Slager, Susan L.

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 Background An association has been identified between the diagnosis-to-treatment interval (DTI) and prognostic clinical factors and outcomes in patients with previously untreated diffuse large B-cell lymphoma (DLBCL) (Maurer et al, JCO 2018). This association can result in inadvertent selection bias in clinical trials to exclude patients with aggressive disease due to the inability to delay treatment long enough to fulfill enrollment criteria, compromising the validity of clinical trial study results to the general population. A similar concern exists in the relapsed/refractory (r/r) DLBCL setting regarding patient selection bias against patients with aggressive disease requiring immediate treatment following relapse or progression. Here we examine the time from progression after immunochemotherapy (IC) to the initiation of first salvage chemotherapy and its association with outcome. Patients and Methods: Newly diagnosed patients were prospectively enrolled within 9 months of diagnosis in the University of Iowa/Mayo Clinic Lymphoma SPORE Molecular Epidemiology Resource (MER), now a subcohort of the Lymphoma Epidemiology of Outcomes (LEO) Cohort Study, and followed for progression/relapse, retreatment, and death. This analysis includes patients with their first r/r DLBCL following frontline IC who initiated aggressive salvage chemotherapy as identified and included in a previous publication (Farooq et al, BJH 2017). The progression-to-treatment interval (PTI) was defined as the time in days from date of progression from IC to initiation of salvage therapy. The date of progression was defined as either a) the date biopsy was obtained for patients who had a biopsy to confirm progressive disease or b) the date of the scan or clinical examination indicating progression in patients who did not have a biopsy performed. Event-free survival (EFS) was defined as time from initiation of first salvage therapy to progression or relapse, initiation of new anti-lymphoma therapy, or death due to any cause; overall survival (OS) was defined as time from initiation of salvage therapy until death due to any cause. Results: 162 patients with r/r DLBCL enrolled in the MER from 2002-2012 who initiated aggressive salvage chemotherapy with intent to transplant and had confirmed dates for both progression after IC and start of salvage therapy were evaluated. Median age at first progression for these patients was 64 years (range 36-76) and 104 (64%) were male. Median time from diagnosis to first progression on IC was 6.7 months (IQR: 4.6-12.7). Initial salvage therapy was R-ICE (80%), R-DHAP (8%), rituximab, oxaliplatin, cytosine arabinoside, and dexamethasone (ROAD) (6%), and other (7%). At a median follow-up of 49 months from initiation of salvage therapy (IQR: 33-74), 116 patients had died (72%). Median PTI was 6 days (IQR: 2-13). 110 patients (68%) had biopsy confirmation of disease prior to initiating salvage therapy; median PTI was 2 days (IQR: 1-7) for patients who had biopsy confirmation vs. 7 days (IQR: 3-16) in patients without biopsy confirmation, Wilcoxon p=<0.0001. There was no difference in OS from initial salvage therapy between patients who did not have biopsy confirmation (HR=1.09, 95% CI: 0.74-1.60, p=0.68) compared to patients with biopsy confirmation. Patients with short PTI (0-6 days) had significantly worse overall survival from initiation of salvage therapy (median OS = 7.6 months, HR=2.10 (95% CI: 1.43-3.08) compared to patients who initiated therapy 7 or more days from progression (median OS = 29.7 months), logrank p<0.0001. This association remained consistent (HR=2.20, 95% CI: 1.48-3.26, p<0.0001) after adjusting for age and biopsy confirmation of progression. Short PTI was also associated with inferior response rate to initial salvage therapy (51% vs. 65%, p=0.058), lower rate of proceeding to transplant after initial salvage therapy (33% vs. 55%, p=0.0063), lower rate of being event-free 24 months from initial salvage therapy (16% vs 33%, p=0.011) and lower rate of ever proceeding to transplant (44% vs. 59%, p=0.057). Conclusions: A short progression-to-treatment interval is strongly associated with inability to proceed to transplant and inferior overall survival in r/r DLBCL. These results have implications for the design and interpretation of clinical trials in the relapsed/refractory setting. Figure. Figure. Disclosures Maurer: Celgene: Research Funding; Nanostring: Research Funding; Morphosys: Research Funding. Ansell:Trillium: Research Funding; Merck & Co: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Pfizer: Research Funding; Celldex: Research Funding; Takeda: Research Funding. Witzig:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Cerhan:Nanostring: Research Funding; Celgene: Research Funding; Jannsen: Other: Scientific Advisory Board.

Abstract Despite recent advances and improvement in treatment options, chronic lymphocytic leukemia (CLL) is still an incurable disease. Recent advancement in parallel sequencing technology has provided better understanding of genetic landscape and clonal architecture underlying disease course. Furthermore, mutation status of TP53, SF3B1, NOTCH1, and BIRC3 has improved current CLL risk stratification. In order to better understand the genetic basis and clonal evolution underlying disease progression we analyzed a homogeneously treated cohort of CLL cases at multiple time points before and after therapy. We performed WES in 30 sequential samples from 12 CLL cases. All 12 cases received PCR (pentostatin, cyclophosphamide and rituximab) based chemoimmunotherapy as initial treatment. Of these 12 cases, two had samples available both >6 months prior to enrollment and at treatment initiation, seven patients who experienced relapse had samples available at both treatment initiation and at and the time of relapse, and three patients had samples >6 months prior to enrollment, at treatment initiation, and at relapse. These cases were previously characterized by aCGH/FISH. R function Mclust and kernel density estimate (KDE) plots were used to identify subclonal heterogeneity. An average of 102-fold depth coverage was obtained with an average of 80% of the targeted regions being covered by at least 30X. Overall, we detected a total of 219 nonsynonymous Single Nucleotide Variants (SNV) and indels (average 19, range 11-33). Del13q (42%) was identified as most prevalent copy number abnormality followed by trisomy 12 (33%), del11q and del17p (17% each). Recurrent mutations were identified in NOTCH1 (33%), DDX3X (25%), TP53, SF3B1 XPO1 and MED12 (17% each). Other tumor implicated genes such as CARD11, NOTCH2, NOTCH4, DIS3, TRAF2, NFKBID, CHK2 and RB1 were found mutated in individual cases. In all 12 cases we identified at least one relevant cytogenetic abnormalities (del11q32, del13q14, del17p13, trisomy 12) and/or a mutated driver gene. Interestingly we found NOTCH1, DDX3X and XPO1 mutated at a significantly higher prevalence than previous studies (10%, 2% and 4%, respectively). Since all cases in our cohort correspond to individuals with progressive disease, a plausible explanation for the higher prevalence of mutations in our cohort is that these mutations are associated with disease progression. By WES we identified multiple subclones in 58% of cases (7 of 12 cases), as compared to previously identified 33% (4 of 12 cases) by CGH/FISH. In 5 chemotherapy naïve cases, no significant increase in genomic complexity was detected with disease progression. In contrast, 5 out of 10 cases with samples analyzed before CIT and at the time of relapse had changes in clonal dynamics, with initially dominant subclones diminishing in response to therapy and the available empty niche being occupied by expansion of another “fitter” subclone at the time of relapse. No acquisition of tumor associated gene mutations was seen post therapy or associated with relapse in 9 of 10 cases. In the remaining case we identified an interesting example of convergent evolution. The pre-therapy sample was comprised of a unique clone characterized with del11q13, mutations in SF3B1 (G742D) and DDX3X (pE196fs). After therapy that clone was either eradicated or reduced to below detection level. Interestingly another clone emerged after relapse that harbored an independent del11q22 and mutations affecting different aminoacids of SF3B1 (K700E) and DDX3X (pL21fs). The strong selection of impairment in these genes during disease progression suggests a key role of these pathways/genes in the pathogenesis of this patient. In conclusion, these studies provide additional insight into the genomic landscape and clonal architecture in a cohort of homogeneously treated CLL cases before and after therapy. Stable clonal architecture was seen in prior to treatment, whereas clonal evolution leading to increased tumor heterogeneity occurred after the under selection selective pressure of therapy in relapsed cases. Early identification of tumoral heterogeneity and underlying genetic abnormalities might provide better targets for therapy to decrease the likelihood of relapse. Disclosures: Fonseca: Medtronic: Consultancy; Otsuka: Consultancy; Celgene: Consultancy; Genzyme: Consultancy; BMS: Consultancy; Lilly: Consultancy; Onyx: Consultancy, Research Funding; Binding Site: Consultancy; Millennium: Consultancy; AMGEN: Consultancy; Cylene: Research Funding; Prognostication of MM based on genetic categorization of the disease: Prognostication of MM based on genetic categorization of the disease, Prognostication of MM based on genetic categorization of the disease Patents & Royalties. Shanafelt:Genentech: Research Funding; Glaxo-Smith-Kline: Research Funding; Cephalon: Research Funding; Hospira: Research Funding; Celgene: Research Funding; Polyphenon E International: Research Funding.

Abstract Background Age has long been recognized as a poor prognostic factor in diffuse large B-cell lymphoma (DLBCL) and more recently, a lower baseline quality of life (QOL) in patients with aggressive lymphomas has been associated with an inferior overall survival (OS) (Thompson et al, 2018). Older patients with DLBCL are a heterogeneous population and commonly have comorbid conditions. A concerted effort to identify prognostic factors specific to this population is needed, including QOL. Event free survival at 24 months (EFS24) is a well validated endpoint in DLBCL (Maurer et al, 2014) and is clinically relevant in older patients. We hypothesized that baseline clinical factors and QOL would be associated with EFS24 this patient group. Methods Newly diagnosed lymphoma patients were prospectively enrolled within 9 months of diagnosis into the University of Iowa/Mayo Clinic Lymphoma SPORE Molecular Epidemiology Resource (MER). Patients ≥70 years of age with DLBCL were utilized in this study. All pathology was reviewed by a lymphoma hematopathologist. Clinical variables were abstracted from the medical record. QOL was assessed at enrollment using a single item Linear Analogue Self-Assessment (LASA) and the 27-item Functional Assessment of Cancer Treatment-General questionnaire (FACT-G), which includes physical, social, emotional, and functional well-being domains. QOL scores were converted to a 0-100 scale to facilitate modeling. QOL was analyzed in patients who completed 80% of the FACT-G questions. Characteristics of the study population were compared using the Wilcoxon rank sum test. Odds ratios (ORs) and 95% confidence intervals (CI) were used to estimate the associations of clinical and continuous QOL variables with failure to achieve EFS24 using logistic regression models, with a stepwise approach to identify parsimonious multivariate models. Results A total of 389 patients ≥70 years were identified with DLBCL in the MER from 2002-2015; patient characteristics are described in the Table. In the entire study population, univariate predictors of EFS24 failure included age (OR per 10 years=1.85, 95% CI: 1.26-2.71), ECOG PS ≥2 (OR=4.78, 95% CI: 2.92-7.83), elevated LDH (OR=3.59, 95% CI: 2.27-5.65), stage III-IV (OR=1.94, 1.25-3.02), bulky disease (OR=2.86, 95% CI: 1.43-5.72), international prognostic index (OR=8.71 for IPI 5 vs IPI 0, 95% CI: 3.04-24.9), and receipt of an anthracycline (OR=0.25, 95% CI: 0.12-0.51), while none of the other variables in the Table, including the QOL variables, were statistically significant. In a stepwise multivariate analysis, only ECOG PS ≥2 (OR= 3.56, 95% CI: 2.09-6.06) and elevated LDH (OR=2.86, 95% CI: 1.78-4.60) remained significant predictors of EFS24 failure. QOL data were available on 220 patients, with 120 completing QOL questionnaires prior to receipt of chemotherapy. Compared to participants who completed their FACT-G before the initiation of chemotherapy, the median FACT-G physical (85.7 v. 71.4, p=0.005) and functional (71.4 vs 57.1, p=0.008) scores were lower in participants completing FACT-G after initiation of therapy, were higher on FACT-G emotional (79.2 vs 83.3, p=0.03), and showed no differences on FACT-G social (89.3 vs 91.7, p=0.60) or FACT-G total score (78.2 vs 74.1, p=0.17). Given these differences, we restricted the QOL analysis to those with pre-treatment data. In univariate analysis, the LASA score (OR= 0.98, 95% CI: 0.84-1.14), the FACT-G total (OR=0.99, 95% CI: 0.96-1.01), physical (OR=0.98, 95% CI: 0.97-1.00), social (OR=1.00, 95% CI: 0.98-1.03), emotional (OR= 1.00, 95% CI: 0.98-1.02), and functional (OR=0.99, 95% CI: 0.98-1.01) scores were not associated with EFS24 failure. In a stepwise multivariate analysis of clinical variables in patients with pretreatment QOL scores, only ECOG PS ≥2 (OR=3.56, 95% CI: 1.30-9.72) and female sex (OR=0.37, 95% CI: 0.16-0.85) were associated with failure to achieve EFS24. Conclusions In patients aged 70 years and older, ECOG PS and LDH were the strongest independent predictors of failure to achieve EFS24. As expected, physical and functional scores were lower in patients who completed their QOL after initiation of treatment. Restricting the analysis to patients with pre-treatment data, QOL was not associated with failure to achieve EFS24. Additional studies are needed to identify the most robust clinical predictors of EFS24, while QOL does not appear to be an important predictor of EFS24. Disclosures Reagan: Seattle Genetics: Research Funding. Maurer:Morphosys: Research Funding; Celgene: Research Funding; Nanostring: Research Funding. Cerhan:Celgene: Research Funding; Nanostring: Research Funding; Jannsen: Other: Scientific Advisory Board.

Abstract Background Approximately 2-8% patients with chronic lymphocytic leukemia (CLL) will transform to diffuse large B-cell lymphoma (DLBCL, Richter syndrome [RS]). Clinical characteristics and molecular markers at the time of CLL diagnosis are associated with the risk of RS; however, there are no data regarding germline genetic variations and the risk of RS. Genomewide association studies (GWAS) have shown several single-nucleotide polymorphisms (SNPs) that are associated with a higher risk of familial CLL. It is not known whether any of these polymorphisms also predispose to RS. Methods Since 2002, all consecutive patients with newly diagnosed (<9 months diagnosis) CLL at Mayo Clinic were offered enrollment into a prospective genetic epidemiology study. Patients completed extensive epidemiologic questionnaires and baseline clinical, laboratory, and biologic data were abstracted using a standard protocol. Genotyping of germline tissue at diagnosis was performed using an Illumina iSelect panel and Affymetrix 6.0 SNP chip. All patients were prospectively and longitudinally followed at defined time-points with systematic collection of data on treatments, second cancers, and RS. All patients with biopsy-proven DLBCL during follow-up were considered to have undergone transformation into RS. Time to RS was calculated from CLL diagnosis date until RS or until last follow-up date for those with no RS. SNPs were modeled in two ways: ordinal and dominant. Cox regression was used to estimate hazard ratio (HR) for individual SNPs with time to transformation. Results Thirteen of the GWAS-discovered SNPs associated with risk of developing CLL were available and genotyped on 620 CLL patients. Median age at diagnosis of CLL was 62 years (range 27-88), and 428 (69%) were male. Three hundred and ten (51%) patients were low (0) Rai stage, 271 (45%) were intermediate (I-II) Rai stage, and 22 (4%) were advanced (III-IV) Rai stage. The immunoglobulin heavy chain gene was unmutated in 189 (40%) patients; 157 (32%) patients expressed ZAP-70, 163 (29%) expressed CD38 and 104 (31%) expressed CD49d. Fluorescence in-situ hybridization (FISH) revealed that 210 (41%) patients had del13q, 90 (18%) patients had trisomy 12, 37 (7%) had del11q, 23 (5%) had del17p and 141 (28%) had no detectable FISH abnormalities. As of last follow-up, 239 (39%) patients received therapy for CLL. After a median follow-up of 5.9 years (range 0-11), 15 (2.4%) patients developed biopsy-proven RS. The median time to RS in these 15 patients was 4.5 years (range 1.0-8.7 years). The ordinal HR for the 13 SNPs tested, their corresponding genes, and p-values are shown in Table 1. Germline polymorphisms in a single SNP, rs4987852, encoding for BCL2 (chromosome 18), was significantly associated with an increased risk of RS (ordinal HR=3.9; 95% CI=1.6-9.8; p-value=0.004). This allele was present in 48/605 (8%) non-transformed CLL patients compared to 4/15 (27%) of patients with RS. Time to RS according to the Kaplan-Meier analysis for rs4987852 is shown in Figure 1. This SNP is located in a region in which t(14;18) translocation breakpoints commonly occur in follicular lymphoma and overexpression of BCL2 leads to an increased incidence of B-cell lymphomas in mice. Conclusion Our results suggest that inherited genetic polymorphisms predispose CLL patients to develop RS. Specifically, SNP (rs4987852) present on the BCL2 gene on chromosome 18 in CLL is associated with an increased risk of transformation to RS. These observations require replication in other CLL cohorts. Disclosures: Shanafelt: Genentech: Research Funding; Glaxo-Smith-Kline: Research Funding; Cephalon: Research Funding; Hospira: Research Funding; Celgene: Research Funding; Polyphenon E International: Research Funding.

Abstract Clonal hematopoiesis of indeterminate potential (CHIP) is defined as somatic mutations in clonal blood cells of individuals without any other hematological abnormalities and have been identified using whole exome sequencing (WES) from pre-specified genes. CHIP is reportedly associated with aging, reduced overall survival, cardiovascular disease, and hematologic disease, mainly myeloid cancers. Additionally, whole genome sequencing and WES studies in CLL cases have identified putative CLL driver genes with recurrent somatic variants or copy number alterations. Here, we investigate the associations of CHIP and mutations in CLL somatic driver genes with risk of CLL and MBL, a precursor to CLL. Using WES, 571 individuals from 132 CLL families were sequenced from two different sites, Mayo Clinic (92 families) and National Cancer Institute (NCI, 40 families), including 251 CLLs, 74 MBLs, and 246 unaffected relatives. DNA was extracted from buccal cells (Mayo Clinic) and peripheral blood (NCI). Allele counts from gVCF files were extracted for specific genes and analyzed. Variants were annotated using the Variant Effect Predictor in Ensembl, genome build GRCh37 (http://useast.ensembl.org/index.html). Highly-sensitive flow cytometry of the blood was used to screen for MBL. CHIP was defined as in Genovese et al., 2014 as the presence of any of the following: A) disruptive mutations in ASXL1, TET2, or PPM1D; B) missense mutation p.617F in JAK2; C) disruptive or missense mutations in DNMT3Alocalized to exons 7 to 23D) any mutations reported at least 7 times in hematological malignancies in the Catalogue of Somatic Mutations in Cancer (COSMIC); and (E) No variant allelic fraction (VAF) threshold. CLL driver genes included disruptive or missense mutations in ATM, BIRC3, MYD88, NOTCH1, SF3B1, TP53, EGR2, POT1, NFKBIE, XPO1, or FBXW7with VAF greater than 0.1. Mutations from COSMIC and CLL driver genes were excluded if the minor allele frequency in 1000 Genomes was greater than 0.5%. Data was analyzed using logistic regression adjusting for age, sex, and site. Odds ratios (OR) and 95% confidence intervals (CI) were estimated. Median age for CLL, MBL, and unaffected was 63, 61.5, and 54, respectively. The unaffected relatives were significantly younger than both the CLL and MBL groups (p<0.0001), with no difference between CLL and MBL (p=0.46). A total of 70 (12%) individuals from 54 different families had CHIP. CHIP was characterized in the majority of samples as mutations in DNMT3A, ASXL1, and TET2with 43, 9 and 8 individuals, respectively. As expected, CHIP was associated with age (p<0.001) but not sex (p=0.87). CHIP was present in 42 (17%) CLLs, 9 (12%) MBLs, and 19 (8%) unaffected relatives. There was a significant association between the presence of CHIP and CLL risk compared to unaffected relatives (OR= 2.19, 95% CI: 1.20 - 4.13, p=0.013) but not with MBL risk (OR=1.34, 95% CI: 0.54 - 3.11, p=0.51). Using the CLL driver genes, we found mutations in 119 (47%) CLL, 20 (27%) MBL, and 70 (29%) unaffected relatives. Mutations in CLL driver genes were associated with CLL risk compared to unaffected relatives (OR = 2.51, 95% CI: 1.64 - 3.87, p<0.0001), but there was no evidence for increased MBL risk (OR = 1.00). These results held when we subset the analysis to the Mayo only data where the DNA was extracted from buccal cells. This study further supports the association between mutations in CHIP related genes and risk of hematologic malignancies, in particular CLL. This is the first report of the prevalence of CHIP in MBLs. Although MBL and CHIP represent clonal population of blood cells, our data suggest the clones are different. Mutations in CLL driver genes are also associated with CLL risk but show little to no evidence with increased MBL risk. Interestingly, our unaffected relatives of CLL cases also had a high frequency of CLL driver mutations. Future studies are needed to understand these acquired mutations (both in CHIP- and CLL driver- related genes) in the unaffected to MBL to CLL continuum. Disclosures Parikh: Abbvie: Honoraria, Research Funding; Pharmacyclics: Honoraria, Research Funding; MorphoSys: Research Funding; AstraZeneca: Honoraria, Research Funding; Gilead: Honoraria; Janssen: Research Funding. Ding:Merck: Research Funding. Cerhan:Jannsen: Other: Scientific Advisory Board; Celgene: Research Funding; Nanostring: Research Funding. Shanafelt:Jansen: Research Funding; Pharmacyclics: Research Funding; Genentech: Research Funding; GlaxoSmithKline: Research Funding. Kay:Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta: Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees.

Abstract Background MBL is a precursor state to CLL, with a prevalence of ~5-8% in the general population and 15-18% in CLL families. To date, 41 single nucleotide polymorphisms (SNPs) have been found to be associated with CLL. We previously showed that a PRS of the weighted average of the number of risk alleles of these SNPs is associated with CLL risk using cases and controls from the International Lymphoma Epidemiology (InterLymph) Consortium. We validated this score in an independent sample of CLL cases and controls from the Genetic Epidemiology of CLL (GEC) Consortium, a cohort of families with ≥2 members with CLL. In the CLL families we also reported an association between the PRS and MBL risk, where 93% of MBLs were low-count MBL. Here we evaluate the PRS in an independent sample ascertained from the Mayo Clinic Biobank. Methods The Mayo Clinic Biobank is a large-scale bio-repository of adult patients assembled to provide a wide array of health-related research studies. Biobank participants (N=2530) were screened for MBL using a highly-sensitive, 8-color (CD38, CD45, Kappa, Lambda, CD19, CD23, CD5 and CD20) flow-cytometry assay on stored cryopreserved peripheral blood mononucleotide cells. Low-count and high-count MBL were defined as those individuals who had <85% and ≥85 clonal B-cells out of total B-cells, respectively. Individuals without MBL ("controls") were frequency matched to MBLs based on age and sex, after excluding non-Caucasian and non-interpretable flow results. Participants were genotyped using the Illumina OmniExpress array. Standard genotyping quality-control metrics were applied. We computed the PRS as previously published and categorized it by quintiles based on values previously used with InterLymph controls. The middle quintile served as the reference category. We used logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CI), adjusting for age at consent, sex, and socioeconomic status. In addition, we compared the median PRS by low-count MBL and high count MBL using the Mann-Whitney U test. Results We identified 276 MBLs overall and 2109 controls; 97% (N=267) of MBLs had low-count MBL and only 3% (N=9) had high-count MBL. We then selected 844 controls frequency matched to the MBL cases on age and sex. Family history of leukemia/lymphoma was 11.3% among MBLs overall and 8.8% among controls. The median PRS was 7.88 and 7.46 (Figure) among MBLs overall and controls, respectively. The continuous PRS had a 1.75-fold increased risk for MBL overall (CI:1.5-2.1, P=2.65x10-12). Compared to the middle quintile, the highest quintile had 2.2-fold increased risk for MBL overall (CI:1.5-3.3, P=1.46x10-4) and the lowest quintile had 0.6-fold decreased risk (CI:0.4-0.96, P=0.03). The median PRS for low-count and high count MBL were 7.86 and 8.04, respectively. The continuous PRS had a 1.75-fold increased risk for low-count MBL (CI:1.5-2.1, P=4.81x10-12) and a 1.89-fold increased risk for high-count MBL (CI:0.8-4.2, P=0.13), however, the effect is not statistically significant between high-count and low-count MBL (P=0.89). Conclusion In this independent sample, we validated our previous findings that the CLL PRS is associated with MBL risk. Although the sample size of high-count MBL was limited (N=9), there is evidence of a weak trend towards a higher PRS compared to low-count MBL and controls Larger sample size of high-count MBL is warranted in order to stratify MBL risk by low-count and high-count MBL. These results may help identify individuals at higher risk of MBL including low-count and high-count MBL, beyond the risk associated with age and family history of CLL. Figure: Polygenic risk score distribution by control and MBL status Figure. Figure. Disclosures Parikh: Janssen: Research Funding; Pharmacyclics: Honoraria, Research Funding; Gilead: Honoraria; Abbvie: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; MorphoSys: Research Funding. Cerhan:Nanostring: Research Funding; Celgene: Research Funding; Jannsen: Other: Scientific Advisory Board. Kay:Janssen: Membership on an entity's Board of Directors or advisory committees; Acerta: Research Funding; Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Morpho-sys: Membership on an entity's Board of Directors or advisory committees. Shanafelt:Genentech: Research Funding; Pharmacyclics: Research Funding; GlaxoSmithKline: Research Funding; Jansen: Research Funding.

Abstract Background: B-cell non-Hodgkin lymphomas (NHL) are common lymphoid malignancies in which malignant B-cells arrested at various stages of differentiation proliferate within lymph nodes and occasionally other tissues. The microenvironment that supports the growth and survival of NHL B cells consists of a complex network of immune cells and cytokines and its specific composition has been shown to have significant clinical implications. The intratumoral immune cells include effector and regulatory T (Treg) lymphocytes that are more than simple residual elements from the normal lymph node structure. We previously explored whether the extent of T cell infiltration played a role in the clinical outcome of patients with B-cell NHL and found that an increased percentage of CD4+ T cells in the diagnostic biopsy of lymphoma patients significantly correlated with an improved 5-year overall survival. We have also shown that intratumoral Treg cells significantly suppress the anti-tumor response. Based on the fact that intratumoral T-cells are important in B-cell NHL, we evaluated whether genetic variation in genes that regulate the T-cell response may be associated with lymphoma risk. Methods: We genotyped 257 single nucleotide polymorphisms (SNPs) from 50 candidate genes related to T-cell differentiation and function in a clinic-based study of 441 Caucasian NHL cases and 475 frequency matched Caucasian controls seen at the Mayo Clinic from 2002–2005. Tagging and nsSNPs were selected from HapMap. The most prevalent homozygous genotype was used as the reference group and each SNP was modeled individually as having a log-additive effect, expressed as ordinal odds ratios (OR) per variant allele and 95% confidence intervals, in an age- and sex-adjusted logistic regression analysis. We also evaluated the consistency of the findings for the most common types of B-cell NHL - diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), and CLL/SLL. For gene-level analyses, we used principal components (PC) analysis using all SNPs from a gene; those PCs that explained > 90% of the variability were then modeled in a logistic regression analysis and significance was determined using a likelihood ratio test. We also evaluated the association of haplotypes from each gene with risk of NHL using the score test implemented from HAPLO.SCORE. Results. The mean age at diagnosis was 60.1 years for cases and 58% were male; among controls, the mean age at enrollment was 61.7 years and 55% were men. In the PC gene analyses, PRF1 (perforin gene - involved in cytotoxicity; p=0.004), CD276 (B7-H3 gene - involved in co-stimulation; p=0.01), TBX21 (T-bet gene – regulates Th1 cell development; p=0.02), and IL6 (p=0.02) were significant at p<0.05; haplotype analysis showed similar results, with the addition of CARD15 (NOD2 gene – regulates Th1 responses; p=0.02). There were 2 SNPs in PRF1, and both the intronic SNP rs3758562 (OR=1.38; 1.13–1.69) and the synonymous coding SNP rs885821 (OR=0.80; 0.63–1.02) were associated with NHL risk overall and for each subtype. There were 2 SNPs in CD276, and both the intronic SNP rs7176654 (OR=1.29; 1.07–1.56) and the mrna-utr SNP rs3816661 (OR=0.82; 0.68–0.99) were associated with NHL risk overall and with the CLL/SLL and follicular NHL subtypes specifically. There were 2 SNPs in TBX21, and only the mrna-utr SNP rs7502875 (OR=0.73; 0.59–0.92) was associated with NHL risk overall and for each subtype. There were 11 SNPs in IL6, and the intronic SNP rs2069835 (OR=1.88; 1.24–2.84) and the locus-region SNP rs2069824 (OR=1.73; 1.20–2.50) were each associated with NHL risk overall and each subtype. Finally, there were 9 SNPs in CARD15, with only the nonsynonymous coding SNP rs5743291 (OR=1.43; 1.02–1.98) being associated with overall NHL risk, and risk was specific to FL. Conclusions. Genetic variation in several genes that play critical roles in T-cell maturation and function was associated with lymphoma risk. These genes appeared to be associated with the differentiation and function of effector T-cells particularly Th1 and Th17 cells. These results provide potentially important insights into the role of T-cells in lymphomagenesis.

Abstract Background: Reactive oxygen species cause DNA damage and altered immunologic responses, and have been linked to the development of NHL. Diets rich in fruits and vegetables are excellent sources of antioxidants, and vitamins C, E, polyphenols, and carotenoids, along with selected micronutrients such as zinc are thought to be responsible for most of the antioxidant activity in foods. We evaluated the hypothesis that vegetables, fruits and nutrients involved in antioxidant activity would protect against developing NHL. In a secondary analysis, we also assessed heterogeneity of this hypothesis for the most common subtypes of NHL: chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL), follicular lymphoma, and diffuse large B cell lymphoma (DLBCL). Methods: We evaluated dietary antioxidant intake and NHL risk in a clinic-based study of 416 newly diagnosed NHL cases and 926 frequency-matched controls enrolled at the Mayo Clinic from 2002–2007. Usual diet two years before diagnosis/enrollment was assessed using a self-administered, 128-item food frequency questionnaire. Dietary intake of antioxidants was estimated using the Food Processor SQL system, further supplemented with data from US Department of Agriculture nutrient databases. Unconditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CI), adjusted for age, sex, residence, and total energy. NHL subtypes were centrally reviewed, and subtype-specific risks were estimated using polychotomous logistic regression. Results: The mean age at diagnosis was 60.8 years for cases and 57% were male; for controls, the mean age at enrollment was 60.8 years and 54% were men. NHL risk was inversely associated with intake of total vegetables (OR for highest compared to lowest quartile, 0.46; 95% CI 0.32–0.68; p-trend<0.001), and specifically green leafy (OR=0.54; 95% CI 0.39–0.76; p-trend<0.001) and cruciferous (OR=0.62; 95% CI 0.43–0.88; p-trend=0.02) vegetables, but not legumes or red/yellow/orange vegetables. There was no association with total fruit intake or intake of citrus fruits in particular. When modeled together, the association for green leafy vegetables was unchanged while the association for cruciferous vegetables attenuated (OR=0.75; 95% CI 0.50–1.13; p-trend=0.3). Higher intake of total vitamin A (OR=0.60; 95% CI 0.41–0.88; p-trend<0.001) and vitamin E (OR=0.53; 95% CI 0.34–0.82; p-trend<0.001), but not vitamin C, were associated with lower NHL risk. For vitamin A, there was no association with preformed Vitamin A, but there was an inverse association with beta-carotene (OR=0.56; 95% CI 0.39–0.81; p-trend<0.001); only weak inverse associations were observed for other carotenoids (lutein/zeaxanthin, lycopene, and cryptoxanthin). When modeled together, the inverse association for vitamin E was unchanged while the association for beta-carotene attenuated. For micronutrients, there were inverse associations for manganese (OR=0.60; 95% CI 0.40–0.90; p-trend=0.02), selenium (OR=0.47; 95% CI 0.27–0.80; p-trend=0.01), and zinc (OR=0.46; 95% CI 0.27–0.80; p-trend<0.001) but not copper; when modeled simultaneously, both manganese (p-trend=0.03) and zinc (p-trend=0.02) remained significantly and inversely associated with NHL risk. Further adjustment for education, family history of NHL, smoking, alcohol use, and body mass index did not alter these results. There was no NHL subtype heterogeneity for the associations with total vegetables, green leafy vegetables or vitamin E, while inverse associations were not observed for manganese and follicular lymphoma or zinc and CLL/SLL. Conclusion: Higher intakes of vegetables, particularly green leafy vegetables, vitamin E, manganese, and zinc were associated with lower risk of NHL. These finding extend a growing literature from case-control and cohort studies that support a protective role of foods and nutrients that function in antioxidant related pathways against development of NHL, and therefore may represent one of the few known modifiable risk factors for this cancer.

Abstract Background: Elevated serum BLyS levels have been found in patients with both malignant and autoimmune diseases when compared to those of healthy donors, suggesting that BLyS may play a pathogenic role. While it is clear that BLyS expression is required for normal B cell development and homeostasis, the mechanistic details underlying control of BLyS expression remain to be defined. In previous work we found that a single nucleotide polymorphisms (SNP) in the BLyS promoter (−871C→T) resulted in increased BLyS transcription suggesting that genetic variation in the BLyS gene may influence its expression. Because elevated BLyS levels have been detected in patients with NHL we wanted to determine if additional SNPs in the BLyS gene were associated with high serum BLyS levels and risk of developing of NHL. Methods: We genotyped 9 tagSNPs within the BLyS gene in a clinic-based study of 441 incident Caucasian NHL cases and 475 frequency matched Caucasian controls seen at the Mayo Clinic from 2002–2005. We evaluated the association of individual SNPs as well as haplotypes from the BLyS gene with risk of NHL. We also jointly tested the main effects for all independent (r2<0.25) SNPs using a multivariate logistic regression (MLR) model. As a secondary analysis, for those SNPs showing significant results (< 5% significance), we evaluated associations between those who had all high risk alleles at the SNPs of interest compared to those who had all low risk alleles at the SNPs of interest. Additionally, we evaluated serum BLyS levels by ELISA in these same subjects. Serum BLyS levels were detemined by ELISA. Results: In the individual single SNP logistic regression analysis, 3 of the 9 tagSNPs were significant at p<0.05. Haplotype and MLR results were nonsignificant. However, when we categorized participants into low and high risk groups based on risk alleles at the three statistically significant SNPs, we found the high risk variant had an odds ratio (OR) of 2.086 (p=0.0001) for risk of B-cell NHL. When the analysis was restricted by histologic subtype we found that diffuse large B-cell lymphoma or follilcular lymphoma grade 3 had an OR of 3.163 (p=0.01), follicular lymphoma grade I/II had an OR= 2.547 (p=0.046), and chronic lymphocytic leukemia (CLL) had an OR=1.238 (p=0.13). Because there was not a significant correlation of the high risk variant with CLL, we performed an additional analysis in which we included all B cell NHL cases excluding CLL and the OR was 2.799 (p=0.0001). We next wanted to determine if serum BLyS levels correlated with either the high or low risk variant. The mean serum BLyS level in those individuals (untreated cases and controls) who carried the low risk variant at all three SNPs was significantly lower (p=0.0061) at 1.3 ng/ml (n=25, range: undetectable-4.4 ng/ml) compared to 4.3 ng/ml in those with the high risk variant (n=74, range: undetectable- 66.8 ng/ml). Conclusions: In summary, we have found that genetic variation in the BLyS gene is significantly associated with an increased risk of developing B-cell NHL, particularly follicular and large B-cell lymphoma. Further, we found that genetic variation is also associated with an increase in serum BLyS levels.Taken together, our data further highlight the role of the BLyS gene in B cell malignancies and characterization of the precise genetic and environmental mechanisms that regulate BLyS may have significant clinical impact.