Biology (Jun 2024)

The Landscape of Pediatric High-Grade Gliomas: The Virtues and Pitfalls of Pre-Clinical Models

  • Liam M. Furst,
  • Enola M. Roussel,
  • Ryan F. Leung,
  • Ankita M. George,
  • Sarah A. Best,
  • James R. Whittle,
  • Ron Firestein,
  • Maree C. Faux,
  • David D. Eisenstat

DOI
https://doi.org/10.3390/biology13060424
Journal volume & issue
Vol. 13, no. 6
p. 424

Abstract

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Pediatric high-grade gliomas (pHGG) are malignant and usually fatal central nervous system (CNS) WHO Grade 4 tumors. The majority of pHGG consist of diffuse midline gliomas (DMG), H3.3 or H3.1 K27 altered, or diffuse hemispheric gliomas (DHG) (H3.3 G34-mutant). Due to diffuse tumor infiltration of eloquent brain areas, especially for DMG, surgery has often been limited and chemotherapy has not been effective, leaving fractionated radiation to the involved field as the current standard of care. pHGG has only been classified as molecularly distinct from adult HGG since 2012 through Next-Generation sequencing approaches, which have shown pHGG to be epigenetically regulated and specific tumor sub-types to be representative of dysregulated differentiating cells. To translate discovery research into novel therapies, improved pre-clinical models that more adequately represent the tumor biology of pHGG are required. This review will summarize the molecular characteristics of different pHGG sub-types, with a specific focus on histone K27M mutations and the dysregulated gene expression profiles arising from these mutations. Current and emerging pre-clinical models for pHGG will be discussed, including commonly used patient-derived cell lines and in vivo modeling techniques, encompassing patient-derived xenograft murine models and genetically engineered mouse models (GEMMs). Lastly, emerging techniques to model CNS tumors within a human brain environment using brain organoids through co-culture will be explored. As models that more reliably represent pHGG continue to be developed, targetable biological and genetic vulnerabilities in the disease will be more rapidly identified, leading to better treatments and improved clinical outcomes.

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