VHL and DNA damage repair pathway alterations as potential clinical biomarkers for first-line TKIs in metastatic clear cell renal cell carcinomas

Abstract

Abstract Purpose Vascular endothelial growth factor receptor tyrosine kinase inhibitors (VEGFR-TKIs) are being used for the first-line treatment of metastatic clear cell renal cell carcinoma (mccRCC). Here, we set out to explore associations between genomic statuses, gene expression clusters and clinical outcomes of mccRCCs upon the application of VEGFR-TKIs. Methods A retrospective study of 56 patients with mccRCC who received first-line VEGFR-TKIs and who underwent genomic profiling and whole transcriptome sequencing was conducted. Survival analysis was carried out using log-rank tests and Cox regression analyses, and Kaplan–Meier curves were plotted. Clustering was performed using the K-means method. Results Among the 56 patients tested, 17 harbored DNA Damage and Repair (DDR) pathway alterations and 35 VHL mutations. The median progression-free survival (PFS) rates for the DDR and VHL alteration groups were 18 and 18 months, respectively, compared with 14 and 10 months for the nonmutant groups. DDR mutations, VHL mutations and co-mutations were identified as prognostic biomarkers of a longer PFS (p = 0.017, 0.04, 0.014). K-means clustering of expressed transcripts revealed three clusters of 40 patients: C_1, C_2 and C_3. The C_1 cluster exhibited the best PFS and objective response rate (ORR) to TKI therapy, with the highest proportion of DDR and VHL mutations. Further analysis of the tumor immune environment revealed that the C_1 cluster was enriched in activated CD8 T cells and effector CD4 T cells, whereas the C_2 cluster was enriched in eosinophils, mast cells and DC cells and, thus, in immunosuppressive cells. Conclusions We found that patients with mccRCC harboring DDR and VHL alterations were more likely to benefit from first-line VEGF-TKI systemic therapy than patients with wild-type disease. In addition, we found that a three-cluster prognostic model based on gene expression can predict PFS and ORR, which was well-matched with activated TIL infiltration.