may have occurred during the PCR step. Additionally enrichment for kmer sequences was observed, this may be due to the high mappability of ATAC reads to the TSS sites of genes and the high bias in mappability to the mitochondrial genome. We aimed to sequence ATAC libraries to a depth of 50 million reads per sample, all library read depths exceeded 50 million reads with the exception of one A2780 parental sample which had a depth of ~36 million reads. Filtering for reads mapping to the mitochondrial genome was conducted, we observed a loss of ~66-77% of the total library reads. As expected, further losses to deduplication of the libraries was minimal with read losses of only ~200,000-500,000, indicating good library complexity for reads mapping to accessible

140 regions of chromatin in the non-mitochondrial genome. Additionally we show read counts for successful mate pair alignments after exclusion of reads which did not successfully map to the genome uniquely to the same locus as its mated pair, resulting in an overall depth of ~10-16 million reads.

6.2.3 Differential gene expression analysis in A2780 lines

Transcriptome assembly and differential gene expression analysis in the A2780 datasets was performed using the Cufflinks suite of tools (Figure 1) (265). Fragments per kilobase per million mapped reads (FPKMs) were calculated per gene transcript. As a recommended quality control measure (266) we performed linear regression between each replicate to generate correlation coefficients for sample FPKMs, we then performed hierarchical clustering to represent similarity between samples (Figure 6.4A). All samples were highly correlated, no pairwise comparisons showed a Pearson’s coefficient lower than 0.94, indicating that the transcriptomic profiles of all these A2780 derived samples, chemosensitive or tolerant were highly similar. The experimental replicates clustered together with the exception of the 4 day cisplatin treated CTPs, two replicates of which clustered closely with the 2 day treated chemosensitive A2780s, the other replicate clustered more closely with the 6 day treated CTPs. This finding was somewhat expanded upon principle components analysis (PCA) of the sample FPKMs (Figure 6.4B/C). PCA is also a recommended quality control methodology for RNA-seq analysis (266). Only one principle component: PC1, which contributed the largest proportion of the variation (29.9%), separated the replicates for response to cisplatin. The chemosensitive untreated and 2 day treated A2780 samples clustered at the lower end of the x axis whereas the 6 day treated CTPs clustered at the upper end. Similarly to the hierarchical clustering analysis, two the 4 day treated CTPs clustered towards the chemosensitive samples and one clustered towards the 6 day treated samples. It is tempting to infer a biological explanation for this, in which changes in expression for key genes required for maintaining a drug tolerant state are seen in a bulk population derived from 4 days of cisplatin treatment, but not from 2 days cisplatin treatment, whereas 6 days cisplatin treatment may be sufficient to observe more expansive changes in gene expression perhaps further supporting survival of the tolerant cells. An alternative, and perhaps more likely, explanation is that 6 days of cisplatin treatment is lethal to a higher proportion of non-tolerant cells, and therefore we are measuring expression from a less heterogenous population, with a higher proportion of drug tolerant cells and therefore have better power to and sensitivity to detect relevant changes in gene expression. We calculated differential transcript abundances via the Cuffdiff tool, calculating log2 fold changes in FPKM and FDR corrected P (Q) values. In comparison to the untreated, chemosensitive parental A2780 cells we identified: 3,703 genes differentially expressed in the 2 day treated chemosensitive samples, 1,758 genes differentially expressed in the 4 days treated drug tolerant population, and 3,881 genes differentially expressed in the 6 day treated CTP A2780s with

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Q<0.05 (Figure 6.4D-F). We selected 489 genes differentially expressed in the two tolerant populations (4 and 6 day treated), compared to untreated A2780s, but showing no significant difference in expression between the two chemosensitive populations (untreated and 2 day treated) as being likely candidates for driving the drug tolerant phenotype, potentially as a consequence of epigenetic deregulation selected by cisplatin treatment (Figure 6.4G).

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Figure 6.4: RNA-seq sample quality control and identification of differentially expressed genes in A2780 parental and CTP populations. A) Linear regression between sample FPKMs, represented with hierarchical clustering, Pearson’s coefficients shown. B) PCA plot for samples FPKMs showing PC1 against PC2 and C) barplot showing % contribution to variation of each PC identified. Volcano plots showing

log2 fold change against FDR corrected p (q) value for expression of each gene for 2 day treated chemosensitive (D), 4 day treated CTPs (E) and 6 day treated CTPs (F) compared to untreated A2780. G) Numbers of unique and over-lapping genes showing significant (q<0.05) differential expression in 2 day chemosensitive, 4 day treated CTPs and 6 day treated CTPs. Genes selected as differentially expressed and unique to the CTP phenotype shown (red circle).

Figure 6.5: ATAC library fragment size distribution H3K27me3, H3K27ac and ATAC peak distribution in positive and negative control genesFigure 6.4: RNA-seq sample quality control and identification of differentially expressed genes in A2780 parental and CTP populations. A) Linear regression between sample FPKMs, represented with 143 hierarchical clustering, Pearson’s coefficients shown. B) PCA plot for samples FPKMs showing PC1 against PC2 and C) barplot showing % 6.2.3 Peak calling in A2780 ChIP-seq and ATAC-seq datasets

We identified the quality of the ATAC-seq libraries by via assessment of the insert fragment size distribution (Figure 6.4A), periodicity was observed at intervals of ~180bp as previously reported in the original protocol (262). Enrichment for H3K27me3 and H3K27ac post-translational chromatin modifications, as well as enrichment for regions of open accessible chromatin, at genomic loci was identified using MACS2 peak calling software, controlling for background read density and normalising to input controls for ChIP-seq experiments. Peaks were called with an FDR-corrected q value cut-off of 0.1. Peaks were called from concatenated genomic libraries, where duplicates were available, as per the MACS2 guidelines. Peak call counts from single libraries are shown for ChIP-seq experiments as well as for concatenated replicates (Table 6.2). The number of peaks called after combining libraries was always lower than in either individual library indicating redundancy in the loci of called peaks, and potentially minimal non-random distribution of called peaks. Unfortunately due to failed QC after library preparation only one A2780 CTP H3K27me3 pull-down library was available. Peak calling control regions are shown (Figure 6.5), GAPDH (Figure 6.5B) shows the expected enrichment for H3K27ac and accessible chromatin and absence of H3K27me3 in all samples, as a highly expressed gene with an FPKM between 1,500 and 2,000, whereas MYOD1, which is un-expressed in all A2780 samples with an FPKM of 0. shows enrichment for H3K27me3 and an absence of ATAC or H3K27ac peaks (Figure 6.5C).

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Figure 6.5: ATAC library fragment size distribution H3K27me3, H3K27ac and ATAC peak distribution in positive and negative control genes. A) Fragment size distribution for ATAC-seq reads. H3K27me, H3K27ac and ATAC peaks fragment genomic distribution and MACS2 peak calls plotted in Integrated Genome Viewer and calculated relative to background and input in the GAPDH (B) and MYOD1 (C) genes.

Figure 6.4: ATAC library fragment size distribution H3K27me3, H3K27ac and ATAC peak distribution in positive and negative control genes. A) Fragment size distribution for ATAC-seq reads. H3K27me, H3K27ac and ATAC peaks fragment genomic distribution and MACS2 peak calls plotted in Integrated Genome Viewer and calculated relative to background and input in the GAPDH (B) and MYOD1 (C) genes. D) GAPDH and MYOD1 expression (mean FPKM of 3 replicates) in untreated A2780 and 4 day-derived CTPs.

Figure 6.4: ATAC library fragment size distribution H3K27me3, H3K27ac and ATAC peak distribution in positive and negative control genes. A) Fragment size distribution for ATAC-seq reads. H3K27me, H3K27ac and ATAC peaks fragment145 genomic distribution and MACS2 peak calls plotted in Integrated Genome Viewer and calculated relative to background and input in the GAPDH (B) and MYOD1 (C) genes. D) GAPDH and MYOD1 expression (mean FPKM of 3 replicates) in untreated A2780 and 4 day-derived CTPs.

6.2.4 Analysis of the H3K27me3 landscape in A2780 CTPs

Due to the re-sensitisation of A2780 CTPs by inhibition of EZH2, we hypothesised that CTPs would have acquired observable H3K27me3 remodelling. We observed a massive increase in the number of called H3K27me3 peaks through the CTP genome (Table 2, Figure 6.6A-D). The parental A2780 libraries showed a total of 1,722 mapped peaks whereas the A2780 CTP library mapped 25,321 peaks (Table 6.2), with only 10 peaks unique to the parental population (Figure 6.6A). This represents a 14.8 fold increase in the number of H3K27me3 enriched regions of the CTP genome, indicative of a massive H3K27me3 reprogramming event, likely mediated by EZH2. Given the likely involvement of EZH2 and the PRC2 complex in catalysing H3K27 trimethylation, we investigated expression of PRC2 components in the RNA-seq dataset for A2780 parental and CTP cells (Figure 6.6E). CTPs derived from 4 and 6 days of cisplatin treatment showed no change in expression of PRC2 components EZH2, EHMT2, EED, EZH1 or SUZ12 when compared to untreated A2780s. However, unaltered expression of PRC2 components especially EZH2, does not preclude its activity in H3K27me3 reprogramming in cisplatin tolerant A2780s.

We analysed whether H3K27me3 deposition was targeted to specific genomic regions in A2780 CTPs and identified a non-random pattern of epigenomic reprogramming. H3K27me3 gain was observed in every annotated region of the genome (Figure 6.6B), however we identified whether increases in peaks in genomic regions represented the expected number of peaks gained in that region in comparison to the total peak gain in the CTP sample, or whether an over or under-representation of H3K27me3 peaks was occurring at specific genomic regions to a statistically significant level (Bonferroni P<0.0015, Table 6.4). An over-representation of H3K27me3 peaks was observed at intergenic, intronic and repeat regions, whereas an under-representation of peaks occurred at gene TSS regions, exons, 5’ UTRs and intergenic CPG sites. Intergenic regions showed a gain of >6,500 peaks in CTPs, representing an e nrichment compared to total peak gain of 2.16 fold and an increase in proportion of total annotated peaks genome wide from 10% to 29%. Peaks within gene introns also increased by >6,500, a fold change over expected of 2.88 and an increase from 13% of annotated peaks to 28%. Repeat regions were identified as regions containing short interspersed nuclear elements (SINES), long interspersed nuclear elements (LINES) or long tandem repeats (LTRs), and were annotated as ancient retroviral elements (ARVEs). Peaks counts in ARVEs increased by ~7,000 in the CTPs, representing a significant fold enrichment over expected increase of 4.83 and an increase in proportion of genomic peak distribution from 6% to 27%. We also observed a peak gain of >500 and a fold enrichment over expected of 5.06 in regions of unknown annotation, which seem likely to also

146 represent intergenic regions. By contrast we see a peak gain of ~500 at the TSS of genes, though a reduction in representation of the overall H3K27me3 peak profile from 23% TSS sites in parental A2780s to 4% in CTPs, representing a depletion from expected ratio of 0.17. Likewise we see a depletion in exonic regions from 14% to 4%, a 0.26 fold under-representation, as well as fold depletions of 0.16 for 5’UTRs and 0.11 for CpG islands, representing a reduction of 26% of A2780 parental genomic representation to 3% in the CTPs.

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Figure 6.6: H3K27 trimethylation peak distribution and expression of PRC2 components. A) Unique and shared H3K27me3 peaks in parental and cisplatin tolerant A2780s (CTPs). B) H3K27me3 peak counts for A2780 parental cells and CTPs per annotated genomic region. C) H3K27me3 peaks per annotated genome region as a percentage of peaks in the total genome for parental (left) and cisplatin tolerant (right) A2780s. D) Integrated genome browser plots showing representative images of H3K27me3 read coverage and peak calls (H3K27ac read coverage included) in A2780s and CTPs for an intergenic region in chromosome 11 and an intronic locus within C3orf70. E) Expression of PRC2 components in 4 day and 6 day treatment derived CTPs compared to untreated A2780s.

Figure 6.7: H3K27 acetylation and ATAC-seq peak distributionFigure 6.6: H3K27 trimethylation peak distribution and expression of PRC2 components. A) Unique and shared H3K27me3 peaks in parental and cisplatin tolerant A2780s (CTPs). B) H3K27me3148 peak counts for A2780 parental cells and CTPs per annotated genomic region. C) H3K27me3 peaks per annotated genome region as a percentage of peaks in the total genome for parental (left) and cisplatin tolerant (right) A2780s. D) Integrated genome browser plots showing representative images of H3K27me3 read coverage and peak calls (H3K27ac read coverage included) in A2780s and CTPs for an intergenic region in chromosome 11 and an intronic locus within C3orf70. E) Expression of PRC2 components in 4 day and 6 day treatment derived 6.2.5 Analysis of the H3K27ac and accessible chromatin landscape in A2780 CTPs

Given the enormous scale of H3K27me3 remodelling observed in A2780 CTPs compared to the parental A2780 population, we expected to see a concomitant loss of H3K27ac and a reduction in the number of accessible chromatin peaks, particularly within genomic regions which showed enrichment for H3K27me3 gain, which would be indicative of a silenced genome with a loss of open chromatin. We characterised H3K27ac genome-wide and identified 21,916 peaks in the parental A2780s and 23,851 peaks in the CTPs (Table 6.2). There was substantial overlap between the loci at which H3K27ac marking occurred with 2,986 peaks unique to the parental A2780s and 4,903 peaks unique to the CTPs (Figure 6.7A). Though the largest changes in the H3K27ac landscape occurred in intergenic, intronic and repeat regions (Figure 6.7B), these all represented increases in H3K27ac coverage, and although these was statistically significant (Bonferroni p<0.0015) for intergenic and ARVEs, the enrichment was very small, less than 10% over expected representation for both. Equally, a small loss in number of peaks was observed at the TSS regions of genes, representing a significant depletion, though again to a very small degree, with less than 10% deviation from the number of expected peaks. These changes in peak numbers and distribution between the A2780 parental and CTP genomes represent very little in the way of proportion of total peaks (Figure 6.7C), with changes in no more than 2% of total genome representation occurring.

A very similar number of peaks representing the regions of accessible chromatin were identified in each sample type: 33,949 in parental A2780s and 34,801 in CTPs (Table 6.3). This does not support a hypothesis of a loss of open chromatin and a more compacted genome in the CTPs, which was tempting to infer from the H3K27me3 data. However, whilst 20,846 ATAC peaks remain consistent – at over-lapping genomic loci in both parental and cisplatin tolerant A2780s, the loci of 13,955 ATAC peaks are unique to the parental populations, and 13,103 peaks are unique to the CTPs (Figure 6.7D). This may indicate substantial remodelling in location of accessible chromatin, without representing a change in the amount of open chromatin genome-wide. Remodelling of accessible chromatin does not appear to be occurring within a genome region focussed manner as with H3K27me3, changes in peak number within region type between A2780 parental and cisplatin tolerant cells were very small (Figure 6.7E), a significant regional change in peak number occurred only in the case of ARVE repeat elements (Table 6.4), with CTPs showing a slight proportional depletion of 8%.

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Figure 6.7: H3K27 acetylation and ATAC-seq peak distribution.. A) Unique and shared H3K27ac peaks in parental and cisplatin tolerant A2780s (CTPs). B) H3K27ac peak counts for A2780 parental cells and CTPs per annotated genomic region. C) H3K27ac peaks per annotated genome region as a percentage of peaks in the total genome for parental (left) and cisplatin tolerant (right) A2780s. D) Unique and shared ATAC peaks in parental and cisplatin tolerant A2780s (CTPs). E) ATAC peak counts for A2780 parental cells and CTPs per annotated genomic region.

150 Table 6.4: Fisher’s Exact test statistics for peak enrichment or depletion relative to total peak counts in A278- CTPs versus A2780 parental cells for MACS2-called H3K27me3, H3K27ac and ATAC peaks.Figure 6.7: H3K27 acetylation and ATAC-seq peak distribution.. A) Unique and shared H3K27ac peaks in parental and cisplatin tolerant A2780s (CTPs). B) H3K27ac peak

Table 6.4: Fisher’s Exact test statistics for peak enrichment or depletion relative to total peak counts in A278- CTPs versus A2780 parental cells for MACS2-called H3K27me3, H3K27ac and ATAC peaks.

Table 6.4: Fisher’s Exact test statistics for peak enrichment or depletion relative to total peak counts in SignificantA278- CTPs enrichment versus A2780 (blue) parental or depletion cells for (orange) MACS2 of-called peaks H3K27me3, indicated according H3K27ac to and Bonferroni ATAC peaks. adjusted α=0.0015

Table 6.4: Fisher’s Exact test statistics for peak enrichment or depletion relative to total peak counts in A278- CTPs versus A2780 parental cells for MACS2-called H3K27me3, H3K27ac and ATAC peaks.

6.2.6 Analysis of the regulation of the A2780 CTP transcriptome by the epigenome Table 6.4: Fisher’s Exact test statistics for peak enrichment or depletion relative to total peak counts in Over-representation of H3K27me3 in intergenic, intronic and repeat element loci as well as statistical A278- CTPs versus A2780 parental cells for MACS2-called H3K27me3, H3K27ac and ATAC peaks. depletion at the TSS and exonic regions of genes, does not imply a modified transcriptional programme mediated by H3K27me3 modifications at, or close to TSS regions, which we had previously Table 6.4: Fisher’s Exact test statistics for peak enrichment or depletion relative to total peak counts in hypothesisedA278- CTPs versus would A2780 occur parental concomitantly cells for MACS2 with gene-called silencing. H3K27me3, In orderH3K27ac to andinvestigate ATAC peaks. the association between gain of H3K27me3 and gene silencing in A2780 CTPs, we isolated the 253 genes down- regulatedTable 6.4: in Fisher’s CTPs derived Exact test from statistics both for4 and peak 6 enrichmentdays of cisplatin or depletion treatment relative and to identified total peak genes counts which in a alsoA278 showed- CTPs versusa gain A2780in H3K27me3 parental near cells thefor MACS2TSS. H3K27me3-called H3K27me3, peak gains H3K27ac were annotatedand ATAC peaks. with the nearest gene TSS, this information was used for identification of associated peak gain irrelevant of the distance fromTable the 6.4: TSS Fisher’s to account Exact testfor epigenomic statistics for changespeak enrichment at distal oras depletionwell as proximal relative regulatoryto total peak elements. counts in Only 17A278 genes- CTPs which vers wereus A2780 down parental-regulated cells forin CTPsMACS2 also-called showed H3K27me3, a gain H3K27acin H3K27me3 and ATAC peak peaks. associated with the promoter (Figure 6.8A-B). We investigated whether this exceeded the ratio of down-regulated genesTable that 6.4: wouldFisher’s be Exact expected test statistics to be marked for peak by enrichment H3K27me3 or due depletion to chance, relative the to genes total gainingpeak counts H3K 27in me3 A278- CTPs versus A2780 parental cells for MACS2-called H3K27me3, H3K27ac and ATAC peaks.

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Table 6.4: Fisher’s Exact test statistics for peak enrichment or depletion relative to total peak counts in A278- CTPs versus A2780 parental cells for MACS2-called H3K27me3, H3K27ac and ATAC peaks.

are significantly under-represented (P=3.3E-08), indicating that the large gains in H3K27me3 distribution in CTPs are not driving a transcriptional reprogramming event resulting in genome –wide silencing of gene expression. However it is possible that focal deposition of H3K27me3 associated with the TSS of some of the 17 genes down-regulated in CTPs, may be contributing to the drug tolerant phenotype.

We had hypothesised based on the re-sensitisation of the CTP populations by HDAC inhibition that HDACs may have been working in concert with EZH2 to maintain a silenced epigenome, suppressing expression of pro-apoptotic genes that would otherwise induce cell death in response to cisplatin treatment. Similarly to the analysis described above we therefore addressed which of the 253 genes down-regulated in CTPs also had a loss of H3K27ac peak associated with the TSS (Figure 6.8A/C), of the 812 H3K27ac peaks lost in A2780 CTPs only 9 were associated with the TSS of genes down-regulated in CTPs, and of those only 2 lost peaks were also associated with a gain of H3K27me3 at the TSS of the same gene. There was no statistically significant enrichment (P=1) for association of H3K27ac peak loss with loss of gene expression indicating that similarly to H3K27me3 modifications, H3K27ac remodelling is not associated with genome-wide transcriptional reprogramming.

Since we observed gain of 4,903 H3K27ac peaks in the cisplatin tolerant A2780 population (Figure 6.7A), we also investigated whether the 245 genes up-regulated in CTPs showed enrichment for gain of H3K27ac peaks associated with their TSS. Of the 936 H3K27ac peaks gained annotated as associated with a TSS, only 7 over-lapped with genes over-expressed in CTPs (Figure 6.8D/E), showing no statistically significant enrichment, and no evidence of programmatic transcriptional activation, mediated by H3K27ac.

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Figure 6.8: Association between H3K27me3/ac and differential gene expression in A2780 CTPs. A) Unique and shared genes for genes down-regulated in CTPs, and with gain of an H3K27me3, or loss of an H3K27ac peak associated with the TSS. Odds ratios, 95% confidence intervals and P values shown for Fishers exact tests. Log fold change of gene FPKMs for CTP versus untreated A2780 for genes showing a 2 gain of H3K27me3 (B) or loss of H3K27ac (C) peak associated with the TSS. D) Unique and shared genes for genes up-regulated in CTPs, and with gain of an H3K27ac peak associated with the TSS. Odds ratio,

95% confidence interval and P value shown for Fishers exact test. E) Log2 fold change of gene FPKMs for CTP versus untreated A2780 for genes showing a gain of H3K27ac peak associated with the TSS.

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6.2.7 Analysis of the transcriptomic profile of A2780 CTP cells

We performed functional gene ontology (GO) analysis (Figure 6.9) and gene set enrichment analysis (GSEA) in the RNA-seq dataset in an attempt to identify functional gene-sets and pathways differentially regulated in CTPs. GSEA was performed by calculating F –statistics across sample FPKMs and identifying enrichments for larger F-statistics for genes associated with KEGG pathways, no pathways were identified as significantly differentially regulated with an FDR corrected p<0.05. Gene ontology analysis was performed using the top 103 differentially regulated genes in CTPs, identified as genes significantly (q<0.05) differentially regulated in CTPs derived from 4 and 6 days treatment (Figure

6.9A), with a log2 fold change greater than 1.3. These genes were entered into the online bioinformatics tool Panther (267), in order to identify whether the genes differentially expressed in this dataset over-represent biological functions. Several biological functions were identified as significantly enriched (corrected p<0.05), mostly comprising developmental functions as well as cell- to-cell signalling (Figure 9B-D). From the developmental functions one gene which seemed particularly interesting was HOXB9, over-expressed in CTPs (Figure 6.9F), likely involved in early embryonic development (268), with changes in expression associated with chemoresistance in ovarian cancer patients (269). Additionally, though not a member of the differentially expressed genes due to some over-expression in the 2 day group, we also see over-expression of another gene in the HOXB cluster, though due to transcript annotation by Cufflinks it is difficult to identify whether this gene is HOXB7, HOXB8 or both.

6.3 Discussion

Our initial hypothesis based on in vitro evidence (presented in the previous chapter) was that a reversibly cisplatin tolerant population of chemosensitive ovarian cancer cells, derived from the cell line A2780 is maintained by EZH2 and HDAC activity, working in concert to remodel the epigenome and silence the expression of genes necessary for maintaining the cisplatin tolerant phenotype. We investigated the extent of transcriptomic regulation via RNA-seq and epigenomic remodelling in the context of EZH2 and HDAC activity by sequencing regions enriched for H3K27me3 and H3K27ac modification as well as sequencing regions of accessible chromatin in chemosensitive and cisplatin tolerant A2780 cells.

We identified substantial reprogramming of the H3K27me3 landscape in cisplatin tolerant CTPs compared to the parental populations. The CTP genome showed massive gain of repressive H3K27me3

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Figure 6.9: Gene ontology analysis of differential gene expression in A2780 CTPs. A) Fold representation and FDR corrected p values for GO biological processes over-represented by genes differentially expressed with a log2 fold change >1.3. Log2 fold change in gene FPKMs for CTPs compared to untreated A2780s for genes associated with GO processes of cell-cell signalling (B), development (C) and other genes differentially expressed with a log2 fold change >1.3. Expression of HOXB9 (D) and HOXB7/8 in 2 day treated chemosensitive, 4 day/6 day cisplatin treatment derived A2780s compared to untreated parental A2780s.

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marks genome-wide, with preferential catalysis of trimethylation in intronic, intergenic and repeat domains, rather than at gene TSS loci or exonic regions. Whilst this seemed to imply repression of distal regulatory elements, which may result in gene silencing events, there seemed to be no statistical association between the genome wide distribution of H3K27me3 and gene expression silencing in CTP cells, or localised loss of H3K27ac peaks which would imply the loss of an enhancer. Therefore, we propose a novel mechanism of resistance to the DNA damaging effects and cisplatin adduct formation in DNA, dependent on H3K27 trimethylation and independent of wide-scale transcriptional reprogramming, whereby formation of compressed heterochromatin, pre-dominantly in intergenic, intronic and repeat regions is protective against the DNA damaging effects of cisplatin and preventative of the induction of replication fork crashes and subsequent apoptotic events occurring as its result (Figure 6.10). Reciprocal loss of ATAC-seq peaks, defining regions of accessible chromatin, were not significantly diminished overall in CTPs or in the annotated genomic regions where H3K27me3 was preferentially acquired, as would be expected to support the hypothesis that formation of heterochromatin was occurring at these regions in CTPs. However, ATAC-seq permits mapping of sequenced reads to regions of accessible chromatin and provides valuable information about nucleosomal positioning and the genomic distance between regions of condensed chromatin (262) but does not reveal information about the 3-dimensional structure of heterochromatin beyond its compaction. It is possible that the extensive trimethylation of H3K27 genome wide results in higher order chromatin compaction which may be more protective from formation of cisplatin adducts throughout the genome, though this is highly speculative. However, the absence of evidence implying heterochromatin formation via loss of sequencable regions of chromatin accessibility, particularly in regions enriched for H3K27me3 modification in CTPs, limits the strength of conclusions we can draw. Additionally, if the model for increased toleration of cisplatin due to genomic deposition of H3K27me3 and potentially wide-scale higher order heterochromatin formation is accurate, it is unclear whether this would be protective of the occurrence of cisplatin adducts in the genome or whether simply the compaction of chromatin around cisplatin adducts which have already occurred would facilitate their repair. This would be easily testable by using an antibody to immunoprecipitate and quantify cisplatin damage genome wide after treating A2780s and CTPs with cisplatin. No difference in the amount of adducts accumulated in the CTPs would suggest a mechanism whereby CTPs are more able to tolerate the effects of cisplatin damage, in this case we would expect inhibition of EZH2 after the treatment of CTPs with high doses of cisplatin to reverse the protective effects of wide-scale H3K27me3 modifications and a severe reduction of cell viability compared to cisplatin treated CTPs with

156 functioning EZH2. On the other hand, a reduction in the occurrence of DNA adducts upon treatment with cisplatin compared to parental A2780 cells would suggest a mechanism protective from the

Figure 6.10: Hypothetical model for H3K27me3 distribution and chromatin structure in CTP cells and the conferring of tolerance to cisplatin treatment for the CTP genome

157 occurrence of cisplatin adducts. It is tempting to think that the swift recovery, proliferation and colony formation observed for cisplatin treated CTPs indicates that these cells simply have less cisplatin induced replication fork crashes and oxidative stress to contend with their ability to grow.

Genes identified as interesting from the RNA-seq dataset, even from focussed gain of H3K27me3 or loss of H3K27ac may contribute to the observed drug tolerant phenotype. One particular gene of interest is WIZ, a zinc finger nuclease which stabilises the G9a protein, preventing proteosomal degradation and stabilising G9a/GLP heterodimers (270), thus potentially playing a role in increased PRC2 activity and catalysation of H3K27 trimethylation genome wide. Aside from the hypothetical model for increased resistance to cisplatin adduct formation in the CTPs as a consequence of extensive H3K27me3 modifications, it is possible that transcriptional alterations in the CTPs, epigenetically driven or otherwise are contributing to the maintenance of a cisplatin tolerant phenotype through deregulation of cellular signalling mechanisms such as inhibition of apoptosis, cell cycle progression and proliferation and the maintenance of a cancer stem cell-like phenotype, a sub-population hypothesised to contribute to enhanced resistance to chemotherapy in ovarian cancer (78), and noted as prevalent in previous studies revealing drug tolerant populations (257, 271).

Several genes differentially expressed in CTPs have been shown to contribute to the described pathways (Figure 6.10). HOXB9 is a particularly interesting candidate, over-expressed in CTPs and flagged by gene ontology analysis due to its involvement in developmental pathways, HOXB9 has previously been implicated in chemoresistance in ovarian cancer, noted as over-expressed in HGSOC patients developing chemoresistance and associating with poor survival (269). The mechanistic role of HOX genes in conferring resistance to treatment has yet to be elucidated however, their maintenance of a stem like mesenchymal phenotype of ovarian cancer has been documented (29).

FOXO1 was down-regulated in CTPs, this is interesting as this gene has been previously been identified as subject to inactivating mutations in paclitaxel resistant ovarian cancer, and appears to be involved in regulation of cell death in response to ROS produced as a result of paclitaxel treatment (272). A potential mechanism by which cisplatin induces apoptosis is through generation of ROS and causation of mitochondrial stress (273), and loss of FOXO1 expression may result in the attenuation of this cytotoxic effect.

LITAF, a downstream effector of p53, was down-regulated in CTPs. This gene has been implicated as a tumour suppressor gene in acute myeloid leukaemia in which it functions as a pro-apoptotic signalling factor (274), and in B cell non-Hodgkins lymphoma, in which it was shown to suppress BCL6 pro-survival signalling (275). LITAF therefore may be a candidate for regulation of the drug tolerant A2780 phenotype.

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Figure 6.11: Hypothetical interactome for maintenance of a drug tolerant phenotype by genes differentially expressed in A2780 CTPs. The potential role of genes observed as differentially expressed in CTPs in the cellular processes of apoptosis, cell cycle progression and proliferation, and maintenance of the cancer stem cell phenotype. H3K27me3/ac-dependent silencing postulated where gain or loss peaks respectively was associated with the TSS of a silenced gene.

TLE3 was down-regulated in CTPs, in which a gain of H3K27me3 enriched region occurred ~50kb from the gene TSS, potentially indicating the silencing of a distal regulatory element. TLE3 is an inhibitor of the Notch pathway and regulates epithelial cell fates – driving them towards terminal differentiation (276). Notch and Wnt are the primary pathways maintaining cancer stem cell sub-populations (277). In addition TLE3 has been shown to prevent proliferation in colorectal cancer by suppressing MAPK and Akt signalling (278) and loss of TLE3 is associated with loss of sensitivity to taxane based chemotherapy in ovarian cancer patients (279).

MAD2L1 was down-regulated in A2780 CTPs, previously loss of MAD2 has been associated with poor prognosis and chemoresistance in serous ovarian cancer patients treated with carboplatin/taxane combination therapy (280) and loss of expression of MAD2 in chemosensitive ovarian cancer cells, specifically A2780 has resulted in increased resistance to paclitaxel therapy due to reduction in deregulated BCL2 expression and reduction in G2/M cell cycle arrest (281).

BMP-7, a gene involved in neural differentiation (282) was down-regulated in CTPs and shown loss of H3K27ac associated with the TSS. This was interesting as this gene has been shown to inhibit the cancer

159 stem cell phenotype in glioblastoma multiformens (283), as well as re-sensitise resistant GM cells to Temozolomide (284) and promote EMT in melanoma (285).

Genes differentially expressed in CTPs were enriched for involvement in neural development, indicating that these pathways may play a role in maintaining the presence of CTPs. Other neurodevelopmental genes deregulated in CTPs were ODZ3, also the most over-expressed gene in the dataset, EFNA5, SLITRK5, EPHA5, PCDH10 and HES1, another repressor of Notch signalling (286). There is a necessity to validate the involvement of these genes in supporting the drug tolerant phenotype via shRNA interference experiments.

6.4 Conclusion

We attempted to characterise transcriptomic and epigenomic changes occurring in chemosensitive ovarian cancer A2780 cells when treated for >4 days with 2µM cisplatin to drive the drug tolerant populations which emerge. RNA-seq profiling provided no definitive driver genes, though we observed deregulation in certain candidate genes of interest, involved either in developmental pathways or pathways which may relate directly to the cell’s ability to resistant the cytotoxic effects of cisplatin, notably developmental gene HOXB9. Validation, however, is required to confirm the involvement of any of these genes in deriving a drug tolerant phenotype. Despite the observation that EZH2 and HDAC inhibition reverse the drug tolerant phenotype we did not observe wide-scale transcriptional changes occur which associated with epigenomic shifts in H3K27me3 or H3K27ac modifications. We did however notably identify genome-wide remodelling of H3K27me3 in the CTPs, which show far higher chromatin H3K27me3 content, particularly in intergenic, intronic and repeat regions which we hypothesise is protective from the occurrence of cisplatin adducts in the CTP genome, and mediated by PRC2. However, this hypothesis was not confirmed by a reciprocal shift towards loss of chromatin accessibility in the CTP epigenome, which would be indicative of wide scale chromatin compaction. It should be noted that the change in genomic distribution of H3K27me3 was observed for a single experimental replicate and that the ATAC-seq data may not be reliable as it failed to pass a sequencing quality measure. Validation and further experiments are required to demonstrate that H3K27me3 is remodelled in CTPs, that this remodelling is dependent on EZH2/PRC2 activity and that the remodelling provides protection from or tolerance for the formation of cisplatin adducts in the A2780 CTP genome.

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Chapter 7: General Discussion

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7.1 Summary of findings

Initially we attempted to identify patient sub-groups within the ICON7 phase III clinical trial (97) with differential clinical outcomes when treated with bevacizumab. Cumulative evidence from 3 phase III clinical trials indicating that bevacizumab has efficacy in prolonging EOC progression of some EOC in patients with advanced stage disease, sub-optimal surgical debulking and/or chemoresistant disease (97, 99, 100), lead to its use in the clinic as a second line therapy. However, as of yet, few studies have linked bevacizumab treatment and improved OS rates in EOC patients (with the exception of “high risk” patients in the ICON7 trial), and patients taking the drug often experience adverse events such as bowel perforation, indicating the necessity for a biomarker to stratify EOC patients at first or second line for beneficial response to bevacizumab. To date promising biomarker studies for predicting response of EOC patients to bevacizumab by showing association with extended PFS have not been validated or convincingly stratified for patient groups showing improved OS. We analysed DNA samples from 399 EOC patients for methylation at the promoters of VEGF-A, VEGF-B and VEGF-C via bisulfite modification pyrosequencing. We chose these loci as previous published work (217) showed associations between promoter methylation of VEGF-B and VEGF-C with clinical patient outcomes, notably PFS, for EOC patients treated with carboplatin and paclitaxel.

Survival analysis revealed that both VEGF-B and VEGF-C methylation associated with clinical outcomes. In all EOC patients across both trial arms, low VEGF-B methylation associated with better patient OS, and loss of VEGF-C methylation was associated with poor PFS. This validated the direction of change in methylation at both loci observed previously as interacting with patient outcomes in two independent patient cohorts (217). Further analysis of VEGF-B methylation and OS in separate trial arms revealed that low VEGF-B methylation was strongly associated with improved OS in patients treated with carboplatin and paclitaxel, passing the threshold HR>2 of clinical significance. However, patients treated with bevacizumab in addition to carboplatin and paclitaxel, showed no difference for OS when split by methylation, and all patients showed similar OS to patients with high VEGF-B methylation when not treated with bevacizumab. We also observed that patients with high VEGF-B expression showed improved OS when treated with standard chemotherapy only, while patients treated with bevacizumab showed no difference in survival while split by expression. These data indicate that a sub-group of EOC patients with low VEGF-B methylation and consequently high expression of the gene benefit from longer survival, and that treatment of these patients with bevacizumab impacts the beneficial effect of VEGF-B, and may indicate that this patient sub-group should not be treated with this drug. There is a need for validation of these findings in an independent bevacizumab treated EOC patient cohort.

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It is unclear why VEGF-B expression is prognostically beneficial for EOC patient outcomes, as the role of VEGF-B in cancer development and angiogenesis is very poorly understood. Some data has shown VEGF-B suppresses neoplastic vascular growth and may mediate anti-angiogenic effects by interaction with VEGFR-1 (107, 287), indicating that VEGF-B expression by tumour or surrounding epithelial cells may suppress pro-tumourigenic angiogenesis. It is possible that epigenetic silencing of VEGF-B in ovarian cancer removes a barrier to angiogenesis, enabling tumour progression and metastasis. It is also unclear why the introduction of bevacizumab into the patient therapeutic regimen may counteract the beneficial outcomes associated with VEGF-B expression. A study was published which investigated binding kinetics of VEGF inhibitors with VEGF family ligands VEGF-A, VEGF-B and PlGF (235). The authors state that bevacizumab does not show cross-reactivity with VEGF-B or PlGF however they only present data on binding kinetics for bevacizumab and PlGF, therefore we cannot exclude the possibility of cross-target affinity of bevacizumab for VEGF-B. It is possible that sequestration of VEGF- B by the bevacizumab antibody may inhibit its action in supressing tumourigenesis or metastasis, additionally the competitive inhibition of bevacizumab:VEGF-A interactions by preferential VEGF-B binding may leave serum VEGF-A available to promote angiogenic growth and metastasis. However, this hypothesis is highly speculative and requires experimental evidence, starting with in vitro experiments examining the binding kinetics of both VEGF-A and VEGF-B with bevacizumab, aiming to identify whether the antibody shows partial or preferential binding efficacy for VEGF-B. This is also the first study that has shown an ovarian cancer patient group to show differential OS when treated with bevacizumb.

The association between VEGF-C methylation and patient survival previously identified (217) was again identified in the ICON7 EOC cohort, with loss of methylation at the promoter associated with poorer PFS as in the previously published study. Multivariable statistical modelling revealed that the association between VEGF-C methylation and PFS was confounded by patient FIGO stage, with a progressive loss of VEGF-C methylation observed in patients with higher stage disease. We hypothesised that deregulation of VEGF-C expression due to a loss of methylation at the promoter may drive OC progression and be causative in poorer patient prognosis. This hypothesis is supported by existing literature indicating that VEGF-C expression is a well known driver of metastasis in ovarian cancer by promoting lymphangiogenesis and angiogenesis (288), as well as promoting a more invasive ovarian cancer tumour cell phenotype via autocrine signalling (114, 126). We observed patterns of VEGF-C methylation and expression in a second ovarian cancer (HGSOC) patient cohort (28) where tumour samples had been taken at primary presentation and ascites samples taken at relapse. These patients had been treated with a standard chemotherapeutic regimen of carboplatin and paclitaxel immediately after primary presentation and cytoreductive surgery and were selected for study based

163 on recurrence of the disease within 6 months of final chemotherapy treatment, thus were designated resistant to therapy. The identification of increased VEGF-C expression and a small loss of methylation at the promoter supported the hypothesis that VEGF-C associates with and may drive OC progression. Since the recurrent OC in these patients was resistant to therapy we further hypothesised that VEGF- C may be a driver of resistance to platinum based therapy. We identified a cell line model of EOC recapitulating the patient samples: the PEO1 line taken at chemosensitive primary presentation from the tumour and the PEO4 line isolated from the ascites of same patient after chemoresistant relapse. We again identified a gain of VEGF-C expression in the PEO4 line compared to the PEO1 as well as loss of methylation at the promoter. Upon Decitabine treatment of the PEO1 line we observed re- expression of VEGF-C indicating that regulation of VEGF-C expression is likely dependent upon methylation in these lines.

Using a recently published CRISPR technique (240) utilising sgRNA-targeted Fok1 dimers to generate DSBs with minimal off-target effects we generated three clonal VEGF-C null PEO4 cell lines to investigate the role of VEGF-C in driving malignant EOC phenotypes. We identified no re-sensitisation of the PEO4 line to cisplatin by VEGF-C knock out, nor did we observe a loss of migration or invasion as observed previously (114), though PEO4 shows a very epithelial-like phenotype with low capacity for migratory and invasive phenotypes. We did observe a reduction in growth of the PEO4 line when VEGF-C was knocked out in both 2D and 3D culture, co-occurring with a reduction in anoikis induced apoptosis. We hypothesise that epigenetic deregulation of VEGF-C in EOC leads to increased expression and drives metastasis and progression of the disease in part via an undiscovered mechanism: promoting survival and growth in conditions of low adherence and cell-cell contacts by suppressing apoptosis induced by anoikis. These data contribute to the body of evidence that inhibition of the VEGF-C:VEGF-R signalling axis may prevent progression and metastasis and extend life for EOC patients.

We sought to further discover the underlying role of selectable epigenetic heterogeneity in ovarian cancer tumours and how this relates to the development of resistance to first line therapies. Selection of epigenetic modifications such as DNA methylation at the promoter of MLH1 have been strongly associated with the acquisition of resistance to cisplatin based therapies (160, 161, 164). Additionally, altered EZH2, which is disregulated and linked to many malignant phenotypes in many cancers (187), is over-expressed in side populations of ovarian cancer cell lines (70) and silencing of genes bivalently marked by H3K4me3 and H3K27me3 at the promoters has been observed in chemoresistant ovarian cancer (289). A number of studies have identified heterogeneity in chemosensitive tumour cell lines from a number of cancers, revealing sub-populations of epigenetically defined multi-drug tolerant cells which can survive high doses of therapy and propagate a sensitive population upon removal of

164 treatment (85, 257, 271, 290). Survival of these populations generally seems to be defined by a slow- cycling or quiescent state, where proliferation is re-established after removal of the drug or with sustained drug treatment. In order to identify whether initial acquisition of resistance to chemotherapy in ovarian cancer could be explained by similar underlying drug tolerant heterogeneous cell populations we attempted to establish an in vitro model of drug tolerance using the A2780 chemosensitive cell line. Initially we established a qualitative association between stem cell content and chemoresistance in A2780 and the highly chemoresistant A2780-derived cell line via tumoursphere initiation and ALDH-high cellular content. This indicated that a level of heterogeneity was likely to exist within these cell lines, with a CSC or side population phenotype previously established as relating to platinum sensitivity (73, 78). We exposed A2780 cells to low doses of cisplatin (2µM) for 48 hour cycles and identified that when cells were treated for 4 days formed colonies after treatment which showed a tripled IC50, the more resistant phenotype persisted until after several months of passaging when the population reverted to the sensitive phenotype, indicating evidence of tolerant plasticity.

In order to identify whether the tolerant cell population is maintained by epigenomic programming we treated the tolerant populations with an EZH2 inhibitor (GSK343), a pan HDAC inhibitor (SAHA) and a BRD4 inhibitor (JQ1). Inhibition of EZH2 and HDACs re-sensitised the populations to cisplatin, indicating that cisplatin is likely to select for a heterogenous sub-population likely to be maintained by epigenomic programming associated with transcriptional silencing, similarly to drug tolerant populations isolated from glioblastoma populations, also maintained by EZH2 (271). We investigated transcriptomic and epigenomic changes in the drug tolerant populations compared to the sensitive A2780 cell line. Using next generation sequencing we identified large scale remodelling of H3K27me3 modifications genome-wide, localised preferentially in non-coding intragenic, intronic and repeat sequences. This remodelling did not seem to overlap significantly with transcriptional changes, therefore we hypothesised a novel mechanism whereby heavy H3K27 trimethylation maintains a state of decreased vulnerability to DNA damaging agents such as cisplatin in sub-populations of chemosensitive ovarian cancer populations by reducing access of the genome to these cytotoxic agents.

7.2 Limitations

7.2.1 Limitations of ICON7 study

A very interesting association was identified between VEGF-B methylation and expression in standard chemotherapy treated EOC patients, where low VEGF-B methylation and high expression was strongly associated with improved patient OS, and the survival benefit was lost in “VEGF-B low” patients treated

165 with Bevacizumab. Whilst this supports a hypothesis for bevacizumab having a negative impact on the survival of EOC patients with low VEGF-B methylation these findings need to be validated in an independent cohort of patients receiving both bevacizumab and standard chemotherapy versus chemotherapy in a control group before VEGF-B methylation could be taken forward as a clinical biomarker to indicate EOC patients that may have clinical outcomes negatively impacted by treatment with bevacizumab. However, bevacizumab is currently in clinical use as a second line therapy for advanced chemoresistant disease, whereas patients in the ICON7 cohort were prescribed bevacizumab as a first line agent. The study would need to be repeated in a cohort treated with bevacizumab at relapse to determine whether it was possible to isolate a sub-group of HGSOC patients with poorer outcomes based on VEGF-B methylation using OS as the clinical endpoint. The observed association in this study cohort was between VEGF-B methylation/expression and OS, but not PFS, though the relationship with PFS was borderline, and a trend was identified for longer time to progression with lower methylation.

Association was observed previously between VEGF-B methylation and PFS (217), OS was not tested in the previous study, however association with the same clinical outcome was not observed. Low VEGF-B methylation in the ICON7 cohort associated with better patient outcomes in the standard chemotherapy arm, and high VEGF-B expression associated with better patient OS, indicating the expected canonical relationship between loss of methylation at the promoter and increased expression, both associating with improved survival. Whilst the direction of association between VEGF- B methylation and patient outcomes were the same is in the published study in the SGCTC and TCGA cohorts (217), the relationship between VEGF-B promoter methylation and expression in the TCGA HGSOC cohort showed negative correlation with increased VEGF-B methylation associating with increased expression. This indicates that further data is required to identify the true direction of association between VEGF-B promoter methylation and expression in ovarian cancer. Additionally, we were unable to identify the relationship between VEGF-C methylation and expression in the ICON7 cohort as we had data only from a single microarray probe which showed no positive expression data for any sample.

7.2.2 Limitations of study with VEGF-C in ICGC patient cohort

We demonstrated a small loss of methylation at the promoter of VEGF-C in ascites collected at chemoresistant relapse compared to the matched tumours at primary presentation as well as a large reciprocal increase in expression. The sample numbers in this study were low: DNA methylation data was presented for 15 patient pairs and expression data was presented for 11 pairs. Low sample numbers leads to reduced power to detect real effects and show higher variability in statistical

166 hypothesis testing. The expression and methylation data presented are from primary tumour for the primary samples and ascites for the relapse samples, unfortunately we cannot dismiss the possibility that the tissue of origin is not the explanatory variable for the change in VEGF-C methylation and expression between primary and relapse tissue, as we do not have an appropriate control for these variables in primary tumour from the patients at relapse or ascites from the patients at primary presentation. We do have methylation from patient ascites at primary presentation and methylation does not appear to change between the two however we only have data from two patients which is insufficient to make a reliable conclusion that methylation is unchanged. Additionally, expression was the variable showing the most convincing change and we do lack an appropriate control for this variable. Similarly, because relapse occurred within 6 months of the last chemotherapy cycle, these samples were deemed chemoresistant, we hypothesised that VEGF-C may drive development of chemoresistance as well as progression. This hypothesis would have been better supported had expression data been obtained and analysed for chemosensitive relapse samples as well, i.e. relapse occurring >12 months after the final chemotherapy cycle, with the hypothesis for how VEGF-C expression associates with relapse versus chemoresistance being dependent on whether we saw gain of expression in the chemosensitive relapse samples.

7.2.3 Limitations of study with VEGF-C knock-out in an ovarian cancer cell line

The same limitation exists in this functional study as for the previous study in the ICGC patient cohort. It should be noted that the cell lines used in the functional VEGF-C knock out study recapitulate the patient cohort i.e. are matched lines derived from primary tumour at primary presentation and ascites at the point of relapse in the same patient, therefore biological differences observed in these lines are potentially also representative of tumour cells extracted from primary tumour versus ascites rather than showing differences unique to tumour cells derived from relapse tissue.

VEGF-C null PEO4 cells showed a reduction in apoptosis in adherent culture as well as non-adherent culture compared to the VEGF-C expressing parental line, indicating that VEGF-C suppresses apoptotic cell death in this line. It was surprising therefore that the loss of VEGF-C did not re-sensitise the line to cisplatin. Our primary observation was that VEGF-C suppresses anoikis induced apoptosis, it is possible that VEGF-C induced signalling is the primary mechanism of escape for anoikis induced apoptosis in the PEO4 line, whereas redundant mechanisms are likely to suppress apoptotic signalling induced by cisplatin damage. It is possible that VEGF-C is sufficient to inhibit cisplatin induced apoptosis but not necessary, however this is impossible to conclude without over-expression studies in the chemosensitive PEO1 cell line.

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Whilst we demonstrated that VEGF-C inhibits apoptosis in the PEO4 cell line, particularly when in non- adherent conditions, we did not demonstrate via which cognate receptor/s and pathway/s VEGF-C is conferring its pro-survival effects. Autocrine VEGF-C signalling in ovarian cancer has previously been identified as occurring via the VEGF-C/VEGFR3 axis (114) and VEGF-C/VEGFR3 axis signalling primarily occurs via the Akt and Erk1 pathways in other cell types (291). Western blot analysis indicated this was not via Akt signalling as hypothesised (data not shown), therefore it seems likely that VEGF-C may promote ovarian cancer cell survival via the MAPK/Erk1 pathways.

Despite observing a role for VEGF-C in promoting survival and tumoursphere initiating growth we saw no association between cancer stem cell gene expression and VEGF-C expression. We hypothesised that the role VEGF-C seems to play in driving survival and proliferation of EOC cells in low adherence culture makes highly expressing VEGF-C cells more akin to “cancer stem cells”. We did not identify over-expression of known EOC cancer stem cell markers in VEGF-C high PEO4 cells compared to its lowly expressing counterpart PEO1 or the VEGF-C null PEO4 lines. Additionally, we lack the necessary in vivo evidence to say that VEGF-C induced growth and resistance to apoptosis in non-adherent culture supports a tumour initiating and metastatic phenotype.

7.2.4 Limitations of drug tolerance in chemosensitive ovarian cancer cell lines study

Although the A2780/CP70 line, the most cisplatin resistant of the clonally derived A2780 lines, showed an enrichment for tumoursphere formation efficiency and ALDH-high cell content, we did not establish a statistical correlation between ALDH content or tumoursphere initiation efficiency and cisplatin resistance in the 5 A2780 cell lines of varying levels of cisplatin resistance, indicating that size of CSC population does not necessarily correlate with level of resistance. However, we did observe that there seemed to be CSC-like sub-populations where response to cisplatin is particularly low.

There are two possibilities for how selection of a CTP population occurs as a result of 4+ days of cisplatin treatment. One is selection for the survival of an underlying tolerant population which then proliferates to form the bulk CTP population which inherits epigenomic changes unique to the underlying tolerant population and conferring tolerance, which is then reversed in the absence of selection. The second is where cisplatin treatment selects for an expansion of the underlying resistant population relative to the sensitive population, which may reverse in the absence of selection due to the less resistant cells having a proliferative advantage in the absence of drug. Cloning cells from the isolated CTP population and testing the resistance of their progeny is the only way to identify this. Either way, the underlying heterogeneity and selectable trait associating with plasticity in cisplatin response appears to be epigenomically defined. This question would also be answered by a pure isolation of the underlying drug tolerant population which we did not achieve. Initially we attempted

168 this via extended cisplatin treatment up to 10 days and testing for enrichment of quiescent cells or ALDH-high cells, we observed neither, though we saw a large G2 phase enrichment, likely because of cisplatin induced cell cycle stalling. It is possible that extended cisplatin treatment would have isolated a “DTP” population as published in the original study (257). We did not isolate a pure DTP population – therefore could not provide evidence of a slow cycling/quiescence or a stem cell sub-population in the A2780 cell line.

We identified a potentially novel epigenomic mechanism by which reversibly resistant A2780 cells can resist the DNA damaging effects of cisplatin. We identified extensive chromatin remodelling of the CTPs, with H3K27me3 being deposited genome-wide, preferentially in non-coding regions. However, the ATAC-seq data does not verify this hypothesis, with minimal remodelling of accessible chromatin occurring genome wide. However this may be because the majority of accessible chromatin detectable by ATAC-seq occurs at the TSS regions of genes (292), therefore it may not be of surprise that we see little change in accessible chromatin as measurable by ATAC-seq. It should be mentioned that the ATAC-seq failed to pass sequencing QC and that H3K27me3 ChIP-seq was only available for one experimental CTP replicate, therefore validation of this finding is necessary.

7.3 Future Perspectives 7.3.1 Elucidation of the role of VEGF-B in promoting EOC patient survival outcomes, interaction with bevacizumab and validation as a potential biomarker for exclusion of patients from bevacizumab therapy

Identifying the role of VEGF-B in promoting beneficial ovarian cancer outcomes is not a trivial matter. Previous studies into the role of this protein in normal angiogenic function and cancer have yielded conflicting results, Li et al., suggest that VEGF-B plays antagonistic roles in an angiogenesis context to promote survival of healthy vasculature and inhibit growth of disorganised, neoplastic vasculature (107). Studies of VEGF-B function in cancer mostly suggest it plays a pro-tumourigenic role, stratification of muscle invasive bladder cancer by VEGF-B expression indicates VEGF-B is prognostic for poor survival (293), VEGF-B expression has been implicated with a metastatic phenotype in melanoma (294) and studies in pancreatic cell lines indicate that resveratrol and metformin reduce tumour growth in part via down-regulation of VEGF-B (295, 296) whilst expression of VEGF-B in neuroendocrine pancreatic tumours in vivo inhibits growth (108). In vivo studies are ideal to capture the complexity of the interaction between VEGF-B expression, angiogenesis and effects on tumour growth in ovarian cancer tumours. It is likely that since VEGF-B seems to play a dual role in angiogenesis it also plays a dual role across cancers. The most intriguing question raised by this study is not only how VEGF-B is implicated with improved patient survival but also how bevacizumab interacts with

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VEGF-B to mitigate its benefit. The original study investigating off-target effects for bevacizumab does not present data on VEGF-B binding, so initially we would need to perform biochemical affinity assays with bevacizumab and VEGF-B as well as in the presence of VEGF-A, to identify whether bevacizumab may bind VEGF-B epitopes preferentially. We could also introduce bevacizumab to VEGF-B over- expressing murine models of ovarian cancer, which would allow us to identify whether VEGF-B inhibits tumour-associated angiogenesis, tumour growth and/or metastasis and whether bevacizumab interferes with these effects. First however, and most importantly, it is necessary to validate these findings in another EOC cohort treated with bevacizumab, after which VEGF-B methylation could be considered as a potential biomarker for the stratification of the clinical application of bevacizumab for the treatment of ovarian cancer. Currently as bevacizumab is prescribed as a second line therapy for chemoresistant disease we would need to perform the same analysis in a cohort of ovarian cancer patients with advanced chemoresistant disease such as the AURELIA phase III trial cohort (100) to identify whether VEGF-B methylation has the same prognostic value for OS at this point in the disease and whether it can still stratify patients who will suffer poorer OS outcomes if prescribed the drug. Refining the group of patients treated with bevacizumab to VEGF-B high patients at relapse may provide better data to allow prognostic studies to identify whether the drug truly has a positive effect on patient OS.

7.3.2 Further elucidation of the role of VEGF-C in platinum resistance, tumoursphere initiation, metastasis and cancer stem cell phenotype

Our study identified that VEGF-C was not necessary to promote resistance to cisplatin in the PEO4 cell line, as the IC50 was not decreased in the VEGF-C knock-out lines. However, we demonstrated its role in mitigating apoptosis in the same line. VEGF-C may therefore be sufficient to inhibit cisplatin induced apoptosis in a chemosensitive cancer cell, which would easily be verified by over-expressing VEGF-C in the PEO1 cell line and performing caspase 3/7 and survival assays. Most interestingly this work elucidated a potentially novel autocrine role for VEGF-C in promoting cell survival and tumoursphere growth in non-adherent culture by inhibiting anoikis induced apoptosis. Though it seems highly likely, this study did not demonstrate that this phenotype is dependent on the VEGF-C/VEGFR signalling axis. Recapitulation of the findings in VEGF-C null PEO4 lines upon inhibition of VEGFRs in the PEO4 line using Cediranib would confirm that these effects are conferred via autocrine signalling instigated by VEGF-C/VEGF-R interactions. It would also be interesting to elucidate the downstream intracellular signalling mechanism by which VEGF-C confers enhanced growth and resistance to apoptosis. Studies have revealed that VEGF-C is able to suppress apoptosis and suppress the efficacy of cytotoxic drugs in several cancers by up-regulating BCL2 (115–117), therefore inhibiting BCL2 in PEO4 cells to identify

170 the impact on growth and apoptosis would be interesting. Ideally to confirm that VEGF-C is sufficient to confer these effects upon a cancer cell, we would over-express VEGF-C in the PEO1 line and measure tumoursphere formation efficiency and apoptosis in non-adherent conditions. Upon confirmation that VEGF-C is sufficient to increase tumoursphere formation efficiency and inhibit anoikis induced apoptosis we would attempt to reverse the phenotype by inhibiting VEGF-R with Cediranib and inhibiting Bcl2, to confirm the involvement of the VEGF/VEGFR signalling axis and the downstream effector of Bcl2 in the phenotype. Importantly at this stage we would take the study into murine models to perform tumour initiation tests using PEO4 and PEO4-VEGF-C null cell lines to confirm that VEGF-C enhances the ability of VEGF-C to initiate a tumour, indicating whether the in vitro findings are relevant to the physiological development of the tumour. Over time we could also identify whether the loss of VEGF-C impairs the ability of the tumour to metastasise as hypothesised.

7.3.2 Further characterisation of the CTP phenotype and its role in the acquisition of platinum resistance in ovarian cancer

In chapter 5 we discuss the necessity of clarification of the role heterogeneity is playing in contributing to the CTP phenotype: a proliferative population with a tripled IC50 for cisplatin. It would be informative to identify whether we are selecting for an expanded sub-population, or a pure population of proliferative cells derived from a sub-population. There are several ways to address this question – the simplest of which is to sustain low dose cisplatin treatment of the A2780 parental cells in order to identify a pure tolerant sub-population, if one exists, as done previously with the NSCLC PC9 cell line (86). This approach combined with cell cycle analysis would permit identification of whether these cells are able to withstand cisplatin treatment due to maintaining a quiescent slow cycling population as in other drug tolerant persistent populations derived from other chemoresistant cancer types (83, 84, 86). Further to this approach we could identify whether survival of A2780 sub-populations was dependent on HDAC and EZH2 activity. It is also important to verify whether genetic heterogeneity plays a part in the isolation of this phenotype using single cell cloning before attempting isolation of further CTP populations from several clonally derived lines.

A powerful approach which could be taken to identify the role of heterogeneity in cisplatin tolerance is to use single cell RNA-seq profiling to identify sub-populations based on signatures and similarities in transcriptional profile and compare to the expanded CTP population as well as the long term treated “pure cisplatin tolerant” population if isolated. These signatures may allow us to identify whether there is an underlying cisplatin tolerant population within the parental A2780 cell line, and whether the signature(s) of this sub-population(s) matches the transcriptional profile of the “pure cisplatin tolerant” population, indicating whether longer term cisplatin treatment is selecting for this sub-

171 population. Similarly, profiling in the expanded CTP population may indicate whether the population is comprised completely or from a larger ratio of the underlying parental population(s). However, this is a highly speculative approach and rests on assumptions that sub-populations are pre-existing within the parental A2780 population, that genome scale differences in transcription exist and are detectable in these populations and that the transcriptional profiles of CTPs play a part in driving the phenotype, though this experiment would provide validation and potentially clarification for candidate transcriptional drivers of the CTP state. HOXA9 seemed of particular interest for knock-down validation studies due to the frequency of occurrence of association between HOX gene regulation and impact on patient prognosis and development of chemoresistance in the literature as well its potential influence over the the networks of genes involved in developmental pathways which were identified as enriched in the deregulated genes in A2780 CTPs.

Most interestingly we identified wide-scale H3K27me3 remodelling and deposition in the CTP populations particularly in intronic and intergenic regions. While this may point to remodelling which affects transcriptional change via silencing of long range enhancer interactions, we observed very little concordance between proximity of H3K27me3 peaks and silencing of gene expression for genes with proximal TSS sites. We also observed very little concordance between gain H3K27me3 and loss of H3K27ac, a strong marker for active transcription, or closure of chromatin via ATAC-seq peaks. These data point towards a genome state which is highly trimethylated at H3K27 residues but which may not influence transcription to a large extent. We hypothesised therefore that wide-scale H3K27me3 remodelling may facilitate protection of the genome by inducing higher levels of heterochromatin compaction, perhaps in areas sensitive to damage, which are particularly key for DNA damage repair signalling or where DNA replication roadblocks are particularly likely to occur. Further data is required to confirm this relationship between H3K27me3 deposition and either tolerance to DNA damage or protection from DNA damage as discussed in chapter 6. Firstly via treatment with EZH2 inhibitors then attempted induction of the CTP phenotype and H3K27me3 ChIP-seq to demonstrate that EZH2 is necessary for the genome wide remodelling of H3K27me3 observed in CTPs. Following this confirmation we would use a combination of cisplatin treatment, EZH2 inhibition and western blotting using an antibody to DNA damage to determine whether EZH2-depedent H3K27me3 remodelling provides either protection from accumulation of platinum adducts in the genome or tolerance to their presence. It is tempting to think that as the cells in the data presented here are able to recover quickly from several days of cisplatin treatment and immediately enter a proliferative state rather than remaining quiescent for a long time, that the genome is protected from damage rather than being merely tolerant to it, in which case we would expect that cells may need to exist in a non-proliferative quiescent/slow-cycling state whilst repairing the adducts. It seems likely therefore that we are likely

172 to see reduced DNA adduct accumulation in the A2780 genome when treated with cisplatin in the absence of EZH2 inhibition.

It has been suggested that in ovarian cancer cisplatin damage may induce DNA methylation occurrences across the genome, particularly in MMR deficient genetic backgrounds (178) and that these changes, where they may induce selectable phenotypes such as de novo silencing of genes necessary for maintaining drug sensitivity. Whilst other studies mostly used EGFR inhibitors and TKIs to derive drug tolerance models (83–86), the data indicated a drug tolerant phenotype within a drug sensitive cancer cell line. However, with DNA damaging agents like cisplatin, it is a possibility that the induction of damage in a small proportion of cells, perhaps dependent on the damage loci, results in wide scale chromatin remodelling as observed in this study. The recruitment of DNMT enzymes to DNA at sites of double strand breaks induced by oxidative damage has been observed, recruited by MMR damage recognition proteins (174) as well as recruitment of DNMTs, PRC2 complexes and SIRT1 to CG rich regions where DSBs occur (177). It follows therefore that epigenetic alterations of DNA methylation and H3K27 trimethylation may be catalysed at the sites of DSBs where DNMTs and PRC2 are recruited. It is tempting to speculate that initial epigenetic changes in a subset of cells in key regions may result in heterochromatin compaction, resulting in increased DNA methylation and H3K27me3 genome wide and heterochromatin compaction, conferring protection for further cisplatin lesions. It may be the case that while there may be stochasticity to where cisplatin lesions occur, only cells in which epigenomic remodelling occurs for certain loci, perhaps intergenically in non-coding regions for example where downstream transcriptional effects will not impact cell viability are selected for. It would be very interesting to localise regions of the genome in which cisplatin adducts occur in A2780 upon treatment and to identify whether DNA methylation and/or H3K27me3 gains are enriched at those loci.

In terms of clinical application, these results could be promising from the therapeutic perspective of preventing the emergence of chemoresistance. These data support other data in ovarian cancer models implicating EZH2 activity in the emergence of chemoresistance (70, 289), and the wider literature implicating EZH2 in sustaining CSC populations (187) – which are implicated in the repopulation of the tumour niche and patient progression as well as the development of drug resistance (73). EZH2 has been suggested as a therapeutic target for the sensitisation of intrinsically drug resistant CSC populations to TKIs in CML (198). However in order to initiate EZH2 as a druggable target at primary presentation before or alongside first-line chemotherapy long term studies in vitro and in vivo are necessary to demonstrate the ability of EZH2 inhibition to prevent the emergence of chemoresistant cell phenotypes. For example similar dosing regimes used to isolate the A2780/MCP and A2780/CP70 cell lines using multiple rounds of cisplatin dosing could be replicated using

173 chemosensitive cancer cell lines and murine models injected with the same cells, combined with or preceded by EZH2 inhibition. Another possibility is to use murine xenograft models of sensitive and platinum resistant ovarian cancer, to identify whether EZH2 can resensitise chemoresistant tumours to platinum. However, based on the models of drug tolerance described for PC9 NSCLC cell lines to Erlotinib (86, 250) it seems the drug tolerant state is likely to be a temporary state which allows for the appearance and selection of multiple redundant resistance conferring mutations and potentially other epigenetic aberrations leading to sustained stable drug resistance which may not be reversible by EZH2 inhibition. Therefore, application in a chemosensitive setting may make more biological sense as well as reduce patient exposure to chemotherapy.

7.4 Summary of findings

The key findings of this study have been validation of VEGF-B and VEGF-C promoter-CGI methylation as associating with ovarian cancer patient outcomes. We identified stage as a confounding variable for the relationship between VEGF-C and methylation and PFS as well as identifying loss of methylation and increased expression of VEGF-C in ovarian cancer patient ascites post relapse supporting the role of deregulation of VEGF-C expression epigenetically in driving cancer stage and progression. We knocked out VEGF-C via CRISPR in an ovarian cancer cell line expressing high levels of VEGF-C, and identified a novel autocrine mechanism by which VEGF-C promotes tumour cell growth and survival in non-adherent conditions as well as suppression of apoptosis, notable apoptosis induced by non- adherence to a surface. Leading us to hypothesise that one mechanism by which deregulated VEGF-C promotes patient progression is by autocrine inhibition of anoikis induced cell death increasing the chances of metastasis. Additionally, we isolated a temporary cisplatin tolerant proliferative population with low dose cisplatin treatment of the chemosensitive A2780 cell line, which showed a tripled IC50 for cisplatin, and resensitisation by EZH2 treatment. ChIP-seq profiling of this population revealed extensive H3K27me3 remodelling in these cells. We propose a novel EZH2 dependent drug tolerant state which is defined by extensive H3K27me3 modification in non-coding regions of the genome and confers protection from formation of cisplatin adducts in the genome of the tumour cell.

174

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