Effects of combined inhibition of MEK and mTOR on downstream signaling and tumor growth in pancreatic cancer xenograft models
Bibliographic record
Abstract
The ERK and mTOR pathways show multiple interconnections that coordinate growth activation and the regulation of protein translation. Although drugs that target these pathways appear to have limited anti-cancer effects as single agents, we hypothesized that the monotherapy anticancer efficacy of these agents could be enhanced by their combination. The MEK inhibitor AZD6244 (ARRY-142886) and the mTOR inhibitor rapamycin were tested as single agents and in combination, using BxPC-3 and MIA PaCa-2 pancreatic cancer models in vivo. In both models, S6 ribosomal protein was almost completely inhibited with combined treatment, but only partially inhibited with the single agents. In addition, 48 h treatment with the drug combination produced greater apoptosis, revealed by caspase 3 cleavage, and growth inhibition measured using bromodeoxyuridine incorporation, compared to the single agents. AZD6244 but not rapamycin exhibited a significant anti-angiogenic effect, as shown by tumor VEGF ELISA assay and CD31 analysis. Plasma and tumor pharmacokinetic analyses indicated that AZD6244 accumulates in tumor tissue at concentrations that produce target inhibition and cell cycle arrest in vitro. In chronic dosing experiments, the drug combination was well tolerated, and showed greater growth inhibition compared to the single agents. These results are consistent with the hypothesis that ERK and mTOR signaling interact at multiple levels to regulate tumor growth in vivo, and support the testing of MEK plus mTOR inhibitor combinations in pancreatic cancer patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".