Intracranial sarcomas can arise secondarily from primary brain tumors, including gliomas and meningiomas, either spontaneously or following radiotherapy. The current WHO classification recognizes sarcomatous transformation in several tumor entities; however, sarcomas arising from meningiomas remain poorly characterized and are regarded as a possible histological manifestation within the spectrum of anaplastic meningiomas. We analyzed nine matched meningioma–sarcoma pairs using integrated histopathological assessment and molecular profiling, including DNA methylation analysis, next-generation sequencing, copy number profiling, and proteomics. Although recurrent sarcomatous tumors were clonally related to their meningioma precursors—sharing identical NF2 alterations and overlapping chromosomal aberrations—they demonstrated pronounced divergence at the histological, immunophenotypic, and epigenetic levels. Importantly, sarcomatous transformation occurred in four cases without prior radiotherapy. Sarcomatous recurrences exhibited loss of meningothelial markers and acquired expression of cytokeratin and myogenic markers. DNA methylation profiling revealed a shift away from canonical meningioma signatures toward profiles resembling non-meningothelial mesenchymal tumors. Proteomic analysis showed consistent upregulation of SOX2 in sarcomatous tumors compared with their primary counterparts, suggesting acquisition of stem-like features during lineage divergence. Clinically, these tumors were associated with aggressive growth, early recurrence, and extracranial metastases, resembling malignant sarcomas more closely than anaplastic meningiomas. In addition, analysis of an institutional cohort of NF2-mutant intracranial tumors (n = 316) suggests that sarcomas with inactivating NF2 mutations may originate from meningiomas even in the absence of a clinically recognized precursor. Together, these findings suggest that sarcomatous transformation represents a rare evolutionary endpoint in NF2-mutant meningiomas, marked by clonal continuity but pronounced biological divergence. These results highlight limitations of morphology-based classification and emphasize the value of integrated molecular diagnostics in distinguishing these tumors from conventional high-grade meningiomas. Given their sarcoma-like behavior despite a meningioma ancestry, these tumors may not be adequately captured by current meningioma grading schemes.

Classification of tumors in neuro-oncology today relies on molecular patterns (mostly DNA methylation) and their machine learning-supported interpretation. Understanding the process of algorithmic interpretation is essential for safe application in clinical routine. This is paradigmatically true for the most common primary intracranial tumor in adults, meningioma. Here, by applying multiomic profiling and multiple lines of orthogonal computational evaluation in multiple independent datasets, we found that not only tumor cell characteristics but also incremental changes in the tumor microenvironment (TME) have impact on epigenetic meningioma classification and clinical outcome. Besides revealing the decisive role of non-neoplastic cells in the CNS methylation classifier, this challenges the model of distinct meningioma subgroups toward a TME-determined risk continuum. This refines current controversies in molecular meningioma subtyping. In addition, we apply these learnings to devise and validate a simple diagnostic approach for increased clinical prediction accuracy based on immunohistochemistry, which is also applicable in resource-limited settings.

BeadChip array-based DNA methylation profiling has been recognized by the World Health Organization (WHO) as a key diagnostic tool for brain tumor classification. While its diagnostic utility has been well established, data on technical reproducibility, interlaboratory comparability, and data interpretation under real-world diagnostic conditions remain limited. Bridging this gap, we here report the results of an international proficiency test using the Infinium MethylationEPIC v2.0 platform and the corresponding Brain Tumor Classifier version 12.8. Tissue slides of eight FFPE brain tumor samples, covering a representative range of CNS tumor entities, were distributed among 24 laboratories in 10 different countries. Participants were asked to report methylation classes and copy number variation (CNV) profiles. Pre-array workflows were left to local procedures and results had to be submitted within 15 working days. Technical data reproducibility was high with a median pairwise beta-value correlation of 0.99 (range 0.93-1.0). In general, participating centers generated high-quality data, reflected by consistently low detection p-values (<0.01). Eighteen of the 24 participating centers (75%) successfully passed the test. Of the six centers that failed the test, two laboratories experienced technical issues that led to misclassification of individual cases and contributed to incorrect CNV reporting. Four additional centers showed substantial discrepancies in the interpretation of diagnostically highly relevant CNVs, whereas methylation classification was not impaired. While accurate DNA quantification proved to be an important pre-array step, the use of the DNA restoration kit had only minor influence on overall results. Taken together, our interlaboratory performance testing on EPIC v2.0 CNS tumor profiling confirms high reproducibility of tumor classification but reveals the need for harmonized CNV reporting.

Chronic obstructive pulmonary disease (COPD) leads to a progressive decline in lung function, making it a leading cause of mortality worldwide. While COPD primarily affects the lungs, emerging research shows the life-threatening role of associated systemic changes affecting other organs, including the brain. However, the complex interplay between COPD and the brain’s microenvironment remains poorly understood. Here, we monitored alterations across several brain regions in mouse models subjected to chronic mild hypoxia (CMH) or tobacco-smoke exposure leading to pulmonary hypertension (S-PH) or also emphysema (S-Em). Brain microvasculature, microglia and hypoxia pathway states were analyzed by immunofluorescence and immunohistochemistry imaging of standard FFPE tissue sections. Quantitative evaluation revealed model-specific changes in microvascular density, with increased density in the CMH and S-Em and decreased density in the S-PH mice. 3D-imaging of cleared thick brain tissue sections by spinning-disc confocal microscopy corroborated these findings and, together with fibrinogen, MMP-9 and TIMP-1 immunoblot analyses, provided a comprehensive view of the distinct microenvironment states. Overall, our findings link lung dysfunction and associated changes in the brain microenvironment, which may be relevant to understanding the systemic comorbidities of chronic lung diseases, particularly altered brain conditions that may influence lung cancer metastasis.

ABSTRACT:BackgroundCurrent treatment strategies for pediatric intracranial ependymoma do not consider molecular heterogeneity. Here, we evaluated molecular group-specific determinants of outcome and developed an improved risk stratification model. METHODS:Patients aged 0-21 years with localized intracranial ependymoma were enrolled into the prospective clinical trial E-HIT2000. Treatment included maximum safe surgery, local radiotherapy, and chemotherapy, stratified according to age, histology and, following a major amendment, residual tumor. Clinical data were analyzed in a pooled molecularly annotated cohort with data from patients treated analogously within subsequent registries. RESULTS:For 291 trial patients, the 5-year progression-free survival (PFS) and overall survival (OS) were 62 ± 3% and 81 ± 2%, respectively. For the molecularly annotated pooled cohort (n = 228), 5-year PFS/OS were: posterior-fossa group A ependymoma (EPN-PFA) (n = 146): 45 ± 4%/77 ± 4%; posterior-fossa group B ependymoma (EPN-PFB) (n = 19): 90 ± 7%/100%; supratentorial ependymoma, ZFTA fusion-positive (EPN-ZFTA) (n = 59): 64 ± 7%/86 ± 5%; supratentorial ependymoma, YAP1 fusion-positive (EPN-YAP1) (n = 4): 50 ± 25%/100%. Patients with EPN-PFA without molecular risk factors (1q gain, and/or subtype EPN-PFA1c/d/e, 2a), with complete resection, and postoperative radiotherapy showed favorable outcomes (5-year PFS/OS 75 ± 10%/92 ± 7%). For patients with EPN-PFA with molecular risk factors, prognosis was poor irrespective of residual tumor status (5-year PFS/OS: 33 ± 6%/64 ± 6%). Among EPN-ZFTA, 11/59 tumors were classified as EPN-ZFTA with alternative fusions, associated with inferior PFS (5-year PFS/OS: 36 ± 15%/91 ± 9%). For EPN-ZFTA-RELA, homozygous deletions of CDKN2A were associated with unfavorable outcomes (4-year PFS/OS: 19 ± 16%/57 ± 18% vs. 79 ± 7%/97 ± 3%, P = .0001). Finally, we developed a novel stratification model that discriminates standard and intermediate risk patients from those at high risk (P < .0001 for PFS and OS). CONCLUSIONS:These results strongly suggest the inclusion of molecular parameters into stratification and the use of distinct treatment strategies within future ependymoma trials.

Abstract Recent advances in neuro-oncology have highlighted the meninges as a key immunological hub at the interface between systemic immunity and the central nervous system (CNS). While meningiomas are primarily benign, ∼20% exhibit high-grade features including brain invasion, recurrence, and treatment resistance. Macrophages in the tumor microenvironment are of particular interest due to their high abundance and phenotypic plasticity. While many studies have investigated macrophage diversity in cancer, this study spatially resolves myeloid activation programs in their native tissue context, exploring correlates with clinical parameters including meningioma molecular subtypes and recurrence. We combine spatial transcriptomics (Xenium) with multiplexed ion beam imaging (MIBI), integrating gene expression and high-dimensional protein data at single-cell resolution. Our large multi-center retrospective cohort of 368 FFPE samples includes cases with detailed molecular and clinical annotations. To enable scalable spatial analysis, we sampled pathologist-annotated regions and imaged tissue-micro-arrays (TMAs). Myeloid activation programs were discovered using cNMF. Delaunay algorithms delineated niches in Xenium data, while CellCharter discovered tissue niches in MIBI data. Spatial protein and transcriptomic data from consecutive sections reveal spatially segregated biological processes. The myeloid compartment in meningioma makes up a significant part of the tumor bulk, especially in low-grade tumors. Protein-level analysis shows clear tissue zonation marked by specific metabolic marker expression such as MCT1 and differential infiltration by microglia-like macrophage subsets. Spatial transcriptomics revealed at least 5 nuanced transcriptional myeloid activation programs that differ across methylation classes of over 320 cases. These differential enrichments were accompanied by distinct infiltration patterns and cell-type co-localization suggesting interaction-driven activation of myeloid programs. Overall, tumor-associated macrophages (TAMs) exhibit context-dependent prognostic significance across cancer types, most evidence supports their association with poor prognosis. In meningiomas, dense TAM infiltration is a key feature of benign subtypes. This study provides spatial context to meningioma methylation classes and addresses critical questions regarding the microenvironmental cues that shape TAM diversity and whether specific TAM activation programs have prognostic value, potentially revealing novel therapeutic avenues. Citation Format: Domenico Calafato, Yiheng Tang, Gleb Rukhovich, Elyas Heidari, Leonille Schweizer, Michael Weller, Christine Haberler, Till Acker, Bhuvic Patel, Abigail Suwala, Felix Sahm, Felix Hartmann, Moritz Gerstung. Large-scale spatial molecular profiling uncovers distinct macrophage activation states across meningioma methylation classes [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 1206.

Molecular testing is essential for classifying central nervous system (CNS) tumors, with methylation profiling providing the highest diagnostic granularity. However, this requires more resources and time than conventional hematoxylin and eosin (H&E) histopathology, which is widely available globally. Here we propose Hetairos, an artificial intelligence algorithm that predicts 102 methylation-based CNS tumor subtypes from digital H&E slides. Built and validated on 9,606 patients and over 11,000 slides from 11 centers across four continents, Hetairos identified 50-70% of cases with high confidence, achieving an accuracy of 0.87 for its highest-rated predictions. Hetairos outperformed five board-certified neuropathologists in a direct histology-only comparison (0.68 versus 0.30). Prospective evaluation in routine diagnostics confirmed its performance, reducing turnaround time from 12 days (molecular testing) to 12 min. Hetairos supports diagnostic decision-making across the full spectrum of pediatric and adult CNS tumors by narrowing differential diagnoses and guiding efficient testing.

BACKGROUND:Replication repair deficiency is associated with increased risk of developing malignant gliomas. The aim of this study was to investigate primary mismatch repair deficient gliomas (PMMRDGs), a group of IDH-wildtype and H3-wildtype gliomas that is enriched among patients with CMMRD and Lynch syndrome. METHODS:We investigated how PMMRDGs differ from other gliomas with respect to DNA methylation profile, genomic alterations, histopathology, and clinical outcomes. RESULTS:PMMRDGs occur in pediatric, adolescents and the elderly, falling in two related methylation clusters and are characterized by a high frequency of replication repair deficiency. Histology showed multinucleated giant cells, and immunohistochemistry demonstrated loss of MMR protein expression. Survival analysis revealed long-term survival in patients with high mutational burden (>50 mut/Mb) and an intact chromosome 9p region, which was validated in an independent reference cohort. CONCLUSIONS:Overall, our findings indicate that PMMRDGs represent a distinct type of IDH-wildtype gliomas with potential for long-term survival likely driven by immune activation.

Meningiomas in pediatric and adolescent/young adult patients are poorly characterized biologically and clinically, and risk stratification is largely extrapolated from adult tumors. We analyze 293 tumors from patients aged 0-39 years using integrated histopathological and molecular profiling. Youth-onset meningiomas are enriched for NF2 and SMARCE1 alterations and exhibit a gain-dominated copy-number landscape, including recurrent chr17q gain, whereas canonical adult high-risk features, such as chr1p loss, lack prognostic significance. Adult-derived prognostic frameworks, including WHO grade, methylation-based stratification and integrated risk scores, fail to predict progression in patients ≤21 years of age. Tumors segregate into age-enriched epigenetic clusters defined by SMARCE1, NF2 and BAP1 alterations. Among NF2-altered tumors, patterns of Merlin inactivation, shaped by germline status and co-occurring copy-number variations, delineate biologically divergent subsets. In patients ≤21 years, extent of resection is the dominant predictor of outcome, while molecular features further refine risk assessment. These findings define pediatric and young adult meningiomas as a distinct molecular entity and support age-adapted risk refinement that integrates molecular features with strong clinical determinants.

Background Mitochondrial dysfunction is an important pathogenic factor in Alzheimer´s disease (AD) progression. Most studies analysed disturbances in the mitochondrial metabolism and oxidative stress or focussed on mitochondrial dynamics such as mitochondrial trafficking, fusion-fission and mitophagy. Objective Very limited data exist regarding changes in the mitochondrial numerical density at different levels of AD neuropathologic changes (ADNC) in human brains. Methods Mitochondrial numerical densities were analysed by morphometry using the marker protein ATP5B in sections of 13 brain areas of 8 patients with either low, mid or high ADNC, 6 patients with tauopathy and 10 control patients. Patient samples were classified according to the ABC score. Results In comparison to control patients, we detected increases in mitochondrial densities at low (not in all cases), mid and high ADNC in neurons of the frontal (25%) and temporal (11%) neocortices, pontine nuclei (30%) and Purkinje neurons of the cerebellum (30%). Contrarily, mitochondrial densities decreased by 20% in hippocampal neurons of the entorhinal cortex and CA3 region at mid and high ADNC. Only minor changes occurred in other brain regions investigated (e.g., parietal and occipital neocortices, inferior olive, substantia nigra, striatum). In tauopathy patients, changes in mitochondrial densities were comparable to those in AD patients, except for a stronger decrease in the entorhinal cortex (40%) and a greater increase in the temporal neocortex (30%). Conclusions In the neocortex, primarily affected by extracellular amyloid-β (Aβ) deposits, mitochondrial densities in neurons increased, whereas they decreased in the hippocampus, at first enriched in intracellular neurofibrillary tangles.

BACKGROUND:Meningiomas are the most common adult brain tumors. While homozygous deletions of CDKN2A/B are linked to early recurrence and hence serve as CNS WHO grade 3 criterion, the clinical impact of hemizygous deletions remains unclear-especially since distinguishing between hemi- and homozygous losses can be technically challenging. METHODS:DNA methylation data, copy-number, and mutation data were evaluated on a multicenter cohort of 970 meningiomas. Each sample's CDKN2A/B status was manually classified by visual inspection in relation to whole chromosomal losses and gains in the copy number profile generated from global methylation array data in relation to other copy number events. Progression probabilities were determined using the Kaplan-Meier method. RESULTS:Among 970 meningiomas, n = 30 had homozygous, n = 114 hemizygous (n = 31 segmental; n = 83 focal), and n = 826 CDKN2A/B balanced status. In cases with hemizygous deletions in general, an association with increased progression risk compared to balanced cases was observed, although this did not reach statistical significance (log-rank P = .074; HR = 1.36, 95% CI [0.97, 1.90]; P = .07). However, segmental hemizygous losses were linked to a significantly worse prognosis (log-rank P = .0023), but focal hemizygous deletions were not (log-rank P = .523). Segmental hemizygous CDKN2A/B deletions were more frequently associated with a higher amount of high-risk copy number variations than focal losses. CONCLUSION:Our findings suggest that hemizygous CDKN2A/B deletions overall do not confer worse risks for progression in meningiomas. The signal for segmental deletions may not be locus-specific but just one representation of the generally instable genome of aggressive meningiomas.

Purpose:Sarcomas pose a severe diagnostic challenge. A wide variety of these distinct entities need to be distinguished from each other and from less aggressive types of mesenchymal tumors, to ensure correct clinical management. A machine learning based classifier for sarcomas utilizing DNA methylation data from 1077 tumors recognizing 62 sarcoma types has already been developed and termed the sarcoma classifier, which we published in 2021. Here we present a major advancement of the scale and precision of the sarcoma classifier. Methods:DNA methylation profiles and histologic data from an unprecedented multi-institutional cohort of mesenchymal tumors were collected and analyzed. Utilizing a machine learning approach, the classifier was rigorously validated through five-fold nested cross-validation, achieving a 98% class-level accuracy and a Brier score of 0.017, indicative of well-calibrated probability estimates. Results:The sarcoma classifier v13.1 was developed based on a training set of 4377 methylation profiles from sarcomas and less aggressive mesenchymal tumors comprising 116 tumor sub-classes and 4 control groups forming 93 distinct methylation classes. Performance was validated using four independent cohorts, comprising a total of 1547 mesenchymal tumors. A methylation-based classifier prediction was obtained in 73% of cases in the validation sets, of which 91% matched the original histopathology diagnosis, thereby increasing diagnostic confidence. The classifier enabled a definitive molecular diagnosis or tumor reclassification in 6% of cases with inconclusive or ambiguous histological findings. Conclusion:Adding new sarcoma types and expanding tumor sample numbers in each methylation class in the new sarcoma classifier decisively increased the number of diagnostic predictions and improved match with histologic evaluation. This substantial advancement will promote clinical implementation of the tool for the diagnosis of mesenchymal tumor lesions.

Machine learning-based molecular classifications, particularly those using DNA methylation data, have greatly advanced diagnostics for meningioma, the most common type of primary intracranial tumor. Meningiomas have historically been classified into NF2-mutant and NF2-wild-type groups, while additional mutations and copy-number variations associated with progression risk have been incorporated into WHO grading. Several genome-wide methylation-based classification systems have been proposed. The systems, such as the random forest Brain Tumour Classifier, have been incorporated into diagnostic guidelines. However, while a number of core archetypes are shared among the different classifications, discrepancies on the definition and granularity of subtypes remain an obstacle to their clinical application. Understanding the underlying heterogeneity driving these classifications is therefore crucial. Through an integrated analysis of single-nuclei and spatially resolved transcriptomic data, as well as DNA methylation array data from multiple meningioma cohorts, we identified cell types and epigenetic signatures that are associated with increased aggressiveness in meningiomas. The results demonstrated that incremental changes in the tumor microenvironment (TME), particularly shifts in compositions and epigenetic-transcriptomic signatures in tumor-associated monocytes/macrophages and microglia-like cells, have a decisive impact on epigenetic classifications alongside tumor cells, and significantly affect clinical outcome. Therefore, we refine the previously proposed distinct molecular subtypes with a TME-determined risk continuum model for NF2-mutant meningiomas. Based on these discoveries, we additionally designed an immunohistochemistry-based diagnostic approach, which also captures intra-tumoral heterogeneities.

Meningiomas are the most common primary intracranial neoplasms, with highly variable patient outcomes. While most meningiomas are benign, a significant subset recurs postoperatively, presenting substantial treatment challenges. BAP1 gene inactivation has been suggested as a marker for aggressive meningiomas, although its precise molecular and clinical roles remain poorly understood. To comprehensively investigate BAP1-altered meningiomas, we used six meningiomas with known BAP1 alterations as a discovery set. Genome-wide DNA methylation profiling of these samples, along 11,151 reference meningiomas, identified a distinct molecular cluster (n = 42) using unsupervised visualization approaches. These tumors were further characterized by DNA/RNA sequencing, histopathological examination, and a retrospective review of clinical data, compared to reference meningioma cohorts, providing a thorough characterization of this rare tumor subtype. Our integrative analysis revealed BAP1-altered meningiomas as a distinct CNS tumor subtype, characterized by recurrent loss of chromosome 3p21 and driven by various BAP1-inactivating alterations. Although rhabdoid morphology is present in some cases, it is not exclusive and should not be used as a grading criterion. Progression-free survival analysis showed a median of 21 months (95% CI: 12-NA), with a 2-year overall survival rate of 79% (95% CI: 60%-100%), highlighting the aggressive nature of these tumors. Gene expression profiling revealed upregulation of PRC target genes, dysregulated Polycomb signaling, and elevated expression in several cellular and growth factor pathways. BAP1-altered meningiomas represent a distinct and aggressive CNS tumor subtype associated with PRC dysregulation and recurrent 3p chromosome loss. These findings support the designation "meningioma, BAP1-altered."

DNA methylation-based classification is now central to contemporary neuro-oncology, as highlighted by the World Health Organization (WHO) classification of central nervous system (CNS) tumors. We present the Heidelberg CNS Tumor Methylation Classifier version 12.8 (v12.8), trained on 7,495 methylation profiles, which expands recognized entities from 91 classes in version 11 (v11) to 184 subclasses. This expansion is a result of newly identified tumor types discovered through our large online repository and global collaborations, underscoring CNS tumor heterogeneity. The random forest-based classifier achieves 95% subclass-level accuracy, with its well-calibrated probabilistic scores providing a reliable measure of confidence for each classification. Its hierarchical output structure enables interpretation across subclass, class, family, and superfamily levels, thereby supporting clinical decisions at multiple granularities. Comparative analyses demonstrate that v12.8 surpasses previous versions and conventional WHO-based approaches. These advances highlight the improved precision and practical utility of the updated classifier in personalized neuro-oncology.

Defective DNA repair and metabolic rewiring are highly intertwined in promoting the development and progression of cancer. However, the molecular players at their interface remain poorly understood. Here we show that an RNF20-HIF1α axis links the DNA damage response and metabolic reprogramming in lung cancer. We demonstrate that RNF20, which catalyzes monoubiquitylation of histone H2B (H2Bub1), controls Rbx1 expression and thereby the activity of the VHL ubiquitin ligase complex and HIF1α levels. Ablation of a single Rnf20 allele significantly increases the incidence of lung tumors in mice. Mechanistically, Rnf20 haploinsufficiency results in inadequate tumor suppression via the Rnf20-H2Bub1-p53 axis and induces DNA damage, cell growth, epithelial-mesenchymal transition (EMT), and metabolic rewiring through HIF1α-mediated RNA polymerase II promoter-proximal pause release, which is independent of H2Bub1. Importantly, decreased RNF20 levels correlate with increased expression of HIF1α and its target genes, suggesting HIF1α inhibition as a promising therapeutic approach for lung cancer patients with reduced RNF20 activity.