Background: Inherited risk of pancreatic cancer has been associated with mutations in several genes, including BRCA2, CDKN2A (p16), PRSS1, and PALB2. We hypothesized that common variants in these genes, single nucleotide polymorphisms (SNP), may also influence risk for pancreatic cancer development.

Methods: A clinic-based case-control study in non-Hispanic white persons compared 1,143 patients with pancreatic adenocarcinoma with 1,097 healthy controls. Twenty-eight genes directly and indirectly involved in the Fanconi/BRCA pathway (includes BRCA1, BRCA2, and PALB2) were identified and 248 tag SNPs were selected. In addition, 11 SNPs in CDKN2A, PRSS1, and PRSS2 were selected. Association studies were done at the gene level by principal components analysis, whereas recursive partitioning analysis was used to investigate pathway effects. At the individual SNP level, adjusted additive, dominant, and recessive models were investigated, and gene-environment interactions were also assessed.

Results: Gene level analyses showed no significant association of any genes with altered pancreatic cancer risk. Multiple single SNP analyses showed associations, which will require replication. Exploratory pathway analyses by recursive partitioning showed no association between SNPs and risk for pancreatic cancer.

Conclusion: In a candidate gene and pathway SNP association study analysis, common variations in the Fanconi/BRCA pathway and other candidate familial pancreatic cancer genes are not associated with risk for pancreatic cancer. Cancer Epidemiol Biomarkers Prev 2009;18(9):2549–52)

The double-stranded break repair pathway is a unique pathway of response to DNA cross-linking and subsequent repair; the exact mechanism of which is as yet undetermined (1). High-penetrance mutations in double-stranded break repair genes such as BRCA1 and BRCA2 increase susceptibility to cancer, most notably breast and ovarian cancers (2, 3), but also have been reported in familial pancreatic cancer kindreds (4-6). Truncating mutations in FANCC and FANCG have been reported in a few cases of sporadic young-onset pancreatic cancer, although their contribution to pancreatic cancer risk is unclear (7-9), whereas truncating mutations in PALB2 have also recently been documented in familial pancreatic cancer kindreds (10). Other genes involved in hereditary susceptibility to pancreatic cancer include CDKN2A (familial melanoma) and PRSS1 (hereditary pancreatitis).

We hypothesized that low-penetrance polymorphisms could confer a modest increase in risk for pancreatic cancer. Unlike highly penetrant truncating mutations or large deletions, these polymorphic variants may be associated with alterations in gene function or expression to a more limited extent.

Written informed consent was obtained from each subject for participation in this study and provision of biospecimens. This study was approved by the Mayo Clinic Institutional Review Board.

Cases

From October 2000 to March 2007, patients with clinically documented pancreatic adenocarcinoma were recruited to a prospective registry during their visit to Mayo Clinic (Rochester, Minnesota or Jacksonville, Florida), as previously described (11).

Controls

From May 2004 to February 2007, healthy controls were recruited from the General Internal Medicine clinics at Mayo Clinic (Rochester). Controls were frequency matched to cases on sex, location of residence, age at time of recruitment (in 5-y increments), and race/ethnicity, as previously described (11).

Single Nucleotide Polymorphism Selection

A linkage disequilibrium–based tag single nucleotide polymorphism (SNP) strategy was used (12). Known genes directly and indirectly involved in double-stranded break repair were identified (n = 28; Table 1), as well as PRSS1, PRSS2, and CDKN2A. Genotype data were compiled from HapMap, SeattleSNPs, and National Institute of Environmental Health Sciences SNPs. We used LdSelect software (version 1.0; ref. 13) for SNP selection from each gene, including 5 kb upstream/downstream, using criteria of r2 ≥ 0.9 and minor allele frequency > 0.05. A total of 259 SNPs in 31 genes were selected.

Table 1.

Demographic and clinical characteristics of cases and controls

VariableCases (n = 1,143)Controls (n = 1,097)P*
Age at diagnosis (cases) or study entry (controls; +SD) 65.5 ±10.7 65.6 ±10.8 0.79 
Age (<60 y) 329 (29%) 297 (27%) 0.37 
Male sex 668 (58%) 557 (51%) <0.001 
Non-Hispanic whites 1,143 (100%) 1,097 (100%)  
Ever smoker 682 (60%) 505 (46%) <0.001 
Smoking status   <0.001 
    Never smoker 455 (40%) 592 (54%)  
    Former smoker 527 (47%) 458 (42%)  
    Current smoker 148 (13%) 41 (4%)  
    Missing 13  
Years smoked (+SD) 22.4 ±16.9 18.2 ±14.0 <0.001 
Pack-years smoked (+SD) 17.0 ±23.0 9.3 ±17.2 <0.001 
BMI (+SD) 27.8 ±5.5 27.2 ±4.7 0.010 
Region   <0.001 
    Minnesota, Iowa, or Wisconsin (Tristate) 579 (51%) 748 (68%)  
    North Dakota or South Dakota 94 (8%) 40 (4%)  
    Other United States 448 (39%) 308 (28%)  
    Other country 22 (2%) 1 (0%)  
Diabetes mellitus   <0.001 
    No 801 (70%) 1,008 (92%)  
    Yes 342 (30%) 89 (8%)  
    Diabetes mellitus (>2 y before pancreatic cancer diagnosis) 224  
Pancreas cancer stage at enrollment 
    Resectable 328 (29%) 0 —  
    Locally advanced 379 (33%) 0 —  
    Metastatic 430 (38%) 0 —  
Not otherwise specified 6 (1%) 0 —  
Family history of pancreatic cancer (first degree) 79 (7%) 43 (4%) 0.002 
VariableCases (n = 1,143)Controls (n = 1,097)P*
Age at diagnosis (cases) or study entry (controls; +SD) 65.5 ±10.7 65.6 ±10.8 0.79 
Age (<60 y) 329 (29%) 297 (27%) 0.37 
Male sex 668 (58%) 557 (51%) <0.001 
Non-Hispanic whites 1,143 (100%) 1,097 (100%)  
Ever smoker 682 (60%) 505 (46%) <0.001 
Smoking status   <0.001 
    Never smoker 455 (40%) 592 (54%)  
    Former smoker 527 (47%) 458 (42%)  
    Current smoker 148 (13%) 41 (4%)  
    Missing 13  
Years smoked (+SD) 22.4 ±16.9 18.2 ±14.0 <0.001 
Pack-years smoked (+SD) 17.0 ±23.0 9.3 ±17.2 <0.001 
BMI (+SD) 27.8 ±5.5 27.2 ±4.7 0.010 
Region   <0.001 
    Minnesota, Iowa, or Wisconsin (Tristate) 579 (51%) 748 (68%)  
    North Dakota or South Dakota 94 (8%) 40 (4%)  
    Other United States 448 (39%) 308 (28%)  
    Other country 22 (2%) 1 (0%)  
Diabetes mellitus   <0.001 
    No 801 (70%) 1,008 (92%)  
    Yes 342 (30%) 89 (8%)  
    Diabetes mellitus (>2 y before pancreatic cancer diagnosis) 224  
Pancreas cancer stage at enrollment 
    Resectable 328 (29%) 0 —  
    Locally advanced 379 (33%) 0 —  
    Metastatic 430 (38%) 0 —  
Not otherwise specified 6 (1%) 0 —  
Family history of pancreatic cancer (first degree) 79 (7%) 43 (4%) 0.002 

*P value unadjusted.

Only Non-Hispanic whites included in the analysis.

Defined as <100 cigarettes in lifetime.

Genotyping

DNA samples were analyzed in the Mayo Clinic Genotyping Shared Resource using an Illumina Golden Gate Custom 768-plex OPA panel using the standard protocol. BeadStudio II software was used to analyze the data and prepare reports. DNA samples from cases and controls were randomly placed on plates.

Quality Control

Positive and negative controls were run in parallel to assess the quality of genotyping. All genotype clusters were manually inspected by a molecular geneticist (J.M.C.). Call rates were high for SNPs overall, at 99.6% rate for samples, and 95.1% for loci. Forty-seven pairs were used for duplicate concordance, with a 99.9% concordance rate. Eighteen SNPs failed to amplify and 91 samples had a call rate of 0.

Statistical Methods

Risk factor questionnaires were completed by 100% of controls and 71% of cases. For cases missing risk factor questionnaires, clinical data were extracted from available medical records, with a high intermethod reliability as previously reported (12). Hardy-Weinberg equilibrium was confirmed in controls for each SNP by χ2 test. Those failing were excluded from the analysis (n = 2). A principal components analysis (14) approach was used to test for an overall association between disease and the multiple SNPs genotyped within each gene. The necessary number of principal components needed for each gene was determined using a 90% explained variance criteria. Once the necessary principal components were determined, multivariable logistic regression models were constructed to assess the significance of each gene. We had 88% power to detect an odds ratio of 1.35 with a minor allele frequency of 0.10 and 90% power to detect an odds ratio of 1.25 with a minor allele frequency of 0.25.

Allele associations were assessed using the Pearson χ2 or Fisher's exact test (when sample sizes were small), and genotype associations were assessed using the Cochran-Armitage trend test. Multivariate analysis compared genotype frequencies in cases and controls adjusted for age, sex, ever/never smoking status, family history of pancreatic cancer (first degree), and body mass index (BMI).

Demographic characteristics of cases and controls are shown in Table 1. There were differences in BMI, sex, percent of ever smokers, percent reporting a first-degree relative with pancreatic cancer, and diabetes. Adjusted principal components analyses for each gene (Table 2) showed no association for any gene with pancreatic cancer risk. Logistic regression analyses at the single SNP level for each gene were also done using multivariable additive, dominant, and recessive models. Statistically significant associations are shown in Supplementary Table S1, although no associations would remain significant after Bonferroni adjustment. The proportion of positive findings (4.0-4.2% for the three models) are within the range expected by chance (α = 0.05). Recursive partitioning analysis was done as an exploratory method to assess SNP-SNP associations within the pathway and SNP-environment interactions. No partitions by SNPs reached statistical significance in these analyses, and no interaction was identified from this analysis (Supplementary Fig. S1).

Table 2.

Gene level principal components analysis of pancreatic cancer risk associations

Gene nameNo. SNPsChromosome locationUnadjusted P*Adjusted PPrincipal components
ATM 11 11q22.3 0.7714 0.70308 
ATR 10 3q22-24 0.7710 0.69677 
BRCA1 17q21 0.7091 0.70542 
BRCA2 22 13q12.3 0.8191 0.69003 
BRIP1 16 17q22 0.2312 0.57038 
CDKN2A 9p21 0.8854 0.80569 
CHEK1 10 11q22-23 0.5427 0.43054 
FANCA 12 16q24.3 0.8330 0.99897 
FANCC 17 9q22.3 0.5472 0.18791 
FANCD2 3p25.3 0.2406 0.19372 
FANCE 6p21-22 0.6380 0.34821 
FANCF 11p15 0.1601 0.16791 
FANCG 9p13 0.7913 0.91465 
FANCL (PHF922 2p16.1 0.3318 0.45418 
FANCM 14q21.3 0.2517 0.19150 
PALB2 16p12 0.1992 0.38942 
MCPH1 8p23 0.1268 0.20708 
MDC1 6pter-p21.3 0.1056 0.12931 
MRE11A 18 11q21 0.3097 0.54468 
NBN (NBS121 8q21 0.3246 0.52926 
PRSS1 7q35 0.3033 0.40413 
PRSS2 7q35 0.8843 0.87790 
RAD50 5q31 0.9222 0.98552 
RAD51 15q15.1 0.5609 0.88217 
RAD51L3 17q11 0.3954 0.42135 
RAD54 8q21.3-22 0.4097 0.17993 
RBBP8 18q11.2 0.5038 0.69827 
SHFM1 17q21 0.8274 0.72815 
TOPBP1 12 3q22.1 0.3638 0.23532 
TP53BP1 15q15-21 0.9888 0.98525 
USP1 1p31-32 0.2560 0.27569 
Total 
31 259     
Gene nameNo. SNPsChromosome locationUnadjusted P*Adjusted PPrincipal components
ATM 11 11q22.3 0.7714 0.70308 
ATR 10 3q22-24 0.7710 0.69677 
BRCA1 17q21 0.7091 0.70542 
BRCA2 22 13q12.3 0.8191 0.69003 
BRIP1 16 17q22 0.2312 0.57038 
CDKN2A 9p21 0.8854 0.80569 
CHEK1 10 11q22-23 0.5427 0.43054 
FANCA 12 16q24.3 0.8330 0.99897 
FANCC 17 9q22.3 0.5472 0.18791 
FANCD2 3p25.3 0.2406 0.19372 
FANCE 6p21-22 0.6380 0.34821 
FANCF 11p15 0.1601 0.16791 
FANCG 9p13 0.7913 0.91465 
FANCL (PHF922 2p16.1 0.3318 0.45418 
FANCM 14q21.3 0.2517 0.19150 
PALB2 16p12 0.1992 0.38942 
MCPH1 8p23 0.1268 0.20708 
MDC1 6pter-p21.3 0.1056 0.12931 
MRE11A 18 11q21 0.3097 0.54468 
NBN (NBS121 8q21 0.3246 0.52926 
PRSS1 7q35 0.3033 0.40413 
PRSS2 7q35 0.8843 0.87790 
RAD50 5q31 0.9222 0.98552 
RAD51 15q15.1 0.5609 0.88217 
RAD51L3 17q11 0.3954 0.42135 
RAD54 8q21.3-22 0.4097 0.17993 
RBBP8 18q11.2 0.5038 0.69827 
SHFM1 17q21 0.8274 0.72815 
TOPBP1 12 3q22.1 0.3638 0.23532 
TP53BP1 15q15-21 0.9888 0.98525 
USP1 1p31-32 0.2560 0.27569 
Total 
31 259     

*Likelihood ratio test.

Likelihood ratio test adjusted for age, sex, ever/never smoking, BMI, diabetes, and first-degree family history of pancreatic cancer.

Number of principal components needed to meet 90% explained variance criteria.

This large case-control study designed to assess common variants in genes associated with hereditary cancer or familial pancreatic cancer did not find associations of polymorphic variants with pancreatic cancer risk. Therefore, we conclude that functional variations of modest effect that might be associated with common polymorphisms in these genes do not seem to confer increased risk for pancreatic cancer. For instance, for the DNA repair genes, it is probable that only somatic loss of heterozygosity in the setting of a defective allele results in a neoplastic transformation, but minor germ-line variation in DNA repair capacity does not seem to meaningfully influence risk for pancreatic cancer. When high-throughput DNA sequencing is practically scaled to large numbers of subjects, it may be possible to identify high-penetrance mutations in key pathways that confer risk in “sporadic” pancreatic cancer patients as well as in the familial pancreatic cancer setting.

In a tag SNP analysis of genes associated with familial pancreatic cancer and genes associated with DNA double-stranded break repair, polymorphic variants were not associated with risk for pancreatic cancer.

No potential conflicts of interest were disclosed.

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.

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Supplementary data