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Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.

  • Author(s): Menden, Michael P;
  • Wang, Dennis;
  • Mason, Mike J;
  • Szalai, Bence;
  • Bulusu, Krishna C;
  • Guan, Yuanfang;
  • Yu, Thomas;
  • Kang, Jaewoo;
  • Jeon, Minji;
  • Wolfinger, Russ;
  • Nguyen, Tin;
  • Zaslavskiy, Mikhail;
  • AstraZeneca-Sanger Drug Combination DREAM Consortium;
  • Jang, In Sock;
  • Ghazoui, Zara;
  • Ahsen, Mehmet Eren;
  • Vogel, Robert;
  • Neto, Elias Chaibub;
  • Norman, Thea;
  • Tang, Eric KY;
  • Garnett, Mathew J;
  • Veroli, Giovanni Y Di;
  • Fawell, Stephen;
  • Stolovitzky, Gustavo;
  • Guinney, Justin;
  • Dry, Jonathan R;
  • Saez-Rodriguez, Julio
  • et al.

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

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