Citations

Abstract B cell maturation antigen (BCMA)-targeted chimeric antigen receptor T (CART) cell therapies have emerged as a potentially curative treatment for multiple myeloma (MM). However, 50-70% of patients relapse within 3 years. The MM tumor microenvironment (TME) is highly immunosuppressive with an abundance of bone marrow (BM) cancer-associated fibroblasts (CAFs), which contribute to BCMA-CART cell failure. Therapeutic targeting of BM-CAFs has been beset by 1) an incomplete understanding of CAF interactions with CART cells and other BM cells and 2) toxicity risks due to prolonged depletion of healthy fibroblasts and inefficient drug delivery to the BM. To elucidate how BM-CAFs drive BCMA-CART resistance, we performed single cell RNA sequencing (scRNAseq) of baseline BM aspirates from responders (R, n = 6) and non-responders (NR, n = 6) to BCMA-CART cell therapy. NR BMs were enriched for inhibitory CAFs (iCAFs) that highly express fibroblast activation protein (FAP). Spatial analyses of BM cells revealed greater iCAF-iCAF and iCAF-inhibitory macrophage interactions in NR. These findings suggest that FAP+ iCAFs in NR are abundant in the BM and exhibit significant interactions with immunosuppressive macrophages. Next, we sought to validate our scRNAseq data in preclinical models. Xenograft mice were intravenously (IV) engrafted with luciferase+ (luc+) BCMA+ OPM2 cells and/or patient-derived (pd) BM-CAFs (1x106 each). Following engraftment, mice received 1x106 BCMA-CART cells (IV). BCMA-CART cells eradicated tumors in mice engrafted with OPM2 cells, but not in mice engrafted with pdBM-CAFs and OPM2 cells (p<0.01). In NSG and humanized mice (NSG mice engrafted with human CD34+ cells in the first 48 hours of life), tumor growth was significantly accelerated in the presence of pdBM-CAFs (NSG: p<0.01; humanized: p<0.001). BCMA-CART, OPM2, and pdBM-CAF co-cultures also showed significant increases in immunosuppressive cytokines including IL-1Rα, IL-4 and IL-10 and inhibited CART cell proliferation compared to co-cultures without pdBM-CAFs (IL-1Rα, IL-10: p<0.01; IL-4: p<0.001; proliferation: p<0.05). To overcome CAF-mediated CART dysfunction, we engineered BCMA-CART cells to secrete molecules to target BM FAP+ iCAFs and remodel the MM-TME (STriKEs). We designed two BCMA-CART-STriKE strategies: 1) a tri-specific composed of IL-15, anti-FAP, and anti-CD16 molecules to activate innate immune cells to kill CAFs and 2) bi-specific anti-FAP, anti-SIRPα molecules linked to an antibody Fc region (Fc fusion) targeting the CD47/SIRPα axis to induce phagocytosis of FAP+ iCAFs. We hypothesized that BCMA-CART-STriKEs would surmount CAF-mediated immunosuppression without toxicity. We generated BCMA-CART-STriKEs from healthy human donor T cells. CAR expression, STriKE secretion, and binding were confirmed via flow cytometry. Co-cultures of BCMA-CART-STriKEs, donor-matched macrophages, OPM2 cells or BCMA+ MM1S cells, and pdBM-CAFs showed significantly higher OPM2 or MM1S killing (p<0.001), immune cell activation (p<0.01), and phagocytosis (p<0.05), compared to control BCMA-CART cells secreting IL-15 or Fc fusions targeting only FAP or only SIRPα. Next, we assessed BCMA-CART-STriKE efficacy in vivo. NOD-SCID-γ-/- (NSG) mice received 1x106 pdBM-CAFs and 1x106 luc+ OPM2 or luc+ MM1S cells via IV or subcutaneous (SQ) injection. Mice were randomized after confirming tumor engraftment (via bioluminescence imaging for IV or tumor caliper for SQ) and received IV treatment with 1x106 BCMA-CART-STriKEs (or controls) and 5x105 donor-matched macrophages or CD16+ immune cells. BCMA-CART-STriKE-treated mice achieved total tumor clearance. Treatment with BCMA-CART cells secreting IL-15 led to lethal weight loss despite tumor clearance while BCMA-CART cells secreting Fc fusions targeting only FAP or SIRPα failed to eradicate tumors. Lastly, we assessed the potential toxicities of BCMA-CART-STriKEs in humanized mice engrafted with OPM2 cells and pdBM-CAFs (1x106 each). CART cells expressing BCMA and FAP CARs (BCMA-FAP CART) served as a control. BCMA-FAP CART treatment resulted in significant toxicity and weight loss while BCMA-CART-STriKE treatment did not (p<0.01). In summary, we have identified iCAF interactions with immunosuppressive immune cells as key contributors to BCMA-CART cell failure and designed novel BCMA-CART-STriKEs that overcome this failure in MM preclinical models. These findings support the translation of BCMA-CART-STriKEs to a phase I clinical trial.

Abstract Introduction Marginal zone lymphoma (MZL) is an indolent and heterogenous disease. Several prognostic models have been proposed to assess outcomes in MZL, including FLIPI, IPI (primarily designed for overall survival [OS]), and MZL-specific scores such as MALT-IPI (event-free survival [EFS]) and MZL-IPI (progression-free survival). FLIPI24 is a recently developed prognostic index for patients with follicular lymphoma (FL) treated with first-line (1L) immunochemotherapy (IC), identifying a high-risk population with inferior survival and increased risk of lymphoma-related death. FLIPI24 has also demonstrated robust performance in the entire FL population, including patients under observation. This study aimed to evaluate the prognostic utility of FLIPI24 in patients with MZL and to compare its performance with other established indices (FLIPI, MZL-IPI, IPI, MALT-IPI). Methods We pooled harmonized data from newly diagnosed MZL patients with complete available FLIPI24 variables (age, white blood cell count [WBC], hemoglobin [HGB], serum lactate dehydrogenase [LDH], and beta-2-microglobulin [B2M] levels) treated with 1L IC. Data were collected from three prospective international cohorts: NiHiL (Czech Republic; n=897; enrolled 2005–2023), LEO (USA; n=62; 2015–2020), and MER (USA; n=32; 2010–2015), forming a pooled cohort of 991 1L IC-treated patients. The analysis was then extended to include all newly diagnosed MZL patients (n=2,116), regardless of treatment. The primary endpoint in the 1L IC-treated cohort was EFS at 24 months (EFS24), and for all MZL patients, it was OS. Secondary endpoints included OS24 for 1L IC treated cohort, lymphoma-related mortality by FLIPI24 risk groups, and comparative prognostic performance of FLIPI, IPI, and MALT-IPI, by C-statistics; performance of MZL-IPI was assessed on a subgroup of NiHiL patients with available data (n=877 all MZL, n=485 1L IC-treated). Results Among the 991 1L IC-treated patients, the median age at diagnosis was 66 years (range 22–94); 47% were male; 11% had ECOG performance status ≥2, 68% clinical stage III–IV, 42% elevated LDH, 40% IPI 3–5, 42% FLIPI 3–5, and 62% MALT-IPI 2–3. The median B2M level was 2.8 mg/L, WBC 7.0 × 10⁹/L, and HGB 13.1 g/dL. MZL subtypes were: 64% extranodal (EMZL), 22% nodal (NMZL), and 14% splenic (SMZL). Most common 1L IC regimens were R-CVP (46%), R-CHOP-like (32%), R-chlorambucil (9%), and B-R (9%). FLIPI24 stratified patients into low- (18%), intermediate- (37%), and high-risk (45%) groups. High-risk patients were older (median 54 vs 65 vs 71 years for low-, intermediate-, and high-risk groups, respectively; P<0.01), had more frequent adverse baseline features, and were more likely to have the SMZL subtype (2% vs 7% vs 25% for low-, intermediate-, and high-risk groups, respectively; P<0.01). After a median follow-up of 7.2 years, EFS24 was 89%, 85%, and 65% (P<0.01) across low-, intermediate-, and high-risk groups; OS at 2 years was 98%, 97%, and 79%; and OS at 5 years was 96%, 91%, and 62%, resp. (P<0.01). The 5-year cumulative incidence of lymphoma-related death was 13% in high-risk vs 6% in low-/intermediate-risk groups. FLIPI24 showed better prognostic accuracy for EFS compared with FLIPI, MZL-IPI, IPI and MALT-IPI (C-index: 0.640 vs 0.630, 0.622, 0.620 and 0.603, resp.), as well as for OS (C-index: 0.710 vs 0.672, 0.641, 0.663, 0.652, resp.). Prognostic performance was consistent across MZL subtypes. In the extended cohort of 2,116 newly diagnosed MZL patients (47% receiving IC, 17% rituximab monotherapy, 11% radiotherapy, 8% surgery, 2% antibiotics; 15% observed), FLIPI24 stratified patients into low- (24%), intermediate- (39%), and high-risk (37%) groups. OS at 2 year was 99%, 97%, and 83%, at 5 years it was 96%, 91%, and 62%, resp. (P<0.01). The 5-year cumulative incidence of lymphoma-related death was 12% in high-risk vs 4% in low-/intermediate-risk patients. FLIPI24 outperformed other indices (OS C-index: 0.754 vs MZL-IPI 0.654, FLIPI 0.700, MALT-IPI 0.694, IPI 0.699). Conclusion FLIPI24 demonstrates strong and consistent prognostic performance across all MZL subtypes, identifying ~40% of patients as high-risk with significantly worse survival and increased lymphoma-related mortality. The FLIPI24 outperforms FLIPI, MZL-IPI, IPI, and MALT-IPI in predicting outcomes and may serve as a practical tool to guide risk-adapted management and selection of patients for novel therapies in MZL. Funding: NU21-03-00411, P50 CA97274, U01 CA195568.

Abstract Introduction Elevated levels of fetal hemoglobin (HbF) ameliorate sickle cell disease (SCD) manifestations. BEAM-101 is an investigational cell therapy comprising ex vivo base-edited autologous CD34+ hematopoietic stem and progenitor cells (HSPCs) that directly target the HBG1/2 promoters to disrupt BCL11A binding, switching production from sickle (HbS) to majority anti-sickling HbF. We present ongoing data from the BEACON study (NCT05456880), a Phase 1/2, single-arm, open-label study evaluating a single dose of BEAM-101 in individuals with SCD and severe vaso-occlusive crises (sVOCs). Methods Eligible patients were aged 12–35 years with SCD and ≥4 sVOCs in the 2-year period prior to informed consent. After mobilization with weight-based or tiered fixed-dose plerixafor, autologous CD34+ HSPCs were collected by leukapheresis and genetically modified with an adenine base editor. Following conditioning with pharmacokinetically adjusted myeloablative busulfan (target cumulative AUC 80 hr*mg/L), patients received a single dose of BEAM-101 (≥3.0×106 viable CD34+ cells/kg). Patients were then monitored for neutrophil and platelet engraftment, adverse events (AEs), total Hb, Hb fractions, % F-cells, hemolysis markers, sVOCs, and additional protocol endpoints for up to 24 months. Results As of June 16, 2025, enrollment has been completed and 46 patients have initiated mobilization. Of the 26 patients who have received BEAM-101, mean duration of follow-up was 6.2 (range 1.0–18.7) months. Baseline demographics were: age 18–34 years, 24 βS/βS genotype, 21 self-reported Black/African American, and 11 female. One patient, who successfully engrafted and obtained stable HbF levels >60%, withdrew consent 5 months post-infusion. For the 26 treated patients, the median number of stem cell collection cycles was 1 (range 1–4); 18 (69.2%) required a single cycle, 4 required 2 cycles, and 4 required >2 cycles. Target cell dose was achieved with a median of 3 (1–12) collection days. Patients received a mean BEAM-101 dose of 8.4 (range 3.2–23.4) ×106 viable CD34+ cells/kg. By the data cut, patients achieved neutrophil (n=26) and platelet (n=24) engraftment at a median of 18 (12–30) days and 19 (11–50) days, respectively, with a median duration of 7 (4–17) days of neutropenia. Of the 24 evaluable patients, 9 (37.5%) achieved platelet engraftment without requiring platelet transfusion through the transplant process. No patients had ≥Grade 3 AEs or serious AEs related to BEAM-101. One patient died due to respiratory failure, assessed as likely related to busulfan conditioning, 4 months post-infusion as previously reported. Mean total Hb increased to 12.1 (range 9.0–14.7) g/dL by Month (M) 1 (n=24), 15.6 (11.9–18.6) g/dL by M6 (n=11), and was sustained through follow-up. Four patients had total Hb levels above the normal range beyond M6 without any associated clinical manifestations or need for therapeutic intervention. Erythropoietin levels trended towards normal in all patients, including those with elevated total Hb. Mean endogenous HbF (HbF/[HbF+HbS]) was >60% and HbS was <40% by M1, which was sustained through last follow-up. At M6, mean % F-cells were 99.2% (97.0–99.9; n=10) and mean HbF/F-cell was 20.8 (18.0–23.9; n=9) pg, exceeding the protective threshold of 10 pg against sickling. Peripheral blood editing was at a mean of 68.5% by M3 (n=16), 69.6% by M6 (n=10), and 72.8% by M12 (n=5). Hemolysis markers and sickling parameters normalized or improved for all patients. No patients experienced any investigator-reported sVOCs post-engraftment. Conclusions The BEAM-101 treatment process is characterized by a low number of mobilization and collection cycles and high editing efficiency, resulting in rapid neutrophil and platelet engraftment and robust pancellular HbF expression, with durability observed up to 18 months. Additionally, there is a corresponding reduction in HbS levels to <40%, and improvements or resolution of hemolysis parameters and anemia, along with resolution of investigator-reported sVOCs post-engraftment. The safety profile of the BEAM-101 treatment regimen is consistent with busulfan conditioning, autologous HSCT, and underlying SCD. These results demonstrate the potential for base editing of the HBG1/2 promoters as a safe and effective therapeutic modality for the treatment of SCD that also has the potential to minimize hospitalization and treatment process burden for patients, families, and sites.

Abstract Background: In R/R DLBCL/FL, T-cell engager therapy (TCET) using chimeric antigen receptor T-cell therapy (CART) or bispecific antibodies (BsAbs) are promising options. Patients (pts) with R/R disease post TCET experience dismal outcomes with overall survival (OS)< 5 months (mos). Lonca, an antibody drug conjugate (ADC) targeting CD19 (FDA approved for R/R DLBCL and beneficial in R/R FL) has been increasingly used post-TCET. RWDA of Lonca is limited, especially regarding: 1) impact of CD19 expression on activity, 2) adverse events (AEs) in non-clinical trial setting, and 3) impact of sequencing Lonca and TCET. Thus, we conducted a RWDA of Lonca efficacy and toxicity in R/R DLBCL and FL pre- and post-TCET. Methods: We conducted a multicenter retrospective study of R/R DLBCL or FL pts ≥18 years who received Lonca from 2017 to 2025 at 10 US academic sites. Demographic, pathological, treatment, and AEs were collected. Efficacy outcomes included overall response rate (ORR), complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). Progression-free survival (PFS) and OS were analyzed by Kaplan-Meier method. CD19 expression by immunohistochemistry (IHC) and/or flow cytometry (FC) prior to Lonca administration was correlated to outcomes. Results: Among 120 pts (DLBCL n= 101, FL n=19), all but 2 pts received Lonca monotherapy. At a median follow-up of 6.1 mos, median PFS and OS were 2.6 mos and 6.7 mos, respectively for the entire cohort. For DLBCL, median age was 66 years (18-89). Median prior lines of therapy (LOT) were 4 (1-11). ORR was 37.7% (CR: 17%, PR: 20.7%), SD: 6%, and PD: 56%. Median PFS was 2.1 mos, and median OS was 5.8 mos. For FL, median age was 62 years (28-88). Median prior LOT was 6 (2-8). ORR was 49.4% (CR: 28%, PR: 21.4%), SD: 14%, and PD: 35.7%. Median PFS was 6.7 mos, and median OS was 6.8 mos. OS by best response (entire cohort) was 11.3 mos and 11.8 mos in those who had CR and PR, respectively, 10.6 mos in SD and 3.5 mos in those with PD. Lonca LOT (2nd-12th line) was not associated with improved OS (p=0.58). Bulky disease (>7.5 cm) was not associated with worse OS (p=0.07). PD to Lonca was associated with PD at 2nd line treatment (OR: 4.3; 95% CI: 1.7–10.9) and 3rd-line treatment (OR: 4.2; 95% CI: 1.5–11.5), elevated lactate dehydrogenase (OR: 1.9; 95% CI: 1.2–2.9), and each subsequent increase of extranodal sites >1 prior to Lonca (OR: 1.5; 95% CI: 1.04–2.1). Among pts with available CD19 status prior to Lonca administration, 28 were FC-positive and 22 were IHC-positive. Of the CD19 FC-positive pts (n=23), 22% (5/23) achieved CR, 26% (6/23) had PR, 8% (2/23) had SD, and 43% (10/23) developed PD. Of the CD19 IHC-positive pts (n=20): 15% (3/20) achieved CR, 20% (4/20) had PR, 5% (1/20) had SD, and 60% (12/20) developed PD. Among all CD19-negative pts (n=7), two achieved CR, one had SD, and four with PD. Any grade AE was seen in 84% of pts. Any grade ≥3 (G3) AE was seen in 62% of pts. Notable G3 AEs included: anemia (35%), lymphopenia (34%), neutropenia (21.6%), thrombocytopenia (20%), infection (17%), fatigue (9%), peripheral edema (5%), pleural effusion (4%), photosensitivity (2.5%), and pruritus (2.5%). Treatment discontinuation due to AEs occurred in 17% (n=21) of pts. Fifty-five pts (50 DLBCL, 5 FL) received both Lonca and CD19 directed CART (Pre-Lonca n=54; Post-Lonca n=1). There was no significant difference in PFS or OS between pts who received CART and those who did not (PFS: 2.7 mos vs 3 mos; OS: 7.4 mos vs 5 mos). BsAb was used in 41 pts (34 DLBCL, 7 FL) (Pre-Lonca n=30; Post-Lonca n=11). No significant difference in PFS or OS was observed between pts who received BsAb pre- or post-Lonca. There was also no significant difference in PFS between pts who received a BsAb (2.2 mos) and those who did not (2.9 mos), nor in OS.Conclusion: In this large RWDA of Lonca, pts who achieved any form of clinical response experienced a significantly longer OS than previously reported for heavily pre-treated pts. Lonca was predominantly administered after TCET; however, there was no significant difference in efficacy between pts who received CART and BsAbs and those who did not. CD19 positivity and earlier utilization of Lonca did not appear to have an impact on response rates. Our findings support a role for Lonca in the treatment of R/R DLBCL pts not candidates for TCET. This RWDA highlights the need to further investigate the sequencing of Lonca in R/R DLBCL and FL.

Abstract Introduction The International Prognostic Index (IPI, NEJM, 1993) has guided risk stratification in large B-cell lymphoma (LBCL) for over three decades. Its simplicity and clinical utility have driven its widespread adoption, even in the era of precision medicine. However, the prognostic value of the IPI and its components may not be uniform across the age spectrum of LBCL patients. We evaluated the contemporary prognostic performance of the IPI in a large international cohort, focusing on age-stratified performance and the relative contributions of its individual components, grouped as patient- and disease-related factors. Methods We harmonized and pooled data from 6,941 patients with newly diagnosed systemic LBCL treated with R-CHOP-like regimens (R-CHOP, R-miniCHOP, DA-EPOCH-R, R-CHOEP, R-CHOP+X) with complete IPI data from three prospective cohorts: NiHiL (Czech Republic, n=4,590; 2010–2023), LEO (USA, n=1,789; 2015–2020), and MER (USA, n=562; 2010–2015). IPI components included age, ECOG performance status (PS) as patient-related, and Ann Arbor clinical stage, serum lactate dehydrogenase (LDH), and extranodal (EN) involvement as disease-related factors. Prognostic performance was evaluated using multivariable Cox regression models and C-statistics. Age-stratified analysis was performed for: ≤40 years (7%; n=492), 41–60 years (28%; n=1,909), 61–80 years (59%; n=4,080), and >80 years (7%; n=460). The primary endpoint was overall survival (OS). Results In the pooled cohort, the median age was 66 years (range 18–95). Compared to the original IPI cohort (n=3,273), our cohort was older (age >60 years: 66% vs 41%) but otherwise comparable: ECOG PS 2–4 (25% vs 24%), clinical stage III–IV (63% vs 66%), elevated LDH (60% vs 52%), and >1 EN involvement (32% vs 30%). In multivariable analysis, age (≤60 vs >60 years) was the strongest predictor of OS (HR=2.78; vs HR=1.96 in original IPI report), followed by ECOG PS (HR=2.07; vs 1.80), clinical stage (HR=1.50; vs 1.47), LDH (HR=1.41; vs 1.85, all P<0.01); EN involvement was not significant (P=0.50, HR=1.03 vs 1.48). Risk group distribution shifted from the original IPI: 28% were low-risk (0–1 factor; vs 35%), 23% low-intermediate (2; vs 27%), 24% high-intermediate (3; vs 22%), and 25% high-risk (4–5; vs 16%). Corresponding 2-year OS rates were 95%, 86%, 78%, and 63%, superior to those of the original IPI cohort (87%, 67%, 55%, 44%; pre-rituximab era). Age was associated with an increasing risk of mortality, with a more pronounced rise beyond 60 years. In a piecewise Cox model, the HR per year was 1.04 for ages ≤60 (linear), 1.05 for 61–69 (accelerated), and 1.06 for ≥70 years (exponential, all P<0.01). ECOG PS retained prognostic significance across age groups: HRs were 1.87 (P=0.06) for ≤40 years, 1.63 (P<0.01) for 41–60, 2.22 (P<0.01) for 61–80, and 1.43 (P<0.01) for >80 years. The strength of other IPI components declined with age. Clinical stage showed decreasing impact: HR 2.08 (P=0.09), 1.97 (P<0.01), 1.49 (P<0.01), and 1.31 (P=0.05). LDH was significant only up to age 80 years: HRs were 2.07 (P=0.04), 1.96 (P<0.01), 1.32 (P<0.01), and 1.21 (P=0.13). EN involvement was non-significant across all groups: HRs were 1.37 (P=0.34), 1.26 (P=0.05), 0.98 (P=0.74), and 1.06 (P=0.67). The IPI C-statistics was 0.676 overall and declined with age: ≤40 years (0.708), 41–60 years (0.683), 61–80 years (0.641), and >80 years (0.598). We next stratified the IPI factors into patient- (age, ECOG PS) and disease-related (clinical stage, LDH, EN involvement). In patients ≤40 years, disease-related factors outperformed patient-related (C-index 0.693 vs 0.602), as in the 41–60 group (C-index 0.677 vs 0.589). In patients aged 61–80, predictive values were comparable (0.613 vs 0.622). In >80-year-olds, both declined substantially (0.580 vs 0.568). Conclusion The IPI remains a valuable prognostic tool in LBCL, particularly among younger patients. However, its overall predictive accuracy declines with age. In patients ≤60 years old, prognosis is primarily driven by disease-related factors, suggesting a role for molecular classifiers to enhance risk stratification. In older patients, incorporation of additional patient-level factors, such as comorbidities and nutritional status could be considered. These findings underscore the need for more individualized prognostic tools for LBCL patients. Funding: NU21-03-00411, P50 CA97274, U01 CA195568, Charles University Haematology-Oncology Cooperatio Program.

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: Although progression-free survival (PFS) in early-stage (ES) classic Hodgkin Lymphoma (cHL) is high, there remains a subset of patients (pts) at higher risk of worse outcomes, particularly in those with unfavorable risk disease. Leveraging pt-level data from within the HoLISTIC (Hodgkin Lymphoma International STudy for Individual Care) Consortium, the E-HIPI was developed in clinical trial pts and validated in real-world registry pts to predict 2-year (y) PFS (Rodday. NEJM Evidence 2025). Continuous, objective, and readily measurable pre-treatment variables were considered, with sex, maximum tumor diameter, albumin, and hemoglobin included as significant predictors. Using the E-HIPI, interactive calculators for pt risk, comparison, and stratification were created (https://rtools.mayo.edu/holistic_ehipi/). Although the E-HIPI was developed and validated in both unfavorable and favorable ES cHL pts, the clinical predictors are primarily markers of unfavorable disease. In the development and initial validation cohorts, 2y PFS was 93.7% and 90.3%, respectively, the C-statistic was 0.63 in both cohorts, calibration was strong with slight underprediction of events in the validation cohort, and the E-HIPI outperformed the historic binary classification of patients as favorable or unfavorable using EORTC criteria. However, since pts in the original E-HIPI development and validation were primarily treated with ABVD chemotherapy protocols, additional validation is needed, particularly among pts with unfavorable ES cHL who were treated initially with more intensive chemotherapeutic platforms (e.g., BEACOPP-based). Thus, we conducted an external validation of the E-HIPI in pts treated on recent unfavorable ES cHL GHSG trials. Methods: The validation cohort included 2367 adults (ages 18 to 60y) with newly-diagnosed unfavorable cHL from randomized phase 3 trials conducted by the GHSG (HD14 von Tresckow. JCO 2012; and HD17 Borchmann. Lancet 2021). We calculated the predicted probability of 2y PFS events based on the E-HIPI model equation (scale 1-100%). Model performance was assessed using discrimination and calibration. Discrimination, which is the model's ability to separate high and low risk pts, was assessed using Harrell's C-statistic. Calibration, which is the model's ability to accurately predict absolute risk, was assessed by comparing observed and predicted probabilities of 2y PFS events within quintiles of predicted probabilities and estimating calibration-in-the-large (predicted minus observed event rate) and the calibration slope. Results: For pts treated on unfavorable GHSG trials, mean age was 33y, 95% had stage II disease, mean baseline E-HIPI score (i.e., predicted 2y PFS event rate) was 6.0% (SD=2.3%), and the observed 2y PFS rate was 96.2% (95%CI: 95.4-97.0%). The C-statistic was 0.65 (95% CI: 0.59-0.70), calibration-in-the-large was 0.02, and the calibration slope was 1.08 (95% CI: 0.52, 1.64). Within the lowest risk E-HIPI quintile, the observed 2y PFS event rate was 1.6%, compared with 6.1% in the highest risk quintile. Conclusions: In external validation of the recently published E-HIPI in pts with cHL treated on unfavorable GHSG trials using more intensive BEACOPP-based protocols, discrimination was similar to the original development and validation cohorts. Collectively, the E-HIPI is a treatment-agnostic prediction tool across varied treatment regimens for unfavorable ES cHL pts. Better 2y PFS in the GHSG trials than in the E-HIPI development and initial validation cohorts may have contributed to mild misclassification, likely reflecting differences in treatment regimens. This may be resolved with recalibration or incorporating granular treatment variables into future models. Additional research is needed to understand E-HIPI performance in pts with favorable disease who have even lower event rates as well as to more fully predict individualized pt outcomes across varied chemotherapy platforms and radiation received for all ES cHL pts.

Abstract Introduction The LEO Comorbidity Index (LCI), comprising ten equally weighted comorbidity categories (respiratory, cardiovascular, digestive, hepatic, renal, autoimmune, diabetes, cancer history, HIV, and stroke), has demonstrated prognostic utility in patients with non-Hodgkin lymphomas. However, its predictive accuracy may vary with increasing age, due to age-related rises in comorbidity burden and physiological vulnerability. To investigate these dynamics, we evaluated the age-stratified predictive performance of LCI in patients with large B-cell lymphoma (LBCL), with a focused analysis on individuals aged 80+ years. We also examined which specific comorbidities most strongly influenced overall survival (OS) and developed a simplified, clinically applicable index tailored to this population. Methods We first included adults ≥40 years with newly diagnosed LBCL and recorded comorbidities from the LEO registry (USA, 2015–2020; n=1,652) to assess age-stratified predictive performance of LCI using the C-index. Next, we focused on patients 80+ years (n=263), pooled from the LEO cohort (n=147) and NiHiL registry (restricted to General Hospital, Prague, Czech Republic, 2010–2023; n=116). Comorbidities were collected from patient self-reports (LEO) and/or medical records (LEO/NiHiL). Univariate and multivariate Cox models were used. The primary endpoint was OS. Results Among the total LBCL population (n=1,652), LCI predictive performance improved by age category with C-index for patients <65 of 0.584, 65–79 years 0.579, and ≥80 years 0.647, indicating stronger discrimination in the 80+ group. Subsequent analysis focused on patients aged 80+ years (n=263; median age 83). Within this cohort, 34% had ECOG performance status (PS) 2–4, 66% stage III–IV disease, 59% elevated lactate dehydrogenase, 35% >1 extranodal site involved, and 58% International Prognostic Index (IPI) score of 3–5. A total of 377 comorbidities were reported, averaging 1.43 per patient (1.22 in LEO; 1.61 in NiHiL). LCI distribution was: 0 points in 24% (n=62), 1 point in 33% (n=88), and 2–5 points in 43% (n=113). Higher LCI scores had worse ECOG PS 2–4: 19% (LCI=0), 33% (LCI=1), and 43% (LCI ≥2; P=0.01), and inferior OS (median 6.9 years LCI=0, 3.0 years LCI=1, and 2.8 years LCI≥2, resp.; P=0.02). Presence of ≥1 comorbidity was associated with shorter OS compared to no comorbidities (HR=1.67, P=0.01). The LCI was an independent predictor of OS after adjusting for age (HR=1.18, P=0.01, C-index 0.623), IPI (HR=1.16, P=0.03, C-index 0.628) as well as for recently proposed SENIOR-IPI (HR=1.14, P=0.049, C-index 0.665). When evaluating individual comorbidities, heart disease (HR=1.54, P=0.01), stroke (HR=1.89, P=0.03), and recent malignancy (HR=1.99, P=0.02) were independent predictors of OS. Based on these findings, we developed a simplified LCI (sLCI) for use in 80+ patients incorporating only the three comorbidities (1 point each). Patients were categorized as sLCI: 0 (49%, n=129), 1 (44%, n=115), and 2–3 points (7%, n=19). sLCI scores 0, 1, 2–3 were associated with ECOG PS 2–4 27%, 37%, and 63%, respectively (P=0.01) and inferior OS: median 5.3 years (0), 2.5 years (1), and 1.4 years (2–3; P<0.01). sLCI remained an independent and strong predictor of OS after adjusting for age (HR=1.71, P<0.01, C-index 0.636), IPI (HR=1.66, P<0.01, C-index 0.635), and SENIOR-IPI (HR=1.60, P<0.04, C-index 0.659). The results were consistent in both NiHiL and LEO cohorts and remained significant among patients treated with anthracyclines (sLCI adjusted for age: HR=1.58, P<0.01, C-index 0.607; for IPI: HR=1.53, P=0.01, C-index 0.623; for SENIOR-IPI: HR=1.51, P=0.01, C-index 0.633). Conclusion Comorbidity burden becomes increasingly prognostic with higher age in LBCL patients. In the 80+ individuals, comorbidities – particularly heart disease, recent cancer, and stroke – have an independent impact on OS. While the original LCI captures the impact of broadly defined comorbidities, its prognostic utility attenuates in the 80+ patients, likely reflecting limited relevance of indolent chronic conditions in this population's constrained survival horizon. The sLCI, focused on the most prognostically relevant comorbidities, offers practical risk stratification. Incorporation of sLCI into clinical decision-making – alongside other prognostic indices – could better identify high-risk individuals and guide treatment strategies. Funding: NU21-03-00411, U01 CA195568.

Abstract Introduction: The CAR-T therapies ide-cel and cilta-cel are approved for patients (pts) with relapsed/refractory multiple myeloma (RRMM). The aim of this study was to determine the incidence, subtypes, and associated risk factors for subsequent primary malignancies (SPMs) in MM pts treated with CAR-T therapy in the real-world setting. Methods: A retrospective, multicenter observational cohort study was conducted of pts who received CAR-T therapy between May 2021 and Dec 2024 at 15 centers within the US Multiple Myeloma Immunotherapy Consortium. Logistic models were used for univariable analyses of invasive SPMs (excluding non-melanoma skin cancers). Variables significant at p < 0.10 were included as candidate variables in the multivariable analysis. Time to event endpoints were analyzed using Kaplan-Meier curves. Results: Of n=1460 CAR-T recipients, median follow-up from the time of CAR-T infusion for the entire cohort, ide-cel, and cilta-cel pts was 13.1, 16.9, and 10 months, respectively. A total of 55 invasive SPMs were noted in 53 pts (SPM incidence 3.6%). The incidence rate of SPMs was 4.55 per 1,000 person-years, based on 53 SPMs occurring over 11,646.14 person-years (calculated from CAR-T to the SPM diagnosis, death, or last follow-up). Baseline characteristics were similar for pts with no SPM versus SPM including: age at CAR-T (median 67 vs 65), female (36% vs 43%), Black (13% vs 16%), prior autologous transplant (87% vs 80%), lenalidomide for ≥2 years (31% vs 27%), high risk cytogenetics (23% vs 29%), and known clonal hematopoiesis (7.5% vs 9.9%). Differences were observed in baseline characteristics among pts with SPM versus no SPM for ≥2 prior alkylator lines of therapy (LOT) (82% vs 60%, p=0.002) and median prior LOT (6 vs 5, p=0.008). There was a trend for more patients with SPMs being age ≥70 (35% vs 37%, p=0.074). Of the 53 SPMs, 26 (49%) were myeloid (MDS or AML), 19 (35.8%) were solid, 7 (13.2%) were of T-cell origin, and 1 was a B-cell lymphoma. Of the T-cell SPMs, three were of T-LGL, though one of the T-LGL pts later developed an invasive CNS T-cell lymphoma (CAR-negative). There were four cases of confirmed CAR-positive T-cell SPMs (all pts treated with cilta-cel), of which two were indolent T-cell lymphoma of the GI tract and two were cutaneous PTCL. There was no difference in total incidence of SPMs among pts who received ide-cel (total=4.4%; myeloid=2.2%, T-cell=0.04%) versus cilta-cel (total=3%; myeloid=1.4%; T-cell=0.5%) (p=0.21). Median OS from the time of CAR-T therapy was 40.8 months for pts without SPM compared to 27.6 months for pts with SPM (p=0.4). Median OS did not differ by myeloid, solid, and T-cell subgroup: 26.4, 37.2, and 24 months, respectively (p=0.21). Median time from CAR-T cell to SPM was 12 months overall, and by subtypes of myeloid, solid, and T-cell was 13.2, 9.6, and 9.6 months, respectively (p=0.74). There was no difference in time to any SPM among pts receiving ide-cel versus cilta-cel (p=0.61). An earlier time to diagnosis of myeloid SPM was observed for cilta-cel versus ide-cel (4.8 vs 20.4 months; p=0.003). Median OS from the time of diagnosis of any SPM, myeloid, solid, or T-cell SPM was 9.6, 7.2, 15.6, and not reached in months, respectively (p=0.006), demonstrating that pts diagnosed with myeloid subtype SPM had inferior OS from the time of their SPM diagnosis compared to other subtypes. The cause of death of 4/11 myeloid SPM pts was the myeloid SPM itself. In univariable analysis for the entire cohort, age ≥70 (p=0.079), ≥5 prior LOT (p=0.012), and ≥2 prior alkylator LOT (p<0.001) were associated with higher risk for SPM. In multivariable analysis, ≥2 LOT containing alkylators was associated with higher risk of SPM (p=0.005). For the univariable analysis of the myeloid subgroup, ≥2 prior alkylator LOT (p=0.016) and ≥2 years of lenalidomide maintenance (p=0.063) were associated with higher risk of SPM. In multivariable analysis, ≥2 years of lenalidomide (p=0.065) was associated with a trend toward increased risk of myeloid SPM. Conclusions: With a median follow up of 13.1 months, the incidence of developing a SPM following CAR-T in pts with RRMM was 3.6%, of which approximately half were myeloid malignancies. There was no difference in SPM incidence when comparing the two CAR-T products. Pts treated with ≥2 alkylator LOT had a higher risk of SPM in multivariable analysis and may warrant additional monitoring for SPM development.

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.