TB-16EXPLORATION OF eEF2K AS A NOVEL THERAPEUTIC TARGET IN MEDULLOBLASTOMA AND NEUROBLASTOMA
Bibliographic record
Abstract
INTRODUCTION: Medulloblastoma and neuroblastoma are aggressive solid pediatric tumors, with 5 year survival rates lower than 50-60%. In addition, more than 80% of the survivors develop permanent neurological impairments. Hence, there is a dire need to identify and validate novel, more effective and less toxic therapeutic approaches. Tumors are continually exposed to acute changes in the micro-environment, including nutrient availability. We previously showed that eukaryotic Elongation Factor-2 Kinase (eEF2K) is a critical regulator of cellular adaptation to acute metabolic stress. Based on those findings, we hypothesize that eEF2K is a marker of outcome and mediates medulloblastoma and neuroblastoma adaptation to acute stress. METHODS: Proprietary gene expression datasets (for medulloblastoma) and the R2 genomic analysis platform (for neuroblastoma) were analyzed for links between eEF2K expression and outcome. Effects of eEF2K knockdown on cell survival were evaluated in BE(2)C neuroblastoma cells. Immunoblotting and immunohistochemistry were performed on neuroblastoma cell lines and tissue microarrays (TMAs) for key molecules in the pathway. Similar studies are underway in medulloblastoma cell lines and TMAs. RESULTS: Low eEF2K mRNA expression is predictive of improved survival in medulloblastoma and neuroblastoma. Low p-eEF2 protein expression, indicative of low eEF2K activity, improves survival in human neuroblastoma. Neuroblastoma cell lines with eEF2K knockdown are more sensitive than controls to nutrient deprivation. CONCLUSIONS: eEF2K may represent a critical mechanism for adaptation to metabolic stress in neuroblastoma and medulloblastoma and is therefore a promising therapeutic target. We are currently exploring the pharmacological inhibition of eEF2K in xenograft tumor models.
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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".