Gene expression profiling predicts clinical outcome of breast cancer View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2002-01

AUTHORS

Laura J. van 't Veer, Hongyue Dai, Marc J. van de Vijver, Yudong D. He, Augustinus A. M. Hart, Mao Mao, Hans L. Peterse, Karin van der Kooy, Matthew J. Marton, Anke T. Witteveen, George J. Schreiber, Ron M. Kerkhoven, Chris Roberts, Peter S. Linsley, René Bernards, Stephen H. Friend

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 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. More... »

PAGES

530-536

Journal

TITLE

Nature

ISSUE

6871

VOLUME

415

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/415530a

    DOI

    http://dx.doi.org/10.1038/415530a

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1043001094

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/11823860


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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, 70\u201380% 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 (\u2018poor prognosis\u2019 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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          "distant metastasis", 
          "breast tumors", 
          "patient-tailored therapy strategies", 
          "local lymph nodes", 
          "breast cancer patients", 
          "primary breast tumors", 
          "poor prognosis signature", 
          "different treatment responses", 
          "hormonal therapy", 
          "adjuvant therapy", 
          "younger patients", 
          "gene expression signatures", 
          "lymph nodes", 
          "clinical outcomes", 
          "clinical parameters", 
          "cancer patients", 
          "BRCA1 carriers", 
          "breast cancer", 
          "treatment response", 
          "disease outcome", 
          "breast cancer gene expression", 
          "patients", 
          "overall outcome", 
          "metastasis", 
          "prognosis signature", 
          "tumor cells", 
          "therapy strategies", 
          "gene expression profiling", 
          "tumors", 
          "expression signatures", 
          "strongest predictor", 
          "DNA microarray analysis", 
          "gene expression profiles", 
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