Comparison of prognostic gene expression signatures for breast cancer

BackgroundDuring the last years, several groups have identified prognostic gene expression signatures with apparently similar performances. However, signatures were never compared on an independent population of untreated breast cancer patients, where risk assessment was computed using the original algorithms and microarray platforms.ResultsWe compared three gene expression signatures, the 70-gene, the 76-gene and the Gene expression Grade Index (GGI) signatures, in terms of predicting distant metastasis free survival (DMFS) for the individual patient. To this end, we used the previously published TRANSBIG independent validation series of node-negative untreated primary breast cancer patients. We observed agreement in prediction for 135 of 198 patients (68%) when considering the three signatures. When comparing the signatures two by two, the agreement in prediction was 71% for the 70- and 76-gene signatures, 76% for the 76-gene signature and the GGI, and 88% for the 70-gene signature and the GGI. The three signatures had similar capabilities of predicting DMFS and added significant prognostic information to that provided by the classical parameters.ConclusionDespite the difference in development of these signatures and the limited overlap in gene identity, they showed similar prognostic performance, adding to the growing evidence that these prognostic signatures are of clinical relevance.

[1]  R. Gelber,et al.  Meeting highlights: updated international expert consensus on the primary therapy of early breast cancer. , 2003, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

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

[3]  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.

[4]  Daniel B. Mark,et al.  TUTORIAL IN BIOSTATISTICS MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS , 1996 .

[5]  Ash A. Alizadeh,et al.  Gene Expression Signature of Fibroblast Serum Response Predicts Human Cancer Progression: Similarities between Tumors and Wounds , 2004, PLoS biology.

[6]  R. Tibshirani,et al.  Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[7]  R. Tibshirani,et al.  Repeated observation of breast tumor subtypes in independent gene expression data sets , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Karen A Gelmon,et al.  Population-based validation of the prognostic model ADJUVANT! for early breast cancer. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

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

[10]  Maqc Consortium.  The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements , 2006, Nature Biotechnology.

[11]  C. Sotiriou,et al.  Taking gene-expression profiling to the clinic: when will molecular signatures become relevant to patient care? , 2007, Nature Reviews Cancer.

[12]  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.

[13]  Gianluca Bontempi,et al.  Biological Processes Associated with Breast Cancer Clinical Outcome Depend on the Molecular Subtypes , 2008, Clinical Cancer Research.

[14]  Christian A. Rees,et al.  Molecular portraits of human breast tumours , 2000, Nature.

[15]  Roman Rouzier,et al.  Breast Cancer Molecular Subtypes Respond Differently to Preoperative Chemotherapy , 2005, Clinical Cancer Research.

[16]  M Markman,et al.  National Institutes of Health Consensus Development Conference Statement: adjuvant therapy for breast cancer, November 1-3, 2000. , 2001, Journal of the National Cancer Institute.

[17]  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.

[18]  A. Witteveen,et al.  Converting a breast cancer microarray signature into a high-throughput diagnostic test , 2006, BMC Genomics.

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

[20]  R. Eils,et al.  From latent disseminated cells to overt metastasis: Genetic analysis of systemic breast cancer progression , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Stella Mook,et al.  Individualization of therapy using Mammaprint: from development to the MINDACT Trial. , 2007, Cancer genomics & proteomics.

[22]  Mads Thomassen,et al.  Comparison of Gene Sets for Expression Profiling: Prediction of Metastasis from Low-Malignant Breast Cancer , 2007, Clinical Cancer Research.

[23]  Lloyd D. Fisher,et al.  Biostatistics: A Methodology for the Health Sciences , 1993 .

[24]  Yi Zhang,et al.  Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer , 2007, BMC Cancer.

[25]  M. J. van de Vijver,et al.  Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. , 2006, Journal of the National Cancer Institute.

[26]  N. Stoecklein,et al.  Genetic heterogeneity of single disseminated tumour cells in minimal residual cancer , 2002, The Lancet.

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

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

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

[30]  Gianluca Bontempi,et al.  Biological mechanisms that trigger breast cancer (BC) tumor progression are molecular subtype dependent , 2007 .

[31]  M. Pencina,et al.  Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation , 2004, Statistics in medicine.

[32]  Robert J. Mayer,et al.  National Institutes of Health Consensus Development Conference Statement: adjuvant therapy for breast cancer, November 1-3, 2000. , 2001, Journal of the National Cancer Institute.