
DOI . ORG {
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Title[redir]:
Gene expression profiling predicts clinical outcome of breast cancer | Nature
Description:
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 behaviour1,2,3. 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 it4,5. None of the signatures of breast cancer gene expression reported to date6,7,8,9,10,11,12 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.
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cancer, breast, article, google, scholar, cas, nature, gene, expression, van, supplementary, access, natl, content, profiling, patients, pdf, ads, cookies, clinical, mao, brca, inst, figure, privacy, data, information, friend, therapy, usa, molecular, res, pubmed, profiles, research, journal, outcome, veer, dai, vijver, roberts, stephen, lymph, tumours, signature, open, prognostic, early, patterns, cancers,
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nature portfolio permissions reprints privacy policy advertising social media nature biotechnol nature med nature 406 nature 382 nature 415 nature patient-tailored therapy strategies ink-jet oligonucleotide synthesizer urokinase-type plasminogen activator early breast cancer author correspondence chromatin accessibility profiling lymph node negative development gene expression profiling gene expression patterns permissions gene expression profiles gene-expression profiles personal data springerlink instant access journals search log gene expression profile invasive breast cancer data protection breast cancer laura human breast cancer primary breast cancer breast cancer cells hereditary breast cancer lymph node status optimizing gene selection breast cancer res privacy hormonal therapy reduces human breast tumours cyclin d1 expression primary breast tumours international consensus panel �poor prognosis’ signature natl cancer inst local lymph nodes netherlands cancer institute van der velde pivotal genes shared
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headline:Gene expression profiling predicts clinical outcome of breast cancer
description: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 behaviour1,2,3. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70â80% of patients receiving this treatment would have survived without it4,5. None of the signatures of breast cancer gene expression reported to date6,7,8,9,10,11,12 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 (âpoor prognosisâ 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.
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description: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 behaviour1,2,3. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70â80% of patients receiving this treatment would have survived without it4,5. None of the signatures of breast cancer gene expression reported to date6,7,8,9,10,11,12 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 (âpoor prognosisâ 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.
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