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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 Monoclonal B-cell lymphocytosis (MBL) is one of the most common pre-malignant conditions and is characterized by a circulating population of clonal B-cells with an absolute clonal B-cell count < 5x10 9/L and no evidence of lymphadenopathy, organomegaly, or cytopenias. MBL can be classified by the immunophenotype: CLL-like MBL (CD5+, CD20dim), atypical MBL (CD5+, CD20+), or non-CLL-like MBL (CD5-, CD20+), as well as by the size of the clone (low-count or high-count) with low-count MBL (LC-MBL) defined as clonal B-cell <0.5x10 9/L or percent clonal B-cell count <85% out of total B-cell count. The prevalence of MBL varies depending on detection methods and population tested. In a Spanish screening study of 608 individuals, they identified 73 MBLs (12%) with CLL-like MBL; moreover, these authors also evaluated overall survival (OS) compared to 290 individuals without MBL and found a significantly shorter OS among these predominantly LC-MBLs. Here, we evaluate the prevalence and OS of LC-MBL in a large screening cohort of 8,297 individuals. Study participants from the Mayo Clinic Biobank, a large-scale biorepository of adult patients, who had no prior history of hematologic malignancy, provided blood samples between 7/2009 to 4/2021. Stored peripheral blood mononuclear cells were screened for MBL with an eight-color flow cytometry assay capable of detecting clonal B-cell event to the 0.005% level. We classified each MBL by immunophenotype as CLL-like MBL, atypical MBL, and non-CLL-like MBL. Individuals with more than one immunophenotype were classified into one immunophenotype based on the following hierarchy: CLL-like MBL, then atypical MBL, then non-CLL like MBL. Based on previously published evidence, individuals were also classified by clonal counts using the percent clonal B-cell count <85% out of the total B-cell count to define LC-MBL. Individuals were followed from sample date to the earliest of death, loss to follow-up, or 5/31/2021. OS for LC-MBL and controls (individuals who screened negative for MBL) was assessed by Kaplan-Meier method. Age- and sex-adjusted OS was analyzed using inverse weights methods. Cox regression was used to calculate hazard ratios (HR) and 95% confidence intervals (CIs), adjusted for age and sex. We screened 8,297 individuals 40 years or older (median age 67 years, 39% male) and identified 1,326 (16%) with LC-MBL and 6,651 (80%) controls. Those individuals detected with high-count MBL (N=90, 1%) or individuals who had insufficient cells for flow cytometry interpretation (N=230, 3%) were excluded from subsequent analyses. The prevalence of LC-MBL was higher in males (21%) than females (14%; p<0.01) and significantly increased with age (p<0.01) ranging from 3% among those 40-49 years, 10% among those 50-59 years, 15% among those 60-69 years, 23% among those 70-79 years, and 29% for those 80+ years. Sixty-one individuals had clones with more than one type of immunophenotype. Based on our hierarchy, CLL-like MBL was the most common (N=1,155), followed by non-CLL-like (N=168) and atypical MBL (N=67). The median follow-up time in our cohort was 26 months (range 0-142), and 432 individuals had subsequently died. The 5-year and 10-year OS adjusted for age and sex among LC-MBLs was 95% and 87%, respectively; control estimates were 95% and 84%, respectively. We did not observe a significant difference in OS between those individuals with LC-MBL versus controls (HR=0.98, CI=0.77-1.24, P=0.84, Fig 1a), nor when we stratified individuals by age <65 (P=0.77) or age >= 65 (P=0.68). Among females, OS was longer among LC-MBL compared to controls (HR=0.61, CI=0.38-0.97, P=0.04); but no evidence of a difference in OS among males was observed (HR=1.22, CI=0.92-1.62, P=0.17). When we evaluated OS by MBL immunophenotype, we observed no statistically significant difference (Fig 1b, log rank P=0.44). When we stratified the MBLs by immunophenotype versus controls, we also did not observe a difference: CLL-like (HR=0.91, CI=0.71-1.18, P=0.5), atypical (HR=1.73, CI=0.92-3.27, P=0.09), non-CLL like (HR=0.98, CI=0.58-1.66, P=0.95). In the largest MBL screening cohort to date, LC-MBL was a common condition among adults 40 years or older reaching a prevalence as high as 29% among individuals 80 years of age or older. OS among those with and without LC-MBL was similar, regardless of immunophenotype and age. The longer survival in females with MBL versus controls requires further evaluation. Figure 1 Figure 1. Disclosures Parikh: Pharmacyclics, AstraZeneca, Genentech, Gilead, GlaxoSmithKline, Verastem Oncology, and AbbVie: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics, MorphoSys, Janssen, AstraZeneca, TG Therapeutics, Bristol Myers Squibb, Merck, AbbVie, and Ascentage Pharma: Research Funding. Kay: Juno Therapeutics: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Targeted Oncology: Membership on an entity's Board of Directors or advisory committees; TG Therapeutics: Research Funding; Sunesis: Research Funding; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; Behring: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; MEI Pharma: Research Funding; Tolero Pharmaceuticals: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; CytomX Therapeutics: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees. Shanafelt: Genentech, Pharmacyclics: Research Funding.

Abstract Introduction: TP53 aberrations, including mutations and deletion of 17p (del17p), are important adverse prognostic markers in chronic lymphocytic leukemia (CLL). Prevalence of TP53 aberrations ranges from 7-11% in untreated CLL and increases with disease progression and treatment. Among CLL patients with TP53 aberrations, co-occurrence of TP53 mutations with del(17p) is common. CLL patients with TP53 mutations or del(17p) have significantly worse outcomes when compared to wild-type patients. Previous studies, focusing on CLL patients at time of treatment, are mixed as to whether a single or more than one TP53 aberration impacts outcomes. TP53 is less well studied in monoclonal B-cell lymphocytosis (MBL), an asymptomatic pre-malignant state of CLL. Del(17p) occurs in 3-4% of MBL individuals, and a study of 54 MBL individuals reported a 2% mutation frequency in TP53. Here we estimated prevalence and evaluated the impact of TP53 aberrations in a large cohort MBL or untreated CLL individuals. Methods : Patients with CLL or MBL diagnosed between 2000 and 2019 from the Mayo Clinic CLL Resource with pre-treatment peripheral blood mononuclear cells (PBMC) collected within two years of diagnosis were considered. DNA was extracted from PBMCs with purity >80% or with sorted CD5+/CD19+ clonal cells. The entire coding regions of 59 CLL driver genes were paired end sequenced. Median coverage depth was >1000x per nucleotide, allowing for detection of mutations with variant allelic fraction (VAF) as low as 1%. Somatic mutations were called using MuTect2 in tumor-only mode, and high impact mutations (frameshift, nonsense, and splicing variants) and missense mutations in CLL hot spots were selected. Somatic mutations and FISH del(17p) were used to define TP53 state for each patient: 1) wild-type [no TP53 mutations and normal del(17p)], 2) single-hit [one TP53 mutation or del(17p)], or 3) multi-hit [multiple TP53 mutations or TP53 mutation and del(17p)]. Time to first treatment (TTFT) and overall survival (OS) were analyzed by TP53 state. TTFT and OS were defined as time from sample collection to first treatment or death, respectively, or last follow-up date. Median TTFT and OS was estimated by the Kaplan-Meier method. We used Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals for TTFT and OS associations. The models were adjusted for known adverse prognostic factors including clinical diagnosis (CLL or MBL), age at diagnosis, Rai stage, b2 microglobulin, and IGHV mutation status. Results: Individuals with CLL (N=597) or MBL (N=285) were analyzed for prevalence of TP53 mutations and del(17p). We found 58 CLL patients (9.7%) and 15 MBL individuals (5.3%) had TP53 mutations. The median VAF in CLL was 30.9% (<10% VAF, n=21) and in MBL was 13.5% (<10% VAF, n=7). Del(17p) was present in 37 patients with CLL (6.2%) and 7 individuals with MBL (2.7%). In total, 86 CLL/MBL patients had a TP53 mutation and/or del(17p): 56.8% (48 patients) were single-hit and 44.2% (38 patients) were multi-hit (Fig. 1a). Patients with any TP53 aberration had shorter TTFT than wild-type patients (median 2.3 vs 9.4 years). Among patients with TP53 aberrations, median TTFT was shorter in multi-hit patients (20 events, 1.8 years) compared to single-hit patients (24 events, 3.2 years) (Fig. 1b). In Cox regression, single-hit (HR = 1.7 [1.1-2.6]) and multi-hit (HR = 1.8 [1.1-2.9]) patients had shorter TTFT compared to wild-type patients after adjusting for covariates (Fig. 1c). Multi-hit patients also had shorter OS compared to wild-type patients, while OS in single-hit patients did not significantly differ from wild-type patients (Fig. 1d). Median OS was 5.5 years in multi-hit patients (22 deaths) compared to 15.1 years in wild-type patients (196 deaths) and 14.3 years in single-hit patients (15 deaths). In the OS model adjusted for covariates, multi-hit patients had a significant increased risk (HR = 2.6 [1.6-4.1]), but single-hit patients did not (HR = 1.4 [0.8-2.5]) compared with wild-type patients (Fig. 1e). OS HRs remained stable after censoring at time of treatment. Both TTFT and OS HRs were consistent when mutations with VAF < 10% were excluded. Conclusions: This study suggests single versus multi-hit TP53 aberrations may be important for prognostic outcomes in untreated CLL and MBL patients. Prognostic metrics may need to consider single versus multi-hit TP53 aberrations and include TP53 mutations with low VAF. Figure 1 Figure 1. Disclosures Cerhan: Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding; Genentech: Research Funding. Parikh: Pharmacyclics, MorphoSys, Janssen, AstraZeneca, TG Therapeutics, Bristol Myers Squibb, Merck, AbbVie, and Ascentage Pharma: Research Funding; Pharmacyclics, AstraZeneca, Genentech, Gilead, GlaxoSmithKline, Verastem Oncology, and AbbVie: Membership on an entity's Board of Directors or advisory committees. Kay: MEI Pharma: Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; TG Therapeutics: Research Funding; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; Juno Therapeutics: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharmaceuticals: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Targeted Oncology: Membership on an entity's Board of Directors or advisory committees; CytomX Therapeutics: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Behring: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees.

Abstract TMB is used to guide PD-1-directed immunotherapy in solid tumor Oncology. However, it has not been systematically studied in AML, where the focus has been on cytogenetic risk and individual driver gene mutations (GM's). TMB contribution to AML epidemiology is also uncertain. We therefore studied its association with epidemiologic risk factors; driver GM's and somatic mutations (SM's) in AML risk genes which we recently demonstrated (ABCB1; CYP1A1; CYP2B6; EPHX1; ERCC1,2,& 5; MEFV; MTRR; and TERT); clinical and cytogenetic features; and outcome after therapy in the MCAEC, a highly annotated clinical epidemiology cohort of consecutive AML pts [Finn, Cancer Epidemiol 39:1084, 2015]. Methods: We obtained somatic leukemia DNA from remnant diagnostic cytogenetic pellets in 98 MCAEC patients (pts), as previously described [Foran, Blood (2017) 130:570a]. Whole exome sequencing (WES) was performed at depth of ~100 million paired end 100bp reads using Agilent SureSelectXT Human All Exon V5 + UTRs target enrichment kit. Reads were mapped to human genome build hg19 using BWA-MEM. Single nucleotide variants (SNVs) and small INDELs were identified using Mayo Clinic (MC) analytic pipeline GenomeGPS 4.0.1 following Broad GATK variant discovery best practices of alignment, realignment and recalibration, and multi-sample joint genotyping; and filtered using both germline whole exome and genome sequencing of ~1200 MC Biobank samples and public germline variant databases of 1000 genome project, 6500 individuals in exome sequencing project, and HapMap phase 3. Remaining variants were annotated using ANNOVAR, and functional variants of non-synonymous, truncating, frame-shift, and splice-sites were used in the statistical association analyses. TMB was defined as the number of functional mutations per Mb of coding region, heterozygous or homozygous. TMB associations with epidemiologic risk, clinical characteristics, and SM's in AML risk genes (listed above) or driver GM's (occurring in 5 or more pts: ASXL1, BCOR, CEBPA, DNMT3A, FLT3, IDH2, KRAS, MLL2-5, NF1, NPM1, NRAS, PHF6, RUNX1, SF3B1, STAG2, TET2, TP53, U2AF1) were evaluated using linear regression models; a rank transformation of TMB was utilized due to its skewed distribution. Multivariable analysis (MVA) models were adjusted for variables with p-value <0.05 in unadjusted analysis. Regression coefficients ("β") were estimated, interpreted as change in mean TMB rank for specified variables or characteristic. Associations with outcomes were examined using logistic regression or Cox proportional hazards regression models. All statistical tests were two-sided (version 3.6.2; R Foundation for Statistical Computing, Vienna, Austria). Results Median age at AML diagnosis was 70 yrs (Range: 19-94 yrs), and 67 pts were male. Cytogenetic risk group was favorable (7%), intermediate-normal (46%) or abnormal (20%), and poor risk (27%). 40/61 pts (66%) achieved complete remission (CR). With a median follow-up of 8.0 months (Range: 0.1 - 186), 80 pts (82%) died and 20 (20%) underwent allogeneic transplantation (AlloBMT). Median TMB was 18.2 (Range: 15.0-70.1). In MVA, significant associations with increased TMB were observed in pts with history of prior immunosuppressive therapy or solid organ transplantation (β=19.48, P=0.015), and with FLT3 (β=21.12, P=0.015), MLL2 (β=20.91, P=0.001), and MLL3 (β=11.31, P=0.031) GM's. A borderline association was observed for U2AF1 (β=16.14, P=0.057). TMB was also associated with SM's in TERT (β=25.13, P=0.028); a borderline association with SM's in ABCB1 was not confirmed in MVA (β=-17.98, P=0.069). Additionally, cytogenetic risk group was associated with TMB in MVA (P=0.005), being highest in intermediate-normal and lowest in poor risk groups. Body Mass Index was inversely associated with TMB (unadjusted β=-16.99, P=0.005), but unconfirmed in MVA (β=-8.29, P=0.12). There was no association with CR (OR=0.93, P=0.46), use of (HR=0.96, P=0.64) or relapse risk (HR=1.00, P=0.98) following AlloBMT, or survival (HR=0.97, P=0.56) (Figure). Conclusions Measurement of TMB is feasible in this AML epidemiologic cohort, and we observed important associations with AML risk factors, risk gene SM's, cytogenetic risk group, and driver GM's. We acknowledge the relatively small sample size and possibility of type II error, and therefore these observations require validation in a large prospective cohort which is planned. Figure 1 Figure 1. Disclosures Foran: OncLive: Honoraria; certara: Honoraria; actinium: Research Funding; boehringer ingelheim: Research Funding; novartis: Honoraria; abbvie: Research Funding; servier: Honoraria; taiho: Honoraria; pfizer: Honoraria; revolution medicine: Honoraria; gamida: Honoraria; takeda: Research Funding; sanofi aventis: Honoraria; trillium: Research Funding; syros: Honoraria; aptose: Research Funding; bms: Honoraria; kura: Research Funding; h3bioscience: Research Funding; aprea: Research Funding; sellas: Research Funding; stemline: Research Funding. Murthy: CRISPR Therapeutics: Research Funding. Finn: Jazz: Consultancy, Speakers Bureau; BMS: Consultancy, Speakers Bureau; BeiGene: Consultancy, Speakers Bureau; ADC Therapeutics: Consultancy, Speakers Bureau. Badar: Pfizer Hematology-Oncology: Membership on an entity's Board of Directors or advisory committees. Cerhan: Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; Genentech: Research Funding; NanoString: Research Funding; Regeneron Genetics Center: Other: Research Collaboration.

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 Introduction Monoclonal B-cell lymphocytosis (MBL), monoclonal gammopathy of undetermined significance (MGUS), and clonal hematopoiesis (CH) are three common premalignant hematological conditions. MBL is a precursor to chronic lymphocytic leukemia (CLL), MGUS is a precursor to both multiple myeloma (MM) and Waldenström macroglobulinemia (WM), and CH is a possible precursor to myeloid disease. All three conditions are independently associated with aging. Furthermore, there is evidence that all three conditions are associated with increased risk of infection. The relationship among these conditions, the prevalence with which they co-exist, and the association between the number of premalignant conditions and risk of infection is unknown. Methods Study participants from the Mayo Clinic Biobank, a large-scale biorepository of adult patients, provided a peripheral blood sample between 7/14/2009 to 06/18/2015. Individuals were screened for MBL using eight-color flow cytometry capable of detecting clonal B-cells to the 0.005% level. MGUS was screened using a matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) assay. CH was screened using a targeted sequencing panel of 42 CH-associated genes with at least 1000x read depth and the ability to detect variant allele fractions down to 1%. All study participants resided in Olmsted County, Minnesota, the county that includes Mayo Clinic. Serious infection, defined as hospitalization due to infection, was abstracted from medical records from the time of sample collection through 12/31/2020 or date of death. The medical record abstractor collecting data was blinded to condition. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association between each of the conditions. Cox regression was used to estimate hazard ratios (HR) and 95% CI to assess the association between number of premalignant conditions with risk of infection. All analyses were adjusted for age at sample collection and sex. Results A total of 407 individuals (55% male, median age 68 years) were screened for all three conditions. The prevalence was 19.9% (n=81) for MBL, 14.5% (n=59) for MGUS, and 25.6% (n=104) for CH. When we evaluated the association between each of the conditions, we found no evidence of an association between the prevalence of CH and risk of MBL (OR=0.79, 95% CI: 0.43-1.42), CH and risk of MGUS (OR=1.24, 95% CI: 0.66-2.27), or MGUS and risk of MBL (OR=1.32, 95% CI:0.66-2.52). The prevalence of co-existence of these conditions was assessed by grouping individuals based on their condition or conditions (Figure 1A). We found that 197 (48.4%) study participants had at least one premalignant condition and 10.6% had two or more. Individuals with 2 or more conditions were the oldest (median age 74 years), followed by 1 condition (median 69 years) and 0 conditions (median 67 years, P-trend <0.001). Sex was not significantly different across the groups, with 55%, 58%, and 47% male for 0, 1, and 2 conditions, respectively. Medical record abstraction identified 97 serious infection events in 80 individuals. Medical records were not available on 7 individuals. The most common infections were urinary tract (20 events), pneumonia (14 events), and cellulitis (14 events). Median follow-up time was 10.6 years (range 0.1-11.5 years). Compared to individuals with no condition, MBL (HR=1.75, 95% CI: 1.06-2.88) and MGUS (HR=1.75, 95% CI: 1.04-2.93) were associated with increased risk of serious infection. We found no evidence of an association with CH (HR=1.15, 95% CI: 0.71-1.87). When grouped by the number of conditions, we observed a significant trend (P-trend <0.01, Figure 1B) in risk of serious infections by the number of conditions: individuals with 1 condition had a 35% increased risk of infections (HR=1.35, 95% CI:0.83-2.20) and individuals with 2 or more conditions had a 2.6-fold increased risk (HR=2.58, 95% CI: 1.37-4.84). Discussion At least one premalignant condition was present in nearly 50% of the study population, with CH being the most prevalent. No evidence of a relationship between any of the three conditions was observed. However, we found increased susceptibility to serious infections as the number of conditions increased with individuals with at least two conditions had 2.6-fold increased risk of infections. Figure 1 Figure 1. Disclosures Parikh: Pharmacyclics, AstraZeneca, Genentech, Gilead, GlaxoSmithKline, Verastem Oncology, and AbbVie: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics, MorphoSys, Janssen, AstraZeneca, TG Therapeutics, Bristol Myers Squibb, Merck, AbbVie, and Ascentage Pharma: Research Funding. Dispenzieri: Takeda: Research Funding; Alnylam: Research Funding; Pfizer: Research Funding; Oncopeptides: Consultancy; Sorrento Therapeutics: Consultancy; Janssen: Consultancy, Research Funding. Murray: Mayo Clinic: Other: Has received patents for the Mass-Fix technology which has been licensed to the Binding Site with potential royalties.. Kumar: Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Oncopeptides: Consultancy; Beigene: Consultancy; Novartis: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Antengene: Consultancy, Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Roche-Genentech: Consultancy, Research Funding; Bluebird Bio: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; Amgen: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Cerhan: Regeneron Genetics Center: Other: Research Collaboration; Genentech: Research Funding; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Kay: TG Therapeutics: Research Funding; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Targeted Oncology: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; MEI Pharma: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; Rigel: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Research Funding; Genentech: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Behring: Membership on an entity's Board of Directors or advisory committees; CytomX Therapeutics: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Juno Therapeutics: Membership on an entity's Board of Directors or advisory committees. Shanafelt: Genentech, Pharmacyclics: 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 Introduction: Novel therapeutic agents such as immune checkpoint inhibitor (ICI) and brentuximab vedotin (BV) are active in classic Hodgkin lymphoma (cHL), including in patients that relapse after autologous stem cell transplant (ASCT). However, optimal management strategy is unclear for patients with relapsed or refractory (RR) cHL post-ASCT. The aim of the study is to determine the impact of novel agents relative to conventional therapy and allogeneic stem cell transplant (allo-SCT) on survival outcomes of patients with cHL who relapsed after ASCT. Methods: Patients with RR cHL who underwent ASCT between 06/1993 and 10/2017 at 3 Mayo Clinic sites were included. Clinical characteristics, treatment information, and outcome data were abstracted. For patients who relapsed after ASCT, the post-relapse progression free survival (PFS) and overall survival (OS) were analyzed using the Kaplan-Meier method and Cox proportional hazards models. Statistical analyses were done in JMP v15.2.1 and EZR v1.54. Results: A total of 332 patients with RR cHL who underwent salvage therapy and ASCT were identified. After a median post-ASCT follow-up of 8.6 years (range 6.8-9.7), 136 (41%) patients had a relapse or disease progression after ASCT. Patient characteristics of the 136 cases are summarized in the Table. The median age at post-ASCT relapse was 34 years (range 20-73), and 77 (57%) were male. 59 (43%) relapsed within 6 months and 77 (57%) relapsed after 6 months following ASCT. 59 (45%) had an extranodal site involvement at relapse. 14 (10%) had therapy with ICI or BV as salvage therapy prior to ASCT or maintenance therapy post-ASCT. The median post-relapse PFS and OS was 0.8 (95% CI 0.6-1.1) and 3.2 years (95% CI 2.2-5.5) years, respectively. Compared to patients who relapse after 6 months, patients who relapsed within 6 months of ASCT had worse post-relapse PFS (median 0.5 [0.3-0.7] vs 1.3 [0.9-1.9] years, p=0.0003) and OS (median 1.3 [0.5-2.2] vs 6.4 [3.7-10.4] years, p=0.0003). Extranodal site involvement at relapse was not associated with post-relapse PFS (median 0.7 [0.5-1.2] vs 0.9 [0.6-1.3] years, p=0.28) but was associated with worse post-relapse OS (median 2.7 [1.5-4.2] vs 6.4 [2.6-NA] years, p=0.006). Prior therapy with ICI or BV was not associated with post-relapse PFS (median 0.6 [0.3-NA] vs 0.8 [0.6-1.1] year, p=0.8) and OS (median NR [1.0-NA] vs 3.2 [2.2-5.5] years, p=0.5). After post-ASCT relapse, the median lines of subsequent therapy were 2 (range 1-12). For first post-ASCT salvage therapy, novel agents (ICI or BV), compared to other therapies, were associated with superior post-relapse PFS (median 1.7 [0.7-3.6] vs 0.7 [0.5-1.0] years, p=0.004) and OS (median 7.6 [4.7-NA] vs 3.2 [2.2-5.6], p=0.02). Allo-SCT following first post-ASCT relapse (n=9) was not associated with improvement in post-relapse PFS (median 2.2 years [0.3-NA] vs 0.8 [0.6-1.1] years, p=0.1) or OS (median NR [0.5-NA] vs 5.1 [3.2-7.3] years, p=0.7). Patients who received ICI or BV at any point post-ASCT relapse had significantly better post-relapse OS (median 7.6 [4.3-16.7] vs 2.2 [1.4-3.7] years, p=0.004) compared to those who never received any novel agent (Figure 1A). In contrast, allo-SCT at any point post-ASCT relapse (n=27) did not improve post-relapse OS (median 5.6 [2.7-NA] vs 4.7 [2.7-7.3] years, p=0.3) (Figure 1B). In multivariate Cox regression models adjusted for age and sex, exposure to ICI and/or BV was associated with superior post-relapse OS (HR 0.5, 95% CI 0.3-0.8, p=0.007); however, allo-SCT was not associated with improvement in post-relapse OS (HR 0.8, 95% CI 0.4-1.5, p=0.5). Conclusions: Patients relapsing within 6 months of ASCT and those with extranodal involvement at relapse had inferior OS after post-ASCT relapse. Prior therapy with novel agents did not impact post-relapse survival outcomes. In the setting of post-ASCT relapse, novel therapeutic agents significantly improved survival outcomes while allo-SCT did not. Future multicenter studies are needed to explore the role of novel agents and allo-SCT in patients with RR cHL post-ASCT relapse. Figure 1 Figure 1. Disclosures Wang: Eli Lilly: Membership on an entity's Board of Directors or advisory committees; InnoCare: Research Funding; MorphoSys: Research Funding; Genentech: Research Funding; Novartis: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees. Paludo: Karyopharm: Research Funding. Tun: Gossamer Bio, Acrotech: Consultancy; Mundipharma, Celgene, BMS, Acrotech, TG therapeutics, Curis, DTRM: Research Funding. Cerhan: Regeneron Genetics Center: Other: Research Collaboration; Genentech: Research Funding; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: 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. Witzig: Karyopharm Therapeutics, Celgene/BMS, Incyte, Epizyme: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene/BMS, Acerta Pharma, Kura Oncology, Acrotech Biopharma, Karyopharm Therapeutics: Research Funding. Nowakowski: Celgene, MorphoSys, Genentech, Selvita, Debiopharm Group, Kite/Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene, NanoString Technologies, MorphoSys: Research Funding. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding.

Abstract Introduction: Non-Hodgkin lymphoma (NHL) constitutes ~40% of hematologic malignancies and, in 2020, resulted in 19,940 deaths in the USA. The most common NHL subtypes are diffuse large B-cell lymphoma (DLBCL), including primary mediastinal large B-cell lymphoma (PMBCL), and follicular lymphoma (FL). Although a majority of patients respond to standard-of-care therapy, many patients with NHL eventually relapse, highlighting the need for additional treatments. Real-world data regarding the safety and efficacy of emerging therapies in the relapsed/refractory (R/R) population, and the association between treatment patterns and patient outcomes, are limited. These data could provide unique insights to clinical and health-related quality of life (HRQoL) outcomes in patients with DLBCL, FL, or PMBCL treated with emerging therapies, especially novel options such as chimeric antigen receptor (CAR) T cell therapies. Methods: The Connect ® Lymphoma Disease Registry (NCT04982471) is a US-based, multicenter, prospective observational cohort study of patients with R/R NHL (DLBCL, FL, and PMBCL). Approximately 2100 patients ≥ 18 years of age from ~200 community oncology (~80%) or academic (~20%) sites will be enrolled over a ~3-year period. Patients with histologically confirmed NHL subtypes will be enrolled into 1 of 4 cohorts: first R/R DLBCL, second R/R DLBCL, first R/R FL, or first R/R PMBCL (Figure). Patients will be required to have begun second- (first R/R) or third- (second R/R) line systemic treatment within 60 days prior to enrollment. Patients receiving treatment for any active malignancy other than DLBCL, FL, or PMBCL at the time of enrollment, or who are diagnosed with any other malignancy in the 6 months prior to enrollment, will be excluded. All treatment and management decisions will be determined by the practicing clinicians. Patients may undergo hematopoietic stem cell transplantation, CAR T cell therapy, or other treatments at other sites while participating in this study. Patients will be followed from enrollment for up to 5 years or until death, withdrawal of consent, loss to follow-up, or study termination, whichever occurs first. Data collection will occur at enrollment (baseline) and then every ~3 months. The main objectives of the Connect ® Registry are to describe patient characteristics, practice patterns, and factors associated with treatment choice, sequencing, and effectiveness in NHL subtypes. Secondary objectives include describing treatment regimen safety, patient-reported outcomes (PROs) including HRQoL, and healthcare resource utilization outcomes. Exploratory objectives include tumor and blood biomarker evaluation and understanding the availability of social support and its impact on long-term treatment decision-making. Case report forms will be used to collect clinical and treatment data, including baseline demographics, clinical characteristics, treatment details and response, and socioeconomic factors. Outcome measures for efficacy will be progression-free survival, event-free survival, objective response rate, time to next treatment, and overall survival. The availability of social support will be assessed via a specific questionnaire administered at baseline. General (EQ-5D-5L) and disease-specific (FACT-Lym) questionnaires will also be administered. Patients may also optionally agree to release tumor biopsies and blood samples for biomarker analysis. Clinicians will be required to report serious adverse events (AEs), secondary primary malignancies, and confirmed COVID-19 infections within 24 hours. Non-serious AEs of interest include grade 1-3 cytokine release syndrome, grade 1-3 neurotoxicity, grade 3 colitis, grade 3 arrhythmia, grade 3 hemorrhage. Other AEs of interest to be collected include grade 3 hypogammaglobulinemia, prolonged grade 3 cytopenia, and grade 3 infections. Data collected in the Connect ® Registry will increase understanding of the association between emerging therapies and patient outcomes for R/R DLBCL, FL, and PMBCL. Study support: Bristol Myers Squibb Figure 1 Figure 1. Disclosures Flowers: Amgen: Research Funding; Janssen: Research Funding; Biopharma: Consultancy; Ziopharm: Research Funding; Burroughs Wellcome Fund: Research Funding; Nektar: Research Funding; Karyopharm: Consultancy; Iovance: Research Funding; Allogene: Research Funding; AbbVie: Consultancy, Research Funding; Cellectis: Research Funding; Pfizer: Research Funding; Sanofi: Research Funding; BeiGene: Consultancy; Kite: Research Funding; EMD: Research Funding; Genentech/Roche: Consultancy, Research Funding; Morphosys: Research Funding; Adaptimmune: Research Funding; Novartis: Research Funding; Epizyme, Inc.: Consultancy; Spectrum: Consultancy; Pharmacyclics/Janssen: Consultancy; Acerta: Research Funding; 4D: Research Funding; Denovo: Consultancy; Celgene: Consultancy, Research Funding; Guardant: Research Funding; Genmab: Consultancy; Gilead: Consultancy, Research Funding; Bayer: Consultancy, Research Funding; SeaGen: Consultancy; Cancer Prevention and Research Institute of Texas: CPRIT Scholar in Cancer Research: Research Funding; Takeda: Research Funding; National Cancer Institute: Research Funding; TG Therapeutics: Research Funding; Eastern Cooperative Oncology Group: Research Funding; Xencor: Research Funding; Pharmacyclics: Research Funding. Andorsky: Celgene/Bristol Myers Squibb: Research Funding; AbbVie: Consultancy; Celgene/Bristol Myers Squibb: Consultancy; AstraZeneca: Other: served on steering committees; Epizyme: Research Funding; AbbVie: Research Funding. Burke: SeaGen: Consultancy, Speakers Bureau; X4 Pharmaceuticals: Consultancy; Bristol Myers Squibb: Consultancy; Verastem: Consultancy; AstraZeneca: Consultancy; MorphoSys: Consultancy; Adaptive Biotechnologies: Consultancy; Roche/Genentech: Consultancy; Epizyme: Consultancy; Kura: Consultancy; AbbVie: Consultancy; Beigene: Consultancy, Speakers Bureau; Kymera: Consultancy. Cerhan: Genentech: Research Funding; NanoString: Research Funding; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; Regeneron Genetics Center: Other: Research Collaboration. Grinblatt: Astellas Pharma, Inc.: Consultancy; Bristol Myers Squibb: Consultancy; Astra Zeneca: Consultancy; AbbVie: Consultancy. Toomey: Bristol Myers Squibb: Consultancy. Zelenetz: Gilead: Honoraria, Research Funding; Verastem: Honoraria; Novartis: Honoraria; MEI Pharma: Honoraria, Research Funding; SecuraBio: Honoraria; Abbvie: Honoraria, Research Funding; MorphoSys: Honoraria; Pharmacyclics: Honoraria; AstraZeneca: Honoraria; LFR: Other; Genentech/Roche: Honoraria, Research Funding; NCCN: Other; MethylGene: Research Funding; Beigene: Honoraria, Other, Research Funding; BMS/Celgene/JUNO: Honoraria, Other; Amgen: Honoraria; Gilead: Honoraria; Janssen: Honoraria. Sullivan: Bristol Myers Squibb: Current Employment, Current holder of individual stocks in a privately-held company. Flick: Bristol Myers Squibb: Current Employment. Kiselev: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company, Current holder of individual stocks in a privately-held company. Kaplan: Bristol Myers Squibb: Current Employment. Ahn: Bristol Myers Squibb: Current Employment.

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.