Subclonal p53 immunostaining in the diagnosis of endometrial carcinoma molecular subtype
Bibliographic record
Abstract
AIMS: The significance of subclonal expression of p53 (abrupt transition from wild-type to mutant-pattern staining) is not well understood, and the arbitrary diagnostic cut-off of 10% between NSMP and p53abn molecular subtypes of endometrial carcinoma (EC) has not been critically assessed. Our aim was to characterise subclonal p53 and discrepant p53 expression/TP53 sequencing results in EC and assess their clinical significance. METHODS AND RESULTS: Subclonal p53 immuostaining on whole sections from 957 ECs was recorded. Agreement between TP53 mutational assessment and p53 immunostaining was evaluated. Subclonal p53 IHC staining was seen in 4.0% (38 of 957) of cases, with 23 of 957 (2.4%) showing mutant-pattern p53 staining in ≥10% of tumour cells. It was most commonly seen in POLEmut (nine of 65, 14%) and MMRd (13 of 274, 4.7%) EC ('multiple classifier' ECs), where subclonal p53 staining does not impact the molecular subtype diagnosis. Excluding POLEmut and MMRd EC, 11 of 957 (1.1%) showed ≥10% subclonal p53 from which four patients died of disease, while there were no deaths due to disease in the five patients with <10% mutant-pattern p53 staining. Agreement between p53 immunostaining and TP53 sequencing was 92.6%; most of the discrepant results were in the ultramutated POLEmut or hypermutated MMRd ECs. In NSMP and p53abn EC the agreement between IHC and sequencing was 95.8%. CONCLUSIONS: Subclonal p53 staining ≥10% is present in only 1.1% of EC after excluding 'multiple classifier' ECs. The cut-off of ≥10% subclonal p53 staining identified patients at increased risk of dying from EC, supporting its use to diagnose p53abn molecular subtype.
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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 | 0.006 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".