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Background Population-wide screening for melanoma is not cost-effective, but genetic characterization could facilitate risk stratification and targeted screening. Common Melanocortin-1 receptor (MC1R) red hair colour (RHC) variants and Microphthalmia-associated transcription factor (MITF) E318K separately confer moderate melanoma susceptibility, but their interactive effects are relatively unexplored. Objectives To evaluate whether MC1R genotypes differentially affect melanoma risk in MITF E318K+ vs. E318K– individuals. Materials and methods Melanoma status (affected or unaffected) and genotype data (MC1R and MITF E318K) were collated from research cohorts (five Australian and two European). In addition, RHC genotypes from E318K+ individuals with and without melanoma were extracted from databases (The Cancer Genome Atlas and Medical Genome Research Bank, respectively). χ 2 and logistic regression were used to evaluate RHC allele and genotype frequencies within E318K+/– cohorts depending on melanoma status. Replication analysis was conducted on 200 000 general-population exomes (UK Biobank). Results The cohort comprised 1165 MITF E318K– and 322 E318K+ individuals. In E318K– cases MC1R R and r alleles increased melanoma risk relative to wild type (wt), P < 0.001 for both. Similarly, each MC1R RHC genotype (R/R, R/r, R/wt, r/r and r/wt) increased melanoma risk relative to wt/wt (P < 0.001 for all). In E318K+ cases, R alleles increased melanoma risk relative to the wt allele [odds ratio (OR) 2.04 (95% confidence interval 1.67–2.49); P = 0.01], while the r allele risk was comparable with the wt allele [OR 0.78 (0.54–1.14) vs. 1.00, respectively]. E318K+ cases with the r/r genotype had a lower but not significant melanoma risk relative to wt/wt [OR 0.52 (0.20–1.38)]. Within the E318K+ cohort, R genotypes (R/R, R/r and R/wt) conferred a significantly higher risk compared with non-R genotypes (r/r, r/wt and wt/wt) (P < 0.001). UK Biobank data supported our findings that r did not increase melanoma risk in E318K+ individuals. Conclusions RHC alleles/genotypes modify melanoma risk differently in MITF E318K– and E318K+ individuals. Specifically, although all RHC alleles increase risk relative to wt in E318K– individuals, only MC1R R increases melanoma risk in E318K+ individuals. Importantly, in the E318K+ cohort the MC1R r allele risk is comparable with wt. These findings could inform counselling and management for MITF E318K+ individuals. [ABSTRACT FROM AUTHOR]

Summary: Background: Previous studies suggest that polygenic risk scores (PRSs) may improve melanoma risk stratification. However, there has been limited independent validation of PRS‐based risk prediction, particularly assessment of calibration (comparing predicted to observed risks). Objectives: To evaluate PRS‐based melanoma risk prediction in prospective UK and Australian cohorts with European ancestry. Methods: We analysed invasive melanoma incidence in the UK Biobank (UKB; n = 395 647, 1651 cases) and a case‐cohort nested within the Melbourne Collaborative Cohort Study (MCCS, Australia; n = 4765, 303 cases). Three PRSs were evaluated: 68 single‐nucleotide polymorphisms (SNPs) at 54 loci from a 2020 meta‐analysis (PRS68), 50 SNPs significant in the 2020 meta‐analysis excluding UKB (PRS50) and 45 SNPs at 21 loci known in 2018 (PRS45). Ten‐year melanoma risks were calculated from population‐level cancer registry data by age group and sex, with and without PRS adjustment. Results: Predicted absolute melanoma risks based on age and sex alone underestimated melanoma incidence in the UKB [ratio of expected/observed cases: E/O = 0·65, 95% confidence interval (CI) 0·62–0·68] and MCCS (E/O = 0·63, 95% CI 0·56–0·72). For UKB, calibration was improved by PRS adjustment, with PRS50‐adjusted risks E/O = 0·91, 95% CI 0·87–0·95. The discriminative ability for PRS68‐ and PRS50‐adjusted absolute risks was higher than for risks based on age and sex alone (Δ area under the curve 0·07–0·10, P < 0·0001), and higher than for PRS45‐adjusted risks (Δ area under the curve 0·02–0·04, P < 0·001). Conclusions: A PRS derived from a larger, more diverse meta‐analysis improves risk prediction compared with an earlier PRS, and might help tailor melanoma prevention and early detection strategies to different risk levels. Recalibration of absolute risks may be necessary for application to specific populations. [ABSTRACT FROM AUTHOR]

The representation of volatile organic compound (VOC) deposition and oxidation mechanisms in the context of secondary organic aerosol (SOA) formation are developed in the United Kingdom Chemistry and Aerosol (UKCA) chemistry–climate model. Impacts of these developments on both the global SOA budget and model agreement with observations are quantified. Firstly, global model simulations were performed with varying VOC dry deposition and wet deposition fluxes. Including VOC dry deposition reduces the global annual-total SOA production rate by 2 %–32 %, with the range reflecting uncertainties in surface resistances. Including VOC wet deposition reduces the global annual-total SOA production rate by 15 % and is relatively insensitive to changes in effective Henry's law coefficients. Without precursor deposition, simulated SOA concentrations are lower than observed with a normalised mean bias (NMB) of -51 %. Hence, including SOA precursor deposition worsens model agreement with observations even further (NMB =-66 %). Secondly, for the anthropogenic and biomass burning VOC precursors of SOA (VOC ANT/BB), model simulations were performed by (a) varying the parent hydrocarbon reactivity, (b) varying the number of reaction intermediates, and (c) accounting for differences in volatility between oxidation products from various pathways. These changes were compared to a scheme where VOC ANT/BB adopts the reactivity of a monoterpene (α -pinene), and is oxidised in a single-step mechanism with a fixed SOA yield. By using the chemical reactivity of either benzene, toluene, or naphthalene for VOC ANT/BB , the global annual-total VOC ANT/BB oxidation rate changes by -3 %, -31 %, or -66 %, respectively, compared to when using α -pinene. Increasing the number of reaction intermediates, by introducing a peroxy radical (RO2), slightly slows the rate of SOA formation, but has no impact on the global annual-total SOA production rate. However, RO2 undergoes competitive oxidation reactions, forming products with substantially different volatilities. Accounting for the differences in product volatility between RO2 oxidation pathways increases the global SOA production rate by 153 % compared to using a single SOA yield. Overall, for relatively reactive compounds such as toluene and naphthalene, the reduction in reactivity for VOC ANT/BB oxidation is outweighed by accounting for the difference in volatility of RO2 products, leading to a net increase in the global annual-total SOA production rate of 85 % and 145 %, respectively, and improvements in model agreement (NMB of -46 % and 56 %, respectively). However, for benzene, the reduction in VOC ANT/BB oxidation is not outweighed by accounting for the difference in SOA yield pathways, leading to a small change in the global annual-total SOA production rate of -3 %, and a slight worsening of model agreement with observations (NMB =-77 %). These results highlight that variations in both VOC deposition and oxidation mechanisms contribute to substantial uncertainties in the global SOA budget and model agreement with observations. [ABSTRACT FROM AUTHOR]

Background To improve melanoma early detection, tools to predict personal risk based on genetic information (polygenic risk scores, PRS) have been developed, but require external validation. Methods We analysed invasive melanoma incidence in UK Biobank (UKB; n = 395,647; 1,651 cases) and the Melbourne Collaborative Cohort Study (MCCS, Australia; n = 4,765; 303 cases). Three PRS were evaluated: 68 genetic variants (SNPs) at 54 loci from a 2020 meta-analysis (PRS68); 50 SNPs significant in the 2020 meta-analysis excluding UKB (PRS50); 45 SNPs at 21 loci known pre-2020 (PRS45). 10-year melanoma risks were calculated from population-level cancer registry data by age group and sex, with and without PRS adjustment. Results All PRS were strongly associated with melanoma incidence, including after adjustment for age, sex, ethnicity, and ease of tanning. Predicted absolute melanoma risks based on age and sex alone underestimated melanoma incidence in UKB (ratio expected/observed cases E/O=0.65, 95% confidence interval 0.62-0.68) and MCCS (E/O=0.65, 0.57-0.73). For UKB, this was reduced by PRS-adjustment, e.g. PRS50-adjusted risks E/O=0.91 (0.87-0.95). Discriminative ability for PRS68- and PRS50-adjusted absolute risks was higher than for risks based on age and sex alone (deltaAUC 0.07-0.1, p < 0.0001), and higher than for PRS45-adjusted risks (deltaAUC 0.02-0.04, p < 0.001). Conclusions A PRS derived from a larger, more diverse meta-analysis improves melanoma risk prediction compared to an earlier PRS. Re-calibration of absolute risks may be necessary for application to specific populations. Key messages A genetic score can improve prediction of melanoma risk and might help tailor melanoma prevention and early detection strategies to different risk levels. [ABSTRACT FROM AUTHOR]

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., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Importance: Identifying individuals at high risk of melanoma can optimize primary and secondary prevention strategies., Objective: To develop and externally validate a risk prediction model for incident first-primary cutaneous melanoma using self-assessed risk factors., Design, Setting, and Participants: We used unconditional logistic regression to develop a multivariable risk prediction model. Relative risk estimates from the model were combined with Australian melanoma incidence and competing mortality rates to obtain absolute risk estimates. A risk prediction model was developed using the Australian Melanoma Family Study (629 cases and 535 controls) and externally validated using 4 independent population-based studies: the Western Australia Melanoma Study (511 case-control pairs), Leeds Melanoma Case-Control Study (960 cases and 513 controls), Epigene-QSkin Study (44 544, of which 766 with melanoma), and Swedish Women's Lifestyle and Health Cohort Study (49 259 women, of which 273 had melanoma)., Main Outcomes and Measures: We validated model performance internally and externally by assessing discrimination using the area under the receiver operating curve (AUC). Additionally, using the Swedish Women's Lifestyle and Health Cohort Study, we assessed model calibration and clinical usefulness., Results: The risk prediction model included hair color, nevus density, first-degree family history of melanoma, previous nonmelanoma skin cancer, and lifetime sunbed use. On internal validation, the AUC was 0.70 (95% CI, 0.67-0.73). On external validation, the AUC was 0.66 (95% CI, 0.63-0.69) in the Western Australia Melanoma Study, 0.67 (95% CI, 0.65-0.70) in the Leeds Melanoma Case-Control Study, 0.64 (95% CI, 0.62-0.66) in the Epigene-QSkin Study, and 0.63 (95% CI, 0.60-0.67) in the Swedish Women's Lifestyle and Health Cohort Study. Model calibration showed close agreement between predicted and observed numbers of incident melanomas across all deciles of predicted risk. In the external validation setting, there was higher net benefit when using the risk prediction model to classify individuals as high risk compared with classifying all individuals as high risk., Conclusions and Relevance: The melanoma risk prediction model performs well and may be useful in prevention interventions reliant on a risk assessment using self-assessed risk factors.