A Landscape of Driver Mutations in Melanoma

Hodis, Eran; Watson, Ian R.; Kryukov, Gregory V.; Arold, Stefan T.; Imieliński, Marcin; Theurillat, Jean Philippe; Nickerson, Elizabeth; Auclair, Daniel; Li, Liren; Place, Chelsea S.; DiCara, Daniel; Ramos, Alex H.; Lawrence, Michael S.; Cibulskis, Kristian; Sivachenko, Andrey; Voet, Douglas; Saksena, Gordon; Stransky, Nicolas; Onofrio, Robert C.; Winckler, Wendy; Ardlie, Kristin; Wagle, Nikhil; Wargo, Jennifer A.; Chong, Kelly K.; Morton, Donald L.; Stemke-Hale, Katherine; Guo, Cheng; Noble, Michael S.; Meyerson, Matthew; Ladbury, John E.; Davies, Michael A.; Gershenwald, Jeffrey E.; Wagner, Stephan N.; Hoon, Dave S.B.; Schadendorf, Dirk; Lander, Eric S.; Gabriel, Stacey; Getz, Gad; Garraway, Levi A.; Chin, Lynda

doi:10.1016/j.cell.2012.06.024

Cited by 2,294 publications

(2,585 citation statements)

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%

“…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%

“…These genes were illustrated in Figure 1A and all genes that fulfilled i) and ii) and tested for multiple hypotheses were listed in Table S1E. The association between BRCA2 nsSNVs and overall nsSNV counts were tested using two-sided Mann-Whitney test and validated in independent WES datasets (Hodis et al, 2012;TCGA, 2015).…”

Section: Mutation Recurrencementioning

confidence: 99%

See 2 more Smart Citations

Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma

Hugo¹,

Zaretsky²,

Sun³

et al. 2017

Cell

361

247

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Section: Enrichment For Brca2 Mutations In Anti-pd-1 Responsive Melanomamentioning

confidence: 99%

Section: Mutation Recurrencementioning

confidence: 99%

See 1 more Smart Citation

Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma

Hugo¹,

Zaretsky²,

Sun³

et al. 2017

Cell

361

247

View full text Add to dashboard Cite

“…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%

NF1‐mutated melanoma tumors harbor distinct clinical and biological characteristics

et al. 2017

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In general, melanoma can be considered as a UV‐driven disease with an aggressive metastatic course and high mutational load, with only few tumors (acral, mucosal, and uveal melanomas) not induced by sunlight and possessing a lower mutational load. The most commonly activated pathway in melanoma is the mitogen‐activated protein kinase (MAPK) pathway. However, the prognostic significance of mutational stratification is unclear and needs further investigation. Here, in silico we combined mutation data from 162 melanomas subjected to targeted deep sequencing with mutation data from three published studies. Tumors from 870 patients were grouped according to BRAF,RAS,NF1 mutation or triple‐wild‐type status and correlated with tumor and patient characteristics. We found that the NF1‐mutated subtype had a higher mutational burden and strongest UV mutation signature. Searching for co‐occurring mutated genes revealed the RASopathy genes PTPN11 and RASA2, as well as another RAS domain‐containing gene RASSF2 enriched in the NF1 subtype after adjustment for mutational burden. We found that a larger proportion of the NF1‐mutant tumors were from males and with older age at diagnosis. Importantly, we found an increased risk of death from melanoma (disease‐specific survival, DSS; HR, 1.9; 95% CI, 1.21–3.10; P = 0.046) and poor overall survival (OS; HR, 2.0; 95% CI, 1.28–2.98; P = 0.01) in the NF1 subtype, which remained significant after adjustment for age, gender, and lesion type (DSS P = 0.03, OS P = 0.06, respectively). Melanoma genomic subtypes display different biological and clinical characteristics. The poor outcome observed in the NF1 subtype highlights the need for improved characterization of this group.

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“…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%

MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration

Hou

Gao

et al. 2018

Advanced Science

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Identification of a few cancer driver mutation genes from a much larger number of passenger mutation genes in cancer samples remains a highly challenging task. Here, a novel method for distinguishing the driver genes from the passenger genes by effective integration of somatic mutation data and molecular interaction data using a maximal mutational impact function (MaxMIF) is presented. When evaluated on six somatic mutation datasets of Pan‐Cancer and 19 datasets of different cancer types from TCGA, MaxMIF almost always significantly outperforms all the existing state‐of‐the‐art methods in terms of predictive accuracy, sensitivity, and specificity. It recovers about 30% more known cancer genes in 500 top‐ranked candidate genes than the best among the other tools evaluated. MaxMIF is also highly robust to data perturbation. Intriguingly, MaxMIF is able to identify potential cancer driver genes, with strong experimental data support. Therefore, MaxMIF can be very useful for identifying or prioritizing cancer driver genes in the increasing number of available cancer genomic data.

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A Landscape of Driver Mutations in Melanoma

Cited by 2,294 publications

References 63 publications

Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma

Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma

NF1‐mutated melanoma tumors harbor distinct clinical and biological characteristics

MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration

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