A morphologic and molecular reappraisal of myoepithelial tumors of soft tissue, bone, and viscera with EWSR1 and FUS gene rearrangements
Bibliographic record
Abstract
Myoepithelial tumors (MET) represent a clinicopathologically heterogeneous group of tumors, ranging from benign to highly aggressive lesions. Although MET arising in soft tissue, bone, or viscera share morphologic and immunophenotypic overlap with their salivary gland and cutaneous counterparts, there is still controversy regarding their genetic relationship. Half of MET of soft tissue and bone harbor EWSR1 or FUS related fusions, while MET arising in the salivary gland and skin often show PLAG1 and HMGA2 gene rearrangements. Regardless of the site of origin, the gold standard in diagnosing a MET relies on demonstrating its "myoepithelial immunophenotype" of positivity for EMA/CK and S100 protein or GFAP. However, the morphologic spectrum of MET in soft tissue and bone is quite broad and the above immunoprofile is nonspecific, being shared by other pathogenetically unrelated neoplasms. Moreover, rare MET lack a diagnostic immunoprofile but shows instead the characteristic gene fusions. In this study, we analyzed a large cohort of 66 MET with EWSR1 and FUS gene rearrangements spanning various clinical presentations, to better define their morphologic spectrum and establish relevant pathologic-molecular correlations. Genetic analysis was carried out by FISH for EWSR1/FUS rearrangements and potential partners, and/or by targeted RNA sequencing. Then, 82% showed EWSR1 rearrangement, while 18% had FUS abnormalities. EWSR1-POU5F1 occurred with predilection in malignant MET in children and young adults and these tumors had nested epithelioid morphology and clear cytoplasm. In contrast, EWSR1/FUS-PBX1/3 fusions were associated with benign and sclerotic spindle cell morphology. Tumors with EWSR1-KLF17 showed chordoma-like morphology. Our results demonstrate striking morphologic-molecular correlations in MET of bone, soft tissue and viscera, which might have implications in their clinical behavior.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".