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Identification of clinically meaningful, reproducible, and widely available prognostic and, more importantly, predictive biomarkers in metastatic renal cell carcinoma (mRCC) is a huge area of need. As Saliby et al. point out in a comprehensive review for the , there are currently no standard, accepted biomarkers predictive of treatment responses or toxicities to vascular endothelial growth factor receptor (VEGFR) targeting tyrosine kinase inhibitors (TKIs) or immuno-oncology (IO) agents.

In fact, predictive biomarkers that are routinely used in other solid malignancies, including programmed death ligand 1 (PD-L1) or tumor mutational burden (TMB), are not validated in mRCC and should not be used in the clinical setting to make treatment decisions. Further, data with PBRM1, which is a commonly mutated gene in mRCC associated with enhanced treatment responses to IO, has been inconsistent and limits its utility as a biomarker.

In fact, the key to identifying predictive biomarkers in mRCC may be looking at this disease through a different lens and utilizing "unexpected" tools compared with other solid tumor types. For instance, certain host factors appear promising in potentially guiding therapeutic decisions with regards to IO agents, including HLA-A*03 carriage, positivity for a single nucleotide polymorphism in the IL7 intronic region, and presence of particular bacterial species within the gut microbiome.

Using mRNA sequencing to understand functional gene expression led to identification of distinct mRCC subtypes/"clusters" based on their intrinsic multigene signatures that are associated with a more "angiogenic" or "inflamed" phenotypes of mRCC, which may lead to future personalized tailoring of therapies.

Other emerging modalities attempt to identify biomarkers using single-cell analysis including single-cell RNA sequencing to understand diverse cells comprising the tumor microenvironment, high-dimensional spacial transcriptomic analysis to understand tumor architecture and spacial relationships between its components, predictive biologic modeling using patient tumor fragments, and molecular imaging.

Although none of these techniques are readily available for clinical use, they hold promise and will hopefully transition to clinical use in the future.

Nataliya Mar, MD, is associate clinical professor in the Division of Hematology/Oncology at the Chao Family Comprehensive Cancer Center, UCI Health, in Orange, California.

Read the review here and an interview about it here.