Citations

The bone marrow immune microenvironment (BMME) is a critical factor in multiple myeloma (MM) pathogenesis. Despite higher incidence of MM among self-identified African Americans, the relationship between African genetic similarity and the BMME remains unclear. We present the largest analysis to date assessing the impact of Ancestry on the BMME in myeloma (N=320, including 64 with ≥50% African genetic similarity, AFR-High) leveraging our recently generated Immune Atlas. AFR-High was associated with enrichment of late T-effector cells, driven by rare cytotoxic CD4⁺ T cells, suggesting a distinct adaptive BMME. AFR-High exhibited expansion of late-differentiated adaptive NK cells with high cytotoxic GNLY and GZMH expression. AFR-High depicted reduced CD14⁺ monocytes, increased CD16⁺ non-classical monocytes, and expansion of macrophage-like monocyte populations with enrichment of metabolic adaptation, efferocytosis and phagocytic clearance programs, and immunoregulatory pathways. These findings reveal a shift from an innate inflammatory immune landscape toward adaptive cytotoxic and immunoregulatory remodeling associated with African genetic similarity in MM.

Objective To assess the effect of Moses™ 2.0 (Lumenis Be Ltd., Yokneam Illit, Israel; Boston Scientific, Marlborough, MA, USA) for lithotripsy of renal stones of 8–20 mm in diameter on operative time, surgeon workload, and retropulsion. Subjects/Patients and Methods In our multicentre, randomised, double‐blind clinical trial, patients were randomised 1:1 to standard or Moses 2.0 laser lithotripsy and stratified by stone size. Patients and surgeons were blinded to laser modality. Primary outcomes were total operative time as well as surgeon workload assessed by the National Aeronautics and Space Administration‐Task Load Index (NASA‐TLX). Secondary outcomes were stone retropulsion evaluated by blinded video review and postoperative clinical outcomes. Linear regression modelling was used for analysis. Results A total of 153 patients (79 Standard, 74 Moses 2.0) with similar clinical, stone, and operative characteristics were evaluated. There was no significant difference in operative times between the Standard and Moses 2.0 groups ( P = 0.73). Moses 2.0 was associated with lower effort, frustration, mental, physical, and temporal workload, as well as higher rated overall performance compared to the standard laser. Video review demonstrated no difference in rates of retropulsion ( P = 0.13). Complication rates (Standard: 13%; Moses 2.0: 10%) and stone‐free rates (Standard: 51%; Moses: 2.0 55%) were comparable. Conclusion Our study evaluated the impact of Moses 2.0 technology on high‐powered renal stone dusting, constituting the largest double‐blind trial of its kind to date. There was insufficient evidence to suggest any difference in operative times or clinical outcomes when using Moses 2.0 compared to standard laser lithotripsy. Moses 2.0 was associated with reduced surgeon workload, which has implicit impact on surgeon wellness.

Abstract INTRODUCTION Plasma p‐tau217 and p‐tau217/Aβ42 reliably identify non‐Hispanic White individuals with symptomatic and pre‐clinical AD. However, performance across ethnoracially diverse groups remains unknown. METHODS Cognitively normal Black, Hispanic, and non‐Hispanic White participants completed cognitive evaluations, brain magnetic resonance imaging (MRI), amyloid and tau positron emission tomography (PET), and measures of kidney function. Plasma p‐tau217 and Aβ42 were measured using Fujirebio Lumipulse assays. Pre‐clinical AD detection was evaluated using symptomatic and cohort‐derived optimized cutoffs. RESULTS Among 191 participants (57 Black, 46 Hispanic White, 88 non‐Hispanic White), age‐adjusted plasma p‐tau217 and p‐tau217/Aβ42 performed similarly in detecting amyloid‐positive individuals (≥ 25 Centiloids), regardless of race/ethnicity. Both measures independently correlated with amyloid deposition, whereas estimated glomerular filtration rate (eGFR) correlated solely with p‐tau217. Pre‐clinical AD optimized cutoffs (p‐tau217 ≥ 0.132 pg/ml; p‐tau217/Aβ42 ≥ 0.0059) outperformed cutoffs established in symptomatic individuals (sensitivity: 83% vs. 57%; 77% vs. 63%; negative predictive value [NPV] > 93%). DISCUSSION Plasma biomarkers performed similarly across ethnoracially diverse cohorts. Optimized cutoffs may improve early detection and trial enrollment.

Abstract Although the human genome encodes a vast repertoire of noncoding RNAs that regulate gene expression, the noncoding genome remains underexplored due to technical challenges. Specifically, during transcriptomic sequencing data alignment, the overlap between noncoding and coding loci can create ambiguous read alignments that are subsequently discarded from downstream analysis. For this reason, most of the noncoding genome is excluded from standard genomic annotations used for sequencing alignment. To address this challenge and enable concurrent profiling of the coding and noncoding transcriptome, we systematically integrated standard coding (GENCODE) and noncoding (LncBook) genome annotations, preserving coding gene annotations and removing overlapping noncoding regions. The resulting integrated genome annotation expanded the number of annotated noncoding genes from 40,785 to 138,296 while preserving all coding genes and reducing ambiguous read assignment. To evaluate the utility of our integrated genome annotation for uncovering novel, biologically relevant noncoding RNAs (ncRNAs), we realigned CD138-positive bulk RNA-seq (N = 942) and CD138-negative single-cell RNA-seq (N = 478) data from the MMRF CoMMpass study, generating a comprehensive coding-noncoding atlas of the myeloma bone marrow microenvironment with noncoding genes representing 51% of highly variable genes and displaying significant cell type specificity. Tumor expression profiling based on this integrated profiling identified 15 clusters, including two enriched for amp(1q21) or t(4;14) and associated with shorter progression-free survival (PFS). Differential expression and systematic filtering yielded 19 candidate high-risk ncRNAs, including previously uncharacterized ENSG00000310209 , which was associated with poor PFS (HR = 1.141, P = 0.0025), increased IRF4 activity, Wnt pathway activation, CCL5 signaling, and the accumulation of anergic-like CD8+ T cells. These findings establish integrated coding-noncoding analysis as a strategic approach for discovering functional ncRNAs from transcriptomic sequencing data.

Importance Elevated low-density lipoprotein cholesterol (LDL-C) is a modifiable risk factor for cardiovascular disease, the leading cause of premature death worldwide. Assessing the LDL-C–related burden is critical for guiding prevention and treatment strategies. Objectives To estimate the global, regional, and national burden of ischemic heart disease and ischemic stroke attributable to elevated LDL-C (relative to 35-54 mg/dL) from 1990 to 2023 and to quantify the contributions of population growth, aging, risk-deleted burden, and exposure changes to burden trends. Design, Setting, and Population This comparative risk assessment, part of the Global Burden of Disease Study 2023, estimated population-level LDL-C exposure and associated health loss in 204 countries and territories. Mean LDL-C levels were estimated using spatiotemporal gaussian process regression based on 806 studies across 161 countries. Relative risks were derived from meta-analyses of 38 randomized clinical trials. Population-attributable fractions for deaths and disability-adjusted life-years (DALYs) were estimated by age and sex for adults aged 25 years or older from 1990 to 2023, with 95% uncertainty intervals. Exposure Population-level LDL-C concentrations. Main Outcomes and Measures Population-attributable fractions, counts, and rates (all ages and age standardized per 100 000) of LDL-C–attributable deaths and DALYs from ischemic heart disease and ischemic stroke, with uncertainty intervals. Results In 2023, elevated LDL-C accounted for 3.6 million deaths (95% uncertainty interval, 2.2-5.4 million; 6.0% of global mortality) and 90.7 million DALYs (95% uncertainty interval, 58.9-123.3 million; 3.2% of DALYs). Although global all-ages rates remained stable, age-standardized death and DALY rates decreased by 45.6% and 39.5%, respectively, since 1990. In 2023, age-standardized LDL-C–attributable DALY rates were highest in Eastern Europe and lowest in high-income Asia-Pacific. One-third of the global LDL-C burden occurred in India and China. Population growth and aging drove the increasing burden, with notable regional disparities in LDL-C exposure and risk-deleted DALY rates shifting toward middle-sociodemographic settings. Conclusions and Relevance Despite declining age-standardized rates, the absolute LDL-C burden has increased since 1990 due to demographic changes and has shifted toward middle-sociodemographic countries. Measurement and surveillance gaps persist. Strengthened prevention, diagnosis, and treatment access strategies are essential to mitigate the health burden of LDL-C.

Importance Ischemic heart disease (IHD), the leading cause of death in the US, is predominantly due to modifiable risk factors. Estimates of IHD mortality attributable to risk factors provide evidence for health policy decision-making. Objective To estimate the burden of IHD death attributable to risk factors in the US from 1990-2023. Design, Setting, and Participants The Global Burden of Disease Study 2023 (GBD 2023) used vital records and a broad set of epidemiologic data to estimate IHD death rates, risk factor exposure, and relative risk curves for risk-outcome pairs for 1990-2023 for the general population. Data analysis was performed from October 2024 to December 2025. Exposure Twelve metabolic, behavioral, and environmental risk factors. Main Outcomes and Measures The primary outcomes were IHD death rates per 100 000 persons, counts, and attributable risks from 1990-2023 by age, sex, and US state. Estimates include 95% uncertainty intervals (UI). IHD death rates were estimated using ensemble modeling methods. Risk exposures were estimated using bayesian meta-regression methods. Relative risks were estimated following the Burden of Proof framework. Results In 2023, there were 473 000 IHD deaths (95% UI, 414 000-510 000) in the US, a decrease of 58.7% (95% UI, 56.8%-61.0%) in age-standardized rate since 1990. Between 2010 and 2023, there was a 19.0% decrease (95% UI, 15.0%-22.9%) for males and a 24.5% decrease (95% UI, 20.3%-29.7%) for females in IHD death rates. High systolic blood pressure (SBP), dietary risks, and high low-density lipoprotein cholesterol (LDL-C) were the leading risk factors for IHD deaths in 2023, accounting for 47.2% (95% UI, 36.4%-57.0%), 38.6% (95% UI, 17.2%-56.8%), and 28.5% (95% UI, 19.3%-39.6%) of IHD deaths, respectively. Increased exposure to several risk factors substantially increased their attributable burden for IHD deaths in 2023, with high fasting plasma glucose (FPG) increasing 38.8% (95% UI, 11.5%-81.1%) and high body mass index (BMI) increasing 54.5% (95% UI, 41.8%-66.3%) since 1990. Smoking and particulate matter pollution had the greatest decrease in attributable IHD mortality since 1990, at 33.3% (95% UI, 23.6%-41.7%) and 74.9% (95% UI, 46.7%-88.8%), respectively. Conclusion and Relevance Per the results of this systematic analysis of GBD 2023, a total of 88.7% (95% UI, 83.4%-92.5%) of IHD deaths were attributable to modifiable risk factors in the US in 2023, with high SBP, dietary risks, and high LDL-C being the greatest contributors. High BMI and high FPG showed the largest attribution increases, while exposure to other risks did not increase significantly for the population.

Abstract To enhance the prediction of traumatic brain injury (mTBI) outcomes, we propose a deep learning approach that integrates brain computed tomography (CT) scans with corresponding synthetic T1-weighted magnetic resonance imaging (T1-MRI). Our method significantly outperforms the prediction using CT scans alone. TRACK-TBI Pilot dataset, which includes imaging and clinical outcome data from patients with TBI, is studied. The hypothesis is brain CT and T1-MRI complement each other and together will improve TBI prognosis compared to using either CT or T1-MRI alone. Since CT and T1-MRI may not be available for the same individual, we employed a specialized version of a generative adversarial network (GAN), known as fixed-point GAN (FP-GAN). FP-GAN was trained using unpaired CT and T1-MRI scans to generate synthetic T1-MRIs from real CT scans. This process produced pseudo-paired CT-MRI data, which was then used to train a deep learning classifier for outcome prediction. The classifier consists of dual parallel 3D ResNet-18 models, each independently processing T1-MRI and CT scans. We used Glasgow Outcome Scale-Extended (GOSE) scores at 3 months post-TBI as the measure of patient outcomes. To avoid data leakage, the subjects used in FP-GAN and ResNet-18 model have no overlap. We further divided the paired data, allocating 69 samples for 5-fold cross-validation and 17 samples for testing. Prognostic performance was evaluated using the area under the receiver operating characteristic curve (AUC), F1-score (the harmonic mean of precision and recall), sensitivity (true positive rate) and specificity (true negative rate). For binary classification, we defined good recovery as GOSE ≥ 7 (positive) and poor recovery (negative) as 3 ≤ GOSE ≤ 6. Accordingly, our training set consists of 24 subjects with poor recovery and 45 subjects with good recovery, while the testing set includes 5 subjects with poor recovery and 12 subjects with good recovery. A DeLong test on AUC confirms that the improvement from incorporating synthetic T1-MRI (AUC = 0.76 ± 0.10) is statistically significant (P < 0.05) compared to using CT alone (AUC = 0.68 ± 0.13). The significant improvement from using the combination of real CT and synthetic T1-MRI in sensitivity (SEN = 0.95 ± 0.07) and overall performance metrics, such as F1-score (F1 = 0.84 ± 0.03), suggests that the proposed approach provides a robust and effective prognostic approach compared to using CT alone (SEN = 0.83 ± 0.18 and F1 = 0.76 ± 0.07). This pilot research demonstrates the potential of a deep learning-based harmonization model to bridge the gap between CT and T1-MRI in TBI assessment. By integrating synthetic T1-MRI with CT, prediction performance is substantially enhanced.

Abstract The CoMMpass study (NCT01454297) is a prospective, longitudinal observational study involving 1,141 newly diagnosed multiple myeloma (NDMM) patients. Bone marrow aspirates from the study participants were collected for comprehensive molecular characterization of tumor (using bulk RNA sequencing and whole genome sequencing) and tumor immune microenvironment using 3’ single-cell RNA sequencing (scRNA-seq). To support exploration of this rich multi-omic dataset, we developed CoMMpass Explorer (CE), an intuitive interactive platform that enables real-time analysis of clinical and genomic data from CoMMpass. A central feature of CE is cohort building, where users can filter and stratify patients by clinical, genomic, and survival data elements to create custom cohorts for analysis. CE provides five main views: Overall Summary for clinical feature distribution, Kaplan-Meier curves, and multivariate Cox proportional hazards models; Mutational Profile for visualizing and comparing somatic mutations; Tumor Profile for bulk differential expression and gene set enrichment; Immune Microenvironment for comparing cell type abundance and cell cycle dynamics between cohorts using scRNA-seq data; and Pseudo-Bulk, which aggregates single-cell expression by cell type to enable cohort-level comparisons of transcriptional programs using DEseq2. CE reproduces prior CoMMpass studies, including WEE1 expression being associated with poorer progression-free survival (Simhal, et al.) and different mutation frequencies between African American and European American patients (Manojlovic, et al.). We further demonstrate CE’s utility by interrogating the 2025 Consensus Genomic Staging (CGS) risk classification system (Avet-Loiseau, et al). We compared CGS high-risk (HR, n=249) vs. CGS standard-risk (SR, n=573) patients. HR patients have significantly worse progression-free survival, overall survival, and time to second-line therapy compared with SR patients. Univariate models recapitulated expected differences in the clinical features that define CGS risk. Bulk tumor RNA-seq and ssGSEA highlighted a more proliferative and genomically unstable transcriptional program in HR group, with upregulation of angiogenesis and cell-cycle-related pathways. In the immune microenvironment, monocytes were more abundant in the SR group. Pseudo-bulk RNA-seq of plasma cells showed overexpression of ANXA1, NSD2, IGF1R, MAF and related genes in HR. CE democratizes access to the CoMMpass multi-omic resource, allowing researchers to interrogate clinical and molecular heterogeneity. By enabling hypothesis generation and replication of published results, CE serves as a valuable tool for identifying prognostic biomarkers and meaningful patterns in gene expression, mutation profiles, and the immune microenvironment. Citation Format: Wanxing Zhang, Chaitanya R. Acharya, Steven M. Foltz, David E. Avigan, Samir Parekh, Ravi Vij, Shaji Kunnathu Kumar, Taxiarchis Kourelis, Sagar Lonial, Hearn Cho, Ioannis S. Vlachos, Sacha Gnjatic, Li Ding, Manoj Bhasin, George Mulligan. CoMMpass Explorer: An interactive platform to explore clinical and genomic data from newly diagnosed multiple myeloma patients from the landmark CoMMpass observational study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5504.