Biomarker Expression in Pelvic High-grade Serous Carcinoma
Bibliographic record
Abstract
Neoadjuvant therapy has an emerging role in the treatment of high-stage ovarian carcinoma. Some ovarian carcinoma subtypes do not respond well to standard chemotherapy, making accurate subtype diagnosis before starting therapy important. This diagnosis is frequently based on omental biopsy specimens. In particular, with very small biopsies, immunostaining for diagnostic biomarkers may be needed. To assess intratumoral heterogeneity of biomarker expression in pelvic high-grade serous carcinoma, we compared the expression of a set of 10 biomarkers between ovarian and omental sites. Tissue microarrays were constructed from 123 high-grade serous carcinomas with paired ovarian and omental tumor samples. These samples were stained with biomarkers that have been used in ovarian carcinoma subtype diagnosis (WT1, TP53/p53, MUC16/CA125, CDKN2A/p16), and with biomarkers of the tumor microenvironment (CD8, CD163, SPARC, PDGFRB), cell adhesion (CDH1/E-Cadherin), and proliferation (Ki67) as well. Expression frequencies in samples from the 2 sites were compared, as was concordance at the 2 sites for individual tumors. The 2 markers of desmoplastic stromal response (PDGFRB, SPARC) were more frequently expressed in the omentum compared with the ovary (P<0.001; McNemar test). The other 8 markers did not show a significant difference in the frequency of expression between sites. Within individual cases, some markers such as Ki67 and CDKN2A showed variability, indicating that these markers are affected by intratumoral heterogeneity. The intratumoral variability for MUC16, TP53, and WT1 was modest. Commonly used diagnostic markers, such as TP53 and WT1, show little variability between ovarian and omental sites, suggesting that they can be successfully used in small biopsy specimens from extraovarian sites. In contrast, markers of host stromal response do vary between sites, suggesting a biologic difference of the microenvironment at different sites that should be taken into account when tissue-based research is carried out.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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 teacher head, 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".