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Introduction: Brexucabtagene autoleucel (brexu-cel) was approved by US FDA for relapsed/refractory (R/R) mantle cell lymphoma (MCL) on July 24, 2020 based on results from the pivotal ZUMA-2 (NCT02601313) study, in which an objective response rate (ORR) of 93% and a complete response (CR) rate of 67% were achieved among the 60 treated patients with at least 7 months of follow-up. The study had stringent eligibility criteria, including prior treatment with a BTK inhibitor (BTKi), and only allowed BTKi and/or corticosteroid for bridging therapy. We report here the safety and efficacy of brexu-cel in standard of care practice among centers in the US Lymphoma CAR-T Consortium. Methods: Fourteen centers participated in this retrospective study. Patients who underwent leukapheresis by 6/15/2021 with an intent to manufacture brexu-cel were included. Baseline clinical characteristics, bridging therapy, adverse events after brexu-cel infusion, and post-infusion outcome data were collected. Eligibility for ZUMA-2 was retrospectively determined based on characteristics at the time of leukapheresis. Duration of response (time from initial response to disease progression or death from any cause), progression-free survival (PFS; time from infusion to disease progression or death from any cause) and overall survival (OS; time from infusion to death from any cause) were analyzed using the Kaplan-Meier method. Results: At the data cut-off date of 7/9/2021, 107 patients underwent leukapheresis, among whom 93 (87%) completed brexu-cel infusion, 2 (2%) were waiting for infusion, and 12 (11%) did not receive infusion (manufacture failure n=6, organ dysfunction n=1, death n=5). Baseline clinical characteristics of the 93 infused patients are shown in Table 1. The median age was 67 years and 81% were male. 32% had high risk simplified MIPI, 77% had Ki-67≥30%, 45% had blastoid or pleomorphic variant, 46% had TP53 alteration, 29% had complex karyotype, and 7% had CNS involvement. The median number of prior lines of therapy was 3. Eighty-two percent had prior BTKi treatment, and 44% had refractory disease to the last line of therapy. Sixty-eight (73%) patients would not have met ZUMA-2 eligibility criteria. Reasons for ineligibility included ECOG PS ≥2 (n=8), CNS involvement by lymphoma (n=6), prior therapies (n=33), cytopenia (n=11), renal or hepatic dysfunction (n=13), other medical conditions (n=18), and active infection (n=2). Sixty (65%) of the 93 patients received bridging therapy, which included BTKi (n=27), venetoclax (n=14), chemotherapy (n=19), CD20 antibody (n=26), lenalidomide (n=3), corticosteroid (n=9), and radiotherapy (n=13). Only 13 (14%) patients received BTKi or corticosteroid alone as in ZUMA-2. Among 93 infused patients, cytokine release syndrome (CRS) rate was 88% (8% grade ≥3), and immune effector cell-associated neurotoxicity syndrome (ICANS) rate was 58% (33% grade ≥3). No grade 5 CRS or ICANS occurred. Medications used to manage CRS and ICANS were 71 (76%) for tocilizumab, 64 (69%) for steroid, and 16 (17%) for anakinra. Twenty-four (26%) patients required ICU admission, 9 patients required vasopressors, and 4 patients required mechanical ventilation. With a median follow-up of 3.0 months (range 0.1-9.6), day 30 response was evaluable in 81 patients, and the ORR was 86%, with 64% CR (Table 2). The ORR/CR rates were 94%/70% for blastoid or pleomorphic variants, 82%/50% for TP53 altered, 84%/61% for BTKi-exposed, 94%/76% for BTKi-naïve, and 88%/67% for those not meeting ZUMA-2 eligibility criteria. The ORR/CR rates were 87%/69% for patients who received bridging therapy and 85%/56% for those who did not. For patients who achieved a response at day 30, the rate of continued response at 3-month was 83.7% (95% CI 68.3%-92.0%). The 3-month PFS rate was 80.6% (95% CI 68.6%-88.4%), and the 6-month OS rate was 82.1% (95% CI 67.7%-90.5%). Conclusions: This multicenter retrospective study demonstrated encouraging safety and efficacy data of brexu-cel in R/R MCL in the real world practice. The CRS and ICANS incidences were comparable to those reported in ZUMA-2, but use of tocilizumab and steroid was more frequent than in ZUMA-2. Although 73% of the patients would have been ineligible for ZUMA-2, the ORR and CR rate were comparable to those reported in ZUMA-2. Longer follow-up is necessary to confirm long term safety and efficacy. YW, PJ and FLL are co-first authors, and YL, MW and MDJ are co-senior authors. Figure 1 Figure 1. Disclosures Wang: Novartis: Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Eli Lilly: Membership on an entity's Board of Directors or advisory committees; InnoCare: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; MorphoSys: Research Funding. Jain: Kite: Consultancy; Lilly: Membership on an entity's Board of Directors or advisory committees. Locke: Gerson Lehrman Group: Consultancy; Emerging Therapy Solutions: Consultancy; EcoR1: Consultancy; Cowen: Consultancy; GammaDelta Therapeutics: Consultancy, Other: Scientific Advisory Role; Kite, a Gilead Company: Consultancy, Other: Scientific Advisory Role, Research Funding; Umoja: Consultancy, Other; Janssen: Consultancy, Other: Scientific Advisory Role; Wugen: Consultancy, Other; Legend Biotech: Consultancy, Other; Takeda: Consultancy, Other; Novartis: Consultancy, Other, Research Funding; Moffitt Cancer Center: Patents & Royalties: field of cellular immunotherapy; Iovance Biotherapeutics: Consultancy, Other: Scientific Advisory Role; Calibr: Consultancy, Other: Scientific Advisory Role; Cellular Biomedicine Group: Consultancy, Other: Scientific Advisory Role; BMS/Celgene: Consultancy, Other: Scientific Advisory Role; Bluebird Bio: Consultancy, Other: Scientific Advisory Role; Amgen: Consultancy, Other: Scientific Advisory Role; Allogene Therapeutics: Consultancy, Other: Scientific Advisory Role, Research Funding. Munoz: Bayer, Gilead/Kite Pharma, Celgene, Merck, Portola, Incyte, Genentech, Pharmacyclics, Seattle Genetics, Janssen, and Millennium: Research Funding; Kite, a Gilead Company, Kyowa, Bayer, Pharmacyclics/Janssen, Seagen, Acrotech/Aurobindo, Beigene, Verastem, AstraZeneca, Celgene/BMS, Genentech/Roche.: Speakers Bureau; Targeted Oncology, OncView, Kyowa Kirin, Physicians' Education Resource, and Seagen: Honoraria; Pharmacyclics/Abbvie, Bayer, Kite, a Gilead Company, Pfizer, Janssen, Juno/Celgene, Bristol Myers Squibb, Kyowa Kirin, Alexion, Fosun Kite, Innovent, Seagen, BeiGene, Debiopharm, Epizyme, Karyopharm, ADC Therapeutics, Servier, and Genmab: Consultancy, Other: advisory role; Alexion, AstraZeneca Rare Disease: Other: Study investigator. Maurer: BMS: Research Funding; Genentech: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite Pharma: 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. Beitinjaneh: Kite/Gilead: Other: Ad Board Event Attendee. Frank: Allogene Therapeutics: Research Funding; Kite-Gilead: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Research Funding. Dahiya: Miltenyi Biotech: Research Funding; Kite, a Gilead Company: Consultancy; Jazz Pharmaceuticals: Research Funding; Atara Biotherapeutics: Consultancy; BMS: Consultancy. McGuirk: Magenta Therapeutics: Consultancy, Honoraria, Research Funding; Gamida Cell: Research Funding; Novartis: Research Funding; Bellicum Pharmaceuticals: Research Funding; Fresenius Biotech: Research Funding; Astelllas Pharma: Research Funding; Novartis: Research Funding; Allovir: Consultancy, Honoraria, Research Funding; Pluristem Therapeutics: Research Funding; Juno Therapeutics: Consultancy, Honoraria, Research Funding; Kite/ Gilead: Consultancy, Honoraria, Other: travel accommodations, expense, Kite a Gilead company, Research Funding, Speakers Bureau; EcoR1 Capital: Consultancy. Goy: Vincerx pharma: Membership on an entity's Board of Directors or advisory committees; Michael J Hennessey Associates INC: Consultancy; Elsevier's Practice Update Oncology, Intellisphere, LLC(Targeted Oncology): Consultancy; Phamacyclics: Research Funding; Vincerx: Honoraria, Membership on an entity's Board of Directors or advisory committees; Rosewell Park: Consultancy; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; AbbVie/Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Research Funding; Incyte: Honoraria; Hoffman la Roche: Consultancy; LLC(Targeted Oncology): Consultancy; Xcenda: Consultancy, Honoraria; OncLive Peer Exchange: Honoraria; Infinity/Verastem: Research Funding; Genentech/Hoffman la Roche: Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Medscape: Consultancy; Xcenda: Consultancy; Bristol Meyers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Acerta: Consultancy, Research Funding; Bristol Meyers Squibb: Membership on an entity's Board of Directors or advisory committees; Janssen: Research Funding; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie/Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Elsevier PracticeUpdate: Oncology: Consultancy, Honoraria; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Genomic Testing Cooperative: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; Physicians' Education Resource: Consultancy, Other: Meeting/travel support; MorphoSys: Honoraria, Other; Novartis: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Constellation: Research Funding; COTA (Cancer Outcome Tracking Analysis): Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; Hackensack Meridian Health, Regional Cancer Care Associates/OMI: Current Employment. Vose: Kite, a Gilead Company: Honoraria, Research Funding. Hill: Kite, a Gilead Company: Consultancy, Honoraria, Other: Travel Support, Research Funding; Celgene (BMS): Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria; Gentenech: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Beigene: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Incyte/Morphysis: Consultancy, Honoraria, Research Funding; AstraZenica: Consultancy, Honoraria; Epizyme: Consultancy, Honoraria. Oluwole: Pfizer: Consultancy; Curio Science: Consultancy; Janssen: Consultancy; Kite, a Gilead Company: Consultancy, Research Funding. Deol: Kite, a Gilead Company: Consultancy. Shah: Jazz Pharmaceuticals: Research Funding; Incyte: Research Funding; Pfizer: Consultancy, Other: Expenses; Novartis: Consultancy, Other: Expenses; Bristol-Myers Squibb/Celgene: Consultancy, Other: Expenses; BeiGene: Consultancy, Honoraria; Amgen: Consultancy; Precision Biosciences: Consultancy; Kite, a Gilead Company: Consultancy, Honoraria, Other: Expenses, Research Funding; Pharmacyclics/Janssen: Honoraria, Other: Expenses; Acrotech/Spectrum: Honoraria; Servier Genetics: Other; Adaptive Biotechnologies: Consultancy. Paludo: Karyopharm: Research Funding. Miklos: Pharmacyclics: Patents & Royalties; Adaptive Biotechnologies, Novartis, Juno/Celgene-BMS, Kite, a Gilead Company, Pharmacyclics-AbbVie, Janssen, Pharmacyclics, AlloGene, Precision Bioscience, Miltenyi Biotech, Adicet, Takeda: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics, Amgen, Kite, a Gilead Company, Novartis, Roche, Genentech, Becton Dickinson, Isoplexis, Miltenyi, Juno-Celgene-Bristol Myers Squibb, Allogene, Precision Biosciences, Adicet, Adaptive Biotechnologies: Research Funding; Kite, a Gilead Company, Amgen, Atara, Wugen, Celgene, Novartis, Juno-Celgene-Bristol Myers Squibb, Allogene, Precision Bioscience, Adicet, Pharmacyclics, Janssen, Takeda, Adaptive Biotechnologies and Miltenyi Biotechnologies: Consultancy. Ghobadi: Wugen: Consultancy; Atara: Consultancy; Amgen: Consultancy, Research Funding; Celgene: Consultancy; Kite, a Gilead Company: Consultancy, Honoraria, Research Funding. Neelapu: Kite, a Gilead Company, Merck, Bristol Myers Squibb, Novartis, Celgene, Pfizer, Allogene Therapeutics, Cell Medica/Kuur, Incyte, Precision Biosciences, Legend Biotech, Adicet Bio, Calibr, Unum Therapeutics and Bluebird Bio: Honoraria; Takeda Pharmaceuticals and related to cell therapy: Patents & Royalties; Kite, a Gilead Company, Merck, Bristol Myers Squibb, Novartis, Celgene, Pfizer, Allogene, Kuur, Incyte, Precision BioSciences, Legend, Adicet Bio, Calibr, and Unum Therapeutics: Other: personal fees; Kite, a Gilead Company, Bristol Myers Squibb, Merck, Poseida, Cellectis, Celgene, Karus Therapeutics, Unum Therapeutics (Cogent Biosciences), Allogene, Precision BioSciences, Acerta and Adicet Bio: Research Funding. Lin: Legend: Consultancy; Novartis: Consultancy; Gamida Cell: Consultancy; Vineti: Consultancy; Bluebird Bio: Consultancy, Research Funding; Sorrento: Consultancy; Takeda: Research Funding; Merck: Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Juno: Consultancy; Kite, a Gilead Company: Consultancy, Research Funding. Wang: Moffit Cancer Center: Honoraria; Chinese Medical Association: Honoraria; InnoCare: Consultancy, Research Funding; Epizyme: Consultancy, Honoraria; Hebei Cancer Prevention Federation: Honoraria; Newbridge Pharmaceuticals: Honoraria; Genentech: Consultancy; OMI: Honoraria; Physicians Education Resources (PER): Honoraria; Clinical Care Options: Honoraria; CAHON: Honoraria; VelosBio: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; CStone: Consultancy; Loxo Oncology: Consultancy, Research Funding; BioInvent: Research Funding; Juno: Consultancy, Research Funding; DTRM Biopharma (Cayman) Limited: Consultancy; BGICS: Honoraria; Bayer Healthcare: Consultancy; Janssen: Consultancy, Honoraria, Research Funding; Kite Pharma: Consultancy, Honoraria, Research Funding; Molecular Templates: Research Funding; Imedex: Honoraria; Miltenyi Biomedicine GmbH: Consultancy, Honoraria; Dava Oncology: Honoraria; Oncternal: Consultancy, Research Funding; Lilly: Research Funding; Celgene: Research Funding; Anticancer Association: Honoraria; AstraZeneca: Consultancy, Honoraria, Research Funding; The First Afflicted Hospital of Zhejiang University: Honoraria; BeiGene: Consultancy, Honoraria, Research Funding; Scripps: Honoraria; Mumbai Hematology Group: Honoraria; Acerta Pharma: Consultancy, Honoraria, Research Funding. Jain: BMS, Kite/Gilead, Novartis, Precision Biosciences, Takeda: Consultancy.

Introduction: DNA mismatch repair (MMR) is a major mechanism cells use to repair erroneous insertion or deletion of bases during DNA replication. MMR deficiency (MMR-d), including rare germline mutations (Lynch syndrome) and somatic inactivation, has been extensively studied in solid tumors, such as colon, endometrial, pancreatic, and ovarian cancers. The prevalence of MMR-d in solid tumors ranges from 1% - 20% of somatic defects, and patients with MMR-d tumors have a longer overall survival. It is also well-known that MMR is an integral part of activation-induced cytidine deaminase (AID)-mediated somatic hypermutation and immunoglobulin class switch processes in normal mature B lymphocytes, while mice deficient in the MMR genes are prone to develop T and B cell lymphomas. It is also known that follicular lymphoma (FL) carries a high degree of somatic hypermutation with some heterogeneity induced by AID. Our understanding of MMR status in human blood cancers, especially in FL remains very limited. One could hypothesize that MMR-d status could permit cells to acquire and sustain high-degree of mutation rates, better sensitivity to DNA damaging chemotherapy while hindering rapid cell proliferation, which could manifest to a better prognosis in patients. Herein, we conducted an exploratory analysis to understand the frequency of MMR-d status and its prognostic significance in FL. Methods: Following approval of the Institution Review Board, we assessed the expression of MMR proteins; MLH1, PMS2 and MSH6 on formalin fixed paraffin embedded (FFPE) lymph node tissue obtained from patients with FL by immunohistochemistry (IHC) using standard methods on Ventana Benchmark XT automated stainers (Ventana Medical Systems, Tucson, AZ). Dako (clone ES05), Cell Marque (clone EPR3947) and Biocare (clone BC/44) antibodies were used to stain MLH1, PMS2 and MSH6, respectively. Fifty cases of FL with existing FFPE blocks were selected randomly. The expression of MLH1, PMS2 and MSH6 proteins in the tumor cells was scored as 0, +1 or +2, (Figure 1A). Cases with absence of the IHC signal (score of 0) in any one of the three proteins were classified as MMR-d. Progression free survival at 12 months (PFS12) or 24 months (PFS24) was defined as being free from disease progression or death at 12 or 24 months, respectively. All time-to-event analyses were done from the time of diagnosis. Results: Fifty patients with FL were included in the study. The median age at diagnosis was 65 years (range 34-85) and 58% were females. At initial diagnosis, 36 (72%) patients had advanced stage (stage 3, n=8, stage 4, n=28) disease. Of the 50 patients, 26 (52%) required treatment; chemotherapy (n=14), radiation treatment alone (n=9), surgery alone (n=3). Out of the 50 patients, 23 (46%) were classified as MMR-d due to lack of expression of one of the three assessed proteins: MLH1 (n=1, 2%), PMS2 (n=11, 22%) and MSH6 (n=19, 38%). The patient who had MLH1 loss had concomitant PMS2 loss. Stage IV disease was observed in 52% of the MMR-d patients and 59% in the MMR proficient (MMR-p) patients, p=0.77. Median age at diagnosis was 62 (range: 34-84) years and 67 (range: 34-85) years in MMR-d and MMR-p patients, respectively, p=0.1. FLIPI score was similar between the MMR-d [low n=10 (43%), intermediate n=9 (39%), high n=4 (18%)] and MMR-p [low n=5 (18%), intermediate n=14 (52%), high n=8 (30%)] patients, p=0.16. Fourteen (61%) of MMR-d patients required initiation of therapy upon diagnosis compared to 12 (44%) of MMR-p patients, p=0.27. The median follow-up for the entire cohort was 17 years [95% confidence interval (CI): 11.7-29.4]. The median overall survival (OS) of MMR-d patients was 10 years (95%CI: 7.8-20.7) compared to 6 years (95%CI: 4.5-NR) in MMR-p patients, p=0.036, Figure 1B. In patients with MMR-d and MMR-p, 83% and 55% achieved PFS12, p=0.04 and 65% and 41% achieved PFS24, p=0.08, respectively. After adjusting for FLIPI score, MMR status remained associated with OS (MMR-d HR: 0.51, 95%CI: 0.2-1.02, p=0.05). Discussion and Conclusion: In this pilot study, MMR-d in the FL tumors was common (46%) and, as predicted, was associated with a favorable OS. Association of MMR-d status with PFS in general and patients with early progression (short PFS12 and PFS24) and relationship of MMR-d with types of therapy need further study in large FL datasets. Figure 1 Figure 1. Disclosures Paludo: Karyopharm: Research Funding. King: Celgene/BMS: Research Funding. Maurer: Celgene: Research Funding; Genentech: Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Nanostring: Research Funding; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees. Nowakowski: Celgene, NanoString Technologies, MorphoSys: Research Funding; Celgene, MorphoSys, Genentech, Selvita, Debiopharm Group, Kite/Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees. Witzig: Karyopharm Therapeutics, Celgene/BMS, Incyte, Epizyme: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene/BMS, Acerta Pharma, Kura Oncology, Acrotech Biopharma, Karyopharm Therapeutics: Research Funding.

Introduction: Clinical trials of novel salvage therapies (ST) have encouraging outcomes for relapsed/refractory classic Hodgkin lymphoma (R/R cHL) after autologous stem cell transplant (ASCT); yet studies comparing novel ST with conventional salvage chemotherapy are lacking. In a single center cohort, we demonstrated that bendamustine/brentuximab (BBV) had higher overall response rates (ORR) and complete response (CR) rates in ASCT-eligible R/R cHL (Desai et al JCO, 2021). Herein we report comparative outcomes of novel and conventional ST in R/R cHL who undergo ASCT, in a large multicenter retrospective cohort. Methods: Consecutive R/R cHL pts who underwent ASCT at 12 institutions across United States were included. Demographics and clinical variables at relapse including age, sex, B symptoms, stage, bulky disease (BD, single mass > 6 cm) extra nodal disease (END), primary refractory disease (PRD) and early relapse (ER, within 1 year) were recorded by electronic health records review. Study objectives were ORR, CR to first ST, post-ASCT PFS and OS by final ST. Time to event endpoints were defined from date of ASCT. Results: From 12 participating institutions, 853 pts of R/R cHL who underwent ASCT were eligible for this study. Median age was 33 (14-72) years, 457 (54%) were male, 446 (52%) had BD, 257 (30%) had advanced stage, 271 (32%) had END, 369 (43%) had B symptoms, 142 (17%) had PRD and 307 (36%) had ER. All 853 received at least 1 ST, 245 received 2 ST, 71 received 3 ST and 26 received 4 ST. Seven groups of ST were identified: 1. Conventional platinum-based chemotherapy (PBC) group including ICE, DHAP and ESHAP 2. BBV 3. Brentuximab Vedotin and nivolumab (BV/Nivo) 4. BV alone (BV) 5. gemcitabine-based chemotherapy (Gem) 6. checkpoint inhibitors (CPI) and 7. other miscellaneous agents (Misc). 1st ST was as follows: 553 had PBC; 69 had BBV; 48 had BV/Nivo; 65 had BV; 49 had Gem; 4 had CPI and 63 had Misc. There was no significant difference in the baseline characteristics by type of 1 st ST (data not shown). BBV had significantly higher ORR (92% vs 79%, p Final ST prior to ASCT was PBC in 451, BBV in 76, BV/Nivo in 48, BV in 87, CPI in 24, Gem in 90 and Misc in 64. Table 1 lists K-M estimates of 2-year survival probabilities for different Final ST groups. Median follow up was 3 (range 0.1-13) years. BV/Nivo group had significantly higher proportion of patients with PRD and BD than PBC, no other differences in baseline characteristics were identified amongst ST groups (data not shown). BV/Nivo (HR: 0.1 (CI 95:0.02-0.4), p 536 pts underwent ASCT in CR, 273 underwent ASCT in partial response (PR) and 31 underwent ASCT with progressive disease (PD). Pre-ASCT PR (HR 1.6 (CI 95:1.3-2.6), p In pts with pre-ASCT CR, all 36 who had pre-ASCT CR after BV/Nivo were alive and relapse free for follow up of 0.1-5 yrs. BV/Nivo was associated with significantly higher PFS (HR 0.1 (CI 95: 0.01-0.7), p Conclusions: BV/Nivo has a higher CR rate and better post-ASCT PFS compared to conventional chemotherapy and can lead to durable remissions in pts with pre-ASCT CR. BBV had a higher response rate and similar post-ASCT survival to conventional chemotherapy. BV had lower response rates compared to chemotherapy. Novel ST such as BV/Nivo and BBV may be preferable to conventional chemotherapy in R/R cHL. Figure 1 Figure 1. Disclosures Spinner: Notable Labs: Honoraria. Bachanova: FATE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Research Funding; KaryoPharma: Membership on an entity's Board of Directors or advisory committees; Gamida Cell: Membership on an entity's Board of Directors or advisory committees, Research Funding. Dorritie: SITC presentation: Honoraria; Genmab: Research Funding; University of Pittsburgh/UPMC Hillman Cancer Center: Current Employment; Janssen: Research Funding; F. Hoffman-La Roche Ltd: Research Funding; Juno/BMS: Research Funding; Kite, a Gilead Company: Research Funding; OncLive/Institutional Perspectives on Cancer presentation: Honoraria. Arai: Magenta Therapeutics: Research Funding. Maurer: Nanostring: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; Celgene: Research Funding; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees. Diefenbach: Genentech, Inc./ F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Celgene: Research Funding; IMab: Research Funding; Morphosys: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; Merck Sharp & Dohme: Consultancy, Honoraria, Research Funding; Perlmutter Cancer Center at NYU Langone Health: Current Employment; AbbVie: Research Funding; IGM Biosciences: Research Funding; Incyte: Research Funding; Trillium: Research Funding; Gilead: Current equity holder in publicly-traded company; MEI: Consultancy, Research Funding. Nowakowski: Kyte Pharma: Consultancy; Ryvu Therapeutics: Consultancy; Selvita: Consultancy; Curis: Consultancy; Karyopharm Therapeutics: Consultancy; Celgene/Bristol Myers Squibb: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; MorphoSys: Consultancy; Bantham Pharmaceutical: Consultancy; Daiichi Sankyo: Consultancy; Zai Labolatory: Consultancy; Incyte: Consultancy; Kymera Therapeutics: Consultancy; TG Therapeutics: Consultancy; Blueprint Medicines: Consultancy; Nanostrings: Research Funding. Advani: Astellas/Agensys: Research Funding; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Bayer: Membership on an entity's Board of Directors or advisory committees; Bristol Myer Squibb: Membership on an entity's Board of Directors or advisory committees; Cell Medica: Membership on an entity's Board of Directors or advisory committees; Forty Seven: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genetech Inc.: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees; Janssen Pharmaceutical: Research Funding; Juno: Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Kura: Research Funding; Kyowa: Membership on an entity's Board of Directors or advisory committees; Merck: Research Funding; Millenium: Research Funding; Pharmacyclics: Consultancy, Research Funding; Portola Pharmaceuticals: Consultancy; Regeneron: Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees.

Background: Event-free survival at 24 months (EFS24) is an important clinical endpoint in newly diagnosed diffuse large B-cell lymphoma and follicular lymphoma, and patients who achieve EFS24 with frontline immunochemotherapy have minimal loss of lifetime compared to age- and sex-matched general population. However, the prognostic role of EFS24 in mantle cell lymphoma (MCL) has not been well studied, possibly due to the perception of continued relapse pattern and poor survival outcome of MCL historically. A recent study from our group demonstrated evolving frontline therapy pattern from 2002-2009 ("Era 1") to 2010-2015 ("Era 2") which was associated with improved EFS and overall survival (OS) in Era 2. In the current study, we sought to explore the prognostic role of EFS24 in the two eras. Methods: Patients with newly diagnosed MCL from 9/2002 through 6/2015 were identified from Molecular Epidemiology Resource (MER), a prospective cohort study of the University of Iowa/Mayo Clinic Lymphoma SPORE. OS was defined as time from diagnosis (or the EFS24 defining event where applicable) to death from any cause and was analyzed using the Kaplan-Meier method. Expected OS accounting for age and sex was generated in R by using the general US population as the reference group. Observed versus expected OS was summarized by using standardized mortality ratio (SMR) and 95% confidence intervals (CI) of observed to expected deaths. Cumulative incidence of lymphoma-specific death was analyzed using Gray's test, with deaths from all other causes treated as competing events. Results: A total of 343 patients were included, 175 from Era 1 (median follow-up 13.0 years) and 168 from Era 2 (median follow-up 6.9 years). Age, sex and simplified MIPI score were similar between the 2 groups. Patients diagnosed in Era 2 had better OS, with a 5-year OS of 68.4% vs 59.2% (simplified MIPI-adjusted hazard ratio 0.68, 95% CI 0.50-0.93). Patients diagnosed in both eras had inferior OS compared to the general population, with an SMR of 3.26 (95% CI 2.70-3.89, P The primary cause of death after diagnosis was lymphoma-related for patients diagnosed in both eras. The 5-year rate of lymphoma-related death was 28.8% in Era 1 and 20.5% in Era 2. In patients who were diagnosed in Era 1 and achieved EFS24, the primary cause of death after achieving EFS24 remained to be lymphoma-related, with a 5-year rate of 19.8% compared to 6.2% for lymphoma-unrelated causes (Figure 1C). In contrast, in patients who were diagnosed in Era 2 and achieved EFS24, the rate of lymphoma-related death was no longer higher than that of lymphoma-unrelated death, with a 5-year rate of 2.1% vs 5.5%, respectively (Figure 1D). In a sensitivity analysis restricted to only patients who received standard frontline immunochemotherapy, similar results were obtained. For example, in patients who achieved EFS24, the SMR compared to the general population was 2.89 (95% CI 2.00-4.04, P Conclusions: MCL survival outcome has improved for patients first diagnosed in the more recent era (2010-2015, compared to 2002-2009), likely due to improved frontline therapy as well as better salvage treatments (such as lenalidomide and BTK inhibitors). In the more recent treatment era, patients who achieved EFS24 had survival approaching the age- and sex-matched general population. In addition, these patients had a low risk of dying from lymphoma and were more likely to die from other causes. Longer follow-up (e.g., in Era 2) and external validation in other series are necessary to confirm the prognostic role of EFS24. With more efficacious salvage treatment options and likely continued improvement of OS in MCL, EFS24 may become an important clinical endpoint in frontline therapy of MCL. Figure 1 Figure 1. Disclosures Wang: TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Eli Lilly: Membership on an entity's Board of Directors or advisory committees; Novartis: Research Funding; InnoCare: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding; MorphoSys: Research Funding. Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Nanostring: Research Funding. Link: Novartis, Jannsen: Research Funding; Genentech/Roche: Consultancy, Research Funding; MEI: Consultancy. Farooq: Kite, a Gilead Company: Honoraria. Paludo: Karyopharm: Research Funding. Witzig: Celgene/BMS, Acerta Pharma, Kura Oncology, Acrotech Biopharma, Karyopharm Therapeutics: Research Funding; Karyopharm Therapeutics, Celgene/BMS, Incyte, Epizyme: Consultancy, Membership on an entity's Board of Directors or advisory committees. Habermann: Seagen: Other: Data Monitoring Committee; Tess Therapeutics: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Cerhan: Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding; Genentech: Research Funding. Nowakowski: Celgene, NanoString Technologies, MorphoSys: Research Funding; Celgene, MorphoSys, Genentech, Selvita, Debiopharm Group, Kite/Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees.

BACKGROUND: Mantle cell lymphoma (MCL) is a B-cell non-Hodgkin lymphoma with variable clinical outcomes. Commonly used risk stratification tools (Ki67 IHC, MIPI) in newly diagnosed MCL are not frequently used when selecting therapy, resulting in treatment choice being dictated by age and co-morbidities rather than disease biology. The MCL35 risk score was developed as a more reliable measure of proliferation and has been shown to be prognostic and can risk stratify younger transplant eligible MCL patients into three groups with significantly different overall survival (OS; Scott et al. 2017; Holte et al. 2018) but has not been evaluated in older transplant ineligible patients. We report results evaluating the prognostic value of the MCL35 assay in older MCL patients (≥65) treated with frontline bendamustine/rituximab (BR). METHODS: Archived tissue samples from 119 patients age ≥65 years treated with BR from collaborating Lymphoma/Leukemia Molecular Profiling Project (LLMPP) sites and the LEO/MER cohort were collected and analyzed using the MCL35 assay and stratified into three distinct risk groups (low, standard, and high risk). Association between MCL35 proliferation scores and OS were estimated by the Kaplan-Meier method and hazard ratios were calculated. Associations between Ki67, s-MIPI, p53 IHC status, morphology and OS were also evaluated. RESULTS: The MCL35 assay was run on tissue samples from 119 patients. Median patient age was 74 (range 65-93) and 69.5% were male. Ki67 was CONCLUSIONS: These results suggest high risk MCL35 score is a prognostic biomarker of poor OS in patients >65 with MCL treated with BR. Conversely, Ki67 was not significantly associated with OS in these patients. Additional clinical validation using a larger sample size from the E1411 study is planned. If similar results are found, the MCL35 assay in combination with s-MIPI and p53 status may have utility in stratifying patients into risk adapted treatment arms in future prospective clinical trial designs. Figure 1 Figure 1. Disclosures Maurer: BMS: Research Funding; Genentech: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite Pharma: 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. Villa: Janssen: Honoraria; Gilead: Honoraria; AstraZeneca: Honoraria; AbbVie: Honoraria; Seattle Genetics: Honoraria; Celgene: Honoraria; Lundbeck: Honoraria; Roche: Honoraria; NanoString Technologies: Honoraria. Habermann: Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Cohen: Janssen, Adicet, Astra Zeneca, Genentech, Aptitude Health, Cellectar, Kite/Gilead, Loxo, BeiGene, Adaptive: Consultancy; Genentech, BMS/Celgene, LAM, BioINvent, LOXO, Astra Zeneca, Novartis, M2Gen, Takeda: Research Funding. Hill: Celgene (BMS): Consultancy, Honoraria, Research Funding; Epizyme: Consultancy, Honoraria; Gentenech: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria; Kite, a Gilead Company: Consultancy, Honoraria, Other: Travel Support, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding; AstraZenica: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Beigene: Consultancy, Honoraria, Research Funding; Incyte/Morphysis: Consultancy, Honoraria, Research Funding. Raess: Scopio Labs: Research Funding. Scott: Celgene: Consultancy; NanoString Technologies: Patents & Royalties: Patent describing measuring the proliferation signature in MCL using gene expression profiling.; BC Cancer: Patents & Royalties: Patent describing assigning DLBCL COO by gene expression profiling--licensed to NanoString Technologies. Patent describing measuring the proliferation signature in MCL using gene expression profiling. ; Rich/Genentech: Research Funding; Janssen: Consultancy, Research Funding; Incyte: Consultancy; Abbvie: Consultancy; AstraZeneca: Consultancy. Rimsza: NanoString Technologies: Other: Fee-for-service contract.

Introduction: Central nervous system (CNS) relapse is an uncommon but devastating complication occurring in about 2-10% of patients with diffuse large B-cell lymphoma (DLBCL). The CNS-IPI is a validated prognostic tool used to identify patients at high risk of CNS relapse. MYC and BCL2/BCL6 rearrangements (double-hit lymphoma- DHL) and non-GCB (NGCB) cell of origin may be at higher risk of CNS relapse. However, the ability of these molecular data to refine CNS-IPI risk prediction has not been assessed. In this study, we aimed to describe the impact of DHL and NGCB cell of origin in the incidence of CNS relapse in patients with newly diagnosed DLBCL. Methods: A retrospective review was conducted to identify patients with newly diagnosed DLBCL from 01/01/2002 - 06/30/2015 using the University of Iowa/Mayo Clinic Lymphoma Molecular Epidemiology Resource database. Patients with primary CNS lymphoma or with CNS involvement at diagnosis were excluded. All patients received chemoimmunotherapy. Clinical, biological, laboratory and radiological data were abstracted including the occurrence of CNS relapse. CNS IPI risk score categories were defined as low- (≤1) intermediate- (2-3) and high-risk (≥4) as originally described (Schmitz et al, JCO 2016). Cell of origin (COO) classification into GCB and non-GCB subtypes was performed using Hans algorithm and/or NanoString data. Characteristics between groups were compared using Kruskal-Wallis or Chi-squared test as appropriate. Survival analysis was performed using Cox proportional hazards model and the Kaplan Meier method. Statistical analysis was performed using SAS version 9.4M5 and R version 4.0.3. Results: We identified a total of 1,360 patients with newly diagnosed DLBCL. Patients had a median age of 62 years (range 18-93 years), were predominantly male (56%), had a performance status The median follow-up for the entire cohort was 84.9 months (IQR: 58.9-120.9). Overall, 50 patients (3.6%) had a CNS relapse during the study period. Lymphoma relapsed in the CNS in 3.0% (95% CI:2.1-4.4), 4.7% (95% CI: 3.4-6.3) and 4.9% (95% CI: 3.7-6.6)% of patients with CNS-IPI score of intermediate/high risk at 1, 3 and 5 years following the diagnosis of DLBCL. In patients with low risk CNS-IPI, 1.1% (95% CI: 0.4-2.5), 1.7% (95% CI: 0.9-3.4) and 1.7% (95% CI: 0.9-3.4) had a CNS relapse at 1, 3 and 5 years following DLBC diagnosis. Patients with CNS relapse were more likely to have elevated LDH (76% vs 56%, p=0.008), advanced stage (78% vs 61%, p=0.01), adrenal involvement (8% vs 2.4%, p=0.014) and have a CNS IPI intermediate risk (68% vs 51%), and high risk (16% vs 12.3%, p=0.014) at the time of initial diagnosis compared to patients without CNS relapse. Patients with CNS relapse had similar rates of DHL (4% vs 2.7%, p=0.17) and NGCB cell of origin (42% vs 36.2%, p=0.52) compared to patients without CNS relapse. In the multivariable analysis, the strongest predictors for CNS relapse were CNS IPI high risk (Hazard Ratio [HR]= 3.6, 95%CI= 1.4-9.7) and CNS IPI intermediate risk (HR= 3.15, 95%CI= 1.46-6.81). DHL and NGCB cell of origin were not associated with a significantly higher risk of CNS relapse (HR= 1.79, [95%CI=0.42-7.59] and HR= 1.26, [95%CI=0.62-2.57], respectively). Conclusion: CNS relapse was an uncommon occurrence in patients with DLBCL who were treated with chemoimmunotherapy. CNS IPI intermediate- and high-risk scores remain the strongest predictors of CNS relapse in patients with DLBCL after adjusting to biological high-risk variables including DHL and NGCB cell of origin. Larger studies are required to validate our findings due to the low incidence of CNS relapse in our cohort. Figure 1 Figure 1. Disclosures Farooq: Kite, a Gilead Company: Honoraria. Maurer: Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Genentech: Research Funding; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Witzig: Karyopharm Therapeutics, Celgene/BMS, Incyte, Epizyme: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene/BMS, Acerta Pharma, Kura Oncology, Acrotech Biopharma, Karyopharm Therapeutics: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Novak: Celgene/BMS: Research Funding.

Background: Follicular lymphoma (FL) exhibits significant clinical, cellular, molecular, and genetic heterogeneity. Our understanding of FL biology and molecular classifications of FL, to date, has largely been driven by pathologic classification (Grade 1-3b), genetic classification (m7-FLIPI), or gene expression profiling (IR-1/2; Huet-23), along with limited studies on small cohorts or targeted panels. In order to further understand the biological underpinnings and complexity of FL, large-scale and integrated whole exome sequencing (WES) and RNA sequencing (RNAseq) studies are needed. Using a highly-annotated cohort of 93 FL tumors with matched RNAseq, WES, and CyTOF data, we have explored the transcriptomic signature of purified FL B cells and identified unique molecular subsets that are defined by distinct pathway activation, immune content, and genomic signatures. Methods: Frozen cell suspensions from 93 untreated FL (Grade 1-3b) patients' tumor biopsies, 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. RNAseq and WES were performed in the Mayo Clinic Genome Analysis Core. RNAseq and WES data were processed using a standard pipeline and novel driver genes were identified using Chasm+ driver analysis. Copy number variants were identified from WES data using GISTIC 2.0. NMF clustering and single sample gene set testing for B cell lineage and tumor microenvironment (TME) signatures were performed in R using the NMF and singscore packages. Results: Unsupervised clustering of RNAseq data identified three distinct expression programs which separated patient B cell samples into 3 groups: Group 1 (G1, n=20), Group 2 (G2, n=24), Group 3 (G3, n=43). While no clinical attributes were defined by any single group, G1 consisted of cases that had less aggressive characteristics (63% Stage I-II, 79% FLIPI 0-1). To identify unique transcriptional pathways driving the three expression programs, we scored gene level contributions to NMF expression programs and employed gene set enrichment analysis. This revealed significant pathway association with type-I IFN signaling (G1), DNA repair and stress response (G2), and epigenetic modulation (G3) as differentiating programs between the 3 groups (FDR q Conclusion: In this study, we have identified three unique FL tumor B cell groups, defined by specific transcriptional programs. Pathways such as inflammation, DNA damage response, and chromatin modification contribute most to the differences between B cell samples and group membership. Additionally, each program associated with specific genetic events as well as TME composition, highlighting potential drivers of these B cell states. This study improves the understanding of the mechanisms driving FL tumors and motivates further investigation into transcriptional consequences of genetic events as well as potential tumor intrinsic factors that may influence the TME. Figure 1 Figure 1. Disclosures Maurer: BMS: Research Funding; Genentech: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite Pharma: 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. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Link: MEI: Consultancy; Genentech/Roche: Consultancy, Research Funding; Novartis, Jannsen: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. King: Celgene/BMS: Research Funding. Cerhan: Genentech: Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Novak: Celgene/BMS: Research Funding.

Background: CD20 antibody plus alkylator and/or anthracycline based immunochemotherapy (IC) is a standard frontline therapy for patients with follicular lymphoma (FL) with 10-year event-free survival (EFS) and overall survival (OS) rates of approximately 50% and 80% respectively in long-term follow-up of clinical trials. Currently available clinical prognostic indices for FL have been designed using PFS and OS endpoints. Early events, commonly defined as progression of disease within 24 months (POD24) or early transformation to a more aggressive histology, are associated with inferior outcomes and increased risk of death due to refractory FL. Timely identification of the minority of patients with elevated mortality risk might enhance clinical management and research strategies. The FLIPI24 Consortium was created to develop a clinical prognostic index using early events as the primary endpoint. We report the outcomes for the pooled cohort and investigate the implications of therapy patterns on potential model development. Methods: Individual patient data were pooled and harmonized from 11 prospective observational cohorts from Europe, North America, and Australia. Patients who were diagnosed with grades 1-3A FL and initiated frontline IC were eligible. EFS was defined as time from start of IC to progression, relapse, retreatment (2nd line), histologic transformation, or death due to any cause. Early events were defined using status at 24 months from start of IC. OS was defined as time from start of IC to death due to any cause. Kaplan Meier curves and Cox proportional hazards models were used to evaluate outcomes by clinical features and therapy types. Results: 9006 patients were abstracted and harmonized, 6111 patients initiated frontline IC between 2002 and 2018 and were included in this analysis. Median age at diagnosis was 61 years (IQR 52-69) and 50% were male. Complete FLIPI data were available in 5637 patients (92%) and 46%, 32%, and 22% were low, intermediate, and high risk, respectively. IC type was 3079 R-CHOP or like (50%) , 1529 R-CVP or like (25%), 918 R-bendamustine (B-R) or like (15%), and 585 fludarabine or other alkylator based IC (10%); 3187 received CD20 antibody maintenance (52%). Patients receiving R-CHOP were younger, more frequently grade 3A, and more frequently had elevated LDH; differences in other characteristics by IC type were not clinically meaningful. At median follow-up of 42 months (IQR 17-72), 2647 patients (43%) had an event (any) and 1494 patients (25%) died. Median survival after an early (non-death) event was 49 months (95% CI: 41-58); 5-year OS was 46% (95% CI: 43-49) compared to 89% (95% CI: 88-90) in patients without POD24. Across all IC types, EFS estimates at 2 and 10 years from start of IC were 80% (95% CI:79-81) and 49% (95% CI:48-51) and OS estimates were 92% (95% CI: 91-92) and 70% (95% CI: 69-72), respectively. FLIPI was highly associated with both EFS (c-statistic=0.61) and OS (c-statistic=0.65) from the initiation of IC (both p Treatment patterns changed significantly over the study timeframe. Use of B-R and/or maintenance increased to 30% and 70% respectively in N=2937 patients treated in 2010-2018 (Era2) compared to Conclusion: EFS and OS from this large pooled analysis of observational cohorts is similar to long-term follow-up of randomized clinical trials in the IC era and support the use of these data for model development. Modeling efforts for early events should adjust for initial IC selection and use of maintenance therapy. Utilization of bendamustine and/or maintenance therapy increased over the study timeframe from 2002-2018, and Era2 was associated with improved EFS but not OS. This cohort provides comprehensive and robust observational data to define clinical predictors in IC treated patients. Figure 1 Figure 1. Disclosures Maurer: Genentech: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Flowers: Janssen: Research Funding; Takeda: Research Funding; National Cancer Institute: Research Funding; Biopharma: Consultancy; BeiGene: Consultancy; Amgen: Research Funding; Celgene: Consultancy, Research Funding; Xencor: Research Funding; Acerta: Research Funding; Bayer: Consultancy, Research Funding; Sanofi: Research Funding; 4D: Research Funding; Adaptimmune: Research Funding; Allogene: Research Funding; EMD: Research Funding; TG Therapeutics: Research Funding; Burroughs Wellcome Fund: Research Funding; Kite: Research Funding; AbbVie: Consultancy, Research Funding; Cellectis: Research Funding; Denovo: Consultancy; Cancer Prevention and Research Institute of Texas: CPRIT Scholar in Cancer Research: Research Funding; Karyopharm: Consultancy; Gilead: Consultancy, Research Funding; Genmab: Consultancy; Epizyme, Inc.: Consultancy; Novartis: Research Funding; Nektar: Research Funding; Morphosys: Research Funding; Iovance: Research Funding; Spectrum: Consultancy; Pfizer: Research Funding; Ziopharm: Research Funding; Guardant: Research Funding; Eastern Cooperative Oncology Group: Research Funding; SeaGen: Consultancy; Pharmacyclics/Janssen: Consultancy; Genentech/Roche: Consultancy, Research Funding; Pharmacyclics: Research Funding. Villa: Janssen: Honoraria; Gilead: Honoraria; AstraZeneca: Honoraria; AbbVie: Honoraria; Seattle Genetics: Honoraria; Celgene: Honoraria; Lundbeck: Honoraria; Roche: Honoraria; NanoString Technologies: Honoraria. Weibull: Jansen-Cilag: Other: part of a research collaboration between Karolinska Institutet and Janssen Pharmaceutica NV for which Karolinska Institutet has received grant support. Ghesquieres: Janssen: Honoraria; Mundipharma: Consultancy, Honoraria; Roche: Consultancy; Celgene: Consultancy, Honoraria; Gilead Science: Consultancy, Honoraria. Kridel: Gilead Sciences: Research Funding. Gandhi: Janssen: Research Funding; Novartis: Honoraria. Cheah: Celgene: Research Funding; TG Therapeutics: Consultancy, Honoraria, Other: advisory; Loxo/Lilly: Consultancy, Honoraria, Other: advisory; AstraZeneca: Consultancy, Honoraria, Other: advisory; AbbVie: Research Funding; Beigene: Consultancy, Honoraria, Other: advisory; Ascentage pharma: Consultancy, Honoraria, Other: advisory; Gilead: Consultancy, Honoraria, Other: advisory; MSD: Consultancy, Honoraria, Other: advisory, Research Funding; Janssen: Consultancy, Honoraria, Other: advisory; Roche: Consultancy, Honoraria, Other: advisory and travel expenses, Research Funding. Hawkes: Gilead: Membership on an entity's Board of Directors or advisory committees; Merck Sharpe Dohme: Membership on an entity's Board of Directors or advisory committees; Antigene: Membership on an entity's Board of Directors or advisory committees; Regeneron: Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squib/Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck KgA: Research Funding; Specialised Therapeutics: Consultancy; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Other: Travel and accommodation expenses, Research Funding, Speakers Bureau; Janssen: Speakers Bureau. Seymour: Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sunesis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: 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, Research Funding, Speakers Bureau; Mei Pharma: Honoraria, Membership on an entity's Board of Directors or advisory committees; Morphosys: Honoraria, Membership on an entity's Board of Directors or advisory committees; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Research Funding, Speakers Bureau. Freeman: Amgen: Honoraria; Celgene: Honoraria; Sanofi: Honoraria, Speakers Bureau; Incyte: Honoraria; Abbvie: Honoraria; Teva: Research Funding; Roche: Research Funding; Janssen: Honoraria, Speakers Bureau; Seattle Genetics: Honoraria; Bristol Myers Squibb: Honoraria, Speakers Bureau. Clausen: Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel expences ASH 2019; Gilead: Consultancy, Other: Travel expences 15th ICML ; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees. Wahlin: Gilead Sciences: Research Funding; Roche: Consultancy, Research Funding. Link: Novartis, Jannsen: Research Funding; Genentech/Roche: Consultancy, Research Funding; MEI: Consultancy. Ekstroem Smedby: Janssen Cilag: Research Funding; Takeda: Consultancy. Sehn: Genmab: Consultancy; Novartis: Consultancy; Debiopharm: Consultancy. Trněný: Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses; Portola: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; 1st Faculty of Medicine, Charles University, General Hospital in Prague: Current Employment; Celgene: Consultancy; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses; Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses; Amgen: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses; MorphoSys: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Honoraria; Gilead Sciences: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses; Incyte: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. El-Galaly: ROCHE Ltd: Ended employment in the past 24 months; Abbvie: Other: Speakers fee. Cerhan: Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Genentech: Research Funding; NanoString: Research Funding.

Background: Differences in outcomes between clinical trial and real-world populations in follicular lymphoma (FL) may be partially explained by differences in characteristics between clinical trial and real-world patients. We hypothesized that certain eligibility criteria may be responsible for populations with distinct demographic/clinical characteristics. In this study, we compiled a list of eligibility criteria from front-line FL trials and evaluated their impact on the potentially eligible FL patients derived from two national cohort studies. We also evaluated whether criteria were duplicative and whether liberalizing criteria might enlarge the pool of eligible patients. Methods: Eligibility criteria were abstracted from 21 first-line clinical trials in FL in the FLASH database. We identified FL patients in two prospective cohort studies: Molecular Epidemiology Resource (MER) and Lymphoma Epidemiology of Outcomes (LEO). Descriptive statistics were used to characterize patients included and excluded by various eligibility criteria. Chi-square tests and two-sample t-tests were used to compare categorical and continuous variables, respectively. To study the relative impact of individual criteria, we used a step-wise approach to quantify the number of additional patients excluded with each individual criterion after applying other criteria. Results: Eligibility criteria included in at least one-third of studies included stage (present in all 21 trials), renal function (18), HIV/AIDS status (17), performance status (16), history of other malignancies (16), hepatic function (16), and cardiac function (15), other serious health conditions (13), pregnancy status (10), neuro/psych function (10), age (10), metabolic disease status (9), CNS involvement (9), birth control (8), pulmonary function (8), WBC count (8), HBV status (8), platelet count (7), measurable disease (7), HCV status (7), active infection status (7), and CD20 status (7). We included 738 patients with newly diagnosed FL undergoing first-line therapy from MER and 703 patients from LEO. In MER, the median age was 60 years; 87% were White vs 2% non-White, 80% were non-Hispanic vs 2% Hispanic, and 68% were stage III/IV. In LEO, the median age was 60 years; 88% were White vs 11% non-White, 87% were non-Hispanic vs 11% Hispanic, and 67% were stage III/IV. Exclusion criteria impacting more than 10% of patients included stage (31 in LEO vs 29% in MER), self-reported serious health conditions (17 vs 31%), prior cancer diagnosis (14 vs 11%), performance status (10%), and platelet count (10%). Patients excluded due to the following criteria tended to be older: renal function (median 70 vs 60 years in MER), prior malignancy (67 vs 58 years), and self-reported serious health conditions (66 vs 58 years). This pattern was consistent in LEO (Table). No eligibility criteria significantly impacted race/ethnicity. Given the potential for multiple eligibility criteria to exclude the same patients, we used a step-wise approach to evaluate the impact of individual criteria on patients remaining after exclusion by the larger group of criteria, and whether liberalizing criteria impacted patient numbers and/or demographics. We found that only self-reported serious health conditions significantly impacted numbers (excluding 43% of patients), whereas hematologic parameters and performance status excluded only 5 and 1% of patients, respectively, with no impact on age or race/ethnicity. We also found that liberalizing stage requirement from III-IV to II-IV could increase enrollment by 14% in MER (12% in LEO), and liberalizing platelet requirement from ≥150,000 to ≥100,000 could increase enrollment by 11% (15% in LEO). Moreover, liberalizing eligibility criteria had no impact on age or race/ethnicity of the patient pool or on EFS (Figure). Conclusions: We found that excluding patients with prior malignancies, poor renal function, and self-reported serious health conditions appears to inadvertently exclude older patients. Other serious health conditions appeared to exclude a significant number of patients but did not impact patient demographics. Liberalizing certain criteria, including stage and platelet requirement, could potentially increase trial accrual, but would not likely correct the older patient deficit. These findings may serve in developing consensus eligibility criteria for future first-line FL trials. Figure 1 Figure 1. Disclosures Flowers: Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; National Cancer Institute: Research Funding; Allogene: Research Funding; Biopharma: Consultancy; Cellectis: Research Funding; EMD: Research Funding; Genentech/Roche: Consultancy, Research Funding; Epizyme, Inc.: Consultancy; Genmab: Consultancy; Iovance: Research Funding; Guardant: Research Funding; Burroughs Wellcome Fund: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; Spectrum: Consultancy; Gilead: Consultancy, Research Funding; Amgen: Research Funding; Xencor: Research Funding; Nektar: Research Funding; SeaGen: Consultancy; Ziopharm: Research Funding; TG Therapeutics: Research Funding; 4D: Research Funding; Acerta: Research Funding; Denovo: Consultancy; Novartis: Research Funding; Pfizer: Research Funding; Eastern Cooperative Oncology Group: Research Funding; Morphosys: Research Funding; Adaptimmune: Research Funding; Pharmacyclics/Janssen: Consultancy; Karyopharm: Consultancy; BeiGene: Consultancy; AbbVie: Consultancy, Research Funding; Kite: Research Funding; Janssen: Research Funding; Cancer Prevention and Research Institute of Texas: CPRIT Scholar in Cancer Research: Research Funding; Pharmacyclics: Research Funding. Link: MEI: Consultancy; Novartis, Jannsen: Research Funding; Genentech/Roche: Consultancy, Research Funding. Friedberg: Acerta: Other: DSMC ; Bayer: Other: DSMC ; Novartis: Other: DSMC . Cohen: Genentech, Takeda, BMS/Celgene, BioInvent, LAM, Astra Zeneca, Novartis, Loxo/Lilly: Research Funding; Janssen, Adaptive, Aptitude Health, BeiGene, Cellectar, Adicet, Loxo/Lilly, AStra ZenecaKite/Gilead: Consultancy. Kahl: AbbVie, Acerta, ADCT, AstraZeneca, BeiGene, Genentech: Research Funding; AbbVie, Adaptive, ADCT, AstraZeneca, Bayer, BeiGene, Bristol-Myers Squibb, Celgene, Genentech, Incyte, Janssen, Karyopharm, Kite, MEI, Pharmacyclics, Roche, TG Therapeutics, and Teva: Consultancy. Lossos: Lymphoma Research Foundation: Membership on an entity's Board of Directors or advisory committees; Stanford University: Patents & Royalties; Seattle Genetics: Consultancy; NCI: Research Funding; Verastem: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; NIH grants: Research Funding; University of Miami: Current Employment. Nastoupil: Bayer: Honoraria; Gilead/Kite: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; ADC Therapeutics: Honoraria; IGM Biosciences: Research Funding; Takeda: Honoraria, Other: DSMC, Research Funding; Janssen: Honoraria, Research Funding; Genentech: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; TG Therapeutics: Honoraria, Research Funding; Epizyme: Honoraria, Research Funding; Denovo Pharma: Other: DSMC; Caribou Biosciences: Research Funding; Bristol Myers Squibb/Celgene: Honoraria, Research Funding; MorphoSys: Honoraria. Maurer: Nanostring: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; BMS: Research Funding. Cerhan: NanoString: Research Funding; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Genentech: Research Funding. Martin: ADCT: Consultancy.

Introduction Central nervous system relapse (secondary central nervous system lymphoma -SCNS) is an uncommon but devastating complication of aggressive B-cell lymphoma. Patients (Pts) with CNS-IPI 4-6 are at greatest risk (10.2% at 2 years). Intravenous high-dose methotrexate (HD-MTX) is widely used to mitigate SCNS risk but data supporting this practice are limited. Methods We performed a multicentre, retrospective study at 21 sites in Australia, Asia, North America and Europe. Chart or registry review was performed for consecutively diagnosed pts with diffuse large B-cell lymphoma (DLBCL) and CNS-IPI 4-6, high grade B-cell lymphoma (HGBL) with rearrangements of MYC+BCL2 and/or BCL6 and primary breast/testicular DLBCL irrespective of CNS-IPI. Pts were diagnosed between 2000-2020, 18-80 years at diagnosis, and treated with curative intent anti-CD20 based chemo-immunotherapy. Pts with CNS involvement at diagnosis were excluded. HD-MTX was defined as at least one cycle of intravenous MTX at any dose. Time to SCNS was calculated from date of diagnosis (all-pts), and from the end of frontline systemic lymphoma therapy, defined as 6x21 days from diagnosis (complete response (CR-pts)), until SCNS, systemic relapse, death, or censoring, whichever came first. Cumulative risk of SCNS was computed using the Aalen-Johansen estimator treating death and systemic relapses as competing events. Adjusted cumulative risks were obtained by using an inverse probability of treatment weighting approach. The average treatment effect was computed as the difference in adjusted 5-year risk of SCNS. Results - 2300 and 1455 pts were included in the all-pts and CR-pts analyses, respectively. Baseline demographics and details of therapy are summarised in Table 1. Except for a predominance of males, pts ≤60 years and pts with ECOG 0-1 in the HD-MTX vs no HD-MTX groups, the demographics and treatments were well balanced. At a median follow up of 5.9 years (range 0.0-19.1) and 5.5 years from diagnosis (range 0.0-18.7), 201/2300 and 84/1455 pts experienced CNS events in the all-pts and CR-pts analyses respectively. For all-pts(n=2300), CNS-IPI was 4-6 in 2052(89.2%), with R-CHOP-like therapy given to 93.8%. 410 pts (17.8%) received HD-MTX (265 HD-MTX alone, 145 in combination with intrathecal methotrexate (IT-MTX);435 received IT-MTX alone;1455 received neither. There were 32/410 and 169/1890 SCNS events, with median time from diagnosis to SCNS of 8.8 and 6.7 months in the HD-MTX and no HD-MTX groups respectively. 5-year OS was 70% (95% CI, 65-76%) and 55% (95% CI 53-57%) in HD-MTX and no HD-MTX groups respectively. There was no difference in the adjusted 5-year risk of SCNS between the HD-MTX and no HD-MTX groups (8.4% vs 9.1%, adjusted hazard ratio [HR] 0.71, p=0.100) (Figure 1). For CR-pts(n=1455), CNS-IPI was 4-6 in 1267(87.0%), with R-CHOP-like therapy given to 93.3%. 284 pts (19.5%) received HD-MTX (170 HD-MTX alone, 114 with IT-MTX);298 received IT-MTX alone;873 received neither. There were 16/284 and 68/1171 SCNS events, with median time from diagnosis to SCNS of 11.0 and 10.3 months in the HD-MTX and no HD-MTX groups respectively. 5-year OS was 74%(95% CI 67-81%) and 75%(95% CI 72-78%) in the HD-MTX groups and no HD-MTX groups respectively (adjusted HR 1.08, p=0.622). There was no difference in the 5-year risk of CNS relapse between the HD-MTX and no HD-MTX groups 5.0% vs 6.0% (adjusted HR 1.03, p=0.903) (Figure 2). Exploratory analysis of the impact of HD-MTX among the highest risk groups CNS IPI 5 (n=368), CNS-IPI 6 (n=59) and CNS-IPI 6 plus all pts with testicular, renal or adrenal involvement (n=349) did not reveal differences in SCNS rates in HD-MTX treated pts. Additional subgroup analyses will be presented at the meeting. Conclusions To our knowledge, this is the largest study of the efficacy of HD-MTX in reducing SCNS events focusing exclusively on high-risk pts. The overall incidence of CNS relapse observed was consistent with previous reports in similar patient cohorts at 9%. The use of HD-MTX did not lower SCNS rates overall or when analysis was confined to CR pts at completion of curative intent therapy to compensate for potential immortal bias associated with HD-MTX therapy. Despite the limitations of the non-randomized and retrospective design, it appears unlikely that HD-MTX is associated with a clinically meaningful reduction in SCNS rates in pts with high risk for SCNS. Figure 1 Figure 1. Disclosures Lewis: AstraZeneca: Consultancy, Honoraria; Novartis: Patents & Royalties: Conference attendance; Janssen: Honoraria, Patents & Royalties: Conference attendance; Roche: Consultancy, Honoraria. Villa: Janssen: Honoraria; Gilead: Honoraria; AstraZeneca: Honoraria; AbbVie: Honoraria; Seattle Genetics: Honoraria; Celgene: Honoraria; Lundbeck: Honoraria; Roche: Honoraria; NanoString Technologies: Honoraria. Bobillo: F. Hoffmann-La Roche Ltd: Consultancy, Speakers Bureau; Gilead: Speakers Bureau. Ekstroem Smedby: Takeda: Consultancy; Janssen Cilag: Research Funding. Savage: Merck: Consultancy, Honoraria, Other: Institutional clinical trial funding; BMS: Consultancy, Honoraria, Other: Institutional clinical trial funding; AbbVie: Consultancy, Honoraria; Roche: Research Funding; Takeda: Other: Institutional clinical trial funding; Servier: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Astra-Zeneca: Consultancy, Honoraria; Beigene: Other: Institutional clinical trial funding; Genentech: Research Funding. Eyre: Beigene: Honoraria, Research Funding; Incyte: Consultancy; Secura Bio: Consultancy, Honoraria; Janssen: Honoraria; Gilead/KITE: Honoraria, Other: Travel support for conferences, Research Funding, Speakers Bureau; Loxo Oncology: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy, Honoraria, Other: Travel to conferences; AstraZeneca: Honoraria, Research Funding; Roche: Consultancy, Honoraria. Cwynarski: Incyte: Consultancy, Speakers Bureau; Gilead: Consultancy, Speakers Bureau; Atara: Consultancy; Celgene: Consultancy; Takeda: Consultancy, Other: travel to scientific conferences, Speakers Bureau; Kite, a Gilead Company: Consultancy, Other: travel to scientific conferences, Speakers Bureau; Janssen: Consultancy, Other: travel to scientific conferences; Roche: Consultancy, Other: travel to scientific conferences, Speakers Bureau; BMS/Celgene: Other: travel to scientific conferences. Stewart: Teva: Honoraria; Sandoz: Honoraria; Amgen: Honoraria; AstraZeneca: Honoraria; Novartis: Honoraria; Celgene: Honoraria; Gilead: Honoraria; Abbvie: Honoraria; Janssen: Honoraria; Roche: Honoraria. Bishton: Gilead: Honoraria, Other: Travel grants; AbbVie: Honoraria, Other: Travel grants; Celgene/BMS: Honoraria, Other: travel grants; Celltrion: Honoraria, Other: Travel grants; Takeda.: Honoraria, Other: Travel grants . Fox: F. Hoffmann-La Roche Ltd: Consultancy, Membership on an entity's Board of Directors or advisory committees. Joffe: Epizyme: Consultancy; AstraZeneca: Consultancy. Eloranta: Janssen Pharmaceutical NV: Other: NV. Sehn: Genmab: Consultancy; Novartis: Consultancy; Debiopharm: Consultancy. Manos: Bristol-Myers Squibb: Other: Travel and meetings. Hawkes: Specialised Therapeutics: Consultancy; Gilead: Membership on an entity's Board of Directors or advisory committees; Merck Sharpe Dohme: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Antigene: Membership on an entity's Board of Directors or advisory committees; Regeneron: Speakers Bureau; Bristol Myers Squib/Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck KgA: Research Funding; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Other: Travel and accommodation expenses, Research Funding, Speakers Bureau; Janssen: Speakers Bureau. Minson: Novartis: Research Funding; Hoffman La Roche: Research Funding. Dickinson: Celgene: Research Funding; Amgen: Honoraria; Takeda: Research Funding; Gilead Sciences: Consultancy, Honoraria, Speakers Bureau; MSD: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Roche: Consultancy, Honoraria, Other: travel, accommodation, expenses, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Research Funding, Speakers Bureau. Øvlisen: Abbvie: Other: Travel expenses. Gregory: Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel fees, Speakers Bureau; Janssen: Consultancy; Novartis: Consultancy. Ku: Roche: Consultancy; Antegene: Consultancy; Genor Biopharma: Consultancy. Talaulikar: Roche: Honoraria, Research Funding; Jansenn: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Takeda: Honoraria, Research Funding; EUSA Pharma: Honoraria, Research Funding. Maurer: Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Genentech: Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Nanostring: Research Funding. El-Galaly: Abbvie: Other: Speakers fee; ROCHE Ltd: Ended employment in the past 24 months. Cheah: Roche: Consultancy, Honoraria, Other: advisory and travel expenses, Research Funding; Janssen: Consultancy, Honoraria, Other: advisory; MSD: Consultancy, Honoraria, Other: advisory, Research Funding; Gilead: Consultancy, Honoraria, Other: advisory; Ascentage pharma: Consultancy, Honoraria, Other: advisory; Beigene: Consultancy, Honoraria, Other: advisory; AbbVie: Research Funding; Celgene: Research Funding; AstraZeneca: Consultancy, Honoraria, Other: advisory; Loxo/Lilly: Consultancy, Honoraria, Other: advisory; TG Therapeutics: Consultancy, Honoraria, Other: advisory.

Introduction: Low-grade B-cell lymphomas (LGBCL), aside from follicular lymphoma and chronic lymphocytic leukemia/small lymphocytic lymphoma, account for approximately 10% of B-cell non-Hodgkin lymphomas and consist of several subtypes. While a majority of LGBCL cases have an overall favorable prognosis, we have previously shown that cases who have an event (relapse or progression, transformation, or re-treatment) within 24 months of diagnosis (EFS24) have an inferior overall survival (OS) compared to those achieving EFS24 (Tracy et al., AJH 2019;94:658-66). However, the underlying biological characteristics associated with early failure and aggressive disease across LGBCL subtypes are unknown. In this study, we used matched transcriptomic, genomic, and immune profiling data from LGBCL cases, the largest cohort to date, and asked whether there were unique biological phenotypes across different LGBCL subtypes and whether we could identify signatures associated with aggressive LGBCL. Validation of the prognostic utility of this signature was performed on a previously published, independent cohort of 63 pre-treatment LGBCL cases. Methods: Tumors from 64 newly diagnosed LGBCL patients from the Molecular Epidemiology Resource of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence were included in this study (SMZL (n = 48), NMZL (n = 6), LPL (n = 5), B-NOS (n = 3), EMZL (n = 2)). RNA sequencing (RNAseq) data from 61 LGBCL tumors and 5 benign CD19+CD27+ memory B samples was subjected to NMF clustering to define groups. Differential expression and pathway analysis were used to identify biological characteristics of each cluster. CIBERSORT was used to identify immune cells in the tumor microenvironment. Whole exome sequencing (WES) was performed on 61 tumor-normal pairs. Singscore was used to assign a single score per patient representing gene expression of the survival-associated transcriptomic signature identified in this study. Results: NMF analysis of RNAseq data identified 5 clusters of patients, denoted LGBCL1-5 (Fig 1A). Patients from the same diagnostic subtype did not exclusively cluster together, with all LGBCL clusters comprised of patients from multiple subtypes (Fig 1B). Exploring the association between patient cluster and outcome, we observed significantly inferior event-free survival (EFS) (HR 2.24; 95% CI 1.01-4.98) and overall survival (OS) (HR 5.59; 95% CI 2.00-15.63) in LGBCL5 patients compared to LGBCL1-4 (Fig 1C). In addition, 80% of the transformation cases in our cohort were classified as LGBCL5 (Fig 1D). Differential expression and pathway analysis showed distinct processes significantly upregulated in each cluster (FDR < 0.05), with LGBCL5 demonstrating enrichment of cell cycle and mitosis pathways. CIBERSORT identified increased immune cell content in LGBCL3 and LGBCL5 compared to other clusters, with high frequencies of mast cells in both (p = 0.0002), increased CD8 T cells in LGBCL3 (p < 0.0001), and increased T follicular helper cells in LGBCL5 (p = 0.004). WES identified previously reported alterations in NOTCH, NFkB, and chromatin remodeling pathways and novel variants in LGBCL, including mutations in HNRNPK, CLTC, HLA-A, HLA-B and HLA-C. Assessment of alterations by cluster showed significant enrichment of TNFAIP3 (OR 5.54; 95% CI 1.20-28.14) and BCL2 alterations (OR 5.49; 95% CI 1.07-32.02) in LGBCL5 cluster. Finally, we identified a cell cycle-related transcriptomic signature of 108 genes upregulated in LGBCL5 and EFS24 failure cases. Cases with high expression of this signature showed significantly inferior EFS (HR 14.25; 95% CI 4.90-41.38) and OS (HR 7.82; 95% CI 2.40-25.44) compared to cases with low expression in our discovery cohort. This observation was reproduced in an independent validation cohort, where patients with high expression of this signature demonstrated significantly inferior EFS (HR 5.70; 95% CI 1.49-21.79) and OS (HR 10.07; 95% CI 2.00-50.61). Conclusions: In this study, we are the first to define mechanisms of pathogenesis in LGBCL with shared transcriptomic, genomic, and immune profiles present across LGBCL subtypes. We then further defined a gene expression signature associated with inferior patient outcome, with application of this signature to an independent validation cohort demonstrating proof of concept and utility of this signature as a prognostic marker in LGBCL patients. Figure 1 Figure 1. Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Nanostring: Research Funding. Paludo: Karyopharm: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Incyte: Other: Scientific Advisory Board; Seagen: Other: Data Monitoring Committee; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Link: MEI: Consultancy; Genentech/Roche: Consultancy, Research Funding; Novartis, Jannsen: Research Funding. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. Cerhan: Genentech: Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Novak: Celgene/BMS: Research Funding.

Background Diffuse large B-cell lymphoma (DLBCL) that fails to achieve a complete response (CR) or relapses early after standard immunochemotherapy (IC, e.g., R-CHOP or similar) is referred to as primary refractory DLBCL and has a poor prognosis. The clinical course with regards to frontline IC is heterogenous, and different definitions have been used in literature, e.g., progressive or refractory during therapy (narrow definition), or not achieving complete remission at the end of IC, or relapsing within 6 or 12 months after completing IC (broad definition). A small proportion of patients may still have chemosensitive disease and be able to achieve durable disease control with salvage chemotherapy followed by autologous stem cell transplant (ASCT). However, it is challenging to identify such patients, with clinical and molecular predictors highly needed. In this study, we examined the association of time to refractory status with survival outcomes in patients with primary refractory disease. Methods Adult patients with newly diagnosed DLBCL between 2002 and 2015 and seen at Mayo Clinic Rochester were identified from the prospective Molecular Epidemiology Resource (MER) cohort study of the University of Iowa/Mayo Clinic Lymphoma SPORE. The current study included patients with primary refractory disease, which was defined as no response to frontline IC (primary progression), partial response (PR) at end of treatment (EOT PR), or relapse with 12 months after achieving CR at EOT (early relapse). Clinical characteristics, treatment and response data, and follow-up data were abstracted from MER and collected by medical record review if needed. Clinical characteristics between groups were compared using Chi-square test. Overall survival (OS) was defined as the time from confirmation of refractory disease to the time of death from any cause and was analyzed using the Kaplan-Meier method. Results Out of 949 newly diagnosed DLBCL patients, 122 (12.8%) had primary refractory disease, 36 with primary progression, 36 with EOT PR, and 50 with early relapse. The baseline clinical characteristics are summarized in Table 1. No significant differences in age, gender, ECOG performance status, number of extranodal sites, stage, International Prognostic Index, or cell of origin were found between the 3 groups. The proportion of MYC2 and BCL2/BCL6 rearrangements or Myc/Bcl2 double expressor was small and not different among the 3 groups. Salvage therapies were mainly platinum based high-dose chemotherapy (e.g., R-ICE, R-DHAP, or similar) for systemic disease, MTX-based therapy for CNS relapse, or radiotherapy or resection for localized disease (Table 1). The response to salvage chemotherapy was significantly different between the 3 groups. The CR/PR rate was 8.6%/28.6% for patients with primary progression, 16.7%/46.7% for patients with EOT PR, and 35.6%/33.3% for patients with early relapse (P At a median follow-up time (of living patients) of 113 months, 93 patients had died. The 2-year OS was 13.9% for patients with primary progression, which was significantly worse compared to that of patients with EOT PR (2-year OS 41.7%) or early relapse (2-year OS 44%), (P=0.001)(Figure 1A). In patients with early relapse, the 2-year OS was not significantly different among those who relapsed with 3 months, between 3-6 months, or between 6-12 months (Figure 1B). Conclusion Our data suggest that broadly defined primary refractory DLBCL has heterogenous survival outcomes. DLBCL patients with primary progressive disease represent an ultra-high risk group that has particularly poor survival outcomes with current standard salvage regimens. Novel therapies such CAR T-cell therapy or targeted agents should be studied in this patient population. In contrast, patient who only achieve PR at EOT and those who relapse within 1 year of achieving CR had better OS. A fraction of these patients may still have chemosensitive disease and benefit from salvage chemotherapy and ASCT. The survival difference in the two groups also has important implications for clinical trial design. The definition of primary refractory DLBCL in clinical trials should be carefully and clearly defined. When evaluating novel therapies in single arm trials, the benchmark for efficacy (i.e., historical outcomes) may differ according to the population included in the trial (e.g., time to refractory status). Figure 1 Figure 1. Disclosures Maurer: BMS: Research Funding; Genentech: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite Pharma: 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. Bennani: Vividion: Other: Advisory Board; Kyowa Kirin: Other: Advisory Board; Daichii Sankyo Inc: Other: Advisory Board; Verastem: Other: Advisory Board; Purdue Pharma: Other: Advisory Board; Kymera: Other: Advisory Board. Cerhan: Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Genentech: Research Funding. Witzig: Celgene/BMS, Acerta Pharma, Kura Oncology, Acrotech Biopharma, Karyopharm Therapeutics: Research Funding; Karyopharm Therapeutics, Celgene/BMS, Incyte, Epizyme: Consultancy, Membership on an entity's Board of Directors or advisory committees. Habermann: Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Wang: Genentech: Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Novartis: Research Funding; Eli Lilly: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding; InnoCare: Research Funding; MorphoSys: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding. Nowakowski: Celgene, MorphoSys, Genentech, Selvita, Debiopharm Group, Kite/Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene, NanoString Technologies, MorphoSys: Research Funding.

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma. Although 5-year survival rates in the first-line setting range from 60% to 70%, up to 50% of patients become refractory to or relapse after treatment. Published analyses of large-scale outcome data from patients with refractory DLBCL are limited. SCHOLAR-1, an international, multicohort retrospective non-Hodgkin lymphoma research study, retrospectively evaluated outcomes in patients with refractory DLBCL which, for this study, was defined as progressive disease or stable disease as best response at any point during chemotherapy (>4 cycles of first-line or 2 cycles of later-line therapy) or relapsed at ≤12 months from autologous stem cell transplantation. SCHOLAR-1 pooled data from 2 phase 3 clinical trials (Lymphoma Academic Research Organization-CORAL and Canadian Cancer Trials Group LY.12) and 2 observational cohorts (MD Anderson Cancer Center and University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence). Response rates and overall survival were estimated from the time of initiation of salvage therapy for refractory disease. Among 861 patients, 636 were included on the basis of refractory disease inclusion criteria. For patients with refractory DLBCL, the objective response rate was 26% (complete response rate, 7%) to the next line of therapy, and the median overall survival was 6.3 months. Twenty percent of patients were alive at 2 years. Outcomes were consistently poor across patient subgroups and study cohorts. SCHOLAR-1 is the largest patient-level pooled retrospective analysis to characterize response rates and survival for a population of patients with refractory DLBCL.

Background: Obesity is increasing worldwide, with the highest prevalence in the United States. High or low body mass index (BMI) is a well-established risk factor for increased all-cause mortality and also has been associated with cancer-specific mortality. However, the impact of BMI on survival following diagnosis with lymphoma currently remains controversial. We leveraged a prospective cohort of lymphoma patients to assess the relationship of BMI two years prior to diagnosis (BMI-2), at diagnosis (BMI-dx), and three-years post-diagnosis (BMI+3) with lymphoma-specific survival (LSS) as the primary endpoint and with event-free survival (EFS) and overall survival (OS) as secondary endpoints. Patient and Method: Patients were prospectively enrolled at lymphoma diagnosis to the SPORE Molecular Epidemiology Resource (MER) cohort at Mayo Clinic and University of Iowa from 2002-2015. BMI-2 and BMI+3 were self-reported in patient questionnaires, while BMI-dx was extracted from the medical chart. Patients with extreme BMI (BMI -5%), and increase (>+5%). Person-time at risk was assessed from lymphoma diagnosis until death or last follow-up, except for analyses of BMI change from BMI-dx to BMI+3, which started person-time at risk when the 3-year (+/- 6 months) follow-up questionnaire was returned. Cause of death was assigned by a study clinician. For all lymphoma patients combined and in the most common subtypes, we evaluated the association of BMI at each time point and change in BMI with EFS, LSS, and OS using hazard ratios (HRs) and 95% confidence intervals (CI) from multivariable adjusted Cox models. Results: A total of 4,009 lymphoma patients (including 670 diffuse large B-cell lymphoma [DLBCL], 689 follicular lymphoma [FL] and 1018 chronic lymphocytic leukemia/small lymphocytic lymphoma [CLL/SLL] and 1,632 others) with data on BMI-dx were included. Among them, 2,955 patients had BMI-2 and 2,004 had BMI+3 and were evaluable for change in BMI. The median age of all patients at diagnosis was 61 years (range 18-92 years), and 94% of patients had ECOG performance status Patients with FL who were obese at BMI-2 had significantly shorter LSS (HR: 3.02, 95%CI: 1.43-6.41, p=0.004). Associations between obesity at BMI-2 and LSS were not evident for DLBCL (HR: 1.04, 95%CI: 0.62-1.76, p=0.879) or CLL/SLL (HR: 1.10, 95%CI: 0.71-1.70, p=0.668) (Table). BMI-dx was not associated with LSS in any lymphoma patients, except that DLBCL patients who were underweight at BMI-dx (n=10) experienced shorter LSS (HR: 3.52, 95%CI: 1.22-10.1, p=0.020). This correlated significantly with presence of B symptoms (p=0.004) and may signify aggressive disease. Across all subtypes, >5% decrease in BMI from BMI-2 to BMI-dx was associated with significantly shorter LSS in patients with (HR: 2.02, 95%CI: 1.65-2.48, p5% increase in BMI from BMI-dx to BMI+3 also was associated with significantly shorter LSS in subsequent years (HR: 3.74, 95%CI: 1.30-10.8, p=0.014). The associations reported for LSS generally were similar for EFS and OS. Conclusions: FL patients with obesity prior to diagnosis or who experienced increasing BMI after the diagnosis had significantly shorter LSS. The impact of weight control after the diagnosis of FL patient outcomes warrants investigation. Figure Disclosures Maurer: Celgene / BMS: Research Funding; Kite: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees. Flowers:Leukemia and Lymphoma Society: Membership on an entity's Board of Directors or advisory committees; Denovo Biopharma: Consultancy; Celgene: Consultancy, Research Funding; BeiGene: Consultancy; Kite: Research Funding; Bayer: Consultancy; Eastern Cooperative Oncology Group: Research Funding; Cancer Prevention and Research Institute of Texas: Research Funding; National Cancer Institute: Research Funding; AbbVie: Consultancy, Research Funding; V Foundation: Research Funding; TG Therapeutics: Research Funding; Burroughs Wellcome Fund: Research Funding; Millennium/Takeda: Consultancy, Research Funding; Acerta: Research Funding; Spectrum: Consultancy; Pharmacyclics/Janssen: Consultancy; Karyopharm: Consultancy; OptumRx: Consultancy; Gilead: Consultancy, Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding. Cerhan:NanoString: Research Funding; BMS/Celgene: 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.

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 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 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.

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 ( 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.

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: 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 (p5% 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 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.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, commonly described by cell-of-origin (COO) molecular subtypes. We sought to identify novel patient subgroups through an unsupervised analysis of a large public dataset of gene expression profiles from newly diagnosed de novo DLBCL patients, yielding 2 biologically distinct subgroups characterized by differences in the tumor microenvironment. Pathway analysis and immune deconvolution algorithms identified higher B-cell content and a strong proliferative signal in subgroup A and enriched T-cell, macrophage, and immune/inflammatory signals in subgroup B, reflecting similar biology to published DLBCL stratification research. A gene expression classifier, featuring 26 gene expression scores, was derived from the public dataset to discriminate subgroup A (classifier-negative, immune-low) and subgroup B (classifier-positive, immune-high) patients. Subsequent application to an independent series of diagnostic biopsies replicated the subgroups, with immune cell composition confirmed via immunohistochemistry. Avadomide, a CRL4(CRBN) E3 ubiquitin ligase modulator, demonstrated clinical activity in relapsed/refractory DLBCL patients, independent of COO subtypes. Given the immunomodulatory activity of avadomide and the need for a patient-selection strategy, we applied the gene expression classifier to pretreatment biopsies from relapsed/refractory DLBCL patients receiving avadomide (NCT01421524). Classifier-positive patients exhibited an enrichment in response rate and progression-free survival of 44% and 6.2 months vs 19% and 1.6 months for classifier-negative patients (hazard ratio, 0.49; 95% confidence interval, 0.280-0.86; P = .0096). The classifier was not prognostic for rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone or salvage immunochemotherapy. The classifier described here discriminates DLBCL tumors based on tumor and nontumor composition and has potential utility to enrich for clinical response to immunomodulatory agents, including avadomide.

Some patients with diffuse large B-cell lymphoma (DLBCL) present with a concurrent indolent lymphoma at diagnosis. Their outcomes in the rituximab era are not fully defined. Using a prospectively followed cohort of 1324 newly diagnosed DLBCL patients treated with immunochemotherapy, we defined the prevalence, characteristics, and outcome of DLBCL with concurrent indolent lymphoma. Compared with patients with DLBCL alone (n = 1153; 87.1%), patients with concurrent DLBCL and follicular lymphoma (FL) (n = 109; 8.2%) had fewer elevations in lactate dehydrogenase, lower International Prognostic Index (IPI), and predominantly germinal center B-cell–like (GCB) subtype, whereas patients with concurrent DLBCL and other indolent lymphomas (n = 62; 4.7%) had more stage III-IV disease and a trend toward higher IPI and non-GCB subtype. After adjusting for IPI, patients with concurrent DLBCL and FL had similar event-free survival (EFS) (hazard ratio [HR] = 0.95) and a trend of better overall survival (OS) (HR = 0.75) compared with patients with DLBCL alone, but nearly identical EFS (HR = 1.00) and OS (HR = 0.84) compared with patients with GCB DLBCL alone. Patients with concurrent DLBCL and other indolent lymphomas had similar EFS (HR = 1.19) and OS (HR = 1.09) compared with patients with DLBCL alone. In conclusion, DLBCL patients with concurrent FL predominantly had the GCB subtype with outcomes similar to that of GCB DLBCL patients. DLBCL patients with concurrent other indolent lymphoma had similar outcomes compared with patients with DLBCL alone. These patients should not be summarily excluded from DLBCL clinical trials.

In this issue of Blood, Luminari et al demonstrate that patients with marginal zone lymphoma (MZL) who experience early progression (progression of disease at 24 months [POD24]) have poor survival.1 Overall survival after POD24 was 53% at 3 years, a stark contrast to the 95% 5-year survival rate from diagnosis in patients without early progression. These results extend the seminal study in follicular lymphoma (FL) from the National LymphoCare Study by Casulo et al2 to include indolent nonfollicular B-cell lymphomas (INFLs).

Peripheral T-cell lymphomas (PTCLs) are generally aggressive non-Hodgkin lymphomas with poor overall survival rates following standard therapy. One-third of PTCLs express interferon regulatory factor-4 (IRF4), a tightly regulated transcription factor involved in lymphocyte growth and differentiation. IRF4 drives tumor growth in several lymphoid malignancies and has been proposed as a candidate therapeutic target. Because direct IRF4 inhibitors are not clinically available, we sought to characterize the mechanism by which IRF4 expression is regulated in PTCLs. We demonstrated that IRF4 is constitutively expressed in PTCL cells and drives Myc expression and proliferation. Using an inhibitor screen, we identified nuclear factor κB (NF-κB) as a candidate regulator of IRF4 expression and cell proliferation. We then demonstrated that the NF-κB subunits p52 and RelB were transcriptional activators of IRF4. Further analysis showed that activation of CD30 promotes p52 and RelB activity and subsequent IRF4 expression. Finally, we showed that IRF4 transcriptionally regulates CD30 expression. Taken together, these data demonstrate a novel positive feedback loop involving CD30, NF-κB, and IRF4; further evidence for this mechanism was demonstrated in human PTCL tissue samples. Accordingly, NF-κB inhibitors may represent a clinical means to disrupt this feedback loop in IRF4-positive PTCLs.

Follicular lymphoma (FL), an indolent neoplasm caused by a t(14;18) chromosomal translocation that juxtaposes the BCL2 gene and immunoglobulin locus, has a variable clinical course and frequently undergoes transformation to an aggressive lymphoma. Although BCL2 mutations have been previously described, their relationship to FL progression remains unclear. In this study, we evaluated the frequency and nature of BCL2 mutations in 2 independent cohorts of grade 1 and 2 FLs, along with the correlation between BCL2 mutations, transformation risk, and survival. The prevalence of BCL2 coding sequence mutations was 12% in FL at diagnosis and 53% at transformation (P.0001). The presence of these BCL2 mutations at diagnosis correlated with an increased risk of transformation (hazard ratio 3.6; 95% CI, 2.0-6.2; P.0001) and increased risk of death due to lymphoma (median survival of 9.5 years with BCL2 mutations vs 20.4 years without; P = .012). In a multivariate analysis, BCL2 mutations and high FL international prognostic index were independent risk factors for transformation and death due to lymphoma. Some mutant Bcl-2 proteins exhibited enhanced antiapoptotic capacity in vitro. Accordingly, BCL2 mutations can affect antiapoptotic Bcl-2 function, are associated with increased activation-induced cytidine deaminase expression, and correlate with increased risk of transformation and death due to lymphoma.

In follicular lymphoma (FL), no prognostic index has been built based solely on a cohort of patients treated with initial immunochemotherapy. There is currently a need to define parsimonious clinical models for trial stratification and to add on biomolecular factors. Here, we confirmed the validity of both the follicular lymphoma international prognostic index (FLIPI) and the FLIPI2 in the large prospective PRIMA trial cohort of 1135 patients treated with initial R-chemotherapy ± R maintenance. Furthermore, we developed a new prognostic tool comprising only 2 simple parameters (bone marrow involvement and β(2)-microglobulin [β(2)m]) to predict progression-free survival (PFS). The final simplified score, called the PRIMA-PI (PRIMA-prognostic index), comprised 3 risk categories: high (β(2)m > 3 mg/L), low (β(2)m ≤ 3 mg/L without bone marrow involvement), and intermediate (β(2)m ≤ 3 mg/L with bone marrow involvement). Five-year PFS rates were 69%, 55%, and 37% in the low-, intermediate-, and high-risk groups, respectively (P < .0001). In addition, achieving event-free survival (EFS) or not at 24 months (EFS24) was a strong posttreatment prognostic parameter for subsequent overall survival, and the PRIMA-PI was correlated with EFS24. The results were confirmed in a pooled external validation cohort of 479 patients from the FL2000 LYSA trial and the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence Molecular Epidemiology Resource. Five-year EFS in the validation cohort was 77%, 57%, and 44% in the PRIMA-PI low-, intermediate-, and high-risk groups, respectively (P < .0001). The PRIMA-PI is a novel and easy-to-compute prognostic index for patients initially treated with immunochemotherapy. This could serve as a basis for building more sophisticated and integrated biomolecular scores.

Anaplastic lymphoma kinase (ALK)-negative anaplastic large cell lymphoma (ALCL) is a CD30-positive T-cell non-Hodgkin lymphoma that morphologically resembles ALK-positive ALCL but lacks chromosomal rearrangements of the ALK gene. The genetic and clinical heterogeneity of ALK-negative ALCL has not been delineated. We performed immunohistochemistry and fluorescence in situ hybridization on 73 ALK-negative ALCLs and 32 ALK-positive ALCLs and evaluated the associations among pathology, genetics, and clinical outcome. Chromosomal rearrangements of DUSP22 and TP63 were identified in 30% and 8% of ALK-negative ALCLs, respectively. These rearrangements were mutually exclusive and were absent in ALK-positive ALCLs. Five-year overall survival rates were 85% for ALK-positive ALCLs, 90% for DUSP22-rearranged ALCLs, 17% for TP63-rearranged ALCLs, and 42% for cases lacking all 3 genetic markers (P < .0001). Hazard ratios for death in these 4 groups after adjusting for International Prognostic Index and age were 1.0 (reference group), 0.58, 8.63, and 4.16, respectively (P = 7.10 × 10−5). These results were similar when restricted to patients receiving anthracycline-based chemotherapy, as well as to patients not receiving stem cell transplantation. Thus, ALK-negative ALCL is a genetically heterogeneous disease with widely disparate outcomes following standard therapy. DUSP22 and TP63 rearrangements may serve as predictive biomarkers to help guide patient management.

Introduction: Axicabtagene Ciloleucel (axi-cel), a CD19 chimeric antigen receptor (CAR) T-cell therapy, was approved for the treatment of relapsed/refractory aggressive B-cell non-Hodgkin lymphoma in October, 2017. In the ZUMA-1 trial leading to axi-cel FDA approval, patients (pts) with prior or active secondary central nervous system (CNS) lymphoma involvement were excluded. A recent publication of 8 pts with secondary CNS lymphoma who underwent Tisagenlecleucel CAR T cell therapy was reported (Frigault M.J.2019). Since the two FDA approved CAR T cell products have a different neurotoxicity profile, understanding outcomes of axi-cel in this setting is important. We report here the real-world experience of 17 pts treated with axi-cel who had a history of secondary CNS involvement or had active CNS disease at time of CAR T infusion. Methods: Seventeen academic centers from the US Lymphoma CAR T Consortium contributed data independently from the manufacturer. Data regarding secondary CNS involvement, management, and outcome were obtained in addition to CAR T therapy outcome for pts who were identified as having active, secondary CNS involvement at the time of evaluation for CAR-T therapy. Nine centers reported data on 17 cases with CNS involvement. Follow-up data was missing for one pt in the CNS cohort. Lee criteria or the modified Lee grading scale were used for cytokine release syndrome (CRS). CTCAEv4 or CARTOX grading were used for immune effector cells associated neurotoxicity syndrome (ICANS). All leukapheresed pts were included in the intention to treat (ITT) analysis for response rate and event-free survival (EFS). EFS was defined as date of leukapheresis until progression or death due to any cause. EFS was evaluated using Kaplan Meier curves with log-rank test. Differences in clinical characteristics and response between CNS and non-CNS pts were not formally tested due to small sample size and multiple comparison concerns. Results: With a data cut-off of 4/30/2019, 300 pts underwent leukapheresis with intention to manufacture standard of care axi-cel. By the time of leukapheresis, 17 (6%) had secondary CNS involvement (4 parenchymal disease, 10 leptomeningeal/CSF, 3 data not available). Compared to the non-CNS cohort, baseline demographics were comparable (Panel A). Manufactured axi-cel was within specification for 100% of the CNS cohort. There was a higher rate of bridging therapy use in the CNS cohort 82% (1 steroids only, 2 radiation therapy, 12 systemic therapy) vs 52% in non-CNS cohort; p=0.022. Time from leukapheresis to CAR T infusion was 3.5 days longer in the CNS cohort as opposed to the non-CNS cohort: median time of 29.5 (range 20-76) vs. 26 days (range 5-67), (p=0.029), respectively. The CAR T infusion rate was 88% for the CNS cohort (15/17) compared to 93% (262/283) in the non-CNS cohort. Among the 15 infused pts in the CNS cohort, 10 had resolution of CNS involvement, and 5 had persistent active CNS disease at time of CAR T infusion. After axi-cel infusion, the incidence of CRS and ICANS, of any grade or grade 3 or higher, were comparable between the CNS and non-CNS cohorts. Tocilizumab and steroid use were comparable between the two groups (Panel B). No seizures or cerebral edema were noted in the CNS cohort. With a median follow-up of 10.1 months from leukapheresis (range 7.6-12.6), the ITT best overall response rates (CR+PR) and ongoing responses at month 6 between CNS and non-CNS cohorts were 75% vs. 59%, and 41% vs. 31%, respectively (Panel B). In the 5 pts with active CNS disease at time of CAR T infusion, the response of CNS disease were 2 CR, 1 PR and 2 PD as best response. In the 10 pts with resolved CNS disease at time of CAR T infusion, 2 PD were seen and both occurred systemically. EFS from leukapheresis was not statistically significantly different between CNS and non-CNS cohorts (6 months EFS: CNS cohort, 36%; non-CNS cohort 57%. HR=1.58, 95% CI: 0.83-3.01, p=0.16, Panel C). Six month EFS from the date of infusion for the CNS cohort was 49.9% (Panel D). Conclusions: Pts attempting CAR T therapy with secondary CNS disease in the real world setting had similar rates of CAR T infusion, toxicity, and outcomes when compared to patients without CNS disease. Small sample size and limited follow-up caution the strength of conclusions for application to clinical practice, but these results support further investigation of CAR T in pts with history of or active secondary CNS lymphoma. Figure Disclosures Bennani: Seattle Genetics: Other: Advisory board; Adicet Bio: Other: Advisory board; Purdue Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Purdue Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board; Kite Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board; Adicet Bio: Other: Advisory board; Adicet Bio: Other: Advisory board; Kite Pharma: Other: Advisory board. Maurer:Celgene: Research Funding; Nanostring: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees. Nastoupil:Bayer: Honoraria; Spectrum: Honoraria; TG Therapeutics: Honoraria, Research Funding; Novartis: Honoraria; Janssen: Honoraria, Research Funding; Gilead: Honoraria; Genentech, Inc.: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Jain:Kite/Gilead: Consultancy. Chavez:Novartis: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Genentech: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Bayer: Membership on an entity's Board of Directors or advisory committees. Cashen:Seattle Genetics: Other: Speaker's Bureau; Novartis: Other: Speaker's Bureau; Celgene: Other: Speaker's Bureau. Reagan:Kite, A Gilead Company: Consultancy; Curis: Consultancy; Seattle Genetics: Research Funding. Oluwole:Pfizer: Consultancy; Spectrum: Consultancy; Gilead Sciences: Consultancy; Bayer: Consultancy. McGuirk:Novartis: Research Funding; Fresenius Biotech: Research Funding; Astellas: Research Funding; Bellicum Pharmaceuticals: Research Funding; Kite Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Gamida Cell: Research Funding; Pluristem Ltd: Research Funding; ArticulateScience LLC: Other: Assistance with manuscript preparation; Juno Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Deol:Novartis: Other: Advisory board; Kite: Other: Advisory board; Agios: Other: Advisory board. Sehgal:Juno/Celgene: Research Funding; Merck: Research Funding; Kite/Gilead: Research Funding. Goy:COTA: Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Other: leadership role for profit healthcare company; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Astrazenca: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; University of Nebraska: Research Funding; Hakensackumc: Research Funding; Takeda: Other: Grants outside of the submitted work; Kite, a Gilead Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants outside of the submitted work; Pharmacyclics/Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants outside of the submitted work, Research Funding; Genentech: Other: Grants outside of the submitted work, Research Funding; Hackensack University Medical Center, RCCA: Employment; Acerta: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants outside of the submitted work, Research Funding. Hill:Abbvie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; TG therapeutics: Research Funding; AstraZeneca: Consultancy, Honoraria; Kite: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Celegene: Consultancy, Honoraria, Research Funding; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Consultancy, Research Funding; Takeda: Research Funding; Amgen: Research Funding. Vu:Celgene: Other: Stock. Andreadis:Genentech: Equity Ownership, Other: Spouse is Employee; Novartis: Honoraria, Research Funding; Amgen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Gilead: Consultancy; Jazz Pharmaceuticals: Consultancy; Bayer: Consultancy. Munoz:Incyte: Research Funding; Portola: Research Funding; Celgene: Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; Bayer: Consultancy, Speakers Bureau; AstraZeneca: Speakers Bureau; Pharmacyclics LLC an AbbVie Company: Consultancy, Research Funding, Speakers Bureau; Kite Pharma: Consultancy, Research Funding, Speakers Bureau; Gilead: Consultancy, Speakers Bureau; Fosunkite: Speakers Bureau; Kyowa: Consultancy, Honoraria, Speakers Bureau. Vose:Acerta Pharma: Honoraria, Other: Grants, Research Funding; Bristol-Meyers Squibb Company: Research Funding; Celgene Corporation: Research Funding; Incyte Corporation: Research Funding; Kite Pharma: Honoraria, Other: Grants, Research Funding; Novartis: Research Funding; Seattle Genetics: Research Funding; AbbVie: Consultancy, Honoraria; Epizyme: Consultancy, Honoraria; Legend Pharmaceuticals: Honoraria. Miklos:Miltenyi Biotech: Membership on an entity's Board of Directors or advisory committees; Becton Dickinson: Research Funding; AlloGene: Membership on an entity's Board of Directors or advisory committees; Precision Bioscience: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees; Kite-Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Juno: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding. Locke:Kite: Other: Scientific Advisor; Novartis: Other: Scientific Advisor; Cellular BioMedicine Group Inc.: Consultancy. Neelapu:Unum Therapeutics: Consultancy, Research Funding; BMS: Research Funding; Pfizer: Consultancy; Incyte: Consultancy; Poseida: Research Funding; Allogene: Consultancy; Merck: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Karus: Research Funding; Precision Biosciences: Consultancy; Cell Medica: Consultancy; Acerta: Research Funding; Cellectis: Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy. Lin:Novartis: Membership on an entity's Board of Directors or advisory committees; Juno: Membership on an entity's Board of Directors or advisory committees; Kite/Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; BlueBird Bio: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sorrento: Membership on an entity's Board of Directors or advisory committees.

Background With lymphoma survival rates increasing and a growing population of long-term survivors, the development of cardiovascular disease (CVD) in this patient population is of increasing importance. Anthracyclines are critical in the management of many lymphoma subtypes. However, there is a risk of developing anthracycline-induced CVD. Here, we estimate the cumulative incidence of CVD in adult lymphoma survivors and investigate risk factors associated with post diagnosis CVD. Methods Participants were from the Mayo component of the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE). From 2002-2015, the MER offered enrollment to all patients with newly diagnosed lymphoma who are US residents and age >18 years. Participants completed a risk factor questionnaire, and clinical and treatment data were abstracted from medical records. Patients were contacted every 6 months for the first 3 years after diagnosis and annually thereafter to assess disease status, re-treatment and new onset morbidity including CVD. CVD events, including congestive heart failure (CHF), coronary artery disease (CAD), valvular heart disease (VHD), and arrhythmia were identified and validated against medical records. CHF was validated with the Cardiovascular Health Study Criteria and/or the Framingham Criteria. CAD, arrhythmia, and VHD were validated using clinical definitions. We calculated the cumulative incidence of CVD, with death modeled as a competing risk. The association of risk factors and treatments with risk of CVD was estimated using hazard ratios (HR) and 95% confidence intervals (CI) from Cox regression with a competing risk of death. Risk factors included age, sex, diabetes, smoking, body mass index (BMI), and treatment with anthracyclines or radiation therapy. Results The study consisted of 3,063 lymphoma patients after excluding those with chronic lymphocytic leukemia and CVD prior to lymphoma diagnosis. The median age at diagnosis was 59 years (range 18-95), and 56% were males. At a median follow-up was 6.9 years (range 0.8-17.1), 640 patients (21%) had died without CVD and 485 patients self-reported CVD post lymphoma diagnosis, of which 280 (57.7%) were validated. Cardiovascular events included 86 CHF, 78 CAD, 40 VHD, and 164 arrhythmias. The cumulative incidence of CVD (Figure 1) at 5 and 10 years was 6.0% (95% 5.2%-7.0%) and 10.7% (95% CI 9.5%-12.1%), respectively. In multivariable analysis, increasing age (HR=3.93 per 5 years, p30 kg/m2 (HR=1.50, p=0.01), and anthracycline treatment (HR=1.49. p6 anthracycline cycles was 5%, 6.9%, 7.4%, and 7.7%, respectively. Adjusting for cardiac risk factors, the number of anthracycline cycles was significantly associated with increased risk of CVD (1-5 cycles HR=1.34, p=0.11; 6 cycles HR=1.51, p6 cycles HR=2.04, p=0.03). Furthermore, the number of anthracycline cycles was associated with CHF (1-5 cycles HR=2.82, p6 cycles HR=5.33, p6 cycles HR=1.65, p=0.27). Conclusions In the modern treatment era, the risk of new onset CVD in patients with lymphoma without a history of CVD is approximately 1% per year after diagnosis. Arrhythmia and CHF were the most commonly occurring CVD events in this cohort. Both traditional CVD risk factors and treatment with anthracyclines was associated with an increased risk of developing CVD, and anthracyclines were a risk factor for arrhythmia and CHF in particular. Prevention of CVD in lymphoma patients will need to address both treatment and traditional lifestyle factors. Figure 1 Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding; Celgene: Research Funding. Nowakowski:Selvita: Membership on an entity's Board of Directors or advisory committees; Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding. Cerhan:NanoString: Research Funding; Celgene: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees.

Background: DLBCL has traditionally been classified by cell of origin (COO) subcategories based on tumor gene expression profiles which include Activated B-Cell (ABC) and Germinal Center B-Cell (GCB). Recently, using tumor samples from patients treated with RCHOP, new classification models have focused on DNA alterations. However, a comprehensive integrative approach using a large transcriptomic data set across both newly diagnosed (nd) and relapsed/refractory (r/r) DLBCL is yet to be accomplished. A robust clustering of this type will allow for identification of biologically driven DLBCL patient subgroups and may predict patient outcome and inform treatment approaches. Methods: RNAseq was performed on a total of 882 DLBCL tumor FFPE biopsies from 2 ndDLBCL cohorts (cohort 1 and 2) and 2 r/r DLBCL cohorts (cohorts 3 and 4). Cohort 1 (N=267) was commercially sourced and served as the discovery cohort. Cohort 2 (N=340) was from the Mayo/Iowa Lymphoma SPORE Molecular Epidemiology Resource (MER) and served as the replication cohort. Cohort 3 (N=189) was from the CC-122-ST-001 and CC-122-DLBCL-001 clinical trials (NCT01421524 and NCT02031419), and cohort 4 (N=86) was from r/r patients from the MER. Clustering input consisted of gene expression data, gene set variation analysis (GSVA) scores computed from the hallmark gene sets of MSigDB gene sets, as well as immune cell abundance estimates from a DLBCL-specific deconvolution method. An integrative clustering method iClusterPlus was applied to the input data to identify patient subgroups. A multinomial generalized linear model classifier was trained on the discovery dataset and applied to cohorts 2, 3, and 4 to assess patterns of gene expression and clinical features among the subgroups. Results: Integrative clustering identified 8 subgroups of ndDLBCL patients (DLBCL1-8; D1-D8) in cohort 1. Classifiers trained on cohort 1 were applied to cohort 2 and the same 8 clusters were identified. Among RCHOP treated patients in cohort 2, subgroups D4 (p Within D4, 92% of patients were ABC, representing a high risk subset of ABC patients. The mEFS in D4 ABCs was 38.2 months, while mEFS of non-D4 ABCs was not reached (p2, compared to 33% of non-D4 with IPI>2 (p D8 represented a high-risk subset which was 73% GCB. The mEFS of D8 GCBs was 5.4 months, while mEFS of non-D8 GCBs was not reached (p In the r/r DLBCL setting, D1-D8 were all present, with an increased prevalence of D4 and D8 in Cohort 3 (30% and 17%, respectively) and Cohort 4 (30% and 14%) compared to the newly diagnosed setting. Mutational data for these cohorts has been collected and is being interpreted in the context of the discovered subgroups. Conclusion: A novel integrative clustering of transformed gene expression data revealed 8 biologically homogeneous groups, two of which had inferior outcomes when treated with RCHOP therapy. Furthermore, these two subgroups were more prevalent in r/r DLBCL. This classification allows for the transcriptomic identification of high-risk patients underserved by RCHOP therapy. *Ortiz, Wenzl and Stokes contributed equally **Gandhi and Novak contributed equally Disclosures Ortiz: Celgene Corporation: Employment, Equity Ownership. Stokes:Celgene Corporation: Employment, Equity Ownership. Huang:Celgene Corporation: Employment, Equity Ownership. Maurer:Celgene: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Towfic:Celgene Corporation: Employment, Equity Ownership. Hagner:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Ratushny:Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment, Equity Ownership. Danziger:Celgene Corporation: Employment, Equity Ownership. Stong:Celgene Corporation: Employment, Equity Ownership. Lata:Celgene Corporation: Employment, Equity Ownership. Kamalakaran:Celgene Corporation: Employment, Equity Ownership. Mavrommatis:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Czuczman:Celgene Corporation: Employment, Equity Ownership. Ansell:Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding. Cerhan:NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. 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. Gandhi:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Novak:Celgene Coorperation: Research Funding.

Introduction: Non-endemic Burkitt lymphoma (BL) is a rare and highly aggressive B-cell malignancy, of whom a substantial number are adolescents and young adults (AYAs). In this particular group of patients, the balance between efficacy and long-term toxicities is of major concern, due to the long expected remaining lifetime. In this study, we investigated the outcomes for AYA patients treated with intensive immunochemotherapy. Patients and methods: Patients were identified through queries to clinic based and population-based lymphoma registries from six countries (Australia, Canada, Denmark, Norway, Sweden, and USA). All diagnoses were confirmed by local investigators; patients with classical BL histology plus detectable MYC translocation, were included. Patients between 18 and 39 years of age at diagnosis treated with intensive immunochemotherapy (DA-EPOCH-R or more intensively) were evaluated in pre-specified age groups. All treatment protocols included rituximab. Overall survival (OS) was defined as the time from diagnosis until death from any cause or censoring, while event-free survival (EFS) was defined as the time from diagnosis until unplanned re-treatment, progression/relapse, death, or censoring, whichever came first. Response evaluation was assessed using established response criteria based on CT and/or PET/CT imaging. Survival curves were computed using the Kaplan-Meier estimator. The cumulative incidence of relapse in patients reaching complete remission (CR) or CR unconfirmed (CRu) was computed by Aalen-Johansen estimator. The 5-year restricted loss of lifetime (RLOL) was defined as the area between the patient and general population survival curves until 5-years. The general population survival was retrieved from publicly available lifetables stratified on age, sex, calendar year, and country. Results: In total, 108 AYA BL patients were included. The median age was 30 years, ranging between 18 and 39 and 82% were male. The majority had advanced stage disease (Ann Arbor stage III-IV, 76%), extranodal involvement (87%), and elevated LDH (67%). Seven patients (6%) had CNS involvement at the time of diagnosis. The chemotherapy regimens used were CODOX-M/IVAC (51%), BFM/GMALL (34%), hyper-CVAD (7%), DA-EPOCH (5%), and others (3%). Among 74 patients for whom data on tumor lysis were available, 10 (14%) developed clinical tumor lysis upon start of chemotherapy, all presenting with advanced stage and extranodal disease. The response rate was 91% (89 in CR/CRu and 2% in partial remission). At a median follow-up of 53 months, 15 had an event and 10 died. The 2-year OS and EFS for the total population was 92% (87-97%, Figure 1) and 88% (82-94%), respectively. For 13 patients aged 18-21 2-year OS and EFS were both 100%, while 2-year OS and EFS for 43 patients aged 22-30 were 98% (93-100%) and 93% (85-100%), respectively. The 2-year OS and EFS for patients aged 31-39 were 86% (76-96%) and 80% (69-91%), respectively. For patients in CR/CRu and available date of response evaluation, the 2-year post-remission OS was 99% (97-100%) and the 2-year relapse risk was 2% (0-5%), with no relapses after 6 months. Compared to the general population, the RLOL was 0.8 months (-0.5-2.2 months). Nine patients did not respond to primary treatment (SD or PD), all which belonged to the age group 31-40 years (P Conclusion: The outcomes of AYA BL patients following intensive immunochemotherapy are excellent with high remission rates and low risk of relapse in general. However, even among young patients age remained prognostic, with patients 31 years or older having inferior OS and lower response rate to first-line treatment. In patients achieving CR, survival was similar to that of the general population over the median follow-up of 4.5 years, illustrating the need to focus on survivorship issues in these individuals. Figure 1: Overall survival for BL patients following intensive immunochemotherapy for all patients and for age groups (18-21, 22-30, and 31-39 years). Figure 1 Disclosures Jørgensen: Gilead: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees. Holte:Novartis: Honoraria, Other: Advisory board. Molin:Roche Holding AG: Honoraria; Merck & Co., Inc.: Honoraria; Bristol-Myers Squibb: Honoraria; Takeda Pharmaceuticals: Honoraria. Ekstroem Smedby:Takeda: Honoraria, Other: Grant funding, Research Funding; Celgene: Honoraria, Other: Grant funding, Research Funding; Janssen Cilag: Honoraria, Other: Grant funding, Research Funding. Song:Takeda: Honoraria; Celgene: Honoraria, Research Funding; Janssen: Honoraria; Amgen: Honoraria. Gerrie:Lundbeck, Seattle Genetics: Consultancy, Honoraria. Cheah:Roche, Janssen, MSD, Gilead, Loxo Oncology, Acerta, BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Other: Travel expenses; Celgene, Roche, Abbvie: Research Funding. El-Galaly:Roche: Employment, Other: Travel support; Takeda: Other: Travel support.

Introduction. The VES-13 is a simple, self-reported, function based tool originally developed to screen community-dwelling populations to identify persons age 65 years and older at risk of death and functional decline, including death in the next 12 months. The VES-13 items include patient's age, self-rated overall health status, functional limitations in physical activity, and functional disabilities in more complex activities of daily living. As part of the Lymphoma Epidemiology of Outcomes (LEO) cohort study, we collected self-reported VES-13 data at study enrollment on all participants regardless of age, and here we report on the prevalence of vulnerable status defined by the VES-13 and its association with 1-year mortality, overall, stratified at age 65 years, and in the subset of diffuse large B-cell lymphoma (DLBCL) treated with immunochemotherapy. Methods. From 7/2015 to 6/2017, 3253 participants with NHL were enrolled within 6 months of their diagnosis into the LEO cohort. 2004 were evaluable on VES-13, and 1183 (59%) completed it before the initiation of therapy. VES-13 scores range from a minimum of 0 (low risk for decline) to a maximum of 10 (greatest risk for decline), and a score ≥3 was classified as vulnerable. Clinical, pathology and treatment data were abstracted using a standard protocol, and participants were contacted every 6 months for the first three years and then annually thereafter to identify outcomes. Medical records were reviewed by LEO clinicians to classify cause of death according to a standard protocol. Therapy was determined by the treating physician, and this was independent of knowledge of the VES-13 score. The association of VES-13 with 1-year mortality from date of diagnosis was estimated using odds ratios (OR) and 95% confidence intervals (CI) from logistic regression models, which also provided model c-statistics. Results. The median age of the 2004 participants in this analysis was 62 years (range 18-94); 57% were male; 54% were ≥65 years; and 28% had a normal body mass index (BMI), 1% were underweight, 35% were overweight, and 36% were obese. Clinically, 59% of participants had an aggressive subtype, 65% were stage III-IV, 24% had B-symptoms, and 11% had a performance status (PS) of ≥2. Overall, 28% of participants were classified as vulnerable (95% CI 26%-30%), with a higher prevalence among those completing the survey after initiation of therapy (38%, 95% 34%-41%) versus before initiation of therapy (22%, 95% CI 20%-24%), and a higher prevalence for those ≥65 years (32%, 95% CI 29%-34%) versus Conclusion. Based on a self-administered VES-13, 28% of newly diagnosed NHL patients in the LEO cohort were classified as vulnerable, including 25% of those Disclosures Martin: Karyopharm: Consultancy; Sandoz: Consultancy; I-MAB: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Teneobio: Consultancy. Casulo:Gilead: Honoraria, Other: Travel, accommodation, expenses; Celgene: Research Funding; Roche: Other: Travel, accommodation, expenses. Cohen:Takeda Pharmaceuticals North America, Inc.: Research Funding; Bristol-Meyers Squibb Company: Research Funding; Hutchison: Research Funding; Astra Zeneca: Research Funding; Lymphoma Research Foundation: Research Funding; ASH: Research Funding; LAM Therapeutics: Research Funding; UNUM: Research Funding; Gilead/Kite: Consultancy; Janssen Pharmaceuticals: Consultancy; Seattle Genetics, Inc.: Consultancy, Research Funding; Genentech, Inc.: Consultancy, Research Funding. Farooq:Kite Pharma: Research Funding; Celgene: Honoraria. Friedberg:Acerta: Other: Data & Safety Monitoring Committee; Bayer: Honoraria, Other: Data & Safety Monitoring Committee. Kahl:Seattle Genetics: Consultancy; ADC Therapeutics: Consultancy, Research Funding; BeiGene: Consultancy; TG Therapeutics: 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. Maurer:Nanostring: Research Funding; Celgene: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees. Nastoupil:Spectrum: Honoraria; Genentech, Inc.: Honoraria, Research Funding; Gilead: Honoraria; Janssen: Honoraria, Research Funding; Novartis: Honoraria; TG Therapeutics: Honoraria, Research Funding; Bayer: Honoraria; Celgene: Honoraria, Research Funding. Rutherford:Heron: Consultancy, Honoraria; Janssen Scientific Affairs: Consultancy, Honoraria; AstraZeneca: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Juno Therapeutics Inc: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Verastem: Consultancy, Honoraria; Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees. Flowers:National Cancer Institute: Research Funding; BeiGene: Consultancy, Research Funding; Eastern Cooperative Oncology Group: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Pharmacyclics/Janssen: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Bayer: Consultancy; Spectrum: Consultancy; Karyopharm: Consultancy; Acerta: Research Funding; TG Therapeutics: Research Funding; Millenium/Takeda: Research Funding; AstraZeneca: Consultancy; Optimum Rx: Consultancy; V Foundation: Research Funding; Celgene: Consultancy, Research Funding; Burroughs Wellcome Fund: Research Funding; Denovo Biopharma: Consultancy; Gilead: Consultancy, Research Funding. Cerhan:NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: 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: Advances in the understanding of disease biology, the introduction of new drugs, and better supportive care have improved outcomes in multiple myeloma (MM). Most improvements have been observed in clinical trial and tertiary referral center populations but questions remain about the generalizability of these findings to patients treated in the community. Methods: We studied all patients diagnosed with MM between 01/01/2001 and 12/31/2015 who had complete demographic and overall survival (OS) data available and were seen at Mayo Clinic (MAYO) or followed in the Surveillance, Epidemiology, and End Results Program (SEER, 18 registry data 2000 - 2016, 11/2018 submission). We retrieved age at diagnosis, sex, date of diagnosis, date of last follow-up, and OS for all patients. OS was defined as the time from diagnosis to death from any cause. Patients who were alive at the end of follow-up (12/31/ 2016) were censored. OS estimates were calculated using the Kaplan-Meier method. Age- and sex-adjusted Cox models were used to assess the association between the 5-year interval of diagnosis and OS. Expected OS estimates were calculated based on United States general population rate tables (Human Mortality Database) using a conditional approach. Standardized mortality ratios (SMR) were calculated by dividing the number of observed deaths by the number of expected deaths in age- and sex-matched general United States population controls. Linear regression models were fit to test for linear trends in early mortality and SMR over time (per 5-year interval). P-values below 0.05 were considered statistically significant. Results: The median age at diagnosis was 3 years lower in patients at MAYO (64 years, 15% ≥ 75 years, 60% male, n = 3293) compared to SEER (67 years, 29% ≥ 75 years, 55% male, n = 61779). After a median follow-up of 2.8 years the median OS was longer in MAYO compared to SEER (5.4 versus 4.0 years, HR 0.82, 95% CI 0.78 - 0.86, p < 0.001) and remained statistically significant after adjusting for age and sex (HR 0.91, 95% CI 0.86 - 0.95, p < 0.001). Early mortality (1-year mortality) decreased between 2001-2005 and 2011-2015: From 20% to 11% at MAYO and from 26% to 19% in SEER. When grouping OS by year of diagnosis (in 5-year-intervals) improvements were seen in both populations (A) and remained statistically significant after adjusting for age and sex. The relative improvements were similar comparing the 5-year period after the introduction of the novel therapies (2006 - 2010) to 2001 - 2005 and more pronounced in MAYO for the most recent 5-year interval (2011 - 2015, A). This trend was reflected in a steady temporal improvement in 5-year OS estimates in MAYO including the most recent 5-year interval (2011 - 2015, B bottom left). In SEER there was a comparable increase between the first two 5-year intervals but a lesser improvement in more recently (2011 - 2015, B bottom left). A diagnosis of MM remained associated with significant excess mortality in all age groups over time in both populations (B top). There was a decrease in excess mortality over time at MAYO (SMR decline per 5-year interval 1.3, 95% CI 0.9 - 1.8, p < 0.001) while there was little change in SEER (SMR decline 0.0, 95% CI -0.3 - 0.3, p = 0.917, B bottom right). Further stratifying by age at diagnosis, the decrease in excess mortality was observed mostly in patients < 75 years at MAYO (SMR decline per 5-year interval 1.7, 95% CI 1.5 - 2.0, p < 0.001, C bottom left) and to a lesser extent in older patients (SMR decline 0.4, 95% CI 0.2 - 0.6, p < 0.001, C bottom right). No such trends towards improvement were observed in either age group in SEER (C bottom). In older patients, early mortality remained approximately 30% in both populations despite continued improvements, while the 5-year OS estimates for the most recent 5-year interval (2011 - 2015) were 37% at MAYO and 26% in SEER (C top). Conclusions: Both early mortality and long-term survival have improved over time. Reductions in excess mortality were largely confined to younger patients with access to specialized care. Patients ≥ 75 years represent more than a quarter of all patients in the community, a third of them died within one year of the diagnosis, and only one in four was alive five years later. Older patients with MM remain a vulnerable population and have derived only limited benefit from recent advances in the field. Safe and effective therapies for older patients with MM remain an unmet need. Figure Disclosures Gertz: Ionis/Akcea: Consultancy; International Waldenstrom Foundation: Research Funding; Alnylam: Consultancy; Prothena Biosciences Inc: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Spectrum: Consultancy, Research Funding; Annexon: Consultancy; Appellis: Consultancy; Amgen: Consultancy; Medscape: Consultancy, Speakers Bureau; Physicians Education Resource: Consultancy; Abbvie: Other: personal fees for Data Safety Monitoring board; Research to Practice: Consultancy; Teva: Speakers Bureau; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Proclara: Membership on an entity's Board of Directors or advisory committees; i3Health: Other: Development of educational programs and materials; Springer Publishing: Patents & Royalties; Amyloidosis Foundation: Research Funding. Dispenzieri:Alnylam: Research Funding; Akcea: Consultancy; Takeda: Research Funding; Pfizer: Research Funding; Janssen: Consultancy; Intellia: Consultancy; Celgene: Research Funding. Lacy:Celgene: Research Funding. Maurer:Nanostring: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. Dingli:alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Rigel: Consultancy; Karyopharm: Research Funding. Kapoor:Takeda: Honoraria, Research Funding; Celgene: Honoraria; Janssen: Research Funding; Amgen: Research Funding; Sanofi: Consultancy, Research Funding; Glaxo Smith Kline: Research Funding; Cellectar: Consultancy. Leung:Takeda: Research Funding; Prothena: Membership on an entity's Board of Directors or advisory committees; Aduro: Membership on an entity's Board of Directors or advisory committees; Omeros: Research Funding. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

Purpose The development of cardiovascular disease (CVD) is a relatively rare but a clinically important adverse event in the treatment of lymphoma, particularly in individuals receiving anthracyclines. There are few studies assessing the role of germline genetic susceptibility as a predictor of CVD in this setting. We evaluated the association of 24 single nucleotide polymorphisms (SNPs) from candidate genes involved in anthracycline-induced cardiotoxicity, CVD, and venous thromboembolism with new-onset CVD in a prospective cohort of lymphoma patients treated in the modern era. Methods All patients were from the Mayo component of the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma SPORE. Enrollment from 2002-2015 was offered to patients with newly diagnosed lymphoma who were age ≥18 years. Clinical, pathology and treatment data were abstracted using a standard protocol, and participants provided a peripheral blood sample, from which DNA was extracted. All patients were prospectively contacted every 6 months for the first 3 years from diagnosis and then annually thereafter to assess disease status, re-treatment and development of new morbidities, including CVD. Reported CVD events included congestive heart failure (CHF), coronary heart disease (CHD), arrhythmia, valvular heart disease (VHD), and other CVD. These events were identified during follow-up and validated against medical records. Genotyping was conducted using a custom Illumina iSelect platform with rigorous quality controls. For each SNP, Cox regression was used to estimate Hazard Ratios (HRs) and 95% confidence intervals (CI) with time to first CVD, using death without CVD as a competing risk. HRs were also obtained for time to CHF, using death without CVD as a competing risk. We also modeled these events for all patients and for patients receiving frontline anthracyclines. Each SNP was modeled as having a log-additive (per minor allele) effect in the regression model. An ordinal test was used to assess the trend, with a nominal P Results There were a total of 3,063 newly diagnosed lymphoma patients (excluding chronic lymphocytic leukemia) with no history of CVD at time of lymphoma diagnosis, of which 1280 had genotype data available for analysis. The median age at diagnosis was 59 years (range, 18-95) and 56% were male. The most common subtypes were follicular (26.3%), diffuse large B-cell (23.2%), Hodgkin (11.3%), marginal zone (11.2%), mantle cell (6.3%) and T-cell (5.4%) lymphoma. Anthracycline-based chemotherapy as initial therapy was used in 52% of individuals. At a median follow-up of 6.9 years (range, 0.1-17.1), 363 (30.7%) patients died, and 173 (13.5%) had new-onset CVD after lymphoma diagnosis. There were 234 incident CVD events in the 173 patients: 49 CHD, 50 CHF, 27 VHD, 103 arrhythmias, and 5 other CVD. Results are shown in the Table. When assessing all CVD, F5 (rs4524) (HR=1.28, 95% CI=1.03-1.58) and F11 (rs4253399) (HR=1.30, 95% CI=1.06-1.61) were associated with CVD at P Conclusions In this exploratory study of candidate SNPs from the literature, we found limited evidence for a role of germline genetic variability in predicting risk of CVD or CHF in a cohort of lymphoma patients, especially after accounting for multiple testing. To fully address this hypothesis, future studies will need larger sample sizes and more comprehensive genetic assessment. Disclosures Maurer: Celgene: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Nowakowski:NanoString: Research Funding; Genentech, Inc.: Research Funding; MorphoSys: Consultancy, Research Funding; Bayer: Consultancy, Research Funding; Curis: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Celgene: Consultancy, Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees. Cerhan:Celgene: Research Funding; NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees.

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

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 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.

WOS: 000313111300022, PubMed ID: 23018644, STAT3 regulates cell growth by upregulating downstream targets, such as Myc. The frequency of phosphorylated STAT3 (pSTAT3) and Myc expression and their prognostic relevance is unknown within diffuse large B-cell lymphoma (DLBCL) germinal center B-cell (GCB) and non-GCB subtypes. pSTAT3 and Myc were studied by immunohistochemistry (IHC) on tumors from 40 DLBCL patients uniformly treated on a clinical trial of epratuzumab/rituximab-CHOP. A total of 35% of cases were pSTAT3-positive, and pSTAT3 positivity was more frequent in the non-GCB (P = .06) type but did not correlate with event-free survival (EFS). Myc expression was observed in 50% of cases and was more frequent in non-GCB type (P = .07). Myc-positive cases had inferior EFS in all patients, including the GCB and pSTAT3-positive cases, were more likely to express Myc (P = .06). Myc translocations involving the major breakpoint regions were found in 10% (3 of 29) of cases, and all 3 cases were GCB and had an inferior EFS (P = .09). pSTAT3, but not Myc expression, was correlated with elevated pretreatment serum cytokines, such as IL-10 (P = .05), G-CSF (P = .03), and TNF-alpha (P = .04). pSTAT3 IHC in DLBCL tumors has the potential to identify patients for STAT3 pathway-directed therapy; Myc IHC is a potential marker for inferior EFS in GCB patients. (Blood. 2012;120(22):4400-4406), University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence [P50 CA097274]; Goodwin Foundation; North Central Cancer Treatment Group [CA25224, CA114740]; Predolin Foundation; [R01-CA127433], This work was supported in part by the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (Career Development Award P50 CA097274; M.G.), Goodwin Foundation Pilot award (M.G.), the North Central Cancer Treatment Group (CA25224; and Biospecimen Resource Grant CA114740), and the Predolin Foundation Award. This work is supported in part by R01-CA127433 to T.E.W.