TY - JOUR T1 - Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. JF - Nat Commun Y1 - 2019 A1 - Menden, Michael P A1 - Wang, Dennis A1 - Mason, Mike J A1 - Szalai, Bence A1 - Bulusu, Krishna C A1 - Guan, Yuanfang A1 - Yu, Thomas A1 - Kang, Jaewoo A1 - Jeon, Minji A1 - Wolfinger, Russ A1 - Nguyen, Tin A1 - Zaslavskiy, Mikhail A1 - Jang, In Sock A1 - Ghazoui, Zara A1 - Ahsen, Mehmet Eren A1 - Vogel, Robert A1 - Neto, Elias Chaibub A1 - Norman, Thea A1 - Tang, Eric K Y A1 - Garnett, Mathew J A1 - Veroli, Giovanni Y Di A1 - Fawell, Stephen A1 - Stolovitzky, Gustavo A1 - Guinney, Justin A1 - Dry, Jonathan R A1 - Saez-Rodriguez, Julio KW - ADAM17 Protein KW - Antineoplastic Combined Chemotherapy Protocols KW - Benchmarking KW - Biomarkers, Tumor KW - Cell Line, Tumor KW - Computational Biology KW - Datasets as Topic KW - Drug Antagonism KW - Drug Resistance, Neoplasm KW - Drug Synergism KW - Genomics KW - Humans KW - Molecular Targeted Therapy KW - mutation KW - Neoplasms KW - pharmacogenetics KW - Phosphatidylinositol 3-Kinases KW - Phosphoinositide-3 Kinase Inhibitors KW - Treatment Outcome AB -

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.

VL - 10 IS - 1 U1 - https://www.ncbi.nlm.nih.gov/pubmed/31209238?dopt=Abstract ER -