The DNA Damage Signaling Pathway Connects Oncogenic Stress to Cellular Senescence
Bibliographic record
Abstract
The mechanisms of tumor suppression must be linked to the oncogenic threats that may affect a normal cell. An important cancer causing mechanism is the accidental activation of genes that stimulate cell proliferation (oncogenes) by a variety of endogenous or environmental mutagens. This event has been experimentally modelled by enforcing the expression of oncogenes in primary cells. The astonishing outcome of these manipulations is that oncogenes trigger antiproliferative responses preventing progression to malignant transformation. These responses bring to an end proliferation due to cell death or a permanent cell cycle arrest called senescence. Here we review evidence indicating that oncogene induced senescence (OIS) involves activation of p53 via the DNA damage response (DDR). These results imply mechanisms of DNA damage in cells expressing oncogenes, that may be secondary to reactive oxygen species and/or some form of "oncogenic stress" that affect normal DNA replication. Interestingly, DNA damage signals persist in cells that escape from senescence. The implications of these signals for tumorigenesis are also discussed. Given that DNA damage signals have now been observed in cells treated with any stimuli known to induce senescence, the process can be redefined as a metabolically viable but permanent cell cycle arrest with persistent DNA damage signaling.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".