Impact of sex on the efficacy of immune checkpoint inhibitors in kidney and urothelial cancers: a systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE: To analyze and summarize the efficacy of immune checkpoint inhibitor (ICI) alone or in combination therapy for renal cell carcinoma (RCC) and urothelial carcinoma (UC) stratified by sex. METHODS: Three databases were queried in October 2022 for randomized controlled trials (RCTs) analyzing RCC and UC patients treated with ICIs. We analyzed the association between sex and the efficacy of ICIs in RCC and UC patients across several clinical settings. The outcomes of interest were overall survival (OS) and progression-free survival for the metastatic setting and disease-free survival (DFS) for the adjuvant setting. RESULTS: Overall, 16 RCTs were included for meta-analyses and network meta-analyses. In the first-line treatment of metastatic RCC (mRCC) and UC (mUC) patients, ICI-based combination therapies significantly improved OS compared to the current standard of care, regardless of sex. Adjuvant ICI monotherapy reduced the risk of disease recurrence in female patients with locally advanced RCC (pooled hazard ratio [HR]: 0.71, 95% confidence interval [CI] 0.55-0.93) but not in male patients, and, conversely, in male patients with muscle-invasive UC (pooled HR: 0.80, 95%CI 0.68-0.94) but not in female patients. Treatment ranking analyses in the first-line treatment of mRCC and mUC showed different results between sexes. Of note, regarding adjuvant treatment for RCC, pembrolizumab (99%) had the highest likelihood of improved DFS in males, whereas atezolizumab (84%) in females. CONCLUSIONS: OS benefit of first-line ICI-based combination therapy was seen in mRCC and mUC patients regardless of sex. Sex-based recommendations for ICI-based regimens according to the clinical setting may help guide clinical decision-making.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.001 | 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".