Comparison of First-Line Anti-PD-1-Based Combination Therapies in Metastatic Renal-Cell Carcinoma: Real-World Experiences from a Retrospective, Multi-Institutional Cohort
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to test for differences in overall (OS) and progression-free survival (PFS) rates and toxicity in first-line immune checkpoint inhibition (IO) combination therapy in metastatic renal-cell carcinoma (mRCC) patients. METHODS: Between November 2017 and April 2021, 104 patients with histologically confirmed mRCC from 6 tertiary referral centers with either IO + IO (nivolumab + ipilimumab, n = 68) or IO + tyrosine kinase inhibitor (TKI) (pembrolizumab + axitinib, n = 36) were included. Kaplan-Meier and Cox regression analyses tested for OS and PFS differences. RESULTS: Of 104 mRCC patients, 68 received IO + IO (65.4%) and 36 IO + TKI (34.6%) therapy, respectively. Median age was 67 years (interquartile range: 57-70.3). Patients receiving IO + TKI were less likely to be poor risk according to the International Metastatic Renal-Cell Carcinoma Database Consortium score (16.7 vs. 30.9%) and presented with lower T-stage, compared to IO + IO treated patients. Median PFS was 9.8 months (CI: 5.3-17.6) versus 12.3 months (CI: 7.7 - not reached) for IO + IO versus IO + TKI treatment, respectively (p = 0.22). Median OS was not reached, survival rates at 12 months being 73.9 versus 90.0% for IO + IO versus IO + TKI patients (p = 0.089). In subgroup analyses of elderly patients (≥70 years, n = 38), IO + TKI treatment resulted in better OS rates at 12 months compared to IO + IO (91.0 vs. 57.0%; p = 0.042). CONCLUSION: IO + IO and IO + TKI as first-line therapies in mRCC patients were both comparable as for the oncological outcome and toxicity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".