Introduction
Cutaneous melanoma (CM) is the third most common skin cancer and is responsible for over 1,300 deaths in Australia annually [
1] and more than 7,000 deaths in the United States of America (USA) [
2]. While survival rates have been improving since 2013, likely due to advances in immunotherapies and BRAF-targeted therapies, management of CM remains a major public health burden, with an annual cost of over AUD 200 million in Australia and USD 24 billion in the US [
3,
4].
CM-susceptibility is driven by host factors including skin pigmentation and number of naevi, as well as environmental factors, most importantly exposure to ultraviolet radiation [
5‐
9]. Germline genetic factors can influence the risk of developing CM through modification of these host risk factors, and other biological pathways; genome‐wide association studies (GWAS) have identified over 50 CM-susceptibility loci [
10].
Although there are well known prognostic factors for melanoma-specific survival (MSS) including primary tumour thickness, ulceration, mitotic rate, melanoma type, anatomical site and the stage of the tumour at diagnosis [
11,
12], the role of host genetic factors in MSS is not well understood. Death of a relative from CM is associated with poorer MSS, raising the possibility that germline genetic factors influence survival [
13]. Higher naevus count has been associated with improved survival [
14]. Naevus count is strongly influenced by germline genetics [
15,
16], and is the strongest risk factor for the development of melanoma [
17], suggesting germline genetic risk for CM may also impact survival. Telomere length is another biological pathway to high genetic CM-susceptibility [
18] and may also influence MSS [
19].
A powerful approach to test whether germline genetic risk for a given disease or trait (e.g. risk for CM) influences another trait (e.g. MSS) is to combine individual genetic effects in a polygenic risk score (PRS). Death from all causes has been associated with the joint effect of PRSs associated with risk of a range of diseases (e.g. coronary artery disease, pancreatic cancer, and lung cancer) or associated with mortality risk factors (e.g. cholesterol, sleep duration) [
20], suggesting that germline risk for development of a disease can help predict outcomes. However, it is not known whether a genetic predisposition to CM influences melanoma outcomes.
To explore these two questions, we first aimed to identify germline genetic factors that influence MSS by performing a large-scale GWAS of MSS. Following this we assessed whether a PRS for CM-susceptibility (referred to as PRS_susceptibility) was associated with MSS.
Discussion
In this study, we performed the largest GWAS for MSS to date using data from Australia and the United Kingdom and potentially have identified two independent, novel, genome-wide significant (P < 5 × 10–8) loci for MSS at 1q42.13 and 7p14.1. While the two loci did not formally replicate in an independent cohort, the confidence intervals (particularly for rs75682113) in the replication set overlap the estimate from the discovery cohorts. Confirmation of these two loci will require replication in larger cohorts. rs75682113 is particularly promising as it was genome-wide significant (P < 5 × 10–8) in our meta-analysis of the discovery and replication samples.
In addition, we report evidence that increased genetic susceptibility for CM, as measured by a one SD increase in a PRS_susceptibility, was significantly associated with improved MSS. However, caution is required as the result was primarily driven by a strong association in the MIA cohort. Genetic susceptibility to CM is primarily driven by loci in the pigmentation and naevus count pathways [
44]. HRs for PRS_susceptibility and MSS were slightly attenuated (but still with a significant association) when we removed SNPs in either pathway. In turn PRS designed specifically for these traits were also associated (though not significantly for naevus count) with MSS. In addition, the PRS for telomere length (another pathway to both CM susceptibility and survival) was not significantly associated with MSS in our sensitivity analysis. These pathway-analysis results suggest that if genetic propensity to CM is associated with improved survival it is not simply due to pigmentation, nevus count or telomere length.
However, this study suggests that if there is a true association, its magnitude may differ across populations, presumably due to environmental factors such as high UV (e.g. in Australia) and other effects. Firstly, the MIA and UKB meta-analysis results did not replicate in the LMC. Secondly, the high heterogeneity metrics (e.g. I
2) indicate that the effect sizes may not be consistent across the three studies, with a very strong result in the MIA cohort (a high UV setting) and weaker associations in the UK samples (Table
1 and Additional file
2: Table S2). Although the fixed-effects model shows a strong statistically significant association, the results are not significant for the random-effects model even when they are of a similar magnitude. The observed heterogeneity may be due to differences in recruitment, where the MIA cohort recruitment was from clinics as opposed to the population-based UKB and LMC. It is also possible that the strong inverse result in Australia is influenced by overdiagnosis for melanoma [
45]. It is estimated that 54% of all melanomas and 15% invasive melanomas in Australia are over-diagnosed [
46]. Thus, patients may be diagnosed with non-lethal melanoma and subsequently exhibit improved survival. However, recent evidence suggests that regular skin checks (which may lead to overdiagnosis for melanoma) are not associated with MSS [
47]. Since sun exposure or high UV exposure is associated with improved MSS [
48,
49], it is also possible that differences in high or long-term sun and ultraviolet-radiation exposure in Australia are in part responsible for the heterogeneity.
Analyses of outcomes such as survival necessitate the inclusion of people based on having the index disease, which can introduce an index event bias [
39]. This can potentially lead to spurious associations between disease risk factors (such as SNPs associated with risk of disease) with survival. The application of a recent method to identify and adjust for this bias in our data did not meaningfully change the results, similar to what has been observed for previous studies [
40].
In a more detailed analysis in the MIA cohort, our study suggests that this inverse association is consistent even after further adjusting for (and testing for interaction with) strong predictors of MSS like tumour stage and primary tumour thickness at diagnosis. The stratified analysis shows that the association is not modified by primary tumour thickness or stage. Thus, if replicated in additional cohorts, a CM-susceptibility PRS is potentially an independent prognostic factor for MSS.
To our knowledge, while no prior study has examined the association of a CM susceptibility PRS and survival outcome, similar inverse relationships have been found in other cancers e.g. higher breast cancer PRSs and better breast cancer prognosis/characteristics [
50,
51]. Also, a follicular lymphoma PRS was associated with improved overall survival among women in a population in the USA [
52]. BRCA1/2 mutations which increase breast cancer risk were associated with better overall survival among triple-negative breast cancer women [
53]. A CAD PRS was inversely associated with all-cause mortality (OR = 0.91; 95% CI = 0.85–0.98), and ischaemic stroke (OR = 0.78; 95% CI = 0.67–0.90) in CAD patients [
40].
The mechanisms underlying this inverse association are unclear. Particularly for MSS, it could be that a higher genetic risk for CM leads to thin melanomas or slow-growing melanomas that are less lethal [
54,
55,
56], and respond better to treatment. However, detailed analysis in the MIA cohort showed no difference in survival for both thin and thick tumour categories. In addition, after excluding the initial two years of follow up, the results were consistent, suggesting there is no survival/ lead-time bias.
As noted in our study above, higher nevus counts may be associated with a lower chance of dying from melanoma [
14]. It is possible however that those with large numbers of naevi are subjected to increased screening, which may lead to overdiagnosis and greater survival relative to those with fewer moles [
57]. However, as already indicated, increased screening is not associated with MSS [
47].
Another possible mechanism could be via gene-environment interaction, where those at highest genetic risk of CM benefit more from treatment (e.g. immunotherapy), as it is the case for those at high genetic risk for coronary artery disease (CAD) and treatment benefits from PCSK9 inhibitors in the FOURIER and ODYSSEY OUTCOMES trials [
58,
59].
This study presents new insights that highlight the potential clinical utility of
PRS_susceptibility for profiling and monitoring patients for melanoma outcomes following diagnosis during the “melanoma follow-up care program” [
60,
61]. In combination with other prognostic factors, it could be used to guide patient care e.g. counselling on modification of mortality-related non-genetic behaviours and lifestyle factors, or guide the direction of patient-specific treatment to help improve survival after diagnosis. It may also be useful for the stratification of patients while recruiting into clinical trials evaluating melanoma treatment and outcomes.
Acknowledgements
This study was conducted using data from UK Biobank (application number 25331), MIA (Australia), QSkin (Australia), Leeds Melanoma Cohort (UK), 23andMe Research (USA) and GWAS summary data from the melanoma meta-analysis consortium. We gratefully acknowledge Simone Cross, as well as Susan List Armitage and the Sample Processing Facility, at QIMR Berghofer Medical Research Institute for their assistance in genotyping MIA samples, and Maria Teresa Landi at the National Cancer Institute for the genotyping them. We would like to thank the research participants and employees of 23andMe for making this work possible. The authors thank and acknowledge the valuable contributions of Hazel Burke and Valerie Jakrot, and their colleagues, in the clinical, data management, and biospecimen banking teams at Melanoma Institute Australia.
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