Immunophenotypic Analysis in Early Müllerian Serous Carcinogenesis
Bibliographic record
Abstract
Studies on the immunophenotypes of early forms of serous carcinoma arising from female genital tract are limited. We aimed to examine p53, p16(Ink4a), estrogen receptor (ER), progesterone receptor (PR), ERBB2, WT1, and Ki-67 protein expression in endometrial intraepithelial carcinoma (n=29), serous tubal intraepithelial lesion (n=4) and carcinoma (STIC, n=10), and the putative precursor p53 signature (n=11). Among endometrial intraepithelial carcinoma, 80% demonstrated p53 overexpression and 10% were consistent with a null phenotype. p16(Ink4a) immunostaining were observed in all endometrial intraepithelial carcinoma cases. ER, PR, ERBB2, and WT1 were positive in 54%, 25%, 11%, and 18% of cases, respectively. STIC cases demonstrated p53 overexpression and null phenotype in 90% and 10%, respectively. All STIC cases were p16(Ink4a) and WT1 positive, whereas ER and PR were positive in 70% and 20%, respectively. All STICs were negative for ERBB2. Among serous tubal intraepithelial lesion cases, 75% demonstrated p53 overexpression and 25% a null phenotype. p53 was positive in all 11 p53 signature cases, whereas p16(Ink4a) was universally negative. Finally, ER and PR were positive in 100% and 73% of p53 signature cases, respectively. These results suggest that p16(Ink4a) has a role in early Müllerian serous carcinogenesis but is absent in the earliest noncommitted lesion. p16(Ink4a) immunohistochemistry can be used as an adjunct confirmatory tool in p53-null cases with limited surface area.
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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 | 0.000 |
| 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".