Biomarker analyses from the phase III randomized CLEAR trial: lenvatinib plus pembrolizumab versus sunitinib in advanced renal cell carcinoma
Bibliographic record
Abstract
BACKGROUND: In CLEAR, lenvatinib + pembrolizumab (L + P) significantly improved efficacy versus sunitinib in first-line treatment of patients with advanced renal cell carcinoma (aRCC). We report results from CLEAR biomarker analyses. PATIENTS AND METHODS: GEP signatures including proliferation and angiogenesis] versus BOR/PFS. Association between mutation status of RCC driver genes and PFS were analyzed for genes for which ≥20 patients per arm had oncogenic alterations. Association of molecular subtypes with outcome was evaluated with baseline KPS adjustments. The set of biomarkers evaluated and statistical significance criteria for PD-L1 CPS, gene signature scores, and molecular subtypes were prespecified. RESULTS: GEP/low-angiogenesis/low-proliferation) clusters. No association between molecular subtypes and PFS for L + P/sunitinib was observed (after adjustment for KPS and gene signatures that were individually associated with PFS). CONCLUSIONS: Improvements in objective response rate and PFS for L + P versus sunitinib in aRCC were observed consistently across a range of biomarker subgroups defined using RCC driver mutations, PD-L1, gene expression signatures, and molecular subtypes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".