Glioma Subtypes Research Articles

P10.09.A BLOOD-BASED CLASSIFICATION OF IDH-STATUS OF NON-ENHANCING GLIOMAS: A MACHINE LEARNING APPROACH

Abstract BACKGROUND Non-invasive determination of IDH mutational status in patients with glioma could offer significant therapeutic opportunities. While IDH wildtype tumors (e.g., GBM) typically show enhancement on MRI, IDH mutant tumors often lack this enhancement. However, relying solely on anatomic radiology may lead to misclassification, and currently, tissue acquisition is the primary method for assessing IDH status in gliomas. This limitation hampers the development of neoadjuvant or intraoperative therapeutic strategies based on IDH status; thus, more minimally invasive methods to determine IDH status are needed. In this study, we assessed immune cell proportions, which vary by glioma subtype, from pre-surgery peripheral blood samples as an alternative method of classifying the IDH status in gliomas without enhancement. MATERIAL AND METHODS First, we employed a highly accurate large language model (GPT-4-Turbo-128k) with over 99% accuracy to read radiology notes and exclude patients with enhancing gliomas. Then, we identified twelve immune cell subtypes from whole blood using deconvolution algorithms based on DNA methylation data. These immune cell subtypes, along with patient age, were integrated into a machine learning model (random forest) to predict IDH status (wildtype vs. mutant), leveraging conditional Generative Adversarial Networks to generate synthetic data and mitigate bias in the datasets. Two independent datasets were included for training and validating the model. RESULTS The random forest model had an area under the ROC curve (AUC) of 0.90 and accurately identified IDH status in 81% of a training set of 287 gliomas (65 wildtype and 222 mutant tumors at varied time points after diagnosis). In an independent validation data set of 99 gliomas (6 wildtype and 93 mutant) from pre-surgery blood samples, the AUC was 0.93, and accuracy predicted IDH status in 92% of cases with a sensitivity of 93% and specificity of 83%. CONCLUSION We could accurately predict IDH status in pre-surgical peripheral blood samples from patients with non-enhancing gliomas. This minimally invasive approach is a promising step toward reducing risk and exploring opportunities for earlier therapies based on IDH status.

Evaluation of an Image-based Classification Model to Identify Glioma Subtypes Using Arterial Spin Labeling Perfusion MRI On the Publicly Available UCSF Glioma Dataset.

Glioma is acomplex cancer comprising various subtypes and mutations, which may have different metabolic characteristics that can potentially be investigated and identified using perfusion imaging. Therefore, the aim of this work was to use radiomics and machine learning analysis of arterial spin labeling MRI data to automatically differentiate glioma subtypes and mutations. Atotal of 495 Arterial Spin Labeling (ASL) perfusion imaging datasets from the UCSF Glioma database were used in this study. These datasets were segmented to delineate the tumor volume and classified according to tumor grade, pathological diagnosis, and IDH status. Perfusion image data was obtained from a3T MRI scanner using pseudo-continuous ASL. High level texture features were extracted for each ASL dataset using PyRadiomics after tumor volume segmentation and then analyzed using amachine learning framework consisting of ReliefF feature ranking and logistic model tree classification algorithms. The results of the evaluation revealed balanced accuracies for the three endpoints ranging from 55.76% (SD = 4.28, 95% CI: 53.90-57.65) for the tumor grade using 25.4 ± 37.21 features, 62.53% (SD = 2.86, 95% CI: 61.27-63.78) for the mutation status with 23.3 ± 29.17 picked features, and 80.97% (SD = 1.83, 95% CI: 80.17-81.78) for the pathological diagnosis which used 47.3 ± 32.72 selected features. Radiomics and machine learning analysis of ASL perfusion data in glioma patients hold potential for aiding in the diagnosis and treatment of glioma, mainly for discerning glioblastoma from astrocytoma, while performance for tumor grading and mutation status appears limited.

Role of Advanced Magnetic Resonance Imaging in Differentiating among Glioma Subtypes and Predicting Tumor-Proliferative Behavior

Abstract Objective Gliomas are a devastating and heterogeneous group of primary brain tumors. Previously, the source of glioma was undetermined. Recent literature indicates that neural stem cells, or progenitors, are proposed to be the source of glioma. The prognosis of different types of gliomas differs due to their various biological tissue types. Besides the histological grade, the two useful immunohistochemistry markers that show the tumor's biological behavior are isocitrate dehydrogenase (IDH) labeling and the Ki-67 labeling index. We sought to determine the magnetic resonance imaging (MRI) characteristics associated with IDH mutational status and ascertain whether MRI combined with IDH mutational status, can better predict the clinical outcomes of gliomas. Materials and Methods This period study was conducted in the Department of Radiology, Sri Ramachandra Institute of Higher Education and Research, Chennai, Tamil Nadu, India for 5 years (May 2016–May 2021). The study cohort included 30 patients diagnosed with gliomas who underwent preoperative MRI followed by surgical resection and histopathological examination. Preoperative MRI images were done to assess qualitative tumor characteristics such as location, margin of tumor, extent, cortical involvement, cystic component, mineralization or hemorrhage, and contrast enhancement. Discussion Differences in MRI features between IDH-mutant (MT) and IDH-wild-type (WT) groups were analyzed using the chi-square test for categorical variables and the Mann–Whitney U test for continuous variables. Statistical analysis was conducted using SPSS software. Results Among the 30 patients evaluated, 18 had IDH-WT and 12 had IDH-MT type gliomas. Male predominance (73.33%) was noted in our study. Brainstem location, indistinct borders (83.33%), less cortical involvement (72.22%), less cystic changes (88.89%), more area of necrotic component (44.44%), significantly increased choline/creatine (Cho/Cr) ratio, and choline/N-acetyl aspartate (Cho/NAA) ratio favors IDH-WT tumors. Positive T2-fluid-attenuated inversion recovery mismatch sign is more frequently seen in IDH-MT (7/12; 58.33%) tumors than in IDH-WT (4/18; 22.22%) tumors. Whereas well-defined contours (66.67%), more cortical involvement (83.33%), more cystic changes (58.33%), and less area of necrotic component favor IDH-MT type tumors. Conclusion MRI is a very promising and valuable tool for differentiating among glioma subtypes and predicting tumor-proliferative behavior in glioma cases. The combination of MRI characteristics with IDH mutation status enhances the predictive accuracy for clinical outcomes in glioma patients. This approach could potentially guide treatment planning and improve prognostic assessments.

MAPPING GLIOMAS: A RADIOGRAPHIC AND SPATIAL ANALYSIS ACCORDING TO THE 2021 WHO CLASSIfiCATION

Abstract AIMS The spatial positioning of gliomas within the brain significantly influences prognosis, potentially mirroring the genetic landscape of their precursor cells. Furthermore, the association between glioma subtypes and their spatial distribution underscores a complex interplay that may illuminate underlying pathogenetic mechanisms. This study explores the intricate relationship between the location of gliomas and their classification according to the 2021 WHO guidelines, aiming to unravel the spatial and genetic nuances that define glioma heterogeneity. METHOD In this study, preoperative anatomical MRI data from 242 glioma patients were meticulously analyzed to elucidate spatial distribution patterns in relation to tumor genetics. The imaging data underwent registration to the MNI-152 standard brain space, facilitating uniform analysis. Subsequent tumor segmentation and division of the brain into 84 distinct subregions allowed for a detailed examination of tumor locations. A statistical analysis was conducted to explore the stereospecific frequency of glioma occurrence across these subregions, correlating findings with tumor grade and pivotal genetic markers, including MGMT promoter methylation, IDH1 mutation, 1p/19q co-deletion, and CDKN2A/B homozygous deletion. RESULTS We developed 21 distinct tumor frequency atlases based on subtypes (Glioblastoma, Oligodendroglioma, Astrocytoma), biomarker status (IDH, 1p19q, MGMT, CDKN2A/B), and grade (2, 3, 4). Employing probability vectors for statistical analysis revealed a significant correlation among different subtypes, locations and overall survival. Furthermore, we used a Visualization tool to build a 3D heatmap for each tumor subtype, more intuitively illustrating spatial correlations. CONCLUSION Our study underscores the prognostic value of tumor location and its correlation with glioma subtypes and patient outcomes, suggesting a potential pathway to personalized treatments. Future efforts will focus on incorporating multi-omics data into spatial radiographic analyses.

INTEGRATING TRANSCRIPTOMICS WITH SPATIAL RADIOGRAPHIC ATLASES FOR GLIOMA ANALYSIS

Abstract AIMS The localization of gliomas in the brain is a critical prognostic factor, reflecting the genetic makeup of their originating cells. Variations in transcriptomic profiles across different regions and subtypes may further shed light on the mechanisms driving tumor development. Our research delves into the complex interplay between glioma locations and their transcriptomic signatures, seeking to dissect the spatial and genetic intricacies contributing to glioma diversity. METHOD In this study, we conducted a comprehensive analysis of preoperative anatomical MRI and transcriptomic data from 242 glioma patients to investigate the relationship between spatial distribution and tumor transcriptomic profiles. The MRI data were standardized by registration to the MNI-152 brain space. Tumors were segmented, and the brain was partitioned into 84 discrete subregions for detailed examination. Transcriptomic data underwent preprocessing, with principal component analysis employed to reduce dimensionality. Statistical analyses were used to assess the relationship between glioma description across different subtypes and PCA results. Additionally, we identify differentially expressed genes and common genes across regions for various glioma subtypes. Comparative analyses with the Allen Brain Atlas (divided into 84 subregions with expression data) were also performed to discern transcriptional differences between healthy individuals and glioma patients, further enhancing our understanding of glioma biology at the molecular level. RESULTS We generated 21 frequency maps detailing regional glioma occurrences, categorized by biomarkers, subtypes, and grades. Notably, some of these maps showed significant correlations with expression principal component analyses (PCAs). Our findings include a set of genes consistently expressed across subregions, alongside region specific differentially expressed genes. Additionally, we identified distinct gene expression patterns when comparing glioma patients to healthy controls in the Allen Brain Atlas, highlighting potential molecular underpinnings of glioma heterogeneity. CONCLUSION Our analysis revealed notable correlations between glioma subtype locations and transcriptomic profiles, underscoring the role of transcriptomic signatures in explaining regional variations in glioma incidence.

Functional germline variants in DNA damage repair pathways are associated with altered survival in adults with glioma treated with temozolomide.

Temozolomide (TMZ) treatment has demonstrated, but variable, impact on glioma prognosis. This study examines associations of survival with DNA repair gene germline polymorphisms among glioma patients who did and did not have TMZ treatment. Identifying genetic markers which sensitize tumor cells to TMZ could personalize therapy and improve outcomes. We evaluated TMZ-related survival associations of pathogenic germline SNPs and genetically predicted transcript levels within 34 DNA repair genes among 1504 glioma patients from the UCSF Adult Glioma Study and Mayo Clinic whose diagnoses spanned pre- and post-TMZ eras within the major known glioma prognostic molecular subtypes. Among those who received TMZ, 5 SNPs were associated with overall survival, but not in those who did not receive TMZ. Only rs2308321-G, in MGMT , was associated with decreased survival (HR=1.21, p=0.019) for all glioma subtypes. Rs73191162-T (near UNG ), rs13076508-C (near PARP3 ), rs7840433-A (near NEIL2 ), and rs3130618-A (near MSH5 ) were only associated with survival and TMZ treatment for certain subtypes, suggesting subtype-specific germline chemo-sensitization. Genetically predicted elevated compared to normal brain expression of PNKP was associated with dramatically worse survival for TMZ-treated patients with IDH -mutant and 1p/19q non-codeleted gliomas (p=0.015). Similarly, NEIL2 and TDG expressions were associated with altered TMZ-related survival only among certain subtypes. Functional germline alterations within DNA repair genes were associated with TMZ sensitivity, measured by overall survival, among adults with glioma, these variants should be evaluated in prospective analyses and functional studies. We observed SNPs associated with glioma survival, specific to cases receiving TMZ An MGMT variant may reduce glioma survival indirectly through myelosuppression Decreased genetic PNKP expression in the brain may sensitize cells to TMZ. The introduction of temozolomide (TMZ) as a part of standard-of-care in the treatment of gliomas marked the last notable increase in patient survival. However, the effectiveness of TMZ is not universal, and can result in serious complications. The mechanism of action behind the drug is the introduction of damaging methyl groups across the tumor genome and leveraging of DNA damage repair (DDR) mechanisms to signal programmed cell death. Previous literature has identified that defects in DDR mechanisms can alter TMZ sensitivity. Using a unique dataset that spans the pre- and post-TMZ eras, we demonstrate that germline variation in DDR-related genes may have significant impact on overall survival for patients treated with TMZ, with no effects observed in the pre-TMZ era. This suggests that germline variants in these DDR genes could be used to personalize TMZ therapy to improve patient survival.

Diffusion MRI is superior to quantitative T2-FLAIR mismatch in predicting molecular subtypes of human non-enhancing gliomas.

This study compared the classification performance of normalized apparent diffusion coefficient (nADC) with percentage T2-FLAIR mismatch-volume (%T2FM-volume) for differentiating between IDH-mutant astrocytoma (IDHm-A) and other glioma molecular subtypes. A total of 105 non-enhancing gliomas were studied. T2-FLAIR digital subtraction maps were used to identify T2FM and T2-FLAIR non-mismatch (T2FNM) subregions within tumor volumes of interest (VOIs). Median nADC from the whole tumor, T2FM, and T2NFM subregions and %T2FM-volume were obtained. IDHm-A classification analyses using receiver-operating characteristic curves and multiple logistic regression were performed in addition to exploratory survival analyses. T2FM subregions had significantly higher nADC than T2FNM subregions within IDHm-A with ≥ 25% T2FM-volume (P < 0.0001). IDHm-A with ≥ 25% T2FM-volume demonstrated significantly higher whole tumor nADC compared to IDHm-A with < 25% T2FM-volume (P < 0.0001), and both IDHm-A subgroups demonstrated significantly higher nADC compared to IDH-mutant oligodendroglioma and IDH-wild-type gliomas (P < 0.05). For classification of IDHm-A vs. other gliomas, the area under curve (AUC) of nADC was significantly greater compared to the AUC of %T2FM-volume (P = 0.01, nADC AUC = 0.848, %T2FM-volume AUC = 0.714) along with greater sensitivity. In exploratory survival analyses within IDHm-A, %T2FM-volume was not associated with overall survival (P = 0.2), but there were non-significant trends for nADC (P = 0.07) and tumor volume (P = 0.051). T2-FLAIR subtraction maps are useful for characterizing IDHm-A imaging characteristics. nADC outperforms %T2FM-volume for classifying IDHm-A amongst non-enhancing gliomas with preserved high specificity and increased sensitivity, which may be related to inherent diffusivity differences regardless of T2FM. In line with previous findings on visual T2FM-sign, quantitative %T2FM-volume may not be prognostic.

Fractal dimension and lacunarity measures of glioma subcomponents are discriminative of the grade of gliomas and IDH status.

Since the overall glioma mass and its subcomponents-enhancing region (malignant part of the tumor), non-enhancing (less aggressive tumor cells), necrotic core (dead cells), and edema (water deposition)-are complex and irregular structures, non-Euclidean geometric measures such as fractal dimension (FD or "fractality") and lacunarity are needed to quantify their structural complexity. Fractality measures the extent of structural irregularity, while lacunarity measures the spatial distribution or gaps. The complex geometric patterns of the glioma subcomponents may be closely associated with the grade and molecular landscape. Therefore, we measured FD and lacunarity in the glioma subcomponents and developed machine learning models to discriminate between tumor grades and isocitrate dehydrogenase (IDH) gene status. 3D fractal dimension (FD3D) and lacunarity (Lac3D) were measured for the enhancing, non-enhancing plus necrotic core, and edema-subcomponents using preoperative structural-MRI obtained from the The Cancer Genome Atlas (TCGA) and University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) glioma cohorts. The FD3D and Lac3D measures of the tumor-subcomponents were then compared across glioma grades (HGGs: high-grade gliomas vs. LGGs: low-grade gliomas) and IDH status (mutant vs. wild type). Using these measures, machine learning platforms discriminative of glioma grade and IDH status were developed. Kaplan-Meier survival analysis was used to assess the prognostic significance of FD3D and Lac3D measurements. HGG exhibited significantly higher fractality and lower lacunarity in the enhancing subcomponent, along with lower fractality in the non-enhancing subcomponent compared to LGG. This suggests that a highly irregular and complex geometry in the enhancing-subcomponent is a characteristic feature of HGGs. A comparison of FD3D and Lac3D between IDH-wild type and IDH-mutant gliomas revealed that mutant gliomas had ~2.5-fold lower FD3D in the enhancing-subcomponent and higher FD3D with lower Lac3D in the non-enhancing subcomponent, indicating a less complex and smooth enhancing subcomponent, and a more continuous non-enhancing subcomponent as features of IDH-mutant gliomas. Supervised ML models using FD3D from both the enhancing and non-enhancing subcomponents together demonstrated high-sensitivity in discriminating glioma grades (~97.9%) and IDH status (~94.4%). A combined fractal estimation of the enhancing and non-enhancing subcomponents using MR images could serve as a non-invasive, precise, and quantitative measure for discriminating glioma grade and IDH status. The combination of 2-hydroxyglutarate-magnetic resonance spectroscopy (2HG-MRS) with FD3D and Lac3D quantification may be established as a robust imaging signature for glioma subtyping.

Inhibiting lncRNA NEAT1 Increases Glioblastoma Response to TMZ by Reducing Connexin 43 Expression.

Glioblastoma multiforme (GBM) is considered the most assailant subtype of gliomas, presenting a formidable obstacle because of its inherent resistance to temozolomide (TMZ). This study aimed to characterize the function of lncRNA NEAT1 in facilitating the advancement of gliomas. The expression level of NEAT1 in glioma tissues and cells was detected by qRT-PCR. RNA interference experiment, cell proliferation assay, FITC/PI detection assay, immunoblotting, bioinformatics prediction, a double luciferase reporter gene assay, RNA immunoprecipitation (RIP) assay, SLDT assay and correlation analysis of clinical samples were performed to explore the regulatory effects of NEAT1, miR-454-3p and Cx43 and their role in malignant progression of GBM. The role of NEAT1 in vivo was investigated by an intracranial tumor formation experiment in mice. The results showed that recurring gliomas displayed elevated levels of NEAT1 compared to primary gliomas. The suppression of NEAT1 led to a restoration of sensitivity in GBM cells to TMZ. NEAT1 functioned as a competitive endogenous RNA against miR-454-3p. Connexin 43 was identified as a miR-454-3p target. NEAT1 was found to regulate gap junctional intercellular communication by modulating Connexin 43, thereby impacting the response of GBM cells to TMZ chemotherapy. Downregulation of NEAT1 resulted in enhanced chemosensitivity to TMZ and extended the survival of mice. Overall, these results indicated that the NEAT1/miR-454-3p/Connexin 43 pathway influences GBM cell response to TMZ and could offer a potential new strategy for treating GBM.

Terahertz meta-biosensor for subtype detection and chemotherapy monitoring of glioma cells

Rapid and precise identification of glioma subtypes can assist surgeons in quickly determining patients’ conditions and developing treatment plans. However, current methods face challenges such as high cost and limited sensitivity, thereby significantly hindering diagnostic efficiency. In this paper, we proposed a spectral sensing strategy by introducing terahertz biosensors for glioma subtype recognition and therapeutic efficacy monitoring. Our findings demonstrated that both the resonance frequency and amplitude of the transmission spectra varied as the cell increased. The two-dimensional fingerprint diagram could help us rapidly recognize cell subtypes. Additionally, we conducted a theoretical analysis of the sensing performance. We confirmed that the high Q-factor (∼27.8) results from toroidal dipole resonance and examined the impact of dielectric loss and the refractive index of the analyte on the resonance dip. Furthermore, we applied the biosensor in therapeutic drug screening and showed that as the concentration of tubastatin A increased, the resonance frequency shifted from 183.1 GHz to 76.2 GHz. This result indicates that the toroidal dipole constructed by the metasurface enables ultrasensitive light-matter interactions that amplify subtle cellular changes to the resonance spectrum. The proposed THz meta-biosensor demonstrates significant potential for rapid intraoperative recognition of glioma cell subtypes and therapeutic efficacy monitoring.

Discovery of GJC1 as a prognostic biomarker in glioma cells: insights into its cell-cycle relationship and differential expression in non-neuronal cells.

Gliomas, originating from the most common non-neuronal cells in the brain (glial cells), are the most common brain tumors and are associated with high mortality and poor prognosis. Glioma cells exhibit a tendency to disrupt normal cell-cycle regulation, leading to abnormal proliferation and malignant growth. This study investigated the predictive potential of GJC1 in gliomas and explored its relationship with the cell cycle. Retrospective analysis of RNA-seq and single-cell sequencing data was conducted using the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. The differential expression of GJC1 in gliomas with various pathological features and in different non-neuronal cell groups was analyzed. Functional data were examined using gene set variation analysis (GSVA). Furthermore, CellMiner was used to evaluate the relationship between GJC1 expression and predicted treatment response across these databases. GJC1 expression was enriched in high-grade gliomas and 1p/19q non-codeletion gliomas. GJC1 enrichment was observed in classical and mesenchymal subtypes within the TCGA glioma subtype group. In single-cell subgroup analysis, GJC1 expression was higher in glioma tissues compared to other non-neuronal cells. Additionally, the TCGA classical subtype of glioma cells exhibited more GJC1 expression than the other subgroups. GJC1 emerged as an independent prognostic factor for overall survival in glioma. GSVA unveiled potential mechanisms by which GJC1 may impact cell-cycle regulation in glioma. Finally, a significant correlation was observed between GJC1 expression and the sensitivity of multiple anti-cancer drugs. These findings confirmed GJC1 as a novel biomarker and provided insights into the differential gene expression in non-neuronal cells and the impact of the cell cycle on gliomas. Consequently, GJC1 may be used to predict glioma prognosis and has potential therapeutic value.

A - 141 The Relationship of Tumor Location and Treatment on Cognition in Long-Term IDH-Mutant Glioma Survivors

Abstract Objective Isocitrate dehydrogenase 1 mutation (IDH-M) denotes a distinct subtype of glioma which has a relatively favorable prognosis but nevertheless requires treatment including surgery, radiation and/or chemotherapy and the tumors inevitably recur. Factors influencing cognition in long-term survivors (LTS) of IDH-M have not been well characterized. We investigated factors affecting cognition in a cohort of well characterized of IDH-M LTS. Method Patients (N = 65; 98.5% Caucasian; 47.7% female; Mage = 47.51; Medu = 16.06) who were at least 3 years post-IDH-M diagnosis who completed neuropsychological assessment in the Neuro-oncology division at Massachusetts General Hospital were included. Tests of processing speed, working memory, episodic memory, and executive function were analyzed along with tumor, treatment, and individual difference factors relevant to cognition. Exploratory analyses were conducted to generate hypotheses regarding factors of interest. Results Tumor hemisphere affected delayed recall of word list learning Z-score (F = 4.00; p = 0.024; left = −1.41; right = −0.59). Tumor lobe affected visual learning (F = 3.11; p = 0.054; frontal = −0.594; temporal = −0.807; parietal = −1.983). There were no effects of treatment type (photon vs proton radiation; single vs multi-agent chemotherapy), presence of seizures, psychiatric diagnosis, tumor subtype (oligodendroglioma/astrocytoma), grade (2,3,4) or disease duration (Mean = 9.5y; min = 3y; max = 28.8y) on cognition. Conclusions In this heavily treated group of IDH-M glioma LTS referred for neuropsychological evaluation, typical lateralizing/localizing cognitive deficits were revealed. However, simple dichotomies may fail to capture relevant contributors to cognitive impairment. Integration with individual factors including premorbid cognition, radiation dosimetry and brain network connectivity may be necessary to predict cognitive trajectories and outcomes.

Integrated analyses reveal two molecularly and clinically distinct subtypes of H3 K27M-mutant diffuse midline gliomas with prognostic significance

H3 K27M-altered diffuse midline gliomas (DMGs) are highly malignant tumours that arise in the midline structures of the CNS. Most DMGs carry an H3 K27M-mutation in one of the genes encoding for histone H3. Recent studies suggested that epigenetic subgroups of DMGs can be distinguished based on alterations in the MAPK-signalling pathway, tumour localisation, mutant H3-gene, or overall survival (OS). However, as these parameters were studied individually, it is unclear how they collectively influence survival. Hence, we analysed dependencies between different parameters, to define novel epigenetic, clinically meaningful subgroups of DMGs. We collected a multifaceted cohort of 149 H3 K27M-mutant DMGs, also incorporating data of published cases. DMGs were included in the study if they could be clearly allocated to the spinal cord (n = 31; one patient with an additional sellar tumour), medulla (n = 20), pons (n = 64) or thalamus (n = 33), irrespective of further known characteristics. We then performed global genome-wide DNA methylation profiling and, for a subset, DNA sequencing and survival analyses. Unsupervised hierarchical clustering of DNA methylation data indicated two clusters of DMGs, i.e. subtypes DMG-A and DMG-B. These subtypes differed in mutational spectrum, tumour localisation, age at diagnosis and overall survival. DMG-A was enriched for DMGs with MAPK-mutations, medullary localisation and adult age. 13% of DMG-A had a methylated MGMT promoter. Contrarily, DMG-B was enriched for cases with TP53-mutations, PDGFRA-amplifications, pontine localisation and paediatric patients. In univariate analyses, the features enriched in DMG-B were associated with a poorer survival. However, all significant parameters tested were dependent on the cluster attribution, which had the largest effect on survival: DMG-A had a significantly better survival compared to DMG-B (p < 0.001). Hence, the subtype attribution based on two methylation clusters can be used to predict survival as it integrates different molecular and clinical parameters.

The tumorigenic effect of the high expression of ABRACL in glioma and its potential as a therapeutic target

Gliomas are the most common malignant intracranial tumors, with no effective treatments. Better understanding and identification of novel targets are urgently warranted. Actin-binding Rho activating C-terminal like (ABRACL) has been reported as an oncogene in several cancer types. However, the potential roles of ABRACL in the tumorigenesis of malignant glioma remain unknown. We discovered that ABRACL is highly expressed in different sub-types of gliomas in both CGGA and TCGA databases, which was further validated in glioblastoma cell lines and normal human astrocyte lines. RT-qPCR, Western blotting and immunohistochemistry demonstrated that ABRACL expression in glioma tissues was upregulated along with the increasing WHO grades. Further survival analysis of glioma patients also revealed that the overall survival of patients in the ABRACL high expression level group were significantly shorter than those in the low expression level group. Knockdown of ABRACL inhibited the proliferation, cell migration, invasion and cytodynamics behaviors in glioma cell lines via activating STAT3 signaling, which also induced apoptosis and cell cycle arrest. Conversely, overexpressing ABRACL promoted cell renewing and migration, enabled more flexible cell deformation, supporting ABRACL being a bona fide oncogene. Intracranial orthotopic xenograft experiment further confirmed that ABRACL downregulation significantly suppressed glioma growth. These results have demonstrated that the tumorigenic effect of ABRACL is partly mediated by STAT3, whose expression also correlates with clinical prognosis. ABRACL facilitates glioma malignancy phenotype through regulating the cytoskeleton by activating STAT3 pathway, suggesting that it may represent a potential therapeutic target for glioblastoma.

Contemporary Prognostic Signatures and Refined Risk Stratification of Gliomas: An Analysis of 4,400 Tumors.

With the significant shift in the classification, risk stratification, and standards of care for gliomas, we sought to understand how the overall survival of patients with these tumors is impacted by molecular features, clinical metrics, and treatment received. We assembled a cohort of patients with a histopathologically diagnosed glioma from The Cancer Genome Atlas, Project Genomics Evidence Neoplasia Information Exchange, and Dana-Farber Cancer Institute/Brigham and Women's Hospital. This incorporated retrospective clinical, histological, and molecular data alongside prospective assessment of patient survival. 4,400 gliomas were identified: 2,195 glioblastoma, 1,198 IDH1/2-mutant astrocytoma, 531 oligodendroglioma, 271 other IDH1/2-wildtype glioma, and 205 pediatric-type glioma. Molecular classification updated 27.2% of gliomas from their original histopathologic diagnosis. Examining the distribution of molecular alterations across glioma subtypes revealed mutually exclusive alterations within tumorigenic pathways. Non-TCGA patients had significantly improved overall survival compared to TCGA patients, with 26.7%, 55.6%, and 127.8% longer survival for glioblastoma, IDH1/2-mutant astrocytoma, and oligodendroglioma respectively (all p<0.01). Several prognostic features were characterized, including NF1 alteration and 21q loss in glioblastoma, and EGFR amplification and 22q loss in IDH1/2-mutant astrocytoma. Leveraging the size of this cohort, nomograms were generated to assess the probability of overall survival based on patient age, the molecular features of a tumor, and the treatment received. By applying modern molecular criteria, we characterize the genomic diversity across glioma subtypes, identify clinically applicable prognostic features, and provide a contemporary update on patient survival to serve as a reference for ongoing investigations.

Self-Assembled Aza-Boron-Dipyrromethene-Based H2S Prodrug for Synergistic Ferroptosis-Enabled Gas and Sonodynamic Tumor Therapies.

Glioblastoma multiforme (GBM) is the most aggressive and lethal subtype of gliomas of the central nervous system. The efficacy of sonodynamic therapy (SDT) against GBM is significantly reduced by the expression of apoptosis-inhibitory proteins in GBM cells. In this study, an intelligent nanoplatform (denoted as Aza-BD@PC NPs) based on the aza-boron-dipyrromethene dye and phenyl chlorothionocarbonate-modified DSPE-PEG molecules is developed for synergistic ferroptosis-enabled gas therapy (GT) and SDT of GBM. Once internalized by GBM cells, Aza-BD@PC NPs showed effective cysteine (Cys) consumption and Cys-triggered hydrogen sulfide (H2S) release for ferroptosis-enabled GT, thereby disrupting homeostasis in the intracellular environment, affecting GBM cell metabolism, and inhibiting GBM cell proliferation. Additionally, the released Aza-BD generated abundant singlet oxygen (1O2) under ultrasound irradiation for favorable SDT. In vivo and in vitro evaluations demonstrated that the combined functions of Cys consumption, H2S production, and 1O2 production induced significant death of GBM cells and markedly inhibited tumor growth, with an impressive inhibition rate of up to 97.5%. Collectively, this study constructed a cascade nanoreactor with satisfactory Cys depletion performance, excellent H2S release capability, and prominent reactive oxygen species production ability under ultrasound irradiation for the synergistic ferroptosis-enabled GT and SDT of gliomas.

Glioma Type Prediction with Dynamic Contrast-Enhanced MR Imaging and Diffusion Kurtosis Imaging-A Standardized Multicenter Study.

The aim was to explore the performance of dynamic contrast-enhanced (DCE) MRI and diffusion kurtosis imaging (DKI) in differentiating the molecular subtypes of adult-type gliomas. A multicenter MRI study with standardized imaging protocols, including DCE-MRI and DKI data of 81 patients with WHO grade 2-4 gliomas, was performed at six centers. The DCE-MRI and DKI parameter values were quantitatively evaluated in ROIs in tumor tissue and contralateral normal-appearing white matter. Binary logistic regression analyses were performed to differentiate between high-grade (HGG) vs. low-grade gliomas (LGG), IDH1/2 wildtype vs. mutated gliomas, and high-grade astrocytic tumors vs. high-grade oligodendrogliomas. Receiver operating characteristic (ROC) curves were generated for each parameter and for the regression models to determine the area under the curve (AUC), sensitivity, and specificity. Significant differences between tumor groups were found in the DCE-MRI and DKI parameters. A combination of DCE-MRI and DKI parameters revealed the best prediction of HGG vs. LGG (AUC = 0.954 (0.900-1.000)), IDH1/2 wildtype vs. mutated gliomas (AUC = 0.802 (0.702-0.903)), and astrocytomas/glioblastomas vs. oligodendrogliomas (AUC = 0.806 (0.700-0.912)) with the lowest Akaike information criterion. The combination of DCE-MRI and DKI seems helpful in predicting glioma types according to the 2021 World Health Organization's (WHO) classification.

Incorporation of Edited MRS into Clinical Practice May Improve Care of Patients with IDH-Mutant Glioma.

Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion classify adult-type diffuse gliomas into 3 tumor subtypes with distinct prognoses. We aimed to evaluate the performance of edited MR spectroscopy for glioma subtyping in a clinical setting, via the quantification of D-2-hydroxyglutarate (2HG) and cystathionine. The delay between this noninvasive classification and the integrated histomolecular analysis was also quantified. Subjects with presumed low-grade gliomas eligible for surgery (cohort 1) and subjects with IDH-mutant gliomas previously treated and with progressive disease (cohort 2) were prospectively examined with a single-voxel Mescher-Garwood point-resolved spectroscopy sequence at 3T. Spectra were quantified using LCModel. The Cramér-Rao lower bounds threshold was set to 20%. Integrated histomolecular analysis according to the 2021 WHO classification was considered as ground truth. Thirty-four consecutive subjects were enrolled. Due to poor spectra quality and lack of histologic specimens, data from 26 subjects were analyzed. Twenty-one belonged to cohort 1 (11 women; median age, 42 years); and 5, to cohort 2 (3 women; median age, 48 years). Edited MR spectroscopy showed 100% specificity for detection of IDH-mutation and 91% specificity for the prediction of 1p/19q-codeletion status. Sensitivities for the prediction of IDH and 1p/19q codeletion were 69% and 33%, respectively. The median Cramér-Rao lower bounds values were 16% (13%-28%) for IDH-mutant and 572% (554%-999%) for IDH wild type tumors. The time between MR spectroscopy and surgery was longer for low-grade than for high-grade gliomas (P = .03), yet the time between MR spectroscopy and WHO diagnosis did not differ between grades (P = .07), possibly reflecting molecular analyses-induced delays in high-grade gliomas. Our results, acquired in a clinic setting, confirmed that edited MR spectroscopy is highly specific for both IDH-mutation and 1p/19q-codeletion predictions and can provide a faster prognosis stratification. In the upcoming IDH-inhibitor treatment era, incorporation of edited MR spectroscopy into clinical workflow is desirable.

IDH1-mutant metabolite D-2-hydroxyglutarate inhibits proliferation and sensitizes glioma to temozolomide via down-regulating ITGB4/PI3K/AKT

The heterogeneous molecular subtypes of gliomas demonstrate varied responses to chemotherapy and distinct prognostic outcomes. Gliomas with Isocitrate dehydrogenase 1 (IDH1) mutation are associated with better outcomes and are more responsive to temozolomide (TMZ) compared to those without IDH1 mutation. IDH1-mutant gliomas elevate D-2-hydroxyglutarate (D-2HG) levels, with potential dual effects on tumor progression. Limited research has explored the potential anti-glioma effects of D-2HG in combination with TMZ. Clinical data from over 2500 glioma patients in our study confirms that those with IDH1 mutations exhibit enhanced responsiveness to TMZ chemotherapy and a significantly better prognosis compared to IDH1 wild-type patients. In subsequent cellular experiments, we found that the IDH1-mutant metabolite D-2HG suppresses Integrin subunit beta 4 (ITGB4) expression, and down-regulate the phosphorylation levels of PI3K and AKT, ultimately inhibiting cell proliferation while promoting apoptosis, thereby improving glioma prognosis. Additionally, we have demonstrated the synergistic effect of D-2HG and TMZ in anti-glioma therapy involved inhibiting the proliferation of glioma cells and promoting apoptosis. Finally, by integrating data from the CGGA and TCGA databases, it was validated that ITGB4 expression was lower in IDH1-mutant gliomas, and patients with lower ITGB4 expression were associated with better prognosis. These findings indicate that ITGB4 may be a promising therapeutic target for gliomas and D-2HG inhibits proliferation and sensitizes glioma to temozolomide via down-regulating ITGB4/PI3K/AKT. These findings drive theoretical innovation and research progress in glioma therapy.

Streamlined Intraoperative Brain Tumor Classification and Molecular Subtyping in Stereotactic Biopsies Using Stimulated Raman Histology and Deep Learning.

Recent artificial intelligence algorithms aided intraoperative decision-making via stimulated Raman histology (SRH) during craniotomy. This study assesses deep learning algorithms for rapid intraoperative diagnosis from SRH images in small stereotactic-guided brain biopsies. It defines a minimum tissue sample size threshold to ensure diagnostic accuracy. A prospective single-center study examined 121 SRH images from 84 patients with unclear intracranial lesions undergoing stereotactic brain biopsy. Unprocessed, label-free samples were imaged using a portable fiber laser Raman scattering microscope. Three deep learning models were tested to (i) identify tumorous/nontumorous tissue as qualitative biopsy control; (ii) subclassify into high-grade glioma (central nervous system World Health Organization grade 4), diffuse low-grade glioma (central nervous system World Health Organization grades 2-3), metastases, lymphoma, or gliosis; and (iii) molecularly subtype IDH and 1p/19q statuses of adult-type diffuse gliomas. Model predictions were evaluated against frozen section analysis and final neuropathologic diagnoses. The first model identified tumorous/nontumorous tissue with 91.7% accuracy. Sample size on slides impacted accuracy in brain tumor subclassification (81.6%, κ = 0.72 frozen section; 73.9%, κ = 0.61 second model), with SRH images being smaller than hematoxylin and eosin images (4.1 ± 2.5 mm2 vs. 16.7 ± 8.2 mm2, P < 0.001). SRH images with more than 140 high-quality patches and a mean squeezed sample of 5.26 mm2 yielded 89.5% accuracy in subclassification and 93.9% in molecular subtyping of adult-type diffuse gliomas. Artificial intelligence-based SRH image analysis is non-inferior to frozen section analysis in detecting and subclassifying brain tumors during small stereotactic-guided biopsies once a critical squeezed sample size is reached. Beyond frozen section analysis, it enables valid molecular glioma subtyping, allowing faster treatment decisions in the future; however, refinement is needed for long-term application.