PNL2: A Useful Adjunct Biomarker to HMB45 in the Diagnosis of Uterine Perivascular Epithelioid Cell Tumor (PEComa)
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it