DNA damage response revisited: the p53 family and its regulators provide endless cancer therapy opportunities
Bibliographic record
Abstract
Antitumor therapeutic strategies that fundamentally rely on the induction of DNA damage to eradicate and inhibit the growth of cancer cells are integral approaches to cancer therapy. Although DNA-damaging therapies advance the battle with cancer, resistance, and recurrence following treatment are common. Thus, searching for vulnerabilities that facilitate the action of DNA-damaging agents by sensitizing cancer cells is an active research area. Therefore, it is crucial to decipher the detailed molecular events involved in DNA damage responses (DDRs) to DNA-damaging agents in cancer. The tumor suppressor p53 is active at the hub of the DDR. Researchers have identified an increasing number of genes regulated by p53 transcriptional functions that have been shown to be critical direct or indirect mediators of cell fate, cell cycle regulation, and DNA repair. Posttranslational modifications (PTMs) primarily orchestrate and direct the activity of p53 in response to DNA damage. Many molecules mediating PTMs on p53 have been identified. The anticancer potential realized by targeting these molecules has been shown through experiments and clinical trials to sensitize cancer cells to DNA-damaging agents. This review briefly acknowledges the complexity of DDR pathways/networks. We specifically focus on p53 regulators, protein kinases, and E3/E4 ubiquitin ligases and their anticancer potential.
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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 | 0.001 |
| 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".