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

Menden, Michael P. and Wang, Dennis and Mason, Mike J. and Szalai, Bence and Bulusu, Krishna C. and Guan, Yuanfang and Yu, Thomas and Kang, Jaewoo and Jeon, Minji and Wolfinger, Russ and Nguyen, Tin and Zaslavskiy, Mikhail and Sharma, Alokanand and Jang, In Sock and Ghazoui, Zara and Ahsen, Mehmet E. and Vogel, Robert and Neto, Elias Chaibub and Norman, Thea and Tang, Eric K. Y. and Garnett, Mathew J. and Di Veroli, Giovanni Y. and Fawell, Stephen and Stolovitzky, Gustavo and Guinney, Justin and Dry, Jonathan R. and Saez-Rodriguez, Julio (2019) Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nature Communications, 10 (2674). Online. ISSN 2041-1723

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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.

Item Type:	Journal Article
Additional Information:	Alokanand Sharma is part of the group of contributing authors listed as an author in this publication, under the name of "AstraZeneca-Sanger Drug Combination DREAM Consortium". Full details of his authorship and affiliation can be found at the back of the article.
Subjects:	Q Science > Q Science (General)
Divisions:	Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
Depositing User:	Fulori Nainoca - Waqairagata
Date Deposited:	13 Aug 2019 01:25
Last Modified:	13 Aug 2019 01:25
URI:	https://repository.usp.ac.fj/id/eprint/11691

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