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

@article{citeulike:2826305, 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.}, author = {Loi, Sherene and Kains, Benjamin H. and Desmedt, Christine and Wirapati, Pratyaksha and Lallemand, Francoise and Tutt, Andrew and Gillet, Cheryl and Ellis, Paul and Ryder, Kenneth and Reid, James and Daidone, Maria and Pierotti, Marco and Berns, Els and Jansen, Maurice and Foekens, John and Delorenzi, Mauro and Bontempi, Gianluca and Piccart, Martine and Sotiriou, Christos}, citeulike-article-id = {2826305}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2164-9-239}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18498629}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18498629}, day = {22}, doi = {10.1186/1471-2164-9-239}, issn = {1471-2164}, journal = {BMC Genomics}, keywords = {breast-cancer, estrogen-receptor, microarray-data, microarray-data-analysis, tamoxifen}, month = {May}, number = {1}, pages = {239+}, posted-at = {2009-08-03 22:21:26}, priority = {2}, title = {Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen}, url = {http://dx.doi.org/10.1186/1471-2164-9-239}, volume = {9}, year = {2008} }

TY - JOUR ID - citeulike:2826305 L3 - citeulike-article-id:2826305 N2 - 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. IS - 1 JF - BMC Genomics SN - 1471-2164 TI - Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen VL - 9 SP - 239 KW - breast-cancer KW - estrogen-receptor KW - microarray-data KW - microarray-data-analysis KW - tamoxifen AU - Loi, Sherene AU - Kains, Benjamin AU - Desmedt, Christine AU - Wirapati, Pratyaksha AU - Lallemand, Francoise AU - Tutt, Andrew AU - Gillet, Cheryl AU - Ellis, Paul AU - Ryder, Kenneth AU - Reid, James AU - Daidone, Maria AU - Pierotti, Marco AU - Berns, Els AU - Jansen, Maurice AU - Foekens, John AU - Delorenzi, Mauro AU - Bontempi, Gianluca AU - Piccart, Martine AU - Sotiriou, Christos PY - 2008/05/22/ UR - http://dx.doi.org/10.1186/1471-2164-9-239 ER -