title: Inferring the temporal order of cancer gene mutations in individual tumor samples.
abstract: The temporal order of cancer gene mutations in tumors is essential for understanding and treating the disease. Existing methods are unable to infer the order of mutations that are identified at the same time in individual tumor samples, leaving the heterogeneity of the order unknown. Here, we show that through a complex network-based approach, which is based on the newly defined statistic -carcinogenesis information conductivity (CIC), the temporal order in individual samples can be effectively inferred. The results suggest that tumor-suppressor genes might more frequently initiate the order of mutations than oncogenes, and every type of cancer might have its own unique order of mutations. The initial mutations appear to be dedicated to acquiring the function of evading apoptosis, and some order constraints might reflect potential regularities. Our approach is completely data-driven without any parameter settings and can be expected to become more effective as more data will become available.
authors.affiliation: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Guo
authors.affiliation: Department of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, United States of America.
authors.forename: Hanliang
authors.initials: H
authors.lastname: Guo
authors.affiliation: Department of Industry and Market Research China Mobile Research Institute, Beijing, China.
authors.forename: Zhanyi
authors.initials: Z
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D053263:Gene Regulatory Networks; D016147:Genes, Tumor Suppressor; D006801:Humans; D008954:Models, Biological; D009154:Mutation; D009369:Neoplasms / Q000235:genetics*; D016571:Neural Networks, Computer; D009857:Oncogenes / Q000235:genetics*