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

Menden, M. P. and Wang, D. and Mason, M. J. and Szalai, Bence and Bulusu, K. C. and Turu, Gábor (2019) Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. NATURE COMMUNICATIONS, 10 (1). ISSN 2041-1723


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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. © 2019, The Author(s).

Item Type: Article
Uncontrolled Keywords: Humans; MUTATION; MUTATION; GENETICS; BIOLOGY; GENOMICS; GENOMICS; human; Treatment Outcome; Treatment Outcome; information processing; drug potentiation; drug effect; Drug Synergism; Cell Line, Tumor; Drug Resistance; Drug Antagonism; Drug Antagonism; antineoplastic agent; Neoplasms; neoplasm; phosphatidylinositol 3 kinase; Antineoplastic Combined Chemotherapy Protocols; Pharmacogenetics; Pharmacogenetics; tumor marker; Drug Resistance, Neoplasm; Computational Biology; Standards; tumor cell line; Molecular Targeted Therapy; molecularly targeted therapy; Benchmarking; Benchmarking; tumor necrosis factor alpha converting enzyme; antagonists and inhibitors; procedures; Biomarkers, Tumor; Datasets as Topic; Phosphatidylinositol 3-Kinases; ADAM17 protein, human; ADAM17 Protein;
Subjects: R Medicine / orvostudomány > RC Internal medicine / belgyógyászat > RC0254 Neoplasms. Tumors. Oncology (including Cancer) / daganatok, tumorok, onkológia
R Medicine / orvostudomány > RM Therapeutics. Pharmacology / terápia, gyógyszertan
Depositing User: MTMT SWORD
Date Deposited: 27 Nov 2019 07:18
Last Modified: 27 Nov 2019 07:18

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