MEDU-42. ELUCIDATING THE ROLE OF STRESS GRANULE FORMATION IN MEDULLOBLASTOMA
Bibliographic record
Abstract
Medulloblastoma (MB) is the most common pediatric brain malignancy, accounting for 20% of diagnosed brain tumors in children. The Group 3 (G3) MB subtype is found to be especially malignant and is commonly associated with MYC over-expression. Current G3 MB treatment regimens provide for 50% 5-year survival rates and are associated with severe side effects, including hearing loss and neurocognitive deficits. High expression of stress granule (SG) proteins such as G3BP1 and PAPBPC1 correlates with poor survival outcome in G3 MB patients. We propose that formation of SGs, mRNP granules assembled under cellular stress, is integral for MB tumor progression, and suggest that inhibition of SG formation is a novel therapeutic strategy. In order to identify key proteins for SG formation we set up a siRNA screen for 95 genes that have previously been linked to SG formation, and that are highly expressed in MB G3 tumors. The screen has been done in two G3 MB cell lines: MED8A and HDMB03. EIF2AK1, also known as HRI, was identified as one of the hits in the screen; it is an eIF2a kinase responsible for cellular response to oxidative stress. Phosphorylation of eIF2a is essential for canonical SG formation and global translation inhibition. We therefore used ISRIB, a known inhibitor of the integrated stress response, to block the effects of eIF2a phosphorylation, thus maintaining active global translation under stress conditions and preventing SG assembly. MB cells exposed to ISRIB combined with vincristine exhibited significantly decreased proliferation as well as increased apoptosis markers compared to cells exposed to vincristine alone. We show that combining SG inhibition with chemotherapy enhances the effects of the latter, which is increasingly relevant for patients suffering from therapy side effects and high chances of metastasis associated with G3 MB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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 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".