First-line (1L) immuno-oncology (IO) combination therapies in metastatic renal-cell carcinoma (mRCC): Results from the international mRCC database consortium (IMDC).
Bibliographic record
Abstract
4577 Background: In mRCC, ipilimumab and nivolumab (ipi-nivo) is a 1L treatment option. Recent data have also shown efficacy of 1L IO-VEGF (IOVE) inhibitor combinations. Comparative data between these two strategies are limited and the efficacy of subsequent therapies remains unknown. Methods: Using the IMDC dataset, patients (pts) treated with any 1L IOVE combination were compared to those treated with ipi-nivo. Multivariable Cox regression analysis was performed to control for imbalances in IMDC risk factors. Results: 188 pts received 1L IO combination therapy: 113 treated with IOVE combinations and 75 with ipi-nivo. Baseline characteristics and IMDC risk factors were comparable between groups. When comparing IOVE combinations vs ipi-nivo, 1L response rate (RR) was 33% vs 40% (p=0.39), time to treatment failure (TTF) was 14.3 (95% CI 9.2-16.1) vs 10.2 months (95% CI 6.7-15.1, p=0.23), and median overall survival (OS) was not reached (NR) (95% CI 22.3-NR) vs NR (95% CI 35.1-NR, p=0.17). When adjusted for IMDC risk factors, the hazard ratio (HR) for TTF was 0.71 (95% CI 0.46-1.12, p=0.14) and the HR for death was 1.74 (95% CI 0.82-3.68, p=0.14). Second-line (2L) treatments were varied. In pts receiving subsequent VEGF-based therapy, 2L RR was lower in the IOVE (n=20) versus ipi-nivo (n=20) cohort (15% vs 45%; p=0.04), though 2L TTF was not significantly different (3.7 vs 5.4 months, p=0.40, n=55). The use of IO post IOVE was uncommon and 3/5 pts had PD as best response; 2/5 had PR/SD but their 1L IOVE exposure was short at <3 months. Conclusions: There does not appear to be a superior 1L IO combination strategy in mRCC, as lOVE combinations and ipi-nivo have comparable 1L RR, TTF and OS. Most pts received VEGF-based therapy in the 2L. In this group, 2L RR was greater in pts who received ipi-nivo, though there was no difference in 2L TTF. [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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".