Association between neutrophil-to-eosinophil ratio and efficacy outcomes with avelumab plus axitinib or sunitinib in patients with advanced renal cell carcinoma: post hoc analyses from the JAVELIN Renal 101 trial
Bibliographic record
Abstract
Objective: We report post hoc analyses of efficacy with first-line avelumab plus axitinib or sunitinib according to baseline neutrophil-to-eosinophil ratio (NER) in patients with advanced renal cell carcinoma (aRCC) from the JAVELIN Renal 101 phase 3 trial. Methods and analysis: Progression-free survival (PFS), overall survival (OS) and objective response per baseline NER were analysed in the overall population and in patients with programmed death ligand 1 (PD-L1+) tumours. Multivariable Cox regression analyses to assess the effect of NER after adjustment for other baseline variables were conducted. Results: In NER <median versus ≥median subgroups of the avelumab plus axitinib arm, HRs for PFS and OS were 0.81 (95% CI 0.630 to 1.035) and 0.67 (95% CI 0.481 to 0.940), and objective response rates (ORRs) were 63.9% vs 55.2%, respectively. The HR for PFS in the PD-L1+ subgroup was 0.72 (95% CI 0.520 to 0.986). Comparing NER-defined subgroups in the sunitinib arm, HRs for PFS and OS were 0.93 (95% CI 0.728 to 1.181) and 0.57 (95% CI 0.424 to 0.779), and ORRs were 32.8% versus 30.8%, respectively. Within NER subgroups, analyses of PFS, OS and ORR favoured avelumab plus axitinib versus sunitinib treatment. Interaction tests that assessed the association between treatment and NER yielded conflicting results when NER was assessed as a dichotomised variable (median cut-off) or continuous variable. Conclusion: Hypothesis-generating analyses suggest that baseline NER may be prognostic for longer OS irrespective of treatment. Analyses of the association between NER level and treatment outcomes with avelumab plus axitinib versus sunitinib were inconclusive. Trial registration number: NCT02684006.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".