The epidemiology of melanoma is complex, and individual risk depends on sun exposure, host factors, and genetic factors, and in their interactions as well. Sun exposure can be classified as intermittent, chronic, or cumulative (overall) exposure, and each appears to have a different effect on type of melanoma. Other environmental factors, such as chemical exposures-either through occupation, atmosphere, or food-may increase risk for melanoma, and this area warrants further study. Host factors that are well known to be important are the numbers and types of nevi and the skin phenotype. Genetic factors are classified as high-penetrant genes, moderate-risk genes, or low-risk genetic polymorphisms. Subtypes of tumors, such as BRAF-mutated tumors, have different risk factors as well as different therapies. Prevention of melanoma has been attempted using various strategies in specific subpopulations, but to date optimal interventions to reduce incidence have not emerged.

Review from JAMA Dermatology — Risk Prediction Models for Incident Primary Cutaneous Melanoma — A Systematic Review

The costs, morbidity, and mortality associated with melanoma diagnosis place a high burden on health care systems in many countries.^1-3 Diagnosis of melanoma at an earlier stage is associated with better survival,⁴ and observational studies have shown that whole-body clinical skin examination is associated with lower melanoma mortality and fewer thick (ie, higher stage) melanomas,^5-8 although the observed mortality decline in the Schleswig-Holstein region of Germany was not replicated after the introduction of population-wide screening in Germany.⁹ Apart from Germany, no country has an organized population-based melanoma screening program because of insufficient evidence that screening reduces melanoma mortality; uncertainty about possible harms, such as overdiagnosis and unnecessary biopsy^10-12; and limited evidence for cost-effectiveness.^1,13,14

It is unclear to what degree genomic and traditional (phenotypic and environmental) risk factors overlap in their prediction of melanoma risk. We evaluated the incremental contribution of common genomic variants (in pigmentation, nevus, and other pathways) and their overlap with traditional risk factors, using data from two population-based case-control studies from Australia (n = 1,035) and the United Kingdom (n = 1,460) that used the same questionnaires. Polygenic risk scores were derived from 21 gene regions associated with melanoma and odds ratios from published meta-analyses. Logistic regression models were adjusted for age, sex, center, and ancestry. Adding the polygenic risk score to a model with traditional risk factors increased the area under the receiver operating characteristic curve (AUC) by 2.3% (P = 0.003) for Australia and by 2.8% (P = 0.002) for Leeds. Gene variants in the pigmentation pathway, particularly MC1R, were responsible for most of the incremental improvement. In a cross-tabulation of polygenic by traditional tertile risk scores, 59% (Australia) and 49% (Leeds) of participants were categorized in the same (concordant) tertile. Of participants with low traditional risk, 9% (Australia) and 21% (Leeds) had high polygenic risk. Testing of genomic variants can identify people who are susceptible to melanoma despite not having a traditional phenotypic risk profile.