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

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.

SUBMITTER: Loi S

PROVIDER: S-EPMC2423197 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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

Loi Sherene S Haibe-Kains Benjamin B Desmedt Christine C Wirapati Pratyaksha P Lallemand Françoise F Tutt Andrew M AM Gillet Cheryl C Ellis Paul P Ryder Kenneth K Reid James F JF Daidone Maria G MG Pierotti Marco A MA Berns Els Mjj EM Jansen Maurice Phm MP Foekens John A JA Delorenzi Mauro M Bontempi Gianluca G Piccart Martine J MJ Sotiriou Christos C

BMC genomics 20080522

<h4>Background</h4>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 traini ...[more]

PMID: 18498629

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Project description:PurposeRecent data have shown that the expression levels of long noncoding RNAs (lncRNAs) are associated with tamoxifen sensitivity in estrogen receptor (ER)-positive breast cancer. Herein, we constructed an lncRNA-based model to predict disease outcomes of ER-positive breast cancer patients treated with tamoxifen.MethodsLncRNA expression information was acquired from Gene Expression Omnibus by re-mapping pre-existing microarrays of patients with ER-positive breast cancer treated with tamoxifen. The distant metastasis-free survival (DMFS) predictive signature was subsequently built based on a Cox proportional hazard regression model in discover cohort patients, which was further evaluated in another independent validation dataset.ResultsSix lncRNAs were found to be associated with DMFS in the discover cohort, which were used to construct a tamoxifen efficacy-related lncRNA signature (TLS). There were 133 and 362 patients with TLS high- and low-risk signatures in the discover cohort. Both univariate and multivariate analysis demonstrated that TLS was associated with DMFS. TLS high-risk patients had worse outcomes than low-risk patients, with a hazard ratio of 4.04 (95% confidence interval, 2.83-5.77; p<0.001). Both subgroup analysis and receiver operating characteristic analysis indicated that TLS performed better in lymph node-negative, luminal B, 21-gene recurrence score high-risk, and 70-gene prognosis signature high-risk patients. Moreover, in a comparison of the 21-gene recurrence score and 70-gene prognosis signature, TLS showed a similar area under receiver operating characteristic curve in all patients. Gene Set Enrichment Analysis indicated that TLS high-risk patients showed different gene expression patterns related to the cell cycle and nucleotide metabolism from those of low-risk patients.ConclusionThis six-lncRNA signature was associated with disease outcome in ER-positive breast cancer patients treated with tamoxifen, which is comparable to previous messenger RNA signatures and requires further clinical evaluation.

Dataset Information

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

Publications

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

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