Second-line tyrosine kinase inhibitor-therapy after immunotherapy-failure
Bibliographic record
Abstract
PURPOSE OF REVIEW: Most contemporary metastatic renal-cell carcinoma patients receive first-line immunotherapy and tyrosine kinase inhibitor (TKI) combination or immunotherapy-immunotherapy combination, as first-line standards of care. However, second-line therapy choices are less well established. To address this void, we examined existing evidence supporting second and subsequent-line treatment options after immunotherapy-based combination therapy. RECENT FINDINGS: Evidence regarding efficacy of second-line therapy after immunotherapy-based combination is mainly retrospective, except for axitinib, which is the only TKI with prospective efficacy data in this setting. Cabozantinib demonstrated excellent second-line progression-free survival (PFS) that remained in third or later line use, albeit based on small numbers of observations. Moreover, pazopanib demonstrated excellent PFS, but showed wider variability in PFS rates. Sunitinib's PFS rates appeared lower than for axitinib, cabozantinib or pazopanib. Finally, inhibitors of the mammalian target of rapamycin pathway appeared to offer even lower efficacy than any TKI after immunotherapy-based therapy combinations. SUMMARY: All available contemporary evidence about TKI efficacy after immunotherapy-based therapy combinations is based on institutional studies. No major differences in efficacy for the examined TKIs after immunotherapy-based combination therapies were recorded. In general, these showed similar efficacy to their efficacy data recorded in first-line.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".