Synergistic Anticancer Activity of HSP70 Inhibitor and Doxorubicin in Gain-of-Function Mutated p53 Breast Cancer Cells
Bibliographic record
Abstract
Background: The mutation rate of p53 in breast cancer is around 20%. Specific p53 mutations exhibit prion-like abnormal misfolding and aggregation and gain oncogenic function, causing resistance to the chemotherapy drug doxorubicin. In this study, we identified key upstream regulatory molecules that inhibit the aggregation of p53 with the aim of increasing the anticancer effect of doxorubicin. Methods: Thioflavin T was employed as a fluorescent probe to detect prion-like protein aggregates within cells, the response to various inhibitors was evaluated using CCK8 assay, and the coefficient of drug interaction was calculated. The cell apoptosis ratio was evaluated using Caspase-3/7 based flow cytometry assay. Results: MDA-MB-231 cells (with p53 R280K mutation) and T47D cells (with p53 L194F mutation) had a strong Thioflavin T staining signal, but MDA-MB-468 cells (with p53 R273H mutation) had a weak Thioflavin T signal. Compared to MDA-MB-468 cells, which had a good response to doxorubicin, both MDA-MB-231 and T47D showed high doxorubicin drug resistance. Co-treatment with various misfolding p53 aggregation inhibitors and doxorubicin found that only the HSP70 inhibitor and doxorubicin had synergistic anticancer activity in both MDA-MB-231 and T47D cells. Furthermore, this co-treatment induced cell apoptosis in MDA-MB-231, which was reversed by a pan-caspase inhibitor. Conclusions: Doxorubicin resistance caused by specific p53 mutants can be resolved by co-treatment with a HSP70 inhibitor in breast 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) | 0.000 | 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".