Link, Brian K.

Background Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive non-Hodgkin lymphoma subtype with a 5-year survival of ~64%. While DLBCL is treated using immunochemotherapy (IC) with curative intent, 20%-40% of patients do not reach remission or relapse post IC. Clonal somatic mutations have been associated with aging, hematologic malignancies (predominately myeloid), and reduced OS in the general population. The objective of this study was to evaluate the association of clonal somatic mutations with event-free survival (EFS) and overall survival (OS). Methods We studied newly diagnosed DLBCL cases treated with IC who were enrolled in the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE). Clinical and treatment data were abstracted from medical records and all patients were systematically followed for disease outcomes. Pre-treatment DNA was extracted from matched peripheral blood and paraffin-embedded tumor tissue, and whole exome sequencing was conducted at 100x coverage. From the peripheral blood, allele counts from GVCF files produced by HaplotypeCaller in GATK were extracted for 42 genes commonly associated with clonal hematopoiesis (e.g. DNMT3A, TET2, and ASXL1). Mutations were deemed clonal somatic if the population minor allele frequency was <0.5% in ExAC and had a variant allele fraction (VAF) that deviated from 0.5 using a binomial distribution test at p<0.001. Furthermore, mutations had to have a minimum alternate read count of at least 5 reads, a VAF greater than or equal to 10%, and met at least one of the following criteria: CAVA impact of "HIGH"; pathogenic/likely pathogenic per ClinVar; or reported in COSMIC at least 10 times. EFS was defined as time from diagnosis to disease progression, relapse, retreatment, or death due to any cause. The association of clonal somatic mutations with prognosis was estimated using Kaplan-Meier curves, and hazard ratios (HR) and 95% confidence intervals (CI) from Cox regression. Associations were adjusted for age at diagnosis, sex, and the International Prognostic Index (IPI). Results The study consisted of 261 DLBCL patients treated with IC. The median age at diagnosis was 65 years (range 20-90) and 56% were male. With a median follow-up time of 5.1 years (range 0.1-15.0), there were 100 events and 80 deaths. A total of 17 (6.5%) patients had clonal somatic mutations, and16 patients were over the age of 60. A total of 8 (of 42) genes had clonal somatic mutations, with SF3B1, ASXL1, and TET2, being the most frequent (4 individuals per gene). VAFs ranged from 0.10 to 0.28 and none of the patients had multiple mutations. Additionally, the clonal somatic variants found in the peripheral blood were abscent in the tumor sample. Of the 17 patients with clonal somatic mutations, 12 had an event while 88 patients without a mutation had an event. In a univariate analysis, clonal somatic mutations were associated with inferior EFS (HR=2.55, 95% CI 1.39-4.68, p=0.002; Figure 1A). After adjusting for age, sex, and IPI, clonal somatic mutations remained associated with inferior EFS (HR=2.02, 95% CI 1.09-3.74, p=0.026). Clonal somatic mutations were also associated with inferior OS in the univariate analysis (HR=2.06, 95% CI 0.99-4.29, p=0.053), which attenuated after multivariate adjustment (HR=1.59, 95% CI: 0.76-3.34, p=0.22, Figure 1B). Although based on small numbers, mutations in SF3B1 were associated with inferior EFS (HR=3.25, 95% CI 1.16-9.12, p=0.025), but did not reach significance for OS (HR=2.56, 95% CI 0.78-8.38, p=0.120). Conclusions In this novel study of newly diagnosed DLBCL patients, clonal somatic mutations were identified in 6.5% of patients and were associated with inferior outcomes. Additional research is required at deeper sequencing to validate these findings and integration with tumor genomics is required to understand the prognosis of DLBCL patients with smaller clonal populations. Disclosures Shah: Dren Bio: Consultancy. Maurer:Celgene / BMS: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Martin:Celgene: Consultancy; Karyopharm: Consultancy, Research Funding; Morphosys: Consultancy; Regeneron: Consultancy; Incyte: Consultancy; Kite: Consultancy; Cellectar: Consultancy; Bayer: Consultancy; Beigene: Consultancy; Sandoz: Consultancy; I-MAB: Consultancy; Janssen: Consultancy; Teneobio: Consultancy. Witzig:AbbVie: Consultancy; Incyte: Consultancy; Acerta: Research Funding; Karyopharm Therapeutics: Research Funding; Immune Design: Research Funding; Spectrum: Consultancy; Celgene: Consultancy, Research Funding; MorphSys: Consultancy. Nowakowski:NanoString: Research Funding; Seattle Genetics: Consultancy; Curis: Consultancy; Kymera: Consultancy; Kite: Consultancy; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees; MorphoSys: Consultancy, Research Funding; Celgene/BMS: Consultancy, Research Funding. Novak:Celgene/BMS: Research Funding. Cerhan:NanoString: Research Funding; BMS/Celgene: Research Funding.

PURPOSE Exclusion of patients needing urgent treatment or requiring novel biomarkers before enrollment has impacted the ability to enroll real-world patients in frontline trials of diffuse large B-cell lymphoma (DLBCL). The impact of baseline organ function–based eligibility criteria on this effect and clinical trial exclusion is less well-understood. METHODS Consecutive patients with newly diagnosed lymphoma were enrolled from 2002 to 2015 into the Molecular Epidemiology Resource (MER) of the University of Iowa and Mayo Clinic Lymphoma Specialized Program of Research Excellence. The current analysis includes 1,265 patients with DLBCL receiving standard immunochemotherapy. Organ function parameters were identified from criteria for hemoglobin, absolute neutrophil count, platelet count, creatinine clearance, and bilirubin, as reported in frontline DLBCL trials. Abstracted laboratory values from MER were used to determine the percent (%) of patients excluded. Outcomes and cause-of-death analyses comparing ineligible and eligible groups in MER were conducted. An interactive online tool was developed to estimate exclusions based on organ function for future trial design. RESULTS Between 9% and 24% of MER patients with DLBCL receiving standard immunochemotherapy were excluded on the basis of baseline organ function alone. Ineligible patients based on organ function had significantly inferior event-free survival (hazard ratios, 1.67-2.16), overall survival (hazard ratios, 1.87-2.56), and event-free survival at 24 months (odds ratio, 1.71-2.16). Ineligible patients were more likely to die from lymphoma progression than increased therapy-related complications. CONCLUSION Current national and international trials exclude up to 24% of patients from participation on the basis of organ function alone. A significant difference in the outcomes, notably lymphoma-related death, suggests issues with generalization and potential exclusion of high-risk patients. These data will help future clinical trial development and meet US Food and Drug Administration and ASCO recommendations to increase trial accrual.

Background: Aggressive lymphomas are potentially curable; however, long-term effects of treatment and the disease may adversely affect patients' quality of life (QoL). We investigated QoL at baseline, and up to nine years after diagnosis in survivors of aggressive lymphomas. Methods: Patients newly diagnosed with lymphoma were prospectively enrolled within nine months of diagnosis in the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE) and systematically followed. Patients were enrolled from September 1, 2002 until June 30, 2015. Eligibility criteria included being a United States resident, English-speaking, no history of HIV, and aged 18 years and older. All pathology was reviewed by a lymphoma hematopathologist. Aggressive lymphoma subtypes included in this analysis were diffuse large B-cell lymphoma (DLBCL), Follicular lymphoma grade III, Hodgkin lymphoma, T-cell lymphoma, other non-Hodgkin lymphoma not otherwise specified (NOS), other B-cell lymphoma NOS, and composite lymphomas. We assessed QoL using the Functional Assessment of Cancer Therapy-General (FACT-G) scale at baseline enrollment, and 1, 2, 3, 6, and 9 years post-diagnosis. FACT-G measures well-being (WB) in four domains-physical (PWB), social/family (SFWB), emotional (EWB), and functional (FWB), which are added to create a total (TOT) score. Those who completed <80% of FACT-G questions at any given time point were excluded. "Survivor" was defined as alive at 1, 2, 3, 6, and 9 year follow-up with no active disease or treatment within six months of a given time point. Because patients could be enrolled at any time within the first 9 months from diagnosis, the timing of initial QoL assessment was variable with respect to the start of lymphoma treatment. We estimated longitudinal change in QoL using mixed models incorporating scores from all available time points. The minimally important difference (MID) of 5 points for TOT and 2 points for each of the domains was used to determine clinical significance. Scores were compared to United States (US) general population normative data. All statistical analyses were performed using SAS (v 9.4). Results: From September 1, 2002 to June 30, 2015, 3,028 patients with aggressive lymphomas were prospectively enrolled in the MER, of which 2,018 completed the FACT-G baseline assessment (47% prior to therapy initiation). Of these patients, 1,144 at 1 year, 1,002 at 2 years, 943 at 3 years, 562 at 6 years, and 289 at 9 years post-diagnosis completed the FACT-G at both baseline and each of the respective time points after diagnosis, and met the definition of survivor. The median age at diagnosis of patients analyzed was 59 years (range 18-93). The cohort included 844 female patients (42%). DLBCL comprised 44% of cases, Hodgkin lymphoma 19%, T-cell lymphoma 11%, composite lymphomas 8%, other B-cell NOS 6%, other non-Hodgkin lymphoma NOS 7%, and Follicular lymphoma grade III 5%. QoL increased from baseline enrollment; the largest increase was seen from baseline to 1 year after diagnosis with a statistically and clinically significant mean TOT score difference of 6.7 points. This mean increase in TOT scores sustained over time with an increase of 7.8 points above baseline at 9 years after diagnosis. At baseline, PWB, EWB, and FWB scores were significantly lower in patients with aggressive lymphomas compared to the US general population, and SFWB scores were significantly higher (all p <0.01); however, only SFWB and EWB score differences were clinically significant as defined by MID. SFWB showed only a mild decline from baseline over the course of 9 years. Despite this, all SFWB scores were statistically and clinically significantly higher than the US general population at 1, 2, 3, 6, and 9 years after diagnosis (all p <0.01 and MID >2). Patients with aggressive lymphomas TOT scores at baseline did not differ from the US general population, while TOT scores at 1, 2, 3, 6, and 9 years post-diagnosis were statistically and clinically significantly higher. Overall, the results in the pre-treatment QoL subset were consistent with the cohort as a whole. Conclusions: In survivors of aggressive lymphomas, QoL improved primarily in the first year after diagnosis, and sustained over the course of nine years. QoL in survivors of aggressive lymphomas is higher than the United States general population at 1, 2, 3, 6, and 9 years after diagnosis. Figure Disclosures Maurer: Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Celgene / BMS: Research Funding; Kite: Membership on an entity's Board of Directors or advisory committees. Farooq:Kite, a Gilead Company: Honoraria. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding.

Background: While HL is a highly curable cancer, patients (pts) with advanced stage disease experience increased risk of relapse. Delineation of prognosis is desired to compare cohorts and outcomes between trials, and to define groups of pts for whom reduction in treatment may be appropriate or where novel therapeutic approaches are needed. The International Prognostic Score (IPS), which was derived from a discovery set of 1,618 HL pts with complete data, was a seminal publication in the field (Hasenclever and Diehl NEJM 1998). However, these data were published >20 years ago with a significant minority of pts having received chemotherapy regimens no longer in clinical use. More contemporary analyses have shown altered utility of the IPS (e.g., Moccia JCO 2012; Diefenbach BJH 2015). In addition, prior studies identified bulk disease as an adverse prognostic factor in advanced stage HL (Laskar JCO 2004; Johnson JCO 2010). Our objective was to leverage individual pt data (IPD) from HoLISTIC (www.hodgkinconsortium.com) to discover a new, robust, and modern prognostication index for advanced-stage HL pts applicable to diverse settings across the world. Methods: We created a data repository of IPD from clinical trials for newly diagnosed HL pts, which includes 4,085 advanced-stage (III or IV) pts treated in 8 large, prospective studies completed in the modern era (ie, IIL HD9601: Gobbi JCO 2005; Italian HD2000: Federico JCO 2009; ECOG 2496: Gordon JCO 2013; SWOG 0816: Press JCO 2016; IIL HD0801: Zinzani JCO 2016; RATHL: Johnson NEJM 2016; GITIL HD0607: Gallamini JCO 2017; and COG AHOD 0831: Kelly BJH 2019) as well as prominent cancer registries (eg, the Mayo/Iowa Molecular Epidemiologic Resource (MER)). The discovery analysis herein included pts from the ECOG 2496, HD0801 IIL, GITIL HD0607, and SWOG 0816 studies. Furthermore, it was restricted to pts (n=1,279) on these trials with complete data for all 9 covariates of interest: age; sex; advanced stage (III vs IV); B symptoms; any bulk; and values of hemoglobin, white blood count (WBC), lymphocyte count, and albumin. Using Cox proportional hazard (PH) models, we evaluated univariate associations between 5-year progression-free survival (PFS) and overall survival (OS) with the aforementioned prognostic variables. Age was categorized based on plots and optimum model fit (c statistic). Lab values were dichotomized using cut-points from the 1998 IPS. Per convention, treatment factors were not included in the model. To identify independent prognostic factors of PFS and OS, a parsimonious Cox PH model was fit using backward selection of all potential risk factors (P<0.05). Hazard ratios (HR) with 95% confidence interval (CI) were reported. Kaplan Meier (KM) plots were also reported for risk factors in the multivariable (MVA) models to visualize differences. Results: Among all pts, characteristics included: median age of 32.9 years (IQR 25.4-44, range 15-83); 55% male; 49% stage IV; 63% B symptoms; 26% bulk >10 cm; 20% hemoglobin <10.5 g/dL; 15% WBC count ≥15,000/mm3; 9% lymphocyte count <600/mm3; and 63% with albumin <4g/dL. For analysis of age, we observed a U-shaped relationship with PFS (Fig A), which helped delineate optimal cut points of 15-24 years (23.1%), 25-49 years (61.4%), and ≥50 years (15.6%). In univariate analysis: age, stage, B symptoms, bulk, anemia, low lymphocyte count, and low albumin were associated with worse survival. In the MVA model, age >50 years, stage IV disease, B symptoms, and bulky disease were associated with worse PFS; and age >50 years, stage IV disease, bulky disease, anemia, and low albumin were associated with worse OS (Fig B). KM plots for age, stage, and bulk are presented in Fig C. Conclusions. In this international, multi-study analysis of advanced stage HL in the modern era, we identified several factors that were associated with both worse PFS and OS on MVA (ie, age, stage IV disease, and bulky disease). The finding of bulky disease as a significant prognostic factor warrants further investigation. In addition, we detected an age-related U-shaped impact on PFS with inferior outcomes for pts ages 15-25 years and ≥50 years, the latter in an increasing linear fashion. Altogether, these data will serve as a training cohort for a modern HL prognostication index that will be augmented and analyzed with a large independent validation cohort (vis-à-vis the remaining HL data in the HoLISTIC consortium), which will be presented at the ASH meeting. Disclosures Parsons: Seattle Genetics: Consultancy. Advani:Astra Zeneca, Bayer Healthcare Pharmaceuticals, Cell Medica, Celgene, Genentech/Roche, Gilead, KitePharma, Kyowa, Portola Pharmaceuticals, Sanofi, Seattle Genetics, Takeda: Consultancy; Celgene, Forty Seven, Inc., Genentech/Roche, Janssen Pharmaceutical, Kura, Merck, Millenium, Pharmacyclics, Regeneron, Seattle Genetics: Research Funding. Federico:Spectrum: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sandoz: Consultancy, Membership on an entity's Board of Directors or advisory committees; Mundipharma s.r.l.: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Millennium/Takeda: Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Cephalon/Teva: Research Funding. Friedberg:Acerta Pharma - A member of the AstraZeneca Group, Bayer HealthCare Pharmaceuticals.: Other; Bayer: Consultancy; Kite Pharmaceuticals: Research Funding; Portola Pharmaceuticals: Consultancy; Roche: Other: Travel expenses; Seattle Genetics: Research Funding; Astellas: Consultancy. Hutchings:Genmab: Research Funding; Janssen: Research Funding; Roche: Consultancy; Genmab: Consultancy; Takeda: Consultancy; Roche: Research Funding; Celgene: Research Funding; Daiichi: Research Funding; Sankyo: Research Funding; Novartis: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; Roche: Honoraria; Genmab: Honoraria; Takeda: Honoraria. Johnson:MorphoSys: Honoraria; Kymera: Honoraria; Kite Pharma: Honoraria; Incyte: Honoraria; Celgene: Honoraria; Epizyme: Consultancy, Research Funding; Novartis: Honoraria; Takeda: Honoraria; Oncimmune: Consultancy; Boehringer Ingelheim: Consultancy; Janssen: Consultancy; Oncimmune: Consultancy; Janssen: Consultancy; Genmab: Honoraria; Bristol-Myers: Honoraria; Epizyme: Consultancy, Research Funding. Radford:Novartis: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Speakers Bureau; ADCT: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Research Funding; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; GlaxoSmithKline: Current equity holder in publicly-traded company, Other: Spouse; AstraZeneca: Current equity holder in publicly-traded company, Other: Spouse; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Zinzani:MSD: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Incyte: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees; EUSA Pharma: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sandoz: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Immune Design: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Eusapharma: Consultancy, Speakers Bureau; Verastem: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kyowa Kirin: Consultancy, Speakers Bureau; TG Therapeutics, Inc.: Honoraria, Speakers Bureau; Kirin Kyowa: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Servier: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen-Cilag: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celltrion: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; ADC Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Portola: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Immune Design: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Merck: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Cerhan:NanoString: Research Funding; BMS/Celgene: Research Funding. Evens:Abbvie: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria, Research Funding; MorphoSys: Consultancy, Honoraria; Research To Practice: Honoraria, Speakers Bureau; Novartis: Consultancy, Honoraria; Epizyme: Consultancy, Honoraria, Research Funding; Merck: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria; Mylteni: Consultancy, Honoraria.

Background: Observation or "wait and watch" (W/W) strategy remains a viable option in the rituximab era for asymptomatic, stage II-IV, low-tumor burden patients with FL grade 1-2, 3A. Studies to date both in pre and post rituximab era have not shown an overall survival benefit from immediate treatment in such low-risk patients.To improve our understanding and to better counsel FL patients on W/W strategy, we sought to estimate the incidence of treatment initiation at landmark time points in our prospectively observed cohort from the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE). We further evaluate the association between the presence of GELF criteria (MER treatment criteria) at diagnosis and initiation of treatment patterns, transformation rates and cause of death in FL patients managed by W/W. Methods: FL patients on W/W strategy were identified from MER of the University of Iowa/Mayo Clinic Lymphoma SPORE. From 2002-2015, consecutive patients with newly diagnosed FL were offered enrollment. Patients were managed per treating physician and followed prospectively. Baseline clinical and pathological data were abstracted using a standard protocol. Cumulative incidence estimates of treatment initiation for follicular lymphoma were calculated using transformation to large cell lymphoma (as the first event) and death due to any cause as competing risks. Transformation was defined based on biopsy-proven disease. Patients were retrospectively considered to meet treatment criteria at diagnosis if they had the presence of any GELF criteria components per available abstracted data in the MER database. Not all GELF criteria were prospectively assessed and captured for all patients, and thus the MER treatment criteria utilized here will be conservative for formal GELF assessment. Results: A total of 401 FL patients were identified in MER on W/W strategy. Baseline characteristics for W/W patients showed a favorable profile such as normal LDH (89%), low and intermediate FLIPI score in 48% and 35% respectively, no B symptoms (97%), low tumor burden (<4 nodal groups) in 72%, and GELF positive vs. negative in 36% and 64% respectively. At a median follow up of 8 years (IQR 5.9-12), 64% initiated treatment for FL only. Cumulative incidence estimates of treatment initiation at sequential time points were calculated from the initiation of observation (Table1&2). At diagnosis, the likelihood of treatment initiation was 15%, 26%, and 42% in the next 1, 2, and 5 years. The incidence of treatment initiation decreased over time as patients remained treatment-free after diagnosis (Figure 1A [blue curve]). Patients that met MER treatment criteria had increased rates of therapy initiation (33.2% vs. 21.9%) in the next two years and untreated transformation to large cell lymphoma [red curve] (8.7% vs. 4.5%) during the next 5 years (Figure 1B). Despite this, the rate of lymphoma related death at 10 years was similar between groups (met treatment criteria = 6.2% vs. 7.1%) (Figure 1C, [green curve]). Discussion: The longer duration of W/W strategy suggests a decreasing need for treatment over time. We provide time point estimates, which would be helpful for counseling patients. Of note, long-term continuous oncologic assessment and follow-up is necessary since approximately 66% of patients in this mature cohort initiated treatment by 8 years median follow up. MER treatment criteria at diagnosis identified patients with higher rates of transformation and therapy initiation in the first two years but did not identify those with worse lymphoma specific survival. Identification of biological differences in patients with early vs. late or no progression is a critical next step in understanding outcomes in W/W patients. Disclosures Ansell: Bristol Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Takeda: Research Funding; AI Therapeutics: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; ADC Therapeutics: Research Funding; Trillium: Research Funding. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding. Wang:Incyte: Research Funding; Innocare: Research Funding; Novartis: Research Funding. Witzig:MorphSys: Consultancy; AbbVie: Consultancy; Incyte: Consultancy; Acerta: Research Funding; Karyopharm Therapeutics: Research Funding; Immune Design: Research Funding; Spectrum: Consultancy; Celgene: Consultancy, Research Funding. Maurer:Morphosys: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding; Kite: Membership on an entity's Board of Directors or advisory committees; Celgene / BMS: Research Funding. Nowakowski:NanoString: Research Funding; Kite: Consultancy; Seattle Genetics: Consultancy; Kymera: Consultancy; Celgene/BMS: Consultancy, Research Funding; MorphoSys: Consultancy, Research Funding; Curis: Consultancy; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees.

Background: Indolent B-cell lymphomas are a group of neoplastic disorders that account for approximately one-third of non-Hodgkin lymphomas in the Western world. They are clinically indolent but pathologically diverse and generally composed of follicular lymphoma (FL), extranodal marginal zone lymphoma (EMZL), lymphoplasmacytic lymphoma (LPL), nodal marginal zone lymphoma (NMZL), splenic marginal zone lymphoma (SMZL), and small lymphocytic lymphoma. The overall prognosis is favorable in most patients, but others can have a more aggressive disease with associated progression, relapse, or histologic transformation. We have previously reported that the most common cause of death in FL was lymphoma. Here we report on the cause of death (COD), in other non-follicular indolent B-cell lymphomas (NFIBL) during the first decade of the rituximab era. Methods: Participants were from the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE). From 2002 -2015, MER offered enrollment to all patients with newly diagnosed lymphoma who were US residents and age >18 years. Patients were treated per physician choice and followed prospectively. An event was defined as progression or relapse, initiation of 2nd line therapy, or death from any cause. EFS24 was defined using event-free survival at 24 months from diagnosis. Overall survival was defined as the time from diagnosis until death due to any cause. Overall survival from EFS24 was defined as time from event defining date (achieving EFS24 or early event). For decendents, copies of the death certificate or medical records associated with death were reviewed. Cumulative incidence estimates of the cause of death in NFIBL were calculated as competing risks utilizing the cuminc function from the cmprsk package in R version 3.6.2. COD was categorized as a result of lymphoma (progression or therapy-related) vs. other causes vs. unknown/missing. Results: 820 patients with newly diagnosed NFIBL were enrolled in this study. The subtypes included extranodal marginal zone (EMZL, N = 362), unclassifiable low-grade B-cell lymphoma (LGBCL-U, N = 202), lymphoplasmacytic lymphoma (LPL, N = 92), nodal marginal zone lymphoma (NMZL, N = 80), and splenic marginal zone lymphoma (SMZL, N = 84). The median age at diagnosis was 63 years (range 18-92), and 50.5% were male. Baseline clinicopathologic characteristics showed that the LDH was abnormal in 19.3%, hemoglobin was abnormal in 33.8%, and 62.8% had stage III-IV disease. The IPI score was 0-1 in 51%, 2 in 36.6%, 3 in 10.6%, and 4-5 in 1.8%. At a median follow-up of 83 months (range 0-193), 172 (21%) patients had died, and the primary cause of death was non-lymphoma related in 87 (50.6%), lymphoma related 48 (27.9%), and unknown in 37 (21.5%). When examining all subtypes combined, the 10-year estimate for lymphoma related death was 7% (95% CI: 5.2-9.5) which was lower than the rate of non-lymphoma related death (10 years estimate 14%, 95% CI: 11.3-17.4), Figure 1A, Table 1. The 10-year estimate of lymphoma related death varied by subtype, ranging from 2.7% in EMZL to 16.3% in LPL (table 1), while non-lymphoma related death at 10 years ranged from 10.2% in EMZL to 22.5% in SMZL; only in LPL was the lymphoma related rate higher than the non-lymphoma. Patients who failed to achieve EFS24 from diagnosis had significantly higher rates of lymphoma related death (18% at 10 years from failure, 95% CI: 11.7-29.1, Figure 1B) compared to patients who achieved EFS24 (3.9% at 10 years from achieving EFS24, 95% CI: 2.3-6.8, Figure 1C). Rates were similar by EFS24 for non-lymphoma related death (14.5%, 95% CI: 11.0-19.0 vs. 11.2, 95% CI: 6.3-19.7). Figure 1B-C. Conclusion: The most common cause of death in NFIBL at 10 years was unrelated to lymphoma, in contrast to our previous data in follicular lymphoma which showed lymphoma as the most common cause of death at 10 years. However, similar to follicular lymphoma, patients with NFIBL who failed to achieve EFS24 to frontline therapy had significantly increased risk of lymphoma related death. Further research into the cause of death in NFIBL is warranted. Disclosures Maurer: Celgene / BMS: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Witzig:Celgene: Consultancy, Research Funding; MorphSys: Consultancy; AbbVie: Consultancy; Incyte: Consultancy; Acerta: Research Funding; Karyopharm Therapeutics: Research Funding; Immune Design: Research Funding; Spectrum: Consultancy. Novak:Celgene/BMS: Research Funding. Farooq:Kite, a Gilead Company: Honoraria. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding.

Background: Follicular Lymphoma (FL) is the second most common non-Hodgkin lymphoma and presents with significant clinical, cellular, molecular, and genetic heterogeneity. Despite the name and defining microanatomic location, the genetic and molecular identity and pathogenesis of the FL tumor cell is largely uncharacterized. Prior clinical and molecular classifications of FL have been primarily driven by pathologic classification (Grade 1-3b), genetic classification (M7-FLIPI), or gene expression profiling (IR-1/2). Using a unique cohort of 93 FL tumors, we have explored the transcriptomic signature of purified FL B cells, along with their matched whole tumor, and identified unique molecular subsets which are defined by distinct pathway activation, immune content, and genomic signatures identified through whole exome sequencing (WES). Methods: Frozen tumor biopsies from 93 untreated FL (Grade 1-3b) patients enrolled in the University of Iowa/Mayo Clinic Lymphoma SPORE were used for the study. DNA was isolated from whole tumor cell suspensions and RNA was isolated from both whole tumor and B cell enriched cell suspensions. RNA sequencing (RNAseq) and WES was performed in the Mayo Clinic Genome Analysis Core. RNAseq and WES data were processed using the Mayo Clinic standard pipeline and novel driver genes were identified using 20/20+ driver analysis. Copy number variants were identified using GISTIC 2.0. NMF clustering and single sample gene set testing, for B cell lineage and tumor microenvironment (TME) signatures, was performed in R using the NMF and SingScore packages. Results: NMF consensus clustering of FL B cell RNAseq data identified two distinct subsets, C1 (n=32) and C2 (n=57). Clinically, C1 was associated with being FL grade 3 (p<0.001) and more aggressive therapy (e.g. immunochemotherapy) at diagnosis while other clinical factors were similar between the groups. To determine the biologic underpinnings that drove the NMF analysis, the top NMF metagenes were incorporated (n=389 for C1 and n=293 for C2) for GO analysis. Among those genes were AICDA, IRF4, and EZH2 in C1, and BCL2 in C2. Additionally, we identified significant gene set enrichment with NMF metagenes and calculated differential gene set expression comparing C1, C2, and normal B cells (n=5). Together, these analyses suggest C1 displays significantly (p<0.05) enhanced DNA replication and metabolic activity, whereas C2 displays significantly enhanced cytoskeleton rearrangement, repressed inflammatory cytokine signaling, and NF-kB activity. Based on recent scRNAseq data suggesting that FL derives from distinct germinal center B cells, the B lineage difference between C1 and C2 was explored. Both subgroups were heterogeneous for dark (DZ), intermediate (INT), light (LZ) zones, and plasmablast (PBL) gene signatures. When compared to benign B cell samples, C1 was enriched for a pre-PBL (p<0.01), and C2 for an INT phenotype related to a LZ signature (p<0.01). To define if a genomic signature was associated with C1 and C2, mutations and copy number variants (>5% frequency) were examined. TNFAIP3, TP53, and BCL6 alterations were enriched in C1 samples, whereas C2 associated with alterations in BCL2, KMT2D, CREBBP, REL, and MYC. Finally, B cell clusters were analyzed for TME signatures. C1 samples displayed significant enrichment of macrophage, cytotoxic cell, gamma-delta-Tcell, and endothelial cell TME elements (p<0.05) consistent with an inflamed/hot tumor. In contrast, C2 samples exhibited significantly lower signatures (p<0.05) in all entities except for the fibroblast signature, compared to C1 and normal, consistent with an immune desert phenotype. Conclusion: Our results suggest that B cells from FL patients display two distinct transcriptomic signatures. C1 identifies an immunologically active tumor, driven by TNFAIP3 alterations, with pre-PBL characteristics, DNA replication and repair, inflammatory cytokine secretion/signaling, and hyper-metabolic characteristics. C2 identifies an immunologically quiet tumor, driven by alterations in BCL2 and chromatin modifiers, with an intermediate GC phenotype, repressed cytokine signaling, and active cell cycle progression and cytoskeleton rearrangement. This study improves our understanding of the mechanisms driving FL tumors and motivates further investigation into the relationship between tumor intrinsic factors that may influence the TME. Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding; Celgene / BMS: Research Funding. Ansell:ADC Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; AI Therapeutics: Research Funding; Takeda: Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding. Cerhan:NanoString: Research Funding; BMS/Celgene: Research Funding. Novak:Celgene/BMS: Research Funding.

Introduction Due to the higher metabolic demand, malignant cells have an increased dependency on the nucleocytoplasmic trafficking of proteins, as compared to normal cells. Chromosome region maintenance protein 1 (CRM1), encoded by the XPO1 gene, is the main protein receptor which facilitates export of molecules, including tumor suppressor proteins, from the nucleus to the cytoplasm thereby making them inactive. Expression of CRM1 in tumor tissue has been shown to be an independent prognostic marker in several solid tumors and in acute myeloid leukemia; high CRM1 expression by immunohistochemistry (IHC) was associated with more aggressive disease and shorter survival. Importantly, selinexor, a first in class small molecule inhibitor of CRM1, was recently approved for the treatment of relapsed diffuse large B-cell lymphoma (DLBCL). The expression of CRM1 on tumor cells and the assessment of its prognostic impact have not been studied in patients (pts) with DLBCL or primary mediastinal B-cell lymphomas (PMBCL). Methods Paraffin embedded tumor tissue from pts with DLBCL or PMBCL treated with immunochemotherapy was assessed for CRM1 expression through IHC on tissue microarray (TMA). CRM1 anti-rabbit monoclonal antibody (Cell Signaling, catalog-no: 46249) was used at 1:100 dilution. Tumor cell grading was based on CRM1 staining in tumor cells compared to background non-malignant lymphocytes and non-malignant lymphocytes in spleen and tonsillar tissue controls. Renal cell carcinoma (RCC) was used as a positive control [known high levels of CRM1 staining (Inoue et al, J Urol, 2012)]. Two expert hematopathologists (RLK & AJW) independently scored CRM1 nuclear staining and assigned a grade of 0-3; 0 (no definitive nuclear staining, equal to background lymphocytes), 1 (dim nuclear staining), 2 (consistent nuclear staining, nuclear detail still visible behind the stain) and 3 (strong nuclear staining obscuring most nuclear detail, staining equivalent to RCC control). The average CRM1 score per case across all available cores on the TMA was calculated. Low CRM1 expression for a case was arbitrarily defined as a score of 0-2.0; high CRM1 expression was score 2.1-3.0. Scoring reliability between reviewers and between cores was assessed using intra-class correlation coefficient; score 0.75-0.90 was considered as a good scoring reliability. Event-free survival (EFS) was defined as time from diagnosis to progression, relapse, retreatment, or death. The association of CRM1 expression and risk of failing to achieve EFS at 24 months after diagnosis (EFS24) was estimated using odds ratios (OR) and 95% confidence intervals (CI) from logistic regression models, while the association of CRM1 expression with continuous EFS and overall survival (OS) was estimated using Kaplan-Meier curves and hazard ratios (HR) and 95% CI from Cox regression models. Results Tumor tissue from 282 pts was studied for CRM1 staining, including 275 pts with DLBCL and 7 pts with PMBCL. Median age of the study population was 61 years (range: 18-93) and 59% were male. The median follow-up for the entire cohort was 88.6 months. The first-line treatment regimens and baseline patient characteristics at diagnosis are outlined in Figure 1A. Of the 282 pts, 200 (71%) had high level of CRM1 expression and 82 (29%) had low CRM1 expression [only 4 (1.4%) had no or 0 staining]. Intra-class correlation coefficient to measure scoring reliability was 0.8. There was no difference in International Prognostic Index (IPI), ECOG performance score, lactate dehydrogenase or age at diagnosis among the groups with high CRM1 expression compared to low CRM1 expression (Figure 1A). The EFS24 failure was 29% for pts with low CRM1 expression while 26% in pts who had high CRM1 expression, OR=1.16, 95% CI 0.63-2.07; p=0.63. Null associations were also observed for EFS (HR=1.21, 95% CI 0.80-1.83; p=0.38) and OS (HR=1.02, 95% CI 0.61-1.69; p=0.95), (Figure 1B, C). Results were similar when adjusted for gender and IPI. Conclusion CRM1 expression by IHC on paraffin embedded tumor tissue is feasible in DLBCL and PMBCL. These data demonstrate that the CRM1 protein, the target for selinexor, is indeed expressed in the vast majority of these tumors; only 1.4% had no staining. However, CRM1 expression by IHC is not a prognostic marker for EFS24, EFS or OS. Whether CRM1 staining predicts selinexor response has not been studied but should be included in any new studies using CRM1 inhibitors. Disclosures Maurer: Nanostring: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; Celgene / BMS: Research Funding. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding. Witzig:Acerta: Research Funding; Immune Design: Research Funding; Incyte: Consultancy; MorphSys: Consultancy; Celgene: Consultancy, Research Funding; Spectrum: Consultancy; Karyopharm Therapeutics: Research Funding; AbbVie: Consultancy.

Introduction Primary mediastinal large B cell lymphoma (PMBCL) is a rare non-Hodgkin lymphoma (NHL) with a female predominance; often presenting with a large anterior mediastinal mass. Though PMBCL has clinical and molecular features overlapping with Hodgkin lymphoma, it is a distinct entity defined by the World Health Organization classification. PMBCL is heterogeneously treated, and most patients receive front line therapy with either rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone (R-CHOP) with radiotherapy (RT), or the more intensive etoposide, prednisone, vincristine, cyclophosphamide, doxorubicin with rituximab (EPOCH-R) regimen. Diagnosis of PMBCL is made using clinicopathologic criteria and radiographic imaging, however gene expression profiling (GEP) studies reveal a characteristic genotypic signature distinct from diffuse large B cell lymphoma (DLBCL). Molecular classification of PMBCL using the Lymph3Cx assay from formalin-fixed paraffin-embedded tissue (FFPE) is feasible, reproducible, and highly concordant in a training and validation cohort (Mottok et al. Blood 2018). Using a multicenter cohort of patients, we sought to estimate the rate of mis-match among patients with a clinical diagnosis of PMBCL using Lymph3Cx, and describe treatment selections and outcomes for each group. Methods Patients were identified from a cohort of patients with newly diagnosed NHL from the University of Iowa and Mayo Clinic Molecular Epidemiology Resource, and the Lymphoma Epidemiology of Outcomes cohort. Patients were enrolled between 2002-2019, and included if they had clinically defined PMBCL. FFPE was retrieved from hematopathology archives of participating academic centers. All diagnoses of PMBCL were based on expert hematopathology review at the time of therapy, and all cases underwent classification by GEP using the Lymph3Cx assay. Lymph3Cx was performed in the clinical lab at the Mayo Clinic in Arizona: Contiguous unstained sections were deparaffinized and macrodissected to enrich for tumor content before RNA isolation;100-200 ng of total RNA was used in an nCounter Elements XT, hybridized, and processed the following day using the nCounter FLEX system. Raw counts were processed through the Lymph3Cx algorithm and results reported as probability of PMBCL (≥0.90 as PMBCL, ≤0.10 as DLBCL all other results "Unclear PMBCL/DLBCL") (A. Mottok et al, Blood, 2018). For cases classified as DLBCL, the Lymph2Cx cell-of-origin classifier results was reported (Scott et al, JCO, 2016). Time to event endpoints were described with Kaplan-Meier plots by groups defined by mismatch status and compared with a logrank test. Binary outcomes will be presented with 90% exact confidence intervals. Results Fifty patients were identified. Median age was 35 years (range 19-70). Sixty four percent were women. Median follow up was 47 months. Treatments included R-CHOP (44%), EPOCH-R (44%), and MACOP-B [methotrexate with leucovorin rescue, doxorubicin, cyclophosphamide, vincristine, prednisone, and bleomycin] (6%), other (4%). Ten patients (20%) had events (defined as progression or death). Three patients in the entire cohort (6%) died. The Kaplan-Meier estimated survival at 47 months (median follow-up) is 92%. The Lymph3Cx assay yielded gene expression data of sufficient quality in 47/50 cases (94%, 90% CI=85.2, 98.3%). Of 47 cases clinically identified as PMBCL, 5 unclear were DLBCL/PMBCL and 1 was Germinal Center B cell subtype of DLBCL. Among these 6 patients, 4 received R-EPOCH (66%), 1 received R-CHOP (16.6%). One patient had missing treatment data. One patient had an event requiring subsequent therapy; all patients remain alive. Conclusions Using 47 patients with PMBCL defined by histology, clinical and radiographic findings, and molecular features, we demonstrate high concordance between clinical phenotype and molecular genotype of PMBCL by Lymph3Cx. Among the 6 patients not classified as PMBCL, most received R-EPOCH. Differences in outcome by mis-match status await longer follow-up and further accrual of subjects to our data base. Our data suggest molecular genotyping may have a role in mediastinal presentations of large cell lymphoma to optimize treatment decision making. Disclosures Maurer: Nanostring: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Celgene / BMS: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding. Flowers:AbbVie: Consultancy, Research Funding; Kite: Research Funding; Burroughs Wellcome Fund: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Denovo Biopharma: Consultancy; Celgene: Consultancy, Research Funding; Cancer Prevention and Research Institute of Texas: Research Funding; TG Therapeutics: Research Funding; Eastern Cooperative Oncology Group: Research Funding; V Foundation: Research Funding; Bayer: Consultancy; National Cancer Institute: Research Funding; Millennium/Takeda: Consultancy, Research Funding; Gilead: Consultancy, Research Funding; Acerta: Research Funding; Spectrum: Consultancy; Pharmacyclics/Janssen: Consultancy; Karyopharm: Consultancy; OptumRx: Consultancy; Leukemia and Lymphoma Society: Membership on an entity's Board of Directors or advisory committees; BeiGene: Consultancy. Friedberg:Acerta Pharma - A member of the AstraZeneca Group, Bayer HealthCare Pharmaceuticals.: Other; Astellas: Consultancy; Bayer: Consultancy; Kite Pharmaceuticals: Research Funding; Portola Pharmaceuticals: Consultancy; Roche: Other: Travel expenses; Seattle Genetics: Research Funding.

Background: Plasmablastic lymphoma (PBL) is a rare subtype of Non-Hodgkin Lymphoma with a well-known association with HIV infection. The outcomes of PBL patients are typically described with high relapse rates and poor prognosis. (Loghavi S, J Hematol Oncol. 2015; Morscio J, Am J of Pathol. 2014; Castillo JJ, Clin Lymphoma Myeloma Leuk. 2011; Castillo JJ, Am J Hematol. 2008) There has been a paucity of data suggesting that limited stage disease (Ann Arbor stage I-II) may have a more favorable prognosis. However, due to the rarity of this disease there have been no large-scale reviews to confirm this. Thus, many patients with limited stage disease are subject to the aggressive therapy recommendations based on the poor outcomes of PBL patients as a whole. (Loghavi S, J Hematol Oncol. 2015; NCCN Guidelines, version 2.2020; Al-Malki MM, BBMT. 2014) We attempted to determine the treatment patterns and outcomes of patients with limited stage PBL. Methods: We conducted a multi-center (13 US academic centers) retrospective study of patients with limited stage (Ann Arbor stage I-II) Plasmablastic lymphoma. Determination of limited stage and diagnosis of PBL was determined by the investigators at each individual center. Patients diagnosed between 1/1/1990 and 6/1/2018 were included. Baseline demographic, clinical, laboratory, pathology, and outcomes data were extracted by retrospective chart review. Kaplan Meier was utilized for time to event analysis. Results: Baseline characteristics are included in table 1. A total of 80 patients were identified with limited stage disease. With a median follow up of 34 months the 3-yr Progression free survival (PFS) and overall survival (OS) was 71.9% and 78.7% respectively (Figure 1A and 1B). Patients that received frontline chemotherapy with (n=29) and without RT (n=36) had a 3-yr PFS and OS of 84.6% and 96.2% as compared to 64.5% and 70.8%, respectively (Figure 2A and 2B; Figure 3A and 3B). The Hazard ratio (HR) for PFS and OS for chemo (reference) vs chemo-RT was 0.47 (95% CI 0.18-1.3; P=0.131) and 0.18 (95% CI 0.04-0.84; p=0.029) respectively. The HR for PFS and OS for CHOP (n=14, reference) vs EPOCH (n=33) based regimens was 0.37 (95% CI 0.11-1.2; p=0.106) and 0.36 (95% CI 0.079-1.6; p=0.182) respectively. Patients with stage I/IE disease (n=56) had a 3-yr PFS and OS of 73.1% and 81.2% respectively. Patients with stage II/IIE disease (n=24) had a 3-yr PFS and OS of 69.3% and 73.4% respectively. Patients that received aggressive treatment (n=17) with Hyper-CVAD based regimens and/or Auto-SCT as consolidation had a 3-yr PFS and OS of 63.6% and 72.7% respectively. Patients with concomitant HIV (n=16) had a 3-yr PFS and OS of 80.8% and 77.4% respectively. Seven patients received RT alone and 6 patients had surgical resection alone as frontline therapy; 1 patient received no therapy; 1 patient received HAART therapy only and remains in CR without any other treatment for PBL 29 months after diagnosis. There were 8 deaths (10%) related to PBL, 3 deaths (4%) related to treatment of PBL (2 during frontline chemo and 1 upon relapse with salvage chemo), and 9 deaths (11%) related to other causes. The 3-yr lymphoma free survival (LFS) of the entire cohort, pts receiving chemo alone, and pts receiving chemo-RT (without including treatment related mortality (TRM) as an event) was 89.1%, 85.2%, and 100% respectively. The 3-yr LFS survival of the entire cohort, pts receiving chemo alone, and pts receiving chemo-RT (including TRM as an event) was 85.1%, 80.0%, and 96.2% respectively. Discussion: Here we report the largest review to our knowledge of limited stage PBL. Outcomes appear to be excellent with 3-yr PFS and OS of 71.9% and 78.7% respectively and a 3-yr LFS of 89.1% (85.1% when attributing TRM as an event). There was no obvious benefit to receiving aggressive therapy with H-CVAD based regimens and/or Auto-SCT. Although this is a small, uncontrolled sample size the HR for OS was improved in patients receiving frontline chemo-RT vs chemo alone 0.18 (95% CI 0.04-0.84; p=0.029). However, this did not take into account TRM with or progression while receiving frontline chemotherapy. Patients who were HIV+ had similar PFS and OS outcomes compared to the entire cohort. This retrospective study clearly demonstrates the favorable outcomes in this patient population, especially in those able to receive definitive therapy for their disease. Disclosures Hess: ADC Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS, AstraZeneca: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Patel:Takeda: Consultancy, Research Funding; Precision Biosciences: Research Funding; Nektar: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Bristol Myers Squibb: Consultancy, Research Funding; Poseida: Research Funding; Cellectis: Research Funding; Oncopeptides: Consultancy; Janssen: Consultancy, Research Funding. Nowakowski:Nanostrings: Research Funding; Seattle Genetics: Consultancy; Curis: Consultancy; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other; Kymera: Consultancy; Denovo: Consultancy; Kite: Consultancy; Celgene/BMS: Consultancy, Research Funding; Roche: Consultancy, Research Funding; MorphoSys: Consultancy, Research Funding. Chavez:Bayer: Consultancy; AbbVie: Consultancy; BeiGene: Speakers Bureau; Morphosys: Consultancy, Speakers Bureau; Merck: Research Funding; AstraZeneca: Speakers Bureau; Celgene: Consultancy; Genentech: Speakers Bureau; Epizyme: Speakers Bureau; Gilead: Consultancy; Novartis: Consultancy; Kite, a Gilead Company: Consultancy, Speakers Bureau; Karyopharm: Consultancy; Verastem: Consultancy; Pfizer: Consultancy. Hill:Beigene: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria, Research Funding; Genentech: Consultancy, Honoraria, Research Funding; Takeda: Research Funding; AstraZenica: Consultancy, Honoraria, Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding. Maddocks:Pharmacyclics: Consultancy, Honoraria; Morphosys: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Karyopharm: Consultancy; ADC Therapeutics, AstraZeneca: Consultancy; BMS: Consultancy, Research Funding. Wagner-Johnston:ADC Therapeutics, Regeneron, CALIB-R, Verastem: Membership on an entity's Board of Directors or advisory committees. Kahl:AstraZeneca Pharmaceuticals LP: Consultancy, Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy; Pharmacyclics LLC: Consultancy; Genentech: Consultancy; Acerta: Consultancy, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Consultancy; ADC Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BeiGene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche Laboratories Inc: Consultancy. Alderuccio:ADC Therapeutics: Membership on an entity's Board of Directors or advisory committees; Forma Therapeutics: Other: Family member; Agios Pharmaceuticals: Other: Family member; Oncinfo: Honoraria; Inovio Pharmaceuticals: Other: Family member; Foundation Medicine: Other: Family member; Puma Biotechnology: Other: Family member; OncLive: Honoraria. Lossos:Janssen Biotech: Honoraria; Verastem: Consultancy, Honoraria; Janssen Scientific: Consultancy, Other; Seattle Genetics: Consultancy, Other; Stanford University: Patents & Royalties; NCI: Research Funding. Portell:Bayer: Consultancy; BeiGene: Consultancy, Research Funding; Kite: Consultancy, Research Funding; Acerta/AstraZeneca: Research Funding; Amgen: Consultancy; Janssen: Consultancy; Pharmacyclics: Consultancy; AbbVie: Research Funding; TG Therapeutics: Research Funding; Infinity: Research Funding; Roche/Genentech: Consultancy, Research Funding; Xencor: Research Funding. Landsburg:Celgene: Membership on an entity's Board of Directors or advisory committees; Triphase: Research Funding; Takeda: Research Funding; Curis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Speakers Bureau. Castillo:TG Therapeutics: Research Funding; Beigene: Consultancy, Research Funding; Kymera: Consultancy; Janssen: Consultancy, Research Funding; Abbvie: Research Funding; Pharmacyclics: Consultancy, Research Funding.