Nuclear β-Catenin Expression in the Context of Abnormal p53 Expression Indicates a Nonserous Histotype in Endometrial Carcinoma
Bibliographic record
Abstract
The interobserver reproducibility is poor for histotyping within the p53-abnormal molecular category of endometrial carcinomas (ECs); therefore, biomarkers that improve histologic classification are useful. β-catenin has been proposed to have prognostic significance in specific clinicopathologic and molecular contexts. The diagnostic utility for β-catenin expression patterns in determining the histotype of p53-abnormal ECs has not been well studied. We identified ECs molecularly classified as "p53-abnormal." The p53-abnormal classification was assigned when (1) no POLE exonuclease domain hotspot mutations identified, (2) mismatch-repair protein expression was retained, and (3) abnormal p53 expression (null or overexpression) was present. Morphology was re-reviewed and β-catenin immunohistochemistry was scored as abnormal (nuclear) or normal (membranous, non-nuclear). Eighty ECs were identified in the "p53-abnormal" category; 27 (33.75%) were uterine serous carcinomas, and 53 were of nonserous histotype: 28 uterine carcinosarcomas (35%), 16 endometrioid carcinomas (20%), 2 clear cell carcinomas (2.5%), and 7 high-grade EC with ambiguous morphology (8.75%). All 27 uterine serous carcinomas demonstrated membranous β-catenin staining. Of the 53 nonserous ECs, 11 (21%) showed abnormal β-catenin expression: 6 endometrioid carcinomas, 4 uterine carcinosarcoma, and 1 high-grade EC with ambiguous morphology. The specificity of abnormal β-catenin expression for nonserous EC is high (100%) but the sensitivity is low (21%) with positive and negative predictive values of 100% and 60%, respectively. Our data shows that abnormal β-catenin expression in the context of p53-abnormal EC is highly specific, but not sensitive, for nonserous ECs and may be of value as part of a panel in classifying high-grade EC, particularly to exclude uterine serous carcinoma when nuclear staining is present.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".