A Proteogenomic View of Synchronous Endometrioid Endometrial and Ovarian Cancer
Bibliographic record
Abstract
PURPOSE: Increasing genomics-based evidence suggests that synchronous endometrial and ovarian cancer (SEOC) represents clonally related primary and metastatic tumors. A systematic analysis of the global protein landscape of SEOCs, heretofore lacking, could reveal functional and disease-specific consequences of known genetic alterations, the directionality of metastasis, and accurate histologic markers to distinguish SEOCs from single-site tumors. EXPERIMENTAL DESIGN: We performed a systematic proteogenomic analysis of 29 patients diagnosed with SEOC at three international gynecologic oncology treatment centers (Chicago, Vancouver, and Tübingen). For direct comparison with single-site tumors, we included 9 patients with single-site endometrioid ovarian and 26 patients with single-site endometrioid endometrial cancer (EEC). For all 64 patients, we performed sequencing of a 275-gene cancer panel combined with compartment-resolved mass spectrometry-based proteomics of consecutive tissue sections to compare global (6,000+ proteins), tumor, and stromal proteomes. RESULTS: DNA-based panel sequencing confirmed that most SEOCs are clonally related. Global proteome profiling uncovered pronounced differences between SEOCs and single tumors and underscored the importance of the stromal proteome in defining and identifying SEOCs. We identified molecularly unique SEOC stromal proteomes, which were globally more related to single endometrial cancers. We finally derived a proteomic predictor distinguishing SEOCs from single-site ovarian and uterine tumors. CONCLUSIONS: The integrated proteogenomic data show that SEOCs are distinguishable from endometrioid endometrial or endometrioid ovarian cancer. Based on their proteogenomic similarity to EECs, we conclude that most SEOCs represent primary EECs that have metastasized to the ovary.
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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 | 0.000 |
| 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 | 0.000 | 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".