Citations

Abstract High-grade gliomas (HGGs) are aggressive tumors with poor outcomes and limited treatment options. Here, we combined genomic and transcriptomic tumor profiling with drug testing in a patient-derived 3-dimensional culture model to identify individualized treatments and predictive biomarkers. Activity of single agents targeting frequently dysregulated glioma pathways was relatively poor ex vivo and generally reflected historical patient data. However, compounds targeting PI3K, epigenetic, and survival/senescence signaling were effective in some cases. Drug sensitivity correlated with transcriptional rather than genomic features and suggested heterogeneity as a resistance mechanism. Bromodomain and extraterminal domain inhibition was particularly effective in tumors enriched in the mesenchymal transcriptional subtype, promoted proneural transition, and was overcome by upregulated PI3K signaling. Notably, combinations were largely effective, with 6 strategies exhibiting stronger efficacy than corresponding single agents in most cases (58-77%). This study identifies HGG vulnerabilities and associated biomarkers, resistance mechanisms, and effective combination strategies that warrant further clinical validation. Graphical Abstract

Abstract High-grade gliomas (HGG) have significant spatial heterogeneity, complicating therapeutic approaches. While multiparametric MRI (mpMRI) enables noninvasive assessment of this heterogeneity, most predictive models lack biological interpretability when used to classify or predict outcomes. Here, we developed an unsupervised framework that identifies novel mpMRI feature patterns through dimensional reduction, linking them to clinically relevant molecular signatures. We analyzed 18 advanced mpMRI features from 73 stereotactic biopsies across 20 HGG patients. Imaging data were normalized, followed by dimensional reduction using an iterative UMAP across varying parameters. Density-based clustering was used to generate stable image-derived clusters through consensus co-clustering. RNA-seq data from matched biopsies were analyzed using DESeq2 and GSEA to identify differentially expressed genes and enriched pathways. We then constructed a network linking specific imaging features to these transcriptional signatures. Validation was performed in an independent cohort, allowing us to extend clusters across full brain mpMRI. Unsupervised clustering of mpMRI features produced nine stable image-based clusters. These clusters were not fully explained by contrast enhancement, metabolic class, or tumor purity, shown by intraclass correlation (ICC) analyses, suggesting they capture novel biological variation. Transcriptomic analysis showed each cluster was associated with distinct gene expression patterns and enriched biological pathways, including neuronal differentiation, immune chemotaxis, and chromatin remodeling. Key imaging features (sRCBV, MTT, K2)—combined in cluster-specific patterns—enabled construction of an interpretable imaging–transcriptomic network. This was validated in an independent cohort. We then applied to full brain mpMRI, mapping spatially distinct regions to biological trends and patient outcomes. Addressing a research gap in imaging HGG, we present an interpretable network that connects specific patterns of mpMRI to clinically meaningful molecular signatures. This approach advances our understanding of tumor heterogeneity and supports future translational work in precision patient care.

Abstract BACKGROUND CNS ependymal tumors rarely demonstrate mixed subependymal and ependymal pathology. The clinical course of these tumors is difficult to predict, but prior studies have identified TERT mutations and loss of chromosome 6 as poor prognostic factors in these tumors when located in the posterior fossa. METHODS In this retrospective single-institution study, patients with a diagnosis of mixed subependymoma/ependymoma were identified and chart reviewed to describe demographics, pathological findings, treatment courses and clinical outcomes. RESULTS Eleven patients (median age 57, 2 female, 9 male) were identified. Ten tumors were in the posterior fossa and 1 in the cervical spine. At diagnosis, 2 underwent gross total resection, 8 underwent subtotal resection, and 1 was only biopsied. Radiotherapy was administered to 5 patients at initial diagnosis (54Gy in 30 fractions). Systemic therapy (temozolomide and bevacizumab) was administered at recurrence to 2 patients. Loss of chromosome 6 was present in 5 /11 patients and TERT mutations were present in 4/11 patients, with both present in 3/11 patients. Median progression-free survival (mPFS) was 90.1 months (95% CI: 90.1–NA), with 3 progression events observed at 22, 90, and 107 months. Median follow-up duration was 27.9 months. Receiving radiotherapy had a longer mPFS (98 vs 22 months, p=0.15), although non-significant. At the time of data cut-off none of the patients were deceased. CONCLUSIONS Mixed subependymoma/ependymoma is a rare diagnosis with varying clinical outcomes. Our case series demonstrated that the majority of patients did not exhibit the previously identified poor prognostic factors. Although non-significant due to limited case numbers, radiotherapy was suggestive of longer mPFS.

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 We describe a 46-year-old male with Neurofibromatosis type 1 (NF1) and a previously known parenchymal medullary mass identified in 2022, initially managed with surveillance. In late 2023, he developed a progressive brainstem syndrome prompting presentation to our center. MRI showed a T2/FLAIR hyperintense, heterogeneously enhancing ventral medullary mass with leptomeningeal involvement, a non-enhancing right pontine lesion, right optic nerve glioma, and nodular leptomeningeal foci at T4 and L4–L5. He received IMRT to the brainstem prior to tissue diagnosis with good local response. Due to progression at T4, biopsy revealed a hypercellular, mitotically active glioma without necrosis or microvascular proliferation. Molecular profiling demonstrated IDH1/2-wildtype, H3K27M-negative, retained ATRX, intact 1p/19q, and CDKN2A/B homozygous deletion. DNA methylation profiling classified the tumor as high-grade astrocytoma with piloid features (HGAP). Spinal lesions responded to proton therapy, but new metastatic foci emerged in the subependymal lateral ventricles, leptomeninges, and cervical spinal cord. He underwent whole-brain radiation, oral temozolomide, and intrathecal cytarabine/etoposide. In June 2024, cell-free DNA analysis showed a similar molecular profile with newly identified 1p/19q codeletion, diverging from tissue findings. The patient remains alive >3 years from initial presentation. HGAP is a novel molecular class of IDH-wildtype gliomas defined by DNA methylation profiling and MAPK pathway alterations (commonly NF1, BRAF, FGFR1), CDKN2A/B codeletion, and ATRX alterations. These tumors often resemble glioblastoma or diffuse midline gliomas on imaging and are typically infratentorial or spinal, complicating biopsy. Prognostic data are limited, with <50% 5-year overall survival and no clear prognostic value of MGMT promoter methylation. This case illustrates multifocal HGAP in a patient with NF1 with favorable survival. The acquired 1p/19q codeletion raises questions about tumor mosaicism, subclonal evolution, and prognostic implications.

BACKGROUND AND OBJECTIVES: While many trials of the last decade have shifted the paradigm of stroke care toward endovascular therapy, a lack of timely access to centers with neurointerventional capabilities for many patients has remained a significant barrier to care. Endovascular robotic systems are poised to address this need for expanded access through telerobotic procedures, while also benefiting clinical team safety and raising the possibility of improved procedural precision. METHODS: A telerobotic setup consisting of a magnetic field generator and a mechanical advancer was placed with a silicone vascular phantom in an interventional suite in Switzerland and connected by wireless internet to an operator console in Arizona. From the operator console, the neurosurgeon navigated a guidewire and aspiration catheter to simulated middle cerebral artery (MCA) (n = 10) and basilar artery (n = 10) clots in consecutive timed procedural trials in evaluation of system feasibility and operator learning curve. RESULTS: From over 5700 miles away, the neurosurgeon navigated an aspiration catheter to 10 MCA and 10 basilar artery simulated large vessel occlusions. Clot aspiration was attempted in 4 trials and successful in each. Endovascular navigational time over 10 procedural trials to the M1 segment of the MCA and 10 trials to the basilar artery were completed on average in 114 seconds (SD 44.4 seconds) and 134 seconds (SD 65.8 seconds), respectively. Sufficient operator mastery was demonstrated in 20 procedural iterations with an average 77% reduction in navigational time from aortic arch initial to final trials. CONCLUSION: To our knowledge, we present the first intercontinental aspiration thrombectomy in the preclinical setting in comparable time to the clinical practice and demonstrate the early potential of telerobotic navigation. A lack of haptic feedback did not pose a hindrance, and the potential for intuitive operation offers to drive future research and adoption of this telesurgery system.

BACKGROUND AND OBJECTIVES: Brainstem cavernous malformations (BSCM) can result in spontaneous intracerebral hemorrhage (ICH), often resulting in significant morbidity. We aimed to assess the functional outcome and identify predictors of functional neurological outcome after single and multiple symptomatic hemorrhages. METHODS: As part of an international multicenter collaboration, institutional databases from 3 different tertiary referral centers included BSCM patients with complete baseline characteristics, MRI Data set, ≥1 ICH, and ≥1 follow-up examination followed at our institutions between 2003 and 2023. Functional neurological outcome was obtained using the modified Rankin Scale at diagnosis, before and after each ICH, and last follow-up. Patients were excluded after surgical removal of the lesion and/or loss of follow-up. RESULTS: A total of 383 patients (41.47 ± 15.51 age; 220 [57.4%] female) were included and followed for an average of 63.16 ± 85.75 months. Functional neurological outcome deteriorated in 47.2% ( P = .010) after the second ICH and in 46.5% ( P = .007) after the third ICH. Moreover, the functional neurological status was impaired in 22.3% ( P < .001) of patients at last available follow-up compared with the time of BSCM diagnosis. CONCLUSION: In our study, we observed that the chance of full recovery might decrease with each ICH. We observed a significantly associated neurological deterioration after each ICH compared with initial ICH.