Clinical Effectiveness of Second-line Sunitinib Following Immuno-oncology Therapy in Patients with Metastatic Renal Cell Carcinoma: A Real-world Study
Bibliographic record
Abstract
BACKGROUND: Limited data exist on the clinical effectiveness of second-line (2L) vascular endothelial growth factor (receptor) targeted inhibitor (VEGF(R)i) sunitinib after first-line (1L) immuno-oncology (IO) therapy for patients with metastatic renal cell carcinoma (mRCC) in real-world settings. METHODS: A retrospective cohort study among adult patients with mRCC treated with 2L sunitinib following 1L IO was conducted from select International mRCC Database Consortium (IMDC) centers. All analyses were performed overall and by 1L ipilimumab + nivolumab (IPI+NIVO) or 1L IO+VEGF(R)i. Median overall survival (mOS) and time-to-treatment discontinuation (mTTD) in 2L were estimated using Kaplan-Meier analysis. The 2L objective response rate (ORR) (complete/partial response) was reported. RESULTS: Among 102 patients on 2L sunitinib, mean age was 61.3 years. IMDC risk scores at 2L initiation was available for 83 patients: 8 (9.6%) were favorable, 45 (54.2%) were intermediate, and 30 (36.1%) were poor risk. The 1L consisted of IPI+NIVO in 62 (60.8%), IO+VEGF(R)i therapy in 27 (26.5%), and IO monotherapy in 13 (12.7%) patients. Among all patients, mOS was 15.6 months (95% confidence interval [CI], 9.8-21.7), with a 1-year OS rate of 57.5% (95% CI, 45.2-68.0). mTTD was 5.4 months (95% CI, 4.2-7.2) and ORR was 22.5%. CONCLUSION: Despite availability of effective 1L therapies in recent years, 2L sunitinib continues to have clinical activity after failure of 1L IO. Further studies on optimal treatment sequencing after 1L IO progression are needed.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".