Vitamin D metabolic pathway genes and pancreatic cancer risk.

Vitamin D signaling is of interest in relation to cancer because of its hypothesized role in inducing immune cell differentiation and inhibiting tumor proliferation and angiogenesis [ 1 ]. In humans, most vitamin D is synthesized endogenously via exposure of the skin to solar ultra-violet B radiation, which converts 7-dehyrocholesterol in skin to vitamin D. Small amounts come also from dietary sources such as fish or fortified dairy products and, in some populations, dietary supplements [ 2 ]. Some previous studies have suggested lower pancreatic cancer risk with proxy markers of higher vitamin D status. Ecologic studies, which are based on population averages rather than individual level data, have shown lower pancreatic cancer death rates in areas with more sun exposure in Spain [ 3 ], the United States [ 4 , 5 ], and Japan [ 6 , 7 ]. A large, prospective study that used a predicted estimate of vitamin D status based on five determinants of serum 25-hydroxyvitamin D (25(OH)D) (dietary and supplemental vitamin D, skin pigmentation, adiposity, geographic residence, and leisure activity) also found an inverse association with pancreatic cancer risk [ 8 ]. Serum 25(OH)D is the most widely used biomarker to assess vitamin D status in epidemiologic studies as it reflects both endogenous synthesis and dietary vitamin D intake [ 9 ]. However, previous studies evaluating measured circulating 25(OH)D concentrations with risk of pancreatic cancer show conflicting results. A large, pooled study of serum concentrations from eight cohorts as part of the Vitamin D Pooling Project (952 cases, 1,333 controls) reported increased pancreatic cancer risk with higher circulating vitamin D concentrations (odds ratio (OR) = 2.12, 95% confidence interval (CI)) comparing serum levels ≥100 nmol/L to the referent 50–75nmol/L [ 10 ]. In contrast, a nested case-control study pooling five prospective cohorts (451 cases, 1,167 controls) suggested an inverse association OR = 0.67, 95% CI 0.46–0.97 comparing plasma 25(OH)D quintiles (>81.05 to <45.64 nmol/L) and no association when using the categories employed in the Vitamin D Pooling Project [ 11 ]. Pathway-based analysis of GWAS can detect associations that might be missed by focusing on single loci or even genes [ 12 ]. To our knowledge only one previous population based case-control study (628 cases, 1,193 controls) evaluated associations between genetic variants related to vitamin D and pancreatic cancer, and reported no single-nucleotide polymorphism (SNP) associations after adjustment for multiple comparisons [ 13 ]. In the present study, we used data from 20 studies in PanScans I-III to examine 11 genes in the vitamin D metabolic pathway and 213 corresponding SNPs with risk of pancreatic cancer. In a subset of the cohorts we assessed effect measure modification by circulating vitamin D concentrations. Some of the samples used in this analysis overlap with those utilized in the Vitamin D Pooling Project [ 10 ]. We hypothesized that the contradictory evidence on circulating vitamin D and risk of pancreatic cancer might be explained by genetic variations in vitamin D-related genes and multiplicative interaction between circulating vitamin D and genetic variation.

Genetic variation in the vitamin D metabolic pathway overall was not associated with risk of pancreatic cancer (pathway ARTP p-value = 0.830, Table 1 ). None of the 11 genes were associated with pancreatic cancer ( Table 1 ). SNPs near the VDR (rs2239186), GC (rs2282679), LRP2 (rs4668123), CYP24A1 (rs2762932), and CUBN (rs1810205) genes were the top SNPs associated with pancreatic cancer (p-values 0.008–0.037) ( Table 2 ), although they did not reach the threshold for statistical significance after adjusting for multiple comparisons. a P-values account for number of SNPs within genes or within the overall pathway, but not for the total number of genes; Models were adjusted for age (≤50, 51–60, 61–70, 71–80, ≥81 years), sex, study and population stratification by 5 eigenvectors for ethnic ancestry. a After bonferroni correction (0.05/213) p-values < 0.0002 were considered significant b Odds ratios (ORs) were adjusted for age (≤50, 51–60, 61–70, 71–80, ≥81 years), sex, study, and population stratification by 5 eigenvectors for ethnic ancestry. c Odds ratios (ORs) are for the number of copies of the minor allele. d Phase refers to participation in PanScan I, II or III. For rs4668123 data was available only from PanScan phases II and III. A test for heterogeneity between the three phases of PanScan indicated no evidence of heterogeneity after adjustment for multiple comparisons. Results stratified by PanScan phase are presented for the overall pathway and 11 genes in S4 Table and for SNPs with nominal p-values <0.05 in S5 Table . No associations were significant after adjustment for multiple comparisons. In analyses stratified by high vs. low circulating vitamin D concentration, no significant differences were observed and tests for interaction between vitamin D concentration and each of the top 20 SNPs were not significant (all p-values > 0.1; data not shown). Of the four variants identified as associated with circulating vitamin D concentration in published GWAS [ 26 , 27 ], in our sample only a tag SNP in GC showed an association (p = 5.30 x10 –7 ) with vitamin D status; tag SNPs in or near DHCR7/NADSYN1 and CYP2R1 did not show an association and rs6013897 in CYP24A1 could not be studied as the Illumina HumanHap550 platform does not include a tag SNP for rs6013897.

Our findings do not support an association between common genetic variants in the vitamin D metabolic pathway and risk of pancreatic cancer, despite the large sample size and ability to assess effect measure modification by circulating vitamin D concentration. Future research should explore other pathways through which vitamin D might be associated with risk of pancreatic cancer; for example through studies of gene-nutrient interactions involving variants in downstream signaling pathways involving vitamin D.

We obtained data from the 20 studies in the PanScan collaboration who agreed to participate in this pathway analysis. PanScan phases I-III have been previously described [ 14 – 16 ]. Our primary analysis included genotype data from 10 cohort studies and 10 case control studies in the PanScan collaboration. Participating cohorts included the Agricultural Health Study, Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC), Give us a Clue to Cancer and Heart Disease Study (CLUE II), Cancer Prevention Study II (CPS-II), Melbourne Collaborative Cohort Study (MCCS), Multiethnic Cohort (MEC), New York University Women’s Health Study (NYU-WHS), Prostate Lung Colorectal and Ovarian Cancer Screening Trial (PLCO), Selenium and Vitamin E Cancer Prevention Trial (SELECT) and the VITamins and Lifestyle cohort (VITAL). The included case-control studies were the Mayo Clinic Molecular Epidemiology of Pancreatic Cancer Study, University of California San Francisco, Yale University, MD Anderson Cancer Center, University of Toronto, Johns Hopkins University, Memorial Sloan-Kettering Cancer Center, PACIFIC Study of Group Health and Northern California Kaiser Permanente, Spanish Pancreatic Cancer Study (PANKRAS II) [ 17 ], and PANcreatic Disease ReseArch (PANDoRA) (Heidelberg, Germany) [ 18 ]. All cases were diagnosed with primary pancreatic adenocarcinoma (ICD-O-3 code C250-C259 or C25.0-C25.3, C25.7-C25.9 ). In short, PanScans-I and II used a nested case-control study design for the cohort studies. Cohort cases were confirmed through cancer registries, death certificates or review of medical records by medical personnel. Cohort controls for PanScan-I were incidence density sampled with a 1:1 ratio and were alive and cancer free at the time of diagnosis of the matched case. In all case-control studies, matching criteria included calendar year of birth within five years, gender, race and ethnicity, while some cohorts also matched on age at baseline or blood draw, smoking, date/time of blood draw, fasting status at time of blood draw, and length of follow-up. All data was de-identified before genotyping and before samples were sent to NCI. Genotyping was performed at the National Cancer Institute’s (NCI’s) Cancer Genomics Research Laboratory (formerly known as the Core Genotyping Facility) using the Illumina HumanHap550 array for PanScan-I, the Illumina Human 610-Quad array for PanScan II, and the Illumina Human 770-Quad chip for PanScan III. In PanScan III controls were previously genotyped using second-generation Illumina SNP microarrays (e.g. OmniExpress, Omni 1M or Omni 2.5M) and drawn from PanScan III prospective cohorts and a Spanish case-control study and thus were not matched to the cases [ 19 ]. SNPs reported here were limited to those with minor allele frequencies (MAF) ≥5%. In total, we used data from 3,583 pancreatic cancer cases and 7,053 controls (1,108 cases and 4,353 controls from the cohorts and 2,475 cases and 2,700 controls from case-control studies) of European descent. The age and sex distribution of cases and controls is described in S1 Table . Written consent was obtained from all study participants. Each participating study was reviewed and approved by their local IRB for appropriateness in PanScan participation ( S2 Table ) [ 15 ]. A subset of the cohort participants with GWAS data also had 25(OH)D measured in serum as part of the Vitamin D Pooling Project (713 cases and 878 controls) [ 20 ]. These subjects were from the following cohorts: ATBC, CLUE-II, CPS-II, NYU-WHS, and PLCO. Methods for assaying 25(OH)D have been previously described [ 10 ]. In short, Heartland Assays, Inc. (Ames, Iowa) performed assays for 25(OH)D for samples from CLUE-II, CPS-II, NYU-WHS, PLCO, and a subset of ATBC samples using the DiaSorin LIAISON 25 OH Vitamin D TOTAL Assay (Diasorin, Inc., Stillwater, Minnesota). The remaining ATBC samples were assayed previously using a similar method in the laboratory of Dr. R. Vieth [ 21 ]. The methods and coefficients of variation percentages for the blinded quality control samples of the Heartland 25(OH)D measures have been previously reported (10). Using a nested components of variance analysis with logarithmically transformed quality control measures across batches, the overall intra- and interbatch coefficients of variation were 16.5% and 4.7% for the previously assayed concentrations in the ATBC and PLCO studies, respectively. DHCR7 (DHC-7 reductase) converts pro-vitamin D 3 (7-dehydrocholesterol) in the skin to cholesterol. Alternatively, pre-vitamin D is formed from 7-dehydrocholestrol following dermal UVB exposure. Vitamin D is also derived from diet or supplements in the form of cholecalciferol (D 3 ) or ergocalciferol (D 2 ). The vitamin D binding protein (DBP, also known as GC), transports provitamin D to the liver, as well as other vitamin D compounds to target tissues. In the liver 25-hydroxylases CYP2R1 and CYP27A1 convert vitamins D 2 and D 3 from diet and sun exposure to 25(OH)D (calcidiol), the major circulating vitamin D metabolite. Calcidiol is then converted to the active form 1,25(OH) 2 D 3 (calcitriol) by 1α-hydroxylase ( CYP27B1 ) in the kidney and other organs. 1α-hydroxylase ( CYP27B1 ) is localized on the inner mitochondrial membrane where it produces the active form of vitamin D which binds the vitamin D receptor ( VDR) with substantially higher affinity than 25(OH)D. The vitamin D binding protein, GC, transports vitamin D metabolites to target organs, where calcitriol binds to VDR and forms a heterodimer with RXRA (retinoid x-receptor alpha). This heterodimer attaches to vitamin D response elements on various target genes, some of which are thought to have anti-carcinogenic properties [ 1 ]. Vitamin D is catabolized by 24-hydroxylase ( CYP24A1 ) to inactive forms [ 22 ]. Three additional genes, cubulin ( CUBN ), megalin ( LRP2) and Calcium Sensing Receptor ( CASR ) were added to our analysis to be consistent with the previous study on vitamin D-related genes and pancreatic cancer [ 13 ]. Both cubulin and megalin are plasma membrane receptors that in combination mediate an endocytic update of GC-bound vitamin D. The CASR membrane protein binds calcium in the extracelluar matrix and plays an important role in calcium homeostasis. In total, genotype data from 213 tag SNPs with MAF > 0.05 in 11 genes involved in the synthesis ( DHCR7 , CYP27A1 , CYP2R1 ), transport ( GC , CASR ), metabolism ( CYP27B1 , LRP2 , CUBN ), signal transduction ( VDR , RXRA ) or catabolism ( CYP24A1 ) of endogenous vitamin D were used in our analysis [ 1 ]. These SNPs are located within a span of 20kb 5′ upstream and 10kb 3′ downstream of the gene coding region as defined by the National Center for Biotechnology Information’s human genome build 36.3. SNPs and associated genes are listed in S3 Table . We used unconditional logistic regression to test the association between individual SNPs and pancreatic cancer risk, adjusting for age (≤50, 51–60, 61–70, 71–80, ≥81 years), sex, study, and 5 eigenvectors capturing ethnic ancestry. We performed the pathway and gene analyses using the R package AdaJoint and the adaptive rank truncated product (ARTP) statistic [ 23 ]. In each analysis 1,000,000 permutations were conducted. This statistic accounts for gene or pathway size and linkage disequilibrium and summarizes joint association signals within a gene or pathway. Analyses were restricted to Caucasians. We also tested for heterogeneity between the three phases (PanScan I, PanScan II and PanScan III) using the R package for fixed effects meta-analysis. We adjusted for multiple comparisons in the p-heterogeneity analysis considering a p-value<0.0002 as significant. Analyses stratified by the three PanScan phases were performed to explore possible differences in associations by phase. To test possible effect modification by vitamin D status, we performed stratified analyses in the subset of cohort studies with measured 25(OH)D; we created a dichotomous variable for circulating vitamin D (≤50 nmol/L or >50 nmol/L), as above this threshold is considered to be adequate for bone and overall health in national recommendations [ 24 , 25 ] and was close to the median for controls in our study population (control median 25(OH)D = 51.5 nmol/L). In this subset analysis we additionally adjusted for smoking (never, former, current), body mass index (BMI), and season of blood draw (fall, winter, spring, summer). To test for multiplicative interaction, we created an interaction term between circulating vitamin D as a dichotomous variable and individual SNPs. Using a Bonferroni correction for multiple comparisons, genes with a p-value<0.006 and SNPs with a p-value<0.0002 were considered statistically significant. We additionally evaluated whether SNPs associated with vitamin D levels in published GWAS (rs2282679, rs12785878, rs10741657, rs6013897) on circulating vitamin D [ 26 , 27 ], or representative tag SNPs, were associated with vitamin D in a subset of our study sample.

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