Glioma Subtypes Research Articles

Long-term outcomes of CNS WHO grade 2 oligodendroglioma in adult patients: a single-institution experience

PurposeOligodendrogliomas (ODGs) are a subtype of diffuse lower-grade gliomas with overall survival of > 10 years. This study aims to analyze long-term outcomes and identify prognostic factors in patients with WHO grade 2 ODG.MethodsWe retrospectively reviewed 138 adult patients diagnosed with 1p/19q co-deleted ODG who underwent surgical resection or biopsy between 1994 and 2021, analyzing clinical data, treatment details, and outcomes. Progression-free survival (PFS) and overall survival (OS) were evaluated using Kaplan–Meier analysis. Univariate and multivariate Cox regression analyses were utilized to identify significant prognostic factors.ResultsIn the gross total resection (GTR) group, 63 (45.7%) underwent observation and 5 (3.6%) received postoperative treatment; in the non-GTR group, 37 (26.8%) were observed and 33 (23.9%) received postoperative treatment. The median PFS and OS were 6.8 and 18.4 years, respectively. Between adjuvant treatment and observation, there was no significant difference in PFS or OS. However, GTR or STR with less than 10% residual tumor exhibited significantly better PFS and OS compared to PR or biopsy (p = 0.022 and 0.032, respectively). Multivariate analysis revealed that contrast enhancement on MRI was associated with worse PFS (HR = 2.36, p < 0.001) and OS (HR = 5.89, p = 0.001). And the presence of seizures at presentation was associated with improved OS (HR = 0.28, p = 0.006).ConclusionThis study underscores favorable long-term outcomes for patients with 1p/19q co-deleted ODG WHO grade 2. Our findings indicate that the EOR plays a crucial role as a significant prognostic factor in enhancing PFS and OS outcomes in WHO grade 2 ODG.

Integrative analysis of genomic and epigenomic regulation reveals miRNA mediated tumor heterogeneity and immune evasion in lower grade glioma

The expression dysregulation of microRNAs (miRNA) has been widely reported during cancer development, however, the underling mechanism remains largely unanswered. In the present work, we performed a systematic integrative study for genome-wide DNA methylation, copy number variation and miRNA expression data to identify mechanisms underlying miRNA dysregulation in lower grade glioma. We identify 719 miRNAs whose expression was associated with alterations of copy number variation or promoter methylation. Integrative multi-omics analysis revealed four subtypes with differing prognoses. These glioma subtypes exhibited distinct immune-related characteristics as well as clinical and genetic features. By construction of a miRNA regulatory network, we identified candidate miRNAs associated with immune evasion and response to immunotherapy. Finally, eight prognosis related miRNAs were validated to promote cell migration, invasion and proliferation through in vitro experiments. Our study reveals the crosstalk among DNA methylation, copy number variation and miRNA expression for immune regulation in glioma, and could have important implications for patient stratification and development of biomarkers for immunotherapy approaches.

Comprehensive analysis of distinct circadian clock subtypes of adult diffuse glioma and their associations with clinicopathological, genetic, and epigenetic profiles.

The circadian clock (CC) has biological and clinical implications in gliomas. Most studies focused on CC effects on the tumor microenvironment and the application of chronotherapy. The present study focused on CC gene expression patterns and intracellular oncogenic activities. Glioma gene expression data were collected from The Human Cancer Genome Atlas (TCGA) project. After applying inclusion and exclusion criteria, we selected 666 patients from TCGA-GBM and TCGA-LGG projects and included important clinicopathological variables. The entire cohort was subjected to clustering analysis and divided into CC1 and CC2 subtypes based on statistical, biological, and clinical criteria. CC2 gliomas showed higher expression of BMAL1 and CRY1 and lower expression of CRY2 and PER2 (adjusted P < .001). CC2 gliomas had q higher activity of cell proliferation, metabolic reprogramming, angiogenesis, hypoxia, and many oncogenic signals (P < .001). The CC2 subtype contained a higher proportion of glioblastomas (P < .001) and had a worse prognosis (P < .001). Stratified Kaplan-Meier and multivariable Cox analyses illustrated that the CC subtype is an independent prognostic factor to clinicopathological characteristics (P < .001), genetic aberrations (P = .006), and biological processes (P < .001). Thus, this study shows statistical evidence of CC subtypes and their biological, and clinicopathological significance in adult gliomas.

Single Nucleus Total RNA Sequencing of Formalin-Fixed Paraffin-Embedded Gliomas.

Gliomas, the predominant form of brain cancer, comprise diverse malignant subtypes with limited curative therapies available. The insufficient understanding of their molecular diversity and evolutionary processes hinders the advancement of new treatments. Technical complexities associated with formalin-fixed paraffin-embedded (FFPE) clinical samples hinder molecular-level analyses of gliomas. Current single-cell RNA sequencing (scRNA-seq) platforms are inadequate for large-scale clinical applications. In this study, automated snRandom-seq is developed, a high-throughput single-nucleus total RNA sequencing platform optimized for archival FFPE samples. This platform integrates automated single-nucleus isolation and droplet barcoding systems with the random primer-based scRNA-seq chemistry, accommodating a broad spectrum of sample types. The automated snRandom-seq is applied to analyze 116492 single nuclei from 17 FFPE samples of various glioma subtypes, including rare clinical samples and matched primary-recurrent glioblastomas (GBMs). The study provides comprehensive insights into the molecular characteristics of gliomas at the single-cell level. Abundant non-coding RNAs (ncRNAs) with distinct expression profiles across different glioma clusters and uncovered promising recurrence-related targets and pathways in primary-recurrent GBMs are identified. These findings establish automated snRandom-seq as a robust tool for scRNA-seq of FFPE samples, enabling exploration of molecular diversities and tumor evolution. This platform holds significant implications for large-scale integrative and retrospective clinical research.

Association between glioma and neurodegenerative diseases risk: a two-sample bi-directional Mendelian randomization analysis.

Earlier observational studies have demonstrated a correlation between glioma and the risk of neurodegenerative diseases (NDs), but the causality and direction of their associations remain unclear. The objective of this study was to ascertain the causal link between glioma and NDs using Mendelian randomization (MR) methodology. Genome-wide association study (GWAS) data were used in a two-sample bi-directional MR analysis. From the largest meta-analysis GWAS, encompassing 18,169 controls and 12,488 cases, summary statistics data on gliomas was extracted. Summarized statistics for NDs, including Alzheimer's disease (AD), multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD) were obtained from the GWAS of European ancestry. Inverse variance weighted (IVW) method was elected as the core MR approach with weighted median (WM) method and MR-Egger method as complementary methods. In addition, sensitivity analyses were performed. A Bonferroni correction was used to correct the results. Genetically predicted glioma had been related to decreased risk of AD. Specifically, for all glioma (IVW: OR = 0.93, 95% CI = 0.90-0.96, p = 4.88 × 10-6) and glioblastoma (GBM) (IVW: OR = 0.93, 95% CI = 0.91-0.95, p = 5.11 × 10-9). We also found that genetically predicted all glioma has a suggestive causative association with MS (IVW: OR = 0.90, 95% CI = 0.81-1.00, p = 0.045). There was no evidence of causal association between glioma and ALS or PD. According to the results of reverse MR analysis, no discernible causal connection of NDs was found on glioma. Sensitivity analyses validated the robustness of the above associations. We report evidence in support of potential causal associations of different glioma subtypes with AD and MS. More studies are required to uncover the underlying mechanisms of these findings.

MoNETA: MultiOmics Network Embedding for SubType Analysis.

Cells are complex systems whose behavior emerges from a huge number of reactions taking place within and among different molecular districts. The availability of bulk and single-cell omics data fueled the creation of multi-omics systems biology models capturing the dynamics within and between omics layers. Powerful modeling strategies are needed to cope with the increased amount of data to be interrogated and the relative research questions. Here, we present MultiOmics Network Embedding for SubType Analysis (MoNETA) for fast and scalable identification of relevant multi-omics relationships between biological entities at the bulk and single-cells level. We apply MoNETA to show how glioma subtypes previously described naturally emerge with our approach. We also show how MoNETA can be used to identify cell types in five multi-omic single-cell datasets.

Multi-scale brain attributes contribute to the distribution of diffuse glioma subtypes.

Gliomas are primary brain tumors and are among the most malignant types. Adult-type diffuse gliomas can be classified based on their histological and molecular signatures as IDH-wildtype glioblastoma, IDH-mutant astrocytoma, and IDH-mutant and 1p/19q-codeleted oligodendroglioma. Recent studies have shown that each subtype of glioma has its own specific distribution pattern. However, the mechanisms underlying the specific distributions of glioma subtypes are not entirely clear despite partial explanations such as cell origin. To investigate the impact of multi-scale brain attributes on glioma distribution, we constructed cumulative frequency maps for diffuse glioma subtypes based on T1w structural images and evaluated the spatial correlation between tumor frequency and diverse brain attributes, including postmortem gene expression, functional connectivity metrics, cerebral perfusion, glucose metabolism, and neurotransmitter signaling. Regression models were constructed to evaluate the contribution of these factors to the anatomic distribution of different glioma subtypes. Our findings revealed that the three different subtypes of gliomas had distinct distribution patterns, showing spatial preferences toward different brain environmental attributes. Glioblastomas were especially likely to occur in regions enriched with synapse-related pathways and diverse neurotransmitter receptors. Astrocytomas and oligodendrogliomas preferentially occurred in areas enriched with genes associated with neutrophil-mediated immune responses. The functional network characteristics and neurotransmitter distribution also contributed to oligodendroglioma distribution. Our results suggest that different brain transcriptomic, neurotransmitter, and connectomic attributes are the factors that determine the specific distributions of glioma subtypes. These findings highlight the importance of bridging diverse scales of biological organization when studying neurological dysfunction.

A novel ARHGAP family gene signature for survival prediction in glioma patients.

ARHGAP family genes are often used as glioma oncogenic factors, and their mechanism of action remains unexplained. Our research entailed a thorough examination of the immune microenvironment and enrichment pathways across various glioma subtypes. A distinctive 6-gene signature was developed employing the CGGA cohort, leading to insights into the disparities in clinical characteristics, mutation patterns, and immune cell infiltration among distinct risk categories. Additionally, a unique nomogram was established, grounded on ARHGAPs, with DCA curves illustrating the model's prospective clinical utility in guiding therapeutic strategies. Emphasizing the role of ARHGAP30, integral to our model, its impact on glioma severity and the credibility of our risk assessment model were substantiated through RT-qPCR, Western blot analysis, and cellular functional assays. We identified 6 ARHGAP family genes associated with glioma prognosis. Analysis using the Kaplan-Meier method indicated a correlation between elevated risk levels and adverse outcomes in glioma patients. The risk score, linked with tumour staging and IDH mutation status, emerged as an independent factor predicting prognosis. Patients in the high-risk category exhibited increased immune cell infiltration, enhanced tumour mutational burden, more pronounced expression of immune checkpoint genes, and a better response to ICB therapy. A nomogram, integrating the risk score with the pathological features of glioma patients, was developed. DCA analysis and cellular studies confirmed the model's potential to improve clinical treatment outcomes for patients. A novel ARHGAP family gene signature reveals the prognosis of glioma.

Genome-wide polygenic risk scores predict risk of glioma and molecular subtypes.

Polygenic risk scores (PRS) aggregate the contribution of many risk variants to provide a personalized genetic susceptibility profile. Since sample sizes of glioma genome-wide association studies (GWAS) remain modest, there is a need to efficiently capture genetic risk using available data. We applied a method based on continuous shrinkage priors (PRS-CS) to model the joint effects of over 1 million common variants on disease risk and compared this to an approach (PRS-CT) that only selects a limited set of independent variants that reach genome-wide significance (P < 5 × 10-8). PRS models were trained using GWAS stratified by histological (10 346 cases and 14 687 controls) and molecular subtype (2632 cases and 2445 controls), and validated in 2 independent cohorts. PRS-CS was generally more predictive than PRS-CT with a median increase in explained variance (R2) of 24% (interquartile range = 11-30%) across glioma subtypes. Improvements were pronounced for glioblastoma (GBM), with PRS-CS yielding larger odds ratios (OR) per standard deviation (SD) (OR = 1.93, P = 2.0 × 10-54 vs. OR = 1.83, P = 9.4 × 10-50) and higher explained variance (R2 = 2.82% vs. R2 = 2.56%). Individuals in the 80th percentile of the PRS-CS distribution had a significantly higher risk of GBM (0.107%) at age 60 compared to those with average PRS (0.046%, P = 2.4 × 10-12). Lifetime absolute risk reached 1.18% for glioma and 0.76% for IDH wildtype tumors for individuals in the 95th PRS percentile. PRS-CS augmented the classification of IDH mutation status in cases when added to demographic factors (AUC = 0.839 vs. AUC = 0.895, PΔAUC = 6.8 × 10-9). Genome-wide PRS has the potential to enhance the detection of high-risk individuals and help distinguish between prognostic glioma subtypes.

Intraoperative rapid molecular diagnosis aids glioma subtyping and guides precise surgical resection.

The molecular era of glioma diagnosis and treatment has arrived, and a single rapid histopathology is no longer sufficient for surgery. This study sought to present an automatic integrated gene detection system (AIGS), which enables rapid intraoperative detection of IDH/TERTp mutations. A total of 78 patients with gliomas were included in this study. IDH/TERTp mutations were detected intraoperatively using AIGS in 41 of these patients, and they were guided to surgical resection (AIGS detection group). The remaining 37 underwent histopathology-guided conventional surgical resection (non-AIGS detection group). The clinical utility of this technique was evaluated by comparing the accuracy of glioma subtype diagnosis before and after TERTp mutation results were obtained by pathologists and the extent of resection (EOR) and patient prognosis for molecular pathology-guided glioma surgery. With NGS/Sanger sequencing and chromosome detection as the gold standard, the accuracy of AIGS results was 100%. And the timing was well matched to the intraoperative rapid pathology report. After obtaining the TERTp mutation detection results, the accuracy of the glioma subtype diagnosis made by the pathologists increased by 19.51%. Molecular pathology-guided surgical resection of gliomas significantly increased EOR (99.06% vs. 93.73%, p < 0.0001) and also improved median OS (26.77 vs. 13.47 months, p = 0.0289) and median PFS (15.90 vs. 10.57 months, p = 0.0181) in patients with glioblastoma. Using AIGS intraoperatively to detect IDH/TERTp mutations to accurately diagnose glioma subtypes can help achieve maximum safe resection of gliomas, which in turn improves the survival prognosis of patients.

DNAJC1 facilitates glioblastoma progression by promoting extracellular matrix reorganization and macrophage infiltration

BackgroundGlioblastoma (GBM) is a high-grade and heterogeneous subtype of glioma that presents a substantial challenge to human health, characterized by a poor prognosis and low survival rates. Despite its known involvement in regulating leukemia and melanoma, the function and mechanism of DNAJC1 in GBM remain poorly understood.MethodsUtilizing data from the TCGA, CGGA, and GEO databases, we investigated the expression pattern of DNAJC1 and its correlation with clinical characteristics in GBM specimens. Loss-of-function experiments were conducted to explore the impact of DNAJC1 on GBM cell lines, with co-culture experiments assessing macrophage infiltration and functional marker expression.ResultsOur analysis demonstrated frequent overexpression of DNAJC1 in GBM, significantly associated with various clinical characteristics including WHO grade, IDH status, chromosome 1p/19q codeletion, and histological type. Moreover, Kaplan‒Meier and ROC analyses revealed DNAJC1 as a negative prognostic predictor and a promising diagnostic biomarker for GBM patients. Functional studies indicated that silencing DNAJC1 impeded cell proliferation and migration, induced cell cycle arrest, and enhanced apoptosis. Mechanistically, DNAJC1 was implicated in stimulating extracellular matrix reorganization, triggering the epithelial-mesenchymal transition (EMT) process, and initiating immunosuppressive macrophage infiltration.ConclusionsOur findings underscore the pivotal role of DNAJC1 in GBM pathogenesis, suggesting its potential as a diagnostic and therapeutic target for this challenging disease.

DIPG-49. SPATIAL TRANSCRIPTOMIC SEQUENCING OF A DIPG PATIENT TUMOUR RESOURCE

Abstract BACKGROUND Diffuse Intrinsic Pontine Glioma (DIPG), the most common subtype of Diffuse Midline Glioma (DMG), is a brainstem high-grade glioma arising in young children and always proves fatal. Major challenges include the disease’s intratumoural genetic and cellular heterogeneity as well as its diffusely invasive phenotype. Research of the last few years has highlighted that interactions between cancer cells, the nervous system and immune cells crucially contribute to tumour cell invasion and disease malignancy. Hence, a better understanding of cancer-promoting interactions is a key requirement to elucidate novel strategies for the treatment of this devastating disease. METHOD We performed spatial transcriptomic sequencing on 10 sequential tumour-infiltrated DIPG patient brainstem regions, using the 10x Genomics Visium platform. RESULTS Spatially-informed analysis and cell type clustering overall highlighted the molecular intratumoural heterogeneity present in the disease. We assessed cell cluster interactions as well as receptor-ligand (RL) pairing, illustrating communication between neuronal, immune cell and tumour clusters. We further determined respective RL pairs involved in such. To address the question whether invasive edges genetically differ from the tumour core, we evaluated genes that are significantly correlated with distal brainstem regions compared to tumour proximal sites. Results revealed gene signatures that are highly associated with neuronal signalling and communication, validating previous findings emphasising the importance of neuron-to-glioma interactions driving DIPG disease progression. CONCLUSION DIPG is a lethal paediatric brainstem glioma for which more durable therapeutic modalities remain to be found. In-depth analysis of this disease is often hampered due to the lack of patient tissue suitable for laboratory studies. Our patient autopsy specimen, spanning the entire tumour-infiltrated brainstem provided a platform to study this aggressive disease in a spatio-temporal manner. This analysis elucidates how DIPG is driven by the tumour microenvironment, including associated receptor-ligand interactions. This has the potential to ultimately discover new treatment targets.

DIPG-53. AN EXCEPTIONALLY RARE CASE OF A DIFFUSE MIDLINE GLIOMA WITH CONCOMITANT H3.1 K27M AND G34R MUTATIONS IN THEHIST1H3C (H3C3) GENE

Abstract BACKGROUND Histone mutations (H3 K27M, H3 G34R/V) define subtypes of paediatric-type diffuse high-grade gliomas (HGG) (diffuse midline glioma (DMG), H3 K27-altered, diffuse hemispheric glioma (DHG), H3 G34-mutant). The WHO classification recognises exceptional cases where these mutations co-occur. We report one such case of a 2-year-old female presenting with neurological symptoms and lethargy. METHODS MRI imaging identified a brainstem lesion; a stereotactic needle biopsy was undertaken, followed by standard diagnostic neuropathology workflows including histology review, panel immunohistochemistry, DNA methylation profiling (Illumina EPIC BeadArrays, brain tumour classifier (MNP v12.5 R package)) and WES/WGS. Patient-derived in vitro cell cultures were established allowing further characterisation using RNAseq and chromatin immunoprecipitation assays with sequencing (ChIP-seq). RESULTS Histology showed diffusely infiltrating pleomorphic astrocytes, multinucleated cells, and conspicuous mitotic activity; the diagnosis was DMG, H3 K27-altered (immunohistochemistry: H3K27me3 loss, H3K27M positivity). DNA methylation profiling classified the tumour as ‘DMG, H3 K27-altered’ (calibrated score=0.99). WES/WGS revealed concurrent H3.1 K27M and G34R mutations (clonal, in the same reads) of HIST1H3C (H3C3), somatic variants in FGF11 and PIK3CA, and a pathogenic germline NBN variant. The RNAseq profile of the primary tumour and cell cultures clustered with H3 K27M-mutant tumours. Unbiased in vitro drug screening showed no selective sensitivities. ChIP-seq (H3K27ac, H3K27me3, H3K36me3, RNApol2 marks) showed features in keeping with DMG H3 K27M-mutant tumours (H3K27ac loci including OLIG2, IRX1/2, PKDCC). The patient was treated with adjuvant radiotherapy and everolimus, but progressed and passed away 13 months post-diagnosis. CONCLUSION This case is an exceptionally rare, complex variant of histone-mutant paediatric HGG, and the first reported occurrence in HIST1H3C (H3C3). It illustrates that in cis, the H3.1 K27M mutation demonstrates a dominance over molecular and clinical profiles compared to G34R, and highlights the importance of broad molecular profiling to identify such examples for further study.

HGG-29. INTEGRATED MOLECULAR PROFILING OF &amp;gt;2000 PAEDIATRIC-TYPE DIFFUSE HIGH-GRADE GLIOMA TUMOURS AND &amp;gt;100 PATIENT-DERIVED MODELS

Abstract BACKGROUND Paediatric-type diffuse high-grade glioma (PDHGG) is recognised in the WHO2021 CNS Tumour Classification as biologically distinct from ‘adult-types’, and comprises four major categories based on distinct biology and clinical presentation, though sub-types within these are less well-defined. METHODS Published and unpublished genome/exome/targeted sequencing from n=1593 cases were integrated with n=2027 cases with methylation array profiling, with an overlap of n=761. These were supplemented with varying availability of bulk/single-cell RNAseq, ChIP-seq and ATAC-seq, and analysed alongside data from ~100 patient-derived models representing 16 PDHGG sub-types. RESULTS Around half of cases were diffuse midline glioma with H3K27 alterations (DMG-H3K27), which can be split into sub-types based upon either the specific histone alteration (DMG-H3.3K27M/-H3.1K27M/-EZHIP) or co-segregating pathway activating mutations (DMG-EGFR/-MAPK/-ACVR1). These showed distinct copy number, transcriptomic and epigenetic signatures depending on the anatomical location, replicated in ~50 models established as 2D/3D cultures and CDX/PDX. By contrast, diffuse hemispheric glioma with H3G34 mutations (DHG-H3G34) presented a homogenous genomic landscape (TP53/ATRX/PDGFRA) but with a higher degree of transcriptional diversity in both tumours and models. In high-grade glioma, H3/IDH-wild-type (HGG-WT) samples, receptor tyrosine kinase (RTK) subtypes are split by the presence or absence of co-segregating mutations (BCOR/SETD2/NF1) and copy number changes. Across methylation-based subtypes, genetic drivers are associated with distinct phenotypes or drug responses independent of location. EGFR mutations are largely confined to the highly infiltrative pedHGG_RTK2 hemispheric (enriched in gliomatosis cerebri) and DMG-EGFR (bithalamic glioma) subtypes, whilst PDGFRAmutations span DMG-H3.3K27M, DHG-H3G34, and pedHGG-RTK1, and exhibit a differential drug response to targeted therapies depending on the nature of the alteration. Hypermutant tumours are split between pedHGG-RTK1A and the emerging ‘HGG-E’ subtype. CONCLUSIONS These data aim to catalogue the diversity of PDHGG subtypes in order to identify biologically rational targets, and credential patient-derived models which can be exploited for mechanistic/preclinical studies.

Diffusion kurtosis imaging-based habitat analysis identifies high-risk molecular subtypes and heterogeneity matching in diffuse gliomas.

High-risk types of diffuse gliomas in adults include isocitrate dehydrogenase (IDH) wild-type glioblastomas and grade 4 astrocytomas. Achieving noninvasive prediction of high-risk molecular subtypes of gliomas is important for personalized and precise diagnosis and treatment. We retrospectively collected data from 116 patients diagnosed with adult diffuse gliomas. Multiple high-risk molecular markers were tested, and various habitat models and whole-tumor models were constructed based on preoperative routine and diffusion kurtosis imaging (DKI) sequences to predict high-risk molecular subtypes of gliomas. Feature selection and model construction utilized Least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM). Finally, the Wilcoxon rank-sum test was employed to explore the correlation between habitat quantitative features (intra-tumor heterogeneity score，ITH score) and heterogeneity, as well as high-risk molecular subtypes. The results showed that the habitat analysis model based on DKI performed remarkably well (with AUC values reaching 0.977 and 0.902 in the training and test sets, respectively). The model's performance was further enhanced when combined with clinical variables. (The AUC values were 0.994 and 0.920, respectively.) Additionally, we found a close correlation between ITH score and heterogeneity, with statistically significant differences observed between high-risk and non-high-risk molecular subtypes. The habitat model based on DKI is an ideal means for preoperatively predicting high-risk molecular subtypes of gliomas, holding significant value for noninvasively alerting malignant gliomas and those with malignant transformation potential.

The molecular signature and prognosis of glioma with preoperative intratumoral hemorrhage: a retrospective cohort analysis

BackgroundIntratumoral hemorrhage, though less common, could be the first clinical manifestation of glioma and is detectable via MRI; however, its exact impacts on patient outcomes remain unclear and controversial. The 2021 WHO CNS 5 classification emphasised genetic and molecular features, initiating the necessity to establish the correlation between hemorrhage and molecular alterations. This study aims to determine the prevalence of intratumoral hemorrhage in glioma subtypes and identify associated molecular and clinical characteristics to improve patient management.MethodsIntegrated clinical data and imaging studies of patients who underwent surgery at the Department of Neurosurgery at Peking Union Medical College Hospital from January 2011 to January 2022 with pathological confirmation of glioma were retrospectively reviewed. Patients were divided into hemorrhage and non-hemorrhage groups based on preoperative magnetic resonance imaging. A comparison and survival analysis were conducted with the two groups. In terms of subgroup analysis, we classified patients into astrocytoma, IDH-mutant; oligodendroglioma, IDH-mutant, 1p/19q-codeleted; glioblastoma, IDH-wildtype; pediatric-type gliomas; or circumscribed glioma using integrated histological and molecular characteristics, according to WHO CNS 5 classifications.Results457 patients were enrolled in the analysis, including 67 (14.7%) patients with intratumoral hemorrhage. The hemorrhage group was significantly older and had worse preoperative Karnofsky performance scores. The hemorrhage group had a higher occurrence of neurological impairment and a higher Ki-67 index. Molecular analysis indicated that CDKN2B, KMT5B, and PIK3CA alteration occurred more in the hemorrhage group (CDKN2B, 84.4% vs. 62.2%, p = 0.029; KMT5B, 25.0% vs. 8.9%, p = 0.029; and PIK3CA, 81.3% vs. 58.5%, p = 0.029). Survival analysis showed significantly worse prognoses for the hemorrhage group (hemorrhage 18.4 months vs. non-hemorrhage 39.1 months, p = 0.01). In subgroup analysis, the multivariate analysis showed that intra-tumoral hemorrhage is an independent risk factor only in glioblastoma, IDH-wildtype (162 cases of 457 overall, HR = 1.72, p = 0.026), but not in other types of gliomas. The molecular alteration of CDK6 (hemorrhage group p = 0.004, non-hemorrhage group p < 0.001), EGFR (hemorrhage group p = 0.003, non-hemorrhage group p = 0.001), and FGFR2 (hemorrhage group p = 0.007, non-hemorrhage group p = 0.001) was associated with shorter overall survival time in both hemorrhage and non-hemorrhage groups.ConclusionsGlioma patients with preoperative intratumoral hemorrhage had unfavorable prognoses compared to their nonhemorrhage counterparts. CDKN2B, KMT5B, and PIK3CA alterations were associated with an increased occurrence of intratumoral hemorrhage, which might be future targets for further investigation of intratumoral hemorrhage.

Radiomics Analysis of Quantitative Maps from Synthetic MRI for Predicting Grades and Molecular Subtypes of Diffuse Gliomas.

To investigate the feasibility of using radiomics analysis of quantitative maps from synthetic MRI to preoperatively predict diffuse glioma grades, isocitrate dehydrogenase (IDH) subtypes, and 1p/19q codeletion status. Data from 124 patients with diffuse glioma were used for analysis (n = 87 for training, n = 37 for testing). Quantitative T1, T2, and proton density (PD) maps were obtained using synthetic MRI. Enhancing tumour (ET), non-enhancing tumour and necrosis (NET), and peritumoral edema (PE) regions were segmented followed by manual fine-tuning. Features were extracted using PyRadiomics and then selected using Levene/T, BorutaShap and maximum relevance minimum redundancy algorithms. Asupport vector machine was adopted for classification. Receiver operating characteristic curve analysis and integrated discrimination improvement analysis were implemented to compare the performance of different radiomics models. Radiomics models constructed using features from multiple tumour subregions (ET + NET + PE) in the combined maps (T1 + T2 + PD) achieved the highest AUC in all three prediction tasks, among which the AUC for differentiating lower-grade and high-grade diffuse gliomas, predicting IDH mutation status and predicting 1p/19q codeletion status were 0.92, 0.95 and 0.86 respectively. Compared with those constructed on individual T1, T2, and PD maps, the discriminant ability of radiomics models constructed on the combined maps separately increased by 11, 17 and 10% in predicting glioma grades, 35, 52 and 19% in predicting IDH mutation status, and 16, 15 and 14% in predicting 1p/19q codeletion status (p < 0.05). Radiomics analysis of quantitative maps from synthetic MRI provides anew quantitative imaging tool for the preoperative prediction of grades and molecular subtypes in diffuse gliomas.

Multi-omics Analysis Revealed that the CCN Family Regulates Cell Crosstalk, Extracellular Matrix, and Immune Escape, Leading to a Poor Prognosis of Glioma.

The CCN family is a group of matricellular proteins associated with the extracellular matrix. This study aims to explore the role of the CCN family in glioma development and its implications in the tumor microenvironment. Through analysis of bulk RNA-seq cohorts, correlations between CCN family expression and glioma subtypes, patient survival, and bioactive pathway enrichment were investigated. Additionally, single-cell datasets were employed to identify novel cell subgroups, followed by analyses of cell communication and transcription factors. Spatial transcriptomic analysis was utilized to validate the CCN family's involvement in glioma. Results indicate overexpression of CYR61,CTGF, and WISP1 in glioma, associated with unfavorable subtypes and reduced survival. Enrichment analyses revealed associations with oncogenic pathways, while CTGF and WISP1 expression correlated with increased infiltration of regulatory T cells and M2 macrophages. Single-cell analysis identified MES-like cells as the highest CCN expression. Moreover, intercellular signal transduction analysis demonstrated active pathways, including SPP1-CD44, in cell subgroups with elevated CYR61 and CTGF expression. Spatial transcriptomic analysis confirmed co-localization of CYR61,CTGF and SPP1-CD44 with high oncogenic pathway activity. These findings suggest that CCN family members may serve as potential prognostic biomarkers and therapeutic targets for glioma.

Frailty in patients with IDH-mutant gliomas: experience from a high-volume tumor center.

Gliomas are increasingly diagnosed in an aging population, with treatment outcomes influenced by factors like tumor genetics and patient frailty. This study focused on IDH-mutant gliomas and assessed how frailty affects 30-day readmission and overall survival (OS). We aimed to address a gap in understanding the impact of frailty on this specific glioma subtype. 136 patients with an IDH-mutant glioma between 2007 and 2021 were identified at our institution. High frailty was classified by scores ≥ 1 on the 5-factor modified frailty index (mFI-5) and ≥ 3 on the Charlson Comorbidity Index (CCI). Patient and tumor characteristics including age, sex, race, Karnofsky Performance Status (KPS), Body Mass Index (BMI), tumor type and location, type of operation, and therapy course were recorded. Outcomes measured included 30-day readmission and overall survival (OS). Analysis was conducted utilizing logistic regression and Kaplan-Meier curves. Of the 136 patients, 52 (38%) had high frailty: 18 with CCI ≥ 3, 34 with mFI-5 ≥ 1. High frailty correlated with increased BMI (CCI: 30.2, mFI-5: 30.1kg/m2), more neurological deficits (CCI: 61%, mFI-5: 56%), and older age at surgery (CCI: 63, mFI-5: 48years). Hospital readmission within 30days occurred in 8 (5.9%) patients. Logistic regression indicated no significant difference in 30-day readmission rates (CCI: p = 0.30, mFI-5: p = 0.62) or median OS between high and low frailty groups. However, patients treated at our institution with newly diagnosed tumors with high mFI-5 had a 6.79 times higher adjusted death hazard than those with low mFI-5 (p = .049). Our analysis revealed that CCI and mFI-5 were not significantly associated with 30-day nor OS. However, in patients with non-recurrent tumors, there was a significant association of mFI-5 with OS. Further study of frailty with larger cohorts is warranted to enhance prognostication of outcome after neurosurgical treatment.

Raman-based machine-learning platform reveals unique metabolic differences between IDHmut and IDHwt glioma.

Formalin-fixed, paraffin-embedded (FFPE) tissue slides are routinely used in cancer diagnosis, clinical decision-making, and stored in biobanks, but their utilization in Raman spectroscopy-based studies has been limited due to the background coming from embedding media. Spontaneous Raman spectroscopy was used for molecular fingerprinting of FFPE tissue from 46 patient samples with known methylation subtypes. Spectra were used to construct tumor/non-tumor, IDH1WT/IDH1mut, and methylation-subtype classifiers. Support vector machine and random forest were used to identify the most discriminatory Raman frequencies. Stimulated Raman spectroscopy was used to validate the frequencies identified. Mass spectrometry of glioma cell lines and TCGA were used to validate the biological findings. Here, we develop APOLLO (rAman-based PathOLogy of maLignant gliOma)-a computational workflow that predicts different subtypes of glioma from spontaneous Raman spectra of FFPE tissue slides. Our novel APOLLO platform distinguishes tumors from nontumor tissue and identifies novel Raman peaks corresponding to DNA and proteins that are more intense in the tumor. APOLLO differentiates isocitrate dehydrogenase 1 mutant (IDH1mut) from wild-type (IDH1WT) tumors and identifies cholesterol ester levels to be highly abundant in IDHmut glioma. Moreover, APOLLO achieves high discriminative power between finer, clinically relevant glioma methylation subtypes, distinguishing between the CpG island hypermethylated phenotype (G-CIMP)-high and G-CIMP-low molecular phenotypes within the IDH1mut types. Our results demonstrate the potential of label-free Raman spectroscopy to classify glioma subtypes from FFPE slides and to extract meaningful biological information thus opening the door for future applications on these archived tissues in other cancers.