A Src/Abl Kinase Inhibitor, SKI-606, Blocks Breast Cancer Invasion, Growth, and Metastasis <i>In vitro</i> and <i>In vivo</i>
Bibliographic record
Abstract
The central role of Src in the development of several malignancies, including breast cancer, and the accumulating evidence of its interaction with receptor tyrosine kinases, integrins, and steroid receptors have identified it as an attractive therapeutic target. In the current study, we have evaluated the effect of a Src/Abl kinase inhibitor, SKI-606, on breast cancer growth, migration, invasion, and metastasis. Treatment of human breast cancer cells MDA-MB-231 with SKI-606 caused a marked inhibition of cell proliferation, invasion, and migration by inhibiting mitogen-activated protein kinase and Akt phosphorylation. For in vivo studies, MDA-MB-231 cells transfected with the plasmid encoding green fluorescent protein (GFP; MDA-MB-231-GFP) were inoculated into the mammary fat pads of female BALB/c nu/nu mice. Once tumor volume reached 30 to 50 mm(3), animals were randomized and treated with vehicle alone or 150 mg/kg SKI-606 by daily oral gavage. Experimental animals receiving SKI-606 developed tumors of significantly smaller volume (45-54%) compared with control animals receiving vehicle alone. Analysis of lungs, liver, and spleen of these animals showed a significant decrease in GFP-positive tumor metastasis in animals receiving SKI-606 at a dose that was well tolerated. Western blot analysis and immunohistochemical analysis of primary tumors showed that these effects were due to the ability of SKI-606 to block tumor cell proliferation, angiogenesis, growth factor expression, and inhibition of Src-mediated signaling pathways in vivo. Together, the results from these studies provide compelling evidence for the role of Src inhibitors as therapeutic agents for blocking breast cancer growth and metastasis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".