Proteomics-Derived Biomarker Panel Improves Diagnostic Precision to Classify Endometrioid and High-grade Serous Ovarian Carcinoma
Bibliographic record
Abstract
Abstract Purpose: Ovarian carcinomas are a group of distinct diseases classified by histotypes. As histotype-specific treatment evolves, accurate classification will become critical for optimal precision medicine approaches. Experimental Design: To uncover differences between the two most common histotypes, high-grade serous (HGSC) and endometrioid carcinoma, we performed label-free quantitative proteomics on freshly frozen tumor tissues (HGSC, n = 10; endometrioid carcinoma, n = 10). Eight candidate protein biomarkers specific to endometrioid carcinoma were validated by IHC using tissue microarrays representing 361 cases of either endometrioid carcinoma or HGSC. Results: More than 500 proteins were differentially expressed (P < 0.05) between endometrioid carcinoma and HGSC tumor proteomes. A ranked set of 106 proteins was sufficient to correctly discriminate 90% of samples. IHC validated KIAA1324 as the most discriminatory novel biomarker for endometrioid carcinoma. An 8-marker panel was found to exhibit superior performance for discriminating endometrioid carcinoma from HGSC compared with the current standard of WT1 plus TP53 alone, improving the classification rate for HGSC from 90.7% to 99.2%. Endometrioid carcinoma–specific diagnostic markers such as PLCB1, KIAA1324, and SCGB2A1 were also significantly associated with favorable prognosis within endometrioid carcinoma suggesting biological heterogeneity within this histotype. Pathway analysis of proteomic data revealed differences between endometrioid carcinoma and HGSC pertaining to estrogen and interferon signalling. Conclusions: In summary, these findings support the use of multi-marker panels for the differential diagnosis of difficult cases resembling endometrioid carcinoma and HGSC.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 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".