PNL2: A Useful Adjunct Biomarker to HMB45 in the Diagnosis of Uterine Perivascular Epithelioid Cell Tumor (PEComa)
Bibliographic record
Abstract
Perivascular epithelioid cell tumors (PEComa) are rare neoplasms characterized by co-expression of melanocytic and muscle markers. HMB45 and Melan-A are used to confirm a PEComa diagnosis; however, both are often focally expressed and sensitivity for Melan-A is low. PNL2 is a reliable biomarker for epithelioid melanoma and renal angiomyolipoma/PEComa. The objective of this study was to determine PNL2 utility in diagnosing uterine PEComas as well as distinguishing PEComas from uterine smooth muscle tumors (SMTs). Twenty-one uterine PEComas and 45 SMTs were analyzed for PNL2; a subset was also stained for HMB45, Melan-A, Cathepsin-K, Desmin, and h-Caldesmon. Cases were scored as negative (0), focal (<10% of tumor cells), or patchy to diffusely positive (>10% of tumor cells). PEComas were positive for PNL2, HMB45, and Melan-A in 86%, 100%, and 57% of cases, respectively. In PEComas, PNL2 was patchy to diffusely positive more frequently (10/18, 56%) than Melan-A (4/12, 33%). In contrast, 2 of 45 (4%) SMTs were focally PNL2 positive; HMB45 was focally positive in 4 SMTs (11%) and all were negative for Melan-A. Desmin and h-Caldesmon were positive in 90% and 57% of PEComas, and 91% and 82% of SMTs. Cathepsin-K was positive in 100% of PEComas and 93% of SMTs. PNL2 is a useful biomarker for the diagnosis of uterine PEComa, with comparable sensitivity and specificity to HMB45. In contrast, PNL2 stains more PEComas when compared with Melan-A. Cathepsin-K, Desmin, and h-Caldesmon are of little utility for distinguishing PEComas and SMTs; however, lack of Cathepsin-K argues against PEComa. These results suggest that PNL2 should be used in conjunction with HMB45 in the diagnosis of PEComa of the uterine corpus.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".