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.

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.

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.

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.

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.

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.

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.

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.

Disease-Associated Risk Variants in ANRIL Are Associated with Tumor-Infiltrating Lymphocyte Presence in Primary Melanomas in the Population-Based GEM Study

BACKGROUND

Genome-wide association studies have reported that genetic variation at ANRIL (CDKN2B-AS1) is associated with risk of several chronic diseases including coronary artery disease, coronary artery calcification, myocardial infarction, and type 2 diabetes mellitus. ANRIL is located at the CDKN2A/B locus, which encodes multiple melanoma tumor suppressors. We investigated the association of these variants with melanoma prognostic characteristics.

METHODS

The Genes, Environment, and Melanoma Study enrolled 3,285 European origin participants with incident invasive primary melanoma. For each of ten disease-associated SNPs at or near ANRIL, we used linear and logistic regression modeling to estimate, respectively, the per allele mean changes in log of Breslow thickness and ORs for presence of ulceration and tumor-infiltrating lymphocytes (TIL). We also assessed effect modification by tumor NRAS/BRAF mutational status.

RESULTS

Rs518394, rs10965215, and rs564398 passed false discovery and were each associated (P ≤ 0.005) with TILs, although only rs564398 was independently associated (P = 0.0005) with TILs. Stratified by NRAS/BRAF mutational status, rs564398*A was significantly positively associated with TILs among NRAS/BRAF mutant, but not wild-type, cases. We did not find SNP associations with Breslow thickness or ulceration.

CONCLUSIONS

ANRIL rs564398 was associated with TIL presence in primary melanomas, and this association may be limited to NRAS/BRAF-mutant cases.

IMPACT

Pathways related to ANRIL variants warrant exploration in relationship to TILs in melanoma, especially given the impact of TILs on immunotherapy and survival.

Exome-Wide Pan-Cancer Analysis of Germline Variants in 8,719 Individuals Finds Little Evidence of Rare Variant Associations

BACKGROUND

Many cancer types show considerable heritability, and extensive research has been done to identify germline susceptibility variants. Linkage studies have discovered many rare high-risk variants, and genome-wide association studies (GWAS) have discovered many common low-risk variants. However, it is believed that a considerable proportion of the heritability of cancer remains unexplained by known susceptibility variants. The "rare variant hypothesis" proposes that much of the missing heritability lies in rare variants that cannot reliably be detected by linkage analysis or GWAS. Until recently, high sequencing costs have precluded extensive surveys of rare variants, but technological advances have now made it possible to analyze rare variants on a much greater scale.

OBJECTIVES

In this study, we investigated associations between rare variants and 14 cancer types.

METHODS

We ran association tests using whole-exome sequencing data from The Cancer Genome Atlas (TCGA) and validated the findings using data from the Pan-Cancer Analysis of Whole Genomes Consortium (PCAWG).

RESULTS

We identified four significant associations in TCGA, only one of which was replicated in PCAWG (BRCA1 and ovarian cancer).

CONCLUSIONS

Our results provide little evidence in favor of the rare variant hypothesis. Much larger sample sizes may be needed to detect undiscovered rare cancer variants.

658Familial aggregation of melanoma by anatomic site of occurrence

Background

Evidence is emerging that melanoma has distinct etiologic pathways and subtypes, characterized by factors like anatomic site. For example, older men have more head and neck melanomas, and younger women more leg melanomas. Family history is a strong risk factor and familial risk genes have been identified. We therefore aimed to investigate familial aggregation of melanoma from an etiologic heterogeneity perspective, to determine whether melanoma subtypes aggregate within families.

Methods

Using population-level linked health data, we examined the extent to which melanomas in first-degree relatives (FDRs) of cases shared the same body site of occurrence. FDR-pairs diagnosed with melanoma were identified using the Western Australian Cancer Registry and genealogical Family Connections System. Site was categorized as head/neck, trunk/arms, or legs. We identified 1,006 pairs of tumours from 677 family pairs and used Chi-Squared tests to evaluate familial aggregation by site. Degrees of etiologic heterogeneity of individual site-pairs were characterized by site concordance odds ratios.

Results

Familial aggregation by site was statistically significant (P = 0.01). However, only the site pairings of head/neck versus trunk/arms showed strong evidence of familial concordance (OR = 1.7, 95% CI 1.1-1.7). Trends were broadly similar in same-sex pairs.

Conclusions

Results show modest evidence of familial aggregation of melanoma by site, with overall evidence of aggregation but inconsistent patterns between sites.

Key messages

Strong etiologic differences in incidence between anatomic sites for age and sex may be more strongly influenced by non-genetic factors, such as familial patterns of sun exposure.

Validity of a method for identifying disease subtypes that are etiologically heterogeneous

A focus of cancer epidemiologic research has become the identification of risk factors that influence specific subtypes of disease, a phenomenon known as etiologic heterogeneity. In previous work we developed a novel strategy to cluster tumor markers and identify disease subtypes that differ maximally with respect to known risk factors for use in the context of case-control studies. The method relies on the premise that unsupervised k-means clustering will find candidate solutions that are closely aligned with the sought-after etiologically distinct clusters, which may not be true in the presence of clusters of tumor markers that are not related to risk of disease. In this article, we investigate in detail the ability of the method to identify the "true" clusters in the presence of clusters that are unrelated to risk factors, what we term "counterfeit" clusters. We find that our method works when the strength of structure is larger in the clusters that truly represent etiologic heterogeneity than in the counterfeit clusters, but when this condition is not met, or when there are many tumor markers that simply represent noise, the method will not find the correct solution without first performing variable selection to identify the tumor markers most strongly related to the risk factors. We illustrate the results using data from a breast cancer case-control study.

Development and Validation of a Breast Cancer Risk Prediction Model for Childhood Cancer Survivors Treated With Chest Radiation: A Report From the Childhood Cancer Survivor Study and the Dutch Hodgkin Late Effects and LATER Cohorts

PURPOSE

Women treated with chest radiation for childhood cancer have one of the highest risks of breast cancer. Models producing personalized breast cancer risk estimates applicable to this population do not exist. We sought to develop and validate a breast cancer risk prediction model for childhood cancer survivors treated with chest radiation incorporating treatment-related factors, family history, and reproductive factors.

METHODS

Analyses were based on multinational cohorts of female 5-year survivors of cancer diagnosed younger than age 21 years and treated with chest radiation. Model derivation was based on 1,120 participants in the Childhood Cancer Survivor Study diagnosed between 1970 and 1986, with median attained age 42 years (range 20-64) and 242 with breast cancer. Model validation included 1,027 participants from three cohorts, with median age 32 years (range 20-66) and 105 with breast cancer.

RESULTS

The model included current age, chest radiation field, whether chest radiation was delivered within 1 year of menarche, anthracycline exposure, age at menopause, and history of a first-degree relative with breast cancer. Ten-year risk estimates ranged from 2% to 23% for 30-year-old women (area under the curve, 0.63; 95% CI, 0.50 to 0.73) and from 5% to 34% for 40-year-old women (area under the curve, 0.67; 95% CI, 0.54 to 0.84). The highest risks were among premenopausal women older than age 40 years treated with mantle field radiation within a year of menarche who had a first-degree relative with breast cancer. It showed good calibration with an expected-to-observed ratio of the number of breast cancers of 0.92 (95% CI, 0.74 to 1.16).

CONCLUSION

Breast cancer risk varies among childhood cancer survivors treated with chest radiation. Accurate risk prediction may aid in refining surveillance, counseling, and preventive strategies in this population.

Differences in Melanoma Between Canada and New South Wales, Australia: A Population-Based Genes, Environment, and Melanoma (GEM) Study

In an effort to understand the difference between melanomas diagnosed in Australia (New South Wales) and Canada, where the incidence in New South Wales is almost three times greater than in Canada, and mortality is twice as high although survival is slightly more favorable, we had one pathologist review 1,271 melanomas from British Columbia and Ontario, Canada, to compare these to melanomas in New South Wales, Australia. We hypothesized that histopathologic characteristics might provide insight into divergent pathways to melanoma development. We found a number of differences in risk factors and tumor characteristics between the two geographic areas. There were higher mole counts and darker phenotypes in the Canadian patients, while the Australian patients had greater solar elastosis, more lentigo maligna melanomas, and more tumor infiltrating lymphocytes. We hypothesize that the differences observed may illustrate different etiologies – the cumulative exposure pathway among Australian patients and the nevus pathway among Canadian patients. This is one of the largest studies investigating the divergent pathway hypothesis and is particularly robust due to the evaluation of all lesions by one dermatopathologist.

Using the "Hidden" genome to improve classification of cancer types

It is increasingly common clinically for cancer specimens to be examined using techniques that identify somatic mutations. In principle, these mutational profiles can be used to diagnose the tissue of origin, a critical task for the 3% to 5% of tumors that have an unknown primary site. Diagnosis of primary site is also critical for screening tests that employ circulating DNA. However, most mutations observed in any new tumor are very rarely occurring mutations, and indeed the preponderance of these may never have been observed in any previous recorded tumor. To create a viable diagnostic tool we need to harness the information content in this "hidden genome" of variants for which no direct information is available. To accomplish this we propose a multilevel meta-feature regression to extract the critical information from rare variants in the training data in a way that permits us to also extract diagnostic information from any previously unobserved variants in the new tumor sample. A scalable implementation of the model is obtained by combining a high-dimensional feature screening approach with a group-lasso penalized maximum likelihood approach based on an equivalent mixed-effect representation of the multilevel model. We apply the method to the Cancer Genome Atlas whole-exome sequencing data set including 3702 tumor samples across seven common cancer sites. Results show that our multilevel approach can harness substantial diagnostic information from the hidden genome.

Association of Known Melanoma Risk Factors with Primary Melanoma of the Scalp and Neck

BACKGROUND

Scalp and neck (SN) melanoma confers a worse prognosis than melanoma of other sites but little is known about its determinants. We aimed to identify associations between SN melanoma and known risk genes, phenotypic traits, and sun exposure patterns.

METHODS

Participants were cases from the Western Australian Melanoma Health Study (n = 1,200) and the Genes, Environment, and Melanoma Study (n = 3,280). Associations between risk factors and SN melanoma, compared with truncal and arm/leg melanoma, were investigated using binomial logistic regression. Facial melanoma was also compared with the trunk and extremities, to evaluate whether associations were subregion specific, or reflective of the whole head/neck region.

RESULTS

Compared with other sites, increased odds of SN and facial melanoma were observed in older individuals [SN: OR = 1.28, 95% confidence interval (CI) = 0.92-1.80, P _trend = 0.016; Face: OR = 4.57, 95% CI = 3.34-6.35, P _trend < 0.001] and those carrying IRF4-rs12203592*T (SN: OR = 1.35, 95% CI = 1.12-1.63, P _trend = 0.002; Face: OR = 1.29, 95% CI = 1.10-1.50, P _trend = 0.001). Decreased odds were observed for females (SN: OR = 0.49, 95% CI = 0.37-0.64, P < 0.001; Face: OR = 0.66, 95% CI = 0.53-0.82, P < 0.001) and the presence of nevi (SN: OR = 0.66, 95% CI = 0.49-0.89, P = 0.006; Face: OR = 0.65, 95% CI = 0.52-0.83, P < 0.001).

CONCLUSIONS

Differences observed between SN melanoma and other sites were also observed for facial melanoma. Factors previously associated with the broader head and neck region, notably older age, may be driven by the facial subregion. A novel finding was the association of IRF4-rs12203592 with both SN and facial melanoma.

IMPACT

Understanding the epidemiology of site-specific melanoma will enable tailored strategies for risk factor reduction and site-specific screening campaigns.

Optimized variable selection via repeated data splitting

Model selection in high-dimensional settings has received substantial attention in recent years, however, similar advancements in the low-dimensional setting have been lacking. In this article, we introduce a new variable selection procedure for low to moderate scale regressions (n>p). This method repeatedly splits the data into two sets, one for estimation and one for validation, to obtain an empirically optimized threshold which is then used to screen for variables to include in the final model. In an extensive simulation study, we show that the proposed variable selection technique enjoys superior performance compared with candidate methods (backward elimination via repeated data splitting, univariate screening at 0.05 level, adaptive LASSO, SCAD), being amongst those with the lowest inclusion of noisy predictors while having the highest power to detect the correct model and being unaffected by correlations among the predictors. We illustrate the methods by applying them to a cohort of patients undergoing hepatectomy at our institution.

Testing clonal relatedness of two tumors from the same patient based on their mutational profiles: update of the Clonality R package

SUMMARY

The Clonality R package is a practical tool to assess the clonal relatedness of two tumors from the same patient. We have previously presented its functionality for testing tumors using loss of heterozygosity data or copy number arrays. Since then somatic mutation data have been more widely available through next generation sequencing and we have developed new methodology for comparing the tumors' mutational profiles. We thus extended the package to include these two new methods for comparing tumors as well as the mutational frequency estimation from external data required for their implementation. The first method is a likelihood ratio test that is readily available on a patient by patient basis. The second method employs a random-effects model to estimate both the population and individual probabilities of clonal relatedness from a group of patients with pairs of tumors. The package is available on Bioconductor.

AVAILABILITY AND IMPLEMENTATION

Bioconductor (http://bioconductor.org/packages/release/bioc/html/Clonality.html).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A risk prediction model for the development of subsequent primary melanoma in a population-based cohort

BACKGROUND

Guidelines for follow-up of patients with melanoma are based on limited evidence.

OBJECTIVES

To guide skin surveillance, we developed a risk prediction model for subsequent primary melanomas, using demographic, phenotypical, histopathological, sun exposure and genomic risk factors.

METHODS

Using Cox regression frailty models, we analysed data for 2613 primary melanomas from 1266 patients recruited to the population-based Genes, Environment and Melanoma study in New South Wales, Australia, with a median of 14 years' follow-up via the cancer registry. Discrimination and calibration were assessed.

RESULTS

The median time to diagnosis of a subsequent primary melanoma decreased with each new primary melanoma. The final model included 12 risk factors. Harrell's C-statistic was 0·73 [95% confidence interval (CI) 0·68-0·77], 0·65 (95% CI 0·62-0·68) and 0·65 (95% CI 0·61-0·69) for predicting second, third and fourth primary melanomas, respectively. The risk of a subsequent primary melanoma was 4·75 times higher (95% CI 3·87-5·82) for the highest vs. the lowest quintile of the risk score. The mean absolute risk of a subsequent primary melanoma within 5 years was 8·0 ± SD 4.1% after the first primary melanoma, and 46·8 ± 15·0% after the second, but varied substantially by risk score.

CONCLUSIONS

The risk of developing a subsequent primary melanoma varies considerably between individuals and is particularly high for those with two or more primary melanomas. The risk prediction model and its associated nomograms enable estimation of the absolute risk of subsequent primary melanoma, on the basis of on an individual's risk factors, and can be used to tailor surveillance intensity, communicate risk and provide patient education. What's already known about this topic? Current guidelines for the frequency and length of follow-up to detect new primary melanomas in patients with one or more previous primary melanomas are based on limited evidence. People with one or more primary melanomas have, on average, a higher risk of developing another primary invasive melanoma, compared with the general population, but an accurate way of estimating individual risk is needed. What does this study add? We provide a comprehensive risk prediction model for subsequent primary melanomas, using data from 1266 participants with melanoma (2613 primary melanomas), over a median 14 years' follow-up. The model includes 12 risk factors comprising demographic, phenotypical, histopathological and genomic factors, and sun exposure. It enables estimation of the absolute risk of subsequent primary melanomas, and can be used to tailor surveillance intensity, communicate individual risk and provide patient education.

Testing tumors from different anatomic sites for clonal relatedness using somatic mutation data

A common task for the cancer pathologist is to determine, in a patient suffering from cancer, whether a new tumor in a distinct anatomic site from the primary is an independent occurrence of cancer or a metastasis. As mutational profiling of tumors becomes more widespread in routine clinical practice, this diagnostic task can be greatly enhanced by comparing mutational profiles of the tumors to determine if they are sufficiently similar to conclude that the tumors are clonally related, that is, one is a metastasis of the other. We present here a likelihood ratio test for clonal relatedness in this setting and provide evidence of its validity. The test is unusual in that there are two possible alternative hypotheses, representing the two anatomic sites from which the single clonal cell could have initially emerged. Although evidence for clonal relatedness is largely provided by the presence of exact mutational matches in the two tumors, we show that it is possible to observe data where the test is statistically significant even when no matches are observed. This can occur when the mutational profile of one of the tumors is closely aligned with the anatomic site of the other tumor, suggesting indirectly that the tumor originated in that other site. We exhibit examples of this phenomenon and recommend a strategy for interpreting the results of these tests in practice.

In Defense of P Values

Recently, a controversy has erupted regarding the use of statistical significance tests and the associated P values. Prominent academic statisticians have recommended that the use of statistical tests be discouraged or not used at all. This has naturally led to a lot of confusion among research investigators about the support in the academic statistical community for statistical methods in general. In fact, the controversy surrounding the use of P values has a long history. Critics of P values argue that their use encourages bad scientific practice, leading to the publication of far more false-positive and false-negative findings than the methodology would imply. The thesis of this commentary is that the problem is really human nature, the natural proclivity of scientists to believe their own theories and present data in the most favorable light. This is strongly encouraged by a celebrity culture that is fueled by academic institutions, the scientific journals, and the media. The importance of the truth-seeking tradition of the scientific method needs to be reinforced, and this is being helped by current initiatives to improve transparency in science and to encourage reproducible and replicable research. Statistical testing, used correctly, has an important and valuable place in the scientific tradition.

An EM algorithm to improve the estimation of the probability of clonal relatedness of pairs of tumors in cancer patients

Background

We previously introduced a random-effects model to analyze a set of patients, each of which has two distinct tumors. The goal is to estimate the proportion of patients for which one of the tumors is a metastasis of the other, i.e. where the tumors are clonally related. Matches of mutations within a tumor pair provide the evidence for clonal relatedness. In this article, using simulations, we compare two estimation approaches that we considered for our model: use of a constrained quasi-Newton algorithm to maximize the likelihood conditional on the random effect, and an Expectation-Maximization algorithm where we further condition the random-effect distribution on the data.

Results

In some specific settings, especially with sparse information, the estimation of the parameter of interest is at the boundary a non-negligible number of times using the first approach, while the EM algorithm gives more satisfactory estimates. This is of considerable importance for our application, since an estimate of either 0 or 1 for the proportion of cases that are clonal leads to individual probabilities being 0 or 1 in settings where the evidence is clearly not sufficient for such definitive probability estimates.

Conclusions

The EM algorithm is a preferable approach for our clonality random-effect model. It is now the method implemented in our R package Clonality, making available an easy and fast way to estimate this model on a range of applications.

Evidence for Etiologic Subtypes of Breast Cancer in the Carolina Breast Cancer Study

BACKGROUND

Distinctions in the etiology of triple-negative versus luminal breast cancer have become well established using immunohistochemical surrogates [notably estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2)]. However, it is unclear whether established immunohistochemical subtypes are the sole or definitive means of etiologically subdividing breast cancers.

METHODS

We evaluated clinical biomarkers and tumor suppressor p53 with risk factor data from cases and controls in the Carolina Breast Cancer Study, a population-based study of incident breast cancers. For each individual marker and combinations of markers, we calculated an aggregate measure to distinguish the etiologic heterogeneity of different classification schema. To compare schema, we estimated subtype-specific case-control odds ratios for individual risk factors and fit age-at-incidence curves with two-component mixture models. We also evaluated subtype concordance of metachronous contralateral breast tumors in the California Cancer Registry.

RESULTS

ER was the biomarker that individually explained the greatest variability in risk factor profiles. However, further subdivision by p53 significantly increased the degree of etiologic heterogeneity. Age at diagnosis, nulliparity, and race were heterogeneously associated with ER/p53 subtypes. The ER-/p53+ subtype exhibited a similar risk factor profile and age-at-incidence distribution to the triple-negative subtype.

CONCLUSIONS

Clinical marker-based intrinsic subtypes have established value, yet other schema may also yield important etiologic insights.

IMPACT

Novel environmental or genetic risk factors may be identifiable by considering different etiologic schema, including cross-classification based on ER/p53.

MC1R variants in childhood and adolescent melanoma: a retrospective pooled analysis of a multicentre cohort

BACKGROUND

Germline variants in the melanocortin 1 receptor gene (MC1R) might increase the risk of childhood and adolescent melanoma, but a clear conclusion is challenging because of the low number of studies and cases. We assessed the association of MC1R variants with childhood and adolescent melanoma in a large study comparing the prevalence of MC1R variants in child or adolescent patients with melanoma to that in adult patients with melanoma and in healthy adult controls.

METHODS

In this retrospective pooled analysis, we used the M-SKIP Project, the Italian Melanoma Intergroup, and other European groups (with participants from Australia, Canada, France, Greece, Italy, the Netherlands, Serbia, Spain, Sweden, Turkey, and the USA) to assemble an international multicentre cohort. We gathered phenotypic and genetic data from children or adolescents diagnosed with sporadic single-primary cutaneous melanoma at age 20 years or younger, adult patients with sporadic single-primary cutaneous melanoma diagnosed at age 35 years or older, and healthy adult individuals as controls. We calculated odds ratios (ORs) for childhood and adolescent melanoma associated with MC1R variants by multivariable logistic regression. Subgroup analysis was done for children aged 18 or younger and 14 years or younger.

FINDINGS

We analysed data from 233 young patients, 932 adult patients, and 932 healthy adult controls. Children and adolescents had higher odds of carrying MC1R r variants than did adult patients (OR 1·54, 95% CI 1·02-2·33), including when analysis was restricted to patients aged 18 years or younger (1·80, 1·06-3·07). All investigated variants, except Arg160Trp, tended, to varying degrees, to have higher frequencies in young patients than in adult patients, with significantly higher frequencies found for Val60Leu (OR 1·60, 95% CI 1·05-2·44; p=0·04) and Asp294His (2·15, 1·05-4·40; p=0·04). Compared with those of healthy controls, young patients with melanoma had significantly higher frequencies of any MC1R variants.

INTERPRETATION

Our pooled analysis of MC1R genetic data of young patients with melanoma showed that MC1R r variants were more prevalent in childhood and adolescent melanoma than in adult melanoma, especially in patients aged 18 years or younger. Our findings support the role of MC1R in childhood and adolescent melanoma susceptibility, with a potential clinical relevance for developing early melanoma detection and preventive strategies.

FUNDING

SPD-Pilot/Project-Award-2015; AIRC-MFAG-11831.

Inherited Genetic Variants Associated with Melanoma BRAF/NRAS Subtypes

BRAF and NRAS mutations arise early in melanoma development, but their associations with low-penetrance melanoma susceptibility loci remain unknown. In the Genes, Environment and Melanoma Study, 1,223 European-origin participants had their incident invasive primary melanomas screened for BRAF/NRAS mutations and germline DNA genotyped for 47 single-nucleotide polymorphisms identified as low-penetrant melanoma-risk variants. We used multinomial logistic regression to simultaneously examine each single-nucleotide polymorphism's relationship to BRAF V600E, BRAF V600K, BRAF other, and NRAS+ relative to BRAF-/NRAS- melanoma adjusted for study features. IRF4 rs12203592*T was associated with BRAF V600E (odds ratio [OR] = 0.59, 95% confidence interval [CI] = 0.43-0.79) and V600K (OR = 0.65, 95% CI = 0.41-1.03), but not BRAF other or NRAS+ melanoma. A global test of etiologic heterogeneity (Pglobal = 0.001) passed false discovery (Pglobal = 0.0026). PLA2G6 rs132985*T was associated with BRAF V600E (OR = 1.32, 95% CI = 1.05-1.67) and BRAF other (OR = 1.82, 95% CI = 1.11-2.98), but not BRAF V600K or NRAS+ melanoma. The test for etiologic heterogeneity (Pglobal) was 0.005. The IRF4 rs12203592 associations were slightly attenuated after adjustment for melanoma-risk phenotypes. The PLA2G6 rs132985 associations were independent of phenotypes. IRF4 and PLA2G6 inherited genotypes may influence melanoma BRAF/NRAS subtype development.

Contralateral breast cancers: Independent cancers or metastases?

A cancer in the contralateral breast in a woman with a previous or synchronous breast cancer is typically considered to be an independent primary tumor. Emerging evidence suggests that in a small subset of these cases the second tumor represents a metastasis. We sought to investigate the issue using massively parallel sequencing targeting 254 genes recurrently mutated in breast cancer. We examined the tumor archives at Memorial Sloan Kettering Cancer Center for the period 1995-2006 to identify cases of contralateral breast cancer where surgery for both tumors was performed at the Center. We report results from 49 patients successfully analyzed by a targeted massively parallel sequencing assay. Somatic mutations and copy number alterations were defined by state-of-the-art algorithms. Clonal relatedness was evaluated by statistical tests specifically designed for this purpose. We found evidence that the tumors in contralateral breasts were clonally related in three cases (6%) on the basis of matching mutations at codons where somatic mutations are rare. Clinical data and the presence of similar patterns of gene copy number alterations were consistent with metastasis for all three cases. In three additional cases, there was a solitary matching mutation at a common PIK3CA locus. The results suggest that a subset of contralateral breast cancers represent metastases rather than independent primary tumors. Massively parallel sequencing analysis can provide important evidence to clarify the diagnosis. However, given the inter-tumor mutational heterogeneity in breast cancer, sufficiently large gene panels need to be employed to define clonality convincingly in all cases.

Cautionary Note on Using Cross-Validation for Molecular Classification

Purpose Reproducibility of scientific experimentation has become a major concern because of the perception that many published biomedical studies cannot be replicated. In this article, we draw attention to the connection between inflated overoptimistic findings and the use of cross-validation for error estimation in molecular classification studies. We show that, in the absence of careful design to prevent artifacts caused by systematic differences in the processing of specimens, established tools such as cross-validation can lead to a spurious estimate of the error rate in the overoptimistic direction, regardless of the use of data normalization as an effort to remove these artifacts. Methods We demonstrated this important yet overlooked complication of cross-validation using a unique pair of data sets on the same set of tumor samples. One data set was collected with uniform handling to prevent handling effects; the other was collected without uniform handling and exhibited handling effects. The paired data sets were used to estimate the biologic effects of the samples and the handling effects of the arrays in the latter data set, which were then used to simulate data using virtual rehybridization following various array-to-sample assignment schemes. Results Our study showed that (1) cross-validation tended to underestimate the error rate when the data possessed confounding handling effects; (2) depending on the relative amount of handling effects, normalization may further worsen the underestimation of the error rate; and (3) balanced assignment of arrays to comparison groups allowed cross-validation to provide an unbiased error estimate. Conclusion Our study demonstrates the benefits of balanced array assignment for reproducible molecular classification and calls for caution on the routine use of data normalization and cross-validation in such analysis.

The interaction between vitamin D receptor polymorphisms and sun exposure around time of diagnosis influences melanoma survival

Evidence on the relationship between the vitamin D pathway and outcomes in melanoma is growing, although it is not always clear. We investigated the impact of measured levels of sun exposure at diagnosis on associations of vitamin D receptor gene (VDR) polymorphisms and melanoma death in 3336 incident primary melanoma cases. Interactions between six SNPs and a common 3'-end haplotype were significant (p < .05). These SNPs, and a haplotype, had a statistically significant association with survival among subjects exposed to high UVB in multivariable regression models and exerted their effect in the opposite direction among those with low UVB. SNPs rs1544410/BsmI and rs731236/TaqI remained significant after adjustment for multiple testing. These results suggest that the association between VDR and melanoma-specific survival is modified by sun exposure around diagnosis, and require validation in an independent study. Whether the observed effects are dependent or independent of vitamin D activation remains to be determined.

Association of Incident Amelanotic Melanoma With Phenotypic Characteristics, MC1R Status, and Prior Amelanotic Melanoma

Importance

We previously reported that survival is poorer from histopathologically amelanotic than pigmented melanoma because of more advanced stage at diagnosis. Identifying patients at risk of amelanotic melanoma might enable earlier diagnosis and improved survival; however, the phenotypic characteristics and underlying genetics associated with amelanotic melanoma are unknown.

Objective

To determine whether phenotypic characteristics, carriage of MC1R variants, and history of amelanotic melanoma are associated with histopathologically amelanotic melanoma.

Design, Setting, and Participants

The Genes, Environment, and Melanoma (GEM) study is an international cohort study that enrolled patients with incident primary cutaneous melanomas from population-based and hospital-based cancer registries (1998 to 2003). The GEM participants included here were 2387 patients with data for phenotypes, MC1R genotype, and primary melanomas scored for histopathologic pigmentation. Of these 2387 patients with incident melanomas scored for pigmentation, 527 had prior primary melanomas also scored for pigmentation.

Main Outcomes and Measures

Associations of phenotypic characteristics (freckles, nevi, phenotypic index) and MC1R status with incident amelanotic melanomas were evaluated using logistic regression models adjusted for age, sex, study center, and primary status (single or multiple primary melanoma); odds ratios (ORs) and 95% CIs are reported. Association of histopathologic pigmentation between incident and prior melanomas was analyzed using an exact logistic regression model.

Results

This study included 2387 patients (1065 women, 1322 men; mean [SD] age at diagnosis, 58.3 [16.1] years) and 2917 primary melanomas. In a multivariable model including phenotypic characteristics, absence of back nevi, presence of many freckles, and a sun-sensitive phenotypic index were independently associated with amelanotic melanoma. Carriage of MC1R variants was associated with amelanotic melanoma but lost statistical significance in a model with phenotype. Further, patients with incident primary amelanotic melanomas were more likely to have had a prior primary amelanotic melanoma (OR, 4.62; 95% CI, 1.25-14.13) than those with incident primary pigmented melanomas.

Conclusions and Relevance

Absence of back nevi, presence of many freckles, a sun-sensitive phenotypic index, and prior amelanotic melanoma increase odds for development of amelanotic melanoma. An increased index of suspicion for melanoma in presenting nonpigmented lesions and more careful examination for signs of amelanotic melanoma during periodic skin examination in patients at increased odds of amelanotic melanoma might lead to earlier diagnosis and improved survival.

A comparison of statistical methods for the study of etiologic heterogeneity

Cancer epidemiologic research has traditionally been guided by the premise that certain diseases share an underlying etiology, or cause. However, with the rise of molecular and genomic profiling, attention has increasingly focused on identifying subtypes of disease. As subtypes are identified, it is natural to ask the question of whether they share a common etiology or in fact arise from distinct sets of risk factors. In this context, epidemiologic questions of interest include (1) whether a risk factor of interest has the same effect across all subtypes of disease and (2) whether risk factor effects differ across levels of each individual tumor marker of which the subtypes are comprised. A number of statistical models have been proposed to address these questions. In an effort to determine the similarities and differences among the proposed methods, and to identify any advantages or disadvantages, we use a simplified data example to elucidate the interpretation of model parameters and available hypothesis tests, and we perform a simulation study to assess bias in effect size, type I error, and power. The results show that when the number of tumor markers is small enough that the cross-classification of markers can be evaluated in the traditional polytomous logistic regression framework, then the statistical properties are at least as good as the more complex modeling approaches that have been proposed. The potential advantage of more complex methods is in the ability to accommodate multiple tumor markers in a model of reduced parametric dimension.

Estimating the probability of clonal relatedness of pairs of tumors in cancer patients

Next generation sequencing panels are being used increasingly in cancer research to study tumor evolution. A specific statistical challenge is to compare the mutational profiles in different tumors from a patient to determine the strength of evidence that the tumors are clonally related, that is, derived from a single, founder clonal cell. The presence of identical mutations in each tumor provides evidence of clonal relatedness, although the strength of evidence from a match is related to how commonly the mutation is seen in the tumor type under investigation. This evidence must be weighed against the evidence in favor of independent tumors from non-matching mutations. In this article, we frame this challenge in the context of diagnosis using a novel random effects model. In this way, by analyzing a set of tumor pairs, we can estimate the proportion of cases that are clonally related in the sample as well as the individual diagnostic probabilities for each case. The method is illustrated using data from a study to determine the clonal relationship of lobular carcinoma in situ with subsequent invasive breast cancers, where each tumor in the pair was subjected to whole exome sequencing. The statistical properties of the method are evaluated using simulations, demonstrating that the key model parameters are estimated with only modest bias in small samples in most configurations.

Defining Cancer Subtypes With Distinctive Etiologic Profiles: An Application to the Epidemiology of Melanoma

We showcase a novel analytic strategy to identify sub-types of cancer that possess distinctive causal factors, i.e. sub-types that are "etiologically" distinct. The method involves the integrated analysis of two types of study design: an incident series of cases with double primary cancers with detailed information on tumor characteristics that can be used to define the sub-types; a case-series of incident cases with information on known risk factors that can be used to investigate the specific risk factors that distinguish the sub-types. The methods are applied to a rich melanoma dataset with detailed information on pathologic tumor factors, and comprehensive information on known genetic and environmental risk factors for melanoma. Identification of the optimal sub-typing solution is accomplished using a novel clustering analysis that seeks to maximize a measure that characterizes the distinctiveness of the distributions of risk factors across the sub-types and that is a function of the correlations of tumor factors in the case-specific tumor pairs. This analysis is challenged by the presence of extensive missing data. If successful, studies of this nature offer the opportunity for efficient study design to identify unknown risk factors whose effects are concentrated in defined sub-types.