Ansell, Stephen M.

Abstract Background Watchful waiting is a common option for pts with asymptomatic FL/MZL. Anti-tumor immunization may delay or even avoid subsequent need for more toxic therapies. EO is designed from non-self-protein sequences derived from gut bacteria, including 4 HLA-A2 CD8 T cell epitopes (synthetic mimic peptides), exhibiting molecular mimicry with specific epitopes on B cell markers (CD20, CD22, CD37, BAFF-receptor). EO also contains a CD4 helper epitope UCP2. EO expands pre-existing memory CD8 T cells recognizing non-self-epitopes from gut bacteria that cross-react with B cell antigens on tumor cells. Methods Cohort 2 (C2; planned accrual 25 pts) of EONHL1-20/SIDNEY includes HLA-A2+ pts with previously untreated FL/MZL not in need of treatment. Pts receive EO (300μg/peptide) SC with adjuvant Montanide, q2 weeks (w) x 4, then q4w, for 12 total doses. For antigen-specific immune responses, peripheral blood mononuclear cells were assayed by flow cytometry using EO-mimic or B cell peptide specific tetramers without in vitro stimulation. IFN-γ ELISPOT was used to evaluate response to UCP2 and anti-CD3. Primary endpoint is objective response rate (ORR) per Lugano 2014. A pre-defined futility boundary was applied (ORR uninteresting=5%/promising=20%). Finding at least 3 of 25 pts with ORR would meet criteria to continue development (chance of 13% and 90% for true ORR of 5% and 20%, respectively). Results As of July 2025, 22 pts had started EO. Median age 59 years (range 32-86); 9 female; 19 ECOG 0/3 ECOG 1; 19 FL/3 extranodal MZL (ENMZL); 3 Ann Arbor stage I-II/8 stage III/11 stage IV; FLIPI low risk 8/intermediate 8/high 6; FLIPI-2 low risk 12/intermediate 7/high 3; 19 GELF neg/3 pos. At a median follow-up for progression of 9.4 months, 9 pts completed EO, 4 stopped for progressive disease (PD), 2 due to PI/pt decision; 7 ongoing. Median treatment duration 38 w (range 4-39). Among 19 evaluable pts, best Lugano response was 3 complete (CR) and 6 partial responses (PR) (ORR 47% [95% CI 24%–71%]; CR-rate 16% [3%-40%]), 6 stable disease (SD), and 4 PD. ORR by PET-CT was 29% (n=14) w6, 39% (n=18) w19, 46% (n=13) w42; corresponding CR rates were 0%, 11%, 15%. Median time to response 17.1 w (range 5.3-41.1). Currently there are only 2 confirmed PD events in pts with prior PR/CR. Among 8 pts with an initial indeterminate response per LyRIC 2016 followed by a confirmatory scan, 4 pts did not have confirmed PD; a possible atypical response pattern rate of 50% on EO (occurring 5.3-41.9 w after start of EO). In 16 immune response evaluable pts, 88% and 81% had expansion of CD8 T cells specific for EO mimic and B cell target peptides, respectively. Expanded specific CD8 T cells had a memory phenotype dominated by effector memory (TEM) and central memory cells. At w5, 7 clinical responders and 7 non-responders were evaluable; more responders had a positive immune response against EO mimic (Chi-square p=0.025) and B cell target (p=0.047) peptides. Max expansion (response sum for 4 EO mimic or 4 B cell target peptides at a single timepoint w3-w8) of specific CD8 T cells (% of all peripheral CD8 T cells) was higher for responders than non-responders both for EO mimic (Mann Whitney p=0.018) and B cell target (p=0.017) peptides. Pts (4) with an initial indeterminate response (LyRIC 2016) had a higher max expansion of B cell target peptide specific CD8 T cells vs 4 pts with a typical progression pattern (Mann Whitney p=0.018) and vs all other 12 pts evaluable for immune response (p=0.029). There was no difference in CD4 T cell response to UCP2 in responders vs non-responders (induction or max immunity w3-w8). Anti-CD3 expansion of T cells indicating T cell intrinsic activation potential did not correlate with clinical response (baseline or max). The most common EO-related adverse events (AEs) were Gr (Grade) 1 (10 pts) and Gr 2 (6 pts) local administration site reactions. Other related AEs in >1 pt were Gr 1 headache (3 pts) and Gr 1-2 fatigue (3 pts). Asthenia lasting 2 days was the only related Gr 3 AE. There were 4 unrelated Gr 3 AEs (gastroenteritis, GI-hemorrhage, inguinal hernia, and syncope). There were no Gr 4-5 AE. ConclusionsEO has a favorable safety profile and is preliminary associated with an ORR of 47%, exceeding the pre-specified boundary for promising activity. The speed and level of expansion of both EO mimic and B cell target peptide specific CD8 memory T cells are associated with objective response. Results will be updated.

Abstract Despite a characteristic indolent course, a substantial subset of follicular lymphoma (FL) patients has an early relapse with a poor outcome. Thus far, efforts to identify factors that predict survival have been unsuccessful. However, we and others have demonstrated the prognostic relevance of CD4+ T cells in the tumor microenvironment (TME) of FL and developed a bio-clinical risk model (called BioFLIPI) that offers improved risk stratification (Mondello et al, BCJ 2021). However, the mechanisms defining different BioFLIPI scores remain to be fully understood. To identify the programs associated with the BioFLIPI scores, we first interrogated the RNA-seq of purified B cells from 67 newly diagnosed FL1-3A (BioFLIPI 1 n=21; BioFLIPI 2-3 n=36; BioFLIPI 4 n=10). We found 1,156 upregulated genes and downregulation of 930 genes in BioFLIPI 4 vs 1, including well-known immune molecules such as CD70, IL10RA, CD69, and CCR6 (FDR<0.05). Gene set enrichment analysis (GSEA) using the MSigDB confirmed a negative enrichment in multiple immune response and interferon gamma signatures and revealed striking positive enrichment for cell proliferation and antigen presentation pathways in BioFLIPI 4 compared to BioFLIPI 1 tumors. The same pathways were dysregulated in BioFLIPI 2-3 but a lower magnitude. To investigate the somatic mutations associated with different BioFLIPI scores, we performed WES of the same 67 FLs and further expanded this cohort for a total of 90 patients (BioFLIPI 1 n=26; BioFLIPI 2-3 n=51; BioFLIPI 4 n=13). We found enrichment for mutations in several regulators of the germinal center in BioFLIPI 4 and 2-3, such as MEF2B (p=0.029), FOXO1 (p=0.038) and IRF8 (p=0.035), suggesting the cooperation of multiple oncogenic events promoting lymphomagenesis. In contrast, BioFLIPI 1 tumors harbored a higher rate of mutations in TNFRSF14 gene, known to enhance anti-lymphoma immune response. To investigate whether BioFLIPI modulates immune signatures of the microenvironment, we performed TME deconvolution by applying CIBERSORTx to bulk RNA-seq of these 67 FLs. We found that FLs with BioFLIPI 4 and 2-3 were enriched for CD4+ Tcm exh (p=0.02), Tem exh (p=0.04), Tfhexh (p=0.07) and NK cells (p=0.01) compared to BioFLIPI 1. In line with this data, CyTOF performed on a subset of these patients (BioFLIPI 1 n=12; BioFLIPI 2-3 n=25; BioFLIPI 4 n=6) confirmed progressive increase of CD4+ Tcm exh (p=0.006), Tem exh (p=0.009) and Tfh exh (p=0.025) cells from BioFLIPI 1 to BioFLIPI 4. Single cell RNA/BCR-seq of 21 FLs (BioFLIPI 1 n=6; BioFLIPI 2-3 n=12; BioFLIPI 4 n=3) corroborated a higher abundance of Tem exh, cytotoxic (CTL) Tem exh, Tfhexh and NK cells in BioFLIPI 4 and 2-3. Conversely, CD8+ naïve T cells were prevalent in BioFLIPI 1 tumors. Differential gene expression in T cell clusters from BioFLIPI 4 vs BioFLIPI 1 FL revealed a marked upregulation of multiple immune genes involved in antigen presentation (e.g., HLA-DRB, HLA-DQA, HLA-DPA/B) and immune response (e.g., ETS1, TNFSF8, IL7R). GSEA showed a positive enrichment for antigen presentation signatures and negative enrichment for TNFα signaling and Oxphos metabolism. Similar findings were observed in BioFLIPI 2-3 FLs. Using CellChat analysis, we found a stronger interaction between malignant B cells and Tem exh and CTL Tem exh cells in BioFLIPI 4 and 2-3 vs BioFLIPI 1 tumors. This was linked to an increased strenght of MHCI-CD8 interaction and concordant decrease of MHCII-CD4. Notably, the suppressive BTLA-TNFRSF14 signaling axis was present in BioFLIPI 4 and 2-3 but not in BioFLIPI 1 FLs. This was accompanied by a decreased signaling through TNF/LTA-TNFRSF1B, CD80/CD86-CD28/CTLA4. Linking the BioFLIPI-specific immunogenicity to lymphomagenesis, we found that FL patients with BioFLIPI 4 had two-three folds higher risk of transformation to an aggressive lymphoma (12% vs 7%) or death (21% vs 3%) than those with BioFLIPI 1, suggesting that this immune dysfunction favors the expansion of a dominant tumor clone and promotes a more aggressive form of FL. Collectively, our findings demonstrate that BioFLIPI scores in FL reflect distinct patterns of tumor immunogenicity that shape the TME. Notably, high BioFLIPI scores are paradoxically associated with enhanced antigen presentation alongside a suppressed immune response. This suggests a sustained antigen-specific T cell response that may drive immune exhaustion, promote tumor progression, and ultimately lead to histologic transformation

Abstract Background: Follicular lymphoma (FL) is a germinal center–derived B cell malignancy characterized by the clonal expansion of a dominant malignant B cell population. While prior studies have identified transcriptional subtypes across patients, most analyses have treated B cells within individual tumors as a transcriptionally uniform population. However, this assumption has not been systematically tested using high-resolution, clonotype-resolved transcriptomic data. The broader landscape of intratumoral B cells—including those not belonging to the dominant clone—remains poorly defined, and their roles in disease biology are not well understood. These unexpanded, intratumoral B (ITB) cells may include residual non-malignant populations, early-stage malignant cells, or immune-modulating cells with distinct roles in the tumor microenvironment. It is also unknown whether their presence varies across patients or correlates with clinical features or immune microenvironmental interactions. Methods: We performed integrated single-cell 5′ RNA sequencing and B cell receptor (BCR) repertoire sequencing on 7 newly diagnosed FL 1-3A fresh-frozen lymph node biopsies (Dasari et al., Blood Cancer Discov 2025). Using a high-confidence approach that incorporates heavy and light chain pairing, V and J gene usage, and CDR3 nucleotide junction identity, we classified B cells into two groups: (1) cells belonging to the dominant expanded clone (FL B cells) and (2) all other B cells with detectable and non-expanded BCRs (ITB). These clonal identities were then integrated with single-cell transcriptomic profiles using Seurat and Immcantation pipelines. Differential expression analysis was conducted using edgeR to compare FL B cells to ITB cells across all FL biopsies. Results: After quality filtering, a total of 44,637 single cells were retained across all 7 samples (minimum per sample: 3,847 cells), including malignant and non-malignant B cells as well as diverse tumor microenvironment populations. The proportion of B cells assigned to the dominant FL clone varied substantially across tumors, ranging from 22% to 89%, indicating that some tumors contain a substantial population of ITB cells. In one tumor, we identified two expanded malignant B cell clones comprising 40.6% and 34.4% of all cells. These clones exhibited non-overlapping V and J gene usage (IGHV3-11/IGHJ5 vs. IGHV3-72/IGHJ3), distinct CDR3 junctions, and different immunoglobulin isotypes (IGHG1 vs. IGM). BCL2 was expressed in most cells within both clones (Clone 1: 87.8%, Clone 2: 67.0%), with mean log-normalized expression levels of 1.79 and 0.95, respectively. Both clones formed distinct UMAP clusters and showed transcriptional divergence from ITBs, with 453 differentially expressed genes (DEGs) identified for Clone 1 and 98 for Clone 2 (adjusted p<0.01, |log₂FC|>1, min.pct≥0.25). Additionally, 748 DEGs were identified between the two clones. This evidence of clonal divergence within a single FL tumor reinforces the need to reconsider intratumoral homogeneity in FL and suggests that multiple malignant clones may sometimes coexist. When comparing FL clones to ITB cells across tumors we found that FL clones exhibited a transcriptional program enriched for proliferation and cell cycle regulators, including MKI67 (p<0.005), E2F1 (p<0.0001), CDKN2A (p<0.0001), and CDKN2B (p<0.01), as well as elevated expression of AICDA (p<0.01), EZH2 (p<0.0001), and PD-L1 (p<0.05). As expected, the malignant cells also had higher expression of BCL2 (p<0.01) and CD10/MME (p<0.0001). In contrast, ITB cells demonstrated relatively higher expression of SELL (p<0.0001) and TNFRSF14 (p<0.05), which were significantly downregulated in the FL clone. Conclusions: Together, these findings challenge the prevailing notion that FL tumors are defined by a single homogeneous malignant B cell population. Instead, our data support a model in which multiple transcriptionally and functionally distinct B cell populations coexist within tumors, including dominant clones and a diverse population of ITB cells. This is, to our knowledge, the first study to comprehensively characterize these populations using integrated, lineage-aware single-cell multiomic profiling across FL patients. These insights may ultimately inform new strategies for understanding tumor evolution, immune evasion, and therapeutic targeting in follicular lymphoma.

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 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 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.

Abstract Anaplastic large cell lymphomas (ALCLs) are CD30+ T-cell lymphomas that share pathologic features but differ in presentation, outcome, and genetics. Current classification incorporates clinical presentation and anaplastic lymphoma kinase (ALK) status but inadequately addresses molecular heterogeneity and therapeutic vulnerabilities. We studied 689 patients with ALCL in the LLMPP (Lymphoma/Leukemia Molecular Profiling Project) and performed expert consensus review, genetic subtyping (ALK, DUSP22, TP63, and triple negative), and immunohistochemistry for phosphorylated STAT3Tyr705. RNA sequencing with unsupervised gene expression profiling in 393 patients identified 2 main molecular types of ALCL that could be predicted with 91% accuracy based on the presence (type I) or absence (type II) of phosphorylated STAT3Y705 expression. Type I ALCLs included ALK+ ALCL and a subset of triple-negative ALCLs (TN-I); type II ALCLs included tumors with DUSP22 and/or TP63 rearrangements and the remaining triple-negative ALCLs (TN-II). Type I ALCLs were enriched for JAK-STAT3, whereas type II ALCLs were enriched for non–tyrosine kinase pathways, particularly epigenetic regulators such as EZH2. Immunohistochemistry showed overexpression of EZH2 and its trimethylated substrate H3K27. Prognosis in systemic ALCL was favorable for DUSP22-rearranged ALCL (5-year overall survival, 95%) and ALK+ ALCL (88%), intermediate for triple-negative ALCL (TN-I, 52% and TN-II, 37%), and poor for TP63-rearranged ALCL (0%). We introduce an integrated molecular classification that preserves currently diagnosed ALCL entities but identifies 4 molecularly distinct ALK− ALCL subtypes (DUSP22-rearranged, TP63-rearranged, TN-I, and TN-II). This classification can be easily implemented on paraffin tissue in routine practice or clinical trials, and stratifies ALCL into diagnostically, prognostically, biologically, and potentially therapeutically relevant subtypes.