Link, Brian K.

Introduction: Current classifications of low-grade B-cell lymphomas (LGL), including splenic marginal zone lymphomas (SMZL), nodal marginal zone lymphomas (NMZL), and Lymphoplasmacytic lymphomas (LPL) are based on a mixture of clinical features and morphologic, immunophenotypic, and genetic findings from the tumor biopsy specimen. While this approach to classification makes pathologic diagnosis more precise, the corresponding clinical impact for the timing and choice of treatment is limited, and differentiating between cases can be challenging. Although LGL are considered indolent and the 10-year overall survival (OS) is about 80%, 70% of cases will eventually require treatment and approximately 30% of patients display a more aggressive phenotype and have a poor prognosis. Consequently, further investigations into the driving genetic, biological, and immune mechanisms of LGL are essential for early identification of high-risk patients and design of personalized treatments. Materials and Methods: RNA-seq was performed on 63 newly diagnosed LGL patient samples from the Mayo Clinic/University of Iowa Lymphoma SPORE: SMZL (N=48), NMZL (N=6), LPL (N=5), MALT (N=2), and BCL (N=2) as well as 5 normal memory B cell controls (CD19+CD27+). For identification of biologic clusters, filtered RNA-seq data was analyzed using the Non-negative Matrix Factorization (NMF) clustering tool from the Broad Institute against the normal samples. Each cluster was analyzed for differential gene expression. This analysis generated cluster-specific T values for each gene. Genes that were significantly associated with a cluster (FDR<0.05) were analyzed for genetic ontology. Cibersort was used to deconvolute the tumor microenvironment (TME). We performed whole exome sequencing (WES) analyses on 60/63 matched LGL to characterize mutations and copy number alterations. Results: WES revealed the presence of previously reported mutations in genes such as MYD88, SPEN, NOTCH, and KLF2 as well as copy number alterations 7q31.2, BCL6, TNFAIP3, and BCL2. NMF clustering of RNA-seq data resulted in a best fit of 5 clusters, named LGL1 through LGL4 and "Normals". Pathologic subtypes were not exclusive to a specific cluster. Differential RNA expression analysis resulted in gene sets that were differentially expressed in each cluster. A cluster signature of the top 1% of associated genes (N=94) was created for each. LGL1 genes were associated with higher BCR signaling. LGL2 was characterized by regulatory dysfunction, particularly concerning mitochondrial integrity. LGL3 genes were associated with high TME and NOTCH and STAT signaling components. Specifically, LGL3 was associated with high CD8+ T-cell and M2 macrophage TME presence. LGL4 was distinguished by the presence of aggressive genetic programs related to B-cell activation such as NF-kB and IRF4. Consequently, LGL4 patients had significantly poorer rates of event-free survival (EFS) (P=0.029) and OS (P=0.006) when compared against the other groups. The top 1% gene signature (N=94) was next validated against an independent cohort of 84 LGL samples with gene expression data (SMZL [N=34], NZML [N=24], and LPL [N=24]). A similar cluster pattern emerged, with pathological subtypes distributing across LGL classifications based on respective transcriptomic signatures. Association of DNA variants within each cluster was also analyzed, but due to the low frequency of genomic variants detected in LGL, nothing was significantly associated. However, we did see an enrichment of BCL2, BCL6, and TNFAIP3 alterations in LGL4 and an overall lower driver gene mutation frequency in LGL2. Conclusions: Gaining a greater understanding of LGL based on their genetic, biologic, and immune profiles will establish a valuable platform for clinicians to identify high-risk cases and make better therapeutic decisions. Using a large cohort of well-annotated cases we identify novel clusters of LGL that present unique genomic and clinical profiles. Our study lays the groundwork for a precision therapy approach in LGL 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 and who would benefit from closer monitoring and targeted agents. Disclosures Anagnostou: American Society of Hematology, Mayo Clinic/Iowa Lymphoma SPORE, Mayo Clinic Immune Monitoring Core, Mayo Clinic Hematology Small Grant: Research Funding. Ansell:Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment. Cerhan:Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; NanoString: Research Funding. Novak:Celgene Coorperation: 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: Front-line immunochemotherapy (IC) with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) is expected to cure 60-70% of newly diagnosed DLBCL. Up to one third of newly diagnosed DLBCL patients will have relapsed or refractory (r/r) disease.1 Current standard of care for these patients is salvage chemotherapy and, if chemosensitive, to be followed by high dose chemotherapy with hematopoietic cell rescue (autoHCT).2,3 Event-free survival at 24 months (EFS24) after frontline IC is associated with excellent long-term outcomes with overall survival (OS) similar to that of age- and sex-matched controls.4 In comparison, those achieving EFS24 following autoHCT for relapsed or refractory disease may have increased risk for late-mortality compared to the general population with advanced age and intensive salvage therapy contributing to the risk of late complications.5 We sought to better characterize EFS24 after autoHCT to determine the utility of this end-point for informed clinical decisions, patient management, and future clinical trials. Methods: Patients were prospectively enrolled onto the Molecular Epidemiology Resource (MER) of the University of Iowa/ Mayo Clinic Specialized Program of Research Excellence (SPORE). Patients were followed for relapse, retreatment, and death; all events were validated by medical record review. For this analysis patients were included if they were consented within 9 months of initial diagnosis of DLBCL between 2002-2012, had received anthracycline-based IC (R-CHOP, or similar), and eventually had undergone autoHCT for r/r DLBCL. Patients with primary central nervous system (CNS) lymphoma or post-transplant lymphoproliferative disorders (PTLD) were excluded. Overall survival (OS) was defined as time from autoHCT until death due to any cause. Event-free survival (EFS) was defined as time from autoHCT until progression, relapse, retreatment, or death due to any cause. OS from achieving EFS24 after autoHCT was compared to age- and sex-matched general US population. Results: 108 patients underwent autoHCT for relapsed DLBCL between 2002 and 2017. Median age at autoHCT of the patients was 60 years (range 20-78) and 72 (67%) were male. The most common transplant conditioning regimen was BEAM (82%). At a median follow-up after autoHCT of 85 months (range 1-171), 72 patients (67%) had an event and 64 patients (59%) had died. Kaplan-Meier estimates for EFS and OS at 24 months from the time of autoHCT were 49% (95% CI: 40-59) and 61% (95% CI: 52-71), respectively. 52 patients had a progression within 24 months of autoHCT; OS from progression was poor (median OS=2.8 months, 95% CI: 1.8-6.0; 5 year OS = 9%, 95% CI: 2-22). 48 patients achieved EFS24 after autoHCT; median OS from achieving EFS24 was 136 months (95% CI: 92-NE) and 5 year OS was 79% (95% CI: 68-93). This was inferior to the background population (Figure 1, SMR=3.64, 95% CI: 2.11-6.27, p<0.0001). Eight patients had a progression after achieving EFS24 from autoHCT. OS from progression in these 8 patients (median OS=27.3 months, 95% CI: 14.4-NE; 5 year OS = 16%, 95% CI: 3-93) was improved compared to progression within 24 months of autoHCT (p=0.072, figure B). Cause of death (COD) in EFS24 achievers was progression of lymphoma (n=6), infection (n=1), secondary malignancy related to therapy (n=3), heart disease (n=1), and unknown (n=1). Conclusions: Patients achieving EFS24 after salvage chemotherapy and autoHCT have a favorable long-term prognosis; however, overall survival remained inferior to the general population. Most common COD after achieving EFS24 was progression of lymphoma. In spite of this, EFS24 remains a valuable end-point for informed clinical decisions, patient management, and future clinical trials. Figure 1 Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Nanostring: Research Funding. Ansell:Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding. Cerhan:Celgene: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding. Farooq:Celgene: Honoraria; Kite Pharma: Research Funding.

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: EMZL is a heterogeneous disease with variable risk for relapse and progression. Based on age ≥70 years, stage III-IV and elevated LDH, Thieblemont et al (Blood. 2017) developed the MALT-IPI to identify high-risk patients. In this index, disease characteristics (stage and LDH) account for 66% while a disease nonspecific characteristic (age) for 33% of the index score. We reported (Am J Hematol. 2019) that EMZL with multiple mucosal sites (MMS) at diagnosis is characterized by shorter survival and increased incidence of higher grade transformation. To better recognize disease-attributable high-risk patients, we developed a new EMZL prognosis score chiefly based on patient's disease characteristics. METHODS: The revised (R)-MALT-IPI was developed using a retrospective data set of 405 EMZL patients treated at the University of Miami (UM) from 1995 to 2017. Cox proportional hazards regression analysis was conducted to evaluate the effect of the potential prognostic variables on progression-free survival (PFS) and overall survival (OS) and to develop the new index R-MALTI-IPI based on PFS. Model validation was performed in two independent cohorts of EMZL patients from the University of Iowa/Mayo Clinic Molecular Epidemiology Resource (MER) database (n=297) and the IELSG-19 study (n=400) used for the development of MALT-IPI. Performance of various prognostic indices was compared using AIC statistics, and concordance c-statistics by Harrell (CH) and by Gonen and Heller (CGH). RESULTS: Among the candidate variables tested in univariable analysis, the following were statistically significant predictors of shorter PFS: age >60, age ≥70, anemia (Hb<12g/dL), stage III-IV, ECOG PS ≥2, elevated serum LDH, number of extranodal sites >1, number of nodal sites >4, and presence of MMS at diagnosis, defined as EMZL with ≥2 different extranodal sites excluding spleen and bone marrow. A stepwise Cox regression analysis yielded a multivariable model with four independent predictors of shorter PFS: age >60 (HR=1.53, p=0.010), elevated LDH (HR=1.73, p=0.004), stage III-IV (HR=2.03, p=0.0003) and presence of MMS (HR=2.78, p<0.0001). Based on this, a new index R-MALT-IPI was developed with scores ranging from 0 to 5, calculated as a sum of 1 point for age >60, elevated LDH, stage III-IV, and 2 points for MMS. The R-MALT-IPI defined 4 risk groups: low-risk (score 0 (35%), reference group), low-medium risk (score 1 (39%), HR=1.91, p=0.005), medium-high risk (score 2 (13%), HR=3.77, p<0.0001), and high-risk (score 3+ (13%), HR=8.54, p<0.0001). When compared with MALT-IPI, R-MALT-IPI better stratifies and separates high risk patients (26%) into medium-high risk and high-risk patients with a median PFS of 5.8 years (2.9-9.1) and 1.8 years (1.3-2.6) respectively, compared to 2.6 years (1.8-4.7) in the high-risk MALT-IPI patients (16.8%). The R-MALT-IPI index also distinguished patients with different OS. For validation, we analyzed R-MALT-IPI index performance in independent Iowa/Mayo Clinic MER and IELSG-19 cohorts. Both R-MALT-IPI and MALT-IPI were useful in distinguishing PFS and OS in all the cohorts. In the UM training cohort, the concordance c-statistics' values for the two indices were similar: for PFS, CH=0.6893 and CGH=0.6611 for R-MALT-IPI, and CH=0.6551 and CGH=0.6367 for MALT-IPI; for OS, CH=0.7017 and CGH=0.6813 for R-MALT-IPI, and CH=0.7029 and CGH=0.67715 for MALT-IPI. In the validation cohorts, the concordance c-statistics' values for the two indices were also similar, but slightly lower than in the UM cohort for PFS. When comparing medium-high to high-risk R-MALT-IPI groups, there was a reduction of 4 years in median PFS in the UM cohort, and reduction in median EFS of 5.6 years in the MER cohort, an important difference between these risk groups identified by the R-MALT-IPI index. CONCLUSIONS: R-MALT-IPI is a new index for EMZL centered principally on disease characteristics. Overall, there is a similar prediction of PFS (EFS) by R-MALT-IPI and MALT-IPI indexes; however, R-MALT-IPI better recognizes a high-risk group accounting for 13% of EMZL patients with short median PFS and thus obviates the waiting period needed to recognize patients with shorter EFS24. Collaborative studies addressing best treatment approach for these high-risk EMZL patients are eagerly needed. Disclosures Alderuccio: Agios: Other: Immediate family member; Foundation Medicine: Other: Immediate family member; OncLive: Consultancy; Targeted Oncology: Honoraria; Puma Biotechnology: Other: Immediate family member; Inovio Pharmaceuticals: Other: Immediate family member. Thieblemont:Cellectis: Membership on an entity's Board of Directors or advisory committees; Kyte: Honoraria; Janssen: Honoraria; Celgene: Honoraria; Roche: Honoraria, Research Funding; Gilead: Honoraria; Novartis: Honoraria. Cerhan:Celgene: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding. Zucca:Kite, A Gilead Company: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees, Other: Travel Grant, Research Funding; AstraZenaca: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Research Funding; Merck: Research Funding; Celltrion Helathcare: Membership on an entity's Board of Directors or advisory committees; Abbvie: Other: Travel Grant. Lossos:NIH: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees; Janssen Scientific: Membership on an entity's Board of Directors or advisory committees.

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 Background Disease characteristics, treatment patterns, and outcomes of follicular lymphoma (FL) patients (pts) aged >80 years (yrs) are infrequently reported. Further, the original Follicular Lymphoma International Prognostic Index (FLIPI) has not been formally studied in pts aged >80 yrs. Also, whether FLIPI is associated with overall survival (OS) in pts aged >80 yrs is unknown. Patients/Methods We used the National LymphoCare Study—a Genentech-sponsored, prospective, multicenter registry of FL pts across the United States without study-specific treatment—to comprehensively analyze FL pts aged >80 yrs. Using Pearson chi-squared tests, associations of age groups with disease characteristics and overall response rate (ORR) were examined. Median progression-free survival (PFS) and OS by treatment regimen were estimated using the Kaplan-Meier method. Cox proportional hazards regression that were adjusted for baseline disease factors were used to assess treatment differences in PFS, lymphoma-related mortality (LRM, which includes both disease and treatment-related deaths), and OS, as well as the significance of age by treatment interactions. FLIPI and a prognostic model that was constructed based on the findings were used to predict outcomes. Results Of 2649 evaluable pts, 209 (8%) were aged >80 yrs. Compared with pts aged ≤60, pts aged >80 yrs were more likely to be Caucasian, to have worse performance status, to have lower hemoglobin (Hgb) value (<12 g/dL), to have less advanced stage, and were less likely to be treated at academic institutions. When treated, pts aged >80 yrs had lower ORR compared with younger pts and were less likely to receive rituximab + chemotherapy (P <.001) or anthracyclines (P <.001). Use of maintenance rituximab was not significantly different between pt groups when rituximab induction was given (61% for pts aged ≤60, 38% for pts aged >80, P=.11), but it was more frequent in pts aged >80 yrs when rituximab + chemotherapy induction was administered (44% for pts aged ≤60, 59% for pts aged >80, P=.04). After adjusting for maintenance use, sex, and baseline factors, age significantly differentiated PFS impact of observation, rituximab monotherapy, and rituximab + chemotherapy (P=.01). No treatment regimen provided superior PFS or OS in pts aged >80 yrs. With a median follow-up of 6.5 yrs for all pts (4.3 yrs for pts aged >80 yrs), 5-year OS was 59% and 92%for patients aged >80 and ≤60 yrs, respectively. OS in pts aged >80 yrs varied based on FLIPI score (log-rank test, P=.01; Figure 1). Interestingly, the percentage of deaths that were disease-related was 40% in pts aged >80 yrs, which was comparable to the percentage in pts aged ≤60 yrs of 48%. Cox modeling showed that lower Hgb, B symptoms, and male sex predicted worse OS (P<.01) but not PFS in pts aged >80 yrs. Recognizing that males have inferior OS compared with females in the general population, we constructed a prognostic model composed only of B symptoms and Hgb level <12 (low risk=no factors; high risk=1 or more factors; P<.0001; Figure 2). FLIPI and the proposed prognostic model significantly differentiated both OS and LRM (Table 1). Conclusions In the largest, prospectively published study of FL pts aged >80 yrs, OS, PFS, ORR, and treatment selection varied compared with younger pts. Surprisingly, the proportion of deaths attributed to lymphoma in these older FL pts was 40%. FLIPI was associated with outcome in pts aged >80 yrs and so was a proposed prognostic model comprising lower Hgb and B symptoms. Independent validation of this model is required, and additional prospective studies that are designed for the oldest old are warranted. Disclosures: Nabhan: Genentech Inc.: Advisory Board Other, Honoraria. Byrtek:Genentech, a member of the Roche group: Employment, Equity Ownership; Roche: Equity Ownership, stock options, stock options Other. Dawson:Genentech: Employment; Roche: Equity Ownership. Link:Genentech: Consultancy; Millenium: Consultancy; Pharmacyclics: Consultancy; Spectrum: Consultancy; Genentech: Research Funding; Millenium: Research Funding; Pharacyclics: Research Funding. Zelenetz:GSK: Consultancy; Celgene: Consultancy; Cephalon: Consultancy; Gilead: Consultancy; Seattle Genetics: Consultancy; Sanofi-Aventis: Consultancy; Genentech: Research Funding; GSK: Research Funding; Roche: Research Funding; Cancer Genetics: Scientific Advisor Other. Cerhan:Genentech: Scientific Advisory Board of the National LymphoCare Study Other. Flowers:Abbott, Celgene, Millennium/Takeda, Sanofi, Spectrum, Janssen: Research Funding; Celgene, Genentech Bio-oncology: Consultancy.

Abstract Background: Bendamustine and rituximab (BR) is a commonly chosen frontline treatment for grade 1-2 (G 1-2) follicular lymphoma (FL). This regimen resulted in significantly longer progression-free survival (PFS) and less toxicity compared to R-CHOP in the STiL trial [Rummel et al., 2013] and similar results were observed when comparing BR vs. R-CHOP/R-CVP in the confirmatory BRIGHT trial [Flinn et al., 2014, updated ASCO 2017]. Importantly, patients (pts) with grade 3A (G 3A) FL were excluded from these studies, yet the conclusions are often extrapolated to the treatment of G 3A pts. Notably, G 3A pts were included in the GALLIUM trial which used both bendamustine as well as CHOP backbones. Several retrospective studies of FL pts treated with frontline R-CHOP have demonstrated equivalent or improved overall survival (OS) for G 3A relative to G 1-2 FL (Koch et al., 2016, Mustafa et al., 2017, Yuan et al., 2017, Maeshima et al., 2013). Early disease progression after diagnosis and treatment with frontline R-CHOP has been identified as a strong predictor of poor OS in FL pts [Casulo et al. 2015], but has not been assessed in pts treated with frontline BR. We retrospectively evaluated outcomes of a large cohort of FL pts treated with frontline BR and stratified the results based on histologic grade and early vs. late progression. Methods: We reviewed medical records of adult (age >18) pts with FL treated with frontline BR at 18 US cancer centers. There was no central pathology review; each academic institution confirmed the diagnosis and grade of FL. Pts with unknown grade, grade 3B, or cutaneous FL were excluded. Baseline characteristics between grades were evaluated with the Chi-Square test for categorical variables and the Mann-Whitney U for continuous variables. Outcomes were calculated from time of initiation of BR. Patients were grouped according to whether or not they had progression of disease within 24 months of BR initiation (POD24). OS and PFS were estimated using the Kaplan-Meier method and compared using the log-rank test. Results: 687 pts were included (616 G 1-2, 71 G 3A). Median age at diagnosis was 60 years (range 21-94) with 53% men. The median time from diagnosis to treatment with BR was 1 month (range 0-139). The median duration of follow up for the entire cohort was 39 months. Characteristics including age, gender, ethnicity, ECOG, stage, LDH, and FLIPI are shown in the Table, and did not significantly differ between G 1-2 and G 3A pts. Rates of complete response (CR) and partial response (PR) to BR were similar between G 1-2 and G 3A (72%/22% vs. 66%/21%, respectively, p=0.114) but progressive disease (PD) was higher in G 3A pts (11.9%) relative to G 1-2 (4.8%, p=0.025). As shown in the Figure (A and B), 3-year PFS and OS was significantly longer for G 1-2 vs. G 3A pts (75% vs. 65%, log rank p= 0.035 and 90% vs. 86%, log rank p=0.007, respectively). Seventy-six (12.3%) of the 616 G 1-2 pts and 13 (18.3%) of the 71 G 3A pts experienced POD24 (p=0.19). For pts with both G 1-2 and G 3A FL, POD24 was associated with inferior OS (3-year OS 95% vs. 57% for G 1-2 pts, log rank p<0.0001 and 3-year OS 93% vs. 37% for G 3A pts, log rank p<0.0001) compared to patients without POD24. Conclusions: G 3A FL pts have an inferior OS and are significantly more likely to have PD to frontline BR compared to G 1-2, which differs from the published experience with R-CHOP. Similar to outcomes after frontline treatment with R-CHOP, POD24 after frontline BR for FL is associated with very poor OS. Future studies are needed to address optimal frontline management of pts with G3A as well as for pts with any grade FL with POD24 after frontline BR. Disclosures Nastoupil: Novartis: Honoraria; Genentech: Honoraria, Research Funding; Gilead: Honoraria; Celgene: Honoraria, Research Funding; TG Therappeutics: Research Funding; Karus: Research Funding; Merck: Honoraria, Research Funding; Spectrum: Honoraria; Janssen: Research Funding; Juno: Honoraria. Cerhan:Jannsen: Other: Scientific Advisory Board; Celgene: Research Funding; Nanostring: Research Funding. Maurer:Celgene: Research Funding; Nanostring: Research Funding; Morphosys: Research Funding. Smith:Genentech: Research Funding; Incyte Corporation: Research Funding; Portola Pharmaceuticals: Research Funding; Pharmacyclics: Research Funding; Merck Sharp and Dohme Corp.: Consultancy, Research Funding; Acerta Pharma BV: Research Funding; Seattle Genetics: Research Funding. Lossos:Affimed: Research Funding. Portell:TG therapeutics: Research Funding; Amgen: Consultancy; Kite: Research Funding; AbbVie: Research Funding; BeiGene: Research Funding; Acerta: Research Funding; Genentech/Roche: Consultancy, Research Funding; Infinity: Research Funding. Calzada:Seattle Genetics: Research Funding. Cohen:Bristol-Myers Squibb: Research Funding; Infinity Pharmaceuticals: 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; Millennium: 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; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Research Funding; Bristol-Myers Squibb: Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; AbbVie: 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; Millennium: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: 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; Pharmacyclics: 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; Takeda: Research Funding; BioInvent: Consultancy. Ghosh:Spectrum: Consultancy; SGN: Consultancy, Research Funding, Speakers Bureau; Pharmacyclics, an Abbvie Company: Consultancy, Research Funding, Speakers Bureau; TG Therapeutics: Honoraria, Research Funding; Celgene: Consultancy; Juno: Consultancy, Research Funding; Abbvie: Consultancy, Speakers Bureau; Genentech: Research Funding; F. Hoffman-La Roche Ltd: Research Funding; PCYC: Consultancy, Research Funding, Speakers Bureau; Gilead: Consultancy, Speakers Bureau; Forty seven Inc: Research Funding. Barta:Janssen: Membership on an entity's Board of Directors or advisory committees; Merck, Takeda, Celgene, Seattle Genetics, Bayer: Research Funding. Caimi:Kite Pharma: Other: Advisory Board Participation; Genentech: Other: Advisory Board PArticipation, Research Funding; Celgene: Speakers Bureau; Kite Pharma: Other: Advisory Board Participation. Danilov:Gilead Sciences: Consultancy, Research Funding; Aptose Biosciences: Research Funding; Bayer Oncology: Consultancy, Research Funding; Astra Zeneca: Consultancy; Genentech: Consultancy, Research Funding; Takeda Oncology: Research Funding; TG Therapeutics: Consultancy; Verastem: Consultancy, Research Funding. Martin:AstraZeneca: Consultancy; Seattle Genetics: Consultancy; Janssen: Consultancy; Kite: Consultancy; Gilead: Consultancy; Bayer: Consultancy. Kamdar:Seattle Genetics: Speakers Bureau; Genentech: Consultancy. Kahl:AstraZeneca: Consultancy; Acerta: Consultancy; Juno: Consultancy; CTI: Consultancy; Celgene: Consultancy; Gilead: Consultancy; Seattle Genetics: Consultancy; Abbvie: Consultancy; Genentech: Consultancy; ADC Therapeutics: Consultancy. Hill:Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Genentech: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees.

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.

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.