Optimal first-line and second-line treatments for metastatic renal cell carcinoma: current evidence
Bibliographic record
Abstract
Since 2005, an abundance of targeted agents has been approved for the treatment of metastatic renal cell carcinoma (mRCC), without any specification as to what may be the most optimal first-line and second-line sequence. Hence, our objective was to critically examine the evidence supporting the use of first-line and second-line agents in the management of mRCC. Our review suggests that in first line, sunitinib and pazopanib represent treatment options for patients with favorable or intermediate-risk features and clear cell histology. Unfortunately, the Phase III trial cannot conclusively prove the noninferiority of pazopanib relative to sunitinib. Hence, the use of sunitinib as first-line standard of care remains justified. Pazopanib represents an option for specific patients in whom sunitinib might not be tolerated. In patients with poor-risk features, temsirolimus represents the only option supported with level 1 evidence. Less optimal alternatives include sunitinib and bevacizumab combined with interferon, based on the minimal inclusion of poor-risk patients in pivotal Phase III studies of these two molecules. In patients with non-clear cell mRCC, the use of temsirolimus is supported by Phase III data, unlike for any other molecule. In second line, the options consist of everolimus and axitinib. However, the axitinib data are substantially more robust given the inclusion of more patients considered as true second-line, and validly justify the choice of axitinib over everolimus. Nonetheless, the Phase III trial of everolimus may be considered as level 1 evidence for use as third-line or subsequent lines of therapy.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".