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. Author manuscript; available in PMC: 2012 Oct 1.
Published in final edited form as: Pharmacogenet Genomics. 2011 Oct;21(10):615–623. doi: 10.1097/FPC.0b013e3283493a57

Xenobiotic metabolizing gene variants, pesticide use, and risk of prostate cancer

Stella Koutros 1, Gabriella Andreotti 1, Sonja I Berndt 1, Kathryn Hughes Barry 1, Jay H Lubin 1, Jane A Hoppin 2, Freya Kamel 2, Dale P Sandler 2, Laurie A Burdette 3, Jeffrey Yuenger 3, Meredith Yeager 1,3, Michael CR Alavanja 1, Laura E Beane Freeman 1
PMCID: PMC3172373  NIHMSID: NIHMS307706  PMID: 21716162

Abstract

Background

To explore associations with prostate cancer and farming, it is important to investigate the relationship between pesticide use and single nucleotide polymorphisms (SNPs) in xenobiotic metabolic enzyme (XME) genes.

Objectives

We evaluated pesticide-SNP interactions between 45 pesticides and 1,913 XME SNPs with respect to prostate cancer among 776 cases and 1,444 controls in the Agricultural Health Study.

Methods

We used unconditional logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Multiplicative SNP-pesticide interactions were calculated using a likelihood ratio test.

Results

A positive monotonic interaction was observed between petroleum oil/petroleum distillate use and rs1883633 in the oxidative stress gene glutamate-cysteine ligase (GCLC) (p-interaction=1.0×10−4); men carrying at least one variant allele (minor allele) experienced an increased prostate cancer risk (OR=3.7, 95% CI: 1.9–7.3). Among men carrying the variant allele for thioredoxin reductase 2 (TXNRD2) rs4485648, microsomal epoxide hyrdolase 1 (EPHX1) rs17309872, or myeloperoxidase (MPO) rs11079344, increased prostate cancer risk was observed with high compared to no petroleum oil/petroleum distillate (OR=1.9, 95% CI: 1.1–3.2, p-interaction=0.01), (OR=2.1, 95% CI: 1.1–4.0, p-interaction=0.01), or terbufos (OR=3.0, 95% CI: 1.5–6.0 p-interaction=2.0×10−3) use, respectively. No interactions were deemed noteworthy at the false discovery rate = 0.20 level; the number of observed interactions in XMEs was comparable to the number expected by chance alone.

Conclusions

We observed several pesticide-SNP interactions in oxidative stress and phase I/phase II enzyme genes and risk of prostate cancer. Additional work is needed to explain the joint contribution of genetic variation in XMEs, pesticide use, and prostate cancer risk.

Keywords: Prostate cancer, pesticides, xenobiotic metabolizing enzymes, single nucleotide polymorphism, interaction

INTRODUCTION

There have been few environmental factors that have been identified to alter prostate cancer risk. However, results from the Agricultural Health Study (AHS), a prospective cohort of licensed private and commercial pesticide applicators in Iowa and North Carolina, show that pesticide applicators have a significantly higher risk of prostate cancer than men in the general population of Iowa and North Carolina [1]. The metabolism and subsequent excretion of pesticides from the body requires a series of chemical reactions that depend on xenobiotic metabolic enzymes (XMEs) [2]. Phase I and phase II enzyme reactions are well-described processes for the clearance of xenobiotic substances. The enzymes responsible for mediating phase I reactions encompass members of the cytochrome P450 (CYPs) superfamily [3]. Substantial literature documents the principal involvement of CYPs in the metabolism of specific xenobiotic substrates including some herbicides and organophosphophate (OP) insecticides [4–8]; still, the metabolism of many pesticides is not well characterized. Phase II enzymes also play a crucial role in xenobiotic metabolism and are a necessary part of the pathway to excretion. Phase II enzymes, such as sulfotransferases (SULTs), N-acetyltransferases (NATs), UDP-glucuronosyltransferases (UGTs), and glutathione S-transferases (GSTs] catalyze conjugation reactions of intermediates directly to form detoxification products, or further metabolize other reactive intermediates for future excretion [3]. In addition, key nuclear receptors including the aryl hydrocarbon receptor (AHR), pregnane X receptor (PXR or NR1I2) and the constitutive active/androstane receptor (CAR or NR1I3) can stimulate gene transcription in response to pesticides and affect their metabolism [9–11]. Accumulation of toxic pesticide intermediates can induce the production of reactive oxygen species (ROS), markers of oxidative stress, which can react with cellular DNA to cause mutation or gross DNA rearrangements [12;13]. For example, the roles of enzymatic antioxidants like superoxide dismutase (SOD) and catalase (CAT), or paraoxonase 1 (PON1) in the metabolism of highly toxic OP oxon metabolites, are well-described in the defense against ROS [12;14].

There is also evidence that genetic susceptibility plays a role in prostate cancer development. For example, twin studies have estimated that 42% of prostate cancer risk may be explained by genetic factors [15]. Recent genome-wide scans of prostate cancer have also identified high susceptibility loci in various gene regions [16;17]. Although none of the genome-wide scans to date have identified susceptibility loci in XMEs, these studies have not addressed the complex interactions that XMEs have with relevant exposures. Thus, in order to fully understand the relationship between pesticide use, genetic susceptibility in XMEs, and prostate cancer risk, it is important to investigate the role of the interaction between pesticides and genetic polymorphisms. Only one study has considered the joint contribution of two single nucleotide polymorphisms (SNPs) in XME genes and pesticide use on subsequent risk of prostate cancer; this study observed an elevated but non-significant risk among carriers of the allele variants [18].

In this study we evaluated the interaction between pesticide use and 1,913 SNPs in genes that code XMEs, and risk of prostate cancer in 2,220 AHS participants.

MATERIALS AND METHODS

Study population

The AHS is a prospective cohort study that includes 55,747 male licensed pesticide applicators in Iowa and North Carolina. Applicators were recruited from 1993 through 1997; a detailed description of this cohort has been previously published [19]. During a follow-up telephone interview conducted in 1999–2003, applicators were asked for a mouthwash rinse sample to provide DNA from buccal cells. Approximately 72% of all applicators who completed the follow-up interview returned a buccal sample. In addition, applicators with incident prostate cancer who did not return a sample at follow-up were asked separately to provide a mouthwash rinse sample, with 307/561 (55%) returning a sample. Men diagnosed with incident prostate cancer between 1993 and 2004 who also provided a buccal cell sample were included in the current nested-case-control study. Eligibility, inclusion and exclusion criteria have been previously described [20]. Breifly, cancer cases were coded using the International Classification of Diseases for Oncology, 2nd edition, and stage (local, regional, distant, unstaged) and grade (well differentiated, moderately differentiated, poorly differentiated, undifferentiated, missing) were abstracted by the state cancer registries in Iowa and North Carolina. Eligible controls were frequency matched 2:1 to cases by date of birth (+/− 1 year). Controls were male applicators who provided buccal cell material, were alive and not lost to follow-up at the time of case diagnosis, and had no previous cancer diagnosis except non-melanoma skin cancer. All participants for the nested case-control study are white. Based on these inclusion criteria, 841 cases (66% of total white cases in the cohort as of 2004) and 1,659 controls were identified (total N= 2,500). Due to genotyping space limitations 164 controls were excluded. Of the remaining samples, 108 were removed due to insufficient or poor DNA quality (N=20; 14 cases, 6 controls) or <90% completion rate (i.e. more than 10% of the SNP assays failed for a given sample, N=88; 47 cases, 41 controls). We further identified 5 individuals who were suspected to be non-white (<80% European ancestry using STRUCTURE software [21] or significant deviation from the first two components in principal components analysis [22]) leaving a final sample size of 776 cases and 1,444 controls. Informed consent was obtained and the study protocol was reviewed by all relevant Institutional Review Boards.

Genotyping, Gene/SNP selection, and Quality Control

DNA was extracted from buccal cells using the Autopure protocol (QIAGEN). Genotyping was performed at NCI’s Core Genotyping Facility (http://cgf.nci.nih.gov/operations/multiplex-genotyping.html) [23], using the Custom Infinium® BeadChip Assays (iSelect™) from Illumina Inc. as part of a collaborative genotyping effort of an array of 26,512 SNPs in 1,291 candidate genes. TagSNPs were chosen to cover candidate genes for the three ancestry populations [Caucasian (CEU), Japanese Tokyo (JPT) + Chinese Beijing (CHB) and Yoruba people of Ibadan, Nigeria (YRI)] in the HapMap Project (Data Release 20/Phase II, NCBI Build 36.1 assembly, dbSNPb126) to allow use of this custom iSelect panel for studies containing different ethnic populations. TagSNPs were chosen using a modified version of the method described by Carlson et al. [24] as implemented in the Tagzilla (http://tagzilla.nci.nih.gov/) software package. For each original candidate gene, SNPs within the region spanning 20 kb 5′ of the start of transcription to 10 kb 3′ of the end of the last exon were grouped using a binning threshold of r2=0.80, and tagSNPs chosen from these bins.

From the available data, we selected 149 candidate genes in the xenobiotic metabolism pathway, defined to include genes known to play a role in the biotransformation of xenobiotic substrates, including pesticides. Selected genes include those that encode phase I and phase II enzymes, receptors mediating the induction of xenobiotic biotransforming enzymes, and key enzymes in the regulation of the intracellular redox environment (oxidative stress). Additional genotyping for two glutathione transferase variants responsible for key conjugation reactions was also conducted; copy number variation assays for GSTM1 (Ex4+10+>-) and GSTT1 (Ex5–49+>-) deletions were performed separately using Applied Biosystems TaqMan® SNP Genotyping Assays. As a result, a total of 2,192 SNPs in 149 candidate genes in the xenobiotic metabolism pathway were identified. Exclusion for SNPs with low completion rate (<90% of samples) and SNPs showing evidence of deviation from Hardy–Weinberg proportions (p<1 x10−6) were made. SNPs with MAF<0.05 in the nested case-control samples were further excluded for analysis due to limited power. The overall genotyping rate was between 96% and 100% in the resultant N=1,913 SNPs in 149 candidate genes (Supplemental Table 1). Blinded duplicate samples (2%) were also included and concordance of these samples ranged from 96–100%.

Statistical Analysis

Unconditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between SNPs and prostate cancer, pesticides and prostate cancer and the interaction between SNPs, pesticide use and prostate cancer risk. For SNP analyses, genotypes were coded as counts of the risk allele assuming a log-additive model and models were adjusted for age (10 yr-intervals) and state (Iowa or North Carolina). Exposure to pesticides was classified from responses to two self-administered questionnaires that were completed at enrollment. These questionnaires collected comprehensive data on lifetime use of 50 pesticides; pesticides with a prevalence of use less than 5% in the current nested case-control subgroup were excluded leaving 45 pesticides for analysis (17 herbicides, 21 insecticides, 2 fumigants, and 5 fungicides). Participants were asked how many years they applied each chemical (1 year or less, 2–5, 6–10, 11–20, 21–30, or more than 30 years) and how many days the applicator personally used it in an average year (less than 5, 5–9, 10–19, 20–39, 40–59, 60–150, or more than 150 days). Pesticides were categorized by lifetime exposure days (years of use × days per year) in this analysis from AHS data release version P1REL0712.04. Lifetime exposure days were categorized as non-exposed, low, and high exposed to a given chemical using the median cut-point based on distribution of lifetime days among cases and controls together for each chemical. Pesticide models were adjusted for confounders of the pesticide-prostate cancer relationship and include age, state and family history of prostate cancer in first degree relative (no, yes, missing).

SNP-pesticide interactions were examined using a multiplicative model. The p-value for each SNP-pesticide interaction was computed by comparing nested models with and without the cross-product terms using a likelihood ratio test. SNP-pesticide interactions with p-interaction≤0.01 were carried further for exploration with stratification by genotype using a dominant model for increased power. The top five interaction results, regardless of the direction of the association, from each XME pathway are presented. We also hypothesized that pesticides increase the risk of prostate cancer; thus in order to highlight the most interesting findings, additional stratified results (with p-interaction≤0.01) are presented if a monotonic increasing risk across pesticide exposure strata was evident. All interaction models were adjusted for age and state. Additional factors that were examined but ultimately not considered in the modeling because they did not change point estimates by more than 10% were family history of prostate cancer in first degree relative (no, yes, missing), type of applicator (private or commercial), and other pesticide adjustment based on the correlations between selected pesticides. We were not able to explore aggressive prostate cancer alone due to small numbers.

We applied the false discovery rate (FDR) (Benjamini–Hochberg adjustment) method to account for the expected proportion of false discoveries [25]. FDR values were calculated separately for each pesticide and separately from the results of 1,913 tests (i.e., total number of SNPs studied) in the evaluation of the association between each SNP-pesticide interaction and the risk of prostate cancer. Interactions were deemed noteworthy at an FDR = 0.20 level.

RESULTS

Cases and controls selected for the nested case-control study were similar to all prostate cancer cases from the cohort in age, state of residence, applicator type, presence of familial prostate cancer, and in prostate cancer disease characteristics for cases (data not shown) [20]. A list of all 149 genes and the number of SNPs in each gene identified in the xenobiotic metabolizing pathway is presented in Supplemental Table 1.

We examined SNPs involved in regulating a number of enzymes, including 1,203 in phase I/phase II enzymes, 61 in key xenobiotic receptors, and 649 in key enzymes that regulate the intracellular redox environment Table 1 shows a summary of the SNP results, as well as the interaction results. The number of observed main effects of SNPs in the oxidative stress pathway (at the α=0.01 level) was slightly greater than the number expected by chance (1% of the 649 SNPs examined in this pathway); in contrast, we did not observe any departure from the expected numbers of main effects for SNPs in Phase I/Phase II enzyme genes or in receptor genes. When crossed with 45 pesticides, the number of observed interactions was comparable to the number expected by chance alone (at the α=0.01 level).

Table 1.

Summary results table for observed and expected single nucleotide polymorphism associations and pesticide interactions in xenobiotic metabolizing enzymes.

XME pathway Total SNPs Expected SNP Associations ≤ 0.01 Observed SNP Associations ≤ 0.01 Pesticides Examined Expected Interactions ≤0.01 Observed Interactions ≤0.01
Phase I/Phase II 1203 12 10 45 541 537
Receptors 61 <1 0 45 28 31
Oxidative Stress 649 7 13 45 292 295
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Xenobiotic Metabolizing Enzyme [XME]; Single Nucleotide Polymorphism (SNP).

Associations between SNPs in XME genes and prostate cancer that were statistically significant at the p ≤ 0.01 level are presented in Table 2. P-values for all 1,913 SNPs are presented in Supplemental Table 2. The strongest association was seen for rs933271 in the oxidative stress gene thioredoxin reductase 2 (TXNRD2) with ORper allele = 1.33 (95% CI: 1.16, 1.52), p-trend = 4.62×10−5. Five additional SNPs in this gene were associated with prostate cancer, rs5993882 (p-trend=0.0014), rs5746847 (p-trend=0.0036), rs4485648 (p-trend=0.0065), rs9606186 (p-trend=0.0078), rs6518591 (9-trend=0.0086). After simultaneous adjustment for these five TXNRD2 SNPs, only rs933271 remained significant (p=0.0075), suggesting a single independent signal. Associations between several other oxidative stress genes and prostate cancer were observed. Two SNPs in myeloperoxidase (MPO) were significantly associated with prostate cancer, rs11079344, ORper allele = 1.59 (95% CI: 1.22, 2.08) and rs8178406, ORper allele = 1.21 (95% CI: 1.07, 1.37). Two SNPs in microsomal epoxide hydrolase 1 (EPHX1) were also significantly associated with prostate cancer, rs2740168 (p-trend=0.0042) and rs2292566, ORper allele = 1.27 (95% CI: 1.07, 1.51). Additional associations in phase I/phase II enzyme genes including CYP2C18, and CYP2C19 were also observed.

Table 2.

Selected results (p-trend ≤ 0.01) for the association between prostate cancer and SNPs in xenobiotic metabolizing enzyme genes in the AHS PNCC

SNP SNP Region Risk allele RAF* Chromosome Gene XME pathway OR†95% CI p-trend
rs933271 −2080T>C C 0.26 22 TXNRD2 Oxidative Stress 1.33 (1.16, 1.52) 4.62E-05
rs11079344 −16363A>G A 0.92 17 MPO Oxidative Stress 1.59 (1.22, 2.08) 0.0007
rs5993882 −8206 T>G G 0.22 22 TXNRD2 Oxidative Stress 1.26 (1.09, 1.45) 0.0014
rs3760808 IVS1-1326C>T C 0.68 19 SULT2B1 Phase I, Phase II Enzymes 1.24 (1.08, 1.42) 0.0023
rs8178406 *3573T>C T 0.58 17 MPO Oxidative Stress 1.21 (1.07, 1.37) 0.0030
rs12979328 −12597A>C C 0.26 19 DHDH Oxidative Stress 1.22 (1.07, 1.40) 0.0034
rs5746847 IVS1-2382A>G C 0.54 22 TXNRD2 Oxidative Stress 1.20 (1.06, 1.36) 0.0036
rs2740168 IVS3+1328G>A G 0.58 1 EPHX1 Phase I, Phase II Enzymes 1.20 (1.06, 1.37) 0.0042
rs2287833 −15250T>A A 0.33 19 DHDH Oxidative Stress 1.20 (1.06, 1.37) 0.0044
rs10424237 IVS2+2144C>G C 0.52 19 SULT2B1 Phase I, Phase II Enzymes 1.20 (1.06, 1.35) 0.0047
rs854555 IVS8+1126C>A C 0.64 7 PON1 Oxidative Stress 1.21 (1.06, 1.38) 0.0049
rs2292566 Ex3-8G>A A 0.13 1 EPHX1 Phase I, Phase II Enzymes 1.27 (1.07, 1.51) 0.0057
rs2547238 IVS3-93G>C G 0.71 19 SULT2A1 Phase I, Phase II Enzymes 1.21 (1.06, 1.40) 0.0060
rs854550 Ex9-255C>T C 0.79 7 PON1 Oxidative Stress 1.25 (1.07, 1.47) 0.0065
rs4485648 IVS1-784A>G G 0.18 22 TXNRD2 Oxidative Stress 1.24 (1.06, 1.44) 0.0065
rs7659526 −17127T>C T 0.85 4 GSTCD Oxidative Stress 1.29 (1.07, 1.56) 0.0071
rs9606186 IVS1-1738G>C G 0.53 22 TXNRD2 Oxidative Stress 1.18 (1.04, 1.33) 0.0078
rs7874112 IVS4-5165A>G G 0.11 9 MTAP Oxidative Stress 1.30 (1.07, 1.57) 0.0079
rs2281891 Ex8+5C>T C 0.83 10 CYP2C18 Phase I, Phase II Enzymes 1.26 (1.06, 1.50) 0.0080
rs1322179 IVS5-5011C>T C 0.83 10 CYP2C19 Phase I, Phase II Enzymes 1.26 (1.06, 1.49) 0.0084
rs6518591 IVS1+5203T>C C 0.18 22 TXNRD2 Oxidative Stress 1.23 (1.05, 1.44) 0.0086
rs8102683 −7433C>T C 0.74 19 CYP2A6 Phase I, Phase II Enzymes 1.22 (1.05, 1.41) 0.0086
rs2296680 IVS5+221G>A G 0.83 10 CYP2C18 Phase I, Phase II Enzymes 1.25 (1.06, 1.49) 0.0089
rs12768009 IVS1+3235G>A G 0.83 10 CYP2C19 Phase I, Phase II Enzymes 1.25 (1.05, 1.48) 0.0100
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Single Nucleotide Polymorphism (SNP); Xenobiotic Metabolizing Enzyme (XME); Odds Ratio (OR); Confidence Interval (CI); Agricultural Health Study (AHS); Prostate Nested Case-Control Study (PNCC).

*

Risk Allele Frequency (RAF) among controls.

†

OR per risk allele assuming a log-additive model. Adjusted for age and state.

Associations between pesticide use and prostate cancer are presented in Table 3. Although no statistically significant positive associations between pesticides and prostate cancer were observed, there was suggestive evidence of increased risk (OR greater than 1.0) with increasing number of days of use of petroleum oil/petroleum distillate used as herbicide, terbufos, fonofos, phorate, and methyl bromide. Among chemicals with a lower prevalence of use (<30 %) in the nested case-control study (dieldrin, lindane, 2,4,5-T and others), some significant (p-trend<0.05) inverse associations were observed. A list of all 45 pesticides, their prevalence of use, and the median level of lifetime days of use of each chemical is presented in Supplemental Table 3.

Table 3.

Odds ratios and 95% CI for the association between prostate cancer and 45 pesticides in the AHS PNCC

Lifetime exposure days None Low High P trend
Ca/Co Ca/Co OR* (95% CI) Ca/Co OR* (95% CI)
Carbaryl 352/633 REF 110/224 0.84 (0.64, 1.10) 107/256 0.62 (0.46, 0.84) 0.003
2,4,5-T 500/898 REF 93/158 1.07 (0.81, 1.42) 50/148 0.60 (0.42, 0.84) 0.004
Lindane 606/1,089 REF 38/94 0.69 (0.47, 1.03) 29/80 0.64 (0.41, 0.99) 0.031
Diazinon 513/964 REF 68/117 1.03 (0.74, 1.42) 47/122 0.67 (0.47, 0.96) 0.032
Phorate 462/846 REF 79/155 0.73 (0.54, 0.99) 76/195 1.16 (0.92, 1.44) 0.041
Dieldrin 660/1,203 REF 21/52 0.78 (0.46, 1.31) 26-Jun 0.44 (0.18, 1.07) 0.044
DDT 373/699 REF 100/205 0.89 (0.68, 1.18) 104/239 0.77 (0.59, 1.01) 0.064
Chlordane 505/888 REF 77/190 0.69 (0.51, 0.92) 52/112 0.79 (0.55, 1.11) 0.092
2,4,5-TP 662/1,222 REF 17/31 0.96 (0.52, 1.75) 13/41 0.58 (0.31, 1.10) 0.095
Cyanazine 391/698 REF 151/309 0.84 (0.65, 1.07) 141/305 0.78 (0.60, 1.00) 0.104
Metolachlor 369/712 REF 179/303 1.14 (0.91, 1.44) 133/303 0.84 (0.65, 1.07) 0.109
Imazethapyr 411/773 REF 143/232 1.16 (0.89, 1.50) 127/293 0.81 (0.62, 1.05) 0.119
Heptachlor 545/1,003 REF 48/108 0.85 (0.59, 1.23) 52/124 0.77 (0.55, 1.10) 0.123
Malathion 225/399 REF 173/351 0.85 (0.67, 1.09) 142/310 0.80 (0.62, 1.04) 0.149
Permethrin (all) 575/1,100 REF 78/117 1.24 (0.92, 1.69) 55/128 0.80 (0.57, 1.12) 0.224
Petroleum oil/Petroleum Distillate 488/964 REF 56/113 1.00 (0.71, 1.41) 57/93 1.24 (0.87, 1.76) 0.236
2,4-D 135/218 REF 309/597 0.83 (0.64, 1.08) 288/582 0.79 (0.61, 1.04) 0.245
Pendimethalin 474/856 REF 73/173 0.74 (0.55, 0.99) 79/165 0.85 (0.63, 1.14) 0.247
Chlorothalonil 711/1,320 REF 23/54 0.74 (0.45, 1.24) 17/39 0.73 (0.40, 1.32) 0.257
Maneb/mancozeb 664/1,238 REF 15/35 0.77 (0.41, 1.45) 18/40 0.72 (0.40, 1.30) 0.279
Paraquat 592/1,082 REF 39/105 0.65 (0.44, 0.96) 34/67 0.82 (0.53, 1.29) 0.296
Alachlor 277/546 REF 223/400 1.08 (0.86, 1.35) 176/378 0.91 (0.72, 1.15) 0.298
Butylate 501/903 REF 43/122 0.64 (0.44, 0.93) 82/169 0.86 (0.64, 1.15) 0.305
Dicamba 324/573 REF 171/368 0.78 (0.61, 1.00) 181/360 0.82 (0.63, 1.05) 0.318
Metribuzin 433/792 REF 83/183 0.84 (0.62, 1.12) 92/192 0.87 (0.66, 1.16) 0.351
Toxaphene 585/1,084 REF 36/77 0.82 (0.54, 1.24) 43/86 0.87 (0.59, 1.28) 0.354
Coumaphos 610/1,144 REF 36/66 1.00 (0.66, 1.52) 30/66 0.83 (0.53, 1.30) 0.426
DDVP 603/1,123 REF 42/90 0.81 (0.55, 1.20) 44/93 0.85 (0.58, 1.24) 0.428
Captan 623/1,144 REF 30/68 0.81 (0.52, 1.27) 31/62 0.85 (0.54, 1.33) 0.491
Chlorimuron-ethyl 487/955 REF 97/173 1.10 (0.83, 1.44) 47/106 0.83 (0.58, 1.20) 0.504
Atrazine 189/375 REF 277/494 1.13 (0.89, 1.42) 275/545 1.00 (0.79, 1.27) 0.512
Benomyl 662/1,242 REF 19/33 1.03 (0.58, 1.86) 17/36 0.84 (0.46, 1.52) 0.556
Fonofos 511/992 REF 104/181 1.12 (0.85, 1.47) 74/132 1.07 (0.78, 1.46) 0.605
Aldicarb 675/1,269 REF 13/23 0.97 (0.48, 1.96) 14/28 0.84 (0.43, 1.64) 0.607
Parathion 627/1,176 REF 28/36 1.36 (0.82, 2.26) 24/50 0.83 (0.50, 1.38) 0.609
Aldrin 481/896 REF 63/148 0.80 (0.57, 1.10) 83/165 0.93 (0.70, 1.26) 0.628
Metalaxyl 590/1,113 REF 46/85 0.98 (0.67, 1.44) 36/67 0.92 (0.58, 1.44) 0.705
Carbon Disulfide/Carbon Tetrachloride 674/1,244 REF 21/39 1.02 (0.59, 1.76) 21/42 0.91 (0.53, 1.55) 0.742
Trifluralin 312/583 REF 186/362 0.95 (0.76, 1.20) 185/357 0.95 (0.75, 1.21) 0.762
EPTC 530/1,063 REF 80/112 1.41 (1.03, 1.93) 63/129 0.95 (0.68, 1.31) 0.781
Methyl bromide 637/1,215 REF 52/108 0.88 (0.60, 1.28) 56/97 1.05 (0.71, 1.53) 0.799
Chlorpyrifos 451/854 REF 137/248 1.02 (0.80, 1.30) 164/313 0.98 (0.78, 1.23) 0.838
Carbofuran 433/857 REF 129/212 1.18 (0.92, 1.51) 121/237 0.98 (0.77, 1.26) 0.845
Glyphosate 182/333 REF 285/561 0.90 (0.71, 1.14) 274/527 0.93 (0.73, 1.18) 0.886
Terbufos 406/803 REF 156/256 1.21 (0.95, 1.54) 124/246 1.01 (0.78, 1.30) 0.927
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Confidence Interval (CI); Agricultural Health Study (AHS); Prostate Nested Case-Control Study (PNCC).

*

Adjusted for age, state, and family history of prostate cancer.

Stratified odds ratios for the association between pesticides and prostate cancer for the top five interactions from each XME pathway are presented in Table 4. In general, the p-values for SNPs in Phase I/II enzymes and for oxidative stress SNPs were smaller compared with those for receptors. Of the fifteen interaction results presented, fourteen were qualitative (involving increased risk with exposure in one genotype stratum and decreased risk in the other) or showed a significant protective association with prostate cancer. However, one interaction showed evidence for a positive interaction with a monotonic trend of increasing risk among variant allele (minor allele) carriers; the risk of prostate cancer associated with low petroleum oil/petroleum distillate use was 1.6 times those with no use (OR=1.6, 95% CI: 0.9, 2.9) and for high petroleum oil/petroleum distillate use was 3.7 times those with no use (OR=3.7, 95% CI: 1.9–7.3) among men carrying at least one variant allele in the glutamate-cysteine ligase (GCLC) SNP rs1883633. None of the interactions presented in Table 4 were noteworthy after adjustment using the FDR method.

Table 4.

Stratified odds ratios and 95% CI, adjusted for age and state, for associations between pesticides and prostate cancer for top five interactions from each pathway

Pesticide Use
PHASE I/PHASE II ENZYEMES None Low High
SNP Gene SNP p-trend Pesticide Genotype Ca/Co Ca/Co OR (95% CI) Ca/Co OR (95% CI) P-interaction
rs1808682 CYP2B6 0.5846 DDT AA 204/432 REF 62/110 1.3 (0.9, 1.8) 60/125 1.0 (0.7, 1.5) 6.3 × 10−5
AG+GG 169/267 REF 38/95 0.6 (0.4, 0.9) 44/114 0.6 (0.4, 0.8)
rs9367980 TPMT 0.4652 Alachlor CC 168/256 REF 116/221 0.8 (0.6, 1.1) 92/199 0.7 (0.5, 1.0) 8.7 × 10−5
CT+TT 105/276 REF 105/170 1.7 (1.2, 2.3) 82/166 1.3 (0.9, 1.9)
rs2331564 UGT2A3 0.5114 Carbofuran CC 312/584 REF 89/149 1.1 (0.8, 1.5) 71/181 0.7 (0.5, 0.9) 1.3 × 10−4
CT+TT 116/264 REF 38/54 1.7 (1.0, 2.7) 47/53 2.0 (1.3, 3.1)
rs4646450 CYP3A5 0.1301 DDVP TT 432/748 REF 31/59 0.9 (0.6, 1.5) 22/73 0.5 (0.3, 0.9) 1.6 × 10−4
CT+CC 170/373 REF 11/31 0.7 (0.4, 1.5) 22/19 2.5 (1.3, 4.8)
rs7659526 GSTCD 0.0071 Cyanazine AA 322/486 REF 115/237 0.7 (0.5, 1.0) 103/235 0.6 (0.5, 0.9) 2.3 × 10−4
AG+GG 69/210 REF 35/71 1.4 (0.8, 2.4) 38/70 1.6 (0.9, 2.6)
RECEPTORS
rs2301677 AHR 0.3914 Malathion AA 106/241 REF 111/197 1.3 (0.9, 1.8) 90/166 1.2 (0.9, 1.7) 3.2 × 10−5
AG+GG 111/141 REF 54/138 0.5 (0.3, 0.7) 49/131 0.5 (0.3, 0.7)
rs3802082 AHR 0.1992 Malathion TT 133/272 REF 122/218 1.1 (0.8, 1.6) 97/193 1.0 (0.7, 1.4) 2.3 × 10−4
CT+CC 88/119 REF 42/116 0.5 (0.3, 0.8) 41/102 0.5 (0.3, 0.9)
rs2501870 NR1I3 0.9349 Glyphosate CC 93/207 REF 166/307 1.2 (0.9, 1.7) 157/275 1.3 (1.0, 1.8) 6.4 × 10−4
CT+TT 89/126 REF 119/254 0.6 (0.5, 0.9) 117/252 0.6 (0.4, 0.9)
rs3802082 AHR 0.1992 Toxaphene TT 371/691 REF 30/52 1.1 (0.7, 1.7) 34/51 1.2 (0.7, 1.9) 8.1 × 10−4
CT+CC 192/352 REF 6/23 0.5 (0.2, 1.2) 9/31 0.5 (0.2, 1.1)
rs2066853 AHR 0.1154 Malathion AA 171/332 REF 149/274 1.1 (0.8, 1.4) 123/247 1.0 (0.7, 1.3) 8.7 × 10−4
AG+GG 54/66 REF 24/76 0.4 (0.2, 0.7) 19/63 0.4 (0.2, 0.7)
OXIDATIVE STRESS
rs2237584 PON1 0.4856 Paraquat AA 544/954 REF 33/96 0.6 (0.4, 0.9) 26/63 0.7 (0.4, 1.1) 4.2 × 10−5
AG+GG 48/128 REF 6/9 1.7 (0.6, 5.1) 8/4 4.8 (1.4, 17.1)
rs3732533 TXNRD3 0.9289 Alachlor CC 258/473 REF 191/354 1.0 (0.8, 1.2) 152/343 0.8 (0.6, 1.0) 8.9 × 10−5
CT+TT 15/72 REF 31/41 4.0 (1.9, 8.3) 22/32 3.6 (1.6, 8.0)
rs1883633 GCLC 0.2716 Petroleum Oil AA 376/687 REF 36/81 0.8 (0.5, 1.3) 34/77 0.8 (0.5, 1.3) 1.0 × 10−4
AG+GG 11/274 REF 20/32 1.6 (0.9, 2.9) 23/16 3.7 (1.9, 7.3)
rs13054371 TXNRD2 0.5143 Aldrin TT 153/227 REF 10/44 0.4 (0.2, 0.9) 20/58 0.6 (0.4, 1.1) 1.9 × 10−4
CT+CC 328/668 REF 52/104 1.0 (0.7, 1.4) 63/107 1.2 (0.8, 1.7)
rs230819 SEPP1 0.0251 2,4,5-T AA 136/360 REF 16/51 0.6 (0.3, 1.0) 11/42 0.5 (0.2, 1.0) 2.5 × 10−4
AG+GG 354/614 REF 76/104 1.3 (0.9, 1.8) 38/99 0.7 (0.5, 1.0)
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In Table 5, we present all results that show a positive monotonic trend with a p-interaction for pesticides and prostate cancer <0.01, regardless of pathway. Several SNP-pesticide combinations in oxidative stress genes and phase I/phase II enzyme genes displayed this pattern, but none in the receptor pathway, although fewer tests were conducted for receptor SNPs. In addition to the positive interaction between petroleum oil/petroleum distillate and rs1883633, in the oxidative stress gene GCLC (p-interaction=1.0×10−4), other notable pesticide interactions among variants which also showed independent associations with prostate cancer were observed. Among men carrying the variant allele for TXNRD2 rs4485648, EPHX1 rs17309872, or MPO rs11079344, increased prostate cancer risk was observed with high compared to no petroleum oil/petroleum distillate (OR=1.9, 95% CI: 1.1–3.2, p-interaction=0.01), (OR=2.1, 95% CI: 1.1–4.0, p-interaction=0.01), or terbufos (OR=3.0, 95% CI: 1.5–6.0 p-interaction=2.0×10−3) use, respectively. None of the interactions presented in Table 5 were noteworthy after adjustment using the FDR method.

Table 5.

Stratified odds ratios for the association between pesticides and prostate cancer stratified by oxidative stress and Phase I/Phase II enzyme genotype where p-interaction ≤ 0.01.

Pesticide Use
OXIDATIVE STRESS None Low High
SNP Gene SNP p-trend Pesticide Genotype Ca/Co Ca/Co OR* (95% CI) Ca/Co OR* (95% CI) P-interaction
rs1883633 GCLC 0.2716 Petroleum Oil AA 376/687 REF 36/81 0.8 (0.5, 1.3) 34/77 0.8 (0.5, 1.3) 1.0 × 10−4
AG+GG 111/274 REF 20/32 1.6 (0.9, 2.9) 23/16 3.7 (1.9, 7.3)
rs10040697 GLRX 0.1111 Petroleum Oil CC 368/663 REF 39/86 0.8 (0.6, 1.2) 33/63 1.0 (0.6, 1.5) 9.0 × 10−3
CT+TT 117/294 REF 17/26 1.7 (0.8, 3.4) 24/28 2.2 (1.2, 4.1)
rs17299478 NQO1 0.4819 Petroleum Oil CC 347/690 REF 39/91 0.9 (0.6, 1.3) 38/75 1.1 (0.7, 1.6) 5.0 × 10−3
CT+TT 141/274 REF 17/22 1.6 (0.8, 3.0) 19/18 2.2 (1.1, 4.3)
rs4485648 TXNRD2 0.0065 Petroleum Oil TT 301/634 REF 31/86 0.8 (0.5, 1.2) 26/61 0.9 (0.6, 1.5) 0.010
CT+CC 184/326 REF 25/27 1.7 (0.9, 3.0) 31/31 1.9 (1.1, 3.2)
rs11079344 MPO 0.0007 Terbufos AA 35/655 REF 133/211 1.2 (0.9, 1.5) 99/204 0.9 (0.7, 1.2) 2.0 × 10−3
AG+GG 32/124 REF 17/36 1.8 (0.9, 3.7) 22/30 3.0 (1.5, 6.0)
rs9332950 MGST1 0.288 Methyl Bromide GG 484/911 REF 41/88 0.8 (0.6, 1.3) 38/84 0.8 (0.5, 1.3) 4.0 × 10−3
GC+CC 145/283 REF 11/19 1.1 (0.5, 2.4) 17/10 3.1 (1.3, 7.5)
rs17309872 GSS 0.6370 Atrazine AA 170/309 REF 242/430 1.0 (0.8, 1.3) 235/480 0.9 (0.7, 1.2) 9.0 × 10−3
AT+TT 18/66 REF 35/64 2.1 (1.1, 4.3) 40/35 2.3 (1.2, 4.6)
PHASE I/PHASE II ENZYEMES
rs7291934 SULT4A1 0.4650 Fonofos AA 399/726 REF 68/135 1.0 (0.7, 1.3) 51/108 0.9 (0.6, 1.3) 5.0 × 10−4
AG+GG 96/248 REF 33/42 1.9 (1.1, 3.3) 19/19 2.4 (1.2, 4.9)
rs2331564 UGT2A3 0.5114 Fonofos CC 361/674 REF 71/135 1.0 (0.7, 1.4) 41/99 0.8 (0.5, 1.2) 6.0 × 10−4
CG+GG 141/304 REF 33/41 1.7 (1.0, 2.9) 32/31 2.2 (1.3, 3.9)
rs8192775 CYP2E1 0.8473 Chlorpyrifos GG 393/713 REF 116/215 1.0 (0.8, 1.3) 131/278 0.9 (0.7, 1.1) 1.0 × 10−3
AG+AA 58/139 REF 20/33 1.5 (0.8, 2.9) 33/33 2.5 (1.4, 4.5)
rs12676857 NAT2 0.1233 Carbofuran TT 325/588 REF 92/157 1.1 (0.8, 1.4) 82/185 0.8 (0.6, 1.1) 3.0 × 10−3
CT+CC 108/268 REF 37/55 1.7 (1.0, 2.7) 38/52 1.8 (1.1, 3.0)
rs5764318 SULT4A1 0.7212 Methyl Bromide TT 449/815 REF 33/78 0.7 (0.5, 1.2) 37/79 0.8 (0.5, 1.3) 6.0 × 10−3
CT+CC 186/396 REF 19/30 1.3 (0.7, 2.5) 19/18 2.2 (1.0, 4.5)
rs2292566 EPHX1 0.0057 Petroleum Oil GG 350/728 REF 41/85 1.0 (0.7, 1.5) 33/70 1.0 (0.7, 1.6) 0.010
AG+AA 137/236 REF 15/27 1.0 (0.5, 2.0) 24/21 2.1 (1.1, 4.0)
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*

Adjusted for age and state.

DISCUSSION

In this nested case-control study, we evaluated the interaction between genes coding for XMEs and pesticide use on the risk of prostate cancer. We used two approaches to present interaction results. The first approach highlights the top five interactions observed in each XME pathway (Table 4). Among these interactions, the majority showed biologically less plausible inverse associations, qualitative interactions, or unstable point estimates due to small cell counts. For the second approach, we presented interactions (Table 5) where at least one genotype stratum showed a significant positive pesticide effect on prostate cancer and a monotonic pattern across pesticide use strata (from low to high use). The interaction between petroleum oil/petroleum distillate and the promoter region SNP rs1883633, in GCLC was identified in both approaches. Several other notable interactions in oxidative stress and phase I/phase II enzyme genes based on the stratified pattern of pesticide use by genotype were observed, including several interactions with SNPs that also showed a main effect on prostate cancer in our study; however, none were deemed noteworthy at the FDR = 0.20 level. Furthermore, the number of observed interactions was comparable to the number expected by chance alone.

The GCLC gene is shown to have oxidative stress-responsive elements in the promoter/enhancer region [26;27] and polymorphisms that are associated with decreased GCLC expression are suggested to be important determinants of susceptibility to oxidative stress and DNA damage [28]. While this SNP was not independently associated with prostate cancer, there does appear to be a modifying effect of GCLC genotype on the association between petroleum oil/petroleum distillate use and prostate cancer. The use of petroleum oil/petroleum distillate has not been previously associated with prostate cancer in AHS analyses [29], however, there appears to be a non-significant but positive association with petroleum oil herbicide use in this subset of nested case-control participants. Historically, petroleum oils and distillates, hydrocarbons derived from petroleum, have been used as herbicides [30]. It is difficult to interpret the effect of the use of petroleum oil-based pesticides on prostate cancer in this study because of the wide variability in use and structure as well as the lack of specificity about its use in the questionnaire data. Nonetheless, several other interactions with this chemical were observed in oxidative stress pathway SNPs, implicating this mechanism in pesticide-related prostate carcinogenesis.

Other interactions with petroleum oil were also observed. We observed an increased risk of prostate cancer associated with increasing levels of petroleum oil use among variant allele carriers of the TXNRD2 SNP rs4485648, which also had an association with prostate cancer (SNP p-trend=0.0065). TXNRD2 is a key enzyme in the regulation of the intracellular redox environment [31]. Thus, polymorphisms in these enzymes may result in an imbalance in the oxidative stress/antioxidant status [32]. These oxygen radicals may cause damage to DNA and chromosomes, induce epigenetic alterations, and interact with oncogenes or tumor suppressor genes [33;34] and increase prostate cancer risk. It is important to consider, however, that alternative mechanisms might explain the increased prostate cancer risk observed with TXNRD2. This gene partially overlaps the catechol-O-methyltransferase (COMT) gene on chromosome 22 that has been well described in androgen and estrogen metabolism [35;36]. The role of androgen biosynthesis and metabolism in prostate cancer growth, proliferation, and progression is well established [37] and the putative role of pesticides as endocrine disruptors [38], suggests that pesticides may act via a hormonal mechanism to influence prostate cancer development.

Several of the pesticides with significant interactions in this study have been previously linked to prostate cancer in the AHS. For example, use of the fumigant methyl bromide was significantly associated with prostate cancer risk in AHS applicators in an early analysis of the cohort [29]. Also, use of the organophosphate insecticides fonofos and terbufos has been linked with excesses of prostate cancer in applicators among those with a family history of prostate cancer [29;39;40]. The association between terbufos and prostate cancer is of particular interest since it is still registered for use and has a relatively high prevalence of ever use (~40% in AHS cohort), unlike fonofos which is no longer registered for use and methyl bromide which has a low prevalence of use. In the present study terbufos use was associated with prostate cancer in the presence of the variant genotype of the MPO promoter SNP rs11079344. This SNP was also significantly associated with prostate cancer in the current study. MPO is an oxidative stress gene that codes for myeloperoxidase which has been described to influence cancer risk in response to xenobiotics, including pesticides [41–43]. Another MPO promoter region SNP which was not genotyped here, with a variant resulting in decreased MPO expression [44], has been extensively studied in lung, bladder, and breast cancer [45]. Thus, our results suggest that when we consider these two potential risk factors together, the risk of prostate cancer may be potentiated.

In addition to variants in oxidative stress related genes, SNPs in Phase I/Phase II enzyme genes showed positive associations with prostate cancer. For example, among men carrying the variant allele in the EPHX1 gene rs2292566, the risk of prostate cancer in the highest category of petroleum oil use was 2.1 times those with no use. In addition, the independent association of rs2292566 appears to be an important risk factor for prostate cancer in this population. Epoxide hydrolase functions in both the activation and detoxification of epoxides and low EPHX1 activity has been associated with increased cytogenetic damage in the presence of pesticide exposure [3;41]. Variations in direct pesticide metabolizing enzymes, including those in the CYP2C family appear also to be important genetic markers of prostate cancer susceptibility in this study. Although we did not observe any positive interactions, several SNP associations were observed in CYP2C18 and CYP2C19. A variety of organophosphate and carbamate compounds have been shown to be directly metabolized by phase I enzymes CYP2C8, CYP2C9, CYP2C18, and CYP2C19 [46]. Thus, polymorphisms in the genes that code for these Phase I/Phase II enzymes may alter levels of toxic pesticide intermediates and influence prostate cancer risk.

Several strengths and limitations of our study should be recognized. High quality genotype and pesticide information is available in the AHS. For many gene-exposure studies, the key limitation is the quality of the exposure information. The quality of information on pesticide use among AHS participants is high; self-reported pesticide use information has been found to be reliable in this cohort [47;48]. Furthermore, the ability of the AHS to look at individual pesticides rather than groups (herbicides or insecticides or chemical classes) is critical because observed cancer risks appear to be chemical-specific. Although the numbers within some strata were small, to our knowledge there are no other studies with more power to examine this interaction. In addition, we considered many interactions and none of the observed interactions were deemed noteworthy at the FDR=0.20 level; it is possible, however, that multiple testing issues have masked any true interactions present in this dataset. The numbers of interactions were what we might have expected by chance alone although some interesting patterns were observed. Similarly, we observed interesting SNP associations, however, the number of significant associations for SNPs in the oxidative stress genes was only slightly greater than would be expected by chance, while the number of significant associations for SNPs in Phase I/Phase II enzyme genes and in receptor genes were as expected. Also, we were not able to examine disease aggressiveness due to small numbers. Finally, all participants in this study were white, which limits the generalizability of the results to other racial/ethnic groups.

In conclusion, we observed several positive interactions in oxidative stress and phase I/phase II enzyme genes. None, however, were deemed noteworthy at the FDR=0.20 level and the overall number of observed interactions was comparable to the number expected by chance alone. Nonetheless, some XMEs did modify the association between pesticide use and prostate cancer. Interactions of pesticides and SNPs in oxidative stress genes and phase I/phase II enzyme genes implicate these mechanisms in particular. More evidence for the pesticide-prostate association is needed to fully explain the mechanisms by which pesticides might influence cancer risk.

Supplementary Material

1
2
3

Acknowledgments

This research was supported by the Intramural Research Program of the NIH, National Cancer Institute, Division of Cancer Epidemiology and Genetics (Z01CP010119) and National Institute of Environmental Health Sciences (Z01ES049030). KHB was supported by National Cancer Institute grant T32 CA105666. We thank the participants in the Agricultural Health Study for their contributions in support of this research.

Abbreviations

SNP

Single Nucleotide Polymorphism

XME

Xenobiotic Metabolizing Enzyme

OR

Odds Ratio

CI

Confidence Interval

AHS

Agricultural Health Study

PNCC

Prostate Nested Case-Control Study

FDR

False Discovery Rate

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Supplementary Materials

1
NIHMS307706-supplement-1.doc (63KB, doc)
2
NIHMS307706-supplement-2.xls (336.5KB, xls)
3
NIHMS307706-supplement-3.doc (74KB, doc)

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