HEG1 Is a Highly Specific and Sensitive Marker of Epithelioid Malignant Mesothelioma
Bibliographic record
Abstract
Malignant mesothelioma can be difficult to distinguish from other malignancies, particularly non-small cell lung carcinomas (NSCLCs), without immunohistochemistry. However, conventional markers of mesothelial lineage all have variable degrees of cross-reactivity with other neoplasms, including NSCLCs, necessitating the use of multiple mesothelioma and carcinoma markers in every case for accurate diagnosis. A recently described monoclonal HEG homolog 1 (HEG1) antibody was proposed to be a specific marker for mesothelioma. Here we performed a large scale assessment of the SKM9-2 HEG1 antibody using tissue microarrays containing 69 epithelioid mesotheliomas, 32 sarcomatoid mesotheliomas, 167 NSCLCs, and 17 ovarian high-grade serous carcinomas. Strong membrane staining, usually diffuse, for HEG1 was seen in 65/69 (94%) epithelioid mesotheliomas, 0/60 pulmonary squamous cell carcinomas, 0/73 pulmonary adenocarcinomas, and 0/13 pulmonary large cell carcinomas. HEG1 showed staining in 14/32 (44%) sarcomatoid mesotheliomas compared with 0/21 sarcomatoid pulmonary carcinomas. Three of 17 (18%) high-grade serous carcinomas demonstrated membrane staining. Ten B3 thymoma whole sections were negative. On the microarrays, the conventional mesothelial markers calretinin, WT1, D2-40, and CK5/6 had sensitivities for epithelioid mesothelioma of 94%, 90%, 96%, and 91%, respectively. We conclude that HEG1 SKM9-2 antibody offers sensitivity comparable to conventional markers for epithelioid mesotheliomas, but provides considerably better specificity, such that the diagnosis of epithelioid mesothelioma versus NSCLC potentially could be confirmed with a combination of HEG1 and a suitable broad spectrum carcinoma marker such as claudin-4. HEG1 is specific but insensitive for separating sarcomatoid mesotheliomas from sarcomatoid lung carcinomas.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".