Real-world outcomes among patients with advanced/metastatic renal cell carcinoma (mRCC) treated with cabozantinib or other tyrosine kinase inhibitors (TKIs) after checkpoint inhibitor (CPI) therapy.
Bibliographic record
Abstract
4530 Background: CPI-based therapy is the first-line standard of care for patients with mRCC, but effectiveness data for subsequent targeted therapies are limited. Cabozantinib, a multi-targeted TKI, is indicated for patients with mRCC in the USA and may be prescribed after doublet CPI therapy. Methods: Retrospective study of cabozantinib vs other TKI treatment after CPI therapy for mRCC using the US Oncology Network electronic health record database and chart review. Patients initiated TKI therapy between May 1, 2016 and Nov 30, 2021, immediately after CPI therapy. The primary endpoint was real-world response rate over the first 6 months of treatment (rwRR6months) based on physician assessment (complete response (CR): documented CR/indication of remission/disappearance of all lesions/no evidence of disease, or partial response (PR): documented PR/improved disease/responding disease). Covariates were adjusted by inverse probability of treatment weighting. Noninferiority was assessed using a 90% confidence interval (CI) and 10% noninferiority margin; if noninferiority was met, superiority was evaluated using a 95% CI and chi-square test. Results: In total, 485 patients were included. Baseline characteristics were similar for the cabozantinib and other TKIs subgroups, including proportions of patients with ≥ 2 metastatic sites (74.7% vs 68.2%) and metastases in bone (45.0% vs 43.5%), brain (8.8% vs 7.1%), liver (18.1% vs 19.5%), lung (35.0% vs 44.2%) and lymph nodes (38.7% vs 31.8%). At 6 months, tumor assessment data were available for 75.5% of patients (cabozantinib, 79.8% of cabozantinib and 66.2% of other TKIs, 66.2%) patients. Adjusted rwRR6months was 62.5% for cabozantinib and 46.0% for other TKIs (rate difference: noninferiority, 16.5% [90% CI, 9.3–23.7], p < 0.0001; superiority, 16.5% [95% CI, 7.8–25.1], p = 0.0002). Conclusions: In patients with mRCC receiving standard of care treatment in the USA, cabozantinib was effective post-CPI therapy, including in patients without prior TKI therapy. Difference in adjusted rwRR6months significantly favored cabozantinib vs other TKIs. These data may inform global jurisdictions that restrict cabozantinib to the post TKI setting. [Table: see text]
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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 | 0.002 | 0.006 |
| 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 | 0.000 | 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".