Citations

AbstractNeurolymphomatosis (NL) is a rare manifestation of lymphoma, with limited evidence for optimal management. The largest patient series, 50 cases of lymphoma and leukemia, was published in 2010 with limited rituximab exposure. This study aims to evaluate the clinical presentation, diagnostic testing, and outcomes of NL in the rituximab era. Forty biopsy-proven cases of NL, in association with non-Hodgkin lymphoma (NHL), at the Mayo Clinic were retrospectively evaluated. B-cell NHL was associated with 97% of NL cases, of which diffuse large B-cell lymphoma (DLBCL) was the most common (68%). Primary NL, defined as neural involvement present at the time of diagnosis of lymphoma, was noted in 52% cases. Seventy percent of patients presented with sensorimotor weakness and neuropathic pain. Magnetic resonance imaging (MRI) was positive in 100% patients. Overall survival (OS) was significantly better for primary NL and NL associated with indolent lymphomas. Relapses were seen in 60% (24/40) of patients; 75% involved the peripheral or central nervous system at relapse. The use of rituximab in the frontline setting significantly impacted progression-free survival (PFS). Transplant consolidation was noted to be associated with improved OS. This study adds to the available literature on NL in the rituximab era. The overall outcomes have improved in recent years. In our experience, MRI and positron emission tomography/computed tomography may be required for accurate assessment of the extent of disease involvement and identification of an optimal biopsy site. The use of rituximab was associated with improvement in PFS, and autologous stem cell transplant was associated with OS.

Abstract Disease progression after frontline therapy for Diffuse large B‐cell lymphoma (DLBCL) is a clinically significant event. Patients who experience early progression or have refractory disease have especially poor outcomes. Simple, clinically applicable prognostic tools are needed for selecting patients for consideration for novel therapies and prognostication in the relapsed/refractory (R/R) setting. Model building was performed in patients from the Surrogate endpoints in aggressive lymphoma (SEAL) consortium with disease progression after frontline immunochemotherapy. The primary endpoint was overall survival (OS) measured from date of progression. Validation was performed in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER) and Danish National Lymphoma Register (LYFO) cohorts. Model performance was assessed using time‐dependent concordance indices (c‐statistic) and calibration with metrics evaluated at 2 years from progression. Note, 1234 of 5112 patients treated with frontline immunochemotherapy in the SEAL consortium developed progressive disease. Time to progression on immunochemotherapy and age at progression were strongly associated with post‐progression OS (both p < 0.001). A prognostic model was developed incorporating spline fit for both variables. The model had good concordance in the discovery (0.67) and validation sets (LYFO c = 0.64, MER c = 0.68) with generally good calibration. Time to progression on frontline therapy is strongly associated with post‐progression OS in DLBCL. We developed and validated a simple to apply clinical prognostic tool in the R/R setting. The useful prediction of expected outcomes in R/R DLBCL and can inform treatment decisions such as considerations for CAR‐T therapy as well as trial designs. The model is available in smartphone‐based point of care applications.

AbstractNon‐steroidal anti‐inflammatory drugs (NSAIDs) and statin drugs may protect against the development of non‐Hodgkin lymphoma (NHL), but data are limited, particularly for NHL subtypes. Furthermore, some in vitro, animal and epidemiologic data suggest there may be a synergistic effect of these two agents, but there has been no test of this hypothesis in NHL. We evaluated the self‐reported use of NSAIDs and statins in a clinic‐based study of 1703 NHL patients and 2199 frequency‐matched controls. Unconditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs), adjusted for potential confounding variables. We observed an inverse association of regular use of low‐dose aspirin with risk of NHL (OR = 0.82; 95% CI 0.70‐0.96) that was stronger with longer duration of use (P < .01). There were no associations for use of regular or extra‐strength aspirin, ibuprofen, other NSAIDs, statins or other cholesterol‐lowering drugs with NHL risk, while an inverse association with COX‐2 inhibitors was equivocal. There was also no interaction of low‐dose aspirin and statins on NHL risk. Inverse associations of similar magnitude to all NHL were observed for regular use of low‐dose aspirin with diffuse large B‐cell, follicular, marginal zone and all other lymphomas, although not all associations were statistically significant. In conclusion, low‐dose aspirin but not regular/extra strength aspirin, other NSAIDs or statin use was associated with lower risk of NHL. Beyond the potential for the primary prevention of NHL, these data also point to a role of anti‐platelet or other effects of low‐dose aspirin in lymphomagenesis that warrant follow‐up.

PURPOSE Lenalidomide combined with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) (R2CHOP) in untreated diffuse large B-cell lymphoma (DLBCL) has shown promising activity, particularly in the activated B-cell–like (ABC) subtype. Eastern Cooperative Oncology Group (ECOG)-ACRIN trial E1412 was a randomized phase II study comparing R2CHOP versus R-CHOP in untreated DLBCL. PATIENTS AND METHODS Patients with newly diagnosed DLBCL, stage II bulky-IV disease, International Prognostic Index (IPI) ≥ 2, and ECOG performance status ≤ 2 were eligible and randomly assigned 1:1 to R2CHOP versus R-CHOP for six cycles. Tumors were analyzed using the NanoString Lymph2Cx for cell of origin. The primary end point was progression-free survival (PFS) in all patients with the co-primary end point of PFS in ABC-DLBCL. Secondary end points included overall response rate (ORR), complete response (CR) rate, and overall survival (OS). RESULTS Three hundred forty-nine patients were enrolled; 280 patients (145 R2CHOP and 135 R-CHOP) were evaluable: 94 were ABC-DLBCL, 122 germinal center B-cell–like-DLBCL, 18 unclassifiable, and 46 unknowns. Baseline characteristics were well-balanced between arms, and the median age was 66 (range, 24-92); 70% of patients had stage IV disease; 34%, 43%, and 24% had IPI 2, 3, and 4 or 5, respectively. Myelosuppression was more common in the R2CHOP arm. The ORR and CR rate were 92% and 68% in R-CHOP and 97% ( P = .06) and 73% ( P = .43) in the R2CHOP arm, respectively. The median follow-up was 3.0 years; R2CHOP was associated with a 34% reduction in risk of progression or death versus R-CHOP (hazard ratio [HR], 0.66 95% CI, 0.43 to 1.01) and 3-year PFS of 73% versus 61%, one-sided P = .03, and an improvement in OS (83% and 75% at 3 years; HR, 0.67; one-sided P = .05). The PFS HR for R2CHOP was 0.67 for ABC-DLBCL, one-sided P = .1. CONCLUSION In this signal-seeking study, the addition of lenalidomide to R-CHOP (R2CHOP) improved outcomes in newly diagnosed DLBCL including patients with ABC-DLBCL.

Introduction: Richter transformation (RT) refers to transformation of chronic lymphocytic leukemia (CLL) to diffuse large B-cell lymphoma (DLBCL) or other aggressive lymphomas. The incidence, clinical characteristics, and outcomes of RT with CNS involvement remain undefined. Methods: Biopsy-confirmed RT diagnosed from 4/1993 to 4/2018 was identified from the Mayo Clinic CLL database. RT cases with CNS involvement were further reviewed, with clinical characteristics, treatment and follow-up data abstracted. Overall survival (OS) was defined as time from CNS involvement to death from any cause and analyzed using the Kaplan-Meier method. Results: Among 204 CLL patients with biopsy-proven RT, 15 patients had documented CNS involvement by imaging (parenchymal n=11, ocular n=1, parenchymal and ocular n=1, leptomeningeal n=1, cranial and spinal nerve roots n=1). Biopsy confirmation of CNS involvement was available in 11 patients. Of these 15 patients, 10 had CNS involvement at initial RT diagnosis, and 5 patients developed CNS involvement at RT progression, with a median time from RT diagnosis to CNS involvement of 14.9 months (range 2.8-41.1). Prior to RT, 11 patients (73.3%) received no treatment for CLL, 3 (53.4%) received chemoimmunotherapy (CIT) only (1-3 lines), and 1 patient (6.7%) received CIT (1 line) and ibrutinib. CLL FISH results were available in 9 patients, with del(17p) in 2, trisomy 12 in 2, del(13q) in 3, and normal results in 2. One patient had TP53 mutation (FISH result unavailable). IgHV mutation status was known in 3 patients and 1 was unmutated. The median time to RT was 4.4 years (range 0-11.4). The median age at RT diagnosis was 70 years (range 59-78), and 9 (60%) were male. The histology of RT was DLBCL in all 15 patients (diagnosed by CNS biopsy in 8 and other biopsies in 7), with GCB subtype in 6 and non-GCB subtype in 3. MYC rearrangement was detected in 4 of 6 patients tested, and 2 of those also had BCL-2 rearrangement (MYC/BCL-2 double hit). Of the 10 patients with CNS involvement at initial RT diagnosis, 4 had isolated CNS involvement (confirmed by brain biopsy [n=2] or vitreous biopsy [n=2]). All 4 patients were treated with high dose (HD) methotrexate (MTX) based regimen, and 3 patients achieved complete remission (CR) and underwent autologous stem cell transplant with continued CR, while 1 patient achieved partial remission and received whole brain radiotherapy (WBRT) at disease progression. Six patients had both systemic and CNS involvement (confirmed by brain biopsy [n=2] or CSF examination [n=2] in 4; the other 2 were diagnosed by imaging). All 6 patients received CNS directed therapy (HD MTX [n=5] or WBRT [n=1]) as well as systemic therapy (R-CHOP [n=5] or HyperCVAD [n=1]), with varied short duration of disease control (Figure 1A). Of the 5 patients who developed CNS involvement at RT progression (after 1-2 lines of therapy including first-line R-CHOP), 2 had isolated CNS involvement (1 was confirmed by CSF examination and the other was by imaging only). Both were treated with HD MTX based regimen, and 1 achieved a durable remission of 2 years but eventually had CNS relapse, while the other had no response and received WBRT with limited benefit. Three patients had both systemic and CNS involvement (2 were confirmed by CSF examination and the other was by imaging only), and all 3 patients were treated with HD MTX based regimen but all had CNS disease progression within 3 months (Figure 1A). The OS after CNS involvement for all 15 RT patients was 9.4 months (95% CI 2.3-19.1). Patients with CNS involvement at initial RT diagnosis had numerically longer OS compared to patients who developed CNS involvement at RT progression (median OS 13.0 vs 4.1 months, P = 0.137; Figure 1B). Patients with isolated CNS involvement had better OS compared to patients with both CNS and systemic involvement (median OS 32.0 vs 5.3 months, P = 0.009; Figure 1C). Three of the 4 patients who only had isolated CNS involvement at the time of RT diagnosis have ongoing long term remission and survival (Figure 1A). Conclusions: In this large series of RT, approximately 1 in 14 patients had CNS involvement. Patients with isolated CNS involvement appeared to have favorable long term outcome; however, RT patients with both systemic and CNS involvement had extremely poor survival. The incidence, molecular characteristics, and optimal management of RT with CNS involvement in the novel agent era warrant future multicenter studies. Disclosures Wang: Incyte: Research Funding; Innocare: Research Funding; Novartis: Research Funding. Parikh:GlaxoSmithKline: Honoraria; Pharmacyclics: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; MorphoSys: Research Funding; TG Therapeutics: Research Funding; Merck: Research Funding; AbbVie: Honoraria, Research Funding; Ascentage Pharma: Research Funding; Genentech: Honoraria; Verastem Oncology: Honoraria. Kay:Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Research Funding; MEI Pharma: Research Funding; Sunesis: Research Funding; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees. Witzig:AbbVie: Consultancy; MorphSys: Consultancy; Immune Design: Research Funding; Karyopharm Therapeutics: Research Funding; Incyte: Consultancy; Spectrum: Consultancy; Celgene: Consultancy, Research Funding; Acerta: Research Funding. Nowakowski:MorphoSys: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Celgene/BMS: Consultancy, Research Funding; Kite: Consultancy; Denovo: Consultancy; Kymera: Consultancy; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other; Curis: Consultancy; Seattle Genetics: Consultancy; Nanostrings: Research Funding. Ansell:Trillium: Research Funding; AI Therapeutics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Takeda: Research Funding; ADC Therapeutics: Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding. Kenderian:MorphoSys: Research Funding; Sunesis: Research Funding; Tolero: Research Funding; BMS: Research Funding; Juno: Research Funding; Gilead: Research Funding; Kite: Research Funding; Novartis: Patents & Royalties, Research Funding; Torque: Consultancy; Humanigen: Consultancy, Patents & Royalties, Research Funding; Lentigen: Research Funding; Mettaforge: Patents & Royalties. Ding:DTRM: Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Membership on an entity's Board of Directors or advisory committees; alexion: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Membership on an entity's Board of Directors or advisory committees; Octapharma: Membership on an entity's Board of Directors or advisory committees; Astra Zeneca: Research Funding; Abbvie: Research Funding.

Background: Obesity is increasing worldwide, with the highest prevalence in the United States. High or low body mass index (BMI) is a well-established risk factor for increased all-cause mortality and also has been associated with cancer-specific mortality. However, the impact of BMI on survival following diagnosis with lymphoma currently remains controversial. We leveraged a prospective cohort of lymphoma patients to assess the relationship of BMI two years prior to diagnosis (BMI-2), at diagnosis (BMI-dx), and three-years post-diagnosis (BMI+3) with lymphoma-specific survival (LSS) as the primary endpoint and with event-free survival (EFS) and overall survival (OS) as secondary endpoints. Patient and Method: Patients were prospectively enrolled at lymphoma diagnosis to the SPORE Molecular Epidemiology Resource (MER) cohort at Mayo Clinic and University of Iowa from 2002-2015. BMI-2 and BMI+3 were self-reported in patient questionnaires, while BMI-dx was extracted from the medical chart. Patients with extreme BMI (BMI &lt;14 and BMI ≥50) were excluded from the analysis. BMI change from BMI-2 to BMI-dx and from BMI-dx to BMI+3 was categorized as no change (-5% to 5%), decrease (&gt;-5%), and increase (&gt;+5%). Person-time at risk was assessed from lymphoma diagnosis until death or last follow-up, except for analyses of BMI change from BMI-dx to BMI+3, which started person-time at risk when the 3-year (+/- 6 months) follow-up questionnaire was returned. Cause of death was assigned by a study clinician. For all lymphoma patients combined and in the most common subtypes, we evaluated the association of BMI at each time point and change in BMI with EFS, LSS, and OS using hazard ratios (HRs) and 95% confidence intervals (CI) from multivariable adjusted Cox models. Results: A total of 4,009 lymphoma patients (including 670 diffuse large B-cell lymphoma [DLBCL], 689 follicular lymphoma [FL] and 1018 chronic lymphocytic leukemia/small lymphocytic lymphoma [CLL/SLL] and 1,632 others) with data on BMI-dx were included. Among them, 2,955 patients had BMI-2 and 2,004 had BMI+3 and were evaluable for change in BMI. The median age of all patients at diagnosis was 61 years (range 18-92 years), and 94% of patients had ECOG performance status &lt;2. At the time of diagnosis, 28% were normal weight (BMI 18.5-25), 1% were underweight (BMI &lt;18.5), 39% were overweight (BMI 25-30) and 32% were obese (BMI ≥30). With a median follow-up of 108 months from diagnosis (IQR 83-143 months), 1320 deaths were observed, 48% of which were due to lymphoma. Patients with FL who were obese at BMI-2 had significantly shorter LSS (HR: 3.02, 95%CI: 1.43-6.41, p=0.004). Associations between obesity at BMI-2 and LSS were not evident for DLBCL (HR: 1.04, 95%CI: 0.62-1.76, p=0.879) or CLL/SLL (HR: 1.10, 95%CI: 0.71-1.70, p=0.668) (Table). BMI-dx was not associated with LSS in any lymphoma patients, except that DLBCL patients who were underweight at BMI-dx (n=10) experienced shorter LSS (HR: 3.52, 95%CI: 1.22-10.1, p=0.020). This correlated significantly with presence of B symptoms (p=0.004) and may signify aggressive disease. Across all subtypes, &gt;5% decrease in BMI from BMI-2 to BMI-dx was associated with significantly shorter LSS in patients with (HR: 2.02, 95%CI: 1.65-2.48, p&lt;0.001). However, only for FL patients, &gt;5% increase in BMI from BMI-dx to BMI+3 also was associated with significantly shorter LSS in subsequent years (HR: 3.74, 95%CI: 1.30-10.8, p=0.014). The associations reported for LSS generally were similar for EFS and OS. Conclusions: FL patients with obesity prior to diagnosis or who experienced increasing BMI after the diagnosis had significantly shorter LSS. The impact of weight control after the diagnosis of FL patient outcomes warrants investigation. Figure Disclosures Maurer: Celgene / BMS: Research Funding; Kite: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees. Flowers:Leukemia and Lymphoma Society: Membership on an entity's Board of Directors or advisory committees; Denovo Biopharma: Consultancy; Celgene: Consultancy, Research Funding; BeiGene: Consultancy; Kite: Research Funding; Bayer: Consultancy; Eastern Cooperative Oncology Group: Research Funding; Cancer Prevention and Research Institute of Texas: Research Funding; National Cancer Institute: Research Funding; AbbVie: Consultancy, Research Funding; V Foundation: Research Funding; TG Therapeutics: Research Funding; Burroughs Wellcome Fund: Research Funding; Millennium/Takeda: Consultancy, Research Funding; Acerta: Research Funding; Spectrum: Consultancy; Pharmacyclics/Janssen: Consultancy; Karyopharm: Consultancy; OptumRx: Consultancy; Gilead: Consultancy, Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding. Cerhan:NanoString: Research Funding; BMS/Celgene: Research Funding.

Introduction: Lymphoma is the most common cancer among adolescents and young adults (AYAs). We examined changes in health-related quality of life (HRQoL) and its predictors in AYA patients (pts). Patients and Methods: We identified AYA pts (aged 18-39) enrolled 2002-2015 in a prospective cohort of pts with newly diagnosed lymphoma from the University of Iowa and Mayo Clinic Molecular Epidemiology Resource, part of the Lymphoma Epidemiology of Outcomes cohort. Enrollment could occur prior to or after initiation of treatment. We measured HRQoL using the Functional Assessment of Cancer Therapy-General (FACT-G) questionnaire at baseline, 12, and 24 months. FACT-G yields five HRQoL domain scores: emotional well-being (EWB), functional WB (FWB), physical WB (PWB), social/family WB (SFWB), and total FACT-G score (a sum of the domains). Pts completing &lt;80% of the FACT-G questions were excluded. Linear mixed models with random subject intercepts estimated changes in FACT-G scores from baseline. The covariates in multivariate analysis were lymphoma subtype, stage, and treatment. Interaction effects between treatment (chemotherapy and/or radiation) and subtype were added to the model. We calculated effect sizes (ES) for the magnitude of mean change scores: 0.2, 0.5, and 0.8 were considered small, medium, and large ESs, respectively. Only ESs for mean score differences with p&lt;0.05 are reported. Results: We identified 467 pts; median age at diagnosis was 30 years, median follow up was 5.9 years. 53% of pts completed the baseline FACT-G assessment pre-treatment, 47% completed after treatment began. Pts assessed after treatment initiation had lower baseline total FACT-G (ES -0.25), FWB (ES -0.27), and PWB scores (-0.46); but baseline EWB was higher in pts assessed prior to treatment (ES 0.20). There was no association between HRQoL scores at baseline or over time and lymphoma subtype, stage, or treatment type, or interactions. Total FACT-G scores modestly improved over time, ES 0.32 at 1 year and ES 0.45 at 2 years after enrollment. EWB, FWB, and PWB also improved over time (ES 0.36, 0.44, 0.30 at 1 year; and 0.49, 0.56, 0.38 at 2 years, respectively). SFWB scores slightly worsened over time (ES -0.24 at 1 year and -0.12 at 2 years). Conclusions: AYA pts with lymphoma had higher baseline total FACT-G scores, FWB and PWB prior to therapy initiation compared to after initiation. HRQoL improved from diagnosis through the first 2 years after diagnosis, except for SFWB. Neither stage, lymphoma subtype, nor treatment type affected change in HRQoL. The lack of improvement in SFWB suggests social interventions and future studies should examine factors impacting SFWB in AYA pts. Disclosures Cohen: Genentech, BMS, Novartis, LAM, BioInvent, LRF, ASH, Astra Zeneca, Seattle Genetics: Research Funding; Janssen, Adicet, Astra Zeneca, Genentech, Aptitude Health, Cellectar, Kite/Gilead, Loxo: Consultancy. Flowers:Leukemia and Lymphoma Society: Membership on an entity's Board of Directors or advisory committees; National Cancer Institute: Research Funding; AbbVie: Consultancy, Research Funding; Cancer Prevention and Research Institute of Texas: Research Funding; Eastern Cooperative Oncology Group: Research Funding; Burroughs Wellcome Fund: Research Funding; Kite: Research Funding; V Foundation: Research Funding; TG Therapeutics: Research Funding; Millennium/Takeda: Consultancy, Research Funding; Acerta: Research Funding; Spectrum: Consultancy; Pharmacyclics/Janssen: Consultancy; Karyopharm: Consultancy; OptumRx: Consultancy; Gilead: Consultancy, Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Denovo Biopharma: Consultancy; Celgene: Consultancy, Research Funding; BeiGene: Consultancy; Bayer: Consultancy. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding.

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

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