MBRS-57. TARGETING METABOLIC ADAPTATION IN MYC/MYCN AMPLIFIED PEDIATRIC MEDULLOBLASTOMA AND NEUROBLASTOMA
Bibliographic record
Abstract
The MYC oncogenes contribute to more than 50% of all human cancers, but their therapeutic targeting has proven challenging. MYC/MYCN amplification in childhood medulloblastoma (MB) and neuroblastoma (NB) determine aggressive disease and high mortality, underlying the need for novel and effective therapies. MYC-driven transformation is energy demanding and impairs cell survival under nutrient deprivation (ND), a characteristic stress condition within the tumor microenvironment. We recently identified eukaryotic Elongation Factor 2 Kinase (eEF2K) as a pivotal mediator of the adaptive response of tumor cells to ND. We therefore hypothesized that eEF2K facilitates the adaptation of MYC/MYCN amplified MB/NB to ND, and that inhibiting this pathway can impair tumor progression. Analyzing publicly available genomic databases and tissue microarrays, we found that high eEF2K expression and activity are strongly predictive of poor outcome in MB and NB (p<0.001), and correlate with MYC/MYCN amplification (p<0.001). Inhibition of eEF2K significantly decreases survival of MYC/MYCN amplified MB/NB cell lines in vitro under ND. Combination of eEF2K knockdown and caloric restriction determines a twofold growth decrease of MYCN amplified NB mouse xenografts. Finally, eEF2K inactivation significantly attenuated the ability of tumor cells to engage fatty acid oxidation under ND, suggesting a link between eEF2K, MYC transformation and lipid metabolism. eEF2K represents a critical mediator for the adaptive response of MYC/MYCN amplified tumors to acute metabolic stress, and is therefore a promising therapeutic target. Future studies will combine eEF2K pharmacological inhibition with caloric restriction mimetics, as eEF2K activity appears to be critical under ND.
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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) | 0.001 | 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".