Habermann, Thomas M.

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.

Abstract Background Factors associated with chronic immune stimulation such as transplantation, autoimmune disorders and chronic infections have been linked to the development of lymphoma, while atopic conditions have been associated with a lower risk of lymphoma. Vaccinations, in part due to their ability to induce immune responses, have been hypothesized to play a role in lymphoma etiology, but there are few studies with mixed results and limited data on lymphoma subtypes. Methods We used a clinic-based study of 2461 newly diagnosed lymphoma cases and 2253 frequency-matched controls enrolled from 2002-2015. Participants self-reported lymphoma risk factors and history of vaccinations for hepatitis A, hepatitis B, chicken pox, yellow fever and influenza. Pathology was centrally reviewed, and included 644 chronic lymphocytic leukemia, 528 follicular (FL), 414 diffuse large B-cell (DLBCL), 170 marginal zone, 127 mantle cell, 119 T-cell, 181 Hodgkin, and 278 other lymphoma subtypes. We used polytomous logistic regression to estimate odds ratio (OR) and 95% confidence intervals (CI) of vaccinations with lymphoma risk overall and for subtypes, adjusting for age, sex, socioeconomic status, body mass, alcohol use, smoking history, recreational sun exposure, and family history of hematological malignancies. Results The median age at diagnosis for lymphoma cases was 64 years (range: 18-93) and 58% were male; the median age at enrollment for controls was 62 years (range: 18-91) and 53% were male. The most common vaccinations among controls were influenza (90%) followed by hepatitis B (41%), hepatitis A (30%), chicken pox (20%), and yellow fever (11%). Vaccination for influenza was inversely associated with lymphoma risk overall (OR=0.75, CI:0.61-0.93, P=0.01), with an inverse trend for increasing number of vaccinations (P=0.0001). In subtype analyses, Influenza vaccination was inversely associated with risk of FL (OR=0.59, CI:0.43-0.80, P=0.001), DLBCL (OR=0.64, CI:0.44-0.92, P=0.02) and other lymphoma subtypes (OR=0.66, CI:0.43-1.00, P=0.05). Vaccination for hepatitis A was inversely associated with lymphoma risk (OR=0.79, CI:0.65-0.96, P=0.02), with no significant subtype-specific associations. No evidence of association with vaccination for hepatitis B (OR=0.95, CI:0.79-1.14) and chicken pox (OR=0.83, CI:0.68-1.02) for lymphoma risk overall or for any subtypes. Although no evidence of an association with vaccination for yellow fever (OR=0.81, CI:0.62-1.06) for lymphoma risk, it was inversely associated with DLBCL risk (OR=0.42, CI:0.22-0.80). Conclusion Vaccination for influenza and hepatitis A were inversely associated with overall lymphoma risk. DLBCL was associated with vaccination for influenza and yellow fever. FL was associated with vaccination for influenza. Understanding the underlying mechanisms for a putative protective effect of certain vaccines could provide new clues to the etiology and prevention of lymphoma. Citation Format: Geffen Kleinstern, Dennis P. Robinson, Carrie A. Thompson, Melissa C. Larson, Cristine Allmer, Grzegorz S. Nowakowski, Timothy G. Call, Stephen M. Ansell, Andrew L. Feldman, Neil E. Kay, Thomas M. Habermann, Susan L. Slager, James R. Cerhan. Vaccination history and risk of lymphoma and its major subtypes [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1054.

8556 Background: In the original PARMA trial it was demonstrated that salvage chemotherapy with DHAP followed by autologous bone marrow transplant resulted in increased overall survival over salvage chemotherapy with DHAP alone in patients with aggressive lymphomas. The current study was designed to assess safety and feasibility of ROAD as a salvage chemotherapy regimen which could be administered as an inpatient or outpatient. Methods: Patients received immunochemotherapy on the following schedule: rituximab 375 mg/m2 weekly × 4, oxaliplatin 130 mg/m2 on day 2, Ara C 2000mg/m2 x 2 doses on day 2 and dexamethasone 40 mg on days 2–5, with OAD repeated at 3 week intervals (up to 6 cycles). Patients were considered for autologous stem cell transplantation after 2 cycles if eligible. Eligible histologies included diffuse large B cell lymphoma, mantle cell lymphoma and transformed lymphoma in first relapse. Results: 50 patients were accrued from Aug 2006 through Jul 2008: 5 patients were deemed ineligible after central pathology review. Baseline characteristics of eligible patients included median age 69 (range 23 - 77), 53% were male, 53% had advanced stage at relapse, LDH was elevated in 58% and all patients had an ECOG PS of 2 or less. Patients received a median of 2 cycles of therapy (range 1–6) with 39/45 receiving treatment in cycle 2, with 12 patients continuing beyond 2 cycles. 31 patients experienced grade III/IV hematologic toxicity and 22 patients had grade III/IV non-hematologic toxicity, primarily febrile neutropenia. One patient developed grade III nephrotoxicity due to disease progression. Twenty patients received their treatments exclusively as outpatients. 26 responses were seen in the 45 eligible patients (58%, 95% CI: 44–74%), with 20 responding patients proceeding to autologous SCT. Conclusions: ROAD is a safe and effective salvage chemotherapy regimen for relapsed aggressive lymphoma, including as a preparatory regimen prior to stem cell transplant. It appears to have similar response rates to R-DHAP in a similar patient population, but without the potential nephrotoxicity (data from prior published study from NCCTG). ROAD can be safely administered as an inpatient or outpatient. No significant financial relationships to disclose.