Abstract 1195: Erk activity promotes Notch-dependent HES1 expression in pancreatic cancer cells
Bibliographic record
Abstract
Abstract The oncogene Ras is well-recognized for its involvement in tumorigenesis in part through activation of downstream pathways including the PI3K/Akt and Mek/Erk pathways. Recent studies have suggested that the ability of Ras to transform cells also depends on cooperation with the Notch signalling pathway. However, the molecular and cellular mechanisms involved in this cooperation remain largely unknown. The aim of this study was to evaluate whether specific activation of the Ras/Raf/Mek/Erk pathway impacts Notch-dependent signalling. METHODS. Experiments were done using the pancreatic cancer cells MIA PaCa-2 and BxPC-3. Both cell lines express endogenous levels of the activated Notch1 (NIC). To inhibit Mek/Erk activity, cells were treated with the Mek inhibitor U0126 (10uM). Activation of the Mek/Erk pathway was achieved by treatment with phorbol 12-myristate 13-acetate (PMA, 100nM). The hes1-luciferase reporter gene was used to evaluate Notch-dependent transcriptional activity. The gamma-secretase inhibitor DAPT (25µM) was used to inhibit Notch activation. RESULTS. 1- Treatment of pancreatic cancer cells with U0126 reduced the activity of the hes1-luciferase reporter gene to the same extent than treatment with the gamma-secretase inhibitor DAPT. 2- Combined treatment (U0126 + DAPT) did not result in an additive inhibition of hes1-luciferase activity suggesting a Notch-dependent impact of the Mek inhibitor on hes1 transcription. 3- Increased HES1 mRNA expression levels were observed following treatment with PMA, an effect prevented by addition of U0126. 4- Massive activation of the Notch receptors by treatment with EGTA promoted HES1 protein expression. 5- Interestingly, strong stimulation of Erk activity by PMA promoted the EGTA-induced HES1 protein expression. 6- Conversely, inhibition of Erk activity completely prevented both the EGTA-induced and EGTA+PMA-induced HES1 protein expression. CONCLUSION. Taken together, our results suggest that Erk activity promotes Notch-dependent transcriptional activity and consequently promotes the expression of Notch target genes such as HES1. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1195. doi:1538-7445.AM2012-1195
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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) | 0.003 | 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".