GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers

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.

• DD
  Doris Duke Charitable Foundation
• NI
  National Institutes of Health

  Award: U24CA143867
• NH
  National Human Genome Research Institute
• NC
  National Cancer Institute
• NI
  National Institute of General Medical Sciences

  Award: T32GM07753