Evaluation of the Expression of Sprouty 1 Protein and Its Clinical Relevance in Epithelial Ovarian Cancer

Evaluation of the expression of Sprouty 1 protein and its clinical relevance in epithelial ovarian cancer

Samar Masoumi-Moghaddam MD

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

Faculty of Medicine St George Clinical School University of New South Wales

Sydney, NSW, Australia, 2014 PLEASE TYPE THE UNIVERSITY OF NEW SOUTH WALES Thesis/Dissertation Sheet

Surname or Family name: Masouml Moghaddam

First name: Samar Other name/s:

Abbreviation for degree as given in the University calendar: PhD

School: StGeorge Clinical School Faculty: Medicine

Title: Evaluation of the expression of Sprouty 1 protein and Its clinical relevance in epithelial ovarian cancer

Abstract 350 words maximum: (PLEASE TYPE)

Human epithelial ovarian cancer (EOC) Is the leading cause of gynaecological cancer-associated death. Most patients have advanced disease at diagnosis. Late presentation and widespread abdominal metastasis account for high death rate. Despite invasive surgery and platinum-based chemotherapy as the standard of care for advanced disease, recurrent disease with progressively shorter disease-free intervals and resistance to chemotherapy develop in most cases. Known as modulators of RTK signalling, Sprouty (Spry) proteins are implicated in regulation of cellular processes central to cancer development, progression and dissemination. Although deregulation of Spry has been investigated in a variety of human cancers, little is known about its status In EOC. ·· In this project, I first evaluated the expression status of Spry1, Spry2 and Spry4 isoforms in a panel of EOC cell lines in vitro where Spry1 was more significantly downregulated at mRNA and protein levels. Next, I investigated the functional outcomes of alterations in Spry1 protein levels induced by Spry1 transfection or silencing of EOC cells in vitro. I observed inverse correlation between the Spry1 expression and cell proliferation, migration, invasion and survival. Finally, to evaluate the clinical relevance of these findings, immunohistochemical expression of Spry1 protein and its association with p-ERK/ERK, Ki67, FGF, VEGF and IL-6 as well as with clinicopathological features and patient outcome were retrospectively explored in a cohort of 100 patients. Similar analysis was performed for Spry2 and Spry4, too. My results indicated significant downregulation of the three isoforms in tumor tissues. Spry1 and Spry2 levels were negatively correlated with p-ERK/ERK, Ki67, tumor stage, grade and recurrence. No significant correlations with growth factors and IL-6 were found. Spry1 and Spry2 were inversely correlated with lymphovascular invasion and post-treatment ascites, respectively. Spry1 and Spry2 low-expressing patients had significantly poorer overall survival (OS) and disease-free survival (DFS) than their high-expressing counterparts. Lastly, multivariate analysis identified Spry1 and Spry2 as independent predictors of OS and DFS. In conclusion, I report for the first time downregulation of Spry1 and Spry2 proteins in EOC with significant impact on tumor behavior and prognostic value as independent predictors of survival and recurrence.

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Date ……………………………………………...... 20/08/2014 ii

Abstract

Human epithelial ovarian cancer (EOC) is the leading cause of gynaecological cancer associated death. Most patients have advanced disease at diagnosis. Late presentation and widespread abdominal metastasis account for high death rate. Despite invasive surgery and platinum-based chemotherapy as the standard of care for advanced disease, recurrent disease with progressively shorter disease-free intervals and resistance to chemotherapy develop in most cases. Known as modulators of RTK signaling, Sprouty (Spry) proteins are implicated in regulation of cellular processes central to cancer development, progression and dissemination. Although deregulation of Spry has been investigated in a variety of human cancers, little is known about its status in EOC.

In this project, I first evaluated the expression status of Spry1, Spry2 and Spry4 isoforms in a panel of EOC cell lines in vitro where Spry1 was more significantly downregulated at mRNA and protein levels. Next, I investigated the functional outcomes of alterations in Spry1 protein levels induced by Spry1 transfection or silencing of EOC cells in vitro. I observed inverse correlation between the Spry1 expression and cell proliferation, migration, invasion and survival. Finally, to evaluate the clinical relevance of these findings, immunohistochemical expression of Spry1 protein and its association with p-ERK/ERK, Ki67, FGF, VEGF and IL-6 as well as with clinicopathological features and patient outcome were retrospectively explored in a cohort of 100 patients. Similar analysis was performed for Spry2 and Spry4, too.

My results indicated significant downregulation of the three isoforms in tumor tissues. Spry1 and Spry2 levels were negatively correlated with p-ERK/ERK, Ki67, tumor stage, grade and recurrence. No significant correlations with growth factors and IL-6 were found. Spry1 and Spry2 were inversely correlated with lymphovascular invasion and post-treatment ascites, respectively. Spry1 and Spry2 low expressing patients had significantly poorer overall survival (OS) and disease-free survival (DFS) than their high-expressing counterparts. Lastly, multivariate analysis identified Spry1 and Spry2 as independent predictors of OS and DFS. In conclusion, I report for the first time downregulation of Spry1 and Spry2 proteins in EOC with significant impact on tumor behavior and prognostic value as independent predictors of survival and recurrence. iii

Acknowledgments

This thesis would have never been possible without:  the leadership, foresight and the outstanding supervision of Professor David Morris who provided unwavering support and guidance.  the continual encouragement, guidance and support I received from my co- supervisor, Dr Ai-Qun Wei who helped me with the progress of this project.

I would like to express my gratitude to Dr Greg Robertson, Dr Leon Vonthethoff and Mr Keith Westbury for their kind contributions and help toward the clinical part of this study. Also, I would like to thank Dr Patrick Lam for his kind advice in relation to the statistical analysis.

With special thanks to Professor George Murrell and my friends and colleagues at the Department of Orthopaedic Surgery, Ms Marina Zimmermann, Ms Tiffany Rankin and Ms Vivienne Underwood for providing such an enjoyable atmosphere to work in.

I would like to thank my colleagues and friends, Ms Samina Badar, Dr Afshin Amini, Dr Ahmed Mekkawy, Dr Javed Akhter, Dr Anahid Ehteda, Dr Jose Perdomo, Dr Peyman Mirarabshahi and Ms Mahshid Rafiee for their help and friendship.

Finally, I wish to convey my appreciation to Professor Michael Grimm, Dr Ashish Diwan and Professor Laura Poole Warren for their support.

To my lovely parents, Soudabeh and Ali, my sisters Sahar and Saman, my brother Saadat, and my dear husband Afshin for their endless love and support

Table of Contents

Abstract ...... ii

Acknowledgments ...... iii

List of Figures ...... xi

List of Tables ...... xv

List of Abbreviations ...... xviii

List of Publications ...... xxiv

Journal articles ...... xxiv

1 Literature Review...... 1

1.1 Ovarian cancer ...... 1 1.1.1 Epidemiology ...... 1 1.1.2 Classification ...... 3 1.1.2.1 Surface epithelial-stromal tumors ...... 4 1.1.2.1.1 Serous carcinoma ...... 5 1.1.2.1.2 Endometrioid adenocarcinoma ...... 6 1.1.2.1.3 Clear cell carcinoma ...... 7 1.1.2.1.4 Mucinous carcinoma ...... 8 1.1.2.2 Non-epithelial tumors ...... 8 1.1.3 Risk factors and protective factors ...... 10 1.1.4 Molecular pathology ...... 10 1.1.4.1 Cell signaling pathways ...... 10 1.1.4.2 Proteins involved in molecular pathology ...... 11 1.1.4.2.1 Proteins associated with cell proliferation ...... 11 1.1.4.2.2 Proteins associated with signaling to the cytoskeleton ...... 12 1.1.4.2.3 Proteins associated with hormonal pathways ...... 12 1.1.4.2.4 Growth factors and cytokines ...... 13 1.1.5 Diagnosis ...... 20 1.1.5.1 Clinical presentation ...... 20 vi

1.1.5.2 Imaging and diagnostic biomarkers ...... 20 1.1.6 Pathological grading and staging ...... 21 1.1.7 Treatment...... 24 1.1.8 Conclusion ...... 25 1.2 Sprouty and cancer ...... 27 1.2.1 MAPK signaling pathway ...... 27 1.2.2 Sprouty protein family ...... 27 1.2.2.1 Sprouty: a versatile modulator with complex functionality ...... 28 1.2.2.1.1 Cell and context dependency ...... 32 1.2.2.1.2 Growth factor dependency and pathway sensitivity ...... 32 1.2.2.1.3 Transcriptional regulation of the Sprouty expression ...... 39 1.2.2.1.4 Modulation of the Sprouty stability by post-translational mechanisms ...... 41 1.2.2.1.5 Regulation of the Sprouty activity ...... 44 1.2.2.1.6 Regulation of RTK activity and stability ...... 46 1.2.2.1.7 Structural variation and functional divergence of the Sprouty proteins ...... 47 1.2.3 Deregulation of Sprouty in cancer ...... 49 1.2.3.1 Breast Cancer ...... 50 1.2.3.2 Prostate cancer ...... 51 1.2.3.3 Liver cancer ...... 53 1.2.3.4 Lung cancer ...... 55 1.2.3.5 Colon cancer ...... 57 1.2.3.6 Melanoma ...... 59 1.2.3.7 Sarcoma ...... 60 1.2.3.8 B-cell lymphoma ...... 62 1.2.3.9 Testicular germ cell cancer ...... 63 1.2.3.10 Endometrial cancer ...... 63 1.2.3.11 Thyroid cancer ...... 63 1.2.3.12 Pituitary tumor ...... 64 1.2.3.13 Ovarian cancer ...... 64 1.2.3.14 Clear cell renal cell carcinomas (ccRCC) ...... 64 1.2.4 Sprouty in cancer: complexity and controversy ...... 69 vii

1.2.5 Conclusion ...... 70

2 Aim and Hypothesis ...... 71

2.1 Introduction ...... 71 2.2 Aims ...... 72 2.2.1 Aim 1 ...... 72 2.2.2 Aim 2 ...... 72 2.2.3 Aim 3 ...... 72 2.2.4 Aim 4 ...... 72 2.3 Hypotheses ...... 72 2.3.1 Hypothesis 1 ...... 72 2.3.2 Hypothesis 2 ...... 72 2.3.3 Hypothesis 3 ...... 73 2.3.4 Hypothesis 4 ...... 73

3 General Materials and Methods ...... 74

3.1 Materials ...... 74 3.1.1 Cell lines ...... 74 3.1.2 Chemicals and reagents ...... 74 3.1.3 Antibodies ...... 76 3.1.4 Instruments and software ...... 77 3.2 Methods ...... 77 3.2.1 Cell culture ...... 77 3.2.2 Western blotting ...... 78 3.2.3 Immunocytochemistry ...... 80 3.2.4 RT-PCR ...... 80 3.2.4.1 RNA isolation ...... 80 3.2.4.2 DNase treatment ...... 81 3.2.4.3 Reverse transcription ...... 82 3.2.4.4 Agarose gel electrophoresis ...... 83 3.2.5 Transfection and silencing ...... 84 3.2.6 MTT assay ...... 85 3.2.7 Trypan Blue assay ...... 85 3.2.8 Scratch assay ...... 86 viii

3.2.9 Cell migration and invasion assays ...... 86 3.2.10 Patients and clinical samples ...... 87 3.2.10.1 Human Ethics Application ...... 87 3.2.10.2 Tissue samples ...... 87 3.2.11 Immunohistochemistry ...... 91 3.2.11.1 Staining ...... 91 3.2.11.2 Scoring ...... 92 3.2.12 Statistical Analysis ...... 93 3.2.12.1 In vitro study ...... 93 3.2.12.2 Clinical study ...... 93

4 Evaluation of the status and functional significance of the Sprouty expression in epithelial ovarian cancer cells: an in vitro study ...... 95

4.1 Introduction ...... 95 4.2 Part I: Evaluation of the Sprouty expression in EOC cells and their normal counterparts ...... 95 4.2.1 Results ...... 96 4.2.1.1 Different human EOC cell lines express different levels of Sprouty proteins as compared with normal epithelial ovarian cells...... 96 4.2.1.2 Spry1 and Spry2 are differentially expressed by EOC cells at mRNA levels...... 100 4.2.1.3 Immunocytochemical staining of Spry1 and Spry2 displays a predominant vesicular cytoplasmic staining in OVCAR-3, SKOV-3, and HOSEpiC cells...... 103 4.3 Part II: Functional significance of the Spry1 expression in EOC cells with distinct levels of the protein ...... 107 4.3.1 Results ...... 107 4.3.1.1 Induced expression of Spry1 is deleterious for viability of SKOV-3 cells. 107 4.3.1.2 Spry1 transfection of SKOV-3 cells diminishes migration and invasion. 112 4.3.1.3 Spry1 knockdown enhances growth and proliferation of the 1A9 human ovarian cancer cells...... 116 ix

4.3.1.4 Spry1 knockdown in 1A9 cells enhances wound healing and augments cell migration and invasion...... 119 4.3.1.5 Induced expression of Spry1 triggers apoptotic events in SKOV-3 cells and inhibits activation of ERK and AKT...... 123 4.4 Discussion ...... 126

5 The status and the clinical relevance of the Spry1 protein expression in EOC: a retrospective study ...... 130

5.1 Introduction ...... 130 5.2 Results ...... 131 5.2.1 Spry1 protein is downregulated in EOC...... 131 5.2.2 Spry1 expression inversely correlates with the expression of p- ERK/ERK and Ki67 in EOC...... 134 5.2.3 Spry1 expression is correlated with clinicopathological characteristics of EOC patients...... 138 5.2.4 Expression of Spry1 is associated with survival in patients with EOC...... 141 5.2.5 Spry1 protein expression cannot predict the development of post- treatment ascites and chemorefractory disease in patients with EOC...... 148 5.2.6 There is no meaningful association between the Spry1 protein expression and tumor size in patients with EOC...... 151 5.3 Discussion ...... 153

6 Retrospective study of the expressions of Spry2 and Spry4 proteins in EOC: correlation with Spry1 and clinicopathological parameters, association with patient outcome ...... 156

6.1 Introduction ...... 156 6.2 Results ...... 156 6.2.1 Spry2 and Spry4 proteins are downregulated in EOC...... 156 6.2.2 The expression of Spry2, but not that of Spry4, correlates with the expression of Spry1, p-ERK/ERK and Ki67 in EOC...... 161 6.2.3 The expression of Spry2, but not that of Spry4, correlates with clinicopathological characteristics of EOC patients...... 164 x

6.2.4 Expression of Spry2, but not that of Spry4, is associated with survival in patients with EOC...... 167 6.2.5 Expression of Spry2 has a predictive value for the development of post- treatment ascites, but not for response to chemotherapy, in EOC patients. . 177 6.2.6 Expressions of Spry2 and Spry4 proteins are not associated with tumor size in patients with EOC...... 179 6.3 Discussion ...... 181

7 Evaluation of the expression status of VEGF, FGF and IL-6 proteins and their correlation with Spry1, Spry2 and Spry4 proteins in EOC: a retrospective study 188

7.1 Introduction ...... 188 7.2 Results ...... 189 7.2.1 VEGF, FGF-2 and IL-6 proteins are upregulated in EOC...... 189 7.2.2 There are no correlations between the expressions of Sprouty isoforms and those of VEGF, FGF-2 and IL-6 in EOC...... 194 7.3 Discussion ...... 197

8 Summary and future potential directions ...... 201

9 References ...... 208

List of Figures

Figure 1-1 Worldwide incidence and mortality rate of different cancers in 2012.. .. 2

Figure 1-2 A proposed model for intraperitoneal dissemination of ovarian cancer ...... 16

Figure 1-3 Representative regulators of the Sprouty cellular content at transcriptional and post-translational level, irrespective of the Sprouty isoform and cell type ...... 43

Figure 1-4 Schematic illustration of the Sprouty-mediated regulation of cell proliferation, differentiation and survival, irrespective of the Sprouty isoform and cell type ...... 66

Figure 1-5 Schematic illustration of the Sprouty-mediated regulation of cell migration, adhesion and cytoskeletal rearrangement, irrespective of the Sprouty isoform and cell type ...... 68

Figure 3-1 Flow chart representing the sequence of steps of the retrospective clinical study ...... 89

Figure 4-1 Analysis of Spry1, Spry2 and Spry4 protein expressions in human epithelial ovarian cancer cell lines and HOSEpiC human ovarian surface epithelial cells by Western blot ...... 98

Figure 4-2 Quantitative analysis of Sprouty protein expressions in malignant and normal epithelial ovarian cells using ImageQuant software...... 99

Figure 4-3 Determination of integrity and purity of RNA samples extracted from malignant and normal epithelial ovarian cells...... 101

Figure 4-4 RT-PCR evaluation of Spry1 and Spry2 mRNA expressions in malignant and normal epithelial ovarian cells ...... 102 xii

Figure 4-5 Immunocytochemical staining of Spry1 and Spry2 proteins in OVCAR- 3 cells ...... 104

Figure 4-6 Immunocytochemical staining of Spry1 and Spry2 proteins in SKOV-3 cells ...... 105

Figure 4-7 Immunocytochemical staining of Spry1 and Spry2 proteins in HOSEpiC cells ...... 106

Figure 4-8 Western blot analysis of Spry1 protein expression in Spry1-transfected SKOV-3 cells ...... 108

Figure 4-9 Spry1 stable transfection and its effect on viability of SKOV-3 cells .. 109

Figure 4-10 Spry1 transient transfection and its effect on viability of SKOV-3 cells detected by trypan blue assay ...... 110

Figure 4-11 Spry1 transient transfection and its effect on viability of SKOV-3 cells detected by MTT assay...... 111

Figure 4-12 Spry1 transfection of SKOV-3 cells and its effect on wound healing 113

Figure 4-13 Effect of Spry1 transfection on SKOV-3 cell migration ...... 114

Figure 4-14 Spry1-induced inhibition of invasion in SKOV-3 cells ...... 115

Figure 4-15 Effect of Spry1 knockdown on viability of 1A9 cells ...... 117

Figure 4-16 MTT assay of 1A9 cells after Spry1 knockdown ...... 118

Figure 4-17 Effect on wound healing of Spry1 knockdown in 1A9 cells ...... 120

Figure 4-18 Effect of Spry1 knockdown on migration of 1A9 cells ...... 121

Figure 4-19 Effect of the Spry1 knockdown on invasion of 1A9 cells ...... 122

Figure 4-20 Effect of Spry1 transfection/knockdown on the expression of apoptosis-related proteins in EOC cells...... 124 xiii

Figure 4-21 Effect of Spry1 transfection/knockdown on the expression of proteins associated with proliferation and survival in EOC cells ...... 125

Figure 5-1 Immunohistochemical analysis of the Spry1 expression in EOC ...... 132

Figure 5-2 Comparison of Spry1 protein expression levels in normal and malignant ovarian epithelial tissues after immunohistochemical scoring...... 133

Figure 5-3 Expression of ERK, phospho-ERK and Ki67 in EOC...... 135

Figure 5-4 Comparison of ERK and phospho-ERK expression scores and their ratio in EOC samples and matched normal tissues...... 136

Figure 5-5 Kaplan-Meier curves of overall survival (OS) and disease-free survival (DFS) probabilities associated with Spry1 expression in EOC ...... 144

Figure 5-6 Association of low or high Spry1 expression with EOC tumor size .... 152

Figure 6-1 Immunohistochemical analysis of Spry2 expression in EOC ...... 158

Figure 6-2 Immunohistochemical analysis of Spry4 expression in EOC ...... 159

Figure 6-3 Comparative analysis of Spry2 and Spry4 expression scores in normal and malignant ovarian epithelial tissues after immunohistochemical scoring ...... 160

Figure 6-4 Kaplan-Meier curves of overall survival (OS) and disease-free survival (DFS) probabilities associated with Spry2 expression in EOC...... 170

Figure 6-5 Kaplan-Meier curves of overall survival (OS) and disease-free survival (DFS) probabilities associated with Spry4 expression in EOC ...... 171

Figure 6-6 Evaluation of associations between Spry2 and Spry4 expressions and tumor size ...... 180

Figure 7-1 Immunohistochemical analysis of VEGF expression in EOC ...... 190

Figure 7-2 Immunohistochemical analysis of FGF-2 expression in EOC ...... 191 xiv

Figure 7-3 Immunohistochemical analysis of the IL-6 expression in EOC ...... 192

Figure 7-4 Comparative analysis of VEGF, FGF-2 and IL-6 expression scores in normal and malignant ovarian epithelial tissues after immunohistochemical scoring...... 193

List of Tables

Table 1-1 WHO histological classification of ovarian tumors ...... 3

Table 1-2 IARC histological groups of ovarian cancer ...... 9

Table 1-3 Ovarian cancer staging according to AJCC TNM system and FIGO staging system ...... 22

Table 1-4 Sprouty implication in developmental and physiological processes reported by some investigators ...... 30

Table 1-5 Responses of different cell types to the Sprouty-induced regulation reported by different investigators ...... 33

Table 3-1 List of cell lines ...... 74

Table 3-2 List of chemicals and reagents ...... 74

Table 3-3 List of antibodies ...... 76

Table 3-4 List of instruments and software ...... 77

Table 3-5 Antibodies dilutions and the relevant blocking buffer ...... 79

Table 3-6 PCR reaction setup for reverse transcription ...... 82

Table 3-7 Optimized PCR program setup used ...... 83

Table 3-8 Clinicopathological characteristics of the participants ...... 90

Table 3-9 Primary antibodies and positive control tissues used for immunohistochemical study ...... 92

Table 5-1 Correlations of Spry1 with p-ERK/ERK and Ki-67 in EOC ...... 137 xvi

Table 5-2 Correlations between the Spry1 expression and clinicopathological characteristics of EOC patients ...... 139

Table 5-3 Univariate (unadjusted) Cox’s proportional hazards analysis of Spry1 and other potential predictors of survival and recurrence in EOC ...... 145

Table 5-4 Multivariate Cox’s proportional hazards analysis of predictors of survival and recurrence in EOC ...... 147

Table 5-5 Univariate logistic regression analysis of potential predictors of response to chemotherapy & post-treatment ascites in EOC ...... 149

Table 5-6 Multivariate logistic regression analysis of potential predictors of response to chemotherapy and post-treatment ascites in EPC ...... 150

Table 6-1 Correlations of the Spry1 expression with the expressions of Spry2 and Spry4 in EOC ...... 162

Table 6-2 Correlations of Spry2 with p-ERK/ERK and Ki-67 in EOC ...... 163

Table 6-3 Correlations of Spry4 with p-ERK/ERK and Ki-67 in EOC ...... 163

Table 6-4 Correlations of the Spry2 and Spry4 expressions with clinicopathological characteristics of EOC patients ...... 165

Table 6-5 Univariate (unadjusted) Cox’s proportional hazards analysis of the predictive value of Spry2 and Spry4 for overall survival (OS) and disease-free survival (DFS) in EOC ...... 172

Table 6-6 Multivariate Cox’s proportional hazards analysis of the predictive value of Spry2 and other predictors of overall survival (OS) and disease-free survival (DFS) in EOC ...... 173

Table 6-9 Univariate logistic regression analysis of the predictive value of Spry2 and Spry4 for response to chemotherapy and post-treatment ascites in EOC ..... 178

Table 6-10 Multivariate logistic regression analysis of the predictive value of Spry2 for post-treatment ascites in EOC ...... 178

Table 7-1 Correlation of Spry1 expression with VEGF, FGF-2 and IL-6 ...... 195

Table 7-2 Correlation of Spry2 expression with VEGF, FGF-2 and IL-6 ...... 195

Table 7-3 Correlation of Spry4 expression with VEGF, FGF-2 and IL-6 ...... 196

xviii

List of Abbreviations

AJCC American joint committee on cancer ARMS alveolar subtype of rhabdomyosarcoma BCR B cell receptor BDNF brain-derived neurotrophic factor bFGF basic fibroblast growth factor β-ME β-mercaptoethanol BSA bovine serum albumin BPH benign prostatic hyperplasia oC degrees celsius Cav-1 caveolin-1 CBD c-Cbl binding domain c-Cbl canonical Casitas B-lineage lymphoma ccRCC clear cell renal cell carcinomas CI confidence interval Cm2 square centimeter CRD cysteine-rich domain CREB cAMP response element-binding protein CtBP C-terminal binding protein DALYs disability-adjusted life years DcR3 decoy receptor-3 DEPC diethyl pyrocarbonate DFS disease-free survival DMEM Dulbecco's modified Eagle's medium DNA deoxyribonucleic acid dSpry Drosophila Sprouty DYRK1A dual-specificity tyrosine phosphorylation-regulated kinase 1A EDTA ethylenediaminetetraacetic acid EGF epidermal growth factor EGFR epidermal growth factor receptor xix

ELISA enzyme-linked immunosorbent assay EMT epithelial–mesenchymal transition EOC Epithelial ovarian cancer ERK extracellular signal-regulated kinase ERMS embryonic subtype of rhabdomyosarcoma EST expressed sequence tag FBS fetal bovine serum FGF-2 fibroblast growth factor-2 FIGO International Federation of Gynaecology and Obstetrics FRS2α fibroblast growth factor receptor substrate 2 g acceleration due to gravity (9.8 m.s-2) g gram Gαo G protein αo GAPDH glyceraldehyde 3-phosphate dehydrogenase gDNA genomic DNA GDNF glial cell line-derived neurotrophic factor GISTs gastrointestinal stromal tumors GnRH gonadotropin-releasing hormone GRIN G potein-regulated inducer of neurite outgrowth GSK3B glycogen synthase kinase 3 beta h hour HCC hepatocellular carcinoma HCCP hepatocellular carcinoma with poorer outcome HCG human chorionic gonadotropin HDM2 human double minute 2 HE4 human epididymis protein-4 HER2 human epidermal growth factor receptor 2 HGF hepatocyte growth factor HNPCC hereditary nonpolyposis colon cancer HR hazard ratio HRP horseradish peroxidase Hrs hepatocyte growth factor-regulated tyrosine kinase substrate xx

hSpry human Sprouty IARC International Agency for Research on Cancer IGF-2 insulin-like growth factor-2 IHC immunohistochemistry IL-6 interleukin 6 IL-8 interleukin-8 IL-10 interleukin-10 KITLG the ligand for the receptor tyrosine kinase c-KIT LOH loss of heterozygosity MAPK mitogen-activated protein kinases MAP2K MAPK-kinases MAP3K MAPK-kinase kinase MEF mouse embryonic fibroblasts MEK MAPK/ERK kinases µg microgram µl microliter ml milliliter mm millimeter MMPs matrix metalloproteinases Mnk1 MAPK-interacting kinase 1 MPNST malignant peripheral nerve sheath tumors mSpry mouse Sprouty MTC medullary thyroid carcinoma MTT 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide myr-Akt myristoylated Akt NEEC nonendometrioid carcinomas NF1 neurofibromin 1 NGF nerve growth factor NICE National Institute for Health and Clinical Excellence No number NSCLC non-small cell lung cancer OD optical density xxi

OEpiCM ovarian epithelial cell medium OS overall survival OSE ovarian surface epithelium PBS phosphate buffer saline pcDNA plasmid driven by the Cytomegalovirus promoter DNA PCR polymerase chain reaction PDCD4 programmed cell death 4 pDCs plasmacytoid dendritic cells PDGF platelet-derived growth factor PEN/STREP penicillin/streptomycin p-ERK phospho-ERK PHD prolyl hydroxylase domain protein PI3K phosphatidylinositol 3-kinase PIN prostatic intraepithelial neoplasia PIP2 phosphatidylinositol 4,5-bisphosphate PIP3 phosphatidylinositol 3,4,5-trisphosphate PKC protein kinase C PKM2 pyruvate kinase M2 PLC phospholipase C PlGF placenta growth factor PMA phorbol 12-myristate-13-acetate PP2A protein phosphatase 2A PPARγ peroxisome proliferator-activated receptor gamma PTEN phosphatase and tensin homolog PTP1B protein tyrosine phosphatase 1B PVDF polyvinylidene fluoride membrane pVHL von Hippel-Lindau protein RIPA buffer radioimmunoprecipitation lysis buffer RMS rhabdomyosarcoma RNA ribonucleic acid ROS reactive oxygen species rpm revolutions per minute xxii

RPMI Roswell Park Memorial Institute medium rRNA ribosomal RNA RTK receptor tyrosine kinase RT-PCR reverse transcription-polymerase chain reaction S1P sphingosine-1-phosphate SDF-1 Stromal cell-derived factor-1 SDS sodium dodecylsulfate SEM standard error of measurment SHP2 phosphotyrosine phosphatase Siah2 Seven in Absentia homolog 2 SP1 specificity protein 1 Spry Sprouty Spry1 Sprouty1 Spry2 Sprouty2 Spry4 Sprouty4 SRM serine-rich motif TAE buffer Tris Acetate-EDTA buffer TAM tamoxifen TCL1-tg T-cell leukemia 1-transgenic TCR T cell receptor TESK1 testicular protein kinase 1 TGCT testicular germ cell tumors TGFβ1 transforming growth factor-beta1 TNF tumor necrosis factor TNF-α tumor necrosis factor-alpha Tsg101 tumor susceptibility gene 101 protein uPA urokinase-type plasminogen activator uPAR urokinase-type plasminogen activator receptor v volt VE-cad vascular endothelial-cadherin VEGF vascular endothelial growth factor VEGFR vascular endothelial growth factor receptor xxiii

WHO World Health Organisation WT wild type WT1 Wilms Tumor suppressor 1 xSpry xenopus Sprouty yr year zSpry zebra fish Sprouty

xxiv

List of Publications

Journal articles

1. Sprouty 2 protein, but not Sprouty 4, is an independent prognostic biomarker for human epithelial ovarian cancer. International Journal of Cancer 2014 (In press). 2. Sprouty 1 predicts prognosis in human epithelial ovarian cancer. American Journal of Cancer Research 2015 (In press). 3. Sprouty Proteins and Cancer. Jacobs Journal of Cancer Science and Clinical Research 2014, 1(1): 002 (Editorial). 4. The expression of the Sprouty 1 protein inversely correlates with growth, proliferation, migration and invasion of ovarian cancer cells. Journal of Ovarian Research 2014, 7, 61. 5. The developing story of Sprouty and cancer. Cancer and Metastasis Reviews 2014, 33 (2-3), 695-720. 6. Initial report on differential expression of sprouty proteins 1 and 2 in human epithelial ovarian cancer cell lines. Journal of Oncology 2012, 2012, 373826. 7. Significance of vascular endothelial growth factor in growth and peritoneal dissemination of ovarian cancer. Cancer and Metastasis Reviews 2012, 31 (1-2), 143- 62.

Conference poster presentations

1. Expression of Sprouty 1 protein (Spry1) is downregulated in human epithelial ovarian cancer and associated with clinicopathological features and patient outcome. 27th Lorne International Cancer Conference, 12-14 February 2015, Lorne, VIC, Australia 2. The expression of the human Sprouty protein-1 (hSpry1) inversely correlates with proliferation, migration and invasion of the SKOV-3 and 1A9 human ovarian cancer cells. 23rd Biennial Congress of the European Association for Cancer Research (EACR-23), 5-8 July 2014, Munich, Germany xxv

3. Effect of the induced expression of human Sprouty protein-1 (Spry1) on SKOV- 3 human ovarian cancer cells’ proliferation, migration, invasion and survival. 3rd World Congress on Cancer Science & Therapy, 21-23 October 2013, San Francisco, USA 4. Spry1-transfected SKOV-3 ovarian cancer cell line shows altered biological behavior. Lowy Cancer Symposium, May 2013, UNSW, Sydney, NSW, Australia 5. Human epithelial ovarian cancer cell lines differentially express Sprouty proteins 1, 2 and 4. 25th Lorne International Cancer Conference, 14-17 February 2013, Lorne, VIC, Australia

Other journals article publications during my candidature

1. Secreted mucins in pseudomyxoma peritonei: pathophysiological significance and potential therapeutic prospects. Orphanet Journal of Rare Diseases 2014, 9, 71. 2. Pseudomyxoma Peritonei: Uninvited Goblet Cells, Ectopic MUC2. Journal of Glycobiology 2013, S1:002. 3. Potent cytotoxic effects of bromelain in human gastrointestinal carcinoma cell lines MKN45, KATO-III, HT29-5F12 and HT29-5M21. Oncotargets and therapy 2013, 6, 403-9. 4. The Critical Role of Vascular Endothelial Growth Factor in Tumor Angiogenesis. Current Cancer Drug Targets 2012, 12 (1), 23-43. 5. Utility of Vascular Endothelial Growth Factor Inhibitors in the Treatment of Ovarian Cancer: From Concept to Application. Journal of Oncology 2012, 2012, 540791.

1 Literature Review

1.1 Ovarian cancer

1.1.1 Epidemiology

Accounting for around 3.6% of female cancers worldwide, ovarian cancer is the seventh leading cancer in women and the second cause of gynaecological cancer death worldwide based on GLOBOCAN database (Figure 1-1). In Australian women, it is the eighth most common cancer and the fifth most common cause of cancer death (GLOBOCAN, 2012). As the second most commonly diagnosed gynaecological cancer, with 1,305 new cases in Australian in 2010, ovarian cancer was the leading cause of death from gynaecological malignancies in Australia, accounting for 4.8% of all cancer deaths in women in 2011. About 82% of all new cases of ovarian cancer in 2008 were women aged 50 years or older, indicating an increase in the risk of the disease with age. As reported in 2009, the average age of diagnosis is 64.4 years. In 2010, a life time risk of developing ovarian cancer before the age of 85 in Australian women was reported to be 1 in 77. The risk of dying from ovarian cancer before the age of 85 was reported 1 in 111 in 2011. A decrease in the age-standardized incidence rate for ovarian cancer was reported in 2010, indicating a reduction from 12.4 per 100,000 in 1982 to 10.4 per 100,000 women in 2010. Moreover, there was also an increase in the five-year relative survival from 32.4% to 43.3% between the periods 1982-1987 and 2006-2010. In 2012, ovarian cancer was estimated to account for 13,200 disability-adjusted life years (DALYs) in Australia; of these, 12,100 were years lost due to premature death and 1,100 were years of healthy life lost due to disease, disability or injury. An estimated 1,470 and 1,640 Australian women are expected to be diagnosed with ovarian cancer in 2014 and 2020, respectively (Cancer Australia, Accessed June 2014 ). 2

Incidence

1,676,633 (25 .2%) 1,924,710 (28 . 9%)-~

'---583,100 (8.8%) - =-----527,624 (7 .9%)

Mortality • Breast • Colorectum 1,215,200 (34 .3%)-- • Lung 320,250 (9.0%) • Cervix uteri • Stomach • Corpus uteri 491,194 (13.8%) • Ovary • Thyroid ~L,__ _/' 265,653 (7.5%) :::...,__ ___ 254,096 (7 .2%) • Liver Other and unspecified 5-year prevalence

3,738,161 (21 . 8%)---~

• Incidence 6,255,391 (36.4%) • Mortality 0 10 20 30 40 50 ASR (W) rate per 100,000 586,624 ( 3.4%) -~-....

1,216,504 ( 7 . 1%)--~ 507,340 (3.0%)-----.J '------1,590,151 (9.3%) 1,547,161 ( 9 . 0%)------=~- :::__-----626,382 (3.6%) Figure 1-1 Worldwide incidence and mortality rate of different cancers in 2012. Ovarian cancer (shown in dark purple) has an incidence of 3.6% and a mortality rate of 4.3%. ASR: Age-standardized rate (Adapted from http://globocan.iarc.fr) 3

1.1.2 Classification

According to the classification by World Health Organisation (WHO) approved by the International Society of Gynecological Pathologists, most ovarian tumors fall into three major categories of surface epithelial cell tumors, germ cell tumors and sex cord stromal tumors (Scully and Sobin, 1999; Scully, 1975) as shown in Table 1-1. To classify histological types of ovarian cancer in comparative studies, a summarized version of this classification (Table 1-2) was proposed later by International Agency for Research on Cancer (IARC) (Parkin et al., 1998).

Table 1-1 WHO histological classification of ovarian tumors Surface epithelial-stromal tumors Serous tumors: benign, borderline, malignant Mucinous tumors, endocervical-like and intestinal-type: benign, borderline, malignant Endometrioid tumors: benign, borderline, malignant, epithelial-stromal and stromal

Clear cell tumors: benign, borderline, malignant Transitional cell tumors: Brenner tumor, Brenner tumor of borderline malignancy, malignant Brenner tumor, transitional cell carcinoma (non-Brenner type) Squamous cell tumors

Mixed epithelial tumors (specify components): benign, borderline, malignant Undifferentiated carcinoma

Sex cord-stromal tumors Granulosa-stromal cell tumors: granulosa cell tumors, thecoma-fibroma group Sertoli-stromal cell tumors, androblastomas: well-differentiated, Sertoli-Leydig cell tumor of intermediate differentiation, Sertoli-Leydig cell tumor poorly differentiated (sarcomatoid), retiform Sex cord tumor with annular tubules

Gynandroblastoma Unclassified Steroid (lipid) cell tumors: stromal luteoma, Leydig cell tumor, unclassified

Germ cell tumors 4

Dysgerminoma: variant-with syncytiotrophoblast cells Yolk sac tumors (endodermal sinus tumors): polyvesicular vitelline tumor, hepatoid, glandular Embryonal carcinoma

Polyembryoma Choriocarcinoma Teratomas: immature, mature, monodermal, mixed germ cell

Others

Gonadoblastoma

Germ cell sex cord-stromal tumor of nongonadoblastoma type Tumors of rete ovarii

Mesothelial tumors Tumors of uncertain origin and miscellaneous tumors Gestational trophoblastic diseases

Soft tissue tumors not specific to ovary

Malignant lymphomas, leukemias, and plasmacytomas

Unclassified tumors Secondary (metastatic) tumors Tumor-like lesions

1.1.2.1 Surface epithelial-stromal tumors

Approximately 90% of ovarian tumors are epithelial in origin. The major morphological subtypes of epithelial ovarian tumors are serous, endometrioid, mucinous and clear cell. Less common subtypes include transitional (Brenner), mixed, and undifferentiated tumors. It is widely believed that epithelial type of ovarian tumors arise from the ovarian surface epithelium (OSE) or more likely from surface epithelial inclusion cysts (Cho and Shih 2009). OSE is histologically similar to the mesothelium, which is the epithelium lining the interior of the peritoneal cavity. In fact, OSE is indistinguishable from and continuous with the mesothelial lining of adjacent organs and tissues. This 5

similarity, as well as the close morphologic resemblance of ovarian epithelial-stromal tumors to some epithelial tumors arising elsewhere within the pelvis and abdomen, may be explained by the shared origin of OSE and the mesothelium, i.e. the primitive coelomic epithelium (Chen et al., 2003). Nevertheless, epithelial ovarian tumor cells are not mesothelioma-like and instead resemble cells derived from the Müllerian duct, an invagination of the coelomic epithelium. As such, serous, endometrioid and mucinous ovarian tumor cells share morphological and biological characteristics with epithelial cells lining the fallopian tubes, uterus and endocervix, respectively, rather than with OSE cells (Dubeau, 2008). Since OSE does not thus seem to be the direct precursor of the epithelial ovarian neoplasms, it is hypothesized that OSE cells initially differentiates to a mesenchymal phenotype that is characteristic of Müllerian duct-derived tissues (Resta et al., 1993). This process is known as epithelial-mesenchymal transition (EMT) that is also implicated in embryo tissue generation and post-ovulatory wound healing (Gubbels et al., 2010). In contrast, a provocative theory by Dubeau suggests that epithelial ovarian tumors arise from Müllerian-duct derived tissues located outside this organ (Dubeau, 1999, 2008). According to Dubeau, most of neoplasms currently classified as primary epithelial ovarian tumors originate from Müllerian epithelium, either in the fimbriated end of the fallopian tubes or in the microscopic structures found in the paratubal and paraovarian areas. These structures belong to the so-called “secondary Müllerian system” which also includes endometriosis, endosalpingiosis, and endocervicosis (Lauchlan, 1994). On this basis, Dubeau argues that the various components of the secondary Müllerian system, with the inclusion of the rete ovarii, provide a source for all the cell types that are present in the major subtypes of tubo- ovarian epithelial tumors. He concludes that ovarian, fallopian tube, and primary peritoneal carcinomas, three apparently distinct diseases with identical morphological features and similar clinical characteristics, are all directly and exclusively derived from precursors with common features of Müllerian differentiation and could thus be regarded as a single entity (Dubeau, 2008).

In this review, I will focus on the epithelial type of ovarian cancer (EOC) and its four major types which represent the area of interest in my research project.

1.1.2.1.1 Serous carcinoma 6

Serous carcinoma accounts for 70% of all EOC tumors. A variety of architectural patterns, including nested, papillary (micropapillary or macropapillary), glandular (slit- like or round spaces), cribriform, solid and single cells, may be present in serous ovarian carcinomas. The perceived relationship between benign, borderline and malignant tumors has been controversial for ovarian serous neoplasms and pathological evidence for such a continuum is lacking (McCluggage, 2011). Hence, a two-tier system is advocated for grading ovarian serous carcinoma in which tumors are morphologically divided into “low-grade” and “high-grade” types. In this system, the distinction between low and high-grade serous carcinoma is based on the degree of nuclear atypia as the chief discriminator and the amount of mitotic activity as a secondary feature (Malpica et al., 2004). High-grade serous carcinoma is much more common than low-grade. These two types of ovarian serous carcinoma are not two grades of the same neoplasm but rather two distinct tumor types with different tumorigenesis, genetic abnormalities, biological behavior and prognostic features. Low-grade type is believed to arise in many cases through a stepwise progression from benign cystadenoma to borderline tumor to invasive low-grade carcinoma. In contrast, high-grade serous carcinoma is not generally related to borderline tumor. Instead, ovarian surface epithelium, the epithelium of cortical inclusion cysts and, more recently, the epithelium of the distal fimbrial portion of the fallopian tube have been implicated as the precursor of this type of ovarian serous carcinoma. While K-Ras, B-Raf and ERBB2 are frequently mutated in low-grade carcinoma, high-grade tumors are also associated, in most cases, with a TP53 mutation or p53 dysfunction, as well as with BRCA1 and BRCA2 abnormalities. As regards chemosensitivity, low-grade carcinoma does not respond well to traditional chemotherapeutic agents, as opposed to high-grade carcinoma which are initially chemosensitive [reviewed by (McCluggage, 2011)]. With respect to clinical outcome, reclassification of serous ovarian carcinomas according to this two-tier system in a Gynecologic Oncology Group (GOG) study revealed that women with high-grade serous tumor and those with low-grade tumor are two distinct populations and that the former have significantly poorer survival (Bodurka et al., 2012).

1.1.2.1.2 Endometrioid adenocarcinoma

Endometrioid adenocarcinoma represents about 10% of EOC tumors. This tumor type is made up of cells resembling those lining the uterus (endometrium) and is sometimes 7

associated with endometriosis. They often, although not always, arise from endometriosis (especially an endometriotic cyst) or a pre-existing borderline adenofibroma in a way similar to that proposed for progression of low-grade serous carcinoma. Most, but not all, ovarian endometrioid adenocarcinomas are low-grade and low-stage. In contrast to the serous carcinomas, over 50% of endometrioid adenocarcinomas are confined to the ovaries at diagnosis and nuclear atypia is usually less pronounced. The presence of glandular confluence and a back-to-back architecture with stromal exclusion should result in a diagnosis of endometrioid adenocarcinoma even when the cytological features are low-grade. High-grade ovarian endometrioid adenocarcinomas exist but are relatively uncommon. The ovarian endometrioid carcinomas share many molecular genetic features with their uterine counterparts. Indeed, mutations in several of the same tumor suppressor genes, oncogenes, and genes involved in DNA repair have been observed in both endometrial and ovarian endometrioid carcinomas. These include PTEN, CTNNB1, β-catenin, TP53, K-Ras and PIK3CA mutations and microsatellite instability. ARID1A is also frequently mutated in low-grade endometrioid carcinomas [reviewed by (Cho and Shih 2009; Jayson et al., 2014; McCluggage, 2011)].

1.1.2.1.3 Clear cell carcinoma

Recent studies suggest that clear cell carcinomas occur with approximate equal frequency to endometrioid adenocarcinomas. Clear cell carcinomas are typically composed of cells with abundant clear cytoplasm and prominent cell membranes with a characteristic morphological appearance typically consisting of an admixture of architectural arrangements including tubulocystic, glandular, solid and papillary. The majority arise in endometriosis and careful pathological sampling usually reveals background endometriosis, often in the form of an endometriotic cyst. On occasions, a component of benign or borderline clear cell adenofibroma is identified, although it may be difficult to ascertain whether this represents a precursor lesion or merely a growth pattern in a clear cell carcinoma. Although most clear cell carcinomas are diagnosed at early stage (stage I or II), these tumors tend to behave aggressively and respond poorly to chemotherapy. It is widely recommended that ovarian clear cell carcinomas be automatically graded as grade 3. Clear cell EOC bears frequent mutations in the 8

ARID1A gene. PIK3CA gene mutations are also found in a third of cases (Jayson et al., 2014; McCluggage, 2011).

1.1.2.1.4 Mucinous carcinoma

Mucinous carcinomas affect a wide age range, including occasionally children and adolescents. While 75% to 80% of mucinous tumors are benign neoplasms developing in only one ovary and are more likely found in younger women, cancerous mucinous tumors are more common in older women. Most ovarian mucinous carcinomas are unilateral and stage I, and advanced stage neoplasms (stage III or IV) are extremely uncommon. According to new studies, primary mucinous carcinomas are relatively uncommon, accounting for approximately 3% of primary EOC tumors. In this context, however, a secondary should always be strongly considered as metastatic mucinous carcinomas in the ovary are still sometimes misdiagnosed as a primary ovarian mucinous carcinoma. Most primary ovarian mucinous carcinomas (and borderline tumors) are of so-called intestinal (enteric or non-specific) type, many of which contain goblet cells and are usually multiloculated, often with thick tenacious mucus. A much more uncommon Müllerian (endocervical) type of ovarian mucinous carcinoma and borderline tumor also exists. Like low grade serous carcinomas, ovarian mucinous neoplasms of intestinal type comprise a spectrum or continuum from benign through borderline to malignant. Similar to low grade serous carcinomas, ovarian mucinous tumors of intestinal type commonly exhibit K-Ras mutations. They also show a high frequency of HER2 amplification [reviewed by (Aletti et al., 2007; Jayson et al., 2014; McCluggage, 2011)].

When divided into early stage (stage I–II) and late stage (stage III–IV), it can be seen that serous, clear cell and endometrioid carcinomas are approximately equally represented in early stage, nearly all mucinous carcinomas are early stage and a very high percentage of advanced stage neoplasms are serous in type. In other words, a high percentage of clear cell, endometrioid and mucinous carcinomas are diagnosed at early stage and these tumor types, in particular endometrioid and mucinous, are usually confined to the ovary at diagnosis (stage I) (McCluggage, 2011).

1.1.2.2 Non-epithelial tumors 9

Non-epithelial types of ovarian tumors consist of the sex cord-stromal tumors (6% of ovarian cancers), germ cell tumors (3%), and other tumors (1%) (Holschneider and Berek, 2000). The sex cord-stromal group includes tumors of mesenchymal and mesonephric origin. Some of these tumors, namely fibromas and thecomas, have a fibrous appearance, and some appear to be derived from the granulosa cells or their testicular sex cord counterparts, the Leydig and Sertoli cells. The ovarian germ cells are the origin of a number of tumors that are identical to testicular germ cell tumors. Germ cells that are stranded or have gone astray during their migration between the yolk sac and the developing gonads may develop into germ cell tumors outside the gonads (Chen et al., 2003).

Table 1-2 IARC histological groups of ovarian cancer Histologic type WHO ICD-O morphology code Carcinoma 8010–8570,a 9014–9015, 9110 Serous carcinomab 8441–8462, 9014 Mucinous carcinomab 8470–8490, 9015 Endometrioid carcinoma 8380–8381, 8560, 8570 Clear cell carcinoma 8310–8313, 9110 Adenocarcinoma NOS 8140–8190, 8211–8231, 8260, 8440 Other specified carcinomas Unspecified carcinoma 8010–8034 Sex cord-stromal tumors 8590–8671 Germ cell tumors 8240–8245, 9060–9102 Other specified cancers (including malignant Brenner tumor, Müllerian mixed tumor, and carcinosarcoma) Unspecified cancer 8000–8004

IARC: International Agency for Research on Cancer, WHO: World Health Organization; ICD-O: International Classification of Diseases for Oncology; NOS: not otherwise specified. a: Excludes 8240–8245, b: Includes tumors of borderline malignancy (low malignant potential). 10

1.1.3 Risk factors and protective factors

Several risk factors have been correlated with ovarian cancer, among which the most significant risk factor is a family history of either breast or ovarian cancer. Approximately 10% to 15% of all EOC have a hereditary predisposition to the disease consisting of at least two distinct groups of individuals as follows: BRCA1 or/and BRCA2 mutations and hereditary nonpolyposis colon cancer (HNPCC), also called Lynch syndrome II, due to mutations in DNA mismatch repair genes (Aletti et al., 2007; Holschneider and Berek, 2000). Women who carry BRCA1 or BRCA2 mutations have an estimated lifetime risk of 60-85% of developing breast cancer, and a lifetime risk of between 26% and 54% of developing ovarian cancer for BRCA1, and between 10% and 23% for BRCA2 (Colombo et al., 2010). Representing a unique phenotype of the hereditary breast-ovarian syndrome linked to mutations in BRCA1, a small number of families called site-specific ovarian cancer families have been reported to have an excess of ovarian cancer but no breast cancer. Family members with HNPCC, accounting for only approximately 1% of all ovarian cancers, are at increased risk of ovarian cancer, with a cumulative incidence of 12%. This group also have an increased lifetime risk of colon cancer (70%), endometrial cancer (40%-60%), and gastric cancer (Aletti et al., 2007).

It has been reported that the risk of ovarian cancer increases with age. In 2008, about 82% of all new cases of ovarian cancer among Australian females were diagnosed as women 50 years or older (Cancer Australia, Accessed June 2014 ). According to epidemiological research, multiple pregnancies, breastfeeding, oral contraceptive use, bilateral tubal ligation or hysterectomy and prophylactic oophorectomy are among factors associated with a reduced risk of developing the disease. On the other hand, nulliparity, early menarche, and late menopause are associated with an increased risk. There are conflicts in reports over the influence of exposure to carcinogens, including diagnostic or therapeutic radiation, asbestos and talcum powder, and dietary factors on the development of ovarian cancer (Aletti et al., 2007; Colombo et al., 2010).

1.1.4 Molecular pathology

1.1.4.1 Cell signaling pathways 11

Cell signaling pathways play a crucial role in cancer cell growth, invasion, survival, metastasis and immune escape. The mitogen-activated protein kinase (MAPK) pathway, the phosphatidylinositol 3-kinases (PI3K) pathway, the nuclear factor kappa-light- chain-enhancer of activated B cells (NF-kB) pathway, the activator of transcription 3 (Jak-STAT 3) pathway, the proto-oncogene tyrosine protein kinase Src pathway, the ErbB activation pathway, the lysophosphatidic acid (LPA) pathway, the Müllerian inhibitory substance receptor pathway, the EGF and VGEF pathways and the ER beta pathway have all been reported as major intracellular signaling pathways associated with ovarian cancer (Longuespee et al., 2012).

Among the above pathways, MAPK/ERK, as the main target of the Sprouty-mediated regulation, is of particular interest in my study. With respect to the implication of ERK in ovarian cancer, Manzano et al found that the downregulation of CL100, an endogenous dual-specificity phosphatase known to inhibit MAPK, could result in progression of human ovarian cancer through promoting MAPK pathway (Manzano et al., 2002). They indicated that malignant ovarian epithelial cells displayed 10-25 times less activity of CL100 compared with that in normal ovarian epithelial cells. Induced expression of CL100 in ovarian cancer cells inhibited intraperitoneal tumor growth in nude mice. In another study by Lengyel et al, the MEK inhibitor PD98059 sensitized ovarian cancer cell lines to Cisplatin. (Lengyel et al., 1995). The involvement of MAPK signaling pathways in regulation of apoptosis induced by chemotherapeutic agents in ovarian carcinoma cells has also been demonstrated in other studies (Lee et al., 1998; Persons et al., 1999).

1.1.4.2 Proteins involved in molecular pathology

A variety of proteins have been shown to play a major role in ovarian cancer development and progress. These proteins are divided into different categories as follows:

1.1.4.2.1 Proteins associated with cell proliferation

Among proteins associated with cell proliferation are the protein family S100, in particular S100 A11 and S100 A12 proteins (El Ayed et al., 2010) . S100 A11 protein known to regulate cell growth through the inhibition of DNA synthesis (Makino et al., 12

2004; Sakaguchi et al., 2004) was detected in ovarian ascites in a study for ovarian cancer biomarker discovery (Gortzak-Uzan et al., 2008). S100 A12 is known to promote leukocyte migration in chronic inflammatory responses (Yang et al., 2001). The expression of oviduct-specific glycoprotein (OGP, Mucin-9), a marker of normal oviductal epithelium has also been indicated as a potential associated protein at early events of ovarian carcinogenesis (El Ayed et al., 2010; Longuespee et al., 2012). In a study by Porcile et al, stromal cell-derived factor-1 (SDF-1) induced proliferation of ovarian cancer cells by increasing the phosphorylation and activation of extracellular signal-regulated kinases (ERK)1/2, which correlates to epidermal growth factor (EGF) receptor transactivation (Porcile et al., 2005). Transforming growth factor-beta (TGF-β) has also been shown to stimulate tumor cell proliferation, to increase the production of matrix metalloproteinase (MMP), and to enhance invasiveness of ovarian cancer cells (Do et al., 2008; Rodriguez et al., 2001; Sood et al., 2004; Sood et al., 2001; Vergara et al., 2010). Tumor growth stimulation has also been linked to the presence of IL-7 (Giuntoli et al., 2009; Lambeck et al., 2007; Xie et al., 2004) as well as TNF-α and IL8 released by plasmacytoid dendritic cells (pDCs) in ovarian tumor environment (Labidi- Galy et al., 2011).

1.1.4.2.2 Proteins associated with signaling to the cytoskeleton

Changes in cell phenotype such as epithelial-mesenchymal transition (EMT) could lead to cytoskeleton reorganization of the cells and thus a more motile phenotype for cancer invasion and metastasis. Several proteins, including profilin-1, cofilin-1, vimentin, and cytokeratin 19 are involved in the intracellular signaling to the cytoskeleton. TGF-β has been described to induce EMT in ovarian adenosarcoma cells (Kitagawa et al., 1996). In a study by Vergara et al on SKOV-3 ovarian cancer cell line, TGF-β induced the expression of cofilin and profilin-1 at mRNA and protein levels and modified its cytoskeletal organization (Vergara et al., 2010).

1.1.4.2.3 Proteins associated with hormonal pathways

Several hormones such as leptin, prolactin, osteopontin, anti-Müllerian hormone (Mor et al., 2005) and human chorionic gonadotropin (HCG) (Guo et al., 2011) have been detected through proteomic studies of ovarian cancer. 13

1.1.4.2.4 Growth factors and cytokines

Insulin-like growth factor (IGF), epidermal growth factor (EGF), transforming growth factor-β (TGF-β), platelet-derived growth factor (PDGF), fibroblast growth factor (FGF) and vascular endothelial growth factor (VEGF) have been reported to be strongly associated with the growth and progression of ovarian cancer (Langdon and Smyth, 1998). There is also evidence indicating the role of cytokines such as tumor necrosis factor (TNF), interleukin-6 (IL-6), interleukin-8 (IL-8) and interleukin-10 (IL-10) in ovarian cancer tumor growth and progression, at least in part through creating an immunosuppressive network (Kulbe et al., 2012; Longuespee et al., 2012; Matte et al., 2012).

Among these, VEGF, FGF and IL-6 evaluated in the current project are discussed below:

1.1.4.2.4.1 Vascular endothelial growth factor

The vascular endothelial growth factor family protein includes seven VEGF peptides (VEGF-A to -F and placenta growth factor (PlGF)) and three receptor tyrosine kinases (RTK) called vascular endothelial growth factor receptor (VEGFR-1 to -3) (Yamazaki and Morita, 2006). As one of the most studied growth factors in ovarian cancer, the association of VEGF-A (hereafter VEGF) with ovarian cancer growth and progression is well established. Playing an important role in the physiology of normal ovaries, VEGF has a major contribution to the pathophysiology of ovarian cancer through induction of tumor angiogenesis and vascular permeability. VEGF promotes tumor growth and facilitates malignant cell invasion and dissemination (Figure 1-2). Essentially by promoting tumor angiogenesis and enhancing vascular permeability, VEGF contributes to the development of peritoneal carcinomatosis associated with malignant ascites formation, the characteristic feature of advanced ovarian cancer at diagnosis, further highlighting the crucial part played by VEGF in the progressive course of the disease (Masoumi Moghaddam et al., 2012).

Constitutive expression of VEGF gene in normal and neoplastic human ovaries (Olson et al., 1994) as well as its differential expression in tumor specimens compared to benign ovarian tissue (Abu-Jawdeh et al., 1996; Hazelton et al., 1999) has been 14

reported. Preclinical experiments have shown that overexpression of VEGF can transform normal, functional ovarian epithelium into ascites-producing, neoplastic tissue (Ramakrishnan et al., 2005; Schumacher et al., 2007). Large amounts of VEGF are secreted in ovarian cancer in vitro and in vivo (Santin et al., 1999). Several studies have indicated elevated immunohistochemical expression of VEGF in ovarian cancer (Duncan et al., 2008; Ravikumar and Crasta, 2013; Shen et al., 2000; Siddiqui et al., 2010; Siddiqui et al., 2011; Smerdel et al., 2009; Wong et al., 2003). Overexpression of intratumoral VEGF, found to correlate with poorer prognosis (Hartenbach et al., 1997; Kassim et al., 2004; Paley et al., 1997) and enhanced odds of progression (Chambers et al., 2010), has been suggested as an independent prognostic factor for overall survival (Siddiqui et al., 2010). VEGF expression in primary tumors was reported to be correlated directly with the extent of disease and inversely with disease-free survival (Goodheart et al., 2005; Raspollini et al., 2004) or overall survival (Duncan et al., 2008; Shen et al., 2000; Smerdel et al., 2009). VEGF expression within omental metastases appeared not only correlated with the extent of omental involvement but also as an independent prognostic indicator (Siddiqui et al., 2001). Elevated levels of VEGF were detected in fluid samples from malignant cysts generated during ovarian cancer development which may represent a useful biomarker of angiogenesis and tumor progression (Abu-Jawdeh et al., 1996; Hazelton et al., 1999). VEGF levels in ovarian cancer-induced malignant ascites are markedly elevated compared with those in ascitic fluids of nonmalignant origin (Zebrowski et al., 1999) being reportedly of prognostic significance (Bamias et al., 2008). VEGF has been suggested as a serological biomarker for clinical diagnosis and a predictor of prognosis in patients with ovarian cancer (Cooper et al., 2002; Hefler et al., 2006; Li et al., 2004). In addition, overexpression of VEGF receptors (Abu-Jawdeh et al., 1996) and co-receptors (Ferrara and Kerbel, 2005; Osada et al., 2006) has been found in ovarian cancer. It has been reported that VEGF gene polymorphisms are an independent adverse prognosticator of overall survival (Hefler et al., 2007). Moreover, immunohistochemical expression of VEGF has been found to predict response to platinum-based chemotherapy in patients with EOC where tumors of patients with platinum resistant disease exhibit higher levels of VEGF (Siddiqui et al., 2011). 15

') -- Mesothelial lining ------1 , Lymph ' \ vessel I ' -- / VEGF (]] Invasion (]) Matrix degradation MMPs ______~ ~ VEGF

'J~Gf ~ ~ e @ Nee-vascularization ------lwMigration/ Disaggregation (&)MET J Tumor ~ VEGF i •-I ------fJ ' ~ VEGF Spheroid fJ ~ VEGF EMT @) EMT Tumor microenvironment ~ jffil Adhesion

Single cell ------~ VEGF t Vascular permeability ------~

l ~: Apoptosis c ~ Blood : vessel _J ~ 16

Figure 1-2 A proposed model for intraperitoneal dissemination of ovarian cancer (Ahmed et al., 2007; Shield et al., 2009) with focus on the role of VEGF. Primary ovarian tumors are generally confined to the ovaries. To metastasize, tumor cells undergo epithelial– mesenchymal transition (EMT) to attain motility (1). Subsequently, ruptured tumor sheds malignant cells into the peritoneum (2), where they often form spheroids to survive (3). Spheroids undergo changes into invasive mesenchymal phenotype to maintain survival and motility (4). These cellular aggregates are transported throughout the peritoneal cavity by normal peritoneal fluid and then adhere to and implant on the peritoneum and mesothelial linings of pelvic and abdominal organs (5), where they undergo a reverse transition (mesenchymal–epithelial transition or MET) (6) and disaggregation (7) to initiate metastatic growth. Through the activity of MMPs, matrix degradation occurs (8) and invasive tumor cells infiltrate the mesothelial lining and the extracellular matrix (9). Cytokines and growth factors, such as VEGF, TNF-α, IL- 6, IL-8, and bFGF, contribute to EMT and invasiveness of spheroids via autocrine and paracrine loops. VEGF is also significantly involved in different steps of the process, including primary tumor angiogenesis, neovascularization at newly seeded sites, MMP-mediated matrix degradation, and malignant ascites formation. Figure created by author published in Cancer and Metastasis Reviews 2012, 31(1-2): 143-162.

1.1.4.2.4.2 Fibroblast growth factor

The fibroblast growth factor (FGF) family consists of twenty two FGF members (FGF-1 to -14 and FGF-16 to -23) and four tyrosine kinase receptors (FGFR-1 to -4) (Finklestein and Plomaritoglou, 2001; Ornitz and Itoh, 2001). As the most studied FGF, the association of basic fibroblast growth factor (bFGF or FGF-2, hereafter FGF) with ovarian cancer has been reported in previous studies. To test the potential role of FGF as a factor with both angiogenic and mitogenic activity in human ovarian cancer, the expression of FGF in a range of ovarian cancer cell lines was demonstrated by Crickard et al using immunoblot analysis and immunofluorescent staining. They also showed increased FGF mRNA level and cell proliferation in response to FGF treatment in vitro. Suramin, a known FGF inhibitor, inhibited the proliferation of ovarian cancer cell lines in accordance with the levels of expression of FGF and its receptor (Crickard et al., 1994). These results were further confirmed by Blasio et al, suggesting that the factor can act in an autocrine manner (Di Blasio et al., 1995; Di Blasio et al., 1993). Fujimoto et al found that the levels of FGF and its mRNA were significantly higher in advanced primary ovarian cancers when compared with normal ovaries, regardless of histological types and some clinical backgrounds (Fujimoto et al., 1997). Intense expression of FGF mRNA (Davidson et al., 2002) as well as raised serum and ascitic FGF concentrations (Barton et al., 1997) have also been demonstrated in advanced ovarian cancers. Elevated levels of FGF have also been reported in serum and tumor tissue of patients with ovarian cancer compared with cancer-free individuals employing ELISA, quantitative PCR and by IHC (Le Page et al., 2006). In addition to proliferation, the ability of FGF in stimulation of migration, invasion and angiogenesis has been indicated in vitro. (Lin et al., 2003a; Lin et al., 2003b; Zhang et al., 2003). Gene expression profiling of advanced ovarian cancer showed the autocrine stimulation of mesenchymal conversion and cohort/scatter migration by FGF, suggesting a central role for FGF signaling in the maintenance of cellular plasticity of ovary-derived cells throughout the carcinogenesis process (De Cecco et al., 2004). FGF could also affect the regulation of other genes and proteins implicated in invasion, metastasis or angiogenesis, including urokinase-type plasminogen activator (uPA) (Li and Jiang, 2010), matrix metalloproteinases (MMPs) (Strutz et al., 2002), VEGF (Giavazzi et al., 2003; Sako et al., 2003), and E-cadherin (Billottet et al., 2004; Lau et al., 2013; Strutz et al., 2002; Wu et al., 2008). The 18

expression of FGF has also been suggested as a biological predictor of response to therapy. Gan et al reported that high FGF expression was inversely correlated with sensitivity to paclitaxel, in part due to the direct effects of FGF on proliferation and apoptosis, and was a strong predictor of resistance to the treatment (Gan et al., 2006) However, the role of FGF in ovarian cancer progression is still controversial. Employing immunoassay (Obermair et al., 1998) and immunoblot (Secord et al., 2007) analyses, high levels of cytoplasmic FGF have been reported to be associated with reduced tumor aggressiveness and increased survival rates. Since tumors with high cytoplasmic expression of FGF had more stromal content, Obermair et al hypothesized that FGF might induce a fibroblastic response that gives rise to a less aggressive phenotype.

1.1.4.2.4.3 Interleukin-6

IL-6 is a classic pro-inflammatory cytokine associated with a variety of diseases, including cancer. Implicated in the ovarian follicle development in normal ovaries and the regulation of chronic inflammation, IL-6 can result in a cellular microenvironment beneficial to cancer cell growth, proliferation, migration and survival through direct and indirect effects on tumor cells or the immune system components, respectively (Heikkila et al., 2008; Nash et al., 1999). In vitro studies have confirmed the production of IL-6 by normal and neoplastic ovarian epithelium (Lidor et al., 1993; Watson et al., 1993). The IL-6 association with EOC carcinogenesis, progression (Lo et al., 2011), enhanced tumor cell survival, increased resistance to chemotherapy (Wang et al., 2010), invasiveness (Obata et al., 1997) and tumor angiogenesis (Coward et al., 2011) has also been indicated. The clinical relevance of IL-6 in ovarian carcinoma was suggested by several observations detecting high levels of IL-6 in serum or ascites of ovarian cancer patients (Acien et al., 1994; Berek et al., 1991; Dobrzycka et al., 2013; Gorelik et al., 2005; Kryczek et al., 2000; Kutteh and Kutteh, 1992; Plante et al., 1994; Scambia et al., 1995). Acien et al suggested high circulating IL-6 concentrations for differentiating malignant ovarian tumors from benign conditions (Acien et al., 1994). These results were further confirmed by Gorelik et al as they demonstrated significant differences in serum concentrations of IL-6 between ovarian cancer and control groups (Gorelik et al., 2005). Several investigations indicated a link between high IL-6 levels and unfavorable clinical outcome. In a study by Berek et al, they concluded that serum IL-6 might be a 19

useful tumor marker for ovarian cancer as it correlated with the tumor burden, clinical disease status, and survival (Berek et al., 1991). Scambia et al found independent prognostic value of serum IL-6 in primary ovarian cancer as well as correlation of elevated serum IL-6 levels with a poor prognosis (Scambia et al., 1995). Dobrzycka et al reported a significant association between elevated IL-6 levels and poor overall and disease-free survival (Dobrzycka et al., 2013). Elevated ascitic IL-6 correlated significantly with ascites volume and initial tumor size in EOC patients as investigated by Plante el al. However, there was no statistical correlation between IL-6 levels in ascites or serum with survival time or other tumor parameters, including tumor stage, grade, histologic findings and residual tumor volume after debulking (Plante et al., 1994). Immunohistochemical expression of IL-6 in ovarian cancer specimens have also been evaluated by different investigators. Glezerman et al assessed the expression levels of IL-6 in normal and cancerous ovarian tissues by immunohistochemical staining. The results indicated that IL-6 was expressed in cancerous ovarian tissue at a higher level than in normal ovarian tissues (Glezerman et al., 1998). Guo et al reported a significant difference in immunohistochemical expressions of IL-6 among the metastatic, drug- resistant recurrent tumors and matched primary tumors with more staining in the drug- resistant and the metastatic tumors (Guo et al., 2010). In another study employing automated immunohistochemistry on tissue microarrays from ovarian cancer cases, intensity of IL-6 staining in malignant cells was significantly associated with poor prognosis (Coward et al., 2011). High IL-6 expression of ovarian tumors was also revealed by Plewka et al. They showed that the malignant serous tumors had higher expressions of IL-6 as compared with serous borderline and benign lesions. Since the presence of IL-6 in the mucinous tumor subtype was observed only in benign lesions, they concluded that the role of IL-6 and its regulators in the pathogenesis of ovarian cancer may depend on the histological type (Plewka et al., 2014). There are evidences suggesting the role of IL-6 in promoting resistance to chemotherapy. Scambia et al reported higher levels of IL-6 in serum of patients unresponsive to chemotherapy as compared with responsive ones when analyzing negative prognostic value of IL-6 in ovarian cancer patients (Scambia et al., 1994). Wang et al found that both exogenous and endogenous IL-6 could induce cisplatin and paclitaxel resistance in vitro via increased expression of both multidrug resistance-related genes and apoptosis inhibitory proteins, as well as activation of ERK and Akt signaling (Wang et al., 2010). They also 20

demonstrated that IL-6 might contribute to the refractoriness of ovarian cancer to tamoxifen (TAM) (Wang et al., 2014).

1.1.5 Diagnosis

1.1.5.1 Clinical presentation

The symptoms of ovarian cancer are fairly nonspecific and often occur when the disease is already spread throughout the abdominal cavity. Patients typically present with 3-4 months of abdominal pain or distension, which might be mistakenly attributed to irritable bowel syndrome (Jayson et al., 2014). The most common symptoms of the disease include abdominal discomfort, fullness or vague pain, bloating or increased abdominal girth, back pain, early satiety, urinary urgency and frequency, gastrointestinal symptoms, vaginal bleeding, (Goff et al., 2007; Gubbels et al., 2010; Yawn et al., 2004). In advanced disease, abdominal swelling, discomfort due to ascites with or without a large abdomino-pelvic mass and weight loss are the most common complaints. Some patients present with a swollen leg secondary to a deep vein thrombosis (Taylor and Kirwan, 2012). Patients may also present with bowel obstruction or shortness of breath due to intra-abdominal masses and pleural effusion, respectively. Although early-stage disease is usually asymptomatic with incidental diagnosis, it may occasionally present with dyspareunia or pelvic pain due to ovarian torsion (Aletti et al., 2007). Symptoms which are more severe or frequent than expected and of recent onset warrant further diagnostic investigation (Goff et al., 2004). The most important sign of ovarian cancer is the presence of a pelvic mass at physical examination with irregularity, solid features, and nodularity characteristics. In advanced stages, abdominal distension due to ascites and abdominal masses, pleural effusion, nodal metastases can be detected. Some paraneoplastic syndromes, including cerebellar degeneration associated with anti–Purkinje cell antibodies as well as superficial thrombophlebitis, dermatomyositis, and polyarthritis, may be present (Aletti et al., 2007). Owing to the vague and non-specific nature of the symptoms, women might delay seeking investigation (Gilbert et al., 2012).

1.1.5.2 Imaging and diagnostic biomarkers 21

Measurement of serum CA-125 (cancer antigen-125) concentration and abdominal and transvaginal ultrasound are the key investigations when ovarian cancer is suspected (Jayson et al., 2014).

Ultrasonography and abdominopelvic computed tomography are the most common diagnostic imaging performed in the evaluation of a pelvic mass. Although other imaging techniques, such as magnetic resonance imaging or positron emission tomography, might be helpful in further assessment of the pelvic tumor, they are not routinely requested in preoperative evaluation (Aletti et al., 2007).

CA-125 measurement was initially employed to monitor the disease status of ovarian cancer patients, including detection of early recurrence or assessment of chemotherapy response. However, it was recommended later as a possible EOC marker in the primary assessment of a pelvic mass. According to the UK National Institute for Health and Clinical Excellence (NICE) guideline, patients who develop symptoms like irritable bowel syndrome or experience persistent or frequent symptoms, in particular those over 50 years, should have their serum CA-125 concentrations measured (Jayson et al., 2014).

Other promising markers for ovarian cancer include lysophosphatidic acid (a lipid found to be elevated in serum and ascites fluid), mesothelin, human epididymis protein-4 (HE4), decoy receptor-3 (DcR3), osteopontin, spondin-2, inhibin, activin, epidermal growth factor receptor (EGFR), VEGF, IL-8, macrophage colony-stimulating factor, and different kallikreins (Aletti et al., 2007; Gubbels et al., 2010).

1.1.6 Pathological grading and staging

Historically, the most commonly used systems for histopathological grading of EOC have been proposed by the International Federation of Gynecology and Obstetrics (FIGO), the World Health Organization (WHO), and the Gynecologic Oncology Group (GOG), of which the two former are universal in that they can be applied to all the major morphological subtypes of EOC. The FIGO system uses 3 grades based on architectural criteria, i.e., the proportion of glandular or papillary structures relative to areas of solid tumor growth. Grades 1, 2, and 3 correspond to <5%, 5–50%, and >50% solid growth, respectively. The WHO system incorporates both architectural and 22

cytological features, but these are not assigned based on quantitative criteria and as a consequence, this system can be considered rather subjective. In the GOG system, the grading method varies depending on the histological type of the tumor. For example, endometrioid adenocarcinomas are graded using FIGO criteria, while clear cell carcinomas are not assigned a grade at all [reviewed by (Cho and Shih 2009)]. More recently, a universal, 3-tier system has been proposed based on the Nottingham system for grading all types of mammary carcinoma. In this system known as Shimizu- Silverberg system, tumors are scored and graded based on the following criteria: predominant architectural pattern, nuclear pleomorphism and mitotic activity. Each parameter is given a score of 1–3 and a grade is derived based on the summation of the scores. Total scores of 3–5, 6–7, and 8–9 then yield the final grades of 1 (well- differentiated), 2 (moderately differentiated), and 3 (poorly differentiated), respectively (Shimizu et al., 1998; Silverberg, 2000). A 2-tier grading system proposed for ovarian serous carcinomas, the predominant subtype of EOC, was described earlier (Bodurka et al., 2012; Malpica et al., 2004).

A surgical staging is conducted in ovarian cancer to evaluate the extent of tumoral spread, to guide treatment and to provide information on prognosis (Taylor and Kirwan, 2012). As shown in Table 1-3, staging of ovarian cancer is performed according to the TNM system used by the American Joint Committee on Cancer (AJCC), that is comparable to an alternative staging system approved by the International Federation of Gynaecology and Obstetrics (FIGO) (Prat, 2014).

Table 1-3 Ovarian cancer staging according to AJCC TNM system and FIGO staging system AJCC FIGO Description TX Primary tumor cannot be assessed. T0 0 No evidence of primary tumor. Stage I T1-N0-M0 I Tumor limited to ovaries (one or both). Tumor limited to one ovary; capsule intact; no tumor T1a-N0-M0 IA on ovarian surface; no malignant cells in the ascites or peritoneal washings. 23

Tumor limited to both ovaries; capsules intact; no T1b-N0-M0 IB tumor on ovarian surface; no malignant cells in the ascites or peritoneal washings. Tumor limited to one or both ovaries, with any of the T1c-N0-M0 IC following: T1c1-N0-M0 IC1 Surgical spill Capsule ruptured before surgery or tumor on ovarian T1c2-N0-M0 IC2 surface T1c3-N0-M0 IC3 Malignant cells in the ascites or peritoneal washings Stage II Tumor involves one or both ovaries with pelvic T2-N0-M0 II extension. Extension and/or implants on uterus and/or fallopian T2a-N0-M0 IIA tubes and/or ovaries T2b-N0-M0 IIB Extension to other pelvic intraperitoneal tissues Stage III Positive retroperitoneal lymph nodes only T1/T2-N1-M0 IIIA1 (cytologically or histologically proven): IIIA1(i) Metastasis up to 10 mm in greatest dimension IIIA1(ii) Metastasis more than 10 mm in greatest dimension Microscopic extrapelvic (above the pelvic brim) T3a2-N0/N1-M0 IIIA2 peritoneal involvement with or without positive retroperitoneal lymph nodes Macroscopic peritoneal metastasis beyond the pelvis T3b-N0/N1-M0 IIIB up to 2 cm in greatest dimension, with or without metastasis to the retroperitoneal lymph nodes Macroscopic peritoneal metastasis beyond the pelvis more than 2 cm in greatest dimension, with or without metastasis to the retroperitoneal lymph T3c-N0/N1-M0 IIIC nodes (includes extension of tumor to capsule of liver and spleen without parenchymal involvement of either organ) 24

Stage IV Any T, any N, M1 IV Stage IVA Pleural effusion with positive cytology Parenchymal metastases and metastases to extra- Stage IVB abdominal organs (including inguinal lymph nodes and lymph nodes outside of the abdominal cavity)

AJCC: American Joint Committee on Cancer, FIGO: International Federation of Gynaecology and Obstetrics

1.1.7 Treatment

Treatment in ovarian cancer depends upon the stage at presentation and the histological subtype. In general, treatment is a combination of staging and debulking laparotomy followed by chemotherapy. Except in very early disease, treatment is rarely curative but it can provide symptom relief and prolong life (Taylor and Kirwan, 2012).

Primary cytoreduction is universally considered the cornerstone of initial management for patients with advanced disease (Aletti et al., 2007). Optimal cytoreductive surgery should reduce the maximum diameter of residual disease to less than 1 cm (Guppy et al., 2005). Several radical surgical procedures, including intestinal resection, splenectomy, diaphragmatic resection, and hepatic resection have been also described as treatments of advanced ovarian cancer with acceptable morbidity. Interval debulking surgery, an operation performed after a course of induction, or neoadjuvant, chemotherapy (usually 2 or 3 cycles) when complete surgical debulking is not deemed feasible at diagnosis, and secondary debulking, considered for selected patients with recurrent disease, are other surgical approaches to advanced invasive disease (Aletti et al., 2007; Jayson et al., 2014). Patients with stage I disease mostly undergo bilateral oophorectomy, hysterectomy, and surgical staging, including peritoneal biopsies, omentectomy, and pelvic and aortic lymph node dissection. In fertile women with a well-differentiated unilateral unruptured stage IA disease, a conservative surgery with removal of only the affected ovary without hysterectomy can be performed to preserve fertility (Jayson et al., 2014). 25

Postoperative treatment with either paclitaxel plus carboplatin or participation in clinical trials is recommended for patients with early-stage disease with poor prognostic features (Aletti et al., 2007). For advanced-stage disease, the current standard first line chemotherapy regimen involves intravenous administration of a platinum based drug (cisplatin/carboplatin) with a taxane, usually paclitaxel, given 3 weekly for six cycles. Although the majority of patients with ovarian cancer (up to 50%) achieve a clinical and radiological complete remission with the above chemotherapy regimen, 20–30% will show no evidence of response and the disease will recur in most patients with advanced ovarian cancer. (Taylor and Kirwan, 2012).

Because advanced ovarian cancer is often limited to the peritoneal cavity, intraperitoneal as opposed to systemic administration of chemotherapy has been proposed as a strategy to increase drug concentrations in the abdominal cavity (Aletti et al., 2007). Neoadjuvant therapy is a strong consideration for those individuals who are deemed to be poor surgical candidates at the time of initial diagnosis. In addition, for patients diagnosed with stage IV disease, especially those with a high burden of metastatic disease, treatment can begin with chemotherapy and response to chemotherapy can dictate subsequent decisions about aggressive surgical debulking (Aletti et al., 2007).

Hormonal therapies, including tamoxifen, aromatase inhibitors and GnRH analogues, which probably act by reducing oestrogen activity are occasionally used. The response rates of 10-15% have been reported in relapsed disease (Taylor and Kirwan, 2012).

Targeted therapies, including VEGF-VEGFR targeted agents, are among the most interesting novel approaches. Interim analysis of the ICON 7 trial of standard therapy with or without bevacizumab (a monoclonal antibody to VEGF) demonstrated a sustained improvement in progression free survival in a subgroup of women with advanced disease and suboptimal surgical debulking (Taylor and Kirwan, 2012).

1.1.8 Conclusion

As the second most common gynaecological cancer in Australian women, ovarian cancer is the leading cause of death from gynaecological malignancies. Late diagnosis due to the lack of specificity of clinical symptoms together with the high rate of disease 26

recurrence and refractoriness are major contributors to the poor survival rates. Lack of novel and specific sets of markers for diagnosis, clinical monitoring, prognosis and prediction of response to treatment is still considered as an unmet need to improve medical management of this disease. Thus, more investigations for identification of new markers are warranted. This may also result in the development of new treatment modalities for better clinical management of this highly refractory and recurrent disease. 27

1.2 Sprouty and cancer

1.2.1 MAPK signaling pathway

Mitogen-activated protein kinase (MAPK) signaling pathways are among the most widespread regulatory mechanisms of the eukaryotic cell biology. The first mammalian MAPK pathway to be identified and entirely mapped is extracellular signal-regulated kinase or MAPK/ERK (hereafter ERK). ERK orchestrates a signal transduction from cell membrane molecules to the transcriptional machinery to promote cell growth, differentiation and survival. As with other MAPKs, ERK represents a three-tiered kinase cascade composed of the sequentially-acting kinases. ERK is activated by a wide range of extracellular signals, including growth factors, cytokines, hormones, and neurotransmitters. Signal transduction is initiated when a ligand binds its transmembrane receptor tyrosine kinase (RTK) and thereby activates Ras, a small G protein anchored to the plasma membrane. Ras subsequently recruits from the cytosol to the cell membrane and activates Raf serine/threonine-specific kinases of MAPK-kinase kinase (MAP3K) family. Through serine/threonine phosphorylation, Raf activates a family of dual specificity kinases known as MAPK kinases (MAP2K) or MAPK/ERK kinases (MEKs). By concomitant tyrosine and threonine phosphorylation, MEKs activate MAPK (Erk). Phosphorylated Erk eventually induces gene expression by direct and indirect targeting of transcription factors. To set up a biologically coordinated infrastructure for physiologically appropriate outcomes, ERK and its core modules are under tight, multilayered control of positive and negative regulators, including the Sprouty protein family (Masoumi-Moghaddam et al., 2014b).

1.2.2 Sprouty protein family

The Sprouty protein family is a downstream modulator of MAPK signaling pathway and thus a major contributor to the regulation of the eukaryotic cells biology. Sprouty was discovered by Hacohen et al who initially described it as a common antagonist of fibroblast growth factor (FGF) and epidermal growth factor (EGF) signaling pathways in Drosophila (Hacohen et al., 1998; Kramer et al., 1999). In a search of the Expressed Sequence Tag (EST) database, they identified three human homologs of the fly gene designated hSpry1-3 (Hacohen et al., 1998). The fourth mammalian homolog, hSpry4, was later discovered in mice (de Maximy et al., 1999) and humans (Leeksma et al., 28

2002). Emerging evidence later showed that Sprouty specifically inhibits activation of ERK in response to a wide range of trophic factors, including FGF (Gross et al., 2001; Impagnatiello et al., 2001), platelet-derived growth factor (PDGF) (Gross et al., 2001), vascular endothelial growth factor (VEGF) (Impagnatiello et al., 2001), nerve growth factor (NGF) (Wong et al., 2002), brain-derived neurotrophic factor (BDNF) (Gross et al., 2007) and glial cell line-derived neurotrophic factor (GDNF) (Ishida et al., 2007). The biological functions of the Sprouty proteins have been attributed to its conserved motifs. These mainly include the N-terminal canonical Casitas B-lineage lymphoma (c- Cbl) binding domain (CBD) containing a key tyrosine residue; the serine-rich motif (SRM); and the C-terminal cysteine-rich domain (CRD) also known as the Sprouty (or translocation) domain. Among the Sprouty isoforms, Spry2 exhibits the highest evolutionary conservation, with the human Spry2 showing 97%, 85% and 51% sequence homology, in the CRD domain, to the mouse, chick and Drosophila protein, respectively (Minowada et al., 1999). Although Spry2 appears to be ubiquitously expressed in embryonic and adult tissues, the expression of other isoforms shows organ/tissue specificity (Ding et al., 2004; Leeksma et al., 2002; Minowada et al., 1999).

The Sprouty proteins are currently recognized as key regulators of ERK signaling that act on different levels of the pathway. Furthermore, they are part of a tightly- orchestrated regulatory mechanism where interactions with a variety of players lay the basis for a crosstalk between ERK and partner cascades. Nevertheless, aberrant activation of ERK and deregulation of Sprouty occur in a variety of pathological conditions, including malignant transformation.

1.2.2.1 Sprouty: a versatile modulator with complex functionality

Since discovery of Sprouty in 1998 (Hacohen et al., 1998), an expanding body of evidence has continued to support its crucial role in regulation of various physiological processes. Initial studies by Minowada et al (Minowada et al., 1999) and Tefft et al (Tefft et al., 1999) revealed that this protein family and its regulatory relationship with FGF-induced signaling are evolutionarily conserved. Through comparative genomic analysis, the linkage between the human Sprouty and FGF genes was later reported (Katoh and Katoh, 2006). Sprouty regulates tubular morphogenesis as a fundamental process in organogenesis and angiogenesis where FGF signaling is particularly involved 29

(Cabrita and Christofori, 2008; Horowitz and Simons, 2008; Warburton, 2008). Apart from its crucial role in embryogenesis, Sprouty has been implicated in regulation of physiological events in adult organs. Table 1-4 summarizes a number of studies in which implication of Sprouty in developmental and physiological events has been documented. 30

Table 1-4 Sprouty implication in developmental and physiological processes reported by some investigators Investigators Sprouty isoform Developmental/Adult physiological event Hacohen et al (Hacohen et al., 1998) dSpry Tracheal development Kramer et al (Kramer et al., 1999) dSpry Eye development Minowada et al (Minowada et al., 1999) mSpry2 and 4 Limb development Tefft et al (Tefft et al., 1999) mSpry2 Lung development Furthauer et al (Furthauer et al., 2001) zSpry4 Midbrain development Zhang et al (Zhang et al., 2001) mSpry1, 2 and 4 Craniofacial and trunk development Gross et al (Gross et al., 2003) mSpry1 Kidney development Chi et al (Chi et al., 2004) hSpry2 Ureteric branching Lo et al (Lo et al., 2004) mSpry1 and 2 Breast development in puberty and pregnancy Anteby et al (Anteby et al., 2005) hSpry1, 2 and 3 Placental villi sprouting Haimov-Kochman et al (Haimov-Kochman et hSpry2 Follicle maturation and corpus luteum formation al., 2005) Lin et al (Lin et al., 2005) mSpry2 Patterning of midbrain and anterior hindbrain Shim et al (Shim et al., 2005) mSpry2 Inner ear development Basson et al (Basson et al., 2006) mSpry1 Ureteric branching Boros et al (Boros et al., 2006) mSpry1 and 2 Ocular lens development Chi et al (Chi et al., 2006) mSpry2 Male sex organogenesis 31

Natanson-Yaron et al (Natanson-Yaron et al., hSpry2 Placental villi sprouting 2007) Price et al (Price et al., 2007) hSpry4 Kidney development Gross et al (Gross et al., 2007) mSpry2 Neuronal differentiation Shaw et al (Shaw et al., 2007) mSpry2 Lung development Laziz et al (Laziz et al., 2007) hSpry1, 2 and 4 Muscle regeneration Hamel et al (Hamel et al., 2008) hSpry2 Oocyte developmental competence Klein et al (Klein et al., 2008) mSpry4 (+ mSpry1 or 2) Growth and development of rodent incisors Jaggi et al (Jaggi et al., 2008) mSpry4 Pancreas development Wang et al (Wang et al., 2008) xSpry1 Gastrulation Pan et al (Pan et al., 2010) mSpry2 Lens and lacrimal gland development Purcell et al (Purcell et al., 2012) mSpry1 and 2 Temporomandibular Joint development Sieglitz et al (Sieglitz et al., 2013) dSpry Neuronal and glial differentiation Kuracha et al (Kuracha et al., 2013) mSpry1 and 2 Eyelid closure Velasco et al (Velasco et al., 2011) hSpry2 Endometrial gland developing and branching Sigurdsson et al (Sigurdsson et al., 2013) hSpry2 Breast morphogenesis Ching et al (Ching et al., 2014) mSpry1 and 2 External genitalia development dSpry: Drosophila Sprouty; hSpry: human Sprouty; mSpry: mouse Sprouty; xSpry: Xenopus Sprouty; zSpry: zebra fish Sprouty 32

At cellular level, Sprouty modulates key processes, including proliferation, differentiation, motility and survival, through regulation of ERK and parallel pathways as well as interaction with a number of effectors and regulators. As listed in Table 1-5, various regulatory effects of the Sprouty proteins in normal and neoplastic cells have been documented in literature. The Sprouty-mediated modulation, however, occurs in a cell- and context-dependent manner where a number of facts and factors, as described below, are involved in the determination of the eventual response.

1.2.2.1.1 Cell and context dependency

It is evident that cellular behavior in response to the growth factor stimulation and Sprouty-mediated regulation varies from cell to cell. For example, while Spry2 inhibits the differentiation of PC12 pheochromocytoma cells in response to FGF (Gross et al., 2001; Wong et al., 2002), it promotes FGF-induced differentiation of C2C12 myoblasts (de Alvaro et al., 2005). Depending on the cellular context and innate physiological characteristics, different components of the RTK signalosome are activated by different ligands (Schlessinger, 2004). Moreover, strength and duration of the signal transduction are among critical determinants of cell fate in response to activation and regulation of the RTK signaling (Marshall, 1995). In NIH3T3 fibroblasts and PC12 cells, for example, whereas transient activation of ERK during mid-G1 phase leads to cell cycle progression and hence proliferation in the former, sustained ERK activity induces cell- cycle withdrawal and neuronal differentiation in the latter (Kim and Bar-Sagi, 2004). Accordingly, Sprouty was shown to inhibit proliferation of NIH3T3 cells and differentiation of PC12 cells in response to growth factor stimulation (Gross et al., 2001). It will be discussed in the following sections that how Sprouty modulates the RTK signaling depending on the intersection point where it interferes as well as on its interplay with other interacting molecules.

1.2.2.1.2 Growth factor dependency and pathway sensitivity

Depending on the RTK activated and the downstream pathway(s) affected, Sprouty differentially modulates growth factor actions and thereby elicits divergent responses. Hence, Sprouty isoforms are able to selectively uncouple growth factor-induced signal transductions. 33

Table 1-5 Responses of different cell types to the Sprouty-induced regulation reported by different investigators Response Investigators Spry Stimulator Cell (×: inhibited, :enhanced, U: unaffected) Pathway/Molecule P M I D A Ap T C Normal cells Impagnatiello et al FGF, VEGF × × ERK 1, 2 HUVEC (Impagnatiello et al., 2001) EGF × Not ERK FGF, NGF, Gross et al (Gross et al., 2001) 1, 2 NIH3T3 × U ERK PDGF Lee et al (Lee et al., 2001) 4 FGF, VEGF HUVEC × × ERK Huebert et al (Huebert et al., 1 VEGF CPAE × ERK 2004) Poppleton et al (Poppleton et × Rac1 GTPase 2 Serum IEC-6 al., 2004) × ERK Zhang et al (Zhang et al., FGF, EGF, 2 VSMC × × N/A 2005) PDGF, serum de Alvaro et al (de Alvaro et × ERK 2 FGF C2C12 al., 2005)  AKT Tsumura et al (Tsumura et al., 4 C2C12 × Cofilin 34

2005) Fong et al (Fong et al., 2006) 2 KD FGF NIH3T3   ERK Wang et al (Wang et al., 2006) 4 - HUVEC × N/A Sutterlüty 2 Serum WI38 × × ERK et al (Sutterluty et al., 2007) Ding et al (Ding et al., 2007) 2 DR TGFβ1 NIH3T3  TGFβ1/Smad Ding et al (Ding and NIH3T3 2 DR TNF-α × TNF-α/P38 MAPK Warburton, 2008) MLE15 Lito et al (Lito et al., 2009) 2 - MSU1.1 × AKT, HDM2, p53 Tennis et al (Tennis et al., 4 - B2B × × × ME Wnt7A/Fzd9 /PPARγ 2010) Jung et al (Jung et al., 2012) 1 KD FGF4 J1-mESCs  ERK Sigurdsson 2 KD - D492 U  EM  EGFR et al (Sigurdsson et al., 2013) 2 KD ×  - U Felfly et al (Felfly and Klein, 4 KD U × hESC N/A 2013) 2 KD  FGF, EGF 4 KD U Mekkawy et al (Mekkawy and 1 uPA, EGF HEK293 × N/A Morris, 2013) 35

Neoplastic cells Gross et al (Gross et al., 2001) 1, 2 NGF, FGF PC12 × N/A Sasaki et al (Sasaki et al., 4 NGF, FGF PC12 × ERK 2001) Yigzaw et al (Yigzaw et al., FGF, EGF, × RTK 2 HeLa 2001; Yigzaw et al., 2003) PDGF, serum × PTP1B/p130Cas Wong et al (Wong et al., EGF  2 PC12 RTK 2002) NGF, FGF × Lee et al (Lee et al., 2004) 2 HGF/SF SK-LMS1 × × ×  × c-Met, ERK, AKT Lo et al (Lo et al., 2004) 2 DN - MCF-7   N/A Fong et al (Fong et al., 2006) 2 HGF SNU449 × c-Met, ERK LNCaP,

Wang et al (Wang et al., 2006) 4 - PC3 U N/A DU145 × Edwin et al (Edwin et al., 2 Serum HeLa × × AKT, PTEN, Rac1 2006) Ishida et al (Ishida et al., 2 GDNF TGW × × Ret 2007) Sutterlüty et al (Sutterluty et VL-8 × × 2 Serum ERK al., 2007) A-549 V/V × V/V × 36

VL-4, VL-2 Edwin and Patel (Edwin and 2 KD Serum SW13  AKT, ERK, Cbl Patel, 2008) Huh7, Lee et al (Lee et al., 2008) 2 - × ERK SNU449 Jaggie et al (Jaggi et al., 2008) 4 FGF, Serum PANC-1 × × PTP1B/p130Cas Frank et al (Frank et al., 2009) 2 - Nalm-6 ×  ERK Lito et al (Lito et al., 2009) 2 EGF PH3MT × Rac1 GTPase Tennis et al (Tennis et al., H157, 4 - × × × × Wnt7A/Fzd9 /PPARγ 2010) H2122 Holgren et al (Holgren et al., 2 HGF, Serum HCT-116 V/V   × V c-Met 2010) Barbachano et al (Barbachano SW480- 2 - × × ME E-cadherin et al., 2010) ADH Schaaf et al (Schaaf et al., RD, 1 KD - ×  Ras/ERK 2010) TE381T Wang et al (Wang et al., 2 DN - HLE  V U AKT, ERK, PKM2 2012a) Alsina et al (Alsina et al., 4 NGF PC12 × TrkA/ERK, 37

2012) Rac1GTPase Saos-2, Mekkawy et al (Mekkawy and MDA- 1 uPA, EGF × × N/A Morris, 2013) MB-231, HCT116 MCF-7, × × ERK Vanas et al (Vanas et al., 2, 4 Serum MDA- 2014) × N/A MB231

Spry: Sprouty; P: proliferation; M: migration; I: invasion; D: differentiation; A: adhesion; Ap: apoptosis; T: transition/transformation; C: colony/foci formation; N/A: not available; ME: mesenchymal-epithelial transition; EM: epithelial-mesenchymal transition; V/V: both in vitro and in vivo; V: in vivo; not otherwise marked: in vitro; FGF: fibroblast growth factor; VEGF: vascular endothelial growth factor; NGF: nerve growth factor; EGF: epidermal growth factor; PDGF: platelet-derived growth factor; TNF-α: tumor necrosis factor alpha; HGF/SF: hepatocyte growth factor/scatter factor; GDNF: glial cell line-derived neurotrophic factor; DR: downregulated Sprouty; KD: knocked down Sprouty; DN: dominant-negative mutant of Sprouty; HUVEC: human umbilical vein endothelial cells; NIH3T3: mouse embryonic fibroblast cell line; CPAE: calf pulmonary artery endothelial cell line; IEC-6: normal rat small intestine epithelial cell line; VSMC: vascular smooth muscle cells; C2C12: mouse myoblast cell line; WI38: normal human embryonic lung fibroblast cell line; MSU1.1: human fibroblast cell line; B2B: human nontransformed lung epithelial cell line; J1 mESCs: mouse embryonic stem cells; D492: breast epithelial stem cell line; hESC: human embryonic stem cells; HEK293: human embryonic kidney cells; PC12: a cell line derived from pheochromocytoma of the rat adrenal medulla; HeLa: human cervical cancer cell line; SK-LMS-1: human leiomyosarcoma cell line; MCF-7: breast cance cell line; LNCaP: human prostate adenocarcinoma cell line; PC3: human prostate adenocarcinoma cell line; DU145: human prostate carcinoma cell line; TGW: human 38

neuroblastoma cell line; VL-8, A-549, VL-4 and VL-2: non–small cell lung cancer (NSCLC) cell lines; SW13: adrenal cortex adenocarcinoma cell line; Huh7 and SNU449: human hepatocellular carcinoma cell lines; PANC-1: human pancreatic epithelioid carcinoma cell line; Nalm-6: pre–B-cell tumor cell line; PH3MT: HRas-expressing derivative of MSU1.1 human fibroblast cell line; H157and H2122: non–small cell lung cancer (NSCLC) cell lines; HCT-116: human colon cancer cell line; SW480-ADH: human colon cancer cell line; RD and TE381T: embryonic rhabdomyosarcoma cell lines; HLE: human hepatocellular carcinoma cell line

In a study by Impagnatiello et al (Impagnatiello et al., 2001) indicating anti-proliferative effects of the overexpressed Spry1 and Spry2 on endothelial cells in the presence of FGF, VEGF and EGF, while FGF- and VEGF-induced activation of ERK were repressed by Sprouty, EGF-activated ERK was left unaffected. It was revealed in a study by Sasaki et al (Sasaki et al., 2001) that the expression of Spry2 and Spry4 in HEK293 cells inhibited FGF-induced ERK signaling but did not affect EGF or PDBu activation of ERK. Conversely, the expression of dominant negative mutants of Spry2 and Spry4 enhanced and prolonged FGF, but not EGF, activation of ERK. They later found that Spry4 suppresses VEGF-induced, Ras-independent activation of Raf1 but does not affect the Ras-dependent cascade induced by EGF (Sasaki et al., 2003). Furthermore, evidence supports a positive feedback loop whereby EGF stimulation of ERK signaling is potentiated and sustained by Sprouty. This paradoxical effect was initially investigated by Wong et al (Wong et al., 2002) in PC12 cells employed as a proliferation/differentiation responsive model. It is known that FGF and NGF induce differentiation of PC12 cells into a neuron-like phenotype via sustained activation of ERK whereas EGF stimulation transiently activates ERK and thus promotes cell proliferation (Kao et al., 2001; Marshall, 1995). Wong et al reported that Spry1 and Spry2 inhibited differentiation of PC12 cells induced by FGF-activated ERK, yet they augmented ERK activity in response to EGF and hence promoted differentiation of PC12 cells (Wong et al., 2002). This effect represents an intriguing role of Sprouty in protecting EGFR which will be discussed later. Using NIH3T3 and PC12 cells transfected with Spry1 or Spry2, Gross et al (Gross et al., 2001) observed that Sprouty restricted cell proliferation and growth factor-induced differentiation, but did not promote apoptosis. The investigators found that Spry1 and Spry2 impeded FGF or PDGF stimulation of ERK but did not affect phosphatidylinositol 3-kinase (PI3K)/AKT pathway through which many RTK-mediated survival signals are relayed. In another study, De Alvaro et al indicated that FGF stimulation of C2C12 myoblasts induces proliferation and a differentiation-defective phenotype associated with sustained activation of ERK and lack of activation of AKT. Overexpressed Spry2, however, conferred myogenic differentiation properties that was accompanied by repression of ERK and activation of AKT (de Alvaro et al., 2005).

1.2.2.1.3 Transcriptional regulation of the Sprouty expression 40

ERK pathway is known to generally upregulate Sprouty (Ozaki et al., 2001). However, inducibility of the Sprouty isoforms in response to the growth factor stimulation may vary in a cell type- and context-dependent manner. In an initial study by Ozaki et al, the expression of the Sprouty genes, including Spry1, was shown to be positively regulated by ERK (Ozaki et al., 2001). Parallel studies consistently reported that the expression of Spry2 and Spry4 was rapidly induced by growth factors in fibroblasts (Gross et al., 2001), endothelial cells (Impagnatiello et al., 2001) and HEK293 cells (Sasaki et al., 2001), yet concomitant downregulation of Spry1 was observed (Gross et al., 2001; Impagnatiello et al., 2001). Moreover, Kral et al (Kral et al., 2013) demonstrated that neither growth factor stimulation nor Ras activation increased the Spry1 protein levels in WI38 normal human lung fibroblasts. Since Spry1 in their cell cycle analysis with WI38 cells was constantly expressed, they concluded that mitogenic signaling is not sufficient to modulate the Spry1 expression and that Spry1, as appeared in earlier studies (Gross et al., 2003; Hausott et al., 2009; Shea et al., 2010), is more likely modulated by differentiation processes. In agreement, partner pathways and mechanisms, too, have been shown to play a role in modulating transcriptional expression of the Sprouty proteins. Choi et al (Choi et al., 2006) indicated that Spry1 is the only Sprouty isoform induced by T cell receptor (TCR) stimulation in murine CD4+ T cells and that ensuing expression of Spry1 with dual output impacts TCR signaling depending on their differentiation state. In contrast, Frank et al observed in mouse splenic B cells that combined activation of CD40 and B cell receptor (BCR), known as a stimulator of B-cell proliferation and survival, induces Spry2, but not Spry1, through an ERK-dependent negative feedback loop which attenuates activation of ERK, thereby implicating Spry2 in regulating antigen-induced expansion of mature B cells (Frank et al., 2009). Ding et al reported that transforming growth factor-beta1 (TGFβ1) signaling downregulates Spry2 protein in Swiss 3T3 cells in a MAPK-independent manner with possible involvement of Smad pathway (Ding et al., 2007). Later, they also implicated tumor necrosis factor-alpha (TNF-α) signaling in the Spry2 downregulation via p38 MAPK led to apoptosis of Swiss 3T3 fibroblasts and MLE15 lung epithelial cells (Ding and Warburton, 2008). Different growth factor isoforms may also variably stimulate RTK induction of the Sprouty expression. Jiang et al observed in granulosa cells that while FGF1, FGF4 and FGF8 enhanced the expression of Spry2 and Spry4, and FGF8 additionally increased the abundance of Spry1 mRNA, FGF10 and FGF18 failed to 41

induce the Sprouty expression (Jiang et al., 2013; Jiang and Price, 2012). Moreover, the presence of GC-rich regions in Spry1 (Gross et al., 2003), Spry2 (Ding et al., 2003) and Spry4 (Ding et al., 2004) promoters suggests spatiotemporal regulation of the Sprouty expression by tissue-specific transcription factors. Accordingly, the transcription factors Wilms Tumor suppressor 1 (WT1) (Gross et al., 2003), cAMP response element- binding protein (CREB) and specificity protein 1 (SP1) (Gross et al., 2007) and peroxisome proliferator-activated receptor gamma (PPARγ) (Tennis et al., 2010) have been implicated in normal development of kidney and central nervous system as well as in inhibition of lung tumorigenesis via activating Spry1, Spry2 and Spry4 promoters, respectively. Also, Sabatel et al identified Spry1 as a target of the angiostatic agent 16K prolactin which was shown to induce NF-kappa B-dependent upregulation of Spry1 in primary and human endothelial cells. They showed that Spry1 silencing protects endothelial cells from apoptosis and induces endothelial cell adhesion, migration, and tube formation and argued that Spry1 acts as an endogenous inhibitor of angiogenesis (Sabatel et al., 2010).

1.2.2.1.4 Modulation of the Sprouty stability by post-translational mechanisms

Apart from transcriptional regulation of the protein expression, intracellular level of Sprouty is post-translationally controlled, as well. Polyubiquitylation and proteasomal degradation of active Sprouty mediated by the E3 ubiquitin ligase c-Cbl is a tyrosine phosphorylation-dependent process that temporally limits the Sprouty intervention (Hall et al., 2003; Rubin et al., 2003). Mason et al showed that although Spry2/c-Cbl complex formation is dispensable for the inhibitory effect of Spry2 on the FGF-activated ERK, it mediates degradation of Spry2 in response to FGF. Thus, Spry2 accumulates to higher levels and inhibits FGF-induced signaling more efficiently in c-Cbl-null mouse embryonic fibroblasts (MEFs) than in control MEFs (Mason et al., 2004; Mason et al., 2006). Reporting bimodal expression of Spry2 along with sustained elevation of Spry4 during cell cycle progression, Mayer et al (Mayer et al., 2010) indicated that second phase in the expression profile of Spry2 as transient attenuation of the protein expression during late G1 is solely dependent on cell cycle-specific ubiquitination by c- Cbl. DaSilva et al (DaSilva et al., 2006) showed that serine phosphorylation on Ser112 and Ser121 by MAPK-interacting kinase 1 (Mnk1) provides Spry2 with balanced phosphorylation of Tyr55 that leads to the protein stabilization. As such, mutation of 42

theses serine residues or inhibition of Mnk1 augmented RTK-mediated phosphorylation of Tyr55, thereby enhancing c-Cbl-mediated degradation of the protein. Edwin et al (Edwin et al., 2010) reported that the HECT domain-containing E3 ubiquitin ligase Nedd4 binds and polyubiquitinates Spry2 to regulates its cellular content along with its ability to modulate RTK signaling. They found that Nedd4 requires Mnk2-dependent phosphorylation of Ser112/Ser121 for its interaction with Spry2. Another E3 ubiquitin ligase, Seven in Absentia homolog 2 (Siah2), has also been implicated in post- translational regulation of the Sprouty content. Nadeau et al (Nadeau et al., 2007) indicated that co-expression of Siah2 resulted in proteasomal degradation of Spry1, Spry2, and, to a lesser extent, Spry4 in a tyrosine phosphorylation-independent manner. As with c-Cbl, it was shown that RING finger domain of Siah2 binds the N-terminal domain of Spry2 to mediate their interaction. Consistently, it was later reported that a dominant-negative Siah2 RING mutant primarily increased the Sprouty content and activity (Qi et al., 2008). Furthermore, it was demonstrated in a study by Ding et al on Swiss 3T3 cells that TGFβ1 not only downregulates the expression of Spry2, but also induces the protein degradation via a lysosome-dependent pathway (Ding et al., 2007). They concluded that downregulation of Spry2 by TGFβ1 at transcriptional and post- translational levels lays a basis for crosstalk between TGFβ1 signaling and EGF- as well as FGF-induced ERK in mesenchymal cells. Haigal et al showed that hypoxia increased the Spry4 expression in several cell types through HIF-dependent transcription as well as increased mRNA stability (Haigl et al., 2010). Furthermore, Anderson et al (Anderson et al., 2011) demonstrated that prolyl hydroxylation of Spry2 by prolyl hydroxylase domain proteins (PHDs) during normoxia targets it for recognition and ubiquitination by von Hippel-Lindau (pVHL)-associated E3 ubiquitin ligase.

Transcriptional and post-translational regulation of the Sprouty cellular content is illustrated in Figure 1-3. 43

Figure 1-3 Representative regulators of the Sprouty cellular content at transcriptional and post-translational level, irrespective of the Sprouty isoform and cell type. MAPK/ERK is the main pathway to upregulate Sprouty. Transcription factors WT1 and PPARγ and Wnt/β-catenin signaling pathway have also been shown to upregulate Sprouty. miR-21 is a cancer-associated microRNA that targets and negatively regulates the Sprouty genes. TGFβ1 not only downregulates the expression of Sprouty, but also induces the protein degradation via a lysosome-dependent pathway. E3 ubiquitin ligases c-Cbl, Siah2, NEDD4 and pVHL induce degradation of Sprouty to regulate its cellular content. PP2A competes with c-Cbl for binding to Sprouty, thereby inhibiting c-Cbl-mediated degradation of Sprouty. Mnk1 is a positive regulator of the Sprouty stability through serine phosphorylation. c-Cbl: canonical Casitas B-lineage lymphoma; FZD receptor: Frizzled receptor; miR- 21: microRNA 21; Mnk1: MAPK-interacting kinase 1; NEDD4: neural precursor cell 44

expressed, developmentally down-regulated 4; PP2A: protein phosphatase 2A; PPARγ: peroxisome proliferator-activated receptor gamma; RTK: receptor tyrosine kinase; Siah2: Seven in Absentia homolog 2; WT1: Wilms Tumor suppressor 1; CS rearrangement: cytoskeletal rearrangement. In this figure, C- and N-terminus of the Sprouty molecule symbol are shown in white and blue, respectively. Figure created by author published in Cancer and Metastasis Reviews 2014, 33(2-3): 695-720.

1.2.2.1.5 Regulation of the Sprouty activity

Sprouty trafficking to and from the plasma membrane regulates subcellular localization of Sprouty and hence keeps the protein functionality under spatiotemporal control. In unstimulated cells, Sprouty is distributed throughout the cytosol, with hSpry2 being co- localized with microtubules, as well. Upon growth factor activation, Sprouty translocates to the plasma membrane, notably ruffles, where it becomes activated in association with phosphatidylinositol 4,5-bisphosphate (PIP2) and the caveolin-1 (Cav- 1) (Hanafusa et al., 2002; Impagnatiello et al., 2001; Lim et al., 2000; Lim et al., 2002). Lim et al (Lim et al., 2002) demonstrated that Sprouty binds PIP2 through its CRD domain and that this phenomenon is an essential process for regulation of ERK signaling. As the major structural protein of caveolae (specialized plasma membrane invaginations involved in multiple cellular functions, including signal transduction), Cav-1 similarly inhibits growth factor activation of ERK in a cell density-dependent manner. At higher cell densities, Sprouty/Cav-1 interaction modulates signaling in a growth factor- and Sprouty isoform-specific manner. At lower cell densities, however, Cav-1 inhibits the Sprouty function (Cabrita et al., 2006). Moreover, Hwangpo et al indicated the interaction between Spry2 and G protein αo/G potein-regulated inducer of neurite outgrowth (Gαo/GRIN) pathway in modulating Spry2 repression of ERK (Hwangpo et al., 2012). While GRIN was shown to bind and sequester Spry2, the activated Gαo interacted with GRIN to release Spry2.

Sprouty phosphorylation on the conserved tyrosine is considered as an indispensible prerequisite for the regulatory function of Spry1 and Spry2 (Fong et al., 2003; Hanafusa et al., 2002), but not for that of Spry4 (Alsina et al., 2012; Mason et al., 2004; Sasaki et al., 2003). This process, however, is variably induced by different growth factors. Using NIH3T3 fibroblasts transfected with Spry1, Spry2 and Spry4 (Mason et al., 2004), Mason et al observed that Spry1, Spry2, but not Spry4, undergo tyrosine 45

phosphorylation after growth factor stimulation. Moreover, while FGF induced tyrosine phosphorylation in both Spry1 and Spry2, PDGF and EGF induced it in Spry1 and Spry2, respectively. Through a time course analysis, they also revealed that FGF- induced tyrosine phosphorylation of Spry1 is kinetically different from that of Spry2. In agreement with earlier reports (Fong et al., 2003; Hall et al., 2003), the investigators found that tyrosine phosphorylation regulates interaction of Sprouty with c-Cbl and concluded that tyrosine phosphorylation serves as a dual feedback loop which, on the one hand, activates Sprouty inhibition of ERK and, on the other hand, promotes c-Cbl- mediated ubiquitination and degradation of Sprouty and thus terminates signaling inhibition. Functional significance of Sprouty phosphorylation on other residues than the conserved tyrosine has also been studied. Rubin et al (Rubin et al., 2005) observed in HEK293T cells that FGF, but not EGF, activation of ERK is inhibited by Spry2 and that only FGF can induce significant phosphorylation of the C-terminal tyrosines, in particular Tyr227. On this basis, they postulated that C-terminal tyrosine phosphorylation modulates the specificity of the Spry2 inhibition of different ERKs. Results from a study by Aranda et al supported a functional interaction between dual- specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) and Spry2 where DYRK1A regulates the phosphorylation status of Spry2. Since mutation of Thr75 on Spry2, identified as a phosphorylation site for DYRK1A, enhanced the repressive function of Spry2 on FGF-induced ERK signaling, they suggested that DYRK1A is a negative regulator of the Sprouty activity by threonine phosphorylation.

Sprouty dephosphorylation by phosphatases, including protein phosphatase 2A (PP2A) and Src homology-2 containing phosphotyrosine phosphatase (SHP2), can differentially regulate the protein activity. In unstimulated cells, Sprouty is phosphorylated predominantly on serine residues (Impagnatiello et al., 2001). Lao et al found that PP2A binds and dephosphorylates Spry2 at Ser112 and Ser115 upon FGF stimulation (Lao et al., 2007). They postulated that Spry2 serine dephosphorylation alters the tertiary structure of the protein and thereby exposes the cryptic proline-rich motif in the Spry2 C-terminus which they had earlier identified as a binding site for Grb2 (Lao et al., 2006), enabling Spry2 to potently inhibit FGF activation of ERK. They also found that PP2A and c-Cbl compete for binding to Spry2 at an overlapping sequence to fine-tune its activity. They later reported that testicular protein kinase 1 (TESK1) attenuates the 46

ability of Spry2 to inhibit growth factor actions in a way independent of its kinase activity and primarily by interfering with Spry2/Grb2 interactions and dephosphorylation of serine residues by PP2A (Chandramouli et al., 2008). SHP2 has been implicated in regulating the Sprouty activity through dephosphorylation of the phosphotyrosines and subsequent dissociation of Sprouty from Grb2 that positively regulates growth factor activation of ERK (Hanafusa et al., 2004; Jarvis et al., 2006; Jung et al., 2012). Pan et al showed that SHP2 regulates Sprouty positively at the transcriptional level and negatively at the post-translational level and concluded that dynamic regulation of Sprouty by SHP2 might be important not only for modulating Ras signaling in developmental processes, but also for RTK signaling in general (Pan et al., 2010). On the other hand, Sprouty may mediate its actions in part by increasing active contents of such phosphatases as protein tyrosine phosphatase 1B (PTP1B) and phosphatase and tensin homolog (PTEN). Regulating subcellular localization of PTP1B, Sprouty increases PTP1B soluble content which has been shown to mediate, and mimic, Sprouty-induced repression of cell adhesion and migration (Jaggi et al., 2008; Yigzaw et al., 2003). Poppleton et al reported that Sprouty regulates cell migration by inhibiting Rac1 activation which they postulated is mediated in part by PTP1B dephosphorylation of cellular proteins and, subsequently, decreased amount of phosphorylated p130Cas or phosphatidylinositol 3,4,5-trisphosphate (PIP3) known as Rac1 activators (Poppleton et al., 2004). In a study by Edwin et al (Edwin et al., 2006), where Spry2 unexpectedly inhibited EGF activation of AKT and exhibited no significant effect on EGF activation of EGFR and ERK, they observed that Spry2 increases the amount and activity of PTEN that was found necessary for Sprouty to attenuate EGF-activated AKT and to inhibit cell proliferation. Beyond its cytoplasmic role in negatively regulating PI3K/AKT, PTEN is phosphorylated and accumulated in the nucleus in response to the Spry2 deficiency to induce p53-mediated growth arrest independently of its phosphatase activity (Patel et al., 2013). This process is part of a regulatory mechanism involving Spry2 interaction with PP2A and PTEN for inhibition of tumorigenesis which will be discussed later.

1.2.2.1.6 Regulation of RTK activity and stability

Sprouty might regulate activity and stability of RTKs through interaction with mechanisms involved in reversible (transient) and irreversible (definitive) inhibition of 47

ERK on the basis of dephosphorylation (inactivation) and degradation (downregulation) of RTKs, respectively. Among the reversible inhibitory mechanisms is the RTK dephosphorylation by PTP1B that provides spatiotemporal regulation of the RTK activity (Haj et al., 2002). Despite the fact that PTP1B, as described earlier, is regulated by Sprouty to control some cellular functions on the basis of protein tyrosine phosphorylation, there is no evidence of direct interaction between Sprouty and PTP1B in RTK dephosphorylation. In contrast, it is well documented that Sprouty interferes with c-Cbl-mediated downregulation of RTK in a growth factor-dependent manner. Sprouty evidently inhibits c-Cbl-induced ubiquitination and degradation of EGFR, thereby sustaining EGF-activated ERK (Egan et al., 2002; Fong et al., 2003; Hall et al., 2003; Wong et al., 2002; Wong et al., 2001). Rubin et al (Rubin et al., 2003) postulated that EGFR activation, followed by Spry2 phosphorylation and its association with c- Cbl, initiates a competitive process where c-Cbl promotes Spry2 polyubiquitination and degradation and Spry2, conversely, sequesters active c-Cbl molecules and impedes receptor ubiquitination and degradation. They concluded that Sprouty fine-tunes EGF signaling through interlinked positive and negative feedback loops. Moreover, Edwin and Patel (Edwin and Patel, 2008) suggested a novel role for Sprouty in regulating cellular apoptosis where endogenous Sprouty, by sequestering c-Cbl, augments EGFR activation of ERK and AKT pathways and the resultant anti-apoptotic signaling. A c- Cbl-independent mechanism for Sprouty-induced upregulation of EGFR was identified by Kim et al (Kim et al., 2007). They reported that Spry2 interferes with the trafficking of activated EGFR from early to late endosomes. To do so, Spry2 was postulated to bind the endocytic regulatory protein hepatocyte growth factor-regulated tyrosine kinase substrate (Hrs) to prevent it from interaction with the tumor susceptibility gene 101 protein (Tsg101) which is required for EGFR transport.

1.2.2.1.7 Structural variation and functional divergence of the Sprouty proteins

The C-terminal cysteine-rich (CRD) domain of Sprouty is a highly conserved region implicated for such key functions of the protein as membrane translocation and ERK inhibition (Hall et al., 2003; Lim et al., 2000; Yigzaw et al., 2001). The N-terminal tyrosines Tyr53 (in Spry1 and Spry4) and Tyr55 (in Spry2) are also conserved residues crucial for the Sprouty functionality to the extent that their corresponding dominant- negative mutants fail to attenuate ERK signaling and even repress the function of the 48

wild type (WT) protein (Alsina et al., 2012; Hanafusa et al., 2002; Mason et al., 2004; Sasaki et al., 2003; Sasaki et al., 2001). Less homologous sequences, however, have been localized in the C-terminal as well as N-terminal regions of the Sprouty isoforms that contribute to their differential interaction with signaling molecules and molecular partners and accounts, in part, for their functional divergence. Sprouty isoforms display differential affinity for different molecular targets upstream or downstream of Ras or even beyond ERK. While interacting with Raf kinases (Aranda et al., 2008; Reich et al., 1999; Tsavachidou et al., 2004; Yusoff et al., 2002), Spry2 exhibits the highest affinity for Grb2. Lao et al identified an exclusive proline-rich sequence in the Spry2 C- terminus which was found as a binding site for Grb2 responsible for differential interaction of Spry2, as compared to Spry1 and Spry4, with Grb2 (Lao et al., 2006). Sasaki et al (Sasaki et al., 2003) demonstrated that the Spry4 mutants lacking the N- terminal conserved tyrosine residue necessary for suppressing FGF signaling still inhibit the VEGF-A-induced activation of ERK in a Ras-independent manner by binding through the CRD domain to Raf1, indicating that Spry4 differentially regulates different ERK pathways through distinct action points. Later in a study by Ayada et al (Ayada et al., 2009), the CRD domain was further implicated for the Spry4 functions in regulating the VEGF-A-induced, protein kinase C- (PKC-) dependent activation of ERK as well as various types of PLC-dependent signaling. The investigators indicated that Spry4 interacts through its CRD domain with PIP2 to inhibit PIP2 hydrolysis and ensuing activation of PKC in response to VEGF-A. Also found to impact the PKC downstream signals, Spry4 was introduced as a general inhibitor of phospholipase C (PLC) and PLC-dependent signaling with regulatory functions broader than previously thought (Ayada et al., 2009). In a parallel study investigating the physiological function of Spry4 as an angiogenic regulator (Taniguchi et al., 2009), they indicated that Spry4 suppresses Ras-independent angiogenesis stimulated by VEGF-A and sphingosine-1- phosphate (S1P) while it does not affect Ras-dependent VEGF-C signaling. TESK1, a cofilin kinase with critical role in integrin-mediated actin cytoskeletal reorganization and cell spreading, was identified by Tsumura et al (Tsumura et al., 2005) as a target for the Spry4 CRD domain through which Spry4 binds TESK1 and inhibits cofilin phosphorylation, thereby negatively regulating cell spreading and migration independently of its regulatory effect on ERK. 49

Variation in the binding sites for such molecular partners as c-Cbl, CIN85 and Cav-1 is also documented. Known to mediate monoubiquitination of activated RTKs (Haglund et al., 2003a; Haglund et al., 2003b; Mosesson et al., 2003), c-Cbl interacts with endocytic scaffold complexes, including CIN85/endophilins, to facilitate RTK endocytosis and degradation (Kowanetz et al., 2003; Petrelli et al., 2002; Soubeyran et al., 2002). The N- terminal c-Cbl binding motif is shared by Sprouty isoforms. However, Spry2 exhibits the highest binding affinity for c-Cbl and Spry4 weakly binds it (Ng et al., 2008; Wong et al., 2001). Mason et al (Mason et al., 2004) found that tyrosine phosphorylation is essential for Sprouty association with c-Cbl and that a less homologous sequence within the c-Cbl binding motif of Spry4 prevents it from phosphorylation and binding to c-Cbl. Furthermore, principal CIN85-binding sites are found only in Spry1 and Spry2. Haglund et al showed that Spry2 associates with c-Cbl and CIN85 upon EGF stimulation to inhibit EGFR endocytosis and degradation whereas Spry4 fails to inhibit downregulation of EGFR (Haglund et al., 2005). Sprouty isoforms have also shown differential interaction with Cav-1. Cabrita et al showed that while all four Sprouty isoforms can bind Cav-1 through their conserved C-terminal domain, they exhibit differential cooperativity with Cav-1 in repressing ERK (Cabrita et al., 2006). When either Spry1 or Spry3 were expressed in the presence of Cav-1, FGF-induced ERK activation was synergistically attenuated. However, when either Spry2 or Spry4 were present along with Cav-1, ERK activation increased slightly compared with when Cav-1 was present by itself, suggesting a decrease in the inhibitory activity of Cav-1. In addition, it was shown in another study that inhibitory function of the Sprouty proteins is enhanced through cooperative interaction among the protein isoforms. Ozaki et al (Ozaki et al., 2005) found that all four Sprouty isoforms are able to form hetero- and homo-oligomers through their C-terminal domains. They observed that while Spry1 and Spry4 interact with Grb2 and Sos1, respectively, the hetero-oligomer formed by the two exhibits the most potent inhibitory effect on FGF-activated ERK.

1.2.3 Deregulation of Sprouty in cancer

Given their critical role as modulators of MAPK/ERK and mediators of the crosstalk between ERK and other signaling pathways for maintaining homeostatic control of cellular behavior, the Sprouty proteins are conceivably expected to be deregulated in malignant conditions. On this basis, deregulation of Sprouty in a variety of cancers has 50

been studied by different investigators and its utility as a biological marker (Barbachano et al., 2010; Faratian et al., 2011; Feng et al., 2010; Frolov et al., 2003; Lo et al., 2004; Patel et al., 2013; Sirivatanauksorn et al., 2012; Song et al., 2012), a tumor suppressor (Dorman et al., 2012; Feng et al., 2011; Lee et al., 2008; Macia et al., 2012; McKie et al., 2005; Schutzman and Martin, 2012; Sutterluty et al., 2007; Velasco et al., 2011; Winn et al., 2005) or even an oncogene (Barbachano et al., 2010; Holgren et al., 2010; Kanetsky et al., 2009; Lito et al., 2009; Lito et al., 2008; Schaaf et al., 2010) with application in targeted approaches (Barbachano et al., 2010; Feng et al., 2010; Katoh and Katoh, 2006; Kwabi-Addo et al., 2004; Minowada and Miller, 2009; Song et al., 2012; Sutterluty et al., 2007; Tennis et al., 2010) has been argued which are discussed below.

1.2.3.1 Breast Cancer

In a study by Lo et al in 2004, while mSpry1 and mSpry2 were implicated in the breast development during puberty and pregnancy (Lo et al., 2004), it was revealed in Cancer Profiling Array containing pairs of cDNAs generated from 50 matched pairs of normal and cancer tissues that hSpry1 and hSpry2 were consistently downregulated in breast cancer. Real-time PCR confirmed that more than 90% of the patient samples demonstrated suppressed expression of Spry1 and Spry2. Neither DNA methylation nor histone hypoacetylation was found to be responsible for the Sprouty downregulation by an epigenetic silencing. They finally indicated that the MCF-7 breast cancer cells transfected with a dominant-negative mutant of Spry2 proliferated faster and exhibited anchorage-independent growth in vitro and formed larger tumors in vivo. Following a meta-analysis of the gene expression profiles of a total of 1107 tumors combined with a further analysis of two single datasets, Faratian et al (Faratian et al., 2011) reported in 2011 that Spry1, Spry2 and Spry4 were differentially expressed across clinicopathological subgroups of the breast cancer and that low Spry2 expression was associated with high expression of the human epidermal growth factor receptor 2 gene (HER2). Spry2 was found as an independent prognostic factor that may identity breast cancer patients with a more favorable outcome even when tumors exhibit poor pathological features. Since Spry2 was shown to act synergistically with the HRE2- targeting trastuzumab to reduce cell viability in vitro, the expression of Spry2 was quantified in a cohort of 122 trastuzumab-treated patients, revealing that low Spry2 51

expression was associated with poor outcome and increased risk of death. Hence, the investigators argued for the usefulness of Spry2 in stratifying patients for treatment with trastuzumab.

Implicating the urokinase-type plasminogen activator receptor (uPAR) as a partner protein interacting with hSpry1 (Mekkawy et al., 2010), Mekkawy et al reported that hSpry1 colocalizes with uPAR upon stimulation with EGF and urokinase-type plasminogen activator (uPA) and suppresses uPAR-mediated migration and invasion of the MDA-MB-231 breast cancer cells (Mekkawy and Morris, 2013). Vanas et al (Vanas et al., 2014) recently indicated that different breast cancer cell lines differentially express Sprouty as compared with normal mammary epithelial cells. However, a correlation between the expression profiles of Spry2 and Spry4 was found. They also reported that ectopic expression of Spry4 inhibited cell proliferation independently of its endogenous expression level. Furthermore, increased Spry4 interfered with serum- induced activation of ERK and inhibited cell migration.

1.2.3.2 Prostate cancer

In 2004, Kwabi-Adoo et al (Kwabi-Addo et al., 2004) reported the result of immunohistochemical analysis of 407 tissue microarrays containing prostate cancer as well as matched normal tissue cores, showing downregulation of hSpry1 in approximately 40% of the prostate cancer cases studied. This finding was corroborated by real-time PCR where Spry1 mRNA levels were significantly decreased in 16 out of 20 prostate cancer tissue samples in comparison with the normal tissue. In their in vitro study, the investigators interestingly observed that the prostate cancer cells LNCaP and PC3, in contrast to primary epithelial cells, did not show induction of the Spry1 expression at mRNA and protein levels in response to FGF2 stimulation. They also reported that Spry1 transfection of LNCaP and PC3 cells had an inhibitory effect on colony formation and cell proliferation. In agreement with earlier studies showing upregulation of FGFs in prostate cancer, Kwabi-Adoo et al concluded that Spry1 downregulation may lead to the unrestrained FGF-induced signal transduction and hence tumor progression. Later, McKie et al (McKie et al., 2005) observed that Spry2 mRNA is downregulated in invasive prostate cancer cell lines as well as in clinically high-grade prostate cancers when compared to benign prostatic hyperplasia (BPH) and 52

well-differentiated prostate tumors. Identifying hypermethylated CpG islands in hSpry2 gene correlated with suppressed expression of hSpry2 mRNA, they implicated epigenetic inactivation as the main mechanism for the hSpry2 downregulation in prostate cancer. As later reported by Kwabi-Adoo et al, this mechanism is also responsible for downregulation of Spry1 in prostate cancer (Kwabi-Addo et al., 2009). Data from an integrated genomic profiling of 218 prostate tumors by Taylor et al revealed that Spry1 and Spry2 genes are inactivated in 15% and 18% of the primary cancer as well as in 42% and 74% of the metastatic disease, respectively (Taylor et al., 2010). Through in situ hybridization on 14 prostate tissue samples and quantitative real- time PCR analysis in 25 pairs of matched normal and tumor tissue samples, downregulation of Spry4 in a subset of prostate cancers was reported by Wang et al (Wang et al., 2006). Their epigenetic analysis revealed methylation of a CpG island in the 5’ regulatory region of Spry4 in more than a half of all prostate cancer DNA samples studied which was significantly correlated with decreased expression of Spry4 mRNA. They also demonstrated that Spry4, unlike Spry1, does not hinder cell growth but rather inhibits cell migration, suggesting that Spry1 and Spry4 perform different functions in prostate cancer. Later, Fritzsche et al (Fritzsche et al., 2006) observed through microarray analysis of microdissected prostate tissue specimens a coordinated, yet modest, downregulation of both Spry1 and Spry2 mRNAs gradually increasing from hyperplasia to severe prostatic intraepithelial neoplasia (PIN) to cancer. Spry2 mRNA downregulation was confirmed in an independent, larger series of macrodissected tumors by quantitative RT-PCR. Unlike McKie et al, however, they reported that Spry2 downregulation in prostate cancer is independent of DNA methylation.

ERK and PI3K/AKT has been identified as the two most commonly altered pathways in prostate cancer with alteration frequency of 42-43% in primary and 90-100% in metastatic prostate cancer (Taylor et al., 2010). Activation of ERK and PI3K/AKT by aberrant RTK signaling has been implicated in the development of aggressive prostate cancer (Yap et al., 2011). On this basis, Sprouty interactions with other feedback regulators of the two pathways and its significance in prostate cancer tumorigenesis and progression have been explored by some investigators. PTEN has been reported to be inactivated in 4% of the primary prostate cancer as well as in 42% of the metastatic disease (Taylor et al., 2010). A key genetic interaction between the Sprouty and PTEN has been reported. While Pten heterozygosity per se results in low-grade PIN in mice 53

(Di Cristofano et al., 1998), Schutzman et al (Schutzman and Martin, 2012) showed that concomitant inactivation of Sprouty accelerated emergence of PIN and promoted development of more extensive, high-grade phenotype along with the transition to invasive cancer. Conversely, expression of a Spry2 gain-of-function transgene in the context of Pten homozygosity suppressed the AKT hyperactivation and the prostate tumorigenesis resulted from Pten loss-of-function, implicating Sprouty genes in regulation of ERK and PI3K/AKT pathways in prostate cancer. They suggested that the expression status of PTEN and Sprouty genes in prostate biopsies from men at risk for prostate cancer could potentially help to risk-stratify patients with PIN. Patel et al later indicated that Sprouty status along with that of PTEN and PP2A collectively represents an important determinant of the prostate cancer progression (Patel et al., 2013). They showed in a coherent set of in vitro and in vivo systems that although Spry2 deficiency is sufficient to activate both PI3K/AKT and ERK cascades, it is insufficient to drive tumorigenesis, with Spry2-deficient cells exhibiting PTEN-mediated growth arrest. As follows, it was shown that the Spry2 deficiency-induced growth arrest mechanistically involves PTEN, PP2A, glycogen synthase kinase 3 beta (GSK3B), p53 and reactive oxygen species (ROS). By enhancing RTK activation, Spry2 deficiency increases intracellular ROS which subsequently activates PP2A. PP2A then dephosphorylates and activates GSK3B that drives phosphorylation and nuclear accumulation of PTEN. Nuclear PTEN eventually promotes growth arrest by induction of p53 and p21, independent of its phosphatase activity. Overall, by introducing a novel, PP2A- dependent tumor suppressor checkpoint, Patel et al identified the cooperative role of concomitantly inactivated Spry2, PTEN, and PP2A to drive the prostate cancer progression. Hence, they postulated that loss of Spry2 may represent an early event in prostate carcinogenesis compensated by nuclear PTEN-mediated growth arrest which might be subsequently overcome by inactivation of PTEN, TP53, or PP2A.

1.2.3.3 Liver cancer

In a gene expression study by Chen et al in 2002, Spry2 was among the top 600 genes found to be differentially expressed in hepatocellular carcinoma (HCC) compared with non-tumor liver tissue (Chen et al., 2002). In 2006, a more stringent and biologically relevant analytic approach to the same database by Fong et al revealed a consistent downregulation of Spry2 in HCC (Fong et al., 2006). Using in situ hybridization on 54

tissue microarrays from an independent set of patients, they confirmed significantly differential expression of Spry2 in HCC compared with normal or cirrhotic liver tissue. While showing the resemblance between the expression pattern of Spry2 and that of several potential tumor markers in hepatocellular carcinoma, the investigators ruled out loss of heterozygosity (LOH) or the promoter hypermethylation as possible mechanisms responsible for Spry2 downregulation. Moreover, it was shown that Spry2 plays functionally important roles in HCC by inhibiting hepatocyte growth factor (HGF)- induced cell proliferation and ERK activation in the Spry2-overexpressing HCC cells. Identifying Spry2 in their genomic analysis as a downregulated and frequently deleted gene in HCC, Lee et al (Lee et al., 2008) observed in vitro that overexpressed Spry2 inhibits HCC cell growth. Their in vivo study using hydrodynamic transfection not only exhibited, in line with earlier studies (Harada et al., 2002; Harada et al., 2004), the cooperative role of activated Wnt/β-catenin and Ras in induction of HCC, but also revealed that dominant negative Spry2 cooperates with β-catenin to induce development of liver cancer in mice, with tumor cells showing upregulation of ERK and deregulation of genes involving in cell proliferation, apoptosis and angiogenesis. This study suggested that Spry2 might function as a candidate tumor suppressor for HCC. They reported later the synergistic role of Spry2 inactivation and c-Met upregulation in mouse and human hepatocarcinogenesis (Lee et al., 2010b). They observed in a collection of human liver tissue samples significant downregulation of Spry2 protein as well as ubiquitously high expression of c-Met (total and activated) and its downstream effectors (activated Erk and Akt) in most cases of HCC with poorer outcome (HCCP) in the context of WT Ras. The expression of Spry2 was found to be downregulated at transcriptional and post-translational level by promoter hypermethylation, LOH and proteasomal degradation by NEDD4. In vitro, Spry2 overexpression inhibited c-Met- induced cell proliferation as well as ERK and AKT activation whereas loss of Spry2 potentiated c-Met signaling. Their in vivo study with mice hydrodynamically transfected with c-Met and/or a dominant negative mutant form of Spry2 indicated that Spry2 inactivation cooperates with c-Met to induce hepatocarcinogenesis by sustaining proliferation and angiogenesis, suggesting a pivotal oncogenic mechanism responsible for unrestrained activation of ERK and AKT pathways in human hepatocarcinogenesis. By hydrodynamic injection and co-expression of an activated/myristoylated form of Akt (myr-Akt) and a dominant negative Spry2 mutant in the mouse liver, Wang et al (Wang 55

et al., 2012a) later indicated that loss of Spry2 accelerated AKT-induced hepatocarcinogenesis which was associated with activation of ERK pathway and pyruvate kinase M2 (PKM2)-induced glycolysis. In vitro, they found that activation of PKM2 in the HCC cell line HLE transfected with Akt and dominant negative Spry2 is independent of ERK and AKT cascades, collectively implying that loss of Spry2 synergizes with activated AKT to induce rapid hepatocarcinogenesis through the activation of ERK and PKM2 pathways.

Differential expression of the Sprouty homologs in HCC was reported by Sirivatanauksorn et al (Sirivatanauksorn et al., 2012) where paired HCC and non-tumor liver tissue samples from 31 patients were examined by quantitative RT-PCR. Most HCC tissues showed upregulation of Spry1 and downregulation of Spry2 and Spry4 at mRNA level. Moreover, mRNA expression of Spry1, Spry2 and Spry4 in cancerous specimens was significantly different from that in nontumor tissues. The expression of Spry3, however, did not show any significant difference among the samples. Studying the association of the Sprouty gene expression with clinical parameters of HCC, they indicated that the expression of Spry2 was significantly lower in patients with advanced disease and angiolymphatic invasion whereas Spry1 was significantly upregulated in cases without underlying cirrhosis compared with cirrhotic patients. Prognostic significance and clinical relevance of the Spry2 protein expression in HCC was later studied by Song et al (Song et al., 2012). Their initial study in vitro showed that the ratio of phospho-ERK to Spry2 in the HCC cell lines MHCC97L, HCCLM3 and HCCLM6 displayed an elevation concordant with their stepwise metastatic potential. Similarly, the Spry2 expression per se inversely correlated with the metastatic potential of the HCC cells. In their immunohistochemical study, they found that 86.3% of a total of 240 patients exhibited Spry2 downregulation. They reported that Spry2 downregulation accompanied highly malignant clinicopathological features like advanced TNM stages and tumors with vascular invasion and poor differentiation. They found that Spry2-negative patients had poorer survival and increased postoperative recurrence and thereby suggested potential implications of Spry2 as a predictor of the disease prognosis and a biomarker of the treatment sensitivity.

1.2.3.4 Lung cancer 56

Using reverse transcription-PCR and immunohistochemical staining of matched tumor and nontumor samples, Sutterluty et al reported in 2007 that Spry2 expression, but not that of Spry1, is consistently reduced at mRNA and protein levels in non-small cell lung cancer (NSCLC) tissues (Sutterluty et al., 2007). Their in vitro analysis with a panel of NSCLC cell lines revealed that high levels of Spry2 expression were exclusively detected in KRAS-mutated cells and that only few cell lines with reduced Spry2 exhibited Spry2 promoter hypermethylation. Moreover, while ectopic expression of Spry2 inhibited ERK activity and diminished cell migration in NSCLC cells with WT KRAS, but not in those with the mutated one, it significantly reduced cell proliferation in all NSCLC cell lines studied in vitro and blocked tumor formation in mice inoculated with the KRAS-mutated cell line A-549. In addition, even a dominant negative Spry2 mutant defective in antagonizing ERK significantly, although less potently, inhibited cell proliferation in NSCLC cells with or without KRAS mutation. Collectively, they demonstrated that Spry2 downregulation contributes to NSCLC tumorigenesis via ERK- dependent and -independent mechanisms and implicated Spry2 as a tumor suppressor in NSCLC. Consistently, Shaw et al reported that Spry2 functions as a tumor suppressor in the context of a germline oncogenic KRAS mutation -KRASG12D- in which loss of Spry2 increased the number and overall burden of lung tumors in mice (Shaw et al., 2007). This was corroborated by a later report whereby lack of Spry2 expression along with high level of ERK activation was evident in putative tumorigenic cells of KRASG12D-induced neoplasia in mouse lungs (Cho et al., 2011). The role of Spry2 in inhibiting lung tumor development was further confirmed by Minowada et al (Minowada and Miller, 2009) who evaluated consequences of Spry2 overexpression in mouse lung epithelium in the context of urethane-induced tumorigenesis. The chemical carcinogen urethane induces KRAS gain-of-function mutations and lung tumors in mice. The investigators observed that Spry2-overexpressing animals developed significantly fewer and smaller tumors compared with their littermate controls. Since the overexpression of Spry2 did not alter KRAS mutational frequencies, it was suggested that the tumor-suppressing activity of the overexpressed Spry2 might be applied at stages of carcinogenesis subsequent to KRAS mutation.

A putative role for Spry4 as part of Wnt7A/Fzd9 tumor-suppressing pathway was initially suggested by Winn et al where Wnt7A and Fzd9 induced the expression of 57

Spry4 in NSCLC cells (Winn et al., 2005). They subsequently identified PPARγ (Bren- Mattison et al., 2005; Winn et al., 2006) and Spry4 (Tennis et al., 2010) as downstream effectors of Wnt7A/Fzd9 that mediate its antitumorigenic effects. Reporting downregulation of Spry4 in a variety of NSCLCs as well as in dysplastic lung cell lines, Tennis et al showed that Spry4 transfection inhibited NSCLC cell growth, migration, invasion and epithelial-mesenchymal transition. They found that Wnt7A/Fzd9 signaling increases Spry4 promoter activity through PPARγ. Corroborated by their earlier reports (Bren-Mattison et al., 2005; Winn et al., 2005; Winn et al., 2006), Tennis et al concluded that Spry4 represents an inducible effector of the Wnt7A/Fzd9 pathway downstream of PPARγ which restores a nontransformed epithelial phenotype while inhibiting NSCLC cell growth, migration and invasion (Tennis et al., 2010).

1.2.3.5 Colon cancer

In their cDNA array study on different cancers in 2004, Lo et al presented differential expression pattern of Spry2 in 38 matched pairs of normal and tumor samples from colon cancer patients (Lo et al., 2004). In a BLAST search of human ESTs followed by in silico expression analysis, Katoh and Katoh (Katoh and Katoh, 2006) observed that Spry4 mRNA is expressed in colon cancer. Based on the comparative genomics analyses, they characterized Spry4 as the evolutionarily conserved target gene of the Wnt/β-catenin signaling pathway. Implicating Spry4 in Wnt/β-catenin regulation of progenitor cells, they suggested that epigenetic silencing and loss-of-function mutations of Spry4 could lead to carcinogenesis. In a study by Barbachano et al (Barbachano et al., 2010), immunofluorescence analysis of human colon cancer biopsies quantitatively confirmed in 34 patients showed high levels of Spry2 and low levels of E-cadherin in undifferentiated, high-grade tumors in contrast to low levels of Spry2 and high levels of E-cadherin in low-grade specimens. In vitro, Spry2 and E-cadherin exhibited an inverse correlation and reciprocal regulation in colon cancer cells. The investigators found that Spry2 induces the expression of the ZEB1 epithelial-to-mesenchymal transition gene and protein and abrogates the induction of an adhesive epithelial phenotype by 1,25(OH)2D3. Supplemented by a meta-analysis of the data available at the Oncomine database (Rhodes et al., 2004) in favor of higher expression of Spry2 in colon tumors compared with other neoplasias, their results suggested a tumorigenic action and a potential role as a tumor marker for Spry2 in colon cancer. Examining a low number of 58

matched colon cancer samples, Holgren et al (Holgren et al., 2010) also reported upregulation of Spry2, as well as c-Met, at mRNA and protein levels. In vitro, Spry2 upregulation in the KRAS-mutated cell line HCT-116 significantly increased cell proliferation, accelerated cell cycle transition and enhanced cell migration and invasion which were attributed, at least in part, to activation of HGF/c-Met axis and its downstream effectors Akt and Erk. They also demonstrated that Spry-2 knockdown significantly inhibited cell invasion in both WT- and mutant KRAS-expressing cell lines. With Spry2 transfectants forming significantly larger xenografts with higher metastatic potential in vivo, they concluded that Spry2 may control metastatic potential of colon cancer cells, at least in part, by c-Met upregulation. Examining primary tumor samples from 113 patients with colorectal cancer, Watanabe et al (Watanabe et al., 2011) later observed that KRAS mutant tumors (31%) exhibited a distinct gene expression signature compared with their WT counterparts (69%) where Spry2 was among the 30 genes upregulated in the KRAS mutants. They found that the discriminating genes identified were related to not only K-Ras/ERK but other signaling pathways such as Wnt/β-catenin, NF-kappa B activation and TGFβ signaling, thereby suggesting a crosstalk between K-Ras-mediated signaling and other pathways in colorectal cancer.

In contrast, Feng et al reported in 2010 that Spry2 may be a potential biomarker in predicting the response to anti-EGFR treatment in colon cancer (Feng et al., 2010). They showed that the expression of Spry2 positively correlates with the sensitivity of colon cancer cells to the EGFR inhibitor gefitinib and that Spry2 can enhance the response of colon cancer cells to gefitinib by increasing the expression of phosphorylated EGFR, total EGFR and PTEN. They later reported downregulation of Spry2 in association with colon cancer progression and suggested a tumor suppressor role for Spry2 (Feng et al., 2011). By real-time quantitative RT-PCR on mRNA isolated from normal and tumor tissues of 67 patients with colon cancer, they showed that Spry2 was downregulated in 72.7% (16/22) of stage II, 91.3% (21/23) of stage III and 100% (22/22) of stage IV tumors examined. A negative correlation was also evident between the expression levels of Spry2 and the microRNA miR-21, an indicator of poor survival and poor response to adjuvant chemotherapy in cancer patients. In vitro, overexpressed Spry2 inhibited the growth and migration of HCT116 human colon cancer cells which 59

was concomitantly accompanied by an increase in the expression of PTEN and reduction in phosphorylation of ERK and Akt. Spry2 also suppressed the growth and tumorigenesis of colon cancer cells in vivo. In line with earlier studies suggesting Sprouty genes as targets of miR-21 (Huang et al., 2010; Sayed et al., 2008; Thum et al., 2008), they proposed that Spry2 is negatively regulated by miR-21 and that such interaction may play a role in colon cancer carcinogenesis. Spry1, too, was later found to inhibit EGF- or uPA-induced migration of HCT116 cells in vitro (Mekkawy and Morris, 2013).

1.2.3.6 Melanoma

Examining a panel of melanocytic and melanoma cell lines, Tsavachidou et al reported in 2004 that Spry2 acts as an inhibitor of ERK signaling in melanocytes and WT BRAF melanoma cells, but not in cell lines with BRAFV600E (previously designated as BRAFV599E) mutation (Tsavachidou et al., 2004). Their genetic and gene expression analyses revealed that Spry2 is downregulated in melanoma cells harboring WT BRAF yet upregulated in the BRAFV600E mutants. They observed that Spry2 directly interacted with WT B-Raf and inhibited ERK but failed to directly bind the mutant B-Raf and did not affect ERK. In conclusion, they proposed that Spry2 may be bypassed in melanoma cells either by downregulation of its expression in WT BRAF cells or through BRAF mutation. In a later study (Bloethner et al., 2005), microarray data validated by real-time PCR indicated upregulation of Spry2 in melanoma cell lines with mutations in BRAF and NRAS. Qi et al (Qi et al., 2008) showed that the expression of a dominant-negative Siah2 RING finger mutant in SW1 mouse melanoma cells reduced their tumorigenesis through the increase of Spry2. Using genomic and gene expression analyses of an animal model of skin neoplasm that produces both benign and malignant tumors, Quigley et al (Quigley et al., 2011) demonstrated that alleles that are not relevant in normal tissue are associated with tumor susceptibility but somatic alterations during tumor progression may reduce the detectable influence of germline polymorphisms. As such, while Spry2 was identified as a susceptibility gene for skin tumors and was expressed at very low levels in normal skin and at elevated levels in tumors, higher Spry2 expression in tumors was found to be associated with greater resistance to tumorigenesis which was ascribed to the role of Spry2 in regulation of ERK. Through an integrative approach analyzing genomic and gene expression changes in relation to in 60

vivo growth aggressiveness, Mathieu et al (Mathieu et al., 2012) found that genomic loss of Spry1, Spry2 -along with altered expression of some other genes- was associated with a more aggressive melanoma phenotype. However, no convincingly enhanced levels of ERK phosphorylation were found in the fast-growing subgroup of their melanoma model compared with its slow-growing counterpart. Given widespread activating mutations in BRAF and, in particular, inability of Spry2 to attenuate ERK in the context of BRAFV600E mutation, their findings argue for a role of Sprouty in regulation of melanoma aggressiveness independent of attenuation of ERK.

1.2.3.7 Sarcoma

In 2005, gene expression profiling of 134 human sarcoma tumors by Baird et al revealed upregulation of Spry2 in 2/7 of fibrosarcomas and 4/5 of dermatofibrosarcomas (Baird et al., 2005). Lito et al reported upregulation of Spry2 protein in the human fibrosarcoma cell lines SHAC, HT1080, VIP:FT and NCI as well as in HRAS- and NRAS-transformed human fibroblasts (Lito et al., 2008). They provided evidence that Spry2 is necessary for sarcoma formation by patient-derived fibrosarcoma cell lines or HRAS oncogene-transformed human fibroblasts through EGFR signaling. Indicating Spry2-dependent interaction of H-Ras with c-Cbl and CIN85 and the ability of Spry2 to sustain EGFR signaling and tumor formation in the context of HRAS activation, they indicated that the positive effect of Spry2 in sarcoma tumor formation by human fibroblasts is specific to HRAS transformation. Contrasting the role of Spry2 in HRAS transformation with that earlier reported in KRASG12D mutation (Shaw et al., 2007), Lito et al raised the possibility of differences between oncogenic Ras isoforms in tumorigenesis through Ras isoform-specific regulation and modes of action (Lito et al., 2008). Using a similar model system, they later showed that oncogenic HRAS requires Spry2 to protect fibroblasts from UV-induced apoptosis and damage and to resist cisplatin cytotoxicity. This antiapoptotic function of Spry2 was found to be mediated by a pathway consisting of Akt, human double minute 2 (HDM2) and p53 recruited through Rac1 (Lito et al., 2009). In another study, gene expression profiling by Schaaf et al (Schaaf et al., 2010) revealed that Spry1, Spry2 and Spry4 were consistently upregulated in the embryonic subtype of rhabdomyosarcoma (ERMS) as compared with its alveolar subtype (ARMS). They indicated that elevated Spry1 in ERMS cells associated with hyperactive ERK signaling is caused by oncogenic RAS mutations 61

which is frequent in ERMS but absent in ARMS. Spry1 was found essential for ERMS cell proliferation and survival in vitro and ERMS tumor formation and maintenance in vivo. Accordingly, silencing of Spry1 abolished tumorigenicity of ERMS cells and caused regression of established ERMS tumors in mice. Thus, they argued that Spry1 functions as an agonist of ERK signaling in rhabdomyosarcoma with RAS mutation.

A microarray analysis of 41 soft tissue tumors reported by Nielsen et al in 2002 (Nielsen et al., 2002) revealed that Spry1, Spry4 and KIT were among the genes that demonstrated specific expression in gastrointestinal stromal tumors (GISTs). Using expression profiling of the GIST882 cells treated with the c-Kit inhibitor imatinib in vitro, Frolov et al later identified Spry4 as an imatinib-responsive gene significantly downregulated in the treated cells and the Spry4 protein as a downstream effector of the c-Kit-activated ERK targeted by the drug (Frolov et al., 2003). In their clinical study, since Spry4 levels were dramatically decreased in patients responsive to the drug compared with non-responsive patients, the authors proposed Spry4 as a reliable marker of the imatinib-responsive treatment.

Sprouty, on the other hand, has reportedly shown inhibitory effects on other types of sarcoma cells and tumors. Identifying Spry2 as an inducible, negative regulator of HGF/SF-induced activation of ERK and AKT, Lee et al (Lee et al., 2004) reported in 2004 that Spry2 inhibits proliferation, anchorage-independent growth, migration and invasion of SK-LMS-1 human leiomyosarcoma cells in vitro. Rathmanner et al reported that Spry2, but not Spry4, potently inhibits proliferation and interfere with migration of human osteosarcoma-derived cells, with implication of the N-terminal sequence variation in the specific inhibitory effect of Spry2 (Rathmanner et al., 2013). Osteosarcoma cell invasion was also shown to be impeded by overexpressed Spry1 as a result of interaction with uPAR (Mekkawy and Morris, 2013). A microarray study by Holtkamp et al (Holtkamp et al., 2004) identified Spry2 as one of the genes differentially upregulated in benign human neurofibroma as compared with malignant peripheral nerve sheath tumors (MPNST) from the same patient. Supporting a role for Sprouty in limiting the development of these benign lesions, Courtois-Cox et al later reported that Sprouty genes were highly expressed in both Raf-expressing and neurofibromin 1 (NF1)-deficient fibroblasts (Courtois-Cox et al., 2006). They argued 62

that Sprouty is part of a multifaceted negative feedback signaling network in response to the aberrant activation of Ras that underlies oncogene-induced senescence.

1.2.3.8 B-cell lymphoma

Reported in 2008, epigenetic silencing of hSpry2 and its clinical relevance in lymphoid/hematopoietic malignancies were investigated by Sanchez et al (Sanchez et al., 2008). Of 16 relevant human cancer cell lines, hSpry2 promoter was methylated only in the B-cell diffuse lymphoma cell line HT. This was found to be associated with and related to hSpry2 downregulation at mRNA and protein levels. The ectopic expression of hSpry2 in HT cells drastically reduced the phorbol 12-myristate-13- acetate (PMA)-induced activation of ERK. The investigators then observed that HT mock cells developed tumors in nude mice seven times larger than those formed by the hSpry2 transfectants. Clinically, they identified hSpry2 hypermethylation in 26 out of 71 patients with B-cell diffuse lymphoma as well as in 10 out of 13 Burkitt’s lymphomas but in no normal B lymphocytes from 37 healthy individuals. As evaluated in 55 out of the initial 71 patients, the authors reported that Spry2 promoter hypermethylation was significantly associated with a lower 5-year survival rate and concluded that Spry2 could be an important regulator in mouse B-cell diffuse lymphomas. In agreement, epigenetic silencing and repressed expression of Spry2 in mouse and human mature B-cell tumor cell lines and a T-cell leukemia 1-transgenic (TCL1-tg) mouse model of B-cell lymphoma as well as in human B-cell lymphoma samples were reported by Frank et al (Frank et al., 2009). 5 out of 7 diffuse large B-cell lymphomas and the only Burkitt’s lymphoma sample studied contained DNA methylation of the Spry2 promoter which was associated with repressed Spry2 expression in 4 out of 6 lymphoma samples. Mechanistically, they demonstrated that Spry2 overexpression reduces ERK activation and induces B-cell apoptosis and Spry2 inactivation, on the other hand, increases ERK-dependent proliferation of B-cells. In conclusion, they implicated Spry2 in regulation of TCL1-augmented ERK signaling and B-cell proliferation and suggested Spry2 epigenetic silencing as an aberration contributing to B-cell lymphoma progression.

1.2.3.9 Testicular germ cell cancer

Results from a genome-wide scan among 277 cases of testicular germ cell tumors (TGCT) and a subsequent replication study on 371 cases were reported by Kanetsky et al in 2009 (Kanetsky et al., 2009) whereby genetic variation of KITLG (gene encoding the ligand for the receptor tyrosine kinase c-KIT) and Spry4 was shown to predispose to testicular germ cell cancer. These findings were found in agreement with an earlier report identifying Spry4 as downstream of c-KIT activation of ERK which is upregulated in GISTs in association with aberrant activation of c-KIT (Frolov et al., 2003).

1.2.3.10 Endometrial cancer

Differential expression of Spry2 in normal endometrium throughout the menstrual cycle as well as in endometrial cancer was reported by Velasco et al (Velasco et al., 2011) in 2011. Indicating complete absence of Spry2 in about 20% of 136 cases immunohistochemically studied, they found that Stage III and IV tumors had the lowest levels of Spry2 immunostaining. Moreover, a strong, inverse correlation between the Spry2 expression and the cell proliferation index Ki67 was revealed, with nonendometrioid carcinomas (NEEC) exhibiting the highest level of cell proliferation and lowest level of the Spry2 expression. They concluded that Spry2 may be involved in regulation of endometrial carcinogenesis through control of cell proliferation.

1.2.3.11 Thyroid cancer

In 2012, Macia` et al (Macia et al., 2012) reported that Spry1 is expressed in mouse thyroid C-cells and that targeted deletion of Spry1 causes C-cell hyperplasia, a precancerous lesion preceding medullary thyroid carcinoma (MTC), in young adult mice. They also found that ectopic expression of Spry1 in a tumorigenic, MTC-derived cell line reduced proliferation of the cancer cells in vitro and inhibited growth of the xenografts in vivo. Furthermore, they indicated that the Spry1 promoter is frequently methylated and that the Spry1 expression is accordingly decreased in human MTC samples, collectively suggesting that Spry1 is a candidate tumor-suppressor gene in MTC. By in vivo analysis of the thyroid glands from the Spry1 knockout mice, they 64

recently described a novel mechanism by which Spry1 induces a senescence-associated secretory phenotype via activation of the NF-kappa B pathway, thereby restricting cell proliferation independently of the ERK pathway (Macia et al., 2014).

1.2.3.12 Pituitary tumor

Investigating the role of the C-terminal binding protein (CtBP), a transcriptional corepressor with known oncogenic properties, in normal and neoplastic pituitary, Dorman et al (Dorman et al., 2012) identified Spry2 as a potential target of CtBP1 and hence a potential tumor suppressor involved in regulation of pituitary cell growth and apoptosis. Gene expression profiling validated by real-time PCR and Western blotting revealed that Spry2 is upregulated in the CtBP1-deficient GH4 pituitary tumor cells that grow slower than their parental cells. Mechanistically, upregulation of Spry2 in CtBP1- deficient GH4 cell was shown to impair phosphorylation of the fibroblast growth factor receptor substrate 2 (FRS2α) in response to FGF.

1.2.3.13 Ovarian cancer

Schaner et al suggested the possible involvement of Sprouty and the MAPK pathway in ovarian cancer through evaluation of the gene expression patterns of serous ovarian cancer tissue by cluster analysis (Schaner et al., 2005). They found the co-expression of the 3 Sprouty transcripts (1, 2, and 4) with the previously identified markers and genes for ovarian cancer. In a study by Polytarchou et al (Polytarchou et al., 2011), Spry1 was identified as a target of miR-21 in Akt2-conferred resistance to hypoxia in both normal and tumor cells. Upon oxygen deprivation, Akt2 was found to induce miR-21 which in turn targets and downregulates Spry1, PTEN and programmed cell death 4 (PDCD4) led to enhanced survival of Akt2-expressing cells during hypoxia. They provided evidence that this hypoxia-activated, Akt2-dependent pathway is present in ovarian cancer through examining a panel of ovarian cancer cell lines in vitro as well as real- time PCR analysis of 31 human ovarian cancer samples.

1.2.3.14 Clear cell renal cell carcinomas (ccRCC)

In an attempt to identify genes effectively discriminating between clinically aggressive and nonaggressive ccRCC, Takahashi et al performed the gene expression profiling of 29 tumors obtained from patients with diverse clinical outcomes. According to their 65

report, Spry1 was exclusively upregulated in the good outcome group (Takahashi et al., 2001).

In conclusion, the contributory role of the Sprouty downregulation in carcinogenesis and/or tumor progression and metastasis in the context of the breast (Lo et al., 2004), prostate (Kwabi-Addo et al., 2009; McKie et al., 2005; Patel et al., 2013; Schutzman and Martin, 2012), liver (Lee et al., 2008; Lee et al., 2010b; Wang et al., 2012a), lung (Bren-Mattison et al., 2005; Minowada and Miller, 2009; Shaw et al., 2007; Sutterluty et al., 2007; Tennis et al., 2010; Winn et al., 2006), colon (Feng et al., 2011; Katoh and Katoh, 2006), melanoma (Mathieu et al., 2012; Qi et al., 2008; Quigley et al., 2011), B cell lymphoid (Frank et al., 2009; Sanchez et al., 2008) and thyroid (Macia et al., 2012; Macia et al., 2014) cancer is documented. This contribution is an apparent reflection of the critical role of Sprouty in regulation of cellular processes central to the development, progression and dissemination of malignant conditions, including cell proliferation, migration, invasion, transformation and survival (Table 1-5). Mechanistically, Sprouty regulates cell behavior through modulation of the ERK activation along with interaction with a wide range of players and ultimate involvement of other regulatory mechanisms and cellular pathways as depicted in Figure 1-4 and Figure 1-5. Nevertheless, context- dependent contribution of Sprouty to cancer tumorigenicity and metastatic potential has also been reported in colon cancer (Barbachano et al., 2010; Holgren et al., 2010) as well as in RAS mutated fibrosarcoma (Lito et al., 2008) and rhabdomyosarcoma (Schaaf et al., 2010) as a result of E-cadherin repression and ensuing inhibition of the adhesive epithelial phenotype (Barbachano et al., 2010), upregulation of c-Met (Holgren et al., 2010) and a concomitant RAS mutation (Lito et al., 2008; Schaaf et al., 2010). 66

Figure 1-4 Schematic illustration of the Sprouty-mediated regulation of cell proliferation, differentiation and survival, irrespective of the Sprouty isoform and cell type. Sprouty activity is resulted from or regulated through interaction with a number of players. This interaction impacts functionality of ERK and other signaling pathways. Sprouty binds c-Cbl and CIN85 and sequestrate c-Cbl to augment and prolong RTK signaling by inhibiting receptor endocytosis. This mechanism has been implicated in cell differentiation. E3 ubiquitin ligase c-Cbl, on the other hand, binds to and induces degradation of Sprouty to restrict ERK activation. Sprouty has also been shown to interact with different phosphatases. It increases active contents of PTEN to mediate antiproliferative actions by inhibiting Akt activation. PTEN is also phosphorylated and accumulated in the nucleus in response to the Sprouty deficiency to induce p53-mediated growth arrest independently of its phosphatase activity. It is likely that the proto-oncogenic potential of NEDD4 is resulted in part from its ability to ubiquitinate both Sprouty and PTEN, resulting in unchecked activation of Akt. Sprouty also increases PTP1B content. PT Phosphatases PP2A and SHP2 differentially regulate the Sprouty activity. While PP2A potentiates Sprouty binding to Grb2 and thus 67

positively regulates Sprouty by serine dephosphorylation, SHP2 promotes dissociation of Sprouty from Grb2 through tyrosine dephosphorylation and checks Sprouty inhibition of ERK. Moreover, interaction between Sprouty and kinases yields different outcomes. DYRK1A is considered a negative regulator of the Sprouty activity by threonine phosphorylation. TESK1 interferes with Sprouty/Grb2 interaction as well as with Sprouty serine dephosphorylation by PP2A, thereby attenuating Sprouty functioning. Sprouty isoforms also exhibit differential cooperativity with Cav-1 to repress growth factor activation of ERK. At low cell density, however, Cav-1 inhibits the Sprouty function. Sprouty is a general inhibitor of PLC-dependent signaling and inhibits various PKC upstream and downstream signals, including PIP2 hydrolysis.

Sprouty is an interacting partner of the Gαo/GRIN pathway. GRIN modulates Sprouty repression of ERK by binding and sequestering Sprouty. Activated Gαo, on the other hand, promotes inhibition of ERK via interacting with GRIN and releasing Sprouty. Finally, different Sprouty isoforms have the potential to interact with one another to form oligomers with more potent activity. Cav: Caveolin-1; c-Cbl: canonical Casitas B-lineage lymphoma; CIN85: Cbl-interacting protein of 85 kDa; DYRK1A: dual-specificity tyrosine-phosphorylated and -regulated kinase 1A; Gαo: G protein αo; GRIN: G protein-regulated inducer of neurite outgrowth; miR-21: microRNA 21; Mnk1: MAPK-interacting kinase 1; NEDD4: neural precursor cell expressed, developmentally down-regulated 4; PAPC: paraxial protocadherin; PIP2: phosphatidylinositol-4,5-bisphosphate; PKC: Protein kinase C; PLC: phospholipase C; PP2A: protein phosphatase 2A; PTEN: phosphatase and tensin homolog; PTP1B: protein tyrosine phosphatase 1B; RTK: receptor tyrosine kinase; SHP2: Src homology-2 containing phosphotyrosine phosphatase; Siah2: Seven in Absentia homolog 2; TESK1: testicular protein kinase 1; HCD: high cell density; LCD: low cell density. In this figure, C- and N-terminus of the Sprouty molecule symbol are shown in white and blue, respectively. Red lines indicate the Sprouty effect, with dashed lines representing indirect influence. Question marks refer to postulated, but not proven, interactions. Figure created by author published in Cancer and Metastasis Reviews 2014, 33(2-3): 695-720. 68

Figure 1-5 Schematic illustration of the Sprouty-mediated regulation of cell migration, adhesion and cytoskeletal rearrangement, irrespective of the Sprouty isoform and cell type. Sprouty is shown to interact with phosphatases. It increases active contents of PTP1B to mediate its antimigrative action by inhibiting activation of Rac1. Sprouty inhibits the kinase activity of TESK1 that plays a critical role in integrin- mediated actin cytoskeletal reorganization and cell spreading. Sprouty is a general inhibitor of PLC-dependent signaling and inhibits various PKC upstream and downstream signals. Protocadherin PAPC implicated in modulating beta-catenin- independent Wnt-signaling has been suggested to mediate its regulatory effect by binding and sequestering Sprouty. 69

FZD receptor: Frizzled receptor; PAPC: paraxial protocadherin; PKC: Protein kinase C; PLC: phospholipase C; PTP1B: protein tyrosine phosphatase 1B; RTK: receptor tyrosine kinase; TESK1: testicular protein kinase 1; CS rearrangement: cytoskeletal rearrangement. In this figure, C- and N-terminus of the Sprouty molecule symbol are shown in white and blue, respectively. Red lines indicate the Sprouty effect. Figure created by author published in Cancer and Metastasis Reviews 2014, 33(2-3): 695-720.

1.2.4 Sprouty in cancer: complexity and controversy

Under physiological conditions, as detailed earlier, Sprouty-mediated regulation is complex and multifaceted. Despite the initial understanding of Sprouty as a negative regulator of ERK, it is now evident that Sprouty has targets beyond ERK and functions, in concert with a variety of interacting molecules, in a cell- and context-dependent manner. Sprouty is differentially expressed by various normal cells not only during development, but also in adult organs in a tissue-specific or ubiquitous manner. Moreover, different Sprouty isoforms exhibit divergent regulatory functions. On this basis, it is not surprising that role of Sprouty in malignant conditions, where physiological homeostasis is altered in favor of neoplastic growth and progression, is fraught with intricacy and controversy. As discussed throughout this article, attempts have been made to shed light on unknown aspects of this story. In sum, our current knowledge indicates that the Sprouty’s implication in cancer, similar to its role under normal circumstances, is cell type- and context-dependent. Although deregulation of the Sprouty genes can indicate a general aspect of the Sprouty status in a given cancer, this needs to be interpreted in relation to the gene expression at the protein level and pertinent functional outcomes. A rewired genetic network with involvement of Sprouty and ERK signaling apparently promotes tumorigenesis. However, the Sprouty gene association with tumor susceptibility or resistance may not be necessarily associated with a consistent phenotype in vivo due to somatic alterations. Thus, a combination of genetic and gene expression analysis has been recommended to complement genetic association methods for identification of susceptibility or resistance factors (Quigley et al., 2011). The expression of the Sprouty proteins, on the other hand, might be variably altered during tumorigenesis based on the pathogenic mechanism involved. Therefore, the expression pattern of Sprouty might be reflecting, for instance, a response to the mutant RAS-induced hyperactivation of ERK or, on the contrary, the epigenetic 70

silencing of the Sprouty promoter. Moreover, Sprouty’s mode of action can be converted under malignant conditions. In the context of the RAS mutation, for example, Sprouty can function as an inhibitor (Courtois-Cox et al., 2006; Shaw et al., 2007; Sutterluty et al., 2007) or facilitator (Holgren et al., 2010; Lito et al., 2009; Lito et al., 2008; Schaaf et al., 2010) of the tumor development and/or progression. This might result in part from different functionality of the RAS isoforms (Lito et al., 2008). Collectively, the expression pattern of Sprouty in different types of cancer is just a reflection of the primary or secondary deregulations incurred under specific circumstances. Since Sprouty is physiologically able to function as both a repressor and an activator of RTK signaling, its specific implication needs to be individually investigated in different cancers where its mode of action be evaluated in relation to the malignant cell behavior. In this regard, while investigation of the Sprouty gene aberrations and relevant oncogenic mutations can provide clues to the underlying mechanisms, evaluation of the effect of the Sprouty expression on cancer cell biology along with analysis of the clinicopathological relevance of the Sprouty deregulation will yield a better understanding of the Sprouty biology in a given cancer with potential application in the Sprouty-based approaches.

1.2.5 Conclusion

Known as a modulator of ERK, Sprouty interacts with a variety of effectors, mediators and adaptor proteins to mediate the crosstalk between ERK and other pathways. In addition, Sprouty isoforms are differentially induced in response to different growth factors to elicit divergent cellular responses. Different patterns of the Sprouty deregulation have been reported in different cancers. Since Sprouty is able to function as both an inhibitor and an activator of RTK signaling, and due to the cell-specific and context-dependent nature of the Sprouty-mediated regulation, its role needs to be individually investigated in each type of cancer. To date, no studies have investigated the status of Sprouty protein in ovarian cancer and its effects on tumor biology. Evaluation of likely deregulation of Sprouty expression and its clinicopathological relevance in ovarian cancer will yield a better understanding of the role of Sprouty in this cancer. It may also lay foundation for further assessment of Sprouty as a protein with potential application in diagnostic, therapeutic and prognostic approaches. 71

2 Aim and Hypothesis

2.1 Introduction

Sprouty proteins are evolutionarily-conserved modulators of MAPK/ERK and RTK signaling. This protein family is largely implicated in developmental and physiological processes. As with normal cells, emerging evidence shows that Sprouty proteins are involved in the regulation of biological processes in malignant cells with eventual impact on cancer cell behavior. Accordingly, a variety of studies has demonstrated the deregulation of Sprouty proteins in different cancers. In addition, the clinical relevance of the Sprouty expression in cancer as a prognostic factor and/or a predictive biomarker of the treatment sensitivity has been proposed (Masoumi-Moghaddam et al., 2014b). In the context of EOC, a possible role for Sprouty gene has been suggested in two in vitro studies (Polytarchou et al., 2011; Schaner et al., 2005). However, to the best of our knowledge, the expression of Sprouty proteins and its clinicopathological relevance in patients with EOC have not been investigated before. On this basis, I aimed in the present project to evaluate the possible implications of Sprouty in EOC.

To exert their regulatory functions, Sprouty isoforms have been shown to interact with each other as well as with other proteins, including a variety of partner molecules and adaptor proteins, to mediate the crosstalk between MAPK/ERK and other pathways, including growth factor-induced signaling. Evidence also indicates that a number of growth factors and cytokines, including VEGF, FGF and IL-6, contribute to the pathophysiology of ovarian cancer as discussed in section 1.1.4.2.4 . Nevertheless, it is unknown if there in an association between Sprouty proteins and these growth factors and cytokines in EOC. Investigation of a possible link between Sprouty isoforms and VEGF, FGF and IL-6 was another aim of this study.

Investigation of the expression status of this protein family and its likely correlation with other crucial growth factors or cytokines involved in the pathogenesis of the disease may open up new research fields that can lead to better understanding of the ovarian cancer development. These findings could be further assessed for the development of new therapeutic modalities or prognostic markers and thus improvement of the current standard of care for patients with EOC. 72

2.2 Aims

The overall aim of this study was to evaluate the expression status of Sprouty1 protein and its clinicopathological relevance in EOC. The specific aims of this study were:

2.2.1 Aim 1

To evaluate the status and functional significance of the Sprouty expression in EOC cells in vitro with regard to the cancer cell behavior, including proliferation, migration, invasion and survival;

2.2.2 Aim 2

To define the status and the clinical relevance of the Spry1 protein expression in tumor samples from patients with EOC in a retrospective study with attention to a possible role of Spry1 as a prognostic factor;

2.2.3 Aim 3

To investigate retrospectively the expression levels of Spry2 and Spry4 proteins in EOC and their possible correlations with Spry1, clinicopathological parameters and patient outcome;

2.2.4 Aim 4

To evaluate the expression status of VEGF, FGF and IL-6 proteins and their correlation with Spry1, Spry2 and Spry4 proteins in EOC in a retrospective study.

2.3 Hypotheses

2.3.1 Hypothesis 1

Sprouty proteins are expressed differentially in EOC cells as compared with their normal counterparts. In addition, alterations in the Spry1 content of EOC cells could inversely affect their proliferation, migration, invasion and survival. This hypothesis was investigated in chapter 4.

2.3.2 Hypothesis 2 73

Spry1 protein is downregulated in EOC tissue samples compared to their matched normal samples and its expression inversely correlates with aggressive clinicopathological features of the disease. In addition, it can predict the patients’ outcome with regard to their overall and disease free survival. This hypothesis was investigated in chapter 5.

2.3.3 Hypothesis 3

Spry2 and Spry4 proteins are downregulated in EOC tissue. The expression of these proteins inversely correlates with aggressive clinicopathological features of the disease and has predictive value for EOC patients’ outcome, including overall and disease free survival. In addition, the expression of these Sprouty isoforms directly correlates with that of Spry1 protein. This hypothesis was investigated in chapter 6.

2.3.4 Hypothesis 4

VEGF, FGF-2 and IL-6 proteins are upregulated in EOC and correlate with the expressions of Spry1, Spry2 and Spry4 proteins in the same cancer tissue samples. This hypothesis was investigated in chapter 7.

3 General Materials and Methods

3.1 Materials

3.1.1 Cell lines

Table 3-1 List of cell lines Cell line Supplier OVCAR-3 American Type Culture Collection (ATCC) SKOV-3 American Type Culture Collection (ATCC) 1A9 American Type Culture Collection (ATCC) CAOV-3 American Type Culture Collection (ATCC) A2780 American Type Culture Collection (ATCC) OV-90 American Type Culture Collection (ATCC) IGROV-1 American Type Culture Collection (ATCC) HOSEpiC SienCell

3.1.2 Chemicals and reagents

Table 3-2 List of chemicals and reagents Reagent Supplier Fetal Bovine Serum (FBS) Sigma-Aldrich RPMI-1640 media Invitrogen DMEM (Dulbecco's Modified Eagle's Medium) Invitrogen 1:1 mixture of MCDB 105/Medium 199 media Sigma-Aldrich OEpiCM (Ovarian Epithelial Cell Medium) SienCell Penicillin/Streptomycin(PEN/STREP) Invitrogen Phosphate buffer saline (PBS) Invitrogen Trypsin-EDTA Solution Gibco, Invitrogen Trypan blue stain Sigma-Aldrich Sodium azide Sigma-Aldrich Formaldehyde Sigma-Aldrich 75

RIPA buffer Sigma-Aldrich BioRad protein assay kit Bio-Rad Sodium dodecylsulfate (SDS) Sigma-Aldrich Bovine serum albumin Sigma-Aldrich polyvinylidene fluoride membrane (PVDF) Millipore Corporation Western Lightning enhanced chemiluminescence GE Healthcare Tween 20 Sigma-Aldrich Tris base Sigma-Aldrich RNeasy Plus Mini Kit Qiagen UltraPure agarose Invitrogen Tris Acetate-EDTA buffer Sigma-Aldrich RNA Sample Loading Buffer Sigma-Aldrich β-mercaptoethanol (β-ME) Sigma-Aldrich DNA-free DNase Treatment and Removal Reagents kit Invitrogen SuperScript III One-Step RT-PCR System with Platinum Invitrogen Taq DNA Polymerase RNAse inhibitor Qiagen 10,000X SYBR Safe DNA gel stain Invitrogen DEPC-treated water Ambion Sprouty1 primer forward Invitrogen Sprouty1 primer reverse Invitrogen Sprouty2 primer forward Invitrogen Sprouty2 primer reverse Invitrogen β-actin primer forward Invitrogen β-actin primer reverse Invitrogen SF Cell Line 4D-Neucleofector Kit Lonza pcDNA3.1/Spry1 construct Invitrogen pcDNA3.1 empty vector Invitrogen G418-Geneticin Gibco Crystal Violet solution Sigma-Aldrich Giemsa Sigma-Aldrich Thiazolyl Blue Tetrazolium Bromide Sigma-Aldrich 76

BioCoat Matrigel Invasion Chamber BD Biosciences Transwell polycarbonate membrane cell culture inserts Corning Life Sciences Spry1 Pre-design Chimera RNAi Abnova Gelatine glycerol Sigma-Aldrich Citrate Buffer Sigma-Aldrich EDTA Buffer Sigma-Aldrich SuperFrost Plus microscope slides Thermo Fisher Scientific EnVision Plus kit DAKO Hematoxylin DAKO

3.1.3 Antibodies

Table 3-3 List of antibodies Antibody Supplier Sprouty1 (mouse monoclonal) Abnova Sprouty2 (mouse monoclonal) Abnova Caspase 3 (rabbit polyclonal) Santa Cruz Bcl2 (rabbit polyclonal) Santa Cruz Caspase 8 (rabbit polyclonal) R&D Systems Caspase 9 (rabbit polyclonal) Cell Signaling PARP (rabbit polyclonal) Cell Signaling Akt (rabbit polyclonal) Cell Signaling Phospho-PTEN (rabbit polyclonal) Cell Signaling Caspase 7 (rabbit monoclonal) Cell Signaling Bcl-xl (rabbit monoclonal) Cell Signaling Bax (rabbit monoclonal) Cell Signaling Phospho-Akt (rabbit monoclonal) Cell Signaling PTEN (rabbit monoclonal) Cell Signaling Rabbit Horseradish peroxidase-conjugated secondary Cell Signaling Thiazolyl Blue Tetrazolium Bromide (MTT) Sigma-Aldrich Sprouty4 (rabbit polyclonal) Abnova p44/42 MAPK (Erk1/2) (rabbit monoclonal) Cell Signaling 77

Phospho- p44/42 MAPK (Erk1/2) (rabbit monoclonal) Cell Signaling Ki67 (mouse monoclonal) Santa cruz FGF-2 (rabbit polyclonal) Santa cruz VEGF (mouse monoclonal) Santa cruz IL-6 (mouse monoclonal) Santa cruz GAPDH (mouse monoclonal) Sigma-Aldrich HRP-conjugated gout anti mouse Santa cruz HRP-conjugated gout anti rabbit Santa cruz Alexa Flour 488 chicken anti-mouse IgG Invitrogen

3.1.4 Instruments and software

Table 3-4 List of instruments and software Instruments and software Supplier FluoView FV500 Laser scanning confocal microscope Olympus ImageQuant LAS 4000 Biomolecular imager GE Healthcare NanoDrop 2000 Spectrophotometer Thermo Fisher Scientific Veriti 96-Well Thermal Cyclers Applied Biosystems Bio-Rad Gel Doc UV Transilluminator 2000 Bio-Rad Leica DMLB microscope, DC200 digital imaging system Leica Microsystems 4D-Neucleofector System Lonza PowerWaveX microplate reader Bio-Tek Instruments Leica DM IRB microscope, DC200 digital imaging system, Leica Microsystems IM50 software ImageJ software RSB, NIH GraphPad InStat (GraphPad Prism 6) GraphPad Statistical package SPSS (version 22) SPSS Inc

3.2 Methods

3.2.1 Cell culture 78

All cell lines were maintained in a humidified 5% CO2 incubator at 37°C in their respective medium as follows: OVCAR-3, SKOV-3, 1A9, A2780 and IGROV-1 cells in RPMI-1640, CAOV-3 in DMEM, OV-90 cells in a 1:1 mixture of MCDB 105/Medium 199 and HOSEpiC in OEpiCM. The culture media used were all supplemented with 10% fetal bovine serum and 1% penicillin–streptomycin mixture.

3.2.2 Western blotting

This experiment was performed for human EOC cell lines, OVCAR-3, SKOV-3, 1A9, A2780, CAOV-3, OV-90, IGROV-1, and normal human ovarian surface epithelial cell line, HOSEpiC. These cells were maintained in their respective medium in 75 cm2 culture flasks until they got subconfluent. For transfected cells, the experiment was done at the 8, 24, 48 and 72h post-transfection for SKOV-3 cells and 24, 48 and 72h post- transfection for 1A9 cell line. At the desired time point, the culture medium was discarded and the cells were washed twice with ice-cold PBS. The cells were then lysed using 80-100 μl of RIPA buffer (150 mM NaCl, 1% NP-40, 0.5% sodium deoxycholate, 0.1% SDS, 50 mM Trizma base pH 8) containing 10% protease inhibitor cocktail per flask while incubated on ice for 10 minutes. After scrapping, the cell lysates were collected in Eppendorf tubes and stored at -80ºC. Prior to performing the protein assay, the tubes were centrifuged at 10,000 g at 4ºC for 10 minutes. Cell debris was discarded and the supernatants, containing the total protein extract of the cells were transferred to a separate tube.

Protein concentrations were quantified by Bio-Rad protein assay kit as per manufacturer’s instruction. Then, the same amounts of the proteins (50 μg) from each sample were mixed with 4× protein loading buffer and incubated for 5 minutes at 95 °C. The proteins were loaded on 12% SDS gels, subjected to sodium dodecyl sulfate– polyacrylamide gel electrophoresis using running buffer (25 mM Tris, 192 mM glycine, 0.1% SDS) for 2 hours at 80V. The proteins bands were then transferred to a polyvinylidene fluoride membrane (PVDF) using transfer buffer (25 mM Tris, 20% methanol) overnight at 30V at 4ºC, or for 2 hours at 60V. The membranes were stained with 0.5% ponceau S solution to confirm transfer efficiency and equal loading of the proteins. The membranes were blocked for 1 hour at room temperature in 5% (w/v) skim milk or BSA in tris-buffer saline containing 0.05% Tween 20 (TBST) followed by 79

incubation with primary antibodies overnight at 4ºC. Membranes were washed with TBST and treated with secondary antibody conjugated to horseradish peroxidase for 1 hour at room temperature. Table 3-5 shows the dilution of primary and secondary antibodies used. The membranes were then washed six times with TBST and the bands were visualized by an enhanced chemiluminescence detection kit.

Using the ImageQuant LAS 4000 Biomolecular imager and ImageQuant software (GE Healthcare, UK), the antigen-antibody reaction was digitized, band densitometry was quantified and the data was normalized against the values of GAPDH protein expression. Quantitative analysis of the protein expression was otherwise performed through normalizing the data from cancer cell lines against those from HOSEpiC cells as the normal control, where the values were expressed in arbitrary units as a percentage of the protein expression in each cell line to that in HOSEpiC.

Table 3-5 Antibodies dilutions and the relevant blocking buffer Antibody Dilution Blocking buffer Sprouty1 1:1000 5% Skim milk

Sprouty2 1:1000 5% Skim milk Caspase 3 1:200 5% BSA

Bcl2 1:200 5% Skim milk Caspase 8 1:2000 5% BSA

Caspase 9 1:1000 5% BSA PARP 1:1000 5% BSA Akt 1:1000 5% BSA

Phospho-PTEN 1:1000 5% BSA

Caspase 7 1:1000 5% BSA

Bcl-xl 1:1000 5% BSA Bax 1:1000 5% BSA

Phospho-Akt 1:1000 5% BSA PTEN 1:1000 5% BSA Horseradish peroxidase-conjugated (Rabbit secondary) 1:2000 5% BSA 80

p44/42 MAPK (Erk1/2) 1:1000 5% BSA Phospho- p44/42 MAPK (Erk1/2) 1:1000 5% BSA

GAPDH 1:20000 5% Skim milk HRP-conjugated gout anti-mouse & anti-rabbit 1:5000 5% BSA

3.2.3 Immunocytochemistry

OVCAR-3, SKOV-3 and HOSEpiC cells were seeded onto sterile glass coverslips in a 6-well tissue culture plate at an initial density of 2.5×105 cells/well and maintained in their respective medium at 37oC in a humidified, 5% CO2 atmosphere. At 50% confluence, the culture medium from each well was aspirated and the cells were gently rinsed twice with ice-cold PBS at room temperature. The cells were then fixed in 0.1% sodium azide plus 0.5% formaldehyde in PBS for one hour at room temperature. This was followed by one hour incubation with 70% ethanol/PBS at 4oC for permeabilization and further fixation.

In order to block nonspecific binding of the antibodies, the coverslips were immersed in 1% bovine serum albumin (BSA) in PBS for one hour at room temperature. Cells were then incubated overnight at 4oC with monoclonal anti-Spry1 and anti-Spry2 antibodies (1:20 in 1% BSA/1x PBS), with the exception of the negative control samples to which no primary antibody was applied. After rinsing the cells five times with ice-cold PBS for 5 minutes each time, incubation with the secondary antibody was subsequently applied to all samples using Alexa Flour 488 chicken anti-mouse IgG (1:500 in 1% BSA/1x PBS) for 1 hour at room temperature in dark. Next, the cells were rinsed with PBS for 6 times, 5 minutes each time (under dim and ambient light source) and counterstained with propidium iodide (1:500) for 3 minutes. This was followed by rinsing with PBS. The coverslips were then mounted with gelatine glycerol and stored at 4oC in dark. The cells were visualized by laser scanning confocal microscope and X60 oil immersion lense. The FluoView software (version 4.3) was used to overlay the images.

3.2.4 RT-PCR

3.2.4.1 RNA isolation 81

1 x 107 cells of OVCAR-3, SKOV-3, 1A9, A2780, CAOV-3, OV-90 and IGROV-1 human EOC cell lines, and HOSEpiC normal human ovarian surface epithelial cell line, were harvested in their respective medium in 75 cm2 culture flasks. After 24 hour, the medium was aspirated and the cells were washed with PBS. 10 ml of 0.10–0.25% trypsin in PBS was added to each flask. When the cells detached from the flask, the medium (containing serum to inactivate the trypsin) was added and the cells were transferred to an RNase-free polypropylene centrifuge tube, and were centrifuged at 300 × g for 5 min at room temperature followed by complete aspiration of the supernatant. The collected cell pellets were then loosened thoroughly by flicking the tubes. The cells were disrupted by adding 350 µl of Buffer RLT Plus, containing 10 μl β- mercaptoethanol (β-ME) per 1 ml Buffer RLT Plus, to each cell pellet. The lysate was then homogenize by pipetting it directly into a QIAshredder spin column placed in a 2 ml collection tube, followed by centrifuging for 2 minutes at 10,000 rpm. Next, the homogenized lysate was transferred to a gDNA Eliminator spin column placed in a 2 ml supplied collection tube and centrifuge for 30 seconds at ≥10,000 rpm. 1 volume (350- 600 μl) of 70% ethanol was added to the saved flow-through from the last step, and mixed well by pipetting. 700 μl of the sample was transferred to an RNeasy spin column placed in a 2 ml supplied collection tube, and centrifuged for 15 seconds at ≥10,000 rpm. After discarding the flow-through, the RNeasy spin column membrane washed by subsequent adding of 700 μl Buffer RW1 and 500 μl Buffer RPE, containing 4 volumes of ethanol 100%, each followed by centrifuging for 15 seconds at ≥10,000 rpm. The washing with 500 μl Buffer RPE was repeated with subsequent centrifuging for 2 minutes at ≥10,000 rpm. The RNeasy spin column was then placed in a new 1.5 ml collection tube and 30–50 μl RNase-free water was added directly to the spin column membrane, and centrifuged for 1 minute at ≥10,000 rpm to elute the RNA.

The extracted RNA yield and purity were then determined by measuring the absorbance at 230, 260 and 280 nm using a NanoDrop 2000 Spectrophotometer followed by the evaluation of RNA integrity through gel electrophoresis for determination of 28S/18S ribosomal RNA (rRNA) ratio as discussed in 3.2.4.4 section.

3.2.4.2 DNase treatment 82

In this experiment, possible contaminating DNA from RNA preparations was digested by DNA-free DNase Treatment and Removal Reagents. RNA samples were first diluted to 10 μg nucleic acid/50 μl reaction volume according to the manufacturer’s instructions. 5μl of 10X DNase I Buffer and 1 μl of rDNase I were added to the RNA, and mixed gently followed by incubation at 37oC for 30 minutes. Next, 5 μl of resuspended DNase Inactivation Reagent was added to the reaction tube, mixed well and incubated at room temperature for 2 minutes. At the end of incubation, the reaction was centrifuged at 10,000 × g for 1.5 min and the supernatant, which contains the RNA, was transferred into a fresh tube.

3.2.4.3 Reverse transcription

Reverse transcription was performed using SuperScript III One-Step RT-PCR System with Platinum Taq DNA Polymerase in a Veriti 96-Well Thermal Cyclers according to the manufacturer’s protocol with the setup shown in Table 3-6. Transcripts amplification of Spry1 and Spry2 employed the following oligonucleotide primers to cross exon/intron regions:

Sprouty1 Primer Forward: 5´-CTGCAGGGGAAGTGCAAGTGTGGAGAA-3´ Sprouty1 Primer Reverse: 5´-AAGCTTAGTTCAGGAGGTACAACCCAC-3´ Sprouty2 Primer Forward: 5´-GGATCCCATTCGCTCATCTGCCAGGAA-3´ Sprouty2 Primer Reverse: 5´-AAGCTTTGCTGGGTGAGGGCGTCTCTG-3´

Oligonucleotide primers of β-actin, 5'-ATATCGCCGCGCTCGTCGTC-3'(forward) and 5'- AGTGGTACGGCCAGAGGCGT-3' (reverse), were designed by NCBI Primer-BLAST (http://www.ncbi.nlm.nih.gov/tools/primer-blast/) and used as a reference in the same amplification conditions. The PCR amplification was carried out as shown in Table 3-7.

Table 3-6 PCR reaction setup for reverse transcription Component Volume 2X Reaction Mix 25 μL Template RNA 200 ng Sense primer (10 μM) 1 μL Anti-sense primer (10 μM) 1 μL 83

SuperScript® III RT/ Platinum® Taq Mix 2 μL Autoclaved distilled water to Up to 50 μL

Table 3-7 Optimized PCR program setup used Stage Step Temperature Time cDNA synthesis 56oC 30 minutes Holding Denaturation (hot start) 94oC 3 minutes Denaturation 94oC 30 seconds Cycling (35 cycles) Annealing 56oC 30 seconds Extension 72oC 30 seconds Holding Final extention 72oC 5 minutes

Minus RT (-RT) controls (samples in which no reverse transcriptase was added) and no template control (NTC) were also included in the experiment to verify absence of genomic DNA in the RNA preparations and possible contamination of kit components, respectively. 10 μl aliquots of the PCR products were separated on 1.5% agarose gel containing a 1:10,000 dilution of 10,000X SYBR Safe DNA gel stain by electrophoresis at 80 V and then observed using Bio-Rad Gel Doc UV Transilluminator 2000.

3.2.4.4 Agarose gel electrophoresis

This experiment was performed for the evaluation of the RNA samples’ integrity through determination of 28S/18S ribosomal RNA (rRNA) ratio as well as evaluation of the RT-PCR products. TEA buffer 1% was first made using “Tris Acetate-EDTA buffer, 10× concentrate” and DEPC-treated water. For gel preparation, 0.75 g of agarose powder was added to 50 ml of TAE buffer 1% to make 50 ml of agarose gel 1.5%. The mouth of the flask was covered with plastic wrap, and the wrap was pierced with a small hole for ventilation. The flask was then placed in the microwave oven and heated until bubbles appeared. Next, it was removed carefully and swirled gently for resuspending any agarose particles. The solution was reheated until the solution came to a boil and all the agarose particles were dissolved. Then, it was mixed gently and cooled to 50–60oC at room temperature for at least 10 minutes. 5 µl of SYBR Safe DNA gel stain was added to the solution before pouring the solution into the casting tray. 84

For evaluation of the RT-PCR products, 10 μl aliquots of the PCR were mixed with 2 µl of loading buffer from which 10 μl was loaded to each well of 8-well comb gel. Next, the products were separated on 1.5% agarose gel containing a 1:10,000 dilution of 10,000X SYBR Safe DNA gel stain by electrophoresis at 80 V for 80 minutes. The gel was then read using Bio-Rad Gel Doc UV Transilluminator 2000.

For RNA integrity investigation, 2 µl of each RNA sample was added to 8 µl of DEPC- treated water. Then, the solution was added to 2 µl of loading buffer and mixed by pipetting. The tank was filled with TAE buffer 1% when the gel got ready and then 5 µl of each prepared sample mix was loaded to each well of 15-well comb gel.

3.2.5 Transfection and silencing

Electroporation-based transfection and silencing were carried out using 4D- Neucleofector System and SF Cell Line 4D-Neucleofector Kit. Cultured cells were subcultured 2 days before the experiment to reach maximum 75% confluency on the day of transfection. On the day of transfection, the required amount of Nucleofector solution (100 µl per Nucleocuvette) was prepared by mixing the supplement and the SF solution in a ratio of 1:4.5. Next, the cultured cells were washed once with PBS and trypsinized followed by neutralization with respective supplemented culture medium. The cells were then counted using a hemocytometer and 2 × 106 cells per Nucleocuvette were transferred into a fresh centrifuge tube and centrifuged at 90 × g for 10 minutes at room temperature. The supernatant was removed completely and the cell pellet was resuspended in room temperature Nucleofector solution. Immediately, the cell suspension was mixed with either 4 μg pcDNA3.1/Spry1 construct (Spry1-transfected group), or pcDNA3.1 empty vector (+vector group) for induction of Spry1 or 2 μg pmaxGFP Vector for evaluaton of transfection efficacy in SKOV-3 cells. For -vector group no plasmid was added. All samples were then transferred into their designated Nucleocuvette (100 µl per Nucleocuvette). With the caps closed, cuvettes were inserted into the 4D-Neucleofector device and the reaction was run with the optimized program (pulse code: EH-100). As soon as the transfection was successfully completed, cuvettes were taken out and 500 μl of pre-equilibrated culture medium was added to each one, followed by 10-minute room temperature incubation. Using the supplied transfer pipettes, samples were then transferred to the culture vessels and cultured for further 85

analysis. Transient silencing of Spry1 in 1A9 cells was performed using 150 nM Spry1 Pre-design Chimera RNAi for silenced group, no RNAi for the control group and 2 μg pmaxGFP Vector for evaluation of transfection efficacy. Reaction was similarly carried out using 4D-Neucleofector System set up for the cell line but with pulse code of EN- 138. Efficiency of the electroporation was evaluated by visualization of the green fluorescent protein (GFP) encoded by the co-transfected control plasmid (pmaxGFP Vector) showing transfection efficiency of >80%, as well as by western blot analysis of the Spry1 expression in the control transfected or silenced cells. The specificity of the constructs and plasmids were confirmed by western blot as the expression of other members of the Sprouty family was found unaffected.

For the stable transfection experiment, exposure to the selection condition culture media containing G418-Geneticin at a final concentration of 300μg/ml for selection of the stably-transfected clones was started two days post-transfection. On day 14 post- selection, Geneticin-resistant clones were fixed with ice-cold 100% methanol for 10 minutes at -20°C. Methanol was then aspirate from the plates and the cells were incubated for 20 minutes with 0.1% crystal violet solution at room temperature. The crystal violet stain was then removed and the cells were washed in water until the dye stops coming off and allowed to dry at room temperature.

3.2.6 MTT assay

Appropriate number of the Spry1-transfected SKOV-3 cells (5000/well for 24h, 3500/well for 48h and 2000/well for 72h) and Spry1-silenced 1A9 cells (8000/well for 24h, 4000/well for 48h and 2000/well for 72h) along with equal number of their respective controls were cultured in 96-well plates at 37oC in 5% CO2 incubator for 24, 48 and 72 hours. At the endpoints, cells were incubated with Thiazolyl Blue Tetrazolium Bromide at a concentration of 0.5 mg/ml for further 4 hours. Next, the medium was discarded carefully. Using a shaker for 10 minutes, resulting formazan crystals were then dissolved with 100 μl of dimethyl sulfoxide (DMSO) and absorbance was read using PowerWaveX microplate reader at the working wavelength of 562 nm.

3.2.7 Trypan Blue assay 86

80,000 of the Spry1-transfected SKOV-3 cells and 150,000 of Spry1-silenced 1A9 cells along with equal number of their respective controls were cultured in 6-well plates. At 24h, 48h and 72h after plating, cells were trypsinized and centrifuged at 1300 rpm for 5 minutes at room temperature. The supernatant was then discarded and the cells were resuspended in medium. Cell suspensions were then diluted 1:10 in trypan blue and the actual number of the cells was calculated using a hemocytometer.

3.2.8 Scratch assay

Control, Spry1-transfected (350,000/well) and Spry1-silenced cells (750,000/well) were grown in 6-well plates for 24 hours post-transfection to confluence. At 24h, the cell monolayers were scraped with the tip of a pipette to create a uniform scratch. The culture media were then replaced with fresh media supplemented with 2% FBS and the reference points for imaging were marked. Using Leica DM IRB microscope equipped with Leica DC200 camera and IM50 software, plates were viewed with a 5X objective and sequential imaging was performed at the given time points. Images were then analyzed with ImageJ software and the results were quantified by measuring the percentage of the scratch closure.

3.2.9 Cell migration and invasion assays

This experiment was carried out 24 hours post transfection. 24-well Transwell system with polycarbonate membranes of 8mm pore size was used for cell migration and invasion assays. For invasion assay, inserts were rehydrated for 2 hours in humidified tissue culture incubator, 37oC, 5% CO2 atmosphere by adding 0.5 ml of warm (37oC) bicarbonate based culture medium to the interior of the inserts and in its respective well of 24-well plate. Next, appropriate amount of cell suspensions (5×104 SKOV-3 cells or 1×105 1A9 cells in 500 µl of 0.1% BSA/RPMI per well) was transferred to the upper compartments of the Boyden chambers either coated with matrigel for invasion assay or without matrigel coating for migration assay. Lower compartments were filled with 750 µl of the same medium supplemented with 10% FBS. Cells were then allowed to migrate and invade at 37oC. At the given time points, content of the upper compartment was discarded and upper surface of the membrane was wiped with a cotton swab. Cells on the lower surface of the membrane were fixed in ice-cold 100% methanol for 10 minutes at -20°C, stained with Giemsa (diluted 1/5 in PBS) for 10 mins at room 87

temperature and counted under the light microscope in at least eight different fields across the membrane.

3.2.10 Patients and clinical samples

3.2.10.1 Human Ethics Application

To obtain clinical samples for this project, the use of human tissue was approved by “South Eastern Sydney and Illawarra Area Health Service Human Research Ethics Committee-Central Network (EC00135)”. In brief, “Human Ethics Application” (HEA) was prepared and submitted to the respective committee along with the following documents: • Study protocol • Participant information statement, consent and revocation of consent (St George Public Hospital) • Participant information statement, consent and revocation of consent (St George Private Hospital) After getting the approval of HREC (HREC reference number: HREC/11/STG/177) for conducting the research in two Australian sites (St George Public Hospital and St George Private Hospital), the “Site Specific Assessment Form” (SSA) was submitted to St George Public Hospital. Accordingly, the letter of authorisation (SSA reference number: SSA/12/STG/43) was provided by South Eastern Sydney Local Health District.

3.2.10.2 Tissue samples

Patients were identified through the databases of the St George Hospital (The University of New South Wales) and the St George Private Hospital, Sydney, New South Wales, Australia. Of a total of 480 cases, 100 patients with primary EOC, who received standard/cytoreductive surgery plus standard adjuvant chemotherapy and had matched evaluable normal tissue, entered the study. The average age of patients at diagnosis was 62.5 years with a range from 35.32 to 84.3 years. Patients lost to follow-up were excluded. Archived formalin-fixed, paraffin-embedded material from surgically resected ovarian cancer specimens containing tumor and the matched normal tissue was then obtained from Department of Pathology, St George Hospital, New South Wales, Australia. Tissue specimens collected between 2001 and 2012 were histologically 88

classified according to the World Health Organization (WHO) classification system (Chen et al., 2003). Demographic and clinical data were collected from patients’ medical charts. The clinical information, including age, date of diagnosis, staging, menstrual status, response to chemotherapy, recurrent disease, date of recurrence, ascites at diagnosis, and development of post-treatment ascites, were gathered. Histopathological findings such as tumor subtype, tumor stage, tumor size, lymphovascular invasion, lymph node involvement and degree of differentiation were obtained from original pathology reports. For tumor size, if more than one dimension was reported, the largest dimension of tumor was recorded. The final staging of the disease was determined on the basis of a combination of surgical and pathological findings in accord with the Federation of Gynecology and Obstetrics (FIGO) guidelines (Odicino et al., 2008). Moreover, haematoxylin and eosin (H & E) stained sections of the entire cohort underwent independent histopathological assessment by different pathologists in South Eastern Area Laboratory Services, Central, Kogarah Campus at St George Public Hospital. A Flow chart demonstrating the sequence of steps for conducting the study has been shown in Figure 3 -1.

Clinicopathological data has been shown in Table 3-8. Patient numbers may differ slightly among IHC studies because of lack of cancer tissue remaining in the paraffin- embedded archival blocks at the time of study. For a number of patients, there was lack of information in relation to lymphovascular invasion and lymph node involvement in their original pathology reports. 89

Figure 3-1 Flow chart representing the sequence of steps of the retrospective clinical study 90

Table 3-8 Clinicopathological characteristics of the participants Clinicopathological Parameter Patients (Number) Patients (%)

Age (year) ≤50 16 16

>50 84 84

Menopause Yes 92 92

No 8 8

Disease stage Early (I-II) 14 14

Advanced (III-IV) 86 86

Tumor grade I-II 23 23 III 77 77

Tumor subtype Serous 81 81

- High-grade 63 63 - Low-grade 18 18 Endometrioid 4 4

- High-grade 2 2 - Low-grade 2 2

Mucinous 2 2

Clear cell 5 5 Others 8 8

Lymphovascular invasion Yes 35 58

No 25 42

Lymph node involvement 91

Yes 38 60 No 25 40

Response to chemotherapy No 21 21 Recurrent 58 58 Yes Non-recurrent 21 21

Ascites at diagnosis Yes 54 54

No 46 46

Post-treatment ascites Yes 42 42 No 58 58

Residual tumor None 48 48

<1 cm 35 35

1-2 cm 0 0 >2 cm 17 17

Deceased Yes 67 67

No 33 33

3.2.11 Immunohistochemistry

3.2.11.1 Staining

The primary antibodies and positive control tissues used for immunohistochemical study are listed in Table 3-9. Formalin-fixed, paraffin-embedded tissue sections (5µm- thick) mounted on SuperFrost slides were deparaffinized with xylene and rehydrated through a series of alcohols. For antigen retrieval, sections were placed in either 10 mM Tris base, 1 mM EDTA solution at pH 9.0 for Ki-67 and IL-6 or 10 mM sodium citrate buffer at pH 6.0 for the rest and exposed to repeated (twice) microwave heating of 10 92

min (or twice heating of 5 min for VEGF) at 750W. After 10 min incubation with 3% hydrogen peroxide in methanol for inactivation of endogenous peroxidase activity, sections were incubated with DAKO protein blocking solution for blocking of nonspecific binding of secondary antibody, followed by incubation with primary antibody at 4oC overnight. Specimens were then incubated with appropriate mouse or rabbit secondary antibody using EnVision Plus kit (DAKO) for 30 min and then with diaminobenzidine chromogen for 5 min. All slides were counterstained with hematoxylin to visualize the nuclei. For negative controls, the same specimens as my positive controls for each antibody were used but the primary antibodies were replaced with the primary antibody diluents.

Table 3-9 Primary antibodies and positive control tissues used for immunohistochemical study

Antibody (Ab) Antigen retrieval Ab dilution Positive Control Spry1 20 mins Citrate 1:500 Kidney Spry2 20 mins Citrate 1:100 Small bowel/ testis Spry4 20 mins EDTA 1:250 Testis ERK 20 mins Citrate 1:200 Breast/kidney/fallopian tube p-ERK 20 mins Citrate 1:100 Fallopian tube/prostate cancer Ki67 20 mins EDTA 1:100 Tonsil FGF-2 20 mins Citrate 1:200 Tonsil/testis VEGF 10 mins Citrate 1:300 Prostate cancer/breast cancer IL-6 20 mins EDTA 1:250 Tonsil

3.2.11.2 Scoring

Under light microscope using Leica DMLB Microsystems, staining of the epithelial cells was evaluated and scored by at least two observers blinded to patient outcome. In case of disagreement, the slides were re-examined and a consensus was reached by the observers. Representative slides were photographed using Leica DC200 digital imaging system. Semi-quantitative scoring was performed based on the average signal intensity and the percentage of immunoreactive cells. A four-value intensity score (0, no immunoreactivity; 1, weak intensity; 2, moderate intensity and 3, strong intensity) was 93

used as well as a four-value quantity score defined as follows: Spry1 (0, none; 1, 1- 33%; 2, 34-66%; and 3, 67-100% ) (Kwabi-Addo et al., 2004), ERK and p-ERK (0, none; 1, less than 10%; 2,10-50%; and 3, greater than 50%) (Handra-Luca et al., 2003), FGF, IL-6 and VEGF (0, none; 1, 1-25%; 2, 26-50%; and 3, greater than 50%) (Mattern et al., 1996; Terris et al., 1998). The average intensity and quantity scores for the five cores were then multiplied yielding a 10-point immunohistochemical score ranging from 0 (no staining) to 9 (extensive, strong staining) for each case. For Ki-67, the percentage of the positively stained cells among the total number of the tumor cells in the area was scored (Dowsett et al., 2011). For p-ERK and Ki-67, the proportion of cells showing a positive nuclear stain was considered as positive staining.

3.2.12 Statistical Analysis

3.2.12.1 In vitro study

For in vitro study, all data presented are representative of three independent experiments performed in triplicate. Statistical analysis was conducted using GraphPad Prism 6. Data are presented as mean ± SE. Student’s t-test was applied for unpaired samples and P values < 0.05 were considered significant. Since no significant difference was found between the data from +vector and -vector controls in the experiments with transiently- transfected SKOV-3, +vector was considered as their negative control for the statistical analysis.

3.2.12.2 Clinical study

All statistical analyses were performed using the statistical package SPSS, version 22 (SPSS Inc., Chicago, IL). The data were summarized using standard descriptive statistics and frequency tabulations. Wilcoxon matched-pairs signed rank test was used for comparison of the Spry1 expression between normal and cancer tissue. Associations between the clinicopathological parameters and the Spry1 expression were evaluated using Spearman correlation coefficient testing. The same test was used to assess the correlation between the expression of Spry1 and other parameters studied. The binary cut-off points of the parameters were identified using the Classification and Regression Tree (CART) algorithm which were near the median values. For outcome analysis, time-to-event variables were defined as the difference between the time of diagnosis and 94

the time of an event (death or disease recurrence). Accordingly, overall survival (OS) was defined as the time from surgery to death or to the end of the study and disease-free survival (DFS) was calculated from the date of surgery to recurrence or to the end of the study. The predictive value of Spry1 for OS and DFS was evaluated using the Kaplan- Meier method and the statistical significance between the survival curves was assessed by the log-rank test. Kaplan-Meier survival curves were constructed and presented as three different models. In model 1, high Spry1 and low Spry1 tumor groups were created based on the CART binary cut-off point of 3.5. Considering the Spry1 maximum score of 6 in the tumor samples (according to the 10-point immunohistochemical scoring method) divided by quartiles, the second model, including high (5, 6), medium (3, 4), low (1, 2) and no (0) Spry1 groups, was created. A third model was similarly created based on a maximum score of 9 which includes high (7-9), medium (4-6), low (1-3) and no (0) Spry1 groups. To assess the independent predictive value of Spry1 in the presence of other clinicopathological variables, the Cox univariate and multivariate proportional hazard models with 95% confidence interval (CI) were constructed. A similar methodology was applied to other parameters analyzed, including Spry2 and Spry4. For the outcome analyses of the Spry2 expression, the above three models were employed. With regard to Spry4, the first model was used along with the third one, namely Spry4 model 2, according to the Spry4 maximum immunohistochemical score of 9. For evaluation of the concomitant expression of Spry1 and Spry2, three further models were constructed based on the binary cut-off point of 3.5 as follows: Model 1: high Spry1 and high Spry2 vs. low Spry1 and low Spry2 vs. low Spry1 and high Spry2 vs. high Spry1 and low Spry2; Model 2: high Spry1 and high Spry2 vs. low Spry1 and low Spry2 vs. low Spry1 and high Spry2 AND high Spry1 and low Spry2; Model 3: high Spry1 and high Spry2 vs. low Spry1 and low Spry2. The binary model of the expressions of Spry1, Spry2 and Spry4 (so-called model 1) was employed for all Spearman correlation coefficient analyses. Univariate and multivariate logistic regression analyses were also conducted to ascertain the effects of Sprouty isoforms and other clinicopathological variables on the likelihood of the development of post-treatment ascites and chemotherapy refractory disease. Independent t-test was used for evaluation of association between tumor size and the Sprouty expression. A P-value of < 0.05 was considered statistically significant for all analyses. 95

4 Evaluation of the status and functional significance of the Sprouty expression in epithelial ovarian cancer cells: an in vitro study

4.1 Introduction

As reviewed earlier, Sprouty (Spry) proteins are evolutionarily-conserved, ligand- inducible modulators of the MAPK/ERK pathway and receptor tyrosine kinase (RTK) signaling. Of the four isoforms identified, Spry1, Spry2 and Spry4 are evidently involved in developmental and physiological processes through regulation of such cellular processes as differentiation, proliferation, migration and survival. Given aberrant activation of MAPK/ERK in cancer, deregulation of the Sprouty expression and its pathophysiological outcomes have been investigated in a number of human malignancies. To the best of our knowledge, however, altered expression of Sprouty proteins in EOC with likely impacts on tumor behavior has not been studied before.

As an introductory part of my project and an initial attempt towards my goal of understanding the role of Sprouty proteins in EOC, I evaluated in this chapter the expression of the Sprouty isoforms in normal and malignant epithelial ovarian cells (Part I) and subsequently explored how the Sprouty expression could impact EOC cell biology (Part II), in vitro.

4.2 Part I: Evaluation of the Sprouty expression in EOC cells and their normal counterparts

First of all, a comprehensive literature review was conducted (Masoumi-Moghaddam et al., 2014b). My literature search revealed that since discovery of Sprouty in 1998, the expression of Sprouty at mRNA and/or protein levels has been examined in various cancer cell lines. These include malignant cells of breast (Lo et al., 2004), prostate (Kwabi-Addo et al., 2004), liver (Fong et al., 2006), lung (Sutterluty et al., 2007), colon (Barbachano et al., 2010), medullary thyroid (Macia et al., 2012) and pituitary (Dorman et al., 2012) carcinoma as well as those derived from melanoma (Tsavachidou et al., 2004), fibrosarcoma (Lito et al., 2008), leiomyosarcoma (Lee et al., 2004), rhabdomyosarcoma (Schaaf et al., 2010), osteosarcoma (Rathmanner et al., 2013), gastrointestinal stromal tumor (Frolov et al., 2003) and lymphoid/hematopoietic 96

malignancies (Sanchez et al., 2008). Although the expression of Sprouty in the mammalian ovarian follicular cells has been reported earlier (Gallardo et al., 2007; Haimov-Kochman et al., 2005; Hamel et al., 2008; Jiang et al., 2011; Robert et al., 2001), it has not been documented before how Sprouty is expressed by EOC cells.

In the early stage of my study, a panel of seven commonly-used EOC cell lines, including OVCAR-3, SKOV-3, 1A9, A2780, CAOV-3, OV-90 and IGROV-1, as well as the primary human ovarian surface epithelial cell line HOSEpiC were selected in which the expression of Spry1, Spry2 and Spry4 was first evaluated and compared at protein level. This was followed by the analysis of the mRNA expression and the protein subcellular localization for two isoforms the expression of which were found to be more significantly altered in malignant cells.

4.2.1 Results

4.2.1.1 Different human EOC cell lines express different levels of Sprouty proteins as compared with normal epithelial ovarian cells.

To evaluate the physiological status of the Sprouty expression in normal epithelial ovarian cells in vitro, cellular contents of Spry1, Spry2 and Spry4 proteins were detected in the HOSEpiC primary epithelial ovarian cells by Western blot. As shown in Figure 4-1, the protein expression analysis revealed that the three isoforms are moderately expressed by HOSEpiC cells. To investigate how differently from their normal counterparts EOC cells express Sprouty proteins, I next performed similar analysis in my panel of EOC cell lines (Figure 4-1). Firstly, it was revealed that Sprouty proteins are differentially expressed by the seven EOC cell lines. In addition, different Sprouty isoforms were found to be differently expressed by the EOC cells. While OVCAR-3 cells expressed high levels of Spry1, it was expressed at moderate levels by 1A9 and A2780 cells, at low levels by IGROV-1 cells and at minimal levels by SKOV- 3, CAOV-3 and OV-90 cells. As for Spry2, whereas OVCAR-3 and 1A9 cells exhibited high levels of the protein expression, and A2780 and IGROV-1 cells expressed it moderately, the expression levels were low for CAOV-3 cells, very low for OV-90 cells and minimal for SKOV-3 cells. Spry4, on the other hand, was expressed by all cell lines, among which the highest and lowest expression levels were presented by OVCAR-3 and SKOV-3 cells, respectively. The other five cell lines indicated moderate 97

to low levels of Spry4 expression. Together, the combined expression of the three isoforms was found to be at the highest and lowest levels in OVCAR-3 and SKOV-3 cells, respectively.

Secondly, when quantitative comparison of the protein expression was carried out using ImageQuant software and the nonuniform expression patterns of Sprouty isoforms across the seven cancer cell lines studied were individually compared against the Sprouty expression pattern in HOSEpiC cells, nonconformity was evident (Figure 4-2). While the expression of Spry1, Spry2 and Spry4 in OVCAR-3 cells was significantly higher than that in the normal control (p values of 0.0012, 0.0004, and 0.0005, respectively), SKOV-3, CAOV-3, and OV-90 cells expressed significantly lower levels of Spry1 (p values of 0.0002, 0.0015, and 0.0002, respectively), Spry2 (p values of <0.0001, 0.0146, and 0.0003, respectively) and Spry4 (p values of 0.0046, 0.0042, and 0.0016, respectively). Compared with HOSEpiC cells, A2780 cells indicated similar expression level of Spry1 while expressing significantly lower levels of Spry2 (p value: 0.0032) and Spry4 (p value: 0.0140). Although IGROV-1 cells expressed Spry1 and Spry4 significantly lower than did the control group (p values of 0.0002 and 0.0490, respectively), decline in the expression of Spry2 was not significant (p value: 0.1074). Finally, 1A9 cells showed similar levels of Spry1, insignificantly higher levels of Spry2 (p value: 0.1657) and significantly lower levels of Spry4 (p value: 0.0089) as compared with the control. Overall, of the three isoforms studied, Spry1 was the one the expression pattern of which indicated the most significant alteration across the EOC cells. Amongst the cancer cell lines studied, the highest and lowest levels of the three isoforms, collectively, were observed in OVCAR-3 and SKOV-3 cells, respectively. In sum, various EOC cell lines were found to differentially express Spry1, Spry2 and Spry4 proteins when the expression of the three isoforms was analyzed in each cell line and when the expression of each individual isoform across the EOC cell lines was compared with that in the HOSEpiC normal cells. 98

Figure 4-1 Analysis of Spry1, Spry2 and Spry4 protein expressions in human epithelial ovarian cancer cell lines and HOSEpiC human ovarian surface epithelial cells by Western blot. As seen, Spry1, Spry2 and Spry4 were differentially expressed by both cancer and normal (HOSEpiC) cells. In addition, altered expression of Sprouty isoforms in cancer cells was evident when the expression of each isoform by individual EOC cell lines was compared to that by HOSEpiC cells used as the normal control. Images are representative of three independent experiments. GAPDH protein was used as a loading control. 99

Figure 4-2 Quantitative analysis of Sprouty protein expressions in malignant and normal epithelial ovarian cells using ImageQuant software. Following Western blot, the expression of Sprouty isoforms by individual EOC cells was quantified in arbitrary units and normalized against that in HOSEpiC cells. As shown, Spry1 (A), Spry2 (B) and Spry4 (C) proteins were differentially expressed by cancer cell lines when the expression of each isoform was compared to that in normal control. Loading amounts and quantified values were normalized against GAPDH values, too. Data are shown as means ± S.E of at least three independent experiments. Significant values (p value <0.05) are indicated with asterisks. 100

4.2.1.2 Spry1 and Spry2 are differentially expressed by EOC cells at mRNA levels.

Since the expression alterations of Spry1 and Spry2 proteins observed across my panel of EOC cell lines were found to be more prominent than that of Spry4, the expressions of Spry1 and Spry2 at mRNA level and their possible correspondence with the protein expression patterns were explored next. For this purpose, the yield and purity of the RNA extracted from the normal and cancer cells were determined by a NanoDrop 2000 Spectrophotometer measuring the absorbance at 230, 260 and 280 nm, and its integrity was evaluated by gel electrophoresis determining the 28S/18S rRNA ratio (Figure 4-3). Then, reverse transcription polymerase chain reaction (RT-PCR) analysis of the total RNA samples was carried out. The expected product size for Spry1 and Spry2 RNAs was 438 bases and 471 bases, respectively (Figure 4-4).

As shown in Figure 4-4, Spry1 mRNA is differentially expressed by EOC cell lines in vitro. While expressed minimally by SKOV-3 and CAOV-3 cells and at a relatively high level by 1A9 cells, Spry1 mRNA was expressed by other EOC cell lines as well as by HOSEpiC cells at low to moderate levels. The expression of Spry2 mRNA, however, was more prominent in cancer cells than in the normal cells and indicated a fairly unified pattern across the whole range of the EOC cell lines studied. Taken together, while the expression of Spry1 mRNA by EOC cells was nonuniform, Spry2 mRNA expression followed a comparatively unified pattern of overexpression in all EOC cells. In comparison with Spry2, Spry1 indicated a more compatible correspondence between the expression patterns at mRNA and protein levels. 101

Figure 4-3 Determination of integrity and purity of RNA samples extracted from malignant and normal epithelial ovarian cells. A. Analysis of total RNA samples from OVCAR-3 (1), SKOV-3 (2), 1A9 (3), A2780 (4), CAOV-3 (5), OV-90 (6), IGROV-1 (7) and HOSEpiC (8) cells separated by denaturing agarose gel electrophoresis demonstrates a 28S/18S rRNA intensity ratio of around 2, indicative of good to very good quality RNA. B. Representative photo of the RNA yield and purity analysis of OV-90 using a NanoDrop 2000 Spectrophotometer shows A260/A280 and A260/A230 OD ratios of very close to 2.0, indicative of pure RNA with no protein and solvent contamination, respectively. Images are representative of three independent experiments. 102

Figure 4-4 RT-PCR evaluation of Spry1 and Spry2 mRNA expressions in malignant and normal epithelial ovarian cells. As seen, Spry1 mRNA (top) was differentially expressed by EOC cells OVCAR-3 (1), SKOV-3 (2), 1A9 (3), A2780 (4), CAOV-3 (5), OV-90 (6), and IGROV-1 (7). Spry1 mRNA was expressed at minimal levels in SKOV-3 and CAOV-3 cells, at a relatively high level in 1A9 cells and at low to moderate levels in the remaining cells. Spry2 mRNA (middle), however, indicated a fairly unified pattern of overexpression across the EOC cell lines. The product size for Spry1 and Spry2 RNAs was 438 bases and 471 bases, respectively. HOSEpiC cells 103

were used as the normal control. Images are representative of three independent experiments. β-actin (bottom) was used as a reference.

4.2.1.3 Immunocytochemical staining of Spry1 and Spry2 displays a predominant vesicular cytoplasmic staining in OVCAR-3, SKOV-3, and HOSEpiC cells.

To determine subcellular distribution of Spry1 and Spry2 proteins in EOC and normal epithelial ovarian cells in vitro, I performed immunocytochemical staining of Spry1 and Spry2 in OVCAR-3, SKOV-3 and HOSEpiC cells using the isoform-specific antibodies. The two cancer cell lines were selected based on the fact that they represent the cells with the highest and lowest expression of both Spry1 and Spry2 proteins amongst the cell lines studied. Confocal immunofluorescence microscopy revealed strong, moderate and weak staining intensity of both isoforms in OVCAR-3, HOSEpiC and SKOV-3 cells, respectively. Moreover, although nuclear staining of Spry1 was observed, the cytoplasmic vesicular localization was the predominant staining pattern observed for both Spry1 and Spry2 (Figure 4-5, Figure 4-6 and Figure 4-7). 104

Figure 4-5 Immunocytochemical staining of Spry1 and Spry2 proteins in OVCAR- 3 cells. Sprouty isoforms were stained with affinity-purified specific antibodies and viewed by confocal immunofluorescence microscopy. Strong green fluorescence represents the expression of Spry1 (panel A) and Spry2 (panel B) in OVCAR-3 cells. As regards subcellular localization, a predominantly cytoplasmic vesicular staining for both isoforms along with a nuclear staining for Spry1 is evident. Nuclei counterstained with propidium iodide are shown in red. Images captured with a 60X oil immersion objective are representative of three independent experiments. Scale bar: 50 μm. 105

Figure 4-6 Immunocytochemical staining of Spry1 and Spry2 proteins in SKOV-3 cells. Spry1 (panel A) and Spry2 (panel B) proteins were stained with affinity-purified specific antibodies and viewed by confocal immunofluorescence microscopy (green). Nuclei were counterstained with propidium iodide (red). As seen, Spry1 and Spry2 were detected with weak intensity in SKOV-3 cells. With respect to subcellular localization, photographs indicate a predominantly cytoplasmic vesicular staining for both isoforms, as well as a nuclear staining for Spry1. Images captured with a 60X oil immersion objective are representative of three independent experiments. Scale bar: 50 μm. 106

Figure 4-7 Immunocytochemical staining of Spry1 and Spry2 proteins in HOSEpiC cells. Using affinity-purified specific antibodies, Spry1 (panel A) and Spry2 (panel B) proteins were detected in HOSEpiC cells by confocal immunofluorescence microscopy (green). Propidium iodide staining was performed for nuclei (red). As seen, the staining intensity of Spry1 and Spry2 proteins was moderate. Immunostaining also displayed a predominantly cytoplasmic localization for both isoforms along with a nuclear staining for Spry1. Images captured with a 60X oil immersion objective are representative of three independent experiments. Scale bar: 50 μm. 107

4.3 Part II: Functional significance of the Spry1 expression in EOC cells with distinct levels of the protein

Sprouty proteins, as modulators of RTK signaling, have been shown to regulate key cellular functions, including proliferation, differentiation, motility and survival. These cellular activities are central to tumor growth, progression and metastasis. Moreover, mutation and/or aberrant activation of RTKs and their downstream cascades are associated with cancer (Hanahan and Weinberg, 2000). On this basis, altered expression of Sprouty is expected to contribute to malignant behavior of cancer cells. Accordingly, as reviewed in Chapter 1, regulatory functions of Sprouty as well as functional consequences of alterations in the Sprouty expression has been studied in a variety of human cancer cells in vitro.

Under physiological conditions, Sprouty has been implicated in developmental and physiological processes in the mammalian ovary, including oocyte competence and female fertility (Gallardo et al., 2007; Haimov-Kochman et al., 2005; Hamel et al., 2008; Jiang et al., 2011; Robert et al., 2001). Nevertheless, the status and functional significance of the Sprouty expression in EOC have not been studied before. In the first part of this chapter, I observed that, among the three Sprouty isoforms studied, Spry1 was associated with more prominent alteration at both mRNA and protein expression levels across my panel of EOC cell lines. Here, I investigated in the second part how Spry1 transfection or silencing could impact the EOC cell behavior. For this purpose, the Spry1-expressing cell line 1A9 and the Spry1-nonexpressing cell line SKOV-3 were employed. In addition, a number of underlying mechanisms responsible for some of the outcomes observed were explored.

4.3.1 Results

4.3.1.1 Induced expression of Spry1 is deleterious for viability of SKOV-3 cells.

To assess the effect of the Spry1 expression on ovarian cancer biology, I intended to examine viability of SKOV-3 cells after transfection with the plasmid encoding the full- length sequence of Spry1. Initially, the expression of the Spry1 protein was detected by Western blot at 8h, 24h, 48h and 72h, post-transfection (Figure 4-8). Next, the impact of sustained as well as transient expression of the protein on the cell viability was 108

evaluated. In my stable transfection experiment under Geneticin selection for 14 days, no Spry1-transfected clones survived whereas numerous colonies of the +vector SKOV- 3 formed (Figure 4-9). Using the trypan blue (Figure 4-10) and MTT (Figure 4-11) assays, I also found that the expression of Spry1 resulted in a significant decrease in the growth and proliferation of the transfected cells 72h post transfection (p value of 0.0003 and 0.0042 for trypan blue and MTT assay, respectively). Taken together, I observed that induced expression of Spry1 adversely impacts the SKOV-3 cell viability, in vitro.

Figure 4-8 Western blot analysis of Spry1 protein expression in Spry1-transfected SKOV-3 cells. SKOV-3 cells were transfected with Spry1 plasmid and the expression of Spry1 was detected at 8 (8h-trans), 24 (24h-trans), 48 (48h-trans) and 72 hours (72h- trans), post-transfection. Two negative control groups, namely +vector and –vector, representing SKOV-3 cells transfected with pcDNA3.1 plasmid and those with no plasmid transfection, respectively, were included. The Spry1-expressing cell line 1A9 was used as a positive control for the protein expression. Images are representative of three independent experiments. GAPDH blot is shown as the loading control of the experiment.

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Figure 4-9 Spry1 stable transfection and its effect on viability of SKOV-3 cells. SKOV-3 cells were transfected with Spry1 plasmid, subjected to Geneticin selection for 14 days and stained with crystal violet. pcDNA3.1-transfected and mock cells, namely +vector and –vector groups, respectively, were included as control. As seen, while numerous colonies were present in +vector group, none formed in Spry1-transfected and -vector groups. Images are representative of three independent experiments. 110

Figure 4-10 Spry1 transient transfection and its effect on viability of SKOV-3 cells detected by trypan blue assay. SKOV-3 cells were transfected with Spry1 plasmid and the number of live cells was calculated and compared to negative control groups (+vector and –vector) 24h, 48h and 72h post transfection. The graph demonstrates significant decrease in growth and proliferation of Spry1-transfected cells on day 3 post- transfection. Data are shown as mean ± SE. Significant values (p value <0.05) are marked by asterisks.

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Figure 4-11 Spry1 transient transfection and its effect on viability of SKOV-3 cells detected by MTT assay. SKOV-3 cells were transfected with Spry1 plasmid and subjected to MTT assay 24h (a), 48h (b) and 72h (c) post transfection. The graph indicates a significant decrease in growth and proliferation of the Spry1-transfected cells on day 3 post-transfection as compared to negative control groups (+vector and – vector). Results are shown as optical density (OD) units which are linearly correlated with the cell number. Data are shown as mean ± SE. Significant values (p value <0.05) are marked by asterisks. 112

4.3.1.2 Spry1 transfection of SKOV-3 cells diminishes migration and invasion.

To investigate the influence of the Spry1 expression on other mitogen-dependent processes, three different assays were employed in the next step to compare the motility and invasion of the Spry1-transfected cells with those of the negative control cells. In the scratch assay, the percent closure of the wounded area in the Spry1 transfection group showed a significant decline measured at 20h (p value: 0.0232) and 24h (p value: 0.0046) after scratch (Figure 4-12). Results from the migration assay (Figure 4-13) indicated that the number of the Spry1-transfected cells migrated was significantly lower than their control counterparts, 6h (p value: 0.0090) and 12h (p value: 0.0002) after plating. The invasion assay (Figure 4 -14) similarly showed reduced number of the invaded cells in the Spry1 transfection group examined at 6h (p value: 0.0159) and 12h (p value: 0.0005). In sum, induced expression of Spry1 was associated with attenuation of the SKOV-3 cell motility and invasion, in vitro. 113

Figure 4-12 Spry1 transfection of SKOV-3 cells and its effect on wound healing. SKOV-3 cells were transiently transfected with Spry1 plasmid. Transfected and control groups were then subjected to scratch assay and imaged at 0h, 20h and 24h after scratch (top). Data analysis showed a significant decrease in the percent closure by Spry1 transfection group as compared to negative controls (+vector and –vector) 20h and 24h after scratch (bottom). Images are representative of three independent experiments. Data are shown as mean ± SE. Significant values (p value <0.05) are marked by asterisks. 114

Figure 4-13 Effect of Spry1 transfection on SKOV-3 cell migration. SKOV-3 cells were transfected with Spry1 plasmid. Transfected and negative control (+vector and – vector) groups were subjected to migration assay using 24-well Transwell system and photographed 6h and 12h after plating (top). Data analysis showed a significantly lower number of the migrated cells in the transfection group at both endpoints (bottom). Images are representative of three independent experiments. Data are shown as mean ± SE. Significant values (p value <0.05) are marked by asterisks. 115

Figure 4 -14 Spry1-induced inhibition of invasion in SKOV-3 cells. SKOV-3 cells were transfected with Spry1 plasmid. Transfected and negative control (+vector and – vector) groups were subjected to invasion assay using matrigel-coated Transwell plates and imaged 6h and 12h after plating (top). Results demonstrate a significantly lower number of the invaded cells in the transfection group assayed at the two endpoints (bottom). Images are representative of three independent experiments. Data are shown as mean ± SE. Significant values (p value <0.05) are marked by asterisks.

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4.3.1.3 Spry1 knockdown enhances growth and proliferation of the 1A9 human ovarian cancer cells.

To evaluate how the inhibited expression of Spry1 could affect the ovarian cancer cell biology, Spry1 was initially silenced in 1A9 cells using the specific siRNA, with the protein expression being examined at 24h, 48h and 72h post transfection (Figure 4-15 A). Both the Spry1-expressing (control) and Spry1-silenced cells were then assayed for their ability to grow and proliferate. In the trypan blue assay (Figure 4-15 B), the diminished expression of Spry1 in the silenced cells was associated with a significant increase in their growth and proliferation assessed at 48h (p value: 0.0365) and 72h (p value: 0.0228) endpoints. MTT assay of the cell viability (Figure 4-16) returned similar results as enhanced growth and proliferation of 1A9 cells in the Spry1 knockdown group, 48h and 72h post silencing (p value of 0.0011 and 0.0024, respectively). Collectively, enhanced viability of 1A9 cells was resulted when the expression of Spry1 was knocked down, in vitro. 117

Figure 4-15 Effect of Spry1 knockdown on viability of 1A9 cells. A. Western blot analysis of the Spry1 expression in 1A9 cells after Spry1 silencing with specific siRNA at 24h (24h-silenced), 48h (48h-silenced) and 72h (72h-silenced) as compared to the control. B. Trypan blue assay on Spry1-silenced cells and their control counterparts showing a significant increase in the growth and proliferation of the silenced cells at 48h and 72h endpoints. Images are representative of three independent experiments. Data are shown as mean ± SE. Significant values (p value <0.05) are marked by asterisks. 118

Figure 4-16 MTT assay of 1A9 cells after Spry1 knockdown. Spry1-silenced cells and their control counterparts were subjected to MTT assay 24h (a), 48h (b), and 72h (c) post transfection. Results indicate a significant increase in the growth and proliferation of the silenced cells at 48h and 72h endpoints. Data are presented as optical density (OD) units which are linearly correlated with the cell number. Data are shown as mean ± SE. Significant values (p value <0.05) are marked by asterisks. 119

4.3.1.4 Spry1 knockdown in 1A9 cells enhances wound healing and augments cell migration and invasion.

To investigate the correlation between the expression of Spry1 and other determinants of a malignant phenotype, I next tested both Spry1-silenced and control 1A9 cells for their capability to migrate and invade in vitro. My scratch assay (Figure 4-17) indicated that Spry1 knockdown led to a significantly increased percent closure at the 40h endpoint (p value: 0.0259). In the migration assay, the Spry1-silenced group showed a significantly higher number of migrated cells, 14h (p value: 0.0125) and 20h (p value: 0.0090) after plating (Figure 4-18). The invasion assay (Figure 4-19) indicated that 1A9 cell invasion through the matrigel-coated membrane at 14h and 20h was significantly higher in the Spry1-silenced group (p value of 0.0298 and 0.0373, respectively). Taken together, the knocked-down expression of Spry1 resulted in enhanced motility and invasion of 1A9 cells, in vitro. 120

Figure 4-17 Effect on wound healing of Spry1 knockdown in 1A9 cells. Spry1- silenced 1A9 cells and their control counterparts were subjected to scratch assay and photographed 0h, 24h and 40h after scratch (top). Data analysis showed that the percent closure developed by the Spry1-silenced cells 40h after scratch was significantly higher than that observed in control (bottom). Images are representative of three independent experiments. Data are shown as mean ± SE. Significant values (p value <0.05) are marked by asterisks. 121

Figure 4-18 Effect of Spry1 knockdown on migration of 1A9 cells. Spry1-silenced 1A9 cells were subjected to migration assay using 24-well Transwell system, imaged 14h and 20h after plating and compared to their control counterparts (top). As a result, a significantly higher number of Spry1-silenced cells migrated at the two endpoints (bottom). Images are representative of three independent experiments. Data are shown as mean ± SE. Significant values (p value <0.05) are marked by asterisks. 122

Figure 4-19 Effect of the Spry1 knockdown on invasion of 1A9 cells. Spry1-silenced 1A9 cells were subjected to invasion assay using matrigel-coated Transwell plates, photographed 14h and 20h after plating (top) and compared to control. The graph (bottom) indicates that the number of Spry1-silenced cells invaded at both endpoints was significantly higher than their control counterparts. Images are representative of three independent experiments. Data are shown as mean ± SE. Significant values (p value <0.05) are marked by asterisks. 123

4.3.1.5 Induced expression of Spry1 triggers apoptotic events in SKOV-3 cells and inhibits activation of ERK and AKT.

To investigate the implication of apoptotic processes in the Spry1-induced growth inhibition found in this study, the expression of a number of apoptosis-associated proteins in the Spry1-transfected SKOV-3 cells was then examined. As detected by Western blot 48 hours post transfection, overexpression of the pro-apoptotic Bax along with decreased expression of the antiapoptotic proteins Bcl-2 and Bcl-xl were observed. Attenuation of procaspases 3, 7, 8 and 9 as well as cleavage of PARP, an indicator of the caspase 3 activity, was also evident (Figure 4-20). Given the pivotal role of MAPK/ERK and AKT pathways in cell proliferation and survival, I next evaluated the activation status of these pathways after Spry1 transfection. My results showed that induced expression of Spry1 markedly reduced activated forms of ERK and AKT. Moreover, the expression of phosphatase and tensin homolog (PTEN), a major negative regulator of AKT signaling, was found to be increased. This was accompanied by a concomitant decrease in phospho-PTEN known as the inactive form of cytoplasmic PTEN (Figure 4-21). No significant change in the expression pattern of the above proteins was found in the Spry1-silenced 1A9 cells. 124

Figure 4-20 Effect of Spry1 transfection/knockdown on the expression of apoptosis-related proteins in EOC cells. Western blot analysis was performed on Spry-1 transfected SKOV-3, Spry-1 silenced 1A9, and their control counterparts 48h post-transfection. Results indicate activation of apoptotic processes as overexpression of pro-apoptotic Bax, decreased expression of antiapoptotic Bcl-2 and Bcl-xl, attenuation of procaspases 3, 7, 8 and 9, and cleavage of PARP in Spry1-transfected cells. No significant change in the expression of these proteins was found in Spry1-silenced cells. Images are representative of three independent experiments. GAPDH protein was used as a loading control. 125

Figure 4-21 Effect of Spry1 transfection/knockdown on the expression of proteins associated with proliferation and survival in EOC cells. Western blot analysis was performed on Spry-1 transfected SKOV-3, Spry-1 silenced 1A9, and their control counterparts 48h post-transfection. As seen, reduced expression of the activated forms of ERK and AKT along with increased expression of PTEN and concomitant decrease of phospho-PTEN were evident in Spry1-transfected cells, indicating repression of ERK and AKT, with involvement of PTEN for the latter. No significant change in the expression pattern of these proteins was found in Spry1-silenced cells. Images are representative of three independent experiments. GAPDH protein was used as a loading control. 126

4.4 Discussion

My initial data indicates that surface epithelial cells of normal ovaries express Spry1, Spry2 and Spry4. Mammalian Spry1, Spry2 and Spry4 are expressed in the embryo and various adult tissues. While Spry2 appears to be ubiquitously expressed, the expression of other isoforms shows organ/tissue specificity, with Spry3 being primarily expressed in adult brain and testis (Minowada et al., 1999). Apart from its crucial role in embryogenesis, Sprouty has been implicated in regulation of physiological processes in adult organs. With regard to the ovarian physiology, Spry2 expression in human pre- ovulatory follicles and granulosa-lutein cells has been suggested to contribute to follicle maturation, ovulation and corpus luteum formation (Haimov-Kochman et al., 2005). Sprouty has also been implicated in mammalian oocyte competence (Robert et al., 2001) and female fertility (Gallardo et al., 2007).

My results also indicate the differential expression of the Spry1 and Spry2 proteins across the EOC cell lines studied with a general trend of downregulation of Spry1 and/or Spry2. These cell lines are the commonly used in vitro representatives of various subtypes of EOC which are originally derived from individual patients demonstrating divergent clinicopathological characteristics. Differential expression patterns of the Sprouty isoforms have been previously reported in a variety of malignant cell types, supporting the notion that the expression of Spry is isoform-, cell- and context- dependent. Kwabi-Addo et al. observed that the three commonly used prostate cancer cell lines (LNCaP, DU145, and PC3) all expressed lower levels of Spry1 protein than did the normal epithelial cells (Kwabi-Addo et al., 2004). Vanas et al reported differential expression of Spry2 and Spry4 in 6 breast cancer cell lines where downregulation of Spry2 and Spry 4 was shown in 4 and 5 lines, respectively. Sutterluty et al (Sutterluty et al., 2007) and Song et al (Song et al., 2012) also reported that Spry2 is expressed at different levels by a panel of non-small cell lung cancer and hepatocellular cancer cell lines, respectively. In contrast, studying four fibrosarcoma cell lines and two malignantly-transformed human fibroblasts, Lito et al (Lito et al., 2008) reported upregulation of Spry2 in all the 6 cell lines. In the present study, the expression of both Spry1 and Spry2 isoforms in OVCAR-3 cells was significantly higher than that in the other cancer cell lines and the control group. All these results reported by others and us imply that although decreased expression of Spry1 and/or 127

Spry2 was observed more frequently amongst the cancer cell lines, decline in the Sprouty expression might not necessarily be required in all cancers.

When compared to the results of the western blot analysis, my evaluation of the Sprouty mRNA expression indicated that the expression of Sprouty at protein level may not necessarily correspond to that at mRNA level. This might be due to the complex regulation of the Sprouty protein family on various levels. As reviewed in Chapter 1, this regulatory mechanism not only includes transcriptional regulation, but also involves post-translational modulation, including differential subcellular localization and/or cellular content modification via interaction with a variety of adaptors, regulators and mediators.

Of the three Sprouty isoforms evaluated in my study, Spry1 was associated with more prominent alteration at both mRNA and protein levels across my panel of EOC cell lines. I also observed that while the expression of Spry1 was moderately positive in 1A9 cells, it was barely detectable in SKOV-3 cells. Meanwhile, it has been shown before that SKOV-3 and 1A9, respectively, exhibit high and low potentials for migration (Bijman et al., 2008) and invasion (Lu et al., 2008; Swaminathan et al., 2011; Xu et al., 2008; Zhang et al., 2007). On this basis and given the key role of Sprouty proteins in regulation of cellular functions, including proliferation, differentiation, motility and survival, I postulated that the cellular content of the Spry1 protein could be a determinant of the EOC cell behavior. To test my hypothesis, I intended to examine how alteration of the Spry1 expression in SKOV-3 and 1A9 cells could impact their malignant phenotype assayable by functional tests. My results demonstrate that while induced expression of Spry1 in the EOC cell line with minimal Spry1 content (SKOV- 3) attenuates cell proliferation and diminishes survival, knockdown of the protein expression in the Spry1-expressing cell line (1A9) enhances cell viability. My findings are in line with the results from earlier studies on a number of normal cells. Gross et al (Gross et al., 2001) showed that Spry1 inhibits growth and differentiation of NIH3T3 fibroblasts. Spry1 negative regulation of the endothelial cell proliferation has been indicated in HUVEC cells by Impagnatiello et al (Impagnatiello et al., 2001) and Lee et al (Lee et al., 2010a). Using CPAE and ABAE endothelial cells, Huebert et al (Huebert et al., 2004) and Sabatel et al (Sabatel et al., 2010) have consistently reported Spry1- induced inhibition of the endothelial cell proliferation. Xiang et al (Xiang et al., 2010) 128

showed that genistein, a phytoestrogen with potential cardioprotective effects, modulates proliferation of quiescent endothelial cells against that of vascular smooth muscle cell (VSMC) through regulating the Spry1 expression. Spry1 capability to inhibit cell growth and proliferation has also been explored in a number of cancer cell lines before. Kwabi-Adoo et al (Kwabi-Addo et al., 2004) reported that overexpression of Spry1 in the prostate cancer cell lines LNCaP and PC3 had an inhibitory effect on colony formation, cell proliferation and viability. In a study by Macia et al (Macia et al., 2012), the expression of Spry1 reportedly restrained the proliferation of the human medullary thyroid carcinoma cell line TT in vitro and significantly inhibited tumor growth in the murine xenografts. Jin et al (Jin et al., 2013) demonstrated that Pokemon- or miR-21-induced suppression of Spry1 stimulated growth and proliferation of the QGY-7703 hepatocellular carcinoma cells while its upregulation inhibited clonogenic growth and proliferation in vitro. Sabatel et al (Sabatel et al., 2010) found that the positive Spry1 regulation induced by 16 K prolactin can delay tumor growth in a chimeric mouse model of human colon carcinoma. Another aspect of the Spry1 function in my study was exhibited when induced expression of Spry1 in SKOV-3 cells attenuated cell motility and invasion while Spry1 knockdown in 1A9 cells, conversely, promoted migration and invasion. In agreement, Sabatel et al found in their study that partial silencing of Spry1 not only protected ABAE endothelial cells from apoptosis and enhanced cell proliferation, but also promoted cell migration in vitro (Sabatel et al., 2010). Our lab also reported earlier that Spry1 is a partner protein of the urokinase-type plasminogen activator receptor (uPAR) (Mekkawy et al., 2010) which is able to inhibit uPAR-stimulated migration and invasion in the Saos-2 osteosarcoma, MDA-MB-231 breast cancer and HCT116 colorectal cancer cell lines (Mekkawy and Morris, 2013).

Exploring mechanisms underlying anti-proliferative and anti-survival effects of the Spry1 transfection in EOC cells, I found that induced expression of Spry1 activates proapoptotic processes, with implication of Bcl-2 protein family and caspase pathways. My results also indicate that Spry1 expression inhibits activation of ERK and AKT in SKOV-3 cells. The role of Sprouty protein family in regulating ERK and AKT stimulation of cell proliferation and survival was discussed in Chapter 1. This regulatory function has been studied in a number of cancer cells, including those derived from leiomyosarcoma (Lee et al., 2004) and cervical (Edwin et al., 2006), liver (Fong et al., 129

2006; Wang et al., 2012a) and breast (Vanas et al., 2014) cancers. Moreover, my results implicate PTEN in the Spry1-induced inhibition of AKT where increased amount and activity of PTEN accompanied attenuation of AKT phosphorylation. It has been shown that Sprouty mediates its anti-proliferative effects, at least in part, by increasing the amount and activity of PTEN that in turn attenuates AKT signaling (Edwin et al., 2006). In agreement, Polytarchou et al. (Polytarchou et al., 2011) have argued for a hypoxia- activated, Akt-dependent pathway promoting tumor resistance where the microRNA miR-21 induced by Akt targets and downregulates Spry1, PTEN and programmed cell death 4 (PDCD4), resulting in enhanced cell survival.

Taken together, my results highlight the role of Spry1 in EOC cell biology. Since cell proliferation, migration, invasion, and survival are central to the development, progression, and dissemination of malignant conditions, I next investigated clinical relevance of the Spry1 protein expression in patients with EOC.

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5 The status and the clinical relevance of the Spry1 protein expression in EOC: a retrospective study

5.1 Introduction

As the second most commonly diagnosed gynaecological cancer, with 1,305 new cases in Australian women in 2010, epithelial ovarian cancer (EOC) was the most common cause of death from gynaecological malignancies in Australia in 2011, accounting for 4.8 per cent of all cancer deaths in women (Cancer Australia, Accessed June 2014 ).

Most women with EOC have advanced disease at diagnosis. The late presentation and widespread abdominal metastasis account for the high death rate. Despite invasive surgery and platinum-based cytotoxic chemotherapy as the standard of care for advanced disease, episodes of recurrent disease, progressively shorter disease-free intervals and resistance to chemotherapy will develop in most cases (Jayson et al., 2014).

Since discovery in 1998 (Hacohen et al., 1998), Sprouty proteins have been increasingly implicated in the multilayered, complex regulation of MAPK/ERK pathway and receptor tyrosine kinase (RTK) signaling. As such, this protein family has been shown to regulate processes central to the development, progression and dissemination of malignant conditions, including cell proliferation, migration, invasion and survival. For the past decade, deregulation of Sprouty has been investigated in a variety of cancers. Nevertheless, the expression status of Sprouty and its clinicopathological significance in EOC have not been investigated before. In my initial study, I demonstrated differential expression of Spry1 and Spry2 proteins in a panel of EOC cell lines where a tendency for downregulation of Spry1 and/or Spry2 was evident. I also investigated the functional outcomes of induced alterations in the expression of Spry1 in EOC cells in vitro and observed inverse correlation between the Spry1 expression and cell growth, proliferation, migration and invasion. To evaluate the clinical relevance of these findings, the expression status of Spry1 protein in a cohort of 100 patients with EOC and its association with clinicopathological features as well as with survival and recurrence were retrospectively explored. In this chapter, results of this study are reviewed and discussed. 131

5.2 Results

5.2.1 Spry1 protein is downregulated in EOC.

To quantitatively compare the expression of Spry1 protein in normal and neoplastic ovarian epithelium, I quantitated Spry1 expression based on the methodology described in Section 3.2.11.2. My data showed variable expression of Spry1 protein in both normal and cancerous tissues. Although some normal tissues had minimum (score 0: 7%) or maximum (score 9: 5%) staining, the vast majority of cases showed mild to moderate staining in their normal epithelium with immunohistochemistry score of 1-3 and 4-6, respectively. Ovarian cancer epithelium also exhibited variable expression of the protein, from minimal (score 0: 14%) to mild to moderate. However, there was no cancerous tissue with maximum staining. While cells with mild staining intensity largely showed a cytoplasmic pattern, perinuclear staining was commonly associated with moderate and strong intensities. When the protein expression in tumor tissue was compared to that in normal tissue, significant downregulation of Spry1 (p value: 0.004) in tumor tissue was revealed (Figure 5-1).

Due to the variability of the protein expression in different samples, I also compared the staining scores of normal and cancer tissues from the same patient for a more meaningful deduction. Overall, 42% of cancer tissues had a lower staining score than their matched normal tissues. In 24% of the EOC samples, however, the cancerous tissue received higher Spry1 expression score than did the matched normal epithelium (Figure 5-2). When the total of 100 tumor samples were stratified by the cut-off point of 3.5 into high- (>3.5) and low- (≤3.5) expressing groups, 62 cases (62%) were identified as patients with Spry1 low-expressing tumors. 132

Figure 5-1 Immunohistochemical analysis of the Spry1 expression in EOC. A. Representative photographs demonstrate high (left) and low (right) levels of the Spry1 expression in EOC tissues using immunohistochemistry (magnification= 40x). B. Comparison of the protein expression scores in normal and tumor tissues indicated significant downregulation of Spry1 expression in EOC. Data are represented as mean expression score ± SE (left), as well as maximum and minimum expression score (right). Significant values (p value <0.05) are marked by asterisks.

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Figure 5-2 Comparison of Spry1 protein expression levels in normal and malignant ovarian epithelial tissues after immunohistochemical scoring. The graph demonstrates that 42% (left bar) and 24% (right bar) of cases showed lower (TN) levels of Spry1 expression in their tumor tissue than in their matched normal tissue, respectively. In 34% of patients (middle bar), tumor and matched normal tissue scored equally (T=N).

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5.2.2 Spry1 expression inversely correlates with the expression of p-ERK/ERK and Ki67 in EOC.

Given the aberrant activation of MAPK/ERK in cancer and the role of Sprouty proteins in regulation of the pathway, immunohistochemical analysis and scoring of the tissue samples for the expression of ERK and p-ERK were then performed (Figure 5-3). Phosphorylation of ERK is the final step in the activation of MAPK/ERK pathway. ERK and p-ERK appeared to be expressed in both the cytoplasm and nucleus. Only nuclear staining was considered in the scoring of p-ERK. My data demonstrated significant upregulation of p-ERK in tumor tissue (p value <0.0001) despite insignificant difference between the expressions of ERK in tumor and matched normal tissue samples. As a result, p-ERK/ERK expression ratio as an indicator of ERK activation was significantly higher (p value <0.0001) in tumor tissues (Figure 5-4). Moreover, the expression of Ki67, known as a tumor proliferation marker with a nuclear staining, was also immunohistochemically analyzed and scored. Finally, possible correlations between the expression of Spry1 and these variables were analyzed whereby significant negative correlations of Spry1 with p-ERK/ERK (p value: 0.045, correlation coefficient= - 0.201) and Ki67 (p value: 0.010, correlation coefficient= - 0.256) were revealed (Table 5-1). 135

Figure 5-3 Expression of ERK, phospho-ERK and Ki67 in EOC. Representative photographs demonstrate high (left) and low (right) levels of ERK (top), phospho-ERK (middle) and Ki67 (bottom) expression in immunohistochemically stained EOC tissues (magnification= 40x). As seen, ERK and phospho-ERK shows both cytoplasmic and nuclear expressions. Nuclear staining of phospho-ERK was considered for scoring. With regard to Ki-67, nuclear staining is evident.

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Figure 5-4 Comparison of ERK and phospho-ERK expression scores and their ratio in EOC samples and matched normal tissues. The graph shows that while ERK expression received similar scores in normal and tumor tissues (top), phospho-ERK expression score (middle) and phospho-ERK/ERK ratio (bottom) were significantly higher in EOC. Data are represented as mean expression score ± SE. Significant values (p value <0.05) are marked by asterisks. 137

Table 5-1 Correlations of Spry1 with p-ERK/ERK and Ki-67 in EOC

Parameter Patients No. High Spry1 Low Spry1 p value * Low 53 25 28 p-ERK/ERK ratio (cut-off: 0.34) 0.045 High 47 13 34 Low 39 21 18 Ki-67 (cut-off: 10%) 0.010 High 60 17 43

No.: number * Statistically significant values (p value <0.05) are shown in bold. 138

5.2.3 Spry1 expression is correlated with clinicopathological characteristics of EOC patients.

To investigate the clinical relevance of the Spry1 expression in EOC, I firstly evaluated the correlation between the expression of Spry1 and clinicopathological characteristics of the EOC patients in my cohort (Table 5 -2). Data analysis showed that the expression of Spry1 was inversely correlated with aggressive clinicopathological features, including the disease stage (p value: 0.029, correlation coefficient= - 0.218), tumor grade (p value: 0.037, correlation coefficient= - 0.209), recurrence (p value: 0.001, correlation coefficient= - 0.379) and lymphovascular invasion (p value: 0.042, correlation coefficient= - 0.263). A correlation between the Spry1 expression and age was also observed (p value: 0.022, correlation coefficient= 0.229). 139

Table 5 -2 Correlations between the Spry1 expression and clinicopathological characteristics of EOC patients

Spry1 * Parameter Patients No. p value High Low Age (year) ≤50 16 2 14 0.022 >50 84 36 48 Menopause Yes 92 37 55 0.124 No 8 1 7 Disease stage Early (I-II) 14 9 5 0.029 Advanced (III-IV) 86 29 57 Tumor grade I-II 23 13 10 0.037 III 77 25 52 Tumor subtype Serous 81 32 49 - High-grade 63 22 41 - Low-grade 18 10 8 Endometrioid 4 2 2 - High-grade 2 0 2 0.647 - Low-grade 2 2 0 Mucinous 2 0 2 Clear cell 5 2 3 Others 8 2 6 Lymphovascular invasion Yes 35 8 27 0.042 No 25 12 13 Lymph node involvement Yes 38 15 23 0.511 No 25 12 13 140

Response to chemotherapy No 21 6 15 0.321 Recurrent 58 17 41 Yes 0.001 Non-recurrent 21 15 6 Ascites at diagnosis Yes 54 18 36 0.302 No 46 20 26 Post-treatment ascites Yes 42 12 30 0.100 No 58 26 32 Residual tumor No 48 17 31 <1 cm 35 15 20 N/A 1-2 cm 0 0 0

>2 cm 17 6 11

No.: number; N/A: not applicable * Statistically significant values (p value <0.05) are shown in bold. 141

5.2.4 Expression of Spry1 is associated with survival in patients with EOC.

Subsequently, the influence of the Spry1 expression on overall survival (OS) and disease-free survival (DFS) was investigated. Firstly, survival probabilities were estimated using the Kaplan-Meier method and differences were compared by the log- rank test in three different models as described in section 3.2.12.2 (Figure 5-5). In model 1, it was found that Spry1 low-expressing patients had significantly poorer OS (p value: 0.010) and DFS (p value: 0.012) than those with high expression of Spry1. The median OS for low-expressing and high-expressing groups was 2.7 and 6.8 years, respectively. The median DFS in Spry1 low-expressing patients was 14.9 months versus 30 months in the high-expressing group. Evaluated by model 2, OS indicated significant difference among the groups (p value: 0.040) with the median OS of 1.7, 3.80, 5 and 7.7 years for no, low, medium and high Spry1 groups, respectively. In this model, a near significant trend was seen for DFS (p value: 0.052). According to the Spry1 maximum expression score of 6 in tumor samples, no high expression group was created by model 3. The Kaplan-Meier analysis in this model yielded significantly different OS (p value: 0.015) and DFS (p value: 0.021) among the groups. The median OS for no, low and medium expression groups was 1.7, 3.2 and 6.8 years, respectively, and the median DFS in patients with no, low and medium expression of Spry1 was 10.2, 25.2 and 30 months, respectively.

To evaluate the predictive value of the Spry1 expression, it was then assessed along with the clinicopathological parameters of the participants (Table 3-8) in univariate and multivariate analyses of the factors associated with survival. In univariate (unadjusted) Cox’s proportional hazards analysis, high Spry1 (Model 1: HR=0.49; 95% CI, 0.28- 0.85; p value: 0.012), low stage (HR=0.28; 95% CI, 0.11-0.71; p value: 0.008), no residual tumor (HR=0.44; 95% CI, 0.23-0.84; p value: 0.013) and no ascites at diagnosis (HR=0.59; 95% CI, 0.36-0.98; p value: 0.045) appeared to be significant predictors of a better OS. Moreover, high Spry1 (model 1: HR=0.48; 95% CI, 0.27- 0.86; p value: 0.014), low stage (HR=0.27; 95% CI, 0.10-0.69; p value: 0.007) and no ascites at diagnosis (HR=0.50; 95% CI, 0.29-0.87; p value: 0.015) were found to significantly affect DFS (Table 5-3). The analysis of the Spry1 expression in model 3 also revealed significant associations between Spry1 and both OS (HR=0.53; 95% CI, 0.30-0.96; p value: 0.036) and DFS (HR=0.52; 95% CI, 0.29-0.95; p value: 0.034). 142

However, the associations of the Spry1 expression in model 2 with OS and DFS were insignificant (Table 5-3).

Multivariate Cox proportional hazards regression analysis was subsequently performed to confirm the prognostic significance of the parameters found to be of significant predicting value in the univariate analysis. Evaluating model 1, my results revealed that high Spry1 (HR=0.53; 95% CI, 0.30-0.94; p value: 0.030), low stage (HR=0.37; 95% CI, 0.14-0.99; p value: 0.048) and no residual tumor (HR=0.40; 95% CI, 0.20-0.78; p value: 0.007) were independent prognostic factors for a better OS. With respect to DFS, high Spry1 (HR=0.53; 95% CI, 0.30-0.95; p value: 0.035) and low stage (HR=0.34; 95% CI, 0.12-0.92; p value: 0.035) remained independent predictors in multivariate analysis. Using model 3, however, results of the multivariate analysis of the Spry1 expression were insignificant for both OS and DFS (Table 5-4). 143

1 .0 1.0 1.0

p value: 0.010 p value: 0.040 p value: 0.015 0.8 0.8 0.8 >. >. >. ~ ~ ~ 0 .6 0.6 0.6 Jl :.0 Jl ns ns ns Jl Jl Jl 3 4 0 0 0 I.. I.. 0 .4 I.. 0.4 0.4 a.. 2 a.. a.. en en en 0 0 0 02 02 0.2

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.00 2.50 5.00 7.50 10.00 12.50 .00 2.50 5.00 7.50 10.00 12.50 .00 2.50 5.00 7.50 10.00 12.50 Time (years) Time (years) Time (years)

1.0 1.0 1.0

p value: 0.052 p value: 0.021 0.8 p value: 0.012 0.8 0.8 >. >. >. ~ ~ ~ 0.6 Jl 0.6 0.6 Jl Jl ns 4 ttl ns Jl Jl Jl 0 I.. 0 3 0 I.. 0.4 a..I.. 0.4 a.. 0.4 a.. en en en u.. u.. u.. c 02 c 0.2 c 0.2

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.0 50.0 100.0 150.0 200.0 .0 50.0 100.0 150.0 200.0 .0 50.0 100.0 150.0 200.0 Time (months) Time (months) Time (months)

Model1 Model2 Model3

144

Figure 5-5 Kaplan-Meier curves of overall survival (OS) and disease-free survival (DFS) probabilities associated with Spry1 expression in EOC. The graphs demonstrate OS (top) and DFS (bottom) probabilities in our cohort using three different models. Model 1 (left) is a binary model, including low expression group with Spry1 expression score ≤3.5 (line 1), and high expression group scored >3.5 (line 2). Model 2 (middle) is a quaternary model and includes no expression group scored 0 (line 1), low expression group scored 1-2 (line 2), medium expression group scored 3-4 (line 3), and high expression group scored 5-6 (line 4). Model 3 (right), also a quaternary model, was created according to a higher maximum score (9) as follows: no expression group scored 0 (line 1), low expression group scored 1-3 (line 2), medium expression group scored 4-6 (line 3), and high expression group with expression score of 7-9 (nil). p values <0.05 are considered statistically significant. 145

Table 5-3 Univariate (unadjusted) Cox’s proportional hazards analysis of Spry1 and other potential predictors of survival and recurrence in EOC Overall survival Disease-free survival Variables HR (95% CI) p value * HR (95% CI) p value * Age (year) (≤50 vs. >50) 0.503 (0.239-1.057) 0.070 0.855 (0.431-1.694) 0.653

Menopause (no vs. yes) 0.395 (0.123-1.267) 0.118 0.861 (0.309-2.398) 0.774 Stage (early vs. late) 0.286 (0.114-0.718) 0.008 0.271 (0.105-0.696) 0.007

Tumor grade (I-II vs. III) 0.623 (0.338-1.148) 0.129 0.529 (0.272-1.026) 0.060 Tumor subtype (HG serous vs. LG serous vs. HG endometrioid vs. LG endometrioid vs. 0.903 (0.401-2.037) 0.807 1.607 (0.495-5.214) 0.430 mucinous vs. clear cell vs. others) Lymphovascular invasion (no vs. yes) 0.625 (0.317-1.230) 0.173 0.629 (0.312-1.272) 0.197

Lymph node involvement (no vs. yes) 0.797 (0.411-1.546) 0.503 0.579 (0.280-1.197) 0.140 Ascites at diagnosis (no vs. yes) 0.599 (0.364-0.988) 0.045 0.509 (0.295-0.878) 0.015 Residual tumor (no vs. <1 cm vs. 1-2 cm vs. 0.440 (0.230-0.844) 0.013 0.611 (0.277-1.350) 0.224 >2 cm) Ki67 (≤10% vs. >10%) 0.604 (0.359-1.018) 0.059 0.936 (0.554-1.580) 0.804

Spry1 (high vs. low) model 1 0.493 (0.284-0.857) 0.012 0.489 (0.277-0.863) 0.014 146

Spry1 (high vs. medium vs. low vs. no) model 2 0.551 (0.255-1.188) 0.128 0.687 (0.324-1.453) 0.326 Spry1 (high vs. medium vs. low vs. no) model 3 0.539 (0.303-0.961) 0.036 0.527 (0.292-0.953) 0.034

Model 1: high (>3.5) vs. low (≤3.5); Model 2: high (5, 6) vs. medium (3, 4) vs. low (1, 2) vs. no (0); Model 3: high (7-9) vs. medium (4-6) vs. low (1-3) vs. no (0) HR: hazard ratio; CI: confidence interval; HG: high-grade; LG: low-grade * Statistically significant values (p value <0.05) are shown in bold. 147

Table 5-4 Multivariate Cox’s proportional hazards analysis of predictors of survival and recurrence in EOC Overall survival Disease-free survival Variables HR (95% CI) p value * HR (95% CI) p value *

Stage (early vs. late) 0.374 (0.141-0.992) 0.048 0.341 (0.126-0.927) 0.035

Ascites at diagnosis (no vs. yes) 0.772 (0.460-1.297) 0.329 0.605 (0.347-1.055) 0.076 Residual tumor (no vs. <1 cm vs. 1-2 cm vs. >2 0.404 (0.208-0.783) 0.007 N/A N/A cm) Spry1 (high vs. low) model 1 0.534 (0.303-0.942) 0.030 0.539 (0.303-0.959) 0.035

Stage (early vs. late) 0.365 (0.137-0.968) 0.043 0.317 (0.115-0.871) 0.026

Ascites at diagnosis (no vs. yes) 0.771 (0.458-1.298) 0.328 0.623 (0.356-1.092) 0.098 Residual tumor (no vs. <1 cm vs. 1-2 cm vs. >2 0.431 (0.219-0.847) 0.015 N/A N/A cm) Spry1 (high vs. medium vs. low vs. no) model 3 0.579 (0.319-1.052) 0.073 0.594 (0.326-1.082) 0.088

Model 1: high (>3.5) vs. low (≤3.5); Model 3: high (7-9) vs. medium (4-6) vs. low (1-3) vs. no (0) HR: hazard ratio; CI: confidence interval; N/A: not applicable * Statistically significant values (p value <0.05) are shown in bold. 148

5.2.5 Spry1 protein expression cannot predict the development of post-treatment ascites and chemorefractory disease in patients with EOC.

The predictive value of the Spry1 expression and the clinicopathological parameters for development of post-treatment ascites and chemorefractory disease was assessed by univariate and multivariate logistic regression tests (Table 5-5). Using the binary model, Spry1 showed no predictive value for response to chemotherapy with carboplatin and taxol (HR=0.58; 95% CI, 0.20-1.67; p value: 0.320) or development of post-treatment ascites (HR=0.49; 95% CI, 0.21-1.14; p value: 0.101). The parameters with significant predictive value for response to chemotherapy in univariate analysis included tumor subtype (HR=0.18; 95% CI, 0.04-0.88; p value: 0.034) and residual disease (HR=0.22; 95% CI, 0.06-0.76; p value: 0.016) which also retained their independent significance in multivariate analysis (tumor subtype: HR=0.10; 95% CI, 0.01-0.53; p value: 0.007; residual tumor: HR=0.14; 95% CI, 0.03-0.56; p value: 0.006). Stage (HR=0.08; 95% CI, 0.01-0.67; p value: 0.020), ascites at diagnosis (HR=0.23; 95% CI, 0.09-0.55; p value: 0.001) and refractory disease (HR=0.15; 95% CI, 0.05-0.46; p value: 0.001) appeared to be significant predictors of post-treatment ascites, of which ascites at diagnosis (HR=0.20; 95% CI, 0.07-0.58; p value: 0.003) and refractory disease (HR=0.09; 95% CI, 0.02-0.37; p value: 0.001) retained their predictive value in multivariate analysis (Table 5-6). 149

Table 5-5 Univariate logistic regression analysis of potential predictors of response to chemotherapy & post-treatment ascites in EOC Response to chemo # (Refractory) Post-treatment ascites Variables HR (95% CI) p value * HR (95% CI) p value *

Age (year) (≤50 vs. >50) 0.489 (0.102-2.343) 0.371 1.089 (0.370-3.203) 0.877 Menopause (no vs. yes) 1.281 (0.239-6.858) 0.773 1.421 (0.334-6.038) 0.634

Stage (early vs. late) 0.588 (0.121-2.857) 0.510 0.084 (0.011-0.674) 0.020 Tumor grade (I-II vs. III) 1.059 (0.341-3.290) 0.921 0.525 (0.194-1.420) 0.204 Tumor subtype (HG serous vs. LG serous vs. HG endometrioid vs. LG endometrioid vs. 0.189 (0.040-0.882) 0.034 0.450 (0.099-2.049) 0.302 mucinous vs. clear cell vs. others) Lymphovascular invasion (no vs. yes) 0.625 (0.184-2.125) 0.452 1.00 (0.351-2.851) 1.00 Lymph node involvement (no vs. yes) 0.439 (0.106-1.817) 0.256 0.484 (0.157-1.491) 0.206

Ascites at diagnosis (no vs. yes) 1.086 (0.414-2.847) 0.867 0.233 (0.098-0.554) 0.001 Residual tumor (no vs. <1 cm vs. 1-2 cm vs. >2 cm) 0.225 (0.067-0.761) 0.016 0.404 (0.130-1.252) 0.116

Refractory disease (no vs. yes) N/A N/A 0.153 (0.051-0.464) 0.001

Spry1 (high vs. low) 0.588 (0.206-1.675) 0.320 0.492 (0.211-1.147) 0.101

HR: hazard ratio; CI: confidence interval; HG: high-grade; LG: low-grade; N/A: not applicable; # Chemotherapy with carboplatin and taxol * Statistically significant values (p value <0.05) are shown in bold. 150

Table 5-6 Multivariate logistic regression analysis of potential predictors of response to chemotherapy and post-treatment ascites in EPC Response to chemo # (Refractory) Variables HR (95% CI) p value * Tumor subtype (HG serous vs. LG serous vs. HG endometrioid vs. LG endometrioid vs. 0.100 (0.019-0.534) 0.007 mucinous vs. clear cell vs. others) Residual tumor (no vs. <1 cm vs. 1-2 cm vs. >2 cm) 0.140 (0.035-0.563) 0.006 Post-treatment ascites

Stage (early vs. late) 0.122 (0.014-1.091) 0.060 Ascites at diagnosis (no vs. yes) 0.205 (0.073-0.582) 0.003

Refractory (no vs. yes) 0.099 (0.026-0.375) 0.001

HR: hazard ratio; CI: confidence interval; HG: high-grade; LG: low-grade; # Chemotherapy with carboplatin and taxol * Statistically significant values (p value <0.05) are shown in bold.

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5.2.6 There is no meaningful association between the Spry1 protein expression and tumor size in patients with EOC.

As presented in Figure 5-6, a possible association between high or low Spry1 expression and EOC tumor size was investigated next. The median tumor size was 30 mm in low expressing group and 45 mm in high expressing group. The mean tumor sizes were 43.89 mm and 54.23 mm in low and high expressing tumors, respectively. Evaluating a possible association with tumor size, independent T-test analysis indicated no significant difference between Spry1 low and high expression groups (p value: 0.259). 152

Figure 5-6 Association of low or high Spry1 expression with EOC tumor size. The stem and leaf plot demonstrates the median (bold line) and minimum and maximum values (T-bars) for tumor size in Spry1 low-expressing (left) and high-expressing (right) groups. The median tumor sizes in low-expressing and high-expressing groups were 30 mm and 45 mm, respectively. The mean tumor size in low Spry1 group (43.89 mm) was not significantly different from that in high Spry1 group (54.23 mm). Circles represent patients in low-expressing group whose tumor sizes were equal to or greater than 125 mm. p values <0.05 are considered statistically significant. 153

5.3 Discussion

For the past 15 years, an expanding body of evidence has continued to support the crucial role of Sprouty proteins in cell biology. Spry1 was the first mammalian homologue of the Drosophila Sprouty to be identified (Hacohen et al., 1998). Regulatory functions of Spry1 in organogenesis and other physiological processes are well documented (Anteby et al., 2005; Basson et al., 2006; Boros et al., 2006; Ching et al., 2014; Gross et al., 2001; Hacohen et al., 1998; Huebert et al., 2004; Impagnatiello et al., 2001; Jung et al., 2012; Kuracha et al., 2013; Purcell et al., 2012). Spry1 regulation of key cellular processes has been shown to also impact biological behavior of different cancer cells (Jin et al., 2013; Kwabi-Addo et al., 2004; Macia et al., 2012; Mathieu et al., 2012; Mekkawy and Morris, 2013; Polytarchou et al., 2011). Consistently, I found in my in vitro study that the Spry1 expression impacts EOC cell behavior (Masoumi- Moghaddam et al., 2014a). On this basis, I anticipated that the expression of Spry1 would be of clinical relevance, clinicopathological significance and prognostic value in EOC and thus tested my hypothesis in the present retrospective study.

My immunohistochemical study revealed significant downregulation of Spry1 protein in EOC tissues that is in line with previous reports of Spry1 inactivation or downregulation at DNA, RNA or protein levels in a number of malignancies, including breast (Faratian et al., 2011; Lo et al., 2004), prostate (Fritzsche et al., 2006; Kwabi-Addo et al., 2004; Taylor et al., 2010) and thyroid (Macia et al., 2012) cancer. Nevertheless, my results contrast with those reported by Schaaf et al (Schaaf et al., 2010) and Sirivatanauksorn et al (Sirivatanauksorn et al., 2012) indicating upregulation of Spry1 in embryonal rhabdomyosarcoma (ERMS) and hepatocellular carcinoma (HCC), respectively. In the first study, however, upregulation of Spry1 was found in an oncogenic RAS background where Spry1 is expected to be transcriptionally upregulated as a result of RAS activation, and increased expression of Spry1 mRNA in the second study was found insignificant when the Spry1 expression in HCC tissues was compared with its expression in cirrhotic tissue, thereby implicating other causes, including aberrant hepatocyte function, in upregulation of Spry1. Nevertheless, it should be noted in my study that in a significant fraction of the EOC samples (24%), the cancerous tissue received higher Spry1 expression score than did the matched normal epithelium (Figure 5-2). Therefore, although decreased expression of Spry1 was seen in a substantial 154

fraction of EOC samples, it is not clearly a biological requirement of all ovarian cancers.

The Spry1 expression in my study was found to inversely correlate with the expression of p-ERK/ERK and Ki67 in EOC tumor tissue. This is in agreement with the results of my in vitro study as well as with earlier findings indicating the inhibitory effects of Spry1 on MAPK/ERK activity and proliferative capacity of cancer cells. In a contradictory report, however, Schaaf et al (Schaaf et al., 2010) argued earlier that Spry1 was essential for ERMS cell proliferation and survival in vitro and tumor formation and maintenance in vivo. Nevertheless, this effect was observed only in oncogenic RAS mutants in the context of which aberrant activation of MAPK/ERK downstream of the Sprouty action point is evident.

In agreement with the results of earlier preclinical studies on other cancer cells, my in vitro findings showed that the Spry1 expression inversely impacts EOC cell proliferation, migration, invasion and survival. These biological processes are central to growth, development and dissemination of malignant conditions. Consistently, I observed in my retrospective study the inverse correlation of the Spry1 expression with aggressive clinicopathological features of the disease and identified Spry1 as a predictor of overall survival and recurrence in EOC. In this regard, the clinical relevance of the Spry1 expression in cancer has been investigated by a number of investigators although its significance as a prognostic factor has not been reported before. In an attempt to identify the genes effectively discriminating between clinically aggressive and nonaggressive types of clear cell renal cell carcinoma in 29 patients with diverse clinical outcomes, Takahashi et al (Takahashi et al., 2001) found Spry1 among exclusively upregulated genes in the good outcome group. Through microarray analysis of 49 microdissected prostate tissue specimens, Fritzsche et al (Fritzsche et al., 2006) observed gradually intensifying downregulation of Spry1 mRNA from hyperplasia to severe prostatic intraepithelial neoplasia (PIN) to cancer. Consistently, Taylor et al (Taylor et al., 2010) found that Spry1 gene is inactivated in 15% of the primary prostate cancer, as well as in 42% of the metastatic disease. In a study by Faratian et al (Faratian et al., 2011), Spry1 gene expression in six Affymetrix datasets representing a total of 1107 breast cancer tumors was found to be higher in normal-like subtype of the cancer and lower in tumors with higher grade. In an additional single dataset containing 143 155

normal and 42 tumor tissues, Spry1 appeared to be downregulated in a panel of invasive ductal carcinomas as compared with normal breast tissue.

In conclusion, I report for the first time that Spry1 protein is downregulated in EOC. My findings also provide the evidence that the expression of Spry1 protein impacts EOC tumor behavior and predicts the patient outcome.

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6 Retrospective study of the expressions of Spry2 and Spry4 proteins in EOC: correlation with Spry1 and clinicopathological parameters, association with patient outcome

6.1 Introduction

Spry2 (Hacohen et al., 1998) and Spry4 (Leeksma et al., 2002) were the second and fourth human homologs of the Drosophila Sprouty to be identified. Like Spry1, these two members of the Sprouty family have been implicated in the modulation of RTK signaling and hence regulation of cell functions. On this basis, deregulation of these isoforms, in particular Spry2, has been investigated in different cancer types (Masoumi-Moghaddam et al., 2014b). As with Spry1, however, the status and significance of the expressions of Spry2 and Spry4 in EOC have not been explored before. In the previous chapters, the effects of the Spry1 protein expression on behavior of the EOC cell and tumor investigated in in vitro and retrospective studies were reviewed. Given the known regulatory functions of Spry2 and Spry4 in normal and cancer cell biology, I next intended to evaluate in a similar retrospective study on the same cohort the immunohistochemical expression of Spry2 and Spry4 proteins in EOC, their possible correlations with the Spry1 expression and other clinicopathological parameters, and their significance as predictors of OS and DFS. The results of this study are reviewed and discussed in this chapter.

6.2 Results

6.2.1 Spry2 and Spry4 proteins are downregulated in EOC.

To quantitatively compare the expression of Spry2 and Spry4 in normal and neoplastic ovarian epithelium, I quantitated the expression of these two Sprouty isoforms based on the methodology described in section 3.2.11.2. My immunohistochemical analysis revealed variable expressions of these proteins in both normal and cancer epithelial cells. Evaluating the Spry2 protein expression in normal epithelial cells, I observed mild (immunohistochemical score of 1-3), moderate (immunohistochemical score of 4-6) and strong (immunohistochemical score of 7-9) expressions in 51%, 45% and 1% of normal tissue samples, respectively. 3% of patients had the staining score of 0 in their normal 157

epithelia. Spry2 variable expression was also seen in cancer epithelium where 58%, 29% and 13% of tumor samples showed mild, moderate and no staining, respectively. Cells with mild staining intensity largely showed cytoplasmic pattern while perinuclear staining was commonly associated with moderate and strong intensities. There were also a few epithelial cancer cells showing nuclear staining associated with strong intensity.

Regarding the expression of Spry4 protein in normal ovarian epithelium, the majority of cases (62%) showed strong staining with immunohistochemical score of 7-9, 30% exhibited moderate expression, 7% indicated mild staining and only one sample (1%) had no staining. When assessed in cancer epithelium, Spry4 showed mild, moderate and strong expressions in 22%, 52% and 23% of tissues, respectively, as well as no staining in 3%. Cells with mild staining intensity largely showed cytoplasmic pattern. Perinuclear staining was commonly associated with moderate intensity whereas nuclear staining was observed only in those with strong intensity. When the expression levels of Spry2 and Spry4 in tumor and normal epithelium were compared, significant downregulation of Spry2 (p value <0.0001) and Spry4 (p value <0.0001) in tumor tissues was revealed (Figure 6-1 and Figure 6-2).

Due to the variability of the protein expression in different samples, I also compared the staining scores of Spry2 and Spry4 in cancer tissue and those in matched normal tissue from the same patient for a more meaningful deduction (Figure 6-3). As a result, in 60% of samples Spry2 staining score in cancer tissue was lower than that in matched normal tissue. However, 12% had higher expression of Spry2 in cancer tissue than in matched normal epithelium. When the Spry4 staining score of cancerous tissue was compared with that in matched normal tissue, lower and higher score of cancer tissue was evident in 49% and 4% of samples, respectively. In 47% of cases, however, cancer tissues and matched normal tissues received similar Spry4 expression scores.

When the total of 100 tumor samples were classified by the cut-off point into Spry2 high- (>3.5) and low- (≤3.5) expressing groups, as well as into Spry4 high- (>6) and low- (≤6) expressing groups, 70 cases were identified as patients with low expression of Spry2 and 76 cases were found to be Spry4 low-expressing. 158

Figure 6-1 Immunohistochemical analysis of Spry2 expression in EOC. A. Representative photographs indicate high (top) and low (bottom) levels of Spry2 expression in EOC tissues (magnification= 40x). B. The graphs demonstrate the comparison of Spry2 expression scores in normal and tumor tissues where significant downregulation of Spry2 in EOC tissues is evident. Data are represented as mean expression score ± SE (top), as well as maximum and minimum expression score (bottom). Significant values (p value <0.05) are marked by asterisks. 159

Figure 6-2 Immunohistochemical analysis of Spry4 expression in EOC. A. Representative photographs indicating high (top) and low (bottom) levels of the Spry4 immunohistochemical expression in the EOC tissue (magnification= 40x). B. Comparison of Spry4 expression scores in normal and tumor tissues, showing significant downregulation of Spry4 protein in EOC tissue. Data are represented as mean expression score ± SE (top), as well as maximum and minimum expression score (bottom). Significant values (p value <0.05) are marked by asterisks. 160

Figure 6-3 Comparative analysis of Spry2 and Spry4 expression scores in normal and malignant ovarian epithelial tissues after immunohistochemical scoring. The top graph demonstrates that 60% (left bar) and 12% (right bar) of cases in our cohort received lower (TN) Spry2 expression score in their tumor tissue than in their matched normal tissue, respectively. In 28% of cases (middle bar), the expression score of normal and tumor tissues was equal (T=N). With respect to Spry4 expression (bottom graph), the majority of tumor samples scored lower than (TN).

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6.2.2 The expression of Spry2, but not that of Spry4, correlates with the expression of Spry1, p-ERK/ERK and Ki67 in EOC.

Given the known interactions among the Sprouty isoforms for a balanced, regulatory output, a possible association among the expressions of Spry1, Spry2 and Spry4 was next explored. As shown in Table 6-1, while a significant correlation between Spry1 and Spry2 was revealed (p value <0.001, correlation coefficient= 0.679), there was no significantly meaningful correlation between the expression of Spry4 with either Spry1 (p value: 0.293) or Spry2 (p value: 0.514).

Considering the role of Sprouty isoforms in the regulation of the MAPK/ERK pathway and cellular functions, including cell proliferation, the possible associations between the expressions of Spry2 and Spry4 and those of p-ERK/ERK and Ki-67 were subsequently analyzed. As a result, significant negative correlations of Spry2 with both p-ERK/ERK (p value: 0.048, correlation coefficient= - 0.199) and Ki67 (p value: 0.011, correlation coefficient= - 0.253) were found (Table 6-2). However, as shown in Table 6-3, there was no correlation between Spry4 and p-ERK/ERK (p value: 0.883) or Ki-67 (p value: 0.350).

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Table 6-1 Correlations of the Spry1 expression with the expressions of Spry2 and Spry4 in EOC

Spry1 Parameter Patients No. p value * Low High Low 70 58 12 Spry2 <0.001 High 29 3 26

Low 76 49 27 Spry4 0.293 High 23 12 11

No.: number * Statistically significant values (p value <0.05) are shown in bold.

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Table 6-2 Correlations of Spry2 with p-ERK/ERK and Ki-67 in EOC

Patients Spry2 Parameter p value * No. Low High Low 53 33 20 p-ERK/ERK ratio 0.048 High 46 37 9 Low 39 22 17 Ki-67 0.011 High 60 48 12

No.: number * Statistically significant values (p value <0.05) are shown in bold.

Table 6-3 Correlations of Spry4 with p-ERK/ERK and Ki-67 in EOC

Patients Spry4 Parameter p value * No. Low High Low 53 41 12 p-ERK/ERK ratio 0.883 High 46 35 11 Low 39 28 11 Ki-67 0.350 High 60 48 12

No.: number * Statistically significant values (p value <0.05) are shown in bold.

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6.2.3 The expression of Spry2, but not that of Spry4, correlates with clinicopathological characteristics of EOC patients.

To investigate the clinical relevance of Spry2 and Spry4 expressions in EOC, I firstly evaluated the correlation between the expression of these two Sprouty isoforms and the clinicopathological characteristics of the EOC patients in my cohort (Table 6-4). Since a strong correlation had been earlier revealed between Spry1 and Spry2 expressions in our EOC tumors, a possible correlation between the expression of Spry2 and the clinicopathological characteristics was anticipated. My data analysis indicated that the expression of Spry2 was inversely correlated with aggressive clinicopathological features, including the disease stage (p value: 0.013, correlation coefficient= - 0.248), tumor grade (p value: 0.003, correlation coefficient= - 0.297), recurrence (p value <0.001, correlation coefficient= - 0.450) and post-treatment ascites (p value: 0.001, correlation coefficient= - 0.316). However, no significant correlation between the Spry4 expression and the clinicopathological characteristics of the patients was found. 165

Table 6-4 Correlations of the Spry2 and Spry4 expressions with clinicopathological characteristics of EOC patients

Patients Spry2 Spry4 Parameter p value* p value* No. Low High Low High Age (year) ≤50 15 11 4 13 2 0.811 0.329 >50 84 59 25 63 21 Menopause Yes 91 65 26 69 22 0.599 0.459 No 8 5 3 7 1 Disease stage Early (I-II) 14 6 8 10 4 0.013 0.614 Advanced (III-IV) 85 64 21 66 19 Tumor grade I-II 22 10 12 18 4 0.003 0.530 III 77 60 17 58 19 Tumor subtype Serous 80 57 23 59 21 - High-grade 63 48 15 45 18 - Low-grade 17 9 8 14 3 Endometrioid 4 2 2 4 0 - High-grade 2 2 0 0.216 2 0 0.094 - Low-grade 2 0 2 2 0 Mucinous 2 1 1 2 0 Clear cell 5 4 1 4 1 Others 8 6 2 7 1 Lymphovascular invasion Yes 35 28 7 28 7 0.298 0.716 No 25 17 8 19 6 Lymph node involvement 166

Yes 38 30 8 29 9 0.053 0.112 No 25 14 11 23 2 Response to chemotherapy No 21 17 4 0.250 16 5 0.944 Recurrent 57 46 11 46 11 Yes Non- <0.001 0.197 21 7 14 14 7 recurrent Ascites at diagnosis Yes 53 39 14 41 12 0.504 0.883 No 46 31 15 35 11 Post-treatment ascites Yes 41 36 5 33 8 0.001 0.466 No 58 34 24 43 15 Residual tumor None 47 33 14 33 14 <1 cm 35 26 9 28 7 N/A N/A 1-2 cm 0 0 0 0 0 >2 cm 17 11 6 15 2

No.: number; N/A: not applicable * Statistically significant values (p value <0.05) are shown in bold.

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6.2.4 Expression of Spry2, but not that of Spry4, is associated with survival in patients with EOC.

Subsequently, the influence of the expressions of Spry2 and Spry4 on overall survival (OS) and disease-free survival (DFS) was investigated in their respective models (section 3.2.12.2). For this purpose, survival probabilities were estimated by the Kaplan-Meier method and differences were compared by the log-rank test (Figure 6-4). It was found that Spry2 low-expressing patients had significantly poorer OS (p value: 0.002) and DFS (p value: 0.004) than those with high expression of Spry2 in model 1. The median OS for low-expressing and high-expressing groups was 2.7 and 6.8 years, respectively. The median DFS in Spry2 low-expressing patients was 24.3 months versus 78.8 months in the high-expressing group. While the results were not significantly different in model 2 (p values of 0.559 for OS and 0.194 for DFS), the log-rank test showed significant differences in model 3 with the p values of 0.009 and 0.007 for OS and DFS, respectively. While the median OS was 3.2 and 2.4 years in patients with no or low expression of Spry2, respectively, patients with Spry2 medium expression levels had a median OS of 6.8 years. No patient expressed high levels of Spry2 (score: 7-9) in their tumor samples. In this model, the median DFS for patients with no, low and medium expression of Spry2 was 32.8, 14.16 and 78.83 months, respectively.

Outcome analysis of the Spry4 expression was next carried out using its two respective models. Although Spry4 low-expressing group in model 1 showed lower OS (3.7 years) and DFS (26.3 months) than did the high-expressing group (OS and DFS of 6.4 years and 30 month, respectively), the results were found statistically insignificant, with p values of 0.407 and 0.393 for OS and DFS, respectively. The results were also insignificant in model 3 with the p values of 0.094 and 0.593 for OS and DFS, respectively (Figure 6-5).

To evaluate the predictive value of the expressions of Spry2 and Spry4, the expressions of these isoforms were then assessed along with the clinicopathological parameters of the participants (Table 3-8) in univariate and multivariate analyses of the factors associated with survival. My univariate (unadjusted) Cox’s proportional hazards analysis yielded different results for Spry2 and Spry4 in different models (Table 6-5). In model 1, high Spry2 appeared to be a significant predictor of an increased OS 168

(HR=0.39; 95% CI, 0.20-0.75; p value: 0.005) and a better DFS (HR=0.41; 95% CI, 0.21-0.78; p value: 0.007). Similar results were revealed with model 3 where Spry2 showed predictive value for OS (HR=0.36; 95% CI, 0.18-0.71; p value: 0.004) and DFS (HR=0.35; 95% CI, 0.18-0.70; p value: 0.003). However, results from univariate analysis of Spry2 in model 2 were not significant (p values of 0.552 and 0.906 for OS and DFS, respectively). As shown in Table 6-5, no significant results were also obtained in univariate analysis of Spry4 in model 1 (p values of 0.408 and 0.395 for OS and DFS, respectively) or model 2 (p value: 0.359 and 0.371 for OS and DFS, respectively).

Factors with predictive significance in univariate analysis were then subjected to multivariate Cox’s proportional hazards analysis (Table 6-6). In model 1, high Spry2 retained its significance for both OS (HR=0.44; 95% CI, 0.23-0.87; p value: 0.018) and DFS HR=0.50; 95% CI, 0.26-0.98; p value: 0.044). Consistently, multivariate analysis of Spry2 in model 3 also indicated significant predictive value of high Spry2 for a better OS (HR=0. 39; 95% CI, 0.19-0.80; p value: 0.011) and DFS (HR=0.43; 95% CI, 0.21- 0.86; p value: 0.018). 169

1.0 1.0 1.0

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0.8 p value: 0.007 0.8 p value : 0.004 0.8 p value: 0.194 >. >. >. ~ ~ ~ 0.6 .Q 0.6 .Q 0.6 :0 3 nJ 2 nJ nJ .Q .Q .Q 0 0 0 0.4 ... 0.4 ... 0.4 ... a. a. a. en en en LL 3 LL LL 0.2 02 c 02 c c 2 1

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Time (months) Time (months) Time (months)

Model1 Model2 Model3

170

Figure 6-4 Kaplan-Meier curves of overall survival (OS) and disease-free survival (DFS) probabilities associated with Spry2 expression in EOC. The graphs demonstrate OS (top) and DFS (bottom) probabilities in our cohort using three different models. Model 1 (left) is a binary model, including low expression group with Spry2 expression score ≤3.5 (line 1), and high expression group scored >3.5 (line 2). Model 2 (middle) is a quaternary model and includes no expression group scored 0 (line 1), low expression group scored 1-2 (line 2), medium expression group scored 3-4 (line 3), and high expression group scored 5-6 (line 4). Model 3 (right), also a quaternary model, was created according to a higher maximum score (9) as follows: no expression group scored 0 (line 1), low expression group scored 1-3 (line 2), medium expression group scored 4-6 (line 3), and high expression group with expression score of 7-9 (nil). p values <0.05 are considered statistically significant. 171

Figure 6-5 Kaplan-Meier curves of overall survival (OS) and disease-free survival (DFS) probabilities associated with Spry4 expression in EOC. The graphs demonstrate OS (top) and DFS (bottom) probabilities in our cohort using two different models. Model 1 (left) is a binary model, including low expression group with Spry4 expression score ≤6 (line 1), and high expression group scored >6 (line 2). Model 2 (right) is a quaternary model and includes no expression group scored 0 (line 1), low expression group scored 1-3 (line 2), medium expression group scored 4-6 (line 3) and high expression group scored 7-9 (line 4). p values <0.05 are considered statistically significant. 172

Table 6-5 Univariate (unadjusted) Cox’s proportional hazards analysis of the predictive value of Spry2 and Spry4 for overall survival (OS) and disease-free survival (DFS) in EOC Overall survival Disease-free survival Variables HR (95% CI) p value * HR (95% CI) p value * Spry2 (high vs. low) model 1 0.397 (0.207-0.759) 0.005 0.415 (0.219-0.788) 0.007 Spry2 (high vs. medium vs. low vs. no) model 2 0.548 (0.075-3.988) 0.552 1.090 (0.260-4.568) 0.906 Spry2 (high vs. medium vs. low vs. no) model 3 0.360 (0.181-0.715) 0.004 0.355 (0.180-0.700) 0.003 Spry4 (high vs. low) model 1 0.752 (0.382-1.478) 0.408 0.751 (0.389-1.451) 0.395 Spry4 (high vs. medium vs. low vs. no) model 2 0.720 (0.358-1.451) 0.359 0.730 (0.367-1.455) 0.371

Spry2; Model 1: high (>3.5) vs. low (≤3.5); Model 2: high (5, 6) vs. medium (3, 4) vs. low (1, 2) vs. no (0); Model 3: high (7-9) vs. medium (4-6) vs. low (1-3) vs. no (0) Spry4; Model 1: high (>6) vs. low (≤6); Model 2: high (7-9) vs. medium (4-6) vs. low (1-3) vs. no (0) HR: hazard ratio; CI: confidence interval * Statistically significant values (p value <0.05) are shown in bold. 173

Table 6-6 Multivariate Cox’s proportional hazards analysis of the predictive value of Spry2 and other predictors of overall survival (OS) and disease-free survival (DFS) in EOC Overall survival Disease-free survival Variables HR (95% CI) p value * HR (95% CI) p value * Stage (early vs. late) 0.376 (0.142-0.994) 0.049 0.358 (0.130-0.982) 0.046 Ascites at diagnosis (no vs. yes) 0.805 (0.477-1.358) 0.416 0.643 (0.367-1.124) 0.121 Residual tumor (no vs. <1 cm vs. 1-2 cm vs. >2 cm) 0.393 (0.203-0.761) 0.006 N/A N/A Spry2 (high vs. low) model 1 0.448 (0.230-0.874) 0.018 0.508 (0.264-0.981) 0.044 Stage (early vs. late) 0.381 (0.144-1.011) 0.053 0.355 (0.128-0.982) 0.046 Ascites at diagnosis (no vs. yes) 0.831 (0.491-1.408) 0.492 0.663 (0.377-1.165) 0.153 Residual tumor (no vs. <1 cm vs. 1-2 cm vs. >2 cm) 0.372 (0.191-0.726) 0.004 N/A N/A Spry2 (high vs. medium vs. low vs. no) model 3 0.397 (0.196-0.806) 0.011 0.434 (0.217-0.869) 0.018

Considering the significant association of Spry1 and Spry2 with each other as well as with OS and DFS, three further models were constructed based on the binary cut-off point of 3.5 (as described in section 3.2.12.2) to evaluate the predictive value of these two isoforms when concomitantly expressed at different levels. Kaplan-Meier survival analysis revealed that concomitantly higher expressions of Spry1 and Spry2 were associated with improved survival in model 1 (p values of 0.017 and 0.032 for OS and DFS, respectively), model 2 (p values of 0.009 and 0.015 for OS and DFS, respectively) and model 3 (p value of 0.003 for both OS and DFS). As shown in Table 6-7, univariate analysis of the concomitant expression of Spry1 and Spry2 did not show a predictive significance in models 1 (p values of 0.139 and 0.129 for OS and DFS, respectively) and 2 (p values of 0.252 and 0.186 for OS and DFS, respectively). In model 3, however, significant results regarding both OS (HR=0.35; 95% CI, 0.17-0.73; p value: 0.005) and DFS (HR=0.35; 95% CI, 0.17-0.72; p value: 0.004) were revealed, with the hazard ratios and p values being more significant than those resulted from the analysis of the expression of each isoform individually. Finally, the concomitant expression of Spry1 and Spry2 retained its predictive significance for both OS (HR=0.38; 95% CI, 0.18- 0.80; p value: 0.011) and DFS (HR=0.40; 95% CI, 0.19-0.82; p value: 0.013) in multivariate analysis (Table 6-8).

175

Table 6-7 Univariate (unadjusted) Cox’s proportional hazards analysis of the predictive value of the concomitant expression of Spry1 and Spry2 for overall survival (OS) and disease-free survival (DFS) in EOC Overall survival Disease-free survival Variables HR (95% CI) p value * HR (95% CI) p value * Spry1/Spry2 model 1 0.486 (0.187-1.265) 0.139 0.472 (0.179-1.243) 0.129 Spry1/Spry2 model 2 0.581 (0.229-1.472) 0.252 0.532 (0.209-1.357) 0.186 Spry1/Spry2 model 3 0.359 (0.175-0.733) 0.005 0.357 (0.177-0.721) 0.004

Model 1: high Spry1 and high Spry2 vs. low Spry1 and low Spry2 vs. low Spry1 and high Spry2 vs. high Spry1 and low Spry2; Model 2: high Spry1 and high Spry2 vs. low Spry1 and low Spry2 vs. low Spry1 and high Spry2, and high Spry1 and low Spry2; Model 3: high Spry1 and high Spry2 vs. low Spry1 and low Spry2 HR: hazard ratio; CI: confidence interval * Statistically significant values (p value <0.05) are shown in bold.

176

Table 6-8 Multivariate Cox’s proportional hazards analysis of the predictive value of the concomitant expression of Spry1 and Spry2 and other predictors of overall survival (OS) and disease-free survival (DFS) in EOC Overall survival Disease-free survival Variables HR (95% CI) p value * HR (95% CI) p value * Stage (early vs. late) 0.502 (0.169-1.489) 0.214 0.533 (0.196-1.454) 0.219 Ascites at diagnosis (no vs. yes) 0.764 (0.435-1.343) 0.350 0.614 (0.339-1.114) 0.109 Residual tumor (no vs. <1 cm vs. 1-2 cm vs. >2 cm) 0.443 (0.215-0.913) 0.027 N/A N/A Spry1/Spry2 model 3 0.381 (0.181-0.805) 0.011 0.401 (0.195-0.824) 0.013

Model 3: high Spry1 and high Spry2 vs. low Spry1 and low Spry2 HR: hazard ratio; CI: confidence interval; N/A: not applicable * Statistically significant values (p value <0.05) are shown in bold.

177

6.2.5 Expression of Spry2 has a predictive value for the development of post- treatment ascites, but not for response to chemotherapy, in EOC patients.

Using the binary model of the Spry2 and Spry4 expressions, the predictive value of these isoforms in relation to the development of post-treatment ascites and chemorefractory disease in our EOC patients was then assessed by univariate and multivariate logistic regression tests (Table 6-9). Univariate analysis revealed the significance of the Spry2 expression for predicting the development of post-treatment ascites (HR=0.23; 95% CI, 0.08-0.64; p value: 0.005). With regard to prediction of the response to chemotherapy with carboplatin and taxol (Table 6-9), the Spry2 expression showed no significantly meaningful value (HR=0.48; 95% CI, 0.14-1.57; p value: 0.225). The Spry4 expression also showed no significant value for predicting the refractory disease (HR=0.76; 95% CI, 0.29-1.99; p value: 0.579) or post-treatment ascites (HR=0.49; 95% CI, 0.22-1.10; p value: 0.085). In multivariate logistic regression analysis, Spry2 (HR=0.25; 95% CI, 0.07-0.83; p value: 0.024), ascites at diagnosis (HR=0.19; 95% CI, 0.06-0.56; p value: 0.003) and refractory disease (HR=0.09; 95% CI, 0.02-0.38; p value: 0.001) were identified as independent predictors of post- treatment ascites in EOC patients (Table 6-10). 178

Table 6-9 Univariate logistic regression analysis of the predictive value of Spry2 and Spry4 for response to chemotherapy and post- treatment ascites in EOC Response to treatment # (Refractory) Post-treatment ascites Variables HR (95% CI) p value * HR (95% CI) p value * Spry2 (high vs. low) 0.480 (0.147-1.570) 0.225 0.236 (0.086-0.648) 0.005

Spry4 (high vs. low) 0.761 (0.290-1.996) 0.579 0.493 (0.220-1.104) 0.085

HR: hazard ratio; CI: confidence interval; # Chemotherapy with carboplatin and taxol * Statistically significant values (p value <0.05) are shown in bold.

Table 6-10 Multivariate logistic regression analysis of the predictive value of Spry2 for post-treatment ascites in EOC Post-treatment ascites Variables HR (95% CI) p value * Stage (early vs. late) 0.150 (0.015-1.547) 0.111

Ascites at diagnosis (no vs. yes) 0.193 (0.066-0.567) 0.003

Refractory disease (no vs. yes) 0.098 (0.025-0.385) 0.001 Spry2 (high vs. low) 0.256 (0.078-0.838) 0.024

HR: hazard ratio; CI: confidence interval * Statistically significant values (p value <0.05) are shown in bold. 179

6.2.6 Expressions of Spry2 and Spry4 proteins are not associated with tumor size in patients with EOC.

Subsequently, a possible association between tumor size and the expressions of Spry2 and Spry4 proteins was investigated (Figure 6-6). The median tumor sizes in Spry2 low- and high-expressing groups were 35 mm and 45 mm, respectively. The mean tumor sizes of low Spry2 (46.39 mm) and high Spry2 (53.20 mm) groups were not significantly different (p value: 0.510). For Spry4, the median tumor sizes were 40 and 42.50 in low- and high-expressing groups, respectively. No significant difference was observed between mean tumor sizes of low Spry4 (45.84 mm) and high Spry4 (59.70 mm) groups (p value: 0.458). 180

Figure 6-6 Evaluation of associations between Spry2 and Spry4 expressions and tumor size. The stem and leaf plots demonstrate the median (bold line) and minimum and maximum values (T-bars) for tumor size in Spry2 (top) and Spry4 (bottom) low- and high-expressing groups. The median tumor sizes in Spry2 low- and high-expressing groups were 35 mm and 45 mm, respectively. The mean tumor size in low Spry2 group (46.39 mm) was not significantly different from that in high Spry2 group (53.20 mm). With respect to Spry4, the median tumor sizes in low- and high-expressing groups were 40 and 42.50, respectively. As with Spry2, there was no significant difference between the mean tumor size of low Spry4 group (45.84 mm) and that of high Spry4 group (59.70 mm). Circles represent patients in low-expressing groups whose tumor sizes were equal to or greater than 125 mm. p values <0.05 are considered significant. 181

6.3 Discussion

As with Spry1, implication of Spry2 and Spry4 in a variety of physiological and developmental processes through the regulation of biological cell behavior has been indicated by different investigators (Table 1-5). In this regard, Spry2 has been shown to inhibit growth factor- and/or serum-induced proliferation (de Alvaro et al., 2005; Gross et al., 2001; Impagnatiello et al., 2001; Sutterluty et al., 2007; Zhang et al., 2005) and migration (Poppleton et al., 2004; Sutterluty et al., 2007; Zhang et al., 2005) of various normal cells. Similar physiological functions have been also ascribed to Spry4 (Lee et al., 2001; Tennis et al., 2010; Tsumura et al., 2005; Wang et al., 2006). Accordingly, functional outcomes of the expression of Spry2 and Spry4 have been explored in a number of malignancies. However, as reviewed in detail in Chapter 1, Sprouty isoforms have proved to exert divergent effects despite functional cooperation and structural interactions. Moreover, as addressed below, role of different Sprouty isoforms in cancer biology is associated with further complexity and even controversy.

Spry2 has been found to inhibit cellular functions central to malignant behavior of cancer cells, including cell proliferation, differentiation, migration and invasion, in leiomyosarcoma (Lee et al., 2004), osteosarcoma (Rathmanner et al., 2013), hepatocellular carcinoma (Fong et al., 2006; Lee et al., 2008), neuroblastoma (Ishida et al., 2007), B-cell lymphoma (Frank et al., 2009) non-small cell lung cancer (NSCLC) (Sutterluty et al., 2007), cervical cancer (Edwin et al., 2006; Yigzaw et al., 2001; Yigzaw et al., 2003) and breast cancer cells. Lo et al (Lo et al., 2004), for example, indicated that the MCF-7 breast cancer cells transfected with a dominant-negative mutant of Spry2 proliferated faster in vitro and formed larger tumors in vivo. Similarly, Sutterluty et al (Sutterluty et al., 2007) demonstrated in their in vitro and in vivo studies that Spry2 inhibits NSCLC cell proliferation and tumorigenesis via ERK-dependent and independent mechanisms. Spry4 has reportedly indicated similar inhibitory effects in prostate cancer (Wang et al., 2006), pancreatic epithelioid carcinoma (Jaggi et al., 2008), NSCLC (Tennis et al., 2010) and breast cancer (Vanas et al., 2014) cells. Reporting downregulation of Spry4 in a variety of NSCLCs as well as in dysplastic lung cell lines, Tennis et al (Tennis et al., 2010), for example, showed that Spry4 transfection inhibited NSCLC cell growth, migration, invasion, and epithelial-mesenchymal transition. In a recent study, Vanas et al (Vanas et al., 2014) observed a significant 182

correlation between the expression of Spry2 and Spry4 and similarly reported that Spry4 interferes with breast cancer cell proliferation and migration. In some other malignancies, on the other hand, paradoxical regulatory functions/roles have been suggested for Spry2 and Spry4. Holgren et al (Holgren et al., 2010) found that Spry2 upregulation in the KRAS-mutated cell line HCT-116 significantly increased cell proliferation, accelerated cell cycle transition and enhanced cell migration and invasion and that Spry2 transfectants formed significantly larger xenografts. These effects were attributed, at least in part, to activation of HGF/c-Met axis and its downstream effectors Akt and Erk. Reporting upregulation of Spry2 protein in the human fibrosarcoma cell lines as well as in HRAS-transformed human fibroblasts, Lito et al (Lito et al., 2008) provided evidence that Spry2 is necessary for their tumorigenicity with the involvement of EGFR signaling. This effect, however, was found specific to HRAS transformation. Using expression profiling of the gastrointestinal stromal tumor (GIST) cells treated with the c-Kit inhibitor imatinib, Frolov et al (Frolov et al., 2003) identified Spry4 as a downstream effector of the c-Kit-activated ERK which was targeted and significantly downregulated in the treated cells.

As described in Chapters 1 and 4, differential expression of Spry1, Spry2 and Spry4 by different cancer cell lines with different functional outcomes has been documented in a number of studies, including the present project. Moreover, variable expression profiles of the Sprouty isoforms with variable clinical significance in different malignant tumors have been reported in the literature. For the first time in EOC, following the evaluation of the Spry1 expression and its significance as a predictive biomarker, I explored in the present study the expression levels of Spry2 and Spry4 proteins and their clinical relevance in my cohort, retrospectively.

Firstly, it was found that Spry2 and Spry4 proteins were significantly downregulated, along with Spry1, in EOC. Different Sprouty isoforms have been shown to be differentially expressed in different malignancies. As such, downregulation of Spry1 and Spry2 in breast cancer (Faratian et al., 2011; Lo et al., 2004), inactivation of Spry1 and Spry2 genes in prostate cancer (Taylor et al., 2010), upregulation of Spry1 and downregulation of Spry2 and Spry4 in hepatocellular carcinoma (Sirivatanauksorn et al., 2012) and upregulation of Spry1, Spry2 and Spry4 in embryonal rhabdomyosarcoma (ERMS) (Schaaf et al., 2010) have been reported. Considering Spry2 and Spry4 183

homologs individually, different reports of the expression status of the corresponding gene and/or protein are available, too. These mainly include deregulations of Spry2 as downregulation/inactivation in NSCLC (Sutterluty et al., 2007), HCC (Fong et al., 2006; Lee et al., 2008), malignant peripheral nerve sheath tumor (MPNST) (Holtkamp et al., 2004), B-cell diffuse lymphoma (Sanchez et al., 2008), prostate (McKie et al., 2005) and endometrial cancer (Velasco et al., 2011) as well as upregulation in fibrosarcoma (Baird et al., 2005). In the context of colorectal cancer (CRC), both upregulation (Barbachano et al., 2010; Holgren et al., 2010; Watanabe et al., 2011) and downregulation (Feng et al., 2011) of Spry2 with different implications -as described below- have been reported. With regard to Spry4, while it was reported to be deactivated and downregulated in a subset of prostate cancer (Wang et al., 2006), it was suggested as a susceptibility gene for testicular germ cell cancer (Kanetsky et al., 2009).

Comparing the expression scores of cancerous tissues with those of matched normal tissues, I found that 28% and 12% of tumors expressed Spry2 at equal and higher levels, respectively. As for Spry4, 47% and 4% of tumors received equal or higher scores, respectively. Therefore, although Spry2 and Spry4 proteins were expressed at significantly lower levels in a substantial fraction of EOC tumors in my cohort, decrease in the expressions of Spry2 or Spry4 is not necessarily required in all EOC tumors.

Of the three isoforms evaluated in this study, Spry1 and Spry2 represented the homologs with significantly correlated expression profiles in EOC tumors. This finding is consistent with the expression profiles of these homologs exhibited by my panel of EOC cells in vitro. This can be justified, at least in part, by functional resemblance and interactions among Sprouty isoforms which have been maily observed and documented for Spry1 and Spry2 (reviewed in Chapter 1). As with Spry1, downregulation of Spry2, too, was found to be of significant clinical relevance -as discussed below- which further support the functional cooperation between the two isoforms in EOC. I also observed a negative correlation between Spry2 and p-ERK/ERK or Ki67. Since Spry2 is known as a typical inhibtor of MAPK/ERK and hence cell proliferation, increased expression of p-ERK/ERK and Ki67 as indicators of ERK activation and cell proliferation are expected to be found in association with Spry2 downregulation. In this regard, a study by Velasco et al (Velasco et al., 2011) on normal endometrial and endometrial 184

carcinoma tissues is a notable example. By immunohistochemical analysis of the normal tissue, including proliferative and secretory endometrium, the investigators detected the highest Ki67 in proliferative endometrium with a very significant correlation with the expression of estrogen and progesterone receptors. Accordingly, the expression of Spry2 protein was inversely correlated with the expression of hormonal receptors and a highly significant decrease in the expression of Spry2 was seen in the proliferative phase of normal endometrium. When similar analysis was carried out with cancerous tissues, they found significant increase of the cell proliferation in tumor tissue compared to the secretory endometrium. As expected, however, the level of cell proliferation in proliferative endometrium was nearly as high as that observed in tumor samples. Finally, tumor tissues with the highest levels of Ki67 showed the lowest levels of Spry2 immunoexpression.

Investigating the clinical significance of Spry2 downregulation in our patients, I firstly found that low expression of Spry2 was significantly correlated with such aggressive clinicopathological features of EOC as high disease stage, high tumor grade, recurrence and post-treatment ascites. In agreement, association of Spry2 downregulation and aggressive disease has been reported in some other cancers before. McKie et al (McKie et al., 2005) observed that Spry2 mRNA is downregulated in clinically high-grade prostate cancers when compared to benign prostatic hyperplasia (BPH) and well- differentiated prostate tumors. Taylor et al (Taylor et al., 2010) later reported that while Spry2 gene inactivation was detected in 18% of the primary prostate cancers studied, it was observed in 74% of the metastatic tumors. In their study on endometrial carcinoma, Velasco et al (Velasco et al., 2011) found that grade III tumors expressed significantly lower levels of Spry2 protein than did grades I and II. By quantitative RT-PCR on paired HCC and non-tumor liver tissues from 31 patients, Sirivatanauksorn et al (Sirivatanauksorn et al., 2012) indicated that the expression of Spry2 was significantly lower in advanced disease and in association with angiolymphatic invasion. In an immunohistochemical study on HCC tumor samples from 240 patients, Song et al (Song et al., 2012) similarly reported that Spry2 downregulation accompanied highly malignant clinicopathological features of HCC, including advanced TNM stages, poorly-differentiated tumors and those with vascular invasion. With regard to CRC, however, contradictory reports can be found in the literature. Feng et al (Feng et al., 185

2011) reported downregulation of Spry2 in association with colon cancer progression and suggested a tumor suppressor role for Spry2. Examining paired tumor and normal tissue samples from 67 patients with colon cancer by real-time quantitative RT-PCR, they reported that Spry2 was downregulated in 72.7 % (16/22) of stage II, 91.3 % (21/23) of stage III and 100 % (22/22) of stage IV tumors. A negative correlation was also evident between the expression levels of Spry2 and the microRNA miR-21, an indicator of poor survival and poor response to adjuvant chemotherapy in cancer patients. They had previously showed that the expression of Spry2 positively correlates with the sensitivity of colon cancer cells to the EGFR inhibitor gefitinib and that Spry2 can enhance the response of colon cancer cells to gefitinib in vitro and in vivo by increasing the expression of EGFR and PTEN (Feng et al., 2010). In contrast, through immunofluorescence analysis of colon cancer biopsies quantitatively confirmed in 34 patients, Barbachano et al (Barbachano et al., 2010) reported high levels of Spry2, along with low levels of E-cadherin, in undifferentiated, high-grade tumors versus low levels of Spry2, and high levels of E-cadherin, in low-grade specimens. In vitro, they found inverse correlation and reciprocal regulation between Spry2 and E-cadherin in colon cancer cells whereby Spry2 was suggested to demonstrate a tumorigenic role by repressing E-cadherin. A similar role was suggested for Spry2 in CRC where Spry2 was implicated to control metastatic potential of colon cancer cells, at least in part, by c-Met upregulation (Holgren et al., 2010). KRAS mutation, however, was found later to play a critical role in CRC. Examining primary tumor samples from 113 patients with CRC, Watanabe et al (Watanabe et al., 2011) found that Spry2 was among the 30 genes which were upregulated in the KRAS mutant CRC tumors. They found that the discriminating genes identified were related to not only KRas/ERK but other signaling pathways such as Wnt/β-catenin, NF-kappa B activation, and TGFβ signaling, thereby suggesting a crosstalk between K-Ras-mediated signaling and other pathways in colorectal cancer.

Secondly, I provided evidence that the expression of Spry2 not only is associated with survival, but might serve as an independent predictor of OS and DFS. Few studies have investigated significance of the Spry2 expression in relation to the clinical outcome of malignancies. Through their meta-analysis of the gene expression profiles of a total of 1,107 breast cancer tumors combined with a further analysis of two single datasets, Faratian et al (Faratian et al., 2011) identified Spry2 as an independent predictor of a 186

more favorable outcome even for tumors with poor pathological features. Since Spry2 gene expression was inversely correlated with that of human epidermal growth factor receptor-2 gene (HER2) and Spry2 was also shown in vitro to act synergistically with the HRE2-targeting trastuzumab to reduce cell viability, the investigators then quantified the expression of Spry2 protein in a cohort of 122 trastuzumab-treated patients using the AQUA fluorescence image analysis system. Their results revealed that low Spry2 expression was associated with poor outcome and increased risk of death, thereby suggesting the usefulness of Spry2 in stratifying patients for trastuzumab therapy. In their immunohistochemical study on HCC addressed earlier, Song et al (Song et al., 2012) found that Spry2-negative patients had poorer survival and increased postoperative recurrence and identified Spry2 as an independent predictor of recurrence. In a study by Sanchez et al (Sanchez et al., 2008) on 55 patients with B-cell diffuse lymphoma, Spry2 promoter hypermethylation was found to be significantly associated with a lower 5-year survival rate.

In addition to exhibiting a negative correlation with development of post-treatment ascites, Spry2 was identified as a marker with predictive value for the condition. To the best of our knowledge, this is the first report showing a link between Sprouty and malignant ascites. I postulate that Spry2 might exert such an inhibitory effect by hindering EOC tumor growth and development and/or through regulation of mechanisms that promote ascites formation. Given the implications of VEGF and FGF in both MAPK/ERK signaling and malignant ascites formation, it is not unlikely that Sprouty proteins play a role in the regulation of this pathological process. Sprouty has shown to regulate angiogenesis and vascular permeability independently of Ras. In this regard, Spry4 was implicated in Ras-independent regulation of VEGF-induced angiogenesis and vascular permeability (Taniguchi et al., 2009). Recently, Spry4 has also been implicated in c-Src-dependent, Ras-independent regulation of angiogenesis and vascular permeability through inhibition of endothelial cell migration and adhesion and accelerated degradation of VE-cadherin (Gong, 2013; Gong et al., 2013). These findings and pertinent hypotheses need to be addressed in separate studies. However, possible correlations between the expression levels of Sprouty isoforms and those of VEGF, FGF and IL-6 in EOC were evaluated in the final part of the present project which will be described in the following chapter. 187

Apart from the significant downregulation in EOC, no other significant results were obtained in this study in relation to Spry4 protein. Of the three Sprouty isoforms studied, my literature review revealed that Spry4 is the least-studied homolog with regard to cancer biology, in particular from the clinical point of view. In a study by Frolov et al (Frolov et al., 2003) exploring biological markers of response to the c-Kit inhibitor imatinib in gastrointestinal stromal tumors (GISTs), they identified Spry4 gene among imatinib-responsive genes and Spry4 protein as a downstream effector of the c- Kit-activated ERK targeted by the drug. In their clinical investigation on 7 patients, they then found that Spry4 levels were dramatically decreased in imatinib- responsive cases and thus proposed Spry4 as a reliable marker of the imatinib-responsive treatment. This article, however, was the only report of the clinical significance of the Spry4 expression in cancer I could find in the literature. On this basis, role of Spry4 in cancer needs to be further elucidated in future research.

In conclusion, I report for the first time that Spry2 and Spry4 proteins are downregulated, along with Spry1, in EOC. My findings also provide the evidence that the expression of Spry2 protein impacts EOC tumor behavior and predicts the patient outcome.

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7 Evaluation of the expression status of VEGF, FGF and IL-6 proteins and their correlation with Spry1, Spry2 and Spry4 proteins in EOC: a retrospective study

7.1 Introduction

Sprouty proteins are evolutionarily conserved modulators of MAPK/ERK pathway. A broad range of extracellular stimuli, including mitogens, growth factors, cytokines, hormones and neurotransmitters, activate MAPK/ERK. Furthermore, growth factors and cytokines have been implicated in molecular pathology of EOC. Insulin-like growth factor (IGF), epidermal growth factor (EGF), transforming growth factor-β (TGF-β), platelet-derived growth factor (PDGF), fibroblast growth factor (FGF) and vascular endothelial growth factor (VEGF) have been reported to be strongly associated with the growth and progression of EOC (Langdon and Smyth, 1998). There are also evidence indicating the role of cytokines such as tumor necrosis factor (TNF), interleukin-6 (IL- 6), interleukin-8 (IL-8) and interleukin-10 (IL-10) in pathophysiology of EOC where presence of an immunosuppressive network protecting tumor from immune system is also implicated in EOC growth and progression (Kulbe et al., 2012; Longuespee et al., 2012; Matte et al., 2012).

With well-documented contributions to the pathophysiology of EOC and with respect to the following facts, FGF, VEGF and IL-6 held special interest in the present project. FGF and VEGF are known as potent activators of MAPK/ERK. Besides, role of VEGF in enhancement of tumor angiogenesis and induction of vascular hyperpermeability is well documented. A proangiogenic role has also been suggested for FGF and IL-6 in EOC. In contrast, Sprouty proteins inhibit FGF as well as VEGF activation of MAPK/ERK and parallel pathways, thereby exerting growth-inhibitory and anti- angiogenic effects. As such, Spry4 was shown to suppress VEGF-stimulated angiogenesis and tumor growth (Taniguchi et al., 2009). In addition, VEGF and IL-6 have also been indicated to be involved in, or associated with, development of EOC- related malignant ascites. Conversely, as described in the previous chapter, my data revealed an inverse correlation between the expression of Spry2 and development of post-treatment ascites where Spry2 also retained a predictive value for the condition. On 189

this basis, the expression status of FGF, VEGF and IL-6 proteins and their associations with Sprouty isoforms in my cohort of EOC patients were retrospectively evaluated which are described in this chapter.

7.2 Results

7.2.1 VEGF, FGF-2 and IL-6 proteins are upregulated in EOC.

To explore the expression status of VEGF, FGF-2 and IL-6 in my cohort, the immunohistochemical expression of these proteins in EOC and normal epithelium was evaluated and quantified based on the methodology described in the section 3.2.11.2. Firstly, a cytoplasmic pattern of expression was observed for the three proteins. Secondly, a comparison of the expression levels of these markers in tumor and normal tissue revealed a significant upregulation of VEGF (p value <0.0001), FGF-2 (p value <0.0001) and IL-6 (p value <0.0001) in EOC as shown in Figure 7-1, Figure 7-2 and Figure 7-3, respectively.

Using the binary cut-off point defined for each marker, the tumor samples were then classified into high- and low-expressing groups. When VEGF was evaluated in a total of 100 cases, 59 cases were identified as patients with high expression of VEGF (score>3.5). In a total of 95 cases, FGF-2 was high (score>3.5) in 31 patients. With regard to IL-6, 30 out of 98 cases were classified as IL-6 high-expressing patients (score>3.5).

Next, the staining scores of each marker in normal and cancer tissue from the same patient were compared in all matched samples. As a result, my analysis indicated higher expression of VEGF, FGF-2 and IL-6 in 77, 91.5 and 75 percent of tumor tissues, respectively, as compared with matched normal tissues (Figure 7-4). 190

Figure 7-1 Immunohistochemical analysis of VEGF expression in EOC. A. Representative photographs indicating high (top) and low (bottom) levels of VEGF expression in EOC tissue (magnification= 40x). B. Comparison of VEGF expression scores in normal and tumor tissues, demonstrating significant upregulation of VEGF protein in EOC tissues. Data are represented as mean expression score ± SE (top), as well as maximum and minimum expression score (bottom). Significant values (p value <0.05) are marked by asterisks. 191

Figure 7-2 Immunohistochemical analysis of FGF-2 expression in EOC. A. Representative photographs indicating high (top) and low (bottom) levels of FGF-2 expression in EOC tissue (magnification= 40x). B. Comparison of FGF-2 expression scores in normal and tumor tissues, demonstrating significant upregulation of FGF-2 protein in EOC tissues. Data are represented as mean expression score ± SE (top), as well as maximum and minimum expression score (bottom). Significant values (p value <0.05) are marked by asterisks. 192

Figure 7-3 Immunohistochemical analysis of the IL-6 expression in EOC. A. Representative photographs indicating high (top) and low (bottom) levels of IL-6 expression in EOC tissue (magnification= 40x). B. Comparison of IL-6 expression scores in normal and tumor tissues, demonstrating significant upregulation of IL-6 protein in EOC tissues. Data are represented as mean expression score ± SE (top), as well as maximum and minimum expression score (bottom). Significant values (p value <0.05) are marked by asterisks.

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Figure 7-4 Comparative analysis of VEGF, FGF-2 and IL-6 expression scores in normal and malignant ovarian epithelial tissues after immunohistochemical scoring. The graphs represent the percentage of cases showing lower (TN) expression scores for VEGF (top), FGF-2 (middle) and IL-6 (bottom) in their tumor tissue as compared to their corresponding values in matched normal tissue. Higher expressions of VEGF, FGF-2 and IL-6 in tumor tissue were found in 77%, 91.5% and 75% of patients, respectively. 194

7.2.2 There are no correlations between the expressions of Sprouty isoforms and those of VEGF, FGF-2 and IL-6 in EOC.

The immunohistochemical expression of VEGF, FGF-2 and IL-6 proteins in EOC samples were then subjected to further analysis for evaluation of any possible association with the expressions of the Sprouty isoforms in a binary model. As shown in Table 7-1, no significant correlation was found between the expression of Spry1 and that of VEGF (p value: 0.284), FGF-2 (p value: 0.193) or IL-6 (p value: 0.468). Spry2 did not show any significant correlation with VEGF (p value: 0.655), FGF-2 (p value: 0.683) or IL-6 (p value: 0.677), either (Table 7-2). Similarly, insignificant results were obtained when possible correlations of the Spry4 expression with the expressions of VEGF (p value: 0.466), FGF-2 (p value: 0.927) and IL-6 (p value: 0.118) proteins were individually analyzed (Table 7-3).

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Table 7-1 Correlation of Spry1 expression with VEGF, FGF-2 and IL-6

Parameter Patients No. Low Spry1 High Spry1 p value Low 41 28 13 VEGF 0.284 High 59 34 25 Low 64 42 22 FGF-2 0.193 High 31 16 15 Low 68 40 28 IL-6 0.468 High 30 20 10 No.: number. p values <0.05 are considered statistically significant.

Table 7-2 Correlation of Spry2 expression with VEGF, FGF-2 and IL-6 Parameter Patients No. Low Spry2 High Spry2 p value Low 41 30 11 VEGF 0.655 High 58 40 18 Low 64 46 18 FGF-2 0.683 High 31 21 10 Low 68 47 21 IL-6 0.677 High 30 22 8 No.: number. p values <0.05 are considered statistically significant. 196

Table 7-3 Correlation of Spry4 expression with VEGF, FGF-2 and IL-6

Parameter Patients No. Low Spry4 High Spry4 p value Low 41 33 8 VEGF 0.466 High 58 43 15 Low 64 49 15 FGF-2 0.927 High 31 24 7 Low 68 49 19 IL-6 0.118 High 30 26 4

No.: number. p values <0.05 are considered statistically significant. 197

7.3 Discussion

Significance of VEGF in EOC is well-established (Masoumi Moghaddam et al., 2012). Playing an important role in the physiology of normal ovaries, VEGF has a major contribution to the growth and development of EOC mainly through induction of tumor angiogenesis and enhancement of vascular permeability. Moreover, it has been argued that VEGF might directly promote growth and proliferation of EOC cells through an autocrine loop (Boocock et al., 1995; Chen et al., 2004; Mattern et al., 1997). Preclinical studies have shown that overexpression of VEGF can transform normally functional ovarian epithelium into neoplastic, ascites-producing tissue (Ramakrishnan et al., 2005; Schumacher et al., 2007). Through similar mechanisms, VEGF also contributes to the development of the characteristic features of the advanced EOC– peritoneal carcinomatosis and malignant ascites. Consistently, elevated immunohistochemical expression of VEGF in EOC has been reported before (Duncan et al., 2008; Ravikumar and Crasta, 2013; Shen et al., 2000; Siddiqui et al., 2010; Siddiqui et al., 2011; Smerdel et al., 2009; Wong et al., 2003). Clinically, intratumoral expression of VEGF has been found to be directly correlated with disease progression (Chambers et al., 2010) and poor survival (Duncan et al., 2008; Goodheart et al., 2005; Raspollini et al., 2004; Shen et al., 2000; Smerdel et al., 2009). In agreement, significance of the immunohistochemical expression of VEGF in EOC tumor as an independent prognostic factor (Siddiqui et al., 2010) or a biomarker of response to platinum-based chemotherapy (Siddiqui et al., 2011) has also been suggested. Moreover, clinical relevance of serum and/or malignant ascitic VEGF levels in EOC patients has been investigated in a number of studies. As such, VEGF has been suggested as a serological biomarker for clinical diagnosis and a predictor of prognosis in patients with EOC (Cooper et al., 2002; Hefler et al., 2006; Li et al., 2004). Similarly, ascitic VEGF was reported to be of prognostic value in EOC (Bamias et al., 2008).

As with VEGF, implication of FGF in the pathophysiology of EOC is well documented. Similarly, FGF has shown both angiogenic and mitogenic activities in EOC. In this regard, FGF has been reported to stimulate proliferation, migration and invasion of EOC cells in vitro and to promote angiogenesis in vivo (Crickard et al., 1994; Lin et al., 2003a; Lin et al., 2003b; Zhang et al., 2003). These effects, at least in part, result from regulation of other genes and proteins implicated in invasive and angiogenic features of 198

the malignant condition, including urokinase-type plasminogen activator (uPA) (Li and Jiang, 2010), matrix metalloproteinases (MMPs) (Strutz et al., 2002), VEGF (Giavazzi et al., 2003; Sako et al., 2003) and E-cadherin (Billottet et al., 2004; Lau et al., 2013; Strutz et al., 2002; Wu et al., 2008). Through stimulation of mesenchymal conversion and cohort/scatter cell migration, FGF signaling was also found to play a central role in the maintenance of cellular plasticity of ovary-derived cells throughout the carcinogenesis process (De Cecco et al., 2004). Accordingly, increased expression of FGF (mRNA and/or protein) in tumor tissue (Davidson et al., 2002; Fujimoto et al., 1997), raised concentration of FGF in serum and/or ascetic fluid (Barton et al., 1997), or elevated levels of FGF in both tumor and serum (Le Page et al., 2006) has been reported in EOC. However, clinical relevance of the FGF expression has been controversial. Gan et al (Gan et al., 2006) found that high FGF expression was inversely correlated with sensitivity to paclitaxel and was a strong predictor of resistance to the drug. In contrast, Obermair et al (Obermair et al., 1998) and Secord et al (Secord et al., 2007) have reported inverse correlation of intratumoral FGF with tumor progression and poor survival. Obermair et al concluded that FGF hypothetically induces a fibroblastic response which results in a less aggressive phenotype.

Known as a pleotropic cytokine, IL-6 is implicated in EOC carcinogenesis. It influences EOC growth and development through direct and indirect effects on tumor cells or their microenvironment, including immune system components, respectively (Heikkila et al., 2008; Nash et al., 1999). As such, IL-6 has been indicated to promote EOC cell proliferation, migration, invasion, survival and resistance to chemotherapeutic agents (Obata et al., 1997; Wang et al., 2012b; Wang et al., 2010). IL-6 also contributes to EOC-induced angiogenesis (Coward et al., 2011) and malignant ascites (Lo et al., 2011). In agreement, increased immunohistochemical expression of IL-6 in tumor specimens (Glezerman et al., 1998) and high levels of IL-6 in serum (Acien et al., 1994; Gorelik et al., 2005) or ascites samples (Plante et al., 1994) from patients with EOC have been reported. Accordingly, clinicopathological relevance of intratumoral, serum or ascitic IL-6 levels has been investigated in EOC. Plewka et al (Plewka et al., 2014) observed that malignant serous tumors had higher expression levels of IL-6 as compared with serous borderline and benign lesions, and that IL-6 in the mucinous subtype was observed only in benign lesions. They thus concluded that the expression 199

of IL-6 might be dependent on the histological subtype of the tumor and the degree of malignancy. Guo et al (Guo et al., 2010) reported a significant difference in immunohistochemical expressions of IL-6 among the metastatic, drug-resistant recurrent tumors and matched primary tumors with more staining in the drug-resistant and the metastatic tumors, respectively. In another study by Coward et al (Coward et al., 2011), intensity of IL-6 staining in malignant cells was found to be significantly associated with poor prognosis. Similarly, a link between high levels of serum or ascitic IL-6 and unfavorable clinical outcome has been reported. As such, serum IL-6 has shown significant association with tumor burden, clinical disease status, and survival (Berek et al., 1991; Dobrzycka et al., 2013) as well as prognostic value (Scambia et al., 1995) in EOC. Scambia et al (Scambia et al., 1994) reported higher levels of serum IL-6 in patients unresponsive to chemotherapy. Ascitic IL-6, too, was found by Plante el al (Plante et al., 1994) to be directly correlated with ascites volume and initial tumor size.

Taken together, VEGF, FGF and IL-6 are known to be largely involved in the pathogenesis of EOC. The three factors all have shown mitogenic as well as proangiogenic activities in EOC. VEGF and IL-6 are also implicated in or associated with the development of EOC-induced malignant ascites. MAPK/ERK signaling cascades activated by VEGF and FGF are among pathways regulated by Sprouty proteins through a negative feedback loop. I showed earlier that Spry1, Spry2 and Spry4 are downregulated in EOC and that the expressions of Spry1 and Spry2 are of clinical significance and prognostic value. Spry2 was also found to be negatively correlated with post-treatment development of malignant ascites and identified as an independent predictor of the condition. On this basis, the expression status of VEGF, FGF and IL-6 proteins as EOC growth promoting factors and their likely correlation with the expression levels of the Sprouty isoforms as EOC growth inhibiting factors were explored in the final part of this project. As anticipated, and in line with earlier reports, intratumoral expression levels of VEGF, FGF and IL-6 were found to be significantly higher than their corresponding levels in normal ovarian tissue. However, no statistically meaningful correlation was found between the expression levels of VEGF, FGF and IL-6 and those of the Sprouty isoforms. It is noteworthy that since Sprouty has been the main focus of this project, clinical relevance of the VEGF, FGF and IL-6 expressions in EOC was not included in this thesis. Using the same cohort, possible 200

associations between the immunohistochemical expression of VEGF, FGF and IL-6 proteins and clinicopathological features as well as disease outcome will be explored in a separate retrospective study.

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8 Summary and future potential directions

Employing in vitro and retrospective clinical studies, this research project has investigated the possible role of Sprouty proteins in EOC disease. The current chapter contains a brief summary of my findings and their potential applications in clinic as well as prospects and likely directions for future research.

Known as an evolutionarily conserved family of proteins implicated in developmental, physiological and pathological processes, Sprouty proteins have been the focus of a variety of studies for the past decade. Despite the fact that Sprouty is widely recognized as an inducible modulator of MAPK/ERK signaling, emerging evidence identifies Sprouty as a versatile regulator with targets even beyond MAPK/ERK. With regard to cancer biology, different patterns of the Sprouty deregulation have been reported in different cancers. As with normal cells, evidence shows that Sprouty in malignancies functions in a cell-specific and context-dependent manner, hence its implication as a negative or, in some instances, a positive regulator of tumor growth and progression. Accordingly, clinicopathological significance of the Sprouty expression has been investigated in a variety of malignant conditions. However, lack of knowledge clearly exists with respect to the role of Sprouty in EOC. Thus, I aimed in the present project to investigate the expression status of Sprouty and its clinical relevance in EOC.

In vitro, my initial data indicates that surface epithelial cells of normal ovaries express Spry1, Spry2 and Spry4, suggesting -in line with earlier reports- the involvement of Sprouty in adult ovarian physiology. I also observed differential expression of the Spry1 and Spry2 proteins, individually, across a panel of EOC cell lines with a general trend of downregulation of Spry1 and/or Spry2. My evaluation of the Sprouty mRNA expression in comparison with the Western blot analysis of the protein expression indicated that the expression of Sprouty at protein level may not necessarily correspond to that at mRNA level. Of the three Sprouty isoforms evaluated in the seven EOC cell lines used, Spry1 indicated more prominent alterations at both protein and mRNA levels. Induced expression of Spry1 in the EOC cell line with minimal Spry1 content (SKOV-3) attenuated cell proliferation, motility and invasion, and diminished survival. Conversely, knockdown of the protein expression in the Spry1-expressing cell line 1A9 202

enhanced cell viability and promoted proliferation, migration and invasion. I also found that induced expression of Spry1 activated proapoptotic processes, with implication of Bcl-2 protein family and caspase pathways. Additionally, the Spry1 expression inhibited activation of ERK and AKT in SKOV-3 cells, with involvement of PTEN in inhibition of the AKT activity. Taken together, my findings highlight the role of Spry1 in EOC cell biology.

Since cell proliferation, migration, invasion, and survival are central to the development, progression, and dissemination of malignant conditions, I next investigated clinical relevance of the Spry1 protein expression in patients with EOC. My immunohistochemical study revealed significant downregulation of Spry1 protein in EOC tissues as compared with their matched normal ovarian samples. I also observed in EOC tumor tissues an inverse correlation between the Spry1 expression and those of p- ERK/ERK and Ki67, the latter known as a tumor proliferation marker. This was in agreement with my in vitro results indicating the inhibitory effects of Spry1 on MAPK/ERK activity and proliferative capacity of EOC cells. Clinically, I found an inverse significant correlation between the Spry1 expression and aggressive clinicopathological features of the disease, including the disease stage, tumor grade, recurrence and lymphovascular invasion. Furthermore, high levels of the Spry1 expression were found to be significantly associated with better OS and DFS. More remarkably, Spry1 was identified in my multivariate analysis as an independent prognostic factor for better OS and DFS both. When the predictive value of the Spry1 expression for development of post-treatment ascites or chemorefractory disease was assessed, no significant results were observed. There was also no statistically meaningful association between the Spry1 protein expression and tumor size.

Considering similar implications of Spry2 and Spry4 in biological and pathological conditions, the expression levels of Spry2 and Spry4 proteins and their clinical relevance were then explored. I found that Spry2 and Spry4 proteins were downregulated along with Spry1 in EOC as compared with their matched normal samples. Given the known interactions among the Sprouty isoforms for a balanced, regulatory output, a possible correlation among the expression levels of Spry1, Spry2 and Spry4 was next explored. While a significant correlation between Spry1 and Spry2 was revealed, Spry4 expression indicated no statistically meaningful correlation with 203

Spry1 or Spry2. Evaluating the possible associations of Spry2 and Spry4 expressions with those of p-ERK/ERK and Ki-67, I noted negative significant correlations of Spry2 with both p-ERK/ERK and Ki67, individually. No significant correlation was found between Spry4 and p-ERK/ERK or Ki-67. Since a strong correlation had been earlier revealed between the expression levels of Spry1 and Spry2, a possible correlation between the expression of Spry2 and clinicopathological characteristics was explored next. As anticipated, Spry2 expression indicated an inverse significant correlation with aggressive clinicopathological features, including the disease stage, tumor grade, recurrence and post-treatment ascites. In contrast, no significant correlation between the expression levels of Spry4 and clinicopathological characteristics of the patients was found. Subsequently, the influence of the Spry2 expression on OS and DFS was evaluated where high Spry2 appeared to be a significant predictor of better OS and DFS both. Moreover, my multivariate analysis revealed predicting value of Spry2 as an independent prognostic factor for both OS and DFS. With regard to Spry4, no statistically significant results were obtained.

Given the significant correlation between the expression levels of Spry1 and Spry2 as well as their significant influence on clinical outcome, I next evaluated the predictive value of the concomitant expression of Spry1 and Spry2 proteins. When patients with high expression levels of both Spry1 and Spry2 were evaluated against those expressing both isoforms at low levels, significantly different outcomes, both in terms of OS and DFS, were observed. Hazard ratios and p values resulted here were more significant than those resulted from the outcome analysis of the expression of each isoform individually. This model of concomitant expression of Spry1 and Spry2 retained its predictive significance for both OS and DFS in multivariate analysis, as well.

Investigating the predictive value of Spry2 and Spry4 isoforms in relation to the development of post-treatment ascites and chemorefractory disease in our EOC patients, Spry2 was surprisingly identified as an independent predictor of post-treatment ascites. Spry2 expression, however, indicated no statistically significant value for prediction of response to carboplatin and taxol chemotherapy. Similar analyses with Spry4 yielded no significant results. Subsequently, a possible association between tumor size and the expressions of Spry2 and Spry4 proteins was investigated where no significant difference between the mean sizes of low- and high-expressing tumors was resulted. 204

By promoting tumor growth and progression and contributing to tumor angiogenesis and malignant ascites formation, FGF, VEGF and IL-6 are largely implicated in the pathophysiology of EOC. In contrast, Sprouty proteins have been shown to inhibit tumor growth and development and to repress angiogenesis. In the present study on EOC, Sprouty expression was found to inhibit invasive behavior of EOC cells and to be inversely associated with aggressive features of the tumor, including malignant ascites. On this basis, intratumoral levels of FGF, VEGF and IL-6 proteins and a likely link between the expressions of Spry1, Spry2 and Spry4 and those of FGF, VEGF and IL-6 were explored next. As anticipated, my immunohistochemical analysis indicated higher expression levels of VEGF, FGF and IL-6 in tumor tissues as compared with matched normal tissues. However, no statistically significant correlation was found between VEGF, FGF and IL-6 levels and those of the Sprouty isoforms.

In sum, I report for the first time downregulation of Spry1, Spry2 and Spry4 in EOC, associations of Spry1 and Spry2 low expression levels with aggressive clinical features and poor survival, and prognostic significance of Spry1 and Spry2 as independent predictors of OS and DFS.

As an initial attempt, this project provides insight into understanding the role of Sprouty proteins in EOC and their potential clinical applications. Nevertheless, considering versatility of Sprouty proteins along with their isoform-specific functionality in a context-dependent manner, Sprouty-mediated regulations in EOC need to be further elucidated in future research. As such, the elaboration of functional significance of the expression of different Sprouty isoforms and pertinent underlying mechanisms in further in vitro as well as in in vivo models are yet to be explored. Preclinically, Sprouty transfection or knockdown in animal models of human EOC will foster a better understanding of how Sprouty proteins influence the pathophysiological processes, including tumorigenesis, tumor angiogenesis, invasion and metastasis, and impact survival. In addition, a combination of genetic and gene expression analyses can provide invaluable data to compliment protein expression studies and to identify susceptibility or resistance factors (Quigley et al., 2011). Furthermore, since RAS (Bloethner et al., 2005; Courtois-Cox et al., 2006; Holgren et al., 2010; Lito et al., 2009; Lito et al., 2008; Schaaf et al., 2010; Shaw et al., 2007; Sutterluty et al., 2007) and RAF (Bloethner et al., 2005; Mathieu et al., 2012; Tsavachidou et al., 2004) oncogenes have 205

been identified as important determinants of the expression status and/or mode of action of the Sprouty isoforms, analysis of possible accompanying mutations can be of notable importance. This could also include mutants with known implication in EOC biology, such as BRCA1 and BRCA2 (Colombo et al., 2010).

A growing body of evidence shows that Sprouty proteins function as inducible regulators of VEGF and FGF activation of MAPK/ERK. My data demonstrate downregulation of Sprouty isoforms as well as upregulation of VEGF and FGF in EOC. Also, p-ERK/ERK expression ratio as an indicator of ERK activation was significantly higher in tumor tissue and inversely correlated with the expression levels of Spry1 and Spry2. Thus, despite the fact that no significant correlation was found between the expression levels of the Sprouty isoforms and those of VEGF and FGF in the present project, functional interactions between these growth factors and Sprouty proteins are expected in EOC that remain to be further investigated in future research. Moreover, Sprouty isoforms have shown to interact with one another as well as with an increasing number of biological players, including partner molecules and adapter proteins, not only to modulate growth factor-stimulated RTKs, but to mediate the crosstalk between MAPK/ERK and other pathways (Masoumi-Moghaddam et al., 2014b). On this basis, another avenue for future research lies in the importance of such functional and/or structural interactions in the pathophysiology of EOC. As such, a functional interaction is postulated to take place between Sprouty and Caveolin-1 (Cav-1) in EOC. Cav-1 is a transmembrane protein that comprises the major architectural component of the so- called caveolae, the specialized plasma membrane invaginations involved in multiple cellular functions, including signal transduction. Cav-1 has been shown to associate and cooperate with Sprouty proteins to modulate MAPK/ERK in a growth factor- and Sprouty isoform-specific manner (Cabrita et al., 2006; Impagnatiello et al., 2001). Cav- 1 inhibits activation of endothelial nitric oxide synthase and vascular endothelial growth factor receptor-2 (VEGFR-2), promotes association of VEGFR-2 with vascular endothelial cadherin (VE-cadherin) and enhances endothelial barrier function with eventual inhibitory effects on tumor growth, angiogenesis and vascular permeability (Bauer et al., 2005; Lin et al., 2007). Thus, cooperative functions of Sprouty and Cav-1 might be a regulatory mechanism in EOC to inhibit EOC tumor growth and angiogenesis as well as tumor vasculature permeability, with a putative role in 206

inhibition of malignant ascites. In addition, Sprouty has shown to regulate angiogenesis and vascular permeability independently of Ras. In this regard, Spry4 was implicated in Ras-independent regulation of VEGF-induced angiogenesis and vascular permeability (Taniguchi et al., 2009). Recently, Spry4 has also been implicated in c-Src-dependent, Ras-independent regulation of angiogenesis and vascular permeability through inhibition of endothelial cell migration and adhesion and accelerated degradation of VE- cadherin (Gong, 2013; Gong et al., 2013). Similar mechanisms mediated by Sprouty proteins might regulate mitogenic and angiogenic activities of biological contributors to the pathophysiology of EOC, including VEGF, FGF and IL-6. Supporting this notion, the present project provides for the first time evidence of a link between Sprouty and malignant ascites where Spry2 was inversely correlated with post-treatment ascites and also identified as an independent predictor of the condition. This was found along with upregulation of VEGF, FGF and IL-6 that are known to be implicated in, or associated with, the development of EOC-induced malignant ascites. Taking into account the inverse correlations of Spry1 and Spry2 with aggressive clinicopathological features of EOC, another theory could imply indirect involvement of Sprouty in regulation of malignant ascites formation where downregulation of Sprouty could lead to tumor progression and invasion and hence development of more aggressive features, including malignant ascites. Further investigations are warranted to test all above hypotheses.

There is an increasing need for the development of novel biomarkers for diagnostic, therapeutic and prognostic purposes in cancer. In the context of EOC, it has now become clear that different histologic subtypes differ with respect to epidemiological and genetic risk factors, precursor lesions, molecular events during oncogenesis, patterns of spread, response to chemotherapy, and outcome. While different subtypes of EOC are thus considered as different diseases, current treatment protocols are not subtype-specific. Hence, there is an emerging consensus on subclassification of and personalized treatment approaches to EOC for which reproducible pathological diagnosis of tumor subtype is critical. Thus, discovery of reliable stratification biomarkers for identification of patients who may benefit from a given therapy is of great value (Gilks and Prat, 2009; Kobel et al., 2008). In this project, I did not find any significant correlation between different subtypes of EOC and the expression Sprouty. The majority of participants in my study had serous EOC, with remaining patients with 207

non-serous subtypes comprising 19% of all cases, collectively. Since non-serous subtypes, including endometrioid, mucinous, clear cell, transitional (Brenner), mixed and undifferentiated, are known to account for 50% of EOC tumors (Taylor and Kirwan, 2012), it would be reasonable to investigate the relevance of Sprouty expression in histopathological subclassification of EOC in a larger cohort with a better subtype distribution.

Of particular note in the present study is the revelation of the value of Spry1 and Spry2 as independent prognostic factors for a better outcome in EOC. Spry2 also showed independent predictive value in relation to the development of post-treatment ascites. In agreement, Spry2 was earlier identified as a prognostic factor in breast cancer with usefulness in stratification of patients for trastuzumab therapy (Faratian et al., 2011). Similarly, Spry4 was proposed as a reliable marker of the imatinib-responsive treatment in patients with gastrointestinal stromal tumors (Frolov et al., 2003). Since clinicopathological predictors such as disease stage, tumor grade and residual disease are helpful but not efficient enough for the management and stratification of EOC patients, development of novel factors with independent predictive and prognostic value will help to improve the current standard of care. In this regard, validation of my findings in independent studies and evaluation of Sprouty proteins in combination with other proposed biological markers (Crijns et al., 2006; Le Page et al., 2010; Zagouri et al., 2010) in a prospective study may provide a more reliable set of biomarkers. A parallel examination of EOC specimens from patients receiving adjuvant chemotherapy could evaluate the utility of a Sprouty-based stratification where Sprouty could serve as a prognostic factor and/or a biomarker of treatment sensitivity. In conclusion, data from the present research further highlights the significance of the Sprouty-mediated regulation as a developing story in cancer. In particular, our findings lay the basis for future studies elaborating on the implications and potential clinical applications of this protein family in EOC, including their utility in targeted strategies.

208

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