Drug Repositioning for Cancer Therapy Based on Large-Scale Drug-Induced Transcriptional Signatures

PLoS One. 2016 Mar 8;11(3):e0150460. doi: 10.1371/journal.pone.0150460. eCollection 2016.


An in silico chemical genomics approach is developed to predict drug repositioning (DR) candidates for three types of cancer: glioblastoma, lung cancer, and breast cancer. It is based on a recent large-scale dataset of ~20,000 drug-induced expression profiles in multiple cancer cell lines, which provides i) a global impact of transcriptional perturbation of both known targets and unknown off-targets, and ii) rich information on drug's mode-of-action. First, the drug-induced expression profile is shown more effective than other information, such as the drug structure or known target, using multiple HTS datasets as unbiased benchmarks. Particularly, the utility of our method was robustly demonstrated in identifying novel DR candidates. Second, we predicted 14 high-scoring DR candidates solely based on expression signatures. Eight of the fourteen drugs showed significant anti-proliferative activity against glioblastoma; i.e., ivermectin, trifluridine, astemizole, amlodipine, maprotiline, apomorphine, mometasone, and nortriptyline. Our DR score strongly correlated with that of cell-based experimental results; the top seven DR candidates were positive, corresponding to an approximately 20-fold enrichment compared with conventional HTS. Despite diverse original indications and known targets, the perturbed pathways of active DR candidates show five distinct patterns that form tight clusters together with one or more known cancer drugs, suggesting common transcriptome-level mechanisms of anti-proliferative activity.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Antineoplastic Agents / pharmacology*
  • Cell Survival / drug effects
  • Cell Survival / genetics
  • Cluster Analysis
  • Computational Biology / methods
  • Computer Simulation
  • Databases, Factual
  • Datasets as Topic
  • Drug Repositioning*
  • Drug Screening Assays, Antitumor
  • Gene Expression Profiling*
  • Gene Expression Regulation, Neoplastic / drug effects*
  • Glioblastoma / drug therapy
  • Glioblastoma / genetics
  • Glioblastoma / metabolism
  • High-Throughput Screening Assays
  • Humans
  • Neoplasms / drug therapy
  • Neoplasms / genetics*
  • Neoplasms / metabolism
  • Reproducibility of Results
  • Signal Transduction / drug effects
  • Transcriptome*


  • Antineoplastic Agents

Grant support

WK was supported by National Research Foundation (NRF) grant (NRF-2014R1A2A2A01007166) and Technology Innovation Program (10050154) funded by Ministry of Trade, industry & Energy of Korea.