Loss of p53 cooperates with K‐ras activation to induce glioma formation in a region‐independent manner
Bibliographic record
Abstract
Gliomas are recognized as a heterogeneous group of neoplasms differing in their location and morphological features. These differences, between and within varying grades of gliomas, have not been explained solely on the grounds of an oncogenic stimulus. Interactions with the tumor microenvironment as well as inherent characteristics of the cell of origin are likely a source of this heterogeneity. There is an ongoing debate over the cell of origin of gliomas, where some suggest a progenitor, while others argue for a stem cell origin. Thus, it is presumed that neurogenic regions of the brain such as the subventricular zone (SVZ) containing large numbers of neural stem and progenitor populations are more susceptible to transformation. Our studies demonstrate that K-ras(G12D) cooperates with the loss of p53 to induce gliomas from both the SVZ and cortical region, suggesting that cells in the SVZ are not uniquely gliomagenic. Using combinations of doxycycline-inducible K-ras(G12D) and p53 loss, we show that tumors induced by the cooperative actions of these genes remain dependent on active K-ras expression, as deinduction of K-ras(G12D) leads to complete tumor regression despite absence of p53. These results suggest that the interplay between specific combinations of genetic alterations and susceptible cell types, rather than the site of origin, are important determinates of gliomagenesis. Additionally, this model supports the view that, although several genetic events may be necessary to confer traits associated with oncogenic transformation, inactivation of a single oncogenic partner can undermine tumor maintenance, leading to regression and disease remission.
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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".