The Growing Complexity of Cancer Cell Response to DNA-Damaging Agents: Caspase 3 Mediates Cell Death or Survival?
Bibliographic record
Abstract
It is widely stated that wild-type p53 either mediates the activation of cell cycle checkpoints to facilitate DNA repair and promote cell survival, or orchestrates apoptotic cell death following exposure to cancer therapeutic agents. This reigning paradigm has been challenged by numerous discoveries with different human cell types, including solid tumor-derived cell lines. Thus, activation of the p53 signaling pathway by ionizing radiation and other DNA-damaging agents hinders apoptosis and triggers growth arrest (e.g., through premature senescence) in some genetic backgrounds; such growth arrested cells remain viable, secrete growth-promoting factors, and give rise to progeny with stem cell-like properties. In addition, caspase 3, which is best known for its role in the execution phase of apoptosis, has been recently reported to facilitate (rather than suppress) DNA damage-induced genomic instability and carcinogenesis. This observation is consistent with an earlier report demonstrating that caspase 3 mediates secretion of the pro-survival factor prostaglandin E₂, which in turn promotes enrichment of tumor repopulating cells. In this article, we review these and related discoveries and point out novel cancer therapeutic strategies. One of our objectives is to demonstrate the growing complexity of the DNA damage response beyond the conventional "repair and survive, or die" hypothesis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".