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PXA-like tumors: prognostic implications.

Supriya Bhardwaj1, Hemlata Jangir1, Swati Singh1

  • 1Neuropathology Laboratory, All India Institute of Medical Sciences, New Delhi, India.

Child'S Nervous System : Chns : Official Journal of the International Society for Pediatric Neurosurgery
|May 31, 2025
PubMed
Summary
This summary is machine-generated.

DNA methylation profiling revealed pleomorphic xanthoastrocytoma (PXA) in pediatric cases, challenging traditional grading. These PXA tumors showed aggressive features and poor survival, indicating a need for revised classification strategies.

Keywords:
BRAF V600E mutationDKFZ methylation classifierDNA methylationPTERTPediatric high-grade gliomasPleomorphic xanthoastrocytoma

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Area of Science:

  • Neuro-oncology
  • Molecular Pathology
  • Pediatric Oncology

Background:

  • Central nervous system (CNS) tumor classification is evolving with molecular markers.
  • DNA methylation profiling offers new insights into tumor heterogeneity.

Purpose of the Study:

  • To investigate discrepancies between epigenetic profiling, histomorphology, and CNS WHO grade.
  • To analyze therapeutic and survival outcomes in pediatric CNS tumors based on molecular classification.

Main Methods:

  • Retrospective analysis of three pediatric cases (ages 9-15) with high-grade glioma diagnosis.
  • Multidisciplinary assessment including histomorphology, DNA methylation profiling (Heidelberg/DKFZ Classifier), and CNS WHO grading.
  • Evaluation of patient survival duration.

Main Results:

  • Histopathological and CNS WHO grading revealed diffuse pediatric-type high-grade gliomas (PHGG) and glioblastoma with BRAF V600E mutation.
  • DNA methylation profiling consistently classified all three cases as pleomorphic xanthoastrocytoma (PXA).
  • Despite the PXA classification, patients had a poor mean survival of 13.7 months.

Conclusions:

  • The methylation class PXA can encompass tumors with aggressive histopathological features.
  • PXA tumors, as defined by methylation profiling, may present with a poor prognosis.
  • This highlights the importance of integrating molecular data for accurate CNS tumor classification and prognostication.