Citations

Abstract Purpose: Inhibitors of the Ataxia telangiectasia and Rad3-related (ATR) protein kinase, a critical component of the DNA damage repair response, have synergistic anti-cancer activity with platinum compounds in preclinical models. We therefore conducted a Phase 2 study of the ATR inhibitor berzosertib+carboplatin vs. docetaxel+carboplatin in metastatic castration-resistant prostate cancer. Patients and Methods: Patients previously treated with at least one androgen receptor pathway inhibitor and taxane underwent mandatory biopsy and were randomized 1:1 to receive Arm A (docetaxel 60 mg/m2+carboplatin AUC4 day 1; or carboplatin AUC5 if not a docetaxel candidate) or Arm B (berzosertib 90 mg/m2 days 2,9+carboplatin AUC5 day 1) every 21 days. The primary endpoint was overall response rate (ORR; ≥50% PSA decline or radiographic response). Results: Of 73 randomized patients, 65 received protocol treatment: 34 on Arm A (26 docetaxel+carboplatin; 8 carboplatin alone) and 31 on Arm B. Thirteen patients (38%) in Arm A and 20 (65%) in Arm B had ≥Grade 3 treatment-related adverse events (TrAEs). ORR was 15% in Arm A (5/34; 5/26[19%] with docetaxel+carboplatin) and 0% in Arm B (0/31). At interim analysis after 65 of planned 130 patients were treated, enrollment was halted due to futility. Homologous recombination repair deficiency status from tissue and blood did not correlate with clinical outcomes, but ATM-deficiency or high levels of phoshpo-KAP1 detected in tissue was associated with clinical benefit across both arms of the trial. Conclusions: Berzosertib+carboplatin demonstrated lower ORR and more frequent ≥grade 3 TrAEs compared to docetaxel+carboplatin in this heavily pre-treated, biomarker-unselected population. NCT03517969

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.

Polycystic kidney disease (PKD) arises from mutations in cilia-associated genes, such as PKD1 and PKD2 , expressed in renal epithelial cells, leading to progressive kidney dysfunction and end-stage kidney disease (ESKD). PKD patients exhibit significant heterogeneity in disease progression, largely due to genetic and environmental modifiers. Like patients, mouse models of PKD also exhibit significant heterogeneity with regards to the gene mutated, age of disease onset, and rate of disease progression. To elucidate the cellular and molecular consequences of these variables, we constructed an integrated single-cell RNA sequencing atlas across mouse models of PKD, mapping changes in cell type composition, gene expression, and intercellular signaling networks across the whole atlas and within individual models. Across models, single cell RNA sequencing (scRNAseq) data revealed increased Spp1 (osteopontin) expression and signaling from PKD-enriched clusters. Global deletion of Spp1 in Pkd1 RC/RC mice resulted in a modest reduction in cyst severity and improved kidney function. From these studies, we created a freely available, searchable website (https://bmblx.bmi.osumc.edu/scPKD/) that can be used to identify cross- and intra-model changes in gene expression, guiding researchers to new therapeutic targets for treating PKD.

Abstract Immune checkpoint inhibitors (ICIs) can produce durable tumor control, yet many patients experience primary or acquired resistance, mixed lesion-level responses, or immune-related adverse events. Tissue and blood biomarkers provide biologically informative but spatially incomplete views of a heterogeneous and time-varying tumor–host system. Radiologic imaging complements these assays by repeatedly measuring whole-body tumor burden, organ-specific disease patterns, lesion-level heterogeneity, and treatment-related change. This critical narrative review evaluates imaging-centered artificial intelligence (AI) for precision cancer immunotherapy, including baseline and serial CT, MRI, and PET; radiomics and learned image representations; lesion-level and whole-body heterogeneity; toxicity assessment; and integration with pathology, genomics, circulating tumor DNA, laboratory measurements, and electronic health records. Representative cohorts range from early radiogenomic studies of approximately 100–200 patients to externally validated and longitudinal models involving hundreds or thousands of patients. Rather than treating all reported performance estimates as equally actionable, we appraise the clinical question, cohort design, endpoint, validation strategy, calibration, and intended clinical action. Current evidence supports noninvasive immune phenotyping, risk stratification, longitudinal monitoring, and multimodal biomarker integration. However, most models predict outcomes among patients already receiving ICIs and therefore remain prognostic under treatment rather than predictive of differential treatment benefit. Radiotherapy (RT) combined with ICI serves as a focused imaging use case because lesion identity, dose distribution, fractionation, sequence, and thoracic and normal-tissue exposure must be linked to defined actions. Existing patient-level AI studies estimate efficacy or toxicity after treatment selection; none has prospectively shown that an AI-selected RT strategy improves patient outcomes. Translation will require patient-level leakage control, calibration, geographic and temporal validation, robustness to missing modalities, and prospective evaluation of prespecified model-guided decisions.

Cytotoxic T cells produce the cytokine interferon-γ (IFN-γ). We sought to determine whether IFN-γ directly kills target cells and found that T cells used IFN-γ to kill murine melanoma cells lacking the kinases TBK1 and IKKε. In the absence of both kinases, IFN-γ induced the production of TNF receptor 1 (TNFR1) and the sensor Z-DNA binding protein 1 (ZBP1) in the tumor cells to stimulate receptor-interacting protein kinase 1 (RIPK1)–dependent apoptosis in a cell-autonomous manner. IFN-γ also enhanced the activation of nuclear factor κB (NF-κB) signaling in a TNFR1-dependent manner in cells deficient in both TBK1 and IKKε. Because IFN-γ–induced apoptosis occurred in a transcription-dependent manner with slow kinetics, STAT1 and NF-κB cooperated to activate the expression of inflammatory genes in the dying cells. Thus, IFN-γ–induced cell death was accompanied by an inflammatory signature in the absence of TBK1 and IKKε. TBK1 and IKKε not only mediated the induction of type I IFN but also inhibited RIPK1-dependent death and NF-κB–dependent inflammation. Therefore, we propose that these kinases may have gained these intertwined functions so that cells infected by pathogens that produce antagonists of TBK1 and IKKε are eliminated by IFN-γ–secreting T cells in an inflammatory manner to compensate for the inhibition of type I IFN production.

Background/Objectives: To develop and evaluate a deep learning-based quality control system for Lumbar Spinal Digital Radiographs (LSDR), designed to automate and improve their evaluation and reduce reliance on manual reviews. Methods: This retrospective study utilized a deep learning workflow comprising image segmentation, feature extraction, and a classification model. The dataset, including anteroposterior (AP) and lateral (LAT) X-ray images, was expanded through data augmentation techniques. Four U-Net-based models were assessed: standard U-Net, Swin-UNet, Attention U-Net, and Attention U-Net with the weight map, with the latter selected for its superior performance. Extracted features, such as brightness, contrast, and anatomical positioning, were used in an XGBoost classifier, which was evaluated using mean intersection over union (mIoU), accuracy, sensitivity, specificity, and AUC. Results: The Attention U-Net with weighted attention outperformed the other models, achieving high mIoU scores in both AP and LAT views. The XGBoost classifier achieved the best performance in classifying images as “qualified” or “unqualified,” with an AUC of approximately 0.9, high accuracy, and balanced sensitivity and specificity. This approach effectively addressed class imbalances and improved model accuracy compared to traditional machine learning models such as MLP and SVM. Conclusions: The developed automated quality control system demonstrated potential for enhancing image quality, enhancing diagnostic reliability, and optimizing clinical workflow efficiency.

Summary Long-read nanopore sequencing enables simultaneous detection of germline variation and native DNA base modifications on individual DNA molecules, providing a unique opportunity to investigate allele-specific epigenetic regulation. Here, we performed whole-genome nanopore sequencing on normal and tumor prostate tissues to characterize differential methylation, methylation entropy, and allele-specific methylation (ASM) associated with noncoding genetic variants. Genome-wide analysis identified extensive cancer-associated differentially methylated regions (DMRs), with hypermethylated DMRs significantly enriched near transcription start sites and transcriptional regulatory regions. Integration with transcriptomic datasets revealed strong inverse relationships between promoter methylation and gene expression, while 5-hydroxymethylcytosine (5hmC) levels positively correlated with transcriptional activity across gene bodies. Using fragment-level methylation patterns enabled by long-read sequencing, we further quantified methylation entropy incorporating both 5mCG and 5hmCG states. Cancer-hypermethylated DMRs exhibited markedly reduced entropy, consistent with clonal fixation of methylation states during tumor progression. Entropy profiling across chromatin annotations demonstrated maximal epigenetic heterogeneity at partially modified enhancer-associated regions. To investigate cis-regulatory genetic effects, we developed a simple ASM framework (nanoASM) that can partition sequencing reads by allelic state and identifies allele-specific DMRs directly from long-read data. Compared with conventional population-level mQTL analysis, ASM demonstrated substantially improved statistical efficiency by leveraging within-individual contrasts and reducing sample-level heterogeneity. Although germline single nucleotide polymorphisms (SNPs) were largely shared between normal and tumor tissues, ASM patterns differed substantially, with tumor-associated ASM regions displaying significantly larger genomic span and stronger allelic methylation differences. Comparative analysis with TCGA prostate mQTL and GTEx prostate eQTL datasets demonstrated substantial concordance between ASM directionality and downstream transcriptional effects, particularly for variants located within DMRs and near transcription start sites. At the IRX4 prostate cancer risk locus, ASM identified an androgen-responsive regulatory domain overlapping AR ChIP-seq and H3K27ac peaks, nominating rs6885084 as a candidate functional variant. At the PSCA locus, ASM anchored by rs4736369 was associated with allele-specific methylation, chromatin activation, transcript abundance, and isoform usage. Together, these findings establish nanopore-based ASM analysis as a powerful approach for resolving functional noncoding variants and their regulatory domains they control in prostate cancer.

Background: Alcohol-associated hepatitis (AH) is a severe inflammatory liver condition driven by dysregulated immune responses. CD8 T cells accumulate in the liver during AH, yet their functional role remains poorly defined. Methods: A murine NIAAA chronic-binge model with lipopolysaccharide (LPS) challenge was used. Single-cell RNA sequencing of liver and blood immune cells defined CD8 T cell subsets, followed by pathway analysis and reanalysis of human AH datasets. Spatial relationships were validated by immunohistochemistry of human AH liver tissue with CD8 T RM and liver sinusoidal endothelial cell (LSEC) co-staining. LSEC–CD8 T cell interactions were examined using in vitro co-culture, and signaling pathways were assessed by bulk RNA sequencing and phospho-protein analyses. IL15 function was evaluated by in vivo neutralization. Results: Single-cell RNA sequencing in the murine AH model revealed a distinct population of CD8 tissue-resident memory T cells (T RM ) with heightened activation and proinflammatory cytokine production. Analysis of human AH liver single-cell RNA sequencing data, along with immunohistochemistry validation, supported CD8 T RM enrichment in diseased tissue. IL15 emerged as a prominent pathway linked to T RM activation, and IL15 blockade reduced T RM abundance and attenuated EtOH/ lipopolysaccharide-induced liver injury. Mechanistically, LSECs not only provided a structural niche for T RM retention but also amplified IL15 signalling. In vitro, coculture experiments demonstrated that LSECs intensified activation in pre-stimulated CD8 T cells in a stimulus-dependent manner: under IL15 stimulation, LSECs boosted effector function without inducing cell death, whereas under T-cell receptor stimulation, LSECs drove hyperactivation and activation-induced cell death. Bulk RNA-seq and phospho-protein analysis identified the PI3K-AKT pathway as a shared pathway enhanced by LSEC coculture in activated CD8 T cells. These findings define a context-dependent mechanism in which LSECs promote IL15-driven signaling through AKT pathway amplification, promoting T RM persistence and inflammatory activity in AH. Conclusions: IL15-associated signaling within the hepatic microenvironment, shaped by LSEC-CD8 T-cell interactions, promotes activation and persistence of CD8 T RM cells in AH. Targeting the IL15-LSEC-AKT axis may disrupt pathogenic T RM niches and represent a promising therapeutic strategy for severe AH.