Meningioma transcriptomic landscape demonstrates novel subtypes with regional associated biology and patient outcome.

Meningiomas, although mostly benign, can be recurrent and fatal. World Health Organization (WHO) grading of the tumor does not always identify high-risk meningioma, and better characterizations of their aggressive biology are needed. To approach this problem, we combined 13 bulk RNA sequencing (RNA-seq) datasets to create a dimension-reduced reference landscape of 1,298 meningiomas. The clinical and genomic metadata effectively correlated with landscape regions, which led to the identification of meningioma subtypes with specific biological signatures. The time to recurrence also correlated with the map location. Further, we developed an algorithm that maps new patients onto this landscape, where the nearest neighbors predict outcome. This study highlights the utility of combining bulk transcriptomic datasets to visualize the complexity of tumor populations. Further, we provide an interactive tool for understanding the disease and predicting patient outcomes. This resource is accessible via the online tool Oncoscape, where the scientific community can explore the meningioma landscape.

Leveraging single-cell sequencing to classify and characterize tumor subgroups in bulk RNA-sequencing data.

PURPOSE: Accurate classification of cancer subgroups is essential for precision medicine, tailoring treatments to individual patients based on their cancer subtypes. In recent years, advances in high-throughput sequencing technologies have enabled the generation of large-scale transcriptomic data from cancer samples. These data have provided opportunities for developing computational methods that can improve cancer subtyping and enable better personalized treatment strategies. METHODS: Here in this study, we evaluated different feature selection schemes in the context of meningioma classification. To integrate interpretable features from the bulk (n = 77 samples) and single-cell profiling (∼ 10 K cells), we developed an algorithm named CLIPPR which combines the top-performing single-cell models, RNA-inferred copy number variation (CNV) signals, and the initial bulk model to create a meta-model. RESULTS: While the scheme relying solely on bulk transcriptomic data showed good classification accuracy, it exhibited confusion between malignant and benign molecular classes in approximately ∼ 8% of meningioma samples. In contrast, models trained on features learned from meningioma single-cell data accurately resolved the sub-groups confused by bulk-transcriptomic data but showed limited overall accuracy. CLIPPR showed superior overall accuracy and resolved benign-malignant confusion as validated on n = 789 bulk meningioma samples gathered from multiple institutions. Finally, we showed the generalizability of our algorithm using our in-house single-cell (∼ 200 K cells) and bulk TCGA glioma data (n = 711 samples). CONCLUSION: Overall, our algorithm CLIPPR synergizes the resolution of single-cell data with the depth of bulk sequencing and enables improved cancer sub-group diagnoses and insights into their biology.

Targeted gene expression profiling predicts meningioma outcomes and radiotherapy responses.

Surgery is the mainstay of treatment for meningioma, the most common primary intracranial tumor, but improvements in meningioma risk stratification are needed and indications for postoperative radiotherapy are controversial. Here we develop a targeted gene expression biomarker that predicts meningioma outcomes and radiotherapy responses. Using a discovery cohort of 173 meningiomas, we developed a 34-gene expression risk score and performed clinical and analytical validation of this biomarker on independent meningiomas from 12 institutions across 3 continents (N = 1,856), including 103 meningiomas from a prospective clinical trial. The gene expression biomarker improved discrimination of outcomes compared with all other systems tested (N = 9) in the clinical validation cohort for local recurrence (5-year area under the curve (AUC) 0.81) and overall survival (5-year AUC 0.80). The increase in AUC compared with the standard of care, World Health Organization 2021 grade, was 0.11 for local recurrence (95% confidence interval 0.07 to 0.17, P < 0.001). The gene expression biomarker identified meningiomas benefiting from postoperative radiotherapy (hazard ratio 0.54, 95% confidence interval 0.37 to 0.78, P = 0.0001) and suggested postoperative management could be refined for 29.8% of patients. In sum, our results identify a targeted gene expression biomarker that improves discrimination of meningioma outcomes, including prediction of postoperative radiotherapy responses.

Integrated clinical genomic analysis reveals xenobiotic metabolic genes are downregulated in meningiomas of current smokers.

INTRODUCTION: Meningiomas are the most common primary intracranial tumor. Recently, various genetic classification systems for meningioma have been described. We sought to identify clinical drivers of different molecular changes in meningioma. As such, clinical and genomic consequences of smoking in patients with meningiomas remain unexplored. METHODS: 88 tumor samples were analyzed in this study. Whole exome sequencing (WES) was used to assess somatic mutation burden. RNA sequencing data was used to identify differentially expressed genes (DEG) and genes sets (GSEA). RESULTS: Fifty-seven patients had no history of smoking, twenty-two were past smokers, and nine were current smokers. The clinical data showed no major differences in natural history across smoking status. WES revealed absence of AKT1 mutation rate in current or past smokers compared to non-smokers (p = 0.046). Current smokers had increased mutation rate in NOTCH2 compared to past and never smokers (p < 0.05). Mutational signature from current and past smokers showed disrupted DNA mismatch repair (cosine-similarity = 0.759 and 0.783). DEG analysis revealed the xenobiotic metabolic genes UGT2A1 and UGT2A2 were both significantly downregulated in current smokers compared to past (Log2FC = - 3.97, padj = 0.0347 and Log2FC = - 4.18, padj = 0.0304) and never smokers (Log2FC = - 3.86, padj = 0.0235 and Log2FC = - 4.20, padj = 0.0149). GSEA analysis of current smokers showed downregulation of xenobiotic metabolism and enrichment for G2M checkpoint, E2F targets, and mitotic spindle compared to past and never smokers (FDR < 25% each). CONCLUSION: In this study, we conducted a comparative analysis of meningioma patients based on their smoking history, examining both their clinical trajectories and molecular changes. Meningiomas from current smokers were more likely to harbor NOTCH2 mutations, and AKT1 mutations were absent in current or past smokers. Moreover, both current and past smokers exhibited a mutational signature associated with DNA mismatch repair. Meningiomas from current smokers demonstrate downregulation of xenobiotic metabolic enzymes UGT2A1 and UGT2A2, which are downregulated in other smoking related cancers. Furthermore, current smokers exhibited downregulation xenobiotic metabolic gene sets, as well as enrichment in gene sets related to mitotic spindle, E2F targets, and G2M checkpoint, which are hallmark pathways involved in cell division and DNA replication control. In aggregate, our results demonstrate novel alterations in meningioma molecular biology in response to systemic carcinogens.

Targeted gene expression profiling predicts meningioma outcomes and radiotherapy responses.

Background: Surgery is the mainstay of treatment for meningioma, the most common primary intracranial tumor, but improvements in meningioma risk stratification are needed and current indications for postoperative radiotherapy are controversial. Recent studies have proposed prognostic meningioma classification systems using DNA methylation profiling, copy number variants, DNA sequencing, RNA sequencing, histology, or integrated models based on multiple combined features. Targeted gene expression profiling has generated robust biomarkers integrating multiple molecular features for other cancers, but is understudied for meningiomas. Methods: Targeted gene expression profiling was performed on 173 meningiomas and an optimized gene expression biomarker (34 genes) and risk score (0 to 1) was developed to predict clinical outcomes. Clinical and analytical validation was performed on independent meningiomas from 12 institutions across 3 continents (N = 1856), including 103 meningiomas from a prospective clinical trial. Gene expression biomarker performance was compared to 9 other classification systems. Results: The gene expression biomarker improved discrimination of postoperative meningioma outcomes compared to all other classification systems tested in the independent clinical validation cohort for local recurrence (5-year area under the curve [AUC] 0.81) and overall survival (5-year AUC 0.80). The increase in area under the curve compared to the current standard of care, World Health Organization 2021 grade, was 0.11 for local recurrence (95% confidence interval [CI] 0.07-0.17, P < 0.001). The gene expression biomarker identified meningiomas benefiting from postoperative radiotherapy (hazard ratio 0.54, 95% CI 0.37-0.78, P = 0.0001) and re-classified up to 52.0% meningiomas compared to conventional clinical criteria, suggesting postoperative management could be refined for 29.8% of patients. Conclusions: A targeted gene expression biomarker improves discrimination of meningioma outcomes compared to recent classification systems and predicts postoperative radiotherapy responses.

Hypermitotic meningiomas harbor DNA methylation subgroups with distinct biological and clinical features.

BACKGROUND: Meningiomas, the most common primary intracranial tumors, can be separated into 3 DNA methylation groups with distinct biological drivers, clinical outcomes, and therapeutic vulnerabilities. Alternative meningioma grouping schemes using copy number variants, gene expression profiles, somatic short variants, or integrated molecular models have been proposed. These data suggest meningioma DNA methylation groups may harbor subgroups unifying contrasting theories of meningioma biology. METHODS: A total of 565 meningioma DNA methylation profiles from patients with comprehensive clinical follow-up at independent discovery (n = 200) or validation (n = 365) institutions were reanalyzed and classified into Merlin-intact, Immune-enriched, or Hypermitotic DNA methylation groups. RNA sequencing from the discovery (n = 200) or validation (n = 302) cohort were analyzed in the context of DNA methylation groups to identify subgroups. Biological features and clinical outcomes were analyzed across meningioma grouping schemes. RESULTS: RNA sequencing revealed differential enrichment of FOXM1 target genes across two subgroups of Hypermitotic meningiomas. Differential expression and ontology analyses showed the subgroup of Hypermitotic meningiomas without FOXM1 target gene enrichment was distinguished by gene expression programs driving macromolecular metabolism. Analysis of genetic, epigenetic, gene expression, or cellular features revealed Hypermitotic meningioma subgroups were concordant with Proliferative or Hypermetabolic meningiomas, which were previously reported alongside Merlin-intact and Immune-enriched tumors using an integrated molecular model. The addition of DNA methylation subgroups to clinical models refined the prediction of postoperative outcomes compared to the addition of DNA methylation groups. CONCLUSIONS: Meningiomas can be separated into three DNA methylation groups and Hypermitotic meningiomas can be subdivided into Proliferative and Hypermetabolic subgroups, each with distinct biological and clinical features.

XCVATR: detection and characterization of variant impact on the Embeddings of single -cell and bulk RNA-sequencing samples.

BACKGROUND: RNA-sequencing has become a standard tool for analyzing gene activity in bulk samples and at the single-cell level. By increasing sample sizes and cell counts, this technique can uncover substantial information about cellular transcriptional states. Beyond quantification of gene expression, RNA-seq can be used for detecting variants, including single nucleotide polymorphisms, small insertions/deletions, and larger variants, such as copy number variants. Notably, joint analysis of variants with cellular transcriptional states may provide insights into the impact of mutations, especially for complex and heterogeneous samples. However, this analysis is often challenging due to a prohibitively high number of variants and cells, which are difficult to summarize and visualize. Further, there is a dearth of methods that assess and summarize the association between detected variants and cellular transcriptional states. RESULTS: Here, we introduce XCVATR (eXpressed Clusters of Variant Alleles in Transcriptome pRofiles), a method that identifies variants and detects local enrichment of expressed variants within embedding of samples and cells in single-cell and bulk RNA-seq datasets. XCVATR visualizes local "clumps" of small and large-scale variants and searches for patterns of association between each variant and cellular states, as described by the coordinates of cell embedding, which can be computed independently using any type of distance metrics, such as principal component analysis or t-distributed stochastic neighbor embedding. Through simulations and analysis of real datasets, we demonstrate that XCVATR can detect enrichment of expressed variants and provide insight into the transcriptional states of cells and samples. We next sequenced 2 new single cell RNA-seq tumor samples and applied XCVATR. XCVATR revealed subtle differences in CNV impact on tumors. CONCLUSIONS: XCVATR is publicly available to download from https://github.com/harmancilab/XCVATR .

Multiple approaches converge on three biological subtypes of meningioma and extract new insights from published studies.

One-fifth of meningiomas classified as benign by World Health Organization (WHO) histopathological grading will behave malignantly. To better diagnose these tumors, several groups turned to DNA methylation, whereas we combined RNA-sequencing (RNA-seq) and cytogenetics. Both approaches were more accurate than histopathology in identifying aggressive tumors, but whether they revealed similar tumor types was unclear. We therefore performed unbiased DNA methylation, RNA-seq, and cytogenetic profiling on 110 primary meningiomas WHO grade I and II). Each technique distinguished the same three groups (two benign and one malignant) as our previous molecular classification; integrating these methods into one classifier further improved accuracy. Computational modeling revealed strong correlations between transcription and cytogenetic changes, particularly loss of chromosome 1p, in malignant tumors. Applying our classifier to data from previous studies also resolved certain anomalies entailed by grouping tumors by WHO grade. Accurate classification will therefore elucidate meningioma biology as well as improve diagnosis and prognosis.

Racial and Socioeconomic Disparities in Patients With Meningioma: A Retrospective Cohort Study.

BACKGROUND: Meningiomas are the most common intracranial neoplasms. Although genomic analysis has helped elucidate differences in survival, there is evidence that racial disparities may influence outcomes. African Americans have a higher incidence of meningiomas and poorer survival outcomes. The etiology of these disparities remains unclear, but may include a combination of pathophysiology and other factors. OBJECTIVE: To determine factors that contribute to different clinical outcomes in racial populations. METHODS: We retrospectively reviewed 305 patients who underwent resection for meningiomas at a single tertiary care facility. We used descriptive statistics and univariate, multivariable, and Kaplan-Meier analyses to study clinical, radiographical, and histopathological differences. RESULTS: Minority patients were more likely to present through the emergency department than an outpatient clinic (P < .0001). They were more likely to present with more advanced clinical symptoms with lower Karnofsky Performance scores, more frequently had peritumoral edema (P = .0031), and experienced longer postoperative stays in the hospital (P = .0053), and African-American patients had higher hospitalization costs (P = .046) and were more likely to be publicly insured. Extent of resection was an independent predictor of recurrence freedom (P = .039). Presentation in clinic setting trended toward an association with recurrence-free survival (P = .055). We observed no significant difference in gross total resection rates, postoperative recurrence, or recurrence-free survival. CONCLUSION: Minority patients are more likely to present with severe symptoms, require longer perioperative hospitalization, and generate higher hospitalization costs. This may be due to socioeconomic factors that affect access to health care. Targeting barriers to access, especially to subspecialty care, may facilitate more appropriate and timely diagnosis, thereby improving patient care and outcomes.

Predictors of postoperative seizure outcome in supratentorial meningioma.

OBJECTIVE: Meningiomas are the most common primary intracranial tumor. Seizures are common sequelae of meningioma development. Meningioma patients with seizures can be effectively treated with resection, with reports of seizure freedom of 60%-90%. Still, many patients manifest persistent epilepsy. Determining factors associated with worsened seizure outcomes remains critical in improving the quality of life for these patients. The authors sought to identify clinical, radiological, and histological factors associated with worse seizure outcomes in patients with supratentorial meningioma and preoperative seizures. METHODS: The authors retrospectively reviewed the charts of 384 patients who underwent meningioma resection from 2008 to 2020. The charts of patients with a documented history of preoperative seizures were further reviewed for clinical, radiological, operative, perioperative, histological, and postoperative factors associated with seizures. Engel class at last follow-up was retrospectively assigned by the authors and further grouped into favorable (class I) and worse (class II-IV) outcomes. Factors were subsequently compared by group using comparative statistics. Univariable and multivariable regression models were utilized to identify independent predictors of worse seizure outcome. RESULTS: Fifty-nine patients (15.4%) were found to have preoperative seizures, of whom 57 had sufficient postoperative data to determine Engel class outcome. Forty-two patients (74%) had Engel class I outcomes. The median follow-up was 17 months. Distinct margins on preoperative imaging (p = 0.012), Simpson grade I resection (p = 0.004), postresection ischemia (p = 0.019), WHO grade (p = 0.019), and recurrent disease (p = 0.015) were found to be the strongest predictors of Engel class outcome in univariable logistic regression. MIB-1 index (p = 0.001) and residual volume (p = 0.014) at last follow-up were found to be the strongest predictors of Engel class outcome in univariable generalized linear regression. Postresection ischemia (p = 0.012), WHO grade (p = 0.022), recurrent disease (p = 0.038), and MIB-1 index (p = 0.002) were found to be the strongest independent predictors of Engel class outcomes in multivariable analysis. CONCLUSIONS: Postresection ischemia, higher WHO grade, elevated MIB-1 index, and disease recurrence independently predict postresection seizure persistence in patients with supratentorial meningioma. Further understanding of the etiology of these markers may aid in elucidation of this complex disease process and guide management to prevent worse outcomes.

Identification of novel fusion transcripts in meningioma.

INTRODUCTION: Meningiomas are the most common primary intracranial tumor. Recent next generation sequencing analyses have elaborated the molecular drivers of this disease. We aimed to identify and characterize novel fusion genes in meningiomas. METHODS: We performed a secondary analysis of our RNA sequencing data of 145 primary meningioma from 140 patients to detect fusion genes. Semi-quantitative rt-PCR was performed to confirm transcription of the fusion genes in the original tumors. Whole exome sequencing was performed to identify copy number variations within each tumor sample. Comparative RNA seq analysis was performed to assess the clonality of the fusion constructs within the tumor. RESULTS: We detected six fusion events (NOTCH3-SETBP1, NF2-SPATA13, SLC6A3-AGBL3, PHF19-FOXP2 in two patients, and ITPK1-FBP2) in five out of 145 tumor samples. All but one event (NF2-SPATA13) led to extremely short reading frames, making these events de facto null alleles. Three of the five patients had a history of childhood radiation. Four out of six fusion events were detected in expression type C tumors, which represent the most aggressive meningioma. We validated the presence of the RNA transcripts in the tumor tissue by semi-quantitative RT PCR. All but the two PHF19-FOXP2 fusions demonstrated high degrees of clonality. CONCLUSIONS: Fusion genes occur infrequently in meningiomas and are more likely to be found in tumors with greater degree of genomic instability (expression type C) or in patients with history of cranial irradiation.

Patient-Derived Orthotopic Xenograft (PDOX) Mouse Models of Primary and Recurrent Meningioma.

BACKGROUND: Meningiomas constitute one-third of all primary brain tumors. Although typically benign, about 20% of these tumors recur despite surgery and radiation, and may ultimately prove fatal. There are currently no effective chemotherapies for meningioma. We, therefore, set out to develop patient-derived orthotopic xenograft (PDOX) mouse models of human meningioma using tumor. METHOD: Of nine patients, four had World Health Organization (WHO) grade I tumors, five had WHO grade II tumors, and in this second group two patients also had recurrent (WHO grade III) meningioma. We also classified the tumors according to our recently developed molecular classification system (Types A, B, and C, with C being the most aggressive). We transplanted all 11 surgical samples into the skull base of immunodeficient (SCID) mice. Only the primary and recurrent tumor cells from one patient-both molecular Type C, despite being WHO grades II and III, respectively-led to the formation of meningioma in the resulting mouse models. We characterized the xenografts by histopathology and RNA-seq and compared them with the original tumors. We performed an in vitro drug screen using 60 anti-cancer drugs followed by in vivo validation. RESULTS: The PDOX models established from the primary and recurrent tumors from patient K29 (K29P-PDOX and K29R-PDOX, respectively) replicated the histopathology and key gene expression profiles of the original samples. Although these xenografts could not be subtransplanted, the cryopreserved primary tumor cells were able to reliably generate PDOX tumors. Drug screening in K29P and K29R tumor cell lines revealed eight compounds that were active on both tumors, including three histone deacetylase (HDAC) inhibitors. We tested the HDAC inhibitor Panobinostat in K29R-PDOX mice, and it significantly prolonged mouse survival (p < 0.05) by inducing histone H3 acetylation and apoptosis. CONCLUSION: Meningiomas are not very amenable to PDOX modeling, for reasons that remain unclear. Yet at least some of the most malignant tumors can be modeled, and cryopreserved primary tumor cells can create large panels of tumors that can be used for preclinical drug testing.

TFEB regulates murine liver cell fate during development and regeneration.

It is well established that pluripotent stem cells in fetal and postnatal liver (LPCs) can differentiate into both hepatocytes and cholangiocytes. However, the signaling pathways implicated in the differentiation of LPCs are still incompletely understood. Transcription Factor EB (TFEB), a master regulator of lysosomal biogenesis and autophagy, is known to be involved in osteoblast and myeloid differentiation, but its role in lineage commitment in the liver has not been investigated. Here we show that during development and upon regeneration TFEB drives the differentiation status of murine LPCs into the progenitor/cholangiocyte lineage while inhibiting hepatocyte differentiation. Genetic interaction studies show that Sox9, a marker of precursor and biliary cells, is a direct transcriptional target of TFEB and a primary mediator of its effects on liver cell fate. In summary, our findings identify an unexplored pathway that controls liver cell lineage commitment and whose dysregulation may play a role in biliary cancer.

Molecular profiling predicts meningioma recurrence and reveals loss of DREAM complex repression in aggressive tumors.

Meningiomas account for one-third of all primary brain tumors. Although typically benign, about 20% of meningiomas are aggressive, and despite the rigor of the current histopathological classification system there remains considerable uncertainty in predicting tumor behavior. Here, we analyzed 160 tumors from all 3 World Health Organization (WHO) grades (I through III) using clinical, gene expression, and sequencing data. Unsupervised clustering analysis identified 3 molecular types (A, B, and C) that reliably predicted recurrence. These groups did not directly correlate with the WHO grading system, which classifies more than half of the tumors in the most aggressive molecular type as benign. Transcriptional and biochemical analyses revealed that aggressive meningiomas involve loss of the repressor function of the DREAM complex, which results in cell-cycle activation; only tumors in this category tend to recur after full resection. These findings should improve our ability to predict recurrence and develop targeted treatments for these clinically challenging tumors.

The Role of Merlin/NF2 Loss in Meningioma Biology.

Mutations in the neurofibromin 2 (NF2) gene were among the first genetic alterations implicated in meningioma tumorigenesis, based on analysis of neurofibromatosis type 2 (NF2) patients who not only develop vestibular schwannomas but later have a high incidence of meningiomas. The NF2 gene product, merlin, is a tumor suppressor that is thought to link the actin cytoskeleton with plasma membrane proteins and mediate contact-dependent inhibition of proliferation. However, the early recognition of the crucial role of NF2 mutations in the pathogenesis of the majority of meningiomas has not yet translated into useful clinical insights, due to the complexity of merlin's many interacting partners and signaling pathways. Next-generation sequencing studies and increasingly sophisticated NF2-deletion-based in vitro and in vivo models have helped elucidate the consequences of merlin loss in meningioma pathogenesis. In this review, we seek to summarize recent findings and provide future directions toward potential therapeutics for this tumor.

Nutrient-sensitive transcription factors TFEB and TFE3 couple autophagy and metabolism to the peripheral clock.

Autophagy and energy metabolism are known to follow a circadian pattern. However, it is unclear whether autophagy and the circadian clock are coordinated by common control mechanisms. Here, we show that the oscillation of autophagy genes is dependent on the nutrient-sensitive activation of TFEB and TFE3, key regulators of autophagy, lysosomal biogenesis, and cell homeostasis. TFEB and TFE3 display a circadian activation over the 24-h cycle and are responsible for the rhythmic induction of genes involved in autophagy during the light phase. Genetic ablation of TFEB and TFE3 in mice results in deregulated autophagy over the diurnal cycle and altered gene expression causing abnormal circadian wheel-running behavior. In addition, TFEB and TFE3 directly regulate the expression of Rev-erbα (Nr1d1), a transcriptional repressor component of the core clock machinery also involved in the regulation of whole-body metabolism and autophagy. Comparative analysis of the cistromes of TFEB/TFE3 and REV-ERBα showed an extensive overlap of their binding sites, particularly in genes involved in autophagy and metabolic functions. These data reveal a direct link between nutrient and clock-dependent regulation of gene expression shedding a new light on the crosstalk between autophagy, metabolism, and circadian cycles.

Newly Diagnosed Optic Pathway Glioma During Pregnancy.

BACKGROUND: Optic pathway gliomas (OPGs) are relatively rare, and their presentation after the first decade of life is even less common. Although many treatment options exist, surgery is typically reserved for tumors significantly compressing surrounding structures. Pregnancy can complicate the management of these tumors, as fetal developmental considerations limit the ways in which they are imaged and treated. CASE DESCRIPTION: In this report we detail the case of a 27-year-old pregnant woman who was found to have an OPG during her third trimester. After a decline in this patient's vision and clinical status, a decision was made to induce labor at 31 weeks so that her disease could be more thoroughly addressed. CONCLUSIONS: While OPGs are typically benign tumors, pregnancy complicates their management significantly. Contrast media and anesthesia pose significant risks to the fetus, while pregnancy may contribute to increased rates of tumor growth and clinical deterioration. Managing OPGs in pregnant patients thus requires balancing the risks to the fetus and patient.