Performance of the pattern‐based interpretation of p53 immunohistochemistry as a surrogate for <i>TP53</i> mutations in vulvar squamous cell carcinoma
Bibliographic record
Abstract
AIMS: The most commonly mutated gene in vulvar squamous cell carcinoma (VSCC) is TP53 and its prognostic value, particularly in HPV-independent VSCC, is uncertain. In other tumours, p53 immunohistochemistry (IHC) is an excellent surrogate marker for TP53 mutations. In order to study this in VSCC, we assigned six p53 IHC patterns into two final classes: 'wild-type' or 'mutant'. We determined the performance and interobserver variability of this pattern-based p53 IHC approach. METHODS AND RESULTS: Two experienced gynaecological pathologists scored the predefined p53 IHC patterns of 59 VSCC, independently and blinded for molecular data. Agreement was calculated by Cohen's kappa. All disagreements regarding p53 IHC patterns were resolved by a consensus meeting. After DNA isolation, the presence of pathogenic TP53 variants was determined by next-generation sequencing (NGS). Sensitivity, specificity and accuracy of p53 IHC as a surrogate marker for TP53 mutation status were calculated. Initial p53 IHC pattern interpretation showed substantial agreement between both observers (k = 0.71, P < 0.001). After consensus, 18 cases (30.5%) were assigned a final p53 IHC class as TP53 wild-type and 41 cases (69.5%) as mutant. The accuracy between the p53 IHC class and TP53 mutation status, after the consensus meeting, was 96.6%. Moreover, the sensitivity and specificity were high 95.3% [95% confidence interval (CI) = 82.9-99.1% and 100% (95% CI = 75.9-100%)]. CONCLUSIONS: Pattern-based p53 IHC classification is highly reproducible among experienced gynaecological pathologists and accurately reflects TP53 mutations in VSCC. This approach to p53 IHC interpretation offers guidance and provides necessary clarity for resolving the proposed prognostic relevance of final p53 IHC class within HPV-independent VSCC.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".