Risk of Non–Hodgkin Lymphoma Associated with Germline Variation in Genes That Regulate the Cell Cycle, Apoptosis, and Lymphocyte Development

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Risk of Non–Hodgkin Lymphoma Associated with Germline Variation in Genes that Regulate the Cell Cycle, Apoptosis, and Lymphocyte Development

Lindsay M. Morton,1 Mark P. Purdue,1 Tongzhang Zheng,2 Sophia S. Wang,1 Bruce Armstrong,3 Yawei Zhang,2 Idan Menashe,1 Nilanjan Chatterjee,1 Scott Davis,6 Qing Lan,1 Claire M. Vajdic,4 Richard K. Severson,7 Theodore R. Holford,2 Anne Kricker,3 James R. Cerhan,8 Brian Leaderer,2 Andrew Grulich,5 Meredith Yeager,9 Wendy Cozen,10 Shelia Hoar Zahm,1 Stephen J. Chanock,9 Nathaniel Rothman,1 and Patricia Hartge1 1Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS, Rockville, Maryland; 2Department of Epidemiology and Public Health, Yale School of Medicine, New Haven, Connecticut; 3School of Public Health, The University of Sydney, 4University of New South Wales Cancer Research Centre, Prince of Wales Clinical School, and 5National Centre in HIV Epidemiology and Clinical Research, University of New South Wales, Sydney, New South Wales, Australia; 6Fred Hutchinson Cancer Research Center and University of Washington, Seattle, Washington; 7Department of Family Medicine and Karmanos Cancer Institute, Wayne State University, Detroit, Michigan; 8Mayo Clinic, College of Medicine, Rochester, Minnesota; 9Core Genotyping Facility, Advanced Technology Center, National Cancer Institute, NIH, DHHS, Gaithersburg, Maryland;and 10Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California

Abstract

Chromosomal translocations are the hallmark genetic associated translocation. Variants in BCL2L11 were aberration in non–Hodgkin lymphoma (NHL), with strongly related to follicular lymphoma only, particularly specific translocations often selectively associated with rs3789068 (ORAG, 1.41; 95% CI, 1.10-1.81; ORGG,1.65; P BCL7A specific NHL subtypes. Because many NHL-associated 95% CI, 1.25-2.19; trend = 0.0004). Variants in translocations involve cell cycle, apoptosis, and lympho- were strongly related to diffuse large B-cell lymphoma cyte development regulatory genes, we evaluated NHL only, particularly rs1880030 (ORAG, 1.34; 95% CI, 1.08- P risk associated with common genetic variation in 1.68; ORAA, 1.60; 95% CI, 1.22-2.08; trend = 0.0004). The 20 candidate genes in these pathways. Genotyping of associations for both variants were similar in all three 203tag single nucleotide polymorphisms (SNP) was studies and supported by haplotype analyses. We also conducted in 1,946 NHL cases and 1,808 controls pooled observed notable associations for variants in BCL6, from 3independent population-based case-control stud- CCND1,andMYC. Our results support the role of ies. We used logistic regression to compute odds ratios common genetic variation in cell cycle, apoptosis, and (OR) and 95% confidence intervals (CI) for NHL and four lymphocyte development regulatory genes in lympho- major NHL subtypes in relation to tag SNP genotypes magenesis, and suggest that effects may vary by NHL and haplotypes. We observed the most striking associ- subtype. Replication of our findings and further study to ations for tag SNPs in the proapoptotic gene BCL2L11 identify functional SNPs are warranted. (Cancer Epi- (BIM)andBCL7A, which is involved in a rare NHL- demiol Biomarkers Prev 2009;18(4):1259–70)

Introduction

Non–Hodgkin lymphomas (NHL) are closely related The strongest known NHL risk factor is severe immu- diseases, each involving the malignant transformation nodeficiency, but the etiologies of most lymphomas of lymphoid cells but with distinctive morphologic, remain unexplained (1, 2). Although no major suscepti- immunophenotypic, genetic, and clinical features (1). bility gene has been identified, several lines of evidence reveal the contributions of genetic predisposition to NHL etiology: NHL risk is elevated among individuals with a Received 11/4/08;revised 1/12/09;accepted 1/28/09;published OnlineFirst 3/31/09. family history of hematopoietic malignancy, migrant Grant support: All genotyping and statistical analysis for this project was supported studies show that migrants tend to retain the NHL by the Intramural Research Program of the NIH (National Cancer Institute). The National Cancer Institute-Surveillance, Epidemiology, and End Results study was also incidence rates and patterns of their country of origin, supported by the Intramural Research Program of the NIH (National Cancer Institute) and common genetic variations have recently been and by Public Health Service contracts N01-PC-65064, N01-PC-67008, N01-PC-67009, associated with NHL risk (3-6). N01-PC-67010, N02-PC-71105. The Connecticut study was also supported by NIH grant CA62006 from the National Cancer Institute. The New South Wales study was Chromosomal translocations are the hallmark genetic also supported by the National Health and Medical Research Council of Australia aberration in NHL, with specific translocations often [(Bruce Armstrong) Project Grant number 990920], The Cancer Council New South Wales, and The University of Sydney Medical Foundation. selectively associated with particular NHL subtypes Requests for reprints: Lindsay M. Morton, Division of Cancer Epidemiology and (7-10). Most translocations occur as a side effect of Genetics, National Cancer Institute, NIH, Department of Health and Human the single- and double-stranded DNA breaks induced Services, 6120 Executive Boulevard, EPS 7040, MSC#7238;Rockville, MD 20852. Phone: 301-435-3972;Fax: 301-402-0207. E-mail: [email protected] during endogenous processes critical to normal lympho- Copyright D 2009 American Association for Cancer Research. cyte development. Specifically, early in lymphocyte doi:10.1158/1055-9965.EPI-08-1037 development, DNA in the variable (V), diversity (D),

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and joining (J) regions of the immunoglobulin heavy The protocols for each study were approved at the chain (IgH) and E light chain (IgL) loci recombines to Institutional Review Boards of the NCI and each SEER form a functioning B-cell receptor in a process known as center for the NCI-SEER study;Yale University, V(D)J recombination. Mature, antigen-stimulated B-cells the Connecticut Department of Public Health, and the undergo two processes that entail DNA breaks. The first, NCI for the Connecticut study;and all participating class switch recombination, involves recombination of institutions for the NSW study. All study participants DNA in the IgH constant region to produce effector provided informed consent. antibody classes. The second, somatic hypermutation, typically induces a high rate of point mutations in the Ig V NHL Pathology Classification. All cases were histo- regions to produce antibodies with improved antigen logically confirmed by the local diagnosing pathologist in affinity. the NCI-SEER study and by central review of diagnostic NHL-associated translocations typically result in slides by two independent expert hematopathologists transcriptional deregulation of a proto-oncogene or in the Connecticut study. In the NSW study, all cases oncogene by juxtaposing it with Ig regulatory sequences, were histologically confirmed by the local diagnosing although some non–Ig translocations can also occur pathologist, and a confirmatory central pathology review (7-11). Many of the genes involved in NHL-associated was done for cases judged to be <90% certain to be NHL translocations regulate the cell cycle, apoptosis, and on review of the diagnostic pathology report by an expert lymphocyte development, such as MYC, BCL2, CCND1, hematopathologist. In the present analyses, we evaluated and BCL6. Genes in these pathways (e.g., MYC, BCL6, NHL overall and specific NHL subtypes, grouping cases and PIM1) also have been identified as targets of according to the WHO classification (1) using the aberrant (non-Ig) somatic hypermutation (12). International Lymphoma Epidemiology Consortium The likely importance of cell cycle, apoptosis, guidelines (19). For analyses by NHL subtype, we and lymphocyte development regulatory genes in evaluated only the 4 most common subtypes: diffuse lymphomagenesis is evident from their participation in large B-cell lymphoma (DLBCL;28%), follicular lympho- NHL-associated translocations and their identification ma (28%), marginal zone lymphoma (8%), and chronic as targets of aberrant somatic hypermutation, yet few lymphocytic leukemia/small lymphocytic lymphoma studies have investigated the relationship between (CLL/SLL;8%;Table 2). Our studies primarily included risk of developing lymphoma and common genetic SLL rather than CLL cases because these diseases were variation in these genes. We therefore investigated not considered the same entity until the WHO classifi- risk of NHL and NHL subtypes associated with common cation was introduced in 2001 (1). genetic variation in 20 candidate genes involved in regulating the cell cycle, apoptosis, and lymphocyte Laboratory Methods development, 7 of them in or near breakpoints for Biological Samples and DNA Extraction. Study partic- lymphoma-associated chromosomal translocations ipants who did not provide a biological specimen, did (Table 1). Our study population included 1,946 patients nothavesufficientmaterialforDNAextractionor with NHL and 1,808 controls derived from pooling 3 sufficient DNA for genotyping, or whose genotyped population-based case-control studies. Combining data sex was discordant from the questionnaire data were from three studies enabled us to evaluate pooled risk excluded from this analysis (Table 2). For the NCI-SEER estimates as well as risk estimates in three independent study, DNA was extracted from blood clots or buffy coats populations, and provided sufficient sample size to (BBI Biotech) using Puregene Autopure DNA extraction investigate risk of NHL overall and the four most kits (Gentra Systems), and from buccal cell samples by common NHL subtypes. phenol-chloroform extraction methods (20). Genotype frequencies for individuals who provided blood compared with buccal cells were equivalent (21). For the Materials and Methods Connecticut study, DNA was extracted from the blood samples using phenol-chloroform extraction methods Study Population. Our study population was derived (20). For the NSW study, DNA was extracted from from pooling three independent population-based buffy coats using Qiagen QIAamp DNA Blood Midi case-control studies, which have been described in detail kits by laboratory staff at the Viral Epidemiology previously: the National Cancer Institute-Surveillance Section, Science Applications International Corporation- Epidemiology and End Results (NCI-SEER) NHL Frederick, NCI-Frederick. Case-Control Study (13, 14), the Connecticut NHL Case-Control Study (15, 16), and the New South Wales Genotyping. Genotyping of tag single nucleotide poly- (NSW) NHL Case-Control Study (17, 18). Selected morphisms (SNP) from 20 candidate genes involved in characteristics for each study are presented in Table 2. regulating the cell cycle, apoptosis, and lymphocyte All three studies included first primary NHL cases development was conducted at the NCI Core Genotyp- only, and population controls were frequency matched ing Facility (Advanced Technology Center11;ref. 22) to cases (Table 2). The pooled study population using a custom-designed GoldenGate assay (Illumina).12 had more women than men because the Connecticut The GoldenGate assay included a total of 1,536 tag SNPs; study was limited to women, and the age distribution thus, this analysis was conducted as part of a panel that was somewhat younger than a typical series of NHL cases because the NCI-SEER and NSW studies were limited to adults younger than age 75 y. Like the underlying populations, the study population was 11 http://snp500cancer.nci.nih.gov predominantly Caucasian and non-Hispanic. 12 http://www.illumina.com

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Downloaded from cebp.aacrjournals.org on September 24, 2021. © 2009 American Association for Cancer Research. Table 1. Candidate genes involved in regulating the cell cycle, apoptosis, and lymphocyte development

Candidate Chromosomal Gene name (aliases)* Comments Total SNPsc Gene b gene* location* (N = 203) coverage (N = 20) Genes involved in cell cycle control and apoptosis BCL10 1p22 B-cell CLL/lymphoma 10 (CLAP; Induces apoptosis, activates nuclear factor-nB. t(1;14)(p22;q32) 12 92% mE10; CIPER; c-E10; CARMEN) [BCL10;IgH] is involved in < 5% marginal zone lymphomas. TP53I3 2p23.3 Tumor protein p53 inducible protein 3 (PIG3) Involved in p53-mediated apoptosis. 5 100% BCL2L11 2q12-q13 BCL2-like 11 (BAM, BIM, BIM-a6, BIM-b6, Induces apoptosis. BCL2 family member. 12 86% BIM-b7, BOD, BimEL, BimL) RIPK1 6p25.2 Receptor (TNFRSF)-interacting serine-threonine Induces apoptosis. 9 90% kinase 1 (FLJ39204, RIP, RIP1) PIM1 6p21.2 Pim-1 oncogene Controls cell growth, differentiation and apoptosis, particularly 1 50%

acrEieilBoakr rv20;84.Arl2009 April 2009;18(4). Prev Biomarkers Epidemiol Cancer for the hematopoietic system. Target of aberrant hypermutation. RIPK2 8q21 Receptor-interacting serine-threonine Induces apoptosis. 6 86% kinase 2 (WUGSC:H_RG437L15.1, CARD3, CARDIAK, CCK, GIG30, RICK, RIP2) MYC 8q24.21 V-myc avian myelocytomatosis viral Promotes cell proliferation. Target of aberrant hypermutation. t(8;14)(q24;q32) 12 100% oncogene homologue (c-myc) [MYC;IgH] is involved in 95-100% Burkitt lymphomas and 10% DLBCLs. t(8;12;14)(q24;q24;q32) [MYC;BCL7A;IgH] is involved in <1% Burkitt lymphomas. CCND1 11q13 Cyclin D1 (BCL1, D11S287E, PRAD1, U21B31) Regulates the cell cycle G1-S transition. t(11;14)(q13;q32) 5 83% [CCND1;IgH] is involved in 95-100% mantle cell lymphomas and <5% multiple myelomas and CLLs. BCL2L2 14q11.2-q12 BCL2-like 2 (BCL-W, BCLW, KIAA0271) Inhibits apoptosis. BCL2 family member. 2 50% BCL2L10 15q21 BCL2-like 10 (BCL-B, Boo, Diva, MGC129810, Inhibits apoptosis. BCL2 family member. 5 83% MGC129811) BCL2A1 15q24.3 BCL2-related protein A1 (ACC-1, ACC-2, Inhibits apoptosis. BCL2 family member. 8 89% BCL2L5, BFL1, GRS, HBPA1) TP53 17p13.1 Tumor protein p53 (Li-Fraumeni syndrome; Induces cell cycle arrest or apoptosis in response to DNA damage. Mutated in 2 22% LFS1, TRP53, p53) DLBCL (25%) and Burkitt lymphoma (40%). BCL2 18q21.3 B-cell CLL/lymphoma 2 Inhibits apoptosis. BCL2 family member. t(14;18)(q32;q21) [IgH;BCL2]is 62 91% acrEieilg,Boakr Prevention & Biomarkers Epidemiology, Cancer involved in 70-90% follicular lymphomas, 30% DLBCLs, and <5% other NHLs. BAX 19q13.3-q13.4 BCL2-associated X protein Induces apoptosis. BCL2 family member. 10 77% BCL2L1 20q11.21 BCL2-like 1 (BCL-XL/S, BCL2L, BCLX, Bcl-X, Inhibits apoptosis. BCL2 family member. 4 80% DKFZp781P2092, bcl-xL, bcl-xS) Genes involved in lymphocyte development LMO2 11p13 LIM domain only 2 (RBTN2, RBTNL1, Highly expressed in germinal center lymphocytes. Near the 11p13 T-cell 22 88% RHOM2, TTG2) translocation cluster. AICDA 12p13 Activation-induced cytidine deaminase (AID, Initiates class switch recombination and somatic hypermutation in germinal 7 70% ARP2, CDA2, HIGM2) center B-cells. BCL6 3q27 B-cell CLL/lymphoma 6 (BCL5, BCL6A, Controls germinal-center formation and T-cell–dependent immune responses. 11 92% LAZ3, ZBTB27, ZNF51) Target of aberrant hypermutation. t(3;14)(q27;q32) [BCL6;IgH] is involved in 10-35% DLBCLs and 5-10% follicular lymphomas. t(3;various)(q27) [BCL6;various] is involved in 5% DLBCLs. Gene function unknown BCL7A 12q24.13 B-cell CLL/lymphoma 7A (BCL7) Function unknown. t(12;14)(q24;q32) [BCL7A;IgH] and t(8;12;14)(q24;q24;q32) 6 86% [MYC;BCL7A;IgH] are involved in <1% Burkitt lymphomas. BCL7C 16p11 B-cell CLL/lymphoma 7C Function unknown. 2 100%

*As defined by Entrez gene (http://www.ncbi.nlm.nih.gov/sites/entrez?db=gene). cMarkers genotyped for each candidate gene based on selection by Tagzilla. For each gene, SNPs within the region spanning 20kb 5¶ of the start of transcription (exon 1) to 10 kb 3¶ of the end of the last exon were grouped using a binning threshold of r2 > 0.8. bEstimated gene coverage is based on the number of SNPs genotyped/number of bins from the designable set of SNPs (r2>0.8, minor allele frequency >5%) genotyped in the HapMap Caucasian (CEU) samples, Build 20 (http://www.hapmap.org). 1261

Table 2. Selected characteristics of the NCI-SEER, Connecticut, and NSW NHL case-control studies and participants

NCI-SEER Connecticut Time period 1998-2000 1996-2000 Eligibility criteria Included ages 20-74 y. Excluded Included ages 21-84 y. known HIV-positive individuals. Excluded males.

Control selection <65 y: random digit dialing, <65 y: random digit dialing, z65 y: Medicare files z65 y: Medicare files Matching variables Age (5 y groups), sex, race, Age (5 y groups) SEER area Risk factor data Self-administered questionnaire, Self-administered questionnaire, in-person interview in-person interview

Controls Cases Controls Cases

n (%)* n (%)* n (%)* n (%)*

Study population c Risk factor data b 1057 (NA) 1321 (NA) 717 (NA) 601 (NA) Genotyped for this analysis 834 (NA) 1001 (NA) 517 (NA) 436 (NA) x Final analytic population 828 (100) 990 (100) 515 (100) 436 (100) Study site Detroit SEER registry 139 (17) 197 (20) — — Iowa SEER registry 246 (30) 301 (30) — — Los Angeles SEER registry 199 (24) 234 (24) — — Seattle SEER registry 244 (29) 258 (26) — — Connecticut SEER registry — — 515 (100) 436 (100) NSW — — — — Australian Capital Territory — — — — Sex Male 443 (53) 536 (54) — — Female 385 (47) 454 (46) 515 (100) 436 (100) Age (y) <50 203 (25) 277 (28) 98 (19) 86 (20) 50-59 177 (21) 235 (24) 97 (19) 89 (20) 60-69 285 (34) 311 (31) 120 (23) 110 (25) 70+ 163 (20) 167 (17) 200 (39) 151 (35) Race/ethnicity White, non-Hispanic 646 (78) 829 (84) 473 (92) 415 (95) Black 112 (13) 64 (6) 14 (3) 13 (3) Asian/other/unknown 70 (8) 97 (10) 28 (5) 8 (2) NHL subtype DLBCL — 294 (30) — 137 (31) Follicular lymphoma — 246 (25) — 103 (24) Marginal zone lymphoma — 82 (8) — 29 (7) CLL/SLL — 101 (10) — 43 (10) Mantle cell lymphoma — 40 (4) — 10 (2) Lymphoplasmacytic lymphoma — 24 (2) — 9 (2) Burkitt lymphoma — 11 (1) — 0 (0) Mycosis fungoides/Se´zary syndrome — 18 (2) — 10 (2) Peripheral T-cell lymphoma — 41 (4) — 14 (3) NHL, not otherwise specified — 133 (13) — 81 (19) DNA source Blood 598 (72) 688 (70) 515 (100) 436 (100) Buccal 230 (28) 302 (30) — —

Abbreviation: NA, not applicable. *Percentage is based on the final analytic population for this pooled analysis. cParticipation (percentage interviewed among those approached) in NCI-SEER was 76% for cases and 52% for controls;in Connecticut was 72% for cases, 69% for controls <65 y, 47% for controls z65 y;and in NSW was 85% for cases and 61% for controls. bStudy participants who did not provide a biological specimen, did not have sufficient material for DNA extraction or sufficient DNA for genotyping, or whose genotyped sex was discordant from the questionnaire data were excluded from this analysis. The Connecticut study also restricted this analysisto participants who provided a blood sample. The NSW also restricted this analysis to participants of European or Asian ethnicity (97% of participants). xThe final analytic population further excluded participants with a low sample completion rate (NCI-SEER: 11 cases, 6 controls;Connecticut: 2 contr ols; NSW: 4 cases, 9 controls).

also included SNPs from candidate genes in other path- Project (Data Release 20/phase II, National Center for ways. Tag SNPs were chosen from the designable set of Biotechnology Information Build 35 assembly, dbSNPb125) common SNPs (minor allele frequency, >5%) genotyped in using the software Tagzilla,13 which implements a tagging the Caucasian (CEU) population sample of the HapMap algorithm based on the pairwise binning method of Carlsonetal.(23).Foreachgene,SNPswithintheregion spanning20kb5¶ of the start of transcription (exon 1) to 10 13 http://tagzilla.nci.nih.gov/ kb 3¶ of the end of the last exon were grouped using a

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Table 2. Selected characteristics of the NCI-SEER, Connecticut, and NSW NHL case-control studies and participants (Cont’d)

NSW Pooled

2000-2001 Included ages 20-74 y. Excluded known HIV-positive individuals and organ transplant recipients. Electoral rolls

Age (5 y groups), sex, state or territory

Self-administered questionnaire, telephone interview

Controls Cases Controls Cases

n (%)* n (%)* n (%)* n (%)*

694 (NA) 694 (NA) 474 (NA) 524 (NA) 465 (100) 520 (100) 1808 (100) 1946 (100)

— — 139 (8) 197 (10) — — 246 (14) 301 (16) — — 199 (11) 234 (12) — — 244 (13) 258 (13) — — 515 (28) 436 (22) 446 (96) 496 (95) 446 (25) 496 (26) 19 (4) 24 (5) 19 (1) 24 (1)

268 (58) 304 (58) 711 (39) 840 (43) 197 (42) 216 (42) 1097 (61) 1106 (57)

107 (23) 121 (23) 408 (23) 484 (25) 135 (29) 171 (33) 409 (23) 495 (25) 151 (32) 154 (30) 556 (31) 575 (30) 72 (16) 74 (14) 435 (24) 392 (20)

459 (99) 507 (98) 1578 (87) 1751 (90) — — 126 (7) 77 (4) 6 (1) 13 (3) 104 (6) 118 (6)

— 169 (33) — 600 (31) — 191 (37) — 540 (28) — 49 (9) — 160 (8) — 17 (3) — 161 (8) — 19 (4) — 69 (4) — 23 (4) — 56 (3) — 3 (1) — 14 (1) — 3 (1) — 31 (2) — 7 (1) — 62 (3) — 39 (7) — 253 (13)

465 (100) 520 (100) 1578 (87) 1644 (85) — — 230 (13) 302 (15)

binning threshold of r2 > 0.8. When there were multiple personnel. SNPs with concordance of <95% in the study- transcripts available for genes, only the primary transcript specific quality control samples were excluded for that was assessed. study (NCI-SEER buccal cell samples, n = 1). We also excluded samples with a low completion rate (<90% Quality Control, Exclusions, and Final Analytic Study of the full panel of 1536 tag SNPs;NCI-SEER, 11 cases, Population. We excluded tag SNPs (n = 3) that failed to 6 controls;Connecticut, 2 controls;NSW, 4 cases, cluster in the genotyping calling algorithm (separately 9 controls). We included in our analyses 5 candidate analyzed for buccal cell and peripheral blood cell SNPs previously genotyped by Taqman assay in at least 2 samples) or did not amplify during the amplification of the 3 studies and located within 1 of the 20 candidate step of the genotyping assay. SNPs with low completion genes in this analysis, some of the results of which have rate (<90% of samples) were excluded by study (NCI- been published previously (24, 25). SEER blood samples, n = 1;NCI-SEER buccal cell The final pooled analytic study population included samples, n = 4). Quality control duplicates and replicates 1,946 cases and 1,808 controls with data for 203 SNPs from each study were genotyped, blinded to laboratory (198 tag SNPs, 5 previously genotyped Taqman SNPs) in

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or near the 20 candidate genes in this analysis (Supple- Multilocus Analyses. For those genes with at least one mentary Table S1;Table 1). Hardy-Weinberg equilibrium SNP with a Ptrend of <0.05 for NHL or an NHL subtype was evaluated among non-Hispanic Caucasian controls (n = 12 genes), we further conducted two multilocus (n = 1,578, 87% of the analytic population) for the pooled tests. The purpose of these tests was to detect stronger study population and by study (Supplementary associations that might have been missed by the single- Table S1). In the pooled study population, 3 SNPs SNP analyses based on linkage disequilibrium between showed evidence (P < 0.001) for deviation from Hardy- the genotyped SNPs and a causally associated SNP. First, Weinberg proportions but were retained in the analysis we conducted a likelihood ratio test, assessing the because the quality control data did not suggest any obvious relative improvement in model fit from the inclusion of genotyping error (rs9392454, rs7941248, rs17757541). parameters for all independent SNPs (r2 < 0.8 among controls) in a particular gene, assuming a codominant model for each single nucleotide polymorphism com- Statistical methods pared with a model with just age, sex, race, and study SNP-Based Analyses. We calculated odds ratios (OR) center. Second, we conducted haplotype analyses among and 95% confidence intervals (CI) estimating the non-Hispanic Caucasians. We evaluated risk of NHL and relative risk of NHL and NHL subtypes in relation to NHL subtypes associated with haplotypes defined by SNP genotype using dichotomous and polytomous SNPs within a sliding window of three loci across a gene unconditional logistic regression models, respectively. (Haplo Stats, version 1.2.1, haplo.score.slide).14 A global The homozygote of the most common allele in the score statistic was used to summarize the evidence of pooled study population was used as the reference association of disease with the haplotypes for each group. Tests for trend under the codominant model window. In addition, we visualized haplotype structures used a three-level ordinal variable for each SNP (0, using Haploview, version 3.11 (30), based on measures homozygote common;1, heterozygote;2, homozygote of pairwise linkage disequilibrium between SNPs. For variant). All models were adjusted for age, race/ blocks of linkage disequilibrium (Supplementary ethnicity, sex, and study center (categories listed in Table S3), we obtained ORs and 95% CIs for the Table 2). We conducted analyses restricted to non- underlying haplotypes under the assumption of an Hispanic Caucasians and stratified by age (<50, z 50 y) additive model (haplo.glm, minimum haplotype fre- and sex to evaluate the consistency of our results by quency 1%). Two SNPs (MYC rs3824120, BCL2 various demographic groups. To evaluate the consis- rs1982673) were excluded from haplotype analyses tency of our results by NHL subtype, we assessed because they were genotyped in only two of three heterogeneity among NHL subtypes in the polytomous studies (Supplementary Table S1). All haplotype analy- multivariate unconditional logistic regression models ses were adjusted for age, sex, and study center. using the Wald m2 statistic (results presented in Supplementary Tables). Analyses were conducted using SAS version 9.1 (SAS Institute). Results We obtained a gene-level summary of association by computing the minimum P value (‘‘minP test’’), which In this analysis of 203 SNPs from 20 candidate genes assesses the true statistical significance of the smallest among 1,946 patients with NHL and 1,808 population Ptrend within each gene (determined by dichotomous controls, the overall statistical significance for NHL of the logistic regression, comparing NHL or NHL subtypes biological pathway(s) captured by all 20 genes was P = to controls;SNPs listed in Supplementary Table S2) by 0.0544 (tail strength statistic, 0.1546). We observed permutation-based resampling methods (10,000 permu- suggestive associations (Ptrend < 0.05) for 15 SNPs with tations) that automatically adjust for the number of tag risk of NHL overall, 17 SNPs with DLBCL, 12 SNPs with SNPs tested within that gene and the underlying linkage follicular lymphoma, 10 SNPs with marginal zone disequilibrium pattern (26, 27). To account for multiple lymphoma, and 13 SNPs with CLL/SLL (Supplementary comparisons with 20 candidate genes in this analysis, Table S4). we applied the false discovery rate (FDR) method of We observed the most striking associations for Benjamini and Hochberg (28) to the minP test separately BCL2L11 (also known as BIM) and BCL7A (FDR value for NHL and each subtype. We considered FDR values of for minP test, <0.2). BCL2L11 was associated with <0.2 for the minP test as the least likely to be due to a follicular lymphoma (minP = 0.0068;Table 3). SNP- false positive finding and thus represent our most based analyses revealed suggestive associations (Ptrend < interesting results. Finally, we summarized the overall 0.05) for 4 SNPs with NHL overall, and 6 SNPs with evidence of association of the 203 SNPs with NHL or an follicular lymphoma but no significant associations with NHL subtype by using the ‘‘tail strength’’ statistic (29), a any other NHL subtype (Supplementary Table S4). Two variants in linkage disequilibrium in our control popu- summary measure for the departure of the observed P lation (D’ = 0.99;r 2 = 0.75) were particularly strongly value distribution from their expected distribution under related to follicular lymphoma (rs7567444: OR , 0.87; the global null hypothesis of no association in the group CT 95% CI 0.70-1.08;OR , 0.60;95% CI, 0.44-0.80; P = of 20 candidate genes in this analysis. We assessed the TT trend 0.0009;rs3789068: OR , 1.41;95% CI, 1.10-1.81;OR , significance of the tail strength statistics by generating AG GG 1.65;95% CI, 1.25-2.19; P = 0.0004), with very similar their null distributions by permutation-based resampling trend risk estimates in all 3 studies (Supplementary Table S5; of the data. Higher tail strength values (and corresponding lower P values) provide stronger evidence of association. Analyses were conducted using the MATLAB Statistics Toolbox 6.2 (The Mathworks, Inc.). 14 http://mayoresearch.mayo.edu/mayo/research/schaid_lab/software.cfm

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Table 3. Results for the minimum P value (minP test) for the 20 candidate genes we evaluated, for NHL overall and by subtype

Candidate gene NHL DLBCL Follicular lymphoma Marginal zone lymphoma CLL/SLL BCL10 0.5447 0.8675 0.2101 0.4424 0.1830 TP53I3 0.6529 0.6996 0.4719 0.6475 0.2091 BCL2L11 0.0489 0.4653 0.0068 0.5141 0.8484 RIPK1 0.7164 0.5040 0.9507 0.9517 0.9248 PIM1 0.04230.0231 0.4853 0.6349 0.9617 RIPK2 0.4872 0.9026 0.4508 0.2340 0.2671 MYC 0.7174 0.4457 0.5316 0.3774 0.0361 CCND1 0.0744 0.0629 0.5965 0.3924 0.3516 BCL2L2 0.6374 0.8009 0.1184 0.6136 0.7437 BCL2L10 0.3458 0.5721 0.5965 0.9450 0.1807 BCL2A1 0.6037 0.6955 0.5907 0.6038 0.7183 TP53 0.4872 0.9042 0.2973 0.7189 0.0849 BCL2 0.1772 0.7520 0.1785 0.0506 0.9962 BAX 0.7085 0.9242 0.4991 0.2834 0.2803 BCL2L1 0.6575 0.7890 0.2660 0.9542 0.7690 LMO2 0.1591 0.3511 0.7938 0.5980 0.4292 AICDA 0.4028 0.1571 0.8852 0.3242 0.1875 BCL6 0.0616 0.1641 0.0452 0.0237 0.0574 BCL7A 0.0211 0.0025 0.1809 0.9871 0.6922 BCL7C 0.8592 0.8298 0.9120 0.6331 0.5307

NOTE: Bold type indicates P value of <0.05. The minP test assesses the true statistical significance of the smallest P trend within each gene (determined by dichotomous logistic regression, comparing NHL or NHL subtypes to controls;SNPs listed in Supplementary Table S2) by permutation-based resamplin g methods (10,000 permutations) that automatically adjust for the number of tag SNPs tested within that gene and the underlying linkage disequilibrium pattern (26, 27).

Table 4;Fig. 1). The multilocus analyses supported the direction. In the pooled data set, CLL/SLL was particu- association of BCL2L11 with follicular lymphoma and larly associated with rs3172469 (ORGT, 1.20;95% CI, did not show stronger evidence of association than the 0.85-1.70;OR GG, 2.29;95% CI, 1.33-3.93; Ptrend = 0.0094), single SNP-based analyses (Supplementary Tables S6-7). with similar risk estimates in all three studies (Table 4; BCL7A was particularly associated with DLBCL Supplementary Table S5). In multilocus analyses, the (minP = 0.0025;Table 3). Three SNPs were significantly likelihood ratio test showed a slightly stronger association related to DLBCL only, with one variant particularly than the minP test with NHL overall (likelihood ratio test, strongly associated with DLBCL (rs1880030: ORAG, 1.34; P = 0.0122;Supplementary Table S6), whereas the 95% CI, 1.08-1.68;OR AA, 1.60;95% CI, 1.22-2.08; Ptrend = analyses of haplotypes defined by SNPs within a sliding 0.0004), which was consistent in all 3 studies (Supple- window of three loci were similar to the SNP-based mentary Table S5;Table 4;Fig. 1). The multilocus analyses and supported a stronger association for CLL/ analyses supported the association of this variant with SLL than other subtypes (Supplementary Table S7). DLBCL and did not show stronger evidence of associa- MYC was associated with CLL/SLL (minP = 0.0361; tion than the single SNP-based analyses (Supplementary Table 3). The two SNPs most strongly associated with Tables S6-7). Another SNP was related to risk of NHL CLL/SLL were in modest linkage disequilibrium in 2 overall (rs12827036: ORGT, 0.83;95% CI, 0.71-0.97;OR TT, our control population (D’ = 0.77;r = 0.45), and the 0.77;95% CI, 0.64-0.93; Ptrend = 0.0044), with similar homozygote was rare (3.0-4.8% among controls). Thus, statistically significant risk estimates for both DLBCL we evaluated risk estimates under the dominant genetic and follicular lymphoma (Table 4), and consistent risk model (rs3891248: ORAT/AA, 0.57;95% CI, 0.38-0.85; estimates in all 3 studies (Supplementary Table S8). P = 0.0060;rs16902359: OR CT/TT, 0.52;95% CI, 0.33-0.82; We also observed notable associations for BCL6, MYC, P = 0.0049), which were similar in all three studies and CCND1 (FDR value for minP test, 0.2-0.5). BCL6 was (Supplementary Table S5;Table 4). The multilocus marginally associated with NHL overall (minP = 0.0616) analyses did not show stronger evidence of association and most subtypes (Table 3). In SNP-based analyses, than the single SNP-based analyses (Supplementary scattered suggestive associations (Ptrend < 0.05) were Tables S6-7). observed for 8 SNPs for NHL overall and/or at least one CCND1 was weakly associated with NHL (minP = NHL subtype (Supplementary Table S4). The SNP most 0.0744;Table 3). Two SNPs in linkage disequilibrium in strongly associated with NHL overall was rs1523475 our control population (D’ = 0.96;r 2 = 0.53) were modestly (ORCT, 1.14;95% CI, 0.99-1.31;OR TT, 1.50;95% CI, 1.07- related to NHL in the pooled study population (rs603965: 2.11; P trend = 0.0079;Table 4). Consistent with our ORGA, 1.10;95% CI, 0.94-1.27;OR AA, 1.25;95% CI, 1.04- previous report on rs1056932 from the Connecticut study 1.52; Ptrend = 0.0203;rs2450254: OR AT, 0.94;95% CI, 0.82- (25), the strongest SNP associations in the pooled data set 1.09;OR TT, 0.83;95% CI, 0.68-1.00; Ptrend = 0.0623), with were observed for CLL/SLL (5 SNPs Ptrend < 0.05, consistent risk estimates across all four subtypes (Table 4). including rs1056932). Compared with the Connecticut The risk estimates were also generally similar across all study, the associations for CLL/SLL in the NCI-SEER and three studies (Supplementary Table S8), although the risk NSW studies tended to be weaker but were in the same estimates for the splice variant G870A (rs603965), which

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Table 4. Selected individual SNP results from the pooled study population for NHL overall and by subtype

Candidate dbSNP ID, Genotype Controls All NHL DLBCL Follicular Marginal zone CLL/SLL gene* SNP500 Alias* (n = 1946) (n = 600) lymphoma (n = 540) lymphoma (n = 160) (n = 161) c c c c c nnOR (95% CI) n OR (95% CI) n OR (95% CI) n OR (95% CI) n OR (95% CI) BCL2L11 rs7567444, CT 871 933 0.87 (0.75, 1.01) 285 0.88 (0.71, 1.09) 271 0.87 (0.70, 1.08) 82 1.00 (0.69, 1.46) 77 0.92 (0.63, 1.33) ACOXL_01 TT 386 368 0.77 (0.64, 0.93) 119 0.83 (0.64, 1.09) 82 0.60 (0.44, 0.80) 29 0.80 (0.49, 1.30) 32 0.87 (0.55, 1.39) Ptrend 0.0055 0.1542 0.0009 0.4109 0.5434 acrEieilBoakr rv20;84.Arl2009 April 2009;18(4). Prev Biomarkers Epidemiol Cancer rs3789068, AG 857 939 1.16 (0.99, 1.35) 293 1.16 (0.92, 1.45) 270 1.41 (1.10, 1.81) 77 1.15 (0.77, 1.71) 82 1.17 (0.79, 1.72) BCL2L11_14 GG 399 486 1.27 (1.06, 1.53) 146 1.21 (0.93, 1.58) 150 1.65 (1.25, 2.19) 39 1.27 (0.80, 2.01) 35 1.04 (0.65, 1.67) Ptrend 0.0093 0.1439 0.0004 0.3095 0.8038 BCL7A rs12827036, GT 853 900 0.83 (0.71, 0.97) 286 0.87 (0.70, 1.08) 245 0.79 (0.63, 0.99) 70 0.91 (0.62, 1.34) 76 0.88 (0.60, 1.28) BCL7A_02 TT 385 377 0.77 (0.64, 0.93) 114 0.75 (0.57, 0.99) 112 0.77 (0.58, 1.02) 37 1.04 (0.66, 1.64) 30 0.80 (0.50, 1.30) Ptrend 0.0044 0.0415 0.495 0.9104 0.3553 rs1880030, AG 858 954 1.09 (0.94, 1.27) 302 1.34 (1.08, 1.68) 253 0.88 (0.71, 1.09) 84 1.18 (0.82, 1.70) 69 0.90 (0.62, 1.31) BCL7A_03 AA 341 383 1.10 (0.91, 1.32) 144 1.60 (1.22, 2.08) 90 0.76 (0.57, 1.02) 25 0.87 (0.53, 1.44) 38 1.27 (0.82, 1.98) Ptrend 0.2681 0.0004 0.0585 0.8114 0.3843 BCL6 rs3172469, GT 757 828 1.08 (0.95, 1.24) 269 1.18 (0.97, 1.43) 214 0.98 (0.80, 1.21) 67 1.02 (0.72, 1.43) 70 1.20 (0.85, 1.70) BCL6_05 GG 120 152 1.34 (1.04, 1.74) 43 1.27 (0.87, 1.85) 45 1.39 (0.95, 2.03) 10 0.98 (0.49, 1.96) 20 2.29 (1.33, 3.93) Ptrend 0.0303 0.0708 0.3063 0.9723 0.0094 rs1523475, CT 581 658 1.14 (0.99, 1.31) 218 1.27 (1.04, 1.55) 185 1.17 (0.95, 1.44) 46 0.85 (0.59, 1.22) 50 1.03 (0.72, 1.48) BCL6_16 TT 64 86 1.50 (1.07, 2.11) 23 1.30 (0.79, 2.14) 22 1.32 (0.79, 2.20) 2 0.34 (0.08, 1.42) 11 2.00 (1.01, 3.96) Ptrend b 0.0079 0.0188 0.0884 0.1255 0.1864 MYC rs3891248, AT/AA 578 605 1.01 (0.87, 1.16) 202 1.18 (0.96, 1.44) 154 0.96 (0.77, 1.19) 64 1.49 (1.06, 2.10) 36 0.57 (0.38, 0.85) MYC_02 b rs16902359, CT/TT 482 462 0.92 (0.79, 1.08) 153 1.06 (0.85, 1.32) 118 0.90 (0.71, 1.14) 42 1.03 (0.70, 1.52) 27 0.52 (0.33, 0.82) MYC_21 CCND1 rs603965, AG 883 967 1.10 (0.94, 1.27) 307 1.20 (0.97, 1.49) 268 1.08 (0.86, 1.35) 86 1.30 (0.88, 1.91) 78 1.05 (0.72, 1.53) CCND1_02 AA 321 403 1.25 (1.04, 1.52) 126 1.35 (1.03, 1.77) 108 1.16 (0.87, 1.54) 31 1.26 (0.77, 2.05) 35 1.38 (0.86, 2.19) Ptrend 0.0203 0.0270 0.3088 0.2868 0.2132 rs2450254, AT 873 943 0.94 (0.82, 1.09) 278 0.83 (0.68, 1.02) 270 1.04 (0.83, 1.29) 73 0.82 (0.58, 1.17) 81 1.03 (0.71, 1.48) CCND1_15 TT 335 312 0.83 (0.68, 1.00) 91 0.73 (0.55, 0.97) 88 0.90 (0.67, 1.21) 24 0.69 (0.42, 1.14) 28 0.96 (0.59, 1.55) Ptrend 0.0623 0.0176 0.6003 0.1195 0.9011 LMO2 rs3824848, CT 804 880 1.10 (0.96, 1.27) 279 1.17 (0.96, 1.43) 247 1.14 (0.93, 1.41) 75 1.15 (0.81, 1.62) 75 1.16 (0.82, 1.65) LMO2_32 TT 168 227 1.35 (1.08, 1.69) 71 1.43 (1.04, 1.97) 57 1.24 (0.88, 1.75) 17 1.10 (0.62, 1.95) 19 1.43 (0.83, 2.48) Ptrend 0.0098 0.0176 0.1247 0.5366 0.1805 BCL2 rs2849377, AT 406 402 0.86 (0.74, 1.01) 121 0.84 (0.67, 1.06) 130 1.04 (0.83, 1.31) 21 0.49 (0.31, 0.79) 30 0.78 (0.51, 1.18) BCL2_22 TT 33 15 0.41 (0.22, 0.76) 7 0.61 (0.27, 1.40) 4 0.38 (0.13, 1.09) 1 0.30 (0.04, 2.20) 1 0.36 (0.05, 2.67) Ptrend 0.0041 0.0634 0.5582 0.0018 0.1267

*Candidate gene: gene of interest. Gene: Gene in which SNP is located, as genotyping included SNPs spanning 20 kb 5’ of the start of transcription (exon 1) to 10kb 3’ of the end of the last exon. SNP500 Alias: SNP500 (http://snp500cancer.nci.nih.gov). cCommon homozygote used as the reference group for each SNP. bThe dominant model is presented because the homozygote was rare (3.0-4.8% among controls).

we previously reported for the NCI-SEER study (24), were effects of BCL2 (31-34). Several isoforms of BCL2L11 attenuated and not significant in the Connecticut and created by both transcriptional and posttranslational NSW studies. The multilocus analyses did not show modification have been identified and shown to have stronger evidence of association than the single SNP- varying proapoptotic activity (35, 36). Furthermore, based analyses (Supplementary Tables S6-7). LMO2 and BCL2 were not statistically significantly associated with NHL or any NHL subtype (FDR value for minP test, >0.5;Table 3). However, in each gene, the association with NHL for at least one SNP could not be disregarded based on a Ptrend of <0.01 and consistency of risk estimates in all 3 studies and across all 4 NHL subtypes (LMO2 rs3824848: ORCT, 1.10;95% CI, 0.96-1.27; ORTT, 1.35;95% CI, 1.08-1.69; Ptrend = 0.0098; BCL2 rs2849377: ORAT, 0.86;95% CI, 0.74-1.01;OR TT, 0.41;95% CI, 0.22-0.76; Ptrend = 0.0041;Supplementary Tables S4, S5, and S8). It was also notable that of the 12 SNPs in BCL2 related to NHL or an NHL subtype, 8 were particularly related to marginal zone lymphoma, although no clear patterns emerged to implicate a particular variant (Supplementary Table S4). The multilocus analyses did not show stronger evidence of association than the single SNP-based analyses (Supple- mentary Tables S6-7). Although we observed suggestive associations (Ptrend <0.05) for the one SNP we genotyped in PIM1 and for one of the two SNPs we genotyped in TP53, we could not explore these findings further because we did not have data for additional SNPs within these genes (Supplementary Table S4). We also observed suggestive associations (Ptrend < 0.05) for individual SNPs in BCL10, AICDA, and BAX, but the minP test, study- specific SNP-based anayses, and multilocus analyses generally did not support an association with risk of NHL overall or any NHL subtype (Supplementary Tables S4-8;Table 3). Risk estimates were similar when we conducted the SNP-based analyses restricted to non-Hispanic Cauca- sians and stratified by age (<50, z 50 years) and sex (data not shown).

Discussion

In this pooled analysis, we showed consistent evidence from three population-based case-control studies that common genetic variation in cell cycle, apoptosis, and lymphocyte development regulatory genes may play a role in lymphomagenesis, and the effects may vary by NHL subtype. In particular, we found that two variants in linkage disequilibrium in the proapoptotic gene BCL2L11 (BIM) were significantly related to follicular lymphoma risk, and one variant in BCL7A, which is involved in a rare NHL-associated translocation, was significantly related to DLBCL risk. We also observed notable associations for variants in BCL6 and CCND1 with risk of NHL overall, and variants in MYC with risk of CLL/SLL. We observed suggestive associations for at least 1 variant in 7 of the remaining 15 genes we evaluated, but overall the findings for these genes were not compelling. BCL2L11 (also known as BIM) is a key proapoptotic Figure 1. Risk estimates in the pooled study population and by member of the BCL2 family that maintains hematopoietic study for the three most significant SNP associations, including cell homeostasis by initiating apoptosis in lymphocytes, the BCL2L11 (BIM) variants rs7567444 (A) and rs3789068 (B) regulating the negative selection of autoreactive lympho- with follicular lymphoma, and the BCL7A variant rs1880030 cytes, and balancing the proliferative and antiapoptotic with DLBCL (C).

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diminished expression of BCL2L11 has been associated for at least one variant in each of 7 genes, but overall, the with melanoma progression (37), renal cell carcinoma findings for these genes were not compelling. For three (38), and glioblastoma (39). We present here the first of these genes (LMO2, BCL2, BCL10), we successfully report on common genetic variation in BCL2L11. The genotyped z85% of the SNPs identified by our tagging two variants in BCL2L11 for which we observed a algorithm from both HapMap Build 20 and the current particularly striking association with follicular lymphoma version of HapMap (Build 22). However, for the (rs7567444, rs3789068) were in linkage disequilibrium in remaining four genes (TP53, PIM1, BAX, AICDA), we our control population and tag variants spanning most successfully genotyped V70% of the SNPs identified by of BCL2L11. If our findings are replicated, it will be our tagging algorithm from both HapMap Build 20 and necessary to conduct additional genotyping across the the current version of HapMap (Build 22). The publica- entire gene to determine which region contains the causal tion of our complete results from all SNPs in all 20 of the variant(s). candidate genes can be used to compare results of future BCL7A was identified by its participation in a three- research on these variants in relation to lymphoma- way chromosomal translocation with MYC and IgH in a genesis (Supplementary Table S4). Burkitt lymphoma cell line and has also been shown to The main strength of this analysis was our ability to be rearranged in a mediastinal B-cell lymphoma cell line evaluate the associations in three independent study (40). Although the function of BCL7A is unknown, the populations. Interpretation of our results should also take protein shows homology with the actin-binding protein, into account several limitations. We did not have data on caldesmon, and is part of an evolutionarily conserved a sufficient number of unlinked, unassociated SNPs to family that also includes BCL7B and BCL7C (41). We quantitatively assess population structure within our present the first report on common genetic variation in data. However, it is unlikely that our results were biased BCL7A, although diminished expression of BCL7A has by population stratification because our results were been associated with mycosis fungoides (42), peripheral similar in three independent study populations, and it is T-cell lymphoma (43), more aggressive clinical behavior unlikely that the same substructure would be repeated in of cutaneous T-cell lymphoma (44), and poorer prog- multiple studies. In addition, our risk estimates were nosis for DLBCL (45). The variant in BCL7A for which similar when we restricted the analytic population to non- we observed a particularly strong association with Hispanic Caucasians (data not shown). Participation DLBCL (rs1880030) tags eight other loci located in or (percentage interviewed among those approached) was near exon 5. More research is needed to discover the low in the three studies, particularly for controls. function of BCL7A and replicate our findings, particu- However, it is unlikely that participation bias would larly focusing on the region of the gene surrounding completely explain our findings because it is unlikely that exon 5. genotype frequencies vary by willingness to participate We also observed notable associations for variants in (21). Survival bias could have influenced our results for BCL6 and CCND1 with risk of NHL overall, and variants those genotypes also associated with prognosis because in MYC with risk of CLL/SLL. All three of these genes some patients with more aggressive disease were too ill to play important roles in the cell cycle and/or lymphocyte participate or died before study investigators could development (46-48) and have been implicated in contact them, and common genetic variants associated lymphomagenesis by several lines of evidence (7-12, 45, with NHL etiology may also be associated with 49, 50). However, there is limited previous research survival (54). Although all cases had histologically associating lymphoma with common genetic variation in confirmed NHL, our results for NHL subtypes could BCL6 and CCND1, and no previous research for MYC. have been biased by disease misclassification among the The BCL6 findings from the pooled data set were subtypes. However, diagnostic accuracy is estimated to consistent with our previous report from the Connecticut be >80% for most NHL subtypes (55, 56), and any disease study only (25) but do not provide support for two other misclassification was likely to be non-differential, thus previous studies of follicular lymphoma in relation to biasing our results toward the null hypothesis. We may SNPs in the regulatory first intronic region of BCL6 have had some false negative results because of inade- (51, 52). The CCND1 splice variant G870A (rs603965), quate coverage of the SNPs identified in HapMap, or which we previously reported for the NCI-SEER study because the genetic variation identified by HapMap does (24), has also been associated with acute lymphoblastic not uniformly cover the genome. Finally, our results leukemia (48). Although no previous research has require replication in other study populations because associated lymphoma with common genetic variation some findings may be the result of false positive in MYC, the two rare variants in MYC (rs3891248, associations. However, by combining data from three rs16902359) associated with CLL/SLL in this pooled studies, we were able to evaluate pooled risk estimates as analysis are singletons located in the promoter and first well as risk estimates in three independent populations, intronic region of MYC. Chromosomal translocation minimizing the chance of false positive associations breakpoints clustered in this region have been shown particularly for our strongest findings. to have a greater effect on MYC overexpression in Burkitt In summary, we found consistent evidence in three lymphomas than breakpoints in other regions of MYC population-based case-control studies that common (53). Because of the importance of BCL6, CCND1, and genetic variation in cell cycle, apoptosis, and lympho- MYC in the cell cycle and/or lymphocyte development cyte development regulatory genes may play a role in as well as carcinogenesis, we believe further study of lymphomagenesis, and the effects may vary by NHL common genetic variation in these genes and lymphoma subtype. Replication of our results, particularly in risk is warranted. studies with sufficient power to evaluate NHL sub- Of the remaining 15 candidate genes we evaluated in types, and further study to identify functional SNPs are this pooled analysis, we observed suggestive associations warranted.

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Disclosure of Potential Conflicts of Interest 18. Purdue MP, Lan Q, Kricker A, et al. Polymorphisms in immune function genes and risk of non-Hodgkin lymphoma: findings from No potential conflicts of interest were disclosed. the New South Wales non-Hodgkin Lymphoma Study. Carcinogen- esis 2007;28:704 – 12. 19. Morton LM, Turner JJ, Cerhan JR, et al. Proposed classification of Acknowledgments lymphoid neoplasms for epidemiologic research from the Interna- The costs of publication of this article were defrayed in part by tional Lymphoma Epidemiology Consortium (InterLymph). Blood 2007;110:695 – 708. the payment of page charges. This article must therefore be 20. Garcia-Closas M, Egan KM, Abruzzo J, et al. Collection of hereby marked advertisement in accordance with 18 U.S.C. genomic DNA from adults in epidemiological studies by buccal Section 1734 solely to indicate this fact. cytobrush and mouthwash. Cancer Epidemiol Biomarkers Prev We thank Mary McAdams, Peter Hui, Michael Stagner, and 2001;10:687 – 96. Zeynep Kalaylioglu of Information Management Services, Inc., 21. Bhatti P, Sigurdson AJ, Wang SS, et al. Genetic variation and for their programming support. For the NCI-SEER study, we willingness to participate in epidemiologic research: data from three acknowledge the contributions of the staff and scientists at studies. Cancer Epidemiol Biomarkers Prev 2005;14:2449 – 53. the SEER centers of Iowa, Los Angeles, Detroit, and Seattle for the 22. Packer BR, Yeager M, Burdett L, et al. SNP500Cancer: a public resource for sequence validation, assay development, and frequency conduct of the study’s field effort. The NSW study was made analysis for genetic variation in candidate genes. Nucl Acids Res possible by access to new notifications to the NSW Central 2006;34:D617 – 21. Cancer Registry, which is funded by the NSW Health Depart- 23. Carlson CS, Eberle MA, Rieder MJ, Yi Q, Kruglyak L, Nickerson DA. ment. Ann-Maree Hughes oversaw conduct of the study and Selecting a maximally informative set of single-nucleotide poly- Melisa Litchfield, Maria Agaliotis, Chris Goumas, Jackie Turner, morphisms for association analyses using linkage disequilibrium. and staff of the Hunter Valley Research Foundation contributed Am J Hum Genet 2004;74:106 – 20. to the data collection. Jenny Turner, study pathologist, reviewed 24. Wang SS, Cozen W, Severson RK, et al. Cyclin D1 splice variant and all pathology reports and original slides as necessary. risk for non-Hodgkin lymphoma. Hum Genet 2006;120:297 – 300. 25. Zhang Y, Lan Q, Rothman N, et al. A putative exonic splicing polymorphism in the BCL6 gene and the risk of non-Hodgkin lymphoma. J Natl Cancer Inst 2005;97:1616 – 8. 26. Westfall P, Zaykin D, Young S. Multiple tests for genetic effects in References association studies. Methods Mol Biol 2002;184:143 – 68. 1. Jaffe ES, Harris NL, Stein H, Vardiman JW, editors. Pathology and 27. Chen B, Sakoda L, Hsing AW, Rosenberg PS. Resampling-based Genetics of Tumours of Haematopoietic and Lymphoid Tissues. multiple hypothesis testing procedures for genetic case-control World Health Organization Classification of Tumours, Volume 3. association studies. Genet Epidemiol 2006;30:495 – 507. Lyon: IARC Press;2001. 28. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a 2. Hartge P, Bracci PM, Wang SS, Devesa SS, Holly EA. Non-Hodgkin practical and powerful approach to multiple testing. J Royal Stat Soc lymphoma. In: Schottenfeld D, Fraumeni JF, Jr., editors. Cancer B 1995;57:289 – 300. Epidemiology and Prevention, 3rd ed. Oxford: Oxford Press;2006. 29. Taylor J, Tibshirani R. A tail strength measure for assessing the 3. Au WY, Gascoyne RD, Klasa RD, et al. Incidence and spectrum of overall univariate significance in a dataset. Biostat 2006;7:167 – 81. non-Hodgkin lymphoma in Chinese migrants to British Columbia. Br 30. Barrett J, Fry B, Maller J, Daly M. Haploview: analysis and J Haematol 2005;128:792 – 6. visualization of LD and haplotype maps. Bioinformatics 2005;21: 4. Iscovich J, Parkin DM. Risk of cancer in migrants and their 263–5. descendants in Israel: I. Leukaemias and lymphomas. Int J Cancer 31. Bouillet P, Metcalf D, Huang DC, et al. Proapoptotic Bcl-2 relative 1997;70:649 – 53. Bim required for certain apoptotic responses, leukocyte homeostasis, 5. Rothman N, Skibola CF, Wang SS, et al. Genetic variation in TNF and and to preclude autoimmunity. Science 1999;286:1735 – 8. IL10 and risk of non-Hodgkin lymphoma: a report from the 32. Bouillet P, Purton JF, Godfrey DI, et al. BH3-only Bcl-2 family InterLymph Consortium. Lancet Oncol 2006;7:27 – 38. member Bim is required for apoptosis of autoreactive thymocytes. 6. Wang SS, Slager SL, Brennan P, et al. Family history of hematopoietic Nature 2002;415:922 – 6. malignancies and risk of non-Hodgkin lymphoma (NHL): a pooled 33. Craxton A, Draves KE, Clark EA. Bim regulates BCR-induced entry analysis of 10 211 cases and 11 905 controls from the International of B cells into the cell cycle. Eur J Immunol 2007;37:2715 – 22. Lymphoma Epidemiology Consortium (InterLymph). Blood 2007; 34. Enders A, Bouillet P, Puthalakath H, Xu Y, Tarlinton DM, Strasser A. 109:3479 – 88. Loss of the pro-apoptotic BH3-only Bcl-2 family member Bim inhibits 7. Kuppers R, Dalla-Favera R. Mechanisms of chromosomal trans- BCR stimulation-induced apoptosis and deletion of autoreactive B locations in B cell lymphomas. Oncogene 2001;20:5580 – 94. Cells. J Exp Med 2003;198:1119 – 26. 8. Dyer MJS. The pathogenetic role of oncogenes deregulated by 35. Marani M, Tenev T, Hancock D, Downward J, Lemoine NR. chromosomal translocation in B-cell malignancies. Int J Hematol Identification of novel isoforms of the BH3 domain protein Bim 2003;77:315 – 20. which directly activate Bax to trigger apoptosis. Mol Cell Biol 2002; 9. Kuppers R. Mechanisms of B-cell lymphoma pathogenesis. Nat Rev 22:3577 – 89. Cancer 2005;5:251 – 62. 36. Harada H, Quearry B, Ruiz-Vela A, Korsmeyer SJ. Survival factor- 10. Bende RJ, Smit LA, van Noesel CJM. Molecular pathways in induced extracellular signal-regulated kinase phosphorylates BIM, follicular lymphoma. Leukemia 2006;21:18 – 29. inhibiting its association with BAX and proapoptotic activity. Proc 11. Ohno H. Pathogenetic and clinical implications of non-immunoglo- Natl Acad Sci U S A 2004;101:15313 – 7. bulin;BCL6 translocations in B-cell non-Hodgkin’s lymphoma. J Clin 37. Dai DL, Wang Y, Liu M, Martinka M, Li G. Bim expression is Exp Hematop 2006;46:43 – 53. reduced in human cutaneous melanomas. J Invest Dermatol 2008; 12. Pasqualucci L, Neumeister P, Goossens T, et al. Hypermutation of 128:403 – 7. multiple proto-oncogenes in B-cell diffuse large-cell lymphomas. 38. Zantl N, Weirich G, Zall H, et al. Frequent loss of expression Nature 2001;412:341 – 6. of the pro-apoptotic protein Bim in renal cell carcinoma: 13. Chatterjee N, Hartge P, Cerhan JR, et al. Risk of non-Hodgkin’s evidence for contribution to apoptosis resistance. Oncogene lymphoma and family history of lymphatic, hematologic, and other 2007;26:7038 – 48. cancers. Cancer Epidemiol Biomarkers Prev 2004;13:1415 – 21. 39. Austin M, Cook SJ. Increased expression of Mcl-1 is required for 14. Wang SS, Cerhan JR, Hartge P, et al. Common genetic variants in protection against serum starvation in phosphatase and tensin proinflammatory and other immunoregulatory genes and risk for homologue on chromosome 10 null mouse embryonic fibroblasts, non-Hodgkin lymphoma. Cancer Res 2006;66:9771 – 80. but repression of Bim is favored in human glioblastomas. J Biol 15. Lan Q, Zheng T, Rothman N, et al. Cytokine polymorphisms in the Chem 2005;280:33280 – 8. Th1/Th2 pathway and susceptibility to non-Hodgkin lymphoma. 40. Zani VJ, Asou N, Jadayel D, et al. Molecular cloning of complex Blood 2006;107:4101 – 8. chromosomal translocation t(8;14;12)(q24.1;q32.3;q24.1) in a Burkitt 16. Zhang Y, Holford TR, Leaderer B, et al. Hair-coloring product use lymphoma cell line defines a new gene (BCL7A) with homology to and risk of non-Hodgkin’s lymphoma: a population-based case- caldesmon. Blood 1996;87:3124 – 34. control study in Connecticut. Am J Epidemiol 2004;159:148 – 54. 41. Jadayel D, Osborne L, Coignet L, et al. The BCL7 gene family: 17. Hughes AM, Armstrong BK, Vajdic CM, et al. Pigmentary character- deletion of BCL7B in Williams syndrome. Gene 1998;224:35 – 44. istics, sun sensitivity and non-Hodgkin lymphoma. Int J Cancer 2004; 42. Tracey L, Villuendas R, Dotor AM, et al. Mycosis fungoides shows 110:429. concurrent deregulation of multiple genes involved in the TNF

Cancer Epidemiol Biomarkers Prev 2009;18(4). April 2009

signaling pathway: an expression profile study. Blood 2003;102: into the immunoglobulin heavy chain locus in human Burkitt 1042 – 50. lymphoma and murine plasmacytoma cells. Proc Natl Acad Sci 43. Martinez-Delgado B, Melendez B, Cuadros M, et al. Expression U S A 1982;79:7837 – 41. profiling of T-cell lymphomas differentiates peripheral and lympho- 51. Jardin F, Ruminy P, Parmentier F, et al. Clinical and biological blastic lymphomas and defines survival related genes. Clin Cancer relevance of single-nucleotide polymorphisms and acquired somatic Res 2004;10:4971 – 82. mutations of the BCL6 first intron in follicular lymphoma. Leukemia 44. van Doorn R, Zoutman WH, Dijkman R, et al. Epigenetic profiling of 2005;19:1824 – 30. cutaneous T-cell lymphoma: promoter hypermethylation of multiple 52. Lossos IS, Jones CD, Zehnder JL, Levy R. A polymorphism in the tumor suppressor genes including BCL7a, PTPRG, and p73. J Clin BCL-6 gene is associated with follicle center lymphoma. Leuk Oncol 2005;23:3886 – 96. Lymphoma 2001;42:1343 – 50. 45. Alizadeh AA, Eisen MB, Davis RE, et al. Distinct types of diffuse 53. Wilda M, Busch K, Klose I, et al. Level of MYC overexpression in large B-cell lymphoma identified by gene expression profiling. pediatric Burkitt’s lymphoma is strongly dependent on genomic Nature 2000;403:503 – 11. breakpoint location within the MYC locus. Genes Chromosomes 46. Adhikary S, Eilers M. Transcriptional regulation and transformation Cancer 2004;41:178 – 82. by Myc proteins. Nat Rev Mol Cell Biol 2005;6:635 – 45. 54. Cerhan JR, Wang SS, Maurer MJ, et al. Prognostic significance of host 47. Jardin F, Ruminy P, Bastard C, Tilly H. The BCL6 proto-oncogene: a immune gene polymorphisms in follicular lymphoma survival. leading role during germinal center development and lymphoma- Blood 2007;109:5439 – 46. genesis. Pathol Biol 2007;55:73 – 83. 55. A clinical evaluation of the International Lymphoma Study Group 48. Knudsen KE, Diehl JA, Haiman CA, Knudsen ES. Cyclin D1: polymor- classification of non-Hodgkin’s lymphoma. The Non-Hodgkin’s phism, aberrant splicing and cancer risk. Oncogene 2006;25:1620–8. Lymphoma Classification Project. Blood 1997;89:3909 – 18. 49. Lossos IS, Jones CD, Warnke R, et al. Expression of a single gene, 56. Clarke CA, Glaser SL, Dorfman RF, Bracci PM, Eberle E, Holly EA. BCL-6, strongly predicts survival in patients with diffuse large B-cell Expert review of non-Hodgkin’s lymphomas in a population-based lymphoma. Blood 2001;98:945 – 51. cancer registry: reliability of diagnosis and subtype classifications. 50. Taub R, Kirsch I, Morton C, et al. Translocation of the c-myc gene Cancer Epidemiol Biomarkers Prev 2004;13:138 – 43.

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