Targeting Focal Adhesion Kinase with Dominant-Negative FRNK or Hsp90 Inhibitor 17-DMAG Suppresses Tumor Growth and Metastasis of SiHa Cervical Xenografts
Bibliographic record
Abstract
Focal adhesion kinase (FAK), a nonreceptor protein tyrosine kinase and key modulator of integrin signaling, is widely expressed in different tissues and cell types. Recent evidence indicates a central function of FAK in neoplasia where the kinase contributes to cell proliferation, resistance to apoptosis and anoikis, invasiveness, and metastasis. FAK, like other signaling kinases, is dependent on the chaperone heat shock protein 90 (Hsp90) for its stability and proper function. Thus, inhibition of Hsp90 might be a way of disrupting FAK signaling and, consequently, tumor progression. FAK is expressed in high-grade squamous intraepithelial lesions and metastatic cervical carcinomas but not in nonneoplastic cervical mucosa. In SiHa, a cervical cancer cell line with characteristics of epithelial-to-mesenchymal transition, the stable expression of dominant-negative FAK-related nonkinase decreases anchorage independence and delays xenograft growth. FAK-related nonkinase as well as the Hsp90 inhibitor 17-dimethylaminoethylamino-17-demethoxygeldanamycin both negatively interfere with FAK signaling and focal adhesion turnover. Short-term 17-dimethylaminoethylamino-17-demethoxygeldanamycin treatment prolongs survival in a SiHa lung metastasis model and chronic administration suppresses tumor growth as well as metastatic spread in orthotopic xenografts. Taken together, our data suggest that FAK is of importance for tumor progression in cervical cancer and that disruption of FAK signaling by Hsp90 inhibition might be an avenue to restrain tumor growth as well as metastatic spread.
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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) | 0.002 | 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".