PURPOSE: Meningiomas are the most frequent primary intracranial tumors. Patient outcome varies widely from benign to highly aggressive, ultimately fatal courses. Reliable identification of risk of progression for individual patients is of pivotal importance. However, only biomarkers for highly aggressive tumors are established ( CDKN2A/B and TERT), whereas no molecularly based stratification exists for the broad spectrum of patients with low- and intermediate-risk meningioma.METHODS: DNA methylation data and copy-number information were generated for 3,031 meningiomas (2,868 patients), and mutation data for 858 samples. DNA methylation subgroups, copy-number variations (CNVs), mutations, and WHO grading were analyzed. Prediction power for outcome was assessed in a retrospective cohort of 514 patients, validated on a retrospective cohort of 184, and on a prospective cohort of 287 multicenter cases.RESULTS: Both CNV- and methylation family–based subgrouping independently resulted in increased prediction accuracy of risk of recurrence compared with the WHO classification (c-indexes WHO 2016, CNV, and methylation family 0.699, 0.706, and 0.721, respectively). Merging all risk stratification approaches into an integrated molecular-morphologic score resulted in further substantial increase in accuracy (c-index 0.744). This integrated score consistently provided superior accuracy in all three cohorts, significantly outperforming WHO grading (c-index difference P = .005). Besides the overall stratification advantage, the integrated score separates more precisely for risk of progression at the diagnostically challenging interface of WHO grade 1 and grade 2 tumors (hazard ratio 4.34 [2.48-7.57] and 3.34 [1.28-8.72] retrospective and prospective validation cohorts, respectively).CONCLUSION: Merging these layers of histologic and molecular data into an integrated, three-tiered score significantly improves the precision in meningioma stratification. Implementation into diagnostic routine informs clinical decision making for patients with meningioma on the basis of robust outcome prediction.

Genome-wide DNA methylation signatures correlate with and distinguish central nervous system (CNS) tumor types. Since the publication of the initial CNS tumor DNA methylation classifier in 2018, this platform has been increasingly used as a diagnostic tool for CNS tumors, with multiple studies showing the value and utility of DNA methylation-based classification of CNS tumors. A Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) Working Group was therefore convened to describe the current state of the field and to provide advice based on lessons learned to date. Here, we provide recommendations for the use of DNA methylation-based classification in CNS tumor diagnostics, emphasizing the attributes and limitations of the modality. We emphasize that the methylation classifier is one diagnostic tool to be used alongside previously established diagnostic tools in a fully integrated fashion. In addition, we provide examples of the inclusion of DNA methylation data within the layered diagnostic reporting format endorsed by the World Health Organization (WHO) and the International Collaboration on Cancer Reporting. We emphasize the need for backward compatibility of future platforms to enable accumulated data to be compatible with new versions of the array. Finally, we outline the specific connections between methylation classes and CNS WHO tumor types to aid in the interpretation of classifier results. It is hoped that this update will assist the neuro-oncology community in the interpretation of DNA methylation classifier results to facilitate the accurate diagnosis of CNS tumors and thereby help guide patient management.

Corpora amylaceaare cell-derived structures that appear physiologically in the aged human brain. While their histological identification is straightforward, their ultrastructural composition and microenvironment at the nanoscale have remained unclear so far, as has their relevance to aging and certain disease states that involve the sequestration of toxic cellular metabolites. Here, we apply correlative serial block-face scanning electron microscopy and transmission electron tomography to gain three-dimensional insight into the ultrastructure and surrounding microenvironment of cerebralCorpora amylaceain the human brainstem and hippocampal region. We find that cerebralCorpora amylaceaare composed of dense labyrinth-like sheets of lipid membranes, contain vesicles as well as morphologically preserved mitochondria, and are in close proximity to blood vessels and the glymphatic system, primarily within the cytoplasm of perivascular glial cells. Our results clarify the nature of cerebralCorpora amylaceaand provide first hints on how they may arise and develop in the aging brain.

The majority of metabolic principles are evolutionarily conserved from nematodes to humans. Caenorhabditis elegans has widely accelerated the discovery of new genes important to maintain organismic metabolic homeostasis. Various methods exist to assess the metabolic state in worms, yet they often require large animal numbers and tend to be performed as bulk analyses of whole worm homogenates, thereby largely precluding a detailed studies of metabolic changes in specific worm tissues. Here, we have adapted well-established histochemical methods for the use on C. elegans fresh frozen sections and demonstrate their validity for analyses of morphological and metabolic changes on tissue level in wild type and various mutant strains. We show how the worm presents on hematoxylin and eosin (H&E) stained sections and demonstrate their usefulness in monitoring and the identification of morphological abnormalities. In addition, we demonstrate how Oil-Red-O staining on frozen worm cross-sections permits quantification of lipid storage, avoiding the artifact-prone fixation and permeabilization procedures of traditional whole-mount protocols. We also adjusted standard enzymatic stains for respiratory chain subunits (NADH, SDH, and COX) to monitor metabolic states of various C. elegans tissues. In summary, the protocols presented here provide technical guidance to obtain robust, reproducible and quantifiable tissue-specific data on worm morphology as well as carbohydrate, lipid and mitochondrial energy metabolism that cannot be obtained through traditional biochemical bulk analyses of worm homogenates. Furthermore, analysis of worm cross-sections overcomes the common problem with quantification in three-dimensional whole-mount specimens.

NUT midline carcinoma is an aggressive neoplasm defined by chromosomal rearrangements of the nuclear protein in testis (NUT) gene (NUTM1). In this article, we present a strategy to detect this rare tumor through a standard DNA methylation array analysis even when occurring in unusual anatomic sites. We illustrate our approach through a case study in which we detected metastatic spread of a NUT midline carcinoma within a bone marrow biopsy that exhibited histological features of a blastoid, undifferentiated neoplasm. Our strategy builds on molecular data derived from The Cancer Genome Atlas and Gene Expression Omnibus as well as computational strategies adopted from the Brain Tumor Methylation Classifier. It is a combined approach that detects the unusual cell lineage of NUT midline carcinomas and makes diagnostic use of the entity-specific copy number alterations.