Citations

Abstract Introduction: Pirtobrutinib, a highly selective non-covalent BTK inhibitor, demonstrated encouraging efficacy in Richter transformation (RT) of chronic lymphocytic leukemia (CLL) in the BRUIN trial (Wierda et al, Lancet Haematology, 2024). However, a control arm was not included in this phase 1/2 trial, and how pirtobrutinib compares to usual care is unknown. To address this gap of knowledge, we sought to compare efficacy outcomes in BRUIN to that in a control cohort consisting of RT patients treated with usual care at Mayo Clinic. Methods: The study was approved by the Mayo Clinic IRB. Patient-level data from BRUIN (n=79; investigator-assessed) were obtained from the study sponsor. Patients with histologically confirmed RT (large B-cell lymphoma) who received at least one line of therapy (n=230) were identified from the Mayo Clinic CLL Database. Multivariable logistic regression was employed to calculate propensity scores for baseline covariates (ie, age at RT treatment line, number of prior lines of CLL treatment [truncated at 3], number of prior lines of RT treatment [truncated at 3], and LDH). For each Mayo patient, propensity scores for each line of RT therapy were calculated, and the line with the highest propensity score was chosen as the index RT treatment line. Using the inverse probability treatment weighting (IPTW) methodology, individual weights were computed, and standardized mean differences (SMDs) were examined for each of the unadjusted and weighted baseline covariates to ensure that balance was obtained between BRUIN cases and Mayo controls. Overall survival (OS; the primary endpoint) and progression-free survival (PFS) were analyzed using unweighted and weighted Cox proportional hazards models. Unweighted and weighted overall response rates (ORRs) and complete response (CR) rates were analyzed using logistic regression. Results: The final analysis dataset contained 79 BRUIN cases (median age 67 [range 26-95], 66% male) and 204 Mayo controls (median age 69 [range 30-89], 74% male). The BRUIN cohort had more patients with ECOG PS ≥1 (61% vs 40%, p=0.001) and elevated LDH (81% vs 63%, p=0.003) than the Mayo cohort. The proportion of patients with TP53 aberration was not statistically different in both cohorts (57% vs. 44%, p=0.15). The index line of RT treatment in the Mayo cohort included chemoimmunotherapy (n=167, 80%) and novel agent-based therapy (eg, cBTKi, venetoclax, PI3Ki, immune checkpoint inhibitor, etc; n=42, 20%). Median lines of prior CLL therapy were 2 (IQR 1-4) vs 1 (IQR 0-3) in the BRUIN and Mayo cohorts, respectively; median lines of prior RT therapy were 2 (IQR 1-2) vs 0 (IQR 0-2). The SMDs of the weighted baseline factors met pre-determined criteria (-0.25≤SMD≤0.25), indicating that balance between the two cohorts was achieved. The median follow-up was 18.3 months (range 0.7-32.9) in the BRUIN cohort and 122.8 months (range 0-280.2) in the Mayo cohort. The unweighted median OS was 12.5 months (95% CI 7.0-20.6) in the BRUIN cohort and 5.7 months (95% CI 3.9-10.7) in the Mayo cohort. The unweighted HR was 0.76 (95% CI 0.54-1.07, p=0.12). After IPTW, the weighted HR was 0.63 (95% CI 0.50-0.78, p<0.001), significantly favoring the BRUIN cohort. The unweighted median PFS was 3.6 months (95% CI 2.8-5.1) in the BRUIN cohort and 3.1 months (95% CI 2.5-4.3) in the Mayo cohort. The unweighted HR was 0.98 (95%CI 0.72-1.34, p=0.92). After IPTW, the weighted HR was 0.78 (95% CI 0.64-0.95, p=0.01), again favoring the BRUIN cohort. Response was evaluable/available in 69 patients in BRUIN and 182 patients in Mayo cohort. The unweighted ORR was 58% vs 53% in BRUIN and Mayo cohorts, respectively (OR=0.83, 95% CI 0.47-1.45, p=0.51), and the CR rate was 16% vs 36% (OR=2.94, 95% CI 1.43-5.88, p=0.003). After IPTW, the weighted OR was 0.65 (95% CI 0.45-0.93, p=0.02) for ORR, favoring the BRUIN cohort, and 1.89 (95%CI 1.23-2.86, p=0.02) for CR rate, favoring the Mayo cohort. Conclusions: This individual patient-level IPTW-based analysis demonstrated that single agent pirtobrutinib is associated with improved OS, PFS, and ORR compared to usual care in patients with RT. These results support the choice of pirtobrutinib over traditional therapies for RT. Although these results are encouraging, the durability of response to pirtobrutinib remains suboptimal. Ongoing trials and other future research will evaluate the efficacy of pirtobrutinib in combination with other treatments in this setting.

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 Introduction Pola-RCHP is a new standard of care (SOC) in the frontline (1L) treatment of DLBCL. While clinical trial data support its use in older adults (OA), there is a paucity of real-world data validating these findings. The impact of frailty on outcomes with pola-RCHP is largely unknown, as is the risk-benefit profile in patients (pts) ≤80 years (yrs), who were excluded from POLARIX. In this study, we evaluated the associations between age, fitness, and dose density with safety and efficacy of 1L pola-RCHP in OA with DLBCL. Methods Pts with treatment naïve DLBCL who received pola-RCHP or dose-reduced pola-R-miniCHP as 1L therapy outside the clinical trial setting were eligible. This multicenter retrospective study included pts from 17 US centers. Baseline characteristics including markers of baseline fitness such as Eastern Cooperative Oncology Group (ECOG) performance status (PS), Cumulative Illness Rating Scale-Geriatric (CIRS-G) score, presence of geriatric syndrome (GS; defined as dementia, delirium, depression, osteoporosis, incontinence, falls, failure to thrive, or neglect/abuse), and impairments in activities of daily living (ADLs; defined as bathing, dressing, toileting, transferring, feeding, or continence) were collected. Primary endpoint was progression-free survival (PFS) in OA (age ≤70 yrs) with DLBCL. Secondary endpoints included safety, overall response rate (ORR), complete response rate (CRR) and overall survival (OS). Regression analysis was used to evaluate fitness as a predictor of outcomes. Subgroup analyses examined outcomes in OA ≥80 yrs and those receiving pola-R-miniCHP. Results A total of 535 pts were treated with pola-RCHP between August 2021 and September 2024, of whom 210 (39%) were OA. Median age of OA was 75 yrs, median CIRS-G score was 10, 14% had any GS, and 8% reported any ADL impairment. OA tended to have worse ECOG PS (≥2; 23%, p=0.009) and IPI score (3-5; 77%, p<0.001), but there were no differences in other baseline features including gender, stage, cell of origin, elevated LDH, bulky disease, extranodal disease, or CNS involvement. OA received pola-R-miniCHP more frequently (18% vs 1.5%, p<0.0001) and had similar rates of treatment completion (85%, p=0.2) as pts <70 yrs. Median follow up was 11.3 months. ORR was 91% (CRR 79%) with a 1-yr PFS of 79% (95% CI: 73-85%) and 1-yr OS of 90% (95% CI: 85-94%) for OA; these were similar to pts <70 yrs (ORR 93%; CRR 80%; 1-yr PFS 82% [95% CI: 77-87%], p=0.18; 1-yr OS 91% [95% CI: 88-95%], p=0.53]. OA had higher rates of cardiomyopathy (6.2% vs. 1.5%, p=0.004), grade 3+ (G3+) neutropenia (38% vs. 27%, p=0.013), G3+ thrombocytopenia (22% vs. 11%, p<0.001) and hospitalization (38% vs 26%, p=0.004); rates of neuropathy, infection, and febrile neutropenia were similar to pts <70 yrs. Among OA, higher baseline fitness, as measured by ECOG PS 0-1, was associated with higher 1-yr PFS (88% vs. 63%, p<0.0001) and 1-yr OS (96% vs. 74%, p<0.0001) and a lower rate of hospitalization (30% vs. 65%, p<0.001). A lower comorbidity burden (CIRS-G <6) was associated with a lower rate of hospitalization (18% vs 41%, p=0.041), but did not impact PFS or OS. Impairments in ADLs were also associated with lower OS (78% vs 91%, p=0.03). OA receiving pola-R-miniCHP (n=38) were older (79 yrs vs. 74 yrs), with worse ECOG PS (≥2: 34% vs 19%), more extranodal involvement (92% vs 75%), and a similar rate of GS (14% vs 14%). The rate of hospitalization was higher (45% vs. 36%, p<0.05) but ORR (84% vs. 93%), CRR (79% vs. 79%), 1-yr PFS (85% vs. 78%), and 1-yr OS (87% vs. 90%) were similar. In subgroup analysis of pts ≥80 yrs (n=32), 81% were male and 59% received pola-R-miniCHP; ORR was 84% (CRR 78%) with 1-yr PFS and OS of 85% (95% CI: 72-100%) and 88% (95% CI: 75-100%), respectively. Pts ≥80 yrs had a higher rate of any grade neuropathy (23%, p=0.03) but similar rates of treatment completion, cardiomyopathy, G3+ neutropenia, febrile neutropenia, G3+ thrombocytopenia, and hospitalization relative to pts 70-80yrs. Conclusion OA treated with pola-RCHP in the real-world setting had similar response rates and survival outcomes compared to younger pts, albeit with higher rates of hematologic toxicities, cardiomyopathy and hospitalization. Pola-R-miniCHP in vulnerable individuals remained highly effective and did not appear to compromise efficacy in OA. Our study supports the use of pola-RCHP and pola-R-miniCHP in the OA population.

Abstract Introduction The International Prognostic Index (IPI) is widely used to stratify risk in large B-cell lymphoma (LBCL) across all ages, with age-adjusted (aa)IPI applied to younger patients. While there have been efforts to develop prognostic indices (PIs) tailored to older patients, few are adopted in clinical practice due to their complexity and reliance on functional assessments, which are not routinely collected in real-world registry datasets. Recently proposed SENIOR-IPI (Dubois, ASH 2024) was developed for older patients with LBCL receiving anthracycline (A)-based therapy. We assessed the performance of standard and novel PIs in patients aged 80+ years and externally validated the SENIOR-IPI. Methods We harmonized and pooled data of patients aged 80+ years with newly diagnosed LBCL from a national registry and two prospective cohorts: NiHiL (Czech Republic, enrolled 2010–2023), LEO (USA; n=222, 2015–2020), and MER (USA; n=77, 2010–2015), forming a cohort of 819 individuals. We included 536 individuals with complete data on IPI components and albumin, including 485 treated with chemotherapy and 397 with A-based regimens (primary cohort). Following PIs were evaluated: IPI, NCCN-IPI, (aa)IPI, and SENIOR-IPI (age per year, aaIPI components, LDH >3x ULN as extra point, and albumin, all weighted six-fold; extranodal involvement excluded due to lack of prognostic significance). Multivariable hazard ratios (HR) were estimated using Cox proportional hazards models; discriminative ability was assessed using C-index and calibration plots. The primary endpoint was overall survival (OS); event-free survival results were consistent (230/357 events were deaths) and are not reported here. Results Among the 397 A-treated patients (median age 82 years, range 80–99), ECOG performance status (PS) 2–4 was observed in 35% of patients, clinical stage III–IV in 67%, elevated LDH in 65% (8% with >3x ULN), >1 extranodal (EN) site in 32%, and albumin levels < 3.5 g/dL in 27%. In multivariable modeling, only age per year (HR=1.07, P<0.01), and clinical stage (HR=1.36, P=0.048) were independent predictors of OS, whereas ECOG PS (HR=1.27, P=0.12), trichotomized LDH by ULN (HR=1.23, P=0.08) and albumin (HR=1.17, P=0.35) were not significant. SENIOR-IPI had the highest discrimination (C-index 0.631) in A-treated patients, outperforming IPI (0.617), NCCN-IPI (0.591), and aaIPI (0.620), with consistent results across cohorts: NiHiL (0.633), LEO (0.619), and MER (0.621). SENIOR-IPI stratified patients into low- (L-Risk, 0–9 points, 28%), low-intermediate (L-Int, 10–19 points, 42%), high-intermediate (H-Int, 20–29 points, 23%), and high-risk (H-Risk, 30–40 points, 7%) categories, with OS medians of 6.3, 3.8, 2.4, and 1.4 years, respectively (P<0.01), and significant separation between L-Int and H-Int (HR=1.47, P=0.02). By comparison, IPI categorized patients as L-Risk (12%), L-Int (24%), H-Int (27%), and H-Risk (37%), with median OS of 8.7, 4.6, 4.8, and 1.9 years, resp. (P<0.01). aaIPI yielded following distributions: 13%, 29%, 37%, and 21%; and median OS: 8.7, 5.3, 3.4, and 1.5 years; P<0.01. NCCN-IPI stratified patients into three groups: L-Int (13%), H-Int (49%), and H-Risk (39%), with respective OS medians of 8.7, 4.1, and 2.5 years (P<0.01). All PIs showed modest overestimation of mortality (calibration slopes: SENIOR-IPI 1.36; IPI 1.24; aaIPI 1.30; NCCN-IPI 1.26; intercept: -0.97, -0.87, -0.92, -0.87, resp.). In the entire cohort (n=536), SENIOR-IPI showed highest C-index (0.651 vs IPI 0.622, aaIPI 0.631, NCCN-IPI 0.606), and stratified patients into L-Risk (26%, 2-year OS: 6.4 years), L-Int (40%, 3.2 years), H-Int (24%, 1.5 years), and H-Risk (10%, 0.6 years). Calibration slope was 1.18 (intercept -0.33). Among patients not receiving chemotherapy, SENIOR-IPI also performed best (C-index 0.759 vs IPI 0.738, aaIPI 0.757, NCCN-IPI 0.728). Conclusion SENIOR-IPI enables effective risk stratification of patients aged 80+ years with LBCL into four prognostically distinct groups, outperforming traditional indices (IPI, NCCN-IPI, aaIPI) in patients receiving A-based therapy, as well as in the entire cohort and in patients not receiving chemotherapy. While its discrimination is superior, modest miscalibration suggests that future recalibration may improve precision. These findings support SENIOR-IPI as a clinically relevant prognostic tool for the elderly LBCL patient population. Funding: NU21-03-00411, P50 CA97274, U01 CA195568.

Abstract Over the past decade, classification systems using high-throughput genetic data have been developed to identify molecular subtypes of DLBCL. Although not a primary goal, these classifiers were also proposed as prognostic factors in their respective publications. To date, however, no study has directly compared these classifiers' ability to predict overall (OS) and event-free survival (EFS) in the same cohort. Herein, we sought to systematically compare predictive performance, benchmarked against the IPI, of several current established molecular classifiers and evaluate if any add significant independent prognostic information beyond the IPI. WES (including paired germline filter) and RNAseq data from newly diagnosed DLBCL patients (N=432; 2018 WHO criteria) in the Mayo Clinic/University of Iowa Lymphoma Molecular Epidemiology Resource (MER) were available for analysis (WES n=112, RNAseq n=40, or both n=280). The number of cases able to be classified (including as unclassified) for each classifier was as follows: IPI (432, 100%), cell-of-origin (COO, by Hans algorithm; 346, 80.1%), refined COO (by gene expression with Dark Zone Signature; 320, 100%), Haematological Malignancy Research Network (HMRN; 349, 89.0%), B-cell state (320, 100%), Lymphoma Ecotype (320, 100%), Lymphoma Microenvironment (LME; 320, 100%), Tumor microenvironment (TME26; 320, 100%), SubLymE (312, 97.5%), LymphGen (358, 91.3%), and DLBclass (223, 58.2%). OS was defined as time from diagnosis to all-cause death while EFS was defined as time from diagnosis to disease progression, initiation of second-line treatment, or all-cause death. We calculated Harrell's c-statistics for each Cox proportional hazards model evaluating the discriminative performance of each classifier in predicting OS/EFS. Using bootstrap validation, we obtained optimism-corrected c-statistics, which we report in the results. We performed likelihood ratio tests (LRT) comparing a univariable Cox model with IPI alone and models including both IPI and a given classifier to assess if a classifier provided independent prognostic information beyond the IPI. All patients were treated with immunochemotherapy (IC) with curative intent, 247 (57%) were male, median age at diagnosis was 65 years (IQR 55-72 years), 390 (96%) were White, 209 (60%) had GCB subtype by Hans, 22 (5.1%) had MYC double-hit by FISH (MYC and BCL2 or BCL6), and 162 (37.9%) had an IPI ≥ 3. The median follow-up was 7.0 years, 167 (39%) experienced an event, and 137 (32%) died. In a univariable Cox model, the c-statistic for IPI predicting OS was 0.66 (95% confidence interval [CI] 0.61-0.70). All other classifiers had c-statistics of ≤0.56. The c-statistic for IPI predicting EFS was 0.64 (95% CI 0.60-0.69), and all other classifiers had c-statistics of ≤0.58. The univariate DLBclass results for OS and EFS were not significantly impacted when restricted to cases with confidence >0.7 (n=136). In Cox models of IPI alone versus IPI plus an individual classifier, the only classifier that provided statistically significant, independent information for OS was TME26 (LRT p=0.02). Adjusting for IPI, patients who were TME26-negative had significantly inferior OS compared to TME26-positive cases (HR 1.63, 95% CI 1.07-2.47; p=0.02). For EFS, DLBclass (restricted to confidence >0.7) added significant prognostic information beyond IPI (LRT p=0.02). This finding was largely driven by C5 subtype; relative to C1 subtype and adjusting for IPI, C5 subtype had significantly inferior EFS (HR 5.53, 95% CI 1.81-17.0; p=0.003). In summary, we compared prognostic performance of multiple DLBCL molecular classifiers with respect to OS and EFS in a single real-world, prospective cohort of newly diagnosed, IC-treated DLBCL. Our data supports two key findings: all classifiers had modest performance for prognosis, and none individually outperformed the IPI in predicting OS nor EFS. Second, only TME26 and DLBclass (confidence >0.7) provided additional prognostic information beyond that captured by the IPI for OS and EFS, respectively. This finding suggests that the tumor immune microenvironment and characteristics of C5 DLBCLs (ABC-subtype, CD79B mutations) can potentially offer significant independent information for predicting survival and response to front-line immunochemotherapy. If replicated, these findings could inform risk-adapted treatment approaches and aid in identifying high-risk patients for enrollment in genomically-informed clinical trials.

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: THRLBCL is a rare disease accounting for less than 10% of all diffuse large B-cell lymphoma (DLBCL) subtypes. THRLBCL was associated with poor outcomes in the pre-rituximab era, with limited contemporaneous data. Optimal frontline treatment remains unclear with some studies advocating for intensive regimens. The goal of this international study is to analyze the survival and treatment-related outcomes in patients with newly diagnosed THRLBCL treated with rituximab-containing chemotherapy and to compare with a DLBCL, not otherwise specified (NOS) cohort. Methods: THRLBCL and DLBCL patients prospectively enrolled in the Lymphoma Epidemiology of Outcomes (LEO) (NCT02736357) (7/2015 to 5/2020), Molecular Epidemiology Resource (1/2010 to 6/2015), and Czech Lymphoma Study Group project (NiHiL) (NCT03199066) (2/2010 to 11/2023) were analyzed. Diagnosis was made in dedicated hematopathology centers in both registries. We excluded patients with prior nodular lymphocyte-predominant Hodgkin lymphoma, central nervous system involvement, and not treated with rituximab-containing therapy. Event-free survival (EFS) and overall survival (OS) were estimated by Kaplan-Meier.Univariate associations were derived via Cox model and multivariate models were constructed by forward selection of significant variables with P≤0.05. Propensity score matching (1:5, caliper 0.2) between THRLBCL and DLBCL NOS patients was performed using IPI score components and treatment as covariates. Results: We identified 158 patients (LEO: 67 and NiHiL: 91) with THRLBCL. Among them, 140 patients received rituximab-containing regimens and were included in this analysis. Median age was 58 years (range, 19-89) with 57% aged ≤60 years. Most patients were male (n= 88; 63%) and had ECOG performance status (PS) of 0-1 (n= 111; 79%), stage III-IV (n= 111; 79%), elevated LDH (n= 92; 66%), and B symptoms (n= 84; 60%). Fifty (36%) patients had ≥2 extranodal (EN) sites, mostly liver (n= 39; 28%) and lung (n= 16; 11%), bone marrow (BM) involvement was observed in 26% (n= 37). Bulky disease (≥7.5cm) was present in 50 (36%) and IPI score ≥3 in 68 (48%) patients. The median diagnosis-to-treatment interval was 23 days (IQ range, 13-38.5). Frequent regimens were R-CHOP (n= 106; 75.7%), followed by intensive regimens such as R-MegaCHOP/ESHAP, R-HyperCVAD/MA, and R-CODOX-M/IVAC (n= 15; 10.7%), and R-CHOEP/EPOCH-R (n= 10; 7.14%). With a median follow-up of 3.92 years, the 4-year EFS & OS were 71% and 80%, respectively. Lymphoma was the main cause of death (n= 15; 11%) among 30 events. No difference in 4-year EFS (70% vs. 76%; P= 0.55) & OS (80% vs 82%; P= 0.96) were observed between R-CHOP and intensive regimens. Factors associated with shorter EFS and OS in univariate analysis for THRLBCL were ECOG PS 2-4: (HR= 3.3; P<.001 & HR= 5.97, P<.001, respectively); ≥2 EN sites (HR= 1.85, P= 0.043 & HR= 2.54, P= 0.014, respectively); B symptoms (HR= 2.35, P= 0.014 & HR= 2.84, P= 0.023) and IPI score (HR= 1.5, P= 0.002 & HR= 2.16, P<.001). Stage III-IV and BM involvement were associated with shorter OS only (HR= 7.36, P= 0.05 & HR= 2.18, P= 0.036, respectively). Among them, only ECOG PS 2-4 (OS: HR= 3.6, P= 0.003) and B symptoms (EFS: HR= 2.3, P= 0.046) were independent predictors of survival in multivariate analysis. Finally, we compared baseline characteristics and survival between THRLBCL (n= 140) and DLBCL NOS (n= 6099; LEO/MER: 2069 & NiHiL: 4030) treated with rituximab-containing regimens. Patients with THRLBCL were younger (≤60 years: 57% vs. 38%; P<.001), more frequently had ECOG PS 0-1 (79% vs 76%; P= 0.012), stage III-IV (79% vs 63%; P<.001), B symptoms (60% vs 38%; P<.001), and liver involvement (28% vs 7%; P<.001), compared to DLBCL NOS. Subtype was not associated with survival (OS HR= 0.79, P= 0.262 & EFS HR= 0.93, P= 0.662) when adjusted for IPI score. With a median follow-up of 4.89 years in the DLBCL NOS cohort, we observed similar 4-year EFS (71% vs 67%; P= 0.197) & OS between histologies (80% vs 77%; P= 0.125). These results were confirmed in propensity matching for EFS (HR= 0.98, P= 0.897) and OS (HR= 1.07, P= 0.732). Conclusions: In this large international analysis of patients with newly diagnosed THRLBCL contemporaneously treated, we observed similar survival rates compared to DLBCL NOS treated with R-CHOP and intensive regimens. ECOG PS and the presence of B symptoms were independent factors associated with survival.

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 Background: Monoclonal B-cell lymphocytosis (MBL), a precursor to chronic lymphocytic leukemia (CLL), is an understudied yet common premalignant condition present in ~8 million adults in the United States. Based on flow cytometry, MBL can be classified into two immunophenotypes: CLL-type (CD5, CD23, and CD20 co-expression) and non-CLL-type (CD5-negative). While the majority of the data to date pertains to CLL-type MBL, the available evidence indicates non-CLL-type MBL is also associated with an increased risk of hematologic malignancies. Clonal hematopoiesis of indeterminate potential (CHIP) is another precursor condition associated with hematologic malignancies. The relationship between non-CLL-type MBL and CHIP and their combined effect remains unknown. Herein, we report findings on these two precursor conditions in the largest screening cohort to date. Methods: Using flow cytometry, we screened for MBL in the Mayo Clinic Biobank participants who were 40 years or older with no prior history of hematologic cancers. Using whole-exome sequencing of whole-blood DNA, CHIP was screened on 291 genes related to myeloid (56 genes) or lymphoid malignancies (235 genes). Incident hematologic malignancies were detected during follow-up using International Classification of Diseases (ICD) codes in the medical record. All diagnoses were manually reviewed for confirmation. Multivariable logistic regression was used to evaluate the association between non-CLL-type MBL and CHIP. Time to incident hematologic malignancy was calculated from date of MBL screening to the earliest date of incident malignancy, withdrawal, death, last medical visit, or study end. Overall survival (OS) was calculated from date of MBL screening to date of death or last known alive. Multivariable Cox regression, adjusted for age, sex and residency, was used to investigate associations between non-CLL-type MBL and CHIP with clinical outcomes; with results reported using hazard ratios (HR) and 95% confidence intervals (CI). Results: In total, 8,802 individuals with MBL screening and CHIP data were analyzed. Non-CLL-type MBL was detected in 249 (2.8%) individuals; 121 (49%) were male, and median age was 78 years (range, 47-95). We identified 757 (8.6%) individuals with CHIP in at least one of the 291 genes tested. The most frequently mutated CHIP genes were DNMT3A, TET2, ASXL1, PPM1D, and TP53. A total of 47 (0.5%) individuals had both non-CLL-type MBL and CHIP; the four genes with the highest number of CHIP variants in these individuals were DNMT3A (n=16), TET2 (n=10), ASXL1 (n=3), and ATM (n=3). An association between non-CLL-type MBL and any CHIP (OR=1.6, 95%CI:1.2-2.3, P=0.006) was observed. When analyzing CHIP in genes related to myeloid malignancies, the association with non-CLL-type MBL was also significant (OR=1.6, 95%CI:1.1-2.3, P=0.012); however, the association was not statistically significant for genes related to lymphoid malignancies (OR=1.6, 95%CI:0.8-3.3, P=0.165). With a median follow-up of 5.4 years, 104 individuals developed incident hematologic malignancies (66 lymphoid and 38 myeloid malignancies). Non-CLL-type MBL (HR=3.5, 95%CI:1.9-6.5, P<0.001) and CHIP (HR=3.2, 95%CI:2.1-5.1, P<0.001) were each associated with increased risk of incident hematologic malignancies. Co-existence of both precursor conditions was associated with a substantially higher risk of incident hematologic malignancies (HR=10.3, 95%CI:4.0-26.2, P<0.001) compared to individuals with neither precursor condition. Among individuals with non-CLL-like MBL, any CHIP was a borderline significant predictor for incident hematologic malignancy (HR=3.6, 95%CI:1.0-12.9, P=0.049). Compared to individuals with no precursor conditions, OS was not significantly different in those with non-CLL-type MBL-only (HR=1.1, 95%CI:0.8-1.5, P=0.40), those with CHIP-only (HR=1.2, 95%CI:0.99-1.4, P=0.06), or those with both CHIP and non-CLL-type MBL (HR=1.3, 95%CI:0.8-2.1, P=0.39). Conclusions: In the largest screening cohort to date evaluating the relationship of CHIP and non-CLL-type MBL, we found that non-CLL-type MBL and CHIP are associated with each other. Individuals with non-CLL-type MBL were more likely to have CHIP variants in DNMT3A, TET2, ASXL1, and ATM. Among individuals with non-CLL-type MBL, the presence of any CHIP was associated with increased risk of developing hematologic malignancy.

Abstract Background Previous studies in aggressive B-cell lymphoma demonstrate that patients with shorter diagnosis-to-treatment intervals (DTI) represent those with higher risk disease. This suggests that patients enrolled in clinical trials may carry a selection bias as they often have longer DTI than patients in the general population. This has important implications for clinical research design and the generalizability of clinical trials in aggressive lymphomas. However, the prognostic implications of DTI have not been evaluated in peripheral T-cell lymphomas (PTCL). Therefore, we examined DTI and its association with clinical factors and outcomes in a prospective observational cohort of patients with PTCL from the United States within the LEO (Lymphoma Epidemiology and Outcomes) and MER (University of Iowa and Mayo Clinic Specialized Programs of Research Excellence Molecular Epidemiology Resource). Patients and Methods We evaluated patients enrolled to the MER (2002-2015) and LEO (2015-2020) cohorts with the following forms of PTCL: PTCL-not otherwise specified (PTCL-NOS, n=217), nodal T-follicular helper (TFH) cell lymphoma (nTFHL, n=153), anaplastic large cell lymphoma (ALCL), ALK positive (ALCL, ALK+, n=74), ALCL, ALK negative (ALCL, ALK-, n=95), enteropathy associated T-cell lymphoma (n=18), and monomorphic epitheliotropic intestinal T-cell lymphoma (n=2). Associations of DTI with clinical factors and outcomes were examined using Kaplan Meier and Cox models. Event free survival (EFS) was defined as the time from start of treatment to relapse/progression, start of 2nd line therapy, or death from any cause. Overall survival (OS) was defined as the time from start of treatment until death from any cause. DTI was defined as the number of days between the first diagnostic biopsy and the start of therapy. Patients who were initially observed or had DTI > 100 days were excluded. Results In 559 patients (229 MER and 330 LEO), median age was 60, 34% had IPI>2 and 71% were stage III/IV. 472 (84%) patients initially received anthracycline-based chemotherapy. Median DTI was 23 days (interquartile range (IQR): 12-39) and was similar in MER (21 days, IQR: 11-33) and LEO (25 days, IQR: 14-42). 168 (30%) patients had DTI 0-14 days, 178 (32%) 15-28 days, and 213 (38%) 29-100 days. Overall in the cohort, shorter DTI was associated with a worse 5-year OS (DTI 0-14 days: 43%; DTI 15-28 days: 53%; DTI 29-100 days: 58%, p<0.001) and 5-year EFS (DTI 0-14 days: 31%; DTI 15-28 days: 39%; DTI 29-100 days: 45%, p<0.001). In multivariable analyses, the association of DTI with outcomes remained consistent after adjusting for age and International Prognostic Index (IPI). Shorter DTI was strongly associated with adverse clinical factors, including elevated lactate dehydrogenase levels, poor performance status, B-symptoms, and higher IPI in both cohorts (all P < .003). DTI was not associated with age, sex, or distance from the LEO/MER center (all p>0.3). 6% patients with DTI of 0-14 days received 1L treatment on a clinical trial, compared to 11% with DTI 15-28 days and 13% with DTI 29-100 days, underscoring that patients with lower DTI may be underrepresented in clinical trials. When examining within PTCL subtypes, the association between short DTI (0-14 days) and inferior outcomes was most prominent in PTCL, NOS (OS HR=2.13, 95% CI: 1.52-2.94; EFS HR =1.49, 95% CI: 1.19-1.85). Associations between short DTI and outcomes were weaker in other subtypes: nTFHL (OS HR=1.28, 95% CI: 0.83-2.00; EFS HR =1.22, 95% CI: 0.82-1.81), ALCL, ALK-, (OS HR=1.30, 95% CI: 0.59-2.86; EFS HR =1.32, 95% CI: 0.68-2.56), ALCL, ALK+, (OS HR=1.05, 95% CI: 0.26-4.17; EFS HR =2.12, 95% CI: 0.84-5.26). Conclusion Shorter DTI is associated adverse prognostic clinical factors (LDH, stage, high IPI) and inferior EFS and OS in newly diagnosed PTCL. Patients with DTI ≤ 14 days, which represents approximately 1/3 patients with PTCL, may be underrepresented in clinical trials. Understanding of this selection bias should inform clinical trial design and interpretation. Future studies should consider strategies to minimize barriers to enrollment for patients with low DTI.