Oncogenic <i>ras</i> and p53 Cooperate To Induce Cellular Senescence
Bibliographic record
Abstract
Oncogenic activation of the mitogen-activated protein (MAP) kinase cascade in murine fibroblasts initiates a senescence-like cell cycle arrest that depends on the ARF/p53 tumor suppressor pathway. To investigate whether p53 is sufficient to induce senescence, we introduced a conditional murine p53 allele (p53(val135)) into p53-null mouse embryonic fibroblasts and examined cell proliferation and senescence in cells expressing p53, oncogenic Ras, or both gene products. Conditional p53 activation efficiently induced a reversible cell cycle arrest but was unable to induce features of senescence. In contrast, coexpression of oncogenic ras or activated mek1 with p53 enhanced both p53 levels and activity relative to that observed for p53 alone and produced an irreversible cell cycle arrest that displayed features of cellular senescence. p19(ARF) was required for this effect, since p53(-/-) ARF(-/-) double-null cells were unable to undergo senescence following coexpression of oncogenic Ras and p53. Although the levels of exogenous p53 achieved in ARF-null cells were relatively low, the stabilizing effects of p19(ARF) on p53 could not explain the cooperation between oncogenic Ras and p53 in promoting senescence. Hence, enforced p53 expression without oncogenic ras in p53(-/-) mdm2(-/-) double-null cells produced extremely high p53 levels but did not induce senescence. Taken together, our results indicate that oncogenic activation of the MAP kinase pathway in murine fibroblasts converts p53 into a senescence inducer through both quantitative and qualitative mechanisms.
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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".