We updated a field synopsis of genetic associations of cutaneous melanoma (CM) by systematically retrieving and combining data from all studies in the field published as of August 31, 2013. Data were available from 197 studies, which included 83,343 CM cases and 187,809 controls and reported on 1,126 polymorphisms in 289 different genes. Random-effects meta-analyses of 81 eligible polymorphisms evaluated in >4 data sets confirmed 20 single-nucleotide polymorphisms across 10 loci (TYR, AFG3L1P, CDK10, MYH7B, SLC45A2, MTAP, ATM, CLPTM1L, FTO, and CASP8) that have previously been published with genome-wide significant evidence for association (P<5 × 10(-8)) with CM risk, with certain variants possibly functioning as proxies of already tagged genes. Four other loci (MITF, CCND1, MX2, and PLA2G6) were also significantly associated with 5 × 10(-8)

Many single nucleotide polymorphisms (SNPs) have been described as putative risk factors for melanoma. The aim of our study was to validate the most prominent genetic risk loci in an independent Greek melanoma case-control dataset and to assess their cumulative effect solely or combined with established phenotypic risk factors on individualized risk prediction. We genotyped 59 SNPs in 800 patients and 800 controls and tested their association with melanoma using logistic regression analyses. We constructed a weighted genetic risk score (GRSGWS) based on SNPs that showed genome-wide significant (GWS) association with melanoma in previous studies and assessed their impact on risk prediction. Fifteen independent SNPs from 12 loci were significantly associated with melanoma (P < 0.05). Risk score analysis yielded an odds ratio of 1.36 per standard deviation increase of the GRSGWS (P = 1.1 × 10(-7)). Individuals in the highest 20% of the GRSGWS had a 1.88-fold increase in melanoma risk compared with those in the middle quintile. By adding the GRSGWS to a phenotypic risk model, the C-statistic increased from 0.764 to 0.775 (P = 0.007). In summary, the GRSGWS is associated with melanoma risk and achieves a modest improvement in risk prediction when added to a phenotypic risk model.

The publicly available online database MelGene provides a comprehensive, regularly updated, collection of data from genetic association studies in cutaneous melanoma (CM), including random-effects meta-analysis results of all eligible polymorphisms. The updated database version includes data from 192 publications with information on 1114 significantly associated polymorphisms across 280 genes, along with new front-end and back-end capabilities. Various types of relationships between data are calculated and visualized as networks. We constructed 13 different networks containing the polymorphisms and the genes included in MelGene. We explored the derived network representations under the following questions: (i) are there nodes that deserve consideration regarding their network connectivity characteristics? (ii) What is the relation of either the genome-wide or nominally significant CM polymorphisms/genes with the ones highlighted by the network representation? We show that our network approach using the MelGene data reveals connections between statistically significant genes/ polymorphisms and other genes/polymorphisms acting as ‘hubs’ in the reconstructed networks. To the best of our knowledge, this is the first database containing data from a comprehensive field synopsis and systematic meta-analyses of genetic polymorphisms in CM that provides user-friendly tools for in-depth molecular network visualization and exploration. The proposed network connections highlight potentially new loci requiring further investigation of their relation to melanoma risk.Database URL: http://www.melgene.org.

The use of real-world evidence (RWE) to support international regulatory decisionmaking is reflected in the growing number of regulatory frameworks and guidelines published by Competent Authorities and international initiatives that accept real-world data (RWD) sources. RWD can be obtained from a range of sources, including electronic health/medical records, pharmacy and insurance claims, patient-reported outcomes, product and disease registries, biobanks, and observational studies. However, the availability of RWD sources depends on the processes/systems implemented by regional healthcare systems, which are limited by the potential of inconsistent data collection, heterogeneity of clinical practices, and an overall lack of standardization. As the analysis of RWD/RWE primarily evaluates association rather than causation, it is still often viewed as a supplement to, rather than a replacement of, data that derives from controlled environments, such as Randomized Controlled Trials (RCT). Despite this, RWE may still be used to support the assessment of safety and effectiveness in regulatory submissions and can facilitate regulatory decisions (including reimbursement) by providing longterm data on safety and performance that could not otherwise be collected during the limited duration of a RCT. However, available RWE frameworks reveal serious challenges to the use of RWE for the support of the assessment of safety and effectiveness, due to biases in data collection, lack of randomization, quality of data collection, and generalizability of results and endpoints. Patient privacy and the need to ensure confidentiality also hinders regulatory stakeholders from establishing and implementing concrete regulations. This is because the collection and management of RWD must be used in accordance with national, and often conflicting, laws on data protection and information governance. This article summarizes all currently available RWE frameworks and discusses potential solutions for future harmonization and cross-stakeholder collaborations. Such harmonization and collaboration will boost the integration of RWE, not only in the post-approval stages of a medicine's lifecycle but also in the development and lifelong post-market surveillance of medical devices (MDs).