Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen.
Loi
S, Haibe-Kains B, Desmedt C, Wirapati P, Lallemand F, Tutt AM, Gillet
C, Ellis P, Ryder K, Reid JF, Daidone MG, Pierotti MA, Berns EMJJ,
Jansen MPHM, Foekens JA, Delorenzi M, Bontempi G, Piccart MJ and
Sotiriou C
Background: Estrogen receptor
positive (ER+) breast cancers (BC) are heterogeneous with regard to
their clinical behavior and response to therapies. The ER is currently
the best predictor of response to the anti-estrogen agent tamoxifen,
yet up to 30-40% of ER+BC will relapse despite tamoxifen treatment. New
prognostic biomarkers and further biological understanding of tamoxifen
resistance are required. We used gene expression profiling to develop
an outcome-based predictor using a training set of 255 ER+ BC samples
from women treated with adjuvant tamoxifen monotherapy. We used
clusters of highly correlated genes to develop our predictor to
facilitate both signature stability and biological interpretation.
Independent validation was performed using 362 tamoxifen-treated ER+ BC
samples obtained from multiple institutions and treated with tamoxifen
only in the adjuvant and metastatic settings.
Results: We developed a gene
classifier consisting of 181 genes belonging to 13 biological clusters.
In the independent set of adjuvantly-treated samples, it was able to
define two distinct prognostic groups (HR 2.01 95%CI: 1.29-3.13;
p=0.002). Six of the 13 gene clusters represented pathways involved in
cell cycle and proliferation. In 112 metastatic breast cancer patients
treated with tamoxifen, one of the classifier components suggesting a
cellular inflammatory mechanism was significantly predictive of
response.
Conclusions: We have developed
a gene classifier that can predict clinical outcome in
tamoxifen-treated ER+ BC patients. Whilst our study emphasizes the
important role of proliferation genes in prognosis, our approach
proposes other genes and pathways that may elucidate further mechanisms
that influence clinical outcome and prediction of response to tamoxifen.