CDPath: Cooperative driver pathways discovery using integer linear programming and Markov clustering
KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2019-10-01
Print Publication Date2019
Permanent link to this recordhttp://hdl.handle.net/10754/658583
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AbstractDiscovering driver pathways is an essential task to understand the pathogenesis of cancer and to design precise treatments for cancer patients. Increasing evidences have been indicating that multiple pathways often function cooperatively in carcinogenesis. In this study, we propose an approach called CDPath to discover cooperative driver pathways. CDPath firstly uses Integer Linear Programming to explore driver core modules from mutation profiles by enforcing co-occurrence and functional interaction relations between modules, and by maximizing the mutual exclusivity and coverage within modules. Next, to enforce cooperation of pathways and help the follow-up exact cooperative driver pathways discovery, it performs Markov clustering on pathway-pathway interaction network to cluster pathways. After that, it identifies pathways in different modules but in the same clusters as cooperative driver pathways. We apply CDPath on two TCGA datasets: breast cancer (BRCA) and endometrial cancer (UCEC). The results show that CDPath can identify known (i.e., TP53) and potential driver genes (i.e., SPTBN2). In addition, the identified cooperative driver pathways are related with the target cancer, and they are involved with carcinogenesis and several key biological processes. CDPath can uncover more potential biological associations between pathways (over 100%) and more cooperative driver pathways (over 200%) than competitive approaches.
CitationYang, Z., Yu, G., Guo, M., Yu, J., Zhang, X., & Wang, J. (2019). CDPath: Cooperative driver pathways discovery using integer linear programming and Markov clustering. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1–1. doi:10.1109/tcbb.2019.2945029
SponsorsThis work is supported by Natural Science Foundation of China (61873214, 61872300, 61871020, 61571163 and 61532014), Fundamental Research Funds for the Central Universities (XDJK2019B024), the National Key Research and Development Plan Task of China (Grant No.2016YFC0901902), Natural Science Foundation of CQ CSTC (cstc2018jcyjAX0228), and the Fundamental Research Funds for the Central Universities, NWSUAF, China (2452015060).