Prognostic and theragnostic biomarkers in ovarian clear cell carcinoma
Bibliographic record
Abstract
Summary In this study, we aimed to test whether prognostic biomarkers can achieve a clinically relevant stratification of patients with stage I ovarian clear cell carcinoma (OCCC) and to survey the expression of 10 selected actionable targets (theragnostic biomarkers) in stage II to IV cases. From the population-based Alberta Ovarian Tumor Type study, 160 samples of OCCC were evaluated by immunohistochemistry and/or silver-enhanced in-situ hybridization for the status of 5 prognostic (p53, p16, IGF2BP3, CCNE1, FOLR1) and 10 theragnostic biomarkers (ALK, BRAF, ERBB2, ER, MET, MMR, PR, ROS1, NTRK1-3, VEGFR2). Kaplan-Meier survival analyses were performed. Cases with abnormal p53 or combined p16/IFG2BP3 abnormal expression identified a small subset of patients (6/54 cases) with stage I OCCC with an aggressive course (5-year ovarian cancer specific survival of 33.3%, compared to 91.5% in the other stage I cases). Among theragnostic targets, ERBB2 amplification was present in 11/158 (7%) of OCCC, while MET was ubiquitously expressed in OCCC similar to a variety of normal control tissues. ER/PR showed a low prevalence of expression. No abnormal expression was detected for any of the other targets. We propose a combination of 3 biomarkers (p53, p16, IGF2BP3) to predict prognosis and the potential need for adjuvant therapy for patients with stage I OCCC. This finding requires replication in larger cohorts. Additionally, OCCC could be tested for ERBB2 amplification for inclusion in gynecological basket trials targeting this alteration.
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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 | 0.001 | 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".