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

Craig H Mermel1234, Steven E Schumacher1234, Barbara Hill1, Matthew L Meyerson1234, Rameen Beroukhim1234* and Gad Getz1*

Author Affiliations

1 Cancer Program, The Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA

2 Department of Medical Oncology, Dana Farber Cancer Institute, 44 Binney Street, Boston, MA 02115, USA

3 Department of Cancer Biology, Dana Farber Cancer Institute, 44 Binney Street, Boston, MA 02115, USA

4 The Center for Cancer Genome Discovery, Dana Farber Cancer Institute, 44 Binney Street, Boston, MA 02115, USA

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Genome Biology 2011, 12:R41  doi:10.1186/gb-2011-12-4-r41

Published: 28 April 2011

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.