Targeting the p53-Family in Cancer and Chemosensitivity: Triple Threat
Bibliographic record
Abstract
The p53-family of transcription factors consists of three genes - p53, p63, and p73 - that share significant structural and functional similarities. Although these genes encode multiple variants that have opposing functions in cancer biology, the full-length, transactivating (TA) p53-family members are potent inducers of apoptosis and tumor suppression. Many anti-cancer agents, from traditional chemo- and radiation therapies to more recently developed small molecules, exert their effects by enhancing the anti-proliferative effects of p53 and TAp63/p73. In this review, we provide an overview of the regulatory pathways controlling the p53-family proteins as a framework for understanding p53-family targeted drug mechanisms. We will also summarize recent work on promising attempts to re-activate p53 in tumors. In addition, we will discuss how p63 and p73 - the two more recently discovered p53-family members - have affected drug discovery and how these two genes may also hold promise as drug targets for recent and future novel therapies. This review will emphasize how targeting multiple members of the family of p53 proteins is likely to provide an increased threat to the growth of cancer cells.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".