The effect of immune checkpoint inhibitor combination therapies in metastatic renal cell carcinoma patients with and without previous cytoreductive nephrectomy: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Recently, immune checkpoint inhibitor (ICI)-combination therapies have radically altered the treatment landscape in metastatic renal cell carcinoma (mRCC). No phase 3 trials have assessed the impact of cytoreductive nephrectomy (CN) for efficacy in mRCC patients treated with ICI-combination therapy. We aimed to assess the role of ICI-combination therapy based on CN status. METHODS: Multiple databases were searched for articles published until June 2021. Studies comparing overall and/or progression-free survival (OS/PFS) in mRCC patients treated with ICI combination-therapy were deemed eligible. RESULTS: Six studies met the eligibility criteria. ICI-combination therapy was associated with significantly better OS/PFS than sunitinib in patients who had undergone CN (hazard ratio [HR], 0.67; 95% confidence interval [CI], 0.59-0.77/HR, 0.57; 95% CI, 0.44-0.74, respectively; both P < 0.001), and in those who had not (HR, 0.69; 95% CI, 0.57-0.85/HR, 0.63; 95% CI, 0.52-0.77, respectively; both P < 0.001). Although the OS and PFS benefits of ICI-combination therapy were larger in those undergoing CN, the HR for OS and PFS indicated that ICI-combination therapy's treatment effect did not differ substantially with or without CN. In network meta-analyses, nivolumab plus cabozantinib was the most effective regimen in those undergoing CN, and pembrolizumab plus lenvatinib for those not undergoing CN. CONCLUSION: The effect of ICI combination therapy did not differ between mRCC patients undergoing and not undergoing CN. As each ICI combination regimen varied widely in its effect in patients undergoing and not undergoing CN, CN may contribute to better treatment decision-making for ICI-combination therapy recipients.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".