GISTIC2.0 Facilitates Sensitive and Confident Localization of the Targets of Focal Somatic Copy-Number Alteration in Human Cancers

Genome Biol. 2011;12(4):R41. doi: 10.1186/gb-2011-12-4-r41. Epub 2011 Apr 28.

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Computational Biology
  • Computer Simulation
  • Gene Dosage / genetics*
  • Humans
  • Models, Theoretical
  • Neoplasms / genetics*
  • Neoplasms / pathology*
  • Software*
  • Tumor Suppressor Proteins / genetics*

Substances

  • Tumor Suppressor Proteins