Integrated transcriptomic landscape of medulloblastoma and ependymoma reveals novel tumor subtype-specific biology

TL;DR

This study maps gene activity in two types of childhood brain tumors to reveal new biological patterns and improve diagnosis.

Contribution

The research provides a unified transcriptomic reference landscape for medulloblastoma and ependymoma with subtype-specific molecular signatures.

Findings

Distinct transcriptomic compartments EPN-E1 and EPN-E2 were identified in ependymoma with unique RNA fusions and signatures.

Group 3/4 medulloblastoma and SHH tumors were precisely stratified by subtype and patient age.

Subtype-specific pathways and gene fusions were discovered, offering insights into tumor biology and outcomes.

Abstract

Medulloblastoma and ependymoma are common pediatric central nervous system tumors with significant molecular and clinical heterogeneity. While molecular subgrouping has enabled classification into molecular subtypes, the extent of heterogeneity within these subgroups remains poorly defined.

We collected bulk RNA sequencing data from 888 medulloblastoma and 370 ependymoma tumors to establish a comprehensive reference landscape. After rigorous batch effect correction, normalization, and dimensionality reduction, we generated a unified landscape to explore gene expression, signaling pathways, RNA fusions, and copy number variations.

Our transcriptional analysis revealed distinct clustering patterns, including two primary ependymoma compartments, EPN-E1 and EPN-E2, each with specific RNA fusions and molecular signatures. In medulloblastoma, we observed precise stratification of Group 3/4 tumors by subtype and in Sonic Hedgehog (SHH) tumors by patient age. We also identified subtype-specific pathways and gene fusions, enriched in each group.

This transcriptomic landscape serves as a resource for biomarker discovery, diagnostic refinement, and prediction of tumor biology and outcome. By enabling projection of new patients’ bulk RNA-seq data onto the reference map using nearest neighbor analysis, the framework supports accurate subtype classification. The landscape is publicly available via Oncoscape, an interactive platform for global exploration and application.

Graphical Abstract

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