Abstract
SummaryThe prediction of anticancer drug response is crucial for achieving a more effective and precise treatment of patients. Models based on the analysis of large cell line collections have shown potential for investigating drug efficacy in a clinically-meaningful, cost-effective manner. Using data from thousands of cancer cell lines and drug response experiments, we propose a drug sensitivity prediction system based on a 47-gene expression profile, which was derived from an unbiased transcriptomic network analysis approach. The profile reflects the molecular activity of a diverse range of cancer-relevant processes and pathways. We validated our model using independent datasets and comparisons with published models. A high concordance between predicted and observed drug sensitivities was obtained, including additional validated predictions for four glioblastoma cell lines and four drugs. Our approach can accurately predict anti-cancer drug sensitivity and will enable further pre-clinical research. In the longer-term, it may benefit patient-oriented investigations and interventions.
Publisher
Cold Spring Harbor Laboratory
Cited by
1 articles.
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