Efficacy of targeted therapy (TT) after checkpoint inhibitors (CPI) in metastatic renal cell carcinoma (mRCC): Results from the Canadian Kidney Cancer Information System (CKCis).
Bibliographic record
Abstract
568 Background: While the use of CPI has demonstrated clinical benefit in patients with mRCC, data showing the efficacy of subsequent TT is limited. This real-world analysis evaluated the efficacy of TT post CPI in mRCC patients. Methods: Data was collected and analyzed from CKCis. Patients with mRCC who received TT after CPI were identified and analyzed based on line of therapy. Time to treatment failure (TTF – time from starting first subsequent TT to stopping TT) and overall survival (OS) were calculated. Hazard Ratio (HR) calculations were adjusted for IMDC group and age. Results: 102 patients were treated with TT post CPI (table). Those who received first-line ipilimumab + nivolumab (I/N) versus a vascular endothelial growth factor inhibitor (VEGFi) + CPI combination prior to second-line TT had a median TTF of 8.0 vs 5.2 months (m) (HR=0.43, 95% CI: 0.13-1.44) and median OS of 16.5 m vs not reached (HR=0.76, 95% CI: 0.11-5.24). Patients who received a VEGFi versus a mammalian target of rapamycin inhibitor (mTORi) as third-line TT had a median TTF of 7.6 vs 4.4 m (HR=0.52, 95% CI: 0.24-1.10) and median OS of 21.7 vs 16.2 m (HR=0.41, 95% CI: 0.16-1.08). All third-line TT patients received first-line VEGFi and second-line nivolumab. Of the third-line VEGFi TT patients, 24 received axitinib (TTF 7.1 m, OS 21.7 m) and 22 received cabozantinib (data immature). Conclusions: Activity of TT in mRCC patients after CPI is demonstrated in multiple lines. In second-line, VEGFi TT had numerically better outcomes after I/N than after VEGFi+CPI combination. Efficacy of third-line TT was seen with a trend favoring VEGFi over mTORi. Axitinib in the third-line has notable activity after CPI, while data on cabozantinib and fourth-line TT are maturing. These results support the use of VEGFi after CPI in mRCC patients. [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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".