Identification of Patients With Ovarian Cancer Experiencing the Highest Benefit From Bevacizumab in the First-Line Setting on the Basis of Their Tumor-Intrinsic Chemosensitivity (KELIM): The GOG-0218 Validation Study
Bibliographic record
Abstract
PURPOSE In patients with high-grade ovarian cancer, predictors of bevacizumab efficacy in first-line setting are needed. In the ICON-7 trial, a poor tumor intrinsic chemosensitivity (defined by unfavorable modeled cancer antigen-125 [CA-125] ELIMination rate constant K [KELIM] score) was a predictive biomarker. Only the patients with high-risk disease (suboptimally resected stage III, or stage IV) exhibiting unfavorable KELIM score < 1.0 had overall survival (OS) benefit from bevacizumab (median: 29.7 v 20.6 months; hazard ratio [HR], 0.78). An external validation study in the GOG-0218 trial was performed. METHODS In GOG-0218, 1,873 patients were treated with carboplatin-paclitaxel ± concurrent-maintenance bevacizumab/placebo. Patient KELIM values were calculated with CA-125 kinetics during the first 100 chemotherapy days by the Lyon University team. The association between KELIM score (favorable ≥ 1.0, or unfavorable < 1.0) and bevacizumab benefit for progression-free survival (PFS)/OS was independently assessed by NGR-GOG using univariate/multivariate analyses. RESULTS KELIM was assessable in 1,662 patients with ≥ 3 CA-125 available values. An unfavorable KELIM score was associated with bevacizumab benefit compared with placebo (PFS: HR, 0.70; 95% CI, 0.59 to 0.82; OS: HR, 0.87; 95% CI, 0.73 to 1.03), whereas a favorable KELIM was not (PFS: HR, 0.96; 95% CI, 0.79 to 1.17; OS: HR, 1.11; 95% CI, 0.89 to 1.39). The highest benefit was observed in patients with a high-risk disease exhibiting unfavorable KELIM, for PFS (median: 9.1 v 5.6 months; HR, 0.64; 95% CI, 0.53 to 0.78), and for OS (median: 35.1 v 29.1 months; HR, 0.79; 95% CI, 0.65 to 0.97). CONCLUSION This GOG-0218 trial investigation validates ICON-7 findings about the association between poor tumor chemosensitivity and benefit from concurrent-maintenance bevacizumab, suggesting that bevacizumab may mainly be effective in patients with poorly chemosensitive disease. Bevacizumab may be prioritized in patients with a high-risk and poorly chemosensitive disease to improve their PFS/OS (patient KELIM score calculator available on the Biomarker Kinetics website).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".