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Conceptualizing Cancer Drugs as Classifiers

Author

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  • Patrick Nathan Lawlor
  • Tomer Kalisky
  • Robert Rosner
  • Marsha Rich Rosner
  • Konrad Paul Kording

Abstract

Cancer and healthy cells have distinct distributions of molecular properties and thus respond differently to drugs. Cancer drugs ideally kill cancer cells while limiting harm to healthy cells. However, the inherent variance among cells in both cancer and healthy cell populations increases the difficulty of selective drug action. Here we formalize a classification framework based on the idea that an ideal cancer drug should maximally discriminate between cancer and healthy cells. More specifically, this discrimination should be performed on the basis of measurable cell markers. We divide the problem into three parts which we explore with examples. First, molecular markers should discriminate cancer cells from healthy cells at the single-cell level. Second, the effects of drugs should be statistically predicted by these molecular markers. Third, drugs should be optimized for classification performance. We find that expression levels of a handful of genes suffice to discriminate well between individual cells in cancer and healthy tissue. We also find that gene expression predicts the efficacy of some cancer drugs, suggesting that these cancer drugs act as suboptimal classifiers using gene profiles. Finally, we formulate a framework that defines an optimal drug, and predicts drug cocktails that may target cancer more accurately than the individual drugs alone. Conceptualizing cancer drugs as solving a discrimination problem in the high-dimensional space of molecular markers promises to inform the design of new cancer drugs and drug cocktails.

Suggested Citation

  • Patrick Nathan Lawlor & Tomer Kalisky & Robert Rosner & Marsha Rich Rosner & Konrad Paul Kording, 2014. "Conceptualizing Cancer Drugs as Classifiers," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-10, September.
  • Handle: RePEc:plo:pone00:0106444
    DOI: 10.1371/journal.pone.0106444
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    References listed on IDEAS

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    1. Shinichi Yachida & Siân Jones & Ivana Bozic & Tibor Antal & Rebecca Leary & Baojin Fu & Mihoko Kamiyama & Ralph H. Hruban & James R. Eshleman & Martin A. Nowak & Victor E. Velculescu & Kenneth W. Kinz, 2010. "Distant metastasis occurs late during the genetic evolution of pancreatic cancer," Nature, Nature, vol. 467(7319), pages 1114-1117, October.
    2. Jordi Barretina & Giordano Caponigro & Nicolas Stransky & Kavitha Venkatesan & Adam A. Margolin & Sungjoon Kim & Christopher J. Wilson & Joseph Lehár & Gregory V. Kryukov & Dmitriy Sonkin & Anupama Re, 2012. "The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity," Nature, Nature, vol. 483(7391), pages 603-607, March.
    3. 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. S, 2002. "Gene expression profiling predicts clinical outcome of breast cancer," Nature, Nature, vol. 415(6871), pages 530-536, January.
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