Abstract
The aim of this study was to investigate whether genetic polymorphisms in cytochrome P450s (CYPs), glutathione S-transferases (GSTs) and N-acetyltransferases (NATs) genes modify the relationship between alcohol consumption and risk of non-Hodgkin's Lymphoma (NHL) in a population-based case-control study including 1,115 Connecticut women. Although we did not find strong evidence that the genetic polymorphisms modify the relationship between alcohol consumption and risk of NHL, we identified significant interactions for multiple GSTs and NATs and alcohol intake among persons with DLBCL. Our results confer support investigation of the gene-environment interaction in a larger study population of DLBCL.
Keywords: Non-Hodgkin lymphoma, alcohol, genetic polymorphisms, GST, CYPs, NATs
Introduction
Numerous epidemiologic studies have investigated the effect of alcohol consumption on non-Hodgkin lymphoma (NHL) risk but the results have been inconsistent [1-12]. Alcohol is a risk factor of interest as it is oxidized to acetaldehyde, which is genotoxic, and it is also thought to induce the activity of metabolic enzymes involved in its excretion and the detoxification of potentially harmful xenobiotic compounds. Polymorphisms in genes that code various types of cytochrome P450s (CYPs), glutathione-S-transferases (GSTs), and N-acetyltransferases (NATs) manifest as decreased or lack of enzyme activity [2], prompting the hypothesis that allelic variants may be associated with an impaired detoxification capacity and subsequently an overall increased susceptibility to cancer. While a limited number of studies have explored the relationship between genetic polymorphisms in CYPs, GSTs and NATs and NHL risk [13-18], no study has been conducted to investigate whether genetic variation in xenobiotic metabolic genes modifies the association between alcohol consumption and NHL risk. We subsequently analyzed data from a population-based case-control study in Connecticut women to explore the relationship between genetic polymorphisms in CYPs, GSTs, and NATs genes, alcohol consumption, and risk of NHL.
Methods
The study population has been reported in detail in the previously published articles [6, 19]. Briefly, a total of 835 histologically confirmed incident female NHL cases from 1996 to 2000 in Connecticut that were aged 21-84 years old, still alive at the time of interview and had never been diagnosed of cancer except for non-melanoma skin cancer, were identified by the Yale Cancer Center's Rapid Case Ascertainment Shared Resource [20]. Among these eligible cases, 601 completed in-person interviews and 461 cases provided blood samples. All cases were histologically confirmed and classified into NHL subtypes according to the WHO classification system [21]. Population-based controls were frequency matched on age (±5year age group) and were identified via random-digit dialing (for those aged less than 65 years) or random selection from Centers for Medicare and Medicaid Services (CMS) records (for those aged 65 years or older). Of 1264 eligible controls, 718 women completed in-person interviews and 535 women provided blood samples.
DNA was extracted from blood samples using phenol-chloroform extraction. A total of 20 single nucleotide polymorphisms (SNPs) in 11 xenobiotic genes, including CYP21A2 (rs6474), CYP1A1 (rs1048943), CYP1A2 (rs762551), CYP1B1 (rs1056836), CYP2C9 (rs1799853), CYP2E1 (rs2070673), GSTM3 (rs1799735), GSTP1 (rs1695, rs1138272), GSTT1 (rs17856199), NAT1 (rs4987076, rs13249533, rs1057126, and rs15561), and NAT2 (rs1041983, rs1801280, rs1799929, rs1799930, rs1208, and rs1799931), were selected and genotyped in blood-based DNA samples. Genotyping was conducted using real-time PCR on an ABI 7900HT sequence detection system as described on the website (http://snp500cancer.nci.nih.gov) at the NCI Core Genotyping Facility [22]. Duplicate samples from 100 study subjects and 40 replicate samples from each of two blood donors were interspersed throughout the plates used for genotype analysis. The concordance rates for quality control samples were 100% for all assays. All genotyping frequencies among control populations were in Hardy-Weinberg equilibrium (P>0.05). The SNP data were used to assign the most likely NAT1 and NAT2 alleles and NAT2 acetylation phenotypes at the University of Louisville (by David W. Hein) [23].
Unconditional logistic regression models were used to estimate the odds ratio (OR), the 95% confidence interval (CI) and P value for associations between drinking of any beverage, wine, beer, or liquor, polymorphisms in CYPs, GSTs and NATs and risk of NHL, adjusting for age (<50, 50-70, >70 years), education (high school or less, some college, college graduate or more), smoke (non-smoker, 1-7, 8-14,15-33, □34 pack-years), and family history of cancer (any cancer, none). All tests were two-sided with significance level of 0.05 using SAS 9.1 (SAS Institute Inc., Cary, NC). We also conducted the same analysis for non-Hispanic Caucasians only.
Results
No significant association was identified between genetic polymorphisms in the xenobiotic genes of interest and risk of NHL overall among either non-drinkers or drinkers of any type of alcoholic beverage or for drinkers of specific alcoholic beverages including wine, beer, or liquor (Table 1).
Table 1.
Association between NHL and polymorphisms in xenobiotic metabolizing enzymes by drinking status
| Non-drinkers | Drinkers | Wine-drinkers | Beer-drinkers | Liquor-drinkers | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SNPs | Controls | Cases | *OR (95%CI) | Controls | Cases | *OR (95%CI) | Controls | Cases | *OR (95%CI) | Controls | Cases | *OR (95%CI) | Controls | Cases | *OR (95%CI) |
| CYP21A2 (rs6474) | |||||||||||||||
| GG | 77 | 69 | 1 | 164 | 122 | 1 | 128 | 96 | 1 | 88 | 54 | 1 | 104 | 78 | 1 |
| AG or AA | 89 | 89 | 1.06(0.68,1.66) | 185 | 160 | 1.14(0.83,1.58) | 156 | 124 | 1.03(0.71,1.47) | 69 | 64 | 1.43(0.87,2.34) | 124 | 109 | 1.10(0.73,1.65) |
| p-interaction | 0.81 | 0.86 | 0.38 | 0.94 | |||||||||||
| CYP1A1 (rs1048943) | |||||||||||||||
| AA | 152 | 149 | 1 | 335 | 269 | 1 | 273 | 208 | 1 | 145 | 112 | 1 | 223 | 180 | 1 |
| AG or GG | 17 | 11 | 0.68(0.31,1.52) | 22 | 22 | 1.21(0.65,2.25) | 19 | 17 | 1.16(0.58,2.31) | 12 | 7 | 0.75(0.28,2.02) | 11 | 13 | 1.43(0.61,3.35) |
| p-interaction | 0.27 | 0.33 | 0.88 | 0.21 | |||||||||||
| CYP1A2 (rs762551) | |||||||||||||||
| AA | 82 | 76 | 1 | 185 | 135 | 1 | 148 | 103 | 1 | 84 | 51 | 1 | 117 | 89 | 1 |
| AC or CC | 90 | 86 | 1.01(0.65,1.57) | 177 | 159 | 1.22(0.89,1.68) | 147 | 125 | 1.20(0.84,1.70) | 76 | 69 | 1.49(0.91,2.46) | 120 | 103 | 1.16(0.78,1.72) |
| p-interaction | 0.5 | 0.56 | 0.29 | 0.69 | |||||||||||
| CYP1B1 (rs1056836) | |||||||||||||||
| CC | 66 | 52 | 1 | 126 | 109 | 1 | 102 | 83 | 1 | 56 | 43 | 1 | 83 | 69 | 1 |
| CG or GG | 115 | 118 | 1.30(0.82,2.04) | 250 | 192 | 0.89(0.64,1.22) | 206 | 149 | 0.90(0.62,1.29) | 111 | 82 | 1.04(0.62,1.72) | 165 | 126 | 0.90(0.60,1.35) |
| p-interaction | 0.16 | 0.2 | 0.42 | 0.23 | |||||||||||
| CYP2C9 (rs1799853) | |||||||||||||||
| CC | 148 | 145 | 1 | 303 | 242 | 1 | 245 | 185 | 1 | 129 | 99 | 1 | 199 | 152 | 1 |
| CT or TT | 41 | 35 | 0.84(0.50,1.40) | 96 | 74 | 0.97(0.68,1.38) | 80 | 57 | 0.95(0.64,1.41) | 45 | 29 | 0.82(0.47,1.43) | 59 | 50 | 1.14(0.73,1.78) |
| p-interaction | 0.58 | 0.67 | 0.99 | 0.32 | |||||||||||
| CYP2E1 (rs2070673) | |||||||||||||||
| TT | 108 | 103 | 1 | 245 | 192 | 1 | 210 | 153 | 1 | 103 | 75 | 1 | 161 | 121 | 1 |
| AT or AA | 63 | 59 | 1.04(0.66,1.64) | 118 | 103 | 1.12(0.81,1.56) | 86 | 76 | 1.24(0.84,1.80) | 57 | 46 | 1.12(0.67,1.88) | 77 | 72 | 1.24(0.82,1.86) |
| p-interaction | 0.73 | 0.52 | 0.69 | 0.54 | |||||||||||
| GSTM3 (rs1799735) | |||||||||||||||
| ++ | 116 | 121 | 1 | 256 | 200 | 1 | 208 | 162 | 1 | 115 | 81 | 1 | 171 | 128 | 1 |
| +- or -- | 55 | 22 | 0.74(0.46,1.21) | 104 | 96 | 1.17(0.84,1.65) | 85 | 68 | 1.01(0.69,1.48) | 44 | 41 | 1.31(0.77,2.22) | 65 | 66 | 1.24(0.81,1.90) |
| p-interaction | 0.14 | 0.33 | 0.12 | 0.11 | |||||||||||
| GSTP1_01 (rs1695) | |||||||||||||||
| AA | 82 | 66 | 1 | 148 | 121 | 1 | 120 | 91 | 1 | 58 | 49 | 1 | 95 | 80 | 1 |
| AG or GG | 90 | 96 | 1.30(0.83,2.03) | 212 | 175 | 1.01(0.73,1.39) | 173 | 139 | 1.08(0.75,1.54) | 101 | 73 | 0.77(0.46,1.28) | 142 | 114 | 0.98(0.66,1.46) |
| p-interaction | 0.42 | 0.57 | 0.18 | 0.41 | |||||||||||
| GSTP1_02 (rs1138272) | |||||||||||||||
| CC | 143 | 133 | 1 | 297 | 251 | 1 | 240 | 195 | 1 | 131 | 100 | 1 | 192 | 162 | 1 |
| CT or TT | 29 | 30 | 1.12(0.63,1.98) | 66 | 45 | 0.85(0.56,1.29) | 56 | 35 | 0.81(0.51,1.30) | 29 | 22 | 1.06(0.56,2.00) | 46 | 32 | 0.93(0.56,1.55) |
| p-interaction | 0.49 | 0.43 | 0.97 | 0.68 | |||||||||||
| GSTT1_02(rs17856199) | |||||||||||||||
| ++ | 61 | 49 | 1 | 125 | 96 | 1 | 102 | 80 | 1 | 52 | 46 | 1 | 82 | 63 | 1 |
| +- or -- | 88 | 87 | 1.31(0.80,2.13) | 175 | 138 | 1.03(0.72,1.47) | 144 | 105 | 0.91(0.61,1.34) | 78 | 48 | 0.64(0.37,1.13) | 109 | 95 | 1.15(0.74,1.79) |
| p-interaction | 0.5 | 0.28 | 0.07 | 0.75 | |||||||||||
| NAT1 | |||||||||||||||
| without * 10 | 111 | 98 | 1 | 215 | 183 | 1 | 173 | 136 | 1 | 100 | 77 | 1 | 138 | 126 | 1 |
| with * 10 | 59 | 65 | 1.26(0.80,1.99) | 143 | 107 | 0.81(0.59,1.12) | 118 | 89 | 0.90(0.62,1.29) | 59 | 41 | 0.83(0.50,1.39) | 97 | 65 | 0.68(0.45,1.03) |
| p-interaction | 0.12 | 0.23 | 0.22 | 0.05 | |||||||||||
| NAT2 | |||||||||||||||
| Rapid | 74 | 70 | 1 | 144 | 129 | 1 | 114 | 108 | 1 | 69 | 62 | 1 | 94 | 79 | 1 |
| Slow | 97 | 92 | 1.02(0.66,1.59) | 218 | 165 | 0.88(0.64,1.21) | 181 | 120 | 0.73(0.51,1.04) | 91 | 59 | 0.75(0.46,1.24) | 143 | 113 | 0.95(0.63,1.42) |
| p-interaction | 0.59 | 0.25 | 0.39 | 0.78 | |||||||||||
Adjusted for age and education, family history and smoking status
When we looked at the association by the DLBCL histologic type (Table 2), we found that a polymorphism in GSTM3 (rs1799735) modified the association between alcohol consumption and risk of DLBCL (P for interaction =0.023). Among never-drinkers, individuals with null alleles of GSTM3 (rs1799735) had a significantly decreased risk of DLBCL (OR=0.39, 95% CI 0.16-0.94) compared to individuals with the alleles, while among drinkers, a slightly increased risk of DLBCL was found for women with null alleles (OR=1.26, 95% CI 0.77-2.04).
Table 2.
Association between DLBCL and polymorphisms in xenobiotic metabolizing enzymes by drinking status
| Diffuse Large B-Cell Lymphoma |
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Non-drinkers | Drinkers | Non-drinkers | Drinkers | Wine-drinkers | Beer-drinkers | Liquor-drinkers | ||||||
| SNP | Controls | Controls | Cases | *OR (95%CI) | Cases | *OR (95%CI) | Cases | *#OR (95%CI) | Cases | *OR (95%CI) | Cases | *OR (95%CI) |
| CYP21A2 (rs6474) | ||||||||||||
| GG | 77 | 164 | 24 | 1 | 37 | 1 | 27 | 1 | 20 | 1 | 27 | 1 |
| AG or AA | 89 | 185 | 23 | 0.73(0.37,1.43 | 60 | 1.38(0.86,2.21) | 45 | 1.35(0.78,2.34) | 30 | 1.83(0.93,3.59) | 39 | 1.10 (0.61,1.99) |
| p-interaction | 0.13 | 0.18 | 0.06 | 0.33 | ||||||||
| CYP1A1 (rs1048943) | ||||||||||||
| AA | 17 | 22 | 44 | 1 | 85 | 1 | 64 | 1 | 44 | 1 | 57 | 1 |
| AG or GG | 82 | 185 | 3 | 0.64(0.18,2.32 | 12 | 2.13(0.99,4.58) | 7 | 1.68(0.66,4.29) | 6 | 1.7 (0.57,5.21) | 10 | 3.22 (1.23,8.44) |
| p-interaction | 0.11 | 0.23 | 0.25 | 0.04 | ||||||||
| CYP1A2 (rs762551) | ||||||||||||
| AA | 66 | 126 | 21 | 1 | 51 | 1 | 35 | 1 | 22 | 1 | 36 | 1 |
| AC or CC | 115 | 250 | 27 | 1.15(0.58,2.16 | 47 | 1.00(0.63,1.59) | 37 | 1.11(0.65,1.90) | 28 | 1.59(0.80,3.17) | 30 | 0.90 (0.50,1.60) |
| p-interaction | 0.82 | 0.98 | 0.57 | 0.58 | ||||||||
| CYP1B1 (rs1056836) | ||||||||||||
| CC | 148 | 303 | 18 | 1 | 40 | 1 | 30 | 1 | 19 | 1 | 24 | 1 |
| CG or GG | 41 | 96 | 32 | 0.99(0.50,1.93 | 60 | 0.77(0.48,1.23) | 44 | 0.78(0.45,1.34) | 33 | 1.03(0.51,2.05) | 43 | 0.88 (0.49,1.58) |
| p-interaction | 0.45 | 0.47 | 0.89 | 0.66 | ||||||||
| CYP2C9 (rs1799853) | ||||||||||||
| CC | 63 | 118 | 46 | 1 | 77 | 1 | 53 | 1 | 40 | 1 | 48 | 1 |
| CT or TT | 116 | 256 | 7 | 0.56(0.23,1.35 | 26 | 1.11(0.66,1.86) | 22 | 1.33(0.75,2.37) | 13 | 0.98(0.47,2.07) | 21 | 1.77 (0.95,3.32) |
| p-interaction | 0.13 | 0.07 | 0.29 | 0.02 | ||||||||
| CYP2E1 (rs2070673) | ||||||||||||
| TT | 82 | 148 | 36 | 1 | 64 | 1 | 46 | 1 | 29 | 1 | 42 | 1 |
| AT or AA | 90 | 212 | 12 | 0.64(0.31,1.34 | 35 | 1.12(0.70,1.81) | 27 | 1.44(0.83,2.52) | 22 | 1.40(0.71,2.78) | 25 | 1.18 (0.65,2.12) |
| p-interaction | 0.2 | 0.08 | 0.11 | 0.19 | ||||||||
| GSTM3 (rs1799735) | ||||||||||||
| ++ | 143 | 297 | 40 | 1 | 65 | 1 | 50 | 1 | 36 | 1 | 44 | 1 |
| +- or -- | 29 | 66 | 8 | 0.39(0.16,0.94 | 34 | 1.26(0.77,2.04) | 23 | 1.12(0.63,1.99) | 15 | 0.98(0.47,2.04) | 23 | 1.32 (0.72,2.44) |
| p-interaction | 0.02 | 0.05 | 0.11 | 0.03 | ||||||||
| GSTP1_01 (rs1695) | ||||||||||||
| AA | 61 | 125 | 15 | 1 | 36 | 1 | 24 | 1 | 19 | 1 | 24 | 1 |
| AG or GG | 88 | 175 | 33 | 2.04(1.00,4.13 | 63 | 1.25(0.78,2.00) | 49 | 1.45(0.83,2.54) | 32 | 0.95(0.48,1.90) | 43 | 1.29 (0.72,2.33) |
| p-interaction | 0.39 | 0.63 | 0.17 | 0.42 | ||||||||
| GSTP1_02 (rs1138272) | ||||||||||||
| CC | 143 | 240 | 39 | 1 | 83 | 1 | 62 | 1 | 42 | 1 | 57 | 1 |
| CT or TT | 29 | 56 | 9 | 1.19(0.51,2.76 | 16 | 0.94(0.51,1.73) | 11 | 0.82(0.40,1.68) | 9 | 1.04(0.43,2.47) | 10 | 0.88 (0.40,1.91) |
| p-interaction | 0.74 | 0.58 | 0.88 | 0.62 | ||||||||
| GSTT1_02(rs17856199) | ||||||||||||
| ++ | 61 | 125 | 15 | 1 | 38 | 1 | 30 | 1 | 19 | 1 | 26 | 1 |
| +- or -- | 88 | 175 | 24 | 1.16(0.55,2.46 | 38 | 0.68(0.41,1.15) | 26 | 0.53(0.29,0.98) | 20 | 0.62(0.29,1.34) | 28 | 0.81 (0.43,1.52) |
| p-interaction | 0.3 | 0.15 | 0.27 | |||||||||
| NAT1 | ||||||||||||
| w/out * 10 | 111 | 215 | 30 | 1 | 73 | 1 | 52 | 1 | 39 | 1 | 52 | 1 |
| with * 10 | 59 | 143 | 17 | 0.98(0.48,1.99 | 24 | 0.42(0.25,0.72) | 20 | 0.48(0.27,0.88) | 10 | 0.40(0.18,0.90) | 13 | 0.30 (0.15,0.60) |
| p-interaction | 0.06 | 0.13 | 0.1 | 0.51 | ||||||||
| NAT2 | ||||||||||||
| Rapid | 74 | 144 | 19 | 1 | 39 | 1 | 30 | 1 | 24 | 1 | 23 | 1 |
| Slow | 97 | 218 | 29 | 1.30(0.66,2.55 | 59 | 1.06(0.66,1.70) | 42 | 0.97(0.57,1.68) | 26 | 0.84(0.43,1.66) | 43 | 1.30 (0.72,2.35) |
| p-interaction | 0.36 | 0.46 | 0.39 | 0.94 | ||||||||
Adjusted for age and education, family history and smoking status
We also observed a two-fold increased risk of DLBCL (OR=2.04, 95% CI 1.00-4.13) for women who carried GSTP1 (rs1695) AG/GG genotypes compared to those with AA genotype among non-drinkers, but not among drinkers. An approximately 60% reduced risk of DLBCL (OR=0.42, 95% CI 0.25-0.72) was found for women with NAT1*10 genotype compared to women without NAT1*10 genotype among drinkers but not among non-drinkers. However, there was no significant interaction was found for these genotypes.
When we looked at the associations of DLBCL and specific types of beverages (Table 2), we found that polymorphisms in CYP1A1 (rs1048943), CYP2C9 (rs1799853), GSTM3 (rs1799735) and NAT1 modified the association between liquor consumption and risk of DLBCL (P for interaction =0.04, 0.02, 0.03 and 0.02, respectively). Among liquor-drinkers, individuals with variant or null alleles of CYP1A1 (rs1048943), CYP2C9 (rs1799853) or GSTM3 (rs1799735) had an increased risk of DLBCL (OR=3.22, 95% CI: 1.23-8.44; 1.77:0.95,3.32; 1.32:0.72,2.44, respectively) compared to individuals with the wildtype alleles. Among each of the three kinds of beverages drinkers, a similar decreased risk of DLBCL was observed (0.48:0.27, 0.88; 0.40:018, 0.90; 0.30:0.15, 0.60 for wine, beer and liquor drinkers, respectively).
When we looked at the association for the marginal zone and T-cell histologic types (Tables 3 and 4), no significant association was identified between genetic polymorphisms in xenobiotic genes of interest among either non-drinkers or drinkers of any type of alcoholic beverage or for drinkers of specific alcoholic beverages including wine, beer, or liquor. However, for the marginal zone histologic type (Table 3), significant interactions were observed for the GSTP1 (rs1695) genotype and wine drinking (p-interaction = 0.02). For the T-cell histologic type (Table 4), significant interactions were observed for the NAT1*10 genotype and drinking (p-interaction = 0.02) and for liquor drinking (p-interaction = 0.02). However, for the rarer subtypes, the results were based on small numbers of cases, some <5.
Table 3.
Association between MZ lymphoma and polymorphisms in xenobiotic metabolizing enzymes by drinking status
| Marginal Zone Lymphoma |
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Non-drinkers | Drinkers | Non-drinkers | Drinkers | Wine-drinkers | Beer-drinkers | Liquor-drinkers | ||||||
| SNP | Controls | Controls | Cases | *OR (95%CI) | Cases | *OR (95%CI) | Cases | *#OR (95%CI) | Cases | *OR (95%CI) | Cases | *OR (95%CI) |
| CYP21A2 (rs6474) | ||||||||||||
| GG | 77 | 164 | 3 | 1.00 | 8 | 1.00 | 6 | 1.00 | 3 | 1.00 | 4 | 1.00 |
| AG or AA | 89 | 185 | 6 | 1.57(0.38,6.60) | 11 | 1.22(0.48,3.11) | 7 | 0.95(0.31,2.94) | 2 | 0.95(0.14,6.66) | 10 | 1.43(0.40,5.06) |
| p-interaction | 0.73 | 0.51 | 0.64 | 0.92 | ||||||||
| CYP1A1 (rs1048943) | ||||||||||||
| AA | 152 | 335 | 10 | - | 20 | 1.00 | 14 | 1.00 | 6 | - | 15 | - |
| AG or GG | 17 | 22 | 0 | - | 1 | 0.74(0.09,5.80) | 1 | 1.04(0.13,8.62) | 0 | - | 0 | - |
| p-interaction | 0.98 | 0.97 | ||||||||||
| CYP1A2 (rs762551) | ||||||||||||
| AA | 82 | 185 | 5 | 1.00 | 11 | 1.00 | 9 | 1.00 | 4 | 1.00 | 7 | 1.00 |
| AC or CC | 90 | 177 | 5 | 1.07(0.28,4.17) | 10 | 0.93(0.38,2.28) | 6 | 0.63(0.21,1.84) | 2 | 0.32(0.05,2.06) | 8 | 1.09(0.36,3.28) |
| p-interaction | 0.82 | 0.51 | 0.33 | 0.95 | ||||||||
| CYP1B1 (rs1056836) | ||||||||||||
| CC | 66 | 126 | 3 | 1.00 | 6 | 1.00 | 4 | 1.00 | 1 | 1.00 | 5 | 1.00 |
| CG or GG | 115 | 250 | 7 | 1.10(0.25,4.76) | 16 | 1.37(0.52,3.62) | 11 | 1.44(0.44,4.73) | 6 | 3.72(0.37,37.13) | 10 | 0.94(0.29,3.03) |
| p-interaction | 0.91 | 0.86 | 0.35 | 0.78 | ||||||||
| CYP2C9 (rs1799853) | ||||||||||||
| CC | 148 | 303 | 8 | 1.00 | 15 | 1.00 | 11 | 1.00 | 4 | 1.00 | 10 | 1.00 |
| CT or TT | 41 | 96 | 4 | 2.09(0.56,7.85) | 6 | 1.28(0.48,3.42) | 3 | 0.82(0.22,3.04) | 2 | 1.98(0.27,14.40) | 4 | 1.82(0.50,6.62) |
| p-interaction | 0.63 | 0.34 | 0.91 | 0.91 | ||||||||
| CYP2E1 (rs2070673) | ||||||||||||
| TT | 108 | 245 | 5 | 1.00 | 15 | 1.00 | 10 | 1.00 | 5 | 1.00 | 11 | 1.00 |
| AT or AA | 63 | 118 | 5 | 1.52(0.39,5.99) | 6 | 0.84(0.32,2.22) | 5 | 1.22(0.40,3.70) | 1 | 0.46(0.05,4.36) | 4 | 0.76(0.22,2.57) |
| p-interaction | 0.87 | 0.37 | 0.46 | |||||||||
| GSTM3 (rs1799735) | ||||||||||||
| ++ | 116 | 256 | 8 | 1.00 | 12 | 1.00 | 9 | 1.00 | 3 | 1.00 | 9 | 1.00 |
| +- or -- | 55 | 104 | 2 | 0.25(0.03,2.08) | 9 | 1.77(0.72,4.35) | 6 | 1.57(0.54,4.62) | 3 | 1.70(0.29,9.89) | 6 | 1.38(0.42,4.48) |
| p-interaction | 0.52 | 0.14 | 0.19 | 0.16 | ||||||||
| GSTP1_01 (rs1695) | ||||||||||||
| AA | 82 | 148 | 2 | 1.00 | 13 | 1.00 | 10 | 1.00 | 5 | 1.00 | 8 | 1.00 |
| AG or GG | 90 | 212 | 8 | 3.50(0.67,18.34) | 8 | 0.43(0.17,1.07) | 5 | 0.34(0.11,1.04) | 1 | 0.07(0.006,0.78) | 7 | 0.60(0.20,1.83) |
| p-interaction | 0.10 | 0.02 | 0.01 | 0.10 | ||||||||
| GSTP1_02 (rs1138272) | ||||||||||||
| CC | 143 | 297 | 6 | 1.00 | 18 | 1.00 | 13 | 1.00 | 6 | - | 12 | 1.00 |
| CT or TT | 29 | 66 | 4 | 4.66(1.07,20.27) | 3 | 0.77(0.22,2.71) | 2 | 0.65(0.14,3.00) | 0 | - | 3 | 1.32(0.33,5.32) |
| p-interaction | 0.03 | 0.09 | 0.24 | |||||||||
| GSTT1_02(rs17856199) | ||||||||||||
| ++ | 61 | 125 | 2 | 1.00 | 6 | 1.00 | 4 | 1.00 | 2 | 1.00 | 3 | 1.00 |
| +- or -- | 88 | 175 | 5 | 2.46(0.26,23.01) | 12 | 1.43(0.52,3.97) | 8 | 1.44(0.42,4.98) | 2 | 0.50(0.04,5.76) | 11 | 5.18(1.14,23.64) |
| p-interaction | 0.09 | 0.50 | 0.28 | 0.89 | ||||||||
| NAT1 | ||||||||||||
| w/out *10 | 111 | 215 | 6 | 1.00 | 13 | 1.00 | 8 | 1.00 | 2 | 1.00 | 10 | 1.00 |
| with *10 | 59 | 143 | 4 | 1.53(0.39,6.02) | 7 | 0.74(0.28,1.93) | 6 | 1.12(0.36,3.46) | 3 | 2.83(0.40,20.2) | 5 | 0.66(0.21,2.08) |
| p-interaction | 0.51 | 0.67 | 0.63 | 0.32 | ||||||||
| NAT2 | ||||||||||||
| Rapid | 74 | 144 | 2 | 1.00 | 9 | 1.00 | 7 | 1.00 | 4 | 1.00 | 6 | 1.00 |
| Slow | 97 | 218 | 8 | 2.77(0.55,13.84) | 12 | 0.93(0.38,2.28) | 8 | 0.75(0.26,2.17) | 2 | 0.41(0.06,2.64) | 9 | 1.03(0.33,3.27) |
| p-interaction | 0.38 | 0.18 | 0.12 | 0.30 | ||||||||
Adjusted for age and education, family history and smoking status
Table 4.
Association between T-Cell lymphoma and polymorphisms in xenobiotic metabolizing enzymes by drinking status
| T Cell Lymphoma |
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Non-drinkers | Drinkers | Non-drinkers | Drinkers | Wine-drinkers | Beer-drinkers | Liquor-drinkers | ||||||
| SNP | Controls | Controls | Cases | *OR (95%CI) | Cases | *OR (95%CI) | Cases | *#OR (95%CI) | Cases | *OR (95%CI) | Cases | *OR (95%CI) |
| CYP21A2 (rs6474) | ||||||||||||
| GG | 77 | 164 | 4 | 1.00 | 6 | 1.00 | 4 | 1.00 | 3 | 1.00 | 6 | 1.00 |
| AG or AA | 89 | 185 | 7 | 1.45(0.39,5.42) | 14 | 1.91(0.70,5.15) | 11 | 1.96(0.59,6.49) | 5 | 1.50(0.30,7.56) | 9 | 1.17(0.38,3.54) |
| p-interaction | 0.73 | 0.76 | 0.94 | 0.78 | ||||||||
| CYP1A1 (rs1048943) | ||||||||||||
| AA | 152 | 335 | 9 | 1.00 | 21 | - | 15 | 1.00 | 7 | - | 16 | - |
| AG or GG | 17 | 22 | 2 | 1.75(0.32,9.67) | 0 | - | 0 | - | 0 | - | 0 | - |
| p-interaction | ||||||||||||
| CYP1A2 (rs762551) | ||||||||||||
| AA | 82 | 185 | 3 | 1.00 | 9 | 1.00 | 5 | 1.00 | 4 | 1.00 | 7 | 1.00 |
| AC or CC | 90 | 177 | 8 | 2.36(0.58,9.57) | 13 | 1.39(0.56,3.43) | 11 | 1.85(0.61,5.63) | 4 | 0.89(0.18,4.48) | 9 | 1.32(0.46,3.84) |
| p-interaction | 0.52 | 0.79 | 0.29 | 0.48 | ||||||||
| CYP1B1 (rs1056836) | ||||||||||||
| CC | 66 | 126 | 2 | 1.00 | 7 | 1.00 | 6 | 1.00 | 3 | 1.00 | 5 | 1.00 |
| CG or GG | 115 | 250 | 9 | 3.11(0.62,15.77) | 15 | 1.01(0.39,2.59) | 10 | 0.74(0.25,2.14) | 5 | 0.72(0.14,3.84) | 11 | 1.04(0.34,3.17) |
| p-interaction | 0.22 | 0.15 | 0.12 | 0.23 | ||||||||
| CYP2C9 (rs1799853) | ||||||||||||
| CC | 148 | 303 | 12 | - | 19 | 1.00 | 13 | 1.00 | 7 | 1.00 | 15 | 1.00 |
| CT or TT | 41 | 96 | 0 | - | 7 | 1.02(0.39,2.65) | 6 | 1.22(0.42,3.56) | 3 | 0.73(0.14,3.83) | 3 | 0.69(0.19,2.52) |
| p-interaction | 0.96 | 0.96 | 0.96 | 0.96 | ||||||||
| CYP2E1 (rs2070673) | ||||||||||||
| TT | 108 | 245 | 4 | 1.00 | 15 | 1.00 | 14 | 1.00 | 5 | 1.00 | 10 | 1.00 |
| AT or AA | 63 | 118 | 7 | 3.40(0.90,12.81) | 7 | 1.03(0.41,2.64) | 2 | 0.36(0.08,1.64) | 3 | 1.59(0.32,7.91) | 6 | 1.12(0.38,3.27) |
| p-interaction | 0.17 | 0.03 | 0.48 | 0.22 | ||||||||
| GSTM3 (rs1799735) | ||||||||||||
| ++ | 116 | 256 | 7 | 1.00 | 12 | 1.00 | 10 | 1.00 | 5 | 1.00 | 8 | 1.00 |
| +- or -- | 55 | 104 | 3 | 0.74(0.17,3.17) | 10 | 2.17(0.88,5.31) | 6 | 1.64(0.55,4.83) | 3 | 1.99(0.39,10.11) | 8 | 2.40(0.82,6.96) |
| p-interaction | 0.26 | 0.51 | 0.36 | 0.19 | ||||||||
| GSTP1_01 (rs1695) | ||||||||||||
| AA | 82 | 148 | 7 | 1.00 | 7 | 1.00 | 4 | 1.00 | 4 | 1.00 | 7 | 1.00 |
| AG or GG | 90 | 212 | 4 | 0.66(0.17,2.51) | 15 | 1.41(0.55,3.61) | 12 | 1.93(0.59,6.26) | 4 | 0.45(0.09,2.33) | 9 | 0.85(0.30,2.44) |
| p-interaction | 0.27 | 0.21 | 0.75 | 0.57 | ||||||||
| GSTP1_02 (rs1138272) | ||||||||||||
| CC | 143 | 297 | 11 | - | 19 | 1.00 | 13 | 1.00 | 6 | 1.00 | 14 | 1.00 |
| CT or TT | 29 | 66 | 0 | - | 3 | 0.75(0.21,2.65) | 3 | 1.04(0.28,3.83) | 2 | 2.45(0.36,16.79) | 2 | 0.65(0.14,3.10) |
| p-interaction | 0.95 | 0.95 | 0.95 | 0.97 | ||||||||
| GSTT1_02(rs17856199) | ||||||||||||
| ++ | 61 | 125 | 4 | 1.00 | 6 | 1.00 | 6 | 1.00 | 3 | 1.00 | 3 | 1.00 |
| +- or -- | 88 | 175 | 4 | 0.58(0.13,2.71) | 13 | 1.47(0.53,4.06) | 9 | 0.99(0.33,2.99) | 4 | 0.61(0.11,3.34) | 10 | 2.71(0.71,10.31) |
| p-interaction | 0.39 | 0.69 | 0.84 | 0.17 | ||||||||
| NAT1 | ||||||||||||
| w/out *10 | 111 | 215 | 2 | 1.00 | 12 | 1.00 | 7 | 1.00 | 4 | 1.00 | 10 | 1.00 |
| with *10 | 59 | 143 | 9 | 8.33(1.67,41.64) | 10 | 0.97(0.39,2.40) | 9 | 1.61(0.55,4.71) | 4 | 1.08(0.21,5.51) | 6 | 0.83(0.28,2.42) |
| p-interaction | 0.02 | 0.07 | 0.08 | 0.02 | ||||||||
| NAT2 | ||||||||||||
| Rapid | 74 | 144 | 7 | 1.00 | 9 | 1.00 | 8 | 1.00 | 4 | 1.00 | 6 | 1.00 |
| Slow | 97 | 218 | 4 | 0.49(0.13,1.83) | 13 | 1.10(0.44,2.74) | 8 | 0.70(0.24,2.01) | 4 | 0.93(0.19,4.49) | 10 | 1.18(0.40,3.44) |
| p-interaction | 0.32 | 0.66 | 0.60 | 0.31 | ||||||||
Adjusted for age and education, family history and smoking status
When we restricted the sample to non-Hispanic Caucasians, the results were unchanged.
Discussion
We evaluated SNPs that were drawn from eleven key genes that play a role in the mediation of carcinogen metabolism. Overall, our results do not confer evidence that the relationship between NHL and alcohol intake is modified by common genetic variation in CYP, GST, and NAT genes. However, when we evaluated the DLBCL histologic type, we identified significant interactions for multiple GSTs and NATs according to alcohol intake. Due to limited case numbers for the DLBCL as well as other subtypes of interest, we recommend that these findings be pursued in a larger study population in the future.
Many of the cytochrome P450 (CYP) enzymes are known to oxidize ethanol into acetaldehyde, and the expression of most of CYPs, especially CYP1A1 and CYP2C9, is inducible by ethanol [20, 24, 25]. As such, the 3-fold increased risk of DLBCL for those with a heterozygous or wildtype CYP1A1 genotype in liquor drinkers may be due to the higher ethanol content of liquor (361mg/ml) compared to wine (79mg/ml) and beer (36mg/ml)and higher CYP expression [2]. CYP1A1 is also associated with the metabolism of many other potential carcinogens, such as nitrosamines, some components of tobacco smoke and many organic chlorinated and non-chlorinated solvents, including benzene [26], which may increase the risk of DLBCL [19, 27]. A potential explanation for our finding is that alcohol consumption may induce the over-expression of CYPs which accelerates the metabolism of toxicants derived from other exposures thus increasing the risk of cancer. The non-significant decreased risk in non-drinkers compared to the increased observed risk in those receiving a heavy dose of ethanol suggests that the alcohol may play an important role in inducing the capacity of those with the impaired genotype.
Glutathione S-transferases (GSTs) consist of a family of isoenzymes that also play an important role in the detoxification of endogenous compounds as they catalyze the conjugation of these compounds to facilitate excretion from the body. In our study, we found a two-fold increased risk of DLBCL among non-drinkers with variant alleles of GSTP1 suggesting that the low catalytic efficiency may impair the detoxification of other harmful substances. However, our results also showed a significant interaction as never drinkers with a null GSTM3 genotype had a significantly decreased risk of DLBCL compared to individuals with the alleles, whereas a slightly increased risk of DLBCL was found for drinkers with the null genotype. It has been shown previously that the GSTM3 null allele has an increased transcription potential and enhances the detoxification activity of GSTM3-encoded protein [28]. A potential explanation for our finding is subsequently that the alcohol-induced expression of oxidases (such as CYPs) creates more reactive intermediates which add to the burden of detoxification.
NATs are also important in the metabolism of toxicants as they catalyze the conjugation of compounds to prepare them for excretion [29]. The rate of acetylation is thought to be related to the toxicity of a compound as it may affect how quickly a chemical is excreted. In our study, we found a significantly decreased risk of DLBCL among drinkers with a NAT1*10 genotype, though this was not observed among non-drinking women. The NAT1*10 allele has been associated with a rapid acetylator phenotype both in vitro [30] and in vivo [31]. Our results suggest that the expression of NAT1*10 genotype is alcohol-inducible and it reduces the risk of DLBCL by increasing the rate at which environmental and cancer-causing agents are acetylated and excreted from the body.
Our study has several strengths. It is a population-based, case-control study with both incident cases that are histologically confirmed and highly accurate genotyping data. The primary limitation of our study is that the sample size is modest and the number of cases in several histologic subgroups was small. This resulted in reduced power to detect associations for SNPs with low allele frequencies. It was limited to women and may be nongeneralizable to the entire population. Information bias, resulting from exposure misclassification is likely to have been nondifferential, thus biasing our risk estimates towards the null. Furthermore, our findings were based on small numbers and could be due to chance. In addition, because of the large number of comparisons, we cannot rule out chance findings due to multiple comparisons. As such, the positive findings in our report require replication in larger studies with greater power, which will be particularly valuable if tagged SNPs with full genomic coverage of the most promising candidate genes are used.
In sum, our study suggested that the polymorphisms in key metabolic pathway genes may be related to the risk of DLBCL, and this association may be modified by alcohol consumption. We did not find this to be true for NHL overall or for the other histologic subtypes. Our results confer support for the need for this hypothesis to be pursued in a larger study population, with a particular focus on DLCBL.
Acknowledgements
Contract grant sponsor: National Institutes of Health; Contract grant numbers: CA62006, 1D43TW007864-01, and 1D43TW008323; Contract grant sponsor: National Center for Research Resources; Contract grant number: UL1 RR024139; Contract grant sponsor: Intramural Research Program of the NIH.
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