Cerhan, James R.

Background: Diffuse large B cell lymphoma (DLBCL) is the most common aggressive lymphoid malignancy in adults. Though standard immunochemotherapy regimens can result in clinical remission and cure in a majority of patients, approximately 30% of patients are primary refractory or eventually relapse, and their prognosis is very poor. Recent progress by large scale genomic and transcriptomic profiling of DLBCL patients has resulted in a deep understanding of disease drivers in newly diagnosed DLBCL (ndDLBCL). However, previous genomic studies of relapsed and/or refractory DLBCL (rrDLBCL) are limited by small sample sizes and much less is known about the genomic landscape or the changes in clonal populations that occur within a patient undergoing R-CHOP therapy. Analysis of a large cohort of rrDLBCL is needed to uncover the importance of known drivers, define clonal shifts that occur in relapsing patients, and discover novel mutations that impact resistance to therapy. Methods: DNA sequencing (whole exome or genome sequencing) was performed on tumor and matching germline samples from two rrDLBCL patient cohorts (N=143). The first rrDLBCL cohort consisted of 68 formalin fixed paraffin embedded (FFPE) tumor samples with matched germline obtained from the Mayo/Iowa Lymphoma SPORE Molecular Epidemiology Resource (MER); including 18 diagnostic/relapse paired samples. The second cohort consisted of 75 FFPE samples from 2 rrDLBCL clinical trials CC-122-ST-001 and CC-122-DLBCL-001 (NCT01421524 and NCT02031419). In addition, a ndDLBCL cohort (n=364) consisting of MER front line samples were also analyzed. CNV and SV calling was performed with Sclust and Manta, respectively. Mutational drivers were identified with cDriver. Cosmic mutational signatures were calculated for all samples. Results: For comparison of overall mutation rates between the ndDLBCL and rrDLBCL we analyzed the frequency of mutations in known driver genes [n=211, Chapuy et al, 2018; Schmitz et al, 2018; Reddy et al 2017]. Of these, the top 5 most mutated genes found in the relapse cohort were KMT2D (34.3%), IGLL5 (25.9%), CREBBP (22.4%), TP53 (21.0%), and HIST1H1E (19.6%) all of which were previously reported as mutated genes in rrDLBCL [Morin et al. 2016; Park et al. 2016;Greenawalt et al. 2017]. None of these frequencies changed significantly from the MER ndDLBCL cohort. However, frequencies of 2 genes NCOR1 and ACTB were increased significantly from 3.3% to 13.3% (p= 5.85 x 10-05) and 0.5% to 6.3% (p=2.56 x 10-04), respectively, in the rrDLBCL cohorts. Analysis of the previously reported driver mutations in 18 paired samples revealed a significant increase in the mean variant allele frequency (VAF) from diagnosis to relapse of mutations in BCL2 and BIRC6 from 18.5% to 28.8%(p=0.016) and 4.3% to 13.7%(p=0.011), respectively. In our analysis of novel drivers in rrDLBCL cohorts, 59 significant driver mutations (FDR 0.1) were identified, 23 were novel driver mutations while the remaining 36 have been previously reported as drivers of newly diagnosed DLBCL. These include novel mutations in genes such as IL4R (8.8%), CDK5R1 (6.7%), LAPTM5 (3.7%) and others whose function in lymphoma are unknown. In a secondary analysis, we performed mutational signature analysis for rrDLBCL and identified groups of patients with COSMIC mutational signatures that are associated with aging, defects in homologous end joining resulting in large insertions/deletions, and with defective mismatch repair often found in microsatellite unstable tumors. Together with the high prevalence of TP53 mutations, these results indicate genetic instability as a hallmark of relapse/refractory DLBCL. Analysis of CNV and SV will also be reported. Conclusion: Here we report to our knowledge the largest genomic dataset in relapsed and /or refractory DLBCL using tumor biopsies. Previously identified mutated genes in rrDLBCL were confirmed and novel drivers identified. Paired samples analysis reveals clonal shifts which occur during treatment and at time of relapse. Whether the novel driver mutations appeared as a function of selection of a rare clone over the course of therapy or a therapy induced mutation will be the focus of ongoing work. Novel variants including SNV, CNV and SV can potentially lead to new insights and deepen our understanding of the mechanisms of resistance in DLBCL. * Wenzl, Ortiz and Stong contributed equally **Novak and Gandhi contributed equally Disclosures Ortiz: Celgene Corporation: Employment, Equity Ownership. Stong:Celgene Corporation: Employment, Equity Ownership. Huang:Celgene Corporation: Employment, Equity Ownership. Maurer:Morphosys: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Nanostring: Research Funding. Towfic:Celgene Corporation: Employment, Equity Ownership. Hagner:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Nikbakht:Celgene Corporation: Employment, Equity Ownership. Stokes:Celgene Corporation: Employment, Equity Ownership. Lata:Celgene Corporation: Employment, Equity Ownership. Kamalakaran:Celgene Corporation: Employment, Equity Ownership. Mavrommatis:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Czuczman:Celgene Corporation: Employment, Equity Ownership. Ansell:Regeneron: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding. Cerhan:Celgene: Research Funding; NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees. Nowakowski:Bayer: Consultancy, Research Funding; Curis: Research Funding; Celgene: Consultancy, Research Funding; MorphoSys: Consultancy, Research Funding; Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding. Novak:Celgene Coorperation: Research Funding. Gandhi:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties.

Background: Follicular lymphoma (FL) is the most frequent indolent lymphoma. While immunochemotherapy (IC) treated FL patients who achieve event-free status at 24 months after diagnosis (EFS24) have the subsequent life expectancy of the general population, those who fail to achieve EFS24 have aggressive disease with poor outcomes. Similarly, in FL patients initially observed or treated with rituximab monotherapy, EFS at 12 months (EFS12) is a strong predictor of subsequent outcome. Thus, an unmet patient need is to predict at diagnosis those at greatest risk of early failure in order to identify better treatments. The lymphoma microenvironment may be a key determinant of early failure. Therefore, we aimed to improve risk stratification of newly diagnosed FL patients by using a discovery and validation study design to identify microenvironment determinants of early failure and then integrate them into the Follicular Lymphoma International Prognostic Index (FLIPI). Patients and Methods: We evaluated 496 patients with FL grade 1-3A who were prospectively enrolled into the Molecular Epidemiology Resource (MER) cohort from 2002-2012. In the discovery set of patients (N=166), we stained tissue microarrays for CD4, CD8, FOXP3, CD32b, CD14, CD68, CD70, SIRPa, TIM3, PD-1 and PDL1. Immunohistochemical staining was determined both within and between malignant follicles. Stains were scored to the nearest decile as the percentages of cells positive. Scores for each stain were dichotomized as 0% vs >0% except for CD32b (<20% vs ≥20%), CD70 (<10% vs ≥10%), and SIRPa (<30% vs ≥30%). EFS was defined as time from diagnosis to progression, relapse, retreatment, or death. Early failure was defined as failing to achieve EFS24 for patients treated with IC at diagnosis and failing to achieve EFS12 for patients who were observed or received other treatments. Risk of early failure was estimated using odds ratios (ORs) and 95% confidence intervals (CI) from logistic regression models. Markers at P≤0.15 were brought forward for validation in a separate set of patients from the MER cohort (N=330), with P<0.005 being declared statistically significant after a Bonferroni correction (0.05/10 tests). In the combined dataset, we also used Cox regression to assess associations with continuous EFS and overall survival (OS). Mass cytometry (CyTOF) was then performed on a subset of patients (N=51) to comprehensively characterize the phenotype of intratumoral immune cells associated with outcome. Results: In the discovery set, CD4, CD8, FOXP3, PD-1, and SIRPα were associated with risk of early failure at P≤0.15 and were subsequently evaluated in the validation set. Only CD4 expression inside the follicle validated. Specifically, lack of CD4 expression inside the follicle was associated with higher risk of early failure in the discovery (OR=1.77, P=0.15) and validation (OR=2.59, P=0.001) sets, with a pooled OR=2.29 (95% CI 1.47-3.58; P<0.001). CyTOF analysis characterized the favorable CD4+ T-cell population associated with achieving EFS12/24 as activated effector memory T cells lacking exhaustion markers (Figure 1A). Adjustment of CD4 expression for FLIPI in the combined dataset did not weaken this association (OR=2.38, 95% CI 1.51-3.79), and lack of CD4 expression was prognostic within each FLIPI group 0-1 (OR=2.65; p=0.036), 2 (OR=2.07; P=0.064) and 3-5 (OR=2.51; P=0.015). We next established a bio-clinical risk model (termed BioFLIPI) that combined CD4 intrafollicular expression and FLIPI into a 1-4 scale, where any expression of intrafollicular CD4 moved a patient up one FLIPI risk group, adding a fourth (high) risk group for FLIPI 3-5 and any CD4 inside expression. The BioFLIPI score was strongly associated with risk of early failure: compared to a BioFLIPI score of 1 (24% of patients), patients with a score of 2 (35% of patients; OR=2.17, 95% CI 1.08-4.69), 3 (31% of patients; OR=3.53, 95% CI 1.78-7.54), and 4 (10% of patients; OR=8.92, 95% CI 4.00-21.1) had increasing risk of early failure. The c-statistic for BioFLIPI was 0.665, which was higher than that for FLIPI alone (0.636). BioFLIPI was also strongly predictive of continuous EFS [Figure 1B], OS [Figure 1C], and for EFS and EFS24 when restricted to IC-treated patients [Figure 1D]. Conclusion: Integration of CD4 expression inside the follicle at diagnosis with FLIPI improves identification of FL patients at risk for early failure. Disclosures Novak: Celgene Coorperation: Research Funding. Nowakowski:Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; Genentech, Inc.: Research Funding; MorphoSys: Consultancy, Research Funding. Cerhan:NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. Ansell:LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment.

Diffuse large B-cell lymphoma is a heterogeneous disease, commonly described by cell-of-origin (COO) molecular subtypes. We sought to identify novel patient subgroups through an unsupervised analysis of a large public dataset of gene expression profiles from newly diagnosed de novo DLBCL patients, yielding two biologically distinct subgroups characterized by differences in the tumor microenvironment. Pathway analysis and immune deconvolution algorithms identified higher B-cell content and a strong proliferative signal in subgroup A and enriched T-cell, macrophage, and immune/inflammatory signals in subgroup B, reflecting similar biology to published DLBCL stratification research. A gene expression classifier, featuring 26 gene expression scores, was derived from the public dataset to discriminate subgroup A (classifier-negative, immune-low) and subgroup B (classifier-positive, immune-high) patients. Subsequent application to an independent series of diagnostic biopsies and replicated the subgroups with immune cell composition confirmed via immunohistochemistry. Avadomide, a CRL4CRBN E3 ubiquitin ligase modulator, demonstrated clinical activity in relapsed/refractory DLBCL patients, independent of COO subtypes. Given the immunomodulatory activity of avadomide and need for a patient selection strategy, we applied the gene expression classifier to pretreatment biopsies from relapsed/refractory DLBCL patients receiving avadomide (NCT01421524). Classifier-positive patients demonstrated an enrichment in response rate and progression-free survival of 44% and 6.2 months versus 19% and 1.6 months for classifier-negative patients (HR=0.49 [0.280, 0.86]; P=.0096). The classifier was not prognostic for R-CHOP or salvage immunochemotherapy. The classifier described here discriminates DLBCL tumors based on tumor and non-tumor composition and has potential utility to enrich for clinical response to immunomodulatory agents including avadomide.

Abstract Background. Recent clinical trials based on immunotherapies targeting the PD-1/PD-L1 pathway have shown striking durable responses in a subset of patients with solid cancers. The Programmed death 1 (PD-1) protein is a key immune checkpoint inhibitor expressed by activated T cells. Its ligand, PD-L1, was reported highly expressed by tumor cells in some diffuse large B-cell lymphomas (DLBCL) of non-Germinal Center phenotype. We recently reported on the French multicenter GOELAMS075 trial that pre-treatment soluble PD-L1 (sPD-L1) in plasma was elevated in DLBCL patients compared to controls, and that elevated sPD-L1 was associated with inferior overall survival (OS), independent of the International Prognostic Index (IPI) and other clinical factors (Rossille et al., Leukemia 2014). Here, we replicate and extend these findings in two independent studies from Australia and the US. Methods. The protein expression of sPD-L1 was evaluated using a commercial ELISA kit. The French discovery cohort consisted of 288 adults with newly diagnosed aggressive DLBCL, age 18 to 60 years, and treated with R-CHOP or high dose chemotherapy plus rituximab followed by autologous stem cell support (clinicaltrials.gov: NCT00561379); there were also 60 controls. The Australian study consisted of 51 DLBCL patients age 18 to 71 years, all stages, treated with R-CHOP14, along with 57 controls. The US study was an observational cohort from the Iowa/Mayo Lymphoma SPORE and consisted of 225 DLBCLs, age 19 to 92 years, all stages, treated with immunochemotherapy, along with 98 controls. Plasma samples were collected pre-treatment using EDTA tubes for the Australian and the US cohorts, BDª P100 tubes for the French cohort. sPD-L1 expression was measured in Rennes, France (French & US samples) and in Brisbane, Australia (Australian samples).The Kaplan-Meier method and Cox regression were used to model the association of sPD-L1 with OS. The 95th percentile of the sPD-L1 levels in each matched control group was used as the cutoff point to define elevated sPD-L1 levels. Results. Replicating the French findings, sPD-L1 levels were significantly higher for DLBCL patients compared to controls in both the US (P<0.0001) and Australian (P<0.0001) studies (Figure 1). The odds ratios for elevated sPD-L1 among patients compared to controls were 10.7 (95% CI 3.3-22.0), 8.9 (95% CI 3.1-25.3) and 12.6 (95% CI 2.7-28.0) for the French, US and Australian studies, respectively. In the French cohort, elevated sPD-L1 levels were associated with inferior OS (Hazard Ratio (HR)=2.05; 95% CI 1.18-3.57), and this was replicated in the US cohort (HR=1.71; 95% CI 0.97-3.01) (Figure 2). Due to the similarity of the results, we next pooled the French and US cohorts to increase power for multivariate and subset analyses. The median age at diagnosis of the 513 patients was 54 years; 29% were more than 60 years old; 69% of the patients had stage III-IV disease; 56% were IPI intermediate-risk and 9% were IPI high-risk patients. The median follow-up was 48 months (range, 0.5-126) overall, and 66 months for the patients still alive; there were 102 deaths. Using a cutpoint of 1729 pg/mL from the combined studies, patients with elevated sPD-L1 had inferior OS compared to patients without elevated sPD-L1, with 3-year OS rates estimated at 78% (95% CI 71.8-84.9) vs. 87.8% (95%CI 84.5-91.4) (P<0.01). In multivariate analysis, elevated sPD-L1 (HR=1.75; 95%CI 1.16-2.64), age (HR=1.03; 95%CI 1.02-1.05), high-risk IPI score (HR=3.71; 95%CI 1.91-7.21) and ALC ² 1 G/L (HR=2.19; 95%CI 1.44-3.33) were all significant at P<0.01. In analyses stratified by IPI, the 3-year OS rates among high-risk patients (IPI 4-5) was 40.9% (95% CI 23.6-71.0) for elevated sPD-L1 vs. 82.1% (95% CI 69.1-97.6) for not elevated sPD-L1 (P<0.01). There was no association of sPD-L1 with 3-year OS among low risk (IPI 0-1) or intermediate-risk (IPI 2-3) patients. Conclusion. We confirmed that sPD-L1 levels are higher in pre-treatment plasma samples of newly diagnosed DLBCL patients compared to controls, and that elevated levels of sPD-L1 are associated with inferior OS in DLBCL patients treated with immunochemotherapy, and this is independent of IPI and other clinical factors. Among high risk patients, we identified a subgroup with less than 50% survival. Figure 1 Figure 1. Figure 2 Figure 2. Disclosures No relevant conflicts of interest to declare.

Abstract Introduction: Follicular lymphoma (FL) is the most common indolent non-Hodgkin lymphoma in the world and has a median age at diagnosis in the seventh decade. FL in young adults (YA; 40 years old or younger) is extremely rare. Currently, there are no standard approaches guiding treatment of YA patients with FL, and very little is known about disease characteristics and outcomes of YA patients with FL given limited research conducted in this vulnerable population. To gain further insight into FL in YA, we analyzed the National LymphoCare Study (NLCS) to describe disease and patient characteristics, as well as features of treatment in YA patients with FL. We previously reported that 2-year progression-free survival (PFS) is an important survival endpoint in patients with FL undergoing chemo-immunotherapy. Hence, we also sought to characterize 2-year PFS in this age group and compare it to older cohorts. Methods: Evaluablepatients were identified in the NLCS, and those between 18–40 years of age with newly diagnosed FL at any stage were classified as YA patients. Patients with mixed histology or transformed disease were excluded, as were patients with progression of disease prior to beginning first-line treatment. Survival probability was estimated by the Kaplan-Meier method. We estimated the association of age group with PFS using hazard ratios (HR) and 95% confidence intervals (CI) from multivariable Cox models. Results: A total of164 YA patients with FL were analyzed, representing 6.2% of the NLCS population, similar to the observed frequency in the Surveillance, Epidemiology, and End Results (SEER) Program data (4.8% of all FL). Sixty nine percent of YA patients had advanced stage disease. The majority of patients (80%) had low-grade histology, and 50% had good risk disease according to the Follicular Lymphoma International Prognostic Index (FLIPI). Nineteen percent of patients (31/164) underwent watchful waiting, 12% received rituximab monotherapy, and 47% received chemo-immunotherapy (61% of whom received R-CHOP [rituximab, doxorubicin, vincristine, prednisone]). There was no significant difference in FLIPI score or other baseline disease characteristics compared to adult patients aged 41–60 years. Eleven deaths occurred among YA with FL; only 5 of these were lymphoma related. Overall survival (OS) at 2 years was 97.4% (95% CI 93.3%, 99.0%), and at 5 years, 93.7% (88.3%, 96.7%), which was similar to patients aged 41–60 (97.2% [96.0%, 98.0%] at 2 years, and 92.0% [90.1%, 93.5%] at 5 years). After a median follow-up of 7.1 years, OS in YA FL was 92%. Through follow-up, there were 64 PFS events. The estimated 2-year PFS (95% CI) for YA and adults 41–60 was 75.9% (67.1%, 82.6%) and 80.9% (78.1%, 83.4%), respectively. After adjusting for FLIPI score, there was no difference in PFS for YA with FL requiring first-line treatment (excluding watchful waiting) compared to adults aged 41–60 years (HR=0.93; 95% CI 0.69, 1.25), and no difference in OS compared to adults aged 41–60 years (HR=1.19; 95% CI 0.64, 2.23). Conclusions: In the largest cohort of YA patients with FL to date, we found few differences in outcomes compared to patients aged 41–60. FLIPI and other disease characteristics were similar to adults aged 41–60 years. There were no differences between YA FL and adults aged 41–60 in PFS for all treated patients. OS in the YA group of patients with FL was outstanding. YA patients with FL have reassuringly similar outcomes to patients aged 41–60. Fertility preservation and survivorship issues should be taken into consideration when defining management strategies, but otherwise these data support that YA patients with FL should not be approached differently from older adults with the same disease. Disclosures Byrtek: Genentech, Inc.: Employment, Equity Ownership. Dawson:Genentech, Inc.: Employment, Equity Ownership. Zhou:RTI-HS: Employee of RTI-HS, which has research contracts with Genentech Other. Flowers:Seattle Genetics: Consultancy; Spectrum: Consultancy, Research Funding; Sanofi: Research Funding; Abbott: Research Funding; Novartis: Research Funding; OptumRx: Consultancy; Millennium/Takeda: Research Funding; Janssen: Research Funding; Celgene: Research Funding; Allos: Consultancy. Farber:Gilead: Speakers Bureau; Janssen/Pharmacyclics: Speakers Bureau; Seattle Genetics: Speakers Bureau; Leukemia Lymphoma Society NJ Chapter: Membership on an entity's Board of Directors or advisory committees; Genentech: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Alexion: Speakers Bureau, Stock ownership Other. Cerhan:Genentech, Inc.: LymphoCare Scientific Advisory Board Other. Link:Genentech, Inc.: Consultancy, Scientific Advisory Board for Genentech Other.

Abstract Background: EBV+ DLBCL not otherwise specified (EBV+ DLBCL NOS) is defined by the 2016 WHO Classification of Tumors of Hematopoietic and Lymphoid Tissues as EBV+ DLBCL in patients without a history of known immunosuppression. Among Asian patients, adverse outcomes have been associated with EBV+ vs EBV- DLBCL, and circulating EBV has been found in the majority of patients. Outcome studies of unselected North American patients are few and non-confirmatory. No formal assessment of immunosuppression status has been incorporated into these analyses, leading to the variable inclusion of patients with a history of immunocompromised states. We sought to determine the prevalence, presenting clinical and pathology characteristics, and outcomes of DLBCL by EBV and immunosuppression status in an unselected prospective cohort study of North American patients. Methods: From 2002-2012, we enrolled consecutive, newly diagnosed patients with lymphoma who were ≥ 18 years and HIV negative into the Molecular Epidemiology Resource (MER). Clinical, laboratory, and treatment data were abstracted from medical records. All DLBCL patients with available tissue microarrays (TMAs), excluding primary mediastinal DLBCL and post-transplant lymphoproliferative disorders, were included. Medical records were reviewed using a standard protocol to classify patients as immunocompromised based on a documented history of primary immunodeficiency or iatrogenic immunosuppression. DLBCL cell of origin was classified as germinal center (GCB) vs non-GCB using the Hans algorithm. CD30 was determined using immunohistochemistry; positivity cutoff was 20% of neoplastic cells. EBV testing was performed by in-situ hybridization for EBV encoded small RNAs (EBERs), and scored by an expert hematopathologist. Event free survival (EFS) was defined as time from diagnosis until recurrence, retreatment, or death due to any cause. Associations with EFS and Overall Survival (OS) were obtained using the methods of Kaplan-Meier (KM). Hazard Ratios (HRs) and 95% confidence intervals (CIs) were estimated from Cox regression models. Results: 1,100 patients with DLBCL were enrolled into the MER; 362 had TMAs available for this analysis. Median age was 63 years (range 20-89) and 59% were male. 89.5% of patients were treated with immunochemotherapy. 16 cases (4.4%) were EBV+, and 39 cases (10.8%) were considered immunocompromised. EBV positivity was more common in immunocompromised vs immunocompetent patients, with a trend towards significance (10.3% vs 3.7%; P = 0.08). The non-GCB subtype was more frequent in EBV+ vs EBV- DLBCL (62.5% vs 34.1%; P < 0.01), but similar between immunocompromised vs immunocompetent patients (48.7% vs 33.7%; P = 0.10). CD30 positivity was also more frequent in EBV+ vs. EBV- DLBCL (25.0% vs 8.1%; P < 0.01), but similar between immunocompromised vs immunocompetent patients (12.8% vs 8.4%, respectively; P = 0.45). With a median follow-up of 59 months (range 0.7-155.2) there were 151 events and 119 deaths. KM curves by EBV and immunosuppression status are shown in the figure. There was no association of EBV positivity with EFS (HR = 0.76; 95% CI 0.34-1.72) or OS (HR = 0.99; 95% CI 0.43-2.25). A history of immunosuppression was not associated with EFS (HR = 1.17; 95% CI 0.72-1.89) or OS (HR = 1.02; 95% CI 0.58-1.78). In the subset of immunocompetent patients, there was no association of EBV positivity with EFS (HR = 0.54; 95% CI 0.17-1.70) or OS (HR = 0.71; 95% CI 0.23-2.25). Similar results were obtained after adjustment for the International Prognostic Index. Circulating viral DNA was detected in 2/5 EBV+ and 4/49 EBV- DLBCL patients. Conclusions: The prevalence of EBV+ DLBCL NOS is <5% among patients in a large North American cohort, similar to other western populations. There was no evidence of an association of EBV or immunosuppression status with EFS or OS, with low power for detecting associations with EBV. Circulating viral DNA was infrequent among either EBV+ or EBV- DLBCL patients. While immunosuppression has been previously shown to be a risk factor for DLBCL, it was not found to be associated with EFS or OS. Overall, these results suggest divergent outcomes among EBV+ DLBCL patients between western and non-western populations, underscoring differences in host factors or circulating viral strains. Disclosures Ansell: BMS, Seattle Genetics, Merck, Celldex and Affimed: Research Funding.

Abstract Introduction. CLL primarily affects elderly individuals who frequently have comorbid health conditions. It is typically assumed non-CLL-related etiologies will be the ultimate cause of death for most CLL patients, particularly those with comorbid conditions at diagnosis. Methods. Between 9/2002 and 11/2014, 1174 patients with newly diagnosed CLL were enrolled in a prospective cohort study evaluating the natural history of CLL. Comorbidities were prospectively recorded at the time of diagnosis. Comorbidities arising during the course of disease were not considered for this analysis. Standardized longitudinal follow-up was performed in all patients every 6 months for the first 3 years after diagnosis and annually thereafter through 04/2015. Internal and external medical records, death certificates, and information from next of kin were centrally reviewed to determine cause of death, using a standardized protocol. Categorical and continuous variables were evaluated using the c2 or Fisher exact tests and the Mann-Whitney test, as appropriate. Results. Baseline characteristics at time of CLL diagnosis are shown in the Table. Over 80% of patients had 2 or more comorbidities at diagnosis (median 3, range 0-11). After a median follow up of 5 years, 224 (19%) patients have died. The cause of death could be accurately determined in 183 (82%) of these patients. The cause of death was due to CLL in 135 (74%), including 84 (46%) CLL progressions, 14 (8%) infections, and 37 (20%) other cancers. Death was due to non-CLL-related causes (such as congestive heart failure, stroke or chronic obstructive pulmonary disease) in the remaining 48 (26%) patients. On univariable analysis, age and number of comorbid health conditions were not related to whether or not the cause of death was related/unrelated to CLL. The only specific co-morbid condition at diagnosis that predicted for non-CLL related death was stroke (8% vs 1%, p=0.04). Unmutated IGHV was the only prognostic factor thatpredicted greater likelihood of CLL-related death (70% vs 50%, p=0.03). Conclusions. The majority of patients with CLL have multiple comorbidities at time of diagnosis. Despite this fact, CLL progression and/or CLL-related complications are the primary cause of death. The number and type of comorbidities at diagnosis have minimal relationship to whether or not the ultimate cause of death was CLL-related. In contrast, the CLL-specific characteristic IGHV status (but interestingly not FISH defects) correlates with cause of death. Collectively, these findings illustrate the need for more effective CLL therapy, particularly treatments that can be tolerated by patients with comorbid health conditions. It is hoped the signaling inhibitors may help address this unmet need. Table. Number (%), median [range] N=1174 Age (years) 63 [23-89] Males Females 791 (67) 383 (33) Creatinine-Clearance > (mL/min) 86 [10-252] B2M (mg/dL) 2.3 [1.1-13.2] Rai stage 0 I-II III-IV 604 (52) 512 (38) 54 (10) CD49d positive negative 277 (30) 638 (70) CD38 positive negative 332 (30) 780 (70) ZAP70 positive negative 380 (37) 650 (63) IGHV unmutated mutated 394 (44) 506 (56) FISH del13q negative +12 del11q del17p 395 (40) 175 (18) 276 (28) 90 (9) 50 (5) Comorbid health conditions Other cancers 237 (20) Stroke 38 (3) Cardiac disease 326 (28) Hypertension 472 (40) Respiratory 210 (18) Endocrinologic 165 (14) Diabetes 118 (10) Hyperlipidemia 485 (41) Rheumatologic 489 (42) Gastrointestinal 384 (33) Genitourinary 412 (35) Psychiatric 197 (17) DVT/PE 33 (3) Substance abuse 58 (5) Sexually transmitted disease 35 (3) Obesity 376 (32) # of Comorbidities 0 1 2 3 4 > 4 66 (6) 148 (13) 203 (17) 220 (19) 206 (17) 331 (28) Disclosures Kay: Hospira: Research Funding; Genentech: Research Funding; Celgene Corporation: 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, Research Funding; Pharmacyclics: Research Funding; Tolero Pharma: Research Funding. Cerhan:Kite Pharma: Research Funding.

Abstract Background: Vitamin D insufficiency (VDI) has been associated with inferior outcomes in patients with follicular lymphoma (FL) treated with chemotherapy and an anti-CD20 monoclonal antibody (immunochemotherapy; IC), but whether this association holds for other FL treatment strategies is not known. Furthermore, the subset of FL patients who experience clinical failure (progression, treatment failure) within 12 months of diagnosis have an inferior outcome compared to those achieving event-free status at 12 months (EFS12). We tested the hypothesis that VDI is predictive of early clinical failure (failure to achieve EFS12), overall survival (OS), and lymphoma-specific survival (LSS) among FL patients overall and in subgroups who were initially observed, treated with IC, rituximab monotherapy, or other therapies. Methods: We evaluated this hypothesis in a prospective cohort study of 642 newly diagnosed patients with grade 1-3a FL enrolled from 2002-2012 in the Molecular Epidemiology Resource of the Iowa/Mayo Lymphoma SPORE. Serum 25-hydroxyvitamin D [25(OH)D] was measured by liquid chromatography-tandem mass spectroscopy, and VDI was defined as serum total 25(OH)D <20 ng/mL (per Institute of Medicine criteria) determined within 120 days of diagnosis. We used logistic regression to estimate Odds Ratios (ORs) and 95% confidence intervals (CIs) and Cox regression to estimate hazard ratios (HRs) and 95% CIs. We adjusted for FLIPI, body mass index, and month of blood draw; for the full cohort, we additionally adjusted for treatment. Results: The mean age at diagnosis was 60 years (range 23-93); 19% of patients had VDI. The median follow-up period was 59 months (range 1-144), with 297 events and 78 deaths (42 due to lymphoma). Treatment included IC (N=252), observation (N=218), rituximab monotherapy (N=76), or other (N=96). OS and EFS curves are shown in figures 1 and 2. In the full cohort, VDI was associated with inferior EFS12 (OR=2.05; 95% CI 1.18-3.54), OS (HR=2.35; 95% CI 1.37-4.02), and LSS (HR=2.97; 95% CI 1.52-5.80). In the IC subset, VDI was associated with inferior EFS12 (OR=3.00; 95% CI 1.26-7.13), OS (HR=2.86; 95% CI 1.39-5.85), and LSS (HR=2.96; 95% CI 1.29-6.79). VDI was associated with inferior OS in patients who were observed (HR=2.25; 95% CI 1.20-6.76) or received other therapies (HR=3.06; 95% CI 1.01-9.24). VDI was positively but not statistically significantly associated with inferior EFS12 in the patients who were observed (OR=1.71; 95% CI 0.70-4.17) or received other therapies (OR=2.31; 95% CI 0.41-13.07), with low power for these associations; LSS could not be estimated due to lack of deaths. There were too few events (N=10) and deaths (N=5) in the R-monotherapy group for analysis. Conclusions: We confirm previous findings that vitamin D insufficiency is associated with adverse long-term prognosis among patients with FL treated with IC, and extend these findings to patients who are initially observed or treated with other therapies. For the first time, we observed an association of vitamin D insufficiency with early clinical failure, suggesting a potentially modifiable factor to address in this subset of patients with poor outcomes. Whether treating VDI improves outcomes in FL warrants assessment. Disclosures Ansell: BMS, Seattle Genetics, Merck, Celldex and Affimed: Research Funding. Nowakowski:Celgene: Research Funding; Morphosys: Research Funding; Bayer: Consultancy, Research Funding.

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.

Introduction. Acquired severe aplastic anemia (SAA) is a life-threatening disorder characterized by severe progressive pancytopenia and hypocellular bone marrow. The etiology of acquired SAA is not understood but believed to be related to abnormal immune responses to environmental exposures. We conducted a genome-wide association study (GWAS) to identify common germline variants associated with SAA. Methods. We identified 895 patients with SAA who underwent related or unrelated hematopoietic cell transplant (HCT) with clinical data and pre-HCT blood samples available in the Center for International Blood and Marrow Transplant Research (CIBMTR) database and biorepository. Pre-HCT DNA was extracted from blood of patients with SAA and genome-wide genotyping was conducted using the Illumina OmniExpress array. We excluded 93 inherited bone marrow failure cases. The SAA cases were grouped cases into discovery and validation sets based on time of batch sample receipt. Analyses were limited to patients of European ancestry based on principal component analyses to minimize the potential effect of population stratification. Controls were genomically matched and selected from previously scanned cancer-free subjects at the Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, NCI. The final analysis included 534 acquired SAA cases (359 in the discovery set and 175 in the validation set), and 2,455 controls (1,396 in the discovery set, and 1,059 in the validation set). Results. Patients with SAA in this study received HCT between 1989-2015 at a median age of 21 years, 56% were male, and the median time between SAA diagnosis and HCT was 11 months. Strong genome-wide association signals were identified across the human leukocyte antigen (HLA) genes encoded at the major histocompatibility complex (MHC) on chromosome 6p21 (Figure 1). The top SNP was located in the P4 binding pocket of the HLA class II gene HLA-DPB1(rs1042151A>G, p.Met105Val, pooled-odds ratio [OR]=1.75, 95% confidence interval [CI]=1.50-2.03, p=1.94x10-13). The expression of HLA-DP in CD19+ cells from 175 healthy donors was significantly different by rs1042151 A>G genotype (p=2.04x10-6) (Figure 2). A second SNP near HLA-B, rs28367832G>A, also reached genome-wide significance (pooled-OR=1.49, 95% CI=1.22-1.78, p=7.27x10-9). Copy-number variant analysis and next generation sequencing also identified somatic, clonal copy-neutral loss-of-heterozygosity affecting class I HLA genes in 8.6% of the SAA cases and none of the controls. Conclusion. This SAA GWAS identified strong association signals between common germline genetic variants in HLA class I and II genes and SAA. The main SNP is associated with changes in HLA-DP expression suggesting a key role for this locus in SAA etiology. This study adds further evidence to the connection between SAA and immune dysregulation. Disclosures Cerhan: Janssen: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; Celgene: Research Funding. Lee:Incyte: Research Funding; Syndax: Research Funding; Amgen: Research Funding; Novartis: Research Funding; Takeda: Research Funding; Kadmon: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; AstraZeneca: Research Funding.