2012
DOI: 10.1016/j.cell.2012.06.024
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A Landscape of Driver Mutations in Melanoma

Abstract: SUMMARY Despite recent insights into melanoma genetics, systematic surveys for driver mutations are challenged by an abundance of passenger mutations caused by carcinogenic ultraviolet (UV) light exposure. We developed a permutation-based framework to address this challenge, employing mutation data from intronic sequences to control for passenger mutational load on a per gene basis. Analysis of large-scale melanoma exome data by this approach discovered six novel melanoma genes (PPP6C, RAC1, SNX31, TACC1, STK1… Show more

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Cited by 2,292 publications
(2,585 citation statements)
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References 63 publications
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“…The background mutation rate of each gene was calculated from the WES data of 469 melanoma tumors (Hodis et al, 2012;TCGA, 2015). Analysis of copy number variation (CNVs) did not identify any recurrent alterations exclusive to either group.…”
Section: Enrichment For Brca2 Mutations In Anti-pd-1 Responsive Melanomamentioning
confidence: 99%
See 2 more Smart Citations
“…The background mutation rate of each gene was calculated from the WES data of 469 melanoma tumors (Hodis et al, 2012;TCGA, 2015). Analysis of copy number variation (CNVs) did not identify any recurrent alterations exclusive to either group.…”
Section: Enrichment For Brca2 Mutations In Anti-pd-1 Responsive Melanomamentioning
confidence: 99%
“…To estimate the statistical significance of the recurrence of gene mutations in the responding or non-responding tumors, we used an independent batch of 469 melanomas' whole exome sequence datasets (Hodis et al, 2012;TCGA, 2015) to estimate each gene's background mutation frequency. Significance was computed by Fisher exact test followed by FDR adjustment for multiple hypothesis testing.…”
Section: Mutation Recurrencementioning
confidence: 99%
See 1 more Smart Citation
“…Hodis et al . found that tumors without recurrent mutations in either BRAF or NRAS had a significant enrichment of NF1 mutations or alterations in KIT (Hodis et al ., 2012). Furthermore, Krauthammer et al .…”
Section: Introductionmentioning
confidence: 99%
“…Some methods such as Mutation_Assessor,7 CHASM,8 transFIC,9 and FATHMM10 predict possible driver mutations by assessing functional impact of missense mutations. Other methods such as MutSig2.0,11 MutSigCV,12 InVEx,13 and MuSiC14 predict as possible driver genes those with extraordinary higher mutation rates than background mutation rates (BMR). A considerable number of genes have been identified as driver genes by these methods.…”
Section: Introductionmentioning
confidence: 99%