Results of TRIO-14, a phase II, multicenter, randomized, placebo-controlled trial of carboplatin-paclitaxel versus carboplatin-paclitaxel-ganitumab in newly diagnosed epithelial ovarian cancer
Bibliographic record
Abstract
PURPOSE: Insulin-like growth factor (IGF) signaling is implicated in pathogenesis and chemotherapy resistance of epithelial ovarian cancer (EOC). We explored efficacy and safety of adding ganitumab, a monoclonal antibody targeting IGF-1R, to carboplatin/paclitaxel (CP) chemotherapy in patients with primary EOC. DESIGN: Patients were randomly assigned to receive CP/ganitumab (18 mg/kg q3w) or CP/placebo for 6 cycles followed by 6 cycles of single agent ganitumab/placebo maintenance therapy as front-line therapy. Primary endpoint was progression free survival. Secondary endpoints were time to progression and overall survival. Pretreatment samples were prospectively collected for retrospective biomarker analyses. RESULTS: 170 patients enrolled. 165 patients assessable for toxicity. Median PFS was 15.7 months with CP/ganitumab and 16.7 months with CP/placebo (HR 1.23; 95% CI 0.82-1.83, P = 0.313). All grade neutropenia (84.1% vs 71.4%), thrombocytopenia (75.3% vs 57.1%) and hyperglycemia (15.9% vs 2.6%) were more common in the ganitumab group compared to the placebo group. Ganitumab/placebo related serious adverse events were reported in 26.1% of the patients with ganitumab and in 6.5% with placebo. Non-progression related fatal events were more common with ganitumab (5 versus 2 patients). The ganitumab group experienced more dose delays which resulted in lower relative dose intensity of chemotherapy in the experimental group. In an exploratory model IGFBP2 expression was predictive of ganitumab response (treatment interaction; PFS, P = 0.03; OS, P = 0.01). CONCLUSION: Addition of ganitumab to CP chemotherapy in primary EOC did not improve PFS. Our results do not support further study of ganitumab in unselected EOC patients.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".