Magill, Stephen T.

Abstract Background: Glioblastoma recurrence is inevitable despite aggressive surgical resection of contrast enhancing (CE) tumor. Unlike the deep molecular characterization of CE glioblastoma, unresected infiltrating tumor, representing minimal residual disease (MRD) has not been well profiled. Increasingly, clinical trials are being conducted in the MRD setting for glioblastoma patients, necessitating understanding of unresected infiltrating tumor for targeted therapeutic development. Methods: Visium HD spatial transcriptomics was used to profile a cohort of newly diagnosed, treatment-naïve glioblastoma patients that underwent supramaximal resection, with both core and infiltrating in situ components. Spatial profiles generated expression signatures of infiltrative and CE tumor. For in silico prediction of therapeutics targeting the MRD, expression signatures of infiltrative and CE tumor were compared with drug perturbation signatures from the NIH L1000 database using the sRGES method to predict reversal of disease expression patterns. IC50 data from human cell lines were aggregated from the Genomics of Drug Sensitivity in Cancer or individual study data while blood-brain barrier (BBB) penetrance was predicted using the CNS-MPO approach. Findings: Infiltrative tumor expression profiles were enriched for genes corresponding to neural and oligodendrocyte progenitor like cells. In contrast CE glioblastoma displayed mesenchymal and astrocytic programs, suggesting spatial concentration of glioblastoma states within MRD and CE tumor. For CE glioblastoma, proteasome inhibitors and glucocorticoid receptor agonists were predicted to have the highest antitumor activity. In contrast, histone deacetylase inhibitors (HDACi) were highly ranked for MRD while exhibiting low predicted efficacy in CE tumor. Synergy analysis of drug candidates provided multiple dual treatment options for both the CE and MRD contexts. Conclusions: Cumulatively, these data indicate that the resected tumor does not reflect the MRD state, providing clarity on why targeted therapeutics for glioblastoma have not been particularly successful. Spatial profiling of the MRD demonstrates vulnerability to unique drug targets, which if utilized in the correct setting could confer greater survival benefit. Citation Format: Harrshavasan Congivaram, Shashwat Tripathi, Mateo Gomez, Katy McCortney, Ching Man Wai, Ruochen Du, Thomas K. Sears, Jianzhong Zhang, Daniel J. Brat, Craig M. Horbinski, Mark W. Youngblood, Jared T. Ahrendsen, Adam M. Sonabend, Stephen T. Magill, Matthew C. Tate, Maciej S. Lesniak, Sean Sachdev, Timothy Sita, Priya Kumthekar, Karan Dixit, Robin Buerki, Ditte Primdahl, Mustafa Khasraw, John de Groot, David A. Reardon, Rimas V. Lukas, Roger Stupp, Amy B. Heimberger. In silico screening of therapeutics candidates targeting minimal residual disease in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2496.

ABSTRACT 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.

Brain tumor-related epilepsy (BTRE) is a common and debilitating symptom of central nervous system (CNS) tumors. The epileptogenic zone, defined as cortex responsible for seizure generation, is located at the peritumoral region for most tumors, and lower-grade intrinsic brain tumors have the highest seizure incidence. Surgery is often the most effective treatment for the reduction in seizures in BTRE. However, surgical decisions have historically often been made exclusively for oncologic reasons, with less emphasis on seizure control. Surgical approaches for all tumor types are reviewed, highlighting relevant risk factors. Adjunctive tools during surgery, such as intraoperative electrocorticography (ECoG), may help identify and remove surrounding brain areas which are epileptogenic. Minimally invasive surgery is also gaining traction, given its utility in treating seizures deep-seated tumors. This review explores epileptogenic brain tumors, surgery for BTRE, and emerging strategies to better achieve seizure control.

Abstract Targeted gene expression profiling improves risk stratification for meningiomas, but the utility of this approach for low grade meningiomas is incompletely understood. To address this, 466 meningiomas that were initially used to validate a 34-gene expression biomarker were combined with 270 previously unpublished samples for an analysis of biomarker performance in newly diagnosed (n=691) or recurrent (n=45) WHO grade 1 meningiomas treated with gross total (GTR) or subtotal resection (STR), and newly diagnosed WHO grade 2 meningiomas treated with GTR. Prediction of local freedom from recurrence (LFFR) or overall survival (OS) was measured using Cox proportional hazards models and the Kaplan-Meier method. The cohort included 736 patients, 69.4% female, with a median age at diagnosis of 58 years. There were 635 WHO grade 1 meningiomas, 498 with GTR and 133 with STR, and 101 WHO grade 2 meningiomas with GTR. The median follow-up was 4.6 years, the local recurrence rate was 12.0% and the rate of death was 8.4%. The biomarker classified 51.5% of meningiomas as intermediate risk and 9.6% as high risk. Combining the biomarker with extent of resection identified 19.7% as unfavorable (intermediate molecular risk with STR or high molecular risk), including 7.4% of newly diagnosed WHO grade 1 meningiomas with GTR. The risk score was prognostic for LFFR overall (hazard ratio per 0.1 increase in risk score 1.89, 95% confidence interval 1.58-2.25) and across WHO grades, extents of resection, and newly diagnosed or recurrent settings. Gene expression risk groups were prognostic for LFFR, with improved outcomes among low molecular risk compared to intermediate molecular risk (Log-rank, p=0.0002) and high molecular risk meningiomas (Log-rank, p<0.0001). These data suggest that a targeted gene expression biomarker can identify low grade meningiomas that are more likely to recur after surgery, possibly changing management for approximately one-fifth of cases.

Abstract The prevalence of asymptomatic, incidental meningiomas is increasing due to greater frequency of brain imaging. This retrospective cohort study of adult patients presenting at a single institution from 1996-2020 integrated clinical data, serial magnetic resonance imaging (MRI) volumetrics, DNA methylation profiling, and targeted gene expression profiling to define the natural history and molecular architecture of incidental meningiomas. Volumetrics were calculated from contrast-enhanced brain MRIs at time of diagnosis compared to serial surveillance MRIs (average 3 MRIs per patient pre-intervention, range: 1-12) DNA methylation groups and gene expression risk scores were defined for 123 samples that underwent resection. Time-to-event analyses were performed for progression-free survival (PFS), symptom-free survival (SFS), and treatment-free survival (TFS). The cohort included 238 meningiomas from 207 patients. Median age at diagnosis was 59 years and 81.5% of patients were female. Median clinical and imaging follow-up were 9.1 and 6.7 years, respectively, during which time 50.8% progressed. Median maximum diameter and volume at diagnosis were 2.20 cm and 4.25 cm3, respectively, and median pre-treatment volumetric growth rate was 19.2% per year. Median pre-treatment PFS and TFS were 3.10 and 1.27 years, respectively, and median SFS and post-treatment PFS were not reached. Cox proportional hazards regression identified maximum diameter at diagnosis as a significant predictor of worse pre-treatment PFS (multivariate HR 1.65 per 1 cm [95% CI 1.19–2.28], p=0.0027) and worse TFS (multivariate HR 1.44 per 1 cm [95% CI 1.17–1.76], p<0.001). Skull base tumors had with worse SFS (multivariate HR 2.48 [95% CI 1.02–6.00], p=0.045). Molecular and histological features among incidental meningiomas that ultimately underwent resection (n=176, 74.0%) were reassuring (89.2% WHO grade 1, 10.2% WHO grade 2). These findings suggest maximum diameter at diagnosis of incidental meningiomas is significantly associated with earlier progression and eventual treatment, and skull base tumors have earlier symptom development.

Abstract Background Surgery is the mainstay of treatment for meningioma, the most common primary intracranial tumor, but improvements in meningioma risk stratification are needed and current indications for postoperative radiotherapy are controversial. Recent studies have proposed prognostic meningioma classification systems using DNA methylation profiling, copy number variants, DNA sequencing, RNA sequencing, histology, or integrated models based on multiple combined features. Targeted gene expression profiling has generated robust biomarkers integrating multiple molecular features for other cancers, but is understudied for meningiomas. Methods Targeted gene expression profiling was performed on 173 meningiomas and an optimized gene expression biomarker (34 genes) and risk score (0 to 1) was developed to predict clinical outcomes. Clinical and analytical validation was performed on independent meningiomas from 12 institutions across 3 continents (N = 1856), including 103 meningiomas from a prospective clinical trial. Gene expression biomarker performance was compared to 9 other classification systems. Results The gene expression biomarker improved discrimination of postoperative meningioma outcomes compared to all other classification systems tested in the independent clinical validation cohort for local recurrence (5-year area under the curve [AUC] 0.81) and overall survival (5-year AUC 0.80). The increase in area under the curve compared to the current standard of care, World Health Organization 2021 grade, was 0.11 for local recurrence (95% confidence interval [CI] 0.07–0.17, P < 0.001). The gene expression biomarker identified meningiomas benefiting from postoperative radiotherapy (hazard ratio 0.54, 95% CI 0.37–0.78, P = 0.0001) and re-classified up to 52.0% meningiomas compared to conventional clinical criteria, suggesting postoperative management could be refined for 29.8% of patients. Conclusions A targeted gene expression biomarker improves discrimination of meningioma outcomes compared to recent classification systems and predicts postoperative radiotherapy responses.