Real-world experience of cabozantinib in Asian patients with advanced renal cell carcinoma following treatment with VEGFR tyrosine kinase inhibitors and/or immune checkpoint inhibitors
Bibliographic record
Abstract
Background: There is a lack of real-world data on the use of cabozantinib in Asian patients with metastatic renal cell carcinoma. Methods: We conducted a retrospective study to investigate the toxicity and efficacy of cabozantinib in this patient population who progressed on tyrosine kinase inhibitors and/or immune-checkpoint inhibitors from six oncology centres in Hong Kong. The primary endpoint was the incidence of serious adverse events (AEs) attributed to cabozantinib. Secondary safety endpoints included dose reductions and AE-led treatment terminations. Secondary effectiveness endpoints included overall survival, progression-free survival, and objective response rate. Results: A total of 24 patients were included. Half received cabozantinib as a third-line or later-line treatment, whilst 50% received prior immune-checkpoint inhibitors, primarily nivolumab. Overall, 13 (54.2%) patients reported at least one cabozantinib-related AE of grades 3-4. The most commonly reported AEs were hand-foot skin reactions (9; 37.5%) and anaemia (4; 16.7%). Fifteen (65.2%) patients required dose reductions. Three patients discontinued treatment because of AEs. The median progression-free survival and overall survival were 10.3 months and 13.2 months, respectively; 6 (25%) patients achieved partial responses, and 8 (33.3%) achieved stable disease. Conclusion: Cabozantinib was generally well tolerated and efficacious in Asian patients with metastatic renal cell carcinoma who were heavily pretreated.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".