Citations

Abstract Polygenic risk scores (PRSs), which quantify inherited susceptibility to complex traits and diseases, have emerged as valuable tools for risk stratification and precision medicine. Despite their promise, PRS developed on European cohorts often demonstrate substantially reduced predictive accuracy in non-European populations, due to differences in genetic architecture. The disproportionate representation of European ancestry cohorts in genome-wide association studies (GWAS) leads to inequitable deployment of PRS technologies across diverse populations. Here, we introduce PRANA (Polygenic Risk Adaptation via Neural-network Architecture), a deep learning framework that adapts an existing PRS developed on one population to other ancestries. Unlike methods that require large-scale GWAS in the target population, PRANA leverages pre-trained PRS models derived from European cohorts and adapts them using modestly sized cohorts from the target population. We evaluated PRANA on seven complex traits in South Asian, East Asian and Ashkenazi Jewish populations, as well as in selected smaller East Asian subpopulations where the scarcity of training data poses a particular challenge. PRANA mostly improved predictive performance of the baseline PRS models by 5%-20% in terms of effect size (β) and Nagelkerke’s R², and, in most cases, outperformed existing cross-ancestry multi-PRS approaches. These results highlight PRANA as a scalable and practical strategy to reduce disparities in genomic risk prediction and advance the equitable application of PRS in diverse populations.

Accumulation of DNA damage drives β-cell dysfunction, senescence, and death in type 1 and type 2 diabetes. Although α-cell dysfunction also contributes to disease pathology, α-cells remain remarkably resistant to senescence and increased cell death under diabetogenic stress. The mechanisms underlying these differential responses to diabetogenic stress, particularly differences in their DNA damage vulnerability, remain unclear. We demonstrate that replication introduces a window of genomic vulnerability in both α- and β-cells during neonatal growth, with α-cells exhibiting higher replication rates and DNA damage. We show that neonatal β-cells resolve DNA damage more efficiently during mitosis and favor error-free repair, whereas α-cells compensate for their greater DNA-damage vulnerability through increased cellular turnover. Using mouse models of overnutrition and diabetes, we show that β-cells exhibit greater vulnerability to terminal DNA damage and impaired repair capacity under diabetogenic stress, with compensatory replication amplifying this vulnerability. We demonstrate that developmental and postnatal epigenetic programs shape the differential DNA damage vulnerability of postnatal β- and α-cells. Loss of de novo DNA methyltransferase Dnmt3a in pancreatic progenitors selectively increases the DNA damage vulnerability of β-cells from neonatal growth through adulthood. Complementing this developmental process, enhancer analyses reveal lineage-specific DNA damage response regulatory landscapes that are remodeled in β-cells under metabolic stress. Collectively, our findings uncover novel developmental mechanisms that shape the distinct DNA damage responses of postnatal β- and α-cells during growth and diabetes. Article Highlights Mechanisms underlying the differential susceptibility of pancreatic β- and α-cells to diabetogenic stress remain unclear. We investigated whether replication, repair fidelity, and developmental epigenetic programs determine the vulnerability of postnatal β- and α-cells to DNA damage, a key driver of β-cell failure in diabetes. Replication introduces DNA damage vulnerability in both neonatal β- and α-cells, yet β-cells resolve damage more efficiently. Loss of DNA methyltransferase 3a in pancreatic progenitors selectively heightens β-cell DNA damage vulnerability that persists into adulthood. Moreover, diabetogenic stress preferentially compromises β-cell repair fidelity. These findings reveal how developmental programs shape β-cell resilience and may influence lifelong diabetes risk.

Importance Amyloid positron emission tomography (PET) is increasingly used in research and clinical settings to determine the etiology of cognitive decline and eligibility for amyloid-targeting therapies. To assist with amyloid PET evaluation and to guide clinical decision-making, images can be quantified in a standardized unit called Centiloid, the interpretation of which can vary according to the method and threshold used. Objective To collect Centiloid values from available studies and determine robust positivity cutoffs using data-driven methods and correspondence with visual reads. Data Sources PubMed search (October 2024) identified studies with Centiloid values. Corresponding authors were invited to share individual participant data. Additional data were obtained through access-controlled repositories and conference outreach (July 2024-July 2025). Study Selection Studies were included if they provided Centiloids, radiotracer, age, and sex. Data Extraction and Synthesis Each study was analyzed using a unified statistical pipeline; study estimates were pooled using random-effects meta-analysis. Main Outcomes and Measures Gaussian mixture models (GMMs) were fitted to Centiloid values for each study. In studies with a bimodal distribution (per integrated completed likelihood), single cutoffs for positivity were set as mean plus 2 SDs of the lower gaussian component. Using GMMs, a double-cutoff approach defined a lower certainty range using a 90% posterior probability cutoff for assignment to the low (amyloid-negative) vs high (amyloid-positive) component. An alternative Centiloid cutoff was derived from maximizing the correspondence (Cohen κ) with the binary visual reads when available. Results This meta-analysis included cross-sectional amyloid PET scans acquired with 5 radiotracers from 49 227 participants across 53 studies from 15 countries (mean age, 71 years; 54% female, 62% cognitively impaired). The data-driven GMM approach identified a bimodal distribution in 51 studies (n = 48 786), resulting in a single cutoff for positivity of 18 Centiloids (95% CI,16-19; I 2 = 97%). The double-cutoff approach revealed high confidence for interpreting scans as negative when Centiloid values were lower than 11 (95% CI, 9-13; I 2 = 95%) and interpreting scans as positive if Centiloid values were higher than 26 (95% CI, 24-28; I 2 = 95%). In analyses of correspondence with binary (positive or negative) visual reads of amyloid PET scans (n = 35 045; 36 studies), Centiloids were highly predictive of visual positivity (Cohen κ, 0.86; 95% CI, 0.83-0.89; I 2 = 96%) with a cutoff of 27 Centiloids (95% CI, 24-30; I 2 = 80%). Conclusions and Relevance In this individual participant data meta-analysis, positivity cutoffs converged around 18 Centiloids (data-driven) and 27 Centiloids (visual reads). Findings from a double-cutoff analysis suggest that scans in the 11 to 26 Centiloid range should be interpreted with caution depending on the context of use.

The NCCN Clinical Practice Guidelines for Breast Cancer include recommendations for clinical management of patients with carcinoma in situ, invasive breast cancer, Paget’s disease, Phyllodes tumor, inflammatory breast cancer, and management of breast cancer during pregnancy. The panel convenes annually to update recommendations based on a review of recently published clinical trials. This selection from the NCCN Guidelines for Breast Cancer focuses on the recommendations for the management of recurrent or stage IV (M1) metastatic breast cancer. It summarizes the treatment strategies stratified by tumor biology, including hormone receptor status and HER2 expression, as well as clinical factors such as prior therapy and disease-free interval. Throughout all treatment lines, supportive care, quality of life preservation, toxicity minimization, and clinical trial participation are emphasized as integral components of patient care.

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.

Background Immune checkpoint inhibitor-based combinations represent the standard of care for advanced renal cell carcinoma. The OMNIVORE trial investigated a response-adaptive strategy, including treatment discontinuation in early nivolumab responders (Arm A) and salvage ipilimumab addition in non-responders (Arm B). Here we report outcomes with extended follow-up. Methods OMNIVORE was a phase II response-adaptive trial in which patients received induction nivolumab monotherapy. Patients achieving a confirmed complete or partial response discontinued nivolumab and entered observation (Arm A), while patients with stable disease or progressive disease received two doses of ipilimumab added to ongoing nivolumab (Arm B). This analysis characterizes long-term overall survival across the full study cohort and durability of response among patients who discontinued nivolumab following an early objective response. Results Of 83 patients who initiated treatment, 12 (14%) were allocated to Arm A and 57 (69%) to Arm B, with a median follow-up among living patients of 59.4 months (range 29.1–85.4 months) in Arm A and 31.4 months (range 4.6–62.6 months) in Arm B. The 3-year overall survival rate from nivolumab initiation was 64% (95% CI 51% to 74%) in the overall cohort, 83% (95% CI 48% to 96%) in Arm A, and 63% (95% CI 47% to 76%) in Arm B. Of the 12 Arm A patients, 6 (50%) remained off nivolumab at 1 year following treatment discontinuation, of whom 5 maintained responses beyond 43 months off therapy, with all remaining alive at last known follow-up, with overall survival ranging from 48.8 to 85.4 months from nivolumab initiation. Of the six patients who resumed treatment, only one achieved a durable complete response remaining on treatment at 83.2 months from nivolumab initiation. All 57 Arm B patients discontinued treatment with a median treatment duration of 3.7 months (range 1–24.8 months) and a median progression-free survival from nivolumab plus ipilimumab initiation of 4.6 months (95% CI 2.7 to 6.5 months). Conclusions With extended follow-up, a meaningful subset of patients achieving an early objective response to nivolumab maintained prolonged treatment-free survival following a short course of treatment. Salvage ipilimumab in nivolumab non-responders demonstrated limited benefit, reinforcing that upfront concurrent dual checkpoint blockade remains the preferred approach. Trial registration number NCT03203473 .

Abstract Lung fibrosis, including idiopathic pulmonary fibrosis (IPF), represents a spectrum of progressive interstitial lung diseases characterized by disrupted epithelial repair, fibroblast activation, and excess extracellular matrix accumulation. A central feature of fibrotic progression is the loss of alveolar type 2 epithelial (AT2) cell identity and the emergence of aberrant transitional states that fail to support normal regeneration. Based on our re-analysis of multiple publicly available scRNA-seq datasets from the IPF Cell Atlas, S100A2 expression is tightly associated with basal-like reprogramming of AT2 cells and is a top upregulated gene at both mRNA and protein levels in IPF lungs. Furthermore, scRNA-seq data from human lung organoid models reveal that AT2 cells co-cultured with fibroblasts acquire a basal-like phenotype and express high levels of S100A2. Functionally, overexpression of S100A2 in human iPSC-derived lung alveolar epithelial type 2 (iAT2) cells leads to loss of AT2 cellular identity, increased generation of reactive oxygen species, and activation of RAGE signaling pathway. We demonstrate that pharmacologic inhibition of RAGE using Azeliragon preserves AT2 cell populations, associated with reduced oxidative stress in iAT2 cells, and significantly attenuates collagen deposition in bleomycin-induced lung fibrosis models. Collectively, our results indicate that S100A2 is a key driver of epithelial dysfunction in lung fibrosis, promoting loss of AT2 identity, aberrant basal fate acquisition, and persistent epithelial injury. Targeting the unrecognized S100A2–RAGE pathway may offer a new therapeutic strategy to restore epithelial homeostasis in lung fibrosis.

Abstract Poly (ADP-ribose) polymerase (PARP) inhibitors (PARPi) are a mainstay therapy for homologous recombination (HR)-deficient cancers; however, resistance remains a major clinical challenge. Previously, through a genome wide CRISPR screen, we identified ZNF251 haploinsufficiency as a novel driver of PARPi resistance. In BRCA1 -mutant ( BRCA1 mut ) cells, ZNF251 deficiency led to HR hyperactivation, conferring PARPi resistance that could be reversed by RAD51 inhibition. In this study, we further show that ZNF251 deficiency induces replication stress and a pseudo-senescence state, in which cells exhibit molecular and phenotypic markers of senescence while retaining proliferative capacity. Because senomorphic and senolytic therapies can target senescent cells, we tested whether these approaches could overcome PARPi resistance in ZNF251 -deficient breast cancer cells. Critically, targeting this senescence-like state with either senomorphic agents, such as cytokine inhibitors, or senolytic agents, such as BCL-2 and BCL-XL inhibitors, overcame PARPi resistance ex vivo and in vivo . Importantly, pseudo-senescence was also observed in other PARPi-resistant contexts driven by HR hyperactivation, including 53BP1 - and Shieldin -mutant cells, suggesting that it may represent a broader mechanism underlying PARPi resistance in breast cancer. Furthermore, in two olaparib-resistant, BRCA -mutant triple-negative breast cancer organoid models, treatment with DT2216, a BCL-XL–targeting PROTAC, sensitized both models to olaparib. Together, our work defines a novel pathway linking HR hyperactivation, replication stress, and pseudo-senescence, and positions both senomorphic and senolytic therapies as promising strategies to overcome PARPi resistance in BRCA1 mut breast cancer. Highlights ZNF251 deficiency drives PARP inhibitor resistance through HR hyperactivation, replication stress, and pseudo-senescence in BRCA1 -mutant breast cancer. Senomorphic and senolytic therapies overcome PARP inhibitor resistance in vitro , in vivo , and in patient-derived organoid models. Pseudo-senescence represents a shared vulnerability of HR-hyperactivated PARPi-resistant cancers and can be therapeutically targeted.