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Molecular Classification of Tamoxifen-Resistant Breast Carcinomas by Gene Expression Profiling

From the Departments of Medical Oncology and Pathology, Erasmus MC/Daniel den Hoed Cancer Center, Rotterdam, the Netherlands

Address reprint requests to Els Berns, PhD, Erasmus MC, Department of Medical Oncology, Josephine Nefkens Institute, Room Be424, PO Box 1738, 3000 DR Rotterdam, the Netherlands; e-mail: p.berns{at}erasmusmc.nl

PURPOSE: To discover a set of markers predictive for the type of response to endocrine therapy with the antiestrogen tamoxifen using gene expression profiling.

PATIENTS AND METHODS: The study was performed on 112 estrogen receptor–positive primary breast carcinomas from patients with advanced disease and clearly defined types of response (ie, 52 patients with objective response v 60 patients with progressive disease) from start of first-line treatment with tamoxifen. Main clinical end points are the effects of therapy on tumor size and time until tumor progression (progression-free survival [PFS]). RNA isolated from tumor samples was amplified and hybridized to 18,000 human cDNA microarrays.

RESULTS: Using a training set of 46 breast tumors, 81 genes were found to be differentially expressed (P .05) between tamoxifen-responsive and -resistant tumors. These genes were involved in estrogen action, apoptosis, extracellular matrix formation, and immune response. From the 81 genes, a predictive signature of 44 genes was extracted and validated on an independent set of 66 tumors. This 44-gene signature is significantly superior (odds ratio, 3.16; 95% CI, 1.10 to 9.11; P = .03) to traditional predictive factors in univariate analysis and also significantly related with a longer PFS in univariate (hazard ratio, 0.54; 95% CI, 0.31 to 0.94; P = .03) as well as in multivariate analyses (P = .03).

CONCLUSION: Our data show that gene expression profiling can be used to discriminate between breast cancer patients with progressive disease and objective response to tamoxifen. Additional studies are needed to confirm if the predictive signature might allow identification of individual patients who could benefit from other (adjuvant) endocrine therapies.

Supported by grant DDHK 2364 of the Dutch Cancer Society, Amsterdam, the Netherlands, and in part by the Netherlands Genomics Initiative/Netherlands Organization for Scientific Research.

Presented in part at the San Antonio Breast Cancer Conference (oral presentation), San Antonio, TX, December 5, 2003; and at the American Association for Cancer Research 2004 (poster presentation), Orlando, FL, March 29, 2004.

Authors' disclosures of potential conflicts of interest are found at the end of this article.

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