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Statistical genomic analysis approach to determine driver mutations in melanoma

A statistical genomic analysis approach to determine driver mutations in melanoma could help identify new targets to treat the disease. Driver mutations in melanoma are difficult to identify due to the large number of random mutations induced by UV damage. Using genomic sequence data from paired tumor and normal tissues of patients with melanoma, a baseline mutation rate was defined and used to help identify five new candidate genes that could drive the disease: protein phosphatase 6 catalytic subunit (PPP6C), ras-related C3 botulinum toxin substrate 1 (rho family, small GTP binding protein Rac1; RAC1), sorting nexin 31 (SNX31), transforming acidic coiled-coil containing protein 1 (TACC1) and serine/threonine kinase 19 (STK19). Next steps include applying the strategy to a larger cohort of patients with melanoma as well as to patients with lung cancer.

SciBX 5(32); doi:10.1038/scibx.2012.849
Published online Aug. 16, 2012

Patent application filed; available for licensing

Hodis, E. et al. Cell; published online July 20, 2012;
Contact: Lynda Chin, The University of Texas MD Anderson Cancer Center, Houston, Texas
Contact: Levi A. Garraway, Broad Institute of MIT and Harvard, Cambridge, Mass.