Equivalent Survival of p53 Mutated Endometrial Endometrioid Carcinoma Grade 3 and Endometrial Serous Carcinoma
Bibliographic record
Abstract
TP53 status is the most important prognostic biomarker in endometrial carcinoma. We asked the question whether p53 mutated endometrial endometrioid carcinomas grade 3 (EEC3) or endometrial serous carcinomas (ESC), the latter ubiquitously harboring TP53 mutation, have different outcomes. TP53 mutation status was assessed by surrogate p53 immunohistochemistry on 326 EEC3 and ESC from 2 major cancer centers in Canada. Mutant-type p53 expression, including overexpression, complete absence, or cytoplasmic expression, was distinguished from the wild-type pattern. Statistical associations with clinico-pathological parameter, other key biomarkers, and survival analyses were performed. P53 mutant-type immunohistochemistry was observed in all 126 ESC and in 47/200 (23.5%) EEC3. ESC and p53 mutated EEC3 had an unfavorable outcome compared with p53 wild-type EEC3 (hazard ratio=2.37, 95% confidence interval=1.48-3.80, P=0.003, hazard ratio=2.19, 95% confidence interval=1.16-4.12, P=0.016, respectively) in multivariable analyses adjusted for age, stage, center, and presence of lymph-vascular invasion. There was no significant difference in survival between ESC and p53 mutated EEC3 in multivariable analysis. Furthermore, p53 mutated EEC3 and ESC almost completely overlapped in univariate survival analysis when mismatch repair (MMR)-deficient cases were excluded, which suggests that EEC3 harboring combined MMR deficiency and TP53 mutations behave more according to the MMR status. Significant differences between p53 mutated MMR-proficient EEC3 and ESC in PTEN and p16 expression status remained. p53 mutated, MMR-proficient EEC3 and ESC have overlapping survival significantly different from p53 wild-type EEC3, which justifies a similar treatment with current non-targeted standard therapy. Although this is so, separate classification should continue due to biological differences that will become important for future targeted therapy.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".