Proteomic analysis of meningiomas reveals clinically distinct molecular patterns

Unveiling a Biomarker Signature of Meningioma: The Need for a Panel of Genomic, Epigenetic, Proteomic, and RNA Biomarkers to Advance Diagnosis and Prognosis

Meningiomas are the most prevalent primary intracranial tumors. The majority are benign but can undergo dedifferentiation into advanced grades classified by World Health Organization (WHO) into Grades 1 to 3. Meningiomas' tremendous variability in tumor behavior and slow growth rates complicate their diagnosis and treatment. A deeper comprehension of the molecular pathways and cellular microenvironment factors implicated in meningioma survival and pathology is needed. This review summarizes the known genetic and epigenetic aberrations involved in meningiomas, with a focus on neurofibromatosis type 2 (NF2) and non-NF2 mutations. Novel potential biomarkers for meningioma diagnosis and prognosis are also discussed, including epigenetic-, RNA-, metabolomics-, and protein-based markers. Finally, the landscape of available meningioma-specific animal models is overviewed. Use of these animal models can enable planning of adjuvant treatment, potentially assisting in pre-operative and post-operative decision making. Discovery of novel biomarkers will allow, in combination with WHO grading, more precise meningioma grading, including meningioma identification, subtype determination, and prediction of metastasis, recurrence, and response to therapy. Moreover, these biomarkers may be exploited in the development of personalized targeted therapies that can distinguish between the 15 diverse meningioma subtypes.

Proteomic interrogation of the meninges reveals the molecular identities of structural components and regional distinctions along the CNS axis

The meninges surround the brain and spinal cord, affording physical protection while also serving as a niche of neuroimmune activity. Though possessing stromal qualities, its complex cellular and extracellular makeup has yet to be elaborated, and it remains unclear whether the meninges vary along the neuroaxis. Hence, studies were carried-out to elucidate the protein composition and structural organization of brain and spinal cord meninges in normal, adult Biozzi ABH mice. First, shotgun, bottom-up proteomics was carried-out. Prominent proteins at both brain and spinal levels included Type II collagen and Type II keratins, representing extracellular matrix (ECM) and cytoskeletal categories, respectively. While the vast majority of total proteins detected was shared between both meningeal locales, more were uniquely detected in brain than in spine. This pattern was also seen when total proteins were subdivided by cellular compartment, except in the case of the ECM category where brain and spinal meninges each had near equal number of unique proteins, and Type V and type III collagen registered exclusively in the spine. Quantitative analysis revealed differential expression of several collagens and cytoskeletal proteins between brain and spinal meninges. High-resolution immunofluorescence and immunogold-scanning electronmicroscopy on sections from whole brain and spinal cord - still encased within bone -identified major proteins detected by proteomics, and highlighted their association with cellular and extracellular elements of variously shaped arachnoid trabeculae. Western blotting aligned with the proteomic and immunohistological analyses, reinforcing differential appearance of proteins in brain vs spinal meninges. Results could reflect regional distinctions in meninges that govern protective and/or neuroimmune functions.

Integrated Meta-Omics Analysis Unveils the Pathways Modulating Tumorigenesis and Proliferation in High-Grade Meningioma

Meningioma, a primary brain tumor, is commonly encountered and accounts for 39% of overall CNS tumors. Despite significant progress in clinical research, conventional surgical and clinical interventions remain the primary treatment options for meningioma. Several proteomics and transcriptomics studies have identified potential markers and altered biological pathways; however, comprehensive exploration and data integration can help to achieve an in-depth understanding of the altered pathobiology. This study applied integrated meta-analysis strategies to proteomic and transcriptomic datasets comprising 48 tissue samples, identifying around 1832 common genes/proteins to explore the underlying mechanism in high-grade meningioma tumorigenesis. The in silico pathway analysis indicated the roles of extracellular matrix organization (EMO) and integrin binding cascades in regulating the apoptosis, angiogenesis, and proliferation responsible for the pathobiology. Subsequently, the expression of pathway components was validated in an independent cohort of 32 fresh frozen tissue samples using multiple reaction monitoring (MRM), confirming their expression in high-grade meningioma. Furthermore, proteome-level changes in EMO and integrin cell surface interactions were investigated in a high-grade meningioma (IOMM-Lee) cell line by inhibiting integrin-linked kinase (ILK). Inhibition of ILK by administrating Cpd22 demonstrated an anti-proliferative effect, inducing apoptosis and downregulating proteins associated with proliferation and metastasis, which provides mechanistic insight into the disease pathophysiology.

Meningioma: A Biography-Tumor Forever Tied to the Origins and "Soul of Neurosurgery"

Meningiomas are neoplasms derived from the arachnoid cap cells of the leptomeninges and are the most common intracranial tumor. In the present historical vignette, the evolution of the management and diagnosis of meningioma is described. We begin with studies of skulls from the prehistoric record, such as the Steinheim skull, which demonstrate morphologic changes (e.g., hyperostosis) now known to occur with meningioma growth. We then continue with the earliest formal descriptions of meningiomas, including that by Platter, who published the first report of meningioma, along with early histopathologic descriptions by Cushing, who divined the cytological origins of the tumor and was the first to use the term meningioma. We conclude with a description of current management of meningiomas and potential avenues for further discovery. This article is effectively a lifetime biography of a tumor known and loved by neurosurgeons, the simple and yet complex meningioma.

A large-scale targeted proteomics of serum and tissue shows the utility of classifying high grade and low grade meningioma tumors

BACKGROUND

Meningiomas are the most prevalent primary brain tumors. Due to their increasing burden on healthcare, meningiomas have become a pivot of translational research globally. Despite many studies in the field of discovery proteomics, the identification of grade-specific markers for meningioma is still a paradox and requires thorough investigation. The potential of the reported markers in different studies needs further verification in large and independent sample cohorts to identify the best set of markers with a better clinical perspective.

METHODS

A total of 53 fresh frozen tumor tissue and 51 serum samples were acquired from meningioma patients respectively along with healthy controls, to validate the prospect of reported differentially expressed proteins and claimed markers of Meningioma mined from numerous manuscripts and knowledgebases. A small subset of Glioma/Glioblastoma samples were also included to investigate inter-tumor segregation. Furthermore, a simple Machine Learning (ML) based analysis was performed to evaluate the classification accuracy of the list of proteins.

RESULTS

A list of 15 proteins from tissue and 12 proteins from serum were found to be the best segregator using a feature selection-based machine learning strategy with an accuracy of around 80% in predicting low grade (WHO grade I) and high grade (WHO grade II and WHO grade III) meningiomas. In addition, the discriminant analysis could also unveil the complexity of meningioma grading from a segregation pattern, which leads to the understanding of transition phases between the grades.

CONCLUSIONS

The identified list of validated markers could play an instrumental role in the classification of meningioma as well as provide novel clinical perspectives in regard to prognosis and therapeutic targets.

Prognostic-Related Biomarkers in Pancreatic Ductal Adenocarcinoma Correlating with Immune Infiltrates Based on Proteomics

BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) accounts for 85% of pancreatic carcinoma cases. Patients with PDAC have a poor prognosis. The lack of reliable prognostic biomarkers makes treatment challenging for patients with PDAC. Using a bioinformatics database, we sought to identify prognostic biomarkers for PDAC. MATERIAL AND METHODS Using proteomic analysis of the Clinical Proteomics Tumor Analysis Consortium (CPTAC) database, we were able to identify core differential proteins between early and advanced pancreatic ductal adenocarcinoma tissue, and then we used survival analysis, Cox regression analysis, and area under the ROC curves to screen for more significant differential proteins. Additionally, the Kaplan-Meier plotter database was utilized to determine the relationship between prognosis and immune infiltration in PDAC. RESULTS We identified 378 differential proteins in early (n=78) and advanced stages (n=47) of PDAC (P<0.05). PLG, COPS5, FYN, ITGB3, IRF3, and SPTA1 served as independent prognostic factors of patients with PDAC. Patients with higher COPS5 expression had shorter overall survival (OS) and recurrence-free survival, and those with higher PLG, ITGB3, and SPTA1, and lower FYN and IRF3 expression had shorter OS. More importantly, COPS5, IRF3 were negatively associated with macrophages and NK cells, but PLG, FYN, ITGB3, and SPTA1 were positively related to the expression of CD8+ T cells and B cells. COPS5 affected the prognosis of PDAC patients by acting on B cells, CD8+ T cells, macrophages, and NK cells immune infiltration, while PLG, FYN, ITGB3, IRF3, and SPTA1 affected PDAC patient prognosis through some immune cells. CONCLUSIONS PLG, COPS5, FYN, IRF3, ITGB3 and SPTA1 could be potential immunotherapeutic targets and valuable prognostic biomarkers of PDAC.

Integrative proteomics and m6A microarray analyses of the signatures induced by METTL3 reveals prognostically significant in gastric cancer by affecting cellular metabolism

METTL3-mediated RNA N6-methyladenosine (m6A) is the most prevalent modification that participates in tumor initiation and progression via governing the expression of their target genes in cancers. However, its role in tumor cell metabolism remains poorly characterized. In this study, m6A microarray and quantitative proteomics were employed to explore the potential effect and mechanism of METTL3 on the metabolism in GC cells. Our results showed that METTL3 induced significant alterations in the protein and m6A modification profile in GC cells. Gene Ontology (GO) enrichment indicated that down-regulated proteins were significantly enriched in intracellular mitochondrial oxidative phosphorylation (OXPHOS). Moreover, the protein-protein Interaction (PPI) network analysis found that these differentially expressed proteins were significantly associated with OXPHOS. A prognostic model was subsequently constructed based on the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, and the high-risk group exhibited a worse prognosis in GC patients. Meanwhile, Gene Set Enrichment Analysis (GSEA) demonstrated significant enrichment in the energy metabolism signaling pathway. Then, combined with the results of the m6A microarray analysis, the intersection molecules of DEPs and differential methylation genes (DMGs) were significantly correlated with the molecules of OXPHOS. Besides, there were significant differences in prognosis and GSEA enrichment between the two clusters of GC patients classified according to the consensus clustering algorithm. Finally, highly expressed and highly methylated molecules regulated by METTL3 were analyzed and three (AVEN, DAZAP2, DNAJB1) genes were identified to be significantly associated with poor prognosis in GC patients. These results signified that METTL3-regulated DEPs in GC cells were significantly associated with OXPHOS. After combined with m6A microarray analysis, the results suggested that these proteins might be implicated in cell energy metabolism through m6A modifications thus influencing the prognosis of GC patients. Overall, our study revealed that METTL3 is involved in cell metabolism through an m6A-dependent mechanism in GC cells, and indicated a potential biomarker for prognostic prediction in GC.

Clinical Proteomics for Meningioma: An Integrated Workflow for Quantitative Proteomics and Biomarker Validation in Formalin-Fixed Paraffin-Embedded Tissue Samples

Clinical proteomics is a rapidly emerging frontier in laboratory medicine. High-throughput proteomic investigations of biopsy tissues provide mechanistic insights into complex human diseases. For large-scale proteomics, formalin-fixed and paraffin-embedded (FFPE) tissue samples offer a viable alternative to fresh-frozen (FF) tissues that have restricted availability. In this context, meningioma is one of the most common primary brain tumors where innovation in diagnostics and therapeutic targets can benefit from clinical proteomics. We present here an integrated workflow for quantitative proteomics and biomarker validation of meningioma FFPE tissues. Applying label-free quantitative (LFQ) proteomics, we reproducibly (Pearson's correlation: 0.84-0.91) obtained an in-depth proteome coverage (nearly 4000 proteins per sample) from 120 min gradient of single unfractionated mass spectrometry run. Furthermore, building upon LFQ data and literature curated set of meningioma-associated proteins, we validated VIM, AHNAK, and CLU from FFPE tissues using selected reaction monitoring (SRM) assay and compared its performance with FF tissues. This study illustrates how knowledge from label-free proteomics can be integrated for selecting peptides for targeted validation and suggests that FFPE tissues are comparable to FF tissues for SRM assays. This quantitative clinical proteomics workflow is scalable for large-scale clinical diagnostics studies in the future, for example, utilizing the global repository of FFPE tissues in meningioma and possibly in other cancers.

Transcriptomic Profiling Revealed Lnc-GOLGA6A-1 as a Novel Prognostic Biomarker of Meningioma Recurrence

BACKGROUND

Meningioma is the most common primary central nervous system neoplasm, accounting for about a third of all brain tumors. Because their growth rates and prognosis cannot be accurately estimated, biomarkers that enable prediction of their biological behavior would be clinically beneficial.

OBJECTIVE

To identify coding and noncoding RNAs crucial in meningioma prognostication and pathogenesis.

METHODS

Total RNA was purified from formalin-fixed and paraffin-embedded tumor samples of 64 patients with meningioma with distinct clinical characteristics (16 recurrent, 30 nonrecurrent with follow-up of >5 years, and 18 with follow-up of <5 years without recurrence). Transcriptomic sequencing was performed using the HiSeq 2500 platform (Illumina), and biological and functional differences between meningiomas of different types were evaluated by analyzing differentially expression of messenger RNA (mRNA) and long noncoding RNA (IncRNA). The prognostic value of 11 differentially expressed RNAs was then validated in an independent cohort of 90 patients using reverse transcription quantitative (real-time) polymerase chain reaction.

RESULTS

In total, 69 mRNAs and 108 lncRNAs exhibited significant differential expression between recurrent and nonrecurrent meningiomas. Differential expression was also observed with respect to sex (12 mRNAs and 59 lncRNAs), World Health Organization grade (58 mRNAs and 98 lncRNAs), and tumor histogenesis (79 mRNAs and 76 lncRNAs). Lnc-GOLGA6A-1, ISLR2, and AMH showed high prognostic power for predicting meningioma recurrence, while lnc-GOLGA6A-1 was the most significant factor for recurrence risk estimation (1/hazard ratio = 1.31; P = .002).

CONCLUSION

Transcriptomic sequencing revealed specific gene expression signatures of various clinical subtypes of meningioma. Expression of the lnc-GOLGA61-1 transcript was found to be the most reliable predictor of meningioma recurrence.

Bone Invasive Meningioma: Recent Advances and Therapeutic Perspectives

Meningioma is the most common primary neoplasm of the central nervous system (CNS). Generally, these tumors are benign and have a good prognosis. However, treatment can be challenging in cases with aggressive variants and poor prognoses. Among various prognostic factors that have been clinically investigated, bone invasion remains controversial owing to a limited number of assessments. Recent study reported that bone invasion was not associated with WHO grades, progression, or recurrence. Whereas, patients with longer-recurrence tended to have a higher incidence of bone invasion. Furthermore, bone invasion may be a primary preoperative predictor of the extent of surgical resection. Increasing such evidence highlights the potential of translational studies to understand bone invasion as a prognostic factor of meningiomas. Therefore, this mini-review summarizes recent advances in pathophysiology and diagnostic modalities and discusses future research directions and therapeutic strategies for meningiomas with bone invasion.

Notch signaling in malignant gliomas: supporting tumor growth and the vascular environment

Glioblastoma is the most malignant form of glioma, which is the most commonly occurring tumor of the central nervous system. Notch signaling in glioblastoma is considered to be a marker of an undifferentiated tumor cell state, associated with tumor stem cells. Notch is also known for facilitating tumor dormancy escape, recurrence and progression after treatment. Studies in vitro suggest that reducing, removing or blocking the expression of this gene triggers tumor cell differentiation, which shifts the phenotype away from stemness status and consequently facilitates treatment. In contrast, in the vasculature, Notch appears to also function as an important receptor that defines mature non-leaking vessels, and increasing its expression promotes tumor normalization in models of cancer in vivo. Failures in clinical trials with Notch inhibitors are potentially related to their opposing effects on the tumor versus the tumor vasculature, which points to the need for a greater understanding of this signaling pathway.

Correlation Between Plasma Proteomics and Adverse Outcomes Among Older Men With Chronic Coronary Syndrome

Background

Chronic coronary syndrome (CCS) is a newly proposed concept and is hallmarked by more long-term major adverse cardiovascular events (MACEs), calling for accurate prognostic biomarkers for initial risk stratification.

Methods

Data-independent acquisition liquid chromatography tandem mass spectrometry (DIA LC-MS/MS) quantitative proteomics was performed on 38 patients with CCS; 19 in the CCS events group and 19 in the non-events group as the controls. We also developed a machine-learning-based pipeline to identify proteins as potential biomarkers and validated the target proteins by enzyme-linked immunosorbent assay in an independent prospective cohort.

Results

Fifty-seven differentially expressed proteins were identified by quantitative proteomics and three final biomarkers were preliminarily selected from the machine-learning-based pipeline. Further validation with the prospective cohort showed that endothelial protein C receptor (EPCR) and cholesteryl ester transfer protein (CETP) levels at admission were significantly higher in the CCS events group than they were in the non-events group, whereas the carboxypeptidase B2 (CPB2) level was similar in the two groups. In the Cox survival analysis, EPCR and CETP were independent risk factors for MACEs. We constructed a new prognostic model by combining the Framingham coronary heart disease (CHD) risk model with EPCR and CETP levels. This new model significantly improved the C-statistics for MACE prediction compared with that of the Framingham CHD risk model alone.

Conclusion

Plasma proteomics was used to find biomarkers of predicting MACEs in patients with CCS. EPCR and CETP were identified as promising prognostic biomarkers for CCS.

Molecular subtypes based on CNVs related gene signatures identify candidate prognostic biomarkers in lung adenocarcinoma

The classical factors for predicting prognosis currently cannot meet the developing requirements of individualized and accurate prognostic evaluation in lung adenocarcinoma (LUAD). With the rapid development of high-throughput DNA sequencing technologies, genomic changes have been discovered. These sequencing data provide unprecedented opportunities for identifying cancer molecular subtypes. In this article, we classified LUAD into two distinct molecular subtypes (Cluster 1 and Cluster 2) based on Copy Number Variations (CNVs) and mRNA expression data from the Cancer Genome Atlas (TCGA) based on non-negative matrix factorization. Patients in Cluster 1 had worse outcomes than that in Cluster 2. Molecular features in subtypes were assessed to explain this phenomenon by analyzing differential expression genes expression pattern, which involved in cellular processes and environmental information processing. Analysis of immune cell populations suggested different distributions of CD4+ T cells, CD8+ T cells, and dendritic cells in the two subtypes. Subsequently, two novel genes, TROAP and RASGRF1, were discovered to be prognostic biomarkers in TCGA, which were confirmed in GSE31210 and Tianjin Medical University Cancer Institute and Hospital LUAD cohorts. We further proved their crucial roles in cancers by vitro experiments. TROAP mediates tumor cell proliferation, cycle, invasion, and migration, not apoptosis. RASGRF1 has a significant effect on tumor microenvironment. In conclusion, our study provides a novel insight into molecular classification based on CNVs related genes in LUAD, which may contribute to identify new molecular subtypes and target genes.

Comprehensive proteomic analysis reveals distinct functional modules associated with skull base and supratentorial meningiomas and perturbations in collagen pathway components

Meningiomas are brain tumors that originate from the meninges and has been primarily classified into three grades by the current WHO guidelines. Although widely prevalent and can be managed by surgery there are instances when the tumors are located in difficult regions. This results in considerable challenges for complete surgical resection and further clinical management. While the genetic signature of the skull base tumors is now known to be different from the non-skull base tumors, there is a lack of information at the functional aspects of these tumors at the proteomic level. Thus, the current study thereby aims to obtain mechanistic insights between the two radiologically distinct groups of meningiomas, namely the skull base & supratentorial (non-skull base-NSB) regions. We have employed a comprehensive mass spectrometry-based label-free quantitative proteomic analysis in Skull base and supratentorial meningiomas. Further, we have used an Artificial Neural Networking employing a sparse Multilayer perceptron (MLP) architecture to predict protein concordance. A patient-derived spectral library has been employed for a novel peptide-level validation of proteins that are specific to the radiological regions using the SRM assay based targeted proteomics approach. The comprehensive proteomics enabled the identification of nearly 4000 proteins with high confidence (1%FDR ≥ 2 unique peptides) among which 170 proteins were differentially abundant in Skull base vs Supratentorial tumors (p-value ≤0.05). In silico analysis enabled mapping of the major alterations and hinted towards an overall perturbation of extracellular matrix and collagen biosynthesis components in the non-skull base meningiomas and a prominent perturbation of molecular trafficking in the skull base meningiomas. Therefore, this study has yielded novel insights into the functional association of the proteins that are differentially abundant in the two radiological subgroups. SIGNIFICANCE: In the current study, we have performed label-free proteomic analysis on fresh frozen tissue of 14 Supratentorial (NSB) and 7 Skull base meningiomas to assess perturbations in the global proteome, we have further employed an in-depth in silico analysis to map the pathways that have enabled functional mapping of the differentially abundant proteins in the Skull base and Supratentorial tumors. The findings from the above were also subjected to a machine learning-based neural networking to find out the proteins that have the most concordance of occurrence to determine the most influential proteins of the network. We further validated the differential abundance of identified protein markers in a larger patient cohort of Skull base and Supratentorial employing targeted proteomics approach to validate key protein candidates emerging from ours and other recent studies. The previous studies that have explored the skull base and convexity meningiomas have been able to reveal alterations in the genetic mutations in these tumor types. However, there are not many studies that have explored the functional aspects of these tumors, especially at the proteome level. We have attempted for the first time to map the functional modules associated with altered proteins in these tumors and have been able to identify that there is a possibility that the Skull base meningiomas to be considerably different from the Non-skull base (NSB) tumors in terms of the perturbed pathways. Our study employed global as well as targeted proteomics to examine the proteomic alterations in these two tumor groups. The study indicates that proteins that were more abundant in Skull base tumors were part of molecular transport components, non-skull base proteins majorly mapped to the components of extracellular matrix remodeling pathways. In conclusion, this study substantiates the distinction in the proteomic signatures in the skull base and supratentorial meningiomas paving way for further investigation of the identified markers for determining if some of these proteins can be used for therapeutic interventions for cases that pose considerable challenges for complete resection.

The role of mRNA in the development, diagnosis, treatment and prognosis of neural tumors

Neural tumors can generally be divided into central nervous system tumors and peripheral nervous tumors. Because this type of tumor is located in the nerve, even benign tumors are often difficult to remove by surgery. In addition, the majority of neural tumors are malignant, and it is particular the same for the central nervous system tumors. Even treated with the means such as chemotherapy and radiotherapy, they are also difficult to completely cure. In recent years, an increasingly number of studies have focused on the use of mRNA to treat tumors, representing an emerging gene therapy. The use of mRNA can use the expression of some functional proteins for the treatment of genetic disorders or tissue repair, and it can also be applied to immunotherapy through the expression of antigens, antibodies or receptors. Therefore, although these therapies are not fully-fledged enough, they have a broad research prospect. In addition, there are many ways to treat tumors using mRNA vaccines and exosomes carrying mRNA, which have drawn much attention. In this study, we reviewed the current research on the role of mRNA in the development, diagnosis, treatment and prognosis of neural tumors, and examine the future research prospects of mRNA in neural tumors and the opportunities and challenges that will arise in the future application of clinical treatment.

Advances in meningioma genomics, proteomics, and epigenetics: insights into biomarker identification and targeted therapies

Meningiomas are a heterogeneous group of tumors, defined histo-pathologically by World Health Organization (WHO) grading. The WHO grade of meningiomas does not always correlate with clinical aggressiveness. Despite maximal surgical resection and adjuvant radiation, a subset of tumors are clinically aggressive; displaying early recurrence and invasion. Current methods for identifying aggressive meningiomas solely focus on genomics, proteomics, or epigenetics and not a combination of all for developing a real-time clinical biomarker. Improved methods for the identification of these outlying tumors can facilitate better classification and potentially adjuvant treatment planning. Understanding the pathways of oncogenesis using multiple markers driving aggressive meningiomas can provide a foundation for targeted therapies, which currently do not exist.

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.

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.

Comprehending Meningioma Signaling Cascades Using Multipronged Proteomics Approaches & Targeted Validation of Potential Markers

Meningiomas are one of the most prevalent primary brain tumors. Our study aims to obtain mechanistic insights of meningioma pathobiology using mass spectrometry-based label-free quantitative proteome analysis to identifying druggable targets and perturbed pathways for therapeutic intervention. Label-free based proteomics study was done from peptide samples of 21 patients and 8 non-tumor controls which were followed up with Phosphoproteomics to identify the kinases and phosphorylated components of the perturbed pathways. In silico approaches revealed perturbations in extracellular matrix remodeling and associated cascades. To assess the extent of influence of Integrin and PI3K-Akt pathways, we used an Integrin Linked Kinase inhibitor on patient-derived meningioma cell line and performed a transcriptomic analysis of the components. Furthermore, we designed a Targeted proteomics assay which to the best of our knowledge for very first-time enables identification of peptides from 54 meningioma patients via SRM assay to validate the key proteins emerging from our study. This resulted in the identification of peptides from CLIC1, ES8L2, and AHNK many of which are receptors and kinases and are difficult to be characterized using conventional approaches. Furthermore, we were also able to monitor transitions for proteins like NEK9 and CKAP4 which have been reported to be associated with meningioma pathobiology. We believe, this study can aid in designing peptide-based validation assays for meningioma patients as well as IHC studies for clinical applications.

Multiomics profiling of paired primary and recurrent glioblastoma patient tissues

Background

Despite maximal therapy with surgery, chemotherapy, and radiotherapy, glioblastoma (GBM) patients have a median survival of only 15 months. Almost all patients inevitably experience symptomatic tumor recurrence. A hallmark of this tumor type is the large heterogeneity between patients and within tumors itself which relates to the failure of standardized tumor treatment. In this study, tissue samples of paired primary and recurrent GBM tumors were investigated to identify individual factors related to tumor progression.

Methods

Paired primary and recurrent GBM tumor tissues from 8 patients were investigated with a multiomics approach using transcriptomics, proteomics, and phosphoproteomics.

Results

In the studied patient cohort, large variations between and within patients are observed for all omics analyses. A few pathways affected at the different omics levels partly overlapped if patients are analyzed at the individual level, such as synaptogenesis (containing the SNARE complex) and cholesterol metabolism. Phosphoproteomics revealed increased STMN1(S38) phosphorylation as part of ERBB4 signaling. A pathway tool has been developed to visualize and compare different omics datasets per patient and showed potential therapeutic drugs, such as abobotulinumtoxinA (synaptogenesis) and afatinib (ERBB4 signaling). Afatinib is currently in clinical trials for GBM.

Conclusions

A large variation on all omics levels exists between and within GBM patients. Therefore, it will be rather unlikely to find a drug treatment that would fit all patients. Instead, a multiomics approach offers the potential to identify affected pathways on the individual patient level and select treatment options.

Proteomics pinpoints alterations in grade I meningiomas of male versus female patients

Meningiomas are among the most common primary tumors of the central nervous system (CNS) and originate from the arachnoid or meningothelial cells of the meninges. Surgery is the first option of treatment, but depending on the location and invasion patterns, complete removal of the tumor is not always feasible. Reports indicate many differences in meningiomas from male versus female patients; for example, incidence is higher in females, whereas males usually develop the malignant and more aggressive type. With this as motivation, we used shotgun proteomics to compare the proteomic profile of grade I meningioma biopsies of male and female patients. Our results listed several differentially abundant proteins between the two groups; some examples are S100-A4 and proteins involved in RNA splicing events. For males, we identified enriched pathways for cell-matrix organization and for females, pathways related to RNA transporting and processing. We believe our findings contribute to the understanding of the molecular differences between grade I meningiomas of female and male patients.

Three-dimensional modeling of human neurodegeneration: brain organoids coming of age

The prevalence of dementia and other neurodegenerative diseases is rapidly increasing in aging nations. These relentless and progressive diseases remain largely without disease-modifying treatments despite decades of research and investments. It is becoming clear that traditional two-dimensional culture and animal model systems, while providing valuable insights on the major pathophysiological pathways associated with these diseases, have not translated well to patients' bedside. Fortunately, the advent of induced-pluripotent stem cells and three-dimensional cell culture now provide tools that are revolutionizing the study of human diseases by permitting analysis of patient-derived human tissue with non-invasive procedures. Specifically, brain organoids, self-organizing neural structures that can mimic human fetal brain development, have now been harnessed to develop alternative models of Alzheimer's disease, Parkinson's disease, motor neuron disease, and Frontotemporal dementia by recapitulating important neuropathological hallmarks found in these disorders. Despite these early breakthroughs, several limitations need to be vetted in brain organoid models in order to more faithfully match human tissue qualities, including relative tissue immaturity, lack of vascularization and incomplete cellular diversity found in this culture system. Here, we review current brain organoid protocols, the pathophysiology of neurodegenerative disorders, and early studies with brain organoid neurodegeneration models. We then discuss the multiple engineering and conceptual challenges surrounding their use and provide possible solutions and exciting avenues to be pursued. Altogether, we believe that brain organoids models, improved with classical and emerging molecular and analytic tools, have the potential to unravel the opaque pathophysiological mechanisms of neurodegeneration and devise novel treatments for an array of neurodegenerative disorders.

Defining Protein Pattern Differences Among Molecular Subtypes of Diffuse Gliomas Using Mass Spectrometry

Molecular characterization of diffuse gliomas has thus far largely focused on genomic and transcriptomic interrogations. Here, we utilized mass spectrometry and overlay protein-level information onto genomically defined cohorts of diffuse gliomas to improve our downstream molecular understanding of these lethal malignancies. Bulk and macrodissected tissues were utilized to quantitate 5,496 unique proteins over three glioma cohorts subclassified largely based on their IDH and 1p19q codeletion status (IDH wild type (IDHwt), n = 7; IDH mutated (IDHmt), 1p19q non-codeleted, n = 7; IDH mutated, 1p19q-codeleted, n = 10). Clustering analysis highlighted proteome and systems-level pathway differences in gliomas according to IDH and 1p19q-codeletion status, including 287 differentially abundant proteins in macrodissection-enriched tumor specimens. IDHwt tumors were enriched for proteins involved in invasiveness and epithelial to mesenchymal transition (EMT), while IDHmt gliomas had increased abundances of proteins involved in mRNA splicing. Finally, these abundance changes were compared with IDH-matched GBM stem-like cells (GSCs) to better pinpoint protein patterns enriched in putative cellular drivers of gliomas. Using this integrative approach, we outline specific proteins involved in chloride transport (e.g. chloride intracellular channel 1, CLIC1) and EMT (e.g. procollagen-lysine, 2-oxoglutarate 5-dioxygenase 3, PLOD3, and serpin peptidase inhibitor clade H member 1, SERPINH1) that showed concordant IDH-status-dependent abundance differences in both primary tissue and purified GSC cultures. Given the downstream position proteins occupy in driving biology and phenotype, understanding the proteomic patterns operational in distinct glioma subtypes could help propose more specific, personalized, and effective targets for the management of patients with these aggressive malignancies.