Biomarker analysis from a phase III trial (GOLD) of dovitinib (Dov) versus sorafenib (Sor) in patients with metastatic renal cell carcinoma after one prior VEGF pathway–targeted therapy and one prior mTOR inhibitor therapy.
Bibliographic record
Abstract
473 Background: In the GOLD trial, Dov did not improve progression-free survival (PFS) or overall survival (OS) over Sor. An exploratory objective of the study was to investigate plasma and tumor biomarkers to predict outcome. Methods: Plasma samples were obtained longitudinally throughout the study, and biomarkers were assessed by immunoassay. Primary archival tumor samples were assessed by immunohistochemistry. Log-rank tests, stratified by baseline MSKCC risk group, for difference in Kaplan-Meier curves between biomarker category (low/high based on </≥ median baseline values) within treatment arm were performed. Hazard ratios (HRs) were estimated from Cox proportional hazards models. Results: Plasma samples were available from 561 patients (Dov, n = 281; Sor, n = 280), and tumor samples were available from 341 patients (Dov, n = 181; Sor, n = 160). Baseline plasma biomarker levels were not predictive of Dov or Sor PFS or OS. However, strong prognostic effects, particularly for OS, were observed. High baseline cKIT and low baseline FGF2, HGF, PlGF, sVEGFR1, VEGFA, and VEGFD were associated with better OS for both Dov and Sor (Table). Changes from baseline in a number of plasma biomarkers were observed following treatment with Dov and Sor, consistent with VEGFR/FGFR inhibitory effects. Prognostic effects were also observed for low FGFR2 (PFS) and low FGF2 (OS) expression in archival tumors. Conclusions: Baseline plasma biomarkers are prognostic but not predictive in the third-line setting. Clinical trial information: NCT01223027. [Table: see text]
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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) | 0.002 | 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".