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

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Authors

Menden, Michael P.
Wang, Dennis
Mason, Mike J.
Szalai, Bence
Bulusu, Krishna C.
Guan, Yuanfang
Yu, Thomas
Kang, Jaewoo
Jeon, Minji
Wolfinger, Russ

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2019

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Article

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Abstract

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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Menden, M. P., Wang, D., Mason, M. J., Szalai, B., Bulusu, K. C., … Saez-Rodriguez, J. (2019). Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nature Communications, 10(1). doi:10.1038/s41467-019-09799-2

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Creative Commons Attribution 4.0 International

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2041-1723

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