Clinical biology of OPCML and its potential therapeutic applications in ovarian and breast cancers
L. S. Louis
A thesis submitted for the degree of Doctor of Philosophy
Department of Surgery and Cancer Faculty of Medicine Imperial College London
August 2014
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2 Abstract
Opioid binding protein cell adhesion molecule like (OPCML), is a glycosyl phosphatidylinositol (GPI) anchored cell adhesion molecule, that was discovered by our lab to be somatically methylated with its expression silenced in over 80% of epithelial ovarian cancers (EOCs) (Sellar, Watt et al. 2003). This was subsequently confirmed and further expanded by other groups to include many other tumours (Czekierdowski, Czekierdowska et al. 2006, Yao, Li et al. 2006, Zhang, Ye et al. 2006, Chen, Ye et al. 2007). OPCML exhibited the functional characteristics of a tumour suppressor gene (TSG) both in vitro and in vivo (Sellar,
Watt et al. 2003). Our lab has defined the function of OPCML, acting on a disparate set of
RTKs (EphA2, FGFR1, FGFR3, HER2, HER4) by being a systems level regulator that ultimately leads to phospho ERK (pERK) and phospho AKT (pAKT) down-regulation, inducing apoptosis and growth inhibition in cancer but not normal cells (McKie, Vaughan et al. 2012).
The overall aim of this PhD was to investigate the use of OPCML as a targeted therapy in ovarian cancer. The key finding of this thesis is the evidence of potential therapeutic effects of OPCML along with other targeted therapies. Here, I report on in vitro sensitising effects of
OPCML to anti-HER2 but not anti-EGFR therapy in ovarian and breast cancer cell lines, thus further confirming earlier group findings regarding OPCML selectivity with HER2 but not
EGFR. I have also explored the association of OPCML and different angiogenic factors; namely VEGFA and the VEGFR family, with evidence that OPCML expression decreases levels of VEGFR3 as demonstrated by western blotting under different stimulation conditions,
FACS and pull down experiments.
Another part of this thesis explores further characterisation of OPCML at the cellular protein level via the use of reverse phase protein microarray (RPPA), and further western blots to
3 validate some of the results as well as proliferation assays confirming the growth inhibition properties of OPCML transiently transfected into a biologically varied group of ovarian cancer cell lines.
The final part shows attempts to further develop a recombinant OPCML (rOPCML) that mimic the function of the original gene in prokaryotic and later in HEK293 mammalian cell vector which proved challenging. A ‘sandwich’ ELISA plate to measure the generated His- tagged rOPCML protein was developed en route.
In conclusion, this PhD thesis progressed our understanding of OPCML biology and its potential future use as targeted therapy in cancer.
4 Table of contents
Abstract
Table of contents
List of tables
List of figures
Declaration
Acknowledgement
Dedication
Chapter one: Introduction
1.1 Background to the PhD project
1.2 Ovarian cancer
1.2.1 Incidence and epidemiology 1.2.2 Is ovarian cancer really ovarian? 1.2.3 Risk factors for ovarian cancer 1.2.4 Familial ovarian cancer 1.2.5 Classification, histology and molecular pathology of ovarian cancer 1.2.5.1 Germ line ovarian tumours 1.2.5.2 Epithelial ovarian tumours 1.2.5.3 Epithelial ovarian cancer and RTKs 1.2.5.3.1 EGF family 1.2.5.3.2 VEGF family 1.2.5.4 Ovarian cancer and integrins 1.2.5.5 Abnormal cellular pathways in ovarian cancer 1.2.5.6 Ovarian cancer and tumour suppressor genes 1.2.6 Staging of ovarian cancer 1.2.7 Screening for ovarian cancer 1.2.8 Diagnosis of ovarian cancer 1.2.9 Treatment of ovarian cancer
5 1.2.9.1 Surgery 1.2.9.2 Chemotherapy 1.2.9.2.1 Chemotherapy for newly diagnosed disease 1.2.9.2.2 Chemotherapy for recurrent disease 1.2.9.2.3 Adjuvant versus neoadjuvant chemotherapy 1.2.9.2.4 Intravenous versus intraperitoneal chemotherapy 1.2.9.2.5 Maintenance chemotherapy 1.2.9.3 Targeted therapy with biological modulating agents 1.3 Breast cancer 1.3.1 Incidence and epidemiology 1.3.2 Risk factors for breast cancer 1.3.3 Prevention of breast cancer 1.3.4 Classification of breast cancer 1.3.5 Diagnosis of breast cancer 1.3.6 Staging of breast cancer 1.3.7 Treatment of breast cancer 1.4 The IgLON family 1.5 OPCML 1.5.1 OPCML as a tumour suppressor gene 1.5.2 OPCML and ovarian cancer 1.5.3 OPCML and breast cancer 1.5.4 OPCML and other cancers 1.5.5 OPCML and receptor tyrosine kinases 1.5.5.1 OPCML binds to HER2 but not to EGFR 1.5.6 OPCML and targeted therapy 1.6 rOPCML 1.6.1 rOPCML inhibits tumour growth in vitro 1.6.2 rOPCML inhibits tumour growth in vivo 1.7 Rationale for the project 1.7.1 Hypothesis and Aims
6 Chapter two: materials and methods
2.1 Cell culture
2.1.1 Cell lines
2.1.1.1 Wild type cell lines
2.1.1.2 Stable cell lines
2.1.2 Cell lines maintenance
2.1.3 Cell line passage
2.1.4 Cryopreservation and thawing of cells
2.2 Cloning and general nucleic acid manipulation
2.2.1 Agarose gel electrophoresis
2.2.2 Plasmid DNA preparation
2.2.2.1 Miniprep
2.2.2.2 Maxiprep
2.2.3 DNA isolation from plasmid glycerol stocks
2.2.4 Restriction endonuclease digestion
2.2.5 DNA plasmid extraction and purification
2.2.6 DNA plasmid ligation
2.2.7 E. coli transformation with plasmid DNA
2.2.8 E. coli propagation and DNA isolation
2.2.9 DNA sequencing
2.2.10 GST-OPCML fusion proteins
2.3 Cell transfection and cloning
2.3.1 OPCML transient transfection
2.3.1.1 OPCML transient transfection into wild type cancer cell lines
2.3.1.2 OPCML transient transfection into HEK293 cells
2.4 General protein analysis and manipulation
7 2.4.1 Protein extraction
2.4.1.1 Protein extraction from cultured cells
2.4.1.2 Protein extraction from culture media
2.4.1.3 Protein extraction for reverse phase protein micro-array (RPPA)
2.4.2 Protein quantification
2.4.3 Western blotting
2.4.4 Protein_Protein interaction
2.4.4.1 Co-immuno-precipitation (Co-IP)
2.4.4.2 Pull-down assay
2.4.5 Reverse phase protein microarray (RPPA)
2.4.6 Recombinant OPCML protein expression in E.coli
2.4.7 Recombinant OPCML protein expression in HEK293 cells
2.4.8 Enzyme-linked immunosorbent assay (ELISA)
2.4.8.1 Creation of α-His-tag OPCML “sandwich” ELISA plates
2.4.8.2 “Grid-iron” ELISA
2.4.8.3 “Spike and recovery” ELISA
2.5 Cell biology assays
2.5.1 Ligand stimulation
2.5.1.1 EGF stimulation
2.5.1.2 VEGF stimulation
2.5.2 Cell proliferation assay
2.5.2.1 MTT proliferation assay
2.5.2.2 WST1 proliferation assay
2.5.3 Caspase-glo apoptosis assay
2.5.4 Florescence-activated cell sorting (FACS)
2.5.4.1 Annexin V/ Propedium iodine apoptosis FACS assay
8 2.5.4.2 Cell surface protein quantification FACS assay
2.5.5 Immunofluorescence confocal microscopy (IFM)
2.6 Targeted cell treatment
2.6.1 Trastuzumab treatment
2.6.2 Lapatinib treatment
2.6.3 Erlotinib treatment
2.6.4 Bevacizumab treatment
2.7 Bioinformatics and statistical analysis
Results
Chapter three: OPCML potentiates anti-HER2 targeted therapy in ovarian and breast cancers through binding and negative regulation of HER2 but not EGFR
3.1 Introduction
3.2 Transient transfection of OPCML into a panel of different ovarian cancer cell lines results in down regulation of multiple RTKs
3.3 OPCML sensitises ovarian and breast cancer cell lines response to anti-HER2 targeted therapy
3.4 OPCML potentiate dual HER2/EGFR inhibitor (Lapatinib) therapy in ovarian and breast cancer cell lines
3.5 OPCML has no added therapeutic effect on ovarian and breast cancer cell line response to anti-EGFR targeted therapy
3.6 Summary
9 Chapter four: OPCML, angiogenic agents and the VEGF family of receptors
4.1 Introduction
4.2 In silico analysis of publically available microarray database shows that high OPCML expression is associated with low levels of VEGFA and other factors that play a role in angiogenesis
4.3 OPCML expression is associated with decrease VEGFA levels in vitro
4.3.1 OPCML expression is associated with decrease VEGFA levels in stably transfected ovarian cancer cell lines
4.3.2 OPCML expression is associated with decreased VEGFA levels in the conditioned media of stably transfected ovarian cancer cell lines
4.4 OPCML abrogates total and phospho VEGFR3 levels, as well as phospho (but not total) VEGFR2 under different stimulation conditions, but it has no effect on total or phospho VEGFR1 expression
4.4.1 Overexpression of OPCML results in decrease in total and phospho VEGFR3 levels, as well as phospho VEGFR2, in stably transfected ovarian cancer cell lines
4.4.2 OPCML knockdown in normal ovarian surface epithelial cells results in increased expression of total and phospho VEGFR3 levels, as well as phospho VEGFR2
4.4.3 OPCML transient transfection into a host of heterogeneously different ovarian cancer cell lines results in a decrease in total and phospho VEGFR3 levels, as well as phospho (but not total) VEGFR2
4.5 OPCML expression is associated with attenuated response to VEGFA stimulation for both total and phospho VEGFR3
4.6 Confocal microscopy supports OPCML-associated depletion of VEGFR3
4.7 Fluorescence-activated-cell-sorting (FACS) analysis of OPCML transfected ovarian cancer cells shows decreased expression of VEGFR3 but not VEGFR2 or VEGFR1
4.8 Pull down assay of OPCML-GST fusion protein in SKOBS-V1.2 shows OPCML direct interaction with VEGFR3
4.9 OPCML has no sensitising effects on ovarian cancer cells response to bevacizumab
4.10 Summary
10 Chapter five: RPPA characterisation of OPCML interactions and down-stream signalling effects
5.1 Introduction
5.2 RPPA
5.2.1 Background
5.2.2 Antibodies
5.2.3 Data quantification, normalisation and analysis
5.2.4 Generalised analysis of OPCML effects on different proteins
5.2.5 Analysis of combined OPCML effects, effects of EGF stimulation and batch effect
5.2.6 Analysis of OPCML effects in two heterogeneous cell lines
5.2.7 Analysis of OPCML impact on EGF ligand stimulation
5.2.8 Analysis of OPCML cell line specific effects
5.2.9 Analysis of OPCML effects in SKOBS-P95R-3.4 cells comparison to OPCML and empty vector controls
5.3 Transient transfection of OPCML into a panel of different ovarian cancer cell lines results in decrease in cell proliferation
5.4 Knockdown of OPCML in nOSE results in elevated expression of pHER3 under different stimulation conditions
5.5 OPCML-P95R mutation results in altered regulation of multiple RTKs in comparison to wild-type OPCML and empty vector controls
5.6 Summary
Chapter six: The production of rOPCML
6.1 Introduction
6.2 Exogenous recombinant OPCML protein accelerates apoptosis in vitro
6.3 The activity of rOPCML varies between batches and degrades with time
11 6.4 rOPCML transfection and expression in mammalian cell vector
6.5 Development of an OPCML Enzyme-linked immunosorbent assay (ELISA) plate
6.5.1 Creation of α-His-tag OPCML “sandwich” ELISA plates
6.5.2 “Grid-iron” ELISA
6.5.3 “Spike and recovery” ELISA
6.6 Summary
Chapter seven: Discussion
7.1 Introduction
7.2 OPCML potentiates anti-HER2 targeted therapy in ovarian and breast cancers through binding and negative regulation of HER2 but not EGFR
7.3 OPCML expression results in decreased VEGFA and altered VEGFRs expression levels
7.4 High throughput microarray analysis shows a potentially varied base for OPCML downstream signalling events including anti-proliferation and apoptosis
7.5 Technical limitations of producing rOPCML as a therapeutic agent
7.6 Overall conclusions
7.7 Limitation of this project
7.7 Future directions
References
Appendix A: Supplementary Tables and Figures
Appendix B: Publication lists
12 List of tables:
1.1: Prognosis of invasive epithelial ovarian cancer
1.2: Classification, tumour precursor and molecular pathway of epithelial ovarian cancers
1.3: TSGs implicated in ovarian cancer
1.4: The new FIGO staging of ovarian cancer
2.1: List of primary antibodies used in western blotting
2.2: List of HRP secondary antibodies
2.3: List of IRDye secondary antibodies
2.4: List of antibodies used for Co-IP and pull down assays
2.5: List of the primary and secondary antibodies in cell surface protein quantification FACS assay
2.6: List of Primary and Secondary antibodies used for confocal microscopy
4.1: OPCML relationship with angiogenic factors
5.1.A: Generalised analysis of RPPA data showing proteins with increased expression associated with increase in OPCML levels.
5.1.B: Generalised analysis of RPPA data showing proteins with decreased expression associated with increase in OPCML levels.
5.2.A: BKS-2.1 – combined OPCML effects
5.2.B: BKS-2.1 – combined EGF effects
5.3: PEO1-OP6 – combined OPCML effects
5.4.A: Consistent OPCML induced effects - significantly increased proteins in two heterogeneous cell lines
5.4.B: Consistent OPCML induced effects - significantly decreased proteins in two heterogeneous cell lines
5.5: Cell line specific – OPCML effects
5.6: SKOBS-P95R-3.4 – OPCML-P95R effects
13 List of figures:
1.1: Members of the EGF receptor family
1.2: Diagrammatic presentation of the effect of EGF stimulation on EGFR
1.3: Structure of the VEGFRs, NRPs and TIE, as well as their respective ligands
1.4: Simple schematic presentation of the result of VEGFA stimulation on VEGFR2 and its downstream effects
1.5: OPCML
1.6: In silico analysis of TCGA data of frequency of OPCML methylation
1.7: An in-silico meta-analysis of expression microarray datasets with survival outcome by OPCML expression status using the KMPlotter for ovarian and breast cancers
1.8: Western blot in two ovarian cancer cell lines and ovarian surface epithelium (OSE), demonstrating that OPCML negatively regulates specific RTKs
1.9: Western blots of total and phospho-HER2 and EGFR protein and down-stream effects from SKOBS-V1.2 (vector control) and BKS-2.1 cells (stable OPCML expression) following EGF and FGF stimulation
1.10: Co-immunoprecipitation experiments in stably transfected OPCML cell line BKS-2.1, direct and reciprocal
1.11: Interaction assays show that OPCML binds to HER2 but not to EGFR
1.12: OPCML sensitises stably transfected OPCML expressing cells (BKS-2.1) to Trastuzumab and Lapatinib, but not Erlotinib
1.13: Targeting of cancer but not normal cells by rOPCML
1.14: The impact of rOPCML protein treatment on cell signalling
1.15: Bar graphs comparison between rOPCML treatment group and BSA control group
2.1: The variation of different variables for the optimisation of the ELISA plate
3.1: Western blots showing the effect of OPCML transient transfection vs. that of empty vector (EV) control in PEA1, PEA2, A2780, PEO1 cell lines
3.2: OPCML sensitises HER2 positive ovarian and breast cancer to Trastuzumab but not breast cancer cells with normal HER2 expression.
14 3.3: OPCML sensitises normal expressing as well as HER2 positive ovarian and breast cancer to dual HER2/EGFR TKI Lapatinib.
3.4: OPCML has no effect on cells response to anti-EGFR small molecule inhibitor Erlotinib regardless of HER2 or EGFR status.
4.1: OPCML and angiogenic factors heatmaps
4.2: Box-plot for OPCML upper and lower quartile and corresponding VEGFA
4.3: Western blot and bar chart of OPCML expressing cells, and their negative control, tested for their VEGFA levels
4.4: VEGFA levels in serum free media of OPCML expressing cells
4.5: Western blot showing SKOBS-V1.2, SKOBS-3.5 and BKS-2.1 under serum free conditions, full media and VEGFA stimulation
4.6: Western blot showing VEGFRs in OSE OPCML knockdowns (PLKO-1.3, sh-464-23 and sh- 339-24) under serum free conditions, full media and VEGFA stimulation
4.7: VEGFA and VEGFRs in transiently transfected OPCML ovarian cancer cell lines
4.8: Variable effects on VEGF receptor family following time point stimulation with 10 ng/ml of VEGFA in SKOV-3 derived cells
4.9: Immunofluorescence confocal microscopy of OPCML and VEGFR3
4.10: Cell surface protein FACS assay of OPCML and VEGFR3 protein in PEO1 cells and PEO1- OP6
4.11: GST-OPCML fusion protein pull-down assay with OPCML and VEGFRs
4.12: OPCML has no effect on cells response to anti-VEGFA monoclonal antibody (Bevacizumab)
5.1: RPPA median centred heatmaps for SKOBS-V1.2, SKOBS-P95R-3.4 and BKS-2.1 cells under serum free media and EGF stimulation.
5.2: RPPA median centred heatmaps for PEO1 and PEO1-OP6 cells under serum free media and EGF stimulation.
5.3: WST1 proliferation assay in PEA1 (A), PEA2 (B), A2780 (C) and PEO1 (D) cell lines
5.4: Bar chart of OPCML effects on cell proliferation
5.5: Knockdown of OPCML results in increase in HER3_pY1289 levels
15 5.6: Western blot comparing the protein expression of different RTKs as well as down-steam signalling AKT and ERK in OPCML empty vector control (SKOBS-V1.2), wild type OPCML expressing stables (SKOBS-3.5 and BKS-2.1) and OPCML-P95R mutant (SKOBS-P95R-3.4).
6.1: rOPCML induces apoptosis in vitro
6.2: Varied activity of rOPCML between batches
6.3: Varied activity of rOPCML with time
6.4: OPCML sequenced construct in pcDNA3.1/V5-His-B
6.5: Western blot showing OPCML and the His-tagged protein expression in HEK 293-F cells in days after the plasmid transfection.
6.6: Diagrams showing the decrease in HEK 293-F cell viability following transfection, and accompanying daily changes in cell count in these cells.
6.7: Coomassie stain of rOPCML purification and elution in HEK 293-F cells
6.8: Western blots showing OPCML and α His-tag
6.9: Coomassie stain from the second batch of rOPCML purification and elution in HEK 293-F cells
6.10: SKOBS-V1.2 cells treated with rOPCML and v/v equivalent PBS
6.11: SKOBS-V1.2 cells treated with dialysed rOPCML and v/v equivalent PBS
6.12: Standard curve for the OPCML ELISA plate
6.13: Optimised standard curve for OPCML ELISA plate
6.14: Spike and recovery ELISA assay
16 Declaration,
The author has composed this thesis in its entirety.
The author undertook the work presented here, and all contributions from different individuals have been duly acknowledged.
Louay Louis
August 2014
17 Acknowledgement,
I would like to thank all the people who supported me during the last four years and helped me pull through what was a challenging yet most wonderful journey. First and for most, I would like to thank Mr Richard Smith for believing in me and for his immense support and advice along the path, in preparation for, and during the research degree. Also, for my supervisors, Professor Hani Gabra, Miss Sadaf Ghaem-Maghami and Dr Euan Stronach, for giving me this opportunity and their guidance throughout.
Special thanks for the chief scientists, Dr Chiara Recchi and Dr Andrew Paterson for their help with the confocal microscopy and the FACS experiments, as well as their support over the past year, in addition to their valuable advice during the writing-up period.
I’d also like to thank other members of our group, past and present, including Seb, Art, Elisa,
Elena, Jane, Haonan, Sushmita and Tommy. Seb, for the initial help in the lab during the start and teaching me different lab techniques, Art for his help during the transfer period, and Elisa for the help with the RPPA experiment.
Equally, I’d like to thank other colleagues for their support throughout, especially Tom,
Lynsey, Raj and Elaina and in particular Dr Ed Curry for his great help with the analysis of the
RPPA data as well as the in silico analysis of OPCML with the various angiogenic agents.
Finally, special thanks to my good friends Camila and Srdjan for all the fun, the highs and the lows we shared together and supported each other during this journey.
18 Dedication
This work is dedicated, first and foremost to my lovely wife Saba: Honey there is no way in this world that I would have been able to do this work without you. Thank you so much for your love.
This work is also for our two wonderful boys, Daniel and Michael, and for my godson Simon.
To my Mum and Dad, Elham and Salim, for making me the man that I am.
For my late father-in-law, Samie, the man who taught me surgery, I know you would have loved to see me finish-up. I hope this will do you proud.
For Evelyn and Bashar, thank you for all your support over the past four years.
19 List of abbreviations
AKT v-akt murine thymoma viral oncogene homolog
BRCA1 Breast cancer antigene 1
BRCA2 Breast cancer antigene 2
BSA Bovine Serum Albumin
CSA Catalysed signal amplification
CMV Cytomegalo-virus
DAB 3, 3’ di aminobenzidine
DMSO Dimethyl sulphoxide
EDTA Ethylenediaminetetraacetic acid
EGF(R) Epidermal Growth Factor (Receptor)
ERK Extracellular-signal-Regulated Kinase
EOC Epithelial Ovarian Cancer
FACS Fluorescence-Activated Cell Sorting
FGF(R) Fibroblast Growth Factor (Receptor)
FIGO International Federation of Gynaecology and Obstetrics
GPI Glycosyl Phosphatidyl Inositol
GST Glutathione-S-Transferase
HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid
HER2 Human Epidermal growth factor Receptor 2
His Histidine
20 HOX Homeobox
Ig Immunoglobulin
IgLON Immunoglobulin Lsamp, Opcml and Neurotrimin
IPTG Isopropyl-b-D1-Thiogalactopyranoside kDa Kilo dalton
L Litre
LOH Loss-of-Heterozygosity
LPA Lysophosphatidic acid
MAPK Mitogen-Activated Protein Kinase mTOR Mammalian target of rapamycin
MWCO Molecular Weight Cut-Off
Ni-NTA Nickel-NitriloTriacetic Acid
NHS National Health Service
NICE National Institute for Health and Clinical Excellence
OPCML Opioid binding Protein/ Cell adhesion Molecule
OSE Ovarian Surface Epithelium
PBS Phosphate Buffered Saline
PCR Polymerase Chain Reaction
PFA Para-formaldehyde
PI3K PhosphoInositide3 Kinase rOPCML Recombinant OPCML
RPM Round per minute
RTK(s) Receptor Tyrosine Kinase(s)
21 SD Standard deviation
SDS-PAGE Sodium Dodecyl Sulphate – Polyacrylamide Gel
Electrophoresis
SEM Standard error of mean
TAE Tris Acetate-EDTA
TMB Tetramethylbenzidine
TSG(s) Tumour-Suppressor Gene(s)
VEGF(R) Vascular Endothelial Growth Factor (Receptor) v/v Volume/volume w/v Weight/volume
22
Chapter one
Introduction
23 1.1 Background to the PhD project
The context of this PhD project will be discussed in this chapter. First, we will start by introducing ovarian cancer, its aetiology, classification, pathology, diagnosis and treatment, in addition to discussing the underlying molecular mechanisms of the disease. Secondly, there will be a similar analysis regarding breast cancer. We will then discuss our protein of interest, namely the opioid binding protein cell adhesion molecule-like (OPCML), part of the
IgLON family, and its target receptor tyrosine kinases (RTKs). This will be followed by a detailed discussion of the available data, both published and our unpublished, concerning the relation of OPCML to ovarian cancer as well as other cancers. We will conclude with the project rationale, hypothesis and aims.
24 1.2 Ovarian cancer
1.2.1 Incidence and epidemiology
Ovarian cancer is the most lethal of all gynaecological cancers. It accounts for more deaths than all the other gynaecological cancers combined (Cancer Research UK 2008, UK 2014) despite being the 5th most common cancer in women overall and the 2nd most common gynaecological cancer after uterine cancer (Statistics 2014). 5984 patients were diagnosed in the UK in 2012, which is slightly less than previous years (6580 patients in 2010, (Office for
National Statistics 2012)). This figure equates to a crude rate of 22.1 new cases per 100 000 population per year with an annual mortality incidence of 4272 cases (Statistics 2014). The
5-year age-standardised relative survival (percentage) for ovarian cancer in women diagnosed during 2007-2011 and followed up to 2012 in England was 46.3% (Statistics
2014). Whilst this represents an improvement from previous years, the prognosis is still bleak. This is largely related to late diagnosis. The median age for patients in ovarian cancer is 60 years, and the average lifetime risk for women is about 1 in 70 (Cannistra 2004).
Although up to 90% of patients in stage I ovarian cancer can be cured with conventional surgery and chemotherapy, less than 30% of ovarian cancers are currently detected in stage
I owing to the absence of an effective screening strategy and lack of clear natural history of the disease. Table 1.1 shows the prognosis of ovarian cancer by stage (see below) of disease
(Cancer.org 2014).
25 Stage Relative 5-year survival rate I 89% IA 94% IB 91% IC 80% II 66% IIA 76% IIB 67% IIC 57% III 34% IIIA 45% IIIB 39% IIIC 35% IV 18%
Table 1.1: Prognosis of invasive epithelial ovarian cancer as per the relative 5-year survival rate in the United States (table adapted from(Cancer.org 2014)) based on pre-2013 staging).
26 1.2.2 Is ovarian cancer really ovarian?
Traditionally it was always believed that epithelial ovarian cancer (EOC) derives from malignant transformation of the simple cuboidal epithelium of the ovarian surface, which is contiguous with the peritoneal mesothelium (Auersperg, Wong et al. 2001, Cannistra 2004).
This theory has been put to scrutiny over the last few years (Medeiros, Muto et al. 2006,
Lee, Miron et al. 2007, Dubeau 2008) with researchers citing evidence from morphological, embryological, molecular biology and histological pathology to try and explain the wide variation between different histological subtypes of ovarian cancer. The current debate on whether the fimbrial end of the fallopian tube or remnants of the mullerian ducts are the precursor for the disease is on-going, and will undoubtedly continue for years to come.
However, a consensus paper from an international meeting of leading experts in the field of ovarian cancer held in Florida in 2011, whilst accepting that the term “ovarian cancer” is misleading, advised against changing the terminology, as it would cause confusion to both the scientific community and the public at large. They recommended that before the term
“ovarian cancer” can be abandoned, the disparate origins of this disease need to be more widely understood by patients, physicians and scientists (Vaughan, Coward et al. 2011).
1.2.3 Risk factors for ovarian cancer
The most important risk factor is a family history of ovarian or breast cancer although an identifiable genetic predisposition (BRCA1 and BRCA2 mutations, Lynch syndrome) is recognised in 10 – 15% of cases (Hennessy, Coleman et al. 2009). Nulliparity, early menarche, late menopause, and increasing age are also associated with increased risk for ovarian cancer, whereas oral contraceptive use, pregnancy, lactation and tubal ligation are
27 associated with reduced risk (Hennessy, Coleman et al. 2009). Combined oral contraceptives
(COC), even after a few months of use, substantially decrease the risk of ovarian cancer
(Herbst and Berek 1993, Rice 2010). The longer a woman has used COCs, the greater this effect. This reduction in risk can persist for more than 30 years after cessation of COCs, though its effect becomes attenuated (Beral, Doll et al. 2008), with an odds ratio (OR) as low as 0.21 of developing ovarian cancer for women who took the pill for more than 10 years.
Hanna and Adams estimated that each year of using the pill brings an approximate 7% reduction in risk of ovarian cancer (Hanna and Adams 2006).
There is some evidence from earlier studies that suggested that infertility treatment, including the use of clomiphene citrate and/or in vitro fertilisation can be associated with increased risk of developing ovarian cancer or borderline tumours of low malignant potential (Harris, Whittemore et al. 1992, Rossing, Daling et al. 1994, Ayhan, Salman et al.
2004, Sanner, Conner et al. 2009). Recent papers (Calderon-Margalit, Friedlander et al.
2009, Jensen, Sharif et al. 2009), as well as a recent literature review (Louis LS 2013), did not substantiate that risk. It is most likely that the increased risk in this group of patients can be attributed to infertility (especially when combined with nulliparity) than to treatment of the latter as those who were treated but not conceived were at the highest risk (Whittemore,
Harris et al. 1992, Whittemore 1994, Mosgaard, Lidegaard et al. 1997, Vlahos,
Economopoulos et al. 2010), whilst those who were treated and conceived showed a decrease in their overall risk (Kallen, Finnstrom et al. 2010, van Leeuwen, Klip et al. 2011).
28 1.2.4 Familial ovarian cancer
Family history of ovarian cancer has long been recognised as the greatest risk factor for developing the disease, accounting for approximately 10-15% of epithelial ovarian cancers
(Bast 2011). This risk is greatly increased in families where more than one relative with breast or ovarian cancer has been identified with BRCA1 and BRCA2 germline mutations
(both genes are responsible for double-strand DNA repair) being the better-known predisposing risk factors (Blackwood and Weber 1998, Venkitaraman 2002). However, the presence of either mutation is associated with better prognosis than non-carriers (Bolton,
Chenevix-Trench et al. 2012) and better prognosis with BRCA2 carriers compared to BRCA1
(Yang, Khan et al. 2011).
Other risk factors also include mismatch repair genes MLH1 and MSH2 in Lynch syndrome, also known as hereditary non-polyposis colorectal cancer (HNPCC).
The risk of ovarian cancer increases to 1 in 30 when a first-degree relative is affected
(parent, sibling, child) and may be as high as 1 in 4 when two or more first-degree relatives are affected by the disease (Schildkraut and Thompson 1988).
The term Hereditary Breast/Ovarian Cancer Syndrome (Narod, Feunteun et al. 1991,
Campeau, Foulkes et al. 2008) is used to describe the families whereby one or more first- degree relative are affected by either breast and/or ovarian cancers (and occasionally other specific cancers).
29 1.2.5 Classification, histology and molecular pathology of ovarian cancer
Ovarian cancer is classified into epithelial (most common type) and germ cell tumours. The latter represent no more than 5 % of all ovarian cancers. In addition, sex cord-stromal tumours (mainly granulosa cell type) represents about 1-2% (Mutch and Prat 2014).
1.2.5.1 Germ line ovarian tumours
All tumours of germ line type arise from the germ cells of the ovary (the oocytes). They are classified according to the type of their exact cell of origin (Nogales, Dulcey et al. 2014). This includes: dysgerminoma (undifferentiated and most common type), yolk sac (endodermal) tumours (yolk sac), immature teratoma (embryo), embryonal carcinoma (whole blastocyst) and choriocarcinoma (trophoblast) (Gershenson 2007). These tumours are usually common in the first two decades of life, most diagnosed at stage I (i.e. confined to the ovary). The tumours are associated with increased levels of alpha-fetoprotein (α-FP) in yolk sac, dysgerminoma and occasionally immature teratoma tumours (Billmire, Cullen et al. 2014), human chorionic gonadotropin (hCG) for choriocarcinoma (Muller and Cole 2009, Lee, Song et al. 2011), and dysgerminoma tumours (Motawy, Szymendera et al. 1992). The latter is also associated with increased levels of lactate dehydrogenase (LDH) (Pressley, Muntz et al.
1992). Germ cell tumours are treated with conservative surgery (usually involves unilateral salpingo-oopherectomy along with peritoneal washings and omental biopsy) followed by chemotherapy, as these tumours are highly chemosensitive. Current standard of chemotherapy includes bleomycin, etoposide and cisplatin for three cycles (STRATOG 2014) and the prognosis is usually good with 5 years cure being 89%.
30 1.2.5.2 Epithelial ovarian tumours
Current classification of EOC, which represents 90% of all ovarian cancers, was classically based on histopathological grade (1–3), with grade 1 being well differentiated, 2 moderately differentiated and 3 poorly differentiated. It is also classified based on appearance into serous (most common, 68%), mucinous (3%), endometrioid (9%), clear cell (13%), mixed epithelial carcinomas (6%) and, less commonly, transitional (Brenner), squamous, and undifferentiated subtypes. Additionally, fallopian tube and primary peritoneal cancers occur that morphologically and clinically resemble epithelial ovarian cancers, as traditionally it was believed that the same embryonic precursor is shared by the ovarian surface epithelium and the peritoneal and fallopian tube epithelia (Hennessy, Coleman et al. 2009, Ricciardelli and
Oehler 2009).
More recently, EOC has been classified into at least five distinct subtypes based on histopathology, immunohistochemistry and molecular genetics: high-grade serous carcinoma (70%); endometrioid carcinoma (10%); clear-cell carcinoma (10%); mucinous carcinoma (3%) and low-grade serous carcinoma (less than 5%) (Sieh, Kobel et al. 2013,
Mutch and Prat 2014).
An interesting thing in ovarian cancer pathology is the fact that unlike most cancers that become less differentiated during their neoplastic transformation, advanced epithelial ovarian cancers develop into four distinct histological types that resemble the well- differentiated normal cells that line the fallopian tube (serous), endometrium
(endometrioid) and endocervix (mucinous), or cells that form nests within the vagina (clear cell) (Bast, Hennessy et al. 2009, Kurman and Shih Ie 2010). At a molecular level, altered patterns of gene expression in different subtypes have correlated with distinctive patterns
31 of gene expression in the normal fallopian tube, endometrial and intestinal mucosa. These subtypes have also correlated with the abnormal re-expression of homeobox (HOX) genes that are normally only expressed during gynaecological organogenesis. HOXA9, HOXA10,
HOXA11 and HOXA13 are associated with the developing fallopian tube, uterus, lower uterine segment and cervix, and upper vagina, respectively. Although these genes are not expressed in the normal ovarian surface epithelium, HOXA9 is expressed in serous, HOXA10 in endometrioid and HOXA11 in mucinous ovarian cancers (Bast, Hennessy et al. 2009,
Hennessy, Coleman et al. 2009). These distinct ovarian cancer subtypes differ with regard to their epidemiology, genetic changes, gene expression, tumour markers and responsiveness to therapy. Clear-cell and mucinous cancers generally do not respond as well as serous and endometrioid cancers to platinum- and taxane based chemotherapeutic regimens (Bast,
Hennessy et al. 2009).
Based on morphological and molecular genetics studies, epithelial ovarian cancers are also divided into two categories that are designated type I and type II tumours, corresponding to two main pathways of tumorigenesis. Type I tumours arise in a gradual manner from borderline tumours and include low-grade serous carcinomas, mucinous, endometrioid, and clear-cell carcinomas. Type II tumours arise anew, and include high-grade serous carcinoma, malignant mixed mesodermal tumours, and undifferentiated carcinomas. Type II tumours are characterised by frequent TP53 mutations, genomic instability, and BRCA mutations in some cases. This classification addresses the relation of borderline tumours to invasive carcinoma and provides a morphological and molecular framework for studies aimed at elucidating pathogenesis of epithelial ovarian cancer (Shih Ie and Kurman 2004, Hennessy,
Coleman et al. 2009)
32 Seminar
in several germline genetic mutation syndromes. The A B C mechanisms of how these genes contribute have not been elucidated. The most common are associated with defective homologous recombination DNA repair, such as the BRCA1 and BRCA2 genes,17,18 or hereditary non- polyposis coli and Lynch syndrome (associated with endometrial and colorectal cancers),19 where the genes are implicated in base mismatch repair. The fi ve times increased prevalence of particular BRCA gene D E F mutations in Ashkenazi Jews compared with the general population places this group at increased risk of ovarian and breast cancer.20 Additional genetic syndromes include Peutz-Jegher and other rarer disorders.21 Figure 1: Examples of diff erent ovarian cancer histotypes Histopathology and molecular pathology (A) Borderline tumour: serous epithelium with progressive branching architectural complexity. Some tufting and Histological interpretation of resected tissue can be budding (epithelial cells becoming detached). No high-grade atypia or stromal invasion. (B) High-grade serous tumour: serous epithelium with increased architectural complexity, becoming solid. Glands are elongated with complex and needs specialist input. Figure 1 shows the narrow cleft-like spaces showing foci of necrosis and exfoliation. The nuclei are uniformly high grade. (C) Grade 2 major epithelial ovarian cancer histotypes. The notion of endometrioid carcinoma: foci of squamous metaplasia, not seen in (pure) variants of other ovarian carcinomas, are ovarian cancer description and diagnosis is changing, evident. The glands are crowded and fused but the glycocalyx is smooth and the epithelium is generally from one disease with several epithelial subtypes to non-exfoliative. The nuclei are stratifi ed and occupy the full thickness of the epithelium. (D) Clear cell carcinoma of 22–24 the ovary: uniform high-grade nuclear features with clear cytoplasm; solid and papillary areas are visible with some several distinct diseases (fi gure 2). Almost 10 years hobnailing and tufting. The carcinoma is invasive. (E) Well diff erentiated mucinous carcinoma (H and E) showing ago, a new classifi cation was proposed that separated progressive archtectural complexity and nuclear atypia. The epithelium towards the benign end of the spectrum ovarian cancers into type I and II tumours.26 Type I shows tall columnar cells with basal nuclei. Areas of defi nite stromal invasion are diffi cult to identify but when tumours were low grade; some (endometrioid, mucinous, tumours are this complex, histopathologists prefer a diagnosis of well diff erentiated carcinoma. (F) Mucinous carcinoma: cytokeratin 7 (CK7)—diff use staining of tumour cells. CK7 staining in conjunction with patchy CK20 and and clear cell types) harboured mutations in BRAF, CDX-2 (CK20 and CDX-2 not shown) are consistent with a primary tumour of ovarian origin. KRAS, and PTEN with microsatellite instability. Type II tumours included high-grade serous and carcinosarcoma, which frequently contain mutations in p53,25 BRCA1, and Epithelial ovarian cancer BRCA2. Integrated genomic analysis of ovarian cancer in High-grade serous Low-grade serous Endometrioid Clear cell Mucinous several hundred tumours, further delineated four TP53 BRAF ARID1A ARID1A KRAS transcriptional subtypes, and identifi ed somatic BRCA1/2 KRAS PI3KCA PI3KCA ERBB2 ampl 26 mutations in NF1, BRCA1, BRCA2, and CDK12. NF1 NRAS PTEN PTEN Importantly, homologous recombination repair of DNA CDK12 ERBB2 PPP2R1A CTNNB1 Homologous PP2R1A damage is defective in roughly 50% of high grade serous Recombination MMR deficiency cancer and NOTCH and FOXM1 signalling are Repair genes implicated in the pathophysiology of serous tumours Pathway alterations (fi gure 3).27 Now that prevalent type-associated underlying PI3/Ras/Notch/ genetic signatures have been identifi ed, the foundations FoxM1 have been laid upon which personalised medicine should be built over the next decade. Figure 2: Epithelial subtypes and associated mutations Adapted from Banerjee and colleagues25 by permission of AACR. Table 1.2: Classification, tumour precursor and molecular pathway of epithelial ovarian High-grade serous and endometrioid ovarian cancers Most patients with epithelial ovarian cancer have high- cancers (adapted fromdistinct and recurrent (Jayson, mutations. Kohn et al. 2014)27 Nearly. all ovarian grade serous cancer, a disease that is characterised by cancers related to deleterious mutations in BRCA1 and nearly universal p53 gene abnormalities,28,29 also seen in BRCA2 are high-grade serous cancer. Genomic studies endometrioid and other high-grade undiff erentiated have subdivided high-grade tumours into four histologies. Although these tumours were believed to Mutation and loss of TP53 function is one of the most frequent genetic abnormalities which subgroups termed proliferative, immunologic, have developed from the ovarian surface epithelium, the mesenchymal, and diff erentiated.27,32 However, this implementation of prophylactic salpingo-oophorectomy is classifi observed cation in 60 has– 80% not of yet sporadic been applied and familial to clinical EOC (Bast, care. Hennessy et al. 2009), albeit for familial risk has shown a high prevalence of tubal earlier High-grade reviews put cancers it at a are modest characterised 51% (Kmet, Cook by et initial al. 2003). On the other hand, carcinoma or precursor serous tubal intraepithelial chemosensitivity with subsequent acquisition of carcinoma in resected tissue, resulting in the hypothesis lysophosphatidic increasing resistance acid (LPA) at each activation recurrence. is observed in 90% of cases (Bast, Hennessy et al. that the tubal fi mbriae might be the site of origin of most 2009). PI3K, and its downstream effector AKT2, were shown to be amplified in significant high-grade serous cancer.30,31 Low-grade serous and endometrioid ovarian cancer High-grade serous cancer is characterised by genomic proportion of ovarian carcinoma Low-grade serous ovarian cancer(Auersperg, Wong et al. 2001) shows more indolent. KRAS is amplified in 5% and instability, DNA copy number abnormalities, but few overexpressed behaviour and in 30 – retrospective 52%, mostly in studies low grade describe type I EOC low(Bast, Hennessy et al. 2009).
PTEN mutations were observed in 15% of EOC (Ricciardelli and Oehler 2009), mainly in low www.thelancet.com Vol 384 October 11, 2014 1377 grade endometrioid subtypes.
33 1.2.5.3 Epithelial ovarian cancer and RTKs
There are approximately 20 classes in which receptor tyrosine kinases (RTKs) are categorised, but those reported to play an important role in ovarian cancer pathogenesis are EGF, FGF, VEGF, Eph, TAM, HGF and PDGF receptor families (Reibenwein and Krainer
2008, Jiao, Ou et al. 2011).
Studies have shown that those ovarian tumours with high malignant potential over-express
HER2 and AKT in 22 – 66% and 12 – 30 % respectively (Landen, Birrer et al. 2008), although recent consensus points out that only 11% of cases have shown amplification of HER2 in recurrent EOC (Reibenwein and Krainer 2008). In addition to the above, EGFR over- expression occurs in 60 – 98% (Blagden and Gabra 2009). However, gefitinib and erlotinib, both EGFR inhibitors, stabilised EOC in 11 – 44% of patients, with objective regression in only 4 – 6% of cases (Bast, Hennessy et al. 2009).
EphA2, as well as other members of the Eph RTK family, have also been associated with advanced ovarian cancer and poor clinical outcome (Konecny, Glas et al. 2009). FGF1 ligand, a potentially important target in EOC (Birrer, Johnson et al. 2007), with upregulation and overexpression being a frequent event that is associated with aggressive disease and reduced survival, occurs in anything from 8% of EOC (Theillet, Adelaide et al. 1993) to 51%
(Bast, Hennessy et al. 2009). Both MET and AXL, and their prospective ligands, HGF and Gas
6, are overexpressed in ovarian cancer (Jiao, Ou et al. 2011) with AXL more overexpressed in recurrent and metastatic cases (Rankin, Fuh et al. 2010), whilst MET was found to have increased copy number in 33% of clear cell subtype in one study (Ross, Ali et al. 2013) and it is associated with poor prognosis (Blumenschein, Mills et al. 2012).
34 1.2.5.3.1 EGF family
The EGF receptor family comprises four members: EGFR (Epithelial Growth Factor Receptor, also known as HER1 and ErbB1), HER2 (Neu/ErbB2), HER3 and HER4 (ErbB3 and ErbB4 respectively). These receptors’ structure includes a glycosylated extracellular domain, a single hydrophobic transmembrane segment, and an intracellular portion with a juxtamembrane segment, a protein kinase domain, and a carboxy-terminal tail (Yarden and
Sliwkowski 2001, Roskoski 2014) (Figure 1.1). EGFR, HER3 and HER4 are activated via ligand- induced dimerisation, whilst HER2 is activated through hetero-dimerisation with other ligand-occupied receptors, for the latter does not have its own soluble ligand. Hence, HER2 is quite unusual among the EGF family of receptors in its inability to homodimerise (Burgess,
Cho et al. 2003). However, it can transform cells in a ligand-independent manner when overexpressed, by intrinsically interacting with any binding partners that become available
(Bose, Molina et al. 2006, Yang, Raymond-Stintz et al. 2007).
With regards to HER3, the receptor has a defective kinase domain and exerts its function only when dimerising with another member of the group (Citri and Yarden 2006, Sorkin and
Goh 2008).
There are eleven ligands that belong to the EGF family and act as agonists for the members of the EGF receptor family, these are: the epidermal growth factor (EGF), the transforming growth factor-a (TGFα), amphiregulin (AR), beta-cellulin (BTC), the heparin-binding EGF-like growth factor (HB-EGF), epiregulin (EPR), epigen (EPG) and the neuregulin (NRGs) of which there are four types numbered neuregulin-1 to neuregulin-4 (Ferguson 2008, Wilson,
Gilmore et al. 2009, Roskoski 2014).
35
Figure 4: The EGF receptor family members – structure and function. (A) Structural Figure 1.1: Members of the EGF receptor family. The diagram shows the structure of each representationmember of the family where the extracellular arrows refer to the ligand of the four members of the EGFR family-binding cleft, the where the black arrows indicate the ligandcircles is where dimerisation takes place and the intracellular arrows shows the ATP binding -binding cleft, the dashed circles show the dimerization arm and the white arrows point tosite in the kinase domain (adapted from the ATP binding site in the(Yarden and Pines 2012) kinase domain )(Adap. ted from Yarden and Pines, 2012). (B)
Schematic view of the mechanism of dimerization of the receptors that undergo conformational changes in order to bind to ligands and interact with the binding partner (from The EGF receptor family downstream signaling passes through at least four independent Burgess et al., 2003). (C) Schematic view of the proposed mechanism of signalling of homo- cellular pathways, these include: the phosphatidylinositol 3-kinase/Akt (PKB) pathway, the andRas/Raf/MEK/ hetero-ERK1/2 pathway, and the phosphoErbB dimers showing thatlipase C gamma (PLCδ) pathway, HER2-containing dimers JAK/Stat are preferentially formed when(signal HER2 transducer is and overexpressed, activator of transcription) causing pathway enhanced(Yarden downstream and Sliwkowski signalling 2001, and outcomes (from
YardenBose, Molina and et Sliwkowski, al. 2006, Roskoski 2001 2014)), . (Figure AR: 1.2). amphiregulin; This means that EGF: the EGF epiderm receptor al growth factor; TGF : transforming growth factor- ; EPGN: epigen; EPR: epiregulin; BTC: -cellulin; HBEGF: 36 heparin-binding EGF-like growth factor; NRG1-4: neuregulin1-4.
family plays an important role in multiple important biological processes such as regulation
of cell cycle, proliferation, apoptosis, survival and motility. In addition there is evidence that
HER2 signaling modulates the equilibrium between pro- and anti-angiogenic factors, namely
VEGFA levels, indirectly (Wen, Yang et al. 2006).
Dutta and Maity Page 17 NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript
Figure 1. Signaling pathways downstream of EGFR activation BindingFigure of 1.2 ligand: Diagrammatic (EGF, TGF-β presentation , etc,) to EGF of receptor the effect leads to of aggregation EGF stimulation of multiple on EGFR receptors (as a (not shown in diagram). Dimerization of an EGF receptor with another ErbB family member representative example of the EGF receptor family) and the resultant downstream signally leads to phosphorylation of tyrosine residues located in the intracellular domain. This allows forchanges (adapted from docking of proteins, leading(Dutta and Maity 2007) to activation of the). Ras/Raf/Erk, PI3K/Akt, PLC-γ, and JAK/ Stat pathways.
37
Cancer Lett. Author manuscript; available in PMC 2008 September 8. 1.2.5.3.2 VEGF family
The vascular endothelial growth factor family of receptors (VEGFRs) consists of a ligand- binding region with seven immunoglobulin-like domains, a trans-membrane domain, and a tyrosine kinase domain with a long kinase insert (also known as a type-V RTK) (Shibuya
2013). VEGFRs consist of VEGFR1, VEGFR2 and VEGFR3. They also include neuropilin one and two (NRP1 and NRP2). Tyrosine kinase with immunoglobulin-like and EGF-like domains one and two (TIE-1 and TIE-2) are closely associated with this set of RTKs (Saharinen, Eklund et al. 2011). Structurally, VEGFRs are distantly related to the members of the M-colony stimulating factor receptor/platelet-derived growth factor receptor (CSFR)/(PDGFR) family
(Shibuya 2013). VEGFR2, and to a less extent VEGFR1, are the major signal transducers for angiogenesis, whereas VEGFR3 is the major signal for lymphangiogenesis (Bergers and
Hanahan 2008, Saharinen, Eklund et al. 2011). The ligands for the VEGF receptor family include: VEGFA, VEGFB, VEGFC, VEGFD, VEGFE, as well as placental growth factor (PlGF) for the VEGFRs. Class three semaphorin (SEMA3) are ligands for NRPs (Sakurai, Doci et al. 2012,
Zachary 2014), whilst angiopoietins (Ang1, Ang2, Ang3 and Ang4) are the ligand for TIE receptors (Augustin, Koh et al. 2009, Saharinen, Eklund et al. 2011) (Figure 1.3).
38
Review Trends in Molecular Medicine July 2011, Vol. 17, No. 7
VEGFC PIGF
(a) VEGF VEGFD VEGFB Ang1 Ang2
NRP2 NRP1 VEGFR3 VEGFR2 VEGFR1 Tie2 Tie1
Lymphangiogenesis Angiogenesis VEGF Sprouting Arteriogenesis Coronary growth Figure 1.3: Structure of the VEGFRs, NRPs and TIE, as well as their respective ligands
(adapted from (Saharinen, Eklund et al. 2011)).
1 2 3i 3ii 4
N (b) VEGFs and VEGFRs play a major role in cancer angiogenesis (c) (Li, Wang et al. 2004). C Angiogenesis is a rate-limiting step in cancer. It has been hypothesised that no solid cancer Ang-2 can spread more than 2 mm without angiogenesis (Hanahan and Weinberg 2000, Hanahan N N’ N and Weinberg 2011)N’ . In ovarian αN cancers, VEGF expression by the tumour cells results in
tumour D2 associated ascites, due to the increase in vascular permeability (Ravikumar and Ig2 D2’ Crasta 2013), and poor prognosis (Burger L2 2011). The resultant effect of VEGFs activation, L3 L1 C and of VEGFA in particular, is the activation of multiple downstream signaling effectors EGF Ig1 including ERKs, Src, PI3K/Akt, FAK, Rho family GTPases, endothelial NO and p38 MAPK with D3 D3’ the resultant effect on cell proliferation, survival, vascular permeability N and adhesion/ C’ C migration (Takahashi and Shibuya 2005, Claesson-Welsh and Welsh 2013), (Figure 1.4).Tie-2
C Ig3
TRENDS in Molecular Medicine
Figure 1. VEGFs, angiopoietins and their receptors regulate blood and lymphatic vessels. (a) VEGFC and VEGFD induce lymphangiogenesis via VEGFR3, but VEGFR3 also
contributes to angiogenesis (dashed arrow). Certain lymphangiogenic signals might also be mediated via VEGFR2 (dashed arrow) [115,161,162]. 39PlGF is involved in
arteriogenesis and pathological angiogenesis, whereas VEGFB induces the growth of the coronary vessels and their branches in rat hearts [163]. VEGFRs have an extracellular Ig
homology domain (red), a single transmembrane region and an intracellular tyrosine kinase domain (gray) interrupted by a kinase insert. VEGFs also bind heparan sulfate
proteoglycans (not illustrated) and the b1b2 domains (blue) of NRP coreceptors. NRPs bind neuronal axon guidance ligands, semaphorins, with the a1a2 domains (green),
whereas domain c (purple) together with the transmembrane domain mediates NRP dimerization. The Tie receptors (Tie1, Tie2) consist of extracellular Ig homology domains
(red), EGF-like repeats (gray), fibronectin type III domains (blue), and an intracellular tyrosine kinase domain interrupted by a kinase insert (gray).
1) Lymphatic vessels in the ears of adult mice stained for LYVE-1. 2) Isolectin-B4 staining of the developing mouse retinal vasculature at P5. A VEGF gradient directs the sprouting
of endothelial tip cells (arrow) at the leading edges of the growing vessels. 3) i: arteries of a wild type rat heart stained with anti-SMC actin; ii: rat coronary vessels induced by a
heart-specific VEGFB transgene. 4) Blood vessels in the mouse ear stained for PECAM-1. The mechanism of VEGFR2 and Tie2 ligand binding (b–c). (b) A cartoon representation
of the crystal structure of the VEGFC homodimer (orange and green) and the two VEGFR2-D23 chains (light and dark blue) in the 2:2 complex (PDB code 2X1X; [164]). Loops L1–
L3 and the extended N-terminal helix (aN) of VEGFC bind to the VEGFR2 domains 2 (D2) and 3 (D3). (c) A cartoon representation of the crystal structure of the Ang2 (orange) in
complex with the extracellular region of Tie2 (light blue; PDB code 2GY7; [165]). The three Ig-like (Ig1–Ig3) and EGF-like domains are labeled. Ang2 binds at the tip of the
arrowhead-shaped Tie2, interacting with only the Ig2 domain. Disulfide bonds (yellow) and amino (N) and carboxy (C) termini are indicated.
on targeting the various cells in the microenvironment of Angiogenesis plays a rate-limiting role in tumor growth
the tumor stroma. and without it tumors display a ‘dormant phenotype’ where
Targeting ECs lining the tumor blood vessels has the rate of cell proliferation reaches equilibrium with the
emerged as a significant strategy to block tumor growth. rate of cell death [6]. Both tumor and stromal cells can
348 230 H. Takahashi and M. Shibuya
Figure 2 SchematicFigure 1.4: diagram illustratingSimple schematic presentation of the result of VEGFA stimul the receptor-binding specificity of VEGF family membersation on VEGFR2 and and the VEGFR-2 signalling pathways The VEGF family of ligandsits downstream effects (adapted from and their receptor-binding patterns are shown at(Takahashi and Shibuya 2005) the top. Downstream VEGFR signalling pathways). focusing on VEGFR-2 are shown at the bottom. Tyr1175 (Y1175) and Tyr1214 (Y1214) are the two major autophosphorylation sites in VEGFR-2. PLC-γ binds to Y1175, leading to the phosphorylation and activation of this protein. Y1214 appears to be required to trigger the sequential activation of Cdc42 and p38 MAPK. Many proteins are activated by VEGFR-2 through an unknown mechanism, including FAK, PI3K and Src. The activation of downstream signal transduction molecules leads to several different endothelial cell functions such as migration, vascular1.2.5.4 permeability, Ovarian cancer and integrins survival and proliferation.
Integrins are the major cellular receptors mediating adhesion to the extracellular matrix. been detected in theInteg heart,rin lung,signalling thyroid regulates gland and cell skeletal proliferation, of PlGF-3, apoptosis, plus gene a heparin-binding expression, differentiation, domain previously muscle [34]. PlGF binds VEGFR-1, but not VEGFR-2 thought to be present only in PlGF-2 [39]. [35,36]. Alternative splicing of the human PlGF gene The crystal structure of human PlGF-1 has shown actin reorganisation, and cell migration (Monniaux, Huet-Calderwood et al. 2006). OSE generates four isoforms which differ in size and binding that this protein is structurally similar to VEGF-A [40]. properties: PlGF-1 (PlGF ), PlGF-2 (PlGF ), PlGF-3 Furthermore, despite this moderate sequence conser- express 131 a large panel of integrins 152 including α2, α3, α6, αv, β1, β3, β4, β5, and αvβ3 (PlGF203)andPlGF-4(PlGF224)[37–39](Figure1). vation, PlGF and VEGF-A bind to the same binding PlGF-1 is the shortest isoform and a non-heparin binding interface of VEGFR-1 in a very similar fashion [41]. protein. PlGF-2(Maubant, is able to Cruet bind-Hennequart heparin and et the al. co- 2005). However, In ovarian recent cancer studies integrin have signalling reported activates that, unlike in receptors NRP-1 and NRP-2 due to the insertion of a VEGF-A, N-glycosylation in PlGF plays an important highly basic 21-aminoproteases acid of sequence the matrix encoded metallopr by exonoteinases role (MMP) in VEGFR-1 family. binding Proteases [42]. co localise with VI near the C-terminus [37] (Figure 2). PlGF-3, which Carmeliet et al. [43] have shown that a deficiency in / contains an insertionintegrins, of 216 and nucleotides regulate coding the interface for a 72- between PlGF integrins (PlGF− and −) does the intracellular not affect embryonic cytoskeleton angiogenesis amino acid sequence between exons 4 and 5 of the PlGF in mice. However, loss of PlGF impairs angio- gene but lacks theresulting in increased intracellular matrix degradation, tumour cell invasion and metastasis coding sequence of exon 6, is unable to genesis, plasma extravasation and collateral growth du- bind heparin [38]. PlGF-4 consists of the same sequence ring ischaemia, inflammation, wound healing and cancer, (Monniaux, Huet-Calderwood et al. 2006).
C ⃝ 2005 The Biochemical Society
40 1.2.5.5 Abnormal cellular pathways in ovarian cancer
As mentioned earlier, multiple pathways are altered in EOC. In high-grade ovarian cancers, a number of signaling abnormalities were observed, such as the retinoblastoma (RB1) pathway, responsible for cell cycle progression, deregulated in 67% of high-grade tumours.
Notch signaling is deregulated in 23% of cases, and PI3K and/or Ras signalling are altered in
45%. FOXM1 is altered in 87% of high-grade serous cancer, probably due to high prevalence of TP53 mutations in this group of cancers (Bast 2011, Cancer Genome Atlas Research
2011).
1.2.5.6 Ovarian cancer and tumour suppressor genes
Many tumour suppressor genes (TSGs) have been implicated in EOC (Table 1.3). In general, a
TSG is inactivated when both of its alleles are affected, with the exception to this being TP53 and PTEN. The cancer genome atlas (TCGA) genomic analysis study of 316 high grade serious ovarian cancers showed that TP53 mutations are prevalent in 96% of these samples (Cancer
Genome Atlas Research 2011).
Other possible defects in TSGs could be due to genetic imprinting when one inherits a silenced allele such as with aplasia Ras homolog member (ARHI), pleiomorphic adenoma gene-like 1 (PLAGL1) and paternally expressed 3 (PEG3). One other mechanism includes epigenetic methylation, such as with RASSF1 and OPCML (Bast, Hennessy et al. 2009).
41
Table 1.3: TSGs implicated in ovarian cancer (adapted from (Bast, Hennessy et al. 2009).
1.2.6 Staging of ovarian cancer
Staging for ovarian cancer (and for that reason that of fallopian tube and primary peritoneal cancers) is done according to the International Federation of Gynaecology and Obstetrics
(FIGO) recently revised paper (Prat and Oncology 2014). The aim of staging patients is to standardise the terminology used, allow comparison between different centres and for prognostic purposes. Table 1.4 shows the up to date staging classification.
42 402 Clinical Commentary
Table 1 2014 FIGO ovarian, fallopian tube, and peritoneal cancer staging system and corresponding TNM.
I Tumor confined to ovaries or fallopian tube(s) T1
IA Tumor limited to one ovary (capsule intact) or fallopian tube, No tumor on ovarian or fallopian tube surface No malignant cells in the ascites or peritoneal T1a washings IB Tumor limited to both ovaries (capsules intact) or fallopian tubes T1b No tumor on ovarian or fallopian tube surface No malignant cells in the ascites or peritoneal washings IC Tumor limited to one or both ovaries or fallopian tubes, with any of the following: T1c IC1 Surgical spill intraoperatively IC2 Capsule ruptured before surgery or tumor on ovarian or fallopian tube surface IC3 Malignant cells present in the ascites or peritoneal washings
II Tumor involves one or both ovaries or fallopian tubes with pelvic extension (below pelvic brim) or peritoneal cancer (Tp) T2 IIA Extension and/or implants on the uterus and/or fallopian tubes/and/or ovaries T2a IIB Extension to other pelvic intraperitoneal tissues T2b
III Tumor involves one or both ovaries, or fallopian tubes, or primary peritoneal cancer, with cytologically or histologically confirmed spread to the peritoneum outside the pelvis and/or metastasis to the retroperitoneal lymph nodes T3 IIIA Metastasis to the retroperitoneal lymph nodes with or without microscopic peritoneal involvement beyond the pelvis T1,T2,T3aN1 IIIA1 Positive retroperitoneal lymph nodes only (cytologically or histologically proven) IIIA1(i) Metastasis ≤ 10 mm in greatest dimension (note this is tumor dimension and not lymph node dimension) T3a/T3aN1 IIIA1(ii) Metastasis N 10 mm in greatest dimension IIIA 2 Microscopic extrapelvic (above the pelvic brim) peritoneal involvement with or without positive retroperitoneal lymph nodes T3a/T3aN1 IIIB Macroscopic peritoneal metastases beyond the pelvic brim ≤ 2 cm in greatest dimension, with or without metastasis to the retroperitoneal lymph nodes T3b/T3bN1 III C Macroscopic peritoneal metastases beyond the pelvic brim N 2 cm in greatest dimension, with or without metastases to the retroperitoneal nodes (Note 1) T3c/T3cN1
IV Distant metastasis excluding peritoneal metastases Stage IV A: Pleural effusion with positive cytology Any T, Any N, Stage IV B: Metastases to extra-abdominal organs (including inguinal lymph nodes and lymph nodes outside of abdominal cavity) (Note 2) M1 (Note 1: includes extension of tumor to capsule of liver and spleen without parenchymal involvement of either organ) T3c/T3cN1) (Note 2: Parenchymal metastases are Stage IV B)
Notes: Table 1. Includes extension 1.4: ofThe tumor to new capsule of liverFIGO and spleen staging without parenchymal of ovarian involvement of canceither organ.er with its equivalent (tumour, node, 2. Parenchymal metastases are Stage IV B. metastasis) TNM classification (from (Mutch and Prat 2014)). chose a staging classification system that takes into account the most relevant prognostic parameters shared by all tumor types. However, it Table 2 was agreed on by all that histologic type should be designated at the 43 Carcinoma of the ovary–fallopian tube–peritoneum — stage grouping. time of diagnosis and staging. The five agreed upon epithelial histologic
FIGO UICC types, as well as much less common malignant germ cell and potentially malignant sex cord-stromal tumors, are listed below. (Designate primary: Tov, Tft, Tp or Tx) Stage T N M Histologic types: IA T1a N0 M0 IB T1b N0 M0 Carcinomas (by order of frequency) IC T1c N0 M0 High-grade serous carcinoma (HGSC) IIA T2a N0 M0 Endometrioid carcinoma (EC) IIB T2b N0 M0 IIIA T3a N0 M0 Clear-cell carcinoma (CCC) T3a N1 M0 Mucinous carcinoma (MC) IIIB T3b N0 M0 Low-grade serous carcinoma (LGSC). T3b N1 M0 IIIC T3c N0–1M0Note: Transitional cell carcinoma is currently interpreted as a mor- T3c N1 M0 phologic variant of HGSC; malignant Brenner tumor is considered a IV Any T Any N M1 Regional nodes (N) low-grade carcinoma which is extremely rare. Nx Regional lymph nodes cannot be assessed Malignant germ cell tumors (dysgerminomas, yolk sac tumors, and N0 No regional lymph node metastasis immature teratomas) Potentially malignant sex cord-stromal tumors N1 Regional lymph node metastasis (mainly rare cases of granulosa cell tumors and Sertoli–Leydig cell tu- Distant metastasis (M) Mx Distant metastasis cannot be assessed mors with heterologous sarcomatous elements). M0 No distant metastasis The issues discussed and concluded by the FIGO committee will be M1 Distant metastasis (excluding peritoneal metastasis) taken one stage at a time, controversial issues raised, and the available Notes: data cited as appropriate. 1. The primary site – i.e. ovary, fallopian tube or peritoneum – should be designated where Staging should be considered fluid. As more prognostic features be- possible. In some cases, it may not be possible to clearly delineate the primary site, and come available these should be used to further predict outcomes and these should be listed as ‘undesignated’. determine treatment. The world is getting smaller in the sense that 2. The histologic type of should be recorded. 3. The staging includes a revision of the stage III patients and allotment to stage IIIA1 is staging systems must be applicable universally and including resource based on spread to the retroperitoneal lymph nodes without intraperitoneal dissemina- rich as well as resource poor regions. Toward this end, three members tion, because an analysis of these patients indicates that their survival is significantly bet- of FIGO will now be on the AJCC staging committee and there is repre- ter than those who have intraperitoneal dissemination. sentation of the UICC on the AJCC. The International Gynecologic Cancer 4. Involvement of retroperitoneal lymph nodes must be proven cytologically or Society and the Society of Gynecologic Oncology now have nonvoting histologically. 5. Extension of tumor from omentum to spleen or liver (Stage III C) should be differentiat- representation on the FIGO committee as well. We need to continue to ed from isolated parenchymal splenic or liver metastases (Stage IVB). gain consensus internationally by having cross representation on our 1.2.7 Screening for ovarian cancer
Many trials have been conducted to test for an effective screening method for ovarian cancer, which on the face of it, is very much needed, as it is hoped that by diagnosing the disease earlier (than current stage III when the majority are diagnosed) patients therefore will have a better prognosis. So far, all trials to date have failed to show that any screening in the general population is associated with an improvement in survival, regardless of the method(s) used.
It is important to note that none of the screening tests used fit the world health organisation definition and principles of screening test (J. M. G. WILSON 1968).
Most of the screening methods to date have focused on the use of Ca125 and/or trans- vaginal ultrasonography (TVS). Ca125 is a glycoprotein that is expressed by the peritoneal mesothelia, as well as the endometrial surface and the fallopian tube epithelia (Bast, Feeney et al. 1981, Kabawat, Bast et al. 1983) and it is used mainly as a marker for follow-up post treatment and to check for disease progression (Bast, Xu et al. 1998, Menon and Jacobs
2001). Whilst 90% of EOC will have raised Ca125 at diagnosis (Bast, Klug et al. 1983), only
50% of patients will have a raised Ca125 in stage I disease (Nustad, Bast et al. 1996). Its worthwhile noting that in EOC the rise in Ca125 is persistent, and continues to do so, whilst in benign disease, Ca125 may be elevated, but tends to be stable (Bast 2011).
TVS on its own will result in decreased test specificity, as well as increase risk of surgical intervention (Partridge, Kreimer et al. 2009). In one study (UKCTOCS), around 35 operations were needed for each case of EOC diagnosed using TVS to start with, followed by Ca125 for those patients with suspicious masses (Menon, Gentry-Maharaj et al. 2009). However, that
44 same study showed that when using serial Ca125, and only referring those patients with raised levels for TVS, then this would decrease the surgical intervention to only 2.9 operations per each case of EOC (Menon, Gentry-Maharaj et al. 2009).
Currently, the recommendation from the National Comprehensive Cancer Network (NCCN) in the United States is to offer genetic testing for those patients with high risk of ovarian cancer e.g. BRCA1 or BRCA2 mutation carriers, or those with strong family history of the disease (or of breast cancer) to look for any mutations present. Those with breast cancer risk will be offered screening as per breast cancer screening guideline, whilst those with risk of ovarian cancer should be offered “research and individualised recommendations”, including prophylactic bilateral salpingo-oopherectomy (BSO) for those found to have the genetic mutations (when they completed their family), or screening with concurrent TVS and Ca125 every six months, starting at 35 years of age for those who decline BSO (NCCN
2011).
It is important to note that it was in high risk patients undergoing prophylactic BSO that high-grade serous tubal intraepithelial carcinoma (STIC) was observed in the fallopian tube and, particularly, in the fimbria, but not in the ovary and changed our prospective view on
EOC (Piek, van Diest et al. 2001).
1.2.8 Diagnosis of ovarian cancer
As mentioned earlier, most patients with EOC present with advanced disease, often reporting non-specific symptoms and appearing well. Symptoms may include abdominal pain, bloating, change in bowel habit, early satiety, urinary symptoms and pelvic symptoms.
45 Many are initially misdiagnosed with irritable bowel syndrome and/or referred initially for general surgical review or general medical review for inflammatory bowel disease. The national institute for health and clinical excellence (NICE) in the UK has issued guidance in relation to recognition and early management of ovarian cancer (NICE 2011).
To confirm the diagnosis, one will require histopathological tissue analysis. Most often this will be obtained at the time of surgery when appropriate staging procedures can also be performed. However, in secondary care, clinical assessment, imaging (with CT scanning and/or ultrasound) and Ca125 measurement are the key elements for diagnosis and formation of an appropriate management plan which usually includes a referral to a tertiary gynaecological oncological centre. To identify which cases with an isolated pelvic mass would benefit most from referral to a cancer centre, a risk of malignancy index (RMI) scoring system is used. This RMI is calculated using menopausal status, the presence/absence of suspicious ultrasound features and the Ca125 result. When the RMI crosses a designated threshold (250 IU/ml or above) then the risk of malignancy is greater and consideration should be given to referral of the patient to a cancer center (NICE 2011).
It is important to note that the use of TVS is associated with good diagnostic sensitivity
(Testa, Kaijser et al. 2014); however, it is operator dependent (Fischerova and Burgetova
2014). Computed tomography (CT) of the chest, abdomen and pelvis is the standard tool to establish the extent of ovarian cancer prior to surgery (NICE 2011). Whilst there is some evidence that the use of integrated positron emission tomography (PET) with CT, or magnetic resonance imaging (MRI) is the optimal tool for cases of suspected recurrence
(Fischerova and Burgetova 2014, Lai, Lin et al. 2014).
46 1.2.9 Treatment of ovarian cancer
The classical treatment of ovarian cancer is a combination of surgical debulking followed by chemotherapy with platinum with or without taxane based chemotherapeutic agents, usually with good response to treatment. However, relapse and disease recurrence as well as drug resistance and rapid progression are common features with few therapeutic options
(Faratian, Um et al. 2011). The potential for targeted therapy based on better understanding of the molecular basis of the disease offers new and fascinating insights for the future.
1.2.9.1 Surgery
The first line of treatment of ovarian cancer is cytoreductive surgery. The aim is to remove all macroscopic disease (complete cytoreduction). Any residual disease less than one cm is classed as optimal cytoreduction (Schorge, McCann et al. 2010).
There is a good evidence to support correlation between complete cytoreduction and overall survival (OS) as well as progression free survival (PFS) (Elattar, Bryant et al. 2011,
Fader, Java et al. 2013). A Cochrane database multivariate analysis of 11 retrospective studies examined the role of cytoreductive surgery in advanced (stage III – IV) EOC and concluded that those patients where complete cytoreduction was achieved (no macroscopic surgery) had significantly OS and PFS compared with those who did not. Equally so, similar effects were noted, albeit attenuated, with optimal (less than one cm residual disease) compared with suboptimal surgery (Elattar, Bryant et al. 2011).
An ancillary study from the gynecological oncology group (GOG 182) that examined the role of complete cytoreduction in grade I advanced (stage III – IV) ovarian cancer showed
47 significant improvement in PFS and OS (33.2 and 96.9 months respectively) when compared with those patients with optimal and suboptimal disease. Complete cytoreduction was achieved in almost 25% of the cohort and optimal was achieved in 51% (Fader, Java et al.
2013).
1.2.9.2 Chemotherapy
Ovarian cancer is a chemo-sensitive solid tumour. The aim of giving chemotherapy, especially after complete cytoreductive surgery, is to achieve cure by eradicating all microscopic disease.
1.2.9.2.1 Chemotherapy for newly diagnosed disease
Adjuvant chemotherapy is given following surgery for ovarian cancer. It is offered to almost all patients with ovarian cancer except those with stage I A, well-differentiated low-grade tumours, who have a high five-year survival and low recurrence risk.
The international collaborative ovarian neoplasm (ICON) 1 study showed significant evidence for giving chemotherapy in patients with early stage disease with no macroscopic residual cancer following surgery. The extent of surgical staging was not specified in the protocol. Patients were randomised to observation or six cycles of chemotherapy, mostly single agent platinum. The results showed significant PFS and OS in patients who had adjuvant treatment immediately after surgery (Colombo, Guthrie et al. 2003).
In advanced disease, the use of platinum-based chemotherapy as a first line strategy improves prognosis with 70 – 80% response rate (Ledermann and Kristeleit 2010). Overall,
48 the combination of carboplatin (AUC 5 to 6) and paclitaxel (175 mg/m2) every three weeks for six cycles has become a standard of care in advanced ovarian cancer (NICE 2003). Single agent carboplatin is also used for patients with a poor performance status, and for those who do want to avoid the additional toxicity of combination chemotherapy, e.g. alopecia and neuropathy. When compared with cisplatin, doxorubicin and cyclophosphamide (CAP), carboplatin showed similar efficacy with less side effects (ICON 2, (1998). The addition of a taxane agent, such as paclitaxel, may provide a benefit in terms of response but at the expense of toxicity. Some studies showed a significant survival advantage for cisplatin and paclitaxel when compared with cisplatin and cyclophosphamide (McGuire, Hoskins et al.
1996, Piccart, Bertelsen et al. 2000), while two other studies failed to show any significant benefit (Muggia, Braly et al. 2000, International Collaborative Ovarian Neoplasm 2002). GOG
158 study showed that carboplatin was equivalent to cisplatin when used in combination with paclitaxel (Ozols, Bundy et al. 2003). Furthermore, the SCOTROC-1 trial demonstrated that docetaxel had equivalent activity to paclitaxel when combined with carboplatin as a first line treatment in EOC post cytoreductive surgery. Docetaxel was associated with less neuropathy, but with more neutropenia (Vasey, Jayson et al. 2004).
1.2.9.2.2 Chemotherapy for recurrent disease
The majority of ovarian cancer patients experience disease recurrence. It is estimated that between 50 to 75% will relapse within two years (NICE 2003). Patients are classified based on their response to platinum based chemotherapy and how soon the disease recurs since the primary treatment is finished (NICE 2005). If progression-free survival is more than six months, then, depending on the duration of PFS, retreating with carboplatin-based chemotherapy provides a response rate of 30 - 50% (Bast 2011). It was postulated that
49 recurrence occurs partly because some cells escape destruction and/ or are resistant to treatment from the start, hence with time they will repopulate and tumour expansion occurs (Davis and Tannock 2000, Davis and Tannock 2002). Single agents have then been administered sequentially, including pegylated liposomal doxorubicin (PLD), topotecan, gemcitabine, etoposide, and hexamethylmelamine, producing response rates of 15%–30%
(Bast 2011). Studies have shown that combinations of carboplatin and gemcitabine have improved progression-free survival, but not overall survival, over carboplatin alone or PLD alone (Pfisterer, Plante et al. 2006).
The GOG 182 study that ran a five-arm trial of different doublets and triplets of chemotherapeutic agents showed that the addition of other drugs to carboplatin and paclitaxel did not improve progression-free or overall survival (Bookman 2009).
Current practice in the UK based on NICE guidelines (NICE 2005, NICE 2011, NICE 2013, NICE
2013) advocate second line treatment with PLD, paclitaxel or topotecan. The latter is currently recommended for the treatment of women with platinum refractory or platinum- resistant ovarian cancer when PDL and paclitaxel are considered inappropriate. Paclitaxel in combination with platinum-based therapy is now recommended as a treatment option for women whose disease is partially sensitive to platinum, i.e. recurs within six months of finishing treatment. Part of these recommendations includes women who have received paclitaxel as part of their first-line treatment. Paclitaxel can also be used as a single agent in platinum resistant and refractory disease. Finally, is recommended as a treatment option for women whose disease does not respond to, and those women whose disease relapses within 12 months from, initial platinum-based therapy, or those with platinum allergy.
50 1.2.9.2.3 Adjuvant versus neoadjuvant chemotherapy
Neoadjuvant chemotherapy is the use of chemotherapy prior to surgery when the diagnosis has been established by cytology of ascitic fluid or histology of a tissue biopsy. Usually there will be further cycles of chemotherapy following surgery.
A meta-analysis of 22 cohorts totaling 835 patients found that neoadjuvant chemotherapy was inferior to upfront surgery in term of overall survival (Bristow and Chi 2006).
There have been two trials (EORTC 55971 and CHORUS) that investigated the role of neoadjuvant chemotherapy in the management of ovarian cancer. Both were randomised control trials (RCTs) with one arm including patients undergoing upfront surgery followed by six cycles of chemotherapy, or to chemotherapy for three cycles followed by surgery then followed by three more cycles of chemotherapy in the other arm (Vergote, Trope et al.
2010, Sean Kehoe 2013).
Both studies (albeit CHORUS only reported in abstract form) have revealed that neoadjuvant chemotherapy results in the same survival outcome, compared with primary surgery and chemotherapy. However, this relates to a specific patient population. Another RCT (Rose,
Nerenstone et al. 2004) that compared secondary-debulking surgery versus no surgery
(after suboptimal primary surgery) did not report any difference in terms of PFS or OS between the two cohorts.
1.2.9.2.4 Intravenous versus intraperitoneal chemotherapy
There is a growing evidence to provide chemotherapy through intraperitoneal (IP) rather that the standard intravenous (IV) route. Three randomised control studies have shown a benefit of IP chemotherapy in stage III optimally debulked ovarian cancer (Alberts, Liu et al.
51 1996, Markman, Bundy et al. 2001, Armstrong, Bundy et al. 2006, Landrum, Java et al.
2013). GOG 172 (Armstrong, Bundy et al. 2006) compared IP with IV cisplatin and paclitaxel and found 16 months increase in median survival. However, IP chemotherapy was associated with more toxicity than IV chemotherapy and must be given in specialist units.
The study by Landrum et al looked at the long term follow up of 428 patients with advanced
EOC who were treated with IP chemotherapy following complete /optimal cytoreduction.
For those patients who had complete cytoreduction, the median PFS was 43 months and the median OS was 110 months.
Despite the evidence and the increasing interest in IP chemotherapy, it faces hurdles in terms of lack of centers able to administer this treatment and the fact that NICE has not fully endorsed the use of intraperitoneal chemotherapy in ovarian cancer outside of clinical trials (NICE 2013).
The GOG 252 study is recruiting patients for IP chemotherapy (including targeted therapy) with any size of residual disease following surgery, the results of which are eagerly awaited.
1.2.9.2.5 Maintenance chemotherapy
Maintenance chemotherapy following a complete response to initial chemotherapy has been investigated. The only trial reported so far showed that maintenance with paclitaxel for 12 months was superior in terms of PFS but with increased incidence of peripheral neuropathy (Markman, Liu et al. 2003). However, there are currently new trials incorporating bevacizumab as maintenance treatment, the results of which are awaited and will further our knowledge regarding maintenance treatment (Gonzalez-Martin, Sanchez-
Lorenzo et al. 2014).
52 1.2.9.3 Targeted therapy with biological modulating agents
Bevacizumab is a humanized monoclonal antibody targeting the VEGFA ligand, preventing its attachment to its main receptor (VEGFR2), and inhibiting tumour angiogenesis (Hurwitz,
Fehrenbacher et al. 2004).
In the field of EOC, two RCTs (ICON7 and GOG218) investigated bevacizumab in combination with carboplatin and paclitaxel as a first line treatment. In the 3-arm GOG218 trial (Burger,
Brady et al. 2011), no significant difference in terms of PFS was noted between the control and concurrent bevacizumab arms, but a statistically significant benefit was seen between the control and concurrent/maintenance bevacizumab arms of four months. In ICON7 trial
(Perren, Swart et al. 2011), two groups were randomised to standard chemotherapy versus standard chemotherapy plus bevacizumab concurrent and maintenance treatment. PFS of around two months was observed. However, the PFS was almost 8 months in those patients with sub-optimal surgery.
53 1.3 Breast Cancer
Breast cancer is derived from the epithelial cells that line the terminal duct of the mammary gland lobular unit. Cancer cells that remain within the basement membrane are classed as carcinoma in-situ or non-invasive, whilst invasive breast carcinoma refers to dissemination of the cancer cells outside of the basement membrane.
1.3.1 Incidence and epidemiology
Breast cancer is the most common cancer in women overall (Office for National Statistics
2011). 40251 patients were diagnosed in the UK in 2010, which represents a 1.8% increase from 2009. The 5-year age-standardised relative survival (percentage) for breast cancer in women diagnosed during 2005-2009 and followed up to 2010 in England was 85% (Office for National Statistics 2011, Statistics 2014). The median age for patients in breast cancer is
60 years, and the average lifetime risk for women is 12% (Dixon 2003).
1.3.2 Risk factors for breast cancer
Risk factors predisposing to increased risk of breast cancer include age (more common in the elderly), living in a developed country, race (more common in Caucasian and African
Americans), early menarche, late menopause, delayed first pregnancy and child birth till after 40 years of age, previous benign disease including atypical hyperplasia, history of cancer in the other breast, high socioeconomic status, sedentary lifestyle, increased BMI
(more than 35), diet high in saturated fat, excessive alcohol consumption, exposure to
54 ionising radiation, taking exogenous hormones (COC pills, HRT) and most importantly familial history of breast cancer, in particular BRCA1 and BRCA2 mutations. It is interesting to note that smoking has no role in the aetiology of breast cancer, unless if the patient was smoking before first live birth (Dixon 2003, Warner 2011).
1.3.3 Prevention of breast cancer
The US preventive services task force (USPSTF) as well as NICE have both issued recommendation for the use of tamoxifen or raloxifene as a chemoprevention agent against breast cancer in high-risk population. Tamoxifen decreases the risk of invasive breast cancer by seven cases per 1000 patients during five years. Obviously the risk stratification means that the medication will be of use only in ER positive patients (NICE 2013, USPSTF 2013).
1.3.4 Classification of breast cancer
Historically, invasive breast cancer is classified into ‘special’ and ‘non-special’ types. The latter is also known as ‘not otherwise specified’. Special types include: tubular, cribriform, medullary, fucoid, papillary and classic lobular. Non-invasive breast carcinoma in-situ is further classified into ductal carcinoma in-situ (DCIS) and lobular carcinoma in-situ (Dixon
2003).
Breast cancer is also classified according to the expression of estrogen receptor (ER), progesterone receptor (PR) and HER2 expression (Foulkes, Smith et al. 2010). Based on DNA microarray studies four main molecular classes of breast cancer have been proposed: basal-
55 like breast cancers, which mostly correspond to ER-negative, PR–negative, and absence of
HER2 overexpression (hence, “triple-negative” tumors); luminal-A cancers, which are mostly
ER-positive and histologically low-grade; luminal-B cancers, which are also mostly ER- positive but may express low levels of hormone receptors and are often high-grade; and
HER2-positive cancers, which show amplification and high expression of the HER2. These subgroups correspond reasonably well to clinical characterisation on the basis of ER and
HER2 status, as well as proliferation markers or histologic grade (Perou, Sorlie et al. 2000,
Brenton, Carey et al. 2005, Pusztai 2009, Sotiriou and Pusztai 2009).
In breast cancer, HER2 overexpression is reported in 15-20% of cases (Di Cosimo and
Baselga 2010). In comparison, EGFR overexpression can be as high as 13 – 52% in triple negative breast cancer (Carey 2010).
1.3.5 Diagnosis of breast cancer
More and more commonly suspected cases of breast cancer in the UK are managed through one-stop clinics. The patient will usually undergo ‘triple-assessment’, a combination of clinical examination, radiology (in the form of either ultrasound scanning or low dose X-ray mammogram), then either cytology in the remits of fine needle aspiration (FNA), or histopathology after a core biopsy. Cytology has a false positive rate of 2:1000. Only two thirds of cancers in women under 50 years of age are suspected by clinical examination and imaging (Dixon 2003). There is evidence that breast MRI is superior and more sensitive, albeit less specific, for the detection of breast cancer in high-risk populations (Shah, Rosso et al. 2014).
56 1.3.6 Staging of breast cancer
Breast cancer is staged according to the TNM classification, with T (Tumour) classified between Tis (in situ) to T4 when the tumour has spread to the chest wall or to the skin. N
(lymph Node) is classified from N0, meaning no lymph node metastasis, to N3, meaning involvement of the regional lymph nodes (axillary, above and below the clavicle). M
(Metastasis), M0 refers to no metastasis and M1 to the presence of metastasis (UK
2014)(CRUK).
1.3.7 Treatment of breast cancer
Treatment of early breast cancer (cancer that has not been detected beyond the breast or lymph nodes) is surgery in the form of a lumpectomy with 2 mm clearance margin; it is the standard of choice for DCIS along with minimal axillary sampling with the use of sentinel lymph node biopsy. Further axillary surgery can be offered for those who are found to be node positive, however adjuvant chemotherapy with docetaxel is the standard following surgery with early invasive disease. Mastectomy is reserved for patients with advanced local disease (i.e. lump larger that 5 cm in size). Endocrine therapy, in the form of an aromatase inhibitor, is offered to postmenopausal patients with ER positive disease. Anti-HER2 therapy with Trastuzumab is offered to patients with HER2 positive cancers. Patients should also be offered radiotherapy after surgery (when the resection margins are clear) and after mastectomy or those with positive lymph nodes and at risk of recurrence (NICE 2014).
Neoadjuvant chemotherapy has been traditionally reserved for locally advanced breast cancer disease, but recently there is evidence that it might be a useful option in the
57 management of operable breast cancer, especially those with EGFR-positive, triple negative cancers (Kummel, Holtschmidt et al. 2014).
Single versus double agent blockade for HER2 positive metastatic breast cancer has shown that adding pertuzumab to trastuzumab and docetaxel in recurrent, unresectable or metastatic HER2 positive breast cancer is associated with better response to treatment, and improved PFS and OS (Baselga, Bradbury et al. 2012). Lapatinib a dual HER2/EGFR molecule inhibitor has shown efficacy and improved PFS when used with capecitabine for the treatment of recurrent HER2 positive breast cancer not responding to chemotherapy or trastuzumab (Geyer, Forster et al. 2006).
58 1.4 The IgLON family
The IgLON family consists of opioid binding protein cell adhesion molecule-like (OPCML, also known as OBCAM), limbic system – associated membrane protein (LSAMP), neurotrimin
(HNT), and neuronal growth regulator 1 (NEGR-1).
The four IgLON family members are glycosyl phosphatidylinositol (GPI)-anchored cell adhesion molecules. They are most highly expressed in the nervous system and associate to form up to six different heterodimeric 'Diglons' that can modify cell adhesion and inhibit axon migration (Reed, Dunn et al. 2007). Each member consists of three C2-like immunoglobulin (Ig) domains that are attached via their C-terminal GPI anchor to the outer leaflet of the plasma membrane. IgLONs are highly glycosylated (Itoh, Hachisuka et al.
2008). All IgLON family members exhibit similar structures of medium sized protein (Reed,
McNamee et al. 2004). Like most GPI-anchored proteins (GPI-APs), they are enriched within the cholesterol-rich lipid rafts (Simons and Toomre 2000) and are thought to modulate protein organisation on the cell surface and downstream signalling (Mishra and Joshi 2007).
IgLONS, being glycoproteins belonging to the Ig superfamily, have roles in cell–cell recognition and binding and modulation of axon migration (Reed, Dunn et al. 2007) binding opioids in the presence of acidic lipids.
In EOC, OPCML, LSAMP and NEGR1 expression was reduced while HNT expression was increased more than seven times in cancer cells compared to the controls (Ntougkos, Rush et al. 2005).
OPCML was mapped to human chromosome 11 by hybridising probe with a somatic cell hybrid panel (Shark and Lee 1995) and was found to be highly expressed in the cortical plate
59 and hippocampus (Struyk, Canoll et al. 1995). Two OPCML splice variants have been identified in humans, termed alpha1 and alpha2 (Reed, Dunn et al. 2007). As with all other
IgLONs, OPCML link to the cell membrane via the GPI anchor is consistent with a role in cell recognition and adhesion, as well as in cellular signalling (Wick, Fan et al. 1996). The expression of OPCML was also prominent at filopodia and cellular processes of type-1 and -2 astrocytes, more so at the growth phase in type-1 than the stationary one. Overexpression of OPCML increased the cell size of type-1 astrocytes, which suggests that OPCML may be responsible for controlling the cell proliferation and growth in cortical astrocytes (Sugimoto,
Maekawa et al. 2010). Furthermore, OPCML immunoreactivity was exclusively seen in the dendrites of (vasopressin) AVP-secreting magnocellular neurons suggesting that they confer to magnocellular neurons the ability to rearrange dendritic connectivity (Miyata, Funatsu et al. 2000). Immunofluorescence microscopy showed that OPCML immune-reactivity was localised mainly at postsynaptic spines with its surface localisation dynamically regulated in response to neuronal activity through a raft-dependent pathway. Inhibition of OPCML function with the specific antibody resulted in a significant decrease in the number of synapses on dendrites compared with control (Yamada, Hashimoto et al. 2007). The structure of OPCML is seen in (Figure 1.5).
LSAMP was initially isolated from the rat limbic system and the developing central nervous system. It has a protein structure of 64-68 kDa that upon deglycosylation decreases to ≈38 kDa, and it is uniquely distributed in limbic structures, such as the cortical and sub-cortical regions of the limbic system (Pimenta, Fischer et al. 1996, Pimenta, Reinoso et al. 1996). In addition to EOC, LSAMP has also been suggested to be a TSG in sporadic and familial clear cell renal cell carcinoma (Chen, Lui et al. 2003) and in human osteosarcomas (Kresse,
60 Ohnstad et al. 2009, Yen, Chen et al. 2009, Pasic, Shlien et al. 2010). LSAMP has also been correlated with behavioral disorders (Catania, Pimenta et al. 2008, Innos, Philips et al. 2011).
NEGR1, also known as Kilon, it is less glycosylated than other IgLON family members (≈46 kDa). Kilon is mainly expressed in the brain, with high to moderate expressions observed in the cerebral cortex, hippocampus and cerebellum (Funatsu, Miyata et al. 1999). In addition to a putative suppressor role in EOC (Ntougkos, Rush et al. 2005, Kim, Hwang et al. 2014),
NEGR1 has also been suggested as TSG in neuroblastoma (Takita, Chen et al. 2011). NEGR1 has also been associated with obesity and dyslexia (Willer, Speliotes et al. 2009, Zhao,
Bradfield et al. 2009, Cheung, Tso et al. 2010, Veerappa, Saldanha et al. 2013).
Finally, Neurotrimin is a 65 KDa protein that decease to 36 KDa after deglycosylation. It has been mainly studied in the brain where it has been shown to promote neuronal outgrowth
(Struyk, Canoll et al. 1995, Gil, Zanazzi et al. 1998). It is upregulated in the developing brain as well as in adults where it promotes the formation and maintenance of synapses, respectively (Chen, Gil et al. 2001).
61
Figure 1.5: In the upper panel, OPCML gene resides at 11q25; its transcript has a length of
1038 bps that corresponds to a protein of 345 residues. In the lower panel, schematic view of OPCML protein with the GPI-anchor that tether it to the outside of the cell membrane
(Figure courtesy of Dr E Zanini, Imperial College London).
62 1.5 OPCML
1.5.1 OPCML as a tumour suppressor gene
Research originally undertaken in the Gabra laboratory (CRUK labs, Edinburgh) first identified OPCML as a TSG at chromosome 11q25, a site highlighted by frequent loss of heterozygosity (LOH). They demonstrated that the peak of LOH at D11S4085 region of
11q25 lies within the OPCML gene but not its immediate centromeric paralogue neurotrimin
(HNT). It was further demonstrated that epigenetic silencing of the remaining OPCML allele or often bi-allelic silencing occurred in 83% of epithelial ovarian cancer (EOC) (Sellar, Watt et al. 2003). These OPCML characteristics have been confirmed in other studies
(Czekierdowski, Czekierdowska et al. 2006, Yao, Li et al. 2006, Zhang, Ye et al. 2006, Chen,
Ye et al. 2007).
1.5.2 OPCML and ovarian cancer
The observation that OPCML expression was abolished in the vast majority of ovarian cancers led to the successful demonstration in our lab that OPCML was inactivated by somatic methylation in these tumours with no evidence of methylation in normal ovarian tissue. OPCML somatic methylation significantly correlated with the loss of OPCML gene expression (Sellar, Watt et al. 2003). Extensive mutation analysis of over 200 ovarian cancer cases yielded only one example of a somatic mutation (substitution of proline at residue 95 with arginine, P95R) at the distal Ig domain 1 of this gene. Transfection of OPCML into the ovarian cancer cell line SKOV-3, which is hypermethylated at the OPCML CpG island and does not express OPCML, resulted in growth inhibition, enhanced intercellular aggregation, and abolition of subcutaneous and intraperitoneal tumorigenicity in vivo (when injected into
63 nude mice) (Sellar, Watt et al. 2003). OPCML achieved this through abrogation of specific repertoire of RTKs (McKie, Vaughan et al. 2012) (see below).
1.5.3 OPCML and breast cancer
Testing for OPCML expression in multiple breast carcinoma cell lines by means of quantitative RT-PCR showed that OPCML was dramatically silenced (by CpG methylation) in
90% of breast cancer cell lines. Furthermore, it was also found that OPCML was silenced in
91% of primary breast cancer tumours, while it was only silenced in 25% (1 out 4 samples) of surgical-margin breast tissue from breast cancer patients (Cui, Ying et al. 2008).
An In silico analysis of the online database of The Cancer Genome Atlas (TCGA) of the relation between OPCML methylation and loss of expression in breast cancer cell lines and breast cancer tissues found that OPCML was methylated in 31% of cases (McKie, Vaughan et al. 2012). Furthermore, using the KMPlotter online program demonstrated that high OPCML expression for breast cancer (above median expression) was highly significant prognostic factor for relapse in 2324 breast cancer patients (McKie, Vaughan et al. 2012), (Figure 1.6 and 1.7).
1.5.4 OPCML and other cancers
In addition to EOC and breast cancer, reduced OPCML expression was identified in mixed human brain tumours (Reed, Dunn et al. 2007). Furthermore, in lung adenocarcinoma,
OPCML was found to be one of the four most frequently methylated gene loci compared with normal lung tissue (Tsou, Galler et al. 2007, Anglim, Galler et al. 2008). More recently, it was demonstrated that OPCML is frequently inactivated by somatic methylation in a wide variety of human cancers including primary nasopharyngeal, oesophageal, gastric,
64 hepatocellular, cholangiocarcinoma, colorectal and cervical cancers, as well as lymphomas
(Cui, Ying et al. 2008, Li, Liu et al. 2010, Sriraksa, Zeller et al. 2011). In one study (Selamat,
Galler et al. 2011) OPCML methylation appeared to be predominantly present in invasive adenocarcinoma of the lung but not in atypical adenomatous hyperplasia. All the above findings indicate that OPCML is a frequently inactivated multi-tissue tumour suppressor, in common human epithelial cancers.
Using the same in silico analysis of publicly available microarray datasets confirmed the clinical relevance of OPCML mentioned earlier (McKie, Vaughan et al. 2012). To confirm the frequency of OPCML methylation in human cancer, our group found that (from the available methylation data) in 44% of cancer patients (678 out of 1537 patients) representing (in addition to breast and ovarian) brain, leukaemia, colon, renal, lung, and endometrial cancers, OPCML was methylated, with site-specific methylation rates ranging from 31% for breast cancer (as mentioned earlier) to 73% for colonic adenocarcinoma (Figure 1.6, A and
B). These findings further underscore the clinical relevance of OPCML inactivation.
An interesting study recently noted that by changing from methylation-specific polymerase chain reaction (MSP) to nested MSP, one could increase the detection of the methylated
OPCML in ovarian cancer patients from 58 to 83% (Zhou, Cao et al. 2011).
65
Figure 1.6: In silico analysis of TCGA data portal analysis
(http://tcgadata.nci.nih.gov/tcga/tcgaHome2.jsp) of frequency of OPCML methylation in
1537 patients with cancer. Methylation27 analysis method was used and a methylation density cut-off of >0.5. (a) Patients tested and methylated by cancer type in numbers (b)
Tumours methylated by cancer type (percentage) (data courtesy of (McKie, Vaughan et al.
2012).
66
Figure 1.7: An in silico meta-analysis of expression microarray datasets with survival outcome by OPCML expression status using the KMPlotter for (a) PFS in ovarian cancer
(n=1090) and (b) relapse free survival in breast cancer (n=2324) patients. Note that a cut-off of highest quartile versus rest was applied for ovarian cancer and median cut-off was applied to breast cancer datasets (McKie, Vaughan et al. 2012).
67 1.5.5 OPCML and receptor tyrosine kinases
As mentioned earlier, OPCML is a non-transmembrane, external lipid leaflet GPI-anchored protein that is frequently lost in cells by somatic inactivation of the gene. Our group hypothesised that OPCML might confer its tumour suppressor function (leading to down regulation of phospho, but not of total AKT and ERK) by interacting with trans-membrane receptor tyrosine kinase (RTK) to abrogate growth factor mediated signalling. Our group first demonstrated that OPCML expression is induced by RTK activation, suggesting that OPCML may represent a delayed early negative feedback regulator of cellular signalling as highlighted from intracellular models (Amit, Wides et al. 2007) studying the RTK system. We analysed the effect of RTK growth factor stimulation on OPCML gene expression. Treatment of 4/4 ovarian cancer cell lines with EGF or FGF 1/2 resulted in rapid OPCML RNA and concomitant protein expression (data not published) suggesting that OPCML maybe a putative suppressor-type immediate-early negative feedback regulator (unpublished data).
We have demonstrated that OPCML was capable of specific interactions with key RTKs, resulting in their ubiquitination and proteasomal downregulation (e.g. HER2 & FGFR1).
Although not all members of a specific RTK family may interact, the result is attenuation of phosphorylation signals of key RTKs, and leads to significant signal attenuation downstream
(McKie, Vaughan et al. 2012). Stable transfection of OPCML in the basal unstimulated or ligand-stimulated SKOV-3 ovarian cancer cells, resulted in the profound protein down- regulation of a specific repertoire of RTKs: EPHA2; FGFR1; FGFR3; HER2; HER4 and more recently AXL (see Figure 1.8, left column, data on AXL not shown) and this RTK down- regulation spectrum is reproducible by transient transfection of a different ovarian cancer cell line, PEO1 (Figure 1.8, middle column). These same RTKs were also reciprocally up-
68 regulated when physiological OPCML was knocked down by siRNA in OSE-C2, a normal ovarian surface epithelial cell line (Davies, Steele et al. 2003) (Figure 1.8, right column). This specific RTKs inactivation by OPCML was not seen for other RTKs we have investigated so far
(Figure 1.8). The phenotypic consequences of these signalling effects were confirmed in growth assays in ligand-supplemented media where OPCML-transfectants were significantly growth-inhibited compared with vector control, SKOBS-V1.2 (data not shown).
It was also noted that RTK degradation induced by OPCML may abrogate the activity of other RTK family members that do not specifically interact with OPCML themselves and this has been seen in the case of decreased level of phosphorylated EGFR whilst the total EGFR did not change ((McKie, Vaughan et al. 2012), (Figure 1.9).
69
Figure 1.8: Western blot in two ovarian cancer cell lines and ovarian surface epithelium
(OSE), demonstrating that OPCML negatively regulates EphA2, FGFR1, FGFR3, HER2, and
HER4, but does not affect EphA10, FGFR3, EGFR, HER3, VEGFR1, and VEGFR3; left column, stably transfected SKOV-3 ovarian cancer cells containing empty vector control (-), and
SKOBS-3.5 (+) and BKS-2.1 (+++) OPCML expression. Transiently transfected PEO1 cancer cells (middle column) with vector only (-) and OPCML (+). OSE-C2 OSE cells (right column), physiologically expressing OPCML were untreated (-), transfected with non-silencing duplex
(Nsi), or OPCML-directed siRNA duplexes (si) demonstrating specific RTKs reciprocal upregulation. β-tubulin control for loading is shown in last panel (data courtesy of (McKie,
Vaughan et al. 2012).
70
Figure 1.9: (a) Western blots of total and phospho-HER2 and EGFR protein from SKOBS-V1.2
(vector control) and BKS-2.1 cells (stable OPCML expression) were subjected to a 60 minute
EGF (50 ng/ml) time course demonstrating loss of HER2 and EGFR phosphorylation (Y1248 and Y1173 respectively) in OPCML transfected cells. (b) Subjecting the same cells to a 60 minutes FGF1 (10 ng/ml) time course demonstrate loss of total and phospho (Y-766) FGFR1.
(c) Transfection of OPCML was associated with abrogation of EGF phospho-activation of
PLCƔ (Y-783), ERK 1 and 2 (T-202/ T-204) and AKT (S-473) (data courtesy of (McKie, Vaughan et al. 2012).
71 1.5.5.1 OPCML binds to HER2 but not to EGFR
As outlined above, HER2, but not EGFR, has been shown to interact with OPCML. This has been demonstrated by co-immunoprecipitation (Co-IP) experiments carried out with the OPCML over-expressing cell line BKS-2.1 by both direct and reciprocal IP
(McKie, Vaughan et al. 2012), (Figure 1.10).
The recombinant GST-tagged OPCML, generated in our lab, was employed in the pull- down experiments conducted in SKOV-3, an ovarian cancer cell line strongly positive for HER2 with no detectable OPCML, and in MDA-231, a breast cancer cell line positive for EGFR but negative for both OPCML and HER2. HER2 was pulled-down from the
SKOV-3 cell lysate by the GST-OPCML but not EGFR (Figure 1.11.A).
As HER2 is naturally forming dimers with EGFR on the cell, our group investigated whether an interaction between OPCML and EGFR was possible but had not been detected by those experiments as the high levels of HER2 had saturated all the OPCML molecules present in the pull-down. The HER2-negative MDA-231 breast cancer cell line was then employed in a pull-down assay where the recombinant GST-OPCML was used. This experiment provided further evidence that OPCML does not interact with
EGFR (Figure 1.11.B), confirming that OPCML exerts its effects through inhibition of
HER2 only.
In order to further confirm these results, mammalian 2-hybrid studies have been carried out. The extracellular domain of HER2 (HER2 NEX) and of EGFR (EGFR ECD)
(Figure 1.11.C) have been used to verify their ability to interact, or not, with the three
Ig domains of OPCML in Cos-1 cells, that have a low basal expression of both HER2 and
EGFR. Negative controls included cells transfected with the empty pM vector and the pVP16 vector containing the target protein or the bait vector containing OPCML co-
72 transfected with the empty pVP16 vector with GAL4 Luc. Transfection with a plasmid
Figure 3 expressing the β-galactosidase gene was used as a transfection control for normalising the luminescence signal. With these experiments it was confirmed that HER2, but not
EGFR, interacts with OPCML (Figure 1.11.D).
(a)
IP: IP: IP: IP:
IB: EphA2 IB:FGFR1 IB: HER2 IB: EGFR
IB: OPCML IB: OPCML IB: OPCML IB: OPCML
Figure 1.10: Co-immunoprecipitation experiments in stably transfected OPCML cell
line BKS-2.1; direct and reciprocal, demonstrated that OPCML directly interacts with
HER2 but not with EGFR. IB (immunoblotting) (data courtesy of (McKie, Vaughan et al.
2012).
73 A
B
74 C
D
75 Figure 1.11 (previous two pages): Interaction assays show that OPCML binds to HER2 but not to EGFR. (A) The pull-down of HER2 and EGFR from SKOV-3 ovarian cancer cell line.
GST-OPCML and GST-only proteins were pulled-down with Magne-GST beads and incubated with SKOV-3 cell lysate. The presence of HER2, EGFR and of the OPCML fusion proteins in this assay was verified by immunoblotting (IB). (B) GST-OPCML pull-down of
EGFR from a breast cancer cell line, MDA-231 was analysed by western blotting. GST-
OPCML and GST-only proteins were pulled-down with Magne-GST beads and incubated with MDA-231 cell lysate. SKOV-3 and His-OPCML were used as positive controls for HER2 and OPCML respectively. The presence of EGFR, HER2 and OPCML fusion proteins in this assay was verified by IB. MDA-231 is a HER2- and OPCML-negative cell line. (C) Schematic view of the domains 1, 2 and 3 of OPCML that were cloned in the pM vector downstream of the SV40 promoter and GAL4 DNA binding domain (DBD) and of the extracellular domains (ECD) of EGFR and HER2 (NEX) that were cloned in the pVP16 vector downstream of the SV40 promoter and VP16 activation domain (AD). (D) The interaction between the three functional Ig domains of OPCML and the extracellular domains of HER2 and EGFR has been analysed. 50ng of OPCML123-pM and 50ng of the EGFR ECD- or HER2 NEX-pVP16 constructs were transfected into COS-1 cells with 50ng of GAL4 Luc, a Gal4-responsive reporter, and 50ng of pCMV-β-gal reporter per 200-mm2 well. Cells were harvested 24 hours. After transfection, assayed for luciferase activity and corrected for β-galactosidase activity to give the relative activity. Results shown are the mean of three independent experiments performed in quadruplicates ± SEM. * p<0.05, *** p<0.0005. (Data courtesy of Dr E Zanini, Imperial College London).
76 1.5.6 OPCML and targeted therapy
Limited experiments were carried out to test if OPCML has any effect on cancer cells response to targeted therapy. Using only stably transfected, HER2 positive, OPCML expressing SKOV-3 ovarian cancer cell lines (BKS-2.1) and their empty vector negative controls (SKOBS-V1.2), OPCML sensitised the cells to anti-HER2 monoclonal antibody
Trastuzumab, and to dual acting HER2/EGFR inhibitor Lapatinib, but not anti-EGFR small molecule inhibitor Erlotinib (data not published), (Figure 1.12).
A
77
B
C
Figure 1.12: OPCML sensitises stably transfected OPCML expressing cells (BKS-2.1) to
Trastuzumab (A) and Lapatinib (B), but not Erlotinib (c), as judged by the levels of phospho
ERK1/2 and phospho AKT (S473). Data courtesy of Dr I Okun, previously of Imperial College
London.
78 1.6 rOPCML
The extra-cellular membrane location and mechanism of action of OPCML raised the possibility of direct extracellular tumour suppressor protein therapy (avoiding the complexity of gene therapy for intracellular tumour suppressor replacement; or intra- cellular delivery of protein therapies). Recombinant human OPCML domain 1-3 protein
(rOPCML) was purified using a bacterial expression vector (pHis-Trx) subcloned with domains 1-3 of OPCML and the signal peptide and excluding the GPI anchor sequences
(McKie, Vaughan et al. 2012).
1.6.1 rOPCML inhibits tumour growth in vitro
The administration of rOPCML could specifically down regulate the growth rate of cancer cells compared to the non-cancer line. The addition of 10 μM rOPCML over 48 hours to a panel of ovarian (7), breast (2) and lung cancer (7) cell lines showed that the proliferation of most of the cell lines was reduced significantly (Figure 1.13, ovarian cell lines only) (McKie,
Vaughan et al. 2012).
Western blot analysis of rOPCML-treated SKOV-3 cells demonstrated that cells had reduced
HER2 protein and pHER2 (Y1248) levels, as seen when the protein is expressed endogenously. Down-stream signalling molecules, ERK 1/2 and AKT (S473) were also shown to be inhibited confirming that the exogenous administration of rOPCML could recapitulate the regulatory control of OPCML in cancer cells (Figure 1.14), (McKie, Vaughan et al. 2012).
79
Figure 1.13: Targeting of cancer but not normal cells by rOPCML (upper panel), OSE-C2 and
SKOV-3 cells were subjected to varying concentrations of rOPCML (0.5, 1, 2, 5, and 10 µM).
Proliferation assay (using MTT) is shown relative to control vehicle-treated (dotted line) at
48 hours. A dose dependent inhibition of SKOV-3 cell growth demonstrates the specificity of rOPCML application (lower panel). The OSEC-C2 line and a panel of ovarian cancer lines
(SKOV-3, IGROV, OVISE, OVCAR-5, A2780, PEA1, and PEA2) were exposed to 10 µM of rOPCML for 24 hours (white bar) and 48 hours (black bar) with MTT cell growth was normalised to vehicle only controls. The data demonstrates profoundly growth suppression after exposure to rOPCML (data courtesy of (McKie, Vaughan et al. 2012).
80
Figure 1.14: The impact of rOPCML protein treatment on cell signalling (as demonstrated by western blotting): rOPCML specifically abrogates total/ phospho-HER2, total/ phospho-
FGFR1, phospho but not total EGFR (only in SKOV-3, as A2780 does not express EGFR) and abrogation of downstream substrates pAKT (S473) and pERK1/2 in both SKOV-3 and A2780
(data courtesy of (McKie, Vaughan et al. 2012).
81 1.6.2 rOPCML inhibits tumour growth in vivo
Nude mice were transfected intraperitoneally (IP) with either SKOV-3 or A2780 ovarian cancer cell lines. Tumours were establishment, and both models received twice-weekly IP injections of equimolar concentrations of either bovine serum albumin (BSA) or rOPCML.
The experiment was terminated after three weeks due to obvious extensive IP tumour growth and deteriorating condition of BSA treated control animals whereas rOPCML treated mice remained well with IP growth and ascites formation being significantly and profoundly suppressed in the treated models. Also in A2780 tumour bearing mice, rOPCML profoundly inhibited the number of intra-peritoneal deposits compared with BSA controls (Figure 1.15),
(McKie, Vaughan et al. 2012).
82
Figure 1.15: Bar graphs comparison between rOPCML treatment group (+) and BSA control group (-), showing the mean tumour weight (left), the mean number of deposits (middle), and the mean volume of ascites removed from the nude mice after termination of the experiment (data courtesy of (McKie, Vaughan et al. 2012).
83 1.7 Rationale for the project
1.7.1 Hypothesis and Aims
The general aims of this project were to further investigate the preliminary results we identified regarding interaction and sensitisation of cancer cells by OPCML to targeted therapy. In doing so, we can determine a potential scope of clinical action for OPCML. The other aim was to further investigate the use of OPCML as a therapy in itself and build on our earlier work on rOPCML and its anti-proliferative effects. In particular, we aim to investigate the basis of ascites inhibition by rOPCML noted in our small in vivo experiment by trying to identify the most likely agent/agents OPCML exerts its functional effects through. We also aim to test that the rOPCML produced is stable, both structurally and biologically, by ensuring the production process is simple, consistent and reproducible.
To set up to do these objectives and aims, we made use of the standardised methods already set up and in use in our lab, and the new methods, particularly, the process of producing rOPCML and the large number of OPCML constructs that have been produced and encode for different combinations of the three functional domains of this TSG. GST-
OPCML proteins have been mainly exploited to investigate RTK interacting partners of
OPCML from members of the VEGF family after we established through in silico a positive association between OPCML and VEGFA. Multiple cell lines, ovarian and breast were tested with increasing levels of targeted therapeutic agents with or without OPCML transiently transfected to confirm sensitising effects with anti-HER2 therapy.
Further throughput analysis of OPCML was also carried out using reverse phase protein microarray to look at interactions at cell surface and down-stream signals with further
84 evaluation of some of these results and comparing them with what we already know from our previous work.
Therefore, this work represents a further step in our understanding and evidence for potentially future clinical use of OPCML in treatment of cancer, especially ovarian cancer.
85
Chapter two
Materials and Methods
86 2.1 Cell culture
2.1.1 Cell lines
All cell lines were obtained from Professor Hani Gabra’s research lab (Imperial College
London) except SKBR-3 breast cancer cell lines (kindly provided by Professor Eric Lam,
Imperial College London), and MDA-MB-231 breast cancer cell lines (kindly provided by
Professor Bob Brown, Imperial College London). Cell lines used include standard ovarian and breast cell lines and stable transfectants developed in house.
2.1.1.1 Wild type cell lines
SKOV-3: (Epithelial ovarian cancer) EOC cell line, originally derived from ascitic high-grade serous adenocarcinoma cells (Fogh, Fogh et al. 1977, Fogh, Wright et al. 1977). This cell line is platinum resistant.
A2780: EOC cell line, originally established from a tumour tissue of a patient prior to chemotherapy, and considered to be platinum sensitive cell line.
PEO1: EOC cell line, originally derived from ascitic serous adenocarcinoma cell prior to chemotherapy treatment, hence, cells are platinum sensitive (Langdon, Lawrie et al. 1988).
PEA1: EOC cell line, originally derived from ascitic serous adenocarcinoma cell prior to chemotherapy treatment, hence, cells are platinum sensitive (Langdon, Lawrie et al. 1988).
PEA2: EOC cell line, and a PEA1 isogeneic cell line originally derived from ascitic serous adenocarcinoma cells from the same patient after treatment and development of chemoresistance to cisplatinum (Langdon, Lawrie et al. 1988).
87 OV-90: EOC cell line, originally derived from ascitic papillary serous adenocarcinoma cells
(Lounis, Mes-Masson et al. 1998). This cell line is positive for HER2 expression.
SKBR-3: Epithelial breast cancer cell line, originally derived from pleural effusion of metastatic mammary adenocarcinoma (Trempe 1976). This cell line is positive for HER2 expression.
MDA-MB-231: Epithelial breast cancer cell line, originally derived from pleural effusion of metastatic mammary adenocarcinoma (Cailleau, Young et al. 1974). This cell line is positive for EGFR expression.
HEK 293-F: Human embryonic kidney (HEK) mammalian cells 293-F that were originally derived from a paternal HEK cells. Cells were purchased from Invitrogen [Invitrogen,
Carlsbad, CA, USA].
2.1.1.2 Stable cell lines
The following cell lines were used:
SKOBS-V1.2: SKOV-3 EOC cell line stably transfected with pcDNA3.1zeo (empty vector).
SKOBS-3.5: SKOV-3 EOC cell line stably transfected with OPCML cloned into pcDNA3.1zeo, expressing approximately 3-fold expression over physiological levels seen in normal ovarian surface epithelium (OSE).
BKS-2.1: SKOV-3 EOC cell line stably transfected with OPCML cloned into pcDNA3.1zeo, expressing approximately 30-fold expression over physiological levels seen in normal OSE.
88 SKOBS-P95R-3.4: SKOV-3 EOC cell line stably transfected with mutant OPCML (P95R) plasmid cloned into pcDNA3.1zeo, expressing approximately 3-folds expression levels of wild-type OPCML seen in normal OSE.
PLKO-1.3: the normal SV-40 Large-T transformed human OSE line (OSE-C2), expressing normal levels of OPCML as previously described (Davies, Steele et al. 2003), were stably transfected with pOLK.1 shRNA plasmid (empty vector control). sh-464-23: The normal SV-40 Large-T transformed human OSE line (OSE-C2), stably transfected with pOLK.1 containing OPCML shRNA achieving 60% knock down of physiological OPCML levels. sh-339-24: The normal SV-40 Large-T transformed human OSE line (OSE-C2), stably transfected with pOLK.1 containing OPCML shRNA achieving 95% knock down of physiological OPCML levels.
PEO1-EV: PEO1 epithelial ovarian cancer (EOC) cell line stably transfected with pcDNA3.1zeo
(empty vector).
PEO1-OP6: PEO1 EOC cell line stably transfected with OPCML cloned into pcDNA3.1zeo, expressing approximately 6-fold expression over physiological levels seen in normal OSE.
89 2.1.2 Cell lines maintenance
All cell lines (except HEK 293-F cells) were cultured and grown at 37 ˚C in a humidified incubator supplemented with 5% CO2.
All cell lines (except HEK 293-F and OV-90 cells) were maintained in RPMI-1640 medium
[Sigma-Aldrich, Ayrshire, UK] supplemented with 10% fetal calf serum (FCS) [First Link, UK],
2 mM GIBCO® L-glutamine [Invitrogen, Carlsbad, CA, USA], and 50 U/ml
Penicillin/Streptomycin [Invitrogen, Carlsbad, CA, USA].
FreeStyle™ HEK 293-F cells [Invitrogen, Carlsbad, CA, USA] were grown at 37 ˚C, 8% CO2, in suspension using GIBCO® FreeStyle™ 293 expression medium [Invitrogen, Carlsbad, CA,
USA] at variable cell densities between 0.1 x 106 and 3.0 x 106 cells/ml using Erlenmeyer tissue culture flasks with vented caps [Corning Inc., Corning, NY, USA] at 135 – 155 rpm on an orbital shaker with universal platform [Grant-bio model PSU-10i, Grant Instruments,
Hillsborough, NJ, USA]. Cell density and viability were checked using Nexcelom Cellometer™
AutoT4 [Nexcelom Bioscience, Lawrence, MA, USA] following the addition of 100 µl of
Trypan Blue solution 0.4% [Sigma-Aldrich, Ayrshire, UK] to 1 ml of the cell suspension as per the manufacturer’s instructions.
OV-90 cells were maintained in a mixture of 1:1 v/v of MCDB 105 media [Sigma-Aldrich,
Steinheim, Germany] with Media 199 [Sigma-Aldrich, Steinheim, Germany]. The mixture was supplemented with 15% fetal calf serum (FCS) [First Link, UK], 2 mM GIBCO® L- glutamine and 50 U/ml Penicillin/Streptomycin. MCDB 105 media was prepared under sterile conditions by dissolving 14.9 g of the media powder in 1 litre of sterile water then filtering the solution using a 0.22 µm membrane filter [Sartorius, Goettingen, Germany].
90 All cell lines were routinely checked for mycoplasma infection using MycoAlert kit [Lonza,
UK].
2.1.3 Cell line passage
All cells (except HEK-293-F cells) were passaged when 95% confluent by trypsinisation using
10% trypsin [Sigma-Aldrich, Steinheim, Germany] in 0.05% ethylenediaminetetraacetic acid
(EDTA) [Sigma-Aldrich, Steinheim, Germany].
HEK293-F cells were passaged when cell density 3.0 x 106 cells/ml by dilution into a fresh culture medium.
2.1.4 Cryopreservation and thawing of cells
Following trypsinisation as described above, cells were resuspended into a “freezing media” mixture of 70% of its’ culture medium, supplemented with 20% FCS and 10% dimethyl sulphoxide (DMSO) [Sigma-Aldrich, Steinheim, Germany] before transfer into 2 ml cryovials
[Corning Inc., Corning, NY, USA]. Vials were initially stored at – 80 ˚C overnight then transferred next morning to liquid nitrogen Dewar vessel for long-term storage. A similar protocol was used to cryopreserve HEK 293-F cells except that the freezing media contained fresh GIBCO® FreeStyle™ 293 expression medium supplemented with 10% DMSO only.
Cells were thawed by adding 1 ml of warm media to the cryovial with pipetting before centrifuging the mixture at 1500 rpm to get rid of the DMSO content and re-suspending the cells into a fresh media.
91 2.2 Cloning and general nucleic acid manipulation
2.2.1 Agarose gel electrophoresis
DNA samples were analysed in 1% agarose gels comprising w/v 1 g agarose [Fisher Scientific,
Loughborough, UK] in 100 ml of 1X Tris Acetate-EDTA (TAE) buffer (pH 8.0, 24.2 g Tris, 5.71 ml glacial acetic acid, 10 ml 0.5 M EDTA, and de-ionised water) containing 1 µg/ml ethidium bromide [Sigma, Steinheim, Germany]. Gels were run at 100V and visualised under UV light.
Molecular sizes were estimated by suitable DNA ladder (HyperLadderTM I-IV) [Bioline, UK].
2.2.2 Plasmid DNA preparation
The OPCML cDNA plasmid constructs in pcDNA3.1zeo used for OPCML transfection were generated by PCR by Dr E Zanini and Dr A McKie (Imperial College London). The three Ig domain structure of OPCML (with the signal peptide and minus the GPI anchor) was also cloned into either pGEX-6P-1 [GE Healthcare, Amersham, UK] or pHis-Trx (kindly provided by
Dr E McKenzie, University of Manchester) expressing the rOPCML used in our initial experiments.
The OPCML cDNA plasmid constructs used for OPCML transfection and rOPCML expression in mammalian cells were prepared with the help of Dr A McKie (Imperial College London).
2.2.2.1 Miniprep
QIAprep spin miniprep kit [Qiagen, Dusseldorf, Germany] was used to isolate DNA from bacterial cultures following the manufacture’s guidelines.
92 2.2.2.2 Maxiprep
QIAprep spin maxiprep kit [Qiagen, Dusseldorf, Germany] was used to expand the isolated
DNA from bacterial cultures following the manufacture’s guidelines.
2.2.3 DNA isolation from plasmid glycerol stocks
3 µl from glycerol reserves of various plasmids prepared in our lab in the past, including pcDNA3.1zeo, pGEX-6P-1 and pHis-Trx OPCML and empty vector controls, were transformed into 15 µl of competent Escherichia coli cells [Bioline, UK] and then diluted into 5 ml of sterile 2YT media (1.6% w/v Bacto-Tryptone, 1% w/v Yeast Extract, 0.5% w/v NaCl in deionised water with pH adjusted to 7.5 with the final volume adjusted to 1 L and autoclaved) and left on an orbital shaker at 37 ˚C overnight. DNA was isolated next morning using QIAprep spin miniprep kit [Qiagen, Dusseldorf, Germany] as described earlier.
2.2.4 Restriction endonuclease digestion
The isolated DNA (as mentioned above) was digested with restriction endonuclease enzymes and their corresponding digestion reagents. The latter were all purchased from
New England Biolabs [NEB, Ipswich, MA, USA]. 6 – 10 µl of DNA was mixed with 15 – 18 µl of deionised water, 3 µl of appropriate restriction buffer and 1.5 µl of restriction enzyme. NEB buffer 3 was used with BamHI enzyme, while NEB buffer 2 was used with XhoI. The digest was incubated at 37 ˚C for 4 hours.
93 2.2.5 DNA plasmid extraction and purification
Following incubation, the digested DNA was separated on an agarose gel electrophoresis and the relevant size bands were visualised and excised using a sterile scalpel under UV light. After weighing the bands, a threefold v/w of QG buffer was added to each band and
DNA was extracted and purified using the QIAquick gel extraction kit [Qiagen, Dusseldorf,
Germany] as per manufacturer’s instructions.
2.2.6 DNA plasmid ligation
A small sample of the purified DNA was run on 1% agarose gel to confirm the DNA presence.
The plasmid ligation mixture contained a DNA insert to vector ratio of 3:1, in addition to 1 µl of T4 DNA ligase [Invitrogen, Carlsbad, CA, USA] and 1 µl of 10X T4 DNA ligase buffer
[Invitrogen, Carlsbad, CA, USA]. The mixture was diluted with deionised water to a total volume of 20 µl and incubated for 3 hours at room temperature before being transformed into competent E. coli cells.
2.2.7 E. coli transformation with plasmid DNA
10 µl of the ligation mixture were added to E. coli chemically competent bacterial aliquots
[Bioline, UK] and left to incubate on ice for 30 minutes followed by heat shock at 42 ˚C for
45 seconds before replacing the cells on ice again for further 2 minutes. The mixture was then added to 100 µl of 2YT media and plated on an agar media under sterile conditions and
94 with appropriate antibiotic selection (ampicillin 100 µg/ml). The plate was incubated at 37
˚C overnight.
2.2.8 E. coli propagation and DNA isolation
Single transformed colonies were picked up under sterile conditions and grown overnight in
5 – 10 ml of 2YT media at 37 ˚C on an orbital shaker and with ampicillin antibiotic selection
(100 µg/ml). Next day, DNA was isolated from the bacterial cultures using QIAprep spin miniprep kit [Qiagen, Dusseldorf, Germany].
2.2.9 DNA sequencing
The isolated miniprep DNA was sequenced by automated DNA sequencing service (Imperial
College London). DNA stocks were made once confirmed the sequencing result was mutation free.
2.2.10 GST-OPCML fusion proteins
Recombinant GST-OPCML proteins were created in our lab by Dr S Vaughan and Dr E Zanini and were used as part of the GST pull down assay (see 2.4.4.2). In brief, the extracellular domains of OPCML were cloned into the pGEX-6P-2 vector encoding Glutathione-S-
Transferase (GST) and expressed in bacterial host.
95 2.3 Cell transfection and cloning
2.3.1 OPCML transient transfection
2.3.1.1 OPCML transient transfection into wild type cancer cell lines
5 x 105 cells/well were seeded in a 6 well plate with 2 ml of appropriate full media
(containing 10% FCS) and left to settle over night. Cells were transfected with either OPCML
(in pcDNA3.1) or pcDNA3.1 empty vector control using the Effectene transfection reagent
[Qiagen, Dusseldorf, Germany] as per the manufacturer’s recommendations. The ratio of the DNA to Effectene used was 1 µg/10 µl. Following incubation for 6 hours at 37 °C the transfection media was removed and cells were washed with warm PBS and 2 ml of fresh media was added to each well. Following further incubation overnight, the media was removed and either the cells were seeded into a 96 well plate (for WST1 proliferation assay, see 2.5.2.2) or the medium was replaced with serum free medium for at least 6 hours before cell were lysed (see 2.4.1.1) or further treated with different medications and/or ligand stimulations as per the experiment design (see 2.5.1 and 2.6) for the purpose of western blotting.
2.3.1.2 OPCML transient transfection into HEK293 cells
In preparation for transfection, one day prior the mammalian cells were counted and passaged to a density of between 0.7 – 0.9 x 106 cells/ml with viability that was always above 90%. Cell density and viability was counted on the day just before transfection with
96 the count adjusted to between 1.0 – 1.25 x 106 cells/ml by adding fresh media. Transfection was carried out using 30 ml culture in 250 ml Erlenmeyer flasks. Transfection complexes were done by mixing 37.5 µl of FreeStyle Max reagent [Invitrogen, Carlsbad, CA, USA] diluted in 0.6 ml of warm Gibco FreeStyle293 Expression medium [Invitrogen Gibco,
Carlsbad, CA, USA] to 30 µg OPCML pcDNA3/V5-His-B that was also diluted in 0.6 ml of similar media. The DNA-transfection reagent mixture was allowed to incubate at room temperature (inside a hood) for 10 minutes before slowly adding it to the HEK 293-F suspension culture whist swirling the flask, which was then placed, back on the orbital shaker inside the incubator.
97 2.4 General protein analysis and manipulation
2.4.1 Protein extraction
2.4.1.1 Protein extraction from cultured cells
Prior to collection, cells were washed twice with cold phosphate buffered saline (PBS) (8g
NaCl, 0.2g KCl, 1.44g Na2HPO4, 0.24 g KH2PO4). Whole cell lysates were collected in 2% sodium dodecyl sulphate (SDS) lysis buffer (2% w/v SDS, 50 mM Tris-HCl; pH 6.8, 1% glycerol, in deionised water) supplemented with 40 µg/ml protease inhibitor cocktail
[Roche, Switzerland] and 10 µg/ml phosphatase inhibitor cocktail II [Calbiochem, Merck,
Darmstadt, Germany].
Alternatively, the SDS lysis buffer was replaced with radio-immunoprecipitation assay
(RIPA™) buffer [Sigma-Aldrich, Ayrshire, UK].
Cultured cell plates were lysed on ice for 20 minutes, with shaking every 5 minutes. Cells were scraped using sterile cell scrapers [Corning Inc., Corning, NY, USA] and collected into eppendorf tubes, centrifuged at 13.3 x 103 rpm, 4 °C for 20 minutes before the supernatant was collected and either proceeded immediately to western blotting or stored at – 20 °C for latter use.
2.4.1.2 Protein extraction from culture media
1x106 OPCML and empty vector control cells were each seeded in a 10 cm plate and left to settle overnight. Media was exchanged with 7 ml fresh serum free media next morning after
98 washing the cells with warm PBS. Following a further 48 hours incubation, the media was aspirated and concentrated to less the 0.5 ml using the Pierce protein concentrators kit,
20KDa molecular weight cut off (MWCO) [Pierce, Thermo-Scientific, Rockford, IL, USA] as per the manufacturer’s recommendations. The concentrated sample was used for western blotting.
2.4.1.3 Protein extraction for reverse phase protein micro-array (RPPA)
Cells were washed with PBS before being lysed on ice for 20 minutes, with shaking every 5 minutes, using 150 µl of RPPA lysis buffer (1% Triton X-100, 50mM HEPES, pH 7.4, 150mM
NaCl, 1.5mM MgCl2, 1mM EGTA, 100mM NaF, 10mM sodium pyrophosphate, 1mM
Na3VO4, 10% glycerol, supplemented with 40 µg/ml protease inhibitor cocktail [Roche,
Switzerland] and 10 µg/ml phosphatase inhibitor cocktail II [Calbiochem, Merck, Darmstadt,
Germany]. Cells were scraped using sterile cell scrapers [Corning Inc., Corning, NY, USA] and collected into eppendorf tubes, centrifuged at 13.3 x 103 rpm, 4 °C for 20 minutes before the supernatant was collected. 50 μl were used for protein quantification and western blotting and 100 µl stored at -80 ˚C for shipment to the US to conduct the experiment after adjusting the concentration to 1 mg/ml.
2.4.2 Protein quantification
Protein concentration was estimated using the BCA assay [Pierce, Thermo Scientific,
Rockford, IL, USA] according to the manufacturer’s recommendations.
99 Occasionally, and specially for repeat western blotting runs, the protein content of each sample was also measured using a NanoDrop 1000 Spectrophotometer [Fisher Scientific,
Wilmington, DE, USA].
2.4.3 Western blotting
Lysates were incubated at 95 °C for five minutes after adding 4X SDS sample buffer (40% glycerol, 8% SDS, 0.25M Tris-HCL, pH 6.8 with 2-mercaptoethanol at 1/10th of the volume), then separated into either 8, 10 or 12% sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) in the conventional manner.
After electrophoretic separation, proteins were transferred to nitrocellulose membranes
(median pore size 0.2 μm) [Bio-Rad, Berkeley, CA, USA]. Membranes were then blocked in either 10% non-fat milk in phosphate buffered saline – Tween (PBS/T), (8 g NaCl, 0.2 g KCl,
1.44 g Na2HPO4, 0.24 g KH2PO4, 0.1% v/v Tween 20) or tris buffered saline – Tween (TBS/T)
(8 g NaCl, 0.2g KCl, 3 g Tris, 0.015 g phenol red, 0.05% v/v Tween 20) for 1 hour.
Antibody dilutions were made up in 5% non-fat milk (or 5% bovine serum albumin (BSA)) dissolved in either PBS/T or TBS/T depending on the antibody used and as per the manufacturers’ recommendations. This was applied to membranes overnight, on the shaker, at 4 °C. Membranes were then washed in either TBS/T or PBS/T three times, five minutes each time. Horseradish peroxidase (HRP) conjugated secondary antibodies [Dako,
Denmark] were prepared in 5% non-fat milk dissolved in PBS/T or TBST as per the manufacturer’s recommendation and applied to membranes for one to two hours.
Membranes were then washed again in TBS/T or PBS/T three times for fifteen minutes each
100 time. Bands were visualised using Immobilon™ Western Chemiluminescent HRP substrate system [Millipore, UK] after three minutes incubation.
Photographic films [Kodak, UK] were applied to the membranes and developed using a
Konica Minolta SRX101 developing machine [Konica Minolta Inc., Tokyo, Japan].
Alternatively, membranes were transferred to polyvinylidene fluoride (PVDF) membranes
[Millipore, UK] instead of the nitrocellulose ones. Membranes were blocked with LI-COR®
Odyssey™ blocking buffer [LI-COR Biosciences, Lincoln, NE, USA] for 1 hour. Primary antibodies were used same as before whilst the HRP secondary antibodies were replaced with IRDye® secondary antibody [LI-COR Biosciences, Lincoln, NE, USA] prepared with 5%
BSA in TBS/T. After 1 hour of secondary antibody blockage in the dark, and a further three membrane washes by TBS/T for 10 minutes each time, bands were visualised using the LI-
COR Odyssey infrared imaging system [LI-COR Biosciences, Lincoln, NE, USA].
Quantitative densitometry analysis was carried out using ImageJ (Fiji) software (National
Institute of Health, Bethesda, MD, USA).
101 Table 2.1: List of primary antibodies used in western blotting, the molecular weight (MW) of their respective immunogenes, concentration in dilution medium and manufacturer
Antibody MW (K Da) Dilution Medium Dilution Manufacturer Polyclonal goat Anti-OBCAM 55 5% milk TBST 1/1000 R&D Systems Rabbit (total) anti-HER2 antibody (OP15L) 185 5% milk TBST 1/1000 Calbiochem Rabbit (total) anti-ERK 1&2 42/44 5% milk TBST 1/1000 Cell Signaling Rabbit anti-phospho ERK 1&2 42/44 5% milk TBST 1/1000 Cell Signaling Rabbit (total) anti-AKT1 60 5% BSA TBST 1/1000 Cell Signaling Rabbit anti-phospho AKT1 (S473) 60 5% BSA TBST 1/1000 Cell Signaling Mouse α/β Tubulin antibody 55 5% milk TBST 1/1000 Cell Signaling Mouse α Actin antibody 44 5% milk TBST 1/1000 Abcam Rabbit (total) anti-VEGFR1 180 5% milk TBST 1/1000 R&D Systems Rabbit anti-phospho VEGFR1 (1213) 180 5% milk TBST 1/1000 R&D Systems Rabbit (total) anti-VEGFR2 230 5% BSA TBST 1/500 Cell Signaling Rabbit anti-phospho VEGFR2 (Tyr1212) 210-230 5% BSA TBST 1/500 Cell Signaling Cell Rabbit (total) anti-VEGFR3 195 5% milk TBST 1/500 Application Rabbit anti-phospho VEGFR3 Cell (Tyr1230/1231) 198/130 5% milk TBST 1/500 Application rabbit VEGFA antibody 27 5% milk TBST 1/500 Abcam Rabbit (total) anti-EphA2 125 5% BSA TBST 1/500 Cell Signaling Rabbit (total) anti-FGFR1 145 5% BSA TBST 1/1000 R&D Systems Rabbit (total) anti-HER3 148 5% BSA TBST 1/1000 Cell Signaling Rabbit anti-phospho HER3 (Tyr 1289) 185 5% BSA TBST 1/1000 Cell Signaling Mouse (total) anti-EGFR 165 5% milk TBST 1/1000 R&D Systems Rabbit anti-phospho EGFR (pY1068) 185 5% milk TBST 1/1000 R&D Systems Same as Rabbit anti-α6xHis tagged 5% milk TBST 1/1000 Abcam Mouse (total) anti-cMET 140 5% milk TBST 1/500 Cell Signaling Rabbit anti-phospho cMET (pY1234/1235) 145 5% BSA TBST 1/250 Cell Signaling
102 Table 2.2: List of HRP secondary antibodies used and their concentration
Antibody Dilution HRP goat anti-mouse 1/1000 HRP goat anti-rabbit 1/2500 HRP mouse anti-goat 1/2000
Table 2.3: List of IRDye secondary antibodies used, their concentration and emission of associated dye.
Antibody Channel Colour IRDye goat anti-rabbit 800CW green IRDye goat anti- mouse 680RD red
103 2.4.4 Protein_Protein interaction
2.4.4.1 Co-immuno-precipitation (Co-IP)
Cells were grown to 80 – 90% confluency in T-150 flasks [Corning Inc., Corning, NY, USA].
Following media aspiration and wash with PBS, 5 ml of EDTA was added and cells were incubated at 37 ˚C for 10 minutes. Cells then were scraped and centrifuged at 1500 g for 5 minutes. The supernatant was discarded and the pellet was twice re-suspended and centrifuged again in PBS before the latter was discarded and the pellet suspended in 1 ml of
Co-IP lysis buffer (10% Triton-X 100, 5M NaCl, 1M Tris HCl, 1M NaF, 0.5M EDTA, 40 µg/ml protease inhibitor, 10 µg/ml phosphatase inhibitor, in deionised water). Cells were lysed on ice for 30 minutes before the lysate was spun at 13.3 x 106 rpm, 4 ˚C. 200 µl of the supernatant was saved as direct control whilst the remainder was divided into 2 halves of
400 µl each that were incubated separately with 30 µl of pre-cleared protein G Sepharose beads [Sigma-Aldrich, Ayrshire, UK]. One of the samples will contain the supernatant and antibody of choice (IP sample), the second will have the supernatant with no antibody
(extract only) and a third beads sample will have the antibody only with fresh lysis buffer
(antibody only). The three samples were incubated at 4 ˚C overnight on a tube rotator
[Stuart, Bibby Scientific, Shropshire, UK]. Next morning, beads were washed twice with lysis buffer, twice with PBS before finally suspended in 1X SDS sample buffer. The 4 samples (the beads plus the direct control sample) were analysed by western blotting.
104 2.4.4.2 Pull down assay
Cell lysates were prepared in a similar method to the Co-IP experiment. Equal volume of cell lysates mixed with GST-OPCML fusion proteins (and their negative controls) were immobilised on the Magne-GST™ pull down system by being bound to magnetic glutathione
4B resin beads [Promega, WI, USA] as per the manufacturer’s recommendations. Final lysates were analysed by western blotting.
Table 2.4: List of antibodies used for Co-IP and pull down assays
Antibody Manufacturer Technique Polyclonal goat Anti-OBCAM R&D Systems WB Monoclonal mouse anti- OBCAM R&D Systems Co-IP Rabbit (total) anti-VEGFR1 R&D Systems WB Rabbit (total) anti-VEGFR2 Cell Signaling WB Rabbit (total) anti-VEGFR3 Cell Applications WB
2.4.5 Reverse phase protein microarray (RPPA)
1 x 106 cells/10 cm plate of OPCML expressing and empty vector controls each were seeded overnight. Cells were serum starved when near confluency for 24 hours followed next day by either lysing the cells under serum starvation conditions, or after stimulation with 50 ng/ ml of EGF [R&D Systems, MN, USA] for 30 minutes as explained earlier (see 2.4.1.3).
Following protein quantification, the lysate concentration was adjusted to 1 mg/ml and samples were shipped on dry ice to Professor Gordon Mills’s laboratory (MD Anderson, TX,
USA) for RPPA to be conducted. The results were analysed back at Imperial College London.
105 Once at MD Anderson, the process of Quantification, normalisation and analysis of data started. The cell lysates were two-fold-serial diluted for 5 dilutions (from undiluted to 1:16 dilution) and arrayed on nitrocellulose-coated slide in 11x11 format. Samples were then probed with antibodies by catalysed signal amplification (CSA) approach and visualised by 3,
3’ diaminobenzidine (DAB) colorimetric reaction before the slides were scanned on a flatbed scanner to produce 16-bit tiff image, spots were identified on the images and the density was quantified by Array-Pro Analyser.
Relative protein levels for each sample were determined by interpolation of each dilution curves from the "standard curve" (supercurve) of the slide (antibody). A Bioinformatics script in R constructs supercurve. These values (given as Log2 values) are defined as
‘Supercurve Log2’ (Raw) value, following which all the data points were normalised for protein loading and transformed to linear value designated as "Linear after normalisation".
This was further transformed to Log2 value, and then median-centred for Hierarchical
Cluster analysis.
Heatmaps were generated for an Unsupervised Hierarchical cluster (unsupervised on both antibodies and samples). The heatmaps included were generated in Cluster 3.0
(http://www.eisenlab.org/eisen/) as a hierarchical cluster using Pearson Correlation and a centre metric. Each resulting heatmap was visualised in Treeview
{http://www.eisenlab.org/eisen/) and presented as a high resolution .bmp format.
The stained slides were analysed on Array-Pro then by supercurve R x64 2.15.1. There were
18 sets of replicated antibodies and 3 negative controls for secondary antibodies. We performed QC test for each antibody staining (slide). QC score above 0.8 indicates good
106 antibody staining and were included in the heatmaps. In the case of antibodies with replicates, the one with the highest QC Score was used.
2.4.6 Recombinant OPCML protein expression in E.coli
Following the method initially used in our lab for expression and refolding of recombinant
OPCML (rOPCML) and under the supervision of Dr S Vaughan (Imperial College London), both pGEX-6P-2- and pHis TRx- OPCML constructs were used to transform BL21 (DE3)pLys bacteria cells [Merck, UK]. Bacteria were then grown in 2YT media at 37 °C until the optical density (O.D.) of the media reached 0.6 at 600 nm and then induced with isopropyl β-D-1- thiogalactopyranoside (IPTG) [Sigma-Aldrich, Ayrshire, UK] at a final concentration of 1 mM for further 4 hours at 37°C. Bacteria cells were harvested at 6000 g for 20 minutes, freeze- thawed at -20 °C over-night or at -80 °C for 15 minutes and then resuspended in 1X PBS. To improve lysis of the cells Triton X-100 [Sigma-Aldrich, Ayrshire, UK] was added at 1% (v/v) final concentration together with 1mg/ml chicken egg lysozyme [Sigma-Aldrich, Ayrshire,
UK]. The solution was then incubated at room temperature for at least 2 hours with DNase and protease inhibitor and inclusion bodies were separated by centrifugation at 15000 g for
30 minutes. White and pure inclusion bodies were obtained after washing repeatedly with
1X PBS and 2% (v/v) final concentration Triton X-100. Inclusion bodies were then denatured in solubilisation buffer (8 M Urea, 10 mM dithiothreitol (DTT)) to reach 1mg/ml protein concentration and mixed until all the inclusion bodies were dissolved. The suspension was first centrifuged at 15000 g for 20 minutes and the supernatant filtered through a 0.45μm membrane filtration unit before being placed in a 10 kDa MWCO dialysis tubing [Sigma-
Aldrich, Ayrshire, UK]. Refolding of the protein was achieved by linear gradient dialysis placing the tube in cold 1X PBS, 1% glycerol at 4˚C with mixing, the solution was replaced
107 with fresh PBS on regular basis. After 3 days the solution inside the dialysis tube was centrifuged at 15000 g for 20 minutes and the supernatant was transferred to a new tube after sterilising it through a 20 µm membrane filter. The final protein was stored at 4 ˚C after its concentration was determined using NanoDrop 1000 Spectrophotometer [Fisher
Scientific, Wilmington, DE, USA].
2.4.7 Recombinant OPCML protein expression in HEK293 cells
Cells were transfected as explained earlier (see 2.3.1.2). Samples were harvested at different time points post transfection for analysis. No medium changes or additions were made after the transfection.
On day 4 post transfection, cells viability and count was checked before the cell culture was spun at 1500 g for 5 minutes. After discarding the supernatant, the pellet was resuspended in lysis buffer (pH 8.0, 50 mM NaH2PO4, 300 mM NaCl, 10 mM imidazole, 0.05% Tween-20) at a rate of 500 µl/ 1x107 cells. The cells were lysed by sonication on ice (15 seconds bursts at 75 Watts with 10 seconds cooling in-between bursts for a total of 10 bursts). Lysed cells were left afterwards on dry ice/ thaw cycle before centrifuging the lysate at 10000 g for 10 minutes at 4 °C. The supernatant was suspended in a Nickel Nitrilotriacetic acid resin (Ni-
NTA superflow) [Qiagen, Dusseldorf, Germany] after the resin was washed with wash buffer
(pH 8.0, 50 mM NaH2PO4, 300 mM NaCl, 20 mM imidazole, 0.05% Tween-20) for a final resin concentration of 5%. The supernatant-resin mixture was left on an orbital shaker at
4°C for 2 hours before running it through a polypropylene column [Pierce, Thermo-
Scientific, Rockford, IL, USA] and further washed with wash buffer 3 times. Finally an elution
108 buffer (pH 8.0, 50 mM NaH2PO4, 300 mM NaCl, 250 mM imidazole, 0.05% Tween-20) was added to the column and the flow-through, which contains the purified protein, was collected. The amount of protein content was determined at each step using NanoDrop
1000 Spectrophotometer [Fisher Scientific, Wilmington, DE, USA]. The final eluted protein and the left over and flow-through from each step was analysed on 10% SDS-PAGE followed by Coomassie Brilliant Blue staining [Sigma-Aldrich, Ayrshire, UK] and also confirmed by western blotting. The final protein was tested on SKOBS V1.2 cells for down-regulation of
HER2 expression (with other cells treated with PBS as a negative control) to confirm the biological activity of the eluted protein.
2.4.8 Enzyme-linked immunosorbent assay (ELISA)
2.4.8.1 Creation of α-His-tag OPCML “sandwich” ELISA plates
100 µl of capture antibody solution per well, comprising α-His-tag monoclonal antibody
[Abcam, CA, USA] diluted in coating buffer (pH 9.6: carbonate-bicarbonate [Sigma-Aldrich,
MO, USA] diluted in PBS) at a concentration of 1 µg/ml, was added in triplicates to
MaxiSorp® 96 well plates [Nunc, GmbH, Germany]. Plates were sealed with transparent adhesive film [Abgene, Thermo-Scientific, Rockford, IL, USA] and incubated overnight at 4
˚C. Next day, the plate was washed three times in wash buffer of 0.05% Tween-20 in PBS.
The plate was inverted after each wash and blotted on an absorbent paper to get rid of any unbound residual buffer. The latter process was repeated after all wash cycles. Following the wash, 300 µl of 5% milk in PBS was added to each well to block any remaining unbound sites. Again, the ELISA plate was sealed and incubated overnight at 4 ˚C. Next morning, the
109 plate was washed again three times with wash buffer and 100 µl of rOPCML standard was added to each well, comprising rOPCML [Sino Biological, Beijing, China] at serial dilutions in cold PBS staring at 10 ng/µl till zero concentration. The plate was sealed, placed on an orbital shaker for 2 minutes before it was incubated at room temperature on the lab bench for 2 hours. The plate was washed five times and 100 µl/well of polyclonal goat anti-OPCML detection antibody [R&D systems, MN, USA] diluted at a concentration of 1 µg/ml in assay diluents (pH 7.0: 10% FCS in PBS) were added and incubated for further 2 hours at room temperature. This was followed by further wash for seven times and 100 µl/well of 1/1000 anti-HRP goat secondary antibody in 5% milk in PBS solution was added and incubated for 1 hour at room temperature. Following five times final wash cycle, a 100 µl of tetramethylbenzidine (TMB) detection substrate was added to each well and incubated for
30 minutes, at room temperature, in the dark. The plate was then read at 570 nm [Optimax,
Siloam Biosciences, OH, USA] before adding 50 µl/well of ELISA stop solution (comprising 4.5
M H2SO4). The plate was read again at 450 nm, with 30 seconds shaking beforehand. For the final result, the first reading at 570 nm was deducted from the latter reading at 450 nm in order to remove the background noise.
2.4.8.2 “Grid-iron” ELISA
Following the creation of a reasonable, stable and reproducible standard curve, efforts were directed into optimising the various variables within the ELISA plate by means of a “grid- iron” experiment. In short, the ELISA plate was divided into 4 quadrants (Figure 2.1.A) with each quad being a “mini-grid”. The 6 columns in each quadrant are varied for capture
110 antibody concentration, whilst the rows represent different standards concentration, with each quad representing a different concentration of detection antibody.
Using this method will yield 16 different combinations which were all analysed and the best signal to noise ratio result was chosen as the optimum result.
The experiment was further repeated but with the slight change of fixing the concentration of the capture antibody and varying the concentrations of the secondary antibody. Hence, the upper 2 quadrants had the same concentration and the lower 2 had a different one (see
Figure 2.1.B). Again the optimised result was chosen as the one that gave the best signal to noise ratio.
111 Figure 2.1: The variation of different variables for the optimisation of the ELISA plate
A: Grid-Iron I
50 ng/ml Detection 100 ng/ml Detection 1 µg/ml Capture 1 µg/ml Capture2 2 µg/ml Capture 2 µg/ml Capture4 4 µg/ml Capture5 4 µg/ml Capture6 1 µg/ml Capture7 1 µg/ml Capture8 2 µg/ml Capture9 2 µg/ml Capture10 4 µg/ml Capture11 4 µg/ml Capture12 A Blank Blank Blank Blank Blank Blank Blank Blank Blank Blank Blank Blank B 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard C 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard D 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard E Blank Blank Blank Blank Blank Blank Blank Blank Blank Blank Blank Blank F 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard G 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard H 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 200 ng/ml Detection 400 ng/ml Detection
B: Grid-Iron II
50 ng/ml Detection 1/1000 Secondary Ab 100 ng/ml Detection 1/1000 Secondary Ab 1 µg/ml Capture 1 µg/ml Capture2 1 µg/ml Capture3 1 µg/ml Capture4 1 µg/ml Capture5 1 µg/ml Capture52 1 µg/ml Capture7 1 µg/ml Capture8 1 µg/ml Capture9 1 µg/ml Capture10 1 µg/ml Capture11 1 µg/ml Capture12 A Blank Blank Blank Blank Blank Blank Blank Blank Blank Blank Blank Blank B 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard C 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard D 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard E Blank Blank Blank Blank Blank Blank Blank Blank Blank Blank Blank Blank F 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard 1 µg/µl Standard G 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard 2 µg/µl Standard H 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 4 µg/µl Standard 200 ng/ml Detection 1/500 Secondary Ab 400 ng/ml Detection 1/500 Secondary Ab
112 2.4.8.3 “Spike and recovery” ELISA
Following the optimisation of all the variables within the ELISA plate, a “spike and recovery” experiment was conducted to test the integrity of the plate in detecting rOPCML produced in our lab. In short, a duplicate of two standards was generated using the optimised method as mentioned earlier with the exception that standard A was diluted in a 1:1 v/v of PBS and fresh FreeStyle 293 expression medium (Dilution A), whilst standard B was exactly the same as before (Dilution B). The “spike” was generated by high, medium and low dilutions of rOPCML generated in our lab in mammalian cells as mentioned above (see 2.4.7), in addition to an empty vector control, each in Dilution A and B respectively and in pentacles.
The final result is achieved by plotting the data points on the standard curve using 4- parameter non-linear fit.
113 2.5 Cell biology assays
2.5.1 Ligand stimulation
2.5.1.1 EGF stimulation
2 x 105 cells/well were seeded in a 6 well plate with RPMI full media and left to settle overnight. Next morning the media was removed and cells washed with PBS, and serum starved for at least 6 hours before either serum free media, full media (10% FCS or 15% FCS in OV-90 cells) or serum free media with 50 ng/ml EGF [R&D Systems, MN, USA] was added.
Cells were further incubated for 30 minutes at 37 °C before they were collected after lysing them in a 2% SDS lysis buffer and checked by western blotting as mentioned earlier.
2.5.1.2 VEGF stimulation
2 x 105 cells/well were seeded in a 6 well plate with RPMI full media and left to settle overnight. Next morning the media was removed and cells washed with PBS, and serum starved for at least 6 hours before either serum free media, full media (10% FCS or 15% FCS in OV-90 cells) or serum free media with 10 ng/ml VEGFA [R&D Systems, MN, USA] was added. Cells were further incubated for 30 minutes at 37 °C before they were collected after lysing them in a 2% SDS lysis buffer and checked by western blotting as mentioned earlier.
2.5.2 Cell proliferation assay
2.5.2.1 MTT proliferation assay
Cell proliferation assays were carried out in quadruplicate using the thiazolyl blue tetrazolium bromide (MTT) assay [Roche, Switzerland]. 2 x 103 cells/well were plated in 96-
114 well plates and cultured in full media (with 10% FCS). Cells were incubated with MTT for 2 hours at 37 °C and the purple fomazan product was solubilised in 100 μl dimethyl sulfoxide
(DMSO), resuspended and read on plate reader at 540 nm.
2.5.2.2 WST1 proliferation assay
Cell proliferation assays were also carried out in hexaplicates using tetrazolium salt WST1
[Roche, Switzerland]. Cells were plated in 96-well plates at a density of 1.5 x 103 cells/well, and cultured in full media (10% FCS), low serum media (0.25% FCS) or serum free media.
Cells were incubated for 24, 48 and 72 hours. Following the addition of WST1 cells were further incubated for 30 minutes at 37° C before each plate was read at 450 nm.
2.5.3 Caspase-Glo apoptosis assay
Casapse-3/7 apoptosis assays were carried out in quadruplicates using Caspase-Glo 3/7- assay kit [Promega, WI, USA]. 1 x 104 cells/well were plated in white 96-well plates [Fisher
Scientific, Wilmington, DE, USA] and duplicated in clear plastic ones for MTT cell viability analysis. Treatment started 24 hours after seeding the cells, with either BSA as a control or rOPCML at concentrations of 2, 5 and 10 μM per well for a further 24 hours. Caspase-Glo reagent was then added to the white-walled plate and the plate gently shaken for 2 hours at room temperature and analysed on a lumistar optima luminometer [BMG Labtech,
Offenburg, Germany]. Cell viability was quantified by MTT assay as described above and was used to normalise Caspase-Glo luminescence.
115 2.5.4 Florescence-activated cell sorting (FACS)
2.5.4.1 Annexin V/ Propedium iodine apoptosis FACS assay
1 x 105 cells were seeded into 6 well plates, with 1 mL RPMI full media for 24 hours. 24 hours later they were either treated with BSA (3mg/mL) or rOPCML (3mg/mL) for 6 hours.
Cells were analysed by flow cytometry after harvesting by gentle cell scraping and dual staining with Annexin V and PI [FITC Annexin V Apoptosis Detection Kit II, BD Pharmingen,
PA, USA] as per manufacturer’s protocol. Briefly, cells were washed twice with cold PBS and diluted in 1X Annexin V binding buffer at a concentration of 1 x 106 cells/ml. Then 5 µL of
Annexin V and 5 µL of PI were added and following gently vortexing and incubation for 15 minutes at room temperature in the dark, further 400 µL of 1X binding buffer was added to each tube and they were analysed immediately by flow cytometry. Flow cytometric analysis was performed using FACS™ Calibur and CellQuest software [Becton Dickinson, BD
Pharmingen, PA, USA] and FlowJo [Tree Star, OR, USA] data analysis. This experiment was done with direct supervision from Dr S Vaughan and kind help from Dr H Gungor (both of
Imperial College London).
2.5.4.2 Cell surface protein quantification FACS assay
Cells were separated using 5 ml of cell dissociation buffer [Sigma-Aldrich, Ayrshire, UK] for
10 minutes. Following spinning at 1500 g at room temperature, cells were counted and 5 x
105 cells were transferred to eppendorf tubes. Cells were washed twice in stain buffer (3%
BSA in PBS) and re-suspended in 100 µl of the same buffer and primary antibody added (no primary antibody added to the unstained and secondary antibody only controls). The eppendorfs were spun on a tube rotator [Stuart, Bibby Scientific, Shropshire, UK] at 4 ˚C for
116 1 hour, then spun at 13000 rpm and washed three times before adding the secondary antibody. Following further spin on the tube rotator for 30 minutes at 4 ˚C, the tubes were spun and washed again three times and all the cells were suspended in 200 µl/tube of 4% w/v para-formaldehyde (PFA) in PBS and stored in the fridge for 20 minutes to fix the cells.
Tubes were then spun and washed one more time before re-suspending them in 500 µl of stain buffer and transferred to FACS tubes [BD Falcon, NJ, USA] to be analysed using the BD
FACS-Calibur machine and software [BD Biosciences, NJ, USA]. The latter was done with the help of Dr Chiara Recchi, Imperial College London.
Table 2.5: List of the primary and secondary antibodies in cell surface protein quantification
FACS assay
Primary Secondary antibody Manufacturer Dilution antibody Manufacturer Dilution Monoclonal mouse anti- Goat anti- OBCAM R&D Systems 1/100 mouse 488 Alexa Fluor® 1/400 Polyclonal rabbit Goat anti-rabbit anti-VEGFR1 Cell Signaling 1/10 546 Alexa Fluor® 1/400 Polyclonal rat Goat anti-rat anti-VEGFR2 Millipore 1/50 633 Alexa Fluor® 1/400 Monoclonal mouse anti- Goat anti- VEGFR3 Millipore 1/100 mouse 488 Alexa Fluor® 1/400
117 2.5.5 Immunofluorescence confocal microscopy (IFM)
Cells grown on glass slides were fixed in 4% PFA solution (20 minutes at room temperature), washed with PBS then permeabilised for 20 minutes with PBS containing 1% BSA, 0.2% saponin (PBS-saponin) before blocking in blocking buffer PBS containing 10% goat serum,
2% BSA and 2% fetal calf serum for 1 hour. Slides were incubated with appropriate combinations of primary antibodies diluted in PBS-saponin buffer for 1 hour at room temperature, followed by washing twice in PBS-saponin buffer before incubation with appropriate secondary antibody. Slides were washed again and mounted with Vectashield mounting medium [Vector Laboratories, Peterborough, UK] and imaged on a Zeiss LSM 510 confocal microscope [Carl Zeiss, Germany]. This experiment was done with the help of Dr
Andrew Paterson and Dr Chiara Recchi, both of Imperial College London.
Table 2.6: List of Primary and Secondary antibodies used for confocal microscopy
Secondary Primary antibody Manufacturer Dilution antibody Manufacturer Dilution Monoclonal mouse anti- Goat anti- OBCAM R&D Systems 1/100 mouse 488 Alexa Fluor® 1/400 Polyclonal rabbit Goat anti- anti-VEGFR1 Cell Signaling 1/100 rabbit 568 Alexa Fluor® 1/400 Polyclonal rat Goat anti-rat anti-VEGFR2 Millipore 1/100 633 Alexa Fluor® 1/400 Monoclonal mouse Goat anti- anti-VEGFR3 Millipore 1/100 mouse 488 Alexa Fluor® 1/400 Phalloidin 546 Alexa Fluor® 1/200
118 2.5.6 Biotin “pulse-chase” labelling of cell surface proteins
Cell surface proteins were “pulsed” with a cell impermeable, cleavable biotinylation reagent
(Sulfo-NHS-SS-Biotin) [Pierce, Thermo-Scientific, Rockford, IL, USA] that was exposed to primary amines of proteins on the cell surface. In short: 2 x 105 cells/well were seeded into a
6 well plate and left to settle overnight, next morning cells were washed in ice cold PBS followed by 30 minute “pulse” incubation with the biotinylation reagent at 4 ˚C on an orbital shaker. Full media was added and cells were re-incubated for required periods at 37 ˚C in
5% CO2. Cells were then harvested, sonicated and lysed on ice for 30 minutes and the labelled surface proteins were affinity-purified using NeutrAvidin Agarose resin column
[Pierce, Thermo-Scientific, Rockford, IL, USA], followed by elution in 2X SDS lysis buffer.
Before spun-column purification of the biotinylated protein, 25% of the cell lysate was kept as a total cellular input sample and added to SDS-gel loading buffer for subsequent Western blotting analysis.
119 2.6 Targeted cell treatment
2.6.1 Trastuzumab treatment
Following OPCML and empty vector control transient transfection as detailed earlier (see
2.3.1.1), cells were serum starved for at least 6 hours before being treated for 3 hours with increasing doses of Trastuzumab (Herceptin®, [Genentech, Roche, CA, USA] reconstituted at
21 mg/ml in sterilised and filtered PBS. This was kindly provided by Dr David Leonard of
Imperial Healthcare clinical services). The final concentrations of Trastuzumab used were 2,
5, 10, 20 and 50 μg/ml. Cells were then stimulated with EGF (50 ng/ml) for 30 minutes before the cells were lysed and tested on an SDS-PAGE.
All experiments were done with positive and negative controls, including no treatment and ligand stimulation only.
2.6.2 Lapatinib treatment
Following OPCML and empty vector control transient transfection as detailed earlier (see
2.3.1.1), cells were serum starved for at least 6 hours before being treated for 3 hours with increasing doses of Lapatinib (Tyverb®, [GlaxoSmithKline, NC, USA] reconstituted in 1 ml dimethyl sulfoxide (DMSO) [Biovea, UK] to a make a stock concentration of 27.0 mM). The final concentrations of Lapatinib used were 2, 5, 10 and 20 nM. Cells were then stimulated with EGF (50 ng/ml) for 30 minutes before the cells were lysed and tested on an SDS-PAGE.
All experiments were done with positive and negative controls, including no treatment,
DMSO only and ligand stimulation only.
120 2.6.3 Erlotinib treatment
Following OPCML and empty vector control transient transfection as detailed earlier (see
2.3.1.1), cells were serum starved for at least 6 hours before being treated for 3 hours with increasing doses of Erlotinib (Tarceva®, [Genentech, Roche, CA, USA] reconstituted in 1 ml
DMSO to a make a stock concentration of 23.3 mM). The final concentrations of Erlotinib used were 2, 5, 10 and 20 nM. Cells were then stimulated with EGF (50 ng/ml) for 30 minutes before the cells were lysed and tested on an SDS-PAGE.
All experiments were done with positive and negative controls, including no treatment,
DMSO only and ligand stimulation only.
2.6.4 Bevacizumab treatment
Stably transfected OPCML and empty vector control cells were seeded at 1.5 x 105 cell/well into a 6 well plates and left to settle overnight. Next morning, cells were serum starved for at least 6 hours before being treated for 3 hours with increasing doses of Bevacizumab
(Avastin®, [Genentech, Roche, CA, USA] at 25 mg/ml and was kindly provided by Dr David
Leonard of Imperial Healthcare clinical services). The final concentrations of Bevacizumab used were 5, 10, 20, 50 and 100 μg/ml. Cells were then stimulated with VEGFA (50 ng/ml) for 30 minutes before the cells were lysed and tested on an SDS-PAGE.
All experiments were done with positive and negative controls, including no treatment and ligand stimulation only.
121 2.7 Bioinformatics and statistical analysis
Raw data and gene expression levels of OPCML and various angiogenic factors were determined using Affymetrix GeneChip® Human Genome U133 set HGU133plus2 microarrays with probes for OPCML being: 206215_at, 214111_at.
Background correction, probe-level summarisation and normalisation of raw data was performed across the full set of samples by Dr Ed Curry using Robust Multichip Average as implemented in the “Affy” package of Bioconductor.
Statistical significance and calculations of P value and adjusted P value of different probes compared to the upper and lower quartile of each OPCML probe from Affymetrix files was investigated using the log rank test calculated by R program (www.r-project.org). The latter was also used by Dr Curry to conduct the further analysis and test the significance of different variable within the RPPA data after the raw data was provided by the MD
Anderson Labs (the results, provided by the latter after running the samples, were quantified and normalised).
Full OPCML amino acid sequence were retrieved from the Uniprot website
(www.uniprot.org), the identifier for OPCML is Q14982 and was used to test the absence of mutations from the sequenced OPCML used for plasmid transfection into mammalian cell vector and rOPCML protein .
Analysis and expression of the results is reported as arithmetic mean across all replicates ± standard error of mean (SEM). Significance was analysed using Student’s t-Test (Microsoft
Excel). The following levels of significance were used: *: p<0.05; **: p<0.01; ***: p<0.001.
122 The statistical significance of comparable data for ELISA plates' measurements was analysed using a standard two-tailed Student’s t-test and errors were expressed as the mean +/- standard deviation (SD) or SEM using PRISM® software program [GraphPad, CA, US].
123 Results
Chapter three: OPCML potentiates anti-HER2 targeted therapy in ovarian and breast cancers through binding and negative regulation of HER2 but not EGFR
Chapter four: OPCML, angiogenic agents and VEGF family of receptors
Chapter five: RPPA characterisation of OPCML interactions and down-stream signalling effects
Chapter six: The production of rOPCML
124
Chapter three
OPCML potentiates anti-HER2 targeted therapy in ovarian and breast cancers through binding and negative regulation of
HER2 but not EGFR
125 3.1 Introduction
Previous work in our lab has showed that OPCML is a tumour suppressor gene that is frequently inactivated in ovarian cancer (Sellar, Watt et al. 2003), and many other cancers
(Cui, Ying et al. 2008). OPCML works by down-regulating a specific repertoire of receptor tyrosine kinases (RTKs) (McKie, Vaughan et al. 2012), namely HER2, FGFR1 and EphA2, the final result leading to reduction in phospho ERK (pERK) and phospho AKT (pAKT). In this chapter, we confirm these findings in other ovarian cancer cells by transient transfection of
OPCML. We show that transfecting OPCML into ovarian and breast cancer cell lines results in sensitising these cells to HER2 targeted therapy.
126 3.2 Transient transfection of OPCML into a panel of different ovarian cancer cell lines
results in down regulation of multiple RTKs
Previous results from our lab showed OPCML effects on SKOV-3 ovarian cell lines in terms of abrogation of specific repertoire of RTKs. We investigated whether transiently transfecting
OPCML in different ovarian cancer cell lines will have a similar effect in these lines, and what changes would occur to their RTKs and down-stream signalling. We chose PEA1, PEA2,
A2780 and PEO1 cell lines, a panel of platinum sensitive and resistant cell lines. Following transient transfection, lysate generation and western blotting, OPCML transfected cells showed abrogation of multiple RTK receptors including HER2, FGFR1 and EphA2 as well as down-stream signals including pERK (but not total ERK) and pAKT (but not total AKT). No effect was noted with regards to total EGFR, with little effect noted on phospho EGFR
(pEGFR) (Figure 3.1).
It was also interesting to note that down-regulation was clearer in PEA2, a platinum resistant cell line, even though the latter’s expression of the OPCML transgene was lower than in the other cell lines.
127 PEA1& PEA2& A2780& PEO1&
OPCML& EV& OPCML& EV& OPCML& EV& OPCML& EV&
OPCML&
tHER2&
tEGFR&
pEGFR&pY1068&&
tFGFR1&
EphA2&
tAKT&
pAKT&S473&
tERK&1/2&
pERK&1/2&
β1Tubulin&
Figure 3.1: Western blots showing the effect of OPCML transient transfection vs. that of empty vector (EV) control in PEA1, PEA2, A2780, PEO1 cell lines exhibiting the broad spectrum of OPCML effects on these cells. Despite many attempts, PEA2 cells did not seem to show any expression for FGFR1, whilst EGFR is not expressed in A2780 cells (McKie,
Vaughan et al. 2012).
128 3.3 OPCML sensitises ovarian and breast cancer cell lines response to anti-HER2 targeted therapy
As OPCML strongly down-regulates HER2 expression in normal epithelial as well cancer cell lines, this led us to examine the potential therapeutic benefits of OPCML when combined with anti-HER2 therapeutic agents. Previous results from our lab showed that stably transfected OPCML sensitises SKOV-3 derived cells to anti-HER2 therapy (unpublished data).
One ovarian and two breast cancer cell lines were transiently transfected with either OPCML plasmid or an empty vector control. The transfected cells were subjected to increasing levels of Trastuzumab with or without EGF stimulation. The experiments were conducted in HER2- positive ovarian (OV-90) and breast (SKBR-3) cancer cells and EGFR-positive, but normal
HER2 expressing, MDA-MB-231 breast cancer cells. Samples were analysed by western blotting for pERK1/2 and pAKT1 (S473). The result showed strong inhibition of ERK1/2 phosphorylation with effective concentration from as little as 2 µg/ml in the OPCML- transfected, HER2-positive OV-90 and SKBR-3 cells (Figure 3.2.A and 3.2.B), but there was no difference between the OPCML transfected and the empty vector MDA-MB-231 EGFR expressing cells (Figure 3.2.C). Similarly, inhibition of AKT1 phosphorylation was noted in the
OPCML expressing HER2-positive cells (when compared to the empty vector controls), but no difference was noted in the MDA-MB-231 cell line. Blotting for total and phospho EGFR
(pEGFR-Y1068) and total HER2 (tHER2) in all three cell lines confirms attenuation of tHER2 levels in the OPCML transfected cells, but no effect on total EGFR or pEGFR. Taken together, this is in keeping with sensitisation to Trastuzumab in the OPCML-transfected cells. We can deduce that OPCML expression confers a therapeutic advantage in anti-HER2 targeted therapy and that OPCML could potentially be a useful mechanism to overcome resistance to such targeted therapy in cancer patients, especially in HER2 positive breast cancer type.
129
130
Figure 3.2 (previous and current page): OPCML sensitises HER2 positive ovarian and breast cancer to Trastuzumab but not breast cancer cells with normal HER2 expression. (A) HER2 positive ovarian cancer cell line OV-90 shows heightened response to Trastuzumab in
OPCML transfected cells (OV-90_OPCML) compared to the empty vector control (OV-90_EV) in term of pERK1/2 and pAKT1. (B) HER2-positive breast cancer cell line SKBR-3 cells transfected with OPCML (SKBR-3_OPCML) showed decrease in its pAKT and pERK levels with a much smaller doses of Trastuzumab (from as little as 2 µg/ml with EGF stimulation). (C)
OPCML did not have any effect on pERK and pAKT expression in Trastuzumab treated normal HER2 expressing breast cancer cells MDA-MB-231. Note that total HER2 (tHER2) expression in A, B, and C lower in OPCML expressing cells compared to empty negative controls, whilst neither total EGFR (tEGFR) nor phospho EGFR (pEGFR) levels were affected.
131 3.4 OPCML potentiate dual HER2/EGFR inhibitor (Lapatinib) therapy in ovarian and breast cancer cell lines
Following on from our earlier experiments with Trastuzumab, we evaluated the effect of
OPCML expression in the cells mentioned earlier (transiently transfected OV-90, SKBR-3 and
MDA-MB-231) with the dual acting anti-HER2/EGFR small molecule tyrosine kinase inhibitor
(TKI) Lapatinib as explained before (chapter two, material and methods, section 2.6.2). Our results demonstrated that OPCML expression sensitises all the cells to Lapatinib therapy, including the EGFR-positive with normal HER2 expression that of MDA-MB-231. Figure 3.3
(A, B and C) shows ERK1/2 and AKT phosphorylation levels were strongly attenuated in all the OPCML-transfected cells (as compared to empty vector controls). The hypothesis for that being that OPCML down-regulating HER2 levels, hence less will be available for hetero- dimerisation with EGFR, thus less phosphorylated levels of the latter will be available (as shown in the relevant blots in Figure 3.3) and lower concentrations of Lapatinib will result in inhibition in pERK/pAKT.
132
133
Figure 3.3: OPCML sensitises normal expressing as well as HER2 positive ovarian and breast cancer to dual HER2/EGFR TKI Lapatinib. (A) HER2-positive ovarian cancer cell line
OV-90_OPCML shows decrease in its pAKT and pERK from as little as 2 nM with EGF stimulation, compared to empty vector control (OV-90_EV). (B) HER2-positive breast cancer cell line SKBR-3 OPCML transfected cells shows heightened response to Lapatinib compared to its empty vector control (SKBR-3_EV) with little decrease in pAKT and more pronounced effect for pERK (C) OPCML did sensitise EGFR-positive, normal HER2 expressing breast cancer cells MDA-MB-231 to Lapatinib with a clear effect on pERK and pAKT compared to empty vector control (MDA-MB-231_EV). Note that HER2 and pEGFR expression in all cells is much lower in OPCML expressing cells compared to empty vector negative controls, whilst tEGFR levels were not affected. Note that D in column two for each cell line refers to DMSO.
134 3.5 OPCML has no added therapeutic effect on ovarian and breast cancer cell line response to anti-EGFR targeted therapy
Following on from our experiments with Trastuzumab and Lapatinib, we evaluated the effect of OPCML expression in the cells mentioned earlier (transiently transfected OV-90, SKBR-3 and MDA-231) with anti-EGFR small molecule TKI Erlotinib as explained before (chapter two, material and methods, section 2.6.3). Our results demonstrated that OPCML has no effect on the response of any of these cells to Erlotinib as defined by pERK and pAKT levels in comparison to empty vector controls (Figure 3.4 A, B and C). Taken together, this result adds further weight to the selective nature of OPCML action through an interaction with HER2 but not EGFR.
135
136
Figure 3.4 (previous and current page): OPCML has no effect on cells response to anti- EGFR small molecule inhibitor Erlotinib regardless of HER2 or EGFR status. (A) HER2- positive ovarian cancer cell-line OV-90_OPCML shows showed no difference in response to Erlotinib compared to empty vector controls. (B) HER2-positive breast cancer cell line SKBR- 3 OPCML transfected cells showed no difference in their response to Erlotinib compared to the empty vector control (SKBR-3_EV). (C) OPCML did not sensitise EGFR-positive, normal HER2 expressing MDA-MB-231 breast cancer cells to Erlotinib.
137 3.7 Summary
In this chapter we reported our findings regarding the selectivity of OPCML for HER2. We confirmed by transient transfection of OPCML into multiple ovarian cancer cells the same finding from earlier experiments in the lab: namely that OPCML exerts its effect through abrogation of a specific repertoire of RTKs.
Further experiments confirmed that OPCML sensitised HER2-positive ovarian and breast cancer cells to anti-HER2 directed therapy. Furthermore, OPCML sensitised EGFR-positive, normal HER2 expressing breast cancer cells to dual acting HER2/EGFR inhibitor Lapatinib, in addition to augmenting the effect of the latter on HER2-positive ovarian and breast cancer cells. OPCML did not seem to have any sensitising effect when the cells were treated with increasing levels of anti-EGFR inhibitor Erlotinib.
The conclusion from the above experiments further add to the evidence that OPCML potentiates its effect through functional relationship with HER2 but not EGFR and may have a potential future use as a modulator to overcome resistance to targeted therapy in ovarian and breast cancers.
138
Chapter four
OPCML, angiogenic agents and the VEGF family of receptors
139 4.1 Introduction
One of the main biological capabilities, and hallmarks of cancer, is the ability of
cancer cells to induce angiogenesis (Hanahan and Weinberg 2011). The increased
microvascular permeability of the tumour vasculature has been well established as
the main factor in the formation of malignant ascites, whose amount of production
correlates with the degree of neovascularisation, with VEGFA playing an important
role in this process (Adam and Adam 2004).
Previous work in our lab (McKie, Vaughan et al. 2012) has showed that the
administration of recombinant OPCML in vivo in mice that had developed ovarian
cancer resulted in almost total disappearance of ascites in the treated murine hosts.
In this chapter, we report on our work investigating the relation between OPCML and
different angiogenis factors in silico, following which; we further investigated the
relation between OPCML and VEGFA at the cellular level and also in the media. We
further studied the association between OPCML and the various VEGF family
receptors in different stimulation conditions using western blotting, confocal
microscopy, FACS analysis, co-immuno-precipitation and OPCML pull-down assays.
We concluded with an experiment in vitro to explore if OPCML sensitises ovarian
cancer cells to bevacizumab.
140 4.2 In silico analysis of publically available microarray database shows that high
OPCML expression is associated with low levels of VEGFA and other factors that
play a role in angiogenesis
We started by investigating relationships between OPCML and various angiogenic
factors (see supplementary figures and tables). We used the online database of the
Cancer Genome Atlas (TCGA) to look for differences in RNA expression of these
factors between patients with high and low OPCML expression (defined as upper and
lower quartile of OPCML expression respectively). The significant results can be seen
in Table 4.1, the 'logFC' column indicates direction: negative values mean as OPCML
goes up, the measurements from that antibody went down. Adjusted P Value 'adj. P
Val' is Benjamini-Hochberg adjusted p-values (multiple testing correction), with
results <0.05 considered to be statistically significant and included in this section.
Table 4.1: OPCML relationship with angiogenic factors
Gene LogFC P Value Adj P Val
FGF14 0.118830368 1.33E-07 1.24E-05
EPHA1 0.4534392 1.43E-05 0.000652185
CSF1 0.09091184 2.45E-05 0.000652185
ANGPT4 0.093246312 2.81E-05 0.000652185
SEMA3C -0.286159816 0.000106863 0.001987643
FGF2 0.102541248 0.000239355 0.003709996
PDGFB 0.13739448 0.000885436 0.00939474
141 FGF5 0.076793496 0.000889739 0.00939474
EPHA1 0.069247472 0.000911902 0.00939474
THBS1 -0.491843456 0.001010187 0.00939474
FGF18 0.070836824 0.001779837 0.015047714
CTNNB1 -0.159440424 0.002530632 0.019612398
VEGFA -0.301377952 0.003922097 0.028058076
FGF12 0.099051208 0.004623927 0.028983399
KITLG 0.046748016 0.004674742 0.028983399
MMP12 -0.597338576 0.005731153 0.032705483
THBS1 -0.474346048 0.005978422 0.032705483
VEGFA -0.268281752 0.006683174 0.034529732
FGF5 0.056737792 0.009423518 0.041279762
SEMA3B 0.048256496 0.009635597 0.041279762
FGF21 0.052626904 0.009706314 0.041279762
MMP9 -0.511029864 0.009794488 0.041279762
TNF -0.246043888 0.010408074 0.041279762
FGF22 0.055413472 0.011126623 0.041279762
EPHA3 0.059503312 0.011202825 0.041279762
SEMA3C -0.348368152 0.011911489 0.041279762
PDGFB 0.098707336 0.011984447 0.041279762
CSF1 0.155203168 0.014369709 0.047727961
142 The results in table 4.1 can also be seen as Figure 4.1.A in a median centred
unsupervised hierarchical cluster, whilst 4.1.B represents median centred samples
arranged in relation to OPCML expression lower quartile. In both heatmaps, green
colour indicates a positive value i.e. the RNA array levels goes up, whilst red indicates
a negative value i.e. the array levels goes down.
One should note that for most arrays there was more than one probe to show the
expression of each factor.
Looking at the table and the heatmaps, we noted that increased OPCML expression
(at probe 206215) was associated with a significant decrease (P value 0.0039 and
adjusted P value of 0.028) in VEGFA levels (at probes 211527 and 212171). Figure 4.2
A and B shows a box-plot of OPCML in relation to OPCML expression quartiles and
VEGFA (probe 211527) in relation to OPCML upper and lower quartiles.
143 Figure 4.1.A: Median centred unsupervised hierarchical cluster of OPCML high
quartile expression and various angiogenic factors
144 Figure 4.1.B: Median centred samples of angiogenic factors arranged in relation to
OPCML expression lower quartile
145 Figure 4.2.A
Figure 4.2.B
146 4.3 OPCML expression is associated with decrease VEGFA levels in vitro
4.3.1 OPCML expression is associated with decrease VEGFA levels in stably
transfected ovarian cancer cell lines
To further test the association between OPCML and VEGFA found in silico, VEGFA
expression was assessed by means of western blotting in OPCML expressing cell lines
SKOBS-3.5, BKS-2.1 and PEO1-OP6, as well as their empty vector controls SKOBS-V1.2
and PEO1 cells, under serum free conditions. The results show that VEGFA level is
decreased in OPCML expressing cells (Figure 4.3).
147
VEGFA Rela ve Intensity 1.8 1.6 1.4 1.2 1 0.8 VEGFA Rela ve Intensity 0.6 0.4 0.2 0 V1.2 3.5 BKS PEO1 OP6
Figure 4.3: Western blot of OPCML expressing cells, and their negative control, tested
for their VEGFA levels. The bar chart underneath represents the densitometry of the
western blots.
148 4.3.2 OPCML expression is associated with decreased VEGFA levels in the
conditioned media of stably transfected ovarian cancer cell lines
After we observed VEGFA reduction in OPCML expressing cells, we measured the
level of soluble VEGFA present in the media to ensure that OPCML is not causing this
reduction by promoting the secretion of a larger proportion of VEGFA extra-cellularly.
The conditioned medium sample was prepared after the medium was incubated with
the cells for 48 hours as described previously (see chapter 2, materials and methods,
section 2.4.1.2). The results are preliminarily and it shows that VEGFA appears to be
decreased in the conditioned medium in a similar fashion to the cells expressing
OPCML (see figure 4.4).
Figure 4.4: VEGFA levels in serum free media of OPCML expressing cells (BKS-2.1 and
PEO1-OP6 as well as their negative controls.
149 4.4 OPCML abrogates total and phospho VEGFR3 levels, as well as phospho (but not
total) VEGFR2 under different stimulation conditions, but it has no effect on total or
phospho VEGFR1 expression
After we established that OPCML decreases VEGFA levels, we tested if OPCML has
any influence on the VEGF receptor family (VEGFRs), including VEGFR1, VEGFR2 and
VEGFR3.
4.4.1 Overexpression of OPCML results in decrease in total and phospho VEGFR3
levels, as well as phospho VEGFR2, in stably transfected ovarian cancer cell lines
Starting with the stably transfected cell lines, VEGFRs were tested by western
blotting, under serum free conditions, full media and with VEGFA stimulation. Using
SKOV-3 ovarian cancer cell lines derived cells, OPCML overexpression in BKS2.1 cells
seems to abrogate total as well as phospho (at Tyrosine (Tyr) 1230 and 1231) VEGFR3
levels (Figure 4.5). It seems also that that OPCML decreases the phosphorylated
levels of VEGFR2 (at Tyr 1212) but has no significant effects on the total expression of
VEGFR2. There was no effect on the expression of total VEGFR1, with less
phosphorylated levels (at tyrosine 1213)) in SKOBS-V1.2 compared with BKS-2.1
(Figure 4.5).
150
Figure 4.5: Western blot showing OPCML-negative SKOBS-V1.2, and OPCML-
expressing SKOBS-3.5 and BKS-2.1 under serum free conditions (S), full media (F) and
VEGFA stimulation at 10 ng/ml.
4.4.2 OPCML knockdown in normal ovarian surface epithelial cells results in
increased expression of total and phospho VEGFR3 levels, as well as phospho
VEGFR2
Ovarian surface epithelial (OSE) cell lines stably knockdown for OPCML were tested
for VEGFRs expression at total and phosphorylated levels. The results confirmed
151 earlier findings from the SKOV-3 derived cell lines, namely, that overexpression of
OPCML is associated with decrease in the levels of total and phospho VEGFR3, as well
as, phospho VEGFR2 in normally expressing OPCML cells (PLKO-1.3) compared to its
60% (sh-464-23) and 95% (sh-339-23) knockdowns (Figure 4.6). Again, no significant
effects were noted for total VEGFR2, and total VEGFR1. However, there seems to be
a drop in pVEGFR1 levels with OPCML knockdown (Figure 4.6).
Figure 4.6: Western blot showing VEGFRs in OSE OPCML knockdowns (PLKO-1.3 (0%),
sh-464-23 (60%) and sh-339-24 (95%)) under serum free conditions (S), full media (F)
and VEGFA stimulation at 10 ng/ml.
152 4.4.3 OPCML transient transfection into a host of different ovarian cancer cell lines
results in a decrease in total and phospho VEGFR3 levels, as well as phospho (but
not total) VEGFR2
To further verify the results that we obtained in stably transfected and knockdown
cells, OPCML was transiently transfected into four ovarian cancer cell lines (PEA1,
PEA2, A2780 and PEO1). The lysed cells were tested on SDS-PAGE. The results
confirmed the finding we noted in stably expressing cells, i.e. OPCML transfection, as
compared to empty vector controls, is associated with reduced expression of total
and phospho VEGFR3 levels (albeit at different level in each cell line), in addition to
pVEGFR2 but not tVEGFR2 or tVEGFR1 under serum free conditions. There did not
seem to be any effect of OPCML expression on pVEGFR1 either (Figure 4.7). The
transfected cells were also tested for VEGFA expression, the results of which
confirmed earlier finding of VEGFA reduction in OPCML expressing cells as it can be
seen in Figure 4.7.
153
Figure 4.7: VEGFA expression, as well as total, phospho VEGFR3 and phospho VEGFR2
levels were reduced in transiently transfected OPCML ovarian cancer cell lines (PEA1,
PEA2, A2780 but not clearly in PEO1) compared to empty vector (EV) controls under
serum free conditions. No effects were noted with regards to total VEGFR2
expression or that of VEGFR1.
154 4.5 OPCML expression is associated with attenuated response to VEGFA stimulation
for both total and phospho VEGFR3
Following ligand stimulation with VEGFA in serum starved SKOBS-V1.2, SKOBS-3.5
and BKS-2.1 cells, the activation of VEGFRs family in all these cells was studied. The
results show that OPCML attenuates the response of the cells to VEGFA in terms of
total and phospho VEGFR3 (at T1230/1231), phospho VEGFR2 (at T1212) and even
phospho VEGFR1 (at T1213), with the peak response being 30 minutes for BKS-2.1
cells. However, in SKOBS-V1.2 cells, they continued to show an increased response
after 60 minutes stimulation for all the phosphorylated proteins tested (Figure 4.8).
Thus, this experiment shows that the expression of OPCML in BKS-2.1 cells resulted in
a stronger inhibition of VEGFR3.
155
Figure 4.8: Variable effects on VEGF receptor family following time point stimulation
with 10 ng/ml of VEGFA. In addition to OPCML inhibition of total and phospho
VEGFR3 at baseline, there was an attenuated response to the stimulation in BKS-2.1
cells with a slight induction peak observed at 30 minutes for both VEGFR2 and
VEGFR3. It also seems that phospho VEGFR1 (at T1213); but not total VEGFR1, do
respond to VEGFA stimulation but at a less accelerated pace with the peak visible at
60 minutes for all the cells.
156 4.6 Confocal microscopy supports OPCML-associated depletion of VEGFR3
To further examine the specific loss of VEGFR3 expression in association with OPCML,
confocal microscopy was used to detect VEGFR3 levels in both SKOBS- V1.2 and BKS-
2.1 cell lines. The cells were grown on glass slides and fixed in 4% paraformaldehyde
(see materials and methods, immunofluorescence confocal microscopy, section
2.5.5). Slides were incubated with appropriate combinations of monoclonal OPCML
and VEGFR3 antibodies in addition to DAPI. Slides incubated with secondary
antibodies only were used as negative controls. Other slides were also tested for
VEGFR1 and VEGFR2.
As expected, OPCML expression is hardly present in SKOBS-V1.2, alongside a stronger
signal for VEGFR3. In BKS-2.1 however, OPCML is strongly expressed, while VEGFR3 is
reduced compared with SKOBS-V1.2 (Figure 4.9). There did not seem to be any
difference between SKOBS-V1.2 and BKS-2.1 in terms of VEGFR1 and VEGFR2
expression (see Appendix A, Supplementary Figures).
This data suggests that the expression of OPCML in the SKOV-3 cell line results in the
negative regulation of VEGFR3 expression and supports earlier observations of
preferential attenuation by OPCML.
157 SKOBS-V1.2 BKS-2.1 OPCML& VEGFR3&
Figure 4.9: Immunofluorescence confocal microscopy of OPCML expressing cells
(BKS-2.1), right panels, and empty vector control (SKOBS-V1.2), left panels. The data
demonstrated almost absent OPCML expression in SKOVS-V1.2 (upper row) and the
decreased expression of VEGFR3 in BKS-2.1 cells (lower row).
158 4.7 Fluorescence-activated-cell-sorting (FACS) analysis of OPCML transfected
ovarian cancer cells shows decreased expression of VEGFR3 but not VEGFR2 or
VEGFR1
The specific loss of VEGFR3 expression in association with OPCML was further
examined using FACS analysis to determine the degree of decreased expression of
VEGFR3 in PEO1 derived ovarian cancer cell lines. The cells were prepared as
explained earlier (see materials and methods, cell surface protein quantification FACS
assay, section 2.5.4.2). Cells were fixed following incubation with monoclonal OPCML
and VEGFR3 antibodies and their appropriate secondary antibodies. Unstained cells
and secondary antibodies only tubes were used as negative controls. Other tubes
were also tested for VEGFR1 and VEGFR2 with the appropriate antibody incubation.
The results show that OPCML expression is hardly present in PEO1 cells, alongside a
stronger signal for VEGFR3. In PEO1-OP6 cells however, OPCML is strongly expressed,
while VEGFR3 is reduced compared with PEO1 cells (see figure 4.10 A and B). There
didn’t seem to be any difference between the two cell lines in terms of VEGFR1 and
VEGFR2 expression (see Appendix A, Supplementary Figures).
This data gives further evidence that the expression of OPCML results in the negative
regulation of VEGFR3 expression and supports earlier observations of preferential
attenuation by OPCML.
159 4 peo1 unstained 2nd exp.016 4 peo1 unstained 2nd exp.016 peo1 unstained 2nd exp.016 10 10 200 3 3 160 10 10 2 2 120 10 10
R1 FL4-H SSC-H 80 Counts 1 1 R3 R2 10 10 40 0 0 0 10 10 0 1 2 3 4 0 200 400 600 800 1000 10 10 10 10 10 100 101 102 103 104 FSC-H FL1-H FL1-H 4 op6 unstained 2nd exp.008 4 op6 unstained 2nd exp.008 op6 unstained 2nd exp.008 10 200 10 3 3 160 10 10 2 2 120 10 10 FL4-H SSC-H 80 Counts 1 1 R3 R2 10 10 40 0 0 0 10 10 0 1 2 3 4 0 200 400 600 800 1000 10 10 10 10 10 0 1 2 3 4 FL1-H 10 10 10 10 10 FSC-H FL1-H
4 peo1 2nd ms only 2nd exp.017 peo1 2nd ms only 2nd exp.017 4 peo1 2nd ms only 2nd exp.017 200 10 10 3 3 160 10 10 2 2 120 10 10 FL4-H 80 SSC-H Figure 4.10.A: FACS analysis confirming OPCML expression levels in OPCML Counts 1 1 R3 R2
expressing cells (PEO110 -OP6) compared to their negative control (PEO1) 10 40 0 0 0 10 10 0 1 2 3 4 0 200 400 600 800 1000 10 10 10 10 10 100 101 102 103 104 FSC-H FL1-H FL1-H
4 op6 opcml 2nd exp.012 4 op6 opcml 2nd exp.012 op6 opcml 2nd exp.012 4 op6 2nd ms only 2nd exp.009 4 op6 2nd ms only 2nd exp.009
200 op6 2nd ms only 2nd exp.009 10 10 10 10 200 3 3 3 3 160 10 10 160 10 10 2 2 120 2 2 10 120 10 10 10 FL4-H SSC-H 80 Counts FL4-H 1 SSC-H 1 80 1 Counts 1 10
10 R3 R2 10 10 40 40 0 0 0 0 0 10 10 10 10 1000 101 1022 1033 1044 0 0 0 200200 400400 600600 800800 10001000 10 10 10 10 10 100 0 101 1 102 2 103 3 104 4 FSC-HFSC-H FL1-H 10 10 10 10 10 FL1-HFL1-H 4 peo1 opcml only 2nd exp.020 4 peo1 opcml only 2nd exp.020 peo1 opcml only 2nd exp.020
4 peo1 V3 only 2nd exp.023 4 10 peo1 V3 only 2nd exp.023 peo1 V3 only 2nd exp.023 10 200 10 200 10 3 3 160 3 10 10 3 160 10 2 10 2 120 10 2 10 2 120 FL4-H SSC-H 80 Counts 1 10 1 10 FL4-H SSC-H 80 Counts 10 10 1 1 40 0 0 10 10 40 10 10 0
0 0 0 1 2 3 4 0 200 400 600 800 1000 10 10 10 10 10 0 1 2 3 4 0
10 FSC-H 10 FL1-H 10 10 10 10 10 0 200 400 600 800 1000 100 101 102 103 104 0 1 FL1-H2 3 4 10 10 10 10 10 FSC-H FL1-H FL1-H
Figure 4.10.A: Cell surface protein FACS assay of OPCML protein in PEO1 cells (top) 4 op6 V3 2nd exp.015 4 op6 V3 2nd exp.015 op6 V3 2nd exp.015 10 200 10 Page 1
3 and PEO1-OP6 (bottom). Analysis of the results shows that only 1.4% of PEO1 cells 3 160 10 10 did stain positive for OPCML (X geo mean of 43.1), whilst 20.1% of PEO1-OP6 cells 2 2 120 10 10 FL4-H SSC-H 80 were positive for OPCML (X geo mean of 361.1). Counts 1 1 10 10 40
0 0 0 10 10 0 1 2 3 4 0 200 400 600 800 1000 10 10 10 10 10 100 101 102 103 104 FSC-H FL1-H FL1-H
4 peo1 2nd ms only 2nd exp.017 peo1 2nd ms only 2nd exp.017 4 peo1 2nd ms only 2nd exp.017 200
10 10 3 3 160 10 10 2 2 120 10 10 160 FL4-H 80 SSC-H Counts 1 1 10 10 40 0 0 0 10 10 0 1 2 3 4 0 200 400 600 800 1000 10 10 10 10 10 100 101 102 103 104 FSC-H FL1-H FL1-H 4 op6 2nd ms only 2nd exp.009 4 op6 2nd ms only 2nd exp.009 op6 2nd ms only 2nd exp.009 10 10 200 3 3 160 10 10 2 2 120 10 10 FL4-H SSC-H 80 Counts 1 1 10 10 40 0 0 10 10 0 1 2 3 4 0 0 200 400 600 800 1000 10 10 10 10 10 0 1 2 3 4 FSC-H FL1-H 10 10 10 10 10 FL1-H
Page 2 4 op6 opcml 2nd exp.012 4 op6 opcml 2nd exp.012 op6 opcml 2nd exp.012 200 10 10 3 3 160 10 10 2 2 120 10 10 FL4-H SSC-H 80 Counts 1 1 10
10 Figure 4.10.B: FACS analysis showing decreased expression of total VEGFR3 levels in
40
0 0 OPCML expressing cells (PEO1-OP6) compared to their negative control (PEO1) 0 10 10 0 1 2 3 4 0 200 400 600 800 1000 10 10 10 10 10 0 1 2 3 4 FL1-H 10 10 10 10 10 FSC-H FL1-H
4 peo1 V3 only 2nd exp.023 4 peo1 V3 only 2nd exp.023 peo1 V3 only 2nd exp.023 10 200 10 3 3 160 10 10 2 2 120 10 10 FL4-H SSC-H 80 Counts 1 1 10 10 40 0 0 0 10 10 0 1 2 3 4 0 200 400 600 800 1000 10 10 10 10 10 100 101 102 103 104 FSC-H FL1-H FL1-H
4 op6 V3 2nd exp.015 4 op6 V3 2nd exp.015 op6 V3 2nd exp.015 10 200 10 3 3 160 10 10 2 2 120 10 10 FL4-H SSC-H 80 Counts 1 1 10 10 40 0 0 0 10 10 0 1 2 3 4 0 200 400 600 800 1000 10 10 10 10 10 100 101 102 103 104 FSC-H FL1-H FL1-H
4 peo1 2nd ms only 2nd exp.017
peo1 2nd ms only 2nd exp.017 4 Figure 4.10.B: peo1Cell surface protein FACS assay of VEGFR3 protein in PEO1 cel 2nd ms only 2nd exp.017 ls (top) 200 10 10 3 3
and PEO1-OP6 (bottom). Analysis of the results 160 shows that PEO1 cells did stain 10 10 2 2 positive for VEGFR3 (mean of 220.4 and geo mean of 111.8), whilst PEO1120 -OP6 cells 10 10 FL4-H 80 SSC-H Counts 1 1 were much less positive for VEGFR3 (mean of 39.6 and geo mean of 26.1) 10 10 40 0 0 0 10 10 0 1 2 3 4 0 200 400 600 800 1000 10 10 10 10 10 100 101 102 103 104 FSC-H FL1-H FL1-H 4 op6 2nd ms only 2nd exp.009 4 op6 2nd ms only 2nd exp.009 op6 2nd ms only 2nd exp.009 10 10 200 3 3
160 10 10 2 2 120 10 10 FL4-H SSC-H 80 Counts 1 1 161 10 10 40 0 0 10 0 10 0 1 2 3 4 0 200 400 600 800 1000 10 10 10 10 10 0 1 2 3 4 FSC-H FL1-H 10 10 10 10 10 FL1-H
Page 2 4.8 Pull down assay of OPCML-GST fusion protein in SKOBS-V1.2 shows OPCML
direct interaction with VEGFR3
Recombinant GST-tagged OPCML, previously produced in our lab, was employed in
the pull-down experiments (as explained before in chapter 2, materials and methods,
pull down assay, 2.4.4.2), conducted in SKOBS-V1.2 cells, an ovarian cancer cell line
with no detectable OPCML. VEGFR3, but not VEGFR1 or VEGFR2, was pulled-down
from the cell lysate by the OPCML-GST fusion protein (Figure 4.11). We further
investigated whether by co-immuno-precipitation (CO-IP) we are able to detect
VEGFR3 and OPCML together. The CO-IP was not successful (see Appendix A,
Supplementary Figures).
The pull down experiment however provided evidence that OPCML might directly
interact with VEGFR3.
162
Figure 4.11: GST-OPCML fusion protein (and their negative controls) was added to
SKOBS-V1.2 cell lysate and then immobilised on the Magne-GST™ pull down system
by being bound to magnetic glutathione 4B resin beads. It confirms VEGFR3 pull
down by OPCML. LO = Lysate Only, LB = Lysate and Beads, LBE = Lysate, Beads and
magneGST-empty vector, LBO = Lysate, Beads and magneGST-OPCML.
163 4.9 OPCML has no sensitising effects on ovarian cancer cells response to
bevacizumab
Further to our earlier findings regarding OPCML and VEGFA, we evaluated the effect
of OPCML expression on the response to VAGFA monoclonal antibody bevacizumab
in BKS 2.1 and SKOBS-V1.2, and PEO1-OP6. The experiment was carried out as
explained previously in chapter two, materials and methods, section 2.6.4. For both
cell types, whilst OPCML expressing cells showed evidence of less VEGFA levels, this
did not yield any significant effects in terms of phospho VEGFR2 (pVEGFR2 – T1212)
response or that of phospho ERK (pERK) and phospho AKT (pAKT) inhibition (Figure
4.12 A and B).
164
Figure 4.12: OPCML has no effect on cells response to anti-VEGFA monoclonal
antibody (Bevacizumab). (A) OPCML expressing cells BKS 2.1 as well as empty vector
controls SKOBS-V1.2 showed no significant difference in response to bevacizumab
treatment as seen in pAKT and pERK response. (B) OPCML expressing PEO1-OP6 and
OPCML negative PEO1 cells did not show any significant difference in response to
bevacizumab treatment as seen in pAKT and pERK response.
165 4.10 Summary
In this chapter, we reported our promising findings on the association between
OPCML and angiogenic factors found in silico. In particular we were interested in
further exploring the relation between OPCML and VEGFA. We proved that OPCML
expression is associated with decrease in VEGFA levels at cellular levels. The results
were confirmed in multiple cell lines. We then further explored the relation of
OPCML to the VEGFA family of receptors. Our results showed that OPCML decreases
the total and phosphorylated levels of VEGFR3, as well as that of phospho (but not
total) VEGFR2 in some cell lines. OPCML did not seem to have any effects on the total
or phosphorylated levels of VEGFR1 receptors.
We concluded by testing if OPCML sensitises ovarian cancer cells to anti-VEGFA
inhibitor bevacizumab. Whilst the results did not show any sensitising effects, this
needs to be interpreted with caution. Further experiments are required to check if
OPCML has any future potential to contribute to anti-angiogenesis therapy with
other therapeutic agents and inhibitors, such as anti-VEGFR3 targeted therapy.
166
Chapter five
RPPA characterisation of OPCML interactions and down-stream signalling effects
167 5.1 Introduction
Previous work in ovarian cancer cell lines in our lab identified that OPCML exerts its effects through the abrogation of a repertoire of multiple RTKs such as HER2 and FGFR1. This leads to the attenuation of the phosphorylation levels of phospho-ERK and phospho-AKT with the resultant effect being apoptosis and inhibition of proliferation. In this chapter, we report on the different effects of OPCML on a panel of proteins involved in signalling events within the cell by the use of reverse phase protein micro-array (RPPA) in two ovarian cancer cell lines.
Important findings from the RPPA data were validated using western blotting and cell proliferation assays.
168 5.2 RPPA
5.2.1 Background
RPPA (Paweletz, Charboneau et al. 2001) use is currently expanding as a high throughput source for screening and systematic assessment of cellular proteins. It can be used to monitor protein expression levels activation patterns, detection of post-translational modifications in a quantitive manner. RPPA analysis has great potential for identifying new directions, targets and biomarker candidates for further evaluation using pathway activation profiling via a specific and well characterised antibodies (Troncale, Barbet et al. 2012).
The benefits of RPPA include the ability for high sample capacity testing and low sample consumption to generate reproducible data, thus guaranteeing comparability between independent sample printing runs and antibody incubation. The process is limited by the quality of the primary antibodies used and the standardisation process for each antibody with further need for secondary confirmation of the results by western blotting (Troncale,
Barbet et al. 2012). Methods developed to overcome some of the difficulties include near infrared detection for the proteins tested (Loebke, Sueltmann et al. 2007), as well as data analysis tools to yield quantitative information on protein abundance and protein activation e.g. serial dilution curve (Zhang, Wei et al. 2009).
RPPA-based proteome profiling was centered on detecting the activation state of key signalling players such as of PI3K/AKT/mTOR, RAS/RAF/MEK/ERK, and STAT, as well as additional cancer-relevant pathways such as p53 signalling, apoptosis, and cell cycle control
(Ummanni, Mannsperger et al. 2014).
169 5.2.2 Antibodies
The experiments were carried out in two batches, in each using stably transfected OPCML lines and an empty vector control under serum free or EGF stimulatory conditions. Batch one used the SK-OV-3 derived cells BKS 2.1 (with up to 30 fold the level of OPCML expression) and SKOBS-V1.2 empty vector control, in addition to OPCML mutant cells
SKOBS-P95R-3.4. The total number of antibodies tested was 172 and further detailed analysis was carried out at our lab in Imperial College London with the help of the bioinformatics group. A second experiment was carried out using the same settings of serum free media and EGF stimulation with PEO1-OP6 cells (with 6 folds the level of OPCML) and PEO1 cells as control. The resulting analysis was carried out at out lab as with the SK-
OV-3 stables. The RPPA of PEO1/ PEO1-OP6 cells reported on 214 antibodies, including 70 new ones, whilst 15 antibodies were excluded from the second experiment (for full details of all the antibodies used in both experiments, as well as the ones added and excluded from the second one, please refer to Appendix A, Supplementary Tables).
170 5.2.3 Data quantification, normalisation and analysis
Using the ‘supercurve’ method, relative protein levels for each sample were determined and constructed by a Bioinformatics script in the statistical software R. Following this, all the data points were normalised for protein loading and transformed to linear values before further transformation into Log2 values, and finally median-centred values for Hierarchical
Cluster analysis and heatmaps were generated.
Figure 5.1 shows heatmaps that were generated for an Unsupervised Hierarchical cluster
(unsupervised on both antibodies and samples of SKOBS-V1.2, SKOBS-P95R-3.4 and BKS-2.1 under serum free media and EGF stimulation). The heatmap included was generated in
Cluster 3.0 (http://www.eisenlab.org/eisen/) as a hierarchical cluster using Pearson
Correlation and a centre metric. The resulting heatmap was visualised in Treeview
{http://www.eisenlab.org/eisen/) and presented as a high resolution .bmp format. The stained slides were analysed on Array-Pro then by supercurve R x64 2.15.1. There were 18 sets of replicated antibodies and 3 negative controls for secondary antibodies. Quality control (QC) test was performed for each antibody staining (slide). QC score above 0.8 indicates good antibody staining and were included in the Heatmaps. In the case of antibodies with replicates, the one with the highest QC Score was used. 5.1.A represents median centred unsupervised hierarchical cluster whilst 5.1.B represents median centred samples arranged in order of cell line and EGF stimulation.
Green colour indicates a positive value i.e. as OPCML levels go up; the measurements from that antibody went up. Red indicates a negative value i.e. as OPCML levels go down; the measurements from that antibody went down.
171 Figure 5.1.A: RPPA median centred unsupervised hierarchical cluster for SKOBS-V1.2,
SKOBS-P95R-3.4 and BKS-2.1 cells under serum free media and EGF stimulation.
172 Figure 5.1.B: RPPA median centred samples for SKOBS-V1.2, SKOBS-P95R-3.4 and BKS-2.1 cells arranged according to cell line and EGF stimulation.
173 Figure 5.2 shows heatmaps that were generated for an Unsupervised Hierarchical cluster
(unsupervised on both antibodies and samples of PEO1 and PEO1-OP6 under serum free media and EGF stimulation). The heatmap were generated, visualised and analysed in a similar fashion as was done with SKOBS-V1.2, SKOBS-P95R-3.4 and BKS-2.1 cell lines. 5.2.A represents median centred unsupervised hierarchical cluster whilst 5.2.B represents median centred samples arranged in order of cell line and EGF stimulation. Again, green colour indicates a positive value i.e. as OPCML levels goes up; the measurements from that antibody went up. Red indicates a negative value i.e. as OPCML levels goes down; the measurements from that antibody went down.
174 Figure 5.2.A: RPPA median centred unsupervised hierarchical cluster for PEO1 and PEO1- OP6 cells under serum free media and EGF stimulation.
175 Figure 5.2.B: RPPA median centred samples for PEO1 and PEO1-OP6 cells arranged according to cell line and EGF stimulation.
176 5.2.4 Generalised analysis of OPCML effects on different proteins
Using both cell line models and serum free and stimulation conditions, the following proteins were found positively associated with OPCML i.e. the protein levels increase with an increase in the level of OPCML (Table 5.1.A).
Table 5.1.A: Generalised analysis of RPPA data showing proteins with increased expression associated with increase in OPCML levels.
14-3-3_beta Cox 2
4E-BP1 EGFR
Akt ETS-1
AR Fibronectin
ATP5H FoxM1
Bad_pS112 GCN5L2
BAP1 GPBB
Bax NF2
Bcl-2 P21
Bim Paxillin
BRCA2 PDCD1L1 c-Jun_pS73 PDCD4 c-Kit PDGFR_beta
Caspase-7_cleavedD198 SCD1
CD31 SF2
CDKN2A_p16INK4a Src
177 Chk1_pS345 Transglutaminase
Chk2_pT68 UBAC1
UGT1A VEGFR2
Under the same criteria, high OPCML expression was negatively correlated with the other proteins, i.e. the protein levels decrease with an increase in OPCML levels (Table 5.1.B)
Table 5.1.B: Generalised analysis of RPPA data showing proteins with decreased expression associated with increase in OPCML levels.
A-Raf IGF1R
Akt_pS473 INPP4B
ATM MAPK_pT202_Y204 c-Met MDM2_pS166
Chk2 MEK1 cIAP MEK1_pS217_S221
Claudin-7 p38 alpha MAPK
ER-alpha p70S6K_pT389
FAK p90RSK
FAK_pY397 PAl-1
FOX03a PCNA
G6PD PDK1
GSK3-alpha-beta_pS21_S9 PDK1_pS241
GYS PI3K-p85
178 GYS_pS641 Porin
HER2 PRAS40_pT246
HER3 Rad51
HER3_pY1298 Rb_pS807_S811
S6_pS240_S244 STAT5-alpha
SDHA Tuberin
Smac TYRO3
TRFC XRCC1
Generalised analysis of the above proteins show that certain groups and biological processes seem to be primarily influenced by OPCML levels. This include proteins responsible for apoptosis, including Akt_pS473, cIAP, FOX03a, MAPK_pT202_Y204, INPP4B,
Porin, MEK1 and MEK1_pS217_S221, p38 alpha MAPK, p70S6K_pT389, p90RSK and Smac;
MAPK/ERK and PI3K/AKT pathways, including increase in levels of Bad-pS112, Bax, Bcl-2,
Bim, Akt, caspase-7_cleavedD198; proteins involved in other pathways such as c-Jun_pS73 of the JNK pathway, NF2 of the Hippo/SWH pathway and PDCD1L1 that leads to T-cell proliferation.
OPCML has previously demonstrated a role as an anti-proliferative regulator through effects downstream of the PI3K/Akt pathway as mentioned earlier in the introduction in chapter one. In addition, here we see an effect through an increase in P21, CDKN2A_p16INK4a as well as cKit and a decrease in ER-alpha, PDK1, PDK1_pS241 and PI3K_p85, PRAS40_pT246 and Tuberin.
179 The other notable feature noted from the above proteins are the ones concerned with signal transduction, cell cycle progression and DNA damage and repair mechanism. High
OPCML was associated with an increase in 14-3-3_beta, Bap1, BRCA2, Chk1_pS345,
Chk2_pT68, SF2, Src, Transglutaminase and UBAC1. High OPCML was also associated with decrease in A-Raf, ATM, total Chk2, MDM2_pS166, PCNA, Rad51, Rb_pS807_S811, STAT5-α and XRCC1. OPCML seems also to play a role in transcription and translation via altering the levels of 4E-BP1, ETS-1, FoxM1, GCN5L2, PDCD4 and S6_pS240_S244.
With regards to proteins that are involved in cell adhesion and metastasis, high levels of
OPCML were associated with increased CD31, Paxillin and Fibronectin, and with decreased levels of c-Met, Claudin-7, FAK and FAK_pY397, PAI-1 in addition to GSK3-∝-β_pS21_S9.
Another thing noted was that OPCML seems to alter the levels of a lot of compounds that play a role in generalised cell functions like fatty acid and glycogen synthesis such as AR,
ATP5H, Cox 2, GPBB, SCD1, UGT1A, G6PD, GYS and GYS_pS641, SDHA and TRFC.
And finally, in addition to the previously known effect of OPCML on abrogating the levels of
HER2, OPCML is associated with a decrease in c-MET (as mentioned earlier), HER3 and
HER3_pY1298, IGF1R and TYRO3. OPCML was also associated with increase in the levels of total EGFR, PDGFR-β and VEGFR2.
180 5.2.5 Analysis of combined OPCML effects, effects of EGF stimulation and batch effect
Using the R program with full input from Dr Ed Curry (Bioinformatics Department, Imperial
College London), more detailed analysis was conducted and modelled out taking into account the effect of batch and the effect of EGF stimulation, with the impact of OPCML overexpression being the variable of interest. This was expressed separately for each cell line due to heterogeneity of these cells but the results represent the consistent changes across both batches. This was achieved by combining the two studies and then corrected for batch with 'combat' as this gave a lot more statistical power than processing each experiment separately.
The second part of the analysis included modelling the OPCML effect across all samples, but taking into account the serum conditions.
The aim of using the above methods was to see if the significant proteins were likely to change in both EGF and serum-free conditions, accepting that their baseline levels may be different across the cell lines.
BKS 2.1 – combined OPCML effects can be seen in table 5.2.A. This shows the overall impact of OPCML in BKS 2.1 cells, with 48 (out of 172) proteins being affected. The 'logFC' and 't' columns indicate direction: negative values mean as OPCML goes up, the measurements from that antibody went down. Adjusted P Value 'adj.P.Val' is Benjamini-Hochberg adjusted p-values (multiple testing correction), with results <0.05 considered to be statistically significant and included in this section.
The results show that OPCML is significantly associated with an increase in the levels of:
Fibronectin (extracellular matrix integrin binding glycoprotein), Dvl3 (Wnt signal
181 transduction pathway), and Heregulin (a ligand for HER3 and HER4), YAP (transcriptional modulator in Hippo pathway, restricting proliferation and promoting apoptosis), IRS1
(activation of PIP3 kinase or GRB2), Snail (epithelial to mesenchymal transition), VEGFR2
(RTK, important player in angiogenesis), B-Raf (mitogenic signal transduction from membrane to nucleus), FASN (catalyses the long chain fatty acid synthesis from acetyl co-A),
Akt (apoptosis), 53BP1 (DNA repair, binds TP53), BRCA2 (dsDNA break repair), caveolin-1
(scaffolding protein for the formation of caveolae), MEK-1 (activation of MAPK/ERK), Src
(non receptor tyrosine kinase), P21 (cell growth arrest through inhibition of cycline dependent kinase), Bim (pro-apoptotic protein inducer of Bax/Bak oligomerisation), PARP- cleaved (single-strand DNA repair), Chk1-pS345 (localises Chk1 to the nucleus following checkpoint activation resulting in DNA damage and tumour suppression), c-Kit (a cytokine receptor RTK whose activation results in cell survival, proliferation & differentiation) and finally cyclin_B1 (mitosis regulatory protein & forms complexes with Cdk1).
Further analysis of the table shows that increased levels of OPCML is associated with significant decrease in: mTOR_pS2448 (central regulator of cell growth, metabolism and survival), G6PD (fatty acid synthesis), ER-alpha (estrogen receptor involved in cell proliferation and differentiation), NDRG1_pT346 (acts as a tumor suppressor in many cell types), Chk2 (induction of cell cycle arrest & DNA damage), TAZ (transcription repressor),
INPP4B (plays a role in activation of PIP3 in the PI3K/AKT pathway), PDK1 and PDK1_pS241
(activation of AKT1 and AKT2), HER2_pY1248 (site of HER2 phosphorylation), eEF2K
(regulation of protein synthesis via peptide chain prolongation control), Notch-1 (trans- membrane receptor controlling cell fate and anti-angiogenic sprouting), c-MET (RTK), p90RSK (promotes cell proliferation downstream of mTOR, represses BAD), PI3K-p85 (PI3K
182 regulatory subunit), rictor and rictor-pT1135 (subunit of mTORC2, mTORC2 plays a critical role in AKT1 'Ser-473' phosphorylation), Smad4 (transcription activator), EGFR_pY1068
(activation of EGFR RTK and its effects downstream), ACC-pS79 (catalyses acetyl CoA; main enzyme in biosynthesis and oxidation of fatty acids), HER3-pY1298 (activation of HER3 RTK),
Rb (regulator of cell entry division and tumour suppressor), Tuberin-pT1462 (inhibits activation of S6 by negatively regulating mTORC1 activity and leading to down-regulation of ribosomal translation of RNA), Src-pY146 (activation of Src), 4E-BP1_pS65 (activation of mRNA translation), AMPK-alpha (increases cell uptake of glucose and fatty acid oxidation) and finally NF-kB_p65_pS536 (major regulator of DNA transcription).
Table 5.2.A: BKS-2.1 – combined OPCML effects
Antibody LogFC t P-Value Adj-P-Val
ER-alpha -0.62090149 -10.22044678 3.75E-06 0.00039372
Dvl3 0.162422265 9.971823357 4.58E-06 0.00039372
Heregulin 0.388277439 8.241513645 2.09E-05 0.001006314
TAZ -0.182688123 -8.102353891 2.39E-05 0.001006314
INPP4B -0.366865247 -7.646445526 3.74E-05 0.001006314 mTOR_pS2448 -0.167565702 -7.507727809 4.31E-05 0.001006314
NDRG1_pT346 -0.610256065 -7.437048442 4.63E-05 0.001006314
G6PD -0.31895285 -7.426799627 4.68E-05 0.001006314
PDK1 -0.100945598 -7.214417682 5.84E-05 0.001009086
PDK1_pS241 -0.298893735 -7.209902004 5.87E-05 0.001009086
183 Fibronectin 0.227438667 6.580436071 0.000116393 0.00181996
HER2_pY1248 -12.57543156 -6.292747545 0.000161673 0.002317312
Chk2 -0.129765411 -6.2197279 0.000176021 0.002328899
YAP 0.136364852 5.758652199 0.000305918 0.003521408 eEF2K -0.160116972 -5.755516188 0.0003071 0.003521408
Notch1 -0.182729513 -5.603769087 0.000370662 0.003984616
IRS1 0.185829682 5.45246168 0.000448537 0.004538142
Snail 0.06750427 5.091251951 0.000716353 0.006617163 c-Met -0.102003826 -5.054043561 0.000752539 0.006617163 p90RSK -0.158396004 -5.037325261 0.000769438 0.006617163
PI3K-p85 -2.036475678 -4.918493585 0.000902046 0.007166818
Rictor -0.410642591 -4.906545933 0.000916686 0.007166818
VEGFR2 3.761011532 4.869117679 0.000964234 0.007210796
Smad4 -0.088120329 -4.83670308 0.001007567 0.007220898
B-Raf 0.519515135 4.759947978 0.001118782 0.007697223
FASN 0.935237753 4.650595119 0.001300688 0.00860455
EGFR_pY1068 -1.83439155 -4.617134747 0.001362525 0.008679788
ACC_pS79 -0.12058646 -4.510266038 0.001582141 0.009477001
Akt 2.266133474 4.50323519 0.001597866 0.009477001
HER3_pY1298 -0.155688633 -4.387411013 0.001882561 0.010793352
53BP1 0.536065837 4.255090397 0.002275814 0.012627099
BRCA2 2.02988422 4.147420917 0.00266064 0.01430094
Caveolin-1 6.534536149 3.966881538 0.003470282 0.018087532
184 MEK1 0.174907277 3.863625644 0.004048024 0.020478239
Rictor_pT1135 -0.532021945 -3.775993098 0.004618471 0.022696486
Rb -0.118748699 -3.72166324 0.005014402 0.023957697
Tuberin_pT1462 -0.282256222 -3.696422359 0.005210412 0.024221373
Src 0.322135095 3.646149561 0.00562532 0.025461973
Src_pY416 -0.562482423 -3.434231357 0.007797024 0.034285633
P21 0.150709083 3.410121439 0.008094839 0.034285633
Bim 0.04707267 3.40290521 0.008186276 0.034285633
PARP_cleaved 0.055871273 3.388498558 0.008372073 0.034285633
Chk1_pS345 0.047828376 3.263390655 0.010183449 0.040733798
4E-BP1_pS65 -0.299341693 -3.230909646 0.010717611 0.041273023
AMPK-alpha -0.065370786 -3.226157513 0.010798174 0.041273023 c-Kit 0.087664715 3.193845524 0.011362946 0.042487538
Cyclin_B1 0.669191355 3.090430987 0.013385876 0.048986611
NF-kB.p65_pS536 -0.474530775 -3.07167942 0.013790971 0.049417645
BKS 2.1 – combined EGF effects can be seen in table 5.2.B. This shows the overall impact of
EGF in BKS 2.1 cells as compared to its effects in SKOBS-V1.2 cells, with 9 proteins being affected. Again the 'logFC' and 't' columns indicate direction: positive values mean as EGF levels goes up, the measurements from that antibody also increases. ‘AveExpr’ refers to background average expression. Adjusted P Value 'adj.P.Val' is Benjamini-Hochberg adjusted
185 p-values (multiple testing correction), with results <0.05 considered to be statistically significant and included in this section.
These proteins include: Akt_pS473 and Akt_pT308 (activated forms of Akt), mTOR_pS2448
(activated mTOR), p70S6K_pT389 (promotes cell proliferation downstream of mTOR, activating EEF2K & repressing BAD), S6_pS235_S236 (up regulation of ribosomal translation of RNA), GSK3-alpha-beta (Wnt pathway regulation of beta-catenin), NDRG1_pT346 (Acts as a tumour suppressor in many cell types, necessary but not sufficient for p53/TP53-mediated caspase activation and apoptosis) and YB1_pS102 (mediates pre-mRNA alternative splicing).
Table 5.2.B: BKS-2.1 – combined EGF effects
Antibody logFC AveExpr t P-Value adj-P-Val p70S6K_pT389 0.49052575 0.749932625 14.06092416 4.93E-05 0.006060875
S6_pS235_S236 4.2229175 4.09859825 12.39621925 8.81E-05 0.006060875
GSK3-alpha-beta 0.87829725 2.144005625 11.91534601 0.000105713 0.006060875
Akt_pS473 2.192335 2.62524375 7.734103981 0.000745549 0.026345539
NDRG1_pT346 0.70687275 1.391527625 7.687269454 0.000765859 0.026345539
GSK3_pS9 0.69541325 2.063755125 7.14715721 0.001055424 0.030255474
Akt_pT308 2.17866425 2.528100625 6.762634776 0.001343381 0.03215722 mTOR_pS2448 0.14060875 0.870590875 6.597169164 0.001495685 0.03215722
YB1_pS102 1.46504175 2.415228625 5.840246283 0.002519812 0.048156409
186 PEO1-OP6 – combined OPCML effects can be seen in table 5.3. This shows the overall impact of OPCML in PEO1-OP6 cells, with 98 (out of 214) proteins being affected. The 'logFC' and 't' columns indicate direction: negative values mean as OPCML goes up, the measurements from that antibody went down. Adjusted P Value 'adj.P.Val' is Benjamini-
Hochberg adjusted p-values (multiple testing correction), with results <0.05 considered to be statistically significant and included in this section.
The results show that OPCML is significantly associated with an increase in the levels of many proteins similar to the ones in BKS-2.1 including: VEGFR2, fibronectin, Dvl3, Akt,
BRCA2, Src, P21, Bim, Chk1-pS345 and c-Kit.
Other proteins that are significantly increased with an increase in OPCML levels in PEO1-OP6 cells are: PKC-alpha (positive and negative regulation of cell proliferation and apoptosis through interaction with ERK1/2), EGFR (RTK), GPBB (glycogen phosphorylase isoenzyme),
Bcl2 (regulation of cell anti-apoptosis and survival), eEF2 (regulation of protein synthesis via peptide chain prolongation control), STAT3_pY705 (IL6 transcription factor), Annexin-I
(inhibitor of NF-kB), Chk2_pT68 (induction of cell cycle arrest & DNA damage), stathmin
(promotes disassembly of microtubules), P53 (powerful TSG), N-Cadherin (cell adhesion molecules indicating epithelial to mesenchymal transition in cancers and hence invasion and metastasis), P27 (cell growth arrest through inhibition of cycline dependent kinase), transglutaminase (cross linking of proteins), c-Met_pY1235 (activated form of c-MET), c-
Jun_pS73 (activation of JNK signalling pathway and prevents apoptosis), eIF4G (DNA transcription activator), Bax (apoptosis regulation by antagonising Bcl-2 protein), Beclin
(regulation of autophagy), GATA3 (transcriptional activation of T-cell receptor alpha),
Bad_pS112 (promotes cell death by competing with Bax for heterodimerisation with Bcl-xl,
187 Bcl-2 and Bcl-w), BAP1 (chromatin deubiquitination), 4E-BP1 (activation of mRNA translation), Bak (pro-apoptotic protein, member of Bcl-2 gene family, involved in initiating apoptosis), GCN5L2 (functions as histone transferase to promote transcriptional activation),
Mcl-1 (regulation of apoptosis), YB-1 (mediates pre-mRNA alternative splicing) and
GSK3_pS9 (regulator of beta-catenin in the Wnt pathway).
It is worth noting that there are few proteins that show increased levels in PEO1-OP6 cells that were actually reduced in BKS-2.1 cells. This includes: NFNDRG1_pT346, NF-kB- p65_pS536, Notch-1, Smad4, 4E-BP1_pS65, eEF2K, Rictor_pT1135, p90RSK, TAZ and mTOR_pS2448.
Further analysis of the table shows that an increased level of OPCML is associated with significant decrease in levels of ER-alpha, INPP4B, PDK1, c-MET and HER3-pY1298.
Other proteins that are significantly reduced with an increase in OPCML levels in PEO1-OP6 cells are: Lck (non RTK for T cells), IGF1R (RTK), beta-catenin (regulation of cell-cell adhesion and gene transcription, over-expressed in many cancers), DJ-1 (cell protection against oxidative stress & death), FOXO3a (trigger of apoptosis in the MAPK/ERK pathway), TSC1
(negatively regulating mTORC1 signalling), Gab2 (adaptor protein involved in cell proliferation), HER2 (RTK), FAK and FAK_pY397 (focal cell adhesion and metastasis), Syk
(non RTK, MEK1, activation of MAPK/ERK), B-Raf_pS445 (mitogenic signal transduction from membrane to nucleus), 14-3-3_zeta (protein C kinase inhibitor protein), PMS2 (post- replicative DNA mismatch repair system), TYRO3 (RTK), GYS (glycogen synthesis), E-Cadherin
(regulation of epithelial cell-cell adhesion, mobility & proliferation), Smac (caspase activation and apoptosis), Tuberin (inhibits activation of S6 by negatively regulating mTORC1 activity), Rab11 (recycling endosomes and membrane trafficking) and Rab25 (regulation of
188 cell survival and promotes invasive migration), XRCC1 (corrects defective DNA strand break),
ERCC1 (DNA repair), HER3 (RTK), SDHA (in mitochondria, plays a role in citric acid and respiratory chain), MIG-6 (inhibition of activation of EGFR family of receptors), PI3K_p110- alpha (signalling response for FGFR family of receptors), ADAR1 (A to I RNA editing of dsRNA), caspase-8 (activation of apoptosis), claudin-7 (plays a major role in obliteration of tight-junction intercellular space), UQCRC2 (part of the mitochondrial respiratory chain), Bcl- xl (pro-survival protein member of the Bcl family responsible for regulation of cell anti- apoptosis and survival), TTF1 (terminates ribosomal gene transcription, mediates replication fork arrest and regulates RNA polymerase I transcription on chromatin), JNK2 (JNK signalling pathway activation leading to cell proliferation, differentiation, migration & programmed cell death), Complex_II_Subunit-30 (adaptor protein complex for protein transport via transport vesicles), eIF4E (initiation factor of protein biosynthesis), CD49b (integrin A2), porin (forms a channel through the mitochondrial outer membrane, responsible for the release of mitochondrial products that triggers apoptosis), p38-alpha-MAPK (essential component of MAPK pathway leading to activation of CREB, Fos and STAT3) and finally
PCNA (RAD6 DNA repair pathway).
There were also few proteins that show decreased levels in PEO1-OP6 cells but were actually amplified in BKS-2.1 cells, it includes: B-Raf, heregulin, FASN, PARP-cleaved, Snail and YAP.
There were also proteins that, unlike in BKS-2.1, were not significantly affected in PEO1-OP6 batch experiment. This includes: IRS1, 53BP1, caveolin-1, cyclin_B1, G6PD, PDK1_pS241,
Chk2, HER2_pY1248, PI3K-p85, rictor, EGFR_pY1068, ACC-pS79, Rb, Tuberin-pT1462, Src- pY146 and AMPK-alpha.
189 Finally, analysis of PEO1-OP6 cells in terms of combined EGF effects did not yield any significant proteins as judged by ‘adj. P-Val’.
Table 5.3: PEO1-OP6 – combined OPCML effects
Antibody logFC t P-Value adj-P-Val
Lck -1.222255618 -26.97168665 1.83E-06 0.000306435
VEGFR2 3.427335761 24.57597803 2.86E-06 0.000306435
IGF1R -0.503253344 -15.10973986 2.94E-05 0.001837361 beta-Catenin -0.585092719 -14.61998758 3.43E-05 0.001837361
PKC-alpha 1.268611127 12.89737117 6.22E-05 0.002661799
DJ-1 -0.340626606 -12.30543264 7.76E-05 0.002768602
FOXO3a -0.217835542 -11.59741875 0.000102583 0.0031361
TSC1 -0.756034809 -11.26793895 0.000117439 0.003141483
HER2 -2.542634922 -10.78087659 0.00014443 0.00317845
Gab2 -6.649715629 -10.71656715 0.000148526 0.00317845
EGFR 1.371191013 10.10036559 0.000195764 0.003452313
FAK_pY397 -0.465575971 -10.06142106 0.000199314 0.003452313
NDRG1_pT346 1.650825052 9.951891628 0.00020972 0.003452313
GPBB 0.905478911 9.733047246 0.000232531 0.003541623 c-Kit 0.406458914 9.596735857 0.000248245 0.003541623
Syk -2.323625701 -9.443995742 0.000267389 0.003572033
190 MEK1 -1.033306386 -9.323428482 0.00028376 0.003572033
Bcl-2 0.270444625 8.998859205 0.000334164 0.003972841 eEF2 0.360612714 8.846118542 0.000361562 0.003981195
NF-kB_ 0.303176587 8.791146163 0.000372074 0.003981195 p65_pS536
Notch1 0.173914398 8.634729093 0.000404049 0.00411745
B-Raf_pS445 -0.565129951 -8.489792462 0.000436649 0.004123754
Smad4 0.487189703 8.427069518 0.000451727 0.004123754
B-Raf -3.940327644 -8.383858384 0.000462477 0.004123754
14-3-3_zeta -6.986117298 -8.013337731 0.000568386 0.004865387
4E-BP1_pS65 1.70232941 7.930391197 0.000595925 0.004904922
STAT3_pY705 0.385083722 7.81519815 0.000636859 0.004927242
Annexin-I 0.321564667 7.794178732 0.000644686 0.004927242
PMS2 -0.739852623 -7.536203047 0.000750745 0.005539977
TYRO3 -0.423881623 -7.214176708 0.000913833 0.006518679
E-Cadherin -2.069484787 -7.126870698 0.00096511 0.00666237
GYS -0.496482442 -6.811434317 0.001181268 0.007812379
Chk2_pT68 0.153271333 6.781399829 0.001204713 0.007812379
Stathmin 0.121426302 6.525989228 0.001428136 0.008988855
Smac -0.984160951 -6.480401524 0.001473021 0.009006473
Tuberin -0.707473077 -6.332237579 0.001630921 0.009323296
P53 0.312332321 6.282213058 0.001688713 0.009323296
Rab25 -0.384304576 -6.223771339 0.001759333 0.009323296
191 FAK -0.365507166 -6.214500003 0.001770856 0.009323296
N-Cadherin 0.404365432 6.212346096 0.001773546 0.009323296
XRCC1 -0.181392499 -6.202233093 0.001786239 0.009323296
HER3 -4.706085393 -6.1295554 0.001880699 0.009436159
HER3_pY1289 -0.455690653 -6.118152502 0.001896051 0.009436159
ERCC1 -0.148466355 -6.034916471 0.002012725 0.009789161
P27 0.120111785 5.966533725 0.002114981 0.009952863
Trans- 2.018845753 5.94821354 0.002143412 0.009952863 glutaminase
Fibronectin 0.22082371 5.921360105 0.002185909 0.009952863
SDHA -1.522609487 -5.701774397 0.002573513 0.011370594
Heregulin -0.5647871 -5.686413325 0.002603547 0.011370594
MIG-6 -0.228169522 -5.616741472 0.002745081 0.011465261
PI3K_ -0.276190546 -5.60738466 0.002764773 0.011465261 p110.alpha
P21 0.129954701 5.577915492 0.002827892 0.011465261
INPP4B -0.135850112 -5.572568023 0.002839527 0.011465261 eEF2K 0.352198067 5.402365394 0.003241175 0.012844655 c-Met_pY1235 0.291971447 5.272282548 0.003593779 0.013983066
PDK1 -0.101999499 -5.137840884 0.00400667 0.015311202 c-Jun_pS73 0.277391882 5.113074882 0.00408869 0.015350519
Src 0.571121612 5.031020776 0.004374897 0.015870804
FASN -2.945205765 -5.030828418 0.004375595 0.015870804
192 ADAR1 -0.088286628 -4.966255153 0.004617519 0.016259578
ER.alpha -3.219522685 -4.95626807 0.004656322 0.016259578
Akt 0.744715941 4.942430995 0.004710719 0.016259578
PARP_cleaved -0.577399426 -4.885953986 0.004940619 0.016694246
Rictor_pT1135 0.282347814 4.873594505 0.004992672 0.016694246
Caspase-8 -1.495259208 -4.82264284 0.005214161 0.017166623 eIF4G 3.080677638 4.767803905 0.005465537 0.01772159
Claudin-7 -6.545617461 -4.684640951 0.005874366 0.018650002
TAZ 0.21557552 4.674588804 0.005926169 0.018650002
UQCRC2 -0.135777122 -4.623751719 0.006196483 0.019218077
Bax 0.099787909 4.59353269 0.006363996 0.019455644
Snail -0.835766518 -4.490187357 0.006978246 0.021033024
Beclin 0.200081588 4.432288343 0.007352558 0.021853436
BRCA2 0.084592674 4.406316199 0.007528044 0.022068513
Bcl-xl -0.722388701 -4.363202294 0.007830252 0.022644243
TTF1 -0.418603326 -4.335818142 0.008029545 0.022910968
JNK2 -0.550543704 -4.31009167 0.008222175 0.023151913
GATA3 0.086537317 4.287767609 0.0083937 0.023327945
Bad_pS112 0.174192417 4.231364819 0.008845944 0.024269642
Chk1_pS345 0.074939916 4.210796242 0.009017873 0.024428163
BAP1 0.217590415 4.128103115 0.00974944 0.025922287
Complex_II_ -0.48053382 -4.121399635 0.009811707 0.025922287
Subunit-30
193 mTOR_pS2448 0.346830867 4.107339568 0.009943811 0.025950922 eIF4E -0.261271036 -3.93806629 0.011707644 0.030008549 c-Met -0.732858607 -3.929989544 0.011800472 0.030008549
4E-BP1 0.440973165 3.912304131 0.012006722 0.030008549
Bak 0.088860347 3.907834049 0.01205951 0.030008549
Bim 0.197385294 3.890085617 0.012271772 0.030185737 p90RSK 0.069480843 3.808733225 0.013301631 0.032347148
YAP -0.149590381 -3.718913262 0.014556423 0.035000837
CD49b -0.06886241 -3.62112871 0.016080189 0.038235115
GCN5L2 0.191481476 3.605385026 0.016342286 0.038431311
Mcl-1 0.184468027 3.564637195 0.017043729 0.039571247
YB-1 0.114162052 3.555997813 0.01719685 0.039571247
Porin -0.109929485 -3.448594388 0.01923822 0.04379765 p38-alpha- -0.126476739 -3.431756128 0.019582778 0.043844549
MAPK
PCNA -0.189275292 -3.427615567 0.019668583 0.043844549
Rab11 -0.059583395 -3.36600933 0.020997335 0.046324017
GSK3_pS9 1.189347372 3.336175456 0.021677416 0.047336398
194 5.2.6 Analysis of OPCML effects in two heterogeneous cell lines
Attempt to combine the two cell lines together to look for consistent effects of OPCML overexpression in both PEO1-OP6 and BKS-2.1 cell lines did not yield any statistically significant results (in terms of adjusted P value < 0.05) even when the baseline differences in protein expression were taken into account. . This is likely to be a result of considerable biological heterogeneity between the two cell lines, such that there is not much statistical power for even this moderately high-throughput assay. However, a consistent effect was noted in both cell lines with significant increase in some proteins (Table 5.4.A) and a significant decrease in others (Table 5.4.B)
Table 5.4.A: Consistent OPCML induced effects - significantly increased proteins in two heterogeneous cell lines
P21 BRCA2
BIM Dvl3
Chk1-pS345 Akt
VEGFR2 Src
Fibronectin c-Kit
Table 5.4.B: Consistent OPCML induced effects - significantly decreased proteins in two heterogeneous cell lines
ER-alpha c-MET
INPP4B HER3-pY1298
PDK1
195 5.2.7 Analysis of OPCML impact on EGF ligand stimulation
Another point of interest to look at was the impact of OPCML on the cellular response to
EGF stimulus. This was done for each cell line system separately, combining (and taking into account in the model) the two batches of each cell line with the aim to look for consistent effects across both cell lines. However, there were not enough data points to give anything meaningful, partly because the results for each cell line do not have much overlap.
Hence, there was no impact of OPCML on EGF induction in BKS-2.1 cells (see Appendix A,
Supplementary Tables), while very little impact on OP6 cells and no significant effects in terms of adjusted p value.
196 5.2.8 Analysis of OPCML cell line specific effects
As an additional consequence of combining the datasets, it was possible to interrogate the
impact of cell line on OPCML-related protein expression, by modelling the interaction
between the OPCML effect and cell line effect terms. Table 5.5 shows the 8 proteins that are
peculiar to OPCML.
Table 5.5: Cell line specific – OPCML effects
Antibody logFC AveExpr t P-Value adj-P-Val
NDRG1_pT346 -2.263643802 1.680578857 -8.281662868 4.11E-06 0.000588344
Gab2 6.685197129 3.335707468 5.774160533 0.000114598 0.008193755
Lck 1.266793368 1.291480516 4.960386384 0.000403631 0.019239734
Syk 2.353008201 1.630726376 4.771994677 0.000547464 0.019571851
HER2_pY1248 -12.99313909 5.645342025 -4.207686684 0.001404017 0.036150482
HER3 4.690735643 3.033611452 4.162455241 0.001516803 0.036150482
X14.3.3_zeta 7.251850048 6.082243681 4.032288711 0.001897124 0.038755541
NF-kB- -0.788231587 0.818732381 -3.937657253 0.002234972 0.039950118 p65_pS536
197 5.2.9 Analysis of OPCML effects in SKOBS-P95R-3.4 cells comparison to OPCML and empty
vector controls
One final analysis for the RPPA data involves the comparison of P95R mutation with the wild type OPCML and empty vector controls under different stimulation conditions. Again, detailed analysis was conducted and modelled out taking into account the effect of the batch and the effect of EGF stimulation, with the impact of OPCML P95R mutation overexpression being the variable of interest. SKOBS-P95R-3.4 – combined OPCML-P95R mutation effects can be seen in table 5.6. This shows the overall impact with 50 (out of 172) proteins being affected. The 'logFC' and 't' columns indicate direction: negative values mean as OPCML-P95R levels goes up, the measurements from that antibody went down. ‘AveExpr’ refers to background average expression. Adjusted P Value 'adj.P.Val' is Benjamini-Hochberg adjusted p-values (multiple testing correction), with results <0.05 considered to be statistically significant and included in this section.
The results show that OPCML-95R is associated with significant increase (compared to
SKOBS-V1.2 levels) in the levels of: fibronectin, heregulin, YAP, IRS1, snail, B-Raf, Akt, 53BP1,
BRCA2, Src, P21, Bim and Chk1-pS345.
Other proteins that did increase with the P95R mutation (compared to BKS-2.1 cells) are: cyclin-E1 (regulation of DC), AMPK_pT172 (increase cell uptake of glucose and fatty acid oxidation), p38_pT180_Y182 (essential component of MAPK signalling pathway regulation of many transcription factors such as NF-kB and STAT3), Src_pY527 (deactivation of Src),
Caspase-7_cleavedD198 (activation of apoptosis), Bak (pro-apoptotic protein, member of
Bcl-2 gene family, involved in initiating apoptosis), c-Jun_pS73 (activation of JNK signalling pathway and prevents apoptosis), TIGAR (inhibits glycolysis of fructose and may protect cells
198 against reactive oxygen species and against apoptosis induced by TP53), smad1
(transcription modulator) beta-catenin, P27, N-Cadherin, Syk, transglutaminase, and finally
TSC1.
It is worth noting that NF-kB_p65_pS536 levels are increased in SKOBS-P95R-3.4 cells but were actually reduced in BKS-2.1 cells. On the other hand Dvl3 and c-Kit levels are decreased in cells with the OPCML-P95R mutation.
Further analysis of the table shows that increased levels of OPCML-P95R are associated with significant decrease in: mTOR_pS2448, ER-alpha, NDRG1_pT346, Chk2, TAZ, INPP4B, PDK1 and PDK1_pS241, HER2_pY1248, eEF2K, Notch-1, c-MET, p90RSK, rictor, EGFR_pY1068, ACC- pS79 and HER3-pY1298.
Other proteins whose levels are significantly reduced are: STAT5-alpha (signal transduction and activation of transcription, mediates cellular actions of HER4 and FGFR family), Rad51
(double strand DNA repair), HER3, Gab2, and ACC1.
There were also proteins that, unlike in BKS-2.1, were not significantly affected by the
OPCML-P95R mutation. This includes: VEGFR2, FASN, caveolin-1, MEK-1, PARP-cleaved, cyclin_B1, G6PD, PI3K-p85, rictor-pT1135, Rb, Tuberin-pT1462, Src-pY146, 4E-BP1_pS65,
Smad4, and AMPK-alpha.
199 Table 5.6: SKOBS-P95R-3.4 – OPCML-P95R effects
Antibody logFC t P-Value adj-P-Val
Fibronectin 0.900481878 26.0534566 1.38E-09 2.38E-07
Cyclin_E1 0.373061079 9.909912335 4.81E-06 0.000414068
53BP1 1.126173808 8.939147076 1.10E-05 0.000545312
NF-kB-p65_pS536 1.356589466 8.781322869 1.27E-05 0.000545312
INPP4B -0.399910572 -8.335197801 1.91E-05 0.000658214
STAT5-alpha 0.315639771 7.668181314 3.66E-05 0.001025117 mTOR_pS2448 -0.168266025 -7.539105579 4.17E-05 0.001025117
ER-alpha -0.446286109 -7.346162799 5.09E-05 0.001093837
ACC_pS79 -0.182744462 -6.835146704 8.77E-05 0.001561752
PDK1 -0.09520051 -6.803825563 9.08E-05 0.001561752 eEF2K -0.184770334 -6.641698495 0.000108667 0.001699155
Chk2 -0.135921705 -6.51480246 0.000125343 0.00179658
TAZ -0.139418636 -6.183319999 0.000183691 0.002430367
PDK1_pS241 -0.252384596 -6.088010529 0.000205551 0.002525336 beta-Catenin 1.580582211 5.847079119 0.000274557 0.003148256
AMPK_pT172 0.198809 5.375536856 0.000494803 0.005193067
Notch1 -0.174027298 -5.336898088 0.000519975 0.005193067
HER2_pY1248 -10.59678872 -5.302634417 0.00054346 0.005193067 p38_pT180_Y182 0.15336862 5.043161875 0.000763492 0.00691161
Akt 2.431518272 4.831886018 0.001014184 0.008323578
NDRG1_pT346 -0.392706193 -4.785818848 0.001079885 0.008323578
200 HER3 -0.094174337 -4.771607592 0.001101068 0.008323578
Gab2 -0.434529148 -4.763707922 0.001113037 0.008323578
P21 0.200543018 4.537722811 0.001522305 0.010909854
Transglutaminase 1.070773622 4.427592156 0.001778073 0.012195585
ACC1 -2.828910597 -4.402138565 0.001843519 0.012195585 p90RSK -0.135769684 -4.317760812 0.002079594 0.013247782
Src_pY527 0.084157377 4.258161257 0.002265752 0.013918189
HER3_pY1298 -0.145998035 -4.114323406 0.002792466 0.016562212
Heregulin -0.192054591 -4.076519445 0.00295161 0.016922564
IRS1 0.132709897 3.893864651 0.003868935 0.021466348
Bak 0.143290477 3.863862435 0.004046588 0.02175041
Rictor -0.316365324 -3.780077925 0.004590071 0.023855918
Caspase.7_cleavedD198 0.084689486 3.762206424 0.004715705 0.023855918
Rad51 -0.052636678 -3.701075688 0.005173676 0.024933974 c.Jun_pS73 0.081804798 3.695372376 0.005218739 0.024933974
N-Cadherin 0.055474215 3.584377829 0.006183378 0.028579456
TIGAR 0.336019288 3.559422591 0.006425107 0.028579456
Dvl3 -0.057885877 -3.553870777 0.006480226 0.028579456
Snail 0.046639551 3.517610165 0.006852674 0.029466497
BRCA2 1.708930689 3.491654754 0.00713305 0.029924015
Syk 0.12297881 3.382829563 0.00844639 0.034589977 c-Kit -0.091744387 -3.342478193 0.008995718 0.035982873
EGFR_pY1068 -1.292816305 -3.253998353 0.010334997 0.040002233
201 Chk1_pS345 0.047573679 3.246012378 0.0104657 0.040002233
Smad1 0.07020857 3.215174067 0.0109868 0.040887629
Src 0.283118719 3.204535016 0.011172782 0.040887629
P27 0.055278152 3.167897879 0.011838676 0.042163056
TSC1 0.906236689 3.158735317 0.012011568 0.042163056
Bim 0.043414297 3.138439695 0.012403932 0.042669527
202 5.3 Transient transfection of OPCML into a panel of different ovarian cancer cell lines
results in decrease in cell proliferation
Previous results from our lab revealed anti-proliferative OPCML effects on stably transfected
SKOV-3 ovarian cell lines (McKie et al 2012). The results from the RPPA studies also point towards OPCML having a possible role as anti-proliferative agent. We investigated whether transiently transfecting OPCML in different ovarian cancer cell lines will show a similar effect in terms of cell proliferation. PEA1, PEA2, A2780 and PEO1 cell lines, a variety of platinum sensitive and resistant cell lines, were used. Following transient transfection, WST1 proliferation assays were undertaken for all the cell lines. The outcomes show that OPCML transfection resulted in significantly less proliferation in all cell lines by 72 hours post transfection (P value < 0.005 in all 4 cell lines). This was also significantly evident in PEA2 by
48 hours (Figure 5.3). Proliferation in the OPCML-transfected cells by 72 hours was 61%,
58%, 77% and 70% that of empty vector controls, in PEA1, PEA2, A2780 and PEO1 respectively (Figure 5.4). It is also noted that by 72 hours, the proliferation curve will go down in all the OPCML transfected cells, which suggests that OPCML could be inducing cell- death in these cells by that stage.
Figure 5.3 (next two pages): WST1 proliferation assay in PEA1 (A), PEA2 (B), A2780 (C) and
PEO1 (D) cell lines. Proliferation was measured at 450 nm wavelength at 6, 24, 48 and 72 hours post transfection. The experiment was repeated three times for each cell line and the graphs represent the combined results of all three assays per each cell line. Asterisks indicate significant results. (*), P value < 0.05 (0.043 for PEA2 at 48 hours). (***), P value <
0.001 (P value: 0.001 for PEA1; 0.00003, for PEA2; 0.000005, for A2780; 0.0002, for PEO1 cells. All at 72 hours).
203 Effects of OPCML on cell proliferation in PEA1 cells
0.3 *** A 0.25
0.2
0.15 PEA1_OPCML PEA1_EV
Absorbance 0.1
0.05
0 6 24 48 72 Hours
Effects of OPCML on cell proliferation in PEA2 cells
0.25 *** B 0.2 *
0.15 PEA2_OPCML
0.1 PEA2_EV
Absorbance 0.05
0 6 24 48 72 Hours
Effects of OPCML on cell proliferation in A2780 cells
0.2 C *** 0.15
0.1 A2780_OPCML A2780_EV 0.05 Absorbance
0 6 24 48 72 Hours
204
Effects of OPCML on cell proliferation in PEO1 cells
0.2 *** D 0.15
0.1 PEO1_OPCML PEO1_EV 0.05 Absorbance
0 6 24 48 72 Hours
Figure 5.4: OPCML effects on cell proliferation. Bar chart representing cell proliferation for
PEA1, PEA2, A2780 and PEO1 cell lines expressed as a percentage of the control in transiently transfected OPCML cells normalised to the empty vector controls 72 hours post transfection.
OPCML cell proliferation as a percentage of total proliferation at 72 hours
120
100
80
60
Prolifera on 40
20
0 Control PEA1 PEA2 A2780 PEO1
205 5.4 Knockdown of OPCML in nOSE results in elevated expression of pHER3 under different
stimulation conditions
As noted from the RPPA, phospho HER3 (HER3_pY1289) was reduced in OPCML expressing ovarian cancer cell lines BKS-2.1 and PEO1-OP6 at basal and ligand stimulation conditions.
To validate this result, we further analysed the effect of growth factor stimulation in relation to OPCML levels in a normal ovarian surface epithelial cell line that was stably transfected with an empty plasmid (PLKO), or partially depleted of OPCML (sh-464-23; 60% OPCML knockdown). Following serum free media, full media and EGF stimulation (50 ng/ml for 30 minutes), we tested the lysates by western blotting. Our results show that higher OPCML levels under non-stimulation physiological conditions are associated with less expression of phosphorylated levels of HER3, an RTKs whose total level is not usually affected by OPCML as previously demonstrated (McKie, Vaughan et al. 2012).
Similar results were noted under stimulation conditions, including full media (10% FCS) and
EGF stimulation (Figure 5.5).
206 Figure 5.5: Knockdown of OPCML by 60% (in sh-464-23 cell line) results in increase in
HER3_pY1289 levels compared to the empty vector control (PLKO) under serum free (S), full media (F) and EGF (E) stimulation conditions.
PLKO& Sh&464123&
S& F& E& S& F& E&
pHER3& (Tyr&1289)&
β1Tubulin&
207 5.5 OPCML-P95R mutation results in altered regulation of multiple RTKs in comparison to
wild-type OPCML and empty vector controls
To further validate the RPPA results, lysates from SKOBS-V1.2, SKOBS-3.5, BKS-2.1 and
SKOBS-P95R-3.4 were generated under serum free conditions and blotted on an SDS-PAGE gel for multiple RTKs that are known to be affected by OPCML and others that are known to be influenced by mutations in OPCML, in addition to total and phospho ERK and AKT (Figure
5.6). The result shows that compared to OPCML expressing SKOBS-3.5 and empty vector control cells, SKOBS-P95R-3.4 is associated with decrease in HER2 and FGFR1, albeit this is less than the abrogation seen in BKS-2.1. No effect is seen in terms of EphA2, but a decrease in the levels of total and phospho EGFR and HER3 is noted. There is no evidence from the western blot of any significant changes to the total or phospho AKT or ERK levels.
208 Figure 5.6: Western blot comparing the protein expression of different RTKs as well as down-steam signalling AKT and ERK in OPCML empty vector control (SKOBS-V1.2), wild type
OPCML expressing stables (SKOBS-3.5 and BKS-2.1) and OPCML-P95R mutant (SKOBS-P95R-
3.4).
SKOBS1V1.2& SKOBS13.5& BKS12.1& SKOBS1P95R13.4&
tHER2&
tFGFR1&
EphA2&
tEGFR&
pEGFR&Y1068&
tHER3&
pHER3&Y1298&
tERK&
pERK&1/2&
tAKT&
pAKT1S473&
OPCML&
β1Tubulin&
209 5.6 Summary
The use of the high throughput process of RPPA gave further credence to the results noted previously in our lab, namely, that OPCML acts through a certain repertoire of RTKs. This will result in effects down-stream on different proteins and players in cell proliferation, pro- survival and apoptosis when looking at each cell line model separately. Other interesting findings include interactions with different proteins in the Wnt pathway. However, due to the general heterogeneity between the cells and the variable baseline levels of different proteins, not many proteins were found to be consistently affected in both cell lines.
The consistent effect for both cell lines is OPCML association with significant increase in P21,
BIM, Chk1-pS345, VEGFR2, Fibronectin, BRCA2, Dvl3, Akt, Src, and c-Kit, as well as a significant decrease in ER-alpha, INPP4B, PDK1, c-MET and HER3-pY1298 (Table 5.4 A and B).
The anti-proliferative effects of OPCML were demonstrated in four biologically different ovarian cancer cell lines following transient transfection and proliferation assays with the use of WST-1.
A knockdown model was used to validate the observed OPCML effect on phospho HER3 levels, while the stable OPCML transfects were used to further check some of the results noted for OPCML-P95R mutation.
210
Chapter six
The production of rOPCML
211 6.1 Introduction
OPCML has a varied and wide biological activity. It exerts its effects as a tumour
suppressor gene and a ‘gate keeper’ across multiple signalling pathways and in many
different cancers (McKie, Vaughan et al. 2012). It logically follows that attempts to re-
generate its effects or to reintroduce its activity could be further explored to develop
a potential drug that can be used as cancer treatment either alone or in combination
with current standard anti-cancer therapy.
In this chapter, we report on our efforts to try and generate a recombinant OPCML
(rOPCML). Dr S Vaughan and Dr E Zanini first developed this process in our lab. The
efficacy of rOPCML as an apoptotic agent was tested in two ovarian cell-lines, where
apoptosis was measured by different assays. We also report on the challenges in
trying to replicate the effects of rOPCML in subsequent batches and the rationale in
switching the production of rOPCML from prokaryotic to mammalian HEK-293F cells.
We adapted a systematic approach to try and generate the recombinant protein, and
to overcome some of the challenges, we developed a “sandwich” OPCML ELISA plate
to accurately measure rOPCML generated by this method.
212 6.2 Exogenous recombinant OPCML protein accelerates apoptosis in vitro
As rOPCML acts in a similar way to wild type OPCML (McKie, Vaughan et al. 2012),
namely down-regulating multiple RTKs and their down-stream signalling, including
pro-proliferation and pro-differentiation, we set to examine the role of rOPCML
treatment on two ovarian cancer cell lines. Using SKOV-3 and A2780, both cell types
were treated with increasing levels of rOPCML (0, 2, 5, 10 µM) with equimolar levels
of bovine serum albumin (BSA) as a control for 24 hours. This was done by means of a
Caspase-Glo Apoptosis assay (Promega) with an MTT proliferation assay as control.
The results showed that rOPCML increases ovarian cancer cell apoptosis in vitro
(Figure 6.1.A). This was significant (P value < 0.001) at 5 and 10 µM concentrations
for A2780, but only at 10 µM for SKOV-3 cells (Figure 6.1.A).
To further confirm the above findings, A2780 and SKOV-3 cells were analysed by flow
cytometry following rOPCML or BSA control treatment, by staining with Annexin V
and propidium iodide (PI), which are markers of apoptosis. The first detects cells that
have expressed phosphatidylserine, a feature found in apoptotic cells, whilst PI binds
to DNA but it is impermeable through the cell surface of viable cells. Our results
demonstrated that rOPCML increased apoptosis compared to control by 28% for
A2780 and 19% for SKOV-3 cells at 6 hours post treatment (Figure 6.1.B).
213 A A2780 6 BSA - control rOPCML 4
2 to non-treated caspase/mtt normailsed caspase/mtt 0 2 5 10 Treatment (µM)
SK-OV-3 5 BSA - Control 4 rOPCML
3
2 to non-treated to 1 casp/mtt normalised casp/mtt
0 2 5 10 Treatment (µM)
B
30 OPCML
20
10 % Annexin positive V cells normalised to untreated cells 0 A2780 SKOV-3
214 Figure 6.1: rOPCML induces apoptosis in vitro: (a) caspase 3/7 assay normalised to
viable cell numbers demonstrates that rOPCML but not equimolar BSA (control)
induce apoptosis in a dose dependent manner for A2780 (upper panel) but only
significantly at the highest concentration of rOPCML (10mM) for SKOV-3 (lower
panel) 24 hours after exposure to these agents. (b) Flow cytometry analysis of SKOV-
3 and A2780 cells stained with annexin V demonstrating the higher number of cells
taking up annexin V (early apoptotic cells (19 and 28% respectively) after incubation
for 6 hours with rOPCML compared to the BSA control. (Data courtesy of (McKie,
Vaughan et al. 2012), Cancer Discovery, 2012).
215 6.3 The activity of rOPCML varies between batches and degrades with time
Following on from the results of the experiments done with rOPCML and before
scaling up protein production and progressing to more advanced in vivo experiments,
a re-run of the initial trial process of rOPCML production in E. coli was undertaken.
This was to make sure that the process was robust with little or no variation in the
quality of the final protein. A new batch was made following the same protocol as
mentioned earlier (see chapter two, Material and Methods, section 2.4.6). The final
protein concentration generated was 240 µg/ml. The protein was analysed by
western blotting using OPCML and α His-tag antibodies. This showed one band for
each antibody at 55 KDa mark (data not shown).
To start with, the biological activity of the newly purified rOPCML was tested as an
anti-proliferative agent using an MTT proliferation assay in SKOV-3 cells. Cells were
treated for 24 hours with increasing levels of rOPCML starting with 0, 2, 5 and 10 µM
with v/v equivalent of PBS as a control (Figure 6.2). The experiment did not show any
significant difference in inhibition of proliferation in SKOV-3 cells between the new
batch of rOPCML and PBS. A repeat of the same experiment by another member of
our group (Dr Suzy Tan), with a new batch of protein that she manufactured in our
lab, also yielded a similar result (data not shown).
216
1.6
1.4
1.2
PBS treated SKOV3 1 cells 0.8 rOPCML treated 0.6 SKOV3 cells Absorbance
0.4 0.2
0 Dose (µM) 0 2 5 10
Figure 6.2: SKOV-3 cells were treated for 24 hours with increasing levels (0, 2, 5, 10
µM) of rOPCML and v/v equivalent of PBS.
A multitude of changes in the production process were undertaken by another
member of our lab (Mr T Wylie) under the directions of Mr S Vaughan (original team
member who pioneered the original rOPCML production with Dr E Zanini) as part of
on-going efforts to supply protein for crystallization studies. This included the use of
GST-OPCML, which was expressed in E. coli as before, denatured in urea and
refolded, before being extracted by GST affinity chromatography. The biological
activity of the protein produced was then tested (within 24 hours of its production)
by comparing the level of total HER2 down-regulation in SKOBS-V1.2 cells following
treatment with 10 µM of rOPCML and v/v equivalent of PBS and checking the lysates
on an SDS-PAGE western blot. The rationale for using HER2 was the well-documented
down-regulatory effect of OPCML on HER2 (McKie et al 2012). The western blot
showed a significant decrease in total HER2 levels in the rOPCML treated cells (Figure
217 6.3.A). A densitometry analysis of the blot showed that the HER2 was decreased by
85% in the rOPCML treated cells compared to the control group (Figure 6.3.B). To test
the stability of rOPCML, the same experiments were repeated two weeks later. The
western blot from the latter experiment did not reveal any change in the total HER2
levels between the rOPCML treated cells and the PBS controls (Figure 6.3.C),
indicating that the rOPCML produced was not stable and its biological activity
deteriorated with time when stored at 4°C.
Figure 6.3 (next page): A) SKOBS V1.2 cells treated with rOPCML and v/v equivalent
PBS within one day of producing the recombinant protein shows a strong biological
activity by down-regulating HER2 levels. B) Densitometry of the same western blot in
(A) showing the decrease in HER2 levels by 85%. C) A repeat of the same experiment
as in (A) after the protein was stored at 4°C for two weeks shows no effect on HER2,
meaning total loss of rOPCML biological activity.
218 A
B Rela ve HER2 expression in SKOBS-V1.2 cells following treatment with PBS & rOPCML
1.2 1 0.8 0.6 0.4 0.2 0
PBS rOPCML
C
219 6.4 rOPCML transfection and expression in mammalian cell vector
Given the issues with producing rOPCML in prokaryotic cells, we decided to change
vectors in order to produce a more stable protein whose biological activity would not
deteriorate over a short period of time. Mammalian cells were chosen due to their
ability to produce appropriately soluble, correctly refolded proteins under native
conditions. The most commonly used mammalian cell host are the human embryonic
kidney (HEK) 293 cells. These can be transiently transfected relatively easy with the
desired plasmid and produce the required recombinant protein over a relatively short
period of time. A new plasmid was constructed in pcDNA3.1/V5-His-B as described in
materials and methods (section 2.2; cloning and nucleic acid manipulation), the
construct was verified by sequencing. The sequence of the final construct can be seen
on next page (Figure 6.4), it revealed two conservative third base C/G mutations that
would not make any difference to the final protein production.
220 Figure 6.4: OPCML sequenced construct in pcDNA3.1/V5-His-B
CCGGTGCGCAGCGGAGATGCCACCTTCCCCAAAGCTATGGACAACGTGACGGTCCGGCAGG
GGGAGAGCGCCACCCTCAGGTGTACCATAGATGACCGGGTAACCCGGGTGGCCTGGCTAAA
CCGCAGCACCATCCTCTACGCTGGGAATGACAAGTGGTCCATAGACCCTCGTGTGATCATCC
TGGTCAATACACCAACCCAGTACAGCATCATGATCCAAAATGTGGATGTGTATGACGAAGGT
CCGTACACCTGCTCTGTGCAGACAGACAATCATCCCAAAACGTCCCGGGTTCACCTAATAGT
GCAAGTTCCTCCTCAGATCATGAATATCTCCTCAGACATCACTGTGAATGAGGGAAGCAGTG
TGACCCTGCTGTGTCTTGCTATTGGCAGACCAGAGCCAACTGTGACATGGAGACACCTGTCA
GTCAAGGAAGGCCAGGGCTTTGTAAGTGAGGATGAGTACCTGGAGATCTCTGACATCAAGC
GAGACCAGTCCGGGGAGTACGAATGCAGCGCGTTGAACGATGTCGCTGCGCCCGATGTGC
GGAAAGTAAAAATCACTGTAAACTATCCTCCCTATATCTCAAAAGCCAAGAACACTGGTGTTT
CAGTCGGTCAGAAGGGCATCCTGAGCTGTGAAGCCTCTGCAGTCCCCATGGCTGAATTCCAG
TGGTTCAAGGAAGAAACCAGGTTAGCCACTGGTCTGGATGGAATGAGGATTGAAAACAAAG
GCCGCATGTCCACTCTGACTTTCTTCAATGTTTCAGAAAAGGATTATGGGAACTATACTTGTG
TGGCCACGAACAAGCTTGGGAACACCAATGCCAGCATCACATTGTATGGGCCTGGC
Once the construct was available, work progressed into transiently transfecting it into
HEK 293-F cells. We used the cationic liposomal transfection reagent, FreeStyle-MAX,
specifically developed for transfection into HEK 293-F cells using the serum-free
suspension culture medium. We started with a pilot run of 30 ml of the suspension
cells to identify the concentration of the plasmid to use and the day of maximum
protein expression. Since there was no ELISA plates to quantify the exact protein
concentration each day post transfection, so the cell count and viability were
221 measured every day following transfection. 1.5 x 106 cells was harvested every 24
hours for seven days post transfection and lysed in a lysis buffer containing 2% SDS.
The lysate was then checked on an SDS-PAGE for OPCML expression, and counter
checked with the α-His-tag antibody. After running the experiment twice, the results
show that day four was the day of maximum protein expression (Figure 6.5). It is
interesting to note that whilst cell viability starts to decrease from day one post
transfection, it is not till after day five where the viability drops steeply and so does
the protein production. Day four is also the day of highest cell count (Figure 6.6).
Figure 6.5: Western blot showing OPCML and the His-tagged protein expression in
HEK 293-F cells in days after the plasmid transfection.
222
Viability
100% 90% 80% 70% 60% 50% Viability 40% 30% 20%
Percentage of cell viability 10% 0% D0 D1 D2 D3 D4 D5 D6 D7 Days
Cell Count 3
2.5
cells 2 6
1.5 Cell Count
1 Cell count per 10 0.5
0 D0 D1 D2 D3 D4 D5 D6 D7 Days
Figure 6.6: Diagrams showing the decrease in HEK 293-F cell viability following
transfection (top), and accompanying daily changes in cell count in these cells
(bottom).
223 Once the timing for maximum protein production was established, we proceeded to
a small run (30 ml) of production and purification of the recombinant protein. Again
using 30 µg of plasmid, the transfection was carried out as explained before
(materials and methods section 2.3.1.2). Four days after transfection, the cells were
collected and centrifuged before being lysed with lysis buffer (containing imidazole),
followed by sonication, as well as freeze and thaw cycles to ensure that the maximum
number of cells was lysed. We used Ni-NTA resin to capture the His-tagged protein
due the high affinity of the former to the tagged proteins. A total of 160 µl of resin
was used, this was washed thoroughly using a wash buffer (which also contains
imidazole) to get rid of the ethanol content in it. Following incubation of the protein
with the Ni-NTA resin then washing on the column, the protein was eluted twice. The
final protein concentration from the first elution was 80 µg/ml, whilst that of the
second elution was 20 µg/ml. Sample of the supernatant from the lysed cells pre and
post sonication, post resin incubation and column run flow through (flow-through),
wash through and elution were run on SDS-PAGE and checked by Coomassie stain
(Figure 6.7). This showed two bands around the 55-60 KDa molecular weight, which
may be an isoforms of OPCML. We also noted another band around 110 kDa. A
western blot for the same parameters was conducted and tested for OPCML and α
His-tag. This showed the same bands as in the Coomassie as well a significant amount
of contaminants (Figure 6.8), which could be other His-tagged proteins that were also
eluted or breakdown products.
224
Figure 6.7: Coomassie stain of rOPCML purification and elution in HEK 293-F cells,
starting from left: marker lane, cell lysis supernatant pre-sonication, cell lysis
supernatant post-sonication, flow-through, washing step one, washing step two,
washing step three, first elution and second elution.
55 kDa
55 kDa
225 Figure 6.8 (previous page): Western blots showing OPCML and α His-tag. Starting
from left: 1, cell lysis supernatant pre-sonication, 2, cell lysis supernatant post-
sonication, 3, flow-through, 4, washing step one, 5, washing step two, 6, washing
step three, 7, first elution.
We attempted a second run at making the recombinant protein at low scale in 30 ml
suspension, this time increasing the amount of plasmid used to 37.5 µg. We followed
the same protocol as before, but we increased the amount of Ni-NTA resin to 350 µl.
The final protein concentration obtained after the first elution was 130 µg/ml, whilst
that after the second elution it was 30 µg/ml. Representative samples from the
supernatant of the lysed cells pre and post sonication, flow-through, wash through
and elution; were run on an SDS-PAGE and stained by Coomassie stain (Figure 6.9). A
western blot to test for OPCML and αHis-tag antibodies was carried out and showed
similar results as shown before (Figure 6.8).
226
Figure 6.9: Coomassie stain from the second batch of rOPCML purification and
elution in HEK 293-F cells, starting from left: marker lane, cell lysis supernatant pre-
sonication, cell lysis supernatant post-sonication, flow-through, washing step one,
washing step two, washing step three, first elution and second elution.
To verify the biological activity of the newly manufactured protein, rOPCML was
tested on SKOBS-V1.2 cells at a concentration of 2 µM and total HER2 (tHER2) down-
regulation was measured with v/v equivalent of PBS as a control. We noted that after
24 hours the majority of the rOPCML treated cells were dead and detached from the
surface, hence the attempted western blot did reveal only minimal HER2 and β-
tubulin signal due to the proportion of dead cells (Figure 6.10). The experiment was
repeated following dialysis of rOPCML in a 20 KDa MWCO column in order to get rid
of the imidazole. While there was less number of cell deaths, the result did not reveal
any changes in HER2 levels between treatment and control, thus confirming that the
newly made protein was not biologically active (Figure 6.11).
227
Figure 6.10: SKOBS-V1.2 cells treated with rOPCML (+) and v/v equivalent PBS (-)
Figure 6.11: SKOBS-V1.2 cells treated with dialysed rOPCML (+) and v/v equivalent
PBS (-)
228 6.5 Development of an OPCML Enzyme-linked immunosorbent assay (ELISA) plate
Following on from our initial steps in manufacturing rOPCML in HEK 293-F cells, we
directed our efforts to create the conditions to optimise the quality of the protein
produced. In order to overcome some of the challenges in making rOPCML, namely
we do not have a reliable process to measure exactly how much rOPCML is produced
per a defined numerical population of HEK-293-F cells, we developed an rOPCML
sandwich ELISA detection assay. We made use of a commercially available antibody
against OPCML, which we have used before in our lab for confocal microscopy and
western blotting (polyclonal goat anti-OPCML, R&D systems), and of another
antibody against hexahistidine as a capture antibody. In addition, we also used a
newly developed, commercially available, recombinant OPCML that is also his-
tagged. Through using the ELISA assay we can quantify the amount of rOPCML
produced. This process in the short term will also help us to define the exact amount
of Ni-NTA resin to use during the purification process.
6.5.1 Creation of α-His-tag OPCML “sandwich” ELISA plates
As described in materials and methods (see chapter two, 2.4.8.1), we used an anti-His
mouse monoclonal capture antibody at 1 μg/ml, a standard range from 10 ng/μl, an
anti-OPCML goat polyclonal detection antibody at 100 ng/ml and a secondary anti-
goat antibody at 1/1000 concentration.
229 Multiple runs were carried out, following which the His-tagged OPCML standard for
the ELISA plate was calculated and plotted using the PRISM graphpad program
(Figure 6.12).
H is-ta g O P C M L 'sa n d w ic h ' E LIS A sta n d a rd
0 .4
y 0 .3 t i s n e d
l 0 .2 a c i t p
O 0 .1
0 .0 -2 -1 0 1 2 L o g o f r O P C M L p ro t e in (n g / ml) p lo tte d a g a in s t o p tic a l d e n s it y
Figure 6.12: Log of rOPCML protein (ng/μl). Standard curve for the OPCML ELISA
plate with rOPCML protein concentration at 0, 0.039, 0.078, 0.15612, 0.3125, 0.625,
1.25, 2.5, 5 and 10 μg/ml. Y = 7.717*X + 34.98, R2 = 0.2011.
6.5.2 “Grid-iron” ELISA
Following the development of the initial standard curve for the ELISA plate, we
optimised the different variables, namely the optimal concentration for the capture,
detection and secondary antibody along different standard concentrations with two
230 grid-iron experiments (see chapter two, materials and methods, 2.4.8.2) and (Figure
2.1). Each ‘minigrid’ (16 in each plate) represents a unique combination of capture,
standard, detection and secondary antibody. After analysing all the results (data not
shown), the combination that gave the best and most optimal signal to noise ratio
was that of a capture antibody of 1 μg/ml, detection antibody of 400 ng/ml and a
secondary antibody dilution of 1/500.
Using the new optimised numbers the new standard curve for our ELISA can be seen
in (Figure 6-13).
H is-ta g O P C M L 'sa n d w ic h ' E LIS A sta n d a rd
0 .8
y 0 .7 t i s n e d
l 0 .6 a c i t p
O 0 .5
0 .4 -2 -1 0 1 2 L o g o f r O P C M L p ro t e in (n g / μ l) p lo tt e d a g a in s t o p tic a l d e n s it y
Figure 6.13: Optimised standard curve for OPCML ELISA plate with rOPCML protein
concentration at 0, 0.039, 0.078, 0.15612, 0.3125, 0.625, 1.25, 2.5, 5 and 10 μg/ml. Y
= 0.07484*X + 0.6428, R2 = 0.6376.
231 6.5.3 “Spike and recovery” ELISA
To test the robustness of the OPCML sandwich ELISA plate, we conducted a spike and
recovery experiment to test the integrity of the assay in detecting rOPCML (see
chapter two, 2.4.8.3). In brief, two standards were generated, one as was detailed
above (dilution B), and the other after we substituted 50% of the PBS used in our
standard with fresh FreeStyle 293 expression medium (dilution A). Four different
types of samples in addition to a negative control were tested in two rows of five
wells each with high, medium and low concentration of rOPCML with one of the rows
being diluted in dilution A and the other in B. The results were plotted on the
standard curve using a 4-parameter non-linear fit. The final result shows good
correlation between the standard and different rOPCML concentration (Figure 6.14),
and confirms the robustness of the developed plate as a tool to measure future
productions of His-tagged rOPCML.
232 rOPCML spike and recovery 0.80
0.75
0.70
0.65
Optical density Optical 0.60
0.55 -1.5 -1.0 -0.5 0.0 0.5 Log of ELISA standard
Figure 6.14: Spike and recovery ELISA assay of diluent and an unknown concentration
of an analyte showing correlation with the standard curve following dilutions in 50%
of standard (solution A) and the original standard curve in full assay diluent (solution
B) with the mean plotted against the original standard curve.
233 6.6 Summary
This work in this chapter pertains to the production of rOPCML. Currently, to make
rOPCML requires a slow meticulous process to achieve a recombinant protein that is
stable, safe and biologically active. The results of this chapter builds on earlier results
that we achieved when making rOPCML, including that of inhibition of growth and
stimulation of apoptosis in two different ovarian cancer cell lines, as well as
replicating the RTKs inhibition seen in vitro. However, the production of rOPCML was
plagued with technical challenges that essentially stemmed from the instability of the
generated protein as proved when several people attempted to remake rOPCML. The
challenges continued when we switched from a prokaryotic to a mammalian cell
vector. We generated an ELISA plate to address some of the issues encountered.
Nonetheless, whilst we remain hopeful, we do understand that this process will be
long and challenging before we can have a fully functional rOPCML.
234
Chapter seven
Discussion
235 7.1 Introduction
This thesis investigated the role of OPCML in clinical biology, its use as a sensitising agent for targeted therapy in ovarian and breast cancer, and attempts to develop OPCML as a therapy. Also OPCML proteomics and the role of OPCML in angiogenesis were examined.
We did this as research undertaken in the Gabra laboratory first identified OPCML as a putative tumour suppressor gene (TSG) at chromosome 11q25 a site highlighted by frequent loss of heterozygosity (LOH). Our lab demonstrated that the peak of LOH at 11q25 lay within the OPCML gene. It was further demonstrated that epigenetic silencing of the remaining
OPCML allele occurred in 83% of epithelial ovarian cancer (EOC) (Sellar, Watt et al. 2003).
Transfection of OPCML into the ovarian cancer cell line SKOV-3, which is hypermethylated at the OPCML CpG island and has greatly reduced OPCML levels, resulted in growth inhibition, enhanced intercellular aggregation, and abolition of subcutaneous and intraperitoneal tumorigenicity in vivo (Sellar, Watt et al. 2003).
Multiple studies confirmed that OPCML is frequently inactivated, multi-tissue specific tumour suppressor, in common human epithelial cancers (Reed, Dunn et al. 2007, Tsou,
Galler et al. 2007, Anglim, Galler et al. 2008, Cui, Ying et al. 2008).
In 2012, we identified the mechanism of OPCML TSG function, by binding to a defined spectrum of (RTKs) including, HER2 and FGFR1, via their extracellular domains. OPCML targets these RTKs for proteasomal degradation via a pathway involving polyubiquitination, therefore inducing a systems level switch-off of a spectrum of RTKs that leads to pERK and pAKT down-regulation, inducing apoptosis and growth inhibition in cancer but not normal cells (McKie, Vaughan et al. 2012).
236 The hallmarks of cancer comprise six biological aptitudes acquired during its development.
This includes sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis, with the catalyst for these events being genomic instability leading to genetic diversity, and inflammation (Hanahan and Weinberg 2000). The rate-limiting role in all of these activities is angiogenesis (Folkman 2004). Both tumour & stromal cells secrete excess pro-angiogenic factors that stimulate the sprouting of new vessels, with VEGF family playing a major role in “turning on” this “angiogenic switch”. Other characteristics that play a role in cancer generation include evading immune destruction, re-programming of energy metabolism (Hanahan and Weinberg 2011). Given these studies, we went on investigating the OPCML relation with various angiogenic agents, and VEGFA in particular, due to the latter’s role in cancer angiogenesis.
7.2 OPCML potentiates anti-HER2 targeted therapy in ovarian and breast cancers through
binding and negative regulation of HER2 but not EGFR
The work from our lab (McKie, Vaughan et al. 2012) established that OPCM regulates RTKs via internalising into lipid rafts, polyubiquitination and non-clathrin dependent proteasomal degradation. Studies conducted in this thesis contributed to the conclusions reported in the paper, namely that OPCML interactions on the cell surface (as we noted from the biotinylation experiment) showed accelerated loss/ non circulation of the biotin pulsed
HER2 from the OPCML expressing cells and confirming the selectivity and the extracellular nature of OPCML interaction (see Appendix A, supplementary Figures).
237 We further evaluated the role of transiently transfecting OPCML into different ovarian cancer cell lines. The result was abrogation of multiple RTKs including HER2, FGFR1 and
EphA2 and their downstream signalling including pERK and pAKT (Figure 3.1). Multiple conclusions are noted from this experiment including: the level of down-regulation of different RTKs varies between different cell lines, and it’s not related to OPCML level. Of note also that pAKT level is not affected as much as that of pERK which may reflect more
OPCML traffic directing downstream to the latter, in fact pAKT was not affected in A2780 which could be due to the biological aberrations within that cell line. Nonetheless, HER2,
FGFR1 and EphA2 were both down-regulated in all the cell lines in keeping with results carried out using SKOV-3 stably transfected cells (McKie, Vaughan et al. 2012).
The concept of “oncogene addiction” in breast cancer (Weinstein and Joe 2008) led to the development of targeted therapy to HER2 in this type of cancer. Trastuzumab (Genentech/
Roche) a monoclonal antibody targeting HER2, and Lapatinib (GlaxoSmithKline) a small molecule inhibitor against HER2 and EGFR have been used successfully in treatment of metastatic HER2 positive breast cancer along with chemotherapy and resulted in increased response rate, with prolongation in progression free survival and overall survival compared to chemotherapy alone (Slamon, Leyland-Jones et al. 2001, Geyer, Forster et al. 2006).
Despite that, most patients will eventually develop recurrence due to drug resistance in addition to suboptimal response rate of only 18 – 35% to Trastuzumab in HER2 positive breast tumours. Several mechanisms have been proposed to try and overcome drug resistance which are currently being evaluated, such as linking Trastuzumab to T-DM1; a microtubule inhibitor (Esteva, Yu et al. 2010).
238 We tested three cancer cell lines (one ovarian and two breast lines) with increasing levels of
Trastuzumab, Lapatinib and Erlotinib each. Theses results showed that OPCML sensitised the cells to anti-HER2 (Figure 3.2) and combined HER2/EGFR inhibition (Figure 3.3), but quite random effects with Erlotinib and definitely no sensitising effects (Figure 3.4). From these experiments we deduce that OPCML expression confer a therapeutic advantage in anti-HER2 targeted therapy (as judged by increased sensitisation of pERK and pAKT) and that OPCML might be a useful mechanism to overcome resistance to targeted therapy in cancer patients, especially HER2 positive breast cancer patients. The hypothesis for that being that OPCML down-regulating HER2 levels, hence less will be available for hetero- dimerisation with EGFR, therefore less phosphorylated levels of the latter will be available
(as shown in the relevant blots in chapter three) and lower concentrations of Lapatinib will result in inhibition in phospho-ERK/AKT. This is in keeping with other studies, which showed that HER2 inhibition is one of the ways of overcoming resistance to anti-EGFR therapy
(Takezawa, Pirazzoli et al. 2012). As with regards to Erlotinib’s random effects, we hypothesise that with OPCML having a functional interaction with HER2, less of the latter will be available to heterodimerise with EGFR. Alteration in the levels of HER1-HER2 heterodimers have been noted to be associated with altered response to TKI influence on the RTK, with Erlotinib having no effect on HER1-HER1 homodimers (DeFazio-Eli, Strommen et al. 2011).
The clear conclusions from the Trastuzumab and Lapatinib experiments further add to the evidence that OPCML potentiates its effect through functional relationship with HER2 but not EGFR. This is in keeping with previous results from our lab, which noted direct interaction between OPCML and HER2, something we never observed with EGFR. The
239 results also confirm our earlier observations of a sensitising effect with these biological agents in stably transfected cells.
7.3 OPCML expression results in decreased VEGFA and altered VEGFRs expression levels
One interesting finding from our pilot in vivo experiment was the abolition of ascites in the rOPCML treated murine group by 95% (McKie, Vaughan et al. 2012). It has been well established that increased microvascular permeability of tumour vasculature is the main factor in malignant ascites formation with the amount of ascites production correlating with the degree of neovascularisation, with VEGFA playing an important role in that (Adam and
Adam 2004).
To investigate if OPCML has any effect on angiogenic factors we started by an in silico analysis using the data from the Cancer Genome Atlas (TCGA). The angiogenic factors were chosen based on our review of the literature. We only included ligands and integrins but no receptors as this will not be affected in the data due to the fact that it is (i.e. the data) is an
RNA not protein data. The other issue we encountered was that there were two probes for
OPCML with the median level for one passing through the 95% centile of the other, therefore attempts to combine the two probes together in the analysis did not yield any significant result. We therefore used each probe separately and assessed the expression against the top and lower quartile of each probe. Using OPCML probe 206215, a total of 28 probe hits scored significant correlation with altered OPCML levels (Table and Figure 4.1).
Some of these probes are in duplicates for the same target, such as EPHA1, THBS1, VEGFA,
SEMA3C and PDGFB, meaning that more than one paired probes had evident result of an
240 association with altered OPCML levels. We chose to concentrate on VEGFA due to its vital role in angiogenesis; unfortunately, we did not have enough time to investigate the other factors. We further investigated the expression of VEGFA in stably transfected SKOV-3 and
PEO1 cells. We found that OPCML expression was associated with a decrease in VEGFA expression in OPCML expressing cells; the reduction was not significant in SKOBS-3.5 cells
(Figure 4.3). It is also important to note, in SKOBS-3.5 cells you see an increase in OPCML
RNA by qT-PCR, but an increase in the protein level of OPCML as measured by western blotting is not consistently seen. Therefore, it is hard to draw conclusions from this cell line.
We further investigated the expression of VEGFA in the media of the OPCML expressing cells with that of negative controls so to ensure that the ligand reduction is not caused by OPCML promoting the secretion of a larger proportion of VEGFA extra-cellularly. The results of this experiment points out towards reduction in soluble VEGFA in medium (Figure 4.4). The mechanism of OPCML interaction in angiogenesis is still to be fully elucidated, but our hypothesis is that this may be indirectly related to the down-regulation of HER2 as it has been shown in other studies that HER2 signalling modulates the equilibrium between pro- and anti-angiogenic factors indirectly via inhibiting the PI3K/AKT pathway with the result of
HER2 down-regulation being a decrease in VEGF (Wen, Yang et al. 2006).
After we established that OPCML decreases VEGFA levels, we tested if OPCML has any influence on the VEGF receptor family (VEGFRs), including VEGFR1, VEGFR2 and VEGFR3.
Using the SKOV-3 derived cell lines, the results show that OPCML expression was associated with a decrease in total VEGFR3 and with it phospho VEGFR3. There was no effect on total
VEGFR2 and VEGFR1, however phospho VEGFR2 was decreased, whilst that of VEGFR1 was increased, with the increase in OPCML expression (Figure 4.5). The results were not very
241 clear with SKOBS-3.5, which is not surprising given the previously mentioned issues. When applying VEGFA time-point stimulation, the results show that OPCML expressing cells showed less response to the ligand stimulation in keeping with a probable role in modulating the cell response to the stimulatory effect of VEGFA (Figure 4.8). When we looked at the VEGF receptors in OSE OPCML knockdown cells under different stimulation conditions, the results showed a similar trend with the results being most evident when comparing the normal OPCML expressing cells PLKO-1.3 with the 95% knockdown sh-339-24
(Figure 4.6). When we extended these test further to various ovarian cancer cell lines, we noted variable results between cells in keeping with the different biology of these cells, with the most evident changes noted in PEA1 and PEA2 cells in terms of VEGFR3 abrogation, whilst that of PEO1 was the least evident. Interestingly, there was no effect on pVEGFR1 in any of these cells, but the effect on pVEGFR2 was noted in all the cells (Figure 4.7). One thing to note with all the experiments is that the different antibodies that we tested and used for the VEGFRs were not all of a good quality and problems with “dirty” antibodies with background and decreased specificity was most evident with VEGFR2. This issue persisted despite many antibodies from different manufacturers; this could be partially due to technical difficulties with western blotting due to the high molecular weight of VEGFRs in general and VEGFR2 in particular.
The specific loss of VEGFR3 expression in association with OPCML was further examined using confocal microscopy and FACS analysis (Figures 4.9 and 4.10), both modalities showed decreased expression of tVEGFR3 in the OPCML expressing cells compared to empty vector control. To test if there was a direct interaction between OPCML and VEGFR3, a CO-IP experiment was set up using the SKOV-3 derived cell line BKS-2.1; unfortunately, this did not
242 yield any evidence for interaction (see Appendix A, Supplementary Figures). There could be many reasons for that, one being that OPCML decreases the VEGFR3 levels, and that
VEGFR3 level is not high is these cells anyway (as we noted from the western blots). Other possibility could be due to technicality, and although this was not evident at the time but in retrospect we noted that using PBS to wash the beads in the penultimate step prior to suspending them in 1X SDS could end up attenuating the interaction. A pull-down assay was used and this time VEGFR3, but not VEGFR1 or VEGFR2, was pulled-down from the cell lysate by the OPCML-GST fusion protein (Figure 4.11). The pull down experiment provided evidence that OPCML might be directly interacting with VEGFR3.
Finally we evaluated the effect of OPCML expression on the response to VEGFA monoclonal antibody Bevacizumab in BKS 2.1 and SKOBS-V1.2, and PEO1-OP6 (Figure 4.12). The results were random and did not show any evidence of sensitisation. There could be a possibility that the Bevacizumab used is suboptimal in terms of potency, as we use the left over medicine passed to us from the oncology pharmacological department at Imperial
Healthcare NHS trust after it has been used to treat patients. There is a possibility the medication was not stored correctly in the intervening time before we received it, hence, this result needs to be interpreted with caution. Further experiments are required to check if OPCML has any future potential to contribute to anti-angiogenesis therapy with other therapeutic agents and inhibitors, such as anti-VEGFR3 targeted therapy. Recent evidence suggests that VEGFR3 inhibition in ovarian cancer cells results in decreased BRCA1 and
BRCA2 levels, anti-proliferative effects and improved response to chemotherapy (Lim, Yang et al. 2014).
243 7.4 High throughput microarray analysis shows a potentially varied base for OPCML
downstream signalling events including anti-proliferation and apoptosis
RPPA is a new high-throughput proteomic profiling technique that is becoming more feasible as a standard method for investigating the various cellular pathway profiles implicated in cancer (Pin, Federici et al. 2014). We ran two experiments in batches, the latter to improve the reliability and the statistical accuracy of the results. We used the
SKOV-3 and the PEO1 derived stably transfected cells under serum free medium and EGF stimulation to further explore OPCML functional relationship at proteomic level with its empty vector control. The first experiment also included batches for OPCML mutation
(OPCML-P95R). Our results showed that certain groups and biological processes seem to be primarily influenced by OPCML levels. These include proteins responsible for apoptosis, anti- proliferation, proteins concerned with signal transduction, cell cycle progression and DNA damage and repair mechanism, cell adhesion and metastasis, as well as a lot of compounds that play a role in generalised cell functions like fatty acid and glycogen synthesis.
The consistent effect for both cell lines is OPCML association with significant increase in P21,
BIM, Chk1-pS345, VEGFR2, Fibronectin, BRCA2, Dvl3, Akt, Src, and c-Kit, as well as a significant decrease in ER-alpha, INPP4B, PDK1, c-MET and HER3-pY1298 (Table 5.4 A and B).
The results show us that two of the most important cellular pathways that OPCML is associated with many of its proteins are the PI3K/AKT and the MAPK/ERK pathways. OPCML is associated with significant increase (especially in PEO1-OP6 cells) in the pro-apoptotic proteins BIM, BAX, BAK and BAD_pS112, part of the BCL-2 family that facilitate apoptosis with BAX and BAK ultimately causing the release of cytochrome C and activation of the mitochondrial cell death pathway. BIM will act directly on BAX and BAK, whilst BAD will
244 regulate the anti-apoptotic proteins within the BCL-2 family, including BCL-XL (Youle and
Strasser 2008).
P21, a Cyclin-dependent kinase inhibitor is induced by both p53-dependent and - independent mechanisms leading to anti-proliferation and cell cycle arrest and may even have a role in inducing apoptosis (Gartel and Tyner 2002).
PDK1, a kinase responsible for activation of Akt, leading to increased cell proliferation (Fyffe and Falasca 2013), is decreased with increased OPCML expression. Previous research from our lab has shown that OPCML has no effect on total AKT expression whilst it decreases that of pAKT (Sellar, Watt et al. 2003, McKie, Vaughan et al. 2012).
BRCA2 protein promotes homologous recombination (HR) repair of damaged DNA in cells, through its interaction with the RAD51 recombinase leading to maintenance of genome integrity and tumour suppression function (Spugnesi, Balia et al. 2013).
Chk1 is activated following DNA damage, which leads to checkpoint signal transduction, facilitates cell cycle arrest and DNA damage repair and may contribute to resistance to anti- cancer therapy (Zhang and Hunter 2014). Increased levels of Fibronectin have been associated with poor prognosis in ovarian cancer (Ricciardelli and Rodgers 2006). Dvl3, a key component of the Wnt pathway, has been implicated in up-regulation of β-catenin, and cellular proliferation in colorectal and cervical cancer (Kwan, Chan et al. 2013). All these could point towards possible pitfalls in using OPCML as a therapy and will need to be further evaluated.
ER-alpha regulates the proliferation of breast cancer through MAPK/ERK and PI3K/Akt signalling pathways (Kaufmann, Jonat et al. 2007).
245 Inositol polyphosphate 4-phosphatase type II (INPP4B) is an important tumour suppressor of the PI3K pathway responsible for de-phosphorylation of PIP2 to PIP, thus inhibiting further signalling (Stjernstrom, Karlsson et al. 2014).
The RPPA results also showed that VEGFR2 levels are increased in OPCML expressing cells, however, the results from our investigations into the role of OPCML with various angiogenic factors points towards OPCML having no obvious effect on tVEGFR2 expression and possible decrease expression of pVEGFR2 (see results, chapter four).
To further investigate OPCML effects on cell proliferation in ovarian cancer we carried out proliferation assays using WST1 (Roche) on four different ovarian cancer cell lines after transient transfection of OPCML. Our results confirmed earlier findings from stably transfected cells (Sellar, Watt et al. 2003, McKie, Vaughan et al. 2012) that OPCML transfection results in proliferation inhibition in all cell lines by 72 hours, with significant inhibition in PEA2 noted by 48 hours (Figure 5.4). It should also be noted that by 72 hours, the proliferation curve did go down in all the OPCML transfected cells, which suggests that
OPCML could be inducing cell-death in these cells by that stage.
To test OPCML effect on HER3-pY1298, we conducted an experiment in OSE cell knockdowns. Knockdown of OPCML by 60% (in sh-464-23 cell line) results in increase in
HER3_pY1289 levels compared to the empty vector control (PLKO) under serum free, full medium and EGF stimulation conditions (Figure 5.5), which supports the RPPA finding.
Testing for OPCML effects on cMET in PEO1 derived cells under serum-free and EGF stimulation also supports the RPPA results whereby OPCML expression is associated with a decrease in cMET levels (see Appendix A, Supplementary Figures).
246 Finally western blotting showed that OPCML-P95R mutation is associated with phenotypic changes compared to OPCML wild type and empty vector controls. SKOBS-P95R-3.4 was associated with decrease in HER2 and FGFR1, albeit this was less than the abrogation seen in BKS-2.1. No effect was seen in terms of EphA2, but a decrease in the levels of total and phospho EGFR and HER3 was noted. There was no evidence from the western blot of any significant changes to the total or phospho AKT or ERK levels (Figure 5.6).
In conclusion, the results noted from the RPPA analysis confirmed previously observed data regarding OPCML being a TSG influence signalling downstream via multiple pathways, some of them were known such as the PI3K/AKT and MAPK/ERK, others are novel such as the JNK and Wnt pathways. It is important to view these results as a guide to direct our efforts and resources towards a more detailed analysis of future targets, e.g. studying the functional relation of OPCML and cMET in detail and how this might influence chemo-resistance, tumour migration and invasion is one of the paths that we can follow in the future based on the preliminary findings from the RPPA data.
247 7.5 Technical limitations of producing rOPCML as a therapeutic agent
With regards to rOPCML, and in order to comprehensively investigate its role as a therapeutic agent; either alone or in combination with chemotherapy, we need to produce a protein that is stable; both structurally and biologically, through a process that is relatively simple, consistent and reproducible. Our original pilot work; including the small in vivo experiment established the potential therapeutic benefits of rOPCML expressed in E. coli as a future biological modulator in the treatment of ovarian cancer (McKie, Vaughan et al.
2012). Our results showed that rOPCML accelerates ovarian cancer cells death in vitro
(McKie, Vaughan et al. 2012). However, subsequent batches of rOPCML prepared in our lab using the same process as before made it quite apparent that there were variations in- between different batches, as well as in the same batch. This was evident both in terms of yield and biological activity. This is due in part to the bacterial expression systems, which on one hand are simple to manipulate, inexpensive, and generates high yield of proteins over a short period of time, but, on the other hand suffer from poor protein solubility and/ or lack post-translational modification (Liu, Dalby et al. 2008). Other systems for protein production including yeasts are generally more technically demanding; whilst baculo-virus protein production is quite time consuming (Liu, Dalby et al. 2008) and not easy to produce a properly folded protein as other members in our lab have experienced (unpublished data).
Against this, the use of mammalian cell vector, namely the HEK-293-F cells, through transiently transfecting the plasmid of interest, provides a protein that is soluble, glycosylated and correctly folded over a relatively short period of time (Baldi, Muller et al.
2005), albeit the protein yield is much smaller. We have used an OPCML His-tagged plasmid transfected into suspension modified HEK-293-F cells. The suspension media is serum-free
248 and of animal-free origin, so was the transfection reagent. As this is novel in terms of
OPCML production, before proceeding to protein production and purification, we investigated the effects of various transient transfection parameters on rOPCML synthesis.
Following identification of the timing of maximal protein expression as confirmed by western blotting (Figure 6.5 and 6.6), we started the process of small-scale protein production. As we are using a His-tagged protein, Ni-NTA is the ideal matrix for capturing and purifying the expressed protein. It was also important to ensure that the process for purification and elution is done under native condition, as we need to protect the biological activity of the protein. One problem with purification under native condition is the fact that non-tagged proteins can potentially interact with the Ni-NTA resin. This non-specific binding might have an impact on the quality of rOPCML generated. Another important and major issue with regards to our protein production is the fact that we do not have a reliable process to measure exactly how much rOPCML is produced per a defined numerical population of HEK-293F cells. We addressed this by developing an rOPCML sandwich ELISA detection assay. This was shown to be both sensitive and specific as we noted from the spike and recovery experiments (Figure 6.14). We made use of a commercially available antibody against OPCML, which we have tried before in our lab for confocal microscopy and western blotting as a detecting antibody, whilst another one against hexahistidine served as a capture antibody. Through using the ELISA assay we can quantify the predicted rOPCML.
This process is a pre-requisite anyway for any future pharmacokinetics (PK) study and in the short term will result in us identifying the exact amount of Ni-NTA resin for use during purification. Other fine tuning options which can be deployed to decrease the non specific protein binding includes: decreasing the amount of imidazole in the lysis buffer and/or increase the amount of NaCl in the elution buffer. We tested the biological activity of the
249 newly produced rOPCML against SKOBS-V1.2 cells at a similar concentration (2 µM) as per the previous prokaryotic produced batches. The results were random and inconsistent
(Figure 6.11). Initially we noted a large number of cell death in the treatment group, this could be due to the quality of rOPCML produced or it could be due to other factors e.g. chemical reaction due to the effect of the alkaline pH of the solution, or one of the constituents of the buffer like imidazole being toxic to the cells. When we dialysed the protein, we did not observe therapeutic effect on HER2 expression. In the future we may also decide to use pERK and pAKT to confirm the biological activity of rOPCML as it is a more robust evidence for the protein’s activity. However, given the importance of OPCML as a
TSG, it would be worthwhile continuing this work to improve the production process of rOPCML. As OPCML expression is reduced in cancerous tissue, a soluble rOPCML protein might be able to be given as an intravenous drug, to replenish OPCML on the surface of cancer cells.
250 7.6 Overall conclusions
The data presented in this thesis is novel and with the exception of the paper by (McKie,
Vaughan et al. 2012), no previous detailed mechanism or functional relationship for OPCML tumour suppression and clinical biological activity with RTK attenuation existed.
Despite numerous challenges, we set about to investigate the use of OPCML as a sensitising agent in targeted therapy. This target was met and our results did clearly show that OPCML sensitised ovarian and breast cancer cells in vitro to Anti-HER2 and dual acting Anti-
HER2/EGFR agents, further adding to the knowledge regarding selectivity of OPCML to
HER2. We also tested generalised OPCML proteomics, which could help us in the future to select clinical target/targets for OPCML clinical biological use.
With regards to our second aim to investigate the relationship and propose a functional mechanism for OPCML effects on angiogenesis, we showed by in silico and in vitro work that
OPCML has a functional relation with VEGFA. Our results concluded that OPCML is associated with decreased VEGFR3 levels. These novel results further added to our knowledge and understanding of OPCML functional interactions.
Finally, this thesis showed the limitations and the various challenges in trying to produce a recombinant OPCML protein. This should stimulate our efforts to try and use novel methods to consistently produce a stable protein, such as virus-like particles.
251 7.7 Limitation of this project
As with some of the details mentioned above with regards to limited published knowledge on the subject, significant time and effort was spent on generating resources that enabled the study of OPCML role in angiogenesis in general and VEGFA and the VEGFR family in particular. This was compounded by poor quality of available antibodies. Assessment of the biological activity of rOPCML and the variation noted in the subsequent batches produced took a lot of time and effort to try and remedy what proved to be a complex and challenging subject.
252 7.8 Future directions
In conclusion, our work shows that OPCML and its recombinant form may potentially play an important part in treatment of human cancer, and in particular ovarian cancer. Our ultimate goal will be producing a biologically active and stable rOPCML that we plan to test vigorously, with the aim of producing an anti-cancer drug that we can test in clinical trials.
Further work would also help our understanding of OPCML with angiogenesis, including that with PDGF and SEMA3C. Also to further investigate relation of OPCML with VEGFR3, including if any sensitisation effects does occur with anti-VEGFR3 therapy. In addition to other experiments to test VEGFR3 response to stimulation with VEGFC and VEGFD in OPCML transfected cells, angiogenesis assays and tumour angiogenesis models.
Studying the various effects noted in the RPPA experiment would help consolidate our hypothesis on OPCML detailed interactions and downstream signalling and test future targets and possible resistance proteins. An example would be to fully test OPCML relation and any interaction with the BCL-2 family may prove useful in understanding the OPCML effect of apoptosis, or the functional relation between OPCML and BRCA2. Ultimately it will be useful to consider if OPCML exerts any sensitisation effects with chemotherapy, especially with platinum-based compounds, which could be done via the use of cells response as measured by an IC50 curve.
253
254 References
• (1998). "ICON2: randomised trial of single-agent carboplatin against three-drug combination of CAP (cyclophosphamide, doxorubicin, and cisplatin) in women with ovarian cancer. ICON Collaborators. International Collaborative Ovarian Neoplasm Study." Lancet 352(9140): 1571-1576.
• Adam, R. A. and Y. G. Adam (2004). "Malignant ascites: past, present, and future." J Am Coll Surg 198(6): 999-1011.
• Alberts, D. S., et al. (1996). "Intraperitoneal cisplatin plus intravenous cyclophosphamide versus intravenous cisplatin plus intravenous cyclophosphamide for stage III ovarian cancer." N Engl J Med 335(26): 1950-1955.
• Amit, I., et al. (2007). "Evolvable signaling networks of receptor tyrosine kinases: relevance of robustness to malignancy and to cancer therapy." Mol Syst Biol 3: 151.
• Anglim, P. P., et al. (2008). "Identification of a panel of sensitive and specific DNA methylation markers for squamous cell lung cancer." Mol Cancer 7: 62.
• Armstrong, D. K., et al. (2006). "Intraperitoneal cisplatin and paclitaxel in ovarian cancer." N Engl J Med 354(1): 34-43.
• Auersperg, N., et al. (2001). "Ovarian surface epithelium: biology, endocrinology, and pathology." Endocr Rev 22(2): 255-288.
• Augustin, H. G., et al. (2009). "Control of vascular morphogenesis and homeostasis through the angiopoietin-Tie system." Nat Rev Mol Cell Biol 10(3): 165-177.
• Ayhan, A., et al. (2004). "Association between fertility drugs and gynecologic cancers, breast cancer, and childhood cancers." Acta Obstet Gynecol Scand 83(12): 1104- 1111.
• Baldi, L., et al. (2005). "Transient gene expression in suspension HEK-293 cells: application to large-scale protein production." Biotechnol Prog 21(1): 148-153.
• Baselga, J., et al. (2012). "Lapatinib with trastuzumab for HER2-positive early breast cancer (NeoALTTO): a randomised, open-label, multicentre, phase 3 trial." Lancet 379(9816): 633-640.
255
• Bast, R. C., Jr. (2011). "Molecular approaches to personalizing management of ovarian cancer." Ann Oncol 22 Suppl 8: viii5-viii15.
• Bast, R. C., Jr., et al. (1981). "Reactivity of a monoclonal antibody with human ovarian carcinoma." J Clin Invest 68(5): 1331-1337.
• Bast, R. C., Jr., et al. (2009). "The biology of ovarian cancer: new opportunities for translation." Nat Rev Cancer 9(6): 415-428.
• Bast, R. C., Jr., et al. (1983). "A radioimmunoassay using a monoclonal antibody to monitor the course of epithelial ovarian cancer." N Engl J Med 309(15): 883-887.
• Bast, R. C., Jr., et al. (1998). "CA 125: the past and the future." Int J Biol Markers 13(4): 179-187.
• Beral, V., et al. (2008). "Ovarian cancer and oral contraceptives: collaborative reanalysis of data from 45 epidemiological studies including 23,257 women with ovarian cancer and 87,303 controls." Lancet 371(9609): 303-314.
• Bergers, G. and D. Hanahan (2008). "Modes of resistance to anti-angiogenic therapy." Nat Rev Cancer 8(8): 592-603.
• Billmire, D. F., et al. (2014). "Surveillance after initial surgery for pediatric and adolescent girls with stage I ovarian germ cell tumors: report from the Children's Oncology Group." J Clin Oncol 32(5): 465-470.
• Birrer, M. J., et al. (2007). "Whole genome oligonucleotide-based array comparative genomic hybridization analysis identified fibroblast growth factor 1 as a prognostic marker for advanced-stage serous ovarian adenocarcinomas." J Clin Oncol 25(16): 2281-2287.
• Blackwood, M. A. and B. L. Weber (1998). "BRCA1 and BRCA2: from molecular genetics to clinical medicine." J Clin Oncol 16(5): 1969-1977.
• Blagden, S. and H. Gabra (2009). "Promising molecular targets in ovarian cancer." Curr Opin Oncol 21(5): 412-419.
256 • Blumenschein, G. R., Jr., et al. (2012). "Targeting the hepatocyte growth factor-cMET axis in cancer therapy." J Clin Oncol 30(26): 3287-3296.
• Bolton, K. L., et al. (2012). "Association between BRCA1 and BRCA2 mutations and survival in women with invasive epithelial ovarian cancer." JAMA 307(4): 382-390.
• Bookman, M. A. (2009). "Dose-dense chemotherapy in advanced ovarian cancer." Lancet 374(9698): 1303-1305.
• Bose, R., et al. (2006). "Phosphoproteomic analysis of Her2/neu signaling and inhibition." Proc Natl Acad Sci U S A 103(26): 9773-9778.
• Brenton, J. D., et al. (2005). "Molecular classification and molecular forecasting of breast cancer: ready for clinical application?" J Clin Oncol 23(29): 7350-7360.
• Bristow, R. E. and D. S. Chi (2006). "Platinum-based neoadjuvant chemotherapy and interval surgical cytoreduction for advanced ovarian cancer: a meta-analysis." Gynecol Oncol 103(3): 1070-1076.
• Burger, R. A. (2011). "Overview of anti-angiogenic agents in development for ovarian cancer." Gynecol Oncol 121(1): 230-238.
• Burger, R. A., et al. (2011). "Incorporation of bevacizumab in the primary treatment of ovarian cancer." N Engl J Med 365(26): 2473-2483.
• Burgess, A. W., et al. (2003). "An open-and-shut case? Recent insights into the activation of EGF/ErbB receptors." Mol Cell 12(3): 541-552.
• Cailleau, R., et al. (1974). "Breast tumor cell lines from pleural effusions." J Natl Cancer Inst 53(3): 661-674.
• Calderon-Margalit, R., et al. (2009). "Cancer risk after exposure to treatments for ovulation induction." Am J Epidemiol 169(3): 365-375.
• Campeau, P. M., et al. (2008). "Hereditary breast cancer: new genetic developments, new therapeutic avenues." Hum Genet 124(1): 31-42.
257 • Cancer Genome Atlas Research, N. (2011). "Integrated genomic analyses of ovarian carcinoma." Nature 474(7353): 609-615.
• Cancer Research UK (2008). "Ovarian cancer, UK mortality statistics.".
• Cancer.org (2014). "Relative 5-year survival rates for ovarian cancer in the United States." from http://www.cancer.org/cancer/ovariancancer/detailedguide/ovarian- cancer-survival-rates.
• Cannistra, S. A. (2004). "Cancer of the ovary." N Engl J Med 351(24): 2519-2529.
• Carey, L. A. (2010). "Directed therapy of subtypes of triple-negative breast cancer." Oncologist 15 Suppl 5: 49-56.
• Catania, E. H., et al. (2008). "Genetic deletion of Lsamp causes exaggerated behavioral activation in novel environments." Behav Brain Res 188(2): 380-390.
• Chen, H., et al. (2007). "Loss of OPCML expression and the correlation with CpG island methylation and LOH in ovarian serous carcinoma." Eur J Gynaecol Oncol 28(6): 464-467.
• Chen, J., et al. (2003). "The t(1;3) breakpoint-spanning genes LSAMP and NORE1 are involved in clear cell renal cell carcinomas." Cancer Cell 4(5): 405-413.
• Chen, S., et al. (2001). "Neurotrimin expression during cerebellar development suggests roles in axon fasciculation and synaptogenesis." J Neurocytol 30(11): 927- 937.
• Cheung, C. Y., et al. (2010). "Obesity susceptibility genetic variants identified from recent genome-wide association studies: implications in a chinese population." J Clin Endocrinol Metab 95(3): 1395-1403.
• Citri, A. and Y. Yarden (2006). "EGF-ERBB signalling: towards the systems level." Nat Rev Mol Cell Biol 7(7): 505-516.
• Claesson-Welsh, L. and M. Welsh (2013). "VEGFA and tumour angiogenesis." J Intern Med 273(2): 114-127.
258 • Colombo, N., et al. (2003). "International Collaborative Ovarian Neoplasm trial 1: a randomized trial of adjuvant chemotherapy in women with early-stage ovarian cancer." J Natl Cancer Inst 95(2): 125-132.
• Cui, Y., et al. (2008). "OPCML is a broad tumor suppressor for multiple carcinomas and lymphomas with frequently epigenetic inactivation." PLoS One 3(8): e2990.
• Czekierdowski, A., et al. (2006). "Opioid-binding protein/cell adhesion molecule-like (OPCML) gene and promoter methylation status in women with ovarian cancer." Neuro Endocrinol Lett 27(5): 609-613.
• Davies, B., et al. (2003). "Immortalisation of human ovarian surface epithelium with telomerase and temperature-sensitive SV40 large T antigen." Experimental Cell Research 288(2): 390-402.
• Davies, B. R., et al. (2003). "Immortalisation of human ovarian surface epithelium with telomerase and temperature-sensitive SV40 large T antigen." Exp Cell Res 288(2): 390-402.
• Davis, A. J. and I. F. Tannock (2002). "Tumor physiology and resistance to chemotherapy: repopulation and drug penetration." Cancer Treat Res 112: 1-26.
• Davis, A. J. and J. F. Tannock (2000). "Repopulation of tumour cells between cycles of chemotherapy: a neglected factor." Lancet Oncol 1: 86-93.
• DeFazio-Eli, L., et al. (2011). "Quantitative assays for the measurement of HER1-HER2 heterodimerization and phosphorylation in cell lines and breast tumors: applications for diagnostics and targeted drug mechanism of action." Breast Cancer Res 13(2): R44.
• Di Cosimo, S. and J. Baselga (2010). "Management of breast cancer with targeted agents: importance of heterogeneity. [corrected]." Nat Rev Clin Oncol 7(3): 139-147.
• Dixon, M. (2003). ABC of breast diseases
• Dubeau, L. (2008). "The cell of origin of ovarian epithelial tumours." Lancet Oncol 9(12): 1191-1197.
259 • Dutta, P. R. and A. Maity (2007). "Cellular responses to EGFR inhibitors and their relevance to cancer therapy." Cancer Lett 254(2): 165-177.
• Elattar, A., et al. (2011). "Optimal primary surgical treatment for advanced epithelial ovarian cancer." Cochrane Database Syst Rev(8): CD007565.
• Esteva, F. J., et al. (2010). "Molecular predictors of response to trastuzumab and lapatinib in breast cancer." Nat Rev Clin Oncol 7(2): 98-107.
• Fader, A. N., et al. (2013). "Survival in women with grade 1 serous ovarian carcinoma." Obstet Gynecol 122(2 Pt 1): 225-232.
• Faratian, D., et al. (2011). "Phosphoprotein pathway profiling of ovarian carcinoma for the identification of potential new targets for therapy." Eur J Cancer 47(9): 1420- 1431.
• Ferguson, K. M. (2008). "Structure-based view of epidermal growth factor receptor regulation." Annu Rev Biophys 37: 353-373.
• Fischerova, D. and A. Burgetova (2014). "Imaging techniques for the evaluation of ovarian cancer." Best Pract Res Clin Obstet Gynaecol 28(5): 697-720.
• Fogh, J., et al. (1977). "One hundred and twenty-seven cultured human tumor cell lines producing tumors in nude mice." J Natl Cancer Inst 59(1): 221-226.
• Fogh, J., et al. (1977). "Absence of HeLa cell contamination in 169 cell lines derived from human tumors." J Natl Cancer Inst 58(2): 209-214.
• Folkman, J. (2004). "Endogenous angiogenesis inhibitors." APMIS 112(7-8): 496-507.
• Foulkes, W. D., et al. (2010). "Triple-negative breast cancer." N Engl J Med 363(20): 1938-1948.
• Funatsu, N., et al. (1999). "Characterization of a novel rat brain glycosylphosphatidylinositol-anchored protein (Kilon), a member of the IgLON cell adhesion molecule family." J Biol Chem 274(12): 8224-8230.
260 • Fyffe, C. and M. Falasca (2013). "3-Phosphoinositide-dependent protein kinase-1 as an emerging target in the management of breast cancer." Cancer Manag Res 5: 271- 280.
• Gartel, A. L. and A. L. Tyner (2002). "The role of the cyclin-dependent kinase inhibitor p21 in apoptosis." Mol Cancer Ther 1(8): 639-649.
• Gershenson, D. M. (2007). "Management of ovarian germ cell tumors." J Clin Oncol 25(20): 2938-2943.
• Geyer, C. E., et al. (2006). "Lapatinib plus capecitabine for HER2-positive advanced breast cancer." N Engl J Med 355(26): 2733-2743.
• Gil, O. D., et al. (1998). "Neurotrimin mediates bifunctional effects on neurite outgrowth via homophilic and heterophilic interactions." J Neurosci 18(22): 9312- 9325.
• Gonzalez-Martin, A., et al. (2014). "First-line and maintenance therapy for ovarian cancer: current status and future directions." Drugs 74(8): 879-889.
• Hanahan, D. and R. A. Weinberg (2000). "The hallmarks of cancer." Cell 100(1): 57- 70.
• Hanahan, D. and R. A. Weinberg (2011). "Hallmarks of cancer: the next generation." Cell 144(5): 646-674.
• Hanna, L. and M. Adams (2006). "Prevention of ovarian cancer." Best Pract Res Clin Obstet Gynaecol 20(2): 339-362.
• Harris, R., et al. (1992). "Characteristics relating to ovarian cancer risk: collaborative analysis of 12 US case-control studies. III. Epithelial tumors of low malignant potential in white women. Collaborative Ovarian Cancer Group." Am J Epidemiol 136(10): 1204-1211.
• Hennessy, B. T., et al. (2009). "Ovarian cancer." Lancet 374(9698): 1371-1382.
• Herbst, A. L. and J. S. Berek (1993). "Impact of contraception on gynecologic cancers." Am J Obstet Gynecol 168(6 Pt 2): 1980-1985.
261
• Hurwitz, H., et al. (2004). "Bevacizumab plus irinotecan, fluorouracil, and leucovorin for metastatic colorectal cancer." N Engl J Med 350(23): 2335-2342.
• Innos, J., et al. (2011). "Lower anxiety and a decrease in agonistic behaviour in Lsamp-deficient mice." Behav Brain Res 217(1): 21-31.
• International Collaborative Ovarian Neoplasm, G. (2002). "Paclitaxel plus carboplatin versus standard chemotherapy with either single-agent carboplatin or cyclophosphamide, doxorubicin, and cisplatin in women with ovarian cancer: the ICON3 randomised trial." Lancet 360(9332): 505-515.
• Itoh, S., et al. (2008). "Glycosylation analysis of IgLON family proteins in rat brain by liquid chromatography and multiple-stage mass spectrometry." Biochemistry 47(38): 10132-10154.
• J. M. G. WILSON, G. J. (1968). PRINCIPLES AND PRACTICE OF SCREENING FOR DISEASE.
• Jayson, G. C., et al. (2014). "Ovarian cancer." Lancet 384(9951): 1376-1388.
• Jensen, A., et al. (2009). "Use of fertility drugs and risk of ovarian cancer: Danish Population Based Cohort Study." BMJ 338: b249.
• Jiao, Y., et al. (2011). "Targeting HSP90 in ovarian cancers with multiple receptor tyrosine kinase coactivation." Mol Cancer 10: 125.
• Kabawat, S. E., et al. (1983). "Tissue distribution of a coelomic-epithelium-related antigen recognized by the monoclonal antibody OC125." Int J Gynecol Pathol 2(3): 275-285.
• Kallen, B., et al. (2010). "Malignancies among women who gave birth after in vitro fertilization." Hum Reprod.
• Kaufmann, M., et al. (2007). "Improved overall survival in postmenopausal women with early breast cancer after anastrozole initiated after treatment with tamoxifen compared with continued tamoxifen: the ARNO 95 Study." J Clin Oncol 25(19): 2664- 2670.
262 • Kim, H., et al. (2014). "Newly Identified Cancer-Associated Role of Human Neuronal Growth Regulator 1 (NEGR1)." J Cancer 5(7): 598-608.
• Kmet, L. M., et al. (2003). "A review of p53 expression and mutation in human benign, low malignant potential, and invasive epithelial ovarian tumors." Cancer 97(2): 389-404.
• Konecny, G. E., et al. (2009). "Activity of the multikinase inhibitor dasatinib against ovarian cancer cells." Br J Cancer 101(10): 1699-1708.
• Kresse, S. H., et al. (2009). "LSAMP, a novel candidate tumor suppressor gene in human osteosarcomas, identified by array comparative genomic hybridization." Genes Chromosomes Cancer 48(8): 679-693.
• Kummel, S., et al. (2014). "Surgical treatment of primary breast cancer in the neoadjuvant setting." Br J Surg 101(8): 912-924.
• Kurman, R. J. and M. Shih Ie (2010). "The origin and pathogenesis of epithelial ovarian cancer: a proposed unifying theory." Am J Surg Pathol 34(3): 433-443.
• Kwan, H. T., et al. (2013). "AMPK activators suppress cervical cancer cell growth through inhibition of DVL3 mediated Wnt/beta-catenin signaling activity." PLoS One 8(1): e53597.
• Lai, C. H., et al. (2014). "Molecular imaging in the management of gynecologic malignancies." Gynecol Oncol.
• Landen, C. N., Jr., et al. (2008). "Early events in the pathogenesis of epithelial ovarian cancer." J Clin Oncol 26(6): 995-1005.
• Landrum, L. M., et al. (2013). "Prognostic factors for stage III epithelial ovarian cancer treated with intraperitoneal chemotherapy: a Gynecologic Oncology Group study." Gynecol Oncol 130(1): 12-18.
• Langdon, S. P., et al. (1988). "Characterization and properties of nine human ovarian adenocarcinoma cell lines." Cancer Res 48(21): 6166-6172.
• Ledermann, J. A. and R. S. Kristeleit (2010). "Optimal treatment for relapsing ovarian cancer." Ann Oncol 21 Suppl 7: vii218-222.
263
• Lee, C. W., et al. (2011). "Residual tumor after the salvage surgery is the major risk factors for primary treatment failure in malignant ovarian germ cell tumors: a retrospective study of single institution." World J Surg Oncol 9: 123.
• Lee, Y., et al. (2007). "A candidate precursor to serous carcinoma that originates in the distal fallopian tube." J Pathol 211(1): 26-35.
• Li, B., et al. (2010). "CpG island methylator phenotype associated with tumor recurrence in tumor-node-metastasis stage I hepatocellular carcinoma." Ann Surg Oncol 17(7): 1917-1926.
• Li, L., et al. (2004). "Correlation of serum VEGF levels with clinical stage, therapy efficacy, tumor metastasis and patient survival in ovarian cancer." Anticancer Res 24(3b): 1973-1979.
• Lim, J. J., et al. (2014). "VEGFR3 inhibition chemosensitizes ovarian cancer stemlike cells through down-regulation of BRCA1 and BRCA2." Neoplasia 16(4): 343-353 e341- 342.
• Liu, C., et al. (2008). "Transient transfection factors for high-level recombinant protein production in suspension cultured mammalian cells." Mol Biotechnol 39(2): 141-153.
• Loebke, C., et al. (2007). "Infrared-based protein detection arrays for quantitative proteomics." Proteomics 7(4): 558-564.
• Louis LS, S. S., Ghaem-Maghami S, Abdalla H, Smith JR (2013). "The relationship between infertility treatment and cancer, including gynaecological cancers." The Obsterician & Gynaecologist 15: 177 - 183.
• Lounis, H., et al. (1998). "Mapping of chromosome 3p deletions in human epithelial ovarian tumors." Oncogene 17(18): 2359-2365.
• Markman, M., et al. (2001). "Phase III trial of standard-dose intravenous cisplatin plus paclitaxel versus moderately high-dose carboplatin followed by intravenous paclitaxel and intraperitoneal cisplatin in small-volume stage III ovarian carcinoma: an intergroup study of the Gynecologic Oncology Group, Southwestern Oncology Group, and Eastern Cooperative Oncology Group." J Clin Oncol 19(4): 1001-1007.
264 • Markman, M., et al. (2003). "Phase III randomized trial of 12 versus 3 months of maintenance paclitaxel in patients with advanced ovarian cancer after complete response to platinum and paclitaxel-based chemotherapy: a Southwest Oncology Group and Gynecologic Oncology Group trial." J Clin Oncol 21(13): 2460-2465.
• Maubant, S., et al. (2005). "Expression of alpha V-associated integrin beta subunits in epithelial ovarian cancer and its relation to prognosis in patients treated with platinum-based regimens." J Mol Histol 36(1-2): 119-129.
• McGuire, W. P., et al. (1996). "Cyclophosphamide and cisplatin compared with paclitaxel and cisplatin in patients with stage III and stage IV ovarian cancer." N Engl J Med 334(1): 1-6.
• McKie, A. B., et al. (2012). "The OPCML tumor suppressor functions as a cell surface repressor-adaptor, negatively regulating receptor tyrosine kinases in epithelial ovarian cancer." Cancer Discov 2(2): 156-171.
• Medeiros, F., et al. (2006). "The tubal fimbria is a preferred site for early adenocarcinoma in women with familial ovarian cancer syndrome." Am J Surg Pathol 30(2): 230-236.
• Menon, U., et al. (2009). "Sensitivity and specificity of multimodal and ultrasound screening for ovarian cancer, and stage distribution of detected cancers: results of the prevalence screen of the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS)." Lancet Oncol 10(4): 327-340.
• Menon, U. and I. J. Jacobs (2001). "Ovarian cancer screening in the general population." Curr Opin Obstet Gynecol 13(1): 61-64.
• Mishra, S. and P. G. Joshi (2007). "Lipid raft heterogeneity: an enigma." J Neurochem 103 Suppl 1: 135-142.
• Miyata, S., et al. (2000). "Expression of the IgLON cell adhesion molecules Kilon and OBCAM in hypothalamic magnocellular neurons." J Comp Neurol 424(1): 74-85.
• Monniaux, D., et al. (2006). "Integrins in the ovary." Semin Reprod Med 24(4): 251- 261.
• Mosgaard, B. J., et al. (1997). "Infertility, fertility drugs, and invasive ovarian cancer: a case-control study." Fertil Steril 67(6): 1005-1012.
265
• Motawy, M. S., et al. (1992). "Serum AFP, hCG and CEA in the management of patients with testicular, ovarian and extragonadal germ cell tumors." Int J Biol Markers 7(2): 80-86.
• Muggia, F. M., et al. (2000). "Phase III randomized study of cisplatin versus paclitaxel versus cisplatin and paclitaxel in patients with suboptimal stage III or IV ovarian cancer: a gynecologic oncology group study." J Clin Oncol 18(1): 106-115.
• Muller, C. Y. and L. A. Cole (2009). "The quagmire of hCG and hCG testing in gynecologic oncology." Gynecol Oncol 112(3): 663-672.
• Mutch, D. G. and J. Prat (2014). "2014 FIGO staging for ovarian, fallopian tube and peritoneal cancer." Gynecol Oncol 133(3): 401-404.
• Narod, S. A., et al. (1991). "Familial breast-ovarian cancer locus on chromosome 17q12-q23." Lancet 338(8759): 82-83.
• NCCN (2011). Genetic/Familial High-Risk Assessment: Breast and Ovarian.
• NICE, C. (2011). Ovarian cancer: The recognition and initial management of ovarian cancer.
• NICE, C. (2013). Familial breast cancer: Classification and care of people at risk of familial breast cancer and management of breast cancer and related risks in people with a family history of breast cancer.
• NICE, C. (2014). Advanced breast cancer (update).
• NICE, I. (2013). Cytoreduction surgery followed by hyperthermic intraoperative peritoneal chemotherapy for peritoneal carcinomatosis.
• NICE, T. (2003). Guidance on the use of paclitaxel in the treatment of ovarian cancer.
• NICE, T. (2005). Paclitaxel, pegylated liposomal doxorubicin hydrochloride and topotecan for second-line or subsequent treatment of advanced ovarian cancer: Review of Technology Appraisal Guidance 28, 45 and 55.
266 • NICE, T. (2011). Trabectedin for the treatment of relapsed ovarian cancer.
• NICE, T. (2013). Bevacizumab in combination with gemcitabine and carboplatin for treating the first recurrence of platinum-sensitive advanced ovarian cancer.
• NICE, T. (2013). "Bevacizumab in combination with paclitaxel and carboplatin for first-line treatment of advanced ovarian cancer."
• Nogales, F. F., et al. (2014). "Germ cell tumors of the ovary: an update." Arch Pathol Lab Med 138(3): 351-362.
• Ntougkos, E., et al. (2005). "The IgLON family in epithelial ovarian cancer: expression profiles and clinicopathologic correlates." Clin Cancer Res 11(16): 5764-5768.
• Nustad, K., et al. (1996). "Specificity and affinity of 26 monoclonal antibodies against the CA 125 antigen: first report from the ISOBM TD-1 workshop. International Society for Oncodevelopmental Biology and Medicine." Tumour Biol 17(4): 196-219.
• Office for National Statistics (2011). "Cancer statistics registrations. Registrations of cancer diagnosed in 2009, England." (MB1 40).
• Office for National Statistics (2011). "Cancer survival in England: Patients diagnosed 2005-2009 and followed up to 2010."
• Office for National Statistics (2012). "Cancer Statistics Registration, England; 2010." from http://www.ons.gov.uk/ons/publications/re-reference- tables.html?edition=tcm%3A77-262496.
• Ozols, R. F., et al. (2003). "Phase III trial of carboplatin and paclitaxel compared with cisplatin and paclitaxel in patients with optimally resected stage III ovarian cancer: a Gynecologic Oncology Group study." J Clin Oncol 21(17): 3194-3200.
• Partridge, E., et al. (2009). "Results from four rounds of ovarian cancer screening in a randomized trial." Obstet Gynecol 113(4): 775-782.
• Pasic, I., et al. (2010). "Recurrent focal copy-number changes and loss of heterozygosity implicate two noncoding RNAs and one tumor suppressor gene at chromosome 3q13.31 in osteosarcoma." Cancer Res 70(1): 160-171.
267
• Paweletz, C. P., et al. (2001). "Reverse phase protein microarrays which capture disease progression show activation of pro-survival pathways at the cancer invasion front." Oncogene 20(16): 1981-1989.
• Perou, C. M., et al. (2000). "Molecular portraits of human breast tumours." Nature 406(6797): 747-752.
• Perren, T. J., et al. (2011). "A phase 3 trial of bevacizumab in ovarian cancer." N Engl J Med 365(26): 2484-2496.
• Pfisterer, J., et al. (2006). "Gemcitabine plus carboplatin compared with carboplatin in patients with platinum-sensitive recurrent ovarian cancer: an intergroup trial of the AGO-OVAR, the NCIC CTG, and the EORTC GCG." J Clin Oncol 24(29): 4699-4707.
• Piccart, M. J., et al. (2000). "Randomized intergroup trial of cisplatin-paclitaxel versus cisplatin-cyclophosphamide in women with advanced epithelial ovarian cancer: three-year results." J Natl Cancer Inst 92(9): 699-708.
• Piek, J. M., et al. (2001). "Dysplastic changes in prophylactically removed Fallopian tubes of women predisposed to developing ovarian cancer." J Pathol 195(4): 451- 456.
• Pimenta, A. F., et al. (1996). "cDNA cloning and structural analysis of the human limbic-system-associated membrane protein (LAMP)." Gene 170(2): 189-195.
• Pimenta, A. F., et al. (1996). "Expression of the mRNAs encoding the limbic system- associated membrane protein (LAMP): II. Fetal rat brain." J Comp Neurol 375(2): 289- 302.
• Pin, E., et al. (2014). "Preparation and use of reverse protein microarrays." Curr Protoc Protein Sci 75: Unit 27 27.
• Prat, J. and F. C. o. G. Oncology (2014). "Staging classification for cancer of the ovary, fallopian tube, and peritoneum." Int J Gynaecol Obstet 124(1): 1-5.
• Pressley, R. H., et al. (1992). "Serum lactic dehydrogenase as a tumor marker in dysgerminoma." Gynecol Oncol 44(3): 281-283.
268 • Pusztai, L. (2009). "Gene expression profiling of breast cancer." Breast Cancer Res 11 Suppl 3: S11.
• Rankin, E. B., et al. (2010). "AXL is an essential factor and therapeutic target for metastatic ovarian cancer." Cancer Res 70(19): 7570-7579.
• Ravikumar, G. and J. A. Crasta (2013). "Vascular endothelial growth factor expression in ovarian serous carcinomas and its effect on tumor proliferation." South Asian J Cancer 2(2): 87-90.
• Reed, J., et al. (2004). "Diglons are heterodimeric proteins composed of IgLON subunits, and Diglon-CO inhibits neurite outgrowth from cerebellar granule cells." J Cell Sci 117(Pt 17): 3961-3973.
• Reed, J. E., et al. (2007). "Expression of cellular adhesion molecule 'OPCML' is down- regulated in gliomas and other brain tumours." Neuropathol Appl Neurobiol 33(1): 77-85.
• Reibenwein, J. and M. Krainer (2008). "Targeting signaling pathways in ovarian cancer." Expert Opin Ther Targets 12(3): 353-365.
• Ricciardelli, C. and M. K. Oehler (2009). "Diverse molecular pathways in ovarian cancer and their clinical significance." Maturitas 62(3): 270-275.
• Ricciardelli, C. and R. J. Rodgers (2006). "Extracellular matrix of ovarian tumors." Semin Reprod Med 24(4): 270-282.
• Rice, L. W. (2010). "Hormone prevention strategies for breast, endometrial and ovarian cancers." Gynecol Oncol 118(2): 202-207.
• Rose, P. G., et al. (2004). "Secondary surgical cytoreduction for advanced ovarian carcinoma." N Engl J Med 351(24): 2489-2497.
• Roskoski, R., Jr. (2014). "The ErbB/HER family of protein-tyrosine kinases and cancer." Pharmacol Res 79: 34-74.
• Ross, J. S., et al. (2013). "Comprehensive genomic profiling of epithelial ovarian cancer by next generation sequencing-based diagnostic assay reveals new routes to targeted therapies." Gynecol Oncol 130(3): 554-559.
269
• Rossing, M. A., et al. (1994). "Ovarian tumors in a cohort of infertile women." N Engl J Med 331(12): 771-776.
• Saharinen, P., et al. (2011). "VEGF and angiopoietin signaling in tumor angiogenesis and metastasis." Trends Mol Med 17(7): 347-362.
• Sakurai, A., et al. (2012). "Semaphorin signaling in angiogenesis, lymphangiogenesis and cancer." Cell Res 22(1): 23-32.
• Sanner, K., et al. (2009). "Ovarian epithelial neoplasia after hormonal infertility treatment: long-term follow-up of a historical cohort in Sweden." Fertil Steril 91(4): 1152-1158.
• Schildkraut, J. M. and W. D. Thompson (1988). "Familial ovarian cancer: a population-based case-control study." Am J Epidemiol 128(3): 456-466.
• Schorge, J. O., et al. (2010). "Surgical debulking of ovarian cancer: what difference does it make?" Rev Obstet Gynecol 3(3): 111-117.
• Sean Kehoe, J. H., Matthew Nankivell, Gordon C. Jayson, Henry Charles Kitchener, Tito Lopes, David Luesley, Timothy Perren, Selina Bannoo, Monica Mascarenhas, Stephen Dobbs, Sharadah Essapen, Jeremy Twigg, Jonathan Herod, W. Glenn McCluggage, Mahesh Parmar, Ann Marie Swart; School of Cancer Sciences, University of Birmingham, Birmingham, United Kingdom; MRC Clinical Trials Unit, London, United Kingdom; Medical Research Council Clinical Trials Unit, London, United Kingdom; Department of Medical Oncology, Christie Hospital and University of Manchester, Manchester Academic Health Sciences Centre, Manchester, United Kingdom; Academic Unit of Obstetrics and Gynaecology, University of Manchester, Manchester, United Kingdom; Royal Cornwall Hospitals NHS Foundation Trust, Truro, United Kingdom; Pan Birmingham Gynaecological Cancer Centre, City Hospital, Birmingham, United Kingdom; St James's Institute of Oncology, St. James's University Hospital, Leeds, United Kingdom; Department of Gynaecological Oncology, Belfast City Hospital, Belfast, Northern Ireland; Royal Surrey County Hospital, Guildford, United Kingdom; Department of Gynaecological Oncology, James Cook University Hospital, Middlesborough, United Kingdom; Department of Gynaecology, Liverpool Women's Hospital, Liverpool, United Kingdom; Department of Pathology, Belfast Health and Social Care Trust, Belfast, Northern Ireland (2013). Chemotherapy or upfront surgery for newly diagnosed advanced ovarian cancer: Results from the MRC CHORUS trial. 2013 ASCO Annual Meeting, J Clin Oncol 31, 2013 (suppl; abstr 5500).
270 • Selamat, S. A., et al. (2011). "DNA methylation changes in atypical adenomatous hyperplasia, adenocarcinoma in situ, and lung adenocarcinoma." PLoS One 6(6): e21443.
• Sellar, G. C., et al. (2003). "OPCML at 11q25 is epigenetically inactivated and has tumor-suppressor function in epithelial ovarian cancer." Nat Genet 34(3): 337-343.
• Shah, R., et al. (2014). "Pathogenesis, prevention, diagnosis and treatment of breast cancer." World J Clin Oncol 5(3): 283-298.
• Shark, K. B. and N. M. Lee (1995). "Cloning, sequencing and localization to chromosome 11 of a cDNA encoding a human opioid-binding cell adhesion molecule (OBCAM)." Gene 155(2): 213-217.
• Shibuya, M. (2013). "Vascular endothelial growth factor and its receptor system: physiological functions in angiogenesis and pathological roles in various diseases." J Biochem 153(1): 13-19.
• Shih Ie, M. and R. J. Kurman (2004). "Ovarian tumorigenesis: a proposed model based on morphological and molecular genetic analysis." Am J Pathol 164(5): 1511- 1518.
• Sieh, W., et al. (2013). "Hormone-receptor expression and ovarian cancer survival: an Ovarian Tumor Tissue Analysis consortium study." Lancet Oncol 14(9): 853-862.
• Simons, K. and D. Toomre (2000). "Lipid rafts and signal transduction." Nat Rev Mol Cell Biol 1(1): 31-39.
• Slamon, D. J., et al. (2001). "Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2." N Engl J Med 344(11): 783-792.
• Sorkin, A. and L. K. Goh (2008). "Endocytosis and intracellular trafficking of ErbBs." Exp Cell Res 314(17): 3093-3106.
• Sotiriou, C. and L. Pusztai (2009). "Gene-expression signatures in breast cancer." N Engl J Med 360(8): 790-800.
271 • Spugnesi, L., et al. (2013). "Effect of the expression of BRCA2 on spontaneous homologous recombination and DNA damage-induced nuclear foci in Saccharomyces cerevisiae." Mutagenesis 28(2): 187-195.
• Sriraksa, R., et al. (2011). "CpG-island methylation study of liver fluke-related cholangiocarcinoma." Br J Cancer 104(8): 1313-1318.
• Statistics, O. f. N. (2014). "All malignant neoplasms (tumours): number of newly diagnosed cases, 2012." Retrieved July 2014, from http://www.ons.gov.uk/ons/dcp171778_367563.pdf.
• Statistics, O. f. N. (2014). "Cancer Survival in England: Patients Diagnosed 2007–2011 and Followed up to 2012." from http://www.ons.gov.uk/ons/dcp171778_333318.pdf.
• Stjernstrom, A., et al. (2014). "Alterations of INPP4B, PIK3CA and pAkt of the PI3K pathway are associated with squamous cell carcinoma of the lung." Cancer Med 3(2): 337-348.
• STRATOG (2014). "Germ cell tumours - chemotherapy." from http://www.rcog.org.uk/stratog/page/chemotherapy.
• Struyk, A. F., et al. (1995). "Cloning of neurotrimin defines a new subfamily of differentially expressed neural cell adhesion molecules." J Neurosci 15(3 Pt 2): 2141- 2156.
• Sugimoto, C., et al. (2010). "OBCAM, an immunoglobulin superfamily cell adhesion molecule, regulates morphology and proliferation of cerebral astrocytes." J Neurochem 112(3): 818-828.
• Takahashi, H. and M. Shibuya (2005). "The vascular endothelial growth factor (VEGF)/VEGF receptor system and its role under physiological and pathological conditions." Clin Sci (Lond) 109(3): 227-241.
• Takezawa, K., et al. (2012). "HER2 amplification: a potential mechanism of acquired resistance to EGFR inhibition in EGFR-mutant lung cancers that lack the second-site EGFRT790M mutation." Cancer Discov 2(10): 922-933.
• Takita, J., et al. (2011). "Aberrations of NEGR1 on 1p31 and MYEOV on 11q13 in neuroblastoma." Cancer Sci 102(9): 1645-1650.
272
• Testa, A., et al. (2014). "Strategies to diagnose ovarian cancer: new evidence from phase 3 of the multicentre international IOTA study." Br J Cancer 111(4): 680-688.
• Theillet, C., et al. (1993). "FGFRI and PLAT genes and DNA amplification at 8p12 in breast and ovarian cancers." Genes Chromosomes Cancer 7(4): 219-226.
• Trempe, G. L. (1976). "Human breast cancer in culture." Recent Results Cancer Res(57): 33-41.
• Troncale, S., et al. (2012). "NormaCurve: a SuperCurve-based method that simultaneously quantifies and normalizes reverse phase protein array data." PLoS One 7(6): e38686.
• Tsou, J. A., et al. (2007). "Identification of a panel of sensitive and specific DNA methylation markers for lung adenocarcinoma." Mol Cancer 6: 70.
• UK, C. R. (2014). "TNM breast cancer staging." from http://www.cancerresearchuk.org/cancer-help/type/breast-cancer/treatment/tnm- breast-cancer-staging.
• UK, C. R. (2014). "UK-ovarian-cancer-statistics." from http://www.cancerresearchuk.org/cancer-info/cancerstats/keyfacts/ovarian- cancer/uk-ovarian-cancer-statistics.
• Ummanni, R., et al. (2014). "Evaluation of reverse phase protein array (RPPA)-based pathway-activation profiling in 84 non-small cell lung cancer (NSCLC) cell lines as platform for cancer proteomics and biomarker discovery." Biochim Biophys Acta 1844(5): 950-959.
• USPSTF (2013). Medications for Risk Reduction of Primary Breast Cancer in Women.
• van Leeuwen, F. E., et al. (2011). "Risk of borderline and invasive ovarian tumours after ovarian stimulation for in vitro fertilization in a large Dutch cohort." Hum Reprod 26(12): 3456-3465.
• Vasey, P. A., et al. (2004). "Phase III randomized trial of docetaxel-carboplatin versus paclitaxel-carboplatin as first-line chemotherapy for ovarian carcinoma." J Natl Cancer Inst 96(22): 1682-1691.
273
• Vaughan, S., et al. (2011). "Rethinking ovarian cancer: recommendations for improving outcomes." Nat Rev Cancer 11(10): 719-725.
• Veerappa, A. M., et al. (2013). "Family-based genome-wide copy number scan identifies five new genes of dyslexia involved in dendritic spinal plasticity." J Hum Genet 58(8): 539-547.
• Venkitaraman, A. R. (2002). "Cancer susceptibility and the functions of BRCA1 and BRCA2." Cell 108(2): 171-182.
• Vergote, I., et al. (2010). "Neoadjuvant chemotherapy or primary surgery in stage IIIC or IV ovarian cancer." N Engl J Med 363(10): 943-953.
• Vlahos, N. F., et al. (2010). "Fertility drugs and ovarian cancer risk: a critical review of the literature." Ann N Y Acad Sci 1205: 214-219.
• Warner, E. (2011). "Clinical practice. Breast-cancer screening." N Engl J Med 365(11): 1025-1032.
• Weinstein, I. B. and A. Joe (2008). "Oncogene addiction." Cancer Res 68(9): 3077- 3080; discussion 3080.
• Wen, X. F., et al. (2006). "HER2 signaling modulates the equilibrium between pro- and antiangiogenic factors via distinct pathways: implications for HER2-targeted antibody therapy." Oncogene 25(52): 6986-6996.
• Whittemore, A. S. (1994). "The risk of ovarian cancer after treatment for infertility." N Engl J Med 331(12): 805-806.
• Whittemore, A. S., et al. (1992). "Characteristics relating to ovarian cancer risk: collaborative analysis of 12 US case-control studies. II. Invasive epithelial ovarian cancers in white women. Collaborative Ovarian Cancer Group." Am J Epidemiol 136(10): 1184-1203.
• Wick, M. J., et al. (1996). "Expression of OBCAM-related cDNA clones in Cos 1 cells: evidence for a phosphatidylinositol linkage to the cell membrane." Brain Res Mol Brain Res 36(2): 322-328.
274 • Willer, C. J., et al. (2009). "Six new loci associated with body mass index highlight a neuronal influence on body weight regulation." Nat Genet 41(1): 25-34.
• Wilson, K. J., et al. (2009). "Functional selectivity of EGF family peptide growth factors: implications for cancer." Pharmacol Ther 122(1): 1-8.
• Yamada, M., et al. (2007). "Synaptic adhesion molecule OBCAM; synaptogenesis and dynamic internalization." Brain Res 1165: 5-14.
• Yang, D., et al. (2011). "Association of BRCA1 and BRCA2 mutations with survival, chemotherapy sensitivity, and gene mutator phenotype in patients with ovarian cancer." JAMA 306(14): 1557-1565.
• Yang, S., et al. (2007). "Mapping ErbB receptors on breast cancer cell membranes during signal transduction." J Cell Sci 120(Pt 16): 2763-2773.
• Yao, D. S., et al. (2006). "[*OPCML gene transferred by recombinant lentiviruses in vitro and its inhibition to ovarian cancer cells]." Zhonghua Fu Chan Ke Za Zhi 41(5): 333-338.
• Yarden, Y. and G. Pines (2012). "The ERBB network: at last, cancer therapy meets systems biology." Nat Rev Cancer 12(8): 553-563.
• Yarden, Y. and M. X. Sliwkowski (2001). "Untangling the ErbB signalling network." Nat Rev Mol Cell Biol 2(2): 127-137.
• Yen, C. C., et al. (2009). "Identification of chromosomal aberrations associated with disease progression and a novel 3q13.31 deletion involving LSAMP gene in osteosarcoma." Int J Oncol 35(4): 775-788.
• Youle, R. J. and A. Strasser (2008). "The BCL-2 protein family: opposing activities that mediate cell death." Nat Rev Mol Cell Biol 9(1): 47-59.
• Zachary, I. (2014). "Neuropilins: role in signalling, angiogenesis and disease." Chem Immunol Allergy 99: 37-70.
• Zhang, J., et al. (2006). "[Deletion of OPCML gene and promoter methylation in ovarian epithelial carcinoma]." Zhongguo Yi Xue Ke Xue Yuan Xue Bao 28(2): 173- 177.
275
• Zhang, L., et al. (2009). "Serial dilution curve: a new method for analysis of reverse phase protein array data." Bioinformatics 25(5): 650-654.
• Zhang, Y. and T. Hunter (2014). "Roles of Chk1 in cell biology and cancer therapy." Int J Cancer 134(5): 1013-1023.
• Zhao, J., et al. (2009). "The role of obesity-associated loci identified in genome-wide association studies in the determination of pediatric BMI." Obesity (Silver Spring) 17(12): 2254-2257.
• Zhou, F., et al. (2011). "A study of the methylation status of opioid binding protein/cell adhesion molecule-like gene in ovarian cancer using nested methylation- specific polymerase chain reaction detection." Clin Lab 57(5-6): 421-424.
276
Appendix A
Supplementary Tables and Figures
277 List of angiogenic factors tested in silico for their relative expression with OPCML
ID Gene
221310_at FGF14
205977_s_at EPHA1
211839_s_at CSF1
221134_at ANGPT4
203788_s_at SEMA3C
204421_s_at FGF2
216061_x_at PDGFB
210310_s_at FGF5
215804_at EPHA1
201109_s_at THBS1
206986_at FGF18
201533_at CTNNB1
211527_x_at VEGFA
214589_at FGF12
207029_at KITLG
204580_at MMP12
201110_s_at THBS1
212171_x_at VEGFA
210311_at FGF5
203070_at SEMA3B
278 221433_at FGF21
203936_s_at MMP9
207113_s_at TNF
221315_s_at FGF22
206071_s_at EPHA3
203789_s_at SEMA3C
204200_s_at PDGFB
207082_at CSF1
207442_at CSF3
201108_s_at THBS1
216055_at PDGFB
219304_s_at PDGFD
214284_s_at FGF18
217112_at PDGFB
205078_at PIGF
209687_at CXCL12
211124_s_at KITLG
203666_at CXCL12
218718_at PDGFC
210513_s_at VEGFA
35666_at SEMA3F
215664_s_at EPHA5
208378_x_at FGF5
279 209730_at SEMA3F
221376_at FGF17
215775_at THBS1
208417_at FGF6
210512_s_at VEGFA
206742_at FIGF
216867_s_at PDGFA
206941_x_at SEMA3E
206980_s_at FLT3LG
206832_s_at SEMA3F
203499_at EPHA2
204422_s_at FGF2
211164_at EPHA3
221166_at FGF23
205609_at ANGPT1
210557_x_at CSF1
216837_at EPHA5
208449_s_at FGF8
206674_at FLT3
210607_at FLT3LG
205117_at FGF1
207501_s_at FGF12
203071_at SEMA3B
280 221374_at FGF16
208240_s_at FGF1
220394_at FGF20
204259_at MMP7
205572_at ANGPT2
206404_at FGF9
215324_at SEMA3D
209716_at CSF1
206114_at EPHA4
205608_s_at ANGPT1
206805_at SEMA3A
205077_s_at PIGF
206070_s_at EPHA3
211148_s_at ANGPT2
205463_s_at PDGFA
201107_s_at THBS1
209946_at VEGFC
205782_at FGF7
207849_at IL2
203683_s_at VEGFB
211485_s_at FGF18
211029_x_at FGF18
206783_at FGF4
281 205110_s_at FGF13
206987_x_at FGF18
214571_at FGF3
219689_at SEMA3G
282 Antibodies used in the first batch RPPA experiment
14-3-3_epsilon-M-C HER2_pY1248-R-V
4E-BP1-R-V HER3-R-V
4E-BP1_pS65-R-V HER3_pY1298-R-C
4EBP1_pT37_T46-R-V HSP70-R-NA
53BP1-R-C IGF-1R-beta-R-C
ACC_pS79-R-V IGF1R_pY980-R-NA
ACC1-R-C IGFBP2-R-V
ACC1-R-C INPP4B-G-C
ACC1-R-C IRS1-R-V
AIB1-M-V JNK_pT183_Y185-R-NA
Akt-R-V JNK2-R-C
Akt_pS473-R-V K-Ras-M-C
Akt_pT308-R-V MAPK_pT202_Y204-R-V alpha-Catenin-M-V Mcl-M-NA alpha-Catenin-M-V MEK1-R-V
AMPK_alpha-R-C MEK1_pS217_S221-R-V
AMPK_pT172-R-V MIG-6-M-V
Annexin_I-R-V MLH1-M-NA
AR-R-V Mre11-R-C
Bak-R-C MSH2-M-C
Bax-R-V MSH6-R-C
Bcl-2-M-V mTOR-R-C
283 Bcl-X-R-C mTOR_pS2448-R-C
Bcl-xL-R-V N-Cadherin-R-V
Beclin-G-V N-Cadherin-R-V beta-Catenin-R-V NF-kB-p65_pS536-R-C
Bid-R-C NF2-R-C
Bid-R-C Notch1-R-V
Bim-R-V Notch3-R-C c-Jun_pS73-R-C P-Cadherin-R-C c-Kit-R-V p21-R-C c-Met-M-C p27-R-V c-Met_pY1235-R-C p27_pT157-R-C c-Myc-R-C p27_pT198-R-V
C-Raf-R-V p38_MAPK-R-C
C-Raf_pS338-R-C p38_pT180_Y182-R-V
Caspase-3_active-R-C p53-R-V
Caspase-7_cleavedD198-R-C p70S6K-R-V
Caspase-8-M-C p70S6K_pT389-R-V
Caspase-9_cleavedD330-R-C p90RSK_pT359_S363-R-C
Caveolin-1-R-V PARP_cleaved-M-C
CD31-M-V Paxillin-R-V
CDK1-R-V PCNA-M-V
Chk1-R-C PDK1_pS241-R-V
Chk1_pS345-R-C PI3K-p110-alpha-R-C
284 Chk2-M-C PI3K-p85-R-V
Chk2_pT68-R-C PKC-alpha-M-V cIAP-R-V PKC-alpha_pS657-R-V
Claudin-7-R-V PR-R-V
Collagen_VI-R-V PRAS40_pT246-R-V
COX-2-R-C PTCH-R-C
Cyclin_B1-R-V PTEN-R-V
Cyclin_D1-R-V Rab11-R-V
Cyclin_D1-R-V Rab25-R-C
Cyclin_E1-M-V Rad50-M-C
DJ-1-R-C Rad51-M-C
Dvl3-R-V Rb-M-V
E-Cadherin-R-V Rb_pS807_S811-R-V eEF2-R-V S6_pS235_S236-R-V eEF2K-R-V S6_pS240_S244-R-V
EGFR-R-C Smac-M-V
EGFR_pY1068-R-V Smad1-R-V
EGFR_pY1173-R-C Smad3-R-V
EGFR_pY992-R-V Smad4-M-V
EGFR_pY992-R-V Snail-M-C eIF4E-R-V Src-M-V
EphA2-M-NA Src_pY416-R-C
EphA2_pS897-R-NA Src_pY527-R-V
285 ER-alpha-R-V STAT3_pY705-R-V
ER-alpha_pS118-R-V STAT5-alpha-R-V
ERCC1-M-C Stathmin-R-V
FAK-R-C Syk-M-V
FAK_pY397-M-NA Tau-M-C
Fibronectin-R-C TAZ_pS89-R-C
Fibronectin-R-C Tuberin-R-C
FOXO3a-R-C VASP-R-C
FOXO3a_pS318_S321-R-C VEGFR2-R-V
GAB2-R-V XIAP-R-C
GATA3-M-V XRCC1-R-C
GATA3-M-V YAP-R-V
GSK3-alpha-beta-M-V YAP_pS127-R-C
GSK3-alpha-beta_pS21_S9-R-V YB-1-R-V
GSK3_pS9-R-V YB-1_pS102-R-V
HER2-M-V Shc_pY317-R-NA
HER2-M-V Vimentin-R-NA
HER2-M-V
286 Additional antibodies used in the second batch RPPA experiment
14-3-3_beta-R-V MYH11-R-V
14-3-3_zeta-R-V Myosin IIa pS1943-R-V
A-Raf-R-V N-Ras-M-V
ACVRL1-R-C Napsin-R-C
ADAR1-M-V NDRG1_pT346-R-V
ARHI-M-C p38 alpha MAPK-M-V
ATM-R-V PAl-1-M-V
ATM_pS1981-R-V PDCD1L1-G-C
ATP5H-M-C PDCD4-R-C
B-Raf-M-C PDGFR_beta-R-V
B-Raf_pS445-R-V PEA15-R-V
Bad_pS112-R-V PEA15_pS116-R-V
BAP1-M-V PKC-delta_pS664-R-V
BRCA2-R-C PKC-pan_BetaII_pS660-R-V
CD29-M-V PMS2-R-V
CD49b-M-V Porin-M-V
CDKN2A_p16INK4a-R-V PREX1-R-V
Complex_II_Subunit 30-M-V Raptor-R-V
Cyclophilin_F-M-V RBM15-R-V
E2F1-M-V Rictor-R-C eIF4G-R-C Rictor_pT1135-R-V
ERCC1-M-QC SCD1-M-V
ETS-1-R-V SDHA-M-V
FASN-R-V SETD2-R-QC
287 FoxM1-R-V SF2-M-V
G6PD-M-V TFRC-R-V
GAPDH-M-C TIGAR-R-V
GCN5L2-R-V Transglutaminase-M-V
GPBB-R-V TSC1-R-C
GYS-R-V TTF1-R-V
GYS_pS641-R-V Tuberin_pT1462-R-V
Heregulin-R-V TYRO3-R-V
Histone H3-R-V UBAC1-R-V
Lck-R-V UGT1A-M-V
MDM2_pS166-R-V UQCRC2-M-C
MEK2-R-V
288 Antibodies excluded from the second batch RPPA experiment alpha-Catenin-M-V alpha-Catenin-M-V
EGFR_pY992-R-V
EphA2-M-NA
EphA2_pS897-R-NA
HSP70-R-NA
K-Ras-M-C
MLH1-M-NA
Mre11-R-C
Notch3-R-C
Tau-M-C
VASP-R-C
XIAP-R-C
YB-1_pS102-R-V
Vimentin-R-NA
289 RPPA BKS-2.1 cells – OPCML impact on EGF stimulation (top 10 results), note none of the results are significant
Antibody logFC AveExpr t P.Value adj.P.Val p27 0.069512 0.6141185 1.821824123 0.148016522 0.998911209 eIF4E 0.0887565 0.787279125 1.606990088 0.188711511 0.998911209
PDCD4 -0.819669 2.00649225 -1.50738939 0.211469839 0.998911209
PEA15_pS116 0.1976495 0.583865875 1.499159172 0.213474114 0.998911209
X4E.BP1_pT37 0.7647545 4.057939625 1.469703268 0.220809603 0.998911209
_T46
Smad3 0.0912855 1.294627875 1.464258836 0.222193617 0.998911209
TSC1 0.9195595 4.058372875 1.435516818 0.22964883 0.998911209
FASN 0.62477 3.027912 1.423440124 0.232857114 0.998911209
STAT3_pY705 0.0518065 0.427308375 1.358302722 0.250961419 0.998911209
290
120
100
80
Vs total HER2 60 tHER2 +OPCML
40 tHER2 -OPCML normalised
20 Rela ve t intensity of HER2 – bio nylated 0 0 2 4 hours
Western blot showing a ‘pulse-chase’ biotinylation in SKOV-3 derived cell lines. The blot demonstrates more rapid loss of HER2 protein in OPCML transfected BKS2.1 than OPCML deficient SKOBS V1.2 cells (top). Bottom: Quantification of relative signal intensity of biotinylated HER2 (expressed as a percentage of the initial pulse) normalised against input of total HER2 in each cell (Figure is courtesy of (McKie, Vaughan et al. 2012), Cancer
Discovery, 2012).
291
Immuno-fluorescence microscopy showing VEGFR1 and VEGFR2 expression in SKOBS-V1.2
(right panel) and BKS-2.1 (left panel)
292
293
294 Western blot showing attempted Co-IP in BKS-2.1 and PEO1-OP6 cells between OPCML and
VEGFR1, VEGFR2, VEGR3
295 Western blot showing OPCML and cMET levels in PEO1 and PEO1-OP6 cells under serum- free and EGF stimulation
296
Appendix B
Publications list
297 Publications related to the PhD thesis:
1. McKie AB, Vaughan S, Zanini E, Okun IS, Louis LS, de Sousa C, Greene MI, Wang Q,
Agarwal R, Shaposhnikov D, Wong JL, Gungor H, Janczar S, El-Bahrawy M, Lam EW,
Chayen NE, Gabra H. The OPCML tumor suppressor functions as a cell surface
repressor-adaptor, negatively regulating receptor tyrosine kinases in epithelial
ovarian cancer. Cancer Discov 2012 Feb; (2): 156 – 71.
2. Louis LS, Zanini E, Okun I S, McKie A, Paterson A, Recchi C, El-Bahrawy M, Stebbing J,
Gabra H. OPCML potentiates anti-HER2 targeted therapy in ovarian and breast
cancer through binding and negative regulation of HER2 but not EGFR (in progress).
Poster presentations at National meetings:
1. LS Louis, E Sokolskaja, M Al-Bakir, AB McKie, S Ghaem-Maghami, H Gabra. OPCML
directly abrogates multiple RTKs, their downstream signalling, and inhibits cell
proliferation in a wide variety of ovarian cancer cell lines. (BGCS, London, 2012)
2. E Sokolskaja, L Louis, E Zanini, A McKie, H Gabra. Recombinant OPCML as a therapy
for ovarian cancer. (BGCS, London, 2012)
3. E Zanini, L S Louis, I Okun, A B McKie, C Recchi, H Gabra. The tumour suppressor
protein OPCML potentiates anti-HER2 targeted therapy in ovarian and breast cancer.
(Genes, Cambridge, 2014).
4. L Louis, E Zanini, M El-Bahrawy, C Recchi, H Gabra. The tumour suppressor protein
OPCML potentiates anti-HER2 targeted therapy in ovarian and breast cancer. (BGCS,
London, 2014).
298 5. L Louis, E Curry, A Paterson, J Antony, C Recchi, H Gabra. High OPCML expression
correlates with decrease in VEGFA levels and abrogation of VEGFR3 in ovarian cancer
cell lines. (BGCS, London, 2014).
Papers published during my PhD that are not related to the thesis:
1. S Saso, K Logan, Y Abdalla, LS Louis, S Ghaem-Maghami, JR Smith, G del Priore. Use
of cyclosporine in Uterine Transplantation. J Transplant 2012; 134936.
2. Louis LS, Saso S, Chatterjee J, Barsoum E, Al-Samarrai M. Adenomyosis and infertility.
Reprod Biomed Online 2012 May; (5): 586.
3. Louis LS, Manzo E, Barsoum EM, Al-Samarrai M. A case report of a successful live
birth following IVF preceded by fertility sparing surgery for a large adenomyomata. J
Obstet Gynaecol 2012 Jul; (5): 496 – 7.
4. Saso S, Ghaem-Maghami S, Louis LS, Ungar L, Del Priore G, Smith JR. Uterine
transplantation: what else needs to be done before it can become a reality? J Obstet
Gynaecol 2013 Apr; (3): 232 – 8.
5. Louis LS, Saso S, Ghaem-Maghami S, Abdalla H, Smith JR. The relationship between
infertility treatment and cancer, including gynaecological cancers. TOG, 2013 Jul; (3)
V15: 177 – 83.
6. Louis LS, Saso S, Ghaem-Maghami S, Abdalla H, Smith JR. Letter to the editor Re “The
relationship between infertility treatment and cancer, including gynaecological
cancers”. TOG Jan (1) V16: 70 – 1.
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