Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
Michael P. Menden (Helmholtz Zentrum München, European Bioinformatics Institute, AstraZeneca)
Dennis Wang (AstraZeneca, University of Sheffield)
Mike J. Mason (Sage Bionetworks)
Bence Szalai (Semmelweis University, RWTH Aachen University, Hungarian Academy of Sciences)
Krishna C. Bulusu (AstraZeneca)
Yuanfang Guan (University of Michigan)
Thomas Yu (Sage Bionetworks)
N.N. Aben (Netherlands Cancer Institute, TU Delft - Pattern Recognition and Bioinformatics)
L. F.A. Wessels (TU Delft - Pattern Recognition and Bioinformatics, Cancer Genomics Netherlands, Netherlands Cancer Institute)
G.B. More Authors (External organisation)
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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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