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.