|Publication Type||Journal Article|
|Year of Publication||2012|
|Authors||Mermel, CH, Schumacher, SE, Hill, B, Meyerson, ML, Beroukhim, R, Getz, G|
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.