Atypical neurofibromas reveal distinct epigenetic features with proximity to benign peripheral nerve sheath tumor entities
Bibliographic record
Abstract
BACKGROUND: Plexiform neurofibromas can transform into atypical neurofibromas (ANF) and then further progress to aggressive malignant peripheral nerve sheath tumors (MPNST). ANF have been described to harbor distinct histological features and frequent loss of CDKN2A/B. However, histological evaluation may be rater-dependent, and detailed knowledge about the molecular mechanisms of malignant transformation is scarce. In general, malignant transformation can be accompanied by significant epigenetic changes, and global DNA methylation profiling is able to differentiate relevant tumor subgroups. Therefore, epigenetic profiling might provide a valuable tool to distinguish and characterize ANF with differing extent of histopathological atypia from neurofibromas and MPNST. METHODS: We investigated 40 tumors histologically diagnosed as ANF and compared their global methylation profile to other peripheral nerve sheath tumors. RESULTS: Unsupervised class discovery and t-SNE analysis indicated that 36/40 ANF cluster with benign peripheral nerve sheath tumors with clear separation from MPNST. 21 ANF formed a molecularly distinct cluster in proximity to schwannomas. Tumors in this cluster had a frequent heterozygous or homozygous loss of CDKN2A/B and significantly more lymphocyte infiltration than MPNST, schwannomas, and NF. Few ANF clustered closely with neurofibromas, schwannomas, or MPNST, raising the question, whether diagnosis based on histological features alone might pose a risk to both over- and underestimate the aggressiveness of these lesions. CONCLUSIONS: Our data suggest that ANF with varying histological morphology show distinct epigenetic similarities and cluster in proximity to benign peripheral nerve sheath tumor entities. Future investigations should pay special respect to correlating this methylation pattern to clinical outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".