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

Bin Chen (), Li Ma, Hyojung Paik, Marina Sirota, Wei Wei, Mei-Sze Chua (), Samuel So and Atul J. Butte ()
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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
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

Nature Communications, 2017, vol. 8, issue 1, 1-12

Abstract: 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.

Date: 2017
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DOI: 10.1038/ncomms16022

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