Feldman, Andrew L.

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 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.

BACKGROUND: Angioimmunoblastic T cell lymphoma (AITL) has a unique histological phenotype with a relatively small number of malignant T-cells and an extensive infiltrate of normal cells including T-cells, B-cells, NK cells and FDCs. The malignant cell has a T follicular helper (Tfh) cell phenotype with increased expression of immune checkpoint molecules including PD-1 and ICOS. With the routine use of antibodies targeting immune checkpoint molecules, clinical results in limited numbers of AITL patients have thus far been mixed with some responding patients but also some who rapidly progress. Because both malignant T-cells and normal T-cells in AITL may be PD-1 positive, we conducted a study to compare the phenotype of normal T-cells and malignant T-cells within the tumor microenvironment. We also evaluated whether soluble immune checkpoint molecules in the peripheral blood differed between AITLs, controls, and other PTCL subgroups. METHODS: We designed a novel CyTOF antibody (Ab) panel comprised of 37 markers to identify and characterize cells of T, B, NK, macrophage and FDC lineages within a cohort of biopsy specimens from 6 AITL patients compared to 6 normal controls (2 LN, 4 tonsil). Soluble immune receptors and ligands were then measured by multiplex ELISA on peripheral blood from 8 AITLs, 5 controls, and 32 additional samples from PTCL subtypes including PTCL-NOS, ALCL and CTCL. RESULTS: Malignant T-cells in AITL specimens could clearly be identified by viSNE mapping based on CyTOF expression of CD4, CD5, BCL6, CD10 (variable), CXCR5 (variable), PD-1 and ICOS. Aberrant loss of CD3 and CD7 on malignant T-cells was commonly seen. Expression of other immune checkpoint molecules was assessed. Malignant cells expressed high levels of TIGIT and moderate levels of CD27, but malignant T-cell populations were negative for expression of TIM3, LAG3, and CTLA-4. A substantial proportion of normal T-cells also expressed immune checkpoint molecules including PD-1 and CD27, and a subset of these cells also expressed TIGIT and ICOS. A small minority of normal T-cells expressed TIM3, LAG3, and CTLA-4. As others have described, the normal Tfh cell population had an immunophenotype that was otherwise relatively similar to the malignant population, but distinguishable via viSNE mapping with our CyTOF Ab panel. Expression of PD-L1 could be detected on NK-cells, macrophages, and a few select populations of normal T cells, but not on B-cells or FDCs. Malignant T-cells did not express PD-L1. Due to the potential importance of immune checkpoint molecules in this disease, we then measured soluble checkpoint receptors and ligands in the serum. Soluble PD-1 was significantly increased in the peripheral blood of AITL when compared to normal controls (average 2637 pg/mL (SE = 512.1) in AITL vs 57.87 (SE = 16.88) in controls). Similarly, soluble PD-L1 was significantly increased in AITL compared to normal controls (13.8 (SE = 10.5) in AITL vs 0.94 (SE = 0.25) in controls). CONCLUSIONS: Malignant T-cells in AITL have a unique phenotype which was able to be reliably identified through use of viSNE mapping with our CyTOF Ab panel. Furthermore, malignant T-cells were found to consistently express additional immune regulatory molecules beyond PD-1 and ICOS, including TIGIT and CD27. However, a substantial subset of normal T-cells have a very similar phenotype suggesting that activation of T-cells in this disease will likely stimulate both normal and malignant T-cells. This may explain the rapid disease progression seen in a subset of AITL patients treated with PD-1 blockade. Furthermore, soluble PD-1 is significantly increased in the serum of AITL patients and may explain the overall lack of clinical efficacy of anti-PD-1 antibodies in treating AITL. Disclosures Cerhan: NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. Ansell:LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; LAM Therapeutics: Research Funding.

Background: While there is evidence in the literature of increased prevalence of hypogammaglobulinemia in chronic lymphocytic leukemia (CLL), there are no studies evaluating the prevalence of hypogammaglobulinemia in newly diagnosed diffuse large B cell lymphoma (DLBCL) or the relationship between hypogammaglobulinemia and presentation or outcomes. The objective of this study was to examine the prevalence of hypogammaglobulinemia in newly diagnosed DLBCL patients and to test the hypothesis that DLBCL patients with baseline hypogammaglobulinemia have a distinct clinical profile and outcome. Methods: We obtained banked frozen sera from 200 newly diagnosed, treatment-naïve, DLBCL patients from the Lymphoma SPORE Molecular Epidemiology Resource (MER), a prospective cohort study conducted at the Mayo Clinic and the University of Iowa. IgG/A/M levels were measured using immunoturbidimetric assay whereas IgE level was measured using electrochemiluminescence immunoassay; deficiency was defined using standard reference ranges. IgE levels were considered deficient if &lt;2 UI/ml. The associations between Ig deficiencies and clinical factors were evaluated with Wilcoxon rank sum and chi-squared (Fisher's exact, where appropriate) tests. Event-free survival (EFS) was defined as time from diagnosis to progression, relapse, retreatment, or death, and EFS24 was defined as EFS at 24 months after diagnosis (achieve or failure to achieve EFS24). The association of Ig levels with EFS24 was estimated using odds ratios (OR) and 95% confidence intervals (CI) from logistic regression, and with EFS, overall survival (OS), and lymphoma-specific survival (LSS) was estimated using Kaplan-Meier curves and hazard ratios (HR) and 95% CI from Cox regression. Results: The mean age (SD) of the cohort was 65.6 (13.4) years, 54% were males and 98% of the patients were white. Over a median follow-up of five years, there were 59 (29.5%) deaths. The prevalence of hypogammaglobulinemia, defined as any deficiency, in newly diagnosed, treatment-naïve DLBCL was 22.1% (44/199) in our cohort, and the most common Ig deficiency was for IgG (&lt;700 mg/dL, 13.5%), followed by IgM (&lt;40 mg/dL, 9.0%), IgE (&lt;2 UI/ml, 7.5%) and IgA (&lt;70 mg/dL, 4.0%) (Table1). There were no statistically significant differences between Ig deficient and non-deficient patients in terms of age at diagnosis, gender, stage, cell of origin, or MYC double hit status. However, median LDH levels were higher in Ig deficient patients (228 vs 194, p&lt;0.01). Any immunoglobulin deficiency was associated with inferior EFS (HR 1.94, 95% CI 1.16-3.24) (Figure 1) and OS (HR 2.02, 95% CI 1.17-3.49), and these associations were not attenuated after adjustment for the international prognostic index (IPI). Any Ig deficiency was also associated with failure to achieve EFS24 (OR=2.13, 95% CI 1.00-4.60) after adjusting for IPI. Conclusions: To the best of our knowledge, this is the first study to report the prevalence of hypogammaglobulinemia in treatment naïve DLBCL. We found that any Ig deficiency was not uncommon in our cohort and it was associated with an inferior EFS and OS in DLBCL patients. The prevalence of hypogammaglobulinemia in DLBCL patients seems to be lower than has been described in CLL patients. While the underlying relationship between these two immunologic disorders deserves further study, our findings highlight the interaction between global immune dysfunction and emergence of a clonal B cell process. Disclosures Nowakowski: Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding. Farooq:Celgene: Honoraria; Kite Pharma: Research Funding. Cerhan:NanoString: Research Funding; Celgene: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees.

Introduction: New Diffuse Large B-cell (DLBCL) treatments remain a clinical need despite the success of rituximab combined with CHOP chemotherapy (RCHOP), which results in durable responses in 60-70% of patients. Those refractory to, or who relapse following, first-line therapy have a very poor outcome, with only 20% surviving beyond 5 years. Rationally-targeted frontline strategies are needed, especially for those with high-risk disease. Significant advances have been made in the genomic classification of DLBCL, but none have impacted the design of phase III trials for untreated DLBCL patients. Building on new genetic profiling studies to personalize clinical treatment, an NCI initiative, could allow clinicians to add targeted therapies to the RCHOP backbone based on individual tumor signatures. The phase II ECOG-ACRIN1412 trial, which compared RCHOP combined with lenalidomide (R2CHOP) versus RCHOP showed significantly superior event free survival (EFS) and overall survival benefits for those treated with R2CHOP. Herein, we report the profile of a high-risk ABC/non-GCB subset of DLBCL driven by genomic alterations in inflammatory genes that are susceptible to front-line R2CHOP, but continue to experience poor outcome with RCHOP alone. Methods: We studied a total of 196 DLBCL patients. 47 were treated with R2CHOP from an investigator-initiated, open-label, single-arm phase II study (NCT00670358). 149 were newly diagnosed DLBCL cases treated with RCHOP, or R-immunochemotherapy (herein called RCHOP), and followed prospectively through the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic SPORE that served as a contemporary cohort. Patients from each treatment group were divided based on their event free survival at 24 months (EFS24). DNA alterations within these populations were identified through whole exome sequencing (WES). Variants were analyzed for their presence in EFS24 achievement or failure groups in both RCHOP and R2CHOP. Both tumor and germline sequencing was performed for 47/47 R2CHOP cases and 49/149 RCHOP cases. Gene expression data from the PanCan panel of 730 B-cell-related genes was analyzed to determine gene expression profiles characteristic of the high-risk/R2CHOP-profile on 59 available non-GCB DLBCL cases (45 RCHOP; 14 R2CHOP). A two-sided comparative marker analysis T statistic test was applied to assess what genes displayed differential expression based on achieving EFS24 in both R2CHOP and RCHOP populations. Positive values represented associations with achieving EFS24 and negative values were associated with cases of EFS24 failure. Results: Three genes were enriched in the RCHOP cases that failed EFS24 but achieved EFS24 with R2CHOP among non-GCB patients: PIM1, SPEN, and MYD88 (L265P), herein referred to as Responder Alterations. In R2CHOP cases, patients with a Responder Alteration had a better overall EFS (P = 0.051) compared to wild type patients. In contrast, RCHOP treated patients with a Responder Alteration in their tumor had a significantly worse overall EFS (P = 0.0004) compared to patients without a mutation. Together, PIM1, SPEN, or MYD88 (L265P) mutations were present in 38.0% (30/79) of all non-GCB cases. Collective R2CHOP and RCHOP EFS24 differential gene expression T values were significantly different for 18 previously-defined signatures. The R2CHOP cases that achieved EFS24 were enriched for genes involved in cell cycle, JAK-STAT, cytokine signaling, and NF-κB pathways based on these signatures and ontology analyses. Lastly, cases with WES and PanCan data were analyzed together to observe differential gene expression patterns between cases with and without signature mutations. These data suggest that R2CHOP disrupts tumors reliant on IRF4, NF-κB, and STAT transcription factors, leading to a loss of proliferative feedback systems. Conclusions: Our combined analysis of DNA and RNA across R2CHOP and RCHOP treatment cohorts identifies a high-risk non-GCB phenotype that encompasses approximately 38% of non-GCB patients, is capable of sustaining JAK-STAT and NF-κB signaling, and is sensitive to R2CHOP. Our study lays the groundwork for a precision therapy approach in DLBCL in which DNA or RNA profiles can be used to identify patients early in treatment who may not benefit from the current standard of care, RCHOP, and who would benefit from the addition of lenalidomide or other targeted agents. Disclosures Gandhi: Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Ansell:LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding. Cerhan:Celgene: Research Funding; NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees. Nowakowski:Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Genentech, Inc.: Research Funding; MorphoSys: Consultancy, Research Funding; NanoString: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees. Novak:Celgene Coorperation: Research Funding.

INTRODUCTION Complete staging via bone marrow assessment is standard of practice for most newly diagnosed non-Hodgkin lymphoma (NHL) patients, though the methods and patterns of bone marrow assessment vary in clinical practice. The 2014 Lugano Classification recommends that in diffuse large B cell lymphoma (DLBCL), positron emission tomography-computed tomography (PET/CT) alone can designated advanced-stage disease in many, but not all cases. For most other NHL subtypes, obtaining bone marrow biopsy (BMB) remains recommended. We aimed to assess clinical practice patterns of marrow assessment and the performance of PET/CT in the clinical setting. METHODS The Lymphoma Epidemiology of Outcomes (LEO) cohort is a prospective cohort from 8 academic medical centers. All patients were enrolled in the cohort within 6 months of diagnosis, and prospectively followed. Patients with lymphoma enrolled in the cohort between July 2015 and June 2017 were included for study. Baseline clinical characteristics at diagnosis, whether BMB and/or PET/CT were performed, and the result of these assessments were extracted from the database. Bone marrow involvement on PET/CT was abstracted via review of radiology reports. No central review of images was utilized. RESULTS In total, 3,251 patients were included, consisting of the following subtypes: DLBCL (n=1,098), follicular lymphoma (FL n=730), mantle cell lymphoma (MCL n=302), T-cell lymphoma (TCL n=278), marginal zone lymphoma (MZL n=269), other NHL (n=505) and composite lymphoma (n=69). Other clinical characteristics are shown in table 1. Staging BMB was obtained in 78% of patients, PET/CT was obtained in 56%, both BMB and PET/CT were obtained in 46% while neither were obtained in 9% (table 1). There were significant differences in lymphoma subtype between patients having any bone marrow assessment vs no bone marrow assessment (p<0.0001). MCL (96%) and DLBCL (94%) most frequently had any bone marrow assessment, while MZL was least frequently assessed (84%). Patients undergoing any bone marrow assessment had higher median lactate dehydrogenase (247 U/L vs 206 U/L, p=0.0002). Among the 8 enrolling medical centers, the frequency of bone marrow biopsy at diagnosis ranged from 53% to 86%, and the frequency of PET/CT at diagnosis ranged from 34% to 94% (p<0.0001, table 1). Among patients with DLBCL who had positive bone marrow by either PET/CT or BMB (N=107), 41% (44/107) were positive by BMB only, 19% (20/107) were positive by PET/CT only, and 40% (43/107) were positive by both. Figure 1 shows results of similar analyses for FL, MCL, MZL, and TCL. Using BMB as a gold standard for marrow involvement, the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of PET/CT for marrow assessment in DLBCL is 49%, 90%, 68% and 91%, respectively. Data for other subtypes are shown in table 2. Patients undergoing both PET/CT and BMB had lower median LDH (231 U/L vs 254 U/L, p=0.0032) than those undergoing one or neither assessments. CONCLUSION Overall, 91% of all patients with newly diagnosed NHL had bone marrow assessments via either PET/CT, BMB or both. The significant variation in the frequency of BMB and PET/CT use among the 8 centers reflects a lack of clear consensus on staging practices regarding bone marrow assessment. The sensitivity of PET/CT for detection of marrow involvement was less than 50% across all subtypes. It is possible the sensitivity of PET/CT could be improved via a centralized review process of the images, as is performed in some clinical trials, however practical approaches for implementing such reviews in clinical practice have not been developed. Between 92-100% of all positive bone marrows were detected by BMB, except in cases with DLBCL in which 19% of positive bone marrows were missed by BMB but detected by PET/CT. Thus, in our analysis, BMB more reliably captured marrow involvement that PET/CT in all subtypes in which both assessments were performed. However, neither PET/CT nor BMB alone were able to identify all cases of bone marrow involvement and use of both modalities may be necessary in cases where marrow involvement would have clinically important implications. Disclosures Rutherford: Juno Therapeutics Inc: Consultancy, Honoraria; Janssen Scientific Affairs: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Verastem: Consultancy, Honoraria; Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Heron: Consultancy, Honoraria. Kahl:ADC Therapeutics: Consultancy, Research Funding; Seattle Genetics: Consultancy; TG Therapeutics: Consultancy; BeiGene: Consultancy. Lossos:Janssen Scientific: Membership on an entity's Board of Directors or advisory committees; NIH: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees. Friedberg:Acerta: Other: Data & Safety Monitoring Committee; Bayer: Honoraria, Other: Data & Safety Monitoring Committee. Casulo:Celgene: Research Funding; Roche: Other: Travel, accommodation, expenses; Gilead: Honoraria, Other: Travel, accommodation, expenses. Cohen:Astra Zeneca: Research Funding; Takeda Pharmaceuticals North America, Inc.: Research Funding; Hutchison: Research Funding; Gilead/Kite: Consultancy; Bristol-Meyers Squibb Company: Research Funding; LAM Therapeutics: Research Funding; UNUM: Research Funding; Lymphoma Research Foundation: Research Funding; ASH: Research Funding; Seattle Genetics, Inc.: Consultancy, Research Funding; Janssen Pharmaceuticals: Consultancy; Genentech, Inc.: Consultancy, Research Funding. Nastoupil:TG Therapeutics: Honoraria, Research Funding; Novartis: Honoraria; Spectrum: Honoraria; Celgene: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Genentech, Inc.: Honoraria, Research Funding; Gilead: Honoraria; Bayer: Honoraria. Flowers:Denovo Biopharma: Consultancy; Spectrum: Consultancy; National Cancer Institute: Research Funding; Optimum Rx: Consultancy; Burroughs Wellcome Fund: Research Funding; Acerta: Research Funding; Bayer: Consultancy; BeiGene: Consultancy, Research Funding; Eastern Cooperative Oncology Group: Research Funding; TG Therapeutics: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Gilead: Consultancy, Research Funding; Karyopharm: Consultancy; AstraZeneca: Consultancy; Pharmacyclics/Janssen: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; V Foundation: Research Funding; Millenium/Takeda: Research Funding; Celgene: Consultancy, Research Funding. Cerhan:Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; NanoString: Research Funding. Martin:Sandoz: Consultancy; Karyopharm: Consultancy; Celgene: Consultancy; Teneobio: Consultancy; I-MAB: Consultancy; Janssen: Consultancy.

Abstract Backgroud. The treatment landscape of mantle cell lymphoma (MCL) has significantly changed in last decades. Improvement in diagnosis and understanding of disease biology has been coupled with emergence of new therapeutic options, including targeted agents. While MCL outcome data comes primarily from clinical trials (CT), the impact of therapeutic advances on pattern of care (POC) and outcome in MCL in the general population is not well characterized. This study sought to characterize changes in pattern of care and outcomes of patients with MCL in a prospective observational series in the rituximab era. Methods. The study included consenting adult patients with newly diagnosed MCL that were prospectively enrolled into the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE) Molecular Epidemiology Resource, from 09/01/2002 to 06/30/2015. Demographic, clinical and prognostic factors were abstracted, and all patients were actively followed for re-treatment, relapse and death. Since bendamustine-rituximab (BR) regimen starting to be used in 2010, we defined patients enrolled from 09/01/2002 to 12/31/2009 as era 1 and those enrolled 01/01/2010 to 06/30/2015 as era 2. Baseline characteristics and outcomes, evaluated in terms of event free survival (EFS), overall survival (OS) and cause of death, were analyzed and compared between the two eras identified. Results. 348 patients with newly diagnosed MCL were enrolled. Five patients had no available data for front-line treatment and were excluded. The analysis was thus conducted on 343 patients: 169 patients were diagnosed in era 1 with a median follow-up of 131.2 months vs. 174 in era 2 with a median follow-up of 58.9 months. Baseline clinical characteristics and MIPI score were similar across the two eras ( Table 1). Frontline induction treatment was significantly different in the two eras. BR use was 0 vs 49 (28.2% ), R-CHOP/CHOP like regimen in 89 (52.7%) vs 45 (25.9%), high-dose cytarabine (HiDAC)-based therapy in 1 (0.6%) vs 28 (16.1%), intensified regimens (HyperCVAD) in 16 (9.5%) vs 8 (4.5%), other regimens (including R-cladribine, R-fludarabine-mitoxantrone, rituximab monotherapy) in 35 (20.7%) vs 13 (7.5%) patients in era 1 vs era 2 respectively. Non-systemic treatment (observation, surgery or radiation only) was performed in 25 (14.8%) vs 31 (17.8%), while 9 (8%) vs 12 (7.7%) patients were enrolled in clinical trials, in era 1 vs era 2 respectively. Autologous stem cell transplantation (ASCT) as consolidation of first line treatment or use of Rituximab maintenance was not different between Era 1 and Era 2. Among the entire cohort of 343 MCL patients, 3y-EFS and 3y-OS were 51.9% (95% IC 46.7-57.6) and 73.5 (95% CI 68.8-78.4), respectively (Figure 1). 3y-EFS was 45.9% (95% CI 39.0-54.1%) vs 58.4 (95% CI, 51.2-66.6%) in Era 1 vs Era 2 (HR 0.69 (0.51-0.92), p=0.006), 3y-OS was 70.9% (95% CI, 63.4-78.1%) vs 76.1% (95% CI, 69.7-83.1%) in Era 1 vs Era 2 (HR 0.87 (0.61-1.24), p=0.26), respectively (Figure 1). In a univariate analysis, high risk simplified MIPI was prognostic of lower EFS and OS in both the era groups. Conclusion. The BR regimen entered front line therapy of MCL resulting in decline of R-CHOP use in the MER. While rates of ASCT remain similar over the two eras, high dose cytarabine usage in induction therapy has increased. While POC in MCL continue to evolve, the introduction of bendamustine and high-dose citarabine based regimens resulted in an improvement in EFS but not OS in this observational cohort-based analysis. Findings in this study are important for design and planning of future clinical trials incorporating novel agents in induction therapy of MCL. Figure 1. Figure 1. Disclosures Cohen: Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Infinity Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BioInvent: Consultancy; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Research Funding; Bristol-Myers Squibb: Research Funding; Infinity Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Millennium: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Research Funding; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Millennium: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BioInvent: Consultancy; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding. Witzig:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Cerhan:Celgene: Research Funding; Jannsen: Other: Scientific Advisory Board; Nanostring: Research Funding.

AbstractNF-κB and Notch signaling can be simultaneously activated in a variety of B-cell lymphomas. Patients with B-cell lymphoma occasionally develop clonally related myeloid tumors with poor prognosis. Whether concurrent activation of both pathways is sufficient to induce B-cell transformation and whether the signaling initiates B-myeloid conversion in a pathological context are largely unknown. Here, we provide genetic evidence that concurrent activation of NF-κB and Notch signaling in committed B cells is sufficient to induce B-cell lymphomatous transformation and primes common progenitor cells to convert to myeloid lineage through dedifferentiation, not transdifferentiation. Intriguingly, the converted myeloid cells can further transform, albeit at low frequency, into myeloid leukemia. Mechanistically, coactivation of NF-κB and Notch signaling endows committed B cells with the ability to self renew. Downregulation of BACH2, a lymphoma and myeloid gene suppressor, but not upregulation of CEBPα and/or downregulation of B-cell transcription factors, is an early event in both B-cell transformation and myeloid conversion. Interestingly, a DNA hypomethylating drug not only effectively eliminated the converted myeloid leukemia cells, but also restored the expression of green fluorescent protein, which had been lost in converted myeloid leukemia cells. Collectively, our results suggest that targeting NF-κB and Notch signaling will not only improve lymphoma treatment, but also prevent the lymphoma-to-myeloid tumor conversion. Importantly, DNA hypomethylating drugs might efficiently treat these converted myeloid neoplasms.

Abstract Background: To address the current and long-term unmet health needs of the growing population of patients diagnosed with non-Hodgkin lymphoma (NHL) and NHL survivors, we established the Lymphoma Epidemiology of Outcomes (LEO) cohort study (NCT02736357; https://leocohort.org/) with recruitment at eight centers (Cornell University, Emory University, Mayo Clinic, MD Anderson, University of Iowa, University of Miami, University of Rochester, and Washington University). Here we report on the first two years of enrollment. Methods: All patients ≥18 years with NHL (exclusive of chronic lymphocytic leukemia) newly diagnosed within 183 days of enrollment are eligible. LEO reviews all pathology diagnoses; annotates and harmonizes all cases with clinical, epidemiologic, pathology and treatment data; builds a NHL tumor bank that includes an H&E slide, a formalin-fixed, paraffin-embedded tissue sample and extracted tumor DNA and RNA; collects peripheral blood and banks DNA, serum, plasma and buffy coat in a central biorepository; and prospectively follows patients regularly to ascertain disease progression/relapse, retreatment, transformation, second cancers, survival, updated exposures, and patient-reported outcomes. We examined the demographics, clinical characteristics, and NHL subtypes for patients enrolled in the first two years (2015-2017) in LEO Cohort and compared these characteristics to population-based registry data from the United States Surveillance, Epidemiology, and End Results (SEER) Program for 2011-2015. We also examined baseline body mass index (BMI), comorbidities and quality of life (QOL) measured with the Functional Assessment of Cancer Therapy - General (FACT-G) scores (0-100 scale) overall and by NHL subtype that were collected in LEO but not available in SEER or any other large, prospective US NHL cohort. Results: From 2015-2017, LEO enrolled 3244 NHL patients across 47 states (Figure) with a median age at diagnosis of 62 years (range, 18-94 years) and 55.9% male. Based on self-identified race/ethnicity, 85% of the participants were White, 7.4% were Black/African American, 2.6% were Asian, and 3.5% other or more than one race; 9.9% were Hispanic (regardless of race). The most common subtypes were diffuse large B-cell (DLBCL, 33.9%), follicular (FL, 22.2%), mantle cell (MCL, 9.3%), marginal zone (MZL, 8.3%), T-cell (TCL, 10.9%), and all other NHL subtypes (other NHL, 15.3%). These distributions were all within 5% of SEER data, except that 6.8% of participant in LEO were in the age 80+ years group compared to 18.5% in SEER (Table). The overall prevalence of self-reported comorbidities at enrollment was 13.5% for heart disease (range, 12.1% for FL to 16.6% for MCL), 8.8% for diabetes (range, 7.1% for FL to 9.8% for DLBCL), 4.6% for osteoporosis (range, 3.0% for MCL to 6.3% for MZL), and 5.9% for autoimmune disease (range, 2.0% for MCL to 12.6% for MZL). The median BMI was 27.9 kg/m2 (range, 26.7 kg/m2 for TCL to 28.3 kg/m2 for MZL). A total of 2151 participants completed the FACT-G with a median score of 78.7 (range, 75.9 for TCL to 83.3 for MZL). Complete IPI data were available on 2296 participants: 40.4% IPI 0-1, 32.7% IPI 2, 18.3% IPI 3, 8.7% IPI 4-5. Overall, 73.7% of participants were initially treated with systemic therapy (range, 40.0% for MZL and 91.8% for DLBCL); 14.5% were initially managed with observation (range, 1.4% DLBCL and 31.8% for FL); and 2.0% were initially manage with radiotherapy (range, 0.2% for MCL and 3.8% for DLBCL). Outcomes are being prospectively collected. Conclusions: LEO is a large and diverse cohort of newly diagnosed NHL patients and the first two years of enrollment reflects the broader United States NHL population. Enrollment is ongoing. This unique resource will enable examination of the interactions among a broad array of clinical and molecular factors and their impact on multiple outcomes, to improve prognostication, identify new approaches to improve outcomes and survivorship, and facilitate clinical trials using this infrastructure. Disclosures Flowers: Acerta: Research Funding; Pharmacyclics/ Janssen: Consultancy; OptumRx: Consultancy; Janssen Pharmaceutical: Research Funding; V Foundation: Research Funding; Burroughs Wellcome Fund: Research Funding; Spectrum: Consultancy; Eastern Cooperative Oncology Group: Research Funding; Abbvie: Consultancy, Research Funding; Bayer: Consultancy; Millennium/Takeda: Research Funding; Abbvie: Research Funding; Celgene: Research Funding; Gilead: Consultancy; BeiGene: Research Funding; Karyopharm: Consultancy; Denovo Biopharma: Consultancy; Genentech/Roche: Consultancy; Gilead: Research Funding; Genentech/Roche: Research Funding; National Cancer Institute: Research Funding; TG Therapeutics: Research Funding; Pharmacyclics: Research Funding. Nastoupil:TG Therappeutics: Research Funding; Merck: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Janssen: Research Funding; Spectrum: Honoraria; Gilead: Honoraria; Genentech: Honoraria, Research Funding; Karus: Research Funding; Novartis: Honoraria; Juno: Honoraria. Kahl:Acerta: Consultancy; Genentech: Consultancy; Seattle Genetics: Consultancy; Juno: Consultancy; CTI: Consultancy; ADC Therapeutics: Consultancy; Gilead: Consultancy; Celgene: Consultancy; AstraZeneca: Consultancy; Abbvie: Consultancy. Casulo:Celgene: Research Funding; Gilead: Honoraria. Friedberg:Bayer: Honoraria. Lossos:Affimed: Research Funding. Martin:Seattle Genetics: Consultancy; Kite: Consultancy; Janssen: Consultancy; Gilead: Consultancy; Bayer: Consultancy; AstraZeneca: Consultancy. Leonard:Bayer: Consultancy; Celgene: Consultancy; Biotest: Consultancy; United Therapeutics: Consultancy; BMS: Consultancy; MEI Pharma: Consultancy; Juno: Consultancy; Sutro: Consultancy; Karyopharm: Consultancy; ADC Therapeutics: Consultancy; Pfizer: Consultancy; AstraZeneca: Consultancy; Gilead: Consultancy; Genentech/Roche: Consultancy; Novartis: Consultancy. Bernal-Mizrachi:Kodikaz Therapeutic Solutions: Consultancy, Equity Ownership; Takeda Pharmaceutical Company: Research Funding. Maurer:Morphosys: Research Funding; Nanostring: Research Funding; Celgene: Research Funding. Cerhan:Nanostring: Research Funding; Jannsen: Other: Scientific Advisory Board; Celgene: Research Funding.