Inhibitory effect of the novel tyrosine kinase inhibitor DCC-2036 on triple-negative breast cancer stem cells through AXL-KLF5 positive feedback loop
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC), an aggressive histological subtype of breast cancer, exhibits a high risk of early recurrence rate and a poor prognosis, and it is primarily associated with the abundance of cancer stem cells (CSCs). At present, the strategies for effectively eradicating or inhibiting TNBC CSCs are still limited, which makes the development of novel drugs with anti-CSCs function be of great value for the treatment of TNBC, especially the refractory TNBC. In this study, we found that the small-molecule tyrosine kinase inhibitor DCC-2036 suppressed TNBC stem cells by inhibiting the tyrosine kinase AXL and the transcription factor KLF5. DCC-2036 downregulated the expression of KLF5 by decreasing the protein stability of KLF5 via the AXL-Akt-GSK3β signal axis, and in turn, the downregulation of KLF5 further reduced the expression of AXL via binding to its promotor (-171 to -162 bp). In addition, p-AXL/AXL levels were positively correlated with KLF5 expression in human TNBC specimens. These findings indicated that DCC-2036 is able to suppress the CSCs in TNBC by targeting the AXL-KLF5 positive feedback loop. Moreover, our findings indicated that DCC-2036 increased the sensitivity of TNBC chemotherapy. Therefore, this study proposes a potential drug candidate and several targets for the treatment of refractory 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".