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Pleomorphic xanthoastrocytoma (PXA) is low-grade glial neoplasm principally affecting children and young adults. Approximately 40% of PXA are reported to recur within 10 years of primary resection. Upon recurrence, patients receive radiation therapy and conventional chemotherapeutics designed for high-grade gliomas. Genetic changes that can be targeted by selective therapeutics have not been extensively evaluated in PXA and ancillary diagnostic tests to help discriminate PXA from other pleomorphic and often more aggressive astrocytic malignancies are limited. In this study, we apply the SNaPshot multiplexed targeted sequencing platform in the analysis of brain tumors to interrogate 60 genetic loci that are frequently mutated in 15 cancer genes. In our analysis we detect BRAF V600E mutations in 12 of 20 (60%) WHO grade II PXA, in 1 of 6 (17%) PXA with anaplasia and in 1 glioblastoma arising in a PXA. Phospho-ERK was detected in all tumors independent of the BRAF mutation status. BRAF duplication was not detected in any of the PXA cases. BRAF V600E mutations were identified in only 2 of 71 (2.8%) glioblastoma (GBM) analyzed, including 1 of 9 (11.1%) giant cell GBM (gcGBM). The finding that BRAF V600E mutations are common in the majority of PXA has important therapeutic implications and may help in differentiating less aggressive PXAs from lethal gcGBMs and GBMs.

Histopathology image evaluation is indispensable for cancer diagnoses and subtype classification. Standard artificial intelligence methods for histopathology image analyses have focused on optimizing specialized models for each diagnostic task 1 , 2 . Although such methods have achieved some success, they often have limited generalizability to images generated by different digitization protocols or samples collected from different populations 3 . Here, to address this challenge, we devised the Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model, a general-purpose weakly supervised machine learning framework to extract pathology imaging features for systematic cancer evaluation. CHIEF leverages two complementary pretraining methods to extract diverse pathology representations: unsupervised pretraining for tile-level feature identification and weakly supervised pretraining for whole-slide pattern recognition. We developed CHIEF using 60,530 whole-slide images spanning 19 anatomical sites. Through pretraining on 44 terabytes of high-resolution pathology imaging datasets, CHIEF extracted microscopic representations useful for cancer cell detection, tumour origin identification, molecular profile characterization and prognostic prediction. We successfully validated CHIEF using 19,491 whole-slide images from 32 independent slide sets collected from 24 hospitals and cohorts internationally. Overall, CHIEF outperformed the state-of-the-art deep learning methods by up to 36.1%, showing its ability to address domain shifts observed in samples from diverse populations and processed by different slide preparation methods. CHIEF provides a generalizable foundation for efficient digital pathology evaluation for patients with cancer. A study describes the development of a generalizable foundation machine learning framework to extract pathology imaging features for cancer diagnosis and prognosis prediction.

Malignant pleural mesothelioma (MPM) has relatively ineffective first/second-line therapy for advanced disease and only 18% five-year survival for early disease. Drug-induced mitochondrial priming measured by dynamic BH3 profiling identifies efficacious drugs in multiple disease settings. We use high throughput dynamic BH3 profiling (HTDBP) to identify drug combinations that prime primary MPM cells derived from patient tumors, which also prime patient derived xenograft (PDX) models. A navitoclax (BCL-xL/BCL-2/BCL-w antagonist) and AZD8055 (mTORC1/2 inhibitor) combination demonstrates efficacy in vivo in an MPM PDX model, validating HTDBP as an approach to identify efficacious drug combinations. Mechanistic investigation reveals AZD8055 treatment decreases MCL-1 protein levels, increases BIM protein levels, and increases MPM mitochondrial dependence on BCL-xL, which is exploited by navitoclax. Navitoclax treatment increases dependency on MCL-1 and increases BIM protein levels. These findings demonstrate that HTDBP can be used as a functional precision medicine tool to rationally construct combination drug regimens in MPM and other cancers. Malignant pleural mesothelioma (MPM) is an aggressive malignancy with few effective treatment options available. Here, the authors use dynamic BH3 profiling to measure drug-induced mitochondrial priming and identify AZD8055 and navitoclax as a pro-apoptotic drug combination in ex vivo and preclinical MPM models.

Medulloblastoma is a malignant childhood cerebellar tumour type that comprises distinct molecular subgroups. Whereas genomic characteristics of these subgroups are well defined, the extent to which cellular diversity underlies their divergent biology and clinical behaviour remains largely unexplored. Here we used single-cell transcriptomics to investigate intra- and intertumoral heterogeneity in 25 medulloblastomas spanning all molecular subgroups. WNT, SHH and Group 3 tumours comprised subgroup-specific undifferentiated and differentiated neuronal-like malignant populations, whereas Group 4 tumours consisted exclusively of differentiated neuronal-like neoplastic cells. SHH tumours closely resembled granule neurons of varying differentiation states that correlated with patient age. Group 3 and Group 4 tumours exhibited a developmental trajectory from primitive progenitor-like to more mature neuronal-like cells, the relative proportions of which distinguished these subgroups. Cross-species transcriptomics defined distinct glutamatergic populations as putative cells-of-origin for SHH and Group 4 subtypes. Collectively, these data provide insights into the cellular and developmental states underlying subtype-specific medulloblastoma biology. Characterization of medulloblastoma tissues using single-cell transcriptomics shows that the different molecular subtypes consist of distinct developmental phenotypes.

Since the 2016 update of the WHO Classification of Tumors of the Central Nervous System, omics data have been officially integrated into the diagnostic process for glioblastoma, the most prevalent and aggressive primary malignant brain tumor in adults. This review will examine the current and future integration of omics data in both the diagnosis and therapy of glioblastomas. The current clinical use of omics data primarily focuses on genomics for determining the IDH‐ and H3‐wildtype status of the tumor, and on epigenomics, such as assessing MGMT promoter methylation status as a prognostic and predictive biomarker. However, it can be anticipated that the usage and importance of omics data will likely increase in the future. This work highlights how omics technologies have significantly enhanced our understanding of glioblastoma, particularly of its extensive heterogeneity. This enhanced understanding has not only improved diagnostic accuracy but has also facilitated the identification of new predictive and/or prognostic biomarkers. It is likely that the ongoing integration of omics data will transform many aspects of the diagnostic process, including sample acquisition. Additionally, omics data will be integrated into future glioblastoma treatment procedures, with possible applications ranging from identifying potential therapeutic targets to selecting individual treatment plans. The implications of the ongoing integration of omics data for clinical routine, future classification systems, and trial design are also discussed in this review, outlining the pivotal role omics data play in shaping future glioblastoma diagnosis and treatment. Integration of omics data in the diagnosis and therapy of glioblastoma.

A primitive neoplasm composed predominantly of nonhost cells was detected in the thoracic spinal cord and thecal sac of a 66-year-old man who had received experimental stem-cell treatment from commercial clinics. To the Editor: Commercial stem-cell clinics have been highly publicized in the lay press and operate worldwide with limited or no regulation. 1 We report the case of a 66-year-old man who underwent intrathecal infusions for the treatment of residual deficits from an ischemic stroke at commercial stem-cell clinics in China, Argentina, and Mexico. He was not taking any immunosuppressive medications. In reports provided to him by the clinics, the infusions were described as consisting of mesenchymal, embryonic, and fetal neural stem cells. Progressive lower back pain, paraplegia, and urinary incontinence subsequently developed. Magnetic resonance imaging (MRI) revealed a lesion of . . .

Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines. Prediction of HLA class I epitopes is improved in accuracy and breath with peptidomes from 95 mono-allelic cell lines.

Fusions involving neurotrophic tyrosine receptor kinase ( NTRK ) genes are detected in ≤2% of gliomas and can promote gliomagenesis. The remarkable therapeutic efficacy of TRK inhibitors, which are among the first Food and Drug Administration-approved targeted therapies for NTRK -fused gliomas, has generated significant clinical interest in characterizing these tumors. In this multi-institutional retrospective study of 42 gliomas with NTRK fusions, next generation DNA sequencing ( n  = 41), next generation RNA sequencing ( n  = 1), RNA-sequencing fusion panel ( n  = 16), methylation profile analysis ( n  = 18), and histologic evaluation ( n  = 42) were performed. All infantile NTRK -fused gliomas ( n  = 7) had high-grade histology and, with one exception, no other significant genetic alterations. Pediatric NTRK -fused gliomas ( n  = 13) typically involved NTRK2 , ranged from low- to high-histologic grade, and demonstrated histologic overlap with desmoplastic infantile ganglioglioma, pilocytic astrocytoma, ganglioglioma, and glioblastoma, among other entities, but they rarely matched with high confidence to known methylation class families or with each other; alterations involving ATRX , PTEN , and CDKN2A/2B were present in a subset of cases. Adult NTRK -fused gliomas ( n  = 22) typically involved NTRK1 and had predominantly high-grade histology; genetic alterations involving IDH1 , ATRX , TP53 , PTEN , TERT promoter, RB1 , CDKN2A/2B , NF1 , and polysomy 7 were common. Unsupervised principal component analysis of methylation profiles demonstrated no obvious grouping by histologic grade, NTRK gene involved, or age group. KEGG pathway analysis detected methylation differences in genes involved in PI3K/AKT, MAPK, and other pathways. In summary, the study highlights the clinical, histologic, and molecular heterogeneity of NTRK -fused gliomas, particularly when stratified by age group.

How the glioma immune microenvironment fosters tumorigenesis remains incompletely defined. Here, we use single-cell RNA-sequencing and multiplexed tissue-imaging to characterize the composition, spatial organization, and clinical significance of extracellular purinergic signaling in glioma. We show that microglia are the predominant source of CD39, while tumor cells principally express CD73. In glioblastoma, CD73 is associated with EGFR amplification, astrocyte-like differentiation, and increased adenosine, and is linked to hypoxia. Glioblastomas enriched for CD73 exhibit inflammatory microenvironments, suggesting that purinergic signaling regulates immune adaptation. Spatially-resolved single-cell analyses demonstrate a strong spatial correlation between tumor-CD73 and microglial-CD39, with proximity associated with poor outcomes. Similar spatial organization is present in pediatric high-grade gliomas including H3K27M-mutant diffuse midline glioma. These data reveal that purinergic signaling in gliomas is shaped by genotype, lineage, and functional state, and that core enzymes expressed by tumor and myeloid cells are organized to promote adenosine-rich microenvironments potentially amenable to therapeutic targeting. The components of the glioma immune microenvironment and their roles in promoting tumourigenesis remain poorly understood. Here, the use of single-cell RNA sequencing and multiplexed tissue-imaging in adult and pediatric high-grade gliomas reveals the activity and spatial organization of the immunomodulatory purinergic signaling pathway.

Rameen Beroukhim, Ian Dunn, William Hahn and colleagues report genome and exome sequencing of meningiomas. They identified recurrent somatic mutations in AKT1 and SMO . Meningiomas are the most common primary nervous system tumor. The tumor suppressor NF2 is disrupted in approximately half of all meningiomas 1 , but the complete spectrum of genetic changes remains undefined. We performed whole-genome or whole-exome sequencing on 17 meningiomas and focused sequencing on an additional 48 tumors to identify and validate somatic genetic alterations. Most meningiomas had simple genomes, with fewer mutations, rearrangements and copy-number alterations than reported in other tumors in adults. However, several meningiomas harbored more complex patterns of copy-number changes and rearrangements, including one tumor with chromothripsis. We confirmed focal NF2 inactivation in 43% of tumors and found alterations in epigenetic modifiers in an additional 8% of tumors. A subset of meningiomas lacking NF2 alterations harbored recurrent oncogenic mutations in AKT1 (p.Glu17Lys) and SMO (p.Trp535Leu) and exhibited immunohistochemical evidence of activation of these pathways. These mutations were present in therapeutically challenging tumors of the skull base and higher grade. These results begin to define the spectrum of genetic alterations in meningiomas and identify potential therapeutic targets.