Mutations involving <scp>TGFB</scp> and <scp>MAPK</scp> may be associated with malignancy in granular cell tumors
Bibliographic record
Abstract
Granular cell tumors (GrCTs) are mesenchymal neoplasms of presumed schwannian differentiation that may present as solitary or multifocal lesions with excision usually being curative. A minority of cases, however, show histological features associated with an increased risk for metastasis and are highly aggressive leading to death in about a third of cases. While benign and malignant cases have been shown to harbor mutations in the H + ATPase genes, there is only limited data examining molecular aberrations associated with malignancy. The departmental archives were searched for cases of atypical/malignant GrCTs. Clinical and histopathological features were noted. Whole-exome sequencing was performed. Three cases of malignant GrCTs and one case of atypical GrCTs were included. All three malignant tumors metastasized to distant sites with a median disease-free survival of 16 months and an overall follow-up time of 35 months. Whole-exome sequencing showed mutations involving TGFβ and MAPK pathways in all four tumors. Although the cohort size is small, our preliminary findings suggest that mutations involving the TGFβ and MAPK pathways may be associated with tumor progression or malignant transformation in GrCT pathogenesis.
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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 | 0.001 |
| 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".