Language
English
Publication Date
1-1-2025
Journal
Neuro-Oncology Advances
DOI
10.1093/noajnl/vdaf016
PMID
40321621
PMCID
PMC12046312
PubMedCentral® Posted Date
1-23-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Background: The reactivation of neurodevelopmental programs in cancer highlights parallel biological processes that occur in both normal development and brain tumors. Achieving a deeper understanding of how dysregulated developmental factors play a role in the progression of brain tumors is therefore crucial for identifying potential targets for therapeutic interventions. Single-cell RNA-sequencing (scRNA-Seq) provides an opportunity to understand how developmental programs are dysregulated and reinitiated in brain tumors at single-cell resolution. The aim of this study is to identify the developmental origins of brain tumors using scRNA-Seq data.
Methods: Here, we introduce COORS (Cell Of ORigin like CellS), a computational tool trained on developmental human brain single-cell datasets that annotates "developmental-like" cell states in brain tumors. COORS leverages cell type-specific multilayer perceptron models and incorporates a developmental cell type tree that reflects hierarchical relationships and models cell type probabilities.
Results: Applying COORS to various brain cancer datasets, including medulloblastoma (MB), glioma, and diffuse midline glioma (DMG), we identified developmental-like cells that represent putative cells of origin in these tumors. Our method provides both cell of origin classification and cell age regression, offering insights into the developmental cell types of tumor subgroups. COORS identified outer radial glia developmental cells within IDHWT glioma cells whereas oligodendrocyte precursor cells (OPCs) and neuronal-like cells in IDHMut. Interestingly, IDHMut subgroup cells that map to OPC show bimodal distributions that are both early and late weeks in development. Furthermore, COORS offers a valuable resource by providing novel markers linked to developmental states within MB, glioma, and DMG tumor subgroups.
Conclusions: Our work adds to our cumulative understanding of brain tumor heterogeneity and helps pave the way for tailored treatment strategies.
Keywords
artificial neural network model, brain tumor, cell of origin, scRNA-Seq
Published Open-Access
yes
Recommended Citation
Wang, Su; Curry, Rachel Naomi; McDonald, Malcolm F; et al., "Inferred Developmental Origins of Brain Tumors From Single-Cell RNA-Sequencing Data" (2025). Faculty and Staff Publications. 5332.
https://digitalcommons.library.tmc.edu/baylor_docs/5332