Predicting prognosis of breast cancer with gene signatures: are we lost in a sea of data?

A large number of prognostic and predictive signatures have been proposed for breast cancer and a few of these are now available in the clinic as new molecular diagnostic tests. However, several other signatures have not fared well in validation studies. Some investigators continue to be puzzled by the diversity of signatures that are being developed for the same purpose but that share few or no common genes. The history of empirical development of prognostic gene signatures and the unique association between molecular subsets and clinical phenotypes of breast cancer explain many of these apparent contradictions in the literature. Three features of breast cancer gene expression contribute to this: the large number of individually prognostic genes (differentially expressed between good and bad prognosis cases); the unstable rankings of differentially expressed genes between datasets; and the highly correlated expression of informative genes.

[1]  Kenneth R Hess,et al.  Molecular profiling of carcinoma of unknown primary and correlation with clinical evaluation. , 2008, 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]  Yuan Qi,et al.  Molecular anatomy of breast cancer stroma and its prognostic value in estrogen receptor-positive and -negative cancers. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[4]  W. Alexander,et al.  American Society of Clinical Oncology , 2020, Definitions.

[5]  Achim Rody,et al.  T-cell metagene predicts a favorable prognosis in estrogen receptor-negative and HER2-positive breast cancers , 2009, Breast Cancer Research.

[6]  A. Nobel,et al.  Supervised risk predictor of breast cancer based on intrinsic subtypes. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[7]  Kevin C. Dorff,et al.  The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models , 2010, Nature Biotechnology.

[8]  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.

[9]  Peter Regitnig,et al.  Genomic index of sensitivity to endocrine therapy for breast cancer. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[10]  Leming Shi,et al.  Effect of training-sample size and classification difficulty on the accuracy of genomic predictors , 2010, Breast Cancer Research.

[11]  Robert Gray,et al.  Prognostic utility of the 21-gene assay in hormone receptor-positive operable breast cancer compared with classical clinicopathologic features. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[12]  Achim Rody,et al.  T cell marker metagene predicts a favourable prognosis in estrogen receptor negative and Her2 positive breast cancers. , 2009 .

[13]  A. Giobbie-Hurder,et al.  Central Review of ER, PgR and HER2 in BIG 1-98 Evaluating Letrozole vs. Letrozole Followed by Tamoxifen vs. Tamoxifen Followed by Letrozole as Adjuvant Endocrine Therapy for Postmenopausal Women with Hormone Receptor-Positive Breast Cancer. , 2009 .

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

[15]  R. Gelber,et al.  Thresholds for therapies: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2009 , 2009, Annals of oncology : official journal of the European Society for Medical Oncology.

[16]  R. Bast,et al.  American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[17]  J. Haerting,et al.  Gene-expression signatures in breast cancer. , 2003, The New England journal of medicine.

[18]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Carsten O. Peterson,et al.  Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. , 2001, Cancer research.

[20]  Renee F Wilson,et al.  Systematic Review: Gene Expression Profiling Assays in Early-Stage Breast Cancer , 2008, Annals of Internal Medicine.

[21]  A. Ashworth,et al.  Breast cancer molecular profiling with single sample predictors: a retrospective analysis. , 2010, The Lancet. Oncology.

[22]  Benjamin Haibe-Kains,et al.  Assessment of an RNA interference screen-derived mitotic and ceramide pathway metagene as a predictor of response to neoadjuvant paclitaxel for primary triple-negative breast cancer: a retrospective analysis of five clinical trials. , 2010, The Lancet. Oncology.

[23]  J. Cuzick,et al.  Prognostic Value of a Combined ER, PgR, Ki67, HER2 Immunohistochemical (IHC4) Score and Comparison with the GHI Recurrence Score – Results from TransATAC. , 2009 .

[24]  Carlos Caldas,et al.  A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the Proliferation, Immune response and RNA splicing modules in breast cancer , 2008, Breast Cancer Research.

[25]  Wim H van Harten,et al.  Use of 70-gene signature to predict prognosis of patients with node-negative breast cancer: a prospective community-based feasibility study (RASTER). , 2007, The Lancet. Oncology.

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

[27]  J. Stec,et al.  Gene expression profiles obtained from fine-needle aspirations of breast cancer reliably identify routine prognostic markers and reveal large-scale molecular differences between estrogen-negative and estrogen-positive tumors. , 2003, Clinical cancer research : an official journal of the American Association for Cancer Research.