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

Michael P. Menden, Dennis Wang, Mike J. Mason, Bence Szalai, Krishna C. Bulusu, Yuanfang Guan, Thomas Yu, Jaewoo Kang, Minji Jeon, Russ Wolfinger, Tin Nguyen, Mikhail Zaslavskiy, In Sock Jang, Zara Ghazoui, Mehmet Eren Ahsen, Robert Vogel, Elias Chaibub Neto, Thea Norman, Eric K. Y. Tang, Mathew J. Garnett, Giovanni Y. Di Veroli, Stephen Fawell, Gustavo Stolovitzky, Justin Guinney (), Jonathan R. Dry () and Julio Saez-Rodriguez ()
Additional contact information
Michael P. Menden: Oncology, IMED Biotech Unit, AstraZeneca
Dennis Wang: Oncology, IMED Biotech Unit, AstraZeneca
Mike J. Mason: Sage Bionetworks
Bence Szalai: Semmelweis University
Krishna C. Bulusu: Oncology, IMED Biotech Unit, AstraZeneca
Yuanfang Guan: University of Michigan
Thomas Yu: Sage Bionetworks
Jaewoo Kang: Korea University
Minji Jeon: Korea University
Russ Wolfinger: SAS Institute, Inc
Tin Nguyen: University of Nevada
Mikhail Zaslavskiy: Owkin, Inc.
In Sock Jang: Sage Bionetworks
Zara Ghazoui: Oncology, IMED Biotech Unit, AstraZeneca
Mehmet Eren Ahsen: Yorktown Heights
Robert Vogel: Yorktown Heights
Elias Chaibub Neto: Sage Bionetworks
Thea Norman: Sage Bionetworks
Eric K. Y. Tang: Oncology, IMED Biotech Unit, AstraZeneca
Mathew J. Garnett: Wellcome Trust Sanger Institute
Giovanni Y. Di Veroli: IMED Biotech Unit, AstraZeneca
Stephen Fawell: AstraZeneca, R&D Boston
Gustavo Stolovitzky: Yorktown Heights
Justin Guinney: Sage Bionetworks
Jonathan R. Dry: AstraZeneca, R&D Boston
Julio Saez-Rodriguez: European Bioinformatics Institute, European Molecular Biology Laboratory

Nature Communications, 2019, vol. 10, issue 1, 1-17

Abstract: 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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09799-2

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DOI: 10.1038/s41467-019-09799-2

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