https://sebastiansauer.github.io/sebastiansaueracademic/tag/prediction/predictionWowchemy (https://wowchemy.com)en-usMon, 17 Jun 2019 00:00:00 +0000https://sebastiansauer.github.io/sebastiansaueracademic/media/icon_hufe30330db5c3dae5647fd2ef1714014b_368463_512x512_fill_lanczos_center_3.pnghttps://sebastiansauer.github.io/sebastiansaueracademic/tag/prediction/https://sebastiansauer.github.io/sebastiansaueracademic/publication/astra/Mon, 17 Jun 2019 00:00:00 +0000https://sebastiansauer.github.io/sebastiansaueracademic/publication/astra/<p><em>Abstract</em> 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 &gt;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.</p>