A Limited Panel of Immunomarkers Can Reliably Distinguish Between Clear Cell and High-grade Serous Carcinoma of the Ovary
Why this work is in the frame
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Bibliographic record
Abstract
The distinction of ovarian clear cell carcinomas (CCCs) from high-grade serous carcinomas (HG-SCs) is sometimes a diagnostic challenge. With the recognition that CCCs respond poorly to conventional chemotherapy there are efforts to initiate clinical trials for CCC, making accurate diagnosis critical. The purpose of this study was to test and validate a set of antibodies that could aid in the diagnosis of CCC, using a series of cases from different centers in North America. Using a test set of 133 CCCs, we identified the following markers: Cyclin E, estrogen receptor, hepatocyte nuclear factor (HNF)-1beta, Ki-67, p21, p53, and Wilms tumor (WT)1 that show significant discrimination from 200 HG-SCs. For validation, these markers were characterized on an independent set of 104 CCCs from 3 other centers. There were no significant differences in expression of these 7 markers between the independent test and validation sets of CCC. Combining all CCC cases (N=237), HNF-1beta showed the highest sensitivity (82.5%) and specificity (95.2%) for CCC, and WT1 for HG-SC (sensitivity: 79.9%, specificity: 97.4%). A diagnostic panel consisting of WT1, ER, and HNF-1beta demonstrated nearly identical performance as a panel using all 7 markers in distinguishing CCCs from HG-SCs, correctly classifying 84% of cases. Three percent of cases were misclassified and 13% carried an uninformative triple negative immunophenotype. CCCs show a distinct, reproducible immunophenotype, compared with HG-SCs, and a panel of 3 immunomarkers can serve as a diagnostic aid in problematic cases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it