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


METHODS
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.


CONCLUSIONS
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.
Wirapati
Pratyaksha
P
Swiss Institute of Bioinformatics, 'Batiment Genopode', University of Lausanne, 1015 Lausanne, Switzerland. Pratyaksha.Wirapati@isb-sib.ch
Sotiriou
Christos
C
Kunkel
Susanne
S
Farmer
Pierre
P
Pradervand
Sylvain
S
Haibe-Kains
Benjamin
B
Desmedt
Christine
C
Ignatiadis
Michail
M
Sengstag
Thierry
T
Schütz
Frédéric
F
Goldstein
Darlene R
DR
Piccart
Martine
M
Delorenzi
Mauro
M
D000970:Antineoplastic Agents / Q000494:pharmacology; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D049109:Cell Proliferation; D016000:Cluster Analysis; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D018734:Genes, erbB-2; D006801:Humans; D011379:Prognosis; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism; D011960:Receptors, Estrogen / Q000378:metabolism; D015398:Signal Transduction
D000970:Antineoplastic Agents; D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen