Journal article
Comprehensive Characterization of Cancer Driver Genes and Mutations
MH Bailey, C Tokheim, E Porta-Pardo, S Sengupta, D Bertrand, A Weerasinghe, A Colaprico, MC Wendl, J Kim, B Reardon, PKS Ng, KJ Jeong, S Cao, Z Wang, J Gao, Q Gao, F Wang, EM Liu, L Mularoni, C Rubio-Perez Show all
Cell | Published : 2018
Abstract
Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%–85% of pre..
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Awarded by National Institutes of Health
Funding Acknowledgements
We thank patients who contributed to this study and the NCI Office of Cancer Genomics and acknowledge NIH grants from the NHGRI (U54 HG003273, U54 HG003067, and U54 HG003079) and grants from the NCI (U24 CA143799, U24 CA143835, U24 CA143840, U24 CA143843, U24 CA143845, U24 CA143848, U24 CA143858, U24 CA143866, U24 CA143867, U24 CA143882, U24 CA143883, U24 CA144025, P30 CA016672, BP 2016- 00296, and U24 CA211006).