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Authors: Xia, Fangfang, Shukla, Maulik, Brettin, Thomas, Garcia-Cardona, Cristina, Cohn, Judith, Allen, Jonathan E., Maslov, Sergei, Holbeck, Susan L., Doroshow, James H., Evrard, Yvonne A., Stahlberg, Eric A., Stevens, Rick L.
TITLE: Predicting Tumor Cell Line Response to Drug Pairs with Deep Learning , BMC Bioinformatics , S18 , 19 : 486 , 2018
PUBLICATION DATE: 12-21-2018
ABSTRACT: Background The National Cancer Institute drug pair screening effort against 60 well-characterized human tumor cell lines (NCI-60) presents an unprecedented resource for modeling combinational drug activity. Results The authors presented a computational model for predicting cell line response to a subset of drug pairs in the NCI-ALMANAC database. Based on residual neural networks for encoding features as well as predicting tumor growth, the model explains 94% of the response variance. While the best result was achieved with a combination of molecular feature types (gene expression, microRNA and proteome), the authors showed that most of the predictive power comes from drug descriptors. To further demonstrate value in detecting anticancer therapy, the authors ranked the drug pairs for each cell line based on model predicted combination effect and recover 80% of the top pairs with enhanced activity. Conclusions The authors presented promising results in applying deep learning to predicting combinational drug response. The authors’ feature analysis indicated the community needs screening data involving more cell lines for the models to make better use of molecular features.
PROJECT: Cellular-Level Pilot
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https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2509-3
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