Assessment of time-to-treatment-failure (TTF) as a surrogate endpoint for overall survival (OS) to immune checkpoint inhibitor (ICI) regimens in metastatic renal cell carcinoma (mRCC): Findings from an IMDC analysis.
Bibliographic record
Abstract
4528 Background: In Phase III trials for mRCC, OS is a gold standard primary endpoint. However, for ICI-based regimens, this requires extended follow-up times, resulting in higher costs and delayed drug approvals. Identification of surrogate or intermediate endpoints for OS would be beneficial in addressing these challenges. In the current study, we investigated 6-month TTF as a potential intermediate endpoint (IE) for OS in mRCC. Methods: We included all patients from the International mRCC Database Consortium (IMDC) who received ICI-based regimens from 2013 to 2023. TTF was defined from ICI start until drug cessation or death or censored at date of last follow-up. The cohort was divided into 10 equal sub-cohorts based on the decile disease risk scores, calculated using multivariable Cox regression for OS, considering all relevant covariates (IMDC risk groups, presence of bone, brain, or liver metastases, histology, age, prior nephrectomy, ICI type, and year of ICI initiation). For these sub-cohorts, we used Kaplan-Meier methods to determine 18-month OS and event-free rates for 6, 9, and 12-month TTF. We then performed linear regression of stratum-specific 18-month OS against stratum-specific 6-month (and 9- and 12-month) TTF. In the landmark analysis, OS was calculated starting at 6 months after therapy initiation, excluding patients who died or had follow-up of less than 6 months. Results: The IMDC cohort consisted of 1667 patients with a median age of 63 years and 83% had clear cell histology. Median follow-up was 15.4 months (IQR: 7.1-28.6). Across the 10 sub-cohorts, 6-month TTF accounted for 76% of the variance in 18-month OS (R 2 = 0.76, 95% CI: 0.26-0.87). Similar patterns were seen for 9 (R 2 = 0.65, 95% CI: 0.11-0.81) and 12-month TTF (R 2 = 0.64, 95% CI: 0.09-0.80). In the 6-month landmark analysis (evaluable n = 1255), mRCC patients experiencing treatment failure at 6 months had an 18-month OS (i.e. 12 months after the landmark time) rate of 68% (95% CI: 63%-72%), compared to 92% (95% CI: 90%-94%) for those without treatment failure (adjusted HR: 2.74, 95% CI 2.15-3.49). Conclusions: 6-month TTF was predictive of 18-month OS in mRCC patients. These findings suggest that 6-month TTF may be a promising intermediate endpoint for OS and provides supportive evidence of future validation in prospective studies.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".