Meta-analysis of Gene-Expression Profiles in Breast Cancer: Toward a Unified Understanding of Breast Cancer Sub-typing and Prognosis Signatures

Wirapati P, Sotiriou C, Kunkel S, Farmer P, Pradervand S, Haibe-Kains B, Desmedt C, Ignatiadis M, Sengstag T, Schutz F, Goldstein DR, Piccart MJ and Delorenzi M

Background: Breast cancer sub-typing and prognosis have been extensively studied 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 examined the contribution of well-known biological processes of breast cancer tumorigenesis to their prognostic performance.
Methods: To address the above issues and to further validate these intial findings, we performed the largest meta-analysis of publicly available breast cancer gene-expression and clinical data totaling 2833 breast tumors. Gene co-expression modules of three key biological processes in breast cancer, namely proliferation, estrogen receptor and HER2 signaling, were used to dissect the role of constituent genes of 9 prognostic signatures.

Results: Using 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 9 prognostic signatures exhibited similar prognostic performance in the entire dataset. Their prognostic abilities are due mostly to detection of proliferation activity. Although ER- (basal-like) and ERBB2+ amplification status correspond to bad outcome, they seem to act through elevated expression of proliferation genes, and thus contain only indirect information about prognosis. Clinical variables measuring the extent of tumor progression, such as tumor size and nodal status, still add independent prognostic information to proliferation genes.

Conclusions: This meta-analysis unifies various results of previous gene-expression studies in breast cancer. It reveals connections between traditional prognostic factors, expression-based sub-typing and prognostic signatures, highlighting the important role of proliferation in breast cancer prognosis.