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Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets

Author

Listed:
  • Bin Chen

    (Institute for Computational Health Sciences, University of California, San Francisco)

  • Li Ma

    (Asian Liver Center, School of Medicine, Stanford University)

  • Hyojung Paik

    (Institute for Computational Health Sciences, University of California, San Francisco
    Biomedical HPC Technology Research Center, Korea Institute of Science and Technology Information)

  • Marina Sirota

    (Institute for Computational Health Sciences, University of California, San Francisco)

  • Wei Wei

    (Asian Liver Center, School of Medicine, Stanford University)

  • Mei-Sze Chua

    (Asian Liver Center, School of Medicine, Stanford University)

  • Samuel So

    (Asian Liver Center, School of Medicine, Stanford University)

  • Atul J. Butte

    (Institute for Computational Health Sciences, University of California, San Francisco)

Abstract

The decreasing cost of genomic technologies has enabled the molecular characterization of large-scale clinical disease samples and of molecular changes upon drug treatment in various disease models. Exploring methods to relate diseases to potentially efficacious drugs through various molecular features is critically important in the discovery of new therapeutics. Here we show that the potency of a drug to reverse cancer-associated gene expression changes positively correlates with that drug’s efficacy in preclinical models of breast, liver and colon cancers. Using a systems-based approach, we predict four compounds showing high potency to reverse gene expression in liver cancer and validate that all four compounds are effective in five liver cancer cell lines. The in vivo efficacy of pyrvinium pamoate is further confirmed in a subcutaneous xenograft model. In conclusion, this systems-based approach may be complementary to the traditional target-based approach in connecting diseases to potentially efficacious drugs.

Suggested Citation

  • Bin Chen & Li Ma & Hyojung Paik & Marina Sirota & Wei Wei & Mei-Sze Chua & Samuel So & Atul J. Butte, 2017. "Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets," Nature Communications, Nature, vol. 8(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms16022
    DOI: 10.1038/ncomms16022
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    Cited by:

    1. KWON Seokbeom & MOTOHASHI Kazuyuki, 2020. "Incentive or Disincentive for Disclosure of Research Data? A Large-Scale Empirical Analysis and Implications for Open Science Policy," Discussion papers 20058, Research Institute of Economy, Trade and Industry (RIETI).
    2. Xiaochu Tong & Ning Qu & Xiangtai Kong & Shengkun Ni & Jingyi Zhou & Kun Wang & Lehan Zhang & Yiming Wen & Jiangshan Shi & Sulin Zhang & Xutong Li & Mingyue Zheng, 2024. "Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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