|Home | About | Journals | Submit | Contact Us | Français|
Non-Hodgkin lymphoma (NHL) is a cancer closely associated with immune function, and the TNF G-308A promotor polymorphism, which influences immune function and regulation, was recently reported by the InterLymph Consortium to be associated with NHL risk. TNF signaling activates the NF-κB canonical pathway, leading to transcriptional activation of multiple genes that influence inflammation and immune response. We hypothesized that in addition to TNF signaling, common genetic variation in genes from the NF-κB canonical pathway may affect risk of NHL. We genotyped 54 SNPs within TNF, LTA, and nine NF-κB genes from the canonical pathway (TNFRSF1A, TRADD, TRAF2, TRAF5, RIPK1, CHUK, IKBKB, NFKB1, REL) in a clinic-based study of 441 incident cases and 475 frequency matched controls. Tagging SNPs were selected from HapMap, supplemented by putative functional SNPs for LTA/TNF. We used principal components and haplo.stats to model gene level associations, and logistic regression to model SNP level associations. Compared to the wildtype (GG), the AA genotype for the TNF promoter polymorphism G-308A (rs1800629) was associated with increased risk of NHL (OR=2.14, 95% CI 0.94-4.85), while the GA genotype was not (OR=1.00, 95% CI 0.74-1.34). This association was similar for follicular lymphoma and DLBCL. A previously reported TNF/LTA haplotype was also associated with NHL risk. In gene-level analysis of the NF-κB pathway, only NFKB1 showed a statistically significant association with NHL (p=0.049), and one NFKB1 tagSNP (rs4648022) was associated with NHL risk overall (ordinal OR= 0.59, 95% CI 0.41-0.84; p-trend=0.0037), and for each of the common subtypes. In conclusion, we provide additional evidence for the role of genetic variation in TNF and LTA SNPs and haplotypes with risk of NHL, and we also provide some of the first preliminary evidence for an association of genetic variation in NFKB1, a downstream target of TNF signaling, with risk of NHL.
Non-Hodgkin lymphoma (NHL) is a heterogeneous malignancy of uncontrolled proliferation of clonal B-cells at different stages of maturation, although clonal T-cell and NK-cell malignancies can also occur. Immune dysfunction has been clearly associated with NHL risk (1), and there is accumulating evidence for polymorphic variation in immune genes that control immune function and regulation as susceptibility loci for NHL (2, 3). The most robust finding to date is for the tumor necrosis factor (TNF) G-308A single nucleotide polymorphism (SNP), which has been associated with higher constitutive and inducible expression of TNF-α in several model systems (4, 5) and increased susceptibility to several infectious and inflammatory conditions (6). In the InterLymph pooled analysis of 3586 NHL patients and 4018 controls, the variant heterozygotes (ORGA=1.18; 95% CI 1.04-1.33) and homozygotes (ORAA=1.25; 95% CI 0.91-1.70) were associated with increased risk of NHL, and this association was specific to DLBCL (ORGA=1.29; 95% CI 1.10-1.51; ORAA=1.65; 95% CI 1.16-2.34) but not follicular lymphoma (7). In addition, several haplotypes involving TNF and lymphotoxin A (LTA) have also been implicated in NHL (7-10).
One important biologic function of TNF is activation of the canonical (“classical”) pathway of nuclear factor kappa B (NF-κB), which leads to the transcription of a large number of target inflammatory and other genes related to cellular growth, differentiation and apoptosis (Figure 1). NF-κB is a family of evolutionarily conserved transcription factors that consists of five members in mammals: reticuloendotheliosis (Rel or c-Rel), RelA (p65), RelB, NF-κB1 (p50) and NF-κB2 (p52). Key canonical pathway NF-κB dimers are composed of p50, p65 and c-Rel and inhibitory proteins IκBα, IκBβ, and IκBε. Inappropriate activation of NF-κB has been linked to lymphomagenesis (11) and to inflammatory processes associated with autoimmune disease, asthma, and atopy (12), which themselves are known NHL risk factors (1).
In a prior analysis (3), we focused on gene discovery, and reported results for the strongest associations of 1253 immune genes with risk of NHL. In the current analysis, we have added additional genotyping data and have focused on a candidate pathway analysis; specifically, we evaluated the association of TNF and LTA SNPs and haplotypes with risk of NHL. Furthermore, since binding of TNF to its receptor triggers the NF-κB canonical pathway, leading to transcriptional activation of multiple genes that influence inflammation and immune response, we evaluated whether genetic variation in this pathway was also associated with risk of NHL.
Full details of this on-going, clinic-based casecontrol study conducted at the Mayo Clinic in Rochester, Minnesota have been previously reported (3). This study was reviewed and approved by the Human Subjects Institutional Review Board at the Mayo Clinic, and all participants provided written informed consent. Briefly, eligible patients were within 9 months of their first NHL diagnosis, aged 20 years or older, HIV negative, and were residents of Minnesota, Iowa or Wisconsin at the time of diagnosis. All cases were reviewed and histologically-confirmed by a Mayo Clinic hematopathologist, and classified according to the WHO criteria (13). Of the 956 eligible cases, 626 (65%) participated, 109 (11%) refused, 19 (2%) were unable to be contacted, and 202 (21%) had their eligibility expire, mainly due to not completing consenting (within 9 months of diagnosis) or data collection (within 12 months of diagnosis) in the required timeframe. The median time from diagnosis to enrollment was 53 days (10th percentile 4 days; 90th percentile 138 days), and only 3% of patients had their eligibility expire due to death. Clinic-based controls were randomly selected from Mayo Clinic Rochester patients aged 20 years or older, who were residents of Minnesota, Iowa or Wisconsin, and were being seen for a prescheduled medical examination in the general medicine divisions of the Department of Medicine. Patients were not eligible if they had a history of lymphoma, leukemia, or HIV infection. Controls were frequency matched to cases by 5-year age group, gender, and county of residence. Of the 818 eligible controls, 572 (70%) participated, 239 (29%) refused, and 7 (1%) had their eligibility expire.
All subjects agreeing to participate were asked to complete a self-administered riskfactor questionnaire and provide a blood sample. DNA was extracted from samples using a standard procedure (Gentra Inc., Minneapolis, MN). This (Phase 1) analysis includes 498 cases and 497 controls enrolled from September 1, 2002 through September 30, 2005 and who had a DNA sample available on October 1, 2005.
This analysis of TNF, LTA, and the NF-κB canonical pathway (Table 1) was part of a larger genotyping project to assess the role of immune and other candidate genes in the etiology and prognosis of NHL (3). Most of the genes and SNPs reported here were from the ParAllele (now Affymetrix) Immune and Inflammation SNP panel that included 1253 genes and 9412 SNPs (14). This genotyping was supplemented by a second round of genotyping using a custom Illumina GoldenGate (15) 384 SNP OPA that included SNPs from 100 candidate genes. For both rounds of genotyping, we used tagging SNPs that covered 5 kb up and downstream of each gene with minor allele frequency (MAF) ≥0.05 and pairwise r2 threshold of 0.8, supplemented by validated non-synonymous SNPs. Across the two platforms, the sample success rate was >98% (similar for both platforms), the assay call rate was >93% (98.8% for the ParAllele platform and 93.5% for the Illumina platform), and the concordance rate for blinded duplicates was >98% (similar for both platforms).
After excluding non-Caucasian or Hispanic subjects (N=16), cases with Hodgkin lymphoma or other diagnoses (N=23), subjects with call rates <95% (N=16), quality control failures (N=1), and subjects not genotyped on both platforms (N=23), there were 916 subjects with combined genotype data. Of the 9796 combined SNPs genotyped, we excluded SNPs with call rates <95% (N=724) and SNPs with two or more non-missing differences or other quality control issues (N=10), and SNPs not mapped to NCBI build 36, dbSNP 126 of the human genome (N=20), leaving 9042 SNPs. There were 72 SNPs that were duplicated across platforms, and genotypes from these SNPs were compared. The concordance was 99.70%, although one SNP was found to have a large number of discordant genotypes (160 out 916 subjects) between the platforms and was subsequently dropped from further analysis; the remaining SNPs had only a total of 40 differences (99.94% concordance). Of the 71 remaining duplicated SNPs, the SNP with the highest platform-specific SNP call rate was chosen, leaving a total of 8,969 SNPs. Finally, SNPs that had a minor allele frequency less than 1% in cases and controls combined were excluded (N=935), leaving a total of 916 subjects (441 cases and 475 controls) and 8034 SNPs available for analysis.
Allele frequencies from cases and controls were estimated using observed genotype frequencies. The genotype frequencies in the controls were compared to allele frequencies expected under Hardy-Weinberg Equilibrium using a Pearson goodness-of-fit test or Fisher's exact test (MAF<0.05). In this analysis, there were 6 SNPs from the candidate genes that had a Hardy-Weinberg p-value of P<0.05 (see supplementary Table 1); none of these SNPs were excluded from the analysis.
Two methods were used when analyzing the association between each gene and risk of NHL: haplotype analysis and principal components. For the haplotype analysis, all SNPs from a gene were used to determine haplotype frequencies, and a global score test was used, as implemented in the S-plus program Haplo.stats (16). As a global gene test, we used principal components to create uncorrelated components that are linear combinations of the SNPs from a gene. These components were then ranked according to the amount of the total SNP variance explained. The resulting smallest subset of components that accounted for at least 90% of the variability amongst the SNPs was included in a multivariable logistic regression model. A likelihood ratio test was then used to jointly test the significance of the selected principal components. This method decreases the dimensionality of the correlated SNPs by reducing the number of independent degrees of freedom (17). Gene level tests with p<0.05 were declared of interest, which is justified for the TNF and LTA genes given their high prior probability (7). We also used a p<0.05 threshold for the NF-κB pathway genes, although to address concerns about multiple testing, we assessed the overall significance of the p-values for our gene-level tests using the tail strength methodology (18).
Individual SNPs were examined using unconditional logistic regression to estimate odds ratios (ORs) and corresponding 95% confidence intervals (CIs) separately for heterozygotes and minor allele homozygotes, using homozygotes for the major allele as the reference. A p-trend was calculated assuming an ordinal (log-additive) genotypic relationship. This scoring scheme has been shown to be robust from departures from an additive model (19) and it makes no assumption about Hardy-Weinberg equilibrium (20). For the analysis of tagSNPs from the NF-κB pathway, a p<0.05 in the setting of a global gene test of p<0.05 was declared of interest. To evaluate the impact of multiple testing for the tagSNPs, we calculated the tail strength for the set of 54 p-values (18). We also calculated individual q-values for each ordinal p-trend tests (21), and declared q-values <0.1 to be of interest.
Analyses were implemented using SAS (SAS Institute, Cary, NC, Version 8, 1999) and SPlus (Insightful Corp, Seattle, WA, Version 7.05, 2005) software systems. All analyses were adjusted for the design variables of age, gender, and county of residence. These analyses were restricted to subjects whose self-reported race was Caucasian. Furthermore, we previously tested and found no evidence of population stratification in our data (3) using STRUCTURE (22).
There were 441 cases and 475 controls available for analysis. Cases were slightly younger and were somewhat less likely to have attended graduate or professional school compared to controls, but were well matched on gender and state of residence (Table 2). The most common NHL subtypes were CLL/SLL (N=123), follicular (N=113), and DLBCL (N=69).
Five previously published SNPs in LTA and TNF with putative functional significance were first evaluated with risk of NHL and each of the common subtypes. As shown in Table 3, none of the SNPs overall were individually associated with risk of NHL, with the exception of a suggestive positive association for TNF G-308A (rs1800629), where there was evidence increased risk for the AA genotype. Formal evaluation of the recessive model gave suggestive evidence (p=0.067) for the A allele being association with overall NHL risk (OR=2.14; 95% CI 0.95-4.83). In subtype analysis, similar results were seen for both follicular lymphoma (OR=2.71; 95% 0.93-7.85) and DLBCL (OR=3.31; 95% CI 0.98-11.1), but not CLL/SLL (OR=0.81; 95% CI 0.17-3.82). While there was no effect of the LTA C-91A SNP with risk of all NHL, there were inverse associations for the AA genotype for both CLL/SLL (p=0.03) and follicular NHL (p=0.05). There was also a positive association for GA heterozygotes (no AA homozygotes observed) in TNF G-238A SNP, but for follicular lymphoma only (p=0.01).
We next conducted a haplotype analysis of the five LTA/TNF SNPs according to a previously published report from a US population (8), but found no global association (p=0.72). As shown in Table 3, our estimated haplotype frequencies were quite similar to the previous study, and we observed an association for the C-G-C-A-G haplotype (OR=1.26; 95% CI 1.07-1.48) compared to the A-A-C-G-G haplotype, which was also seen for CLL/SLL (OR=1.37; 95% CI 1.08-1.75) and follicular lymphoma (OR=1.35; 95% CI 1.06-1.72), but not DLBCL. In addition to the previously reported haplotype, we also observed suggestive positive associations with the other two remaining common haplotypes. The latter haplotype associations were also observed for follicular lymphoma and CLL/SLL, but were not observed for DLBCL.
We also evaluated the restricted haplotype based on the TNF G-308A and the LTA A252G loci, which was reported in the InterLymph pooled analysis (7). Compared to the GA haplotype, there was a significantly elevated risk for the AG haplotype (OR=1.16; 95% CI 1.00-1.33) but not the GG haplotype (OR=1.08; 95% 0.91-1.27); full details are provided in Supplemental Table 2. In subtype analysis, the AG haplotype showed a suggestive association with both CLL/SLL (OR=1.20; 95% 0.99-1.45) and DLBCL (OR=1.20; 95% CI 0.93-1.54), but not follicular lymphoma (OR=1.05; 95% 0.83-1.31).
We next conducted gene level tests for TNF, LTA, and nine genes from the NF-κB canonical pathway. The candidate genes in this pathway are shown in Table 4, and coverage ranged 6% to 100%. Only NFKB1 showed evidence for an association with NHL (p=0.049) by principal components analysis, while none of the haplotype tests approached statistical significance at p<0.05. There were no individual level SNP tests that achieved statistical significance outside of the gene level test (Supplementary Table 1). Of the 10 SNPs in NFKB1 (Table 5), only rs4648022 showed a statistically significant association with NHL (p=0.0037), although rs10489114 showed a suggestive association (p=0.063) but was uncommon (MAF in controls, 0.01). The LD structure of NFKB1 is shown in Figure 2. SNP rs4648022 is an intronic SNP, with a MAF of 0.06 in cases and 0.10 in controls. The ordinal OR was 0.59 for each variant allele (95% CI 0.41-0.84), and this association was similar for CLL/SLL (OR=0.58; 95% CI 0.32-1.02), follicular lymphoma (OR=0.58; 95% CI 0.32-1.05), and DLBCL (OR=0.56; 95% CI 0.26-1.20).
To assess the impact of multiple testing on our results, we calculated the tail strength (TS) of p-values from the gene-level tests (N=10; TS = -0.14) and 54 SNP-level tests (N=54; TS = -0.15), and for both we found that the distribution of p-values was no different from chance. We also calculated q-values for the 54 SNPs, and found that all q-values were >0.10 except rs4648022, which had a q=0.071, suggesting that the association for that SNP was noteworthy.
We provide additional evidence for a role of genetic variation in TNF and LTA SNPs and haplotypes in the etiology of NHL, and we also provide some of the first data to support an association of genetic variation in NFKB1 with NHL risk. While our sample size was modest for a genetic study, we were reasonably powered to detect main effects for more common alleles in the setting of evaluating hypotheses with relatively high prior probabilities for NHL associations with TNF (7) and genes in the canonical NF-κB pathway (11). Furthermore, the most significant SNP from NFKB1 remained noteworthy after accounting for multiple testing. Nevertheless, these data should be interpreted with caution and viewed as an initial step in the evaluation of the role of genetic variation in canonical NF-κB pathway in the etiology of NHL.
Our finding of an association with the TNF G-308A SNP (ordinal OR=1.14, 95% CI 0.89-1.46) is consistent for all NHL with the InterLymph pooled results (ordinal OR=1.16; 95% CI 1.04-1.28), although closer inspection of our data suggests a recessive model and positive associations for both DLBCL and follicular lymphoma, as opposed to the association specific to DLBCL observed in the InterLymph results (7). In similar fashion, our LTA A252G SNP (ordinal OR=1.10; 95% CI 0.90-1.34) was consistent with the InterLymph results (ordinal OR=1.05; 95% CI 0.95-1.15), and the TNF-LTA AG haplotype (compared to the GA haplotype) was associated risk in our study (OR=1.16; 95% CI 1.00-1.33) and InterLymph (OR=1.29; 95% CI 1.14-1.47). As with InterLymph, this association was seen for DLBCL but not follicular lymphoma, although we also saw an association for CLL/SLL, which was not specifically evaluated in InterLymph.
Our findings for other candidate TNF (C-857T and G-238A) and LTA (C-91A and C-857T) SNPs were notable only for the LTA C-91A SNP, where we saw a weak and not statistically significant inverse association with all NHL (ordinal OR=0.87; 95% CI 0.72-1.06), which is consistent with prior reports (8, 9, 23). However, we also observed stronger inverse associations for this SNP with CLL/SLL (OR=0.71; 95% CI 0.52-0.96) and follicular lymphoma (OR=0.73; 95% CI 0.53-1.00), but not DLBCL, which was not consistent with other studies that found either no subtype difference (8, 23) or an association specific to DLBCL (9). Adding these additional TNF and LTA SNPs into a previously published haplotype (8), our strongest results were seen for the C-G-C-A-G haplotype (compared to the most common A-A-C-G-G haplotype) based on 2 LTA and 3 TNF SNPs (OR=1.26; 95% CI 1.07-1.48), which was nearly identical to the haplotype estimate from the NCI-SEER study (OR=1.31; 95% CI 1.06-1.63) (8). However, unlike the NCI-SEER study, our haplotype associations were evident for CLL/SLL and follicular lymphoma, but were not seen for DLBCL. Overall, SNP and haplotype analysis suggest that TNF G-308A, LTA C-91A or LTA A252G, or a SNP in LD with one or more of these SNPs, plays a role in the etiology of NHL, although the specifics of NHL subtype associations is still evolving. Given that all of these studies were conducted in white populations of mainly northern European ancestry and the MAFs were all nearly identical, the discrepancies in specific associations may be due to statistical variability that planned pooling studies will be able to more robustly address.
Of the NF-κB canonical pathway genes that we evaluated, we observed an association only for NFKB1. NFKB1 encodes two proteins, a non-DNA-binding protein, p105, and a DNA-binding protein, p50; the major form of NF-κB is composed of p50 along with the p65 (RelA). Karban et al. (24), identified a common insertion/deletion promoter polymorphism (-94ins/delATTG) in NFKB1, and provided evidence for functionality in a reporter assay as well as an association with ulcerative colitis, although in a meta-analysis there was no association for either ulcerative colitis or Crohn's disease (25). The same polymorphism has also been associated with bladder cancer (26) and melanoma risk (27), but not CLL, renal cell carcinoma or colon cancer (26). We did not have data on the -94ins/delATTG polymorphism; the significant SNP in our study was an intronic tagSNP, and thus if an association of NFKB1 is replicated, fine mapping would be required to identify the causal variant(s).
NF-κB signaling has essential roles in lymphocyte development, activation, proliferation, and survival, and aberrant signaling has been linked to lymphomagenesis, including Hodgkin lymphoma, T and B-cell NHLs, and multiple myeloma (11). It is well known that TNF signaling through TNF-R1 leads to activation of the canonical NF-κB pathway, and p50 (encoded by NFKB1) is a component of the major NF-κB dimer (along with RelA) from the canonical pathway (28). The p50-RelA dimer translocates to the nucleus to activate gene transcription, leading to a broad amplification of the inflammatory response, including an increase in pro-inflammatory cytokines (e.g., IL-6, IL-4, IL-5, TNF-α and IL-1b), as well as chemokines (e.g., IL-8, RANTES), and adhesion molecules (e.g., VCAM-1, ICAM-1, E-selectin). Additional effects include activation of positive cell cycle regulators (e.g., cyclin D1 cyclin D2, c-myc, and c-myb) and antiapoptotic factors (e.g., caspases and BCL2 family members) (11). While we observed an association with NFKB1, we cannot rule fully out other genes in this pathway as tagSNP coverage was less than 70% for several genes that we studied. In addition, there are other genes within the canonical pathway that we did not evaluate. We also cannot rule out that our findings are false positives. Finally, there is evidence that other cytokines, including CD40L (29) and BLyS (30), are also associated with NHL risk, but these cytokines signal mainly through the NF-κB alternative pathway, which we were not able to evaluate.
There are several strengths to our study, including the careful characterization of cases and controls, central pathology review, extensive genotyping quality controls, and use of the common HapMap SNPs to tag the genes of interest. We have previously evaluated the potential for population stratification in our study population, and found this is not likely (3). Limitations of our study include the relatively small sample size for NHL subtypes, lack of high gene coverage for some of the genes, and an all white population, limiting generalizability. While our study was not population-based, by restricting to regional cases and controls (i.e., Minnesota, Iowa and Wisconsin), we decrease referral bias and ensure that our cases and controls are derived from the same underlying population. Of note, the MAF for the TNF and LTA genotypes and haplotypes in our study were concordant with population-based data (8).
In summary, our study provides further evidence of a role for genetic variation in the TNF G-308A SNP with risk of NHL, as well as TNF/LTA haplotypes. Additionally, we provide new evidence that genetic variation in NFKB1 is also associated with risk, which will require replication. These findings provide additional clues to the etiology of this cancer and support identifying additional genes and environmental exposures that impact NF-κB signaling, with the ultimate goal of identifying novel prevention approaches.
We thank Sondra Buehler for her editorial assistance.
Grant support: R01 CA92153
Disclosure of Potential Conflicts of Interest No potential conflicts of interest were disclosed.