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Methods for quantifying gene expression 1 and chromatin accessibility 2 in single cells are well established, but single-cell analysis of chromatin regions with specific histone modifications has been technically challenging. In this study, we adapted the CUT&Tag method 3 to scalable nanowell and droplet-based single-cell platforms to profile chromatin landscapes in single cells (scCUT&Tag) from complex tissues and during the differentiation of human embryonic stem cells. We focused on profiling polycomb group (PcG) silenced regions marked by histone H3 Lys27 trimethylation (H3K27me3) in single cells as an orthogonal approach to chromatin accessibility for identifying cell states. We show that scCUT&Tag profiling of H3K27me3 distinguishes cell types in human blood and allows the generation of cell-type-specific PcG landscapes from heterogeneous tissues. Furthermore, we used scCUT&Tag to profile H3K27me3 in a patient with a brain tumor before and after treatment, identifying cell types in the tumor microenvironment and heterogeneity in PcG activity in the primary sample and after treatment. An improved method for single-cell analysis of histone modifications is applied to stem cell differentiation and cancer.

Astrocytomas that harbor recurrent genomic alterations in MYB or MYBL1 are a group of Pediatric-type diffuse low-grade gliomas that were newly recognized in the 2021 WHO Classification of Tumors of the Central Nervous System. These tumors are described in the WHO classification as harboring fusions in MYB or MYBL1 . In this report, we examine 14 consecutive cases in which a MYB or MYBL1 alteration was identified, each with diagnostic confirmation by genome-wide DNA methylation profiling (6 Angiocentric gliomas and 8 Diffuse astrocytomas, MYB- or MYBL1 -altered), for their specific genomic alterations in these genes. Using RNA sequencing, we find productive in-frame fusions of the MYB or MYBL1 genes in only 5/14 cases. The remaining 9 cases show genomic alterations that result in truncation of the gene, without evidence of an in-frame fusion partner. Gene expression analysis showed overexpression of the MYB ( L1 ) genes, regardless of the presence of a productive fusion. In addition, QKI, a recognized fusion partner common in angiocentric glioma, was generally up-regulated in these 14 cases, compared to a cohort comprising >1000 CNS tumors of various types, regardless of whether a genomic alteration in QKI was present. Overall, the results show that truncations, in the absence of a productive fusion, of the MYB ( L1 ) genes can likely drive the tumors and have implications for the analysis and diagnosis of Angiocentric glioma and Diffuse astrocytoma, MYB- or MYBL1 -altered, especially for cases that are tested on panels designed to focus on fusion detection.

YAP1 gene fusions are found in a multitude of human tumors and are the likely tumor-initiating events in these tumors. We have previously shown that YAP1 fusion proteins exert TEAD-dependent oncogenic YAP1 activity. However, the contributions of the C-terminal fusion partners to the oncogenic functions of YAP1 fusion proteins are understudied. Here, we expressed eight different YAP1 gene fusions in vivo. Tumors induced by YAP1::TFE3 displayed a significantly different histomorphology compared to tumors induced by other YAP1 fusions or activated non-fusion YAP1. To assess the extent to which TFE3 activity and the functional TFE3 domains (DNA binding: leucine zipper (LZ) and basic-helix-loop-helix (bHLH); activation domain (AD)) contribute to the oncogenic functions of YAP1::TFE3, we generated several mutant variants and performed functional in vitro and in vivo assays. In vitro, mutation or deletion of the TFE3 DNA binding domains (LZ, bHLH) resulted in reduced TFE3 activity but increased YAP1 activity of YAP1::TFE3. In vivo, deletion of the LZ and bHLH domains did not result in a decrease in tumor incidence but induced the formation of more YAP1-like tumors that lacked prominent features of YAP1::TFE3-driven tumors. By contrast, loss of the TFE3 AD almost completely abrogated tumor formation. Our results suggest that the TFE3 domains significantly contribute to the oncogenic activity of YAP1::TFE3.

Hemangioblastoma is a benign, highly vascularized neoplasm of the central nervous system (CNS). This tumor is associated with loss of function of the VHL gene and demonstrates frequent occurrence in von Hippel-Lindau (VHL) disease. While this entity is designated CNS World Health Organization grade 1, due to its predilection for the cerebellum, brainstem, and spinal cord, it is still an important cause of morbidity and mortality in affected patients. Recognition and accurate diagnosis of hemangioblastoma is essential for the practice of surgical neuropathology. Other CNS neoplasms, including several tumors associated with VHL disease, may present as histologic mimics, making diagnosis challenging. We outline key clinical and radiologic features, pathophysiology, treatment modalities, and prognostic information for hemangioblastoma, and provide a thorough review of the gross, microscopic, immunophenotypic, and molecular features used to guide diagnosis.

Independent scientific achievements have led to the discovery of aberrant splicing patterns in oncogenesis, while more recent advances have uncovered novel gene fusions involving neurotrophic tyrosine receptor kinases (NTRKs) in gliomas. The exploration of NTRK splice variants in normal and neoplastic brain provides an intersection of these two rapidly evolving fields. Tropomyosin receptor kinase B (TrkB), encoded NTRK2 , is known for critical roles in neuronal survival, differentiation, molecular properties associated with memory, and exhibits intricate splicing patterns and post-translational modifications. Here, we show a role for a truncated NTRK2 splice variant, TrkB.T1, in human glioma. TrkB.T1 enhances PDGF-driven gliomas in vivo, augments PDGF-induced Akt and STAT3 signaling in vitro, while next generation sequencing broadly implicates TrkB.T1 in the PI3K signaling cascades in a ligand-independent fashion. These TrkB.T1 findings highlight the importance of expanding upon whole gene and gene fusion analyses to include splice variants in basic and translational neuro-oncology research. Tropomyosin receptor kinase B (TrkB), encoded by the neurotrophic tyrosine receptor kinase 2 ( NTRK2 ) gene, exhibits intricate splicing patterns and post-translational modifications. Here, the authors perform whole gene and transcript-level analyses and report the TrkB.T1 splice variant enhances PDGF-driven gliomas in vivo and augments PI3K signaling cascades in vitro.

Intraoperative consultations, used to guide tumor resection, can present histopathological findings that are challenging to interpret due to artefacts from tissue cryosectioning and conventional staining. Stimulated Raman histology (SRH), a label-free imaging technique for unprocessed biospecimens, has demonstrated promise in a limited subset of tumors. Here, we target unexplored skull base tumors using a fast simultaneous two-channel stimulated Raman scattering (SRS) imaging technique and a new pseudo-hematoxylin and eosin (H&E) recoloring methodology. To quantitatively evaluate the efficacy of our approach, we use modularized assessment of diagnostic accuracy beyond cancer/non-cancer determination and neuropathologist confidence for SRH images contrasted to H&E-stained frozen and formalin-fixed paraffin-embedded (FFPE) tissue sections. Our results reveal that SRH is effective for establishing a diagnosis using fresh tissue in most cases with 87% accuracy relative to H&E-stained FFPE sections. Further analysis of discrepant case interpretation suggests that pseudo-H&E recoloring underutilizes the rich chemical information offered by SRS imaging, and an improved diagnosis can be achieved if full SRS information is used. In summary, our findings show that pseudo-H&E recolored SRS images in combination with lipid and protein chemical information can maximize the use of SRS during intraoperative pathologic consultation with implications for tissue preservation and augmented diagnostic utility.

Recent advances in artificial intelligence (AI) and computer vision empower deep-learning models to infer molecular features from histopathological images to classify CNS tumours. The aim of this study was to test the classification accuracy of a molecular inference-based AI assistant for CNS tumour diagnosis. In this multi-institutional, retrospective study, we used data from whole slide images of samples from patients aged 0–95 years, diagnosed with primary or recurrent CNS tumours. Reference diagnostic labels were determined by DNA methylation-based tumour classification to match one of 52 tumour types selected to encompass most types of gliomas, embryonal tumours, and meningeal and mesenchymal tumours encountered in clinical practice. The Neuropath-AI model was trained on 5835 samples from the National Cancer Institute (NCI; USA), the Children's Brain Tumor Network (USA), and the Digital Brain Tumour Atlas (Austria) to infer molecular features from whole slide images and to use these to predict tumour types with associated confidence scores. The test cohort comprised 5516 samples identified in laboratory archives between May 17, 2024, and May 13, 2025, from the NCI, Northwestern Medicine (USA), University of Pittsburgh Medical Center (USA), and University College London (UK). There were 2753 (50%) female and 2763 (50%) male patients, median age 43 years (IQR 25–59). The primary objective was to measure the classification accuracy of the model family-level and terminal classification predictions in test samples, with coprimary endpoints of sample coverage and prediction and balanced accuracy. Sample coverage was defined as samples receiving a model prediction with a confidence score above a specified threshold. Prediction accuracy and balanced accuracy were analysed in the covered samples (ie, those meeting the confidence criterion) and evaluated by comparing the top-1 or top-2 predictions with reference labels. Family-level classifications were reached in 5299 (96%) of 5516 samples. Predictions to one of the terminal classifications with at least moderate confidence were reached for 4772 (87%) samples. The single highest-scoring classification matched the reference label in 3817 (95% CI 3770–3865; 80% [95% CI 79–81]) of 4772 samples (balanced accuracy 66% [95% CI 63–70]). The two highest-scoring classifications contained the reference label in 4103 (95% CI 4056–4152; 86% [95% CI 85–87]) of 4772 samples (balanced accuracy 75% [95% CI 71–78]). Our model provides the basis for a clinically applicable deep-learning assistant to improve human efficiency and accuracy of CNS tumour diagnosis. The model will be made publicly available and could be implemented to assist human pathologists in future prospective studies. The Intramural Research Program of the National Institutes of Health.

Recent updating of the World Health Organization (WHO) classification of central nervous system (CNS) tumors in 2016 demonstrates the first organized effort to restructure brain tumor classification by incorporating histomorphologic features with recurrent molecular alterations. Revised CNS tumor diagnostic criteria also attempt to reduce interobserver variability of histological interpretation and provide more accurate stratification related to clinical outcome. As an example, diffuse gliomas (WHO grades II–IV) are now molecularly stratified based upon isocitrate dehydrogenase 1 or 2 (IDH) mutational status, with gliomas of WHO grades II and III being substratified according to 1p/19q codeletion status. For now, grading of diffuse gliomas is still dependent upon histological parameters. Independent of WHO classification criteria, multidimensional scaling analysis of molecular signatures for diffuse gliomas from The Cancer Genome Atlas (TCGA) has identified distinct molecular subgroups, and allows for their visualization in 2-dimensional (2D) space. Using the web-based platform Oncoscape as a tool, we applied multidimensional scaling-derived molecular groups to the 2D visualization of the 2016 WHO classification of diffuse gliomas. Here we show that molecular multidimensional scaling of TCGA data provides 2D clustering that represents the 2016 WHO classification of diffuse gliomas. Additionally, we used this platform to successfully identify and define novel copy-number alteration-based molecular subtypes, which are independent of WHO grading, as well as predictive of clinical outcome. The prognostic utility of these molecular subtypes was further validated using an independent data set of the German Glioma Network prospective glioblastoma patient cohort.

Merkel cell carcinoma is a highly aggressive cutaneous neuroendocrine tumor that has been associated with Merkel cell polyomavirus in up to 80% of cases. Merkel cell polyomavirus is believed to influence pathogenesis, at least in part, through expression of the large T antigen, which includes a retinoblastoma protein-binding domain. However, there appears to be significant clinical and morphological overlap between polyomavirus-positive and polyomavirus-negative Merkel cell carcinoma cases. Although much of the recent focus of Merkel cell carcinoma pathogenesis has been on polyomavirus, the pathogenesis of polyomavirus-negative cases is still poorly understood. We hypothesized that there are underlying human somatic mutations that unify Merkel cell carcinoma pathogenesis across polyomavirus status, and to investigate we performed whole exome sequencing on five polyomavirus-positive cases and three polyomavirus-negative cases. We found that there were no significant differences in the overall number of single-nucleotide variations, copy number variations, insertion/deletions, and chromosomal rearrangements when comparing polyomavirus-positive to polyomavirus-negative cases. However, we did find that the retinoblastoma pathway genes harbored a high number of mutations in Merkel cell carcinoma. Furthermore, the retinoblastoma gene (RB1) was found to have nonsense truncating protein mutations in all three polyomavirus-negative cases; no such mutations were found in the polyomavirus-positive cases. In all eight cases, the retinoblastoma pathway dysregulation was confirmed by immunohistochemistry. Although polyomavirus-positive Merkel cell carcinoma is believed to undergo retinoblastoma dysregulation through viral large T antigen expression, our findings demonstrate that somatic mutations in polyomavirus-negative Merkel cell carcinoma lead to retinoblastoma dysregulation through an alternative pathway. This novel finding suggests that the retinoblastoma pathway dysregulation leads to an overlapping Merkel cell carcinoma phenotype and that oncogenesis occurs through either a polyomavirus-dependent (viral large T antigen expression) or polyomavirus-independent (host somatic mutation) mechanism.

Knowledge of 1p/19q-codeletion and IDH1/2 mutational status is necessary to interpret any investigational study of diffuse gliomas in the modern era. While DNA sequencing is the gold standard for determining IDH mutational status, genome-wide methylation arrays and gene expression profiling have been used for surrogate mutational determination. Previous studies by our group suggest that 1p/19q-codeletion and IDH mutational status can be predicted by genome-wide somatic copy number alteration (SCNA) data alone, however a rigorous model to accomplish this task has yet to be established. In this study, we used SCNA data from 786 adult diffuse gliomas in The Cancer Genome Atlas (TCGA) to develop a two-stage classification system that identifies 1p/19q-codeleted oligodendrogliomas and predicts the IDH mutational status of astrocytic tumors using a machine-learning model. Cross-validated results on TCGA SCNA data showed near perfect classification results. Furthermore, our astrocytic IDH mutation model validated well on four additional datasets (AUC = 0.97, AUC = 0.99, AUC = 0.95, AUC = 0.96) as did our 1p/19q-codeleted oligodendroglioma screen on the two datasets that contained oligodendrogliomas (MCC = 0.97, MCC = 0.97). We then retrained our system using data from these validation sets and applied our system to a cohort of REMBRANDT study subjects for whom SCNA data, but not IDH mutational status, is available. Overall, using genome-wide SCNAs, we successfully developed a system to robustly predict 1p/19q-codeletion and IDH mutational status in diffuse gliomas. This system can assign molecular subtype labels to tumor samples of retrospective diffuse glioma cohorts that lack 1p/19q-codeletion and IDH mutational status, such as the REMBRANDT study, recasting these datasets as validation cohorts for diffuse glioma research.