Salles, Gilles A

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 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 <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.

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 < 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 < 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<0.0001) with OS24 ranging from 18% in patients with TTP <6 months compared to 69% in patients with TTP > 36 months (Figure A). Additionally, age at PD (spline p-value<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.

Background Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin's lymphoma (NHL) in adults. R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone) remains the standard of care for newly diagnosed DLBCL, with cure rates of 60-70%. However, more effective front-line options are needed to further improve outcomes, particularly in high-risk patients (Sehn LH, Gascoyne RD. Blood 2015;125:22). Approximately 15-20% of treatment-naïve patients with DLBCL have CD20-low expressing tumors, while CD19 is expressed in >90% of DLBCL. CD20-low DLBCL is associated with poor response to rituximab-based regimens (Johnson NA, et al. Blood 2009;113:3773; Prevodnik VK, et al. Diagnostic Pathol 2011;6:33). CD19 is a B-lymphocyte lineage-specific surface antigen that is widely expressed in mature B-cell malignancies, including DLBCL. CD19 functions as a positive regulator of B-cell receptor signaling and is important for B-cell activation and proliferation, and is, therefore, an attractive therapeutic target in addition to CD20. Tafasitamab (MOR208) is a humanized anti-CD19 monoclonal antibody with an engineered constant region (Fc) that enhances Fc-γ receptor binding affinity on effector cells, thereby enhancing antibody-dependent cellular cytotoxicity (ADCC) and phagocytosis (ADCP). Preliminary data in DLBCL cell lines suggest that combined targeting of CD19 and CD20 with tafasitamab and rituximab, respectively, could have synergistic cytotoxic effects. Monotherapy with tafasitamab has shown clinical activity and acceptable safety in a Phase I study in relapsed/refractory (R/R) chronic lymphocytic leukemia/small lymphocytic lymphoma (NCT01161511) and in a Phase IIa study in R/R NHL (NCT01685008). In patients with R/R DLBCL, treatment with single agent tafasitamab until progression led to a 26% objective response rate (ORR) with several long-term responses (Jurczak W, et al. Ann Oncol 2018; 29:1266). Preclinical in vitro and in vivo data have demonstrated increased combinatorial antitumor effects with tafasitamab and the immunomodulatory agent lenalidomide (LEN). In the Phase II, single-arm L-MIND study (NCT02399085) in patients with R/R DLBCL, treatment with tafasitamab/LEN achieved an ORR of 60%, a complete response (CR) rate of 42.5% and a median progression-free survival (PFS) of 12.1 months (Salles GA, et al. ICML 2019; Abstr 124). This combination received breakthrough therapy designation by the US Food and Drug Administration. Study design and methods First-MIND is a Phase Ib, open-label, multicenter, randomized trial of tafasitamab/R-CHOP or tafasitamab/LEN/R-CHOP in patients with newly diagnosed DLBCL (Figure 1). Patients must be aged ≥18 years, treatment naïve, with histologically confirmed DLBCL not otherwise specified and have intermediate- to high-risk disease (International Prognostic Index 2-5). Key exclusion criteria include known double- or triple-hit lymphoma, and transformed or composite lymphoma. Treatment consists of six 21-day cycles of tafasitamab (12 mg/kg intravenously [IV], on Days [D] 1, 8 and 15) in addition to R-CHOP (Arm A) or tafasitamab (12 mg/kg IV, on D1, 8 and 15) and LEN (25 mg orally, on D1-10) in addition to R-CHOP (Arm B). The trial includes a safety run-in phase and a main phase. In the safety run-in phase, 12 patients will be enrolled in each arm. If no unexpected safety signals suspected to be related to the addition of tafasitamab ± LEN to R-CHOP are observed, an additional 18 patients will be enrolled in each arm in the main phase. The primary objective of the trial is to assess safety; secondary objectives include evaluation of efficacy (ORR and PET-assessed CR rate at end of treatment, PFS, overall survival, event-free survival, time to next anti-lymphoma treatment), long-term safety and pharmacokinetics, and immunogenicity of tafasitamab in each arm. Exploratory objectives will include the assessment of biomarkers in peripheral blood (natural killer [NK] cell count, cell-free circulating tumor DNA) and tumor tissue (DLBCL cell of origin, NK cell or macrophage count/gene expression profile, CD19 and CD20 expression) that may be relevant to the mechanism of action and/or response to study treatment. As this is a Phase Ib study to primarily explore safety, no formal statistical hypothesis is considered for the sample size calculation; approximately 60 patients will be recruited across Europe and the US. Disclosures Burke: Gilead: Consultancy; Roche/Genentech: Consultancy; Celgene: Consultancy. André:Celgene: Other: Travel grants, Research Funding; Chugai: Research Funding; Takeda Millenium: Research Funding; Johnson & Johnson: Research Funding; Amgen: Other: Travel grants, Research Funding; Roche: Other: Travel grants, Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers-Squibb: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees, Other: Travel grants. Cheson:Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Research Funding; Acerta: Consultancy, Research Funding; Kite: Research Funding; Epizyme: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Symbios: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Trillium: Research Funding; TG Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding; Portola: Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Duell:Regeneron Pharmaceuticals, Inc.: Research Funding. Nowakowski:Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding. Rosenwald:MorphoSys: Consultancy. Salles:Roche, Janssen, Gilead, Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; BMS: Honoraria; 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; Amgen: Honoraria, Other: Educational events; Merck: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Sharman:Acerta: Consultancy, Honoraria, Research Funding; TG Therapeutics: Consultancy, Honoraria, Research Funding; Pharmacyclics LLC, an AbbVie Company: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Genentech: Consultancy, Honoraria, Research Funding; AstraZeneca: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Research Funding. Staber:Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda-Millenium: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Celgene: 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, Speakers Bureau; MSD: Honoraria, Speakers Bureau; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Trněný:Abbvie: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Gilead Sciences: Consultancy, Honoraria; Incyte: Consultancy, Honoraria; MorphoSys: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; F. Hoffmann-La Roche: Consultancy, Honoraria; Celgene: Consultancy; Janssen: Consultancy, Honoraria. Westin:Novartis: Other: Advisory Board, Research Funding; Juno: Other: Advisory Board; Unum: Research Funding; Curis: Other: Advisory Board, Research Funding; Genentech: Other: Advisory Board, Research Funding; Kite: Other: Advisory Board, Research Funding; Celgene: Other: Advisory Board, Research Funding; 47 Inc: Research Funding; MorphoSys: Other: Advisory Board; Janssen: Other: Advisory Board, Research Funding. Brugger:MorphoSys: Employment; AstraZeneca: Equity Ownership. Fingerle-Rowson:MorphoSys AG: Employment. Klanova:MorphoSys AG: Employment. Würth:MorphoSys AG: Employment. Truemper:Janssen Oncology: Consultancy; Nordic Nanovector: Consultancy; Takeda: Consultancy, Research Funding; Seattle Genetics, Inc.: Research Funding; Roche: Research Funding; Mundipharma: Research Funding.

Introduction: Most patients with newly diagnosed FL treated with rituximab (R) alone or R + chemotherapy will experience prolonged progression-free survival and overall survival (OS), but it remains unclear what factors have the greatest influence on FL-associated and other causes of death in this patient population. We utilized individual patient data from 13 first-line randomized clinical trials from the FLASH database to perform a comprehensive multistate survival analysis to examine and quantify the relationships between clinical characteristics, treatment response, and early, intermediate, and late FL outcomes. Methods: The multistate survival analysis model defined states for "Alive after beginning Induction Treatment (TX)", "Alive after beginning Maintenance TX", "Death due to FL", and "Death from Other Causes" (Figure 1). We used the Aalen-Johansen estimator, a generalization of the Kaplan-Meier estimator, to calculate the likelihood of being in each model state and estimate the course of FL over time. Making no assumptions on the probability distributions and capable of coping with censored observations, the Aalen-Johansen estimator is a convenient and reliable nonparametric estimator for clinical data. Results: Among 7,465 FL patients with median age 56 (range 18-90) years, 49.2% were female; 28.7% Stage I-III, 71.3% Stage IV, and FLIPI was 0-1 (20.0%), 2 (36.8%), ≥ 3 (43.2%). Following initiation of induction treatment, 2-, 5- and 10-year death rates were 1.7%, 3.8%, and 5.8% due to FL, and 0.7%, 2.1%, and 4.8% from other causes (Figure 2). Death rates at 2, 5, and 10 years due to FL and other causes for subgroups based on clinical characteristics and treatment response are shown in Table 1. Notably, patients > 70 years and patients with FLIPI ≥ 3 had worse outcomes and patients achieving CR at 18, 24, and 30 months experienced improved outcomes. Conclusion: This is the largest study using data from randomized trials to quantify the impact of clinical factors on early, intermediate and late mortality by cause of death. We demonstrated that age > 70 years and FLIPI ≥ 3 were linked to increased FL-associated death and response to TX distinguished patients with favorable and poor outcomes. Future analyses should quantify the impact of predictors on the rate/time of FL outcomes in multivariable models. Disclosures Salles: Roche, Janssen, Gilead, Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; 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; Epizyme: Consultancy, Honoraria; 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. Hoster:Janssen: Research Funding; Roche Pharma AG: Other: Travel Support. Hiddemann:Bayer: Research Funding; Gilead: Consultancy, Honoraria; Vector Therapeutics: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria. Herold:Roche: Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Gilead: Honoraria; Celgene: Honoraria. Morschhauser:Servier: Consultancy; Gilead: Consultancy; Janssen: Honoraria; Roche/Genentech: Consultancy; BMS: Honoraria; Celgene: Honoraria. Rummel:Roche Pharma AG: Honoraria, Research Funding; Celgene: Honoraria; Janssen: Honoraria; Sandoz: Honoraria. Kimby:AbbVie,: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Gilead: Other: educational lectures. Vitolo:Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kite: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Juno Therapeutics: Membership on an entity's Board of Directors or advisory committees; F. Hoffmann-La Roche: Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Gyan:Pfizer: Honoraria. Ladetto:Celgene: Honoraria; Roche: Honoraria; Janssen: Honoraria; Abbvie: Honoraria; Acerta: Honoraria; Sandoz: Honoraria. Nielsen:F. Hoffmann-La Roche Ltd: Employment, Equity Ownership. Flowers:AstraZeneca: Consultancy; BeiGene: Consultancy, Research Funding; Eastern Cooperative Oncology Group: Research Funding; Burroughs Wellcome Fund: Research Funding; AbbVie: Consultancy, Research Funding; Optimum Rx: Consultancy; V Foundation: Research Funding; Pharmacyclics/Janssen: Consultancy, Research Funding; Bayer: Consultancy; Gilead: Consultancy, Research Funding; TG Therapeutics: Research Funding; Denovo Biopharma: Consultancy; Acerta: Research Funding; National Cancer Institute: Research Funding; Millenium/Takeda: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Spectrum: Consultancy;

Introduction: XmAb13676 is a humanized bispecific antibody that binds both CD20 and CD3 in order to recruit cytotoxic T cells to kill CD20 expressing malignant cells. Interim results of an ongoing first-in-human, dose-escalation study (XmAb13676-01; NCT02924402) in subjects with relapsed/refractory (R/R) non-Hodgkin's lymphoma (NHL) and chronic lymphocytic leukemia (CLL) are reported here. Methods: The study is a first-in-human, multi-center, open-label, phase 1, dose-escalation study in subjects with R/R NHL and R/R CLL with a standard 3 + 3 design. The primary objectives are to determine safety, tolerability, and the maximum tolerated dose (MTD) or recommended dose of XmAb13676. Secondary objectives include preliminary anti-tumor activity and PK/PD of XmAb13676. This study is designed in two parts: Part A, escalating dose cohorts that establish an initial priming dose as part of repeated weekly infusions at a fixed dose in a 28-day cycle; and Part B, with a dosing schedule consisting of a priming dose on C1D1 , established in Part A, followed by escalated dose(s) on subsequent weeks. Cytokine Release Syndrome (CRS) prophylaxis with dexamethasone was mandated prior to each administration of XmAb13676. Treatment was continued for 2 cycles or longer if there was evidence of therapeutic benefit. Results: At data cut-off, 44 subjects have been treated, 36 with NHL and 8 with CLL. NHL: Subjects with R/R NHL had a median age of 61.5 years (range 32-89), a median of 3.5 prior therapies (range 1-9) and had been diagnosed a median of 24.6 months (range 6.3-181.2) prior to treatment in the study. Treatment-emergent adverse events (TEAEs) related to treatment occurring in ˃ 3 subjects are shown in Table 1A. Nine treatment-related serious adverse events (SAE) occurred in 6 subjects. The most common treatment-related SAE was CRS, which occurred in 4 (11.1%) subjects with 1 of the events being Grade 4 and the other events being ≤ Grade 2. Treatment responses were assessed by the Lugano criteria or International Working Group criteria for Waldenström Macroglobulinemia (WM). There have been 7 objective responses: 2 complete responses (CR; DLBCL), 1 Very Good PR (VGPR; WM), and 4 partial responses (PR; 1FL, 3 DLBCL) at doses of 20-125 µg/kg. In the efficacy-evaluable population, at doses of 80-125 µg/kg, objective responses were observed in 6/18 patients. A priming dose of 45 µg/kg has been chosen for Part B. An MTD has not been reached and dose escalation is ongoing in Part B in NHL. CLL: Subjects with R/R CLL had a median age of 76 years (range 62-81), a median of 4.5 prior therapies (range 2-6) and had been diagnosed a median of 76.1 months (range 17.5-328.9) prior to treatment in the study. Treatment-related TEAEs occurring in ˃ 1 subject are shown in Table 1B. Three treatment-related serious adverse events (SAE) occurred in 2 subjects. The treatment related SAEs were CRS (Grade 3), hepatocellular injury (Grade 3), and jaundice cholestatic (Grade 2), each of which occurred in 1 (12.5%) subject. There has been 1 CR reported (Richter transformation) in 5 subjects at 20 µg/kg, the highest dose administered thus far. The treatment response was assessed by the Lugano criteria. An MTD has not been reached and dose escalation is ongoing in Part A in CLL. Conclusions: XmAb13676 demonstrated evidence of clinical activity in heavily pretreated subjects with R/R NHL and R/R CLL treated at doses between 20 and 125 µg/kg. CRS was generally manageable with premedication. The study is ongoing with further optimization of dose and schedule. Disclosures Patel: AstraZeneca: Consultancy, Research Funding, Speakers Bureau; Celgene: Consultancy, Speakers Bureau; Genentech: Consultancy, Speakers Bureau; Pharmacyclics/Janssen: Consultancy, Speakers Bureau; Sunesis: Consultancy. Chanan-Khan:AbbVie: Research Funding; Xencor: Research Funding; Pharmacyclics: Research Funding; Merck: Research Funding; Jansen: Research Funding; Mayo Clinic: Employment; Ascentage: Research Funding; Millennium: Research Funding. Salles:Novartis, Servier, AbbVie, Karyopharm, Kite, MorphoSys: 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; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Other: Educational events; Merck: Consultancy, Honoraria, 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; BMS: Honoraria. Cartron:Gilead: Honoraria; Jansen: Honoraria; Celgene: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Sanofi: Honoraria. Thomas:Celgene: Research Funding; Amgen: Research Funding; Xencor: Research Funding; BMS: Research Funding. Wierda:KITE pharma: Research Funding; Sunesis: Research Funding; Miragen: Research Funding; Gilead Sciences: Research Funding; Acerta Pharma Inc: Research Funding; GSK/Novartis: Research Funding; Cyclcel: Research Funding; Loxo Oncology Inc.: Research Funding; AbbVie: Research Funding; Genentech: Research Funding; Juno Therapeutics: Research Funding; Oncternal Therapeutics Inc.: Research Funding; Pharmacyclics LLC: Research Funding; Xencor: Research Funding; Janssen: Research Funding. Liebowitz:Xencor: Employment, Equity Ownership. Pagel:AstraZeneca: Consultancy; Gilead Sciences: Consultancy; Pharmacyclics: Consultancy. Ribrag:MSD: Membership on an entity's Board of Directors or advisory committees; Roche: Other: Travel, accommodations, and expenses ; Nanostring: Membership on an entity's Board of Directors or advisory committees; Epizyme: Consultancy, Research Funding; Servier: Consultancy, Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Infinity: Membership on an entity's Board of Directors or advisory committees; AZ: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees, Other: Travel, accommodations, and expenses ; Incyte: Membership on an entity's Board of Directors or advisory committees; ArgenX: Research Funding. Saville:Xencor: Employment, Equity Ownership. Johnson:Xencor: Employment, Equity Ownership. Ly:Xencor: Employment, Equity Ownership. Phillips:Pharmacyclics: Consultancy, Research Funding; Bayer: Consultancy; Abbvie: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Genentech: Consultancy; Seattle Genetics: Consultancy; Gilead: Consultancy; Incyte: Membership on an entity's Board of Directors or advisory committees.

Abstract Background Relatively few studies have evaluated the role of host (germline) genetics in predicting outcome in diffuse large B-cell lymphoma (DLBCL), and of these, most were small candidate gene studies without replication and/or from the pre-R-CHOP era. We report the first genome-wide association study (GWAS) of outcome in DLBCL patients treated with rituximab and anthracycline-based chemotherapy. We used two independent studies for discovery, and validated the results in two additional independent studies. Methods In the discovery phase, we conducted a meta-analysis of two GWAS studies of DLBCL patients treated with rituximab and anthracycline-based chemotherapy, the first study as part of the prospective clinical trials of the LNH2003B program (N=540) of the Lymphoma Study Association (LYSA) and the second as part of the Molecular Epidemiology Resource (N=316), a prospective observational study from the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE). Genotyping for the GWAS was performed using the Illumina 610 (LYSA) and 660 Quad (SPORE) BeadArrays (Illumina, San Diego, USA). HapMap2 was used to impute additional SNPs. Association testing was conducted assuming a log-additive genetic model and using Cox regression analyses. We calculated the hazard ratios (HRs) and 95% CIs for event-free survival (EFS), with adjustment for age, sex and age-adjusted IPI. The two studies were then combined using the fixed effects inverse variance method based on the beta estimates and standard errors from each study. From the meta-analysis, 89 SNPs were selected for validation, based on a minor allele frequency (MAF) >0.05 and LD pattern for multiple SNPs from the same region. Validation cohorts were an independent set of DLBCL patients from the SPORE (SPORE-2, N=366) and DLBCL patients from the prospective GOELAMS 075 phase III study (N=294), and validation genotyping was conducted using the Illumina VeraCode platform. We then conducted a meta-analysis of the 4 studies. We report SNPs with both a statistical significance of p<5x10-6 and effect sizes in the same direction across the 4 studies in the meta-analysis. For the latter SNPs, we also report the results for overall survival (OS), adjusted for age, sex and age-adjusted IPI. Results In the final meta-analysis of all four studies for the prediction of EFS, the top SNP was rs7765004 (MAF=0.30; HR=1.45; p=9.7 x 10-8) in the 6q21 region, which is near the MARCKS (myristoylated alanine-rich protein kinase C substrate) and HDAC2 genes. MARCKS encodes for a protein involved in cell mobility, while HDAC2 belongs to the histone deacetylase family. Additional SNPs from this region included rs6918103 (MAF=0.25; HR=1.38; p=7.6 x 10-6), which marks an EZH2 histone binding site, and rs11969684 (MAF=0.12; HR=1.43; p=3.3 x 10-5). The next hit was rs7712513 (MAF=0.31; HR=1.40; p=3.2 x 10-7), which is near arginine-fifty homeobox pseudogene 1 (ARGFXP1) on chromosome 5q23.2. This was followed by a region on chromosome 8q13.3, with the SNP rs9298183 (MAF=0.29; HR=1.41; p=1.4 x 10-6) from LOC100132891 showing the strongest association, along with 3 other SNPs from this region (rs2035376, rs6472637, and rs1600797; all p<5 x 10-6). These SNPs are near the musculin gene (MSC) a transcriptional repressor of E2A frequently silenced by promoter methylation in DLBCL. Finally, rs2253986 (MAF=0.27; HR=1.39; p=3.4 x 10-6) at chromosome 16q12.2, along with rs2253971 and rs6499810, were associated with EFS. These SNPs are located in CES5A (carboxylesterase 5A), which encodes a member of the carboxylesterase family. The family members are responsible for the hydrolysis or transesterification of various xenobiotics. The top SNPs associated with EFS also predicted OS, including rs7765004 (HR=1.41; p=1.2 x 10-5), rs7712513 (HR=1.39; p=8.4 x 10-6), rs9298183 (HR=1.36; p=3.5 x 10-5), and rs2253986 (HR=1.35; p=5.2 x 10-5). Conclusions Although our top SNPs on did not reach genome-wide significance (p<5x10-8), they showed clear consistency across the four large prospective studies and predicted both EFS and OS independently of IPI. The strongest findings were from loci on 6q21, 8q13.3, 5q23.2, and 16q12.2, and several genes from these regions are involved in methylation and xenobiotic metabolism, supporting a role for host genetic variation in these pathways in DLBCL prognosis. Disclosures: No relevant conflicts of interest to declare.

Abstract Background: Follicular lymphoma (FL) is the most common indolent NHL with a heterogenous natural history of disease and a median survival of 8 to 12 y, albeit ranging between 1 to 20 y. Casulo et al. (2015) identified a high-risk FL cohort of patients with progression of disease (POD) at ≤24 months following initiation of immunochemotherapy with R-CHOP. Idelalisib (Zydelig) is a first-in-class, highly selective, oral inhibitor of PI3Kd which is indicated for relapsed FL or SLL following receipt of at least two lines of systemic chemotherapy. Retrospective subgroup analysis of the idelalisib registrational trial NCT01282424 (101-09) of a cohort with early POD following immunochemotherapy was performed to assess possible activity of idelalisib in this population. Methods: A subset of 46 patients enrolled in study 101-09 were identified as having been diagnosed with FL and having received first-line immunochemotherapy, of which 37 experienced early POD, defined as starting second-line treatment within 24 months of initial first-line treatment. For the latter group, descriptive statistics of demographic and baseline characteristics and inter-treatment intervals in months as well as Kaplan-Meier estimates of progression-free survival (PFS) and overall survival (OS) following initiation of immunochemotherapy and idelalisib were calculated. Population: Demographic characteristics of these 37 patients included median (range) age at initiation of idelalisib of 64 (33-84) y and 18 (48.6%) females. Histologic grade at diagnosis included 33 (89.2%) with grades 1 or 2 as well as 4 (10.8%) with grade 3A, while 21 (56.8%) patients had a FLIPI score ≥3. The mean (s.d.) number of prior therapies was 3.4±1.4, with a range of 2 to 8, while first-line therapies included 21 (56.8%) patients who received R-CHOP-based regimens, 7 (18.9%) who received BR, and 5 (13.5%) who received R-CVP. Mean (s.d.) inter-treatment intervals included 12.5±6.1 months between first- and second-line for all patients, 9.7±9.3 months between second- and third-line for all patients, 11.9±12.0 months between third- and fourth-line for 24 (64.9%) patients, and 11.8±7.6 months between fourth- and fifth-line for 15 (40.5%) patients. Median (range) time from first-line therapy to idelalisib initiation was 30.3 (8.9-94.7) months; no patient received idelalisib as second-line therapy. Results: Best responses included 5 (13.5%) patients with CR, 16 (43.2%) with PR, 2 (5.4%) with SD, and 1 (2.7%) with PD; median duration of response for those with CR or PR was 11.8 months (95% CI: 3.8 months, not evaluable). There were 7 (18.9%) deaths and 21 (56.8%) PFS events in this group. Estimated probabilities of survival (s.e.) and progression-free status at 2 y following initiation of idelalisib were 79%±7% and 29%±10%, respectively. Median PFS was 11.1 months (95% CI: 5.5, 19.3 months). Estimated probability of survival (s.e.) at 5 years following initiation of first-line treatment was 79%±8%. Median overall survival from both initiation of first-line immunochemotherapy as well as with idelalisib was not reached during the course of this study. Conclusions: Idelalisib may have significant clinical activity in high-risk and doubly-refractive FL following early relapse status post first-line immunochemotherapy. Given the small size of the studied subset population which may not be representative, further characterization in additional patients is warranted to ensure the generalizability of this finding, including consideration of further investigational protocols featuring targeted therapies employed both as single agents and in combination. Figure 1. Overall Survival From Initiation of First-line Treatment Figure 1. Overall Survival From Initiation of First-line Treatment Figure 2. Overall Survival From Initiation of Idelalisib Figure 2. Overall Survival From Initiation of Idelalisib Figure 3. Progression-Free Survival From Initiation of Idelalisib Figure 3. Progression-Free Survival From Initiation of Idelalisib Disclosures Gopal: Gilead, Spectrum, Pfizer, Janssen, Seattle Genetics: Consultancy; Spectrum, Pfizer, BioMarin, Cephalon/Teva, Emergent/Abbott. Gilead, Janssen., Merck, Milennium, Piramal, Seattle Genetics, Giogen Idec, BMS: Research Funding; Millennium, Seattle Genetics, Sanofi-Aventis: Honoraria. Kahl:Roche/Genentech: Consultancy; Seattle Genetics: Consultancy; Millennium: Consultancy; Cell Therapeutics: Consultancy; Celgene: Consultancy; Infinity: Consultancy; Pharmacyclics: Consultancy; Juno: Consultancy. Flowers:Infinity Pharmaceuticals: Research Funding; Genentech: Research Funding; Spectrum: Research Funding; Millennium/Takeda: Research Funding; Seattle Genetics: Consultancy; Spectrum: Research Funding; Pharmacyclics: Research Funding; AbbVie: Research Funding; AbbVie: Research Funding; Gilead Sciences: Research Funding; Pharmacyclics: Research Funding; Onyx Pharmaceuticals: Research Funding; Celegene: Other: Unpaid consultant, Research Funding; OptumRx: Consultancy; Gilead Sciences: Research Funding; Janssen: Research Funding; Acerta: Research Funding; Millennium/Takeda: Research Funding; Acerta: Research Funding; Infinity Pharmaceuticals: Research Funding; Janssen: Research Funding; Onyx Pharmaceuticals: Research Funding. Martin:Janssen: Consultancy, Honoraria; Acerta: Consultancy; Gilead: Consultancy; Celgene: Consultancy; Novartis: Consultancy; Bayer: Consultancy. Link:Genentech: Consultancy, Research Funding; Kite Pharma: Research Funding. Ansell:Bristol-Myers Squibb: Research Funding; Celldex: Research Funding. Ye:Gilead: Employment. Koh:Gilead: Employment. Abella:Gilead: Employment. Barr:Pharmacyclics LLC, an AbbVie Company: Consultancy, Research Funding; Abbvie: Consultancy; Gilead: Consultancy. Salles:Calistoga Pharmaceuticals, Inc.; Celgene Corporation; Genentech, Inc.; Janssen Pharmaceutica Products, L.P.; Roche: Consultancy; Celgene Corporation; Roche and Gilead Sciences: Research Funding; Celgene Corporation; Roche: Speakers Bureau.

Abstract Background: Limited data exist to describe the clinical features and outcomes for elderly pts with FL. The FLASH group performed a prospectively planned pooled analysis of IPD from first-line RCTs and examined associations between age (<= 70 vs. > 70 years), clinical characteristics and FL outcomes. Methods: We identified 18 randomized controlled multicenter clinical trials in the FLASH database which enrolled elderly pts (> 70 yrs). From these 18 studies, 5922 previously untreated FL pts were included for this analysis. Complete response (CR) at 24 and 30 months were defined as whether the pt achieved and remains in CR at 24 months and 30 months after initiation of induction treatment. PFS24 is defined as proportion of pts progression-free and alive at 24 months post-randomization. Time to progression (TTP) and overall survival (OS) were defined as time from randomization to the date of progression (censoring for death without progression) and date of death due to any cause, respectively. Progression-free survival (PFS) was defined as time from randomization to the date of progression or death, due to any cause, whichever comes first. Non-lymphoma death was defined as death without prior progression. Early disease outcomes, i.e. CR24, CR30, and PFS24 were primary outcomes; secondary outcomes were TTP, OS, and PFS. For time-to-event outcomes, Kaplan-Meier method and log-rank test were used for univariate estimation and comparison; stratified Cox proportional hazard modeling was used for multivariable analyses. Cumulative incidence methods were used to model PFS while treating disease progression as the primary event of interest and non-lymphoma death as a competing risk. For binary outcomes, multivariable logistic regression was used to estimate the association between age groups and outcomes. Variable adjusted in the regression models are FLIPI score without the age component, ECOG performance status (>= 2 vs < 2), and rituximab use. Results: Among 5922 previously untreated FL pts from 18 RCTs, 63% (n = 3728) were <= 60, 28% (n = 1652) 61-70, and 9% (n = 542) > 70 yrs. Pts age > 70 (vs. <= 70) more commonly had elevated LDH (42% vs 36%, p = 0.02), hemoglobin < 12 g/dL (27% vs 19%, p < 0.0001), ECOG PS >= 2 (8.8% vs 5.0%, p = 0.0004), and elevated β2-microglobulin (68% vs 49%, p < 0.001), less often had > 4 lymph nodes involved (54% vs 65%, p < 0.001), and had similar FLIPI scores without age component (p = 0.17). There were no significant differences between groups in: Ann Arbor stage (94% vs 95% stage III/IV) or rituximab use (62% vs 58%). Median survival follow-up was 5.6 yrs. Pts > 70 yrs did not differ from pts <= 70 in rates of CR24 (27% vs 30%, p=0.32), CR30 (29% vs 31%, p=0.55) or PFS24 (63% vs. 67%, p=0.10). Median TTP was 3.8 and 4.0 years for pts > 70 and <= 70, respectively (HR=1.08, 95% CI 0.96-1.23, p=0.20). With a median OS of 14.6 years for all pts, median OS was 7.4 and 15.7 years for pts > 70 and <= 70, respectively (HR=2.35, 95% CI=2.03-2.73, p<0.001). PFS was statistically significantly different but clinically similar at median 3.1 and 3.8 years for pts > 70 and <= 70, respectively (HR=1.26, 95% CI =1.12-1.41, p<0.001). Further investigation showed the difference in PFS was due to higher incidence of non-lymphoma death in pts > 70 with no difference in disease progression (Figure). In regression models, adjusting for rituximab use, ECOG PS >= 2, and FLIPI score without the age component, age > 70 was a significant predictor of OS and PFS (due to higher incidence of non-lymphoma death), but not PFS24, CR24 or CR30 (Table). Conclusions: FL pts > 70 yrs treated on trials have similar early disease outcomes to younger patients. There is no disease-specific outcome difference between age groups. Age alone should not disqualify patients from standard treatments or RCTs. Disclosures Flowers: Genentech: Consultancy, Research Funding; NIH: Research Funding; Mayo Clinic: Research Funding; TG Therapeutics: Research Funding; Millenium/Takeda: Research Funding; ECOG: Research Funding; Acerta: Research Funding; Pharmacyclics, LLC, an AbbVie Company: Research Funding; Roche: Consultancy, Research Funding; AbbVie: Research Funding; Infinity: Research Funding; Gilead: Consultancy, Research Funding. Ou:Mayo Clinic: Employment. Shi:Mayo Clinic: Employment. Hochster:Genentech: Consultancy, Other: Consultancy fees for advisory boards for GI cancer and bevacizumab with amounts within Yale University de minims guidelines. Brice:Roche: Honoraria; Takeda Pharmaceuticals International Co.: Honoraria, Research Funding; Bristol Myers-Squibb: Honoraria; Gilead: Honoraria; Seattle Genetics: Research Funding. Hiddemann:Roche: Other: Grants; Roche: Membership on an entity's Board of Directors or advisory committees; Genentech: Other: Grants. Marcus:Takeda: Consultancy, Speakers Bureau; Roche: Consultancy, Speakers Bureau. Kimby:Abbvie: Consultancy, Honoraria; Jansen: Consultancy, Honoraria; Celgene: Honoraria; Baxalta: Consultancy; Gilead: Honoraria. Herold:Gilead: Other: Personal fees from member advisory board; Celgene: Honoraria; Genentech: Other: Grants; Roche: Honoraria, Other: Grants. De Bedout:Celgene: Employment. Nielsen:Hoffmann-La Roche: Employment. Morschhauser:Roche: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Servier: Consultancy, Honoraria; Gilead Sciences: Consultancy, Honoraria; Janssen: Honoraria. Rummel:Mundipharma GmbH: Other: Personal fees, Research Funding; Roche Pharma AG: Other: Personal fees, Research Funding. Vitolo:Gilead: Other: Honoraria for lectures; Takeda: Other: Honoraria for lectures; Roche: Membership on an entity's Board of Directors or advisory committees, Other: Honoraria for lectures; Janssen: Membership on an entity's Board of Directors or advisory committees, Other: Honoraria for lectures. Salles:Celgene: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Gilead: Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Roche/Genentech: Consultancy, Honoraria, Research Funding; Mundipharma: Honoraria. Sargent:Celgene: Research Funding.