Nowakowski, Grzegorz S.

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: Relapse is expected when treating patients with follicular lymphoma (FL) and marginal zone lymphoma (MZL), with a shortened response associated with each relapse. Lenalidomide combined with rituximab (R2) has shown enhanced activity, with a recently reported median progression free-survival (PFS) of 39.4 months in patients with relapsed/refractory (R/R) indolent non-Hodgkin lymphoma, including those with FL and MZL (AUGMENT; J Clin Oncol. 2019;37:1188). Methods: MAGNIFY is a multicenter, phase IIIb trial in patients with R/R FL grades 1-3b, transformed FL (tFL), MZL, or mantle cell lymphoma (MCL; NCT01996865) in which optimal lenalidomide duration is being explored. Patients received 12 cycles of R2 (lenalidomide 20 mg/d, d1-21/28 + rituximab 375 mg/m2 weekly in cycle 1 and then on day 1 of cycles 3, 5, 7, 9, and 11) followed by 1:1 randomization in patients with stable disease, partial response, or complete response/complete response unconfirmed (CR/CRu) to R2 versus rituximab maintenance for 18 months. Data presented here focus on induction R2 in efficacy-evaluable MZL patients compared with the overall population of FL grades 1-3a+MZL (not including FL grade 3b, tFL, or MCL) receiving ≥ 1 treatment with baseline/post-baseline assessments. The primary end point is progression-free survival (PFS) by 1999 International Working Group criteria. Results: As of November 30, 2019, 393 patients with FL grades 1-3a and MZL enrolled; 76 (19%) had MZL. The median age of MZL patients was 68 years (range, 46-90), 68 (89%) had stage III/IV disease, and 72 (95%) had prior rituximab-containing therapy. Overall response rate was 68% with 39% CR/CRu, and median duration of response was 38.6 months (95% CI, 29.4-not reached [NR]). The median PFS was 41.2 months (95% CI, 38.4-NR). Efficacy results for MZL subtypes and the overall population (FL grades 1-3a + MZL) are shown in the table. Forty-two patients (55%) have completed 12 cycles of R2, and 41 (54%) have been randomized and entered maintenance. Twenty-eight patients (37%) prematurely discontinued both lenalidomide and rituximab, primarily due to adverse events (AEs; n = 11; 14%) and progressive disease (n = 6; 8%). The most common (≥ 20%) all-grade AEs were neutropenia (49%), diarrhea (37%), anemia (25%), thrombocytopenia (24%), and constipation (24%). Most common (≥ 10%) grade 3/4 AEs were neutropenia (41%; 1 patient [1%] had febrile neutropenia), thrombocytopenia (13%), and leukopenia (11%). Conclusions: R2 is active with a tolerable safety profile in patients with R/R MZL. The efficacy and safety profile of R2 in MZL patients were similar to results observed in the overall MAGNIFY population. Disclosures Coleman: Seattle Genetics: Research Funding; EMD Serono Research and Development Institute Inc.: Research Funding; Innocare: Research Funding; ARCUS Biosciences: Research Funding; AstraZeneca Pharmaceuticals, LP (Acerta Pharma BV Trials): Research Funding; AstraZeneca Pharmaceuticals, LP: Research Funding; BMS (Celgene Corporation): Research Funding; Eli Lilly and Company: Research Funding; Genetech (F. Hoffman-LaRoche Ltd): Research Funding; Hutchinson MediPharma, LTD: Research Funding; Ipsen Group: Research Funding; Karyopharma Therapeutics, Inc.: Research Funding; Klus Pharma, Inc.: Research Funding; MeiPharma, Inc.: Research Funding; Merck Sharp & Dohme Corp.: Research Funding; Novartis Pharmaceuticals: Research Funding; Incyte Corporation: Research Funding; Boston BIoMedical, Inc.: Research Funding; BeiGene: Research Funding; Acerta: Research Funding. Andorsky:CTI: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca: Research Funding. Yacoub:Roche: Other: Support of parent study and funding of editorial support; Dynavax: Current equity holder in publicly-traded company; Ardelyx: Current equity holder in publicly-traded company; Incyte: Speakers Bureau; Hylapharm: Current equity holder in private company; Cara Therapeutics: Current equity holder in publicly-traded company; Agios: Honoraria, Speakers Bureau; Novartis: Speakers Bureau. Melear:Janssen: Speakers Bureau; AstraZeneca: Speakers Bureau. Fanning:Takeda: Consultancy, Speakers Bureau; Bristol Myers Squibb: Consultancy, Speakers Bureau; TG Therapeautics: Consultancy; Abbvie: Consultancy; Prisma Health: Current Employment; Sanofi Aventis: Speakers Bureau. Kolibaba:Atara Biotech: Consultancy; Compass Oncology: Ended employment in the past 24 months; Verastem: Honoraria; Seattle Genetics: Research Funding; Pharmacyclics: Research Funding; Novartix: Research Funding; Janssen: Research Funding; Gilead: Research Funding; Genentech: Research Funding; Celgene: Research Funding; Acerta: Research Funding; AbbVie: Research Funding; Sumitomo Dainippon Pharma Oncology: Consultancy, Other: TRAVEL, ACCOMMODATIONS, EXPENSES (paid by any for-profit health care company; TG Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Lansigan:BMS: Consultancy; Seattle Genetics: Consultancy; BMS Steering Committee for MAGNIFY Program: Membership on an entity's Board of Directors or advisory committees; Spectrum Pharma: Consultancy, Research Funding. Reynolds:IHA Hematology/Oncology Consultants: Current Employment. Nowakowski:Kymera: Consultancy; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other; Curis: Consultancy; Kite: Consultancy; MorphoSys: Consultancy, Research Funding; Denovo: Consultancy; Seattle Genetics: Consultancy; Nanostrings: Research Funding; Celgene/BMS: Consultancy, Research Funding; Roche: Consultancy, Research Funding. Gharibo:Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company; Bayer: Ended employment in the past 24 months. Ahn:BMS: Current Employment, Current equity holder in publicly-traded company. Li:BMS: Current Employment, Current equity holder in publicly-traded company. Rummel:Roche: Honoraria; Amgen GmbH: Honoraria; Celgene: Consultancy, Honoraria; Janssen: Honoraria; Novartis Pharma GmbH: Honoraria. Sharman:BeiGene: Research Funding; Bristol Meyers Squibb: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; AstraZeneca: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; TG Therapeutics: Consultancy, Research Funding; Acerta: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Celgene: Consultancy, Research Funding.

300 Background: Current methods to assess site feasibility for industry-funded clinical trials are onerous and delay patient access to novel treatment options and high-quality clinical trials. Industry sponsors and contract research organizations (CROs) often probe for unnecessary and/or duplicative information. These burdens prolong trial start-up times and are a barrier to site participation in oncology trials. The American Society of Clinical Oncology (ASCO) Research Community Forum convened a task force to identify ways to improve the site feasibility assessment process. Methods: Data were collected in 3 steps: 1) survey to assess site burdens, 2) collation of sample feasibility questionnaires (FQs), and 3) stakeholder meeting to discuss potential solutions. The task force then developed recommendations for process improvements and obtained stakeholder feedback through a survey. Results: 113 oncology practices (66 community, 47 academic) reported completing a median 5 FQs and 2 pre-study site visits (PSSVs) per month. FQs took a median 2 hours to complete whereas PSSVs took a median 4 hours to complete. Most considered FQ (81%) and PSSV (91%) content redundant to information previously provided, and FQs similar between different sponsors (86%). The median time from first contact to first patient enrolled was 6 months. The 40 respondents to the stakeholder survey represented 19 academic- and 9 community-based sites, 8 industry sponsors, and 4 CROs. Most preferred a model with a short FQ plus a PSSV when there was not a prior relationship. If there was a prior relationship, either a PSSV or teleconference was preferred. All stakeholders identified time savings, expedited start-up, fewer staff resources, and cost savings as the greatest benefits. The greatest barriers to adoption were buy-in from sponsors and CROs, and insufficient information about site capabilities. Conclusions: Site feasibility assessments for industry-sponsored trials are important to ensure patient safety and access to high quality clinical trials. However, current methods are inefficient and time and resource intensive. This initiative provided insights about challenges for sites and the viability of a fundamental change to site feasibility assessments. ASCO recommendations are forthcoming on improving processes, standardizing and minimizing questions, and using portals that are effective across all trials and clinical research scenarios.

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.

TPS8073 Background: Response rates to salvage immunochemotherapy in patients with DLBCL relapsing after or refractory (R/R DLBCL) to front line therapy remain unsatisfactory. Lenalidomide (Len) has significant single agent activity in relapsed/refractory DLBCL. The addition of lenalidomide (Len) days 1-7 to rituximab plus ifosfamide-carboplatin-etoposide (RICE) was shown to be feasible with promising efficacy in phase 1b study (Feldman T, et al. BJH, 2014). We developed phase I/II study to evaluate the safety and efficacy of the addition of Len (extended to 14 day schedule) to RICE (R2-ICE) for R/R-DLBCL patients who are candidates for stem cell transplant. Methods: The phase I portion was designed to determine the maximally tolerated dose Len in combination with RICE using the standard cohort 3+3 design. The escalation dose levels were 15 mg and 20 mg daily x 14 days. Prophylactic aspirin and growth factor support is mandatory. After 2 cycles of therapy response is evaluated with a PET/CT scan; the responding patients are eligible for 1-2 additional cycles of R2ICE as a bridging before HDC/SCT. The estimated overall response rate for two cycles of R-ICE in R/R DLBCL to RCHOP was estimated to be approximate 45%. We hypothesize that the addition of lenalidomide in the relapse setting could increase the overall response rate by approximately 20%. The one-stage design with an interim analysis being utilized in phase 2 requires 45 evaluable patients (one sided alpha = 0.09, power 90%). For Phase I, all types of B-cell lymphomas were eligible. For phase II portion only DLBCL patients are eligible per central pathology review. Other eligibility criteria include: received one line of previous anti-lymphoma therapy, ≥ 2 weeks from completion of prior anti-lymphoma therapy, candidate for HDC and SCT, adequate organ (creatinine clearance ≥ 60ml/min by Cockcroft-, total bilirubin ≤ 2 × ULN) and bone marrow function (ANC) ≥1500/mm3; platelet count ≥75,000/mm3). The use of steroids and/or rituximab up to 1 week prior to registration for management of symptoms is allowed. 9 patients cleared phase 1 without DLT and dose of 20 mg days 1 -14 was recommend for phase 2 part (RP2D) of the study. The phase 2 study passed interim futility analysis and accrual continues. Correlatives include cell of origin by Nanostring, Myc/bcl2 expression and by FISH and minimal residual disease. PET scans are centrally reviewed including metabolic tumor volume. Clinical trial information: NCT02628405 .