SKI-606 (bosutinib), a novel Src kinase inhibitor, suppresses migration and invasion of human breast cancer cells
Bibliographic record
Abstract
Src family kinase activity is elevated in many human tumors, including breast cancer, and is often associated with aggressive disease. We examined the effects of SKI-606 (bosutinib), a selective Src family kinase inhibitor, on human cancer cells derived from breast cancer patients to assess its potential for breast cancer treatment. Our results show that SKI-606 caused a decrease in cell motility and invasion of breast cancer cell lines with an IC50 of approximately 250 nmol/L, which was also the IC50 for inhibition of cellular Src kinase activity in intact tumor cells. These changes were accompanied by an increase in cell-to-cell adhesion and membrane localization of beta-catenin. By contrast, cell proliferation and survival were unaffected by SKI-606 at concentrations sufficient to block cell migration and invasion. Analysis of downstream effectors of Src revealed that SKI-606 inhibits the phosphorylation of focal adhesion kinase (FAK), proline-rich tyrosine kinase 2 (Pyk2), and Crk-associated substrate (p130Cas), with an IC50 similar to inhibition of cellular Src kinase. Our findings indicate that SKI-606 inhibits signaling pathways involved in controlling tumor cell motility and invasion, suggesting that SKI-606 is a promising therapeutic for breast cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".