Meningiomas are the most common primary intracranial tumor in adults. Traditional bulk genomic and histopathological analyses have provided valuable insights into meningioma biology. Recent advances in single-cell sequencing technologies have enabled the comprehensive study of a tumor’s transcriptome and epigenome at a single-cell resolution, along with spatially resolved data and functional genomics approaches. These strategies allow for the profiling of complex intratumoral and intertumoral heterogeneity, the identification of gene regulatory networks, and the characterization of distinct cell populations within the tumor microenvironment that drive tumor behavior. In this review, we examine the current landscape of single-cell omics in meningioma research and highlight opportunities for future discovery.

BACKGROUND:The introduction of genomic profiling as a tool for molecular classification and clinical outcome prediction has revolutionised the care of patients with brain tumours. Artificial intelligence (AI) provides advanced avenues to convert complex genomic information into routinely available patient-level information. In this study, we aimed to evaluate whether deep learning could robustly characterise molecular subtypes, predict risk of recurrence, and identify salient chromosome copy number alterations in haematoxylin and eosin (H&E)-stained tissue samples of the most common brain tumour, meningioma. METHODS:For this retrospective, multicentre, model development and validation study, we created a cohort of meningioma cases with paired DNA methylation and matched digitised H&E images. The cohort included a training dataset consisting of a cross-section of real-world clinical cases from the National Cancer Institute (USA; n=439) and a dataset comprising real-world clinical cases from the University Health Network (Canada; n=166). A second test dataset consisting of WHO grade 2 meningiomas after gross total resection (from the Mayo Clinic, USA; n=67) was used for additional clinical validation. We trained and validated five dedicated deep learning models to use H&E staining alone to predict the following: molecular classification of meningiomas (ie, molecular groups 1-4 [MG1-4], which are associated with homogeneous biology and clinical outcomes); three relevant chromosome arm aneuploidies (1p loss, 1q gain, and 22q loss); and DNA methylation-based 5-year progression-free survival risk group (high vs low). Area under the receiver operating characteristic curve (AUC) and balanced accuracy were used to assess classifier performance, and differences in progression-free survival between groups predicted to be at high or low risk of recurrence were compared using the log-rank test. FINDINGS:Our deep learning classifier achieved balanced accuracies of 87-97% for predicting meningioma molecular group from H&E staining when an output probability threshold of 0·4 was used. AUCs were 0·98 (95% CI 0·97-1·00) for MG1 versus other molecular groups, 0·96 (0·94-0·99) for MG2 versus other molecular groups, 0·81 (0·74-0·88) for MG3 versus other molecular groups, and 0·88 (0·83-0·94) for MG4 versus other molecular groups. AUCs for chromosome-level alterations were 0·86 (0·80-0·92) for chromosome 1p loss, 0·86 (0·79-0·93) for chromosome 22q loss, and 0·79 (0·65-0·91) for chromosome 1q gain. The dedicated outcome prediction model was prognostic for progression-free survival after adjusting for WHO grade, extent of resection, and patient age (hazard ratio 3·49, 95% CI 1·54-7·91; p=0·0028). INTERPRETATION:To our knowledge, this study is the first to apply deep learning models that can, beyond diagnosis, identify molecular subtypes and predict outcomes in a single brain tumour entity (meningioma) using H&E staining alone. To date, treatment tailoring and individualised risk prediction for meningioma have only been possible using resource-intensive genomic profiling. The study shows the broad clinical utility of applying AI modelling to readily available H&E-stained samples to democratise access to genomic information globally. FUNDING:Canadian Institutes of Health Research and Brain Tumour Charity.

PURPOSE:Radiation-induced meningiomas (RIMs) are an uncommon late complication of cranial irradiation that frequently display aggressive behavior. Although recent genomic and epigenomic studies have redefined sporadic meningiomas into four molecular groups with distinct biological and clinical characteristics, the same analysis has not yet been conducted on RIMs. This study sought to contextualize RIMs within the current methylation-based meningioma classification. METHODS:DNA methylation data from RIMs (n = 20) were integrated with a reference cohort of sporadic (n = 121) meningiomas previously used to define molecular subgroups. Molecular group membership was assigned using a supervised machine-learning approach. Copy-number alterations and pathway enrichment analyses were derived from methylation data, and clinical features were compared between RIMs and sporadic meningiomas. RESULTS:Supervised molecular classification assigned 70% RIMs to the hypermetabolic subtype. The RIM cohort demonstrated broad DNA hypomethylation enriched for metabolic and biosynthetic pathways. Copy-number profiling revealed widespread chromosomal instability, including recurrent 22q loss involving NF2 and SMARCB1 as well as PTEN, MYB, and C19MC, consistent with the copy number alterations observed in hypermetabolic meningiomas. CONCLUSIONS:RIMs predominantly align with the hypermetabolic molecular group, characterized by metabolic pathway activation and genomic instability. This distribution indicates a distinct molecular profile compared with sporadic meningiomas.

BACKGROUND:Meningiomas exhibit clinical heterogeneity. Radiotherapy (RT) remains the only adjuvant therapy, but tumor-control is variable, and biomarkers are limited. NRG/RTOG-0539 is the first prospective phase 2 trial to stratify meningioma patients for adjuvant RT based on clinical risk. Here, we apply modern molecular tools to this cohort and identify correlates of RT response. METHODS:Tumor tissue from 100 RTOG-0539 patients was profiled using DNA methylation arrays, RNA sequencing, and whole-exome sequencing. Recurrence scores, Molecular Groups, gene expression, and copy number alterations were compared across clinical groups and between RT responders and non-responders; non-response was defined as progression or death within 3 years. RESULTS:Modern grading criteria, including brain invasion, TERT mutation, CDKN2A/B deletion, and 1p/1q status, would reclassify 10% of tumors and alter treatment group assignment in 7%. Non-responders to RT exhibited more frequent 1p and 14q loss, and more copy number alterations. Transcriptomic and epigenetic profiling revealed immune-related signatures in responders and cell cycle-related pathways in non-responders, several of which overlapped with targets of vorinostat, a histone deacetylase inhibitor previously validated in aggressive meningioma models. The Proliferative Molecular Group was an independent predictor of post-RT recurrence in multivariable analysis, outperforming WHO grade. CONCLUSION:Multi-omic analysis of the NRG/RTOG-0539 cohort shows that updated WHO grading criteria, incorporating molecular and cytogenetic features, improve risk stratification. However, molecular classification, particularly the Proliferative group, remains an independent and stronger predictor of RT response. These findings support integrating molecular biomarkers alongside modern grading frameworks to guide treatment and trial design in meningioma. CLINICAL TRIAL INFORMATION:NCT00895622.

Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive sarcomas arising from Schwann cells and characterized by marked cellular and molecular heterogeneity. Although bulk multi-omic studies have provided valuable insights into MPNST biology, recent advances in single-cell profiling have deepened our understanding of the tumor microenvironment and molecular mechanisms underlying malignant transformation. Single cell analyses have revealed distinct Schwann cell-like, malignant neural crest-like, immune, and stromal cellular subpopulations within MPNSTs and their precursor lesions. Comparative profiling of MPNSTs, neurofibromas, and atypical neurofibromatous neoplasms of uncertain biologic potential, suggest that MPNST progression involves Schwann cell dedifferentiation into a more primitive, stem-like state. In this review, we summarize key discoveries from single-cell characterization studies, and discuss how these findings illuminate MPNST tumorigenesis, cellular plasticity, and potential therapeutic vulnerabilities.

The classification and management of central nervous system (CNS) tumours have undergone substantial transformation over the past two decades, with the recognition of an expanding set of molecularly defined disease entities. Genome-wide DNA methylation profiling has emerged as an important complement to histopathology and targeted molecular testing, providing an epigenetic framework that captures tumour lineage, biological state and large-scale genomic alterations within a single assay. Accumulating evidence demonstrates that methylation-based approaches improve diagnostic precision, resolve biologically heterogeneous entities and support clinically meaningful risk stratification across a broad spectrum of CNS tumours. Methylation profiling has also increasingly been applied to outcome prediction, liquid biopsy-based analyses and the resolution of diagnostically challenging or misclassified cases. In addition, advances in multi-omic integration, computational inference from routinely acquired clinical data and evolving implementation frameworks are extending the clinical applicability of epigenetic profiling. This review synthesises the current state of DNA methylation profiling within the diagnostic and clinical framework of CNS tumours and considers how emerging technologies and practice models are shaping its ongoing integration into neuro-oncological care.

Recent advances in our understanding of the molecular landscape of meningioma have generated new insights into the biology and heterogeneity of this disease, with demonstrated clinical value. However, there remains a need to understand tumor-intrinsic heterogeneity at single-cell resolution to inform potential therapeutic avenues. In this study, we examined the breadth of cell types and states in meningioma using a large cohort profiled with single-nuclear RNA sequencing and high-resolution spatial transcriptomics, as well as bulk DNA methylation and RNA sequencing (n = 712), bulk proteomics (n = 88) and plasma methylation (n = 59). We demonstrated that myeloid cell states differ across molecular groups of meningiomas and evolve meaningfully from dura to tumor. Myeloid cell states were also associated with unique myeloid-neoplastic interactions and neoplastic gene programs, suggesting a role in shaping the microenvironment. Finally, multiple non-neoplastic cell states refined outcome prediction beyond molecular group, suggesting a role in meningioma progression.

Background Adolescent and young adult (AYA) patients remain underrepresented in neuro-oncology research. Despite being the second most common primary brain tumor in this population, meningiomas have not been studied using age-specific molecular analyses. DNA methylation-based classification and prognostic tools have transformed meningioma care. This study aimed to evaluate the performance of these tools across age groups.Methods We analyzed 1,568 meningiomas with DNA methylation and clinical data, including 18 pediatric patients (<15 years), 195 AYA patients (15-39 years), and 1,355 adult patients (>39 years). Pediatric and AYA (P/AYA) tumors were combined and compared with adult tumors. The performance of established molecular classifiers and recurrence predictors, as well as differences in chromosomal copy number alterations were compared across age groups.Results While histologic grading was comparable between cohorts, P/AYA tumors displayed significantly fewer aggressive molecular groups and lower frequencies of chromosomal arm losses, including 1p, 6q, and 14q. The adult-trained recurrence predictor failed in the P/AYA population (AUC 0.57), despite similar score distributions. Retraining the model on an age-specific cohort using an identical analytic framework improved performance (AUC 0.79) and enabled effective stratification of progression-free survival (P = 0.00054). Importantly, 1p loss retained prognostic significance within the P/AYA group, supporting its clinical utility.Conclusions Molecular tools developed in adult-dominant cohorts do not generalize to younger patients due to both biological divergence and exclusion from model development. These findings underscore the need for age-specific molecular frameworks and highlight the imperative of including P/AYA populations in precision neuro-oncology research to ensure lifespan-equitable care.

BACKGROUND:DNA methylation profiling can be used to robustly predict postsurgical outcomes and response to radiotherapy (RT) for meningioma patients. To allow for seamless integration of these complementary models into clinical practice, a practical framework is needed. METHODS:We leveraged a cohort of nearly 2000 surgically-treated meningiomas with DNA methylation profiling and clinical outcomes data. Existing methylation-based prediction models were dichotomized to yield four risk groups: low and high recurrent risk, each with RT sensitive and resistant subgroups. Risk groups were correlated with progression-free survival in the context of existing biomarkers including extent of resection and WHO grade. RESULTS:We first demonstrated that all risk groups benefit from gross total resection. All "high-risk, RT sensitive" tumors (n = 306, 15.7%) also benefited from adjuvant RT: after GTR, median PFS increased from 4.68 (4.13-9.48) years to not reached (P = .003); after subtotal resection (STR), from 2.12 (1.59-3.02) to 4.09 (3.41-not reached) years (P = .004). "Low-risk, RT sensitive cases" (n = 1207, 61.8%) also benefited from RT after STR (median PFS 7.39 (6.66-12.8) vs. 16.53 (10.35-not reached) years, P = .03), suggesting that RT be considered in these patients. Neither "low-risk RT resistant" (n = 84, 4.3%) nor "high-risk RT resistant" (n = 356, 18.2%) cases benefited from RT, and the latter group was associated with universally poor outcomes. CONCLUSIONS:We identify methylation-defined risk groups of meningioma for which additional benefit is gained from adjuvant RT, leading to a clinical decision-making framework for straightforward integration of molecular models into clinical practice.

A major barrier in our understanding of glioblastoma (GBM) is the difficulty in tracking tumour development in real-time. We adopted a novel approach that combines the principles of classic cellular barcoding with CRISPR/Cas-9 technology and single-cell RNA sequencing known as continuous lineage tracing to apply a phylogenetic approach to studying tumour development. Patient derived glioma initiating cell lines were engineered with expressed DNA barcodes with CRISPR/Cas-9 targets and subsequently implanted to create an intracranial xenograft model. Tumors were sent for single cell RNA sequencing; clonal relationships were surmised through identification of expressed barcodes, and cells were characterized by their transcriptional profiles. Phylogenetic lineage trees were created utilizing lineage reconstructive algorithms to define cell fitness and expansion. Our work has revealed a significant amount of intra-clonal cell state heterogeneity, suggesting that tumour cells engage in phenotype switching prior to therapeutic intervention. We defined a consistent transcriptional pattern for tumour engraftment and in vivo clonal advantage. Phylogenetic lineage trees allowed us to define gene signatures of both cell fitness and expansion, which correlate strongly with published neural-mesenchymal and developmental-injury response phenotypes. GBMs exist along a transcriptional gradient between undifferentiated but “high-fit” cells and terminally differentiated, “low-fit” cells: cells with highly fitness appear to represent cells undergoing cell state transition. We successfully employed a novel lineage tracing technique in GBM, creating a powerful tool for real-time tracing of tumour growth through the analysis of a unique set of highly detailed transcriptional data with associated clonal and phylogenetic relationships.

BACKGROUND:TERT promoter mutation is a rare biomarker in meningiomas associated with aberrant TERT expression and reduced progression-free survival. Although high TERT expression is characteristic of tumours with TERT promoter mutations, it has also been observed in tumours with wildtype TERT promoters. This study aimed to investigate the prevalence and prognostic association of TERT expression in meningiomas. METHODS:This multi-institutional cohort study retrospectively collected clinical and molecular data from 1241 meningiomas surgically resected between Jan 1, 2000, and Dec 31, 2024, at Toronto Western Hospital, Canada (n=380; discovery cohort) and external institutions in Canada, Germany, and the USA (n=861; validation cohort). All patients were aged 18 years and older. TERT promoter mutation and TERT expression were determined by Sanger and bulk RNA sequencing. The primary outcomes were TERT expression (presence or absence) in meningiomas with and without TERT promoter mutations, and the difference in progression-free survival between tumours expressing TERT and those not expressing TERT. Survival analysis was assessed using Cox regression and Kaplan-Meier analysis. FINDINGS:Between Jan 1, 2000, and Dec 31, 2024, clinical demographics and tumour characteristics were collected. Median follow-up was 6·2 years (IQR 1·7-12·5) in the discovery cohort and 3·3 years (1·3-3·8) in the validation cohort. 777 (65·8%) of 1181 patients with sex data in the overall cohort were female; 404 (34·2%) were male. TERT was expressed in 157 (28·7%) of 547 wildtype TERT promoter meningiomas and in 193 (32·0%) of 604 overall with RNA data. TERT expression overall conferred an intermediate progression-free survival, shorter than that in patients with TERT-negative tumours but longer than in those with TERT promoter mutations. In the discovery cohort, median progression-free survival was 3·2 years (95% CI 1·7-6·5) in patients with wildtype TERT promoter tumours expressing TERT, 16·0 years (7·1 to not reached; p=0·0021) in patients with TERT-negative wildtype TERT promoter tumours, and 1·6 years (0·9 to not reached; p=0·039) in patients with TERT promoter mutations. These findings were replicated in the validation cohort. Within each WHO grade, TERT expression conferred a progression-free survival equivalent to TERT-negative meningiomas of one grade higher. Grade 1 tumours with TERT expression had a progression-free survival similar to TERT-negative grade 2 tumours (median not reached [95% CI 16·0 to not reached] vs 8·2 years [95% CI 4·5 to not reached]; p=0·59). Grade 2 tumours with TERT expression had a similar progression-free survival to TERT-negative grade 3 tumours (median 3·6 years [2·4 to 5·3] vs 3·8 years [2·3 to not reached]; p=0·42). Multivariable regression showed that TERT expression remained associated with shorter progression-free survival even after adjusting for TERT promoter mutations, CDKN2A/B loss, chromosome 1p/22q status, and WHO grade (hazard ratio 1·85 [95% CI 1·33-2·57]; p=0·0002). INTERPRETATION:TERT expression in meningiomas predicted earlier disease progression, independent of TERT promoter mutation and other markers, and might warrant reclassification of meningiomas that express TERT to a higher WHO grade. FUNDING:Canadian Institutes of Health Research, Brain Tumour Charity UK, University Health Network Foundation, Mary Hunter Meningioma Research Fund, V Foundation, and National Institutes of Health.

Brain metastases (BMs) are a common and often fatal progression of systemic cancers, affecting up to 25% of patients. Despite advances in surgical resection and radiotherapy, the median survival remains limited to 10–16 months. While genomic profiling has enabled the development of targeted therapies, there remains a paucity of prognostic tools for clinical use. To address this, we explored the utility of DNA methylation profiling—an epigenetic marker increasingly recognized for its diagnostic and prognostic value. We profiled fresh-frozen tissue from 327 surgically resected BM samples of lung, breast, melanoma, and gastrointestinal (GI) origin using the Illumina Infinium MethylationEPIC BeadChip array. Unsupervised analysis via partitioning around medoids (PAM) clustering identified five robust groups. Four of these correlated closely with primary tumor origin. However, a distinct “Poly-Origin Cluster” emerged, comprising tumors from multiple primary sites with a shared epigenetic signature. Poly-Origin tumors showed improved survival outcomes compared to origin-aligned clusters (20.2 vs. 10.1 months, p=0.0018), independent of clinical covariates. Notably, these tumors were enriched for immune cell infiltration—including CD14+ macrophages, CD19+ B-cells, and CD56+ NK cells—based on deconvolution analysis, and exhibited reduced genomic instability as measured by copy number variation. These tumors were enriched for pathways related to cell signaling. We validated the prognostic significance of the Poly-Origin methylation signature in a publicly available independent cohort (n=96), and found that high signature concordance correlated with extended survival. Finally, we demonstrated the feasibility of detecting this signature in plasma-derived cell-free DNA using cfMeDIP-seq, with high classification accuracy (AUC = 0.98). These findings reveal a clinically meaningful subtypes of BM not captured by tissue origin or mutation status alone. Our work suggests that DNA methylation profiling may be a powerful tissue- and liquid biopsy–based tool that holds promise as a non-invasive prognostic biomarker in the management of brain metastasis.

Recurrent IDH-mutant gliomas pose a significant therapeutic challenge, with limited treatment options following progression after standard therapy. Combining PARP inhibitors with immune checkpoint blockade has been proposed as a synergistic strategy in IDH-mutant high-grade gliomas, leveraging vulnerabilities in homologous recombination repair and increased PD-L1 expression following PARP inhibition. This phase II trial (NCT03991832) evaluated the combination of the PARP inhibitor olaparib and the PD-L1 inhibitor durvalumab in patients with recurrent IDH-mutant glioma. We also investigated the potential of the plasma tumor methylome as a non-invasive biomarker of treatment response. Twenty-nine patients (median age 40.5 years; 41% female) were enrolled between January 2020 and February 2023. All patients received olaparib (300 mg twice daily) and durvalumab (1,500 mg IV every four weeks) until radiographic or clinical progression. Plasma samples were collected at baseline and monthly, and cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) was performed. The objective response rate was 10%, and median overall survival was 9.3 months. Longitudinal cfMeDIP-seq profiling enabled development of a circulating methylome classifier that accurately distinguished responders from non-responders. Integration with matched tumor transcriptomic and methylation data revealed enrichment of immune and DNA repair pathways in responders. Whole-exome sequencing identified no consistent mutational correlates. Spatial transcriptomic analysis demonstrated a more interactive, immune-rich tumor microenvironment and reduced malignant cell state diversity in responders. This study supports the safety of combined PARP and PD-L1 blockade in recurrent IDH-mutant glioma and highlights the plasma methylome as a promising non-invasive biomarker of therapeutic response.

TERT promoter mutation (TPM) is a rare but established biomarker in meningiomas associated with aberrant TERT expression, reduced progression-free survival (PFS), and reduced overall survival. While TERT is highly expressed in tumors with promotor mutations, its expression has also been observed even in tumors with wildtype TERT promoters (TP-WT). This study aimed to assess the prevalence of TERT expression and its association with clinical outcome in meningiomas. Methods: Bulk RNA sequencing (n=604), Sanger sequencing of the TERT promoter (n=1095), and methylation profiling (n=1218) of a multi-institutional cohort of meningiomas (total n=1241) were performed to determine TERT expression, TERT promoter mutation status, and TERT promoter methylation. A cohort of 380 meningiomas from Toronto was used for discovery, and 861 meningioma samples from external institutions were compiled as a validation cohort. Results: TERT expression was significantly higher in meningiomas with TPMs compared to TP-WT. However, TERT was still expressed in 30.4% of meningiomas that lacked TPM. TERT expression increased with higher WHO grades and was associated with shorter progression-free survival, even among TP-WT tumors. WHO grade 1 tumors that expressed TERT had PFS similar to those of WHO grade 2, while WHO grade 2 meningiomas expressing TERT had a PFS similar to those of WHO grade 3 meningiomas. Among grade 3 meningiomas, tumors expressing TERT had PFS similar to those with TPMs. Conclusions: Our findings highlight the prognostic significance of TERT expression in meningiomas, even in the absence of TPMs. Its presence may identify patients at greater risk of rapid progression. These results support the inclusion of TERT expression in risk stratification models and management strategies, including future WHO classification and cIMPACT-NOW molecular testing criteria. Chloe Gui, Justin Z. Wang, Vikas Patil, Andrew Ajisebutu, Jeff Liu, Zeel Patel, Rebeca Yakubov, Ramneet Kaloti, Yosef Ellenbogen, Christopher Wilson, Aaron Cohen-Gadol, Ghazaleh Tabatabai, Marcos Tatagiba, Felix Behling, Eric C. Holland, Jill S. Barnholtz-Sloan, Andrew E. Sloan, Craig Horbinski, Silky Chotai, Lola B. Chambless, Andrew Gao, Serge Makarenko, Stephen Yip, Kenneth Aldape, Farshad Nassiri, Gelareh Zadeh. TERT expression predicts progression-free survival in meningiomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4592.