%0 Journal Article
%A Menden, Michael P
%A Wang, Dennis
%A Mason, Mike J
%A Szalai, Bence
%A Bulusu, Krishna C
%A Guan, Yuanfang
%A Yu, Thomas
%A Kang, Jaewoo
%A Jeon, Minji
%A Wolfinger, Russ
%A Nguyen, Tin
%A Zaslavskiy, Mikhail
%A AstraZeneca-Sanger Drug Combination DREAM Consortium
%A Jang, In Sock
%A Ghazoui, Zara
%A Ahsen, Mehmet Eren
%A Vogel, Robert
%A Neto, Elias Chaibub
%A Norman, Thea
%A Tang, Eric K Y
%A Garnett, Mathew J
%A Veroli, Giovanni Y Di
%A Fawell, Stephen
%A Stolovitzky, Gustavo
%A Guinney, Justin
%A Dry, Jonathan R
%A Saez-Rodriguez, Julio
%T Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.
%D 2019
%U https://hdl.handle.net/10668/25009
%X 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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