Molecular Biomarkers in Metastatic Clear Cell Renal Cell Carcinoma Treated with First-Line Immune Combinations: a Systematic Review of Phase III Randomized Clinical Trials by Meet-URO Group

Immune checkpoint inhibitor (ICI)–based combinations represent the standard first-line treatment for metastatic clear cell renal cell carcinoma (mRCC), although robust biomarkers for treatment selection remain undefined. We conducted a systematic review to identify biomarkers assessed in randomized clinical trials (RCTs) on first-line ICI-based regimens. Following PRISMA guidelines, we searched PubMed, Web of Science and Scopus (January 2018–October 2025) for phase III RCTs investigating first-line ICI-based therapies in mRCC with molecular or circulating biomarker analyses. Given heterogeneity across studies, a qualitative synthesis was performed. Sixteen reports were included. Biomarkers were assessed using immunohistochemistry, transcriptomics, genomic sequencing and blood analyses. PD-L1 expression did not reliably discriminate benefit across ICI-based combinations, although it revealed a negative prognostic influence with sunitinib and a potential predictive role for nivolumab-ipilimumab. Tumours with angiogenic signatures were consistently associated with improved outcomes, suggesting prognostic relevance, while derived limited additional benefit from ICIs. Immune-related signatures were associated with ICI response, whereas proliferation and MYC-related signatures identified disease with poor prognosis across treatments. Individual mutations (e.g. PBRM1, VHL, BAP1, PTEN) showed heterogeneous and mainly prognostic associations, whereas composite gene panels (e.g. rDM) may offer better predictive value. Circulating biomarkers, including inflammatory cytokines and KIM-1 dynamics, demonstrated promising prognostic and early predictive signals. No validated biomarker currently supports treatment selection among first-line ICI-based combinations in mRCC. Future research should prioritize adaptive, biomarker-guided trial designs integrating longitudinal multi-omic profiling and circulating biomarkers to generate clinical evidence and support personalized treatments.