Imatinib and Dasatinib Inhibit Hemangiosarcoma and Implicate PDGFR-β and Src in Tumor Growth
Bibliographic record
Abstract
Hemangiosarcoma, a natural model of human angiosarcoma, is an aggressive vascular tumor diagnosed commonly in dogs. The documented expression of several receptor tyrosine kinases (RTKs) by these tumors makes them attractive targets for therapeutic intervention using tyrosine kinase inhibitors (TKIs). However, we possess limited knowledge of the effects of TKIs on hemangiosarcoma as well as other soft tissue sarcomas. We report here on the use of the TKIs imatinib and dasatinib in canine hemangiosarcoma and their effects on platelet-derived growth factor receptor β (PDGFR-β) and Src inhibition. Both TKIs reduced cell viability, but dasatinib was markedly more potent in this regard, mediating cytotoxic effects orders of magnitude greater than imatinib. Dasatinib also inhibited the phosphorylation of the shared PDGFR-β target at a concentration approximately 1000 times less than that needed by imatinib and effectively blocked Src phosphorylation. Both inhibitors augmented the response to doxorubicin, suggesting that clinical responses likely will be improved using both drugs in combination; however, dasatinib was significantly (P < .05) more effective in this context. Despite the higher concentrations needed in cell-based assays, imatinib significantly inhibited tumor growth (P < .05) in a tumor xenograft model, highlighting that disruption of PDGFR-β/PDGF signaling may be important in targeting the angiogenic nature of these tumors. Treatment of a dog with spontaneously occurring hemangiosarcoma established that clinically achievable doses of dasatinib may be realized in dogs and provides a means to investigate the effect of TKIs on soft tissue sarcomas in a large animal model.
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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".