Nanoparticulate drug combination inhibits DNA damage repair and PD-L1 expression in BRCA-mutant and wild type triple-negative breast cancer
Bibliographic record
Abstract
The high mortality rate associated with metastatic breast cancer presents a significant global challenge. Inherent and chemotherapy-induced DNA damage repair, alongside immunosuppression, drastically contribute to triple-negative breast cancer (TNBC) relapse and metastasis. While poly (ADP-ribose) polymerase (PARP) inhibitors such as olaparib show effectiveness against BRCA1-mutant TNBC, they may lead to drug resistance and reduced efficacy due to increased programmed death-ligand 1 (PD-L1) expression. Our study explored the use of polymer-lipid nanoparticles (PLN) loaded with doxorubicin (DOX) and oligomeric hyaluronic acid (oHA), functionalized iRGD-peptide for integrins targeting (iRGD-DOX-oHA-PLN), to prevent TNBC immunosuppression, DNA repair, and metastasis. The results demonstrate that the iRGD-DOX-oHA-PLNs efficiently downregulated single and double-strand DNA repair proteins and enhanced DNA damage while decreasing PD-L1 expression compared to olaparib. Accordingly, iRGD-DOX-oHA-PLN treatment showed significantly higher efficiency in reducing levels of primary tumor growth and numbers of metastases to the lung and liver compared to olaparib in vitro and in vivo in both BRCA1-mutant and wild type TNBC orthotopic xenograft models. • iRGD-DOX-oHA-PLN nanoparticles effectively target triple-negative breast cancer. • Suppressed tumor growth and metastasis more than olaparib regardless of BRCA1 mutation • Inhibited DNA double- and single-strand break repair proteins and PD-L1 expression. • Exhibited superior efficacy over olaparib in BRCA1-mutant and wild-type TNBC models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".