Ghesquières, Hervé

Abstract Introduction: Plamotamab (XmAb13676) is a humanized bispecific antibody that binds both CD20 and CD3 to recruit cytotoxic T cells to kill CD20 expressing malignant cells. Interim results of an ongoing first-in-human (FIH), dose-escalation study (XmAb13676-01; NCT02924402) in subjects with relapsed/refractory (R/R) non-Hodgkin's lymphoma (NHL) are reported. Methods: The study is an FIH, multi-center, open-label, phase 1, dose-escalation study in subjects with R/R NHL with a standard 3 + 3 design. The primary objectives are to determine safety, tolerability, and the maximum tolerated dose (MTD) or recommended dose of plamotamab. Secondary objectives include preliminary anti-tumor activity and pharmacokinetics/pharmacodynamics of plamotamab. This study has 3 Parts: Part A, escalating dose cohorts that establish an initial priming dose as part of repeated weekly infusions at a fixed, weight-based dose in a 28-day cycle; Part B, with a dosing schedule consisting of a priming dose on Cycle 1 Day 1, established in Part A, followed by step-up dosing (SUD) on subsequent weeks; and Part C is an SUD regimen with flat and less frequent dosing. Cytokine release syndrome (CRS) prophylaxis with dexamethasone, antihistamine, and acetaminophen was mandated prior to each administration of plamotamab. Treatment was continued for 2 cycles or longer if there was evidence of therapeutic benefit. Results: At data cut-off (01Jul2021), 80 subjects with NHL have been treated. Subjects had a median age of 62 years (range 32-89), a median of 4 prior therapies (range 1-10), and had been diagnosed a median of 28 months (range 6-353) prior to treatment in the study. The most common treatment-related adverse event was CRS. Overall, 50 subjects (62.5%) experienced CRS, with 4 (5.0%) experiencing Grade ≥ 3 CRS. No related neurotoxicity >Grade 2 has been observed. Treatment responses for NHL were assessed by the Lugano criteria or International Working Group criteria for Waldenström Macroglobulinemia. There have been 23 objective responses (43.4% overall response rate [ORR]) and 13 (38.2%) at doses of ≥ 80 µg/kg or flat dosing in all evaluable subjects with NHL and diffuse large cell B-cell lymphoma, respectively. In the efficacy-evaluable, follicular lymphoma population, at doses of 80-360 µg/kg or flat dosing, objective responses were observed in 8/10 (80%) subjects. After implementation of flat dosing, as opposed to weight-based dosing, a 50% ORR (4/8 evaluable subjects) has been observed in NHL subjects in that cohort. An MTD has not been reached, and dose escalation is ongoing. Overall, 4 out of 16 (25%) evaluable subjects with prior CAR-T therapy responded to plamotamab. Conclusions: Plamotamab demonstrated evidence of clinical activity in heavily pretreated subjects with R/R NHL. CRS was generally manageable with premedication. The study is ongoing with further optimization of dose and schedule. Figure 1 Figure 1. Disclosures Patel: Kite Pharma: Consultancy, Speakers Bureau; Genentech: Consultancy; Morphosys: Consultancy; Bristol Myers Squibb: Consultancy, Speakers Bureau; BeiGene: Consultancy; Janssen: Consultancy; AstraZeneca: Consultancy, Research Funding, Speakers Bureau; Pharmacyclics: Consultancy; Abbvie: Consultancy; TG Therapeutics: Consultancy, Speakers Bureau; MEI Pharma: Consultancy; ADC Therapeutics: Consultancy; Lilly: Consultancy. Michot: GSK: Honoraria; Celgene: Honoraria; MSD: Consultancy, Honoraria; Innate Pharma: Research Funding; Incyte: Research Funding; H3 biomedecine: Research Funding; GSK: Consultancy, Honoraria, Research Funding; Genentech: Research Funding; Gamamabs: Research Funding; Forma: Research Funding; Exelixis: Research Funding; Eos: Research Funding; Eisai: Research Funding; Debiopharm: Research Funding; Daiichi Sankyo,: Research Funding; Clovis: Research Funding; Chugai: Research Funding; Boeringer Ingelheim: Research Funding; Celgene: Research Funding; Blueprint: Research Funding; Beigene: Research Funding; Bayer: Research Funding; Argen-x: Research Funding; Amgen: Research Funding; Agios: Research Funding; Aduro: Research Funding; Abbvie: Research Funding; ASTEX: Research Funding, Speakers Bureau; Astra Zeneca: Honoraria, Research Funding; Roche: Honoraria; Bristol Myers Squibb: Consultancy, Honoraria, Research Funding. Chanan-Khan: Alpha2 Pharmaceuticals, NonoDev, Starton: Current holder of stock options in a privately-held company; Ascentage: Research Funding; Cellectar: Current equity holder in publicly-traded company; Alpha2 Pharmaceuticals: Patents & Royalties: Tabi; Ascentage, Starton, Cellectar, NonoDev, Alpha2 Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; BeiGene, Jansen, Ascentage: Honoraria; BieGene, Jansen, Ascentage: Consultancy. Ghesquieres: Janssen: Honoraria; Mundipharma: Consultancy, Honoraria; Roche: Consultancy; Celgene: Consultancy, Honoraria; Gilead Science: Consultancy, Honoraria. Byrd: Vincerx Pharmaceuticals: Current equity holder in publicly-traded company, Membership on an entity's Board of Directors or advisory committees; Novartis, Trillium, Astellas, AstraZeneca, Pharmacyclics, Syndax: Consultancy, Honoraria; Newave: Membership on an entity's Board of Directors or advisory committees. Cartron: Roche, Celgene-BMS: Consultancy; Danofi, Gilead, Novartis, Jansen, Roche, Celgene-BMS, Abbvie, Takeda: Honoraria. Portell: Acerta/AstraZeneca: Research Funding; Xencor: Research Funding; SeaGen: Research Funding; Targeted Oncology: Honoraria; Aptitude Health: Honoraria; Abbvie: Research Funding; Pharmacyclics: Honoraria; Kite: Honoraria, Research Funding; Merck: Honoraria, Research Funding; BeiGene: Honoraria, Research Funding; Morphosys: Honoraria; TG Therapeutics: Honoraria, Research Funding; Genentech: Research Funding; VelosBio: Research Funding. Solh: Jazz Pharmaceuticals: Consultancy; Partner Therapeutics: Research Funding; BMS: Consultancy; ADCT Therapeutics: Consultancy, Research Funding. Wierda: Loxo Oncology, Inc.: Research Funding; GSK/Novartis: Research Funding; Pharmacyclics LLC, an AbbVie Company: Research Funding; Miragen: Research Funding; Sunesis: Research Funding; Gilead Sciences: Research Funding; KITE Pharma: Research Funding; Genentech: Research Funding; Janssen: Research Funding; Juno Therapeutics: Research Funding; Acerta Pharma Inc.: Research Funding; Xencor: Research Funding; AstraZeneca: Research Funding; Cyclacel: Research Funding; Oncternal Therapeutics, Inc.: Research Funding; Karyopharm: Research Funding; Genzyme Corporation: Consultancy; AbbVie: Research Funding. Johnson: Xencor: Current equity holder in publicly-traded company, Divested equity in a private or publicly-traded company in the past 24 months, Ended employment in the past 24 months. Garcha: Xencor: Current Employment, Current equity holder in publicly-traded company, Current holder of stock options in a privately-held company; Promedim: Ended employment in the past 24 months. Ribrag: AstraZeneca: Membership on an entity's Board of Directors or advisory committees; GSK: Research Funding; MSD Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees; Infinity Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Servier: Consultancy, Membership on an entity's Board of Directors or advisory committees; Argen-X: Research Funding; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Astex Pharmaceuticals: Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees; Nanostring: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; PharmaMar: Honoraria, Membership on an entity's Board of Directors or advisory committees; Epizyme: Honoraria, Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees. Liebowitz: Xencor: Current Employment, Current equity holder in publicly-traded company, Divested equity in a private or publicly-traded company in the past 24 months. Phillips: Incyte: Consultancy, Other: received travel expenses from Incyte, Research Funding; ADCT, BeiGene, Bristol Myers Squibb, Cardinal Health, Incyte, Karyopharm, Morphosys, Pharmacyclics, Seattle Genetics: Consultancy; AbbVie: Consultancy, Research Funding; AstraZeneca: Consultancy; BMS: Consultancy, Research Funding; Genentech: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bayer: Consultancy, Research Funding.

Abstract Background: CD20 antibody plus alkylator and/or anthracycline based immunochemotherapy (IC) is a standard frontline therapy for patients with follicular lymphoma (FL) with 10-year event-free survival (EFS) and overall survival (OS) rates of approximately 50% and 80% respectively in long-term follow-up of clinical trials. Currently available clinical prognostic indices for FL have been designed using PFS and OS endpoints. Early events, commonly defined as progression of disease within 24 months (POD24) or early transformation to a more aggressive histology, are associated with inferior outcomes and increased risk of death due to refractory FL. Timely identification of the minority of patients with elevated mortality risk might enhance clinical management and research strategies. The FLIPI24 Consortium was created to develop a clinical prognostic index using early events as the primary endpoint. We report the outcomes for the pooled cohort and investigate the implications of therapy patterns on potential model development. Methods: Individual patient data were pooled and harmonized from 11 prospective observational cohorts from Europe, North America, and Australia. Patients who were diagnosed with grades 1-3A FL and initiated frontline IC were eligible. EFS was defined as time from start of IC to progression, relapse, retreatment (2nd line), histologic transformation, or death due to any cause. Early events were defined using status at 24 months from start of IC. OS was defined as time from start of IC to death due to any cause. Kaplan Meier curves and Cox proportional hazards models were used to evaluate outcomes by clinical features and therapy types. Results: 9006 patients were abstracted and harmonized, 6111 patients initiated frontline IC between 2002 and 2018 and were included in this analysis. Median age at diagnosis was 61 years (IQR 52-69) and 50% were male. Complete FLIPI data were available in 5637 patients (92%) and 46%, 32%, and 22% were low, intermediate, and high risk, respectively. IC type was 3079 R-CHOP or like (50%) , 1529 R-CVP or like (25%), 918 R-bendamustine (B-R) or like (15%), and 585 fludarabine or other alkylator based IC (10%); 3187 received CD20 antibody maintenance (52%). Patients receiving R-CHOP were younger, more frequently grade 3A, and more frequently had elevated LDH; differences in other characteristics by IC type were not clinically meaningful. At median follow-up of 42 months (IQR 17-72), 2647 patients (43%) had an event (any) and 1494 patients (25%) died. Median survival after an early (non-death) event was 49 months (95% CI: 41-58); 5-year OS was 46% (95% CI: 43-49) compared to 89% (95% CI: 88-90) in patients without POD24. Across all IC types, EFS estimates at 2 and 10 years from start of IC were 80% (95% CI:79-81) and 49% (95% CI:48-51) and OS estimates were 92% (95% CI: 91-92) and 70% (95% CI: 69-72), respectively. FLIPI was highly associated with both EFS (c-statistic=0.61) and OS (c-statistic=0.65) from the initiation of IC (both p<0.0001). There were significant differences in EFS and OS by IC type (both p<0.0001) and use of maintenance was associated with prolonged EFS in landmark analyses at both 6 and 12 months from initiation of IC (both p<0.0001). Treatment patterns changed significantly over the study timeframe. Use of B-R and/or maintenance increased to 30% and 70% respectively in N=2937 patients treated in 2010-2018 (Era2) compared to <1% and 40% respectively in N=3174 patients treated 2002-2009 (Era1). EFS was significantly higher for Era2 compared to Era1 (HR=0.77, 95% CI: 0.71-0.83), which remained significant after adjustment for FLIPI (EFS HR=0.82, 95% CI: 0.76-0.89). However, the association between treatment eras and overall survival was weaker (HR=0.89, 95% CI: 0.79-0.99) and not significant after adjusting for baseline FLIPI (OS HR=0.99, 95% CI: 0.88-1.10). Conclusion: EFS and OS from this large pooled analysis of observational cohorts is similar to long-term follow-up of randomized clinical trials in the IC era and support the use of these data for model development. Modeling efforts for early events should adjust for initial IC selection and use of maintenance therapy. Utilization of bendamustine and/or maintenance therapy increased over the study timeframe from 2002-2018, and Era2 was associated with improved EFS but not OS. This cohort provides comprehensive and robust observational data to define clinical predictors in IC treated patients. Figure 1 Figure 1. Disclosures Maurer: Genentech: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Flowers: Janssen: Research Funding; Takeda: Research Funding; National Cancer Institute: Research Funding; Biopharma: Consultancy; BeiGene: Consultancy; Amgen: Research Funding; Celgene: Consultancy, Research Funding; Xencor: Research Funding; Acerta: Research Funding; Bayer: Consultancy, Research Funding; Sanofi: Research Funding; 4D: Research Funding; Adaptimmune: Research Funding; Allogene: Research Funding; EMD: Research Funding; TG Therapeutics: Research Funding; Burroughs Wellcome Fund: Research Funding; Kite: Research Funding; AbbVie: Consultancy, Research Funding; Cellectis: Research Funding; Denovo: Consultancy; Cancer Prevention and Research Institute of Texas: CPRIT Scholar in Cancer Research: Research Funding; Karyopharm: Consultancy; Gilead: Consultancy, Research Funding; Genmab: Consultancy; Epizyme, Inc.: Consultancy; Novartis: Research Funding; Nektar: Research Funding; Morphosys: Research Funding; Iovance: Research Funding; Spectrum: Consultancy; Pfizer: Research Funding; Ziopharm: Research Funding; Guardant: Research Funding; Eastern Cooperative Oncology Group: Research Funding; SeaGen: Consultancy; Pharmacyclics/Janssen: Consultancy; Genentech/Roche: Consultancy, Research Funding; Pharmacyclics: Research Funding. Villa: Janssen: Honoraria; Gilead: Honoraria; AstraZeneca: Honoraria; AbbVie: Honoraria; Seattle Genetics: Honoraria; Celgene: Honoraria; Lundbeck: Honoraria; Roche: Honoraria; NanoString Technologies: Honoraria. Weibull: Jansen-Cilag: Other: part of a research collaboration between Karolinska Institutet and Janssen Pharmaceutica NV for which Karolinska Institutet has received grant support. Ghesquieres: Janssen: Honoraria; Mundipharma: Consultancy, Honoraria; Roche: Consultancy; Celgene: Consultancy, Honoraria; Gilead Science: Consultancy, Honoraria. Kridel: Gilead Sciences: Research Funding. Gandhi: Janssen: Research Funding; Novartis: Honoraria. Cheah: Celgene: Research Funding; TG Therapeutics: Consultancy, Honoraria, Other: advisory; Loxo/Lilly: Consultancy, Honoraria, Other: advisory; AstraZeneca: Consultancy, Honoraria, Other: advisory; AbbVie: Research Funding; Beigene: Consultancy, Honoraria, Other: advisory; Ascentage pharma: Consultancy, Honoraria, Other: advisory; Gilead: Consultancy, Honoraria, Other: advisory; MSD: Consultancy, Honoraria, Other: advisory, Research Funding; Janssen: Consultancy, Honoraria, Other: advisory; Roche: Consultancy, Honoraria, Other: advisory and travel expenses, Research Funding. Hawkes: Gilead: Membership on an entity's Board of Directors or advisory committees; Merck Sharpe Dohme: Membership on an entity's Board of Directors or advisory committees; Antigene: Membership on an entity's Board of Directors or advisory committees; Regeneron: Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squib/Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck KgA: Research Funding; Specialised Therapeutics: Consultancy; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Other: Travel and accommodation expenses, Research Funding, Speakers Bureau; Janssen: Speakers Bureau. Seymour: Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sunesis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Mei Pharma: Honoraria, Membership on an entity's Board of Directors or advisory committees; Morphosys: Honoraria, Membership on an entity's Board of Directors or advisory committees; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Research Funding, Speakers Bureau. Freeman: Amgen: Honoraria; Celgene: Honoraria; Sanofi: Honoraria, Speakers Bureau; Incyte: Honoraria; Abbvie: Honoraria; Teva: Research Funding; Roche: Research Funding; Janssen: Honoraria, Speakers Bureau; Seattle Genetics: Honoraria; Bristol Myers Squibb: Honoraria, Speakers Bureau. Clausen: Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel expences ASH 2019; Gilead: Consultancy, Other: Travel expences 15th ICML ; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees. Wahlin: Gilead Sciences: Research Funding; Roche: Consultancy, Research Funding. Link: Novartis, Jannsen: Research Funding; Genentech/Roche: Consultancy, Research Funding; MEI: Consultancy. Ekstroem Smedby: Janssen Cilag: Research Funding; Takeda: Consultancy. Sehn: Genmab: Consultancy; Novartis: Consultancy; Debiopharm: Consultancy. Trněný: Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses; Portola: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; 1st Faculty of Medicine, Charles University, General Hospital in Prague: Current Employment; Celgene: Consultancy; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses; Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses; Amgen: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses; MorphoSys: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Honoraria; Gilead Sciences: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses; Incyte: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. El-Galaly: ROCHE Ltd: Ended employment in the past 24 months; Abbvie: Other: Speakers fee. Cerhan: Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Genentech: Research Funding; NanoString: Research Funding.

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

Abstract Great heterogeneity in survival exists for patients newly diagnosed with diffuse large B-cell lymphoma (DLBCL). Three scoring systems incorporating simple clinical parameters (age, lactate dehydrogenase, number/sites of involvement, stage, performance status) are widely used: the International Prognostic Index (IPI), revised IPI (R-IPI), and National Comprehensive Cancer Network IPI (NCCN-IPI). We evaluated 2124 DLBCL patients treated from 1998 to 2009 with frontline rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP; or variant) across 7 multicenter randomized clinical trials to determine which scoring system best discriminates overall survival (OS). Median age was 63 years, and 56% of patients were male. Five-year OS estimates ranged from 54% to 88%, from 61% to 93%, and from 49% to 92% using the IPI, R-IPI, and NCCN-IPI, respectively. The NCCN-IPI had the greatest absolute difference in OS estimates between the highest- and lowest-risk groups and best discriminated OS (concordance index = 0.632 vs 0.626 [IPI] vs 0.590 [R-IPI]). For each given IPI risk category, NCCN-IPI risk categories were significantly associated with OS (P ≤ .01); the reverse was not true, and the IPI did not provide additional significant prognostic information within all NCCN-IPI risk categories. Collectively, the NCCN-IPI outperformed the IPI and R-IPI. Patients with low-risk NCCN-IPI had favorable survival outcomes with little room for further improvement. In the rituximab era, none of the clinical risk scores identified a patient subgroup with long-term survival clearly &lt;50%. Integrating molecular features of the tumor and microenvironment into the NCCN-IPI or IPI might better characterize a high-risk group for which novel treatment approaches are most needed.

7544 Background: Great heterogeneity in survival exists for patients (pts) newly diagnosed with aggressive BCL. Three scoring systems based on simple clinical parameters (age, lactate dehydrogenase, number/sites of involvement, stage, performance status) are widely used: the international prognostic index (IPI), revised-IPI (R-IPI), and National Comprehensive Cancer Network IPI (NCCN-IPI). We studied BCL pts treated with R-CHOP to determine which scoring systems best identifies subgroups with poor outcomes that might benefit from new approaches. Methods: Individual pt data from 7 multicenter trials (1998-2009) of pts with BCL (86% DLBCL) treated front-line with R-CHOP (or variant) were analyzed to determine whether IPI, R-IPI, or NCCN-IPI best discriminated overall survival (OS). The concordance index (c-index) from a proportional hazards model, stratifying on trial and induction therapy, quantified predictive accuracy of each scoring system. Results: 2561 pts (median age 63 yrs, 56% male) were classified into IPI, R-IPI, and NCCN-IPI risk groups (Table). With a median follow-up of 5 yrs, NCCN-IPI had the greatest absolute difference in OS estimates between the highest and lowest risk groups at 1, 3, and 5 yrs, and best discriminated OS (c-index = 0.631, Table). Conclusions: In an independent and large cohort of pts, NCCN-IPI performs best in risk-stratifying pts with aggressive BCL, readily distinguishing pts at high and low risk for treatment failure using clinical parameters (5-yr OS between 48 and 92%). Improvement over the simpler IPI appears incremental, and IPI may remain a valuable alternative. Work integrating molecular features of the tumor into the (NCCN-) IPI is in progress to define high risk groups where targeted novel approaches are needed most. [Table: see text]

8504 Background: Diffuse large B-cell lymphoma (DLBCL) is an aggressive lymphoma. The optimal follow-up strategy for patients (pts) in remission is not clear. The goal of this study is to determine the utility of surveillance scans in a large, prospective, multi-institutional cohort of DLBCL pts. Methods: Patients were enrolled in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER), a prospective cohort of newly diagnosed lymphoma pts. All pts were followed for events including relapse, re-treatment, and death with events verified by medical records. Patients eligible for this study had biopsy proven DLBCL and were treated with anthracycline based immunochemotherapy (IC). Initial and post-treatment management was per treating physician. Medical records were re-reviewed in pts with events for clinical details at relapse and relationship to planned follow-up visits and surveillance scans. Results: 644 pts with DLBCL treated with IC were enrolled in MER from 2002-2009. Median age was 63 years (range 18-92), 54% were men, and median f/u was 59 months (range 8-116). 537 pts entered post-treatment observation; 109 (20%) of the 537 pts relapsed and 41 died from other causes. 42% of relapses were in the first 12 months following diagnosis, 27% between 12-24 months, and 31% >24 months. In the 109 who relapsed, 62% of pts (62/100, 9 unknown) presented to their physician earlier than a planned follow-up visit due to symptoms. At the time of relapse, 68% were symptomatic, 42% of pts had abnormal physical exam, and 55% had elevated LDH; 87% of pts had ≥1 of these features. Of the 38 pts with relapse detected at a planned visit, 26 had clinical features of relapse and 12 pts had relapse detected solely by planned surveillance scan; 4 pts had relapse of low-grade or other subtype and 8 had DLBCL relapse (4 of whom had equivocal/positive PET at the end of IC). Thus, surveillance scanning detected DLBCL relapse prior to clinical manifestations in only 8/537 pts (1.5%) observed post DLBCL therapy. Conclusions: The vast majority of DLBCL relapses occur outside of planned follow-up visits and are accompanied by symptoms, physical exam, or laboratory abnormalities. Routine surveillance scans post-therapy add little to detection of DLBCL relapse.

Abstract Background: Lipid traits have been inconsistently linked to risk of non-Hodgkin lymphoma (NHL). We examined the association of genetically predicted lipid traits with risk of diffuse large B-cell lymphoma (DLBCL), chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and marginal zone lymphoma (MZL) using Mendelian randomization (MR) analysis. Methods: Genome-wide association study data from the InterLymph Consortium were available for 2,661 DLBCLs, 2,179 CLLs, 2,142 FLs, 824 MZLs, and 6,221 controls. SNPs associated (P &lt; 5 × 10−8) with high-density lipoprotein (HDL, n = 164), low-density lipoprotein (LDL, n = 137), total cholesterol (TC, n = 161), and triglycerides (TG, n = 123) were used as instrumental variables (IV), explaining 14.6%, 27.7%, 16.8%, and 12.8% of phenotypic variation, respectively. Associations between each lipid trait and NHL subtype were calculated using the MR inverse variance–weighted method, estimating odds ratios (OR) per standard deviation and 95% confidence intervals (CI). Results: HDL was positively associated with DLBCL (OR = 1.14; 95% CI, 1.00–1.30) and MZL (OR = 1.09; 95% CI, 1.01–1.18), while TG was inversely associated with MZL risk (OR = 0.90; 95% CI, 0.83–0.99), all at nominal significance (P &lt; 0.05). A positive trend was observed for HDL with FL risk (OR = 1.08; 95% CI, 0.99–1.19; P = 0.087). No associations were noteworthy after adjusting for multiple testing. Conclusions: We did not find evidence of a clear or strong association of these lipid traits with the most common NHL subtypes. While these IVs have been previously linked to other cancers, our findings do not support any causal associations with these NHL subtypes. Impact: Our results suggest that prior reported inverse associations of lipid traits are not likely to be causal and could represent reverse causality or confounding.

Background: Disease progression (PD) after frontline IC therapy for DLBCL is a clinically significant event and only 20-40% of patients achieve durable remissions with salvage chemotherapy. Patients who experience early PD or have disease refractory to IC have especially poor outcomes. Chimeric antigen receptor T-cell therapy has emerged as a novel effective therapy for selected cases of relapsed or refractory (r/r) DLBCL, although its availability is limited by cost and logistic challenges. Simple, clinically applicable prognostic tools would be useful for selecting patients for consideration for novel therapies vs those who are more likely to be successfully managed by conventional therapies, but few have been developed for r/r DLBCL. Methods: Model building was performed in patients with PD after IC from 13 frontline, multicenter, randomized DLBCL clinical trials from the SEAL Consortium. All patients received rituximab and an anthracycline-based combination IC and were followed systematically; however therapy received, clinical, and laboratory data at progression were not available. OS was defined as time from first progression until death from any cause. Associations between variables and OS were evaluated using Cox models; splines were used to model functional forms. Validation was performed in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER) and Danish LYFO cohorts. Model performance was assessed using time-dependent concordance indices (c-stat), Brier scores, and calibration with metrics evaluated at two years from progression. Results: Model Development (SEAL): 1125 of 5112 patients had PD after IC. Median time to PD (TTP) was 11.8 months (IQR 6.6-23.5); median age at PD was 68 years (IQR 60-73). At a median follow-up of 21 months from PD (IQR 7-43), 732 patients (65%) had died and 24 month overall survival after PD (OS24) was 35% (95% CI: 32-38). TTP was strongly associated with post PD OS (spline p-value&lt;0.0001) with OS24 ranging from 18% in patients with TTP &lt;6 months compared to 69% in patients with TTP &gt; 36 months (Figure A). Additionally, age at PD (spline p-value&lt;0.0001) was associated with post PD OS. A model consisting of age at PD including patients 40-80 years and TTP ranging 0-60 months was created (n=1011, Figure B) due to sparse data outside these intervals. C-stat was 0.67, and the model was well calibrated (predicted OS24 = 34% vs. actual OS24 = 34%). External Validation (MER): The model was validated in 290 patients with DLBCL who initiated 2nd line therapy after PD to IC. Median age at PD was younger than the SEAL cohort at 62 years (IQR 57-70). Patients with biopsy proven indolent histology at relapse were excluded. Median TTP was 8.2 months (IQR 5.1-17.0). At a median follow-up of 87 months from PD, 201 patients (69%) had died. OS24 was 47% (95% CI: 41-53). The model showed similar concordance (c-stat=0.65), but underestimated OS24 (predicted =33% vs. 47% actual, Figure C). External Validation (LYFO): The model was validated in 599 patients with DLBCL and PD after frontline IC. Median age at PD was 67 years (IQR 60-73). Median TTP was 10.1 months (IQR 6.0-19.4). At a median follow-up of 82 months from PD, 435 patients (73%) had died. OS24 was 38% (95% CI: 34-42). The model showed similar concordance (c-stat=0.67) when applied to the LYFO cohort, but again underestimated OS24 (predicted =32% vs. 38% actual, Figure D). Case vignettes: A 74 year old patient who progressed 9 months after diagnosis would have a predicted OS24 of 20%. A 58 year old patient who progressed 32 months after diagnosis would have a predicted OS24 of 65% Conclusions: TTP to following IC is strongly associated with post-progression survival in DLBCL. We developed a model from the largest frontline clinical trial dataset in DLBCL and validated a simple to apply clinical prognostic tool in the r/r setting. The model allows better understanding of expected outcomes in r/r DLBCL and can aid design and interpretation of trial results in this setting. The model underestimated the actual survival probability when applied to non-trial validation cohorts. Recalibration of the model for transplant eligible patients and development of smartphone based point-of-care application of the model is ongoing. Figure Disclosures Maurer: Celgene: Research Funding; Nanostring: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees. Schmitz:Riemser: Consultancy, Honoraria; Celgene: Equity Ownership; Gilead: Honoraria; Novartis: Honoraria. Farooq:Kite Pharma: Research Funding; Celgene: Honoraria. Flowers:Optimum Rx: Consultancy; Burroughs Wellcome Fund: Research Funding; BeiGene: Consultancy, Research Funding; Karyopharm: Consultancy; AstraZeneca: Consultancy; Gilead: Consultancy, Research Funding; Eastern Cooperative Oncology Group: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Bayer: Consultancy; TG Therapeutics: Research Funding; Acerta: Research Funding; Celgene: Consultancy, Research Funding; National Cancer Institute: Research Funding; Denovo Biopharma: Consultancy; Pharmacyclics/Janssen: Consultancy, Research Funding; Spectrum: Consultancy; Millenium/Takeda: Research Funding; AbbVie: Consultancy, Research Funding; V Foundation: Research Funding. Frederiksen:Alexion: Research Funding; Gilead: Research Funding; Abbvie: Research Funding; Janssen Pharmaceuticals: Research Funding; Novartis: Research Funding. Cunningham:Eli Lilly: Research Funding; 4SC: Research Funding; Bayer: Research Funding; MedImmune: Research Funding; Celgene: Research Funding; AstraZeneca: Research Funding; Merrimack: Research Funding; Sanofi: Research Funding; Amgen: Research Funding; Janssen: Research Funding; Merck: Research Funding; Clovis: Research Funding. Jørgensen:Gilead: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees. Poeschel:Amgen: Other: Travel, accommodations, expenses; Abbvie: Other: Travel, accomodations, expenses; Hexal: Speakers Bureau; Roche: Other: Travel, accomodations, expenses. Nowakowski:F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding; Genentech, Inc.: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding. Seymour:Takeda: Consultancy; Celgene: Consultancy, Research Funding, Speakers Bureau; AbbVie: Consultancy, Honoraria, Research Funding, Speakers Bureau; Acerta: Consultancy; Janssen: Consultancy, Research Funding; Roche: Consultancy, Research Funding, Speakers Bureau. Merli:Takeda: Honoraria, Other: Travel Expenses; Gilead: Honoraria; Mundipharma: Honoraria; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel expenses, Research Funding; Sandoz: Membership on an entity's Board of Directors or advisory committees; Teva: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Other: Travel Expenses; Janssen: Honoraria. Haioun:novartis: Honoraria; celgene: Honoraria; roche: Consultancy; celgene: Consultancy; gilead: Consultancy; takeda: Consultancy; janssen cilag: Consultancy; amgen: Honoraria; servier: Honoraria. Tilly:roche: Membership on an entity's Board of Directors or advisory committees; servier: Honoraria; merck: Honoraria; Gilead: Honoraria; Janssen: Honoraria; BMS: Honoraria; Karyopharm: Consultancy; Astra-Zeneca: Consultancy; Celgene: Consultancy, Research Funding; Roche: Consultancy. Salles:Amgen: Honoraria, Other: Educational events; BMS: Honoraria; Merck: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis, Servier, AbbVie, Karyopharm, Kite, MorphoSys: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Epizyme: Consultancy, Honoraria; Roche, Janssen, Gilead, Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events. El-Galaly:Roche: Employment, Other: Travel support; Takeda: Other: Travel support.

Abstract Background. Recent clinical trials based on immunotherapies targeting the PD-1/PD-L1 pathway have shown striking durable responses in a subset of patients with solid cancers. The Programmed death 1 (PD-1) protein is a key immune checkpoint inhibitor expressed by activated T cells. Its ligand, PD-L1, was reported highly expressed by tumor cells in some diffuse large B-cell lymphomas (DLBCL) of non-Germinal Center phenotype. We recently reported on the French multicenter GOELAMS075 trial that pre-treatment soluble PD-L1 (sPD-L1) in plasma was elevated in DLBCL patients compared to controls, and that elevated sPD-L1 was associated with inferior overall survival (OS), independent of the International Prognostic Index (IPI) and other clinical factors (Rossille et al., Leukemia 2014). Here, we replicate and extend these findings in two independent studies from Australia and the US. Methods. The protein expression of sPD-L1 was evaluated using a commercial ELISA kit. The French discovery cohort consisted of 288 adults with newly diagnosed aggressive DLBCL, age 18 to 60 years, and treated with R-CHOP or high dose chemotherapy plus rituximab followed by autologous stem cell support (clinicaltrials.gov: NCT00561379); there were also 60 controls. The Australian study consisted of 51 DLBCL patients age 18 to 71 years, all stages, treated with R-CHOP14, along with 57 controls. The US study was an observational cohort from the Iowa/Mayo Lymphoma SPORE and consisted of 225 DLBCLs, age 19 to 92 years, all stages, treated with immunochemotherapy, along with 98 controls. Plasma samples were collected pre-treatment using EDTA tubes for the Australian and the US cohorts, BDª P100 tubes for the French cohort. sPD-L1 expression was measured in Rennes, France (French & US samples) and in Brisbane, Australia (Australian samples).The Kaplan-Meier method and Cox regression were used to model the association of sPD-L1 with OS. The 95th percentile of the sPD-L1 levels in each matched control group was used as the cutoff point to define elevated sPD-L1 levels. Results. Replicating the French findings, sPD-L1 levels were significantly higher for DLBCL patients compared to controls in both the US (P<0.0001) and Australian (P<0.0001) studies (Figure 1). The odds ratios for elevated sPD-L1 among patients compared to controls were 10.7 (95% CI 3.3-22.0), 8.9 (95% CI 3.1-25.3) and 12.6 (95% CI 2.7-28.0) for the French, US and Australian studies, respectively. In the French cohort, elevated sPD-L1 levels were associated with inferior OS (Hazard Ratio (HR)=2.05; 95% CI 1.18-3.57), and this was replicated in the US cohort (HR=1.71; 95% CI 0.97-3.01) (Figure 2). Due to the similarity of the results, we next pooled the French and US cohorts to increase power for multivariate and subset analyses. The median age at diagnosis of the 513 patients was 54 years; 29% were more than 60 years old; 69% of the patients had stage III-IV disease; 56% were IPI intermediate-risk and 9% were IPI high-risk patients. The median follow-up was 48 months (range, 0.5-126) overall, and 66 months for the patients still alive; there were 102 deaths. Using a cutpoint of 1729 pg/mL from the combined studies, patients with elevated sPD-L1 had inferior OS compared to patients without elevated sPD-L1, with 3-year OS rates estimated at 78% (95% CI 71.8-84.9) vs. 87.8% (95%CI 84.5-91.4) (P<0.01). In multivariate analysis, elevated sPD-L1 (HR=1.75; 95%CI 1.16-2.64), age (HR=1.03; 95%CI 1.02-1.05), high-risk IPI score (HR=3.71; 95%CI 1.91-7.21) and ALC ² 1 G/L (HR=2.19; 95%CI 1.44-3.33) were all significant at P<0.01. In analyses stratified by IPI, the 3-year OS rates among high-risk patients (IPI 4-5) was 40.9% (95% CI 23.6-71.0) for elevated sPD-L1 vs. 82.1% (95% CI 69.1-97.6) for not elevated sPD-L1 (P<0.01). There was no association of sPD-L1 with 3-year OS among low risk (IPI 0-1) or intermediate-risk (IPI 2-3) patients. Conclusion. We confirmed that sPD-L1 levels are higher in pre-treatment plasma samples of newly diagnosed DLBCL patients compared to controls, and that elevated levels of sPD-L1 are associated with inferior OS in DLBCL patients treated with immunochemotherapy, and this is independent of IPI and other clinical factors. Among high risk patients, we identified a subgroup with less than 50% survival. Figure 1 Figure 1. Figure 2 Figure 2. Disclosures No relevant conflicts of interest to declare.

Abstract Background Recent genome wide association studies (GWAS) of Hodgkin lymphoma (HL) have identified several associations at both HLA and non-HLA loci. However, much of HL heritability remains unexplained. Methods To identify novel risk loci, we performed a meta-analysis of 3 HL GWAS including a total of 1,810 cases and 7,879 controls. Results were replicated in an independent set of 1,163 cases and 2,580 controls, for a total of 3,097 and 11,097 cases and controls combined, respectively. participants in discovery and replication stages were of European descent. quality control and imputation we conducted a meta-analysis addressing 1,004,829 variants (λ= 1.10, λ1000= 1.03). Associations between SNP genotypes and HL risk were evaluated under a log-additive model of inheritance adjusting for sex, study center and significant principal components to control for population stratification. We performed an analysis with all HL cases and then conducted stratified analyses by histological subtype (classical, nodular sclerosis and mixed cellularity), age at diagnosis (nodular sclerosis among those diagnosed at 15- 35 years in all studies, and those diagnosed at 35 and older years in the European Study only) and EBV tumor status (negative and positive). We then used a bioinformatic approach (FunciSNP) to identify potential functional variants associated with HD risk correlated with risk loci of interest. We extracted publically available ENCODE data on biofeatures to identify potential functional motifs associated with the index SNP or correlated SNPs. Finally, we measured expression levels of the two alternative mRNA transcripts in lymphoblastoid cell lines (LCLs) from 49 post-therapy HL patients and 25 unaffected controls. RT-PCR was carried out in triplicate. Relative expression levels were calculated relative to TBP as housekeeping gene. Linear models were used to assess correlation between genotype and TCF3expression levels. Results The meta-analysis identified a novel susceptibility variant at chromosome 19p13.3 (rs1860661) associated with HL risk (Odds Ratio [OR]= 0.78, P=2.0*10-8, I2=0%). variant is located in intron 2 of TCF3 (also known as E2A), a regulator of B- and T-cell lineage commitment. was also significantly associated with HL (OR= 0.85, P=0.002) in the replication series of 1,281 cases and 3,218 controls. the combined analysis consisting of the discovery and replication sets, rs1860661was strongly associated with HL (OR=0.81,=3.5*10-10), with no evidence of heterogeneity between contributing studies (Phom=0.41, I2=0%). The number of G alleles defined by rs1860661 was significantly associated with a reduced risk of each HL subtype except EBV positive HL. rs1860661 and two correlated SNPs, rs10413888 (r2=0.90) and rs8103453 (r2=0.89) identified by FunciSNP analysis map in or near marks of open chromatin and in DNAse hypersensitivity sites in TCF3 in CD20+ B cell lines., the protective minor alleles of these SNPs as defined by the G-G-G haplotype map to the binding sites for ZBTB7a (rs10413888 and rs1860661) and (rs8103453) transcription factors, likely improving the binding efficiency to the sites which may result in increased transcription rates of TCF3. TCF3 is encoded by two alternative transcripts (E12 and E47). Higher expression levels of TCF3-E47, whose transcription start site is located close to rs1860661, was associated with the rs1860661-G allele in controls (P=0.02), but not in HL patients (P=0.22). Conclusion/Discussion TCF3 is essential for the commitment of lymphoid progenitors to both B-cell and T-cell lineage development. A molecular and phenotypic hallmark of classical HL is the loss of the B-cell phenotype in HRS cells, including lack of demonstrable B-cell receptor and most B-cell specific markers such as CD19 or CD20. HRS cells have a low level of TCF3, particularly homodimers of the isoform E47, due to expression of the ABF-1 and ID2 inhibitors that bind to TCF3. Thus, higher TCF3 levels in HRS precursor cells may lead to enhanced retention of the B cell phenotype, thereby conferring a protective effect. These data suggest a link between the 19p13.3 locus including TCF3 and HL risk, indicating that TCF3 could be relevant to HL etiology and pathogenesis. Disclosures: Link: Genentech: Consultancy; Millenium: Consultancy; Pharmacyclics: Consultancy; Spectrum: Consultancy.