The Future of Precision Prevention for Advanced Melanoma

Precision prevention of advanced melanoma is fast becoming a realistic prospect, with personalized, holistic risk stratification allowing patients to be directed to an appropriate level of surveillance, ranging from skin self-examinations to regular total body photography with sequential digital dermoscopic imaging. This approach aims to address both underdiagnosis (a missed or delayed melanoma diagnosis) and overdiagnosis (the diagnosis and treatment of indolent lesions that would not have caused a problem). Holistic risk stratification considers several types of melanoma risk factors: clinical phenotype, comprehensive imaging-based phenotype, familial and polygenic risks. Artificial intelligence computer-aided diagnostics combines these risk factors to produce a personalized risk score, and can also assist in assessing the digital and molecular markers of individual lesions. However, to ensure uptake and efficient use of AI systems, researchers will need to carefully consider how best to incorporate privacy and standardization requirements, and above all address consumer trust concerns.

HLA-DRB1 ∗ 16:01 and HLA-DQB1 ∗ 05:02 Alleles Influence the Susceptibility and Progression of Cutaneous Malignant Melanoma

Background. The influence of HLA class I and II loci on the susceptibility to melanoma remains an area of intense debate. This study aimed to examine whether the HLA system was related to melanoma susceptibility and prognosis in a southern Spanish population. Methods. In this study, HLA class I and class II genotyping were performed using polymerase chain reaction sequence-specific oligonucleotides (PCR-SSO) in 237 Spanish melanoma patients and 636 ethnically matched controls. Data were analyzed according to the clinical characteristics of the defined subgroups. Results. Compared to the control group, DRB1 ∗ 16:01 (4% vs. 1.3%, p = 0.001 , Pc = 0.035, OR = 3.28) and DQB1 ∗ 05:02 (4.9% vs. 2%, p = 0.001 , Pc = 0.017, OR = 2.54) were positivity associated with the susceptibility to melanoma. Both DRB1 ∗ 16:01 (5.4% vs. 1.3%, p = 0.001 , Pc = 0.035, OR = 4.46) and DQB1 ∗ 05:02 (6.5% vs. 2%, p = 0.001 , Pc = 0.017, OR = 3.44) also showed a positive correlation with Breslow thickness >1.5 mm, most notably at an early age of diagnosis (≤58 years), DRB1 ∗ 16:01 (4.2% vs. 1.3%, p = 0.001 , Pc = 0.035, OR = 3.41) and DQB1 ∗ 05:02 (5.4% vs. 2%, p = 0.002 , Pc = 0.034, OR = 2.86). Conclusion. These findings established HLA-DRB1 ∗ 16:01 and HLA-DQB1 ∗ 05:02 loci as melanoma risk factors in the southern Spanish population.

Parkinson’s Disease as a Risk Factor for Melanoma: A Review

Objective: To review the literature and place into a quantified context the relationship of Parkinson’s disease diagnosis to a subsequent diagnosis of malignant melanoma, and to briefly explore potential molecular associations between the two diseases. Methods: The Medline database was queried with terms related to Parkinson’s disease (PD) and malignant melanoma, with use of Boolean operator AND to identify studies involving both diseases. Studies were divided into primary and meta-analyses, with exclusive evaluation of those quantifying risk of malignant melanoma after an established diagnosis of Parkinson’s disease. Critical studies were identified using Medline searches to identify established quantified risk metrics between classic melanoma risk factors and subsequent development of malignant melanoma. Results: Twelve primary studies and three meta-analyses were evaluated and their risk metrices tabulated. Three studies offered estimated risk of development of malignant melanoma in patients with classic melanoma risk factors. These metrices were also tabulated and compared with the metrices established by the twelve primary studies. This demonstrated a similarity in overall risk of developing malignant melanoma in a patient with a diagnosis of Parkinson’s disease as compared to a patient with classical melanoma risk factors. Limitations: Relatively few studies identified specifically quantified the classic risk factors for melanoma, and relatively few studies specifically quantified the degree of risk for developing melanoma after an established Parkinson’s disease diagnosis. Conclusion: It is wise to consider the presence of Parkinson’s disease in a patient as one factor when clinicians decide on the appropriateness of regular full body screening examinations.

1036Independent evaluation of melanoma polygenic risk scores in UK and Australian prospective cohorts

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.

Autoimmune Alleles at the Major Histocompatibility Locus Modify Melanoma Susceptibility

Autoimmunity and cancer represent two different aspects of immune dysfunction. Autoimmunity is characterized by breakdowns in immune self-tolerance, while impaired immune surveillance can allow for tumorigenesis. The class I major histocompatibility complex (MHC-I), which displays derivatives of the cellular peptidome for immune surveillance by CD8+ T cells, serves as a common genetic link between these conditions. As melanoma-specific CD8+ T-cells have been shown to target melanocyte-specific peptide antigens more often than melanoma-specific antigens, we investigated whether vitiligo and psoriasis predisposing MHC-I alleles conferred a melanoma protective effect. In individuals with cutaneous melanoma from both The Cancer Genome Atlas (N = 451) and an independent validation cohort (N = 586), MHC-I autoimmune allele carriers are significantly associated with a later age of melanoma diagnosis. MHC-I autoimmune allele contributions to melanoma risk are not captured by current polygenic risk scores (PRS) and incorporation of autoimmune MHC allele presence can improve relative risk stratification. Mechanisms of autoimmune protection were neither associated with improved melanoma-driver mutation association nor improved gene-level conserved antigen presentation relative to common alleles (population frequency > 1%). However, autoimmune alleles showed a marked affinity relative to common alleles for particular windows of melanocyte conserved antigens suggesting a potential relationship between antigen processing, binding, and cell-surface presentation. Overall, this study presents evidence that MHC-I autoimmune risk alleles modulate melanoma risk currently unaccounted for by current PRS.