Cabozantinib in the Routine Management of Renal Cell Carcinoma: A Systematic Literature Review of Real-World Evidence
Bibliographic record
Abstract
Real-world cabozantinib use has increased since its approval to treat patients with advanced renal cell carcinoma (RCC) in 2016. We reviewed cabozantinib use in real-world clinical practice and compared outcomes with pivotal cabozantinib randomized control trials (RCTs). This PRISMA-standard systematic literature review evaluated real-world effectiveness and tolerability of cabozantinib in patients with RCC (PROSPERO registration: CRD42021245854). Systematic MEDLINE, Embase, and Cochrane database searches were conducted on November 2, 2022. Eligible publications included ≥ 20 patients with RCC receiving cabozantinib. After double-screening for eligibility, standardized data were abstracted, qualitatively summarized, and assessed for risk of bias using the Newcastle-Ottawa Scale. Of 353 screened publications, 41 were included, representing approximately 11,000 real-world patients. Most publications reported cabozantinib monotherapy cohort studies (40/41) of retrospective (39/41) and multicenter (32/41) design; most included patients from North America and/or Europe (30/41). Baseline characteristics were demographically similar between real-world and pivotal RCT populations, but real-world populations showed greater variation in prevalence of prior nephrectomy, multiple-site/brain metastasis, and nonclear-cell RCC histology. Cabozantinib activity was reported across real-world treatment lines and tumor types. Overall survival, progression-free survival, and objective response rate values from pivotal RCTs were within the ranges reported for equivalent outcomes across real-world studies. Common real-world grade ≥ 3 adverse events were consistent with those in pivotal RCTs (fatigue, palmar-plantar erythrodysesthesia syndrome, diarrhea, hypertension), but less frequent. No new tolerability concerns were identified. Real-world RCC survival outcomes for cabozantinib monotherapy were broadly consistent with pivotal RCTs, despite greater heterogeneity in real-world populations.
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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 | 0.018 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".