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PMTDS： a computational method based on genetic interaction networks for Precision Medicine Target-Drug Selection in cancer

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Frontiers of Electrical and Electronic Engineering in China 2017年第4期5卷 380-394页

作者： Varshini Vasudevaraja jamie renbarger Ridhhi Girish Shah Garrett Kinnebrew Murray Korc Limei Wang Yang Huo Enze Liu Lang Li Lijun Cheng Department of Biomedical Informatics College of Medicine Ohio State University Columbus OH 43210 USA Department of Medical and Molecular Genetics School of Medicine Indiana University Indianapolis IN 46202 USA Department of Pediatrics hematology/oncology School of Medicine Indiana University Indianapolis IN 46202 USA Department of Medicine Biochemistry and Molecular Biology School of Medicine Indiana University Indianapoiis IN 46202 USA Pancreatic Cancer Signature Center School of Medicine Indiana University Indianapolis IN 46202 USA

Background： Precision medicine attempts to tailor the right therapy for the right patient. Recent progress in large- scale collection of patents＇ tumor molecular profiles in The Cancer Genome Atlas （TCGA） provides a foundation for systematic discovery of potential drug targets specific to different types of cancer. However, we still lack powerful computational methods to effectively integrate multiple omics data and protein-protein interaction network technology for an optimum target and drug recommendation for an individual patient. Methods： In this study, a computation method, Precision Medicine Target-Drug Selection （PMTDS） based on genetic interaction networks is developed to select the optimum targets and associated drugs for precision medicine style treatment of cancer. The PMTDS system includes three parts： a personalized medicine knowledgebase for each cancer type, a genetic interaction network-based algorithm and a single patient molecular profiles. The knowledgebase integrates cancer drugs, drug-target databases and gene biological pathway networks. The molecular profiles of each tumor consists of DNA copy number alteration, gene mutation, and tumor gene expression variation compared to its adjacent normal tissue. Results： The novel integrated PMTDS system is applied to select candidate target-drug pairs for 178 TCGA pancreatic adenocarcinoma （PDAC） tumors. The experiment results show known drug targets （EGFR, IGF1R, ERBB2, NRII2 and AKR1B1） of PDAC treatment are identified, which provides important evidence of the PMTDS algorithm＇s accuracy. Other potential targets PTK6, ATF, SYK are, also, recommended for PDAC. Further validation is provided by comparison of selected targets with, both, cell line molecular profiles from the Cancer Cell Line Encyclopedia （CCLE） and drug response data from the Cancer Therapeutics Response Portal （CTRP）. Results from experimental analysis of forty six individual pancreatic cancer samples show that drugs selected by PMTDS ha

关键词： precision medicine drug target algorithm pancreatic adenocarcinoma biological pathway cancer

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