Real-world treatment outcomes in patients with advanced renal cell carcinoma who receive axitinib plus pembrolizumab in the first-line setting in Canadian cancer centers.
Bibliographic record
Abstract
382 Background: The landscape of management of advanced renal cell carcinoma (aRCC) in the first line setting has changed dramatically over the past decade. Axitinib with Pembrolizumab (AP) is one of the combinations which demonstrated improved outcomes in the KEYNOTE 426 study. We report real-world outcomes and safety with this combination. Methods: The Canadian Kidney Cancer information system (CKCis) is a multi-institutional prospective RCC cohort. Patients ≥18 years with aRCC and clear cell histology who received AP as first-line therapy from January 1, 2017 to June 30, 2022 were included. Descriptive and actuarial statistics were reported for the following: progression free survival (PFS), overall survival (OS) and adverse events. Results: The cohort includes 222 patients.165 (74.3%) were male with a median age of 64.5 (37.0-87.7) years. Sixty-three (28.3%) patients were IMDC favourable-risk, 69 (31.0%) intermediate-risk, 45 (20.3%) poor-risk and 45 (20.2%) unknown. Median follow-up was 26.6 (range: 0.1 – 77.2) months. Of 222 patients, 119 discontinued treatment (53.6%) due to disease progression in 45.4% or toxicity in 29.4%. Median duration of treatment was 18.5 (range 0.1 to 72.4) months. PFS probability at 12 and 24 m was 63.8% and 49.9% respectively with a median PFS of 22.6 months (95% CI: 15.9-30.1). Survival probability at 12 and 24 m was 89.1% and 80.2% respectively with a median OS of 51.5 months (95% CI 38.0-NR). By IMDC criteria, intermediate risk median OS was 44.8 m (95% CI 41.5-61.6) and poor risk 33.6 m (18.5-NR). Most patients experienced toxicity requiring dose interruption/delay or discontinuation (n=180; 81.4%) The most common toxicities were diarrhea (73.3%), fatigue (54.4%), hepatotoxicity (50.0%), anorexia (26.1%), mucositis (22.2%), nausea (21.1%), palmar plantar erythrodysesthesia (15.6%), hypertension (13.3%), weight loss (13.3%) and proteinuria (11.7%). Others included pneumonitis (8.9%), thyroid dysfunction (6.7%) and colitis (4.4%). Conclusions: The real-world experience of patients with aRCC receiving AP in Canada is similar to the KEYNOTE-426 study in both outcomes and safety. These data continue to support its position as a standard of care in the first line setting for aRCC. Longer follow up and characterization of these patients is warranted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".