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Record W2211386458

Inhibition of EphA2 receptor tyrosine kinase activity by dasatinib in pancreatic cancer.

2007· article· en· W2211386458 on OpenAlexaff
Qing Chang, Claus Jørgensen, Tony Pawson, David W. Hedley

Bibliographic record

VenueMolecular Cancer Therapeutics · 2007
Typearticle
Languageen
FieldNeuroscience
TopicAxon Guidance and Neuronal Signaling
Canadian institutionsLunenfeld-Tanenbaum Research InstituteOntario Institute for Cancer Research
Fundersnot available
KeywordsEPH receptor A2Erythropoietin-producing hepatocellular (Eph) receptorDasatinibCancer researchEphrinTyrosine kinaseReceptor tyrosine kinasePancreatic cancerProto-oncogene tyrosine-protein kinase SrcBiologyAutophosphorylationCancerKinaseCell biologySignal transductionInternal medicineMedicineProtein kinase A
DOInot available

Abstract

fetched live from OpenAlex

C174 Eph receptors constitute the largest family of receptor tyrosine kinases (RTKs) in the human genome. Their ligands, which fall into the ephrin A and ephrin B classes, are surface bound, and bidirectional ephrin receptor/ligand interactions play an important role in normal tissue development. Aberrant Eph receptor function is implicated in cellular transformation, metastasis, and angiogenesis. EphA2 is one prominent member that is over-expressed and functionally altered in many invasive cancers, including pancreatic cancer. Although the mechanisms by which EphA2 contributes to tumor cell malignancy are far from clear, it potentially represents a therapeutic target for novel anticancer agents. 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 (Huang et al. Cancer Res 2007; 67: 2226-38). Since selective small molecule EphA2 inhibitors are not currently available, we investigated the therapeutic potential to target EphA2 by dasatinib in BxPC-3, PANC-1, and MIA PaCa-2 pancreatic cancer cell lines. Using an in vitro kinase assay, we found that EphA2 receptor tyrosine kinase was inhibited directly by dasatinib in a dose-dependent manner. In all three pancreatic cancer cell lines, low basal levels of EphA2 tyrosine phosphorylation were detected by immunoprecipitation. Stimulation with ephrinA1-Fc ligand produced rapid increases of EphA2 phosphorylation, associated with activation of Akt in all three cell lines. In BxPC-3, but not in PANC-1 or MIA PaCa-2 cells, we also observed increased Y705-STAT3, whereas PANC-1 and MIA PaCa-2 cells showed increased ERK phosphorylation and a transient loss of focal adhesion kinase phosphorylation following EphA2 activation. These results suggest that the effects of EphA2 activation on cell behavior differ among the pancreatic cancer cell lines.
 In addition to Src, dasatinib inhibited EphA2 tyrosine kinase activity as well as downstream effectors of EphA2 in pancreatic cancer cell lines. Furthermore, dasatinib caused G1 arrest, not seen with the well characterized Src inhibitor PP2 except at the highest concentration, suggesting that EphA2 receptor tyrosine kinase inhibition but not Src inhibition resulted in cell cycle arrest. Previous work has shown that ligand binding results in the internalization and proteasomal degradation of EphA2 (Walker-Daniels et al. Mol Cancer Res 2002; 1: 79-87). We observed that dasatinib inhibited ligand-induced internalization and degradation of EphA2, suggesting that this is dependent on kinase activity. Preliminary in vivo experiments showed that treatment with dasatinib results in a transient decrease of EphA2 phosphorylation in BxPC-3 xenografts, suggesting that this drug might have activity in pancreatic cancer due to EphA2 inhibition, in addition to its effects on Src. We conclude that EphA2 is a promising therapeutic target in pancreatic cancer, and that the development of more selective inhibitors for clinical testing is indicated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.044
GPT teacher head0.319
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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