Table 1.

Renal cancer biomarker types, potential clinical applications, and challenges.

Biomarker	Clinical utility	Challenges
Histology: subtyping based on sarcomatoid or rhabdoid features	Prognostication and treatment consideration	Applicable only to the group of patients presenting with sarcomatoid or rhabdoid features
Histology: nomograms	Risk stratification	The current risk stratification based on nomograms is not sufficient
Tumor-centric genetic biomarkers	Prognostication	Lack of treatment options based on the targets. Lack of evidence for efficacy
Plasma: ctDNA	Diagnosis, evaluation of treatment options, prediction of treatment outcome, monitoring, and detection of relapse	Low level of ctDNA released from renal cancers; methods need optimization
Plasma: CTCs	Knowledge of cancer biology and metastatic process, prediction of therapy response, and prognostication	Technical optimization of CTC enumeration
TCR	Immune health	Determination of neoantigen targets
Microbiome	Understanding the impact on tumorigenesis, immunity and immune response in relation to immunotherapies, and new treatment options	Multiple bacterial species that may have similar agonistic/antagonistic roles. Interactions with immune and other cell types are very complex
Single cell/spatial omics	Characterize RCC cell populations and immune composition to discover new biomarkers	Comprehensive data analysis, high levels of technical noise, lack of good reference material, and data standardization
Radiomics	Diagnosis of small tumors from benign cases, grading, subtyping, prediction of treatment efficacy, and prognostication	Lack of standardization of scanning protocols and analysis tools across studies