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Open Access Highly Accessed Research article

Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen

Sherene Loi12, Benjamin Haibe-Kains13, Christine Desmedt1, Pratyaksha Wirapati4, Françoise Lallemand1, Andrew M Tutt5, Cheryl Gillet5, Paul Ellis5, Kenneth Ryder5, James F Reid68, Maria G Daidone8, Marco A Pierotti68, Els MJJ Berns7, Maurice PHM Jansen7, John A Foekens7, Mauro Delorenzi4, Gianluca Bontempi3, Martine J Piccart1 and Christos Sotiriou1*

Author Affiliations

1 Functional Genomics Unit, Jules Bordet Institute, Brussels, Belgium

2 Peter MacCallum Cancer Center, East Melbourne, Victoria, Australia

3 Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium

4 NCCR Molecular Oncology, Swiss Institute of Cancer Research and Swiss Institute of Bioinformatics, Epalinges, Switzerland

5 Guys Hospital, London, UK

6 Molecular Cancer Genetics, Fondazione Istituto FIRC di Oncologia Molecolare (IFOM), Milan, Italy

7 Erasmus MC-Daniel-JNI, Rotterdam, The Netherlands

8 Department of Experimental Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy

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BMC Genomics 2008, 9:239  doi:10.1186/1471-2164-9-239

Published: 22 May 2008

Abstract

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.

Conclusion

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.