Type Of Glioma Research Articles

Targeted Glioma Therapy-Clinical Trials and Future Directions.

Glioblastoma multiforme (GBM) is the most common type of glioma, with a median survival of 14.6 months post-diagnosis. Understanding the molecular profile of such tumors allowed the development of specific targeted therapies toward GBM, with a major role attributed to tyrosine kinase receptor inhibitors and immune checkpoint inhibitors. Targeted therapeutics are drugs that work by specific binding to GBM-specific or overexpressed markers on the tumor cellular surface and therefore contain a recognition moiety linked to a cytotoxic agent, which produces an antiproliferative effect. In this review, we have summarized the available information on the targeted therapeutics used in clinical trials of GBM and summarized current obstacles and advances in targeted therapy concerning specific targets present in GBM tumor cells, outlined efficacy endpoints for major classes of investigational drugs, and discussed promising strategies towards an increase in drug efficacy in GBM.

Expression, Prognostic Value, and Biological Function of CTHRC1 in Different Types of Gliomas: A Bioinformatic Analysis and Experiment Validation.

In recent years, abnormal expression of collagen triple helix repeat containing 1 (CTHRC1) has been found in some tumors, closely related to the poor prognosis of cancer patients. However, the clinical significance of CTHRC1 in gliomas is not completely understood. We investigated the expression, prognostic value, and potential biological function of CTHRC1 in different types of gliomas through bioinformatics analysis and experimental verification. Bioinformatics analysis revealed several key findings regarding the expression and clinical significance of CTHRC1 in gliomas. First, the analysis demonstrated a positive correlation between CTHRC1 expression and the World Health Organization (WHO) grading of gliomas, a relationship that was validated through immunohistochemistry experiments. In addition, a trend was observed in which CTHRC1 expression increased with the extent of glioma invasion, as supported by Western blot experiments. Subsequent bioinformatics analysis identified the mesenchymal subtype of gliomas as having the highest levels of CTHRC1 expression, a finding reinforced by immunohistochemical staining. Moreover, high CTHRC1 expression was associated with poor prognosis in gliomas and emerged as an independent prognostic factor, with varying impacts on prognosis between low-grade gliomas (LGGs) and glioblastoma (GBM) subgroups. Notably, comparative analysis unveiled distinct patterns of immune infiltration of CTHRC1 in LGG and GBM. Furthermore, alterations in copy number variations and DNA methylation were identified as potential mechanisms underlying elevated CTHRC1 levels in gliomas. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that CTHRC1 and its associated genes mainly function in the extracellular matrix and participate in tumor-related signaling pathways. The CTHRC1 has shown significant clinical utility as a prognostic marker and mesenchymal subtype marker of glioma.

Integration of Multi-Omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers

Glioma is currently one of the most prevalent types of primary brain cancer. Given its high level of heterogeneity along with the complex biological molecular markers, many efforts have been made to accurately classify the type of glioma in each patient, which, in turn, is critical to improve early diagnosis and increase survival. Nonetheless, as a result of the fast-growing technological advances in high-throughput sequencing and evolving molecular understanding of glioma biology, its classification has been recently subject to significant alterations. In this study, we integrate multiple glioma omics modalities (including mRNA, DNA methylation, and miRNA) from The Cancer Genome Atlas (TCGA), while using the revised glioma reclassified labels, with a supervised method based on sparse canonical correlation analysis (DIABLO) to discriminate between glioma types. We were able to find a set of highly correlated features distinguishing glioblastoma from lower-grade gliomas (LGGs) that were mainly associated with the disruption of receptor tyrosine kinases signaling pathways and extracellular matrix organization and remodeling. Concurrently, the discrimination of the LGG types was characterized primarily by features involved in ubiquitination and DNA transcription processes. Furthermore, we could identify several novel glioma biomarkers likely helpful in both diagnosis and prognosis of the patients, including the genes PPP1R8, GPBP1L1, KIAA1614, C14orf23, CCDC77, BVES, EXD3, CD300A, and HEPN1. Collectively, this comprehensive approach not only allowed a highly accurate discrimination of the different TCGA glioma patients but also presented a step forward in advancing our comprehension of the underlying molecular mechanisms driving glioma heterogeneity. Ultimately, our study also revealed novel candidate biomarkers that might constitute potential therapeutic targets, marking a significant stride toward personalized and more effective treatment strategies for patients with glioma.

The Prognostic Value of Preoperative Inflammatory Markers for Pathological Grading of Glioma Patients.

Introduction: The independent diagnostic value of inflammatory markers neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) and the diagnostic efficacy of NLR, derived neutrophil to lymphocyte ratio (dNLR), PLR, and lymphocyte-to-monocyte ratio (LMR) in glioma cases remain unclear. We investigated the correlation of preoperative peripheral blood inflammatory markers with pathological grade, Ki-67 Proliferation Index, and IDH-1 gene phenotype in patients with glioma, focusing on tumor grade and prognosis. Methods: We retrospectively analyzed the clinical, pathological, and laboratory data of 334 patients with glioma with varying grades and 345 with World Health Organization (WHO I) meningioma who underwent initial surgery at the Affiliated Hospital of Jining Medical University from December 2019 to December 2021. The diagnostic value of peripheral blood inflammatory markers for glioma was investigated. Results: The proportion of men smoking and drinking was significantly higher in the glioma group than in the meningioma group (P < .05); in contrast, the age and body mass index (Kg/m2) were significantly lower in the glioma group (P = .01). Significant differences were noted in the pathological grade (WHO II, III, and IV), Ki-67 Proliferation Index, and peripheral blood inflammatory markers such as lymphocyte median, NLR, dNLR, and PLR between the groups (P < .05). No significant correlation existed between peripheral blood inflammatory factors and IDH-1 gene mutation status or tumor location in patients with glioma (P > .05). LMR, NLR, dNLR, and PLR, varied significantly among different glioma types (P < .05). White blood cell (WBC) count, neutrophil, NLR, and dNLR correlated positively with glioma risk. Further, WBC, neutrophil, NLR, dNLR, and LMR had a high diagnostic efficiency. Conclusion: Peripheral blood inflammatory markers, serving as noninvasive biomarkers, offer high sensitivity and specificity for diagnosing glioma, differentiating it from meningioma, diagnosing GBM, and distinguishing GBM from low-grade glioma. These markers may be implemented as routine screening tools.

GRIK1 promotes glioblastoma malignancy and is a novel prognostic factor of poor prognosis.

Primary tumors of the central nervous system (CNS) are classified into over 100 different histological types. The most common type of glioma is derived from astrocytes, and the most invasive glioblastoma (WHO IV) accounts for over 57% of these tumors. Glioblastoma (GBM) is the most common and fatal tumor of the CNS, with strong growth and invasion capabilities, which makes complete surgical resection almost impossible. Despite various treatment methods such as surgery, radiotherapy, and chemotherapy, glioma is still an incurable disease, and the median survival time of patients with GBM is shorter than 15 months. Thus, molecular mechanisms of GBM characteristic invasive growth need to be clarified to improve the poor prognosis. Glutamate ionotropic receptor kainate type subunit 1 (GRIK1) is essential for brain function and is involved in many mental and neurological diseases. However, GRIK1's pathogenic roles and mechanisms in GBM are still unknown. Single-nuclear RNA sequencing of primary and recurrent GBM samples revealed that GRIK1 expression was noticeably higher in the recurrent samples. Moreover, immunohistochemical staining of an array of GBM samples showed that high levels of GRIK1 correlated with poor prognosis of GBM, consistent with The Cancer Genome Atlas database. Knockdown of GRIK1 retarded GBM cells growth, migration, and invasion. Taken together, these findings show that GRIK1 is a unique and important component in the development of GBM and may be considered as a biomarker for the diagnosis and therapy in individuals with GBM.

CAR-T Therapy can be a Useful Treatment Modality for more than Just Hematologic Malignancies.

Chimeric antigen receptor-T-cell (CAR-T) therapy for hematologic malignancies has made significant advancements over the years, and it is now incorporated as a treatment algorithm. Early phase clinical trials are underway for various solid tumors, and the effectiveness of CAR-T cell therapy has been demonstrated for specific types of glioma and several solid tumors. However, its efficacy does not match that observed in hematological malignancies. Recently, a case series reported CAR-T therapy targeting CD19 for autoimmune diseases such as systemic lupus erythematosus, leading to a dramatic improvement in the clinical symptoms and the possibility of discontinuing immunosuppressive agents. Furthermore, CAR-T cell therapy is expected to be effective against various viruses and Aspergillus spp. Finally, attempts have been made to introduce CAR constructs into regulatory T cells to target their immunosuppressive effects. This article introduces the current progress in CAR-T cell therapy beyond the treatment of only hematologic malignancies and discusses future directions, considering the current medical situation in Japan.

Aggrephagy-related gene signature correlates with survival and tumor-associated macrophages in glioma: Insights from single-cell and bulk RNA sequencing.

Aggrephagy is a lysosome-dependent process that degrades misfolded protein condensates to maintain cancer cell homeostasis. Despite its importance in cellular protein quality control, the role of aggrephagy in glioma remains poorly understood. To investigate the expression of aggrephagy-related genes (ARGs) in glioma and in different cell types of gliomas and to develop an ARGs-based prognostic signature to predict the prognosis, tumor microenvironment, and immunotherapy response of gliomas. ARGs were identified by searching the Reactome database. We developed the ARGs-based prognostic signature (ARPS) using data from the Cancer Genome Atlas (TCGA, n = 669) by Lasso-Cox regression. We validated the robustness of the signature in clinical subgroups and CGGA cohorts (n = 970). Gene set enrichment analysis (GSEA) was used to identify the pathways enriched in ARPS subgroups. The correlations between ARGs and macrophages were also investigated at single cell level. A total of 44 ARGs showed heterogeneous expression among different cell types of gliomas. Five ARGs (HSF1, DYNC1H1, DYNLL2, TUBB6, TUBA1C) were identified to develop ARPS, an independent prognostic factor. GSEA showed gene sets of patients with high-ARPS were mostly enriched in cell cycle, DNA replication, and immune-related pathways. High-ARPS subgroup had higher immune cell infiltration states, particularly macrophages, Treg cells, and neutrophils. APRS had positive association with tumor mutation burden (TMB) and immunotherapy response predictors. At the single cell level, we found ARGs correlated with macrophage development and identified ARGs-mediated macrophage subtypes with distinct communication characteristics with tumor cells. VIM+ macrophages were identified as pro-inflammatory and had higher interactions with malignant cells. We identified a novel signature based on ARGs for predicting glioma prognosis, tumor microenvironment, and immunotherapy response. We highlight the ARGs-mediated macrophages in glioma exhibit classical features.

Decoding the secret of extracellular vesicles in the immune tumor microenvironment of the glioblastoma: on the border of kingdoms.

Over the last decades, extracellular vesicles (EVs) have become increasingly popular for their roles in various pathologies, including cancer and neurological and immunological disorders. EVs have been considered for a long time as a means for normal cells to get rid of molecules it no longer needs. It is now well established that EVs play their biological roles also following uptake or by the interaction of EV surface proteins with cellular receptors and membranes. In this review, we summarize the current status of EV production and secretion in glioblastoma, the most aggressive type of glioma associated with high mortality. The main purpose is to shed light on the EVs as a universal mediator of interkingdom and intrakingdom communication in the context of tumor microenvironment heterogeneity. We focus on the immunomodulatory EV functions in glioblastoma-immune cross-talk to enhance immune escape and reprogram tumor-infiltrating immune cells. We critically examine the evidence that GBM-, immune cell-, and microbiome-derived EVs impact local tumor microenvironment and host immune responses, and can enter the circulatory system to disseminate and drive premetastatic niche formation in distant organs. Taking into account the current state of the art in intratumoral microbiome studies, we discuss the emerging role of bacterial EV in glioblastoma and its response to current and future therapies including immunotherapies.

Advancing noninvasive glioma classification with diffusion radiomics: Exploring the impact of signal intensity normalization.

This study investigates the influence of diffusion-weighted Magnetic Resonance Imaging (DWI-MRI) on radiomic-based prediction of glioma types according to molecular status and assesses the impact of DWI intensity normalization on model generalizability. Radiomic features, compliant with image biomarker standardization initiative standards, were extracted from preoperative MRI of 549 patients with diffuse glioma, known IDH, and 1p19q-status. Anatomical sequences (T1, T1c, T2, FLAIR) underwent N4-Bias Field Correction (N4) and WhiteStripe normalization (N4/WS). Apparent diffusion coefficient (ADC) maps were normalized using N4 or N4/z-score. Nine machine-learning algorithms were trained for multiclass prediction of glioma types (IDH-mutant 1p/19q codeleted, IDH-mutant 1p/19q non-codeleted, IDH-wild type). Four approaches were compared: Anatomical, anatomical + ADC naive, anatomical + ADC N4, and anatomical + ADC N4/z-score. The University of California San Francisco (UCSF)-glioma dataset (n = 409) was used for external validation. Naïve-Bayes algorithms yielded overall the best performance on the internal test set. Adding ADC radiomics significantly improved AUC from 0.79 to 0.86 (P = .011) for the IDH-wild-type subgroup, but not for the other 2 glioma subgroups (P > .05). In the external UCSF dataset, the addition of ADC radiomics yielded a significantly higher AUC for the IDH-wild-type subgroup (P ≤ .001): 0.80 (N4/WS anatomical alone), 0.81 (anatomical + ADC naive), 0.81 (anatomical + ADC N4), and 0.88 (anatomical + ADC N4/z-score) as well as for the IDH-mutant 1p/19q non-codeleted subgroup (P < .012 each). ADC radiomics can enhance the performance of conventional MRI-based radiomic models, particularly for IDH-wild-type glioma. The benefit of intensity normalization of ADC maps depends on the type and context of the used data.

Inferring Diagnostic and Prognostic Gene Expression Signatures Across WHO Glioma Classifications: A Network-Based Approach.

Tumor heterogeneity is a challenge to designing effective and targeted therapies. Glioma-type identification depends on specific molecular and histological features, which are defined by the official World Health Organization (WHO) classification of the central nervous system (CNS). These guidelines are constantly updated to support the diagnosis process, which affects all the successive clinical decisions. In this context, the search for new potential diagnostic and prognostic targets, characteristic of each glioma type, is crucial to support the development of novel therapies. Based on The Cancer Genome Atlas (TCGA) glioma RNA-sequencing data set updated according to the 2016 and 2021 WHO guidelines, we proposed a 2-step variable selection approach for biomarker discovery. Our framework encompasses the graphical lasso algorithm to estimate sparse networks of genes carrying diagnostic information. These networks are then used as input for regularized Cox survival regression model, allowing the identification of a smaller subset of genes with prognostic value. In each step, the results derived from the 2016 and 2021 classes were discussed and compared. For both WHO glioma classifications, our analysis identifies potential biomarkers, characteristic of each glioma type. Yet, better results were obtained for the WHO CNS classification in 2021, thereby supporting recent efforts to include molecular data on glioma classification.

Differential Expression of SRY-Related HMG-Box Transcription Factor 2, Oligodendrocyte Lineage Transcription Factor 2, and Zinc Finger E-Box Binding Homeobox 1 in Serum-Derived Extracellular Vesicles: Implications for Mithramycin Sensitivity and Targeted Therapy in High-Grade Glioma.

Glioblastoma multiforme (GBM) is the most aggressive type of glioma and is often resistant to traditional therapies. Evidence suggests that glioma stem cells (GSCs) contribute to this resistance. Mithramycin (Mit-A) targets GSCs and exhibits antitumor activity in GBM by affecting transcriptional targets such as SRY-related HMG-box transcription factor 2 (SOX2), oligodendrocyte lineage transcription factor 2 (OLIG2), and zinc finger E-box binding homeobox 1 (ZEB1). However, its clinical use has been limited by toxicity. This study explored the diagnostic potential of serum extracellular vesicles (EVs) to identify Mit-A responders. Serum EVs were isolated from 70 glioma patients, and targeted gene expression was analyzed using qRT-PCR. Using chemosensitivity assay, we identified 8 Mit-A responders and 17 nonresponders among 25 glioma patients. The M-score showed a significant correlation (p = 0.045) with isocitrate dehydrogenase 1 mutation but not other clinical variables. The genes SOX2 (p = 0.005), OLIG2 (p = 0.003), and ZEB1 (p = 0.0281) were found to be upregulated in the responder EVs. SOX2 had the highest diagnostic potential (AUC = 0.875), followed by OLIG2 (AUC = 0.772) and ZEB1 (AUC = 0.632).The combined gene panel showed significant diagnostic efficacy (AUC = 0.956) through logistic regression analysis. The gene panel was further validated in the serum EVs of 45 glioma patients. These findings highlight the potential of Mit-A as a targeted therapy for high-grade glioma based on differential gene expression in serum EVs. The gene panel could serve as a diagnostic tool to predict Mit-A sensitivity, offering a promising approach for personalized treatment strategies and emphasizing the role of GSCs in therapeutic resistance.

Identification of efferocytosis-related subtypes in gliomas and elucidating their characteristics and clinical significance.

Introduction: Gliomas, the most prevalent tumors of the central nervous system, are known for their aggressive nature and poor prognosis. The heterogeneity among gliomas leads to varying responses to the same treatments, even among similar glioma types. In our study, we efferocytosis-related subtypes and explored their characteristics in terms of immune landscape, intercellular communication, and metabolic processes, ultimately elucidating their potential clinical implications. Methods and Results: We first identified efferocytosis-related subtypes in Bulk RNA-seq using the NMF algorithm. We then preliminarily demonstrated the correlation of these subtypes with efferocytosis by examining enrichment scores of cell death pathways, macrophage infiltration, and the expression of immune ligands. Our analysis of single-cell RNA-seq data further supported the association of these subtypes with efferocytosis. Through enrichment analysis, we found that efferocytosis-related subtypes differ from other types of gliomas in terms of immune landscape, intercellular communication, and substance metabolism. Moreover, we found that the efferocytosis-related classification is a prognostic factor with robust predictive performance by calculating the AUC values. We also found that efferocytosis-related subtypes, when compared with other gliomas in drug sensitivity, survival, and TIDE scores, show a clear link to the effectiveness of chemotherapy, radiotherapy, and immunotherapy in glioma patients. Discussion: We identified efferocytosis-related subtypes in gliomas by analyzing the expression of 137 efferocytosis-associated genes, exploring their characteristics in immune landscape, intercellular communication, metabolic processes, and genomic variations. Moreover, we discovered that the classification of efferocytosis-related subtypes has a strong prognostic predictive power and holds potential significance in guiding clinical treatment.

Individualized discrimination of tumor progression from treatment-related changes in different types of adult-type diffuse gliomas using [11C]methionine PET.

This study aimed to assess the ability of [11C]methionine (MET) PET in distinguishing between tumor progression (TP) and treatment-related changes (TRCs) among different types of adult-type diffuse gliomas according to the 2021 World Health Organization classification and predict overall survival (OS). We retrospectively selected 113 patients with adult-type diffuse gliomas with suspected TP who underwent MET PET imaging. Maximum and mean tumor-to-background ratios (TBRmax, TBRmean) and metabolic tumor volume (MTV) were calculated. Diagnoses were verified by histopathology (n = 50) or by clinical/radiological follow-up (n = 63). The diagnostic performance of MET PET parameters was evaluated through receiver operating characteristic (ROC) analysis and area under the curve (AUC) calculation. Survival analysis employed the Kaplan-Meier method and Cox proportional-hazards regression. TP and TRCs were diagnosed in 76 (67%) and 37 (33%) patients, respectively. ROC analysis revealed TBRmax had the best performance in differentiating TP from TRCs with a cut-off of 1.96 in IDH-mutant astrocytoma (AUC, 0.87; sensitivity, 93%; specificity 69%), 1.80 in IDH-mutant and 1p/19q-codeleted oligodendroglioma (AUC, 0.96; sensitivity, 100%; specificity, 89%), and 2.13 in IDH wild-type glioblastoma (AUC, 0.89; sensitivity, 89%; specificity, 78%), respectively. On multivariate analysis, higher TBRmean and MTV were significantly correlated with shorter OS in all IDH-mutant gliomas, as well as in IDH-mutant astrocytoma subgroup. This work confirms that MET PET has varying diagnostic performances in distinguishing TP from TRCs within three types of adult-type diffuse gliomas, and highlights its high diagnostic accuracy in IDH-mutant and 1p/19q-codeleted oligodendroglioma and potential prognostic value for IDH-mutant gliomas, particularly IDH-mutant astrocytoma.

The unique immune ecosystems in pediatric brain tumors: integrating single-cell and bulk RNA-sequencing.

The significant progress of immune therapy in non-central nervous system tumors has sparked interest in employing the same strategy for adult brain tumors. However, the advancement of immunotherapy in pediatric central nervous system (CNS) tumors is not yet on par. Currently, there is a lack of comprehensive comparative studies investigating the immune ecosystem in pediatric and adult CNS tumors at a high-resolution single-cell level. In this study, we comprehensively analyzed over 0.3 million cells from 171 samples, encompassing adult gliomas (IDH wild type and IDH mutation) as well as four major types of pediatric brain tumors (medulloblastoma (MB), ependymoma (EPN), H3K27M-mutation (DIPG), and pediatric IDH-mutation glioma (P-IDH-M)). Our approach involved integrating publicly available and newly generated single-cell datasets. We compared the immune landscapes in different brain tumors, as well as the detailed functional phenotypes of T-cell and myeloid subpopulations. Through single-cell analysis, we identified gene sets associated with major cell types in the tumor microenvironment (gene features from single-cell data, scFes) and compared them with existing gene sets such as GSEA and xCell. The CBTTC and external GEO cohort was used to analyze and validate the immune-stromal-tumor patterns in pediatric brain tumors which might potentially respond to the immunotherapy. From the perspective of single-cell analysis, it was observed that major pediatric brain tumors (MB, EPN, P-IDH-M, DIPG) exhibited lower immune contents compared with adult gliomas. Additionally, these pediatric brain tumors displayed diverse immunophenotypes, particularly in regard to myeloid cells. Notably, the presence of HLA-enriched myeloid cells in MB was found to be independently associated with prognosis. Moreover, the scFes, when compared with commonly used gene features, demonstrated superior performance in independent single-cell datasets across various tumor types. Furthermore, our study revealed the existence of heterogeneous immune ecosystems at the bulk-RNA sequencing level among different brain tumor types. In addition, we identified several immune-stromal-tumor patterns that could potentially exhibit significant responses to conventional immune checkpoint inhibitors. The single-cell technique provides a rational path to deeply understand the unique immune ecosystem of pediatric brain tumors. In spite of the traditional attitudes of "cold" tumor towards pediatric brain tumor, the immune-stroma-tumor patterns identified in this study suggest the feasibility of immune checkpoint inhibitors and pave the way for the upcoming tide of immunotherapy in pediatric brain tumors.

Vision transformer based classification of gliomas from histopathological images

Early and accurate detection and classification of glioma types is of paramount importance in determining treatment planning and increasing the survival rate of patients. At present, diagnosis in neuropathology is based on molecular and histological characteristic information provided with microscopic visual examinations of biopsies. However, the traditional method is not only laborious, and time-consuming but also needs experience. Furthermore, the subjective diagnosis causes inter-/intra variability and late or inaccurate diagnosis. To overcome those issues by automated methods, Convolutional Neural Networks (CNNs) and, more recently, transformer-based models have been used. However, they have their own drawbacks. For instance, CNNs ignore global information by focusing on pixel-wise information, although they are good at the extraction of local characteristic features using several convolution and pooling layers. Vision transformers are problematic in the extraction of details and local features, although they are good in the extraction of global features using global receptive fields in the early layers. Therefore, in this work, their advantages have been utilized in designing a new architecture to classify gliomas. Obtaining high performance from the proposed architecture has been achieved by (i) using a combined version of CNN and transformer stages, and (ii) integrating effectively designed feature-combining and smart-joining modules appropriately. Experiments have indicated the effectiveness of the proposed approach in classifying four glioma subtypes from histopathological images in terms of several evaluation metrics (i.e., accuracy (96.75%), recall (97.00%), precision (96.75%), F1-score (96.80%)). Comparative evaluations of the performances of the state-of-the-art techniques have shown better capability of the proposed approach.

Unexplained Causes of Glioma-Associated Epilepsies: A Review of Theories and an Area for Research

Approximately 30% of glioma patients are able to survive beyond one year postdiagnosis. And this short time is often overshadowed by glioma-associated epilepsy. This condition severely impairs the patient’s quality of life and causes great suffering. The genetic, molecular and cellular mechanisms underlying tumour development and epileptogenesis remain incompletely understood, leading to numerous unanswered questions. The various types of gliomas, namely glioblastoma, astrocytoma and oligodendroglioma, demonstrate distinct seizure susceptibility and disease progression patterns. Patterns have been identified in the presence of IDH mutations and epilepsy, with tumour location in cortical regions, particularly the frontal lobe, showing a more frequent association with seizures. Altered expression of TP53, MGMT and VIM is frequently detected in tumour cells from individuals with epilepsy associated with glioma. However, understanding the pathogenesis of these modifications poses a challenge. Moreover, hypoxic effects induced by glioma and associated with the HIF-1a factor may have a significant impact on epileptogenesis, potentially resulting in epileptiform activity within neuronal networks. We additionally hypothesise about how the tumour may affect the functioning of neuronal ion channels and contribute to disruptions in the blood–brain barrier resulting in spontaneous depolarisations.

TMIC-33. UNDERSTAND AND TARGET ASTROCYTE REACTIVITY IN BRAIN TUMORS TO PREVENT RADIORESISTANCE

Abstract Glioblastoma multiforme (GBM) is the most aggressive type of glioma and deadliest brain tumor. Despite aggressive treatments including surgery, chemo-, and radiotherapy, tumors invariably recur as incurable lesions, and the median survival is approximately 15 months after diagnosis. Recurrence is tightly linked to tumor cell resistance to ionizing radiation (IR), a feature that in turn is linked to stem cell characteristics of tumor cells. Previous results from our lab indicate that stromal astrocytes respond to IR with a reactive phenotype that in turn promotes therapeutic resistance of neighboring tumor cells. Our objectives were to i) identify pathways involved in IR-induced astrocyte reactivity; ii) identify compounds able to inhibit IR-induced astrocyte reactivity. We used two complementary approaches to meet our objectives. Using an antibody array we found that JNK and ABL pathways were activated following IR in primary human astrocytes. To validate their involvement in the induction of astrocyte reactive phenotype we used an image-based readout combining the changes in the expression of reactivity markers and in the morphology of astrocytes. For the second approach, we used a similar readout to perform an image-based drug screen which aimed to identify compounds able to inhibit IR-induced astrocyte reactivity. The drug libraries used consisted of approved drugs to get candidates with potential for drug repurposing. We identified 20 compounds inhibiting IR-induced astrocyte reactivity and amongst them, 10 are brain-penetrant compounds that could make them good candidates for repurposing in GBM. An in vivo test is now ongoing with 2 of these compounds in a PDGFA/PDGFB-driven mouse model of GBM to validate their efficiency to increase radiosensitivity of the tumor cell by inhibiting astrocyte reactivity

CNSC-21. INVESTIGATING THE EVOLUTION OF NEURON-GLIOMA CIRCUIT DYNAMICS USING AN IN VIVO IMAGING METHOD

Abstract Pediatric high-grade gliomas (pHGG) are aggressive primary brain neoplasms with a dismal prognosis, making them the leading cause of brain tumor-related deaths in children. pHGGs occur in specific anatomical locations at specific ages underscoring their origin in neurodevelopment and the critical importance of the brain tumor microenvironment. Activity-regulated mechanisms are major regulators of neural development and plasticity. Many pHGGs originate from oligodendroglial precursor cells (OPC) and, like OPCs, neuronal activity promotes pHGG proliferation. Recent work has demonstrated that pHGG cells form calcium-permeable AMPAR-mediated synapses with neurons analogous to the axo-glial synapses that form between neurons and OPCs. Neuronal activity drives pHGG growth through secretion of activity-regulated mitogens and through electrochemical communication with pHGG cells that integrate into neural circuits. In turn, pHGG increases neuronal excitability and remodels functional neural circuits. The interconnected network of glioma cells and neurons is fundamental to pHGG progression. However, how these neuron-glioma networks evolve over time remains to be fully understood. We hypothesize that as gliomas progress, the neuron-glioma malignant circuitry evolves chiefly through synaptogenesis to promote activity that fosters glioma progression. Thus, we developed a two-color in vivo imaging method to study neuron-glioma cell interactions over time in freely behaving mice enabling us to record the frequency, pattern, and synchronicity of calcium transients in neurons, glioma cells, and their coactivity giving us a comprehensive view of the changes in neuron-glioma circuit dynamics over time. We have observed increasing neuronal activity throughout the disease, consistent with increasing neural hyperexcitability over time, and have observed that different types of gliomas exhibit unique patterns of calcium transients (rise time, peak amplitude, decay time, and half-width). Our imaging paradigm offers a unique ability to study neuron-glioma interactions at the systems level and will allow us to study how pharmacological inhibitors and neuronal experience shape neuron-glioma malignant circuit dynamics.

IMMU-11. NLRP3+ TUMOR-ASSOCIATED MYELOID CELLS ARE ENRICHED IN ASTROCYTIC GLIOMAS AND REPRESENT A THERAPEUTIC VULNERABILITY

Abstract BACKGROUND Tumor-associated myeloid cells (TAMs) are known drivers of immunosuppression and glioma progression. Hence novel approaches for repolarizing TAMs from a protumorigenic to an antitumorigenic phenotype is an active area of investigation. While single-cell RNA sequencing (scRNA-seq) studies have characterized glioma TAMs in great detail, their uniqueness or similarity to TAMs in other tumors remains unknown. Cross-comparison of glioma TAMs with TAM subsets from other tumor types will aid in identifying novel TAM-targeted immunotherapies. METHODS We performed scRNA-seq of ~26K myeloid cells from nine patients with high-grade astrocytoma (n=3) and oligodendroglioma (n=6). The scRNA-seq findings were validated using TCGA RNA-seq data, public scRNA-Seq data, and immunohistochemistry (IHC) analysis. RESULTS The TAM clusters in gliomas predominantly exhibited a microglia-specific gene signature, while macrophage and monocyte clusters were also identified. Utilizing canonical TAM markers, we identified three major TAM subtypes, SPP1+ regulatory, FOLR2+ tissue-resident, and NLRP3+ inflammatory, enriched in astrocytomas. A gene set enrichment analysis of the three TAM subtype genes confirmed their enrichment in astrocytomas (IDHMut-non-codel) (P=0.0005; normalized enrichment score [NES]=1.79) in the TCGA bulk RNAseq data from IDH1-mutant LGGs. Investigation of the three TAM subtype markers in public scRNA-seq data confirmed their enrichment in immune/myeloid clusters across multiple glioma types (gangliogliomas, pilocytic astrocytomas, astrocytomas, diffuse midline gliomas, and glioblastomas). Finally, IHC analysis of 14 astrocytomas demonstrated regional variation in NLRP3 and FOLR2 expression in all cases, with a trend toward an inverse association between NLRP3 and FOLR2 expression. CONCLUSIONS 1) Astrocytomas (IDHMut-non-codel) comprise TAM subsets common across multiple tumor types. 2) The FOLR2+ TAMs predict favorable outcomes, while the NLRP3+ TAMs are immunosuppressive and predict adverse outcomes in breast cancer. Given the inverse expression of NLRP3 and FOLR2 in astrocytomas, we hypothesize that NLRP3 TAMs+ are immunosuppressive and a potential immunotherapeutic target in astrocytomas.

IMMU-19. MUTANT ISOCITRATE DEHYDROGENASE 1 IN GLIOMA CAUSES A REDUCTION IN ADENOSINERGIC PATHWAY SIGNALING WITHIN THE TUMOR MICROENVIRONMENT

Abstract Glioma is one of the most aggressive cancers and represents approximately 80% of malignant brain tumors. The majority of CNS World Health Organization (WHO) grades 2-3 gliomas and a subset of high-grade gliomas (CNS WHO grade 4) harbor mutated isocitrate dehydrogenase 1 (IDH1R132H; mIDH1). Mutant IDH1 results in the accumulation of 2-hydroxyglutarate (2HG) which elicits profound changes in the glioma transcriptome/biology. Consequently, mIDH1 glioma patients have a significantly increased median survival compared to wildtype IDH1 patients. Recent data from our lab showed that mIDH1 gliomas exhibit increased immune-reactivity of tumor-infiltrating immune cells. A critical pathway that mediates reduction of anti-tumor immune cell reactivity is the adenosinergic pathway (AP), which converts ATP into adenosine via the enzymes CD39 and CD73. Adenosine binds to adenosine receptors, A2AR and A2BR, on immune cells resulting in a decreased immunoreactivity. Although mIDH1 has been implicated in altering the AP, the extent to which the AP is affected by mIDH1 in gliomas is largely unknown. Here we used human and mouse mIDH1 glioma cells in vitro along with in vivo mIDH1 glioma mouse models to examine changes in the AP. Our results show reduced levels of the enzyme, CD73, on the surface of mIDH1 glioma cells, and the ATP concentration in the mIDH1 glioma cell growth media is lower than from wildtype IDH1 glioma cells. Additionally, we discovered that mIDH1 glioma-infiltrating macrophages have decreased expression of CD39 and A2AR compared to their wildtype IDH1 glioma counterparts. Taken together, these data suggest a reduction of the AP mediated-immune suppression in mIDH1 gliomas. Uncovering these differences in the AP provides novel insights on the use of AP inhibitors for glioma treatment, revealing whether the AP is a preferred target in specific glioma types based on the IDH1 mutation status.