Citations

Importance Timely identification of aortic stenosis (AS) is essential for appropriate clinical management, yet screening remains limited by dependence on comprehensive echocardiography and trained imaging personnel. Objective To develop and validate a deep learning algorithm for detection of moderate or greater AS and prospectively evaluate its performance using artificial intelligence (AI)–guided focused cardiac ultrasound (FoCUS) acquired by novice operators. Design, Setting, and Participants This diagnostic study included retrospective algorithm development and validation and prospective evaluation of AI-guided FoCUS across Mayo Clinic sites in the Midwest, Arizona, and Florida. The model was developed using 6753 patients and evaluated in internal validation (n = 852), internal test (n = 844), and validation (n = 1912) cohorts. Performance was assessed on FoCUS acquired by experienced sonographers (n = 602) and prospectively by novice operators (n = 1302). The retrospective model development and validation cohorts comprised studies performed from January 2005 through September 2022. Prospective study was conducted in 2 enrollment periods from June to August 2024 and from June to September 2025. Participants from both periods were combined to comprise the final prospective cohort. Exposure AI-guided FoCUS acquisition and automated deep learning–based assessment for detection of moderate or greater AS. Main Outcomes and Measures Detection of moderate or greater AS. Performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and positive predictive value. Results The model demonstrated excellent discrimination in the internal test cohort (AUROC, 0.99; 95% CI, 0.98-1.00) and geographically distinct validation cohorts in Arizona (AUROC, 0.99; 95% CI, 0.97-1.00) and Florida (AUROC, 0.99; 95% CI, 0.96-1.00). Among FoCUS examinations acquired by experienced sonographers, sensitivity was 95% (95% CI, 82-99) and specificity was 97% (95% CI, 95-98). In the prospective novice-operator cohort, 1258 of 1302 examinations (96.6%) were suitable for automated analysis. Sensitivity was 93% (95% CI, 82-99) and specificity was 96% (95% CI, 95-97). Expert review of AI-positive and uninterpretable examinations increased the positive predictive value from 49.4% to 91.1%, with sensitivity of 85.4%. Conclusions and Relevance A deep learning algorithm accurately detected moderate or greater AS across validation cohorts in this study. In prospective evaluation, novice operators were able to acquire AI-guided FoCUS examinations that enabled accurate detection of moderate or greater AS. These findings support a scalable strategy that may expand access to AS detection in settings with limited echocardiography resources.

MOGAD is a rare autoimmune encephalomyelitis that shares similar clinical manifestations with other central nervous system demyelinating diseases. However, they must be distinguished due to their distinct pathogenesis and treatment methods. Currently, the clinical diagnosis of MOGAD primarily relies on patients’ clinical manifestations and imaging features, with biomarker application being relatively limited. Meanwhile, research on MOGAD biomarkers has advanced rapidly, with findings mainly focusing on imaging features, antibody titers, nerve cell markers, cytokines, and inflammatory cell ratios. These biomarkers can distinguish among various autoimmune with similar clinical manifestations and exhibit certain specificity. If these biomarker findings are applied to the clinical diagnosis of MOGAD, they could enhance diagnostic accuracy and support earlier and more accurate diagnosis, while providing a scientific basis for evaluating disease progression and prognosis. We discuss the biomarkers and emerging biomarkers of MOGAD, emphasizing the specific changes and aspects that may be applicable to clinical diagnosis and treatment.

Abstract Purpose: Homozygous deletion of the 9p21 locus, containing CDKN2A/B and MTAP [CDKN2A/MTAP deletion (CMdel)], underlies emerging therapeutic strategies, including PRMT5/MAT2A inhibition. The clinicogenomic context and treatment outcome associations of CMdel in non–small cell lung cancer (NSCLC) remain incompletely defined. Experimental Design: We analyzed Tempus Lens records with paired DNA (xT) and RNA (xR) sequencing. After quality filtering (purity ≥40%), 16,947 patients were included in the primary dataset, and 10,996 were included in the validation dataset and stratified by deletion status [CMdel vs. CM wild-type (CMwt)]. IFN status was defined by IFN-kappa (IFNK) expression [log2(TPM + 1)] using the 25th percentile of CMwt as the threshold. Immune composition was inferred by quanTIseq. First-line regimens were grouped as immune checkpoint inhibitor (ICI) monotherapy, ICI chemotherapy, chemotherapy, or tyrosine kinase inhibitors (TKI). Best response and time to next treatment (TTNT) with risk-set adjustment and 18-month truncation were assessed. Results: CMdel occurred in 11.7% (n = 1,991) and 15% (n = 1,676), respectively, and was enriched in mucinous histology, stage IV disease, and never smokers. CMdel was associated with EGFR, TERT, and SMARCA4 alterations, whereas CMwt was associated with RB1, SETD2, and ARID2. More than 50% of CMdel tumors harbored targetable coalterations, enriched in EGFR/ALK/RET and relatively depleted in KRAS. CMdel tumors exhibited lower PD-L1, mutation burden, and immune infiltration. TTNT did not differ by CM status for ICI monotherapy or TKI but was shorter in CMdel for ICI chemotherapy and chemotherapy. Discordant and broad deletions were less common and had weaker TTNT association. IFNK-low expression marked immune-excluded subgroups with worse treatment outcomes. Conclusions: CDKN2A/MTAP loss defines a prevalent, genomically distinct NSCLC subset enriched in actionable drivers. CMdel associates with worse outcomes to ICI/chemotherapy-containing regimens. These findings highlight convergent biology at 9p21 and support biomarker-guided development of PRMT5/MAT2A inhibitors in MTAP-deleted NSCLC.

Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood ( n = 6454) and lung ( n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank–derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.

Abstract Glioblastoma (GBM) remains a deadly cancer driven in part by invasion of tumor cells into the brain. Transcriptomic analyses have identified distinct molecular subtypes, but mechanistic differences that account for clinical differences are not clear. In this study, we show that, as predicted by the motor-clutch model of cell migration, mesenchymal glioma cells are more spread, generate larger traction forces, and migrate faster in brain tissue compared with proneural cells. Despite their rapid migration and comparable proliferation rates in vitro, mice with mesenchymal tumors survive longer than those with proneural tumors. This improved survival correlated with an immune response in mesenchymal tumors, including T cell–mediated. Consistently, inducing mesenchymal tumors in immunodeficient mice resulted in shorter survival, supporting a protective immune role in mesenchymal tumors. Thus, mesenchymal tumors have aggressive migration but are immunologically “hot,” which suppresses net proliferation. These two features counteract each other and may explain the lack of a strong survival difference between subtypes clinically, while also opening up new opportunities for subtype-specific therapies. Significance: This study highlights new mechanical and immunologic insights into GBM molecular subtypes using an integrated modeling–genome engineering strategy, which can potentially facilitate GBM subtype-specific therapeutic strategies.

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.

Background Irritable bowel syndrome (IBS) is a complex disorder of gut-brain interaction, with heterogeneous symptoms, no available biomarkers and limited pathogenetic insight. Objective To identify genetic risk factors and actionable mechanisms for future clinical translation in IBS. Design We conducted a genome-wide association study (GWAS) meta-analysis of IBS in 2 775 539 individuals from 22 biobanks. IBS genetics was studied across multiple ancestries, different case definitions and symptom-related subtypes. Heritability and genetic correlations with other traits were estimated, and Mendelian randomisation was used to test causal relationships. GWAS data were functionally annotated and fine-mapped to prioritise tissues, cell types, pathways, candidate genes, specific mechanisms and druggable targets. Results Significant heritability was only detected in individuals of European ancestry, with near-identical genetic architecture across case definitions. Genetic correlations with GI, psychiatric and cardiometabolic traits were observed, including causal relationships with triglyceride (TG) levels. Functional annotation of IBS risk loci highlighted cell types and pathways relevant to brain, enteric neuro-glial and cardiometabolic domains, as well as actionable targets like GCKR, a regulator of TG metabolism. Druggability analyses converged on cardiometabolic mechanisms, including TG modulation. IBS polygenic risk scores were derived and showed a significant association with case status in an independent case-control dataset, supporting further evaluation in external population-based and clinically ascertained cohorts. Conclusions This study provides the most comprehensive assessment of IBS genetics to date, demonstrating reproducible polygenic inheritance. We link IBS risk to convergent neurogastrointestinal and novel cardiometabolic mechanisms, highlight specific biological pathways and actionable mechanisms and outline translational opportunities emerging from integrated computational analyses.

Abstract Purpose: The addition of MEK inhibition (MEKi) to programmed cell death ligand 1 (PD-L1) blockade improves progression-free survival (PFS) in patients with advanced biliary tract cancer. Although MEK inhibitors may increase tumor cell immunogenicity, they can impair T-cell priming/effector function, limiting combination efficacy. We hypothesized that the addition of a CD27 agonist could restore T-cell function and enhance antitumor immunity in this combination. Patients and Methods: We conducted a randomized, phase II trial evaluating atezolizumab (840 mg, intravenously, days 1 and 15) in combination with the CD27 costimulatory monoclonal antibody [CDX-1127/varlilumab (3 mg/kg, intravenously, days 1 and 15)], with/without the addition of an MEK inhibitor [cobimetinib (60 mg, orally, daily, days 1–21, off days 22–28)] in unresectable biliary tract cancer following at least one metastatic therapy. Overall response rate (ORR) and PFS were coprimary endpoints. Treatment-related changes in CD8+ tumor-infiltrating lymphocytes (TIL) were the primary correlative outcomes. Results: The trial was closed early following interim preplanned ORR analysis. At closure, 57 patients had been enrolled [n = 29 in the cobimetinib + atezolizumab + varlilumab (CAV) arm; n = 28 in the atezolizumab + varlilumab (AV) arm]. A majority (67%) had intrahepatic cholangiocarcinoma, and 32% were immunotherapy experienced. Both regimens were well tolerated without new safety signals. Objective responses were rare [0% (CAV); 3.8% (AV)]. The median PFS (mPFS) was 2.40 (CAV) and 1.84 (AV) months [hazard ratio (HR), 0.67; 95% confidence interval (CI), 0.38–1.18]. Among immunotherapy-experienced patients, the mPFS was 3.62 (CAV) and 1.84 (AV) months (HR, 0.54; 95% CI, 0.18–1.62). Treatment with CAV increased intratumoral CD8+ T-cell density compared with treatment with AV. Conclusions: The combinations of atezolizumab and varlilumab with/without cobimetinib were safe, but neither meaningfully improved outcomes in biliary tract cancer treated in the later lines. Correlative tissue studies validated preclinical work that MEKi increases CD8+ TILs.

Abstract Background Rare pediatric and young adult brain tumors pose major diagnostic and therapeutic challenges due to limited evidence and scattered expertise. The International Rare Brain Tumor Board was established to enable multidisciplinary case review, shared learning, and collaborative decision support. Methods Launched in February 2025, the Rare Brain Tumor Board is a monthly collaborative initiative led by Children’s National Hospital and the National Institutes of Health (NIH). Close partnership with the European Pediatric Rare CNS Tumor Board enhances transatlantic exchange and alignment of diagnostic and treatment recommendations. De-identified cases are presented with clinical history, neuroimaging, histopathology, and molecular profiling, including DNA methylation classification, available for the majority of cases. Sessions are structured to support integrated diagnosis and evidence-informed management recommendations through multidisciplinary discussion (neuropathology, neuroradiology, neurosurgery, radiation oncology, and pediatric as well as adult neuro-oncology). Findings Since its inception, 12 sessions have been held averaging 138 participants per session, reviewing a total of 32 cases (mean 3 cases/session; range 1–4) from 7 different countries and 3 different continents. Entities reviewed include DICER1-intracranial sarcoma (n = 5), astroblastoma (n = 4), FOXR2-activated neuroblastoma (n = 4), CNS tumors with BCOR internal tandem duplication (n = 3) and, BCOR-fused tumors (n = 3), CIC-rearranged-sarcoma (n = 3), intracranial mesenchymal tumor FET::CREB fusion positive (n = 3), MN1::PATZ1 fusion tumors (n = 2), undifferentiated sarcoma (n = 2), PLAG1-fusion tumor (n = 1), medullomyoblastoma (n = 1), and EZHIP-altered diffuse midline glioma (DMG) (n = 1). Multidisciplinary review has supported diagnostic clarification, strengthened familiarity with ultra-rare entities, and informed real-world treatment discussions in settings where prospective data are limited. Discussions have also increased awareness of rare brain tumor registries and opportunities for systematic case capture. Conclusion An international multidisciplinary rare brain tumor board is a feasible, scalable model to advance integrated diagnosis and collaborative management of ultra-rare pediatric and young adult brain tumors, while building a shared knowledge base across institutions.