Organic solvents are a group of solvents that have carbon in their structure and have been used in many industrial settings. As a substantial increase in mortality due to non-Hodgkin lymphoma (NHL) was first observed among men with potential exposure to solvents (Vianna and Polan, 1979), numerous efforts have been made to explore the association between occupational exposure to solvents and the risk for NHL (Smith and Lickiss, 1980; Cartwright et al., 1988; Wong and Raabe, 2000; Rego et al., 2002; Kato et al., 2005; Zhao et al., 2005; Mandel et al., 2006; Wang et al., 2009; Purdue et al., 2011). Although some studies have reported an increased risk for NHL associated with organic solvents (Cartwright et al., 1988; Rego et al., 2002; Kato et al., 2005), such as benzene (Smith and Lickiss, 1980; Zhao et al., 2005), trichloroethylene (Purdue et al., 2011), carbon tetrachloride, and formaldehyde (Wang et al., 2009), other studies did not support the associations (Wong and Raabe, 2000; Mandel et al., 2006; Seidler et al., 2007; Tranah et al., 2009; Alexander and Wagner, 2010). A meta-analysis including 26 cohorts of petroleum workers exposed to benzene (Wong and Raabe, 2000) and a meta-analysis including 14 cohort and four case–control studies on workers exposed to trichloroethylene (Mandel et al., 2006) did not observe an increased risk for NHL. The inconsistent results from previous studies could be because of the potential misclassification of exposure to solvents. In addition, genetic susceptibility in different populations may have also contributed toward the conflicting results (Barry et al., 2010).
NHL is a heterogeneous group of diseases arising from the immune system. The only established risk factor for NHL is immune dysregulation resulting from various medical conditions, medication use, and infections (La Vecchia et al., 1992; Muller et al., 2005). Genetic variations in immune genes have been reported to be associated with the risk for NHL (Lan et al., 2006; Purdue et al., 2007; Colt et al., 2009). For example, interleukin 10 (IL10) and tumor necrosis factor (TNF) are genes coding for immunoregulatory cytokines that are key mediators of inflammation, apoptosis, and Th1/Th2 balance, and function as autocrine growth factors in lymphoid tumors (Khatri and Caligiuri, 1998; Aggarwal, 2003). A pooled analysis of eight case–control studies suggested that the TNF (rs1800629) AA genotype was associated with 25 and 65% increased risks for NHL overall and diffuse large B-cell lymphoma (DLBCL), respectively, compared with those for the GG genotype (Rothman et al., 2006). The study also found that the IL10 (rs1800890) AA genotype was associated with 19 and 28% increased risks for NHL overall and DLBCL, respectively, compared with those in the TT genotype (Rothman et al., 2006). The association was further confirmed by updated pooled analyses with additional studies (Skibola et al., 2010).
Given the identified role of genetic polymorphisms of immune genes in NHL tumorigenesis and the potential association between occupational exposure to solvents and the risk for NHL, we analyzed data from a population-based case–control study in Connecticut to examine whether the relationship between occupational exposure to solvents and the risk for NHL varies according to polymorphisms in a number of immune genes.
A detailed description of the study population has been published elsewhere (Zhang et al., 2004; Wang et al., 2009). Briefly, NHL cases were histologically confirmed incident cases (International Classification of Diseases for Oncology, 2nd edition, codes M-9590-9642, 9690-9701, 9740-9750) that were diagnosed between 1996 and 2000; the patients were aged between 21 and 84 years without a history of any cancers, except nonmelanoma skin cancer, were female residents of Connecticut, and were alive at the time of interview. A total of 601 of 832 patients identified with incident NHL (72%) completed in-person interviews. All NHL cases were histologically confirmed by study pathologists and were classified according to the 2001 World Health Organization classification (Jaffe and Vardiman, 2001).
Population-based controls were recruited through random digit dialing for women aged younger than 65 years and through Centers for Medicare and Medicaid services (CMMS) files for those aged 65 years or older, with participation rates of 69 and 47%, respectively. A total of 717 controls completed in-person interviews. Patients and controls were frequency matched by age within 5-year groups. Of the 601 patients and 717 controls, 518 patients and 597 controls donated blood or buccal-cell samples. The study was approved by the Institutional Review Boards at Yale University, the Connecticut Department of Public Health, and the National Cancer Institute.
Exposure assessment of solvents has been described previously (Wang et al., 2009). Briefly, a standardized, structured questionnaire was administered to study participants to collect information on employment history including job/industry titles and employment dates. These self-reported jobs and industries were coded on the basis of the 1980 Standard Occupational Classification Manual (US Department of Commerce, 1980) and the 1987 Standard Industrial Classification Manual (Office of Management and Budget, 1987) and then linked to a generic job-exposure matrix developed by the National Cancer Institute’s industrial hygienists to assess solvent exposures (Dosemeci et al., 1994). The job-exposure matrix included the probability and intensity of exposure (ordinal categories) for each job and industry for any organic solvent, any chlorinated solvent, benzene, formaldehyde, and a number of individual chlorinated solvents, including chloroform, carbon tetrachloride, dichloromethane, dichloroethane, methyl chloride, and trichloroethylene.
Phenol–chloroform extraction was used to isolate DNA from the blood and buccal-cell samples. Genotyping was carried out at the National Cancer Institute’s Core Genotyping Facility (http://cgf.nci.nih.gov). All TaqMan assays (Applied Biosystems, Foster City, California, USA) for this study were optimized on the ABI 7900HT detection system with 100% concordance with sequence analysis of 102 individuals as listed on the SNP500Cancer website (http://snp500cancer.nci.nih.gov). A total of 39 single nucleotide polymorphisms (SNPs) in 20 Th1/Th2 immune genes were selected for genotyping on the basis of the following criteria: minor allele frequencies more than 5%, laboratory evidence of function, or previous association with human disease studies. Because of the limited amount of DNA available from participants who provided only buccal cells, we first genotyped participants who provided a blood sample. If there was suggestive evidence, or if we had a relatively high suspicion that a given SNP was associated with a risk for NHL, genotype analysis would include participants who provided only buccal-cell samples.
Duplicate samples from 100 study participants and 40 replicate samples from each of two blood donors were interspersed throughout the plates used for genotype analysis. As reported previously, the concordance rates for quality control samples were between 99 and 100% for all assays. The genotype frequencies for four SNPs (rs1059293, rs231775, rs2243250, and rs2070874) analyzed using a χ2-test (P<0.05) were not consistent with Hardy–Weinberg equilibrium among non-Hispanic white controls and were excluded from the final analysis. Another five SNPs (rs2069822, rs2069818, rs2069807, 3024509, and rs361525) with a minor allele frequency less than 10% were also excluded from the final analysis. A total of 30 SNPs in 17 Th1/Th2 genes, IFNG (rs1861494, rs2069705), IFNGR1 (rs3799488), IFNGR2 (rs9808753), IL10RA (rs9610), IL12A (rs568408, rs582054), IL13 (rs20541, rs1800925, rs1295686), IL15 (rs10833), IL15RA (rs2296135), IL2 (rs2069762), IL4 (rs2243248, rs2243290, rs2243268), IL4R (rs2107356), IL5 (rs2069812), IL6 (rs1800795, rs1800797), IL7R (rs1494555), JAK3 (rs3008), IL10 (rs1800871, rs1800872, rs1800896, rs3024496, rs3024491, rs1800890), and TNF (rs1800629, rs1799724), were included in the final analysis.
Solvent exposure variables were presented as dichotomous ever/never exposure metrics because of the small cell counts for some solvents within genotype strata. In addition, the numbers with exposure to several individual solvents were low; the final analyses were carried out for any organic solvents, any chlorinated solvents, trichloroethylene, benzene, and formaldehyde. To increase the statistical power, heterozygous and homozygous variant genotypes were combined for all genes. Unconditional logistic regression models were used to determine odds ratios (ORs) and 95% confidence intervals (CIs) for associations between various solvents and the risk for NHL within each genotype stratum adjusted for age (continuous) and race (white/nonwhite). Additional adjustment for a family history of hematopoietic cancers, alcohol consumption, tobacco smoking, education, and medical history of immune-related disease did not markedly alter effect estimates, and thus, these variables were not included in the final models. Statistical significance was defined at the 0.05 level on the basis of two-sided tests. The significance of immune gene–solvent interaction was assessed by including an interaction term in the logistic regression models. The false discovery rate (FDR) method set at 0.2 was used to control for multiple comparisons (Benjamini, 1995). All statistical analyses were performed using the SAS software (version 9.1; SAS Institute Inc., Cary, North Carolina, USA).
As shown in Table 1, genetic polymorphism in IL15RA (rs2296135) significantly modified the association between occupational exposure to organic solvents and the risk for NHL overall (Pfor interaction=0.0195), whereas IL10 (rs1800890), IL15RA (rs2296135), IL4 (rs2243248), and IL10 (rs1800896) significantly modified the association between organic solvents and the risk for DLBCL (Pfor interaction=0.0058, 0.0275, 0.0262, and 0.0240, respectively). After adjustment for FDR, an interaction with the IL10 (rs1800890) polymorphism in the risk for DLBCL remained statistically significant. Compared with women who were never occupationally exposed to organic solvents, women who were exposed to organic solvents at least once had a significantly increased risk for DLBCL if they carried the IL10 (rs1800890) TT genotype (OR=3.31, 95% CI: 1.80–6.08), but not if they carried AT/AA genotypes (OR=1.14, 95% CI: 0.72–1.79).
For occupational exposure to chlorinated solvents, a significant interaction with IL12A (rs582054) was observed for NHL overall (Pfor interaction=0.0169), DLBCL (Pfor interaction=0.0423), and follicular lymphoma (Pfor interaction=0.0201; Table 1). A significant interaction with IL7R (rs1494555) was only observed for NHL overall (Pfor interaction=0.0275). However, none of the interactions remained statistically significant after adjustment for FDR.
Significant associations among individual solvents (i.e. trichloroethylene, benzene, and formaldehyde), immune gene polymorphisms, and the risk for NHL are presented in Table 2. Compared with women without occupational exposure to trichloroethylene, women who had undergone occupational exposure to trichloroethylene had an increased risk for NHL overall (OR=2.09, 95% CI: 1.28–3.42) and DLBCL (OR=2.66, 95% CI: 1.42–4.96) if the women carried IL12A (rs582054) AT/AA genotypes; however, this risk was not observed among women who carried the IL12A (rs582054) TT genotype. A significant interaction was observed between exposure to trichloroethylene and IL12A (rs582054) for NHL overall and DLBCL (Pfor interaction=0.0090 and 0.0119, respectively). After adjustment for FDR, none of these interactions, however, remained statistically significant.
For benzene exposure, significant interactions were observed for IL10 (1800890) for DLBCL (Pfor interaction=0.0415); IL12A (rs582054) for NHL overall (Pfor interaction=0.0201) and follicular lymphoma (Pfor interaction=0.0468); TNF (rs1800629) for follicular lymphoma (Pfor interaction=0.0206); and IL15 (rs10833) for follicular lymphoma (Pfor interaction=0.0406). However, none of these interactions remained statistically significant after adjustment for FDR.
Significant interactions were also observed between occupational exposure to formaldehyde and polymorphisms in IL6 (rs1800795, rs1800797) and IL10 (rs1800890) for DLBCL (Pfor interaction=0.0279, 0.0311, and 0.0373, respectively). Significant interactions were observed between exposure to formaldehyde and IL6 (rs1800795, rs1800797) polymorphisms for follicular lymphoma risk (Pfor interaction=0.028). Again, none of the interactions remained statistically significant after adjustment for FDR.
Nonsignificant interactions were observed for other immune gene polymorphisms and various solvents in relation to NHL overall and its major subtypes (data are not shown but are available directly from the corresponding author).
For the first time, we examined the potential interactions between occupational exposure to solvents and genetic variations in 17 immune genes. Our analysis suggested that IL10 (rs1800890), which has previously been found to be associated with the risk for NHL in several studies (Lan et al., 2006; Rothman et al., 2006; Skibola et al., 2010), modified the association between occupational exposure to an organic solvent and the risk for DLBCL. IL10 (rs1800890) is located in the distal region of the IL10 promoter and is believed to be associated with IL10 expression (Wieczorek et al., 2008). The IL10 (rs1800890) T allele results in high IL10 production (Gibson et al., 2001). High IL10 expression can hinder the inflammatory response by inducing apoptosis in mast cells and macrophages as an immunoregulatory cytokine of T cells (Bailey et al., 2006). Thus, the immunosuppressive property of IL10 could increase susceptibility to tumorigenicity (Hori et al., 2003). A cohort study reported that high serum levels of IL10 were associated with an increased risk for B-cell NHL among individuals with acquired immunodeficiency syndrome (Breen et al., 2003). A high serum level of IL10 was also found to be associated with a poor prognosis of DLBCL (Lech-Maranda et al., 2004).
However, it has been suggested that an immunologic mechanism could be involved in lymphomagenesis from solvents (Vineis et al., 2007). Occupational benzene exposure has been found to impair the immune system and may contribute to the process of carcinogenesis (Brandao et al., 2005). Occupational exposure to solvents (i.e. hydrocarbons and toluene) has been reported to result in abnormal values of lymphocyte subpopulations and impaired lymphocyte functions (Lange et al., 1973; Zeman et al., 1990). We speculated that the impaired immune system resulting from the IL10 (rs1800890) T allele could be more susceptible to occupational solvent exposures and act synergistically with solvents in lymphomagenesis.
The major strengths of the study include the population-based study design and histologically confirmed incident NHL cases, which minimized potential misclassification of disease. Solvent exposure was assessed by linking generic job-exposure matrixes with self-reported occupational histories; thus, possible exposure misclassification was unavoidable. Because we used dichotomous solvent variables in the current study, the effect of misclassification was expected to be attenuated. We examined a total of 30 SNPs, which increased the likelihood of false-positive findings. Therefore, we used the FDR approach to control for multiple comparisons (Hochberg, 1990). Although the study had a relatively large sample size, the power was limited for NHL subtypes. Finally, the study included only women; the findings may not be generalizable to men.
Our study provided preliminary evidence supporting the role of immune gene variation in modulating the association between occupational solvent exposure and the risk for NHL. A possible role of IL10 with organic solvents warrants further investigation in larger populations.
This research was supported by the NIH grants CA62006 and CA165923, the Intramural Research Program of the National Institutes of Health (NIH), the National Cancer Institute, and the National Institutes of Health training grants 1D43TW008323-01, 1D43TW007864-01, and HD70324-01.
Conflicts of interest
There are no conflicts of interest.
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