Evolving landscape of first-line combination therapy in advanced renal cancer: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Renal cell carcinoma (RCC) is a common malignancy with approximately 30% of cases diagnosed at the advanced or metastatic stage. While single-agent vascular endothelial growth factor-targeted therapy has been a mainstay of treatment, data from multiple phase III trials assessing first-line immune checkpoint inhibitor (ICI) combinations have demonstrated a significant survival benefit. Methods: A systematic search of the published and presented literature was performed to identify phase III trials assessing ICI combination regimens in RCC using search terms ‘immune checkpoint inhibitors’ AND ‘renal cell carcinoma,’ AND ‘advanced’. Results: Six phase III trials showed significant benefits for ICI combinations compared with sunitinib. Nivolumab plus ipilimumab significantly improved overall survival [OS; median, 47.0 versus 26.6 months, hazard ratio (HR) = 0.68, 95% confidence interval (CI) = 0.58–0.81, p < 0.0001) and progression-free survival (PFS; median 11.6 versus 8.3 months, HR = 0.73, 95% CI = 0.61–0.87, p = 0.0004) in International Metastatic renal cell carcinoma Database Consortium intermediate and poor-risk patients. OS was also significantly improved for ICI plus tyrosine kinase inhibitor combinations regardless of risk, including pembrolizumab plus either axitinib (HR = 0.73, 95% CI = 0.60–0.88, p < 0.001) or lenvatinib (HR = 0.66, 95% CI = 0.49–0.88, p = 0.005) and nivolumab plus cabozantinib (HR = 0.66, 95% CI = 0.50–0.87, p = 0.003). No new safety signals were identified. Conclusions: Phase III first-line trials of ICI combinations showed survival benefits compared with a control arm of sunitinib. Global access to these combinations should be made available to patients with advanced RCC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it