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. Author manuscript; available in PMC: 2015 Oct 1.
Published in final edited form as: Mol Nutr Food Res. 2014 Jul 28;58(10):2023–2035. doi: 10.1002/mnfr.201400068

Consortium analysis of gene and gene-folate interactions in purine and pyrimidine metabolism pathways with ovarian carcinoma risk

Linda E Kelemen 1,2,*, Kathryn L Terry 3,4, Marc T Goodman 5,6, Penelope M Webb 7, Elisa V Bandera 8, Valerie McGuire 9, Mary Anne Rossing 10,11, Qinggang Wang 1, Ed Dicks 12, Jonathan P Tyrer 12, Honglin Song 12, Jolanta Kupryjanczyk 13, Agnieszka Dansonka-Mieszkowska 13, Joanna Plisiecka-Halasa 13, Agnieszka Timorek 14, Usha Menon 15, Aleksandra Gentry-Maharaj 15, Simon A Gayther 16, Susan J Ramus 16, Steven A Narod 17, Harvey A Risch 18, John R McLaughlin 19, Nadeem Siddiqui 20, Rosalind Glasspool 21, James Paul 21, Karen Carty 21, Jacek Gronwald 22, Jan Lubiński 22, Anna Jakubowska 22, Cezary Cybulski 22, Lambertus A Kiemeney 23,24,25, Leon F A G Massuger 26, Anne M van Altena 26, Katja K H Aben 23,25, Sara H Olson 27, Irene Orlow 27, Daniel W Cramer 3,4, Douglas A Levine 28, Maria Bisogna 28, Graham G Giles 29,30,31, Melissa C Southey 32, Fiona Bruinsma 29, Susanne Krüger Kjær 33,34, Estrid Høgdall 33,35, Allan Jensen 33, Claus K Høgdall 34, Lene Lundvall 34, Svend-Aage Engelholm 36, Florian Heitz 37,38, Andreas du Bois 37,38, Philipp Harter 37,38, Ira Schwaab 39, Ralf Butzow 40,41, Heli Nevanlinna 41, Liisa M Pelttari 41, Arto Leminen 41, Pamela J Thompson 5,6, Galina Lurie 42, Lynne R Wilkens 42, Diether Lambrechts 43,44, Els Van Nieuwenhuysen 45, Sandrina Lambrechts 45, Ignace Vergote 45, Jonathan Beesley 46; AOCS Study Group/ACS Investigators7,46,47, Peter A Fasching 48,49, Matthias W Beckmann 48, Alexander Hein 48, Arif B Ekici 50, Jennifer A Doherty 10,51, Anna H Wu 16, Celeste L Pearce 16, Malcolm C Pike 16,27, Daniel Stram 16, Jenny Chang-Claude 52, Anja Rudolph 52, Thilo Dörk 53, Matthias Dürst 54, Peter Hillemanns 55, Ingo B Runnebaum 54, Natalia Bogdanova 53, Natalia Antonenkova 56, Kunle Odunsi 57, Robert P Edwards 58, Joseph L Kelley 58, Francesmary Modugno 58,59, Roberta B Ness 60, Beth Y Karlan 61, Christine Walsh 61, Jenny Lester 61, Sandra Orsulic 61, Brooke L Fridley 62, Robert A Vierkant 63, Julie M Cunningham 64, Xifeng Wu 65, Karen Lu 66, Dong Liang 67, Michelle AT Hildebrandt 65, Rachel Palmieri Weber 68, Edwin S Iversen 69,70, Shelley S Tworoger 4,71, Elizabeth M Poole 4,71, Helga B Salvesen 72,73, Camilla Krakstad 72,73, Line Bjorge 72,73, Ingvild L Tangen 72,73, Tanja Pejovic 74,75, Yukie Bean 74,75, Melissa Kellar 74,75, Nicolas Wentzensen 76, Louise A Brinton 76, Jolanta Lissowska 77, Montserrat Garcia-Closas 78, Ian G Campbell 32,79, Diana Eccles 80, Alice S Whittemore 9, Weiva Sieh 9, Joseph H Rothstein 9, Hoda Anton-Culver 81,82, Argyrios Ziogas 81, Catherine M Phelan 83, Kirsten B Moysich 84, Ellen L Goode 85, Joellen M Schildkraut 68,69, Andrew Berchuck 86, Paul DP Pharoah 12,87, Thomas A Sellers 83, Angela Brooks-Wilson 88, Linda S Cook 1,89, Nhu D Le 90; on behalf of the Ovarian Cancer Association Consortium.
PMCID: PMC4197821  NIHMSID: NIHMS621878  PMID: 25066213

Abstract

Scope

We re-evaluated previously reported associations between variants in pathways of one-carbon (folate) transfer genes and ovarian carcinoma (OC) risk, and in related pathways of purine and pyrimidine metabolism, and assessed interactions with folate intake.

Methods and Results

Odds ratios (OR) for 446 genetic variants were estimated among 13,410 OC cases and 22,635 controls and among 2,281 cases and 3,444 controls with folate information. Following multiple testing correction, the most significant main effect associations were for DPYD variants rs11587873 (OR=0.92, P=6x10−5) and rs828054 (OR=1.06, P=1x10−4). Thirteen variants in the pyrimidine metabolism genes, DPYD, DPYS, PPAT and TYMS, also interacted significantly with folate in a multi-variant analysis (corrected P=9.9x10−6) but collectively explained only 0.2% of OC risk. Although no other associations were significant after multiple testing correction, variants in SHMT1 in one-carbon transfer, previously reported with OC, suggested lower risk at higher folate (Pinteraction=0.03-0.006).

Conclusions

Variation in pyrimidine metabolism genes, particularly DPYD, which was previously reported to be associated with OC, may influence risk; however, stratification by folate intake is unlikely to modify disease risk appreciably in these women. SHMT1 SNP-byfolate interactions are plausible but require further validation. Polymorphisms in selected genes in purine metabolism were not associated with OC.

Keywords: case-control, DPYD, folate, polymorphism, SHMT1

Introduction

Global statistics estimated that ovarian carcinoma (OC) afflicted 225,000 women and resulted in 140,000 deaths in 2008 [1]. There are no specific screening methods or unique symptoms to detect OC in early stages [2]. Risk stratification strategies may have appreciable impact in reducing the incidence and suffering from OC by identifying those women at greatest risk of developing the disease who would benefit from preventive measures [3]. Germline mutations in high-risk genes (e.g., BRCA1 and BRCA2) remain the best-defined genetic risk factors [4], but explain just 10-15% of all OC [5-7]. About 4% of the polygenic risk is explained by common, but poorly understood, low-risk polymorphisms [8-11] and most non-genetic risk factors (oral contraceptive use [12-14], parity [15, 16], breast-feeding [15, 17], tubal ligation [18], endometriosis [19] and smoking [20]) are not conducive to public health recommendations for risk modification.

Stratification of genetic risk by dietary factors may prevent some cancers. For example, folates participate in one-carbon (1-C) transfer reactions that are essential for the biosynthesis of purines (adenine and guanine) and pyrimidines (cytosine, thymine and uracil), which are incorporated into DNA and RNA, as well as for the biosynthesis of methyl groups for DNA methylation [21] (Figure 1). Perturbation of the coenzymatic role of folate, or of key enzymes in 1-C transfer or purine or pyrimidine metabolism, can have broad consequences that lead to tumor initiation [22] and progression [23] and thus could alter risk among a substantial proportion of individuals. Numerous genetic disorders of purine and pyrimidine metabolism have been characterized in humans [24]. Although these are rare and inherited in Mendelian fashion, genes encoding enzymes in these pathways have been associated with various cancers [25-30]. We previously examined associations between 180 tagging common single nucleotide polymorphisms (tagSNPs) in 21 genes involved in 1-C transfer and risk of OC in 1,770 participants [31] and also reported risk modification by multivitamin intake, a proxy for folate intake [31, 32]. Ten SNPs in eight genes (AHCYL1, DNMT3A, DPYD, MTHFD1, MTHFS, SHMT1, SLC19A1 and TYMS) were associated with OC at P≤0.05 in either ordinal or co-dominant genetic risk models [31] and eight SNPs in five genes (DNMT3A, DNMT1, MTHFR, MTHFD1 and ATIC) were associated with OC at P≤0.05 in interaction analyses with multivitamin use [31, 32]. In those studies, the strongest evidence for association was for a haplotype and a single SNP (rs9909104) in SHMT1 for which we calculated a false positive report probability of 9% to 16%, respectively [31], and for two SNPs in ATIC interacting with multivitamin use at a false discovery rate <0.25 and P<0.05 [32]. SNPs in DNMT3A, DPYD, MTHFD1 and MTHFS did not replicate in a subsequent genotyping effort among 16,000 participants [33].

Figure 1.

Figure 1

Overview of the role of folate and key enzymes involved in one-carbon transfer for DNA synthesis and methylation reactions. DHFR, dihydrofolate reductase; DNMTs, DNA methyltransferases; MTHFR, methylenetetrahydrofolate reductase; MTR, 5-methyltetrahydrofolate-homocysteine methyltransferase; MTRR, 5-methyltetrahydrofolatehomocysteine methyltransferase reductase; TYMS, thymidylate synthase.

Our objectives in the current investigation were to re-evaluate previously reported genetic associations in a larger sample size, evaluate additional variants in the related pathways of purine and pyrimidine metabolism and to assess effect modification by dietary folate intake. We performed these analyses in over 36,000 women contributing DNA in the Ovarian Cancer Association Consortium (OCAC).

Materials and Methods

Gene and SNP selection

Genes were selected according to two categories. The first category consisted of six genes (ATIC, DNMT3B, DPYD, MTR, SHMT1 and TYMS) with previously observed SNP associations with OC [31, 32] and were included for replication. These SNPs were selected with high gene coverage using minor allele frequency (MAF) ≥0.01 and pair-wise linkage disequilibrium (LD) threshold of r2<0.8 (ATIC, DNMT3B, DPYD and MTR) or <0.9 (SHMT1 and TYMS). The second category consisted of nine genes related to purine metabolism (ADSL, ADSS, DCK, GART, GMPS, IMPDH1, IMPDH2, PAICS and PFAS) and 11 genes involved in pyrimidine metabolism (AK3, CAD, CMPK1, CTPS, DHODH, DPYS, NME6, PPAT, PRPS2, RRM2B and UMPS) (Supplementary Figure 1). These SNPs were selected with MAF≥0.05 and LD threshold of r2<0.8. All SNPs within 5kb up- and downstream of the largest cDNA isoform (Human Genome build 36) of each gene was selected using information from 60 unrelated individuals of European ancestry sequenced in the pilot phase of the 1000 Genomes Project [34] and binned using the Haploview program [35]. We prioritized tagSNPs for genotyping that were coding SNPs, had the highest MAF in each bin and, if available, met criteria for predicted likelihood of successful genotyping based on Illumina quality score metrics. In January 2010, 803 tagSNPs were submitted for genotyping: 31% of these SNPs were unique to the 1,000 Genomes Project and not found in dbSNP.

Study Subjects

Subjects (n=47,630) from 43 individual studies participating in OCAC were grouped into 34 geographically similar study strata [11]. Of 44,308 subjects whose DNA passed genotyping quality control criteria (see below), we further excluded subjects with borderline tumors, subjects of non-European ancestry and those with prior history of cancer other than non-melanoma skin cancer, leaving 36,045 eligible subjects (13,410 cases and 22,635 controls) for analysis. Informed consent was obtained in each of the individual studies and local human research investigations committees approved each study.

Genotyping and Quality Control (QC)

Details of the genotyping have been described elsewhere [11]. In brief, we used an Illumina Infinium custom iSelect BeadChip developed for the international Collaborative Oncology Gene-environment Study (iCOGS). Centralized genotyping calls and QC were performed at the University of Cambridge. Quality control for samples has been detailed previously [11]. We excluded SNPs that failed genotyping, had call rates <95% and MAF >0.05 or call rates <99% and MAF <0.05, departed from Hardy-Weinberg equilibrium (P value <10−7), had discordant genotypes >2% between duplicates and monomorphic SNPs. Of 803 tagSNPs submitted for genotyping, 203 SNPs failed genotyping, 127 were monomorphic and 27 had MAF <0.01 leaving 446 SNPs that passed QC. Genotyping failures and monomorphic SNPs reflected the large number of polymorphisms that were subsequently found to be falsely positive in the pilot phase sequencing data of the 1000 Genomes Project.

Covariate and Dietary Data

Key clinical, demographic and questionnaire data were harmonized across study centers and merged into a common dataset. Dietary intakes of folate and total energy were estimated with validated food frequency questionnaires (FFQs) in six studies (AUS [36], DOV [37], HAW and STA [38], NEC [39] and NJO [40]) pertaining to the year preceding recruitment or for the time period approximately four years before the reference date (DOV). Data on the use of multivitamins and single vitamin and mineral supplements were also available and total folate intake was estimated by summing intakes from both food sources and from supplements. Nutrient and genotype data were available for 2,281 cases and 3,444 controls of European ancestry.

Statistical Analysis

Genotypes were used to estimate allele frequencies and pair-wise LD between SNPs was estimated with r2 values using Haploview [35]. Data from the 34 study strata were combined into a single dataset following confirmation of no statistical heterogeneity in SNP associations across study sites. We estimated odds ratios (OR) and 95% confidence intervals (CI) for each SNP using unconditional logistic regression treating the number of variant alleles carried as an ordinal (log-additive) variable. Secondary analyses also considered co-dominant (non-additive) risk models. Interactions between each SNP and total folate intake were evaluated with the Wald test in models that also included a one degree-of-freedom product term for the ordinal coding for genotype and total folate intake group (below/above the energy-adjusted median intake: ≤484 μg/d vs >484 μg/d ≈ approximately the dietary reference intake of 400 μg/d for folate, which is also the folic acid content of a typical multivitamin supplement). Risk models were adjusted for age (continuous), study stratum and the first five eigenvalues from principal components analysis to account for sub-strata of European ancestry across the 34 international studies (see ref [11]). Additional adjustment for non-genetic risk factors did not change estimates and these variables were excluded from the models (data not shown).

Multi-variant analysis

Because some of the genes selected for replication belonged to either the purine (ATIC) or pyrimidine (DPYD and TYMS) metabolism pathways, while DNMT3B, MTR and SHMT1 belonged to 1-C transfer, we evaluated associations according to these three pathways. However, we considered evidence for replication if SNPs in these six genes reached statistical significance according to the criteria described below.

To assess the likelihood of false-positive findings, we performed a multi-variant analysis that accounted for the potential correlations between SNPs within genes in a pathway. Since our primary interest was to evaluate SNP-by-folate interactions, we prioritized these associations for evaluation of multiple testing as follows. A likelihood ratio test (LRT) statistic was calculated by comparing a regression model with and without significant SNP-by-folate interaction terms. Permutation-based tests were then used to compute P-values from a null distribution of the LRT statistic generated by permuting case status 10,000 times. The generation of a null distribution was performed five times, each time with a different seed. For evaluation of individual genes that showed SNP associations at P<0.05, we applied a conservative Bonferroni correction of the Type I error using the number of SNPs tested in that gene’s pathway (44 SNPs in 1-C transfer, 100 SNPs in purine metabolism and 302 SNPs in pyrimidine metabolism). The corresponding thresholds were P=0.001 for 1-C transfer, P=5x10-4 for purine metabolism and P=1.6x10−4 for pyrimidine metabolism.

We also estimated haplotype frequencies of >1% for selected genes with and without stratification by total folate intake using an expectation-maximization algorithm [41] as described in detail elsewhere [32]. The generation of haplotypes using 129 DPYD tagSNPs resulted in an infinite recursion so we selected 29 tagSNPs, one for each haplotype block constructed according to the Gabriel criteria [42] in Haploview [35] and two located outside of a haplotype block. These tagSNPs were selected based on significant P values in main effect or interaction analyses, highest MAF or highest D' values with other tagSNPs in the haplotype block. Individual haplotype associations were interpreted carefully in the absence of global haplotype significance.

To assess the population importance of the SNP-by-folate interactions, we used the Genome-wide Complex Trait Analysis (GCTA) program to estimate the percent variance in risk of OC explained by the SNP-by-folate interaction terms [43]. In principle, the GCTA program can be used to evaluate a subset of SNPs or SNP-by-environment interactions and has been used by the developers in this context (J Yang, personal communication, December 2013). Briefly, we first estimated the pairwise genetic relationship matrix (GRM) of the subjects using the SNPs of interest and then fitted the GRM in a regression model that also included age, study stratum, five eigenvalues, total folate intake group and SNP-by-folate interaction terms. Restricted maximum likelihood was applied to deconstruct the phenotypic variance into the percentages explained by the SNPs, the SNP-by-folate interaction terms and residual environmental component.

Statistical tests were two-sided and, unless stated otherwise, were implemented with SAS version 9 (SAS Institute, NC), R [44] and Plink v1.07 [45] software.

Results

The distribution of cases and controls stratified by study is shown in Supplementary Table 1. Descriptive information on the 446 SNPs is provided in Supplementary Table 2.

Twenty-three SNPs were associated with risk of OC at P<0.05, including two SNPs in SHMT1 (1-C transfer) and 12 SNPs in DPYD (pyrimidine metabolism) (Table 1). We reported associations with SNPs in both of these genes previously, although with different variants [31]. In the current study, the two SNPs with the smallest P value were in DPYD in pyrimidine metabolism: rs11587873 (OR, 0.92; 95% CI, 0.89-0.96; P=6x10−5) and rs828054 (OR, 1.06; 95% CI, 1.03-1.10; P=1x10−4). These two SNPs remained statistically significant at the corrected P=1.6x10−4. The other 10 DPYD SNPs were correlated with either rs11587873 or rs828054. There was no statistical heterogeneity in ORs across study strata. Associations were similar when restricted to high-grade serous OC histology (Table 1). Associations for the remaining SNPs are shown in Supplementary Table 3.

Table 1.

Per-allele (log-additive) associationsa) between variants in genes in 1-C transfer and purine and pyrimidine metabolism pathways and risk of ovarian carcinoma: 13,410 cases (5,813 high-grade serous-only) and 22,635 controls of European ancestry in OCAC.

All cases High-Grade Serous cases
Gene SNP OR 95% CI lower 95% CI upper P-value P-value (SNP*site interaction) OR 95% CI lower 95% CI upper P-value P-hetb)
1-C transfer
DNMT3B rs4911256 1.04 1.00 1.07 0.025 0.88 1.04 0.99 1.09 0.082 0.26
SHMT1 rs669340 0.96 0.93 0.99 0.021 0.58 0.94 0.90 0.98 0.006 0.09
SHMT1 rs4925179 0.97 0.93 1.00 0.040 0.69 0.95 0.91 1.00 0.037 0.21
Purine metabolism
ATIC pos215884573 1.05 1.00 1.10 0.036 0.34 1.08 1.01 1.14 0.016 0.08
IMPD2 rs4974081 0.96 0.93 1.00 0.043 0.68 0.96 0.92 1.01 0.169 0.66
Pyrimidine metabolism
AK3c) rs691941 0.95 0.91 0.99 0.020 0.13 0.94 0.89 1.00 0.061 0.11
DPYD rs828054 1.06 1.03 1.10 0.0001 0.80 1.07 1.03 1.12 0.002 0.02
DPYD rs11587873 0.92 0.89 0.96 0.00006 0.08 0.93 0.88 0.98 0.008 0.01
DPYD rs12120388 0.94 0.91 0.97 0.0004 0.83 0.94 0.90 0.98 0.004 0.02
DPYD rs676686 1.06 1.02 1.09 0.001 0.85 1.07 1.02 1.12 0.003 0.01
DPYD rs914959 1.05 1.01 1.08 0.007 0.65 1.05 1.00 1.10 0.028 0.08
DPYD rs7537668 1.04 1.01 1.08 0.011 0.61 1.05 1.01 1.10 0.026 0.03
DPYD rs4128474 0.95 0.90 1.00 0.031 0.99 0.96 0.90 1.02 0.178 0.34
DPYD rs494271 1.04 1.00 1.07 0.034 0.80 1.04 1.00 1.10 0.052 0.02
DPYD rs6678858 0.95 0.91 1.00 0.034 1.00 0.97 0.91 1.03 0.321 0.70
DPYD rs7555294 0.96 0.92 1.00 0.041 0.90 0.97 0.91 1.02 0.229 0.73
DPYD rs4434871 0.95 0.91 1.00 0.041 0.97 0.98 0.92 1.04 0.491 0.65
DPYD rs7522938 1.03 1.00 1.07 0.048 0.72 1.02 0.98 1.07 0.330 0.26
DHODHd) rs3213423 1.05 1.01 1.09 0.018 0.10 1.01 0.96 1.06 0.757 0.77
DHODH rs11864453 0.97 0.93 1.00 0.037 0.19 0.97 0.93 1.01 0.170 0.54
NME6 rs7651161 0.97 0.94 1.00 0.048 0.73 0.96 0.92 1.01 0.089 0.49
RRM2B pos103299244 2.67 1.15 6.19 0.022 0.92 2.42 0.78 7.48 0.126 0.27
TYMS pos656897 1.27 1.09 1.48 0.002 0.27 1.31 1.07 1.62 0.010 0.008
a)

Adjusted for age (continuous), study stratum and the first five eigenvalues from principal components analysis; P < 0.05.

b)

P-value for tumor heterogeneity comparing odds ratios between all controls and each of high-grade serous OC, low-grade serous OC, mucinous OC, endometrioid OC and clear cell OC.

c)

SNP is located 3′ upstream of gene.

d)

SNP is located in flanking 5′ region of gene; all other SNPs are located in introns; ‘pos’ SNPs are novel and identified by chromosomal position.

When SNPs were examined by interactions with total folate intake (Table 2), 22 SNPs showed interactions at P<0.05, including two SNPs that were associated with OC risk overall (SHMT1 rs4925179 and DPYD rs7522938). Three of four SHMT1 SNPs in 1-C transfer (rs56001517, rs7216214 and rs2273026) were associated with a 23% to 30% decreased risk of OC at higher total folate intake (smallest P=0.006) and these three SNPs were correlated with each other (r2 = 0.61 to 0.92) but did not pass the multiple testing significance threshold of P=0.001. Fifteen of the 22 SNPs (60%) were in pyrimidine metabolism genes and a large proportion of these were in four genes (DPYS, DPYD, PPAT and TYMS) that encode enzymes in the sub-pathway of fluoropyrimidine metabolism, which is an important pharmacogenomics pathway targeted by anti-folate chemotherapy. We, therefore, evaluated the 13 SNP-by-folate interactions in these four genes collectively in a multi-variant analysis. Despite generating five null distributions, none achieved a LRT statistic that included the observed LRT statistic: permuted maximum χ2 = 37.95 to 45.74 with 13 degrees-of-freedom (df; smallest P=1.6x10−5) compared to observed χ2 = 46.92 with 13 df (P=9.9x10−6). This suggested the observed value was more extreme than would be expected. Associations for the remaining SNP-by-folate interactions are shown in Supplementary Table 4. We estimated the percentage of variance in risk of OC explained by the 13 SNP-by-folate interaction terms in pyrimidine metabolism to be 0.1994 % (95% CI, 0.1991 to 0.1997) compared to 1x10-4 % (95% CI, 9.9x10−5 to 1x10−4) explained by the 13 SNPs alone.

Table 2.

Associationsa) between variants in genes in 1-C transfer and purine and pyrimidine metabolism pathways and risk of ovarian carcinoma stratified by total folate intake: 2,281 cases and 3,444 controls of European ancestry participants in OCAC.

Total folate Intake ≤ 484 μg/d Total folate intake > 484 μg/d
Controls Cases Controls Cases
Gene SNP AAb) AB BB AA AB BB OR 95%
CI
lower
95%
CI
upper
P
value
AA AB BB AA AB BB OR 95%
CI
lower
95%
CI
upper
P
value
P
intc)
1-C transfer
SHMT1d) rs56001517 1506 232 8 991 162 5 1.05 0.85 1.29 0.656 1475 215 6 1011 108 2 0.70 0.55 0.89 0.004 0.012
SHMT1 rs7216214 1394 335 18 907 237 15 1.08 0.91 1.28 0.396 1341 340 13 925 182 12 0.76 0.63 0.92 0.004 0.006
SHMT1 rs4925179 711 786 250 501 526 131 0.88 0.79 0.99 0.03 703 775 217 469 509 144 1.03 0.92 1.16 0.606 0.048
SHMT1 rs2273026 1409 323 15 931 213 15 1.03 0.86 1.22 0.767 1363 322 11 938 172 11 0.77 0.64 0.94 0.008 0.027
Purine metabolism
IMPD1d) rs6948333 991 653 101 624 453 77 1.09 0.96 1.23 0.171 950 637 105 638 435 46 0.91 0.80 1.03 0.139 0.039
PFAS rs11649742 1049 606 92 721 378 60 0.93 0.82 1.06 0.257 1020 592 84 624 443 55 1.14 1.00 1.30 0.045 0.029
PFAS pos8110739 1659 85 0 1122 37 0 0.64 0.43 0.96 0.03 1630 64 2 1068 51 1 1.18 0.81 1.71 0.384 0.034
Pyrimidine metabolism
CTPS rs6675122 1030 611 106 615 475 69 1.18 1.04 1.33 0.009 960 619 118 624 442 56 0.98 0.86 1.11 0.706 0.034
CTPS rs41268101 1121 521 92 677 402 61 1.16 1.02 1.32 0.022 1026 568 88 670 387 45 0.95 0.83 1.09 0.441 0.034
DPYD rs667565 671 810 266 378 573 207 1.18 1.06 1.31 0.003 614 814 268 439 508 175 0.92 0.82 1.03 0.127 0.001
DPYD rs4520446 501 879 363 318 566 274 1.09 0.98 1.21 0.135 464 824 397 331 555 234 0.90 0.81 1.00 0.057 0.016
DPYD rs7522938 752 745 216 440 540 164 1.15 1.03 1.29 0.011 712 760 208 498 469 145 0.96 0.86 1.08 0.482 0.019
DPYD rs2811182 562 887 297 413 538 208 0.96 0.86 1.07 0.43 617 784 296 360 553 209 1.12 1.00 1.25 0.041 0.042
DPYS rs1962267 550 872 325 417 555 187 0.87 0.78 0.97 0.014 586 802 309 378 535 209 1.02 0.92 1.14 0.689 0.033
DPYS rs2853160 489 889 369 303 580 276 1.08 0.97 1.20 0.158 460 839 398 317 569 236 0.93 0.83 1.03 0.169 0.036
DPYS rs17834440 1295 427 25 908 239 12 0.82 0.69 0.97 0.018 1318 359 20 853 250 19 1.11 0.94 1.31 0.223 0.008
DPYS rs2853178 859 750 138 601 475 83 0.93 0.83 1.05 0.242 889 680 128 571 448 103 1.10 0.97 1.24 0.142 0.049
DPYS rs13263121 751 782 212 530 492 136 0.93 0.84 1.04 0.227 764 751 181 480 496 145 1.13 1.01 1.27 0.034 0.02
DPYS rs1319371 1053 613 81 672 422 65 1.10 0.97 1.25 0.157 1011 592 94 683 387 52 0.90 0.79 1.02 0.106 0.03
DPYS rs35450967 1034 626 87 654 435 70 1.11 0.98 1.26 0.1 982 616 99 671 397 54 0.88 0.77 1.01 0.061 0.012
PPAT rs13135046 516 857 374 362 579 216 0.91 0.82 1.01 0.079 519 837 338 314 569 239 1.08 0.96 1.20 0.198 0.023
TYMS rs2298582 1336 393 18 914 234 11 0.86 0.73 1.02 0.09 1356 327 14 864 235 22 1.21 1.02 1.44 0.027 0.006
a)

Adjusted for age (continuous), study stratum and the first five eigenvalues from principal components analysis. SNP effect was determined using a log-additive logistic regression model.

b)

Allele counts: AA=homozygous wildtype allele carriers; AB=heterozygous allele carriers; BB=homozygous variant allele carriers.

c)

P-value for interaction.

d)

SNP is located 5′ downstream of gene; all other SNPs are located in introns; ‘pos’ SNPs are novel and identified by chromosomal position.

There were no significant associations of haplotypes at the global (gene) level with risk of OC for DPYD, DPYS or SHMT1 (Supplementary Table 5). Interestingly, SHMT1 haplotype #6 comprised minor alleles of the three correlated SHMT1 SNPs (rs56001517, rs7216214 and rs2273026) mentioned above and showed a decreased risk with OC at higher total folate intake (haplotype OR=0.68, 95% CI=0.53-0.87, P=0.002) that mirrored those of the individual SNP findings in Table 2. The selection of 29 haplotype block tagSNPs produced a single haplotype that was not significant, while several individual haplotypes of low frequency in DPYS were observed at P<0.05. Folate intake was not independently associated with risk of OC in multivariable-adjusted models (OR for >484 μg/d vs ≤484 μg/d = 1.04, 95% CI=0.91-1.18), nor when using different total folate intake cutpoints (OR for >400 μg/d vs ≤400 μg/d = 1.00, 95% CI=0.88-1.13 and OR for >683 μg/d [>75% percentile] vs ≤683 μg/d = 0.98, 95% CI=0.84-1.14).

Discussion

The results of the current study suggested a potential role for inherited variation in DPYD in pyrimidine metabolism with risk of OC. Folate intake may modify genetic risk of OC in the pyrimidine metabolism pathway, but the population effect is likely to be small. A possible role for SHMT1 SNP-by-folate interactions in the one-carbon transfer pathway may exist, but requires further validation. Selected genes in purine metabolism were not associated with risk of OC.

In the current study, 12 SNPs with additive effects in DPYD were found to be associated with OC and the strongest association (rs11587873) suggested a modest 8% decreased risk. We previously reported that the main effect of another DPYD SNP, rs1801265 (Arg29Cys), was associated with increased risk of OC among homozygous rare allele carriers [31], although that association was not replicated elsewhere [33] or in the current study. DPYD was represented by five SNPs in our earlier study of 1,770 participants [31] and these participants were also included in the current investigation of 129 SNPs. A haplotype analysis did not support an association of a single DPYD haplotype with risk of OC; however, this may be due, in part, from selecting 29 haplotype block tagSNPs to overcome the infinite recursion when using all 129 tagSNPs. We, therefore, cannot rule out a role for DPYD in risk of OC.

During pyrimidine metabolism, uracil and thymine concentrations are determined, in part, by the availability of 1-C units from folates and are catabolized to β-alanine and to valine/leucine/isoleucine, respectively (Supplementary Figure 1). DPYD encodes dihydropyrimidine dehydrogenase, the initial and rate-limiting step, whereas DPYS encodes dihydropyrimidinase, which catalyzes the secondary step. DPYD and DPYS enzyme deficiency or inhibition can cause decreased production of β-alanine or accumulation of the pyrimidines, uracil/dihydrouracil and thymine/dihydrothymine, and has shown considerable phenotypic variation ranging from severe neurological and developmental disorders associated with inborn errors to milder symptoms of lethargy, dizziness [46-48] and gastrointestinal abnormalities (gastroesophageal reflux, malabsorption) [49]. The pyrimidine metabolism pathway is identical for the degradation of fluoropyrimidines including 5-fluorouracil (5-FU), one of the most commonly prescribed chemotherapeutic agents in cancers [50, 51]. DPYD or DPYS enzyme deficiency results in toxicity among cancer patients from the inability to metabolize 5-FU [52, 53]. Screening programs for inborn errors of pyrimidine degradation have also identified individuals without symptoms, indicating an incomplete knowledge of the full spectrum of genetic, gene-environment, biochemical and clinical manifestations of DPYD and DPYS impairment [46, 49]. It will, therefore, be important to also investigate these associations with survival outcomes.

We had also previously reported increased risk with the main effect of SHMT1 variant rs9909104 [31], but could not replicate the main effect association here. The expanded analysis of SHMT1 SNPs and haplotypes in the present study suggested that risk may be modified by higher folate intake, although these associations did not meet the criteria of significance following multiple testing correction. SHMT1 encodes the serine hydroxymethyltransferase 1 (soluble) enzyme that catalyzes the reversible conversion of glycine and tetrahydrofolate to serine and 5,10 methylenetetrahydrofolate in the cytoplasm for the synthesis of methionine, pyrimidines (e.g., thymidylate) and purines [54]. Serine synthesis, nucleotide synthesis and the pentose phosphate pathway, which generates ribose-5-P (see Supplementary Figure 1), are implicated as important mechanisms of metabolic reprogramming in cancer cells [55]. Polymorphisms in SHMT1 have also been associated with carcinomas of the lung [56] and head and neck [57] and have been shown to interact with dietary folate to alter risk of non-Hodgkin lymphoma [58].

The strengths of this investigation include the gene-environment risk analysis in a targeted pathway using a large assembly of women with OC and the rigorous centralized genotyping and quality control standards. We improved upon our previous work [31, 32] by refining the associations using total folate intake instead of multivitamin supplement use. The median cutpoint for total folate intake approximated the dietary reference intake of 400 μg/d; therefore, the SNP-by-folate interactions can be interpreted as comparing women who meet or exceed recommendations to those who do not. There are also limitations to the current study. Folate intake was assessed at time of diagnosis using a FFQ that asked about average intake over the last year that may not represent habitual intake and may be affected by recall bias. Another potential limitation of the SNP-by-folate interactions is the analysis of all tumor types without stratification by tumor histology. This was decided a priori to maximize statistical power, although the evaluation of SNP main effects suggested no significant differences in risk estimates across the histological types for most SNPs. Another limitation is the inability to distinguish potentially causal SNPs at this time. We genotyped tagSNPs and some genes, such as DPYD, were large and were represented by several correlated tagSNPs that suggested either decreased or increased risk. The evaluation of a DPYD haplotype did not satisfactorily overcome this challenge and will need further clarification.

Conclusions

SNPs in DPYD may have modest effects on risk of OC and will require further evaluation in order to disentangle putative causal variants. Our findings suggest that exceeding the recommendations for folate intake does not negatively modify susceptibility in selected genes in pyrimidine metabolism to influence risk of OC. A possible role for SHMT1 SNP-by-folate interactions in the one-carbon transfer pathway may exist, but will require further validation. Polymorphisms in selected genes in purine metabolism do not appear to be associated with OC.

Supplementary Material

Supporting Information

Acknowledgments

This study would not have been possible without the contributions of the following: P. Hall, (COGS); D. F. Easton, A. M. Dunning and A. Lee (Cambridge); J. Benitez, A. Gonzalez-Neira and the staff of the CNIO genotyping unit; D. C. Tessier, F. Bacot, D. Vincent, S. LaBoissière and F. Robidoux and the staff of the Genome Quebec genotyping unit; S. E. Bojesen, S. F. Nielsen, B. G. Nordestgaard, and the staff of the Copenhagen DNA laboratory; and S. A. Windebank, C. A. Hilker, J. Meyer and the staff of Mayo Clinic Genotyping Core Facility. We thank all the individuals who took part in this study and all the researchers, clinicians and technical and administrative staff who have made possible the many studies contributing to this work. In particular, we thank: D. Bowtell, A. deFazio, D. Gertig, A. Green, P. Parsons, N. Hayward, and D. Whiteman (AUS); G. Peuteman, T. Van Brussel, and D. Smeets (BEL); U. Eilber (GER); L. Gacucova (HMO); P. Schurmann, F. Kramer, W. Zheng, T.W. Park-Simon, K. Beer-Grondke, and D. Schmidt (HJO); J. Vollenweider (MAY); the MD Anderson Center for Translational and Public Health Genomics (MDA); the state cancer registries of AL, AZ, AR, CA, CO, CT, DE, FL, GA, HI, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, and WY (NHS); L. Paddock, M. King, L. Rodriguez-Rodriguez, A. Samoila, and Y. Bensman (NJO); M. Sherman, A. Hutchinson, N. Szeszenia-Dabrowska, B. Peplonska, W. Zatonski, A. Soni, P. Chao, and M. Stagner (POL); C. Luccarini, P. Harrington, the SEARCH team and ECRIC (SEA); the Scottish Gynaecological Clinical Trails group and SCOTROC1 investigators (SRO); I. Jacobs, M. Widschwendter, E. Wozniak, N. Balogun, A. Ryan, and J. Ford (UKO); and Carole Pye (UKR). We thank Jian Yang for assistance with the GCTA program.

Funding

The COGS project is funded through a European Commission's Seventh Framework Programme grant (agreement number 223175 - HEALTH-F2-2009-223175). The Ovarian Cancer Association Consortium is supported by a grant from the Ovarian Cancer Research Fund thanks to donations by the family and friends of Kathryn Sladek Smith (PPD/RPCI.07). The scientific development and funding for this project were supported by the Canadian Institutes of Health Research (MOP-86727) and the US National Cancer Institute GAME-ON Post-GWAS Initiative (U19-CA148112). Funding of the constituent studies was provided by the Canadian Institutes of Health Research (MOP-84340), WorkSafeBC 14, and OvCaRe: BC’s Ovarian Cancer Research Team; the American Cancer Society (CRTG-00-196-01-CCE); the California Cancer Research Program (00-01389V-20170, N01-CN25403, 2II0200); Cancer Council Victoria; Cancer Council Queensland; Cancer Council New South Wales; Cancer Council South Australia; Cancer Council Tasmania; Cancer Foundation of Western Australia; the Cancer Institute of New Jersey; Cancer Research UK (C490/A6187, C490/A10119, C490/A10124, C536/A13086, C536/A6689); the Celma Mastry Ovarian Cancer Foundation ; the Danish Cancer Society (94-222-52); the ELAN Program of the University of Erlangen-Nuremberg; the Eve Appeal (Oak Foundation); the Fred C. and Katherine B. Andersen Foundation; the German Cancer Research Center; the German Federal Ministry of Education and Research of Germany, Program of Clinical Biomedical Research (01GB 9401); the Helsinki University Central Hospital Research Fund; Helse Vest; Imperial Experimental Cancer Research Centre (C1312/A15589); the L & S Milken Foundation; the Lon V. Smith Foundation (LVS-39420); the Mayo Foundation; the Mermaid I project; the Minnesota Ovarian Cancer Alliance; the National Health and Medical Research Council (NHMRC) of Australia (199600, 209057, 251533, 396414, 400281, and 504715); Nationaal Kankerplan of Belgium; the Norwegian Cancer Society; the Norwegian Research Council; the OHSU Foundation; the Polish Ministry of Science and Higher Education (4 PO5C 028 14, 2 PO5A 068 27); Pomeranian Medical University; Radboud University Medical Center; the Roswell Park Cancer Institute Alliance Foundation; the Royal Marsden Hospital; the Rudolf-Bartling Foundation; the Sigrid Juselius Foundation; the state of Baden-Württemberg through Medical Faculty of the University of Ulm (P.685); the UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge and the University College London Hospitals; the US Army Medical Research and Material Command (DAMD17-98-1- 8659, DAMD17-01-1-0729, DAMD17-02-1-0666, DAMD17-02-1-0669, W81XWH-10-1-0280); the US National Cancer Institute (K07-CA095666, K07-CA143047, K22-CA138563, N01-CN55424, N01-PC067010, N01-PC035137, P01-CA017054, P01-CA087696, P30-CA15083, P50-CA105009, P50- CA136393, R01-CA014089, R01-CA016056, R01-CA017054, R01-CA049449, R01-CA050385, R01-CA054419, R01- CA058598, R01-CA058860, R01-CA061107, R01-CA061132, R01-CA063682, R01-CA064277, R01-CA067262, R01-CA071766, R01-CA074850, R01-CA076016, R01-CA080742, R01-CA080978, R01-CA083918, R01-CA087538, R01- CA092044, R01-095023, R01-CA106414, R01-CA122443, R01-CA112523, R01-CA114343, R01-CA126841, R01- CA136924, R01-CA149429, R03-CA113148, R03-CA115195, R37-CA070867, R37-CA70867, U01-CA069417, U01- CA071966 and Intramural research funds); the US National Institutes of Health/National Center for Research Resources/General Clinical Research Center (MO1-RR000056); and the US Public Health Service (PSA-042205).

L.E.K. was supported by a Canadian Institutes of Health Research Investigator award (MSH-87734). P.M.W. is supported by the NHMRC of Australia. B.Y.K. holds an American Cancer Society Early Detection Professorship (SIOP-06-258-01- COUN). F.M. is supported by a K-award from the National Cancer Institute (K07-CA080668).

List of Abbreviations

1-C

one-carbon

ADSL

adenylosuccinate lyase

ADSS

adenylosuccinate synthase

AK3

adenylate kinase 3

ATIC

5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase

CAD

carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase

CMPK1

cytidine monophosphate (UMP-CMP) kinase 1, cytosolic

CTPS

CTP synthase 1

DCK

deoxycytidine kinase

DHODH

dihydroorotate dehydrogenase (quinone)

DNMT3B

DNA (cytosine-5-)-methyltransferase 3 beta

DPYD

dihydropyrimidine dehydrogenase

DPYS

dihydropyrimidinase

GART

phosphoribosylglycinamide formyltransferase, phosphoribosylglycinamide synthetase, phosphoribosylaminoimidazole synthetase

GCTA

Genome-wide Complex Trait Analysis

GMPS

guanine monphosphate synthase

iCOGS

international Collaborative Oncology Gene-environment Study

IMPDH1

IMP (inosine 5'-monophosphate) dehydrogenase 1

IMPDH2

IMP (inosine 5'-monophosphate) dehydrogenase 2

LD

linkage disequilibrium

LRT

likelihood ratio test

MAF

minor allele frequency

NME6

NME/NM23 nucleoside diphosphate kinase 6

OC

Ovarian carcinoma

OCAC

Ovarian Cancer Association Consortium

OR

odds ratio

PAICS

phosphoribosylaminoimidazole carboxylase, phosphoribosylaminoimidazole succinocarboxamide synthetase

PFAS

phosphoribosylformylglycinamidine synthase

PPAT

phosphoribosyl pyrophosphate amidotransferase

PRPS2

phosphoribosyl pyrophosphate synthetase 2

QC

quality control

RRM2B

ribonucleotide reductase M2 B (TP53 inducible)

SHMT1

serine hydroxymethyltransferase 1 (soluble)

SNP

single nuctleotide polymorphism

TYMS

thymidylate synthase

UMPS

uridine monophosphate synthetase

Footnotes

Conflict of Interest Statement

The authors declare that there are no financial or commercial conflicts of interest.

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