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

TitleCommunity assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.
Publication TypeJournal Article
Year of Publication2019
AuthorsMenden, MP, Wang, D, Mason, MJ, Szalai, B, Bulusu, KC, Guan, Y, Yu, T, Kang, J, Jeon, M, Wolfinger, R, Nguyen, T, Zaslavskiy, M, Jang, ISock, Ghazoui, Z, Ahsen, MEren, Vogel, R, Neto, EChaibub, Norman, T, K Y Tang, E, Garnett, MJ, Di Veroli, GY, Fawell, S, Stolovitzky, G, Guinney, J, Dry, JR, Saez-Rodriguez, J
Corporate AuthorsAstraZeneca-Sanger Drug Combination DREAM Consortium
JournalNat Commun
Volume10
Issue1
Pagination2674
Date Published2019 06 17
ISSN2041-1723
KeywordsADAM17 Protein; Antineoplastic Combined Chemotherapy Protocols; Benchmarking; Biomarkers, Tumor; Cell Line, Tumor; Computational Biology; Datasets as Topic; Drug Antagonism; Drug Resistance, Neoplasm; Drug Synergism; Genomics; Humans; Molecular Targeted Therapy; mutation; Neoplasms; pharmacogenetics; Phosphatidylinositol 3-Kinases; Phosphoinositide-3 Kinase Inhibitors; Treatment Outcome
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.

DOI10.1038/s41467-019-09799-2
Alternate JournalNat Commun
PubMed ID31209238
PubMed Central IDPMC6572829
Grant ListR01 CA204856 / CA / NCI NIH HHS / United States
R25 CA180993 / CA / NCI NIH HHS / United States
204735/Z/16/Z / / Wellcome Trust / United Kingdom
T32 ES007329 / ES / NIEHS NIH HHS / United States
/ / Wellcome Trust / United Kingdom
MC_UU_00002/13 / / Medical Research Council / United Kingdom
MC_UU_00002/2 / / Medical Research Council / United Kingdom