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Background Limited stage (stage I−II) disease is an uncommon presentation of mantle cell lymphoma (MCL), and its optimal management remains controversial. A British Columbia study (Leitch, Ann Oncol, 2003) and a Surveillance, Epidemiology and End Results (SEER) study (Murthy, Clin Lymphoma Myeloma Leuk, 2014) suggested the importance of radiotherapy (RT). A National Cancer Database (NCDB) study in the pre-BTK inhibitor (BTKi) era showed that chemotherapy (Chemo) plus RT (Chemo+RT) improved overall survival (OS) compared to Chemo or RT alone (Gill, Int J Radiat Oncol Biol Phys, 2015). However, a retrospective International Lymphoma Radiation Oncology Group study (Dabaja, Ann Oncol, 2017) reported comparable OS with Chemo+RT vs Chemo or RT alone. The potential differences in OS with different frontline therapy modalities may be modified by subsequent therapies for relapsed or refractory (R/R) disease. In this study, we used the NCDB data to analyze frontline treatment patterns and outcomes of limited stage MCL in the pre-BTKi vs BTKi eras. Methods The NCDB was queried to identify patients with stage I−II MCL diagnosed in 2004−2020. Because the first BTKi ibrutinib was approved by the FDA for R/R MCL in November 2013, we defined 2004−2013 as pre-BTKi era and 2014−2020 as BTKi era. Frontline treatment modalities were categorized to RT alone, Chemo alone, or Chemo+RT. Survival analysis was performed using the Kaplan-Meier method and the Cox proportional hazards model. Results Among 32,746 patients with newly diagnosed MCL in 2004−2020, 3,836 (11.7%) presented with stage I−II disease, with 2,428 having treatment information available. For patients diagnosed in the pre-BTKi era (n = 2005), 195 (9.7%) received RT alone, 1472 (73.4%) received Chemo alone, and 338 (16.9%) received Chemo+RT. For patients diagnosed in the BTKi era (n = 423), 45 (10.6%) received RT alone, 320 (75.7%) received Chemo alone, and 58 (13.7%) received Chemo+RT. Patients in the RT alone group were older (P <0.001) and had more females (P <0.001), but there were no statistically significant differences in Charlson-Deyo score, international prognostic index, or treating facility type (academic vs non-academic). For patients with stage I disease, in the pre-BTKi era, patients treated with Chemo+RT (n = 182) had improved OS compared to those treated with RT alone (n = 141) or Chemo alone (n = 647), with a 5-year OS of 75.4% vs 71.8% vs 61.4% (P <0.001). Similar results were observed in the BTKi-era, with a 5-year OS of 83.6% vs 69.5% vs 62.9% (P <0.001) for patients treated with Chemo+RT (n = 31), RT alone (n = 30), or Chemo alone (n = 130), respectively. After adjusting for age and sex, Chemo+RT was associated with improved OS compared to Chemo alone (pre-BTKi era, HR = 0.59, 95% CI = 0.46−0.75, P <0.001; BTKi era, HR = 0.34, 95% CI = 0.14−0.79, P = 0.013), but not compared to RT alone (pre-BTKi era, HR = 0.62, 95% CI = 0.23−1.68, P = 0.343; BTKi era, HR = 0.77, 95% CI = 0.56−1.05, P = 0.10) (Figure 1A). For patients with stage II disease, in the pre-BTKi era, patients treated with Chemo+RT (n = 156) had improved OS compared to those treated with RT alone (n = 54) or Chemo alone (n = 825), with a 5-year OS of 70.0% vs 47.3% vs 58.4% (P <0.001). In the BTKi-era, patients treated with Chemo+RT (n = 27) or RT alone (n = 15) both had improved OS compared to those treated with Chemo alone (n = 190), with a 5-year OS of 77.2% vs 78.8% vs 61.9% (P = 0.004) . After adjusting for age and sex, Chemo+RT was associated with improved OS compared to Chemo alone in BTKi era only (pre-BTKi era, HR = 0.85, 95% CI = 0.67−1.07, P = 0.165; BTKi era, HR = 0.49, 95% CI = 0.25−0.95, P = 0.034), but not compared to RT alone (pre-BTKi era, HR = 0.78, 95% CI = 0.53−1.13, P = 0.185; BTKi era, HR = 0.97, 95% CI = 0.35−2.67, P = 0.952) (Figure 1B). Conclusions Treatment pattern for limited stage MCL has not changed significantly from the pre-BTKi era to the BTKi era, with approximately 15% of patients receiving Chemo+RT, 10% RT alone and 75% Chemo alone. In the BTKi era, Chemo+RT appeared to be associated with better OS compared to Chemo alone but not compared to RT alone. Interpretation of the results requires caution because (1) patient numbers are small in Chemo+RT and RT alone groups, (2) longer follow-up may be needed, and (3) selection of therapy was not random, eg, patients receiving Chemo alone may have had higher disease burden such as bulky disease and/or non-contiguous stage II involvement but these details are not available in NCDB.

Background: A subset of follicular lymphoma (FL) patients will experience a ‘high-risk defining event’ (HRDE) that portends poor survival due to early progression of disease or transformation to diffuse large B cell lymphoma (TFL). Progression of disease within 24 months (POD24) from front-line immunochemotherapy (ICT) identifies patients with a subsequent five-year OS of 50% compared to a reference group of 90% (Casulo et al. JCO 2015). Although a robust surrogate endpoint for OS in clinical trials (Casulo et al. Blood 2022), it remains to be established if the OS of patents is influenced by the nature of the HRDE. In this study, we examined whether characteristics and clinical trajectory of those with relapsed/progressive FL is the same as that of patients experiencing TFL. Methods: We conducted an international retrospective analysis of newly diagnosed FL patients requiring treatment across 14 academic centres between 2002 and 2021 in the era of rituximab and PET-CT staging. Patients were included with grade 1-3A FL requiring first-line (1L) treatment (ICT, rituximab monotherapy or radiotherapy). We excluded patients with untreated FL, de novo TFL and those with <24 months follow up provided they did not experience a HRDE. Baseline clinical, imaging and laboratory values, treatment details and outcomes were collected. We defined HRDE groups as persistent (relapse or progression) FL within 24 months of 1L treatment (FL24), early TFL (<24 months of 1L treatment) or late TFL (>24 months of 1L treatment). Patients not experiencing a HRDE formed the FL reference group. Landmark overall survival (OS) analysis was performed by Cox regression using risk status as a time-varying covariate for time of HRDE. For the FL reference group, landmark was defined at 24 months after 1L treatment. Results: 501 patients met the inclusion criteria and were categorised as: reference FL (n=378), FL24 (n=50), early TFL (n=28), late TFL (n=15). 24 patients who died within 24 months of 1L treatment without documentation of preceding HRDE and were omitted. Median follow up for patients alive beyond 24 months was 5.1 years. Median age at diagnosis was 60 years, 91% had ECOG performance status 0-1 and 38% were high-risk (>3) FLIPI. 1L chemotherapy regimens included: bendamustine (59%), CHOP (24%), CVP (8%), other (9%); a majority received rituximab (80%) vs obinutuzumab (20%). Maintenance therapy was used in 54% of pts. Expectedly, those experiencing HRDE had an inferior OS compared to the reference group (HR 5.23, p<0.001)(Figure 1). However, among HRDE group, median OS significantly differed: early TFL patients (3.0 yrs) and FL24 (12.6yrs)(HR 2.27, p=0.02) (Table 1). There was no difference in OS after HRDE between early TFL and late TFL ( p=0.81). Biopsy was performed in 84% of TFL cases with no difference in OS between biopsy-proven vs clinically suspected cases ( p=0.23). Multivariate OS analysis assessing HRDE and treatment demonstrated no significant interaction between HRDE group and 1L chemotherapy ( p=0.52), monoclonal antibody ( p=0.29) or maintenance therapy ( p=0.27). Several established prognostic features at baseline were enriched in patients who went on to experience HRDE including: ECOG >1 ( p<0.001), raised LDH ( p<0.001), advanced-stage disease (( p<0.011) and high risk FLIPI ( p=0.005). Baseline FDG-PET was available in 259 patients. Adverse PET metrics were enriched in only early TFL, and not FL24 or late TFL patients, including high SUV max( p=0.009), total metabolic tumor volume ( p=0.015) and total lesional glycolysis ( p=0.01). Conclusions: HRDEs after 1L treatment are associated with inferior OS in FL compared with standard-risk patients. Unlike POD24, this study demonstrates disparate clinical outcomes between patients experiencing early progression with persistent FL and those experiencing TFL. Adverse metabolic characteristics on baseline FDG-PET also suggest biological distinction between HRDE subsets. These findings have important implications for epidemiological studies, biomarker development and clinical trials aiming to improve outcomes in this underserved high-risk FL cohort.

Background: We previously reported that treatment with the anti-CD30 antibody-drug conjugate brentuximab vedotin (BV) in combination with the checkpoint inhibitors targeting CTLA-4 and/or PD-1 (ipilimumab (I) and nivolumab (N)) led to high overall response rates (ORR) and complete response (CR) rates in relapsed/refractory (R/R) Hodgkin's lymphoma patients in a Phase 1 ECOG-ACRIN sponsored clinical trial. As previously presented in the Phase 1 component of NCT01896999 (E4412), the ORR for the treatment arms were: BV+I 76%, BV+N 89%, and BV+I+N 82%, with CR rates of 57%, 61%, and 73% across the groups, respectively. This suggested that not all patients who responded derived equal benefit. Moreover, with a median follow-up of 2.24 years, the PFS of 1.1 year, NR, and 2.49 years across the groups suggest that even some patients who achieved CR progressed on or subsequent to treatment. Currently biomarkers have no ability to predict which patients will maximally benefit from these therapies. We investigated blood-based immune biomarkers mechanistically across all cohorts which may explain differential responses and guide future treatment decisions for R/R HL patients. Methods: Peripheral blood plasma for 54 of 61 (89%) patients evaluable for response was collected at up to 4 time points and tested for 92 immuno-oncology soluble analytes with Olink proximity extension assay and for antibody titers to 20 known tumor antigens by ELISA. Matching peripheral blood mononuclear cells (PBMC) were analyzed by CyTOF mass cytometry for major immune cell subsets and marker surface expression, and for T cell receptor diversity by Immunoseq®. Mixed effect linear models were used to identify significantly associated changes (P<0.05) related to treatment longitudinally within groups and to ORR between groups. Results: Analysis of plasma demonstrated durable increase in soluble plasma PD-1 and decreases in CCL17, ANGPT2, MMP12, IL13, and CXCL13 in N-containing regimens, and higher GZMA and CAIX in BV+I (P<0.05). Despite baseline heterogeneity in PBMC composition, we found durable increases in plasmacytoid dendritic cells (pDC) after N-containing treatments, and transient increases in plasmablasts and memory B cells, as well as early decreases in naïve CD4+ T cells and neutrophils across treatments. Treatment with BV+I+N demonstrated late increases in neutrophils, and changes in memory B cell markers. Clinical benefit analyses revealed that elevated soluble CD5, ADA, CXCL13, and CCL17 at baseline or after two treatment cycles were associated with worse ORR (P<0.05), and that a module of co-regulated soluble analytes at baseline, including MUC16, was associated with worse progression free survival (PFS) (P<0.05). NY-ESO-1 autoantibodies were more frequent in non-responders, and the top 10% of expanded TCR clonotypes were increased in responders after one treatment cycle (P<0.05). Conclusion: This data reveals differential immune activation based on treatment modality for patients with R/R HL receiving checkpoint-based regimens in combination with an ADC. Our data highlights potential tumor- and immune-derived predictive and pharmacodynamic biomarker candidates of response and resistance. These novel findings have relevance for R/R HL patients receiving immunotherapy and other immunotherapy combinations such as immuno-chemotherapy platforms. Prospective evaluation of these biomarkers in the Phase II component of this study, a randomized comparison of BV+N vs. BV+N+I which has completed adult accrual, is planned. Figure Legend Clinical responders and non-responder patient samples were analyzed using ELISA (auto antibodies), CyTOF (Cell populations), TCRseq (T cell clonality) and Olink (proteomics). The results showed that pDCs, Neutrophils and B-Plasmablasts increased with specific drug combinations independently of clinical response. While T Cell expansion was associated with clinical response, the increase of auto antibodies against NY-ESO-1 and circulating proteins CD5, ADA, MUC16, CXCL13, CXCL9 and CCL17 were associated with worse prognosis

Introduction: Non-Follicular Low-Grade B-Cell Lymphomas (LGBCL) are a subset of lymphomas that share overlapping clinical and histologic features. While most cases of LGBCL have an indolent course, some experience aggressive disease with no definitive biomarkers to identify aggressive cases at diagnosis. Significant advances have been made in the molecular classification of other lymphomas, yet molecular profiling and identification of mechanisms driving aggressive disease in LGBCL remain an unmet need. We previously used a multi-omic approach to characterize the molecular and immune landscapes of LGBCL tumors and identified a transcriptomic signature associated with cell proliferation and inferior overall survival (OS) in two independent cohorts (HR 7.82; 95% CI 2.4-25.4; p < 0.001 & HR 10.07; 95% CI 2.00-50.61; p = 0.005) and cases that transformed to DLBCL (Hopper et al., 2023). Here, we identify the oncoprotein DEK as a novel transcriptional regulator of this signature and use a functional genomic approach to further interrogate the role of DEK in B-cell lymphoma. Methods: Tumors from 64 newly-diagnosed/untreated LGBCL cases enrolled in the University of Iowa/Mayo Clinic Molecular Epidemiology Resource were included in this study (SMZL = 48, NMZL = 6, LPL = 5, B-NOS = 3, EMZL = 2). Tumor RNA-seq and paired tumor-normal WES were used to assess DEK expression and identify copy number alterations (CNAs), respectively. DEK deficient (KO) cell line models were generated using CRIPSR/Cas9. DEK expression was assessed by western blot (WB) and immunofluorescence (IF) using standard techniques. Proliferation was assessed by MTT assay. Results: To identify transcriptional regulators of our previously identified signature, we used the tool MAGIC to mine publicly available ChIP-seq data and identified DEK, an oncoprotein associated with tumorigenesis and poor prognosis in several cancers, as a key regulator of genes in our signature (p = 0.008). RNA-seq indicated that high levels of DEK expression associated with inferior OS in LGBCL (HR 2.99; 95% CI 1.21-7.36; p = 0.02). Additionally, we identified DEK as a target of copy number gains/amps in LGBCL (7% of cases), which corresponded to significantly higher DEK expression (p = 0.04). The association of DEK with aggressive lymphoma was confirmed when we assessed its expression in public cell line RNA-seq data and observed the highest expression in lines derived from aggressive lymphomas, which we further validated at the protein level in a panel of B-cell lymphoma cell lines by WB and IF confocal microscopy. Additionally, we detected nuclear localization of DEK, consistent with its role as a transcriptional regulator. As a model of DEK overexpression for functional characterization, we used the SUDHL6 lymphoma cell line, which has an amplification of DEK at 6p22.3 and expresses high levels of DEK protein. Using CRISPR technology, we generated DEK deficient SUDHL6 and tested the impact of loss of DEK expression. DEK KO cells proliferated significantly slower than WT SUDHL6 cells in vitro (p < 0.0001)(Panel A), indicating DEK plays a role in regulating proliferative capacity in lymphoma. To identify mechanisms responsible for reduced proliferation in DEK KOs, we assessed baseline protein expression levels of apoptosis proteins in WT versus KO cells. We found near complete abrogation of anti-apoptotic protein Bcl-2 and reduction of Bcl-xL expression in DEK KO cells (Panel B). These results indicate DEK may contribute to tumor proliferative capacity by regulating susceptibility to apoptosis. Conclusion: DEK has been implicated in regulation of gene expression, but its precise role has remained elusive, with little known about its role in lymphoma. Using an integrative molecular and functional genomics approach, we identified DEK as a novel oncoprotein in LGBCL that is upregulated in cases with inferior clinical outcome and is regulated by copy number gains/amps. DEK plays a role in regulating tumor proliferative capacity by altering susceptibility to apoptosis through regulation of Bcl-2 and Bcl-xL. Collectively, this data suggests high-risk LGBCL tumors may enrich for cells with DEK overexpression or CNAs as a mechanism driving oncogene addiction, highlighting DEK, and its downstream targets, as potential therapeutic targets in a subset of high-risk LGBCL.

Background Diffuse large B cell lymphoma (DLBCL) is the most common aggressive lymphoid malignancy in adults. Though standard immunochemotherapy regimens can result in clinical remission and cure in a majority of patients, approximately 30% of patients are primary treatment resistant or eventually relapse (rrDLBCL), and their prognosis is very poor. Recent molecular classification by large scale genomic and transcriptomic profiling of newly diagnosed DLBCL tumors has resulted in a deeper understanding of disease drivers, however, the molecular heterogeneity of rrDLBCL is yet to be fully explored. Therefore, in this study, we used an unsupervised approach to better define transcriptional and genetic programs in rrDLBCL samples hypothesizing that molecular stratification within this high-risk group may lead to a better understanding of this disease, which remains an unmet need, and a more personalized therapeutic approach. Methods Data used in this study included RNA (n=143) and whole exome (n=126) sequencing data from available FFPE tumor samples at the time of a relapse (any line of treatment, r1-r10 relapse timepoints included in analysis, one per patient), consented to the Molecular Epidemiology Resource (n=61), banked in the Mayo Lymphoma Biobank (n=50), or consented to the CC-122-ST-001 clinical trial (n=32, NCT01421524). Unsupervised clustering was performed on protein coding gene expression values using the non-negative matrix factorization (NMF) approach. NMF was carried out by performing 200 runs on cluster sizes from 2 to 6. Cell of origin (COO) was determined using the method described by Reddy et al. The tumor microenvironment was analyzed using CIBERSORTx and Lymphoma Microenvironment Classification (LME). Genetic classification was done using LymphGen. Results In order to identify unique transcriptional programs in rrDLBCL, we implemented NMF and consensus clustering to define an ideal quantity of stable patient clusters. We identified 4 rrDLBCL patient clusters (rrC1-rrC4) as the most stable solution, based on consensus clustering selection criteria of iterative NMF runs. While relapse timepoint was not associated with individual clusters, we did observe significant differences in tumor characteristics between the clusters. COO classification found that rrC1 and rrC4 were enriched for GCB-DLBCL while rrC3 was enriched for ABC-DLBCL classification (P < .001). To explore the biologic programs in each cluster, we used pathFindR, which leverages protein-protein interaction networks to identify disease related pathways, rrC1 and rrC4 were defined by pathways involved in cell cycle and mismatch repair; oxidative phosphorylation in rrC2; and MAPK and NF-kB in rrC3. Next, LME classification was done to define patterns in the tumor microenvironment and we found a significant difference between groups (P < .001), with rrC2 and rrC4 tumors enriched for a depleted TME, while rrC1 and rrC3 had a more immune rich or inflammatory TME. This was supported by CIBERSORTx analysis identifying significant differences (P > .001) in abundance of CD8 T cells, memory CD4 T cells, and M1 macrophages between clusters, with rrC4 having the lowest abundance. We then explored genetic patterns in each cluster. Gene level mutation enrichment analysis showed BCL7A mutation exclusively in C4 (6/6, P = .03). The clusters were not associated with LymphGen classification. Lastly, our recently published high risk signature classification that is associated with early clinical failure and is characterized by metabolic dysregulation, a depleted TME, T53 alterations, and poor outcome, was significantly different between the groups (P = .03) with enrichment for high-risk cases in rrC4. In summary, we show for the first time that rrDLBCL patients can be classified into four gene expression clusters that are associated with distinct pathway, TME, and genetic programs. These clusters should now be tested to learn if they can help select patients for newer therapies for rrDLBCL such as CAR-T and bispecific antibodies.

Background: Cytopenias are a common complication of and prolonged (beyond month 3 post CAR-T infusion), severe (grade 3 or higher) cytopenias can be difficult to manage. Stem cell boosts have been reported to restore hematopoiesis; however, stem cells would need to be available prior to CAR-T therapy for potential use post CAR-T. Most lymphoma patients and limited number of myeloma patients may have pre-collected stem cells in storage. Identifying patients at high risk for prolonged and severe cytopenia post CAR-T at the time of CAR-T evaluation (baseline), before leukapheresis, can help identify the patients for coordination of stem cell collection during CAR-T manufacturing and increase the likelihood that the collected stem cells would be used. In this study, we aim to identify clinical variables at the time of CAR-T evaluation, prior to leukapheresis, that are associated with severe cytopenias at month 3 post CAR-T infusion and identify patients who could be considered for pre-emptive stem cell collection prior to CAR-T therapy. Methods: We conducted a retrospective analysis of patients with multiple myeloma (MM) and non-Hodgkin lymphoma (NHL) who received CAR-T products at Mayo Clinic from 01/2016 to 06/2022. Patients with progressive disease in the first year post CAR-T were excluded from the analysis. Baseline (at CAR-T evaluation, prior to leukapheresis) variables were compared between those with and without severe cytopenia at month 3 post CAR-T. Severe cytopenias were defined as hemoglobin (Hg) < 8 g/dL, absolute neutrophil count (ANC) < 0.5 x 10 9/L, and/or platelet count (PLT) < 50 x 10 9/L. Statistically significant variables (p<0.05) by analysis were examined for statistical significance in multivariate analysis (MVA). Results: Among the 166 patients who received CAR-T (88 NHL, 78 MM) during the study period, 31 (18.67%) had a severe cytopenia at month . Twenty baseline variables were examined; HGB, PLT, ANC, and c-reactive protein (CRP) We devised a cytopenia risk score using 1 point each for Hg<10 or PLT<100k at baseline, with a range of 0 to 2. Demographics and clinical outcome by cytopenia risk score is shown in Table 1B. A higher percentage of patients with a score of 1 or 2 had a severe cytopenia at month 3 (Figure 1). In particular, there was a . In addition Conclusion: Baseline cytopenias before leukapheresis may identify patients with increased risk for severe cytopenias at month 3 post CAR-T. These patients can have continued slow hematopoietic recovery in the first year. Stem cell collection, if feasible, could be considered before CAR-T to alleviate post-CAR-T severe prolonged cytopenias.

Background: Understanding the symptoms and quality of life (QOL) over time of adults with follicular lymphoma (FL) is important for treatment decision-making and clinical management. However, there are limited population-level data on the long-term QOL of adults with FL. We aim to describe the real-world, long-term QOL of adults with newly diagnosed FL up to 5/6 years after diagnosis. Methods: We used the Mayo Clinic/Iowa Molecular Epidemiology Resource (MER) and the multi-institutional Lymphoma Epidemiology of Outcomes (LEO) to identify adults with grade 1-3A FL who completed QOL surveys at baseline and follow-up. Participants with lymphoma were prospectively enrolled within 9 months of diagnosis in the MER cohort from 2002-2015 and within 6 months of diagnosis in the multi-institutional LEO cohort study across 8 cancer centers from 2015-2020. The LEO/MER cohort studies systematically collected information on disease status, QOL, health behaviors, and functional assessment. Treatments, disease relapses, and deaths were verified by medical record review. Treating physicians determined clinical management. QOL was measured using the Functional Assessment of Cancer Therapy-General (FACT-G) at years 1, 2, 3, and 5/6 post-diagnosis. A higher FACT-G total score (range 0-108) indicated better QOL across 4 subscales (range): physical (0-28), social/family (0-28), emotional (0-24), and functional (0-28) well-being. We categorized participants based on their frontline management at time of diagnosis: “observation” (surveillance), “treatment” (systemic treatment with immunotherapy +/- chemotherapy), and “local” (radiation) groups. We employed a generalized linear mixed model to evaluate and compare the changes in QOL scores (as a continuous variable) from baseline for the observation, treatment, and local groups, adjusting for sex, race/ethnicity, age, FLIPI risk, and cohort (i.e., LEO or MER). We also evaluated QOL changes by FLIPI risk. Results: Our study included 1,544 participants with FL and QOL data. At the time of enrollment, median age was 61 years (range 19-91), 88% were non-Hispanic White, and 49% were female. Based on initial management, 529 (34%) were in the observation group, 880 (57%) were in the treatment group, and 135 (9%) were in the local group. FLIPI risk was high for 17% in the observation group, 29.3% in the treatment group, and 3% in the local group. The mean (standard deviation) baseline FACT-G total score was lowest in the treatment group with the following baseline scores: 86 (13) for the observation group, 83 (14) for the treatment group, and 88 (12) for the local group (Table 1). The observation and local groups reported a greater worsening of their baseline FACT-G total score vs the treatment group at both the 2-year (-1.2, -0.8 vs +1.8, respectively) and 5/6 year (-5.4, -6 vs. -1.6, respectively) timepoints (Figure 1). This appeared to be driven by worsened physical and functional well-being in the observation (-1.3, -2.2, respectively) and local (-0.9, -2.4, respectively) groups vs the treatment group (0, -0.3, respectively). These differences were statistically significant between the observation vs treatment groups. There were no statistically significant differences in the other subscales, i.e., social/family and emotional well-being, by frontline management. Social/family well-being decreased across all groups by 5/6 years (-2.3 in observation, -2.2 in treatment, and -3 in local, p<0.05). Regardless of FLIPI risk, the total FACT-G scores decreased within each FLIPI risk group at 5/6 years (-4.4 for low, -3.8 for intermediate, and -4.9 for high, p<0.05). This was primarily due to worsened social/family well-being (-2.4 for low, -2.2 for intermediate, and -3 for high, p<0.05) at 5/6 years. There was no statistically significant difference in QOL between FLIPI risk groups. Conclusion: This is one of the first and largest studies with real-world longitudinal QOL data for FL. Our study suggests that frontline systemic treatment initially improved QOL and resulted in a lower degree of QOL decline over time. Social/family well-being decreased over time regardless of frontline management or FLIPI risk. Further research is warranted to explore the impact of timing and specific systemic treatments on QOL, incorporate the patient/caregiver experience to address social/family well-being, and identify the clinical significance of our findings.

A substantial subset of follicular lymphoma (FL) patients has an early relapse with a poor outcome. We showed that this unfavorable group displays increased expression of IRF4, dysregulated immune signaling and an immunosuppressive microenvironment. At the molecular level, IRF4 controls expression of antigen presentation and immune molecules (e.g. HLA-DR, CD40, PDL1). Furthermore, silencing of IRF4 triggers anti-lymphoma immunity ( Mondello P et al, Blood 2022;140 Suppl 1:168-169). However, little is known on the molecular mechanisms whereby IRF4 controls the crosstalk between B and T cells. To address this question, first we performed RNA-seq, ATAC-seq and CUT&TAG of active (H3K27Ac, H3K4me3) and repressive (H3K27me3, H3K4me1) histone marks in TMD8 and HBL1 (lymphoma cells with high IRF4 expression) transfected with two pairs of siIRF4 or siCtr for 72 hours. Gene expression revealed a broad change in transcription, with 432 upregulated genes and 136 downregulated genes in siIRF4 compared to siCtr. Notably, the genes with increased expression following IRF4 silencing coded for antigen presentation, immune molecules and cytokines (e.g. HLA-DR, CD86, IL6, IL10). Accordingly, we observed significant enrichment of antigen presentation, interferon and immune response signatures ( Fig 1A). Integrating RNA-seq with chromatin accessibility showed a significant shift towards transcriptional upregulation at genes nearest to upregulated ATAC peaks, with significant enrichment at enhancers (n=771). Conversely, transcriptional suppression was enriched at enhancers (n=2,458) of downregulated peaks. k-means analyses of all union peaks, generated by merging ATAC with CUT&TAG peaks, revealed a significant gain of H3K4me3, with concordant loss of H3K4me1 at enhancers with upregulated gene expression. In contrast, H3K4me3 was severely decreased at suppressed peaks. TF motif analysis revealed an enrichment for PU.1 and E2A at IRF4-bound regions, suggesting their cooperation in regulating immune signaling. Since antigen presentation and other immune molecules are critical components of the immune synapse, we explored the role of IRF4 on the interaction between germinal center (GC) B and T cells by crossing irf4 fl/fl mice with conditional deletion of irf4 ( Klein et al, Nat Immunol 2006) and Cγ 1cre mice ( Cγ 1cre; irf4 fl/fl) to induce recombination in GC B cells. After 10 days of immunization (GC reaction peak), Cγ 1cre; irf4 fl/fl and Cγ 1cre; irf4 fl/+ GC B cells showed significant increase of HLA-DR and CD40 expression compared to WT. As expected, Cγ 1cre; irf4 fl/fl mice displayed a significantly skewed light-zone:dark-zone ratio in favor of increased dark-zone, which are the cells that most reflect the action of BCL6, with concordant suppression of plasma cells. Additionally, we observed a significant increase of CD4 + T cells after partial or complete deletion of irf4, with upregulation of T FH cells and decrease of T reg cells ( Fig 1B). Notably, mice with loss of irf4 showed a significant decrease in exhausted T FH cells. To test if this effect was dependent on MHC:TCR interaction, we cocultured CD4 + T cells with TMD8 cells transfected with two siIRF4 or siCtr in presence of blocking antibodies for MHC class I, MHC class II or both. Blocking one or the other MHC class I or II partially rescued the IRF4 effect on T reg and T FH cells respectively as well as reduced anti-lymphoma cytotoxicity. Further experiments blocking IRF4-dependent IL6 and IL10 identified these two cytokines as responsible for the additional effects on T cells. To confirm this as an antigen-dependent immune response we coculture sorted GC B cells from NP-OVA-immunized Cγ 1cre; irf4 fl/fl, Cγ 1cre; irf4 fl/+ and WT mice with CSFE-labeled OVA-specific OT-II CD4 + T cells at a ratio of 2:1 in an organoid system for 5 days. We observed induced antigen specific proliferation of CD4 + T cells in the presence of Cγ 1cre; irf4 fl/fl and Cγ 1cre; irf4 fl/+ GC B cellscompared to WT,as indicated by decrease in CSFE expression . We confirmed an increase of T FH cells and a decrease of T reg cells, supporting the conclusion that IRF4 directly controls the antigen-dependent immune response . In summary, IRF4 expression in GC and lymphoma B cells regulates immune signaling with PU.1 and E2A, thereby modulating antigen-dependent immune responses with potential implications for lymphomagenesis. Future studies investigating targeting of IRF4 in lymphoma are warranted.

Background: Relapsed DLBCL occurs in up to 40% of patients and has a poor prognosis. Current prognostic tools such as the international prognostic index (IPI) and newer forms (NCCN-IPI) only include baseline clinical features. PET/CT features have not yet been incorporated into prognostic risk models despite being essential for staging and response assessment. Deep learning computer algorithms have consistently demonstrated the ability to analyze imaging data and identify features that are not readily apparent to the human eye. Using a deep learning computer vision model that automatically extracted radiomic features from PET/CTs, we examined the association of changes between baseline PET and interim PET (PET2) radiomic features on survival outcomes and compared the prognostic performance with NCCN-IPI. Methods: Adult patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL) between 2005 and 2015 and enrolled in the prospective MER cohort were included. Native DICOM images from FDG PET/CT scans at baseline and interim PET (PET2, after 2-3 cycles of chemotherapy) were preprocessed for regularity and then segmented using the nnU-Net architecture with an external lymphoma-specific model. Feature extraction and standard uptake value (SUV) characterization were accomplished with PyRadiomics and NiBabel. Our computational method included no manual segmentation correction. Individual radiomics features (n=115) and the % change above (Δ High) and below (Δ low) 50 th quartile from baseline to PET2 were evaluated for association with event free survival (EFS) and overall survival (OS) using a univariate Cox regression model and by Kaplan Meier analysis. EFS was defined as time from PET2 to first systemic relapse or death from any cause. OS was defined as time from PET2 to death from any cause. Additionally, Spearman and Pearson correlation matrices were used to guide feature selection while minimizing collinearity. We then assessed the association of the highest scoring radiomics features with EFS in a multivariate cox regression model. Results: A total of 506 patients had baseline and PET2 scans available for radiomic feature extraction. Median age at diagnosis was 61 years (range 18-90), 43% were females. NCCN-IPI composition and outcomes of our cohort was comparable to the previously published NCCN cohort, with patient distribution and 5-year OS as follows: Low (9%, 5-y OS 83%), Low-intermediate (38%, 5-y OS 49%), High-intermediate (41%, 5-y OS 31%), High (11%, 5-y OS 12%). At a median follow up of 81 months,there were 308 events, and272 patients had died.Several radiomics features were associated with EFS including change in tumor surface area (SA Δ) (HR: 1.75, 95% CI 1.36-2.24, p<0.005, median of 26,927 mm 2, Figure 1A) and change in total metabolic tumor volume (TMTV Δ) (HR: 1.64, 95% CI 1.27-2.10, p<0.005, median of 120,473 mm 3, Figure 1B). As a reference, NCCN-IPI univariate Cox regression analysis of EFS produced a Harrell's C-statistic (c) of 0.624. Single radiomics features of SA Δ and TMTV Δ individually produced unadjusted c-statistics of 0.616 and 0.614 respectively. Combinations of radiomics features (SA Δ, TMTV Δ and NCCN-IPI classification) demonstrated cumulative benefit (c=0.652). Conclusion: Automatic PET-CT radiomic feature extraction using deep learning models is feasible among patients with newly diagnosed DLBCL. The change in tumor SA and TMTV appear to be promising radiomic biomarkers. A low decline in either tumor SA or TMTV by PET2 is associated with higher risk of relapse and poor survival outcomes. As individual characteristics, these features are similar prognostic indicators of EFS as the NCCN-IPI. Further investigation into risk stratification when combining radiomics with clinical and genomic variables is warranted. Figure 1. Event free survival for DLBCL patients with A) a change in tumor surface area by PET2 less than 50% percentile (SA_Δ Low) and greater than 50 th percentile (SA_Δ High) and B) a change in TMTV by PET2 less than 50% percentile (TMTV_Δ Low) and greater than 50 th percentile (TMTV_Δ High).

Background Chimeric antigen receptor (CAR) T-cell therapies have recently been established as an important option for the management of relapsed/refractory (R/R) DLBCL. However, real-world studies suggest that approximately half of all patients (pts) receiving commercial CAR-T therapies will relapse within 6 months. These pts have a poor prognosis, with estimated overall survival (OS) of only 5 months, indicating a significant unmet need. Odronextamab is a novel, off-the-shelf, CD20×CD3 bispecific antibody that has demonstrated activity in both R/R follicular lymphoma and R/R DLBCL. We have previously reported encouraging results with odronextamab from the Phase 1 ELM-1 study (NCT02290951) in pts with R/R DLBCL post CAR-T therapy (Bannerji R, et al. Lancet Haematol. 2022). These results were consistent with the antitumor activity seen in pts with R/R DLBCL from the Phase 2 ELM-2 study (Kim WS, et al. ASH 2022). Here, we present an updated analysis of outcomes in a prespecified cohort of post CAR-T patients from ELM-1. Methods Intravenous odronextamab was administered weekly in 21-day cycles during Cycles (C) 1-4. Revisions to the step-up regimen were reported previously (Bannerji R, et al. Lancet Haematol. 2022). Odronextamab was administered with steroid prophylaxis and step-up doses of 0.7/4/20 mg during C1 to mitigate the risk of cytokine release syndrome (CRS), followed by 160 mg on Days (D) 1, 8, and 15 of C2-4. After C4, odronextamab maintenance treatment continued at 320 mg every 2 weeks until disease progression or unacceptable toxicity. Those pts who achieved a complete response (CR) that was durable for ≥9 months transitioned to dosing with 320 mg every 4 weeks. The primary endpoint was objective response rate (ORR), assessed by independent central review (ICR) according to the Lugano classification. Key secondary endpoints included duration of response (DoR), progression-free survival (PFS), and OS. Immune biomarker assessment was an exploratory endpoint. Results As of Dec 20, 2022, 46 pts were treated (safety-evaluable), with 44 pts evaluable for efficacy. For safety-evaluable pts, median age was 63 years (range 27-82), 67% male, 74% Ann Arbor stage III-IV, and 72% were refractory to CAR-T therapy administered in any prior line. 44 pts were evaluable for efficacy after 4.9 months median duration of follow-up. The ORR and CR rate by ICR were 48% (21/44) and 30% (13/44), respectively. Responses were durable, and both median DoR and median duration of CR were not reached. The probability of maintaining a response for 12 months was 62% and the probability of maintaining a CR for 9 months was 86%.A responder analysis of the subgroup of pts who achieved a CR will be presented. The CAR-T treated pts with DLBCL who were evaluable for biomarker analysis (n=10) had lower T-cell counts at baseline compared with CAR-T naive DLBCL pts (n=41 comparable pts who received odronextamab in ELM-2), yet the fold-change of T-cell expansion at C4D15 was greater in CAR-T treated pts versus CAR-T naive pts. T-cell dynamics, such as changes in activation, exhaustion, and memory subsets, will be presented. Safety was generally consistent with that previously reported. Six (13%) pts permanently discontinued odronextamab due to a treatment-emergent AE (device-related infection, pneumonia, dysphagia, gait disturbance, leukemia, and encephalopathy [n=1 each]). The most common treatment-emergent AE was CRS (any grade, 52%). The highest grade of CRS reported was Grade 2, and low-grade CRS events occurred in 46% of pts with the 0.7/4/20 mg step-up regimen. No cases of ICANS were reported in pts with the 0.7/4/20 mg regimen. Grade ≥3 infections occurred in 10 (22%) pts, with no Grade 5 events. No cases of tumor flare were reported. Conclusions Odronextamab monotherapy demonstrates encouraging antitumor activity in heavily pretreated pts who have progressed after CAR-T therapy, with a generally manageable safety profile. Durable CRs were achieved in this difficult-to-treat setting. These data support the potential role of odronextamab in the treatment paradigm for R/R DLBCL. Further studies to evaluate the optimal sequencing and combinations of odronextamab with CAR-T therapy are warranted.