Paludo, Jonas

ABSTRACT 162 patients with relapsed secondary central nervous system lymphoma (R‐SCNSL) (median age, 65 years; male, 59.9%) including central nervous system (CNS)‐only ( n = 120) and concomitant CNS/systemic relapse ( n = 42) were retrospectively analyzed. Overall, 21.9% of patients were classified as high risk according to the CNS International Prognostic Index (CNS‐IPI). Several biological and clinical features were significantly associated with leptomeningeal involvement, including double‐ or triple‐hit (DHL/THL) status, MYC rearrangement, negative BCL6 expression by IHC, bone marrow involvement, and concomitant R‐SCNSL. Multivariable analysis showed that leptomeningeal involvement independently predicted inferior OS and was associated with a 98% increase in the hazard of death compared with parenchymal relapse (HR = 1.98, 95% CI: 1.20–3.26, p = 0.008). Based on anatomical localization, R‐SCNSL was classified into four subtypes: parenchymal‐only involvement (parenchymal‐CNS [P‐CNS], 68/42%) and parenchymal involvement plus systemic relapse (parenchymal‐concomitant [P‐concomitant], 17/10.5%); and leptomeningeal with or without parenchymal involvement (leptomeningeal‐CNS [LM‐CNS], 52/32.1%) and leptomeningeal with systemic relapse (leptomeningeal‐concomitant [LM‐concomitant], 25/15.4%). This anatomical classification significantly impacted OS and PFS ( p < 0.001). Two‐year OS and PFS were 58.2% and 29.1% for P‐CNS, 32.4% and 17.7% for P‐concomitant, 22% and 13.9% for LM‐CNS, and 7.1% and 0% for LM‐concomitant, respectively. ASCT showed a trend toward improved survival among patients with a response (CR/PR) in a 4‐month landmark analysis. These findings support the clinical application of the anatomical classification in the management of R‐SCNSL.

Abstract Waldenström macroglobulinemia (WM) is characterized by recurrent MYD88 and CXCR4 mutations, whose prognostic value in patients treated with chemoimmunotherapy remains unclear. Moreover, the typically prolonged progression-free survival (PFS) correlates inconsistently with overall survival (OS), underscoring the importance of examining other surrogates. Progression of disease within 24 months (POD24), an established early end point, delineates functionally high-risk patients in other indolent lymphomas. This international study evaluated 253 patients receiving frontline fixed-duration bendamustine-rituximab (BR), a common chemoimmunotherapy for WM. At a median follow-up of 5.9 years, 5-year PFS and OS were 65% and 87%, respectively; 5-year PFS was similar between MYD88L265P (90%) and MYD88wild-type (WT) subcohorts (64% each; P = .4). Among 89 patients with known CXCR4 status, the subcohort with CXCR4mutation (28%) had shorter PFS (median 3.3 vs 8.8 years; hazard ratio [HR], 2.8; P = .0036) and OS (HR, 2.6; P = .036) compared with CXCR4WT. POD24 occurred in 11.5% of patients who demonstrated inferior subsequent OS (5-year OS, 71% vs 86%; HR, 3.1; P = .005) and higher mortality (standardized mortality ratio [SMR], 3.7), unlike the non-POD24 group, whose mortality was comparable to the matched general population (SMR, 1.1). In conclusion, BR is effective irrespective of the MYD88 status, but CXCR4 mutations and POD24 portend worse outcomes. Patients without POD24 represent a cohort with distinctly favorable outcome.

Background: Ibrutinib is the only Bruton tyrosine kinase inhibitor (BTKi) with once-daily oral capsule, tablet, and oral suspension formulations approved in the United States across indications of chronic lymphocytic leukemia/small lymphocytic lymphoma, Waldenström macroglobulinemia, and previously treated chronic graft-versus-host disease, and for mantle cell lymphoma in Europe. Patients with difficulty swallowing capsules or tablets may require alternative formulations or administration options to optimize treatment. Objectives: To evaluate the relative bioavailability of ibrutinib oral suspension relative to capsule or tablet formulations and to examine compatibility for optimal administration methods. Methods: Relative bioavailability of ibrutinib oral suspension was evaluated in healthy volunteers or patients in comparison to capsule or tablet formulations. Dose recovery and delivery, presence of impurities, particle size, and hold time were evaluated after in vitro mock oral suspension administration via syringe and nasogastric and percutaneous endoscopic gastrostomy (PEG) tubes. Results: Clinical bioavailability was comparable for ibrutinib oral suspension and capsules (420 mg/day) under the fasted state in healthy volunteers. Dose-normalized pharmacokinetic values were similar across ibrutinib formulations in patient studies. All evaluated enteral tubes achieved 90%–110% ibrutinib dose recovery regardless of tube type or syringe after 2 × 3 mL water rinses. Oral suspension preservative may be adsorbed into enteral tubes after a 60-minute hold time. Conclusion: Ibrutinib oral suspension bioavailability was comparable with ibrutinib tablet and capsule formulations. When dosed via standard enteral tube administration methods, ibrutinib oral suspension is stable and compatible with polyurethane, silicone, or polyvinyl nasogastric or PEG tubes. To ensure full dose recovery and prevent drug preservative adsorption into tubes, ibrutinib oral suspension should be administered immediately and followed by two rinses of 3 mL of water each. Ibrutinib oral suspension is a viable BTKi treatment option for patients requiring or preferring alternatives to oral capsules and tablets.

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 &lt;100, ANC&lt; 1, CrCl&lt; 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 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 &gt;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 &gt;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 &gt;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: Waldenström Macroglobulinemia (WM) is an indolent B-cell lymphoplasmacytic lymphoma characterized by heterogeneous clinical behavior and variable progression patterns. While MYD88L265Pand CXCR4 mutations are frequently encountered, the broader genomic landscape underpinning disease evolution from smoldering WM (SWM) to active treatment naïve WM (TNWM), post-treatment remission WM (PTWM) and relapsed/refractory WM (RRWM) remains incompletely understood. To address this gap, we studied the mutational profile across the WM disease spectrum. Methods We performed whole exome sequencing (WES) on sorted (CD19 and/or CD138) bone marrow aspirates of patients across the spectrum of WM. Paired germline sequencing was performed using peripheral blood mononuclear cells (PBMCs). Unique molecular identifier-tagged reads were deduplicated using fgbio. WES data were processed through the Sarek pipeline. Any valid mutation from either caller, present on the index or in COSMIC was considered. Copy number variations (CNVs) were inferred via CNVkit and summarized at the gene and arm level (≥50% directional change). Mean tumor and germline coverages were 174X and 74X, respectively. Fisher's exact test was used for enrichment and hypergeometric test for overlap analyses. Results A total of 78 high-quality WM samples [15 SWM, 44 TNWM, 13 RRWM, 6 PTWM in remission (all with paired TNWM samples)] were sequenced. The mutational burden varied across disease states (median SWM=1.48 m/Mb, TNWM= 1.12 m/Mb, RRWM = 1.59 m/Mb; p=0.04). Density plots revealed bimodal VAF distributions, consistent with clonal and subclonal architectures in WM. Overall, 77% harbored a MYD88L265P mutation by WES. Among MYD88^WT^ patients, AS-PCR identified MYD88L265P in 4 (25%) not detected by WES, raising the overall mutation rate to 83%, plus one with a non-L265P variant. Low VAF (&lt;10%) MYD88L265P mutations were seen in 6% of matched PBMCs. MYD88 copy number gain was seen in 2 (17%) MYD88WT TNWM patients. CXCR4 mutations were detected in 38% by WES (18% each frameshift and nonsense). Two (4%) CXCR4WT samples were positive by PCR, giving an overall rate of 40%. CXCR4 mutations were more frequent in RRWM (54%) vs TNWM (41%) and SWM (27%), though not statistically significant. Del(6q) and Del(17p) were noted in 16% (n=12) and 7% (n=5) of patient samples. All del(17p) events were noted in TNWM samples and in one paired post-treatment sample. Overall rate of del(17p) and TP53 mutation was 14% (n=11). RRWM was enriched in ARID1A alterations, a gene implicated in chromatin remodeling and plasma cell identity formation [38% (n=5) in RRWM vs. 11% (n=6) in TNWM and SWM], compared to TNWM and SWM p=0.03]. Alterations in NF-kB signaling gene MAP3K14 were more frequent in RRWM (31%, n=4) compared to the SWM/TNWM cohort [(7%), n=4; p=0.036). We noted an enrichment in the TP53 mutation rate in RRMW [n=3, 23%, OR 7.7] compared to TNWM and SWM cohorts [n=2, 4%), p=0.04]. Mutations in FUBP1 (p=0.03), an upstream regulator of MYC, and CARD11 (p=.043), a B-cell signaling scaffold protein were also more frequent in RRWM. Notably, none of the SWM patients harbored an ARID1A alteration (p=0.057), suggesting acquisition during progression. CNV analysis revealed that MAP3K14 amplifications were frequent in RRWM (n=3, 23%) compared to SWM/TNWM [n=2 (4%), p=0.043]. RRWM samples showed increased CD79B mutations (15%) and significant overlap with the C5/ABC diffuse large B-cell lymphoma (DLBCL) genomic cluster (Chapuy et al., Nat. Med. 2018; p=0.007). This overlap remained significant after excluding MYD88L265P (p=0.019), driven by CARD11, CD79B, and IGLL5, suggesting that a subset of RRWM may acquire an ABC DLBCL-like mutational profile. These findings warrant further investigation using transcriptional profiling. Conclusions This study provides a genomic map of WM evolution from precursor to relapse. Increasing somatic mutations and CNVs support stepwise progression. Enrichment of genes involved in chromatin remodeling and plasma cell identity formation (ARID1A), NF-kB or BCR signaling (CARD11, MAP3K14), and DNA damage repair (TP53) were noted at relapse, all of which have been shown to drive aggressive disease biology. Emergence of an ABC-like genotype in a proportion of RRWM indicates convergent evolution with aggressive lymphomas. Transcriptional studies are warranted to explore this further.

Abstract Introduction: The introduction of the Bruton tyrosine kinase inhibitor acalabrutinib combined with bendamustine and rituximab (ABR) as first-line (1L) therapy for mantle cell lymphoma (MCL) in older patients (pts) is reshaping the approach to treatment sequencing in this disease. The primary analysis of the phase 3 ECHO trial (NCT02972840) with data cutoff Feb 15, 2024 (median time on study 44.9 mo) demonstrated improved progression-free survival (PFS) with ABR vs placebo plus BR (PBR; hazard ratio [HR] 0.73; 95% confidence interval [CI] 0.57–0.94; P=0.0160) in pts ≥65 y with treatment-naive (TN) MCL. Here we present updated results after 50 mo of follow-up, including time to next treatment 2 (TTNT2; ie, initiation of third-line [3L] therapy). Methods: Pts aged ≥65 y with TN MCL and ECOG performance status ≤2 were randomly assigned 1:1 to receive ABR or PBR. Randomization was stratified according to geographic region and simplified MIPI score. BR was given for 6 cycles followed by rituximab maintenance for 2 y in pts achieving a partial or complete response (CR). Acalabrutinib (100 mg twice daily) or placebo was administered at study start until progressive disease (PD) or unacceptable toxicity. Crossover to acalabrutinib was permitted at PD. The primary endpoint was PFS per independent review committee. TTNT2, which was used as a surrogate for PFS2 (time from initial treatment to second disease progression), was defined as time from randomization to second subsequent (3L) antilymphoma therapy after discontinuation of randomized treatment or death. Results: In total, 299 pts were randomized to each arm; median age was 71 y. At data cutoff (February 15, 2025), median (range) time on study was 51.86 (0.03–93.04) mo and median (range) follow-up through reverse Kaplan-Meier method for PFS was 60.8 (0–88.5) mo. Median TTNT2 was not reached (NR) in the ABR arm and 73.8 mo in the PBR arm, establishing a 24% reduction in the risk of initiating 3L therapy or death with ABR vs PBR (stratified HR 0.76; 95% CI 0.59–0.98). In total, 33 (11.0%) and 100 (33.4%) pts received second-line [2L] treatment (median time to 2L therapy, 26.3 vs 19.6 mo) and 10 (3.3%) and 37 (12.4%) received 3L treatment in the ABR and PBR arms, respectively. When COVID-19 deaths were censored (in alignment with prespecified analyses from the primary study), the HR further improved (stratified HR 0.67; 95% CI 0.50–0.89; median TTNT2 NR in any arm). In total, 71.6% and 78.3% of pts discontinued acalabrutinib and placebo, respectively; the most common reasons (&gt;5%) for discontinuation were adverse events (AEs; 44.5%, ABR; 32.4%, PBR) and objective PD (12.7%, ABR; 30.8%, PBR). Median PFS was 72.5 and 47.8 mo in the ABR and PBR arms, respectively (stratified HR 0.68; 95% CI 0.53–0.87; P=0.002). In total, 108 (36.1%) and 116 (38.8%) pts in each arm died; median overall survival (OS) was NR in both arms (stratified HR 0.87; 95% CI 0.67–1.13), with a 36-mo OS rate of 73.8% with ABR vs 68.3% with PBR. With 1 additional year of follow-up since the primary analysis, rates of grade ≥3 AEs and serious AEs in the ABR arm were comparable to previously reported rates, indicating that prolonged acalabrutinib exposure does not appear to result in cumulative toxicities. Three new cases of grade 3/4 neutropenia were reported, with no marked changes in the rates of grade ≥3 or serious infections. The rate of grade ≥3 cardiac events remained consistent with the primary analysis. In the ABR arm, 1 new case of grade 3/4 atrial fibrillation and 1 new case of grade 3/4 hypertension were reported. Rates of thrombocytopenia, major hemorrhage, and ventricular tachyarrhythmia remain unchanged. Conclusion: In this updated analysis of the ECHO trial, despite crossover to acalabrutinib being permitted in the PBR arm, fewer pts in the ABR arm were in need of subsequent treatment (2L and 3L), with longer TTNT2 in the ABR arm. These results suggest that 1L treatment with ABR provides a 24% reduction in risk of initiating 3L therapies or death. Further follow-up has demonstrated that ABR offers improved efficacy (ie, PFS) vs PBR, with no long-term safety concerns with acalabrutinib maintenance. The updated safety findings further support the favorable benefit–risk profile of acalabrutinib in TN MCL. Altogether, these data support the potential of acalabrutinib in combination with BR as 1L treatment, rather than in later lines, to modify long-term treatment outcomes in pts with TN MCL.

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: The landscape of healthcare delivery in hematology has undergone significant transformations with the increasing adoption of telehealth services for appointments and communications in conjunction with the 2016 21st Century Cures Act providing patients complete access to their health information. Digital healthcare services in hematology offer benefits, including convenience and equitable access to those limited by travel or financial burdens. However, they also have inherent risks, including loss of in-person communication, disparities in access to technology, differences in digital literacy, and increased provider workload and burnout. Thus, we analyzed contemporary utilization patterns of hematology healthcare and provider preferences to optimize implementation of evolving hybrid care models. Methods: We analyzed data from the Mayo Clinic (Rochester, MN) Hematology Appointments Office and the Hematology Call Center as well as digital messaging practices using Epic's SlicerDicer tool. Data was collected between January 2023 and January 2025, during which patients had access to the electronic health record (EHR) and direct messaging capabilities. An optional survey was distributed to Hematology providers including physician consultants, fellows, and advanced practice providers (APP) to assess practice preferences. Results: During the analyzed period, a total of 85,899 hematology appointments were completed and captured all adult ages, including 18-34 (4.6%), 35-49 (9.2%), 50-64 (25.3%), 65-79 (48.3%), and 80+ (12.6%). Although in-person, video, and telephone visit options were available, in-person visits predominated across all age groups, with 80.6% of all completed appointments occurring in-person, 12.9% via video, and 6.5% via telephone. Notably, patients aged 80+ had the second highest combined use of video and telephone visits, with over 20% of their appointments conducted virtually. There was a total of 6,893 no-show and cancelled appointments, with 79.9% of these scheduled in-person, 14.8% via video, and 5.3% via telephone. There were over 4,000,000 portal messages in the hematology division with over 1,000,000 sent directly to hematology providers. Patients sent 105,284 new messages, of which 47.2% were new patient advice requests to providers which cascaded to 343,103 additional messages. Patients made 155,036 telephone calls, which cascaded to 407,578 messages over the portal, 78.5% of which were sent directly to providers. Notably, utilization of both portal messaging and telephone calls was similar across all age cohorts, including those 80+ years. Providers only initiated 14,159 new messages to patients yet sent a total of 354,172 messages. The hematology provider survey had a response rate of 42% (53 of 129). Respondents included 45.3% consultants, 45.3% fellows, and 9.4% APPs. 92.5% and 90.6% of providers preferred in-person appointments for new visits and established visits on therapy, respectively. In contrast, 52.8% preferred virtual appointments for established patients not currently receiving therapy. 73.6% and 52.8% of survey respondents either strongly agreed or agreed that virtual appointments are advantageous for patients and providers, respectively. Additionally, only 49.1% felt portal messages were necessary to provide high quality care and 64.2% preferred appointments over communicating through messages. 86.8% of providers reported spending more than 30 minutes daily responding to in-basket messages, however, 94.3% reported not routinely billing for their time. Conclusions: In the digital era, hematology healthcare delivery is multifaceted and increasingly demanding with high rates of patient engagement in their care via various mediums. To our knowledge, this is the largest analysis combining EHR utilization and appointment data with provider preferences to better understand digital hematology care. Digital appointments and communications were common amongst all age groups, including older patients, and had relatively low cancellations rates. Promotion of virtual visits to facilitate patient-provider conversations in addition to standardization of digital communication workflows are low-risk, high-yield interventions to mitigate the cascading of EHR-based communications, especially for those not actively receiving therapy. Together, these could decrease provider burnout while increasing healthcare system operational capacities to provide optimal patient care and satisfaction.