Cited by 215Computational Drug Discovery MethodsBioinformatics and Genomic NetworksCancer Genomics and Diagnostics

A genome-wide positioning systems network algorithm for in silico drug repurposing

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Cheng, Feixiong, Lu, Weiqiang, Liu, Chuang, Fang, Jiansong, Hou, Yuan, Handy, Diane E., Wang, Ruisheng, Zhao, Yuzheng +6 more·2019·Nature Communications

Abstract

Recent advances in DNA/RNA sequencing have made it possible to identify new targets rapidly and to repurpose approved drugs for treating heterogeneous diseases by the ‘precise’ targeting of individualized disease modules. In this study, we develop a Genome-wide Positioning Systems network (GPSnet) algorithm for drug repurposing by specifically targeting disease modules derived from individual patient’s DNA and RNA sequencing profiles mapped to the human protein-protein interactome network. We investigate whole-exome sequencing and transcriptome profiles from ~5,000 patients across 15 cancer types from The Cancer Genome Atlas. We show that GPSnet-predicted disease modules can predict drug responses and prioritize new indications for 140 approved drugs. Importantly, we experimentally validate that an approved cardiac arrhythmia and heart failure drug, ouabain, shows potential antitumor activities in lung adenocarcinoma by uniquely targeting a HIF1α/LEO1-mediated cell metabolism pathway. In summary, GPSnet offers a network-based, in silico drug repurposing framework for more efficacious therapeutic selections.

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  • Graph representation learning has been successful in characterizing drugs at the systems level without patient data to predict interactions with other drugs, protein targets, side effects, and diseases.

    “Graph representation learning has been successful in characterizing drugs at the systems level without patient data to make predictions about interactions with other drugs, protein targets, side effects, diseases [6, 38, 39, 40, 48, 165].”

    From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [48]

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Authors
Cheng, F., Lu, W., Liu, C., Fang, J., Hou, Y., Handy, D. E., Wang, R., Zhao, Y., Yang, Y., Huang, J., Hill, D. E., Vidal, M., Eng, C., & Loscalzo, J.
Affiliations
  • Cleveland Clinic (US)
  • Hangzhou Normal University (CN)
  • Brigham and Women's Hospital (US)
  • East China University of Science and Technology (CN)
  • Harvard University (US)
Corresponding author
Loscalzo, Joseph
Venue
Nature Communications
Published
2019-08-02
Volume
10
Issue
1
Pages
3476-3476
ISSN
2041-1723 (ISSN-L)
OpenAlex
W2965506096
Cited by
215
References
68
Citations by year
2026: 9 · 2025: 29 · 2024: 33 · 2023: 25 · 2022: 30 · 2021: 55 · 2020: 29 · 2019: 5
FWCI
20.24
Type
journal article
Language
en
Open access
Locations
  • Nature Communications (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
Foundation for the National Institutes of Health (HG007690, HL119145, GM107618)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
Keywords
drug repurposing, protein-protein interaction network, disease modules, exome sequencing, gene expression profiling, cancer genomics, drug response prediction, ouabain, lung adenocarcinoma, cellular metabolism
MeSH terms
Adenocarcinoma of Lung, Algorithms, Arrhythmias, Cardiac, Computer Simulation, Feasibility Studies, Heart Failure, Holistic Health, Humans, Lung Neoplasms, Ouabain, Transcription Factors, Systems Biology, Hypoxia-Inducible Factor 1, alpha Subunit, Gene Regulatory Networks, Metabolic Networks and Pathways, Drug Repositioning, Molecular Targeted Therapy, Transcriptome, Protein Interaction Maps, Datasets as Topic

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