Dasatinib sensitises triple negative breast cancer cells to chemotherapy by targeting breast cancer stem cells
Bibliographic record
Abstract
BACKGROUND: Patients with triple negative breast cancer (TNBC) exhibit poor prognosis and are at high risk of tumour relapse, due to the resistance to chemotherapy. These aggressive phenotypes are in part attributed to the presence of breast cancer stem cells (BCSCs). Therefore, targeting BCSCs is a priority to overcoming chemotherapy failure in TNBCs. METHODS: We generated paclitaxel (pac)-resistant TNBC cells which displayed higher sphere forming potential and percentage of BCSC subpopulations compared to the parental cells. A screen with various kinase inhibitors revealed dasatinib, a Src kinase family inhibitor, as a potent suppressor of BCSC expansion/sphere formation in pac-resistant TNBC cells. RESULTS: We found dasatinib to block pac-induced BCSC enrichment and Src activation in both parental and pac-resistant TNBC cells. Interestingly, dasatinib induced an epithelial differentiation of the pac-resistant mesenchymal cells, resulting in their enhanced sensitivity to paclitaxel. The combination treatment of dasatinib and paclitaxel not only decreased the BCSCs numbers and their sphere forming capacity but also synergistically reduced cell viability of pac-resistant cells. Preclinical models of breast cancer further demonstrated the efficiency of the dasatinib/paclitaxel combination treatment in inhibiting tumour growth. CONCLUSIONS: Dasatinib is a promising anti-BCSC drug that could be used in combination with paclitaxel to overcome chemoresistance in TNBC.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".