Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/60862
Título: | Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen |
Autor(es): | Menden, Michael P. Wang, Dennis Mason, Mike J. Szalai, Bence Bulusu, Krishna C. Guan, Yuanfang Yu, Thomas AstraZeneca-Sanger Drug Combination DREAM Consortium Baptista, Delora Machado, D. Rocha, Miguel et. al. |
Data: | Jun-2019 |
Editora: | Springer Nature |
Revista: | Nature Communications |
Citação: | Menden, M. P., Wang, D., Mason, Baptista, Delora, B., Machado, D., Rocha, Miguel, et. al. (2019). Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nature communications, 10(1), 2674 |
Resumo(s): | 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. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/60862 |
DOI: | 10.1038/s41467-019-09799-2 |
ISSN: | 20411723 |
e-ISSN: | 20411723 |
Versão da editora: | https://www.nature.com/articles/s41467-019-09799-2#article-info |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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document_51797_1.pdf | 1,61 MB | Adobe PDF | Ver/Abrir |