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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.

Original publication




Journal article


Nature communications

Publication Date





Oncology, IMED Biotech Unit, AstraZeneca, Cambridge, SG8 6EH, UK.


AstraZeneca-Sanger Drug Combination DREAM Consortium, Cell Line, Tumor, Humans, Neoplasms, Antineoplastic Combined Chemotherapy Protocols, Treatment Outcome, Computational Biology, Genomics, Pharmacogenetics, Drug Antagonism, Drug Synergism, Drug Resistance, Neoplasm, Mutation, Benchmarking, Phosphatidylinositol 3-Kinases, Molecular Targeted Therapy, Datasets as Topic, Biomarkers, Tumor, ADAM17 Protein, Phosphoinositide-3 Kinase Inhibitors