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

Researchers 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, they improve the estimation of background rates for each category. These researchers additionally describe a probabilistic method for defining the boundaries of selected-for SCNA regions with user-defined confidence. Here they detail this revised computational approach, GISTIC2.0, and validate its performance in real and simulated datasets.

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

Mermel Craig H, Schumacher Steven E, Hill Barbara, Meyerson Matthew L, Beroukhim Rameen (Rameen_Beroukhim@dfci.harvard.edu), Getz Gad (gadgetz@broadinstitute.org). Genome Biol. 2011 Apr 28;12(4):R41.

Free article at: http://genomebiology.com/content/pdf/gb-2011-12-4-r41.pdf

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Des chercheurs décrivent des méthodes possédant une puissance et une spécificité accrues pour identifier les gènes cibles de modifications somatiques du nombre de copies (MSNCs) qui dirigent la croissance du cancer. Ils détaillent une approche computationnelle révisée (GISTIC2.0), et valident ses performances dans des ensembles de données réelles et simulées.

Article (en anglais) librement disponible à l’adresse: http://genomebiology.com/content/pdf/gb-2011-12-4-r41.pdf