Classification Of Gliomas Research Articles

IMPA-Net: Interpretable Multi-Part Attention Network for Trustworthy Brain Tumor Classification from MRI.

Deep learning (DL) networks have shown attractive performance in medical image processing tasks such as brain tumor classification. However, they are often criticized as mysterious "black boxes". The opaqueness of the model and the reasoning process make it difficult for health workers to decide whether to trust the prediction outcomes. In this study, we develop an interpretable multi-part attention network (IMPA-Net) for brain tumor classification to enhance the interpretability and trustworthiness of classification outcomes. The proposed model not only predicts the tumor grade but also provides a global explanation for the model interpretability and a local explanation as justification for the proffered prediction. Global explanation is represented as a group of feature patterns that the model learns to distinguish high-grade glioma (HGG) and low-grade glioma (LGG) classes. Local explanation interprets the reasoning process of an individual prediction by calculating the similarity between the prototypical parts of the image and a group of pre-learned task-related features. Experiments conducted on the BraTS2017 dataset demonstrate that IMPA-Net is a verifiable model for the classification task. A percentage of 86% of feature patterns were assessed by two radiologists to be valid for representing task-relevant medical features. The model shows a classification accuracy of 92.12%, of which 81.17% were evaluated as trustworthy based on local explanations. Our interpretable model is a trustworthy model that can be used for decision aids for glioma classification. Compared with black-box CNNs, it allows health workers and patients to understand the reasoning process and trust the prediction outcomes.

Novel Imaging Approaches for Glioma Classification in the Era of the World Health Organization 2021 Update: A Scoping Review.

The 2021 WHO classification of CNS tumors is a challenge for neuroradiologists due to the central role of the molecular profile of tumors. The potential of novel data analysis tools in neuroimaging must be harnessed to maintain its role in predicting tumor subgroups. We performed a scoping review to determine current evidence and research gaps. A comprehensive literature search was conducted regarding glioma subgroups according to the 2021 WHO classification and the use of MRI, radiomics, machine learning, and deep learning algorithms. Sixty-two original articles were included and analyzed by extracting data on the study design and results. Only 8% of the studies included pediatric patients. Low-grade gliomas and diffuse midline gliomas were represented in one-third of the research papers. Public datasets were utilized in 22% of the studies. Conventional imaging sequences prevailed; data on functional MRI (DWI, PWI, CEST, etc.) are underrepresented. Multiparametric MRI yielded the best prediction results. IDH mutation and 1p/19q codeletion status prediction remain in focus with limited data on other molecular subgroups. Reported AUC values range from 0.6 to 0.98. Studies designed to assess generalizability are scarce. Performance is worse for smaller subgroups (e.g., 1p/19q codeleted or IDH1/2 mutated gliomas). More high-quality study designs with diversity in the analyzed population and techniques are needed.

Classifying Glioma via Liquid Biopsy: Progress toward an Unmet Clinical Need.

The diagnosis and classification of glioma by liquid biopsy represent a critical unmet need in neuro-oncology. A recent study demonstrates targeted next-generation sequencing of cell-free DNA from cerebrospinal fluid as an evolving option for liquid biopsy in patients with glioma. See related article by Iser et al., p. 2974.

A novel method for glioma segmentation and classification on pre-operative MRI scans using 3D U-Nets and transfer learning

A novel method for glioma segmentation and classification on pre-operative MRI scans using 3D U-Nets and transfer learning

2-Hydroxyglutarate as an MR spectroscopic predictor of an IDH mutation in gliomas.

The mutated enzyme isocitrate dehydrogenase (IDH) 1 and 2 has been detected in various tumor entities such as gliomas and can convert α-ketoglutarate into the oncometabolite 2-hydroxyglutarate (2-HG). This neuro-oncologically significant metabolic product can be detected by MR spectroscopy and is therefore suitable for noninvasive glioma classification and therapy monitoring.This paper provides an up-to-date overview of the methodology and relevance of 1H-MR spectroscopy (MRS) in the oncological primary and follow-up diagnosis of gliomas. The possibilities and limitations of this MR spectroscopic examination are evaluated on the basis of the available literature.By detecting 2-HG, MRS can in principle offer a noninvasive alternative to immunohistological analysis thus avoiding surgical intervention in some cases. However, in addition to an adapted and optimized examination protocol, the individual measurement conditions in the examination region are of decisive importance. Due to the inherently small signal of 2-HG, unfavorable measurement conditions can influence the reliability of detection. · MR spectroscopy enables the non-invasive detection of 2-hydroxyglutarate.. · The measurement of this metabolite allows the detection of an IDH mutation in gliomas.. · The choice of MR examination method is particularly important.. · Detection reliability is influenced by glioma size, necrotic tissue and the existing measurement conditions.. · Bauer J, Raum HN, Kugel H et al. 2-Hydroxyglutarate as an MR spectroscopic predictor of an IDH mutation in gliomas. Fortschr Röntgenstr 2024; DOI 10.1055/a-2285-4923.

Identifying Diffuse Glioma Subtypes Based on Pathway Enrichment Evaluation.

Gliomas are highly heterogeneous in molecular, histology, and microenvironment. However, a classification of gliomas by integrating different tumor microenvironment (TME) components remains unexplored.Based on the enrichment scores of 17 pathways involved in immune, stromal, DNA repair, and nervous system signatures in diffuse gliomas, we performed consensus clustering to uncover novel subtypes of gliomas. Consistently in three glioma datasets (TCGA-glioma, CGGA325, and CGGA301), we identified three subtypes: Stromal-enriched (Str-G), Nerve-enriched (Ner-G), and mixed (Mix-G). Ner-G was charactered by low immune infiltration levels, stromal contents, tumor mutation burden, copy number alterations, DNA repair activity, cell proliferation, epithelial-mesenchymal transformation, stemness, intratumor heterogeneity, androgen receptorexpression and EGFR, PTEN, NF1 and MUC16 mutation rates, while high enrichment of neurons and nervous system pathways, and high tumor purity, estrogen receptor expression, IDH1 and CIC mutation rates, temozolomide response rate and overall and disease-free survival rates. In contrast, Str-G displayed contrastive characteristics to Ner-G. Our analysis indicates that the heterogeneity between glioma cells and neurons is lower than that between glioma cells and immune and stromal cells. Furthermore, the abundance of neurons is positively associated with clinical outcomes in gliomas, while the enrichment of immune and stromal cells has a negative association with them. Our classification method provides new insights into the tumor biology of gliomas, as well as clinical implications for the precise management of this disease.

Signature Construction and Disulfidptosis-Related Molecular Cluster Identification for Better Prediction of Prognosis in Glioma.

Disulfidptosis is a newly discovered form of regulatory cell death. However, the identification of disulfidptosis-related molecular subtypes and potential biomarkers in gliomas and their prognostic predictive potential need to be further elucidated. RNA sequencing profiles and the relevant clinical data were obtained from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Disulfidptosis-related clusters were identified by unsupervised clustering analysis. Immune cell infiltration analysis and drug sensitivity analysis were used to explore the differences between clusters. Gene set enrichment analysis (GSEA) of differential genes between clusters was performed to explore the potential biological functions and signaling. A disulfidptosis-related scoring system (DRSS) was constructed based on a combined COX and LASSO analysis. Mendelian randomization (MR) analyses were used to further explore the causal relationship between levels of genes in DRSS and an increased risk of glioma. A prognosis nomogram was constructed based on the DRSS and 3 clinical features (age, WHO stage, and IDH status). The accuracy and stability of the prognosis nomogram were also validated in different cohorts. We identified two clusters that exhibited different prognoses, drug sensitivity profiles, and tumor microenvironment infiltration profiles. The overall survival (OS) of Cluster2 was significantly better than Cluster1. Cluster1 had an overall greater infiltration of immune cells compared to Cluster2. However, the Monocytes, activated B cells had higher infiltration abundance in Cluster2. GSEA results showed significant enrichment of immune-related biological processes in Cluster1, while Cluster2 was more enriched for functions related to neurotransmission and regulation. PER3, RAB34, NKX3-2, GPX7, FRA10AC1, and TGIF1 were finally included to construct DRSS. DRSS was independently related to prognosis. There was a significant difference in overall survival between the low-risk score group and the high-risk score group. Among six genes in DRSS, GPX7 levels were demonstrated to have a causal relationship with an increased risk of glioma. GPX7 may become a more promising biomarker for gliomas. The prognosis nomogram constructed based on the DRSS and three clinical features has considerable potential for predicting the prognosis of patients with glioma. Free online software for implementing this nomogram was established: https://yekun-zhuang.shinyapps.io/DynNomapp/ . Our study established a novel glioma classification based on the disulfidptosis-related molecular subtypes. We constructed the DRSS and the prognosis nomogram to accurately stratify the prognosis of glioma patients. GPX7 was identified as a more promising biomarker for glioma. We provide important insights into the treatment and prognosis of gliomas.

Diagnostic Value/Accuracy of Diffusion-weighted Magnetic Resonance Imaging in Low-grade Glioma: Systematic Review

Tumor grading plays a decisive role in glioma classification, which impacts the treatment strategy and prognosis of the patient. Diffusion weighted imaging (DWI) technique is a noninvasive technique used in tumor grading. This study explores whether DWI can be effectively used in tumor grading and the identification of low-grade glioma (LGG) by analyzing apparent diffusion coefficient (ADC) values. Publicly available English databases, PubMed and Google Scholar, were queried from 2015 to 2021. We mined a total of 465 research documents; of which nine articles were selected based on the inclusion and exclusion criteria. The overall findings show cutoff threshold min ADC values with higher b value playing a significant role in the differentiation of high-grade gliomas and LGGs, tumor grading, and determining tumor progression and stability. This systematic review suggests the development of unified DWI imaging parameters with better sensitivity and specificity for grading LGG tumors.

1243 FOXM1*3 Overexpression Correlates to Staging of Astrocytic Glioma

INTRODUCTION: Diffuse gliomas are a group of primary brain tumors characterized by the presence of neoplastic glial-lineage cells. WHO 2021 classification of gliomas recognizes the mutational status of the isocitrate dehydrogenase-1(IDH-1,G395A) gene and the 1p/19q codeletion as identification of cell lineage and a prognosis marker. Transcription factors are binding proteins of genomic DNA and can activate or suppress gene expression. Forkhead box M1(FOXM1) is a transcription factor for Forkhead box(Fox) proteins, a superfamily which is defined by a conserved winged helix DNA binding domain. FOXM1 is a key regulator of both G1/S and G2/M phases of cell cycle and mitotic spindle integrity, it also plays an important role in angiogenesis, DNA damage repair and tissue regeneration. The human FOXM1 gene consists of 10 exons, two of them alternatively spliced. This splicing gives rise to three isoforms: FOXM1*1,*2 and *3. FOXM1*3 is the smallest isoform and is overexpressed in proliferative tissue such as malignant tumors. METHODS: A control group of 10 brain cortex, 22 A2, 13 O2, 11 A3, 3 O3, 6 A4 and 46 GBM with histological and molecular diagnosis of astrocytoma, oligodendroglioma and glioblastoma from 2011 to 2022 were included. The diagnosis correlated with patient outcome. mRNA FOXM1*3 and its housekeeping gen IPO8 was quantified by qPCR with Taqman probes. Data analysis was conducted by MannWhitney U test. RESULTS: There was a statistically significant difference between expression of mRNA FOXM1*3 in normal brain tissue compared to tumor tissue (p < 0.001). The transcript allowed differentiation between grade 2, 3 and 4 astrocytomas (p < 0.005), nevertheless there was no difference between astrocytic versus oligodendrocytic lineage, and grade 4 astrocytoma versus glioblastoma. CONCLUSIONS: The normalized expression of transcript FOXM1*3 allows differentiation of astrocytic gliomas grades.

MODERN IMAGING TECHNIQUES IN THE ASSESSMENT
 OF MALIGNANCY IN GLIAL BRAIN TUMORS:
 LITERATURE REVIEW

Relevance: Gliomas represent a major group of primary central nervous system tumors, accounting for approximately 80% of all
 malignant brain neoplasms. Recent advances in molecular diagnostics, including the identification of mutations in the isocitrate dehydrogenase(IDH) gene, offer new opportunities to improve the classification of gliomas and to understand the mechanisms of their development. IDH
 mutations, which are found in a large proportion of gliomas, are associated with a better prognosis and represent an important biomarker
 for the development of targeted therapeutic strategies. Diagnostic techniques, particularly magnetic resonance imaging (MRI), also play
 an important role, providing additional ways to assess the morphological and biological characteristics of gliomas, contributing to a more
 accurate determination of tumor malignancy and molecular profile, which is key to treatment decisions.
 The purpose: To study the possibilities of magnetic resonance imaging in assessing the degree of malignancy of glial brain tumors.
 Method: A literature review was performed using the keywords: glioma, IDH mutation, IDH wild-type, MRI, magnetic resonance
 spectroscopy (MRS), apparent diffusion coefficient (ADC), diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), diffusion kurtosis
 image (DKI), Susceptibility Weighted Imaging (SWI) in PubMed, BMC Medicine, and Google Scholar databases. The review includes the results
 of an analysis of 45 literature reviews on the diagnostic capabilities of molecular diagnostics and MRI to assess the malignancy of gliomas.
 Results: This review highlights the importance of current approaches to the diagnosis of gliomas, demonstrating that different grades of
 malignancy and IDH mutation status are associated with unique features on MR imaging, including differences in localization, signal, and
 enhancement patterns. DTI and DWI extend the ability of MRI to assess tissue microstructure, allowing for more precise tumor characterization.
 Studies combining MRI morphologic features, ADC and DTI parameters offer a non-invasive approach to glioma prognosis, highlighting the
 importance of integrated use of imaging modalities to improve diagnosis and therapy.
 Conclusion: The use of magnetic resonance imaging allows for more accurate morphologic and metabolic characterization of gliomas
 in assessing the degree of malignancy, allowing physicians to make more accurate prognostic assessments and select the most appropriate
 treatment strategy.
 Keywords: Glioma, IDH mutation, wild-type IDH, MRI, magnetic resonance spectroscopy, ADC, DWI, DTI, DKI, SWI

Utilizing the amide proton transfer technique to characterize diffuse gliomas based on the WHO 2021 classification of CNS tumors.

Diffuse gliomas present a significant challenge for healthcare systems globally. While brain MRI plays a vital role in diagnosis, prognosis, and treatment monitoring, accurately characterizing gliomas using conventional MRI techniques alone is challenging. In this study, we explored the potential of utilizing the amide proton transfer (APT) technique to predict tumor grade and type based on the WHO 2021 Classification of CNS Tumors. Forty-two adult patients with histopathologically confirmed brain gliomas were included in the study. They underwent 3T MRI imaging, which involved APT sequence. Multinomial and binary logistic regression models were employed to classify patients into clinically relevant groups based on MRI findings and demographic variables. We found that the best model for tumor grade classification included patient age along with APT values. The highest sensitivity (88%) was observed for Grade 4 tumors, while Grade 3 tumors showed the highest specificity (79%). For tumor type classification, our model incorporated four predictors: APT values, patient's age, necrosis, and the presence of hemorrhage. The glioblastoma group had the highest sensitivity and specificity (87%), whereas balanced accuracy was the lowest for astrocytomas (0.73). The APT technique shows great potential for noninvasive evaluation of diffuse gliomas. The changes in the classification of gliomas as per the WHO 2021 version of the CNS Tumor Classification did not affect its usefulness in predicting tumor grade or type.

Stratification of glioma based on stemness scores in bulk and single-cell transcriptomes

BackgroundBrain tumours are known to have a high mortality and morbidity rate due to their localised and frequent invasive growth. The concept that glioma resistance could originate from the dissimilarity in the vulnerability of clonogenic glial stem cells to chemotherapeutic drugs and radiation has driven the scientific community to reexamine the comprehension of glioma growth and strategies that target these cells or modify their stemness. MethodsBased on the enrichment scores of 12 stemness signatures, we identified glioma subtypes in both tumour bulks and single cells by clustering analysis. Furthermore, we comprehensively compared molecular and clinical features among the glioma subtypes. ResultsConsistently, in seven different datasets, hierarchical clustering uncovered three subtypes of glioma, termed Stem-H, Stem-M, and Stem-L, with high, medium, and low stemness signatures, respectively. Stem-H and Stem-L exhibited the most unfavorable and favourable overall and disease-free survival, respectively. Stem-H showed the highest enrichment scores of the EMT, invasion, proliferation, differentiation, and metastasis processes signatures, while Stem-L displayed the lowest. Stem-H harboured a greater proportion of late-stage tumours compared to Stem-L. Moreover, Stem-H manifested higher tumour mutation burden, DNA damage repair and cell cycle activity, intratumour heterogeneity, and a more frequent incidence of TP53 and EGFR mutations than Stem-L. In contrast, Stem-L had higher O6-Methylguanine-DNA Methyltransferase (MGMT) methylation levels. ConclusionThe classification of glioma based on stemness may offer new insights into the biology of the tumour, as well as more accurate clinical management of the disease.

Abstract 3497: Glioma Immune Microenvironment Composition Calculator (GIMiCC): A method of estimating the proportions of eighteen key cell types from human brain tissue assayed using Illumina DNA methylation microarray technology

Abstract Precise detection of both tumor subclass and immune composition is critical in the pursuit of personalized immunotherapeutic treatment strategies for glioma. DNA methylation (DNAm) biomarkers are promising on both fronts. DNAm-based biomarkers for molecular classification have been developed and integrated into World Health Organization (WHO) diagnostic criteria. However, prediction of the tumor microenvironment is not as well defined. To fill this gap, we present GIMiCC, an approach for cellular deconvolution of DNAm microarray data for four WHO 2021 tumor subclasses: glioblastoma, oligodendroglioma, grade 2/3 astrocytoma and grade 4 astrocytoma. Using data from 17 isolated cell types, we describe the derivation of the deconvolution libraries and test the method on independent datasets from the Cancer Genome Atlas (TCGA) and the DKFZ molecular neuropathology classifier (PMID: 29539639). We utilize GIMiCC to illustrate that DNAm-based glioma classification is unlikely to be biased by compositional variation. In addition, we utilize GIMiCC to identify composition independent DNAm alterations that are associated with high immune infiltration. Our future work aims to optimize GIMiCC and advance the clinical evaluation of glioma with this new tool. Citation Format: Steven C. Pike, John K. Wiencke, Ze Zhang, Annette M. Molinaro, Devin C. Koestler, Brock C. Christensen, Karl T. Kelsey, Lucas A. Salas. Glioma Immune Microenvironment Composition Calculator (GIMiCC): A method of estimating the proportions of eighteen key cell types from human brain tissue assayed using Illumina DNA methylation microarray technology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3497.

Prognostic Value of ATRX and p53 Status in High-Grade Glioma Patients in Morocco.

Glioblastoma and astrocytoma, grade 4, are the most common and aggressive brain tumors. Several biomarkers, such as the isocitrate dehydrogenase mutation (IDH-1), alpha-thalassemia/mental retardation, and the X-linked mutation (ATRX), enable more accurate glioma classification and facilitate patient management. This study aimed to determine the prognostic value of clinical and molecular factors (IDH, TP53, and ATRX mutations). We also studied the relationship between these molecular markers and the overall survival (OS) of 126 patients with grade 4 glioblastoma/astrocytoma. The immunohistochemical study was conducted using antibodies namely, IDH1, R132H, p53, and ATRX. Statistical tests were used to investigate factors that might influence overall survival using IBM SPSS Statistics, version 25.0 (IBM Corp., Armonk, NY). The median age at diagnosis was 51.5 years. Patients with a Karnofsky performance score (KPS) <70 presented less favorable survival outcomes compared to those with a KPS ≥70. The median OSfor patients was found to be 11.17 months. Expression ofIDH1 R132H was found in 13.5% of patients, p53 overexpression was identified in 55.6% of cases, and loss of ATRX expression was detected in 11.9%. The group of patients with IDH mutant/ATRX mutant/p53 wild-typehad the best prognosis (OS= 27.393 months; p = 0.015). Our results were in line with previous studies. The clinical value of IDH and ATRX mutations in prognostic assessment was confirmed (p ≤0.05). The overexpression of p53 had no significant impact on OS (p = 0.726). Therefore, p53 alone cannot predict survival in glioblastoma patients. Based on the results, these biomarkers may be a potential therapeutic target to prolong patient survival, hence the need for further investigations.

Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study

We provide in this paper a comprehensive comparison of various transfer learning strategies and deep learning architectures for computer-aided classification of adult-type diffuse gliomas. We evaluate the generalizability of out-of-domain ImageNet representations for a target domain of histopathological images, and study the impact of in-domain adaptation using self-supervised and multi-task learning approaches for pretraining the models using the medium-to-large scale datasets of histopathological images. A semi-supervised learning approach is furthermore proposed, where the fine-tuned models are utilized to predict the labels of unannotated regions of the whole slide images (WSI). The models are subsequently retrained using the ground-truth labels and weak labels determined in the previous step, providing superior performance in comparison to standard in-domain transfer learning with balanced accuracy of 96.91% and F1-score 97.07%, and minimizing the pathologist's efforts for annotation. Finally, we provide a visualization tool working at WSI level which generates heatmaps that highlight tumor areas; thus, providing insights to pathologists concerning the most informative parts of the WSI.

Circulating serum miR-362-3p and miR-6721-5p as potential biomarkers for classification patients with adult-type diffuse glioma.

According to the fifth edition of the WHO Classification of Tumours of the Central Nervous System (CNS) published in 2021, grade 4 gliomas classification includes IDH-mutant astrocytomas and wild-type IDH glioblastomas. Unfortunately, despite precision oncology development, the prognosis for patients with grade 4 glioma remains poor, indicating an urgent need for better diagnostic and therapeutic strategies. Circulating miRNAs besides being important regulators of cancer development could serve as promising diagnostic biomarkers for patients with grade 4 glioma. Here, we propose a two-miRNA miR-362-3p and miR-6721-5p screening signature for serum for non-invasive classification of identified glioma cases into the highest-grade 4 and lower-grade gliomas. A total of 102 samples were included in this study, comprising 78 grade 4 glioma cases and 24 grade 2-3 glioma subjects. Using the NanoString platform, seven miRNAs were identified as differentially expressed (DE), which was subsequently confirmed via RT-qPCR analysis. Next, numerous combinations of DE miRNAs were employed to develop classification models. The dual panel of miR-362-3p and miR-6721-5p displayed the highest diagnostic value to differentiate grade 4 patients and lower grade cases with an AUC of 0.867. Additionally, this signature also had a high AUC = 0.854 in the verification cohorts by RT-qPCR and an AUC = 0.842 using external data from the GEO public database. The functional annotation analyses of predicted DE miRNA target genes showed their primary involvement in the STAT3 and HIF-1 signalling pathways and the signalling pathway of pluripotency of stem cells and glioblastoma-related pathways. For additional exploration of miRNA expression patterns correlated with glioma, we performed the Weighted Gene-Co Expression Network Analysis (WGCNA). We showed that the modules most associated with glioma grade contained as many as six DE miRNAs. In conclusion, this study presents the first evidence of serum miRNA expression profiling in adult-type diffuse glioma using a classification based on the WHO 2021 guidelines. We expect that the discovered dual miR-362-3p and miR-6721-5p signatures have the potential to be utilised for grading gliomas in clinical applications.

Cerebrospinal Fluid cfDNA Sequencing for Classification of Central Nervous System Glioma.

Primary central nervous system (CNS) gliomas can be classified by characteristic genetic alterations. In addition to solid tissue obtained via surgery or biopsy, cell-free DNA (cfDNA) from cerebrospinal fluid (CSF) is an alternative source of material for genomic analyses. We performed targeted next-generation sequencing of CSF cfDNA in a representative cohort of 85 patients presenting at two neurooncological centers with suspicion of primary or recurrent glioma. Copy-number variation (CNV) profiles, single-nucleotide variants (SNV), and small insertions/deletions (indel) were combined into a molecular-guided tumor classification. Comparison with the solid tumor was performed for 38 cases with matching solid tissue available. Cases were stratified into four groups: glioblastoma (n = 32), other glioma (n = 19), nonmalignant (n = 17), and nondiagnostic (n = 17). We introduced a molecular-guided tumor classification, which enabled identification of tumor entities and/or cancer-specific alterations in 75.0% (n = 24) of glioblastoma and 52.6% (n = 10) of other glioma cases. The overlap between CSF and matching solid tissue was highest for CNVs (26%-48%) and SNVs at predefined gene loci (44%), followed by SNVs/indels identified via uninformed variant calling (8%-14%). A molecular-guided tumor classification was possible for 23.5% (n = 4) of nondiagnostic cases. We developed a targeted sequencing workflow for CSF cfDNA as well as a strategy for interpretation and reporting of sequencing results based on a molecular-guided tumor classification in glioma. See related commentary by Abdullah, p. 2860.

Frequency of IDH1 mutation in adult-type diffuse astrocytic gliomas in a tertiary hospital in Kenya.

The 2021 WHO classification of gliomas has separated gliomas based on their IDH mutation status, reflecting differences in their pathogenesis and clinical characteristics. There is a paucity of data on the prevalence of IDH mutations in gliomas in this region. This study aimed to determine the frequency of the IDH1 mutation in adult-type diffuse astrocytic gliomas in a tertiary hospital in Kenya. Approximately half of the gliomas were positive for the IDH1 mutation, with a slight male predominance. Our study provides crucial insights into the frequency of IDH1 mutations in gliomas in Kenya.

AllergoOncology: Biomarkers and refined classification for research in the allergy and glioma nexus-A joint EAACI-EANO position paper.

Epidemiological studies have explored the relationship between allergic diseases and cancer risk or prognosis in AllergoOncology. Some studies suggest an inverse association, but uncertainties remain, including in IgE-mediated diseases and glioma. Allergic disease stems from a Th2-biased immune response to allergens in predisposed atopic individuals. Allergic disorders vary in phenotype, genotype and endotype, affecting their pathophysiology. Beyond clinical manifestation and commonly used clinical markers, there is ongoing research to identify novel biomarkers for allergy diagnosis, monitoring, severity assessment and treatment. Gliomas, the most common and diverse brain tumours, have in parallel undergone changes in classification over time, with specific molecular biomarkers defining glioma subtypes. Gliomas exhibit a complex tumour-immune interphase and distinct immune microenvironment features. Immunotherapy and targeted therapy hold promise for primary brain tumour treatment, but require more specific and effective approaches. Animal studies indicate allergic airway inflammation may delay glioma progression. This collaborative European Academy of Allergy and Clinical Immunology (EAACI) and European Association of Neuro-Oncology (EANO) Position Paper summarizes recent advances and emerging biomarkers for refined allergy and adult-type diffuse glioma classification to inform future epidemiological and clinical studies. Future research is needed to enhance our understanding of immune-glioma interactions to ultimately improve patient prognosis and survival.

Diagnostic utility of genetic alterations in distinguishing IDH-wildtype glioblastoma from lower-grade gliomas: Insight from next-generation sequencing analysis of 479 cases.

The accurate diagnosis and classification of gliomas are essential for appropriate treatment planning and prognosis prediction. This study aimed to investigate the molecular diagnostics of IDH-wildtype diffuse astrocytic gliomas and identify potential genetic variants that could differentiate glioblastoma (GBM) from lower-grade gliomas when DNA methylation analysis is not feasible. In total, 479 H3-and IDH-wildtype diffuse astrocytic gliomas were included in this study. All the cases were diagnosed according to the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors. Panel sequencing data were collected, and clinicopathological information was retrieved from medical records. Genetic alterations and histological findings were analyzed to determine their diagnostic utility and prognostic implications. Out of 479 cases, 439 (91.6%) were diagnosed with GBM, including 28 cases that were molecularly diagnosed as GBM. However, 40 (8.4%) cases could not be classified according to the 2021 WHO classification and were diagnosed as lower-grade diffuse astrocytic glioma, IDH-wildtype, not elsewhere classified (LGNEC). In addition to the three genetic alterations included in the diagnostic criteria of GBM, PTEN and EGFR mutations were found to be enriched in GBM. Patients harboring mTOR pathway mutations demonstrated a more favorable prognosis and often exhibited morphology resembling subependymal giant cell astrocytoma, along with a high tumor mutational burden. Among patients with mTOR pathway mutations, those lacking molecular diagnostic features of GBM exhibited outstanding survival outcomes, even in the presence of grade 4 histology. Integration of molecular features enhanced the diagnostic accuracy of IDH-wildtype gliomas. Some molecular alterations enriched in GBM offer valuable insights for molecular diagnosis and glioma classification. Furthermore, high-grade diffuse astrocytic gliomas featuring mTOR pathway mutations in the absence of molecular diagnostic features of GBM could represent more favorable tumor types distinct from GBM.