Abstract 3895: Tumor suppressor activity of the ERK/MAPK signaling: inhibition of cell reprogramming by degradation of specific proteins
Bibliographic record
Abstract
Abstract In this study, we investigated the contribution of the ERK signals to tumor initiation and the differentiation state of cancer cells. Oncogenic forms of RAS are found in up to 30% of all human cancers and are established drivers of tumor initiation and maintenance. However, strong expression of these oncogenes in normal cells induces cellular senescence, a putative tumor-suppressive barrier. RAS activates several signaling pathways, such as the PI3K/AKT pathway, the RAL pathway and the classical RAF/MEK/ERK MAP Kinase pathway. We previously found that RASV12-induced senescence of primary cells is prevented by attenuation of ERK signaling. Mechanistically, strong ERK signaling promotes senescence by inducing selective proteasome-dependent protein degradation. This “Senescence-Associated Protein Degradation” (SAPD) targets proteins required for cell cycle progression, mitochondrial functions, cell migration and cell signaling. Here we show that in addition to abrogating RAS-induced senescence, a moderate ERK activity allows transformation of primary human cells stably expressing RASV12 and hTERT as well as transformation of RasV12-expressing rodent cells. Furthermore, in a Kras-driven mouse model of multistage pancreatic cancer progression, decreased p-ERK levels correlate with tumor initiation. We found that transformed cells with low p-ERK levels express markers of pluripotency and demonstrate phenotypes of tumor initiating cells, such as formation of free-floating tumor spheres, and show the expression of a gene module associated to stem cells. This depends on moderate p-ERK levels, since increasing the activity of the pathway by the pharmacological and genetic inhibition of the Dual-Specificity Phosphatases 1 and 6 (DUSP1/6) completely abrogates the stem-like cell phenotype. Our results suggest that strong ERK signals could circumvent this phenotype by promoting the degradation of key transcription factors that regulate expression of stem cell-associated genes, and this, even if the activity of the pathway is not sufficient to induce cellular senescence. Taken together, these results demonstrate a novel anti-tumor effect of strong ERK signaling and suggest that processes attenuating ERK levels and/or activity may contribute to tumor initiation and aggressiveness of oncogenic RAS-driven cancers. Therefore, we propose a model where a moderated level of activated ERK (p-ERK) in RAS-expressing cells promotes transformation and dedifferentiation whereas higher levels limit cancer initiation and maintenance by activating tumor-suppressive mechanisms like senescence and differentiation. Considering the increased resistance of tumor-initiating cells to chemotherapy and their capacity to initiate tumor development, this model suggests a need to target cancer cells with low pERK levels within a tumor. Citation Format: Xavier Deschênes-Simard, Filippos Kottakis, Frédéric Lessard, Emmanuelle Saint-Germain, Véronique Bourdeau, Nabeel Bardeesy, Gerardo Ferbeyre. Tumor suppressor activity of the ERK/MAPK signaling: inhibition of cell reprogramming by degradation of specific proteins. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3895. doi:10.1158/1538-7445.AM2014-3895
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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 | 0.001 |
| 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".