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Abstract Cyclin D1, encoded by CCND1, is a cell cycle regulator, overexpressed in ~15% of multiple myeloma (MM) cases via CCND1::IGH super-enhancer juxtaposition through t(11;14). Cyclin D1 also plays a non-canonical role in transcriptional regulation, but in t(11;14) MM its function in gene regulation remains undefined. We investigated the global transcriptional and epigenomic consequences of CCND1overexpression in t(11;14) MM. To define a CCND1 signature, RNA-seq from two independent primary MM patient cohorts, CoMMpass and Mayo, was utilized to compare high CCND1 t(11;14) cases (≥300 transcripts-per-million [TPM], n=271) vs. low CCND1 hyperdiploid cases (<30 TPM, n=233). Two CCND1 knockout (KO) clones in t(11;14) MM U266B1 were also generated by CRISPR/Cas9, introducing a frameshift insertion (A) after chr11:69641478 (hg38) in exon 1. ATAC-seq and ChIP-seq (H3K4me3, H3K4me1, and H3K27ac) were performed in duplicates, and RNA-seq in triplicates in U266B1 wild-type (WT) and KO. Libraries were sequenced on Illumina NextSeq 2000 (2x51 bp for ChIP/ATAC, 2x150 bp for RNA-seq). Reads were mapped to hg38 by BWA (ChIP/ATAC) or STAR (RNA-seq), peaks called by MACS2 (FDR< 0.01), peak and gene raw counts estimated by featureCounts. Differential peaks (fold-change ≥1.5) and differentially expressed genes (fold-change ≥2) were identified by edgeR (FDR<0.05), including peaks detected in ≥2 samples and genes with TPM ≥0.1 in ≥1 sample. Interactions between promoters and distal regulatory regions were identified using publicly available chromatin interaction data in blood cell types. Pathway analysis was performed using Enrichr. Co-immunoprecipitation was performed in t(11;14) KMS12BM cells. Differential analysis comparing CCND1-high t(11;14) and CCND1-low MM cases in the two patient cohorts identified 21 upregulated and 21 downregulated genes associated with CCND1 expression which were also captured in the wild-typevs. CCND1 KO U266 cells. Enriched pathways (p<0.05) included Notch signaling, early/late estrogen response, epithelial-mesenchymal transition and reduced mitotic spindle and myogenesis. Dysregulated genes included those related to apoptosis, ubiquitin-proteosome, extracellular matrix, adhesion, and immune response, demonstrating a broader role for cyclin D1 in MM beyond cell cycle control. To explore the epigenomic basis of this transcriptional regulation, for each of the 42 differentially expressed genes we examined the changes of histone marks and chromatin accessibility in promoter and the interacting distal regulatory regions in the U266 KO model. Ten of the 21 genes upregulated in CCND1-high (downloaded in KO; e.g., argininosuccinate synthase 1 [ASS1], PMAIP1, MYEOV, CCR7, and DTX1) and 12 of the 21 genes downregulated in CCND1-high (upregulated in KO; e.g., UACA, RASAL2, PTPRZ1, PPT2, and IL23R) showed concordant changes in histone marks and/or chromatin accessibility, predominantly in distal regulatory regions, suggesting a chromatin-mediated mechanism of gene regulation by cyclin D1. The most prominent changes involved H3K27ac, a mark defining active enhancers. As H3K27ac is deposited by histone acetyltransferases CBP, we tested for the possible cyclin D1-CBP interaction. Co-IP confirmed this interaction, suggesting a role for this complex in chromatin remodeling and transcriptional control in t(11;14) MM. One gene notably upregulated in association with CCND1 is ASS1, which encodes a urea cycle enzyme responsible for endogenous arginine synthesis. Metabolomic studies demonstrated KO cells had elevated aspartate and citrulline and lowered arginine and ornithine compared to WT cells (p<0.01) demonstrating impaired urea cycle function. Further, KO cells displayed significantly reduced ASS1 protein expression, cell growth and S-phase entry compared to WT cells in arginine-replete conditions, with both WT and KO cell types further inhibited by arginine depletion (p<0.05). Together, a cyclin D1-associated gene signature was identified supporting its role in t(11;14) MM beyond the CDK4/6 mediated cell cycle control, including a role in metabolic plasticity and survival under arginine depleted conditions. These findings highlight cyclin D1 as a therapeutic vulnerability and open new avenues for personalized treatment strategies.

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

Abstract Introduction: Patients diagnosed with chronic lymphocytic leukemia or small lymphocytic lymphoma (CLL/SLL) exhibit an elevated risk of developing other cancers (OC) relative to the general population. Historically, chemoimmunotherapy (CIT) represented the cornerstone of CLL/SLL management. A systematic review of clinical trials of CIT for CLL that included 28 studies reported 352/3297 (12.6%) patients developed OC (Csanadi 2022). The introduction of targeted agents beginning in the mid-2010's, including Bruton tyrosine kinase inhibitors (BTKi) and BCL2 inhibitors, has shifted the standard of care toward these novel therapies. In this study, we determined the risk of OC in newly diagnosed CLL/SLL patients cared for at the US Department of Veteran Affairs (VA), The University of Texas MD Anderson Cancer Center, and Mayo Clinic in Rochester, MN. Methods: We identified patients with newly diagnosed CLL/SLL after 1/1/2016 using the VA Central Cancer Registry, MD Anderson, and the Mayo Clinic CLL Databases. The baseline clinical characteristics, CLL treatments, and the occurrence of OC were abstracted by electronic health record (EHR) review. Receipt of CLL directed therapy was categorized into a) CIT only; b) targeted therapy only; c) CIT and targeted therapy; and d) other (including monotherapy with monoclonal antibody therapy). The occurrence of OC was modeled using CLL treatment as a time-dependent variable. Prior and concurrent OC diagnosed within 3 months of CLL/SLL were excluded from analyses, to reduce ascertainment bias. Results: We identified 6844 patients with CLL/SLL who met inclusion criteria. Median follow up was 3.6 years (interquartile range [IQR] 1.9-5.8). The median age at diagnosis of CLL/SLL was 71 years (range, 23 - 102); most patients were white (81%) and male (90%). Although many patients had missing values, among those with available information, IGHV genes were unmutated in 884 (51%), CLL FISH panel showed del17 in 213 (7%), del11q in 389 (13%), trisomy 12 in 674 (23%), and del13q in 1508 patients (50%). A total of 2344 patients received CLL directed therapy during follow-up: 278 (12%) received CIT only, 1809 (77%) received targeted therapy (+/- monoclonal antibody), only 99 (4%) received CIT and targeted therapy and 150 (6%) monoclonal antibody therapy only. Among patients treated with targeted therapies, 950 (50%) received ibrutinib, 381 (20%) received acalabrutinib, 164 (9%) received zanubrutinib, 4 (<1%) received orelabrutinib, 280 (15%) received venetoclax, and 3 (<1%) received sonrotoclax. Other treatments included combinations of a covalent BTKi (cBTKi) and BCL2i in 52 (3%) patients, among others. A total of 583 patients were diagnosed with OC during the study period. Types of OC observed included neoplasms of the prostate (n=129, 22%), lung (n=118, 20%), hematologic malignancies (n=91, 16%), gastrointestinal tract (n=68, 12%), melanoma (n=66, 11%), kidney and bladder (n=44, 8%), head and neck (n=19, 3%), and other/unknown (n=48, 8%). Estimated risk of occurrence of OC was 4.2% (95%CI 3.8-4.8%) at 24 months and 9.7% (95%CI 8.9-10.6%) at 60 months after a diagnosis of CLL/SLL. Multivariable analyses demonstrated that older age (HR: 1.013, 95%CI: 1.00-1.021), male sex (HR: 1.6, 95%CI: 1.1-2.3), and receipt of CLL directed therapy (HR: 1.4, 95%CI: 1.2-1.7) were associated with an increased risk of OC. When considering different types of CLL directed treatments, those who received CIT with or without targeted therapies had the highest risk of development of OC (HR: 1.6, 95%CI: 1.2-2.1). Risk of OC were also increased for those who received CIT only (HR: 1.41, 95%CI: 0.97-2.05) and those who received targeted treatments alone (HR: 1.27, 95%CI: 1.02-1.58). Conclusion: In this large dataset of newly diagnosed patients with CLL/SLL in the past ten years, approximately 10% patients developed OC at 5 years. Receipt of CLL directed therapy was associated with a 40% increased risk of development of OC compared to those patients who did not receive therapy. The risk was highest among patients who received CIT with or without targeted therapy and was still elevated in those who received CIT only or targeted therapy only.

Abstract Introduction: While the incidence of multiple myeloma (MM) is 2-3 fold higher in Black compared to white individuals, the impact of racial differences on treatment outcomes remains unclear due to the confounding effect of socioeconomic barriers and disparities in access to effective therapies and clinical trials. In this study, we explored the relationship between race, social deprivation, and outcomes in newly diagnosed MM (NDMM) patients (pts) receiving modern therapies with triplet or quadruplet regimens. Methods: Pts with NDMM diagnosis, who received first line treatment between 2015-2025, and had data available on response and survival outcomes were identified through the ASH Research Collaborative MM Data Hub (a real-world database with pts across 11 U.S. institutions). Data on patient demographics (age, race, ethnicity, sex), disease risk status (ISS/R-ISS stage, FISH cytogenetics), treatment regimens (triplet vs quadruplet, anti CD38 monoclonal antibody-based vs not), and disparity measures were abstracted from electronic medical records. Race and ethnicity were self-reported by pts. Social Deprivation Index (SDI) score was calculated based on ZIP code, where higher scores reflect greater deprivation. Progression free survival (PFS) was defined as time from start of treatment until progression or death. Overall survival (OS) was defined as time from start of treatment until death from any cause. Kaplan-Meier methods were used to compare PFS and OS between groups, and cox regression models with log-rank tests were used to evaluate the influence of various factors on outcomes. Results: A total of 1,743 patients were identified; 1,116 (63.8%) identified as non-Hispanic White (NHW) and 402 (23.3%) as non-Hispanic Black (NHB) [note: 85 pts (4.9%) were Hispanic but not included due to limited numbers]. The median age (years) at the start of treatment (61.8 vs. 65.6) was lower for NHB compared to NHW pts (p<0.001). The proportion of females was greater in NHBthan NHW pts(53.5% vs. 41.1%, p=0.0016). Median SDI scores were significantly higher for NHBcompared to NHW pts (68.4 vs. 39.4; respectively; p<0.001), reflecting significantly higher rates of social deprivation. While NHB ptshad higher rates of advanced stage disease (ISS III 26.9% vs. 20.9%, p=0.009), lower rates of autologous stem cell transplant (ASCT) (56.7% vs. 63.6%, p=0.014), and longer median time from treatment initiation to ASCT (6.5 vs. 5.9 months, p=0.043), the rates of standard risk cytogenetics on FISH (72.3% vs. 77.6%), and utilization of CD38 monoclonal antibody-based regimen (29.9% vs. 27.7%) were similar between the two groups. Best response after treatment initiation was similar between the NHB and NHW pts (p=0.12) with complete response 45.7% vs. 52.6% and very good partial response 37.3% vs 34.7%; respectively. However, time to best response (months) was longer in NHB pts (7.2 vs. 6.5, p=0.005). Median follow-up for OS was 4.2 years (95% CI 3.9-4.4; 182 OS events) and for PFS was 1.3 years (95% CI: 1.1-1.6; 302 PFS events) across the two groups. Unadjusted PFS was similar for NHB vs NHW pts (HR=1.05, 95% CI: 0.81- 1.36; median: 5.0 vs 4.9 years; 3-year PFS: 66% vs. 71%). Similarly, unadjusted OS was not significantly different in NHB vs NHW pts (HR=1.12, 95% CI: 0.79-1.59; 5-year OS: 86.4% vs 85.6%). Higher SDI scores were significantly associated with worse OS (HR=1.07, 95% CI: 1.02-1.12; p=0.0065) but not PFS (HR=0.99, 95% CI: 0.95-1.02; p=0.46). Though not statistically significant, univariate analyses indicated a greater impact of high SDI scores (≥70, 75th percentile across all pts) in NHW (HR=1.45, p=0.068) than in NHB pts (HR=0.78, p=0.35) for OS. Adjusting for age, sex, ISS stage, and SDI, NHB did not appear to have worse OS (HR=0.75, 95% CI: 0.35-1.60; p=0.46) or PFS (HR=1.23, 95% CI: 0.78-1.96; p=0.37) compared to NHW pts. Conclusions: Our study represents a diverse population enriched for pts who received modern day standard of care frontline therapies including triplet and quadruplet regimens with or without ASCT. Based on SDI scores, higher social deprivation levels were seen in NHB compared to NHW pts without a difference in outcomes between the two races. Our analysis supports that higher social deprivation among different races rather than race and ethnicity alone may drive differential outcomes and adjustment for factors like SDI is critical to interpretation of survival outcomes by race.

Abstract Introduction LINVO, a human B-cell maturation antigen (BCMA)xCD3 bispecific antibody, was recently approved for triple-class exposed (TCE) RRMM after ≥3 prior therapies (EU) or ≥4 prior lines of therapy (US), demonstrating high efficacy and a generally manageable safety profile. Combining LINVO with an anti-CD38 mAb, such as DARA or ISA, may provide additional benefit to pts with RRMM. Here we report pooled safety and efficacy results from the LINVO + DARA and LINVO + ISA cohorts during the dose-finding portion of the Phase 1b LINKER-MM2 trial (NCT05137054). Methods Eligible pts were aged ≥18 years with RRMM that progressed after ≥3 lines of therapy (LoTs), or ≥2 LoTs if TCE or double-class refractory (immunomodulatory drug [IMiD] + proteasome inhibitor [PI]). Prior treatment (tx) with DARA or ISA was allowed if previously tolerated and ≥6 months (mos) or ≥3 mos washout, respectively, had elapsed since last exposure. Tx began with LINVO monotherapy (Cycle [C]0; 28-day cycle) consisting of 2 step-up doses (5 mg, 25 mg) and 2 full doses (dose level [DL]1 100 mg; DL1b 150 mg; DL2 200 mg) before standard dosing of DARA or ISA was added at C1. LINVO was given once weekly in C1–3 and once every 2 weeks in C4+. At the end of 2024, dosing once every 4 weeks was implemented after C6 if a very good partial response or better (≥VGPR) was achieved. Dexamethasone premedication was required during the first 2 cycles and then given per label for DARA or ISA. Primary endpoints: dose-limiting toxicities (DLTs) and tx-emergent adverse events (TEAEs). Key secondary endpoints: overall response rate (ORR), duration of response (DOR), progression-free survival (PFS), and rate of minimal residual disease (MRD) negativity. The analyses reported here combine the LINVO + DARA and LINVO + ISA cohorts. Results As of May 7, 2025, 42 pts were enrolled. All pts were included in the safety analysis set: DL1 (DARA, n=12; ISA, n=6), DL1b (n=6; n=10), or DL2 (n=8; n=0); 37 were included in the efficacy analysis set: DL1 (DARA, n=11; ISA, n=5), DL1b (n=5; n=9), or DL2 (n=7; n=0). At baseline, all pts had received ≥1 PI (refractory to ≥1 PI: 76%) or ≥1 IMiD (refractory to ≥1 IMiD: 86%), and 21 pts (50%) had received prior DARA or ISA (refractory to DARA or ISA: 33% [DARA, n=4; ISA, n=10]). Median number of prior LoTs was 3 (range 2–8), including 50% of pts with TCE and 21% with triple-class refractory disease. Median age was 68 years (range 42–86); male, 57%; ISS stage III at study entry, 12%; high-risk cytogenetics, 31%; bone marrow plasma cells ≥50%, 31%; and soft tissue plasmacytomas, 33% (extramedullary, 12%). Median follow-up duration was 14.8 mos for DL1 (DARA 21.3; ISA 11.3), 7.6 mos for DL1b (DARA 13.5; ISA 5.2), and 5.9 mos for DL2 (all DARA), with 44%, 50%, and 63% of pts still receiving tx, respectively. The most common TEAEs were anemia (any grade [Gr] 48%; Gr 3/4 26%), neutropenia (41%; 41%), diarrhea (38%; 2%), and thrombocytopenia (38%; 17%). Cytokine release syndrome was reported in 33% of pts (all Gr 1/2 events except 1 Gr 3 event during the step-up period that fully resolved). No pts experienced immune effector cell-associated neurotoxicity syndrome (ICANS). Infections were reported in 32 pts (any Gr 76%; Gr ≥3 40%), and 3 resulted in death (1 tx related: cytomegalovirus [CMV] pneumonia; 2 tx unrelated: C. diff colitis and lower respiratory infection). One DLT was observed at DL1b in the ISA cohort (Gr 3 Epstein–Barr virus pharyngitis) and 1 was observed at DL2 in the DARA cohort (Gr 5 CMV pneumonia). Among efficacy evaluable pts, ORR was 75% at DL1 (12/16; ≥VGPR rate 75%), 93% at DL1b (13/14; ≥VGPR rate 57%), and 86% at DL2 (6/7; ≥VGPR rate 86%). Of 10 MRD-evaluable pts, all were MRD negative (<10−5by clonoSEQ or EuroFlow). The 12-mo DOR rate was 79% (95% confidence interval [CI] 52–92; median DOR not reached) and 12-mo PFS rate was 73% (95% CI 53–86; median PFS not reached). Pharmacokinetic analyses showed LINVO concentrations were not affected by the addition of DARA or ISA. ConclusionsIn pts with heavily pretreated RRMM, half of whom had prior anti-CD38 mAb exposure, the combination of LINVO and DARA or ISA was feasible, inducing a high rate of deep and durable responses, and exhibiting a safety profile similar to that reported for each individual drug. These preliminary data support continued development of LINVO plus an anti-CD38 mAb for the tx of pts with MM.

Abstract Background Accurate reconstruction of electronic health record (EHR) treatment sequences is essential for leveraging real-world data (RWD) in clinical research and trial-eligibility platforms in multiple myeloma (MM). However, inconsistencies in RWD treatment documentation and the complexity of line-of-treatment (LOT) assignments in MM hinder reproducibility and limit comparisons against published trials. To address these limitations, we developed HEAL-MM, an automated rule-based algorithm that systematically parses structured medication data to calculate LOTs and segment treatment into standardized, clinically meaningful phases. The algorithm is designed to be interpretable, adaptable to evolving therapeutic frameworks, and compatible with widely adopted EHR data standards. Here, we report its real-world validation against expert-adjudicated treatment sequences to assess concordance, identify edge-case failure points, and support RWD integration. Methods A stratified cohort of 100 MM patients (9,136 structured treatment events) was curated from 114 unique healthcare systems compiled from the multi-institutional HealthTree Foundation harmonized registry. Cohort selection was randomized to ensure coverage across diagnostic eras (pre-2010, 2015–2020, 2022–2025) and treatment exposures, including CAR T-cell therapies, bispecific antibodies, and anti-CD38-based regimens. HEAL-MM sequenced MM regimens chronologically and applied predefined logic rules to identify LOT transitions, the initiation of a new therapeutic course based on drug changes, treatment gaps, and procedural anchors such as stem cell transplant or CAR T. Within each LOT, the algorithm segments and labels therapy into phases, clinically distinct intervals (e.g., induction and maintenance) using rule-based clinical logic. Outputs were benchmarked against expert-labeled adjudications. Concordance was evaluated using concordance index (C-index), stratified by patient, LOT depth and diagnostic era. Results: HEAL-MM demonstrated high concordance with expert-labeled treatment sequences: 0.94 for LOT identification, 0.98 for treatment phase segmentation, and 0.96 for phase label assignment. Achieving complete agreement with all expert annotations in 90% of patients for LOT assignment, 94% for the number of treatment phases identified, 77% for the specific labels applied to each phase, and 75% across all three dimensions simultaneously. Among discordant patients, the absolute difference in final LOT count was 0.5 ± 0.9. Performance was highest in early lines (LOTs 1–3; C-index: 0.97), with seven patients showing discrepancies at this stage. Concordance declined in later lines (LOT ≥4; C-index: 0.87), where 50% of the discordant cases (n=4) were attributable to misclassifications in earlier LOTs, such as medication restarts following >3-month gaps, demonstrating the compounding effect of early errors on longitudinal accuracy. Temporal stratification revealed improved accuracy in post-2020 cases (C-index: 0.94) compared to 2015–2020 cohorts (0.91), reflecting the increasing adoption of structured treatment standards and EHR data quality improvements. To assess HEAL-MM performance at clinically significant junctures, we analyzed treatment transitions that initiated a new LOT or marked a change in phase, achieving C-indices of 0.97 for LOT transitions, 0.97 for treatment phase shifts, and 0.94 for phase label assignment. Conclusions: HEAL-MM provides a validated, scalable solution for standardizing MM treatment structures in RWD environments, as it accurately detects the exact treatment entry of a new LOT 97% of the time. This rule-based framework enables reproducible LOT and phase assignment across heterogeneous EHR systems, supporting real-time cohort identification across institutions and EHR platforms. The algorithm's modular design allows for continuous integration of novel therapeutic categories and evolving treatment logic. While the algorithm maintained high fidelity overall, early-stage misclassifications consistent with increased heterogeneity and documentation ambiguity occasionally propagated downstream, underscoring the compounding effect of upstream errors in longitudinal reconstruction. With its generalizable structure and compatibility with industry-standard data models, HEAL-MM lays the foundation for structured treatment reconstruction in MM across RWD infrastructure and serves as a transferable blueprint for other hematologic malignancies.

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.

ABSTRACT Introduction Hepatocellular carcinoma (HCC) development in patients with metabolic dysfunction‐associated steatotic liver disease (MASLD) is a growing health concern, but the underlying mechanisms are not fully understood. Epigenetic aging biomarkers, reflecting cellular and tissue aging, have been linked to various age‐related pathologies, but their association with MASLD‐HCC is unknown. We investigated associations between five epigenetic aging biomarkers and MASLD‐HCC risk. Methods We performed whole blood DNA methylation assay (Infinium 850k array) and calculated principal components‐based (PC) versions of HorvathAge, HannumAge, PhenoAge and GrimAge and the DunedinPACE aging rate. We further calculated relative age accelerations for PCHorvathAge, PCHannumAge, PCPhenoAge and PCGrimAge. The aging biomarkers were modelled as continuous variables and categorised into tertiles based on distributions among controls. Associations between each aging biomarker and MASLD‐HCC were examined using logistic regression, calculating odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for covariates. Results Data on 272 MASLD‐HCC cases and 316 cancer‐free MASLD controls recruited from six sites and matched on chronological age, sex and study site were analysed. Higher relative age accelerations of PCPhenoAge (OR T3 vs. T1 = 2.25, 95% CI: 1.45–3.50; OR continuous = 1.04, 95% CI: 1.02–1.07, p = 0.009), PCGrimAge (OR T3 vs. T1 = 3.97, 95% CI: 2.41–6.64; OR continuous = 1.16, 95% CI: 1.10–1.24, p = 8.76 × 10 −07 ) and DunedinPACE (OR T3 vs. T1 = 3.45, 95% CI: 2.17–5.55; OR continuous = 1.72, 95% CI: 1.43–2.10, p = 2.58 × 10 −08 ) were associated with MASLD‐HCC, but not PCHorvathAge or PCHannumAge. Conclusion Higher relative age accelerations of PCPhenoAge, PCGrimAge and DunedinPACE aging rate are associated with risk of MASLD‐HCC. These aging biomarkers could improve HCC risk assessment and facilitate risk stratification in patients with MASLD.

ABSTRACT The American College of Clinical Pharmacy (ACCP) first described the role and history of the pharmacist as principal investigator (PI) in 2000. An update was published in 2010, citing evidence from NIH and the ClinicalTrials.gov registry that showed an increase in the number of individuals with a Pharm.D. degree engaging in pharmacist‐directed research. The 2025 ACCP Publications Committee was charged with providing an update to the 2010 paper. Key findings include a substantial increase in the total amount of federal funding awarded to schools of pharmacy (SoP) between 2011 and 2023, a modest increase in the number of pharmacists receiving federal grants when comparing 2010 with 2024, and evidence of pharmacist leadership and engagement in Clinical and Translational Science Award networks and service on national grant review panels. It remains challenging, however, to ascertain accurate and reliable data on pharmacists serving in PI roles because of the limitations of currently available data sources. This is especially true for pharmacist PIs who receive non‐federal funding. Future opportunities include a continued emphasis on training programs to prepare pharmacists for PI roles, greater institutional support (e.g., research infrastructure, mentoring programs) for pharmacists serving in PI roles, better promotion of research career tracks to Pharm.D. students and trainees, enhanced efforts at SoP to pursue institutional training grants, and more collaborative efforts across professional organizations to advocate for pharmacist‐scientists. An urgent call to action is necessary to ensure the future growth and success of pharmacist‐directed research.