2019年12月1日
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
Nature Communications
- 巻
- 10
- 号
- 1
- 開始ページ
- 2674
- 終了ページ
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1038/s41467-019-09799-2
© 2019, The Author(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.
- リンク情報
-
- DOI
- https://doi.org/10.1038/s41467-019-09799-2
- PubMed
- https://www.ncbi.nlm.nih.gov/pubmed/31209238
- Scopus
- https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85067453487&origin=inward 本文へのリンクあり
- Scopus Citedby
- https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85067453487&origin=inward
- ID情報
-
- DOI : 10.1038/s41467-019-09799-2
- eISSN : 2041-1723
- PubMed ID : 31209238
- SCOPUS ID : 85067453487