Oncogenic RAC1 and NRAS drive resistance to endoplasmic reticulum stress through MEK/ERK signalling
Bibliographic record
Abstract
Cancer cells are able to survive under conditions that cause endoplasmic reticulum stress (ER-stress), and can adapt to this stress by upregulating cell-survival signalling pathways and down-regulating apoptotic pathways. The cellular response to ER-stress is controlled by the unfolded protein response (UPR). Small Rho family GTPases are linked to many cell responses including cell growth and apoptosis. In this study, we investigate the function of small GTPases in cell survival under ER-stress. Using siRNA screening we identify that RAC1 promotes cell survival under ER-stress in cells with an oncogenic N92I RAC1 mutation. We uncover a novel connection between the UPR and N92I RAC1, whereby RAC1 attenuates phosphorylation of EIF2S1 under ER-stress and drives over-expression of ATF4 in basal conditions. Interestingly, the UPR connection does not drive resistance to ER-stress, as knockdown of ATF4 did not affect this. We further investigate cancer-associated kinase signalling pathways and show that RAC1 knockdown reduces the activity of AKT and ERK, and using a panel of clinically important kinase inhibitors, we uncover a role for MEK/ERK, but not AKT, in cell viability under ER-stress. A known major activator of ERK phosphorylation in cancer is oncogenic NRAS and we show that knockdown of NRAS in cells, which bear a Q61 NRAS mutation, sensitises to ER-stress. These findings highlight a novel mechanism for resistance to ER-stress through oncogenic activation of MEK/ERK signalling by small GTPases.
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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) | 0.002 | 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".