Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
Journal article, Peer reviewed
Published version
Åpne
Permanent lenke
http://hdl.handle.net/11250/2636615Utgivelsesdato
2019Metadata
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- Institutt for bioingeniørfag [208]
- Institutt for biologi [2638]
- Institutt for klinisk og molekylær medisin [3598]
- Publikasjoner fra CRIStin - NTNU [38937]
Originalversjon
10.1038/s41467-019-09799-2Sammendrag
The effectiveness of most cancer targeted therapies is short-lived. Tumors often developresistance that might be overcome with drug combinations. However, the number of possiblecombinations is vast, necessitating data-driven approaches tofind optimal patient-specifictreatments. Here we report AstraZeneca’s large drug combination dataset, consisting of11,576 experiments from 910 combinations across 85 molecularly characterized cancer celllines, and results of a DREAM Challenge to evaluate computational strategies for predictingsynergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensivemethodological development and benchmarking. Winning methods incorporate priorknowledge of drug-target interactions. Synergy is predicted with an accuracy matching bio-logical replicates for >60% of combinations. However, 20% of drug combinations are poorlypredicted by all methods. Genomic rationale for synergy predictions are identified, includingADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting tosynergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.