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Enabling personalized cancer medicine through analysis of gene-expression patterns

Author

Listed:
  • Laura J. van 't Veer

    (Agendia BV, Louwesweg 6
    the Netherlands Cancer Institute, Plesmanlaan 121
    Cancer Genomics Centre, the Netherlands Cancer Institute, Plesmanlaan 121)

  • René Bernards

    (Agendia BV, Louwesweg 6
    Cancer Genomics Centre, the Netherlands Cancer Institute, Plesmanlaan 121
    Centre for Biomedical Genetics, the Netherlands Cancer Institute, Plesmanlaan 121
    the Netherlands Cancer Institute, Plesmanlaan 121)

Abstract

Therapies for patients with cancer have changed gradually over the past decade, moving away from the administration of broadly acting cytotoxic drugs towards the use of more-specific therapies that are targeted to each tumour. To facilitate this shift, tests need to be developed to identify those individuals who require therapy and those who are most likely to benefit from certain therapies. In particular, tests that predict the clinical outcome for patients on the basis of the genes expressed by their tumours are likely to increasingly affect patient management, heralding a new era of personalized medicine.

Suggested Citation

  • Laura J. van 't Veer & René Bernards, 2008. "Enabling personalized cancer medicine through analysis of gene-expression patterns," Nature, Nature, vol. 452(7187), pages 564-570, April.
    • Handle: RePEc:nat:nature:v:452:y:2008:i:7187:d:10.1038_nature06915
    DOI: 10.1038/nature06915
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    Cited by:

    1. Ioannidis, John P.A., 2009. "Limits to forecasting in personalized medicine: An overview," International Journal of Forecasting, Elsevier, vol. 25(4), pages 773-783, October.
    2. Tailiang Xie & Zhuoxin Yu, 2017. "N-of-1 Design and Its Applications to Personalized Treatment Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 662-675, December.
    3. Jiarong Chen & Canhong Yang & Bin Guo & Emily S Sena & Malcolm R Macleod & Yawei Yuan & Theodore C Hirst, 2016. "The Efficacy of Trastuzumab in Animal Models of Breast Cancer: A Systematic Review and Meta-Analysis," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-17, July.
    4. Dimitris Bertsimas & Allison O’Hair & Stephen Relyea & John Silberholz, 2016. "An Analytics Approach to Designing Combination Chemotherapy Regimens for Cancer," Management Science, INFORMS, vol. 62(5), pages 1511-1531, May.
    5. Pablo Martinez-Lozano Sinues & Malcolm Kohler & Renato Zenobi, 2013. "Human Breath Analysis May Support the Existence of Individual Metabolic Phenotypes," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-5, April.
    6. Yujin Hoshida, 2010. "Nearest Template Prediction: A Single-Sample-Based Flexible Class Prediction with Confidence Assessment," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-8, November.

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