Effectiveness of first-line therapy in patients with advanced non-clear renal cell carcinoma (nccRCC).
Bibliographic record
Abstract
304 Background: Current treatment principles for advanced nccRCC have been largely extrapolated from guidelines for clear cell RCC. Given the emerging randomized data for select nccRCC subtypes, real-world outcomes for these patients are informative particularly in the contemporary checkpoint inhibitor era. Methods: We performed an analysis using the Canadian Kidney Cancer information system (CKCis), a prospective database involving 14 academic centers, on nccRCC patients undergoing first-line systemic therapy between January 2011 – December 2019. Treatment groups were defined as receipt of: vascular endothelial growth factor receptor tyrosine kinase inhibitors (VEGF-TKI), mammalian target of rapamycin inhibitors (mTORi), and PD-1/PD-L1 immune checkpoint inhibitors (ICI, mono- or combination therapy). Primary outcome was 1-yr overall survival (OS) rate. Secondary outcomes were median time to treatment failure ((TTF, months), defined as treatment discontinuation, change or death) and objective response rate (ORR, %). Results: We identified 265 nccRCC patients: 204 (77.0%) received VEGF-TKI, 19 (7.2%) received mTORi and 42 (15.8%) received ICI-based first-line therapy (Table). Overall, median age was 64 years, 75% were male, 84% were classified as IMDC intermediate/poor risk, and 16% underwent prior nephrectomy. Twenty-three percent of patients were enrolled in clinical trials. Patients received primarily sunitinib (81%) or pazopanib (15%) in the VEGF-TKI group (other: 4%), while mTORi-treated patients received temsirolimus (74%) or everolimus (26%). For the ICI-based treatment group, most patients received combination therapy as ipilimumab-nivolumab (71%) or pembrolizumab-axitinib (26%), with 3% receiving ICI monotherapy. 1-yr OS was 65.2% for VEGF-TKI, 57.9% for mTORi and 69.0% for ICI-treated patients. Median TTF was 3.3 for VEGF-TKI, 3.5 for mTORi and 7.1 mos for ICI-treated patients. ORR was 17%, 5%, and 37% respectively for the VEGF-TKI, mTORi and ICI-treated groups. Conclusions: We describe the effectiveness of first-line therapy for patients with nccRCC from a national database. This real-world data suggests an association between first-line ICI-based therapies and improved outcomes, albeit with cabozantinib not available for the indication during this time. Our data supports consensus recommendations for preferred use of ICI-based or VEGF-TKI over mTORi as first-line therapy in nccRCC.[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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".