OS08.7 The somatic landscape of Schwannoma
Bibliographic record
Abstract
To date, clinical trials for schwannomas, which are common cranial and spinal nerve tumors, have generated dismal results. This is largely due to a lack of fully understanding the somatic alterations that occur in schwannoma. To gain a comprehensive molecular understanding of Schwannoma, we performed an integrative multiplatform analysis to determine the genetic landscape of sporadic schwannomas. Exome sequence analysis with validation by targeted DNA-sequencing of 125 samples uncovered, in addition to expected NF2 disruption, recurrent mutations in ARID1A, ARID1B and DDR1. Genome wide methylation profiling revealed that schwannomas consists of two molecular subgroups with unique gene signatures and anatomical location. RNA sequencing revealed a recurrent in-frame gene fusion on chromosome 10q in 12/125 (10%) cases involving two gene partners, SH3PXD2A and HTRA1. Further genomic analysis identified the mechanism to be from a balanced 19Mb chromosomal inversion. The presence of the fusion was associated with male gender predominance, occurring in one out of every six men with schwannoma. Expression of the fusion in both schwannomas and normal schwann cells resulted in elevated phosphorylated-ERK, increased proliferation, increased invasion and resulted in the formation of in vivo xenografts. Furthermore, the fusion increased cell invasion and enhanced protease activity. Targeting of the MEK/ERK pathway was effective in fusion-positive Schwann cells, suggesting a possible therapeutic approach for this subset of tumors.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".