Data from AZD6244 (ARRY-142886) enhances the therapeutic efficacy of sorafenib in mouse models of gastric cancer
Bibliographic record
Abstract
Abstract Gastric cancer is a deadly disease for which current therapeutic options are extremely limited. Vascular endothelial growth factor receptors and platelet-derived growth factor receptors regulate gastric cancer cell proliferation, invasion, and tumor angiogenesis. In the present study, we report that sorafenib therapy effectively inhibited tumor growth and angiogenesis in tumor xenografts. These were associated with reduction in the phosphorylation of vascular endothelial growth factor receptor-2 Tyr951, c-Kit Tyr568/570, platelet-derived growth factor receptor-β Tyr1021, and Akt Ser473 and Thr308, down-regulation of positive cell cycle regulators, increased apoptosis, and up-regulation of p27. Sorafenib treatment also caused up-regulation of p-c-Raf Ser338 and p-extracellular signal-regulated kinase (ERK) Thr202/Tyr204 in gastric cancer xenografts. The combination of sorafenib and MAP/ERK kinase inhibitor AZD6244 enhances the effectiveness of each compound alone. Potential effect of sorafenib/AZD6244 included increase in proapoptotic Bim. Our data show that MAP/ERK kinase inhibition enhances the antitumor activity of sorafenib in vivo, supporting a rationale for multitargeted suppression of the angiogenesis and ERK signaling network in gastric cancer therapy. [Mol Cancer Ther 2009;8(9):2537–45]
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".