BAP1 Immunohistochemistry and p16 FISH to Separate Benign From Malignant Mesothelial Proliferations
Bibliographic record
Abstract
A variety of immunohistochemical (IHC) stains have been proposed to mark either benign or malignant mesothelial proliferations. Loss of the p16 tumor suppressor (CDKN2A), through homozygous deletions of 9p21, is a good marker of mesotheliomas but lacks sensitivity. Recent reports indicate that some mesotheliomas are associated with loss of BRCA-associated protein 1 (BAP1) expression. Here we investigate BAP1 and p16 as potential markers of malignancy and compare test characteristics with previously proposed markers using a well-characterized tissue microarray. BAP1 protein expression was interrogated by IHC. The p16 locus was examined by fluorescence in situ hybridization (FISH) directed toward chromosome 9p21. Loss of BAP1 was identified in 7/26 mesotheliomas and 0/49 benign proliferations. Loss of p16 was identified in 14/27 mesotheliomas and 0/40 benign proliferations, yielding 100% specificity and positive predictive value for each marker. Together, BAP1 IHC and p16 FISH were 58% sensitive for detecting malignancy. Various combinations of p53, EMA, IMP3, and GLUT1 showed reasonably high specificity (96% to 98%) but poor to extremely poor sensitivity. Combined BAP1 IHC/p16 FISH testing is a highly specific method of diagnosing malignant mesotheliomas when the question is whether a mesothelial proliferation is benign or malignant and is particularly useful when tissue invasion by mesothelial cells cannot be demonstrated. However, combined BAP1/p16 FISH testing is not highly sensitive, and negative results do not rule out a mesothelioma. The test characteristics of previously proposed markers EMA, p53, GLUT1, IMP3 suggest that, even in combination, these markers are not useful tools in this clinical setting.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".