title: Identifying Network Perturbation in Cancer.
abstract: We present a computational framework, called DISCERN (DIfferential SparsE Regulatory Network), to identify informative topological changes in gene-regulator dependence networks inferred on the basis of mRNA expression datasets within distinct biological states. DISCERN takes two expression datasets as input: an expression dataset of diseased tissues from patients with a disease of interest and another expression dataset from matching normal tissues. DISCERN estimates the extent to which each gene is perturbed-having distinct regulator connectivity in the inferred gene-regulator dependencies between the disease and normal conditions. This approach has distinct advantages over existing methods. First, DISCERN infers conditional dependencies between candidate regulators and genes, where conditional dependence relationships discriminate the evidence for direct interactions from indirect interactions more precisely than pairwise correlation. Second, DISCERN uses a new likelihood-based scoring function to alleviate concerns about accuracy of the specific edges inferred in a particular network. DISCERN identifies perturbed genes more accurately in synthetic data than existing methods to identify perturbed genes between distinct states. In expression datasets from patients with acute myeloid leukemia (AML), breast cancer and lung cancer, genes with high DISCERN scores in each cancer are enriched for known tumor drivers, genes associated with the biological processes known to be important in the disease, and genes associated with patient prognosis, in the respective cancer. Finally, we show that DISCERN can uncover potential mechanisms underlying network perturbation by explaining observed epigenomic activity patterns in cancer and normal tissue types more accurately than alternative methods, based on the available epigenomic data from the ENCODE project.
authors.affiliation: Department of Computer Science & Engineering, University of Washington, Seattle, Washington, United States of America.
authors.forename: Maxim
authors.identifier: 0000-0003-2265-3881
authors.initials: M
authors.lastname: Grechkin
authors.affiliation: Sage Bionetworks, Seattle, Washington, United States of America.
authors.forename: Benjamin A
authors.initials: BA
authors.lastname: Logsdon
authors.affiliation: Center for Cancer Systems Biology, Department of Radiology, Stanford University, Stanford, California, United States of America.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Gentles
authors.affiliation: Department of Computer Science & Engineering, University of Washington, Seattle, Washington, United States of America.|Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America.
authors.forename: Su-In
authors.initials: SI
authors.lastname: Lee
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics; D019295:Computational Biology; D003198:Computer Simulation; D030541:Databases, Genetic; D044127:Epigenesis, Genetic; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000235:genetics; D016013:Likelihood Functions; D008175:Lung Neoplasms / Q000235:genetics; D008957:Models, Genetic; D009369:Neoplasms / Q000235:genetics*; D011379:Prognosis