Aggregation of melanoma tumour site within Western Australian families.

BACKGROUND: Evidence is emerging that melanoma has distinct aetiologic pathways and subtypes, characterized by factors like anatomic site of the tumour. To explore genetic influences on anatomic subtypes, we examined the extent to which melanomas in first-degree relatives shared the same body site of occurrence. METHODS: Population-level linked data was used to identify the study population of over 1.5 million individuals born in Western Australia between 1945 and 2014, and their first-degree relatives. There were 1009 pairs of invasive tumours from 677 family pairs, each categorised by anatomic site. Greater than expected representation of site-concordant pairs would suggest the presence of genetic factors that predispose individuals to site-specific melanoma. RESULTS: Comparing observed versus expected totals, we observed a modest increase in site concordance for invasive head/neck and truncal tumours (P=0.02). A corresponding analysis including in situ tumours showed a similar concordance (P=0.05). No further evidence of concordance was observed when stratified by sex. CONCLUSION: In conclusion, modest evidence of aggregation was observed but with inconsistent patterns between sites. Results suggest that further investigation into the familial aggregation of melanoma by tumour site is warranted, with the inclusion of genetic data in order to disentangle the relative contributions of genetic and environmental factors.

Identifying somatic fingerprints of cancers defined by germline and environmental risk factors.

Numerous studies over the past generation have identified germline variants that increase specific cancer risks. Simultaneously, a revolution in sequencing technology has permitted high-throughput annotations of somatic genomes characterizing individual tumors. However, examining the relationship between germline variants and somatic alteration patterns is hugely challenged by the large numbers of variants in a typical tumor, the rarity of most individual variants, and the heterogeneity of tumor somatic fingerprints. In this article, we propose statistical methodology that frames the investigation of germline-somatic relationships in an interpretable manner. The method uses meta-features embodying biological contexts of individual somatic alterations to implicitly group rare mutations. Our team has used this technique previously through a multilevel regression model to diagnose with high accuracy tumor site of origin. Herein, we further leverage topic models from computational linguistics to achieve interpretable lower-dimensional embeddings of the meta-features. We demonstrate how the method can identify distinctive somatic profiles linked to specific germline variants or environmental risk factors. We illustrate the method using The Cancer Genome Atlas whole-exome sequencing data to characterize somatic tumor fingerprints in breast cancer patients with germline BRCA1/2 mutations and in head and neck cancer patients exposed to human papillomavirus.

Topical hidden genome: discovering latent cancer mutational topics using a Bayesian multilevel context-learning approach.

Inferring the cancer-type specificities of ultra-rare, genome-wide somatic mutations is an open problem. Traditional statistical methods cannot handle such data due to their ultra-high dimensionality and extreme data sparsity. To harness information in rare mutations, we have recently proposed a formal multilevel multilogistic "hidden genome" model. Through its hierarchical layers, the model condenses information in ultra-rare mutations through meta-features embodying mutation contexts to characterize cancer types. Consistent, scalable point estimation of the model can incorporate 10s of millions of variants across thousands of tumors and permit impressive prediction and attribution. However, principled statistical inference is infeasible due to the volume, correlation, and noninterpretability of mutation contexts. In this paper, we propose a novel framework that leverages topic models from computational linguistics to effectuate dimension reduction of mutation contexts producing interpretable, decorrelated meta-feature topics. We propose an efficient MCMC algorithm for implementation that permits rigorous full Bayesian inference at a scale that is orders of magnitude beyond the capability of existing out-of-the-box inferential high-dimensional multi-class regression methods and software. Applying our model to the Pan Cancer Analysis of Whole Genomes dataset reveals interesting biological insights including somatic mutational topics associated with UV exposure in skin cancer, aging in colorectal cancer, and strong influence of epigenome organization in liver cancer. Under cross-validation, our model demonstrates highly competitive predictive performance against blackbox methods of random forest and deep learning.

Adaptation of a mutual exclusivity framework to identify driver mutations within oncogenic pathways.

Distinguishing genomic alterations in cancer-associated genes that have functional impact on tumor growth and disease progression from the ones that are passengers and confer no fitness advantage have important clinical implications. Evidence-based methods for nominating drivers are limited by existing knowledge on the oncogenic effects and therapeutic benefits of specific variants from clinical trials or experimental settings. As clinical sequencing becomes a mainstay of patient care, applying computational methods to mine the rapidly growing clinical genomic data holds promise in uncovering functional candidates beyond the existing knowledge base and expanding the patient population that could potentially benefit from genetically targeted therapies. We propose a statistical and computational method (MAGPIE) that builds on a likelihood approach leveraging the mutual exclusivity pattern within an oncogenic pathway for identifying probabilistically both the specific genes within a pathway and the individual mutations within such genes that are truly the drivers. Alterations in a cancer-associated gene are assumed to be a mixture of driver and passenger mutations with the passenger rates modeled in relationship to tumor mutational burden. We use simulations to study the operating characteristics of the method and assess false-positive and false-negative rates in driver nomination. When applied to a large study of primary melanomas, the method accurately identifies the known driver genes within the RTK-RAS pathway and nominates several rare variants as prime candidates for functional validation. A comprehensive evaluation of MAGPIE against existing tools has also been conducted leveraging the Cancer Genome Atlas data.

Adaptation of a Mutual Exclusivity Framework to Identify Driver Mutations within Biological Pathways.

Distinguishing genomic alterations in cancer genes that have functional impact on tumor growth and disease progression from the ones that are passengers and confer no fitness advantage has important clinical implications. Evidence-based methods for nominating drivers are limited by existing knowledge on the oncogenic effects and therapeutic benefits of specific variants from clinical trials or experimental settings. As clinical sequencing becomes a mainstay of patient care, applying computational methods to mine the rapidly growing clinical genomic data holds promise in uncovering novel functional candidates beyond the existing knowledge-base and expanding the patient population that could potentially benefit from genetically targeted therapies. We propose a statistical and computational method (MAGPIE) that builds on a likelihood approach leveraging the mutual exclusivity pattern within an oncogenic pathway for identifying probabilistically both the specific genes within a pathway and the individual mutations within such genes that are truly the drivers. Alterations in a cancer gene are assumed to be a mixture of driver and passenger mutations with the passenger rates modeled in relationship to tumor mutational burden. A limited memory BFGS algorithm is used to facilitate large scale optimization. We use simulations to study the operating characteristics of the method and assess false positive and false negative rates in driver nomination. When applied to a large study of primary melanomas the method accurately identified the known driver genes within the RTK-RAS pathway and nominated a number of rare variants with previously unknown biological and clinical relevance as prime candidates for functional validation.

Association of functional, inherited vitamin D-binding protein variants with melanoma-specific death.

BACKGROUND: It is unclear whether genetic variants affecting vitamin D metabolism are associated with melanoma prognosis. Two functional missense variants in the vitamin D-binding protein gene (GC), rs7041 and rs4588, determine 3 common haplotypes, Gc1s, Gc1f, and Gc2, of which Gc1f may be associated with decreased all-cause death among melanoma patients based on results of a prior study, but the association of Gc1f with melanoma-specific death is unclear. METHODS: We investigated the association of the Gc1s, Gc1f, and Gc2 haplotypes with melanoma-specific and all-cause death among 4490 individuals with incident, invasive primary melanoma in 2 population-based studies using multivariable Cox-proportional hazards regression. RESULTS: In the pooled analysis of both datasets, the patients with the Gc1f haplotype had a 37% lower risk of melanoma-specific death than the patients without Gc1f (hazard ratio [HR] = 0.63, 95% confidence interval [CI] = 0.47 to 0.83, P = .001), with adjustments for age, sex, study center, first- or higher-order primary melanoma, tumor site, pigmentary phenotypes, and Breslow thickness. Associations were similar in both studies. In pooled analyses stratified by Breslow thickness, the corresponding melanoma-specific death HRs for those patients with the Gc1f haplotype compared with those without Gc1f were 0.89 (95% CI = 0.63 to 1.27) among participants with tumor Breslow thickness equal to or less than 2.0 mm and 0.40 (95% CI = 0.25 to 0.63) among participants with tumor Breslow thickness greater than 2.0 mm (Pinteraction = .003). CONCLUSIONS: Our findings suggest that individuals with the GC haplotype Gc1f may have a lower risk of dying from melanoma-specifically from thicker, higher-risk melanoma-than individuals without this Gc1f haplotype.

Sex-Specific Associations of MDM2 and MDM4 Variants with Risk of Multiple Primary Melanomas and Melanoma Survival in Non-Hispanic Whites.

MDM2-SNP309 (rs2279744), a common genetic modifier of cancer incidence in Li-Fraumeni syndrome, modifies risk, age of onset, or prognosis in a variety of cancers. Melanoma incidence and outcomes vary by sex, and although SNP309 exerts an effect on the estrogen receptor, no consensus exists on its effect on melanoma. MDM2 and MDM4 restrain p53-mediated tumor suppression, independently or together. We investigated SNP309, an a priori MDM4-rs4245739, and two coinherited variants, in a population-based cohort of 3663 primary incident melanomas. Per-allele and per-haplotype (MDM2_SNP309-SNP285; MDM4_rs4245739-rs1563828) odds ratios (OR) for multiple-melanoma were estimated with logistic regression models. Hazard ratios (HR) for melanoma death were estimated with Cox proportional hazards models. In analyses adjusted for covariates, females carrying MDM4-rs4245739*C were more likely to develop multiple melanomas (ORper-allele = 1.25, 95% CI 1.03-1.51, and Ptrend = 0.03), while MDM2-rs2279744*G was inversely associated with melanoma-death (HRper-allele = 0.63, 95% CI 0.42-0.95, and Ptrend = 0.03). We identified 16 coinherited expression quantitative loci that control the expression of MDM2, MDM4, and other genes in the skin, brain, and lungs. Our results suggest that MDM4/MDM2 variants are associated with the development of subsequent primaries and with the death of melanoma in a sex-dependent manner. Further investigations of the complex MDM2/MDM4 motif, and its contribution to the tumor microenvironment and observed associations, are warranted.

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19.

Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte-macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).

Subsampling based variable selection for generalized linear models.

A novel variable selection method for low-dimensional generalized linear models is introduced. The new approach called AIC OPTimization via STABility Selection (OPT-STABS) repeatedly subsamples the data, minimizes Akaike's Information Criterion (AIC) over a sequence of nested models for each subsample, and includes in the final model those predictors selected in the minimum AIC model in a large fraction of the subsamples. New methods are also introduced to establish an optimal variable selection cutoff over repeated subsamples. An extensive simulation study examining a variety of proposec variable selection methods shows that, although no single method uniformly outperforms the others in all the scenarios considered, OPT-STABS is consistently among the best-performing methods in most settings while it performs competitively for the rest. This is in contrast to other candidate methods which either have poor performance across the board or exhibit good performance in some settings, but very poor in others. In addition, the asymptotic properties of the OPT-STABS estimator are derived, and its root-n consistency and asymptotic normality are proved. The methods are applied to two datasets involving logistic and Poisson regressions.

InterMEL: An international biorepository and clinical database to uncover predictors of survival in early-stage melanoma.

INTRODUCTION: We are conducting a multicenter study to identify classifiers predictive of disease-specific survival in patients with primary melanomas. Here we delineate the unique aspects, challenges, and best practices for optimizing a study of generally small-sized pigmented tumor samples including primary melanomas of at least 1.05mm from AJTCC TNM stage IIA-IIID patients. We also evaluated tissue-derived predictors of extracted nucleic acids' quality and success in downstream testing. This ongoing study will target 1,000 melanomas within the international InterMEL consortium. METHODS: Following a pre-established protocol, participating centers ship formalin-fixed paraffin embedded (FFPE) tissue sections to Memorial Sloan Kettering Cancer Center for the centralized handling, dermatopathology review and histology-guided coextraction of RNA and DNA. Samples are distributed for evaluation of somatic mutations using next gen sequencing (NGS) with the MSK-IMPACTTM assay, methylation-profiling (Infinium MethylationEPIC arrays), and miRNA expression (Nanostring nCounter Human v3 miRNA Expression Assay). RESULTS: Sufficient material was obtained for screening of miRNA expression in 683/685 (99%) eligible melanomas, methylation in 467 (68%), and somatic mutations in 560 (82%). In 446/685 (65%) cases, aliquots of RNA/DNA were sufficient for testing with all three platforms. Among samples evaluated by the time of this analysis, the mean NGS coverage was 249x, 59 (18.6%) samples had coverage below 100x, and 41/414 (10%) failed methylation QC due to low intensity probes or insufficient Meta-Mixed Interquartile (BMIQ)- and single sample (ss)- Noob normalizations. Six of 683 RNAs (1%) failed Nanostring QC due to the low proportion of probes above the minimum threshold. Age of the FFPE tissue blocks (p<0.001) and time elapsed from sectioning to co-extraction (p = 0.002) were associated with methylation screening failures. Melanin reduced the ability to amplify fragments of 200bp or greater (absent/lightly pigmented vs heavily pigmented, p<0.003). Conversely, heavily pigmented tumors rendered greater amounts of RNA (p<0.001), and of RNA above 200 nucleotides (p<0.001). CONCLUSION: Our experience with many archival tissues demonstrates that with careful management of tissue processing and quality control it is possible to conduct multi-omic studies in a complex multi-institutional setting for investigations involving minute quantities of FFPE tumors, as in studies of early-stage melanoma. The study describes, for the first time, the optimal strategy for obtaining archival and limited tumor tissue, the characteristics of the nucleic acids co-extracted from a unique cell lysate, and success rate in downstream applications. In addition, our findings provide an estimate of the anticipated attrition that will guide other large multicenter research and consortia.

Pathway Alterations in Stage II/III Primary Melanoma.

PURPOSE: Genomic classification of melanoma has thus far focused on the mutational status of BRAF, NRAS, and NF1. The clinical utility of this classification remains limited, and the landscape of alterations in other oncogenic signaling pathways is underexplored. METHODS: Using primary samples from the InterMEL study, a retrospective cohort of cases with specimens collected from an international consortium with participating institutions throughout the United States and Australia, with oversampling of cases who ultimately died of melanoma, we examined mutual exclusivity and co-occurrence of genomic alterations in 495 stage II/III primary melanomas across 11 cancer pathways. Somatic mutation and copy number alterations were analyzed from next-generation sequencing using a clinical sequencing panel. RESULTS: Mutations in the RTK-RAS pathway were observed in 81% of cases. Other frequently occurring pathways were TP53 (31%), Cell Cycle (30%), and PI3K (18%). These frequencies are generally lower than was observed in The Cancer Genome Atlas, where the specimens analyzed were predominantly obtained from metastases. Overall, 81% of the cases had at least one targetable mutation. The RTK-RAS pathway was the only pathway that demonstrated strong and statistically significant mutual exclusivity. However, this strong mutual exclusivity signal was evident only for the three common genes in the pathway (BRAF, NRAS, and NF1). Analysis of co-occurrence of different pathways exhibited no positive significant trends. However, interestingly, a high frequency of cases with none of these pathways represented was observed, 8.4% of cases versus 4.0% expected (P < .001). A higher frequency of RTK-RAS singletons (with no other pathway alteration) was observed compared with The Cancer Genome Atlas. Clonality analyses suggest strongly that both the cell cycle and RTK-RAS pathways represent early events in melanogenesis. CONCLUSION: Our results confirm the dominance of mutations in the RTK-RAS pathway. The presence of many mutations in several well-known, actionable pathways suggests potential avenues for targeted therapy in these early-stage cases.

Predicting Cancer Risk from Germline Whole-exome Sequencing Data Using a Novel Context-based Variant Aggregation Approach.

Many studies have shown that the distributions of the genomic, nucleotide, and epigenetic contexts of somatic variants in tumors are informative of cancer etiology. Recently, a new direction of research has focused on extracting signals from the contexts of germline variants and evidence has emerged that patterns defined by these factors are associated with oncogenic pathways, histologic subtypes, and prognosis. It remains an open question whether aggregating germline variants using meta-features capturing their genomic, nucleotide, and epigenetic contexts can improve cancer risk prediction. This aggregation approach can potentially increase statistical power for detecting signals from rare variants, which have been hypothesized to be a major source of the missing heritability of cancer. Using germline whole-exome sequencing data from the UK Biobank, we developed risk models for 10 cancer types using known risk variants (cancer-associated SNPs and pathogenic variants in known cancer predisposition genes) as well as models that additionally include the meta-features. The meta-features did not improve the prediction accuracy of models based on known risk variants. It is possible that expanding the approach to whole-genome sequencing can lead to gains in prediction accuracy. Significance: There is evidence that cancer is partly caused by rare genetic variants that have not yet been identified. We investigate this issue using novel statistical methods and data from the UK Biobank.

Methylation of nonessential genes in cutaneous melanoma - Rule Out hypothesis.

Differential methylation plays an important role in melanoma development and is associated with survival, progression and response to treatment. However, the mechanisms by which methylation promotes melanoma development are poorly understood. The traditional explanation of selective advantage provided by differential methylation postulates that hypermethylation of regulatory 5'-cytosine-phosphate-guanine-3' dinucleotides (CpGs) downregulates the expression of tumor suppressor genes and therefore promotes tumorigenesis. We believe that other (not necessarily alternative) explanations of the selective advantages of methylation are also possible. Here, we hypothesize that melanoma cells use methylation to shut down transcription of nonessential genes - those not required for cell survival and proliferation. Suppression of nonessential genes allows tumor cells to be more efficient in terms of energy and resource usage, providing them with a selective advantage over the tumor cells that transcribe and subsequently translate genes they do not need. We named the hypothesis the Rule Out (RO) hypothesis. The RO hypothesis predicts higher methylation of CpGs located in regulatory regions (CpG islands) of nonessential genes. It also predicts the higher methylation of regulatory CpGs linked to nonessential genes in melanomas compared to nevi and lower expression of nonessential genes in malignant (derived from melanoma) versus normal (derived from nonaffected skin) melanocytes. The analyses conducted using in-house and publicly available data found that all predictions derived from the RO hypothesis hold, providing observational support for the hypothesis.

Adapting an Undergraduate Summer Internship to a Virtual Format: Implementing a Mentored Cancer Research Experience to Meet Rising Demand for Flexible Learning Environments.

To meet the rising demand for flexible learning in data-driven health research, we adapted an in-person undergraduate research program (Quantitative Sciences Undergraduate Research Experience (QSURE)) to an all-virtual framework in summer 2020 and 2021. We used Web-conferencing and remote computing to implement virtual hands-on research training within a comprehensive cancer center. We designed the program to achieve research and career development goals: students completed faculty-mentored quantitative research projects and received education in the responsible conduct of research and practical skills, such as oral and written presentation. We assessed virtual program efficacy using pre- and post-program quantitative and qualitative student feedback. Eighteen students participated (nine each year); they reported high satisfaction with the virtual format. Compared with baseline, students reported improved perceived competence in quantitative skills and research knowledge post-program; these improvements were comparable to the in-person program. Defined benchmarks and consistent communication (with mentors, program directors, other students) were crucial to students' success; however, students noted challenges in building camaraderie online. With adequate resources, Web-based technology can be leveraged as an effective format for hands-on quantitative research training. Our framework can be tailored to an institution's needs, particularly those for which available resources better align with a virtual research program.

Clinical Trial Participation Among Older Adult Medicare Fee-for-Service Beneficiaries With Cancer.

Importance: Clinical trials play a critical role in the development of novel cancer therapies, and precise estimates of the frequency with which older adult patients with cancer participate in clinical trials are lacking. Objective: To estimate the proportion of older adult Medicare Fee-for-Service (FFS) beneficiaries with cancer who participate in interventional cancer clinical trials, using a novel population-based methodology. Design, Setting, and Participants: In this retrospective cohort study evaluating clinical trial participation among older adult patients with cancer from January 1, 2014, through June 30, 2020, claims data from Medicare FFS were linked with the ClinicalTrials.gov to determine trial participation through the unique National Clinical Trial (NCT) identifier. The proportion of patients with newly diagnosed or newly recurrent cancer in 2015 participating in an interventional clinical trial and receiving active cancer treatment from January 2014 to June 2020 was estimated. Data analysis was performed from November 18, 2020, to November 1, 2021. Exposures: Patients with cancer aged 65 years or older with Medicare FFS insurance, with and without active cancer treatment. Main Outcomes and Measures: Enrollment in clinical trials among all patients with cancer 65 years and older and among patients receiving active cancer treatments as defined by the presence of at least 1 NCT identifier corresponding to an interventional cancer clinical trial in Medicare claims. Results: Among 1 150 978 patients (mean [SD] age, 75.7 [8.4] years; 49.9% men and 50.1% women) with newly diagnosed or newly recurrent cancer in 2015, 12 028 (1.0%) patients had a billing claim with an NCT identifier indicating enrollment in an interventional cancer clinical trial between January 2014 and June 2020. In a subset of 429 343 patients with active cancer treatment, 8360 (1.9%) were enrolled in 1 or more interventional trials. Patients enrolled in a trial tended to be younger, male, a race other than Black, and residing in zip codes with high median incomes. Conclusions and Relevance: Findings of this cohort study show that clinical trial enrollment among older adult patients with cancer remains low, with only 1.0% to 1.9% of patients with newly diagnosed or recurrent cancer in 2015 participating in an interventional cancer clinical trial as measured by the presence of NCT identifiers in Medicare claims. These data provide a contemporary estimate of trial enrollment, persistent disparities in trial participation, and only limited progress in trial access over the past 2 decades.

Landscape of mutations in early stage primary cutaneous melanoma: An InterMEL study.

It is unclear why some melanomas aggressively metastasize while others remain indolent. Available studies employing multi-omic profiling of melanomas are based on large primary or metastatic tumors. We examine the genomic landscape of early-stage melanomas diagnosed prior to the modern era of immunological treatments. Untreated cases with Stage II/III cutaneous melanoma were identified from institutions throughout the United States, Australia and Spain. FFPE tumor sections were profiled for mutation, methylation and microRNAs. Preliminary results from mutation profiling and clinical pathologic correlates show the distribution of four driver mutation sub-types: 31% BRAF; 18% NRAS; 21% NF1; 26% Triple Wild Type. BRAF mutant tumors had younger age at diagnosis, more associated nevi, more tumor infiltrating lymphocytes, and fewer thick tumors although at generally more advanced stage. NF1 mutant tumors were frequent on the head/neck in older patients with severe solar elastosis, thicker tumors but in earlier stages. Triple Wild Type tumors were predominantly male, frequently on the leg, with more perineural invasion. Mutations in TERT, TP53, CDKN2A and ARID2 were observed often, with TP53 mutations occurring particularly frequently in the NF1 sub-type. The InterMEL study will provide the most extensive multi-omic profiling of early-stage melanoma to date. Initial results demonstrate a nuanced understanding of the mutational and clinicopathological landscape of these early-stage tumors.

Validation of a Population-Based Data Source to Examine National Cancer Clinical Trial Participation.

Importance: The Centers for Medicare & Medicaid Services requires health care organizations to report the National Clinical Trial (NCT) identifier on claims for items and services related to clinical trials that qualify for coverage. This same NCT identifier is used to identify clinical trials in the ClinicalTrials.gov registry. If linked, this information could facilitate population-based analyses of clinical trial participation and outcomes. Objective: To evaluate the validity of a linkage between fee-for-service (FFS) Medicare claims and ClinicalTrials.gov through the NCT identifier for patients with cancer enrolled in clinical trials. Design, Setting, and Participants: This cohort study included 2 complementary retrospective analyses for a validation assessment. First, billing data from 3 health care institutions were used to estimate the missingness of the NCT identifier in claims by calculating the proportion of known participants in cancer clinical trials with no NCT identifier on any submitted Medicare claims. Second, the Surveillance Epidemiology and End Results-Medicare data set, which includes a subset of all FFS Medicare beneficiaries for whom health insurance claims are linked with cancer registry data, was used to identify adult patients diagnosed with cancer between 2006 and 2015 with an NCT identifier in claims corresponding to an interventional cancer clinical trial. To estimate the accuracy of the NCT identifier when present, the proportion of NCT identifiers that corresponded to trials that were aligned with the patients' known primary or secondary diagnoses was calculated. Data were analyzed from March 2020 to March 2021. Exposures: An NCT identifier present in Medicare claims. Main Outcomes and Measures: The main outcome was participating in a clinical trial relevant to patient's cancer diagnosis. Results: A total of 1 171 816 patients were included in analyses. Across the 3 participating institutions, there were 5061 Medicare patients enrolled in a clinical trial, including 3797 patients (75.0%) with an NCT identifier on at least 1 billing claim that matched the clinical trial on which the patient was participating. Among 1 171 816 SEER-Medicare patients, 29 138 patients (2.5%) had at least 1 claim with a value entered in the NCT identifier field corresponding to 32 950 unique patient-NCT identifier pairs. There were 26 694 pairs (81.0%) with an NCT identifier corresponding to a clinical trial registered in ClinicalTrials.gov, of which 10 170 pairs (38.1%) were interventional cancer clinical trials. Among these, 9805 pairs (96.4%) were considered appropriate. Conclusions and Relevance: In this cohort study, this data linkage provided a novel data source to study clinical trial enrollment patterns among Medicare patients with cancer on a population level. The presence of the NCT identifiers in claims for Medicare patients participating in clinical trials is likely to improve over time with increasing adherence with the Centers for Medicare & Medicaid Services mandate.

Association of Melanoma-Risk Variants with Primary Melanoma Tumor Prognostic Characteristics and Melanoma-Specific Survival in the GEM Study.

Genome-wide association studies (GWAS) and candidate pathway studies have identified low-penetrant genetic variants associated with cutaneous melanoma. We investigated the association of melanoma-risk variants with primary melanoma tumor prognostic characteristics and melanoma-specific survival. The Genes, Environment, and Melanoma Study enrolled 3285 European origin participants with incident invasive primary melanoma. For each of 47 melanoma-risk single nucleotide polymorphisms (SNPs), we used linear and logistic regression modeling to estimate, respectively, the per allele mean changes in log of Breslow thickness and odds ratios for presence of ulceration, mitoses, and tumor-infiltrating lymphocytes (TILs). We also used Cox proportional hazards regression modeling to estimate the per allele hazard ratios for melanoma-specific survival. Passing the false discovery threshold (p = 0.0026) were associations of IRF4 rs12203592 and CCND1 rs1485993 with log of Breslow thickness, and association of TERT rs2242652 with presence of mitoses. IRF4 rs12203592 also had nominal associations (p < 0.05) with presence of mitoses and melanoma-specific survival, as well as a borderline association (p = 0.07) with ulceration. CCND1 rs1485993 also had a borderline association with presence of mitoses (p = 0.06). MX2 rs45430 had nominal associations with log of Breslow thickness, presence of mitoses, and melanoma-specific survival. Our study indicates that further research investigating the associations of these genetic variants with underlying biologic pathways related to tumor progression is warranted.