:: fusion is a driver, potentially targetable, genetic alteration identified in approximately 4% of high-grade diffuse gliomas and rare cases with low-grade histology. Herein, we review the genetic and epigenetic features of these tumors and highlight the challenges in their classification and grading. Diffuse gliomas with :: fusion display unique histopathological and molecular features, including an oligodendroglioma-like appearance, calcifications, and CD34 extravascular immunoreactivity. High-grade tumors exhibit molecular alterations and a DNA methylation profile typical of glioblastoma, suggesting that they may represent a subtype clinically characterized by a slightly better prognosis. Tumors with low-grade morphology are genetically and epigenetically heterogeneous. Some, exclusive to adults, have molecular alterations typical of glioblastoma, although most do not match any methylation classes, using version 12.5 of the Heidelberg classifier. Another group, which mostly affects children or adolescents, lacks the molecular features of glioblastoma and has a DNA methylation profile similar to that of low-grade glioneuronal tumors. In conclusion, diffuse gliomas with :: fusion do not constitute a distinct nosological entity, owing to their genetic and epigenetic diversity. Further studies are warranted to clarify the biological aggressiveness of tumors with low-grade histology to refine the grading and determine the optimal treatment strategy.
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http://dx.doi.org/10.3390/cancers16091644 | DOI Listing |
J Clin Med
January 2025
Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands.
Diffusion weighted imaging (DWI) is used for monitoring purposes for lower-grade glioma (LGG). While the apparent diffusion coefficient (ADC) is clinically used, various DWI models have been developed to better understand the micro-environment. However, the validity of these models and how they relate to each other is currently unknown.
View Article and Find Full Text PDFInt J Mol Sci
January 2025
Department of Pharmaceutical Biochemistry, Poznan University of Medical Sciences, Rokietnicka 3, 60-806 Poznań, Poland.
Adult-type diffuse gliomas are characterized by inevitable recurrence and very poor prognosis. Novel treatment options, including multimodal drugs or effective drug combinations, are therefore eagerly awaited. Tinostamustine is an alkylating and histone deacetylase inhibiting molecule with great potential in cancer treatment.
View Article and Find Full Text PDFEur J Cancer
January 2025
ACCELERATE, Europe; Gustave Roussy Cancer Centre, Paris, France.
Fewer than 10 % of children with diffuse midline glioma (DMG) survive 2 years from diagnosis. Radiation therapy remains the cornerstone of treatment and there are no medicinal products with regulatory approval. Although the biology of DMG is better characterized, this has not yet translated into effective treatments.
View Article and Find Full Text PDFJ Neurosurg
January 2025
Departments of1Neurological Surgery and.
The infiltrative and diffuse nature of gliomas makes complete resection unfeasible. Unfortunately, regions of brain parenchyma with residual, infiltrative tumor are protected by the blood-brain barrier (BBB), making systemic chemotherapies, small-molecule inhibitors, and immunotherapies of limited efficacy. Low-frequency focused ultrasound (FUS) in combination with intravascular microbubbles can be used to disrupt the BBB transiently and selectively within the tumor and peritumoral region.
View Article and Find Full Text PDFBioengineering (Basel)
December 2024
Department of Pathology, University of Yamanashi, Yamanashi 409-3898, Japan.
The latest World Health Organization (WHO) classification of central nervous system tumors (WHO2021/5th) has incorporated molecular information into the diagnosis of each brain tumor type including diffuse glioma. Therefore, an artificial intelligence (AI) framework for learning histological patterns and predicting important genetic events would be useful for future studies and applications. Using the concept of multiple-instance learning, we developed an AI framework named GLioma Image-level and Slide-level gene Predictor (GLISP) to predict nine genetic abnormalities in hematoxylin and eosin sections: , , mutations, promoter mutations, homozygous deletion (CHD), amplification (amp), 7 gain/10 loss (7+/10-), 1p/19q co-deletion, and promoter methylation.
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