Real-World Outcomes in Patients with Advanced/Metastatic Renal Cell Carcinoma Receiving Cabozantinib or Other Tyrosine Kinase Inhibitors After Checkpoint Inhibitor-Based Therapy
Bibliographic record
Abstract
BACKGROUND: Checkpoint inhibitor (CPI)-based therapy is recommended for first-line treatment of advanced/metastatic renal cell carcinoma (mRCC). Cabozantinib is a tyrosine kinase inhibitor (TKI) approved in the USA for treating mRCC, including after CPI-based therapy. However, data on the benefits of subsequent TKI therapy are limited. OBJECTIVE: To study the real-world use and outcomes of cabozantinib versus other TKIs after CPI-based therapy for mRCC. METHODS: This retrospective study used data from the US Oncology Network electronic health record database supplemented by chart review. Patients initiated TKI therapy between 2016 and 2021 after CPI-based therapy. The primary endpoint was real-world response rate in the first 6 months of treatment (RR-6m; physician assessment). Secondary endpoints included overall response rate (ORR), progression-free survival (PFS) and overall survival (OS). Covariates were adjusted by inverse probability of treatment weighting. RESULTS: Of 485 included patients, 331 received cabozantinib and 154 another TKI. Baseline characteristics were generally similar between arms. For cabozantinib versus other TKIs, adjusted RR-6m (available for 69.3% of patients) was 62.5% versus 46.0% (rate difference: superiority, 16.5% [95% CI: 7.8–25.1], p = 0.0002), adjusted ORR was 62.4% versus 49.4% ( p = 0.0020), adjusted median OS was 19.2 versus 19.1 months ( p = 0.7353) and adjusted median PFS was 7.9 versus 9.2 months ( p = 0.8752). CONCLUSIONS: Cabozantinib following CPI-based therapy was effective for treating mRCC in the US real-world setting. Differences in adjusted RR-6m and ORR significantly favored cabozantinib versus other TKIs. The lack of OS difference may reflect differences in post-index therapy.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".