IGF:VN Complexes and their Role in Breast Cell Migration

By

Brett G Hollier

Bachelor of Applied Science (Hons), QUT

School of Life Sciences

Queensland University of Technology

Brisbane, Australia

A thesis submitted for the degree of Doctor of Philosophy of the Queensland University of Technology 2007

STATEMENT OF ORIGINALITY

The work contained in this thesis has not been previously submitted for a degree or diploma at any other higher education institution. To the best of this authors knowledge this thesis contains no material which has been previously published or written by another person except where due references are made.

SIGNED:

DATE:

ii ACKNOWLEDGEMENTS

First and foremost I would like to thank my beautiful wife Sophie. While there have been some challenging times, you have always been there to support me through my ups and downs. The encouragement and reassurance you have given me over the last few years has made all the difference and I wouldn’t have made it without you.

Secondly, I would like to thank Zee for being the best supervisor a student could hope for. I have really enjoyed being mentored by someone who has let me have enough independence to direct my own research, but has given me much needed direction at times when I was getting ‘side-tracked’. Zee has always had an open door policy and no matter how busy she was, always found time for a quick update. I have always respected Zee’s drive and enthusiasm, especially her desire to help others achieve their goals. I’m particularly grateful for Zee’s support in helping me attend a number of international conferences, whereby she has been known to “put in a good word” or “I’ll see what I can do” and help me establish contacts with numerous researchers from around the world. While perhaps not seeing eye-to-eye on all issues we’ve discussed, there has always been respect and its been a blast!

I would also like to thank David for all his support and encouragement during my studies. At times David has given some much needed perspective via his witty (to David anyway) one liners and never lets a person get too full of themselves. He has been a vast wealth of knowledge and has always made me laugh when things get a bit chaotic. Big thanks has to also go out to the ‘original lab posse’ of Jenny, Carolyn and Tony. Jenny and Carolyn have always looked out for me in (and out) of the lab since I first began my Dean Scholars project. It’s always been fun whether it be working, drinking or dropping them on the dance floor. They’ve both been missed a lot since moving on from QUT. Then there’s Tony. There’s not enough that can be said about how much help and inspiration Tony has been since I first met him. Tony’s heard it all before whether he’s wanted to or not, so I’ll just say “You’re the man, respect!”

There are also many other people from the TRR team I would like to acknowledge for their friendship and help in the lab over the years, especially, Damien, Gary,

iii Sean, Louise, Derek, Murphy, Jacqui and Mel. I have also received a lot of advice from numerous people outside of the TRR program, in particular the HDC program and would like to thank current and former members, including Andrew, Steve, Johnnie, Mitch, Mel, Yaowu, Pete and Hoops. Big thanks to Andrew and Steve for introducing me to the “molecular world” and being good mates. I’m also grateful to Shea for her help with the real-time PCR and “looking out for me” on a Friday night. I must also thank Agnes for all her help with the microarray experiments and having time for me to pester her with questions over at UQ.

My scholarship has been funded by the Australian Postgraduate Award, QUT Vice- chancellor’s Top-up funds and originally from the Institute of Health and Biomedical Innovation (IHBI). I would also like to thank the School of Life Sciences, IHBI, QUT grants-in-aid and Prof. Zee Upton for provision of financial support for both national and international conference attendance. This project was also funded in part by a National Health and Medical Research Council grant, 290511.

A big thanks also goes to my family who have given me endless support and encouragement. While at times it hasn’t been the easiest to stay in contact, everyone has always had faith I would get to the end and I hope I’ve made you proud. Lastly, I will end where I started and thank Sophie again. There are not enough words I can say to thank you enough for your support and just being who you are. You’ve always been there for me no matter how ‘grumpy’ I was and have been the inspiration I needed to get through. I’ve been privileged to share this journey with you and look forward to sharing many more.

“Work Hard, Play Harder”

B.Hollier

“Water is your friend”

S.Hollier

iv ABSTRACT

Members of the -like (IGF) family are mitogenic growth factors which have been shown to play critical roles in both normal growth and development, and tumour biology. The IGF system is complex and the biological effects of the IGFs are determined by diverse interactions between many molecules, including interactions with the extracellular matrix (ECM). Recent observations have demonstrated that IGFs can associate with the ECM protein vitronectin (VN) and this interaction can modulate IGF-stimulated biological functions. It has been demonstrated previously that IGF-II can bind directly to VN, while IGF-I associates with VN indirectly via the involvement of IGF-binding proteins (IGFBPs) -2, -3, -4 and -5. As the IGF system plays important roles in both normal breast development and in the transformation and progression of breast cancer, this study aimed to describe the effects of substrate-bound IGF-I:IGFBP:VN complexes on breast cell functions and to dissect the mechanisms underlying these responses. The studies reported in this thesis demonstrate that substrate-bound IGF- I:IGFBP:VN complexes, containing IGFBP-3 and IGFBP-5, are potent stimulators of proliferation and migration in the “normal”, non-tumourigenic MCF-10A breast epithelial and MCF-7 breast carcinoma cell lines. Interestingly, substrate-bound IGF- I:IGFBP:VN complexes were less effective in increasing the migration of the metastatic MDA-MB-231 breast cancer cell line. This, however, is due to these cells expressing the αvβ3 which can support a highly migratory phenotype independent of IGF-I-stimulation. Taken together this suggests a particularly important role for these complexes in stimulating a highly migratory phenotype in pre-invasive or poorly metastatic breast cells. Studies using IGF-I analogues were also undertaken to establish if there was a requirement for ternary complex formation and the type-1-IGF (IGF-1R) in the enhanced migration responses observed. These studies determined IGF- I:IGFBP:VN-stimulated migration to be dependent upon both heterotrimeric IGF- I:IGFBP:VN complex formation and activation of the IGF-1R. Furthermore, the enhanced cellular migration was abolished upon incubation of MCF-7 and MCF- 10A cells with function blocking antibodies directed at VN-binding and the IGF-IR. In addition, analysis of the signal transduction pathways underlying the enhanced cell migration revealed that the complexes stimulate a transient activation

v of the ERK/MAPK signaling pathway, while simultaneously producing a sustained activation of the PI3-K/AKT pathway. Optimal intracellular signaling required activation of both the IGF-1R and VN-binding integrins, as antibody mediated inhibition of either receptor led to substantial decreases in both ERK/MAPK and PI3-K/AKT pathway activation. Furthermore, experiments using pharmacological inhibitors of these pathways determined a pivotal role for PI3-K/AKT activation in substrate-bound IGF-I:IGFBP:VN-stimulated cell migration. In order to confirm an important role for the PI3-K/AKT pathway in these responses, wild-type and activated-AKT was transiently overexpressed in MCF-10A cells. Overexpression of both wild-type and activated-AKT further enhanced cellular migration in response to substrate-bound IGF-I:IGFBP:VN complexes. However, these responses still required co-activation of the IGF-1R and VN-binding integrins. In an attempt to obtain a global view of the possible molecular mechanisms underpinning IGF-I:IGFBP:VN-stimulated cell migration, oligonucleotide microarrays were used to screen for candidate genes important for the observed migratory responses. The microarray studies identified 165 genes which were differentially expressed in cells migrating in response to substrate-bound IGF- I:IGFBP:VN complexes. Gene ontology and functional analysis revealed many of these genes to be significantly associated with biological functions relevant to cancer transformation and progression, including cell growth and proliferation, cell death and cellular movement. In regard to cell migration, a number of the genes identified have previously reported roles in cellular movement, migration and metastasis, which may provide future targets to augment IGF-I:IGFBP:VN-stimulated cell migration. Taken together, the studies reported throughout this thesis have provided the first mechanistic insights into the action of IGF-I:IGFBP:VN complexes and add further evidence to support the involvement of VN-binding integrins and their co-operativity with the IGF-IR in the promotion of tumour cell migration. Importantly, identifying the molecular mechanisms by which IGF:VN complexes enhance breast cell function will lead to not only a better understanding of this critical interaction, but also aid in developing diagnostic tests and therapeutics directed at treating breast cancer.

vi TABLE OF CONTENTS

Statement of originality …….ii Acknowledgements …….iii Abstract …….v Table of contents …….vii List of figures …….xiii List of tables …….xvi List of abbreviations ……xvii List of publications and presentations ..…...xix

CHAPTER 1 INTRODUCTION 1.1 Introduction ……….2 1.2 Insulin-like growth factor (IGF) system – Introduction ……….2 1.3 IGF system ligands – IGF-I and IGF-II ……….3 1.4 IGF receptors ……….5 1.4.1 The IGF-1R – structure ……….5 1.4.2 IGF-1R – signaling ……….6 1.4.3 The IGF-IIR ……….8 1.5 IGFBPs ……….9 1.5.1 IGFBPs – structure ……….9 1.5.2 IGFBP – functions ………10 1.5.3 IGFBP:ECM interactions ………12 1.6 Mammary gland development ………13 1.7 IGF system expression in the normal mammary gland ………16 1.8 The role of the IGF system in normal mammary development ………17 1.9 Breast cancer ………18 1.10 The IGF system in breast cancer ………21 1.11 Vitronectin (VN) – Introduction ………25 1.11.1 Structure and Functions of VN ………26 1.12 VN and breast cancer ………28 1.13 Integrins – structure ………29 1.14 Integrin signaling ………30 1.15 Cross-talk between growth factor receptors and integrins ………32 1.16 IGF/IGF-1R interaction with integrins ………33 1.17 Interactions between IGF/IGF-IR, VN and VN-binding integrins ………34

vii 1.18 Recent findings from the TRR program to support the IGF:VN interaction in breast cancer metastasis ………37 1.19 Conclusion ………38 1.20 Outline of project ………40 1.20.1 Hypotheses ………40 1.20.2 Aims ………40

CHAPTER 2 MATERIALS AND METHODS 2.1 Introduction ………42 2.2 Proteins ………42 2.3 Cell culture ………42 2.4 Treatment strategy for in vitro assays ………43 2.4.1 Pre-binding of VN, IGFBPs and IGF-I to cultureware ………43 2.4.2 Pre-binding of VN, IGFBPs and IGF-I to 96- and 24-well plates ………43 2.4.3 Pre-binding of VN, IGFBPs and IGF-I to Transwell® inserts ………44 2.4.4 Pre-binding of VN, IGFBPs and IGF-I to 6-well plates ………44 2.5 Functional assays ………45 2.5.1 MTS assay for determination of cellular proliferation ………45 2.5.2 Attachment assays using [3H]-leucine labelled cells ………46 2.5.3 Attachment assays with function blocking antibodies ………46 2.5.4 Transwell® migration assays ………47 2.5.5 Alpha/Beta integrin-mediated cell adhesion array ………47 2.5.6 Assessment of cell viability ………48 2.6 Protein isolation, SDS-polyacrylamide gel electrophoresis (SDS-PAGE) and western immunoblotting ………48 2.6.1 Protein isolation from cell lines ………48 2.6.2 SDS-PAGE ………49 2.6.3 Western immunoblotting ………49 2.7 Transient over-expression of wild type and activated-AKT ………50 2.7.1 Eukaryotic expression vectors ………50 2.7.2 Transformation of competent JM109 E. coli ………50 2.7.3 Plasmid purification – minipreps ………50 2.7.4 Transient transfections ………51 2.8 Microarray analysis of differential gene expression ………52 2.8.1 Extraction of RNA from migrated MCF-10A cells ………52

viii 2.8.1.1 Total RNA isolation – Part 1 (RNA separation and precipitation) ………52 2.8.1.2 Total RNA isolation – Part 2 (DNase treatment and clean-up) ………53 2.8.2 Target synthesis, In vitro transcription (IVT) to synthesize biotin-labelled aRNA, aRNA purification and fragmentation ………54 2.8.2.1 The MessageAmp™ II-Biotin Enhanced Single Round aRNA Amplification Kit – Overview ………54 2.8.2.2 Target synthesis – RT to synthesize 1st and 2nd strand cDNA ………55 2.8.2.3 cDNA purification ………55 2.8.2.4 IVT to synthesize biotin-labelled aRNA ………56 2.8.2.5 aRNA purification ………56 2.8.2.6 Fragmentation of biotin-labelled aRNA ………56 2.8.3 GeneChip® hybridisation and scanning ………57 2.8.3.1 Affymetrix GeneChip® Human Genome U133 Plus 2.0 (HG-U133 Plus 2.0) array ………57 2.8.3.2 Hybridisation ………57 2.8.3.3 Washing and scanning of arrays ………58 2.8.3.4 Processing and Quality control (QC) using GCOS ………58 2.9 Microarray data analysis ………59 2.9.1 Data analysis and presentation using GeneSpring GX 7.3 ………59 2.9.2 Gene ontology, canonical pathway, and functional network analysis ………60 2.9.3 KEGG pathway analysis using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online functional annotation tool ………61 2.10 Confirmation of differential gene expression using quantitative real-time RT-PCR (qRT-PCR) ………61 2.10.1 Standard PCR conditions ………61 2.10.2 Primer design ………62 2.10.3 Reverse transcription (RT) for qRT-PCR ………62 2.10.4 PCR and amplicon purification ………62 2.10.5 qRT-PCR ………63 2.11 Statistical Analysis ………64

ix CHAPTER 3 INVESTIGATIONS INTO THE FUNCTIONAL RESPONSES OF BREAST CELLS TO SUBSTRATE-BOUND IGF- I:IGFBP:VN COMPLEXES 3.1 Introduction ……….66 3.2 Experimental procedures ……….67 3.2.1 Materials ……….68 3.2.2 Cell lines ……….68 3.2.3 Treatment strategy for in vitro assays ……….68 3.2.4 Pre-binding of proteins ……….69 3.2.5 Proliferation assays ……….69 3.2.6 Attachment assays ……….69 3.2.7 Transwell® migration assays ……….70 3.2.8 Confirmation of αvβ3 integrin expression ……….70 3.2.9 Statistical analysis ……….70 3.3 Results ……….71 3.3.1 Investigations into the effects of substrate-bound and solution–phase IGF-I:IGFBP complexes on breast cell proliferation ……….71 3.3.2 Investigations into the effects of IGF-I:IGFBP:VN complexes on cell attachment ……….76 3.3.3 IGF-I:IGFBP:VN complexes synergistically increase cellular migration in MCF-7 and T47D breast cancer cell lines ……….78 3.3.4 IGF-I:IGFBP:VN complexes synergistically increase cellular migration in MCF-10A cells ……….81 3.3.5 IGF-II:VN complexes also synergistically increase cellular migration in MCF-10A cells ……….81 3.3.6 IGF-I:IGFBP:VN complexes are less potent stimulators of migration in the MDA-MB-231 breast cancer cell line due to αvβ3 integrin expression ……….85 3.3.7 IGF-I:IGFBP complexes containing FN and COL IV stimulate increased MCF-7 and MCF-10A cellular migration ……….89 3.4 Discussion ……….93

CHAPTER 4 INVESTIGATIONS INTO THE MECHANISMS OF SUBSTRATE-BOUND IGF-I:IGFBP:VN COMPLEX STIMULATED CELL MIGRATION 4.1 Introduction ………104

x 4.2 Experimental procedures ………105 4.2.1 Materials ………105 4.2.2 Treatment strategy for in vitro assays ………106 4.2.3 Pre-binding of proteins ………106 4.2.4 Transwell® migration assays ………107 4.2.5 Analysis of signal transduction ………107 4.2.6 Assessment of cell viability after treatment with pharmacological inhibitors ………108 4.2.7 Transient transfections ………108 4.2.8 Statistical analysis ………109 4.3 Results ………110 4.3.1 IGF-I:IGFBP:VN-stimulated breast cell migration requires heterotrimeric complex formation and the IGF-1R ………110 4.3.2 Blocking VN-binding integrins and the IGF-1R inhibits IGF-I:IGFBP:VN-stimulated migration ………112 4.3.3 Enhanced by IGF-I:IGFBP:VN complexes ………115 4.3.4 IGF-I:IGFBP:VN-stimulated cell signaling involves both αv-integrins and the IGF-1R ………119 4.3.5 IGF-I:IGFBP:VN complex-stimulated migration is mediated via the PI3-K/AKT pathway ………124 4.3.6 Overexpression of wild type and activated AKT enhances IGF-I:IGFBP:VN-stimulated migration ………127 4.3.7 Substrate-bound complexes containing IGF-I and IGFBPs can stimulate equivalent functional responses to those added in the solution phase ………130 4.4 Discussion ………134

CHAPTER 5 MICROARRAY ANALYSIS OF GENES INVOLVED IN IGF-I:IGFBP:VN- STIMULATED CELL MIGRATION 5.1 Introduction ………144 5.2 Experimental procedures ………145 5.2.1 Materials ………145 5.2.2 Pre-binding of proteins ………146 5.2.3 Transwell® migration assays ………146 5.2.4 Microarray analysis of differential gene expression ………146 5.2.4.1 Extraction of RNA from migrated MCF-10A cells…146

xi 5.2.4.2 Total RNA isolation ………147 5.2.4.3 Target synthesis, In vitro transcription to synthesize biotin-labeled aRNA, aRNA purification and fragmentation ………147 5.2.4.4 Affymetrix GeneChip® Human Genome U133 Plus 2.0 (HG-U133 Plus 2.0) array ………148 5.2.4.5 GeneChip® hybridisation and scanning ………149 5.2.4.6 Processing and quality control (QC) using GCOS.…149 5.2.5 Microarray data analysis ………150 5.2.5.1 Data analysis and presentation using GeneSpring GX 7.3 ………150 5.2.5.2 Functional analysis of target genes ………151 5.2.6 Confirmation of differential gene expression using quantitative real-time RT-PCR (qRT-PCR) ………151 5.3 Results ………152 5.3.1 Experimental strategy for isolation of RNA for Microarray analysis of substrate-bound IGF-I:IGFBP-5:VN-stimulated breast cell migration ………152 5.3.2 HG-U133 Plus 2.0 GeneChip® Array Quality Control (QC).153 5.3.3 Identification of differential gene expression ………156 5.3.4 Functional analysis of genes differentially regulated in migratory cells by substrate-bound IGF-I:IGFBP:VN complexes using IPA ………165 5.3.4.1 Gene ontology and functional analysis ………165 5.3.5 Functional network analysis ………170 5.3.6 qRT-PCR validation of differentially expressed genes…..…177 5.4 Discussion ………184

CHAPTER 6 GENERAL DISCUSSION ……....195

CHAPTER 7 APPENDICES

Appendix 1 ……….207

Appendix 2 ……….211

CHAPTER 8 REFERENCES ……….225

xii TABLE OF FIGURES

1.1 IGF-1R signaling pathways activated by IGF-I and IGF-II ligand binding ……...7 1.2 IGFBP modulation of IGF/IGF-1R signaling ……..11 1.3 Mammary gland structure and development ……..15 1.4 Breast cancer development and metastasis ……..20 1.5 Paracrine role for IGFs in breast tissue ……..23 1.6 Representation of the domain structure of vitronectin ……..27 1.7 The numerous biological functions mediated by VN ……..28 1.8 Schematic representations of A) Major signaling pathways stimulated upon ligand binding to integrin receptors, B) Example of signaling pathways that are co-ordinately regulated by integrins and growth factor receptors ……..31 1.9 Cross-talk between IGF-IR and VN-binding integrins ……..38 3.1.1 Effect of IGF-I:IGFBP:VN complexes on MCF-10A cellular proliferation ……...72 3.1.2 Effect of IGF-I:IGFBP:VN complexes on MCF-7 cellular proliferation ……...75 3.2.1 Stimulation of cellular adhesion in MCF-10A cells in response to substrate-bound IGF-I and IGFBP complexes ……...77 3.2.2 Stimulation of cellular adhesion in MCF-7 cells in response to substrate-bound IGF-I and IGFBP complexes ……...79 3.3.1 Effect of substrate-bound IGF-I:IGFBP:VN complexes on MCF-7 cellular migration ……...80 3.3.2 Effect of substrate-bound IGF-I:IGFBP:VN complexes on T47D cellular migration ……...82 3.4.1 Effect of substrate-bound IGF-I:IGFBP:VN complexes on MCF-10A cellular migration ……...83 3.4.2 Effect of substrate-bound IGF-II:VN complexes on MCF-10A cellular migration ……...84 3.5.1 Effect of substrate-bound IGF-I:IGFBP:VN complexes on MDA-MB-231 cellular migration ……...86 3.5.2 Relative migration levels of MCF-10A, MCF-7 and MDA-MB-231 cell lines in response to VN ……...87 3.5.3 Effect of substrate-bound IGF-I:IGFBP:VN complexes on MCF-7-β3 cellular migration ……...88 3.5.4 Confirmation of αvβ3 integrin expression in MDA-MB-231 and MCF-7- β3 cell lines ……...90

xiii

3.6 Effect of IGF-I:IGFBP-3/-5 on FN- and COL IV-stimulated cell migration ……...91 4.1 Substrate-bound IGF-I:IGFBP:VN-stimulated migration requires heterotrimeric complex formation ……..111 4.2.1 Involvement of VN-binding integrins and the IGF-IR in IGF-I:IGFBP:VN-stimulated migration of MCF-7 cells ……..113 4.2.2 Involvement of VN-binding integrins and the IGF-IR in IGF-I:IGFBP:VN-stimulated migration of MCF-10A cells ……..114 4.2.3 Involvement of VN-binding integrins and the IGF-IR in VN, IGFBP-3:VN, IGFBP-5:VN and IGF-I:VN-stimulated migration of MCF-7 and MCF-10A cells ……..116 4.3 Involvement of VN-binding integrins and the IGF-IR in VN, IGFBP-3:VN, IGFBP-5:VN and IGF-I:VN-stimulated attachment of MCF-7 cells ……..117 4.4.1 Activation of ERK/MAPK and PI3-K/AKT pathways by IGF-I:IGFBP:VN complexes in MCF-7 cells ……..118 4.4.2 Activation of ERK/MAPK and PI3-K/AKT pathways by IGF-I:IGFBP:VN complexes in MCF-10A cells ……..120 4.5 Involvement of αv-subunit containing integrins and the IGF-1R on IGF-I:IGFBP:VN-stimulated ERK/MAPK and PI3-K/AKT pathway activation ……..121 4.6 Involvement of ERK/MAPK and PI3-K/AKT pathways in IGF-I:IGFBP:VN-stimulated migration ……..125 4.7 Overexpression of wild type and activated AKT enhances IGF-I:IGFBP:VN-stimulated migration ……..128 4.8.1 VN-bound IGF-I:IGFBP complexes stimulate migratory responses equivalent to solution-phase complexes ……..131 4.8.2 Comparison of ERK 1/2 and AKT activation by substrate-bound and solution-phase complexes in MCF-10A and MCF-7 cells ……..132 5.1 Confirmation of MCF-10A cell migration stimulated by substrate-bound IGF-I:IGFBP-5:VN complexes ……..154 5.2 Relative Log Expression (RLE) Signal Box Plot ……..157 5.3 Histogram of Signal Values ……..158 5.4 Probe sets identified as differentially expressed in pair wise comparisons of VN alone, IGFBP-5+VN and IGF-I+VN treated cells when compared to the expression of the control group ……..161 5.5 PCA plot of treatment and control samples ……..162 5.6.1 Functional network analysis (Network 1) ……..173

xiv 5.6.2 Functional network analysis (Network 2) ……..175 5.6.3 Functional network analysis (Networks 1 and 2 merged) ……..178 5.7.1 qRT-PCR validation of up-regulated target genes identified by microarray analysis ……..181 5.7.2 qRT-PCR validation of down-regulated target genes identified by microarray analysis ……..182 5.7.3 Comparison of the expression levels of 13 genes determined with micoarrays and with qRT-PCR ……..183

xv LIST OF TABLES

1.1 Evidence for crosstalk between growth factor receptors and integrins. Direct associations ……..33 1.2 Evidence for crosstalk between growth factor receptors and integrins. Integrin-mediated growth factor responses .……..33 2.1 Primers used for qRT-PCR .……..63 5.1 QC performance characteristics of samples hybridised to HG-U133 Plus 2.0 arrays .…….155 5.2 Number of probe sets identified as differentially regulated in Pairwise Comparisons ……..159 5.3.1 The twenty most significantly up-regulated genes in cells migrating in response to IGF-I:IGFBP-5:VN complexes ……..164 5.3.2 The twenty most significantly down-regulated genes in cells migrating in response to IGF-I:IGFBP-5:VN complexes……..164 5.4 Ontology of genes differentially expressed in migratory cells in response to IGF-I:IGFBP-5:VN complexes ……..166 5.5 Focus genes associated with biological processes relevant to Cellular Movement ……..168 5.6 Canonical pathways associated with genes differentially expressed in migratory cells in response to IGF-I:IGFBP-5:VN complexes ……...169 5.7 KEGG pathways over-represented in genes identified as differentially expressed in migratory cells in response to IGF-I:IGFBP-5:VN complexes ……..169 5.8 Functional network analysis of genes differentially regulated by migratory cells in response to IGF-I:IGFBP-5:VN complexes ……...171

xvi LIST OF ABBREVIATIONS

°C Degrees Celsius µCi Micro Curie µg/µL Micrograms per microliter µg/mL Micrograms per milliliter µL Microliter µM Micromoles ACTN1 Alpha-actinin ALS Acid labile subunit AKT Protein kinase B bFGF Basic BSA Bovine serum albumin cDNA Complimentary DNA CK-II Casein kinase II CLDN1 Claudin 1 CO2 Carbon dioxide COL IV type IV collagen CTGF Connective tissue growth factor CXCL14 CXC chemokine ligand 14 CXCR4 CXC chemokine receptor 4 DEPC Diethyl pyrocarbonate DMEM Dulbecco’s modified Eagles medium DMEM/F-12 Dulbecco’s modified Eagles medium/Ham’s F-12 DNA Deoxyribonucleic acid ECM Extracellular matrix EDTA Ethylenediamine tetraacetic acid EFNB2 -B2 EGF ER Estrogen receptor ERK Extracellular signal-regulated kinase F3 Tissue factor FAK Focal adhesion kinase FARP2 FERM-RhoGEF and pleckstrin domain protein 2 FBS Foetal bovine serum FN Fibronectin g Grams GCOS GeneChip® operating software GH Grb2 Growth factor receptor-bound protein 2 HBB HEPES binding buffer HBD Heparin binding domain HEPES 4-2(2-hydroxethyl)-1-piperazineethanesulfonic acid HS Horse serum IGF Insulin-like growth factor IGF-1R type-1-IGF receptor IGFBP IGF-binding protein IR Insulin receptor IRS Insulin receptor substrate

xvii IVT In vitro transcription JNK Jun N-terminal kinase kDa Kilo dalton LB Lysogeny Broth LY294002 2-(4-Morpholinyl)-8-phenyl-4H-1-benzopyran-4-one hydrochloride MAPK Mitogen-activated protein kinase mRNA messenger ribonucleic acid min Minutes mL Milliliter mTOR Mammalian target of rapamycin MTS [3-(4,5-dimethylthiazol-2-yl)-5-(3- carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H- tetrazolium, inner salt; MW Molecular weight ng Nanogram ng/mL Nanogram per milliliter NTPs Nucleotide tri-phosphates ng/µL Nanogram per microliter OCLN Occludin PAI-1 Plasminogen activator inhibitor-1 Pax Paxillin PBS Phosphate buffered saline PCR Polymerase chain reaction PDGF Platelet derived growth factor pDNA Plasmid DNA PI3-K Phosphoinositol-3-kinase RGD Arginine-Glycine-Aspartate RT Reverse transcription RTK Receptor SFM Serum-free medium SFN Stratifin Shc SH2-containing collagen-related protein SHP-2 SH2-containing phosphatise 1 SMC Smooth muscle cell Tal Talin TAT Thrombin-anti-thrombin TBS Tris buffered saline TEB Terminal end bud TFPI Tissue factor pathway inhibitor Triton X-100 Iso-octylphenoxypolyethoxyethanol TGF-β Transforming growth factor beta uPAR Urokinase plasminogen activator receptor uPA Urokinase plasminogen activator U0126 1,4-diamino-2,3-dicyano-1,4-bis[2-aminophenylthio] butadiene U/µL Units per microliter VEGF Vascular endothelial growth factor VN Vitronectin v/v Volume per volume

xviii LIST OF PUBLICATIONS AND PRESENTATIONS

PUBLICATIONS

1) Hollier, B.G., Kricker, J., Van Lonkhuyzen, D.R., Leavesley, D.I. & Upton, Z (2008). Substrate-bound IGF-I:IGFBP:Vitronectin-stimulated breast cell migration is enhanced by co-activation of the PI3-K/AKT pathway by alpha v-integrins and the IGF-IR. Endocrinology, 149(3), 1075-1090.

2) Van Lonkhuyzen, D.R., Hollier, B.G., Shooter, G.K., Leavesley, D.I., and Upton, Z (2007). Chimeric vitronectin:insulin-like growth factor proteins enhance cell migration through co-activation of receptors. Growth factors, Oct, 25(5), 295-308.

3) Hollier, B., Harkin, D., Leavesley, D.I. & Upton, Z. (2005). Responses of Keratinocytes to substrate-bound vitronectin and growth factor complexes. Experimental Cell Research, 305(1), 221-232.

4) Hyde, C.E., Hollier, B., Harkin, D., Anderson, A. & Upton, Z. (2004). Insulin-like growth factors (IGFs) and IGF-binding proteins bound to vitronectin enhance keratinocyte protein synthesis and migration. Journal of Investigative Dermatology, 122(5), 1198-1206.

5) Leavesley, D.I., Harkin, D., Dawson, R., Gillies, P., Hollier, B., Ainscough, L., Hyde, C., Richards, S., Noble, A. & Upton, Z. (2004). Novel vitronectin:growth factor complexes for wound repair and tissue regeneration. Biotechnology and Agricultural Production. Eds Chang-Hung Chou & Shang-Shyng Yang. Dept Biochemistry, National University of Taiwan, Taipei. Pp171-179. [ISBN: 957017482X].

PUBLICATIONS UNDER REVIEW/PREPARATION

1) Kricker, J., Hollier, B.G., Van Lonkhuyzen, D.R., Shooter, G.K., Herington, A.C. & Upton, Z. Impact of heparin-binding regions on the interaction of IGF-I and IGFBPs with vitronectin. Currently under preparation for submission to Molecular and Cellular Endocrinology.

xix PRESENTATIONS

Hollier, B., Leavesley, D., and Upton, Z. (2007). IGF-I:IGFBP:VN complex enhanced cell migration involves both VN-binding integrins and the IGF-1R through activation of the AKT/PI3-K signalling pathway. International conference, Gordon Research Conference, ‘Insulin-like growth factors in Physiology and Disease’, Ventura, USA, Mar 18-23. (Poster presentation).

Hollier, B., Upton, Z. (2006). Development of novel IGF:IGFBP:vitronectin complexes for application in cell based biomedical therapies. International conference, The Third International Congress of the GRS and the IGF Society, Kobe, Japan, Nov 11-15. (Substitute lead session speaker for Prof Upton).

Hollier, B., Leavesley, D.I. & Upton, Z. (2006). IGF:VN complexes in breast cell migration. IGFs Down Under Conference, Brisbane, Australia, Sep 29. (Oral presentation).

Hollier, B., Leavesley, D.I. & Upton, Z. (2006). IGF-I:IGFBP:VN complex enhanced breast cancer cell migration involves both VN-binding integrins and the IGF-1R via activation of the PI3-K/AKT signalling pathway. ComBio 2006, Brisbane, QLD, September 24-28. (Poster presentation).

Hollier, B., Leavesley, D.I. & Upton, Z. (2006). IGF-I:IGFBP:VN complex enhanced breast cancer cell migration involves both VN-binding integrins and the IGF-1R via activation of the PI3-K/AKT signalling pathway. Endocrine Society of Australia Annual Scientific Meeting, Gold Coast, QLD, August 31-September 3rd. (Poster presentation)

Hollier, B., Leavesley, D.I. & Upton, Z. (2005). Evidence for the co-operative regulation between the IGF-1R and vitronectin-binding integrins in enhanced breast cell migration. International Conference, The role of the IGF System in Cancer, Taormina, Italy, Nov 10-12. (Poster Presentation).

Hollier, B., Leavesley, D.I. & Upton, Z. (2005). Evidence for the co-operative regulation between the IGF-1R and vitronectin-binding integrins in enhanced breast cancer cell migration. Australian Society for Medical Research (ASMR) Postgraduate Conference, Wesley Hospital, Brisbane. (Oral Presetation)

Hollier, B., Harkin, D., Leavesley, D.I. & Upton, Z (2004). Responses of Keratinocytes to substrate-bound vitronectin and growth factor complexes. Australian Society for Medical Research (ASMR) Postgraduate Conference, Wesley Hospital, Brisbane. (Poster Presetation).

Hollier, B., Harkin, D., Leavesley, D.I. & Upton, Z (2004). Responses of Keratinocytes to substrate-bound vitronectin and growth factor complexes containing IGFs, IGFBPs, EGF and bFGF. The 2nd International GH-IGF Symposium. Cairns, Australia. April. (Poster Presentation)

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CHAPTER 1

Literature Review

1 1.1 INTRODUCTION Two factors which have been demonstrated to play important roles in the transformation and progression of the malignant phenotype are exposure of cells to elevated levels of mitogenic hormones and growth factors, and altered cellular interactions with the extracellular matrix (ECM). Members of the insulin-like growth factor (IGF) family are mitogenic growth factors which have been shown to play critical roles in both normal growth and development, and tumour biology. This is exemplified by IGF actions in the mammary gland, whereby IGFs can modulate processes important in normal gland development and the progression of breast cancer. The IGF system is complex and the biological effects of the IGFs are determined by diverse interactions between many molecules, including interactions with the ECM. Recent observations have demonstrated that IGFs can associate with the ECM protein vitronectin (VN) and this interaction can modulate IGF-stimulated biological functions. As both IGFs and VN are implicated in breast cancer biology, the studies reported throughout this thesis aim to further investigate the role of the association of IGFs with VN in breast cell function. Therefore, this review will focus on components of the IGF system, specifically IGF-I, IGF-binding protein-3 (IGFBP-3) and IGF-binding protein-5 (IGFBP-5), and interactions with VN with particular relevance to normal and malignant breast cell biology.

1.2 The Insulin-like growth factor (IGF) system – Introduction The association between IGFs and cancer has been a subject of intense investigation for many years. In recent years, both laboratory and population studies have provided strong evidence that IGF physiology influences cancer risk and progression (Samani et al. 2007). Similar to other growth factors, the IGF system is composed of an interacting network of ligand and receptors. The family is comprised of the two ligands, insulin-like growth factor-I (IGF-I) and insulin-like growth factor-II (IGF- II), with the action of these growth factor regulated by at least two receptors and six high affinity IGFBPs (Wood 1995). The IGF system is capable of influencing a wide range of cellular functions through endocrine, paracrine and autocrine mechanisms.

2 1.3 IGF system ligands – IGF-I and IGF-II The IGF family includes the peptides IGF-I, IGF-II, and insulin. IGF-I and IGF-II, also known as , are peptide growth factors which have been demonstrated to have important roles in normal growth and development (Wood 1995). IGF-I and IGF-II are single chain polypeptides of approximately 7.5 kDa which occur in blood plasma at concentrations of 20 – 100 nM and at lower concentrations in most tissues of the body (Humbel 1990). IGF-I and IGF-II share a 62% homology in amino acid sequence and there is 40% structural homology between the IGFs and pro-insulin. The single chain peptides are composed of peptide domains A, B, C and D, whereby the A and B domain are structural homologs of the insulin A and B domains. Both IGF-I and IGF-II have a disulfide linkage between the amino and carboxy termini (A and B domains), but unlike insulin, both IGF-I and IGF-II retain the middle portion of the molecule, the C domain (Humbel 1990).

The IGFs are products of two different genes and their expression is regulated independently. IGF-I is encoded by a single gene located on 12q22 – 23, spanning 5 exons, and alternative splicing produces two distinct mRNA isoforms (IGF-IA and IGF-IB) (Rotwein et al. 1986). Translation then produces an IGF-I precursor consisting of a signal peptide (of between 22 – 48 residues), four domains (BCAD) of the processed growth factor (70 residues) and a C-terminal propeptide domain (E peptide) containing 35 (IGF-IA) and 77 (IGF-IB) residues (Daughaday and Rotwein 1989). The IGF-II gene is located on 11p15.5 and spans approximately 30 kb, consisting of three promoters, five non-coding exons (1-4 and 4B) and three protein coding exons (Daughaday and Rotwein 1989). The IGF-II precursor contains a 24 residue signal peptide, the 67 amino acid residues of the mature growth factor and an 89 residue E peptide region. Further post-translational cleavage of the E peptide domain yields the mature IGF-I and IGF-II proteins, formed by the remaining BCAD domains, of 70 and 67 amino acids, respectively.

The majority of circulating IGF-I and IGF-II peptides are produced by the liver, although extra-hepatic tissues also possess the ability to synthesize the peptides locally. The hepatic synthesis of IGF-I is mainly under the influence of growth hormone (GH) released from the pituitary. Pituitary release of GH results in increased gene expression of IGF-I by the liver, and people who have mutations in

3 the GH receptor (GHR) develop dwarfism with no detectable circulating IGF-I (Laron 1984). The extra-hepatic expression of IGF-I in tissues such as bone, muscle and adipose tissues, is regulated by GH as well as tissue-specific factors (LeRoith and Roberts 2003). However, the synthesis of IGF-II is relatively independent of GH and is regulated by complex mechanisms involving other hormones and tissue- specific growth factors.

Extensive studies using transgenic mouse models, in which genes for IGF-I, IGF-II or the type-1-IGF receptor (IGF-1R), were deleted by homologous recombination, provided insights into the involvement of the IGF system in somatic growth. IGF-I deletion results in growth retardation, with significantly reduced birth weights of IGF-I null mice compared to their wild-type littermates (Baker et al. 1993). IGF-II deletion also inhibits in utero growth, with IGF-II null mice smaller at birth than wild-type littermates, but continue to grow post-natally (DeChiara et al. 1990). Thus, taken together with other studies reported in the literature investigating IGF expression in rodents, the initial concept developed was that IGF-I was the primary regulator of postnatal growth, while IGF-II acted as a fetal growth factor. However, the expression patterns reported in rodents are not observed in humans with both IGF-I and IGF-II produced in multiple tissues throughout life (LeRoith and Roberts 2003). Interestingly, in the human fetus and adult sera, the concentration of IGF-II is 3.5 – 5 fold higher than that of IGF-I (Bennett et al. 1983).

Both IGF-I and IGF-II have been shown to play key roles in numerous cellular and metabolic processes. These effects can be broadly classified into short-term insulin- like metabolic effects, such as stimulation of glucose uptake and glycogen and lipid synthesis in adipose tissue, and long-term cellular effects, such as stimulation of DNA synthesis, cell cycle progression, differentiation, angiogenesis, apoptosis and cell migration (Humbel 1990). These diverse biological actions are carried out through endocrine, paracrine and autocrine mechanisms, which are mediated primarily through the IGF-1R (Cohick and Clemmons 1993).

4 1.4 IGF receptors 1.4.1 The IGF-1R - structure The IGF-1R is a type 2 tyrosine kinase receptor (RTK) which shares 60% amino acid sequence homology with the insulin receptor (IR) and is expressed by the majority of cell types. The IGF-IR is transcribed from a single gene located on the distal arm of chromosome 15 with its expression regulated by numerous physiological stimuli and can be altered in certain pathological conditions including diabetes and cancer (Surmacz 2000). A major 11 kb transcript is translated into a single 1,367 amino acid (180 kDa) pre-protein which is post-translationally cleaved at a furin cleavage site to form 135 kDa alpha (α) and 90 kDa beta (β) subunits (Ullrich et al. 1986). To form the mature receptor, the αβ dimers are assembled into a tetrameric structure linked by disulphide bonds (β-α-α-β) which is necessary for receptor function, as transphosphorylation of one β-chain by the other is necessary for activation of downstream signaling pathways (Tollefsen et al. 1991). The α-subunits are extra- cellular and form the ligand binding domain that binds one ligand molecule (Garrett et al. 1998). The IGF-1R can bind both IGF-I and IGF-II with an approximate four- fold higher affinity for IGF-I (~ 4.45 nM) than IGF-II (~ 17.8 nM) (Forbes et al. 2002), however these affinities can vary greatly depending on the specific cell type and experimental conditions used.

IGF-1R beta chains are comprised of 3 major domains: a juxtamembrane region, a tyrosine kinase domain and C-terminal domain, each containing critical residues for different IGF-1R functions (Surmacz et al 2000). The juxtamembrane domain contains a binding site for phosphorylated substrates (Tyr 950) and is flanked by a NPEY motif which is essential for the recruitment of major signaling proteins such as src/collagen-homology (Shc) and insulin-receptor substrates (IRS) 1-4. The tyrosine kinase domain contains an ATP-binding site at lysine 1003, as well as critical tyrosine residues at positions 1131, 1135 and 1136; all of which are critical for the catalytic activity of the receptor (Hongo et al. 1996; Sepp-Lorenzino 1998). Furthermore, the C-terminal domain contains several residues which play a key role in IGF-1R signaling, such as tyrosine residues 1250, 1251 and 1316 and serine residues at 1280-1283 (Li et al. 1996; Surmacz 2000). Interestingly, specific residues have been identified in the β-chains which have been associated with the development of phenotypes important for cancer, such as transformation,

5 proliferation and protection from apoptosis (Baserga et al. 1994; O'Connor 2003). For example, it has been demonstrated that cells expressing a Y1250F/Y1251F mutant IGF-1R had impaired Shc, Ras and mitogen activated protein kinase (MAPK) activation but normal phosphatidyl inositol-3 kinase (PI3-K) activation in response to IGF-I (Leahy et al. 2004). Moreover, expression of this mutant IGF-1R resulted in impaired functions associated with cellular transformation and cell migration in fibroblasts and epithelial tumours (Leahy et al. 2004).

1.4.2 IGF-1R - signaling Ligand binding to the IGF-IR induces a conformational change in the transmembrane β-subunits, resulting in trans-autophosphorylation of cytoplasmic tyrosine kinase domains. The activated receptor then autophosphorylates additional tyrosine residues in the juxtamembrane and C-terminal domains flanking the tyrosine kinase domain. Phosphorylation of specific Tyr and Ser residues within β-subunits creates binding sites for a number of IGF-1R signaling substrates. Two of the most well studied substrates are insulin-receptor substrate-1 (IRS-1) and Shc. Phosphorylation of Tyr 950 within the juxtamembrane domain provides a docking site for IRS-1 and Shc, and phosphorylation of these proteins by the enables IRS-1 and Shc to recruit and activate other proteins essential in IGF-IR signal cascades (Figure 1.1). Phosphorylated IRS proteins can recruit the p85 regulatory subunit of PI-3K thereby activating the PI-3K signaling cascade, while activated Shc can interact with growth factor receptor-binding protein 2/SOS (Grb2/SOS) to activate the classical ras/Raf/MAPK signaling pathway.

Conversely, IRS through interactions with Grb2/SOS can also activate the MAPK pathway, with Shc associations with the IRS homologs Gab-1 or Gab-2 activating the PI-3K pathway. IRS proteins contain many tyrosine phosphorylation sites capable of binding numerous signaling molecules containing phosphotyrosine binding domains (PTB). In addition to the p85 regulatory subunit of PI3-K and Grb2/SOS, IRS-1 can also recruit adapter proteins Nck, Crk, SHP2 phosphotyrosine phosphatase and Fyn, to further activate a myriad of downstream signaling molecules and enable ligand binding to the IGF-1R to modulate diverse cellular functions.

6

Figure 1.1: IGF-1R signaling pathways activated by IGF-I and IGF-II ligand binding. A schematic representation of the major signaling pathways activated by the autophosphorylated IGF-1R. The numbered boxes (1 – 15) of the IGF-IIR indicate extracellular cysteine-rich regions and repeat sequences.

Of particular relevance to the studies reported in the following chapters, the major signal transduction cascade regulated by IRS-1 is the PI3-K pathway. Through recruitment of the p85 subunit of PI3-K, phosphorylated IRS-1 can activate PI-3K signaling, converting the plasma membrane lipid phosphatidylinositol-4-5- bisphosphate [PI(4,5)P2] to phosphatidylinositol-3,-4-5-trisphosphate [PI(3,4,5)P3] which in turn recruits pleckstrin homology (PH) domain-containing signaling proteins, including the serine-threonine kinase, protein kinase B (AKT), to the membrane where they are activated (Cantley 2002). Through activation of downstream effectors such as AKT, p70 S6 kinase (p70S6K) and some isoforms of protein kinase C (PKC), PI-3K can initiate complex downstream signaling events

7 which can lead to changes in cell growth, cell metabolism, cell movement and cell survival (Cantley 2002). For example, the activation of AKT promotes cell survival by antagonizing apoptosis via inhibition of Bad, caspase-9, and forkhead transcription factor family members. Similarly, AKT via the involvement of p70S6K can activate the expression of cyclin D1 to initiate cell cycle progression. In addition, AKT has been suggested to be able to potentially phosphorylate over 9000 proteins in mammalian cells (Lawlor and Alessi 2001), therefore adding a complex and diverse set of interactions IGF-I could theoretically regulate via PI3-K/AKT pathway activation.

1.4.3 The IGF-IIR The IGF-IIR is a monomeric, multifunctional receptor that binds IGF-II, as well as lysosomal enzymes bearing mannose-6-phosphate moieties (Kiess et al. 1994). Thus, the IGF-IIR is also known as the cation-independent mannose-6-phosphate receptor. Although the biological function of the IGF-IIR remains unclear, it has been proposed that the receptor is an inhibitor of IGF-II action. The 300 kDa receptor is composed of a small intracellular domain and a large extracellular domain containing 15 repeated motifs (Figure 1.1). Ligands which bind the IGF-IIR are sorted to lysosomal compartments, however, there is evidence that distinct domains of the receptor are responsible for binding lysosomal enzymes or IGF-II, with IGF-II binding localized to the eleventh repeat (Schmidt et al. 1995). Binding of IGF-II to the IGF-IIR results in receptor internalization and degradation, thus the IGF-IIR may function as a sink for IGF-II, regulating the availability of IGF-II for interactions with the IGF-IR. In contrast to the IGF-IR, the IGF-IIR does not contain intrinsic tyrosine kinase activity and is generally not involved in intracellular signaling. However, a recent study has demonstrated that siRNA mediated down-regulation of the IGF-IIR significantly reduced IGF-II, but not IGF-I-stimulated ERK1/2 activation, suggesting signaling directly through the IGF-IIR (El-Shewy et al. 2007). Activation of ERK 1/2 was shown to be mediated via interactions involving sphingosine kinase-dependent activation of sphingosine-1-phosphate receptors (El- Shewy et al. 2007). This therefore provides a possible mechanism whereby IGF-II binding to the IGF-IIR can induce intracellular signaling via heterotrimeric G protein signaling pathways.

8 1.5 IGFBPs The majority of IGFs present in the circulation and throughout the extracellular space are bound to members of the IGF-binding protein (IGFBP) superfamily. To date six specific high affinity IGFBPs (IGFBP 1-6) have been well characterized (Kiefer et al. 1991) which have been shown to both enhance and inhibit IGF actions at the cellular level. The IGFBPs share structural homology with each other and bind IGFs while having little affinity for insulin. In general it is believed that the IGFBPs play important roles in regulating the biological activities of the IGFs. Specifically, IGFBPs can act to: 1) transport IGFs in the circulation and control their extra- vascular uptake into tissues; 2) prolong the half-lives of IGFs and slow their clearance; 3) modulate the extra-cellular tissue distribution of the IGFs; and 4) modulate the biological actions of the IGFs by regulating their availability to activate the IGF-1R.

1.5.1 IGFBPs - structure The IGF binding protein gene family consists of six well characterized members that encode a family of related multifunctional proteins, IGFBP-1 to IGFBP-6. The IGFBPs are closely related proteins, although each is a distinct gene product transcribed from four conserved exons located within genes ranging from 5 kb (IGFBP-1) to greater than 30 kb (IGFBP-2 and IGFBP-5) (Baxter 1997). The precursor forms of all IGFBPs contain a secretory signal peptide and the mature proteins, with molecular masses ranging from 22.8 to 31.3 kDa, are secreted extracellularly (Firth and Baxter 2002). Despite having a highly conserved domain structure, IGFBPs display quite distinct functional properties (Firth and Baxter 2002). The conserved N- and C-terminal domains share approximately 40% sequence similarity and contain residues important for IGF binding. The C-terminal domain is cysteine rich and contains residues important for heparin-binding, binding to the acid labile subunit (ALS), and cell surface and matrix associations (Firth and Baxter 2002). The mid-region domain shows essentially no homology between IGFBPs, however it is the location of a number of post-translational modifications which can modulate IGFBP function (Firth and Baxter 2002) and has been postulated to be the region responsible for the non-shared specific actions of IGFBPs (Holly and Perks 2006). In addition, several low affinity binding proteins, termed IGFBP-related proteins (IGFBP-rp) have been described (Baxter et al. 1998). These

9 IGFBP-related proteins share sequence homology with the “classical” IGFBPs, predominantly at the N-terminus, and some can bind IGFs, albeit with a much lower affinity than IGFBPs (Kim et al. 1997).

1.5.2 IGFBP - functions In the circulation approximately 75 - 80% of IGF-I and IGF-II are bound into a ternary complex composed of IGF-I or IGF-II, IGFBP-3 and the non-IGF binding ALS (Leong et al. 1992; Jones and Clemmons 1995). The formation of this ternary complex prevents IGFs from readily leaving the vascular compartment and prolongs the half-life of bound IGFs to 12 -15 hours, as compared to free IGF-I in the plasma which has a half-life of less than 10 minutes (Guler et al. 1989; Hodgkinson et al. 1989). This ternary complex slows the clearance of IGFs and forms a large reservoir of IGF-I and IGF-II in the circulation (approximately 100 nM), which some investigators have hypothesized can function as a pool of material available to organisms during stress (Jones and Clemmons 1995; Holly and Perks 2006). Proteolytic degradation and/or association of IGFBP-3 with proteoglycans on the capillary endothelium cell surface reduces the affinity of IGFBP-3 for IGF-I and disrupts ternary complex formation. This can lead to the formation of binary complexes and allow an equilibrium for the transport of IGFs from the vascular compartment into the tissue (Holly and Perks 2006).

IGFs are not stored intracellularly and a large pool of IGF is maintained in tissues complexed with IGFBPs in the extracellular compartment. The IGFBPs have a higher affinity for IGFs than the IGF-IR and can regulate the availability of IGFs to interact with the IGF-1R. Therefore, at least at the tissue level, IGFBPs are thought to be a major determinant of IGF action. It is generally accepted that IGFBPs inhibit IGF actions by sequestering IGFs, thereby, reducing levels of free IGF available to interact with the IGF-1R (Figure 1.2). However, protease activity present in the circulation and extravascular interstitial fluids, may lead to IGFBP proteolysis and an increase in free IGF available for receptor interactions (Figure 1.2) (Firth and Baxter 2002). Nevertheless, it is now recognized that IGFBPs can also potentiate IGF actions, with the response to IGFBPs depending on the experimental conditions used (Jones and Clemmons 1995; Firth and Baxter 2002). The presence or absence of IGFBP proteases, IGFBP post-translational modifications and differential cellular

10 localization of IGFBPs have all been demonstrated to modulate the effect IGFBPs have on IGF actions (Firth and Baxter 2002). Moreover, IGFBPs can have IGF- independent actions to directly effect cell functions.

Figure 1.2: IGFBP modulation of IGF/IGF-1R signaling. Interactions between IGF and IGFBPs can reduce free IGF levels, decreasing IGF-1R activation and inhibiting cellular responses. Associations of IGFBPs with the ECM can trap IGFs in the pericellular environment. A reduction in the affinity of ECM-bound IGFBP for IGFs can release IGFs to activate the IGF- 1R. Proteolytic degradation of IGFBPs can also increase free IGF levels.

Most of the IGFBPs have been demonstrated to have IGF-independent effects on cell function. The most clearly defined mechanism for IGF-independent actions involve IGFBP-1 and IGFBP-2 which contain an integrin binding arginine-glycine-aspartic acid (RGD) motif. Through interaction with integrins, such as α5β1 and αvβ3, IGFBP-1 and IGFBP-2 have been shown to modulate intracellular signaling, cell migration, attachment and apoptosis independently of IGFs (Jones et al. 1993; Perks et al. 1999; Pereira et al. 2004; Schutt et al. 2004). IGFBP-3 and IGFBP-5 can also trigger specific intracellular pathways independent of IGFs (Kim et al. 2004; Ricort and Binoux 2004) and IGFBP-3 has been reported to “prime” non-tumourgenic breast epithelial cells to respond optimally to EGF (Martin et al. 2003). Similarly, IGFBP-3 and IGFBP-5 have IGF-independent effects on cellular attachment and

11 ceramide-induced breast cancer cell apoptosis (Perks et al. 1999; McCaig et al. 2002). Moreover, the specific effects of these binding proteins were dependent on the matrix components present, as culturing cells on FN could reverse many of the IGF-independent actions (Perks et al. 1999; McCaig et al. 2002; Burrows et al. 2006). Indeed, there is accumulating evidence to support an important role for the interaction of IGFBPs with components of the ECM in not only modulating their own function, but also that of the IGFs.

1.5.3 IGFBP:ECM interactions Each of the IGFBPs, in addition to IGFs, can interact with numerous proteins within the ECM or on the cell surface (Firth and Baxter 2002). As the IGFBPs have a higher affinity for IGF-I and IGF-II than the IGF-1R, it is possible that through interactions with the ECM, IGFBPs can localise IGFs within the extracellular space in close proximity to cell surface receptors (Figure 1.2). IGFBPs, such as IGFBP-3 and IGFBP-5, can bind with high affinity to many ECM proteins including fibrinogen (Campbell et al. 1999), fibronectin (Gui and Murphy 2001; Martin et al. 2002), type IV collagen (Jones et al. 1993), VN (Nam et al. 2002) and thrombospondin and osteopontin (Nam et al. 2000). A recent study has reported that IGFBP-3 can bind to the matrix deposited by both normal and cancerous breast epithelial cells, with matrix-bound IGFBP-3 displaying a 25-fold reduction in affinity for IGF-I. (Martin and Jambazov 2006). Furthermore, purified IGFBP-3 can bind to FN and strong co-localisation of these two proteins has been reported in human articular cartilage (Martin et al. 2002). Moreover, IGFBP-3 association with the matrix is functionally significant as matrix-bound IGFBP-3 enhanced breast epithelial cell adhesion and MAPK pathway activation (Martin and Jambazov 2006).

IGFBP-5 has also been reported to bind with high affinity to ECM proteins including VN (Nam et al. 2002), plasminogen activation-inhibitor-I (PAI-1) (Nam et al. 1997), osteopontin and thrombospondin-1 (Nam et al. 2000). In a similar manner to IGFBP- 3, association of IGFBP-5 with the ECM reduces its affinity for IGF-I by 8- to 15- fold, while also protecting it from proteolytic degradation (Jones et al. 1993; Nam et al. 1997). Furthermore, the ECM binding of IGFBP-5 is functionally significant as the IGF-I that is bound to IGFBP-5 within the ECM is in better equilibrium with receptors, as demonstrated by an increase in ECM-associated IGFBP-5 potentiating

12 the effects of IGF-I (Jones et al. 1993; Parker et al. 1998). Thus, localization of IGFBPs, particularly IGFBP-3 and -5, within the ECM could create a reservoir of IGF-I within the pericellular environment capable of modulating IGF-I action on cells. For example, IGFBP-5 binding to VN can enhance IGF-I-stimulated DNA synthesis and migration of porcine smooth muscle cells (Nam et al. 2002). This study therefore provided the first published direct link between IGF-I, IGFBPs and VN in modulating celluar functions. Studies from our own laboratory have demonstrated that IGFBP-2, -3, -4 and -5 can bind to VN (Kricker et al. 2003) and in the presence of IGF-I can form a heterotrimeric complex capable of enhancing proliferation and migration in a range of cell types (Kricker et al. 2003; Hyde et al. 2004; Hollier et al. 2005; Ainscough et al. 2006). Taken together these reports provide evidence for an important functional involvement between the IGF system and VN.

The complex interactions between IGF ligands, IGFBPs, IGF receptors and associations with the ECM allow the IGF system to regulate a diverse range of biological functions in numerous tissues. The IGF system is known to play an important role in normal mammary gland development and its components have been implicated as risk factors associated with the development of breast cancer (Hankinson et al. 1998; Pollak 2004; Renehan et al. 2004). As such, the IGF system has become a subject of intense investigation for breast cancer research, leading to the development of several reagents aimed at disrupting IGF signaling for use as potential therapeutics. To understand the actions of IGFs in breast cancer, there needs to be an understanding of the IGF system in normal mammary gland development.

1.6 Mammary gland development Most vertebrate organs are patterned during embryogenesis and then maintain their basic structure throughout adult life. Breast tissue is distinct in that it continually changes in structure throughout the lifetime of reproductively active females. Development of the mammary gland is influenced by many factors, particularly the interplay of endocrine hormones with the actions of various growth factors on epithelial and mesenchymal constituents of the developing gland. During its development the mammary gland progresses through a number of distinct stages:

13 including embryonic and fetal stages, the neonatal and pre-pubertal periods of isometric growth, the peripubertal period when the gland undergoes rapid growth and ducts elongate and branch, and sexual maturity when branching continues and alveolar buds form (Hovey et al. 2002). The final stage of mammary development begins at pregnancy, with dramatic increases in epithelial proliferation and alveolar budding, which leads to the formation of secretory alveoli required for milk synthesis and to support lactation (Brisken 2002).

During fetal development the specified mammary epithelium invades from the nipple into a pad of fatty tissue called the mammary fat pad, and forms a small branched ductal network in the proximal corner of the fat pad (Figure 1.3). Around the time of puberty when ovarian hormones are released, rapid growth of the epithelium occurs at a rate of 3 to 4 times that of the whole body (Cowie 1949). The distal ends of the mammary ducts swell into bulbous structures composed of multiple layers of cubodial epithelial cells, called terminal end buds (TEBs) (Figure 1.3). The TEBs mediate the invasion and proliferation of the ducts into the fat pad, and branch by bifurcation until the ducts reach the limits of the fat pad, whereupon they regress (Kleinberg et al. 2000; Wiseman and Werb 2002) (Figure 1.3). Further structural changes occur during pregnancy and lactation, when epithelial cells at the ends of terminal ducts proliferate to prepare for milk secretion. Once lactation has ceased, there is a massive increase in apoptosis and tissue remodeling within the gland to remove cells required for milk production and to produce structures resembling that prior to pregnancy and lactation.

With these developmental stages in mind, many parallels can be drawn between the processes involved in normal mammary gland development and the progression of breast cancer. Processes such as invasion, proliferation, angiogenesis and resistance to apoptosis are critical for both normal gland development and the progression of breast cancer. For example, the TEB is a rapidly proliferating mass of epithelial cells that invades stromal tissue, much like a tumour. Thus, due to its highly invasive nature and 5-fold higher proliferation rate than that of mature ducts, cells within the TEB are among the most important targets for carcinogens within the mammary gland (Russo and Russo 1980; Russo et al. 1983). The lactating mammary gland also has mechanisms in which it is protected from premature involution, and therefore

14 has inherent processes to actively resist apoptosis. Moreover, as the mammary gland undergoes its profound structural and morphological changes throughout the developmental cycle, the blood supply must be adjusted, and thus, like tumours, induces angiogeneic remodelling (Djonov et al. 2001). As the mammary gland retains most of these properties throughout its lifetime many of the processes essential for normal gland development are also associated with cancer.

A)

Adipocyte / fat cell Body cell Stroma B) Cap cell

Basement membrane Epithelial cells Stromal Side branching cell Bifurcation of TEB

Figure 1.3: Mammary gland structure and development. A) Stages of mammary gland development. The mammary epithelium invades the fat pad and forms a small, branched ductal network. With the onset of puberty, terminal end buds (TEBs) form and the ducts invade, branch, and eventually fill the fat pad. In the first stage of pregnancy, ducts branch laterally and form side branches with concomitant epithelial proliferation. Alveolar structures then form on the expanded ductal tree and differentiate into lobular alveoli. Finally, the lobular alveoli terminally differentiate and the epithelium becomes secretory, ready to provide milk upon parturition. Upon involution, the secretory epithelium of the mammary gland dies by apoptosis and the gland is remodeled back to a state resembling that before pregnancy. B) The two distinct mechanisms of branching morphogenesis in the pubertal mammary gland: Side branching and Bifurcation of the TEB. Figure adapted from Wiseman and Werb, (2002).

While it is cells from an epithelial origin that proliferate, invade and therefore have the most tumourgenic potential, the mammary stroma contributes many factors that are critical for normal development, which are also associated with cancer. Indeed, it has become well recognized that the tumour microenvironment can promote tumourigenesis (Park et al. 2000; Schedin and Elias 2004). The stroma is comprised of a number of components, including: adipocytes, pre-adipocytes, fibroblasts, blood vessels, inflammatory cells and the ECM, which like the epithelium, are also regulated throughout the developmental cycle. The mammary microenvironment is

15 very dynamic and physiological changes in the mammary ECM can contribute to tumourigenesis, with involution stroma reported to promote tumour cell invasion and metastasis (McDaniel et al. 2006). Therefore, the cross-talk between the mammary epithelium and stroma is critical for the proper patterning and function of the normal mammary gland, with changes in stromal-epithelial interactions capable of both inducing and promoting breast cancer.

It should be noted that the vast majority of information concerning normal mammary gland development, particuarly the endocrine and growth factor regulation of ductal morphogenesis, has been obtained in studies using genetically engineered mouse models. As species differences exist with respect to gland morphology and endocrine regulation of development, much of the information gained may be irrelevant to studies of the human breast. Therefore, with a lack of knowledge regarding normal human mammary development, much is still to be learnt about the complex interactions between endocrine hormones and growth factors controlling human mammary gland development and function.

1.7 IGF system expression in the normal mammary gland In normal human breast tissue, studies indicate that IGF-I is expressed locally in breast tissue mainly by stromal cells (Cullen et al. 1991; Cullen et al. 1992; Paik 1992), with IGF-I mRNA expression decreased in tumour compared to normal breast stroma (Paik 1992; Singer et al. 1995). These studies also report that IGF-I mRNA was not detectable in either normal epithelium or malignant epithelial cells (Cullen et al. 1991; Cullen et al. 1992; Paik 1992). Studies in rodents have revealed that IGF-I is present in breast epithelial cells during specific times such as during pubertal growth in TEBs and in alveoli during late pregnancy (Richert and Wood 1999; Wood et al. 2000). However, studies have yet to determine whether, like in mouse mammary glands, IGF-I is also present in human breast epithelial cells during specific stages. In contrast to IGF-I, IGF-II has been reported to be low or undetectable in normal breast tissue, in either epithelial or stromal cells, but was expressed by fibroblasts associated with breast tumours (Cullen et al. 1992; Paik 1992; Singer et al. 1995) or in malignant epithelium (Cullen et al. 1991; Cullen et al. 1992; Paik 1992). Moreover, using immunohistochemical approaches the IGF-1R was found to be expressed in both normal and malignant breast tissue and is

16 predominantly localized to the epithelial compartment (Happerfield et al. 1997). Therefore, as IGF-I and –II are produced by cells within the stroma, whereas the IGF-1R is expressed by epithelial cells, this highlights the importance of stromal- epithelial interactions in the developing mammary gland. While there has been limited investigations into the expression of IGFBPs in human breast tissue, analysis of tumour tissues and adjacent normal tissue has revealed that both normal and malignant tissues express detectable levels of IGFBP-1-5 mRNAs, with IGFBP-2, -3 and -5 appearing the most prominent in both tissue types (Pekonen et al. 1992).

1.8 The role of the IGF system in normal mammary development The postnatal growth of the mammary epithelium is governed by many complex interactions and is under the control of circulating hormones and locally produced growth factors. Components of the IGF system have well recognized roles in breast cancer development and progression (which will be reviewed in section 1.10), but also have demonstrated roles in fetal and postnatal growth of the mammary gland (Baker et al. 1993; Liu et al. 1993; Hadsell 2003). Moreover, IGF-I is the primary mediator of growth hormone signaling and controls ductal development and TEB formation (Kleinberg et al. 2000). A requirement for IGF-I in normal ductal development was first demonstrated in transgenic mice having a mutated IGF-I gene where reduced ductal development and number of TEBs were observed (Ruan and Kleinberg 1999). Moreover, a number of studies have also reported the need for the IGF-1R in mammary ductal development (Hadsell and Bonnette 2000; Bonnette and Hadsell 2001). A role for IGF’s in alveologenesis has also been demonstrated with overexpression of IGFBP-5 in transgenic mice during pregnancy inducing premature cell death and decreased lobulo-alveolar development, with exogenous administration of R3-IGF-I (an IGF-I mutant with reduced affinity for IGFBPs) improving mammary gland development and lactational performance (Tonner et al. 2002). Likewise, an important role for IGF-II in alveolar development has also been reported (Brisken et al. 2002). The overexpression of both IGF-I and IGF–II can inhibit mammary involution (Neuenschwander et al. 1996; Moorehead et al. 2001) and induce mammary tumours in transgenic mouse models (Bates et al. 1995; Hadsell et al. 2000). Similarly, a recent study has reported that the overexpression of the IGF-1R in a transgenic mouse model disrupts ductal morphogenesis by inducing mammary epithelial hyperplasia and tumour formation (Jones et al. 2007). Indeed,

17 the IGF system plays a critical role in many of the normal developmental stages and functions of the mammary gland, mainly through its effects on cell cycle progression and cell survival (Hadsell 2003). Therefore, it is not surprising that the IGF system has been the subject of intense focus for breast cancer research and, given its role in cancer biology, has become an attractive target for therapeutic interventions.

1.9 Breast cancer Breast cancer is the most commonly diagnosed form of cancer in Australian women, accounting for 26% of diagnosed cancers and 21% of cancer deaths among women. One in fourteen Australian and one in nine women worldwide will develop breast cancer in their lifetime. Although approximately one in four of those diagnosed will die from their disease, public education campaigns for early diagnosis and the widespread use of adjuvant systemic treatments is increasing survival (Berry et al. 2005). However, the benefit of adjuvant systemic treatment for women with good prognosis is statistically small and exposes these women to complications associated with the treatment without any concomitant survival benefit.

The incidence of breast cancer increases with age, although the rate is less in women after menopause (Colditz 1993; Madigan et al. 1995). Other factors, such as early menarche, nulliparity and late menopause, also increase the risk of breast cancer (Colditz 1993; Madigan et al. 1995). While there have been many risk factors associated with breast cancer, a family history of breast cancer remains as one of the strongest risk factors for developing breast cancer (Anderson 1992; Colditz et al. 1993; King et al. 1993; Eng et al. 1994). Germline mutations in BRCA1 and BRCA2 genes has been associated with a 50 to 85% lifetime risk of breast cancer, ovarian cancer or both (Marcus et al. 1996; Casey 1997). In sporadic breast cancer, abnormalities have been identified in several genes (including p53, bcl-2, c-myc, and c-myb) and in some normal genes or gene products (HER-2/neu and cyclin DI) which are overexpressed (McKenzie and Sukumar 1996; Brenner and Aldaz 1997). Furthermore, many factors have been demonstrated to modulate the growth and proliferation of breast cancer cells. Steroid hormones (estrogens, progestins, and androgens), growth factors (including EGF, TGF-α and –β, and IGF-I and –II) and various cytokines and lymphokines have shown to influence the behaviour and

18 phenotypic expression of breast cells (Dickson and Lippman 1995). Breast cancer also induces neovascularisation, which, in turn, facilitates the metastatic spread of malignant cells (Engels et al. 1997).

Breast cancer develops as a multi-step process and can be conceptualized to develop from a normal epithelial cell or its precursor, progressing through non-proliferative diseases (including apocrine change and duct ectasia), to epithelial hyperplasia and atypical hyperplasia, progressing further to ductal carcinoma in situ and ultimately invasive cancer (Kopans 1998) (Figure 1.4). It has been well established that hormonal regulation is the primary control of normal breast development, neoplastic transformation and breast cancer. In particular, hormones released from the pituitary, ovaries and adrenal glands are involved (Topper and Freeman 1980). It is believed that approximately 95% of breast cancers are estrogen dependent and estrogen plays many important roles in estrogen-sensitive breast cancer, including carcinogenesis, growth stimulation, production of growth factors, angiogenesis and progression (Stewart et al. 1990; Pollak 1998). As such, strategies aimed at either inhibiting the action of estrogen via inhibition of the estrogen receptor (ER), or inhibiting its synthesis by the use of aromatase inhibitors, has led to better survival outcomes for patients with estrogen-sensitve breast cancer (Goss et al. 2003; Coombes et al. 2004). Moreover, blockade of steroid hormone receptors or inhibition of ligand synthesis are relatively successful and nontoxic therapeutics for the treatment of most stages of breast cancer (Hortobagyi 1998). However, there is a definite need to explore other growth regulatory pathways important for both normal and transformed breast cells, with the long term goal to develop additional targeted therapies.

19 Metastatic cells able to Distant organ grow at distant organ

B) Metastases

Blood vessel or lymphatic

Metastases

Atypical A) DCIS Hyperplasia hyperplasia

Normal epithelium

Invasive carcinoma

Figure 1.4: Breast cancer development and metastasis. A) The stages in the development of invasive carcinoma from normal mammary epithelium. B) Model for breast cancer metastasis, whereby invasive cells disseminate from the primary site and can establish secondary tumours at distant organs. Proliferation, migration, invasion, cell survival and angiogenic remodelling are all important processes for the metastasis of malignant cells to distant sites. DCIS, Ductal carcinoma in situ. Adapted from Kopans, (1998).

The primary factor that determines survival is early diagnosis and treatment. Indeed, the primary tumour is rarely the cause for the high mortality associated with breast cancer, which rather, arises from the metastasis of malignant cells and their establishment in critical sites in the body. Metastatic dissemination depends on the ability of tumour cells to traverse tissue barriers, migrate and invade and survive in secondary target tissues (Weinberg 2006). Therefore, cancer metastasis is a multi- step process which results from several independent processes, including: • detachment of cancer cells from their original localization; • cancer cell migration; • invasion of cancer cells into the surrounding tissue, requiring adhesion to and subsequent degradation of extracellular components; • access of cancer cells to blood and lymphatic vessels; and • adhesion to and invasion through the endothelium, allowing colonization at distant sites (Figure 1.4).

20 Understanding the processes that lead to the establishment of secondary tumour bodies and strategies to halt the spread of cancer beyond the primary site are therefore highly valuable and is becoming a major focus of research world-wide.

In breast cancer, malignant epithelial cells also proliferate, migrate and invade surrounding normal tissue. Furthermore, despite the accumulation of multiple genetic abnormalities, the cancer cell is still responsive to extracellular signals that were previously required for normal mammary gland development. Since normal mammary gland development requires the co-ordinated expression of hormones and growth factors, it is not surprising that transformed breast cells continue to respond to these same extracellular signals. As already mentioned, the IGF system is a highly relevant component of normal mammary gland development, but it also has well documented roles in the transformation, development and progression of numerous cancers, including breast cancer (Surmacz 2000; Baserga et al. 2003). Moreover, as there is accumulating evidence implicating the IGF system in numerous human cancers, the IGF system, in particular the IGF-1R, has become an attractive therapeutic target for treatment of cancer (Miller and Yee 2005).

1.10 The IGF system in breast cancer Accumulating epidemiological evidence implicates the IGFs in breast cancer risk (Pollak 2004), with expression of IGF family members often altered in breast cancer (Ellis et al. 1998; Surmacz 2000). Elevated levels of serum IGF-I and reduced levels of serum IGFBP-3 have been associated with an increased risk of breast cancer among premenopausal women (Hankinson et al. 1998), suggesting that high plasma IGF-I levels may be a useful tool in identifying young women at high risk of developing breast cancer. Moreover, in a systematic review, circulating levels of IGF-I have been related to the risk of several epithelial cancers (Renehan et al. 2004). IGF-I has also been shown to act in a synergistic manner with estrogen in stimulating breast cancer cells (Dupont et al. 2000). Indeed, there is evidence that anti-estrogens, such as Tamoxifen, can reduce plasma levels of IGF-I (Pollak et al. 1990), with in vitro evidence suggesting that part of the action of anti-estrogens may be through anti-IGF mechanisms (Westley and May 1994; Guvakova and Surmacz 1997). Moreover, numerous studies to date have demonstrated a role for the IGF system in the malignant transformation, mitogenicity, cell survival/anti-apoptosis,

21 (Cullen et al. 1990; Coppola et al. 1994; Adams et al. 2000; Baserga et al. 2003), increased metastatic potential (Guerra et al. 1996), higher relapse rate (Bonneterre et al. 1990), endocrine resistance (Nicholson et al. 2004) and multi-drug resistance (Geier et al. 1995) characteristics of breast cancer.

It has been reported that IGF-II is expressed in the smooth muscle walls of blood vessels and ducts, as well as in the stroma tightly adjacent to and surrounding tumour epithelium, whereas IGF-II expression in normal mammary epithelium is very low or undetectable (Giani et al. 1996). IGF-I and –II are mitogenic for a number of breast cancer cell lines, with this stimulation inhibited by blocking IGF-1R function (Arteaga and Osborne 1989; Cullen et al. 1990). Moreover, inhibition of IGF-1R function by dominant negative mutants, antisense approaches or antibodies against the IGF-1R inhibit xenograft growth of breast cancer cells in athymic mice (Arteaga et al. 1989; Salatino et al. 2004). IGF-I is a well established cell survival factor and can inhibit normal mammary gland involution and rescue breast cancer cells from chemotherapy-induced cell death (Neuenschwander et al. 1996; Dunn et al. 1997; Gooch et al. 1999). Furthermore, overexpression of the IGF-1R confers radioresistance in breast cancer cells (Turner et al. 1997), supporting an important role of the IGF system in anti-apoptotic signaling in breast cancer cells. However, in addition to IGF/IGF-1R interactions, IGFBPs have also been demonstrated to have IGF-independent effects on cellular attachment and ceramide-induced breast cancer cell apoptosis (Perks et al. 1999; McCaig et al. 2002; Burrows et al. 2006). Moreover, the specific effects on cell survival were dependent on the matrix components present as the actions of IGFBP-3 and IGFBP-5 can be reversed when cells are grown on fibronectin (FN).

Recent in vitro and in vivo evidence suggests IGF-II promotes angiogenesis (Bae et al. 1998; Lee et al. 2000), a process critical in the metastasis of cancer. The role of IGF-II in neovascularisation is thought to be mediated via stromal-epithelial interactions. This is evidenced by highly vascularised tumours displaying increased IGF-II expression in the stroma at the transition from normal epithelium to dysplastic and neoplastic breast disease (Heffelfinger et al. 1999). This stromal- epithelial interaction is also thought to be critical in IGF action on breast cancer cells, as IGF-I and IGF-II are predominantly of stromal origin, with IGF-II expressed

22 by some malignant epithelial cells (Yee et al. 1989; Giani et al. 1996) (Figure 1.5). Studies using immunohistochemical approaches have confirmed this stromal origin, suggesting a paracrine role for IGFs in breast cancer (Toropainen et al. 1995). Supporting the paracrine role of IGFs on breast cancer cells are reports demonstrating that malignant breast epithelial cells can induce IGF-II expression in breast stroma (Singer et al. 1995). This increased IGF-II expression by the stroma is associated with malignancy, compared to benign or normal breast tissue (Cullen et al. 1990).

Stroma

IGF-I Development Progression IGF-II IGF-1R

IGF-1R IGF-II

Transformation Normal Malignant epithelium epithelium

Figure 1.5: Paracrine role for IGFs in breast tissue. IGFs released by stromal cells play roles in normal mammary gland development, malignant transformation and progression of the malignant phenotype.

It is clear that the IGF system is complex and the biological effects of the IGFs are determined by diverse interactions between many different molecules. However, this complex regulatory system is disrupted in breast cancer, which ultimately leads to excess IGF-IR signaling. The IGF-IR and IGF-IIR are both expressed by a variety of breast cancer cell lines as well as in breast cancer biopsies, including those taken from patients with metastatic breast disease (Peyrat and Bonneterre 1992; Gebauer et al. 1998). The IGF-IR has been shown to be overexpressed (up to 14-fold) in breast tumours compared to normal or benign tissue, with breast cancer cells containing

23 higher intrinsic tyrosine kinase (TK) activity than that of normal breast tissue (Resnik et al. 1998).

There is also evidence linking IGF-1R hyperphosphorylation with the early stages of breast cancer and primary breast tumours with high IGF-1R levels being associated with shorter disease-free survival (Surmacz 2000). In vitro studies of breast cancer cell lines have demonstrated that less aggressive ER +ve cells correlate with substantial expression of IGF-1R and IRS-1 (Guvakova and Surmacz 1997; Lee et al. 1999). In contrast, more aggressive metastatic ER –ve breast cancer cell lines express lower levels of the IGF-1R and IRS-1 and often do not respond to IGF-I with growth (Sepp-Lorenzino et al. 1994; Lee et al. 1999). Despite these results, these aggressive cell lines still rely on the IGF-1R for invasion and metastasis. For example, antibody inhibition of the IGF-1R in MDA-MB-231 breast cancer cells substantially reduced migration in vitro and tumourigenesis in vivo (Doerr and Jones 1996), while expression of a soluble form of the IGF-1R was reported to inhibit the attachment, invasion and in vivo metastasis of breast cancer cell lines (Dunn et al. 1998). Moreover, a dominant negative C-terminal truncated IGF-1R, lacking critical autophosphorylation sites, was demonstrated to inhibit the in vivo metastasis of MDA-MB-435 breast cancer cells when injected into the mammary fat pad of mice, without affecting primary tumour growth (Sachdev et al. 2004). This suggests, that IGF-I signaling can regulate the metastatic phenotype independent of tumour growth.

Similarly, the IGF-IIR has also been implicated in breast cancer. The IGF-IIR is generally thought of as a tumour suppressor gene, as it sequesters and degrades IGF- II, therefore not allowing IGF-II to interact with the IGF-IR. However, the IGF-IIR is often mutated in breast cancer, leading to decreased degradation of IGF-II at the cell surface (Ellis et al. 1998). This, combined with the loss of imprinting of the IGF- II gene in many breast cancers (Yballe et al. 1996; Wu et al. 1997), provides evidence for an important role for not only IGF-II, but also the IGF-IIR in breast cancer. Indeed, high expression of IGF-II is associated with poor prognostic features (Yu et al. 1996). The important role for IGF-II/IGF-IIR in breast cancer was supported by a recent study whereby increased expression of the IGF-IIR in MDA- MB-231 breast cancer cells led to an inhibition of cellular invasiveness and motility

24 in vitro, and also in vivo tumour formation (Lee et al. 2003). Concomitantly decreased phosphorylation of IGF-IR and downstream signaling molecules was also observed.

With this extensive evidence in mind, it would appear that the IGF system is a highly relevant growth regulatory system in both normal mammary gland development and breast cancer. Indeed, a number of therapeutics aimed at disrupting IGF signaling have been developed and are currently under investigation in clinical trials (Sachdev and Yee 2006). However, as cancers are regulated by numerous factors, it would be naive to consider only the IGF system in isolation when investigating the development and maintenance of the malignant phenotype. While elevated levels of mitogenic hormones and growth factors do play important roles, altered cellular contact with the ECM also has a critical role in normal development and malignancy. As such, recent findings within the Tissue Repair and Regeneration (TRR) laboratory at QUT reporting that IGFs and IGFBPs can bind to VN, a protein abundant in the ECM, have provided evidence that this interaction will be important in tumours where components of the IGF system are overexpressed. Previous studies have already shown that the interaction of IGF-II with VN significantly enhances the migration of the MCF-7 breast cancer cell line (Noble et al. 2003). Furthermore, we have also demonstrated that IGF-I bound to VN indirectly via IGFBP-5 also enhances the migration of MCF-7 cells (Kricker et al. 2003). Significantly, VN has also been reported to be highly expressed in tumours (Seiffert 1997), suggesting that the association of IGFs with VN will be important in modulating breast cancer cell function.

1.11 Vitronectin (VN) - Introduction VN is a multifunctional adhesive glycoprotein which was first discovered as a protein that induced the immediate growth of unadapted cells in vitro (Holmes 1967). Due to its diverse biological roles VN has previously been known as ‘serum spreading factor’, ‘epibolin’ and ‘S protein’. It is synthesized predominantly by the liver and is present at high concentrations in the circulation (200 to 400 µg/mL), but is also deposited extravascularly into many tissues where it is abundant within the ECM (Preissner and Seiffert 1998). VN shows a high degree of conformational flexibility and can exist in both monomeric (native) and multimeric (denatured)

25 forms, which along with its numerous binding domains, can endow VN with multivalent properties, especially when associated with the ECM (Stockmann et al. 1993; Seiffert and Loskutoff 1996).

1.11.1 Structure and functions of VN VN is a 75 kDa multifunctional adhesive glycoprotein containing 459 amino acids, which are preceded by a 19-amino acid signal peptide (Schvartz et al. 1999). It contains three glycosylation sites and its carbohydrate moiety accounts for approximately 30% of this molecular mass. While its three dimensional structure is unknown, VN contains multiple independently folded domains and binding sites for cells and other components of the ECM (Schvartz et al. 1999). These include, starting from the N-terminus, the 44-amino acid residue -B domain, a connecting region and two hemopexin-like domains (Figure 1.6). The somatomedin B domain (residues 1- 44) contains amino acid residues involved in binding plasminogen activator inhibitor-1 (PAI-1) and urokinase-type plasminogen activator receptor (uPAR) via which VN can regulate cell adhesion and migration (Andreasen et al. 2000). Adjacent to the somatomedin B domain, VN contains an Arginine- Glycine-Aspartate (RGD) recognition sequence (residues 45 – 47) through which it binds a variety of integrin receptors, including αvβ1, αvβ3, αvβ5, αvβ6, αvβ8 and αIIbβ3 (Felding-Habermann and Cheresh 1993). Downstream of the RGD sequence there is a stretch of acidic amino acids (residues 53 – 64) which are involved in binding thrombin-anti-thrombin (TAT) complexes (Gechtman et al. 1997), collagen (Izumi et al. 1988) and also help to neutralize the basic region present at its carboxy- terminal end (residues 348 – 379). An ionic interaction between these acidic and basic stretches help to stabilize VNs three-dimensional structure and is involved in the ability to form multimers.

The amino acids within the carboxy-terminus (residues 332 – 379) mediate the binding of plasminogen to VN and the presence of two consensus heparin binding sequences within the polycationic region (residues 348 – 379) mediate the glycosaminoglycan binding ability of VN. In human blood, VN occurs in two molecular forms: a single chain 75 kDa polypeptide and a clipped form comprised of two chains of 65 kDa and 10 kDa held together by a disulphide bond between Cys274 and Cys453 (Dahlback and Podack 1985). Under physiological conditions, such as

26 binding TAT complexes or components of the complement system, VN becomes “denatured” or unfolded, and exposes both the heparin binding domain and amino terminal domain to form disulphide-linked VN multimers (multimeric VN). Therefore, ligand binding to VN can alter its structural conformation, which in turn affects the ability of VN to bind other ligands by exposing previously cryptic binding domains. VN exists within the ECM in the multimeric form which can exert preferential binding to several ligands including collagen, PAI-1 and uPAR (Seiffert 1997).

Signal peptide

RGD Plasminogen binding (residues 332-348) PAI-1 and uPAR binding (residues 1-44) Heparin binding Integrin binding (residues 45-47) (residues 348-361)

TGF-β binding (residues 43-62) PAI-1 binding (residues 348-370) Thrombin-antithrombin III binding (residues 53-64) Cys274 Cys453

-19 1 45 47 64 332 348 361 370

Aminoterminal Carboxyterminal Domain Domain

Figure 1.6. Schematic representation of the domain structure of vitronectin. The domain structure and localisation of the ligand binding domains on VN for; Plasminogen activator inhibitor-1 (PAI-1), urokinase plasminogen activator receptor (uPAR), integrins, TGF- β, thrombin-antithrombin III complex, plasminogen and heparin. Localisation of the cystine residues responsible for maintenance of multimeric quaternary structure via disulphide bond formation. Adapted from Schvartz et al (1999).

VN interacts with numerous components of the ECM, as well as members of the complement system through which it can modulate numerous biological functions, including thrombosis, hemostasis, fibrinolysis and immune defence (Figure 1.7) (Preissner and Seiffert 1998; Schvartz et al. 1999). VN is found in a variety of tissues anchored to the ECM via its collagen, glycosaminoglycan or heparin binding domains. Through interactions with uPAR and specific integrins, including αvβ1, αvβ3, αvβ5 and αvβ6, VN can mediate the attachment and migration of cells within the ECM. Binding of PAI-1 competes with the binding of uPAR and integrins to VN, enabling PAI-1 to regulate cell attachment and migration. Indeed it has been shown that active PAI-1 dissociates bound VN from uPAR, preventing VN binding

27 to its integrin receptors, thereby reducing adhesion to VN, which has been demonstrated to both inhibit (Stefansson and Lawrence 1996) and increase (Waltz et al. 1997) migration of cells in the presence of VN. This suggests a physical linkage between uPAR and VN-binding integrins, a concept supported by the finding that uPAR and the αvβ5 integrin co-localise in focal contacts of human keratinocytes (Reinartz et al. 1995). Moreover, the interaction between uPAR and the αvβ5 VN- receptor enhances breast cancer cell migration and invasion (Carriero et al. 1999). Taken together, these findings indicate an important role for the interaction between uPAR, VN and its integrin receptors in modulating cell function.

Apoptosis (increased cell survival) Cell adhesion, (Integrins) spreading and Extracellular anchoring migration (GAG, Collagen) (Integrins, uPAR)

Fibrinolysis Immune defence (PAI-1, uPAR) VITRONECTIN (Complement)

Hemostasis Cell proliferation (Thrombin, Factor Xa) (Integrins, Growth factors)

Figure 1.7: The numerous biological functions mediated by VN. Recreated from Schvartz et al (1999).

1.12 VN and breast cancer VN deposition has been detected in areas of fibrosis and necrosis in a variety of diseases, including membranous nephropathy, arteriosclerosis and degenerative central nervous system disorders and is expressed at high levels in tumours (Seiffert 1997). VN has been identified in breast carcinomas where it has been found in the peritumoural stroma, blood vessel walls, fibroblasts (Carriero et al. 1997) and as deposits in the connective tissue matrix around tumour cells (Loridon-Rosa et al. 1988; Niculescu et al. 1992). Aaboe and co-workers (2003) have reported one of the differences in tissue architecture between normal breast tissue and carcinomas was

28 correlated with a distinct VN distribution pattern, independent of differences in VN concentration. Immunohistochemical approaches of ductal breast carcinomas found prominent accumulations of VN in ECM structures surrounding cancer cell islands and in ducts which were infiltrated with cancer cells (Aaboe et al. 2003). In contrast, normal tissue displayed a much more homogeneous distribution with VN associated with mammary ducts and occasionally with small blood vessels (Aaboe et al. 2003). Similarly, a strong correlation has been observed between expression of both PAI-1 and uPAR with the invasiveness of cells derived from normal and malignant mammary tissue (Tong et al. 1999; Zannetti et al. 2000). These interactions may be critically important in tumour cell metastasis, as increased expression of VN and VN-binding integrins has been reported at the leading edge of migrating cancer cells (Gladson and Cheresh 1991; Uhm et al. 1999; Bello et al. 2001). Therefore, it has been suggested that as VN is a ligand for uPAR and several integrins, it may play an important role in tumour invasion, by providing traction and direction to migrating tumour cells during invasion, and/or migrating endothelial cells during angiogenesis (Aaboe et al. 2003).

1.13 Integrins - structure It is well established that the ECM profoundly influences major cellular programs of growth, differentiation and apoptosis. Deciding which of these programs a cell will elicit is ultimately determined by the composition of the surrounding ECM. The influence of the ECM on cell function is mediated predominantly by integrins, a family of heterodimeric transmembrane proteins. Integrins are comprised of non- covalently associated α and β glycoprotein type I heterodimers, with extracellular domains which bind the ECM and cytoplasmic domains which associate with the actin cytoskeleton and affiliated proteins. The integrins therefore provide a link between the ECM and the intracellular cytoskeleton. In mammals at least 16 α and 8 β subunits have been identified, with different combinations of α and β subunits dimerising to form at least 22 different receptors with distinct and often overlapping specificity for ECM proteins (Giancotti and Ruoslahti 1999).

Integrins were originally described as mediators of cell-cell and cell-ECM adhesion, but in recent years it has become apparent that they also function as true cell-surface receptors which can transmit signals to the cell interior upon ligation of ECM

29 components (Jones and Walker 1999). In this way, the extracellular environment can influence cellular functions as diverse as migration, differentiation, survival and tissue (re)modelling, both in normal and pathological states (Hynes 1992). Interestingly, integrins have also been shown to signal in the opposite direction, or “inside-out”, as ECM binding activity can be modulated from the interior of the cell (Giancotti and Ruoslahti 1999).

1.14 Integrin signaling Integrin signaling is complex as the cytoplasmic domains of these receptors are extremely short and lack kinase activity. However, ligation of integrins causes the phosphorylation of a number of cytoplasmic proteins via the association of adapter proteins with integrins. The majority of signaling molecules implicated in ECM- integrin interactions appear to be rather ubiquitous mediators of signal transduction. For example, proteins including focal adhesion kinase (FAK), Rho GTPase, Raf, Ras and MAPKs such as ERKs, can be recruited to the ECM-integrin binding site (Miyamoto et al. 1995) (Figure 1.8). However, the major adapter protein involved in integrin signaling is FAK which can interact directly or indirectly through cytoskeletal proteins, with the cytoplasmic tail of integrins.

Upon ligation, FAK is autophosphorylated, leading to recruitment and phosphorylation of Grb2/SOS and subsequent phosphorylation of the MAPK pathway. In this way, FAK is analogous in function to the IRS in IGF-IR signaling. Interestingly, however, FAK may also recruit other proteins such as Src kinase, which may then signal through the PI3-K/AKT and c-jun N-terminal kinase (JNK) pathways. FAK-independent integrin signaling also occurs as caveolin-1 has been shown to mediate Shc-stimulated activation of the Grb2/SOS complex upon integrin activation (Schaller 2001). Specialised cells are surrounded by different combinations of ECM proteins and express an array of tissue-specific integrin receptors. In the case of breast cancer, there is considerable evidence of altered integrin expression in comparison with that of normal tissue. The VN-binding integrins, or αv integrins, are highly expressed on breast cancer cells, with the “classical” VN-receptor, αvβ3, shown to be crucial in tumour progression and expression of the malignant phenotype (Marshall and Hart 1996; Meyer et al. 1998). Indeed, migration of the highly metastatic MDA-MB-231 and MDA-MB-435 breast

30 cancer cell lines is inhibited when αvβ3 and αvβ5 VN-receptors are blocked with antagonists (Bartsch et al. 2003). The αvβ3 and αvβ5 VN-receptors are also associated with tumour angiogenesis as αvβ3 is significantly upregulated during angiogenesis in vivo (Gasparini et al. 1998), with both integrins shown to be key targets to block tumour angiogenesis (Varner and Cheresh 1996; Eliceiri and Cheresh 1999). In addition, the αvβ3 and αvβ5 integrins, through their interactions with VN, have been reported to protect tumour cells from chemically-induced apoptosis (Uhm et al. 1999).

Vitronectin αβ Integrins Growth Factor Cav αβ Receptors

Cav-Shc IRS Ras Sos Grb2 P Shc Shc

Tal FAK-Src RAS Sos Grb2 P Raf Pax FAK PI3-K P Vin Src CAS Raf MEK p85 p110 Rac Actin AKT MEK MAPK JNK JNK AKT Rac MAPK

various intracellular responses A) B) various intracellular responses

Figure 1.8: Schematic representations of A) Major signaling pathways stimulated upon ligand binding to integrin receptors, B) Example of signaling pathways that are co-ordinately regulated by integrins and growth factor receptors. Stimulation of integrins and subsequent phosphorylation of intracellular targets causes integrins to become clustered in the plane of the cell membrane. This results in the formation of large clusters of integrins, cytoskeletal proteins and signaling molecules, know as focal adhesion contacts. Focal adhesion contacts intimately link the cell to the ECM, thus these contacts facilitate integrin signaling and are critical for cell survival and proliferation. It is well recognized that the majority of non- malignant cell types require integrin-mediated adhesion to the surrounding ECM environment to survive. Indeed, optimal cell stimulation by growth factors, such as epidermal growth factor (EGF), platelet derived growth factor (PDGF), vascular endothelial growth factor (VEGF) or insulin, depends on integrin-mediated cell

31 adhesion to the appropriate ECM (Giancotti and Ruoslahti 1999; Eliceiri 2001). This “cross-talk”, may not seem all that surprising when considering the pathways co- ordinately regulated by integrins and growth factors (Figure 1.8). Indeed, it is hypothesized that the requirement for integrin activation through the formation of focal adhesion contacts may result from cross-talk between integrin pathways and those involved in growth factor signaling.

1.15 Cross-talk between growth factor receptors and integrins Numerous growth factors interact with ECM molecules and modulate cellular function, suggesting an important interaction between growth factors and components of the ECM. For example, EGF has been demonstrated to act in a synergistic manner with the glycoprotein thrombospondin in stimulating smooth muscle cell DNA synthesis (Majack et al. 1986). Likewise, FGFs bound by ECM heparan sulfates (HS) have been reported to stimulate proliferation or migration depending on the HS configuration (Nurcombe et al. 2000). In addition, several other growth factors have been demonstrated to have their activities modulated by ligand binding to integrin receptors (Eliceiri 2001). Ligand occupancy of integrin receptors is thought to trigger integrin clustering and the association with the cytoskeleton appears to give rise to integrin-growth factor receptor complexes. Indeed, a direct association with integrins has been demonstrated for a number of growth factor receptors (Table 1.1). The αvβ3 integrin has been reported to directly associate with platelet-derived growth factor (PDGF), transforming growth factor beta (TGF-β), vascular endothelial growth factor (VEGF) and insulin receptors (Eliceiri 2001; Galliher and Schiemann 2006). Similarly, the epidermal growth factor receptor (ErB- 2) can directly associate with α6β1 and α6β4 integrins (Falcioni et al. 1997). The association between integrins and growth factor receptors can lead to partial activation of the growth factor receptor, thereby bringing growth factor signaling closer to a threshold point of initiating activity (Moro et al. 1998).

Interactions between β3 integrins and the TGF-βIIR lead to enhanced TGF-β- stimulated MAPK activation, Smad-2/-3-mediated gene transcription and induction of EMT (Galliher and Schiemann 2006). Similarly, via PI3-K-dependent mechanisms VEGF promotes adhesion and migration of endothelial cells mediated by αvβ3, αvβ5 and β1 integrins (Byzova et al. 2000). Indeed, numerous studies have

32 shown integrins to be involved in mediating a range of cellular responses stimulated by growth factors, including insulin and IGF-I (Table 1.2).

Table 1.1. Evidence for crosstalk between growth factor receptors and integrins. Direct growth factor receptor associations.

Growth Factor Receptor Integrins PDGFR αvβ3 VEGFR-2, -3 αvβ3 IR αvβ3 ErB-2 α6β4 TGF-βIIR αvβ3

Table 1.2. Evidence for crosstalk between growth factor receptors and integrins. Integrin-mediated growth factor responses..

Growth Factor Integrin Response PDGF αvβ3 Proliferation, migration bFGF αvβ3, α5β1 Angiogenesis, migration VEGF αvβ5 Angiogenesis VEGF αvβ3, αvβ5, β1 Angiogenesis, migration VEGF α2β1, α1β1, αvβ3 Angiogenesis, migration EGF αvβ5 Migration, metastasis EGF αvβ3, β1 Proliferation αvβ3, αvβ5, β1 Migration, metastasis, IGF/insulin proliferation

NB. Tables adapted from Eliceiri 2001.

One mechanism by which growth factors, integrins and ECM proteins can co-operate is via synergistic responses in their respective signaling pathways. This was illustrated by Miyamoto and co-workers (1996) who found synergistic increases in MAPK activation and phosphorylation of growth factor receptors, when integrin aggregation and receptor occupancy were combined with each of EGF, bFGF and PDGF. Moreover, ligand occupancy of the αvβ3 is required for optimal IGF-1R signaling in SMCs (Maile et al. 2006; Maile et al. 2006).

1.16 IGF/IGF-1R interaction with integrins Ligand occupancy of integrins has been shown to regulate IGF-I-stimulated responses in a variety of cell types, including smooth mucle cells (SMC), preadipocytes, osteoblasts, and transformed cells (Goel et al. 2004; Kapur et al.

33 2005; Sekimoto et al. 2005). A direct physical association of the IGF-IR with integrins has been demonstrated, although it is cell type specific. Co-precipitation of the IGF-IR and α6 integrin subunit following dual ligand stimulation was demonstrated in lens epithelial cells (Walker et al. 2002) and an association of α5β1 or α1β1 integrins and the IGF-IR in chondrocytes (Shakibaei et al. 1999). Moreover, the IGF-IR and β1 integrin have been shown to co-localise to lipid rafts on the plasma membrane after IGF-I stimulation with subsequent polymerization of F-actin, phosphorylation of AKT, ERK1/2, FAK and paxillin, while enhancing the association of the β1 subunit with focal adhesion proteins (Tai et al. 2003). Upon IGF-I stimulation, the scaffolding protein RACK1 can mediate the formation of an IGF-1R:RACK1:β1 integrin complex leading to enhanced cell migration in transformed cell types (Kiely et al. 2006). The β1 integrin can also bind to IRS-1 and a Grb-2/Shp2 complex to modulate IGF-1R signaling and proliferation in response to IGF-I (Goel et al. 2004). Furthermore, increases in IGF-IR have been reported to decrease β1 integrin expression and regulate neuroblastoma cellular attachment and migration (Meyer et al. 2004). Taken together, these data highlight the complex interactions that can occur between growth factor receptors, including the IGF-1R, and integrins to modulate cellular functions. Indeed, VN association with VN- binding integrins (αv integrins), including αvβ3 and αvβ5, have been documented to have an important role in modulating cellular responses to IGF-I (Doerr and Jones 1996; Brooks et al. 1997; Maile et al. 2006).

1.17 Interactions between IGF/IGF-IR, VN and VN-binding integrins The response of cells to IGF-I is not only dependent on the ability to activate the IGF-1R, but also on the activation state of integrins. As such, it has been proposed that the enhanced cellular responses to IGFs in the presence of VN may be a result of “cross-talk” between the IGF-IR and αv integrins. It has been shown that VN binding with αvβ3 is necessary for IGF-I-stimulated biological responses in smooth muscle cells (SMCs) (Jones et al. 1996; Zheng and Clemmons 1998). Moreover, blocking VN binding to αvβ3 results in the abolishment of IGF-I-stimulated responses such as DNA synthesis, cell migration, IGF-IR autophosphorylation and phosphorylation of downstream signaling components such as IRS-1 and PI3-K (Jones et al. 1996; Zheng and Clemmons 1998; Clemmons et al. 1999). It seems,

34 however, that this regulation is not unidirectional as IGF-IR activation can modulate integrin signaling. For example, IGF-I stimulation was shown to increase the affinity of αvβ3 for VN via a re-distribution of integrin associated protein (IAP) which increased IAP-αvβ3 association, resulting in enhanced cell migration (Maile et al. 2002). Moreover, several studies have reported that IGF-IR downstream signaling components, such as IRS-1, Grb2 and PI3-K, associate with αvβ3 integrins after stimulation with IGF-I (Vuori and Ruoslahti 1994; Jones and Clemmons 1995).

One of the major mechanisms whereby integrins can collaborate with growth factors is via integration of downstream signaling pathways. As mentioned in section 1.4.2, the major downstream pathways activated in response to IGF-I stimulation involve the recruitment and activation of either Shc or IRS proteins at the cell membrane. These proteins can then recruit and activate other signaling substrates, such as the Grb-2/SOS complex or the p85-regulatory subunit of PI3-K, thereby activating the MAPK and PI3-K pathways, respectively. These proteins therefore provide a mechanism whereby integrins can regulate IGF-stimulated signaling and biological functions. Indeed, the co-regulation of downstream signaling between the IGF-1R and the αvβ3 integrin has been extensively studied in SMCs. Ligand occupancy of αvβ3 induces β3 phosphorylation which has been reported to regulate IGF-1R signaling (Ling et al. 2003). In concert with IGF-1R activation, ligand binding to αvβ3 induces β3 phosphorylation, which facilitates the recruitment and transfer of the tyrosine phosphatase SHP-2 to the transmembrane protein SHP substrate-1 (SHPS-1) (Ling et al. 2003; Ling et al. 2005). SHP-2 transfers to SHPS-1 then recruits Shc to SHPS-1 and facilitates Shc activation (Ling et al. 2005). This results in the formation of a SHPS-1:SHP-2:Shc complex which causes sustained Shc phosphorylation and MAPK activation (Clemmons and Maile 2005; Ling et al. 2005). This effect is mediated by the interaction between the HBD of VN binding to a cysteine loop sequence within the extracellular region of the β3 integrin subunit (C-loop region) (amino acids 177 – 184), which is required for enhanced IGF-1R signaling (Maile et al. 2006; Maile et al. 2006). The interaction between the VN- HBD and the C-loop region of β3 was also reported to be distinct from the interaction between the VN-RGD and αvβ3. This was demonstrated by blocking VN-HBD binding to the C-loop region of β3, resulting in inhibition of IGF-I signaling and stimulation of SMC proliferation and migration (Maile et al. 2006);

35 thus providing a mechanism by which VN binding to its cell surface integrin receptors can regulate IGF-I-stimulated responses.

While the mechanisms outlined in SMCs may be specific for αvβ3 in modulating IGF-I responses, there is also evidence that other VN-binding integrins regulate cellular responses to IGFs. It has been reported that VN binding to the αvβ5 integrin is required for IGF-I-stimulated cell migration of the MCF-7 breast carcinoma cell line (Doerr and Jones 1996). Indeed, a number of studies have identified a critical role for crosstalk between growth factor receptors, including the IR and IGF-1R, and the αvβ5 integrin (Eliceiri 2001). The αvβ5-mediated adhesion and migration of human pancreatic carcinoma cells on VN has been demonstrated to be dependent on EGF or insulin pre-stimulation (Klemke et al. 1994). Similarly, melanoma cells expressing αvβ5 required insulin or IGF-I stimulation for migration on VN, whereas cells expressing αvβ3 supported IGF-independent migration (Brooks et al. 1997). Likewise, studies undertaken using both chicken and mouse models have reported that melanoma cells expressing αvβ5 required the ex vivo pre-stimulation with IGF for metastasis, whereas αvβ3 expressing cells can metastasize in the absence of pre- stimulation (Klemke et al. 1994). These studies provide evidence that αvβ5, but not αvβ3, mediated in vitro cell migration and in vivo metastasis requires growth factor stimulation, and provides further evidence to support the important interaction between IGFs and VN-binding integrins in regulating metastasis.

Taken together, there is accumulating evidence that there is co-operation occurring between integrins and the IGF-IR. The literature reported above highlights the numerous interactions between the IGF system and components of the ECM. Moreover, many growth factors have their activities modulated by interactions with a variety of ECM proteins and their respective cell surface integrin receptors. However, to date the majority of detailed studies investigating the mechanisms behind VN regulation of IGF actions has been in SMCs, which express the αvβ3 integrin. Therefore, it will be important to determine the role of VN in modulating IGF actions in other cell types, especially in cells which don’t express αvβ3. As the IGF-IR, VN and VN-binding integrins all play important roles in tumour biology, we therefore hypothesized that the interaction between these components will also be relevant to breast cancer cell function and metastasis.

36

1.18 Recent findings from the TRR program to support the IGF:VN interaction in breast cancer metastasis The above findings strongly support coordinate regulation of the IGF1-R and the VN-binding integrins and suggest that the binding of their ligands, IGFs and VN, in the ECM has a functional role (Figure 1.9). Following the initial discovery that the ECM protein VN binds to insulin-like growth factor-II (IGF-II) (Upton et al. 1999), initial functional studies revealed that IGF-II:VN complexes could stimulate synergistic proliferative and migratory responses in HaCAT human keratinocytes (Upton and Kricker 2002.). Given that expression of IGFs, VN and their cell surface receptors correlate with metastatic potential and histological tumour grade of a variety of cancers (Gladson and Cheresh 1991; Tomasini-Johansson et al. 1994; Giovannucci 1999), the ability of IGF-II:VN complexes to modulate cellular functions in MCF-7 breast carcinoma cells has also been examined. In contrast to studies in HaCAT cells, IGF-II:VN complexes had little effect on cell attachment and de novo protein synthesis. However, pre-binding IGF-II to immobilised VN was found to significantly enhance MCF-7 cell migration in the Transwell® assay system (Noble et al. 2003). Furthermore, using IGF analogues with reduced affinity for IGFBPs it was reported that IGF-II bound to VN enhanced migration independently of IGFBPs (Noble et al. 2003).

Novel links between IGF-I, IGFBP and VN have also been identified. Previous studies have demonstrated that IGFBP-2, -3, -4 and -5 can bind to VN (Upton 2002; Kricker et al. 2003). Subsequent studies revealed that IGFBP-3 and IGFBP-5 bind to VN via their HBD associating with the acidic region of VN (residues 53 – 64) (Kricker et al, unpublished data). As IGFBP-4 lacks a HBD, IGFBP-4 binding to VN has been proposed to be mediated via acidic residues within its N-terminal domain associating with the HBD contained within VN (residues 348 – 379) (Kricker et al, unpublished data). Furthermore, it was determined that in the presence of IGFBP-2, - 3, -4 and -5, IGF-I could interact with VN to form a heterotrimeric IGF-I:IGFBP:VN complex (Kricker et al. 2003).

37 Vitronectin IGF Vitronectin IGF αβ α αβ Integrin α IGF1R Integrin IGF1R

β β

independent signal transduction cross-talk

pERK 1/2 pERK 1/2 pERK 1/2 pAKT pAKT pAKT

Figure 1.9: Cross-talk between IGF-IR and VN-binding integrins. One possible mechanism by which the association of IGFs with VN results in enhanced cell migration, is the co-operation or “cross-talk” between the IGF-IR and integrin signaling pathways, leading to enhanced intracellular signaling.

The critical involvement of the IGFBPs in mediating IGF-I binding to VN was shown to be specific for IGFBP-3 and IGFBP-5, as IGF-I analogues which bind poorly to IGFBPs, were unable to bind to VN (Kricker et al. 2003). In the same study, substrate-bound IGF-I:IGFBP-5:VN complexes were observed to stimulate significant increases in MCF-7 cell migration (Kricker et al. 2003), while other studies have also reported IGF-I:IGFBP-5:VN complexes to be potent stimulators of migration and proliferation of HaCAT human skin keratinocytes (Hyde et al. 2004; Hollier et al. 2005). Taken together with the findings by others reviewed above, this provides further support for the notion of intimate coordinate regulation between the IGF-1R and αv integrins.

1.19 Conclusion It is well established that the composition of the extracellular microenvironment can have profound effects on the way cells respond to peptide hormones and growth factors. Indeed, the way in which a particular cell type responds to a growth factor can vary greatly depending on its expression of specific cell surface integrin receptors via which it mediates cell adhesion to the surrounding ECM. This review has outlined numerous instances whereby members of the IGF system have been reported to interact with components of the ECM to modulate their biological

38 functions. VN is one such ECM protein which has established roles in regulating IGF action on cells and there is accumulating evidence for the co-ordinate regulation between the IGFs/IGF-IR, VN and VN-binding integrins. However, as these interactions are often cell type specific and the regulation between VN-binding integrins and the IGF-1R remains unclear, there is still a need to investigate this interaction in other cell types. This is of particular relevance to recently described substrate-bound IGF:VN complexes as to date there have been only limited reports on the functional significance and mechanisms underpinning their actions.

Thus, we propose that the IGF:VN interaction is an important mechanism promoting breast cell dissemination, especially as increased VN expression has been reported at the leading edge of migrating cancer cells (Gladson and Cheresh 1991; Uhm et al. 1999). As the IGF system plays important roles in both normal breast development and in the transformation and progression of breast cancer, there is a need to further investigate the role of the association of IGFs with VN in breast cell functions. Indeed, identifying the molecular mechanisms by which IGF:VN complexes enhance breast cell function will lead to not only a better understanding of this critical interaction, but also aid in developing diagnostic tests and therapeutics directed at treating breast cancer.

39 1.20 OUTLINE OF PROJECT

1.20.1 Hypotheses The underlying hypotheses explored in my PhD studies were: 1) The association of IGF-I with VN via the involvement of IGFBP-3 and -5, will stimulate increased cellular functions in MCF-7 breast carcinoma and MCF-10A non-tumourgenic breast cell lines; 2) Enhanced cellular responses induced by IGF-I:IGFBP:VN complexes will be a result of enhanced activation of key intracellular signaling pathways mediated via “cross-talk” or co-operation between the IGF-1R and VN-binding integrins; and 3) IGF-I:IGFBP:VN complexes will induce differential expression of candidate genes critical to enhanced cell migration.

1.20.2 Aims Thus, the aims of my PhD studies were to: 1) Examine the functional responses of MCF-7 and MCF-10A breast cell lines to substrate-bound IGF-I:IGFBP:VN complexes; 2) Investigate the mechanisms behind IGF-I:IGFBP:VN complex-stimulated migration; and 3) Use gene microarrays to screen for candidate genes involved in IGF- I:IGFBP:VN complex-stimulated migration.

40

CHAPTER 2

Materials and Methods

41 2.0 MATERIALS AND METHODS 2.1 Introduction All materials and methods referred to in the following results chapters are outlined in this chapter. Any deviations from the standard protocols are described in the individual results chapters where they appear. All general reagents were supplied from various commercial suppliers, unless otherwise stated, and were of the highest laboratory grade. Suppliers of method-specific reagents are detailed in the appropriate method section.

2.2 Proteins Human IGF-I, Epidermal growth factor (EGF), Long R3 IGF-I (LR3 IGF-I), Des (1- 3)-IGF-I and [Leu24][Ala31]-IGF-I ([L24][A31]-IGF-I) were purchased from Novozymes (Adelaide, SA, Australia). Human VN and anti-ERK 1/2 polyclonal antibody were from Promega (Annandale, NSW, Australia), with IGFBP-3 (N109D) and anti-AKT polyclonal antibody from Upstate Biotech (Lake Placid, NY, USA). IGFBP-5 was purchased from Dr Sue Firth, Kolling Institute of Medical Research (University of Sydney, NSW, Australia). Mouse monoclonal antibodies directed against the αv-integrin subunit (AV1), αvβ5 (P1F6), αvβ6 (10D5), β1-subunit (P4C10) and the IgG matched control antibody were purchased from Chemicon (Temecula, CA, USA) and a monoclonal antibody raised against the IGF-1R (αIR3) was purchased from Merck Biosciences (Kilsyth, VIC, Australia). For detection of phosphorylated signalling intermediates, anti-phospho ERK1/2 MAPK (Thr 202/ Tyr 204) (E10), anti-phospho-AKT (S473) (587F11), anti-phospho-AKT (T308) and anti-phospho-p70S6K (T389) monoclonal antibodies were from Cell Signaling Technology (Beverly, MA, USA). All other reagents were purchased from Sigma- Aldrich (Castle Hill, NSW, Australia) unless otherwise stated.

2.3 Cell culture The MCF-7 and T47D human breast carcinoma cell lines (HTB-22 and HTB-133 respectively, ATCC, Manassas, VA) were obtained from Dr Steven Meyers (Science Research Centre, Queensland University of Technology, Brisbane, Australia) and MDA-MB-231 cells (HTB-26, ATCC) were a kind gift from Dr Chris Schmidt (Queensland Institute of Medical Research, Brisbane, Australia). MCF-7 cells stably overexpressing the β3 integrin (MCF-7-β3) were a gift from Dr John Price (Monash

42 University, VIC, Australia). All three breast cancer cell lines were grown in DMEM/Ham’s F-12 (DMEM/F12) media (1:1) (Invitrogen, Mulgrave, VIC, Australia) containing 10% fetal bovine serum (FBS) (HyClone, South Logan, UT, USA). The MCF-10A cells, a spontaneously immortalised phenotypically normal breast epithelial cell line (Soule et al. 1990), were a kind gift from Dr Robert Pauley (Karmanos Cancer Institute, Detroit, MI) and Dr Janet Martin (Kolling Institute of Medical Research) and were maintained routinely in DMEM/F12 (1:1) containing 15 mM HEPES, 5% horse serum (HS) (Invitrogen), 10 µg/mL bovine insulin, 20 ng/mL EGF, 50 ng/mL cholera enterotoxin and 0.5 µg/mL hydrocortisone. All cultures were passaged every 2-3 days by trypsin/EDTA detachment.

2.4 Treatment strategy for in vitro assays The standard approach to studying the effects of growth factors is to add them to the culture medium. However, in vivo it is more likely that cells respond to growth factors bound to the ECM. This study therefore adopted the strategy of “pre-binding” growth factors and VN to tissue culture plastic in multi-well plates (Nagle Nunc International, Roskilde, Denmark) and to the lower chamber and membrane surface of 12-µm pore Transwells®(Costar, New York, NY, USA) in an effort to more accurately reflect the in vivo cellular environment.

2.4.1 Pre-binding of VN, IGFBPs and IGF-I to cultureware Preliminary studies in our laboratory have shown that optimal VN-mediated cell attachment to multiwell plates is achieved by “pre-binding” human VN at a concentration of 1 µg/mL to multi-well plates and lower chamber and membrane surface of 12-well Transwell® plates.

2.4.2 Pre-binding of VN, IGFBPs and IGF-I to 96- and 24-well plates 24-well culture plates were prepared using procedures described previously (Kricker et al. 2003; Noble et al. 2003; Hyde et al. 2004; Hollier et al. 2005). Thus, 300 µL of DMEM/F-12 serum-free and phenol red-free medium (DMEM-SFM) containing VN at a concentration of 1 µg/mL (300 ng/well) was added to each well of 24-well culture dishes and incubated for three hours at 37 °C. Medium containing unbound VN was removed and the wells were washed with 1 mL DMEM-SFM + 0.5%

43 Bovine Serum Albumin (BSA) (RIA grade, Sigma-Aldrich), before the addition of 300 µL of HBB + 1% BSA to wells and incubation at 37 °C for 30 min to block non- specific binding to the tissue culture plastic. Following a further wash, as previously described, 250 µL of DMEM-SFM + 0.05% BSA containing IGF-I (10 - 100 ng/mL), IGFBP-3 and IGFBP-5 (30 - 300 ng/mL), either alone or in combination, were added to wells of 24-well plates and incubated at 37 °C for 2 hours. The solution containing unbound growth factors was removed and wells were washed with 1 mL DMEM-SFM and air-dried. Identical procedures were used for coating of 96-well plates, with the exception that 100 µL volumes were used in steps for pre- binding of VN (1µg/mL), IGF-I (10 - 100 ng/mL) and IGFBP-3 or IGFBP-5 (30 - 300 ng/mL), either alone or in combination. Wash steps were performed with 200 µL volumes for 96-well plates. Importantly, the “pre-bound” growth factors retain their biological activity after coating procedures and are stable for up to three weeks after storage at 4 °C (unpublished data).

2.4.3 Pre-binding of VN, IGFBPs and IGF-I to Transwell® inserts Transwell® inserts were prepared by pre-binding the lower well and under-surface of 12-µm pore membranes with substrate-bound complexes as previously described (Kricker et al. 2003; Noble et al. 2003; Hyde et al. 2004; Hollier et al. 2005). Therefore, 1 mL of DMEM-SFM containing 1 µg/mL of VN was added to the lower chambers of 12-well Transwell™ plates and incubated for 3 hours at 37 °C. Unbound VN was then removed and the wells were washed twice with 1 mL of DMEM-SFM + 0.5% BSA. For pre-binding of growth factors, 1 mL of DMEM-SFM + 0.05% BSA containing native IGF-I (10 - 100 ng), IGF-I analogues (30 ng), IGF-II (100 - 1000 ng), IGFBP-3 and IGFBP-5 (30 - 300 ng), either alone or in combination, was added to the lower chamber of Transwells® and incubated overnight at 4 °C. The solution containing unbound growth factors was removed and the wells were washed twice with 1 mL DMEM-SFM + 0.05% BSA and 1 mL of DMEM-SFM + 0.05% BSA placed into the lower well.

2.4.4 Pre-binding of VN, IGFBPs and IGF-I to 6-well plates 6-well plates were pre-coated with the optimal concentrations of substrate-bound IGF-I and IGFBP complexes determined by stimulation of cell migration. The pre-

44 binding procedures and concentrations of growth factors used in 6-well plates were identical to those used in Transwell® migration assays, with the exception that the volume added was adjusted for the approximate relative differences in culture surface areas (approx 2.5:1, 6-well:Transwell® surface area ratio). Therefore, 2.5 mL of DMEM-SFM containing 1 µg/mL of VN was added to each well of 6-well plates and incubated for 3 hours at 37 °C. Unbound VN was then removed and the wells were washed twice with 3 mL of DMEM-SFM + 0.5% BSA. For pre-binding of growth factors, 2.5 mL of DMEM-SFM + 0.05% BSA containing IGF-I (30 ng), IGFBP-3 and IGFBP-5 (90 ng), either alone or in combination, was added into 6- well plates and incubated overnight at 4 °C. The solution containing unbound growth factors was removed and the wells were washed twice with 3 mL DMEM-SFM + 0.05% BSA and air-dried before addition of cells.

2.5 Functional assays 2.5.1 MTS assay for determination of cellular proliferation ® The assessment of cell proliferation was performed using the CellTiter 96 AQueous ® One Solution Cell Proliferation Assay (MTS) (Promega). The CellTiter 96 AQueous

One Solution Cell Proliferation Assay is a colorimetric method for determining the number of viable cells in proliferation, cytotoxicity or chemosensitivity assays. The ® CellTiter 96 AQueous One Solution Reagent contains a tetrazolium compound [3- (4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H- tetrazolium, inner salt; MTS(a)] and an electron coupling reagent (phenazine ethosulfate; PES). To assess cellular proliferation, 5 x 103 cells that had been serum- starved by incubation in serum-free and phenol red-free DMEM/F12 (DMEM/F12- SFM) for 4 hours were harvested by trypsinization and seeded into each well of pre- coated 96-well plates in 100 µL of DMEM/F12-SFM. The cells were then incubated for 72 hours at 37ºC, 5% CO2, before adding 20 µL of MTS solution to each well.

The plates were then incubated for 2 hours at 37°C, 5% CO2 to allow for color development and the absorbance was then recorded at 490 nm using a 96-well plate reader (Benchmark Plus, Bio-Rad, Gladesville, NSW). The quantity of formazan product as measured by the amount of 490 nm absorbance is directly proportional to the number of viable cells in culture (Cory et al. 1991). The use of MTS reagents has

45 been previously demonstrated to correlate well with assays using [H3]-thymidine incorporation (www.promega.com/tbs/tb112/tb112.pdf).

2.5.2 Attachment assays using [3H]-leucine labelled cells Attachment assays were performed as previously described (Leavesley et al. 1999; Noble et al. 2003). MCF-7 and MCF-10A cells were grown for 18 hours in the presence of 2 µCi/mL of [3H]-leucine (Amersham Biosciences, Castle Hill, NSW, Australia), thus allowing the cells to be labelled via the incorporation of [3H]-leucine into newly synthesized protein. Following serum starvation for 4 hours, the cells were harvested by trypsinisation and 1 x 105 cells were seeded into pre-coated wells

of 24-well plates and incubated for 4 hours at 37ºC, 5% CO2 to allow cell attachment. The medium and unattached cells were then removed and the wells washed twice with 2 mL of Hanks Balanced Salt Solution (Sigma), twice with 2 mL of ice cold 5% trichloroacetic acid for 5 minutes each time and once with 2 mL distilled water. The cell monolayer was then solubilised in 0.1% triton X-100/0.5M NaOH for 1 hour. After triturating the cells, 100 µL sub-samples were added to 5 mL of Ready Safe Scintillation fluid (Beckman Coulter, Gladesville, NSW) and counted in a β-Counter LS5000TA liquid scintillation spectrometer (Beckman Coulter).

2.5.3 Attachment assays with function blocking antibodies Cells which had been serum-starved for four hours were harvested and incubated with the indicated concentrations of inhibitory antibodies for 30 minutes at room temperature. The cells were then seeded into pre-coated wells at a density of 1 x 105

cells/well and incubated for 4 hours at 37ºC, 5% CO2 to allow cell attachment. Following two washes with 1 mL of phosphate buffered saline (PBS), the attached cells were fixed in 3.7% para-formaldehyde and stained with 0.01% Crystal Violet in PBS. The number of cells which had attached in response to each treatment was then quantified by extracting the crystal violet stain in 10% acetic acid and determining the optical density of these extracts at 595 nm using a 96-well plate reader (Benchmark Plus, Bio-Rad).

46 2.5.4 Transwell® migration assays Migration assays were performed as previously described by our laboratory (Noble et al. 2003; Hyde et al. 2004; Hollier et al. 2005). Cells which had been serum- starved for four hours were harvested and seeded at a density of 2 x 105 cells/well in DMEM/F12-SFM + 0.05% BSA into the upper chamber of pre-coated Transwell® inserts (12-µm pores). In some experiments, the cells were pre-incubated with the indicated concentrations of inhibitory antibodies for 30 minutes at room temperature, or with pharmacological inhibitors LY294002 and U0126 for 60 minutes at 37°C, ® ® 5% CO2 before seeding into the Transwell . Plates containing the Transwell inserts were then incubated for 5 hours at 37°C, 5% CO2. Cells which had migrated to the lower surface of the membrane were fixed in 3.7% para-formaldehyde and stained with 0.01% Crystal Violet in phosphate buffered saline (PBS). The number of cells which had migrated to the lower surface of the porous membrane was then quantified by extracting the crystal violet stain in 10% acetic acid and determining the optical density of these extracts at 595 nm using a 96-well plate reader (Benchmark Plus, Bio-Rad) (Leavesley et al. 1993).

2.5.5 Alpha/Beta integrin-mediated cell adhesion array The alpha/beta integrin-mediated cell adhesion array was purchased from Chemicon International (Temecula, CA, USA) to determine the expression of specific integrins/subunits by cell lines used throughout these studies. The 96-well plate array uses mouse monoclonal antibodies generated against human alpha and beta integrins/subunits that are immobilized onto a goat anti-mouse antibody-coated microtiter plate. The plate is then used to capture cells expressing these integrins on their cell surface. Assays were performed as described by the manufacturer’s instructions provided with the array. Thus, the required number of wells were rehydrated with 200 µL of PBS for 10 minutes at room temperature (RT). A single cell suspension containing 1 x 106 cells/mL was then prepared by harvesting cells using a non-enzymatic cell dissociation buffer (Sigma). One hundred microlitres of the cell suspension was then added to each well of the mouse anti-alpha or anti-beta integrin capture and control wells and incubated at 37°C, 5% CO2 for 2 hours to allow cell attachment. Each well was then washed three times with 200 µL of assay buffer before staining the cells with 100 µL of Cell Stain Solution (Crystal Violet)

47 for five minutes at RT. Plates were then washed gently with deionized water and left to air dry. The cell stain was then extracted with 100 µL of Extraction Buffer for ten minutes at RT followed by determining the optical density of these extracts at 595 nm using a 96-well plate reader (Benchmark Plus, Bio-Rad).

2.5.6 Assessment of cell viability ® The assessment of cell viability was performed using the CellTiter 96 AQueous One Solution Cell Proliferation Assay (MTS) (Promega). To assess cell viability, serum- starved cells which had been pre-treated with pharmacological inhibitors LY294002

(20 µM) and U0126 (10 µM) for 60 minutes at 37°C, 5% CO2, were seeded into 96- well plates at a density of 1 x 104 cells/well. Cells were then incubated for 5 hours at

37°C, 5% CO2 (i.e. the same time used for migration assays), before adding of 20 µL of MTS solution to each well. The plates were then incubated for 2 hours at 37°C,

5% CO2 to allow for color development and the absorbance was then recorded at 490 nm using a 96-well plate reader (Benchmark Plus, Bio-Rad). The quantity of formazan product as measured by the amount of 490 nm absorbance is directly proportional to the number of viable cells in culture.

2.6 Protein isolation, SDS-polyacrylamide gel electrophoresis (SDS-PAGE) and western immunoblotting 2.6.1 Protein isolation from cell lines Following desired incubation of cells, the medium was removed and the cell monolayer was washed with PBS containing 2 mM Na3VO4 and 10 mM NaF. The cells were then lysed in Radio Immuno-Precipitation Assay (RIPA) buffer containing 150 mM NaCl, 1.0% Igepal CA-630, 0.5% sodium deoxycholate, 0.1%

SDS, 50 mM Tris, pH 8.0, 2 mM Na3VO4 and 10 mM NaF with a complete protease inhibitor cocktail (Roche Diagnostics, Cactle Hill, NSW, Australia). Lysates were then homogenized by passing through a 26-gauge needle and syringe at least five times before being centrifuged at 4°C (14,000g, 20 minutes). The supernatants were then collected and their protein concentrations determined using the bicinchoninic acid assay (Pierce, Rockford, IL, USA).

48 2.6.2 SDS-PAGE SDS-PAGE gels were prepared using the BioRad Mini-Protean 3 system. In general, the SDS-PAGE gels contained a 10% resolving gel layer with an overlying 4% stacking gel layer to enhance band resolution. Protein samples (10 µg total protein) were mixed 1:4 with a reducing Laemmli loading buffer (0.05% bromophenol blue, 0.225M Tris-HCL, 5% SDS, 50% glycerol, 100 mM DTT) and heated for 10 minutes at 95°C. The gels were then placed into Tris-glycine buffer (25 mM Tris, 0.2 M glycine, 0.1% SDS) and the samples loaded into their appropriate wells. The samples were then electrophoresised at 90 V for 20 minutes through the 4% stacking gel and 1-2 hours at 120V through the 10% resolving gel depending on the size of the target protein. A pre-stained broad range molecular weight marker (Bio-Rad) was included on all gels to monitor the appropriate electrophoresis time and to estimate the size of the cell proteins.

2.6.3 Western immunoblotting Protein samples were resolved by SDS-PAGE and transferred to BioTrace®NT nitrocellulose membranes (Pall Corporation, FL, USA) at 4°C in 25 mM Tris, 40 mM glycine, 10% methanol for 2 hours at 200 mA. Transfer of the proteins was confirmed by visual inspection of pre-stained molecular weight marker bands on the nitrocellulose membranes. The membranes were then blocked in either 5% non-fat milk or 5% BSA in Tris-buffered saline (10 mM Tris, 0.5 M NaCl, pH 7.6) with 0.1% Tween-20 (TBST) for 1 hour at room temperature. The blocked membranes were then incubated with either anti-phospho ERK1/2 MAPK (1:2000), anti- phospho AKT (1:2000), anti-phospho p70S6K (1:2000) or anti-Myc (1:5000) mouse monoclonal antibodies overnight at 4°C in blocking buffer. The membranes were then washed six times for 5 minutes each in TBST before incubation with horseradish peroxidase-conjugated goat anti-mouse or anti-rabbit secondary antibodies (1:5000) for 1 hour at RT. Following a further six, five minute washes, the protein bands were then visualized using enhanced chemiluminescence following the manufacturer’s instructions (GE Healthcare). The same membranes were subsequently stripped and total levels of ERK 1/2, AKT or GAPDH detected as outlined above using anti-ERK 1/2, anti-AKT or anti-GAPDH antibodies to validate equal loading.

49 2.7 Transient over-expression of wild type and activated-AKT

2.7.1 Eukaryotic expression vectors

The AKT1/PKBα cDNA Allelic pack was purchased from Upstate (Lake Placid, NY, USA). This pack included pUSEamp eukaryotic expression vectors under the control of the cytomegalovirus promoter, containing Myc-His tagged mouse wild type AKT-1 (WT-AKT), N-terminal myristoylated AKT-1 (MYR-AKT), which produces an activated form of AKT and the empty vector control (see Appendix 1, Figure A1-1 for vector schematics).

2.7.2 Transformation of competent JM109 E. coli

One 50 µL aliquot of competent 2nd generation XL-10 Gold (JM109) E. coli cells (108 cells/mL) was thawed on ice for each transformation. Upon thawing, 50 ng of each plasmid (0.5 µL) was added to the competent cells, mixed gently, and incubated on ice for 30 minutes. Cells were then heat shocked for 90 seconds at 42°C and then returned to ice for 15 minutes. Nine hundred and fifty microlitres of lysogeny broth (LB) medium (5g/L bactotryptone, 2.5 g/L yeast extract and 2.5 g/L NaCl) was added to each tube and the cells were allowed to recover at 37°C for 1 hour with shaking. For all vectors, 20 µL was then streaked out onto LB agar plates containing 100 µg/mL ampicillin and incubated overnight at 37°C. Up to 5 colonies were picked from each plate using a p200 tip, which was then placed into 5 mL of LB medium containing 100 µg/mL ampicillin and incubated overnight at 37°C with shaking. Glycerol stocks were then made by mixing 800 µL of each overnight culture with 200 µL of 80% glycerol and storing at -80°C. The remaining volume of cultures was then purified using procedures detailed below. All glycerol stocks were subsequently analysed with restriction enzyme digests and confirmed to contain the appropriate plasmid inserts.

2.7.3 Plasmid purification – minipreps

Plasmids were purified using the QIAprep miniprep kit (Qiagen, Doncaster, VIC) which contained all the solutions and spin columns required for plasmid purification.

50 All steps were performed at room temoerature (RT) following the manufacturer’s kit directions. Briefly, 5 mL overnight growth cultures were centrifuged at 3000 rpm for 5 minutes to pellet the cells. The pelleted cells were resuspended in 250 µL buffer P1 and were transferred to microcentrifuge tubes. Two hundred and fifty microlitres of buffer P2 was added into each tube and mixed several times by inversion. After no longer than 5 minutes, 350 µL of buffer N3 was added and again mixed by inversion. The tubes were then centrifuged for 10 minutes at 13,000 rpm (~ 18,000 g) in a desktop centrifuge and the resulting supernatants were applied to QIAprep spin columns. The spin columns were then centrifuged for 1 minute (13,000 rpm) and the flow-through discarded. QIAprep columns were washed with 500 µL buffer PB, before being centrifuged for 1 minute and the flow-through discarded. The columns were then washed with 750 µL buffer PE and centrifuged for 1 minute. The flow- through was again discarded and the column centrifuged for an additional 1 minute to remove residual PE buffer. The columns were transferred to new 1.5 mL microfuge tubes. To elute DNA, 50 µL of buffer EB (10 mM Tris-Cl, pH 8.5) was added to the center of each column and incubated at RT for 1 minute. Eluted DNA was then collected by centrifugation for 1 minute. The quality and quantity of the extracted plasmid DNA was then assessed by agarose gel electrophoresis and spectrophotometric UV absorbance at 260/280 nm, respectively.

2.7.4 Transient transfections

The day before transfection 3 x 105 cells were plated into wells of 6-well plates and incubated overnight at 37°C, 5% CO2 in normal growth medium. The following morning, for each well to be transfected, 2 µg of pDNA was diluted into 50 µL of serum-free Opti-MEM (Opti-MEM) (Invitrogen) and incubated at RT for 5 minutes. During the 5 minute incubation, 6 µL of GeneJuice transfection reagent (Merck, Colchester, VIC) was diluted into 50 µL of Opti-MEM. Fifty microlitres of diluted GeneJuice reagent was then added to each of the pDNA samples, mixed gently by pipetting, and incubated at RT for 20 minutes. This resulted in a final pDNA concentration of 2 µg/100 µL, with a 3:1 ratio of GeneJuice:pDNA. The volumes were scaled up appropriately for transfection of multiple wells with the same pDNA. During the 20 minute incubation, plates containing cells to be transfected were removed from the incubator and washed twice with 3 mL of Opti-MEM, leaving a 2

51 mL volume remaining in each well after the last wash. The GeneJuice/pDNA mixture (100 µL containing 2 µg pDNA) was then added drop-wise to each well and the plates gently rocked back and forth to ensure even distribution. The plates were then incubated for 7 hours at 37°C, 5% CO2 . The Opti-MEM was then replaced with normal growth media and the cells were incubated at 37°C, 5% CO2 for 24 h before harvesting to confirm protein expression or for use in Transwell® migration assays.

2.8 Microarray analysis of differential gene expression

2.8.1 Extraction of RNA from migrated MCF-10A cells

Total RNA was isolated using TRI Reagent from MCF-10A cells which had migrated through the 12-µm pores onto the under-surface of Transwell® membranes in response to VN, IGFBP-5 + VN, IGF-I+VN and IGF-I+IGFBP-5+VN treatments (Treatment samples) or from non-migrated MCF-10A cells which remained on the upper-surface of the membrane in response to serum-free medium (SFM) (No Treatment sample, non-migrated control). For Treatment samples, this was achieved by briefly removing the non-migrated cells from the upper surface of the membrane with a cotton bud and transferring the Transwell® insert into a fresh 12-well plate containing 200 µL of TRI reagent to lyse the migrated cells. For control samples (No Treatment), any migrated cells were removed from the under-surface of the membrane and the inserts were transferred to fresh plates. Two hundred microlitres of TRI reagent was then placed into the Transwell® inserts to lyse the non-migrated cells. Twelve 200 µL TRI reagent samples, representing cells lysed from twelve Transwell® inserts, were pooled for each of the Treatments and No Treatment controls. Each sample (~ 2.4 mL) was then divided into 2 x 1 mL aliquots to facilitate RNA isolation in microcentrifuge tubes, with the remainder (~ 400 µL) stored at -80°C. To obtain replicate biological samples, the whole migration assay and RNA extraction was repeated three times on separate days.

2.8.1.1 Total RNA isolation – Part 1 (RNA separation and precipitation) Two hundred microlitres of chloroform was added to each 1 mL TRI-reagent aliquot, shaken vigorously, and incubated for 5 minutes at RT to allow phase separation. The RNA was then separated into the aqueous phase by centrifugation at maximum speed

52 (~ 18,000 g) in a desktop centrifuge for 15 minutes at 4°C. The aqueous phase (~ 600 µL) was then transferred into new tubes and the RNA precipitated with 1 volume of isopropanol, 0.1 volume 7.5 M ammonium acetate and 5 µg/ml linear polyacrylamide (Ambion, Austin, TX, USA) at -20°C overnight. Precipitated RNA was then pelleted by centrifugation for 15 minutes at 4°C. The supernatant was carefully poured off and the pellet was washed briefly with cold 70% ethanol. The tubes were centrifuged at low speed (~ 7500 g) at 4°C for 10 minutes, after which the ethanol was poured off and the pellets were allowed to air dry. RNA pellets were then resuspended in 20 µL of diethyl pyrocarbonate (DEPC)-treated sterile water and the RNA samples from each of the original two TRI reagent aliquots were combined for each treatment (40 µL final volume/sample). To assess the quality of the extracted RNA, 1 µL of each sample was separated on a 1.5% agarose/tris-acetate- EDTA (TAE) gel and electrophoresed for 30 minutes at 115 V, to ensure RNA stability. Quality RNA was determined with gel analysis by the presence of two distinct 28S rRNA and 18S rRNA bands, with the former at least twice as intense as the latter. One µL of each sample was also diluted 1/60 with TE buffer (pH 8.0) and analysed in a BioPhotometer UV spectrophotometer (Eppendorf, North Ryde, NSW, Australia) with readings taken at 260 nm, 280 nm and 230 nm to determine the quantity and purity of RNA samples and to ensure the lack of solvent carryover into subsequent procedures.

2.8.1.2 Total RNA isolation – Part 2 (DNase treatment and clean-up) To ensure the RNA samples were free from DNA, any residual contaminating DNA was then removed by DNase treatment. The remaining volume of RNA samples (~ 38 µL) was treated with 10 U/mL rDNase (Ambion) in a final volume of 200 µL and incubated at 37°C for 30 minutes. DNase was then inactivated by the addition of an equal volume of phenol:chloroform:isoamyl alcohol (25:24:1), mixed by inversion and left to stand at RT until phase separation (~ 5 minutes). The tubes were then centrifuged at RT for 5 minutes (~ 18,000 g) and the aqueous phase containing the RNA was transferred to new tubes. Two hundred microlitres of chloroform was then added to each tube and centrifuged again for 5 minutes at RT. The aqueous phase (~ 200 µL) was then transferred into new tubes and the RNA precipitated with 2.5 volumes of 100% ethanol and 0.1 volume 7.5 M ammonium acetate at -20°C

53 overnight. The precipitated RNA was then pelleted by centrifugation for 15 minutes at 4°C. The supernatant was carefully poured off and the pellet was washed briefly with cold 70% ethanol. The tubes were centrifuged at low speed (~ 7500 g) at 4°C for 10 minutes, after which the ethanol was poured off and the pellets were allowed to air dry. The RNA pellets were then resuspended in 20 µL of DEPC-treated sterile water. Two microlitres of each sample was used to assess RNA quality and quantity as described in Part-1. Only RNA samples which passed gel analysis and had

A260/A280 and A260/A230 ratios of at least 1.9 and 2.0, respectively, were used in downstream applications.

2.8.2 Target synthesis, In vitro transcription (IVT) to synthesize biotin-labeled anti-sense RNA (aRNA), aRNA purification and fragmentation

2.8.2.1 The MessageAmp™ II-Biotin Enhanced Single Round aRNA Amplification Kit - Overview All steps from target synthesis through to fragmentation of biotin-labeled aRNA were performed using the MessageAmp™ II-Biotin Enhanced Single Round aRNA Amplification Kit (Ambion), following the manufacturer’s protocols. This method is based on the RNA amplification protocol developed by James Eberwine (Van Gelder et al. 1990). The procedure consists of reverse transcription (RT) with an oligo(dT) primer bearing a T7 promoter using ArrayScript™, a reverse transcriptase engineered to produce higher yields of 1st-strand cDNA than wild type enzymes. ArrayScript™ catalyzes the synthesis of virtually full-length cDNA, which helps to ensure production of reproducible microarray samples. The cDNA then undergoes 2nd-strand synthesis and clean-up to become a template for in vitro transcription in a reaction containing biotin-modified UTP and T7 RNA polymerase. To maximise biotin-labeled aRNA yield, this kit uses an optimised mixture of biotin-labeled and unlabeled nucleotide tri-phosphates (NTPs) and the proprietary MEGAscript® in vitro transcription technology to generate hundreds to thousands of antisense RNA copies (or aRNA, also commonly called cRNA) of each mRNA in a sample. The biotin-labeled aRNA is then purified and after fragmentation is suitable for hybridisation to Affymetrix GeneChip® Arrays (Santa Clara, CA, USA).

54 It should be noted that the MessageAmp™ II-Biotin Enhanced Single Round aRNA Amplification Kit (Ambion, Catalogue No. AM1791), contains proprietary components and their concentrations. Therefore, despite contacting the manufacturers for more details on the composition and concentrations of kit components, no specific enzyme or buffer concentrations other than the volume of each used can be provided in the following protocols. All steps and centrifugations are performed at RT unless otherwise stated.

2.8.2.2 Target synthesis – RT to synthesize 1st and 2nd strand cDNA To prepare double-stranded cDNA, 1 µg of total RNA was diluted in a maximum volume of 9 µL. Each sample was spiked with a poly-A control, including lys, phe, thr, and dap at a final concentration of 1:100,000, 1:50,000, 1:25,000, and 1:7,500 (ratio of copy number), to monitor the labeling process. The controls, lys, phe, thr, and dap, are B. subtilis genes that have been modified by the addition of poly-A-tails (Affymetrix GeneChip® Poly-A control kit, Affymetrix, Santa Clara, CA, USA). One microlitre of T7 oligo(dT) primer was added to each tube and incubated at 70°C for 10 minutes. The reaction was then cooled on ice and RT was performed by incubation at 42°C for 2 hours with each reaction containing 1 µL of ArrayScript, in a final volume of 20 µL reverse transcription master mix (see Appendix 1, Table A1- 2 for components). The contents of tubes were then spun down and tubes placed on ice for at least 5 minutes before proceeding. Second strand cDNA was then synthesized by adding 80 µL of second strand master mix (see Appendix 1, Table A1-3 for components) and incubating for 2 hours at 16°C.

2.8.2.3 cDNA purification To purify cDNA for the following reactions, 250 µL of cDNA binding buffer was added to each sample, mixed thoroughly, and loaded onto the center of a cDNA filter cartridge. The filter cartridge was then centrifuged at ~ 10,000 g for 1 minute and the flow-through discarded. The filter was then washed with 500 µL of wash buffer and centrifuged for a further 1 minute. The flow through was again discarded and the filters spun for another 1 minute to remove residual wash buffer. The cDNA filter was then transferred to a clean elution tube. Twelve microlitres of pre-heated (50°C) nuclease-free water was applied to the center of each filter and left to stand at RT for

55 2 minutes. The filters were then centrifuged for 90 seconds to elute the cDNA. The elution process was then repeated with a second 12 µL volume of pre-heated nuclease-free water, for a final total elution volume of ~ 20 µL. The samples were then placed on ice before proceeding to In vitro transcription (IVT) procedures.

2.8.2.4 IVT to synthesize biotin-labelled aRNA IVT master mix was prepared at RT (see Appendix 1, Table A1-4 for components) and 20 µL was transferred to each 20 µL purified cDNA sample. The tubes were mixed gently by pipetting, spun down briefly and then incubated at 37°C for 14 hours to allow the IVT reaction to proceed. This reaction mixture includes biotin- labeled UTP which becomes incorporated into the newly synthesized aRNA, producing biotin-labeled aRNA. The next day the IVT reactions were stopped by adding 60 µL of nuclease-free water to each sample, producing 100 µL aRNA samples.

2.8.2.5 aRNA purification To purifiy aRNA, 350 µL of aRNA binding buffer was added to each sample and mixed briefly before adding 250 µL of 100% ethanol and pipette mixing. Each sample was then applied to an aRNA filter cartridge and centrifuged for 1 minute at ~ 10,000 g. The flow-through was discarded and the filters were washed with 650 µL of wash buffer, centrifuged again and the flow-through discarded. An additional centrifugation was performed to remove any residual wash buffer and the filters were transferred to clean elution tubes. One hundred microlitres of pre-heated nuclease- free water was added to the center of each filter and left to stand at RT for 2 minutes. The filters were then centrifuged for 90 seconds to elute the aRNA. Two microlitres of the purified aRNA samples were then assessed by agarose gel electrophoresis and UV spectrophotometry to check the quality and quantity of the biotin-labeled aRNA. The samples were then separated into smaller aliquots to avoid freeze thawing of aRNA samples and stored at -80°C.

2.8.2.6 Fragmentation of biotin-labelled aRNA Biotin-labeled aRNA has to be fragmented before hybridisation to Affymetrix GeneChip® Arrays. To have an adequate amount of aRNA to hybridise to both Test3

56 Arrays and GeneChip® Arrays, 20 µg of biotin-labeled aRNA was fragmented. Therefore, 20 µg of biotin-labeled aRNA was diluted to a maximum volume of 32 µL. Eight microlitres of 5 X fragmentation buffer was then added to each sample and incubated at 94°C for 35 minutes. One microlitre of each reaction was then run on a 1.5% agarose gel to confirm fragmentation of aRNA (30-200bp smear).

2.8.3 GeneChip® hybridisation and scanning

2.8.3.1 Affymetrix GeneChip® Human Genome U133 Plus 2.0 (HG-U133 Plus 2.0) array The HG-U133 Plus 2.0 array is one microarray comprised of 1,300,000 unique oligonucleotide features covering over 47,000 transcripts and variants, representing approximately 39,000 of the best characterized human genes (54,675 individual probe sets). The majority of the probe sets used in the design of the array are selected from GenBank®, dbEST, and RefSeq. Sequence clusters are created from Build 133 of UniGene (April 20, 2001) and refined by analysis and comparison with a number of other publicly available databases, including the Washington University EST trace repository and the University of California, Santa Cruz Golden-Path human genome database (April 2001 release). Oligonucleotide probes are synthesized in situ complementary to each corresponding sequence. Eleven pairs of oligonucleotide probes are used to measure the level of transcription of each sequence represented. Additional features of the HG-U133 Plus 2.0 array include: 11 μm feature size, hybridization controls (bioB, bioC, bioD, cre), poly-A-controls (dap, lys, phe, thr), GAPDH and β-actin housekeeping/control genes and an additional 100 probe sets used for normalization controls. For full details see www.affymetrix.com.

2.8.3.2 Hybridisation Fifteen micrograms of fragmented aRNA probe samples were diluted to a total volume of 300 µL with 1 X hybridisation buffer (100 mM MES, 1 M NaCl, 20 mM EDTA and 0.01% Tween-20), in the presence of 50 pM control oligonucleotide B2, 0.1 mg/ml Herring Sperm DNA, 0.5 mg/ml acetylated BSA. A eukaryotic hybridization control mix was used at 1.5, 5, 25, and 100 pM for bioB, bioC, bioD, and cre, respectively. The hybridisation cocktail was then heated at 99°C for 5 min

57 and immediately further incubated at 45°C for 5 minutes. The samples were then centrifuged for 5 minutes at maximum speed in a bench-top microfuge prior to hybridisations. All hybridisations were performed for 16 hours at 45°C in a rotary incubator (60 rpm). To test the quality of fragmented aRNA and assess hybridisation performance, a preliminary hybridisation of each sample to Affymetrix Test3 Array chips was performed. Samples which passed specific performance parameters, described below, were subsequently hybridised to HG-U133 Plus 2.0 Arrays following an identical protocol.

2.8.3.3 Washing and scanning of arrays Following hybridisation, samples were removed and arrays equilibrated to room temperature before washing and staining on the Affymetrix Fluidics Station 400 operated by the GeneChip Operating Software (GCOS) version 1.4. Affymetrix Test3 and HG-U133 Plus 2.0 Arrays were washed and stained following the Mini_euk2v3 and EukGE-WS2v5 fluidic protocols, respectively (for details see: http://www.affymetrix.com/support/technical/fluidics_scripts.affx). Probe arrays were then scanned with an Affymetrix GeneChip® Scanner 3000 at a wavelength of 570nm. Each array was scanned twice to increase the reproducibility and accuracy of probe intensity measurements. The data for each GeneChip® was then processed using GCOS.

2.8.3.4 Processing and quality control (QC) using GCOS Cell summary reports and single-array expression analysis were performed for scanned images to assess the performance of the hybridisation and grid alignment of arrays. For single-array expression analysis, each image file (CEL file) for individual samples was scaled to a target signal of 150 across all probe sets. For each transcript represented on the probe array, the algorithm computes a Detection call, Detection p- value, and signal. The image and data quality was then evaluated using a series of quality control parameters associated with assay and hybridisation performance which ensure comparability of samples across arrays. These include the: • average background (should be within 20 - 100); • the percentage of “present” calls (should be within 10% for all samples);

58 • 3’/5’ ratio of β-actin and GAPDH housekeeping genes (ideally 1, but below 3 is acceptable); • scale factor (SF) was also assessed to determine the compatibility of individual arrays for subsequent multiple-array comparisons (samples within 3-fold of each other); • “present” calls and increasing signal intensity of hybridisation controls, bioB, bioC, bioD, and cre; and • “present” calls and increasing signal intensity of “spiked” poly-A controls, lys, phe, thr, dap. The integrity of total RNA, the performance of cRNA labeling and microarray hybridization, and quality of the microarray data was assessed according to the procedures outlined in Guidelines for Assessing Data Quality, contained within the Affymetrix Data Analysis Fundamentals Manual, www.affymetrix.com/support/technical/manual/expression_manual.affx, as briefly summarised in Appendix 2, Table A2-1 to A2-3. All HG-U133 Plus 2.0 arrays hybridised passed performance parameters and were used in subsequent analysis.

2.9 Microarray data analysis

2.9.1 Data analysis and presentation using GeneSpring GX 7.3 The CEL files containing individual raw array data (probe intensities) were imported to GeneSpring GX 7.3 and pre-processed using Robust Multi-chip Average, with GC-content background correction (GC-RMA). Data which had been normalised with GC-RMA was then further normalised using the ‘per gene normalisation’ step in which all the samples were normalised against the median of the control samples (No Treatment). Therefore, the expression value for one gene across the different conditions is centred on 1, by dividing the expression value by the median expression value for that gene across the conditions (Treatments). This ensures that genes which do not change across conditions received a normalised expression value of 1, allowing for easier visual detection of differentially expressed genes. Statistically significant differentially expressed genes were then identified from pair wise comparisons using a fold change threshold of 1.8 and one-way ANOVA (parametric test, assuming unequal variances) with ‘Benjamini and Hochberg false

59 discovery rate’ (BHFDR) as the multiple testing correction (p = 0.05). Therefore, lists of probe sets were generated whose expression was significantly increased or decreased by at least 1.8-fold.

2.9.2 Gene ontology, canonical pathway, and functional network analysis Gene ontology, canonical pathway, and functional network analyses were undertaken using Ingenuity Pathway Analysis (IPA) tools (Ingenuity® Systems, www.ingenuity.com). The program is a web-delivered application that enables the discovery, visualisation, and exploration of molecular interaction networks in gene expression or proteomic data sets. A data set containing gene identifiers and corresponding expression values of probe sets determined to be significantly expressed by at least +/- 1.8-fold in response to IGF-I:IGFBP-5:VN complexes compared to VN alone, was uploaded into the application. Each gene identifier was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base. The IPA Knowledge Base has been described in detail (Calvano et al. 2005). Briefly, functions of, and interactions between, genes/gene products are mined from the peer-reviewed literature and encoded into an ontology by post-doctoral level scientists. Genes which map to the IPA knowledge Base or so-called “focus genes” are then used as the starting point for generating biological networks. Networks of focus genes were then algorithmically generated based on their connectivity. IPA computes a score for each network according to the fit of the original set of significant genes. This score reflects the negative algorithm of P that indicates the likelihood of the focus genes in a network being found together due to random chance. A score of 3 indicates a 1 in 1000 chance that the focus genes are together in a network by random chance. Therefore, scores of 3 or higher have a 99.9% chance of not being generated by random chance alone. This score was used as the cut-off for identifying gene networks significantly affected by IGF-I:IGFBP-5:VN complexes. Networks are ranked and a graphical representation of the molecular relationships between genes/gene products is generated. The functional analysis also identified biological functions and/or diseases that were most significant to the data set. Fischer’s exact test was used to calculate a p-value determining the probability that each biological function and/or disease assigned to that data set is due to chance alone. The same statistical approach was used to determine the Canonical pathways which were most significant to the data set.

60

2.9.3 Kyoto Encyclopedia of Genes and genomes (KEGG) pathway analysis using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online functional annotation tool To confirm well established functional pathways over-represented by differentially expressed genes, a gene list containing genes differentially expressed by at least +/- 1.8-fold was uploaded into the DAVID online program (http://david.niaid.nih.gov/david/ease.htm). Input genes were then allocated on static KEGG pathway maps. Only KEGG pathways containing 5 or more input genes and an expression analysis systematic explorer (EASE) score of less than 0.05 were considered to be over-represented. The theoretical basis and algorithm of EASE has been fully described previously (Hosack et al. 2003).

2.10 Confirmation of differential gene expression using quantitative real-time RT-PCR (qRT-PCR)

2.10.1 Standard PCR conditions All PCR reactions were performed using the Platinum Taq PCR kit (Invitrogen). The standard reaction conditions included: 1 U Platinum Taq, 1.5 mM MgCl2, 0.2 mM of each dNTP, 0.2 μΜ forward and reverse primers and 0.5 μL cDNA template in a total volume of 25 μL. PCR reactions were then run on a MJ Research Thermocycler (Geneworks, Hindmarsh, SA) with the following temperature cycling conditions: 94°C for an initial 2 minutes, then 35 cycles of 94°C for 30 seconds, 55°C for 30 seconds and 72°C for 1 minute. Amplification of PCR products of the correct size were then confirmed with agarose gel electrophoresis and ethidium bromide/UV visualization.

2.10.2 Primer design All primers were designed using primer express version 2.0 software (Applied Biosystems, Scoresby, VIC). Primer express default parameter settings were used to define acceptable primer sets which included: minimum primer melting temperature

(Tm) of 57°C, maximum of 63°C and optimal Tm of 60°C, with no more than 2°C difference in Tm between primers; GC content between 45-55%; primer length

61 between 18-22 bases; minimum amplicon Tm of 75°C and maximum of 85°C; with an amplicon length between 100 and 200 base pairs.

2.10.3 Reverse transcription (RT) for qRT-PCR First strand cDNA synthesis was performed with the Superscript III first-strand cDNA synthesis kit (Invitrogen) to reverse-transcribe total RNA in 20 μL reactions. One microgram of total RNA, from the samples which were hybridized to the microarrays, was added to 200 ng random hexamers and diluted to 13 μL with water. This was then incubated at 65ºC for 5 minutes, followed by incubation at room temperature for 5 minutes before proceeding. First strand synthesis was carried out using 200 U SuperScript III reverse transcriptase (Invitrogen), 10 mM DTT, 0.5 mM dNTPs, 1 X first strand buffer (250 mM Tris-Cl pH 8.3, 375 mM KCl, 15 mM

MgCl2) to a final volume of 20 μL. Each reaction was then left at R/T for 5 minutes, before incubating at 50ºC for 60 minutes and 70ºC for 15 minutes to inactivate the SuperScript III enzyme. The resulting cDNA samples were then either stored at - 80ºC or diluted 1:10 with sterile nuclease free water for qRT-PCR analysis. All cDNA samples were checked for genomic DNA contamination by PCR amplification of a positive control, the β2-microglobulin gene, using primers (β2-F:

5’–TGAATTGCTATGTGTGTCTGGGT-3’, β2-R:- CCTCCATGATGCTGCTTACAT–3’, annealing temperature 55ºC) which span a 615 base pair (bp) intron. The expected specific PCR product size is 250 bp and can be easily distinguished with agarose gel electrophoresis from an additional 864 bp product if genomic DNA contamination was present in the cDNA sample.

2.10.4 PCR and amplicon purification PCR was performed as described in section 2.10.1 using Platinum Taq with sequence specific primers detailed in Table 2.1, for each transcript to be analysed by qRT-PCR. PCR amplicons were electophoresed on 2% agarose gels and visualized with ethidium bromide staining under UV illumination. The band of interest was then excised from the agarose gel using a sterile scalpel blade. PCR amplicons were then purified from the agarose using the WIZARd PCR product Purification kit according to the manufacturers instructions (Promega). PCR products were then quantified by UV spectrophotometry at 260 nm and the yields converted to absolute

62 cDNA transcript copy numbers. cDNA copy numbers were determined based on 1 DNA bp having a molecular mass of 660 g/n and calculated per μL of cDNA sample.

Table 2.1: Primers used for qRT-PCR

Affymetrix Gene Sequence GeneBank Amplicon probe Symbol Accession # Position identifier 204363_at F3 F: TTACCTGGAGACAAACCTCGGA NM_001993 525-659 R: AAAACATCCCGGAGGCTTAGG 213258_at TFPI F: ACTGAAACCTCCCACCTAGCTG BF511231 2738-2869 R: GGAAGATTGCTTGAGCCCA 217028_at CXCR4 F: TCCTCTATGCTTTCCTTGGAGC AJ224869 1253-1373 R: ACAGATGAATGTCCACCTCGC 204686_at IRS-1 F: TCATTGACTGAACTGCACGTTC NM_005544 4930-5091 R: AAACCCATTCTCTCATGACACG 204359_at FLRT2 F: CCATATGCTGATTTGCTTCTGG NM_013231 6996-7100 R: AAGTGTGCGACATGGTAGGTCT 1568765_at Serpine1 F: CCCCATCACCCAGTAACAAGAA BC020765 336-455 R: CTCTTAATGCTTTCCCAGCGAT 218182_s_at CLDN1 F: TGAAGTGCTTGGAAGACGATGA NM_021101 534-708 R: TGACCAAATTCGTACCTGGCA 204005_s_at PAWR F: CCACCTAGAACAGTTTCAGGCA NM_002583 938-1068 R: GTACCTGAAACATTTGCATCCC 201170_s_at BHLHB2 F: CAACGGCATATGGAGTGTCCT NM_003670 2444-2593 R: CACGATCAGCAATCAGGCAT

218002_s_at CXCL14 F: ATGAAGCCAAAGTACCCGCAC NM_004887 631-773 R: TTCCAGGCGTTGTACCACTTG 209260_at SFN F: ACTCTTCTTGC AGCTGTTGAGC BC000329 990-1140 R: TCAATTCCTACGATCAGAGGCA 202668_at EFNB2 F: AGAGTTCCCTGCAACCAATTG BF001670 3587-3730 R: GACGATCATACAAGCAAGGCAT 218559_s_at MAF-B F: TGGCATCAGAACTGGCAATGA NM_005461 2190-2345 R: CTGCACGGCGTGCTCATG

2.10.5 qRT-PCR qRT-PCR was used to validate microarray expression data by measuring absolute expression levels of selected genes of interest. Primer Express (Applied Biosystems) was used to design all primers used in qRT-PCR as already outlined in Table 2.1. Absolute quantification was carried out using standard curves covering at least 6 logs of amplicon copy number, generated by 10-fold serial dilutions of purified PCR target amplicons. All reactions were done in triplicate in 20 μL volumes in a 96-well format using SYBR green (Applied Biosystems) and an ABI Prism 7300 Sequence

63 Detection System (Applied Biosystems). Reactions contained 1 X SYBR-green PCR mix, 0.25 μM of each forward and reverse primers and 2.5 μL of the cDNA dilutions. PCR amplification followed a two step cycling protocol with an initial 10 minute denaturation at 95 ºC, with 40 cycles of 95 ºC for 15 seconds and 60 ºC for 1 minute. All real-time reactions included a post-amplification melt curve analysis to

determine the melting temperature (Tm) of the amplified PCR product, indicating amplification of the correct sequence. Real-time curves were analysed with ABI Sequence Detection System software version 1.2 (Applied Biosystems) using the automatic option for baseline and threshold values. The software determines the PCR cycle at which each reaction reached its log-linear phase and is directly proportional to the amount of starting cDNA transcript. The cDNA copy number for each reaction was then calculated by direct comparison to the known standards for each gene, which are run concomitantly. Target gene expression for each sample was then

normalised to 18S rRNA (F: 3’-TTCGGAACTGAGGCCATGAT-5’; R: R: 3’- CGAACCTCCGACTTTCGTTCT-5’) .

2.11 Statistical Analysis Data are expressed as a percentage of the response observed in wells containing VN alone, unless otherwise stated. The data was pooled from multiple experiments with each treatment tested in at least duplicate wells in each experiment. Data analysis was performed using one-way ANOVA with Tukeys post hoc tests unless otherwise stated. Statistically significant differences were considered to be present at p<0.05.

64

CHAPTER 3

Investigations into the functional responses of breast cells to substrate-bound IGF-I:IGFBP:VN complexes

65 3.1 INTRODUCTION The primary tumour is rarely the cause for the high mortality associated with breast cancer, which rather, arises from the metastatic dissemination of malignant cells and their establishment in critical sites in the body. Unrestrained cancer cell proliferation does not by itself appear to lead to metastasis. Instead, metastatic dissemination depends on the ability of tumour cells to invade tissue boundaries, migrate and survive in secondary target tissues (Bogenrieder and Herlyn 2003). Two factors thought to be pivotal in breast cancer metastasis are exposure to elevated levels of mitogenic hormones and growth factors, and altered cellular interactions with the extracellular matrix (ECM). Insulin-like growth factor-I (IGF-I) is one such mitogenic growth factor which has been shown to play critical roles in breast cancer biology and as such is a target for novel therapeutic applications (Sachdev and Yee 2006).

As discussed in Chapter 1, IGF-I is a potent mitogen involved in normal growth and development and carries out its diverse biological actions through endocrine, paracrine and autocrine mechanisms (Wood 1995). In addition to its role in normal development, IGF-I can also mediate growth, metastasis and apoptosis in cancer (Samani et al. 2007). The actions of IGF-I are mediated primarily through the IGF- 1R and activation of this receptor can modulate processes such as DNA synthesis, cell cycle progression, differentiation, angiogenesis, apoptosis, invasion and migration (Baserga 1999). IGF-I also interacts with six IGF-binding proteins (IGFBPs 1-6), which bind IGF-I with a higher affinity than the IGF-1R, increasing the in vivo half life of IGF-I and also modulating its specificity and availability to bind the IGF-1R (Firth and Baxter 2002). In addition to IGFBPs, ECM proteins have also been shown to modulate cellular responses to IGF-I (Nam et al. 2000; Woodward et al. 2000; Maile et al. 2006). Indeed, growth factor:ECM interactions are critical for most processes essential to cancer cell metastasis, namely cell attachment, proliferation, migration, differentiation, cell survival and angiogenesis (Giancotti and Ruoslahti 1999).

Recent studies within our laboratory are among the few that have investigated the functional effects of growth factors and ECM proteins when presented to cells as a substrate-bound complex (Kricker et al. 2003; Noble et al. 2003; Hyde et al. 2004;

66 Hollier et al. 2005). Thus, we initially discovered that IGF-II binds to the ECM protein vitronectin (VN) (Upton et al. 1999). Following this, novel links between IGF-I, IGFBPs and VN were also identified. Specifically, our laboratory has shown that while IGF-II can bind directly to VN, IGF-I associates with VN indirectly through IGFBP-2, -3, -4, and -5 (Kricker et al. 2003). These recent studies demonstrating that IGFs and IGFBPs can bind to VN have led to the key hypothesis underlying this thesis; namely, that this interaction will be important in breast cancer biology where components of the IGF system play crucial roles.

It has already been reported that the interaction of IGF-II with VN significantly enhances the migration of the poorly metastatic MCF-7 breast carcinoma cell line (Noble et al. 2003). More recently IGF-II:VN and IGF-I:IGFBP-5:VN complexes have been demonstrated to enhance the proliferation and migration of HaCAT human skin keratinocytes (Hyde et al. 2004). However, to date investigations into the effects of IGF-I:IGFBP:VN complexes in modulating functional responses of breast cancer cells that are relevant to metastasis in vivo are limited. The studies outlined in this chapter aimed to characterize the functional consequences of the interaction of IGFs with VN in breast cell lines which differ in their tumourgenic and invasive capacity. As such, the following experiments were designed to assess MCF- 7 breast cancer cell function, namely: 1) cell proliferation; 2) cell attachment; and 3) cell migration in response to pre-bound combinations of VN, IGFBP-3, IGFBP-5 and IGF-I. Furthermore, as IGF-I has an important role in normal mammary gland development, with many of these processes reflected in breast cancer progression (Hadsell 2003), the responses of the “normal” non-tumourgenic MCF-10A breast epithelial cells were also examined for comparative purposes.

3.2 EXPERIMENTAL PROCEDURES Full details of the materials and methods used in experimental procedures for this chapter are described in Chapter 2. The following is a brief summary of the materials and procedures used to generate the data presented in sections 3.3.1 - 3.3.7.

67 3.2.1 Materials Purified human VN was purchased from Promega Corporation. IGFBP-5 was produced as described previously by Firth et al. (1999) and purchased from Dr Sue Firth, Kolling Institute of Medical Research (University of Sydney, NSW, Australia), while human receptor grade IGF-I was purchased from Novozymes. IGFBP-3 (N109D) was purchased from Upstate Biotech. Type IV Collagen (COL IV), Fibronectin (FN) and 1-(4,5-Dimethylthiazol-2-yl)-3,5-diphenylformazan (MTT) reagent were all purchased from Sigma. Fraction V RIA grade Bovine Serum Albumin was supplied by Sigma and Calbiochem, while [3H]-leucine was purchased from Amersham Biosciences (now GE Healthcare). The alpha/beta integrin- mediated cell adhesion array was purchased from Chemicon International and 12- well Transwells® were purchased from Corning Corporation. All other plastic cultureware was purchased from Nalge Nunc International.

3.2.2 Cell lines In the following studies, responses of MCF-7 breast carcinoma cells were examined as a model for breast cancer. MCF-7 cells express relatively high levels of the IGF- 1R, display a number of VN-binding integrins and are poorly metastatic, making them an ideal cell line to investigate the effects of IGF-I:IGFBP:VN complexes on breast cancer cell function (Meyer et al. 1998; Bartucci et al. 2001). Functional responses in the phenotypically “normal”, tissue-derived MCF-10A mammary epithelial cell line were also investigated to allow comparison of the effects of IGFs bound to VN in a non-invasive, non-metastatic cell line. In more specific studies, the metastatic MDA-MB-231 cell line and MCF-7 cells stably transfected with the αvβ3 integrin (MCF-7-β3) were also used.

3.2.3 Treatment strategy for in vitro assays In vivo it is thought that cells within organized tissues respond to growth factors bound within the ECM. Therefore, in an effort to more accurately mimic the in vivo cellular environment this study adopted the strategy of “pre-binding” growth factors and VN to polystyrene cultureware and to the lower chamber and membrane surfaces of 12-µm pore Transwells®. Hence tissue culture wells and Transwell® chambers were pre-coated with select combinations of VN, IGFBP-3 or -5 and IGF-I using procedures previously reported by our laboratory (Kricker et al. 2003; Noble et al.

68 2003; Hyde et al. 2004; Hollier et al. 2005), forming what we term substrate-bound complexes. It should be noted that in all the following experiments, treatments were only tested in the presence of VN as both cell lines migrate poorly in the absence of serum and VN (data not shown). As migration was the main focus of these studies, the same treatments were also used for proliferation and attachment assays.

3.2.4 Pre-binding of proteins For details regarding the approach used to pre-bind proteins to 96-well plates for use in proliferation and 24-well plates for attachment assays, please refer to section 2.4.2. Transwell® inserts were prepared by pre-binding the lower well and under- surface of 12-µm pore membranes with proteins as described in section 2.4.3. For all functional assays described throughout this chapter, concentrations of proteins added into the culture wells for the pre-binding steps were as follows: VN, 1 μg/mL; IGF-I, 10 – 100 ng/mL; IGFBP-3/-5, 30 – 300 ng/mL; COL IV, 1 μg/mL; and FN, 1 μg/mL.

3.2.5 Proliferation assays Cellular proliferation was assessed using the CellTiter 96® Aqueous One Solution Cell Proliferation Assay (Promega). The use of MTS reagents to measure proliferation has previously been demonstrated to correlate well with methods using [H3]-thymidine incorporation (www.promega.com/tbs/tb112/tb112.pdf). For full details of this method please refer to section 2.5.1. Briefly, 5 x 103 cells that had been serum-starved by incubation in serum-free and phenol red-free DMEM/F12 (DMEM/F12-SFM) for 4 hours were harvested by trypsinisation and seeded into each well of pre-coated 96-well plates in 100 µL of DMEM/F12-SFM. The cells were then incubated for 72 hours at 37ºC, 5% CO2, after which 20 µL MTS solution was added to each well. The plates were then incubated for 2 hours at 37°C, 5% CO2 to allow for color development and the absorbance was then recorded at 490 nm using a 96-well plate reader (Benchmark Plus, Bio-Rad). The quantity of formazan product, as measured by the amount of 490 nm absorbance, is directly proportional to the number of viable cells in culture (Cory et al. 1991).

3.2.6 Attachment assays

69 For full details of this method please refer to section 2.5.2. MCF-7 and MCF-10A cells were grown in normal growth medium for 18 hours in the presence of 2 µCi/mL of [3H]-leucine (Amersham Biosciences, Castle Hill, NSW, Australia), thus allowing the cells to be labelled via the incorporation of [3H]-leucine into newly synthesized protein. Following serum-starvation for 4 hours, the cells were harvested by trypsinisation and 1 x 105 cells were seeded into pre-coated wells of 24-well

plates and incubated for 4 hours at 37ºC, 5% CO2 to allow cell attachment. After washing and trichloroacetic acid precipitation of cellular protein, cellular attachment was determined by sub-sampling solubilized protein precipitate for β-scintillation counting.

3.2.7 Transwell® migration assays For full details of this method please refer to section 2.5.4. Briefly, cells which had been serum-starved for four hours were harvested and seeded at a density of 2 x 105 cells/well in DMEM/F12-SFM + 0.05% BSA into the upper chamber of pre-coated Transwell® inserts (12-µm pores). Plates containing the Transwell® inserts were then

incubated for 5 hours at 37°C, 5% CO2. The cells which had migrated to the lower surface of the membrane were fixed in 3.7% para-formaldehyde and stained with 0.01% Crystal Violet in phosphate buffered saline (PBS). The number of cells which had migrated to the lower surface of the porous membrane was then quantified by extracting the crystal violet stain in 10% acetic acid and determining the optical density of these extracts at 595 nm.

3.2.8 Confirmation of αvβ3 integrin expression To confirm the expression of the αvβ3 integrin in the MDA-MB-231 and MCF-7- β3 cell lines an alpha/beta integrin-mediated cell adhesion array was used. For details of this protocol please refer to section 2.5.5.

3.2.9 Statistical analysis Data are expressed as a percentage of the response observed in wells containing VN alone, unless otherwise stated. The data was pooled from multiple experiments with each treatment tested in at least duplicate wells in each experiment. Data analysis

70 was performed using one-way ANOVA with Tukeys post hoc tests, unless otherwise stated. Statistically significant differences were considered to be present at p<0.05.

71 3.3 RESULTS 3.3.1 Investigations into the effects of substrate-bound and solution–phase IGF- I:IGFBP complexes on breast cell proliferation We have previously shown that IGF-II bound to VN does not elicit significant changes in protein synthesis in MCF-7 cells above that induced by VN alone (Noble et al. 2003). However, little is known about the effect complexes containing IGF-I bound to VN via either IGFBP-3 or IGFBP-5 has on breast cell proliferation. As such, the effects of IGF-I:IGFBP:VN complexes on MCF-10A and MCF-7 cellular proliferation were assessed. The responses of serum-starved MCF-10A and MCF-7 cells were investigated over 72 hours after seeding the cells into 96-well culture plates pre-coated with 100 µL volumes containing 10 – 100 ng/mL of IGF-I (1 – 10 ng/well), 30 – 300 ng/mL of IGFBP-3 or IGFBP-5 (3 – 30 ng/well) and 1 µg/mL of VN (100 ng/well), thus forming substrate-bound complexes. For comparative purposes the effect of solution-phase treatments was also investigated by adding IGF-I and IGFBPs directly into the media of VN-coated wells immediately prior to seeding the cells.

VN-bound IGF-I:IGFBP complexes were found to be potent stimulators of MCF- 10A cellular proliferation with the two highest concentrations of IGF-I bound to either IGFBP-3 or IGFBP-5 stimulating significant increases in cellular proliferation (p<0.05) (Figure 3.1.1 A). The addition of 30 and 100 ng/mL of IGF-I in combination with either IGFBP-3 or IGFBP-5 (forming IGF-I:IGFBP-3:VN and IGF-I:IGFBP-5:VN complexes, respectively) significantly enhanced the proliferation of MCF-10A cells on VN (p<0.05). These responses ranged from 226.4 ± 25.8% (30 ng/mL) to 309.3 ± 25.4% (100 ng/mL) of VN controls for IGF-I:IGFBP-3:VN complexes and 197.4 ± 26.9% (30 ng/mL) to 270.1 ± 36.6% (100 ng/mL) of VN controls for VN:IGFBP-5:IGF-I complexes (p<0.05) (Figure 3.1.1 A). Importantly, these responses were substantially greater than those obtained with the corresponding concentrations of either IGFBP or IGF-I in combination with VN (Figure 3.1.1 A). The addition of 10 ng/mL of IGF-I in combination with IGFBP-3 or IGFBP-5 was observed to increase proliferation of MCF-10A cells but not to statistically greater levels than VN.

72 A) Substrate-bound † 350 All with VN * † * 300 † † 250 * * 200

150

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% stimulation of control(VN only) 50

0 30 90 300 30 90 300 10 30 100 30 90 300 30 90 300 10 30 100 10 30 100 SFM BP-3 BP-5 IGF-I BP-3 BP-5 IGF-I IGF-I

B) Solution-phase

350 All with VN

300 * * * * 250 * *

200 * *

150

100

% stimulationof control (VN only) 50

0 30 90 300 30 90 300 10 30 100 30 90 300 30 90 300 10 30 100 10 30 100 SFM BP-3 BP-5 IGF-I BP-3 BP-5 IGF-I IGF-I

Figure 3.1.1: Effect of IGF-I:IGFBP:VN complexes on MCF-10A cellular proliferation. Responses of MCF-10A cells seeded into 96-well plates pre-coated with combinations of IGF-I and IGFBP-3 or -5 pre-bound to VN (A) or IGF-I and IGFBP-3 or -5 added in solution to VN-coated wells (100 ng/well) (B). The data are expressed as the average percent stimulation of control wells containing VN alone (SFM). The data are pooled from three experiments with treatments tested in triplicate wells in each replicate experiment. The asterisk indicates treatments which significantly increased or decreased proliferation compared to the response of VN only wells (p<0.05). In some instances IGF-I:IGFBP:VN complexes were also observed to increase proliferation above the individual components of the complex (†)(p<0.05). Error bars indicate SEM. SFM = serum-free media. Treatments were applied in a final volume of 100 µL containing the indicated concentrations of IGF-I, IGFBP-3/-5 (ng/mL) and VN (1 µg/mL).

73 For comparative purposes and to determine the potency of substrate-bound complexes, the responses of cells to solution-phase IGF-I and IGFBPs was also examined. In these assays, factors remain in solution throughout the 72 hour period. In contrast to the substrate-bound treatments, solution-phase IGF-I in combination with VN was observed to significantly increase proliferation at the two highest concentrations tested, with responses of 222.7 ± 31.7% (30 ng/mL) and 247.0 ± 20.6% (100 ng/mL) of VN control wells observed (p<0.05) (Figure 3.1.1 B). Importantly, all concentrations of IGF-I in combination with either IGFBP-3 or IGFBP-5 stimulated significant increases in cellular proliferation (p<0.05) (Figure 3.1.1 B). These responses ranged from 184.8 ± 20.8% to 288.7 ± 15.6% of the VN controls for IGF-I:IGFBP-3:VN complexes and 179.3 ± 23.9% to 253.7 ± 19.4% of the VN controls for VN:IGFBP-5:IGF-I complexes (p<0.05) (Figure 3.1.1 B). However, the response of MCF-10A cells to solution-phase IGF-I:IGFBP:VN complexes was similar to levels induced by the corresponding concentration of solution-phase IGF-I in the presence of VN (Figure 3.1.1 B). Interestingly, substrate- bound and solution-phase IGF-I:IGFBP:VN complexes were observed to stimulate comparable overall levels of cellular proliferation in MCF-10A cells (Figure 3.1.1 A and B).

In MCF-7 cells, all concentrations of IGF-I pre-bound with either IGFBP-3 or IGFBP-5 stimulated significant increases in cellular proliferation (p<0.05) (Figure 3.1.2 A). While overall these treatments were not quite as potent stimulators of proliferation as seen in MCF-10A cells, responses in MCF-7 cells ranged from 150.3 ± 7.4% to 198.4 ± 3.6% of VN controls for IGF-I:IGFBP-3:VN complexes, and 137.4 ± 6.0% to 189.6 ± 3.9% of VN controls for IGF-I:IGFBP-5:VN complexes (p<0.05) (Figure 3.1.2 A). Furthermore, these responses were significantly greater than those stimulated by the corresponding concentrations of IGFBP-3 or IGFBP-5 and IGF-I in combination with VN (Figure 3.1.2 A).

In solution-phase treatments, all concentrations of IGF-I were observed to significantly increase the proliferation of MCF-7 cells in combination with VN, with responses ranging from 161.3 ± 7.6% to 170.5 ± 7.8% of VN controls (Figure 3.1.2 B). In a similar fashion, all concentrations of IGF-I tested in the presence of either IGFBP-3 or IGFBP-5 stimulated significant increases in cellular proliferation

74 (p<0.05) (Figure 3.1.2 B). These responses ranged from 170.2 ± 5.8% to 182.1 ± 9.4% of VN controls for IGF-I:IGFBP-3:VN complexes, and 165.2 ± 9.2% to 185.1 ± 5.8% of VN controls for VN:IGFBP-5:IGF-I complexes (p<0.05) (Figure 3.1.2 B). As found with results observed in MCF-10A cells, the response of MCF-7 cells to solution-phase IGF-I:IGFBP:VN complexes was similar to the levels induced by the corresponding concentration of solution-phase IGF-I in combination with VN (Figure 3.1.2 B). Moreover, substrate-bound and solution-phase IGF-I:IGFBP:VN complexes stimulated comparable overall levels of cellular proliferation in MCF-7 cells (Figure 3.1.2 A and B), as observed in MCF-10A cells (Figure 3.1.2 A and B). Taken together, these results indicate that substrate-bound IGF-I:IGFBP:VN complexes are potent stimulators of both non-tumourgenic MCF-10A breast epithelial and MCF-7 breast cancer cell proliferation.

75 A) Substrate-bound

225 † All with VN †

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225 All with VN 200 * * * * * 175 * * * *

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50 % stimulation of control (VN only) 25

0 30 90 300 30 90 300 10 30 100 30 90 300 30 90 300 10 30 100 10 30 100 SFM BP-3 BP-5 IGF-I BP-3 BP-5 IGF-I IGF-I

Figure 3.1.2: Effect of IGF-I:IGFBP:VN complexes on MCF-7 cellular proliferation. Responses of MCF-7 cells seeded into 96-well plates pre-coated with combinations of IGF-I and IGFBP-3 or -5 pre-bound to VN (A) or IGF-I and IGFBP-3 or -5 added in solution to VN-coated wells (B). The data are expressed as the average percent stimulation of control wells containing VN alone (SFM). The data are pooled from three experiments with treatments tested in triplicate wells in each replicate experiment. The asterisk indicates treatments which significantly increased or decreased proliferation compared to the response of VN only wells (p<0.05). In some instances IGF-I:IGFBP:VN complexes were also observed to increase proliferation above the individual components of the complex (†)(p<0.05). Error bars indicate SEM. SFM = serum-free media. Treatments were applied in a final volume of 100 µL containing the indicated concentrations of IGF-I, IGFBP-3/-5 (ng/mL) and VN (1 µg/mL).

76 3.3.2 Investigations into the effects of IGF-I:IGFBP:VN complexes on cell attachment VN is abundant in the ECM where it is likely to modulate cellular functions important in both breast cancer cell metastasis and normal breast development. Cellular attachment is one such biological process and VN has been shown to modulate attachment of cells to the ECM via an RGD-dependent interaction with cell surface integrin receptors (Felding-Habermann and Cheresh 1993). In view of this, attachment of MCF-7 and MCF-10A cells was determined 4 hours after seeding on pre-coated culture wells. Experiments were also undertaken where MCF-7 cells were pre-incubated with function blocking antibodies directed toward specific integrin subunits in an effort to determine their relative contribution in breast cancer cell adhesion to VN.

In MCF-10A cells, all treatments which contained IGF-I, either alone or in combination with IGFBP-3 or IGFBP-5, were observed to significantly increase adhesion of cells to VN (p<0.05) (Figure 3.2.1). Surprisingly, IGF-I treatment alone stimulated the highest levels of cellular adhesion to VN with responses of 149.6 ± 8.1% (10 ng), 180.2 ± 7.7% (30 ng) and 167.8 ± 7.3% of the VN alone control wells. Complexes of IGF-I:IGFBP-3:VN, containing 10 or 30 ng of IGF-I, induced significant increases in cell adhesion with responses of 133.2 ± 4.1% and 142.1 ± 2.5 % of the VN alone containing control wells, respectively (p<0.05) (Figure 3.2.1). All IGF-I complexes containing IGFBP-5 also significantly increased adhesion on VN in MCF-10A cells with responses ranging from 130.4 ± 6% to 142.1 ± 6.9% of the VN control wells (p<0.05) (Figure 3.2.1). It was also demonstrated that 30 and 90 ng of IGFBP-3 induced significant increases in cellular adhesion on VN to levels comparable to those observed with IGF-I:IGFBP-3 containing complexes (responses of 139.8 ± 7.5% (30 ng) and 134.1 ± 7.7% (90 ng) of the VN control wells, respectively) (p<0.05) (Figure 3.2.1).

77

225 All with VN 200 * * 175 * * 150 * * * * * * * 125

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50 % stimulation of control (VN only)

25

0 90 30030 90 30030 10 30 100 30 90 300 30 90 300

SFM 10 30 100 10 30 100 BP-3 BP-5 IGF-I 5% HS BP-3 BP-5 ng/well IGF-I IGF-I

Figure 3.2.1: Stimulation of cellular adhesion in MCF-10A cells in response to substrate-bound IGF-I and IGFBP complexes. Responses of MCF-10A cells seeded into 24-well plates pre-coated with combinations of IGF-I and IGFBP-3 or -5 pre-bound to VN (300 ng/well). The data are expressed as the average percent stimulation of control wells containing VN alone (SFM). For comparative purposes 5% Horse Serum in solution (5% HS) was also measured. The asterisk indicates treatments which significantly increased or decreased attachment compared to the response of VN only wells (p<0.05). The data are pooled from three experiments with treatments tested in at least triplicate wells in each replicate experiment. Error bars indicate SEM. SFM = serum-free media.

78 Overall there was less modulation of adhesion to VN by substrate-bound IGF-I and IGFBPs in MCF-7 cells compared to that observed in MCF-10A cells. Only 30 ng of IGFBP-3 alone and 30 ng of IGF-I alone significantly increased cellular adhesion to VN with responses of 139.3 ± 6% and 140.4 ± 7.3% of the VN control wells, respectively(p<0.05) (Figure 3.2.2). Substrate-bound IGF-I:IGFBP complexes had little effect on modulating adhesion to VN, indicating that the binding of IGF-I to VN via IGFBPs does not block or sterically hinder access of cells to the RGD- integrin binding site present in VN.

3.3.3 IGF-I:IGFBP:VN complexes synergistically increase cellular migration in MCF-7 and T47D breast cancer cell lines To determine if IGF-I bound to VN in the presence of IGFBPs could modulate breast cell migration, the lower chamber and underside of 12-µm pore membranes of Transwell® inserts were coated with combinations of IGF-I and IGFBP-3 or -5 pre- bound to VN. The cells were allowed to migrate over 5 hours, after which the number of cells that had migrated to the under surface of the membrane was quantitated. Only treatments pre-bound to VN were tested as all cell lines migrated poorly in the absence of serum and VN (data not shown).

MCF-7 cells express relatively high levels of the IGF-1R, display a number of VN- binding integrins and are poorly metastatic, making them an ideal cell line to investigate the effects of IGF-I:IGFBP:VN complexes on breast cancer cell migration (Meyer et al. 1998; Bartucci et al. 2001). While the VN only control wells were observed to stimulate substantial cellular migration in MCF-7 cells, the addition of IGF-I in combination with either IGFBP-3 or -5 (forming IGF-I:IGFBP- 3:VN and IGF-I:IGFBP-5:VN complexes, respectively) significantly enhanced the migration of MCF-7 cells on VN, (p<0.05) (Figure 3.3.1). The responses ranged from 143.6 ± 4.7% to 173.6 ± 4.3% of the VN controls for IGF-I:IGFBP-3:VN complexes and 164.6 ± 6.3% to 184.8 ± 6% of the VN controls for VN:IGFBP- 5:IGF-I complexes (p<0.05) (Figure 3.3.1). Importantly, these responses were substantially greater than those obtained with either IGFBP or IGF-I alone with VN, indicating that all three components of the complex are required for optimally enhanced cell migration.

79

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10% FBS BP-3 BP-5 ng/well IGF-I IGF-I

Figure 3.2.2: Stimulation of cellular adhesion in MCF-7 cells in response to substrate-bound IGF-I and IGFBP complexes. Responses of MCF-7 cells seeded into 24-well plates pre-coated with combinations of IGF-I and IGFBP-3 or -5 pre-bound to VN (300 ng/well). The data are expressed as the average percent stimulation of control wells containing VN alone (SFM). For comparative purposes 5% Horse Serum in solution (5% HS) was also measured. The asterisk indicates treatments which significantly increased or decreased attachment compared to the response of VN only wells (p<0.05). The data are pooled from three experiments with treatments tested in at least triplicate wells in each replicate experiment. Error bars indicate SEM. SFM = serum-free media.

80

†

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% stimulation of control (VN only) control (VN % stimulation of 25

0 30 90 300 30 90 300 10 30 100 30 90 300 90 30030 30 10010 SFM 10 30 100 BP-3 BP-5 IGF-I BP-3 BP-5 ng/well IGF-I IGF-I

Figure 3.3.1: Effect of substrate-bound IGF-I:IGFBP:VN complexes on MCF-7 cellular migration. MCF-7 cells were seeded into Transwells® that had been coated with VN and increasing concentrations of IGF-I pre-bound in the presence or absence of IGFBP-3 or -5. The cells were allowed to migrate for 5 hours. The number of cells transversing the membrane in response to each treatment was then expressed as a percentage of those that migrated on VN only (SFM). The data are pooled from three experiments with treatments tested in triplicate wells in each replicate experiment. The asterisk indicates treatments which significantly increased migration above the VN only wells (p<0.05). IGF-I:IGFBP:VN complexes were also observed to increase migration above the individual components of the complex (†)(p<0.05). Error bars indicate SEM. SFM = serum-free media.

81 To confirm that the IGF-I:IGFBP:VN complexes are also potent stimulators of poorly metastatic breast carcinoma cell migration, an additional experiment was performed using the T47D cell line. Similar to the responses observed in MCF-7 cells, the IGF-I:IGFBP-3:VN complex was able to induce significantly increased migration above both the VN control wells and IGF-I or IGFBP-3 alone with VN, with a response of 206.2 ± 21.2% of VN only control wells (p<0.05) (Figure 3.3.2). This therefore, confirms an important role for IGF-I:IGFBP:VN complexes in stimulating migration of poorly metastatic breast cancer cells.

3.3.4 IGF-I:IGFBP:VN complexes synergistically increase cellular migration in MCF-10A cells In order to assess whether similar responses occur in normal breast epithelial cells, migration assays were undertaken using MCF-10A cells. While the overall level of migration in terms of cell numbers was reduced in MCF-10A cells, responses were similar to those observed in MCF-7 cells. The complex of IGF-I:IGFBP-3:VN induced significant increases in cell migration ranging from 175.8 ± 8.6% to 194.9 ± 4.5% of the VN alone control wells (p<0.05) (Figure 3.4.1). IGF-I complexes containing IGFBP-5 also significantly increased cell migration on VN in MCF-10A cells with responses of 145.6 ± 6.2 to 183.0 ± 10% (p<0.05) (Figure 3.4.1). As observed in MCF-7 cells, these responses were also significantly greater than the migration induced by IGFBP-3, IGFBP-5 or IGF-I alone with VN (p<0.05) (Figure 3.4.1).

3.3.5 IGF-II:VN complexes also synergistically increase cellular migration in MCF-10A cells Previous studies from our laboratory have demonstrated IGF-II:VN complexes can stimulate increased MCF-7 cell migration (Noble et al. 2003). As IGF-II:VN complexes have not been investigated for their ability to promote cell migration in normal breast cells a preliminary experiment was undertaken to determine the effect of IGF-II:VN complexes on MCF-10A cell migration. It was observed that IGF-II at all concentrations tested stimulated significantly increased migration of MCF-10A cells on VN; IGF-II at concentrations of 100 ng, 300 ng and 1000 ng induced reponses of 183.4 ± 17.8%, 206.4 ± 15.0% and 206.5 ± 15.5% of the VN control wells, respectively (Figure 3.4.2). Therefore demonstrating that IGF-II:VN

82

† 250 All with VN 225 *

200

175

150

125

100

75

50

% stimulation of control (VN only) (VN control of stimulation % 25

0 90 30 90 30 SFM BP-3 IGF-I BP-3 IGF-I ng/well

Figure 3.3.2: Effect of substrate-bound IGF-I:IGFBP:VN complexes on T47D cellular migration. T47D cells were seeded onto Transwells® that had been coated with VN and IGF-I pre- bound in the presence or absence of IGFBP-3. The cells were allowed to migrate for 5 hours. The number of cells transversing the membrane in response to each treatment was then expressed as a percentage of those that migrated on VN only (SFM). The data are from one experiment with treatments tested in triplicate wells. The asterisk indicates treatments which significantly increased migration above the VN only wells (p<0.05). The IGF-I:IGFBP-3:VN complex was also observed to increase migration above the individual components of the complex (†)(p<0.05). Error bars indicate SEM. SFM = serum-free media.

83

† † † † † 200 * All with VN * * * * 175 †

150 *

125

100

75

50

% stimulation of control (VN only) % stimulation of 25

0 30 90 300 30 90 300 10 30 100 30 90 300 90 30030

SFM 10 30 100 10 30 100 BP-3 BP-5 IGF-I BP-3 BP-5 ng/well IGF-I IGF-I

Figure 3.4.1: Effect of substrate-bound IGF-I:IGFBP:VN complexes on MCF-10A cellular migration. MCF-10A cells were seeded onto Transwells® that had been coated with VN and increasing concentrations of IGF-I pre-bound in the presence or absence of IGFBP-3 or -5. The cells were allowed to migrate for 5 hours. The number of cells transversing the membrane in response to each treatment was then expressed as a percentage of those that migrated on VN only (SFM). The data are pooled from three experiments with treatments tested in triplicate wells in each experiment. The asterisk indicates treatments which significantly increased migration above the VN only wells (p<0.05). IGF-I:IGFBP:VN complexes were also observed to increase migration above the individual components of the complex (†)(p<0.05). Error bars indicate SEM. SFM = serum-free media.

84

225 All with VN * *

200 *

175

150

125

100

75

50 % stimulation of control (Vn only) 25

0 100 300 1000

SFM IGF-II IGF-II IGF-II

ng/well

Figure 3.4.2: Effect of substrate-bound IGF-II:VN complexes on MCF-10A cellular migration. MCF-10A cells were seeded onto Transwells® that had been coated with VN and increasing concentrations of IGF-II. After cells were allowed to migrate for 5 hours, the number of cells transversing the membrane in response to each treatment was then expressed as a percentage of those that migrated on VN only (SFM). The data are from one experiment with treatments tested in triplicate wells. The asterisk indicates treatments which significantly increased migration above the VN only wells (p<0.05). Error bars indicate SEM. SFM = serum free media.

85 complexes are also capable of stimulating significant increases in cellular migration of both tumourgenic and non-tumourgenic breast epithelial cells.

3.3.6 IGF-I:IGFBP:VN complexes are less potent stimulators of migration in the MDA-MB-231 breast cancer cell line due to αvβ3 integrin expression To determine whether the enhanced migration observed in MCF-7 and MCF-10A cells would occur in highly metastatic breast cancer cells, parallel migration assays were performed using the MDA-MB-231 cell line (Figure 3.5.1). MDA-MB-231 cells were found to be less responsive to IGF-I, with only the highest concentration of IGF-I:IGFBP-3 and the two highest concentrations of IGF-I:IGFBP-5 combinations able to increase migration over that found with VN alone (responses of 137.1 ± 5.67% (100 ng IGF-I) for the IGF-I:IGFBP-3:VN complex and 123.0 ± 4.9% (30 ng IGF-I) and 125.2 ± 6.1% (100 ng IGF-I) of the VN control wells, for IGF-I:IGFBP-5:VN complexes) (p<0.05) (Figure 3.5.1). The magnitude of this increase was substantially less than observed in MCF-7 and MCF-10A cells; however, this may be due to the very high level of MDA-MB-231 cell migration observed in response to VN alone. This can be seen in Figure 3.5.2, whereby it is observed that MDA-MB-231 cells are highly motile on VN alone, with significantly higher levels of migration induced by VN in comparison to either MCF-10A or MCF-7 cells.

The response of the MDA-MB-231 cells to these complexes was hypothesized to be due to the expression of the αvβ3 integrin, which is not expressed by MCF-7 or MCF-10A cells. To determine if this was the case, migration assays were performed using MCF-7-β3 cells, which are MCF-7 cells engineered to overexpress the αvβ3 integrin and have been described previously (Pereira et al. 2004). The MCF-7-β3 cells were also found to have a high level of basal migration on VN. While the majority of the IGF-I:IGFBP:VN complexes significantly increased migration above the effect of VN alone, the induction of migration by IGF-I-containing complexes was substantially reduced compared to the wild-type αvβ3 deficient MCF-7 cells (Figure 3.5.3). Responses to IGF-I:IGFBP-3:VN complexes ranged from 116.3 ± 4.4% to 120.4 ± 2.7% of the VN control wells, while the responses to IGF-I:IGFBP- 5:VN complexes ranged from 121.8 ± 4.9% to 132.6 ± 4.4% of the VN alone control wells (Figure 3.5.3).

86

200 All with VN

175 † 150 † * † * 125 *

100

75

50

% stimulation of control (VN only) (VN control of stimulation % 25

0 90 30030 90 30030 30 10010 90 30030 90 30030 30 10010 SFM 30 10010 BP-3 BP-5 IGF-I BP-3 BP-5 ng/well IGF-I IGF-I

Figure 3.5.1: Effect of substrate-bound IGF-I:IGFBP:VN complexes on MDA-MB-231 cellular migration. MDA-MB-231 cells were seeded onto Transwells® that had been coated with VN and increasing concentrations of IGF-I pre-bound in the presence or absence of IGFBP-3 or -5. The cells were allowed to migrate for 5 hours. The number of cells transversing the membrane in response to each treatment was then expressed as a percentage of those that migrated on VN only (SFM). The data are pooled from three experiments with treatments tested in triplicate wells in each replicate experiment. The asterisk indicates treatments which significantly increased migration above the VN only wells (p<0.05). IGF-I:IGFBP:VN complexes were also observed to increase migration above the individual components of the complex (†)(p<0.05). Error bars indicate SEM. SFM = serum-free media.

87

# 500 MCF-10A MCF-7 * 450 MDA-MB-231

400

350

300 *

250

200

150

100 % stimulation of MCF-10A cell migration

50

VN

Figure 3.5.2: Relative migration levels of MCF-10A, MCF-7 and MDA-MB-231 cell lines in response to VN. MCF-10A, MCF-7 and MDA-MB-231 cells were seeded onto Transwells® that had been coated with VN (1 µg/mL) and the cells were allowed to migrate for 5 hours. The number of cells transversing the membrane in response to VN for each cell line was then expressed as a percentage of the MCF-10A cell response. The data are pooled from two experiments with cell lines tested in six wells in each replicate experiment. Error bars indicate SEM. MCF-7 and MB-MDA-231 cells had significantly higher levels of migration than the MCF-10A cell line (*), with the MB-MDA- 231 migratory response also significantly greater than MCF-7 cells (#) (p<0.05).

88

200 All with VN

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† 150 * * * 125 * *

100

75

50

% stimulation of control (VN only) (VN control of stimulation % 25

0 90 30030 90 30030 30 10010 90 30030 90 30030 30 10010

SFM 30 10010 BP-3 BP-5 IGF-I BP-3 BP-5 ng/well IGF-I IGF-I

Figure 3.5.3: Effect of substrate-bound IGF-I:IGFBP:VN complexes on MCF-7-β3 cellular migration. MCF-7-β3 cells were seeded onto Transwells® that had been coated with VN and increasing concentrations of IGF-I pre-bound in the presence or absence of IGFBP-3 or -5. The cells were allowed to migrate for 5 hours. The number of cells transversing the membrane in response to each treatment was then expressed as a percentage of those that migrated on VN only (SFM). The data are pooled from three experiments with treatments tested in triplicate wells in each replicate experiment. The asterisk indicates treatments which significantly increased migration above the VN only wells (p<0.05). IGF-I:IGFBP:VN complexes were also observed to increase migration above the individual components of the complex (†)(p<0.05). Error bars indicate SEM. SFM = serum-free media.

89 Indeed the responses obtained with the MCF-7-β3 cells were similar to those observed for MDA-MB-231 cells (Figure 3.5.3). The expression of the αvβ3 integrin was confirmed in MDA-MB-231 and MCF-7-β3 cells using the alpha/beta integrin- mediated cell adhesion array kit (for details of this method refer to section 2.5.5). As expected, both MDA-MB-231 and MCF-7-β3 cell lines expressed significant levels of the αvβ3 integrin, while MCF-7 cells did not express the αvβ3 integrin (Figure 3.5.4).

3.3.7 IGF-I:IGFBP complexes containing fibronectin and type IV collagen stimulate increased MCF-7 and MCF-10A cellular migration A major component of the basement membrane (BM) in the normal mammary gland is type IV collagen (COL IV) and forms a continuous structure that separates epithelial cells from the surrounding stroma. The BM is the first barrier which must be traversed for epithelial cell invasion into the stroma. In many breast tumours there is disruption of the normal tissue architecture and epithelial cells can become exposed to ECM proteins within the stroma, such as VN and fibronectin (FN). Increased expression of FN in breast tumours has also been associated with poor prognosis and metastasis (Castronovo and Sobel 1990; Oda et al. 1992). These studies with reports from the literature demonstrating that IGFBP-3 and IGFBP-5 can bind to COL IV and FN (Jones et al. 1993; Parker et al. 1996; Martin and Buckwalter 2000; Gui and Murphy 2001), prompted migration assays to be undertaken using FN and COL IV, two proteins abundant within the stroma and BM, respectively. These assays aimed to determine if IGF-I:IGFBP complexes can modulate cell migration on other ECM proteins expressed in the mammary gland in addition to VN. Therefore, Transwell® migration assays were performed as previously described, with the exception that FN and COL IV were used in the pre- binding steps in place of VN.

In MCF-7 cells when FN was used as the substratum, IGF-I:FN, IGF-I:IGFBP-3:FN and IGF-I:IGFBP-5:FN treatments were able to stimulate significantly increased migration above that of the FN alone control wells (responses of 143.3 ± 9.4%, 142.7± 12.69% and 145.5 ± 11.9% of FN control wells, respectively) (p<0.05) (Figure 3.6 A).

90

0.8 MCF-7 MCF-7-β3 * 0.7 MDA-MB-231

0.6 * * 0.5 * 0.4 * * O.D 560 nm * 0.3 * * * 0.2 *

0.1

0.0 Neg Control αv αvβ3 αvβ5 β1

Figure 3.5.4: Confirmation of αvβ3 integrin expression in MDA-MB-231 and MCF-7- β3 cell lines. Adhesion of cells to 96-well microtiter plates immobilized with mouse monoclonal antibodies generated against human alpha and beta integrins/subunits. Each cell line was harvested using non- enzymatic cell dissociation buffer and seeded at a density of 5 x 104 cells/well and incubated at 37°C, 5% CO2 for 2 hours to allow cell attachment. Plates were then washed extensively before staining the cells with crystal violet. Quantification of cellular adhesion to specific integrins/subunits was performed by extracting the cell stain and measuring the optical density (O.D) of extracts at 560 nm. Results are from one representative experiment with each cell line tested in triplicate wells for each specific antibody. Cells were determined to express specific integrins/subunits if the O.D of the extracts was significantly increased above that of the corresponding control antibody-containing wells (Neg control) (p<0.05) (*).

91 A) MCF-7 B) MCF-7

225 All with FN 225 All with COL IV 200 200

175 175 * * * * * 150 150 * 125 125

100 100

75 75

50 50

25 25 % stimulation of control (FN only) % stimulation of control (COL IV only) IV (COL control of stimulation % 0 0 90 90 30 90 90 90 90 30 90 90 30 30 30 30 SFM BP-3 BP-5 IGF-I SFM BP-3 BP-5 IGF-I BP-3 BP-5 BP-3 BP-5 IGF-I IGF-I IGF-I IGF-I C) MCF-10A D) MCF-10A

† † 225 All with FN 225 All with COL IV 200 * * 200 † 175 * 175 † 150 150 * * 125 125

100 100

75 75

50 50

25 25 % stimulation of control (FN only) 0 only) IV (COL control of stimulation % 0 90 90 30 90 90 90 90 30 90 90 30 30 30 30 SFM SFM BP-3 BP-5 IGF-I BP-3 BP-5 IGF-I BP-3 BP-5 BP-3 BP-5 IGF-I IGF-I IGF-I IGF-I

Figure 3.6: Effect of IGF-I:IGFBP-3/-5 on FN- and COL IV-stimulated cell migration. (A, B) MCF-7 and (C, D) MCF-10A cells were seeded onto Transwells® that had been coated with either FN (1 μg/ml) or COL IV (1 μg/ml) and increasing concentrations of IGF-I pre-bound in the presence or absence of IGFBP-3 or -5. The cells were allowed to migrate for 5 hours. The number of cells transversing the membrane in response to each treatment was then expressed as a percentage of those that migrated on FN or COL IV only (SFM). MCF-7 data are pooled from two experiments with treatments tested in six wells in each replicate experiment. MCF-10A data are pooled from three experiments with treatments tested in four wells in each replicate experiment. The asterisk indicates treatments which significantly increased migration above the FN or COL IV only wells (p<0.05). Some IGF-I:IGFBP:matrix protein treatments were also observed to increase migration above the individual components (†)(p<0.05). Error bars indicate SEM. SFM = serum-free media.

92 Similarly, when COL IV was used as the substratum, these same three treatments were also observed to increase migration of MCF-7 cells significantly above that of control wells containing COL IV alone (responses of 129.4 ± 2.3% (IGF-I:COL IV), 142.3± 7.9% (IGF-I:IGFBP-3:COL IV) and 148.9 ± 9.7% (IGF-I:IGFBP-5:COL IV) of FN control wells, respectively) (p<0.05) (Figure 3.6 B). However, unlike the responses observed for VN, IGF-I:IGFBP:FN or IGF-I:IGFBP:COL IV treatments did not lead to any further significant increases in cell migration above the response induced by IGF-I in combination with FN or COL IV (Figures 3.6 A and B).

Similar to the responses observed in MCF-7 cells, when migration assays were undertaken using MCF-10A cells, IGF-I:FN, IGF-I:IGFBP-3:FN and IGF-I:IGFBP- 5:FN treatments were able to stimulate significantly increased migration above that of FN alone control wells (responses of 153.7 ± 7.3%, 192.5 ± 6.8% and 187.5 ± 6.5% of the FN control wells, respectively) (p<0.05) (Figure 3.6 C). The response of MCF-10A cells to IGF-I:IGFBP-3:FN and IGF-I:IGFBP-5:FN treatments was also significantly greater than those obtained with either IGFBP or IGF-I alone with FN (p<0.05) (Figure 3.6 C). When MCF-10A cells migrated on COL IV, the addition of IGF-I in combination with either IGFBP-3 or IGFBP-5 significantly enhanced migration of cells, with responses of 130.5 ± 3.1% and 135.3 ± 6.3% of the COL IV control wells, respectively for IGF-I:IGFBP-3:COL IV and IGF-I:IGFBP-5:COL IV combinations (p<0.05) (Figure 3.6 D). These two responses were also significantly greater than the response stimulated by IGF-I or either IGFBP alone with COL IV.

93 3.4 DISCUSSION Since the initial discovery by Upton et al., (1999) that IGF-II binds to the ECM protein vitronectin (VN), novel links between IGF-I, IGFBPs and VN have also been identified. Specifically, it has been demonstrated that while IGF-II can bind directly to VN, IGF-I associates with VN indirectly through IGFBP-2, -3, -4, and -5 (Kricker et al. 2003). Given that expression of IGFs, VN and their cell surface receptors correlate with metastatic potential and histological tumour grade of a variety of cancers (Gladson and Cheresh 1991; Tomasini-Johansson et al. 1994; Giovannucci 1999), initial studies investigating the functional consequences of these novel complexes focused on the ability of IGF-II:VN to modulate cellular functions in MCF-7 breast carcinoma cells (Noble et al. 2003). In this early study it was reported that IGF-II:VN complexes had little effect on cell attachment and de novo protein synthesis. However, pre-binding IGF-II to immobilised VN was found to significantly enhance MCF-7 cell migration in the Transwell® assay system through involvement of the IGF-1R and was independent of IGFBPs (Noble et al. 2003). Concurrent studies were also undertaken in HaCAT human skin keratinocytes, the results of which were published in 2004 (Hyde et al. 2004). These revealed that IGF- II:VN complexes could also stimulate synergistic proliferative and migratory responses in HaCAT human keratinocytes, therefore demonstrating that the IGF- II:VN complexes could modulate important cell functions in two different epithelial cell lines. However, prior to the commencement of the present study, investigations into the functional responses of cells to IGF-I containing trimeric complexes were limited. Indeed, at the time I commenced the studies described herein the only functional data reported was that by Kricker et al., (2003) who demonstrated that IGF-I bound indirectly to VN via IGFBP-5 could enhance the migration of the MCF- 7 cell line.

Since this time, Hyde et al., (2004) have also reported that IGF-I:IGFBP-5:VN complexes increase the proliferation and migration of HaCAT skin keratinocytes. These data, taken together with other studies reported in the literature (Jones et al. 1996; Maile et al. 2001; Nam et al. 2002), provided compelling evidence in support of the overall concept of co-ordinate regulation between the IGF-IR and αv integrins. Nevertheless, there was still a need to further investigate the functional consequences of the interaction of IGF-I with VN, and in particular studies examining the

94 responses of breast cells which differ in their tumourgenic and invasive capacity were warranted. Furthermore, studies comparing IGFBP-3 and IGFBP-5 in mediating IGF-I association with VN were pursued as these binding proteins can have differential effects on cellular functions in normal and malignant breast cells via both IGF-dependent and IGF-independent mechanisms (Holly and Perks 2006). Thus, it was hypothesised that the interaction of IGF-I and VN, via the involvement of IGFBP-3 and IGFBP-5, would modify many of the biological actions associated with these proteins in breast cells.

The studies reported herein demonstrate that substrate-bound IGF-I:IGFBP:VN complexes containing IGFBP-3 or IGFBP-5, can significantly increase the migration of the poorly metastatic MCF-7 and T47D breast carcinoma cell lines (Figures 3.3.1 and 3.3.2). Interestingly, this response was also observed in the non-tumourgenic MCF-10A breast cell line (Figure 3.4.1). Further, these complexes were able to stimulate significant increases in the proliferation of both MCF-10A and MCF-7 cell lines (Figure 3.1.1 and 3.1.2). While previous studies have shown that IGF:VN complexes can modulate cellular protein synthesis, attachment and migration (Noble et al. 2003; Hyde et al. 2004; Hollier et al. 2005) the data here however, is the first significant study to investigate the effects of substrate-bound IGF-I:IGFBP:VN complexes over a range of functional assays in cells which differ in their tumourigenic and invasive capacity.

Traditionally, in vitro approaches to studying the effects of growth factors on cells have involved adding these soluble growth factors in solution, whereby they are free to rapidly diffuse throughout the culture medium. As cells in vivo are imbedded in a complex ECM it is likely that soluble growth factors would interact with components of the ECM before binding to their respective cell surface receptors (Ruoslahti 1989; Nathan and Sporn 1991). The binding of growth factors to matrix components can lead to the stabilization of local growth factor concentrations, modulate receptor interactions, alter diffusion rates through the matrix or protect the growth factors from proteolytic degradation. Therefore, the response of cells to growth factors bound within the local ECM are likely to be quite different from the same proteins in solution. Indeed, ECM-bound IGFBP-5 has been shown to potentiate the effects of IGF-I on smooth muscle cell DNA synthesis, whereas

95 IGFBP-5 added into the culture medium had no effect on IGF-I-stimulated growth (Jones et al. 1993). As such, we believe that the pre-binding approach adopted in this study more accurately reflects events in vivo, thus making our results particularly relevant to the microenvironment cells are likely to encounter in vivo.

Processes such as invasion, migration, proliferation, resistance to apoptosis and angiogenesis are critical for both normal mammary gland development and breast cancer metastasis in vivo. In view of this, the responses of the non-tumourigenic MCF-10A breast cell line to IGF-I:IGFBP:VN complexes were investigated. It was observed that substrate-bound IGF-I:IGFBP:VN complexes, containing either IGFBP-3 or IGFBP-5, were able to enhance the proliferation and attachment of MCF-10A cells (Figure 3.1.1 and 3.2.1). Moreover, the proliferation and attachment responses observed in MCF-10A cells are very similar to those found previously in HaCAT keratinocytes, where both substrate-bound IGF-II:VN and IGF-I:IGFBP- 5:VN complexes enhanced protein synthesis (Hyde et al. 2004). This suggests substrate-bound IGF-I:IGFBP:VN complexes play an important role stimulating the growth of non-malignant epithelial cell types.

A number of studies using in vitro culture and animal models have demonstrated an important role for IGFs in mammary gland function. IGF-I and IGF-II can stimulate proliferation of mammary epithelial cells (MECs) both in vitro and in vivo (Strange et al. 2002; Stull et al. 2002), and IGF-I in combination with mammogenic hormones can induce ductal growth in mammary gland explant cultures (Richert and Wood 1999). Indeed, studies using transgenic mouse models have determined IGF-I to be critical in GH-mediated TEB development during puberty. IGF-I null mice have decreased numbers of ducts and TEBs resulting in deficient mammary development (Ruan and Kleinberg 1999; Kleinberg et al. 2000). IGFs also play a significant role in survival of the mammary epithelium during involution, illustrated in the delayed involution characterized by reduced apoptosis and tissue remodelling in IGF-I and IGF-II transgenic mice (LeRoith et al. 1995; Neuenschwander et al. 1996; Moorehead et al. 2001). Thus, the inhibition of mammary involution by IGFs may provide a mechanism for breast cancer development as overexpression of IGF-I and IGF-II can also induce mammary tumours in transgenic mouse models (Bates et al. 1995; Hadsell et al. 2000). Moreover, in a 3D-culture system IGF-I or

96 supraphysiological doses of insulin can promote early lesions of breast cancer in IGF-1R overexpressing MCF-10A cells via increased proliferation and survival signaling (Yanochko and Eckhart 2006).

In addition to its role in normal mammary gland function, IGF-I has well documented roles in the transformation, growth and survival of a number of cancers, including breast cancer (Surmacz 2000; Baserga et al. 2003; Samani et al. 2007). We report for the first time that substrate-bound IGF-I:IGFBP:VN complexes are potent stimulators of MCF-7 breast carcinoma cell proliferation (Figure 3.1.2). This supports previous studies reported in the literature demonstrating the mitogenic effects of IGF-I on MCF-7 cells (Arteaga and Osborne 1989; Cullen et al. 1990; McGuire et al. 1992; Surmacz et al. 1998). This stimulation is mediated via the IGF- 1R which is often overexpressed and hyperphosphorylated in breast cancer cells and tissue (Resnik et al. 1998; Surmacz 2000). Indeed, IGF-I can protect breast cancer cells from chemotherapy-induced cell death (Dunn et al. 1997; Gooch et al. 1999) and overexpression of the IGF-1R confers radioresistance in breast cancer cells which can be reversed with antisense oligonucleotides targeting the IGF-1R (Turner et al. 1997). Given the roles of IGF-I in both normal mammary gland function and breast cancer, our results demonstrating substrate-bound IGF-I:IGFBP:VN complexes to be potent stimulators of non-tumourgenic MCF-10A breast epithelial and MCF-7 breast cancer cells are of particular relevance. Thus, IGF-I localised within the ECM may aid in the transformation and progression of breast cancer by inducing and/or maintaining a highly proliferative phenotype in both pre-malignant and malignant breast cells. While not the main focus of my PhD, future more extensive studies are planned to investigate the role of substrate-bound IGF:VN complexes on proliferation and apoptosis in breast cells.

It is well accepted that cell migration is one of the key events leading to tumour dissemination. IGF-I stimulates the migration of a variety of cancer cells, including breast cancer cells. Further, disruption of the IGF-1R using antibodies or chemical inhibitors blocks IGF-I-stimulated cell migration (Doerr and Jones 1996; Bartucci et al. 2001). Similarly, VN through binding to cell surface integrins can regulate the attachment and migration of breast cancer cells (Bartsch et al. 2003). In the studies reported here we show that the IGF-I:IGFBP-3:VN and IGF-I:IGFBP-5:VN

97 complexes are potent stimulators of both MCF-7 and MCF-10A migration with responses significantly greater than the response to either VN, IGFBP-3:VN, IGFBP-5:VN or IGF-I:VN treatments. This therefore supports previous data from our laboratory which demonstrated enhanced migration of HaCAT human keratinocytes and MCF-7 cells in response to IGF-I:IGFBP-5:VN complexes (Kricker et al. 2003; Hyde et al. 2004). Evidence for an important functional interaction between IGF-I and VN in stimulating cellular migration has previously been reported by others. The solution-phase addition of IGF-I has been reported to promote migration of MCF-7 cells on VN and was observed to be mediated by VN binding to the αvβ5 integrin (Doerr and Jones 1996). Moreover, blocking VN binding to the αvβ3 integrin abolishes IGF-I-stimulated cell migration and proliferation in SMCs, with ligand occupancy of αvβ3 required for optimal IGF-1 signaling (Clemmons and Maile 2004).

It is reported here for the first time that IGF-II:VN complexes are also potent stimulators of MCF-10A cell migration and when taken together with data reported previously by Noble et al., (2003) for MCF-7 cells, demonstrates that substrate- bound IGF-II:VN complexes enhance the migration of two types of human breast epithelial cell lines. Together with the data discussed above, this suggests an important physiological role for the interaction between IGF-I and IGF-II with VN in both MCF-10A and MCF-7 cells, particularly with respect to inducing and maintaining a migratory phenotype. Moreover, IGF-I was also demonstrated to promote migration of MCF-7 and MCF-10A cells on COL IV and FN (Figure 3.6). These results may well prove to be biologically relevant as COL IV is one of the major components of the BM and the first barrier epithelial cells must overcome in order to invade the surrounding stroma. Similarly, increased expression of FN in breast cancer has been associated with poor prognosis and metastasis (Castronovo and Sobel 1990; Oda et al. 1992). Disruptions in tissue architecture during breast cancer can expose breast epithelial cells to FN, which has been reported to be involved in the reversal of IGFBP-3 from having inhibitory actions to enhancing cell survival (Burrows et al. 2006). Given that IGFBP-3 and IGFBP-5 have been reported to associate with FN and COL IV (Jones et al. 1993; Parker et al. 1996; Martin and Buckwalter 2000; Gui and Murphy 2001), IGFBP-binding to multiple proteins within the ECM may localise IGFs in the pericellular environment and in turn impact

98 significantly on IGF functions in vivo. Indeed, there is increasing evidence that the ECM has an important role in regulating the functions of growth factors (Eliceiri 2001), and we contend that our data indicates this is true for IGFs as well.

Accumulating evidence indicates that IGF system components associating with the ECM can have a major effect on the biological actions of IGFs. Early studies demonstrated that IGFBPs, including IGFBP-3 and IGFBP-5, could bind to the ECM produced by cultured fibroblasts (Jones et al. 1993). IGFBP-5 was further reported to bind glycosaminoglycans present in several proteoglycans localised to ECM of cultured fibroblasts and/or porcine aortic smooth muscle cells and to specific proteins including VN, COL IV, tenascin C, thrombospondin, osteopontin and PAI-1 (Camacho-Hubner et al. 1992; Parker et al. 1998; Nam et al. 2002). Importantly, investigators observed ECM-associated IGFBP-5 to have an 8- to -15 fold reduced affininty for IGF-I and –II and IGFBP-5 is protected from proteolytic cleavage (Jones et al. 1993). The association of IGFBP-5 with the ECM is functionally significant as ECM-bound IGFBP-5 can potentiate the effects of IGF-I-stimulated fibroblast growth (Jones et al. 1993) and has been reported to be a major determinant of smooth muscle cell responses to IGF-I (Parker et al. 1998). In a recent study by Martin and Jambazov (2006), IGFBP-3 was also shown to associate with the matrix deposited by both normal and malignant breast cells, with matrix-bound IGFBP-3 displaying a 25-fold reduction in affinity for IGF-I. Therefore, it is possible that ECM-associated IGFBPs can act as a reservoir for the IGFs and slow their clearance from the local pericellular environment. Moreover, the IGF-I that is bound to IGFBPs within the ECM may be in a better equilibrium to interact with receptors, thereby providing a mechanism whereby ECM-bound IGFBPs potentiate the effects of IGF-I on cellular functions; thus demonstrating an important functional role for the interaction between IGFs/IGFBPs and the ECM.

Numerous growth factors, such as EGF, bFGF, PDGF and VEGF, can have their activities modulated by interacting with components of the ECM (Eliceiri 2001). Further, it is well recognised that optimal cellular responses to growth factors depends on integrin mediated cell adhesion to the appropriate matrix (Giancotti and Ruoslahti 1999; Eliceiri 2001). While the mechanisms behind the enhanced cellular responses to IGFs in the presence of VN remain unclear, it has been proposed that

99 this may be a result of the “cross-talk” between the IGF-IR and αv integrins (ie. VN- binding integrins). Supporting our hypothesis that VN can collaborate with IGFs are the findings which demonstrate co-ordinate regulation between the IGF-IR and the αvβ3 integrin in SMCs. For instance, blocking VN binding to αvβ3 by disintegrin antagonists results in the abolition of IGF-I-stimulated responses such as DNA synthesis, cell migration, IGF-IR autophosphorylation, IGFBP-5 synthesis and phosphorylation of downstream signaling components such as IRS-1 and PI3-K (Clemmons and Maile 2004). It has since been reported that this effect is mediated by the heparin binding domain (HBD) of VN binding to an extracellular C-loop region of the β3 integrin subunit, which is required for enhanced IGF-1R signaling (Maile et al. 2006; Maile et al. 2006). This provides a possible mechanism by which VN, through binding to integrin receptors, can modulate IGF-I actions.

It was observed that IGF-I:IGFBP:VN complexes were not as potent stimulators of cell migration in the highly metastatic MDA-MB-231 cell line as observed in the less aggressive MCF-10A and MCF-7 cells. This may be explained in part by the fact that MDA-MB-231 cells were highly motile on VN alone. Indeed, we hypothesize that this response is due to these metastatic breast cancer cells expressing the αvβ3 integrin and are hence less dependent on growth factors to induce migration. This contrasts with previous studies using SMCs, which require ligation of the αvβ3 integrin for IGF-I-stimulated cell migration (Jones et al. 1996; Maile et al. 2006). However, previous studies using MDA-MB-231 cells to examine responses to IGF- II:VN complexes found similar results to those we report (Noble et al. 2003). Noble et al., (2003) hypothesized the relatively low level of IGF-1R expression by these cells (~7 x 103 IGF-1R/cell) or the presence of the αvβ3 integrin which is not present in MCF-7 cells accounted for these differences, supporting the role of the αvβ3 integrin in this response. We demonstrate here that MCF-7-β3 cells, which express the αvβ3 integrin, deficient in the parental cell line, showed similar responses to those found with MDA-MB-231 cells. This clearly illustrates the importance of the αvβ3 integrin and its ligand VN for highly invasive breast cancer cells. Furthermore, these data suggest that as tumour cells acquire αvβ3 they become growth factor- independent; a characteristic also reported by others (Klemke et al. 1994; Brooks et al. 1997).

100

It has been reported that αvβ3 ligation by VN can modulate phosphorylation of signaling components downstream of the IGF-1R such as IRS-1 and PI3-K (Zheng and Clemmons 1998). Similarly, MDA-MB-231 cells can activate p38 MAPK in an αv-integrin-dependent manner when plated onto VN in the absence of growth factors (Chen et al. 2001). However, MCF-7 cells require additional growth factor stimulation (Chen et al. 2001). Furthermore, p38 MAPK activity is elevated in MDA-MB-231 cells and higher p38 MAPK activity has been observed to be important for the invasiveness of breast cancer cells by up-regulating uPA expression (Huang et al. 2000; Chen et al. 2001). Therefore, in MDA-MB-231 cells expressing αvβ3, VN alone appears sufficient to induce high levels of migration independent of growth factor stimulation. However, in αvβ3-deficient MCF-10A and MCF-7 cells, growth factor stimulation is required to support substantial levels of cell migration on VN. Thus, we propose that in MCF-10A and MCF-7 cells the enhanced migration responses to substrate-bound IGF-I:IGFBP:VN complexes is mediated via “cross-talk” between the IGF-1R and alternative VN-binding integrins expressed by these cells, including αvβ1 and/or αvβ5.

In support of this hypothesis, a number of studies have reported a growth factor- dependent mechanism for αvβ5-mediated migration and metastasis (Eliceiri 2001). For example, αvβ5-mediated migration of human pancreatic carcinoma cells on VN requires activation of receptor tyrosine kinase signaling via pre-stimulation with growth factors, such as EGF or insulin (Klemke et al. 1994). In a similar fashion, αvβ5-expressing melanoma cells require insulin or IGF-I stimulation for migration on VN, whereas cells expressing αvβ3 supported IGF-independent migration (Brooks et al. 1997). Furthermore, it has been observed that melanoma cells expressing αvβ3 can metastasize in vivo independent of growth factor stimulation, while αvβ5 expressing cells required the ex vivo pre-stimulation with IGF to metastasize (Brooks et al. 1997). These studies provide evidence that αvβ5- mediated, but not αvβ3-mediated, in vitro cell migration and in vivo metastasis requires growth factor co-stimulation, and provides further evidence to support the important interaction between IGFs and VN-binding integrins in regulating cell migration.

101 In summary, our laboratory has reported previously that IGFBPs, specifically IGFBP-2, -3, -4 and -5, can bind to VN, thus facilitating the indirect association of IGF-I with VN by forming heterotrimeric complexes comprised of IGF-I:IGFBP:VN (Kricker et al. 2003). The most significant findings in the studies reported in this chapter, demonstrate that substrate-bound IGF:IGFBP:VN complexes, containing IGFBP-3 and IGFBP-5, are potent stimulators of cellular proliferation and migration in the “normal”, non-tumourgenic MCF-10A epithelial and MCF-7 breast carcinoma cell lines. Thus, we propose the IGF:VN interaction to be an important mechanism for breast cancer metastasis, particularly in tumour cells prior to acquisition of αvβ3 (ie prior to the acquisition of the highly invasive, metastatic phenotype). We hypothesize that IGF-I through the involvement of IGFBP-3 and IGFBP-5 can be captured by VN, providing a local reservoir of IGF-I in the pericellular ECM which can then interact with the IGF-1R. While the mechanisms behind the enhanced cellular responses to IGFs in the presence of VN remain to be determined, there is accumulating evidence for co-operation or “cross-talk” between the IGF-1R and VN- binding integrins. However, the signaling pathways between integrins and growth factor receptors are clearly interconnected (Eliceiri 2001). Therefore, studies investigating the impact of IGF/VN interactions on intracellular signaling will be of particular relevance for determining the mechanisms underlying enhanced cell migration in response to IGF:VN complexes.

102

CHAPTER 4

Investigations into the mechanisms of substrate-bound IGF-I:IGFBP:VN complex stimulated cell migration

103 4.1 INTRODUCTION As outlined in Chapter 1, it is clear that the IGF system is complex and the biological effects of the IGFs are determined by diverse interactions involving many different molecules. However, this complex regulatory system is disrupted in breast cancer and ultimately leads to dysregulated IGF-IR signaling and the development of the invasive phenotype. The IGF-1R has been reported to be over-expressed in breast tumours (Resnik et al. 1998) and has well established roles in the transformation and maintenance of the malignant phenotype (Yu and Rohan 2000; Baserga et al. 2003; O'Connor 2003). However, experimental data suggests the IGF-1R is also essential for normal breast development, thus demonstrating the importance of the IGF-1R in breast cell biology (Hadsell 2003; Stull and Wood 2003). Similarly, VN has been associated with breast tumours in vivo, displaying a distinct distribution pattern compared to normal tissue (Aaboe et al. 2003). With increased expression of VN and VN-binding integrins reported at the leading edge of migrating tumour cells (Gladson and Cheresh 1991; Uhm et al. 1999; Bello et al. 2001), the interactions between the IGF-1R, VN and VN-binding integrins would appear to have a particularly relevant role in breast cancer metastasis. However, to date the mechanisms behind the enhanced migration of breast epithelial cells in response to IGFs associated with VN have remained unclear.

The data presented in this chapter builds upon the studies reported in Chapter 3, which demonstrate that substrate-bound IGF-I:IGFBP:VN complexes are potent stimulators of both normal and malignant breast cell migration. As outlined in chapter 1, numerous growth factors and/or their cognate cell surface receptors can interact with ECM components to modulate cellular functions, suggesting an important interaction between growth factor receptors and the ECM (Eliceiri 2001). In particular, there is accumulating evidence for important interactions between the IGF-1R and αv-integrins in modulating cellular functions (Brooks et al. 1997; Clemmons and Maile 2004; Maile et al. 2006). While the specific mechanisms behind this interaction are still to be fully elucidated, extensive data demonstrate the co-ordinate regulation of intracellular signaling by these receptors (Eliceiri 2001; Clemmons and Maile 2004). Indeed, it is recognized that one mechanism by which growth factor receptors and integrins can

104 collaborate is via synergistic responses in their respective down-stream signaling pathways (Plopper et al. 1995; Miyamoto et al. 1996; Eliceiri 2001).

The data presented in Chapter 3 indicates that when IGF-I is bound to VN via IGFBP-3 or IGFBP-5 to form a heterotrimeric substrate-bound complex, it is a potent stimulator of MCF-7 and MCF-10A cell migration. It was proposed that through the involvement of IGFBP-3 and IGFBP-5, IGF-I can be captured by VN, providing a local reservoir of IGF-I in the pericellular ECM available to interact with the IGF-1R. Furthermore, it was hypothesized that this enhanced migration is a result of these novel complexes facilitating co-operation between the IGF-1R and VN-binding integrins, resulting in enhanced intracellular signaling. Therefore, the following studies were designed to provide the first mechanistic insights into the action of substrate-bound IGF- I:IGFBP:VN complexes in promoting breast cell migration. More specifically, these studies aimed to investigate: 1) a requirement for heterotrimeric complex formation in substrate-bound IGF-I:IGFBP:VN-stimulated migration; 2) the involvement of the IGF- 1R and VN-binding integrins in enhanced migration responses; and 3) the activation and contribution of the MAPK/ERK and PI3-K/AKT pathways in enhanced migration responses stimulated by substrate-bound IGF-I:IGFBP:VN complexes. Furthermore, cellular responses to “pre-bound” growth factors was compared with the classical approach of the solution phase addition of growth factors, in order to investigate the different cellular responses to the two experimental strategies.

4.2 EXPERIMENTAL PROCEDURES Full details of the materials and methods used in experimental procedures for this chapter are described in chapter 2. The following is a brief summary of the materials and procedures used to generate the data presented in sections 4.3.1 – 4.3.7.

4.2.1 Materials Human IGF-I, Long R3 IGF-I (LR3 IGF-I), Des-(1-3)-IGF-I and [Leu24][Ala31]-IGF-I ([L24][A31]-IGF-I) were purchased from Novozymes. Human VN and anti-ERK 1/2 polyclonal antibody were from Promega, with IGFBP-3 (N109D) and anti-AKT

105 polyclonal antibody from Upstate Biotech. IGFBP-5 was purchased from Dr Sue Firth, Kolling Institute of Medical Research. Mouse IgG monoclonal antibodies directed against the αv-integrin subunit (AV1), αvβ5 (P1F6), αvβ6 (10D5), β1-subunit (P4C10) and the IgG matched control antibody were purchased from Chemicon. The monoclonal IGF-1R antibody (αIR3) and pharmacological inhibitors LY294002 and U0126 were purchased from Merck Biosciences. For detection of phosphorylated signaling intermediates, anti-phospho ERK1/2 MAPK (Thr 202/ Tyr 204) (E10), anti- phospho-AKT (S473) (587F11), anti-phospho-AKT (T308) and anti-phospho-p70S6K (T389) monoclonal antibodies were from Cell Signaling Technology. The AKT1/PKBα cDNA allelic pack was purchased from Upstate. Multiplates containing Transwell® inserts were purchased from Corning Corporation. All other plastic cultureware was purchased from Nalge Nunc International.

4.2.2 Treatment strategy for in vitro assays The majority of studies reported in this chapter adopted the strategy of “pre-binding” growth factors and VN to polystyrene cultureware and to the lower chamber and membrane surfaces of 12-µm pore Transwells® to form substrate-bound complexes. In select studies, the more conventional solution-phase strategy of adding growth factors directly into the culture medium for the duration of the assay was also employed. The concentration of IGF-I and IGFBPs used in the solution-phase treatments was identical to the concentration added to substrate-bound treatments prior to the pre-binding and wash steps.

4.2.3 Pre-binding of proteins Transwell® inserts were prepared by pre-binding the lower well and under-surface of 12-µm pore membranes with proteins as described in chapter 2.4.3. For details of pre- binding proteins to 6-well plates for analysis of signal transduction please refer to chapter 2.4.4. For all functional assays described throughout this chapter, concentrations of proteins added into culture wells for pre-binding steps were as follows: VN, 1 μg/mL; IGF-I, 30 ng/mL; and IGFBP-3/-5, 90 ng/mL. These were the

106 concentrations observed in the studies presented in Chapter 3 to be optimal for inducing cell migration.

4.2.4 Transwell® migration assays Cells which had been serum-starved for 4 hours were harvested and seeded at a density of 2 x 105 cells/well in DMEM/F12-SFM + 0.05% BSA into the upper chamber of pre- coated Transwell® inserts (12-µm pores). In some experiments, the cells were pre- incubated with the indicated concentrations of inhibitory antibodies for 30 minutes at room temperature, or with pharmacological inhibitors LY294002 and U0126 for 60 ® minutes at 37°C, 5% CO2, before seeding into the Transwell . Plates containing the ® Transwell inserts were then incubated for 5 hours at 37°C, 5% CO2. Cells which had migrated to the lower surface of the membrane were fixed in 3.7% para-formaldehyde and stained with 0.01% Crystal Violet in phosphate buffered saline (PBS). The number of cells which had migrated to the lower surface of the porous membrane was then quantified by extracting the crystal violet stain in 10% acetic acid and determining the optical density of these extracts at 595 nm (Leavesley et al. 1993).

4.2.5 Analysis of signal transduction Subconfluent cells were serum-starved for 4 hours, detached using a non-enzymatic cell dissociation buffer, resuspended in DMEM/F12-SFM + 0.05% BSA and allowed to recover for 30 minutes at 37°C. The cells were then seeded onto pre-coated 6-well plates and incubated for the times indicated at 37°C, 5% CO2. Following each incubation, the medium was removed and the cell monolayer washed with PBS containing 2 mM Na3VO4 and 10 mM NaF. Cells were then extracted in lysis buffer containing 10 mM Tris pH 7.4, 1% Triton X-100, 150 mM NaCl, 5 mM EDTA, 2 mM

Na3VO4 and 10 mM NaF with a complete protease inhibitor cocktail. Lysates were centrifuged (14,000g, 20 minutes), the supernatants collected and their protein concentrations determined using the bicinchoninic acid assay kit (Pierce, Rockford, IL, USA). Samples (10 µg total protein) were resolved by SDS-PAGE and transferred to nitrocellulose membranes. The membranes were blocked in 5% non-fat milk in Tris-

107 buffered saline with 0.1% Tween-20 (TBST) for 1 hour at room temperature. Membranes were then incubated with either anti-phospho ERK1/2 MAPK (1:2000), anti-phospho AKT (1:2000), anti-phospho p70S6K (1:2000) or anti-Myc (1:5000) mouse monoclonal antibodies overnight at 4°C in blocking buffer. Membranes were then washed six times for 5 minutes each in TBST before incubation with horseradish peroxidase-conjugated goat anti-mouse secondary antibody. Following further washes, protein bands were then visualized using enhanced chemiluminescence following the manufacturer’s instructions (Amersham Biosciences). The same membranes were subsequently stripped and total levels of ERK 1/2, AKT or GAPDH detected as outlined above to validate equivalent loading of each sample.

4.2.6 Assessment of cell viability after treatment with pharmacological inhibitors For full details of this procedure please refer to chapter 2.5.6. The assessment of cell ® viability was performed using the CellTiter 96 AQueous One Solution Cell Proliferation Assay (MTS) (Promega), which can be used to determine the number of viable cells in proliferation, cytotoxicity or chemosensitivity assays. Serum-starved cells which had been pre-treated with pharmacological inhibitors LY294002 (20 µM) and U0126 (10 4 µM) for 60 minutes at 37°C, 5% CO2, were seeded at a density of 1 x 10 cells/well into 96-well plates which had been coated with IGF-I:IGFBP-3:VN complexes. The cells

were then incubated for 5 hours at 37°C, 5% CO2 (ie the same period of the migration assays), before adding 20 µL of MTS/PES solution to each well. Plates were then incubated for 2 hours at 37°C, 5% CO2 to allow for color development and the absorbance was then recorded at 490 nm using a 96-well plate reader. The quantity of formazan product, as measured by the amount of 490 nm absorbance, is directly proportional to the number of viable cells in culture.

4.2.7 Transient transfections For full details of plasmid vectors, purification and transfection protocols please refer to sections 2.7.1 - 2.7.4. MCF-10A cells were transiently transfected with pUSEamp eukaryotic expression vectors containing wild type and activated Myc-His tagged

108 mouse AKT1 under the control of the cytomegalovirus promoter or the empty vector control. Two micrograms of each construct were transfected into MCF-10A cells (3 x 105 /well) using GeneJuice transfection reagent (Merck) for 7 h in serum-free Opti- MEM (Invitrogen). Opti-MEM was then replaced with normal growth media and cells incubated at 37°C, 5% CO2 for 24 h before harvesting.

4.2.8 Statistical analysis Data are expressed as a percentage of the response observed in wells containing VN alone, unless otherwise stated. The data was pooled from multiple experiments with each treatment tested in at least duplicate wells in each replicate experiment. Data analysis was performed using one-way ANOVA with Tukeys post hoc tests, unless otherwise stated. Statistically significant differences were considered to be present at p<0.05.

109 4.3 RESULTS

4.3.1 IGF-I:IGFBP:VN-stimulated breast cell migration requires heterotrimeric complex formation and the IGF-1R To examine whether the observed increases in MCF-7 and MCF-10A cell migration reported in chapter 3 were a result of heterotrimeric complex formation between IGF-I, IGFBPs and VN, the migration responses of MCF-7 cells obtained using native IGF-I and the IGF-I analogues Long R3-IGF-I (LR3-IGF-I) and Des-(1-3)-IGF-I were compared. Both of the IGF-I analogues have a reduced affinity for IGFBPs but retain their ability to activate the IGF-1R (Francis et al. 1992; Tomas et al. 1993). Native IGF- I+VN was able to stimulate migration to 126.8 ± 4.8% of the VN control wells, and the addition of IGFBP-3 or IGFBP-5 significantly increased this response to 173.6 ± 4.3% and 184.8 ± 6.0%, respectively (p<0.05) (Figure 4.1). While LR3-IGF-I and Des (1-3)- IGF-I induced similar levels of migration on VN as observed with native IGF-I (132.4 ± 5.0 and 132.3 ± 2.0% respectively), the addition of either IGFBP-3 or -5 did not result in any further increase in migration above that of LR3-IGF-I or Des-(1-3)-IGF-I alone with VN (Figure 4.1). This suggests that the formation of the heterotrimeric complex (ie. the binding of IGF-I to VN via IGFBP-3 or IGFBP-5) is required for IGF- I:IGFBP:VN complex-stimulated migration. In a like manner, the involvement of the IGF-1R in IGF-I:IGFBP:VN-stimulated migration was assessed using the IGF-I analogue [L24][A31]-IGF-I. This analogue binds to IGFBPs but exhibits a reduced affinity for the IGF-1R (Milner et al. 1995; Forbes et al. 2002). [L24][A31]-IGF-I+VN failed to enhance migration above that observed for the VN alone control wells (103.9 ± 2.8%) or in the presence of IGFBP-3 or IGFBP-5 (102.9 ± 5.1% and 98.5 ± 7.0%, respectively) (Figure 4.1). These responses were significantly lower than those induced by native IGF-I alone with VN or IGF-I:IGFBP:VN complexes (p<0.05), thus demonstrating that activation of the IGF-1R is crucial in IGF-I:IGFBP:VN-stimulated migration.

110

Figure 4.1: Substrate-bound IGF-I:IGFBP:VN-stimulated migration requires heterotrimeric complex formation. MCF-7 cells were seeded onto Transwells® that had been pre-coated with either native IGF-I or IGF-I analogues with reduced binding to IGFBPs (LR3-IGF-I and Des(1-3)-IGF-I) or reduced affinity for the IGF-IR ([L24][A31]-IGF-I) in the presence and absence of IGFBP-3 and -5. The number of cells traversing the membrane in response to each treatment was then expressed as a percentage of those cells that migrated on VN only. The data are pooled from two experiments with treatments tested in six wells for each replicate experiment. The asterisk indicates treatments which significantly increased migration above the VN only wells (p<0.05). The addition of IGFBP-3 or IGFBP- 5 significantly increased the response to native IGF-I+VN (†). A hash indicates IGF-I analogues which significantly reduced migration in response to the same treatment using native IGF-I (p<0.05). Transwells® were pre-coated with native IGF-I or IGF-I analogues (30 ng/mL), IGFBP-3/-5 (90 ng/mL) and VN (1 μg/mL). Error bars indicate SEM.

111 4.3.2 Blocking VN-binding integrins and the IGF-1R inhibits IGF-I:IGFBP:VN- stimulated migration As the enhanced migration response requires IGF-I binding to VN via IGFBPs, the next step in elucidating the mechanisms behind IGF-I:IGFBP:VN complex-stimulated migration was to determine the role of integrin receptors in this response and to reconfirm the importance of IGF-1R activation in this process. Meyer and co-workers (1998) have shown that MCF-7 cells express the αv and β1 subunits and the αvβ5 heterodimer. In addition to these integrins, MCF-10A cells also express the αvβ6 integrin (Meyer et al. 1998). We investigated the contributions of these integrins, as well as the IGF-1R, to the cell migration response by pre-incubating the cells with monoclonal antibodies with established function-blocking properties. As shown in Figure 4.2.1, pre-incubation of MCF-7 cells with antibodies directed against the αv- integrin subunit, IGF-1R, a combination of both αv + IGF-1R antibodies and αvβ5, caused significant inhibition of IGF-I:IGFBP-3:VN complex-stimulated migration, with responses of 27.5 ± 2.7% (αv), 65.1 ± 3.2% (IGF-1R), 15.8 ± 0.8% (αv + IGF-1R) and 44.4 ± 5.1% (αvβ5) of responses observed in wells containing isotype-matched IgG control antibodies, respectively (p<0.05) (Figure 4.2.1). Similar effects were also observed for complexes containing IGFBP-5. Thus IGF-I:IGFBP-5:VN complex- induced migration was inhibited to 22.2 ± 2.8% (αv), 61.6 ± 5.0% (IGF-1R), 14.3 ± 0.6% (αv + IGF-1R) and 42.1 ± 5.1% (αvβ5) of the isotype-matched IgG control antibody wells (Figure 4.2.1). Pre-incubation of MCF-7 cells with anti-β1 integrin subunit antibodies had little effect on cell migration (Figure 4.2.1).

When MCF-10A cells were similarly pre-incubated with the above mentioned antibodies, IGF-I:IGFBP-3:VN-stimulated migration was found to be decreased to 37.6 ± 3.0% (αv), 42.8 ± 3.1% (IGF-1R), 31.1 ± 3.0% (αv + IGF-1R) and 58.6 ± 4.4% (αvβ5) of control wells containing isotype matched IgG control antibodies, respectively (p<0.05) (Figure 4.2.2). As seen in MCF-7 cells, similar effects were also observed for complexes containing IGFBP-5, with IGF-I:IGFBP-5:VN complex-induced migration inhibited to 45.5 ± 3.0% (αv), 40.5 ± 2.6% (IGF-1R), 38.8 ± 4.3% (αv + IGF-1R) and

112

Figure 4.2.1: Involvement of VN-binding integrins and the IGF-IR in IGF-I:IGFBP:VN-stimulated migration of MCF-7 cells. IGF-I:IGFBP:VN-stimulated migration of MCF-7 cells was determined in the presence of monoclonal anti-integrin blocking antibodies against αv (1:10), αvβ5 (25 ug/mL), αvβ6 (25 ug/mL), β1 (10 ug/mL) and the IGF-IR (10 ug/mL) or control mouse isotype-matched IgG antibody (25 ug/mL). Cells were allowed to migrate for 5 hours, after which the migration in the presence of control mouse IgG antibody was taken as 100%. The asterisk indicates antibody treatments which significantly inhibited IGF-I:IGFBP:VN-stimulated migration (p<0.05). The data are pooled from three experiments with treatments tested in duplicate wells in each replicate experiment. Transwells® were pre- coated with IGF-I (30 ng/mL), IGFBP-3/-5 (90 ng/mL) and VN (1 ug/mL). Error bars indicate SEM.

113

Figure 4.2.2: Involvement of VN-binding integrins and the IGF-IR in IGF-I:IGFBP:VN-stimulated migration of MCF-10A cells. IGF-I:IGFBP:VN-stimulated migration of MCF-10A cells was determined in the presence of monoclonal anti-integrin blocking antibodies against αv (1:10), αvβ5 (25 µg/mL), αvβ6 (25 µg/mL), β1 (10 µg/mL) and the IGF-IR (10 µg/mL) or control mouse isotype-matched IgG antibody (25 µg/mL). Cells were allowed to migrate for 5 hours, after which the migration in the presence of control mouse IgG antibody was taken as 100%. The asterisk indicates antibody treatments which significantly inhibited IGF-I:IGFBP:VN-stimulated migration (p<0.05). The data are pooled from three experiments with treatments tested in duplicate wells in each replicate experiment. Transwells® were pre-coated with IGF-I (30 ng/mL), IGFBP-3/-5 (90 ng/mL) and VN (1 µg/mL). Error bars indicate SEM.

114 70.4 ± 6.6% (αvβ5) of isotype-matched IgG control antibody wells, respectively (Figure 4.2.2). In addition, pre-incubation of MCF-10A cells with anti-β1 integrin subunit and anti-αvβ6 integrin antibodies also significantly inhibited IGF-I:IGFBP- 3:VN-stimulated migration, with responses of 74.1 ± 3.6% (β1) and 66.1 ± 3.5% (αvβ6) of isotype-matched antibody control wells, respectively (p<0.05) (Figure 4.2.2). These two antibodies were also found to decrease IGF-I:IGFBP-5:VN-stimulated migration, however, not to statistically significant levels. While the main focus of this study is responses to IGF-I:IGFBP:VN complexes, similar inhibitory effects of specific antibodies, as seen in Figures 4.2.1 and 4.2.2, were also observed for each cell line in response to VN alone, IGFBP-3+VN, IGFBP-5+VN and IGF-I+VN treatments (Figure 4.2.3). It should be noted that the inhibitory effects on cell migration of specific antibodies cannot be attributed solely to a decrease in cell adhesion to Transwell® inserts, as separate assays using MCF-7 cells, revealed that any inhibition of cell adhesion in the presence of function blocking antibodies was far less than the observed decreases in cell migration (Figure 4.3). In fact, only blockade of the αvβ5 integrin led to any significant inhibition of cell adhesion (p<0.05) (Figure 4.3).

4.3.3 Enhanced cell signaling by IGF-I:IGFBP:VN complexes To investigate possible mechanisms underlying the enhanced cell migration in response to IGF-I:IGFBP:VN complexes we examined the activation of intracellular signaling proteins. The ERK/MAPK and PI3-K/AKT pathways were targeted as they are known to be activated downstream of integrins and the IGF-1R. MCF-7 and MCF-10A cells were serum-starved and then seeded into 6-well culture plates which had been coated with VN and combinations of IGF-I and IGFBP-3 or IGFBP-5. After incubation for the indicated times, cell lysates were then analysed by western immunoblotting using phospho-specific antibodies against either dually phosphorylated ERK 1/2 (Thr202 and Tyr204), or AKT-1 (AKT) when phosphorylated on Thr308 or Ser473 residues. Activation of ERK 1/2 and AKT were both substantially increased by the IGF- I:IGFBP-3:VN and IGF-I:IGFBP-5:VN complexes in MCF-7 cells (Figure 4.4.1 A). This effect was greater than that induced by VN alone and IGF-I or IGFBP-3/-5 with VN at all times tested.

115

Figure 4.2.3: Involvement of VN-binding integrins and the IGF-IR in VN, IGFBP-3:VN, IGFBP- 5:VN and IGF-I:VN-stimulated migration of MCF-7 and MCF-10A cells. Cell migration of MCF-7 (A) and MCF-10A (B) cells was determined in the presence of monoclonal anti-integrin blocking antibodies against αv (1:10), αvβ5 (25 µg/mL), αvβ6 (25 µg/mL), β1 (10 µg/mL) and the IGF-IR (10 µg/mL) or control mouse isotype-matched IgG antibody (25 µg/mL). Cells were allowed to migrate for 5 hours, after which the migration in the presence of control mouse IgG antibody was taken as 100%. The asterisk indicates antibody treatments which significantly inhibited migration of each treatment (p<0.05). The data are pooled from three experiments with treatments tested in duplicate wells in each replicate

116 experiment. Transwells® were pre-coated with IGF-I (30 ng/mL), IGFBP-3/-5 (90 ng/mL) and VN (1 µg/mL). Error bars indicate SEM.

Figure 4.3: Involvement of VN-binding integrins and the IGF-IR in VN, IGFBP-3:VN, IGFBP- 5:VN and IGF-I:VN-stimulated attachment of MCF-7 cells. Cell attachment of MCF-7 cells was determined in the presence of monoclonal anti-integrin blocking antibodies against αv (1:10), αvβ5 (25 µg/mL), αvβ6 (25 µg/mL), β1 (10 µg/mL) and the IGF-IR (10 µg/mL) or control mouse isotype-matched IgG antibody (25 µg/mL). Cells were allowed to attach for 4 hours, after which the attachment in the presence of control mouse IgG antibody was taken as 100%. The asterisk indicates antibody treatments which significantly inhibited attachment of each treatment (p<0.05). The data are pooled from three experiments with treatments tested in triplicate wells in each replicate experiment. Transwells® were pre- coated with IGF-I (30 ng/mL), IGFBP-3/-5 (90 ng/mL) and VN (1 µg/mL). Error bars indicate SEM.

117 A) IGFBP-3 IGFBP-5 + SFM + IGF-I + IGF-I + IGF-I + BP-3 + SFM+ SFM + BP-5 + BP-5 + IGF-I+BP-3 + IGF-I+BP-3 + IGF-I+BP-5 ++ IGF-I+BP-5 IGF-I+BP-5 + SFM + IGF-I + SFM + IGF-I + SFM+ SFM + IGF-I + IGF-I + SFM + IGF-I + BP-3 + BP-3 + BP-5 + BP-5 + BP-5 + IGF-I+BP-3 + IGF-I+BP-3 ++ IGF-I+BP-5 IGF-I+BP-5 + IGF-I+BP-3 + IGF-I+BP-3 + IGF-I+BP-5 10 min 30 min 60 min 10 min 30 min 60 min 0 min + VN 0 min + VN pERK 1/2 (T202/Y204)

ERK 1/2

pAKT (T308)

pAKT (S473)

AKT

pp85S6K (T412)

pp70S6K (T389)

GAPDH

B)

IG F-I+BP -3 +VN IG F -I+B P -5 +VN

0 min 10’ 30’ 60’ 2h 3h 4h 5h 0 min 10’ 30’ 60’ 2h 3h 4h 5h pERK 1/2 (T202/Y204)

ERK 1/2

pAKT (S473)

AKT

Figure 4.4.1: Activation of ERK/MAPK and PI3-K/AKT pathways by IGF-I:IGFBP:VN complexes in MCF-7 cells. MCF-7 cells were seeded on wells pre-coated with IGF-I (30 ng/mL), IGFBP-3/-5 (90 ng/mL) and VN (1 µg/mL) combinations for the indicated times. Cells were then lysed and levels of phosphorylated ERK 1/2 and AKT determined by immunoblot analysis. Membranes were subsequently stripped and re-probed to determine total levels of ERK 1/2, AKT or GAPDH. Representative results of multiple experiments are shown. SFM = serum-free media.

118 IGF-I:IGFBP:VN complexes were also observed to activate the PI3-K/AKT axis downstream of AKT, indicated by the phosphorylation of p70S6K in a similar pattern to AKT activation (Figure 4.4.1 A). The IGF-I:IGFBP:VN complex-induced activation of ERK 1/2 was rapid and transient, attaining maximal activation after 10 minutes and returning to near basal levels after 30 minutes. In contrast, increased AKT activation induced by both IGFBP-3 and IGFBP-5 containing IGF-I complexes was sustained over 5 hours (Figure 4.4.1 B).

Unlike MCF-7 cells, MCF-10A cells were found to have relatively low cellular levels of ERK 1 and a higher basal level of ERK 2 phosphorylation (Figure 4.4.2 A). Maximal ERK 2 activation also occurred after 10 minutes in MCF-10A cells, and declined to near basal levels after 30 minutes (Figures 4.4.2 A and B). In contrast to MCF-7 cells, IGF-I:IGFBP:VN complexes did not induce synergistic increases in ERK 1/2 activation over VN alone and IGF-I or IGFBP-3/-5 with VN. However, similar to that observed in MCF-7 cells, AKT activation was substantially increased by the combination of IGF-I, IGFBP-3/-5 and VN with responses greater than those induced by either VN alone and IGF-I or IGFBP-3/-5 alone with VN at all time points tested in MCF-10A cells (Figure 4.4.2 A). Furthermore, the activation of AKT was increased and sustained over 5 hours (Figure 4.4.2 B). In all cases, effects on cell signaling were found to be similar in response to IGF-I:IGFBP-3:VN and IGF-I:IGFBP-5:VN complexes in both cell types, especially with regard to activation of the PI3-K/AKT pathway (Figures 4.4.1 and 4.4.2).

4.3.4 IGF-I:IGFBP:VN-stimulated cell signaling involves both αv-integrins and the IGF-1R In order to determine if VN/integrin interactions, in addition to the IGF-1R, were playing an active role in substrate-bound IGF-I:IGFBP stimulated cell signaling, serum starved MCF-10A cells were harvested and pre-incubated with monoclonal function blocking antibodies directed against the αv-integrin subunit and the IGF-1R, prior to seeding into culture wells coated with IGF-I:IGFBP-5:VN complexes (Figure 4.5 A).

119

A) IGFBP-3 IGFBP-5 + SFM + IGF-I + SFM + IGF-I + SFM + IGF-I + BP-5 + BP-3 + BP-3 + IGF-I+BP-3 + IGF-I+BP-3 + IGF-I+BP-3 + IGF-I+BP-3 + SFM + IGF-I+BP-5 + SFM + SFM + SFM + SFM+ SFM + IGF-I + IGF-I + IGF-I + IGF-I + IGF-I + IGF-I + BP-3 + BP-3 + BP-3 + BP-5 + BP-5 + BP-3 + IGF-I+BP-3 + IGF-I+BP-3 + IGF-I+BP-3 + IGF-I+BP-3 + IGF-I+BP-3 ++ IGF-I+BP-3 IGF-I+BP-3 + IGF-I+BP-5 + IGF-I+BP-5 + IGF-I+BP-5 10 min 30 min 60 min 10 min 30 min 60 min 0 min + VN 0 min + VN pERK 1/2 (T202/Y204)

ERK 1/2

pAKT (T308)

pAKT (S473)

AKT

B) IGF-I+BP-3 +VN IGF-I+BP-5 +VN

0 min 10’ 30’ 60’ 2h 3h 4h 5h 0 min 10’ 30’ 60’ 2h 3h 4h 5h

pERK 1/2 (T202/Y204)

ERK 1/2

pAKT (S473)

AKT

Figure 4.4.2: Activation of ERK/MAPK and PI3-K/AKT pathways by IGF-I:IGFBP:VN complexes in MCF-10A cells. MCF-10A cells were seeded on wells pre-coated with IGF-I (30 ng/mL), IGFBP-3/-5 (90 ng/mL) and VN (1 µg/mL) combinations for the indicated times. Cells were then lysed and levels of phosphorylated ERK 1/2 and AKT determined by immunoblot analysis. Membranes were subsequently stripped and re-probed to determine total levels of ERK 1/2 and AKT. Representative results of multiple experiments are shown. SFM = serum free media.

120

Figure 4.5: Involvement of αv-subunit containing integrins and the IGF-1R on IGF-I:IGFBP:VN- stimulated ERK/MAPK and PI3-K/AKT pathway activation. A, Serum-starved MCF-10A cells were harvested and pre-incubated with monoclonal blocking antibodies against the αv-integrin subunit (1:10), the IGF-IR (10 µg/ml) or control mouse IgG (25 µg/ml) for 30 minutes at room temperature, prior to seeding into 6-well culture dishes pre-coated with IGF-I:IGFBP-5:VN complexes. Cells were then incubated for 30 minutes at 37°C, 5% CO2, lysed and levels of phosphorylated ERK 1/2 and AKT determined by immunoblot analysis. B, MCF-10A cells were grown to confluency in 12-well plates, serum-starved and incubated with monoclonal blocking antibodies against the αv-integrin subunit (1:10), the IGF-IR (10 µg/ml) or control mouse IgG (25 µg/ml) for 2 hours at 37°C, 5% CO2. Cells were then stimulated with IGF-I:IGFBP-5 added into the culture medium for 30 minutes, then lysed and levels of phosphorylated ERK 1/2 and AKT determined by immunoblot analysis. In A and B, membranes were subsequently stripped and re-probed to determine total levels of ERK 1/2 and AKT. Histograms represent results from densitometric analysis of immunoblots, with the ratio of ERK and AKT activation in the presence of function blocking antibodies expressed relative to the activation in cells incubated with isotype-matched control IgG antibody. Representative blot from a single experiment is shown in A and B. In all cases, IGF-I (30 ng/ml), IGFBP-5 (90 ng/ml) and VN (1 µg/ml).

121 A) IGF-I:IGFBP-5:VN IGF-I:IGFBP-5:VN

0’ 30’ 0’ 30’ 0’ 30’ 0’ 30’30’0’30’ 0’ 30’30’0’30’ 0’ 30’30’0’30’

pERK 1/2 pAKT (T202/Y204) (S473)

ERK 1/2 AKT

n v v R n n v v R on α α α α 1 C Co Co Co F- IGF-1RIGF-1 IGF-1RIG

1.25 1.25

1.00 1.00

0.75 0.75

0.50 0.50

0.25 0.25 pAKT / total AKT (relative levels) pERK / total ERK (relative levels)

0.00 0.00 Con αv IGF-1R Con αv IGF-1R 30' 30' 30' 30' 30' 30'

B) IGF-I:IGFBP-5 IGF-I:IGFBP-5

0’ 30’0’30’ 0’ 30’0’30’ 0’ 30’ 0’ 30’0’30’ 0’ 30’0’30’ 0’ 30’

pERK 1/2 pAKT (T202/Y204) (S473)

ERK 1/2 AKT

n v v R v v R α α on α α 1 Co Con C Con F- IGF-1RIGF-1 IG IGF-1R

1.25 1.25

1.00 1.00

0.75 0.75

0.50 0.50

0.25 0.25 pAKT / total AKT (relative levels) pERK / total ERK (relative levels)

0.00 0.00 Con αv IGF-1R Con αv IGF-1R 30' 30' 30' 30' 30' 30'

122

As can be seen in Figure 4.5 A, inhibition of the αv-integrin subunit reduced ERK 1/2 and AKT activation to approximately 62% and 59%, respectively, of the activation stimulated in cells pre-incubated with isotype-matched IgG control antibodies (Figure 4.5 A). Surprisingly, blocking the IGF-1R resulted in only a modest reduction in ERK activation, to approximately 74% of control cells (Figure 4.5 A). However, inhibition of the IGF-1R led to a more prominent reduction in AKT activation (~17% of control) in response to substrate-bound complexes (Figure 4.5 A). This provides the first evidence that interactions with both VN-binding integrins and the IGF-1R can modulate signaling stimulated by substrate-bound IGF-I:IGFBP:VN complexes.

To further validate the involvement of VN-binding integrins in IGF-I-stimulated cell signaling, an alternative approach was also adopted based on studies undertaken in SMCs in which blockade of the αvβ3 integrin has been extensively demonstrated to inhibit IGF-I signaling (Maile et al. 2006; Maile et al. 2006). In this approach, MCF- 10A cells were allowed to grow to confluency before being serum-starved and incubated with αv-integrin subunit and IGF-1R antibodies. The adherent cells were then stimulated with IGF-I:IGFBP-5 added directly to the culture medium, for 30 minutes prior to cell lysis and protein isolation. As expected, blockade of the IGF-1R led to substantial inhibition of IGF-I-stimulated signaling; ERK and AKT activation were reduced to approximately 28% and 6%, respectively, of the activation observed in control cells (Figure 4.5 B). Interestingly, inhibition of αv-integrins also led to substantial reductions in both ERK and AKT activation levels, to approximately 32% and 49% respectively, of that observed by control cells (Figure 4.5 B). This occurred in the absence of any added VN, indicating that blockade of αv-subunit containing integrins can regulate IGF-1R signaling. This finding is of additional significance, given that MCF-10A cells lack the αvβ3 integrin, which has been the main focus of ECM/integrin interactions in modulating IGF-I signaling, specifically involving the β3- subunit. These data provide the first evidence for the important interaction between VN-binding integrins and the IGF-1R in regulating IGF-I:IGFBP:VN complex stimulated cell signaling in breast cells.

123

4.3.5 IGF-I:IGFBP:VN complex-stimulated migration is mediated via the PI3- K/AKT pathway To determine the relative contributions of the two signaling pathways in IGF- I:IGFBP:VN complex-stimulated migration, cells were pre-incubated with specific pharmacological inhibitors of the ERK/MAPK and PI3-K/AKT pathways, U0126 and LY294002 respectively (Figure 4.6). Doses of 20 µM LY294002 and 10 µM U0126 were shown to be specific for inhibition of AKT and ERK 1/2 respectively, with no significant non-specific or “cross” pathway inhibition by either inhibitor observed in both MCF-7 and MCF-10A cell lines (Figure 4.6 A and B). Inhibition of ERK 1/2 by U0126 decreased MCF-7 migration induced by IGF-I:IGFBP-3:VN and IGF-I:IGFBP- 5:VN complexes to 86.0 ± 3.4% and 78 ± 3.6% of that observed for vehicle controls, respectively (Figure 4.6 C). A greater decrease in migration was observed in cells treated with LY294002, with responses of 43.2 ± 2.3% and 35.9 ± 2.8% of vehicle controls observed for IGF-I:IGFBP-3:VN and IGF-I:IGFBP-5:VN complexes, respectively (p<0.05) (Figure 4.6 C). Furthermore, incubation of the cells with a combination of both inhibitors led to a further inhibition of IGF-I:IGFBP:VN- stimulated migration with responses of 11.9 ± 0.75% and 11.9 ± 0.36% of vehicle controls wells, respectively, for IGFBP-3 and IGFBP-5-containing complexes (p<0.05) (Figure 4.6 C).

Incubation of MCF-10A cells with 10 µM U0126 had no significant effect on either IGF-I:IGFBP-3:VN or IGF-I:IGFBP-5:VN complex-stimulated migration, with responses of 96.1 ± 3.6% and 96.8 ± 4.2% of vehicle controls, respectively (Figure 4.6 D). However, incubation of MCF-10A cells with 20 µM LY294002 significantly inhibited migration in response to both IGF-I:IGFBP-3:VN (46.9 ± 2.2%) and IGF- I:IGFBP-5:VN (44.8 ± 2.8%) complexes (Figure 4.6 D). Incubation of MCF-10A cells with both U0126 and LY294002 led to a greater inhibition of migration, than found with either inhibitor alone, in response to IGF-I:IGFBP-3:VN complexes (Figure 4.6 D). Importantly, the attenuation of cell migration in response to LY294002 was not caused by effects on cell viability, as LY294002 was determined to have no significant

124 effect on cell viability in either MCF-7 or MCF-10A cell lines (Figure 4.6 E and F). Taken

Figure 4.6: Involvement of ERK/MAPK and PI3-K/AKT pathways in IGF-I:IGFBP:VN- stimulated migration. MCF-7 and MCF-10A cells were pre-treated with the MEK/MAPK inhibitor (U0126, 10 µM) and/or the PI3-K/AKT inhibitor (LY294002, 20 µM) for 1 hour before seeding into wells pre-coated with IGF-I:IGFBP:VN complexes for 10 minutes (A and B). Cells were then lysed for immunoblot analysis of specific inhibition of ERK 1/2 and AKT activation by pharmacological inhibitors. Representative blots of three replicates are shown on the top of histograms. Histograms represent results from densitometric analysis of immunoblots, with the ratio of ERK (A) and AKT (B) activation in the presence of pharmacological inhibitors expressed relative to activation of vehicle control treated cells. Pre-treated MCF-7 (C) and MCF-10A cells (D) were seeded into Transwell® inserts which had the lower well and membrane surface pre-coated with IGF-I (30 ng/ml), IGFBP-3 or -5 (90 ng/ml) and VN (1 µg/ml) and the cells were allowed to migrate for 5 hours. Data are expressed as a percentage of the response observed in vehicle control wells containing an equivalent concentration of DMSO. The asterisks indicates treatments in which migration was significantly inhibited (p<0.05). The data presented is pooled from three experiments with treatments tested in at least triplicate wells in each replicate experiment. Cell viability of serum-starved MCF-7 (E) and MCF-10A cells (F) after pre-treatment with LY294002 (LY 20 µM) and U0126 (U 10 µM). Cells were pre-treated as described above, then seeded into 96-well plates which had been pre-coated with IGF-I:IGFBP-5:VN complexes and incubated for 5 hours at 37°C, 5% CO2. Cell viability was then assessed using MTS reagent as described in “Materials and Methods”. The asterisk indicates treatments which significantly inhibited cell viability compared to vehicle control treated cells (p<0.05). The data presented is pooled from two experiments with treatments tested in six wells in each replicate experiment. In all cases, error bars indicate SEM.

125 A) IGF-I:IGFBP-5:VN B) IGF-I:IGFBP-5:VN

M M M M l µ M l µ M l µ M l µ M o 0 µ o 0 µ o 0 µ o 0 µ tr 2 0 tr 2 0 tr 2 0 tr 2 0 n 1 n 1 n 1 n 1 o Y o Y o Y o Y C L U C L U C L U C L U pERK 1/2 pAKT (T202/Y204) (S473) ERK 1/2 AKT

1.25 1.25

1.00 1.00

0.75 0.75

0.50 0.50

0.25 0.25 pAKT / total AKT (relative levels) (relative AKT total / pAKT pERK / total ERK (relative levels) ERK (relative total / pERK * * 0.00 * * 0.00 l M M M M M M ol M M trol μ μ trol μ μ tro μ μ tr μ μ 0 0 n 0 n 0 on on 1 o o 2 C U 10 C U C U 10 C Y U 10 LY 20 LY 2 LY 2 L

MCF-7 MCF-10A MCF-7 MCF-10A

C) D)

Vehicle control Vehicle control LY 20 μM LY 20 μM U 10 μM U 10 μM LY 20 μM + U 10μM LY 20 μM + U 10μM 125 125

100 100 * * 75 75

* 50 * 50 * * * *

25 25 %stimulation of vehicle control %stimulation of vehicle control * *

0 0

IGF-I:IGFBP-3:VN IGF-I:IGFBP-5:VN IGF-I:IGFBP-3:VN IGF-I:IGFBP-5:VN

MCF-7 MCF-10A

E) F)

120 120 110 110 100 100 * 90 90 80 80 70 70 60 60 50 50 % viability % viability % 40 40 30 30 20 20 10 10 0 0 M M M M μ μ μ μ 0 0 0 2 1 Control U 1 Control U LY LY 20

MCF-7 MCF-10A

126 together, these data suggest a central role for PI3-K/AKT activation in the enhanced migration observed in response to IGF-I:IGFBP:VN complexes in both MCF-7 and MCF-10A cell types.

4.3.6 Overexpression of wild type and activated AKT enhances IGF-I:IGFBP:VN- stimulated migration To confirm the importance of AKT in IGF-I:IGFBP:VN-stimulated migration, expression constructs containing Myc-His tagged mouse wild type AKT-1 (WT-AKT) and N-terminal myristoylated AKT-1 (MYR-AKT), which encodes an activated form of AKT, were transiently transfected into MCF-10A cells. Transient transfections produced an approximate 200% increase in AKT levels, with equivalent levels of both WT-AKT and MYR-AKT expressed (Figure 4.7 A). While IGF-I:IGFBP:VN complexes are already potent stimulators of MCF-10A cell migration, the expression of WT-AKT and MYR-AKT led to further significant increases in migration of 127.8 ± 8.5% (WT-AKT) and 151.7 ± 8.3% (MYR-AKT) of empty vector control transfected cells (pUSEamp), respectively (p<0.05) (Figure 4.7 B). Expression of activated AKT in MCF-10A cells was also observed to increase migration above that of wild type AKT, with this effect approaching statistical significance (p=0.053). The effect of AKT overexpression was found to be independent of an overall increase in basal migration, as subsequent assays revealed no difference in the level of migration in response to VN alone, for cells expressing either WT-AKT or MYR-AKT compared to control cells (Figure 4.7 C). Moreover, the increase in migration observed with overexpression of AKT still requires activation of the IGF-1R, as [L24][A31]-IGF-I significantly reduced cellular migration in both control and MYR-AKT expressing cells compared to the migration induced by IGF-I:IGFBP-5:VN complexes containing native IGF-I (p<0.05) (Figure 4.7 D). Similarly, optimal migration of both control and MYR-AKT expressing cells also required the involvement of VN-binding integrins, indicated by significant decreases in cell migration in response to IGF-I:IGFBP-5:VN complexes in the presence of antibodies to αv-integrins (Figure 4.7 E). These results provide further evidence for an important interaction between the IGF-1R and VN-binding integrins in IGF-I:IGFBP:VN-stimulated migration mediated via PI3-K/AKT pathway activation.

127

Figure 4.7: Overexpression of wild type and activated AKT enhances IGF-I:IGFBP:VN-stimulated migration. A, Western immunodetection of wild type (WT) and activated (MYR) AKT expression levels in MCF-10A cells after transfection with pUSEamp expression constructs. Transiently transfected MCF- 10A cells expressing WT-AKT, MYR-AKT and empty vector (pUSEamp) were seeded into Transwell® inserts which had the lower well and membrane surface pre-coated with IGF-I:IGFBP-5:VN (B) or VN alone (C) and the cells were allowed to migrate over 5 hours. Data are expressed as a percentage of the response stimulated in empty vector control cells. Expression of both wild type and activated AKT significantly increased migration in response to the IGF-I:IGFBP-5:VN complex as compared to empty vector control cells (*) (p<0.05). Data are pooled from three experiments (n = 15). D, MCF-10A cells expressing MYR-AKT or empty vector (pUSEamp) were seeded into Transwell® inserts pre-coated as above, with either native IGF-I or [L24][A31]-IGF-I in the presence of IGFBP-5 and the cells were allowed to migrate for 5 hours. Data are expressed as a percentage of the response stimulated by native IGF-I containing complexes in empty vector control cells. E, MCF-10A cells expressing MYR-AKT or empty vector (pUSEamp) were pre-incubated with monoclonal blocking antibodies against the αv- integrin subunit (1:10) or isotype-matched IgG control antibody (25 µg/ml) for 30 minutes at room temperature, prior to seeding into Transwell® inserts pre-coated with IGF-I:IGFBP-5:VN complexes. The cells were allowed to migrate for 5 hours, after which the migration of empty vector control cells in the presence of isotype-matched IgG control antibody was taken as 100%. In all cases (B-E), the asterisk indicates treatments which significantly increased migration above empty vector control cells (p<0.05). A cross indicates significant inhibition of migration in comparison to native IGF-I (D) or isotype- matched IgG control antibody (E) (p<0.05). In D and E, results are from two duplicate experiments with each treatment tested in six wells in each replicate experiment (n = 12). In all cases where appropriate, the lower well and under-membrane surface of Transwell® inserts were pre-coated with native IGF-I or [L24][A31]-IGF-I (30 ng/ml), IGFBP-3/-5 (90 ng/ml) and VN (1 µg/ml). Error bars indicate SEM.

128

A)

p T m T K a K -A E A S - R U T Y p W M

myc

Myc-tag-AKT 1 AKT Endogenous AKT

GAPDH

B) C) IGF-I+BP-5+VN VN

175 175 * 150 150 * 125 125

100 100

75 75

50 50

25 25 % stimulation of empty vector (pUSEamp) % stimulation of empty vector control (pUSEamp)

0 0 pUSEamp WT-AKT MYR-AKT pUSEamp WT-AKT MYR-AKT

D) E)

175 175 * Native IGF-I * Control IgG [L24][A31]-IGF-I αv 150 150

125 125

100 100 † 75 † 75 † †

50 50 % stimulation of control IgG control of stimulation %

25 25 % stimulation of emptyvector control(pUSEamp)

0 0 pUSEamp MYR-AKT pUSEamp MYR-AKT

IGF-I:IGFBP-5:VN IGF-I:IGFBP-5:VN

129 4.3.7 Substrate-bound complexes containing IGF-I and IGFBPs can stimulate equivalent functional responses to those added in the solution phase As outlined earlier, traditional in vitro approaches to studying the effects of growth factors on cells have involved adding growth factors in solution, where they are free to rapidly diffuse throughout the culture medium. As cells in vivo are imbedded in a complex ECM, it is likely that soluble growth factors would interact with components of the ECM before binding to their respective cell surface receptors to modulate cell function. As such, we wanted to compare the functional effects of both solution-phase and substrate-bound IGF-I:IGFBP complexes. Thus, serum-starved MCF-10A cells were seeded into Transwell® inserts in which the lower chamber had either been pre- coated with IGF-I and IGFBPs (Substrate-bound), or had IGF-I and IGFBPs added to the media immediately prior to seeding of cells in the upper chamber (Solution-phase). The concentration of IGF-I and IGFBPs used in the solution-phase treatments was identical to the concentration added to substrate-bound treatments prior to the pre- binding and wash steps. As expected, the addition of IGF-I in solution-phase produced a significant increase in MCF-10A cell migration compared to substrate-bound IGF-I, with responses of 118.9 ± 6.3% and 158.6 ± 5.5 % of the control wells, respectively (p<0.05) (Figure 4.8.1). This is not surprising as in the absence of IGFBPs, minimal IGF-I would remain bound to VN in the substrate-bound approach. However, no significant differences were observed in the response of MCF-10A cells to IGF-I in the presence of either IGFBP-3 or IGFBP-5 in both treatment strategies (Figure 4.8.1).

Analysis of downstream signal transduction further demonstrated similar activation of ERK 1/2 and AKT by substrate-bound and solution-phase treatments. As can be seen in Figure 4.8.2, overall comparable levels of ERK 1/2 and AKT were activated by substrate-bound and solution-phase treatments in both MCF-10A and MCF-7 cells. Indeed, the only major difference in stimulation of either ERK 1/2 or AKT activation by IGF-I between the substrate-bound and solution-phase strategies was in the absence of either IGFBP-3 or IGFBP-5 (Figure 4.8.2). This result was predicted as IGF-I cannot bind directly to VN, instead requiring the involvement of IGFBPs to bind VN. Therefore, insufficient IGF-I would remain substrate-bound in the absence of IGFBPs

130

Substrate-bound Solution-phase 200 175 * 150

125

100

75

50 % stimulation of control (VN only)

25

0 IGF-I+VN IGF-I+BP-3+VN IGF-I+BP-5+VN

Treatment

Figure 4.8.1: VN-bound IGF-I:IGFBP complexes stimulate migratory responses equivalent to solution-phase complexes. Stimulation of MCF-10A migration in response to both substrate-bound and solution phase IGF-I:IGFBP complexes. Transwells® were pre-coated with substrate-bound complexes as described in chapter 2.4.3. For solution-phase treatments, wells were treated as above except IGF-I and IGFBPs were not added to VN-coated lower wells until immediately prior to seeding of cells in the upper chamber. Cells were then allowed to migrate for 5 hours and quantified as already described. The asterisk indicates a significant increase in migration between substrate-bound and solution-phase strategies for each treatment (p<0.05). Data are pooled from two experiments with each treatment tested in quadruplicate wells in each replicate experiment. Transwell® inserts were pre-coated with IGF-I (30 ng/mL), IGFBP-3 or -5 (90 ng/mL) and VN (1 µg/mL)Error bars indicate SEM.

131

MCF-10A -I -I -I -I -I -I -I+BP-5 -I -I -I+BP-3-I+BP-3 -I+BP-3-I+BP-3-I+BP-3 -I+BP-5-I+BP-5 -I -I -I -I -I -I -I -I -I+BP-3-I+BP-3 -I+BP-3-I+BP-3-I+BP-3 -I+BP-5 -I+BP-5-I+BP-5 + Pre IGF IGF + Pre + Pre IGF IGF + Sol + Sol + SFM + SFM + SFM IGF IGF IGF + Pre + Pre + Pre IGF IGF IGF + Sol + Sol + Sol IGF + Pre IGF + Sol IGF + Pre + SFM + SFM IGF IGF + Pre + Pre IGF IGF + Sol + Sol + SFM + SFM IGF IGF + Pre + Pre IGF IGF + Sol + Sol IGF IGF IGF + Pre + Pre + Pre IGF IGF IGF + Sol + Sol + Sol + SFM IGF + Sol IGF IGF + Pre + Pre IGF IGF + Sol + Sol

10 min 60 min 10 min 60 min 0 min + VN 0 min + VN p-ERK1/2 (T202/Y204)

ERK1/2

p-AKT (S473)

AKT

MCF-7 -I -I -I+BP-5 -I -I -I -I -I -I+BP-5 -I -I -I+BP-5 -I+BP-5 -I+BP-3 -I+BP-3 -I+BP-5 -I+BP-3 -I+BP-3 -I -I -I -I -I -I -I -I+BP-5 -I -I+BP-3 -I+BP-3 -I+BP-5 -I -I+BP-3 -I+BP-3 -I+BP-5 -I+BP-5 -I+BP-5 + SFM IGF + Sol + SFM + Sol IGF + Sol + Pre IGF + Pre IGF + Sol + Pre IGF + Pre IGF + Sol + Pre IGF + Pre + Pre IGF + Pre IGF + Sol IGF + Pre IGF + Pre IGF + Sol + Pre IGF + Pre IGF + Pre IGF + Sol IGF + Pre + Pre IGF + Pre IGF + Pre + SFM + SFM IGF + Pre + SFM + SFM IGF + Sol IGF + Pre + SFM IGF + Sol IGF + Pre IGF + Sol IGF + Pre IGF + Sol + SFM IGF + Sol IGF + Pre + SFM IGF + Sol IGF + Pre IGF + Sol IGF + Pre IGF + Sol + Sol IGF + Sol + Sol IGF + Sol + Sol IGF + Sol

10 min 60 min 10 min 60 min 0 min + VN 0 min + VN p-ERK1/2 (T202/Y204)

ERK1/2

p-AKT (S473)

AKT

Figure 4.8.2: Comparison of ERK 1/2 and AKT activation by substrate-bound and solution-phase complexes in MCF-10A and MCF-7 cells. Six-well plates were pre-coated with substrate-bound complexes as described in chapter 2.4.4. Solution-phase IGF-I and IGFBPs were added to VN-coated wells immediately prior to the addition of cells. After incubation of cells for the indicated times, the cells were lysed and levels of phosphorylated ERK 1/2 or AKT determined by immunoblot analysis. Membranes were subsequently stripped and re-probed to determine total levels of ERK 1/2 or AKT. Representative results of multiple experiments are shown.

132 able to stimulate the IGF-1R and activate downstream signaling. A slight decrease in activation of ERK 1/2 by substrate-bound IGF-I:IGFBP treatments compared to solution-phase IGF-I:IGFBP treatments at the 60 minute time point was also observed (Figure 4.8.2). However, similar levels of AKT activation were induced by IGF-I in the presence of IGFBP-3 and IGFBP-5 by substrate-bound or solution-phase factors in both the MCF-10A and MCF-7 cell lines at 10 minutes and 60 minutes (Figure 4.8.2).

133 4.4 DISCUSSION Experimental observations by Kricker et al., (2003) demonstrated that IGF-I can bind to the ECM protein VN through the involvement of IGF-binding proteins-2, -3, -4 and -5. As IGF-I and VN have established roles in breast cancer metastasis, the studies undertaken in Chapter 3 were aimed at determining the functional consequences of the interaction of IGF-I, IGFBPs and VN in breast cell biology. Hence, the functional responses of MCF-7 breast carcinoma cells and “normal” non-tumourgenic MCF-10A mammary epithelial cells were investigated to allow side-by-side comparisons of the responses of malignant and “normal” breast epithelial cells to these complexes. The data presented in Chapter 3 demonstrated that substrate-bound IGF-I:IGFBP:VN complexes stimulated synergistic increases in cellular migration in both cell types. However, prior to the studies reported herein (Chapter 4) there had been little investigation into the potential mechanisms underpinning the enhanced migration observed in response to substrate-bound IGF:VN complexes, despite a number of recent publications reporting the functional significance of these complexes in a range of cell types (Kricker et al. 2003; Noble et al. 2003; Hyde et al. 2004; Hollier et al. 2005; Ainscough et al. 2006; Dawson et al. 2006). As such, the studies reported in this chapter comprise the first significant investigation into the mechanisms of IGF- I:IGFBP:VN-stimulated cell migration.

In particular, studies using IGF-I analogues determined IGF-I:IGFBP:VN-stimulated cell migration is dependent upon both heterotrimeric IGF-I:IGFBP:VN complex formation and the involvement of the IGF-1R. Furthermore, the enhanced cellular migration was abolished upon incubation of MCF-7 and MCF-10A cells with function blocking antibodies directed at VN-binding integrins and the IGF-IR. Analysis of the signal transduction pathways underlying the enhanced cell migration revealed that the complexes stimulate a transient activation of the ERK/MAPK signaling pathway, and simultaneously induce a sustained activation of the PI3-K/AKT pathway. Experiments using pharmacological inhibitors of these pathways revealed a critical requirement for PI3-K/AKT activation in the observed response. Indeed, overexpression of wild type and activated AKT 1 further increase substrate-bound IGF-I:IGFBP:VN-stimulated

134 migration, independently of an overall increase in basal levels of cellular migration. The data reported throughout this chapter therefore provide the first mechanistic insights into the action of IGF-I:IGFBP:VN complexes and add further evidence in support of the involvement of VN-binding integrins and their co-operation with the IGF-IR in the promotion of tumour cell migration.

As described in detail in Chapter 1, the IGF system is a highly relevant growth regulatory system in the mammary gland (Hadsell 2003). However, dysregulation or overexpression of elements of the IGF system can lead to aberant IGF-1R signaling and aid the development of the invasive phenotype. Similarly, VN has been associated with breast tumourigenesis in vivo and an increased expression of VN and VN-binding integrins has been reported at the leading edge of migrating tumour cells (Gladson and Cheresh 1991; Uhm et al. 1999; Bello et al. 2001). Therefore, interactions between the IGF-1R, VN and VN-binding integrins would appear to have a particular relevance for breast cancer metastasis. However, to date the mechanisms behind the enhanced migration in response to IGFs associated with VN have remained unclear. Using IGF-I analogues which bind IGFBPs poorly, we demonstrate a critical functional involvement of IGFBP-3 and IGFBP-5 in heterotrimeric complex-stimulated cell migration (Figure 4.1). These results support the hypothesis that IGF-I, through the involvement of IGFBPs, can be captured by the ECM and provide a local reservoir of IGF-I in the pericellular environment which can interact with the IGF-1R. Indeed, activation of the IGF-1R is critical to this response as the [L24][A31]-IGF-I analogue, which binds to IGFBPs but has a reduced affinity for the IGF-1R, failed to increase cell migration above that observed for VN alone (Figure 4.1). Thus, IGF-I associated with VN via IGFBPs, needs to be capable of activating the IGF-1R to enhance cell migration. This was further confirmed as antibody inhibition of the IGF-1R drastically decreased migration of MCF-10A and MCF-7 cells in response to substrate-bound IGF- I:IGFBP:VN complexes (Figures 4.2.1 and 4.2.2).

Activation of the IGF-1R can regulate many of the cellular functions associated with breast cancer progression and metastasis (Surmacz 2000) making it an attractive

135 therapeutic target for breast cancer treatment (Sachdev and Yee 2006). IGF-1R hyperactivation has been implicated in the early stages of breast cancer and increased IGF-1R expression in primary breast tumours has been associated with shorter disease- free survival (Surmacz 2000). This suggests the IGF-1R has a role in the dissemination of breast cancer cells (Sachdev and Yee 2006). Others have also reported that antibody- mediated inhibition of the IGF-1R substantially reduces IGF-I stimulated in vitro migration of MCF-7 and MDA-MB-231 cells on ECM proteins, including VN (Doerr and Jones 1996). In addition, inhibiting IGF/IGF-1R interactions via the expression of a soluble form of the IGF-1R, inhibits the attachment, invasion and in vivo metastasis of aggressive breast cancer cell lines (Dunn et al. 1998). In a similar fashion, a dominant negative C-terminal truncated IGF-1R, that lacked critical autophosphorylation sites, was demonstrated to inhibit the in vivo metastasis of MDA-MB-435 breast cancer cells injected into the mammary fat pad of mice (Sachdev et al. 2004). Interestingly, these studies both report IGF-1R inhibition had no significant effect on primary tumour growth (Dunn et al. 1998; Sachdev et al. 2004) and suggest that IGF-I signaling can regulate the metastatic phenotype independent of tumour growth.

As already mentioned, studies reported here demonstrate that antibody inhibition of the IGF-1R substantially reduced the migration of both MCF-10A and MCF-7 cells in response to substrate-bound IGF-I:IGFBP:VN complexes (Figures 4.2.1 and 4.2.2). This effect was most prominent in MCF-10A cells compared to MCF-7 cells, and may reflect the lower level of IGF-1R expression in MCF-10A cells. Studies were also undertaken to determine the role of integrins in IGF-I:IGFBP:VN-stimulated cell migration as integrin/matrix associations are required for optimal growth factor signaling (Eliceiri 2001). Overall, antibody inhibition of the αv-integrin subunit led to the greatest reduction in cell migration responses to IGF-I:IGFBP:VN complexes in both cell types (Figure 4.2.1 and 4.2.2). More specifically, the αvβ5 integrin was determined to be the major VN-binding integrin involved in mediating IGF- I:IGFBP:VN-stimulated cell migration in both cell types, supporting previous data demonstrating the importance of αvβ5 in IGF-I stimulated migration of MCF-7 cells on VN (Doerr and Jones 1996). Thus, while both the IGF-1R and VN-binding integrins are

136 involved in IGF-I:IGFBP:VN-stimulated cell migration, the relative contribution of these receptors to this response is likely to be due to the cell specific expression levels of these receptors. Nevertheless, these results demonstrate the importance of the IGF- 1R and VN-binding integrins (predominantly αvβ5) in the MCF-7 and MCF-10A cell migration responses. Furthermore, these data support the critical involvement of integrin receptors in IGF-stimulated cell migration.

As outlined in Chapters 1 and 3, numerous growth factor receptors interact with ECM components to modulate cellular functions, suggesting an important interaction between growth factor receptors and the ECM (Eliceiri 2001; Stupack and Cheresh 2002). There is accumulating evidence for direct co-operation between the IGF-1R and VN-binding integrins as the signaling pathways stimulated by these receptors are clearly interconnected (Clemmons and Maile 2004; Maile et al. 2006). Indeed, numerous studies from the Clemmon’s laboratory (University of North Carolina, Chapel Hill, USA) over several years have demonstrated an important interaction between the IGF-1R and αvβ3 integrin in mediating reponses of SMCs to IGF-I (as reviewed in chapter 1.17). These studies have demonstrated a requirement for VN binding to αvβ3 in order for vascular cells to respond optimally to IGF-I; blocking ligand occupancy of αvβ3 inhibits IGF-I-stimulated responses, such as cell proliferation and migration, via regulating IGF-1R signaling (Clemmons and Maile 2004; Clemmons and Maile 2005; Ling et al. 2005; Maile et al. 2006; Clemmons et al. 2007). It seems, however, that this regulation is not unidirectional, as IGF-1R activation by IGF-I can modulate integrin signaling (Maile et al. 2002). Furthermore, co-operation between the IGF-1R and αvβ5 integrin has already been demonstrated to promote tumour cell metastasis in vivo independent of tumour cell growth (Brooks et al. 1997). Our study provides additional evidence for the important interaction between the IGF-1R and VN- binding integrins, as blockade of these receptors inhibits IGF-I:IGFBP:VN-stimulated cellular migration. In addition, these results corroborate previous findings where inhibition of the IGF-1R and αvβ5 integrin reduced IGF-I-stimulated migration on VN in MCF-7 cells (Doerr and Jones 1996).

137 One mechanism by which growth factor receptors and integrins may collaborate is via their respective post-receptor signaling pathways (Plopper et al. 1995; Miyamoto et al. 1996; Eliceiri 2001). Therefore, preliminary studies investigating the activation of ERK/MAPK and PI3-K/AKT pathways were undertaken to determine if this was a possible mechanism for the enhanced cell migration responses observed in the presence of substrate-bound IGF-I:IGFBP:VN complexes. Substrate-bound IGF-I:IGFBP:VN complexes were demonstrated herein to induce synergistic increases in intracellular signal transduction, in particular, an increased and sustained activation of the PI3- K/AKT pathway (Figures 4.4.1 and 4.4.2). In both MCF-7 and MCF-10A cell lines, synergistic increases in AKT activation required all components of the complex to be present (Figures 4.4.1 and 4.4.2). Moreover, optimal IGF-I:IGFBP:VN-stimulated signaling required activation of both the IGF-1R and VN-binding αv-integrins (Figure 4.5). It was also observed that antibody blockade of the αv-integrin subunit led to reductions in IGF-I:IGFBP-stimulated MAPK and PI3-K/AKT pathway activation (Figure 4.5). Therefore demonstrating that even in the absence of exogenously added VN, inhibition of αv-integrins can decrease IGF-I signaling via the IGF-1R. This was a significant finding as these cells do not express the αvβ3 integrin, the focus of several studies investigating integrin regulation of IGF-I-stimulated signaling. Nevertheless, my data support previous findings in SMCs, whereby ligation of the αvβ3 integrin is required for optimal IGF-I-stimulated intracellular signaling, DNA synthesis and migration (Jones et al. 1996; Ling et al. 2003; Maile et al. 2006). Thus, my data suggest that other integrins in addition to αvβ3, also have a role in modulating IGF-I signaling.

The results reported herein also indicate a pivotal role for AKT in promoting breast cell migration as the pharmacological inhibition of the PI3-K/AKT pathway attenuated IGF-I:IGFBP:VN-stimulated cell migration (Figure 4.6). Inhibition of the ERK/MAPK pathway had less of an effect, but in combination with PI3-K/AKT inhibition further reduced cell migration, particularly in MCF-7 cells. The importance for the PI3-K/AKT pathway in the enhanced migration responses is confirmed by increased cell migration observed in response to IGF-I:IGFBP:VN complexes in cells overexpressing wild type and activated AKT 1 (Figure 4.7 B). Importantly, this was not due to an overall

138 increase in basal cell migration, as AKT overexpression failed to increase cell migration in response to VN alone (Figure 4.7 C). Significantly, it was demonstrated that activation of the IGF-1R and αv-integrins was still required to enhance the migration of cells in response to IGF-I:IGFBP:VN complexes via a PI3-K/AKT mediated mechanism (Figure 4.7 D and E). Taken together, these results support previous studies demonstrating the importance of the PI3-K/AKT pathway in IGF-I- dependent motility of cancer cells (Bartucci et al. 2001; Tanno et al. 2001).

AKT’s well established role in cell survival, proliferation and metabolism has important pathological implications for tumourigenesis and metastasis. Indeed, AKT is hyperactivated in many human cancers (Hay 2005). There is accumulating evidence indicating that AKT promotes cell motility in fibroblasts and tumour cells (Higuchi et al. 2001; Kim et al. 2001; Onishi et al. 2007), including interactions with proteins such as Rac, Cdc42 and Girdin known to modulate actin organisation and microtubule stabilisation at the leading edge of migrating cells (Higuchi et al. 2001; Enomoto et al. 2005). Moreover, a number of clinical studies correlate increased AKT expression and activation with more invasive and metastatic disease, which in turn, is associated with poor prognosis (Scheid and Woodgett 2001; Vivanco and Sawyers 2002; Hay 2005; Tokunaga et al. 2006). Conversely, in a study using an in vivo mouse model of breast cancer progression, activation of AKT accelerates ErbB-2-mediated mammary tumourigenesis, but reduced the invasion of cells into the surrounding tissue and the development of metastatic lesions (Hutchinson et al. 2004). Intriguingly, AKT activation has also been reported to block the in vitro motility and invasion of breast cancer cells (Yoeli-Lerner et al. 2005). Isoform-specific roles of AKT 1 and AKT 2 have been demonstrated in MCF-10A cells overexpressing the IGF-1R, where down- regulation of AKT 1 increased migration and epithelial-mesenchymal transition (EMT) in response to IGF-I (Irie et al. 2005). However, in a recent report Ju et al., (2007) show a requirement for AKT 1 in ErbB-2-induced mammary tumourigenesis as mice homozygously deleted of the AKT 1 gene had delayed tumour growth and reduced lung metastases in vivo (Ju et al. 2007). Therefore, it is possible that AKT’s role in the migration and invasion of cells may be regulated by contributions from other factors,

139 such as specific growth factors or matrix proteins. Nevertheless, our results support a role for AKT in promoting cellular migration in response to IGF-I:IGFBP:VN complexes.

Traditional in vitro approaches to studying the cellular effects of growth factors have involved adding these soluble growth factors in solution. However, we wanted to compare the functional responses to both solution-phase and substrate-bound IGF- I:IGFBP complexes. It was observed that solution-phase and substrate-bound IGF- I:IGFBP complexes induce comparable levels of cell migration in MCF-10A cells (Figure 4.8.1). Indeed, similar results have subsequently been demonstrated for MCF-7 cell migration (Dr Derek Van Lonkhuyzen, personal communication). In a similar fashion, comparable levels of ERK 1/2 and AKT activation were also observed (Figure 4.3.7(іi)). Apart from a decrease in ERK activation by substrate-bound IGF- I:IGFBP:VN complexes after 60 minutes, the only major difference in IGF-I-stimulated functional responses between the two treatment strategies was observed in the absence of IGFBPs. In the absence of IGFBPs, IGF-I cannot associate with VN during the pre- binding steps used to form the substrate-bound complexes. Thus, very little IGF-I would remain substrate-bound in the absence of IGFBPs to activate the IGF-1R. Conversely, in the solution-phase strategy all of the IGF-I added into the wells remains in solution to activate the IGF-1R. However, given the role for AKT in IGF- I:IGFBP:VN-stimulated migration, the comparable cell migration responses between solution-phase and substrate-bound IGF-I:IGFBP complexes may reflect the similar levels of AKT activation induced by these two strategies (Figure 4.8.2).

It is hypothesized that two possible mechanisms may account for these equivalent functional responses. Firstly, the majority of IGF-I added into the wells during the pre- binding step, in the presence of IGFBP-3 and IGFBP-5, is incorporated into the substrate-bound VN complexes. This would therefore make available amounts of IGF-I equivalent to that present in the solution-phase treatments. However, recent preliminary findings in our laboratory indicate that depending on the molar ratio of VN, IGFBPs and IGF-I used in the pre-binding steps, only 20-75% of the IGF-I added into the

140 solution phase remains in substrate-bound complexes (data not shown). This suggests that less IGF-I would be present in the substrate-bound complexes in the study reported here than was present in the solution-phase treatments. Yet despite this, similar responses were observed. We hypothesize that the capture of IGF-I within the pericellular environment provides a local reservoir of IGF-I, and promotes increased interactions with the IGF-1R. Indeed, it has previously been reported that IGFBPs associating with the ECM have significantly reduced affinity for IGF-I (Jones et al. 1993; Martin and Jambazov 2006). Thus, ECM-bound IGFBPs may stabilize matrix concentrations of IGF-I and establish an equilibrium which favours IGF-I/IGF-1R interactions, rather than the sequestration of IGF-I away from the IGF-1R by IGFBPs in solution. In addition, a number of growth factor receptors can have their activity regulated by clustering into focal adhesion contacts upon integrin ligation (Eliceiri 2001). Thus, the association of IGF-I with VN in close proximity to cell surface receptors may promote interactions between the IGF-1R and VN-binding integrins, thereby enhancing intracellular signaling. Therefore, reduced amounts of IGF-I would be required to induce comparable functional effects, at least in these short-term assays. However, future detailed studies to determine the quantities of substrate-bound growth factors and indeed, specific receptor interactions, will be critical in fully elucidating these mechanisms.

In summary, the data presented in this chapter indicates that IGF-I when bound to VN via IGFBP-3 or IGFBP-5 to form a heterotrimeric substrate-bound complex is a potent stimulator of MCF-7 and MCF-10A cell migration. These novel complexes appear to facilitate co-operation between the IGF-1R and VN-binding integrins, resulting in enhanced and sustained PI3-K/AKT pathway activation which is pivotal to the increased migratory response. As both IGF-I and VN are implicated in tumour biology, we propose the IGF-I:IGFBP:VN interaction is an important mechanism promoting breast cancer metastasis. Understanding the processes that lead to the establishment of secondary tumours and strategies to halt the spread of cancer beyond the primary site are therefore highly valuable, yet few interventions have targeted this aspect of breast disease. As such, future studies investigating the contribution of dysregulation, or over-

141 expression, of these receptors and their ligands to the dissemination of tumours will be of particular importance. Furthermore, as cell migration is a critical process in wound healing and tissue re-modelling, these interactions may also offer significant promise for the emerging field of tissue engineering for application in a range of tissue repair therapies.

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CHAPTER 5

Microarray analysis of genes involved in IGF-I:IGFBP:VN-stimulated cell migration

143 5.1 INTRODUCTION The IGF system plays an important role in the development and growth of many tissues. In the mammary gland, IGF-I is the primary mediator of growth hormone signaling and controls ductal development and TEB formation (Kleinberg et al. 2000). The IGF-1R is frequently overexpressed in mammary tumours (Resnik et al. 1998) and there is epidemiological evidence associating high circulating levels of IGF-I with a risk of developing breast cancer (Hankinson et al. 1998; Pollak 2004). Moreover, in line with its potent mitogenic and anti-apoptotic effects, dysregulated IGF signaling has been implicated in the initiation and progression of breast cancer (Marshman and Streuli 2002; LeRoith and Roberts 2003). However, as the manner in which cells respond to hormones and growth factors can be modulated by its surrounding microenvironment, the impact of specific ECM components on IGF-stimulated responses needs to be considered. Indeed, the data presented in Chapter 3 and 4 have demonstrated that substrate-bound IGF:IGFBP:VN complexes are potent regulators of several cellular processes with key relevance to breast cancer transformation, growth and metastasis. In particular, IGF-I:IGFBP:VN complexes stimulate increased activation of MAPK/ERK and PI3-K/AKT signaling pathways via co-activation of the IGF-1R and VN-binding integrins. Furthermore, these complexes stimulate synergistic increases in cell migration via the IGF-1R- and αv-integrin-mediated activation of the PI3-K/AKT pathway. These studies represent the first mechanistic insights into the functional effects of substrate- bound IGF-I:IGFBP:VN complexes and provide further evidence for the important role the ECM has on IGF actions. It is well recognised that functional responses to IGFs can be mediated by MAPK and PI3-K signaling cascades, which are rapidly activated in response to IGFs. However, while the intracellular signaling pathways relaying IGF signals to the nucleus are relatively well understood, the IGF target genes which mediate their biological actions are largely unknown.

The emergence of gene expression profiling using DNA microarray approaches has added valuable new information to our understanding of the molecular events underpinning IGF actions, which to date has focused on identifying mitogenic and anti- apoptotic IGF target genes in specific cell types (Dupont et al. 2001; Mulligan et al.

144 2002; Kuemmerle et al. 2004). However, few global gene expression studies aimed at identifying genes involved in IGF-stimulated cell migration and metastasis have been undertaken. While genes studied in isolation have been reported to modulate IGF- stimulated cell migration, a global picture of differential gene expression remains to be elucidated. Additionally, the impact of the ECM on IGF-induced gene expression also needs to be investigated. With results reported in the previous chapters in mind, it was hypothesized that IGF-I:IGFBP:VN complexes would induce differential expression of genes critical to enhanced cell migration. The changes in gene expression identified will not only increase our understanding of the mechanisms responsible for IGF- I:IGFBP:VN-stimulated migration, but may also provide insights into the early molecular events promoting breast cell metastasis. Consequently, studies have been performed to screen for candidate genes involved in IGF-I:IGFBP:VN-stimulated breast cell migration. In contrast to previous gene expression studies, in which pre-plated adherent cells are stimulated with hormones or growth factors, we adopted the novel strategy of isolating RNA from cells which had migrated to the undersurface of Transwell® membranes in response to substrate-bound treatments. This approach was employed to increase the number of target genes relevant to the migratory phenotype, while reducing genes differentially expressed in a “non-specific” manner.

5.2 EXPERIMENTAL PROCEDURES

Full details of the materials and methods used in experimental procedures for this chapter are described in chapter 2. The following is a brief summary of the materials and procedures used to generate the data presented in sections 5.3.1 - 5.3.6.

5.2.1 Materials For full details of the materials used in generating the data for this chapter please refer to Chapter 2. Purified human VN (Promega), IGFBP-5 (Dr Sue Firth) and IGF-I (Novozymes), were obtained as detailed in the previous chapters. Multiplates containing Transwell® inserts were purchased from Corning Corporation. All other plastic cultureware was purchased from Nalge Nunc International.

145

Other key reagents used to generate the data presented in this chapter include Fraction IV RIA grade BSA (Calbiochem), TRI Reagent (Sigma-Aldrich), MessageAmp™ II- Biotin Enhanced Single Round aRNA Amplification Kit (Ambion), Platinum Taq PCR kit (Invitrogen), Superscript III first-strand cDNA synthesis kit (Invitrogen), SYBR- green PCR master mix (Applied Biosystems). The Affymetrix GeneChip Poly-A control kit, Test3 Arrays and GeneChip® Arrays were all from Affymetrix.

5.2.2 Pre-binding of proteins Transwell® inserts were prepared by pre-binding the lower well and under-surface of 12- µm pore membranes with proteins as described in section 2.4.3. For migration assays described throughout this chapter, concentrations of proteins added into the culture wells for the pre-binding steps were as follows: VN, 1μg/mL; IGF-I, 30 ng/mL; and IGFBP-5, 90 ng/mL. These concentrations were observed to be the optimal concentrations for inducing cell migration in the studies presented in Chapter 3.

5.2.3 Transwell® migration assays Cells which had been serum-starved for 4 hours were harvested and seeded at a density of 2 x 105 cells/well in DMEM/F12-SFM + 0.05% BSA into the upper chamber of pre- coated Transwell® inserts (12-µm pores). Plates containing the Transwell® inserts were

then incubated for 5 hours at 37°C, 5% CO2.

5.2.4 Microarray analysis of differential gene expression

5.2.4.1 Extraction of RNA from migrated MCF-10A cells

Total RNA was isolated using TRI Reagent from MCF-10A cells which had migrated through the 12-µm pores onto the under-surface of Transwell® membranes in response to VN, IGFBP-5 + VN, IGF-I+VN and IGF-I+IGFBP-5+VN treatments (Treatment samples) or from non-migrated MCF-10A cells which remained on the upper-surface of the membrane in response to serum-free medium (SFM) (No Treatment sample, non- migrated control). For Treatment samples, this was achieved by briefly removing the

146 non-migrated cells from the upper surface of the membrane with a cotton bud and transferring the Transwell® insert into a fresh 12-well plate containing 200 µL of TRI reagent to lyse the migrated cells. For non-migrated control samples, any migrated cells were removed from the under-surface of the membrane and the inserts were transferred to fresh plates. Two hundred microlitres of TRI reagent was then placed into the Transwell® inserts to lyse the non-migrated cells. Twelve 200 µL TRI reagent samples, representing cells lysed from twelve Transwell® inserts, were pooled for each of the Treatments and control samples. Each sample (~ 2.4 mL) was then divided into 2 x 1 mL aliquots to facilitate RNA isolation in microcentrifuge tubes, with the remainder (~ 400 µL) stored at -80°C. To obtain biological replicates, the entire migration assay and RNA extraction procedure was repeated three times on separate days.

5.2.4.2 Total RNA isolation For full details of RNA isolation procedure please refer to sections 2.8.1.1 to 2.8.1.2. In brief, total RNA was isolated using TRI reagent following the manufacturers protocol. Chloroform was added to the cell lysate to separate RNA, DNA and protein during centrifugation. RNA was precipitated from the aqueous layer with 1 volume of isopropanol, 0.1 volume 7.5 M ammonium acetate and 5 µg/mL linear polyacrylamide at -20°C overnight. Pelleted RNA was washed with 70% ethanol and resuspended in DEPC-treated water. RNA samples were then treated with 10 U/mL rDNase (Ambion), before being purified and concentrated with phenol:chloroform:isoamyl alcohol (25:24:1) extraction. Total RNA quality was determined by running a non-denaturing 1.5% agarose TAE buffered gel and analysing the integrity of the 28S and 18S ribosomal bands. In addition, RNA purity and quantity was determined by A260/A280 and A260/A230 ratios using UV spectroscopy.

5.2.4.3 Target synthesis, In vitro transcription to synthesize biotin-labeled anti-sense RNA (aRNA), aRNA purification and fragmentation For full details of these procedures please refer to chapter 2.8.2.1 to 2.8.2.6. All steps from target synthesis through to the fragmentation of the biotin-labeled aRNA were performed using the MessageAmp™ II-Biotin Enhanced Single Round aRNA

147 Amplification Kit (Ambion), following the manufacturer’s protocols. This method is based on the RNA amplification protocol developed by James Eberwine (Van Gelder et al. 1990). The procedure consists of reverse transcription (RT) with an oligo(dT) primer bearing a T7 promoter using ArrayScript™. The cDNA then undergoes 2nd-strand synthesis and purification to become a template for in vitro transcription in a reaction containing biotin-modified UTP and T7 RNA polymerase. To maximise biotin-labeled aRNA yield, this kit uses an optimised mixture of biotin-labeled and unlabeled NTPs and the proprietary MEGAscript® in vitro transcription technology to generate hundreds to thousands of antisense RNA copies (or aRNA, also commonly called cRNA) of each mRNA in a sample. The biotin-labeled aRNA was then purified and fragmented in preparation for hybridisation to Affymetrix GeneChip® Arrays.

5.2.4.4 Affymetrix GeneChip® Human Genome U133 Plus 2.0 (HG-U133 Plus 2.0) array The HG-U133 Plus 2.0 array is one microarray comprised of 1,300,000 unique oligonucleotide features covering over 47,000 transcripts and variants, representing approximately 39,000 of the best characterized human genes (54,675 individual probe sets). The majority of the probe sets used in the design of the array are selected from GenBank®, dbEST, and RefSeq. Sequence clusters are created from Build 133 of UniGene (April 20, 2001) and are refined by analysis and comparison with a number of other publicly available databases, including the Washington University EST trace repository and the University of California, Santa Cruz Golden-Path human genome database (April 2001 release). Oligonucleotide probes are synthesized in situ complementary to each corresponding sequence. Eleven pairs of oligonucleotide probes are used to measure the level of transcription of each sequence represented. Additional features of the HG-U133 Plus 2.0 array include: 11 μm feature size, hybridization controls (bioB, bioC, bioD, cre), poly-A-controls (dap, lys, phe, thr), GAPDH and β- actin housekeeping/control genes and an additional 100 probe sets used for normalization controls. For full details see www.affymetrix.com.

148 5.2.4.5 GeneChip® hybridisation and scanning For full details of these procedures please refer to chapter 2.8.3.1 to 2.8.3.3. In brief, 15 µg of fragmented aRNA probe samples were combined with hybridization buffer, containing hybridization controls, and hybridized to Test3 and HG-U133 Plus 2.0 Arrays for 16 hours at 45°C in a rotary incubator. Following hybridisation, the samples were removed and the arrays were equilabrated to room temperature before washing and staining on the Affymetrix Fluidics Station 400 operated by the GeneChip® Operating Software (GCOS) version 1.4. Test3 Arrays and HU133 Plus 2.0 Genechip® Arrays were washed and stained following the Mini_euk2v3 and EukGE-WS2v5 fluidic protocols, respectively (for details see: http://www.affymetrix.com/support/technical/fluidics_scripts.affx). Probe arrays were then scanned with an Affymetrix GeneChip® Scanner 3000 at a wavelength of 570 nm. Each array was scanned twice to increase the reproducibility and accuracy of the probe intensity measurements. The data for each Test3 and HG-U133 Plus 2.0 arrays was then processed using GCOS.

5.2.4.6 Processing and quality control (QC) using GCOS Cell summary reports and single-array expression analysis were performed for scanned images to assess the performance of the hybridisation and grid alignment of arrays. For single-array expression analysis, each image file (CEL file) for individual samples was scaled to a target signal of 150 across all probe sets. For each transcript represented on the probe array, the algorithm computes a Detection call, Detection p-value, and signal. The image and data quality was then evaluated using a series of quality control parameters associated with assay and hybridisation performance; this ensures comparability of samples across arrays. These include the: • average background (should be 20 - 100); • the percentage of “present” calls (should be within 10% for all samples); • 3’/5’ ratio of β-actin and GAPDH housekeeping genes (ideally 1, but below 3 is acceptable);

149 • scale factor (SF) was also assessed to determine the compatibility of individual arrays for subsequent multiple-array comparisons (samples within 3-fold of each other); • “present” calls and increasing signal intensity of hybridisation controls, bioB, bioC, bioD, and cre; and • “present” calls and increasing signal intensity of “spiked” poly-A controls, lys, phe, thr, dap. The integrity of total RNA, the performance of cRNA labeling and microarray hybridization, and quality of the microarray data was assessed according to the procedures outlined in Guidelines for Assessing Data Quality, contained within the Affymetrix Data Analysis Fundamentals Manual, www.affymetrix.com/support/technical/manual/expression_manual.affx, as briefly summarised in Appendix 2, Table A2-1 to A2-3. All HG-U133 Plus 2.0 arrays hybridised passed the performance parameters and were used in subsequent analysis.

5.2.5 Microarray data analysis

5.2.5.1 Data analysis and presentation using GeneSpring GX 7.3 The CEL files containing individual raw array data (probe intensities) were imported to GeneSpring GX 7.3 and pre-processed using Robust Multi-chip Average, with GC- content background correction (GC-RMA). Data which had been normalised with GC- RMA was then further normalised using the ‘per gene normalisation’ step in which all the samples were normalised against the median of the control samples (No Treatment). Therefore, the expression value for one gene across the different conditions is centred on 1, by dividing the expression value by the median expression value for that gene across the conditions (Treatments). This ensures that genes which do not change across conditions received a normalised expression value of 1, allowing for easier visual detection of differentially expressed genes. Statistically significant differentially expressed genes were then identified from pair-wise comparisons using a fold change threshold of 1.8 and one-way ANOVA (parametric test, assuming unequal variances) with ‘Benjamini and Hochberg false discovery rate’ (BHFDR) as the multiple testing

150 correction (p = 0.05). Together this generates lists of probe sets whose expression are significantly increased or decreased by at least 1.8-fold.

5.2.5.2 Functional analysis of target genes A data set containing gene identifiers and corresponding expression values of probe sets determined to be significantly expressed by at least +/- 1.8-fold in response to IGF- I:IGFBP-5:VN complexes compared to VN alone, was used for functional analysis. Gene ontology, canonical pathway, and functional network analyses were undertaken using Ingenuity Pathway Analysis (IPA) tools (Ingenuity® Systems, www.ingenuity.com) as described in chapter 2.9.2. KEGG pathway analysis was also performed on the same data set using the DAVID online functional annotation tool as described in chapter 2.9.3.

5.2.6 Confirmation of differential gene expression using quantitative real-time RT- PCR (qRT-PCR) For full details of these protocols please refer to chapter 2.10.1 to 2.10.5. qRT-PCR was used to validate microarray expression data by measuring absolute expression levels of selected genes of interest. Primer Express (Applied Biosystems) was used to design all primers used in qRT-PCR as already outlined in Chapter 2 (Table 2.1). All RNA was reverse transcribed into cDNA using SuperScript III (Invitrogen) following the manufacturer’s protocol. Absolute quantification was carried out using standard curves covering at least 6 logs of amplicon copy number, generated by 10-fold serial dilutions of purified PCR target amplicons. All reactions were performed in triplicate in 20 μL volumes in a 96-well format using SYBR green (Applied Biosystems) and an ABI Prism 7300 Sequence Detection System (Applied Biosystems). Reactions contained 1 X SYBR-green PCR mix, 0.25 μM forward and reverse primers and 2.5 μL of the cDNA dilutions. PCR amplification followed a two step cycling protocol with an initial 10 minute denaturation at 95ºC, with 40 cycles of 95ºC for 15 seconds and 60ºC for 1 minute. All real-time reactions included a post-amplification melt curve analysis to determine the melting temperature (Tm) of the amplified PCR product, indicating amplification of the correct sequence. Real-time curves were analysed with ABI

151 Sequence Detection System software version 1.2 (Applied Biosystems) using the automatic option for baseline and threshold values. The software determines the PCR cycle at which each reaction reached its log-linear phase and is directly proportional to the amount of starting cDNA transcript. The cDNA copy number for each reaction was then calculated by direct comparison to the known standards for each gene, which are run concomitantly. Target gene expression for each sample was then normalised to 18S rRNA.

5.3 RESULTS

5.3.1 Experimental strategy for isolation of RNA for microarray analysis of substrate-bound IGF-I:IGFBP-5:VN-stimulated breast cell migration In order to identify candidate genes involved in substrate-bound IGF-I:IGFBP-5:VN- stimulated cell migration, a novel strategy for the isolation of RNA for use in downstream array analysis was adopted. The vast majority of microarray studies investigating the effects of hormones or growth factors on gene expression stimulate adherent pre-plated cells for a desired time period before isolating RNA for subsequent microarray analysis. Differentially expressed genes are then identified and data sets containing genes of interest uploaded into a variety of functional or gene ontology analysis packages in an attempt to identify the most significant biological functions and/or diseases represented by the target genes. Using this approach, there can be large numbers of genes identified which may have no particular relevance to the specific phenotype of interest. Since stimulation of cell migration in response to substrate-bound IGF-I:IGFBP-5:VN complexes was the focus of the studies in this thesis, RNA was isolated from MCF-10A cells which had migrated to the undersurface of Transwell® insert membranes containing 12-µm pores in response to VN alone, IGFBP-5+VN, IGF- I+VN and IGF-I+IGFBP-5+VN treatments (Treatment arrays). As a control, RNA was also isolated from non-migrated cells which remained on the uppersurface of the insert membrane in response to SFM (Control or No Treatment arrays). This strategy aimed to increase the likelihood of identifying candidate genes involved in cell migration, while keeping irrelevant genes to a minimum. To obtain biological replicates, RNA was

152 isolated from identical experiments performed on three separate days. To confirm that substrate-bound IGF-I:IGFBP-5:VN complexes were inducing MCF-10A cell migration in the experiments from which RNA was isolated, three random wells were fixed and stained with crystal violet to quantify relative levels of cell migration (Figure 5.1). Stimulation of cell migration by substrate-bound IGF-I:IGFBP-5:VN complexes was confirmed with a more than 2-fold increase in migration observed compared to that induced by VN alone (Figure 5.1).

5.3.2 HU133 Plus 2 GeneChip® Array Quality Control (QC) The Affymetrix GeneChip® system is one of the most reliable and reproducible microarray platforms due to the use of multiple fixed probe sets that interrogate each probe sequence hybridised to the array. GeneChip® arrays also contain a variety of internal controls which ensure the comparability of samples across arrays. The integrity of total RNA, the performance of aRNA (or cRNA) labelling and hybridisation, and the quality of the microarray data was assessed according to the procedures detailed in the Affymetrix Data Analysis Fundamentals manual (summarised in Appendix 2). Initial data analysis to monitor array performance was carried out using GCOS, employing a global scaling strategy to normalise all data prior to analysis. As seen in Table 5.1, initial analysis using GCOS revealed that among the 54,675 probe sets contained on the HG- U133 Plus 2.0 arrays, 36.6 – 44.1% of the probes were called “present” across all arrays hybridised. The percent “present” calls across all samples hybridised to the arrays was within a user defined range of 10%, and scale factors were confirmed to be within 3-fold to allow comparisons between arrays (Table 5.1). All 3’ to 5’ ratios of GAPDH and β- actin housekeeping genes were also determined to be below the cut-off ratio of 3, validating that the samples were not degraded and there was no bias toward the increased presence of 3’ probes (Table 5.1).

153

†

225 *

200

175

150 *

125

100

75

% stimulation of VN alone 50

25

0

VN VN +VN I+ 5 F- IG FBP- IGFBP-5+VN G F-I+I IG

Figure 5.1: Confirmation of MCF-10A cell migration stimulated by substrate-bound IGF-I:IGFBP- 5:VN complexes. MCF-10A cells were seeded onto Transwell® inserts which had the undersurface and lower well coated with VN, and IGF-I pre-bound in the presence or absence of IGFBP-5. The cells were allowed to migrate for 5 hours. The number of cells transversing the membrane in response to each treatment was then expressed as a percentage of those that migrated on VN only (VN). The data are pooled from triplicate wells in each of the three experiments in which RNA was isolated for microarray analysis (n = 9). The asterisk indicates treatments which significantly increased migration above the VN only wells (p<0.05). The IGF-I:IGFBP-5:VN complex was also observed to increase migration above the individual components of the complex (†)(p<0.05). Error bars indicate SEM.

154

Table 5.1. QC performance characteristics of samples hybridised to HG-U133 Plus 2.0 arrays.

3'/5' ratio Array SF % Present GAPDH 3'/5' ratio β-actin Exp-1 Control 3 36.6 1.03 0.98 Exp-1 VN 2.83 40.1 1.05 1.57 Exp-1 BP-5+VN 3.37 39.7 1.03 1.46 Exp-1 IGF-I+VN 3.33 39.8 1.02 1.34 Exp-1 IGF-I+BP-5+VN 3.37 39 1.02 1.37 Exp-2 Control 1.54 43.8 0.96 1.28 Exp-2 VN 1.41 43.3 0.95 1.31 Exp-2 BP-5+VN 1.5 42.7 0.96 1.42 Exp-2 IGF-I+VN 1.34 42.3 0.92 1.23 Exp-2 IGF-I+BP-5+VN 1.24 41.4 0.89 1.21 Exp-3 Control 1.49 44.1 0.94 1.58 Exp-3 VN 1.54 42.6 0.93 1.43 Exp-3 BP-5+VN 1.59 43.5 0.96 1.55 Exp-3 IGF-I+VN 1.41 42.7 0.94 1.45 Exp-3 IGF-I+BP-5+VN 1.94 42.7 0.93 1.37

155 The data sets from HG-U133 arrays were also analysed in Expression Console (Affymetrix) to further assess the quality of each array. HG-U133 arrays were analysed for their relative log expression signal (RLE) after normalisation with RMA and box plots were used to easily visualise potential outlier arrays (Figure 5.2). The RLE for individual arrays is compared to the median expression level across the whole group, thus identifying possible outliers that may need to be further investigated or excluded from the data set. The variability between arrays using the RMA algorithm was confirmed to be relatively small and consistent across all sample arrays, with no outstanding outliers detected (Figure 5.2). The median expression level for the majority of the individual arrays (represented by the black bar inside each box) was consistent with the overall group median expression level (represented by the red zero line), with only minor small deviations observed for some arrays (Figure 5.2). The Experiment-1 Control array showed a minor increase in variability, however, this was determined to be within normal acceptable limits (personal communication, Dr Agnes Lichanska, University of Queensland). The variability in signal values from the samples hybridised to HG-U133 Plus 2.0 arrays was also visualised by generating histogram plots (Figure 5.3). Histogram plots display a graphical representation of probeset signal values which can also be used to assess the variability of individual arrays in comparison to all arrays within a data set. As can be seen in Figure 5.3, all arrays showed similar variability in expression values, with no outlier arrays observed. At the completion of QC assessment all arrays (15 in total) were deemed suitable for subsequent gene expression analysis.

5.3.3 Identification of differential gene expression The fifteen CEL files generated in GCOS containing individual raw array data (probe intensities) were imported into GeneSpring GX 7.3 software and pre-processed using Robust Multi-chip Average, with GC-content background correction (GC-RMA). An additional ‘per gene normalisation’ step was performed in which the expression of each probeset across treatments was normalised against the median expression of the corresponding gene on the control arrays. This ensures that genes which do not change across conditions received a normalised expression value of 1. Replicates for each

156

Figure 5.2: Relative Log Expression (RLE) Signal Box Plot. HG-U133 Plus 2.0 arrays were assessed for their quality of relative expression values. The variability between arrays using the RMA algorithm, indicated no prominent outliers. The median for each array (black line within each box) is either on or very close to the median RLE of the group (red zero line). The Box plot was generated in Expression Console (Afymetrix).

157

Figure 5.3: Histogram of Signal Values. The graphical representation of probeset signal values across individual arrays confirms the similar variability for all arrays analysed. No outlier arrays are observed. The Histogram was generated in Expression Console (Affymetrix) using the RMA algorithm.

158 sample array were combined by “Treatment type”, effectively forming five treatment groups, each containing the combined normalized data generated from the hybridization of the 3 RNA isolations/biological replicates; 1) Control or No Treatment (un-migrated cells), 2) VN alone, 3) IGFBP-5+VN, 4) IGF-I+VN, and 5) IGF-I+IGFBP-5+VN.

In order to identify differentially expressed genes in response to each treatment, pair- wise comparisons between each “treatment” group and the “control” group were performed (Table 5.2). Comparisons between each “treatment’ group and “VN alone” were also performed to identify IGFBP-5-, IGF-I- and IGF-I+IGFBP-5-induced changes in gene expression (Table 5.2). Statistically significant differentially expressed genes were then identified using a fold change threshold of 1.8 and one-way ANOVA (parametric test, assuming unequal variances) with Benjamini and Hochberg False Discovery Rate (BHFDR) as the multiple testing correction (p = 0.05). This, generated lists of probe sets whose expression was significantly increased or decreased by at least 1.8-fold. The selection criteria of 1.8-fold was used to ensure critical genes which may not have a large magnitude of change were identified, while also keeping data sets to a manageable size.

Table 5.2: Number of probe sets identified as differentially regulated in Pairwise Comparisons. The table shows the number of statistically significant probe sets which were identified to be at least ± 1.8-fold differentially expressed in each pairwise comparison (p<0.05). BP-5 = IGFBP-5.

Pairwise Comparison VN BP-5+VN IGF-I+VN IGF-I+BP-5+VN Control 14 17 20 231 VN - 0 0 194

To identify genes that may be involved in the induction of cell migration in response to each treatment, initial pair-wise comparisons between each of the “treatment” groups and “control” group were undertaken. As can be seen in Table 5.2, only a small number of transcripts were identified to be differentially expressed by VN alone (14), IGFBP- 5+VN (17) and IGF-I+VN (20) treatments when compared to the control arrays. In contrast, a far greater number of transcripts (231) were identified to be differentially regulated by the IGF-I+IGFBP-5+VN treatment when compared to the control arrays. Since the control arrays represent RNA isolated from un-migrated cells, the number of

159 genes differentially regulated in cells which had migrated in response to each treatment was minimal, with the exception of the IGF-I+IGFBP-5+VN treatment. As expected in view of the similar levels of migration induced by VN, IGFBP-5+VN and IGF-I+VN treatments (Figure 5.1 and Figure 3.4.1), there was considerable overlap in genes differentially expressed by these treatments (Venn diagram, Figure 5.4), whereby the majority of transcripts regulated by VN alone, IGFBP-5+VN or IGF-I+VN, were shared by all three treatments (Figure 5.4). Indeed, thirteen of the genes were shared by all three treatments, with only COL8A1 and PDK4 uniquely expressed in response to VN alone and IGFBP-5+VN, respectively (Figure 5.4). Four genes were determined to be uniquely expressed by IGF-I+VN, namely KLF6, CLDN1, BACH2 and HIST1H2BD (Figure 5.4). This indicates that the majority of the transcripts differentially regulated in migratory cells by these three treatments, in comparison to un-migrated cells, are most likely due to the effect of VN which is present in all treatments. The commonality in gene expression induced by VN alone, IGFBP-5+VN and IGF-I+VN treatments was highlighted in direct pair-wise comparisons between IGFBP-5+VN and IGF-I+VN with VN alone (Table 5.2). No genes were identified to be significantly expressed by at least ± 1.8-fold in IGFBP-5+VN or IGF-I+VN treatments when compared to those induced by VN alone (Table 5.2). This therefore, indicates the commonality in gene expression between VN alone, IGFBP-5+VN and IGF-I+VN treatments, whereby VN is responsible for the majority, albeit small, changes in gene expression.

To further analyze the similarity between the samples, Principal Component Analysis (PCA) was performed in Partek Genomics Suite (Partek Inc, St Charles, MO). PCA is a form of multidimensional scaling using the first three principal components to reduce the complex dimensionality of microarray data and create a three-dimensional plot that visualizes the relatedness of the samples. PCA plots were generated and biological replicates for each sample type were clustered together indicating the absence of outlier arrays within the data set (Figure 5.5). PCA analysis showed a clear separation of the samples into three distinct groupings. The first contained only the control samples (Red), while the second group contained only the IGF-I+IGFBP-5+VN samples (Green).

160

Affymetrix Gene Symbol VN* BP-5+VN* IGF-I+VN* Probe I.D 209101_at CTGFa 4.27 3.85 3.81 229357_at ADAMTS5a 3.86 4.06 3.15 220014_at LOC51334a 2.88 2.71 2.66 219935_at ADAMTS5a 2.67 2.31 2.48 222486_s_at ADAMTS1a 2.08 2.19 1.83 201242_s_at ATP1B1a 1.88 1.81 2.22 201243_s_at ATP1B1a 1.83 1.87 2.15 1555996_s_at EIF4A2a -1.93 -2.01 -1.89 227949_at PHACTR3a -2.20 -1.90 -2.79 1559739_at CHPT1a -2.26 -2.29 -2.62 218723_s_at RGC32a -2.86 -2.73 -3.92 1562321_at PDK4a -3.18 -2.98 -3.60 228193_s_at RGC32a -3.51 -3.30 -3.05 226237_at COL8A1b 1.83 - - 227556_at NME7c - 1.81 2.20 222719_s_at PDGFCc - 1.92 2.00 219799_s_at DHRS9c - -1.85 -2.10 205960_at PDK4d - -2.56 - 208960_s_at KLF6e - - -1.83 218182_s_at CLDN1 e - - -1.85 221234_s_at BACH2 e - - -1.90 235456_at HIST1H2BD e - - -1.96

Figure 5.4. Probe sets identified as differentially expressed in pair-wise comparisons of VN alone, IGFBP-5+VN and IGF-I+VN treated cells when compared to the expression of the control group. All genes passed a +/- 1.8-fold change threshold compared to the control samples and one-way ANOVA with BHFDR, p<0.05. The Venn diagram shows the number of genes in common, or uniquely expressed, by each treatment when compared to the expression of the control group. Probe identifiers, gene symbols and relative fold changes are displayed.

161

Figure 5.5: PCA plot of treatment and control samples. PCA analysis of all 15 samples was performed using Partek Genomics Suite; 49.1% of the data are represented in the scatterplot and are divided among the three main basic vectors, PC #1 (x-axis, 12.4%), PC #2 (y-axis, 16.1%) and PC #3 (z-axis, 20.6%). Biological replicates for each sample clustered together with no outlier arrays. The samples were separated into 3 distinct groupings based on their similarity of expression profiles. Group 1, Control samples; Group 2, IGF-I+IGFBP-5+VN samples; Group 3, VN, IGFBP-5+VN and IGF-I+VN samples. NB. All treatments, except controls, contain VN in addition to the components listed in the color key.

162 The third grouping contained all samples from VN alone, IGFBP-5+VN and IGF-I+VN treatments, indicating the high degree of similarity in gene expression between these treatments. This also supports the commonality in gene expression by all treatment types, with the exception of IGF-I:IGFBP-5:VN samples which formed their own distinct group. Due to the commonality between VN, IGFBP-5+VN and IGF-I+VN treatments and to reduce the complexity of analysis, differential gene expression by the IGF-I+IGFBP-5+VN treatment was identified in comparison to VN alone.

The main purpose of the microarray studies was to identify candidate genes involved in the enhanced migratory response stimulated by IGF-I:IGFBP-5:VN complexes in breast epithelial cells. In order to identify transcripts differentially expressed in a unique manner by IGF-I:IGFBP-5 associating with VN, the gene expression induced by IGF:IGFBP-5:VN treatments was directly compared to that of the VN alone treatments. This revealed 194 probe sets to be significantly expressed by at least ± 1.8-fold in cells migrating in response to the IGF-I:IGFBP-5:VN complex (Table 5.2). These represented 165 unique genes once the presence of repetitive probe sets for a number of the genes identified were taken into consideration. The Affymetrix probe I.D, gene symbols, GenBank accession numbers and relative fold changes for the 165 genes identified are shown in Appendix 2, Table A2-4. Overall, it was observed that 40 of the genes were upregulated, while 125 were downregulated. The twenty genes that were most significantly up- and down-regulated are shown in Table 5.3.1 and Table 5.3.2, respectively. Included among theses genes were the IGF related genes SFN (+2.3), IRS-1 (-5.4), CTGF (-2.8), the proteases TF (+2.7), MMP13 (-3.5), ADAMTS5 (-4.8), protease inhibitors PAI-1/SERPINE1 (+2.0), TFPI (-5.0), cell adhesion proteins FLRT2 (+2.6), CLDN1 (-5.3), cytoskeleton proteins ACTN1 (+1.8), TUBA1 (+1.9), and a variety of genes involved in transcriptional regulation such as BHLHB2 (+2.7), MAFB (+2.4), BACH2 (-3.9) and BCL6 (-4.0). Together this demonstrates a substantial number of genes are differentially expressed in cells in a unique manner in response to IGF- I:IGFBP-5:VN complexes.

163

Table 5.3.1. The twenty most significantly up-regulated genes in cells migrating in response to IGF- I:IGFBP-5:VN complexes.

Fold Change Affy ID Genbank Gene Symbol Description 2.766 209446_s_at BC001743 C7ORF44 chromosome 7 open reading frame 44 2.699 204363_at NM_001993 F3 tissue factor 2.671 201170_s_at NM_003670 BHLHB2 basic helix-loop-helix domain containing, class B, 2 2.602 204359_at NM_013231 FLRT2 fibronectin leucine rich transmembrane protein 2 2.435 202668_at BF001670 EFNB2 ephrin-B2 2.432 205074_at NM_003060 SLC22A5 solute carrier family 22, member 5 2.417 225424_at AB046780 GPAM glycerol-3-phosphate acyltransferase 2.357 218559_s_at NM_005461 MAFB v-maf musculoaponeurotic fibrosarcoma oncogene homolog B (avian) 2.335 209260_at BC000329 SFN stratifin 2.29 200799_at NM_005345 HSPA1A heat shock 70kDa protein 1A 2.256 223062_s_at BC004863 PSAT1 phosphoserine aminotransferase 1 2.187 232060_at AK000776 ROR1 receptor tyrosine kinase-like orphan receptor 1 2.16 206753_at AF086735 RDH16 retinol dehydrogenase 16 2.138 227556_at AI094580 NME7 non-metastatic cells 7, protein expressed in 2.108 229125_at AA456955 ANKRD38 ankyrin repeat domain 38 2.078 209834_at AB017915 CHST3 carbohydrate (chondroitin 6) sulfotransferase 3 2.045 242138_at BF060783 DLX1 distal-less homeobox 1 2.031 1568765_at BC020765 SERPINE1 plasminogen activator inhibitor type 1 2.025 213562_s_at BF979497 SQLE squalene epoxidase 2.014 229105_at AV717094 GPR39 G protein-coupled receptor 39

Table 5.3.2. The twenty most significantly down-regulated genes in cells migrating in response to IGF-I:IGFBP-5:VN complexes.

Fold Change Affy ID Genbank Gene Symbol Description -17.762 218723_s_at NM_014059 C13ORF15 chromosome 13 open reading frame 15 -14.451 205960_at NM_002612 PDK4 pyruvate dehydrogenase kinase, isozyme 4 -9.346 219799_s_at NM_005771 DHRS9 dehydrogenase/reductase (SDR family) member 9 -8.13 217028_at AJ224869 CXCR4 chemokine (C-X-C motif) receptor 4 -7.092 235419_at AW612461 ERRFI1 ERBB receptor feedback inhibitor 1 -6.173 218002_s_at NM_004887 CXCL14 chemokine (C-X-C motif) ligand 14 -5.376 204686_at NM_005544 IRS1 insulin receptor substrate 1 -5.263 218182_s_at NM_021101 CLDN1 claudin 1 -5.051 229357_at BF060767 ADAMTS5 ADAM metallopeptidase with thrombospondin type 1 motif, 5 -5.025 213258_at BF511231 TFPI tissue factor pathway inhibitor -5 205798_at NM_002185 IL7R 7 receptor -4.63 212558_at BF508662 SPRY1 sprouty homolog 1, antagonist of FGF signaling -4.484 220936_s_at NM_018267 H2AFJ H2A histone family, member J -4.149 242281_at AW665656 -4 203140_at NM_001706 BCL6 B-cell CLL/lymphoma 6 -3.968 1559739_at AK025141 CHPT1 choline phosphotransferase 1 -3.953 221234_s_at NM_021813 BACH2 BTB and CNC homology 1, basic leucine zipper transcription factor 2 -3.65 231930_at AL359601 ELMOD1 ELMO/CED-12 domain containing 1 -3.534 205959_at NM_002427 MMP13 matrix metallopeptidase 13 -3.534 231929_at AI458439 IKZF2 IKAROS family zinc finger 2

164 5.3.4 Functional analysis of genes differentially regulated in migratory cells by substrate-bound IGF-I:IGFBP:VN complexes using IPA Genome-wide microarray investigations produce large data sets of differentially regulated genes in response to a specific factor or condition. Often these genes are from a diverse range of gene families with a variety of functions. Therefore, with any microarray analysis the major cellular and biological themes represented by a given data set need to be determined. In order to place the genes identified to be differentially expressed by cells in response to IGF-I:IGFBP-5:VN complexes in a biological context, we utilized the IPA knowledge base. A data set containing the 165 genes identified as being significantly expressed in cells by at least +/- 1.8-fold in response to IGF- I:IGFBP-5:VN complexes compared to VN alone, was uploaded into the application. Out of the 165 genes uploaded, 121 were identified and mapped to corresponding gene objects in the Ingenuity Pathways Knowledge Base (termed Focus Genes).

5.3.4.1 Gene ontology and functional analysis Biological functions were assigned to the whole data set by using the IPA knowledge base as a reference set and a proprietary ontology representing over 300,000 classes of biological objects and consisting of millions of individually modelled relationships between proteins, genes, complexes, cells, tissues, small molecules and diseases. The biological functions assigned to each data set are ranked according to the significance of that biological function to the data set. Functional analysis identified a number of biological functions that were most significant to the differentially regulated genes. Molecular and Cellular functions with a Fischer’s exact test score below 0.05 and containing at least 5 focus genes were considered significant (Table 5.4). Ranked in order of significance, Table 5.4 classifies the genes in the data set by function, including processes involved in, cell growth/proliferation, cell morphology, cell death and cellular movement. Processes involved with cell growth/proliferation were the most significant functional category, containing 48 out of the 121 mapped focus genes (9.6 x 10-7 to 0.014) (Table 5.4). Importantly, Cell Movement was one of the highest ranking functional categories containing 24 focus genes.

165

Tabl Focus e 5.4 Molecular and Cellular Functions Significance p-value Genes Onto Cellular growth and proliferation 9.60 x 10-7 to 0.014 48 logy -5 of Cell morphology 1.09 x 10 to 0.014 24 gene Cell death 1.21 x 10-5 to 0.014 42 s Cell cycle 1.24 x 10-5 to 0.014 24 diffe -5 renti Cell movement 2.04 x 10 to 0.014 24 ally -5 Cellular function and maintenance 5.18 x 10 to 0.014 8 expr Cell signaling 1.64 x 10-4 to 0.014 43 essed Cell-to-Cell signaling and Interaction 5.10 x 10-4 to 0.014 23 in migr Lipid metabolism 5.10 x 10-4 to 0.014 9 -4 ator Small molecule biochemistry 5.10 x 10 to 0.014 18 y Tumour morphology 5.13 x 10-4 to 0.014 10 cells Gene expression 2.12 x 10-3 to 0.014 19 in resp Carbohydrate metabolism 7.23 x 10-3 to 0.014 7 -3 onse Cellular assembly and organization 7.23 x 10 to 0.014 8 to Molecular transport 7.23 x 10-3 to 0.008 7 IGF- I:IG FBP-5:VN complexes

166 Within each of these quite broad ranging Molecular and Cellular classifications, IPA further categorizes genes to more specific functional roles relevant to each classification. Because these studies are focused on cell migration, we also investigated genes with more specific functions within the broader Cell Movement category. As can be seen in Table 5.5, a number of focus genes were significantly associated with biological processes relevant to Cellular Movement, including cellular migration, movement and chemotaxis of a variety of cell types. A number of genes, such as EFNB2, F3, MAFB, SERPINE1, CTGF, IRS1, CXCL14 and CXCR4 were associated with a number of biological processes associated with Cellular Movement (Table 5.5).

Canonical pathway analysis was performed to relate the differentially expressed genes to well documented biological pathways. Canonical pathway analysis identified the pathways from the IPA library of canonical pathways that were most significant to the genes expressed differentially by migratory cells in response to IGF-I:IGFBP-5:VN complexes. In addition to calculating a significance p-value using a Fisher’s exact test, a ratio determining the number of focus genes to overall genes in each pathway is also used to associate canonical pathways to the data set. Table 5.6 shows the five most significant canonical pathways associated with the 121 genes mapped to the IPA knowledge base. When ranked according to p-value, leukocyte extravasation signaling was the most significant canonical pathway associated with the data set (p = 2.24 x 10-3), followed closely by IGF-I signaling (p = 3.78 x 10-3) and axonal guidance (p = 6.81 x 10-3). Alternatively, if ranked by the ratio of focus genes present in each pathway, IGF-I signaling was the highest ranking canonical pathway (0.044), followed by Complement and coagulation cascades (0.043) and leukocyte extravasation signaling (0.032).

In order to further analyze well established pathways which may be modulated in cells migrating in response to the IGF-I:IGFBP-5:VN complexes, KEGG pathway over- representation analysis was performed using the DAVID online functional annotation tool. Only KEGG pathways containing 5 or more input genes and an EASE score of less than 0.05 were considered to be over-represented. Three KEGG pathways passing these

167 Table 5.5. Focus genes associated with biological processes relevant to Cellular Movement. Cellular Movement Significance Focus Genes Sub-category p-value ADM, ANGPT1, CTGF, CXCL14, CXCR4, DLX1, EFNB2, ERBB3, ERRFI1, F3, GATA3, MAFB, MMP7, migration of eukaryotic cells 2.04 x 10-5 MMP13, MXD1, NEDD9, PLD1, SERPINE1, TFPI, TNFSF10, VEGFC migration of neurons 5.13 x 10-4 CXCR4, DLX1, EFNB2, ERRFI1, GATA3, MAFB migration of neuroblastoma 7.62 x 10-4 CXCR4, SERPINE1 cell lines ANGPT1, EFNB2, MMP13, MXD1, SERPINE1, migration of endothelial cells 1.18 x 10-3 VEGFC migration of motor neurons 1.80 x 10-3 GATA3, MAFB

ANGPT1, CXCR4, ERBB3, ERRFI1, NEDD9, PLD1, migration of cell lines 6.64 x 10-3 SERPINE1, TNFSF10, VEGFC

arrest in migration of facial 7.23 x 10-3 MAFB branchiomotor neurons

migration of tumour cells CXCR4, F3, TFPI 1.26 x 10-2

migration of facial 1.44 x 10-2 MAFB branchiomotor neurons

cell movement of endothelial ANGPT1, CXCL14, CXCR4, EFNB2, MMP13, MXD1, 4.82 x 10-5 cells SERPINE1, VEGFC

ADM, ANGPT1, CTGF, CXCL14, CXCR4, DLX1, cell movement of eukaryotic EFNB2, ERBB3, ERRFI1, F3, GATA3, IRS1, MAFB, 1.00 x 10-4 cells MMP7, MMP13, MXD1, NEDD9, NPC1, PLD1,

SERPINE1, TFPI, TNFSF10, VEGFC

ADM, ANGPT1, CTGF, CXCL14, CXCR4, DLX1, EFNB2, ERBB3, ERRFI1, F3, GATA3, IRS1, MAFB, cell movement 2.82 x 10-4 MMP7, MMP13, MXD1, NEDD9, NPC1, PLD1, SERPINE1, STAT1, TFPI, TNFSF10, VEGFC cell movement of neurons 6.89 x 10-4 CXCR4, DLX1, EFNB2, ERRFI1, GATA3, MAFB cell movement of tumour cells 4.43 x 10-3 CXCR4, F3, SERPINE1, TFPI cell movement of 4.45 x 10-3 CXCR4, SERPINE1 neuroblastoma cell lines ADM, ANGPT1, CXCR4, ERBB3, ERRFI1, IRS1, cell movement of cell lines 6.27 x 10-3 NEDD9, PLD1, SERPINE1, TNFSF10, VEGFC chemotaxis of endothelial cells 6.13 x 10-3 ANGPT1, CXCL14, CXCR4, VEGFC

168

Table 5.6. Canonical pathways associated with genes differentially expressed in migratory cells in response to IGF-I:IGFBP-5:VN complexes.

Canonical Pathway Significance p-value Ratio* Focus Genes Leukocyte extravasation 6/186 ACTN1, CLDN1, CXCR4, 2.24 x 10-3 signaling (0.032) MMP7, MMP13, PRKCH 4/90 CSNK2A2, CTGF, IRS1, SFN IGF-I signaling 3.78 x 10-3 (0.044) CXCR4, EFNB2, FZD7, 8/403 Axonal guidance signaling 6.81 x 10-3 PRKCH, PTCH1, ROR1, (0.020) SEMA6A, VEGFC ANGPTI, CXCR4, EFNB2, 5/207 signaling 1.25 x 10-2 ROR1, VEGFC (0.024)

Complement and coagulation F3, SERPINE1, TFPI 3/70 cascades 1.36 x 10-2 (0.043)

* Ratio of focus genes present in each pathway.

Table 5.7. KEGG pathways over-represented in genes identified as differentially expressed in migratory cells in response to IGF-I:IGFBP-5:VN complexes.

Significance Rank KEGG pathway* Genes p-value 1 Adherens junction 9.8 x 10-3 ACTN1, IR, TCF7L2, FARP2, CSNK2A2 Leukocyte transendothelial 2 3.9 x 10-2 ACTN1, OCLN, CLDN1, CXCR4, CXCL14 migration 3 Tight junction 4.1 x 10-2 ACTN1, PRKCH, OCLN, CLDN1, CSNK2A2

* Only KEGG pathways containing a minimum of 5 genes, with an EASE score <0.05 were considered significant.

169 criteria were associated with the data set (Table 5.7). Differentially expressed genes were determined to be highly associated with the processes involved in adherens junction formation (p = 9.8 x 10-3), followed by leukocyte transendothelial migration (p = 3.9 x 10-2) and formation of tight junctions (p = 4.1 x 10-2). It is of note that ACTN1, CLDN1, CXCR4 and CSNK2A2 genes present in the over-represented KEGG pathways, were also present in one or more high ranking canonical pathways. These data indicate that in addition to their role in IGF signaling, genes identified as being differentially expressed by IGF-I:IGFBP-5:VN complexes may potentially regulate the migration of MCF-10A cells via processes associated with leukocyte extravasation/transendothelial migration and regulation of tight and/or adherens junction formation.

5.3.5 Network analysis To further investigate the functional role of the genes identified as being differentially expressed, we next investigated which of the genes are known to interact in a biological context. To achieve this we applied network analysis to the 121 genes which mapped to the Ingenuity Pathways Knowledge Base. These 121 focus genes were then used as the starting point for generating biological networks. To start building networks, IPA queries the Ingenuity Pathways Knowledge Base for interactions between focus genes and all other gene objects stored in the knowledge base, and generates a set of networks to describe the functional relationships between gene products based on known interactions in the literature. The output, displayed graphically as nodes (genes) and edges (the biological relationship between the nodes), provides a detailed representation of a number of biological pathways and functions implicated by the data set. The tool then associates these networks with known biological pathways.

Using a 99.9% confidence level (score ≥ 3) that the genes present in a particular network are not due to random chance, the tool identified eight networks found to be highly significant in the data set of differentially expressed genes (Table 5.8). The networks generated ranged in score from 39 and containing 21 focus genes, to a score of 18 containing 12 focus genes (Table 5.8). In addition, high-level functions were calculated and assigned to each network if the significance of the association between

170

Table 5.8. Functional network analysis of genes differentially regulated by migratory cells in response to IGF-I:IGFBP-5:VN complexes

Network Focus Molecules in Network* Score I.D † Genes 1,3,4,5-IP4, ALK, BCL6, BCL11A, BHLHB2, BTF3, Caspase, CBLB, CEBPD, Ck2, CSNK2A2, ERBB3, FGF13, GATA3, GPAM, HPR, 1 IRS1, KLF6, MAF, MAFB, Mapk, MXI1, NEU3, NEXN, PAWR, 39 21 PDGF BB, PP2A, PPP1R12C, PTN, SERPINE1, SFRS7, SH2B1, SPRY1, STAT1, ZFP36L1 ADM, Akt, ANGPT1, Ap1, COL8A1, CTGF, CXCR4, EFNB2, ERRFI1, F3, IL1, IL7R, Jnk, Mek, Mmp, MMP7, MMP13, NOX4, P38 2 31 18 MAPK, Pdgf, Pdgf Ab, PI3K, Pkc(s), PLD1, PRKCH, Ras, RTK, STAT5a/b, TCR, TFPI, Tgf beta, TNFSF10, TSC22D3, Vegf, VEGFC 20-alpha-hydroxyprogesterone, ABHD5, ADFP, AGTR1B, ATP12A, ATP1A2, ATP1B1, beta-estradiol, BHLHB2, C7ORF44, CCNB1, CCR2, CLEC2B, CXCR4, epinephrine, GJB2, GLCCI1, GNRH1, 3 27 16 HBP1, HIST2H2AA3, IL7R, INPP4B, KLF10, KLK2, NPC1, NR3C1, PKIB, PLIN, PPARGC1A, progesterone, SFRS1, SLC22A5, SMPDL3A, THBD, ZFP36L2 CAMP, CCR2, CENTD1, CHPT1, CLDN1, CMIP, DDR2, EVL, FARP2, FLRT2, GBP2, H2AFJ, HDAC2, IFNG, IKZF2, IKZF3, IL6, 4 IL4R, IL7R, MIRN21, MXD1, NFYB, OCLN, PHACTR3, PLSCR1, 25 15 PPARD, PPP1CA, PSME2, SEMA6A, SRC, TJP1, TJP2, TJP3, WARS, ZBTB32 ACTN1, ADAMTS5, ADFP, ANKRD28, C10ORF10, CAMP, CAPN8, CCL6, CCR2, CEBPG, cholesterol, ECGF1, GBP2, GULP1, HP, 5 HSPA1A, IL16, IL1B, KPNA3, MYC, NEDD9, NFKBIE, OCLN, 22 14 ORM1, P4HB, PAPPA, PSAT1, SLK, SQLE, TGTP, TIE1, TMEM49, TNF, TRIM35, tryptophan ADAMTS1, amino acids, AMPK, BACH2, BCL9L, CBFB, CPM, CTNNB1, CXCL14, CXCR4, EPHB4, FER, FGF2, FZD7, GKAP1, 6 Gsk3, HOXB7, hydrogen peroxide, ID3, KIAA0999, KITLG, KLHL24, 22 14 PCSK6, PDK4, PPARD, PRKG1, PTN, RIOK3, ROCK2, RPS6KB2, STK16, TBC1D8, TFPI2, TUBA4A, VEGFA 3-alpha,17-beta-androstanediol, androsterone, APC, CBR3, CSE1L, DGAT2, DHRS9, dihydrotestosterone, DLX1, DSP, ERRFI1, F2, HSD17B1, IER2, IGF2, IRS4, KCTD11, KLK2, LBH, LY6E, MYH9, 7 20 13 PKP2, PSME3, PTCH1, RDH16, retinoic acid, Retinol dehydrogenase, ROCK2, ROR1, SCD, SLC25A37, TMSB4X, WNT7B, YWHAZ, ZFAND5 ANAPC1, ANAPC2, ANAPC5, ANAPC7, APC, BUB1B, C12ORF5, C13ORF15, CCNB1, CDC2, CDC16, CDC20, CDC27, Cdc2- 8 CyclinB-Sfn, CDC23, CDKN2A, CPA4, FZR1, GAST, HMGA2, 18 12 HSPA2, ID3, MCM3, MDM2, NCL, P4HA1, P4HA2, PHC2, SEC14L1, SESN1, SFN, TCF7L2, TP53, ZAP70, ZBTB10

* Underlined genes are those identified by the microarray analysis. The other genes were either not on the expression array or not significantly regulated. † A score of > 3 was considered significant (p<0.001). Red, up-regulated; Green, down-regulated.

171 the network and the biological function had a p-value < 0.05 (Table 5.8). Graphical representations of the two highest scoring networks (designated Network 1 and Network 2, respectively) and their associated high-level functions are displayed in Figures 5.6.1 and 5.6.2 (for Networks 3-8 see Appendix 2, Figures A-3 to A-8).

The nodes for Network 1 and 2 were comprised largely of focus genes (21/35 and 18/35, respectively), indicating the relevance of these networks to the data set. Network 1 included focus genes involved in transcriptional regulation (BCL6, KLF6, MAF, MAFB, BHLHB2, ZFP36L1, PAWR, STAT1), adapter proteins and kinases (CSNK2A2, IRS1, CBLB), the transmembrane receptor ERBB3, the F-actin binding protein NEXN and the multifunctional protease inhibitor SERPINE1 or PAI-1 (Figure 5.6.1). High-scoring functions in Network 1 were related to processes involved in cell morphology, cell growth, cell death, cell cycle and gene expression (Figure 5.6.1). Although not differentially regulated, MAPK and a member of the PDGF family (PDGF-BB) mapped to the core of Network 1 and were the most prominent interaction partners in this network (Figure 5.6.1). This indicates the possibility that growth factor signaling via MAPK may regulate the activity of a number of genes present in Network 1 through mechanisms other than altered gene expression.

Network 2 contained focus genes for a number of growth factors/cytokines (CTGF, ANGPT1, VEGFC, TNFSF10), transmembrane receptors (IL7R, CXCR4), transmembrane proteins (EFNB2, F3), matrix metalloproteases (MMP7, MMP13), enymes (PLD1, NOX4), signaling intermediates (PRKCH, ERRFI1) and tissue factor pathway inhibitor (TFPI) (Figure 5.6.2). High-scoring functions in Network 2 were related to processes involved in cellular movement, cell growth, cell morphology, cell- to-cell signaling and cell death (Figure 5.6.2). Although not part of the uploaded data set, prominent interaction partners in Network 2 included members of key intracellular signaling pathways, including PI3-K, AKT, p38 MAPK, JNK and PKC family members (Figure 5.6.2). Thus, similar to Network 1, it is possible the activity of several genes within Network 2 could be regulated by interactions with PI3-K/AKT and MAPK signaling pathways involving mechanisms other than altered gene expression.

172

Figure 5.6.1: Functional network analysis (Network 1). The top ranking network significantly associated with genes identified as differentially regulated by IGF- I:IGFBP-5:VN complexes (Network 1). Nodes represent genes, with their shape representing the functional class of the gene product. Nodes are color coded according to their gene expression determined by the microarray analysis (Red, up-regulated; Green, down-regulated). Nodes not shaded were either not on the expression array or not significantly regulated. High-level functions significantly associated with the network are reported in the table below the network diagram.

acts on directly

acts on indirectly

binding only

173 Network 1 (Score 39)

High-Level Functions Significance p-value Focus genes

BCL6, BCL11A, ERBB3, IRS1, KLF6, MAF, MXI1, Cell morphology 2.72 x 10-8 to 0.012 PAWR,SERPINE1, GATA3, BHLHB2

Cellular growth and BCL6, CBLB, CEBPD, CSNK2A2, ERBB3, IRS1, KLF6, proliferation 2.44 x 10-7 to 0.012 MXI1, PAWR, SERPINE1, SPRY1, STAT1, ZFP36L1, BHLHB2 Cell death 3.87 x 10-7 to 0.012 BCL6, BHLHB2, CBLB, CEBPD, CSNK2A2, ERBB3, IRS1, KLF6, MAF, MAFB, PAWR, SERPINE1, STAT1 Cell cycle 1.33 x 10-6 to 0.012 BCL6, BHLHB2, CEBPD, CSNK2A2, ERBB3, IRS1, KLF6, MXI1, PAWR, STAT1 Gene expression 1.41 x 10-6 to 0.012 BCL6, BHLHB2, CEBPD, GATA3, KLF6, MAF, MAFB, MXI1, PAWR, STAT1

174

Figure 5.6.2: Functional network analysis (Network 2). The second-top ranking network significantly associated with genes identified as differentially regulated by IGF- I:IGFBP-5:VN complexes (Network 2). Nodes represent genes, with their shape representing the functional class of the gene product. Nodes are color coded according to their gene expression determined by the microarray analysis (Red, up-regulated; Green, down-regulated). Nodes not shaded were either not on the expression array or not significantly regulated. High-level functions significantly associated with the network are reported in the table below the network diagram.

acts on directly

acts on indirectly

binding only

175 Network 2 (Score 31)

High-Level Significance p- Focus genes Functions value

3.01 x 10-8 to 0.011 ADM, ANGPT1, CTGF, CXCR4, EFNB2, ERRFI1, F3, Cellular movement MMP7, MMP13, PLD1, TFPI, TNFSF10, VEGFC Cellular growth and 5.27 x 10-6 to 0.010 ADM, ANGPT1, COL8A1, CTGF, CXCR4, EFNB2, IL7R, proliferation MMP7, NOX4, PLD1, TFPI, TNFSF10, TSC22D3, VEGFC, ERRFI1, PRKCH NOX4, PLD1, PRKCH, TNFSF10, VEGFC, ADM, Cell morphology 2.55 x 10-5 to 0.010 ANGPT1 Cell-to-Cell signaling and 4.13 x 10-5 to 0.012 ANGPT1, CTGF, CXCR4, F3, PLD1, TFPI, VEGFC, Interaction EFNB2, TNFSF10, ADM, MMP13, MMP7

-5 ADM, ANGPT1, CTGF, CXCR4, EFNB2, F3, IL7R, Cell death 6.19 x 10 to 0.012 MMP7, NOX4, PLD1, PRKCH, TNFSF10, VEGFC,

MMP13

176 In order to further investigate interactions between not only individual nodes, but between networks, Network 1 and 2 were merged together (Figure 5.6.3). Similar high- level functions for the merged network were determined, with cellular growth and proliferation, cell death and cellular movement the most significantly associated biological functions (Figure 5.6.3). In respect of data reported in previous chapters, it was noted that PI3-K, AKT and MAPK mapped to the core of the merged network and were amongst the most prominent interaction partners. This indicates an important role for these intracellular signaling intermediates in regulation of genes within the highest ranking networks generated from IGF-I:IGFBP-5:VN target genes.

5.3.6 qRT-PCR validation of differentially expressed genes Several genes deemed biologically interesting because of their differential expression induced by IGF-I:IGFBP-5:VN complexes and/or their relevance to cellular movement (determined by functional knowledge-based analysis) were validated by qRT-PCR. Total RNA used for qRT-PCR was from the original samples isolated and used for the microarray analysis. The differential expression of 13 transcripts identified by microarray analysis were validated by SYBR-based qRT-PCR, using 18S rRNA as an internal control for normalization (please refer to Table 2.1 for transcript identifiers and oligonucleotide primer sequences). The transcripts of 7 up-regulated genes, including fibronectin leucine rich transmembrane protein-2 (FLRT2), tissue factor (F3), serpin peptidase inhibitor, clade E (SERPINE1) (more commonly referred to as PAI-1), basic helix-loop-helix domain containing class B2 (BHLHB2), ephrin-B2 (EFNB2), stratifin (SFN or 14-3-3σ) and v-maf musculoaponeurotic fibrosarcoma oncogene homolog B (avian) (MAFB) as identified in the microarray analysis were quantitated (Figure 5.3.6(i)). In addition, the transcripts of 6 down-regulated genes, namely chemokine (C- X-C motif) receptor 4 (CXCR4), chemokine (C-X-C motif) ligand 14 (CXCL14), insulin receptor substrate 1 (IRS1), claudin 1 (CLDN1), tissue factor pathway inhibitor (TFPI), and PRKC, apoptosis, WT1 regulator (PAWR) were quantitated (Figure 5.3.6(ii)).

177

Figure 5.6.3: Functional network analysis (Networks 1 and 2 merged). The two highest ranking networks (Network 1 and 2) were merged to produce a cellular network which is differentially regulated by IGF-I:IGFBP-5:VN complexes. Nodes represent genes, with their shape representing the functional class of the gene product. Nodes are color coded according to their gene expression determined by the microarray analysis (Red, up-regulated; Green, down-regulated). Nodes not shaded were either not on the expression array or not significantly regulated. High-level functions significantly associated with the network are reported in the table below the network diagram.

acts on directly

acts on indirectly

binding only

178 Network 1 and 2 merged

High-Level Functions Significance p-value Focus genes

ADM, ANGPT1, BCL6, BHLHB2, CBLB, CEBPD, COL8A1, CSNK2A2, CTGF, CXCR4, EFNB2, Cellular growth and 1.18 x 10-11 to 0.008 ERBB3, IL7R, IRS1, KLF6, MMP7, MMP13, MXI1, proliferation NOX4, PAWR, PLD1, SERPINE1, SPRY1, STAT1, TFPI, TNFSF10, TSC22D3, VEGFC, ZFP36L1 ADM, ANGPT1, BCL6, BHLHB2, CBLB, CEBPD, CSNK2A2, CTGF, CXCR4, EFNB2, ERBB3, F3, Cell death 1.36 x 10-9 to 0.008 IL7R, IRS1, KLF6, MAF, MAFB, MMP7, NOX4, PAWR, PLD1, PRKCH, SERPINE1, STAT1, TNFSF10, VEGFC ADM, ANGPT1, CTGF, CXCR4, EFNB2, ERBB3, Cellular movement 6.61 x 10-9 to 0.008 ERRFI1, F3, GATA3, IRS1, MAFB, MMP7, MMP13, PLD1, SERPINE1, STAT1, TFPI, TNFSF10, VEGFC ADM, BCL6, BCL11A, ERBB3, ERRFI1, IRS1, Cell morphology 5.93 x 10-8 to 0.004 KLF6, MAF, MXI1, PAWR, TSC22D3 ADM, ANGPT1, BCL6, BHLHB2, CEBPD, Cell cycle 4.43 x 10-7 to 0.008 CSNK2A2, ERBB3, ERRFI1, IRS1, KLF6, MXI1, PAWR, PRKCH, STAT1, TNFSF10, VEGFC

179 To fully validate the microarray data, relative mRNA levels were assessed for target genes across VN alone, IGFBP-5+VN, IGF-I+VN and IGF-I+IGFBP-5+VN treatments. Target gene expression for each sample was normalised to 18S rRNA and fold expression relative to VN alone samples determined (Figures 5.7.1 and 5.7.2). Importantly, differential expression of all target genes selected from microarray analysis of cells which migrate in response to IGF-I:IGFBP-5:VN treatments was confirmed. Furthermore, as reflected in the microarray results, the expression of the target genes remained relatively unchanged across VN alone, IGFBP-5+VN and IGF-I+VN treatments, with the exception of IGF+VN significantly reducing the expression of CXCR4. This, however, was not below the 1.8-fold threshold (1.8-fold threshold indicated by the red dashed line in each figure). The results of qRT-PCR for all the target genes were highly consistent with the microarray analysis (Figure 5.7.3). This therefore, aids in validating the data set of genes identified to be differentially regulated by migratory cells in response to the IGF-I:IGFBP-5:VN complexes.

180

FLRT2 F3 3.5 3.5 * 3.0 * 3.0 2.5 2.5 2.0 2.0 1.5 1.5 (F3/18S)

(FLRT2/18S) 1.0 1.0 0.5 0.5 Relative foldRelative expression foldRelative expression 0.0 0.0 N VN V VN 5+ I+VN 5+VN I+VN I+5+VN I+5+VN

PAI-1 BHLHB2 3.5 3.5 3.0 * 3.0 * 2.5 2.5 2.0 2.0 1.5 1.5 (PAI-1/18S)

1.0 (BHLHB2/18S) 1.0 0.5 0.5 Relative foldRelative expression foldRelative expression 0.0 0.0 N VN V VN 5+ I+VN 5+VN I+VN I+5+VN I+5+VN

EFNB2 SFN 3.5 3.5 3.0 * 3.0 2.5 2.5 * 2.0 2.0 1.5 1.5 (SFN/18S)

(EFNB2/18S) 1.0 1.0 0.5 0.5 Relative fold expression Relative fold expression 0.0 0.0 N VN V VN 5+ I+VN 5+VN I+VN I+5+VN I+5+VN

MAFB 3.5 3.0 * 2.5 2.0 1.5

(Maf-B/18S) 1.0 0.5 Relative foldRelative expression 0.0 N VN V 5+ I+VN 5+VN I+

Figure 5.7.1: qRT-PCR validation of up-regulated target genes identified by microarray analysis. mRNA expression for each target gene was normalized to the 18S ribosomal gene and data from each treatment were expressed as the fold difference relative to the VN alone treatment (VN). The asterisk indicates treatments in which there was a statistically significant difference in expression compared to VN (p<0.05). Data are represented as mean ± standard error from three independent experiments. The red dashed line indicates a 1.8-fold increase in expression relative to VN. 5 = IGFBP-5, I = IGF-I.

181 CXCR4 PAWR 1.25 1.25

1.00 1.00 * 0.75 0.75

0.50 0.50 * (PAWR/18S) (CXCR4/18S) 0.25 * 0.25 Relative foldexpression Relative foldexpression 0.00 0.00 N N N N N V V VN V V + 5+VN I+ 5 I+V I+5+VN I+5+

IRS-1 CXCL14 1.25 1.25

1.00 1.00

0.75 0.75

0.50 0.50 (IRS-1/18S)

(CXCL14/18S) * 0.25 * 0.25 Relative foldRelative expression foldRelative expression 0.00 0.00 N N N VN V V V VN + +VN + + +VN 5 I 5 5+VN I 5 I+ I+

CLDN1 TFPI 1.50 1.50

1.25 1.25

1.00 1.00

0.75 0.75

0.50 (TFPI/18S) 0.50 * (CLDN1/18S) * 0.25 0.25 Relative fold expression Relative fold expression 0.00 0.00 N N N N N V V VN V V + 5+VN I+ 5 I+V I+5+VN I+5+

Figure 5.7.2: qRT-PCR validation of down-regulated target genes identified by microarray analysis. mRNA expression for each target gene was normalized to the 18S ribosomal gene and data from each treatment were expressed as the fold difference relative to the VN alone treatment (VN). The asterisk indicates treatments in which there was a statistically significant difference in expression compared to VN (p<0.05). Data are represented as mean ± standard error from three independent experiments. The red dashed line indicates a 1.8-fold decrease in expression relative to VN. 5 = IGFBP-5, I = IGF-I.

182

3.25 Microarray 3.00 QRT-PCR 2.75

2.50

2.25

2.00

1.75

1.50

1.25

1.00

Relative fold expression fold Relative 0.75

0.50

0.25

0.00 1 B I T2 F3 - - R4 N1 P R NB2 SFN af C TF WR PAI F M X IRS-1 CL14 A FL E CLD X P C C BHLHB2

Figure 5.7.3: Comparison of the expression levels of 13 genes determined with micoarrays and with qRT-PCR. Fold changes relative to VN for 13 genes differentially regulated by IGF-I:IGFBP-5:VN treatment as determined by micoarray analysis (shaded bars) and qRT-PCR (open bars).

183 5.4 DISCUSSION The data presented in Chapters 3 and 4 indicated that substrate-bound IGF-I:IGFBP:VN complexes are potent stimulators of cell migration. Moreover, the enhanced migration observed involves increased intracellular signaling regulated via interactions between the IGF-1R and VN-binding integrins. To further understand the mechanisms underpinning these responses, the studies reported in this chapter attempted to identify candidate genes involved in cell migration stimulated by IGF-I:IGFBP-5:VN complexes. To this end we have identified 165 unique genes differentially expressed by cells migrating in response to IGF-I:IGFBP-5:VN complexes when compared to VN alone, IGFBP-5:VN and IGF- I:VN treatments. Moreover, gene ontology and functional analysis revealed this set of genes to be significantly associated with biological functions such as cell growth and proliferation, cell death and cellular movement. Of particular relevance to our studies, many of the identified genes have previously reported roles in cellular movement, migration and metastasis. Importantly, the expression of 13 genes identified as differentially expressed in the microarray analysis was confirmed by qRT-PCR, thus, validating the data set.

To identify genes relevant to cell migration stimulated by substrate-bound-IGF-I:IGFBP- 5:VN complexes, we adopted the novel strategy of isolating RNA from cells which had migrated to the undersurface of porous Transwell® membranes in response to each treatment. This strategy was adopted in the hope that the genes identified would more accurately represent the actual cellular and molecular processes involved in stimulated cell migration in comparison to the more common and relatively static pre-plated culture methodologies previously adopted by others. It was envisioned our approach would also reduce the number of genes identified that may be irrelevant to our phenotype of interest and thus, make the data sets obtained more manageable for further analysis.

Using the approach described only a small number of genes were identified as being differentially expressed in response to most treatments. The exception was IGF- I:IGFBP-5:VN which induced substantial changes in gene expression (Table 5.2). This may partly be explained by the stringency of the gene selection parameters used in this

184 study (i.e 1.8-fold change in addition to one-way ANOVA with BHFDR, p<0.05). Alternatively, this could also arise if the majority of gene expression detected was related to stimulation of cell migration. This is likely, given that high levels of MCF- 10A cell migration are generally only observed in response to IGF-I:IGFBP-5:VN complexes. Moreover, as VN is the component responsible for the majority of cell migration induced by IGFBP-5:VN and IGF-I:VN treatments, it was no surprise that VN was also the component which induced the majority of differential gene expression stimulated by IGFBP-5:VN and IGF-I:VN treatments. This was represented by the high degree of commonality in gene expression induced by VN, IGFBP-5:VN and IGF-I:VN treatments (Figure 5.4 and 5.5). This most likely reflects the similar levels of cell migration stimulated by these three treatments.

In contrast to the gene expression stimulated in cells in response to the VN, IGFBP- 5:VN and IGF-I:VN treatments, a substantial number of probe sets were differentially expressed in response to IGF-I:IGFBP-5:VN complexes when compared to both un- migrated control (231) and VN-stimulated cells (194). Furthermore, 165 unique genes were identified to be differentially expressed by the IGF-I:IGFBP-5:VN complex when compared to all other treatments; therefore, creating a data set of genes potentially involved in the enhanced migration observed in response to this complex. The number of genes revealed was substantial considering not only the stringency of the gene selection parameters, but also the relatively short 5 hour period in which the cells were stimulated and allowed to migrate. Furthermore, cell migration can be modulated by processes which do not involve changes in gene expression, including protein phosphorylation/de- phosphorylation events, protein trafficking and protein-protein binding interactions, especially those involving components of the cytoskeleton. Therefore, the contribution of gene expression in modulating cell migration over this duration could be relatively small and thus, we believe the number of differentially expressed genes identified is significant and are clearly worthy of further investigation.

185 Of the 165 genes identified as being differentially expressed in response to IGF- I:IGFBP-5:VN complexes, 40 were up-regulated while 125 were down-regulated by at least 1.8-fold. To place these observations into a biological context, functional and network analysis was performed utilizing the IPA knowledge base. Overall, the entire data set of differentially expressed genes was determined to be highly associated with a number of important cellular and molecular functions, the most significant of which included functions such as cellular growth and proliferation, cell morphology, cell death, cell cycle and cellular movement (Table 5.4). This is in accordance with the well documented effects of IGF-I on DNA synthesis, cell cycle progression, differentiation, apoptosis and cell migration (Humbel 1990; Samani et al. 2007). Cellular Movement, containing 24 focus genes, ranked as one of the most significant biological functions represented by the data set, thereby improving the confidence of our approach and resulting microarray data. Network analysis identified 8 highly significant networks in the genes identified to be differentially regulated by IGF-I:IGFBP-5:VN complexes (Table 5.8). Thus high-level functions associated with the two highest scoring networks included cell growth, cell death, cellular movement and cell morphology. Moreover, when Network 1 and 2 were merged to determine interactions between these networks, cellular movement was found to be the third most significant functional category (Figure 5.6.3.

It is acknowledged that a number of important biological functions were highly associated with our data set of differentially expressed genes and warrant extensive overview. However, the remainder of this discussion will focus on pathways and interactions of candidate gene/gene products identified to be differentially expressed in cells in response to IGF-I:IGFBP-5:VN complexes in regard to their role in cell migration and metastasis; the core focus of this thesis. It is also acknowledged that our studies, in accordance with those undertaken in previous chapters, were of relatively short duration and changes at the mRNA level may not necessarily translate to changes in protein levels within this period. Indeed, this will be the focus of future studies validating the observed changes in transcript levels over longer time periods and at the protein level. However, these early transcriptional events may well provide important

186 insights into the processes involved in the transition of epithelial cells toward a highly motile and possibly metastatic phenotype.

A combination of canonical and KEGG pathway analysis determined IGF-I:IGFBP- 5:VN target genes to be highly associated with processes involving leukocyte extravasation from the circulation and the formation of tight and adherens junctions. These pathways are of particular relevance as studies of leukocyte extravasation during the inflammatory response have been used as a model for transendothelial migration (TEM) of cancer cells during hematogenous metastasis (Miles et al. 2007). In a similar fashion, altered cell-to-cell adhesion via regulation of tight and adherens junction formation can have roles in promoting metastasis of cells from an epithelial origin.

Amongst the genes associated with these pathways, we observed the Rho-GTPase, FERM-RhoGEF and pleckstrin domain protein 2 (FARP2) and the actin binding protein α-actinin (ACTN1) to be up-regulated in cells in response to IGF-I:IGFBP-5:VN treatment. Rho-family GTPases serve as molecular switches by cycling between an inactive GDP-bound state and an active GTP-bound state, and activated GTPases can bind to their specific effectors that in turn lead to a variety of biological functions. FARP2 has been reported to activate the Rac1 signaling pathway and induce the formation of actin-rich lamellipodia protrusions, which serve as a major driving force of cell movement (Nobes and Hall 1999; Kubo et al. 2002). In a similar fashion, α-actinin has roles in cytoskeletal dynamics via linking cell adhesion receptors to actin filaments and facilitating the formation of focal adhesions (Juliano 2002). Through its association with integrins, α-actinin has been reported to have a critical role in PI3-K-induced reorganization of the actin cytoskeleton and can disrupt FAK/Src complex formation leading to increased cell migration (Zhang et al. 2006). Up-regulation of these genes is of particular relevance as TEM involves cytoskeletal changes mediated by actin and microtubule filaments, the dynamics of which are controlled by the family of Rho- GTPases.

187 Epithelial cell-to-cell adhesion is controlled by apical junctional complexes at the apex of the basolateral membrane and includes both tight and adherens junctions. Epithelial cells that are held together by intercellular junctions can disassemble their contacts with neighboring cells, lose their polarity and become motile during embryonic development, wound healing and cancer invasion/metastasis. These processes are characterized by cell scattering involving the disassembly of cell-to-cell junctions and the acquisition of a migratory phenotype. Expression of proteins that form cell-cell junctions can be altered via growth factor receptor mediated activation of signaling cascades, including PI3-K and MAPK (Braga 2002), and altered expression and function of junctional proteins has been implicated in the ability of epithelial cells to migrate and metastasize (Andre et al. 1999; Martin and Jiang 2001). The studies reported herein, identified the integral membrane proteins claudin-1 (CLDN1) and occludin (OCLN) to be down-regulated in response to IGF-I:IGFBP-5:VN. While down-regulation of CLDN1 has not been reported previously in response to IGF-I, the autocrine production of IGF-I and activation of the IGF-1R has been demonstrated to decrease OCLN levels and disrupt the function of tight junctions (Spoerri et al. 2006; Paye et al. 2007). As CLDNs and OCLN are two of the major proteins constituting tight junctions (Tsukita and Furuse 1999), down-regulation of these proteins would disrupt or prevent tight junction formation and enhance the motility of epithelial cells. It has also been reported that down-regulation of CLDNs and OCLN is implicated in the process of epithelium-to-mesenchyme transition (EMT); a process associated with the acquisition of an invasive phenotype (Ikenouchi et al. 2003). Moreover, CLDN1 expression is lost in breast cancer (Swisshelm et al. 1999; Hewitt et al. 2006), with a recent report associating decreased CLDN1 expression with the recurrence status and malignant potential of breast cancer (Morohashi et al. 2007).

Gene ontology and functional analysis of the 165 genes differentially expressed in response to IGF-I:IGFBP-5:VN complexes identified 24 genes with previously reported roles in processes related to Cellular Movement (Table 5.4). Importantly, the expression of nine of these genes, namely EFNB2, CXCL14, CXCR4, F3, MAFB, SERPINE1 (PAI- 1), TFPI and IRS-1, were amongst those confirmed with qRT-PCR (Figures 5.7.1 and 5.7.2). The significance of these genes was highlighted by network analysis which

188 placed all of these genes within the two highest scoring networks (Table 5.8). It was observed that many of these genes, in particular PAI-1, CXCR4, F3, and EFNB2, had roles in a number of biological functions relevant to cellular movement and/or migration in a range of cells and cell lines including neuronal, endothelial and tumour cells. Interestingly, when the top two functional networks were merged to investigate the interrelatedness of these networks, PI3-K/AKT and MAPK pathways mapped to the core of these networks even though they themselves were not differentially expressed. Therefore, through activation of these two key signaling pathways substrate-bound IGF- I:IGFBP:VN complexes may regulate the expression and/or function of a number of genes within these networks to modulate cellular migration.

PAI-1, F3 and EFNB2 were all up-regulated in response to IGF-I:IGFBP-5:VN complexes and have roles in promoting cell motility and invasion. Plasminogen activator inhibitor type-1 (PAI-1 or SERPINE 1) functions as a fibrinolytic inhibitor but also plays an important role in signal transduction, cell adherence and cell migration (Harbeck et al. 2001). PAI-1 can competitively inhibit the uPAR-dependent attachment of cells to VN (Waltz et al. 1997; Loskutoff et al. 1999) which has been demonstrated to increase migration of cells in the presence of VN (Waltz et al. 1997). PAI-1 increases both filopodia formation and migration of invasive breast cancer cells (Chazaud et al. 2002) and high PAI-1 mRNA expression levels indicate a poor prognosis for survival in patients with breast cancer (Leissner et al. 2006). Moreover, there is abundant clinical evidence implicating PAI-1 as a key factor for tumour invasion and metastasis (Harbeck et al. 2001).

Up-regulation of F3 (or Tissue factor, TF) is correlated with tumour progression in several cancers (Ruf and Mueller 2006). TF is the cell surface receptor for the serine protease coagulation factor VIIa, and the cell associated TF-VIIa complex is the major initiator of the coagulation pathway in vivo. TF supports metastatic tumour dissemination by fibrin-dependent pathways by aiding thrombin-dependent tumour cell survival and through signaling involving the short TF cytoplasmic domain (Palumbo et al. 2000; Ruf and Mueller 2006). TF has been found at the leading edge of invasive

189 tumours in close association with cytoskeletal structures where it can play a role in tumour cell adhesion and migration (Ott et al. 1998; Fischer et al. 1999). TF function is regulated by a specific inhibitor, TF-pathway inhibitor (TFPI) and the relative expression of TF and TFPI can modulate malignant transformation and metastatic potential (Kageshita et al. 2002). In contrast to TF, IGF-I:IGFBP-5:VN complexes down-regulated TFPI in cells, with the expression of TF and TFPI confirmed with qRT- PCR analysis. Thus, it seems that IGF-I stimulation is not only capable of inducing TF expression in cells, but may also enhance its activity via the concomitant down- regulation of TFPI.

Eph-receptor tyrosine kinases (Eph-RTKs) and their membrane-bound receptor-like ligands, the , represent a cell-cell signaling system that directs cellular migration during development. IGF-IGFBP-5:VN was shown to increase expression of the membrane-bound ephrin-B2 (EFNB2) ligand in cells. Ephrin-B2 mRNA is overexpressed in advanced malignant melanomas (Meyer et al. 2005) and via ligation of its cell surface receptor EphB4, functions as a survival factor in breast cancer (Kumar et al. 2006). Ephrin-B2/EphB4 can promote cell migration by mechanisms including modulating integrin activity, actin cytoskeleton reorganization, the formation of multiple lamellipodia and constitutive activation of FAK (Meyer et al. 2005; Yang et al. 2006). Furthermore, activation of EphB4 by ephrin-B2 has been reported to increase the migration of microvascular endothelial cells through the PI3-K/AKT pathway (Steinle et al. 2002) which was observed to be one of the most prominent interaction patterns determined by network analysis.

Two of the most prominently down-regulated genes in cells in response to IGF-I:IGFBP- 5:VN treatment were the genes encoding the CXC chemokine ligand 14 (CXCL14) and the CXC chemokine receptor 4 (CXCR4). The CXC motif containing members comprise one of four categories of chemokines which primarily induce directed migration and TEM of leukocytes through their interactions with their cognate cell surface G protein- coupled receptors (Koizumi et al. 2007). Identification of these genes in our analysis was particularly interesting, as chemokines are thought to play an important role in organ-

190 selective cancer metastasis, due to cancer cell migration/metastasis and leukocyte trafficking sharing many similarities (Tanaka et al. 2005). CXCR4 is highly expressed in human breast cancer cells and is a predictor of poor prognosis and decreased overall survival of patients with breast cancer (Muller et al. 2001; Kato et al. 2003; Li et al. 2004). Activation of CXCR4 by its ligand, SDF-1α (CXCL12), has been reported to increase the migration of epitheloid carcinoma cells in a PI3-K/AKT pathway dependent manner (Peng et al. 2005). Furthermore, CXCR4 has been shown to complex with the IGF-1R and modulate IGF-I-induced chemotaxis of MDA-MB-231 breast cancer cells through activation of PI3-K activity (Akekawatchai et al. 2005). However, we report the down-regulation of CXCR4 in cells migrating in response to IGF-I:IGFBP-5:VN complexes. This is in contrast with the well documented role of CXCR4 in promoting cell migration and metastasis (Koizumi et al. 2007). To date we cannot offer an explanation for this observation but future studies overexpressing CXCR4 will aid in elucidating a role for this receptor in IGF-I:IGFBP:VN-stimulated migration.

We also confirmed the down-regulation of breast and kidney-expressed chemokine (BRAK) CXCL14, a new CXC chemokine with relatively unknown function and receptor selectivity. While little is known about its physiologic functions, CXCL14 is expressed ubiquitously in normal tissues but has significantly decreased expression in many cancerous cell lines and tissues, suggesting a possible role in prevention of oncogenesis (Hromas et al. 1999; Frederick et al. 2000). CXCL14 has recently been reported to be a chemoattractant for activated natural killer (NK) cells and loss of CXCL14 expression immediately after oncogenic transformation of normal tissue may allow malignant cells to escape immune detection (Starnes et al. 2006). Thus, IGF- I:IGFBP-5:VN-stimulated down-regulation of CXCL14 gene expression, in concert with reductions in CLDN1 and OCLN gene expression, may promote the transition of non- tumourgenic MCF-10A cells toward a more malignant phenotype.

One of the most unexpected results from the microarray study was the substantial down- regulation of IRS-1 in response to the IGF-I:IGFBP-5:VN treatment, and suggests a possible shift away from IRS-1 activation in IGF-I-stimulated cell migration. Indeed,

191 studies have demonstrated IRS-1 to be responsible for IGF-I-stimulated proliferation, but not motility, while IRS-2 can enhance IGF-I-stimulated motility, but not proliferation in breast cancer cells (Byron et al. 2006). Together these data indicate divergent roles for IRS-1 and IRS-2 in regulating cell motility. Moreover, the relative activation of IRS-1 to IRS-2 can determine whether MCF-7 cells migrate in response to IGF-I, where increased IRS-2 phosphorylation promotes MCF-7 cell migration via IRS-2 movement to the leading edge of filopodia (Zhang et al. 2004). IRS-1 and IRS-2 are expressed in normal mammary epithelial cells and breast cancer cells (Gibson et al. 2007) and it has been reported that the incidence of lung metastasis increases for IRS-1-deficient tumours and decreases for IRS-2-deficient tumours, as compared to mammary tumours with a wildtype IRS genotype (Nagle et al. 2004; Ma et al. 2006). Thus, through down- regulation of IRS-1, IGF-I:IGFBP-5:VN stimulation may modulate cell motility by favouring IRS-2 activation and promotion of cell migration. Indeed, future studies assessing the relative phosphoylation of IRS-1 and IRS-2 in IGF-I:IGFBP:VN-stimulated cells are warranted.

In summary, the studies reported in this chapter were focused on identifying potential candidate genes involved in the enhanced cell migration observed in response to substrate-bound IGF-I:IGFBP-5:VN complexes. The microarray analysis identified 165 genes to be differentially expressed in MCF-10A cells in response to this complex. Moreover, functional and network analysis found these genes to be highly relevant to processes such as TEM, cell-to-cell adhesion, cell growth and survival, and cellular movement. A number of the IGF-I:IGFBP-5:VN target genes were determined to have previously described roles in cell migration and metastasis, however, the exact functional impact of these genes still remains to be determined. Indeed, functional testing using RNAi or overexpression studies will be needed for the rigorous evaluation of the identified candidate genes important in IGF-I:IGFBP-5:VN-stimulated migration. Nevertheless, these studies have added valuable new knowledge to our understanding of the mechanisms underpinning the responses of breast cells to substrate-bound IGF-I- containing complexes. Furthermore, these results provide new insights into the induction

192 of a highly motile phenotype and thus, early events involved the metastatic spread of breast cancer cells.

193

CHAPTER 6

General Discussion

195 6.1 GENERAL DISCUSSION

Members of the IGF family are mitogenic growth factors which have been shown to play critical roles in both normal growth and development, and tumour biology (LeRoith and Roberts 2003; Samani et al. 2007). The IGF system is complex and the biological effects of the IGFs are determined by diverse interactions between many molecules, including interactions with the ECM. As outlined earlier in this thesis initial investigations from our laboratory demonstrated that IGFs can associate with the ECM protein vitronectin (VN) (Upton et al. 1999; Kricker et al. 2003) and this interaction can modulate IGF-stimulated biological functions (Kricker et al. 2003; Noble et al. 2003; Hyde et al. 2004; Hollier et al. 2005). Since both IGFs and VN are implicated in breast cancer biology, we hypothesized that the IGF:VN interaction would be an important mechanism promoting breast cell dissemination. As such, the underlying aim of the studies undertaken in this thesis was to describe the effects of substrate-bound IGF-I:IGFBP:VN complexes on breast cell functions, in particular cellular migration, and to dissect the mechanisms critical to these responses. The results from these studies, as presented in Chapters 3 to 5 have provided valuable new knowledge regarding not only the functional impact and mechanisms underpinning the action of these novel complexes, but also new insights into IGF/matrix interactions.

The majority of studies undertaken herein adopted the strategy of pre-binding IGFs ± IGFBP-3/-5 to VN-coated tissue culture dishes to form “substrate-bound” complexes prior to the addition of cells. This strategy is in contrast to traditional in vitro approaches to studying the cellular effects of growth factors, which in the main have involved adding these soluble growth factors in solution. In this solution-phase situation the growth factors are freely diffusible throughout the culture medium for the duration of the assay. However, in vivo it is thought that cells within organized tissues respond to growth factors bound within the ECM. As such, we believe that the pre-binding approach adopted in these studies may more accurately reflect the environment cells would encounter in vivo. Using this approach a number of significant findings are reported in this thesis which supports the hypotheses proposed in Chapter 1.

196 Two key studies undertaken by members of our laboratory previously, demonstrated that IGF-II could bind directly to VN, while IGF-I associated with VN indirectly via the involvement of IGFBP-2, -3, -4 and -5 (Upton et al. 1999; Kricker et al. 2003). While early studies demonstrated a functional role for these IGF:VN complexes (Kricker et al. 2003; Noble et al. 2003; Hyde et al. 2004), prior to the studies reported in Chapter 3 only very limited investigation of the effects of substrate- bound IGF-I:IGFBP:VN complexes in modulating the functional responses of breast cells had been undertaken. It was therefore hypothesised that the association of IGF-I with VN via IGFBP-3 and/or IGFBP-5, would stimulate increased cellular functions in non-tumourgenic MCF-10A breast epithelial and MCF-7 breast carcinoma cell lines. This hypothesis was supported by the findings reported in Chapter 3 indicating that substrate-bound IGF-I:IGFBP-3:VN and IGF-I:IGFBP-5:VN complexes are potent stimulators of proliferation and migration in both MCF-10A and MCF-7 cell lines. These results concur with previous findings reported by Hyde et al., (2004) who demonstrated that IGF-II:VN and IGF-I:IGFBP-5:VN complexes were capable of stimulating increased protein synthesis and migration of the HaCAT human skin keratinocyte cell line.

The data presented in Chapter 3 is the first report of substrate-bound IGF- I:IGFBP:VN complexes stimulating increased breast cancer cell proliferation, as a previous study by Noble et al., (2003) observed IGF-II:VN complexes have little effect on MCF-7 cell de novo protein synthesis. We also report for the first time that IGF-I:IGFBP:VN complexes are potent stimulators of proliferation in non- tumourgenic MCF-10A breast epithelial cells. In contrast to the effects on cell proliferation, both substrate-bound IGF-II:VN and IGF-I:IGFBP-5:VN complexes have been observed previously to enhance MCF-7 cell migration in the Transwell® assay system (Kricker et al. 2003; Noble et al. 2003). We can now also report that substrate-bound IGF-I:VN complexes containing either IGFBP-3 or IGFBP-5 also stimulate enhanced MCF-7 breast cancer cell migration. Moreover, substrate-bound IGF-I:IGFBP-3:VN and IGF-I:IGFBP-5:VN complexes stimulate increased migration of MCF-10A breast epithelial cells. These data, taken together with other studies reported in the literature (Jones et al. 1996; Maile et al. 2001; Nam et al. 2002), provide evidence supporting the overall concept of co-ordinate regulation between the IGF-IR and αv integrins. As members of the IGF system and VN can

197 play important roles in a number of human malignancies, future studies investigating the functional role of IGF:VN complexes in other cell types will be crucial to fully elucidating their physiological relevance to cancer progression.

We believe that substrate-bound IGF-I:IGFBP:VN complexes may play a particularly important role inducing a highly migratory phenotype in pre-invasive or poorly metastatic cells, as these complexes were observed to have less of an effect in stimulating the migration of the highly metastatic MDA-MB-231 breast cancer cell line. These cells, however, were highly motile on VN alone due to their expression of the αvβ3 integrin, thus, supporting a highly migratory phenotype independent of IGF-I-stimulation. Moreover, tumour cells which acquire αvβ3 integrin expression are known to become growth factor-independent (Klemke et al. 1994; Brooks et al. 1997), indicating an important role of the αvβ3 integrin and its ligand VN in invasive tumours. Nevertheless, the interaction of IGF-I with VN appears to be highly relevant to cancer metastasis, as increased expression of VN and VN-binding integrins has been reported at the leading edge of migrating tumour cells (Gladson and Cheresh 1991; Uhm et al. 1999; Bello et al. 2001). As proposed in Chapter 3, we believe the IGF:VN interaction to be an important mechanism for breast cancer progression and metastasis, particularly in tumour cells prior to acquisition of αvβ3 (i.e. prior to the acquisition of the highly invasive, metastatic phenotype).

We report that the enhanced cellular functions stimulated by substrate-bound IGF- I:IGFBP:VN complexes results from IGF-I being captured by VN through the involvement of IGFBPs. We propose this provides a local reservoir of IGF-I in the pericellular ECM which can then interact with the IGF-1R to regulate cell function. Moreover, the matrix proteins FN and COL IV, proteins abundant within the ECM and basement membrane structures, respectively, are also capable of associating with IGFBPs (Jones et al. 1993; Parker et al. 1996; Martin and Buckwalter 2000; Gui and Murphy 2001). Thus FN and COL IV may provide additional “anchorage” sites for IGF-I within the ECM to modulate cell functions. Indeed, as the majority of IGF-I and IGF-II in the mammary gland is produced by cells within the stroma, the interaction of IGFs with components of the matrix is likely to play an important role in the distribution of IGFs to cells within epithelial structures. The binding of growth factors to the matrix is likely to prolong the exposure of cells to these factors, either

198 by slowing their clearance rate from the tissue and/or protecting them from degradation. In the mammary gland IGF-1R expression is predominantly localized to the epithelial compartment (Happerfield et al. 1997) and the ability of the matrix to “store” bound growth factors within epithelial structures may promote increased IGF/IGF-1R interactions and sustained activation of IGF signaling. Therefore, given the role of the IGF system in the mitogenicity, cell survival and malignant transformation of breast cancer (Cullen et al. 1990; Coppola et al. 1994; Adams et al. 2000; Baserga et al. 2003), increased IGF/IGF-1R interactions may promote the cellular transition from normal epithelium to dysplastic and neoplastic breast disease. Increased local concentrations and exposure to IGFs captured within the matrix would also enhance the survival and progression of malignant cells.

It is well established that the composition of the extracellular microenvironment can have profound effects on the way cells respond to peptide hormones and growth factors, with several growth factors demonstrated to have their activities modulated by ligand binding to integrin receptors (Eliceiri 2001). Indeed, there is accumulating evidence for co-operation or “cross-talk” between the IGF-1R and VN-binding integrins (Brooks et al. 1997; Clemmons and Maile 2004; Maile et al. 2006). As such, we hypothesized that the enhanced cell migration of MCF-10A and MCF-7 cells in response to IGF-I:IGFBP:VN complexes is a product of these novel complexes facilitating co-operation between the IGF-1R and VN-binding integrins, resulting in enhanced intracellular signaling. In order to determine if the enhanced cell migration responses observed in Chapter 3 were a result of co-operation between the IGF-1R and VN-binding integrins, the aim of the studies undertaken in Chapter 4 was to investigate the mechanisms behind IGF-I:IGFBP:VN complex-stimulated cell migration. As such, the studies undertaken in Chapter 4 represent the first significant investigations into the mechanisms underlying the enhanced migratory responses observed to IGFs associated with VN. Studies using IGF-I analogues which bind IGFBPs poorly demonstrated that heterotrimeric complex formation (i.e. the association of IGF-I with VN via IGFBPs) is required for substrate-bound IGF- I:IGFBP:VN-stimulated cell migration. This supports our previous proposal, that IGF-I via the involvement of IGFBPs, can be captured by VN, localising IGF-I in the pericellular environment to modulate cell function. Furthermore, IGF-I associated with VN still needs to activate the IGF-1R, as the IGF-I analogue

199 [L24][A31]-IGF-I which binds IGFBPs, but has a reduced affinity for the IGF-1R, failed to enhance cell migration above that observed with VN alone.

It has been shown previously that cellular responses to IGF-I can be mediated by IGF-I association with the ECM via IGFBP-5, thereby stabilizing IGF-I matrix concentrations and promoting receptor interactions (Jones et al. 1993). Substrate- bound IGF-I:IGFBP:VN complexes were demonstrated herein to induce synergistic increases in intracellular signal transduction; in particular, an increased and sustained activation of the PI3-K/AKT pathway via co-operation between the IGF-1R and the VN-binding alpha-v integrins (αv-integrins). Indeed, optimal activation of intracellular signaling by substrate-bound IGF-I:IGFBP:VN complexes required activation of both the IGF-1R and αv-integrins, as antibody inhibition of either receptor type led to reductions in ERK 1/2 and AKT activation and cell migration. Together these observations indicate an important role for both the IGF-1R and VN- binding integrins, most prominently αvβ5, in the MCF-7 and MCF-10A cell migration responses observed. This is a particularly novel finding as previous studies investigating the regulation of IGF-I signaling by VN/integrin interactions have focused on the αvβ3 integrin in endothelial and SMC cell types (Maile et al. 2006; Maile et al. 2006; Clemmons et al. 2007; Miller et al. 2007). Interestingly, the cell lines used in the studies reported herein do not express the αvβ3 integrin. Future studies investigating the regulation of IGF-1R and αvβ5 receptor activation, and phosphorylation of downstream signaling intermediates, such as Shc, SHP-2, and SHPS-1, reported to be involved in αvβ3-mediated regulation of IGF-I signaling (Ling et al. 2003; Ling et al. 2005), will be important in elucidating common mechanisms involved in αvβ5 and/or αvβ3 integrin regulation of IGF-I signaling.

The studies reported in Chapter 4 indicate a pivotal role for AKT in promoting breast cell migration. Pharmacological inhibition of the PI3-K/AKT pathway significantly attenuated IGF-I:IGFBP:VN complex-stimulated cell migration. In contrast, the over-expression of wild type and activated AKT 1 increased cell migration further in response to IGF-I:IGFBP:VN complexes, supporting previous studies demonstrating the importance of the PI3-K/AKT pathway in IGF-I-dependent motility of cancer cells (Bartucci et al. 2001; Tanno et al. 2001). The increased and sustained activation of AKT in response to IGF-I:IGFBP:VN complexes is significant as AKT is

200 frequently hyperactivated in human cancers (Hay 2005). For cells to migrate they must first establish a polarised morphology. Key signaling molecules regulating cell polarisation include PI3-K and Rho-family GTPases, whereby PI3-K activity helps to define the leading edge of the cell, whereas Rho-family GTPases regulate cytosketal remodelling, both of which are required for efficient cell migration (Fenteany and Glogauer 2004). Furthermore, AKT has previously reported roles in modulating cell migration through rearrangements of the cytoskeleton mediated via interactions with proteins such as the Rho-family GTPases, Rac and Cdc42 (Higuchi et al. 2001). I therefore propose that studies investigating the dynamics of actin reorganisation mediated via PI3-K/AKT activation and interactions with proteins such as Rho-family GTPases, will be of particular value in further understanding the precise mechanisms underpinning IGF-I:IGFBP:VN-stimulated cell migration.

It was hypothesized that in addition to modulation of intracellular signaling, substrate-bound IGF-I:IGFBP:VN complexes would induce the differential expression of genes important to the enhanced migration responses observed in Chapters 3 and 4. In an attempt to obtain a more ‘holistic’ view of the mechanisms responsible for substrate-bound IGF-I:IGFBP:VN complex-stimulated cell migration, studies undertaken in Chapter 5 employed oligonucleotide microarrays to screen for candidate genes important for the observed migratory responses. Using the novel approach of isolating RNA from cells which had migrated to the underside of porous Transwell® inserts, microarray analysis revealed 165 genes to be differentially expressed by at least 1.8-fold in cells that had migrated in response to substrate-bound IGF-I:IGFBP:VN-complexes. Importantly a number of the genes identified, including, TF, TFPI, PAI-1, EFNB2, CXCR4, CXCL14, CLDN1 and IRS1, have previously reported roles in regulating cellular movement and cancer progression. Interestingly, while not differentially expressed by cells in response to IGF-I:IGFBP:VN complexes, network analysis revealed PI3-K, AKT and MAPK pathway members to play a central role in regulating the expression and/or function of many of the genes identified. This indicates that IGF-I:IGFBP:VN-stimulated MAPK and PI3-K/AKT pathway activation, as described in Chapter 4, has a significant role in regulating the expression of genes important for tumour progression and metastasis. However, extensive studies using RNAi and/or overexpression strategies will be required to determine the relative contributions of

201 the candidate genes and their protein products in IGF-I:IGFBP:VN-stimulated migration.

Apart from leukocytes, most cells in the human body are relatively non-motile, except during development and wound healing when a variety of cell types express a migratory phenotype. During initial stages of cancer progression cells can move while attached to each other, however, it is generally when cell-cell adhesions are lost and cancer cells can migrate individually that they become highly motile. Thus, during cancer progression subsets of tumour cells change from a relatively non- motile to a motile phenotype, which can eventually lead to invasion and metastasis. As such, a significant finding from the microarray studies was the identification of a number of genes differentially expressed in cells migrating in response to IGF- I:IGFBP:VN complexes that are associated with biological processes highly relevant to tumour progression and/or metastasis. These included functions such as leukocyte extravasation signaling/TEM and the disruption of cell-to-cell adhesion junctions, biological processes which play key roles in the progression and metastatic spread of tumour cells in vivo. Thus, via the downregulation of genes, such as CLDN1 and OCLN, whose protein products are involved in the formation of cell-cell adhesion junctions, IGF-I:IGFBP:VN-stimulation may promote cell scattering and the acquisition of a more motile phenotype in both normal and malignant breast cells. Furthermore, the lateral migration of epidermal keratinocytes into the wound-bed is critical in the process of skin re-epithelialization after injury. The increased disassembly of cell-cell adhesions between keratinocytes in peripheral intact epidermal tissue, allows keratinocytes to become more motile and increase their lateral migration into the wound-bed. Thus in addition to direct modulation of migration by ligand/receptor interactions, this provides another mechanism by which epithelial cell types can become more motile in response to IGF:VN complexes. Indeed, a recent study in our laboratory has determined that VN:growth factor complexes enhance re-epithelialization after burn injuries using a 3D in vitro skin equivalent model through mechanisms including increases in keratinocyte lateral and net migration distances into the wounded area (Upton et al. 2007).

In a similar manner, leukocyte TEM or diapedsis has been used to model cancer cell extravasation from the vasculature during hematogenous metastasis (Miles et al.

202 2007). Indeed, integrin-mediated adhesion, rearrangements of the cytoskeleton and motility are essential for TEM and would also be critical for migration of cells to the undersurface of porous Transwell® inserts. Thus, targeting genes which have roles in cytoskeletal dynamics and TEM, such as ACTN1 and FARP2, upregulated in cells in response to IGF-I:IGFBP:VN-stimulation, may prove useful for inhibiting the metastatic potential of cells. Future studies evaluating the effects of IGF- I:IGFBP:VN complexes on TEM using in vitro models of tumour cell invasion through endothelial cell monolayers would provide a more accurate representation of the in vivo situation and thus, the effect of IGF-I:IGFBP:VN complexes on tumour cell extravasation. I believe these results taken together, have provided evidence that the novel approach used to assess the early transcriptional events involved in IGF- I:IGFBP:VN-stimulated cell migration may provide a useful model of the in vivo processes governing metastasis. Indeed, a similar approach could be used to investigate the mechanisms underlying a host of hormones or growth factors known to induce a metastatic phenotype.

Recent research within the Tissue Repair and Regeneration (TRR) program at QUT has focused on the development of multimeric complexes comprising growth factors and ECM proteins. Indeed, since starting my PhD studies, a number of key investigations have discovered that complexes comprising IGFs, IGFBPs and EGF in combination with VN can significantly enhance the proliferation and migration of human keratinocyte cell lines (Hyde et al. 2004; Hollier et al. 2005), as well as the proliferation of keratinocytes derived from human tissues (Hollier et al. 2005; Dawson et al. 2006). These complexes also have potential applications in replacing the requirement for serum and high concentrations of insulin for the ex vivo expansion and propagation of cells for human skin (Dawson et al. 2006) and corneal grafts (Ainscough et al. 2006). Furthermore, recent studies demonstrating enhanced re-epithelialization after burns injuries, in vitro and in vivo, indicates the potential of these novel complexes in wound therapy approaches (Upton et al. 2007). These key findings have led to the commericalisation of IGF:IGFBP:VN complexes (generically referred to as VitroGro®) by the biotechnology company Tissue Therapies Ltd. Tissue Therapies is focused primarily on applying the VitroGro® technology to situations in which cell migration and growth is the desired outcome, such as in wound healing and industrial medicine applications. Therefore, key

203 findings from the studies reported herein have contributed significantly to the commercial development of these complexes, particularly to dissecting the molecular mechanisms associated with these complexes, and are pertinent to many of the current and future projects at QUT investigating these novel complexes.

In view of the significant functional responses induced by IGF-I:IGFBP:VN complexes via co-activation of the IGF-1R and VN-binding integrins, chimeric proteins have been developed incorporating the key functional domains of VN linked to IGF-I, which mimic the functions of the multimeric complex. Indeed, cellular functions stimulated by these chimeric proteins are also dependent on receptor co- activation (Van Lonkhuyzen D, Hollier B, Shooter G, Leavesley D, Upton Z; Chimeric vitronectin:insulin-like growth factor proteins enhance cell growth and migration through co-activation of receptors, In Press, Growth Factors). In addition, results reported herein have contributed significantly to enhancing our understanding of IGF-I:IGFBP:VN interactions and provided the basis for a successful Australian Research Council (ARC) linkage grant. In collaboration with the University of Adelaide and the University of Melbourne, these studies aim to design and develop novel antagonists to block IGF-I:IGFBP:VN complex formation, thereby preventing subsequent activation of cell surface receptors. It is proposed that if antagonists can be developed to block cellular pathways normally stimulated by VitroGro® complexes, new treatments may be developed to prevent cell metastasis in cancer and plaque formation in atherosclerosis, diseases in which VN, IGF-I and IGFBPs have demonstrated roles (Gladson and Cheresh 1991; Uhm et al. 1999; Bello et al. 2001; Aaboe et al. 2003; Kricker et al. 2003; Clemmons 2007).

As both IGF-I and VN are implicated in tumour biology, we propose the IGF- I:IGFBP:VN interaction is an important mechanism promoting breast cancer metastasis. Understanding the processes that lead to the establishment of secondary tumour bodies and strategies to halt the spread of cancer beyond the primary site are therefore highly valuable, yet few interventions have targeted this aspect of breast disease. Indeed, findings from the studies undertaken throughout this thesis suggest that IGF-I:IGFBP:VN complexes stimulate not only the migration of breast cells but also function to modulate the expression of genes associated with promoting breast cancer cell metastasis. We intend to build upon the mechanistic studies reported in

204 this thesis by undertaking a pilot study in which we will examine clinical breast cancer samples using tissue microarrays and high through-put immunohistochemistry; the overall goal being to correlate a selection of cell- associated molecular markers identified through my thesis studies with the progression and outcome of breast disease. As such, future experiments investigating the dysregulation, or over-expression, of these receptors and their ligands in contributing to the dissemination of tumours will be of particular importance.

In summary, the studies undertaken throughout this thesis aimed to describe the effects of substrate-bound IGF-I:IGFBP:VN complexes on functions relevant to breast cancer metastasis and to dissect the mechanisms underlying these responses. I believe the data presented in this thesis has contributed valuable new information to our understanding of the role IGF-I:IGFBP:VN complexes play in modulating cellular functions. Additionally, these studies provide the first comprehensive mechanistic insights into the actions of IGF:VN complexes. An increased understanding of the interactions between growth factors and the ECM on modulating cellular functions and in this instance, IGF-I with VN, will be of particular importance to the metastasis of breast cancer in vivo, as these proteins have been demonstrated to be associated with the dissemination of tumours. Furthermore, as cell migration is a critical process in wound healing and tissue re- modelling, knowledge gained from these studies will also contribute significant value in the emerging field of tissue engineering for application in a range of tissue repair therapies.

205

206

CHAPTER 7

Appendix 1

207 APPENDIX 1

Figure A1-1: Schematic diagrams of pUSEamp expression vectors used in transient transfections. pUSEamp eukaryotic expression vectors under the control of the cytomegalovirus promoter, containing Myc-His tagged mouse wild type AKT-1 (WT-AKT), N-terminal myristoylated AKT-1 (MYR-AKT), which produces an activated form of AKT and the empty vector control.

208 Table A1-2. Reverse Transcription Master Mix (for a single 20 µL reaction)

Amount (µL) Component 2 10 X first strand buffer 4 dNTP mix 1 RNase inhibitor 1 ArrayScript

Table A1-3. Second Strand Master Mix (for a single 100 µL reaction)

Amount (µL) Component 63 Nuclease-free water 10 10 X second strand buffer 4 dNTP mix 2 DNA polymerase 1 RNase H

Table A1-4. IVT Master Mix (for a single 40 µL reaction)

Amount (µL) Component 20 double-stranded cDNA 12 Biotin-NTP mix 4 T7 10 X reaction buffer 4 T7 enzyme mix

209

210

Appendix 2

211 APPENDIX 2

Guidelines for Assessing microarray hybridization and data quality

The integrity of total RNA, the performance of cRNA labeling and microarray hybridization, and quality of the microarray data was assessed according to the procedures outlined in Guidelines for Assessing Data Quality, contained within the Affymetrix Data Analysis Fundamentals Manual, www.affymetrix.com/support/technical/manual/expression_manual.affx. This involved the following: 1) Probe Array Image (.dat files) were inspected and no image artifacts (i.e., high/low intensity spots, scratches, high regional, or overall background, etc.) were observed on each of the 15 arrays hybridized (Figure A2-1* 1-15). 2) The boundaries of the probe area, identified from hybridization of the “spiked” B2 oligos, displayed an alternating intensity pattern along the border of the probe array and the array name was observed in the upper left hand corner of the probe area. The checkerboard pattern of B2 oligo hybridiastion in each corner also allowed for correct grid alignment, ensuring precise data collection from individual probe sets on all arrays. 3) Average background was confirmed to be within the acceptable range of 20 – 100 for arrays scanned with the GeneChip® Scanner 3000 (Table A2-1*). 4) To ensure the quality of RNA and efficiency of reverse transcription, the 3’ to 5’ ratios of GAPDH and β-actin the house-keeping genes were assessed. The ratio between signal values of the 3’ and 5’ probe sets should be lower than 3, with around 1 ideal. 3’ to 5’ ratios across all 15 arrays ranged from 0.89 – 1.05 for GAPDH, and 0.98 – 1.57 for β-actin (Table A2-1*). 5) Scale factors (SF) for all arrays were within 3-fold to allow array comparisons (Table A2-1*). 6) Successful and efficient target labeling was confirmed by the 4 poly-A controls being called “present” with increasing signal values in the order of lys, phe, thr and dap (Table A2-2*). 7) The hybridization controls were all called “present” with increasing signal values in the order of bioB, bioC, bioD, and cre (Table A2-3*). 8) All arrays exhibited satisfactory levels of “% present calls”, with all arrays within 10% of each other (Table A2-1*).

212

*Key for Figure A2-1 and Tables A2-1 to A2-3.

1 Exp-1 Control 2 Exp-1 VN 3 Exp-1 BP-5+VN 4 Exp-1 IGF-I+VN 5 Exp-1 IGF-I+BP-5+VN 6 Exp-2 Control 7 Exp-2 VN 8 Exp-2 BP-5+VN 9 Exp-2 IGF-I+VN 10 Exp-2 IGF-I+BP-5+VN 11 Exp-3 Control 12 Exp-3 VN 13 Exp-3 BP-5+VN 14 Exp-3 IGF-I+VN 15 Exp-3 IGF-I+BP-5+VN

213

Figure A2-1: Images of scanned HG-U133 arrays.

214 Table A2-1. Individual array data for internal controls used to assess RNA sample and assay quality*

Control gene Parameter 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 GAPDH Signal 11227 9965 9950 10129 10287 10189 11435 10184 11224 11261 9757 10025 11552 9096 10787 Detection P P P P P P P P P P P P P P P Ratio 3' to 5' 1.03 1.05 1.03 1.02 1.02 0.96 0.95 0.96 0.92 0.89 0.94 0.93 0.96 0.94 0.93

β-actin Signal 7563 7733 6326 7735 7246 7054 8885 7622 7193 8928 7927 6576 9772 7816 7950 Detection P P P P P P P P P P P P P P P Ratio 3' to 5' 0.98 1.57 1.46 1.34 1.37 1.28 1.31 1.42 1.23 1.21 1.58 1.43 1.55 1.45 1.37

Avg.Background 49 35 33 30 37 34 30 36 34 30 34 35 31 38 32

% Present Calls 36.6 40.1 39.7 39.8 39 43.8 43.3 42.7 42.3 41.4 44.1 42.6 43.5 42.7 42.7 Scale Factor 3 2.83 3.37 3.33 3.37 1.54 1.41 1.5 1.34 1.24 1.49 1.54 1.59 1.41 1.94 * The signal values have been normalized following a global scaling strategy. P = Present call.

Table A2-2. Data for poly-A controls used to monitor labeling reactions during sample preparation*

Control gene Parameter 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Lys Signal 160 158 166 162 167 190 200 170 189 168 168 167 183 142 149 Detection P P P P P P P P P P P P P P P Phe Signal 251 262 261 272 252 277 285 253 278 242 254 268 268 231 265 Detection P P P P P P P P P P P P P P P Thr Signal 335 292 288 355 333 344 391 323 356 323 339 322 357 317 271 Detection P P P P P P P P P P P P P P P Dap Signal 1238 1298 1211 1273 1291 1473 1365 1339 1433 1249 1305 1268 1243 1092 1187 Detection P P P P P P P P P P P P P P P * The signal values have been normalized following a global scaling strategy. P = Present call.

Table A2-3. Individual array data for hybridization controls*

Control gene Parameter 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 bioB Signal 328 283 282 290 302 269 370 220 366 330 333 278 384 330 271 Detection P P P P P P P P P P P P P P P bioC Signal 745 625 578 601 680 538 840 501 754 704 677 569 800 734 548 Detection P P P P P P P P P P P P P P P bioD Signal 3457 3275 3177 3232 3747 3088 4291 2783 4180 3788 3607 3042 4571 3660 2840 Detection P P P P P P P P P P P P P P P Cre Signal 12735 11114 10255 11934 11105 10410 14741 10184 12513 13708 10949 10078 14812 10643 10332 Detection P P P P P P P P P P P P P P P * The signal values have been normalized following a global scaling strategy. P = Present call.

215 Table A2-4: Genes identified as differentially expressed by IGF-I:IGFBP-5:VN complexes. Table contains genes identified as differentially expressed by at least +/- 1.8 by IGF-I:IGFBP-5:VN complexes when compared to the expression by VN alone. All genes passed ANOVA with BHFDR, p<0.05.

Fold Change Affy ID Genbank Gene Symbol Description 2.766 209446_s_at BC001743 C7ORF44 chromosome 7 open reading frame 44 2.699 204363_at NM_001993 F3 tissue factor 2.671 201170_s_at NM_003670 BHLHB2 basic helix-loop-helix domain containing, class B, 2 2.602 204359_at NM_013231 FLRT2 fibronectin leucine rich transmembrane protein 2 2.435 202668_at BF001670 EFNB2 ephrin-B2 2.432 205074_at NM_003060 SLC22A5 solute carrier family 22, member 5 2.417 225424_at AB046780 GPAM glycerol-3-phosphate acyltransferase 2.357 218559_s_at NM_005461 MAFB v-maf musculoaponeurotic fibrosarcoma oncogene homolog B (avian) 2.335 209260_at BC000329 SFN stratifin 2.29 200799_at NM_005345 HSPA1A heat shock 70kDa protein 1A 2.256 223062_s_at BC004863 PSAT1 phosphoserine aminotransferase 1 2.187 232060_at AK000776 ROR1 receptor tyrosine kinase-like orphan receptor 1 2.16 206753_at AF086735 RDH16 retinol dehydrogenase 16 2.138 227556_at AI094580 NME7 non-metastatic cells 7, protein expressed in 2.108 229125_at AA456955 ANKRD38 ankyrin repeat domain 38 2.078 209834_at AB017915 CHST3 carbohydrate (chondroitin 6) sulfotransferase 3 2.045 242138_at BF060783 DLX1 distal-less homeobox 1 2.031 1568765_at BC020765 SERPINE1 plasminogen activator inhibitor type 1 2.025 213562_s_at BF979497 SQLE squalene epoxidase 2.014 229105_at AV717094 GPR39 G protein-coupled receptor 39 1.962 201242_s_at BC000006 ATP1B1 ATPase, Na+/K+ transporting, beta 1 polypeptide 1.961 227616_at BG481877 BCL9L B-cell CLL/lymphoma 9-like 1.957 214141_x_at BF033354 SFRS7 splicing factor, arginine/serine-rich 7, 35kDa 1.937 212242_at AL565074 TUBA4A tubulin, alpha 4a 1.931 227164_at AL521786 SFRS1 splicing factor, arginine/serine-rich 1 1.884 211965_at BE620915 ZFP36L1 zinc finger protein 36, C3H type-like 1 1.882 211538_s_at U56725 HSPA2 heat shock 70kDa protein 2 1.866 232375_at AI539443 STAT1 signal transducer and activator of transcription 1, 91kDa 1.859 1565689_at BG400570 KPNA3 karyopherin alpha 3 1.854 242857_at AA748613 FARP2 FERM, RhoGEF and pleckstrin domain protein 2 1.854 205832_at NM_016352 CPA4 carboxypeptidase A4 1.847 225521_at AL137586 ANAPC7 anaphase promoting complex subunit 7 1.831 227599_at AU157304 C3ORF59 chromosome 3 open reading frame 59 1.825 209852_x_at BC001423 PSME3 proteasome activator subunit 3 1.822 224634_at AI911518 GPATCH4 G patch domain containing 4 1.82 212333_at AL049943 FAM98A family with sequence similarity 98, member A 1.811 230179_at N52572 LOC285812 hypothetical protein LOC285812 1.811 219099_at NM_020375 C12ORF5 chromosome 12 open reading frame 5 1.802 208637_x_at BC003576 ACTN1 actinin, alpha 1 1.801 226064_s_at AW469523 DGAT2 diacylglycerol O-acyltransferase homolog 2 -1.802 202446_s_at AI825926 PLSCR1 phospholipid scramblase 1 -1.802 219583_s_at NM_018418 SPATA7 spermatogenesis associated 7 -1.805 213792_s_at AA485908 INSR -1.808 220014_at NM_016644 PRR16 proline rich 16 -1.808 202083_s_at AI017770 SEC14L1 SEC14-like 1 -1.818 205379_at NM_001236 CBR3 carbonyl reductase 3 -1.818 227949_at AL357503 PHACTR3 phosphatase and actin regulator 3 -1.821 241739_at N62791 OGFOD1 2-oxoglutarate and iron-dependent oxygenase domain containing 1 -1.821 226688_at AW003508 C3ORF23 chromosome 3 open reading frame 23 -1.821 222528_s_at BG251467 SLC25A37 solute carrier family 25, member 37 -1.821 218346_s_at NM_014454 SESN1 sestrin 1 -1.845 227102_at AA115933 TRIM35 tripartite motif-containing 35 -1.845 234192_s_at AK026487 GKAP1 G kinase anchoring protein 1 -1.848 244787_at AI420959 -1.848 228846_at AW071793 MXD1 MAX dimerization protein 1 -1.852 202679_at NM_000271 NPC1 Niemann-Pick disease, type C1 -1.855 218603_at NM_016217 HECA headcase homolog -1.859 227370_at AW043602 KIAA1946 KIAA1946 -1.869 224469_s_at BC006173 C14ORF173 chromosome 14 open reading frame 173 -1.883 204157_s_at NM_025164 KIAA0999 KIAA0999 protein -1.89 221011_s_at NM_030915 LBH limb bud and heart development homolog -1.908 226025_at AV740426 ANKRD28 ankyrin repeat domain 28 -1.912 207001_x_at NM_004089 TSC22D3 TSC22 domain family, member 3 -1.923 218268_at NM_022771 TBC1D15 TBC1 domain family, member 15 -1.931 205376_at NM_003866 INPP4B inositol polyphosphate-4-phosphatase, type II, 105kDa -1.938 1569003_at AL541655 TMEM49 transmembrane protein 49 -1.938 214329_x_at AW474434 TNFSF10 tumor necrosis factor superfamily, member 10 -1.953 203575_at NM_001896 CSNK2A2 casein kinase 2, alpha prime polypeptide -1.953 213624_at AA873600 SMPDL3A sphingomyelin phosphodiesterase, acid-like 3A -1.961 227341_at AW195407 C10ORF30 chromosome 10 open reading frame 30 -1.965 1552309_a_at NM_144573 NEXN nexilin (F actin binding protein) -1.965 201368_at U07802 ZFP36L2 zinc finger protein 36, C3H type-like 2

Table A2-4 (continued)

216 Fold Change Affy ID Genbank Gene Symbol Description -1.969 202130_at AA725102 RIOK3 RIO kinase 3 (yeast) -1.976 235456_at AI810266 HIST1H2BD -1.98 200919_at NM_004427 PHC2 polyhomeotic homolog 2 (Drosophila) -1.992 241379_at W32922 C2ORF13 chromosome 2 open reading frame 13 -2 229050_s_at AL533103 SNHG7 small nucleolar RNA host gene 7 -2.02 244739_at AI051769 -2.028 235463_s_at AI081356 LASS6 LAG1 homolog, ceramide synthase 6 -2.049 228054_at BF593660 TMEM44 transmembrane protein 44 -2.066 208960_s_at BE675435 KLF6 Kruppel-like factor 6 -2.066 218764_at NM_024064 PRKCH protein kinase C, eta -2.066 227900_at AV701750 CBLB Cas-Br-M (murine) ecotropic retroviral transforming sequence b -2.096 219492_at NM_012110 CHIC2 cysteine-rich hydrophobic domain 2 -2.101 209102_s_at AF019214 HBP1 HMG-box transcription factor 1 -2.101 209815_at BG054916 PTCH1 patched homolog 1 -2.105 225327_at AB037791 KIAA1370 KIAA1370 -2.119 224917_at BF674052 MIRN21 microRNA 21 -2.123 228441_s_at BE550153 -2.128 202364_at NM_005962 MXI1 MAX interactor 1 -2.141 219736_at NM_018700 TRIM36 tripartite motif-containing 36 -2.151 219905_at NM_018538 ERMAP erythroblast membrane-associated protein -2.151 231928_at AK023754 HES2 hairy and enhancer of split 2 -2.155 202912_at NM_001124 ADM -2.174 212761_at AI949687 TCF7L2 transcription factor 7-like 2 -2.198 212677_s_at BG530481 CEP68 centrosomal protein 68kDa -2.203 210347_s_at AF080216 BCL11A B-cell CLL/lymphoma 11A (zinc finger protein) -2.217 244433_at AI950023 -2.247 226763_at AW409611 SESTD1 SEC14 and spectrin domains 1 -2.262 229817_at AI452715 ZNF608 zinc finger protein 608 -2.262 223551_at AF225513 PKIB protein kinase inhibitor beta -2.262 238476_at AA481560 LOC153222 adult retina protein -2.268 207543_s_at NM_000917 P4HA1 proline 4-hydroxylase, alpha polypeptide I -2.268 242907_at BF509371 GBP2 guanylate binding protein 2, interferon-inducible -2.273 213618_at AB011152 CENTD1 centaurin, delta 1 -2.273 219312_s_at NM_023929 ZBTB10 zinc finger and BTB domain containing 10 -2.288 227525_at AA058770 GLCCI1 glucocorticoid induced transcript 1 -2.299 226925_at AW069729 ACPL2 acid phosphatase-like 2 -2.331 227492_at AI829721 OCLN occludin -2.364 209946_at U58111 VEGFC vascular endothelial growth factor C -2.387 228088_at AI092265 -2.404 204526_s_at NM_007063 TBC1D8 TBC1 domain family, member 8 -2.41 228528_at AI927692 -2.41 203705_s_at AI333651 FZD7 frizzled homolog 7 -2.415 222486_s_at AF060152 ADAMTS1 ADAM metallopeptidase with thrombospondin type 1 motif, 1 -2.415 226213_at AV681807 ERBB3 v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 -2.427 209732_at BC005254 CLEC2B C-type lectin domain family 2, member B -2.427 202459_s_at U55968 LPIN2 lipin 2 -2.433 223388_s_at AF251025 ZFYVE1 zinc finger, FYVE domain containing 1 -2.457 204259_at NM_002423 MMP7 matrix metallopeptidase 7 -2.475 219773_at NM_016931 NOX4 NADPH oxidase 4 -2.519 242088_at AI961401 KLHL24 kelch-like 24 -2.584 229327_s_at BE674528 MAF v-maf musculoaponeurotic fibrosarcoma oncogene homolog -2.618 226636_at AI378587 PLD1 phospholipase D1, phosphatidylcholine-specific -2.632 204235_s_at AF200715 GULP1 GULP, engulfment adaptor PTB domain containing 1 -2.66 217741_s_at AW471220 ZFAND5 zinc finger, AN1-type domain 5 -2.695 213805_at AI692428 ABHD5 abhydrolase domain containing 5 -2.695 223276_at AF313413 NID67 MSTP150 -2.703 204720_s_at AV729634 DNAJC6 DnaJ (Hsp40) homolog, subfamily C, member 6 -2.732 217047_s_at AK027138 FAM13A1 family with sequence similarity 13, member A1 -2.732 205609_at NM_001146 ANGPT1 1 -2.755 202149_at AL136139 NEDD9 neural precursor cell expressed, developmentally down-regulated 9 -2.809 209101_at M92934 CTGF connective tissue growth factor -2.849 209602_s_at AI796169 GATA3 GATA binding protein 3 -2.857 204005_s_at NM_002583 PAWR PRKC, apoptosis, WT1, regulator -2.865 226237_at AL359062 COL8A1 collagen, type VIII, alpha 1 -2.882 230720_at AI884906 RNF182 ring finger protein 182 -2.907 57715_at W72694 FAM26B family with sequence similarity 26, member B -2.967 209183_s_at AL136653 C10ORF10 chromosome 10 open reading frame 10 -2.976 219316_s_at NM_017791 FLVCR2 feline leukemia virus subgroup C cellular receptor family, member 2 -3.012 207717_s_at NM_004572 PKP2 plakophilin 2 -3.086 220933_s_at NM_024617 ZCCHC6 zinc finger, CCHC domain containing 6 -3.096 203973_s_at NM_005195 CEBPD CCAAT/enhancer binding protein, delta -3.195 223449_at AF225425 SEMA6A semaphorin 6A

Table A2-4 (continued)

217 Fold Change Affy ID Genbank Gene Symbol Description -3.534 205959_at NM_002427 MMP13 matrix metallopeptidase 13 -3.534 231929_at AI458439 IKZF2 IKAROS family zinc finger 2 -3.65 231930_at AL359601 ELMOD1 ELMO/CED-12 domain containing 1 -3.953 221234_s_at NM_021813 BACH2 BTB and CNC homology 1, basic leucine zipper transcription factor 2 -3.968 1559739_at AK025141 CHPT1 choline phosphotransferase 1 -4 203140_at NM_001706 BCL6 B-cell CLL/lymphoma 6 -4.149 242281_at AW665656 -4.484 220936_s_at NM_018267 H2AFJ H2A histone family, member J -4.63 212558_at BF508662 SPRY1 sprouty homolog 1, antagonist of FGF signaling -5 205798_at NM_002185 IL7R interleukin 7 receptor -5.025 213258_at BF511231 TFPI tissue factor pathway inhibitor -5.051 229357_at BF060767 ADAMTS5 ADAM metallopeptidase with thrombospondin type 1 motif, 5 -5.263 218182_s_at NM_021101 CLDN1 claudin 1 -5.376 204686_at NM_005544 IRS1 insulin receptor substrate 1 -6.173 218002_s_at NM_004887 CXCL14 chemokine (C-X-C motif) ligand 14 -7.092 235419_at AW612461 ERRFI1 ERBB receptor feedback inhibitor 1 -8.13 217028_at AJ224869 CXCR4 chemokine (C-X-C motif) receptor 4 -9.346 219799_s_at NM_005771 DHRS9 dehydrogenase/reductase (SDR family) member 9 -14.451 205960_at NM_002612 PDK4 pyruvate dehydrogenase kinase, isozyme 4 -17.762 218723_s_at NM_014059 C13ORF15 chromosome 13 open reading frame 15

218

Figure A-3: Network 3 (Score 27). Molecules*: 20-alpha-hydroxyprogesterone, ABHD5, ADFP, AGTR1B, ATP12A, ATP1A2, ATP1B1, beta-estradiol, BHLHB2, C7ORF44, CCNB1, CCR2, CLEC2B, CXCR4, epinephrine, GJB2, GLCCI1, GNRH1, HBP1, HIST2H2AA3, IL7R, INPP4B, KLF10, KLK2, NPC1, NR3C1, PKIB, PLIN, PPARGC1A, progesterone, SFRS1, SLC22A5, SMPDL3A, THBD, ZFP36L2. * Underlined genes are those identified by the microarray analysis. The other genes were either not on the expression array or not significantly regulated. Red, up- regulated; Green, down-regulated.

219

Figure A-4: Network 4 (Score 25). Molecules*: CAMP, CCR2, CENTD1, CHPT1, CLDN1, CMIP, DDR2, EVL, FARP2, FLRT2, GBP2, H2AFJ, HDAC2, IFNG, IKZF2, IKZF3, IL6, IL4R, IL7R, MIRN21, MXD1, NFYB, OCLN, PHACTR3, PLSCR1, PPARD, PPP1CA, PSME2, SEMA6A, SRC, TJP1, TJP2, TJP3, WARS, ZBTB32. * Underlined genes are those identified by the microarray analysis. The other genes were either not on the expression array or not significantly regulated. Red, up-regulated; Green, down-regulated.

220

Figure A-5: Network 5 (Score 22). Molecules*: ACTN1, ADAMTS5, ADFP, ANKRD28, C10ORF10, CAMP, CAPN8, CCL6, CCR2, CEBPG, cholesterol, ECGF1, GBP2, GULP1, HP, HSPA1A, IL16, IL1B, KPNA3, MYC, NEDD9, NFKBIE, OCLN, ORM1, P4HB, PAPPA, PSAT1, SLK, SQLE, TGTP, TIE1, TMEM49, TNF, TRIM35, tryptophan. * Underlined genes are those identified by the microarray analysis. The other genes were either not on the expression array or not significantly regulated. Red, up-regulated; Green, down-regulated.

221

Figure A-6: Network 6 (Score 22). Molecules*: ADAMTS1, amino acids, AMPK, BACH2, BCL9L, CBFB, CPM, CTNNB1, CXCL14, CXCR4, EPHB4, FER, FGF2, FZD7, GKAP1, Gsk3, HOXB7, hydrogen peroxide, ID3, KIAA0999, KITLG, KLHL24, PCSK6, PDK4, PPARD, PRKG1, PTN, RIOK3, ROCK2, RPS6KB2, STK16, TBC1D8, TFPI2, TUBA4A, VEGFA. * Underlined genes are those identified by the microarray analysis. The other genes were either not on the expression array or not significantly regulated. Red, up-regulated; Green, down-regulated.

222

Figure A-7: Network 7 (Score 20). Molecules*: 3-alpha,17-beta-androstanediol, androsterone, APC, CBR3, CSE1L, DGAT2, DHRS9, dihydrotestosterone, DLX1, DSP, ERRFI1, F2, HSD17B1, IER2, IGF2, IRS4, KCTD11, KLK2, LBH, LY6E, MYH9, PKP2, PSME3, PTCH1, RDH16, retinoic acid, Retinol dehydrogenase, ROCK2, ROR1, SCD, SLC25A37, TMSB4X, WNT7B, YWHAZ, ZFAND5. * Underlined genes are those identified by the microarray analysis. The other genes were either not on the expression array or not significantly regulated. Red, up- regulated; Green, down-regulated.

223

Figure A-8: Network 8 (Score 18). Molecules*: ANAPC1, ANAPC2, ANAPC5, ANAPC7, APC, BUB1B, C12ORF5, C13ORF15, CCNB1, CDC2, CDC16, CDC20, CDC27, Cdc2-CyclinB-Sfn, CDC23, CDKN2A, CPA4, FZR1, GAST, HMGA2, HSPA2, ID3, MCM3, MDM2, NCL, P4HA1, P4HA2, PHC2, SEC14L1, SESN1, SFN, TCF7L2, TP53, ZAP70, ZBTB10. * Underlined genes are those identified by the microarray analysis. The other genes were either not on the expression array or not significantly regulated. Red, up-regulated; Green, down-regulated. .

224

CHAPTER 8

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