Citations

Abstract Chimeric antigen receptor (CAR) T-cell therapy has demonstrated remarkable response rates in relapsed/refractory aggressive B-cell non-Hodgkin's lymphoma. However, durable remissions occur in only 40% of treated patients, highlighting mechanisms of therapeutic resistance and immune escape. While cytotoxicity is principally executed by CAR T-cells, monocytes shape CAR-T cell efficacy and orchestrate cytokine-driven immunity. In this study, we defined distinct metabolic profiles of T-cell and monocyte subsets based on clinical response. Single-cell RNA sequencing (scRNA-seq) and T-cell receptor sequencing (scTCR-seq) were performed on peripheral blood mononuclear cells at three timepoints: before lymphodepletion (BL), at peak CAR-T expansion (PK), and one-month post-infusion (M1). Data were processed with CellRanger v7.0.1 and analyzed with the immunopipe pipeline (Wang et al., 2025). Seurat v4.3.0 was utilized for unsupervised hierarchical clustering analysis; subsets identified included classical monocytes (CM), intermediate monocytes (IM), non-classical monocytes (NM), and monocytic myeloid derived suppressor cells (mMDSC). Metabolic profiles were compared across patients achieving ≥6-month complete remission (CR, n=16), primary refractory disease (PD1, n=4), or relapsed after a prior response (PD2, n=12). A total of 107035 T cells and 129981 monocytes from 77 serial samples in 32 patients were analyzed. CR patients had a higher T-cell:monocyte ratio than PD1/PD2 at all time points, especially at BL and PK (BL 1.2 vs 0.4, p=0.011; PK 1.3 vs 0.3, p=0.027). Clinical responses were compared with metabolic pathway analysis and identified oxidative phosphorylation (OXPHOS) as the dominant pathway. OXPHOS was consistently enriched in PD cohorts across T-cell subsets, particularly in PD1 vs CR at BL (mucosal-associated invariant T-cell (MAIT) and T peripheral memory (Tpm)), and at PK (CD4/CD8 memory precursor effector cells (MPEC) and activated MPECs (MPECa), CD4 T-helper 1 proliferating (Th1p), CD4 Th2 activated (Th2a), regulatory T-cell (Treg), CD8 naive senescent (CD8NS), CD8 T effector memory (Tem) tumor recirculating (TR), CD8 Tpm, CD8 T resident memory (Trm) TR and MAIT Tpm). By M1, OXPHOS enrichment in CD4/CD8MPECa, CD4Th2a, CD8NS, CD8TemTR reversed, now predominant in CR although CD8Tpm and CD8TrmTR remained enriched in PD1. In the monocyte subsets at BL, PD2 showed OXPHOS enrichment in CD163+ monocytes (mono), IM IL1, mMDSC SIRPA, mMDSC TGFβ, and CM IL6 subsets compared to PD1 and CR, while PD1 showed OXPHOS enrichment in IM CD38, CM TGFβ, and mMDSC HIF1A vs CR and PD2. This persisted at PK. By M1, the pattern reversed and mirrored that of T-cells, with CR patients showing OXPHOS enrichment in CD163+mono, CM IL6, CM TGFβ, IM CD38, mMDSC HIF1A, mMDSC SIRPA and mMDSC TGFβ, and additionally in CM EMT, CM p53, NM, NM CD36 vs PD1/PD2. Glycolysis/gluconeogenesis (GLY) was also enriched across subsets. In T-cells, PD1 vs CR showed GLY enrichment in CD4Th2a, CD8Tpm, CD8Tpm proliferating and MAIT Tpm at PK. In monocytes, GLY was enriched in PD1/2 vs CR at BL (CD163+mono, CM IL6) and PK (CD163+mono, IM CD18, mMDSC SIRPA). By M1, GLY metabolism was enriched in CR vs PD1/PD2 in CD163+mono, CM IL6, mMDSC SIRPA. Additional GLY enriched subsets at CR vs PD1 included CM, CM p53, EMT, IM CD38, IM IL1, mMDSC HIF1A, NM, NM CD36. Inositol phosphate metabolism (IP) was predominantly enriched in PD2 vs CR at BL (CD8NS, CD8Tcm, CD8Tpm activated), PK (CD4Th2, CD8NS, CD8Tem TR, CD8Tpm proliferating, γδT Tem) and M1 (CD4/CD8 MPECa, CD4 Th1/Th2, CD8NS, CD8Tpm, CD8 Tpm proliferating, Treg and MAIT Tpm). Notably, Treg was the only T-cell subset enriched for IP at PK in CR vs PD1/PD2. Additionally, the TCA cycle was enriched in PD1 Tregs and Th2 cells at BL and PK vs CR/PD2. A higher T-cell:monocyte ratio in CR vs PD1/PD2 supports prior evidence that elevated pre-lymphodepletion lymphocyte:monocyte ratios predict better outcomes. We identified distinct metabolic signatures in T-cells and monocytes between CR and PD1/PD2. OXPHOS enrichment in T-cells and monocytes at M1 in CR patients may reflect oxidative programming as a hallmark of sustained anti-tumor immunity. Conversely, GLY enrichment in PD1 T-cells at PK may reflect short-lived effector states lacking persistence. Overall, these findings highlight metabolic profiling as a promising predictive biomarker and therapeutic programming target to enhance CAR T-cell outcomes.

Abstract Introduction: CAR-T therapy has transformed cancer therapy. with its first Food and Drug Administration (FDA) approved product in August 2017. Given the expanding indications and growing experience, particularly with management of immediate toxicities requiring specialized expertise such as Cytokine Release Syndrome (CRS) and Immune Effector Cell-Associated Neurotoxicity syndrome (ICANS), FDA recently removed the Risk Evaluation and Mitigation Strategies (REMS) for CD19 and BCMA targeting CAR-T on June 26th2025. In particular, given that CRS and ICANS typically occur in the first 2 weeks post CAR-T infusion, the minimum required stay near CAR-T treatment center is reduced from one month to 2 weeks. While this could reduce treatment burden for patients (pts) who need to travel away from home to receive CAR-T, there is a need to update the transition of care from CAR-T center to pts' local oncology practice. Pts carry in their rate of recovery and have varying supportive care needs in the second half of the month post CAR-T infusion. To assist with care transition and planning, we examine the use of AI multimodal large language model (LLM) to predict patients' supportive care needs 2 weeks after CAR-T infusion based on the clinical data at the time of CAR-T evaluation, prior to leukapheresis. This timepoint was selected to also maximize travel, lodging and caregiver planning for pts and family as well. Methodology: Pts who were treated with FDA-approved CAR-T at Mayo Clinic Rochester between December 2022 and June 2025 were examined. Based on experiences with referral practices in the Midwest region, we defined patients with high monitoring needs as those who between day 14-30 post CAR-T had either hospital admission due to recurrent CRS or ICANS or required transfusion of more than 1 blood product per week (red blood cell or platelets). Others were classified as low monitoring needs. Clinical notes, labs, vital signs and electrocardiogram at the time of CAR-T evaluation were abstracted and summarized by Gemini LLM for association with patients with high versus low monitoring needs post CAR-T. Model was developed and tested for predicted needs and concordance with actual outcome of these pts. Results: Among the 362 pts who received FDA approved CAR-T in the timeframe, 84 pts had high monitoring needs. An additional 84 pts with low monitoring needs were randomly selected from 278 pts. These were divided into a training cohort and test cohort of 84 pts each, with 42 pts with high and 42 pts with low monitoring needs. Demographics of the two cohorts displayed no statistical difference. LLM analysis of the clinical data at the time of CAR-T eval identified 5 categories associated with risk for high monitoring needs: 1) Disease status (clinical notes about bulky, progressive disease or CNS involvement); 2) inflammatory and tumor burden markers (CRP, LDH); 3) hematologic status (platelet, plt; neutrophil count ANC); 4) renal function; 5) performance status. Using common clinical lab parameter convention (platelets <100, ANC< 1, CrCl< 30ml/min), and prioritizing for not missing pts with high monitoring needs, LLM model was defined to predict patients for high monitoring needs if high risk feature was identified in category 1 or any category combinations; whereas patients with no high risk feature or only 1 category that is not disease status was predicted as low monitoring needs. In this model, the sensitivity was 85.7%, specificity 23.8%, F1 score 65.5%. Applying this model to test cohort 2, the sensitivity was 83.3%, specificity 28.6%, and F1 score 65.4%. Discussion: We demonstrate proof-of-concept that LLM can be used to analyze clinical data at the time of evaluation for CAR-T eligibility to predict for supportive care needs post treatment. Not surprisingly, features identified by LLM to be associated with high monitoring needs are those previously reported by us and others at the time of CAR-T evaluation and or pre-lymphodepletion to be associated with risk for cytopenia, infection, hospitalization and non-relapse mortality. Based on the summary output generated by LLM, additional opportunities for classification methods were identified to improve specificity and balanced accuracy of the LLM model. This is a promising tool for further development to assist clinicians and patients to plan their treatment journey.

Abstract Background Chimeric antigen receptor T cell (CART) therapy has revolutionized treatment for hematologic malignancies. However, durable remission following anti-CD19 CART therapy (CART19) remains limited to 40–50% of patients, often due to tumor relapse and CART dysfunction (PMID:31501612). We previously demonstrated that tumor-derived extracellular vesicles (EVs) from patients with chronic lymphocytic leukemia (CLL) can directly impair CART function (PMID:33388419). In this study, we aimed to identify tumor-derived EV microRNA (miRNA) cargo associated with clinical CART failure in B cell malignancies and define how they mechanistically drive CART cell dysfunction. Methods To achieve these aims, we profiled tumor-derived EV miRNA cargo using small RNA sequencing (RNAseq) in two independent patient cohorts. Cohort 1 compared plasma-derived EVs from untreated CLL patients to those from healthy donor EVs to identify miRNAs enriched in CLL (n=23). Cohort 2 included patients with diffuse large B cell lymphoma (DLBCL) treated with axicabtagene ciloleucel (CART19, n=12). Here, plasma EV miRNAs were analyzed at baseline (pre-CART treatment) and 1-month post-CART treatment and compared between CART responders (complete remission at 6 months) and nonresponders (no response at 1 month). Enriched miRNAs from both cohorts were identified following miRNA count normalization and differential expression analysis using adjusted p values of ≤0.05. Functional studies were conducted by transfecting CART19-28ζ cells with synthetic miRNA mimics or scrambled controls. CART cells were also generated with stable overexpression of miRNA via lentiviral shRNA delivery. CART function was assessed via in vitro killing, proliferation, apoptosis assays, RNAseq, and in vivo tumor xenograft models. Results In Cohort 1, we identified several miRNAs including miR-125a to be significantly enriched in CLL-derived EVs relative to healthy controls. Similarly, in Cohort 2, miRNA-125a was also identified among the miRNAs enriched in nonresponders vs. responders to CART treatment; Notably, miR-125a was significantly enriched in nonresponders at baseline (pre-CART treatment) and 1-month post-CART treatment (p=0.0177, p=0.0196). To define the functional effects of miR-125a, we delivered synthetic mimics or stable shRNA constructs encoding miR-125a into CART cells. Delivery of miR-125a was confirmed by RT-qPCR (p<0.0001 for mimic vs. scramble, p=0.0069 for shRNA vs. scramble). Here, miR-125a delivery and overexpression impaired CART cytotoxicity/killing of target tumor cells (~25% reduction, p= 0.0039), decreased antigen-specific proliferation (~47% reduction, p=0.0002), and increased apoptosis following antigen stimulation (1.8-fold increase, p=0.0002) as compared to scramble controls in vitro. We next studied how miR-125a impacts CART functions in vivo. Here, tumor-bearing mice treated with miR-125a–overexpressing CART cells (CART sh125a) exhibited accelerated tumor progression (4.1-fold increase, p=0.0019) and significantly reduced survival as compared to mice treated with scramble control CART treatment (CART shScr) (20% vs 80% survival at day 35, p=0.0064), underscoring the dramatic detrimental impact of miR-125a on therapeutic efficacy. To uncover changes in CART gene expression induced by miR-125a delivery, RNAseq was also performed across stimulated CART sh125a vs. CART shScr cells. CART sh125a cells demonstrated broad transcriptional changes, with gene set enrichment of E2F targets, G2/M checkpoint, and apoptosis pathways (FDR p<0.0001, p<0.0001, and p=0.006) further validating the role of miR-125a in disrupting CART cell cycle control, proliferation, and survival. Conclusion In summary, our study identifies miR-125a as a tumor-derived EV cargo that drives CART dysfunction in two separate B cell malignancies (CLL and DLBCL). miR-125a was enriched in both CLL EVs and nonresponders to CART19 therapy at baseline prior and post CART treatment. Functional studies demonstrate that miR-125a delivery and overexpression directly impair CART cytotoxicity and proliferation, increase CART apoptosis, reduce therapeutic efficacy in tumor xenograft models, and disrupt key pathways related to CART cell cycle regulation and survival. These findings position miR-125a as both a key mechanistic driver of CART failure and a potential predictive biomarker for treatment response, while highlighting it as a possible therapeutic target to improve CART outcomes in the clinic.

Abstract Introduction: Reporting patient outcomes by CAR-T treatment centers is part of the accreditation requirement under the FACT (Foundation for Accreditation of Cell Therapy) Immune Effector Cell (IEC) Program standard. Currently, this is a time-consuming and manual process, making it increasingly difficult to sustain with the growing CAR-T applications and patient volume. The objective of this study is to evaluate how a Large Language Model (LLM) can be used to automate the abstraction and summarization of medical record for CAR-T toxicities. Methods: Clinical data from patients treated with CAR-T between January 2018 and June 2025, including hematology clinical notes, lab results, flowsheets, vital signs, and medication administration record, were used as the source of patient information. The American Society of Transplant and Cell Therapy (ASTCT) CAR-T toxicity grading guideline for cytokine release syndrome (CRS) and immune effector cell associated neurologic syndrome (ICANS) were used to define these toxicities. With the large context window of the LLM (Gemini 2.5 pro), we created a prompt by embedding both the patient data and the ASTCT guidelines for CRS and ICANS, along with an outline of specific information we aimed to abstract from the data. Additionally, the LLM was instructed to provide contextual justification for the data it extracted from patient information. The LLM-generated output included event onset date, resolution date, maximum grade, date of maximum grade, and medications given for management. The CRS and ICANS outcome reports generated by the LLM were then compared to the IEC compliance program data. Any discrepancies identified were reviewed manually. Results: Among patients (pt) treated with CAR-T in the specified timeframe, 47 were selected as training cohort. This cohort included pt with toxicities events, no events, and a mix of events of varying grades. For the test cohort, 66 pts who received CAR-T from March to June 2025 were included. Demographics for the training and test cohorts are reflective of evolving clinical practice (training, test cohorts: median age 69, 65; male 55%, 74%; lymphoma/leukemia 66%, 48%; multiple myeloma 34%, 52%; CD19 CAR-T 46%, 50%). Compared to data from the IEC compliance program, the LLM-generated summaries for CRS events demonstrated an accuracy of 90%, precision 95%, sensitivity 100%, specificity 80%, F1 score 97%, and Matthew's correlation coefficient 87%. For ICANS event, the LLM achieved an accuracy of 91%, precision 92%, sensitivity 92%, specificity 90%, F1 score 92%, Matthew's correlation coefficient 83%. Maximum grade, for both CRS and ICANS, were identified with >90% accuracy. However, dates for CRS and ICANS were less accurate, with the lowest accuracy noted in identifying the event resolution date, 45% for CRS and 67% for ICANS. Medical records were reviewed for all discrepancies between the LLM and the IEC Compliance Program data. The most common discrepancy was a mismatch in event resolution date, determined to be due to differing criteria used by the compliance team and the LLM prompt. Of note, a few discrepancies were attributed to missed information by the IEC Compliance Team, which was correctly identified and categorised by the LLM. An updated LLM prompt (LLMv2) was developed and applied to the test cohort. In version 2, the prompt was revised to include clearer instructions on how to summarize CRS and ICANS events, as well as new instructions on additional data elements to be summarized. The performance of LLMv2 on this cohort for CRS event identification was 100% across all categories (accuracy, precision, sensitivity, specificity, F1 score, and Matthew's correlation coefficient). For ICANS the LLMv2 achieved accuracy of 96%, precision 69%, sensitivity 100%, specificity 93%, F1 score 82%, Matthew's correlation coefficient 80%. Accuracy in identifying event resolution date improved significantly from 64% to 99%. Conclusions: Our study demonstrates that LLMs can significantly reduce the time required to abstract and summarize CAR-T toxicities, while maintaining high accuracy. One limitation of our study is that it is based on data from a single institution. However, plans are already underway to evaluate the use of LLMs in a multi-institutional setting.

Abstract Introduction: Liso-cel is an autologous, CD19-directed, 4-1BB CAR T cell product. TRANSFORM (NCT03575351) is a global, randomized, open-label, phase 3 study of liso-cel versus SOC as second-line therapy in adults with R/R LBCL. In the TRANSFORM study, liso-cel showed superior, more durable efficacy versus SOC, with a potential long-term survival benefit and a favorable safety profile, highlighting the curative potential of liso-cel for second-line R/R LBCL. Upon completion of TRANSFORM, patients who received liso-cel could enroll into a separate, long-term follow-up (LTFU) study (NCT03435796). Here, we report results after approximately 4 years of follow-up in patients from TRANSFORM who consented to the LTFU study. Methods The TRANSFORM study compared liso-cel versus SOC (chemotherapy [R-DHAP, R-ICE, or R-GDP] followed by high-dose chemotherapy [HDCT] + ASCT) in adults aged ≤ 75 years with LBCL primary refractory to or relapsed within 12 months of first-line therapy and eligible for ASCT. Patients in the liso-cel arm underwent lymphodepletion followed by liso-cel (100 × 106 CAR+ T cells). Crossover to receive liso-cel was allowed for patients in the SOC arm. The LTFU study enrolled patients who received liso-cel and discontinued early or completed TRANSFORM; liso-cel was not administered during the LTFU study and subsequent therapies were administered at investigator discretion. Selected AEs considered related to liso-cel (malignancies, neurologic, hematologic, or rheumatologic/autoimmune disorders) and OS would be assessed at each visit (month 3 and then every 6 months from years 1 to 5, and then annually) for up to 15 years from liso-cel infusion until study withdrawal or death, whichever occurred first. OS for the combined study was defined as the time from randomization to death due to any cause. PFS for the combined TRANSFORM and LTFU studies incorporated data from TRANSFORM, assessed by an independent review committee, and from LTFU, assessed by investigators, and was defined as the time from randomization to disease progression or death from any cause. For LTFU only, OS and PFS were measured from LTFU consent to event or censoring. In cases where the patient was censored at last assessment date (without receiving new therapy) in TRANSFORM and enrolled in LTFU, LTFU data were used. For patients from the liso-cel arm of TRANSFORM who did not enroll in the LTFU study, the data from TRANSFORM were considered (ie, either an event or censored) at their last adequate assessment date or before starting new therapy. Results are reported descriptively. Results In total, 184 patients were randomized in TRANSFORM (92 per arm); key demographics and baseline characteristics were previously reported (Abramson, et al. Blood 2023). The LTFU study enrolled 67 patients who received liso-cel (liso-cel arm, n = 43; crossover from SOC arm, n = 24). The median follow-up (range) for the liso-cel arm was 16.9 months (1.3–29.7) in the LTFU study and 40.1 months (2.2–61.9) combining the TRANSFORM and LTFU studies. The median follow-ups for OS and PFS for the liso-cel arm in the LTFU study were 17.1 (16.1–21.9) and 16.9 months (16.0–21.8), respectively. In patients from the LTFU study in the liso-cel arm (n = 43), median PFS and OS were both not reached (NR) with 95% CIs of NR–NR. The 24-month rates (95% CI) of PFS and OS were 94.7% (87.4–100.0) and 95.1% (88.4–100.0), respectively. In the 92 patients randomized to the liso-cel arm, using combined data from the TRANSFORM and LTFU studies, median (95% CI) PFS was NR (12.6–NR); 48-month PFS rate was 52.2% (41.5–62.8). Median (95% CI) OS was NR (NR–NR); 48-month OS rate was 61.5% (51.2–71.7). Safety results from the LTFU study in the liso-cel arm showed no new signals compared with previous reports from TRANSFORM. AEs of any grade occurred in 2 patients (hypogammaglobulinemia and dyspnea; n = 1 each), with no second primary malignancies or serious infections. AEs of grade 3 or 4 occurred in 1 patient (dyspnea). No AEs led to death.Conclusions: After a median follow-up of approximately 4 years combining data from the TRANSFORM and LTFU studies, liso-cel continued to demonstrate long-term clinical benefit with high PFS and OS rates in patients with second-line R/R LBCL. The safety profile of liso-cel continued to be manageable with no new safety signals observed during LTFU. These results further support liso-cel as an effective second-line treatment with curative potential for R/R LBCL.

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 Neurolymphomatosis (NL) is a rare and frequently underrecognized manifestation of lymphoma, characterized by infiltration of peripheral or cranial nerves by malignant lymphocytes. Limited data exist to guide optimal management. We present the largest single-enterprise (included the 3 Mayo Clinic sites) cohort to date, evaluating disease characteristics, treatment patterns, and outcomes in patients (pts) with NL. Methods We conducted a retrospective analysis of pts ≥18 years (yrs) diagnosed with clinical, radiographic, or biopsy-confirmed NL at Mayo Clinic between January 2002 and December 2024. Baseline characteristics were compared using independent t-tests for continuous variables and Fisher's exact or Pearson's chi-square tests for categorical variables. Primary endpoints were overall survival (OS) and progression-free survival (PFS). Objective response rate (ORR) was a secondary endpoint. Kaplan–Meier methods, log-rank tests, and Cox proportional hazards regression were used to assess survival outcomes and associated factors. Results We included 149 pts. Median age at diagnosis was 64 yrs (range 22–83), and 23% were female. Most presented with stage IV disease (89%), and NL was part of the initial lymphoma presentation in 42% (primary NL). Diagnostic confirmation was based on histopathology and imaging in 55%, imaging alone in 26%, histopathology alone in 16%, and clinical features in 4%. Diffuse large B-cell lymphoma was the most common histology (61%), followed by marginal zone lymphoma (7%), and T-cell lymphoma (7%). Clinical presentation included painful neuropathy (65%), concurrent CNS involvement (37%), systemic disease (43%), elevated LDH (46.9%), and ECOG ≥2 (38%). Pattern of involvement included cranial nerves (29%), peripheral nerves (28%), and spinal nerve roots (27%). Median follow-up was 92 months (95% CI, 63–119). Among 137 treatment-evaluable pts, first-line regimens included methotrexate-based (MTX) (62%), bendamustine-based (B) (11%), cytarabine-based (Ara-C) (4%), and rituximab-based (R) (excluding MTX, B, or Ara-C) (15%) therapies. Radiation was used in 18%, and 21% underwent autologous stem cell transplant (ASCT). Among 103 response-evaluable pts, the ORR was 67% (52% complete response [CR], 16% partial response [PR]). ORRs by regimen were 78% (MTX), 92% (B), and 86% (R). On univariate analysis, primary NL (OR 4.3) and frontline ASCT (OR 8.47) were associated with higher odds of response (p<0.05), while ECOG ≥2 (OR 0.24) and localized disease (OR 0.33) were associated with lower odds. On multivariable analysis (MVA), primary NL (OR 5.0 [1.5–12.1], p=0.02) and ECOG >1 (OR 0.2 [0.06–0.79], p=0.018) remained significant predictors of response. Median PFS was 13 months (95% CI, 9–36), with a 2-yr PFS of 43% (95% CI, 35–53). On MVA, concurrent CNS involvement (HR 2.4, p=0.05), systemic disease (HR 2.7, p=0.02), and ECOG >1 (HR 2.4, p=0.02) were associated with inferior PFS, while primary NL was associated with improved PFS (HR 0.3, p=0.002). Patients undergoing frontline ASCT had a median PFS of 108 months (95% CI, 58–not reached) with a 7-yr PFS of 62% (95% CI, 46–84). Among those receiving second line and beyond (2+) therapy, 24 received MTX-based regimens, 10 received CAR-T, and 8 received tyrosine kinase inhibitors. Post-ASCT median PFS was 30 months (95% CI, 15–NR). Among CAR-T recipients, evaluable responses (n=8) included 4 CR, 1 PR, and 3 PD. Median OS was 56 months (95% CI, 29–133), with a 5-yr OS of 50% (95% CI, 41–60). On MVA, ECOG ≥2 (HR 2.5, p=0.02) predicted worse OS, while receipt of ASCT (HR 0.4, p=0.04) and primary NL (HR 0.5, p=0.04) were associated with improved OS. Conclusion This study represents the largest single-enterprise cohort of pts with NL, providing comprehensive insight into its clinical presentation, treatment patterns, and outcomes. Primary NL and preserved PS were independently associated with improved response rates and survival, while CNS and systemic involvement at diagnosis portended poorer PFS. MTX and bendamustine-based regimens achieved high ORR, and frontline ASCT was associated with significantly prolonged remission duration and OS. These findings highlight the prognostic importance of baseline disease burden and functional status, support the early incorporation of high-intensity regimens in eligible patients, and underscore the urgent need for prospective studies to guide evidence-based management in this rare and understudied population.

Abstract Introduction: Anti-CD19 chimeric antigen receptor T-cell (CART) therapy is feasible with an acceptable safety profile in older patients (pts) with relapsed/refractory (RR) large B-cell lymphoma (LBCL) (Tun et al, AJH 2024). However, immune effector cell-associated neurotoxicity syndrome (ICANS) is a frequent and challenging toxicity in older pts, and its characteristics and associated risk factors are not well defined. Methods: Seven centers participated in this retrospective study. Pts aged ≥65 years (yrs) with RR LBCL treated with a commercial CART product were included. Baseline characteristics, treatment, CART toxicity, and outcomes were collected. ICANS was graded by the ASTCT criteria in most pts. Cumulative incidences of ICANS were estimated using deaths/progressions as competing risks, and associations between incidences and variables were assessed by Gray's test. A 30-day landmark was set after infusion to reduce immortal time bias. Progression-free survival (PFS; time from the landmark to disease progression or death) and overall survival (OS; time from the landmark to death) were analyzed with the Kaplan-Meier method. Statistical analyses were done in JMP v18. Results: A total of 224 pts were included. Median age at infusion was 71 yrs (range 65–89; ≥80 yrs, 12%. 142 (63%) were male and 27 (13%) had an ECOG PS scale of ≥2. The median HCT CI was 1 (range 0–6). LDH was elevated in 110 (51%) pts, 23 (19%) had bulky disease ≥7 cm, 176 (82%) had advanced-stage, and 145 (71%) had extranodal disease. Median number of prior treatments was 2 (range 1–8), and 44 (20%) had prior bendamustine. Prior stem cell therapy was done in 47 (21%) pts (autologous stem cell transplant [SCT], n = 45; allogeneic SCT, n=2). Axicabtagene ciloleucel (axi-cel) was most frequently used (58%; n=131) followed by lisocabtagene maraleucel (liso-cel) (16%, n=36) and tisagenlecleucel (tisa-cel) (25%, n=57). Median duration of ICANS was 6 days (interquartile range [IQR] 3–11; range 1–303). Median time to ICANS onset was 6 days (IQR 3.8–8.0; range 0–24). The 30-day cumulative incidence of all grade ICANS was 50.9% (95% confidence interval [CI] 44.7–57.9) and grade ≥3 ICANS was 29.8% (95% CI 24.0–37.0). There was no significant difference in incidence of all grade (P=0.39) and grade ≥3 (P=0.49) ICANS as well as duration of ICANS (≥14 vs <14 days; P=0.08) according to the age groups (≥65–69 vs ≥70–74 vs ≥75–79 vs ≥80–89). In contrast, pts with an ECOG PS scale of ≥2 were associated with a higher incidence of all grade (30-day estimate, 66.7% vs 47.0%; P=0.02) and grade ≥3 ICANS (30-day estimate, 53.8% vs 25.6%; P=0.002). In addition, pts with an elevated LDH level had a higher incidence of grade ≥3 ICANS than those without (30-day estimate, 37.5% vs 18.8%;P=0.004). The incidence of ICANS varied among various CART products, with 30-day estimates of 64.9% vs 33.3% vs 29.8% (P<0.0001) for all grades and 40.1% vs 13.2% vs 18.0% (P=0.004) for grade ≥3 ICANS in pts treated with axi-cel vs tisa-cel vs liso-cel, respectively. A trend toward higher grade ≥3 ICANS was seen in pts treated with ≥3 prior lines than those treated with 2 or 1 (30-day estimate, 35.7% vs 28.1% vs 10.5%; P = 0.07). ICANS was managed with steroids in 47 (41%) pts, levetiracetam +/- other agents in 46 (40%) pts, biologic agents +/- other drugs in 15 (13%) pts, and supportive care in 6 (5%) pts. Median follow-up from the 30-day landmark post-infusion was 17.2 months (95% CI 14.2–22.4). Median PFS was 10.5 months (95% CI 5.7–18.2) and OS was 18.2 months (95% CI 13.3 vs 27.5). There was no difference in PFS and OS in pts who experienced ICANS vs no ICANS (median PFS 14.7 vs 10.1 months; P=0.65 and median OS 18.2 vs 24.7 months; P=0.49). However, there was a trend toward inferior PFS and OS in pts who experienced high-grade ICANS compared to those who had grade 0-2 ICANS (median PFS 9.2 vs 21.2; P=0.14 and median OS 11.7 =vs 22.9; P=0.39). Conclusions: This large study of older pts with RR LBCL demonstrated acceptable risks of ICANS, comparable to that in pivotal registration trials, with a relatively higher rate of grade ≥3 ICANS. The risks of ICANS were similar regardless of patient age, including those aged ≥80 yrs, but different among CART products. Similar to younger pts, high tumor burden disease and poor performance status at CART infusion were associated with higher risks of ICANS. A comprehensive neurologic and frailty assessment is warranted to identify suitable CART candidates.

Abstract Background: De novo secondary central nervous system (CNS) lymphoma (SCNSL), characterized by concurrent systemic and CNS aggressive B-cell lymphoma at diagnosis, is a rare entity with a dismal prognosis. The prognostic significance of the initial anatomical compartment of CNS involvement (parenchymal vs. leptomeningeal) is not well-defined. We sought to analyze outcomes based on CNS localization in a single-institution cohort. Methods We retrospectively reviewed 34 patients with de novo SCNSL (including diffuse large B-cell lymphoma [DLBCL], high-grade B-cell lymphoma, not otherwise specified (HGBCL NOS), and double-/triple-hit lymphoma) treated at the Mayo Clinic between 2002 and 2025. Clinicopathological features, radiologic findings, treatment details, and survival outcomes were abstracted from the medical records. Patients were categorized by the primary site of CNS involvement. Overall survival (OS) and progression-free survival (PFS) were estimated using the Kaplan-Meier method, and groups were compared with the log-rank test. Results The study cohort had a median age of 61.5 years (range 19-79) and a median International Prognostic Index score of 3 (range 2-4). Pathological subtypes included double-/triple-hit lymphoma (n=14, 41.2%), non-germinal center B-cell like (non-GCB) DLBCL (n=11, 32.4%), germinal center B-cell like (GCB) DLBCL (n=8, 23.5%), and HGBCL NOS (n=1, 2.9%). Among patients with available data, 14 (43.8%) were positive for MYC rearrangement, 13 (54.2%) for BCL2 rearrangement, and 10 (45.5%) for BCL6 rearrangement. First-line regimens received by patients included MR-CHOP (n=18, 52.9%), DA-R-EPOCH (n=5, 14.7%), combined CODOX-M/IVAC (n=4, 11.7%), R-CHOP with intrathecal methotrexate (n=3, 8.8%), miscellaneous therapies (n=3, 8.8%), and MRT (n=1, 2.9%). The complete response rate was 58.8% (n=20). Intrathecal chemotherapy was administered to 61.8% of patients, and consolidation with autologous stem cell transplant was performed in 35.3%. Based on the CNS compartment involved, de novo SCNSL (DN-SCNSL) were classified into two subtypes: the leptomeningeal subtype, defined as leptomeningeal involvement with or without parenchymal CNS disease (LM-DN-SCNSL; n=22, 64.7%), and the parenchymal subtype, defined as parenchymal-only CNS involvement (P-DN-SCNSL; n=12, 35.3%). Parenchymal disease was multifocal in 75% of cases, supratentorial in 58.3%, and associated with a midline shift in 30%. Although no tested factors (including pathological subtype, age, sex, and bone marrow involvement) were significantly associated with the anatomical compartment, a non-significant trend was noted for BCL6 rearrangement to be more common in the leptomeningeal group (8/13, 61.5%) than the parenchymal group (2/8, 25.0%) (P = 0.18). Survival outcomes were analyzed for both anatomical and pathological classifications. For the parenchymal versus leptomeningeal cohorts, 2- and 5-year Overall Survival (OS) rates were 75.0% vs. 45.1% and 41.0% vs. 38.7%, respectively (P = 0.34). For Progression-Free Survival (PFS), the 2- and 5-year rates were 48.6% vs. 32.5% and 29.2% vs. 26.0%, respectively (P = 0.53). The 2-year OS rates by pathological subtype were 81.8% for non-GCB DLBCL, 45.8% for DHL/THL, and 37.5% for GCB DLBCL (P = 0.27). Survival outcomes (OS/PFS) were not significantly associated with patient age, sex, pathological subtype, or anatomical compartment. Conclusion DN-SCNSL can be anatomically classified into two subtypes: LM-DN-SCNSL and P-DN-SCNSL. While a prognostic trend favoring the parenchymal subtype was observed, the difference did not achieve statistical significance, a finding likely attributable to the small cohort size. This classification may hold clinical utility for guiding risk stratification, and larger studies are needed to validate these findings and identify the most optimal treatments for each subtype of DN-SCNSL.

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.