Abstract
Background: Melanoma cases may exist in pancreatic cancer kindreds, whereas there is increased risk of pancreatic cancer in familial melanoma. The two cancers may share genetic susceptibility variants in common.
Methods: Three dbGaP (datasets in Genotypes and Phenotypes)-deposited GWAS (genome-wide association study) datasets (MD Anderson melanoma, PanScan 1, and PanScan 2 for pancreatic cancer) were used. Thirty-seven melanoma susceptibility variants in 22 genomic regions from published GWAS, plus melanoma-related genes and pathways were examined for pancreatic cancer risk in the PanScan datasets. Conversely, nine known pancreatic cancer susceptibility variants were examined for melanoma risk in the MD Anderson dataset.
Results: In the PanScan data, initial associations were found with melanoma susceptibility variants in NCOA6 [rs4911442; OR, 1.32; 95% confidence interval (CI), 1.03–1.70; P = 0.03], YWHAZP5 (rs17119461; OR, 2.62; 95% CI, 1.08–6.35; P = 0.03), and YWHAZP5 (rs17119490; OR, 2.62; 95% CI, 1.08–6.34; P = 0.03), TYRP1 (P = 0.04), and IFNA13 (P = 0.04). In the melanoma dataset, two pancreatic cancer susceptibility variants were associated: NR5A2 (rs12029406; OR, 1.39; 95% CI, 1.01–1.92; P = 0.04) and CLPTM1L-TERT (rs401681; OR, 1.16; 95% CI, 1.01–1.34; P = 0.04). None of these associations remained significant after correcting for multiple comparisons.
Conclusion: Reported variants of melanoma genes and pathways do not play a role in pancreatic cancer predisposition. Reciprocally, pancreatic cancer susceptibility variants are not associated with melanoma risk.
Impact: Known melanoma-related genes and pathways, as well as GWAS-derived susceptibility variants of melanoma and pancreatic cancer, do not explain the shared genetic etiology of these two cancers. Cancer Epidemiol Biomarkers Prev; 23(6); 1121–4. ©2014 AACR.
Introduction
Certain subsets of pancreatic cancer kindreds have members with increased risk of melanoma (1); in parallel, there is increased risk for pancreatic cancer in melanoma kindreds (2, 3). Hypothesizing that these two cancers have common genetic susceptibility, we examined whether known melanoma-related genes and pathways, or susceptibility variants of melanoma and pancreatic cancer found in previous genome-wide association studies (GWAS), have shared genetic etiology.
Materials and Methods
Three public GWAS datasets in the database of Genotypes and Phenotypes (dbGaP) were used: (i) the MD Anderson Cancer Center melanoma GWAS (4), (ii) PanScan 1(5), and (iii) PanScan 2 (PanScan datasets; ref. 6). These datasets, quality control procedures, selection of candidate variants, genes and pathways, and methods are provided in Supplementary Materials and Methods. Candidate susceptibility variants from existing GWAS and known melanoma-related genes were selected. Pathways included genes known to be related to melanoma (26 genes), chromosome 9p21 (44 genes), cell cycle (8 genes), eye color (7 genes), freckling (5 genes), nevi (3 genes), pigmentation (12 genes), and sun sensitivity (8 genes; Supplementary Tables S1 and S2). For candidate genes and pathway association analysis, single-nucleotide polymorphisms (SNP) were selected for each gene using a boundary of 20 kb upstream and 10 kb downstream of the transcriptional sites. Data from genotyping and imputation were analyzed using unconditional multivariable logistic regression assuming an additive model. For the PanScan data, covariates in the model included age, sex, study site, genotypic race from EIGENSTRAT analysis (principal components PC1 and PC2), and other significant principal components (PC4 and PC9 for PanScan1, and PC3 for PanScan 2). In the Mayo Clinic subset, we also included additional covariate data: smoking status, family history of cancer (first degree), body mass index, and long-standing diabetes. We performed a similar adjusted analysis of the melanoma data with publicly available covariates: age, sex, two significant principal components (4), family history of cancer, and sun exposure parameters (sunburn, nevi, moles, freckling, tanning, skin color, hair color, and eye color). ORs and 95% confidence intervals (CI) were computed using Plink 1.07. The gene-based association analysis was conducted using the logistic regression model fit for genotype trend effects (1 degree of freedom) adjusted for study, age, sex, self-described ancestry and principal components as previously described (6). The gene-based P value was evaluated through a bootstrap procedure using the minP test statistic. We then conducted the pathway analysis based on the adaptive rank truncated product (ARTP) method, which combines gene-level P values within a pathway into the test statistic and uses a bootstrap procedure to estimate its P value (7). The P value for both the gene-based and pathway analyses was estimated by 30,000 parametric bootstrap steps.
Results
Of 37 melanoma susceptibility variants included in this analysis, 28 were present in the PanScan GWAS data (n = 23) or were represented by SNPs in high linkage disequilibrium (LD) (r2 > 0.5; ref. 8) as determined by Haploview (n = 5). Nine variants could not be tagged (rs16891982, rs17305573, rs1805006, rs1805007, rs28777, rs35391, rs35391, rs1129038, and rs1805008). Several SNPs were shown to be associated with pancreatic cancer risk in the Mayo Clinic subset with covariate adjustment: NCOA6 (rs4911442; OR, 1.32; 95% CI, 1.03–1.70; P = 0.03), YWHAZP5 (rs17119461; OR, 2.62; 95% CI, 1.08–6.35; P = 0.03), and YWHAZP5 (rs17119490; OR, 2.62; 95% CI, 1.08–6.34; P = 0.03; Table 1). The association analysis of melanoma pathways and genes in the PanScan data is shown in Supplementary Table S2. Examination of the 44 genes at chromosome 9p21, in which CDKN2A is located, revealed marginal evidence for significant associations with pancreatic cancer risk: IFNA13 (P = 0.044) and IFNA6 (P = 0.059). Evaluation of all 9p21 SNPs showed that the top three SNPs with the lowest P values were observed in LINGO2, which is associated with Parkinson disease and essential tremor disorder. Although the gene-based P value of LINGO2 is 0.13, this gene had several SNPs (including those with the lowest P values) with P < 0.001 located in two approximately 3-kb regions of relatively high LD (r2 > 0.5; ref. 8) within this large gene (total number of SNPs evaluated = 294). Evaluation of the 26 melanoma candidate genes produced only one nominally significant gene, TYRP1. The top five SNPs with the lowest P values were in PTPRD, located at 9p. CDKN2A and CDKN2B were not significant in this analysis (P = 0.60 and 0.45, respectively). Of the nine known pancreatic cancer susceptibility variants, one SNP showed moderate association with melanoma risk: NR5A2 (rs12029406; OR, 1.40; 95% CI, 1.01–1.93; P = 0.04; Table 2). None of the detected associations were significant after adjusting for multiple comparisons.
Associations of melanoma susceptibility variants, genes, and pathways with pancreatic cancer risk
Association of pancreatic cancer susceptibility variants with melanoma risk
Discussion
Genetic variants associated with risk for pancreatic cancer and melanoma and known melanoma-related pathways and genes do not account for the shared genetic etiology between melanoma and pancreatic cancer. The shared etiology of these cancers, clearly involves factors beyond SNPs.
Conclusion
Reported variants of melanoma genes and pathways do not play a role in pancreatic cancer predisposition. Conversely, pancreatic cancer susceptibility variants are not associated with melanoma risk.
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
Authors' Contributions
Conception and design: L. Wu, A.M. Goldstein, L.T. Amundadottir, G.M. Petersen
Development of methodology: L. Wu, G.M. Petersen
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): L. Wu, X.R. Yang, F. Canzian, B.M. Wolpin, R. Stolzenberg-Solomon, L.T. Amundadottir, G.M. Petersen
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): L. Wu, A.M. Goldstein, K. Yu, L.T. Amundadottir, G.M. Petersen
Writing, review, and/or revision of the manuscript: L. Wu, A.M. Goldstein, K.G. Rabe, A.A. Arslan, F. Canzian, B.M. Wolpin, R. Stolzenberg-Solomon, L.T. Amundadottir, G.M. Petersen
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): A.A. Arslan, R. Stolzenberg-Solomon, G.M. Petersen
Study supervision: L.T. Amundadottir, G.M. Petersen
Grant Support
This study was supported in part by Mayo Clinic SPORE in Pancreatic Cancer (P50CA102701), R01CA97075, and the Intramural Research Program of the NIH, National Cancer Institute (NCI), Division of Cancer Epidemiology and Genetics (DCEG). L. Wu is a trainee in the program funded by NIH/NCRR CTSA grant number TL1 RR024152.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Acknowledgments
The authors thank the investigators and participants in the Pancreatic Cancer Cohort Consortium (PanScan), the Pancreatic Cancer Case–Control Consortium (PanC4) studies (listed in Supplementary Table S2), and the MD Anderson Cancer Center melanoma case–control study. dbGaP provided access to the datasets (dbGaP Study Accession, phs000187.v1.p1). The authors also thank Martha Matsumoto for assistance with PanScan data preparation.
Footnotes
Note: Supplementary data for this article are available at Cancer Epidemiology, Biomarkers & Prevention Online (http://cebp.aacrjournals.org/).
- Received January 22, 2014.
- Accepted February 18, 2014.
- ©2014 American Association for Cancer Research.
Volume 23, Issue 6
