Citations

Abstract Inherited genetic risk factors play a role in multiple myeloma (MM), yet considerable missing heritability exists. Rare risk variants at genome-wide association study (GWAS) loci are a new avenue to explore. Pleiotropy between lymphoid neoplasms (LNs) has been suggested in family history and genetic studies, but no studies have interrogated sequencing for pleiotropic genes or rare risk variants. Sequencing genetically enriched cases can help discover rarer variants. We analyzed exome sequencing in familial or early-onset MM cases to identify rare, functionally relevant variants near GWAS loci for a range of LNs. A total of 149 distinct and significant LN GWAS loci have been published. We identified six recurrent, rare, potentially deleterious variants within 5 kb of significant GWAS single nucleotide polymorphisms in 75 MM cases. Mutations were observed in BTNL2, EOMES, TNFRSF13B, IRF8, ACOXL and TSPAN32. All six genes replicated in an independent set of 255 early-onset MM or familial MM or precursor cases. Expansion of our analyses to the full length of these six genes resulted in a list of 39 rare and deleterious variants, seven of which segregated in MM families. Three genes also had significant rare variant burden in 733 sporadic MM cases compared with 935 control individuals: IRF8 (P = 1.0 × 10−6), EOMES (P = 6.0 × 10−6) and BTNL2 (P = 2.1 × 10−3). Together, our results implicate six genes in MM risk, provide support for genetic pleiotropy between LN subtypes and demonstrate the utility of sequencing genetically enriched cases to identify functionally relevant variants near GWAS loci.

Abstract Background: Lipid traits have been inconsistently linked to risk of non-Hodgkin lymphoma (NHL). We examined the association of genetically predicted lipid traits with risk of diffuse large B-cell lymphoma (DLBCL), chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and marginal zone lymphoma (MZL) using Mendelian randomization (MR) analysis. Methods: Genome-wide association study data from the InterLymph Consortium were available for 2,661 DLBCLs, 2,179 CLLs, 2,142 FLs, 824 MZLs, and 6,221 controls. SNPs associated (P < 5 × 10−8) with high-density lipoprotein (HDL, n = 164), low-density lipoprotein (LDL, n = 137), total cholesterol (TC, n = 161), and triglycerides (TG, n = 123) were used as instrumental variables (IV), explaining 14.6%, 27.7%, 16.8%, and 12.8% of phenotypic variation, respectively. Associations between each lipid trait and NHL subtype were calculated using the MR inverse variance–weighted method, estimating odds ratios (OR) per standard deviation and 95% confidence intervals (CI). Results: HDL was positively associated with DLBCL (OR = 1.14; 95% CI, 1.00–1.30) and MZL (OR = 1.09; 95% CI, 1.01–1.18), while TG was inversely associated with MZL risk (OR = 0.90; 95% CI, 0.83–0.99), all at nominal significance (P < 0.05). A positive trend was observed for HDL with FL risk (OR = 1.08; 95% CI, 0.99–1.19; P = 0.087). No associations were noteworthy after adjusting for multiple testing. Conclusions: We did not find evidence of a clear or strong association of these lipid traits with the most common NHL subtypes. While these IVs have been previously linked to other cancers, our findings do not support any causal associations with these NHL subtypes. Impact: Our results suggest that prior reported inverse associations of lipid traits are not likely to be causal and could represent reverse causality or confounding.

Abstract Background Recent genome wide association studies (GWAS) of Hodgkin lymphoma (HL) have identified several associations at both HLA and non-HLA loci. However, much of HL heritability remains unexplained. Methods To identify novel risk loci, we performed a meta-analysis of 3 HL GWAS including a total of 1,810 cases and 7,879 controls. Results were replicated in an independent set of 1,163 cases and 2,580 controls, for a total of 3,097 and 11,097 cases and controls combined, respectively. participants in discovery and replication stages were of European descent. quality control and imputation we conducted a meta-analysis addressing 1,004,829 variants (λ= 1.10, λ1000= 1.03). Associations between SNP genotypes and HL risk were evaluated under a log-additive model of inheritance adjusting for sex, study center and significant principal components to control for population stratification. We performed an analysis with all HL cases and then conducted stratified analyses by histological subtype (classical, nodular sclerosis and mixed cellularity), age at diagnosis (nodular sclerosis among those diagnosed at 15- 35 years in all studies, and those diagnosed at 35 and older years in the European Study only) and EBV tumor status (negative and positive). We then used a bioinformatic approach (FunciSNP) to identify potential functional variants associated with HD risk correlated with risk loci of interest. We extracted publically available ENCODE data on biofeatures to identify potential functional motifs associated with the index SNP or correlated SNPs. Finally, we measured expression levels of the two alternative mRNA transcripts in lymphoblastoid cell lines (LCLs) from 49 post-therapy HL patients and 25 unaffected controls. RT-PCR was carried out in triplicate. Relative expression levels were calculated relative to TBP as housekeeping gene. Linear models were used to assess correlation between genotype and TCF3expression levels. Results The meta-analysis identified a novel susceptibility variant at chromosome 19p13.3 (rs1860661) associated with HL risk (Odds Ratio [OR]= 0.78, P=2.0*10-8, I2=0%). variant is located in intron 2 of TCF3 (also known as E2A), a regulator of B- and T-cell lineage commitment. was also significantly associated with HL (OR= 0.85, P=0.002) in the replication series of 1,281 cases and 3,218 controls. the combined analysis consisting of the discovery and replication sets, rs1860661was strongly associated with HL (OR=0.81,=3.5*10-10), with no evidence of heterogeneity between contributing studies (Phom=0.41, I2=0%). The number of G alleles defined by rs1860661 was significantly associated with a reduced risk of each HL subtype except EBV positive HL. rs1860661 and two correlated SNPs, rs10413888 (r2=0.90) and rs8103453 (r2=0.89) identified by FunciSNP analysis map in or near marks of open chromatin and in DNAse hypersensitivity sites in TCF3 in CD20+ B cell lines., the protective minor alleles of these SNPs as defined by the G-G-G haplotype map to the binding sites for ZBTB7a (rs10413888 and rs1860661) and (rs8103453) transcription factors, likely improving the binding efficiency to the sites which may result in increased transcription rates of TCF3. TCF3 is encoded by two alternative transcripts (E12 and E47). Higher expression levels of TCF3-E47, whose transcription start site is located close to rs1860661, was associated with the rs1860661-G allele in controls (P=0.02), but not in HL patients (P=0.22). Conclusion/Discussion TCF3 is essential for the commitment of lymphoid progenitors to both B-cell and T-cell lineage development. A molecular and phenotypic hallmark of classical HL is the loss of the B-cell phenotype in HRS cells, including lack of demonstrable B-cell receptor and most B-cell specific markers such as CD19 or CD20. HRS cells have a low level of TCF3, particularly homodimers of the isoform E47, due to expression of the ABF-1 and ID2 inhibitors that bind to TCF3. Thus, higher TCF3 levels in HRS precursor cells may lead to enhanced retention of the B cell phenotype, thereby conferring a protective effect. These data suggest a link between the 19p13.3 locus including TCF3 and HL risk, indicating that TCF3 could be relevant to HL etiology and pathogenesis. Disclosures: Link: Genentech: Consultancy; Millenium: Consultancy; Pharmacyclics: Consultancy; Spectrum: Consultancy.

Abstract Introduction Multiple myeloma (MM) is a result of a malignant transformation of plasma cells that is preceded by the presence of an asymptomatic clonal plasma cell expansion, a condition referred to as monoclonal gammopathy of undetermined significance (MGUS). We and others have shown familial aggregation of MM and MGUS. Evidence from epidemiologic, family and genome-wide association studies (GWAS) suggests a genetic component underlying MM etiology. GWAS have successfully established 17 common genetic risk loci for MM to date and recently, rare inherited susceptibility variants in the LSD1 / KDM1A and USP45 genes were identified in familial MM / MGUS kindreds. Family-based approaches may be used to elucidate genetic variation contributing to familial MM. Genetic linkage analysis has historically been used to detect the chromosomal location of disease genes. The objective of this study was to conduct a linkage analysis of MM / MGUS families to identify genomic regions for MM / MGUS. Methods Linkage analysis was performed on whole exome sequencing (WES) data generated from germline DNA extracted from peripheral blood from MM / MGUS families ascertained at four sites, of which 79 were selected with 2 or more affected members with any combination of MM or MGUS. Whole exome capture was performed using Agilent SureSelect 38 Mb paired end sequencing and ran on Illumina HiSeq 2000s/2500s; standard analyses for alignment (GRCh37) and quality control were conducted. The Genome Analysis Toolkit's (GATK) HaplotypeCaller in per-sample mode was used to jointly call all the samples together. Quality control included removing variants that had <75% call rate, <8x coverage, or minor allele frequency <0.01 (1KGenomes). The jointly called WES data from the 79 families consisted of 230 individuals, including 119 MM, 93 MGUS and 18 unaffected relatives. Additional early onset (defined as age <60) MM cases (n=400), sporadic MM cases (n=879) and controls (n=2389) were also included in the joint calling to be used for follow-up of linkage regions. We included independent variants defined as linkage disequilibrium (LD) r2 <0.05 using both PLINK 1.07's LD based variant pruner and also MERLIN's pairwise r2 cluster marker-marker modeling to ensure variant independence. After quality control and LD filtering: 12,946 variants remained. We conducted multipoint linkage analysis using MERLIN (Multipoint Engine for Rapid Likelihood Inference using the Lander-Green approach) and considered both non-parametric and parametric (dominant and recessive) models to test for co-segregation of chromosomal regions with MM / MGUS. A logarithm of the odds (LOD) score greater than 3.3 was significant evidence for linkage. Within the same families, we interrogated any suggestive regions by performing gene-level association tests using the PEDGENE R package to calculate burden and kernel statistics, accounting for family relationships. Follow-up of these genes in the early onset and sporadic MM cases and controls and interrogation of single variants driving the associations of these genes is ongoing. Results Among the 79 families, 28 consisted of two or more MM, 41 had two or more MM and MGUS cases and 10 had two or more MGUS. Analyses of the 79 MM / MGUS families identified chromosome 6 at 123420001-149070000 BP region, with evidence for linkage by the non-parametric model (LOD score=3.3) and supportive evidence for linkage by the dominant parametric model (LOD=2.5) (Figure). The chromosome 6 region, which is outside the HLA-region, contains a total of 72 genes. Using gene-level association tests, we identified four genes within the region (MOXD1, TRDN, ADAT2, LTV1) that were associated within MM / MGUS in the family cases (p<0.05). Discussion We found evidence for a locus on chromosome 6 linked to MM / MGUS. Four genes in this region may be associated with MM / MGUS in these families. Follow-up of these genes/variants in high-risk individuals (early-onset / age <60), sporadic cases and controls will help elucidate genetic mechanisms contributing to familial MM / MGUS. In addition, we demonstrate the benefit of applying a linkage analysis framework to familial WES data for discovery of genomic regions influencing MM / MGUS. Disclosures Dumontet: Roche: Research Funding; Merck: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria; Sanofi: Honoraria. Kumar:AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Roche: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract We previously identified five single nucleotide polymorphisms (SNPs) at four susceptibility loci for diffuse large B-cell lymphoma (DLBCL) in individuals of European ancestry through a large genome-wide association study (GWAS). To further elucidate genetic susceptibility to DLBCL, we sought to validate two loci at 3q13.33 and 3p24.1 that were suggestive in the original GWAS with additional genotyping. In the meta-analysis (5662 cases and 9237 controls) of the four original GWAS discovery scans and three replication studies, the 3q13.33 locus (rs9831894; minor allele frequency [MAF] = 0.40) was associated with DLBCL risk [odds ratio (OR) = 0.83, P = 3.62 × 10−13]. rs9831894 is in linkage disequilibrium (LD) with additional variants that are part of a super-enhancer that physically interacts with promoters of CD86 and ILDR1. In the meta-analysis (5510 cases and 12 817 controls) of the four GWAS discovery scans and four replication studies, the 3p24.1 locus (rs6773363; MAF = 0.45) was also associated with DLBCL risk (OR = 1.20, P = 2.31 × 10−12). This SNP is 29 426-bp upstream of the nearest gene EOMES and in LD with additional SNPs that are part of a highly lineage-specific and tumor-acquired super-enhancer that shows long-range interaction with AZI2 promoter. These loci provide additional evidence for the role of immune function in the etiology of DLBCL, the most common lymphoma subtype.

AbstractEpidemiologic studies show an increased risk of non‐Hodgkin lymphoma (NHL) in patients with autoimmune disease (AD), due to a combination of shared environmental factors and/or genetic factors, or a causative cascade: chronic inflammation/antigen‐stimulation in one disease leads to another. Here we assess shared genetic risk in genome‐wide‐association‐studies (GWAS).Secondary analysis of GWAS of NHL subtypes (chronic lymphocytic leukemia, diffuse large B‐cell lymphoma, follicular lymphoma, and marginal zone lymphoma) and ADs (rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis). Shared genetic risk was assessed by (a) description of regional genetic of overlap, (b) polygenic risk score (PRS), (c)"diseasome", (d)meta‐analysis.Descriptive analysis revealed few shared genetic factors between each AD and each NHL subtype. The PRS of ADs were not increased in NHL patients (nor vice versa). In the diseasome, NHLs shared more genetic etiology with ADs than solid cancers (p = .0041). A meta‐analysis (combing AD with NHL) implicated genes of apoptosis and telomere length.This GWAS‐based analysis four NHL subtypes and three ADs revealed few weakly‐associated shared loci, explaining little total risk. This suggests common genetic variation, as assessed by GWAS in these sample sizes, may not be the primary explanation for the link between these ADs and NHLs.

Abstract Lung cancer (LC) is the primary cause of cancer-related deaths in the United States. Whereas smoking and other environmental factors strongly increase LC risk, multiple genetic variants also contribute to risk in smokers. Furthermore, among smokers, some families have been identified with an abnormally high prevalence of LC, suggesting that unknown genetic factors can greatly increase LC risk in smokers. The typically short survival after LC diagnosis impedes collection of detailed genotypic information on any single large family pedigree, impairing the identification of putative high-risk factors. Therefore, the Genetic Epidemiology of LC Consortium has collected epidemiological and genetic data from a number of families with high numbers of LC cases from eight different sites across the US. In this study, we have obtained whole exome sequences (WES) from 290 members of 28 families, including 66 LC cases. We used a gene-based approach to allow for the possibility that different families may contain different variants of the same gene. Variants were filtered for i) allele frequency, ii) functional effect using combined annotation-dependent depletion (CADD), and, iii) affecting a gene with either a known or suspected role in cancer. We further selected variants based on their segregation in family pedigrees in a pattern consistent with a large effect on LC risk. Candidate LC risk genes were then identified as those represented in at least two families by the same or different variants. We further culled the list of genes by requiring the presence of at least one rare, functional variant enriched in the WES of 1060 cases relative to 899 controls from the Transdisciplinary Research on Cancer of the Lung consortium. This analysis narrowed our results to two genes, one being E2A, a member of the E family of bHLH transcription factors. Whereas loss-of-function mutations in E2A drive lymphoid cancers, the E2A protein also participates in an oncogenic heterodimer with TWIST1 that promotes the epithelial-mesenchymal transition and is implicated in multiple cancer types. Furthermore, the E2A/TWIST1 heterodimer is the primary TWIST1-containing complex implicated in oncogenesis, and silencing of E2A in KRAS-mutant non-small cell lung cancer (NSCLC) cell lines results in oncogene-stimulated senescence and apoptosis. Our data identified three distinct E2A variants present in all 10 sequenced LC cases in the 5 families in which those variants are found. Finally, two of these E2A variants are located only 57 nucleotides from each other, immediately adjacent to sequences encoding a transcription activation domain, suggesting that both variants alter the same specific protein function. These data identify E2A variants as likely high risk factors for LC in smokers and validate our general approach for identifying genetic factors with a large impact on LC risk. Citation Format: Claudio W. Pikielny, Anthony M. Musolf, Mariza de Andrade, Diptasri Mandal, Colette Gaba, Ping Yang, yafang Li, Ming You, Richard Wilson, Elena Y. Kupert, Marshall W. Anderson, Ann G. Schwartz, Susan M. Pinney, Ambrose I. Granizo-Mackenzie, Yanhong Liu, Ramaswamy Govindan, James McKay, Rayjean Hung, John K. Field, David C. Christiani, Joan E. Bailey-Wilson, Christopher I. Amos. Familial studies identify variants in the E2A transcription factor as putative risk factors for lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-053.

Abstract Background: We previously identified 5 SNPs at 4 susceptibility loci for diffuse large B-cell lymphoma (DLBCL) in individuals of European ancestry through a large genome-wide association study (Cerhan et al, Nat Gen 2014;46:1233-8); however, much of the heritability remains unexplained. To further elucidate genetic susceptibility to DLBCL, we sought to validate 2 loci at 3q13.33 and 3p24.1 that were suggestive in the original report. Methods: We selected two SNPs rs9831894 (3q13.33) and rs6773363 (3p24.1) for de novo replication from studies at the Mayo Clinic, MD Anderson, and Memorial Sloan Kettering. Logistic regression was used to estimate odds ratios (ORs), using a log-additive model, and adjusting for age, gender and significant eigenvectors for each genotyping center separately. For both SNPs, we then conducted a meta-analysis of all replication studies (n=3) and our original GWAS studies (n=4), encompassing in total 5662 cases and 9237 controls for rs9831894, and 5510 cases and 12,817 controls for rs6773363. Meta-analysis was conducted using the fixed-effects inverse variance method based on the β estimates and standard errors from each study. Results: In a meta-analysis of the 4 original GWAS scans (3856 cases and 7666 controls), rs9831894 (MAF=0.40) was associated with DLBCL risk (OR=0.84, P=4.52 x 10-9) and this replicated in a meta-analysis of the 3 studies with de novo genotyping (OR=0.80, P=4.17 x 10-5); the overall meta-analysis showed a strong association with DLBCL risk (OR=0.83, P=3.62 x 10-13). This locus maps near a plausible candidate gene, CD86, a protein coding gene and a member of the immunoglobulin superfamily that encodes a type I membrane protein. Binding of CD86 with cytotoxic T-lymphocyte-associated protein 4 negatively regulates T-cell activation and diminishes the immune response, while binding of CD86 with CD28 antigen is a costimulatory signal for activation of the T-cell. In a meta-analysis of the same 4 GWAS scans, rs6773363 (MAF=0.45) was tentatively associated with DLBCL risk (OR=1.17, P= 3.68 x 10-7) and this replicated in a meta-analysis of the studies with de novo genotyping (OR=1.27, P=3.78 x 10-7); the overall meta-analysis showed a strong association with DLBCL risk (OR=1.20, P=2.31 x 10-12). This locus also maps near a plausible candidate gene, eomesodermin (EOMES), a transcription factor crucial for embryonic development of the central nervous system and also putatively involved in T-cell differentiation in viral infection defense. Conclusion: In this follow-up analysis of our initial GWAS, we have identified two additional loci associated with risk of DLBCL, the most common lymphoma subtype. These loci provide additional evidence for the role of immune function in the etiology of DLBCL. Citation Format: Geffen Kleinstern, Michelle Hildebrand, Vijai Joseph, Sonja I. Berndt, Hervé Ghesquières, James McKay, Sophia S. Wang, Alexandra Nieters, David Cox, Alain Monnereau, Angela R. Brooks-Wilson, Qing Lan, Mads Melbye, Rebecca D. Jackson, Lauren R. Teras, Mark P. Purdue, Claire M. Vajdic, Demetrius Albanes, Anne Zeleniuch-Jacquotte, Simon Crouch, Yawei Zhang, Susan L. Slager, Xifeng Wu, Karin E. Smedby, Gilles Salles, Christine F. Skibola, Nathaniel Rothman, Stephen J. Chanock, James R. Cerhan. Inherited variants at 3q13.33 and 3p24.1 influences risk of diffuse large B-cell lymphoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 221.

Abstract A growing number of loci within the human leukocyte antigen (HLA) region have been implicated in non-Hodgkin lymphoma (NHL) etiology. Here, we test a complementary hypothesis of “heterozygote advantage” regarding the role of HLA and NHL, whereby HLA diversity is beneficial and homozygous HLA loci are associated with increased disease risk. HLA alleles at class I and II loci were imputed from genome-wide association studies (GWAS) using SNP2HLA for 3,617 diffuse large B-cell lymphomas (DLBCL), 2,686 follicular lymphomas (FL), 2,878 chronic lymphocytic leukemia/small lymphocytic lymphomas (CLL/SLL), 741 marginal zone lymphomas (MZL), and 8,753 controls of European descent. Both DLBCL and MZL risk were elevated with homozygosity at class I HLA-B and -C loci (OR DLBCL = 1.31, 95% CI = 1.06–1.60; OR MZL = 1.45, 95% CI = 1.12–1.89) and class II HLA-DRB1 locus (OR DLBCL = 2.10, 95% CI = 1.24–3.55; OR MZL = 2.10, 95% CI = 0.99–4.45). Increased FL risk was observed with the overall increase in number of homozygous HLA class II loci (P trend &lt; 0.0001, FDR = 0.0005). These results support a role for HLA zygosity in NHL etiology and suggests that distinct immune pathways may underly the etiology of the different NHL subtypes. Significance: HLA gene diversity reduces risk for non-Hodgkin lymphoma. Cancer Res; 78(14); 4086–96. ©2018 AACR.