期刊
PLOS COMPUTATIONAL BIOLOGY
卷 13, 期 1, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1005308
关键词
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资金
- National Science Foundation [DB1054964]
- National Institutes of Health [R01CA194547]
- Starr Cancer Foundation
- Institute for Computational Biomedicine
- Department of Pathology, Weill Computational Biology and Medicine [T32GM083937]
- PhRMA Foundation Pre Doctoral Informatics Fellowship
A promising alternative to address the problem of acquired drug resistance is to rely on combination therapies. Identification of the right combinations is often accomplished through trial and error, a labor and resource intensive process whose scale quickly escalates as more drugs can be combined. To address this problem, we present a broad computational approach for predicting synergistic combinations using easily obtainable single drug efficacy, no detailed mechanistic understanding of drug function, and limited drug combination testing. When applied to mutant BRAF melanoma, we found that our approach exhibited significant predictive power. Additionally, we validated previously untested synergy predictions involving anticancer molecules. As additional large combinatorial screens become available, this methodology could prove to be impactful for identification of drug synergy in context of other types of cancers.
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