Risk estimation of distant metastasis in node-negative, estrogen receptor-positive breast cancer patients using an RT-PCR based prognostic expression signature

BackgroundGiven the large number of genes purported to be prognostic for breast cancer, it would be optimal if the genes identified are not confounded by the continuously changing systemic therapies. The aim of this study was to discover and validate a breast cancer prognostic expression signature for distant metastasis in untreated, early stage, lymph node-negative (N-) estrogen receptor-positive (ER+) patients with extensive follow-up times.Methods197 genes previously associated with metastasis and ER status were profiled from 142 untreated breast cancer subjects. A "metastasis score" (MS) representing fourteen differentially expressed genes was developed and evaluated for its association with distant-metastasis-free survival (DMFS). Categorical risk classification was established from the continuous MS and further evaluated on an independent set of 279 untreated subjects. A third set of 45 subjects was tested to determine the prognostic performance of the MS in tamoxifen-treated women.ResultsA 14-gene signature was found to be significantly associated (p < 0.05) with distant metastasis in a training set and subsequently in an independent validation set. In the validation set, the hazard ratios (HR) of the high risk compared to low risk groups were 4.02 (95% CI 1.91–8.44) for the endpoint of DMFS and 1.97 (95% CI 1.28 to 3.04) for overall survival after adjustment for age, tumor size and grade. The low and high MS risk groups had 10-year estimates (95% CI) of 96% (90–99%) and 72% (64–78%) respectively, for DMFS and 91% (84–95%) and 68% (61–75%), respectively for overall survival. Performance characteristics of the signature in the two sets were similar. Ki-67 labeling index (LI) was predictive for recurrent disease in the training set, but lost significance after adjustment for the expression signature. In a study of tamoxifen-treated patients, the HR for DMFS in high compared to low risk groups was 3.61 (95% CI 0.86–15.14).ConclusionThe 14-gene signature is significantly associated with risk of distant metastasis. The signature has a predominance of proliferation genes which have prognostic significance above that of Ki-67 LI and may aid in prioritizing future mechanistic studies and therapeutic interventions.

[1]  I. Ellis,et al.  Pathological prognostic factors in breast cancer. , 1999, Critical reviews in oncology/hematology.

[2]  J. Foekens,et al.  HOXB13-to-IL17BR expression ratio is related with tumor aggressiveness and response to tamoxifen of recurrent breast cancer: a retrospective study. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[3]  I. Ellis,et al.  Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. , 2002, Histopathology.

[4]  M. Cronin,et al.  A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. , 2004, The New England journal of medicine.

[5]  D. Cameron,et al.  Pathological features of breast cancer response following neoadjuvant treatment with either letrozole or tamoxifen. , 2003, European journal of cancer.

[6]  Yudong D. He,et al.  A cell proliferation signature is a marker of extremely poor outcome in a subpopulation of breast cancer patients. , 2005, Cancer research.

[7]  A. Vincent-Salomon,et al.  Proliferation markers predictive of the pathological response and disease outcome of patients with breast carcinomas treated by anthracycline-based preoperative chemotherapy. , 2004, European journal of cancer.

[8]  M. Gilcrease,et al.  Ki-67 as Prognostic Marker in Early Breast Cancer: A Meta-analysis of Published Studies Involving 12 155 Patients , 2008 .

[9]  J. Bergh,et al.  Strong Time Dependence of the 76-Gene Prognostic Signature for Node-Negative Breast Cancer Patients in the TRANSBIG Multicenter Independent Validation Series , 2007, Clinical Cancer Research.

[10]  T. Wu,et al.  Prospects of RNA interference therapy for cancer , 2006, Gene Therapy.

[11]  M. Fernö,et al.  "Good Old" clinical markers have similar power in breast cancer prognosis as microarray gene expression profilers. , 2004, European journal of cancer.

[12]  D. Collet Modelling Survival Data in Medical Research , 2004 .

[13]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[14]  E. Kaplan,et al.  Nonparametric Estimation from Incomplete Observations , 1958 .

[15]  T. Lumley,et al.  Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker , 2000, Biometrics.

[16]  J. Foekens,et al.  Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer , 2005, The Lancet.

[17]  Russ B. Altman,et al.  Missing value estimation methods for DNA microarrays , 2001, Bioinform..

[18]  J. Foekens,et al.  Multicenter validation of a gene expression-based prognostic signature in lymph node-negative primary breast cancer. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[19]  Kevin Coombes,et al.  Prognostic Role of a Multigene Reverse Transcriptase-PCR Assay in Patients with Node-Negative Breast Cancer Not Receiving Adjuvant Systemic Therapy , 2005, Clinical Cancer Research.

[20]  R. Tibshirani,et al.  Prediction by Supervised Principal Components , 2006 .

[21]  Rohit Bhargava,et al.  Histopathologic variables predict Oncotype DX™ Recurrence Score , 2008, Modern Pathology.

[22]  Mitch Dowsett,et al.  Prognostic value of Ki67 expression after short-term presurgical endocrine therapy for primary breast cancer. , 2007, Journal of the National Cancer Institute.

[23]  Howard Y. Chang,et al.  Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[24]  R. Tibshirani,et al.  Semi-Supervised Methods to Predict Patient Survival from Gene Expression Data , 2004, PLoS biology.

[25]  Jan Mous,et al.  Touching base , 2000, Nature Genetics.

[26]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[27]  A. Bednarek,et al.  Ki-67 expression in operable breast cancer: a comparative study of immunostaining and a real-time RT-PCR assay. , 2006, Pathology, research and practice.

[28]  C Sotiriou,et al.  Proliferative markers as prognostic and predictive tools in early breast cancer: where are we now? , 2005, Annals of oncology : official journal of the European Society for Medical Oncology.

[29]  R. Spang,et al.  Predicting the clinical status of human breast cancer by using gene expression profiles , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[30]  M. Akritas Nearest Neighbor Estimation of a Bivariate Distribution Under Random Censoring , 1994 .

[31]  David R. Cox,et al.  Regression models and life tables (with discussion , 1972 .

[32]  Robert Langer,et al.  A combinatorial library of lipid-like materials for delivery of RNAi therapeutics , 2008, Nature Biotechnology.

[33]  L. Sobin,et al.  Histological Typing of Breast Tumors 1 , 1982 .

[34]  L. V. van't Veer,et al.  Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. , 2006, Journal of the National Cancer Institute.

[35]  Thomas D. Schmittgen,et al.  Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. , 2001, Methods.

[36]  Daniel F Hayes,et al.  Prognostic and predictive factors revisited. , 2005, Breast.

[37]  P. Tan,et al.  Immunohistochemical detection of Ki67 in breast cancer correlates with transcriptional regulation of genes related to apoptosis and cell death , 2005, Modern Pathology.

[38]  P. Hall,et al.  An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[39]  G. Devi,et al.  siRNA-based approaches in cancer therapy , 2006, Cancer Gene Therapy.

[40]  C. Perou,et al.  Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study. , 2006, JAMA.

[41]  M. Cronin,et al.  Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[42]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

[43]  F. Couch,et al.  A Two-Gene Expression Ratio of Homeobox 13 and Interleukin-17B Receptor for Prediction of Recurrence and Survival in Women Receiving Adjuvant Tamoxifen , 2006, Clinical Cancer Research.

[44]  Gavin D. Grant,et al.  Common markers of proliferation , 2006, Nature Reviews Cancer.

[45]  A. Nobel,et al.  Concordance among Gene-Expression – Based Predictors for Breast Cancer , 2011 .

[46]  Andreas Makris,et al.  Evaluation of Ki-67 proliferation and apoptotic index before, during and after neoadjuvant chemotherapy for primary breast cancer , 2006, Breast Cancer Research.

[47]  S. Hilsenbeck,et al.  The HOXB13:IL17BR expression index is a prognostic factor in early-stage breast cancer. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[48]  R. W. Scarff,et al.  Histological typing of breast tumors. , 1982, Tumori.

[49]  Yudong D. He,et al.  A Gene-Expression Signature as a Predictor of Survival in Breast Cancer , 2002 .

[50]  Van,et al.  A gene-expression signature as a predictor of survival in breast cancer. , 2002, The New England journal of medicine.

[51]  Sunil J Rao,et al.  Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2003 .