CS-16 * THE eEF2 KINASE IS CRITICAL FOR BRAIN TUMOURS ADAPTATION TO METABOLIC STRESS
Bibliographic record
Abstract
During tumour progression, brain tumour cells are exposed to metabolic stress, such as nutrient deprivation, due to abnormal tumour vasculature. The ability of tumour cells to respond and manage reduced nutrient availability has a strong impact on tumour outcome. The molecular pathways supporting metabolic adaptation of brain tumour cells to nutrient stress represent potential therapeutic targets which are still not well defined. We report that the translation elongation factor 2 (eEF2) kinase mediates a protective response under nutrient starvation by restraining mRNA translation at the step of elongation. In aggressive human tumour cells, such as medulloblastoma (MB) cells, ablation of eEF2K expression increases sensitivity to nutrient removal. In addition, gene expression analysis in patient samples show that eEF2K expression is upregulated in the most aggressive subgroup of MB, namely group 3, and that high eEF2K expression is strongly associated with poor survival in both MB and glioblastoma (GBM). In vivo, eEF2K overexpression confers resistance of tumour xenografts to calorie restriction. Finally, our data reveal that eEF2K is an evolutionarily conserved mediator of the physiological response to nutrient starvation, as genetic removal of eEF2K compromises survival of C. elegans in absence of nutrients. Overall, our works highlight a novel pro-survival factor which is hijacked by brain tumour cells to support their adaptation to nutrient stress. The potential for therapeutic targeting of eEF2 kinase in brain tumors will be discussed.
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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".