Differential transformation capacity of neuro-glial progenitors during development
Bibliographic record
Abstract
Gliomas represent the most common type of brain tumor, but show considerable variability in histologic appearance and clinical outcome. The phenotypic differences between types and grades of gliomas have not been explained solely on the grounds of differing oncogenic stimuli. Several studies have demonstrated that some phenotypic differences may be attributed to regional differences in the neural stem cells from which tumors arise. We hypothesized that temporal differences may also play a role, with tumor phenotypic variability reflecting intrinsic differences in neural stem cells at distinct developmental stages. To determine how the tumorigenic potential of lineally related stem cells changes over time, we used a conditional transgenic system that integrates Cre-Lox-mediated and Tet-regulated expression to drive K-ras(G12D) expression in neuro-glial progenitor populations at different developmental time points. Using this model, we demonstrate that K-ras(G12D)-induced transformation is dependent on the developmental stage at which it is introduced. Diffuse malignant brain tumors develop during early embryogenesis but not when K-ras(G12D) expression is induced during late embryogenesis or early postnatal life. We show that differential expression of cell-cycle regulators during development may be responsible for this differing susceptibility to malignant transformation and that loss of p53 can overcome the transformation resistance seen at later developmental stages. These results highlight the interplay between genetic alterations and the molecular changes that accompany specific developmental stages; early progenitors may lack the regulatory mechanisms present at later, more lineage-restrictive, developmental time points, making them more susceptible to transformation.
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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) | 0.000 | 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".