Link, Brian K.

Abstract Background: Differences in outcomes between clinical trial and real-world populations in follicular lymphoma (FL) may be partially explained by differences in characteristics between clinical trial and real-world patients. We hypothesized that certain eligibility criteria may be responsible for populations with distinct demographic/clinical characteristics. In this study, we compiled a list of eligibility criteria from front-line FL trials and evaluated their impact on the potentially eligible FL patients derived from two national cohort studies. We also evaluated whether criteria were duplicative and whether liberalizing criteria might enlarge the pool of eligible patients. Methods: Eligibility criteria were abstracted from 21 first-line clinical trials in FL in the FLASH database. We identified FL patients in two prospective cohort studies: Molecular Epidemiology Resource (MER) and Lymphoma Epidemiology of Outcomes (LEO). Descriptive statistics were used to characterize patients included and excluded by various eligibility criteria. Chi-square tests and two-sample t-tests were used to compare categorical and continuous variables, respectively. To study the relative impact of individual criteria, we used a step-wise approach to quantify the number of additional patients excluded with each individual criterion after applying other criteria. Results: Eligibility criteria included in at least one-third of studies included stage (present in all 21 trials), renal function (18), HIV/AIDS status (17), performance status (16), history of other malignancies (16), hepatic function (16), and cardiac function (15), other serious health conditions (13), pregnancy status (10), neuro/psych function (10), age (10), metabolic disease status (9), CNS involvement (9), birth control (8), pulmonary function (8), WBC count (8), HBV status (8), platelet count (7), measurable disease (7), HCV status (7), active infection status (7), and CD20 status (7). We included 738 patients with newly diagnosed FL undergoing first-line therapy from MER and 703 patients from LEO. In MER, the median age was 60 years; 87% were White vs 2% non-White, 80% were non-Hispanic vs 2% Hispanic, and 68% were stage III/IV. In LEO, the median age was 60 years; 88% were White vs 11% non-White, 87% were non-Hispanic vs 11% Hispanic, and 67% were stage III/IV. Exclusion criteria impacting more than 10% of patients included stage (31 in LEO vs 29% in MER), self-reported serious health conditions (17 vs 31%), prior cancer diagnosis (14 vs 11%), performance status (10%), and platelet count (10%). Patients excluded due to the following criteria tended to be older: renal function (median 70 vs 60 years in MER), prior malignancy (67 vs 58 years), and self-reported serious health conditions (66 vs 58 years). This pattern was consistent in LEO (Table). No eligibility criteria significantly impacted race/ethnicity. Given the potential for multiple eligibility criteria to exclude the same patients, we used a step-wise approach to evaluate the impact of individual criteria on patients remaining after exclusion by the larger group of criteria, and whether liberalizing criteria impacted patient numbers and/or demographics. We found that only self-reported serious health conditions significantly impacted numbers (excluding 43% of patients), whereas hematologic parameters and performance status excluded only 5 and 1% of patients, respectively, with no impact on age or race/ethnicity. We also found that liberalizing stage requirement from III-IV to II-IV could increase enrollment by 14% in MER (12% in LEO), and liberalizing platelet requirement from ≥150,000 to ≥100,000 could increase enrollment by 11% (15% in LEO). Moreover, liberalizing eligibility criteria had no impact on age or race/ethnicity of the patient pool or on EFS (Figure). Conclusions: We found that excluding patients with prior malignancies, poor renal function, and self-reported serious health conditions appears to inadvertently exclude older patients. Other serious health conditions appeared to exclude a significant number of patients but did not impact patient demographics. Liberalizing certain criteria, including stage and platelet requirement, could potentially increase trial accrual, but would not likely correct the older patient deficit. These findings may serve in developing consensus eligibility criteria for future first-line FL trials. Figure 1 Figure 1. Disclosures Flowers: Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; National Cancer Institute: Research Funding; Allogene: Research Funding; Biopharma: Consultancy; Cellectis: Research Funding; EMD: Research Funding; Genentech/Roche: Consultancy, Research Funding; Epizyme, Inc.: Consultancy; Genmab: Consultancy; Iovance: Research Funding; Guardant: Research Funding; Burroughs Wellcome Fund: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; Spectrum: Consultancy; Gilead: Consultancy, Research Funding; Amgen: Research Funding; Xencor: Research Funding; Nektar: Research Funding; SeaGen: Consultancy; Ziopharm: Research Funding; TG Therapeutics: Research Funding; 4D: Research Funding; Acerta: Research Funding; Denovo: Consultancy; Novartis: Research Funding; Pfizer: Research Funding; Eastern Cooperative Oncology Group: Research Funding; Morphosys: Research Funding; Adaptimmune: Research Funding; Pharmacyclics/Janssen: Consultancy; Karyopharm: Consultancy; BeiGene: Consultancy; AbbVie: Consultancy, Research Funding; Kite: Research Funding; Janssen: Research Funding; Cancer Prevention and Research Institute of Texas: CPRIT Scholar in Cancer Research: Research Funding; Pharmacyclics: Research Funding. Link: MEI: Consultancy; Novartis, Jannsen: Research Funding; Genentech/Roche: Consultancy, Research Funding. Friedberg: Acerta: Other: DSMC ; Bayer: Other: DSMC ; Novartis: Other: DSMC . Cohen: Genentech, Takeda, BMS/Celgene, BioInvent, LAM, Astra Zeneca, Novartis, Loxo/Lilly: Research Funding; Janssen, Adaptive, Aptitude Health, BeiGene, Cellectar, Adicet, Loxo/Lilly, AStra ZenecaKite/Gilead: Consultancy. Kahl: AbbVie, Acerta, ADCT, AstraZeneca, BeiGene, Genentech: Research Funding; AbbVie, Adaptive, ADCT, AstraZeneca, Bayer, BeiGene, Bristol-Myers Squibb, Celgene, Genentech, Incyte, Janssen, Karyopharm, Kite, MEI, Pharmacyclics, Roche, TG Therapeutics, and Teva: Consultancy. Lossos: Lymphoma Research Foundation: Membership on an entity's Board of Directors or advisory committees; Stanford University: Patents & Royalties; Seattle Genetics: Consultancy; NCI: Research Funding; Verastem: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; NIH grants: Research Funding; University of Miami: Current Employment. Nastoupil: Bayer: Honoraria; Gilead/Kite: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; ADC Therapeutics: Honoraria; IGM Biosciences: Research Funding; Takeda: Honoraria, Other: DSMC, Research Funding; Janssen: Honoraria, Research Funding; Genentech: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; TG Therapeutics: Honoraria, Research Funding; Epizyme: Honoraria, Research Funding; Denovo Pharma: Other: DSMC; Caribou Biosciences: Research Funding; Bristol Myers Squibb/Celgene: Honoraria, Research Funding; MorphoSys: Honoraria. Maurer: Nanostring: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; BMS: Research Funding. Cerhan: NanoString: Research Funding; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Genentech: Research Funding. Martin: ADCT: Consultancy.

Abstract Background: Event-free survival at 24 months (EFS24) is an important clinical endpoint in newly diagnosed diffuse large B-cell lymphoma and follicular lymphoma, and patients who achieve EFS24 with frontline immunochemotherapy have minimal loss of lifetime compared to age- and sex-matched general population. However, the prognostic role of EFS24 in mantle cell lymphoma (MCL) has not been well studied, possibly due to the perception of continued relapse pattern and poor survival outcome of MCL historically. A recent study from our group demonstrated evolving frontline therapy pattern from 2002-2009 ("Era 1") to 2010-2015 ("Era 2") which was associated with improved EFS and overall survival (OS) in Era 2. In the current study, we sought to explore the prognostic role of EFS24 in the two eras. Methods: Patients with newly diagnosed MCL from 9/2002 through 6/2015 were identified from Molecular Epidemiology Resource (MER), a prospective cohort study of the University of Iowa/Mayo Clinic Lymphoma SPORE. OS was defined as time from diagnosis (or the EFS24 defining event where applicable) to death from any cause and was analyzed using the Kaplan-Meier method. Expected OS accounting for age and sex was generated in R by using the general US population as the reference group. Observed versus expected OS was summarized by using standardized mortality ratio (SMR) and 95% confidence intervals (CI) of observed to expected deaths. Cumulative incidence of lymphoma-specific death was analyzed using Gray's test, with deaths from all other causes treated as competing events. Results: A total of 343 patients were included, 175 from Era 1 (median follow-up 13.0 years) and 168 from Era 2 (median follow-up 6.9 years). Age, sex and simplified MIPI score were similar between the 2 groups. Patients diagnosed in Era 2 had better OS, with a 5-year OS of 68.4% vs 59.2% (simplified MIPI-adjusted hazard ratio 0.68, 95% CI 0.50-0.93). Patients diagnosed in both eras had inferior OS compared to the general population, with an SMR of 3.26 (95% CI 2.70-3.89, P<0.001) in Era 1 and 2.63 (95% CI 2.03-3.36, P<0.001) in Era 2. Patients diagnosed in Era 1 who achieved EFS24 (n=98) still had inferior OS compared to the general population, with an SMR of 2.23 (95% CI 1.67-2.92, P<0.001) (Figure 1A). In contrast, for patients diagnosed in Era 2 who achieved EFS24 (n=99), no evidence of OS difference was found compared to the general population, with an SMR of 1.31 (95% CI 0.78-2.07, P=0.31) (Figure 1B). The primary cause of death after diagnosis was lymphoma-related for patients diagnosed in both eras. The 5-year rate of lymphoma-related death was 28.8% in Era 1 and 20.5% in Era 2. In patients who were diagnosed in Era 1 and achieved EFS24, the primary cause of death after achieving EFS24 remained to be lymphoma-related, with a 5-year rate of 19.8% compared to 6.2% for lymphoma-unrelated causes (Figure 1C). In contrast, in patients who were diagnosed in Era 2 and achieved EFS24, the rate of lymphoma-related death was no longer higher than that of lymphoma-unrelated death, with a 5-year rate of 2.1% vs 5.5%, respectively (Figure 1D). In a sensitivity analysis restricted to only patients who received standard frontline immunochemotherapy, similar results were obtained. For example, in patients who achieved EFS24, the SMR compared to the general population was 2.89 (95% CI 2.00-4.04, P<0.001) in Era 1 and 0.89 (95% CI 0.43-1.64, P=0.82) in Era 2. The 5-year rates of deaths due to lymphoma-related vs lymphoma-unrelated causes were 20.7% vs 6.2% in Era 1 and 1.3% vs 4.9% in Era 2. Conclusions: MCL survival outcome has improved for patients first diagnosed in the more recent era (2010-2015, compared to 2002-2009), likely due to improved frontline therapy as well as better salvage treatments (such as lenalidomide and BTK inhibitors). In the more recent treatment era, patients who achieved EFS24 had survival approaching the age- and sex-matched general population. In addition, these patients had a low risk of dying from lymphoma and were more likely to die from other causes. Longer follow-up (e.g., in Era 2) and external validation in other series are necessary to confirm the prognostic role of EFS24. With more efficacious salvage treatment options and likely continued improvement of OS in MCL, EFS24 may become an important clinical endpoint in frontline therapy of MCL. Figure 1 Figure 1. Disclosures Wang: TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Eli Lilly: Membership on an entity's Board of Directors or advisory committees; Novartis: Research Funding; InnoCare: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding; MorphoSys: Research Funding. Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Nanostring: Research Funding. Link: Novartis, Jannsen: Research Funding; Genentech/Roche: Consultancy, Research Funding; MEI: Consultancy. Farooq: Kite, a Gilead Company: Honoraria. Paludo: Karyopharm: Research Funding. Witzig: Celgene/BMS, Acerta Pharma, Kura Oncology, Acrotech Biopharma, Karyopharm Therapeutics: Research Funding; Karyopharm Therapeutics, Celgene/BMS, Incyte, Epizyme: Consultancy, Membership on an entity's Board of Directors or advisory committees. Habermann: Seagen: Other: Data Monitoring Committee; Tess Therapeutics: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Cerhan: Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding; Genentech: Research Funding. Nowakowski: Celgene, NanoString Technologies, MorphoSys: Research Funding; Celgene, MorphoSys, Genentech, Selvita, Debiopharm Group, Kite/Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees.

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 Background: In the ELARA (E2202) trial (NCT03568461), tisagenlecleucel (tisa-cel) demonstrated high response rates in patients with relapsed/refractory follicular lymphoma (r/r FL), including those with high-risk disease, with an overall response rate (ORR) of 86% and complete response rate (CRR) of 66%. As ELARA did not include a comparator arm, an adjusted indirect treatment comparison (ITC) using patient-level data from a global retrospective cohort study was conducted. This study aimed to provide comparative, contextual evidence to the efficacy outcomes of tisa-cel from ELARA relative to standard of care (SOC) in routine practice. Methods: ELARA is an ongoing, single-arm, global, multicenter, phase II trial which evaluates the efficacy and safety of tisa-cel in adult patients with r/r FL. As of March 29, 2021, 98 patients were enrolled with a median follow-up of 15 months. SOC data were obtained from ReCORD-FL, a global retrospective cohort study of clinical outcomes in patients with r/r FL meeting the ELARA eligibility criteria who were treated per routine practice at 10 academic centers across North America and Europe; 7 ReCORD-FL sites are also participating in ELARA, but no patients are enrolled in both studies. In ReCORD-FL, with a data cutoff date of December 31, 2020, a total of 187 patients with ≥2 lines of previous treatment were identified for inclusion, with a median follow-up from third-line of 57 months. A complete-case comparison analysis was performed for 97 ELARA apheresed patients and 143 ReCORD-FL patients with complete data on all baseline variables and prognostic factors used for adjustment. For the comparative analysis, one line of therapy (LoT) per patient was selected using a propensity score model to identify the LoT which had the highest chance of being enrolled in ELARA conditional on baseline characteristics and prognostic factors available at the start of the LoT (i.e., the selected LoT was the one with the highest propensity score to be in ELARA). After selection of LoT, an adjusted ITC was performed to assess the effect of tisa-cel versus SOC by measuring CRR, ORR, progression-free survival (PFS), overall survival (OS), and time to next treatment (TTNT). A subgroup analysis of SOC patients with ≥1 eligible LoT initiated from 2014 on (coinciding with the introduction of the Lugano response criteria as well as regulatory approval of idelalisib) was performed for all endpoints. Results: Baseline characteristics for the tisa-cel and SOC cohorts are described in Table 1; after weighting, baseline variables, including number of previous lines of systemic therapy (median: 4 lines) were well balanced between the tisa-cel and SOC cohorts. Treatment regimens observed for ReCORD-FL patients at LoT selection were: anti-CD20 antibody plus alkylator (31.5% of patients), anti-CD20 antibody without alkylator (25.9%), alkylator without anti-CD20 antibody (17.5%), and regimens other than anti-CD20 antibody and alkylator (25.2%). After LoT selection and adjusting for differences in baseline variables, tisa-cel was associated with improvement over SOC in CRR (69.1% vs. 37.3%), ORR (85.6% vs. 63.6%), as well as PFS, TTNT and OS (Table 2, Figures 1-2), with a numerically higher 6-month PFS rate vs. SOC (85.3% vs. 66.5%), as well as higher 24-month OS rate (87.8% vs. 64.8%). Further, there was an estimated 80% reduction in death risk in favor of tisa-cel over SOC, a 40% reduction in risk of progression in favor of tisa-cel over SOC and a 69% reduction in risk of death or requiring a new anticancer therapy (Table 1). In the sub-analysis of SOC patients with lines of therapy initiated in or after 2014, the superiority of tisa-cel over SOC was confirmed in all the efficacy outcomes (CRR: 69.1% vs. 30.5%; ORR: 85.6% vs. 58.8%; hazard ratios substantially < 1 for OS, PFS, TTNT). Conclusion: The ITC results suggest that tisa-cel has superior efficacy over SOC in r/r FL for all evaluated endpoints. A key limitation of this study is that response assessment criteria and schedule was more heterogeneous in ReCORD-FL than in ELARA. However, the sub-analysis on SOC patients assessed with a LoT selected in 2014 or later (when more patients could have been assessed using the Lugano response criteria) showed similar favorability for tisa-cel over SOC in all the efficacy outcomes. Moreover, outcome parameters independent of response criteria, namely OS and TTNT, were also significantly better for tisa-cel vs. SOC. Figure 1 Figure 1. Disclosures Salles: Abbvie, Epizyme, Morphosys, Regeneron: Consultancy, Honoraria; Bayer: Honoraria; Beigene, BMS/Celgene, Debiopharm, Genentech/Roche, Genmab, Incyte, Ipsen, anssen, Novartis. Kite/Gilead, Loxo, Miltneiy, Rapt, TAKEDA, Velosbio, Allogene: Consultancy. Schuster: Acerta Pharma/AstraZeneca: Consultancy; Alimera Sciences: Consultancy; BeiGene: Consultancy; Juno Theraputics: Consultancy, Research Funding; Loxo Oncology: Consultancy; Tessa Theraputics: Consultancy; Genentech/Roche: Consultancy, Research Funding; Merck: Research Funding; Pharmacyclics: Research Funding; Adaptive Biotechnologies: Research Funding; Incyte: Research Funding; TG Theraputics: Research Funding; Novartis: Consultancy, Honoraria, Patents & Royalties, Research Funding; Abbvie: Consultancy, Research Funding; Nordic Nanovector: Consultancy; Celgene: Consultancy, Honoraria, Research Funding. Dreyling: BeiGene: Consultancy; Celgene: Consultancy, Research Funding, Speakers Bureau; Amgen: Speakers Bureau; Genmab: Consultancy; Gilead/Kite: Consultancy, Research Funding, Speakers Bureau; Incyte: Consultancy, Speakers Bureau; Janssen: Consultancy, Research Funding, Speakers Bureau; Novartis: Consultancy, Speakers Bureau; Roche: Consultancy, Research Funding, Speakers Bureau; Abbvie: Research Funding; Astra Zeneca: Consultancy, Speakers Bureau; Bayer HealthCare Pharmaceuticals: Consultancy, Research Funding, Speakers Bureau. Kuruvilla: AbbVie: Honoraria; Amgen: Honoraria; AstraZeneca: Honoraria, Research Funding; Antengene: Honoraria; Roche: Honoraria, Research Funding; Incyte: Honoraria; BMS: Honoraria; Merck: Honoraria; Janssen: Honoraria, Research Funding; Karyopharm: Honoraria, Other: Data and Safety Monitoring Board; Novartis: Honoraria; Medison Ventures: Honoraria; Pfizer: Honoraria; Gilead: Honoraria; Seattle Genetics: Honoraria; TG Therapeutics: Honoraria. Patten: ROCHE: Research Funding; GILEAD SCIENCES: Honoraria, Research Funding; ABBVIE: Honoraria; NOVARTIS: Honoraria; ASTRA ZENECA: Honoraria; JANSSEN: Honoraria. Von Tresckow: AbbVie: Other: Congress and travel support; Amgen: Consultancy, Honoraria; AstraZeneca: Honoraria, Other: Congress and travel support; BMS-Celgene: Consultancy, Honoraria, Other: Congress and travel support; Kite-Gilead: Consultancy, Honoraria; MSD: Consultancy, Honoraria, Other: Congress and travel support, Research Funding; Novartis: Consultancy, Honoraria, Other: Congress and travel support, Research Funding; Pentixafarm: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Other, Research Funding. Smith: Alexion, AstraZeneca Rare Disease: Other: Study investigator; Celgene, Genetech, AbbVie: Consultancy. Davis: Novartis, Vertex Pharmaceuticals, Pfizer, Eisai, Eli Lilly, AstraZeneca: Research Funding. Anjos: Novartis: Current Employment. Chu: Novartis: Current Employment, Current equity holder in publicly-traded company. Zhang: Novartis: Current Employment, Current equity holder in publicly-traded company. Lobetti Bodoni: Spouse: Harlcok Healthcare: Current holder of individual stocks in a privately-held company; Spouse: Takeda: Consultancy, Honoraria, Speakers Bureau; Spouse: Celgene: Honoraria; Spouse: Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Spouse: Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Spouse: Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees; Spouse: NHS: Ended employment in the past 24 months; Spouse: F. Hoffmann-La Roche: Current Employment, Current equity holder in publicly-traded company; Gilead: Other: Travel sponsorship in June 2019; Novartis: Current Employment, Current equity holder in publicly-traded company. Thieblemont: Kyte: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses ; Gilead Sciences: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses ; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses , Research Funding; Takeda: 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, Other: Travel, Accommodations, Expenses ; Bristol Myers Squibb/Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses ; Incyte: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses ; Cellectis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses ; Hospira: Research Funding; Bayer: Honoraria; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses . Fowler: BostonGene, Corp: Current Employment, Current holder of stock options in a privately-held company; Bristol Myers Squibb, F. Hoffmann-La Roche Ltd, TG Therapeutics and Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding. Dickinson: Janssen: Consultancy, Honoraria; Takeda: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria; MSD: Consultancy, Honoraria, Research Funding, Speakers Bureau; Gilead Sciences: Consultancy, Honoraria, Speakers Bureau; Amgen: Honoraria; Celgene: Research Funding; Roche: Consultancy, Honoraria, Other: travel, accommodation, expenses, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Research Funding, Speakers Bureau. Martinez-Lopez: Novartis: Consultancy, Speakers Bureau; BMS: Consultancy, Research Funding, Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Incyte: Consultancy, Research Funding, Speakers Bureau; Roche: Consultancy, Research Funding, Speakers Bureau; Astellas: Research Funding, Speakers Bureau. Wang: Eli Lilly: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; InnoCare: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; MorphoSys: Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees. Link: Genentech/Roche: Consultancy, Research Funding; MEI: Consultancy; Novartis, Jannsen: Research Funding.

Abstract Introduction: Low-grade B-cell lymphomas (LGBCL), aside from follicular lymphoma and chronic lymphocytic leukemia/small lymphocytic lymphoma, account for approximately 10% of B-cell non-Hodgkin lymphomas and consist of several subtypes. While a majority of LGBCL cases have an overall favorable prognosis, we have previously shown that cases who have an event (relapse or progression, transformation, or re-treatment) within 24 months of diagnosis (EFS24) have an inferior overall survival (OS) compared to those achieving EFS24 (Tracy et al., AJH 2019;94:658-66). However, the underlying biological characteristics associated with early failure and aggressive disease across LGBCL subtypes are unknown. In this study, we used matched transcriptomic, genomic, and immune profiling data from LGBCL cases, the largest cohort to date, and asked whether there were unique biological phenotypes across different LGBCL subtypes and whether we could identify signatures associated with aggressive LGBCL. Validation of the prognostic utility of this signature was performed on a previously published, independent cohort of 63 pre-treatment LGBCL cases. Methods: Tumors from 64 newly diagnosed LGBCL patients from the Molecular Epidemiology Resource of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence were included in this study (SMZL (n = 48), NMZL (n = 6), LPL (n = 5), B-NOS (n = 3), EMZL (n = 2)). RNA sequencing (RNAseq) data from 61 LGBCL tumors and 5 benign CD19+CD27+ memory B samples was subjected to NMF clustering to define groups. Differential expression and pathway analysis were used to identify biological characteristics of each cluster. CIBERSORT was used to identify immune cells in the tumor microenvironment. Whole exome sequencing (WES) was performed on 61 tumor-normal pairs. Singscore was used to assign a single score per patient representing gene expression of the survival-associated transcriptomic signature identified in this study. Results: NMF analysis of RNAseq data identified 5 clusters of patients, denoted LGBCL1-5 (Fig 1A). Patients from the same diagnostic subtype did not exclusively cluster together, with all LGBCL clusters comprised of patients from multiple subtypes (Fig 1B). Exploring the association between patient cluster and outcome, we observed significantly inferior event-free survival (EFS) (HR 2.24; 95% CI 1.01-4.98) and overall survival (OS) (HR 5.59; 95% CI 2.00-15.63) in LGBCL5 patients compared to LGBCL1-4 (Fig 1C). In addition, 80% of the transformation cases in our cohort were classified as LGBCL5 (Fig 1D). Differential expression and pathway analysis showed distinct processes significantly upregulated in each cluster (FDR < 0.05), with LGBCL5 demonstrating enrichment of cell cycle and mitosis pathways. CIBERSORT identified increased immune cell content in LGBCL3 and LGBCL5 compared to other clusters, with high frequencies of mast cells in both (p = 0.0002), increased CD8 T cells in LGBCL3 (p < 0.0001), and increased T follicular helper cells in LGBCL5 (p = 0.004). WES identified previously reported alterations in NOTCH, NFkB, and chromatin remodeling pathways and novel variants in LGBCL, including mutations in HNRNPK, CLTC, HLA-A, HLA-B and HLA-C. Assessment of alterations by cluster showed significant enrichment of TNFAIP3 (OR 5.54; 95% CI 1.20-28.14) and BCL2 alterations (OR 5.49; 95% CI 1.07-32.02) in LGBCL5 cluster. Finally, we identified a cell cycle-related transcriptomic signature of 108 genes upregulated in LGBCL5 and EFS24 failure cases. Cases with high expression of this signature showed significantly inferior EFS (HR 14.25; 95% CI 4.90-41.38) and OS (HR 7.82; 95% CI 2.40-25.44) compared to cases with low expression in our discovery cohort. This observation was reproduced in an independent validation cohort, where patients with high expression of this signature demonstrated significantly inferior EFS (HR 5.70; 95% CI 1.49-21.79) and OS (HR 10.07; 95% CI 2.00-50.61). Conclusions: In this study, we are the first to define mechanisms of pathogenesis in LGBCL with shared transcriptomic, genomic, and immune profiles present across LGBCL subtypes. We then further defined a gene expression signature associated with inferior patient outcome, with application of this signature to an independent validation cohort demonstrating proof of concept and utility of this signature as a prognostic marker in LGBCL patients. Figure 1 Figure 1. Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Nanostring: Research Funding. Paludo: Karyopharm: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Incyte: Other: Scientific Advisory Board; Seagen: Other: Data Monitoring Committee; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Link: MEI: Consultancy; Genentech/Roche: Consultancy, Research Funding; Novartis, Jannsen: Research Funding. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. Cerhan: Genentech: Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Novak: Celgene/BMS: Research Funding.

Abstract Background: Novel therapies for multiply relapsed follicular lymphoma (FL) are often evaluated in single arm trials with no comparative data on patients receiving usual care. This study (ReCORD-FL) therefore sought to construct a historical control cohort to augment current and future single arm trials in relapsed/refractory (r/r) FL. The analytic aims were to document patient characteristics, treatment patterns and clinical outcomes in a r/r FL population treated with standard therapies in routine practice. Methods: This was a retrospective cohort study via medical record review in 10 oncology centers across North America and Europe. Adult patients were required to meet several criteria defining multiply r/r FL (i.e., r/r after ≥2 lines of therapy, or relapsed during or within 6 months after completion of anti-CD20 antibody maintenance, or relapsed after autologous HSCT). Patients were also required to have ≥1 line of systemic therapy (i.e., first qualifying salvage therapy) after first meeting the r/r FL criteria; the date of first qualifying salvage therapy defined the study index date. At index, patients were required to have grade 1-3A FL, Eastern Cooperative Oncology Group (ECOG) performance status of 0 or 1, and no evidence of prior histological transformation. Outcomes were observed from the index date and from the start of each therapy line until the earliest of death, last follow-up, or December 31, 2020 (data cutoff). Endpoints were complete response (CR) rate, overall response rate (ORR), time to next treatment (TTNT), progression-free survival (PFS), and overall survival (OS). In a subgroup analysis, endpoints were examined by double refractoriness (r/r to both an anti-CD20 mAb and an alkylator) and POD24 status at index (best response of stable/progressive disease or relapsed within 24 months of front-line anti-CD20 mAb-containing therapy). Time to event outcomes were analyzed using the Kaplan-Meier method. Results: A total of 187 patients were identified for inclusion (Table 1). Most patients' (80.2%) first qualifying salvage (index) therapy occurred in third line (3L) (range: 3L - 6L). Anti-CD20 mAb plus chemotherapy (including alkylating and/or non-alkylating agents) was the most common index regimen (64.2% of patients); 8% received anti-CD20 mAb monotherapy, 11.2% received alkylator-based chemotherapy alone (i.e., an alkylator-containing regimen without anti-CD20 mAb), and 16.6% received other therapies (i.e., other regimens containing neither anti-CD20 mAb nor alkylator). Median follow-up from FL diagnosis was 9 years (range: 1 - 21 years), over which a median of 5 (range: 3 - 11) lines of therapy were observed per patient. CR rate and ORR to the index treatment were 39.0% and 70.6%, respectively (Table 2). Median (95% confidence interval [CI]) TTNT and PFS from index were 14.4 (11.8 - 18.6) and 14.6 (11.0 - 18.0) months, respectively; median OS from index was 128 months (10.6 years). Compared with non-double refractory, those with double refractory disease at index had numerically lower CR (34.3% vs. 45.1%) and ORR (67.6% vs. 74.4%) and substantially shorter median (95% CI) TTNT (11.8 [9.0 - 15.2] vs. 20.9 [14.4 - 26.2] months), PFS (10.7 [7.7 - 14.5] vs. 20.1 [14.4 - 25.4] months), and OS (78.1 [45.8 - 146.7] vs. [Not Reached] months). Outcomes were similarly less favorable for patients who were POD24 at index. All outcomes steadily worsened across successive treatment lines (Table 2, Figures 1a-c). ORR, for example, decreased from 69.5% in 3L (n = 187) to 45.8% in 5L (n = 96) and 41.2% in ≥8L (n = 51), while median (95% CI) PFS decreased from 11.8 (10.1 - 16.6) months in 3L to 9.4 (6.8 - 13.1) months in 5L and 4.4 (1.7 - 5.9) months in ≥8L; median (95% CI) OS had a similar trend: 133.7 (78.1 - 232.4), 46.3 (31.7 - 76.5), and 11.4 (5.9 - 21.2) months in 3L, 5L, and ≥8L, respectively. Conclusions: Our findings further demonstrate the poor outcomes and limited survival in FL patients with multiply r/r, double refractory, or POD24 disease. In assessing response rates and PFS, it is important to consider that response assessment criteria and schedules were more heterogenous in the routine practice settings of ReCORD-FL than in clinical trials. Based on comparability of results with another similar study (SCHOLAR-5), the robustness of data collected, and continued low likelihood of randomized trials in this area, ReCORD-FL provides valuable historical control data for new r/r FL therapies in development. Figure 1 Figure 1. Disclosures Salles: Beigene, BMS/Celgene, Debiopharm, Genentech/Roche, Genmab, Incyte, Ipsen, anssen, Novartis. Kite/Gilead, Loxo, Miltneiy, Rapt, TAKEDA, Velosbio, Allogene: Consultancy; Abbvie, Epizyme, Morphosys, Regeneron: Consultancy, Honoraria; Bayer: Honoraria. Schuster: Abbvie: Consultancy, Research Funding; Acerta Pharma/AstraZeneca: Consultancy; Alimera Sciences: Consultancy; BeiGene: Consultancy; Juno Theraputics: Consultancy, Research Funding; Loxo Oncology: Consultancy; Tessa Theraputics: Consultancy; Genentech/Roche: Consultancy, Research Funding; Merck: Research Funding; Pharmacyclics: Research Funding; Adaptive Biotechnologies: Research Funding; Incyte: Research Funding; TG Theraputics: Research Funding; Novartis: Consultancy, Honoraria, Patents & Royalties, Research Funding; Nordic Nanovector: Consultancy; Celgene: Consultancy, Honoraria, Research Funding. Kuruvilla: Seattle Genetics: Honoraria; Merck: Honoraria; Novartis: Honoraria; Gilead: Honoraria; BMS: Honoraria; Antengene: Honoraria; Amgen: Honoraria; AbbVie: Honoraria; Karyopharm: Honoraria, Other: Data and Safety Monitoring Board; Janssen: Honoraria, Research Funding; Incyte: Honoraria; Medison Ventures: Honoraria; Roche: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; Pfizer: Honoraria; TG Therapeutics: Honoraria. Patten: GILEAD SCIENCES: Honoraria, Research Funding; ROCHE: Research Funding; ASTRA ZENECA: Honoraria; NOVARTIS: Honoraria; JANSSEN: Honoraria; ABBVIE: Honoraria. von Tresckow: Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Other, Research Funding; Pentixafarm: Consultancy, Honoraria; AbbVie: Other: congress and travel support; BMS-Celgene: Consultancy, Honoraria, Other: congress and travel support; AstraZeneca: Honoraria, Other: congress and travel support; Pfizer: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Kite-Gilead: Consultancy, Honoraria; MSD: Consultancy, Honoraria, Other: congress and travel support, Research Funding; Novartis: Consultancy, Honoraria, Other: congress and travel support, Research Funding. Smith: Alexion, AstraZeneca Rare Disease: Other: Study investigator; Celgene, Genetech, AbbVie: Consultancy. Davis: Novartis, Vertex Pharmaceuticals, Pfizer, Eisai, Eli Lilly, AstraZeneca: Research Funding. Nagar: Novartis, AstraZeneca, Eisai: Research Funding. Zhang: Novartis: Current Employment, Current equity holder in publicly-traded company. Bollu: Novartis: Current Employment, Current equity holder in publicly-traded company. Jousseaume: Novartis: Current Employment, Current equity holder in publicly-traded company. Ramos: Novartis: Current Employment, Current equity holder in publicly-traded company. Wang: Eli Lilly: Membership on an entity's Board of Directors or advisory committees; Novartis: Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; MorphoSys: Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding; InnoCare: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding. Link: Genentech/Roche: Consultancy, Research Funding; MEI: Consultancy; Novartis, Jannsen: Research Funding.

Abstract Background: Follicular lymphoma (FL) exhibits significant clinical, cellular, molecular, and genetic heterogeneity. Our understanding of FL biology and molecular classifications of FL, to date, has largely been driven by pathologic classification (Grade 1-3b), genetic classification (m7-FLIPI), or gene expression profiling (IR-1/2; Huet-23), along with limited studies on small cohorts or targeted panels. In order to further understand the biological underpinnings and complexity of FL, large-scale and integrated whole exome sequencing (WES) and RNA sequencing (RNAseq) studies are needed. Using a highly-annotated cohort of 93 FL tumors with matched RNAseq, WES, and CyTOF data, we have explored the transcriptomic signature of purified FL B cells and identified unique molecular subsets that are defined by distinct pathway activation, immune content, and genomic signatures. Methods: Frozen cell suspensions from 93 untreated FL (Grade 1-3b) patients' tumor biopsies, enrolled in the University of Iowa/Mayo Clinic Lymphoma SPORE, were used for the study. DNA was isolated from whole tumor cell suspensions, and RNA was isolated from both whole tumor and B cell-enriched cell suspensions. RNAseq and WES were performed in the Mayo Clinic Genome Analysis Core. RNAseq and WES data were processed using a standard pipeline and novel driver genes were identified using Chasm+ driver analysis. Copy number variants were identified from WES data using GISTIC 2.0. NMF clustering and single sample gene set testing for B cell lineage and tumor microenvironment (TME) signatures were performed in R using the NMF and singscore packages. Results: Unsupervised clustering of RNAseq data identified three distinct expression programs which separated patient B cell samples into 3 groups: Group 1 (G1, n=20), Group 2 (G2, n=24), Group 3 (G3, n=43). While no clinical attributes were defined by any single group, G1 consisted of cases that had less aggressive characteristics (63% Stage I-II, 79% FLIPI 0-1). To identify unique transcriptional pathways driving the three expression programs, we scored gene level contributions to NMF expression programs and employed gene set enrichment analysis. This revealed significant pathway association with type-I IFN signaling (G1), DNA repair and stress response (G2), and epigenetic modulation (G3) as differentiating programs between the 3 groups (FDR q<0.001). VIPER master regulator activity inferencing revealed that these pathways were likely being controlled by differential activity in NF-kB, IRFs, STAT1, BCL6, and FOXO1. Each program significantly enriched for, but were not defined by, portions of specific germinal center programs, such as pre-memory (G1), light-zone-to-dark-zone transition (G2), and a pre-light-zone intermediate (G3). We next assessed the connection between B cell programs and the tumor microenvironment (TME) using available paired CyTOF data on 67 cases, which revealed an active TME in G1, with an abundance of CD8 T cell and NK cell populations, a wide variety of immune content in G2 that consisted mostly of Tfh and myeloid cells, and a poorly populated immune compartment in G3 compared to G1 and G2. Finally, somatic driver mutations and copy number alterations from WES were identified and associated with the three clusters. The three groups distinguished themselves by significant enrichment of copy number alterations (TNFAIP3-loss , 1q23-gain, 1q32-gain) in G2, while 10q-loss and mutations in BCL2 and chromatin modifiers (KMT2D and CREBBP) enriched in G3. G1, overall, had lower alteration burden and had weak associations with any specific alterations, suggesting an alternative mechanism for driving the G1 program. Conclusion: In this study, we have identified three unique FL tumor B cell groups, defined by specific transcriptional programs. Pathways such as inflammation, DNA damage response, and chromatin modification contribute most to the differences between B cell samples and group membership. Additionally, each program associated with specific genetic events as well as TME composition, highlighting potential drivers of these B cell states. This study improves the understanding of the mechanisms driving FL tumors and motivates further investigation into transcriptional consequences of genetic events as well as potential tumor intrinsic factors that may influence the TME. Figure 1 Figure 1. Disclosures Maurer: BMS: Research Funding; 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; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Link: MEI: Consultancy; Genentech/Roche: Consultancy, Research Funding; Novartis, Jannsen: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. King: Celgene/BMS: Research Funding. Cerhan: Genentech: Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Novak: Celgene/BMS: Research Funding.

AbstractBecause there have been a dvances in frontline treatment for mantle cell lymphoma (MCL) over the last 2 decades, we sought to characterize the changes in frontline treatment patterns and their association with outcomes. Patients with newly diagnosed MCL from September 2002 through June 2015 were enrolled in a prospective cohort study, and clinical characteristics, treatment, and clinical outcomes were compared between patients diagnosed from 2002 to 2009 (Era 1) compared with 2010 to 2015 (Era 2). Patient age, sex, and simplified MCL International Prognostic Index (sMIPI) score were similar between the 2 groups. In patients age 65 years or younger, there was less use of rituximab plus hyperfractionated cyclophosphamide, vincristine, doxorubicin, and dexamethasone (R-Hyper-CVAD) (16.1% vs 8.8%) but more use of rituximab plus maximum-strength cyclophosphamide, doxorubicin, vincristine, and prednisone (R-maxi-CHOP) alternating with rituximab plus high-dose cytarabine (R-HiDAC), also known as the Nordic regimen, and R-CHOP alternating with rituximab plus dexamethasone, high-dose cytarabine, and cisplatin (R-DHAP) (1.1% vs 26.4%) and less use of R-CHOP or R-CHOP-like regimens (64.5% vs 35.2%) but more use of R-bendamustine (0% vs 12.1%) in Era 2 (P < .001). These changes were associated with improved event-free survival (EFS; 5-year EFS, 34.3% vs 50.0%; P = .010) and overall survival (OS; 5-year OS, 68.8% vs 81.6%; P = .017) in Era 2. In patients older than age 65 years, there was less use of R-CHOP or R-CHOP-like therapy (39.0% vs 14.3%) and nonstandard systemic therapy (36.6% vs 13.0%) but more use of R-bendamustine (0% vs 49.4%). These changes were associated with a trend for improved EFS (5-year EFS, 25.4% vs 37.5%; P = .051) in Era 2. The shift from R-CHOP or R-CHOP-like regimens to R-bendamustine was associated with improved EFS (5-year EFS, 25.0% vs 44.6%; P = .008) in Era 2. Results from this prospective cohort study provide critical real-world evidence for improved outcomes with evolving frontline patterns of care in patients with MCL.

BackgroundAlthough many patients with follicular lymphoma (FL) undergo routine radiographic surveillance during their first remission, no consensus exists on the modality, duration, frequency, or need for routine imaging studies. The authors retrospectively examined the effect of surveillance imaging on relapse detection and overall survival (OS) in patients with FL.MethodsPatients with newly diagnosed FL who had a response to induction therapy were identified from the Lymphoid Malignancies Enterprise Architecture Database (LEAD) at Emory University and from the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic. Patients were evaluated for both relapse and method of relapse detection (ie, clinical concerns vs radiologic detection through surveillance imaging in an asymptomatic patient).ResultsOf 148 patients in the LEAD cohort, 55 (37%) relapsed, and the majority (n = 35; 64%) of relapses were detected clinically. In the MER cohort, 63 of 177 relapses (54%) were detected clinically. There was no significant difference in OS from the date of diagnosis between the 2 methods of relapse detection in the LEAD (hazard ratio [HR], 0.61; 95% CI, 0.13‐2.94; P = .54) and MER (HR, 1.02; 95% CI, 0.47‐2.21; P = .96) cohorts. Similarly, there was no significant difference in OS from the date of relapse between the 2 methods of relapse detection in the LEAD (HR, 0.47; 95% CI, 0.10‐2.27; P = .35) and MER (HR, 1.02; 95% CI, 0.47‐2.21; P = .96) cohorts.ConclusionsThese findings suggest a limited role for routine surveillance imaging in patients with FL who complete front‐line therapy. Future studies should evaluate which patients may benefit from a more aggressive surveillance approach and should explore novel methods of relapse detection.