Inhibition of Src and insulin/insulin-like growth factor-1 receptor (IR/IGF-1R) on tumors and bone turnover in prostate cancer (PCa) models in vivo compared with inhibition of either kinase alone.
Bibliographic record
Abstract
61 Background: The Src and IGF-1R axes are aberrantly activated in both PCa and the microenvironment of bone metastases. Dasatinib and BMS-754807 are clinically promising small molecule inhibitors with high potency against Src family kinases (SFK) and IR/IGF-1R, respectively. Based on a phase I/II clinical trial in which 9/19 pts treated with docetaxel + dasatinib were increased in serum IGF-1 levels after one cycle, the aim of this study was to establish potential antitumor cooperativity of inhibiting both IGF-1R and Src in experimental PCa models in vitro and in mice. Methods: Inhibition of Src and IGF-1R pathways was accomplished by pharmacologic agents (dasatinib against Src and BMS-754807 against IR/IGF-1R) as well as by shRNA, in PC3 and LNCaP cells. In vivo studies were done after orthotopic and intratibial injection of PC3 cells in nude mice. Results: SFK inhibition decreased proliferation and migration of PCa cells whereas IGF-1R blockade induced apoptosis. All anti-tumor effects were enhanced by dual blockade. IGF-1 induced phosphorylation of Akt1 and 2. Only Akt 1 phosphorylation was decreased by dasatinib; whereas Akt 1 and 2 phosphorylation were completely abrogated by the combination. Dasatinib and BMS-754807 inhibited orthotopic in vivo tumor growth of PC3 cells more potently than either inhibitor alone. Similarly, intratibial tumor growth and bone destruction was significantly reduced with the drug combination, accompanied by a decrease in serum bone turnover markers alkaline phosphatase and N-telopeptide. Conclusions: Dual inhibition of Src and IGF-1R has greater anti-tumor effect in PCa cells compared to inhibiting either alone. In the presence of IGF-1, dasatinib and BMS-754807 are necessary to inhibit IGF-1-induced phosphorylation of Akt1 and 2 in tumor cells in culture. In intratibial models, decreased bone turnover markers in serum support the concept of targeting both the epithelial and bone microenvironment. The combination of dasatinib and BMS-754807 may be a rational therapeutic approach in PCa by blocking complementary processes of tumor growth and progression.
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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".