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Page 2 of 13 Briggs et al. J Cancer Metastasis Treat 2021;7:46 https://dx.doi.org/10.20517/2394-4722.2021.84

Keywords: Carcinoma, renal cell, biomarkers, precision medicine, patient-specific modeling, neoplasms

INTRODUCTION
Biomarkers are objective indicators of disease states that can be observed from outside the patient . With
[1]
advancements in proteomic and genomic analytics, biomarkers hold increasing promise for diseases with
variable prognoses or treatment regimens, where they may predict outcomes and inform individualized
[2,3]
medicine . One such common disease is renal cell carcinoma (RCC), the eighth-most incident cancer in
the United States , responsible for 430,000 new cases and 180,000 deaths in 2020 worldwide . While the
[4]
[5]
prognosis for localized RCC is favorable, with 5-year survival rates up to 95% after surgical treatment,
metastatic RCC (mRCC) is present in up to 16% of new RCC diagnoses and carries a poor prognosis with 5-
year survival rates as low as 12% [4,6,7] .

Historical treatment of mRCC can be broken into three eras. The initial treatments consisted of
immunotherapy with agents such as interferon-alpha or high-dose interleukin-2, which were highly toxic
[8]
and produced durable complete responses in a very small fraction (< 10%) of patients . Further
understanding of RCC cell growth pathways and immunogenicity of RCC led to further development. The
second era of mRCC treatment includes targeted therapy such as mTOR inhibitors and anti-angiogenic
tyrosine kinase inhibitors (TKIs) against vascular endothelial growth factor (VEGF) or the VEGF receptor
(VEGFR). Most recently, immunotherapy or immune checkpoint inhibitors (ICIs), which are monoclonal
antibodies against immune checkpoint proteins such as programmed cell death 1 (PD-1), PD-ligand 1
(PDL1), and anti-cytotoxic T-lymphocyte-associate protein-4 (CTLA-4), have been employed with
[9]
improved ORR and survival .
Prognostic models have been developed and validated to estimate survival in the setting of mRCC. The
most widely used models include the Memorial Sloan Kettering Cancer Center (MSKCC), validated by the
Cleveland Clinic Foundation (CCF) [10,11] , and the International Metastatic RCC Database Consortium
(IMDC) Heng model and validation [12,13] , which predict poorer prognosis with elevated neutrophils or
platelets, lower hemoglobin counts or Karnofsky performance status, and other similar metrics. While these
models provide useful survival estimates, there has been rapid advancement in biomarker research
predicting more specific clinical outcomes such as overall survival (OS), cancer-specific survival (CSS),
progression-free survival (PFS), disease-free survival (DFS), or metastasis. Additional work has explored
biomarkers capable of predicting a patient’s overall response rate (ORR) or time to treatment failure (TTF)
to a specific regimen.

We reviewed biomarkers associated with OS, CSS, PFS, DFS, TTF, and ORR in adults with metastatic RCC.
Data were abstracted via standardized form, then reported with hazard ratios and confidence intervals
where appropriate, subdivided by biomarker type (serum, gene mutation, genetic expression, and
histologic). For the purposes of our review, we followed the convention of referring to biomarkers that are
associated with PFS, DFS, OS, or other broad clinical outcomes independent of treatment received as
“prognostic biomarkers”. This contrasts with biomarkers that predict a response (or absence of a response)
to a specific treatment, which are referred to as “predictive biomarkers”. Included tables are limited to
statistically significant findings, with both significant and non-significant findings found in supplemental
materials. A list of abbreviations for included biomarkers can also be found in the supplement.