Holford, Theodore R.

Objective Determinants of the increased risk of diffuse large B-cell lymphoma (DLBCL) in SLE are unclear. Using data from a recent lymphoma genome-wide association study (GWAS), we assessed whether certain lupus-related single nucleotide polymorphisms (SNPs) were also associated with DLBCL. Methods GWAS data on European Caucasians from the International Lymphoma Epidemiology Consortium (InterLymph) provided a total of 3857 DLBCL cases and 7666 general-population controls. Data were pooled in a random-effects meta-analysis. Results Among the 28 SLE-related SNPs investigated, the two most convincingly associated with risk of DLBCL included the CD40 SLE risk allele rs4810485 on chromosome 20q13 (OR per risk allele=1.09, 95% CI 1.02 to 1.16, p=0.0134), and the HLA SLE risk allele rs1270942 on chromosome 6p21.33 (OR per risk allele=1.17, 95% CI 1.01 to 1.36, p=0.0362). Of additional possible interest were rs2205960 and rs12537284. The rs2205960 SNP, related to a cytokine of the tumour necrosis factor superfamily TNFSF4, was associated with an OR per risk allele of 1.07, 95% CI 1.00 to 1.16, p=0.0549. The OR for the rs12537284 (chromosome 7q32, IRF5 gene) risk allele was 1.08, 95% CI 0.99 to 1.18, p=0.0765. Conclusions These data suggest several plausible genetic links between DLBCL and SLE.

Abstract Caspases play a critical role in regulation of apoptosis, cell differentiation, inflammation, and innate immunity, and several are mutated or have altered expression in non-Hodgkin lymphoma (NHL). To study the impact of genetic variation in caspases on NHL risk, we analyzed tag single nucleotide polymorphisms (SNPs) in 12 caspase and related genes in 3 population-based case-control studies (1946 cases and 1808 controls). Gene-based analysis, adjusting for the number of tagSNPs genotyped in each gene, showed significant associations for CASP8, CASP9, and CASP1. SNP-based analysis showed that CASP8 rs6736233 (odds ratio (OR) CG = 1.21; ORCC = 2.13; P trend = .011); CASP9 rs4661636 (ORCT = 0.89; ORTT = 0.77; P trend = .011); and CASP1 rs1785882 (ORAT = 1.12; ORAA = 1.30; P trend = .0054) were significantly associated with NHL risk and consistent across studies. It is noteworthy that genetic variants in CASP8 were associated with risk of all major NHL subtypes. Our findings suggest that genetic variation in caspases may play an important role in lymphomagenesis.

AbstractChromosomal translocations are the hallmark genetic aberration in non–Hodgkin lymphoma (NHL), with specific translocations often selectively associated with specific NHL subtypes. Because many NHL-associated translocations involve cell cycle, apoptosis, and lymphocyte development regulatory genes, we evaluated NHL risk associated with common genetic variation in 20 candidate genes in these pathways. Genotyping of 203 tag single nucleotide polymorphisms (SNP) was conducted in 1,946 NHL cases and 1,808 controls pooled from 3 independent population-based case-control studies. We used logistic regression to compute odds ratios (OR) and 95% confidence intervals (CI) for NHL and four major NHL subtypes in relation to tag SNP genotypes and haplotypes. We observed the most striking associations for tag SNPs in the proapoptotic gene BCL2L11 (BIM) and BCL7A, which is involved in a rare NHL-associated translocation. Variants in BCL2L11 were strongly related to follicular lymphoma only, particularly rs3789068 (ORAG, 1.41; 95% CI, 1.10-1.81; ORGG, 1.65; 95% CI, 1.25-2.19; Ptrend = 0.0004). Variants in BCL7A were strongly related to diffuse large B-cell lymphoma only, particularly rs1880030 (ORAG, 1.34; 95% CI, 1.08-1.68; ORAA, 1.60; 95% CI, 1.22-2.08; Ptrend = 0.0004). The associations for both variants were similar in all three studies and supported by haplotype analyses. We also observed notable associations for variants in BCL6, CCND1, and MYC. Our results support the role of common genetic variation in cell cycle, apoptosis, and lymphocyte development regulatory genes in lymphomagenesis, and suggest that effects may vary by NHL subtype. Replication of our findings and further study to identify functional SNPs are warranted. (Cancer Epidemiol Biomarkers Prev 2009;18(4):1259–70)

Abstract Background: The International Lymphoma Epidemiology Consortium (InterLymph) provides an opportunity to analyze the relationship between cigarette smoking and non-Hodgkin lymphoma with sufficient statistical power to consider non-Hodgkin lymphoma subtype. The results from previous studies of this relationship have been inconsistent, likely due to the small sample sizes that arose from stratification by disease subtype. To clarify the role of cigarette smoking in the etiology of non-Hodgkin lymphoma, we conducted a pooled analysis of original patient data from nine case-control studies of non-Hodgkin lymphoma conducted in the United States, Europe, and Australia. Methods: Original data were obtained from each study and uniformly coded. Risk estimates from fixed-effects and two-stage random-effects models were compared to determine the impact of interstudy heterogeneity. Odds ratios (OR) and 95% confidence intervals (95% CI) were derived from unconditional logistic regression models, controlling for study center, age, sex, and race. Results: In our pooled study population of 6,594 cases and 8,892 controls, smoking was associated with slightly increased risk estimates (OR, 1.07; 95% CI, 1.00-1.15). Stratification by non-Hodgkin lymphoma subtype revealed that the most consistent association between cigarette smoking and non-Hodgkin lymphoma was observed among follicular lymphomas (n = 1452). Compared with nonsmokers, current smokers had a higher OR for follicular lymphoma (1.31; 95% CI, 1.12-1.52) than former smokers (1.06; 95% CI, 0.93-1.22). Current heavy smoking (≥36 pack-years) was associated with a 45% increased OR for follicular lymphoma (1.45; 95% CI, 1.15-1.82) compared with nonsmokers. Conclusions: Cigarette smoking may increase the risk of developing follicular lymphoma but does not seem to affect risk of the other non-Hodgkin lymphoma subtypes we examined. Future research is needed to determine the biological mechanism responsible for our subtype-specific results.