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.

Menden, M.p., Wang, D., Mason, M.j., Szalai, B., Bulusu, K.c., Guan, Y., et al. (2019). Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. NATURE COMMUNICATIONS, 10(1), 2674 [10.1038/s41467-019-09799-2].

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

Calderone A.;Castagnoli L.;Cesareni G.;Helmer Citterich M.;Palmeri A.;Perfetto L.;Pirro S.;
2019-01-01

Abstract

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
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore BIO/11 - BIOLOGIA MOLECOLARE
Settore BIO/18 - GENETICA
English
Con Impact Factor ISI
ADAM17 Protein; Antineoplastic Combined Chemotherapy Protocols; Benchmarking; Biomarkers, Tumor; Cell Line, Tumor; Computational Biology; Datasets as Topic; Drug Antagonism; Drug Resistance, Neoplasm; Drug Synergism; Genomics; Humans; Molecular Targeted Therapy; Mutation; Neoplasms; Pharmacogenetics; Phosphatidylinositol 3-Kinases; Phosphoinositide-3 Kinase Inhibitors; Treatment Outcome
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6572829/
Menden, M.p., Wang, D., Mason, M.j., Szalai, B., Bulusu, K.c., Guan, Y., et al. (2019). Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. NATURE COMMUNICATIONS, 10(1), 2674 [10.1038/s41467-019-09799-2].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/230361
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