Acta Neuropathologica CommunicationsCancer BiologyPeer-reviewed journal article
Cellular Reprogramming of H3K27M Pediatric High-Grade Glioma to Neuron-like State
Abicumaran UthamacumaraniD, Cynthia HorthiD, Eric BarekeiD, Michel Gravel, Jacek MajewskiiD
Abstract
This study explores the cell fate reprogrammability of H3K27M-mutant pediatric high-grade gliomas (pHGG) using neuronal transdifferentiation as a potential targeted therapy. We treated the BT245 patient-derived glioma cell line with pharmacological combinations targeting neuronal differentiation pathways and performed bulk RNA sequencing to characterize gene expression patterns driving cell fate transitions. Our findings reveal that the drug combinations induce transcriptomic changes consistent with differentiation towards neuronal phenotypes, including the upregulation of synaptic and dendritic signaling genes and the downregulation of malignant signatures. In comparison, astrocytic differentiation media (DM) and H3K27M knockout (KO) promote residual astrocytic phenotypes, suggesting neuronal transdifferentiation as a more effective strategy for mitigating tumor aggressiveness and progression. Differentially expressed genes such as GRIK1, GRIN1, NRXN3, NRXN1, CALB2, SCGN, SLC32A1, SLC1A2, KCNC3, and neurodevelopmental regulators including WNT7A, DLX6, ERBB4, ARX, BCL11B, SEMA3C, and FGFBP3 were identified as key markers regulating the neuron-like lineage transition. This study demonstrates that pHGGs can be phenotypically redirected toward neuronal-like identities through modulating cell fate differentiation programs. These findings advance the concept of differentiation therapy as a promising intervention to reduce phenotypic plasticity and malignancy in pHGG ecosystems. While these are early in vitro findings, the ability to steer and control glioma cells toward stable, less malignant fates offers promising translational potential for patient-centered targeted therapies.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
- Citations
- 2
- FWCI
- 1.28
- Field percentile
- top 17%
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