Gliomagenesis mimics an injury response orchestrated by neural crest-like cells
Bibliographic record
Abstract
Glioblastoma is an incurable brain malignancy. By the time of clinical diagnosis, these tumours exhibit a degree of genetic and cellular heterogeneity that provides few clues to the mechanisms that initiate and drive gliomagenesis1,2. Here, to explore the early steps in gliomagenesis, we utilized conditional gene deletion and lineage tracing in tumour mouse models, coupled with serial magnetic resonance imaging, to initiate and then closely track tumour formation. We isolated labelled and unlabelled cells at multiple stages—before the first visible abnormality, at the time of the first visible lesion, and then through the stages of tumour growth—and subjected cells of each stage to single-cell profiling. We identify a malignant cell state with a neural crest-like gene expression signature that is highly abundant in the early stages, but relatively diminished in the late stage of tumour growth. Genomic analysis based on the presence of copy number alterations suggests that these neural crest-like states exist as part of a heterogeneous clonal hierarchy that evolves with tumour growth. By exploring the injury response in wounded normal mouse brains, we identify cells with a similar signature that emerge following injury and then disappear over time, suggesting that activation of an injury response program occurs during tumorigenesis. Indeed, our experiments reveal a non-malignant injury-like microenvironment that is initiated in the brain following oncogene activation in cerebral precursor cells. Collectively, our findings provide insight into the early stages of glioblastoma, identifying a unique cell state and an injury response program tied to early tumour formation. These findings have implications for glioblastoma therapies and raise new possibilities for early diagnosis and prevention of disease. A study using glioblastoma mouse models, serial magnetic resonance imaging and single-cell profiling details changes in the identity and balance of cellular states from initiation of tumorigenesis to the end point.
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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 | 0.001 |
| 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".