![]() Computational approaches for predicting drug synergy are critical to guide experimental approaches for discovery of rational combination therapy 5.Ī number of approaches have been developed to model drug combination synergy using chemical, biological, and molecular data from cancer cell lines 6, 7 but with limited translatability to the clinic. This is further complicated by the influence of disease and cellular contexts, rendering it impractical to cover all possibilities with undirected experimental screens 4. While empirical experiments are important for observing potential synergistic properties across drug pairs, the possible number of combinations grows exponentially with the number of drugs under consideration. High-throughput preclinical approaches are crucial to determine and evaluate effective combination strategies. The molecular makeup of cancer cells and the mechanisms driving resistance will influence the optimal combination of mechanisms to target 1, 2, 3. Any single therapy may be limited in its effectiveness, but drug combinations are hypothesized to potentially overcome drug resistance and lead to more durable responses in patients. There are multiple mechanisms that may lead to drug resistance 1 that include genetic and non-genetic heterogeneity inherent in advanced cancers, coupled with complex feedback and regulatory mechanisms, and dynamic interactions between tumor cells and their microenvironment. Unfortunately, most patients’ tumors develop resistance leading to disease relapse. ![]() Personalized treatment matching targeted drugs to a tumor’s genetics has resulted in remarkable responses. 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. However, 20% of drug combinations are poorly predicted by all methods. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. Winning methods incorporate prior knowledge of drug-target interactions. 160 teams participated to provide a comprehensive methodological development and benchmarking. 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. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Tumors often develop resistance that might be overcome with drug combinations. The effectiveness of most cancer targeted therapies is short-lived. Nature Communications volume 10, Article number: 2674 ( 2019) AstraZeneca-Sanger Drug Combination DREAM Consortium,.Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
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