TY  - JOUR
AU  - Okonechnikov, Konstantin
AU  - Ghasemi, David R
AU  - Schrimpf, Daniel
AU  - Tonn, Svenja
AU  - Mynarek, Martin
AU  - Koster, Jan
AU  - Milde, Till
AU  - Zheng, Tuyu
AU  - Sievers, Philipp
AU  - Sahm, Felix
AU  - Jones, David T W
AU  - von Deimling, Andreas
AU  - Pfister, Stefan M
AU  - Kool, Marcel
AU  - Pajtler, Kristian W
AU  - Korshunov, Andrey
TI  - Biglycan-driven risk stratification in ZFTA-RELA fusion supratentorial ependymomas through transcriptome profiling.
JO  - Acta Neuropathologica Communications
VL  - 13
IS  - 1
SN  - 2051-5960
CY  - London
PB  - Biomed Central
M1  - DKFZ-2025-00064
SP  - 4
PY  - 2025
N1  - #EA:B062#LA:B300#
AB  - Recent genomic studies have allowed the subdivision of intracranial ependymomas into molecularly distinct groups with highly specific clinical features and outcomes. The majority of supratentorial ependymomas (ST-EPN) harbor ZFTA-RELA fusions which were designated, in general, as an intermediate risk tumor variant. However, molecular prognosticators within ST-EPN ZFTA-RELA have not been determined yet. Here, we performed methylation-based DNA profiling and transcriptome RNA sequencing analysis of 80 ST-EPN ZFTA-RELA investigating the clinical significance of various molecular patterns. The principal types of ZFTA-RELA fusions, based on breakpoint location, demonstrated no significant correlations with clinical outcomes. Multigene analysis disclosed 1892 survival-associated genes, and a metagene set of 100 genes subdivided ST-EPN ZFTA-RELA into favorable and unfavorable transcriptome subtypes composed of different cell subpopulations as detected by deconvolution analysis. BGN (biglycan) was identified as the top-ranked survival-associated gene and high BGN expression levels were associated with poor survival (Hazard Ratio 17.85 for PFS and 45.48 for OS; log-rank; p-value < 0.01). Furthermore, BGN immunopositivity was identified as a strong prognostic indicator of poor survival in ST-EPN, and this finding was confirmed in an independent validation set of 56 samples. Our results indicate that integrating BGN expression (at mRNA and/or protein level) into risk stratification models may improve ST-EPN ZFTA-RELA outcome prediction. Therefore, gene and/or protein expression analyses for this molecular marker could be adopted for ST-EPN ZFTA-RELA prognostication and may help assign patients to optimal therapies in prospective clinical trials.
KW  - Humans
KW  - Ependymoma: genetics
KW  - Ependymoma: metabolism
KW  - Ependymoma: pathology
KW  - Supratentorial Neoplasms: genetics
KW  - Supratentorial Neoplasms: pathology
KW  - Supratentorial Neoplasms: metabolism
KW  - Male
KW  - Female
KW  - Gene Expression Profiling: methods
KW  - Transcription Factor RelA: genetics
KW  - Transcription Factor RelA: metabolism
KW  - Adult
KW  - Middle Aged
KW  - Adolescent
KW  - Child
KW  - Young Adult
KW  - Child, Preschool
KW  - Aged
KW  - Prognosis
KW  - Risk Assessment: methods
KW  - Oncogene Proteins, Fusion: genetics
KW  - Oncogene Proteins, Fusion: metabolism
KW  - BGN (Other)
KW  - ZFTA-RELA fusion (Other)
KW  - Ependymoma (Other)
KW  - Expression (Other)
KW  - Prognosis (Other)
KW  - Transcription Factor RelA (NLM Chemicals)
KW  - RELA protein, human (NLM Chemicals)
KW  - Oncogene Proteins, Fusion (NLM Chemicals)
LB  - PUB:(DE-HGF)16
C6  - pmid:39762990
DO  - DOI:10.1186/s40478-024-01921-w
UR  - https://inrepo02.dkfz.de/record/296134
ER  -