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

Abstract

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, NR1I2 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 have more sample-specific efficacy than the current clinical PDAC therapies.

Conclusions: A novelty target and drug priority algorithm PMTDS is developed to identify optimum target-drug pairs by integrating the knowledgebase base with a single patient’s genomics. The PMTDS system provides an accurate and reliable source for target and off-label drug selection for precision cancer medicine.

Graphical abstract

Keywords

precision medicine / drug target / algorithm / pancreatic adenocarcinoma / biological pathway / cancer

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SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at DOI 10.1007/s40484-017-0126-1.

ACKNOWLEDGEMENTS

This work was supported by NIH Funding 1U54HD090215-01.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Varshini Vasudevaraja, Jamie Renbarger, Ridhhi Girish Shah, Garrett Kinnebrew, Murray Korc, Limei Wang, Yang Huo, Enze Liu, Lang Li and Lijun Cheng declare they have no conflict of interests.

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

RIGHTS & PERMISSIONS

2017 Higher Education Press and Springer-Verlag GmbH Germany