Characterization of the molecular differences between ovarian endometrioid carcinoma and ovarian serous carcinoma
Bibliographic record
Abstract
The histopathological diagnosis of high-grade endometrioid and serous carcinoma of the ovary is poorly reproducible under the current morphology based classification system, especially for anaplastic, high-grade tumours. The transcription factor Wilms' tumour-1 (WT1) is differentially expressed among the gynaecological epithelia from which epithelial ovarian cancers (EOCs) are believed to originate. In EOCs, WT1 protein is observed in the majority of serous carcinomas and in up to 30% of endometrioid carcinomas. It is unclear whether the latter is a reflection of the actual incidence of WT1 protein expression in endometrioid carcinomas, or whether a significant number of high-grade serous carcinomas have been misclassified as endometrioid carcinoma. Several genetic aberrations are reported to occur in EOCs. These include mutation of the TP53 gene, aberrant activation of beta-catenin signalling and loss of PTEN protein expression, among others. It is unclear whether these aberrations are histotype-specific. The aim of this study was to better define the molecular characteristics of serous and endometrioid carcinomas in an attempt to address the problems with the current histopathological classification methods. Gene expression profiles were analysed to identify reproducible gene expression phenotypes for endometrioid and serous carcinomas. Tissue microarrays (TMA) were used to assess the incidence of TP53, beta-catenin and PTEN aberrations in order to correlate their occurrence with WT1 as an immunohistochemistry based biomarker of serous histotype. It was found that nuclear WT1 protein expression can identify misclassified high-grade endometrioid carcinomas and these tumours should be reassigned to serous histotype. Although low-grade endometrioid carcinomas rarely progress to high-grade carcinomas, a combined WT1-negative, TP53-positive immunophenotype may identify an uncommon high-grade subtype of ovarian endometrioid carcinoma. GEO database: array data accession number GSE6008.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".