Expanding the spectrum of mesenchymal neoplasms with <i>NR1D1</i>‐rearrangement
Bibliographic record
Abstract
Undifferentiated mesenchymal neoplasms can be morphologically subclassified based on cell shape; epithelioid tumors may be diagnostically challenging, particularly since they can show morphologic and immunohistochemical overlap with epithelial neoplasms. Following the recent report of an NR1D1::MAML1 gene fusion in an undifferentiated pediatric neoplasm, we performed a retrospective archival review and identified four additional cases of undifferentiated mesenchymal neoplasms with NR1D1-rearrangement. All four tumors occurred in adult women. The tumors involved superficial and/or deep soft tissues of the extremities or abdomen. Morphologically, they showed a spectrum of overlapping features. In addition to epithelioid cells, two cases also had a prominent spindle cell component. Two cases also had admixed polygonal cells containing prominent cytoplasmic vacuoles with amorphous debris. The immunophenotype was nonspecific but all cases had at least focal keratin expression; this was extensive in two tumors. Targeted RNA-sequencing revealed two cases each with NR1D1::MAML1 and NR1D1::MAML2 gene fusions. One patient developed lung and liver metastases, and one patient required amputation due to multifocal disease and underlying bone involvement. This study confirms undifferentiated NR1D1-rearranged sarcoma represents a distinct mesenchymal neoplasm with an epithelioid morphology and potential for aggressive behavior. Further, we offer new insight into the spectrum of clinical, morphologic, immunohistochemical, and molecular findings possible in these rare neoplasms. An awareness of this entity is especially important given the potential for misclassification as a carcinoma.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".