Biomarker analyses in patients with advanced renal cell carcinoma (aRCC) from the phase 3 CLEAR trial.
Bibliographic record
Abstract
4504 Background: In the primary analysis of CLEAR, lenvatinib + pembrolizumab (L+P) significantly improved efficacy vs sunitinib (S) in treatment-naïve patients with aRCC (Motzer 2021). Results were confirmed at the final prespecified OS analysis (Motzer 2024). We report biomarker analyses from CLEAR. Methods: PD-L1 IHC 22C3 pharmDx and NGS assays (ImmunoID NeXT platform: WES and RNA-Seq) were performed on archival tumor specimens. To identify somatic alterations including mutations and copy-number variations, paired PBMC samples were used as reference. For RNA-Seq/IHC-derived analyses, a continuous value analysis was performed adjusting by KPS score for: each gene-signature score (T-cell inflamed gene-expression profile [GEP], and non-GEP signatures including proliferation, angiogenesis, hypoxia, MYC, WNT, and other signatures [Cristescu 2022]) vs best overall response (BOR); non-GEP signatures vs BOR adjusted by GEP; and PD-L1 CPS vs BOR. Cutoff analyses were performed for biomarkers that showed significant association in the continuous value analysis. Cutoff values (1st tertile of GEP, or median of non-GEP, signatures) were determined based on combined L+P and S arms. WES analyses were descriptively summarized if TMB/INDEL burden and mutation status of key RCC driver genes were associated with BOR. Results: There were no notable differences in baseline characteristics and tumor responses in biomarker analysis sets vs the ITT population. In the L+P arm, the continuous GEP signature score was not associated with BOR. The MYC signature score was negatively associated with BOR (2-sided test, significance criteria 0.1; FDR-adjusted p=0.013/0.012 with/without adjustment by GEP signature score, respectively). The ORRs (95% CI) for the MYC-high and -low groups were 66.3% (56.1-75.6) and 84.0% (75.0-90.8), respectively. In the S arm, the continuous GEP signature score was positively associated with BOR (2-sided test, significance criteria 0.05; p=0.010). The ORRs (95% CI) for the GEP-high and -low groups were 46.9% (38.1-55.9) and 28.8% (18.3-41.3), respectively. The angiogenesis signature was positively associated with BOR (2-sided test, significance criteria 0.1; FDR-adjusted p=0.046/0.088 with/without adjustment by GEP signature score, respectively). The ORRs (95% CI) for the angiogenesis-high and -low groups were 52.1% (41.6-62.5) and 30.4% (21.7-40.3), respectively. PD-L1 CPS and TMB/INDEL burden were not associated with BOR in L+P or S arms. ORR was higher with L+P vs S, regardless of the deleterious mutation status of BAP1, VHL, PBRM1, SETD2, and KDM5C—frequently mutated genes in RCC. Conclusions: The superiority of L+P vs S in ORR does not appear to be impacted by gene-expression signatures for tumor-induced proliferation, angiogenesis, hypoxia, MYC, or WNT, or by PD-L1 status, TMB/INDEL burden or mutation status of RCC driver genes. Clinical trial information: NCT02811861 .
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".