Accurate Drug Repositioning through Non-tissue-Specific Core Signatures from Cancer Transcriptomes
Chi Xu,
Daosheng Ai,
Shengbao Suo,
Xingwei Chen,
Yizhen Yan,
Yaqiang Cao,
Na Sun,
Weizhong Chen,
Joseph McDermott,
Shiqiang Zhang,
Yingying Zeng,
Jing-Dong Jackie Han
Affiliations
Chi Xu
Key Laboratory of Computational Biology, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
Daosheng Ai
Key Laboratory of Computational Biology, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
Shengbao Suo
Key Laboratory of Computational Biology, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
Xingwei Chen
Key Laboratory of Computational Biology, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
Yizhen Yan
Key Laboratory of Computational Biology, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
Yaqiang Cao
Key Laboratory of Computational Biology, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
Na Sun
Key Laboratory of Computational Biology, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
Weizhong Chen
Key Laboratory of Computational Biology, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
Joseph McDermott
Key Laboratory of Computational Biology, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
Shiqiang Zhang
Key Laboratory of Computational Biology, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
Yingying Zeng
Key Laboratory of Computational Biology, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
Jing-Dong Jackie Han
Key Laboratory of Computational Biology, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Corresponding author
Summary: Experimental large-scale screens for drug repositioning are limited by restriction to in vitro conditions and lack of applicability to real human conditions. Here, we developed an in silico screen in human in vivo conditions using a reference of single gene mutations’ non-tissue-specific “core transcriptome signatures” (CSs) of 8,476 genes generated from the TCGA database. We developed the core-signature drug-to-gene (csD2G) software to scan 3,546 drug treatment profiles against the reference signatures. csD2G significantly outperformed conventional cell line-based gene perturbation signatures and existing drug-repositioning methods in both coverage and specificity. We highlight this with 3 demonstrated applications: (1) repositioned category of psychiatric drugs to inhibit the TGF-β pathway; (2) antihypertensive calcium channel blockers predicted to activate AMPK and inhibit AKT pathways, and validated by clinical electronic medical records; and (3) 7 drugs predicted and validated to selectively target the AKT-FOXO and AMPK pathways and thus regulate worm lifespan. : Xu et al. generated core transcriptome signatures representing single-gene mutations for 8,476 human genes from the cancer transcriptomes in the TCGA database and developed a drug-repositioning method to predict high-specificity candidate target genes for 1,938 drugs. Applied to targeting aging pathways, 7 lifespan-extending drugs are found and functionally validated. Keywords: drug repositioning, drug target, cancer genomics, data integrative analysis