Abstract
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Genetic variation in Th1/Th2 pathway genes and risk of non-Hodgkin lymphoma: A pooled analysis of three population-based case-control studies
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Abstract
The balance between Th1 and Th2 activity is critical in lymphoid cell development and differentiation. Immune dysfunction underlies lymphomagenesis, so an alteration in the regulation of key Th1/Th2 cytokines may lead to the development of non-Hodgkin lymphoma (NHL). To study the impact of polymorphism in Th1/Th2 cytokines on NHL risk, we analyzed 145 tag single nucleotide polymorphisms (SNPs) in 17 Th1/Th2 cytokine and related genes in three population-based case-control studies (1,946 cases and 1,808 controls). Logistic regression was used to compute odds ratios (OR) for NHL and four major NHL subtypes in relation to tag SNP genotypes and haplotypes. A gene-based analysis adjusting for the number of tag SNPs genotyped in each gene showed significant associations with risk of NHL combined and one or more NHL subtypes for Th1 (IL12A and IL12RB1) and Th2 (IL4, IL10RB, and IL18) genes. The strongest association was for IL12A rs485497, which plays a central role in bridging the cellular and humoral pathways of innate resistance and antigen-specific adaptive immune responses (allele risk OR=1.17; P(trend)=0.00099). This SNP was also associated specifically with risk of follicular lymphoma (allele risk OR=1.26; P(trend)=0.0012). These findings suggest that genetic variation in Th1/Th2 cytokine genes may contribute to lymphomagenesis.
Introduction
Disruption of host immunity is one of the strongest known risk factors for non-Hodgkin lymphoma (NHL). An essential aspect of immune regulation involves maintaining homeostasis between T-helper 1 (Th1) and T-helper 2 (Th2) activation, controlling both lymphoid cell development and differentiation (Chiu and Weisenburger, 2003; Hartge et al., 1994; Neurath et al., 2002). Th1 cells are primarily responsible for the development of cell mediated immunity, whereas Th2 cells are involved in antibody production and humoral immune responses. Cytokines mediate the immune responses by Th1 or Th2 type. Because immune dysfunction is thought to be the underlying basis of lymphomagenesis, alteration in the regulation and expression of key Th1/Th2 cytokines could play an important role in the pathogenesis of NHL.
Single nucleotide polymorphisms (SNPs) in Th1/Th2 cytokines (IL4, IL10, IL12, IL18) have been shown to influence gene expression (Arimitsu et al., 2006; Hoffmann et al., 2001; Keen, 2002) and have been associated with risk of a number of autoimmune conditions (Lee et al., 2007; Pawlik et al., 2005; Hirschfield et al., 2009) and cancers (Wei et al., 2007; Rothman et al., 2006; Chen et al., 2009b; Lan et al., 2006). For example, we previously reported that variants in IL10 and IL4 were significantly associated with risk of NHL and/or its subtypes in a population-based case-control study among women in Connecticut (Yale NHL study) (Lan et al., 2006) and that variants in IL10 were associated with risk of NHL in a population-based case-control study of NHL in New South Wales, Australia (Purdue et al., 2007) and in pooled analyses conducted by the International Lymphoma Consortium study that included data from the three studies in the current report (Rothman et al., 2006). To comprehensively evaluate the role that genetic variation in Th1/Th2 genes plays in lymphomagenesis, we genotyped tag SNPs in a candidate gene study of 17 key cytokine genes in 1,946 NHL cases and 1,808 controls pooled from three independent population-based case-control studies conducted in the US and Australia.
Materials and methods
Study population
Three population-based case-control studies of NHL participated in this pooled analysis: the National Cancer Institute (NCI)-Surveillance Epidemiology and End Results (SEER) NHL case-control study, conducted within the SEER registry catchment areas of Iowa, Detroit, Los Angeles and Seattle (Wang et al., 2006); the Connecticut case-control NHL study, conducted among female residents of Connecticut (Lan et al., 2006); and the New South Wales (NSW) case-control study, conducted among residents of New South Wales and the Australian Capital Territory, Australia (Purdue et al., 2007). All three studies included first primary NHL cases only, and population controls that were frequency-matched to cases. Selected characteristics for each study are presented in Table 1. The protocols for each study were approved by all relevant institutional review boards. All study participants provided informed consent, in accordance with the Declaration of Helsinki.
Table 1
Demographic and pathology characteristics of study participants in the NCI-SEER, Connecticut, and New South Wales NHL case-control studies, by study and pooled
| NCI-SEER* | Yale | New South Wales | Pooled | |||||
|---|---|---|---|---|---|---|---|---|
| Control | Case | Control | Case | Control | Case | Control | Case | |
| n=828 | n=990 | n=515 | n=436 | n=465 | n=520 | n=1808 | n=1946 | |
| N (%) | N (%) | N (%) | N (%) | N (%) | N (%) | N (%) | N (%) | |
| Sex | ||||||||
| Male | 443 (53) | 536 (54) | - | - | 268 (58) | 304 (58) | 711 (39) | 840 (43) |
| Female | 385 (47) | 454 (46) | 515 (100) | 436 (100) | 197 (42) | 216 (42) | 1097 (61) | 1106 (57) |
| Age (years) | ||||||||
| < 50 | 203 (25) | 277 (28) | 98 (19) | 86 (20) | 107 (23) | 121 (23) | 408 (23) | 484 (25) |
| 50–59 | 177 (21) | 235 (24) | 97 (19) | 89 (20) | 135 (29) | 171 (33) | 409 (23) | 495 (25) |
| 60–69 | 285 (34) | 311 (31) | 120 (23) | 110 (25) | 151 (32) | 154 (30) | 556 (31) | 575 (30) |
| 70+ | 163 (20) | 167 (17) | 200 (39) | 151 (35) | 72 (16) | 74 (14) | 435 (24) | 392 (20) |
| Race | ||||||||
| White | 669 (81) | 858 (87) | 484 (94) | 420 (96) | 459 (99) | 507 (98) | 1612 (89) | 1785 (92) |
| Black | 112 (13) | 64 (6) | 14 (3) | 13 (3) | - | - | 126 (7) | 77 (4) |
| Asian | 16 (2) | 32 (3) | 3 (0.6) | 1 (0.2) | 6 (1) | 13 (2) | 25 (1) | 46 (2) |
| Other/Unknown | 31 (4) | 36 (4) | 14 (3) | 2 (0.5) | - | - | 45 (3) | 38 (2) |
| Ethnicity | ||||||||
| Hispanic | 41 (5) | 52 (5) | 18 (4) | 6 (1) | - | - | 59 (3) | 58 (3) |
| Non-Hispanic | 787 (95) | 936 (95) | 491 (95) | 427 (98) | 465 (100) | 520 (100) | 1743 (96) | 1883 (97) |
| Unknown | 2 (0.2) | 6 (1) | 3 (1) | 6 (0.3) | 5 (0.3) | |||
| Study Site | ||||||||
| Detroit | 139 (17) | 197 (20) | - | - | - | - | 139 (8) | 197 (10) |
| Iowa | 246 (30) | 301 (30) | - | - | - | - | 246 (14) | 301 (16) |
| L.A. | 199 (24) | 234 (24) | - | - | - | - | 199 (11) | 234 (12) |
| Seattle | 244 (29) | 258 (26) | - | - | - | - | 244 (13) | 258 (13) |
| Connecticut | - | - | 515 (100) | 436 (100) | - | - | 515 (28) | 436 (22) |
| N.S.W. | - | - | - | - | 446 (96) | 496 (95) | 446 (25) | 496 (26) |
| A.C.T. | - | - | - | - | 19 (4) | 24 (5) | 19 (1) | 24 (1) |
| NHL Subtype | ||||||||
| DLBCL | - | 294 (30) | - | 137 (31) | - | 169 (33) | - | 600 (31) |
| Follicular | - | 246 (25) | - | 103 (24) | - | 191 (37) | - | 540 (28) |
| CLL/SLL | - | 101 (10) | - | 43 (10) | - | 17 (3) | - | 161 (8) |
| Mantle Cell | - | 40 (4) | - | 10 (2) | - | 19 (4) | - | 69 (4) |
| Marginal Zone | - | 82 (8) | - | 29 (7) | - | 49 (9) | - | 160 (8) |
| LPL | - | 24 (2) | - | 9 (2) | - | 23 (4) | - | 56 (3) |
| MF/SS | - | 18 (2) | - | 10 (2) | - | 3 (1) | - | 31 (2) |
| Burkitt | - | 11 (1) | - | 0 | - | 3 (1) | - | 14 (1) |
| Peripheral T | - | 41 (4) | - | 14 (3) | - | 7 (1) | - | 62 (3) |
| NOS | - | 133 (13) | - | 81 (19) | - | 39 (7) | - | 253 (13) |
| DNA Source | ||||||||
| Blood | 598 (72) | 688 (70) | 515 (100) | 436 (100) | 465 (100) | 520 (100) | 1578 (87) | 1644 (85) |
| Buccal | 230 (28) | 302 (30) | - | - | - | - | 230 (13) | 302 (15) |
NHL Pathology Classification
In the NCI-SEER study, all cases were histologically confirmed by the local diagnosing pathologist. In the Connecticut study, all cases were confirmed by central review of diagnostic slides by two independent expert hematopathologists. In the NSW study, all cases were histologically confirmed by the local diagnosing pathologist, and a confirmatory central pathology review was performed for cases whose diagnosis was judged by an expert hematopathologist to be <90% certain on review of the diagnostic pathology report. NHL pathology subtypes were classified based on the World Health Organization classification using the International Lymphoma Epidemiology Consortium (InterLymph) guidelines (Morton et al., 2007). In the present analyses, we evaluated NHL overall and the four most common NHL subtypes: diffuse large B-cell lymphoma (DLBCL) (31%), follicular lymphoma (28%), marginal zone lymphoma (8%), and chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) (8%) (Tables 1).
Biological samples, genotyping, and quality control
DNA was extracted from blood or buccal cell samples. Genotyping was conducted at the National Cancer Institute Core Genotyping Facility (Advanced Technology Center, Gaithersburg, MD; http://snp500cancer.nci.nih.gov). Tag SNPs were chosen from the designable set of common SNPs (minor allele frequency (MAF)>5%) genotyped in the Caucasian (CEU) population sample of the HapMap Project (Data Release 20/Phase II, NCBI Build 35 assembly, dbSNPb125) using the software Tagzilla (http://tagzilla.nci.nih.gov/), which implements a tagging algorithm based on the pair wise binning method of Carlson et al. (Carlson et al., 2004). For each gene, SNPs within the region spanning 20kb 5’ of the start of transcription (exon 1) to 10kb 3’ of the end of the last exon were grouped using a binning threshold of r2>0.8. When there were multiple transcripts available for genes, only the primary transcript was assessed. SNPs with a low completion rate (<90% of the full panel of 1536 tag SNPs) were excluded. QC duplicates and replicates from each study were genotyped and blinded to laboratory personnel. SNPs with a concordance of <95% in the study-specific QC samples as well as a low completion rate (<90% of the samples) were excluded. In total, 145 tag SNPs in 17 Th1/Th2 cytokine genes were analyzed (Supplementary Table 1).
Statistical methods
Unconditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CI) for the genotype-specific risks of NHL for each SNP for the heterozygote and less common homozygote genotypes, with the more common homozygote as the baseline category. Models were adjusted for age, race, sex and study center. Polytomous multivariate unconditional logistic regression models were used to evaluate the effect among different NHL subtypes.
To evaluate the significance of the association between each gene region and NHL risk, we used a minimum p-value (“minP test”) to assess the smallest p-trend within each gene region. The minP test uses permutation-based re-sampling methods (10,000 permutations) (Chen et al., 2006) that adjusts for both the number of tag SNPs in each gene, as well as for correlations between SNPs genotyped for each gene region, while taking into account the underlying linkage disequilibrium pattern
To obtain a gene-level summary of association, the minP value was computed (“minP test”), which evaluates the statistical significance of the smallest p-trend within each gene region (determined by dichotomous logistic regression, comparing NHL or NHL subtypes to controls) by permutation-based resampling methods (10,000 permutations) that automatically adjusts for both the number of tag SNPs in each gene, as well as for correlations between SNPs genotyped for each gene region, while taking into account the underlying linkage disequilibrium pattern (Chen et al., 2006). Further, to assess the robustness of our findings for each gene and NHL risk, false discovery rates (FDR) (Benjamini and Hochberg, 1995) were calculated for each minP test for all NHL to take into account multiple comparisons across the 17 Th1/Th2 genes that were tested. A SNP association with a FDR value < 0.2 was considered a noteworthy finding with a relatively lower probability of being a false discovery. The FDR was also calculated for the trend test for each SNP genotyped.
A haplotype analysis among non-Hispanic Caucasians was carried out using an expectation-maximization (EM) algorithm(Excoffier and Slatkin, 1995) and HaploStats (R version 1.2.0) (R Development Core Team, 2004; Schaid et al., 2002), but did not reveal additional insights beyond those obtained from the SNP and gene-based analyses (data not shown).
Results
Cases and controls were comparable both overall and in each study with respect to age, sex, race, and study. Population characteristics and histologic subtype frequencies were generally similar across the three studies (Table 1). Results for each SNP for all NHL sorted by P(trend), for all NHL by study, and for major NHL subtypes are shown in Supplementary Tables 2, 3 and 4, respectively.
We observed evidence for association at the gene-level for IL12A and IL12RB1, Th1 genes, and IL4, IL10RB, and IL18, Th2 genes, with significance levels <0.05 by the minP test (Table 2). In addition, there was evidence for associations at the gene level for Th2 genes with DLBCL (IL4 and IL18) and marginal zone lymphoma (IL4) and the Th1 gene IL12A with follicular lymphoma (Table 2). Results for the most significant SNP in each gene that was significant based on the minP test are presented in Table 3 and Table 4. Within genes showing an association with NHL, L12A rs485497 and IL4 rs2243263 were associated with increased risk, and IL12RB1 rs12564159, IL10RB rs1058867, and IL18 rs243908 were associated with a decreased risk of NHL overall and/or with one or more of the subtypes (Tables 3 and and4).4). Risk estimates were in the same direction and moderately consistent in magnitude across the three studies for SNPs in IL12A, IL4, and IL18 (Table 3). All of these SNPs except IL18 rs243908 were associated with altered risk of follicular lymphoma (Table 4).
Table 2
Results for the minimum P value (minP test)* for 17 candidate Th1 and/or Th2 genes evaluated in the pooled analysis for NHL overall and by subtype
| Candidate gene | NHL | DLBCL | FL | MZL | CLL |
|---|---|---|---|---|---|
| Th1 | |||||
| IL2 | 0.75 | 0.64 | 0.87 | 0.70 | 0.39 |
| IL12A¶ | 0.021¥ | 0.69 | 0.015 | 0.14 | 0.83 |
| IL12B | 0.59 | 0.51 | 0.58 | 0.99 | 0.86 |
| IL12RB1† | 0.012¥ | 0.15 | 0.092 | 0.43 | 0.12 |
| IL12RB2 | 0.23 | 0.14 | 0.39 | 0.11 | 0.076 |
| Th2 | |||||
| IL13/IL4‡ | 0.046¥ | 0.041 | 0.35 | 0.019 | 0.13 |
| IL4R | 0.56 | 0.59 | 0.36 | 0.83 | 0.32 |
| IL5 | 0.15 | 0.26 | 0.70 | 0.15 | 0.19 |
| IL10/IL19 | 0.23 | 0.14 | 0.25 | 0.44 | 0.57 |
| IL10RA | 0.36 | 0.38 | 0.33 | 0.20 | 0.89 |
| IL10RB§ | 0.038¥ | 0.40 | 0.08 | 0.41 | 0.72 |
| IL18€ | 0.044¥ | 0.023 | 0.92 | 0.15 | 0.31 |
| Th1/Th2 | |||||
| CD5 | 0.17 | 0.28 | 0.11 | 0.48 | 0.90 |
| CD28 | 0.98 | 0.63 | 0.97 | 0.15 | 0.59 |
| IL9 | 0.079 | 0.14 | 0.18 | 0.17 | 0.61 |
Significant p-trend in logistic regression analyses:
Table 3
Results for individual SNP for NHL in the pooled analysis, overall and by study*
| NCI SEER | Connecticut | New South Wales | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Genotype | Controls (%) | Cases (%) | OR (95% CI) | p | FDR value | Control (%) | cases (%) | OR (95% CI) | P | Control (%) | cases (%) | OR (95% CI) | p | Control (%) | cases (%) | OR (95% CI) | p |
| IL12A (rs485497) | |||||||||||||||||
| GG | 506 (28) | 461 (24) | 242 (29) | 240 (24) | 134 (26) | 103 (24) | 130 (28) | 118 (23) | |||||||||
| AG | 894 (50) | 952 (49) | 1.12 (0.96–1.31) | 0.16 | 423 (51) | 471 (48) | 1.04 (0.83–1.31) | 0.75 | 252 (49) | 211 (48) | 1.08 (0.78–1.49) | 0.65 | 219 (47) | 270 (52) | 1.35 (0.99–1.84) | 0.06 | |
| AA | 403 (22) | 526 (27) | 1.37 (1.14–1.65) | 0.00097 | 162 (20) | 273 (28) | 1.51 (1.15–1.99) | 0.0033 | 127 (25) | 122 (28) | 1.29 (0.9–1.86) | 0.17 | 114 (25) | 131 (25) | 1.24 (0.87–1.78) | 0.23 | |
| AG or AA | 1297 (72) | 1478 (76) | 1.19 (1.03–1.39) | 0.020 | 585 (71) | 744 (76) | 1.16 (0.94–1.45) | 0.18 | 379 (74) | 333 (76) | 1.15 (0.85–1.55) | 0.38 | 333 (72) | 401 (77) | 1.31 (0.98–1.76) | 0.06 | |
| Trend | 0.00099 | 0.14 | 0.0039 | 0.17 | 0.22 | ||||||||||||
| IL12RB1 (rs2305742) | |||||||||||||||||
| AA | 1143 (64) | 1287 (67) | 509 (63) | 659 (68) | 332 (65) | 292 (67) | 302 (65) | 336 (65) | |||||||||
| AC | 563 (31) | 566 (29) | 0.86 (0.75–1) | 0.046 | 263 (32) | 276 (29) | 0.76 (0.61–0.93) | 0.009 | 159 (31) | 129 (30) | 0.92 (0.69 1.22) | 0.56 | 141 (30) | 161 (31) | 1.04 (0.79–1.38) | 0.76 | |
| CC | 83 (5) | 66 (3) | 0.67 (0.48–0.94) | 0.020 | 40 (5) | 30 (3) | 0.52 (0.32–0.86) | 0.010 | 21 (4) | 14 (3) | 0.8 (0.4–1.62) | 0.53 | 22 (5) | 22 (4) | 0.9 (0.49–1.66) | 0.74 | |
| AC or CC | 646 (36) | 632 (33) | 0.84 (0.73–0.96) | 0.013 | 303 (37) | 306 (32) | 0.73 (0.59–0.89) | 0.0018 | 180 (35) | 143 (33) | 0.91 (0.69–1.19) | 0.48 | 163 (35) | 183 (35) | 1.02 (0.79–1.33) | 0.86 | |
| Trend | 0.0047 | 0.20 | 0.00070 | 0.43 | 0.99 | ||||||||||||
| IL4 (rs2243263) | |||||||||||||||||
| GG | 1425 (79) | 1458 (76) | 636 (78) | 737 (76) | 399 (78) | 319 (74) | 390 (84) | 402 (77) | |||||||||
| CG | 351 (20) | 444 (23) | 1.28 (1.09–1.51) | 0.0022 | 169 (21) | 223 (23) | 1.21 (0.96–1.52) | 0.11 | 109 (21) | 107 (25) | 1.22 (0.9–1.66) | 0.20 | 73 (16) | 114 (22) | 1.53 (1.1–2.12) | 0.011 | |
| CC | 20 (1) | 19 (1) | 1.03 (0.54–1.97) | 0.92 | 12 (1) | 11 (1) | 0.94 (0.4–2.19) | 0.89 | 6 (1) | 5 (1) | 1 (0.3–3.31) | 1.00 | 2 (0) | 3 (1) | 1.49 (0.25–9) | 0.66 | |
| CG or CC | 371 (21) | 463 (24) | 1.27 (1.09–1.49) | 0.0028 | 181 (22) | 234 (24) | 1.19 (0.95–1.49) | 0.13 | 115 (22) | 112 (26) | 1.21 (0.9–1.64) | 0.22 | 75 (16) | 117 (23) | 1.53 (1.11–2.11) | 0.010 | |
| Trend | 0.0056 | 0.20 | 0.19 | 0.26 | 0.012 | ||||||||||||
| IL10RB (rs1058867) | |||||||||||||||||
| AA | 566 (31) | 683 (35) | 237 (29) | 346 (35) | 162 (32) | 130 (30) | 167 (36) | 207 (40) | |||||||||
| AG | 897 (50) | 964 (50) | 0.91 (0.79–1.06) | 0.23 | 425 (51) | 491 (50) | 0.83 (0.67–1.03) | 0.10 | 249 (48) | 239 (55) | 1.15 (0.86–1.55) | 0.34 | 223 (48) | 234 (45) | 0.85 (0.64–1.12) | 0.24 | |
| GG | 343 (19) | 295 (15) | 0.74 (0.61–0.9) | 0.0024 | 166 (20) | 151 (15) | 0.68 (0.51–0.9) | 0.0070 | 103 (20) | 66 (15) | 0.8 (0.54–1.18) | 0.25 | 74 (16) | 78 (15) | 0.86 (0.59–1.25) | 0.43 | |
| AG or GG | 1240 (69) | 1259 (65) | 0.87 (0.75–1) | 0.044 | 591 (71) | 642 (65) | 0.79 (0.64–0.97) | 0.025 | 352 (68) | 305 (70) | 1.05 (0.79–1.39) | 0.73 | 297 (64) | 312 (60) | 0.85 (0.66–1.1) | 0.22 | |
| Trend | 0.0037 | 0.20 | 0.0060 | 0.43 | 0.30 | ||||||||||||
| IL18 (rs243908) | |||||||||||||||||
| AA | 831 (46) | 958 (49) | 376 (45) | 483 (49) | 247 (48) | 223 (51) | 208 (45) | 252 (49) | |||||||||
| AG | 777 (43) | 803 (41) | 0.88 (0.76–1) | 0.057 | 370 (45) | 413 (42) | 0.85 (0.7–1.04) | 0.11 | 209 (41) | 176 (40) | 0.89 (0.68–1.17) | 0.42 | 198 (43) | 214 (42) | 0.89 (0.68–1.16) | 0.39 | |
| GG | 194 (11) | 175 (9) | 0.77 (0.61–0.97) | 0.025 | 81 (10) | 90 (9) | 0.86 (0.61–1.2) | 0.36 | 59 (11) | 36 (8) | 0.64 (0.41–1.02) | 0.06 | 54 (12) | 49 (10) | 0.75 (0.49–1.15) | 0.18 | |
| AG or GG | 971 (54) | 978 (51) | 0.85 (0.75–0.97) | 0.018 | 451 (55) | 503 (51) | 0.85 (0.71–1.03) | 0.10 | 268 (52) | 212 (49) | 0.84 (0.65–1.09) | 0.18 | 252 (55) | 263 (51) | 0.86 (0.67–1.11) | 0.24 | |
| Trend | 0.0093 | 0.22 | 0.14 | 0.070 | 0.16 | ||||||||||||
Table 4
Results for selected individual SNP from the pooled analysis for NHL subtypes*
| Genotype | Controls (%) | DLBCL | Follicular | Marginal zone lymphoma | CLL/SLL | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Cases (%) | OR (95% CI) | P | Cases (%) | OR (95% CI) | P | Cases (%) | OR (95% CI) | P | Cases (%) | OR (95% CI) | P | P† | ||
| IL12A (rs485497) | ||||||||||||||
| GG | 506 (28) | 148 (25) | 116 (22) | 39 (24) | 41 (26) | |||||||||
| AG | 894 (50) | 294 (49) | 1.08 (0.86–1.35) | 0.52 | 268 (50) | 1.27 (0.99–1.63) | 0.062 | 81 (51) | 1.1 (0.73–1.64) | 0.65 | 75 (47) | 1.02 (0.68–1.52) | 0.93 | 0.67 |
| AA | 403 (22) | 156 (26) | 1.25 (0.96–1.63) | 0.10 | 153 (28) | 1.59 (1.2–2.11) | 0.0012 | 40 (25) | 1.18 (0.74–1.89) | 0.49 | 44 (28) | 1.33 (0.84–2.11) | 0.22 | 0.47 |
| AG or AA | 1297 (72) | 450 (75) | 1.13 (0.91–1.4) | 0.27 | 421 (78) | 1.37 (1.08–1.73) | 0.0093 | 121 (76) | 1.12 (0.77–1.65) | 0.55 | 119 (74) | 1.11 (0.76–1.62) | 0.58 | |
| Trend | 0.11 | 0.0012 | 0.50 | 0.22 | 0.47 | |||||||||
| IL12RB1 (rs2305742) | ||||||||||||||
| AA | 1143 (64) | 394 (67) | 364 (68) | 106 (67) | 107 (67) | |||||||||
| AC | 563 (31) | 171 (29) | 0.84 (0.69–1.04) | 0.11 | 149 (28) | 0.79 (0.64–0.99) | 0.038 | 50 (31) | 0.94 (0.66–1.34) | 0.74 | 47 (30) | 0.86 (0.6–1.23) | 0.41 | 0.85 |
| CC | 83 (5) | 22 (4) | 0.73 (0.45–1.19) | 0.21 | 23 (4) | 0.82 (0.51–1.33) | 0.42 | 3 (2) | 0.38 (0.12–1.23) | 0.11 | 5 (3) | 0.61 (0.24–1.55) | 0.30 | 0.65 |
| AC or CC | 646 (36) | 193 (33) | 0.83 (0.68–1.01) | 0.068 | 172 (32) | 0.8 (0.65–0.98) | 0.033 | 53 (33) | 0.87 (0.61–1.23) | 0.43 | 52 (33) | 0.83 (0.58–1.17) | 0.29 | |
| Trend | 0.057 | 0.050 | 0.21 | 0.22 | 1.00 | |||||||||
| IL4 (rs2243263) | ||||||||||||||
| GG | 1425 (79) | 436 (74) | 410 (76) | 123 (78) | 118 (74) | |||||||||
| CG | 351 (20) | 144 (25) | 1.42 (1.13–1.78) | 0.0023 | 124 (23) | 1.33 (1.05–1.69) | 0.018 | 34 (22) | 1.12 (0.75–1.68) | 0.57 | 39 (25) | 1.33 (0.91–1.96) | 0.14 | 0.77 |
| CC | 20 (1) | 7 (1) | 1.31 (0.54–3.17) | 0.55 | 4 (1) | 0.82 (0.27–2.45) | 0.72 | 1 (1) | 0.69 (0.09–5.22) | 0.72 | 2 (1) | 1.29 (0.29–5.69) | 0.74 | 0.85 |
| CG or CC | 371 (21) | 151 (26) | 1.41 (1.13–1.76) | 0.0021 | 128 (24) | 1.31 (1.03–1.65) | 0.025 | 35 (22) | 1.1 (0.74–1.64) | 0.63 | 41 (26) | 1.33 (0.91–1.94) | 0.14 | |
| Trend | 0.0031 | 0.047 | 0.72 | 0.15 | 0.68 | |||||||||
| IL10RB (rs1058867) | ||||||||||||||
| AA | 566 (31) | 203 (34) | 211 (39) | 58 (36) | 54 (34) | |||||||||
| AG | 897 (50) | 305 (51) | 0.99 (0.81–1.22) | 0.94 | 246 (46) | 0.79 (0.63–0.97) | 0.029 | 82 (51) | 0.91 (0.64–1.3) | 0.61 | 85 (53) | 0.97 (0.68–1.4) | 0.88 | 0.34 |
| GG | 343 (19) | 91 (15) | 0.78 (0.59–1.04) | 0.087 | 82 (15) | 0.69 (0.51–0.92) | 0.012 | 20 (13) | 0.6 (0.35–1.02) | 0.057 | 22 (14) | 0.65 (0.39–1.09) | 0.10 | 0.76 |
| AG or GG | 1240 (69) | 396 (66) | 0.93 (0.77–1.14) | 0.51 | 328 (61) | 0.76 (0.62–0.93) | 0.0080 | 102 (64) | 0.83 (0.59–1.17) | 0.28 | 107 (66) | 0.88 (0.62–1.25) | 0.49 | |
| Trend | 0.14 | 0.0055 | 0.077 | 0.15 | 0.65 | |||||||||
| IL18 (rs243908) | ||||||||||||||
| AA | 831 (46) | 312 (52) | 251 (47) | 85 (53) | 74 (46) | |||||||||
| AG | 777 (43) | 235 (39) | 0.78 (0.64–0.95) | 0.015 | 225 (42) | 0.91 (0.74–1.12) | 0.38 | 62 (39) | 0.77 (0.55–1.09) | 0.14 | 68 (43) | 0.96 (0.68–1.36) | 0.83 | 0.51 |
| GG | 194 (11) | 50 (8) | 0.68 (0.49–0.96) | 0.028 | 62 (12) | 1.04 (0.75–1.44) | 0.80 | 12 (8) | 0.6 (0.32–1.12) | 0.11 | 18 (11) | 1 (0.58–1.72) | 1.00 | 0.12 |
| AG or GG | 971 (54) | 285 (48) | 0.76 (0.63–0.92) | 0.0047 | 287 (53) | 0.94 (0.77–1.14) | 0.51 | 74 (47) | 0.74 (0.53–1.02) | 0.070 | 86 (54) | 0.97 (0.7–1.35) | 0.86 | |
| Trend | 0.0042 | 0.82 | 0.052 | 0.92 | 0.092 | |||||||||
The strongest association for tag SNPs was in IL12A rs485497, which plays a central role in bridging the cellular and humoral pathways of innate resistance and antigen-specific adaptive immune responses (allele risk OR 1.17; 95% CI, 1.07–1.28, P(trend) = 0.00099) (Supplementary Table 2). This variant was related to follicular lymphoma in particular (allele risk OR 1.26; 95% CI, 1.10–1.45, P(trend) = 0.0012), although there was not statistically significant heterogeneity for this SNP across the four subtypes (Supplementary Table 4).
Discussion
We carried out the first comprehensive evaluation of genetic variation in Th1 and Th2 genes and risk of NHL. The comparison of 1946 cases and 1808 controls from three population-based case-control studies of NHL suggests that variants in Th1/Th2 genes may play a role in lymphomagenesis. In particular, SNPs in the Th1 genes IL12A, and IL12RB1 and in the Th2 genes IL4, IL10RB, and IL18 were associated with risk of NHL overall and with one or more subtypes.
IL12 plays a central role in bridging the cellular and humoral pathways of innate resistance and antigen-specific adaptive immune responses (Trinchieri, 1995). IL12 is produced primarily by antigen-presenting cells and exerts immunoregulatory effects on T and natural killer (NK) cells by inducing rapid IFN-γ production (Trinchieri, 1995). Its immunological functions are mediated through high-affinity binding to the IL12 receptor, IL12RB1 and IL12RB2. Inherited deficiencies of IL12, IL12 receptor, and IFN-γ have been associated with increased susceptibility to severity of mycobacterial and other infectious diseases (Filipe-Santos et al., 2006). Mutations in the IL12RB1 gene encoding the IL12Rβ1 chain is the most common genetic disorder associated with mycobacterial disease (Fieschi et al., 2003).
IL18 is a critical cytokine regulator that stimulates both Th1 and Th2-type immune responses, depending on its cytokine milieu (Nakanishi et al., 2001). Working together with IL12, IL18 can stimulate T cells, produce IFN-γ (Yamanaka et al., 2006), and suppress IL4-dependent immunoglobulin E (IgE) production from B cells. However, in the absence of IL12, IL18 seems to favor a Th2 response. High levels of IL18 alone can lead to increased IgE levels and induce IL4 and IL13 production by basophils, mast cells, and CD4+ T cells (Yamanaka et al., 2006). It has been reported that polymorhpisms in IL18 significantly affect IL18 production (Arimitsu et al., 2006). Further, IL18 variants have been associated in some reports with risk of asthma (Higa et al., 2003; Harada et al., 2009), lupus nephritis (Chen et al., 2009a), childhood lymphomas (Andrie et al., 2009), epithelial ovarian cancer (Palmieri et al., 2008), colorectal cancer (Nikiteas et al., 2007), prostate cancer (Liu et al., 2007), and esophageal squamous cell carcinoma (Wei et al., 2007). The IL18 variant identified in our study is in moderate LD with variants that have been associated with these diseases.
IL4 is a key regulator in the inhibition of Th1 cells and stimulation of a Th2 type of immune response. IL4 also has many additional biological roles, including stimulation of activated B-cell and T-cell proliferation and differentiation of CD4+ T-cells into Th2 cells, which are capable of producing both IL4 and IFN-γ. IL4 polymorphisms have been shown to alter gene expression in vivo and in vitro (Rockman et al., 2003) and have been associated in some reports with juvenile idiopathic arthritis, severity of infection with respiratory syncytial virus in young children, asthma, fungal infection with Candida albicans in patients with leukemia, atopy, and inflammatory bowel disease (Murtaugh et al., 2009). We previously reported that a SNP in IL4 (rs2243248) was associated with risk of NHL in the Connecticut study (Lan et al., 2006). In the current study, IL4 rs2243248 was also associated with risk of NHL even though the p trend from this SNP is not the lowest p value among all SNPs we genotyped in this gene. It is in moderate LD with the IL4 SNP (rs2243263) that has the lowest p value for trend in this report (r = 0.38, D’ = 0.8). The magnitude of risk estimates for these two SNPs were very similar. The results from this study provide additional evidence that genetic variation in IL4 plays a role in the etiology of NHL and warrants further investigation.
There is substantial evidence in support of a role for IL10 in lymphomagenesis. We previously contributed data from the three case-control studies in this report to pooled analyses of the International InterLymph Consortium of case-control studies of NHL and results suggest an association between variants in IL10 and risk of NHL overall and for DLBCL in particular and possibly other subtypes (Rothman et al., 2006; Skibola et al., 2010). We found a similar association for IL10 rs1800890 and DLBCL in the three studies included in this report (Supplementary Table 4). Polymorphisms in genes encoding the IL10 receptor could interrupt IL10 mediated immune regulation and alter risk for NHL. Our findings in IL10RB support this hypothesis.
In summary, Th/Th2 cytokine genes play a critical role in mediating Th1/Th2 pathways, apoptotic potential, and regulation of inflammation. Any perturbation of homeostasis of the immune system could potentially alter risk for NHL. Our study provides evidence that common genetic variants in Th1 (IL12A and IL12RB1) and Th2 (IL4, IL10RB, and IL18) genes are associated with risk of NHL and one or more subtypes. Additional studies are needed to replicate and extend these findings, which will be facilitated by genome-wide scan efforts of NHL currently underway (Di Bernardo et al., 2008; Skibola et al., 2009;Conde et al., 2010) and being planned. If these findings are replicated in a larger study population, a comprehensive strategy of fine mapping across risk-related genes to identify potential functional SNPs should be pursued.
Acknowledgements
DNA extraction, genotyping and statistical analysis for this project were supported by the Intramural Research Program of the National Institutes of Health (NIH) (National Cancer Institute (NCI)). The NCI-SEER study was also supported by the Intramural Research Program of the NIH (NCI), and by Public Health Service (PHS) contracts N01-PC-65064, N01-PC-67008, N01-PC-67009, N01-PC-67010, and N02-PC-71105. The Connecticut study was supported by NIH grant CA62006 (TZ) from the NCI. The NSW study was supported by the National Health and Medical Research Council of Australia Project Grant number 990920 (BA), The Cancer Council NSW, and The University of Sydney Medical Foundation. We gratefully acknowledge the assistance of Peter Hui (Information Management Services, Inc., Silver Spring, MD) for programming support.
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SNPs (Showing 10 of 10)
- (5 citations) dbSNP - rs243908
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- (1 citation) dbSNP - rs2243291
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Grant ID: Z01 CP010123-12
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Grant ID: N01 PC067008
Grant ID: N01-PC-67009
Grant ID: N02-PC-71105
Grant ID: N01 PC065064
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Grant ID: R01 CA062006
Grant ID: N01 PC067009
