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Publication Detail

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

Authors: Menden, Michael P; Wang, Dennis; Mason, Mike J; Szalai, Bence; Bulusu, Krishna C; Guan, Yuanfang; Yu, Thomas; Kang, Jaewoo; Jeon, Minji; Wolfinger, Russ; Nguyen, Tin; Zaslavskiy, Mikhail; AstraZeneca-Sanger Drug Combination DREAM Consortium; Jang, In Sock; Ghazoui, Zara; Ahsen, Mehmet Eren; Vogel, Robert; Neto, Elias Chaibub; Norman, Thea; Tang, Eric K Y; Garnett, Mathew J; Veroli, Giovanni Y Di; Fawell, Stephen; Stolovitzky, Gustavo; Guinney, Justin; Dry, Jonathan R; Saez-Rodriguez, Julio

Published In Nat Commun, (2019 06 17)

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.

PubMed ID: 31209238 Exiting the NIEHS site

MeSH Terms: ADAM17 Protein/antagonists & inhibitors; Antineoplastic Combined Chemotherapy Protocols/pharmacology*; Antineoplastic Combined Chemotherapy Protocols/therapeutic use; Benchmarking; Biomarkers, Tumor/genetics; Cell Line, Tumor; Computational Biology/methods*; Computational Biology/standards; Datasets as Topic; Drug Antagonism; Drug Resistance, Neoplasm/drug effects; Drug Resistance, Neoplasm/genetics; Drug Synergism; Genomics/methods; Humans; Molecular Targeted Therapy/methods; Mutation; Neoplasms/drug therapy*; Neoplasms/genetics; Pharmacogenetics/methods*; Pharmacogenetics/standards; Phosphatidylinositol 3-Kinases/genetics; Phosphoinositide-3 Kinase Inhibitors; Treatment Outcome

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