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The cancer biomarker problem

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  • Charles L. Sawyers

    (Howard Hughes Medical Institute, Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center)

Abstract

Genomic technologies offer the promise of a comprehensive understanding of cancer. These technologies are being used to characterize tumours at the molecular level, and several clinical successes have shown that such information can guide the design of drugs targeted to a relevant molecule. One of the main barriers to further progress is identifying the biological indicators, or biomarkers, of cancer that predict who will benefit from a particular targeted therapy.

Suggested Citation

  • Charles L. Sawyers, 2008. "The cancer biomarker problem," Nature, Nature, vol. 452(7187), pages 548-552, April.
    • Handle: RePEc:nat:nature:v:452:y:2008:i:7187:d:10.1038_nature06913
    DOI: 10.1038/nature06913
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    Cited by:

    1. Jixiong Wang & Ashish Patel & James M.S. Wason & Paul J. Newcombe, 2022. "Two‐stage penalized regression screening to detect biomarker–treatment interactions in randomized clinical trials," Biometrics, The International Biometric Society, vol. 78(1), pages 141-150, March.
    2. Jagannath Swaminathan & Alexander A Boulgakov & Edward M Marcotte, 2015. "A Theoretical Justification for Single Molecule Peptide Sequencing," PLOS Computational Biology, Public Library of Science, vol. 11(2), pages 1-17, February.
    3. Anders Dahl Henriksen & Mikkel Wennemoes Hvitfeld Ley & Henrik Flyvbjerg & Mikkel Fougt Hansen, 2015. "Configurational Statistics of Magnetic Bead Detection with Magnetoresistive Sensors," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-16, October.
    4. Xiaohong Li & Patricia L Blount & Thomas L Vaughan & Brian J Reid, 2011. "Application of Biomarkers in Cancer Risk Management: Evaluation from Stochastic Clonal Evolutionary and Dynamic System Optimization Points of View," PLOS Computational Biology, Public Library of Science, vol. 7(2), pages 1-7, February.
    5. Anyou Wang & Ying Du & Qianchuan He & Chunxiao Zhou, 2013. "A Quantitative System for Discriminating Induced Pluripotent Stem Cells, Embryonic Stem Cells and Somatic Cells," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-10, February.

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