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Open Access Publications from the University of California

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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