Gene expression profiling predicts clinical outcome of breast cancer

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 · 2002
DOI: 10.1038/415530a

Pioneer work, 11K+ citations.

  • Sample size: 117 young patients
    • Initial research: 98
      • 34 from patients who developed distant metastases within 5 years
      • 44 from patients who continued to be disease-free after a period of at least 5 years
      • 18 from patients with BRCA1 germline mutations
      • 2 from BRCA2 carriers
    • :rating-4: Additional conformational research: 19

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.

Reference

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 “Gene expression profiling predicts clinical outcome of breast cancer” (2002) DOI: 10.1038/415530a

@Article{van2002,
  title = {Gene expression profiling predicts clinical outcome of breast cancer},
  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.},
  volume = {415},
  issn = {1476-4687},
  url = {http://dx.doi.org/10.1038/415530a},
  doi = {10.1038/415530a},
  number = {6871},
  journal = {Nature},
  publisher = {Springer Science and Business Media LLC},
  author = {van ’t Veer, Laura J. and Dai, Hongyue and van de Vijver, Marc J. and He, Yudong D. and Hart, Augustinus A. M. and Mao, Mao and Peterse, Hans L. and van der Kooy, Karin and Marton, Matthew J. and Witteveen, Anke T. and Schreiber, George J. and Kerkhoven, Ron M. and Roberts, Chris and Linsley, Peter S. and Bernards, René and Friend, Stephen H.},
  year = {2002},
  month = {jan},
  pages = {530–536}
}