PRIMA-1 synergizes olaparib-induced cell death in p53 mutant triple negative human breast cancer cell line via restoring p53 function, arresting cell cycle, and inducing apoptosis
Bibliographic record
Abstract
This study concerned with assessing the effect of restoring p53 using PRIMA-1 on the anti-cancer activity of olaparib against TP53-mutant triple negative breast cancer (TNBC) cells and exploring the optimum synergistic concentrations and the underlying mechanism. Human BC cell lines, MDA-MB-231 with mutated TP53 gene, and MCF-7 with wild-type TP53 gene were treated with olaparib and/or PRIMA-1. The IC 50 value for olaparib was significantly decreased by PRIMA-1 in MDA-MB-231 cells compared to MCF-7 cells. Contrary to MCF-7 cells, co-treatment with olaparib and PRIMA-1 had a synergistic anti-proliferative effect in MDA-MB-231 at all tested concentrations with the best synergistic combination at 45 and 8.5 µM, respectively, and furthermore PRIMA-1 enhanced olaparib-induced apoptosis. This synergistic apoptotic effect was associated with a significant boost in mRNA expression of TP53 gene, cell cycle arrest at G2/M phase, modulation of BRCA-1, BAX and Bcl2 proteins expressions, and induction of active caspase-3. These results present a clue for the utility of combined olaparib and PRIMA-1 in treatment of TP53-mutant TNBC invitro. PRIMA-1 triggers olaparib-induced MDA-MB-231 cell death in a synergistic manner via restoring TP53, decreasing BRCA-1 expression, cell cycle arrest, and enhancement of apoptosis via p53/BAX/Bcl2/caspase 3 pathway.
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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".