Targeting focal adhesion turnover in invasive breast cancer cells by the purine derivative reversine
Bibliographic record
Abstract
BACKGROUND: The dynamics of focal adhesion (FA) turnover is a key determinant for the regulation of cancer cell migration. Here we investigated FA turnover in a panel of breast cancer models with distinct invasive properties and evaluated the impact of reversine on this turnover in relation to cancer cell invasion in in vitro and in vivo conditions. METHODS: Live imaging and immunofluorescence assays were used to investigate FA turnover in breast cancer cells. Biochemical studies were used to investigate the impact of reversine on FA signalling and turnover. In vivo activity was investigated using orthotopic breast cancer mouse models. RESULTS: Accelerated FA disassembly from plasma membrane protrusions was observed in invasive compared with non-invasive breast cancer cells or non-immortalised mammary epithelial cells. Reversine significantly inhibited FA disassembly leading to stable FAs, which was associated with reduced cell motility and invasion. The inhibitory effect of reversine on FA turnover accounted for a large part on its capacity to interfere with FAK function on regulating its downstream targets. In orthotopic breast cancer mouse models, reversine revealed a potent inhibitory activity on tumour progression to metastasis. CONCLUSION: These results support the utility of targeting FA turnover as a therapeutic approach for invasive breast cancer.
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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 | 0.000 |
| 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 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".