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Menden, M.P. ; Wang, D.* ; Mason, M.J.* ; Szalai, B.* ; Bulusu, K.C.* ; Guan, Y.* ; Yu, T.* ; Kang, J.* ; Jeon, M.* ; Wolfinger, R.* ; Nguyen, T.* ; Zaslavskiy, M.* ; Jang, I.S.* ; Ghazoui, Z.* ; Ahsen, M.E.* ; Vogel, R.* ; Neto, E.C.* ; Norman, T.* ; Tang, E.K.Y.* ; Garnett, M.J.* ; Veroli, G.Y.D.* ; Fawell, S.* ; Stolovitzky, G.* ; Guinney, J.* ; Dry, J.R.* ; Saez-Rodriguez, J.* ; AstraZeneca-Sanger Drug Combination DREAM Consortium (Kurz, C.F.)

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

Nat. Commun. 10:2674 (2019)

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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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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Androgen Receptor; Breast-cancer; Gene; Cell; Inhibition; Resistance; Pathway; Mutations; Landscape; Resource

ISSN (print) / ISBN 2041-1723

e-ISSN 2041-1723

Journal Nature Communications

Quellenangaben Volume: 10, Issue: 1, Article Number: 2674

Publisher Nature Publishing Group

Publishing Place London

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Computational Biology (ICB)