Cabozantinib real‐world effectiveness in the first‐through fourth‐line settings for the treatment of metastatic renal cell carcinoma: Results from the International Metastatic Renal Cell Carcinoma Database Consortium
Bibliographic record
Abstract
BACKGROUND: Cabozantinib is approved for metastatic renal cell carcinoma (mRCC) based on the METEOR and CABOSUN trials. However, real-world effectiveness and dosing patterns of cabozantinib are not well characterized. METHODS: Patients with mRCC treated with cabozantinib between 2011 and 2019 were identified and stratified using the International mRCC Database Consortium (IMDC) risk groups. First- (1L), second- (2L), third- (3L), and fourth-line (4L) overall response rate (ORR), time to treatment failure (TTF), and overall survival (OS) were analyzed. Dose reduction rates and their association with TTF and OS were determined. RESULTS: A total of 413 patients were identified. The ORRs across 1L to 4L were 32%, 26%, 25%, and 29%, respectively, and the median TTF rates were 8.3, 7.3, 7.0, and 8.0 months, respectively. The median OS (mOS) rates in 1L to 4L were 30.7, 17.8, 12.6, and 14.9 months, respectively. For patients treated with 1L PD(L)1 combination agent (n = 31), 2L cabozantinib had ORR of 22%, median TTF of 5.4 months, and mOS of 17.4 months. About 50% (129/258) of patients required dose reductions. The TTF and mOS were significantly longer for patients who required dose reduction vs. patients who did not, with an adjusted hazard ratio of 0.37 (95% CI 0.202-0.672, p < 0.01) and 0.46 (95% CI 0.215-0.980, p = 0.04), respectively. Limitations include the retrospective study design and the lack of central radiology review. CONCLUSION: The ORR and TTF of cabozantinib were maintained from the 1L to 4L settings. Dose reductions due to toxicity were associated with improved TTF and OS. Cabozantinib has clinical activity after 1L Immuno-oncology combination agents.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".