MBRS-24. INVESTIGATING THE ROLE OF THE RNA BINDING PROTEIN, MUSASHI 1 IN PEDIATRIC GROUP 3 MEDULLOBLASTOMA
Bibliographic record
Abstract
Pediatric brain tumours are the leading cause of solid cancer mortality with medulloblastoma (MB) representing the most frequent malignant solid central nervous system (CNS) tumour. To date, most experimental efforts to understand the biological mechanism of solid cancers have been directed towards transcriptional regulatory mechanisms that modulate fate determination in stem and progenitor cells. However, little is known regarding post-transcriptional pathways that contribute to mRNA stability, translation into protein, and functional effects on cell fate. The mRNA binding protein, Musashi (Msi) has been observed to play a crucial role in promoting stem cell self–renewal and is implicated in CNS tumours including glioma and MB with associated poor clinical prognosis. METHODS AND Since increasing brain tumour stem cell or brain tumour-initiating cell (BTIC) frequency is associated with tumor aggressiveness and poor patient outcome, we probed for Msi1 within 251 primary human MBs from four transcriptional databases and 74 NanoString-subgrouped MBs, and found it was enriched in aggressive MB subgroups. Applying gene knockdown and overexpression of Msi1 in Group 3 MB, we have performed in vitro and in vivo assays elucidating how these RNA-binding proteins help to maintain the BTIC state promote an aggressive tumour phenotype. Though transcriptional manipulation has uncovered novel druggable targets to treat MB, bench-to-bedside clinical translation of these targets has yet to yield significant patient benefit. For the first time, Msi analysis in MB provides a novel paradigm for treatment of MB through targeting post-transcriptional pathways and regulators.
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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) | 0.001 | 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".