DOWNSTREAM TARGETS OF THE OESTROGEN RECEPTOR AND ENDOCRINE RESISTANCE
C.M.McNEIL
A thesis submitted in fulfilment
of the requirements for the degree of
Doctor of Philosophy
Garvan Institute of University of NSW, Medical Research Australia
i
ORIGINALITY STATEMENT
‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project's design and conception or in style, presentation and linguistic expression is acknowledged.’
Signed ……………………………………………......
Date ……………………………………………......
ii Abstract
ABSTRACT
The transcription factor c-Myc is an early downstream target of oestrogen action in breast cancer cells in culture and it has been speculated that aberrant c-Myc expression may mediate anti-oestrogen resistance. However, studies of c-Myc protein expression as either a prognostic or predictive marker in human breast cancer have been limited and contradictory, as have been studies of c-Myc expression during breast cancer evolution. In order to assess the relationship between c-Myc protein expression and outcome from breast cancer, a representative cohort of 292 women with invasive ductal carcinoma (IDC) and linked clinicopathological data was assembled and tissue microarrays (TMA) generated from the archived breast cancer specimens. Detailed assessments of the expression of cyclin D1, cyclin E, p21WAF1/Cip1 and p27Kip1 were also conducted and analysed in relation to c-Myc expression using immunohistochemistry. Changes in c-Myc protein expression in a TMA model of breast cancer evolution were also conducted. Finally the cell-cycle effects of low-level constitutive c-Myc expression and high-level inducible c-Myc expression were evaluated in MCF-7 cells in vitro.
Key novel results obtained were that c-Myc protein expression changed from preferentially nuclear to preferentially cytoplasmic during the evolution of breast cancer. In women with early invasive breast carcinoma, a “high-risk” cytoplasmic predominant c-Myc expression pattern was defined (~13% of cases) that independently predicted for poor outcome generally, among ER positive cases and in ER postive cases treated with endocrine therapy. In vitro studies confirmed that c-Myc overexpression was associated with resistance to the anti-proliferative effects of anti-oestrogens with persistence of both cyclin D1-cdk4 and cyclin E-cdk2 activities in the face of anti- oestrogen treatment. Further novel findings were that high cyclin D1 expression (upper 10% of expressors) was an independent predictor of poor outcome among ER positive breast cancer cases. Amongst ER + PR positive cases, both “high-risk” c-Myc expression and high level cyclin D1 expression were independent predictors of poor outcome. In summary, these data indicate that aberrant expression of the cell cycle proteins c-Myc and cyclin D1 may result in poor breast cancer outcomes in hormone receptor positive breast cancer and reinforces the importance of the cell cycle as a potential site of therapeutic manipulation in endocrine-resistant breast cancer.
iii Acknowledgements
ACKNOWLEDGEMENTS
At the beginning of 2004 I somewhat naively left clinical medicine and embarked upon a Ph.D in molecular biology at the Garvan Institute of Medical Research in Darlinghurst, Sydney. To see it finally completed seems miraculous.
I owe an enormous debt of gratitude to my supervisor Prof. Robert Sutherland, for his support, advice and forbearance during my less stellar moments (and there were quite a few). My co-supervisor A/Prof. Liz Musgrove was a tremendous source of knowledge and guidance particularly in relation to the design and implemention of the tissue culture and flow cytometry experiments in this thesis.
Mr. C. Marcello Sergio made the constitutive and inducible c-Myc overexpressing cells used in Chapters 5 and 6 of this thesis, and was an amazing resource in all matters related to tissue culture, as well as the personality traits of the FACScalibur. Thankyou to Mrs. Gillian Lehrbach, Dr. Liz Caldon, Dr. Will Hughes and Ms. Christine Lee who collectively taught me many of the techniques of molecular biology used in the production of this thesis. A number of other individuals provided useful suggestions in relation to the c-Myc project including Dr. Tilman Brummer, A/Prof. Sue Clarke, Dr. Alison Butt, A/Prof. Susan Henshall, and Dr. Alex Swarbrick.
Much of this thesis relates to the use of human breast cancer tissue cohorts. Thankyou to Dr. Niamh Murphy for the assembly of the Garvan-RPAH progression series, and the combined efforts of Dr. Dave Segara, Dr. Di Adams, Dr. Andrew Field, Dr. Niamh Murphy and Dr. Sandra O’Toole for their contributions over several years in the assembly of the SVCBCOC. Ms. Edwina Wing-Lun, Ms. Sarah Sutherland and Ms. Liz Todd assisted with data entry. Thankyou to Ms. Sarah Eggleton for her assistance with the optimisation of the c-Myc, cyclin D1, ER and PR antibodies for immunohistochemistry. A particular thankyou to Drs James Kench, Sandra O’Toole and Ewan Millar who collectively co-scored over 10,000 cores of breast cancer tissue, and A/Prof. Adrienne Morey who kindly performed the FISH analyses for both HER2 and MYC amplification at St. Vincent’s Hospital. Many thanks must go to Dr. Elena Lopez-Knowles who performed the DNA extractions from the St. Vincent’s Campus Breast Cancer Outcome Series (SVCBCOC), and provided guidance in the c-Myc
iv Acknowledgements
sequencing experiments. Dr. Niamh Murphy shared her expertise in the use of Excel spreadsheets that made an enormous difference to the task of collating the IHC scores. Dr. Lisa Horvath taught me how to perform survival analyses and provided motherly advice as I traversed the ups and downs of PhD life.
Finally, this thesis would not have been completed without my friends and family. My childhood friends Ben, Katherine and Rachel have been steadfast, even through their own tumultuous times. My partner Stephen has been a constant source of love and support over the last four years, having put up with more discussion on the intricacies of oestrogen action than is probably reasonable to expect of any Australian male. Lastly, my love and thanks go to my amazing parents Jane and Cliff, who have sacrificed much in life to provide their three daughters with education and opportunity. Their contibution to the completion of this thesis is immeasurable.
v Acknowledgements
In memory of my grandfather, James Woods
vi Financial support
FINANCIAL SUPPORT
During my PhD I was grateful to receive financial support through the following awards:
• NH&MRC Medical Postgraduate Scholarship, 2004-2007
• Cancer Institute Research Scholar Award, 2005-2007
• Australian Cancer Technologies Scholarship, 2005
vii Table of Contents
TABLE OF CONTENTS
Abstract ...... iii
Acknowledgements ...... iv
Financial support ...... vii
Table of contents ...... viii
List of abbreviations...... xiv
List of figures...... xvi
List of tables ...... xxii
Manuscripts published or submitted during the course of this thesis ...... xxvii
CHAPTER 1: INTRODUCTION ...... 1
1.1 Breast cancer – clinical overview ...... 2 1.1.1 Epidemiology ...... 2 1.1.2 Histological subtypes of breast cancer ...... 3 1.1.3 The evolution of breast cancer ...... 6 1.1.4 Gene expression profiling and molecular subtypes of breast cancer ...... 9 1.1.5 Current treatment consensus guidelines ...... 13
1.2 The oestrogen receptor and anti-oestrogens ...... 15 1.2.1 The oestrogen receptor...... 15 1.2.2 Anti-oestrogens and breast cancer – pharmacology and clinical outcome...... 21
1.3 The cell cycle in breast cancer...... 24 1.3.1 The cell cycle and breast cancer...... 24 1.3.2 Cyclin D1 ...... 26 1.3.3 Cyclin E ...... 31 1.3.4 p21WAF1/Cip1 ...... 33 1.3.5 p27Kip1 ...... 35 1.3.6 c-Myc ...... 36 1.3.7 Molecular effects of oestrogen and anti-oestrogens on the cell cycle...... 42 1.3.8 Growth factor receptor signalling pathways and proliferation in breast cancer ...... 45
1.4 The molecular basis of endocrine resistance...... 47 1.4.1 Loss of ER expression or function ...... 47 1.4.2 Loss of ER expression ...... 48 1.4.3 Adaptive hypersensitivity to oestrogen deprivation and cross-talk with growth factor signalling pathways...... 49
viii Table of Contents
1.4.4 Polymorphisms in tamoxifen-metabolising enzymes ...... 50 1.4.5 Aberrations in cell cycle control ...... 54
1.5 Summary ...... 56
CHAPTER 2: MATERIALS AND METHODS...... 58
2.1 Archival tissue cohorts...... 59 2.1.1 The Garvan/ Royal Prince Alfred Hospital Progression Cohort (GRPAHPC)...... 59 2.1.2 The St. Vincent’s Campus Breast Cancer Outcome Cohort (SVCBCOC) ...... 61
2.2 Formalin-fixed paraffin-embedded cell blocks...... 62
2.3 Tissues from a murine model of mammary carcinogenesis...... 62
2.4 Immunohistochemistry...... 63 2.4.1 Slide preparation, antigen retrieval and immunohistochemistry ...... 63 2.4.2 Immunohistochemical scoring ...... 65
2.5 In situ hybridisation...... 65
2.6 c-MYC mutational analysis...... 66
2.7 c-Myc overexpressing cell lines and culture ...... 67 2.7.1 Plasmid construction, cell culture, and transfection...... 67 2.7.2 Optimisation of zinc induction and timecourse...... 68 2.7.3 Anti-oestrogen treatment ...... 68
2.8 Functional assessment of cell lines...... 69 2.8.1 Growth curve analysis...... 69 2.8.2 Recovery of lysates from monolayer culture ...... 69 2.8.3 Nuclear/cytoplasmic separation...... 69 2.8.4 Western blot analysis ...... 70 2.8.5 Kinase assays...... 71 2.8.6 Flow cytometry...... 71
2.9 Confocal microscopy ...... 72
2.10 Statistical evaluation...... 73
CHAPTER 3: DEMOGRAPHIC AND CLINICOPATHOLOGICAL FEATURES OF THE ST. VINCENT’S CAMPUS BREAST CANCER OUTCOME COHORT...... 74
3.1 Introduction ...... 75
3.2 Patients and Methods ...... 76 3.2.1 Assembly of the SVCBCOC ...... 76 3.2.2 TMA construction...... 77
ix Table of Contents
3.2.3 CANSTO ...... 78 3.2.4 Clinicopathological data review and verification of death records ...... 79 3.2.5 ER and PR staining ...... 79 3.2.6 HER2 Amplification...... 80
3.3 Results...... 80 3.3.1 Patient demographics...... 80 3.3.2 Selection of the cohort ...... 85 3.3.3 Clinicopathological characteristics ...... 87 3.3.4 Clinicopathological markers of disease outcome ...... 87 3.3.5 Molecular markers of disease outcome ...... 93 3.3.6 Influence of adjuvant endocrine therapy and adjuvant chemotherapy on clinical outcome ...... 100 3.3.7 Clinicopathological features of the cohort subgroups ...... 103 3.3.8 Univariate Cox proportional hazards analysis of the clinicopathological factors in the cohort and its subgroups ...... 103 3.3.9 Molecular phenotyping of the SVCBCOC...... 110
3.4 Discussion ...... 113 3.4.1 The role of human tissue cohorts in biomarker evaluation...... 113 3.4.2 Types of human tissue cohorts...... 115 3.4.3 The SVCBCOC as a representation of the population of breast cancer patients in NSW...... 117 3.4.4 The SVCBCOC as a representation of similar published early breast cancer cohorts ...... 118 3.4.5 Molecular phenotyping of the SVCBCOC...... 121
3.5 Summary ...... 121
CHAPTER 4: RELATIONSHIP BETWEEN EXPRESSION OF CELL CYCLE PROTEINS AND PATIENT OUTCOME ...... 122
4.1 Introduction ...... 123
4.2 Results...... 125 4.2.1 Cyclin D1 ...... 125 4.2.2 Cyclin E ...... 140 4.2.3 p21WAF1/Cip1 ...... 153 4.2.4 p27Kip1 ...... 164 4.2.5 Summary of Cox proportional hazards analyses for cyclin D1, cyclin E, p21WAF1/Cip1 and p27Kip1 ...... 177 4.2.6 Inter-relationship between cell cycle protein expression and clinicopathological variables...... 178
x Table of Contents
4.2.7 Multivariate analysis ...... 184 4.2.8 Relationship between cell cycle protein expression and the phenotypic subtypes of breast cancer...... 188
4.3 Discussion ...... 193 4.3.1 Cyclin D1 ...... 193 4.3.2 Cyclin E ...... 197 4.3.3 p21WAF1/Cip1 ...... 200 4.3.4 p27Kip1 ...... 201
4.4 Summary ...... 203
CHAPTER 5: RELATIONSHIP BETWEEN C-MYC EXPRESSION AND BREAST CANCER EVOLUTION AND OUTCOME ...... 205
5.1 Introduction ...... 206
5.2 Results...... 208 5.2.1 Validation of c-Myc staining in cell pellets with inducible c-Myc expression ...... 208 5.2.2 c-Myc expression and localisation changes during the evolution of breast cancer..208 5.2.3 c-Myc expression is predominantly cytoplasmic during c-Myc-induced mammary carcinogenesis in mice ...... 215 5.2.4 A “high-risk” c-Myc staining pattern associated with poor prognosis is definable by high cytoplasmic staining in the setting of low nuclear staining ...... 215 5.2.5 “High-risk” c-Myc staining pattern is correlated with high tumour grade, large tumour size and oestrogen receptor negativity...... 222 5.2.6 The “high-risk” c-Myc expression is an independent predictor of outcome on multivariate analysis with clinicopathological variables...... 230 5.2.7 The “high-risk” c-Myc expression pattern is an independent predictor of outcome on multivariate analysis with clinicopathological and cell cycle variables ...... 233 5.2.8 “High-risk” c-Myc expression is associated with the “Basal” breast cancer phenotype, inversely associated with the “Luminal A” phenotype and predicts adverse outcome among the “Luminal” breast cancer phenotypes ...... 235 5.2.9 “High-risk” c-Myc expression increases in prevalence with increasing grade of both in situ and invasive breast carcinoma...... 240 5.2.10 “High-risk” (predominantly cytoplasmic) c-Myc is not associated with decreased apoptosis ...... 240 5.2.11 Predominantly cytoplasmic c-Myc expression is not associated with mutations in the nuclear localisation signal, or N-terminal phosphorylation region of c-Myc...... 244 5.2.12 “High-risk” c-Myc expression is not correlated with c-Myc amplification ...... 244 5.2.13 Upregulation of c-Myc expression at the transcriptional level does not result in dramatic cytoplasmic c-Myc expression in human breast cancer cells ...... 245
xi Table of Contents
5.3 Discussion ...... 251
5.4 Summary ...... 257
CHAPTER 6: C-MYC OVEREXPRESSION AS A MEDIATOR OF ANTI-OESTROGEN RESISTANCE...... 258
6.1 Introduction ...... 259
6.2 Results...... 260 6.2.1 Low-level constitutive c-Myc expression causes resistance to the anti-proliferative effects of the pure steroidal anti-oestrogen ICI 182,780 ...... 260 6.2.2 Low-level constitutive c-Myc overexpression per se does not cause increased proliferation rates in exponentially proliferating breast cancer cells...... 262 6.2.3 Low-level constitutive c-Myc overexpression causes reduced sensitivity of exponentially proliferating breast cancer cells to anti-oestrogen...... 263 6.2.4 Low-level constitutive c-Myc overexpression attenuates anti-oestrogen-induced changes in the expression of key cell cycle proteins...... 263 6.2.5 The induction of c-Myc expression in two c-Myc-inducible cell lines is transient and maximal at 3 - 6 hours ...... 268 6.2.6 Zinc-induced c-Myc overexpression results in concentration-dependent resistance to the anti-proliferative effects of ICI 128,780 ...... 270 6.2.7 High-level c-Myc expression following zinc-induction results in attenuation of S phase-suppression by ICI 182,780, tamoxifen and raloxifene...... 272 6.2.8 High-level c-Myc expression maintains cyclin E-cdk2 activity and cyclin D1-cdk4 activity in the face of anti-oestrogen treatment ...... 274 6.2.9 High-level inducible c-Myc overexpression attenuates anti-oestrogen-induced changes in the expression of key cell cycle proteins...... 276 6.2.10 Combining data from both constitutive and inducible models suggests that c-Myc overexpression may prevent anti-oestrogen-mediated down-regulation of cyclin D1, and upregulation of p21WAF1/Cip1...... 281
6.3 Discussion ...... 281 6.3.1 Low-level constitutive c-Myc overexpression is not associated with increased proliferation per se in exponentially growing breast cancer cells ...... 281 6.3.2 Low-level constitutive, and high-level inducible, c-Myc overexpression induces resistance to the anti-proliferative effects of anti-oestrogens in breast cancer cells...... 283 6.3.3 c-Myc overexpression attenuates the ICI 182,780-induced decline in cyclin D1 expression and increase in p21WAF1/Cip1 expression...... 284 6.3.4 c-Myc overexpression attenuates the ICI 182,780-induced inhibition of cyclin D1-cdk4 and cyclin E-cdk2 activities ...... 286
6.4 Summary ...... 287
xii Table of Contents
CHAPTER 7: DISCUSSION AND FINAL COMMENTS...... 290
7.1 Perspectives on the management of early breast cancer ...... 291
7.2 Clinical challenges and biomarker development in the management of breast cancer...... 293
7.3 Cell cycle proteins as breast cancer biomarkers...... 294
7.4 Multi-parameter biomarker signatures and the link to the cell cycle ...... 295
7.5 c-Myc pathways are important in relapse signatures in ER positive breast cancer ...... 296
7.6 Therapeutic targeting c-Myc and cyclin D1...... 297
7.7 Future studies ...... 300
7.8 Conclusion ...... 301
REFERENCES ...... 302
APPENDICES ...... 343
Appendix 1...... 344
Appendix 2...... 345
Appendix 3...... 346
Appendix 4...... 350
xiii List of Abbreviations
LIST OF ABBREVIATIONS
Abbreviation Expanded term AF-1 Activation-function 1 AF-2 Activation-function 2 ADH Atypical ductal hyperplasia ALH Atypical lobular hyperplasia ANCOVA Analysis of covariance ANOVA Analysis of variance ATP Adenosine triphosphate BSA Bovine serum albumin CAK Cdk-activating kinase CCL Columnar cell lesion cdk Cyclin dependent kinase cdk inhibitor Cyclin dependent kinase inhibitor DCIS Ductal carcinoma in situ DMSO Dimethlysulfoxide DNA Deoxyribonucleic acid DTT Dithiothreitol EDTA Ethylenediamine tetraacetic acid EGF Epidermal growth factor EGFR Epidermal growth factor receptor ER Oestrogen receptor ERE Oestrogen receptor element FACS Fluorescence activated cell sorting FCS Foetal calf serum FFPE Formalin-fixed paraffin-embedded GRPAHPC Garvan-Royal Prince Alfred Hospital Progression Cohort GSK-3 Glycogen synthase kinase – 3 h Hour(s) HCl Hydrochloric acid HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid HLH Helix-loop-helix HRT Hormone replacement therapy
xiv List of Abbreviations
IDC Invasive ductal carcinoma IGF-1 Insulin-like growth factor-1 ILC Invasive lobular carcinoma LCIS Lobular carcinoma in situ MAPK Mitogen-activated protein kinase min Minute(s) MMTV Murine mammary tumour virus mRNA Messenger RNA PAGE Polyacrylamide gel electrophoresis PBS Phosphate buffered saline PCR Polymerase chain reaction PI3K Phosphatidylinositol-3-OH kinase PIP3 Phosphatidylinositol-3,4,5-triphosphate PMSF Phenylmethylsulfoylfluoride PR Progesterone receptor PVDF Polyvinylidene difluoride RNA Ribonucleic acid RNase Ribonuclease SDS Sodium dodecyl sulfate Simplified H Percentage of positively staining cells x intensity Score SVCBCOC St. Vincent’s Campus Breast Cancer Outcome Cohort TMA Tissue microarray TGF- Transforming growth factor- UDH Usual ductal hyperplasia VEGF Vascular endothelial growth factor
xv List of Figures
LIST OF FIGURES
Figure 1.1: The traditional model of breast cancer evolution based on histological features and epidemiological studies...... 7
Figure 1.2: The multi-step model of breast cancer evolution ...... 8
Figure 1.3: The “Perou” molecular phenotypes of breast cancer ...... 11
Figure 1.4: The structure of ER and ER , and interactions with ER and its cofactors ...... 17
Figure 1.5: The genomic (classical nuclear action) of ER ...... 18
Figure 1.6: The eukaryotic cell cycle and detail of G1-S phase...... 25
Figure 1.7: Structure of c-Myc ...... 39
Figure 1.8: The molecular effects of oestrogen at the G1-S interface...... 44
Figure 1.9: The PI3K/AKT and Ras/Raf/MEK/ERK pathways ...... 46
Figure 1.10: Cross-talk between ER and growth factor signalling pathways...... 52
Figure 1.11: Tamoxifen metabolism...... 53
Figure 3.1: Age-standardised breast cancer incidence and mortality rates per 100,000 persons, by local government area ...... 83
Figure 3.2: Biennial screening rate per 100,000 population 2003 to 2005 by health area, females aged 50 – 69 years ...... 84
Figure 3.3: Flow-chart of case accrual and selection for the development of the SVCBCOC ....86
Figure 3.4: Kaplan-Meier curves for tumour grade ...... 89
Figure 3.5: Kaplan-Meier curves for tumour size ...... 90
Figure 3.6: Kaplan-Meier curves for lymph node status...... 91
Figure 3.7: Kaplan-Meier curves for patient age...... 92
Figure 3.8: Patterns of ER expression...... 94
Figure 3.9: Kaplan-Meier curves for ER status ...... 95
xvi List of Figures
Figure 3.10: Patterns of PR expression...... 96
Figure 3.11: Kaplan-Meier curves for PR status ...... 97
Figure 3.12: Representative images of HER2 non-amplified and amplified breast cancer ...... 98
Figure 3.13: Kaplan-Meier curves for HER2 amplification status ...... 99
Figure 3.14: Kaplan-Meier curves for endocrine treatment amongst ER positive cases ...... 101
Figure 3.15: Kaplan-Meier curves for adjuvant chemotherapy treatment ...... 102
Figure 4.1: Patterns of cyclin D1 expression...... 126
Figure 4.2: Descriptive statistics for cyclin D1 expression ...... 127
Figure 4.3: Kaplan-Meier curves for cyclin D1 expression...... 128
Figure 4.4: Kaplan-Meier curves for cyclin D1 expression in ER positive cases...... 130
Figure 4.5: Kaplan-Meier curves for cyclin D1 expression in ER negative cases ...... 131
Figure 4.6: Kaplan-Meier curves for cyclin D1 expression in ER positive cases that were treated with adjuvant endocrine therapy...... 132
Figure 4.7: Kaplan-Meier curves for cyclin D1 expression in cases that were treated with adjuvant chemotherapy...... 133
Figure 4.8: Patterns of cyclin E expression ...... 141
Figure 4.9: Descriptive statistics for cyclin E expression...... 143
Figure 4.10: Kaplan-Meier curves for cyclin E expression in the whole cohort ...... 144
Figure 4.11: Kaplan-Meier curves for cyclin E expression in the ER positive subgroup...... 145
Figure 4.12: Kaplan-Meier curves for cyclin E expression in the ER negative subgroup ...... 146
Figure 4.13: Kaplan-Meier curves for cyclin E expression in the ER positive cases that were treated with endocrine therapy ...... 147
Figure 4.14: Kaplan-Meier curves for cyclin E expression in the subgroup that was treated with chemotherapy ...... 148
Figure 4.15: Patterns of p21WAF1/Cip1 expression ...... 154
xvii List of Figures
Figure 4.16: Descriptive statistics for p21WAF1/Cip1 expression ...... 156
Figure 4.17: Kaplan-Meier curves for p21WAF1/Cip1 expression in the whole cohort...... 157
Figure 4.18: Kaplan-Meier curves for p21WAF1/Cip1 expression in the ER positive cases ...... 158
Figure 4.19: Kaplan-Meier curves for p21WAF1/Cip1 expression in the ER negative cases...... 159
Figure 4.20: Kaplan-Meier curves for p21WAF1/Cip1 in the ER positive cases that were treated with endocrine therapy...... 160
Figure 4.21: Kaplan-Meier curves for p21WAF1/Cip1 expression in the cases that were treated with chemotherapy ...... 161
Figure 4.22: Patterns of p27Kip1 expression...... 165
Figure 4.23: Descriptive statistics for p27Kip1 expression...... 166
Figure 4.24: Kaplan-Meier curves for p27Kip1 expression in the whole cohort ...... 167
Figure 4.25: Kaplan-Meier curves for p27Kip1 expression in the ER positive cases ...... 168
Figure 4.26: Kaplan-Meier curves for p27Kip1 expression in the ER negative cases...... 169
Figure 4.27: Kaplan-Meier curves for p27Kip1 expression in the ER positive cases that were treated with endocrine therapy ...... 170
Figure 4.28: Kaplan-Meier curves for p27Kip1 expression in the cases that were treated with adjuvant chemotherapy...... 171
Figure 4.29: Expression of ER and PR as continuous variables by cyclin D1 expression level180
Figure 4.30: Expression of cyclin E, p21WAF1/Cip1 and p27Kip1 as continuous variables by cyclin D1 expression level...... 181
Figure 4.31: Expression of ER and PR as continuous variables by cyclin E expression level..182
Figure 4.32: Expression of cyclin D1, p21WAF1/Cip1, and p27Kip1 as continuous variables by cyclin E expression level ...... 183
Figure 4.33: Expression of cell cycle proteins by tumour subtype (modified Perou definition) .190
Figure 5.1: Validation of c-Myc immunohistochemistry ...... 209
Figure 5.2: Spectrum of c-Myc expression patterns in the evolution of breast cancer ...... 210
xviii List of Figures
Figure 5.3: Spectrum of c-Myc expression in the evolution of breast cancer ...... 213
Figure 5.4: c-Myc expression in DCIS encroaching upon an otherwise normal duct...... 214
Figure 5.5: Expression of c-Myc in the nucleus and cytoplasm during breast cancer progression ...... 216
Figure 5.6: Expression of c-Myc in the nucleus and cytoplasm during breast cancer progression ...... 217
Figure 5.7: c-Myc immunohistochemistry in a mouse model of c-Myc-induced carcinogenesis ...... 218
Figure 5.8: c-Myc expression in IDC...... 219
Figure 5.9: Kaplan-Meier curves for c-Myc expression in the entire cohort...... 221
Figure 5.10: Kaplan-Meier curves for c-Myc expression in the ER positive cases...... 223
Figure 5.11: Kaplan-Meier curves for c-Myc expression in the ER negative cases ...... 224
Figure 5.12: Kaplan-Meier curves for c-Myc expression in the ER positive cases that were treated with adjuvant endocrine therapy...... 225
Figure 5.13: Kaplan-Meier curves for c-Myc expression in the cases that were treated with adjuvant chemotherapy...... 226
Figure 5.14: Expression of ER and PR as continuous variables by c-Myc ...... 228 expression pattern...... 228
Figure 5.15: Expression of cell cycle proteins as continuous variables by c-Myc expression pattern ...... 229
Figure 5.16: Percentage of the modified Perou phenotypic breast cancer subtypes displaying the “high-risk” c-Myc expression pattern ...... 236
Figure 5.17: Kaplan-Meier curves for c-Myc expression in the “Luminal A” subgroup ...... 237
Figure 5.18: Kaplan-Meier curves for c-Myc expression in the “Luminal B” subgroup ...... 238
Figure 5.19: Changes in the percentage of lesions with c-Myc “high-risk” staining during the evolution of breast cancer...... 241
xix List of Figures
Figure 5.20: Expression of cleaved PARP as a continuous variable by c-Myc expression pattern ...... 243
Figure 5.21: Amplification of the c-Myc nuclear localisation signal (NLS) and N-terminal phosphorylation site region...... 246
Figure 5.22: Representative sequencing results for potential c-Myc mutations ...... 247
Figure 5.23: MYC amplification in a predominantly high-grade subset of the SVCBCOC (“Outcome Series”)...... 248
Figure 5.24: MYC amplification in a predominantly high-grade subset of the SVCBCOC (“Outcome Series”)...... 249
Figure 5.25: c-Myc protein localisation after transcriptional upregulation ...... 250
Figure 6.1: Dose response and timecourse of the effect of ICI182,780 on two MCF-7 clones constitutively expressing c-Myc and empty vector control ...... 261
Figure 6.2: Proliferation curves for constitutively overexpressing c-Myc cell lines in comparison to an empty vector control ...... 264
Figure 6.3: Proliferation curves for constitutively overexpressing c-Myc cell lines (B and C) and empty vector control (A), after treatment on day 2 with 10nM ICI182,780 or ethanol vehicle...265
Figure 6.4: Changes in key cell cycle proteins after treatment of constitutive c-Myc overexpressing cells with ICI182,780 ...... 266
Figure 6.5: Timecourse of c-Myc induction in MCF-7 cells with zinc-inducible c-Myc expression ...... 269
Figure 6.6: Titration of zinc dose in two clones with zinc-inducible c-Myc ...... 271 expression in comparison to empty vector ...... 271
Figure 6.7: Effect of zinc-mediated c-Myc induction on sensitivity to tamoxifen, raloxifene and ICI182,780 ...... 273
Figure 6.8: Changes in cyclin-cdk activity after treatment with ICI182,780 in two c-Myc inducible cell lines and an empty vector cell line ...... 275
Figure 6. 9: Changes in key cell cycle proteins after treatment with ICI182,780 in two c-Myc inducible cell lines and an empty vector cell line ...... 277
xx List of Figures
Figure 6.10: Changes in key cell cycle proteins after treatment with ICI182,780 in cells with inducible c-Myc expression...... 278
Figure 6.11: Changes in key cell cycle proteins after treatment with tamoxifen and raloxifene in two c-Myc inducible cell lines and an empty vector cell line ...... 279
Figure 6.12: Graphical representation of the changes in protein expression of cyclinD1, p21WAF1/Cip1, p27Kip1 and cyclin E in two c-Myc-inducible cell lines and empty vector controls after treatment with tamoxifen and raloxifene (see Figure 6.11 for Western blots) .....280
Figure 6.13: Summary of anti-oestrogen-induced changes in cyclin D1, p21WAF1/Cip1, p27Kip1 and cyclin E in breast cancer cells that constitutively or inducibly overexpress c-Myc ...... 282
Figure 6.14: Proposed model of oestrogen signalling via c-Myc and cyclin D1 ...... 289
xxi List of Tables
LIST OF TABLES
Table 1.1: Risk factors for breast cancer...... 4
Table 1.2: Major histological subtypes of invasive breast carcinoma...... 5
Table 1.3: Selected studies of CCND1 amplification in human breast cancer cohorts...... 27
Table 1.4: Selected studies of cyclin D1 RNA and protein expression in human breast cancer cohorts (1996 - 2000)...... 28
Table 1.5: Selected studies of cyclin D1 RNA and protein expression in human breast cancer cohorts (2001 – 2007)...... 29
Table 1.6: Selected studies of cyclin E RNA and protein expression in human breast cancer cohorts...... 32
Table 1.7: Selected studies of p21WAF1/Cip1 protein expression in human breast cancer cohorts 34
Table 1.8: Selected studies of p27Kip1 protein expression in human breast cancer cohorts...... 37
Table 1.9: Selected c-Myc target genes with roles in cell proliferation ...... 38
Table 1.10: Selected studies of MYC gene amplification and c-Myc RNA and protein expression in human breast cancer cohorts...... 41
Table 2.1: Clinicopathological characteristics of patients in the Garvan/ Royal Prince Alfred Progression Cohort (n = 222) ...... 61
Table 2.2: Immunohistochemistry protocols used in human tissues ...... 64
Table 2.3: Immunohistochemistry protocols used in mouse tissues ...... 64
Table 2.4: PCR primer sequences ...... 67
Table 2.5: Steroid antagonists and working concentrations...... 68
Table 3.1: Demographics of the New South Wales population in 2001 ...... 82
Table 3.2: Clinicopathological characteristics of the SVCBCOC ...... 88
Table 3.3: Clinicopathological characteristics of the SVCBCOC and its subgroups ...... 104
Table 3.4: Univariate Cox proportional hazards analysis - entire cohort (n = 292) ...... 105
xxii List of Tables
Table 3.5: Univariate Cox proportional hazards analysis - ER positive subgroup (n = 192) ....106
Table 3.6: Univariate Cox proportional hazards analysis - ER negative subgroup (n = 88).....107
Table 3.7: Univariate Cox proportional hazards analysis - ER positive + endocrine therapy subgroup ...... 108
(n = 109)...... 108
Table 3.8: Univariate Cox proportional hazards analysis - chemotherapy treated subgroup (n = 111) ...... 109
Table 3.9: Surrogate signatures of the Perou molecular phenotypes of breast cancer ...... 111
Table 3.10: Comparison of the prognostic value of the Carey, Livasy and Garvan surrogate signatures...... 112
Table 3.11: Clinicopathological characteristics of the Perou molecular phenotypes ...... 112
Table 3.12: Composition of the SVCBCOC in relation to other contemporaneous breast cancer cohorts...... 120
Table 4.1: Univariate Cox proportional hazards analysis for low cyclin D1 expression...... 134
Table 4.2: Univariate Cox proportional hazards analysis for high cyclin D1 (dichotomised data) ...... 135
Table 4.3: Contingency table of standard clinicopathological variables and cyclin D1 expression ...... 136
Table 4.4: Multivariate Cox proportional hazards modelling - entire cohort...... 137
Table 4.5: Multivariate Cox proportional hazards modelling - ER positive subgroup...... 138
Table 4.6: Multivariate Cox proportional hazards modelling - ER positive subgroup treated with endocrine therapy...... 139
Table 4.7: Multivariate Cox proportional hazards modelling - chemotherapy treated patients..140
Table 4.8: Univariate Cox proportional hazards analysis for high cyclin E expression...... 142
Table 4.9: Contingency table of standard clinicopathological variables and cyclin E expression ...... 149
Table 4.10: Multivariate Cox proportional hazards modelling - entire cohort...... 150
xxiii List of Tables
Table 4.10 Continued...... 151
Table 4.11: Multivariate Cox proportional hazards modelling - ER positive subgroup...... 152
Table 4.12: Multivariate Cox proportional hazards modelling - ER negative subgroup ...... 152
Table 4.13: Multivariate Cox proportional hazards modelling - chemotherapy treated patients153
Table 4.14: Univariate Cox proportional hazards analysis for high p21 expression ...... 162
Table 4.15: Contingency table of standard clinicopathological variables and p21 expression..162
Table 4.16: Multivariate Cox proportional hazards modelling - entire cohort...... 163
Table 4.17: Multivariate Cox proportional hazards modelling - ER positive subgroup treated with endocrine therapy...... 164
Table 4.18: Univariate Cox proportional hazards analysis for low p27 expression...... 172
Table 4.19: Contingency table of standard clinicopathological variables and p27 expression..173
Table 4.20: Multivariate Cox proportional hazards modelling - entire cohort...... 173
Table 4.21: Multivariate Cox proportional hazards modelling - ER positive subgroup...... 175
Table 4.22: Multivariate Cox proportional hazards modelling - ER positive subgroup treated with endocrine therapy...... 175
Table 4.23: Multivariate Cox proportional hazards modelling - chemotherapy-treated subgroup ...... 176
Table 4.24: Summary of Cox proportional hazards analyses ...... 177
Table 4.25: Correlation matrix of clinicopathological and cell cycle protein expression ...... 178
Table 4.26: Clinicopathological characteristics of the ER +ve/PR +ve and ER-ve/PR-ve subgroups ...... 184
Table 4.27: Multivariate Cox proportional hazards modelling - ER + PR positive subgroup (n=150)...... 185
Table 4.28: Multivariate Cox proportional hazards modelling - ER and PR negative subgroup (n=78) ...... 187
xxiv List of Tables
Table 4.29: Correlation matrix of Perou immunohistochemical phenotypes and cell cycle protein expression...... 188
Table 4.30: Univariate predictors of outcome in Perou the molecular phenotypes...... 191
Table 4.31: Multivariate predictors of outcome in the “Luminal A” molecular phenotypes (final resolved models) ...... 192
Table 5.1: Changes in c-Myc protein expression (ANOVA) ...... 212
Table 5.2: Contingency table of c-Myc expression and standard clinicopathological variables + cell cycle proteins ...... 227
Table 5.3: Univariate Cox proportional hazards analysis for the "high-risk" c-Myc expression pattern ...... 230
Table 5.4: Multivariate Cox proportional hazards analysis for the "high-risk" c-Myc expression pattern ...... 231
Table 5.5: Multivariate Cox proportional hazards analysis for the "high-risk" c-Myc expression pattern in the ER positive subgroup...... 232
Table 5.6: Multivariate Cox proportional hazards analysis for the "high-risk" c-Myc expression pattern in the ER positive + endocrine therapy subgroup ...... 233
Table 5.7: Univariate and multivariate Cox proportional hazards modelling in the ER + PR positive cases ...... 234
Table 5.8: Contingency table of c-Myc expression and Perou phenotypic subtypes ...... 239
Table 5.9: Univariate predictors of outcome in the Luminal A phenotype...... 239
Table 5.10: Multivariate predictors of outcome in the “Luminal A” molecular phenotypes (final resolved models) ...... 240
Table A3.1: Multivariate Cox proportional hazards analysis - entire cohort (n = 292) ...... 346
Table A3.2: Multivariate Cox proportional hazards analysis - ER positive subgroup (n = 192)347
Table A3.3: Multivariate Cox proportional hazards analysis - ER negative subgroup (n = 88) 348
Table A3.4: Multivariate Cox proportional hazards analysis - ER positive + endocrine therapy subgroup ...... 348
(n = 109)...... 348
xxv List of Tables
Table A3.5: Multivariate Cox proportional hazards analysis - chemotherapy treated subgroup (n = 111)...... 349
xxvi List of Publications
MANUSCRIPTS PUBLISHED OR SUBMITTED DURING THE COURSE OF THIS THESIS
Butt, A.J., McNeil, C.M., Musgrove, E.A. and Sutherland, R.L. Downstream targets of growth factor and oestrogen signalling and endocrine resistance: the potential roles of c-Myc, cyclin D1 and cyclin E. Endocrine-Related Cancer 12, S47 – S59 (2005).
McNeil, C.M., Sergio, C.M., Anderson, L.R., Inman, C.K., Eggleton, S.A., Murphy, N.C., Millar, E.K., Crea, P., Kench, J.G., Alles, M.C., Gardiner-Garden, M., Ormandy, C.J., Butt, A.J., Henshall, S.M., Musgrove, E.A. and Sutherland, R.L. c-Myc overexpression and endocrine resistance in breast cancer. Journal of Steroid Biochemistry and Molecular Biology 102, 147-55 (2006).
Wang, Y., Millar, E.K.A., Tran, T.H., McNeil, C.M., Burd, C.J., Henshall, S.M., Utama, F.E., Witkiewicz, A., Rui, H., Sutherland, R.L., Knudsen, K.E. and Knudsen, E.S. Cyclin D1b is aberrantly regulated in response to therapeutic challenge and promotes resistance to estrogen antagonists. Cancer Research (In press).
Murphy, N.C., Biankin, A.V., Millar, E.K.A., McNeil, C.M., O’Toole, S.A., Pinese, M., Segara, D., Crea, P., Olayioye, M.A., Soon Lee, C., Hamilton, A., Spillane, A.J., Morey, A.L., Christie, M., Musgrove, E.A., Daly, R.J., Lindeman, G.J., Henshall, S.M., Visvader, J.E. and Sutherland, R.L. StarD10 expression identifies a poor prognosis group of breast cancers independent of HER2/Neu and triple negative status (Submitted).
xxvii
CHAPTER 1: INTRODUCTION
1 INTRODUCTION
1.1 BREAST CANCER – CLINICAL OVERVIEW
1.1.1 Epidemiology
In Western countries, malignancy represents a major cause of morbidity and mortality, with an estimated lifetime risk of developing cancer of 1 in 2 for men, and 1 in 3 for women 1. In New South Wales (NSW), which is likely representative of the Australian community, the most common cancer diagnosis in females is breast cancer, accounting for 27% of malignancies 2. Furthermore, breast cancer accounts for 16% of female deaths attributable to cancer2. Although the age-standardised incidence of breast cancer increased by 7% in the decade spanning 1994 – 2003, the mortality over the same period declined by 22% 3. More recent figures spanning the years 1996 – 2005 report a decline in mortality of 18%, while incidence rates have stabilised 2. This improvement is of the same magnitude as that reported in other Western industrialized countries, and is likely to represent the combined effects of improved screening, earlier diagnosis and improved therapy 4. Indeed, the 5-year survival of women diagnosed with breast cancer in NSW between 1994 and 2000 was 85% and between 1996 and 2005 was 88%. Nonetheless, each year over 800 women in NSW continue to succumb to breast cancer, and thus it remains a major public health problem and the focus of medical research.
Importantly, a considerable body of evidence now exists that links cumulative exposure to oestrogens, both endogenous and exogenous, to the development and progression of breast cancer. Associations exist between the development of breast cancer, and early age of menarche, late menopausal age, and pregnancy 5. The relative risk of breast cancer increases with circulating levels of endogenous oestrogen 6 and there are links with obesity in post-menopausal women as a consequence of the increased production of oestrogen from adipose tissue 7. Exogenous hormones also contribute, with a number of high-profile studies into the use of hormone replacement therapy (HRT) suggesting a relative risk of breast cancer of around 1.3 with long-term use 8,9. Indeed, as a corollary to the HRT studies, the incidence of sceen-detected breast cancer in the USA decined by 5% between 2002 - 2003 subsequent to the release of the Women’s Health Initiative data in July 2002, and coincided with a marked drop in the number of HRT prescriptions 10.The effect appears to be observed particularly with
2 INTRODUCTION
combined oestrogen and progestogen HRT, although there is some variability in the degree of risk depending on the progestagen used, with oestrogen-progesterone and oestrogen-dydrogesterone combinations showing lower associated risk than other progestin combinations 11. The biological basis of this effect is complicated and difficult to dissect, as in vitro studies indicate that progesterone may have growth-promoting, or anti-proliferative effects on breast tissue 12. Nevertheless, even in the absence of co- administered progestin therapy, there is evidence from a recent Europen study of over 80,000 women indicating that oestrogen treatment alone may also increase breast cancer risk ~1.3 fold 11.
The use of the oral contraceptive pill has also been linked to a small increase in the risk of developing breast cancer 13. The argument that oestrogen is a key player in the development of breast cancer is further strengthened by studies demonstrating the benefit of anti-oestrogenic strategies in the prevention of hormone receptor positive breast cancer among high-risk populations 14,15. A summary of the key risk factors for the development of breast cancer is presented in Table 1.1 (adapted from Singletary 16). The role of oestrogen as a mitogenic stimulus in breast cancer cells will be discussed further (Section 1.4).
1.1.2 Histological subtypes of breast cancer
Invasive breast carcinoma is a malignant tumour of the breast epithelium. The majority are adenocarcinomas and thought to be derived from the terminal duct lobular unit. Despite this common site of cellular origin, breast cancer is a remarkably heterogenous disease. Historically, breast carcinoma has been divided into a number of distinct morphological subtypes that differ in their clinical characteristics and prognosis. Further prognostic information is conveyed through the overlay of features such as tumour size, tumour grade, the presence or absence of lymph node metastases, and by predictors of therapeutic responsiveness such as hormone receptor status and HER2 gene amplification. Such predictors form the basis of algorithms for predicting outcome after diagnosis in current clinical use such as the Nottingham Prognostic Index, the St. Gallen recommendations and the web-based Adjuvant! Online 17-19.
The most common histological subtypes of breast cancer are invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC), comprising 47 - 79% and
3 INTRODUCTION
Table 1.1: Risk factors for breast cancer
4 INTRODUCTION
2 – 15% of cases respectively (reviewed by 20). Other pathological subtypes each comprising less than 10% of cases are medullary, invasive tubular, and mucinous carcinomas, which will not be discussed further here. IDC and ILC have quite different histological structures and natural history, although their overall survival outcome is similar when matched for standard clinicopathological factors such as nodal status and hormone receptor expression 21. While IDC is characterised by tubule formation or solid sheets of cells, the classical invasion pattern of ILC is characterised by cords of cells diffusely infiltrating through the stroma in single file. The metastatic pattern differs between IDC and ILC, with metastases to the bone marrow, meninges, gastrointestinal tract, peritoneum, ovaries and adrenal gland more commonly observed in ILC 20,22. At a molecular level there are clear differences in the expression of the oestrogen receptor (ER) and progesterone receptor (PR), (which are higher in ILC) and Human Epidermal Growth Factor Receptor 2 (Her2) (which is lower in ILC) 22. Expression of the Vascular Endothelial Growth Factor (VEGF) and markers of cell adhesion, notably E-cadherin are also generally lost in ILC 22,23(Table 1.2).
Table 1.2: Major histological subtypes of invasive breast carcinoma
Subtype Molecular features
Invasive Ductal Carcinoma ER positive (70%) PR positive (60 - 70%) HER2 amplified (15-30%)
Invasive Lobular Carcinoma Loss of E-cadherin (80-100%) ER positive (70 - 95%) PR positive (60 - 70%) Less HER2 amplification Less VEGF
Furthermore, gene expression profiling of ductal versus lobular carcinomas reveals distinct differences between IDC and typical ILC, particularly in genes required for lipid/fatty acid transport and metabolism. However, there also appears to be a group of ILC with ductal-like gene expression profiles suggesting that at a molecular level, the distinction between IDC and ILC is discordant with histology 20. The presence of mixed lobular and ductal pathologies in ~3 - 5% of cases may also indicate that perhaps in some profiling studies that both histological entities are represented simultaneously 24.
5 INTRODUCTION
1.1.3 The evolution of breast cancer
At a histological level, the dominant model of breast cancer evolution has been one in which there was a transition from normal epithelium to invasive carcinoma via hyperplasia without atypia, atypical hyperplasia, and in situ carcinoma in a manner analagous to that observed in other malignancies such as colorectal and endometrial carcinoma (Figure 1.1) (reviewed by 25). Not only do the putative precursor lesions co- occur in the same anatomical location as invasive carcinoma the notion that precursor and preinvasive lesions are clonally related is supported by studies which show that similar genetic changes are observed in low grade ductal carcinoma in situ (DCIS) and atypical ductal hyperplasia (ADH). Furthermore, epidemiological studies indicate that there is an increased risk of subsequent invasive carcinoma with increasing degrees of atypia on tumour biopsy (Table 1.1). Nonetheless, DCIS is the only proven direct breast cancer precursor lesion. Between 14 - 75% of patients with DCIS will develop invasive malignancy, however rates and latency vary with disease subtype with high- grade DCIS progressing over a shorter time course than low grade DCIS 26.
As a consequence of increasing molecular studies there has been a growing recognition that the evolution of breast cancer is likely to be far more complex, whereby there are a series of distinct and diverging pathways, rather than one or two paths defined by histological subtype. The majority of breast cancers arise from the terminal duct lobular unit, after which molecular analyses indicate that grade is the strongest determinant of the genomic aberrations observed. In particular, genetic studies, and the recent description of the pleomorphic variant of lobular carcinoma indicate that the distinction between lobular and ductal carcinomas is now less clear 27.
Simpson et al 25 have provided a multi-step model of breast cancer evolution, as depicted in Figure 1.2. The salient feature of this model is that there are two major arms: one in which well-differentiated DCIS progresses to grade 1 IDC, and another in which poorly differentiated DCIS evolves into grade 3 IDC. The low-grade tumours are usually ER and PR positive, HER2 negative, basal-marker negative, and display recurrent 16q loss. High-grade lesions tend to be ER and PR negative, may be HER2 positive or basal-marker positive, and show multiple genetic changes including loss of 8p, 11q, 13q, and 14q, gain of 1q, 5p, 8q, and 17q and amplification at 6q22, 8q22,
6 INTRODUCTION
Figure 1.1: The traditional model of breast cancer evolution based on histological features and epidemiological studies
7 INTRODUCTION
Fig
ure 1.2: The multi-step model of breast cancer evolution
8 INTRODUCTION
11q13, 17q12, 17q22-24 and 20q13. Notably, in this schema, lobular carcinoma in situ and ILC are classified in the low-grade arm on account of their pathological and genetic features (reviewed by 25).
The consensus of opinion is that upstream of in situ carcinoma in the breast cancer progression model lie ADH and atypical lobular hyperplasia (ALH), with genetic and morphological similarities suggestive of a close link between low-grade DCIS, and lobular carcinoma in situ (LCIS) respectively. However, the place of usual ductal hyperplasia (UDH) and columnar cell lesions (CCL) in the evolution of breast cancer is under debate. UDH has traditionally been seen as the precursor of ADH and DCIS however, the mixed nature of the cell population comprising this lesion, and rarity of associated chromosomal aberrations has led to some speculation that it is a precursor lesion only rarely. In contrast, CCLs show an immunoprofile that is similar to ADH and well-differentiated DCIS. Furthermore, loss of heterozygosity studies indicate that in some cases there may be a progressive accumulation of allelic damage in columnar cell lesions with atypia, DCIS and IDC 28. Thus at a molecular level, CCL may represent a more plausible precursor to ADH and low-grade DCIS.
Clearly, debate still exists as to the precise sequence of events and histologies in breast cancer progression. On the basis of the coexistance of multiple grades, biomarker profiles and molecular phenotypes within the same cases of DCIS, Allred et al 29 have recently proposed a different model in which poorly differentiated DCIS gradually evolves from well-differentiated DCIS. It is hoped that further developments in molecular technology will bring more clarity, with the promise of improved diagnostic accuracy and prognostication for patients with breast cancer precursor lesions. Furthermore, the use of accurate breast cancer progression modelling affords the opportunity to define early molecular lesions that may potentially be targets for chemoprevention, or therapy.
1.1.4 Gene expression profiling and molecular subtypes of breast cancer
The advent of cDNA microarray technology has facililated research aimed at further defining a molecular classification of breast cancer, and the prediction of outcome on the basis of gene expression profiles. The work of Perou et al 30demonstrated that a
9 INTRODUCTION
number of breast cancer subtypes could be defined on the basis of the expression levels of 496 genes. The first subtype was distinguished by the relatively high expression of genes usually expressed by “luminal” cells, and largely corresponded to phenotypically ER positive tumours. However, the ER negative group could be subdivided into 3 subtypes, each with distinct molecular profiles – the “basal-like”, the “Erb-B2-positive” and the “normal breast-like” (Figure 1.3).
Subsequent work from the same group using a larger cohort of 78 breast carcinomas from 77 individuals again identified similar molecularly-defined subtypes of breast cancer 31. However, it was recognised that there were 3 subgroups within the ER positive (“luminal”) tumours; luminal subgroup A, which was characterised by the highest ER expression, as well as high expression of luminal specific genes (e.g. GATA 3, X-box binding protein, trefoil factor 3, hepatocyte nuclear factor 3 , and LIV- 1), and luminal subgroups B and C which expressed these genes at low to moderate levels. Furthermore, the luminal A group displayed a lower p53 mutation rate (13% vs 40-80%) than the other luminal subtypes, and superior clinical outcome.
A similar study from another group of investigators defined similar subgroups in a cohort of 99 patients, each with distinct clinical outcomes 32. Within the luminal/ER positive subgroup, the “luminal-1” group displayed an 80% 10-year relapse-free survival rate, unlike the “luminal-2” and “luminal-3” tumours with survivals of 40% and 60% respectively. Other studies have attempted to validate the various gene lists in independent cohorts, and although not precisely concordant, the distinction between outcomes for patients with “luminal A/-1” subtype and other subtypes is reproducible 33,34.
Within the non-luminal subclasses of breast cancer are at least two further phenotypes worthy of mention. These are the Erb-B2-positive group, associated with increased expression of genes related to HER2 and corresponding to those patients who are amenable to treatment with molecularly targeted therapies directed against Her2, and the basal-like subtype which is associated with particularly poor outcomes from breast cancer. Basal-like breast cancers shows considerable, although not invariable cross- over with the “triple-negative” breast cancer subtype (i.e.ER/ PR/ HER2 negative). As such, there is a lack of effective tailored therapies for this group of cancers, which display high tumour grade and aggressive clinical behaviour 35,36. Furthermore, many of
10 INTRODUCTION
Figure 1.3: The “Perou” molecular phenotypes of breast cancer
11 INTRODUCTION
the molecular features of basal-like tumours, such as cytokeratin 5/6 expression, high cyclin E and p53 expression, and low p27Kip1 expression, are observed in BRCA-1 – related breast cancers 37. Further analysis of the biology of this breast cancer phenotype with a view to the development of targeted therapies is of critical importance. Potential targets identified to date include the epidermal growth factor receptor (EGFR), Her3, Her4, c-Kit and poly(ADP-ribose) polymerase (PARP) 38.
In addition to defining the broad phenotypic breast cancer classifications described above, gene expression profiling has been used to define “molecular signatures” predictive of outcome and need for adjuvant therapy. Two dominant research groups have produced gene expression profile signatures. The Netherlands Cancer Institute identified a 70-gene poor prognosis signature in a cohort of women with early breast cancer39, while a study from the Erasmus Medical Centre in Rotterdam generated a 76- gene signature that could predict the development of distant metastases within 5 years in node negative cases who had not been treated with systemic therapy 40. Both signatures have been validated in further series of 307 patients 41 and 180 patients 42 respectively. The 70-gene signature (also known as Mammaprint) is the subject of the Microarray in Node-Negative Disease May Avoid Chemotherapy (MINDACT) trial.
Others have used microarray technology or databases from microarray experiments to define a small number of genes analysable by polymerase chain reaction (PCR) that are predictive of relapse after treatment e.g. the Oncotype DX 21-gene prognosis signature and the 2-gene ratio 43,44. Indeed, the Oncotype DX 21-gene prognosis assay has now been recommended for clinical use in the recent ASCO guidelines for the use of biomarkers in the management of breast cancer 45 and is the subject of the Trial Assigning Individual Options for Treatment (TAILORx).
Review of the gene lists generated by multiple expression profiling studies demonstrates little cross-over, and has led to some debate as to their reproducibility. However, a study in which several signatures was compared including the Perou “Intrinsic” gene set, the “70-gene” prognostic signature, the “21-gene” prognosis signature has shown that despite little concordance in the precise genes identified, such signatures do appear to uniformly stratify patients on the basis of their ultimate outcome, and suggests that they are defining a common cellular phenotype 46.
12 INTRODUCTION
A further important finding from gene expression profiling studies is the identification of a suite of genes, predominantly involved in cell cycle regulation and progression, that define tumour grade 47. This genomic grade index (GGI) has been validated in 666 ER positive breast cancer cases, showing comparability to the luminal A vs B classification, and predicting clinical outcome in both tamoxifen-treated and –untreated subgroups 48. Thus, gene expression profiling promises to define particular functional classes of genes that determine patient outcome and treatment sensitivity. In a futher refinement of the analyses associated with this technology, meta-analyses of multiple gene expression profiling experiments in cancer suggest that molecular “pathways” of deregulation, rather than discrete aberrations of a handful of genes, may be defined which not only provide insight into the biological processes at play but provide direction for the rational development and use of targeted therapeutics 49. It is likely that this technology will continue to influence the evolution of our understanding of breast cancer biology and its treatment.
1.1.5 Current treatment consensus guidelines
The management of breast cancer in the 21st century is ideally conducted with a multidisciplinary and evidence-based approach. The physical and psychosocial impact of current breast cancer treatment protocols is such that this treatment must be conducted with appropriate consideration of the wishes of the patient.
The cornerstone of breast cancer treatment is appropriate surgical care. There is now a substantial body of evidence demonstrating equivalent overall survival for women who undergo breast conservation (lumpectomy, with levels I and II axillary dissection, plus radiotherapy) compared with women who undergo mastectomy 50.Women who are eligible for breast conserving surgery (BCS) should therefore be offered the choice of BCS or modified radical mastectomy. In women choosing BCS, the addition of adjuvant radiotherapy is particularly important, reducing the absolute risks of local recurrence and death at 15 years by 19% and 5%, respectively 51. It recent years it has also been demonstrated that women with high-risk disease (large tumours, heavy nodal burden, or invasion of the skin, muscle or chest wall) who have undergone mastectomy, benefit from post-mastectomy chest wall irradiation 52. Further refinements in the surgical care of women with early breast cancer have led to the adoption of sentinel lymph node biopsy as an alternative to axillary clearance 53. This technique reduces the morbidity
13 INTRODUCTION
associated with axillary clearance, and although somewhat operator-dependent, promises to become the standard of care in centres where it is available.
The subsequent systemic treatment recommendations for women with early breast cancer depend on the assessment of disease risk, or prognosis, in the light of consideration of the likely effectiveness of treatment, and known treatment-related toxicities. For many years the dogma in relation to prognostication has maintained that the principal determinant of outcome from breast cancer is the presence (and degree) or absence of regional nodal involvement 54. However, over the last few years there has been a growing recognition of the importance of hormone receptor positivity, to the extent that the St. Gallen guidelines from 2005 and 2007 now primarily stratify along endocrine lines 18,55. Indeed, anti-hormonal manoeuvres such as treatment with tamoxifen, surgical or pharmacological ovarian ablation, and aromatase inhibition improve outcome, reducing the annual breast cancer death rate among women with ER positive disease by ~ 30% 56. Detailed discussion of various aspects of the endocrine treatment of breast cancer is provided in Section 1.2.
Adjuvant anthracycline-based polychemotherapy reduces the annual breast cancer death rate by ~38% in women under the age of 50, and ~20% in women aged 50 – 69 years, an effect that is generally independent of the use of tamoxifen and ER status, nodal status, or other tumour characteristics 56. The benefit from chemotherapy in absolute terms for an individual patient is dependent on the degree of pre-treatment risk, as estimated by factors such as degree of nodal involvement, grade and tumour size. Importantly, the degree of predicted benefit does not have to be large for patients to accept adjuvant therapy, even in the knowledge of the associated side effects. Clearly the stakes are high in the treatment of a potentially lethal disease, and a survey of breast cancer patients conducted after the completion of breast cancer chemotherapy indicated that ~50% of women would undergo adjuvant chemotherapy for a predicted benefit of as little as 1% 57. Standard chemotherapy is at least 4 cycles of anthracycline-based chemotherapy and although there is evidence for improved outcomes with the addition of taxanes (in ER negative patients), or dose-dense scheduling, the added toxicity of such regimens restricts their use to women with higher risk disease (reviewed in 58).
The identification of HER2 amplification as a prognostic factor and therapeutic target has led to the integration of adjuvant therapy with trastuzumab, a monoclonal antibody
14 INTRODUCTION
directed against HER2, into treatment schedules in conjuction with adjuvant chemotherapy. Data emerging from the first few years of follow-up of clinical trials of such schedules suggests that in patients with HER2-amplified tumours, trastuzumab therapy reduces the risk of recurrence by ~40-50% 59-62.
Although ER, PR and HER2 status, in conjunction with degree of regional lymph node involvement, tumour size and tumour grade are key factors facilitating decision-making in the management of early breast cancer, it is clear not only that many women continue to succumb to their disease, but that many are undergoing toxic and unpleasant treatments when such measures are unnecessary. One of the challenges of the study of molecular biology of breast cancer is to define molecular markers to allow identification of those patients likely to gain maximum benefit from current adjuvant therapies. In addition, such endeavours promise to direct the discovery of new molecular targets and the development of new therapeutic agents.
1.2 THE OESTROGEN RECEPTOR AND ANTI- OESTROGENS
1.2.1 The oestrogen receptor
1.2.1.1 Oestrogen receptor structure
There are 2 distinct ERs, ER and ER . Both are steroid hormone receptors of the nuclear receptor gene family that includes the receptors for retinoic acid, thyroid hormone and glucocorticoid. While both function as ligand-activated transcription factors, the predominant mediator of the mitogenic effects of oestrogen is ER . ERs and related molecules may be divided into 4 structural domains with distinct functions: the N-terminal that contains the activation-function 1 domain (AF-1); the DNA binding domain (DBD) that contains 2 C4-type zinc fingers that are responsible for DNA binding; the activation-function 2 domain (AF-2) that contains the ligand-binding domain (LBD) and which is responsible for both hormone binding and homodimerisation with other ER monomers; and the short-variable C-terminal region
15 INTRODUCTION
(Figure1.4) (reviewed in 63-65). The AF-1 and AF-2 domains are important regions in mediating the transcription of ER target genes.
1.2.1.2 Transcriptional modulation by ER
The binding of hormone to ER results in increased receptor phosphorylation, dissociation of chaperone proteins such as heat-shock protein 90 and a resultant conformational change that exposes both the DNA binding domain and the transcriptional activation domains (reviewed in 66). Until recently it was held that oestrogen-bound ER then homodimerises with another receptor before binding to oestrogen response elements (EREs) in the promoter regions of target genes whereupon co-regulatory molecules such as AIB-1 are recruited to positively or negatively modulate the transcription of oestrogen-responsive genes (Figure 1.5A). The coregulatory molecules themselves may also serve to recruit further regulators to the protein complex, such as acetyltransferases, which alter chromatin structure to facilitate transcription (reviewed in 66).
The selective oestrogen-receptor modulator tamoxifen, also binds to the ER, but preferentially recruits corepressive regulators (Figure 1.5B). Importantly, tamoxifen possesses both agonist properties (through the AF-1 domain) and antagonist properties (through the AF-2 domain). Thus, in cell types that are primarily AF-2 dependent the overall effect of tamoxifen is antagonistic, however, in cell types in which AF-1 signalling is dominant (e.g. the endometrium), tamoxifen may potentially function as an agonist (reviewed in 67).
However, in the last 2 years, detailed genome-wide analysis of ER binding sites has been conducted using chromatin immunoprecipitation combined with microarrays (ChIP-on-Chip), revealing that the promoter-proximal regions do not constitute the majority of ER target sites 68. Rather, other remote sites in cis-regulatory elements of the genome may be important. Furthermore, co-operating factors such as C-EBP, Oct and the forkhead protein FoxA1 may constitute important “pioneer factors” in directing the ER to these sites 68.
16 INTRODUCTION
Figure 1.4: The structure of ER and ER , and interactions with ER and its cofactors
17 INTRODUCTION
Figure 1.5: The genomic (classical nuclear action) of ER
18 INTRODUCTION
ER can regulate gene expression without interacting directly with DNA, through direct protein-protein interactions with other transcription factors such as the Fos/Jun activating protein (AP-1) complex and Sp-1 (Figure 1.5C) 69. In this setting, ER functions as a co-regulatory molecule, stabilising the binding of the transciption factor complex, or recruiting other regulatory molecules to the complex. ER regulates the expression of a number of key proteins in this manner including insulin-like growth factor-1 (IGF-1), cyclin D1 and c-Myc (reviewed in 66,69). ER can mediate gene transcription in a ligand-independent manner, as a consequence of cross-talk from growth factor signalling cascades. Growth factors such as transforming growth factor-a (TGF- ), epidermal growth factor (EGF), IGF-1 and heregulin can activate the ER and increase the expression of ER target genes in breast cancer cells 70-72. These effects are mediated by the downstream modulation of growth factor signalling e.g. by the mitogen-activated protein kinase (MAPK) and phosphatidyl inositol 3-kinase (PI3K)/AKT pathways 73-76. The role of such cross-talk in the context of anti-oestrogen resistance is further discussed in Section 1.5.3.
Oestrogen has very rapid effects on responsive cells that precede those anticipated by transcriptional activation, while confocal microscopic studies show that there is a small pool or ERs that are located in the plasma membrane and cytoplasm 77,78. Thus, in addition to the well-described nuclear ER signalling pathway, a further mechanism of ER action involves membrane-initiated steroid signalling. While the precise mechanisms behind this type of signalling remain to be fully defined, ER is known to interact with a number of membrane signalling molecules including IGF-1 receptor, Shc, Src and PI3K, which in turn positively regulate ER signalling by phosphorylation of ER and its coregulators (reviewed in 79,80). Through c-Src activation in particular, ER is also able to mediate the release of EGF from the cell membrane, thus allowing it to interact with the EGFR in an autocrine or paracrine manner 81.
1.2.1.3 Oestrogen receptor-
A second functionally distinct oestrogen receptor, ER has also been identified 82. When compared to ER , ER is expressed comparatively weakly, and with a different tissue distribution. Although ER has been identified in some breast cancer cell lines and tumours, its predominant location is in the ovaries and prostate and it has different
19 INTRODUCTION
ligand-binding specificity 83,84. Like ER , ER binds oestradiol and activates gene transcription. In general ER appears to mediate opposing effects to ER in respect of cell proliferation 85,86. This molecular antagonism appears to be mediated via a combination of altered co-regulator recruitment and increased proteolytic degradation of ER 87,88. However there are in vitro data to support the role of ER in both anti- proliferative and pro-proliferative effects 89,90. It has also been demonstrated that ER is essential for terminal differentiation of murine mammary gland epithelium 91.
In recent years a number of investigators have evaluated the role of ER in human breast tissue samples. A study using real-time RT-PCR has shown that the expression of ER is correlated with ER and inversely correlated with cyclin D1 expression, but not the known ER target genes, PR and pS2 92. There was no association with c-Myc and the expression of either ER or ER in this study. Furthermore, the results suggest that ER exerts its effects via the AP-1 element, rather than the ERE, as those genes that are correlated with ER expression have AP-1 binding sites in their promoters rather than EREs, and those genes that are classically regulated via the ERE (pS2 and PR) are not correlated with ER expression 92.
The effect of aberrant ER expression on outcome is controversial. In a recent study of 512 breast cancer patients, ER expression was not prognostic for outcome in the entire patient group. However, among an ER positive subgroup treated with endocrine therapy, loss of ER was associated with inferior outcome 93. In another study, a gene expression signature of ER activation previously generated in T-47D breast cancer cells linked ER with improved outcome 94. Further complexity has arisen through the recognition that there exist a number of isoforms of ER . Five splice variants of ER have been described - ER , ER 2 (also known as ER cx), ER 5, ER 4 and ER 5 although it is unclear whether all of the variants are expressed as biologically functional proteins (reviewed in 95). However, ER 2 is capable of dimerizing with ER thereby silencing signalling through this ER isoform 96. A recent study of ER 2 expression in 141 tamoxifen-treated breast cancer patients indicated that high mRNA, but not protein expression was associated with an improved outcome 97. The potential role of ER 2 in anti-oestrogen resistance will be discussed in Section 1.4.2.
20 INTRODUCTION
1.2.2 Anti-oestrogens and breast cancer – pharmacology and clinical outcome
1.2.2.1 Selective oestrogen receptor modulators (SERMs)
The archetypal SERM is the widely used drug tamoxifen. At a molecular level, the binding of tamoxifen to the ER results in conformation changes that not only exclude the coactivators SRC1, GRIP1 and AIB1 from their binding site on the AF-2 domain, but also in preferential recruitment of the co-repressors NCOR1 and SMRT (reviewed in 64).Tamoxifen used in the adjuvant setting reduces the risk of breast cancer death for women with lymph node positive, hormone receptor positive breast cancer by ~30% 56,98. Furthermore, treatment with this agent prevents the development of contralateral breast cancer by 30-50% depending on the duration of tamoxifen treatment (an effect that has been verified in chemoprevention trials)15,98,99. Indeed, it has been argued that in epidemiological terms i.e. years of life saved, that tamoxifen has been the single most effective anti-cancer drug developed.
However, a number of problems are associated with the long-term use of tamoxifen. As a result of the partial agonist properties of the drug, pro-proliferative effects have been associated with tamoxifen use. While these effects are regarded as advantageous in some tissues, for example bone, where tamoxifen increases bone density, there are clinically relevant deleterious effects observed with use of the drug. In particular, tamoxifen has been associated with increased endometrial thickening, leading to an increased risk of endometrial cancer that is two- to four-times the baseline risk (corresponding to an excess of 4 cases per 1000 women treated), as well as a tendency towards thromboses and their life-threatening sequelae98.
Further to these concerns regarding the long-term toxicity of tamoxifen, it is apparent that a significant percentage of ER positive tumours treated with this agent will relapse. In the metastatic setting, the use of tamoxifen eventually leads to the development of resistance after an average period of 15 months100. Studies in the adjuvant setting show that increased duration of tamoxifen therapy is associated with improved outcomes, whereby 5 years of tamoxifen is superior to 2 years treatment, which in turn is better than 1 year of treatment98. Nonetheless, many of those women treated with
21 INTRODUCTION
endocrine therapy in the adjuvant context eventually relapse and die as a result of their disease (in the order of 10-15% in the ATAC trial which enrolled a cohort of relatively good prognosis) suggesting that many of these tumours inherently resist, or acquire resistance to tamoxifen 14.
Consequently, much effort has been directed towards the development or more selective compounds to supplant tamoxifen. To this end, the triphenylethylene agents clomiphene, nitromifene, toremifene, droloxifene and idoxifene have been developed. None of these agents has been demonstrated to be superior to tamoxifen 101. The SERM raloxifene belongs to a separate structural class of compounds, the benzothiopines. Raloxifene is in clinical use as an osteoporotic agent as a consequence of its beneficial effects in bone, anti-proliferative action in breast tissue, and lack of endometrial toxicity102,103. Although it has been shown to prevent breast cancer 104,105, it has no current clinical application in the management of established breast cancer.
1.2.2.2 “Pure” steroidal anti-oestrogens
All of the aforementioned agents are classified as non-steroidal anti-oestrogens, and the failure of the majority of these compounds to represent a viable alternative to tamoxifen has led to the development of the “pure” steroidal anti-oestrogens. This class includes ICI 182,780 (Faslodex, Fulvestrant) and ICI 164,384. ICI 182,780 is the more potent of these agents and has therefore been developed for clinical use 106. ICI 182,780 increases ER turnover, and downregulates the receptor, and consequently it abrogates not only the AF-2, but also the AF-1 activities of the ER, unlike tamoxifen. In vitro studies have shown that ICI 182,780 has greater inhibitory effects on breast cancer cells, and less agonistic effects in non-breast tissues, consistent with a lack of AF-1 activation 101,106. Furthermore it demonstrates anti-proliferative activity in patients with tamoxifen-resistant breast cancer that is at least equivalent to that seen with the aromatase inhibitors 101,107 (discussed in section 1.2.2.3). Despite the superior outcomes seen with ICI 182,780, this agent has yet to be adopted into widespread clinical practice, perhaps as a consequence of its route of administration (intramuscular), and the development of the aromatase inhibitors.
22 INTRODUCTION
1.2.2.3 Aromatase inhibitors
The anti-oestrogenic therapies discussed previously block the interaction between oestrogen and its receptor, or in the case of the “pure” steroidal anti-oestrogens, down- regulate the receptor. An alternative strategy involves deprivation of oestrogen. The oldest demonstration of this as a treatment strategy was reported by Beatson in 1896 108, who noticed breast cancer responses in women with advanced breast cancer after surgical oophorectomy. Indeed, surgical or pharmacological oophorectomy (e.g. with Leutenising Hormone - Releasing Hormone agonists) in premenopausal women remains a valid endocrine manoeuvre in the treatment of breast cancer. Interestingly, the 2005 Early Breast Cancer Trialists’ Collaborative Group meta-analysis suggests that the absolute survival benefit after 15 years of ovarian ablation is more modest than previously thought at around 3%, compared to ~9% for tamoxifen 56. A possible explanation for this discrepancy relates to non-ovarian oestrogen which would not be removed by oophorectomy, but which would still be inhibited by tamoxifen.
In post-menopausal women a significant amount of non-ovarian oestrogen is synthesized by the aromatase enzyme from androgens produced in the adrenal glands, and from peripheral adipose tissue. Modulation of non-ovarian oestrogen synthesis through adrenalectomy, or through inhibition of aromatase (as well as other steroidogenic enzymes) by aminoglutethimide is effective, but associated with substantial toxicity 109,110. Consequently, 3 relatively non-toxic aromatase inhibitors have been developed: two triazole compounds (anastrazole and letrozole) and a steroidal inhibitor (exemestane). While anastrazole and letrozole reversibly inhibit aromatase, exemestane acts as a suicide inhibitor of the enzyme. Despite differences in the mode of action of these compounds, there is little to separate these agents in terms of efficacy and toxicity 111.
Aromatase inhibitors are more effective than megestrol acetate, a synthetic progestin, in the treatment of tamoxifen-resistant breast cancer 112, and are superior to tamoxifen in the adjuvant treatment of breast cancer with a difference in disease-free survival at 4 years in the ER positive group of 2.9% 14. Furthermore, in the ATAC trial anastrazole was more effective than tamoxifen at preventing contralateral breast cancer, thus providing the basis for the IBIS-II prevention trial 14.
23 INTRODUCTION
However, these agents are not without risk, and while drugs such as anastrazole have better side effect profiles than tamoxifen with respect to thromboses, and endometrial cancer, there is concern regarding the increased incidence of musculoskeletal disorders, particularly fractures 14. The latter toxicity is particularly relevant given the potential utility of this drug in the adjuvant, and chemopreventive settings, as increasing osteoporosis and fracture rates will potentially have important implications for long-term morbidity.
1.3 THE CELL CYCLE IN BREAST CANCER
1.3.1 The cell cycle and breast cancer
Dysregulation of the cell cycle is the basis of the replicative advantage conferred upon malignant cells 113. The cell cycle is comprised of 4 phases through which a cell must progress in order to divide (Figure 1.6, adapted from 114). In the G1 phase, cells respond to extracellular signals that drive advancement through to S phase, or withdrawal from the cell cycle into a resting G0 state. The decision to divide occurs at the G1-S restriction point, which commits the cell to completion of the cell cycle. During S phase the genetic material of the cell is replicated. The second gap phase G2 then ensues in which the cell prepares to partition its cellular components symmetrically into daughter cells.
Successful transit through the phases of the cell cycle involves a complex interplay of pro-proliferative (predominantly, the cyclin-dependent kinases [cdks] and their regulatory cyclins) and anti-proliferative molecules (the cdk inhibitors). In early G1 phase, mitogenic signalling results in cyclin D1 synthesis, and the formation of complexes with either cdk4 or cdk6. These complexes result in the initial phosphorylation of pRb. Subsequently complexes form between between cyclin E and cdk2, and through co-operation with cyclin D1-cdk4, pRb is completely phosphorylated. This allows the release of the E2F transcription factors necessary for the activation of S phase specific genes, and progression of the cell into S phase. During S phase the activity of cyclin A-cdk2 increases, followed by cyclin A-cdk1 and cyclin B – cdk1 during G2 and mitosis. The transition from G0 to G1 is regulated by cyclin C-cdk3. (Figure 1.6).
24 INTRODUCTION
Figure 1.6: The eukaryotic cell cycle and detail of G1-S phase
25 INTRODUCTION
The cell cycle is further regulated by the cdk inhibitors. These can be grouped into two distinct families, the INK proteins, such as p16INK4A, and the Cip/Kip family that includes p21WAF1/Cip1 and p27Kip1. While p16INK4A acts to maintain pRb in an un-phosphorylated state through inhibiting the activity of cdk4 and cdk6, p21WAF1/Cip1 and p27Kip1 display activity against the functions of cyclin E- and cyclin A-dependent kinases, in particular cdk2. p21WAF1/Cip1 and p27Kip1 also bind to and stabilise cyclin D1-cdk4 complexes, thus sequestering them, preventing repression of cyclin E-cdk2 complexes and further promoting cell cycle progression (reviewed by 115,116).
The transition between the G1 and S phases is considered the primary point of growth regulation in mammalian cells, and is an important focus of deregulation in cancer (Figure 1.6B). Alterations in the expression of a number of cell cycle genes and their protein products critical to transition through the G1-S phase transition have been implicated in the aetiology of breast cancer and thus the remainder of this section will focus on the key players in this part of the cell cycle. Of particular interest are the cyclins D1 and E, the cdks p21WAF1/Cip1and p27Kip1. In addition, the product of the c-Myc proto-oncogene also maintains a central role in the regulation of proliferation at the G1- S interface in addition to its involvement in multiple other cellular processes including differentiation, growth and apoptosis and will be discussed further.
1.3.2 Cyclin D1
Cyclin D1 is the product of the CCND1 gene localised on chromosome 11q13. The CCND1 gene is amplified in ~15% of breast cancers117 and reported to be overexpressed at the protein or mRNA level in around 50% of cases. Thus the majority of cyclin D1 overexpression is likely to occur as a result of mechanisms other than gene amplification, such as transcriptional and post-transcriptional deregulation.
Multiple studies have been conducted with the intent of addressing the role of cyclin D1 amplification 118-128 and overexpression in breast cancer outcome (Table 1.3 – 1.5) 118,120,121,123-125,127,129-147. The results generated from these studies have been conflicting, particularly where RNA and protein expression are concerned. These studies are described in further detail in Chapter 4 and will only be discussed briefly here. Cyclin D1 overexpression has generally been associated with ER positivity, and
26 INTRODUCTION
Table 1.3: Selected studies of CCND1 amplification in human breast cancer cohorts
27 INTRODUCTION
Table 1.4: Selected studies of cyclin D1 RNA and protein expression in human breast cancer cohorts (1996 - 2000)
28 INTRODUCTION
Table 1.5: Selected studies of cyclin D1 RNA and protein expression in human breast cancer cohorts (2001 – 2007)
29 INTRODUCTION
prognostic studies in unstratified cohorts have indicated either an association with favourable outcome 124,138,145,146or no association. In two studies, cyclin D1 was predictive only on univariate analysis 120,130. However Kenny et al 141 found that cyclin D1 overexpression predicted adverse outcome among ER positive patients, while Umekita et al 135 found that overexpression predicted for adverse outcome among ER negative patients. Amplification studies have tended to find either no prognostic role for CCND1, or that amplification is associated with a poor outcome, at least on univariate analysis 118,122,124. CCND1 amplification is also associated with ER positive status.
Apart from cyclin D1, there are two other D-type cyclins designated cyclin D2 and D3, both with the capacity to induce mammary carcinomas in mice, although cyclin D3- induced mammary tumours in mice are squamous cell carcinomas rather than adenocarcinomas 148. Cyclin D2 preferentially binds cdk2, is frequently methylated in breast cancer, and therefore infrequently expressed 149,150. Cyclin D3 is overexpressed in ~10% of breast cancers although information regarding the implications of this on outcome are limited 151,152. The main distinction between the various D-type cyclins appears to be one of differentiation, rather than proliferation (reviewed in 153), and their roles will not be discussed further here.
Cyclin D1 is transcriptionally regulated by a number of mitogens such as steroids and growth factors (section 1.3.7 and 1.3.8) and may be regulated by phosphorylation, relocalisation and degradation. The phosphorylation of cyclin D1-cdk4/6 complexes is mediated by cdk-activating kinase (CAK), which is an enzyme complex comprised of the cyclin-cdk pair, cyclin H-cdk7 154-157, while phosphorylation at Thr-286 by glycogen synthase kinase 3- (GSK-3 ) facilitates nuclear export and ubiquitin-mediated degradation 158. GSK-3 mediated degradation is inhibited by the Ras/PI3K/AKT pathway, while growth-factor stimulated synthesis is mediated via Ras/ Raf/MAPK, indicating some co-operativity in these pathways of cyclin D1 regulation 159. Cyclin D1- cdk complexes are further stabilised by binding to p21WAF1/Cip1and p27Kip1 160. This prevents the nuclear export of p21WAF1/Cip1and p27Kip1 and sequesters them away from cyclin E-cdk2 complexes towards which they are inhibitory.
The major direct functional effect of cyclin D1-cdk4/6 is to phosphorylate the tumour suppressor pRb. This catalytic activity is inhibited by the cdk inhibitor p16INK4, a tumour suppressor that is frequently deleted or mutated in cancer 161,162. Cyclin D1 also
30 INTRODUCTION
possesses non-cell cycle effects, including direct binding to the c-Myb-like transcription factor DMP1, thus preventing DMP1 mediated cell cycle arrest, while other targets include the androgen receptor, and histone acetylases and acetylases (reviewed in 163). Importantly, cyclin D1 can activate the ER by direct binding, and can recruit co- activators of the SRC1 family to the ER in the absence of ligand, and thus can function as a modulator of transcription 164.
1.3.3 Cyclin E
Cyclin E1 and the more recently described cyclin E2 are proteins with high sequence homology, and which interact in the cell cycle in much the same manner. In general, much of the work conducted on the E-type cyclins pertains to cyclin E1, and therefore, unless otherwise specified, “cyclin E” refers to the E1 variant.
The role of aberrant expression of cyclin E in breast cancer outcome has been evaluated in numerous studies 37,131,133,136,165-176 (Table 1.6). These studies are described in further detail in Chapter 4, and will be described only in brief here. Cyclin E is overexpressed in ~30% of breast cancers, and shows an association with ER negativity. Notably, high cyclin E expression has been frequently associated with adverse breast cancer outcomes, particularly in larger studies. However, a number of studies have failed to demonstrate that cyclin E is prognostic in breast cancer including a recent study of over 2032 patients 165 while others have linked cyclin E overexpression to favourable outcome on univariate analysis 131.
During G1 phase of the cell cycle, cyclin E binds to cdk2, thus facilitating the phosphorylation of pRb, and progression into S phase. In cyclin D1-knockout mice, cyclin E can replace the cell cycle effects of cyclin D1 suggesting that while cyclin D1 is an upstream sensor of mitotic signals, it is cyclin E-cdk2 activation that is primarily responsible for the transition from G1-S phase 177.
The activity of cyclin E-cdk2 is regulated by phosphorylation of the cdk2 by CAK and other protein kinases 178,179 and depletion of p21WAF1/Cip1and p27Kip1 from cyclin E-cdk2 complexes through sequestration or cytoplasmic relocalisation180-182. An additional and important layer of regulation is imposed by the accumulation of the cyclin E protein, as
31 INTRODUCTION
Tab
le 1.6: Selected studies of cyclin E RNA and protein expression in human breast cancer cohorts
32 INTRODUCTION
cyclin E expression is upregulated as a consequence of pRb phosphorylation. This leads to a positive feedback loop for cyclin E transcription and maximal cyclin E expression at the G1/S boundary 183,184. A further layer of positive regulation is imposed by the ability of the cyclin E-cdk2 complex to phosphorylate and precipitate the degradation of the cdk inhibitors p21WAF1/Cip1and p27Kip1 181,185. Further, cyclin E is degraded at the completion of G1 through the activities of the SCFFbw7 ubiquitin-ligase complex which directs cyclin E for proteosome-mediated degradation 181.
At a molecular level, cyclin E2 appears to function in the same manner as cyclin E1. However, cyclin E2 has been relatively understudied in human tissue cohorts in general and in breast cancer in particular. In gene expression profiling studies cyclin E2 expression contributes to the 70- and 76-gene poor prognosis, and high histological grade signatures 39,40,47. Cyclin E2 is overexpressed in 38% of breast tumours relative to normal breast tissue controls and is correlated with markers of proliferation and ER negativity 186. In addition, cyclin E2 mRNA expression has been associated with inferior outcomes in ER positive patients 187. Finally, in a recent study of 205 early breast cancer patients, cyclin E2 expression was associated with grade and ER status, and was an independent marker of outcome on multivariate analysis 166.
1.3.4 p21WAF1/Cip1
p21WAF1/Cip1 is involved in multiple cellular processes including differentiation, the induction of senescence, the prevention of apoptosis and induction of growth arrest after DNA damage in response to activated p53 (reviewed in 188). In the mammalian cell cycle p21WAF1/Cip1 inhibits the activity of cyclin E-cdk2, with the effect of inhibiting cell cycle progression. However, p21WAF1/Cip1 also assists in the stabilisation of cyclin D1-cdk4 complexes. Taken together, these data suggest that p21WAF1/Cip1 may have different effects on the cell cycle depending on its expression level, whereby a low level of expression is required for the stabilisation of cyclin-cdk complexes, and high-level expression inhibits cdk activity 189.
Given its diverse effects, it is not surprising that studies of p21WAF1/Cip1 protein expression have yielded conflicting results in relation to outcome from breast cancer 131,140,190-203 (Table 1.7). The majority of studies failed to find any association with
33 INTRODUCTION
Table 1.7: Selected studies of p21WAF1/Cip1 protein expression in human breast cancer cohorts
34 INTRODUCTION
outcome, although a handful found that that p21WAF1/Cip1 predicts for improved 201,202 or poor outcome 191,193,194,199,203. These studies are discussed further in Chapter 4.
Interestingly, two studies in the last few years have found a poorer outcome in association with cytoplasmic localisation of p21WAF1/Cip1 191,193. These data areconsistent with molecular studies indicating that cytoplasmic localisation of p21WAF1/Cip1 results in loss of its cell cycle inhibitory properties 204,205.
The transcription of p21WAF1/Cip1 is induced after DNA damage through the effect of p53 on the p21WAF1/Cip1 promoter. A variety of other factors have also been shown to activate p21WAF1/Cip1 transcription including Sp1/3, Smads, Ap2, STAT, E2F-1/E2F-3, and BRCA1, while p21WAF1/Cip1 transcription is repressed by c-Myc, and a variety of other factors by both p53-dependent and independent mechanisms (reviewed by 206). Although the predominant mechanism of regulation of p21WAF1/Cip1 is transcriptional, further regulation it imposed through ubiquitin-dependent and -independent proteosomal degradation 207,208.
1.3.5 p27Kip1
In respect of cell cycle effects, p27Kip1 performs similar molecular tasks to p21WAF1/Cip1 whereby it functions as a stabiliser of cyclin D1-cdk assembly when in low abundance 209,210, while at higher levels it inhibits cyclin-cdk activity. However, unlike p21WAF1/Cip1 the dominant mechanism of p27Kip1 regulation is post-translational. Thus, in early G1 mitogenic stimuli result in the phosphorylation of p27Kip1 at multiple sites and subsequent cytoplasmic relocalisation (reviewed in 211). Cytoplasmic p27Kip1 is then ubiquitinated via the Kip-ubiquitination-promoting-complex (KPC) and subject to proteosomal degradation 212. Later, in S phase, the cyclin E-cdk2 complexes themselves induce phosphorylation of p27Kip1 at Thr-187, which again promotes the ultimate polyubiquitination and degradation of p27Kip1. This latter degradation process appears dependent on the recognition of Thr-187 by its SCF-type E3 ligase (containing Skp1, Cul1, Skp2 and Roc1) and the cofactor Cks1. In contrast it is likely that the early G1 phase of p27Kip1 degradation is Thr-187 and Skp2-independent (reviewed in 211).
Analysis of p27Kip1 protein expression in breast cancer outcome cohorts has demonstrated a reasonably consistent association with low expression and both ER
35 INTRODUCTION
negativity and poor outcome 133,140,165,174,213-223(Table 1.8). These studies will be discussed further in Chapter 4. Interestingly, low p27Kip1 expression was associated with a tendency towards chemo- or endocrine-sensitivity in a handful of studies although the significance of these findings is unclear 214,215,223.
Thus p27Kip1 is consistently downregulated in breast cancers in vivo. This downregulation of p27Kip1 is likely to be mediated via alterations in the pathways responsible for p27Kip1 degradation, through amplification of key components of the pathways targeting p27Kip1 for ubiquitination, such as Skp2 and Cks1 211. In addition, growth factor receptor signalling pathways, that are frequently dysregulated in breast cancer, may impinge upon p27Kip1 regulation via PI3K-mediated phosphorylation 180,224,225 and the effects of sequestration by cyclin D-cdk4 (ultimately leading to an increase in cyclin E-cdk2 activity) as a consequence of upregulated cyclin D1 and c- Myc (reviewed in 211).
1.3.6 c-Myc
The proto-oncogene c-Myc is a nuclear phosphoprotein of the helix-loop-helix family of transcription factors with pleotropic effects on cellular biology. In particular there is evidence to show that c-Myc drives cell proliferation, inhibits differentiation, drives vasculogenesis, promotes genetic instability, reduces cell adhesion, promotes metastasis, and under certain circumstances promotes apoptosis (reviewed in 226,227). Amplification or overexpression of c-Myc is observed in numerous forms of human carcinoma including breast cancer and is often associated with aggressive and poorly differentiated tumours 228,229, while the 8:14 chromosomal translocation involving c-Myc is central to the pathogenesis of Burkitt’s lymphoma 230. c-Myc is composed of a number of functional domains. The N-terminus contains three highly conserved elements known as Myc boxes I – III, which mediate the transcriptional activities of the protein 227. A fourth Myc box has also been identified that possesses DNA binding activity as well as some transcriptional activity 231. At the C- terminus reside the basic region/ helix-loop-helix/ leucine zipper regions that mediate heterodimerisation with other helix-loop-helix transcription factors, including the
36 INTRODUCTION
Table 1.8: Selected studies of p27Kip1 protein expression in human breast cancer cohorts
37 INTRODUCTION
transcription factor Max. Myc/Max complexes bind to specific sequences, known as E- boxes which contain a central CAC(G/A)TG motif (Figure 1.7). Through this motif, the c-Myc/Max complex transactivates or transrepresses its target genes 227. A list of key c- Myc target genes with roles in proliferation is presented in Table 1.9 (adapted from 227). In addition, c-Myc may mediate its transcriptional effects indirectly through binding to other transcriptional regulators such as Miz-1 232. Other proteins that are bound by c- Myc include Skp2 and Fbw7 which are important components of the E3 ubiquitin ligase involved in c-Myc degradation, TIP48/49 which are involved in chromatin remodelling and TRRAP, a molecule that forms part of a multiprotein complex with histone acetyl- transferase activity 233. Three important phosphorylation sites for c-Myc are located in the N-terminal region, threonine-58, serine-62 and serine-71 227. These residues are particularly important in regulating the stability of c-Myc.
Table 1.9: Selected c-Myc target genes with roles in cell proliferation
Target gene Regulation Outcome
p21WAF1/Cip1 Down DNA damage checkpoint failure Cell cycle inhibition
p15INK4B Down Resistance to TGF- mediated proliferative arrest
CCND1, CCND2, CDK4 Up G1 progression in response to mitogenic signals
E2F2 Up Proliferation
IRP2, H-ferretin Up, Down Required for proliferation
SHMT Up Proliferation and growth
CCND1 - cyclin D1; CCND2 - cyclin D2; CDK4 - cyclin dependent kinase 4; E2F2 - E2F family member 2; IRP2 - iron regulatory protein-2; SHMT - serine hydroxymethyl transferase
38 INTRODUCTION
Figure 1.7: Structure of c-Myc
39 INTRODUCTION
In quiescent cells (G0), the expression of c-Myc mRNA is rapidly induced by mitogenic signalling or serum stimulation, after which cells enter G1 of the cell cycle. Subsequently c-Myc levels decline to lower steady state levels 229. The levels of c-Myc protein are regulated by post-transcriptional mechanisms that lie downstream of Ras. In particular, Ras induces c-Myc stabilisation in advance of recognition by the E3 ligase SCFFBW7, and consequent ubiquitination and proteosomal degradation 234-236.
A number of studies have now been conducted evaluating the prognostic role of MYC amplification 237-239 and c-Myc overexpression in human breast cancer cohorts 240-247 (Table 1.10). These studies will be discussed in further detail in Chapter 5. Briefly, to date the results from these studies indicate that MYC amplification occurs in around 15 – 20% of cases, and is associated with poor outcome 239.Data from the few c-Myc overexpression studies, either at the RNA level or protein level is less clear with the majority of studies failing to demonstrate any prognostic impact. A notable exception is the study by Scorilas et al 243 in which c-Myc RNA overexpression was associated with poorer disease free survival. In particular, studies using immunohistochemistry have been limited in number and have rarely analysed the prognostic impact of c-Myc overexpression on outcome. Nonetheless, these studies suggest that c-Myc overexpression is observed at higher levels than can be accounted for by gene amplificaton and point to other potential mechanisms of dysregulation including transcriptional, and post-translational regulation.
Importantly, c-Myc is a key transcriptional target of oestrogenic signalling in vitro, supporting the role of this protein in breast cancer despite these inconclusive clinical studies 248. The role of c-Myc as a downstream target of oestrogen action is discussed in section 1.3.7.
40 INTRODUCTION
Table 1.10: Selected studies of MYC gene amplification and c-Myc RNA and protein expression in human breast cancer cohorts
41 INTRODUCTION
1.3.7 Molecular effects of oestrogen and anti-oestrogens on the cell cycle
Oestrogen is a mitogen with respect to breast cancer cells. In particular, oestrogen drives early entry into, progression through, and exit from the G1 phase of the cell cycle. It achieves this by regulating a number of molecules necessary for S phase entry including c-Myc, cyclin D1 and cyclin E. Anti-oestrogens such as ICI 182,780 generally exert opposite effects on breast cancer cells, functioning as competitive inhibitors of the ER-mediated actions of oestrogens, thereby inhbiting cell proliferation.
Oestrogen treatment of breast cancer cells results in increased c-Myc transcription by as early as 30 minutes, with protein levels peaking at between 1 and 3 hours 249. Conversely, c-Myc expression is downregulated by treatment with anti-oestrogens 250. Induction of c-Myc expression is able to mimic the effects of oestrogen on cyclin E- cdk2 activation, and leads to S-phase entry in growth arrested cells 251. Further evidence of the role that c-Myc plays in the action of oestrogen is provided by data showing that c-Myc anti-sense oligonucleotides inhibit oestrogen-stimulated breast cancer proliferation in a manner analogous to anti-oestrogen treatment 250,252.
Like c-Myc, cyclin D1 overexpression can recapitulate the effects of oestrogen, allowing cell-cycle progression in anti-oestrogen arrested breast cancer cells 251,253. In anti-oestrogen arrested MCF-7 cells, cyclin D1 mRNA expression is induced within 1 - 3 hours of oestrogen treatment, followed by an increase in protein synthesis within 3 – 6 hours 254-256. Cyclin D1 expression activates cdk4 and is also able to stimulate the activation of cyclin E-cdk2 in a manner analogous to that seen with c-Myc overexpression 251. However, in the model system employed by Prall et al 251, cyclin D1 overexpression did not result in the overexpression of c-Myc and vice versa. Despite the similarities in the end effects of either c-Myc or cyclin D1 on cyclin E-cdk2 activity, it appears that the c-Myc and cyclin D1 pathways are at least in part, distinct from one another (Figure 1.8).
The point of convergence of the c-Myc and cyclin D1 pathways is cyclin E-cdk2, although the dependence of c-Myc proliferative activity on cyclin E-cdk2 has been called into question as c-Myc may induce E2F-transcription and G1 progression in the
42 INTRODUCTION
absence of cyclin E-cdk2 activation 257,258. The oestrogen-induced activation of cyclin E- cdk2 complexes precedes S phase entry by about 3 hours. This is in contrast to the situation for other mitogens, in which the activation of these complexes coincides with the G1-S transition. The mechanism for such activation appears not to reside with changes in the expression levels of cyclin E, cdk2, p21WAF1/Cip1 or p27Kip1 per se, but rather on the distribution of the cdk inhibitors p21WAF1/Cip1 and p27Kip1. In particular, it has been shown that oestrogen treatment, as well as cyclin D1 and c-Myc induction result in the binding of the pRb-related protein p130 to cyclin-cdk complexes, thereby competing with p21WAF1/Cip1 for binding, and thus relieving the inhibitory eftect of this molecule 251,259,260.
Both cyclin D1 and c-Myc induction are independently capable of stimulating the generation of cyclin E-cdk2 complexes of high specific activity. These complexes are comparatively deficient in p21WAF1/Cip1 and p27Kip1 251,254. Furthermore, oestrogen stimulation of breast cancer cells results in repression of p21WAF1/Cip1 activity through a decline in the synthesis of new p21WAF1/Cip1 261. This decline in synthesis is likely to result from transcriptional repression by c-Myc, as there is evidence that p21WAF1/Cip1 is a c-Myc target 262,263. Thus, there is now a considerable body of evidence identifying c- Myc, cyclin D1, cyclin E, p21WAF1/Cip1 and p27Kip1 as key mediators of the proliferative effects of oestrogen. The effect of anti-oestrogen treatment on these proteins, and the role of their aberrant expression in breast cancer outcomes and resistance to anti- oestrogen therapy is discussed in section 1.4.5.
43 INTRODUCTION
Figure 1.8: The molecular effects of oestrogen at the G1-S interface
44 INTRODUCTION
1.3.8 Growth factor receptor signalling pathways and proliferation in breast cancer
A feature of breast cancer of key importance is the dysregulation of growth factor signalling pathways. The most well-characterised of these pathways are those of the ErbB receptor tyrosine kinase family, also known as the epidermal growth factor receptor (EGFR) or HER family. This family consists of 4 receptors, HER1 (EGFR/ErbB1), HER2 (ErbB2), HER3 (ErbB3), and HER4. TGF- , amphiregulin, and EGF bind to HER1; betacellulin, heparin-binding EGF and epiregulin bind to both HER1 and HER4; the neuregulins bind to HER3 and HER4; no soluble ligand is known for HER2, which acts via dimerisation with other HER family members 264,265. The ErbBreceptors have 3 domains: an extracellular domain that recognises and binds the ligand, a hydrophobic transmembrane region, and an intracellular domain with tyrosine kinase activity that has the capacity to phosphorylate tyrosine residues on adaptor proteins 264. Upon ligand binding, homo- or hetero-dimerisation takes place, resulting in phosphorylation of tyrosines on the receptor itself, with resultant activation of diverse downstream signalling cascades particularly the Ras-Raf-MAPK pathway, PI3K-AKT, and c-Src 264 (Figure 1.9). Ultimately, such activation leads to diverse effects on survival, proliferation, angiogenesis and invasion. It is clear therefore, that deregulated expression of components of these signalling pathways could promote the development of these “hallmarks of cancer” 113.
Indeed, the oncogenic effects of deregulated growth factor signalling have been borne out in the clinical setting. HER2 gene amplification or protein overexpression has been identified in 10-34% of invasive cancers (reviewed in 266), and HER2 positivity (defined as either amplification as measured by fluorescence in situ hybridisation, or strong immunohistochemical staining) correlates with resistance to tamoxifen 267 and poor prognosis 268,269. There are conflicting data to suggest possible chemoresistance in association with HER2 positivity, although HER2 positive patients probably respond better to anthracycline-based regimens than the combination of cyclophosphamide, methotrexate and 5-fluorouracil (CMF) (reviewed in 270). Importantly, the identification of HER2 has facilitated the development of trastuzumab (Herceptin), a monoclonal IgG1 humanised murine antibody that binds the extracellular portion of HER2. The use of this agent in patients with HER2 positive tumours has demonstrated striking results not only in the metastatic setting 271, but also in the adjuvant setting 59-62.
45 INTRODUCTION
Figure 1.9: The PI3K/AKT and Ras/Raf/MEK/ERKpathways
46 INTRODUCTION
In addition to HER2, a number of growth factor signalling pathways have relevance to breast cancer. Within the ErbB signalling family, EGFR expression has been elevated in a number of studies, with a wide range of expression levels reported, and an association with poor prognosis 265,272. There are a large number of growth factor signalling pathways of relevance to breast cancer which are not reviewed here due to the lack of evidence for immediate therapeutic application. These include the insulin- like growth factor receptor family, the fibroblast growth factor receptor family, the platelet-derived growth factor, and transforming growth factor- family that have been reviewed elsewhere 273.
1.4 THE MOLECULAR BASIS OF ENDOCRINE RESISTANCE
1.4.1 Loss of ER expression or function
Lack of ER expression is seen in approximately 30 – 40% of breast cancers, and accounts for the majority of de novo resistance to anti-oestrogen treatment 274. The predominant mechanism behind this lack of ER expression is epigenetic, as a result of DNA methylation or histone deacetylation 275,276. The majority of ER negative/PR negative tumours will not respond to anti-oestrogen treatment, and therefore patients with these tumours are generally not offered anti-oestrogenic therapy 98. Those patients whose tumours express ER are generally offered anti-oestrogenic therapy (usually tamoxifen) as part of their management.
In general, those tumours that are ER positive at initial diagnosis remain so. However, 15 - 25% lose ER expression on relapse after an initial period of response to tamoxifen 277. It is clear therefore that as the majority of breast tumours continue to express measurable ER, that loss of ER expression is not the dominant mechanism through which breast cancers acquire resistance to tamoxifen.
47 INTRODUCTION
Rather than a loss of receptor expression per se, there is some in vitro evidence to suggest that ER mutation represents a mechanism of de novo anti-oestrogen resistance. Although the majority of such mutations are infrequent and of debatable clinical significance 278, a recent publication in which 267 early breast cancers were analysed indicated that the A908G mutation is present in 50% of invasive breast carcinomas, and is associated with large tumour size, node positivity, and increased likelihood of recurrence on univariate analysis 279.
1.4.2 Loss of ER expression
Data from experiments in which HeLa cells were transfected with ER or ER suggests that in tissues where signalling occurs via the AP-1 element, that the two ER isoforms display opposing effects on transcription in response to ligand-binding 83. Consistent with this, it has been suggested that ER may exert a protective role against the mitogenic effects of oestrogen, as decreased protein expression of ER is observed in pre-invasive as compared to benign mammary lesions 280. Furthermore, in murine model systems, ER expression is essential for terminal differentiation of the mammary gland epithelium 91, and co-expression of ER and ER during lactation is associated with oestradiol insensitivity 281.
When co-expressed with ER , ER will form heterodimers, although when expressed alone, forms homodimers. Notably, studies of the effect of oestrogen and 4-hydroxy- tamoxifen in osteosarcoma cell lines engineered to express ER alone, ER alone or both ER and ER , show that ER heterodimers result in patterns of gene regulation that are unique from those regulated by ER homodimers 282. Thus, it is likely that loss of ER expression in breast cancer cells may result in distinct changes in the suite of genes regulated by oestrogens and anti-oestrogens.
The potential influence of ER- expression in clinical anti-oestrogen resistance is supported by a clinical cohort study of 50 patients whose tumours were treated with tamoxifen in which ER expression was measured by IHC. This study demonstrated that ER expression was the major variable determining tamoxifen sensitivity 283. Similar findings have been generated by other groups and again indicate that aberrant ER- expression may play a role in tamoxifen resistance 284,285.
48 INTRODUCTION
1.4.3 Adaptive hypersensitivity to oestrogen deprivation and cross- talk with growth factor signalling pathways
There are now many studies that provide in vitro evidence for cross-talk between ER signalling, growth factor signalling and other kinase pathways. These interactions provide the basis for proliferative independence from oestrogen signalling, and influence resistance to anti-oestrogenic therapeutic strategies.
This cross-talk can occur at the level of the ER itself. The ER can be phosphorylated at Ser-118 and Ser-167 by downstream effectors of growth factor signalling such as ERK1/2 and MAPK thereby potentially leading to ligand–independent activation 70,76,286. These in vitro data are supported by a clinical study suggesting a shorter duration of response to tamoxifen in breast cancers displaying increased ERK activity 287. Importantly, it has also been demonstrated that long-term oestogen deprivation of breast cancer cells results in upregulation of MAPK and activity and consequent hypersensitivity to oestradiol 288.
Direct association may also occur between the ER and IGFR, EGFR and HER2 with resultant activation of their downstream signalling pathways 288-290, thus leading to further amplification of the effect. In vitro studies demonstrate upregulation of both EGFR and HER2 during the acquisition of anti-oestrogen resistance 291,292. Clinical studies suggest not only that HER2 overexpression/amplification is associated with an attenuated response to endocrine therapy 267, but that aromatase inhibitors are more effective than tamoxifen in patients with EGFR and/or Her2 overexpression 293. One could speculate that the partial agonist activites of tamoxifen (as opposed to aromatase inhibitors), may allow continued cross-talk and amplification of the proliferative signal driven by growth factor receptor overexpression which might explain this result.
Cross-talk with the PI3K cell survival pathway has also been implicated in the development of resistance to tamoxifen 75. The PI3K pathway is activated by tyrosine kinase receptors, but can also be activated by ER in a ligand-dependent manner 294(ref 103). Notably, upregulation of the downstream protein kinase of this pathway, AKT reduces the inhibition of MCF-7 cells by tamoxifen 75. In addition, not only is AKT a target of several growth factor signalling pathways, such as IGF-1R, EGFR and HER2,
49 INTRODUCTION
but AKT can also phosphorylate ER at Ser-167, leading to further activation and cross- talk 75,295.
Furthermore, upregulation of EGFR has been documented in in vitro models of endocrine resistance, with concomitant increases in the expression of key downstream signalling elements such as phosphorylated forms of ERK1/2, MAPK, AKT and PKC 296 (Section 1.5.3). Treatment with gefitinib, a small molecule EGFR-selective tyrosine kinase inhibitor, blocks these effects, and represents a promising direction for the treatment and prevention of tamoxifen-resistant breast cancer 297. In this regard, a recent study in the clinical setting has linked elevated EGFR expression to resistance to tamoxifen 298.
Cross-talk between growth factor and ER signalling pathways also occurs at the level of ER co-activators. Phosphorylation of the ER co-activator AIB1 enhances its activity 299, while phosphorylation of the co-repressor SMRT results in export from the nucleus thereby preventing transcriptional repression 300.
Thus it can be seen that cross-talk exists between the ER signalling pathway and growth factor signalling pathways at multiple levels, with in vitro evidence of endocrine resistance that is the basis of recent and ongoing trials evaluating the combination of growth factor signalling inhibitors such as gefitinib and tamoxifen (Figure 1.10).
1.4.4 Polymorphisms in tamoxifen-metabolising enzymes
Preliminary studies indicate that impaired metabolism of the pro-drug tamoxifen, to its more active metabolites may play a role in “resistance” to tamoxifen therapy. The maximum pharmacological activity of tamoxifen is dependent upon demethylation to N- desmethyltamoxifen, and then subsequent transformation to the hydroxymetabolite, endoxifen 301,302. The latter step in this process is catalysed by the CYP2D6 enzyme system (Figure 1.11). This cytochrome P450 is subject to genetic polymorphisms whereby ~10% of the population will have impaired drug-metabolising capacity, and thus may be artefactually resistant to tamoxifen due to ineffective generation of the active tamoxifen derivative. Indeed, tamoxifen-treated individuals lacking any functional CYP2D6 allelles, or possessing only one functional allele have significantly lower
50 INTRODUCTION
serum endoxifen levels, and endoxifen/N-desmethyltamoxifen ratios than individuals with two functional alleles302,303.
In addition to genetic variation in activity, CYP2D6 is subject to inhibition by other commonly used pharmaceuticals including the selective serotonin reuptake inhibitors (SSRIs) 303. These agents are in common use for the treatment of depression, and have also been found to be helpful for the treatment of the vasomotor side effects of anti-oestrogen therapy. Thus is it a potential concern that the treatment of tamoxifen- induced side effects may lead to reduced efficiency of the drug. Further studies are awaited to confirm these findings and establish their clinical importance.
51 INTRODUCTION
Figure 1.10: Cross-talk between ER and growth factor signalling pathways
52 INTRODUCTION
Figure 1.11: Tamoxifen metabolism
53 INTRODUCTION
1.4.5 Aberrations in cell cycle control
1.4.5.1 c-Myc
There is evidence to suggest that c-Myc may play a role in the development of anti- oestogen resistance. Oestrogen and anti-oestrogens exert opposite effects on the expression of c-Myc, whereby oestrogen upregulates c-Myc expression, while anti- oestrogens down-regulate c-Myc expression. It has been noted that MCF-7 cells maintained in oestrogen-deprived media upregulate oestrogen-regulated genes, including c-Myc 304, and therefore it is logical to speculate that c-Myc may play a role in the acquisition of the oestrogen/anti-oestrogen independent phenotype. Furthermore, there is now an accumulating body of evidence supporting this hypothesis.
Inducible expression of c-Myc is able to at least partially attenuate the anti-proliferative effects of anti-oestrogen treatment with ICI 182,780 on MCF-7 cells 251,262,305, an effect that it mediated through transcriptional repression of p21WAF1/Cip1 expression.
In addition to the cell cycle effects described above, amplified growth factor signalling cascades may also converge on c-Myc through the activities of MAPK and PI3K (Figure 1.10), and may represent a potential molecular conduit mediating the clinically observed anti-oestrogen resistance seen in the setting of Her2 overexpression. Furthermore, Ras-mediated phosphorylation of c-Myc at Ser-62 can stabilise the protein, potentially contributing to elevated expression as a result of amplified growth factor receptor signalling 235,236,306.
However, clinical studies have been less convincing in reliably demonstrating a role for c-Myc in anti-oestrogen resistance. MYC is frequently amplified in human cancers, and associates with indices of poor prognosis 239,240. Indeed, a number of studies have linked c-MYC amplification with an increased risk or relapse or death 122,238,239 (Table 1.10). However, data evaluating the specific relationship between c-Myc and response to anti-oestrogen therapy are scant, and thus this remains an important unanswered clinical question.
54 INTRODUCTION
1.4.5.2 Cyclin D1
Like c-Myc, there is evidence to implicate cyclin D1 in the development of anti- oestrogen resistance. In vitro studies have shown that reduction in cyclin D1 mRNA and protein expression is an early event in anti-oestrogen action 307,308. Furthermore, constitutive and inducible expression of cyclin D1 rescues breast cancer cells from oestrogen-mediated growth arrest 251,253. While some investigators have demonstrated that constitutive cyclin D1 overexpression mediates a short-term decrease in sensitivity to anti-oestrogen (up to 48 hours) 309, other data suggests that the effects may be more long-lived. Specifically, sustained cyclin D1 overexpression has been observed in breast cancer cells during the acquisition of an enduring tamoxifen-resistant phenotype 310-312. It should be noted however, that these tamoxifen-resistant cells do remain senstitive to ER downregulation by pure anti-oestrogen, and thus the partial agonist nature of tamoxifen may influence these results. Furthermore, there are data to indicate that cyclin D1 may positively feedback on the ER itself in a cdk- and pRb-independent manner 313,314, providing another potential resistance mechanism in the setting of a functional tamoxifen-modulated ER, as opposed to an ICI 182,780-downregulated ER.
Studies addresssing the question of the role of cyclin D1 in clinical endocrine responsiveness have yielded contradictory results. A summary of selected studies evaluating cyclin D1 in breast cancer outcome in general, and in endocrine responsiveness in particular is presented in Tables 1.3 – 1.5. Two large studies have demonstrated a reduced response to tamoxifen treatment in the setting of overexpression at the mRNA and protein level respectively 130,141, and one study has shown that amplification of the cyclin D1 gene may predict for an agonistic tamoxifen effect 121. However, others have shown a trend towards a superior response to tamoxifen in metastatic ER positive tumours that overexpress cyclin D1 147. Thus the impact of cyclin D1 expression on the response to endocrine treatment remains the subject of debate.
1.4.5.3 Cyclin E
There also exist data supporting the role of cyclin E as a mediator of anti-oestrogen resistance. Overexpression of cyclin E abrogates tamoxifen-mediated growth arrest in MCF-7 cells, and confers partial resistance to the acute inhibitory effects of ICI 182,780
55 INTRODUCTION
in a similar fashion to that observed for cyclin D1 309,315. A number of studies using clinical cohorts suggest that adverse outcome is associated with cyclin E overexpression. However, cyclin E overexpression is also linked to ER negativity, as well as to the basal-like breast cancer phenotype and BRCA1 mutations 37, and thus fails to demonstrate independent predictive power on multivariate analysis. There is however, some evidence to suggest that in ER positive cases that cyclin E expression is associated with poor relapse-free survival, or lack of responsiveness to endocrine therapy 169,173, and thus cyclin E remains a potential candidate for ongoing evaluation in the elucidation of the molecular mechanisms underlying endocrine resistance.
1.5 SUMMARY
Anti-oestrogen resistance is an important clinical challenge in the management of breast cancer. Loss or mutation of the ER itself accounts for a minority of acquired anti- oestrogen resistance, with other factors such as cross-talk with growth factor signalling pathways, hypersensitivity to oestradiol, loss of ER , and polymorphisms in tamoxifen- metabolising enzymes (cytochrome P450) being other potential contributors. As outlined in the preceding text, it is clear that there is a close link between cell cycle regulation and the responses of ER positive breast cancer cells to oestrogens and anti- oestrogens. In vitro and clinical evidence points to a potential role for dysregulation of the cell cycle in resistance to the anti-proliferative effects of anti-oestrogens as it is manifest in the clinic. Key molecular players in this process are cyclins D1 and E, the cdk inhibitors p21WAF1/Cip1 and p27Kip1, and the proto-oncogene c-Myc. Furthermore, as the cell cycle is a “final common pathway” of mitogenic signalling, dysregulated growth factor signalling pathways may also impinge on these downstream targets of oestrogen action.
Importantly, these mediators, particularly c-Myc and p21WAF1/Cip1, also interact with other cellular processes such as the apoptotic pathway, and thus the ultimate effects of their dysregulated expression in terms of pro- or anti-cancer phenotype, are likely to be dependent on interactions with a number of other factors such e.g. p53 status, tumour hypoxia.
56 INTRODUCTION
While the immunohistochemical expression of cyclins D1 and E, and the cdk inhibitors p21WAF1/Cip1 and p27Kip1 has been studied in several clinical cohorts, the data in relation to their prognostic value is conflicting, and worthy of clarification, particularly in relation to subgroups defined by ER status. While c-Myc has been extensively studied at the gene and mRNA level, there are few studies that evaluate its expression by IHC either in models of breast cancer progression or outcome. Furthermore, as in vitro studies suggest that c-Myc overexpression rescues breast cancer cells from cell cycle arrest, the role aberrant c-Myc protein expression in the context of ER postive breast cancer treated with endocrine-therapy requires clarification.
Thus the aims of my PhD are to: 1. Develop and characterise a representative early breast cancer tissue microarray cohort for the evaluation of biomarkers of breast cancer outcome, 2. Evaluate the breast cancer outcome cohort for the protein expression of cell cycle proteins cyclin D1, cyclin E, p21WAF1/Cip1 and p27Kip1, in relation to their individual impact on prognosis, and interaction with one another, 3. Evaluate the changes in protein expression of c-Myc in a model of breast cancer evolution, 4. Evaluate the breast cancer outcome cohort for aberrant expression of c-Myc in relation to prognosis, and interaction with other cell cycle proteins, 5. Evaluate the role of c-Myc in modulating the sensitivity of exponentially proliferating breast cancer cells to anti-oestrogen treatment.
In this thesis I aim to clarify the role of aberrant protein expression of c-Myc, a cell cycle marker relatively understudied by immunohistochemistry, in breast cancer evolution and outcome. In addition I aim to establish the relationship of aberrant c-Myc expression to the expression of cyclin D1, cyclin E, p21WAF1/Cip1 and p27Kip1 both in in vitro models of c-Myc overexpression and in human breast cancer tissue.
57
CHAPTER 2: MATERIALS AND METHODS
58 MATERIALS AND METHODS
2.1 ARCHIVAL TISSUE COHORTS
Two human tissue cohorts were used in the experiments in this thesis. The first of these was the Garvan/ Royal Prince Alfred Hospital Progression Cohort (GRPAHPC) that was designed to evaluate changes in protein expression during breast cancer evolution. The second cohort was the St. Vincent’s Hospital Breast Cancer Outcome Cohort (SVCBCOC), and was designed to evaluate the prognostic and predictive impact of the expression of key proteins in breast cancer patients of known clinical outcome. Both cohorts were processed into tissue microarrays (TMAs) on site, and these TMAs were used in all subsequent immunohistochemical assessments. The TMA construction process is detailed in section 2.1.1.
2.1.1 The Garvan/ Royal Prince Alfred Hospital Progression Cohort (GRPAHPC)
The GRPAHPC was constructed by Dr. Niamh Murphy under the supervision of Prof. C.S. Soon Lee from RPAH and Prof. Robert L. Sutherland from the Garvan Institute of Medical Research. A total of 222 patients who were diagnosed with either invasive ductal carcinoma (IDC) or ductal carcinoma in situ (DCIS) between 1996 and 2005 were retrospectively identified, and corresponding archival formalin-fixed paraffin- embedded (FFPE) tumour material retrieved from the pathology archive files of the Royal Prince Alfred Hospital, Camperdown, NSW, Australia. The use of these clinical samples and associated pathology report data for research was approved by the Ethics Committee of the RPAH (Ethics approval number X05-0115).
Representative Haematoxylin and Eosin (H&E) slides of all blocks were prepared at the Garvan Institute of Medical Research. Pathology review was undertaken by three pathologists: Dr. Duncan McLeod from the Department of Anatomical Pathology, RPAH, Camperdown, Sydney, Australia; Dr. Ewan Millar from the Department of Anatomical Pathology, South Eastern Area Health Service, St. George Hospital and the University of New South Wales, Sydney, Australia; and Dr. Sandra O’Toole from the Garvan Institute of Medical Research, Sydney, Australia. Histological grade and tumour type were reviewed and where present areas of normal ducts and lobules, usual ductal
59 MATERIALS AND METHODS
hyperplasia (UDH), atypical ductal hyperplasia (ADH), columnar cell lesions (CLL), DCIS and IDC were “marked-up” by ringing with a coloured marker pen.
Tumours were graded using the Elston and Ellis modification of the Bloom and Richardson classification 316 as part of the routine diagnostic assessment at RPAH. Due to the requirement to assess several cancer fields for mitotic counts (more than assessable by TMA core review), this reported grade was used for our analyses. However, DCIS was graded by nuclear grade during the analysis at the Garvan Institute of Medical Research.
A total of nine TMAs were assembled by making a 1.0 mm cylindrical core in the recipient TMA block using the MTA-1 Manual Tissue Arrayer (Beecher Instruments). The “marked-up” H&E slides were used as a guide to obtain representative sample cores of the various pathological lesions that were then deposited in the recipient array blocks. Four representative cores were taken for DCIS and three were taken for IDC. In addition, where noted at least 2 cores were taken for rarer associated lesions such as ADH, UDH and normal ducts and lobules. Cores were distributed across each array to avoid a predominance of peripheral or central locations or clustering of the different cores for each lesion. Six cores of renal tissue were scattered across the arrays as orientation markers, and normal breast cores derived from patients who had undergone reduction mammoplasty were also scattered across the arrays to allow comparison between normal ducts and lobules in morphologically normal breast tissue, and that associated with carcinoma. When all cores were in place, the recipient block was warmed to 37 °C for 15 minutes to ensure that the tissue cores had adhered to the walls of the cylindrical holes in the recipient block, and then compressed under a glass slide to make all cores flush with the surface. The TMA was then processed into 4 uM sections which were cut and stored at -80 °C to preserve tissue antigenicity. H&E slides were prepared for all TMAs and these were reviewed by Dr. Ewan Millar (pathologist). The locations of the cores and associated pathology were documented using an array map, the details of which were entered into the breast cancer database, CanSto (Section 3.2.3; Appendix 1) to facilitate linkage of pathology data and subsequent immunohistochemical studies. The clinical features of the GRPAHPC are detailed in Table 2.1.
60 MATERIALS AND METHODS
Table 2.1: Clinicopathological characteristics of patients in the Garvan/ Royal Prince Alfred Progression Cohort (n = 222)
Characteristics IDC with associated DCIS DCIS Total
Number of patients 59 (27%) 163 (73%) 222 (100%)
Mean age, years (range) 56 (26 - 82)
IDC Mean size, mm (range) 13 (1 - 66) Nuclear grade, no. (%) I 22 (37%) II 16 (27%) III 21 (36%)
DCIS Mean size, mm (range) 25 (1 - 100) Nuclear grade, no. (%) Low 12 (20%) 30 (19%) 42 (19%) Intermediate 12 (20%) 70 (43%) 82 (37%) High 36 (60%) 62 (38%) 98 (44%)
2.1.2 The St. Vincent’s Campus Breast Cancer Outcome Cohort (SVCBCOC)
The development of the SVCBCOC is the subject of the third chapter of this thesis and will be dealt with in detail there. Briefly, the SVCBCOC is comprised of 292 cases of IDC from the practice of a single surgical oncologist (Dr Paul Crea) whose practice is located at the St. Vincent’s Hospital Campus, Darlinghurst, NSW, Australia. The cohort has comprehensive matched clinicopathological and follow-up data and studies using both the tissue and clinicopathological data were granted ethics approval by the St. Vincent’s Hospital Human Research Ethics Committee (SVH Reference Number H00/036). A total of 18 TMAs were made from archival FFPE tissue blocks as above, with the exception that only invasive ductal carcinoma was to be sampled. At least two (and up to six) cores of IDC were distributed across the arrays, along with orientating cores of renal, hepatic or colonic tissue, and cores of normal ducts and lobules from reduction mammoplasty specimens. Pathology review, array mapping and slide storage were as detailed in section 2.1.1. In addition, the CanSto database (Section 3.2.3; Appendix 1) was used to store associated clinicopathological and follow-up data on the patients in the cohort allowing linkage to immunohistochemical studies and detailed survival analysis.
61 MATERIALS AND METHODS
2.2 FORMALIN-FIXED PARAFFIN-EMBEDDED CELL BLOCKS
The MCF-7-derivative cell lines, MCF-7-p MT or MCF-7-p MT-Myc were generated and maintained as described in Section 2.7. Cells were grown in monolayers in T125 flasks until 70-80% confluent. Cells were trypsinised whereby growth medium was discarded, the cell monolayer was washed with phosphate buffered saline (PBS) and the cells were enzymatically removed from the culture vessel using 0.5% trypsin/ethylenediamine tetraacetic acid (EDTA). The trypsin was then neutralised with foetal calf serum (Theromtrace, Noble Park, Victoria, Australia) (FCS)-containing medium and the suspension subjected to centrifugation at 173 x g to form a cell pellet. The cell pellet was washed with phosphate-buffered saline (PBS). After discarding the supernatant the cells were mixed with 150 uL of human plasma. One hundred and fifty uL of bovine thrombin (DADE® Thrombin Reagent, Marburg, Germany) was added to the cell/plasma mixture to form a clot. The clot was then fixed in 10X the volume of 10% neutral buffered formalin and sent to the St. Vincent’s Hospital pathology department (SydPath, Darlinghurst, NSW, Australia) for paraffin-embedding. The paraffin-embedded specimen was then sectioned by microtome and subjected to immunohistochemical processing.
2.3 TISSUES FROM A MURINE MODEL OF MAMMARY CARCINOGENESIS
The expression of c-Myc was also examined in a mouse model of mammary carcinogenesis. Transgenic mice were generated by crossing the murine mammary tumour virus (MMTV)-rtTA transgenic with the TETO-Myc transgenic mouse 317,318. The mice were administered doxycycline in their food 700mg/kg (Bioserve, Beltsville, MD, USA) constantly. Mammary tissues were harvested at varying time points after transgene induction and fixed in 4% paraformaldehyde, paraffin-embedded and sections at 4 microns prepared. In this model hyperplastic mammary gland lesions develop by 30 days post transgene induction, and by 6 months, high-grade tumours develop. Paraffin-embedded tissues from control mice, mice after 6 days of
62 MATERIALS AND METHODS
doxtetracycline, those with hyperplastic lesions, and those with tumours were a kind gift of Dr. Alex Swarbrick (Garvan Institute of Medical Research).
2.4 IMMUNOHISTOCHEMISTRY
2.4.1 Slide preparation, antigen retrieval and immunohistochemistry
Immunohistochemistry was performed using a DAKO autostainer (DAKO, Carpinteria, CA, USA). The antibodies and protocols used for the human tissue are shown in Table 2.2, while those used for mouse tissues are shown in Table 2.3. Optimisation of the protocols was undertaken such that known negative control tissues and IgG controls showed no staining, positive controls demonstrated convincing staining, and there was variation in staining across a range of intensities in breast cancer tissue test arrays. Furthermore, the results from each optimised protocol had to be reproducible in repeat runs on test arrays prior to evaluation in the TMA series. Decisions in relation to appropriate adjustments in the immunohistochemistry protocols (for example, changes to the primary antibody concentration used, duration of antigen retrieval by waterbath or pressure cooker, or changes to the pH of the retrieval solutions used) were made in consultation with one of the pathologists affiliated with the project (A/Prof. James Kench, Dr. Ewan Millar and Dr. Sandra O’Toole).
Four-micron sections of tissue paraffin blocks were cut on a Leica microtome and placed on coated slides. All slides were heated in a 70ºC oven for 2 hours to ensure adherence. Prior to antigen retrieval the slides were dewaxed and rehydrated in a graded series of ethanol. Antigen retrieval was performed for each antibody as shown below using a boiling waterbath or DAKO Pascal decloaker (pressure cooker), and the sections placed on a DAKO autostainer, where endogenous peroxidase activity was quenched with 3% hydrogen peroxide. A serum-free protein block (DAKO X090930) was then applied for 10 minutes. In human tissue samples and cell line pellets the primary antibody was incubated in a solution of DAKO antibody diluent (SO80983) on the section for one hour at room temperature as indicated below. The Envision+ detection system of the appropriate species (the same as the primary antibody, either mouse or rabbit) was used as a secondary solution (DAKO, CA mouse K400111 and
63 MATERIALS AND METHODS
Table 2.2: Immunohistochemistry protocols used in human tissues Antibody Conc. /Incubation Antigen Retrieval Controls ER (ID5) DAKO, 1:100 / 30 minutes 60 seconds at Positive – breast Carpinteria, CA, USA; maximum temperature cancer; Negative – mouse monoclonal and pressure in a mouse IgG1 pressure cooker in DAKO pH 9.0 solution (s2367) PgR (636), DAKO; 1:200/ 30 minutes 60 seconds at Positive – breast mouse monoclonal maximum temperature cancer; Negative – and pressure in a mouse IgG1 pressure cooker in DAKO pH 9.0 solution (s2367) CK5/6 MAB1602, 1:100/ 60 minutes 60 seconds at Positive – myoepithelial Chemicon International, maximum temperature cells; Negative – non- Temecula USA; mouse and pressure in a epithelial tissues, polyclonal pressure cooker in stroma, vessels DAKO pH 6.1 solution (s1699) Cyclin D1 (SP4), 1:100/ 30 minutes 20 minutes in a boiling Positive – mantle cell LabVision, Freemont, waterbath in DAKO pH lymphoma; Negative – CA, USA; rabbit 6.1 solution (s1699) rabbit IgG monoclonal Cyclin E (13A3), 1:40/ 60 minutes 2 minutes at maximum Positive – placenta; Novocastra, Newcastle, temperature and Negative – lung and UK; mouse monoclonal pressure in a pressure mouse IgG1 cooker in DAKO pH 6.1 solution (s1699) p21WAF1/Cip1, 1:100/ 60 minutes 2 minutes at maximum Positive – HMEC 184 Calbiochem, San temperature and 319; Negative – SKBR3 Diego, CA, USA; pressure in a pressure 319, mouse IgG1 mouse monoclonal cooker in DAKO pH 6.1 solution (s1699) p27Kip1, K25020, BD 1:100/ 60 minutes 10 seconds at Positive – MDA-MB- Transduction maximum temperature 134 319; Negative – Laboratories, and pressure in a HMEC 184 319, mouse Lexington, KY, USA; pressure cooker in IgG1 mouse monoclonal DAKO pH 6.1 solution (s1699) c-Myc (9E10), DAKO; 1:100/45 minutes 20 minutes in a boiling Positive – breast mouse monoclonal waterbath in DAKO pH cancer; Negative – 6.1 solution (s1699); 10 mouse IgG1 minutes for cell pellets Cleaved PARP (p85), 1:50/ 60 minutes 90 seconds at Positive – tonsil Promega, Madison, WI, maximum temperature (germinal centres); USA; rabbit polyclonal and pressure in a Negative – rabbit IgG pressure cooker in DAKO pH 6.1 solution (s1699) Unless specified, controls were chosen on the basis of manufacturers instructions
Table 2.3: Immunohistochemistry protocols used in mouse tissues Antibody Conc. /Incubation Antigen Retrieval Controls c-Myc (9E10) DAKO; 1:100 / 45 mins 2 minutes at maximum Positive – breast mouse monoclonal temperature and cancer; Negative – pressure in a pressure mouse IgG1 cooker in DAKO pH 9.0 solution (s2367)
64 MATERIALS AND METHODS
rabbit 400311) for 30 minutes at room temperature. For the mouse tissues, the DAKO ARK (Animal Research Kit) (K3954) was used in conjunction with the c-Myc 9E10 antibody (DAKO). After rinsing with buffer (DAKO S300685), the reaction was visualised with DAB+ chromagen (DAKO K346811). Sections were then rinsed in water, counterstained with haematoxylin, dehydrated in a series of graded alcohols, cleared in xylene and coverslipped.
2.4.2 Immunohistochemical scoring
Each core was assessed by light microscopy for both nuclear and cytoplasmic staining intensity (0 representing no staining, 1+ representing mild staining, 2+ representing moderate staining and 3+ representing strong staining) and percentage of cells staining (0-100%) by 2 independent observers, one of who was a pathologist. Scores that differed in intensity score, or by more than 10% in percentage of cells staining positive were subject to further consensus scoring to reach an agreed score. All other cores were averaged. A simplified “H score” for each core was then generated by multiplying the final staining intensity by the percentage of positive cells 137. As up to 6 separate cores per breast cancer were present on the TMAs, the scores for each tissue sampled were then averaged to generate a summary score (percentage of cells stained, intensity and H score). These data were then linked to the clinicopathological data for the cohort. The average was chosen (rather than the highest score) to reduce the potential effects of overstaining on the edge of the section, and understaining where some areas of the slide may potentially have been underexposed to antibody using the autostainer.
2.5 IN SITU HYBRIDISATION
HER2 and c-MYC Fluorescence In Situ Hybridisation (FISH) was performed by Dr Adrienne Morey and colleagues in the HER2 FISH Reference Lab at St Vincent’s Hospital, Sydney, according to routine protocols employed for diagnostic cases. Samples were evaluated for HER2 gene amplification using the Vysis PathVysion HER-2 DNA dual colour probe kit (Abbott Molecular, Abbott Park, IL, USA). HER-2
65 MATERIALS AND METHODS
FISH was performed on 4 micron TMA sections according to the manufacturers instructions, with the exception of the pre-treatment step, which utilised 30 min incubation in DAKO Target Retrieval Solution (TRS) at 90 ºC (R.Tubbs, personal communication). Co-denaturation was performed using a HYBrite thermal cycler (90 degrees for 6 mins) and hybridization continued overnight at 37 degrees. Following post-hybridization washes and DAPI counterstaining, the signal was analysed using a Zeiss Axioscope II microscope with digital AxioCam and AxioVision software. In accordance with the recommendations for HER2 assessment at the time of analysis, gene amplification was defined as a HER-2/chr17 ratio >2.0 (low level if ratio >2 but <4). Polysomy was indicated by a raised HER-2 gene copy number (>2.5) with a normal HER-2/Chr17 ratio.
Samples were evaluated for c-MYC amplification using the Vysis LSI c-MYC (8q24.12- q24.13) Spectrum Orange Probe (Abbott Molecular) and Vysis CEP 8 (D8Z2) Spectrum Green probe (Alpha Satellite DNA, 8p11.1-q11.1) (Abbott Molecular). C-MYC FISH was performed in the same manner is described above for HER2 amplification. C-MYC gene amplification was defined as a c-MYC/chr8 ratio >2.0 (low level if ratio >2 but <4). Polysomy was indicated by a raised c-MYC gene copy number (>2.5) with a normal c-MYC/chr8 ratio
2.6 C-MYC MUTATIONAL ANALYSIS
Two cores of breast cancer tissue were punched from each of 287 of the 292 paraffin blocks from the SVCBCOC (5 cases had insufficient material left). DNA was extracted from these cores by Dr. Elena Lopez-Knowles using the Agencourt Forampure kit (Beckman Coulter, Beverly, MA, USA). The N-terminal phosphorylation region and nuclear localisation signal were then amplified by myself using the Expand High Fidelity PCR System (Roche, Mannheim, Germany; primers from Integrated DNA Technologies Coralville, IA, USA) and product amplification verified on a 2% agarose gel. Primer sequences are presented in Table 2.4. The TA was 58 C and the magnesium concentration was 3.5 mM for both PCR reactions. Product purification was undertaken using Exonuclease I (New England Biolabs, Ipswich, MA USA) and SAP dephosphorylation buffer (Roche). Sequencing was then performed at the Australian Cancer Research Foundation sequencing facility (Garvan Institute of
66 MATERIALS AND METHODS
Medical Research, Darlinghurst, NSW, Australia). Sequences were then analysed using Macvector software (Macvector, Cary, NC, USA).
Table 2.4: PCR primer sequences Region Direction Sequence N-terminal phosphorylation region Forward 5’-TAC GAC TCG GTG CAG CCG TAT TT-3’ Reverse 5’-AAG GGA GAA GGG TGT GAC CGC AA-3’ Nuclear localisation signal Forward 5’-TCC ACA CAT CAG CAC AAC TAC GCA-3’ Reverse 5’-TTT CCA ACT CCG GGA TCT GGT CAC-3’
2.7 C-MYC OVEREXPRESSING CELL LINES AND CULTURE
2.7.1 Plasmid construction, cell culture, and transfection.
The plasmids p MT and p MT-Myc, which contain a metal-inducible metallothionein promoter have been previously described 251,320. Stock cultures of MCF-7 cells were obtained from the Michigan Cancer Foundation, Detroit, USA and were maintained in RPMI 1640 supplemented with 5% FCS, sodium bicarbonate (14 mM, Gibco, Grand Island, NY, USA), HEPES (N-[2-hydroxylethyl]piperizine-N-[2-ethanesulfonic acid], pH 7.2, 20 mM, Gibco), 200 mM L-Glutamine (6 mM, Gibco), insulin (10 μg/mL Actrapid, Novo Nordisk, Baulkham Hills, NSW, Australia), gentamicin (10 μg/mL Pfizer, Bentley, WA, Australia), 1M sodium hydroxide (9 mM, Gibco) as previously described 321. Tissue culture flasks were from Corning (NSW, Australia).
Prior to the commencement of this thesis, MCF-7 cells were transfected by Mr C. Marcelo Sergio with either p MT or p MT-c-Myc together with a plasmid containing a selectable marker (puromycin), and individual colonies (10-20 for each construct) were isolated using puromycin selection (0.7 μg/μl, Sigma, MO, USA), expanded and characterised. These studies identified two clones with strongly zinc-inducible c-Myc expression as well as two clones in which c-Myc was constitutively overexpressed in the absence of exogenous zinc (Chapter 6, Figures 6.1 and 6.5). These four clones were used to test the effect of constitutive and inducible c-Myc expression on
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proliferation and sensitivity to anti-oestrogen treatment as described below. One clone transfected with the empty vector (p MT) was used as a negative control in all studies.
2.7.2 Optimisation of zinc induction and timecourse
Increasing concentrations of zinc resulted in progressively increasing levels of c-Myc protein expression as measured by western blot. Previous experiments in the laboratory had used a concentration of 65 μM Zn (as ZnSO4) for the induction of c-Myc expression as previously described 251 and thus zinc concentrations between 0 – 75 μM were evaluated. Studies evaluating the attenuation of anti-oestrogen effect with increasing zinc concentration indicated that the response to c-Myc induction appeared to plateau above a concentration of 50 μM, and that the incremental increase in c-Myc expression as measured by western blot was small between 40-60 μM (Chapter 6). Therefore a concentration of 50 μM zinc was chosen for the majority of the in vitro experiments in this thesis.
2.7.3 Anti-oestrogen treatment
Steroid antagonists (Table 2.5) were each dissolved in ethanol at 1,000-fold final concentration and added to cells in exponential growth. Control cultures received ethanol vehicle to the same final concentration.
Table 2.5: Steroid antagonists and working concentrations
Steroid hormone/ Alternate and scientific name Working conc. Ref Source antagonist
4-OH-Tamoxifen 4-OH–Tam, 1 μM 322 Sigma, MO, USA trans-4(1-(2-(dimethylamino) ethoxy)phenyl)-2-phenyl-1-butenyl) phenol ICI 182780 7 -[9-(4,4,5,5,5- 10 nM 322 Dr Alan Wakeling, Astra- pentafluoropentylsulfinyl) nonyl] estra- Zeneca Pharmaceuticals, 1,3,5,(10)-triene-3,17 -diol Alderly Park, Cheshire, UK Raloxifene [6-hydroxy-2-(4-hydroxyphenyl)- 1 uM 322 Eli Lilly, IN, USA benzothiophen-3-yl]- [4-[2-(1- piperidyl)ethoxy]phenyl] -methanone
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2.8 FUNCTIONAL ASSESSMENT OF CELL LINES
2.8.1 Growth curve analysis
Cells were set up in T25 flasks at 1 x 105 cells per flask, with triplicate flasks set up for each time point. At the timepoints indicated for each experiment the cells were trypsinised as above (Section 2.2), washed with PBS and resuspended in 250 μL- 1 mL of medium. Cell number was quantitated using haemocytometer counting. Relative proliferation of cultures was analysed by plotting cell number on a log scale versus time. Statistical comparisons were performed by Analysis of Covariance (ANCOVA) across the linear period of growth (usually between day 2 and day 5 of the experiment). All growth curve experiments were repeated independently in triplicate.
2.8.2 Recovery of lysates from monolayer culture
Whole cell lysates were collected by trypsinisation as above (Section 2.2). Cell pellets were collected by centrifugation at 173 x g for 3 min and washed with ice-cold PBS. The cells were lysed by resuspension in ice-cold normal lysis buffer (50 mM HEPES (pH 7.4), 1% (v/v) Triton X-100, 0.5% (w/v) sodium deoxycholate, 0.1% (w/v) SDS, 50 mM sodium fluoride, 5 mM EDTA), containing protease inhibitors (1 mM phenylmethylsulfonyl fluoride, 10 g/mL aprotinin, 10 g/ mL leupeptin, 1 mM sodium orthovanadate and 20 mM MG-132, 1 mM DTT [dithiothreitol]). The pellets were then incubated on ice for 5 min prior to transfer to 1.5 mL tubes. The lysates were briefly vortexed, and then centrifuged at 17,530 x g for 10 min and the supernatants stored at -80°C.
2.8.3 Nuclear/cytoplasmic separation
Where nuclear and cytoplasmic lysates were collected the pellet of cells was resuspended in 60 μL of nuclear lysis buffer (20 mM HEPES (pH 7.4), 10 mM NaCl, 1.5 mM MgCl2, 20% (v/v) glycerol, 0.1% (v/v) Triton X-100, 1 mM DTT, protease inhibitors) and incubated at 4°C for 5 min. The suspension was centrifuged at 700 x g for 1 min at
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4°C, and the cytoplasmic supernatant removed to a fresh tube and stored at -80°C. The remaining nuclear pellet was resuspended in 60 μL of nuclear lysis buffer and 9 μL of 5 M NaCl, and incubated a further μL of nuclear lysis buffer and 9 μL of 5 M NaCl, and incubated a further 1 h with occasional vortexing. Centrifugation at 17530 x g for 10 min separated the nuclear supernatant from the pellet and it was removed to a fresh tube and stored at -80°C.
2.8.4 Western blot analysis
Twenty μg of protein lysates were prepared with 4x LDS sample buffer and 10x sample reducing agent (NuPAGE, Invitrogen, Mt Waverley, Victoria, Australia) by heating at 70°C for 10 min. The lysates were separated using NuPage polyacrylamide gels (Invitrogen, Mt Waverley, Victoria, Australia) and transferred to polyvinylidene difluoride (PVDF) membranes (Bio-Rad Laboratories, Hercules, CA, USA). The following gels and buffers were required to resolve particular proteins: 4-12% Bis-Tris with MOPS buffer (c-Myc, cyclin D1, cyclin E, p21WAF1/Cip1, p27Kip1 and pRb-P-Ser249-Thr252), and 3-8% Tris-Acetate with Tris-Acetate Buffer (total pRb). The membranes were washed in a protein blocking solution of 5% skim milk powder in TBS/0.01% Tween for one hour prior to incubation with primary antibody. The membranes were incubated with the following primary antibodies: -actin (AC-15, Sigma); cyclin D1 (DCS6, Novocastra, Newcastle-upon-Tyne, UK); cyclin E (HE12, Santa Cruz Biotechnology, Santa Cruz, CA, USA)), c-Myc (9E10, Santa Cruz Biotechnology); p21WAF1/Cip1 (C24420 BD Transduction Laboratories, Lexington, KY, USA) and p27Kip1 (K25020 BD Transduction Laboratories); total pRb (554136, BD Pharmingen, North Ryde, NSW, Australia), and pRb-P-Ser249-Thr252 (CA1007, Oncogene Research Products, San Diego, CA, USA). The secondary antibodies were horseradish peroxidase-conjugated sheep anti-mouse or donkey anti-rabbit (Amersham Biosciences) and specific proteins were visualized by chemiluminescence (Perkin-Elmer, Rowville, Vic., Australia) and exposure to X-ray film. Relative band intensities were quantitated by performing densitometry on images of the X-ray films scanned into Adobe Photoshop using IPLab Gel software (Scanalytics Inc., Fairfax, VA, USA).
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2.8.5 Kinase assays
The activity of cyclin E/cdk2 complexes was determined as previously described 254. Lysates were prepared in normal lysis buffer as described above. One hundred μg lysates were precleared with protein A-Sepharose (Zymed, CA, USA) (1 h, 4°C) and then immunoprecipitated with protein A-Sepharose conjugated to an anti-cyclin E polyclonal antibody (C-19; Santa Cruz Biotechnology) for 3 h at 4°C. The immunoprecipitates were washed once with normal lysis buffer, twice with ice-cold normal lysis buffer containing 1 M NaCl, once again with normal lysis buffer, and then three times with ice-cold 50 mM HEPES, pH 7.5, 1 mM DTT buffer. The kinase reactions were initiated by resuspending the beads in 30 l of kinase buffer (50 mM
HEPES, pH 7.5, 1 mM DTT, 2.5 mM EGTA, 10 mM MgCl2, 20 m ATP, 10 Ci of [gamma-32P] ATP, 0.1 mM orthovanadate, 1 mM NaF, 10 mM -glycerophosphate) containing 10 g of histone H1 (Sigma, MO, USA) as a substrate. After incubation for 15 min at 30°C, the reactions were terminated by the addition of 9 l of 5 SDS sample buffer. The samples were then heated at 95°C for 5 min and separated using 4-12% Bis-Tris gels (Invitrogen, Mt Waverley, Victoria, Australia), and the dried gel was exposed to X-ray film. Relative band intensities were quantitated by performing densitometry on images of the X-ray films scanned into Adobe Photoshop using IPLab Gel software (Scanalytics Inc., Fairfax, VA, USA).
2.8.6 Flow cytometry
2.8.6.1 Cell recovery from monolayer culture and fixation
Cells grown in monolayers were trypsinised as above. The cells were then collected by centrifugation at 173 x g, and resuspended in growth medium to a cell count of 3 – 5 x 105 cells/mL. To 1 mL of this suspension was added 250 uL of ethidium bromide (Pfizer, North Ryde, NSW, Australia) to give a final concentration of 12.5 μg/mL. Triton X-100 (Sigma, MO, USA) was included in the DNA stain to give a final concentration of 0.2% (v/v). The cells were then incubated at 4°C overnight and then incubated with 50 μL RNaseA at a final concentration of 0.4 mg/mL (Sigma) for 1 hour at room temperature prior to flow cytometry.
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2.8.6.2 Analysis of cell cycle phase distribution using ethidium bromide staining
In order to monitor the S phase fraction during changes in cell proliferation, ethidium bromide stained cells were subjected to DNA flow cytometry on a FACSCalibur (Becton Dickinson, CA, USA). A total of 20,000 to 30,000 events were collected. Co-efficients of variation were 2-5%. Under these staining conditions DNA content is proportional to ethidium bromide staining thus the cell population visually separated into three components – a peak representing early S phase with little increase in DNA content, a trough representing mid-S phase or continuing DNA synthesis, and a peak representing late S phase with duplicated DNA content just prior to cell division. The three populations were quantitated from each DNA histogram, using ModFit LT (Verity Software House, ME, USA) and the proportion of cells in S phase throughout a timecourse was then graphed to compare different populations.
2.9 CONFOCAL MICROSCOPY
The MCF-7-derivative cell lines, MCF-7-p MT or MCF-7-p MT-Myc were maintained as before. Cells cultured as a monolayer were washed with PBS prior to fixation with 4% paraformaldehyde for 15 min. The slides were rinsed in PBS and then permeabilised with 0.2% Triton X-100 for 15 min. Following another PBS rinse, the slides were blocked with 1% (w/v) BSA/PBS for 1 h. The slides were subsequently incubated in the primary antibody -c-Myc Ab-2 (9E10.3) (Neomarkers, Fremont, CA. USA) diluted in 1% (w/v) BSA/PBS for at least 1 h or overnight. After 2-3 PBS washes the slides were incubated with Cy2-labeled anti-mouse secondary antibody and TRITC- Phalloidin as a cell membrane counterstain (Sigma) and ToPro3 as a DNA counterstain (Jackson ImmunoResearch Laboratories, Inc., Westgrove, PA, USA) for 1 h. The slides were washed 2-3 times with PBS and then mounted using a 50% (v/v) glycerol/H20 solution or Prolong Antifade (Molecular Probes, Invitrogen, Mt Waverley, Vic., Australia). Confocal microscopy then was carried out on a Leica DMRBE confocal microscope using a 63x APO oil objective.
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2.10 STATISTICAL EVALUATION
Statistical evaluation was performed using Statview 5.0 Software (Abacus Systems, Berkeley, CA). A p-value of < 0.05 was accepted as statistically significant for all analyses. Experiments involving the c-Myc inducible and overexpressing clones were analysed using the Student’s t test or Analysis of Variance (ANOVA) where appropriate. The expression of c-Myc in the GRPAHPC was initially analysed by ANOVA, and as the data was not normally distributed, the Kruskall-Wallis test.
The prognostic impact of the expression of various proteins as measured by immunohistochemistry in the SVCBCOC was analysed by Kaplan-Meier and Cox proportional hazards analyses. Analyses of correlations between clinicopathological factors was performed using the 2 test, while differences in the expression of various proteins by the SVCBCOC subgroups was conducted using non-parametric tests, the Mann-Whitney and Kruskall-Wallis tests. These analyses are discussed in detail in Chapters 3-5.
All analyses were exploratory in nature and with the exception of the optimal cut-point determination process in which Bonnferroni correction was undertaken (Section 4.1), no attempt was made to adjust for multiple comparisons. It is therefore possible that some of the significant associations identified are false positives, and consequently care should be taken in the interpretation of the results. Independent studies would be required to verify particular findings of interest.
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CHAPTER 3: DEMOGRAPHIC AND CLINICOPATHOLOGICAL FEATURES OF THE ST. VINCENT’S CAMPUS BREAST CANCER OUTCOME COHORT
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3.1 INTRODUCTION
The expected benefits of adjuvant therapy for early breast cancer are dependent on the known clinicopathological prognostic features of regional lymph node metastasis, tumour histology, and grade, and on markers of therapeutic response, namely the expression of ER and PR, and more recently the presence of HER2 amplification. While more sophisticated gene expression profiling methodologies seek to predict the behaviour of breast cancer on the basis of the expression of a suite of molecular markers 39,44, current algorithms lack the ability to predict with perfect precision who will and will not derive benefit from adjuvant therapy.
The expansion of gene expression profiling and molecular phenotyping of human breast cancer, breast cancer cell-lines and tissues from mouse models of mammary carcinoma has the potential to generate numerous potential biomarkers predictive of outcome and treatment response, as well as to identify new therapeutic targets for disease that is unresponsive to current therapeutic approaches 323. Ultimately these molecular markers require validation in representative human tissue cohorts to confirm biological relevance, and potential clinical utility. Studies using cohorts of breast cancer tissues collected from patients with resectable, “early” breast cancer rely on monitoring for disease recurrence and death as their endpoint. Such human tissue cohorts may be collected prospectively or retrospectively, the former with the advantage of optimising tissue handling, collection and archiving, the latter with the advantage of substantially condensing the time required for patient accrual and follow-up to weeks or months instead of years.
The use of tissues from patients treated with neoadjuvant systemic therapy provides a unique opportunity to monitor tumour response and its associated molecular changes in situ over a relatively short period of time by comparing a breast cancer biopsy before neoadjuvant therapy, and the remaining cancer when it is ultimately resected, thus providing useful predictive information about the behaviour of the tumour well in advance of the time to relapse and death. At present relatively few patients are treated in this fashion thus limiting the numbers of patients available to accrue to such studies. Regardless of the method of accrual to studies in which response or outcome are end- points, high quality clinicopathological information is essential to the analysis of any biological data derived.
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For the purposes of the present study, the most feasible approach was retrospective whereby with ethics committee approval, archival FFPE breast cancer tissue blocks were collected in conjunction with corresponding clinicopathological data from patients treated by a single surgical oncologist, Dr. Paul Crea to form the SVCBCOC. The breast cancer tissue blocks were then utilised for the construction of TMAs as described in Chapter 2. Data regarding tissue block archiving and sampling, and the linked clinicopathological data were entered into a password-protected database, CanSto (Appendix 1).
It is important in biomarker validation studies that the cohort being analysed is representative of breast cancer patients within a particular population, and atypical features identified as sources of potential bias. The demographic features of the present cohort were inferred from census data from the Australian Bureau of Statistics, and compared to data from NSW, which reports breast cancer outcome figures similar to other Western industrialized nations. The cohort was then analysed with respect to known clinicopathological prognostic features (nodal involvement, grade, size and age) to address whether or not the cohort behaved as would be expected from the demographics. Finally, the representative nature of the cohort was assessed with respect to the frequency of positivity, and prognostic impact of the three key biomarkers in current routine use for breast cancer, ie. ER and PR expression and HER2 amplification. In order to standardise the determination of hormone receptor positivity (as the cohort spans 12 years of accrual with expected differences in assay technique and interpretation over time), the TMAs were reassessed for ER and PR employing standard IHC techniques. In additon HER2 amplification was assessed by the national reference laboratory using FISH.
3.2 PATIENTS AND METHODS
3.2.1 Assembly of the SVCBCOC
Ethics approval was initially sought from the Human Research Ethics Committee of St. Vincent’s Hospital for the construction of the SVCBCOC for the purposes of high throughput molecular analyses in 2000. Ethics approval was granted (Approval
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Number H036/00), and the requirement for consent waived due to the archival nature of the tissue specimens. Patient records were then sourced from the consulting rooms of a single surgeon, Dr. Paul Crea. Each patient was ascribed a unique “Garvan Identification Number” to allow linkage of clinicopathological data, the location of the tissue block, the location of cores pertaining to the patient on the TMAs, and ultimately, the results of the molecular analyses performed in-house. The demographic and clinicopathological data was initially entered onto a Filemaker database but was subsequently transferred to the CanSto database (Appendix 1). Password protection was utilised to preserve patient confidentiality. The databases were searched for patients with infiltrating ductal carcinoma as classified according to the World Health Organization (WHO) schema 22, and representative tissue blocks were requested from the two pathology services utilised in Dr. Crea’s practice, SydPath (St. Vincent’s Hospital, Darlinghurst, NSW, Australia) and Douglass Hanly Moir, North Ryde, NSW, Australia). The tumours were graded using the Nottingham combined histologic grading scheme 316. Non-ductal carcinoma pathologies were excluded to remove the effects of tumour subtypes of inheritantly different prognosis.
The blocks were then reviewed by a pathologist (initially Dr. Andrew Field and subsequently Dr. Sandra O’Toole) to determine if sufficient tumour would be left for further review. A section was then cut from each block for staining with haematoxylin and eosin (H&E) for further review. Each H&E section was then reviewed by one of three pathologists (Dr. Andrew Field, Dr. Ewan Millar, or Dr. Sandra O’Toole) and the area of carcinoma was ringed (with a marking pen). Using the marking as a guide, TMAs were constructed in which between 2 to 6 cores per patient were distributed across 18 arrays (depending on how much tissue was available).
3.2.2 TMA construction
Breast cancer TMAs were constructed by re-locating tissue cores of invasive cancer from conventional histological paraffin blocks into recipient blocks so that tissue from multiple patients could be assessed on the same slide. TMAs were constructed in the same manner as described for the GRPAHPC (Section 2.1.1). A total of 18 arrays, each with approximately 50-100 cores were constructed. Sections (4 μM) were cut and stored at – 80 °C to preserve tissue antigenicity. One section was stained with H&E and the cores were re-evaluated by a pathologist to confirm that the each core was
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representative of the case. The locations of the cores and their associated pathology were documented using an array map and corresponding entries in an electronic database that allows linkage to clinicopathological data (CanSto).
3.2.3 CANSTO
CanSto is a native Macintosh and Windows application that manages a Sybase database of Clinical and Histological information that was specifically designed for the Cancer Research Program by Mr Jim McBride and Dr Gerard Henderson of the Information Technology Department at the Garvan Institute of Medical Research. It allows linkage and retrieval of clinicopathological data derived from manual review of patient medical records and the results of tissue studies performed in the laboratory. The CanSto application allows simultaneous multi-user data-entry of patient information, automatic data auditing, security and reporting. The first release, CanSto v1.0 was released in May 2001. This was a tissue microarray (TMA) managing software. Between November 2003 to April 2005, CanSto v2.x, v3.x and v4.x were released which integrated the clinical information from the lung group, breast group and prostate group. Today, there is a breast specific version of the application (CanSto Breast), which has been developed in conjunction with the author of this thesis to account for some of the unique features of breast cancer disease behaviour (including bilaterality and multiple episodes) and monitoring.
Detailed data has been collated including, but not limited to standard clinicopathological factors such as histological diagnosis, tumour size, tumour grade, extent of regional lymph node involvement and hormone receptor status. In addition, the database allows documentation of surgery dates and extent, follow-up dates, relapse and death dates, treatment provided and the results of investigations such a mammography, and comuted tomography (CT) scans. A representative computer screen “snapshot” is presented in Appendix 1.
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3.2.4 Clinicopathological data review and verification of death records
In order to ensure data quality, all of the medical records of patients in the cohort were re-reviewed by the author of this thesis, a medical oncologist with 6 years experience in the clinical management of breast cancer for accuracy of data entry, and for interpretation of subsequent events as either new primaries or recurrences. This review also allowed the determination of which patients required censoring from the analysis. For survival analyses, patients who had a breast cancer of any type within 10 years of diagnosis of the cancer that was sampled on the TMAs were censored for death or recurrence as these events might potentially be attributed to either malignancy. Patients with another cancer (such as lung cancer or endometrial cancer) were censored for distant recurrence and death unless there was clear evidence (eg: from a biopsy) that the recurrence was due to one malignancy versus another. Patients who were lost to follow-up were also censored from the survival analyses.
3.2.5 ER and PR staining
Nuclear ER expression in the cohort was measured using the TMAs as outlined in the Chapter 2. While several methods of classifying ER staining as either positive or negative exist, a modified H-score approach was adopted as this offered the best approximation of the National Health and Medical Research Council guidelines for determining hormone receptor positivity which state that positivity is defined as greater than 10% of cells staining with weak intensity (1) or greater than 1% of cells staining with moderate or strong intensity (2 and 3) 324. By using a simplified H-score (percentage of cells staining x the predominant intensity) 137 averaged across the cores assessed per patient with a cut-off of greater than 10, an ER positivity rate for the cohort was defined at 68.6%.
Nuclear PR expression in the cohort was measured using the TMAs as outlined in the Chapter 2. The immunohistochemical protocol was identical to that used for ER except that the PR antibody (DAKO) was used at a dilution of 1: 200. Using similar scoring methodology, PR expression was determined to be positive if the simplified H score
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averaged across the cores assessed per patient was greater than 10. This cut-off identified a PR positivity rate for the cohort of 57.1%.
3.2.6 HER2 Amplification
HER2 Fluorescence In Situ Hybridisation (FISH) was performed and evaluated by Dr Adrienne Morey and colleagues in the HER2 FISH Reference Lab at St Vincent’s Hospital, Sydney, according to routine protocols employed for diagnostic cases as described in Chaper 2.
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3.3.1 Patient demographics
3.3.1.1 Cancer Demographics in New South Wales
Australia reports breast cancer incidence rates that are comparable to those of the United States of America (USA), Canada and the United Kingdom (UK) 1. In the most populous state of New South Wales (NSW) in 2005 there were 4070 new cases of breast cancer (99% of these in females) and 877 deaths, making it the most common cancer in women and the most common cause of cancer death in women. The risk of developing breast cancer in NSW females is 1 in 11 to the age of 75 and the median age at diagnosis is 59 years 1.
Between 1999-2003, in NSW the 5-year survival after diagnosis was 88%. During a similar time period 5-year survival rates across Australia as a whole, the USA and UK were 84%, 88% and 78% respectively. NSW females with localised (i.e. lymph node negative) disease had a 5-year survival of 97% in comparison to the USA figure of 98% 1. Thus the frequency and outcome of breast cancer patients in NSW is comparable to the USA.
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3.3.1.2 Demographic data for the St. Vincent’s Hospital Campus
The SVCBCOC is comprised of breast cancers from patients derived from the practice of a single surgical oncologist Dr. Paul Crea. The tumours were surgically removed in either the St. Vincent’s Public Hospital or the St. Vincent’s Private Hospital, which are located in adjacent premises in the inner city suburb of Darlinghurst, Sydney. While both hospitals are administered by the Sisters of Charity Health Services, St Vincent’s Public Hospital also functions as part of the South Eastern Sydney and Illawarra Area Health Service. There are two other large teaching hospitals within a 10 km radius of the St. Vincent’s Campus, Prince of Wales Hospital in Randwick (also part of South Eastern Sydney and Illawarra Area Health Service) and Royal Prince Alfred Hospital (part of the Central Sydney Area Health Service) in Camperdown servicing demographic areas that overlap with those of the St. Vincent’s Campus.
The St. Vincent’s Hospital Campus is located in the Eastern Suburbs of Sydney, in proximity to the local councils of Woollahra, Waverley, and Randwick. Australian patients are free to choose their treating hospital and doctor, and while 33% of patients in the SVCBCOC live in one of these three council areas, 14% live in wealthy suburbs on the North Shore, and 15% are from remote/rural areas several hours from Sydney, with the remainder of the patients in the cohort living in suburbs scattered across Sydney. The demographics of the dominant drawing regions of Woollahra, Waverley and Randwick, in comparison to Sydney as a whole, and NSW are summarised in Table 3.1. In addition, it is notable that in the Woollahra, Waverley and Randwick areas 13.4%, 16.3% and 3.1% of the population report that they are Jewish in comparison to 0.55% of the state as a whole 325.
Data sourced from the NSW Cancer Registry 326 indicates that the age-standardised breast cancer incidence is statistically higher in the Woollahra local council area with trends towards higher and lower incidences in Waverley and Randwick, respectively. Mortality rates are not significantly different between the three local council regions and NSW as a whole (Figure 3.1). In addition, rates of breast cancer screening in the target population of women aged 50 – 69 years are statistically higher in the South East Sydney and Illawarra Area Health Service at 53.1% in comparison to the NSW rate of 50.2% 326(Figure 3.2).
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Table 3.1: Demographics of the New South Wales population in 2001
New South Wales Sydney Randwick Waverley Woollahra Unemployment % (2002) 6.2 5.3 5.7 6.3 3.6 Average taxable income $AUD 41,623 44,881 45,093 54,885 90,327 Recipients of government cash benefit % 10.0 7.5 6.2 3.8 1.4 Females aged 30-69 years % 49.1 49.0 48.7 49.9 51.4
Indigenous population - % of total population 2.1 1.1 1.3 0.4 0.2
Overseas born population - % of total population Total born overseas 23.2 31.1 36.0 36.8 31.5 Born in Oceania and Antarctica 2.4 3.2 3.3 3.9 3.7 Born in North-West Europe 5.9 6.3 7.5 11.1 10.4 Born in Southern and Eastern Europe 4.1 5.4 6.8 8.5 5.4 Born in North Africa and the Middle East 2.0 3.1 1.9 2.0 1.4 Born in South-East Asia 3.1 4.6 6.6 1.9 1.9 Born in North-East Asia 2.7 4.1 5.4 2.3 2.4 Born in Southern and Cental Asia 1.2 1.8 1.2 0.8 0.6 Born in Americas 1.1 1.5 2.1 2.4 2.3 Born in Sub-Saharan Africa 0.7 1.0 1.2 3.9 3.4 Speaks a language other than english at home 18.7 27.3 28.5 19.2 13.0
Education - % of population aged 15 and over Post-graduate Degree 2.2 2.9 4.9 4.8 7.3 Graduate Diploma and Graduate Certificate 1.2 1.3 1.6 2.0 2.2 Bachelor Degree 10.1 12.4 16.9 21.7 26.0 Advanced Diploma or Diploma 6.3 6.9 7.2 8.5 8.5 Certificate 16.4 15.4 12.7 11.7 8.0
Occupation - % of total employed persons Managers and Administrators 9.5 9.0 8.9 11.9 17.4 Professionals 19.1 21.2 27.3 32.2 35.2 Associate Professionals 11.6 11.9 13.2 13.9 15.8 Tradespersons and Related Workers 11.9 11.1 8.8 7.3 3.6 Advanced Clerical and Service Workers 4.2 4.6 4.8 4.7 5.1 Intermediate Clerical and Service Workers 16.5 17.2 17.1 15.1 12.2 Intermediate Production and Transport Workers 7.8 7.3 4.7 2.8 1.2 Elementary Clerical, Sales and Service Workers 9.3 9.0 8.8 7.1 5.5 Labourers and Related Workers 8.0 6.6 4.5 3.3 1.6 Inadequately described or not stated 2.0 2.1 1.9 1.8 2.5
Adapted from the Australian Bureau of Statistics 2007, 2006 census 325
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Figure 3.1: Age-standardised breast cancer incidence and mortality rates per 100,000 persons, by local government area
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Figure 3.2: Biennial screening rate per 100,000 population 2003 to 2005 by health area, females aged 50 – 69 years
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3.3.2 Selection of the cohort
The accrual, selection and processing of the SVCBCOC involved the input of several dedicated individuals within a multidisciplinary team over a 7 year period (Appendix 2). Development and curation of the cohort was, and continues to be an ongoing process undertaken by a number of individuals within the Cancer Research Program, Garvan Institute of Medical Research and the Departments of Surgery and Pathology of the St. Vincent’s Hospital Campus, including the author of this thesis as outlined in Chapter 2. An important facet of the development of the cohort as a representative model of an early breast cancer population relates to the selection of cases for the production of tissue microarrays and the possible introduction of bias within the cohort as a result. In addition, at the outset of utilisation of the cohort for the studies contained in this thesis, it was determined that in order to reduce the effects of particularly poor or particularly favourable prognostic pathological subtypes, only cases of the commonest invasive cancer type, IDC would be evaluated.
The major factors determining case selection were: i. access to FFPE donor blocks from the pathology departments affiliated with St. Vincent’s Public and Private Hospitals i.e.SydPath and Douglass Hanly Moir, respectively ii. medicolegal imperatives mandating that adequate representative tissue blocks remained with the pathology departments for any subsequent review iii. confirmation that the tissue blocks contained IDC iv. confirmation that the tissue blocks were derived from the primary cancer, as opposed to a local recurrence or distant relapse v. confirmation that the patients from whom the breast cancers were taken had not been pre-treated in a neoadjuvant fashion with radiotherapy, endocrine therapy (for example tamoxifen) or chemotherapy.
Figure 3.3 represents the assembly of the cohort. Predominant reasons for the relatively low block yield (331 cases of IDC from an anticipated 736) were that approximately 100 cases were in use by unrelated investigators elsewhere, and the relatively small size of many of the lesions (leaving insufficient material for review by the pathologist should the need arise) prevented sampling.
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Figure 3.3: Flow-chart of case accrual and selection for the development of the SVCBCOC
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3.3.3 Clinicopathological characteristics
The baseline characteristics of the SVCBCOC are tabulated in Table 3.2. The cases date from 1992 until 2004, and the median follow-up of the cohort is 64 months. The median age at diagnosis was 54 years. 59.8% of the tumours were T1 (up to 20 mm in size), 56.7% were lymph node negative and 54.6% of the tumours were grade I/II. 49.3% of the cohort received adjuvant endocrine therapy (mostly tamoxifen), and 38% received adjuvant chemotherapy (mostly a mixture of cyclophosphamide/methotrexate/5-fluorouracil and anthracycline-based regimens). Close to a quarter of the cohort received both treatment modalities. At the time of analysis 23.3% of the cases had experienced a distant recurrence and 17.8% of the cases had died of their disease. The 5-year disease-free, metastasis-free and breast cancer-specific survivals for the whole cohort were 74.7%, 76.8% and 86.0% respectively.
3.3.4 Clinicopathological markers of disease outcome
The cohort was evaluated for the prognostic effects of well-described clinicopathological factors using univariate Cox proportional hazards analysis. Factors assessed were - lymph node status (positive versus negative), grade (grade 3 versus grades 1 and 2), tumour size (size > 20 mm versus < 20 mm), and age > 50. All factors were analysed with respect to recurrence, distant metastasis and breast cancer-related death. The analysis conducted on the cohort as a whole is detailed in Figures 3.4 – 3.7. Consistent with previously published data 327, high tumour grade, large tumour size and lymph node positivity were all strong predictors of disease recurrence, distant metastasis and breast cancer-related death (p<0.002 for all measures). Age >50 (used as a surrogate marker or menopausal status) did not predict outcome.
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Table 3.2: Clinicopathological characteristics of the SVCBCOC
Characteristic Number of patients % of cohort Median Range Length of follow-up (months) 292 64 0 - 152.1
Age (years) 54 24 - 87
Tumour size (mm) 18 0.9 - 80 0 - 10 mm 50/291 17.2 11 - 20 mm 124/291 42.6 21 - 50 mm 106/291 36.4 > 50 mm 11/291 3.8
Tumour grade I 49/291 16.8 II 110/291 37.8 III 132/291 45.4
Lymph node metastasis 0 164/289 56.7 1 - 3 83/289 28.7 4 - 10 28/289 9.7 >10 14/289 4.8
ER +ve (H score > 10)* 192/280 68.6 PR +ve (H score > 10)* 161/282 57.1 HER2 amplified (FISH) 51/273 18.7
Endocrine therapy 144/292 49.3 Chemotherapy 111/292 38.0 Both endocrine 71/292 24.3 and chemotherapy
Patient with recurrences 75/292 25.7 (local or distant) Patients with distant metastases 68/292 23.3 Deaths 67/292 22.9 Breast cancer-related deaths 52/292 17.8
5 year disease-free survival 74.7 5 year metastasis-free survival 76.8 5 years breast cancer- 86.0 specific survival *H score = % of positively staining cells x predominant intensity; Discrepancies between total patient numbers and clinicopathological parameters are the result of missing data, and between total patient numbers and ER, PR and HER2 are the result of loss of cases from the TMAs during processing.
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Figure 3.4: Kaplan-Meier curves for tumour grade
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Figure 3.5: Kaplan-Meier curves for tumour size
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Figure 3.6: Kaplan-Meier curves for lymph node status
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Figure 3.7: Kaplan-Meier curves for patient age
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3.3.5 Molecular markers of disease outcome
ER and PR status was assessed as described in section 3.2.5. ER and PR staining patterns are presented in Figures 3.8 and 3.10, and show a range of expression levels between negative and strongly positive. By using a simplified H-score 137 averaged across the cores assessed per patient with a cut-off of greater than 10, an ER positivity rate for the cohort was defined at 68.6% and PR positivity rate for the cohort was defined at 57.1%
Overall, the concordance between the in-house hormone-receptor assessments and those from the definitive pathology reports (derived from National Association of Testing Authorities-accredited diagnostic laboratories and from more extensive full section, rather than the TMA assessments) was 88% for ER and 83% for PR. For the purposes of the studies presented in this thesis the in-house simplified H-score assessments were used to enable direct comparison between the ER and PR expression in a particular core of tissue with the expression of other immunohistochemical markers in the same cores of tissue.
The prognostic strength of ER and PR expression was then analysed by Kaplan-Meier analysis, using log-rank statistics (Figures 3.9 and 3.11). ER positively predicted improved outcome for the end points of recurrence, distant metastasis and breast cancer-related death (p=0.0008, p=0.0001 and p<0.0001, respectively), as did PR positivity (p<0.0001 for all end-points).
HER2 amplification was measured by FISH on the TMAs at the St. Vincent’s Hospital Laboratory which is a reference laboratory for assessment of HER2 amplification as described in section 3.2.6. Representative images of HER2 FISH amplification and non-amplification are presented in Figure 3.12. Overall, 18.7% of the cohort displayed HER2 amplification. The prognostic strength of HER2 amplification was analysed by Kaplan-Meier analysis using log-rank statistics (Figure 3.13). HER2 amplification was strongly predictive of recurrence, distant metastasis and breast cancer-related death (p=0.0002, p=0.0004 and p<0.0001 respectively).
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Figure 3.8: Patterns of ER expression
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Figure 3.9: Kaplan-Meier curves for ER status
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Figure 3.10: Patterns of PR expression
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Figure 3.11: Kaplan-Meier curves for PR status
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Figure 3.12: Representative images of HER2 non-amplified and amplified breast cancer
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Figure 3.13: Kaplan-Meier curves for HER2 amplification status
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3.3.6 Influence of adjuvant endocrine therapy and adjuvant chemotherapy on clinical outcome
Manual review of the medical records of the patients in the SVCBCOC was undertaken to quantify the number of patients who were treated with adjuvant endocrine therapy and chemotherapy. In total 49.3% of the cohort were treated with endocrine therapy as documented in the surgeon’s record (56.8% of the ER positive subgroup). Notably, 18 patients that were defined as ER and PR negative on their original pathology report (28.6% of this group of patients), and 29 patients that were defined as ER and PR negative on TMA assessment (37% of this group of patients) were treated with some form of endocrine therapy. It is now known that endocrine therapy is of little benefit in hormone receptor negative patients 56 and thus, in order to evaluate the influence of endocrine therapy on patient outcome, these analyses were restricted to those patients demonstrated to be ER positive on TMA assessment. Kaplan-Meier curves for recurrence, distant metastasis and breast cancer-related death are presented in Figure 3.14 and demonstrate that adjuvant endocrine therapy did not predict improved outcome.
In total, 38% of the cohort received adjuvant chemotherapy (predominantly doxorubicin plus cyclophosphamide, or cyclophosphamide plus methotrexate plus 5-fluorouracil). On logrank analyses, chemotherapy treatment was associated with adverse outcome (p=0.0016, p=0.0004 and p=0.0023 for recurrence, distant metastasis and breast cancer-related death respectively) (Figure 3.15).
Among ER positive patients, the use of adjuvant endocrine therapy was significantly associated with large tumour size (p=0.0012), lymph node positivity (p<0.0001) and the use of chemotherapy (p<0.0001) on 2 analyses. In the entire cohort, use of chemotherapy was significantly associated with high tumour grade (p<0.0001), large tumour size (p<0.0001) and lymph node positivity (p<0.0001) on 2 analyses.
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Figure 3.14: Kaplan-Meier curves for endocrine treatment amongst ER positive cases
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Figure 3.15: Kaplan-Meier curves for adjuvant chemotherapy treatment
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3.3.7 Clinicopathological features of the cohort subgroups
The prognostic value of aberrant expression of a number of key cell cycle proteins was evaluated and detailed in Chapters 4 and 5. For the purposes of these studies, analyses were undertaken in the cohort as a whole, and as the principal aims of this thesis pertain to ER positive breast cancer, also in ER positive versus ER negative cases. In addition, “hypothesis-generating” analyses were conducted in ER positive cases that were treated with endocrine therapy and chemotherapy-treated cases. It should be noted that these latter analyses cannot address the question endocrine- and chemo-resistance per se due to the retrospective non-randomised nature of the study, relatively small cohort size and lack of matched controls (i.e. patients selected for adjuvant therapies are an inherently higher risk cohort of patients). The clinicopathological features of the study cohort and subcohorts are detailed in Table 3.3.
In comparison to the ER negative subgroup, the ER positive subgroup tended to contain less high-grade tumours (30.2% vs 77.3%), fewer tumours greater than 20 mm in size (31.3% vs 54.5%), and less HER2 amplification (13.4% vs 30.1%), although the rates of lymph node positivity were similar (43.2% vs 44.3%). Consistent with the associations described in Section 3.3.6, the two adjuvant therapy subgroups contained higher numbers of lymph node positive tumours, as well as more tumours greater than 20 mm in size. In addition, 64% chemotherapy-treated patients vs 45.4% of the whole cohort were grade III.
3.3.8 Univariate Cox proportional hazards analysis of the clinicopathological factors in the cohort and its subgroups
In order to define baseline prognostic factors for subsequent multivariate analyses, univariate Cox proportional hazards analyses were undertaken for each of the standard prognostic factors in each of the cohort subgroups (Tables 3.4 – 3.8). In addition, the predictive value of endocrine therapy was assessed, although endocrine therapy and chemotherapy were not analysed in any of the subsequent multivariate models. The rationale for excluding the two treatment variables was that endocrine therapy was not predictive of outcome among ER positive patients on Kaplan-Meier analysis (i.e. in this
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cohort endocrine therapy was not predictive of outcome when used in the correct patient population), and chemotherapy was predictive of worse outcome on Kaplan- Meier analysis of the whole cohort as a result of its close relationship with other clinical variables such as grade (i.e. this variable was likely to be representative of a poor prognostic group rather than a predictor of better outcome in this small patient group). Grade, size, lymph node positivity, hormone receptor positivity and HER2 amplification were predictive in the cohort as a whole and among chemotherapy-treated patients. Grade, PR expression and HER2 amplification remained consistently predictive amongst ER positive cases, and HER2 amplification was a consistent predictor in ER positive patients treated with endocrine therapy. Only tumour size and lymph node status were predictive in ER negative cases.
Table 3.3: Clinicopathological characteristics of the SVCBCOC and its subgroups
Variable Whole cohort ER +ve ER -ve ER +ve and Chemotherapy endocrine (n = 292) (n = 192) (n = 88) therapy (n = 111) (n = 109)
Grade I 49/291 (16.8%) 44/192 (22.9%) 5/88 (5.7%) 19/109 (17.4%) 8 (7.3%) Grade II 110/291 (37.8%) 90/192 (46.9%) 15/88 (17.0%) 54/109 (49.5%) 32 (28.8%) Grade III 132/291 (45.4%) 58/192 (30.2%) 68/88 (77.3%) 36/109 (33.0%) 71/111 (64.0%)
Size > 20 mm 117/292 (40.2%) 60/192 (31.3%) 48/88 (54.5%) 48/109 (44%) 62/111 (55.9%)
Lymph node +ve 125/289 (43.2%) 82/190 (43.2%) 39/88 (44.3%) 70/108 (64.8%) 78/111 (70.3%)
Age > 50 184/292 (63.0%) 119/192 (62.0%) 59/88 (67.0%) 72/109 (66.1%) 52/111 (46.8%)
ER +ve 192/280 (68.6%) 192/192 (100%) 0/88 (0%) 109/109 (100%) 65/107 (60.7%) PR +ve 161/282 (57.1%) 150/192 (78.1%) 10/88 (11.4%) 83/109 (76.1%) 54/108 (50%)
HER2 amplified 51/273 (18.7%) 25/187 (13.4%) 25/83 (30.1%) 12/105 (11.4%) 25/106 (23.6%)
111/111 Chemotherapy 111/292 (38.0%) 65/192 (33.9%) 42/88 (47.7%) 54/109 (49.5%) (100%)
Endocrine therapy 144/292 (49.3%) 109/192 (56.5%) 31/88 (35.2%) 109/109 (100%) 71/111 (64%)
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Table 3.4: Univariate Cox proportional hazards analysis - entire cohort (n = 292)
RECURRENCE p Value Hazard Ratio 95% CI Clinicopathological variables Grade III <0.0001 3.32 1.93 - 5.10 Size > 20 mm 0.0013 2.11 1.34 - 3.33 LN +ve <0.0001 3.22 1.98 - 5.23 Age > 50 NS 1.15 0.72 - 1.85 ER +ve 0.0011 0.46 0.29 - 0.73 PR +ve <0.0001 0.27 0.17 - 0.45 HER2 amplification 0.0004 2.46 1.50 - 4.04
Endocrine therapy NS 1.08 0.69 - 1.70 Chemotherapy 0.0020 2.04 1.30 - 3.22
DISTANT METASTASIS p Value Hazard Ratio 95% CI Clinicopathological variables Grade III <0.0001 3.10 1.87 - 5.17 Size > 20 mm <0.0001 2.73 1.68 - 4.44 LN +ve <0.0001 3.99 2.35 - 6.77 Age > 50 NS 1.27 0.77 - 2.10 ER +ve 0.0002 0.40 0.24 - 0.64 PR +ve <0.0001 0.24 0.14 - 0.41 HER2 amplification 0.0007 2.46 1.46 - 4.14
Endocrine therapy NS 1.22 0.75 - 1.96 Chemotherapy 0.0005 2.33 1.45 - 3.76
BREAST CANCER-RELATED DEATH p Value Hazard Ratio 95% CI Clinicopathological variables Grade III <0.0001 3.52 1.93 - 6.42 Size > 20 mm 0.0015 2.47 1.42 - 4.30 LN +ve <0.0001 3.69 2.03 - 6.73 Age > 50 NS 1.43 0.80 - 2.55 ER +ve <0.0001 0.30 0.17 - 0.52 PR +ve <0.0001 0.17 0.09 - 0.33 HER2 amplification <0.0001 3.49 1.96 - 6.28
Endocrine therapy NS 1.11 0.64 - 1.91 Chemotherapy 0.0030 2.29 1.33 - 3.96
CI - confidence interval.
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Table 3.5: Univariate Cox proportional hazards analysis - ER positive subgroup (n = 192)
RECURRENCE p Value Hazard Ratio 95% CI Clinicopathological variables Grade III <0.0001 3.48 1.88 - 6.46 Size > 20 mm NS 1.42 0.76 - 2.64 LN +ve 0.016 2.16 1.15 - 4.04 Age > 50 NS 0.78 0.42 - 1.45 PR +ve 0.0002 0.31 0.17 - 0.58 HER2 amplification 0.0007 3.22 1.64 - 6.33
Endocrine therapy NS 0.97 0.53 - 1.81 Chemotherapy 0.0198 2.07 1.12 - 3.83
DISTANT METASTASIS p Value Hazard Ratio 95% CI Clinicopathological variables Grade III 0.0006 3.21 1.65 - 6.25 Size > 20 mm 0.0481 1.95 1.01 - 3.79 LN +ve 0.0053 2.70 1.34 - 5.43 Age > 50 NS 0.89 0.46 - 1.74 PR +ve 0.0003 0.29 0.15 - 0.57 HER2 amplification 0.0035 2.99 1.43 - 6.24
Endocrine therapy NS 1.17 0.59 - 2.30 Chemotherapy 0.0032 2.72 1.40 - 5.30
BREAST CANCER-RELATED DEATH p Value Hazard Ratio 95% CI Clinicopathological variables Grade III 0.0050 3.27 1.43 - 7.46 Size > 20 mm NS 1.52 0.66 - 3.49 LN +ve NS 1.76 0.77 - 4.01 Age > 50 NS 1.10 0.47 - 2.54 PR +ve 0.0003 0.22 0.10 - 0.50 HER2 amplification <0.0001 6.19 2.58 - 14.83
Endocrine therapy NS 0.89 0.39 - 2.02 Chemotherapy NS 2.11 0.93 - 4.80
CI - confidence interval.
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Table 3.6: Univariate Cox proportional hazards analysis - ER negative subgroup (n = 88)
RECURRENCE p Value Hazard Ratio 95% CI Clinicopathological variables Grade III NS 1.68 0.65 - 4.37 Size > 20 mm 0.0251 2.36 1.11 - 5.00 LN +ve <0.0001 5.34 2.39 - 11.96 Age > 50 NS 2.05 0.89 - 4.74 PR +ve - - - HER2 amplification NS 1.34 0.64 - 2.77
Endocrine therapy NS 1.50 0.75 - 3.02 Chemotherapy NS 1.50 0.74 - 3.02
DISTANT METASTASIS p Value Hazard Ratio 95% CI Clinicopathological variables Grade III NS 1.61 0.62 - 4.20 Size > 20 mm 0.015 2.63 1.20 - 5.73 LN +ve <0.0001 6.27 2.69 - 14.64 Age > 50 NS 1.95 0.84 - 4.52 PR +ve - - - HER2 amplification NS 1.59 0.73 - 3.49
Endocrine therapy NS 1.62 0.80 - 3.29 Chemotherapy NS 1.43 0.70 - 2.92
BREAST CANCER-RELATED DEATH p Value Hazard Ratio 95% CI Clinicopathological variables Grade III NS 1.80 0.61 - 5.25 Size > 20 mm 0.0454 2.33 1.02 - 5.34 LN +ve <0.0001 9.72 3.34 - 28.28 Age > 50 NS 1.77 0.75 - 4.20 PR +ve - - - HER2 amplification NS 1.59 0.73 - 3.49
Endocrine therapy NS 1.82 0.84 - 3.90 Chemotherapy NS 1.89 0.87 - 4.11
CI - confidence interval; - = monotone likelihood (cannot analyse).
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Table 3.7: Univariate Cox proportional hazards analysis - ER positive + endocrine therapy subgroup (n = 109)
RECURRENCE p Value Hazard Ratio 95% CI Clinicopathological variables Grade III 0.0036 3.48 1.50 - 8.04 Size > 20 mm NS 1.99 0.87 - 4.55 LN +ve NS 1.45 0.57 - 3.67 Age > 50 NS 0.64 0.28 - 1.46 PR +ve NS 0.52 0.23 - 1.22 HER2 amplification 0.0046 3.88 1.52 - 9.90
Chemotherapy NS 1.52 0.65 - 3.53
DISTANT METASTASIS p Value Hazard Ratio 95% CI Clinicopathological variables Grade III 0.0163 2.87 1.22 - 5.85 Size > 20 mm 0.0463 2.45 1.02 - 5.92 LN +ve NS 1.65 0.61 - 4.53 Age > 50 NS 0.62 0.26 - 1.46 PR +ve NS 0.52 0.21 - 1.25 HER2 amplification 0.0299 3.06 1.12 - 8.42
Chemotherapy NS 1.69 0.70 - 4.10
BREAST CANCER-RELATED DEATH p Value Hazard Ratio 95% CI Clinicopathological variables Grade III NS 2.87 0.91 - 9.06 Size > 20 mm NS 2.90 0.87 - 9.68 LN +ve NS 0.84 0.25 - 2.81 Age > 50 NS 0.61 0.19 - 1.89 PR +ve NS 0.65 0.20 - 2.18 HER2 amplification 0.0002 10.64 3.04 - 37.25
Chemotherapy NS 2.18 0.66 - 7.27
CI - confidence interval.
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Table 3.8: Univariate Cox proportional hazards analysis - chemotherapy treated subgroup (n = 111)
RECURRENCE p Value Hazard Ratio 95% CI Clinicopathological variables Grade III 0.0134 2.67 1.23 - 5.82 Size > 20 mm 0.0197 2.25 1.14 - 4.46 LN +ve 0.0046 5.50 1.69 - 17.86 Age > 50 NS 1.87 0.99 - 3.54 ER +ve NS 0.57 0.30 - 1.09 PR +ve 0.0001 0.23 0.11 - 0.49 HER2 amplification 0.0441 2.04 1.02 - 4.08
Endocrine therapy 0.0462 0.62 0.28 - 0.99
DISTANT METASTASIS p Value Hazard Ratio 95% CI Clinicopathological variables Grade III 0.0238 2.47 1.13 - 5.4 Size > 20 mm 0.0084 2.66 1.29 - 5.51 LN +ve 0.0042 8.01 1.93 - 33.31 Age > 50 NS 1.88 0.98 - 3.62 ER +ve NS 0.58 0.30 - 1.11 PR +ve 0.0003 0.25 0.12 - 0.53 HER2 amplification 0.0050 3.09 1.41 - 6.81
Endocrine therapy NS 0.53 0.28 - 1.02
BREAST CANCER-RELATED DEATH p Value Hazard Ratio 95% CI Clinicopathological variables Grade III 0.0201 3.16 1.20 - 8.33 Size > 20 mm NS 2.19 0.96 - 5.00 LN +ve 0.016 11.56 1.57 - 85.13 Age > 50 NS 1.80 0.85 - 3.80 ER +ve 0.0059 0.34 0.16 - 0.73 PR +ve 0.0004 0.18 0.07 - 0.46 HER2 amplification 0.0050 3.09 1.41 - 6.81
Endocrine therapy NS 0.55 0.26 - 1.18
CI - confidence interval.
Baseline multivariate analyses in which the least contributory non-significant variables were sequentially removed from the model (“backwards modelling”) are detailed in Appendix 3. It is notable that ER and grade lost prognostic significance on multivariate analyses of the entire cohort and in the ER positive subgroup (for the outcomes of distant metastasis and breast cancer-related death). The superiority of PR over ER has been observed elsewhere in early breast cancer cohorts in models that included HER2 328-331. Unfortunately, many studies of pre-trastuzumab early breast cancer cohorts fail to model ER, PR, HER2, lymph node status and grade together 331. In a study of 972 patients (94% of which were ER positive) treated with endocrine therapy, multivariate analysis was performed that included PR expression, Her2 expression, nodal status, tumour size, Ki-67 and tumour grade. The variables that retained prognostic significance were nodal status, lack of PR expression, and tumour size (with Her2
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showing a trend to significance at p=0.05), with grade and Ki-67 falling out of the model 332. Such findings are consistent with the data from the multivariate analyses in this thesis.
3.3.9 Molecular phenotyping of the SVCBCOC
The work of Perou and colleagues using gene expression profiling and hierarchical clustering, has defined a number of molecular phenotypic subtypes of breast cancer based on their similarities to known breast tissue cell types, namely the luminal epithelial-like (which is further stratified in luminal-A and luminal-B types), Her2 overexpressing, basal epithelial-like and normal breast-like (discussed in further detail in Chapter 1). These phenotypic subtypes have been validated by independent studies and their definition is now informing clinical practice, particularly in relation to the identification of the basal epithelial-like subgroup, a group with particularly poor prognosis and lack of specific targeted therapy 34. The Perou molecular phenotypes were originally defined using gene expression profiling, a technique that is not routinely available in community diagnostic pathology laboratories. However, immunohistochemical surrogates of these molecular phenotypes have been developed to approximate the subgroups defined by gene expression profiling 333,334, using a combination of ER, PR, Her1/EGFR, Her2 and CK5/6 expression (Table 3.9). As part of her doctoral thesis, my colleague Dr. Sandra O’Toole developed a modified version of this stratification system based on ER, PR and CK5/6 expression, and HER2 amplification (as FISH rather than IHC is the current standard in Australia in defining HER2 positivity for therapeutic purposes in the adjuvant setting). Her1 was omitted from the modified stratification system as this marker is not currently performed routinely in Australian diagnostic laboratories, unlike CK5/6.
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Table 3.9: Surrogate signatures of the Perou molecular phenotypes of breast cancer
Penotypic subtype Carey et al 333 Livasy et al 334 Garvan modification
Luminal A ER &/or PR positive, ER positive, ER &/or PR positive, Her2 (IHC)* negative Her2 (IHC)** negative HER2 non-amplified
Luminal B ER &/or PR positive. ER positive. ER &/or PR positive. Her2 (IHC)* positive Her2 (IHC)** positive HER2 amplified
Her2 ER and PR negative, ER negative, ER and PR negative, Her2 (IHC)* positive Her2 (IHC)** positive HER2 amplified
Basal ER, PR and Her2 (IHC)* ER and Her2 (IHC)** ER and PR negative negative, with any negative, with any and HER2 non-amplified expression of CK5/6 expression of CK5/6 with any expression &/or Her1 (IHC) &/or Her1 (IHC) of CK5/6
Unclassified Negative for all above Negative for all above Negative for all above markers markers markers
* - membaneous or membraneous plus cytoplasmic staining with weak or greater intensity in > 10% of tumour cells; ** - membaneous or membraneous plus cytoplasmic staining with 3+ intensity in > 10% of tumour cells
Dr. O’Toole’s modified stratification system was the most predictive of poor outcome among the “Basal” subgroup in particular, and thus was adopted as the preferred scheme for defining the Perou molecular phenotypes in our cohort (Table 3.10). The clinicopathological characteristics of these subgroups are presented in (Table 3.11).
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Table 3.10: Comparison of the prognostic value of the Carey, Livasy and Garvan surrogate signatures
Penotypic subtype Carey et al 333 Livasy et al 334 Garvan modification
Luminal A p<0.0001 p<0.0001 p=0.001 HR 0.2 HR 0.2 HR 0.1 95% CI 0.1 - 0.4 95% CI 0.1 - 0.4 95% CI 0.1 - 0.3
Luminal B p=0.02 p=0.02 p=0.02 HR 2.6 HR 2.6 HR 2.4 95% CI 1.2 - 5.9 95% CI 1.2 - 5.9 95% CI 1.2 - 5.0
Her2 p<0.0001 p<0.0001 p<0.0001 HR 4.3 HR 4.3 HR 3.7 95% CI 2.1 - 8.9 95% CI 2.1 - 8.9 95% CI 1.8 - 7.4
Basal NS NS p=0.006 HR 2.6 95% CI 1.3 - 5.2
Unclassified p=0.03 p=0.03 p=0.02 HR 2.3 HR 2.3 HR 2.5 95% CI 1.1 - 4.9 95% CI 1.1 - 4.9 95% CI 1.1 - 5.6
HR - hazard ratio; CI - confidence interval; Analyses are Cox proportional hazards analyses of dichotimised variables i.e. Luminal A vs non-Luminal A etc.
Table 3.11: Clinicopathological characteristics of the Perou molecular phenotypes Luminal A Luminal B HER2 Basal Unclassified Variable (n = 171) (n = 27) (n = 24) (n = 30) (n = 19)
Grade I 44/171 (25.7%) 0 (0%) 0/24 (0%) 0/30 (0%) 1/19 (5.3%) Grade II 85/171 (49.7%) 6 (22.2%) 3/24 (12.5%) 0/30 (0%) 9/19 (47.4%) 21/24 30/30 Grade III 42/171 (24.6%) 21/27 (77.8%) (87.5%) (100%) 9/19 (47.4%)
13/24 18/30 Size > 20 mm 51/171 (29.8%) 17/27 (63.0%) (54.2%) (60%) 11/19 (57.9%)
Lymph node 11/24 13/30 +ve 74/169 (43.8%) 12/27 (44.4%) (45.8%) (43.3%) 9/19 (47.4%)
17/24 20/30 Age > 50 104/171 (60.8%) 14/27 (51.9%) (70.8%) (66.7%) 15/19 (78.9%)
ER +ve 162/171 (94.7%) 25/26 (96.2%) 0/24 (0%) 0/30 (0%) 0/19 (0%) PR +ve 137/171 (80.1%) 20/27 (74.1%) 0/24 (0%) 0/30 (0%) 0/19 (0%)
HER2 amplified 0/171 (0%) 27/27 (100%) 24/24 (100%) 0/30 (0%) 0/19 (0%)
17/30 Chemotherapy 59/171 (34.5%) 12/27 (44.4%) 13/24(54.2%) (56.7%) 5/19 (26.3%)
Endocrine 10/24 therapy 95/171 (55.6%) 13/27 (48.1%) (41.7%) 9/30 (30%) 9/19 (47.4%)
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3.4 DISCUSSION
3.4.1 The role of human tissue cohorts in biomarker evaluation
While in epidemiological terms adjuvant therapy for breast cancer has resulted in significant survival gains for breast cancer patients, at an individual level the predicted benefit is modest 56. Consequently, the prediction of which patients will derive benefit from current therapeutic strategies, as well as the validation of new discoveries in breast cancer biology and new therapeutic targets in particular, remains a core concern of breast cancer translational research.
The advent of molecular profiling of human cancer using gene expression microarray technology has facilitated the definition of new subclasses of breast cancer based on molecular phenotype 30,32, as well as “signatures” of poor outcome 39,40. Such multi- biomarker signatures are currently under prospective evaluation in clinical trials such as the MINDACT trial 335 although these studies have been slow to accrue patients as a consequence of technical factors related to the acquisition of fresh-frozen tissue for the required RNA studies.
Gene expression profiling technology has generated an explosion of potential prognostic and predictive markers for further study. However, there are a number of caveats in using multi-gene expression profiles in isolation to derive single-marker predictors of outcome or treatment response, and by extension, new therapeutic targets. Due to the high number of genes under simultaneous analysis there is a high false discovery rate, and significant heterogeneity in gene lists generated by different studies. In the absence of labour-intensive strategies such as laser-capture microdissection to select only tumour for profiling, there also exists the potential for significant contamination from stromal tissue, a factor that has been shown to significantly influence the accuracy of gene expression profiles 336. Thus it is clear that ultimately any putative markers of breast cancer outcome must be subject to further validation.
Historically, breast cancer cell lines have been the backbone of molecular studies investigating the biology of breast cancer. In general, breast cancer cell lines are seen
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as reasonable models in which to study breast cancer biology 337 and remain a major tool for dissecting the biological effects of dysregulated gene expression. Breast cancer cell lines have also been utilised in cDNA microarray studies to add further precision to the molecular profiling of breast cancer under various experimental conditions. They have the advantage of feasibility of RNA extraction for microarray studies, and are readily amenable to analysis of phenotype, protein and gene expression studies using conventional molecular techniques. Nonetheless, cell line studies should be viewed with the following reservations. The majority of available cell lines are derived from metastatic tumours (rather than primary tumours), with studies involving three cell lines in particular (MCF-7, T-47D and MDA-MB-231) accounting for approximately two-thirds of published abstracts on Medline 337. Many available breast cancer cell lines are ER negative in contrast to the ER positive predominance in tumour cohorts. Analysis of several different MCF-7 cell lines indicates variable sensitivity to oestrogens, anti- oestrogens and thymidylate synthetase inhibitors consistent with the acquisition of genetic changes over time. Importantly, studies using breast cancer cell lines do not take into account the multiple and complex interactions between breast cancer cells and their stromal milieu.
Mouse models represent attractive systems for the validation of biological effects of altered biomarker expression on tumourigenesis. However, mouse mammary glands differ from human breast tissue in several respects including genetic, morphological and architectural. Again the stromal environment is different from that seen in the human breast due to a relative lack of fibrous tissue. In addition, mouse mammary tumours are generally not hormone dependent, unlike the situation in human breast cancer 338.
It is therefore necessary that potential biomarkers identified through the discovery technologies described above are validated in representative patient cohorts through the assessment of tumour samples and linkage to high quality clinicopathological data. The standard method of histopathological analysis of tissue for many decades is sectioning and staining of paraffin-embedded tissue. Strategies to utilise tissues processed in this manner therefore allow the integration of translational research and standard diagnostic pathology services and in particular, tissues archived over many years.
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3.4.2 Types of human tissue cohorts
Biomarker validation studies may be broadly viewed in 3 different contexts. For the purposes of clinical studies it is generally held that an ideal design is one that is prospective. Increasingly, biomarker studies are being incorporated as adjuncts to prospective clinical trials, thus facilitating links between good-quality clinical data collection and prospectively optimised tissue handling strategies, e.g. tissue fixation methods, the collection of fresh frozen tissue for RNA extraction, and matched blood samples for serum biomarker analyses (although logistics and cost may limit the extent of these studies). In practical terms there are a number of limitations to prospective breast cancer tissue collection strategies. A corollary of the improvement in breast cancer outcomes in recent years is that in order to generate enough events to detect differences in outcome between treatment groups, large numbers of patients (several hundred to thousands) are now required in many adjuvant clinical trials, and accrual and follow-up times are lengthy (several years). Such clinical trials are generally run by large co-operative groups, with centralised pathology review and biomarker determination. Prospective biomarker studies can also be integrated with therapeutic trials in metastatic disease, thus reducing the number of patients required and the duration of the study. However, such patients have generally been pretreated with chemotherapy and/or endocrine therapy prior to enrolment in the trial with consequent potential alterations in the gene expression patterns of their tumours. Re-biopsy of metastases (i.e. an otherwise unnecessary invasive intervention for the purposes of the study, often in sites such as the lung or liver) is ethically debatable and likely to limit accrual. Accrual to such studies may also be compromised by the presence of other competing studies.
An alternative to prospective adjuvant studies in early breast cancer, in which response to treatment is measured by disease relapse and death, is the use of neoadjuvant treatment with chemotherapy or endocrine therapy which provides a unique opportunity to measure the relationship between gene expression and response to systemic therapy over a relatively short period of time. In such studies, the cancer is biopsied prior to systemic therapy. Following systemic therapy (over weeks or months) the cancer is surgically removed at which time the cancer can be assessed for gross measures of response (tumour shrinkage or pathological complete response), and more precise changes in cell proliferation, apoptosis, and gene expression 339. The
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advantages to this study design are that the cancer is readily accessible for biopsy and the study duration is shorter when using pathological changes as endpoints. Nonetheless it should be noted that the markers measured are often merely “surrogate markers” of response and survival. Further limitations to this type of study accrual are that in mammographically screened populations, large tumours that require downstaging prior to surgery are relatively uncommon, and while there are some data to support neoadjuvant therapy as at least equivalent to adjuvant therapy 340, the use of neoadjuvant treatment for the purposes of the biomarker studies may involve a significant deviation from usual practice thereby limiting the acceptance of the study by both patients and clinicians. Inclusion of recurrence and death as endpoints would protract the study duration in a similar manner to adjuvant trials.
In practical terms, an effective alternative strategy for the use of human breast cancer tissue to validate biomarkers is within the context of retrospective analyses of archival tissue. This method takes advantage of the legal requirement of pathology laboratories to archive their pathological specimens after review. By accruing patients previously treated over a number of years, collection of archival tissue and associated clinical follow-up data (through medical record review) can be achieved over a relatively short period of time. In addition, as many antigens are preserved by FFPE processing over a number of years 341 combining a retrospective study model with high throughput tissue microarray technology allows rapid, centralised assessment of a number of biomarkers by immunohistochemistry, while the emergence of technologies to extract DNA and RNA from such archival tissue opens up new avenues for study. The limitations to this type of study are the dependence on the quality of tissue archiving, record keeping and data management, as well as potential evolution in tissue handling techniques, surgery, radiotherapy and systemic adjuvant therapy over the duration of the study with consequent introduction of bias.
Despite its limitations, the use of retrospective cohorts of archival tissue with high quality follow-up data has provided an important resource for a number of important studies into the biology of breast cancer 342,343. Therefore, with ethical approval, for the purposes of the present study the assembly of a retrospective cohort of archival tissue was undertaken. Subsequently analyses were undertaken to evaluate whether the cohort was a representative reflection of breast cancer in a Western population with respect to demographics, and standard clinicopathological predictors of outcome.
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3.4.3 The SVCBCOC as a representation of the population of breast cancer patients in NSW
NSW cancer incidence and outcome data are similar to those for the rest of Australia, and other first world nations 1. In order to evaluate whether the SVCBCOC is a demographically “typical” reflection of the population of NSW, the socio-economic and ethnic profile was inferred from postcode analyses in relation to the most recent census data from the Australian Bureau of Statistics.
One third of the cohort resides within the local council areas of Woollahra, Waverley or Randwick where the population displays relatively high levels of affluence and education, with a further 14% of the cohort derived from wealthy suburbs on the North Shore of Sydney. In particular, the differences in demographics relative to NSW as a whole are most notable in the Woollahra Municipal Council Area where the average taxable income is more than double the state average, the rate of receipt of government cash benefits is less than a seventh, and the rate of university education is more than doubled.
Nearly a quarter of the NSW population are migrants, originating predominantly in North-West Europe (5.9%), Asia (6.9%), Southern and Eastern Europe (4.1%) and more recently North Africa and the Middle East (2.0%). While the local council areas of Woollahra, Waverley and Randwick are similarly multicultural the ethnicity of the inhabitants does differ from the rest of the state. Rates of origin in North-West, and Southern and Eastern Europe are higher, ranging from 7.4 – 11.1% and 5.4 – 8.5% respectively. Woollahra and Waverley local council areas have fewer Asian migrants (4.9% and 5% respectively) with Asian migrants comprising 13.2% of the population in Randwick, although this region is also serviced by the Prince of Wales Hospital. Strikingly, 3.4% and 3.9% of the Woollahra and Waverley areas were born in Sub- Saharan Africa (i.e. Caucasian South Africans), over 5 times the percentage reported for the state.
The eastern suburbs of Sydney, particularly Woollahra and Waverley, are home to one of the major Jewish communities in Sydney, many individuals migrating from Europe between 1933 and 1961, and more recently from the former Soviet Union and South Africa. As a result, the cohort is likely to contain a higher percentage of patients of
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Jewish ancestry than would be expected in NSW as a whole, and consequently may have higher rates of associated familial breast cancer mutations for example BRCA1, although this was not specifically evaluated in the cohort.
Thus, the patient population for the SVCBCOC is drawn from a demographic region that displays high affluence, high levels of education, high European and South African migration and a high rate of Jewish ancestry relative to the remainder of NSW. While the percentage of females aged 30-69 (around 50% of the female population) is comparable between NSW and the drawing area of the St. Vincent’s Campus, such a demographic profile is likely to have implications for both breast cancer incidence and mortality. In particular, affluent Caucasian societies display higher breast cancer incidence rates due to a mixture of genetic factors and socio-reproductive factors such as obesity and late childbearing. Conversely, it would be expected that mortality from breast cancer would be relatively reduced in the cohort (albeit with the caveat that some of the inherited cancers may be associated with poor outcome for biological reasons), as it is known that affluence and education are associated with superior health outcomes as a consequence of improved access to health care. The uptake of mammographic screening in the local Area Health Service is higher than average for the state and therefore it is possible that the breast cancer population of the area is weighted towards smaller sized lesions.
3.4.4 The SVCBCOC as a representation of similar published early breast cancer cohorts
The process of assembly of the SVCBCOC reveals that under 50% of patients from the practice of Dr. Paul Crea with IDC contributed to the final cohort composition. This relatively low case yield resulted from the necessity to leave adequate tissue for potential review with the original diagnostic pathologists, (as well as the loss of about 100 potential cases to other unrelated research projects). As a consequence, small cancers tended to be excluded from the TMA cohort. In addition, small tumours from which short donor cores of tissue are derived are potentially more likely to be “cut-out “ as the TMAs are serially sectioned for study. Therefore there is a potential towards a preponderance of relatively larger lesions in the study cohort in comparison to the entire surgical load of the surgeon affiliated with the study. Although published series (Table 3.12) do not report the relative numbers of very small tumours (i.e.sub-
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centimetre), the relative percentage of larger tumours 343,344 is similar to that observed in the SVCBCOC. The SVCBCOC also has a tendency towards a greater percentage of grade III cancers. Indeed the percentage of high grade cancers is nearly twice that seen a recent Spanish mammography series that evaluated only IDC, and a descriptive French series in which over 80% of cases were IDC 344,345. Neither of these studies required the collection and processing of tissue. A recent Australian series composed of over 90% IDC in which whole tissue sections were evaluated for ER, PR and Her2 reported less high-grade tumours but also less small tumours than reported here for the SVCBCOC 346. Importantly, the SVCBCOC clinicopathological characteristics strongly resemble those of a very large TMA series from Nottingham in relation to tumour grade, size, lymph node status and ER positivity despite the latter series including a large proportion of non-IDC cases, and in particular, 11% invasive lobular carcinomas. This Nottingham cohort reported superior overall survival figures to the other cohorts reviewed despite higher numbers of grade III tumours 343. Possible explanations for the superior outcomes observed are the slightly lower rate lymph node positivity, and the fact that the institution at which this study was performed is a centre of excellence, with consequently better treatment and overall management.
Centralised re-testing of ER, PR and HER2 status revealed positivity rates that are consistent with the known prevalence. On univariate analysis, the known adverse prognosticators of breast cancer outcome (regional lymph node metastasis, high tumour grade, larger tumour size, hormone receptor negativity and HER2 positivity) behaved as expected, again indicating that the cohort is likely to be a representative one 22.
Thus, with the caveats outlined above, the SVCBCOC displays a distribution of clinicopathological features that is generally consistent with other published cohorts (Table 3.12). One important reservation with these data lies in the reported rates of adjuvant endocrine and chemotherapies, which were quite different between studies. In particular, relatively fewer patients in the SVCBCOC were documented to have had endocrine therapy in comparison to the French 344 and British 343 studies and thus data derived from analyses of treatment subgroups should be viewed as hypothesis- generating only.
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Table 3.12: Composition of the SVCBCOC in relation to other contemporaneous breast cancer cohorts
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3.4.5 Molecular phenotyping of the SVCBCOC
Using a combination of ER, PR, and CK5/6 expression and HER2 amplification, a FFPE surrogate of the Perou molecular classification was developed that allowed the greatest discrimination between the outcome of the “Basal” tumours and non-basal tumours. The Garvan modification of the published surrogates of Carey et al 333 and Livasy et al 334 was simple to perform, and utilised tests in routine Australian diagnostic practice. This modified Perou classification system was later employed in comparisons of cell cycle marker expression (Chapters 4 and 5).
3.5 SUMMARY
Thus, with caveats discussed above, in general the study cohort is representative of breast cancer populations in NSW, and reports outcomes that are similar to those of other first world nations. The distribution of clinicopathological factors is generally similar to other reported series, and in particular to another TMA series from a high- profile group in Nottingham. Finally the known clinicopathological breast cancer prognostic factors behave as expected in univariate and multivariate analyses. Subsequent biomarker analyses using the SVCBCOC should therefore be evaluated in this context.
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CHAPTER 4: RELATIONSHIP BETWEEN EXPRESSION OF CELL CYCLE PROTEINS AND PATIENT OUTCOME
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4.1 INTRODUCTION
Disordered cell proliferation is one of the hallmarks of cancer 113. Not only do mitotic counts constitute central components in the conventional determination of pathological tumour grade 316, proliferation markers are key components of the gene expression signatures derived from microarray studies, as well as evolving prognostic signatures such as the 21-gene prognosis (ie: Oncotype DX) score 44.
In normal cells, changes in the expression or activity of cell cycle proteins regulating the G1-S phase transition such as cyclins D1 and E, and the cdk inhibitors p21WAF1/Cip1 and p27Kip1 are central to the mitogenic response. This observation has led to the hypothesis that altered expression of these proteins may be important in the dysregulated proliferation observed in cancer.
Several studies in human breast cancer tissue series have been undertaken in which WAF1/Cip1 Kip1 the expression of the G1-S cyclins and the inhibitors cdk p21 and p27 have been evaluated by immunohistochemistry. These studies have often presented apparently contradictory results (summarised in Chapter 1 in table format), and thus the role of these mediators as prognostic and predictive factors for breast cancer outcome remains the subject of debate.
Overexpression of cyclin D1 at the mRNA and protein levels has been associated with both adverse and improved outcomes from breast cancer 120,130,132,135,138,141,144-146. A possible explanation for the lack of consistency in these data stems from the association with ER positivity, a known good prognostic factor. In studies in which ER status is removed from the equation through subgroup analyses, cyclin D1 expression has been associated with poor outcome 135,141. Futhermore, there are recent data suggesting a different outcome in CCND1 amplified versus cyclin D1 overexpressing tumours, despite the significant overlap between these two groups 121.
Cyclin E expression has generally been associated with poor outcome from breast cancer 347, although a recent study has been contradictory in this regard 165. In addition, the role of low molecular weight (LMW) isoforms of cyclin E as mediators of tumorigenesis, therapeutic resistance and poor outcome from breast cancer has become an emerging area of investigation in recent years 348. However, the role of
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these cyclin E variants in breast cancer genesis and outcome remains far from resolved with a recent study identifying these LWM isoforms in normal human mammary cells, in similar ratios to that observed in cancer 349.
The expression of the cdk inhibitors p21WAF1/Cip1 and p27Kip1 in human breast cancer has also been evaluated in multiple studies, and while p27Kip1 loss has been reported as an adverse prognostic factor in the majority of studies (reviewed in 350), the prognostic role of p21WAF1/Cip1 defies consensus, perhaps as a consequence of the dual role of p21WAF1/Cip1 as a mediator of both anti-proliferative signals and pro-survival signals, and the interaction of p21WAF1/Cip1 with p53 status 200,202,203.
Key reasons for the lack of agreement between the numerous studies of cell cycle protein expression as prognostic indicators in human breast cancer are the lack of consistency in immunohistochemical staining methodology, scoring protocols and cut- points for positivity. In addition, many of the published studies were undertaken on small cohorts and relate to specific breast cancer sub-populations eg: ER positive only, or lymph node negative only, making true comparisons difficult.
An approach to evaluate the role (if any) of a particular protein as a biomarker of patient outcome is to use an optimal cut-point determination technique to derive the best cut-point in the data 351. This method uses serial survival analyses (eg: logrank tests) to dichotomise the protein expression data, thereby identifying the point that is most significant in discriminating the outcome of “high” and “low” expressors. This technique has potential limitations as a consequence of the need to perform multiple testing (with the inherent risk of falsely identifying a cut-point that suggests a difference in outcome purely by chance). However, when statistical corrections for multiple testing are applied 351,352 it is an informative technique, particularly when little consensus exists in the literature as to what constitutes clinically relevant “high” or “low” expression.
Therefore, we evaluated the expression of cyclin D1, cyclin E, and the cdk inhibitors p21WAF1/Cip1 and p27Kip1 using immunohistochemistry in the SVCBCOC, a retrospective cohort of nearly 300 patients with early invasive ductal carcinoma. This cohort is well- characterised, of reasonable size, has a median follow-up of over 5 years and is representative of other published cohorts in terms of clinicopathological parameters (Chapter 3). In addition, the overall outcome of patients in the cohort is comparable to that reported in Australia as a whole, and in other Western developed nations. Optimal
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cut-point determination was undertaken to define “high” versus “low” expressors for all proteins tested, and survival analyses performed to define any relationships with prognosis and outcome for each marker individually, and in combination. Key analyses that were undertaken were those in which outcome was evaluated in the cohort as a whole, and among ER positive and ER negative patients. In addition, exploratory analyses were undertaken to evaluate the outcome amongst ER positive patients treated with endocrine therapy, and those patients who were treated with chemotherapy.
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4.2.1 Cyclin D1
Cyclin D1 immunohistochemistry was optimised and assessed using protocols as detailed in Chapter 2. Representative images of cyclin D1 staining are depicted in Figure 4.1. Mantle cell lymphoma tissue was used as a positive control, while mantle cell lymphoma in which rabbit IgG was substituted for the cyclin D1 antibody was used as a negative control. In breast cancer tissues, a spectrum of nuclear cyclin D1 expression was observed ranging from negative to strongly positive. Frequency distribution of the cyclin D1 simplified H score (percentage of positively staining cells x intensity) across the cohort revealed that the staining was weighted towards low-level expression (Figure 4.2). Optimal cut-point determination using serial logrank tests was undertaken at values close to the 10th, 25th, 50th, 75th and 90th centiles, and two cut- points were apparent – one at an H score of > 0 and another at an H score of > 120. Due to the broad range of values tested for significance, these cut-points did not survive Bonferroni correction for multiple testing, and the subsequent analyses should be viewed with this caveat borne in mind.
On logrank analysis, patients with either a cyclin D1 H score of 0 (low expressors), or greater than 120 (high expressors) had an inferior prognosis to moderate expressors when assessed for recurrence (p=0.0235 and p=0.0088 respectively) and distant metastasis (p=0.0056 and p=0.0075 respectively) (Figure 4.3). When the end-point of breast cancer-related death was used, only the difference between low and moderate expressors remained significant (p=0.0029).
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Figure 4.1: Patterns of cyclin D1 expression
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Figure 4.2: Descriptive statistics for cyclin D1 expression
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Figure 4.3: Kaplan-Meier curves for cyclin D1 expression
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A number of subgroups within the cohort were also analysed – ER positive cases, ER negative cases, ER positive cases treated with adjuvant endocrine therapy and chemotherapy-treated cases. Kaplan-Meier curves corresponding to these analyses are presented in Figures 4.4 – 4.7. In the ER positive subgroup only the difference between moderate and high expression was statistically significant (p=0.0006, p=0.0002 and p=0.0131 for the end-points of recurrence, distant metastasis and breast cancer-related death respectively). In the ER negative subgroup, the number of high expressors was negligible leaving a comparison between the moderate and low expressors. In this subgroup low cyclin D1 expression was statistically significant for breast cancer-related death only (p=0.0443). In ER positive patients who were treated with endocrine therapy, high cyclin D1 expression was predictive of increased breast cancer-related death (p=0.0481). In those cases treated with chemotherapy, both high and low cyclin D1 expression predicted for breast cancer recurrence (p=0.0029 and p<0.0001 respectively) and distant metastasis (p=0.0071 and p<0.0001 respectively) when compared to moderate expressors. For the end-point of breast cancer-related death, only the low expressors had a significantly worse outcome than moderate expressors (p<0.0001).
4.2.1.1 Cyclin D1 univariate Cox proportional hazards analysis
For the purposes of Cox proportional hazards analysis, the data were dichotomised at each of the two cut-points in turn (ie: cyclin D1 low vs cyclin D1 moderate/high, and cyclin D1 high vs cyclin D1 low/moderate), and despite the likely dilution of the effects observed by Kaplan-Meier analysis (due to the combination of the low and moderate expressors, and moderate and high expressors), cyclin D1 high and low expression predicted for adverse outcome in much the same manner as seen by logrank analysis (Tables 4.1 and 4.2).
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Figure 4.4: Kaplan-Meier curves for cyclin D1 expression in ER positive cases
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Figure 4.5: Kaplan-Meier curves for cyclin D1 expression in ER negative cases
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Figure 4.6: Kaplan-Meier curves for cyclin D1 expression in ER positive cases that were treated with adjuvant endocrine therapy
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Figure 4.7: Kaplan-Meier curves for cyclin D1 expression in cases that were treated with adjuvant chemotherapy
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In particular, high cyclin D1 expression was associated with adverse outcome among ER positive patients, predicting an over 3-fold increased risk of recurrence (p=0.0008, HR 3.10, 95% CI 1.60 – 6.00), distant metastasis (p=0.0004, HR 3.52, 95% CI 1.75 – 7.07) and breast cancer-related death (p=0.0124, HR 3.16, 95% CI 1.28 – 7.80). In addition, high expression predicted for recurrence amongst ER positive patients treated with adjuvant endocrine therapy (p=0.0464, HR 2.47, 95% CI 1.02 – 6.03), and recurrence (p=0.0217, HR 2.41, 95% CI 1.14 – 5.11) and distant metastasis among patients treated with adjuvant chemotherapy (p=0.0451, HR 2.24, 95% CI 1.02 – 4.93). Low cyclin D1 expression was associated with an increased risk of breast cancer- related death in ER negative patients (p=0.0482, HR 2.23, 95% CI 1.01 – 4.94) and an increased risk of recurrence (p=0.0017, HR 3.33, 95% CI 1.57 – 7.06), distant metastasis (p=0.0009, HR 3.62, 95% CI 1.69 – 7.74) and breast cancer-related death (p=0.0002, HR 4.92, 95% CI 2.12 – 11.38) in those patients who were treated with adjuvant chemotherapy.
Table 4.1: Univariate Cox proportional hazards analysis for low cyclin D1 expression
p value Hazard Ratio 95% Cl Entire cohort (n = 276) All recurrence NS 1.80 0.97 - 3.35 Distant metastasis 0.0207 2.09 1.12 - 3.92 Breast cancer-related death 0.0075 2.59 1.29 - 5.21 ER +ve (n = 189) All recurrence NS 0.43 0.06 - 3.17 Distant metastasis NS 0.52 0.07 - 3.81 Breast cancer-related death * * * ER –ve (n = 85) All recurrence NS 1.82 0.88 - 3.78 Distant metastasis NS 1.93 0.92 - 4.04 Breast cancer-related death 0.0482 2.23 1.01 - 4.94 ER +ve/ Endocrine treatment (n = 107) All recurrence * * * Distant metastasis * * * Breast cancer-related death * * * Chemotherapy treatment (n = 106) All recurrence 0.0017 3.33 1.57 - 7.06 Distant metastasis 0.0009 3.62 1.69 - 7.74 Breast cancer-related death 0.0002 4.92 2.12 - 11.38
* Unable to analyse due to monotone likelihood; NS - not statistically significant: CI - confidence interval; Low cyclin D1 expression - cyclin D1 H score = 0.
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Table 4.2: Univariate Cox proportional hazards analysis for high cyclin D1 (dichotomised data)
p Value Hazard Ratio 95% CI Entire cohort (n = 276) All recurrence 0.0261 1.98 1.08 - 3.60 Distant metastasis 0.0283 2.02 1.08 - 3.77 Breast cancer-related death NS 1.53 0.68 - 3.41 ER +ve (n = 189) All recurrence 0.0008 3.10 1.60 - 5.99 Distant metastasis 0.0004 3.52 1.75 - 7.07 Breast cancer-related death 0.0124 3.16 1.28 - 7.80 ER -ve * (n = 85) All recurrence _ _ _ Distant metastasis _ _ _ Breast cancer-related death _ _ _ ER +ve/ Endocrine treatment (n = 107) All recurrence 0.0464 2.47 1.02 - 6.03 Distant metastasis NS 2.38 0.92 - 6.14 Breast cancer-related death NS 3.42 1.00 - 11.72 Chemotherapy treatment (n = 106) All recurrence 0.0217 2.41 1.14 - 5.11 Distant metastasis 0.0451 2.24 1.02 - 4.93 Breast cancer-related death NS 1.26 0.43 - 3.65
* there were insufficent cases in this subgroup to analyse; NS - not statistically significant; CI -confidence interval; High cyclin D1 expression - cyclin D1 H score > 120.
On 2 analyses high level cyclin D1 expression was correlated with high tumour grade (p=0.0025), while high and moderate cyclin D1 expression was correlated with endocrine receptor positivity (p<0.003) (Table 4.3).
4.2.1.2 Cyclin D1 multivariate Cox proportional hazards against standard clinicopathological factors
There are a number of approaches to the selection of variables that may be undertaken in the conduct of multivariate analyses 353. One method uses a “backwards” selection technique, whereby all of the clinicopathological factors predictive of outcome on univariate analysis are entered into the model at the start. Then, those variables contributing least to the model (i.e. the ones with the highest p values) are sequentially removed from the model until a basic “resolved” model is generated 354. Another type of model involves the prospective selection of variables based on theoretical understanding 353 taking care to minimise the inclusion of related variables that will be knocked out of the model as a result of multicollinearity 353, and limiting the number of variables such that there are at least 10 events per covariate entered into the model 355.
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For the studies presented here, “backwards” modelling was undertaken as this method is thought to be preferable in the identification of important variables 354.
Table 4.3: Contingency table of standard clinicopathological variables and cyclin D1 expression
Variable Cyclin D1 expression level p value Interpretation (between groups) Low Mod. High Total Grade III 19 87 19 125 low v mod - p=NS High cyclin D1 Grade I and 2 16 124 11 151 mod v high - p=0.0025 correlates with Total 35 211 30 276 low v high - p=NS high grade
LN +ve 16 90 15 121 low v mod - p=NS LN -ve 19 118 15 152 mod v high - p=NS Total 35 208 30 273 low v high - p=NS
Size > 20 mm 16 86 9 111 low v mod - p=NS Size < 20 mm 19 125 21 165 mod v high - p=NS Total 35 211 30 276 low v high - p=NS
ER +ve 11 149 29 189 low v mod - p<0.0001 High/mod ER -ve 24 60 1 85 mod v high - p=0.0029 cyclin D1 correlates Total 35 209 30 274 low v high - p<0.0001 with ER +ve
PR +ve 6 136 16 158 low v mod - p<0.0001 High/mod PR-ve 29 74 14 117 mod v high - p=NS cyclin D1 correlates Total 35 210 30 275 low v high - p=0.0021 with PR +ve
HER2 +ve 7 39 4 50 low v mod - p=NS HER2 +ve 27 168 24 219 mod v high - p=NS Total 34 207 28 269 low v high - p=NS
NS - not statistically significant; mod – moderate; Low cyclin D1 expression - cyclin D1 H score =0; Moderate cyclin D1 expression - cyclin D1 H score >0 and <120; High cyclin D1 H score – cyclin D1 H score > 120; Analyses were performed usng the X2 test.
Multivariate analyses examining the effect of cyclin D1 on outcome in entire cohort and its subgroups are are collated in Tables 4.4 – 4.7. In these analyses, only those conditions where high cyclin D1 expression was predictive on univariate analysis were evaluated. Low cyclin D1 expression was not predictive of outcome on multivariate analysis (data not shown). Thus, in the cohort as a whole, high cyclin D1 expression was not an independent prognostic factor for recurrence or metastasis (Table 4.4). However, in the ER positive subgroup, high cyclin D1 expression was an independent prognostic factor for recurrence (p=0.0123, HR 2.47, 95% CI 1.22 – 5.00) and metastasis (p=0.0013, HR 3.22, 95% CI 1.58 – 6.56) (Table 4.5). For the outcome of breast cancer-related death (Table 4.5) high cyclin D1 expression was not prognostic.
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Table 4.4: Multivariate Cox proportional hazards modelling - entire cohort
p Value Hazard Ratio 95% CI RECURRENCE Clinicopathological variables + high cyclin D1 in multivariate model (n = 265) Grade III 0.1780 1.51 0.83 - 2.74 Size > 20 mm 0.3967 1.23 0.76 - 2.01 Lymph node +ve 0.0005 2.50 1.50 - 4.18 HER2 amplified 0.0181 1.88 1.11 - 3.19 ER +ve 0.4526 0.78 0.40 - 1.51 PR +ve 0.0159 0.46 0.25 - 0.87 High cyclin D1 0.1433 1.73 0.83 - 3.62
Resolved clinicopathological + high cyclin D1 model (after sequential removal of least significant variables) (n = 270) Lymph node +ve <0.0001 2.80 1.71 - 4.58 HER2 amplified 0.0052 2.07 1.24 - 3.44 PR +ve <0.0001 0.33 0.20 - 0.54 (High cyclin D1 falls out of the model)
DISTANT METASTASIS p Value Hazard Ratio 95% CI Clinicopathological variables + high cyclin D1 in multivariate model (n = 265) Grade III 0.5532 1.21 0.64 - 2.30 Size > 20 mm 0.0874 1.57 0.94 - 2.64 Lymph node +ve <0.0001 3.09 1.77 - 5.40 HER2 amplified 0.0195 1.93 1.11 - 3.35 ER +ve 0.1575 0.60 0.29 - 1.22 PR +ve 0.0177 0.44 0.22 - 0.87 High cyclin D1 0.0707 2.07 0.94 - 4.55
Resolved clinicopathological + high cyclin D1 model (after sequential removal of least significant variables) (n = 270) Lymph node +ve <0.0001 3.47 2.03 - 5.93 HER2 amplified 0.0070 2.08 1.22 - 3.54 PR +ve <0.0001 0.28 0.16 - 0.49 (High cyclin D1 falls out of the model)
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Table 4.5: Multivariate Cox proportional hazards modelling - ER positive subgroup
p Value Hazard Ratio 95% CI RECURRENCE Clinicopathological variables + high cyclin D1 in multivariate model (n = 183) Grade III 0.0733 1.89 0.94 - 3.81 Lymph node +ve 0.1556 1.63 0.83 - 3.21 HER2 amplified 0.0151 2.47 1.19 - 5.11 PR +ve 0.0805 0.53 0.26 - 1.08 High cyclin D1 0.0936 1.90 0.90 - 4.03
Resolved clinicopathological + high cyclin D1 model (n = 185) HER2 amplified 0.0021 2.94 1.48 - 5.83 PR +ve 0.0091 0.41 0.21 - 0.80 High cyclin D1 0.0123 2.47 1.22 - 5.00
p Value Hazard Ratio 95% CI DISTANT METASTASIS Clinicopathological variables + high cyclin D1 in multivariate model (n = 183) Grade III 0.2431 1.58 0.73 - 3.42 Size > 20 mm 0.3057 1.46 0.71 - 3.01 Lymph node +ve 0.1166 1.85 0.86 - 3.97 HER2 amplified 0.0704 2.15 0.94 - 4.93 PR +ve 0.0924 0.52 0.24 - 1.11 High cyclin D1 0.0440 2.33 1.02 - 5.28
Resolved clinicopathological + high cyclin D1 model (n = 183) Lymph node +ve 0.0125 2.47 1.22 - 5.05 HER2 amplified 0.0024 3.16 1.51 - 6.63 High cyclin D1 0.0013 3.22 1.58 - 6.56
p Value Hazard Ratio 95% CI BREAST CANCER-RELATED DEATH Clinicopathological variables + high cyclin D1 in multivariate model (n = 185)
Grade III 0.4567 1.45 0.55 - 3.84 HER2 amplified 0.0010 5.01 1.91 - 13.11 PR +ve 0.0067 0.29 0.12 - 0.71 High cyclin D1 0.3088 1.72 0.61 - 4.87 Resolved clinicopathological + high cyclin D1 model (n = 187)
HER2 amplified <0.0001 6.06 2.49 - 14.73 PR +ve 0.0004 0.23 0.10 - 0.52 (Cyclin D1 falls out) CI - Confidence Interval; High cyclin D1 - cyclin D1 H score >120.
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In the ER positive cases that were treated with endocrine therapy (n = 109) high cyclin D1 expression did not predict for adverse outcome (Table 4.6), while in the chemotherapy-treated group (n = 111), high cyclin D1 expression predicted for recurrence only (p=0.0273, HR 2.47, 95% CI 1.11 – 5.49) (Table 4.7).
Table 4.6: Multivariate Cox proportional hazards modelling - ER positive subgroup treated with endocrine therapy
p Value Hazard Ratio 95% CI RECURRENCE Clinicopathological variables + high cyclin D1 in multivariate model (n = 104) Grade III 0.0505 2.43 1.00 - 6.16 HER2 amplified 0.0325 2.92 1.09 - 7.79 High cyclin D1 0.1843 1.88 0.74 - 4.77
Resolved clinicopathological + high cyclin D1 model (n = 105) Grade III 0.0162 2.90 1.22 - 6.90 HER2 amplified 0.0390 2.80 1.05 - 7.43 (High cyclin D1 falls out first, and grade III remains in the model)
CI - Confidence Interval; High cyclin D1 - cyclin D1 H score >120.
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Table 4.7: Multivariate Cox proportional hazards modelling - chemotherapy treated patients
p Value Hazard Ratio 95% CI RECURRENCE Clinicopathological variables + high cyclin D1 in multivariate model (n = 105) Grade III 0.2978 1.64 0.65 - 4.13 Size > 20 mm 0.1755 1.67 0.80 - 3.51 Lymph node +ve 0.0017 6.98 2.08 - 23.43 HER2 amplified 0.0266 2.42 1.11 - 5.29 PR +ve 0.0069 0.33 0.15 - 0.74 High cyclin D1 0.0116 2.86 1.27 - 6.47
Resolved clinicopathological + high cyclin D1 model (n = 105) Lymph node +ve 0.0014 7.09 2.13 - 23.63 HER2 amplified 0.0080 2.77 1.30 - 5.87 PR +ve 0.0003 0.25 0.12 - 0.53 High cyclin D1 0.0273 2.47 1.11 - 5.49
p Value Hazard Ratio 95% CI METASTASIS Clinicopathological variables + high cyclin D1 in multivariate model (n = 105) Grade III 0.4660 1.42 0.55 - 3.66 Size > 20 mm 0.1035 1.91 0.88 - 4.17 Lymph node +ve 0.0017 10.31 2.41 - 44.15 HER2 amplified 0.0166 2.65 1.19 - 5.86 PR +ve 0.0070 0.33 0.15 - 0.74 High cyclin D1 0.0239 2.63 1.14 - 6.10
Resolved clinicopathological + high cyclin D1 model (n = 106) Lymph node +ve 0.0009 11.53 2.73 - 48.68 HER2 amplified 0.0122 2.52 1.22 - 5.19 PR +ve 0.0003 0.24 0.11 - 0.52 (High cyclin D1 falls out)
CI - Confidence Interval; High cyclin D1 - cyclin D1 H score >120.
4.2.2 Cyclin E
Cyclin E immunohistochemistry was optimised and assessed using protocols detailed in Chapter 2. Nuclear staining only was observed (Figure 4.8). Placental tissue was used as a positive control, while bronchial epithelium stained with cyclin E, and placental tissue in which mouse IgG2a was substituted for the cyclin E antibody were used as negative controls. In breast cancer, cyclin E was expressed across a range of intensities, from negative to strongly positive although frequency distribution of
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Figure 4.8: Patterns of cyclin E expression
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percentage of cells staining positively across the cohort revealed that the staining was weighted towards very low-level expression using our staining methodology and controls (Figure 4.9). As with all of the other IHC analyses performed on the SVCBCOC, optimal cut-point determination using serial logrank tests was performed using the simplified H score. However the same optimum cut-point was generated using the simpler parameter of % of cells staining. Thus, optimum cut-point determination was performed at increments of 1% of cells staining at any intensity up to 5% (ie: 6 times). A high cyclin E expressing group could be defined in which cyclin E expression > 0% was associated with an outcome that was significantly inferior for recurrence (p<0.0001), distant metastasis (p<0.0001) and death (p=0.0009), and this cut-point withstood Bonferroni correction for multiple testing (Figure 4.10). This group defined approximately 25% of the cohort. In addition, the cut-point remained prognostic in a series of subgroups including ER positive patients (recurrence only; p=0.0353), ER negative patients (recurrence p=0.0281; distant metastasis p=0.0183), and the group treated with chemotherapy (distant metastasis p=0.0239; breast cancer-related death p=0.0286). Kaplan-Meier curves for the cohort subgroups are detailed in Figures 4.11 – 4.14. On univariate Cox proportional hazards analysis, high cyclin E expression was prognostic for the same outcome measures determined by logrank test, with p values of a similar magnitude (Table 4.8).
Table 4.8: Univariate Cox proportional hazards analysis for high cyclin E expression
p Value Hazard Ratio 95% CI Entire cohort (n = 280) All recurrence 0.0001 2.50 1.56 - 3.99 Distant metastasis 0.0002 2.56 1.57 - 4.19 Breast cancer-related death 0.0030 2.40 1.34 - 4.16 ER +ve (n = 189) All recurrence 0.0394 2.07 1.04 - 4.14 Distant metastasis NS 1.83 0.86 - 3.91 Breast cancer-related death NS 1.32 0.49 - 3.56 ER -ve (n = 88) All recurrence 0.0320 2.15 1.07 - 4.34 Distant metastasis 0.0218 2.31 1.13 - 4.73 Breast cancer-related death NS 2.10 0.980 - 4.515 ER +ve/ Endocrine treatment (n = 108) All recurrence NS 1.41 0.553 - 3.566 Distant metastasis NS 1.62 0.628 - 4.184 Breast cancer-related death NS 1.21 0.325 - 4.471 Chemotherapy treatment (n = 108) All recurrence NS 1.90 0.982 - 3.680 Distant metastasis 0.0273 2.13 1.088 - 4.167 Breast cancer-related death 0.0334 2.31 1.068 - 4.975
NS - not statistically significant; CI - confidence interval; High cyclin E expression - cyclin E % cells staining > 0.
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Figure 4.9: Descriptive statistics for cyclin E expression
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Figure 4.10: Kaplan-Meier curves for cyclin E expression in the whole cohort
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Figure 4.11: Kaplan-Meier curves for cyclin E expression in the ER positive subgroup
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Figure 4.12: Kaplan-Meier curves for cyclin E expression in the ER negative subgroup
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Figure 4.13: Kaplan-Meier curves for cyclin E expression in the ER positive cases that were treated with endocrine therapy
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Figure 4.14: Kaplan-Meier curves for cyclin E expression in the subgroup that was treated with chemotherapy
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On 2 analysis high cyclin E expression was correlated with high tumour grade and hormone receptor negativity (Table 4.9).
Table 4.9: Contingency table of standard clinicopathological variables and cyclin E expression
Variable Cyclin E (high) Cyclin E (low) Total p value Interpretation Grade III 47 81 128 <0.0001 High cyclin E Grade I and 2 21 131 152 expression is correlated Total 68 212 280 with high tumour grade
LN +ve 35 87 122 NS LN -ve 32 123 155 Total 67 210 277
Size > 20 mm 31 83 114 NS Size < 20 mm 37 128 165 Total 68 211 279
ER +ve 32 157 189 <0.0001 High cyclin E ER -ve 36 52 88 expression is correlated Total 68 209 277 with ER negativity
PR +ve 23 136 159 <0.0001 High cyclin E PR-ve 45 74 119 expression is correlated Total 68 210 278 with PR negativity
HER2 +ve 12 39 51 NS HER2 -ve 54 166 220 Total 66 205 271
NS - not statistically significant; High cyclin E expression - cyclin E % > 0; Analyses are performed usng the X2 test.
4.2.2.1 Cyclin E multivariate Cox proportional hazards against standard clinicopathological factors
Cyclin E expression was evaluated by multivariate Cox proportional hazards analysis using the same techniques as those employed for cyclin D1 (Tables 4.10 – 4.13). Within the cohort as whole, high cyclin E expression predicted for recurrence (p=0.0135, HR 1.84, 95% CI 1.13 – 3.00) and metastasis (p=0.0140, HR 1.89, 95% CI 1.14 – 3.14), but was not prognostic for breast cancer-related death (Table 4.10). High cyclin E expression was not predictive of recurrence in the ER positive subgroup (Table 4.11). However, in the ER negative subgroup high cyclin E expression independently predicted for recurrence (p=0.0140, HR 2.43, 95% CI 1.20 – 4.95) and metastasis (p=0.0076, HR 2.69, 95% CI 1.30 – 5.56) (Table 4.12).
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Table 4.10: Multivariate Cox proportional hazards modelling - entire cohort
Hazard p Value Ratio 95% CI RECURRENCE Clinicopathological variables + high cyclin E in multivariate model (n = 267) Grade III 0.1467 1.54 0.86 - 2.78 Size > 20 mm 0.3766 1.24 0.77 - 2.00 Lymph node +ve 0.0004 2.53 1.52 - 4.22 HER2 amplified 0.0299 1.80 1.06 - 3.04 ER +ve 0.8305 1.07 0.59 - 1.91 PR +ve 0.0048 0.42 0.23 - 0.77 High cyclin E 0.0311 1.73 1.05 - 2.85
Resolved clinicopathological + high cyclin D1 model (n = 268) Lymph node +ve <0.0001 2.68 1.64 - 4.38 HER2 amplified 0.0102 2.00 1.17 - 3.26 PR +ve 0.0001 0.36 0.22 - 0.61 High cyclin E 0.0135 1.84 1.13 - 3.00
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Table 4.10 Continued Hazard p Value Ratio 95% CI METASTASIS Clinicopathological variables + high cyclin E in multivariate model (n = 267) Grade III 0.4105 1.30 0.70 - 2.42 Size > 20 mm 0.0839 1.57 0.94 - 2.61 Lymph node +ve <0.0001 3.20 1.84 - 5.58 HER2 amplified 0.0349 1.82 1.04 - 3.16 ER +ve 0.6950 0.88 0.48 - 1.63 PR +ve 0.0039 0.39 0.20 - 0.74 High cyclin E 0.0248 1.81 1.08 - 3.05
Resolved clinicopathological + high cyclin E model (n = 268) Lymph node +ve <0.0001 3.38 1.98 - 5.77 HER2 amplified 0.0143 1.95 1.14 - 3.33 PR +ve <0.0001 0.31 0.18 - 0.55 High cyclin E 0.0140 1.89 1.14 - 3.14
Hazard p Value Ratio 95% CI BREAST CANCER-RELATED DEATH Clinicopathological variables + high cyclin E in multivariate model (n = 267) Grade III 0.4643 1.32 0.63 - 2.75 Size > 20 mm 0.3515 1.32 0.74 - 2.37 Lymph node +ve 0.0001 3.52 1.85 - 6.70 HER2 amplified 0.0017 2.70 1.45 - 5.02 ER +ve 0.2532 0.67 0.33 - 1.34 PR +ve 0.0068 0.33 0.15 - 0.74 High cyclin E 0.2091 1.47 0.81 - 2.68
Resolved clinicopathological + high cyclin E model (n = 270) Lymph node +ve 0.0001 3.37 1.82 - 6.24 HER2 amplified 0.0004 2.97 1.63 - 5.41 PR +ve <0.0001 0.22 0.11 - 0.43 (High cyclin E falls out)
CI - Confidence Interval; High cyclin E - cyclin E % > 0.
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Table 4.11: Multivariate Cox proportional hazards modelling - ER positive subgroup
p Value Hazard Ratio 95% CI RECURRENCE Clinicopathological variables + high cyclin E in multivariate model (n = 184) Grade III 0.0431 2.02 1.02 - 3.99 Lymph node +ve 0.1513 1.64 0.84 - 3.21 HER2 amplified 0.0124 2.54 1.22 - 5.26 PR +ve 0.0171 0.45 0.23 - 0.87 High cyclin E 0.1825 1.64 0.79 - 3.38
Resolved clinicopathological + high cyclin E model (n = 187) Grade III 0.0167 2.29 1.16 - 4.52 HER2 amplified 0.0279 2.26 1.09 - 4.66 PR +ve 0.0030 0.38 0.20 - 0.72 (High cyclin E falls out)
CI - Confidence Interval; High cyclin E - cyclin E % > 0.
Table 4.12: Multivariate Cox proportional hazards modelling - ER negative subgroup
p Value Hazard Ratio 95% CI RECURRENCE Clinicopathological variables + high cyclin E in multivariate model (n = 88) Size > 20 mm 0.1662 1.73 0.80 - 3.76 Lymph node +ve 0.0001 5.14 2.24 - 11.80 High cyclin E 0.0091 2.60 1.27 - 5.34
Resolved clinicopathological + high cyclin E model (n = 88) Lymph node +ve <0.0001 5.76 2.55 - 12.99 High cyclin E 0.0140 2.43 1.20 - 4.95
METASTASIS Clinicopathological variables + high cyclin E in multivariate model (n = 88) Size > 20 mm 0.1097 1.92 0.86 - 4.29 Lymph node +ve <0.0001 6.11 2.55 - 14.60 High cyclin E 0.0043 2.94 1.40 - 6.15
Resolved clinicopathological + high cyclin E model (n = 88) Lymph node +ve <0.0001 6.88 2.92 - 16.20 High cyclin E 0.0076 2.69 1.30 - 5.56
CI - Confidence Interval; High cyclin E - cyclin E % > 0.
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Finally, in the chemotherapy-treated subgroup, high cyclin E expression failed to predict adverse outcome (Table 4.13).
Table 4.13: Multivariate Cox proportional hazards modelling - chemotherapy treated patients
p Value Hazard Ratio 95% CI METASTASIS Clinicopathological variables + high cyclin E in multivariate model (n = 106) Grade III 0.3541 1.56 0.61 - 4.01 Size > 20 mm 0.1657 1.73 0.80 - 3.73 Lymph node +ve 0.0013 10.78 2.54 - 45.79 HER2 amplified 0.1220 1.93 0.84 - 4.43 PR +ve 0.0141 0.34 0.15 - 0.81 High cyclin E 0.3875 1.41 0.65 - 3.07
Resolved clinicopathological + high cyclin E model (n = 106) Lymph node +ve 0.0009 11.53 2.73 - 48.68 HER2 amplified 0.0122 2.52 1.22 - 5.19 PR +ve 0.0003 0.24 0.11 - 0.52 (High cyclin E falls out)
BREAST CANCER-RELATED DEATH Clinicopathological variables + high cyclin E in multivariate model (n = 105) Grade III 0.4109 1.73 0.47 - 6.44 Lymph node +ve 0.0026 22.64 2.97 - 172.63 HER2 amplified 0.0117 3.24 1.30 - 8.09 ER +ve 0.3398 0.63 0.25 - 1.62 PR +ve 0.0219 0.27 0.09 - 0.83 High cyclin E >0.9999 1 0.43 - 2.34
Resolved clinicopathological + high cyclin E model (n = 106) Lymph node +ve 0.0038 19.53 2.61 - 146.19 HER2 amplified 0.0016 3.78 1.66 - 8.64 PR +ve 0.0004 0.17 0.06 - 0.45 (High cyclin E falls out)
CI - Confidence Intervals; High cyclin E - cyclin E % > 0.
4.2.3 p21WAF1/Cip1
p21WAF1/Cip1 immunohistochemistry was optimised and assessed using protocols detailed in Chapter 2. Nuclear staining only was observed (Figure 4.15). Cell pellets generated from exponentially proliferating HMEC-184 cells were used as a positive control, while SKBR3 cells stained with anti-p21WAF1/Cip1 antibody, and HMEC-184 cells stained with a protocol in which mouse IgG1 was substituted for anti-p21WAF1/Cip1
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Figure 4.15: Patterns of p21WAF1/Cip1 expression
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antibody were used as negative controls. Breast cancer tissues displayed a range of staining intensities from negative to strongly positive, although the frequency distribution of percentage of cells staining positively across the cohort was weighted towards low-level expression using our staining methodology and controls (Figure 4.16). Optimal cut-point determination was performed for both percentage of cells staining, maximum intensity and simplified H score. A weak optimal cut-point was determined at >10% of cells staining at intensity for recurrence only (p=0.0304) (Figure 4.17). Due to the number of testing increments used to determine the cut-point, significance was lost if Bonferroni correction was applied, and therefore subsequent analyses using this cut-point should be viewed in the context of this caveat.
Trends to adverse outcome were observed among ER positive patients (p=0.0627 and p=0.0559 for recurrence and breast cancer-related death respectively), and ER negative cases (p=0.0675 for distant metastasis) (Figures 4.18 – 4.19). The >10% cut- point was significantly predictive of breast cancer–related death in the ER positive cases that were treated with endocrine therapy (p= 0.0104) and there was a trend to significance for distant metastasis (p=0.0555) (Figure 4.20), while the analyses among chemotherapy-treated patients were visually suggestive of adverse outcome with high p21WAF1/Cip1 expression, and were likely limited from a statistical perspective by the small sample size analysed (Figure 4.21). Univariate Cox proportional hazards analysis revealed similar results to that determined on logrank testing (Table 4.14).
On 2 analyses, p21WAF1/Cip1 expression was not correlated with any other clinicopathological factors (Table 4.15).
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Figure 4.16: Descriptive statistics for p21WAF1/Cip1 expression
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Figure 4.17: Kaplan-Meier curves for p21WAF1/Cip1 expression in the whole cohort
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Figure 4.18: Kaplan-Meier curves for p21WAF1/Cip1 expression in the ER positive cases
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Figure 4.19: Kaplan-Meier curves for p21WAF1/Cip1 expression in the ER negative cases
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Figure 4.20: Kaplan-Meier curves for p21WAF1/Cip1 in the ER positive cases that were treated with endocrine therapy
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Figure 4.21: Kaplan-Meier curves for p21WAF1/Cip1 expression in the cases that were treated with chemotherapy
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Table 4.14: Univariate Cox proportional hazards analysis for high p21 expression
p Value Hazard Ratio 95% CI Entire cohort (n = 280) All recurrence 0.0336 1.96 1.05 – 3.05 Distant metastasis NS 1.70 0.86 – 3.34 Breast cancer-related death NS 1.86 0.87 – 3.98 ER +ve (n = 191) All recurrence NS 2.06 0.95 – 4.46 Distant metastasis NS 1.65 0.68 – 3.98 Breast cancer-related death NS 2.57 0.94 – 7.02 ER -ve (n = 87) All recurrence NS 2.45 0.84 – 7.17 Distant metastasis NS 2.63 0.90 – 7.73 Breast cancer-related death NS 1.77 0.53 – 5.96 ER +ve/ Endocrine treatment (n = 108) All recurrence NS 2.28 0.84 – 6.15 Distant metastasis NS 2.58 0.94 – 7.07 Breast cancer-related death 0.0190 4.37 1.27 – 14.95 Chemotherapy treatment (n = 107) All recurrence NS 1.96 0.82 – 4.70 Distant metastasis NS 2.15 0.89 – 5.20 Breast cancer-related death NS 2.31 0.87 – 6.13
NS - not statistically significant; CI - confidence interval; High p21 expression - p21 % cells staining > 10.
Table 4.15: Contingency table of standard clinicopathological variables and p21 expression
Variable p21 (high) p21 (low) Total p value Interpretation Grade III 18 108 126 NS Grade I and 2 13 141 154 Total 31 249 280
LN +ve 16 105 121 NS LN -ve 15 141 156 Total 31 246 272
Size > 20 mm 12 101 113 NS Size < 20 mm 19 148 167 Total 31 249 280 No correlations ER +ve 24 167 191 NS ER -ve 7 80 87 Total 31 247 278
PR +ve 6 31 37 NS PR-ve 23 217 240 Total 29 248 277
HER2 +ve 7 44 51 NS HER2 -ve 22 199 221 Total 29 243 272
Low p21 expression - p21 % < 10; High p21 expression - p21 % > 10; Analyses are performed usng the X2 test.
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4.2.3.1 p21WAF1/Cip1 multivariate Cox proportional hazards analysis against standard clinicopathological variables
Multivariate analyses were undertaken as described for cyclin D1. As p21WAF1/Cip1 expression was only prognostic on univariate analysis for recurrence in the entire cohort, and breast cancer-related death in the subgroup of ER positive patients that were treated with endocrine therapy, the analysis was limited to these two circumstances.
In the cohort as a whole, high p21WAF1/Cip1 expression was not prognostic (Table 4.16).
Table 4.16: Multivariate Cox proportional hazards modelling - entire cohort
RECURRENCE p Value Hazard Ratio 95% CI Clinicopathological variables + high p21 in multivariate model (n = 268) Grade III 0.0772 1.68 0.95 - 3.00 Size > 20 mm 0.4136 1.22 0.76 - 1.97 Lymph node +ve 0.0003 2.58 1.55 - 4.31 HER2 amplified 0.0273 1.82 1.07 - 3.08 ER +ve 0.7363 0.90 0.50 - 1.63 PR +ve 0.0074 0.44 0.24 - 0.80 High p21 0.1237 1.67 0.87 - 3.21
Resolved clinicopathological + high p21 model (n = 270) Lymph node +ve <0.0001 2.80 1.71 - 4.58 HER2 amplified 0.0052 2.07 1.24 - 3.44 PR +ve <0.0001 0.33 0.20 - 0.54 (High p21 falls out)
CI - Confidence Interval; High p21 - p21 % > 10.
The ER positive group that was treated with endocrine therapy (n=109) was about one- third the size of the entire cohort, and HER2 amplification was the only other clinical predictor of outcome. Thus the variables entered into the model were limited. Nonetheless, for the outcome measure of breast cancer-related death, high p21WAF1/Cip1 expression was an independent prognostic factor, along with HER2 status (p=0.0045, HR 6.48, 95% CI 1.78 - 23.51) (Table 4.17).
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Table 4.17: Multivariate Cox proportional hazards modelling - ER positive subgroup treated with endocrine therapy
BREAST CANCER-RELATED DEATH p Value Hazard Ratio 95% CI Clinicopathological variables + high p21 in multivariate model (n = 105) HER2 amplified <0.0001 14.20 3.80 - 53.02 High p21 0.0045 6.48 1.78 - 23.51
Resolved clinicopathological + high p21 model (n = 105) HER2 amplified <0.0001 14.20 3.80 - 53.02 High p21 0.0045 6.48 1.78 - 23.51
CI - Confidence Interval; High p21 - p21 % > 10.
4.2.4 p27Kip1
p27Kip1 immunohistochemistry was optimised and assessed using protocols as detailed in Chapter 2. Both nuclear staining and low-level cytoplasmic staining was observed (Figure 4.22). Only the nuclear staining was scored, as the cytoplasmic blush was not visually robust, and the majority of previous studies have scored nuclear expression alone. Cell pellets generated from exponentially proliferating MDA-MB-134 cells were used as a positive control, while HMEC-184 cells stained with anti-p27Kip1 antibody, and MDA-MB-134 cells stained with a protocol in which mouse IgG1 was substituted for anti-p27Kip1 antibody were used as negative controls. Frequency distribution of percentage of staining across the cohort revealed that the staining had a wider range of distribution than observed with cyclins D1, E and p21WAF1/Cip1 using our staining methodology and controls (Figure 4.23). Optimal cut-point determination was undertaken focussing on the values close to the 10th, 25th, 50th 75th and 90th centiles and an optimal cut-point was determined at a simplified H score of > 110, in which outcome was superior for the end-points of recurrence, distant metastasis and death (p=0.0011, p=0.0002, and p<0.0001 respectively) (Figure 4.24). This cut-point also withstood Bonnferroni correction for multiple testing. The cut-point was predictive of outcome in the ER positive patients (distant metastasis p=0.0382 and breast cancer- related death p=0.0121), ER positive patients treated with adjuvant endocrine therapy (recurrence p=0.0250) and patients treated with adjuvant chemotherapy (recurrence p=0.0236, distant metastasis p=0.0227, breast cancer-related death p=0.0034). Kaplan-Meier curves for the cohort subgroups are detailed in Figures 4.25 – 4.28.
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Figure 4.22: Patterns of p27Kip1 expression
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Figure 4.23: Descriptive statistics for p27Kip1 expression
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Figure 4.24: Kaplan-Meier curves for p27Kip1 expression in the whole cohort
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Figure 4.25: Kaplan-Meier curves for p27Kip1 expression in the ER positive cases
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Figure 4.26: Kaplan-Meier curves for p27Kip1 expression in the ER negative cases
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Figure 4.27: Kaplan-Meier curves for p27Kip1 expression in the ER positive cases that were treated with endocrine therapy
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Figure 4.28: Kaplan-Meier curves for p27Kip1 expression in the cases that were treated with adjuvant chemotherapy
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On univariate Cox proportional hazards analysis, p27Kip1 expression was prognostic for the same outcome measures determined by logrank test, with p values of a similar magnitude (Table 4.18).
Table 4.18: Univariate Cox proportional hazards analysis for low p27 expression
p Value Hazard Ratio 95% CI Entire cohort (n = 276) All recurrence 0.0015 2.27 1.37 – 3.78 Distant metastasis 0.0004 2.70 1.56 – 4.69 Breast cancer-related death 0.0002 3.68 1.84 – 7.37 ER +ve (n = 190) All recurrence NS 1.72 0.93 – 3.19 Distant metastasis 0.0422 2.00 1.03 – 3.92 Breast cancer-related death 0.0165 2.86 1.21 – 6.75 ER -ve (n = 85) All recurrence NS 2.73 0.65 – 11.44 Distant metastasis NS 2.61 0.62 – 10.95 Breast cancer-related death NS 2.07 0.49 – 8.75 ER +ve/ Endocrine treatment (n = 108) All recurrence 0.0300 2.54 1.09 – 5.88 Distant metastasis NS 2.29 0.96 – 5.44 Breast cancer-related death NS 2.04 0.65 – 6.43 Chemotherapy treatment (n = 108) All recurrence 0.0273 2.22 1.09 – 4.50 Distant metastasis 0.0266 2.30 1.10 – 4.79 Breast cancer-related death 0.0065 3.91 1.46 – 10.41
NS - not statistically significant; CI - confidence interval; Low p27 - p27 H score < 110.
On 2 analyses, low p27Kip1 expression was correlated with high tumour grade, large tumour size and hormone receptor negativity and HER2 amplification (Table 4.19).
4.2.4.1 p27Kip1 multivariate Cox proportional hazards analysis against standard clinicopathological variables
Mutlivariate Cox proportional hazards analysis was performed as described for cyclin D1 (Section 4.2.1.1). In the cohort as a whole, low p27Kip1 was predictive of metastasis and breast cancer-related death (p=0.0197, HR 1.97, 95% CI 1.11 – 3.49 and p=0.0143, HR 2.48, 95% CI 1.20 – 5.13), in a final model comprised of p27Kip1, lymph node status, HER2 amplification and PR positivity (Table 4.20).
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Table 4.19: Contingency table of standard clinicopathological variables and p27 expression
Variable p27 (high) p27 (low) Total p value Interpretation Grade III 37 89 126 <0.0001 Low p27 expression Grade I and 2 84 66 150 is correlated with Total 121 155 276 high tumour grade
LN +ve 49 73 122 NS LN -ve 70 82 152 Total 119 155 274
Size > 20 mm 39 74 113 0.0093 Low p27 expression Size < 20 mm 82 81 163 is correlated with Total 121 155 276 large tumour size
ER +ve 110 80 190 <0.0001 Low p27 expression ER -ve 11 74 85 is correlated with Total 121 154 275 ER negativity
PR +ve 89 69 158 <0.0001 Low p27 expression PR-ve 32 86 118 is correlated with Total 121 155 276 PR negativity
HER2 +ve 11 39 50 0.0005 Low p27 expression HER2 -ve 107 111 218 is correlated with Total 118 150 268 HER2 amplification
NS - not statistically significant, Low p27 expression - p27 H score < 110; Analyses are performed usng the X2 test.
Table 4.20: Multivariate Cox proportional hazards modelling - entire cohort
RECURRENCE p Value Hazard Ratio 95% CI Clinicopathological variables + low p27 in multivariate model (n = 265) Grade III 0.0847 1.65 0.93 - 2.93 Size > 20 mm 0.5256 1.17 0.72 - 1.89 Lymph node +ve 0.0002 2.61 1.57 - 4.35 HER2 amplified 0.0288 1.79 1.06 - 3.02 ER +ve 0.4876 1.25 0.67 - 2.34 PR +ve 0.0018 0.38 0.21 - 0.70 Low p27 0.0672 1.71 0.96 - 3.03
Resolved clinicopathological + low p27 model (n = 270) Lymph node +ve <0.0001 2.80 1.71 - 4.58 HER2 amplified 0.0052 2.07 1.24 - 3.44 PR +ve <0.0001 0.33 0.20 - 0.54 (Low p27 falls out)
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Table 4.20 Continued METASTASIS p Value Hazard Ratio 95% CI Clinicopathological variables + low p27 in multivariate model (n = 265) Grade III 0.2849 1.39 0.76 - 2.55 Size > 20 mm 0.1404 1.47 0.88 - 2.46 Lymph node +ve <0.0001 3.19 1.84 - 5.55 HER2 amplified 0.0286 1.83 1.07 - 3.17 ER +ve 0.7140 1.13 0.58 - 2.21 PR +ve 0.0012 0.34 0.18 - 0.65 Low p27 0.0354 1.95 1.05 - 3.64
Resolved clinicopathological + low p27 model (n = 266) Lymph node +ve <0.0001 3.50 2.05 - 5.99 HER2 amplified 0.0243 1.85 1.08 - 3.17 PR +ve <0.0001 0.31 0.18 - 0.53 Low p27 0.0197 1.97 1.11 - 3.49
BREAST CANCER-RELATED DEATH p Value Hazard Ratio 95% CI Clinicopathological variables + low p27 in multivariate model (n = 265) Grade III 0.3402 1.41 0.70 - 2.87 Size > 20 mm 0.4316 1.27 0.70 - 2.29 Lymph node +ve <0.0001 3.58 1.88 - 6.80 HER2 amplified 0.0019 2.64 1.43 - 4.89 ER +ve 0.8418 0.93 0.44 - 1.97 PR +ve 0.0044 0.31 0.14 - 0.70 Low p27 0.0418 2.27 1.03 - 5.00
Resolved clinicopathological + low p27 model (n = 266) Lymph node +ve <0.0001 3.63 1.95 - 6.76 HER2 amplified 0.0018 2.61 1.43 - 4.76 PR +ve <0.0001 0.26 0.13 - 0.51 Low p27 0.0143 2.48 1.20 - 5.13
CI - Confidence Interval; Low p27 - p27H score < or = 110.
However, in the ER positive subgroup, p27Kip1 was no longer predictive of distant metastasis (Table 4.21), although remained predictive for breast cancer-related death (p=0.0342; HR 2.61; 95% CI 1.07 – 6.34). In the ER positive subgroup that was treated with endocrine therapy low p27Kip1 expression was predictive of increased tumour recurrence (p=0.0392; HR 2.41; 95% CI 1.05 – 5.55) (Table 4.22).
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Table 4.21: Multivariate Cox proportional hazards modelling - ER positive subgroup
METASTASIS p Value Hazard Ratio 95% CI Clinicopathological variables + low p27 in multivariate model (n = 184) Grade III 0.0783 1.88 0.93 - 3.81 Size > 20 mm 0.4538 1.31 0.65 - 2.63 Lymph node +ve 0.0383 2.19 1.04 - 4.61 HER2 amplified 0.0780 2.03 0.92 - 4.47 PR +ve 0.0038 0.36 0.18 - 0.72 Low p27 0.0451 2.04 1.02 - 4.09
Resolved clinicopathological + low p27 model (n = 185) Lymph node +ve 0.0216 2.34 1.13 - 4.84 HER2 amplified 0.0030 3.09 1.47 - 6.49 PR +ve 0.0056 0.38 0.19 - 0.75 (Low p27 falls out)
BREAST CANCER-RELATED DEATH p Value Hazard Ratio 95% CI Clinicopathological variables + low p27 in multivariate model (n = 184) Grade III 0.2626 1.67 0.68 - 4.08 HER2 amplified 0.0022 4.32 1.69 - 11.00 PR +ve 0.0003 0.22 0.09 - 0.50 Low p27 0.0330 2.62 1.08 - 6.34
Resolved clinicopathological + low p27 model (n = 186) HER2 amplified 0.0004 5.08 2.08 - 12.44 PR +ve 0.0002 0.21 0.09 - 0.47 Low p27 0.0342 2.61 1.07 - 6.34
CI - Confidence Interval; Low p27 - p27 H score = or < 110.
Table 4.22: Multivariate Cox proportional hazards modelling - ER positive subgroup treated with endocrine therapy
RECURRENCE p Value Hazard Ratio 95% CI Clinicopathological variables + low p27 in multivariate model (n = 105) Grade III 0.0112 3.02 1.29 - 7.11 HER2 amplified 0.0856 2.35 0.89 - 6.21 Low p27 0.0585 2.25 0.97 - 5.23
Resolved clinicopathological + low p27 model (n = 108) Grade III 0.0051 3.32 1.44 - 7.68 Low p27 0.0392 2.41 1.05 - 5.55
CI - Confidence Interval; Low p27 - p27 H score = or < 110.
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In the chemotherapy-treated subgroup, low p27Kip1 expression was not independently predictive of outcome on multivariate analysis (Table 4.23).
Table 4.23: Multivariate Cox proportional hazards modelling - chemotherapy-treated subgroup
RECURRENCE p Value Hazard Ratio 95% CI Clinicopathological variables + low p27 in multivariate model (n = 106) Grade III 0.4368 1.49 0.55 - 4.06 Size > 20 mm 0.4314 1.34 0.65 - 2.74 Lymph node +ve 0.0008 7.76 2.34 - 25.76 HER2 amplified 0.0795 1.95 0.93 - 4.10 PR +ve 0.0048 3.34 1.44 - 7.71 Low p27 0.4295 1.37 0.63 - 2.99
Resolved clinicopathological + low p27 model (n = 106) Lymph node +ve 0.0008 7.73 2.34 - 25.56 HER2 amplified 0.0244 2.72 1.11 - 4.65 PR +ve 0.0002 0.23 0.11 - 0.49 (Low p27 falls out)
METASTASIS p Value Hazard Ratio 95% CI Clinicopathological variables + low p27 in multivariate model (n = 106) Grade III 0.6437 1.27 0.46 - 3.56 Size > 20 mm 0.2399 1.58 0.74 - 3.40 Lymph node +ve 0.0010 11.34 2.67 - 48.16 HER2 amplified 0.0417 2.21 1.03 - 4.75 PR +ve 0.0061 3.24 1.40 - 7.52 Low p27 0.3637 1.46 0.65 - 3.29
Resolved clinicopathological + low p27 model (n = 106) Lymph node +ve 0.0009 11.53 2.73 - 48.68 HER2 amplified 0.0122 2.52 1.22 - 5.19 PR +ve 0.0003 0.24 0.11 - 0.52 (Low p27 falls out)
BREAST CANCER-RELATED DEATH p Value Hazard Ratio 95% CI Clinicopathological variables + low p27 in multivariate model (n = 105) Grade III 0.5579 1.49 0.40 - 5.58 Lymph node +ve 0.0026 22.53 2.98 - 170.59 HER2 amplified 0.0071 3.30 1.39 - 7.89 ER +ve 0.9559 0.97 0.33 - 2.83 PR +ve 0.0183 0.26 0.08 - 0.79 Low p27 0.1515 2.49 0.72 - 8.68
Resolved clinicopathological + low p27 model (n = 106) Lymph node +ve 0.0038 19.53 2.61 - 146.19 HER2 amplified 0.0016 3.78 1.66 - 8.64 PR +ve 0.0004 0.17 0.06 - 0.45 (Low p27 falls out)
CI - Confidence Interval; Low p27 - p27H score < or = 110.
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4.2.5 Summary of Cox proportional hazards analyses for cyclin D1, cyclin E, p21WAF1/Cip1 and p27Kip1
Table 4.24: Summary of Cox proportional hazards analyses
HIGH CYCLIN D1 All ER+ve ER-ve ER+ve Chemo Endocrine Analysis End-point Univariate Recurrence Distant Metastasis Breast Cancer-Related Death
Multivariate Recurrence Distant Metastasis Breast Cancer-Related Death
HIGH CYCLIN E All ER+ve ER-ve ER+ve Chemo Endocrine Analysis End-point Univariate Recurrence Distant Metastasis Breast Cancer-Related Death
Multivariate Recurrence Distant Metastasis Breast Cancer-Related Death
HIGH p21 All ER+ve ER-ve ER+ve Chemo Endocrine Analysis End-point Univariate Recurrence Distant Metastasis Breast Cancer-Related Death
Multivariate Recurrence Distant Metastasis Breast Cancer-Related Death
LOW p27 All ER+ve ER-ve ER+ve Chemo Endocrine Analysis End-point Univariate Recurrence Distant Metastasis Breast Cancer-Related Death
Multivariate Recurrence Distant Metastasis Breast Cancer-Related Death
indicates p<0.05 by Cox proportional hazards analysis; High D1 - cyclin D1 H score > 120; High E - cyclin E% cell staining> 0; High p21 - p21 % cells staining > 10; Low p27 - p27 H score < 110; Note: cyclin D1 was analysed as a dichotomous variable in each case; ER +ve - ER H score > 10.
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4.2.6 Inter-relationship between cell cycle protein expression and clinicopathological variables.
The expression of each of the cell cycle proteins, cyclins D1 and E, p21WAF1/Cip1 and p27Kip1, as dichotomised variables was evaluated with respect to the others, and to standard clinicopathological factors using 2 analysis. A summary of the relationships is depicted in Table 4.25. Variables are represented in their negatively prognostic form (i.e. ER negative, HER2 amplified, p27Kip1 low) to allow consistency in the comparison between variables. All correlations are significant to at least p<0.05.
Table 4.25: Correlation matrix of clinicopathological and cell cycle protein expression
Variable Grade LN Size ER -ve PR - HER2 High Low High High Low ve +ve D1 D1 E p21 p27 Grade + + + + + + NS + NS + LN + + NS NS NS NS NS NS NS NS Size + + + + + NS NS NS NS + ER -ve + NS + + + - + + NS + PR -ve + NS + + + NS + + NS + HER2 + NS + + + NS NS NS NS + +ve High D1 + NS NS - NS NS NS + - Low D1 NS NS NS + + NS + NS + High E + NS NS + + NS NS + + NS High p21 NS NS NS NS NS NS + NS + NS Low p27 + NS + + + + - + NS NS
+ - correlation between two negative effects; - - inverse correlation between two negative effects; Grade - grade 3; LN - lymph node positive; Size - size > 20mm; HER2 +ve - HER2 amplified; High D1 - cyclin D1 H score > 120; Low D1 - cyclin D1 H score = 0; High E - cyclin E% staining> 0; High p21 - p21 % staining > 10; Low p27 - p27 H score < 110; NS - not statistically significant; Note: cyclin D1 was analysed as a dichotomous variable in each case; Analyses are performed usng the X2 test.
Consistent with previously published data, the majority of variables that correlated with a poor prognosis were correlated with each other, or failed to reach significance. However high cyclin D1 expression was negatively correlated with both ER negativity and low p27Kip1 expression (ie: this poor prognostic factor was associated with markers of good prognosis, ER positivity and high p27Kip1 expression), and was positively correlated with high p21WAF1/Cip1, a factor that was also correlated with high cyclin E expression (which displayed an association with low rather than high cyclin D1 expression as well as ER and PR negativity).
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To further examine these relationships with the aim of better understanding their potential molecular basis, further analyses were undertaken to determine differences in the expression of the steroid hormone receptors, and cell cycle proteins as continuous, rather than dichotomised variables by both cyclin D1 expression status and cyclin E expression status. As the expression patterns of all of the proteins evaluated were non- normal in distribution, the Mann-Whitney and Kruskall-Wallis tests were used for statistical evaluation. As expected, there was an incremental increase in ER expression between low, medium and high cyclin D1 expressors (p<0.0001) (Figure 4.29). However, the highest level of PR expression was observed in the moderate cyclin D1 expressing (p<0.0001). Thus, while the high cyclin D1 expressing group were strongly ER positive, they were relatively PR poor. Cyclin E expression was highest in the low cyclin D1 expressing group (p=0.0089), while striking increases in the expression of p21WAF1/Cip1 and p27Kip1 were observed in the high cyclin D1 expressing group when compared to the low and moderate cyclin D1 expressing groups (p<0.0001 for all comparisons) (Figure 4.30).
Consistent with the data derived from the 2 analyses, ER and PR expression were lower (p=0.0038 and p<0.0001 respectively), and p21WAF1/Cip1 expression was higher (p<0.0001) in the high cyclin E group (Figures 4.31 – 4.32). No differences were observed between high and low cyclin E expressors in the mean levels of cyclin D1 and p27Kip1 expression. (Figure 4.31 – 4.32).
Thus, both high cyclin D1 and cyclin E overexpression are associated with high tumour grade and poor prognosis, relatively less PR expression and increased expression of the inhibitor p21WAF1/Cip1. In addition, high cyclin D1 expressors demonstrate high expression of the inhibitor p27Kip1 in conjunction with high ER expression.
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Figure 4.29: Expression of ER and PR as continuous variables by cyclin D1 expression level
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Figure 4.30: Expression of cyclin E, p21WAF1/Cip1 and p27Kip1 as continuous variables by cyclin D1 expression level
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Figure 4.31: Expression of ER and PR as continuous variables by cyclin E expression level
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Figure 4.32: Expression of cyclin D1, p21WAF1/Cip1, and p27Kip1 as continuous variables by cyclin E expression level
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4.2.7 Multivariate analysis
To further evaluate the interaction between the various cell cycle markers and clinicopathological factors, further modelling was undertaken in which several cell cycle proteins were modelled together with clinicopathological factors.
The contingency tables (as summarised in Table 4.25) indicated a close link between hormone receptor status and all of the cell cycle proteins studied, with the exception of the least robust of the markers, p21 WAF1/Cip1. As the most reliable of the analyses conducted on the cell cycle markers had been performed in the subgroups defined by hormone receptor status, the analysis was streamlined by conducting it in two groups only - those cases that were both ER and PR positive and those cases that were both ER and PR negative. Thus, this allowed the strong effects of both ER and PR status on cyclin D1, cyclin E and p27Kip1 to be accounted for by stratification into oestrogen- responsive and oestrogen-non-responsive groups. The clinicopathological features of these cohorts are presented in Table 4.26.
Table 4.26: Clinicopathological characteristics of the ER +ve/PR +ve and ER-ve/PR-ve subgroups
Variable ER +ve/PR +ve ER-ve/PR-ve (n = 150) (n = 78)
Grade I 38/150 (25.3%) 2/78 (2.6%) Grade II 75/150 (50%) 13/78 (16.7%) Grade III 37/150 (24.7%) 63/78 (80.8%)
Size > 20 mm 48/150 (32%) 44/78 (56.4%)
Lymph node +ve 57/148 (38.5%) 34/78 (43.6%)
Age > 50 89/150 (59.3%) 56/78 (71.8%)
HER2 amplified 18/146 (12.3%) 24/73 (32.9%)
Chemotherapy 47/150 (31.3%) 36/78 (46.2%)
Endocrine therapy 83/150 (55.3%) 29/78 (37.2%)
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The only cell-cycle protein to predict outcome in the ER and PR positive group (n=150) was cyclin D1 (Table 4.27). On bivariate analyses tumour grade and HER2 status knocked one another out of the model, however when high cyclin D1 was added to the model, HER2 status was predictive of outcome, with grade being the weakest variable (Table 4.27 B and C). In fact high cyclin D1 expression was predictive, along with HER2 amplification for recurrence (p=0.0011, HR 5.24, 95% CI 1.94 – 14.14), metastasis (p=0.0004, HR 6.36, 95% CI 2.28 – 17.71) and breast cancer-related death (p=0.0108, HR 6.66, 95% CI 1.55 – 28.57).
Table 4.27: Multivariate Cox proportional hazards modelling - ER + PR positive subgroup (n=150)
RECURRENCE p Value Hazard Ratio 95% CI A.Variables predictive on univariate analysis* Grade III 0.0067 3.10 1.37 - 7.04 HER2 amplified 0.0042 3.68 1.51 - 8.96 High cyclin D1 0.0082 3.52 1.38 - 8.94
B. Correlations between variables within the ER + PR positive subgroup ( 2 test) Grade III and HER2 amplified <0.0001 (+ve) Grade III and high cyclin D1 0.0153 (+ve) HER2 amplified and high cyclin D1 NS
C. Bivariate Cox proportional hazards analysis Grade III 0.0577 2.37 0.97 - 5.76 HER2 amplified 0.0586 2.56 0.97 - 6.74
Grade III 0.0201 2.68 1.17 - 6.17 High cyclin D1 0.0267 2.93 1.13 - 7.58
HER2 amplified 0.0009 4.97 1.93 - 12.84 High cyclin D1 0.0011 5.24 1.94 - 14.14
D. Model all variables (n = 144) Grade III 0.1434 1.95 0.80 - 3.55 HER2 amplified 0.0125 3.71 1.33 - 10.39 High cyclin D1 0.0031 4.61 1.68 - 10.67
E. Final resolved model (n = 144) HER2 amplified 0.0009 4.97 1.93 - 12.84 High cyclin D1 0.0011 5.24 1.94 - 14.14
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Table 4.27 Continued METASTASIS p Value Hazard Ratio 95% CI A.Variables predictive on univariate analysis* HER2 amplified 0.0462 2.83 1.02 - 7.88 High cyclin D1 0.0020 4.63 1.76 - 12.21
B. Correlations between variables within the ER + PR positive subgroup ( 2 test) HER2 amplified and high cyclin D1 NS
C. Bivariate Cox proportional hazards analysis HER2 amplified 0.0123 3.99 1.35 - 11.77 High cyclin D1 0.0004 6.36 2.28 - 17.71
D. Final resolved model (n = 144) HER2 amplified 0.0123 3.99 1.35 - 11.77 High cyclin D1 0.0004 6.36 2.28 - 17.71
BREAST CANCER DEATH p Value Hazard Ratio 95% CI A.Variables predictive on univariate analysis* HER2 amplified 0.0080 5.55 1.56 - 19.74 High cyclin D1 0.0395 4.15 1.07 - 16.06
B. Correlations between variables within the ER + PR positive subgroup ( 2 test) HER2 amplified and high cyclin D1 NS
C. Bivariate Cox proportional hazards analysis HER2 amplified 0.0030 7.89 2.02 - 30.85 High cyclin D1 0.0108 6.66 1.55 - 28.57
D. Final resolved model (n = 144) HER2 amplified 0.0030 7.89 2.02 - 30.85 High cyclin D1 0.0108 6.66 1.55 - 28.57
CI - Confidence Intervals; NS - not statistically significant; High cyclin D1 - cyclin D1 H score > 120; * All other clinicopathological variables and cell cycle proteins were non-significant for outcome on univariate analysis.
Strikingly, amongst the ER and PR negative cases, no cell cycle marker was predictive of outcome (Table 4.28). Instead, lymph node status was the only predictive variable for recurrence (p<0.0001, HR 5.58, 95% CI 2.49 – 12.47), metastasis (p<0.0001, HR 6.56, 95% CI 2.82 – 15.29) and breast cancer-related death (p<0.0001, HR 10.10, 95% CI 3.47- 29.38).
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Table 4.28: Multivariate Cox proportional hazards modelling - ER and PR negative subgroup (n=78)
RECURRENCE p Value Hazard Ratio 95% CI A.Variables predictive on univariate analysis* Size > 20 mm 0.0446 2.16 1.02 - 4.57 Lymph node +ve <0.0001 5.58 2.49 - 12.47
B. Correlations between variables within the ER + PR positive subgroup ( 2 test) Size > 20 mm and lymph node +ve 0.0074 (+ve)
C. Bivariate Cox proportional hazards analysis (n = 78) Size > 20 mm 0.5163 1.30 0.59 - 2.85 Lymph node +ve 0.0001 5.13 2.21 - 11.90
D. Final resolved model (n = 78) Lymph node +ve <0.0001 5.58 2.49 - 12.47
METASTASIS p Value Hazard Ratio 95% CI A.Variables predictive on univariate analysis* Size > 20 mm 0.0276 2.40 1.10 - 5.23 Lymph node +ve <0.0001 6.56 2.82 - 15.29
B. Correlations between variables within the ER + PR positive subgroup ( 2 test) Size > 20 mm and lymph node +ve 0.0074 (+ve)
C. Bivariate Cox proportional hazards analysis (n = 78) Size > 20 mm 0.4155 1.40 0.62 - 3.16 Lymph node +ve <0.0001 5.90 2.45 - 14.25
D. Final resolved model (n = 78) Lymph node +ve <0.0001 6.56 2.82 - 15.29
BREAST CANCER DEATH p Value Hazard Ratio 95% CI A.Variables predictive on univariate analysis* Lymph node +ve <0.0001 10.10 3.47 - 29.38 B. Final model (n = 78) Lymph node +ve <0.0001 10.10 3.47 - 29.38
CI - Confidence Intervals; * All other clinicopathological variables and cell cycle proteins were non-significant for outcome on univariate analysis.
These data would indicate that in presumed oestrogen-responsive breast cancer (ie: double positive), outcome is determined by biomarkers (HER2 and cyclin D1) with links to high grade, whereas in this small group of oestrogen-non-responsive tumours traditional indicators of metastasis (i.e. lymph node status) prevail.
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4.2.8 Relationship between cell cycle protein expression and the phenotypic subtypes of breast cancer
The optimum immunohistochemical surrogates for the Perou molecular phenotypes of breast cancer for the SVCBCOC were derived by Dr. Sandra O’Toole as described in Chapter 3. Thus the “Luminal A” group was defined as ER and/or PR positive and HER2 non-amplified, the “Luminal B” group was defined as ER and/or PR positive and HER2 amplified, the “HER2” group was defined as ER and PR negative and HER2 amplified and the “Basal” group was defined as ER and PR negative, HER2 non- amplified and CK5/6 positive. Those patients that did not fit into one of these four groups were designated as “Unclassified”.
2 analyses were undertaken to evaluate the association between the Perou subtypes and cyclin D1 expression as a trichotomised variable (defined in Section 4.2.1). Low cyclin D1 expression was inversely correlated with the two luminal subtypes, and positively correlated with the “HER2” and “Basal” subtypes in comparison to moderate + high cyclin D1 expression, while high cyclin D1 expression was correlated with the “Luminal A” phenotype (Table 4.29).
Table 4.29: Correlation matrix of Perou immunohistochemical phenotypes and cell cycle protein expression
Variable High D1 Low D1 High E High p21 Low p27 Luminal A + - - NS - Luminal B NS - NS NS NS HER2 NS + NS NS + Basal NS + + NS + Unclassified NS NS NS NS +
+ = positive correlation ; - = inverse correlation; NS - not statistically significant; Note: cyclin D1 was analysed as a dichotomous variable in each case.
When 2 analyses were undertaken to evaluate the association between the Perou subtypes and cyclin E expression as a dichotomised variable (defined in section 4.2.2), high cyclin E expression was inversely correlated with the “Luminal A” subtype, and positively correlated with the “Basal” subtype. No relationship was identified between high cyclin E expression and the “Luminal B” or “HER2” subgroups (Table 4.29).
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No relationships were detected between p21WAF1/Cip1 expression and the Perou subgroups (Table 4.29). When analysed as a dichotomised variable (defined in section 4.2.4) low 27Kip1 expression was inversely correlated with the “Luminal A” subgroup (p<0.0001), however no relationship was detected with the “Luminal B” subgroup, despite the relationship between low p27Kip1 expression and HER2 amplification (p<0.0001) (Table 4.29). In addition p27Kip1 expression was correlated with the “Basal” and “Unclassified” subgroups (p=0.0011 and p=0.0023 respectively) (Table 4.29).
The expression of cyclin D1, cyclin E, p21WAF1/Cip1 and p27Kip1 was also analysed as a continuous variable (Figure 4.33). These data indicate a similar relationship between the “Luminal” phenotypes and higher levels of cyclin D1 and p27Kip1 expression and lower cyclin E expression (p<0.0001), as well as a relationship bewteen high cyclin E levels and the “Basal” phenotype. High p21WAF1/Cip1 expression was associated with the “Luminal B” phenotype in this analysis (p=0.0212).
In the light of the data from section 4.2.7 suggesting a relationship between cyclin D1 and the outcome of hormone receptor positive disease independent of HER2 status, the prognostic value of high cyclin D1 expression was evaluated in the “Luminal A” and “Luminal B” modified Perou subtypes, in which HER2 amplification status is defined. In the “Luminal A” subtype (n=171), univariate predictors of recurrence were grade, lymph node status, PR expression, high cyclin D1 expression and p21WAF1/Cip1 expression. Univariate predictors of distant metastasis were grade, lymph node status, PR expression and high cyclin D1 expression, and of breast cancer-related death were PR expression and high cyclin D1 expression (Table 4.30).
In the much smaller “Luminal B” subtype (n=27), lymph node status was prognostic for distant metastasis and death, and high cyclin E expression was prognostic for recurrence and distant metastasis. High cyclin D1 expression was not prognostic on univariate analysis, perhaps as a consequence of the small sample size in this group. Of the four high cyclin D1 expressors in the “Luminal B” group (14%), three recurred and two died of their breast cancer. Nonetheless cyclin E appeared to have a dominant effect in this subgroup. There were five high cyclin E expressors in the “Luminal B” subtype comprising 19% of this group, a figure similar to the 16% of the “Luminal A” subtype displaying this expression pattern. However, while 22% of the “Luminal A” high cyclin E cases recurred and 7.4% died, 100% of the “Luminal B” high cyclin E cases
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Figure 4.33: Expression of cell cycle proteins by tumour subtype (modified Perou definition)
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Table 4.30: Univariate predictors of outcome in Perou the molecular phenotypes
p Value Hazard Ratio 95% CI Luminal A Recurrence (n = 171) Grade III 0.0102 2.61 1.26 - 5.43 Lymph node +ve 0.0465 2.15 1.01 - 4.54 PR +ve 0.0006 0.28 0.13 - 0.58 High cyclin D1 0.0004 3.98 1.85 - 8.56 High p21 0.0228 2.85 1.16 - 7.03 Metastasis Grade III 0.0187 2.58 1.17 - 5.69 Lymph node +ve 0.0386 2.37 1.05 - 5.37 PR +ve 0.0027 0.30 0.13 - 0.66 High cyclin D1 0.0005 4.24 1.87 - 9.60 Breast cancer-related death PR +ve 0.0057 0.02 0.08 - 0.65 High cyclin D1 0.0060 4.85 1.57 - 14.98
Luminal B Recurrence (n = 27) High cyclin E 0.0010 9.65 2.51 - 37.15 Metastasis Lymph node +ve 0.0351 4.31 1.11 - 16.75 High cyclin E 0.0095 6.50 1.58 - 26.74 Breast cancer-related death Lymph node +ve 0.0420 4.24 1.05 - 17.09
HER2 Recurrence (n = 24) Lymph node +ve 0.0046 9.37 1.99 - 44.05 High p21 0.0237 5.37 1.25 - 23.02 Metastasis Lymph node +ve 0.0048 9.28 1.97 - 43.67 High p21 0.0242 5.33 1.24 - 22.82 Breast cancer-related death Lymph node +ve Monotone estimation likely
Basal Recurrence (n = 30) Lymph node +ve 0.0059 6.26 1.69 - 23.10 Metastasis Lymph node +ve 0.0031 10.11 2.19 - 46.78 Breast cancer-related death Lymph node +ve 0.0051 19.53 2.44 - 156.54
Unclassified (n = 19) Recurrence High cyclin E 0.0141 6.32 1.45 - 27.51 Metastasis High cyclin E 0.0141 6.32 1.45 - 27.51 Breast cancer-related death High cyclin E 0.0118 8.55 1.61 - 45.43 Low cyclin D1 0.0332 5.33 1.14 - 24.82
High cyclin D1 = cyclin D1 H score > 120; High cyclin E = cyclin E % staining > 0; High p21 = p21 % staining > 10; PR +ve = PR H score > 10. The Perou molecular phenotypes are as defined in Chapter 3.
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recurred and 60% died, suggestive of some co-operativity between cyclin E expression and HER2 amplification. Indeed when all patients with HER2 amplification were examined, high cyclin E expression was predictive of recurrence (p=0.0390, HR 2.43) along with lymph node status (p=0.0007, HR 4.72).
On univariate analysis p21WAF1/Cip1 expression was predictive of recurrence and metastasis in the “HER2” group, while cyclin E expression was predictive of recurrence, metastasis and breast cancer-related death in the “Unclassified” group. No cell cycle marker was predictive in the “Basal” group (Table 4.30).
On multivariate analysis, PR expression and high cyclin D1 expression were independently prognostic for recurrence (p=0.0054 HR 0.34 95% CI 0.16 – 0.73 and p=0.0043 HR 3.15 95% CI 1.44 - 6.94 respectively), distant metastasis (p=0.0160 HR 0.37 95% CI 0.16 – 0.83 and p=0.0036 HR 3.47 95% CI 1.50 – 8.01 respectively), and breast cancer-related death (p=0.0038 HR 0.30 95% CI 0.10 – 0.91 and p=0.0457 HR 3.32 95% CI 1.02 – 10.76 respectively) within the “Luminal A” subgroup (Table 4.31). Multivariate analyses for the “Luminal B”, “HER2”, “Basal” and “Unclassified” groups were not conducted due to the small number of cases in each group.
Table 4.31: Multivariate predictors of outcome in the “Luminal A” molecular phenotypes (final resolved models)
p Value Hazard Ratio 95% CI Luminal A Recurrence (n = 169) PR +ve 0.0054 0.34 0.16 - 0.73 High cyclin D1 0.0043 3.15 1.44 - 6.94 Metastasis PR +ve 0.0160 0.37 0.16 - 0.83 High cyclin D1 0.0036 3.47 1.50 - 8.01 Breast cancer-related death PR +ve 0.0338 0.30 0.10 - 0.91 High cyclin D1 0.0457 3.32 1.02 - 10.76 High cyclin D1 = cyclin D1 H score > 120; Final resolved models are shown following the sequential removal of variables contributing least to the model.
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4.3 DISCUSSION
4.3.1 Cyclin D1
The protein product of the CCND1 gene, cyclin D1, is overexpressed in ~30 to 80% of breast cancers, the variation in reported expression levels being attributable to differences in the composition of patient cohorts evaluated, immunohistochemistry methodologies, schema used to score expression and cut-points for positivity. The CCND1 gene itself is amplified in only ~13-20% of cases 117 indicating that the majority of the observed overexpression of cyclin D1 protein occurs by other mechanisms eg: transcriptional and post-transcriptional dysregulation.
While studies in parathyroid adenomas and mantle-cell lymphoma confirm that CCND1 is an oncogene 356-359 its role as a prognostic and predictive factor in human breast cancer remains the subject of considerable debate. A multitude of disparate studies have now been published with the intent of evaluating the prognostic and predictive impact of cyclin D1 in clinical breast cancer cohorts (refer to Chapter 1). These studies have used a variety of methods to quantitate cyclin D1 expression in a range of patient populations, and have often yielded inconsistent results and interpretations.
Nonetheless, certain findings are reported with some consistency. Cyclin D1 protein expression is associated with ER positivity in the majority of studies, and consequently has been associated with more differentiated, less aggressive cancers 120,123,133,140,144. Thus in some studies, cyclin D1 expression is linked to a superior prognosis 120,130,132,138,145,146. However, others studies in which ER status has been used to define the study cohort report a negative association with outcome. The study by Umekita et al135 determined that high cyclin D1 expression by immunohistochemistry was associated with a poor prognosis among ER negative patients, and the study by Kenny et al 141 showed that cyclin D1 mRNA expression by northern blot was associated with inferior patient outcomes in ER positive patients. Furthermore, the latter authors showed that cyclin D1 overexpression appeared to be associated with resistance to tamoxifen. Stendahl and Landberg reported a similar finding that high expression of cyclin D1 was associated with anti-oestrogen resistance in patients with high ER expression 130. The same group have also reported a discordant response to tamoxifen
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in patients with non-CCND1 amplified but cyclin D1 overexpressing tumours, as compared to patients with CCND1 amplification 121.
Many of the studies evaluating the prognostic value of cyclin D1 have based their cut- points on the studies of Gillett et al 145 in which overexpression was defined relative to the expression in normal epithelium. In their 1996 study of 345 patients, approximately 50% of tumours showed excessive nuclear staining, and around 25% of tumours were judged to be negative for cyclin D1. The use of this as a threshold for overexpression is problematic due to the close relationship between cyclin D1 and ER expression, and the tendency of normal breast epithelium to be ER negative. Others have determined a cut-point for positivity of 5% of cells with nuclear staining by comparison to Western blots of breast cancer cell line lysates 360. Using this 5% cut-point, cyclin D1 expression levels of 48.3 - 67.4% have been reported 132,360.
In order to address the conflict in the literature in relation to the prognostic and predictive value of cyclin D1 expression in human breast cancer, the SVCBCOC was evaluated for cyclin D1 expression by immunohistochemistry. In this cohort, 63.8% of cases would be defined as cyclin D1 positive using a cut-point of 5% of cells staining positive, a rate that is similar to that reported by 132, and suggestive that the spread of expression of cyclin D1 in our cohort is representative of that observed in other studies. Nonetheless, existing studies on the role of cyclin D1 in breast cancer fail to reach consensus, and therefore an optimal cut-point determination technique was used to evaluate the relationship between cyclin D1 expression and outcome.
These results suggest that cyclin D1 expression may be categorised into low (negative), moderate and high expressors. Low and high expressors each comprise approximately 10% of the total cohort and on univariate analyses demonstrate inferior outcomes in the cohort as a whole, among ER positive patients and in those treated with chemotherapy. Marginal effects of uncertain clinical importance were also observed for low expressors in ER negative patients, and high expressors among ER positive patients treated with endocrine therapy, both of which had an increased risk of breast cancer-related death. It is noteworthy that while the p values associated with the determination of the definition of high, moderate and low expression become non- significant when corrected for multiple testing, among the ER positive and chemotherapy subgroups, the differences in outcome are statistically robust suggesting that these cut-points are likely to be valid.
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On multivariate Cox proportional hazards analysis (as a dichotomised variable) against a resolved clinicopathological outcome model, high cyclin D1 expression remained independently predictive of both recurrence and metastasis among ER positive cases, and recurrence in chemotherapy cases, but lost significance in the cohort as a whole. While the relationship between high cyclin D1 expression and adverse outcome in ER positive patients raises the question of the role of cyclin D1 in anti-oestrogen resistance, in contrast to previous studies 141 no predictive effect was apparent on multivariate analysis among hormone receptor positive patients treated with endocrine therapy, perhaps as a consequence of reduced statistical power in this smaller subgroup. It is interesting to note that the high cyclin D1 group comprises 11% of the SVCBCOC, a figure close to the estimated rates of CCND1 amplification in clinical breast cancer cohorts, and although this was not tested in the present study, it is tempting to speculate that these particularly high cyclin D1 expressors may in fact represent those with gene amplification, a group previously thought to demonstrate poor outcomes when treated with endocrine therapy 121.
High cyclin D1 expression was associated with high expression of ER, p27Kip1, p21WAF1/Cip1. Those cases with high cyclin D1 expression displayed less PR expression than observed in those with moderate levels of cyclin D1 expression. High cyclin D1 expression was also associated with high tumour grade. Low cyclin D1 expression was associated with low expression of ER, PR, and p27Kip1, and high expression of cyclin E on 2 analysis, while on Kruskall-Wallis analysis, low cyclin D1 expressors had lower p21WAF1/Cip1 levels.
Thus although high cyclin D1 expressors tend to be ER positive, they display high risk associations with grade and relatively less PR expression, a key indicator of intact (and therefore modulatable) ER signalling. Interestingly, the association with high p27Kip1 and p21WAF1/Cip1 expression poses a number of questions in relation to the role of these inhibitors in regulating the growth of breast cancer cells in vivo. In vitro studies indicate that cyclin D1-cdk4 complexes retain catalytic activity when bound to p27Kip1 and p21WAF1/Cip1 , unlike cyclin E-cdk2 complexes 210,361-363 and therefore given that the high cyclin D1 expressing tumours in our study are also highly proliferative, it is theoretically possible that p27Kip1 and p21WAF1/Cip1 are non-inhibitory in where co-expressed with high levels of cyclin D1. While the expectation that high levels of regulators designed to slow proliferation would be associated with a good rather than poor prognosis phenotype, it
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is conceivable that these cell cycle inhibitors are in fact being sequestered by the high levels of cyclin D1 thus preventing inhibition of cyclin E-cdk2 activity 364.
Low cyclin D1 expressors on the other hand display associations with hormone receptor negativity, and a concomitant phenotype of low p27Kip1 and p21WAF1/Cip1 expression, and high cyclin E expression. Although low cyclin D1 expression was not significantly associated with high tumour grade per se, ER negativity, low p27Kip1 and high cyclin E expression are, and thus the fact that an association with low cyclin D1 and high grade was not detected is surprising, and perhaps a consequence of small sample size. Indeed, no difference in grade was detected by 2 comparisons between the high cyclin D1 and low cyclin D1 expressors (Table 4.3).
The correlations delineated above are entirely consistent with the modified Perou breast cancer phenotypes as a consequence of the dependence of this classification system on ER and PR status. Thus high cyclin D1 expression is associated with the “Luminal A” phenotype when compared to other levels of cyclin D1 expression, while low cyclin D1 expression is associated with the “Basal” and “HER2” phenotypes, and is inversely associated with the two luminal phenotypes. While “Luminal A” breast cancers are regarded as having a particularly good prognosis, those with high cyclin D1 expression within this group are significantly more likely to recur and die of their disease (accounting for over a third of recurrences and deaths) independent of their PR status.
Thus in summary, cyclin D1 expression can be trichotomised into low (negative), moderate and high expressors. While low (negative) cyclin D1 expression is correlated with hormone receptor negativity, low p27Kip1 expression, and high cyclin E expression, high cyclin D1 expression associates with high tumour grade, high p21WAF1/Kip1 expression and ER positivity. Low (negative) cyclin D1 expression is not an independent predictor of breast cancer outcome. However high cyclin D1 expression portends both recurrence and distant metastasis, particularly among ER positive patients, where it is an independent predictor of outcome. Among patients whose tumours are both ER and PR positive, cyclin D1, along with HER2 amplification is an independent prognostic variable. Consistent with these data, high cyclin D1 expression also predicts adverse outcome amongst patients with “Luminal A”-type breast cancer.
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High cyclin D1 expression is predictive of increased risk of breast cancer-related death (and trends towards increased risk of recurrence) among ER positive patients treated with endocrine therapy (largely tamoxifen). In this small group of patients (n = 109), high cyclin D1 expression is not an independent predictor of adverse outcome on multivariate analysis. However, only 109 of 192 ER positive patients (57%) were documented to have received adjuvant endocrine therapy (a low rate of endocrine therapy use in comparison to other series) (Table 3.12). It is possible that this reflects a lack of documentation rather than lack of adjuvant treatment (as patients may have been treated with adjuvant tamoxifen by their medical oncologist or radiation oncologist, rather than the surgeon whose records were reviewed in this study). Thus while these data raise interesting questions in relation to the potential role of cyclin D1 overexpression in anti-oestrogen resistance, in the absence of a matched cohort of definitely non-endocrine-treated ER positive patients it is difficult to draw firm conclusions in relation to the value of cyclin D1 assessment in the prediction of endocrine therapy failure.
Similarly, both low and high cyclin D1 expression (as opposed to moderate expression) were predictive of adverse outcome among the chemotherapy-treated subgroup. These data suggest that in this high-risk group of patients that cyclin D1 expression may provide further prognostic information (which in the case of high cyclin D1 expression remained predictive on multivariate analysis for the outcome of recurrence). Whether this is a specific chemo-resistance effect or a more general adverse effect of aberrant cyclin D1 expression in this high-risk subgroup requires further well-controlled study.
4.3.2 Cyclin E
Cyclin E is overexpressed in ~30% of breast cancers and like cyclin D1 its relationship to outcome is the subject of debate with a number of studies drawing contradictory conclusions (refer to table in the Chapter 1). Nonetheless, the bulk of larger studies indicate that cyclin E overexpression is associated with adverse outcome, with a recent meta-analysis of published data 347 indicating that high cyclin E expression is predictive of poorer overall survival. Despite these data, the prognostic role of cyclin E expression has recently been called into question by a very large study of 2032 patients 165 in which cyclin E protein expression failed to predict for adverse outcome. Importantly, many of the studies performed to date use different methodologies to measure cyclin E
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(RT-PCR, Western blot, immunohistochemistry), and set differing thresholds for overexpression.
A further layer of complexity is added by the studies of Keyomarsi’s group, that have identified a number of low molecular weight (LMW) isoforms of cyclin E in human breast cancer. These LMW isoforms display increased activity in comparison to full length cyclin E, perhaps as a consequence of increased binding to cdk2, and resistance to the inhibitory effects of p21Waf1/Cip1 and p27Kip1 348,365. Furthermore, the expression of cyclin E or its LMW isoforms was a stronger predictor of outcome than lymph node status. Further studies in transgenic mice indicate that overexpression of the LMW isoforms is sufficient to induce mammary tumours with a shorter latency than full length cyclin E, and that such tumours possess greater metastatic potential 366. However, the significance of these LMW forms of cyclin E has been challenged by recent studies in which they have been identified in normal mammary epithelial cells in a similar ratio to full length cyclin E as is found in breast cancer 349.
Cyclin E expression is correlated with increasing disease stage, and with indices of proliferation including Ki67, PCNA and mitotic index. It is preferentially overexpressed in ER negative tumours 175, although not exclusively so and in ER positive cases may mediate resistance to the effects of anti-oestrogens 166,169. Importantly, cyclin E overexpression (along with loss of p27Kip1) is associated with the “basal” breast cancer phenotype, a subgroup of breast cancers of particularly poor prognosis that lack identified targeted therapies 37
In the determination of cyclin E mRNA and protein expression thresholds various techniques and cut-points have been used including optimal cut-point determination 169,174, comparison with normal breast tissue 172,173,367, cutting at the median point in the distribution of the data 170 and ad hoc methods 133,136. In the majority of these studies, the percentage of the cohort determined to be cyclin E overexpressing was between 21.8% and 32%, although two studies using immunohistochemistry found that around 40% of the cohort was cyclin E positive 170,174, and another study found only 10.6% of cases were overexpressors 175.
Using optimal cut-point determination 24.2% of the SVCBCOC were cyclin E overexpressing, which is consistent with previous reports. This point of stratification was highly significant and survived correction for multiple testing. On univariate
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analysis, those patients with high cyclin E expression were significantly more likely to suffer a disease recurrence, distant metastasis or breast cancer-related death. Subgroup analyses further showed that high cyclin E expression was predictive of recurrence amongst ER positive patients, recurrence and distant metastasis among ER negative cases and metastasis and breast cancer-related death among those patients treated with adjuvant chemotherapy. On multivariate analysis high cyclin E expression remained independently predictive of recurrence and distant metastasis in the whole cohort, and in the ER negative subgroup only. However, high cyclin E expression was not predictive of outcome in the ER and PR negative cases. Whether this represents an effect of the small sample size in this latter group, or is a manifestation of the correlation with PR status is unclear.
Nonetheless, these data indicate similar prognostic findings for high cyclin E expression to the bulk of previously published studies in this area. In addition, consistent with previous studies, cyclin E overexpression was correlated with ER negativity, low PR expression, and high tumour grade. High cyclin E expression was also associated with high p21WAF1/Cip1 expression on both 2 and Mann-Whitney analysis, which in a manner similar to that seen for the high cyclin D1 group, may be a reflection of sequestration into cyclin E-cdk2 complxes.
On 2 and non-parametric analyses, the “Basal” and “Luminal A” groups displayed significant relationships with cyclin E expression (positive and inverse respectively). These relationships are consistent with the strong association between cyclin E expression and hormone receptor status (that define the Perou subtypes) and with previous reports. In addition, amongst “Luminal B” cases, high cyclin E expression was prognostic for recurrence and distant metastasis on univariate analysis, with 100% of the high cyclin E/ “Luminal B” cases dying of their disease. This observation is suggestive of a potential interaction between cyclin E and HER2 amplification in these patients, as a similar effect was not observed in the HER2 non-amplified “Luminal A” group. Indeed cyclin E overexpression was associated with an increased risk of recurrence amongst all HER2 amplified patients although this was not prognostic on multivariate analysis (data not shown). However, cyclin E expression was not prognostic in the “HER2” Perou subtype. With only 24 cases in this group, it is difficult to draw definitive conclusions as to whether or not a prognostic relationship between HER2 amplification and high cyclin E expression exists, or if such as relationship is exclusive to hormone receptor positive cases. There are now several in vitro studies
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that link HER2 signalling to the modulation of cyclin E-cdk2 activity through changes in the distribution of p27Kip1 368,369, and in the light of studies demonstrating cross-talk between the HER2 and oestrogen receptor signalling pathways 370 of which cyclin E is a key effector, it is tempting to speculate that the latter scenario may be the case.
Thus, in the SVCBCOC high cyclin E expression is associated with hormone receptor negativity and high tumour grade, and portends poor patient outcome in the entire cohort and amongst ER negative cases. High cyclin E expression is associated with the “Basal” breast cancer subtype and is inversely associated with the “Luminal A” subtype. Furthermore, high cyclin E expression predicts poor outcome in the “Luminal B” subtype of breast cancer.
4.3.3 p21WAF1/Cip1
The p21WAF1/Cip1 protein has been evaluated in several immunohistochemistry-based studies of breast cancer tissue cohorts. These studies have conflicted substantially in both methodology and outcome and at present it is difficult to draw firm conclusions in relation to the prognostic and predictive power of p21WAF1/Cip1 expression (refer to summary tables in Chapter 1).
Various cut-points have been used for the determination of p21WAF1/Cip1 positivity, including > 5% of nuclei staining 192, >10% of nuclei staining 200,202,203, and > 25% of nuclei staining 201, and have defined vastly different proportions of their study populations. For example, the >10% cut-point used by Caffo et al 203, Wakasugi et al 202 and Diab et al 200, defined 32%, 49% and 4.3% of their cohorts as overexpressors respectively. The first of these studies showed an association between p21WAF1/Cip1 overexpression and adverse outcome, the second with improved outcome, and the latter showed no association with outcome.
Further complicating matters, a number of more recent studies indicate that p21WAF1/Cip1 may be relocalised to the cytoplasm in association with Her2 overexpression where it is associated with an adverse prognosis 191,193. In the study by Winters et al 193, p21WAF1/Cip1 expression was graded semiquantitatively to generate separate intensity distribution scores for nuclear and cytoplasmic expression. In the study by Xia et al 191 nuclear p21WAF1/Cip1 was defined as the fraction of tumour cells with positive nuclear
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staining greater than that of cytoplasmic staining and vice versa. By this method, 26% of cases were p21WAF1/Cip1 nuclear expressors, and 33% were cytoplasmic expressors.
Thus a consistent threshold for p21WAF1/Cip1 overexpression or underexpression remains undefined. Aside from the lack of consensus in the measurement of p21WAF1/Kip1 expression, the biology of p21WAF1/Kip1 is complex, with both anti-proliferative effects via the negative regulation of cyclin E-cdk2 activity 371, and anti-apoptotic effects via cytoplasmic relocalisation 372. Thus the prognostic and predictive role of p21WAF1/Cip1 expression in human breast cancer clinical populations remains unresolved.
In the evaluation of p21WAF1/Kip1 in the SVCBCOC, nuclear expression only was observed. Cytoplasmic expression was not seen, possibly as a result of differences in the staining protocol used in comparison to those of other studies 191,193. Furthermore, robust cytoplasmic expression has not been described in the majority of p21WAF1/Kip1 studies (Table 1.7). Optimal cut-point determination was performed however no robust dichotomies could be observed. A weak cut-point was apparent at a percentage expression of >10% (corresponding to 11% of the cohort), the same cut-point used by several other investigators 200,202,203, and while high expression predicted a higher risk of recurrence on univariate analysis, it was not predictive on multivariate analysis,
Amongst ER positive cases treated with endocrine therapy, high p21WAF1/Cip1 expression predicted for increased breast cancer-related death that remained statistically significant on multivariate analysis. Given the failure of p21WAF1/Cip1 expression to predict patient outcome for recurrence or metastasis (end-points which precede and predict death) one should be careful not to over-interpret the significance of these data. Nonetheless, the association between high p21WAF1/Cip1 expression and high levels of both cyclin D1 expression and cyclin E expression are reflective of the role of the role p21WAF1/Cip1 in binding to cyclin-cdk complexes. The fact that in our cohort cyclin E overexpressing tumours displayed a high grade phenotype and were associated with poor outcomes despite high p21WAF1/Cip1 expression is difficult to explain and may in fact relate to the contradictory functions of p21WAF1/Cip1 in vivo, as both a negative cell cycle regulator and down-stream effector of the p53 response to cellular damage 188.
4.3.4 p27Kip1
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The majority of studies addressing the value of p27Kip1 assessment by immunohistochemistry indicate that p27Kip1 loss is a significant adverse prognostic factor in breast cancer (see Chapter 1). High p27Kip1 levels are generally associated with low tumour grade, ER positivity and high cyclin D1 expression, and are inversely correlated with cyclin E expression 221. p27Kip1 is normally expressed at high levels in normal breast epithelial cells, but undergoes downregulation in breast carcinoma, through a variety of mechanisms, including increased degradation by the SCFSkp2 complex 373. A key component of this complex is the S phase kinase associated protein Skp2 that is amplified or overexpressed in breast cancer, and correlated with low p27Kip1 expression. In addition, Skp2 has been linked to the so-called “triple negative” (ER-/ PR-/ HER2 non-amplified) or basal phenotype of breast cancer raising the possibility that through its regulation of p27Kip1, Skp2 may be an appropriate target for this subtype of breast cancers 374.
Review of a number of studies indicates reasonable uniformity in the thresholds used to determine p27Kip1 expression, or if not the cut-point per se, a similar percentage of cases with loss of p27Kip1 expression. Using a cut-point of <50% of nuclei staining, p27Kip1 loss has been observed in ~50% of cases 133,215 and where set at a combination of <50% strong staining or <75% moderate staining, defines a subgroup of 56% of patients 221. In a study of Ashkenazi Jewish women in which there was a relatively high rate of 10.9% BRCA1 mutation, the rate of p27Kip1 loss was slightly higher at 61.3%, consistent with the known association of p27Kip1 loss with the basal phenotype, which in turn is associated with BRCA1-mutant breast cancer 37.
In the SVCBCOC, the optimally determined cut-point of p27Kip1 H score >110 defined the percentage of patients with p27Kip1 loss to be 56%, which is similar to previous studies (Table 1.8) and suggests that the distribution of expression in our studies is similar to that observed in other cohorts. The differences in outcome between high and low expressing cases were statistically significant on both univariate and multivariate analysis in the cohort as a whole (metastasis and breast cancer-related death), among ER positive cases (breast cancer-related death), and ER positive cases treated with adjuvant endocrine therapy (recurrence). However, p27Kip1 expression was not prognostic on univariate analysis in the ER/PR positive subgroup as a likely consequence of the close association between p27Kip1 expression and endocrine status
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i.e. where ER and PR status are both positive or both negative, p27Kip1 expression provides no further prognostic information.
Low p27Kip1 expression was correlated with ER and PR negativity, large tumour size, high tumour grade and inversely correlated with moderate and high cyclin D1 expression which is consistent with previous studies 221. Low p27Kip1 was also correlated with the non-luminal Perou breast cancer phenotypes. In addition low p27Kip1 expression in our cohort was associated with HER2 amplification. Interestingly, p27Kip1 expression was higher among “Luminal A” than “Luminal B” patients on Kruskall-Wallis analysis, and “Luminal B” cases did not show a significant relationship with p27Kip1 expression when dichotomised into high versus low expressors, perhaps as a consequence of the association with HER2 amplification, as Her2 is known to mediate enhancement of p27Kip1 ubiquitination and degradation in vitro 375.
Thus in summary, low p27Kip1 expression is associated with ER, PR and cyclin D1 negativity, HER2 amplification, high tumour grade, large tumour size and non-luminal phenotype. Low p27Kip1 expression is associated with adverse outcome, particularly in patients with ER positive tumours, and when modelled with cyclin D1 expression.
4.4 SUMMARY
These studies present a comprehensive analysis of aberrant expression of several of the key players in the G1-S phase of the cell cycle and their relationships between one another and standard clinicopathological variables. Key findings are that high level cyclin D1 is an independent predictor of outcome in ER positive patients, and high cyclin E is an independent predictor of outcome in all patients and ER negative disease. Low level p27Kip1 expression predicts poor outcome from early breast cancer in all patients, ER positive patients, and ER positive patients treated with endocrine therapy. p21WAF1/Cip1 is overexpressed in the high-grade high cyclin D1 and high cyclin E groups, perhaps as a result of sequestration. High cyclin D1 expression and high cyclin E expression identified cases of poor outcome within the “Luminal A” and “Luminal B” phenotypic subtypes respectively. In addition, high p21WAF1/Cip1 expression identified cases with a poor outcome in the “HER2” subtype.
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Among cases that are both ER and PR positive (i.e. the most likely to be endocrine responsive), cyclin D1 and HER2 amplification status independently predicted outcome. In the small (and potentially underpowered) ER and PR negative group, which are most likely to be endocrine non-responsive, aberrant expression of cell cycle proteins did not add further prognostic information.
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CHAPTER 5: RELATIONSHIP BETWEEN C-MYC EXPRESSION AND BREAST CANCER EVOLUTION AND OUTCOME
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5.1 INTRODUCTION
c-Myc is a nuclear transcription factor of the basic helix-loop-helix leucine zipper (HLH- LZ) family. As a dimer with its binding partner Max it binds to E-boxes in the DNA sequence of target gene promoters, thereby mediating transcriptional activation. c-Myc is a key cell cycle regulator whose expression is rapidly upregulated by oestrogen and down-regulated by anti-oestrogens in breast cancer cells with consequent associated induction of proliferation and quiescence, respectively 251,376. As a result of its role as an oestrogen/anti-oestrogen-regulated gene, speculation has arisen that c-Myc may mediate resistance to anti-oestrogen therapies. Indeed, there is now increasing although conflicting evidence linking dysregulated c-Myc expression with breast cancer outcome, and resistance to endocrine therapy (Table 1.10). c-Myc is thought to be activated in as many as 70% of cancers 377. This increase in c- Myc activity is mediated by a number of mechanisms including gene amplification, gene translocation, enhanced translation, increased protein stability and alterations in transcription 377. Beyond the pro-proliferative effects described above, the cellular effects of c-Myc are diverse and also include replication, growth, metabolism, differentiation and apoptosis. It is notable that the proliferative and apoptotic functions appear to be contradictory in relation to whether c-Myc ultimately promotes a pro- cancer or anti-cancer phenotype.
Breast cancer arises from the epithelium of the terminal duct lobular unit, the site of origin of the majority of intraductal proliferative lesions 22. Breast carcinogenesis involves a multistep process in which benign epithelium becomes hyperplastic before progressing to in situ and then invasive carcinoma 378. However, the relationship is not strictly linear 25, with immunophenotyping and molecular genetic studies indicating that ADH shares many similarities with low grade DCIS, while low grade and high grade DCIS represent genetically distinct disorders that lead to distinct forms of invasive breast carcinoma. While there is some debate as to whether UDH constitutes a precursor to breast cancer, CGH studies suggest it can be a precursor to ADH 379. Clinical follow-up studies suggest that intraductal proliferative lesions are associated with increased risk of development of IDC. UDH is associated with 1.5x the risk of developing breast cancer compared to a reference population while the risk associated with ADH is increased 4-5 fold, and DCIS 8-10 fold 380.
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The evolution of one precursor lesion to another is due to the accumulation of a number of genetic events including the activation of oncogenes such as cyclin D1 and the inactivation of tumour suppressor genes such as BRCA1. c-Myc amplification has been evaluated in a number of studies of invasive breast cancer, and breast cancer progression. These studies suggest that c-Myc amplification is seen preferentially in invasive carcinoma or higher grade cases of DCIS 381-383 with no amplification observed in associated benign or hyperplastic lesions. These data suggest that c-Myc amplification occurs relatively late in breast cancer progression, being found mainly in tumour cells that are genetically unstable and able to tolerate the presence of the oncogene.
Gene amplification accounts for a minority of c-Myc overexpression (~15%), while mRNA and protein expression levels average 50% in various studies 384. Despite this, there are few studies addressing the change in c-Myc protein expression during breast cancer development and therefore, to evaluate the changes in protein expression throughout the evolution of breast cancer c-Myc was assessed in a breast cancer progression series, the GRPAHPC (Refer to Chapter 2). In addition, c-Myc expression was assessed in a mouse model of c-Myc-induced carcinogenesis, in which c-Myc is induced under the control of the MMTV promoter. Finally, the studies conducted to date evaluating the prognostic role of c-Myc in breast cancer patient cohorts have failed to conclusively determine if aberrant expression at the protein level is ultimately prognostic for patient outcome. To address this question c-Myc expression was analysed by immunohistochemistry in the SVCBCOC.
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5.2 RESULTS
5.2.1 Validation of c-Myc staining in cell pellets with inducible c-Myc expression
As c-Myc immunohistochemistry is not a widely used technique, the staining was validated in a MCF-7 breast cancer cell line with zinc-inducible c-Myc expression, (described in Chapter 2). These cells were grown from a starting seeding density of 5 x 105 cells per T75 flask in RPMI 1640 supplemented with 5% FCS for 2 days. At this point they were treated with increasing concentrations of ZnSO4 to final concentrations of 0, 25, 50 and 100 μM for 6 hours. After treatment cells were harvested for cell pelleting and concurrent total cell lysates as described in Chapter 2. The cell pellets were stained for c-Myc using an anti-c-Myc antibody (9E10) (DAKO) as described in Chapter 2 then counterstained with Shandon’s Haematoxylin, dehydrated and mounted for microscopic analysis. Corresponding total cell lysates were separated using SDS- PAGE, transferred to PVDF membrane and immunoblotted for c-Myc using the Santa- Cruz anti-c-Myc antibody as described in Chapter 2. This latter antibody is widely used and validated in our laboratory for Western analysis of c-Myc expression 251.
The results indicate that as c-Myc induction as detected by Western blot increases with increasing concentrations of zinc, so does the intensity and frequency of c-Myc staining by immunohistochemistry. MCF-7 cells bearing the empty vector show no such induction of staining, and thus the staining is not due to the effect of zinc itself (Figure 5.1). Therefore, the staining detected by the DAKO anti-c-Myc antibody in paraffin- embedded samples is likely to truly represent c-Myc protein expression.
5.2.2 c-Myc expression and localisation changes during the evolution of breast cancer
The expression of c-Myc in the GRPAHPC was analysed in both the nuclear and cytoplasmic compartments. Representative images of the staining observed in the various pathological lesions are depicted in Figure 5.2 while the changes in simplified H
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Figure 5.1: Validation of c-Myc immunohistochemistry
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Figure 5.2: Spectrum of c-Myc expression patterns in the evolution of breast cancer
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score in each cellular compartment are presented in Figure 5.3. Significant p values (ANOVA) are tabulated in Table 5.1. Nuclear expression increased dramatically between normal ducts and lobules from reduction mammoplasty patients, and normal ducts and lobules in association with carcinoma (p=0.0032), and again between the latter and UDH (p<0.0001). Nuclear expression then declined in the in situ and invasive carcinoma lesions with a stepwise decline in expression between DCIS grade 2 and 3 (p=0.0040) and between IDC grades 1 and 3 (p=0.0188). A dramatic representation of the change observed in c-Myc expression between “normal” epithelium and DCIS is shown in Figure 5.4, where it can be observed that the apical cell layer of the normal ductal epithelium displays strong nuclear staining while the adjacent DCIS shows loss of nuclear staining, and occasional cells have developed a weak cytoplasmic blush. Notably, the expression on normal breast tissue appeared to be predominantly in the basal/myoepithelial cell layer, while a more luminal expression pattern was observed in association with cancer, and upon the development of hyperplasia (Figure 5.2).
Concurrent with the changes in nuclear expression, several changes in the expression of cytoplasmic c-Myc were also noted (Figures 5.2 - 5.3; Table 5.1). Dramatic increases in c-Myc expression were apparent with the transition from UDH to grade 2 and grade 3 DCIS (p<0.0001). There was a significant decline between grades 2 and 3 DCIS and grades 1 and 2 invasive carcinoma (p<0.02 for all comparisons) although the decline in expression between grade 3 DCIS and grade 3 IDC was not statistically significant. Thus, in general, there was an increase in cytoplasmic staining with the transition to invasive carcinoma, and with higher tumour grade. Although there was less pronounced cytoplasmic expression in those lesions that had acquired the invasive phenotype, the association with higher grade was preserved. There was no statistically significant difference in cytoplasmic c-Myc expression between grade 1 DCIS and IDC, and between grade 3 DCIS and IDC (Table 5.1) suggestive that lesions linked by grade are more similar than those linked by the presence or absence of invasion.
It is notable that the distribution pattern of c-Myc expression was not normal, and thus the ANOVA results described in the preceding paragraphs may not be the optimum statistical test. Log-transformation of the data did not normalise the distribution and therefore, non-parametric tests were also performed on these data. The results from these analyses indicate similar patterns of c-Myc localisation although do not provide
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Table 5.1: Changes in c-Myc protein expression (ANOVA)
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Figure 5.3: Spectrum of c-Myc expression in the evolution of breast cancer
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Figure 5.4: c-Myc expression in DCIS encroaching upon an otherwise normal duct
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detailed statistical discrimination between pathological subgroups (Figures 5.5 and 5.6).
5.2.3 c-Myc expression is predominantly cytoplasmic during c-Myc- induced mammary carcinogenesis in mice
The expression of c-Myc was evaluated within a mouse model of c-Myc-mediated carcinogenesis. In this model, c-Myc is induced under the control of a tetracycline- inducible promoter 318. After oral administration of tetracycline, mice develop hyperplastic mammary lesions at around 2 months, and then mammary tumours at around 6 months. Paraformaldehyde-fixed, paraffin-embedded mammary gland tissue from mice treated with tetracycline for varying periods of time was stained for c-Myc using a modification of the staining methodology employed for human tissue to account for the use of a murine antibody on mouse tissue as described in Chapter 2. Representative photomicrographs of the staining patterns identified are shown in Figure 5.7. While c-Myc expression was apparent at the 6-day point, intense c-Myc staining was clearly visible in the hyperplastic mammary glands harvested after 2 months of c-Myc induction. Notably, the expression pattern was entirely cytoplasmic, unlike that observed in positive control human breast cancer tissue where nuclear staining was also observed. In the c-Myc-induced mammary tumour harvested after 6 months, cytoplasmic expression was clearly apparent, although was less intense than that observed in the hyperplastic lesions. Thus c-Myc-induced mammary proliferative lesions display cytoplasmic rather than nuclear c-Myc expression, and show some decline in expression with the development of invasive carcinoma.
5.2.4 A “high-risk” c-Myc staining pattern associated with poor prognosis is definable by high cytoplasmic staining in the setting of low nuclear staining
The SVCBCOC was used to assess the impact of c-Myc expression on outcome from early breast cancer. Examination of the TMAs by light microscopy demonstrated a number of different c-Myc staining patterns (Figure 5.8). Cut-offs for c-Myc expression
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Figure 5.5: Expression of c-Myc in the nucleus and cytoplasm during breast cancer progression
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Figure 5.6: Expression of c-Myc in the nucleus and cytoplasm during breast cancer progression
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Figure 5.7: c-Myc immunohistochemistry in a mouse model of c-Myc-induced carcinogenesis
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Figure 5.8: c-Myc expression in IDC
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were determined using an optimal cut-point technique, in which c-Myc nuclear and cytoplasmic simplified H score (in increments of 10 between the 10th and 90th centiles) were tested for predictive strength by Kaplan-Meier analysis, using logrank statistics (Harvey et al 1999). At a cut-off of a nuclear H score >20, expression was associated with a better outcome (p=0.0496 for recurrence) however this cut-point fell out of multivariate analysis due to correlations with low tumour grade (p=0.0042), ER positivity (p=0.0030) and PR positivity (p=0.0224). Consistent with the association with hormone receptor positivity, nuclear c-Myc expression was associated with high p27Kip1 expression (p=0.0007) and cyclin D1 expression at the cyclin D1 > 0 cut-point (p=0.0022) with 93% of those with high nuclear c-Myc expression also showing cyclin D1 expression above an H score of 0 (refer to Chapter 4).
At a cut-point of a cytoplasmic H score >10, expression was associated with a poorer prognosis (p=0.0413 for recurrence) however this cut-point fell out of multivariate analysis due to correlations with high tumour grade (p=0.0020), larger tumour size (p=0.0094) and lymph node positivity (p=0.0164). No associations were apparent between cytoplasmic expression and hormone receptor expression.
In the light of the data from the GRPAHPC indicating a shift in c-Myc localisation from nuclear to cytoplasmic during breast cancer evolution, we elected to evaluate a combination of nuclear and cytoplasmic staining. By combining low nuclear expression and high cytoplasmic expression at the two H score cut-offs determined above, a group with significantly worse prognosis was defined in which there was a combination of nuclear H score < 20, and cytoplasmic expression > 10 (p=0.0005 for recurrence). A number of combinations of nuclear and cytoplasmic H scores close to these values were then tested and the most predictive cut-point determined to be a combination of nuclear H score < 20 and cytoplasmic H score > 20. In addition, these tests for significance withstood Bonferroni correction for multiple testing.
Thus a cohort of 37 patients (~13% of the cohort) was defined in which there was high cytoplasmic staining (H score >20) concurrent with low nuclear staining (H score < 20). This pattern of staining was highly predictive of all recurrence, distant metastasis and breast cancer-related death in the cohort as a whole with p values of 0.0001, 0.0008 and 0.0004, respectively on logrank analysis (Figure 5.9). Thus this staining pattern was designated “high-risk” c-Myc expression. The pattern of staining was also highly
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Figure 5.9: Kaplan-Meier curves for c-Myc expression in the entire cohort
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predictive of all recurrence, distant metastasis and breast cancer-related death in the ER positive (Figure 5.10), and ER positive cases treated with endocrine therapy (Figure 5.12). The pattern of staining did not predict outcome in the ER negative subgroup (Figure 5.11), however there was a trend towards significance for disease recurrence, distant metastasis and breast cancer-related death in the chemotherapy- treated subgroup (p = 0.0629, 0.0500 and 0.0520 respectively) (Figure 5.13).
5.2.5 “High-risk” c-Myc staining pattern is correlated with high tumour grade, large tumour size and oestrogen receptor negativity
Analysis of the entire cohort using contingency tables to demonstrate correlations between “high-risk” c-Myc staining pattern and other clinicopathological variables showed highly significant associations between “high-risk” c-Myc staining pattern and high tumour grade (p<0.0001), size > 20 mm (p=0.0011) and ER negativity (p=0.0038) (Table 5.2).
No associations were observed with the expression of other cell cycle proteins when analysed as dichotomous variables. However, when analysed as continuous variables (Figures 5.14 - 5.15), “high-risk” c-Myc expressors displayed less p27Kip1 expression (p=0.0018), consistent with the close relationship between p27Kip1 loss and ER negativity. In addition “high-risk” c-Myc expression was associated with lower ER (p=0.0018) and PR (p=0.0013) expression levels. In the ER positive subgroup “high- risk” c-Myc staining pattern was correlated with high tumour grade ( 2 analysis: p=0.0005). There were no significant correlations in the cohort that was ER positive and treated with endocrine therapy as this group is likely to be uniformly associated with higher risk clinicopathological features as a requisite for selection for adjuvant therapy. In the chemotherapy-treated subgroup “high-risk” c-Myc staining was correlated with high tumour grade, large tumour size and HER2 amplification ( 2 analysis: p= 0.0177, 0.0084 and 0.0221 respectively).
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Figure 5.10: Kaplan-Meier curves for c-Myc expression in the ER positive cases
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Figure 5.11: Kaplan-Meier curves for c-Myc expression in the ER negative cases
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Figure 5.12: Kaplan-Meier curves for c-Myc expression in the ER positive cases that were treated with adjuvant endocrine therapy
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Figure 5.13: Kaplan-Meier curves for c-Myc expression in the cases that were treated with adjuvant chemotherapy
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Table 5.2: Contingency table of c-Myc expression and standard clinicopathological variables + cell cycle proteins c-Myc c-Myc Variable (High-risk) (Low-risk) Total p value Interpretation Grade III 30 96 126 <0.0001 c-Myc "High-risk" expression is Grade I and 2 7 145 152 correlated with high tumour grade Total 37 241 278
Lymph node +ve 21 100 121 NS Lymph node -ve 16 138 154 Total 37 238 275
Size > 20 mm 24 88 112 0.0011 c-Myc "High-risk" expression Size < 20 mm 13 153 166 is correlated with large tumour size Total 37 241 278
ER +ve 18 172 190 0.0038 c-Myc "High-risk" expression is inversely correlated with ER ER -ve 19 66 85 positivity Total 37 238 275
c-Myc "High-risk" expression PR +ve 16 144 160 NS (0.0511) trends PR-ve 21 95 116 towards an inverse correlation Total 37 239 276 with PR positivity
c-Myc "High-risk" expression HER2 +ve 11 40 51 NS (0.0677) trends HER2 -ve 26 194 220 towards a correlation Total 37 234 271 with HER2 amplification
Cyclin D1 (high) 7 28 35 NS Cyclin D1 (mod) 25 186 211 Cyclin D1(low) 5 23 28 Total 37 237 274
Cyclin E (high) 13 54 67 NS Cyclin E (low) 24 185 209 Total 37 239 276 p21 (high) 6 23 29 NS p21 (low) 31 217 248 Total 37 240 277 p27 (high) 11 108 119 NS p27 (low) 26 128 154 Total 37 236 273
NS - not statistically significant. Mod – moderate; Analyses are performed usng the X2 test. High cyclin D1 - cyclin D1 H score > 120; Low cyclin D1 - cyclin D1 H score = 0; High cyclin E - cyclin E% cell staining> 0; High p21 - p21 % cells staining > 10; Low p27 - p27 H score < 110; HER2+ve - HER2 amplified; HER2 -ve - HER2 non-amplified; "High-risk" c-Myc expression pattern - nuclear H score < 20 and cytoplasmic H score > 20.
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Figure 5.14: Expression of ER and PR as continuous variables by c-Myc expression pattern
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Figure 5.15: Expression of cell cycle proteins as continuous variables by c-Myc expression pattern
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5.2.6 The “high-risk” c-Myc expression is an independent predictor of outcome on multivariate analysis with clinicopathological variables
Consistent with the earlier logrank analysis, on univariate Cox proportional hazards analysis “high-risk” c-Myc expression was predictive of increased likelihood of recurrence, distant metastasis and breast cancer-related death for the entire cohort, the ER positive subgroup, and the ER positive subgroup that was treated with endocrine therapy (Table 5.3).
Table 5.3: Univariate Cox proportional hazards analysis for the "high-risk" c-Myc expression pattern Hazard 95% Confidence p Value Ratio Interval Entire cohort (n = 278) All recurrence 0.0003 2.66 1.58 - 4.49 Distant metastasis 0.0012 2.47 1.44 - 4.34 Breast cancer-related death 0.0007 2.92 1.57 - 5.42 ER +ve (n = 190) All recurrence 0.0011 3.44 1.64 - 7.23 Distant metastasis 0.0144 2.82 1.23 - 6.48 Breast cancer-related death 0.0098 3.77 1.38 - 10.33 ER -ve (n = 85) All recurrence NS 1.56 0.74 - 3.30 Distant metastasis NS 1.59 0.75 - 3.37 Breast cancer-related death NS 1.68 0.75 - 3.76 ER +ve/ Endocrine treatment (n = 108) All recurrence 0.0098 3.44 1.35 - 8.80 Distant metastasis 0.0086 3.59 1.38 - 9.31 Breast cancer-related death 0.0006 8.91 2.55 - 31.14 Chemotherapy treatment (n = 107) All recurrence NS 1.96 0.95 - 4.05 Distant metastasis NS 2.05 0.99 - 4.25 Breast cancer-related death NS 2.23 0.97 - 5.11
NS - not statistically significant; "High-risk" c-Myc expression pattern - nuclear H score < 20 and cytoplasmic H score > 20.
Multivariate Cox proportional hazards analysis of the entire cohort could define an optimum model predictive of outcome which included lymph node status, PR status, HER2 amplification and “high-risk” c-Myc expression pattern in which c-Myc remained independently prognostic for recurrence (p=0.0434, HR 1.73, 95% CI 1.02 – 2.96), and breast cancer-related death (p=0.0404, HR 1.94, 95% CI 1.03 – 3.60) (Table 5.4).
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Table 5.4: Multivariate Cox proportional hazards analysis for the "high-risk" c-Myc expression pattern
Variable p value Hazard 95% Confidence Ratio Interval RECURRENCE Multivariate analysis grade 3 0.1260 1.58 0.88 - 2.83 (n = 267) size > 20 mm 0.6716 1.11 0.68 - 1.82 LN +ve 0.0003 2.56 1.53 - 4.28 HER2 +ve 0.0156 1.93 1.13 - 3.28 ER +ve 0.8173 1.07 0.60 - 1.93 PR +ve 0.0025 0.40 0.22 - 0.72 c-Myc (high-risk) 0.1665 1.50 0.84 - 2.68
Resolved model LN +ve 0.0001 2.65 1.61 - 4.36 (n = 268) HER2 +ve 0.0044 2.10 1.26 - 3.50 PR +ve <0.0001 0.35 0.21 - 0.58 c-Myc (high-risk) 0.0434 1.73 1.02 - 2.96
DISTANT METASTASIS Multivariate analysis grade 3 0.3211 1.37 0.74 - 2.53 (n = 267) size > 20 mm 0.1715 1.44 0.85 - 2.44 LN +ve <0.0001 3.21 1.83 - 5.61 HER2 +ve 0.0191 1.94 1.11 - 3.38 ER +ve 0.6548 0.87 0.47 - 1.61 PR +ve 0.0020 0.36 0.19 - 0.69 c-Myc (high-risk) 0.4995 1.23 0.67 - 2.26
Resolved model LN +ve <0.0001 3.45 2.03 - 5.93 (n = 270) HER2 +ve 0.0070 2.58 1.22 - 3.54 PR +ve <0.0001 0.28 0.16 - 0.49
BREAST CANCER-RELATED DEATH Multivariate analysis grade 3 0.3798 1.38 0.67 - 2.84 (n = 267) size > 20 mm 0.5728 1.19 0.65 - 2.19 LN +ve 0.0001 3.49 1.83 - 6.65 HER2 +ve 0.0012 2.80 1.50 - 5.23 ER +ve 0.3784 0.72 0.35 - 1.49 PR +ve 0.0043 0.31 0.14 - 0.69 c-Myc (high-risk) 0.2416 1.51 0.76 - 3.00
Resolved model LN +ve 0.0002 3.28 1.77 - 6.08 (n = 268) HER2 +ve 0.0003 3.03 1.66 - 5.54 PR +ve <0.0001 0.23 0.12 - 0.46 c-Myc (high-risk) 0.0404 1.94 1.03 - 3.60
Resolved models were generated after sequential removal of least significant variables; "High-risk" c-Myc expression pattern - nuclear H score < 20 and cytoplasmic H score > 20.
In the ER positive subgroup, c-Myc was independently prognostic for recurrence (p=0.0004, HR 3.92, 95% CI 1.84 – 8.33), and breast cancer-related death (p=0.0081, HR 4.10, 95% CI 1.44 – 11.66) in a similarly generated model that contained PR status, HER2 amplification, and “high-risk” c-Myc expression, and for distant metastasis (p=0.0292, HR 2.58, 95% CI 1.10 – 6.04) in a model that contained lymph
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node status, PR status, HER2 amplification, and “high-risk” c-Myc expression (Table 5.5).
Table 5.5: Multivariate Cox proportional hazards analysis for the "High-risk" c-Myc expression pattern in the ER positive subgroup
Variable p value Hazard 95% Confidence Ratio Interval RECURRENCE Multivariate analysis grade 3 0.1480 1.70 0.83 - 3.47 (n = 185) LN +ve 0.1492 1.64 0.84 - 3.20 HER2 +ve 0.0066 2.77 1.33 - 5.76 PR +ve 0.0058 0.39 0.20 - 0.76 c-Myc (high-risk) 0.0138 2.78 1.23 - 6.28
Resolved model HER2 +ve 0.0008 3.24 1.64 - 6.43 (n = 187) PR +ve <0.0001 0.29 0.15 - 0.54 c-Myc (high-risk) 0.0004 3.92 1.84 - 8.33
DISTANT METASTASIS Multivariate analysis grade 3 0.2165 1.62 0.75 - 3.49 (n = 185) size > 20 mm 0.5309 1.26 0.62 - 2.55 LN +ve 0.0681 2.02 0.95 - 4.29 HER2 +ve 0.0357 2.41 1.06 - 5.45 PR +ve 0.0066 0.37 0.18 - 0.76 c-Myc (high-risk) 0.1206 2.04 0.83 - 5.02
Resolved model LN +ve 0.0414 2.15 1.03 - 4.48 (n = 185) HER2 +ve 0.0035 3.03 1.44 - 6.38 PR +ve 0.0026 0.34 0.17 - 0.69 c-Myc (high-risk) 0.0292 2.58 1.10 - 6.04
BREAST CANCER-RELATED DEATH Multivariate analysis grade 3 0.6719 1.24 0.46 - 3.34 (n = 187) HER2 +ve 0.0006 5.44 2.06 - 14.37 PR +ve 0.0002 1.94 0.08 - 0.46 c-Myc (high-risk) 0.0220 3.73 1.21 - 11.50
Resolved model HER2 +ve 0.0001 5.91 2.41 - 14.46 (n = 187) PR +ve 0.0001 0.19 0.08 - 0.44 c-Myc (high-risk) 0.0081 4.10 1.44 - 11.66
Resolved models were generated after sequential removal of least significant variables; "High-risk" c-Myc expression pattern - nuclear H score < 20 and cytoplasmic H score > 20.
In the ER positive patients who were treated with endocrine therapy, the optimum multivariate model for all recurrence and breast cancer-related death was comprised of HER2 status and “high-risk” c-Myc expression pattern (Table 5.6) (for c-Myc expression, p=0.0045, HR 4.05, 95% CI 1.54 – 10.65 and p=0.0003, HR 13.25, 95% CI 3.28 – 53.51 for recurrence and breast cancer-related death respectively). For the end-
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point of distant metastasis, the best predictive model was comprised of c-Myc alone (p=0.0086, HR 3.59, 95% CI 1.38 – 53.51).
Table 5.6: Multivariate Cox proportional hazards analysis for the "high-risk" c-Myc expression pattern in the ER positive + endocrine therapy subgroup
Variable p value Hazard 95% Confidence Ratio Interval RECURRENCE Multivariate analysis grade 3 0.0563 2.39 0.98 - 5.82 (n = 105) HER2 +ve 0.0161 3.47 1.26 - 9.55 c-Myc (high-risk) 0.0250 3.12 1.15 - 8.42
Resolved model HER2 +ve 0.0018 4.65 1.77 -12.21 (n = 105) c-Myc (high-risk) 0.0045 4.05 1.54 - 10.65
DISTANT METASTASIS Multivariate analysis grade 3 0.1102 2.10 0.83 - 5.29 (n = 105) size > 20 mm 0.1065 2.11 0.85 - 5.24 HER2 +ve 0.2223 1.97 0.66 - 5.86 c-Myc (high-risk) 0.0385 2.81 1.06 - 7.49
Resolved model c-Myc (high-risk) 0.0086 3.59 1.38 - 9.31 (n = 108)
BREAST CANCER-RELATED DEATH Multivariate analysis HER2 +ve <0.0001 16.35 4.01 - 66.72 (n = 105) c-Myc (high-risk) 0.0003 13.25 3.28 - 53.51
Resolved models were generated after sequential removal of least significant variables; "High-risk" c-Myc expression pattern - nuclear H score < 20 and cytoplasmic H score > 20.
5.2.7 The “high-risk” c-Myc expression pattern is an independent predictor of outcome on multivariate analysis with clinicopathological and cell cycle variables
As noted in chapter 4, the expression of the cell cycle proteins cyclin D1, cyclin E and p27Kip1 were all correlated to endocrine receptor status. The “high-risk” c-Myc expression pattern was also correlated with ER negativity (with a trend towards an association with PR negativity). Thus to evaluate the predictive role of c-Myc expression in comparison to other cell cycle proteins multivariate analyses were conducted in those patients that were both ER and PR positive (n=149 patients). c-Myc was an independent predictor of recurrence (p=0.0002, HR 5.57, 95% CI 2.28 – 13.61), distant metastasis (p=0.0008, HR 5.40, 95% CI 2.01 – 14.48) and breast cancer-
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related death (p<0.0001, HR 14.22, 95% CI 3.99 – 50.66) (Table 5.7). Furthermore, high cyclin D1 also remained an independent predictor for all outcomes measured in this ER and PR positive group. c-Myc expression was not predictive of outcome in the ER and PR negative subgroup and thus further multivariate analyses were not undertaken in this group. .
Table 5.7: Univariate and multivariate Cox proportional hazards modelling in the ER + PR positive cases
Recurrence Variable p value Hazard Ratio 95% Confidence Interval Univariate Grade 3 0.0067 3.10 1.37 - 7.04 HER2 +ve 0.0042 3.68 1.51 - 8.96 High cyclin D1 0.0082 3.52 1.38 - 8.94 High-risk c-Myc <0.0001 6.06 2.70 - 15.20
Multivariate Grade 3 0.5107 1.38 0.53 - 3.55 (n = 144) HER2 +ve 0.0123 3.70 1.33 - 10.31 High-risk c-Myc 0.0007 5.04 1.98 - 12.87 High cyclin D1 0.0024 4.63 1.72 - 12.47
Resolved model HER2 +ve 0.0029 4.22 1.64 - 10.87 (n = 144) High-risk c-Myc 0.0002 5.57 2.28 - 13.61 High cyclin D1 0.0021 4.71 1.76 - 12.64
Distant Metastasis Univariate HER2 +ve 0.0462 2.83 1.02 - 7.88 High cyclin D1 0.0020 4.63 1.76 - 12.21 High-risk c-Myc 0.0009 5.24 1.98 - 13.89
Multivariate HER2 +ve 0.0525 2.96 0.99 - 8.83 (n = 144) High-risk c-Myc 0.0048 4.40 1.57 - 12.31 High cyclin D1 0.0006 5.89 2.14 - 16.19
Resolved model High-risk c-Myc 0.0008 5.40 2.01 - 14.48 (n = 147) High cyclin D1 0.0016 4.82 1.81 - 12.84
Breast cancer-related death Univariate HER2 +ve 0.008 5.55 1.56 - 19.78 High cyclin D1 0.0395 4.15 1.07 - 16.06 High-risk c-Myc <0.0001 12.04 3.59 - 40.44
Multivariate HER2 +ve 0.0646 3.79 0.92 - 15.60 (n = 144) High-risk c-Myc 0.0011 9.73 2.48 - 38.15 High cyclin D1 0.0150 6.06 1.42 - 25.90
Resolved model High-risk c-Myc <0.0001 14.22 3.99 - 50.66 (n = 147) High cyclin D1 0.0196 5.47 1.31 - 22.82
HER2+ve = HER2 amplified; High cyclin D1 = cyclin D1 H score > 120; "High-risk" c-Myc expression pattern - nuclear H score < 20 and cytoplasmic H score > 20.; Only those variables that were prognostic on univariate analysis were entered into the model; Resolved models were generated by sequential removal of least significant variables from the model.
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5.2.8 “High-risk” c-Myc expression is associated with the “Basal” breast cancer phenotype, inversely associated with the “Luminal A” phenotype and predicts adverse outcome among the “Luminal” breast cancer phenotypes
The “high-risk” c-Myc expression pattern was evaluated in the modified Perou phenotypic subtypes in a manner similar to that performed for the cell cycle proteins in Chapter 4. The “high-risk” c-Myc staining pattern was present in 30% of “Basal” cases, 25% of “HER2” cases, 19% of “Luminal B” cases and 21% of “Unclassified” cases, but was only seen in 7.6% of the “Luminal A” cases (Figure 5.16). When analysed by 2 analysis, the “high-risk” pattern was correlated with the “Basal” phenotype in comparison to the rest of the cohort (p=0.0031) and inversely correlated with the “Luminal A” phenotype in comparison to the rest of the cohort (p=0.0002) (Table 5.8).
Kaplan-Meier analysis demonstrated that in the “Luminal A” subgroup (n=171) of which 75% were in the ER and PR positive group, “high-risk” c-Myc staining pattern predicted recurrence (p=0.0362) and breast cancer-related death (p=0.0096), while there was a trend towards increased distant metastasis (p=0.0872) (Figure 5.17). Consistent findings were observed by Cox proportional hazards analysis (Table 5.9). In the smaller “Luminal B” subtype (n=27), “high-risk” c-Myc expression was predictive of increased recurrence (p=0.0038) on Kaplan-Meier analysis, but was not significantly predictive for distant metastasis or breast cancer-related death (Figure 5.18). The “high-risk” staining pattern was not predictive of outcome in the “HER2”, “Basal” or “Others” groups (Data not shown).
Limited multivariate analysis was undertaken in the “Luminal A” group only, as the cohort size in the Luminal B subgroup (and event rate of 12 recurrences) was too small for effective analysis. This analysis showed that the “high-risk” c-Myc expression pattern independently predicted for breast cancer-related death in this subgroup, along with PR status, and knocked cyclin D1 out of the model. However, “high-risk” c-Myc expression was not an independent predictor of recurrence (Table 5.10).
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Figure 5.16: Percentage of the modified Perou phenotypic breast cancer subtypes displaying the “high-risk” c-Myc expression pattern
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Figure 5.17: Kaplan-Meier curves for c-Myc expression in the “Luminal A” subgroup
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Figure 5.18: Kaplan-Meier curves for c-Myc expression in the “Luminal B” subgroup
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Table 5.8: Contingency table of c-Myc expression and Perou phenotypic subtypes
c-Myc c-Myc Variable (High-risk) (Low-risk) Total p value Interpretation Luminal A 13 158 171 0.0002 c-Myc "High-risk" expression is Non-Luminal A 24 78 102 inversely correlated with the Total 37 236 273 "Luminal A" phenotype
Luminal B 5 22 27 NS Non-Luminal B 32 215 247 Total 37 237 274
HER2 6 18 24 NS Non-HER2 31 219 250 Total 37 237 274
Basal 9 20 29 0.0031 c-Myc "High-risk" expression is Non-Basal 28 220 248 correlated with the "Basal" Total 37 240 277 phenotype
Others 4 15 19 NS Non-Others 33 218 251 Total 37 233 270
NS - not statistically significant; "High-risk" c-Myc expression pattern - nuclear H score < 20 and cytoplasmic H score > 20.; Analyses were performed usng the X2 test.
Table 5.9: Univariate predictors of outcome in the Luminal A phenotype
p Value Hazard Ratio 95% CI Luminal A Recurrence (n = 171) Grade III 0.0102 2.61 1.26 - 5.43 Lymph node +ve 0.0465 2.15 1.01 - 4.54 PR +ve 0.0006 0.28 0.13 - 0.58 High cyclin D1 0.0004 3.98 1.85 - 8.56 High p21 0.0228 2.85 1.16 - 7.03 “High-risk” c-Myc 0.0443 2.69 1.03 – 7.07 Metastasis Grade III 0.0187 2.58 1.17 - 5.69 Lymph node +ve 0.0386 2.37 1.05 - 5.37 PR +ve 0.0027 0.30 0.13 - 0.66 High cyclin D1 0.0005 4.24 1.87 - 9.60 “High-risk” c-Myc NS (0.982) 2.47 0.85 – 7.20 Breast cancer-related death PR +ve 0.0057 0.23 0.08 - 0.65 High cyclin D1 0.0060 4.85 1.57 - 14.98 “High-risk” c-Myc 0.0189 4.76 1.29 – 17.55
"High-risk" c-Myc expression pattern - nuclear H score < 20 and cytoplasmic H score > 20; High cyclin D1 - cyclin D1 H score > 120; High p21 - p21 % cells staining > 10.
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Table 5.10: Multivariate predictors of outcome in the “Luminal A” molecular phenotypes (final resolved models)
p Value Hazard Ratio 95% CI Luminal A Recurrence (n = 169) (n = 171) (29 events) PR +ve 0.0054 0.34 0.16 - 0.73 High cyclin D1 0.0043 3.15 1.44 - 6.94 Breast cancer-related death (n = 171) (14 events) PR +ve 0.0052 0.22 0.08 – 0.64 “High-risk” c-Myc 0.0169 4.95 1.33 – 18.36 High cyclin D1 = cyclin D1 H score > 120; “High-risk” c-Myc expression = c-Myc nuclear H score <20 + cytoplasmic H score > 20; final resolved models are shown following the sequential removal of variables contributing least to the model.
5.2.9 “High-risk” c-Myc expression increases in prevalence with increasing grade of both in situ and invasive breast carcinoma
The percentage of lesions within each pathological subtype of the GRPAHPC displaying the “high-risk” staining pattern previously defined in section 5.2.3 (i.e. high cytoplasmic staining (H score >20) concurrent with low nuclear staining (H score < 20) was determined. This pattern of high cytoplasmic expression with low nuclear expression became increasingly prevalent with increasing grade of both in situ and invasive carcinoma as seen in Figure 5.19. Although the pattern appeared to become less prevalent in the transition from in situ to invasive carcinoma for grade 1 and 2 lesions, the effect was not consistent, with increasing prevalence between grade 3 DCIS and IDC.
5.2.10 “High-risk” (predominantly cytoplasmic) c-Myc is not associated with decreased apoptosis
The data presented in the preceding sections indicate that low nuclear c-Myc expression in association with high cytoplasmic c-Myc is associated not only with the evolution of carcinoma, but also with poor outcome, particularly in hormone receptor expressing cancers, and those ER positive cancers that are treated with adjuvant endocrine therapy. Given that c-Myc is a pro-proliferative nuclear transcription factor, the fact that loss of nuclear c-Myc expression is associated with disease progression
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Figure 5.19: Changes in the percentage of lesions with c-Myc “high-risk” staining during the evolution of breast cancer
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and poor outcome is counterintuitive. However, c-Myc also has pro-apoptotic functions particularly in the setting of cellular stress and thus we hypothesized that the poor outcome associated with the “high-risk” c-Myc staining pattern may be due to loss of apoptotic function, particularly in those cancers treated with tamoxifen, an agent that with both anti-proliferative and pro-apoptotic effects 385. It should be noted that the induction of apoptosis is concentration dependent with in vitro studies indicating that at nanomolar concentrations tamoxifen only induces growth arrest, while at micromolar concentrations, apoptotic cell death is observed 386. Notably, the steady-state concentrations of tamoxifen in clinical studies can be up to 1 μM in serum at 4 μM in tumours387.
Therefore, the expression of the apoptotic marker, cleaved PARP was evaluated in the SVCBCOC using protocols detailed in Chapter 2. The expression was quantified by counting positive apoptotic figures per high power field (hpf), in a manner analogous to the method used to count mitoses in tumour grading. In addition, the size of each field examined was corrected for the amount of stroma, so that the areas assessed were comparable in terms of the area of cancer evaluated. On the microscope used for scoring (Olympus BX41) the field size was 0.5 mm at 400x magnification. While the majority of cores assessed displayed infrequent cleaved-PARP positive apoptotic figures, a small number of cases displayed florid apoptosis (Figure 5.20 A-D). When the mean cleaved-PARP positive count was compared by c-Myc expression pattern, the “high-risk” c-Myc expressors showed markedly higher levels of apoptosis, with a mean apoptotic count of close to 40 figures per 10 hpf (p=0.0039) (Figure 5.20E). Thus, the breast cancers with cytoplasmic-predominant (i.e. “high-risk”) c-Myc expression were not only associated with high tumour grade, they also displayed high levels of apoptosis. The association with high apoptotic counts and high tumour grade is likely to be related to the high cell turnover in these cancers and argues against a defect in c-Myc-regulated apoptosis.
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Figure 5.20: Expression of cleaved PARP as a continuous variable by c-Myc expression p
attern
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5.2.11 Predominantly cytoplasmic c-Myc expression is not associated with mutations in the nuclear localisation signal, or N- terminal phosphorylation region of c-Myc
In order to search for a potential mechanism responsible for the cytoplasmic localisation of c-Myc, mutation analysis was undertaken focussing on the major c-Myc nuclear localisation signal (NLS) located at residues 320 - 328 in exon 3, and the N- terminal phosphorylation sites, particularly Thr-58 and Ser-62 (Figure 5.21). These phosphorylation sites undergo mutation in Burkitt’s lymphoma, and play important roles in c-Myc function, ubiquitination, and tethering to cytoplasmic -tubulin. In addition a further site, Ser-71 was also assessed. Twenty-two of the known “high-risk” cases and twenty of the non-“high-risk” cases were selected for analysis. Due to the age of many of the tumour samples, the quality of the DNA was variable, and thus successful PCR for the regions of interest was only possible for 30 cases for the NLS region (13 “high- risk” and 17 non-“high-risk”), and 29 cases for the N-terminal phosphorylation region (12 “high-risk” and 17 non-“high-risk”) (Figure 5.21 - 5.22). From these a total of 16 sequences were analysable for mutations NLS mutations (7 “high-risk” and 9 non- “high-risk”) and 25 were analysable for mutations in the N-terminal phosphorylation region (9 “high-risk” and 16 non-“high-risk”). No mutations were detected for either region in any of the cases. Thus, although the number of cases evaluated was ultimately small, the series was enriched for cases with aberrant c-Myc localisation, and thus mutations in either of the regions assessed are unlikely to account for the expression pattern observed by IHC.
5.2.12 “High-risk” c-Myc expression is not correlated with c-Myc amplification
The c-MYC amplification status of the “high-risk” cases and a similar number of grade- matched non-“high-risk” cases, was evaluated by FISH as described in Chapter 2. Of the 37 “high-risk” cases, 35 were assessable by FISH. Thirty-two non-“high-risk” cases were also assessable. A number of amplification patterns were observed, including no amplification, low level amplification (~2-fold), high-level amplification, polysomy, and loss of the centromeric signal with a resulting high c-MYC to centromere ratio. For the
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purposes of this analysis, only those cases with amplification (with or without concurrent polysomy were evaluated). Cases with a high c-MYC to centromere ratio on the basis of centromeric loss were not considered amplified.
In total, 24% of the 87 cases displayed c-MYC amplification, the majority of which was low-level amplification (around 2-fold) (Figure 5.23). While 17% of the “High-risk” c-Myc expression pattern cases showed c-MYC amplification, 30% of the matched cases displayed this phenotype. On 2 analysis no relationship between the “high-risk” expression pattern and MYC amplification was apparent (Figure 5.24). When c-Myc amplification was evaluated by logrank analysis in the cohort, it did not predict outcome, despite clear separation between the Kaplan-Meier curves, probably as a result of small sample size (Figure 5.24C).
5.2.13 Upregulation of c-Myc expression at the transcriptional level does not result in dramatic cytoplasmic c-Myc expression in human breast cancer cells
The localisation of c-Myc protein was evaluated using a MCF-7 breast cancer cell line with zinc-inducible c-Myc expression (described in Chapter 2). These inducible cells, and negative-control empty vector cells, were treated with 50 μM of ZnSO4 for 6 hours before analysis by immunofluorescence confocal microscopy. The images obtained show an increase in nuclear expression of c-Myc in the inducible clone, in comparison to the small zinc-effect seen in the empty vector cell line (Figure 5.25). Western blots performed after fractionation of protein lysates from the inducible cell line did not demonstrate cytoplasmic expression at this early time point. In addition analysis of c- Myc expression in a battery of breast cancer cell lines did not identify any with predominant cytoplasmic localisation.
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Figure 5.22: Representative sequencing results for potential c-Myc mutations
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Figure 5.23: MYC amplification in a predominantly high-grade subset of the SVCBCOC (“Outcome Series”)
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Figure 5.24: MYC amplification in a predominantly high-grade subset of the SVCBCOC (“Outcome Series”)
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Figure 5.25: c-Myc protein localisation after transcriptional upregulation
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5.3 DISCUSSION
Human breast carcinoma evolves from benign epithelium through to invasive cancer as a consequence of the accumulation of a number of genetic changes. These changes collectively confer the proliferative, survival, invasive and metastatic features of malignancy. Ductal carcinoma in situ is often found adjacent to invasive ductal carcinoma, and has a high-risk of recurrence as invasive ductal carcinoma 22,388,389. Genetic analyses evaluating loss of heterozygosity and CGH profiles also support the idea that invasive breast carcinomas are derived from their in situ counterparts 390,391. c- Myc amplification is observed in approximately 15% of breast carcinomas 239 and in a similar percentage of in situ carcinomas in some studies 382,392. However, other studies using small numbers of cases in which invasive carcinoma and adjacent DCIS were evaluated suggest that c-Myc amplification may occur relatively late in the disease process (i.e. at the switch from in situ to invasive carcinoma) 381,383, and may be the result of genetic instability. Importantly, genetic instability is a known consequence of c-Myc overexpression, and thus it is unclear whether the c-Myc amplification is a cause or a consequence of c-Myc overexpression. Amplification of c-Myc in breast cancer occurs with considerably less frequency than is observed for mRNA or protein overexpression which occurs in approximately 50% of cases 393. Therefore, evaluation of protein expression is critical to understanding the role of c-Myc in breast oncogenesis.
The data from the GRPAHPC (“Progression Series”) indicate that nuclear c-Myc overexpression occurred as early as in benign ducts and lobules and peaked in hyperplastic (UDH) lesions. The progression to in situ carcinoma was accompanied by loss of nuclear c-Myc, with further nuclear loss between low and higher grades of IDC. In the cytoplasm, c-Myc expression began increasing with UDH, and then increased dramatically with grade of carcinoma (in situ and invasive). No consistent change was seen in the transition from in situ to invasive carcinoma within grades, although the combined data (all DCIS vs all cancer) suggest that c-Myc cytoplasmic staining declined in invasive carcinoma.
The data derived from the SVCBCOC (“Outcome Series”) indicate that preferential cytoplasmic localisation of c-Myc was associated with an inferior outcome from breast
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cancer. This “high-risk” c-Myc expression pattern was correlated with ER and PR negativity, HER2 amplification, high tumour grade and large tumour size, and less p27Kip1 expression. In addition, this pattern was correlated with the “Basal” Perou phenotype as assessed by IHC and inversely correlated with the “Luminal A” phenotype. When this “high-risk” pattern was quantitated in the GRPAHPC (“Progression Series”), the percentage of lesions displaying the “high-risk” pattern of high cytoplasmic c-Myc in association with low nuclear c-Myc increased consistently with increasing grade of both in situ and invasive carcinoma.
Although clearly associated with hormone receptor negativity, the “high-risk” c-Myc expression pattern was most strikingly predictive of outcome in ER positive breast cancer, and those cases treated with adjuvant therapy. Despite the associations with other indices of poor outcome such as higher grade and larger tumour size, among the ER positive cases the “high-risk” c-Myc expression pattern remained independently prognostic when modelled against clinical variables (all outcomes). Among the ER positive cases that were treated with adjuvant endocrine therapy the “high-risk” expression pattern remained independently prognostic when modelled against clinical variables (all outcomes). When the modelled against the cell cycle proteins evaluated in Chapter 4 in patients who were both ER and PR positive, c-Myc was independently prognostic for all outcome measures, along with high cyclin D1 expression (and HER2 amplification for recurrence only).
In addition to suggesting that the “high-risk” c-Myc expression pattern may represent a biomarker of adverse outcome amongst ER positive (and ER positive + endocrine therapy-treated) cases these data raise a number of questions in relation to c-Myc localisation and protein activity. While the dogma pertaining to c-Myc states that it is a predominantly nuclear protein, its cytoplasmic localisation in clinical samples is not without precedent. Studies in colorectal neoplasia in the early 1990s described an association between cytoplasmic accumulation of c-Myc and tumour progression from normal to adenoma to carcinoma 394,395. Numerous studies in breast cancer have evaluated Myc at the gene, RNA and protein levels. While in general, amplification studies suggest that MYC amplification is associated with an increased risk of relapse and death, and may be independently prognostic 239, other studies, particularly those evaluating protein expression have generated conflicting results in relation to prognostication and prediction of treatment efficacy (reviewed in 226). At least 4 of these studies describe cytoplasmic localisation of c-Myc protein with and without nuclear
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expression of c-Myc 241,247,396,397, with two studies linking cytoplasmic expression to better survival. Another recent study has identified a small subset of breast cancers (13.6%) in which a high cytoplasmic/nuclear ratio of c-Myc expression as measured by immunofluorescence staining predicts for adverse outcome 398. The present study is the first to the author’s knowledge in which immunohistochemistry is used to describe an association between high cytoplasmic expression of c-Myc protein in the setting of low nuclear expression and adverse outcome from early infiltrating duct carcinoma.
Altered levels of c-MYC at the gene, RNA and protein levels are commonly observed in various human malignancies, however the balance between the actions of c-Myc as a pro-cancer versus an anti-cancer factor remains unclear 377. In particular, the seemingly contradictory roles of c-Myc as a pro-proliferative and pro-apoptotic factor are difficult to reconcile, especially in the context of clinical studies. At first glance, the changing pattern of localisation described here is consistent with the “hit and run” hypothesis of Liao and Dickson 226, whereby nuclear c-Myc may be mediating tumour onset and early growth as indicated by the high expression in UDH. After this point, high c-Myc may be detrimental to the growth and survival of the cancer through its pro-apoptotic effects (reviewed in 399) and therefore in order to ensure cell survival, c-Myc expression may be attenuated or re-localised. In order to address this question, levels of apoptosis were quantitated in the SVCBCOC by measuring cleaved PARP positive apoptotic bodies. Strikingly, the “high-risk” c-Myc expressors were highly apoptotic, rather than the converse. In fact the average cleaved PARP counts for the “high-risk” group were in the upper 10% observed for the cohort, and thus the likelihood that attenuated apoptosis was the cause of the poor patient outcomes seems remote, and argues against the “hit and run” hypothesis in this instance.
The Myc protein contains a number of discrete functional domains that mediate different activities. The C-terminus contains the DNA-binding domain that is critical for gene activation while the N-terminus contains four evolutionarily conserved transactivation domains designated Myc Homology Boxes (MB) I-IV. MBII is necessary for full activation and repression, and MBIII is thought to mediate apoptosis, transformation and tumorigenesis. MBIV was identified relatively recently in N-Myc and is highly conserved between species and across Myc subtypes, including c-Myc 231. Data from mutational analyses of this region in N-Myc suggest roles for MBIV in
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forming Myc/Mad complexes, DNA binding, transactivation, transrepression, and apoptosis.
In the light of the cleaved-PARP data, it is the N-terminal phosphorylation-site rich MBI region, a locus that is subject to mutations in Burkitt’s lymphoma 400,401, that perhaps provides the best link between c-Myc cellular compartmentalisation and phenotype 402,403. In particular, the Thr-58 and Ser-62 sites appear critical in the regulation of c- Myc 404 via a series of phosphorylation, and dephosphorylation events that ultimately target c-Myc for ubiqitin-mediated degradation.
Under the influence of Ras/Raf/ERK, Ser-62 is phosphorylated, leading to stabilisation of c-Myc. This first phosphorylation step appears necessary for the phosphorylation of Thr-58. The latter step is mediated by glycogen-synthase kinase 3 (GSK-3) and targets c-Myc for ubiquitin- mediated degradation 236. GSK-3 is itself inactivated by PI3K/AKT 405, downstream of Ras, and thus Ras can serve to both stabilise, and prevent degradation of c-Myc.
The importance of GSK-3 in mediating degradation has recently been examined in primary effusion lymphoma, where c-Myc is abnormally stable due to underphosphorylation at Thr-58, as a consequence of interference in the function of GSK-3 by the Human Herpes Virus 8 latency associated nuclear antigen 406,407. Others have found that redistribition of GSK-3 to the nucleus is a mechanism for negative regulation of degradation with consequent accumulation of the similarly-regulated protein, -catenin 408. In addition to the sequential phosphorylation of Ser-62 and Thr- 58, there is further evidence that Ser-62 subsequently becomes dephosphorylated through the combined action of the prolyl cis/trans isomerase Pin1 and protein phosphatase 2A (PP2A), resulting in rapid c-Myc degradation 306. Notably, mutations in the alpha subunit of PP2A are present in lung, breast and colon cancers as well as in melanoma, and it is thought to function as a tumour suppressor 409,410.
The ubiquitination of c-Myc subsequent to these phosphorylation events is mediated by the F-box protein Fbw7, a component of the SCF-class of ubiquitin ligase (E3) complexes 234,411,412. Crucially this process is dependent on phosphorylation of Thr-58, although whether the Pin1/PP2A-mediated dephosphorylation of Ser-62 occurs before or after binding is unclear. Nevertheless, the role of Fbw7 in mediating protein degradation has become increasingly recognised, and it is now considered a tumour
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suppressor gene 413,414 with mutations identified in ovarian, breast, endometrial and colon cancers as well as lymphoma (reviewed in 414). In addition, Fbw7 contributes to the degradation of a number of other cell-cycle proteins and oncogenes including cyclin E, Notch, Aurora-A and and c-Jun 414.
Importantly, previous studies indicate that phosphorylation of both Ser-62 and Thr-58 occurs in the cytoplasm, unlike other c-Myc phosphorylation sites 402 and Thr-58 phosphorylated c-Myc accumulates in the cytoplasm 415. Furthermore, there are data linking the phosphorylation state of the Thr-58 residue to the binding of c-Myc to cytoplasmic -tubulin in Burkitt’s lymphoma. However, unlike the situation in Burkitt’s lymphoma, mutational analysis of a subgroup of cases from our cohort that were enriched for the “high-risk” c-Myc expressors showed no mutations in Thr-58, or Ser- 62. This is consistent with a previous study of 94 ductal carcinomas that failed to detect any point mutations in the region spanning amino acids 38 - 63 416. In addition, there were no mutations in the nuclear localisation sequence to account for the cytoplasmic localisation of the protein.
The FISH analysis of a subgroup of the SVCBCOC found that the “high-risk” c-Myc expression pattern was not preferentially associated with c-MYC gene amplification. Furthermore, the upregulation of c-Myc expression in the ER positive MCF-7 cell line using a zinc-inducible promoter resulted in only nuclear c-Myc expression at the relatively early time-point of 6 hours, although it should be noted that this result differs to that seen in the tetracycline-inducible transcriptional upregulation of c-Myc in the mouse model of mammary carcinogenesis. This latter model differs from the former not only by species and the fact that it is an in vivo model, but also in the time-course over which c-Myc was evaluated (several months), and the ER negative status of the mouse mammary tumours. These factors may have contributed to the difference in cellular localisation of c-Myc in these two transcriptional induced models.
Nonetheless, the weight of the data presented above suggests that it is conceivable that cytoplasmic c-Myc is primarily a manifestation of a dysregulated protein degradation pathway as opposed to gene amplification or transcriptional upregulation. In particular, disordered expression or activity of GSK-3, PP2A and Fbw7 are interesting targets for further study in relation to the regulation of c-Myc degradation.
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It is particularly interesting to note that Fbw7 also mediates the degradation of cyclin E (reviewed in 414), as the “high-risk” c-Myc group had a mean cyclin E expression that was more than double that of the non-“high-risk” group, although it did not reach statistical significance. In addition, both cyclin E and c-Myc have been associated with genomic instability 417,418, and the c-MYC amplification studies by FISH in a subgroup of the SVCBCOC demonstrated unusually high rates of loss of the centromeric signal, as well as polysomy.
What is unclear, is whether the overexpressed cytoplasmic c-Myc is functional, and able to access the nucleus to perform is role as a transcription factor. In general terms, the region encompassing the Thr-58 and Ser-62 residues may not be critical for the activities of c-Myc, as deletion mutants demonstrate potentiation of transactivation and transrepression to a similar extent as wild-type c-Myc 404,419, although other studies have shown that the Thr-58 and Ser-62 sites are required for high-level gene activation, as mutation to Ala residues causes marked decreases in activity 420. In any case, c-Myc activity is likely to be preserved in the scenario where Thr-58 and Ser-62 are phosphorylated but associated with impaired degradation, for example if there was a defect in Fbw7 function. Studies using a GFP-Myc fusion protein show that c-Myc may shuttle between the cytoplasmic and nuclear compartments 421 and thus it is conceivable that high levels of cytoplasmic c-Myc may be able to modulate the expression of nuclear c-Myc target genes.
In breast cancer cells c-Myc is a key downstream target of ER signalling. In the light of our IHC data, in which high nuclear expression was associated with ER positivity and good outcome, one could speculate that high nuclear c-Myc expression actually represents intact ER signalling, and therefore is associated with superior outcome, whereas the “high-risk” expression pattern represents autonomous c-Myc (perhaps as a result of impaired degradation) that is disconnected from the usual ER-mediated regulatory processes. Such a hypothesis would explain the association between the “high-risk” staining pattern and ER negativity (including the “Basal” phenotype) as well as the poor outcome observed among ER positives and is consistent with recent data from our laboratory indicating that ER negative breast cancers display overexpression of a “c-Myc signature” of genes that are observed at lower levels in ER positive cases (Alles et al unpublished data). In addition, the “high-risk” c-Myc expression pattern does not appear to be a surrogate for gene amplification, as similar levels of gene amplification were seen in “high-risk” and non-“high-risk” c-Myc expressors.
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5.4 SUMMARY
These combined data indicate that c-Myc nuclear and cytoplasmic expression is observed early in the evolution of breast cancer in morphologically normal breast tissue, with marked nuclear expression observed in hyperplasia. The development of frank carcinoma is associated with nuclear loss of c-Myc, and increasing cytoplasmic localisation. Those cases in which there is preferential localisation of c-Myc to the cytoplasm display inferior outcomes generally and in response to adjuvant therapies, suggestive of resistance to ER modulation. “High-risk” c-Myc expression is associated with higher grade and larger tumour size but remains independently prognostic for outcome in multivariate analyses, particularly for ER positive patients, and ER positive patients treated with endocrine therapy. This pattern was not associated with MYC amplification and may be the result of disordered protein degradation. Potential mediators worthy of further investigation are Fbw7, PP2A and GSK-3.
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CHAPTER 6: C-MYC OVEREXPRESSION AS A MEDIATOR OF ANTI-OESTROGEN RESISTANCE
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6.1 INTRODUCTION
While treatments with anti-oestrogenic agents such as tamoxifen reduce the relative risk of breast cancer relapse and breast cancer-related death by ~40% and ~30%, respectively 56, many women treated with adjuvant endocrine therapy eventually relapse and die as a result of their breast cancer (in the order of 10-15%) 14. The biological basis of de novo or acquired anti-oestrogen resistance as it is manifest in the clinic remains to be fully defined, although there is evidence implicating cross-talk between upregulated growth factor signalling pathways and ER-signalling pathways, polymorphisms in tamoxifen metabolising enzymes, adaptive hypersensitivity to oestrogen deprivation, changes in the co-expression of ER- , and loss or mutation of ER- (reviewed in Chapter 1). A less studied area is the role played by aberrant expression of cell cycle protein targets of ER- signalling.
Oestrogens and anti-oestrogens regulate a number of genes important in the processes of cell cycle entry and progression, including cyclin D1, cyclin E, and the cdk inhibitors, p21WAF1/Cip1 and p27Kip1. Deregulated expression of these targets facilitates cellular proliferation independently of oestrogen or in the presence of anti-oestrogens 254,315,348,422-425. The transcription factor c-Myc regulates the expression of cyclin D1, cyclin E, p21WAF1/Cip1 , p27Kip1 and the activities of cyclin D1-cdk4 and cyclin E-cdk2, and is itself an important downstream target of ER signalling 263,426-430. Thus, through its downstream cell cycle effects, dysregulated c-Myc potentially may play a role in the development of independence from ER signalling. Indeed, ectopic expression of c- Myc induces the proliferation of oestrogen-responsive breast cancer cells pretreated with the pure anti-oestrogen ICI 182,780 251,262,305, an effect that is likely to be mediated at least in part by repression of p21WAF1/Cip1 expression 262.
In the light of the data presented in Chapter 5, indicating that aberrant c-Myc protein expression is associated with adverse outcome, particularly in ERpositive cancers and ER-positive cancers treated with endocrine therapy, further in vitro studies were undertaken to evaluate the effect of c-Myc overexpression on sensitivity to anti- oestrogens in exponentially proliferating cells. In addition, the effect of c-Myc overexpression on the expression of cyclin D1, cyclin E, p21WAF1/Cip1 and p27Kip1, and the activities of cyclin D1-cdk4 and cyclin E-cdk2 was also evaluated.
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6.2 RESULTS
6.2.1 Low-level constitutive c-Myc expression causes resistance to the anti-proliferative effects of the pure steroidal anti-oestrogen ICI 182,780
MCF-7 cells were transfected with c-myc under the control of the metallothionein promoter by Mr. C. Marcello Sergio as described in Chapter 2. Two clones were identified in which c-Myc protein expression was ~1.5 – 2 fold higher than that of empty vector cells, even in the absence of exogenous zinc i.e. these cells constitutively overexpressed c-Myc. These two clones were evaluated in comparison to empty vector control cells to determine the experimental conditions best suited for evaluating the effects of anti-oestrogen treatment in these cells, and for subsequent experiments involving MCF-7 cells with zinc-inducible c-Myc expression (see Section 6.2.5).
The two c-Myc-overexpressing clones, and empty vector control cells, were treated with increasing concentrations of ICI 182,780 in ethanol vehicle while in exponential growth phase. Forty-eight hours later (a time–point chosen on the basis of previous studies in this laboratory), the cells were harvested and stained with ethidium bromide before being subjected to analysis by flow cytometry. DNA histograms were generated and the proportion of cells in S phase of the cell cycle determined. The two c-Myc- overexpressing clones displayed attenuated anti-proliferative responses to treatment with ICI 182,780 in comparison to the empty vector cell line. This effect was clearly apparent at 10 nM ICI 182,780, with empty vector cells displaying an average S phase of around 20% of that observed in untreated cells, in comparison to 60% in the c-Myc- overexpressing clones (p<0.0001) (Figure 6.1A). No further incremental difference between the c-Myc overexpressing and empty vector control cells was observed at higher concentrations of ICI 182,780. Thus, for all further experiments 10 nM ICI 182,780 was used.
To further determine the optimum time-point for the assessment of the anti-proliferative effects of ICI 182,780, the two c-Myc-overexpressing clones and empty vector control cells, were treated with 10 nM ICI 182,780 in ethanol vehicle while in exponential
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Figure 6.1: Dose response and timecourse of the effect of ICI182,780 on two MCF-7 clones constitutively expressing c-Myc and empty vector control
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growth phase, and harvested at time-points spanning 9 – 48 hours. Cells were stained with ethidium bromide and evaluated for cell cycle phase distribution. These experiments revealed good discrimination between the overexpressing and control clones as early as 16 hours post treatment with ICI 182,780, and at 24 hours, empty vector cells displayed an average S phase of around 20% of that observed in untreated cells, in comparison to 50% in the c-Myc-overexpressing clones (p<0.03) (Figure 6.1B), similar levels to that observed at 48 hours in Figure 6.1A. By 48 hours, the S phase in the empty vector cells was ~7% of control untreated cells, while the S phase in the c- Myc overexpressing cells was 25-30% of untreated cells. At this 48 hour time-point, the c-Myc overexpressing cells showed little reduction in c-Myc protein expression following ICI 182,780 treatment in comparison to the empty vector cells, indicating that in these cells, the ectopically expressed c-Myc is largely ER-autonomous (Figure 6.1C).
Overall, these data indicate low-level constitutive c-Myc overexpression is associated with partial resistance to the anti-proliferative effects of ICI 182,780 (as indicated by the percentage of cells in S phase). Good discrimination between the ICI 182,780 responses in empty vector control cells and c-Myc overexpressing cells was apparent at 24 – 48 hours, using a concentration of 10 nM ICI 182,780. As discussed later (section 6.2.4), zinc-induced c-Myc overexpression in the zinc-inducible clones was reasonably short-lived (~24 hours), and therefore, to allow comparison between the experiments using the constitutive and inducible c-Myc-overexpressing cells, a time- point of 24 hours was used to measure anti-oestrogen responses in all subsequent experiments.
6.2.2 Low-level constitutive c-Myc overexpression per se does not cause increased proliferation rates in exponentially proliferating breast cancer cells
In order to evaluate whether c-Myc overexpression causes a generic proliferative advantage in human breast cancer cells, the two constitutive c-Myc overxpressing cell lines were compared to empty vector cells in a “growth curve” assay. In this assay, exponentially proliferating stock cells were trypsinised, counted and plated out at low density, and then counted every day using a haemocytometer in at least triplicate for 6
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days (Figure 6.2A). These data indicated that c-Myc overexpressing clones did not proliferate at a faster rate than the empty vector controls. Thus low-level constitutive c- Myc overexpression does not cause a generic proliferative advantage in exponentially growing breast cancer cells.
6.2.3 Low-level constitutive c-Myc overexpression causes reduced sensitivity of exponentially proliferating breast cancer cells to anti- oestrogen
Using the “growth curve” assay, exponentially proliferating, constitutive c-Myc- overexpressing cells and empty vector controls were plated out as above. One day later, the cells were treated with 10 nM ICI 182,780 or ethanol vehicle, and the cells counted by haemocytometer for the next 4 days (Figure 6.2B). While the empty vector cells effectively stopped proliferating after treatment with ICI 182,780, the two overexpressing clones continued to proliferate (p<0.02). However, the attenuation of the anti-oestrogen effect in the two c-Myc-overexpressing clones was only partial, as ethanol vehicle-treated cells displayed higher proliferation rates than ICI 182,780- treated cells (p<0.02) (Figure 6.3). Thus, low-level c-Myc overexpression is associated with partial-resistance to the anti-proliferative effects of ICI 182,780 in human breast cancer cells as would have been predicted from the S phase data presented in Figure 6.1.
6.2.4 Low-level constitutive c-Myc overexpression attenuates anti- oestrogen-induced changes in the expression of key cell cycle proteins
In order to dissect further the molecular changes occurring in response to constitutive c-Myc overexpression, whole-cell protein lysates were extracted after 24 hours treatment with 10 nM ICI 182,780. The protein lysates were separated by SDS-PAGE and cell cycle proteins visualised by western blotting (Figure 6.4). These data demonstrated that c-Myc expression was repressed by ICI 182,780 treatment in the empty vector clone, while in the constitutive c-Myc-overexpressing cells c-Myc expression was maintained. Consistent with the S phase and “growth curve” data,
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Figure 6.2: Proliferation curves for constitutively overexpressing c-Myc cell lines in comparison to an empty vector control
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Figure 6.3: Proliferation curves for constitutively overexpressing c-Myc cell lines (B and C) and empty vector control (A), after treatment on day 2 with 10nM ICI182,780 or ethanol vehicle
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Figure 6.4: Changes in key cell cycle proteins after treatment of constitutive c- Myc overexpressing cells with ICI182,780
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empty vector cells showed clear reductions in the intensity of the upper phosphorylated band in the total pRb blot, in comparison to untreated cells, or cells with c-Myc overexpression.
On review of the cyclin D1 western blot, (Figure 6.4A) it can be seen that 24 hours of ICI 182,780 treatment resulted in a reduction in the expression of cyclin D1 in the empty vector cells that is not apparent in the c-Myc overexpressing cells. These data suggest that c-Myc overexpression leads to maintenance of cyclin D1 expression in the face of anti-oestrogen treatment, however this was not statistically significant (67% in empty vector cells vs 86% in clone 2 cells) on densitometric assessment of triplicate experiments.
Anti-oestrogen treatment resulted in an increase in the expression of the cdk inhibitors p21WAF1/Cip1 and p27Kip1 to ~140% of control in the empty vector cells. The increase in p21WAF1/Cip1 expression appeared to be attenuated in c-Myc overexpressing cells, approaching significance in constitutive c-Myc clone 2, where p21WAF1/Cip1 expression remained at 99% of that observed in cells not treated with anti-oestrogen (p=0.0786). In the case of p27Kip1, constitutive c-Myc clone 1 displayed upregulation of expression (to 139% of control), while constitutive c-Myc clone 2 showed no change (98% of control). The variance in expression levels measured by densitometry over three experiments was wide, and no statistically significant differences were detected between the control cells and c-Myc overexpressing cells.
Similarly, ICI182,780 treatment of empty vector cells resulted in a slight increase in cyclin E expression to 116%, as a probable consequence of reduced protein turnover. This effect was less marked in constitutive c-Myc clone 2 (97%), although constitutive c-Myc clone 1 also showed a slight increase in cyclin E expression to 115%. Good quality cyclin E blots were only available from duplicate experiments, and thus statistical analyses were not performed on these data. Nonetheless, the effect (if any) of c-Myc overexpression on cyclin E protein expression appears small.
In summary, in exponentially proliferating MCF-7 human breast cancer cells with low- level c-Myc overexpression, resistance to the anti-proliferative effects of ICI 182,780 (as shown in Figure 6.1) is associated with higher levels of pRb phosphorylation, and a trend towards suppression of p21WAF1/Cip1. A non-significant but consistent elevation in
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cyclin D1 expression was also observed in both c-Myc overexpressing clones, while the data for p27Kip1 and cyclin E were inconclusive.
6.2.5 The induction of c-Myc expression in two c-Myc-inducible cell lines is transient and maximal at 3 - 6 hours
MCF-7 cells were transfected with c-myc under the control of the metallothionein promoter as described in Chapter 2. Two clones were identified in which c-Myc protein expression was inducible following zinc treatment. These clones showed c-Myc overexpression at considerably higher levels than those observed in the constitutive c- Myc overexpressing clones and were studied to address the question of whether c- Myc-mediated anti-oestrogen resistance was dependent on the level of expression of c-Myc, and if complete resistance to the anti-oestrogens was possible in the setting of high-level c-Myc overexpression.
In order to determine the optimum timecourse of zinc-induced c-Myc overexpression, exponentially growing, inducible c-Myc clones 1 and 2, and empty vector controls were treated with 0, 25, 50 and 75 μM zinc, and whole-cell protein lysates collected over the ensuing 48 hours. Western blots for c-Myc protein over this timecourse are shown in Figure 6.5A and demonstrate concentration-dependent c-Myc induction by as early as 3 hours. No such c-Myc induction was apparent in the empty vector cells (data not shown). When an average baseline c-Myc expression was calculated from the densitometry values in the 0 μM (water vehicle) arm of the experiment at all time- points, the maximum fold-increases in c-Myc expression were 6-fold and 18-fold for the 50 μM and 75 μM zinc concentrations in inducible clone 1, and 11.5-fold and 20.5-fold for the 50 μM and 75 μM zinc concentrations in inducible clone 2 (Figure 6.5).
Although high levels of c-Myc expression were achieved rapidly using this system, both inducible clones showed a decline in c-Myc expression to near baseline by 24 hours post-zinc induction. Therefore in further experiments aimed at evaluating the effect of high-level c-Myc expression on responses to anti-oestrogen treatment, pre-treatment with zinc was performed for 3 hours prior to treatment with anti-oestrogen. In addition, in order that anti-oestrogen treatment corresponded to the period of maximum c-Myc expression, cell cycle responses were evaluated after 24 hours of treatment with anti-
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Figure 6.5: Timecourse of c-Myc induction in MCF-7 cells with zinc-inducible c- Myc expression
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oestrogen (ie: 27 hours after zinc treatment) rather than at 48 hours (see Section 6.2.1, and Figure 6.1).
6.2.6 Zinc-induced c-Myc overexpression results in concentration- dependent resistance to the anti-proliferative effects of ICI 128,780
Having determined that c-Myc induction by zinc occurred as early as 3 hours post treatment, the optimum concentration of zinc for the purposes of evaluating c-Myc as a mediator of anti-oestrogen resistance was determined. Previous investigators in the laboratory, using a slightly different anti-oestrogen rescue model, had used a dose of 62.5 μM zinc to induce c-Myc in the two inducible clones 251, with higher doses causing cell toxicity. Inducible c-Myc clones 1 and 2, and empty vector controls were pre- treated with zinc concentrations between 0 and 60 μM for three hours, then treated with 10 nM ICI 182,780 or ethanol vehicle for 24 hours. The cells were harvested and stained with ethidium bromide and the percentage of cells in S phase evaluated by flow cytometry.
The addition of zinc resulted in a small but significant attenuation of the ICI 182,780- effect in the empty vector cells (Figure 6.6A) (p=0.0325), while the S phase at 0 μM in both of the inducible clones was higher than that in the empty vector suggestive of some leakiness of the metallothionein promoter (Figure 6.6A). In the inducible clones there was an incremental increase in post-ICI 182,780 S phase with increasing zinc (i.e. c-Myc) concentration. However, when corrected for the increase in S phase observed in the empty vector cells, the response to c-Myc induction began to plateau at around 50 μM, particularly in inducible clone 1 (Figure 6.6B). Furthermore, the incremental rise in c-Myc protein expression by western blot was minor between zinc concentrations of 40 and 60 μM (Figure 6.6B). Therefore, a maximal concentration of 50 μM zinc was chosen for the rest of the inducible c-Myc experiments.
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Figure 6.6: Titration of zinc dose in two clones with zinc-inducible c-Myc expression in comparison to empty vector
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6.2.7 High-level c-Myc expression following zinc-induction results in attenuation of S phase-suppression by ICI 182,780, tamoxifen and raloxifene
The two inducible c-Myc clones, and empty vector cells were pretreated with 0 or 50 μM zinc for three hours, prior to treatment for 24 hours with 10 nM ICI 182,780 (or ethanol vehicle), I μM tamoxifen (or ethanol vehicle), or I μM raloxifene (or dimethyl sulfoxide [DMSO] vehicle). The cells were harvested and stained with ethidium bromide prior to evaluation of cell cycle phase distribution. Zinc treatment resulted in an approximately 10% attenuation of anti-oestrogen-induced S phase suppression in empty vector cells (i.e. in the absence of c-Myc induction).
In the absence of zinc treatment all of the cell lines showed a similar magnitude of S phase suppression in response to anti-oestrogen treatment. Thus treatment with ICI 182,780 suppressed the percentage of cells in S phase to 15 - 25 % of control, while treatment with the less potent anti-oestrogens raloxifene and tamoxifen suppressed the percentage of cells in S phase to ~30 - 40% and ~40 - 40% of control, respectively.
Induction of c-Myc by zinc resulted in significant attenuation of the anti-proliferative effect of all three anti-oestrogens (Figures 6.6 and 6.7), increasing the fraction of cells in S phase by ~30-50% relative to control. Thus, in the presence of c-Myc induction tamoxifen or raloxifene treatment reduced the fraction of zinc-treated empty vector cells in S phase to 40 - 50% of that seen in ethanol/DMSO treated cells, but only reduced the fraction of zinc-treated c-Myc inducible cells in S phase to 70 - 80% of that seen in ethanol/DMSO treated cells (p<0.007). Most strikingly, ICI 182,780 treatment of either water-treated c-Myc inducible cells, or zinc-treated empty vector cells reduced the fraction of cells in S phase to 25% of that seen in cells not treated with ICI 182,780. In contrast, treatment of the zinc-pretreated inducible clones with ICI 182,780 reduced the S phase to ~70% of that observed in untreated cells respectively, a difference in percentage S phase of approximately 45 - 50%.
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Figure 6.7: Effect of zinc-mediated c-Myc induction on sensitivity to tamoxifen, raloxifene and ICI182,780
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Importantly, despite high-level c-Myc induction, anti-oestrogen treatment was still able to effect an approximately 20-30% reduction in the percentage of cells in S phase in the two inducible clones.
6.2.8 High-level c-Myc expression maintains cyclin E-cdk2 activity and cyclin D1-cdk4 activity in the face of anti-oestrogen treatment
In order to further dissect the potential mechanism mediating c-Myc-mediated resistance to anti-oestrogen treatment, the activities of the key downstream effector kinases of the cell cycle (cdk2 and cdk4) were evaluated. Duplicate experiments were performed in which inducible c-Myc expressing cells were pretreated with 0 or 50 μM of zinc for 3 hours and then treated with 10 nM ICI 182,780 (or ethanol vehicle) for 24 hours. Protein lysates were used to assess the activity of cyclin E-cdk2 and cyclin D1- cdk4 by cyclin E-cdk2 kinase assays and phosphorylation of pRb at Ser-249/Thr-252, respectively (Figure 6.8). Overexpression of c-Myc resulted in maintenance of both cyclin E-cdk2 activity and cyclin D1-cdk4 activity at ~80% of levels seen in the absence of ICI 182,780 treatment, in comparison to non-c-Myc overexpressing conditions where both cyclin E-cdk2 activity and cyclin D1-cdk4 activity were suppressed to 40% of levels seen in the absence of ICI 182,780. The magnitude of the c-Myc-mediated change in cyclin D1-cdk4 activity (40-50%) was higher than that observed for cyclin D1 expression (20-30%) under the same conditions (Figure 6.10; refer to Section 6.2.9), and while c-Myc overexpression resulted in slight reductions in cyclin E protein expression after ICI 182,780 treatment, cyclin E-cdk2 activity was increased by ~40%. Despite high-level c-Myc upregulation, the activities of both cyclin E-cdk2 and cyclin D1-cdk4 could be suppressed by ~20% by ICI 182,780 treatment in keeping with the lack of complete attenuation of the growth inhibitory responses.
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Figure 6.8: Changes in cyclin-cdk activity after treatment with ICI182,780 in two c-Myc inducible cell lines and an empty vector cell line
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6.2.9 High-level inducible c-Myc overexpression attenuates anti- oestrogen-induced changes in the expression of key cell cycle proteins
Whole cell protein lysates were prepared in conjunction with the S phase experiments in Section 6.2.7. Proteins were separated by SDS-PAGE and key cell cycle proteins visualised by western blot (Figures 6.9 – 6.12). Consistent with the S phase data, c- Myc overexpression resulted in resistance to the anti-oestrogen-mediated dephosphorylation of pRb. In the absence of zinc-induced c-Myc overexpression ICI 182,780 treatment resulted in clear repression of c-Myc expression. However, in zinc- treated c-Myc-inducible cells, ICI 182,780 treatment resulted in barely any discernable change in c-Myc expression.
Zinc-mediated c-Myc overexpression resulted in attenuation of ICI 182,780-mediated suppression of cyclin D1 expression at 24 hours (p=0.0309 and 0.0104 for inducible c- Myc clone 1 and clone 2 respectively) (Figures 6.9 and 6.10). In addition, empty vector cells showed less repression of cyclin D1 by ICI 182,780, than the two inducible clones in the absence of c-Myc induction (p=0.0209). When the expression of p21WAF1/Cip1, p27Kip1 and cyclin E was evaluated by Western blot, no statistically significant changes were observed (Figure 6.10).
The effect of zinc-mediated c-Myc induction on cell cycle protein responses to tamoxifen and raloxifene was also evaluated in a single series of western blots (Figure 6.11 and 6.12). Clear and reproducible total pRb blots in which the upper phosphorylated band was clearly discernable were elusive, and thus it is difficult to draw definitive conclusions in relation to the effect of c-Myc overexpression on pRb phosphorylation after treatment with these two anti-oestrogens. While the increase in p21WAF1/Cip1 expression associated with anti-oestrogen treatment appeared to be attenuated by around 20-40% in both c-Myc overexpressing clones, consistent changes in the expression of cyclin D1, p27Kip1 and cyclin E were not observed.
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Figure 6. 9: Changes in key cell cycle proteins after treatment with ICI182,780 in two c-Myc inducible cell lines and an empty vector cell line
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Figure 6.10: Changes in key cell cycle proteins after treatment with ICI182,780 in cells with inducible c-Myc expression
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Figure 6.11: Changes in key cell cycle proteins after treatment with tamoxifen and raloxifene in two c-Myc inducible cell lines and an empty vector cell line
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Figure 6.12: Graphical representation of the changes in protein expression of cyclinD1, p21WAF1/Cip1, p27Kip1 and cyclin E in two c-Myc-inducible cell lines and empty vector controls after treatment with tamoxifen and raloxifene (see Figure 6.11 for Western blots)
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6.2.10 Combining data from both constitutive and inducible models suggests that c-Myc overexpression may prevent anti-oestrogen- mediated down-regulation of cyclin D1, and upregulation of p21WAF1/Cip1
The numerous assessments of cell cycle protein expression in both the constitutive and inducible c-Myc overexpression models led to inconclusive results, as the effects seen were relatively small, and the measurement error was large. Thus, to determine if any consistent effects were associated with c-Myc overexpression, the expression of cyclin D1, p21WAF1/Cip1, p27Kip1 and cyclin E after anti-oestrogen treatment (as a percentage of untreated) was plotted for the low- and high-c-Myc expressors from both the constitutive and inducible models, concentrating on the results for the highest expressing clones (constitutive c-Myc clone 2 and inducible c-Myc clone 2). This comparison suggests similar trends in the relative upregulation of cyclin D1 expression and lack of increase of p21WAF1/Cip1 expression after anti-oestrogen treatment in the situation of c-Myc overexpression (Figure 6.13). The results for both cyclin E and p27Kip1 were too variable for any trends to be observed.
6.3 DISCUSSION
6.3.1 Low-level constitutive c-Myc overexpression is not associated with increased proliferation per se in exponentially growing breast cancer cells
Cell proliferation experiments (“growth curves”) demonstrated that MCF-7 cells with low-level constitutive overexpression of c-Myc did not proliferate at a faster rate than wild-type MCF-7 cells. This would suggest that, at least at low levels, c-Myc does not confer a proliferative advantage upon cells already in exponential growth phase under optimal growth conditions. The impact of high-level c-Myc expression on the
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Figure 6.13: Summary of anti-oestrogen-induced changes in cyclin D1, p21WAF1/Cip1, p27Kip1 and cyclin E in breast cancer cells that constitutively or inducibly overexpress c-Myc
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proliferation of MCF-7 cells was not evaluated due to the transient nature of the c-Myc overexpression achieved in our system in comparison to the duration of the growth curve experiments, and therefore it is conceivable that sustained high-level c-Myc expression may confer a proliferative advantage upon breast-cancer cells, even in the absence of anti-mitotic signals such as anti-oestrogen treatment. Nonetheless, these data indicate that the anti-oestrogen resistance subsequently observed in low-level c- Myc overexpressing cells is not merely a manifestation of a cell line that is inherently more proliferative.
6.3.2 Low-level constitutive, and high-level inducible, c-Myc overexpression induces resistance to the anti-proliferative effects of anti-oestrogens in breast cancer cells
Constitutive expression of c-Myc reduces growth factor requirements, prevents growth arrest by a variety of anti-proliferative signals, and can block cellular differentiation, while c-Myc activation in quiescent cells induces entry into the cell cycle in the absence of mitogens 431.
In ER-positive breast cancer cells, oestrogen is a mitogen, with modulation of ER action forming the mainstay of systemic treatment for breast cancer through strategies such as tamoxifen treatment and oestrogen suppression. Thus, the question of whether constitutive or inducible c-Myc overexpression confers resistance to the anti- proliferative effects of the pure anti-oestrogen ICI 182,780 was addressed in subclones of the ER-positive MCF-7 breast cancer cell line in which c-Myc was overexpressed constitutively, or inducibly under the control of a zinc-inducible promoter.
The combined data derived from these experiments indicate that either low-level constitutive or high-level inducible c-Myc overexpression attenuates the anti- proliferative effects of ICI 182,780 as assessed by cell cycle phase distribution and/or growth curves. These data are consistent with previous studies, indicating that c-Myc induction can rescue breast cancer cells from ICI 182,780-mediated cell-cycle arrest 251 or enable proliferation in the presence of suppressive doses of ICI 182,780 305. Furthermore, a study published during the course of this work indicated not only that inducible c-Myc expression could rescue MCF-7 breast cancer cells from ICI 182,780-
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mediated arrest, but that this effect was mediated through the repression as early as 12 hours post c-Myc-induction of p21WAF1/Cip1 expression 262.
When the studies in the inducible c-Myc clones were extended to the anti-oestrogens tamoxifen and raloxifene, c-Myc-overexpressing cells maintained an S phase at ~75- 80% of untreated cells, compared to ~40-50% in low c-Myc cells, a difference of around 30%. The incremental change seen in S phase among high c-Myc expressing cells treated with the pure anti-oestrogen ICI 182,780 was higher than that observed for tamoxifen and raloxifene, and may relate to the higher potency of this anti-oestrogen, down-regulation of the ER itself and lack of agonist effects.
6.3.3 c-Myc overexpression attenuates the ICI 182,780-induced decline in cyclin D1 expression and increase in p21WAF1/Cip1 expression
The role of cyclin D1 as a downstream target of c-Myc has yet to be fully resolved. Several previous studies indicate that c-Myc does not upregulate cyclin D1 expression 251, and in fact may act as a repressor of cyclin D1 transcription 432,433. However, other studies have demonstrated that cyclin D1 protein levels can be upregulated by c-Myc 434. Converse experiments show down-regulation of cyclin D1 after treatment of breast cancer cells with c-Myc anti-sense oligonucleotides or siRNA 250,435 and antibodies to cyclin D1 can prevent c-Myc-induced S phase entry 436. Another study in c-Myc-null fibroblasts showed that loss of c-Myc was associated with a decrease in cyclin D1 levels 437 and c-Myc has been found to drive proliferation through increasing the expression of cyclins D1, D2 and D3 when each is expressed in isolation in mouse fibroblasts 438.
In the study presented here, the anticipated decline in cyclin D1 that is usually observed with ICI 182,780 treatment 307 was attenuated in the two clones with inducible c-Myc expression. These data imply that in our model system c-Myc may play a role in maintaining cyclin D1 expression. Surprisingly, the empty vector cells displayed less anti-oestrogen-mediated suppression of cyclin D1 than the inducible clones (reaching statistical significance for the difference between empty vector and inducible c-Myc clone 2) in the absence of zinc treatment. This finding is difficult to explain, particularly
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as the two inducible clones displayed subtle constitutive c-Myc protein overexpression in addition to high-level inducible c-Myc expression.
The mechanism behind c-Myc-mediated regulation of cyclin D1 expression is unclear although a potential mechanism may relate to the fact that like p21WAF1/Cip1, the cyclin D1 promoter contains several Sp1/Sp3 binding sites 439 known to be modulated by c- Myc 263.
Despite some maintenance of cyclin D1 expression in the setting of c-Myc overexpression, cyclin D1 expression could still be suppressed by treatment with ICI 182,780 to ~60-70% of the levels seen in cells untreated with anti-oestrogen. This implies that despite high-level c-Myc expression, cyclin D1 may still be partially modulated by anti-oestrogen treatment, and suggests that at least to some degree, c- Myc and cyclin D1 may lie on independent paths downstream of ER , as previously suggested by other data from this laboratory 251.
This independence of c-Myc and cyclin D1 has also been observed in the scenario of oncogenic transformation in mouse studies that indicate mammary tumours induced by MMTV c-Myc can develop in cyclin D1 knock-out mice 440 and that c-Myc and cyclin D1 are synergistic in the development of mammary tumours in immunocompromised mice 441.
A further effect of c-Myc overexpression appeared to be the suppression of ICI 182,780-mediated upregulation of p21WAF1/Cip1, an effect that was most apparent in the experiments involving the constitutive c-Myc overexpressing cells. The expression of p21WAF1/Cip1 in MCF-7 cells increases by ~3-fold at 18-24 hours post ICI-treatment 307. In the experiments presented in this thesis, the changes in p21WAF1/Cip1 expression in low c-Myc expressing MCF-7 derivative cell lines were more modest, at around 1.1-1.6 fold. Nonetheless, those clones bearing high c-Myc expression showed a trend towards lower p21WAF1/Cip1 induction, consistent with other studies demonstrating transcriptional repression of p21WAF1/Cip1 by oestrogen and c-Myc 261-263.
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6.3.4 c-Myc overexpression attenuates the ICI 182,780-induced inhibition of cyclin D1-cdk4 and cyclin E-cdk2 activities
The activation of cyclin E-cdk2 complexes has been implicated as the major function of cyclin D1-cdk4 complexes via phosphorylation of Rb and sequestration of p21WAF1/Cip1 and p27Kip1 177. Although some authors suggest that both cyclin D1-cdk4/6 and cyclin E- cdk2 are required sequentially to elicit phosphorylation (and hence inactivation) of pRb 442,443, others have questioned the absolute requirement for cyclin D1-cdk4 in pRb phosphorylation, as ectopic expression of cyclin E obviates the need for cyclin D-cdk4 in pRb phosphorylation in Chinese hamster embryo fibroblasts 444, and cyclin E-cdk2 is able to phosphorylate pRb in the absence of prior phosphorylation by cyclin D1-cdk4 in vitro 445 and in vivo 446. Furthermore, cyclin E “knock-in” is able to rescue phenotypic deficiencies in cyclin D1-null mice 447.
In breast cancer cells ICI 182,780 suppresses cyclin E-cdk2 activity, at least in part, via decreased cyclin D1, and the liberation of p21WAF1/Cip1 and p27Kip1. ICI 182,780 results in decreased cyclin D1 expression beginning at 2-4 hours after anti-oestrogen treatment, and declining to a minimum by 6 hours 307, a time-point that precedes the decline in percentage of cells in S phase by some 12 hours. In rat fibroblasts cyclin D1 expression is limiting for cyclin D1-cdk4 activity 448, and in breast cancer cells the number of cyclin D1-cdk4-p21WAF1/Cip1 complexes declines in parallel with loss of cyclin D1 protein expression and is associated with decreased cyclin E-cdk2 activity and cell proliferation 376.
The decline in cyclin E-cdk2 activity seen with ICI 182,780 treatment occurs after 8 hours of treatment, which is later than the decline in cyclin D1 expression, and precedes the decline in S phase by 3-5 hours 376. This change in cyclin E-cdk2 activity is related to the recruitment of p27Kip1 and in particular p21WAF1/Cip1 to the complexes. At these early time-points, the total cellular level of p21WAF1/Cip1 remains stable, consistent with the idea that p21WAF1/Cip1 is sequestered by cyclin D1. However, total cell p21WAF1/Cip1 levels do decline once S phase has begun, suggesting that different mechanism (eg: transcriptional repression) comes into play at this later time-point.
While c-Myc is a known effector of mitogenic signalling, it’s relationship to and independence from cyclin D1, and cyclin D1-cdk4 activity is less clear. During the entry
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of quiescent immortalised rat fibroblasts into the cell cycle, the earliest and largest defect in c-Myc-null cells is a greater than 10-fold reduction in cyclin D1-cdk4 activity 429,449. In the present studies, c-Myc overexpression resulted in relatively high cyclin D1 expression and high cyclin D1-cdk4 activity in the face of 24 hours of anti-oestrogen treatment, suggesting that in our model system, regulation of c-Myc may be an important intermediate step between ER and cyclin D1-cdk4 activity in ICI 182,780- mediated anti-proliferative effects.
In addition, c-Myc overexpression resulted in high cyclin E-cdk2 activity after 24 hours of anti-oestrogen treatment, consistent with the anti-oestrogen rescue model of Prall et al 251. While cyclin E levels did not change dramatically, as discussed earlier, there was a trend towards relatively lower total p21WAF1/Cip1 expression at this time point. These data are in agreement with the findings of Mukerjee and Conrad 262, although it is also possible that sequestration of p21WAF1/Cip1 and p27Kip1 by cyclin D1-cdk4 complexes secondary to cyclin D1 upregulation may have played a role in the ultimate derepression of cyclin E-cdk2 activity.
6.4 SUMMARY
Together, the data presented in this chapter provide strong evidence that c-Myc overexpression results in resistance to the anti-proliferative effects of anti-oestrogens in ER positive breast cancer cells and this is associated with relatively high cyclin D1- cdk4 activity, cyclin E-cdk2 activity and phosphorylation of pRb. In addition, c-Myc overexpression prevents anti-oestrogen-mediated cyclin D1 repression and may also prevent anti-oestrogen-mediated p21WAF1/Cip1 upregulation.
Despite high-level transient overexpression of c-Myc, cyclin D1 expression, cyclin D1- cdk4 activity, cyclin E-cdk2 activity and proliferation remain partially suppressible by anti-oestrogen treatment. Although this lack of total anti-oestrogen resistance may be a manifestation of the transient nature of the c-Myc induction achieved in our model, the minor changes in cyclin D1 expression relative to the large changes in c-Myc expression are consistent with previous reports indicating that c-Myc and cyclin D1 lie at least in part, on separate, parallel pathways downstream of ER that converge on or
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prior to cyclin E-cdk2 251. In this case, complete resistance to the inhibitory effects of anti-oestrogens will also depend on further cell-cycle lesions in the cyclin D1 pathway, or downstream of this point of convergence. A proposed model incorporating the present data in the context of the published literature is detailed in Figure 6.14.
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Figure 6.14: Proposed model of oestrogen signalling via c-Myc and cyclin D1
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CHAPTER 7: DISCUSSION AND FINAL COMMENTS
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7.1 PERSPECTIVES ON THE MANAGEMENT OF EARLY BREAST CANCER
In Western societies breast cancer is the leading cause of female cancer deaths. With increasing affluence and westernisation, breast cancer is also becoming an important public health issue in the developing world 450. In Australia, the 5 year survival rate for breast cancer is ~85% however unlike the majority of solid tumours where 5 year survival is generally a surrogate measure for cure, breast cancer (and particularly ER positive breast cancer) continues to relapse beyond 10 years post diagnosis.
The global effort in breast cancer research, diagnosis and treatment over the last 30-40 years has dissected some of the basic biology underpinning the behaviour of breast cancer and its response to treatment. Key developments have included the recognition that breast cancer can be viewed essentially as a systemic disease from relatively early in the disease course, and thus overly aggressive primary surgical treatment of breast cancer is often futile from the perspective of systemic relapse. The work of Umberto Veronesi 451 and others in developing breast conservation as an alternative to the more disfiguring total mastectomy has revolutionised the surgical treatment of breast cancer, with refinements such as sentinel node biopsy further reducing surgical morbidity. In combination with advances in radiotherapy, less aggressive surgery has delivered superior patient outcomes without loss of overall survival.
While locoregional disease control is of major importance in the overall management of breast cancer, and poor local control does impact on overall survival 452, it is also clear that the biological processes governing locoregional and systemic disease, are in many ways, quite separate 453 and in the vast majority of cases of systemic disease relapse, there is never any recurrence of the tumour at the primary site. In many respects, from a systemic control point of view, it is the prognostic information gained from surgical treatment (tumour size, grade, local lymph node burden, ER, PR and HER2 status) in conjunction with patient factors (such as age, menopausal status and comorbid illness) that predicts the likelihood of ultimately succumbing to breast cancer.
The importance of adjuvant systemic treatment of breast cancer is clearly demonstrated by the Oxford (Early Breast Cancer Trialists’Collaborative Group) meta-
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analyses which demonstrate that in ER positive disease tamoxifen therapy causes proportional reductions the annual breast cancer death rate of 31%, while chemotherapy results in proportional reductions the annual death rate of 20-38% depending on the age of the patient 56. In the early days of systemic treatment patients were essentially unselected, and consequently response rates were modest. In the case of the earliest targeted therapy tamoxifen, which inhibits the ER, and more lately, trastuzumab which inhibits Her2, the recognition that responses to therapy are contingent on treating patients whose tumours bear the relevant target, has resulted in impressive improvements in patient outcome. Thus, in the case of trastuzumab therapy, early trial results indicate that patients with HER2-amplified tumours have a ~40 - 50% less risk of recurrence when treated in an adjuvant setting 59,60,62.
The importance of biological predeterminants of tumour behaviour and therapeutic response in defining treatment choices is highlighted by the St. Gallen consensus guidelines from 2005 and 2007 18,55. These guidelines suggest that suggest hormone receptor status is now the initial therapeutic decision point (rather than lymph node status). Indeed current recommendations suggest that low risk ER positive patients with low nodal burden, may forgo chemotherapy altogether in favour of endocrine therapy alone. In addition, the biomarkers in current use also appear to predict the likelihood of response to agents with effects beyond direct modulation of the biomarker target itself. For example, ER positive breast cancers may be less responsive to taxane-based chemotherapy, while HER2-amplified breast cancers are more responsive to anthracycline-based chemotherapy.
Thus, it is becoming increasingly clear that the future of systemic breast cancer treatment will depend on the identification, biological characterisation and reliable measurement of new target molecules that will ultimately facilitate the development of new therapeutics, and the patient subpopulations most likely to benefit from them. As we learn more of the biological basis of breast cancer, and in particular of the redundancy in many of the molecular pathways at play, it is clear that drugs targeting multiple pathways will be required to overcome de novo and acquired therapeutic resistance, while the nature of the putative new targets themselves remains the subject of debate and continuing research.
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7.2 CLINICAL CHALLENGES AND BIOMARKER DEVELOPMENT IN THE MANAGEMENT OF BREAST CANCER
In 2007, of a survey of registrants of the 2005 San Antonio Breast Cancer Symposium and the 2005 St. Gallen Consensus Meeting on the Primary Therapy of Early Breast Cancer was conducted to determine the top 10 priorities for translational breast cancer research 454. The resulting list (of 15) included two questions in relation to the use of molecular signatures to optimise patient selection and chemotherapy schedule, as well as research questions relating to the biology and therapeutic targeting of triple negative breast cancer, new growth factor targets and the role of stem cells (Appendix 4). The most highly represented research category pertained to endocrine questions, and included the search for drugable targets to overcome primary and secondary resistance to anti-hormonal agents.
The overall aim of this thesis was to examine the role of cell cycle genes, particularly c- Myc, in breast cancer outcome and endocrine sensitivity. The outcomes presented were that: 1. A representative early breast cancer cohort was assembled and characterised for standard clinicopathological factors, 2. The cohort was evaluated for the expression of several cell cycle proteins, with the generation of new information in relation to the stratification of cyclin D1 expression into three levels (low/ moderate/ high), 3. High cyclin D1 expression was found to be an independent predictor of poor outcome in women with hormone receptor positive breast cancers, and predicted poor outcome among the otherwise good-prognosis “Luminal A” subgroup, 4. The immunolocalisation of c-Myc was observed to shift from predominantly nuclear, to predominantly cytoplasmic with the evolution to carcinoma, 5. An immunohistochemical c-Myc expression signature was defined in which there was a predominant cytoplasmic localisation of c-Myc protein, and that independently predicted for poor outcome in hormone receptor positive breast cancers (and those ER positive cancers treated with adjuvant endocrine therapy),
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6. The “high-risk” immunohistochemical c-Myc expression signature was highly associated with the “Basal” breast cancer phenotype, 7. Testing of the functional consequences of c-Myc overexpression in vitro confirmed that c-Myc overexpression results in resistance to the anti- proliferative effects of anti-oestrogens.
It is therefore clear that the work presented in this thesis addresses several key research concerns of stakeholders in the sphere of breast cancer treatment and research, notably biomarkers and biology of both endocrine resistance, and the triple- negative breast cancer subtype (which is largely, although not entirely synonymous with the “basal” molecular phenotype). The key cell cycle regulator in both of these scenarios is c-Myc. In addition, high-level cyclin D1 expression appears particularly relevant in the outcome of ER positive breast cancer patients.
7.3 CELL CYCLE PROTEINS AS BREAST CANCER BIOMARKERS
As outlined in the Introduction, multiple studies have been performed with the intent of investigating the prognostic role of cell cycle proteins in human breast cancer outcome cohorts. In the recently released “Recommendations for the Use of Tumour Markers in Breast Cancer” from the American Society of Clinical Oncology (ASCO) 45 it was stated that present data are insufficient to recommend measurement of immunohistochemically-based markers of proliferation, including cyclin D1, cyclin E, p21WAF1/Cip1 and p27Kip1. Due to the limited number and inconclusive nature of studies involving immunohistochemical measurement of c-Myc expression, the role of this marker was not discussed in these recommendations.
Many of the studies evaluating cell cycle proteins as predictive markers of outcome in breast cancer are based on small retrospective studies with disparate results and a lack of standardisation in experimental procedures and scoring, and thus it is of little surprise that these single markers are not recommended for use in clinical practice. The data presented in this thesis identify a novel approach to the stratification of cyclin D1 protein expression that explains at least some of the heterogeneity seen in prognostic studies of this marker. In addition, these studies indicate that aberrant c-Myc
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protein expression portends poor outcome in ER positive and ER positive/ endocrine therapy-treated patients. Thus, the role of aberrant expression of cyclin D1 and c-Myc as biomarkers of outcome and treatment response warrants further exploration and validation in the context of ER positive patients treated with anti-oestrogen therapy in large, well-controlled retrospective and prospective patient series.
7.4 MULTI-PARAMETER BIOMARKER SIGNATURES AND THE LINK TO THE CELL CYCLE
The use of biomarkers directed towards particular patient subgroups (such as ER positive patients) has precedence within the current ASCO recommendations which state that the “Oncotype DX” assay can be used to predict the risk of recurrence in patients with node-negative, oestrogen-receptor positive breast cancer that are treated with tamoxifen. It is notable that the “Oncotype DX” assay is a composite score of the expression (by RT-PCR) of 21-genes (16 cancer-related and 5 reference genes) including several “proliferation” genes as well as “metastasis” genes, “Her2” genes, ER and PR. This type of multi-parameter signature differs from single marker studies in the recognition of some of the complexity in the molecular make-up of breast tumours that predicts their ultimate behaviour 44. Several other gene expression signatures also exist, including that derived at the Netherlands Cancer Institute (also known as MammaPrint) 39 and the Rotterdam signature 42 neither of which are recommended for routine clinical use at present, although validation studies are underway.
Notably, in addition to practical considerations such as cost and the need for fresh tissue in some series (eg: the NCI and Rotterdam studies), there is often little overlap between individual gene expression signatures from different studies leading to speculation as to their ultimate clinical utility. Despite these reservations in relation to individual studies, the plethora of microarray studies that have now been performed in many tumour types has allowed the development of meta-analyses to define essential unifying “meta-signatures” of neoplastic transformation and differentiation across multiple tumour types 455. Whether, such a unifying pan-cancer signature is of ultimate utility in informing researchers of tumour biology and directing the development of therapeutic targets remains to be determined. It may be that the use of meta-analyses on patient subgroups stratified on the basis of existing clinical measures (such as
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histological grade and ER status) may prove more informative in defining pathways that are amenable to drug-targeting, or predictive of therapeutic resistance to both targeted agents (including endocrine therapy) and chemotherapy.
7.5 C-MYC PATHWAYS ARE IMPORTANT IN RELAPSE SIGNATURES IN ER POSITIVE BREAST CANCER
The oncogenic Myc pathway is activated not only in IgG-Myc mutation lymphoma, but a number of other malignancies, including N-Myc amplification positive neuroblastoma, metastatic versus localised prostate cancer, colon adenocarcinoma versus normal colon, chronic myeloid leukaemia in blast crisis versus chronic phase, and grade III breast cancer versus grades I and II. Biostatistical approaches have recently been utilised to combine “molecular concepts” from thousands of cancer-related gene expression signatures, thus allowing the role of c-Myc in human breast cancer, and in particular in relation to ER status and response to treatment, to be further defined in terms of downstream pathway alteration.
This approach has demonstrated that in ER positive breast cancer, the molecular signature of disease relapse shows evidence of E2F and Myc pathway activation and associations with the fibroblast serum response and cell cycle signatures 49. Furthermore, the chromosomal cytoband 8q24 (the location of c-Myc) was one of several cytobands showing an association with the ER positive relapse signature. Interestingly, compounds that repress the ER positive relapse signature include resveratrol (which inhibits the cell cycle) and PI3K inhibitors (which also repress the c- Myc transcriptional program).
In these studies, the ER positive and ER negative relapse signatures were quite distinct with the ER negative relapse signature instead showing significant associations with independent Her2 signatures (although not invariably in association with Her2 overexpression). Unlike the ER positive relapse signature, which was closely related to c-Myc and E2F pathway activation, the ER negative relapse signature was associated with the c-Src and Ras pathway 49.
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Another meta-analysis of 5 independent breast cancer cohorts, in which grade III ER positive and ER negative breast cancers were compared, was recently conducted in this laboratory. Unlike the relapse signatures above which were stratified by outcome (and thus address a different question), these analyses showed that ER negative tumours show higher expression of c-Myc regulated genes than ER positive tumours of the same grade 456. This suggests that downstream oestrogen targets may be active in ER negative breast cancers, but dissociated from the regulation by ER signalling.
The relationship between the transcriptional responses of breast cancer cells to oestrogen and c-Myc has also been examined in another recent study from this laboratory demonstrating that ~50% of oestrogen-regulated genes are also c-Myc regulated 457. When the transcriptional profile of genes regulated by oestrogen was further stratified, the “sub-signatures” for the cell cycle, transcriptional regulation, and cell growth were particularly enriched for c-Myc regulated genes.
Thus, the use of global profiling technologies provides compelling evidence of upregulation of the c-Myc pathway both downstream of ER signalling and in ER positive breast cancer that is destined to recur. Furthermore, specific studies evaluating the role of c-Myc in in vitro systems, and the studies presented in this thesis implicate aberrant expression of c-Myc in anti-oestrogen resistance 251,262,305. In the light of these data, c-Myc may represent an attractive future therapeutic target.
7.6 THERAPEUTIC TARGETING C-MYC AND CYCLIN D1
There are several strategies to inhibit c-Myc action that are under pre-clinical investigation, and that demonstrate anti-proliferative activity. Treatment of MCF-7 cells, HeLa cervical carcinoma cells and MES-SA uterine sarcoma cell with a polypeptide inhibitor of c-Myc (inhibiting the dimerisation of c-Myc and Max) enhanced the accumulation of cells in G0/G1 in response to treatment with the topoisomerase II inhibitors doxorubicin and etoposide 458, while the c-Myc small molecule inhibitor 10058-F4 (which also blocks c-Myc-Max dimerisation) resulted in decreased c-Myc protein levels, and inhibited proliferation of hepatocellular carcinoma cells as well as increasing chemosensitivity in association with downregulation of human telomerase reverse transcriptase (hTERT) 459. The majority of 10058-F4-mediated growth inhibition
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was caused by cell cycle arrest in this latter study. In acute myeloid leukaemia 10058- F4 induced cell cycle arrest at G0/G1, down-regulated c-Myc expression and upregulated p21WAF1/Cip1 and p27Kip1 in addition to activation of the mitochondrial apoptotic pathway 460.
Transcriptional approaches to c-Myc repression have also been evaluated. Triplex- forming oligonucleotides targeted to the promoter region of c-Myc inhibit gene expression by ~40% and increase gemcitabine incorporation at the targeted site 461, with greatly reduced cell survival and capacity for anchorage-independent growth. Anti- sense oligonucleotides increase cisplatin sensitivity and mimic the effects of ant- oestrogens 250,462,463 however, this technology is potentially limited by difficulties in delivery to the cancer cells.
It is interesting that several of these studies indicate that inhibition of c-Myc action increases chemosensitivity and has pro-apoptotic effects as previous authors have expressed concerns that c-Myc inhibition may be potentially problematic due to the suppression of c-Myc’s apoptotic function. These concerns have been further strengthened by data indicating that MYC amplification is associated with improved outcomes from trastuzumab, an effect attributed to c-Myc’s role in potentiating apoptosis 464. Indeed, c-Myc amplification status has been integrated as a concurrent biomarker study into current adjuvant breast cancer trials of trastuzumab or lapatinib in combination with chemotherapy, NCT00486668 (NSABP B-41), and NCT00542451 (Dana-Faber Cancer Institute) 465.
Thus, the pleiotropic molecular effects of c-Myc leave investigators with uncertainty as to whether c-Myc modulation will ultimately aid or hinder further efforts to improve breast cancer outcomes, or whether the effects of c-Myc are context dependent i.e. c- Myc inhibition might augment anti-oestrogen action, but interfere with trastuzumab action. It is clear that the role of c-Myc amplification and overexpression in the response of breast cancer to therapeutic interventions can only be addressed by further well-designed clinical studies.
Another potential therapeutic strategy is to target further downstream in the c-Myc pathway e.g. at the level of cdk inhibition. Furthermore, as there is evidence that cyclin D1 overexpression may mediate in vitro anti-oestrogen resistance 253,309 and poor outcomes from breast cancer 141. Therefore, modulation of both cyclin E-cdk2 activity
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downstream of c-Myc, and cyclin D1-cdk4 activity may be worthwhile therapeutic endeavours to reduce cell-cycle mediated anti-oestrogen resistance. Furthermore, as data from this thesis implicate cyclin E expression in adverse outcomes from ER negative breast cancer, modulation of cdk2 activity may present a therapeutic opportunity in this subgroup of breast cancers. Importantly the “Basal” molecular phenotype of breast cancer showed a particular correlation with cyclin E overexpression consistent with previous studies, as well as “high-risk” c-Myc expression. Patients with this type of breast cancer suffer particularly poor outcomes, and thus modulation of cyclin E-cdk2 activity represents a novel therapeutic strategy for this type of breast cancer.
A number of small-molecule inhibitors of cdks have now been developed as potential anti-cancer therapeutics, with the caveats that such a strategy may be complicated by the non-cell cycle effects of some cdks and in the light of data indicating that cdk2 may be dispensable in cell cycle progression 466,467.
Flavopiridol is a pan-cdk inhibitor that inhibits cell proliferation and induces cell cycle arrest in G1 by inhibition of cdk2 and cdk4 468,469. In addition it enhances chemotherapy-induced apoptosis and has already entered phase I and II clinical trials as a single agent and in combination with other chemotherapeutics 470-475 albeit with significant toxicity. The more selective cdk inhibitor, roscovitine, is most active against cdks 1, 2, 5, 7 and 9, as well as pyridoxal kinase, the enzyme responsible for phosphorylation and activation of vitamin B6 and other targets such as ERK1/2 476.
Lack of specificity and toxicity of these agents has precipitated the development of analogues and new chemical agents as potential therapeutics. These include BMS- 387032 (also known as SNS-032), an agent that has demonstrated superior efficiacy to flavopiridol in A2780 ovarian tumour xenografts, and which has entered phase I clinical trials 477. Another promising cdk inhibitor that is selective for cdks 4 and 6 is PD0332991. This agent causes rapid and reversible inhibition of pRb phosphorylation at the cdk4- and cdk6-specific sites Ser780 and Ser795 in MDA MB-435 breast cancer cells, and efficacy with oral administration in colon cancer and glioblastoma xenografts 478. This agent will be evaluated in the phase I clinical trial NCT00141297. The recently developed agent, P276-00, is a derivative of Rohitukine (a compound related to flavopiridol), that preferentially inhibits cdks 4, 1 and 9 479. This agent is currently being evaluated in the phase I clinical trial NCT00408018. In addition a cdk2-selective
299 FINAL DISCUSSION
inhibitor PHA533533 has been developed which shows strong growth inhibition of A2780 tumour xenografts in association with reduced pRb phosphorylation 480.
7.7 FUTURE STUDIES
The studies of Perou and others that define biologically distinct molecular subtypes of breast cancer have change the way in which breast cancer should be conceptualised, particularly in the context of biomarker and drug target discovery. The studies presented in this thesis and elsewhere indicate that the activities of the c-Myc pathway, cyclin D1-cdk4 and cyclin E1-cdk2 represent attractive potential therapeutic targets in the systemic management of breast cancer. However, it is also clear that these targets may only be relevant within discrete subgroups of breast cancer patients (such as those defined through molecular phenotyping), and treatment paradigms.
To further evaluate the role of aberrant c-Myc and cyclin D1 expression in mediating anti-oestrogen resistance, studies utilising archival tissue from large clinical trials of tamoxifen treatment with extensive follow-up data are planned, using tissue from the International Breast Cancer Study Group (IBCSG) trials VIII and IX. To date, over 1600 breast cancer specimens have been processed into TMAs for this purpose.
While the role of c-Myc and cyclin E in “Basal” breast cancer was not the primary concern of this thesis, strategies to identify new drug targets for this type of breast cancer are a key concern identified in the top 15 priorities for translational breast cancer research 454. Further collaborative studies will be required to develop cohorts of sufficient size to validate both the “high-risk” c-Myc expression pattern and high cyclin E expression as immunohistochemical markers of the “Basal” phenotype. While in vitro models of “Basal” breast cancer are somewhat limited, the agents discussed above that are currently in development as therapeutics will need to be evaluated in this specific context, and in specific “Basal” breast cancer populations. Obviously the utility of such studies will ultimately rely on the development of consensus guidelines for the immunohistochemical (or other) determination of molecular phenotype in the context of routine diagnostic pathology services.
300 FINAL DISCUSSION
7.8 CONCLUSION
In conclusion, the studies in this thesis provide new information in relation to the prognostic role of aberrant c-Myc protein expression in breast cancer outcome, and endocrine resistance. These data are consistent with emerging bioinformatic analyses pointing to c-Myc pathway activation as an important component of oestrogen action and signatures of adverse outcome. Furthermore these clinical and in vitro studies indicate that the effects of c-Myc expression on outcome and response to anti- oestrogens respectively, occur at least in part, independently of cyclin D1. Finally, the aberrant localisation of c-Myc observed in the studies presented here provides some clue to a potential mechanism mediating c-Myc overexpression in human breast cancer. Thus further planned studies include the immunohistochemical assessment of mediators of c-Myc localisation/degradation in the SVCBCOC. These studies will hopefully shed further light on the mechanism behind c-Myc overexpression and provide new targets for rational drug development.
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APPENDICES
343 APPENDICES
Appendix 1
CanSto snapshot – representative screen images of the pathology and follow-up fields
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Appendix 2
Contributors to the development of the SVCBCOC
1.Surgeon: Dr. Paul Crea
2. Pathologists (slide mark-up and review): Dr. Andrew Field
Dr. Sandra O’Toole
Dr. Ewan Millar
3. Cohort assembly (gathering of cases): Dr. Davendra Segara
Dr. Diane Adams
Dr. Sandra O’Toole
4. Tissue microarray construction: Dr. Davendra Segara
Dr. Catriona McNeil
5. Medical record review: Dr. Catriona McNeil
6. CanSto redesign: Dr. Catriona McNeil
Ms. Sarah Eggleton
Mr. Jim McBride
Dr. Gerard Henderson
7. Data Entry: Ms. Edwina Wing-Lun
Ms. Elizabeth Todd
Ms. Sarah Sutherland
8. Block management: Ms. Sarah Eggleton
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Appendix 3
Table A3.1: Multivariate Cox proportional hazards analysis - entire cohort (n = 292)
RECURRENCE p Value Hazard Ratio 95% CI All clinicopathological variables in model (n = 268) Grade III 0.0790 1.68 0.94 - 2.99 Size > 20 mm 0.4872 1.19 0.74 – 1.92 LN +ve 0.0002 2.64 1.58 – 4.40 ER +ve 0.9813 0.99 0.56 – 1.76 PR +ve 0.0028 0.40 0.22 – 0.73 HER2 amplification 0.0213 1.86 1.10 – 3.14
Resolved clinicopathological model (n = 270) LN +ve <0.0001 2.80 1.71 – 4.58 PR +ve <0.0001 0.33 0.20 – 0.54 HER2 amplification 0.0052 2.07 1.24 – 3.44
DISTANT METASTASIS p Value Hazard Ratio 95% CI All clinicopathological variables in model (n = 268) Grade III 0.2760 1.41 0.76 – 2.59 Size > 20 mm 0.1244 1.49 0.90 – 2.49 LN +ve <0.0001 3.27 1.88 – 5.71 ER +ve 0.5549 0.84 0.46 – 1.52 PR +ve 0.0021 0.37 0.19 – 0.70 HER2 amplification 0.0211 1.91 1.10 – 3.32
Resolved clinicopathological model (n = 270) LN +ve <0.0001 3.47 2.03 – 5.93 PR +ve <0.0001 0.28 0.16 – 0.49 HER2 amplification 0.0070 2.08 1.22 – 3.54
BREAST CANCER-RELATED DEATH p Value Hazard Ratio 95% CI All clinicopathological variables in model (n = 268) Grade III 0.3076 1.45 0.71 – 2.97 Size > 20 mm 0.3949 1.29 0.72 – 2.32 LN +ve <0.0001 3.59 1.89 – 6.82 ER +ve 0.2369 0.66 0.33 – 1.32 PR +ve 0.0051 0.32 0.15 – 0.71 HER2 amplification 0.0015 2.73 1.47 – 5.07
Resolved clinicopathological model (n = 270) LN +ve 0.0001 3.37 1.82 – 6.24 PR +ve <0.0001 0.22 0.11 – 0.43 HER2 amplification 0.0004 2.97 1.63 – 5.41
CI - confidence interval.
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Table A3.2: Multivariate Cox proportional hazards analysis - ER positive subgroup (n = 192)
RECURRENCE p Value Hazard Ratio 95% CI All clinicopathological variables in model (n = 185) Grade III 0.0249 2.15 1.10 – 4.20 LN +ve 0.0871 1.78 0.92 – 3.45 PR +ve 0.0152 0.45 0.23 – 0.86 HER2 amplification 0.0136 2.50 1.21 – 5.15
Resolved clinicopathological model (n = 187) Grade III 0.0167 2.29 1.16 – 4.52 PR +ve 0.0030 0.38 0.20 – 0.72 HER2 amplification 0.0279 2.26 1.09 – 4.66
DISTANT METASTASIS p Value Hazard Ratio 95% CI All clinicopathological variables in model (n = 185) Grade III 0.0802 1.91 0.93 – 3.92 Size > 20 mm 0.4899 1.28 0.63 – 2.60 LN +ve 0.0485 2.12 1.01 – 4.48 PR +ve 0.0116 0.41 0.20 – 0.82 HER2 amplification 0.0437 2.31 1.02 – 5.19
Resolved clinicopathological model (n = 185) LN +ve 0.0216 2.34 1.13 – 4.84 PR +ve 0.0056 0.38 1.90 – 0.75 HER2 amplification 0.0030 3.09 1.47 – 6.50
BREAST CANCER-RELATED DEATH p Value Hazard Ratio 95% CI All clinicopathological variables in model (n = 187) Grade III 0.2720 1.67 0.67 – 4.19 PR +ve 0.0008 0.24 0.11 – 0.55 HER2 amplification 0.0011 4.94 1.90 – 12.86
Resolved clinicopathological model (n = 187) PR +ve 0.0004 0.23 0.10 – 0.52 HER2 amplification <0.0001 6.06 2.49 – 14.73
CI - confidence interval.
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Table A3.3: Multivariate Cox proportional hazards analysis - ER negative subgroup (n = 88)
RECURRENCE p Value Hazard Ratio 95% CI All clinicopathological variables in model (n = 88) Size > 20 mm 0.2769 1.54 0.71 – 3.35 LN +ve 0.0003 4.73 2.05 – 10.88
Resolved clinicopathological model (n = 88) LN +ve <0.0001 5.34 2.39 – 11.96
DISTANT METASTASIS p Value Hazard Ratio 95% CI All clinicopathological variables in model (n = 88) Size > 20 mm 0.2100 1.67 0.75 – 3.74 LN +ve 0.0001 5.45 2.27 – 13.04
Resolved clinicopathological model (n = 88) LN +ve <0.0001 6.27 2.69 – 14.64
BREAST CANCER-RELATED DEATH p Value Hazard Ratio 95% CI All clinicopathological variables in model (n = 88) Size > 20 mm 0.3770 1.46 0.63 – 3.41 LN +ve <0.0001 8.86 2.99 – 26.26
Resolved clinicopathological model (n = 88) LN +ve <0.0001 9.72 3.34 – 28.28
CI - confidence interval
Table A3.4: Multivariate Cox proportional hazards analysis - ER positive + endocrine therapy subgroup (n = 109)
RECURRENCE p Value Hazard Ratio 95% CI All clinicopathological variables in model (n = 105) Grade III 0.0162 2.90 1.22 – 6.90 HER2 amplification 0.0390 2.80 1.05 – 7.43
Resolved clinicopathological model (n = 105) Grade III 0.0162 2.90 1.22 – 6.90 HER2 amplification 0.0390 2.80 1.05 – 7.43
DISTANT METASTASIS p Value Hazard Ratio 95% CI All clinicopathological variables in model (n = 105) Grade III 0.0521 2.44 0.99 – 6.00 Size > 20 mm 0.0855 2.20 0.90 – 5.40 HER2 amplification 0.2549 1.86 0.64 – 5.37
Resolved clinicopathological model (n = 109) Grade III 0.0163 2.89 1.22 – 6.85
BREAST CANCER-RELATED DEATH p Value Hazard Ratio 95% CI All clinicopathological variables in model (n = 105) HER2 amplification 0.0002 10.64 3.04 - 37.25
Resolved clinicopathological model (n = 105) HER2 amplification 0.0002 10.64 3.04 - 37.25
CI - confidence interval.
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Table A3.5: Multivariate Cox proportional hazards analysis - chemotherapy treated subgroup (n = 111)
RECURRENCE p Value Hazard Ratio 95% CI All clinicopathological variables in model (n = 106) Grade III 0.2548 1.72 0.68 – 4.36 Size > 20 mm 0.3845 1.37 0.67 – 2.81 LN +ve 0.0008 7.79 2.35 – 25.87 PR +ve 0.0045 0.30 0.13 – 0.69 HER2 amplification 0.0719 1.98 0.94 – 4.16
Resolved clinicopathological model (n = 106) LN +ve 0.0008 7.73 2.34 – 25.56 PR +ve 0.0002 0.23 0.11 – 0.49 HER2 amplification 0.0244 2.27 1.11 – 4.65
DISTANT METASTASIS p Value Hazard Ratio 95% CI All clinicopathological variables in model (n = 106) Grade III 0.3840 1.52 0.59 – 3.91 Size > 20 mm 0.2098 1.63 0.76 – 3.48 LN +ve 0.0010 11.41 2.69 – 48.45 PR +ve 0.0058 0.31 0.14 – 0.71 HER2 amplification 0.0383 2.24 1.04 – 4.81
Resolved clinicopathological model (n = 106) LN +ve 0.0009 11.53 2.73 – 48.68 PR +ve 0.0003 0.24 0.11 – 0.52 HER2 amplification 0.0122 2.52 1.22 – 5.19
BREAST CANCER-RELATED DEATH p Value Hazard Ratio 95% CI All clinicopathological variables in model (n = 105) Grade III 0.4090 1.73 0.47 – 6.40 LN +ve 0.0024 22.64 3.01 – 170.22 ER +ve 0.3392 0.63 0.25 – 1.62 PR +ve 0.0216 0.27 0.09 – 0.83 HER2 amplification 0.0076 3.24 1.37 – 7.69
Resolved clinicopathological model (n = 106) LN +ve 0.0038 19.53 2.61 – 146.19 PR +ve 0.0004 0.17 0.06 – 0.45 HER2 amplification 0.0016 3.78 1.66 – 8.64
CI - confidence interval.
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Appendix 4
The top 15 research questions/topics
1 Identify molecular signatures to select patients who could be spared chemotherapy
2 Identify molecular features that indicate the optimal chemotherapy regimen (for example, combination or sequential, anthracycline or not, and taxane or not)
3 Determine the factors in DCIS and/or atypical ductal hyperplasia which lead to progression into invasive carcinoma
4 Determine the role of stem cells in breast cancer development, progression, and treatment sensitivity
5 Identify response/resistance mechanisms and thereby therapeutic targets for triple-negative breast cancer
6 Develop a system (computer and so on) that will integrate all the information gathered so far about breast cancer to build robust models for understanding the aetiopathogenesis, treatment, and prognosis of breast cancer
7 Identify which low-risk patients require no adjuvant therapy
8 Determine whether other growth factor pathways (such as epidermal growth factor receptor, insulin-like growth factor receptor, Notch, Hedeghog, Wnt, and other angiogenic pathways) are important targets for therapy
9 Investigate which gene mutations in a tumour lead to metastases
10 Identify drugable targets that can be developed/exploited for therapeutic gain to overcome primary/secondary endocrine resistance
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11 Define consensus phenotyping procedures for specific molecular subtypes of breast cancer (immunohistochemistry, expression array, or reverse transcription- polymerase chain reaction signature genes)
12 Search for a more accurate and validated score of hormone sensitivity
13 (tie) Develop non-invasive techniques to diagnose and characterise primary breast tumours
13 (tie) Determine whether there is a molecular profile (including PgR and HER2) that can distinguish patients likely to respond to tamoxifen versus an aromatase inhibitor
15 Identify markers of the optimal duration of trastuzumab therapy
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