HGG-18. ALTERNATIVE SPLICING OF NEUROFIBROMIN 1 IS ASSOCIATED WITH ELEVATED MAPK ACTIVITY AND POOR PROGNOSIS IN HIGH-GRADE GLIOMA
Bibliographic record
Abstract
Pediatric high-grade gliomas (pHGG) are invasive tumors with poor prognosis. In particular, diffuse intrinsic pontine glioma (DIPG), arising in the brainstem, is incurable and the leading cause of brain-tumor death in children. Previous genomic studies have uncovered many driving mutations of pHGG, including frequent activating alterations of the RAS/MAPK/PI3K pathway. Here we performed RNA-Seq analysis on a large sample of pHGG (n=63) and normal brain (n=20), finding that, regardless of mutation status, RAS pathway activation was near universal. To try to understand the mechanism behind this, we focussed on alternative splicing, finding a network of alterations converging on the RAS pathway. One of the most significantly spliced genes was neurofibromin 1 (NF1), which switches from the NF1-I transcript in normal brain to NF1-II in pHGG. NF1-II splices the 21 aa exon23a into the GAP-related domain of NF1, rendering NF1 10 times less active in inhibiting RAS thus elevating MAPK signaling. Increased exon23a inclusion was associated with increased RAS activity. The same pattern was seen in adult glioma, where exon23a inclusion was associated with worse patient outcome independent of RAS pathway mutation. Exon23a splicing is regulated by the CELF and ELAV-like families of splice regulators, which are highly downregulated in pHGG leading to increased NF1-II levels. Together, our results identify a novel mechanism by which HGG can activate RAS signaling and promote tumorigenesis independently from mutations.
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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".