Villasboas, José C.

Background: Anti CD-19 chimeric antigen receptor T-cell therapy (CAR-T) is approved to treat relapsed/refractory (R/R) aggressive NHL after 2 or more lines of therapy. Randomized clinical trials are ongoing comparing CAR-T and autologous stem cell transplant (ASCT) in second line. In standard of care practice, patients refractory to second line chemotherapy are sometimes treated with third line chemotherapy and taken to ASCT if disease is chemo-sensitive. Current study aims to evaluate the impact of prior lines of therapy on the efficacy of CAR-T and ASCT. Methods: A retrospective review of patients who received ASCT and/or axicabtagene ciloleucel (axi-cel) for R/R aggressive NHL from June 2016 - January 2020 at Mayo Clinic, Rochester was performed. The lymphoma types included in the study were DLBCL, transformed lymphoma (TFL), high grade B-cell lymphoma (HGBL), and primary mediastinal large B-cell lymphoma (PMBCL). Response to all lymphoma-directed therapy was evaluated using 2014 Lugano criteria. Overall survival (OS) was defined as time from CAR-T or ASCT infusion to death, and progression free survival (PFS) was defined as time from CAR-T or ASCT infusion to disease progression, or next treatment. Categorical data was analyzed as Chi-square or fishers exact test as appropriate. Continuous variables were analyzed using Wilcoxon Rank Sum test. Analysis was performed in R3.6.3. Results: A total of 105 patients underwent ASCT and 53 patients underwent CAR-T therapy in the study period with at least 6 months follow-up. Of the 105 ASCT patients, 87 received ASCT after 2 lines of chemotherapy, ASCT(2), and 18 received ASCT after 3 lines of chemotherapy, ASCT(3). Twenty six of the ASCT [n=19, ASCT(2) and n=7, ASCT(3)] patients received CART in subsequent therapy. Baseline characteristics were comparable between the two groups except ASCT(3) had higher percent of refractory disease after first line (table 1). With a median follow up of 17.4 months, the PFS was significantly longer for the ASCT(2) group with a trend toward longer OS (Figure 1; PFS @ 1 year: ASCT(2) 67%, 95%CI 58-79%; ASCT(3) 27%, 95%CI 27-74%; p = 0.036). Among the CAR-T recipients, only 18 (33%) patients received CAR-T in the earliest possible indication, CAR-T(2): after 2 lines of chemotherapy (n=12) and/or after ASCT(2) (n=6), table 1. Compared to patients who received CAR-T in later lines (CAR-T 3+), OS was significantly longer for patients who received CAR-T(2), with a trend toward longer PFS (Figure 1; OS @ 1 year: CAR-T (2) 82%, 95%CI 65-100%; CAR-T later lines 62%, 95%CI 47-82%; p = 0.05). Additionally, 12 (23%) of patients received CAR-T immediately after ASCT (9 after ASCT(2), 3 after ASCT(3)). Baseline demographics in these 12 patients, CAR-T(ASCT), were comparable to the others who had chemoimmunotherapy as the last salvage therapy prior to CAR-T [CAR-T(chemo)], except a smaller proportion received bridging therapy (33% vs. 73%, p = 0.01). The CAR-T (ASCT) group had significantly prolonged PFS (median PFS - not reached [95%CI 3-not reached (NR)] vs. 3 months [95% CI 3-4 months]), and OS (median OS - NR, [95%CI 10.34-NR] vs. 15.1 months, [95%CI 11.9 - NR], p = 0.03). Updated analysis will be presented at the meeting with longer follow up (Figure 1). Conclusion: CAR-T use at the earliest current approved indication may confer survival advantage. Referral for earlier use of CAR-T remains low at only one third of our CAR-T practice volume. CAR-T use for relapse immediately after ASCT appear to have longer PFS and OS than after other systemic salvage therapy, although contributing parameters such as the need for bridging therapy requires further study. While patients who are chemo-refractory to second line chemotherapy may demonstrate chemo sensitivity with third line chemotherapy, PFS is shorter for ASCT(3). Comparison between ASCT(3) and CAR-T (2, chemorefractory) require larger cohorts with longer follow-up. Disclosures Bennani: Affimed: Research Funding; Purdue Pharma: Other: Advisory Board; Kite/Gilead: Research Funding; Verastem: Other: Advisory Board. Ansell:Takeda: Research Funding; ADC Therapeutics: Research Funding; Trillium: Research Funding; Bristol Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; AI Therapeutics: Research Funding. Lin:Juno: Consultancy; Legend BioTech: Consultancy; Merck: Research Funding; Takeda: Research Funding; Gamida Cells: Consultancy; Sorrento: Consultancy, Membership on an entity's Board of Directors or advisory committees; Vineti: Consultancy; Bluebird Bio: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy; Janssen: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding.

BACKGROUND: Angioimmunoblastic T cell lymphoma (AITL) has a unique histological profile comprised of a relatively small number of malignant CD4+ T-cells of TFH phenotype inter-mixed with an extensive infiltrate of multi-lineage immune cells. In our study, we have utilized mass cytometry, high-dimensional analysis, and single-cell transcriptome analysis to provide novel insights into the unique phenotypes that comprise this intra-tumoral microenvironment. We then extended this work to explore clinical associations including peripheral serum analysis of AITL patients and normal controls. To our knowledge, this represents the first such analysis of its kind in AITL. METHODS: We designed two novel CyTOF antibody (Ab) panels to identify and characterize cells of T, B, NK, monocyte and fDC lineages. Samples analyzed included a cohort of 25 biopsy specimens from 8 histologically confirmed AITL patients (5 lymph node (LN), 3 spleen (SP)) and 17 normal controls across key comparator immune tissue types (7 LN, 6 SP, 4 tonsil (TL)). Extensive high-dimensional analysis of CyTOF data was then performed to provide novel insights into key phenotypes and trends of malignant and non-malignant populations in AITL. We then performed CITE-Seq on control and AITL samples to gain further insight into the RNA transcriptome of key T cell populations at the cellular level. Finally, peripheral serum analysis of cytokines, soluble immune receptors, and ligands were then measured by multiplex ELISA from a separate cohort of 22 samples (5 AITL, 17 control) distinct from the individuals analyzed in the original high-dimensional study cohort. RESULTS: While the presence of "reactive" CD8+ populations is a known histologic hallmark of AITL, we describe the gross expansion of novel CD8+ populations with distinctive immunophenotypic features which have not previously been detailed in this malignancy. Using single-cell protein expression data from CyTOF, these expanded CD8+ populations can be broadly categorized as "effector memory" (CCR7-, CD45RO+, CD45RA-) and further characterized phenotypically by markers of progressive exhaustion, checkpoint inhibition, and terminal differentiation (PD1++, TIGIT++, ICOS+, TIM3+). Further analysis of the single-cell transcriptome from these expanded CD8+ populations via CITE-Seq revealed an expression signature consistent with dysfunction and limited cytotoxic activity (including significant down-regulation of granzyme, perforin, and IFN-g) when compared to benign and malignant controls. Interestingly, when compared to CD8+ populations of identical phenotype found in control tissues, these cells also featured marked upregulation of XCL2 and XCL1 in AITL. Additionally, global shifts in infiltrating CD19+ B cell phenotypes were seen in AITL, marked specifically by diminished expression of both CXCR5 and CD73. Finally, soluble PD-1 and other key immune molecules implicated in the expanded tumor microenvironment were found to be significantly increased in the peripheral serum of AITL patients compared to controls (1567.9 pg/mL (1109.3 S.E.) in AITL vs 29.79 (8.84 S.E.) in controls; P<0.0001). CONCLUSIONS: High-dimensional and single-cell transcriptome analysis of the AITL microenvironment yielded several novel insights which have not been previously described in this malignancy. Highlights include the gross expansion of distinct CD8+ populations - the majority of which are of an exhausted, dysfunctional phenotype featuring marked upregulation of XCL2 and XCL1 - and the global loss of CXCR5 and CD73 expression among AITL CD19+ B cell populations. Taken together, this suggests the presence of aberrant non-malignant immune subsets within the AITL microenvironment which may contribute to novel mechanisms of immune escape. Disclosures Cerhan: NanoString: Research Funding; BMS/Celgene: Research Funding. Ansell:Bristol Myers Squibb: Research Funding; ADC Therapeutics: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; AI Therapeutics: Research Funding; Trillium: Research Funding; Takeda: Research Funding.

Introduction Day 15 absolute lymphocyte count (ALC-15) recovery after autologous peripheral blood hematopoietic stem cell transplantation (APBHSCT) is recognized as a prognostic factor for survival. ALC-15 recovery is directly dependent on the amount of collected and infused autograft-absolute lymphocyte count (A-ALC). Our group published a double-blind phase III study which showed that patients infused with an A-ALC ≥ 0.5 x 109 cells/kg experienced superior survival post-APBHSCT. Based on the results from our phase III study, on April 1, 2017, our Bone Marrow Transplant Program changed our standard clinical practice to collect an A-ALC ≥ 0.5 x 109 cells/kg in addition to stem cells for lymphoma patients undergoing APBHSCT. To continue to assess the prognostic ability of A-ALC, we evaluated the overall survival (OS) and progression-free survival (PFS) of diffuse large B-cell lymphoma (DLBCL) patients that underwent APBHSCT after April 1, 2017 compared with a matched-control group in a 1:1:1 ratio with DLBCL patients infused with an A-ALC < 0.5 x 109 cells/kg or an A-ALC ≥ 0.5 x 109 cells/kg prior to April 1, 2017. Methods Eight-five consecutive DLBCL patients after our practice changed on April 1, 2017 received an A-ALC ≥ 0.5 x 109 cells/kg. For each DLBCL patient infused with an A-ALC ≥ 0.5 x 109 cells/kg after April 1, 2017, the set of potential controls was restricted to those DLBCL patients infused with an A-ALC < 0.5 x 109 cells/kg and an A-ALC ≥ 0.5 x 109 cells/kg prior to April 1 2017 . Within this group of potential controls, the subjects were chosen who best matched for the cases in terms of clinical risk variables, including gender, age, stage, lactate dehydrogenase (LDH), performance status, extra-nodal disease, International prognostic index (IPI), and disease status prior to APBHSCT (complete or partial response), using the GREEDY algorithm. Cases were defined as DLBCL patients infused with an A-ALC ≥ 0.5 x 109 cells/kg starting from April 1 2017. Control 1 was defined as DLBCL patients infused with an A-ALC < 0.5 x 109 cells/kg prior to April 1 2017. Control 2 was defined as DLBCL patients infused with an A-ALC ≥ 0.5 x 109 cells/kg prior to April 1 2017. We truncated the survival follow-up to 3 years starting from the date of transplant to match follow-up time between cases and controls. Both matched cases and control groups patients received BEAM conditioning. This retrospective study was approved by the Mayo Clinic Institutional Review Board according to the regulation of the Declaration of Helsinki. Results Cases, control 1 and control 2 were balanced to the clinical risk variables; age (p = 0.8); gender (p = 0.9); LDH (p = 0.6); stage (p = 0.3); performance status (p = 0.5); extra-nodal disease (p = 0.2); IPI (p = 0.6); and clinical status prior to transplant (p = 0.2). Figure 1A showed the OS and figure 1B the PFS. Both the cases and control 2 showed superior OS and PFS in comparison to control 1. Cases and control 2 showed overall similar OS and PFS. Multivariate analysis including all patients continue to depict the A-ALC ≥ 0.5 x 109 cells/kg as an independent predictor for OS (HR = 0.382, 95%CI=0.241-0.605, p < 0.0001) and PFS (HR = 0.437, 95%CI = 0.279-0.629, p <0.0001). Subgroup analysis, including all patients, shows superior OS and PFS for patients infused with an A-ALC ≥ 0.5 x 109 cells/kg for each of the following clinical variables: age [> 60 years: OS (p <0.0001); PFS (p <0.0001) or ≤ 60 years: OS (p <0.01); PFS (p , 0.02)]; extra-nodal disease [> 1: OS (p <0.008); PFS (p <0.01) or ≤1: OS (p < 0.001): PFS (p <0.0005)]; IPI [> 2: OS (p <0.01); PFS (p <0.02) or ≤ 2: OS (p <0.0008); PFS (p <0.0006)]; LDH [abnormal: OS (p <0.006); PFS (p <0.005) or normal: OS (p <0.002); PFS (p <0.002)]; performance status [> 1: OS (p <0.04); PFS (p <0.03) or ≤ 1: OS (p <0.0001); PFS (p <0.0001)]; stage [I/II: OS (p <0.006); PFS (p <0.006) or III/IV: OS (p <0.002); PFS (p <0.001)]; response prior to transplant [complete response: OS (p <0.0007); PFS (p <0.0001) or partial response: OS (p <0.01); PFS (p <0.02)]. Conclusion Our match-control study continues to support the results of our Phase III study showing that the infusion of A-ALC is a prognostic factor for survival of DLBCL patients undergoing APBHSCT. Our studies support the practice of not only collecting enough stem cells (i.e., CD34) for hematologic engraftment, but also enough immune effector cells (i.e., A-ALC) to improve clinical outcomes in DLBCL patients post-APBHSCT. Figure 1 Disclosures Ansell: Regeneron: Research Funding; Bristol Myers Squibb: Research Funding; Trillium: Research Funding; ADC Therapeutics: Research Funding; Takeda: Research Funding; AI Therapeutics: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding.

Key Points Systemic immune microenvironment signatures in CMML indicate an altered T- and natural killer cell balance. CMML bone marrow dendritic cell aggregates associate with disease progression and systemic regulatory T-cell phenotypic switch.

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

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

Magnesium is an essential element that is involved in the regulation of cell growth, division and differentiation. Epidemiologic studies have demonstrated that a diet poor in magnesium is associated with an increased risk of developing cancer. There are few studies investigating whether magnesium deficiency is associated with a poor prognosis in lymphomas. The recent discovery of X-linked immunodeficiency with magnesium defect, Epstein-Barr virus, and neoplasia (XMEN) disease, suggests a role of magnesium in the development of hematologic malignancies and lymphocyte function. Furthermore, a recently published study found that hypomagnesemia prior to chemotherapy is associated with inferior overall survival (OS) in patients with Burkitt Lymphoma. We hypothesized that low magnesium levels prior to autologous hematopoietic stem cell transplant (AHSCT) may lead to inferior outcomes via impaired lymphocyte recovery. We evaluated whether low serum magnesium levels were associated with inferior survival in patients with relapsed or refractory diffuse large B cell lymphoma (DLBCL) who underwent autologous hematopoietic stem cell transplant (AHSCT) between January 1998 and May 2020 using a retrospective study design. We cross-referenced our search with patients who had a magnesium level as part of their pre-transplant workup between 7 and 28 days prior to their first transplant. Two-sided Wilcoxon rank sum test and Chi square/Fischer's exact test were used to compare the continuous and categorical variables, respectively. Kaplan-Meier and log-rank tests were used to perform all time to event analysis. Hazard ratios (HR) with confidence intervals (CI) were calculated using Cox-proportional hazards. Our study included 581 patients of which 82 (14.1%) had hypomagnesemia (magnesium < 1.7 mg/dL) prior to AHSCT. Our study found that hypomagnesemia was associated with inferior OS and event-free survival (EFS) defined as re-transplant, relapse or death, compared to those with a normal magnesium level; median OS: 7.3 years (95% CI: 2.91 - upper limit not estimable) versus 9.7 years (95% CI: 6.92 - 12.3 years), p=0.0041; and median EFS: 3.9 years (95% CI: 1.63 - 8.98 years) versus 6.29 years (95% CI: 4.73 - 8.95 years), p = 0.016 respectively(B). Higher serum magnesium (HR 0.199 per doubling, 95% CI 0.078 - 0.503; p<0.001) and higher albumin (HR 0.090 per doubling, 95% CI 0.021 - 0.394; p=0.001) were associated with lower mortality rates, and higher creatinine (HR 1.669 per doubling, 95% CI 1.03 - 2.52; p=0.04) was associated with greater mortality up to 1 year post-transplantation. In a multivariate Cox regression excluding albumin, hypomagnesemia (p < 0.001) and elevated creatinine (p = 0.038) were independent predictors of poor overall survival(A). There was no significant difference between day 15 absolute lymphocyte count between patients with normal magnesium levels and low serum magnesium levels prior to their conditioning regimen. Although we hypothesized that we would find a significant difference between 15 day absolute lymphocyte count recovery and hypomagnesemia, this was not apparent. However, magnesium is an important nutrient for immune function and immune recovery is important after the bone marrow is restored post ablative therapy. Thus, low serum magnesium levels may lead to impaired OS and EFS via impaired lymphocyte function. In vitro studies will be necessary to understand this mechanism further. Magnesium may simply be a marker of illness and replacement of magnesium may not improve outcomes. However, further investigations are needed to determine whether outcomes would improve with replacement of magnesium in patients with hypomagnesemia, which our study did not address. Overall, our preliminary data suggests that hypomagnesemia is an independent prognostic factor and predictor of inferior OS and EFS in this cohort of patients. Figure Disclosures Ansell: ADC Therapeutics:Research Funding;AI Therapeutics:Research Funding;Trillium:Research Funding;Affimed:Research Funding;Regeneron:Research Funding;Seattle Genetics:Research Funding;Bristol Myers Squibb:Research Funding;Takeda:Research Funding.Witzig:Spectrum:Consultancy;Celgene:Consultancy, Research Funding;Immune Design:Research Funding;Karyopharm Therapeutics:Research Funding;Acerta:Research Funding;Incyte:Consultancy;AbbVie:Consultancy;MorphSys:Consultancy.

Background: Real world multi-institutional data of anti-CD19 CAR-T therapy from across the US shows similar response rates and toxicities as that of the registration trials. For axicabtagene ciloleucel (axi-cel), bridging therapy (BT) during CAR-T manufacturing was not allowed. Real world data shows that patients who received BT had lower complete remission (CR) rates, duraiton of response (DOR) and overall survival (OS). Whether the poorer outcome is due to the effect of the BT or the aggressive biology of the disease is unknown. Patients with lower disease burden had higher response rate in the registration study ZUMA-1 for axi-cel. We examined the impact of response to BT on CAR-T outcome. Specifically, we also describe our experience using polatuzumab vedotin, bendamustine and rituximab (Pola-BR), also a recently approved therapy for relapsed/refractory diffuse large B-cell lymphoma (DLBCL), prior to CAR-T as salvage (ST) and BT. Methods: A retrospective review of 63 patients who received axi-cel for NHL from June 2016 - March 2020 at Mayo Clinic, Rochester was performed. ST was defined as the last therapy prior to leukapheresis and grouped into categories - autologous stem cell transplant (ASCT), and non-ASCT (chemotherapy and immunotherapy) . BT was defined as lymphoma-directed therapy given between leukapheresis and CAR-T infusion. Response to all lymphoma-directed therapy was evaluated using 2014 Lugano criteria. Event free survival (EFS) was defined as time from axi-cel infusion to progression, next treatment, or death. Overall survival (OS) was defined as time from axi-cel infusion to death. Results: Patients received BT if there were concerns for symptomatic progression of disease during CAR-T manufacturing that would reduce the likelihood of eligibility to receive CAR-T. The decision and choice of BT was at the discretion of the treating physician. Sixty-eight percent (n=43) of the patients received BT (19 chemo, 24 immunotherapy). Demographics of the patients based on their response to BT are shown in table 1. More patients in the BT responder group had received chemotherapy as type of bridge (58% vs. 33%). With a median follow up of 12-months for those still alive, significant improvement in EFS was seen in patients who had partial response (PR) or stable disease (SD) to BT than patients who continued to progress (median 12 months vs. 3 months, p = 0.01) [Figure 1A]. OS however, was not significant between the two groups (median OS 18 months - responders vs. 12 months non-responders, p = 0.19) [Figure 1B]. We also describe in the case series below 11 patients who received Pola-BR in salvage setting (ST) and/or BT (Table 2). Due to the lymphopenic effects of bendamustine, it was avoided before T-cell collection if possible. Two patients, however, received bendamustine as part of Pola-BR for salvage therapy as its need was determined per the managing physician. Axi-cel was successfully manufactured for all 10 patients. One patient did not have measurable disease after Pola-BR BT and remains on observation. A second patient died before CAR-T infusion from progressive disease. Four of the nine patients treated with CAR-T remained in response. Updated outcome will be presented. Conclusion: While patients who received BT have worse EFS than those who didn't, having some control of lymphoma after BT was associated with better EFS compared to patients who continued to have progressive disease after BT. Preliminary data from Pola-BR case series shows 60% (6/10) patients achieved PR/SD before CAR-T infusion, but requires longer follow up for its impact on survival. Disclosures Bennani: Purdue Pharma: Other: Advisory Board; Kite/Gilead: Research Funding; Affimed: Research Funding; Verastem: Other: Advisory Board. Ansell:AI Therapeutics: Research Funding; Takeda: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding; ADC Therapeutics: Research Funding; Trillium: Research Funding. Lin:Janssen: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Novartis: Consultancy; Celgene: Consultancy, Research Funding; Bluebird Bio: Consultancy, Research Funding; Juno: Consultancy; Legend BioTech: Consultancy; Merck: Research Funding; Takeda: Research Funding; Gamida Cells: Consultancy; Sorrento: Consultancy, Membership on an entity's Board of Directors or advisory committees; Vineti: Consultancy.

Introduction The molecular basis for the clinical heterogeneity observed in DLBCL was initially elucidated with gene expression profiling, identifying three distinct subgroups with prognostic relevance based on COO namely activated B-cell like (ABC), germinal center B-cell like (GCB) and a type 3 subtype. Shortly afterwards the Han's algorithm was developed allowing for widespread applicability and correlation of gene expression with protein expression. GCB profile at diagnosis is generally associated with superior long term outcomes compared to non-GCB in patients treated with conventional chemo-immunotherapy. Patients with relapsed/refractory disease (R/R) after front-line therapy undergo salvage treatment and, upon demonstration of objective response, proceed with high dose therapy and autologous transplantation (auto-HCT). Whether COO by Han's algorithm retains its prognostic significance post auto-HCT in patients with R/R DLBCL is not well established. Methods A retrospective analysis was carried out using the Mayo database across all three sites. It included all patients between 18-75 years of age who were diagnosed with de-novo DLBCL and underwent first auto-HCT for R/R disease. Patients must have received salvage treatment and shown chemotherapy sensitive disease. Patients with transformed DLBCL or other variants and those who underwent auto-HCT in first complete remission for high risk disease were excluded. Patients who had less than a partial response at time of transplantation and still underwent auto-HCT were also excluded. Primary endpoints included overall survival (OS); relapse and relapse-free survival (RFS). Continuous variables were summarized as mean (standard deviation) and median (range). Categorical variables were reported as frequency (percentage). Continuous baseline variables were compared between GCB and non-GCB using Wilcoxon rank sum test and categorical baseline variables were compared with Chi-squared test. Kaplan-Meier method was used to estimate 5 and 10-year rates of freedom from long-term events and draw corresponding survival curves. Multivariate Cox regression models were used to evaluate the impact of cell of origin on long-term survival after adjusting for baseline factors. All tests were two-sided with alpha level set at 0.05 for statistical significance. Results A total of 357 patients underwent auto-HCT between 2005 and 2018. Cell of origin was determined for 284 patients and these were included in the analysis. Median age was 61 (range: 19-78) years, and 64% patients were male. GCB and non-GCB patients did not differ significantly with regards to baseline factors (Table 1). During a median follow up of 1.7 years, 139 patients died, 151 had relapse and 175 either died or had relapse. Five year OS for GCB patients versus (vs) non-GCB was at 44% vs 64% (p=0.004) (Figure 1); relapse was at 67% vs 49% (p=0.012) and RFS was at 28% vs 50% (p=0.003). The difference between GCB and non-GCB groups remained statistically significant in multivariate analysis, with GCB patients 60% more likely to die and 50% more likely to experience relapse compared to non-GCB patients. The only other factor that retained significance in multivariate analysis for both endpoints was response at the time of transplant (Table 2A-C). Conclusions In this analysis, GCB DLBCL results in worse OS and higher relapse vs. non-GCB DLBCL following an auto-HCT. It is unclear if relapse/progression of GCB subtype after front-line therapy alters its natural history into a more aggressive disease. Disclosures Ansell: Trillium: Research Funding; ADC Therapeutics: Research Funding; Affimed: Research Funding; Bristol Myers Squibb: Research Funding; AI Therapeutics: Research Funding; Takeda: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding. Foran:Xencor: Research Funding; Trillium: Research Funding; Takeda: Research Funding; Kura Oncology: Research Funding; Aptose: Research Funding; Aprea: Research Funding; Actinium: Research Funding; Boehringer Ingelheim: Research Funding; Abbvie: Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Servier: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Revolution Medicine: Consultancy; H3Biosciences: Research Funding; Agios: Honoraria, Research Funding. Tun:Celgene: Research Funding; Mundipharma: Research Funding; Curis: Research Funding; TG Therapeutics: Research Funding; Acrotech: Research Funding; DTRM Biopharma: Research Funding; Bristol-Myers Squibb: Research Funding. Kharfan-Dabaja:Daiichi Sankyo: Consultancy; Pharmacyclics: Consultancy.

Background The importance of the immune system in modulating the trajectory of lymphoma outcomes has been increasingly recognized. We recently showed that CD4+ cells are associated with clinical outcomes in a prospective cohort of almost 500 patients with follicular lymphoma (FL). Specifically, we showed that the absence of CD4+ cells inside follicles was independently associated with increased risk of early clinical failure. These data suggest that the composition, as well as the spatial distribution of immune cells within the tumor microenvironment (TME), play an important role in FL. To further define the architecture of the TME in FL we analyzed a FL tumor section using the Co-Detection by Indexing (CODEX) multiplex immunofluorescence system. Methods An 8-micron section from a formalin-fixed paraffin-embedded block containing a lymph node specimen from a patient with FL was stained with a cocktail of 15 CODEX antibodies. Five regions of interest (ROIs) were imaged using a 20X air objective. Images underwent single-cell segmentation using a Unet neural network, trained on manually segmented cells (Fig 1A). Cell type assignment was done after scaling marker expression and clustering using Phenograph. Each ROI was manually masked to indicate areas inside follicles (IF) and outside follicles (OF). Relative and absolute frequencies of cell types were calculated for each region. Cellular contacts were measured as number and types of cell-cell contacts within two cellular diameters. To identify proximity communities, we clustered cells based on number and type of neighboring masks using Phenograph. The number of cell types and cellular communities were calculated inside and outside follicles after adjustment for total IF and OF areas. The significance of cell contact was measured using a random permutation test. Results We identified 13 unique cell subsets (11 immune, 1 endothelial, 1 unclassified) in the TME of our FL section (Fig. 1A). The unique phenotype of each subset was confirmed using a dimensionality reduction tool (t-SNE). The global composition of the TME varied minimally across ROIs and consisted primarily of B cells, T cells, and macrophages subsets - in decreasing order of frequency. Higher spatial heterogeneity across ROIs was observed in the frequency of T cell subsets in comparison to B cells subsets. Inspecting the spatial distribution of T cell subsets (Fig. 1B), we observed that cytotoxic T cells were primarily located in OF areas, whereas CD4+ T cells were found in both IF and OF areas. Notably, the majority of CD4+ T cells inside the follicles expressed CD45RO (memory phenotype), while most of the CD4+ T cells outside the follicles did not. Statistical analysis of the spatial distribution of CD4+ memory T cell subsets confirmed a significant increase in their frequency inside follicles compared to outside (20.4% vs 11.2%, p < 0.001; Fig. 1D). Cell-cell contact analysis (Fig 1C) showed increased homotypic contact for all cell types. We also found a higher frequency of heterotypic contact between Ki-67+CD4+ memory T cells and Ki-67+ B cells. Pairwise analysis showed these findings were statistically significant, indicating these cells are organized in niches rather than randomly distributed across image. Analysis of cellular communities (Fig. 1C) identified 13 niches, named according to the most frequent type of cell-cell contact. All CD4+ memory T cell subsets were found to belong to the same neighborhood (CD4 Memory community). Analysis of the spatial distribution of this community confirmed that these niches were more frequently located inside follicles rather than outside (26.3±4% vs 0.004%, p < 0.001, Fig. 1D). Conclusions Analysis of the TME using CODEX provides insights on the complex composition and unique architecture of this FL case. Cells were organized in a pattern characterized by (1) high degree of homotypic contact and (2) increased heterotypic interaction between activated B cells and activated CD4+ memory T cells. Spatial analysis of both individual cell subsets and cellular neighborhoods demonstrate a statistically significant increase in CD4+ memory T cells inside malignant follicles. This emerging knowledge about the specific immune-architecture of FL adds mechanistic details to our initial observation around the prognostic value of the TME in this disease. These data support future studies using modulation of the TME as a therapeutic target in FL. Figure 1 Disclosures Galkin: BostonGene: Current Employment, Patents & Royalties. Svekolkin:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Postovalova:BostonGene: Current Employment, Current equity holder in private company. Bagaev:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Ovcharov:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Varlamova:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Novak:Celgene/BMS: Research Funding. Witzig:AbbVie: Consultancy; MorphSys: Consultancy; Incyte: Consultancy; Acerta: Research Funding; Karyopharm Therapeutics: Research Funding; Immune Design: Research Funding; Spectrum: Consultancy; Celgene: Consultancy, Research Funding. Nowakowski:Nanostrings: Research Funding; Seattle Genetics: Consultancy; Curis: Consultancy; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other; Kymera: Consultancy; Denovo: Consultancy; Kite: Consultancy; Celgene/BMS: Consultancy, Research Funding; Roche: Consultancy, Research Funding; MorphoSys: Consultancy, Research Funding. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding. Ansell:Trillium: Research Funding; Takeda: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding; AI Therapeutics: Research Funding; ADC Therapeutics: Research Funding.