Gene expression profiling predicts clinical outcome of breast cancer.

@article{citeulike:504894, abstract = {Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80\% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.}, address = {Division of Diagnostic Oncology, The Netherlands Cancer Institute, 121 Plesmanlaan, 1066 CX Amsterdam, The Netherlands.}, author = {van 't Veer, L. J. and Dai, H. and van de Vijver, M. J. and He, Y. D. and Hart, A. A. and Mao, M. and Peterse, H. L. and van der Kooy, K. and Marton, M. J. and Witteveen, A. T. and Schreiber, G. J. and Kerkhoven, R. M. and Roberts, C. and Linsley, P. S. and Bernards, R. and Friend, S. H. }, citeulike-article-id = {504894}, doi = {10.1038/415530a}, issn = {0028-0836}, journal = {Nature}, keywords = {breast-cancer, data-integration, gene-expression, systems-biology}, month = {January}, number = {6871}, pages = {530--536}, posted-at = {2008-04-29 18:26:04}, priority = {2}, title = {Gene expression profiling predicts clinical outcome of breast cancer.}, url = {http://dx.doi.org/10.1038/415530a}, volume = {415}, year = {2002} }

TY - JOUR ID - citeulike:504894 L3 - citeulike-article-id:504894 TI - Gene expression profiling predicts clinical outcome of breast cancer. JF - Nature VL - 415 IS - 6871 SP - 530 EP - 536 AD - Division of Diagnostic Oncology, The Netherlands Cancer Institute, 121 Plesmanlaan, 1066 CX Amsterdam, The Netherlands. SN - 0028-0836 N2 - Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy. KW - breast-cancer KW - data-integration KW - gene-expression KW - systems-biology AU - van 't Veer, LJ AU - Dai, H AU - van de Vijver, MJ AU - He, YD AU - Hart, AA AU - Mao, M AU - Peterse, HL AU - van der Kooy, K AU - Marton, MJ AU - Witteveen, AT AU - Schreiber, GJ AU - Kerkhoven, RM AU - Roberts, C AU - Linsley, PS AU - Bernards, R AU - Friend, SH PY - 2002/01/31/ UR - http://dx.doi.org/10.1038/415530a ER -