Efficacy and Safety of Nivolumab and Ipilimumab for Advanced or Metastatic Renal Cell Carcinoma: A Multicenter Retrospective Cohort Study
Bibliographic record
Abstract
We conducted a multicenter, retrospective study to evaluate the efficacy and safety of combination nivolumab plus ipilimumab (NIVO+IPI) in 35 patients with advanced or metastatic renal cell carcinoma (mRCC). In this study, we focused on patients who received NIVO+IPI and were stratified into intermediate- or poor-risk disease according to the International Metastatic Renal Cell Carcinoma Database Consortium model at five institutions in Japan. The primary endpoint was overall survival (OS). Secondary endpoints were disease control rate (DCR), best overall response (BOR), objective response rate (ORR), and progression-free survival (PFS). In addition, we evaluated the role of inflammatory cell ratios, namely neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), as predictive biomarkers in patients with mRCC. The median follow-up period was 1 year, and the 1-year OS rate was 95.8%. The ORR and DCR were 34.3% and 80.0%, respectively. According to BOR, four patients (11.4%) achieved complete response. According to NLR stratification, the 1-year PFS rates were 82.6% and 23.7% when the NLR was ≤4.6 and >4.6, respectively (p = 0.04). Based on PLR stratification, the 1-year PFS rates were 81.7% and 34.3% when the PLR was ≤188.1 and >188.1, respectively (p = 0.033). Although 71.4% of the patients experienced treatment-related adverse events (TRAEs) with NIVO+IPI, only four patients discontinued NIVO+IPI due to grade 3/4 TRAEs. Patients treated with NIVO+IPI as a first-line therapy for advanced or mRCC achieved relatively better oncological outcomes. Therefore, NIVO+IPI may have potential advantages and may lead to a treatment effect compared to those receiving targeted therapies. In addition, PLR >188.1 may be a useful predictive marker for mRCC patients who received NIVO+IPI.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".