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Breast Cancer Research
Published in: Breast Cancer Research 4/2008

Open Access 01-08-2008 | Research article

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

Authors: 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

Published in: Breast Cancer Research | Issue 4/2008

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Abstract

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 the entire dataset. Their prognostic abilities are due mostly to the detection of proliferation activity. Although ER- status (basal-like) and ERBB2+ expression 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.

Conclusion

This meta-analysis unifies various results of previous gene expression studies in breast cancer. It reveals connections between traditional prognostic factors, expression-based subtyping, and prognostic signatures, highlighting the important role of proliferation in breast cancer prognosis.
Appendix
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Metadata
Title
Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures
Authors
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
Publication date
01-08-2008
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 4/2008
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/bcr2124

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