UNASSIGNED: Infrared (IR) spectroscopy allows intraoperative, optical brain tumor diagnosis. Here, we explored it as a translational technology for the identification of aggressive meningioma types according to both, the WHO CNS grading system and the methylation classes (MC).
UNASSIGNED: Frozen sections of 47 meningioma were examined by IR spectroscopic imaging and different classification approaches were compared to discern samples according to WHO grade or MC.
UNASSIGNED: IR spectroscopic differences were more pronounced between WHO grade 2 and 3 than between MC intermediate and MC malignant, although similar spectral ranges were affected. Aggressive types of meningioma exhibited reduced bands of carbohydrates (at 1024 cm-1) and nucleic acids (at 1080 cm-1), along with increased bands of phospholipids (at 1240 and 1450 cm-1). While linear discriminant analysis was able to discern spectra of WHO grade 2 and 3 meningiomas (AUC 0.89), it failed for MC (AUC 0.66). However, neural network classifiers were effective for classification according to both WHO grade (AUC 0.91) and MC (AUC 0.83), resulting in the correct classification of 20/23 meningiomas of the test set.
UNASSIGNED: IR spectroscopy proved capable of extracting information about the malignancy of meningiomas, not only according to the WHO grade, but also for a diagnostic system based on molecular tumor characteristics. In future clinical use, physicians could assess the goodness of the classification by considering classification probabilities and cross-measurement validation. This might enhance the overall accuracy and clinical utility, reinforcing the potential of IR spectroscopy in advancing precision medicine for meningioma characterization.

This study adopts an innovative approach to utilize the DNA methylation class (MC) by prioritizing the understanding of discrepancies over traditional direct comparisons with the pathological diagnosis (PD). The aim is to clarify the morphological criteria for pleomorphic xanthoastrocytoma (PXA). Using the Children\'s Brain Tumor Network online database, PXA-diagnosed cases were sourced. MCs and CDKN2A/B statuses were ascertained using the Heidelberg methylation brain tumor classifier v12.5 (v12.8 for selected cases). Three distinct groups emerged: Group 1 confirmed PXA through both PD and MC (7 cases); Group 2 identified PXA via PD alone (7 cases); and Group 3 diagnosed PXA using MC (5 cases). Key insights from the study include the frequent local infiltration of PXA into gray matter structures, mirroring infiltrative astrocytoma. The MC for PXA stands out for its sensitivity. Cases with a PXA morphological diagnosis diverging from the DNA class warrant attention to newer differential diagnoses such as high-grade astrocytoma with piloid features, pilocytic astrocytoma NF1-associated, and NET-PATZ1. Tumors with a MC indicative of PXA but lacking its typical features may, if high-grade, behave as grade 4 gliomas. In contrast, their low-grade counterparts could belong to the PXA morphological continuum. Further research is pivotal for cementing these findings.

Rare glial, neuronal and glioneuronal tumours in adults form a heterogeneous group of rare, primary central nervous system tumours. These tumours, with a glial and/or neuronal component, are challenging in terms of diagnosis and therapeutic management. The novel classification of primary brain tumours published by the WHO in 2021 has significantly improved the diagnostic criteria of these entities. Indeed, diagnostic criteria are nowadays multimodal, including histological, immunohistochemical and molecular (i.e., genetic and methylomic). These integrated parameters have allowed the specification of already known tumours but also the identification of novel tumours for a better diagnosis.

OBJECTIVE: Anaplastic ganglioglioma is a rare tumour, and diagnosis has been based on histological criteria. The 5th edition of the World Health Organization Classification of Tumours of the Central Nervous System (CNS WHO) does not list anaplastic ganglioglioma as a distinct diagnosis due to lack of molecular data in previous publications. We retrospectively compiled a cohort of 54 histologically diagnosed anaplastic gangliogliomas to explore whether the molecular profiles of these tumours represent a separate type or resolve into other entities.
METHODS: Samples were subjected to histological review, desoxyribonucleic acid (DNA) methylation profiling and next-generation sequencing. Morphological and molecular data were summarised to an integrated diagnosis.
RESULTS: The majority of tumours designated as anaplastic gangliogliomas resolved into other CNS WHO diagnoses, most commonly pleomorphic xanthoastrocytoma (16/54), glioblastoma, isocitrate dehydrogenase protein (IDH) wild type and diffuse paediatric-type high-grade glioma, H3 wild type and IDH wild type (11 and 2/54), followed by low-grade glial or glioneuronal tumours including pilocytic astrocytoma, dysembryoplastic neuroepithelial tumour and diffuse leptomeningeal glioneuronal tumour (5/54), IDH mutant astrocytoma (4/54) and others (6/54). A subset of tumours (10/54) was not assignable to a CNS WHO diagnosis, and common molecular profiles pointing to a separate entity were not evident.
CONCLUSIONS: In summary, we show that tumours histologically diagnosed as anaplastic ganglioglioma comprise a wide spectrum of CNS WHO tumour types with different prognostic and therapeutic implications. We therefore suggest assigning this designation with caution and recommend comprehensive molecular workup.

We aimed to characterise glioblastomas of adolescents and young adults (AYAs) that were isocitrate dehydrogenase (IDH) wild type (wt) and H3wt.
Fifty such patients (aged 16-32) were studied by methylation profiling, targeted sequencing and targeted RNA-seq.
Tumours predominantly clustered into three methylation classes according to the terminology of Capper et al. (2018): (anaplastic) pleomorphic xanthoastrocytoma (PXA) (21 cases), GBM_midline (15 cases) and glioblastoma RTK/mesenchymal (seven cases). Two cases clustered with ANA_PA, four cases with LGG classes and one with GBM_MYCN. Only fifteen cases reached a calibrated score >0.84 when the cases were uploaded to DKFZ Classifier. GBM_midline-clustered tumours had a poorer overall survival (OS) compared with the PXA-clustered tumours (p = 0.030). LGG-clustered cases had a significantly better survival than GBM_midline-clustered tumours and glioblastoma RTK/mesenchymal-clustered tumours. Only 13/21 (62%) of PXA-clustered cases were BRAF V600E mutated. Most GBM_midline-clustered cases were not located in the midline. GBM_midline-clustered cases were characterised by PDGFRA amplification/mutation (73.3%), mutations of mismatch repair genes (40.0%), and all showed H3K27me3 and EZH1P loss, and an unmethylated MGMT promoter. Across the whole cohort, MGMT promoter methylation and wt TERT promoter were favourable prognosticators. Mismatch repair gene mutations were poor prognosticators and together with methylation class and MGMT methylation, maintained their significance in multivariate analyses. BRAF mutation was a good prognosticator in the PXA-clustered tumours.
Methylation profiling is a useful tool in the diagnosis and prognostication of AYA glioblastomas, and the methylation classes have distinct molecular characteristics. The usual molecular diagnostic criteria for adult IDHwt glioblastoma should be applied with caution within the AYA age group.

Our understanding of the genetic and epigenetic alterations in meningioma and the underlying tumor biology of meningioma has significantly changed over the past decade and resulted in revision of prognostically relevant meningioma subclasses within and beyond the WHO classification of CNS tumors.
The 2016 WHO classification of CNS tumors recognizes WHO grade I, II, and III based on histopathological features. Recent work has identified genetic alterations with prognostic implications, including mutations of the TERT promoter, loss of function of the DMD gene, and inactivation of the tumor suppressor BAP-1. Studies of DNA methylation patterns in meningiomas have resulted in a novel and prognostically relevant meningioma subclassification schema. There have been major advances in our understanding of prognostically relevant genetic and epigenetic changes in meningioma which will hopefully allow for improvement in clinical trial design and the development of more effective therapies for meningioma.

DNA methylation is often analyzed by reporting the average methylation degree of each cytosine. In this study, we used a single molecule methylation analysis in order to look at the methylation conformation of individual molecules. Using D-aspartate oxidase as a model gene, we performed an in-depth methylation analysis through the developmental stages of 3 different mouse tissues (brain, lung, and gut), where this gene undergoes opposite methylation destiny. This approach allowed us to track both methylation and demethylation processes at high resolution. The complexity of these dynamics was markedly simplified by introducing the concept of methylation classes (MCs), defined as the number of methylated cytosines per molecule, irrespective of their position. The MC concept smooths the stochasticity of the system, allowing a more deterministic description. In this framework, we also propose a mathematical model based on the Markov chain. This model aims to identify the transition probability of a molecule from one MC to another during methylation and demethylation processes. The results of our model suggest that: 1) both processes are ruled by a dominant class of phenomena, namely, the gain or loss of one methyl group at a time; and 2) the probability of a single CpG site becoming methylated or demethylated depends on the methylation status of the whole molecule at that time.