Mutant IDH in Gliomas: Role in Cancer and Treatment Options

Solomou, Georgios; Finch, Alina; Asghar, Asim; Bardella, Chiara

doi:10.20944/preprints202304.0478.v2

Cited by 3 publications

References 167 publications

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Epitranscriptome Mapping of m⁶A RNA Modifications in Glioma Tumor Tissue

Batool,

Khan,

Muralidharan

et al. 2024

Preprint

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Glioma represents the most common primary central nervous system (CNS) neoplasm in adults. IDH1 mutation is recognized as a hallmark alteration with important diagnostic and prognostic implications. There is considerable evidence for global DNA hypermethylation induced secondary to IDH mutation. However, there is limited understanding of the RNA methylation patterns and its role in glioma biology. In this study, we performed transcriptome wide profiling of N6-methyladenosine (m6A) modifications across IDH mutant (n = 8) and wild-type (n = 7) gliomas using Oxford Nanopore Technologies direct RNA sequencing platform. Our approach enabled high-depth coverage of native transcripts, revealing nearly twice as many full-length transcripts in IDH mutant gliomas compared to wild-type. Notable differences in alternative splicing were observed across glioma subtypes, with truncated and non-coding isoforms more prevalent in glioblastoma (GBM). We further identified significant changes in isoform usage within key metabolic (NAMPT, PKM) and immune (CD63, CD151, CD81) pathways. Chromosomal distribution of m6A sites showed a higher prevalence of m6A modifications in IDH mutant gliomas, with the most pronounced differences on chromosomes 19 and 16. Further stratification by TERT, MGMT, and TP53 mutations revealed similar patterns of increased m6A site numbers in mutant groups, highlighting the importance of integrating epigenomic and epitranscriptomic data in glioma research. These findings highlight the role of m6A modifications in the metabolic reprogramming unique to IDH mutant gliomas, providing insights into potential mechanisms of tumorigenesis and therapeutic resistance.

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Epitranscriptome Mapping of m⁶A RNA Modifications in Glioma Tumor Tissue

Batool,

Khan,

Muralidharan

et al. 2024

Preprint

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Comparison of MRI Sequences to Predict IDH Mutation Status in Gliomas Using Radiomics-Based Machine Learning

Kasap,

Mora,

Blömer

et al. 2024

Biomedicines

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Objectives: Regarding the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors, the isocitrate dehydrogenase (IDH) mutation status is one of the most important factors for CNS tumor classification. The aim of our study is to analyze which of the commonly used magnetic resonance imaging (MRI) sequences is best suited to obtain this information non-invasively using radiomics-based machine learning models. We developed machine learning models based on different MRI sequences and determined which of the MRI sequences analyzed yields the highest discriminatory power in predicting the IDH mutation status. Material and Methods: In our retrospective IRB-approved study, we used the MRI images of 106 patients with histologically confirmed gliomas. The MRI images were acquired using the T1 sequence with and without administration of a contrast agent, the T2 sequence, and the Fluid-Attenuated Inversion Recovery (FLAIR) sequence. To objectively compare performance in predicting the IDH mutation status as a function of the MRI sequence used, we included only patients in our study cohort for whom MRI images of all four sequences were available. Seventy-one of the patients had an IDH mutation, and the remaining 35 patients did not have an IDH mutation (IDH wild-type). For each of the four MRI sequences used, 107 radiomic features were extracted from the corresponding MRI images by hand-delineated regions of interest. Data partitioning into training data and independent test data was repeated 100 times to avoid random effects associated with the data partitioning. Feature preselection and subsequent model development were performed using Random Forest, Lasso regression, LDA, and Naïve Bayes. The performance of all models was determined with independent test data. Results: Among the different approaches we examined, the T1-weighted contrast-enhanced sequence was found to be the most suitable for predicting IDH mutations status using radiomics-based machine learning models. Using contrast-enhanced T1-weighted MRI images, our seven-feature model developed with Lasso regression achieved a mean area under the curve (AUC) of 0.846, a mean accuracy of 0.792, a mean sensitivity of 0.847, and a mean specificity of 0.681. The administration of contrast agents resulted in a significant increase in the achieved discriminatory power. Conclusions: Our analyses show that for the prediction of the IDH mutation status using radiomics-based machine learning models, among the MRI images acquired with the commonly used MRI sequences, the contrast-enhanced T1-weighted images are the most suitable.

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Association of Schimmelpenning Syndrome with Astrocytoma (WHO Grade 3): Case Report

Tumova,

Auslands,

Millers

et al. 2024

Medicina

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Schimmelpenning syndrome, or epidermal nevus syndrome, is a rare, neurocutaneous disorder characterized by skin abnormalities, such as epidermal nevi, and involvement of the central nervous system, including intracranial tumors. There are only a few reported cases of intracranial tumors associated with Schimmelpenning syndrome. In most cases, a single nucleotide mutation in the RAS family proto-oncogenes, like HRAS or KRAS genes, can result in the genetic mosaicism that is responsible for the clinical manifestations of this syndrome. The authors present a case report of a woman with Schimmelpenning syndrome who sought medical help with complaints of progressive headache and dizziness. The radiological and histopathological findings indicated an astrocytoma, IDH-mutant (WHO grade 3). The molecular analysis revealed pathogenic changes in the oncogenic HRAS gene with a prevalence of 31%. The patient underwent surgical treatment and had no neurological sequelae. By presenting such a clinical case, attention is paid to the interrelationship between genetic syndromes and intracranial tumors.

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Mutant IDH in Gliomas: Role in Cancer and Treatment Options

Cited by 3 publications

References 167 publications

Epitranscriptome Mapping of m⁶A RNA Modifications in Glioma Tumor Tissue

Epitranscriptome Mapping of m⁶A RNA Modifications in Glioma Tumor Tissue

Comparison of MRI Sequences to Predict IDH Mutation Status in Gliomas Using Radiomics-Based Machine Learning

Association of Schimmelpenning Syndrome with Astrocytoma (WHO Grade 3): Case Report

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Mutant IDH in Gliomas: Role in Cancer and Treatment Options

Cited by 3 publications

References 167 publications

Epitranscriptome Mapping of m6A RNA Modifications in Glioma Tumor Tissue

Epitranscriptome Mapping of m6A RNA Modifications in Glioma Tumor Tissue

Comparison of MRI Sequences to Predict IDH Mutation Status in Gliomas Using Radiomics-Based Machine Learning

Association of Schimmelpenning Syndrome with Astrocytoma (WHO Grade 3): Case Report

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Epitranscriptome Mapping of m⁶A RNA Modifications in Glioma Tumor Tissue

Epitranscriptome Mapping of m⁶A RNA Modifications in Glioma Tumor Tissue