Frequent <i>PLAG1</i> gene rearrangements in skin and soft tissue myoepithelioma with ductal differentiation
Bibliographic record
Abstract
A subset of cutaneous and superficial soft tissue myoepithelial (ME) tumors displays a distinct ductal component and closely resembles mixed tumors/pleomorphic adenomas of salivary gland. As PLAG1 and HMGA2 rearrangements are the most common genetic events in pleomorphic adenomas, we sought to investigate if these abnormalities are also present in the skin/soft tissue ME lesions. In contrast, half of the deep-seated soft tissue ME tumors lacking ductal differentiation are known to be genetically unrelated, showing EWSR1 rearrangements. FISH analysis to detect PLAG1 and HMGA2 abnormalities was performed in 35 ME tumors, nine skin and 26 soft tissue, lacking EWSR1 and FUS rearrangements. For the PLAG1-rearranged tumors, FISH and RACE were performed to identify potential fusion partners, including CTNNB1 (beta-catenin) on 3p21 and LIFR (leukemia inhibitory factor receptor) on 5p13. Recurrent PLAG1 rearrangement by FISH was detected in 13 (37%) lesions, including three (33%) in the skin and 10 (38%) in the soft tissue. All were classified as benign and all except one showed abundant tubulo-ductal differentiation (comprising 12/24 [50%] of all tumors with ductal structures). A LIFR-PLAG1 fusion was detected by RACE and then confirmed by FISH in one soft tissue ME tumor with tubular formation. No CTNNB1 or LIFR abnormalities were detected in any of the remaining PLAG1-rearranged tumors. No structural HMGA2 abnormalities were detected in any of the 22 ME lesions tested. A subset of cutaneous and soft tissue ME tumors appears genetically linked to their salivary gland counterparts, displaying frequent PLAG1 gene rearrangements and occasionally LIFR-PLAG1 fusion.
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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 | 0.000 |
| 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".