Association of Neutrophil-to-Lymphocyte Ratio with Efficacy of First-Line Avelumab plus Axitinib vs. Sunitinib in Patients with Advanced Renal Cell Carcinoma Enrolled in the Phase 3 JAVELIN Renal 101 Trial
Bibliographic record
Abstract
PURPOSE: To evaluate the association between neutrophil-to-lymphocyte ratio (NLR) and efficacy of avelumab plus axitinib or sunitinib. EXPERIMENTAL DESIGN: Adult patients with untreated advanced renal cell carcinoma (RCC) with a clear-cell component, ≥1 measurable lesions, Eastern Cooperative Oncology Group performance status of 0 or 1, fresh or archival tumor specimen, and adequate renal, cardiac, and hepatic function were included. Retrospective analyses of the association between baseline NLR and progression-free survival (PFS) and overall survival (OS) in the avelumab plus axitinib or sunitinib arms were performed using the first interim analysis of the phase 3 JAVELIN Renal 101 trial (NCT02684006). Multivariate Cox regression analyses of PFS and OS were conducted. Translational data were assessed to elucidate the underlying biology associated with differences in NLR. RESULTS: Patients with below-median NLR had longer observed PFS with avelumab plus axitinib [stratified HR, 0.85; 95% confidence interval (CI), 0.634-1.153] or sunitinib (HR, 0.56; 95% CI, 0.415-0.745). In the avelumab plus axitinib or sunitinib arms, respectively, median PFS was 13.8 and 11.2 months in patients with below-median NLR, and 13.3 and 5.6 months in patients with median-or-higher NLR. Below-median NLR was also associated with longer observed OS in the avelumab plus axitinib (HR, 0.51; 95% CI, 0.300-0.871) and sunitinib arms (HR, 0.30; 95% CI, 0.174-0.511). Tumor analyses showed an association between NLR and key biological characteristics, suggesting a role of NLR in underlying mechanisms influencing clinical outcome. CONCLUSIONS: Current data support NLR as a prognostic biomarker in patients with advanced RCC receiving avelumab plus axitinib or sunitinib.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".