Real-Word Experience of Cabozantinib in Metastatic Renal Cell Carcinoma (mRCC): Results from the Canadian Kidney Cancer information system (CKCis)
Bibliographic record
Abstract
BACKGROUND: Cabozantinib is an oral multitargeted tyrosine kinase inhibitor (TKI) that has demonstrated efficacy in metastatic renal-cell carcinoma (mRCC) randomized trials. OBJECTIVE: To explore the real-world effectiveness of cabozantinib in pretreated patients with mRCC, including patients who progressed on immune-oncology checkpoint inhibitor (ICI) therapy. METHODS: Using the Canadian Kidney Cancer information system (CKCis), patients with mRCC treated with cabozantinib monotherapy as second-line or later from January 1, 2011 to September 1, 2019 were identified. Patients were stratified based on line of cabozantinib received. We reported overall survival (OS), time to treatment failure (TTF) and disease control rate (DCR). Prognostic variables were analyzed using multivariable analysis. RESULTS: 157 patients received cabozantinib (median TTF 8.0 months; median OS 15.8 months): 37 (24%) in the second line (median TTF 10.4 months; median OS 18.9 months) 66 (42%) in third line (median TTF 5.9 months; median OS 13.3 months) and 54 (34%) in either 4th or 5th line (median TTF 9.4 months; median OS 16.8 months). One hundred sixteen patients (74%) received cabozantinib after prior ICI therapy (median TTF of 7.6 months; median OS of 15.8 months). DCR in all patients was 63% with 46%, 65% and 72% in 2nd line, 3rd line and 4th/5th line patients respectively. DCR in patients who received cabozantinib after prior ICI therapy was 64%. CONCLUSIONS: Cabozantinib is effective in a real-world, unselected population of mRCC patients, including in those who have progressed on prior ICI therapy, and in those exposed to multiple lines of therapy.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".