Asmann, Yan W.

Abstract Monoclonal gammopathies, including multiple myeloma (MM), represent a group of plasma cell (PC) disorders that comprise of mostly incurable hematopoietic malignancies with an increasing incidence in the US. Previous epidemiological studies demonstrated a 2-3 fold higher incidence of monoclonal gammopathy of undetermined significance (MGUS) and a similarly higher incidence of MM along with a ~4-year younger age of onset among African Americans (AA) compared to European Americans (EAs) (Fonseca, Leukemia, 2017). This suggests a possible ancestral-associated genetic predisposition of AAs to the development of monoclonal gammopathies (Landgren, Blood, 2006). When access to care is equal, AAs have a better overall survival compared to EAs suggesting that AAs may have a genetic predisposition that renders them better responders to treatment or have more favorable cytogenetic subtypes of MM (Waxman, Blood, 2010). Previous efforts to understand this disparity have relied on self-reported race rather than genetic ancestry. We hypothesize that quantifying genetic ancestry is necessary to fully understand the genetic mechanisms of racial disparities of monoclonal gammopathies. We describe our study of 881 patients with monoclonal gammopathies who had undergone uniform testing to identify MM-specific cytogenetic abnormalities. DNA from the bone marrow samples was genotyped on the Precision Medicine Research Array. Biogeographical ancestry was assessed using the Geographic Population Structure Origins tool, which provides the ancestral breakdown of 36 admixture components for each individual representing different geographic regions and outputs admixture proportions corresponding to those ancestries (Elhaik, Nat Commun, 2014). We evaluated whether an increase in the percentage of African Ancestry altered the odds of any primary cytogenetic abnormality. We demonstrated that a 10% increase in the percentage of African ancestry was associated with a 6% increase in the odds of detecting either an t(11;14), t(14;16) or t(14;20) (odds ratio = 1.06, 95% CI: 1.02 - 1.11; p-value = 0.05). Although we observed an increase in the odds of each of the individual translocations with 10% increase in African ancestry, l t(11;14) (OR=1.03) t(14;16) (OR=1.11) and t(14;20) (OR=1.10), these three abnormalities were combined to achieve greater statistical power. The differences in cytogenetic abnormalities were most striking among individuals with ≥80.0% African (n=120) compared to individuals with <0.1% African ancestry (n=235). Using these cutoff values comparing the ≥80.0% and <0.1% African ancestry cohorts, there was a higher prevalence of t(11;14), t(14;16) and t(14;20) (51% vs. 33%, respectively, p-value=0.008) and a lower prevalence of trisomies (with or without IgH translocations) (48.3% vs. 61.3%, respectively, p-value=0.066). In addition, the ≥80% African ancestry cohort also displayed a slightly lower prevalence of monosomy 13/13q deletion compared to the cohort with <0.1% African ancestry (34.2% vs. 38.7%, respectively, p-value=0.021). Further, we observed a significantly higher prevalence in the proportion of females with monoclonal gammopathies among the ≥80% African ancestry cohort compared to the cohort with <0.1% African ancestry (56.7% vs. 42.1%, respectively, p-value=0.028). We complement our past studies that relied on self-reported race and characterized patients' demographic and uniformly collected cytogenetic data in relation to genetically defined African ancestry. We demonstrate the prevalence of having one of three specific subtypes (i.e., t(11;14), t(14;16), or t(14;20)) was significantly higher in those with highest (≥80%) compared to lowest (<0.1%) African ancestry demonstrating African ancestry is associated with a subtype distribution of MM that differs from non-African individuals. These differences were only revealed after analysis of individuals with the highest and lowest percentage of African ancestry. Since 38.3% of the individuals with ≥80% African ancestry have the favorable t(11;14) translocation (in comparison to 26.8% of <0.1% African individuals), this finding may explain improved survival with equal access to care. Understanding the genetic mechanisms of this disparity is a fundamental step to understanding differences in incidence and outcomes of patients with monoclonal gammopathies. Disclosures Elhaik: DNA Diagnostics Center: Consultancy. Baird:DNA Diagnostics Center: Employment. Cerhan:Jannsen: Other: Scientific Advisory Board; Nanostring: Research Funding; Celgene: Research Funding. Stewart:Amgen Inc., BMS, Celgene, Takeda, Roche, Seattle Genetics, Janssen, Ono: Consultancy; Amgen Inc., Celgene, Roche, Seattle Genetics: Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Kumar:Novartis: Research Funding; KITE: 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; Takeda: Membership on an entity's Board of Directors or advisory committees; AbbVie: 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; 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; 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; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Research Funding.

Abstract Abstract 709 Background: A recent analysis of merged genome-wide and candidate gene genotypes in VTE cases and controls identified multiple tag SNPs that were strongly associated with VTE. Objective: To identify rare and/or novel functional variants by sequencing the implicated genes. Methods: Cases (n=1488) were Mayo Clinic European-American patients of non-Hispanic ancestry with objectively-diagnosed VTE in the absence of active cancer, venous catheter or antiphospholipid antibodies. Controls (n=1439) were Mayo Clinic outpatients without VTE who were frequency-matched on case age, gender, race, MI/stroke status and state of residence. For this analysis, we selected a subset of these cases and controls for sequencing to take advantage of the joint configuration of two ABO SNPs of primary interest, rs8176719 (ABO exon 6 deletion determining type O blood group) and rs2519093 (ABO intron 1 tag SNP), which were previously shown to be strongly associated with VTE (p=5.7E-12 and p=3.0E-16, respectively). We randomly sampled 82 cases and 14 controls within 3 of the 9 potential allele frequency cells (Figure). The rs8176719 alleles are -−/−- (double deletion is the common allele), –/G, and G/G (the rare allele). The rs2519093 alleles are GG (G is the common allele), AG, and AA (A is the rare allele). For each SNP, the genotypes are represented as 0, 1, or 2 copies of the minor allele. We represented the joint allelic configuration of the two SNPs with the number of copies of the rs8176719 given first as 0/0 (both with 0 copies of the minor allele), 0/1, 0/2 (0 copies of the rs8176719 SNP), 1/0 (1 copy of the rare rs8176719 SNP), 1/1/, and 1/2, and 2/0 (2 copies of the rare allele for the rs8176719 SNP and 0 copies of the rare allele of the rs2519093 SNP), 2/1, and 2/2. From the Figure one observes discrepancies between cases and controls at the 0/0, 1/1 and 2/2 combinations. We randomly sampled from these three combinations, taking one third of the case series. For each SNP, we had 28 cases with 0/0 copies of the rare allele, 27 cases with 1/1 copies of the rare allele; and 27 cases with the combination of 2/2 copies of the rare allele. We compared these 82 cases with 14 controls that do not have any of these combinations. Sixteen genes were selected for deep sequencing, including 5 genes harboring SNPs significantly associated with VTE (F5, SLC19A2, ABO, NME7, ATP1B1), 10 genes with SNPs marginally associated with VTE (C1orf114, KLKB1, SELP, F11, SCUBE1, PRKCB1, CD44, ITPR1, GFRA1, BLZF1), and CYP4V2 which reportedly confounds F11 and KLKB1. Agilent SureSelect probes were designed to capture and enrich the ∼2 Mb genomic regions of these 16 genes. Samples were multiplexed (12-plex) and sequenced using Illumina HiSeq 2000. The sequence reads were aligned to the human genome build 36 using Burrows-Wheeler Aligner, and the single nucleotide variants (SNVs) and small INDELs were called using SNVMix and GATK, respectively. For this analysis, novel ABO SNVs were tested for an association with VTE using age-, sex-adjusted logistic regression and Fisher's Exact Test. Results: 98% of the targeted regions were sequenced with > 20X coverage. On average, ∼2500 SNVs and ∼200 INDELs were detected in each sample. Fifteen novel SNVs in intron 6 and 3' of the ABO gene were associated with VTE (p<E-06) and belonged to 3 distinctive LD blocks; none were in LD with the coding or tag ABO SNPs (rs8176719; rs2519093). SNVs inside the middle LD block at the 3' of ABO are located within an enhancer and promoter histone marked with putative transcription factor binding sites. In addition, strong evidence from both ENCODE and dbEST support the middle LD block as lying within a novel transcript, probably an extension of the 3' of ABO. In addition, we discovered a novel, significant, protective, frame-shifting single base (G) deletion at ABO chr9:135120877. Conclusion: Novel ABO functional variants that are associated with VTE were identified by deep sequencing. Disclosures: Heit: Daiichi Sankyo: Consultancy, Honoraria.

Abstract Abstract 1242 Background: While bivariate interactions between Factor V Leiden, prothrombin G20201A and hereditary deficiency of antithrombin, protein C and protein S compound VTE risk, whether other gene-gene interactions are associated with VTE is largely unknown. Objective: To test novel gene-gene interactions for an association with VTE. Methods: Genome-wide scan (Illumina 660Q [557,112 SNPs]) and candidate gene (12,551 SNPs in 764 genes within the anticoagulant, procoagulant, fibrinolytic and innate immunity pathways) genotypes from 1270 non-Hispanic adults of European ancestry with objectively-diagnosed VTE (cases; no cancer, venous catheter or antiphospholipid antibodies) and 1302 controls (frequency-matched on case age, gender, race, MI/stroke status) were merged and imputed to about 2.5 million SNPs with MACH using HapMap Phase 2 (60 CEU). We tested all pairwise SNP-SNP interactions using a 2-stage procedure (fast epistasis in the first stage and age, gender, MI/stroke status and state of residence adjusted analysis using logistic regression in the second stage). Results: The mean ± standard deviation case and control ages were 54.4 ± 16.2 and 55.6 ± 15.8 years, respectively and 51% were female. Among 2.299×10^12 pairwise interactions tested, 900 million reached a p-value<E-06 and were be tested in the second stage. Of these, 2417, 278 and 22 SNP-SNP interactions reached p-values ≤1E-9, 1E–10, and1E-11 respectively; 121 SNP-SNP interactions reached the Bonferroni statistical significance threshold (≤5.555659E-11). The gene-gene pairwise interactions with p-values≤1E-11 were: (1) chromosome 3 open reading frame 70 (C3orf70) – short-chain dehydrogenase/reductase (SDR family) member 4 (DHRS4), (2) 1-acylglycerol-3-phosphate O-acyltransferase 4 (AGPAT4) - NIMA (never in mitosis gene a)-related kinase 6 (NEK6), and (3) TatD DNase domain containing 2 (TATDN2) – WAS protein family, member 3 (WASF3). No gene function data are available for C3orf70. DHRS4 encodes for a 3-ketosteroid reductase important for active steroid hormone regulation, AGPAT4 is important for signal transduction and lipid biosynthesis, NEK6 encodes for Nek6 which downregulates p53-induced cancer cell senescence, TATDN2 encodes a deoxyribonuclease, WASF3 encodes for a member of the Wiskott-Aldrich syndrome protein family which are key regulators of signal transduction and cytoskeletal reorganization in hematopoietic cells. Conclusion: Pairwise gene-gene interaction analyses suggest potential associations between VTE and novel genes for future testing in replication studies. Disclosures: Heit: Daiichi Sankyo: Consultancy, Honoraria.

Abstract Background Relatively few studies have evaluated the role of host (germline) genetics in predicting outcome in diffuse large B-cell lymphoma (DLBCL), and of these, most were small candidate gene studies without replication and/or from the pre-R-CHOP era. We report the first genome-wide association study (GWAS) of outcome in DLBCL patients treated with rituximab and anthracycline-based chemotherapy. We used two independent studies for discovery, and validated the results in two additional independent studies. Methods In the discovery phase, we conducted a meta-analysis of two GWAS studies of DLBCL patients treated with rituximab and anthracycline-based chemotherapy, the first study as part of the prospective clinical trials of the LNH2003B program (N=540) of the Lymphoma Study Association (LYSA) and the second as part of the Molecular Epidemiology Resource (N=316), a prospective observational study from the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE). Genotyping for the GWAS was performed using the Illumina 610 (LYSA) and 660 Quad (SPORE) BeadArrays (Illumina, San Diego, USA). HapMap2 was used to impute additional SNPs. Association testing was conducted assuming a log-additive genetic model and using Cox regression analyses. We calculated the hazard ratios (HRs) and 95% CIs for event-free survival (EFS), with adjustment for age, sex and age-adjusted IPI. The two studies were then combined using the fixed effects inverse variance method based on the beta estimates and standard errors from each study. From the meta-analysis, 89 SNPs were selected for validation, based on a minor allele frequency (MAF) >0.05 and LD pattern for multiple SNPs from the same region. Validation cohorts were an independent set of DLBCL patients from the SPORE (SPORE-2, N=366) and DLBCL patients from the prospective GOELAMS 075 phase III study (N=294), and validation genotyping was conducted using the Illumina VeraCode platform. We then conducted a meta-analysis of the 4 studies. We report SNPs with both a statistical significance of p<5x10-6 and effect sizes in the same direction across the 4 studies in the meta-analysis. For the latter SNPs, we also report the results for overall survival (OS), adjusted for age, sex and age-adjusted IPI. Results In the final meta-analysis of all four studies for the prediction of EFS, the top SNP was rs7765004 (MAF=0.30; HR=1.45; p=9.7 x 10-8) in the 6q21 region, which is near the MARCKS (myristoylated alanine-rich protein kinase C substrate) and HDAC2 genes. MARCKS encodes for a protein involved in cell mobility, while HDAC2 belongs to the histone deacetylase family. Additional SNPs from this region included rs6918103 (MAF=0.25; HR=1.38; p=7.6 x 10-6), which marks an EZH2 histone binding site, and rs11969684 (MAF=0.12; HR=1.43; p=3.3 x 10-5). The next hit was rs7712513 (MAF=0.31; HR=1.40; p=3.2 x 10-7), which is near arginine-fifty homeobox pseudogene 1 (ARGFXP1) on chromosome 5q23.2. This was followed by a region on chromosome 8q13.3, with the SNP rs9298183 (MAF=0.29; HR=1.41; p=1.4 x 10-6) from LOC100132891 showing the strongest association, along with 3 other SNPs from this region (rs2035376, rs6472637, and rs1600797; all p<5 x 10-6). These SNPs are near the musculin gene (MSC) a transcriptional repressor of E2A frequently silenced by promoter methylation in DLBCL. Finally, rs2253986 (MAF=0.27; HR=1.39; p=3.4 x 10-6) at chromosome 16q12.2, along with rs2253971 and rs6499810, were associated with EFS. These SNPs are located in CES5A (carboxylesterase 5A), which encodes a member of the carboxylesterase family. The family members are responsible for the hydrolysis or transesterification of various xenobiotics. The top SNPs associated with EFS also predicted OS, including rs7765004 (HR=1.41; p=1.2 x 10-5), rs7712513 (HR=1.39; p=8.4 x 10-6), rs9298183 (HR=1.36; p=3.5 x 10-5), and rs2253986 (HR=1.35; p=5.2 x 10-5). Conclusions Although our top SNPs on did not reach genome-wide significance (p<5x10-8), they showed clear consistency across the four large prospective studies and predicted both EFS and OS independently of IPI. The strongest findings were from loci on 6q21, 8q13.3, 5q23.2, and 16q12.2, and several genes from these regions are involved in methylation and xenobiotic metabolism, supporting a role for host genetic variation in these pathways in DLBCL prognosis. Disclosures: No relevant conflicts of interest to declare.

Abstract Gene expression profiling has shown that diffuse large B-cell lymphoma (DLBCL) clusters into three major subtypes based on similarity in expression patterns to their cell of origin (COO): germinal center B-cell-like (GCB-DLBCL), activated B-cell-like DLBCL (ABC-DBLCL) and primary mediastinal B-cell lymphoma (PMBCL). These subtypes of DLBCLs are associated with distinctly different overall survival rates after standard immunochemotherapy. However, clinical and prognostic heterogeneity remains within COO subsets and strategies are needed to further stratify patients to identify and target high-risk subsets. A comprehensive genomic analysis of COO on a clinically defined set of DLBCL cases has not been performed and the aim of this study was to use whole-exome sequencing (WES) data from 58 paired tumor-normal DLBCL samples to assess association of known DLBCL genomic alterations with cell COO as well as for identification of novel and relevant genetic biomarkers. To investigate genomic alterations associated with DLBCL subsets, we analyzed WES and genome wide copy number data from 58 paired tumor-normal DLBCL tumors. Gene expression profiling or Hans classification was performed to determine DLBCL COO subtype; 31 patients were classified as GCB and 27 as non-GCB. The WES data were used to 1) assess the association of known DLBCL genomic alterations with COO, and 2) identify novel alternations associated with COO. Statistical analysis was performed and the data were ranked by significance (p≤ 0.05) within each DLBCL subtype. In total, 45 genomic abnormalities were analyzed for their association with either GCB, non-GCB or both. Mutations in CREBBP, EZH2, MEF2B, FOXO1 and REL have also been reported as GCB driver mutations and we observed GCB patients with these mutations, but the mutation clustering was not wholly associated with GCB. MYC double-hits were exclusively found in the GCB-subtype group. For the non-GCB cases we found that mutations in MAP2K3 and MYD88 were significantly associated with this subtype(p< 0.05). In addition to mutational patterns, we identified several copy number alterations (CNA) across both groups. Chromosomal losses in GCB patients were found at chromosomes 10q11.21-10q24.23, 4q12-4q35.2, 3q12.1-3q29, 4p12-4p16.3, 10p11.21-10p15.3, and 14q11.2-14q24.3 whereas gains were localized to 7q11.1-7q36.3, 7p11.2-7p22.3, and 1q21-1q32.1 (p < 0.05). No CNA was observed to directly associate with non-GCB patients, however, a loss at 9p21 and gains at 9q24.1 and 18q21.33 trended with the non-GCB subtype, supporting previous reports. Loss at 10q23.31 or a gain in 2p13-2p12 have been reported as being specific for GCB and our data confirmed the association of 10q23.31 with GCB while a gain at 2p13-2p12 (REL)was found in both subtypes. To further understand genomic differences between DLBCL subtypes, we evaluated the relative percentage of each genomic feature. 18/45 (40%) were only observed in GCB patients whereas 2/45 (5%) were specific to the non-GCB subtype. The majority (25/45, 55%) overlapped between the two subtypes. Throughout our analysis we noted that 7 non-GCB cases lacked any of the driver mutations analyzed in the study. While all cases carried mutations, they consisted of low frequency mutations that were not specifically associated with COO. 2/7 cases had a gain at 9p24.1 that included CD274 and JAK2. Because 9p24.1 gains have not been fully defined in DLBCL, we reviewed all cases and identified 4 (7%) with a 9p24.1 gain, 3 of which were non-GCB and 1 GCB. One non-GCB case was EBV+ and none of the cases showed evidence of PMBCL. Of the 9p24.1 cases, three had RNAseq data available and we found that PDL1 and JAK2 expression was elevated (12 fold p< 0.01 and 7 fold p< 0.01, respectively) when compared to the 9p24.1 normal cases (n=32). While outcome was not the focus of the study, we did note that 6/7 cases that lacked driver mutations achieved event free survival at 24 months (EFS24). Taken together, this analysis has further characterized the genetic profile of each COO subtype and has identified novel GCB CNAs which require independent replication. Additionally, we identify a subgroup of non-GCB DLBCL patients that do not harbor known driver mutations and require further genomic study to better resolve the biology of these tumors. Together, these data provide insight on the genetic heterogeneity of DLBCL and identify genetic variants that may inform subtype specific therapy. Disclosures Nowakowski: Morphosys: Research Funding; Celgene: Research Funding; Bayer: Consultancy, Research Funding. Rimsza:NCI/NIH: Patents & Royalties: L.M. Rimsza is a co-inventor on a provisional patent, owned by the NCI of the NIH, using Nanostring technology for determining cell of origin in DLBCL..

Abstract Abstract 2295 Background: While interaction between Factor V Leiden and other VTE risk exposures compound VTE risk, whether other gene-environment interactions are associated with VTE is largely unknown. Objective: To test novel gene-environment interactions for an association with VTE. Methods: Genome-wide scan (Illumina 660Q [557,112 SNPs]) and candidate gene (12,551 SNPs in 764 genes within the anticoagulant, procoagulant, fibrinolytic and innate immunity pathways) genotypes from 1270 non-Hispanic adults of European ancestry with objectively-diagnosed VTE (cases; no cancer, venous catheter or antiphospholipid antibodies) and 1302 controls (frequency-matched on case age, gender, race, MI/stroke status) were merged and imputed to 2.5 million SNPs with MACH using HapMap Phase 2 (60 CEU). We tested pairwise interactions between these SNPS and each of 7 exposures (“stress” [defined as ever hospitalized, ever surgery, ever trauma, ever transfemoral procedure or ever leg paresis], ever surgery, ever neurosurgery, ever orthopedic surgery; and among women, ever pregnant, ≥3 pregnancies and ever oral contraceptives/hormone therapy), adjusted for age, gender, MI/stroke status and state of residence, using logistic regression. Results: The mean ± standard deviation case and control ages were 54.4 ± 16.2 and 55.6 ± 15.8 years, respectively and 51% were female. Among the 7 models, a significant interaction was found between stress and A2BP1 (rs735919, OR=0.2, p=2.8E-07), ever surgery and ABCG8 (rs6709904, OR=0.27, p=2.1E-07), orthopedic surgery and SLC24A3 (rs6081599, OR=0.53, p=2.8E-07), neurosurgery and TBC1D4 (rs17064485, OR=8.5, p=3.5E-07), every pregnant and MICALCL (rs11022321, OR=0.17, p=8.3E-07), >3 pregnancies and KLRB1 (rs2536929, OR=0.31, p=1.1E-07) and ever oral contraceptives/hormone therapy and LINGO2 (rs10969259, OR=2.02, p=4.9E-07). A2BP1 plays a role in RBC shape and size, ABCG8 is associated with severe hypercholesterolemia, SLC24A3 is important for intracellular calcium homeostasis, TBC1D4 participates in cell signaling in CD4+/CD45RO+ memory T-cells, MICALCL is an extracellular signal-regulated kinase 2-binding protein, KLRB1 regulates natural killer cell function, and LINGO2 encodes a leucine-rich repeat signaling protein. Our observed interaction of VTE risk factors with these genes to compound the risk of VTE requires replication in future studies. If confirmed, functional studies will be important to determine the roles of these gene products in the causal pathway to VTE. Conclusion: VTE risk factors and SNPs within several novel genes may interact to compound the risk of VTE. Disclosures: Heit: Daiichi Sankyo: Consultancy, Honoraria.

Abstract Abstract 1814 Multiple myeloma (MM) is an incurable hematologic disorder characterized by the accumulation of plasma cells (PCs) in the bone marrow (BM). Like normal B cells and PCs, MM cells express a distinctive immunoglobulin (Ig) molecular fingerprint resulting from heavy and light chain gene rearrangements and unique mutations introduced by the somatic hypermutation (SHM) process. Detailed examination of the Ig molecule may offer insight into the etiology of B cell malignancies as well as the ontogeny of malignant transformation and ongoing genetic evolution. Because prior studies of the Ig fingerprint of MM cells have been limited in size and scope, our current understanding of MM Ig repertoire is minimal. We used massively parallel sequencing of normal and malignant PCs to interrogate: 1) DNA versus RNA (cDNA) as starting material for studies of Ig repertoire; 2) the Ig repertoire of normal and malignant PCs; and 3) intraclonal heterogeneity in MM. Bone marrow PCs (BMPCs) from an untreated MM patient and from a normal control subject were isolated by magnetic bead separation. DNA and RNA were isolated from purified cells and amplified in a multiplex PCR reaction with primers designed to capture the entire IGHV region including the heavy chain complementarity determining region 3 (HCDR3) necessary for defining clonal cells. Using Roche 454 GS-FLX Titanium chemistry and a GS FLX Titanium Genome Sequencer, over 30,000 sequence reads were obtained from each sample. Following a detailed algorithm to minimize Taq and sequencing related errors and removal of non-Ig or non-productive Ig sequences, the resulting sequences were grouped based on HCDR3 amino acid identity. Each unique HCDR3 represented one member of the repertoire and all the sequences with the same HCDR3 defined the clone size. This measurement showed that there was nearly a five-fold higher level of unique HCDR3s in normal BMPCs when the starting PCR template was DNA vs. cDNA. Thus, DNA is clearly a better starting template for repertoire analysis using deep sequencing methods. Thorough analysis of the MM DNA sample identified PCs expressing 37 different IGHV genes, and predictably, the vast majority of sequence reads (83%) had exact nucleotide identity in the IGHV and HCDR3 region (IGHV3-74 with 18 mutations in the IGHV region) thereby defining the dominant MM clone. There were also ∼5000 additional sequences that either shared an identical HCDR3 region but varied within the IGHV3-74 gene sequence (n=3975) or were identical within the IGHV3-74 sequence yet varied within the HCDR3 (n=862). These sequences were further analyzed, with sequences not present 10 or more times or not found in both the 5' and 3' directions eliminated from further scrutiny. This process revealed a total of 64 putative subclones with single point mutations in the IGHV or HCDR3 region (10–56 copies of each unique sequence representing a total of 1583 clonally related sequences). When we used a more stringent subclone frequency of ≥0.1%, 22 subclones remained. Of note, conventional Ig repertoire analysis would require sequencing ≥1000 colonies to detect MM subclones present at this frequency. Finally, 11 subclones displayed a significant number of IGHV gene nucleotide differences from the dominant MM clone despite exhibiting an identical HCDR3 region. Notably, a number of these distinguishing IGHV gene mutations are silent and thus may preserve the antigenic specificity of these clonally related cells. These subclones failed our frequency criteria but the type and quality of deviations from the clonal sequence suggest that they are not artifact and warrant further investigation. Notably, all of the putative subclones were also found in the MM cDNA cohort providing further evidence as genuine subclones. The very large number of identical MM clonal sequences is consistent with the idea that MM is a post germinal center malignancy that can no longer undergo SHM. However, we present clear evidence of MM-related subclones albeit relatively small in number. The subclone sequence deviations discovered may reflect on-going genetic evolution independent of the formal SHM mechanism. Alternatively, these subclones may indeed reflect “sister” cells generated during the original germinal center reaction that resulted in the malignant clone. In summary, our studies demonstrate the extraordinary potential of this methodology to track clonal evolution in MM and its precursor conditions over time. Disclosures: No relevant conflicts of interest to declare.

Abstract While extensive, the genetic studies of DLBCL tumors to date have primarily focused on somatic mutations that are associated with development of lymphoma, i.e., driver mutations. Identification of key genetic events that impact and/or predict response to therapy and clinical outcome of lymphoma patients has not been accomplished. In an exploratory study, we used whole-exome sequencing to identify novel biomarkers that can predict which patients have an aggressive form of DLBCL, and therefore would likely benefit from a different and more aggressive treatment plan. Using whole-exome sequencing data from 54 newly diagnosed DLBCL patients, we evaluated the association of somatic coding single nucleotide (cSNV) and copy number (CNV) variants with aggressive DLBCL. All patients were treated with R-CHOP or immunochemotherapy, and disease aggressiveness was based on relapse, with patients classified as having aggressive disease (AD) (n=13, defined as treatment failure, relapse, or progression within 24 months of diagnosis) versus non-aggressive disease (n=41, defined as achieving at least 24 months of relapse-free survival). An in-house algorithm, patternCNV was used to detect somatic CNVs. Logistic regression was used to assess statistical significance. In the cSNV analysis, 24 genes were found to be significantly (p ≤ 0.05) associated with AD. Of these genes, CIITA (p=0.01) was previously identified as a mutational target in DLBCL and is a recurrent gene fusion partner in lymphoid cancers. Metacore analyses showed that seven of these genes, including CIITA, can be placed in the same regulatory network centered around CREB1. Interestingly, the CREB binding protein (CREBBP) is a known target of pathogenic mutations in DLBCL. In the CNV analyses, we identified 245 gene amplifications and 209 gene deletions associated with AD (p ≤ 0.05). Deleted genes were localized in three major chromosomal regions, 6p22.3-6p22.1 (26 genes), 6q13-6q16.3 (31 genes), and 6q21-6q24.2 (86 genes). In the CNV amplification analysis, the genes were localized in two major chromosomal regions, 3q12.3-3q29 (90 genes) and 19q13.12-19q13.43 (122 genes). We also quantified the association of each regional deletion/amplification with patient outcome. As expected, each deleted region was univariately associated with time to relapse: 6p22.3-6p22.1 (HR = 7.19, 95% CI: 2.66-19.46, p=1.33x10-5), 6q13-6q16.3 (HR = 4.44, 95% CI: 1.74-11.32, p=6.84x10-4), and 6q21-6q24.2 (HR = 5.42, 95% CI: 2.12-13.87, p=9.23x10-5) as well as the two amplification regions: 3q12.3-3q29 (HR = 4.60, 95% CI: 1.49-14.24, p=4.27x10-3) and 19q13.12 -19q13 (HR = 13.79, 95% CI: 4.39-43.34, p=2.81x10-8). Deletions at 6q21-6q24.2 have been previously reported in DLBCL and two tumor suppressor genes associated with lymphoma pathogenesis are localized in the region, TNFAIP3 (p=0.14) and PRDM1 (p=0.02), the latter of which was significantly associated with AD. However, the genes in the 6p21 locus that were most significant (p=0.002) were SLC22A16, GPR6, SOBP, MATTL24, DDO, and REV3L. SLC22A16, which also had cSNVs in two tumors, is an organic cation transporter that has been shown to transport chemotherapeutic drugs including doxorubicin. Successful drug response has been correlated with the level of activity and expression of this transporter in tumor cells. To determine if expression of SLC22A16 would have an impact on doxorubicin transport, and may therefore impact response to R-CHOP we overexpressed either an empty vector control or SLC22A16 in OCI-Ly7 DLBCL cells, which do not express endogenous SLC22A16 mRNA. We found that OCI-Ly7 cells expressing SLC22A16 have increased 14C-doxorubicin uptake and are more sensitive to doxorubicin-induced cell death when compared to the empty vector control cells. These data suggest that loss of SLC22A16 expression through gene deletion could impact the ability of DLBCL cells to respond to therapy and indicates an aggressive form of the disease. In summary, our data are the first to use whole exome sequencing to identify genetic variants associated with aggressive DLBCL, which will require replication in additional samples. Nevertheless, our data highlight the role for somatic CNVs in patient outcome and we biologically validate the potential impact of deletions in SLC22A16 at 6p21. Disclosures: No relevant conflicts of interest to declare.

Abstract Abstract 2702 Background: CXCR5 [chemokine (C-X-C motif) receptor 5; also known as Burkitt lymphoma receptor 1 (BCR1)] is expressed on mature B-cells, subsets of CD4+ and CD8+ T-cells, and skin-derived migratory dendritic cells. CXCL13 is the ligand for CXCR5, and together they are involved in guiding B-cells into the B-cell zones of secondary lymphoid organs as well as T-cell migration. CXCR5 is also expressed in malignant cells in NHL patients. Single nucleotide polymorphisms (SNPs) in CXCR5 have not been extensively studied, although two studies reported that SNPs from this chromosome 11 gene were associated with risk of primary biliary cirrhosis in a European population and with risk of NHL in an Asian population. This study evaluated the role of common germline genetic variation in the risk and prognosis of the common NHL subtypes of diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), mantle cell lymphoma (MCL), marginal zone lymphoma (MZL) and peripheral T-cell lymphoma (PTCL). Methods: Ten SNPs in CXCR5 were genotyped using a clinic-based study of 2694 NHL cases (including 586 DLBCL, 588 FL, 137 MCL, 230 MZL, and 158 PTCL) and 1521 controls enrolled from 2002–2009 in the Molecular Epidemiology Resource of the University of Iowa/Mayo Clinic Lymphoma SPORE. Clinical data were abstracted for all NHL patients and all were prospectively followed for disease progression, retreatment, transformation and death. SNPs were selected from HapMap using a standard tagging approach (r2≥0.80 and minor allele frequency (MAF) ≥0.05) and included SNPs 5kb upstream and downstream from the gene. Genotyping was conducted using a custom Illumina iSelect panel. All SNPs were in Hardy-Weinberg equilibrium in the control group. The most prevalent homozygous genotype was used as the reference group, and each SNP was modeled as having a log-additive effect in the regression model. For risk of NHL, odds ratios (ORs) and 95% confidence intervals were calculated using unconditional logistic regression, adjusted for age and gender. For prognosis, hazard ratios (HR) were calculated for both event-free (EFS) and overall (OS) survival using Cox proportional hazards regression, adjusting for age, treatment type, and IPI (DLBCL, PTCL), FLIPI (FL), or MIPI (MCL). To determine risk of FL transformation to DLBCL, a competing risk Cox model was used. A p<0.05 was declared as statistically significant. Results: Of the 10 CXCR5 tag SNPs in our study, 5 were associated with risk of NHL, with rs1790192 (MAF=0.39 among controls) having the strongest association (OR=1.19, 95%CI 1.08–1.30; p=0.0003). This SNP was strongly associated with FL (OR=1.44, 95%CI 1.25–1.66; p=3.1×10−7), and was also associated with DLBCL (OR=1.16, 95%CI 1.01–1.33; p=0.038) and PTCL (OR=1.29, 95%CI 1.02–1.64; p=0.037) but not MCL or MZL. For FL, none of the SNPs were associated with EFS or OS, although rs1790192 was associated with better outcome among FL patients that were observed (HR=0.64; 95%CI 0.47–0.87; p=0.0039). For DLBCL, none of the SNPs were associated with EFS, while three SNPs were associated with OS: rs523604 (HR=0.79; 95%CI 0.64–0.97; p=0.028), rs12363277 (HR=0.57; 95%CI 0.33–0.99; p=0.046), and rs3922 (HR=0.79; 95%CI 0.65–0.97; p=0.027). The latter SNP was also associated with risk of transformation from FL to DLBCL (HR=0.61; 95%CI 0.40–0.91; p=0.016). For MCL, MZL and PTCL, none of the SNPs were associated with either EFS or OS. Conclusions: Several germline CXCR5 polymorphisms were associated with NHL risk, particularly rs1790192 with risk of FL as well as DLBCL and PTCL. CXCR5 SNPs did not appear to be important in prognosis for most NHL subtypes, although rs1790192 was associated with a better outcome in untreated FL and rs3922 was associated with OS in DLBCL patients and with risk of FL transformation. These results provide additional evidence for a role of host genetic variation in CXCR5 in lymphomagenesis, mainly in risk of disease development, although there may also be a role for CXCR5 in FL and DLBCL prognosis. Replications of these findings in additional studies are needed. Disclosures: Ansell: Seattle Genetics, Inc.: Research Funding.