RT Journal Article
T1 Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.
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 AstraZeneca-Sanger Drug Combination DREAM Consortium, 
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
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.
YR 2019
FD 2019-06-17
LK https://hdl.handle.net/10668/25009
UL https://hdl.handle.net/10668/25009
LA en
DS RISalud
RD Apr 12, 2025