Associations of Non-Hodgkin Lymphoma (NHL) Risk With Autoimmune Conditions According to Putative NHL Loci

American Journal of Epidemiology, Mar 2015

Autoimmune conditions and immune system–related genetic variations are associated with risk of non-Hodgkin lymphoma (NHL). In a pooled analysis of 8,692 NHL cases and 9,260 controls from 14 studies (1988–2007) within the International Lymphoma Epidemiology Consortium, we evaluated the interaction between immune system genetic variants and autoimmune conditions in NHL risk. We evaluated the immunity-related single nucleotide polymorphisms rs1800629 (tumor necrosis factor gene (TNF) G308A), rs1800890 (interleukin-10 gene (IL10) T3575A), rs6457327 (human leukocyte antigen gene (HLA) class I), rs10484561 (HLA class II), and rs2647012 (HLA class II)) and categorized autoimmune conditions as primarily mediated by B-cell or T-cell responses. We constructed unconditional logistic regression models to measure associations between autoimmune conditions and NHL with stratification by genotype. Autoimmune conditions mediated by B-cell responses were associated with increased NHL risk, specifically diffuse large B-cell lymphoma (odds ratio (OR) = 3.11, 95% confidence interval (CI): 2.25, 4.30) and marginal zone lymphoma (OR = 5.80, 95% CI: 3.82, 8.80); those mediated by T-cell responses were associated with peripheral T-cell lymphoma (OR = 2.14, 95% CI: 1.35, 3.38). In the presence of the rs1800629 AG/AA genotype, B-cell-mediated autoimmune conditions increased NHL risk (OR = 3.27, 95% CI: 2.07, 5.16; P-interaction = 0.03) in comparison with the GG genotype (OR = 1.82, 95% CI: 1.31, 2.53). This interaction was consistent across major B-cell NHL subtypes, including marginal zone lymphoma (P-interaction = 0.02) and follicular lymphoma (P-interaction = 0.04).

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Associations of Non-Hodgkin Lymphoma (NHL) Risk With Autoimmune Conditions According to Putative NHL Loci

Am J Epidemiol. Original Contribution Associations of Non-Hodgkin Lymphoma (NHL) Risk With Autoimmune Conditions According to Putative NHL Loci Sophia S. Wang ) 0 Claire M. Vajdic 0 Martha S. Linet 0 Susan L. Slager 0 Jenna Voutsinas 0 Alexandra Nieters 0 Silvia de Sanjose 0 Wendy Cozen 0 Graciela S. Alarcón 0 Otoniel Martinez-Maza 0 Elizabeth E. Brown 0 Paige M. Bracci 0 Tracy Lightfoot 0 Jennifer Turner 0 Henrik Hjalgrim 0 John J. Spinelli 0 Tongzhang Zheng 0 Lindsay M. Morton 0 Brenda M. Birmann 0 Christopher R. Flowers 0 Ora Paltiel 0 Nikolaus Becker 0 Elizabeth A. Holly 0 Eleanor Kane 0 Dennis Weisenburger 0 Marc Maynadie 0 Pierluigi Cocco 0 Lenka Foretova 0 Anthony Staines 0 Scott Davis 0 Richard Severson 0 James R. Cerhan 0 Elizabeth C. Breen 0 Qing Lan 0 Angela Brooks-Wilson 0 Anneclaire J. De Roos 0 Martyn T. Smith 0 Eve Roman 0 Paolo Boffetta 0 Anne Kricker 0 Yawei Zhang 0 Christine Skibola 0 Stephen J. Chanock 0 Nathaniel Rothman 0 Yolanda Benavente 0 Patricia Hartge 0 Karin E. Smedby 0 0 City of Hope , 1500 East Duarte Road, Duarte, CA 91010 ( Initially submitted July 7, 2014; accepted for publication September 19, 2014. Autoimmune conditions and immune system-related genetic variations are associated with risk of non-Hodgkin lymphoma (NHL). In a pooled analysis of 8,692 NHL cases and 9,260 controls from 14 studies (1988-2007) within the International Lymphoma Epidemiology Consortium, we evaluated the interaction between immune system genetic variants and autoimmune conditions in NHL risk. We evaluated the immunity-related single nucleotide polymorphisms rs1800629 (tumor necrosis factor gene (TNF) G308A), rs1800890 (interleukin-10 gene (IL10) T3575A), rs6457327 (human leukocyte antigen gene (HLA) class I), rs10484561 (HLA class II), and rs2647012 (HLA class II)) and categorized autoimmune conditions as primarily mediated by B-cell or T-cell responses. We constructed unconditional logistic regression models to measure associations between autoimmune conditions and NHL with stratification by genotype. Autoimmune conditions mediated by B-cell responses were associated with increased NHL risk, specifically diffuse large B-cell lymphoma (odds ratio (OR) = 3.11, 95% confidence interval (CI): 2.25, 4.30) and marginal zone lymphoma (OR = 5.80, 95% CI: 3.82, 8.80); those mediated by T-cell responses were associated with peripheral T-cell lymphoma (OR = 2.14, 95% CI: 1.35, 3.38). In the presence of the rs1800629 AG/AA genotype, B-cell-mediated autoimmune conditions increased NHL risk (OR = 3.27, 95% CI: 2.07, 5.16; P-interaction = 0.03) in comparison with the GG genotype (OR = 1.82, 95% CI: 1.31, 2.53). This interaction was consistent across major B-cell NHL subtypes, including marginal zone lymphoma (P-interaction = 0.02) and follicular lymphoma (P-interaction = 0.04). autoimmune conditions; environment; genetics; interaction; human leukocyte antigen; lymphoma, non-Hodgkin; tumor necrosis factor Abbreviations: CI, confidence interval; DLBCL, diffuse large B-cell lymphoma; HLA, human leukocyte antigen gene; IL10, interleukin-10 gene; InterLymph, International Lymphoma Epidemiology; MZL, marginal zone lymphoma; NHL, non-Hodgkin lymphoma; OR, odds ratio; SNP, single nucleotide polymorphism; TNF, tumor necrosis factor gene. - Non-Hodgkin lymphomas (NHLs) are a histologically and genetically heterogeneous group of malignancies originating from B- and T-lymphocytes. They account for approximately 3% of the worldwide cancer burden and show global variations in patterns of incidence ( 1 ). Populations at high risk of developing NHL include persons with severe immune system dysregulation—resulting, for example, from human immunodeficiency virus infection, immunosuppressive therapy following solid organ transplantation, or an inherited immunodeficiency syndrome ( 2 ). In the absence of these high-risk conditions, there are relatively few established risk factors for NHL; however, autoimmune conditions have been shown to confer approximately 2- to 10-fold increased risks of NHL in both clinical and epidemiologic studies ( 2–5 ). Autoimmune conditions affect approximately 3% of the general population and represent a group of over 80 disorders in which an individual elicits an immune response to his/her own tissues, resulting in inflammation and chronic antigenic stimulation ( 6, 7 ). Autoimmune conditions can be broadly categorized on the basis of whether they are mediated by predominantly B-cell responses or T-cell responses ( 8–12 ), although there is overlap in the immune effector mechanisms that mediate different autoimmune conditions. These diseases can also be categorized as those that affect multiple organs (e.g., systemic lupus erythematosus) and those that primarily target specific organs or tissues (e.g., celiac disease). A large epidemiologic evaluation of autoimmune conditions and NHL risk comprising 12,982 NHL cases and 16,441 controls was conducted by the International Lymphoma Epidemiology (InterLymph) Consortium (1983–2004) (12). In line with other analyses ( 3, 13, 14 ), it implicated Sjögren syndrome and systemic lupus erythematosus in increased B-cell NHL risk and celiac disease and psoriasis in increased T-cell NHL risk. A variety of factors have been proposed that may mediate the associations between autoimmune conditions and NHL risk, including those inherent to the pathology of autoimmune conditions or inherent to external factors such as disease-modifying immunosuppressive drugs or EpsteinBarr virus infection ( 2 ). Recent evidence favors an important role for inflammatory activity and disease severity in both organ-specific and systemic autoimmune processes in determining the risk of NHL ( 15, 16 ). The InterLymph Consortium investigators have also identified common susceptibility immune-response gene loci that are associated with NHL, including 4 variants located in 6p21.3 and rs1800890 (interleukin-10 gene (IL10) T3575A) ( 17–21 ). The 4 single nucleotide polymorphisms (SNPs) in the 6p21.3 region include rs1800629 (tumor necrosis factor gene (TNF) G308A), located in the human leukocyte antigen gene (HLA) class III region; rs10484561 and rs2647012 ( 19, 20 ), which are in high linkage disequilibrium with the extended haplotypes DRB1*01:01-DQA1*01:01DQB1*05:01 and DRB1*15-DQA1*01-DQB1*06:02, respectively (19), in the HLA class II region; and rs6457327 ( 21 ), which is near HLA-C in the HLA class I region. The high relevance of HLA loci in the risk of several autoimmune conditions is well established ( 22 ). Although genome-wide association studies conducted by our research group and others have continued to uncover novel loci ( 23–29 ), these efforts have been accomplished primarily with genotyping restricted to NHL cases and have been derived from a combination of study designs, including cohort studies and clinical case series where exposure data on autoimmune conditions are not systematically collected. Thus, in the present analysis our efforts were focused on the first 5 putative NHL loci identified in the published literature, for which we have complete exposure (e.g., harmonized autoimmune conditions) and genotyping data on both NHL cases and controls from participating InterLymph studies. This analysis combined cleaned and harmonized data derived from previous InterLymph studies that evaluated the main associations of autoimmune conditions ( 12 ) and genetic associations ( 17, 18 ) with NHL etiology. Thus, a case-control study of gene-environment interaction that comprised research included in the current genome-wide association studies would not be possible because of the lack of uniform information on autoimmune conditions from the majority of cohort and clinical studies and because of the lack of genotyping or exposure information from matched controls. Our goal in the present study was to determine whether a positive history of autoimmune disease and variation in rs1800890, rs1800629, rs10484561, rs2647012, or rs6457327 contributed to NHL risk either independently, thereby suggesting distinct pathways of pathogenesis, or jointly, thereby suggesting a common pathway. We conducted a pooled analysis of 8,692 NHL cases and 9,260 controls from 14 participating studies with data available on both genetic variation and autoimmune conditions. We evaluated gene-environment interaction by testing whether the associations between autoimmune conditions and NHL risk differed among persons who possessed 1 (or 2) of the implicated risk variants and those who had no risk alleles ( 30, 31 ). Specifically, we evaluated the risk of autoimmune conditions and NHL according to putative NHL risk loci. These analyses were conducted within participating InterLymph Consortium studies, for which data on both autoimmune conditions and the 5 genetic loci were readily available and had been harmonized in cases and controls. METHODS Study population We included data from 14 individual case-control studies in the InterLymph Consortium (www.epi.grants.cancer.gov/ InterLymph) (Table 1). Case participants from these studies were included if they met the following criteria for eligibility: diagnosis of incident NHL between 1988 and 2007; age 17 years or older; no known human immunodeficiency virus positivity; no history of organ transplantation; available data regarding personal history of 1 or more autoimmune conditions; and genotype information for 1 or more genetic variants of interest. Details on the design methods for each of the participating studies have been previously provided ( 3, 12, 17– 20, 32–43 ). Selected demographic characteristics of study participants are summarized in Table 1 and Web Table 1 (available at http://aje.oxfordjournals.org/). This analysis was approved by the City of Hope Institutional Review Board Committee (Duarte, California). Each participating study’s investigators obtained approval from human subjects review committees and informed consent from all participants. A deidentified pooled data set with study-level individual information on self-reported autoimmune conditions, genotypes, study-specific matching variables, demographic characteristics, potential confounders, and NHL subtypes was provided by the InterLymph Data Coordinating Center (Mayo Clinic, Rochester, Minnesota). A Im ied LH x x x x x x x x x x d d n tu a 7 S 0 ) 1 x x x x x sn 200 (s IL io – e itd 88 en F n 9 G N x x x x x o 1 T un itu g s , , , , , , , , , , y m o h b se io se io se io se io se io se io se io se io e y xse trun r in le x n x n x n x n x n x n x n x n x , x n x n se io se io ,seg tsud itse , g , g ,ge co ge re ge re ge w A A A A A A A A A A A A ) ly n n e (o om A h f p o m , se yL e eg rs 08 ly te gA anR eay –20 s r a n n I 2 2 2 5 7 6 5 4 8 8 8 8 7 9 7 7 – – – – – – – – 9 8 8 1 5 7 8 0 1 1 1 2 2 1 1 2 3 8 1 , 1 C , e m A ,) ) ed II l oo nad P I a s n e i s d s de la u C l c A In L s (H e i 7 d 2 tu 3 S 7 f 5 o 4 s s6 ic r t is d r n e a t c , a 21 r a 0 h 7 C 4 t 6 c 2 le rs e , 0 9 8 1 3 2 5 7 6 6 6 3 9 7 9 3 2 1 6 1 1 5 0 9 , 1 C C to N U r a ;e ; i n e p e n s g e re 5 g r 8 0 r 1 o - to , in c ic a l k f o u e is b l r s ta 60 te ro e 5 in c m , e , 0 n e n 1 r i L o r I c x ; m o nee t,u dn g F e N ,e x n e T iv g ; t it ts s n l e u g x a s i te e d y R ,y c r o d o k n t , x n u E la se io le d cu , g n n ir ge re a ,a ,c m y s A u g u ,h lo o A io rv 4 L m ne 7 H e , – ;s id l 0 2 e p ta c E n iv , e r e m 1 e c , 0 S an ic 0 id ll it –02 ica ive rsaa 0 ed ru p 0 S , 2 M , s d R u n E ito a E c re S fe a ; c y in i g n d o a e l o r M it o r E lf o f a ta s i r m p a te o s il n h o a e p h r t C m a s , y u S L to M n d M ia te t v i C a :s in m n d ad th ito n a ca ts ou se i n v S e S la re , it E a ew W bb L P D o w n l o a d e d f r o m h t t p : / / a j e . o x f o r d j o u r n a l s . o r g / b y g u e s t o n a r c h 3 0 , 2 0 1 6 M Exposure assessment Self-reported history of autoimmune conditions was recorded in each participating study using structured questionnaires during in-person or telephone interviews ( 12 ). In most studies (70%), respondents were asked whether any autoimmune condition had been diagnosed by a physician. Consistent with the previous InterLymph Consortium study ( 12 ), we examined the following: primary Sjögren syndrome, systemic lupus erythematosus, rheumatoid arthritis, systemic sclerosis or scleroderma, poly- or dermatomyositis, immune thrombocytopenic purpura, type 1 diabetes (defined as diabetes diagnosed at age ≤30 years), pernicious anemia, multiple sclerosis, myasthenia gravis, celiac disease, psoriasis, sarcoidosis, Crohn’s disease, ulcerative colitis, autoimmune hemolytic anemia, and Hashimoto’s thyroiditis. Rheumatoid arthritis was restricted to participants who indicated that they were receiving treatment (any treatment) for their disease, to improve the specificity of self-reported diagnoses of rheumatoid arthritis ( 44–46 ). We assessed the duration of autoimmune conditions as the interval (in years) between self-reported age at onset of the autoimmune condition and age at diagnosis of NHL (cases) or age at interview (controls). Participants with an unknown date of autoimmune disease diagnosis or those diagnosed within 2 years of their NHL diagnosis (or 2 years of interview for controls) were excluded from the analysis, to minimize the inclusion of autoimmune paraneoplastic phenomena arising due to incipient, as-yet-undiagnosed NHL. To facilitate the interpretation of any identified associations and to improve statistical power for evaluating geneenvironment interactions, we categorized autoimmune conditions on the basis of the type of primary immune response involved in mediating autoimmunity: specifically, predominance of B-cell activation versus predominance of T-cell activation, based on a consensus panel comprised of rheumatologists, immunologists, and hematologist-oncologists. Conditions included in each pathway category are delineated in Table 2. Autoimmune conditions were also categorized by organ involvement as multiple-organ-targeted versus primarily single-organ-targeted, with further organ-specific evaluations for pancreatic, gastrointestinal/hepatobiliary, dermatological, hematological, neurological, and endocrine organs (Table 2). In other words, conditions in which multiple organ systems were the targets of the autoimmune process were classified as having multiple-organ involvement, whereas those in which the target of the autoimmune process was 1 organ or system (regardless of whether the disease itself might then affect multiple organs, as in diabetes) were classified as having single-organ involvement. Genotyping Genotyping data were collected for rs1800890 (IL10 T3575A), rs1800629 (TNF G308A), rs10484561, rs2647012, and rs6457327 (Table 1). Genotyping methods have been previously described ( 18–20 ). Briefly, rs1800629 and rs1800890 were genotyped using either TaqMan (Applied Biosystems, Inc., Foster City, California) (all studies except EpiLymph) or Pyrosequencing (Qiagen NV, Hilden, Germany) (EpiLymph). Assay conditions for TaqMan assays are available on the National Cancer Institute’s SNP500Cancer website (http:// snp500cancer.nci.nih.gov). For quality control, each laboratory analyzed the same set of genotypes in DNA samples from 102 ethnically diverse individuals, obtained from the Coriell Institute for Medical Research (Camden, New Jersey; http://www.coriell.org/). For HLA SNPs (rs6457327, rs10484561, and rs2647012), genotyping was conducted using the Illumina 317K (Illumina, Inc., San Diego, California; Scandinavian Lymphoma Etiology (SCALE) study), the Illumina Human CNV370Duo BeadChip (Illumina, Inc.; University of California, San Francisco, study), TaqMan (Applied Biosystems, Inc.; National Cancer Institute–Surveillance, Epidemiology, and End Results (SEER) study, New South Wales study, Yale University study, British Columbia study, and University of California, San Francisco, study), the Illumina GoldenGate 1536 SNP Oligo Pool Assay (Illumina, Inc.; Mayo Clinic study), the Sequenom MassARRAY iPLEX (SF1B) (Sequenom, Inc., San Diego, California; University of California, San Francisco, d e l o o P a n i a m o h p m y L r a l u c lli o F d n a , a m o h p m y L ll e C B e g r a L e s u iff D ,) s e p y T ll A ( a m o h p m y L n i k g d o H n o N d n a s n o iit d n o C e n u m m i o t u A f iseo 0207 ro – g 8 te 8 aC ,91 n m teew itruo senoaB snohpC it i m sco ry L sA tIen .3 i,s s le ly aTb naA study), and ( for rs10484561) the Illumina GoldenGate or Pyrosequencing (Qiagen NV; all EpiLymph studies), where call rates were ≥95% and sample completion rates were ≥90% ( 19, 20 ). NHL classification NHL subtypes were grouped using the InterLymph Pathology Working Group guidelines ( 47, 48 ), which are based on the World Health Organization classification (49). We present results for all NHL and for common subtypes, including diffuse large B-cell lymphoma (DLBCL), follicular lymphoma, chronic lymphocytic leukemia/small lymphocytic lymphoma, marginal zone lymphoma (MZL), and peripheral T-cell lymphoma. Statistical methods Confirming the main associations. We first evaluated the main associations between autoimmune conditions and all NHL and major NHL subtypes and between gene variants and NHL/NHL subtypes in the subset of 8,692 NHL cases and 9,260 controls from the InterLymph Consortium study population who had available genotyping data (of the original 12,982 NHL cases and 16,441 controls included in the previous evaluation of autoimmune conditions ( 12 ) (Web Tables 2 and 3)). We further evaluated the main association between autoimmune conditions and NHL according to immunology and pathology: autoimmune conditions largely mediated by B-cell responses versus those largely mediated by T-cell responses, based on our a priori categorization, and multipleorgan-targeted involvement versus single-organ-targeted involvement (Tables 3 and 4). We calculated pooled odds ratios and 95% confidence intervals for NHL risk using joint fixed-effects unconditional logistic regression models adjusting for age, sex, race/ethnicity, and region/study center. Other potential confounders, such as socioeconomic status, smoking status, and family history, did not change risk estimates by ≥10% and thus were not retained in the final model. For each of the main analyses, we conducted χ2 tests for heterogeneity between the studies to ensure that the data from disperse studies could be pooled. Independence of genotypes and autoimmune conditions. We examined the association between the (dichotomized) variant alleles and risk of autoimmune conditions (as defined by B-cell- or T-cell-mediated immune response) among controls using unconditional logistic regression to calculate odds ratios and 95% confidence intervals (Web Table 4). Associations with autoimmune conditions by genotype and P-interaction. We first evaluated interaction on the multiplicative scale. For each grouping of autoimmune conditions, we calculated odds ratios and 95% confidence intervals for all NHL and the major NHL subtypes according to dichotomized genotype. For this dichotomization, we modeled the genotypes in the dominant fashion based on our previous publications, which clearly indicated a dominant model of association ( 17, 18 ). The P value for interaction was estimated using the Wald test for homogeneity of the associations between autoimmune conditions and NHL risk according to genotype strata (Tables 5–9). I5C% ,.6138 ,.9450 ,.5338 ,.8422 ,.3339 9 .31 .05 .13 .06 .13 GG Cases Controls Everb Neverb Ever Never ORa Autoimmune Condition Category Abbreviations: CI, confidence interval; CLL, chronic lymphocytic leukemia; OR, odds ratio; SLL, small lymphocytic lymphoma; TNF, tumor necrosis factor gene. a ORs and 95% CIs were calculated using joint fixed-effects unconditional logistic regression models. Results were adjusted for age, sex, race/ ethnicity, and region/study center. b Number of participants ever or never diagnosed with the specified condition(s). We further evaluated interaction on an additive scale and calculated odds ratios and 95% confidence intervals using a common referent group to evaluate joint associations between autoimmune conditions and variant genotypes, whereby persons without a variant genotype and without autoimmune conditions were the referent group (Table 10). Abbreviations: CI, confidence interval; CLL, chronic lymphocytic leukemia; IL10, interleukin-10 gene; OR, odds ratio; SLL, small lymphocytic lymphoma. a ORs and 95% CIs were calculated using joint fixed-effects unconditional logistic regression models. Results were adjusted for age, sex, race/ ethnicity, and region/study center. b Number of participants ever or never diagnosed with the specified condition(s). Analyses were conducted using SAS 9.3 (SAS Institute, Inc., Cary, North Carolina). All tests were 2-sided, and P-interaction values less than 0.05 were considered statistically significant. To account for multiple comparisons, we applied a conservative Bonferroni correction for 5 tests at an overall α level of 0.05 to all analysis (P = 0.01). For evaluation of rs1800629 Autoimmune Condition Category and rs200890, sensitivity analyses restricting the subjects to persons of European ancestry was conducted; because results did not vary by race ( 18 ), we used the data for all participants to maximize statistical power. Because the prior genotyping efforts for rs10484561, rs2647012, and rs6457327 were restricted to persons of European ancestry ( 19–21 ), all analyses with these HLA SNPs were similarly restricted to persons of European ancestry. Additional sensitivity analyses were also applied to autoimmune conditions whereby more prevalent conditions that contributed to the distinct categories were excluded individually (e.g., rheumatoid arthritis, Sjögren’s syndrome, systemic lupus erythromatosus), in an attempt to Autoimmune Condition Category ensure that our associations based on these categories (e.g., B-cell response) were robust and not driven by any single condition. RESULTS These analyses comprised 8,692 NHL cases and 9,260 controls from 14 participating InterLymph Consortium studies NHL and Autoimmune Conditions by Putative NHL Loci 417 Abbreviations: CI, confidence interval; OR, odds ratio; TNF, tumor necrosis factor gene. a ORs and 95% CIs were calculated using joint fixed-effects unconditional logistic regression models. Results were adjusted for age, sex, race/ ethnicity, and region/study center. carried out in North America, Europe, and Australia that had collected information on autoimmune conditions and had genotyped at least one of the 5 SNPs of interest (Table 1). Thirteen of the 14 studies provided data on rs1800629 (TNF G308A) or rs1800890 (IL10 T3575A) variants, and 13 of the 14 studies contributed data on the 3 HLA class I and class II SNPs (see Table 1). The included studies comprised 9 population-based case-control studies, 4 hospital-based case-control studies, and 1 clinic-based case-control study (Table 1). In our evaluation of associations between autoimmune conditions and genetic variants among controls (Web Table 4), 2 statistically significant associations were identified: a relationship between hemolytic anemia and rs10484561 and a relationship between dermatomyositis/polymyositis and rs1800629. However, this is within the number of associations that one would expect to observe on the basis of chance alone. Because their inclusion did not alter the main associations between the immunity categories and NHL risk, we continued to include these conditions to retain a consistent definition of the immuneresponse categories throughout our analyses. Main autoimmune condition–gene associations In our subset of cases and controls, our results remained consistent with previously published autoimmune condition (Web Table 2) and genetic (Web Table 3) main associations. We found statistically significant increased NHL risks among participants with autoimmune conditions that were mediated predominantly by B-cell responses (odds ratio (OR) = 2.25, 95% confidence interval (CI): 1.74, 2.90), that had multiple organ involvement (OR = 1.78, 95% CI: 1.40, 2.28), and that targeted hematological organs (OR = 3.32, 95% CI: 1.27, 8.63) (Table 3). These associations were consistent for DLBCL and MZL and were most pronounced for autoimmune conditions mediated by B-cell responses (OR = 3.11 (95% CI: 2.25, 4.30) and OR = 5.80 (95% CI: 3.82, 8.80), respectively) and single-organ-targeted (hematological) conditions (for DLBCL, OR = 6.13, 95% CI: 2.10, 17.9) (Tables 3 and 4). Associations with peripheral T-cell lymphoma were observed predominantly for autoimmune conditions mediated primarily by T-cell responses (OR = 2.14, 95% CI: 1.35, 3.38) and autoimmune conditions targeting specific organs (OR = 2.12, 95% CI: 1.33, 3.39), such as the gastrointestinal/hepatobiliary organ system (OR = 3.24, 95% CI: 1.71, 6.13) and the skin (i.e., psoriasis) (OR = 2.12, 95% CI: 1.08, 4.17) (Table 4). Although associations with hematological targeted conditions were also observed for MZL and peripheral T-cell lymphoma, those associations were based on only 1 and 2 cases, respectively. Autoimmune condition–gene interactions (rs1800629 (TNF G308A) and rs1800890 (IL10 T3575A)) Among persons with the rs1800629 (TNF G308A) AG/ AA genotype, those with autoimmune conditions predominantly mediated by B-cell responses had a 3.27-fold increased NHL risk (95% CI: 2.07, 5.16), as compared with a 1.82-fold (95% CI: 1.31, 2.53) increased risk among persons with the GG genotype (P-interaction = 0.03) (Table 5). The increased risk of B-cell response conditions among persons with the AG/AA genotype was consistently observed across the major NHL subtypes, with significant interaction for follicular lymphoma (P = 0.04) and MZL (P = 0.02). Interactions observed for MZL were particularly pronounced, with 8-fold increased risks for autoimmune diseases mediated by B-cell responses among persons with the variant allele (or AG/AA genotype), compared with a 3-fold risk for persons with the GG genotype. Although no significant interaction was observed, increased risks of DLBCL and chronic lymphocytic leukemia/small lymphocytic lymphoma were also observed among persons with autoimmune conditions who harbored the rs1800629 (TNF G308A) allele, but not among those who did not carry the variant allele. These results were consistent in sensitivity analysis where individual autoimmune conditions were excluded, ensuring that our results were not driven by any single condition. Consistent with results from the stratified analysis, in analysis using a single common referent group, the greatest risk of NHL was observed among persons who had both the rs1800629 AG/AA genotype and an autoimmune condition mediated by B-cell responses (OR = 3.86, 95% CI: 2.36, 6.31), as compared with those with the GG genotype who did not have an autoimmune condition (Table 10). The increased risks among persons with both the AG/AA genotype and B-cell-mediated autoimmune conditions were particularly pronounced for DLBCL (OR = 4.43, 95% CI: 2.41, 8.16) and MZL (OR = 13.7, 95% CI: 6.86, 27.5). No statistically significant interactions between rs1800890 (IL10 T3575A) genotypes and autoimmune conditions were observed with either NHL overall or any NHL subtype (Table 6). The elevated risks of NHL, DLBCL, MZL, and peripheral T-cell lymphoma observed for autoimmune conditions were similar across rs1800890 genotypes. HLA class I (rs6457327) and class II SNPs (rs2647012, rs10484561) In general, we observed little evidence of interaction between autoimmune conditions and the rs6457327 SNP located in the HLA class I region near HLA-C. The main associations for relationships of any autoimmune condition, B-cell response conditions, and autoimmune conditions involving multiple organs with NHL and DLBCL did not differ by rs6457327 genotype status (Table 7). We observed no evidence of interaction between the rs2647012 or rs10484561 SNP and autoimmune conditions in the risk of NHL or any NHL subtype (Tables 8 and 9). Although we observed elevated risk for MZL when we restricted the analysis to the rs10484561 TT genotype, the low frequency of the GT/GG genotype and the few exposed MZL cases (n = 1–3) made these results tenuous. DISCUSSION We identified possible interactions between the rs1800629 (TNF G308A) allele and autoimmune conditions that predominantly involve B-cell responses in the risks of DLBCL and MZL. Because tumor necrosis factor α activates the nuclear factor-kB pathway, a central mechanism for inflammation and immune system status, interactions between TNF and autoimmune conditions would suggest a shared biological pathway linked to immune system activation ( 50 ). If this were true, our results would support possible synergy between a genetic propensity toward a chronic inflammatory state in TNF G308A carriers through heightened tumor necrosis factor α overexpression ( 51, 52 ) and the chronic inflammation and B-cell activation seen in persons with autoimmune conditions. Recently, it has been noted that a distinct form of immune response characterized by inflammation, B-cell activation, and the production of inflammatory cytokines plays a central role in driving autoimmune responses ( 53–55 ). This immune response pattern has been termed T-helper 17 and is distinct from the earlier defined T-helper 1 and T-helper 2 immune response patterns. Interleukin-23 is involved in the promotion of T-helper 17 responses, which are mediated by the production of interleukin-17, interleukin-6, and tumor necrosis factor α. Thus, the potential interaction between autoimmune conditions characterized by B-cell-mediated responses and autoantibody production with the TNF genotype may suggest a potential role for T-helper 17 immune responses in the pathogenesis of both NHL and autoimmunity. Clinical studies that directly measure levels of inflammatory markers among persons with and without the TNF G308A allele and autoimmune conditions that involve B-cell activation would also be particularly useful and would aid in the understanding of NHL etiology. The 3 HLA class I/II SNPs identified to date as susceptibility loci for NHL subtypes, including the SNP tagging HLADRB1*01:01 (rs10484561), have largely been associated with follicular lymphoma ( 56, 57 ), although rs10484561 has also been implicated in DLBCL etiology ( 19–21 ). The SNPs rs10484561, rs2647012, and rs6457327 yielded no interactions with autoimmune conditions for DLBCL or follicular lymphoma, a finding that is consistent with results from a previously published analysis within a single study (31). Given the lack of association between autoimmune conditions and follicular lymphoma, the absence of an observed interaction is not unexpected. Additionally identified SNPs in chronic lymphocytic leukemia/small lymphocytic lymphoma etiology ( 23–25, 28, 58 ) were not included in the present analysis because of the lack of data among controls, the lack of data on autoimmune conditions among cases in non-InterLymph studies, and the lack of association between autoimmune conditions and chronic lymphocytic leukemia/small lymphocytic lymphoma, providing little justification for their inclusion. Strengths of this analysis included the large sample size compiled in our international effort, permitting analysis by NHL subtype. To further enhance our statistical power and to address our biological hypothesis, we created biologically based categories for autoimmune conditions. These provided specificity by demonstrating that autoimmune conditions mediated by B-cell effector mechanisms increased risks of DLBCL and MZL and that those involving T-cell-mediated responses increased risk of peripheral T-cell lymphoma. When autoimmune conditions were categorized by whether or not they targeted a single organ and by the specific organs targeted, differential risks were observed; conditions affecting multiple organs were implicated in the risks of DLBCL and MZL, whereas single-organ-targeted autoimmune conditions were implicated in peripheral T-cell lymphoma. Our large sample size and novel use of biology-based categories allowed for the evaluation of statistically significant interactions between genotypes and autoimmune conditions. Given the rarity of autoimmune conditions, pooled analysis of individual data provide an optimal means of conducting such an investigation. Several limitations of this study should be considered, including the use of self-reported data on autoimmune conditions (although most studies queried participants about their personal history of physician-diagnosed conditions ( 3 )) and the relatively low prevalence of autoimmune conditions, which restricted our ability to evaluate interactions with individual autoimmune conditions. Similarly, we were unable to examine associations with the rarer B-cell and T-cell NHL subtypes. In addition, given the large number of tests conducted, we cannot exclude the possibility that our results were due to chance, as our P-interaction values did not reach the level of significance (P = 0.01) set for Bonferroni correction. Nevertheless, we believe that our results remain of high interest, as the differences in risk by genotype (e.g., higher risk among TNF G308A variant genotypes AG/AA) and the subtypes for which differences were observed (e.g., DLBCL, MZL) were consistent with our strong a priori hypotheses. Finally, we acknowledge that survival bias is a concern in case-control studies of NHL, from which this analysis was derived. Epidemiologic studies that include more aggressive NHL subtypes such as DLBCL probably do suffer from survival bias, since some persons have died and others are too ill to participate. We acknowledge that the antigenic stimulus or other stimuli from autoimmune diseases that promote lymphomagenesis are probably part of a chronic process and may be limited to autoimmune diseases that progress over a longer time course. Therefore, patients with more advanced autoimmune diseases or more rapidly progressing NHL may have been less likely to participate in such epidemiologic studies. It is possible that participation based on severity of autoimmune diseases would be applicable to both cases and controls, in which case the participation/survival bias would have been nondifferential and would have biased our risk estimates toward the null. In the scenario in which aggressive lymphomas from severe autoimmune conditions were not included in our analysis, the bias would have been differential and would have pointed toward the null. Our results support the need for expanded research on the potential overlap of autoimmune-condition biological pathways with lymphomagenesis ( 59 ). Future fine mapping efforts and functional studies of implicated SNPs (including that for TNF, where the functional role of the putative loci has not yet been fully deciphered) are also warranted ( 52 ). Future efforts that incorporate haplotype analyses, particularly within the HLA region, in which there is tight linkage disequilibrium, are also warranted. Expanded gene-environment evaluation of established risk factors may further prove fruitful in delineating key biological mechanisms involved in lymphomagenesis. Finally, follow-up of our results among large-scale cohorts with celiac disease or Sjögren’s syndrome could also prove fruitful. ACKNOWLEDGMENTS Author affiliations: Department of Population Sciences, Beckman Research Institute and the City of Hope, Duarte, California (Sophia S. Wang, Jenna Voutsinas); Adult Cancer Program, Lowy Cancer Research Centre, Prince of Wales Clinical School, University of New South Wales, Sydney, Australia (Claire M. Vajdic); Division of Cancer Epidemiology and Genetics, National Cancer Institute, US National Institutes of Health, Gaithersburg, Maryland (Martha S. Linet, Lindsay M. Morton, Patricia Hartge, Qing Lan, Nathaniel Rothman, Stephen J. Chanock); Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota (Susan L. Slager, James R. Cerhan); Centre for Chronic Immunodeficiency, University Medical Center Freiburg, Freiburg, Germany (Alexandra Nieters); Unit of Infections and Cancer, Cancer Epidemiology Research Programme, Institut Català d’ Oncologia, Barcelona, Spain (Yolanda Benavente, Silvia de Sanjose); Biomedical Research Centre Network for Epidemiology and Public Health (CIBERESP), Barcelona, Spain (Yolanda Benavente, Silvia de Sanjose); Norris Comprehensive Cancer Center, Department of Preventive Medicine, NHL and Autoimmune Conditions by Putative NHL Loci 419 Keck School of Medicine, University of Southern California, Los Angeles, California (Wendy Cozen); Department of Medicine, Division of Rheumatology and Clinical Immunology, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama (Graciela S. Alarcón, Elizabeth E. Brown); Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama (Graciela S. Alarcón, Elizabeth E. Brown); Departments of Microbiology, Immunology and Molecular Genetics, and Obstetrics and Gynecology, David Geffen School of Medicine, University of California, Los Angeles (UCLA), Los Angeles, California (Otoniel Martinez-Maza); Department of Epidemiology, Fielding School of Public Health, UCLA, Los Angeles, California (Otoniel Martinez-Maza); Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California (Paige M. Bracci, Elizabeth A. Holly); Epidemiology and Cancer Statistics Group, University of York, York, United Kingdom (Eleanor Kane, Tracy Lightfoot, Eve Roman); Australian School of Advanced Medicine, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia (Jennifer Turner); Department of Histopathology, Douglass Hanly Moir Pathology, Macquarie University Hospital, Sydney, Australia (Jennifer Turner); Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark (Henrik Hjalgrim); British Columbia Cancer Research Center, Vancouver, British Columbia, Canada (John J. Spinelli); Department of Epidemiology and Public Health, School of Medicine, Yale University, New Haven, Connecticut (Yawei Zhang, Tongzhang Zheng); Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts (Brenda M. Birmann); Winship Cancer Institute, Emory University, Atlanta, Georgia (Christopher R. Flowers); Braun School of Public Health and Community Medicine, Hematology Department, Hadassah Medical Center, Hebrew University, Jerusalem, Israel (Ora Paltiel); Division of Clinical Epidemiology, German Cancer Research Centre, Heidelberg, Germany (Nikolaus Becker); Department of Pathology, City of Hope, Duarte, California (Dennis Weisenburger); Registry of Hematological Malignancies of Cote d’Or, Burgundy University and University Hospital, Dijon, France (Marc Maynadie); Department of Public Health, Occupational Health Section, University of Cagliari, Cagliari, Italy (Pierluigi Cocco); Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic (Lenka Foretova); School of Nursing and Human Sciences, Dublin City University, Dublin, Ireland (Anthony Staines); Fred Hutchinson Cancer Research Center and School of Public Health and Community Medicine, University of Washington, Seattle, Washington (Scott Davis); Department of Family Medicine and Karmanos Cancer Institute, School of Medicine, Wayne State University, Detroit, Michigan (Richard Severson); Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, UCLA, Los Angeles, California (Elizabeth C. Breen); Department of Biomedical Physiology and Kinesiology, Faculty of Science, Simon Fraser University, Vancouver, British Columbia, Canada (Angela Brooks-Wilson); Department of Environmental and Occupational Health, School of Public Health, Drexel University, Philadelphia, Pennsylvania (Anneclaire J. De Roos); Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California (Martyn T. Smith); Institute for Translational Epidemiology and Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York (Paolo Boffetta); Sydney School of Public Health, University of Sydney, Sydney, Australia (Anne Kricker); Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama (Elizabeth E. Brown, Christine Skibola); Clinical Epidemiology Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden (Karin E. Smedby); and Karolinska University Hospital, Stockholm, Sweden (Karin E. Smedby). This work was supported by National Institutes of Health (NIH) grants CA17955801 (S. S. Wang, Principal Investigator (PI), City of Hope) and CA033572 (M. Friedman, PI, City of Hope); NIH grants CA45614, CA89745, and CA87014 (E. A. Holly, PI, University of California, San Francisco/ University of California, Berkeley studies); the National Cancer Institute of Canada, the Canadian Institutes of Health Research, and the Michael Smith Foundation for Health Research (British Columbia study); the Intramural Research Program of the NIH (P. Hartge, PI, National Cancer Institute– Surveillance, Epidemiology, and End Results study); the European Commission ( grant QLK4-CT-2000-00422, EpiLymph study); NIH contract NO1-CO-12400 (EpiLymph Italy study); the Fondazione Cariverona (2004: A. Scarpa, PI, EpiLymph Italy study; 2005: P. S. Moore, PI, EpiLymph Italy study); the Compagnia di San Paolo—Programma Oncologia (P. Cocco, PI, EpiLymph Italy study); José Carreras Leukemia Foundation grant DJCLS-R04/08 (A. Nieters, PI, EpiLymph Germany study); Federal Office for Radiation Protection grants StSch4261 and StSch4420 (N. Becker, PI, EpiLymph Germany study); Fondo Investigaciones Sanitarias grant PI040091 (S. de Sanjose, PI, EpiLymph Spain study); Network for Research in Epidemiology and Public Health grant 03/09 and Fondo Investigaciones Sanitarias grant PI041467 (R. Bosch, PI, EpiLymph Spain study); the Leukaemia Research Fund (E. Roman, PI, United Kingdom study); the National Health and Medical Research Council of Australia, Cancer Council New South Wales, and the University of Sydney Medical Foundation Program (B. Armstrong, PI, New South Wales study); National Cancer Institute grant CA62006 (T. Zheng, PI, Yale University study); NIH grant CA92153 (J. Cerhan, PI, Mayo Clinic study); the Health Research Board, Ireland (EpiLymph); and Cancer Research, Ireland (InterLymph) (A. 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Sophia S. Wang, Claire M. Vajdic, Martha S. Linet, Susan L. Slager, Jenna Voutsinas, Alexandra Nieters, Silvia de Sanjose, Wendy Cozen, Graciela S. Alarcón, Otoniel Martinez-Maza, Elizabeth E. Brown, Paige M. Bracci, Tracy Lightfoot, Jennifer Turner, Henrik Hjalgrim, John J. Spinelli, Tongzhang Zheng, Lindsay M. Morton, Brenda M. Birmann, Christopher R. Flowers, Ora Paltiel, Nikolaus Becker, Elizabeth A. Holly, Eleanor Kane, Dennis Weisenburger, Marc Maynadie, Pierluigi Cocco, Lenka Foretova, Anthony Staines, Scott Davis, Richard Severson, James R. Cerhan, Elizabeth C. Breen, Qing Lan, Angela Brooks-Wilson, Anneclaire J. De Roos, Martyn T. Smith, Eve Roman, Paolo Boffetta, Anne Kricker, Yawei Zhang, Christine Skibola, Stephen J. Chanock, Nathaniel Rothman, Yolanda Benavente, Patricia Hartge, Karin E. Smedby. Associations of Non-Hodgkin Lymphoma (NHL) Risk With Autoimmune Conditions According to Putative NHL Loci, American Journal of Epidemiology, 2015, 406-421, DOI: 10.1093/aje/kwu290