Mutant p53 reactivation restricts the protumorigenic consequences of wild type p53 loss of heterozygosity in Li-Fraumeni syndrome patient-derived fibroblasts
Bibliographic record
Abstract
The p53 tumor suppressor, encoded by the TP53 gene, serves as a major barrier against malignant transformation. Patients with Li-Fraumeni syndrome (LFS) inherit a mutated TP53 allele from one parent and a wild-type TP53 allele from the other. Subsequently, the wild-type allele is lost and only the mutant TP53 allele remains. This process, which is termed loss of heterozygosity (LOH), results in only mutant p53 protein expression. We used primary dermal fibroblasts from LFS patients carrying the hotspot p53 gain-of-function pathogenic variant, R248Q to study the LOH process and characterize alterations in various pathways before and after LOH. We previously described the derivation of mutant p53 reactivating peptides, designated pCAPs (p53 Conformation Activating Peptides). In this study, we tested the effect of lead peptide pCAP-250 on LOH and on its associated cellular changes. We report that treatment of LFS fibroblasts with pCAP-250 prevents the accumulation of mutant p53 protein, inhibits LOH, and alleviates its cellular consequences. Furthermore, prolonged treatment with pCAP-250 significantly reduces DNA damage and restores long-term genomic stability. pCAPs may thus be contemplated as a potential preventive treatment to prevent or delay early onset cancer in carriers of mutant p53.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".