The glioblastoma tumor-immune microenvironment (TIME) is an immunosuppressive barrier to therapy that encumbers glioblastoma responses to immune checkpoint inhibition (ICI). Immunosuppressive cytokines, protumor macrophages and myeloid cells, and exhausted T cells are all hallmarks of the glioblastoma TIME. In this study, we integrated spatial and single-cell analyses of patient-matched human glioblastoma samples before and after ICI treatment with genetic, immunologic, single-cell, and pharmacologic studies in preclinical models to show that interleukin 6 (IL6) neutralization reprograms the glioblastoma TIME to sensitize mouse glioblastoma allografts to ICI and radiotherapy. Rare human glioblastomas that achieved clinical responses to ICI had lower pretreatment IL6 levels compared with glioblastomas that did not respond to ICI. Diverse immunostimulatory gene therapies suppressed local IL6 levels in mouse glioblastoma allografts, and IL6 from glioblastoma cells and the tumor microenvironment was associated with reduced survival in preclinical models and in patients. IL6 blockade with a neutralizing antibody transiently sensitized mouse glioblastoma allografts to ICI by decreasing immunosuppressive regulatory T cells and increasing MHCII+ monocytes, CD103+ migratory dendritic cells (DC), CD11b+ conventional DCs, and effector CD8+ T cells. IL6 blockade plus ICI sensitized mouse glioblastoma allografts to immunostimulatory ablative radiotherapy. Together, these data suggest that IL6 signaling contributes to ICI resistance in glioblastoma and provides a combination treatment strategy that could be used for patients. Significance: Integration of spatial and single-cell analyses of glioblastoma samples with immunologic, single-cell, and pharmacologic studies showed that IL6 neutralization reprograms the immune microenvironment to sensitize glioblastomas to ICI and radiotherapy.

Background: The 5-Cog paradigm is a 5-minute brief cognitive assessment coupled with a clinical decision support tool designed to improve clinicians’ early detection of cognitive impairment, including dementia, in their diverse older primary care patients. The 5-Cog battery uses picture- and symbol-based assessments and a questionnaire. It is low cost, simple, minimizes literacy bias, and is culturally fair. The decision support component of the paradigm helps nudge appropriate care provider response to an abnormal 5-Cog battery. Objective: The objective of our study is to evaluate the effectiveness, implementation, and cost of the 5-Cog paradigm. Methods: We will enroll 6600 older patients with cognitive concerns from 22 primary care clinics in the Bronx, New York, and in multiple locations in Indiana for this hybrid type 1 effectiveness-implementation trial. We will analyze the effectiveness of the 5-Cog paradigm to increase the rate of new diagnoses of mild cognitive impairment syndrome or dementia using a pragmatic, cluster randomized clinical trial design. The secondary outcome is the ordering of new tests, treatments, and referrals for cognitive indications within 90 days after the study visit. The 5-Cog’s decision support component will be deployed as an electronic medical record feature. We will analyze the 5-Cog’s implementation process, context, and outcomes through the Consolidated Framework for Implementation Research using a mixed methods design (surveys and interviews). The study will also examine cost-effectiveness from societal and payer (Medicare) perspectives by estimating the cost per additional dementia diagnosis. Results: The study is funded by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health (2U01NS105565). The protocol was approved by the Albert Einstein College of Medicine Institutional Review Board in September 2022. A validation study was completed to select cut scores for the 5-Cog battery. Among the 76 patients enrolled, the resulting clinical diagnoses were as follows: dementia in 32 (42%); mild cognitive impairment in 28 (37%); subjective cognitive concerns without objective cognitive impairment in 12 (16%); no cognitive diagnosis assigned in 2 (3%). The mean scores were Picture-Based Memory Impairment Screen 5.8 (SD 2.7), Symbol Match 27.2 (SD 18.2), and Subjective Motoric Cognitive Risk 2.4 (SD 1.7). The cut scores for an abnormal or positive result on the 5-Cog components were as follows: Picture-Based Memory Impairment Screen ≤6 (range 0-8), Symbol Match ≤25 (range 0-65), and Subjective Motoric Cognitive Risk >5 (range 0-7). As of December 2024, a total of 12 clinics had completed the onboarding processes, and 2369 patients had been enrolled. Conclusions: The findings of this study will facilitate the rapid adaptation and dissemination of this effective and practical clinical tool across diverse primary care clinical settings. Trial Registration: ClinicalTrials.gov NCT05515224; https://www.clinicaltrials.gov/study/NCT05515224 International Registered Report Identifier (IRRID): DERR1-10.2196/60471

Many complex traits in humans exhibit sex differences. Recent advances in understanding context-specific genetic effects underscore the imperative for sex-aware genetic analyses to assess the role of sex in the genetic basis of human phenotypes. Despite increasing recognition of the importance of sex as a biological variable, current computational tools for genome-wide association studies (GWAS) lack functionality for performing nuanced sex-aware quality control and analysis. In response, we present GXwasR, a comprehensive R package that implements statistical genetic models for performing both GWAS and X-Chromosome-Wide Association Studies (XWAS). Designed for both sex-combined and sex-stratified analyses, GXwasR can account for unique features of chromosome X, including X-chromosome inactivation. Beyond enabling association analysis and meta-analysis, the tool has functions to a) test for sex differences in SNP-level genetic effects and heritability, b) assess genetic correlation, c) compute sex-aware polygenic risk scores (PRS), and d) support enhanced quality control protocols expressly designed for these purposes. GXwasR thus serves as a computational resource to enable investigations into the role of sex in the genetic basis of complex human phenotypes.

BACKGROUND AND PURPOSE: Newly developed zero-shot segmentation algorithms, like Meta’s “Segment Anything Model 2” (SAM2), have the potential to automate segmentation processes more efficiently than existing solutions. The goal of this study was to assess the ability of SAM2 to segment meningioma MRIs and suggest paradigms to enhance and assess performance. MATERIALS AND METHODS: We used SAM2 to produce segmentation masks using T1-weighted MRIs within the 2023 BraTS Preoperative Meningioma Dataset. We also proposed interactive click-based and contour-based augmentation strategies to simulate a neuroradiologist’s workflow, alongside a novel ensembling method. Analyses evaluated performance across model iterations both overall and within clinical subgroups of interest using standard statistical techniques and measures. RESULTS: Our cohort included a total of 690 meningiomas, the majority being WHO Grade 1 (75%). SAM2 achieved an overall zero-shot segmentation average Dice score of 0.785. Both click-based and contour-based augmentation strategies provided significant model improvement (0.876 and 0.872, respectively, p < 0.001). Layering a directional consensus approach on top of the contour-based model further enhanced performance (0.921, p < 0.001). Across all model iterations, smaller tumor volumes and tumors without peritumoral edema proved more difficult for SAM2 to segment (p < 0.001). CONCLUSIONS: SAM2 demonstrated reasonable zero-shot segmentation performance on meningioma MRIs, with observable improvements seen with contour-based prompting and directional ensembling. These results suggest that zero-shot segmentation models, with some degree of radiologist assistance or intervention, are promising tools for aiding in image segmentation for meningioma. Future work can investigate methods to improve segmentation performance for small tumor volumes and tumors without peritumoral edema. ABBREVIATIONS: SAM2 - Segment Anything Model 2; ML - Machine Learning; CNN - Convolutional Neural Network; DSC - Dice Similarity Coefficient; GT - Ground Truth.

Meningiomas are the most common primary brain tumors and, despite their benign reputation, often behave aggressively. Meningiomas are morphologically heterogeneous, yet the full significance of their histologic diversity is unclear. This is in large part because many features are not readily quantifiable by traditional observer-based light microscopy. Molecular testing improves prognostic stratification, but is not universally accessible. We therefore sought to determine whether an artificial intelligence (AI)-trained program could predict specific genomic and epigenomic patterns in meningiomas, and whether it could extract more prognostic information out of standard hematoxylin and eosin (H&E) histopathology than the current WHO classification. To do this, we developed Morphologic Set Enrichment (MSE), an interpretable computational pathology framework that quantifies statistical enrichment of morphologic patterns, cells, and tissue architecture from H&E whole-slide images. The MSE meningioma histology program was able to accurately predict DNA methylation subtypes and concurrent chromosome 1p/22q losses, in the process identifying specific morphologic patterns associated with key genomic and epigenomic alterations. It also added prognostic value independent of standard clinical and pathological variables. These results demonstrate that AI-based quantitative morphologic profiling can capture clinically and biologically relevant information that redefines risk stratification for meningiomas, incorporating histological information not included in existing grading schemes.