EPCO-04. GENOMIC AND EPIGENOMIC HALLMARKS OF SCHWANNOMATOSIS SCHWANNOMAS
Bibliographic record
Abstract
Abstract Schwannomatosis (SWNTS) is a genetic cancer predisposition syndrome that manifests as multiple and often, painful neuronal tumors called schwannomas (SWNs). Very little is known about the epigenomic and genomic alterations in SWNTS related SWNs (SWNTS-SWNs) other than germline mutations in SMARCB1 and LZTR1 plus somatic mutations in NF2 and loss of heterozygosity in chromosome 22q. Herein, we have comprehensively established the specific molecular signatures of SWNTS-SWNs. We found that tumor anatomic location was associated with pain and distinct DNA methylation and transcriptional signatures. DNA sequencing revealed several novel non-22q deletions, specifically in LZTR1-mutant cases. Whole-genome sequencing identified novel recurrent structural rearrangements. Further, chromosomal aberrations in SWNTS-SWNs were accompanied by increased transcription of mismatch repair genes. Our transcriptome analysis detected the SH3PXD2A-HTRA1 gene fusion in SWNTS-SWNs, more commonly in LZTR1-mutant tumors. In addition, we identified the specific genetic, epigenetic, and transcriptional hallmarks of painful SWNs that may be harnessed to develop new treatments for this debilitating syndrome.
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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) | 0.004 | 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".