Dual inhibition of Wnt and Yes‐associated protein signaling retards the growth of triple‐negative breast cancer in both mesenchymal and epithelial states
Bibliographic record
Abstract
Triple‐negative breast cancer (TNBC), the most refractory subtype of breast cancer to current treatments, accounts disproportionately for the majority of breast cancer‐related deaths. This is largely due to cancer plasticity and the development of cancer stem cells (CSCs). Recently, distinct yet interconvertible mesenchymal‐like and epithelial‐like states have been revealed in breastCSCs. Thus, strategies capable of simultaneously inhibiting bulk andCSCpopulations in both mesenchymal and epithelial states have yet to be developed. Wnt/β‐catenin and Hippo/YAPpathways are crucial in tumorigenesis, but importantly also possess tumor suppressor functions in certain contexts. One possibility is thatTNBCcells in epithelial or mesenchymal state may differently affect Wnt/β‐catenin and Hippo/YAPsignaling andCSCphenotypes. In this report, we found thatYAPsignaling andCD44high/CD24−/lowCSCs were upregulated while Wnt/β‐catenin signaling andALDH+CSCs were downregulated in mesenchymal‐likeTNBCcells, and vice versa in their epithelial‐like counterparts. Dual knockdown ofYAPand Wnt/β‐catenin, but neither alone, was required for effective suppression of bothCD44high/CD24−/lowandALDH+CSCpopulations in mesenchymal and epithelialTNBCcells. These observations were confirmed with cultured tumor fragments prepared from patients withTNBCafter treatment with Wnt inhibitorICG‐001 andYAPinhibitor simvastatin. In addition, a clinical database showed that decreased gene expression of Wnt andYAPwas positively correlated with decreasedALDHandCD44 expression in patients’ samples while increased patient survival. Furthermore, tumor growth ofTNBCcells in either epithelial or mesenchymal state was retarded, and bothCD44high/CD24−/lowandALDH+CSCsubpopulations were diminished in a human xenograft model after dual administration ofICG‐001 and simvastatin. Tumorigenicity was also hampered after secondary transplantation. These data suggest a new therapeutic strategy forTNBCvia dual Wnt andYAPinhibition.
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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 | 0.001 |
| 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".