Abstract 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. This ongoing study will target 1,000 melanomas within the international InterMEL consortium. We also evaluated tissue-derived predictors of extracted nucleic acids’ quality and success in downstream testing. 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-IMPACT™ assay, methylation-profiling (array), and miRNA expression (Nanostring nCounter). 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.
MotivationMicrobial biomarker identification has become a key application for variable selection methods, yet real-world studies present new challenges in linking high-dimensional longitudinal microbiome data with complex time-to-event outcomes. To our knowledge, these challenges have not been sufficiently addressed in the literature. The nature of survival endpoints complicates the definition of patient groups, which is necessary for direct comparisons of longitudinal trajectories via differential abundance testing methods. Additionally, existing log-ratio lasso regression methods have not been systematically extended to Cox and Fine-Gray models, particularly with respect to incorporating longitudinal microbial features.Highlights•FLORAL correlates microbial features with continuous, binary, or survival outcomes•FLORAL utilizes longitudinal data to improve feature selection in survival models•False discoveries are controlled by FLORAL's two-step selection procedure•FLORAL identifies meaningful microbial markers in allo-HCTsSummaryIdentifying predictive biomarkers of patient outcomes from high-throughput microbiome data is of high interest, while existing computational methods do not satisfactorily account for complex survival endpoints, longitudinal samples, and taxa-specific sequencing biases. We present FLORAL, an open-source tool to perform scalable log-ratio lasso regression and microbial feature selection for continuous, binary, time-to-event, and competing risk outcomes, with compatibility for longitudinal microbiome data as time-dependent covariates. The proposed method adapts the augmented Lagrangian algorithm for a zero-sum constraint optimization problem while enabling a two-stage screening process for enhanced false-positive control. In extensive simulation and real-data analyses, FLORAL achieved consistently better false-positive control compared to other lasso-based approaches and better sensitivity over popular differential abundance testing methods for datasets with smaller sample sizes. In a survival analysis of allogeneic hematopoietic cell transplant recipients, FLORAL demonstrated considerable improvement in microbial feature selection by utilizing longitudinal microbiome data over solely using baseline microbiome data.Graphical abstract
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 (P
Cigarette smoking is a risk factor for developing nonmuscle invasive bladder cancer, and continued smoking exposure after diagnosis may increase the likelihood of adverse clinical outcomes. We compare self-reported vs biochemically verified nicotine exposure to determine the accuracy of self-report among recently diagnosed nonmuscle invasive bladder cancer patients.This cross-sectional analysis consisted of 517 nonmuscle invasive bladder cancer patients who contributed a urine or saliva specimen the same day as self-reporting their smoking, use of e-cigarettes, nicotine replacement therapy and whether they lived with a smoker. Cotinine, the primary metabolite of nicotine, was used as an objective biomarker of recent nicotine exposure.The prevalence of high, low and no cotinine exposure was 13%, 54% and 33%, respectively. Overall, 7.3% of patients (38/517) reported being a current cigarette smoker, while 13% (65/517) had cotinine levels consistent with active smoking exposure. Of these 65 patients 27 denied current smoking, resulting in a sensitivity of self-reported current smoking of 58%. After considering other sources of nicotine exposure such as e-cigarettes, cigars, nicotine replacement therapy and living with a smoker, the sensitivity was higher, at 82%. Nearly all patients with low cotinine denied any smoking-related exposure.Our findings suggest either biochemical verification with cotinine or additional questions about other sources of nicotine are needed to accurately identify nonmuscle invasive bladder cancer patients who have smoking-related exposures. Accurate classification of active and passive smoking exposure is essential to allow clinicians to advise cessation and help researchers estimate the association between post-diagnosis smoking-related exposure and nonmuscle invasive bladder cancer recurrence risk.
Using a genome-wide association study of familial melanoma pedigrees (excluding CDKN2A+ pedigrees) and genetically matched controls, Teerlink et al., 2012Teerlink C. Farnham J. Allen-Brady K. Camp N.J. Thomas A. Leachman S. et al.A unique genome-wide association analysis in extended Utah high-risk pedigrees identifies a novel melanoma risk variant on chromosome arm 10q.Hum Genet. 2012; 131: 77-85Crossref PubMed Scopus (23) Google Scholar identified three single nucleotide polymorphisms (SNPs) in close proximity and high linkage disequilibrium in the 10q25.1 region (rs17119434, rs17119461, and rs17119490) associated with melanoma (Teerlink et al., 2012Teerlink C. Farnham J. Allen-Brady K. Camp N.J. Thomas A. Leachman S. et al.A unique genome-wide association analysis in extended Utah high-risk pedigrees identifies a novel melanoma risk variant on chromosome arm 10q.Hum Genet. 2012; 131: 77-85Crossref PubMed Scopus (23) Google Scholar). These SNPs had low minor allele frequencies of 0.005 among controls utilized by Teerlink et al., 2012Teerlink C. Farnham J. Allen-Brady K. Camp N.J. Thomas A. Leachman S. et al.A unique genome-wide association analysis in extended Utah high-risk pedigrees identifies a novel melanoma risk variant on chromosome arm 10q.Hum Genet. 2012; 131: 77-85Crossref PubMed Scopus (23) Google Scholar, making detection of associations via traditional case–control methods challenging. We sought to confirm the relationship between these SNPs and melanoma utilizing the population-based Genes, Environment, and Melanoma (GEM) Study, designed to detect associations of rare genetic variants with melanoma (Begg et al., 2006Begg C.B. Hummer A.J. Mujumdar U. Armstrong B.K. Kricker A. Marrett L.D. et al.A design for cancer case-control studies using only incident cases: experience with the GEM study of melanoma.Int J Epidemiol. 2006; 35: 756-764Crossref PubMed Scopus (62) Google Scholar). The GEM Study is an international population-based case–control study of melanoma in which controls are those diagnosed with an invasive single primary melanoma (SPM) and cases are those diagnosed with multiple primary melanoma (MPM) ascertained between 1998 and 2003 in Australia, Canada, Italy, and the United States (Begg et al., 2006Begg C.B. Hummer A.J. Mujumdar U. Armstrong B.K. Kricker A. Marrett L.D. et al.A design for cancer case-control studies using only incident cases: experience with the GEM study of melanoma.Int J Epidemiol. 2006; 35: 756-764Crossref PubMed Scopus (62) Google Scholar, Millikan et al., 2006Millikan R.C. Hummer A. Begg C. Player J. de Cotret A.R. Winkel S. et al.Polymorphisms in nucleotide excision repair genes and risk of multiple primary melanoma: the Genes Environment and Melanoma Study.Carcinogenesis. 2006; 27: 610-618Crossref PubMed Scopus (86) Google Scholar). Per GEM protocol, in situ melanomas were considered to be incident melanomas if patients had prior invasive melanomas, in view of the careful surveillance that such patients would have received. The institutional review board at each participating recruitment site approved the study. Participants provided written informed consent. Patient characteristics were collected from phone interviews and self-completed questionnaires. DNA was collected from buccal brushes (Begg et al., 2005Begg C.B. Orlow I. Hummer A.J. Armstrong B.K. Kricker A. Marrett L.D. et al.Lifetime risk of melanoma in CDKN2A mutation carriers in a population-based sample.J Natl Cancer Inst. 2005; 97: 1507-1515Crossref PubMed Scopus (168) Google Scholar). SNPs were genotyped using the MassArray iPLEX platform (Agena Bioscience, San Diego, CA) with quality-control measures described previously (Orlow et al., 2016Orlow I. Reiner A.S. Thomas N.E. Roy P. Kanetsky P.A. Luo L. et al.Vitamin D receptor polymorphisms and survival in patients with cutaneous melanoma: a population-based study.Carcinogenesis. 2016; 37: 30-38Crossref PubMed Scopus (44) Google Scholar). The tumor characteristics were obtained from the diagnostic pathology reports or centralized pathology review as described previously (Kricker et al., 2013Kricker A. Armstrong B.K. Goumas C. Thomas N.E. From L. Busam K. et al.Survival for patients with single and multiple primary melanomas: the Genes, Environment, and Melanoma Study.JAMA Dermatol. 2013; 149: 921-927Crossref PubMed Scopus (27) Google Scholar, Taylor et al., 2015Taylor N.J. Busam K.J. From L. Groben P.A. Anton-Culver H. Cust A.E. et al.Inherited variation at MC1R and histological characteristics of primary melanoma.PLoS One. 2015; 10: e0119920Google Scholar). Logistic regression models estimated the odds ratios (ORs) and 95% confidence intervals (CIs) for each SNP adjusted for study features (age, sex, and study center) and an age by sex interaction. Participants with SPM who developed MPM during the ascertainment period (n = 96) were included as both cases and controls. All tests were two-sided with P < 0.05 considered significant. All data were analyzed using Stata, version 15 (StataCorp, College Station, TX). The demographics and tumor characteristics of the 2,458 controls and 1,205 cases in GEM are in Supplementary Table S1 online, excluding 12 participants not of European descent. The SNPs were in high linkage disequilibrium with each other: D′ = 0.92 for rs17119434 and rs17119461, 0.95 for rs17119434 and rs17119490, and 1.00 for rs17119461 and rs17119490. Minor allele frequencies were between 0.012 and 0.013 for cases and 0.008 and 0.009 for controls, and the genotype frequency of homozygous minor allele carriage was zero for all three SNPs. The associations of these SNPs with MPM compared to SPM are in Table 1, and reported ORs reflect the comparison of heterozygous versus homozygous major allele genotypes. SNPs rs17119461 and rs17119490 were significantly associated with MPM (P < 0.05), and rs17119434 approached significance (P < 0.08). rs17119461 had the strongest independent association with MPM (OR = 1.77, 95% CI = 1.06–2.97).Table 1Associations of genotypes from the 10q25.1 chromosomal region with multiple primary melanoma (n = 1,205) compared with single primary melanoma (n = 2458) patients in the Genes, Environment, and Melanoma Study1Limited to participants of European origin.SNP (hg19)A/aGenotype Frequency, n (%)MAFSingle Primary Melanoma (n = 2,458)Multiple Primary Melanoma (n = 1,205)Aa Versus AA, OR (95% CI)2We used logistic regression models to estimate the ORs and 95% CIs adjusted for study features (age at diagnosis [continuous], sex, and study center) and an age by sex interaction. The genotype frequency of homozygous minor allele carriage was zero for all three SNPs, and the ORs reflect the comparison of heterozygous versus homozygous major allele genotypes.P-ValueControlsCasesMissingAAAaMissingAAAars17119434 (107,505,161)A/G0.0090.01373 (3.0)2344 (95.4)41 (1.7)21 (1.7)1154 (95.8)30 (2.5)1.59 (0.94–2.67)0.08rs17119461 (107,516,352)T/C0.0090.01364 (2.0)2353 (95.7)41 (1.7)18 (1.5)1156 (95.9)31 (2.6)1.77 (1.06–2.97)0.03rs17119490 (107,522,927)G/A0.0080.01284 (3.4)2334 (95.0)40 (1.6)28 (2.3)1148 (95.3)29 (2.4)1.70 (1.00–2.88)0.05Bold type indicates the SNP with the strongest association.Abbreviations: A, major allele; a, minor allele; CI, confidence interval; hg19, human genome reference version 19; MAF, minor allele frequency; OR, odds ratio; SNP, single nucleotide polymorphism.1 Limited to participants of European origin.2 We used logistic regression models to estimate the ORs and 95% CIs adjusted for study features (age at diagnosis [continuous], sex, and study center) and an age by sex interaction. The genotype frequency of homozygous minor allele carriage was zero for all three SNPs, and the ORs reflect the comparison of heterozygous versus homozygous major allele genotypes. Open table in a new tab Bold type indicates the SNP with the strongest association. Abbreviations: A, major allele; a, minor allele; CI, confidence interval; hg19, human genome reference version 19; MAF, minor allele frequency; OR, odds ratio; SNP, single nucleotide polymorphism. To our knowledge, we provide the first confirmation of associations between SNPs in the 10q25.1 region and melanoma occurrence. The ORs (1.6–1.8) for MPM versus SPM were lower in GEM than the ORs (6.8–8.4) for familial melanoma cases versus genetically matched controls in Teerlink et al., 2012Teerlink C. Farnham J. Allen-Brady K. Camp N.J. Thomas A. Leachman S. et al.A unique genome-wide association analysis in extended Utah high-risk pedigrees identifies a novel melanoma risk variant on chromosome arm 10q.Hum Genet. 2012; 131: 77-85Crossref PubMed Scopus (23) Google Scholar. As previously found for CDKN2A mutations, melanoma risk variants in the general population can have a lower relative risk of melanoma than in a high-risk population (Begg et al., 2005Begg C.B. Orlow I. Hummer A.J. Armstrong B.K. Kricker A. Marrett L.D. et al.Lifetime risk of melanoma in CDKN2A mutation carriers in a population-based sample.J Natl Cancer Inst. 2005; 97: 1507-1515Crossref PubMed Scopus (168) Google Scholar). Teerlink et al., 2012Teerlink C. Farnham J. Allen-Brady K. Camp N.J. Thomas A. Leachman S. et al.A unique genome-wide association analysis in extended Utah high-risk pedigrees identifies a novel melanoma risk variant on chromosome arm 10q.Hum Genet. 2012; 131: 77-85Crossref PubMed Scopus (23) Google Scholar proposed a common ancestor to explain the high risk related to the 10q25.1 SNPs among their familial melanoma cases. A more plausible explanation, perhaps, is that the Teerlink et al., 2012Teerlink C. Farnham J. Allen-Brady K. Camp N.J. Thomas A. Leachman S. et al.A unique genome-wide association analysis in extended Utah high-risk pedigrees identifies a novel melanoma risk variant on chromosome arm 10q.Hum Genet. 2012; 131: 77-85Crossref PubMed Scopus (23) Google Scholar estimate is simply an overestimate, a common feature of many initial epidemiologic discoveries (Xiao and Boehnke, 2009Xiao R. Boehnke M. Quantifying and correcting for the winner's curse in genetic association studies.Genet Epidemiol. 2009; 33: 453-462Crossref PubMed Scopus (131) Google Scholar). An advantage of the GEM study is that low-frequency genetic variants are more likely to be observable in SPMs than normal controls (Begg et al., 2005Begg C.B. Orlow I. Hummer A.J. Armstrong B.K. Kricker A. Marrett L.D. et al.Lifetime risk of melanoma in CDKN2A mutation carriers in a population-based sample.J Natl Cancer Inst. 2005; 97: 1507-1515Crossref PubMed Scopus (168) Google Scholar). Further, the ORs found in the GEM study are more likely to represent the impact of these SNPs in the general population than the ORs found for multiple case families. The 10q25.1 gene region lacks genes known to be associated with malignancy. A pseudogene, YWHAZP5, is the closest at 65 kb away. SORCS3 and SORCS1 genes, both involved with vacuolar protein production, fall within 1 Mb in either direction of the SNPs. Thus, the mechanism for these SNP associations with melanoma risk remains unknown. Notably, rs17119461 and rs17119490 were found to be nominally associated with pancreatic cancer, which shares genetic risk with familial melanoma (Wu et al., 2014Wu L. Goldstein A.M. Yu K. Yang X.R. Rabe K.G. Arslan A.A. et al.Variants associated with susceptibility to pancreatic cancer and melanoma do not reciprocally affect risk.Cancer Epidemiol Biomarkers Prev. 2014; 23: 1121-1124Crossref PubMed Scopus (14) Google Scholar). Some melanoma genetic testing panels for patients meeting specific criteria include intermediate risk variants, such as MITF c.952 G>A that have a low minor allele frequency (∼0.0015) (Delaunay et al., 2017Delaunay J. Martin L. Bressac-de Paillerets B. Duru G. Ingster O. Thomas L. Improvement of genetic testing for cutaneous melanoma in countries with low to moderate incidence: the rule of 2 vs the rule of 3.JAMA Dermatol. 2017; 153: 1122-1129Crossref PubMed Scopus (6) Google Scholar). Thus, if validated in additional studies, rs17119461 may be a potential candidate for genetic testing in populations at high risk for melanoma. Further, additional studies investigating the mechanism for the 10q25.1 SNP associations with melanoma risk are warranted. KJB has received minor royalties from editing a textbook with Elsevier. The remaining authors state no conflict of interest. This work was supported by the National Cancer Institute (P01CA206980 to NET and MB, R01CA112243 to NET, U01CA83180 and R01CA112524 to MB, R01CA098438 to CBB, R03CA125829 and R03CA173806 to IO, P30CA016086 (to Henry Shelton Earp), P30CA014089 (to SBG), and P30CA008748 (to Craig B. Thompson); National Institute of Environmental Health Sciences (P30ES010126 to James A. Swenberg). AEC was supported by Career Development Fellowships from the National Health and Medical Research Council is (1147843) and Cancer Institute of New South Wales (15/CDF/1-14). GEM Study Group: Coordinating Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA: Marianne Berwick (principal investigator [PI], currently at the University of New Mexico, Albuquerque, NM, USA), Colin Begg (co-PI), Irene Orlow (co-investigator), Klaus J. Busam (dermatopathologist), Pampa Roy (senior laboratory technician), Siok Leong (research assistant), Sergio Corrales-Guerrero (senior research technician), Keimya Sadeghi (senior laboratory technician), Anne Reiner (biostatistician). University of New Mexico, Albuquerque, NM, USA: Marianne Berwick (PI), Li Luo (biostatistician), Tawny W. Boyce (data manager). Study centers: The University of Sydney and The Cancer Council New South Wales, Sydney, Australia: Anne E. Cust (PI), Bruce K. Armstrong (former PI), Anne Kricker (former co-PI); Menzies Institute for Medical Research University of Tasmania, Hobart, Australia: Alison Venn (current PI), Terence Dwyer (PI, currently at University of Oxford, Oxford, UK), Paul Tucker (dermatopathologist); British Columbia Cancer Research Centre, Vancouver, Canada: Richard P. Gallagher (PI), Agnes Lai, Research Coordinator, Cancer Care Ontario, Toronto, Canada: Loraine D. Marrett (PI), Lynn From (dermatopathologist); CPO, Center for Cancer Prevention, Torino, Italy: Roberto Zanetti, M.D (PI), Stefano Rosso (co-PI); University of California, Irvine, CA, USA: Hoda Anton-Culver (PI); University of Michigan, Ann Arbor, MI, USA: University of Michigan, Ann Arbor, MI, USA: Stephen B. Gruber (PI, currently at University of Southern California, Los Angeles, CA, USA), Shu-Chen Huang (co-investigator, joint at University of Southern California–University of Michigan); University of North Carolina, Chapel Hill, NC, USA: Nancy E. Thomas (PI), Kathleen Conway (co-investigator), David W. Ollila (co-Investigator), Pamela A. Groben (dermatopathologist), Sharon N. Edmiston (research analyst), Honglin Hao (laboratory specialist), Eloise Parrish (laboratory specialist), Jill S. Frank (Research Assistant), David C. Gibbs (Research Assistant, Emory University, Atlanta, GA, USA); University of Pennsylvania, Philadelphia, PA, USA: Timothy R. Rebbeck (former PI), Peter A. Kanetsky (PI, currently at H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA); UV data consultants: Julia Lee Taylor and Sasha Madronich, National Centre for Atmospheric Research, Boulder, CO, USA. Download .pdf (.04 MB) Help with pdf files Supplementary Table S1
Abstract Purpose To determine whether older breast cancer survivors score lower on neuropsychological tests compared to matched non-cancer controls and to test the hypotheses that survivors who were APOE ε4 carriers would have the lowest cognitive performance, but that smoking history would decrease the negative effect of ε4 on cognition. Methods Female breast cancer survivors who had been diagnosed and treated at age 60 or older and were 5 – 15 year survivors (N=328) and age and education matched non-cancer controls (N=160) were assessed at enrollment and at 8, 16 and 24 month follow ups with standard neuropsychological and psychological assessments. Blood for APOE genotyping was collected and smoking history was assessed at enrollment. Participants were purposely recruited so that approximately 50% had a history of treatment with chemotherapy or no chemotherapy and approximately 50% had a smoking history. Results After adjusting for age, cognitive reserve, depression and fatigue, breast cancer survivors scored significantly lower on all domains of cognitive function. A significant two-way interaction demonstrated that the negative effect of ε4 on cognitive performance was stronger among survivors. A significant three-way interaction supported the hypothesis that smoking history had a protective effect on cognitive function in ε4 carriers that was more pronounced in the controls than the survivors. Conclusions The results support the long-term cognitive impact of breast cancer diagnosis and treatments on older, disease-free survivors, particularly for ε4 carriers. The results also emphasize the importance of assessing smoking history when examining APOE and cognition and are an example of the complex interactions of age, genetics, health behaviors and disease history in determining cognitive function.
Public access to genetic information is increasing, and community dermatologists may progressively encounter patients interested in genetic testing for melanoma risk. Clarifying potential utility will help plan for this inevitability. We determined interest and uptake of genetic risk feedback based on melanocortin receptor gene (MC1R) variants, immediate (two weeks) responses to risk feedback, and test utility at three months in patients (age ≥ 18, with a history of nonmelanoma skin cancer). Participants (N = 50) completed a baseline survey and were invited to consider MC1R testing via the study website. Testing interest and uptake were assessed through registration of test decision, request of a saliva test kit, and kit return (all yes/no). Immediate responses to risk feedback included feedback-relevant thoughts, emotions, communication, and information seeking after result receipt; test utility outcomes included family and physician communication and information seeking. Results indicated good retention at both time points (76%; 74%). Half (48%) logged onto the study website, and of these, most (92%) chose testing and (95%) returned a saliva sample. After two weeks, most (94%) had read all the risk feedback information and distress was low (M = 8.81, 7-28, SD = 2.23). Many (69%) had talked with their family about the results. By three months, most had spoken with family (92%) and physicians (80%) about skin cancer risk. Physician communication was higher (70%) in those tested versus those not tested (40%, p = 0.02). The substantial interest and promising outcomes associated with MC1R genetic testing in dermatology patients inform intervention strategies to enhance benefits and minimize risks of skin cancer genetic testing.
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