Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures
1 Swiss Institute of Bioinformatics, 'Batiment Genopode', University of Lausanne, 1015 Lausanne, Switzerland
2 Translational Research and Medical Oncology Unit, Université Libre de Bruxelles, Institut Jules Bordet, 121 Boulevard de Waterloo, 1000 Brussels, Belgium
3 National Centers for Competence in Research, Molecular Oncology, Swiss Institute for Experimental Cancer Research, Ch. des Boveresses 155, 1066 Epalinges, Switzerland
4 DNA Array Facility, Center for Integrative Genomics, 'Batiment Genopode', University of Lausanne, 1015 Lausanne, Switzerland
5 Machine Learning Group, Université Libre de Bruxelles, boulevard du Triomphe, CP212, 1050 Bruxelles, Belgium
6 Institut de Mathématiques, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
Breast Cancer Research 2008, 10:R65 doi:10.1186/bcr2124Published: 28 July 2008
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.
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.
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.
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.