Clinical outcomes by baseline metastases in patients with renal cell carcinoma treated with lenvatinib plus pembrolizumab versus sunitinib: Post hoc analysis of the <scp>CLEAR</scp> trial
Bibliographic record
Abstract
Lenvatinib plus pembrolizumab significantly improved efficacy versus sunitinib in treatment of advanced renal cell carcinoma (aRCC) in the phase 3 CLEAR study. We report results of an exploratory post hoc analysis of tumor response data based on baseline metastatic characteristics of patients who received lenvatinib plus pembrolizumab versus sunitinib, at the final overall survival analysis time point of CLEAR (cutoff: July 31, 2022). Treatment-naïve adults with aRCC were randomized to: lenvatinib (20 mg PO QD in 21-day cycles) plus pembrolizumab (n = 355; 200 mg IV Q3W); lenvatinib plus everolimus (not reported here); or sunitinib (n = 357; 50 mg PO QD; 4 weeks on/2 weeks off). The most common (lenvatinib plus pembrolizumab; sunitinib, respectively) metastatic site was lung (71.0%; 63.9%), followed by lymph node (45.6%; 43.7%), bone (22.5%; 24.9%), and liver (17.7%; 19.6%). Across treatment arms, ≥65% had two or more metastatic organs/sites involved, >80% of patients had nontarget lesions, and ~45% had baseline sums of diameters of target lesions ≥60 mm. Lenvatinib plus pembrolizumab demonstrated greater progression-free survival, objective response rate, and duration of response versus sunitinib across evaluable subgroups regardless of site or size of baseline metastasis or number of metastatic sites at baseline. Overall survival generally trended to favor lenvatinib plus pembrolizumab versus sunitinib; and tumor shrinkage was greater across sites (lung, lymph node, liver, and bone) for patients in the lenvatinib-plus-pembrolizumab arm versus the sunitinib arm. These results further support lenvatinib plus pembrolizumab as a standard-of-care in patients with aRCC regardless of site or size of baseline metastasis or the number of metastatic sites.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".