TRPS1 Expression Is Frequently Seen in a Subset of Cutaneous Mesenchymal Neoplasms and Tumors of Uncertain Differentiation: A Potential Diagnostic Pitfall
Bibliographic record
Abstract
Although extensively studied in cutaneous epithelial neoplasms, the TRPS1 immunoreactivity in cutaneous mesenchymal neoplasms and tumors of uncertain differentiation (CMNTUDs), such as atypical fibroxanthoma (AFX), remains largely unexplored. We assessed TRPS1 immunoreactivity in 135 CMNTUDs, comprising 46 fibrohistiocytic/fibroblastic tumors, 28 vascular tumors, 24 peripheral nerve sheath tumors (PNSTs), 21 tumors of uncertain differentiation, and 16 smooth muscle tumors. Additionally, we included selected cases of melanoma with spindled cell morphology or desmoplastic features (n = 9) and sarcomatoid squamous cell carcinoma (SSCC) (n = 5) to compare TRPS1 expression patterns with those of AFX. TRPS1 expression was prevalent in dermatofibromas (24/24), leiomyomas (8/8), AFXs/pleomorphic dermal sarcoma (PDS) (20/21), dermatofibrosarcomas protuberans (14/22), and leiomyosarcomas (6/8). It was uncommon in angiosarcomas (3/20), Kaposi sarcomas (2/8), and neurofibromas (5/17) and absent in perineuriomas (0/2). AFXs/PDS exhibited the highest median H-score of 240, contrasting with minimal TRPS1 immunoreactivity in vascular neoplasms and PNSTs, with median H-scores consistently below 10. Significant differences in H-score were observed between AFXs/PDS and angiosarcomas (p < 0.001), melanomas (p < 0.001), and leiomyosarcomas (p = 0.029). However, no significant difference was found compared to SSCCs, suggesting limited discriminatory power of TRPS1 in this context. This study sheds light on TRPS1 expression patterns in a subset of CMNTUDs, extending beyond prior studies primarily focused on epithelial tumors, while underscoring potential pitfalls associated with TRPS1 immunohistochemistry.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".