AGene Expression Signature that Can Predict the Recurrence ofTamoxifen-Treated Primary Breast Cancer

Purpose:The identification of a molecular signature predicting the relapse of tamoxifen-treated primary breast cancers should help the therapeutic management of estrogen receptor ^ positive cancers. Experimental Design: A series of 132 primary tumors from patients who received adjuvant tamoxifen were analyzed for expression profiles at the whole-genome level by 70-mer oligonucleotide microarrays. A supervised analysis was done to identify an expression signature. Results:We defined a 36-gene signature that correctly classified 78% of patients with relapse and 80% of relapse-free patients (79% accuracy). Using 23 independent tumors, we confirmed the accuracy of the signature (78%) whose relevance was further shown by using published microarray data from 60 tamoxifen-treated patients (63% accuracy). Univariate analysis using the validation set of 83 tumors showed that the 36-gene classifier is more efficient in predicting disease-free survival than the traditional histopathologic prognostic factors and is as effective as the Nottingham Prognostic Index or the ‘‘Adjuvant! ’’ software. Multivariate analysis showed that the molecular signature is the only independent prognostic factor. A comparison with several already published signatures demonstrated that the 36-gene signature is among the best to classify tumors from both training and validation sets. Kaplan-Meier analyses emphasized its prognostic power both on the whole cohort of patients and on a subgroup with an intermediate risk of recurrence as defined by the St. Gallen criteria. Conclusion: This study identifies a molecular signature specifying a subgroup of patients who do not gain benefits from tamoxifen treatment. These patients may therefore be eligible for alternative endocrine therapies and/or chemotherapy. Breast cancer is the most common female cancer in the Western world and the leading cause of death by cancer among women. Although the mortality rate is now stabilized or decreasing, breast cancer incidence is still on the rise through all European countries (1). About two thirds of breast cancers are hormone (estrogen) dependent as they are positive for estrogen receptor (ER) and/ or progesterone receptor (PR). Because estrogen is a major activator of proliferation in these tumors, its receptor and downstream signaling are excellent targets for the hormonal therapy in patients with ER+ (and/or PR+) breast cancers. Over the past three decades, the antiestrogen tamoxifen, which prevents the binding of estrogen to its receptor, has been the golden standard for the endocrine treatment of all stages of these cancers. In particular, large-scale randomized trials have shown that, in early-stage ER+ breast cancers, a 5-year course of tamoxifen, started immediately after surgery, reduces recurrence by 51% and mortality by 28% (2). However, the success of tamoxifen therapy is limited by intrinsic or acquired tumor resistance. Approximately 40% of patients with ER+ breast cancers will not respond to tamoxifen. This is mostly because this selective ER modulator is not a pure antiestrogen and, indeed, shows some agonist activity. The balance between agonist and antagonist properties differs among cell types and seems to depend on several molecular events that can influence ER signaling. These include the level of coactivators and corepressors and the effect of cross-signaling in growth factor transduction pathways (3). Imaging, Diagnosis, Prognosis Authors’Affiliations: U868 Institut National de la Sante et de la Recherche Medicale,Tumoral Identity and Plasticity, Cancer Research Center of Montpellier, Universite¤ Montpellier 1, CRLC Val d’Aurelle-Paul Lamarque; Department of Informatics, UMR5160 Centre National de la Recherche Scientifique, Montpellier, France; Department of Pathology, Institut Bergonie¤ , Bordeaux, France; and Department of Obstetrics and Gynecology, University ofTurin,Turin, Italy Received 7/26/07; revised11/30/07; accepted12/11/07. Grant support: Institut National de la Sante et de la RechercheMedicale, GEFLUC Montpellier-Languedoc-Roussillon, and the Canceropole Grand-Sud-Ouest, France. M. Chanrion was a recipient of a fellowship from the Ligue contre le Cancer-Comite¤ He¤ rault and V. Negre was a recipient of a fellowship from Institut National de la Sante et de la RechercheMedicale. The costs of publication of this article were defrayed in part by the payment of page charges.This article must therefore be hereby marked advertisement in accordance with18 U.S.C. Section1734 solely to indicate this fact. Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/). Requests for reprints: Jean-Marie Darbon, Institut National de la Sante et de la Recherche Medicale U868, Centre de Recherche en Cance¤ rologie de Montpellier, CRLC Val d’Aurelle-Paul Lamarque, F-34298 Montpellier cedex 5, France. Phone: 33-467-612-556; Fax: 33-467-613-787; E-mail: jmdarbon@valdorel.fnclcc. fr. F2008 American Association for Cancer Research. doi:10.1158/1078-0432.CCR-07-1833 www.aacrjournals. org Clin Cancer Res 2008;14(6)March15, 2008 1744 Research. on May 2, 2017. © 2008 American Association for Cancer clincancerres.aacrjournals. org Downloaded from Recently, third-generation aromatase inhibitors have been shown to be more effective than tamoxifen to treat both advanced and early hormone-sensitive breast cancers in menopausal women (4). However, the absolute reduction in relapse-free survival for adjuvant aromatase inhibitors over tamoxifen is modest, and the issue of long-term adverse effects, in particular in bone, remains to be addressed. Moreover, aromatase inhibitors are also prone to develop resistance, although different mechanisms may be responsible for that. As a consequence, the ability to accurately predict the response to tamoxifen should facilitate the choice of the best endocrine treatment and improve the management of primary ER+ breast cancers. Attempts to identify individual predictive markers failed to do that thus far. Recent publications on largescale analysis of gene expression in breast cancers have underscored the considerable potential of DNA array technology. Hierarchical clustering of gene expression patterns has been successfully used to identify subtypes of breast tumors that exhibit distinct clinical behaviors (5–8). Supervised classifications of gene expression profiles have been done to identify poor prognostic signatures, predictive of recurrence in primary breast cancers (9–11). Such analysis has been used to identify genes that can help to discriminate between responders and nonresponders to chemotherapy agents in breast cancer (12, 13). Similar approaches have been tentatively used to define gene expression signatures that are predictive of recurrence in breast cancer patients treated with tamoxifen (14–16). However, the study by Jansen et al. concerned metastatic breast cancers (i.e., the response to a recurrent disease), whereas that of Ma et al. reported a twogene signature whose performance has been debated (17–19). Both studies used a limited cohort of tumors as a training set (46 and 60 tumors, respectively). Finally, the study by Paik et al. (15) defined a recurrence score predictor for lymph node–negative breast cancer that also predicted the magnitude of chemotherapy benefit (20). However, this classifier was constructed from 250 candidate genes that were previously selected from the published literature. Finally, several studies have also reported molecular prognostic signatures able to classify ER+ breast cancer whatever the patient treatment (21–25). To identify a gene expression signature that might predict recurrence of tamoxifen-treated primary breast cancers, we did a genome-wide microarray analysis of ER+ and/or PR+ primary breast tumors from 132 patients who had been treated with adjuvant tamoxifen. This analysis led to the identification of a 36-gene molecular signature that is highly predictive of clinical outcome.