Citations

Abstract Introduction: The clinical presentation of mantle cell lymphoma (MCL) is heterogeneous, ranging from indolent disease that does not require immediate treatment to rapidly progressive disease that responds poorly to aggressive immunochemotherapy. Several small retrospective studies suggested that active surveillance or delayed treatment is feasible in select patients with an indolent disease course. A shorter diagnosis-to-treatment interval (DTI) is a strong predictor for inferior survival in patients with aggressive non-Hodgkin lymphomas such as diffuse large B-cell lymphoma (DLBCL) and peripheral T-cell lymphoma (PTCL), which likely correlates with an aggressive disease biology and clinical presentation. However, the impact of DTI on survival has not been well studied in patients with MCL. In this study, we utilized the National Cancer Database to investigate the feasibility of active surveillance or delayed treatment in patients with MCL and to explore the potential impact of DTI on survival of MCL patients requiring treatment. Methods: The National Cancer Database was used to identify adult patients (≥ 18 years) with MCL diagnosed from 2004 through 2017. Patients were divided into three groups based on their time to initial treatment: early treatment [ET] (treatment started within 3 months), delayed treatment [DT] (treatment started after 3 months) and surveillance [SUR] (watch and wait without treatment). Baseline clinical characteristics were compared between the 3 groups using Chi-square test. Survival analysis was performed using the Kaplan-Meier method and log-rank test. Statistical analysis was performed using SPSS version 25. Results: A total of 27,867 patients with newly diagnosed MCL were included in this study, 995 patients (4%) in the SUR group, 21,425 patients (77%) in the ET group, and 1,052 patients (4%) in the DT group. There were no major differences in the baseline characteristics among the three groups including demographics (age, sex, ethnicity), socioeconomic factors (annual income, insurance status, place of residence), comorbidity scores, and Ann Arbor stage. Compared to patients in the SUR and DT groups, patients in the ET group were more likely to have B symptoms (ET=27% vs DT=16% vs SUR=8%, p<0.001) and were less likely to be managed at an academic center (ET=40% vs DT=53% vs SUR= 51%, p<0.001) (Table 1). The median follow-up was 38.2 months for the entire cohort. There was a significant difference in OS among the three groups, with a median OS of 107 months (95% CI = 92.7-122.7) in the SUR group, 84.9 months (95% CI = 74.2-95.7) in the DT group, and 68.9 months (95% CI = 66.8-70.9) in the ET group (p=0.001) (Figure 1A). Among patients in the DT group, a shorter DTI was associated with a trend of inferior OS, with a median OS of 82 months (95% CI = 73.2-90.8) for those with a DTI of 3-12 months and not reached (95% CI not calculable) for those with a DTI > 12 months (p=0.095) (Figure 1B). Among the ET group, a shorter DTI was associated with shorter OS, with a median OS of 53 months (95% CI = 50.2-55.8) for patients with a DTI ≤ 15 days, 71.7 months (95% CI = 67.1-76.3) for those with a DTI of 16-30 days, and 87.1 months (95% CI= 82.8-91.4) for patients with a DTI of 31-90 days (p<0.001) (Figure 1C) Conclusion: In this retrospective analysis, patients with newly diagnosed MCL who were managed with an active surveillance strategy or delayed therapy had a better survival compared to patients who underwent early therapy. These findings validate the feasibility of delayed therapy in select patients with MCL. In patients who undergo early therapy, a shorter DTI is associated with an inferior survival outcome, mirroring findings in DLBCL and PTCL. A shorter DTI is likely associated with more aggressive disease biology and clinical presentation. Figure 1 Figure 1. Disclosures Munoz: Pharmacyclics/Abbvie, Bayer, Gilead/Kite Pharma, Pfizer, Janssen, Juno/Celgene, BMS, Kyowa, Alexion, Beigene, Fosunkite, Innovent, Seattle Genetics, Debiopharm, Karyopharm, Genmab, ADC Therapeutics, Epizyme, Beigene, Servier: Consultancy; Gilead/Kite Pharma, Kyowa, Bayer, Pharmacyclics/Janssen, Seattle Genetics, Acrotech/Aurobindo, Beigene, Verastem, AstraZeneca, Celgene/BMS, Genentech/Roche.: Speakers Bureau; Millennium: Research Funding; Janssen: Research Funding; Seattle Genetics: Research Funding; Pharmacyclics: Research Funding; Genentech: Research Funding; Incyte: Research Funding; Portola: Research Funding; Merck: Research Funding; Celgene: Research Funding; Gilead/Kite Pharma: Research Funding; Bayer: Research Funding; Physicians' Education Resource: Honoraria; Seattle Genetics: Honoraria; Targeted Oncology: Honoraria; OncView: Honoraria; Kyowa: Honoraria. Paludo: Karyopharm: Research Funding. 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. Nowakowski: Nanostrings: Research Funding; Blueprint Medicines: Consultancy; TG Therapeutics: Consultancy; Kymera Therapeutics: Consultancy; Incyte: Consultancy; Zai Labolatory: Consultancy; Daiichi Sankyo: Consultancy; Bantham Pharmaceutical: Consultancy; Karyopharm Therapeutics: Consultancy; Curis: Consultancy; Selvita: Consultancy; Ryvu Therapeutics: Consultancy; Kyte Pharma: Consultancy; Genentech: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Celgene/Bristol Myers Squibb: Consultancy, Research Funding; MorphoSys: Consultancy. 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; MorphoSys: Research Funding; Genentech: Research Funding; Novartis: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding; InnoCare: Research Funding.

Abstract BACKGROUND: E4402 was a randomized phase III study comparing two different rituximab dosing strategies for patients with previously untreated, low tumor burden follicular lymphoma (FL). The primary endpoint was time to treatment failure. The initial publication (Kahl, JCO 2014) demonstrated that a retreatment strategy utilized less drug and produced comparable time to treatment failure compared to a maintenance strategy. Here we provide long term follow up results, focusing on response duration, time to first cytotoxic therapy, overall survival, and risk of histologic transformation. METHODS: Eligible patients had untreated, low tumor burden (GELF criteria) FL. Patients received R 375 mg/m 2 weekly x 4 and responders were randomized to maintenance rituximab (MR) (single dose R q 3 months) or retreatment rituximab (RR) (R weekly x 4 doses at disease progression). Each strategy was continued until treatment failure. The primary endpoint, time to treatment failure, was defined as progression within 6 months of last R, no response to R retreatment, initiation of alternative therapy, or inability to complete protocol therapy. Secondary endpoints included time to first cytotoxic therapy, quality of life and safety. The ECOG Data Monitoring Committee recommended release of study results at a planned interim analysis in September 2011 and patients and providers were notified of results. Time to treatment failure data collection halted with release of the results but limited data collection on time to first cytotoxic therapy, response duration, and risk of histologic transformation continued. INITITIAL RESULTS: From 11/03 to 9/08, 384 patients with FL were enrolled. Complete or partial response was achieved in 289 patients (71%), who were then randomized to MR (n=146) or RR (n=143). Demographic features were similar in the two arms: median age 59 years; ECOG PS 0-1 in all patients, and FLIPI low-risk (14.9 vs. 16.4%), intermediate-risk (46.3 vs. 42.9%) and high-risk (38.8 vs. 40.7%) for MR vs. RR, respectively. At initial publication, with a median follow-up of 3.8 years, the time to treatment failure was 3.9 years for MR vs. 3.6 years for RR (p=NS). LONG TERM FOLLOW UP RESULTS: Immunoglobulin levels and risks for serious infections/late complications in MR patients will be updated at the annual meeting. For the endpoint of time to first cytotoxic therapy, the median follow up is 8.7 years. At 7 years, 83% of MR and 63% of RR remained free from first cytotoxic therapy (HR 2.37; 95% CI 1.50 - 3.76) [Figure 1]. For the endpoint of response duration, the median follow up is 12.1 years. At 10 years, 66% of the MR patients remained in their 1 st remission compared to 30% of the RR patients who remained in their 1 st remission [Figure 2]. The median response duration for RR patients receiving a single 4-week course of rituximab was 3.25 years. There was no difference in the overall survival at 10 years, 84% for MR vs. 83% for RR. There was a trend towards a lower risk of histologic transformation for patients receiving MR (n = 4) compared to RR (n = 11) (p = 0.11). CONCLUSIONS: With long term follow up, the RESORT data indicates that in previously untreated, low tumor burden, follicular lymphoma, MR was superior to RR for delaying time to first cytotoxic therapy and for response duration, with a trend towards reducing the risk of histologic transformation. MR did not improve the overall survival. The original publication concluded that the time to treatment failure is similar between the two dosing strategies. Due to study design, time to treatment failure could not be analyzed in this long term follow up analysis. Compared to the historical benchmark of 3 years median time to first cytotoxic therapy when watch and wait is utilized, single agent rituximab, administered by either dosing strategy, was highly effective at delaying the time to first cytotoxic therapy. Figure 1 Figure 1. Disclosures Kahl: AbbVie, Adaptive, ADCT, AstraZeneca, Bayer, BeiGene, Bristol-Myers Squibb, Celgene, Genentech, Incyte, Janssen, Karyopharm, Kite, MEI, Pharmacyclics, Roche, TG Therapeutics, and Teva: Consultancy; AbbVie, Acerta, ADCT, AstraZeneca, BeiGene, Genentech: Research Funding. Habermann: Incyte: Other: Scientific Advisory Board; Tess Therapeutics: Other: Data Monitoring Committee; Seagen: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Schuster: Abbvie: Consultancy, Research Funding; Acerta Pharma: Consultancy; AstraZeneca: Consultancy; Adaptive Biotechnologies: Research Funding; BeiGene: Consultancy; Celgene: Consultancy, Honoraria, Research Funding; DTRM: Research Funding; Genetech: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Incyte: Research Funding; Juno Theraputics: Consultancy, Research Funding; Loxo Oncology: Consultancy; Merck: Research Funding; Nordic Nanovector: Consultancy; Novartis: Consultancy, Honoraria, Patents & Royalties, Research Funding; Pharmaclcyclics: Research Funding; Tessa Theraputics: Consultancy; TG Theraputics: Research Funding. Fenske: TG Therapeutics: Consultancy, Speakers Bureau; Servier Pharmaceuticals: Consultancy; Seattle Genetics: Speakers Bureau; Sanofi: Speakers Bureau; Pharmacyclics: Consultancy; MorphoSys: Consultancy; Kite (Gilead): Speakers Bureau; KaryoPharm: Consultancy; CSL Therapeutics: Consultancy; Bristol-Myers Squibb: Speakers Bureau; Biogen: Consultancy; Beigene: Consultancy; AstraZeneca: Speakers Bureau; ADC Therapeutics: Consultancy; Adaptive Biotechnologies: Consultancy; AbbVie: Consultancy.

Abstract 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.

Abstract Introduction: Novel genetic classifications of diffuse large B-cell lymphoma (DLBCL) highlight the molecular complexity beyond cell of origin and provide new therapeutic implications. Next generation trials may incorporate novel agents for different genetic subtypes based on the pathogenesis mechanisms. For example, with the LymphGen classification, the MCD, BN2, ST2, and EZB subtypes are predicted to be susceptive to PI3K/mTOR targeting (Wright 2020). However, PI3K inhibitors have not been tested in the frontline, although data on the mTOR inhibitor everolimus were encouraging (Alliance 1085). We launched a phase 1/1b trial (NCT04323956) to investigate the feasibility of combining a novel PI3K inhibitor parsaclisib with standard R-CHOP immunochemotherapy and to seek an efficacy signal. Methods: Adult patients with newly diagnosed DLBCL were eligible if any of the following was present: 1) non-GCB subtype per the Hans algorithm; 2) expression of either Myc (≥40%) or Bcl2 (≥50%) by immunohistochemistry; or 3) MYC rearrangement by FISH. High grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements, regardless of morphology, was also eligible. All patients received parsaclisib plus R-CHOP. Phase 1 followed a 3+3 design and the primary endpoint was maximum tolerated dose (MTD) of parsaclisib. Dose levels tested were level 1 (20 mg QD, d1-10; starting level) and level 2 (20 mg QD, d1-14). MTD was defined as the dose level below the lowest dose that induced dose limiting toxicity (DLT) in at least one-third of patients. The primary endpoint in phase 1b is complete response (CR) rate by PET. Results: From July 2020 to June 2021, 15 patients were enrolled, 9 in phase 1 and 6 in phase 1b. The median age at diagnosis was 56 years (range 20-79), and 7 (47%) were female. One patient (7%) had ECOG PS ≥2, 7 (47%) had elevated LDH, 7 (47%) had >1 extranodal site, 13 (87%) had stage III/IV, and 7 (47%) had high-intermediate or high risk International Prognostic Index. Pathology was DLBCL in 13 patients (2 with concurrent follicular lymphoma) and high grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements in 2 patients. Four (27%) had non-GCB subtype, 7 (47%) had Myc or Bcl2 single expression, 8 (53%) had Myc/Bcl2 double expression, and 5 (33%) had MYC rearrangement (2 with concurrent BCL2 and/or BCL6 rearrangements). In phase 1, 3 patients were enrolled at dose level 1 and 6 patients were enrolled at dose level 2. No DLT was observed. Therefore, MTD was not reached, and dose level 2 was selected for phase 1b dose expansion. Six patients were enrolled in phase 1b to date. At the data cutoff date of 7/22/2021, 8 patients completed all 6 cycles of treatment, 1 competed 5 cycles, 3 completed 3 cycles, 2 completed 2 cycles, and 1 completed 1 cycle. Treatment-related adverse events (AE) are summarized in Table 1. The most common hematological AE included lymphopenia (60%), anemia (53%), neutropenia (53%), and thrombocytopenia (27%), and the most common non-hematological AE included nausea (67%), alopecia (40%), constipation (33%), fatigue (33%), dyspepsia (20%), and peripheral sensory neuropathy (20%). The most common grade 3 or 4 AE included lymphopenia (47%), neutropenia (33%), and anemia (13%). One 80-year-old female required parsaclisib dose reduction in cycle 3 and subsequent parsaclisib discontinuation as well as cyclophosphamide and doxorubicin dose reductions due to febrile neutropenia (no source of infection was identified). No other patients required dose reductions. The median follow-up was 3.7 months (range 0.8-10.7). Thirteen patients were evaluable for interim response by PET. The objective response rate was 92%, with 8 (62%) CR and 4 (31%) partial response (PR). One (8%) patient progressed before cycle 2 following a transient clinical response (shrinking palpable mass). Among those who achieved an objective response at interim, 7 patients were evaluable for end of treatment response and all 7 maintained a response, with 6 (86%) CR and 1 (14%) PR. Conclusions: Parsaclisib and R-CHOP combination therapy was generally well tolerated, with no DLT observed in phase 1 and no major safety concerns in both phase 1 and the ongoing phase 1b expansion. The preliminary efficacy signal of objective response appears encouraging in a small cohort of high risk patients. Parsaclisib plus R-CHOP can be an experimental arm for future frontline DLBCL trials investigating genetic subtype-driven novel therapies. Figure 1 Figure 1. Disclosures Wang: Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding; InnoCare: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Genentech: Research Funding; MorphoSys: Research Funding; Eli Lilly: Membership on an entity's Board of Directors or advisory committees; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees. King: Celgene/BMS: Research Funding. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. Tun: Gossamer Bio, Acrotech: Consultancy; Mundipharma, Celgene, BMS, Acrotech, TG therapeutics, Curis, DTRM: Research Funding. 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. 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. 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. OffLabel Disclosure: Parsaclisib is an investigational agent used in this clinical trial.

Abstract Introduction: Novel therapeutic agents such as immune checkpoint inhibitor (ICI) and brentuximab vedotin (BV) are active in classic Hodgkin lymphoma (cHL), including in patients that relapse after autologous stem cell transplant (ASCT). However, optimal management strategy is unclear for patients with relapsed or refractory (RR) cHL post-ASCT. The aim of the study is to determine the impact of novel agents relative to conventional therapy and allogeneic stem cell transplant (allo-SCT) on survival outcomes of patients with cHL who relapsed after ASCT. Methods: Patients with RR cHL who underwent ASCT between 06/1993 and 10/2017 at 3 Mayo Clinic sites were included. Clinical characteristics, treatment information, and outcome data were abstracted. For patients who relapsed after ASCT, the post-relapse progression free survival (PFS) and overall survival (OS) were analyzed using the Kaplan-Meier method and Cox proportional hazards models. Statistical analyses were done in JMP v15.2.1 and EZR v1.54. Results: A total of 332 patients with RR cHL who underwent salvage therapy and ASCT were identified. After a median post-ASCT follow-up of 8.6 years (range 6.8-9.7), 136 (41%) patients had a relapse or disease progression after ASCT. Patient characteristics of the 136 cases are summarized in the Table. The median age at post-ASCT relapse was 34 years (range 20-73), and 77 (57%) were male. 59 (43%) relapsed within 6 months and 77 (57%) relapsed after 6 months following ASCT. 59 (45%) had an extranodal site involvement at relapse. 14 (10%) had therapy with ICI or BV as salvage therapy prior to ASCT or maintenance therapy post-ASCT. The median post-relapse PFS and OS was 0.8 (95% CI 0.6-1.1) and 3.2 years (95% CI 2.2-5.5) years, respectively. Compared to patients who relapse after 6 months, patients who relapsed within 6 months of ASCT had worse post-relapse PFS (median 0.5 [0.3-0.7] vs 1.3 [0.9-1.9] years, p=0.0003) and OS (median 1.3 [0.5-2.2] vs 6.4 [3.7-10.4] years, p=0.0003). Extranodal site involvement at relapse was not associated with post-relapse PFS (median 0.7 [0.5-1.2] vs 0.9 [0.6-1.3] years, p=0.28) but was associated with worse post-relapse OS (median 2.7 [1.5-4.2] vs 6.4 [2.6-NA] years, p=0.006). Prior therapy with ICI or BV was not associated with post-relapse PFS (median 0.6 [0.3-NA] vs 0.8 [0.6-1.1] year, p=0.8) and OS (median NR [1.0-NA] vs 3.2 [2.2-5.5] years, p=0.5). After post-ASCT relapse, the median lines of subsequent therapy were 2 (range 1-12). For first post-ASCT salvage therapy, novel agents (ICI or BV), compared to other therapies, were associated with superior post-relapse PFS (median 1.7 [0.7-3.6] vs 0.7 [0.5-1.0] years, p=0.004) and OS (median 7.6 [4.7-NA] vs 3.2 [2.2-5.6], p=0.02). Allo-SCT following first post-ASCT relapse (n=9) was not associated with improvement in post-relapse PFS (median 2.2 years [0.3-NA] vs 0.8 [0.6-1.1] years, p=0.1) or OS (median NR [0.5-NA] vs 5.1 [3.2-7.3] years, p=0.7). Patients who received ICI or BV at any point post-ASCT relapse had significantly better post-relapse OS (median 7.6 [4.3-16.7] vs 2.2 [1.4-3.7] years, p=0.004) compared to those who never received any novel agent (Figure 1A). In contrast, allo-SCT at any point post-ASCT relapse (n=27) did not improve post-relapse OS (median 5.6 [2.7-NA] vs 4.7 [2.7-7.3] years, p=0.3) (Figure 1B). In multivariate Cox regression models adjusted for age and sex, exposure to ICI and/or BV was associated with superior post-relapse OS (HR 0.5, 95% CI 0.3-0.8, p=0.007); however, allo-SCT was not associated with improvement in post-relapse OS (HR 0.8, 95% CI 0.4-1.5, p=0.5). Conclusions: Patients relapsing within 6 months of ASCT and those with extranodal involvement at relapse had inferior OS after post-ASCT relapse. Prior therapy with novel agents did not impact post-relapse survival outcomes. In the setting of post-ASCT relapse, novel therapeutic agents significantly improved survival outcomes while allo-SCT did not. Future multicenter studies are needed to explore the role of novel agents and allo-SCT in patients with RR cHL post-ASCT relapse. Figure 1 Figure 1. Disclosures Wang: Eli Lilly: Membership on an entity's Board of Directors or advisory committees; InnoCare: Research Funding; MorphoSys: Research Funding; Genentech: Research Funding; Novartis: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees. Paludo: Karyopharm: Research Funding. Tun: Gossamer Bio, Acrotech: Consultancy; Mundipharma, Celgene, BMS, Acrotech, TG therapeutics, Curis, DTRM: Research Funding. Cerhan: Regeneron Genetics Center: Other: Research Collaboration; Genentech: Research Funding; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: 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. 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. 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. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding.

Abstract Introduction: Mantle cell lymphoma (MCL) is an uncommon hematological malignancy with an estimated incidence of 1 per 100,000 persons per year in the United States and represents only about 5% of all non-Hodgkin lymphomas. Several studies have shown that treatment at academic centers and a higher hospital case volume are associated with improved outcomes for uncommon hematological malignancies, probably due to increased provider expertise and access to novel therapies. Treatment of MCL can be complex given the heterogenous nature of the disease and a frequent need for autologous stem cell transplantation in eligible patients. However, the impact of treatment at an academic center and facility patient volume on the survival of patients with MCL has not been well studied in large cohorts. In this study, we utilized the National Cancer Database (NCDB) to investigate the impact of treatment at an academic center and treatment facility volume on the overall survival (OS) of patients with MCL. Methods: The NCDB was used to identify adult patients (≥ 18 years) with newly diagnosed MCL from 2004 through 2017. For facility patient volume analysis, patients were divided into groups based on the average number of new MCL patients seen annually: Tercile 1 [T1] (1-3 patients/year), Tercile 2 [T2] (4-5 patients/year) and Tercile 3 [T3] (≥6 patients/year). Treating centers were divided into Academic and Non-academic using the NCDB definitions. Academic centers were defined as centers that accessions more than 500 newly diagnosed cancer cases per year, participate in postgraduate medical education in at least four program areas including internal medicine and surgery and participates in cancer-related clinical trials. The primary endpoint was overall survival (OS). Survival analysis was performed using the Kaplan-Meier method and Cox hazards proportional model. Statistical analysis was performed using SPSS version 25. Results: We identified 22,752 patients with MCL during the study period. 9,484 (42%) patients were treated at academic centers and 13,070 (57%) were treated at non-academic centers. In terms of facility patient volume 10,948 patients (48%) were in the T1 group, 4,637 (20%) were in the T2 group and 7,166 (31%) were in the T3 group. No significant differences were found in baseline demographics (age, gender, race/ethnicity, comorbidity scores), socioeconomical variables (insurance type, median income, area of residence) and disease-related factors (B-symptoms, Ann Arbor stage) between patients treated academic vs nonacademic centers, or between patients in T1 vs T2 vs T3 groups. Notably, compared to lower volume facilities, T3 facilities were more likely to be academic centers (T3: 81% vs T2: 42% vs T1: 16%, p<0.001) . After a median follow-up of 3.4 years, the median overall survival (OS) was 5.6 years for the entire cohort. The median OS was inferior for patients treated at lower volume facilities (4.1 years for T1, 5.1 years for T2 and 9.0 years for T3, p<0.001) (Figure 1A). Similarly, the median OS was shorter for patients treated at non-academic centers vs academic centers (4.3 years vs 7.5 years respectively, p<0.001) (Figure 1B). In a multivariate analysis, treatment at a lower patient volume facility (Hazard ratio [HR] Q1= 1.26 [95%CI = 1.18-1.34]) and treatment at a non-academic center (HR = 1.1, 95%CI = 1.01-1.12) were both independent prognostic factors of inferior OS, after adjusting for demographics (age, gender, ethnicity, area of residence) and socioeconomic variables (income and insurance status). Conclusion: Patients with MCL treated at academic and higher volume facilities had a higher OS compared to patients treated at non-academic and lower volume facilities.. Additional research is needed to fully understand the mechanisms behind these differences. Patients with MCL may benefit from an early referral to academic and high-volume centers. Figure 1 Figure 1. Disclosures Munoz: Merck: Research Funding; Portola: Research Funding; Genentech: Research Funding; Incyte: Research Funding; Janssen: Research Funding; Seattle Genetics: Research Funding; Pharmacyclics/Abbvie, Bayer, Gilead/Kite Pharma, Pfizer, Janssen, Juno/Celgene, BMS, Kyowa, Alexion, Beigene, Fosunkite, Innovent, Seattle Genetics, Debiopharm, Karyopharm, Genmab, ADC Therapeutics, Epizyme, Beigene, Servier: Consultancy; Gilead/Kite Pharma, Kyowa, Bayer, Pharmacyclics/Janssen, Seattle Genetics, Acrotech/Aurobindo, Beigene, Verastem, AstraZeneca, Celgene/BMS, Genentech/Roche.: Speakers Bureau; Millennium: Research Funding; Pharmacyclics: Research Funding; Celgene: Research Funding; Physicians' Education Resource: Honoraria; Gilead/Kite Pharma: Research Funding; Kyowa: Honoraria; Bayer: Research Funding; Seattle Genetics: Honoraria; OncView: Honoraria; Targeted Oncology: Honoraria. Paludo: Karyopharm: Research Funding. 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. Nowakowski: Daiichi Sankyo: Consultancy; Zai Labolatory: Consultancy; TG Therapeutics: Consultancy; Blueprint Medicines: Consultancy; Nanostrings: Research Funding; MorphoSys: Consultancy; Kymera Therapeutics: Consultancy; Incyte: Consultancy; Ryvu Therapeutics: Consultancy; Kyte Pharma: Consultancy; Genentech: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Celgene/Bristol Myers Squibb: Consultancy, Research Funding; Selvita: Consultancy; Curis: Consultancy; Karyopharm Therapeutics: Consultancy; Bantham Pharmaceutical: Consultancy. Wang: Novartis: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; Eli Lilly: Membership on an entity's Board of Directors or advisory committees; MorphoSys: Research Funding; InnoCare: Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract Introduction: Intravascular lymphoma (IVL) is an extra nodal non-Hodgkin lymphoma with tropism for vascular endothelium. It is characterized by growth of large cells within the lumen of small to medium sized blood vessels. Central nervous system (CNS) and skin are predominantly involved. This report represents a retrospective single-institution review of IVL. Methods: We identified patients (pts) with IVL evaluated at Mayo Clinic Cancer Center between January 2003 and December 2018. Demographic, clinical, radiologic, pathologic, and therapeutic data were extracted. Statistical analysis of overall survival (OS) and progression free survival (PFS)] was performed using Kaplan-Meier method. Results: Total number of pts was 55; 22% (12/55) had CNS-only IVL, 14.5% (8/55) had CNS and non-CNS IVL, and 63.6% (35/55) had non-CNS IVL. Eighty seven percent (47/54) pts were B cell type, 11% (6/54) were T cell type, one pt had NK cell type IVL and another was unknown. Four pts were diagnosed by autopsy. Median age at diagnosis was 68 years (range, 40-85). Sixty-four percent were males. ECOG performance status was <2 in 66%. The median follow-up time from diagnosis was 63 months [CI 95%, 9-NR], and 47% (26/55) were alive. The most common diagnostic biopsy sites were bone marrow (BM) 45% (25/55), skin 25% (14/55), and brain 29% (16/55). Twenty-nine patients had a PET scan. Seventy nine percent (23/29) had abnormal PET findings, with mean SUV of 8.6 (range 2.5-19.1). Of the 35 pts with non-CNS IVL, 76% (16/21) had abnormal PET; furthermore, the diagnosis was made with biopsies of the following sites: bone marrow 54% (19/35), skin 40% (14/35), lung 14% (5/35), liver 5.7% (2/35), spleen 2.8% (1/35), and omentum 2.8% (1/35). Forty-six percent (13/28) received CNS prophylaxis and ten percent (3/55) had relapse in CNS. Two out of the three pts who had CNS relapse had received CNS prophylaxis. The median time to CNS relapse in non-CNS IVL was 9 months. The most common first-line regimen was high-dose methotrexate+ rituximab containing regimen 62% (10/16) in IVL with CNS involvement and RCHOP (60%) (17/28) in non-CNS IVL. Seventeen percent of (8/48) pts received autologous stem cell transplant (ASCT) and 63% (5/8) pts were transplanted in first complete remission (CR1), and 3 pts after the first relapse. Median OS (mOS) for the whole cohort was 57 months, [CI 95%, 9-NR], and median PFS was 7 months [CI 95%, 2-NR]. There was no significant difference in mOS between groups; CNS-only IVL- 9 months (CI 95%, 1-NR), non-CNS IVL -62 months (CI 95%, 20-NR) vs combined CNS and non-CNS IVL- 4 months (CI 95%, 3-NR). mOS for those who received ASCT in CR1 was not reached (CI 95%, 10-NR) vs 51 months in non-transplant group (CI 95%, 3-NR) p=0.24. In pts with non-CNS IVL, there was no significant difference in mOS between CNS prophylaxis subgroup (NR: CI 95%, 57-NR) vs no-CNS prophylaxis subgroup (20 months: CI 95%, 0-NR), p=0.12. In those with CNS IVL mOS for early diagnosis (0-30 days from symptom onset to diagnosis was NR (CI 95%, 3-NR) vs mOS for late diagnosis (>30 days {31-14,440})-5months (CI 95% 1-NR), p=0.29]. Conclusion: 1. BM was most frequently involved in our patients. We suggest that BM biopsy should be part of diagnostic testing when IVL is suspected. 2. Most cases are of B-cell linage, consistent with reported literature. All non-B cell cases were in non-CNS locations. 3. PET scans were abnormal in more than 70% of cases indicating that this imaging modality is vital in the diagnosis due to odd location and small size of lesions. 4.Overall prognosis in the literature was poor with most patients surviving <1 year. Our cohort has mOS of 57 months. The reason(s) for better survival in our cohort could not be definitively determined. 5. CNS involvement had an overall trend towards poor prognosis; however, those diagnosed early had better outcomes; this did not reach statistical significance due to small sample size. 6. mOS was not reached for those transplanted CR1. There was a trend towards a better survival associated with CNS prophylaxis versus no prophylaxis in non-CNS IVL. 7. We suggest that CNS-centric therapeutic approach and intensive consolidation with ASCT should be considered in managing IVL. Figure 1 Figure 1. Disclosures 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. Habermann: Incyte: Other: Scientific Advisory Board; Seagen: Other: Data Monitoring Committee; Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. 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. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. Tun: Mundipharma, Celgene, BMS, Acrotech, TG therapeutics, Curis, DTRM: Research Funding; Gossamer Bio, Acrotech: Consultancy.

Abstract 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 <2 (84%), had stage III/IV disease (61%) and an elevated LDH (56%) at diagnosis. Fluorescent in situ hybridization data was present for 65% of patients. Among patients with available data, DHL and NGCG COO represented 2.8% and 36% of the cohort, respectively. CNS IPI high- and intermediate-risk categories were present in 12.4% and 52% of the patients, respectively. R-CHOP was the most common upfront chemotherapy (n=980, 72%). 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.

Abstract 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<0.001). VIPER master regulator activity inferencing revealed that these pathways were likely being controlled by differential activity in NF-kB, IRFs, STAT1, BCL6, and FOXO1. Each program significantly enriched for, but were not defined by, portions of specific germinal center programs, such as pre-memory (G1), light-zone-to-dark-zone transition (G2), and a pre-light-zone intermediate (G3). We next assessed the connection between B cell programs and the tumor microenvironment (TME) using available paired CyTOF data on 67 cases, which revealed an active TME in G1, with an abundance of CD8 T cell and NK cell populations, a wide variety of immune content in G2 that consisted mostly of Tfh and myeloid cells, and a poorly populated immune compartment in G3 compared to G1 and G2. Finally, somatic driver mutations and copy number alterations from WES were identified and associated with the three clusters. The three groups distinguished themselves by significant enrichment of copy number alterations (TNFAIP3-loss , 1q23-gain, 1q32-gain) in G2, while 10q-loss and mutations in BCL2 and chromatin modifiers (KMT2D and CREBBP) enriched in G3. G1, overall, had lower alteration burden and had weak associations with any specific alterations, suggesting an alternative mechanism for driving the G1 program. 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.

Abstract 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<0.01, Table 1). 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.