Utility of BAP1 Immunohistochemistry and p16 (CDKN2A) FISH in the Diagnosis of Malignant Mesothelioma in Effusion Cytology Specimens
Bibliographic record
Abstract
The diagnosis of malignant mesothelioma in effusion cytology specimens is controversial. BAP1 immunohistochemistry and p16 fluorescence in situ hybridization (FISH) have recently been reported as reliable markers of malignancy in biopsies of mesothelioma. To determine whether these markers, singly or in combination, might also be useful in effusion cytology specimens, we examined 15 biopsies of epithelial mesotheliomas and 3 benign mesothelial reactions and corresponding effusion cytology paraffin-embedded cell blocks. Four cytology specimens were too scanty for p16 FISH analysis but were interpretable for BAP1 immunohistochemistry. Overall, loss of BAP1 and/or deletion of p16 was seen in 11/11 (100%) of matched cytology and tissue biopsy specimens. BAP1 loss alone was seen in 10/15 (67%) biopsies and 10/15 (67%) cytology specimens. Homozygous deletion of p16 by FISH was found in 12/15 (80%) biopsy specimens and 8/11 (73%) evaluable cytology specimens. Seven of 15 (47%) biopsies and 5/11 (42%) cytology specimens showed loss of both markers. All mesothelioma biopsy/cytology pairs showed exactly the same pattern of BAP1 or p16 retention or loss in the biopsy and cytology specimens. The 2 peritoneal mesothelioma cases demonstrated loss of BAP1 but not p16. None of the benign mesothelial reactions or corresponding cytology specimens showed loss of either marker. We conclude that both BAP1 immunohistochemistry and p16 FISH analysis provide reliable markers of mesothelial malignancy in effusion cytology specimens, especially where the atypical mesothelial proliferation is well sampled. BAP1 is easier to interpret with scanty specimens. On the basis of small numbers of cases, use of both markers appears to increase sensitivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".