EPCO-55. ALTERNATIVE RNA SPLICING ANALYSIS IDENTIFIES PATTERNS UNDERLYING MALIGNANT TRANSFORMATION AND THERAPEUTIC RESPONSE IN PERIPHERAL NERVE TUMORS
Bibliographic record
Abstract
Abstract People with neurofibromatosis type 1 (NF-1) develop benign plexiform neurofibromas (pNFs) which can transform into malignant peripheral nervous sheath tumors (MPNSTs). While genetic underpinnings of this transformation have been described, alternative RNA splicing (AS) underlying malignant transformation or therapeutic response are not well understood. Using a discovery cohort from UCSF, we identify 660 AS events differentially spliced during transformation from pNFs to MPNSTs. Of these, 22 events were detected in two independent validation cohorts from the University of Toronto and GeM consortium, all of which had similar direction and magnitude of changes. AS transcripts were enriched in genes related to mitotic spindle and epithelial-mesenchymal transition signatures. We validate increased inclusion of an alternative cassette exon in Fibronectin (FN1) in MPNSTs compared to pNFs, and forced skipping of this exon using custom designed antisense oligonucleotides (ASOs) decreased proliferation, migration, and invasion of MPNST cells. We further investigate AS changes in response to the FDA approved MEK inhibitor selumetinib or radiotherapy in pNF and MPNST cells. Interestingly, while we identify 1035 and 682 AS events induced by radiotherapy in pNF and MPNST cells respectively, only 45 were common to both cell types. Similarly, we identify 1408 and 1107 AS events induced by selumetinib in pNF and MPNST cells respectively, with only 98 common to both cell lines. Together, suggesting biological differences, at the transcriptome level, in response to standard therapies. Finally, we validate ASOs targeting known and novel AS events in key RAS/MAPK regulators HRAS, KRAS, RRAS, and RASA3 that alter proliferation of MPNST cells and synergize with the MEK inhibitor selumetinib. Taken together, our work defines AS differences underlying malignant transformation and treatment response in peripheral nerve tumors, laying the foundation for a new class of rationale therapy design.
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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 | 0.001 | 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".