Meta-analysis
of gene-expression profiles in breast cancer: towards a unified
understanding of breast cancer sub-typing and prognosis signatures
C.
Sotiriou, P. Wirapati, S. Kunkel, P. Farmer, S. Pradervand, Haibe-Kains
B, C. Desmedt, T. Sengstag, F. Schütz, D. R. Goldstein, M. Delorenzi,
M. Piccart
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. The biological roles of
individual genes in a signature, the equivalence of several signatures
and their relation to conventional prognostic factors are still
unclear.
Methods: Here we undertook a
comprehensive meta-analysis of publicly available gene-expression and
clinical data from 18 studies totaling 2833 breast tumor samples. The
concept of co-expression modules (comprehensive lists of genes with
highly correlated expression) was used extensively to reveal the common
thread connecting molecular sub-typing and several prognostic
signatures, as well as conventional clinico-pathological prognostic
factors.
Results: Breast tumors were
consistently grouped into three main subtypes corresponding roughly to
ER-/ERBB2- (basal), ERBB2+ and ER+ (luminal) tumors. ERBB2+ tumors
showed an intermediate estrogen receptor module score which is not
obvious from the traditional ER and ERBB2 marker status combination.
Both, ER-/ERBB2- and ERBB2+ subtypes were characterized by high
proliferation, whereas the ER+ subtype appeared to be more
heterogeneous. Using our meta-analytical approach we were able to
identify 524 genes which were significantly associated with survival.
Of the 524 prognostic genes, 65% were strongly co-expressed with
proliferation, 14% with ER, 0.6% with ERBB2, 2.7% with tumor invasion,
1.5% with immune response and 16% with none of our co-expression
modules. All previously reported prognostics signatures examined in
this meta-analysis (N=9) , despite the disparity in their gene lists,
carried similar information with regard to prognostication, with
proliferation genes being the common driving force. They were all very
useful for determining the risk of recurrence in the ER+ subgroup and
much less informative for ER- and ERBB2+ disease. Combining the
signatures did not improve their performances. Finally, in multivariate
analysis nodal status and tumor size still retained independent
prognostic information.
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.