Effects of dasatinib on EphA2 receptor tyrosine kinase activity and downstream signalling in pancreatic cancer
Bibliographic record
Abstract
Eph receptors constitute the largest family of receptor tyrosine kinases in the human genome. EphA2 is one prominent member that is overexpressed and functionally altered in many invasive cancers, including pancreatic cancer. Dasatinib, which is a multi-targeted kinase inhibitor mainly developed for Bcr-Abl and Src family kinases, has recently been shown to have significant activity against EphA2. As selective small molecule EphA2 inhibitors are not currently available, we investigated the therapeutic potential to target EphA2 by dasatinib in pancreatic cancer cell lines. Using in vitro kinase assays, we found that EphA2 receptor tyrosine kinase was inhibited directly by dasatinib in a dose-dependent manner. Stimulation with ephrinA1 produced rapid increases of EphA2 phosphorylation that were inhibited by dasatinib, although the effects on activation of downstream signalling differed among the pancreatic cancer cell lines. Dasatinib also inhibited ligand-induced binding of EphA2 to the ubiquitin ligase Cbl, and the internalisation and degradation of EphA2, suggesting that these processes are dependent on kinase activity. Treatment with dasatinib decreased EphA2 phosphorylation in BxPC-3 xenografts, suggesting that dasatinib might have activity in pancreatic cancer due to EphA2 inhibition, besides its effects on Src.
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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) | 0.001 | 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".