First-line (1L) immuno-oncology (IO) combination therapies in metastatic renal cell carcinoma (mRCC): Preliminary results from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC).
Bibliographic record
Abstract
584 Background: In mRCC, ipilimumab and nivolumab (ipi-nivo) is a 1L treatment option. Recent data have also shown efficacy of 1L PD(L)1-VEGF (PV) inhibitor combinations. The efficacy of these two strategies has not been compared. Methods: Using the IMDC dataset, patients (pts) treated with any 1L PV combination were compared to those treated with ipi-nivo. Multivariable Cox regression analysis was performed to control for imbalances in IMDC risk factors. Results: 164 pts received 1L IO combination therapy: 104 treated with PV combinations and 60 with ipi-nivo. Baseline characteristics and IMDC risk factors were comparable between groups (Table). When comparing PV combinations vs ipi-nivo, 1L response rates (RR) were 30% vs 39% (p = 0.29), time to treatment failure (TTF) was 13.2 (95% CI 8.3-16.1) vs 8.5 months (95% CI 5.7-14.0, p = 0.31), and median overall survival (OS) was not reached (NR) (95% CI 19.7-NR) vs NR (95% CI 27.6-NR, p = 0.39). When adjusted for IMDC risk factors, the hazard ratio (HR) for TTF was 0.77 (95% CI 0.44-1.35, p = 0.36) and the HR for death was 0.94 (95% CI 0.33-2.71, p = 0.91). Similar results were seen when restricting the cohort to IMDC intermediate/poor risk pts only. In pts receiving subsequent VEGF TKI monotherapy, second-line (2L) RR (13% vs 45%, p = 0.07) and TTF (5.5 vs 5.4 months, p = 0.80) for PV combinations (n = 15) vs ipi-nivo (n = 20) were not significantly different. Conclusions: There does not appear to be a superior 1L IO combination strategy in mRCC, as PV combinations and ipi-nivo have comparable RR, TTF and OS. Although there is a trend towards differences in RR, there does not appear to be a significant difference in TTF for patients receiving 2L VEGF TKI therapy. [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 | 0.002 |
| 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 | 0.000 |
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".