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An integrated approach to anti-cancer drug sensitivity prediction
PrePrint
ISSN: 1545-5963
Noah Berlow, Noah Berlow is with the Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409.
A framework for design of personalized cancer therapy requires the ability to predict the sensitivity of a tumor to anticancer drugs. The predictive modeling of tumor sensitivity to anti-cancer drugs has primarily focused on generating functions that map gene expressions and genetic mutation profiles to drug sensitivity. In this paper, we present a new approach for drug sensitivity prediction and combination therapy design based on integrated functional and genomic characterizations. The modeling approach when applied to data from the Cancer Cell Line Encyclopedia shows a significant gain in prediction accuracy as compared to elastic net and random forest techniques based on genomic characterizations. Utilizing a Mouse Embryonal Rhabdomyosarcoma cell culture and a drug screen of 60 targeted drugs, we show that predictive modeling based on functional data alone can also produce high accuracy predictions. The framework also allows us to generate personalized tumor proliferation circuits to gain further insights on the individualized biological pathway.
Index Terms:
Drugs,Sensitivity,Bioinformatics,Tumors,Genomics,Predictive models
Citation:
Noah Berlow, Ranadip Pal, Qian Wan, Saad Haider, Mathew Geltzeiler, Lara Davis, Charles Keller, "An integrated approach to anti-cancer drug sensitivity prediction," IEEE/ACM Transactions on Computational Biology and Bioinformatics, 22 May 2014. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/TCBB.2014.2321138>
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