Restoration of the Tumor Suppressor Function of Y220C-Mutant p53 by Rezatapopt, a Small-Molecule Reactivator
Bibliographic record
Abstract
Restoration of the tumor suppressor function of tumor-associated p53 mutants, including the Y220C substitution, has posed a significant challenge for therapeutic discovery. In this study, we describe rezatapopt (PC14586), part of a series of compounds designed to reactivate the p53 Y220C mutant. These compounds restore p53 tumor suppressor function by correcting its conformation and enabling it to bind DNA and activate downstream target genes, thus inducing antiproliferative changes in tumor cells. Our findings are supported by biochemical and structural analysis, in vitro and in vivo transcriptomics, and functional data, revealing the recovery of multiple aspects of the wild-type p53 program. These compounds demonstrate potent antitumor activity in preclinical models as single agents and in combination with immunotherapy. Currently, rezatapopt is being evaluated in a registrational phase II clinical trial for patients with advanced solid tumors harboring the TP53 Y220C mutation. SIGNIFICANCE: Rezatapopt is a clinical-stage compound that offers a promising treatment option for TP53-mutant cancers. This study details the characterization of rezatapopt and its related compounds, which can reinstate the tumor suppressor activity of the p53 Y220C mutant. These results emphasize the potential for targeting p53 mutations in cancer therapy.
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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 | 0.001 |
| 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".