A simple gene set-based method accurately predicts the synergy of drug pairs

  • Yu Ching Hsu (Contributor)
  • Yu Chiao Chiu (Contributor)
  • Yidong Chen (Contributor)
  • Tzu Hung Hsiao (Contributor)
  • Eric Y. Chuang (Creator)



Abstract Background The advance in targeted therapy has greatly increased the effectiveness of clinical cancer therapy and reduced the cytotoxicity of treatments to normal cells. However, patients still suffer from cancer relapse due to the occurrence of drug resistance. It is of great need to explore potential combinatorial drug therapy since individual drug alone may not be sufficient to inhibit continuous activation of cancer-addicted genes or pathways. The DREAM challenge has confirmed the potentiality of computational methods for predicting synergistic drug combinations, while the prediction accuracy can be further improved. Methods Based on previous reports, we hypothesized the similarity in biological functions or genes perturbed by two drugs can determine their synergistic effects. To test the feasibility of the hypothesis, we proposed three scoring systems: co-gene score, co-GS score, and co-gene/GS score, measuring the similarities in genes with significant expressional changes, enriched gene sets, and significantly changed genes within an enriched gene sets between a pair of drugs, respectively. Performances of these scoring systems were evaluated by the probabilistic c-index (PC-index) devised by the DREAM consortium. We also applied the proposed method to the Connectivity Map dataset to explore more potential synergistic drug combinations. Results Using a gold standard derived by the DREAM consortium, we confirmed the prediction power of the three scoring systems (all P-values 
Date made available2016

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