Motzer R J, Porta C, Eto M, Hutson T E, Rha S Y, Merchan J R, Winquist E, Gurney H, Grünwald V, George S, Markensohn J, Burgents J E, Cristescu R, Sachdev P, Narita Y, Huang J, Zhao Z, Okpara C E, Minoshima Y, Choueiri T K
Memorial Sloan Kettering Cancer Center, New York, USA.
Interdisciplinary Department of Medicine, University of Bari "A. Moro" and Division of Medical Oncology, Policlinico Consorziale di Bari, Bari, Italy.
Ann Oncol. 2025 Apr;36(4):375-386. doi: 10.1016/j.annonc.2024.12.003. Epub 2024 Dec 11.
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.
Programmed death-ligand 1 (PD-L1) immunohistochemistry (IHC) and next-generation sequencing assays (whole exome sequencing/RNA sequencing) were carried out on archival tumor specimens. For IHC-derived/RNA sequencing analyses, a continuous analysis was carried out adjusting by Karnofsky performance status (KPS) score for: PD-L1 combined positive score (CPS) versus best overall response (BOR)/progression-free survival (PFS); and each gene signature score [T-cell inflamed gene expression profile (TcellGEP)/non-TcellGEP 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.
Within-arm analyses using continuous values showed no association between PD-L1 levels and BOR/PFS for either treatment. PFS hazard ratios between arms were similar regardless of the mutant or wild-type subgroups of RCC driver genes (VHL, PBRM1, SETD2, BAP1, KDM5C). No associations between PFS and gene signature scores were observed for L + P. With sunitinib, high proliferation and MYC signature scores showed shorter PFS; high angiogenesis and microvessel density signature scores showed longer PFS. Six new molecular subtypes were defined. Tumors of patients with favorable/intermediate risk were enriched in angiogenesis and angiogenesis/stromal clusters; those with poor risk were enriched in proliferative and unclassified (low-TcellGEP/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).
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.
在CLEAR研究中，与舒尼替尼相比，乐伐替尼联合帕博利珠单抗（L+P）在晚期肾细胞癌（aRCC）一线治疗中显著提高了疗效。我们报告了CLEAR生物标志物分析的结果。
对存档的肿瘤标本进行程序性死亡配体1（PD-L1）免疫组化（IHC）和新一代测序分析（全外显子组测序/RNA测序）。对于基于IHC/RNA测序的分析，进行连续分析，并根据卡诺夫斯基体能状态（KPS）评分进行调整，以分析：PD-L1联合阳性评分（CPS）与最佳总体缓解（BOR）/无进展生存期（PFS）；以及每个基因特征评分[T细胞炎症基因表达谱（TcellGEP）/非T细胞GEP特征，包括增殖和血管生成]与BOR/PFS。对每个治疗组中≥20例患者存在致癌性改变的基因，分析肾细胞癌驱动基因的突变状态与PFS之间的关联。通过对基线KPS进行调整，评估分子亚型与预后的相关性。预先设定了评估的生物标志物集以及PD-L1 CPS、基因特征评分和分子亚型的统计学意义标准。
使用连续值进行的组内分析显示，两种治疗的PD-L1水平与BOR/PFS之间均无关联。无论肾细胞癌驱动基因（VHL、PBRM1、SETD2、BAP1、KDM5C）的突变型或野生型亚组如何，两组之间的PFS风险比相似。对于L+P，未观察到PFS与基因特征评分之间的关联。对于舒尼替尼，高增殖和MYC特征评分显示PFS较短；高血管生成和微血管密度特征评分显示PFS较长。定义了六种新的分子亚型。中低风险患者的肿瘤在血管生成和血管生成/基质簇中富集；高风险患者的肿瘤在增殖性和未分类（低T细胞GEP/低血管生成/低增殖）簇中富集。未观察到分子亚型与L+P/舒尼替尼的PFS之间存在关联（在对与PFS单独相关的KPS和基因特征进行调整后）。
在使用肾细胞癌驱动基因突变、PD-L1、基因表达特征和分子亚型定义的一系列生物标志物亚组中，均一致观察到L+P对比舒尼替尼在aRCC中的客观缓解率和PFS有所改善。
