Neurodevelopmental hijacking of oligodendrocyte lineage programs drives glioblastoma infiltration
Bibliographic record
Abstract
Glioblastoma (GBM) is an aggressive brain tumor with a highly invasive nature. Despite the clinical relevance of this behavior, the molecular underpinnings of infiltrating GBM cells in the peritumoral zone remain underexplored in patients. Here, we show that peritumoral progenitor-like GBM cells activate transcriptional programs associated with increased invasivity, synaptic activity, and NOTCH signaling. These cells spatially colocalize with neurons and exhibit an increased propensity for neuronal crosstalk. The epigenetic encoding of these infiltrative cells mirrors that of uncommitted oligodendrocyte progenitor cells (OPCs) in the developing human brain, a neurodevelopmental state marked by increased synaptic and migratory potential. Functional perturbation of a nominated regulatory factor, ZEB1, confirmed its role in maintaining the invasive and uncommitted developmental potential of infiltrative GBM cells. Our findings provide insights into the neurodevelopmental hijacking that drives GBM infiltration in patients, rationalizing further investigation into targeting differentiation potential as a therapeutic strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".