title: GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers.
abstract: We describe methods with enhanced power and specificity to identify genes targeted by somatic copy-number alterations (SCNAs) that drive cancer growth. By separating SCNA profiles into underlying arm-level and focal alterations, we improve the estimation of background rates for each category. We additionally describe a probabilistic method for defining the boundaries of selected-for SCNA regions with user-defined confidence. Here we detail this revised computational approach, GISTIC2.0, and validate its performance in real and simulated datasets.
authors.affiliation: Cancer Program, The Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA.
authors.forename: Craig H
authors.initials: CH
authors.lastname: Mermel
authors.forename: Steven E
authors.initials: SE
authors.lastname: Schumacher
authors.forename: Barbara
authors.initials: B
authors.lastname: Hill
authors.forename: Matthew L
authors.initials: ML
authors.lastname: Meyerson
authors.forename: Rameen
authors.initials: R
authors.lastname: Beroukhim
authors.forename: Gad
authors.initials: G
authors.lastname: Getz
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D003198:Computer Simulation; D018628:Gene Dosage / Q000235:genetics*; D006801:Humans; D008962:Models, Theoretical; D009369:Neoplasms / Q000235:genetics* / Q000473:pathology*; D012984:Software; D025521:Tumor Suppressor Proteins / Q000235:genetics*
chemical_list: D025521:Tumor Suppressor Proteins