The biological and clinical value of p53 expression in pelvic high‐grade serous carcinomas
Bibliographic record
Abstract
Studies on the p53 expression and outcome for women with ovarian carcinoma have produced conflicting results. The observed heterogeneity may be due to the range of cut-offs used to define overexpression and the mix of histotypes of the study cohorts. We aimed to examine the association between p53 expression and biological properties of tumours as well as outcome in 502 pelvic high-grade serous carcinomas (HGSCs) derived from two population-based cohorts from British Columbia representing cases with or without residual tumour after initial surgery, respectively, and one clinical trial cohort from Germany (AGO-OVAR-3). p53 expression was assessed on tissue microarrays by immunohistochemistry using the DO-7 antibody. p53 expression was scored in three tiers as complete loss of expression, focal expression or overexpression (defined as more than 50% positive tumour cell nuclei) and correlated with survival using multivariate Cox regression models. p53 was completely absent in 30.3%, focally expressed in 12.0%, and overexpressed in 57.7% of HGSCs, which was an inverse pattern compared to clear cell and endometrioid types of ovarian carcinomas, where 76% and 69% of cases showed focal expression, respectively (p < 0.001, chi square test). Pelvic HGSCs show either complete absence of p53 expression or p53 overexpression in 88% of cases; thus, aberrant p53 expression is a ubiquitous feature of HGSCs. HGSCs with p53 overexpression were associated with a reduced risk of recurrence compared to cases with complete absence of p53 in the British Columbia cohort with residual tumour (HR = 0.71, 95% CI 0.51-0.99) and for a combination of all three cohorts (HR = 0.70, 95% CI 0.55-0.89) in multivariate analysis including age, stage, residual tumour, and stratification by cohort. The association of complete absence of p53 expression with unfavourable outcome suggests functional differences of TP53 mutations underlying overexpression, compared to those underlying complete absence of expression.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".