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

Journal article

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.

Authors: Menden, MP; Wang, D; Mason, MJ; Szalai, B; Bulusu, KC

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer Nature
• Journal: Nature Communications
• Volume: 10
• Issue: 1
• Article number: 2674
• Publication date: 2019-06-17
• Acceptance date: 2019-04-01
• DOI: 10.1038/s41467-019-09799-2
• EISSN: 2041-1723
• Pmid: 31209238
• Language: English
• Keywords: FFR