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

Abstract

Limited validated prognostic biomarkers are available to guide treatment decisions for patients with metastatic clear cell renal cell carcinoma (mccRCC). 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) <i>VHL</i>, <i>SETD2</i>, <i>PBRM1</i>, <i>KDM5C</i>, and <i>NFE2L2</i>; (C2) <i>TP53</i>, <i>TSC1</i>, <i>TERT,</i> and <i>DNMT3A</i>; and (C3) <i>CDKN2A</i>, <i>CDKN2B</i>, <i>BAP1</i>, <i>NF2</i>, and <i>MTAP</i>. 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. <i>TERT</i>, <i>TSC1</i>, and <i>TET2</i> 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 <i>SETD2</i> alterations, and less effective in mutant <i>TSC1</i>. These findings require prospective validation to confirm their clinical utility.