Breast Cancer Therapy by Small-Molecule Reactivation of Mutant p53
Bibliographic record
Abstract
Tumor suppressor p53 is essential for maintaining DNA stability and preventing cancer. Under normal conditions, the p53 protein is either degraded or bound to a negative regulator, rendering it inactive, but when DNA damage occurs, p53 is activated, causing cell cycle arrest and allowing time for cellular repair. If, however, DNA damage is too severe, the cell undergoes apoptosis and is eliminated. Mutations in the p53 gene are linked to various types of cancer and are present in 30-40% of human breast cancers, leading to loss of tumor suppressor function and uncontrolled tumor growth. Moreover, in triple-negative breast cancer (TNBC), a particularly deadly form of the disease, the incidence of p53 mutations increases to 70-80%. Many p53 mutations occur in the DNA binding domain of the p53 gene, leading to accumulation of mutant p53 (mtp53) within the cell, and tumor development. Converting mtp53 back to its functional wild-type form (wtp53) is consequently a rational approach to preventing or even reversing tumor growth. Mechanisms of action of tumor suppressor p53 are widely discussed elsewhere; hence, we will focus on our own studies, using small molecule activators of mtp53 to combat breast cancer. We will show that specific small molecules, such as PRIMA-1 (p53 reactivation and induction of mass apoptosis), reactivate mtp53 in hormone-dependent human breast cancer cells. Furthermore, we will demonstrate the effectiveness of PRIMA-1 at arresting xenograft growth in an animal model and go on to show that the PRIMA-1 analog APR-246 effectively restores wtp53 tumor suppressor activity in TNBC cells. A brief overview of current clinical trials aimed at reactivating p53 to treat certain cancers is provided. Finally, we discuss the possible use of naturally occurring compounds, which are generally non-toxic, to reactivate mutant p53 and control TNBC progression.
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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".