Integrating Domain Specific Knowledge and Network Analysis to Predict Drug Sensitivity of Cancer Cell Lines

Sebo Kim, Varsha Sundaresan, Lei Zhou and Tamer Kahveci

PLOS ONE, 2016, vol. 11, issue 9, 1-27

Abstract: One of fundamental challenges in cancer studies is that varying molecular characteristics of different tumor types may lead to resistance to certain drugs. As a result, the same drug can lead to significantly different results in different types of cancer thus emphasizing the need for individualized medicine. Individual prediction of drug response has great potential to aid in improving the clinical outcome and reduce the financial costs associated with prescribing chemotherapy drugs to which the patient’s tumor might be resistant. In this paper we develop a network based classifier (NBC) method for predicting sensitivity of cell lines to anticancer drugs from transcriptome data. In the literature, this strategy has been used for predicting cancer types. Here, we extend it to estimate sensitivity of cells from different tumor types to various anticancer drugs. Furthermore, we incorporate domain specific knowledge such as the use of apoptotic gene list and clinical dose information in our method to impart biological significance to the prediction. Our experimental results suggest that our network based classifier (NBC) method outperforms existing classifiers in estimating sensitivity of cell lines for different drugs.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0162173

DOI: 10.1371/journal.pone.0162173

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