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

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

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

Mikhail Zaslavskiy; AstraZeneca-Sanger Drug Combination DREAM Consortium
In Sock Jang
Zara Ghazoui
Thomas Yu
Stephen Fawell
Eric K.Y. Tang
Giovanni Y. Di Veroli
Gustavo Stolovitzky
Minji Jeon
Justin Guinney
Krishna C. Bulusu
Mike J. Mason
Bence Szalai
Yanfang Guan
Elias Chaibub Neto
Robert Vogel
Jonathan R. Dry
Dennis Wang
Tin Nguyen
Michael P. Menden
Thea Norman
Mehmet Eren Ahnsen
Julio Saez-Rodriguez
Jaewoo Kang
Mathew J. Garnett
Russ Wolfinger
Katso/Avaa
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NATURE PUBLISHING GROUP
doi:10.1038/s41467-019-09799-2
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Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2021042825289
Tiivistelmä
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.
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