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Introduction: FL is the most common indolent non-Hodgkin lymphoma in the Western world. FL may cause disease-related symptoms, and patients with high-risk disease usually require systemic therapy. BR is commonly used as first-line therapy for high-risk FL, and the addition of bortezomib to a BR backbone has been studied (Evens A et al. Clin Can Res 2020). Little is known about how these therapeutic options impact patients' health-related quality of life (HRQL). To fill this gap, patient-reported outcomes (PROs) were administered to patients enrolled on E2408 to quantify symptom burden and effects of BR versus BVR induction on HRQL. Methods: Patients (n=258) randomized to receive 6 cycles of BR or BVR induction completed PROs assessing neurotoxicity (FACT/GOG-Ntx) at the beginning of each treatment cycle. Additional PROs measuring fatigue (FACT-F), lymphoma-specific concerns (FACT-Lym) and HRQL (Functional Assessment of Cancer Therapy-General; FACT-G) were completed at baseline, mid-treatment (MT; cycle 3/4) and end of induction (EOI; cycle 6). Paired t-test was used to assess PRO score changes from baseline to MT and EOI within BR or BVR group. Two-sample t-test was used to compare change scores between groups at MT and EOI, respectively. Univariate analyses with a linear model identified patient baseline characteristics and clinical factors (age, sex, stage, performance status, # extra nodal sites, FLIPI, GELF, bone marrow involvement, elevated LDH, palpable splenomegaly, B-symptoms, CRIS) associated with PRO change scores from baseline to EOI, adjusting for treatment group. A multivariate model was built with backward variable selection approach for each of the PROs. Results: As shown in Figure 1, compared with baseline, patients randomized to BVR reported significantly worse FACT/GOG-Ntx scores at cycle 4, which continued to end of induction (FACT/GOG-Ntx change scores -2.91 to -3.73; p < 0.001). Neurotoxicity remained stable for patients treated with BR (Ntx change scores -0.02 to -0.55). FACT-Fatigue scores indicated worse fatigue at MT compared to baseline for patients receiving BVR (-2.7, p<0.05), yet they remained stable for BR. FACT-Lym scores were significantly higher at MT (BR: 2.69, p<0.001; BVR: 2.71, p=0.007) and end of induction (BR: 3.32, p<0.001; BVR: 3.55, p<0.001) compared to baseline, signifying a reduction in lymphoma-related symptoms in both arms. FACT-G scores were comparable between treatment arms at each time-point and did not change significantly from baseline to end of induction. Univariate analyses among all patients identified older age and the absence of palpable splenomegaly at baseline as associated with worse FACT-Lym, FACT-Fatigue, and FACT-G change scores, signifying less improvement in lymphoma-related symptoms, fatigue and HRQL from baseline to end of induction (p<0.01). The presence of B-symptoms at baseline was associated with a greater reduction in lymphoma-related symptoms and fatigue from baseline to end of induction (p<0.01). Higher ECOG performance status was only associated with higher FACT-Fatigue change score, suggesting more improvement in fatigue for those with worse performance status at baseline. A multivariate model generated similar results. Conclusions: Despite worse treatment-related symptoms throughout induction, the addition of bortezomib was associated with comparable overall HRQL to those treated with BR. Both treatments were associated with a reduction in lymphoma-related symptoms from baseline to end of induction, likely contributing to stable HRQL throughout treatment despite treatment-related symptoms. Findings also suggest that a subgroup of patients, particularly those who are older, may experience fewer improvements in lymphoma-related symptoms. This underscores the potential need for closer monitoring and clinical management of these patients. Collectively, results are likely to be encouraging for patients experiencing lymphoma-related symptoms, for whom the symptom burden associated with treatment may be worth the trade-off given the potential for improved disease-related symptom control. Disclosures Evens: Merck: Consultancy, Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria; Pharmacyclics: Consultancy, Honoraria; MorphoSys: Consultancy, Honoraria; Research To Practice: Honoraria, Speakers Bureau; Epizyme: Consultancy, Honoraria, Research Funding; Mylteni: Consultancy, Honoraria; Novartis: Consultancy, Honoraria. Advani:Celgene, Forty Seven, Inc., Genentech/Roche, Janssen Pharmaceutical, Kura, Merck, Millenium, Pharmacyclics, Regeneron, Seattle Genetics: Research Funding; Astra Zeneca, Bayer Healthcare Pharmaceuticals, Cell Medica, Celgene, Genentech/Roche, Gilead, KitePharma, Kyowa, Portola Pharmaceuticals, Sanofi, Seattle Genetics, Takeda: Consultancy. Ansell:Trillium: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; AI Therapeutics: Research Funding; Takeda: Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding; ADC Therapeutics: Research Funding. Winter:Norvartis: Consultancy, Other: DSMB; Ariad/Takeda: Consultancy; CVS/Caremark: Consultancy; Delta Fly Pharma: Consultancy; Amgen: Consultancy; Epizyme: Other: DSMB; Merck: Membership on an entity's Board of Directors or advisory committees, Other: advisory board; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Other: advisory board. Cella:FACIT.org: Membership on an entity's Board of Directors or advisory committees, Other: President; Astellas: Consultancy, Honoraria; Pled Pharma: Research Funding; Janssen: Research Funding; Clovis: Research Funding; Alexion: Research Funding; Apellis: Consultancy; Pfizer: Consultancy, Research Funding; Novartis: Consultancy; Kiniksa: Consultancy; IDDI: Consultancy; BMS: Consultancy, Research Funding; ASAHI KASEI PHARMA CORP.: Consultancy; Oncoquest: Consultancy; Mei Pharma: Consultancy; Ipsen: Consultancy, Research Funding; Evidera: Consultancy; DSI: Consultancy, Research Funding; BlueNote: Consultancy; Abbvie: Consultancy, Research Funding; PROMIS Health Org: Membership on an entity's Board of Directors or advisory committees, Other. Kahl:ADC Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; BeiGene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche Laboratories Inc: Consultancy; Pharmacyclics LLC: Consultancy; Genentech: Consultancy; Celgene Corporation: Consultancy; AstraZeneca Pharmaceuticals LP: Consultancy, Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy; Acerta: Consultancy, Research Funding. Wagner:Celgene Inc.: Membership on an entity's Board of Directors or advisory committees; Connect Multiple Myeloma Registry: Membership on an entity's Board of Directors or advisory committees.

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

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

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

Background: Hodgkin's lymphoma (HL) is the model of curative care with radiation therapy, combination chemotherapy, staging approaches, peripheral blood stem cell transplantation, and immunotherapy. However, the value of the novel anti cancer drugs has been recently analyzed and questioned in view of the results in the real improvement of overall survival (OS). Material and methods: All consecutive patients seeking medical care after 1986 in our institution as a result of HL and followed for at least 3 months were entered in the study. A diagnosis of HL was based on the histological study of a pathology specimen, mainly a lymph node; the same pathologist analyzed all the specimens and defined the histological subtype. Clinical stage was defined according to the Ann Arbor classification. Bone marrow biopsies were done only in patients with clinical stages III or IV. Computed tomography (CT) scans were done in all cases, prior to starting the treatment. Fluorodeoxyglucose positron emission tomography (FDG-PET) scans were performed since 2002. Between 1986 and 1997, patients were treated with MOPP, and after 1997 with ABVD as frontline therapy. For stages I and II, four cycles of chemotherapy were delivered and a computerized tomography (CT) scan was performed; if lymph node enlargement were present at this point in time, four additional cycles were given, whereas two additional cycles were given if the CT scan was negative. For stages III and IV, the CT scans were performed at the end of six cycles and two or four more cycles. Mediastinal radiotherapy was delivered only to persons with a positive FDG-PET scan at the end of the treatment. Patients showing activity after these treatments were considered as refractory and treated with four courses of ICE (ifosfamide, carboplatin and etoposide). Autologous or allogeneic peripheral blood hematopoietic stem cell transplants (HSCT) were given to refractory patients after achieving a complete remission (CR): High-dose melphalan (200mg/m2) was employed in autologous transplants, whereas cyclophosphamide, fludarabine and busulfan were employed in allogeneic transplants, all of them from HLA-identical siblings. After the completion of the treatment, patients were every two months for one year and every four months later on. No FDG-PET scans were done during the follow up, unless clinically indicated. Results: Among 91 patients with HL identified between 1986 and 2020, 88 were followed three months or more and were included in the analysis. There were 37 females and 51 males. The median age was 29years (range5-73years). There were 62 patients with nodular sclerosing HL (70%), 19 mixed cellularity (HL), 2 lymphocyte depleted HL, and one lymphocyte predominant HL; in 4 cases the histologic variant could not be defined. According to Ann Arbor classification, 5 patients were found in stage I, 48 in stage II, 19 in stage III and 16 in stage IV. Ten patients presented with a mediastinal mass larger than 10 cm. in the chest X-ray film. Three cases presented with relapsed disease. Patients have been followed for a median of 114 months (range4-402). 44 patients are alive, 10 have died and 34 were lost to follow-up. Median OS for all the patients has not been reached, being above 402 months, the OS at 310 months is 88% and at 402 months 77%. Median OS has not been reached and is above 94, 109, 90 and 98 months for stages I, II, III and IV, respectively (p=0.2). The 310-month OS was 83% for patients treated with MOPP and 88% for those treated with ABVD (HR:0.76, 95% CI 0.2-2.8; p=0.6).Sixteen patients (18%) were refractory to the treatment and 9 (10%) relapsed; they were treated with ICE followed by HSCT (autologous 15 patients and allogeneic 10 patients). Patients who underwent auto-HSCT had a median survival of 329.1 months and an OS of 92.3%, whereas those given allo-HSCT had a median survival of 59.3 months and an OS of 45.7% (HR0.2, 95%CI 0.04-1.3, p=0.057). The OS of patients given or not HSCT was 73.5% and 92.9% at 266 and 404 months, respectively (HR4.09, 95%CI 1.0-16.6, p=0.01). The OS was similar. The causes of death were breast carcinoma in 2 cases, liver carcinoma in one and uncontrolled lymphoma activity in the remaining. Conclusion: HL may be less aggressive in Mexican population than in Caucasians. Combined chemotherapy renders acceptable results, irrespective of clinical stage. Disclosures No relevant conflicts of interest to declare.

Introduction: Transformation of low grade follicular lymphoma (FL) to diffuse large B cell lymphoma (DLBCL) carries a poor prognosis. In retrospective studies, 5-year survival of transformed DLBCL treated with rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP) without autologous stem cell transplant is 40-60%, underscoring need to improve frontline treatment of transformed DLBCL beyond R-CHOP. The overall response rate (ORR) to lenalidomide used as a single agent for relapsed transformed non Hodgkin lymphoma was 45% with 21% complete response (CR) rate and a median duration of response of 12 months (Witzig et al, Ann Onc 2011). These data provided the rationale to include patients with transformed DLBCL (with historical and concurrent FL) in MC078E, a phase II clinical trial testing lenalidomide plus R-CHOP (R2CHOP) for patients with new and untreated de novo and transformed DLBCL (NCT00670358). Here we present analysis of the subset of transformed DLBCL patients. Methods: Adult patients with transformed DLBCL and either historical or concurrent FL, stage >=2, measurable disease by Positron Emission Tomography/computed tomography (PET/CT) and adequate organ function were included. Patients with Central Nervous System (CNS) involvement, significant comorbidities, active non-lymphomatous malignancy, life-threatening thromboembolism (TE) and contraindication to aspirin prophylaxis were excluded. Study participants received up to 6 cycles of rituximab (375 mg/m2), cyclophosphamide (750 mg/m2), doxorubicin (50 mg/m2), and vincristine (1.4 mg /m2) on day 1, prednisone (100 mg) on day 1-5, pegfilgrastim on day 2, and Lenalidomide 25 mg day 1-10 of 21 day cycle. Tumor lysis prophylaxis was per local practice; patients also received TE prophylaxis with aspirin. Primary outcome was event free survival (EFS) at 12 months, where an event was defined as death, progression or subsequent anti-lymphoma therapy. Secondary outcomes included ORR, CR, progression free survival (PFS), and overall survival (OS). Response was evaluated by PET/CT after cycle 2 and cycle 6 with revised response criteria (Cheson et al, 2007). Adverse events were recorded according to CTCAE version 3.0. The Kaplan-Meier method was used to estimate time to event endpoints. Results: Thirty-nine patients were accrued from August 5, 2013 to July 28, 2020 and 33 were eligible by central pathology review. Median age was 64 (range 24-80) years and 18 (54%) were >60 years old. Eighteen (54%) were male, and 32 (97%) had ECOG performance status <2. Twenty-three (70%) had historical FL and 10 (30%) had concurrent FL. Twenty-six (79%) had advanced stage (III-IV). Median number of extra nodal sites were 1 (0-4). Thirteen (39%) had high international prognostic index (IPI) (4-5). Thirty-two (97%) completed at least 2 cycles (30 completed all 6 cycles) and were evaluable for response. ORR was 97% (32/33), 29 (88%) had CR and 3 had PR. EFS at 12 months was 87.9% (95% CI: 71.8, 96.6). Two-year PFS and OS were 84.5% (95% CI: 72.8%-98%) and 96.9% (95% CI: 91-100%) (Figure 1). Twenty nine completed study protocol, 4 discontinued protocol early for disease progression (1), adverse event (AE) (1), refusal (1) and noncompliance (1). Thirty (91%) had hematologic AE of grade 3 or above, 27 (82%) had neutropenia, 16 (48%) had thrombocytopenia, and 7 (21%) had anemia. Sixteen (48%) had grade 3 or above non-hematologic AE. Eight (24%) had febrile neutropenia. There were 3 deaths on this study, 1 due to progressive DLBCL, 1 due to AML and 1 due to malignant melanoma. Conclusion: R2CHOP appears effective in transformed DLBCL with high response rates, event free, progression free and overall survival seen in current study. This study supports the inclusion of anthracycline naive patients with transformed DLBCL in future randomized studies of lenalidomide or other novel immunomodulatory (IMiD) analogues. Disclosures Wang: Novartis: Research Funding; Innocare: Research Funding; Incyte: Research Funding. Ansell:Bristol Myers Squibb: Research Funding; Takeda: Research Funding; AI Therapeutics: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; ADC Therapeutics: Research Funding. Witzig:Spectrum: Consultancy; Immune Design: Research Funding; Karyopharm Therapeutics: Research Funding; Acerta: Research Funding; Incyte: Consultancy; AbbVie: Consultancy; MorphSys: Consultancy; Celgene: Consultancy, Research Funding. Nowakowski:Celgene/BMS: Consultancy, Research Funding; MorphoSys: Consultancy, Research Funding; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy; Kymera: Consultancy; Curis: Consultancy; Seattle Genetics: Consultancy; NanoString: Research Funding.

Background: Patients with Waldenström Macroglobulinemia (WM) tend to present at an older age. Frailty, resulting from comorbidities and reduced organ function, poses unique challenges to the management of WM in the elderly. Age above 65 years, is an established unfavorable prognostic factor in WM. However, data specifically pertaining to the very elderly patient population are scant. The impact of advanced age (≥75 years at diagnosis) on the clinical features and outcomes of patients with symptomatic/active WM were studied. Methods: Patients with active WM, evaluated at Mayo Clinic, Rochester between January 1996 and December 2018 were included in the study. The characteristics and laboratory parameters at presentation of the cohort of patients with the age ≥75 years were compared to those of the younger (age <75 years) cohort. The median follow-up and overall survival (OS) were assessed from active disease to last follow-up or death using the Kaplan Meier method. Because older age is a confounding factor for deaths from unrelated causes, the cause-specific survival (CSS) was calculated. Deaths were deemed WM-related if patients died from progressive disease, transformation with antecedent WM, concomitant light chain (AL) amyloidosis, infection associated with WM or anti-WM therapy related toxicity, while patients who died from other causes were considered to have a WM-unrelated death. The patients who died from causes unrelated to WM were censored at death for CSS analysis. The OS of the cohort of the very elderly patients was compared to that of a cohort of the general US population, matched for age, sex as well as follow-up from the calendar-year of diagnosis, to assess the degree to which WM impacts survival. Results: Among 949 consecutive patients, with a median follow-up of 8.4 years [95% Confidence Interval (CI): 7.7-9.3 years] from active disease, the median age at diagnosis was 65 years (range: 31-93 years). Of 177 patients (19%) who were ≥75 years of age at the onset of active WM, 173 (98%) received anti-WM therapy. The baseline characteristics of the 2 cohorts (patients ≥75 years of age versus <75 years) are outlined in Table 1. The patients in the age ≥75 years cohort were noted to have lower IgM levels and serum albumin, and higher serum LDH and serum β-2 microglobulin compared to the age <75 years cohort. A higher proportion of patients in the age <75 years cohort had coexisting AL amyloidosis at diagnosis. The frequency of MYD88L265P mutation was similar between the 2 cohorts (p=0.2). During the 23-year period, there was an increase in the proportion of patients ≥75 years who were diagnosed with active WM (12% during the 1996-2003 period, 18% during 2004-2010, 25% during 2011-2018; P<0.0001). In the elderly WM cohort, the most frequently used frontline treatment was rituximab monotherapy (36%), followed by rituximab-alkylating agent-based regimen (31%). Of 177 patients with age ≥75 years, 89 (50%) patients were deceased at the time of last follow-up and 58% (52/89) of all deaths in age ≥75 years were attributable to WM. By contrast, 303 (39%) out of the 772 patients in the age <75 years cohort were deceased at last follow-up, with 73% (224/303) deaths attributable to WM. The CSS for patients with age ≥75 years was 10.9 years (95% CI: 7.3-11.7 years) versus 14.7 years (95% CI: 13.2-15.7 years) for age <75 years [Risk ratio 2.1(95% CI: 1.5-2.8); p<0.0001]. At the 1 and 2 year follow-up, WM-related mortality was 6.8% and 11.9%, respectively, for patients with age ≥75 years versus 2.6% and 4.9%, respectively, for the younger cohort. The median OS of patients with age ≥75 years from the diagnosis of active WM was 5.9 years (95% CI: 4.2-7.3 years) compared to the OS of 8.3 years for an age, sex and calendar-year-matched cohort, derived from the general US population (p<0.001), Figure 1. Conclusion: Patients who are ≥75 years of age at the time of diagnosis of active WM present with certain poor prognostic features, including higher serum LDH and β-2 microglobulin and lower serum albumin compared to their younger counterparts. The impact of advanced age is underscored by a poorer WM-specific survival for the subset of patients with the age ≥75 years compared to the younger cohort. Furthermore, although active/symptomatic WM is typically an indolent malignancy, it adversely affects OS of the very elderly patients as suggested by the comparison with an age, sex and calendar-year matched cohort of the general US population. Disclosures Ansell: Bristol Myers Squibb: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; ADC Therapeutics: Research Funding; Seattle Genetics: Research Funding; AI Therapeutics: Research Funding; Takeda: Research Funding. Gertz:Ionis/Akcea: Other: personal fee; Alnylam: Other: personal fee; Prothena: Other: personal fee; Spectrum: Other: personal fee, Research Funding; Annexon: Other: personal fee; Appellis: Other: personal fee; Amgen: Other: personal fee; Medscape: Other: personal fee, Speakers Bureau; Physicians Education Resource: Other: personal fee; Abbvie: Other; Celgene: Other; Research to Practice: Other; Sanofi: Other; Teva: Speakers Bureau; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Proclara: Other; Springer Publishing: Patents & Royalties; Aurora Bio: Other; Janssen: Other: personal fee. 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. Witzig:Spectrum: Consultancy; Immune Design: Research Funding; Karyopharm Therapeutics: Research Funding; Acerta: Research Funding; Incyte: Consultancy; MorphSys: Consultancy; AbbVie: Consultancy; Celgene: Consultancy, Research Funding. Dispenzieri:Pfizer: Research Funding; Janssen: Research Funding; Intellia: Research Funding; Alnylam: Research Funding; Takeda: Research Funding; Celgene: Research Funding. Dingli:Sanofi-Genzyme: Consultancy; Janssen: Consultancy; Bristol Myers Squibb: Research Funding; Rigel: Consultancy; Apellis: Consultancy; Karyopharm Therapeutics: Research Funding; Alexion: Consultancy; Millenium: Consultancy. Kumar:Sanofi: Research Funding; Kite Pharma: Consultancy, Research Funding; Cellectar: Other; Genecentrix: Consultancy; Novartis: Research Funding; Karyopharm: Consultancy; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Carsgen: Other, Research Funding; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Merck: Consultancy, Research Funding; MedImmune: Research Funding; Adaptive Biotechnologies: Consultancy; Tenebio: Other, Research Funding; Dr. Reddy's Laboratories: Honoraria; BMS: Consultancy, Research Funding; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments. Kapoor:Cellectar: Consultancy; Janssen: Research Funding; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; GlaxoSmithKline: Research Funding; Celgene: Honoraria.

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.

Background and Aims Platelet‐stored serotonin critically affects liver regeneration in mice and humans. Selective serotonin reuptake inhibitors (SSRIs) and serotonin noradrenalin reuptake inhibitors (SNRIs) reduce intraplatelet serotonin. As SSRIs/SNRIs are now one of the most commonly prescribed drugs in the United States and Europe and given serotonin’s impact on liver regeneration, we evaluated whether perioperative use of SSRIs/SNRIs affects outcome after hepatic resection. Approach and Results Consecutive patients undergoing hepatic resection (n = 754) were retrospectively included from prospectively maintained databases from two European institutions. Further, an independent cohort of 495 patients from the United States was assessed to validate our exploratory findings. Perioperative intake of SSRIs/SNRIs was recorded, and patients were followed up for postoperative liver dysfunction (LD), morbidity, and mortality. Perioperative intraplatelet serotonin levels were significantly decreased in patients receiving SSRI/SNRI treatment. Patients treated with SSRIs/SNRIs showed a higher incidence of morbidity, severe morbidity, LD, and LD requiring intervention. Associations were confirmed in the independent validation cohort. Combined cohorts documented a significant increase in deleterious postoperative outcome (morbidity odds ratio [OR], 1.56; 95% confidence interval [CI], 1.07‐2.31; severe morbidity OR, 1.86; 95% CI, 1.22‐2.79; LD OR, 1.96; 95% CI, 1.23‐3.06; LD requiring intervention OR, 2.22; 95% CI, 1.03‐4.36). Further, multivariable analysis confirmed the independent association of SSRIs/SNRIs with postoperative LD, which was closely associated with postoperative 90‐day mortality and 1‐year overall survival. Conclusions We observed a significant association of perioperative SSRI/SNRI intake with adverse postoperative outcome after hepatic resection. This indicates that SSRIs/SNRIs should be avoided perioperatively in patients undergoing hepatic resections.