Annals of Oncology | Biomarker analyses from the CLEAR study reveal efficacy comparison between L+P and Sunitinib in aRCC treatment

Through biomarker analyses from the CLEAR trial, this article systematically evaluates the efficacy differences between L+P and Sunitinib in the treatment of advanced renal cell carcinoma (aRCC) based on PD-L1 expression, RCC driver gene mutations, gene expression profiles, and molecular subtypes. It provides new biomarker evidence for precision treatment strategies for aRCC patients.

Literature Overview
The article, 'Lenvatinib plus pembrolizumab versus sunitinib in first-line treatment of advanced renal cell carcinoma: biomarker analyses from the CLEAR trial', published in the 'Annals of Oncology', reviews and summarizes the efficacy comparison between L+P and Sunitinib in the first-line treatment of advanced renal cell carcinoma (aRCC) from the CLEAR trial. It further reveals potential biological mechanisms of treatment response through multiple biomarker analyses, including PD-L1 expression levels, RCC driver gene mutations, gene expression profiles, and molecular subtypes. The study explores how different molecular features affect treatment outcomes and provides scientific evidence for personalized treatment strategies.

Background Knowledge
Advanced renal cell carcinoma (aRCC) is the most common subtype of kidney cancer, and its treatment strategies have gradually shifted toward combination targeted and immunotherapy in recent years. PD-L1 inhibitors combined with TKIs (such as pembrolizumab and lenvatinib) have become an important treatment option; however, the biomarkers for predicting efficacy are not yet fully defined. Mutational status of RCC driver genes (such as VHL, PBRM1, SETD2, BAP1, KDM5C) is associated with tumor biology, while gene expression features (e.g., T-cell inflamed gene expression profile, angiogenesis, and proliferation features) may reflect the tumor microenvironment and mechanisms of treatment response. The classification of molecular subtypes (e.g., angiogenic, immune/proliferative, unclassified) further refines the stratified management of aRCC patients, although their prognostic value across different treatments remains to be fully elucidated. Therefore, this article systematically compares the efficacy of L+P versus Sunitinib across different gene mutations and molecular subtypes through biomarker analyses, offering reference for future personalized treatment strategies.

Research Methods and Experiments
This research is based on biomarker analyses from the CLEAR phase 3 trial, evaluating PD-L1 immunohistochemistry (IHC), whole-exome sequencing, and RNA sequencing data. PD-L1 expression levels were assessed using the Combined Positive Score (CPS) and analyzed as continuous variables, adjusting for baseline Karnofsky Performance Score (KPS). Gene expression features, including the T-cell inflamed gene expression profile (TcellinfGEP) and non-T-cell inflamed features (e.g., proliferation, angiogenesis), were associated with clinical outcomes (BOR and PFS) through continuous scoring. The association between RCC driver gene mutations (VHL, PBRM1, SETD2, BAP1, KDM5C) and PFS as well as ORR was analyzed in cohorts with at least 20 mutation cases per group. Molecular subtypes were classified based on published gene expression features (TcellinfGEP, angiogenesis, stroma, proliferation), and their relationships with treatment efficacy were analyzed.

Key Conclusions and Perspectives

• PD-L1 CPS scores did not show significant association with BOR or PFS in either the L+P or Sunitinib treatment groups, suggesting limited predictive value of PD-L1 as a standalone biomarker in aRCC.
• The mutational status of RCC driver genes (VHL, PBRM1, SETD2, BAP1, KDM5C) showed similar impact on PFS between the L+P and Sunitinib groups, indicating these mutations may not serve as strong predictors for treatment selection.
• In the gene expression feature analysis, high proliferation and MYC features were associated with shorter PFS in the Sunitinib group, whereas high angiogenesis and microvascular density features were associated with longer PFS, suggesting Sunitinib efficacy may be linked to specific gene expression features.
• In the L+P group, no significant association was observed between gene expression features and PFS, suggesting that L+P efficacy may not depend on individual gene expression features but may involve multiple mechanisms working in synergy.
• Molecular subtype analysis showed low-risk patients enriched in the angiogenesis or angiogenesis/stroma clusters, while high-risk patients enriched in the proliferation or unclassified clusters. However, no significant association between molecular subtypes and PFS was observed in either the L+P or Sunitinib groups.
• Although molecular subtypes did not reveal treatment-specific prognostic differences, L+P showed superior efficacy across all subtypes compared with Sunitinib, suggesting broad applicability across patient populations with different molecular features.

Conclusion
This study, based on biomarker analyses from the CLEAR phase 3 trial, systematically evaluated the predictive value of PD-L1 expression, RCC driver gene mutations, gene expression features, and molecular subtypes in the efficacy of L+P versus Sunitinib. Results showed that PD-L1 CPS scores, mutational status of genes, and gene expression features did not show significant differences in efficacy within the L+P treatment group. However, Sunitinib showed reduced efficacy in patients with high proliferation or high MYC features, but better efficacy in those with high angiogenesis or high microvascular density features. Furthermore, molecular subtypes did not show a significant association with PFS in either treatment group. The study confirmed that L+P consistently outperforms Sunitinib across different biomarker-defined subgroups, supporting its broad applicability as a first-line treatment. Future studies should explore additional potential biomarkers to further optimize individualized treatment strategies for aRCC patients.

Nanobody Humanization (Llamanade)

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Nanobodies (Nbs) have recently emerged as a promising class of antibody fragments for biomedical and therapeutic applications. Despite their notable physicochemical properties, Nbs, which are derived from camelids, may require “humanization” to enhance their translational potential for clinical trials. The authors of Llamanade systematically analyzed the sequence and structural properties of Nbs based on next-generation sequencing (NGS) databases and high-resolution structures. Their analysis revealed substantial framework diversities and highlighted key differences between Nbs and human Immunoglobulin G (IgG) antibodies. They identified conserved residues that may contribute to improved solubility, structural stability, and antigen-binding, providing valuable insights into the humanization of Nbs.