Transcriptome signatures associated with meningioma progression

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.

Inhibition of BMP signaling pathway induced senescence and calcification in anaplastic meningioma

PURPOSE

Meningiomas are the most common type of brain tumors and are generally benign, but malignant atypical meningiomas and anaplastic meningiomas frequently recur with poor prognosis. The metabolism of meningiomas is little known, so few effective treatment options other than surgery and radiation are available, and the targets for treatment of recurrence are not well defined. The Aim of this paper is to find the therapeutic target.

METHODS

The effects of bone morphogenetic protein (BMP) signal inhibitor (K02288) and upstream regulator Gremlin2 (GREM2) on meningioma's growth and senescence were examined. In brief, we examined as follows: 1) Proliferation assay by inhibiting BMP signaling. 2) Comprehensive analysis of forced expression GREM2.3) Correlation between GREM2 mRNA expression and proliferation marker in 87 of our clinical samples. 4) Enrichment analysis between GREM2 high/low expressed groups using RNA-seq data (42 cases) from the public database GREIN. 5) Changes in metabolites and senescence markers associated with BMP signal suppression.

RESULTS

Inhibitors of BMP receptor (BMPR1A) and forced expression of GREM2 shifted tryptophan metabolism from kynurenine/quinolinic acid production to serotonin production in malignant meningiomas, reduced NAD + /NADH production, decreased gene cluster expression involved in oxidative phosphorylation, and caused decrease in ATP. Finally, malignant meningiomas underwent cellular senescence, decreased proliferation, and eventually formed psammoma bodies. Reanalyzed RNA-seq data of clinical samples obtained from GREIN showed that increased expression of GREM2 decreased the expression of genes involved in oxidative phosphorylation, similar to our experimental results.

CONCLUSIONS

The GREM2-BMPR1A-tryptophan metabolic pathway in meningiomas is a potential new therapeutic target.

CTAT-LR-fusion: accurate fusion transcript identification from long and short read isoform sequencing at bulk or single cell resolution

Gene fusions are found as cancer drivers in diverse adult and pediatric cancers. Accurate detection of fusion transcripts is essential in cancer clinical diagnostics, prognostics, and for guiding therapeutic development. Most currently available methods for fusion transcript detection are compatible with Illumina RNA-seq involving highly accurate short read sequences. Recent advances in long read isoform sequencing enable the detection of fusion transcripts at unprecedented resolution in bulk and single cell samples. Here we developed a new computational tool CTAT-LR-fusion to detect fusion transcripts from long read RNA-seq with or without companion short reads, with applications to bulk or single cell transcriptomes. We demonstrate that CTAT-LR-fusion exceeds fusion detection accuracy of alternative methods as benchmarked with simulated and real long read RNA-seq. Using short and long read RNA-seq, we further apply CTAT-LR-fusion to bulk transcriptomes of nine tumor cell lines, and to tumor single cells derived from a melanoma sample and three metastatic high grade serous ovarian carcinoma samples. In both bulk and in single cell RNA-seq, long isoform reads yielded higher sensitivity for fusion detection than short reads with notable exceptions. By combining short and long reads in CTAT-LR-fusion, we are able to further maximize detection of fusion splicing isoforms and fusion-expressing tumor cells. CTAT-LR-fusion is available at https://github.com/TrinityCTAT/CTAT-LR-fusion/wiki.

Impact of a cell cycle and an extracellular matrix remodeling transcriptional signature on tumor progression and correlation with EZH2 expression in meningioma

OBJECTIVE

The authors searched for genetic and transcriptional signatures associated with tumor progression and recurrence in their cohort of patients with meningiomas, combining the analysis of targeted exome, NF2-LOH, transcriptome, and protein expressions.

METHODS

The authors included 91 patients who underwent resection of intracranial meningioma at their institution between June 2000 and November 2007. The search of somatic mutations was performed by Next Generation Sequencing through a customized panel and multiplex ligation-dependent probe amplification for NF2 loss of heterozygosity. The transcriptomic profile was analyzed by QuantSeq 3' mRNA-Seq. The differentially expressed genes of interest were validated at the protein level analysis by immunohistochemistry.

RESULTS

The transcriptomic analysis identified an upregulated set of genes related to metabolism and cell cycle and downregulated genes related to immune response and extracellular matrix remodeling in grade 2 (atypical) meningiomas, with a significant difference in recurrent compared with nonrecurrent cases. EZH2 nuclear positivity associated with grade 2, particularly with recurrent tumors and EZH2 gene expression level, correlated positively with the expression of genes related to cell cycle and negatively to genes related to immune response and regulation of cell motility.

CONCLUSIONS

The authors identified modules of dysregulated genes in grade 2 meningiomas related to the activation of oxidative metabolism, cell division, cell motility due to extracellular remodeling, and immune evasion that were predictive of survival and exhibited significant correlations with EZH2 expression.

Paths of Evolution of Progressive Anaplastic Meningiomas: A Clinical and Molecular Pathology Study

Grade 3 meningiomas are rare malignant tumors that can originate de novo or from the progression of lower grade meningiomas. The molecular bases of anaplasia and progression are poorly known. We aimed to report an institutional series of grade 3 anaplastic meningiomas and to investigate the evolution of molecular profile in progressive cases. Clinical data and pathologic samples were retrospectively collected. VEGF, EGFR, EGFRvIII, PD-L1; and Sox2 expression; MGMT methylation status; and TERT promoter mutation were assessed in paired meningioma samples collected from the same patient before and after progression using immunohistochemistry and PCR. Young age, de novo cases, origin from grade 2 in progressive cases, good clinical status, and unilateral side, were associated with more favorable outcomes. In ten progressive meningiomas, by comparing molecular profile before and after progression, we identified two subgroups of patients, one defined by Sox2 increase, suggesting a stem-like, mesenchymal phenotype, and another defined by EGFRvIII gain, suggesting a committed progenitor, epithelial phenotype. Interestingly, cases with Sox2 increase had a significantly shortened survival compared to those with EGFRvIII gain. PD-L1 increase at progression was also associated with worse prognosis, portending immune escape. We thus identified the key drivers of meningioma progression, which can be exploited for personalized treatments.

Genomic markers of recurrence risk in atypical meningioma following gross total resection

Background

Meningiomas are the most common primary central nervous system (CNS) tumor in adults and CNS World Health Organization grade 2 (atypical) meningiomas show an intermediate risk of recurrence/progression. Molecular parameters are needed to better inform management following gross total resection (GTR).

Methods

We performed comprehensive genomic analysis of tumor tissue from 63 patients who underwent radiologically confirmed GTR of a primary grade 2 meningioma, including a CLIA-certified target next-generation sequencing panel (n = 61), chromosomal microarray (n = 63), genome-wide methylation profiling (n = 62), H3K27me3 immunohistochemistry (n = 62), and RNA-sequencing (n = 19). Genomic features were correlated with long-term clinical outcomes (median follow-up: 10 years) using Cox proportional hazards regression modeling and published molecular prognostic signatures were evaluated.

Results

The presence of specific copy number variants (CNVs), including -1p, -10q, -7p, and -4p, was the strongest predictor of decreased recurrence-free survival (RFS) within our cohort (P < .05). NF2 mutations were frequent (51%) but did not show a significant association with RFS. DNA methylation-based classification assigned tumors to DKFZ Heidelberg benign (52%) or intermediate (47%) meningioma subclasses and was not associated with RFS. H3K27 trimethylation (H3K27me3) was unequivocally lost in 4 tumors, insufficient for RFS analysis. Application of published integrated histologic/molecular grading systems did not improve prediction of recurrence risk over the presence of -1p or -10q alone.

Conclusions

CNVs are strong predictors of RFS in grade 2 meningiomas following GTR. Our study supports incorporation of CNV profiling into clinical evaluation to better guide postoperative patient management, which can be readily implemented using existing, clinically validated technologies.

Advances in Central Nervous System Tumor Classification

Historically, the classification of tumors of the central nervous system (CNS) relies on the histologic appearance of cells under a microscope; however, the molecular era of medicine has resulted in new diagnostic paradigms anchored in the intrinsic biology of disease. The 2021 World Health Organization (WHO) reformulated the classification of CNS tumors to incorporate molecular parameters, in addition to histology, to define many tumor types. A contemporary classification system with integrated molecular features aims to provide an unbiased tool to define tumor subtype, the risk of tumor progression, and even the response to certain therapeutic agents. Meningiomas are heterogeneous tumors as depicted by the current 15 distinct variants defined by histology in the 2021 WHO classification, which also incorporated the first moelcular critiera for meningioma grading: homozygous loss of CDKN2A/B and TERT promoter mutation as criteria for a WHO grade 3 meningioma. The proper classification and clinical management of meningioma patients requires a multidisciplinary approach, which in addition to the information on microscopic (histology) and macroscopic (Simpson grade and imaging), should also include molecular alterations. In this chapter, we present the most up-to-date knowledge in CNS tumor classification, particularly in meningioma, in the molecular era and how it could affect their future classification and clinical management of patients with these diseases.

Gene expression analysis during progression of malignant meningioma compared to benign meningioma

OBJECTIVE

Meningioma is the most common primary intracranial neoplasm. Only 1%-3% of meningiomas are malignant according to the 2016 WHO criteria (WHO grade III). High-grade meningiomas present specific gene expression signatures indicating aggressive growth or recurrence. However, changes in gene expression and in neuroinflammatory gene expression signatures in WHO grade III meningiomas and during progression from WHO grade I or II to grade III are unknown.

METHODS

The authors used a NanoString targeted gene expression panel with focus on 787 genes relevant in meningioma pathology and neuroinflammatory pathways to investigate patients with grade III meningiomas treated at Rigshospitalet from 2000 to 2020 (n = 51). A temporal dimension was added to the investigation by including samples from patients' earlier grade I and II meningiomas and grade III recurrences (n = 139 meningiomas). The authors investigated changes in neuroinflammatory gene expression signatures in 1) grade I meningiomas that later transformed into grade III meningiomas, and 2) grade III meningiomas compared with nonrecurrent grade I meningiomas.

RESULTS

The authors' data indicate that FOXM1, TOP2A, BIRC5, and MYBL2 were enriched and the HOTAIR regulatory pathway was enriched in grade III meningiomas compared with nonrecurrent grade I meningiomas. They discovered a separation of malignant and benign meningiomas based only on genes involved in microglia regulation with enrichment of P2RY12 in grade I compared with grade III meningiomas. Interestingly, FOXM1 was upregulated in premalignant grade I meningioma years before the grade III transformation.

CONCLUSIONS

The authors found gene expression changes in low-grade meningiomas that predated histological transformation to grade III meningiomas. Neuroinflammation genes distinguished grade III from grade I meningiomas.

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.

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.

Molecular Signature Expands the Landscape of Driver Negative Thyroid Cancers

Thyroid cancer is the most common endocrine malignancy. However, the cytological diagnosis of follicular thyroid carcinoma (FTC), Hürthle cell carcinoma (HCC), and follicular variant of papillary thyroid carcinoma (FVPTC) and their benign counterparts is a challenge for preoperative diagnosis. Nearly 20-30% of biopsied thyroid nodules are classified as having indeterminate risk of malignancy and incur costs to the health care system. Based on that, 120 patients were screened for the main driver mutations previously described in thyroid cancer. Subsequently, 14 mutation-negative cases that are the main source of diagnostic errors (FTC, HCC, or FVPTC) underwent RNA-Sequencing analysis. Somatic variants in candidate driver genes (ECD, NUP98,LRP1B, NCOR1, ATM, SOS1, and SPOP) and fusions were described. NCOR1 and SPOP variants underwent validation. Moreover, expression profiling of driver-negative samples was compared to 16 BRAF V600E, RAS, or PAX8-PPARg positive samples. Negative samples were separated in two clusters, following the expression pattern of the RAS/PAX8-PPARg or BRAF V600E positive samples. Both negative groups showed distinct BRS, ERK, and TDS scores, tumor mutation burden, signaling pathways and immune cell profile. Altogether, here we report novel gene variants and describe cancer-related pathways that might impact preoperative diagnosis and provide insights into thyroid tumor biology.

Discovering the Molecular Landscape of Meningioma: The Struggle to Find New Therapeutic Targets

Meningiomas are the most common primary CNS tumors. They are usually benign but can present aggressive behavior in about 20% of cases. The genetic landscape of meningioma is characterized by the presence (in about 60% of cases) or absence of NF2 mutation. Low-grade meningiomas can also present other genetic alterations, particularly affecting SMO, TRAF7, KLF4 AKT1 and PI3KCA. In higher grade meningiomas, mutations of TERT promoter and deletion of CDKN2A/B seem to have a prognostic value. Furthermore, other genetic alterations have been identified, such as BAP1, DMD and PBRM1. Different subgroups of DNA methylation appear to be correlated with prognosis. In this review, we explored the genetic landscape of meningiomas and the possible therapeutic implications.

Glutamine anaplerosis is required for amino acid biosynthesis in human meningiomas

BACKGROUND

We postulate that meningiomas undergo distinct metabolic reprogramming in tumorigenesis and unravelling their metabolic phenotypes provide new therapeutic insights. Glutamine catabolism is key to the growth and proliferation of tumors. Here, we investigated the metabolomics of freshly resected meningiomas and glutamine metabolism in patient-derived meningioma cells.

METHODS

1H NMR spectroscopy of tumor tissues from 33 meningioma patients was used to differentiate the metabolite profiles of grade-I and grade-II meningiomas. Glutamine metabolism was examined using 13C/ 15N glutamine tracer, in five patient-derived meningioma cells.

RESULTS

Alanine, lactate, glutamate, glutamine, and glycine were predominantly elevated only in grade-II meningiomas by 74%, 76%, 35%, 75% and 33% respectively, with alanine, and glutamine being statistically significant (p ≤ 0.02). 13C/ 15N glutamine tracer experiments revealed that both grade-I and -II meningiomas actively metabolize glutamine to generate various key carbon intermediates including alanine and proline that are necessary for the tumor growth. Also, it is shown that glutaminase (GLS1) inhibitor, CB-839 is highly effective in downregulating glutamine metabolism and decreasing proliferation in meningioma cells.

CONCLUSION

Alanine and glutamine/glutamate are mainly elevated in grade-II meningiomas. Grade-I meningiomas possess relatively higher glutamine metabolism providing carbon/nitrogen for the biosynthesis of key nonessential amino acids. GLS1 inhibitor (CB-839) would be very effective in downregulating glutamine metabolic pathways in grade-I meningiomas leading to decreased cellular proliferation.

Molecular alterations in meningioma: prognostic and therapeutic perspectives

PURPOSE OF REVIEW

To discuss recent advances in the meningioma biology and their clinical implications.

RECENT FINDINGS

Meningioma is the most common primary intracranial tumor. Mostly benign, 20% of cases display an aggressive behavior despite best standard of care. The genetic landscape of meningiomas is divided according to NF2 mutational status. Although about 60% of meningiomas display NF2 mutations, the other share is more heterogenous. Mutations in TRAF7, SMO, v-akt murine thymoma viral oncogene homolog 1 (AKT1), PI3KCA and KLF4 are seen mostly in WHO grade 1 meningiomas. In higher grade meningiomas, mutations of the TERT promoter and deletions of CDKN2A/B emerge and have prognostic value. Moreover, mutations in DMD, BAP1 and PBRM1 have recently been discovered and are being further explored. DNA methylation subgroups offer valuable insight into meningioma prognosis and its implementation in clinical setting is under evaluation. Moreover, the study of distinct meningioma populations such as radiation-induced meningioma and progestin-associated meningioma may provide further insight into meningioma oncogenesis and potential therapeutic targets.

SUMMARY

The mutational landscape of meningioma has expanded following the use of the new genetic sequencing approaches. Novel mutations have been characterized and reveal their prognostic and therapeutic applications. This improved understanding of meningioma biology has promising implications for novel treatment strategies.

MPscore: A Novel Predictive and Prognostic Scoring for Progressive Meningioma

Simple Summary

Subtyping for meningioma is urgently required to stratify the patients with high risks of recurrence and progression due to the intertumoral heterogeneity in meningioma. Here, we performed a consensus clustering of 179 meningiomas and identified progressive subtype (subtype 3) based the transcriptome profiles. Loss of chromosome 1q along with Neurofibromin 2 (NF2) mutation or loss of chromosome 22p is exclusively presented in subtype 3 meningioma. DNA methylation analyses of meningioma subtypes also suggested hypermethylation was observed in subtype 3 meningioma. Our findings identified low expression of Alkaline Phosphatase (ALPL) is the most significant feature in progressive subtype of meningioma. We constructed and validated a meningioma progression score (MPscore) to characterize the progressive phenotype in meningioma. The predictive accuracy has also been validated in three independent cohorts. Therefore, MPscore can be potentially useful for meningioma recurrence prediction and stratification.

Abstract

Meningioma is the most common tumor in central nervous system (CNS). Although most cases of meningioma are benign (WHO grade I) and curable by surgical resection, a few tumors remain diagnostically and therapeutically challenging due to the frequent recurrence and progression. The heterogeneity of meningioma revealed by DNA methylation profiling suggests the demand of subtyping for meningioma. Therefore, we performed a clustering analyses to characterize the progressive features of meningioma and constructed a meningioma progression score to predict the risk of the recurrence. A total of 179 meningioma transcriptome from RNA sequencing was included for progression subtype clustering. Four biologically distinct subtypes (subtype 1, subtype 2, subtype 3 and subtype 4) were identified. Copy number alternation and genomewide DNA methylation of each subtype was also characterized. Immune cell infiltration was examined by the microenvironment cell populations counter. All anaplastic meningiomas (7/7) and most atypical meningiomas (24/32) are enriched in subtype 3 while no WHO II or III meningioma presents in subtype 1, suggesting subtype 3 meningioma is a progressive subtype. Stemness index and immune response are also heterogeneous across four subtypes. Monocytic lineage is the most immune cell type in all meningiomas, except for subtype 1. CD8 positive T cells are predominantly observed in subtype 3. To extend the clinical utility of progressive meningioma subtyping, we constructed the meningioma progression score (MPscore) by the signature genes in subtype 3. The predictive accuracy and prognostic capacity of MPscore has also been validated in three independent cohort. Our study uncovers four biologically distinct subtypes in meningioma and the MPscore is potentially helpful in the recurrence risk prediction and response to treatments stratification in meningioma.

Alteration of FOXM1 expression and macrophage polarization in refractory meningiomas during long-term follow-up

Malignant progression of grade I meningioma with a long latency period is rare. We experienced grade II/III meningiomas with refractoriness and recurrence from grade I meningiomas through multiple surgeries. Three patients with atypical/anaplastic meningioma experienced long-latent recurrence after initial surgery for grade I (meningothelial) meningioma without following adjuvant radiotherapy were included in the present study. Histological findings of the initial tumors in all cases (case 1, 2, and 3) revealed meningothelial meningioma with 1%, 5%, and 0.1% MIB-1 positive cells, respectively. Surprisingly, magnetic resonance imaging (MRI) detected a recurrent tumor 2, 12, and 12 years after the initial operation, respectively. Case 1 was atypical meningioma after third recurrence, and case 2 and 3 were anaplastic meningioma after second and third recurrence, respectively. The patient in case 2 received adjuvant radiotherapy. In case 2, the tumor recurred intracranial and distant metastasis to the lung with huge substantial pleural effusion was detected. To investigate the pathogenesis of malignant progression from benign to malignant meningioma, CD163/CD68 expression by immunohistochemically and FOXM1 mRNA expression by RT-PCR were compared using surgical specimens from initial and recurrent tumors in all three patients. The ratio of CD163/CD68 positivity and FOXM1 mRNA expression were increased in recurrent tumors compared with matched initial tumors. CD163 and FOXM1 expression levels were induced even in recurrent grade I meningioma, suggesting that macrophage polarization and pro-mitotic transcriptional factor might be associated with clinical behavior of meningioma and be useful as a prediction marker for malignant progression. Careful long-term follow-up is important for early diagnosis of malignant progression in meningiomas, even if grade I meningioma is completely resected. Development of a multidisciplinary approach including radiation and novel molecular targeted therapy is expected for recurrent and malignant meningiomas.

Integration and Comparison of Transcriptomic and Proteomic Data for Meningioma

Meningioma are the most frequent primary intracranial tumour. Management of aggressive meningioma is complex, and development of effective biomarkers or pharmacological interventions is hampered by an incomplete knowledge of molecular landscape. Here, we present an integrated analysis of two complementary omics studies to investigate alterations in the "transcriptome-proteome" profile of high-grade (III) compared to low-grade (I) meningiomas. We identified 3598 common transcripts/proteins and revealed concordant up- and downregulation in grade III vs. grade I meningiomas. Concordantly upregulated genes included FABP7, a fatty acid binding protein and the monoamine oxidase MAOB, the latter of which we validated at the protein level and established an association with Food and Drug Administration (FDA)-approved drugs. Notably, we derived a plasma signature of 21 discordantly expressed genes showing positive changes in protein but negative in transcript levels of high-grade meningiomas, including the validated genes CST3, LAMP2, PACS1 and HTRA1, suggesting the acquisition of these proteins by tumour from plasma. Aggressive meningiomas were enriched in processes such as oxidative phosphorylation and RNA metabolism, whilst concordantly downregulated genes were related to reduced cellular adhesion. Overall, our study provides the first transcriptome-proteome characterisation of meningioma, identifying several novel and previously described transcripts/proteins with potential grade III biomarker and therapeutic significance.

Multi-Omics Analysis in Initiation and Progression of Meningiomas: From Pathogenesis to Diagnosis

Meningiomas are common intracranial tumors that can be cured by surgical resection in most cases. However, the most disconcerting is high-grade meningiomas, which frequently recur despite initial successful treatment, eventually conferring poor prognosis. Therefore, the early diagnosis and classification of meningioma is necessary for the subsequent intervention and an improved prognosis. A growing body of evidence demonstrates the potential of multi-omics study (including genomics, transcriptomics, epigenomics, proteomics) for meningioma diagnosis and mechanistic links to potential pathological mechanism. This thesis addresses a neglected aspect of recent advances in the field of meningiomas at multiple omics levels, highlighting that the integration of multi-omics can reveal the mechanism of meningiomas, which provides a timely and necessary scientific basis for the treatment of meningiomas.

Deep Learning Model for the Automated Detection and Histopathological Prediction of Meningioma

The volumetric assessment and accurate grading of meningiomas before surgery are highly relevant for therapy planning and prognosis prediction. This study was to design a deep learning algorithm and evaluate the performance in detecting meningioma lesions and grade classification. In total, 5088 patients with histopathologically confirmed meningioma were retrospectively included. The pyramid scene parsing network (PSPNet) was trained to automatically detect and delineate the meningiomas. The results were compared to manual segmentations by evaluating the mean intersection over union (mIoU). The performance of grade classification was evaluated by accuracy. For the automated detection and segmentation of meningiomas, the mean pixel accuracy, tumor accuracy, background accuracy and mIoU were 99.68%, 81.36%, 99.88% and 81.36% for all patients; 99.52%, 84.86%, 99.93% and 84.86% for grade I meningiomas; 99.57%, 80.11%, 99.92% and 80.12% for grade II meningiomas; and 99.75%, 78.40%, 99.99% and 78.40% for grade III meningiomas, respectively. For grade classification, the accuracy values of the training and test datasets were 99.93% and 81.52% for all patients; 99.98% and 98.51% for grade I meningiomas; 99.91% and 66.67% for grade II meningiomas; and 99.88% and 73.91% for grade III meningiomas, respectively. The automated detection, segmentation and grade classification of meningiomas based on deep learning were accurate and reliable and may improve the monitoring and treatment of this frequently occurring tumor entity. Furthermore, the method could function as a useful tool for preassessment and preselection for radiologists, offering auxiliary information for clinical decision making in presurgical evaluation.

Multiplatform genomic profiling and magnetic resonance imaging identify mechanisms underlying intratumor heterogeneity in meningioma

Meningiomas are the most common primary intracranial tumors, but the molecular drivers of meningioma tumorigenesis are poorly understood. We hypothesized that investigating intratumor heterogeneity in meningiomas would elucidate biologic drivers and reveal new targets for molecular therapy. To test this hypothesis, here we perform multiplatform molecular profiling of 86 spatially-distinct samples from 13 human meningiomas. Our data reveal that regional alterations in chromosome structure underlie clonal transcriptomic, epigenomic, and histopathologic signatures in meningioma. Stereotactic co-registration of sample coordinates to preoperative magnetic resonance images further suggest that high apparent diffusion coefficient (ADC) distinguishes meningioma regions with proliferating cells enriched for developmental gene expression programs. To understand the function of these genes in meningioma, we develop a human cerebral organoid model of meningioma and validate the high ADC marker genes CDH2 and PTPRZ1 as potential targets for meningioma therapy using live imaging, single cell RNA sequencing, CRISPR interference, and pharmacology.

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.

High Copy-Number Variation Burdens in Cranial Meningiomas From Patients With Diverse Clinical Phenotypes Characterized by Hot Genomic Structure Changes

Meningiomas, as the most common primary tumor of the central nervous system, are known to harbor genomic aberrations that associate with clinical phenotypes. Here we performed genome-wide genotyping for cranial meningiomas in 383 Chinese patients and identified 9,821 copy-number variations (CNVs). Particularly, patients with diverse clinical features had distinct tumor CNV profiles. CNV burdens were greater in high-grade (WHO grade II and III) samples, recurrent lesions, large tumors (diameter >4.3 cm), and those collected from male patients. Nevertheless, the level of CNV burden did not relate to tumor locations, peritumoral brain edema, bone invasion, or multiple lesions. Overall, the most common tumor CNVs were the copy-number gain (CNG) at 22q11.1 and the copy-number losses (CNLs) at 22q13.2, 14q11.2, 1p34.3, and 1p31.3. Recurrent lesions were featured by the CNLs at 1p31.3, 6q22.31, 9p21.3, and 11p12, and high-grade samples had more CNVs at 4q13.3 and 6q22.31. Meanwhile, large tumors were more likely to have the CNVs at 1p31.3 and 1p34.3. Additionally, recurrence prediction indicated the CNLs at 4p16.3 (p = 0.009, hazard ratio = 5.69) and 10p11.22 (p = 0.037, hazard ratio = 4.53) were candidate independent risk factors.

Molecular Advances in Central Nervous System Mesenchymal Tumors

Mesenchymal tumors of the central nervous system (CNS) comprise an array of neoplasms that may arise from or secondarily affect the CNS and its immediate surroundings. This review focuses on meningiomas and solitary fibrous tumors, the most common primary CNS mesenchymal tumors, and discusses recent advances in unveiling the molecular landscapes of these neoplasms. An effort is made to underscore those molecular findings most relevant to tumor diagnostics and prognostication from a practical perspective. As molecular techniques become more readily used at the clinical level, such alterations may strengthen formal grading schemes and lend themselves to treatment with targeted therapies.

A Rapid Robust Method for Subgrouping Non-NF2 Meningiomas According to Genotype and Detection of Lower Levels of M2 Macrophages in AKT1 E17K Mutated Tumours

The majority of meningiomas are grade I, but some grade I tumours are clinically more aggressive. Recent advances in the genetic study of meningiomas has allowed investigation into the influence of genetics on the tumour microenvironment, which is important for tumorigenesis. We have established that the endpoint genotyping method Kompetitive Allele Specific PCR (KASP™) is a fast, reliable method for the screening of meningioma samples into different non-NF2 mutational groups using a standard real-time PCR instrument. This genotyping method and four-colour flow cytometry has enabled us to assess the variability in the largest immune cell infiltrate population, M2 macrophages (CD45+HLA-DR+CD14+CD163+) in 42 meningioma samples, and to suggest that underlying genetics is relevant. Further immunohistochemistry analysis comparing AKT1 E17K mutants to WHO grade I NF2-negative samples showed significantly lower levels of CD163-positive activated M2 macrophages in meningiomas with mutated AKT1 E17K, signifying a more immunosuppressive tumour microenvironment in NF2 meningiomas. Our data suggested that underlying tumour genetics play a part in the development of the immune composition of the tumour microenvironment. Stratifying meningiomas by mutational status and correlating this with their cellular composition will aid in the development of new immunotherapies for patients.