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We have entered a new era for patients with mRCC with multiple treatment options including VEGF(Vascular Endothelial Growth Factor)-, MET(Mesenchymal-eptihelial Transition)-, AXL(AXL-Rezeptortyrosinkinase)-, mTOR(mechanistic Target of Rapamycin)-targeted TKIs (tyrosinekinaseinhibitors) as well as PD-1(programmed cell death protein 1)-, PD-L1(Programmed cell death 1 ligand)- and CTLA4(cytotoxic T-lymphocyte-associated Protein 4)-targeted IOs and their combinations, respectively. However, the possibility to select patients based on predictive biomarkers for the different treatment options is still lacking. The TCGA (Cancer Genome Atlas) consortium conducted comprehensive analyses of genomic and metabolic features of RCC (renal cell carcinoma) and these findings demonstrated significant differences between the major histological subtypes of RCC like differences in immune signatures and their course of disease. The increasing knowledge on the genomic landscape of RCC supports stratification of patients for targeted therapies. Biomarker development for future therapeutic approaches will require integration of multiple biologic components like PD-L1 expression, tumor-infiltrating lymphocytes and mutations in addition to the prognostic risk scores. However, no single molecular marker has been shown to improve the accuracy of MSKCC (Memorial Sloan Kettering Cancer Center) or IMDC (International Metastatic RCC Database Consortium) prognostic risk scores. Large-scale biomarker-driven prospective trials with consensus methodologies on biomarker assessment and scoring are needed to obtain clinically validated new prognostic and predictive biomarkers as described above. The integration of routinely available parameters and new biomarkers could hold the key for personalized treatment strategies of patients with RCC.