CTNNB1 Mutations and Aberrant β-Catenin Expression in Ovarian Endometrioid Carcinoma
Bibliographic record
Abstract
CTNNB1 mutations and aberrant β-catenin expression have adverse prognosis in endometrial endometrioid carcinoma, and recent evidence suggests a prognostic role of β-catenin in ovarian endometrioid carcinoma. Thus, we aimed to determine the prognostic value of the CTNNB1 mutational status, and its correlation with β-catenin expression, in a well-annotated cohort of 51 ovarian endometrioid carcinomas. We performed immunohistochemistry for β-catenin and developed an 11-gene next-generation sequencing panel that included whole exome sequencing of CTNNB1 and TP53. Results were correlated with clinicopathologic variables including disease-free and disease-specific survival. Tumor recurrence was documented in 14 patients (27%), and cancer-related death in 8 patients (16%). CTNNB1 mutations were found in 22 cases (43%), and nuclear β-catenin in 26 cases (51%). CTNNB1 mutation highly correlated with nuclear β-catenin (P<0.05). Mutated CTNNB1 status was statistically associated with better disease-free survival (P=0.04, log-rank test) and approached significance for better disease-specific survival (P=0.07). It also correlated with earlier International Federation of Gynecology and Obstetrics stage (P<0.05). Nuclear β-catenin, TP53 mutations, age, ProMisE group, surface involvement, tumor grade and stage also correlated with disease-free survival. There was no association between membranous β-catenin expression and disease-free or disease-specific survival. CTNNB1 mutations and nuclear β-catenin expression are associated with better progression-free survival in patients with OEC. This relationship may be in part due to a trend of CTNNB1-mutated tumors to present at early stage. β-catenin immunohistochemistry may serve as a prognostic biomarker and a surrogate for CTNN1B mutations in the evaluation of patients with ovarian endometrioid neoplasia, particularly those in reproductive-age or found incidentally without upfront staging surgery.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".