UHRF1 Immunohistochemical Staining Separates Benign Reactive Spindle Cell Mesothelial Proliferations From Sarcomatoid Mesotheliomas
Bibliographic record
Abstract
The separation of benign from malignant mesothelial proliferations is often a difficult pathologic problem. UHRF1 (ubiquitin-like with plant homeodomain and ring finger domains-1) is a regulator of DNA methylation and an epigenetic driver of various human cancers. It has recently been reported that UHRF1 is overexpressed in mesotheliomas. We asked whether UHRF1 immunohistochemistry could be used to separate benign from malignant mesothelial proliferations. Initial studies showed that UHRF1 stained mesothelial cells but also endothelial and other non-neoplastic cells, so that accurate counting of positive mesothelial cells was difficult. Therefore, we ran dual UHRF1-AE1/AE3 stains on 2 tissue microarrays containing 40 reactive mesothelial proliferations and 61 mesotheliomas and only counted UHRF1 staining in keratin-positive cells. On average 10.3±8.6% (mean±SD; range: 0% to 36, median: 6.8%) of epithelioid mesothelioma cells stained compared with 5.3±4.8% (range: 0% to 15%, median: 4.1%) of reactive epithelial mesothelial cells. This difference was statistically significant but there was too much overlap to use diagnostically. In contrast, 37±26% (range: 2.5% to 95%, median: 31%) of cells in sarcomatoid mesotheliomas compared with 1.2±1.2% (range: 0% to 3.0%, median: 1.0%) of cells in reactive spindle cell mesothelial proliferations stained. To confirm this difference we stained whole sections of 21 sarcomatoid mesotheliomas and 19 cases of organizing pleuritis. Staining of mesothelial cells was seen in 2.1±2.4% (range: 0% to 6.8%, median: 1.0%) of organizing pleuritis cases and 44±22% (range: 14% to 90%, median: 41%) of sarcomatoid mesotheliomas. We conclude that dual UHRF1-AE1/AE3 immunohistochemistry is very useful for separating benign spindle cell mesothelial proliferations from sarcomatoid mesotheliomas.
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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 | 0.000 | 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".