Glioma Subtypes Research Articles

Oligodendroglioma patient survival is associated with circulating B-cells and age.

Variations in survival among patients with oligodendroglioma are unexplained by known prognostic factors. To assess the impact of peripheral immune profiles on prognosis, we applied immunomethylomics analyses-DNA methylation of archived whole blood samples, to characterize immune cells. We compared the proportions of immune cells from patients with oligodendroglioma to other glioma subtypes and controls. We used recursive partitioning analysis (RPA) within the oligodendrogliomas to correlate with survival. Patients with oligodendrogliomas (141) were median age at diagnosis of 44 years; 57% male; 75% White; 60% prior chemotherapy; and 25% on dexamethasone at sample collection. Patients with oligodendrogliomas had immune profiles more similar to controls than other glioma subtypes, though with notably lower B-cells. RPA of patients with oligodendrogliomas delineated 2 survival groups based on an interaction between age and B-naïve cells. Patients with longer survival (median 24.2 years) were ≤42 years of age with higher B-naïve cells versus worse survival (median 16.9 years) who were ≤42 years of age with lower B-naïve cells or >42 years of age (P = .00032). Patients with worse survival also had lower CD4- and CD8-naïve T-cells. Similar immune profiles were observed in an independent cohort of oligodendroglioma patients prior to surgery. Peripheral blood immune profiles in oligodendroglioma suggested that younger patients with lower B-naïve cells experienced shorter survival. Though our findings lack of validation cohort and use a heterogenous patient population, they suggest peripheral blood immune profiles may be prognostic for patients with glioma and warrant further investigation.

Role of Histogram Features on Arterial Spin Labeling Perfusion Magnetic Resonance Imaging in Identifying Isocitrate Dehydrogenase Genotypes and Glioma Malignancies.

To explore the use of histogram features on noninvasive arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) in differentiating isocitrate dehydrogenase mutant-type (IDH-mut) from isocitrate dehydrogenase wild-type (IDH-wt) gliomas, and lower-grade gliomas (LGGs) from glioblastomas. This retrospective study included 131 patients who underwent ASL MRI and anatomic MRI. Cerebral blood flow (CBF) maps were calculated, from which 10 histogram features describing the CBF distribution were extracted within the tumor region. Correlation analysis was performed to determine the correlations between histogram features as well as tumor grades and IDH genotypes. The independent t-test and Fisher's exact test were used to determine differences in the extracted histogram features, age at diagnosis, and sex in different glioma subtypes. Multivariate binary logistic regression analysis was performed, and diagnostic performances were evaluated with the receiver operating characteristic curves. CBF histogram features were significantly correlated with tumor grades and IDH genotypes. These features can effectively differentiate LGGs from glioblastomas, and IDH-mut from IDH-wt gliomas. The area under the receiving operating characteristic curve of the model calculated using combined CBF 30th percentile and age at diagnosis in differentiating LGGs from glioblastomas was 0.73. Integrating age at diagnosis and CBF 10th percentile could be more effective in differentiating IDH-mut from IDH-wt gliomas. Furthermore, the combined model had a better area under the receiving operating characteristic curve at 0.856 (sensitivity: 84.4%, specificity: 82.9%). The histogram features on ASL were significantly correlated with tumor grade and IDH genotypes. Moreover, the use of these features could effectively differentiate glioma subtypes. The combined application of age at diagnosis and perfusion histogram features resulted in a more comprehensive identification of tumor subtypes. Therefore, ASL can be a noninvasive tool for the pre-surgical evaluation of gliomas.

Elevated SH3 and Multiple Ankyrin Repeat Domains 2 Expression Correlates With Improved Glioma Prognosis.

This study investigates the prognostic significance of SH3 and multiple ankyrin repeat domains 2 (SHANK2) gene expression in glioma patients, using data from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) project, and the Gene Expression Omnibus (GEO). Through comprehensive analysis, we found a significant association between higher SHANK2 expression and improved survival outcomes across various glioma subtypes. To further validate the clinical relevance of SHANK2, we conducted cellular experiments involving siRNA-mediated knockdown of SHANK2 in U87 and A172 glioma cell lines. Quantitative real-time PCR (qPCR) and Western blot analyses confirmed the successful knockdown of SHANK2, and subsequent MTT assays revealed that silencing SHANK2 significantly promoted glioma cell proliferation. These findings underscore the potential role of SHANK2 as a tumor suppressor in glioma. The study also introduces a multivariate prognostic model incorporating SHANK2, providing a novel perspective on glioma prognosis. While the retrospective nature of the study presents limitations, our results suggest that SHANK2 expression could serve as a valuable biomarker for glioma prognosis and inform future therapeutic strategies.

Isocitrate Dehydrogenase and Alpha Thalassemia/Mental Retardation, X-linked Mutation Status in Human Gliomas at a Tertiary Care Hospital in Southern India: A Cross-sectional Study

Introduction: The molecular subtyping of human gliomas in the current World Health Organisation (WHO) 2021 classification highlights the diagnostic and prognostic significance of neoplastic entities. This subsequently contributes to a better understanding of immunohistochemistry markers in various glioma patients. Aim: The study aimed to determine the Isocitrate Dehydrogenase (IDH1) gene mutation and Alpha Thalessemia/Mental retardation X-linked (ATRX) mutation in gliomas through immunohistochemistry and clinically categorise them into various subgroups. Materials and Methods: The present cross-sectional study was conducted in the Department of Pathology and Neurosurgery on 50 glioma samples collected from patients who underwent surgery at ESIC Medical College and Super Speciality Hospital in Hyderabad, Telangana, India from June 2018 to June 2023. Histopathological evaluation and Immunohistochemistry (IHC) study for IDH1 and Alpha Thalessemia/Mental retardation X-linked were performed on all patients. Clinical variables such as age, gender, duration of symptoms, and clinical presentation were collected from medical records and tabulated. Results: Total 50 cases of glioma were studied. The expression of IDH1 wild type was observed in 30 (60%) cases, which was more common than IDH1 mutation observed in 20 (40%) cases. The mean age in the study was 41.9 years, with a majority of male patients 33 (66%). IDH1 mutation was commonly observed in diffuse fibrillary astrocytomas (34%) followed by 12% of cases in anaplastic Astrocytomas. Glioblastoma was the most common histopathological diagnosis in 40% of cases, but the observed IDH mutation was least frequent in glioblastomas. Out of the 20 glioblastoma cases, only one tested positive for IDH mutation. In contrast, ATRX loss was predominantly seen in glioblastomas, with three out of the five positive cases. IDH1 mutation was more common in adults than in children, and a female preponderance (60%) was noted. ATRX mutation was observed in only 10% of cases. Conclusion: The IDH1 and ATRX are reliable prognostic markers, and their analysis through immunohistochemistry provides a practical and dependable method for categorising gliomas compared to molecular analysis, thus assisting clinicians in achieving promising treatment outcomes.

Network Pharmacology and Bioinformatics Analysis to Identify the Molecular Targets and its Biological Mechanisms of Sciadopitysin against Glioblastoma.

Glioblastoma multiform (GBM) is categorized as the most malignant subtype of gliomas, which comprise nearly 75% of malignant brain tumors in adults. Increasing evidence suggests that network pharmacology will be a novel method for identifying the systemic mechanism of therapeutic compounds in diseases like cancer. The present study aimed to use a network pharmacology approach to establish the predictive targets of sciadopitysin against GBM and elucidate its biological mechanisms. Firstly, targets of sciadopitysin were obtained from the SwissTargetPrediction database, and genes associated with the pathogenesis of GBM were identified from the DiGeNET database. Sixty-four correlative hits were identified as anti-glioblastoma targets of sciadopitysin. Functional enrichment and pathway analysis revealed significant biological mechanisms of the targets. Interaction of protein network and cluster analysis using STRING resulted in two crucial interacting hub genes, namely, HSP90 and AKT1. Additionally, the in vitro cytotoxic potential of sciadopitysin was assessed on GBM U87 cells. The findings indicate that the pharmacological action of sciadopitysin against GBM might be associated with the regulation of two core targets: HSP90 and AKT1. Thus, the network pharmacology undertaken in the current study established the core active targets of sciadopitysin, which may be extensively applied with further validations for treatment in GBM.

Identification of PANoptosis-related predictors for prognosis and tumor microenvironment by multiomics analysis in glioma.

PANoptosis is a newly described inflammatory programmed cell death, that highlights coordination between pyroptosis, apoptosis and necroptosis. However, the functions of PANoptosis-related genes in glioma progression still remain to be explored. This study aims to identify PANoptosis-related predictors that may be utilized for prognosis prediction and development of new therapeutic targets. Firstly, bulk and single-cell RNA-seq (scRNA-seq) data of glioma patients were extracted from TCGA, CGGA and GEO database. Genetic analysis indicates a considerably high mutation frequency of PANoptosis-related genes (PANRGs) in glioma. Consensus clustering was applied to reveal different subtypes of glioma based on PANRGs. Two PANoptosis subtypes with distinct prognostic and TME characteristics were identified. Then, with LASSO-Cox regression analysis, four PANoptosis-related predictors (MYBL2, TUBA1C, C21orf62 and KCNIP2) were determined from bulk and scRNA-seq analysis. Predictive PANRG score model was established with these predictors and its correlation with tumor microenvironment (TME) was investigated. The results showed that patients with low PANRG score, had higher infiltration of anti-tumor immune cells, higher MSI score and lower TIDE score, which are more likely to benefit from immunotherapy. Further analysis identified 16 potential drugs associated with PANoptosis-related predictors. Moreover, the expression levels of four PANoptosis-related predictors were examined in clinical samples and the results were consistent with those analyzed in the database. Besides, we also confirmed the biological functions of two oncogenic predictors (MYBL2 and TUBA1C) by cell experiments, which revealed that knockdown of MYBL2 or TUBA1C could significantly inhibit the proliferation and migration of glioma cells. These findings highlight the prognostic value and biological functions of PANRGs in glioma, which may provide valuable insights for individualized treatment.

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.

Эпилептический статус в раннем послеоперационном периоде у пациентов с диффузными глиомами головного мозга

Study objective. To develop knowledge of the epileptic status (ES) in early post-op period in patients with diffuse brain gliomas. Design. Single-centre retrospective study. Materials and methods. The study group included 280 patients with diffuse brain gliomas: 156 males (55.71%) and 124 females (44.29%). The average age of subjects was 45.81 ± 15.90 years. To identify the diffuse glioma sub-type, 118 patients underwent an examination of frequent mutations of genes IDH1 (exon 4) and IDH2 (exon 4) in tumour tissue using a high-resolution analysis of PCR product melting profile and sequencing. Results. ES in early post-op period developed in 3.57% of cases (n = 10) and manifested predominantly with motor symptoms (paroxysmal, myoclonic, focal motor symptoms). Epilepsy diagnosed before surgery was not a risk factor of ES in early post-op period. Awake surgeries, aiming at determination of the functionally significant (verbal, motor) areas of the brain, as well as the use of intraoperative neurophysiological monitoring (direct cortex stimulation during surgery) did not increase the rate of ESs. It has been demonstrated that ES in patients with diffuse brain gliomas can develop after a longer surgery (p = 0.0129) or longer anaesthesia (p = 0.0251). The association between ES in early post-op period and cerebral complications has been reported: in patients with complications, the rate of ESs was 17.07% vs. 1.26% (p < 0.00001). in patients without complications. The rate of early ESs did not drop with preventive antiepilepsy drugs prescribed to patients with diffuse brain gliomas without pre-op epilepsy, as well as with prolonged sedation with propofol and artificial lung ventilation. Conclusion. ES in early post-op period requires separate diagnostic and treatment algorithms. Keywords: diffuse brain gliomas, post-op period, epilepsy, epileptic status, antiepilepsy drugs.

The global immune-nutrition-inflammation index (GINI) as a robust prognostic factor in glioblastoma patients treated with the standard stupp protocol.

Systemic inflammation can significantly impact gliomas' onset, progression, and prognosis. Glioblastoma multiforme (GBM) represents the glioma subtype characterized by the most profound inflammatory and immunosuppressive states. Consequently, various blood-borne biomarkers have been scrutinized concerning their prognostic value in GBM patients. We sought to investigate whether the recently introduced Global Immune-Nutrition-Inflammation Index (GINI) holds prognostic significance for GBM patients treated with the standard Stupp protocol. We retrospectively analyzed the data from a cohort of newly diagnosed GBM patients receiving the standard Stupp regimen using the propensity score-matching methodology. The GINI was computed using the original formula: GINI = [(C-reactive protein × Monocytes × Platelets × Neutrophils) ÷ (Albumin × Lymphocytes)]. We employed receiver operating characteristic (ROC) curve analysis to identify the optimal cutoff values for GINI, which could help distinguish between different survival outcomes. The primary and secondary objectives were the differences in overall survival (OS) and progression-free survival (PFS) between the GINI groups. The optimal GINI cutoff value was 1350. Out of 294 eligible patients, 211 were PSM-matched: GINI<1350 (N = 95) and GINI≥1350 (N = 116). Comparative Kaplan-Meier estimates indicated that the GINI≥1350 patients had substantially worse median PFS (8.0 vs 16.8months; p < .001) and OS (14.3 vs 22.9months; p < .001) durations than their GINI<1350 counterparts. High pretreatment GINI values are robustly and independently associated with inferior PFS and OS outcomes in selected GBM patients who receive standard Stupp protocol. These findings suggest that if further confirmed, the novel GINI could serve as a valuable biological marker for the prognostic stratification of GBM patients.

Identification of prognostic risk score of disulfidptosis-related genes and molecular subtypes in glioma

BackgroundProgrammed cell death is closely related to glioma. As a novel kind of cell death, the mechanism of disulfidptosis in glioma remains unclear. Therefore, it is of great importance to study the role of disulfidptosis-related genes (DRGs) in glioma. MethodsWe first investigated the genetic and transcriptional alterations of 15 DRGs. Two consensus cluster analyses were used to evaluate the association between DRGs and glioma subtypes. In addition, we constructed prognostic DRG risk scores to predict overall survival (OS) in glioma patients. Furthermore, we developed a nomogram to enhance the clinical utility of the DRG risk score. Finally, the expression levels of DRGs were verified by immunohistochemistry (IHC) staining. ResultsMost DRGs (14/15) were dysregulated in gliomas. The 15 DRGs were rarely mutated in gliomas, and only 50 of 987 samples (5.07 %) showed gene mutations. However, most of them had copy number variation (CNV) deletions or amplifications. Two distinct molecular subtypes were identified by cluster analysis, and DRG alterations were found to be related to the clinical characteristics, prognosis, and tumor immune microenvironment (TIME). The DRG risk score model based on 12 genes was developed and showed good performance in predicting OS. The nomogram confirmed that the risk score had a particularly strong influence on the prognosis of glioma. Furthermore, we discovered that low DRG scores, low tumor mutation burden, and immunosuppression were features of patients with better prognoses. ConclusionThe DRG risk model can be used for the evaluation of clinical characteristics, prognosis prediction, and TIME estimation of glioma patients. These DRGs may be potential therapeutic targets in glioma.

Comprehensive characterization and database construction of immune repertoire in the largest Chinese glioma cohort

Immune receptor repertoire is valuable for developing immunotherapeutic interventions, but remains poorly understood across glioma subtypes including IDH wild type, IDH mutation without 1p/19q codeletion (IDHmut-noncodel) and IDH mutation with 1p/19q codeletion (IDHmut-codel). We assembled over 320,000 TCR/BCR clonotypes from the largest glioma cohort of 913 RNA sequencing samples in the Chinese population, finding that immune repertoire diversity was more prominent in the IDH wild type (the most aggressive glioma). Fewer clonotypes were shared within each glioma subtype, indicating high heterogeneity of the immune repertoire. The TRA-CDR3 was longer in private than in public clonotypes in IDH wild type. CDR3 variable motifs had higher proportions of hydrophobic residues in private than in public clonotypes, suggesting private CDR3 sequences have greater potential for tumor antigen recognition. Finally, we developed GTABdb, a web-based database designed for hosting, exploring, visualizing, and analyzing glioma immune repertoire. Our study will facilitate developing glioma immunotherapy.

A new subtype of diffuse midline glioma, H3 K27 and BRAF/FGFR1 co-altered: a clinico-radiological and histomolecular characterisation

Diffuse midline gliomas (DMG) H3 K27-altered are incurable grade 4 gliomas and represent a major challenge in neuro-oncology. This tumour type is now classified in four subtypes by the 2021 edition of the WHO Classification of the Central Nervous System (CNS) tumours. However, the H3.3-K27M subgroup still appears clinically and molecularly heterogeneous. Recent publications reported that rare patients presenting a co-occurrence of H3.3K27M with BRAF or FGFR1 alterations tended to have a better prognosis. To better study the role of these co-driver alterations, we assembled a large paediatric and adult cohort of 29 tumours H3K27-altered with co-occurring activating mutation in BRAF or FGFR1 as well as 31 previous cases from the literature. We performed a comprehensive histological, radiological, genomic, transcriptomic and DNA methylation analysis. Interestingly, unsupervised t-distributed Stochastic Neighbour Embedding (tSNE) analysis of DNA methylation profiles regrouped BRAFV600E and all but one FGFR1MUT DMG in a unique methylation cluster, distinct from the other DMG subgroups and also from ganglioglioma (GG) or high-grade astrocytoma with piloid features (HGAP). This new DMG subtype harbours atypical radiological and histopathological profiles with calcification and/or a solid tumour component both for BRAFV600E and FGFR1MUT cases. The analyses of a H3.3-K27M BRAFV600E tumour at diagnosis and corresponding in vitro cellular model showed that mutation in H3-3A was the first event in the oncogenesis. Contrary to other DMG, these tumours occur more frequently in the thalamus (70% for BRAFV600E and 58% for FGFR1MUT) and patients have a longer overall survival with a median above three years. In conclusion, DMG, H3 K27 and BRAF/FGFR1 co-altered represent a new subtype of DMG with distinct genotype/phenotype characteristics, which deserve further attention with respect to trial interpretation and patient management.

Histogram analysis of quantitative susceptibility mapping and apparent diffusion coefficient for identifying isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas.

Accurate preoperative identification of isocitrate dehydrogenase (IDH) genotypes and tumor subtypes is highly important for proper treatment planning and prognosis evaluation in patients with glioma. This study aimed to differentiate IDH genotypes and tumor subtypes of adult-type diffuse gliomas using histogram features of quantitative susceptibility mapping (QSM) and apparent diffusion coefficient (ADC). This prospective study enrolled patients with suspected gliomas between March 2019 and January 2022 in a random series. Histogram features of QSM and ADC were extracted from the tumor parenchyma. The Mann-Whitney U test was used to compare the difference in histogram features between different IDH genotypes and among tumor subtypes. Receiver operating characteristic (ROC) curves were constructed to assess the corresponding diagnostic performance. This study included 47 patients with histopathologically confirmed adult-type diffuse gliomas. Totals of seven QSM features including 10th percentile (P10), 90th percentile (P90), interquartile range (IQR), maximum, mean absolute deviation (MAD), root mean squared (RMS), and variance, and five ADC features including P10, mean, median, RMS, and skewness exhibited significant differences between different IDH genotypes (P<0.05 for all), with the IQR of QSM demonstrating the highest area under curve (AUC) of 0.774 [95% confidence interval (CI): 0.635-0.913]. For separating tumor subtypes, the IQR of QSM also showed the highest AUC of 0.745 (95% CI: 0.566-0.924) for glioblastoma (GBM) versus astrocytoma and 0.848 (95% CI: 0.706-0.989) for GBM versus oligodendroglioma, but none of the features could discriminate astrocytoma from oligodendroglioma. The combination of the IQR of QSM, P10 of ADC, and age achieved the highest AUC of 0.910 (95% CI: 0.826-0.994) for IDH genotypes, and 0.939 (95% CI: 0.859-1.000) and 0.967 (95% CI: 0.904-1.000) for GBM versus astrocytoma and GBM versus oligodendroglioma, respectively. QSM and ADC histogram features may serve as potential imaging markers for noninvasively assessing IDH genotypes and tumor subtypes of adult-type diffuse gliomas. Combining significant features may enhance the diagnostic performance substantially.

Adult-type Diffuse Gliomas.

This article highlights key aspects of the diagnosis and management of adult-type diffuse gliomas, including glioblastomas and IDH-mutant gliomas relevant to the daily practice of the general neurologist. The advances in molecular characterization of gliomas have translated into more accurate prognostication and tumor classification. Gliomas previously categorized by histological appearance solely as astrocytomas or oligodendrogliomas are now also defined by molecular features. Furthermore, ongoing clinical trials have incorporated these advances to tailor more effective treatments for specific glioma subtypes. Despite recent insights into the molecular aspects of gliomas, these tumors remain incurable. Care for patients with these complex tumors requires a multidisciplinary team in which the general neurologist has an important role. Efforts focus on translating the latest data into more effective therapies that can prolong survival.

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.

DeepAutoGlioma: a deep learning autoencoder-based multi-omics data integration and classification tools for glioma subtyping

Background and objectiveThe classification of glioma subtypes is essential for precision therapy. Due to the heterogeneity of gliomas, the subtype-specific molecular pattern can be captured by integrating and analyzing high-throughput omics data from different genomic layers. The development of a deep-learning framework enables the integration of multi-omics data to classify the glioma subtypes to support the clinical diagnosis.ResultsTranscriptome and methylome data of glioma patients were preprocessed, and differentially expressed features from both datasets were identified. Subsequently, a Cox regression analysis determined genes and CpGs associated with survival. Gene set enrichment analysis was carried out to examine the biological significance of the features. Further, we identified CpG and gene pairs by mapping them in the promoter region of corresponding genes. The methylation and gene expression levels of these CpGs and genes were embedded in a lower-dimensional space with an autoencoder. Next, ANN and CNN were used to classify subtypes using the latent features from embedding space. CNN performs better than ANN for subtyping lower-grade gliomas (LGG) and glioblastoma multiforme (GBM). The subtyping accuracy of CNN was 98.03% (± 0.06) and 94.07% (± 0.01) in LGG and GBM, respectively. The precision of the models was 97.67% in LGG and 90.40% in GBM. The model sensitivity was 96.96% in LGG and 91.18% in GBM. Additionally, we observed the superior performance of CNN with external datasets. The genes and CpGs pairs used to develop the model showed better performance than the random CpGs-gene pairs, preprocessed data, and single omics data.ConclusionsThe current study showed that a novel feature selection and data integration strategy led to the development of DeepAutoGlioma, an effective framework for diagnosing glioma subtypes.

BIOM-36. PREOPERATIVE IMMUNE AND INFLAMMATORY FACTORS IN PERIPHERAL BLOOD PREDICT PROGNOSIS OF GLIOMA AND CORRELATE WITH GRADES AND SUBTYPES: A PROSPECTIVE MULTI-INSTITUTIONAL STUDY OF 1282 PATIENTS

Abstract BACKGROUND Peripheral blood immune and inflammatory factors can reflect glioma patients' immune status and inform anti-tumor immunotherapy, but research in this field is limited compared to tumor microenvironment studies. We aim to investigate the relationship between these circulating factors and glioma grades, subtypes, and prognosis. METHODS From October 2006 to April 2022, 1282 patients with primary gliomas were enrolled in this study, and their preoperative peripheral blood was collected. Correlations between immune and inflammatory factors and different glioma grades and subtypes were analyzed using ANOVA, t-test, and Ordinal Logistic Regression. ROC curve analysis was used to evaluate the diagnostic efficiency of these factors. In addition, the Cox regression model, K-M survival analysis, and other methods were used to study the relationship between these factors and the prognosis and survival of patients. RESULTS The results showed that with the higher clinical grade, the percentage of NK cells in peripheral blood increases while the percentage of Th and T lymphocytes decreases. C4 levels and percentage of B lymphocytes were higher in grade I gliomas than in other grades. The percentage of NK and Th cells also correlated with glioma subtypes. Immunoglobulin light chains were associated with a favorable prognosis, whereas a higher percentage of peripheral B lymphocytes in grade IV patients was associated with a poor prognosis. CONCLUSIONS The relationship between immune and inflammatory factors and the grades and subtypes of glioma can offer potential opportunities to intervene in these factors that could improve the prognosis of glioma patients. Additionally, it may be helpful to screen glioma and anti-tumor immunotherapy.

PATH-62. RAMAN-BASED AI PLATFORM TO ACCELERATE THE DIAGNOSIS AND MANAGEMENT OF GLIOMAS VIA RAPID METHYLATION PROFILING

Abstract BACKGROUND DNA methylation profiling and subtyping of gliomas has become an integral part of the diagnosis and treatment of glioma patients but remains a challenge due to the sample preparation and time requirements of the existing methylation profiling technology. We hypothesized that machine learning and Raman spectroscopy, which creates fast molecular fingerprints of samples in situ based on chemical signatures rather than the use of exogenous dyes, can provide a fast alternative to the existing approaches for methylation profiling and classification of gliomas. METHODS Raman spectroscopy at the spatial resolution of less than 300nm was used for molecular fingerprinting of the regions of interest using 1mm2 Formalin-Fixed Paraffin-Embedded (FFPE) tissue spots from 45 patient samples with known LGm1 to LGm6 methylation subtypes. Spectral information (over 1738 frequencies) was used to construct tumor/non-tumor, IDH1WT/IDH1mut, and methylation-subtype classifiers. Oversampling was used to obtain subtype-balanced data distributions. Supervised wrapper methods and random forests were used to identify the top 20 most discriminatory Raman frequencies. Stimulated Raman spectroscopy was used to validate the findings. RESULTS We developed APOLLO - a novel platform based on spontaneous Raman spectroscopy and machine learning -- for predicting the DNA methylation subtypes of FFPE glioma tissue specimens. APOLLO discriminates tumors from non-tumor areas with 98% accuracy and discriminates IDH1mut versus IDH1WT tumors with 82% accuracy. APOLLO also achieved high discriminatory power between G-CIMP-high and G-CIMP-low molecular phenotypes (ROC of 0.75), subtypes of lower-grade IDH1mut gliomas with significantly different clinical outcomes. We determined that the Raman shifts important for discriminating IDH1mut versus IDH1wt tumors are associated with novel lipid-metabolism signatures of IDH1mut glioma. CONCLUSIONS The development of APOLLO allows fast, reliable, and accurate prediction of methylation subtypes of glioma which can speed diagnosis and, once validated on fresh tissue, can be implemented in the operating room.

ANGI-02. CELL MIGRATION SIMULATOR-BASED BIOMARKERS FOR GLIOBLASTOMA

Abstract Glioblastoma is the most aggressive malignant brain tumor with the poor survival due to its invasive nature driven by cell migration. There is still no effective treatment targeting glioma cell migration based on their transcriptomic information, due to the lack of understanding in the links to the fundamental mechanics of cell migration. In the previous studies, we developed a physics-based motor-clutch model, the cell migration simulator (CMS), to identify the basic mechanisms of cell migration in various cell types. In this study, we found that glioblastoma patient-derived (xenograft) (PD(X)) cell lines across mesenchymal (MES), proneural (PN), classical (CL) subtypes and two institutions (N = 13 patients) had optimal motility and traction force on stiffnesses around 9.3kPa, with otherwise heterogeneous and uncorrelated motility, traction, and F-actin flow. We applied the CMS as a consistent framework to describe the physical parameters of glioblastoma patient cell migration. Using single cell migration and force generation data on compliant 2D surfaces, we found all glioma PD(X) lines (11 patients) had balanced myosin/adhesion ratios resulting in effective migration. Mesenchymal (MES) lines had enhanced F-actin polymerization resulting in higher motility. Adherent PDX lines had increased myosin-adhesion amount resulting in higher traction force. The CMS also predicted the differential cell migration under cytoskeletal drugs between glioma subtypes. Finally, we found 18 genes in actin cytoskeleton and focal adhesion pathways significantly correlated with the glioma physical parameters, which were being used to estimate the physical parameters of a larger cohort (66 patients) and to strategize targeting their cell migration. Overall, we described a consistent physics-based framework for parameterizing individual glioblastoma patients, connecting to clinical transcriptomic data, and predicting drug effects, that can potentially be used to develop subtype and patient-specific anti-migratory therapeutic strategies.

EPCO-31. THE PROGNOSTIC IMPLICATIONS OF THE RELATIVE ABUNDANCE OF DECONVOLVED TUMOR AND NON-TUMORAL SUBPOPULATIONS IN MULTI-REGIONAL BIOPSIES OF HIGH GRADE GLIOMA WITH BULK RNA-SEQ

Abstract High-grade glioma continues to have dismal survival owing in part to its intra- and inter-patient heterogeneity. Standard clinical protocol collects tumor samples with the aim of providing or confirming a diagnosis and determining the status of a few key genes (e.g. IDH1, MGMT). However, this protocol is unable to capture the diversity within tumor regions, immune expression or normal cell abundances that play key roles in the development of the disease. To overcome this, during surgery we collect image-localized multi-regional biopsies to characterize this disease heterogeneity. Data collection is ongoing, and we currently have 202 samples from 58 patients with available bulk RNA-Seq. With a single-cell reference dataset from Columbia University, we used CIBERSORTx, a deconvolution method, to predict relative abundances of 7 normal, 6 glioma, and 5 immune cell subpopulations for each sample. We used Cox proportional hazard models with first-order statistics (mean, minimum, maximum) of abundances within patients to determine whether these are prognostic, then used TCGA RNA-Seq data to validate these findings. We found that one glioma proneural subtype was significantly beneficial for patient survival relative to other glioma subtypes across all statistics (and showed significance in TCGA). Proliferative and mesenchymal glioma subtypes also showed significance for one or two of the calculated statistics. Oligodendrocyte progenitor cell abundances were consistently significantly beneficial for patient survival in our cohort, while an increased abundance of abnormal (reactive) astrocytes is associated with poor prognosis. We also ran these analyses within invasive margin and core tumor regions to determine location-specific population abundances associated with patient survival. In conclusion, understanding the in vivo diversity of cellular subpopulations within high grade glioma is important for treatment stratification and patient benefit.