Treatment-free Survival After First-line Therapies for Metastatic Renal Cell Carcinoma: An International Metastatic Renal Cell Carcinoma Database Consortium Analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Patients receiving immune checkpoint blockade (ICB) therapy may experience periods of prolonged disease control without a need for systemic therapy. Treatment-free survival (TFS) is an important measure for this period, but no data are available for patients with metastatic renal cell carcinoma (mRCC) starting first-line agents. Our aim was to analyze TFS outcomes for patients with mRCC starting first-line therapy. METHODS: We analyzed data for patients with mRCC starting first-line systemic therapy with VEGFR-targeted monotherapy, an ICB + VEGFR combination, or an ICB doublet from February 1, 2014 to February 1, 2023 from the multicenter International Metastatic RCC Database Consortium (IMDC) database. We estimated 36-mo TFS as the difference in restricted mean survival time between (1) the time to first-line therapy discontinuation and (2) the time to subsequent systemic therapy initiation. KEY FINDINGS AND LIMITATIONS: The study population included 3758 patients receiving either first-line VEGFR monotherapy (n = 2635), an ICB + VEGFR combination (n = 354), or doublet ICB (n = 769) were included. For the IMDC favorable-risk cohort, the 36-mo TFS estimate was 3.1 mo (95% confidence interval [CI] 1.5-4.6) for the VEGFR monotherapy group and 3.7 mo (95% CI 0.2-7.2) for the ICB + VEGFR group. For the IMDC intermediate-/poor-risk cohort, TFS was 2.1 mo (95% CI 1.4-2.8) for the VEGFR monotherapy group, 3.7 mo (95% CI 1.0-6.4) for the ICB + VEGFR group, and 5.3 mo (95% CI 3.8-6.8) for ICB doublet group. Limitations include the retrospective design and an inability to quantify time spent with adverse events. CONCLUSIONS AND CLINICAL IMPLICATIONS: Our study demonstrates that patients with IMDC intermediate or poor risk treated with ICB doublet therapy experienced longer TFS than those treated with VEGFR monotherapy in the first-line setting. These results emphasize the utility of TFS as an informative endpoint and provide survival estimates to inform decision-making in mRCC. PATIENT SUMMARY: For patients with metastatic kidney cancer, we compared the survival time free from a second treatment line for different first-line treatment options. The results show that the time free from second-line treatment was longer when first-line treatment was with a combination of two immunotherapy drugs (ipilimumab and nivolumab) in comparison to other treatment options.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".