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Abstract Background: Prognostication for patients with non-transformed relapsed/refractory (R/R) follicular lymphoma (FL) is not well defined. Progression of disease within 24 months (POD24) and number of lines of prior therapy are commonly used to benchmark outcomes and for trial eligibility in this space. However, a comprehensive model for risk prediction in R/R FL is lacking. Here we utilize patients from the LEO Consortium for Real World Evidence (CReWE) and Iowa/Mayo Clinic Lymphoma SPORE MER to develop a clinical prediction model for R/R FL. Methods: Patients with grade 1-3A FL were prospectively enrolled at diagnosis in the MER and/or initiated second line or later therapy (2L+) for non-transformed FL at one of the 8 LEO Centers from a previous LEO CReWE study (Casulo et al, Lancet Haem 2022). All available lines of therapy were abstracted; lines of therapy after histologic transformation were not used in the analyses. A management plan of observation was considered as a type of therapy in the 1L setting but not in the 2L+ setting. Clinical variables and outcomes were abstracted at each line of therapy. Variables considered for modeling included demographics, clinical labs, treatment history, and pertinent clinical variables (including FLIPI and FLIPI24 components). Multiple imputation was used to address missing data in modeling; beta-2 microglobulin (B2M) was unable to be imputed due to the amount of missing data. Index therapy was defined as the date of initiation of a line of therapy (2L, 3L or 4L) for non-transformed FL. The primary endpoint for model development was the time from index therapy until the histologic transformation or lymphoma-related death (HT/LD). Cox proportional hazards models were used for model development. Modeling was first performed separately for each line of therapy and then evaluated across lines. Bootstrapping on imputed datasets was used to assess optimism in model performance. Results: N=1001 patients initiated 2L+ therapy and were utilized for modeling. Of these patients, N=654 initiated 3L and N=344 initiated 4L therapy. Median age at index therapy was (60, 61, and 62) for lines 2-4, respectively. Median follow-up by line of therapy was 8.2, 6.1, and 6,1 years and number of HT/LD events for modeling was 272, 154, and 88, respectively. Observed outcomes decreased modestly as the line therapy increased: median event-free (1.6, 1.5, and 1.2 years) and overall survival (17.6 years, 14.5 years, and 12.3 years), for lines 2-4, respectively. 5-year estimates of HT/LD by line of therapy were 21.6% (95% CI: 18.9-24.3), 23.7% (95% CI: 19.9-27.4) and 29.3% (95% CI: 23.3-34.7), respectively. A clinical prediction model was independently developed for each line of therapy; the final model for each line consisted of the same variables: increasing age, male sex, higher LDH, lower hemoglobin, higher white blood cell count, prior immunochemotherapy exposure, and shorter time since initial diagnosis. The parameter estimates for these variables were broadly similar across the different lines of therapy. The model output is an individual patient's risk of HT/LD. Patients with high-risk FLP-R, as defined using the upper quartile of FLP-R risk scores for each line, had observed 5 year HT/LD rates of 43%, 44% and 51% in lines 2-4, respectively. In contrast, patients with the lower quartile of FLP-R risk scores had observed 5 year HT/LD rates of 10%, 6% and 8% in lines 2-4, respectively. The optimism-corrected c-statistic for the FLP-R model across all lines of therapy was 0.685. This added significant information beyond FLIPI diagnosis (c=0.533) FLIPI at index line (c=0.563), the line of therapy (c=0.525), POD24 status (c=0.578), or all 4 variables combined (c=0.614). Conclusions: The FLP-R model identifies a group of patients with non-transformed FL in the R/R setting who are at very high risk of transformation and/or lymphoma-related death across lines of therapy. FLP-R features 4 of the 5 components of the 1L FLIPI24 prediction model (B2M was unavailable for modeling) with the addition of sex, treatment history and time since initial diagnosis. The number of prior therapies is not sufficient to identify high-risk populations for clinical trial enrichment in the R/R FL setting and model discrimination for FLP-R was greatly improved over FLIPI and/or POD24 across lines of therapy. A Shiny app will be provided for model implementation; external validation is planned.

Abstract Background Chimeric-antigen-receptor (CAR) T-cell therapy has transformed outcomes for relapsed or refractory hematologic malignancies, yet early identification of patients at highest risk for death remains elusive. A prognostic model that leverages only the information routinely available at the time of CAR-T evaluationcould guide pre-infusion counseling, intensity of follow-up, and resource allocation. Methods We analyzed a prospectively maintained cohort of CAR-T recipients treated at Mayo Clinic to develop and test our model. For each patient, data (vital signs, lab results, cardiac assessments [ECG and echocardiography], and PET/CT images) from initial CAR-T evaluation (up to 60 days prior to CAR-T infusion) was considered. Vital signs and labs were left un-imputed, while ECG, echocardiographic parameters, and PET/CT features were forward-filled from the preceding 60 days. Each patient-day was labeled as “outcome present” or “no outcome,” enabling supervised training of gradient-boosting models to predict overall survival (OS) at 6 months post CAR-T infusion. Model performance was evaluated using the area under the ROC curve (AUC) with 10,000-fold bootstrapping with data shuffling between each iteration to derive confidence intervals for stability. To enhance interpretability, we also developed a simplified model using the top 19 features (determined by Shapley value-based importance), aiming to preserve predictive accuracy with a smaller feature set. Results A total of 570 CAR-T cell treated patients were included in this study (476 patients in the training cohort and 94 patients in the validation cohort). The median age was 64 years and 61% males. The primary diagnoses were B-cell lymphoma (324 patients) and multiple myeloma (246 patients). A total of 4,851 individual vital-sign readings, 11,781 laboratory measurements were included. PET/CT images were available in 318 patients. The ML model demonstrated high discriminative ability for predicting OS at 6 months post CAR-T infusion. Vital signs alone produced a mean AUC of 0.63; radiomics features alone produced a mean AUC of 0.71, while laboratory tests alone produced a mean AUC of 0.84. The features most contributory to each single-modality model were then combined into a simplified multimodal model with a total of 19 features, which produced a mean AUC of 0.87, with a 95% CI of 0.73 - 0.97. The 19 features included in the simplified model were: LDH, ferritin, uric acid, eosinophils, fibrinogen, D-dimer, heart rate, mean arterial blood pressure, QTc, QT interval, and the radiomics features of sphericity, first-order Kurtosis, first-order mean, first-order energy, first-order total energy, shape maximum2D diameter (sagittal plane), SUV CoV, SUVp90, shape flatness. This multimodal predictive model was then applied to the validation cohort (uninvolved with model training or testing), which produced an AUC of 0.93, with a 95% CI of 0.84 - 0.98. Conclusions Our ML-driven approach successfully predicts overall survival 6 months post CAR-T infusion, highlighting those individuals at most risk of poor outcomes at the time of CAR-T evaluation. The strong performance of the multimodal version of the model suggests that each data domain contributes complementary resolving power, and by including novel data types, such as PET CT radiomics features, the most accurate predictions could be realized. By providing accurate, near-real-time risk stratification for poor outcomes within 6 months of CAR-T infusion, the model could facilitate proactive patient management. Prospective studies of the integration of this ML model into CAR-T treatment workflows will be crucial to confirm its clinical benefits and to facilitate its adoption in practice.

Abstract Background Single-agent R is a common 1st-line therapy for patients (pts) with FL, especially those with low-tumor burden advanced stage disease or comorbidities. EO is a therapeutic vaccine generated from non-self-protein sequences from gut bacteria, including 4 HLA-A2 CD8 T cell epitopes that mimic B cell-specific markers: CD20, CD22, CD37 and BAFF-receptor. EO also contains a CD4 helper epitope UCP2. EO expands pre-existing memory CD8 T cells recognizing non-self-protein sequences from gut bacteria which can cross-react with B cell antigens on tumor cells. The aim adding EO to R is to safely increase the depth and duration of responses. Methods Cohort 3 (C3) of trial EONHL1-20/SIDNEY includes pts with HLA-A2 and previously untreated low-tumor burden (GELF) FL grade 1-3A in need of treatment (per pt/treating physician). Pts receive EO (300μg/peptide) SC with adjuvant Montanide, q2 weeks (w) x 4, then q4w for a total of 12 doses, combined with R starting at w7 (375 mg/m2 IV q1w x4, then q8w x4). For immune responses blood mononuclear cells were assayed by flow cytometry and EO-mimic or B cell peptide specific tetramers without in vitro stimulation. The primary objective is to assess safety; secondary objectives include EO immunogenicity and preliminary efficacy. Results As of July 2025, the 6 planned pts had started EO. Median age was 66 years (range 46-73); 5 ECOG 0/1 ECOG 1; 1 Ann Arbor stage III/5 stage IV; FLIPI low risk 2/intermediate 2/high 2; FLIPI-2 low risk 2/intermediate 4. At median follow-up 10.6 months (mo) 4 pts completed and 2 are ongoing on EO. Median treatment duration was 43 w (range 2-43). Best response by Lugano in the 6 pts included 4 complete (CR) and 2 partial responses (PR). Median time to PR/CR was 17.1 w (range 6.0-18.0). Currently there is only 1 (not yet confirmed) progression at 20.5 mo. All 5 immune response evaluable pts had expansion of CD8 T cells specific for EO mimic and B cell target peptides during treatment; 4 of 5 had detectable expansions at w5 (1st testing time). Currently, the longest tested immune response is at w65 and positive for both EO mimic and B cell target peptides. Expanded specific CD8 T cells had a memory phenotype predominantly composed of effector memory (TEM) cells but also including central memory (TCM) and terminally differentiated effector memory CD8 T (TEMRA) cells. Across the 5 pts, the medians of max % (sum of specific CD8 T cells targeting either the 4 EO mimic peptides or the 4 B cell antigen epitopes at a specific timepoint) among all peripheral CD8 T cells were 0.71% (range 0.19-3.97) for EO mimic and 0.33% (0.20-1.30) for B cell target peptides. The most common EO-related adverse events (AEs) were local administration site reactions (erythema, pain, induration). These included Gr (Grade) 1 reactions in 3 pts, Gr 2 in 1 pt, and Gr 3 in 1 pt; latter being ulceration with EO-interruption at w10.1, and in context of a strong immune response against EO mimic [3.97% of all peripheral CD8 T cells at w5] and B cell target [1.30%] peptides, and CR from w16.7. Other EO related AEs were Gr 1 lymph node pain (1 pt), and related to both EO and R were Gr 2 urticaria (1 pt) and Gr 1 fatigue (1 pt). Only R related were Gr 2 infusion related reaction (3 pts), Gr 2 enterocolitis infection (1 pt), and in 1 pt each Gr 1 flushing, anemia, headache and diarrhea. There were no further Gr 3-4 AE and no death on treatment/follow-up. Complete B cell depletion was seen in 5 pts tested at w18 (R added w7); no recovery in the range of w30-65. Only 3 infections were reported, per above enterocolitis, and 2 cases of COVID-19 both resolved without sequalae. ConclusionsThe combination of EO with R has a predictable and manageable safety profile, with EO only adding local administration site reactions to the well-known R safety profile. In this feasibility cohort all evaluable pts developed a specific immune response against EO and B cell targets, and a Lugano objective response. The combination of EO and R is feasible and can be evaluated in further trials. Updated results will be presented at the meeting.

Abstract Background: Recent advances in computer vision have given rise to image foundation models (FMs), large-scale neural networks pre-trained on millions of unlabeled histopathology patches, which can learn generalizable representations of tissue morphology. In many cancers, these FMs have predicted genetic mutations and prognoses directly from histology, suggesting that morphology encodes “hidden” molecular information. In lymphoma, this field is nascent but growing.General-purpose histopathology FMs have limited representation of lymphoid morphology. We developed and evaluated LymphoVision, a lymphoma-specialized image foundation model pretrained with self-supervised learning, to extract clinically meaningful features from routine hematoxylin and eosin (H&E) histology. Methods: LymphoVision was pre-trained using knowledge distillation and consistency (DINOv2) on over 37 million multi-resolution patches (5x, 10x, 20x, 40x) sampled using a density-aware strategy from 31,211 H&E whole slide images (WSIs) from 9,155 archival lymphoma and reactive lymphoproliferative cases. To ensure diverse, representative samples, our patch sampling method uses an efficient graph-based clustering algorithm and prioritizes morphologically informative regions by clustering patch embeddings and performing density sampling within each cluster. The pretrained encoder was evaluated using a clustering-constrained attention multiple instance learning (MIL) framework on three diagnostic tasks: (1) cell-of-origin (COO) classification in diffuse large B-cell lymphoma (DLBCL), distinguishing germinal center B-cell (GCB) from non-GCB subtypes based on the Hans immunohistochemistry algorithm; (2) follicular lymphoma (FL) grading, differentiating low-grade (grades 1–2) from high-grade (grade 3); and (3) multiclass lymphoma subtype classification across six categories: DLBCL, FL, marginal zone lymphoma (MZL), mantle cell lymphoma (MCL), classical Hodgkin lymphoma (CHL), and benign/reactive lymphoid conditions. WSI-level labels were derived from pathology reports, and none of the evaluation cases were included in pretraining. Models used a ViT-Giant LymphoVision backbone to generate 20x patch embeddings, followed by MIL using 50% training data and 2-fold cross-validation (25% validation, 25% test). Performance was assessed using area under the receiver operating characteristic curve (AUC), accuracy, recall (sensitivity) and F1 score. Results: For DLBCL COO classification (total n=358 cases), LymphoVision achieved a mean test AUC of 0.93 (range 0.92–0.94), accuracy of 84.5% and F1 of 0.85 across two independent test folds (n=91 per fold, 50% GCB). These results outperformed general-purpose FMs on the same task and cases, including Virchow-v2 (AUC 0.85) and UNI-v2 (AUC 0.88), despite those models being pretrained on 10–100 times more images. For FL grading (total n=660 cases), the test set in each fold included 166 cases (18% grade 3), yielding a mean test AUC of 0.88 (range 0.87–0.90), accuracy of 89.2% and F1 of 0.80. In multiclass lymphoma subtype classification (total n=1,176 cases), the test set included 296 cases per fold. LymphoVision achieved a mean test AUC of 0.98 (range 0.97-0.99), mean test accuracy of 87.1% and mean F1 of 0.84. Class-wise recall was 92.0% for DLBCL (n=112), 88.2% for FL (n=72), 77.2% for MZL (n=29), 70.8% for MCL (n=35), 87.5% for CHL (n=24), and 95.8% for benign/reactive lymphoid conditions (n=24). Conclusion: LymphoVision is a lymphoma-specialized FM trained using self-supervision that achieves high diagnostic accuracy across multiple clinically meaningful tasks using weak supervision and standard H&E images, suggesting the encoder has learned fine-grained lymphoma morphology. Based on historical benchmarks from the 1997 International Lymphoma Study Group classification project, expert hematopathologists achieved diagnostic agreement rates of 55% to 84% using H&E morphology alone for the non-Hodgkin lymphoma (NHL) entities included in our multiclass task. LymphoVision approached or exceeded these benchmarks. These findings underscore the value of our approach using disease-specialized pretraining and intelligent patch sampling for data-efficient histopathology learning. LymphoVision has the potential to augment diagnostics, enable scalable AI-powered classification in real-world workflows, and serve as a foundation for prognostic and theragnostic applications. External validation and multimodal integration are ongoing.

Abstract Background The benefit of high-dose chemotherapy and autologous stem cell transplantation (ASCT) as a consolidation strategy after initial chemotherapy in peripheral T-cell lymphoma (PTCL) is currently unclear, and a randomized trial (EA4232/PTCL-STAT) to answer this question is currently underway. Angioimmunoblastic T-cell lymphoma (AITL) is a rare, aggressive subtype of nodal PTCL with poor prognosis and unique features. Here, we report a large-scale, real-world evidence on the role of ASCT in frontline AITL management. Methods We analyzed a national cohort of 5,827 patients diagnosed with AITL between 2004 and 2020 from the National Cancer Database (NCDB). Baseline demographic, clinical, and socioeconomic characteristics were assessed. Treatment modalities included chemotherapy alone, chemotherapy with ASCT, and no treatment. Overall survival (OS) was evaluated using Kaplan-Meier method and multivariable Cox proportional hazards regression. Propensity score matching (PSM) was performed to validate ASCT's treatment effect. Results Among patients with complete treatment information (n = 5,224), the median age at diagnosis was 68 years (range 20–90). Frontline treatments included chemotherapy alone (68.6%, n = 3,584), chemotherapy with ASCT (14.6%, n = 761), and no treatment (16.8%, n = 879). Compared with patients who did not receive ASCT, those who underwent ASCT were significantly younger (median 60.0 vs 68.0 years), more frequently treated at academic centers (81.5% vs 57.9%, p < 0.001), and more likely to reside in areas with higher educational attainment (p < 0.001). They exhibited lower Charlson-Deyo comorbidity scores (CDS), with a higher proportion having a score of 0 (82.8% vs 73.6%, p < 0.001), and were more likely to have advanced-stage disease (93.0% vs 86.9%, p < 0.001). Furthermore, a higher proportion of patients who received ASCT were diagnosed in more recent years (p < 0.001). Sex, race, and IPI score distribution showed no significant baseline differences. Median follow-up time was 81.1 months (95% CI: 77.4–84.9), and the median OS was 23.3 months (95% CI: 21.3–25.2) for the entire cohort. For patients receiving chemotherapy with ASCT, the median OS was 139.2 months (95% CI: 104.2–174.2). In comparison, chemotherapy alone yielded a median OS of 23.6 months (95% CI: 21.4–25.8), while patients who received no treatment had a median OS of 4.0 months (95% CI: 2.7–5.2); overall p < 0.001. Median OS also significantly improved with more recent diagnosis eras: 20.1 months for patients diagnosed in 2004–2009 (95% CI: 16.8–23.4), 23.9 months for those diagnosed in 2010–2015 (95% CI: 21.0–26.7), and 26.7 months for patients diagnosed in 2016–2020 (95% CI: 23.1–30.4) (p < 0.001). Analyses consistently demonstrated longer median OS in female patients (27.5 vs. 20.7 months in males, p <0.001), and in patients treated at academic centers (27.5 vs. 16.4 months at non-academic centers, p < 0.001). Median OS varied by race (White: 24.0 months vs. Black: 15.2 months vs. Others: 30.3 months; p = 0.017). Patients with higher household income and higher educational attainment experienced significantly improved survival (p = 0.005 and p = 0.017, respectively). Patients with early-stage disease, lower IPI scores, and lower CDS also showed significantly longer survival (p < 0.001 for all comparisons). Multivariate Cox regression showed that chemotherapy with ASCT significantly reduced mortality risk compared to chemotherapy alone (HR = 0.438, 95% CI: 0.364–0.527, p < 0.001). No treatment significantly increased mortality risk (HR = 1.941, 95% CI: 1.634–2.305, p < 0.001). PSM analysis further validated that chemotherapy with ASCT significantly lowered mortality risk compared to chemotherapy alone (HR = 0.608, 95% CI: 0.503–0.736, p < 0.001), after matching for age at diagnosis, sex, race, income, education level, academic center, stage, CDS, and diagnosis era. Conclusion This large-scale, real-world analysis suggests that chemotherapy followed by ASCT is associated with significantly improved overall survival in patients with AITL, supported by unadjusted Kaplan-Meier analysis, adjusted multivariate Cox regression, as well as PSM analysis. These data support ASCT as an important consolidation strategy for AITL in real-world practice.

Abstract Background Secondary central nervous system lymphoma (SCNSL) is a rare but significant complication, affecting approximately 2–10% of patients with diffuse large B-cell lymphoma (DLBCL), often leading to poor outcomes. The CNS-International Prognostic Index (CNS-IPI) remains a widely adopted tool for estimating CNS relapse risk; however, recent advances in molecular profiling and evolving treatment paradigms have challenged the adequacy of CNS-IPI alone in guiding risk assessment. This study evaluates the prognostic relevance of CNS-IPI in conjunction with molecular and clinical factors, specifically cell-of-origin (COO), gene expression, and frontline treatment regimens. The aim is to refine risk stratification and identify patients at highest risk for SCNSL. Method Building on our previous study, we analyzed data from patients with newly diagnosed DLBCL between 2002 and 2023, enrolled in the Mayo Clinic/University of Iowa Lymphoma Molecular Epidemiology Resource. Patients with primary CNS lymphoma, CNS involvement at diagnosis, or who received CNS-directed therapy were excluded. COO was determined using the Hans algorithm and/or Nanostring profiling. Multivariate cause-specific Cox regression models were used to estimate hazard ratios (HR) and 95% confidence intervals (CIs) for predictors of CNS relapse that were measured at the time of initial DLBCL diagnosis. Cumulative incidence with competing risks and cause-specific Cox models were used to analyze relapse risk. Result We included 2137 patients in our analysis, median age at diagnosis was 63 (IQR 53-72), with 41.6% (n=890) females. The albumin level was below normal in 32.5% (n=350), 64.0% (n=1367) had CNS-IPI of intermediate/high, 37.5% (n=586) had non-germinal center B-cell (GCB) COO, and 14.5% (n=309) were double expressor positive. At a median follow-up of 83 months (IQR 42 -143), 82 (3.8%) SCNSL relapses were observed; the 3 and 5 yrs cumulative incidence for SCNSL from time of DLBCL diagnosis were 3.7% (95% CI 3.0-4.7) and 3.8% (95% CI 3.1-4.8) respectively. In models accounting for death as a competing risk, the 5-year cumulative SCNSL incidence estimate for patients with a CNS-IPI low was 1.5% (95% CI 0.8-2.7), Intermediate was 4.9% (95% CI 3.7-6.4), and high was 6.1% (95% CI 3.8-9.6). The 5-year cumulative SCNSL incidence estimate for patients with non-GCB COO was 5.2% (95% CI 3.6-7.4) compared to 2.4% (95% CI 1.6-3.6; p=0.001) for GCB patients, and the 5-year cumulative SCNSL incidence for double expressor positive patients was 8.6% (95% 5.8-12.6) compared to 1.5% (95% CI 0.8-2.7; p<0.001) for double expressor negative patients. In a multivariable model (including CNS-IPI, COO, double expressor, treatment group, extranodal involvement, and albumin group), CNS-IPI intermediate (HR 2.85 [1.58-5.14]; p<0.001) and high (HR 4.31 [2.15-8.65]; p<0.001), non-GCB COO (HR 2.36 [1.37-4.06]; p=0.002], double expressor (HR 5.56 [2.88-10.73]; p<0.001), and below normal albumin (HR 1.89 [1.02-3.50]; p=0.042) were independently associated with increased risk of SCNSL. The majority of the SCNSL relapse occurred within the first 2 years after DLBCL diagnosis; 81.7% (n=67), with only 6.1% (n=5) occurring after 5 years. For patients who developed SCNSL, the median OS from the time of SCNSL diagnosis was 24 months (95% CI 17-34), with 5 years OS of 31% (95% CI 22-43). These findings are being validated using an external, international cohort, and final analysis will be presented at the time of presentation at ASH. Conclusion In this large, prospectively followed cohort with long-term follow-up data, SCNSL remains a rare but devastating complication of DLBCL, with a 5-year cumulative incidence of 3.8% and it is associated with very poor long-term survival. While CNS-IPI remains a strong predictor of SCNSL risk, our study identifies non-GCB COO and double expressor phenotype as independent, additive molecular risk factors. These findings suggest that current risk stratification tools may underestimate the risk of CNS relapse in molecularly defined high-risk subgroups. Considering the poor outcomes linked to SCNSL and the absence of a connection with frontline treatment types, future research should aim to enhance risk prediction models through molecular profiling and explore prophylactic strategies to reduce the likelihood of SCNSL.

Abstract Diffuse Large B Cell Lymphoma (DLBCL), the most common subtype of aggressive B-cell Non-Hodgkin Lymphoma (NHL), accounts for 30-40% of newly diagnosed NHL cases. Despite recent progress improving our understanding of molecular features of DLBCL, there is still a high relapse rate (~45%) among patients who undergo standard treatment (R-CHOP). Several groups have identified reproducible molecular clusters of DLBCL although, the biologic significance of many of the specific gene mutations within these clusters is poorly understood. DTX1 mutations are a feature of the BN2/C1 DLBCL classifiers and are among the mutations with unknown significance. BN2/C1 DLBCL are a mixture of ABC, GCB, and Unclassified cell of origin (COO) with a common signature being the presence of genetic alterations in genes known to regulate the NOTCH pathway; yet, these tumors lack transcriptional signatures of traditional NOTCH activation, suggesting a unique and undefined biology driven through non-canonical NOTCH pathway activation. DTX1 is an E3 ubiquitin ligase which directly binds the intracellular domain of the NOTCH receptor (NICD), targeting it for degradation, resulting in suppression of NOTCH pathway activity. We therefore hypothesize that DTX1 mutations lead to NOTCH pathway dysregulation and are a critical driver for a subset of BN2/C1 DLBCLs. Using whole exome sequencing (WES) data from 404 newly diagnosed DLBCL patients, we found 8% of patients with mutations in DTX1. Alterations in DTX1 were localized to the NICD binding domain, WWE1. In silico Alphafold modeling of DTX1 WWE1-interaction with NICD revealed that patient mutations clustered within predicted interacting surfaces with NICD. These findings lead to the hypothesis that mutations in the DTX1 WWE1 domain suppress its ability to bind to and degrade NICD, enhancing NOTCH pathway activation in DLBCL. To address this question, we first compared DTX1 protein expression by immunoblot in DTX1 WWE1 mutant (OCI-Ly3 and SUDHL2) and wildtype (WT) (OCI-Ly7, OCI-Ly19, SUDHL6, and U2932) DLBCL cell lines. We found higher expression of NICD1 (p=0.03) in OCI-Ly3 and NICD2 (p<0.0001) in SUDHL2 cell lines compared to WT. This increase was accompanied by an increase in NOTCH surface expression with higher NOTCH1 (p<0.0001) and NOTCH2 (p<0.0001) expression on OCI-Ly3 and SUDHL2 cells, respectively, compared to WT. To remove variability across cell lines we next used CRISPR to generate DTX1 knock out (KO) models using WT DTX1 DLBCL cell lines U2932 and OCI-Ly19. Analysis of NICD and NOTCH receptor protein expression confirmed our initial finding, showing an 8.5 and 4.8-fold increase of NICD1 and a 6 and 2.5-fold increase of NICD2 expression in the U2932 and OCI-Ly19 DTX1 KO cell lines, respectively, compared to empty vector (EV) controls. An increase in surface NOTCH1, and to a lesser extent NOTCH2, was seen in U2932 (p=0.01 and p=0.07, respectively) and OCI-Ly19 (p=0.001 and p=0.2, respectively) KO cells compared to EV controls. To identify pathways affected by DTX1 mutations, we next performed RNAseq on U2932 EV and KO cell lines. Of interest, we did not detect any significant changes in classical NOTCH driven genes (Hes1, Hey1, etc.), supporting prior studies on BN2/C1 tumors. However, we did identify potential non-canonical NOTCH-target genes that may contribute to lymphomagenesis. Gene expression in KO cells showed upregulation of genes involved in germinal center (GC) processes (AICDA, IRF4; p<0.001) and cellular growth and survival (IL6R, CCND2, STAT5B, BCL2; p<0.0001). Measurement of IL6R by flow cytometry and CyclinD2 by immunoblot further verified DTX1-mediated upregulation of IL6R (p<0.01) and CyclinD2 (p<0.01) for U2932 KO cells compared to EV control. We also observed downregulation of genes contributing to cell death (ex. BAX, CASP8, FAS; p<0.05) in KO cells compared to EV. Together, these novel findings support our hypothesis that DTX1 WWE1 mutations lead to dysregulation of the NOTCH pathway by driving overexpression of NICD and NOTCH, resulting in dysregulated expression of proteins involved in cell cycle, GC reaction, and apoptosis. We further provide new biologic insight into BN2/C1 DLBCL, ultimately highlighting a path toward novel therapeutic targets and the advancement of personalized medicine, as patients harboring these mutations may benefit from new therapies targeting inflammatory pathways, such as CELMoDs.

Abstract Background: Immune checkpoint inhibitors (ICIs) have been established as standard of care for patients with classical Hodgkin lymphoma (cHL), including relapsed or refractory (r/r) disease. However, ICIs discontinued in some patients due to treatment-related toxicities or disease progression may later be re-initiated. The outcomes of this common clinical practice of ICI re-initiation, or ‘rechallenge,‘ have not been evaluated. Here, we present real-world efficacy and safety data of ICI rechallenge in patients with cHL. Methods: We conducted a retrospective cohort study of patients with cHL treated with ICIs between 2015 and 2024 at Mayo Clinic sites. Patients were identified via institutional records using diagnostic and treatment codes. Included patients were adults ≥18 years with histologically confirmed diagnosis of cHL receiving ≥1 cycle of ICI therapy. Patients in the rechallenge cohort received a second course of ICI therapy following discontinuation of the initial ICI for any reason. The control cohort included patients with ICI discontinuation without subsequent rechallenge. Primary outcomes included progression-free survival (PFS), event-free survival (EFS), and duration of response (DOR), defined respectively as time from first ICI initiation to disease progression, time from first ICI initiation to disease progression, adverse events, treatment change, or death, and time from first recorded response per positron emission tomography–computed tomography until the occurrence of disease progression. Secondary outcomes included overall survival (OS), and incidence and severity of irAEs following ICI rechallenge. Survival outcomes were compared using propensity-score-matched (PSM) cohorts with a proportional Cox model. Results: A total of 145 patients were included. Of these, 30 (21%) underwent ICI rechallenge, while 115 (79%) received no subsequent ICI treatments. The median age was 40 years (interquartile range [IQR] 26-66). Most were male (59%), predominantly nodular sclerosis subtype (71%), Ann Arbor stage III-IV disease (65%), and ECOG ≤ 1 (96%). ICIs were utilized for r/r disease in 66%, with median 6 (IQR 4-13) cycles and follow up of 40.8 months (IQR 21.1–78.1). Patients received median 1 prior line of therapy (IQR 0-2.5). During initial ICI treatment, 34/145 patients (23%) experienced irAEs, including 17 patients (57%) in the rechallenge group and 17 (15%) in the non-rechallenge group. Most irAEs were of grade 1–2 severity, though 10 (30%) experienced grades ≥3 irAEs. irAE severity did not differ between groups. Among patients who underwent ICI rechallenge, the majority (53%) discontinued due to irAEs. The decision to pursue ICI rechallenge was driven predominantly by disease progression (63%). Among the 1:1 PSM cohorts using bone marrow transplant status and the International Prognostic Score, PFS did not significantly differ between rechallenged vs. non-rechallenged groups (HR 1.7, 95% CI 0.60–4.5, p=0.32). Similarly, no difference was noted among the groups in EFS and DOR using 1:3 PSM cohorts using the number of ICI cycles (HR 1.6, 95% CI 0.83–3.0, p = 0.16 and HR 1.6, 95% CI 0.37–7.0, p = 0.53, respectively). In the 1:1 matched cohort, ICI rechallenge was not associated with inferior OS compared to those without rechallenge (HR 1.6, 95% CI 0.48–5.4, p=0.43). irAE occurred in 20%, including pneumonitis (n=2), hepatitis (n=1), and pancreatitis (n = 1). Of the patients who had experienced irAEs during their initial ICI course, only two developed recurrent irAEs of the same type upon rechallenge, both at reduced severity. Overall, 80% of rechallenged patients did not experience any irAE with their second ICI course. Conclusions: ICI rechallenge was not associated with inferior PFS, EFS, DOR or OS compared patients without rechallenge. Among patients receiving ICI rechallenge after irAE, recurrence of same irAE was rare. ICI rechallenge may be a feasible and potentially effective strategy in selected patients with cHL.

Abstract Introduction: Brentuximab vedotin (BV) was approved by the US FDA for relapsed/refractory (R/R) classical Hodgkin lymphoma (cHL) in 2011 and for previously untreated advanced stage cHL in 2018. The immune checkpoint inhibitors (ICIs) nivolumab and pembrolizumab were approved for R/R cHL in 2016 and 2017, respectively. These novel agents have significantly changed the landscape of cHL management in recent years. In this study, we analyzed data from the National Cancer Database (NCDB) to investigate whether survival outcomes have improved in the novel agent era (2011 and beyond) for patients with cHL in the US. Methods: Patients diagnosed with cHL between 2004 and 2020 were identified in the NCDB. Those with missing survival data were excluded. Three treatment eras were defined based on the timing of FDA approval of novel agents: Chemo era (2004–2010), BV era (2011–2015), and ICI era (2016–2020). Patient and disease characteristics were compared using the Chi-square test. Survival analyses were conducted using the Kaplan-Meier method and Cox proportional hazards model. Results: A total of 85,488 patients with newly diagnosed cHL were identified: 36,150 in the Chemo era, 27,058 in the BV era, and 22,280 in the ICI era. Compared to the chemo era, the BV and/or ICI eras included slightly more patients aged ≥66 (17.3% vs 17.8% vs 19.5%), male (53.6% vs 54.6% vs 53.8%, p=0.042), Black (11.3% vs 12.9% vs 12.5%), Hispanic (9.0% vs 9.9% vs 10.9%), with Charlson-Deyo score ≥1 (14.2% vs 16.1% vs 17.2%), B symptoms (44.1% vs 46.9% vs 46.9%), treated at academic centers (32.4% vs 35.9% vs 36.4%), on Medicaid (9.5% vs 12.2% vs 13.7%) or Medicare (19.3% vs 20.3% vs 21.7%), and with higher income (≥4th quartile by 2008-2012 standard; 34.5% vs 34.3% vs 36.3%) (all p<0.001 except for sex, noted above). Ann Arbor stage data were not available in the ICI era. There were slightly more patients with advanced-stage disease in the BV era compared to the Chemo era (45.4% vs 40.3%, p<0.001). IPS scores were available for only 2,344 patients, with a similar distribution (0–2 vs 3–7) across the three eras (p=0.957). The median follow-up was 6.9 years (95% CI 6.9-7.0). Overall survival (OS) significantly improved in the novel agent eras: the 5-year OS rate was 79.8% (95% CI, 79.4%–81.2%) for patients diagnosed in the Chemo era, 81.9% (95% CI, 81.9%–82.4%) in the BV era, and 84.1% (95% CI, 83.5%–84.8%) in the ICI era (p<0.001). Statistically significant OS improvements were observed across all subgroups, including age (≤40: 92.8% vs 94.9% vs 96.5%, 41-65: 80.5% vs 81.8% vs 84.4%, ≥66: 41.4% vs 46.6% vs 52.7%), sex (male: 78.0% vs 80.0% vs 81.9%, female: 81.9% vs 84.1% vs 86.7%), race (White: 79.9% vs 81.6% vs 83.7%, Black: 77.2% vs 81.2% vs 84.6%, others: 84.5% vs 87.0% vs 89.3%), ethnicity (Hispanic 79.7% vs 82.3% vs 85.1%, Non-Hispanic 80.0% vs 81.9% vs 84.1%), Charlson-Deyo score (0: 83.5% vs 85.4% vs 87.9%, 1: 62.8% vs 68.2% vs 73.2%, 2: 42.5% vs 52.6% vs 58.3%, 3+: 49.0% vs 54.4% vs 51.7%), B symptoms (yes: 74.7% vs 78.9% vs 81.5%, no: 84.3% vs 85.1% vs 87.5%), treatment facility type (academic: 69.8% vs 74.0% vs 77.1%, non-academic: 66.4% vs 67.6% vs 70.2%, unknown: 92.9% vs 95.0% vs 96.5%), insurance type (private: 89.9% vs 91.2% vs 93.4%, Medicare: 46.5% vs 52.3% vs 57.2%, Medicaid: 79.1% vs 84.5% vs 87.7%) and income level (Q1: 74.1% vs 77.1% vs 81.1%, Q2 77.7% vs 80.1% vs 81.3%, Q3 79.8% vs 81.6% vs 85.2%, Q4: 82.8% vs 84.9% vs 87.0%) (all p<0.001). In multivariate Cox regression analysis adjusting for age, sex, race, ethnicity, Charlson-Deyo score, B symptoms, treatment facility type, insurance type, and income level, diagnosis in the BV era (HR = 0.856; 95% CI, 0.827–0.885; p<0.001) and ICI era (HR = 0.666; 95% CI, 0.636–0.698; p<0.001) was independently associated with improved OS compared to the Chemo era. Conclusions: In this large NCDB-based analysis, survival outcomes for patients with cHL significantly improved in the BV and ICI eras compared to the Chemo era. OS improvement was consistent across all subgroups and remained independent of age, sex, race, ethnicity, Charlson-Deyo score, B symptoms, treatment facility type, insurance type, and income level in multivariate analysis. These findings may reflect the positive impact of BV and ICIs on survival in patients with cHL.

Abstract Follicular lymphoma (FL) is the most common indolent non-Hodgkin lymphoma. Although patients with FL have high response rates to therapy, most develop increasingly resistant disease. In addition, transformation into an aggressive lymphoma is associated with unfavorable outcomes. Many novel agents are under investigation, and early clinical data are encouraging. Aligning treatment with the underlying tumor biology and sequencing of therapies remain key clinical challenges. At the Lymphoma Research Foundation’s biannual 2024 Follicular Lymphoma Scientific Workshop, experts convened to discuss the role of chemotherapy in the context of new therapies, the impact of early progression on treatment sequencing, novel end points in clinical trials, disease biology and the tumor microenvironment, and new treatments on the horizon. This report focuses on updates in FL biology, first-line treatment, the role of progression of disease in 24 months, clinical trial design, and redefining cure in FL.