Citations

Abstract High-grade gliomas (HGGs) are aggressive, infiltrative brain tumors with poor survival outcomes and limited therapeutic options. Their spatial and molecular heterogeneity necessitates large, well-curated datasets integrating imaging, clinical, and genomic information to enable precision medicine and drive novel treatment development. Using the Medtronic StealthStation neuronavigation system, we collected over 1,000 spatially localized intraoperative biopsy samples from more than 250 patients undergoing resection for glioma at Mayo Clinic and partnering sites. Each biopsy site was recorded with 3D coordinates, labeled in real time, flash-frozen, and annotated with metadata including collection time, anatomical location, and surgical plan. Samples were processed and entered into a secure database along with the corresponding sampling coordinates, frozen pathology, whole-exome and RNA sequencing data, 5-ALA fluorescence status, patient demographics, and T1, T1Gd, T2, and T2-FLAIR MRI. This multimodal dataset supports radiogenomic research by enabling precise mapping of imaging features to molecular characteristics. A subset of this data, including 268 biopsies from 52 patients (25 male, 27 female) with reported intraoperative 5-ALA fluorescence, was used to develop a radiomics model predicting locoregional patterns of 5-ALA positivity based on preoperative MRI. Image features were extracted from the biopsy sample coordinates across multiple MRI sequences and paired with corresponding 5-ALA fluorescence status to train the model for regional fluorescence prediction. The final model achieved 83% accuracy on the training set and 86% on the validation set using a 70-15-15 data split; this included 208 samples for training and 42 for validation, stratified by patient to ensure no overlap of samples from the same individual across sets. These findings demonstrate the feasibility of predicting 5-ALA fluorescence using radiomic features from standard preoperative MRI, offering a promising noninvasive strategy for improving intraoperative decision-making and enabling more precise, personalized resections in patients with high-grade gliomas.

Abstract BACKGROUND Glioblastoma (GBM) is a molecularly and phenotypically heterogeneous tumor, with regional invasion patterns driving treatment resistance and universal recurrence. In infiltrative regions, GBM cells adopt at least two predominant invasive phenotypes: Neuronal-enriched (NEU), with synaptic and neurodevelopment programs, and Glycolytic/Plurimetabolic (GPM), with heightened immune and metabolic activity. These subtypes differ in therapeutic vulnerability and microenvironmental interactions; features not captured by conventional contrast-enhanced (T1+C) MRI due to minimal blood-brain barrier (BBB) disruption. Our group sought to develop predictive models to distinguish variability of NEU/GPM subtypes within infiltrative GBM, utilizing graph convolutional networks (GCN), advanced multiparametric MRI (mpMRI), and spatially-matched image-localized biopsies with transcriptomic profiling. METHODS We analyzed 18 mpMRI features —including conventional, diffusion-weighted, and Dynamic Susceptibility Contrast (DSC) perfusion MRI— of 101 image-localized biopsies from MRI non-enhancing infiltrative margins of 64 GBM patient tumors. Features were hierarchically clustered per modality; each cluster was represented as a node in a Neo4j graph. Edges connected nodes sharing overlapping samples. Nodes were annotated with NEU or GPM subtype proportion. GCNs were trained to classify subtype-enriched nodes using graph topology and node features. RESULTS GCNs achieved high predictive performance on held-out test data (AUC = 0.95 for NEU, 0.88 for GPM; 70:30 train-test split) with similar 5-fold cross-validation performance and generalized well to an independent cohort (AUC > 0.85, n = 45). Importantly, performance was robust without relying on T1+C imaging, enabling prediction beyond BBB-disrupted regions. GCNs outperformed traditional classifiers (logistic regression, random forest). CONCLUSION Our results demonstrate that GCN-modeling of mpMRI features enables non-invasive prediction of invasive GBM subtypes at the tumor margin. Unlike convolutional neural networks (CNN), which rely on spatial continuity, GCNs leverage relational structure between heterogeneous imaging features, making them ideal for modeling sparse, non-grid data. This may support non-invasive regional vulnerability detection, surgical targeting, and improved response assessment.

Abstract BACKGROUND Bevacizumab, an anti-vascular endothelial growth factor (VEGF) monoclonal antibody, has emerged as a treatment for radiation necrosis. Doses ranging from 2.5 mg/kg to 15 mg/kg have been utilized however the optimal dosing remains inadequately studied. METHODS Retrospective review of adult patients with central nervous system tumors or brain metastasis treated with bevacizumab from 6/29/2021 to 7/31/2024. Patients were divided into those who received bevacizumab 7.5 mg/kg every 21 days and those who received bevacizumab 10 mg/kg every 21 days. The primary outcome was radiographic response as assessed by a blinded neuro-oncologist. Secondary outcomes included clinical response as documented in clinical notes, major adverse events including hemorrhage and thromboembolic events and re-dosing for recurrence of radiation necrosis beyond the 4 initially planned doses. RESULTS 104 patients were included (median age 59.3 years, 55% male); 53 patients in the 7.5 mg/kg group and 51 patients in the 10 mg/kg group. In the 7.5 mg/kg group, 1 patient had a complete response (CR), 16 patients had a partial response (PR), 32 patients had stable disease (SD), 3 patients had progression of disease, and 1 patient did not have repeat imaging. Of the 51 patients in the 10 mg/kg group, 3 patients had a CR, 10 patients had a PR, 27 patients had SD, 5 patients had progression of disease, and 6 patients did not have repeat imaging. There was no statistically significant difference in radiographic response between the two dosing groups (p = 0.42). Clinical response rates were higher in the 7.5 mg/kg group (65.2% vs 37.8%, p = 0.013). Major adverse events and need for re-dosing for radiation necrosis recurrence were comparable. CONCLUSION Bevacizumab 7.5 mg/kg was equally as efficacious as 10 mg/kg in terms of radiographic response and may offer a better clinical response for patients with radiation necrosis.

Abstract Background Central nervous system (CNS) tumors are a significant cause of morbidity and mortality in sub-Saharan Africa (SSA). To date, no large-scale efforts have focused on brain tumor genetics within this region. This study aimed to assess and map the current neuro-oncology capacity in SSA, including workforce availability and existing services for brain tumor registries and biobanks. The results represent the final analysis representing respondents from 70 healthcare institutions. Methods This cross-sectional study utilized an online survey to collect data from healthcare professionals involved in CNS tumor care across SSA through the Society for Neuro-Oncology SSA (SNOSSA). The survey gathered information on the availability of neuro-oncology practitioners, neuropathology and molecular diagnostic capacity, brain tumor management, cancer registries, and biobanking of specimens. Purposive sampling was used to identify participants, with the questionnaire provided in English, French, and Swahili. Results A total of 145 respondents from 22 countries and 70 healthcare institutions participated. Among the 70 institutions, neurosurgery was available in 89%, anatomic pathology in 74%, and medical oncology in 71%. For radiologic evaluation, 64% of institutions reported having both CT and MRI, while 17% had CT only. Molecular testing through IHC or NGS was available in 46% of institutions. Multidisciplinary tumor boards for CNS cases were present in 86% of institutions. General cancer registries were available in 66% of institutions, while 29% had brain tumor-specific registries, and 10% maintained brain tumor biorepositories. Conclusion This study provides a comprehensive summary of the neuro-oncology capacity in SSA. Next steps are to reach out to institutions who have registries and determine what data are being collected, with the hopes of defining a set of common elements to expand registry efforts across SSA. Similarly, to reach out to institutions that have ongoing brain tumor biobanks and CT/MRI capacity to determine what infrastructure exists.

Background: People living with chronic heart conditions often experience poor quality of life (QoL) and reduced well-being; however, the intervention preferences to promote well-being among these individuals are not well known. Purpose: The purpose of this study was to describe QoL and preferences for wellbeing interventions in people with chronic heart conditions and to describe differences by heart condition diagnosis. Method: We conducted a cross-sectional study and recruited participants from a national online support group for people with heart conditions from April 29 to June 3, 2025. We measured QoL (WHO-QOL 28-item) and priorities for interventions via an investigator-developed questionnaire. Descriptive and bivariate analyses were conducted by heart condition. Results: In our preliminary findings among 107 participants [mean age=65.7 years (SD=12.5)], female (53%), White (82%), 67% ≥ 2 heart conditions] most reported being diagnosed with arrhythmias (36%), congenital heart disease (35%), and heart failure (34%), and the fewest reported cardiomyopathy (22%). Participants reported their physical health QoL as the lowest (M = 56.8±12.6), followed by psychological health (M = 62.5±13.1), and social relationships (M = 65.7±19.0). Most participants preferred non-pharmacological wellness interventions (79%) over pharmacological (23%). The highest interest in interventions was for nutrition/weight (80%), exercise (78%), relaxation (78%), mind-body (yoga or tai-chi) (72%), and meditation/mindfulness (69%). Most preferred delivery in-person (64%) or self-guided app/Website (60%). WHO-QOL ratings of Very Satisfied/Satisfied for Health Satisfaction were compared by heart condition (with and without) and were lower for two conditions: cardiomyopathy, (35% vs 63% p=0.015) and arrhythmias (44% vs 65%, p = 0.033). Participants with arrhythmias prioritized social support interventions (74% vs 48%, p = 0.010), while those with cardiomyopathy preferred interventions for depression (70% vs 43%, p = 0.025) (Table 1). Conclusion: We found that people were highly interested in behavioral interventions to promote their well-being. However, preferences for wellness interventions differed by diagnosis. Findings can guide interventions tailored based on condition.

Introduction/Background: CHIP is a risk factor for cardiovascular (CV) disease, cancer, and all-cause mortality. Previous work has shown that CHIP, and particularly larger CHIP clones, are associated with adverse CV outcomes, yet the molecular pathways through which CHIP impacts CV risk are poorly defined. Hypothesis/Research Question: We hypothesize that the integration of whole blood transcriptomics and methylomics will provide novel insights into the pathophysiology of CHIP. Methods/Approach: Whole blood DNA methylation profiling, transcriptomics, and whole exome sequencing with CHIP calling for variant allele frequencies (VAF) of ≥2% (CHIP) and ≥10% (large CHIP) were performed for 507 ISCHEMIA and ISCHEMIA-CKD participants with moderate-severe ischemia. We identified transcriptomic and methylomic differences between participants with CHIP and large CHIP vs no CHIP using DESeq2 and limma, adjusted for age, sex, and race/ethnicity. Gene set enrichment analysis (GSEA) and probe set enrichment analysis (PSEA) were performed to identify pathway-level alterations in transcription and methylation, respectively. Results/Data: Clinical characteristics of study participants are described in Fig 1A . Compared to no CHIP (n=391), transcriptomics identified 6 differentially expressed genes (DEGs) in CHIP (n=116) and 27 DEGs in large CHIP (n=35) (p-adj<0.05; abs(logFC)>0.25) (Fig 1B) . Compared to no CHIP, methylation identified no differentially methylated probes in CHIP and 6 in large CHIP (padj<0.20, abs(logFC)>0.03). GSEA identified 137 pathways significantly different in both CHIP and large CHIP vs. no CHIP (padj<0.05), while PSEA identified 724 and 2356 pathways (padj<0.20), respectively. Given its stronger relationship with methylation and transcription, downstream analyses focused on large CHIP. Integrating these data, we found 58 pathways to be both hypomethylated and transcriptionally upregulated in large CHIP, including azurophil granule-related pathways implicated in neutrophil degranulation (Fig 1C) . Further investigation into the gene-probe pairs driving the azurophil granule pathway enrichment in large CHIP revealed hypomethylation and increased transcription of genes implicated in neutrophil extracellular trap formation, including ELANE, PTRN3, AZU1, and CTSG (Fig 1D) . Conclusions: Integration of the methylome and transcriptome suggests large CHIP is linked to neutrophil-mediated immune pathways in patients with stable coronary artery disease.

Introduction: Higher mtDNA-CN reflects better mitochondrial function, while lower levels indicate mtDNA release and increased inflammation. Although mtDNA-CN serves as a marker of cardiovascular health, the importance and mechanism of risk associated with mtDNA in high-risk individuals with chronic coronary artery disease (CAD) remains poorly understood. To address this gap, we leveraged the ISCHEMIA Biorepository to: (1) evaluate the association between mtDNA-CN and all-cause death (ACD); and (2) define a transcriptomic profile associated with high mtDNA-CN and ACD risk for high-risk patients with CAD. Hypothesis: Higher mtDNA-CN is associated with reduced ACD risk and inflammation in high-risk patients enrolled in the ISCHEMIA Biorepository. Methods: Low pass whole genome sequencing and whole blood RNA-Seq were performed on 594 ISCHEMIA and ISCHEMIA-CKD participants with moderate-severe ischemia. Data were processed using the NYU deciphEHR pipeline and mtDNA was quantified using mitoAnalyzer. mtDNA-CN was median split, with samples having values ≤ median classified as the 'low' group and those > median as the 'high' group. Unadjusted group differences were assessed using Chi-squared tests. Survival analysis was performed using Cox proportional hazard models, adjusted for age, sex, diabetes, eGFR, dialysis status, ischemia severity, and left ventricular ejection fraction. Adjusted differential gene expression (DGE) and gene set enrichment analysis (GSEA) were performed to explore molecular mechanisms underlying mtDNA-CN. Results: Median age was 67 (IQR: 57–78); 19% were female, 84% white, and 6% Hispanic. Hypertension (84%), diabetes (43%), and obesity (45%) were common; 26% had eGFR <60 mL/min/1.73 m 2 . Median mtDNA-CN was 153 counts per sample. After multivariate adjustment , mtDNA-CN > median was associated with a 48% reduction in ACD (aHR = 0.52, 95% CI: 0.30–0.90; P = 0.020) (Figure 1A) . DGE revealed downregulation of inflammatory genes in the mtDNA-CN group, including S100A9 (FC=-.41, p=3.32e-10) and S100A12 (FC=-.56, p=3.32e-10). Inflammatory pathways such as the inflammasome complex (NES=-2.17, p=9.32e-03) and interleukin-1 production (NES= -2.02, p=6.60e-03) were downregulated among participants in the high mtDNA-CN ( Figure 1B) . Conclusion: Higher mtDNA-CN is associated with lower risk of ACD and reduced inflammasome activity, suggesting that enhanced mitochondrial health may mitigate inflammasome-related pathology linked to ACD in patients with CAD.

Abstract The incidence of venous thromboembolism (VTE) in patients with acute lymphoblastic leukemia (ALL) receiving asparaginase-based induction is high despite primary thromboprophylaxis. Our aim was to derive and externally validate a VTE risk prediction model in patients with ALL receiving asparaginase-based induction. We conducted a multicenter retrospective cohort study of patients (aged ≥18 years) with newly diagnosed ALL receiving asparaginase-based induction. The derivation and external validation cohorts included 306 and 94 patients, respectively. Primary outcome was VTE at any site. A cause-specific Cox proportional hazards model stratified by thromboprophylaxis and center was performed to identify VTE risk factors in the derivation cohort. A risk prediction model for VTE at 30 days was derived using variables with P value &lt; .05 in the multivariable model and was tested in the validation cohort. VTE risk factors on multivariable analysis in the derivation cohort included D-dimer ≥1 μg fibrinogen equivalent unit per mL (hazard ratio [HR], 2.64; 95% confidence interval [CI], 1.07-6.5) and hemoglobin (HR for each 1 g/dL increment, 1.19; 95% CI, 1.06-1.34) at ALL diagnosis. A VTE risk score based on these variables distinguished between a 4% (95% CI, 0.72-12) and 20% (95% CI, 14-27) 30-day cumulative incidence of VTE in the derivation cohort, with similar findings in the validation cohort (area under the curve, 0.56). The negative predictive value for VTE at 30 days was 96% and 93% in the derivation and validation cohorts, respectively, and the positive predictive value was 20% in both. We derived and validated a model using D-dimer and hemoglobin, which stratifies VTE risk in patients with ALL receiving asparaginase-based induction.

Background Outcome selection in regional anesthesia and acute pain research is inconsistent, often lacking patient-centered priorities and validated instruments. We aimed to prioritize key outcomes and propose suitable measurement tools to improve the quality, consistency, and relevance of regional anesthesia research. Methods We conducted a multiround Delphi process, including two electronic voting rounds, a multiday in-person meeting, and a patient panel. Experts and patients evaluated existing and proposed outcomes for importance, validity, and relevance. Outcomes reaching ≥70% agreement were prioritized, and appropriate measurement instruments were identified. Results Thirty-two experts and three patients participated. Across three Delphi rounds, 10 outcomes were prioritized for future regional anesthesia research: seven efficacy outcomes (pain scores; opioid consumption; Brief Pain Inventory; functional outcomes; cognitive function; length of stay; block duration); two multidimensional outcomes (quality of recovery and development of a regional anesthesia-specific quality-of-recovery instrument); one safety outcome (chronic postsurgical pain). Patients prioritized cognitive recovery, function, chronic pain, and pain scores. Conclusions This consensus-based, patient-centered framework defines the core priorities for future regional anesthesia and acute pain research. Future work should focus on developing and validating a regional anesthesia-specific quality-of-recovery instrument, refining multidimensional measures such as functional recovery, cognitive outcomes, and the Brief Pain Inventory, and standardizing the reporting of chronic pain and opioid-related outcomes. Adoption of these priorities will enhance methodological consistency, comparability, and patient relevance in future clinical trials.

Abstract Introduction: While evaluation approaches for community-academic research groups are established, few tools exist for academic institutional advisory groups across multi-core centers and research, education, and clinical care missions. Institutional advisory group evaluation should consider group processes and their impact on community-centered outcomes. This study describes the community-engaged development of a mixed-method evaluation approach to address this gap and presents pilot outcomes across an NIH-funded center. Methods: We utilized a Community of Practice model to co-develop a survey with 14 community and academic representatives of four advisory groups. The final survey included five categories of group process and four categories of outcomes. Storytelling sessions with community partners explored areas where the survey identified discrepancies in perspectives between community and academic team members, as well as areas with lower scores. Results: Nine community and 14 academic (staff and faculty) partners completed the survey. Respondents positively assessed group process outcomes (shared values, leadership, community-centeredness, and decision-making), and slightly less positive assessments of institutional outcomes. Storytelling sessions confirmed the overall satisfaction of community partners but highlighted actionable concerns within power-sharing, decision-making, funding equity, and trust-building. Conclusions: The results of this equity-centered evaluation suggest the utility and importance of participatory, mixed-methods approaches to evaluating community-academic institutional advisory groups.