New Insights into p53 Signaling and Cancer Cell Response to DNA Damage: Implications for Cancer Therapy
Bibliographic record
Abstract
Activation of the p53 signaling pathway by DNA-damaging agents was originally proposed to result either in cell cycle checkpoint activation to promote survival or in apoptotic cell death. This model provided the impetus for numerous studies focusing on the development of p53-based cancer therapies. According to recent evidence, however, most p53 wild-type human cell types respond to ionizing radiation by undergoing stress-induced premature senescence (SIPS) and not apoptosis. SIPS is a sustained growth-arrested state in which cells remain viable and secrete factors that may promote cancer growth and progression. The p21(WAF1) (hereafter p21) protein has emerged as a key player in the p53 pathway. In addition to its well-studied role in cell cycle checkpoints, p21 regulates p53 and its upstream kinase (ATM), controls gene expression, suppresses apoptosis, and induces SIPS. Herein, we review these and related findings with human solid tumor-derived cell lines, report new data demonstrating dynamic behaviors of p53 and p21 in the DNA damage response, and examine the gain-of-function properties of cancer-associated p53 mutations. We point out obstacles in cancer-therapeutic strategies that are aimed at reactivating the wild-type p53 function and highlight some alternative approaches that target the apoptotic threshold in cancer cells with differing p53 status.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".