Pathway-Based Drug Repurposing with DPNetinfer: A Method to Predict Drug-Pathway Associations via Network-Based Approaches.

作者: Guixia Liu , Zengrui Wu , Yun Tang , Weihua Li , Yayuan Peng

DOI: 10.1021/ACS.JCIM.1C00009

关键词: Identification (information)External validationDrug repositioningDrug PathwayTumor heterogeneityCellular pathwaysComputational biologyCancer therapyArea under curveComputer science

摘要: Identification of drug-pathway associations plays an important role in pathway-based drug repurposing. However, it is time-consuming and costly to uncover new experimentally. The drug-induced transcriptomics data provide a global view cellular pathways tell how these change under different treatments. These enable computational approaches for large-scale prediction associations. Here we introduced DPNetinfer, novel method predict potential based on substructure-drug-pathway networks via network-based approaches. results demonstrated that DPNetinfer performed well pan-cancer network with AUC (area curve) = 0.9358. Meanwhile, was shown have good capability generalization two external validation sets (AUC 0.8519 0.7494, respectively). As case study, used repurposing cancer therapy. Unexpected anticancer activities some nononcology drugs were then identified the PI3K-Akt pathway. Considering tumor heterogeneity, seven primary site-based models constructed by networks. In word, provides powerful tool A web freely available at http://lmmd.ecust.edu.cn/netinfer/.

参考文章(60)
Feixiong Cheng, Chuang Liu, Jing Jiang, Weiqiang Lu, Weihua Li, Guixia Liu, Weixing Zhou, Jin Huang, Yun Tang, Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference PLoS Computational Biology. ,vol. 8, pp. e1002503- ,(2012) , 10.1371/JOURNAL.PCBI.1002503
Bert Vogelstein, Kenneth W Kinzler, Cancer genes and the pathways they control. Nature Medicine. ,vol. 10, pp. 789- 799 ,(2004) , 10.1038/NM1087
Jiao Li, Zhiyong Lu, Pathway-based drug repositioning using causal inference. BMC Bioinformatics. ,vol. 14, pp. 1- 10 ,(2013) , 10.1186/1471-2105-14-S16-S3
Feixiong Cheng, Weihua Li, Xichuan Wang, Yadi Zhou, Zengrui Wu, Jie Shen, Yun Tang, None, Adverse drug events: database construction and in silico prediction. Journal of Chemical Information and Modeling. ,vol. 53, pp. 744- 752 ,(2013) , 10.1021/CI4000079
Andrew L Hopkins, Network pharmacology: the next paradigm in drug discovery. Nature Chemical Biology. ,vol. 4, pp. 682- 690 ,(2008) , 10.1038/NCHEMBIO.118
Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He, clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters Omics A Journal of Integrative Biology. ,vol. 16, pp. 284- 287 ,(2012) , 10.1089/OMI.2011.0118
Jordi Barretina, Giordano Caponigro, Nicolas Stransky, Kavitha Venkatesan, Adam A Margolin, Sungjoon Kim, Christopher J Wilson, Joseph Lehár, Gregory V Kryukov, Dmitriy Sonkin, Anupama Reddy, Manway Liu, Lauren Murray, Michael F Berger, John E Monahan, Paula Morais, Jodi Meltzer, Adam Korejwa, Judit Jané-Valbuena, Felipa A Mapa, Joseph Thibault, Eva Bric-Furlong, Pichai Raman, Aaron Shipway, Ingo H Engels, Jill Cheng, Guoying K Yu, Jianjun Yu, Peter Aspesi, Melanie De Silva, Kalpana Jagtap, Michael D Jones, Li Wang, Charles Hatton, Emanuele Palescandolo, Supriya Gupta, Scott Mahan, Carrie Sougnez, Robert C Onofrio, Ted Liefeld, Laura MacConaill, Wendy Winckler, Michael Reich, Nanxin Li, Jill P Mesirov, Stacey B Gabriel, Gad Getz, Kristin Ardlie, Vivien Chan, Vic E Myer, Barbara L Weber, Jeff Porter, Markus Warmuth, Peter Finan, Jennifer L Harris, Matthew Meyerson, Todd R Golub, Michael P Morrissey, William R Sellers, Robert Schlegel, Levi A Garraway, None, The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity Nature. ,vol. 483, pp. 603- 607 ,(2012) , 10.1038/NATURE11003
Albert-László Barabási, Natali Gulbahce, Joseph Loscalzo, Network medicine: a network-based approach to human disease Nature Reviews Genetics. ,vol. 12, pp. 56- 68 ,(2011) , 10.1038/NRG2918
Meiyue Song, Yan Yan, Zhenran Jiang, Drug–pathway interaction prediction via multiple feature fusion Molecular BioSystems. ,vol. 10, pp. 2907- 2913 ,(2014) , 10.1039/C4MB00199K