Efficacy and Safety of Checkpoint Inhibitors in Clear Cell Renal Cell Carcinoma: A Systematic Review of Clinical Trials
Bibliographic record
Abstract
Renal cell carcinoma (RCC) is the most common kidney cancer in adults (approximately 90%), and clear cell RCC (ccRCC) is the most frequent histologic subtype (approximately 75%). We reviewed the safety and efficacy of checkpoint inhibitors (CPIs) in ccRCC, identifying 5927 articles in PubMed, Embase, Cochrane, and Web of Science. Ten randomized control (N = 7765) and 10 non-randomized (N = 572) studies were included. Overall, 4819 patients treated with CPI combinations were compared with everolimus, sunitinib, or placebo. Overall response rates (ORR) were 9-25% with nivolumab (niv), 42% with niv + ipilimumab (ipi), 55.7% with niv + cabozantinib, 56% with niv + tivozanib vs. 5% with everolimus. ORR was 51.5-58% with avelumab + axitinib vs. 25.5% with sunitinib. ORR was 59.3-73% with pembrolizumab + tyrosine kinase inhibitor vs. 25.7% with sunitinib. ORR was 32-36% with atezolizumab + bevacizumab vs. 29-33% with sunitinib. In patients with PD-L1+ve and -ve ccRCC, niv, atezolizumab, ipi, and pembrolizumab were safe and effective alone and when combined with cabozantinib, tivozanib, axitinib, levantinib, and pegilodecakin. Atezolizumab + bevacizumab was safe and effective in ccRCC with high PD-L1 expression. Pembrolizumab was safe and effective in preventing recurrence in ccRCC patients with nephrectomy. Additional randomized, double-blind, multicenter clinical trials are needed to confirm these results.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".