Targeting HER2-positive breast cancer cells by a combination of dasatinib and BMS-202: Insight into the molecular pathways
Bibliographic record
Abstract
BACKGROUND: Recent investigations have reported the benefits of using a tyrosine kinase inhibitor, dasatinib (DA), as well as programmed death-ligand 1 (PD-L1) inhibitors in the management of several solid tumors, including breast cancer. Nevertheless, the outcome of the combination of these inhibitors on HER2-positive breast cancer is not explored yet. METHODS: Herein, we investigated the impact of DA and PD-L1 inhibitor (BMS-202) combination on HER2-positive breast cancer cell lines, SKBR3 and ZR75. RESULTS: Our data reveal that the combination significantly inhibits cell viability of both cancer cell lines as compared to monotreatment. Moreover, the combination inhibits epithelial-mesenchymal transition (EMT) progression and reduces cancer cell invasion by restoring E-cadherin and β-catenin expressions and loss of vimentin, major biomarkers of EMT. Additionally, the combination reduces the colony formation of both cell lines in comparison with their matched control. Also, the combination considerably inhibits the angiogenesis of the chorioallantoic membrane model compared with monotreatment. Molecular pathway analysis of treated cells shows that this combination blocks HER2, AKT, β-catenin, and JNK1/2/3 activities. CONCLUSION: Our findings implicate that a combination of DA and BMS-202 could have a significant impact on the management of HER2-positive breast cancer.
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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 | 0.000 | 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 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".