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Whole-genome sequencing identifies a novel germline variant in the oncogene MITF , which is associated with the development of melanoma. MITF , a melanoma predisposition gene Two papers in this issue of Nature demonstrate that missense substitutions in the gene encoding for microphthalmia-associated transcription factor (MITF) are associated with susceptibility to melanoma and renal cell carcinoma. Functional analysis shows that the variant has impaired sumoylation that leads to differential regulation of several MITF targets, and promotes tumour cell clonogenicity, migration and invasion. So far, two genes associated with familial melanoma have been identified, accounting for a minority of genetic risk in families. Mutations in CDKN2A account for approximately 40% of familial cases 1 , and predisposing mutations in CDK4 have been reported in a very small number of melanoma kindreds 2 . Here we report the whole-genome sequencing of probands from several melanoma families, which we performed in order to identify other genes associated with familial melanoma. We identify one individual carrying a novel germline variant (coding DNA sequence c.G1075A; protein sequence p.E318K; rs149617956) in the melanoma-lineage-specific oncogene microphthalmia-associated transcription factor ( MITF ). Although the variant co-segregated with melanoma in some but not all cases in the family, linkage analysis of 31 families subsequently identified to carry the variant generated a log of odds (lod) score of 2.7 under a dominant model, indicating E318K as a possible intermediate risk variant. Consistent with this, the E318K variant was significantly associated with melanoma in a large Australian case–control sample. Likewise, it was similarly associated in an independent case–control sample from the United Kingdom. In the Australian sample, the variant allele was significantly over-represented in cases with a family history of melanoma, multiple primary melanomas, or both. The variant allele was also associated with increased naevus count and non-blue eye colour. Functional analysis of E318K showed that MITF encoded by the variant allele had impaired sumoylation and differentially regulated several MITF targets. These data indicate that MITF is a melanoma-predisposition gene and highlight the utility of whole-genome sequencing to identify novel rare variants associated with disease susceptibility.

Germline mutations in CDKN2A greatly increase risk of developing cutaneous melanoma. We have constructed a risk prediction model, Familial Risk Assessment of Melanoma (FRAMe), for estimating the likelihood of carrying a heritable CDKN2A mutation among Australian families, where the prevalence of these mutations is low. Using logistic regression, we analysed characteristics of 299 Australian families recruited through the Sydney site of GenoMEL (international melanoma genetics consortium) with at least three cases of cutaneous melanoma (in situ and invasive) among first-degree blood relatives, for predictors of the presence of a pathogenic CDKN2A mutation. The final multivariable prediction model was externally validated in an independent cohort of 61 melanoma kindreds recruited through GenoMEL Queensland. Family variables independently associated with the presence of a CDKN2A mutation in a multivariable model were number of individuals diagnosed with melanoma under 40 years of age, number of individuals diagnosed with more than one primary melanoma, and number of individuals blood related to a melanoma case in the first degree diagnosed with any cancer excluding melanoma and non-melanoma skin cancer. The number of individuals diagnosed with pancreatic cancer was not independently associated with mutation status. The risk prediction model had an area under the receiver operating characteristic curve (AUC) of 0.851 (95% CI 0.793, 0.909) in the training dataset, and 0.745 (95%CI 0.612, 0.877) in the validation dataset. This model is the first to be developed and validated using only Australian data, which is important given the higher rate of melanoma in the population. This model will help to effectively identify families suitable for genetic counselling and testing in areas of high ambient ultraviolet radiation. A user-friendly electronic nomogram is available at www.melanomarisk.org.au.

Plant responses to herbivory and links to belowground nitrogen cycling were investigated at Wind Cave National Park, South Dakota. Laboratory estimates of net nitrogen mineralization were highest in soils from the more altered areas of prairie dog colonies (Cynomys ludovicianus) and lowest in the adjacent, lightly grazed, uncolonized grassland. The ratio of CO\"2: net nitrogen mineralized, as index of immobilization, was highest in the uncolonized grassland and lowest in the altered core areas. Soil moisture was an important modifier of in situ field estimates of net nitrogen mineralization. Root biomass, an important carbon source for decomposers in perennial grasslands, was lowest in the altered core area and highest in the adjacent uncolonised grassland. Decreased nitrogen immobilization and increased net nitrogen mineralization in the laboratory incubations likely resulted from decreased root carbon inputs in grazed areas, which limited carbon availability to decomposers. Such increases in plant-available nitrogen may partially explain the frequently reported grazing-induced increases in shoot nitrogen concentrations. These studies suggest that carbon allocation to roots is a key link determining nitrogen-cycling responses to herbivory.

Melanocortin-1 receptor (MC1R) gene variants are very common and are associated with melanoma risk, but their contribution to melanoma risk prediction compared with traditional risk factors is unknown. We aimed to 1) evaluate the separate and incremental contribution of MC1R genotype to prediction of early-onset melanoma, and compare this with the contributions of physician-measured and self-reported traditional risk factors, and 2) develop risk prediction models that include MC1R, and externally validate these models using an independent dataset from a genetically similar melanoma population. Using data from an Australian population-based, case-control-family study, we included 413 case and 263 control participants with sequenced MC1R genotype, clinical skin examination and detailed questionnaire. We used unconditional logistic regression to estimate predicted probabilities of melanoma. Results were externally validated using data from a similar study in England. When added to a base multivariate model containing only demographic factors, MC1R genotype improved the area under the receiver operating characteristic curve (AUC) by 6% (from 0.67 to 0.73; P < 0.001) and improved the quartile classification by a net 26% of participants. In a more extensive multivariate model, the factors that contributed significantly to the AUC were MC1R genotype, number of nevi and previous non-melanoma skin cancer; the AUC was 0.78 (95% CI 0.75-0.82) for the model with self-reported nevi and 0.83 (95% CI 0.80-0.86) for the model with physician-counted nevi. Factors that did not further contribute were sun and sunbed exposure and pigmentation characteristics. Adding MC1R to a model containing pigmentation characteristics and other self-reported risk factors increased the AUC by 2.1% (P = 0.01) and improved the quartile classification by a net 10% (95% CI 1-18%, P = 0.03). Although MC1R genotype is strongly associated with skin and hair phenotype, it was a better predictor of early-onset melanoma than was pigmentation characteristics. Physician-measured nevi and previous non-melanoma skin cancer were also strong predictors. There might be modest benefit to measuring MC1R genotype for risk prediction even if information about traditional self-reported or clinically measured pigmentation characteristics and nevi is already available.

Background Mutations in the CDKN2A and CDK4 genes predispose to melanoma. From three case-control studies of cutaneous melanoma, we estimated the prevalence and predictors of these mutations for people from regions with widely differing latitudes and melanoma incidence. Methods Population-based cases and controls from the United Kingdom (1586 cases, 499 controls) and Australia (596 early-onset cases, 476 controls), and a hospital-based series from Spain (747 cases, 109 controls), were screened for variants in all exons of CDKN2A and the p16INK4A binding domain of CDK4 . Results The prevalence of mutations for people with melanoma was similar across regions: 2.3%, 2.5% and 2.0% for Australia, Spain and the United Kingdom respectively. The strongest predictors of carrying a mutation were having multiple primaries (odds ratio (OR) = 5.4, 95% confidence interval (CI: 2.5, 11.6) for 2 primaries and OR = 32.4 (95% CI: 14.7, 71.2) for 3 or more compared with 1 primary only); and family history (OR = 3.8; 95% CI:1.89, 7.5) for 1 affected first- or second-degree relative and OR = 23.2 (95% CI: 11.3, 47.6) for 2 or more compared with no affected relatives). Only 1.1% of melanoma cases with neither a family history nor multiple primaries had mutations. Conclusions There is a low probability (<2%) of detecting a germline CDKN2A mutation in people with melanoma except for those with a strong family history of melanoma (≥2 affected relatives, 25%), three or more primary melanomas (29%), or more than one primary melanoma who also have other affected relatives (27%).

BackgroundCDKN2A mutations confer a substantial risk of cutaneous melanoma; however, the magnitude of risk is uncertain.MethodsThe study estimated the hazard ratio (HR) and the average age specific cumulative risk (ie, penetrance) of reported melanoma for CDKN2A mutation carriers in case families using a modified segregation analysis of the first and higher degree relatives of 35 population-based cases. The study sample included 223 relatives of 13 melanoma cases diagnosed when aged 18–39 years from Melbourne, Sydney and Brisbane, Australia, and 322 relatives of 22 melanoma cases diagnosed at any age from Yorkshire, UK.ResultsThe estimated HR for melanoma for mutation carriers relative to the general population decreased with regions of increasing ambient ultraviolet (UV) irradiance, being higher for the UK than Australia (87, 95% CI 50 to 153 vs 31, 95% CI 20 to 50, p=0.008), and across Australia, 49 (95% CI 24 to 98) for Melbourne, 44 (95% CI 22 to 88) for Sydney, and 9 (95% CI 2 to 33) for Brisbane (p=0.02). Penetrance did not differ by geographic region. It is estimated that 16% (95% CI 10% to 27%) of UK and 20% (95% CI 13% to 30%) of Australian CDKN2A mutation carriers would be diagnosed with melanoma by age 50 years, and 45% (95% CI 29% to 65%) and 52% (95% CI 37% to 69%), respectively, by age 80 years.ConclusionsContrary to the strong association between UV radiation exposure and melanoma risk for the general population, CDKN2A mutation carriers appear to have the same cumulative risk of melanoma irrespective of the ambient UV irradiance of the region in which they live.

Brown et al . report results of a genome-wide association study for melanoma. Their screen, which used a pooling strategy, identified common variants on 20q11.22 associated with melanoma susceptibility. In two separate studies, Sulem et al . and Gudbjartsson et al . report that the same region on 20q11.22, near ASIP , influences pigmentation and confers risk of cutaneous melanoma and basal cell carcinoma. We conducted a genome-wide association pooling study for cutaneous melanoma and performed validation in samples totaling 2,019 cases and 2,105 controls. Using pooling, we identified a new melanoma risk locus on chromosome 20 (rs910873 and rs1885120), with replication in two further samples (combined P < 1 × 10 −15 ). The per allele odds ratio was 1.75 (1.53, 2.01), with evidence for stronger association in early-onset cases.

A proportion of melanoma‐prone individuals in both familial and non‐familial contexts has been shown to carry inactivating mutations in either CDKN2A or, rarely, CDK4. CDKN2A is a complex locus that encodes two unrelated proteins from alternately spliced transcripts that are read in different frames. The alpha transcript (exons 1α, 2, and 3) produces the p16INK4A cyclin‐dependent kinase inhibitor, while the beta transcript (exons 1β and 2) is translated as p14ARF, a stabilizing factor of p53 levels through binding to MDM2. Mutations in exon 2 can impair both polypeptides and insertions and deletions in exons 1α, 1β, and 2, which can theoretically generate p16INK4A‐p14ARF fusion proteins. No online database currently takes into account all the consequences of these genotypes, a situation compounded by some problematic previous annotations of CDKN2A‐related sequences and descriptions of their mutations. As an initiative of the international Melanoma Genetics Consortium, we have therefore established a database of germline variants observed in all loci implicated in familial melanoma susceptibility. Such a comprehensive, publicly accessible database is an essential foundation for research on melanoma susceptibility and its clinical application. Our database serves two types of data as defined by HUGO. The core dataset includes the nucleotide variants on the genomic and transcript levels, amino acid variants, and citation. The ancillary dataset includes keyword description of events at the transcription and translation levels and epidemiological data. The application that handles users' queries was designed in the model‐view‐controller architecture and was implemented in Java. The object‐relational database schema was deduced using functional dependency analysis. We hereby present our first functional prototype of eMelanoBase. The service is accessible via the URL www.wmi.usyd.edu.au:8080/melanoma.html. Hum Mutat 21:2–7, 2002. © 2002 Wiley‐Liss, Inc.

Background Individuals from melanoma-prone families have similar or reduced sun-protective behaviors compared to the general population. Studies on trends in sun-related behaviors have been temporally and geographically limited. Methods Individuals from an international consortium of melanoma-prone families (GenoMEL) were retrospectively asked about sunscreen use, sun exposure (time spent outside), sunburns, and sunbed use at several timepoints over their lifetime. Generalized linear mixed models were used to examine the association between these outcomes and birth cohort defined by decade spans, after adjusting for covariates. Results A total of 2407 participants from 547 families across 17 centers were analyzed. Sunscreen use increased across subsequent birth cohorts, and although the likelihood of sunburns increased until the 1950s birth cohort, it decreased thereafter. Average sun exposure did not change across the birth cohorts, and the likelihood of sunbed use increased in more recent birth cohorts. We generally did not find any differences in sun-related behavior when comparing melanoma cases to non-cases. Melanoma cases had increased sunscreen use, decreased sun exposure, and decreased odds of sunburn and sunbed use after melanoma diagnosis compared to before diagnosis. Conclusions Although sunscreen use has increased and the likelihood of sunburns has decreased in more recent birth cohorts, individuals in melanoma-prone families have not reduced their overall sun exposure and had an increased likelihood of sunbed use in more recent birth cohorts. These observations demonstrate partial improvements in melanoma prevention and suggest that additional intervention strategies may be needed to achieve optimal sun-protective behavior in melanoma-prone families.

Background Melanocortin-1 receptor ( MC1R ) gene variants are very common and are associated with melanoma risk, but their contribution to melanoma risk prediction compared with traditional risk factors is unknown. We aimed to 1) evaluate the separate and incremental contribution of MC1R genotype to prediction of early-onset melanoma, and compare this with the contributions of physician-measured and self-reported traditional risk factors, and 2) develop risk prediction models that include MC1R , and externally validate these models using an independent dataset from a genetically similar melanoma population. Methods Using data from an Australian population-based, case-control-family study, we included 413 case and 263 control participants with sequenced MC1R genotype, clinical skin examination and detailed questionnaire. We used unconditional logistic regression to estimate predicted probabilities of melanoma. Results were externally validated using data from a similar study in England. Results When added to a base multivariate model containing only demographic factors, MC1R genotype improved the area under the receiver operating characteristic curve (AUC) by 6% (from 0.67 to 0.73; P  < 0.001) and improved the quartile classification by a net 26% of participants. In a more extensive multivariate model, the factors that contributed significantly to the AUC were MC1R genotype, number of nevi and previous non-melanoma skin cancer; the AUC was 0.78 (95% CI 0.75-0.82) for the model with self-reported nevi and 0.83 (95% CI 0.80-0.86) for the model with physician-counted nevi. Factors that did not further contribute were sun and sunbed exposure and pigmentation characteristics. Adding MC1R to a model containing pigmentation characteristics and other self-reported risk factors increased the AUC by 2.1% ( P  = 0.01) and improved the quartile classification by a net 10% (95% CI 1-18%, P = 0.03). Conclusions Although MC1R genotype is strongly associated with skin and hair phenotype, it was a better predictor of early-onset melanoma than was pigmentation characteristics. Physician-measured nevi and previous non-melanoma skin cancer were also strong predictors. There might be modest benefit to measuring MC1R genotype for risk prediction even if information about traditional self-reported or clinically measured pigmentation characteristics and nevi is already available.