Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures

作者：Pratyaksha Wirapati, Christos Sotiriou, Susanne Kunkel, Pierre Farmer, Sylvain Pradervand, Benjamin Haibe‐Kains, Christine Desmedt, Michail Ignatiadis, Thierry Sengstag, Frédéric Schütz, Darlene R Goldstein, Martine Piccart, Mauro Delorenzi · 发表于：Breast Cancer Research · 年份：2008 · DOI：10.1186/bcr2124 · 被引用次数：895 · 研究领域：Breast Cancer Treatment Studies、HER2/EGFR in Cancer Research、Biomarkers in Disease Mechanisms

INTRODUCTION: Breast cancer subtyping and prognosis have been studied extensively by gene expression profiling, resulting in disparate signatures with little overlap in their constituent genes. Although a previous study demonstrated a prognostic concordance among gene expression signatures, it was limited to only one dataset and did not fully elucidate how the different genes were related to one another nor did it examine the contribution of well-known biological processes of breast cancer tumorigenesis to their prognostic performance. METHOD: To address the above issues and to further validate these initial findings, we performed the largest meta-analysis of publicly available breast cancer gene expression and clinical data, which are comprised of 2,833 breast tumors. Gene coexpression modules of three key biological processes in breast cancer (namely, proliferation, estrogen receptor [ER], and HER2 signaling) were used to dissect the role of constituent genes of nine prognostic signatures. RESULTS: Using a meta-analytical approach, we consolidated the signatures associated with ER signaling, ERBB2 amplification, and proliferation. Previously published expression-based nomenclature of breast cancer 'intrinsic' subtypes can be mapped to the three modules, namely, the ER-/HER2- (basal-like), the HER2+ (HER2-like), and the low- and high-proliferation ER+/HER2- subtypes (luminal A and B). We showed that all nine prognostic signatures exhibited a similar prognostic performance in...