Cellular reprogramming of H3K27M pediatric high-grade glioma to neuron-like state
Bibliographic record
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 potential ability to steer and control glioma cells toward stable, less malignant fates offers promising translational potential for patient-centered targeted therapies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".