Identification of Predictive Genomic Biomarkers in Metastatic Clear Cell Renal Cell Carcinoma: A Comprehensive Analysis of Real-World Clinicogenomic Data

PURPOSE Limited validated prognostic biomarkers are available to guide treatment decisions for patients with metastatic clear cell renal cell carcinoma (mccRCC). METHODS This study used a US-based renal cell cancer clinicogenomic database to analyze the predictive value of genomic alterations in patients treated with immune checkpoint inhibitors (ICI) and antiangiogenic (AA) therapies. Three co-occurring gene clusters (C) were considered: (C1) VHL , SETD2 , PBRM1 , KDM5C , and NFE2L2 ; (C2) TP53 , TSC1 , TERT, and DNMT3A ; and (C3) CDKN2A , CDKN2B , BAP1 , NF2 , and MTAP . RESULTS In first line, among 493 patients who underwent systemic therapy, 201 (40.8%) and 172 (34.9%) received AA monotherapy (AAm) and ICI combinations (ICI-C), respectively. TERT , TSC1 , and TET2 and C2 were identified as positive predictors of response to ICI-C versus AAm. Conversely, C1 was a predictive marker for enhanced AAm efficacy. Among ICI-C, ICI + AA was more effective than ICI + ICI in patients with SETD2 alterations, and less effective in mutant TSC1 . CONCLUSION These findings require prospective validation to confirm their clinical utility.