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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".