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© 2023 The Authors. Société Internationale d'Urologie Journal, published by the Société Internationale d'Urologie, Canada.

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Key Words Competing Interests Article Information

Renal cell carcinoma, biomarkers, ctDNA None declared. Received on March 14, 2023 
Accepted on April 26, 2023 
This article has been peer reviewed.

Soc Int Urol J. 2023;4(4):287–292

DOI: 10.48083/JESK5072

287

REVIEW — LIQUID BIOPSY

Circulating Tumor DNA (ctDNA) in Kidney Cancer:  
A Narrative Review
Wesley Yip, A. Ari Hakimi

Memorial Sloan Kettering Cancer Center, New York, United States

Abstract

Circulating tumor DNA (ctDNA) has been investigated as a potential noninvasive biomarker for disease 
prognostication, monitoring, and treatment selection in various tumor types, including renal cell carcinoma (RCC). 
In this narrative review, we explore the current methods of ctDNA analysis and the use of ctDNA in both localized 
and metastatic RCC, focusing on plasma and urine samples. Additionally, we discuss several ongoing as well as 
upcoming clinical trials that incorporate ctDNA analyses into their study designs and outcomes. Despite the exciting 
potential of ctDNA in RCC, current assays still face significant limitations in sensitivity and detection limits.  

Introduction

Kidney cancer ranks as the sixth most common cancer in men and the ninth most common in women[1]. Although 
most cases of renal cell carcinoma (RCC) present with localized disease, 25% to 50% of these patients eventually 
develop metastases[2]. Approximately 30% have metastatic disease at initial presentation[3]. Survival outcomes differ 
significantly between localized and metastatic disease, with overall survival ranging from 6 months to 5 years in the 
latter group[4]. Given these discrepant outcomes, accurate risk-stratification and disease prognostication are crucial 
for appropriate management. The Memorial Sloan Kettering Cancer Center (MSKCC)[5] and International Metastatic 
RCC Database Consortium (IMDC)[6] models are commonly used prognostic tools in the metastatic setting and 
were developed from retrospective studies examining clinical and laboratory factors in patients treated with systemic 
therapy. However, with an increasing number of investigations on the molecular landscape of RCC[7], these factors 
are not included in classic prognostic models. Moreover, while a multitude of therapeutic options are now available for 
advanced disease, there is minimal guidance for treatment selection.

Circulating tumor DNA (ctDNA) has emerged as a noninvasive method that can potentially influence both the 
diagnosis and treatment of RCC, offering an alternative or complement to tissue biopsies[8]. Liquid biopsies can be 
performed serially over the course of treatment and may better represent the tumor profile as a whole, compared to a 
single biopsy section, which can be limited by spatial heterogeneity within tumors[9,10]. A proposal exists to poten-
tially incorporate ctDNA detection into standard TNM staging[11]. In this review, we discuss the current methods of 
ctDNA evaluation, the use of ctDNA in localized and metastatic settings, and future directions in urine-based studies 
and clinical trial correlatives.

ctDNA Overview
Human blood contains various materials, including cell-free nucleic acids (DNA and RNA), proteins, cells, and 

exosomes, which can be measured as a “liquid biopsy.” These materials can originate from both benign and malignant 
sources, but the rapid turnover of malignant cells and the shedding of viable tumor cells from the tumor itself may 
increase the detection of these circulating tumor cells[12]. The half-life of ctDNA in the circulation is approximately  

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patients had genomic alterations, with a median of one 
(interquartile range [IQR], 0-3) alteration per patient. 
The most frequent alterations included TP53, VHL, 
EGFR, NF1, and ARID1A. Patients receiving targeted 
therapy following first-line treatment had a higher 
mutation frequency compared to those who had first-
line therapy alone, particularly for TP53, VHL, EGFR, 
NF1, and PIK3CA[25]. Notably, the frequency of several 
of these alterations was much higher than in prior 
tissue-based studies, such as The Cancer Genome Atlas 
(TCGA), although this may be due to a lower propor-
tion of advanced disease in the TCGA cohort. However, 
mutations in several significant genes[26], such as 
PBRM1, BAP1, and KDM5C, were not included in the 
Guardant360 panel.

There may also be an association between ctDNA 
and RCC tumor burden. In a study of 34 patients with 
metastatic RCC, detectable ctDNA was found to be asso-
ciated with a higher sum of longest diameter of measur-
able lesions, a surrogate for tumor burden[27]. A similar 
finding was reported in the study by Kim et al., where a 
statistically significant correlation (P = 0.048) was found 
between ctDNA and tumor burden based on primary 
tumor diameter on CT scans at the time of diagnosis[23].

Concordance with tissue-based genomic profiling 
was investigated by Kotecha et al. The study evaluated 
the cell-free DNA profiles of 110 patients with meta-
static RCC compared to tumor tissue using established 
panel-based assays (cfDNA-IMPACT[28] and MSK-IM-
PACT[29], respectively). Tissue testing revealed 554 
genomic alterations overall, of which 60% were from 
primary tumors and 40% from metastatic sites. The 
median number of genomic alterations per patient was 
4, and all patients had at least one alteration. The most 
commonly identified alterations included VHL, PBRM1, 
SETD2, BAP1, and TP53. However, only 24 alterations 
were detected in 7 of 110 patients (6%) using standard 
thresholds. In a repeat analysis using expanded detec-
tion thresholds, the authors found 210 alterations in 74 
of 110 patients (67%), predominantly in VHL, PBRM1, 
and TP53. The median time between tissue and blood 
analysis was 23 months (IQR 14 to 48)[30].

The time elapsed between assays may be important, 
as determined by Zengin et al. In their study of 839 
patients with advanced RCC and ctDNA testing, the 
authors found that 112 patients also had tissue-based 
genomic alterations assessed by either a targeted NGS 
panel (FoundationOne or Tempus xT) or whole-exome 
sequencing. ctDNA assessment revealed at least one 
genomic alteration in 72% of patients, with TP53, VHL, 
and TERT being the most frequently mutated genes. 
Tissue samples showed that VHL, PBRM1, and SETD2 
were the most commonly mutated genes. Of note, several 
commonly mutated genes in tissue samples were not 

2 hours, compared with 16 minutes for nonmalignant 
cell-free DNA[13,14]. In an initial study evaluating 
cell-free DNA detection in RCC, 35 patients with RCC 
(all stages) were compared with 54 healthy controls. 
The study used quantitative real-time polymerase 
chain reaction (PCR) with two primer sets targeting a 
sequence of the actin-beta gene (ACTB). One primer set 
amplified both short and long DNA fragments (ACTB-
106), while the other set amplified only long DNA frag-
ments (ACTB-384). ACTB-106 levels represented DNA 
of apoptotic origin, whereas ACTB-384 levels indi-
cated DNA from nonapoptotic cells. The study found 
that median ACTB-106 levels were two times higher 
and ACTB-384 levels were three times higher in RCC 
patients compared to healthy controls[15].

Several assays are available for assessing ctDNA, 
which typically target a small number of variants or aim 
for broad coverage[16]. Targeted assays can be useful for 
detecting specific variants associated with drug response 
and often use PCR, while broad coverage assays can 
detect large numbers of variants across multiple genes 
and typically use next-generation sequencing (NGS).  
It is crucial to draw blood samples into cell stabiliza-
tion or EDTA tubes and process them within 6 hours of 
collection to avoid lysis of white blood cells and subse-
quent ctDNA dilution by normal leukocyte DNA[17]. 
NGS methods do not require prior knowledge of molec-
ular mutations but are most expensive, time-consuming, 
and prone to false positives from sequencing artifacts. 
However, they are also more sensitive than PCR and 
can be more efficient by running multiple tests[18].  
In addition, NGS may be better suited for assessing 
tumor mutational burden (TMB) compared to candidate 
gene analysis[19].

Clinical Applications—Localized
As CpG island hypermethylation has been found widely in 
cancer cells, Hauser et al. investigated hypermethylation 
in cell-free DNA in 8 genes (APC, GSTP1, ARF, p16, 
RAR-B, RASSF1, TIMP3, and PTGS2) known to be 
involved in RCC development and progression. Thirty-
five patients with organ-confined RCC were compared 
to 54 healthy controls, and at least one gene was found 
to be methylated within serum cell-free DNA in 30 of 35 
patients. Most genes were more frequently methylated 
in RCC patients than in the controls, with the exception 
of p16 and TIMP3. Combining the analysis of multiple 

Abbreviations 
cfDNA cell-free DNA
NAC neoadjuvant chemotherapy
NMIBC non-muscle invasive bladder cancer

genes increased diagnostic sensitivity[20]. Thus, CpG 
island hypermethylation could potentially have a role in 
the initial diagnosis of RCC. 

In another study examining cell-free DNA methyl-
ation profiling, Nuzzo et al. used a cell-free methylated  
DNA immunoprecipitation and high-throughput 
sequencing (cfMeDIP-seq) assay with high sensitivity in 
both localized and metastatic disease across a range of 
tumor types[21]. The authors analyzed 148 samples, of 
which 99 were RCC (88 clear cell, 11 papillary), 21 were 
bladder cancer, and 28 were from healthy controls. Of 
the RCC samples, 69 were derived from plasma, with 
23 of these from patients with stage I-II disease. The 
investigators identified differentially methylated regions 
between patient groups and constructed a classifier 
based on the top 300 regions to assign a methylation 
score. They found that 97% of RCC samples had a higher 
median methylation score compared to control samples. 
While localized cases exhibited the lowest methylation 
scores, there was no statistically significant association 
between stage and methylation score (P = 0.09)[22].

In a study using a targeted deep sequencing platform, 
Kim et al. profiled genetic alterations from plasma and 
tumor tissue samples from 20 patients with RCC (10 
localized, 10 metastatic) who underwent nephrectomy. 
Plasma samples exhibited variants in 40% of localized 
samples and 50% of metastatic samples, while tissue 
samples showed variants in 80% of localized samples 
and 70% of metastatic samples. The mutation patterns 
did not differ significantly between localized and meta-
static samples. However, the investigators found that 
53% of patients with mutations in tumor tissue had 
corresponding mutations in plasma samples across the 
entire cohort. Of the patients with metastatic disease, 
this concordance percentage was 71%, with the most 
common mutations occurring in VHL, PBRM1, and 
KDM5C. Patients with RCC demonstrated a higher 
median value of cell-free DNA compared to healthy 
controls (P < 0.03), but ctDNA levels could not differen-
tiate between localized and metastatic disease[23].

Wan et al. evaluated 92 patients with clear cell RCC 
and measured plasma cell-free DNA levels before and 
after surgery using quantitative PCR. They found that 
patients with metastatic disease had significantly higher 
pretreatment levels than those with localized disease. 
Moreover, of patients with localized disease, those who 
experienced recurrence had higher pretreatment levels 
compared to those without recurrence[24].

Clinical Applications—Advanced
Pal et al. conducted a study involving 220 patients 

with advanced RCC who underwent ctDNA assessment 
using the Guardant360 platform, which encompasses 
73 cancer-related genes. The authors found that 79% of 

assessed in the ctDNA panels (eg, PBRM1, SETD2, and 
KDM5C). In patients with gene mutations detected in 
both ctDNA and tissue, 34% of the alterations in tissue 
were also found in ctDNA. The percentage increased to 
51% when samples were collected within 6 months of 
each other and further increased to 61% when collected 
within 3 months of each other[31].

ctDNA may find utility in disease monitoring and 
prognostication, as demonstrated in a study of 53 
patients with clear cell RCC from the Osaka University 
Hospital. Thirty-nine of these patients had metastatic 
disease, of which 13 were untreated at the time of ctDNA 
analysis. The study investigators found that changes 
in mutant allele frequency of ctDNA closely mirrored 
the overall tumor burden over the course of treatment. 
Moreover, patients with short fragment sizes of cell-free 
DNA and/or positive ctDNA had a worse response to 
tyrosine kinase inhibitors compared to those with long 
fragment sizes and/or negative ctDNA. The size of cell-
free DNA fragment size and ctDNA status showed a 
statistically significant correlation with cancer-specific 
survival but not progression-free survival[32].

The potential of ctDNA to predict tumor response 
to treatment is of particular interest, but limited data 
is available. A small study that followed 4 patients who 
received ipilimumab/nivolumab for metastatic RCC 
found that 3 patients had detectable ctDNA levels 
prior to treatment. Two of these patients had decreased 
ctDNA levels, along with a partial response to immune 
checkpoint inhibition. One of these patients with a 
partial response had a TP53 mutation, and the other had 
MTOR and ARID1A mutations. The remaining patient, 
who progressed during treatment, had increased levels 
of ctDNA over the course of therapy, with mutations 
in TP53, VHL, and PIK3CA[23]. The authors suggest 
that ctDNA may have efficacy as an early predictor of 
immune checkpoint blockade response, although the 
study’s small size limits its conclusions.

Future Directions
Several groups are exploring the possibility of assess-

ing ctDNA through urine, providing an even less 
invasive form of liquid biopsy. In a multicenter study 
involving 91 patients with renal tumors of all stages, 
urine samples were collected from 37 patients prior to 
surgery. The study found low overall ctDNA detection 
in plasma at 28% using an untargeted assay, which 
improved only to 55% with a targeted method. ctDNA 
was detected in urine in only 22% of patients. While 
plasma ctDNA detection correlated with lesion size and/
or tumor thrombus, urine ctDNA detection did not. 
However, the study suggests that ctDNA results may 
overcome intratumoral heterogeneity in patients with 
detectable ctDNA in either plasma or urine[33].

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Conclusion
ctDNA holds promise as a noninvasive biomarker for disease prognostication, monitoring, and treatment selection.  
It may also provide valuable insights into changes in tumor profiles over time and overcome intratumoral 
heterogeneity. However, improvements in assay design are needed to increase sensitivity and detection  
limits. Prospective evaluation of ctDNA is being investigated through correlative studies in ongoing and 
upcoming clinical trials. 

In the study by Nuzzo et al., cf MeDIP-seq was 
performed on 30 urine samples from patients with 
RCC, with the majority of patients being at stage I–
II (67%). When comparing the urine cell-free DNA 
in these patients to healthy controls, the authors 
found a mean area under receiver operating charac-
teristic curve of 0.86 (95% CI 0.83 to 0.89). Although 
these results are not as accurate as those from plasma, 
the authors believe that the assay performance can  
be enhanced by enriching for tumor-derived DNA  
and incorporating tumor methylation data into the 
analysis[22].

Clinical Trials
Completed
NCT02960906: The BIOmarker Driven Trial with 
Nivolu mab a nd Ipi limu mab or VEGFR tK i in 
Naïve Metastatic Kidney Cancer (BIONIKK) was 
a randomized phase 2 study involving 202 patients 
with treatment-naïve metastatic RCC who received 
nivolumab, ipilimumab/nivolumab, or TKI based on 
molecular subgroups. Among the exploratory outcome 
measures, the study aimed to determine whether the 
mutation and methylation status of ctDNA can be 
correlated with the clinical evolution and progression-
free survival rates. The primary endpoint of investigator-
assessed objective response rates per molecular/
treatment group has been reported, while the results of 
the exploratory analyses, including ctDNA, are planned 
for future publication[34].

NCT03469713: In the phase 2 study of nivolumab 
and stereotactic body radiotherapy in patients with 
metastatic RCC (NIVES), 69 patients were enrolled and 
received nivolumab for at least 6 months with 30 Gy of 
radiation in 3 consecutive fractions (10 Gy per fraction) 
to a metastatic site. A planned exploratory analysis is 
evaluating JAK1, JAK2, and B2M mutations in ctDNA 
from plasma samples, as these may be associated with 
acquired resistance to checkpoint inhibition. Similar to 
the BIONIKK trial, the primary outcomes of the NIVES 
trial have been published[35], but the ctDNA data are 
eagerly awaited.

Recruiting
NCT05059444: Guardant Health is currently recruiting 
approximately 1000 patients with a wide range of cancer 

types including RCC to their Observation of ResiduAl 
Cancer with Liquid Biopsy Evaluation (ORACLE) study 
to determine whether their Guardant Reveal assay can 
be used for detecting recurrences of early-stage solid 
tumors. The study is recruiting patients with high-
risk RCC, defined as grade 3–4 and stage II-IV who 
are being treated with curative intent. Patients with 
limited/resectable distant metastases are also eligible for 
inclusion.

NCT04295174: The KIDSTAGE observational study 
conducted by Odense University Hospital has completed 
patient recruitment. The study uses positron emis-
sion tomography/computed tomography (PET/CT) 
and ctDNA for monitoring tumor burden and disease 
progression in patients with RCC of any stage. The 
primary outcomes include investigating the utility of 
ctDNA for disease monitoring and whether dual time 
point fluorodeoxyglucose (FDG) PET/CT can be used 
for staging. The goal of 70 patients recruited has been 
met, and with a study timeframe of 3 years, the results 
are expected soon. 

NCT03786796: The ORCHID phase 2 study of olapa-
rib plans to enroll 20 patients with metastatic RCC 
harboring specific DNA repair gene mutations who 
have received prior treatment with either an immune 
checkpoint inhibitor or anti-VEGF therapy. The primary 
outcome measure is objective response or stable disease 
at 6 months, and ctDNA reversion mutations at clinical 
progression will also be evaluated.

NCT05329532: The phase 1/2 study of the Modi-1/
Modi-1v vaccine (ModiFY) is recruiting patients with 
triple negative breast cancer, human papillomavirus–
negative head and neck squamous cell carcinoma, high-
grade serous ovarian carcinoma, or RCC and evaluating 
treatment as monotherapy and in combination with 
immune checkpoint inhibitor therapy. ctDNA will be 
measured throughout the study and up to 12 weeks after 
the final treatment dose.

NCT04609293: An observational study in China aims 
to evaluate the efficacy of combination therapy with 
camrelizumab, apatinib, and hypofractionated radio-
therapy in patients with locally advanced, metastatic, or 
recurrent RCC. Secondary outcome measures include 
ctDNA analysis and whole exome sequencing of the 
primary tumor before and after treatment.

References

1. Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. 
CA Cancer J Clin.2023;73(1):17–48. doi: 10.3322/caac.21763. PMID: 
36633525.

2. Janzen NK, Kim HL, Figlin RA, Belldegrun AS. Surveillance after 
radical or partial nephrectomy for localized renal cell carcinoma and 
management of recurrent disease. Urol Clin North Am.2003;30(4):843–
852. doi: 10.1016/s0094-0143(03)00056-9. PMID: 14680319.

3. Hsieh JJ, Purdue MP, Signoretti S, Swanton C, Albiges L, Schmidinger 
M, et al. Renal cell carcinoma. Nat Rev Dis Primers.2017;3:17009. doi: 
10.1038/nrdp.2017.9. PMID: 28276433; PMCID: PMC5936048.

4. Assi HI, Patenaude F, Toumishey E, Ross L, Abdelsalam M, Reiman T. 
A simple prognostic model for overall survival in metastatic renal cell 
carcinoma. Can Urol Assoc J.2016;10(3–4):113–119. doi: 10.5489/
cuaj.3351. PMID: 27217858; PMCID: PMC4839992.

5. Motzer RJ, Bacik J, Murphy BA, Russo P, Mazumdar M. Interferon-alfa 
as a comparative treatment for clinical trials of new therapies against 
advanced renal cell carcinoma. J Clin Oncol.2002;20(1):289–296. doi: 
10.1200/JCO.2002.20.1.289. PMID: 11773181.

6. Heng DY, Xie W, Regan MM, Warren MA, Golshayan AR, Sahi C, et 
al. Prognostic factors for overall survival in patients with metastatic 
renal cell carcinoma treated with vascular endothelial grow th 
factor-targeted agents: results from a large, multicenter study.  
J Clin Oncol.2009;27(34):5794–5799. doi: 10.1200/JCO.2008.21.4809. 
PMID: 19826129.

7. C ancer Genome Atlas Research Net work . Comprehensive 
molecular characterization of clear cell renal cell carcinoma. 
Nature.2013;499(7456):43– 49. doi: 10.1038/nature12222. PMID: 
23792563; PMCID: PMC3771322.

8. Bergerot PG, Hahn AW, Bergerot CD, Jones J, Pal SK. The role of 
circulating tumor DNA in renal cell carcinoma. Curr Treat Options 
Oncol. 2018;19(2):10. doi: 10.1007/s1186 4 -018-05 30 - 4. PMID: 
29464405.

9. Cimadamore A, Gasparrini S, Massari F, Santoni M, Cheng L, Lopez-
Beltran A, et al. Emerging molecular technologies in renal cell 
carcinoma: liquid biopsy. Cancers (Basel).2019;11(2):196. doi: 10.3390/
cancers11020196. PMID: 30736478; PMCID: PMC6407029.

10. Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to 
cancer therapies. Nat Rev Clin Oncol.2018;15(2):81–94. doi: 10.1038/
nrclinonc.2017.166. PMID: 29115304.

11. Yang M, Forbes ME, Bitting RL, O’Neill SS, Chou PC, Topaloglu U, et 
al. Incorporating blood-based liquid biopsy information into cancer 
staging: time for a TNMB system? Ann Oncol.2018;29(2):311–323. 
doi: 10.1093/annonc/mdx766. PMID: 29216340; PMCID: PMC5834142.

12. Siravegna G, Marsoni S, Siena S, Bardelli A. Integrating liquid biopsies 
into the management of cancer. Nat Rev Clin Oncol.2017;14(9):531–
548. doi: 10.1038/nrclinonc.2017.14. PMID: 28252003.

13. Lo YM, Zhang J, Leung TN, Lau TK, Chang AM, Hjelm NM. 
Rapid clearance of fetal DNA from maternal plasma. Am J Hum 
Genet.1999;64(1):218–224. doi: 10.1086/302205. PMID: 9915961; 
PMCID: PMC1377720.

14. Diehl F, Schmidt K, Choti MA, Romans K, Goodman S, Li M, 
et al. Circulating mutant DNA to assess tumor dynamics. Nat 
Med.2008;14(9):985–990. doi: 10.1038/nm.1789. PMID: 18670422; 
PMCID: PMC2820391.

15. Hauser S, Zahalka T, Ellinger J, Fechner G, Heukamp LC, VON Ruecker 
A, et al. Cell-free circulating DNA: diagnostic value in patients with 
renal cell cancer. Anticancer Res.2010;30(7):2785 –2789. PMID: 
20683013.

16. Oxnard GR, Paweletz CP, Sholl LM. Genomic analysis of plasma cell-
free DNA in patients with cancer. JAMA Oncol.2017;3(6):740–741. doi: 
10.1001/jamaoncol.2016.2835. PMID: 27541382.

17. Merker JD, Oxnard GR, Compton C, et al. Circulating tumor DNA 
analysis in patients with cancer: American Society of Clinical Oncology 
and College of American Pathologists Joint Review. Arch Pathol Lab 
Med.2018;142(10):1242–1253. doi: 10.5858/arpa.2018-0901-SA. 
PMID: 29504834.

18. Maia MC, Salgia M, Pal SK. Harnessing cell-free DNA: plasma 
circulating tumour DNA for liquid biopsy in genitourinary cancers. 
Nat Rev Urol.2020;17(5):271–291. doi: 10.1038/s41585-020-0297-9. 
PMID: 32203306.

19. Stenzinger A, Allen JD, Maas J, Stewart MD, Merino DM, Wempe 
MM, et al. Tumor mutational burden standardization initiatives: 
recommendations for consistent tumor mutational burden assessment 
in clinical samples to guide immunotherapy treatment decisions. Genes 
Chromosomes Cancer.2019;58(8):578–588. doi: 10.1002/gcc.22733. 
PMID: 30664300; PMCID: PMC6618007.

291290

REVIEW — LIQUID BIOPSY Circulating Tumor DNA (ctDNA) in Kidney Cancer: A Narrative Review

http://SIUJ.org
http://SIUJ.org


SIUJ  •  Volume 4, Number 4  •  July 2023 SIUJ.ORG

20. Hauser S, Zahalka T, Fechner G, Müller SC, Ellinger J. Serum DNA 
hypermethylation in patients with kidney cancer: results of a 
prospective study. Anticancer Res.2013;33(10):4651–4656. PMID: 
24123044.

21. Shen SY, Singhania R, Fehringer G, Chakravarthy A, Roehrl MHA, 
Chadwick D, et al. Sensitive tumour detection and classification using 
plasma cell-free DNA methylomes. Nature.2018;563(7732):579–583. 
doi: 10.1038/s41586-018-0703-0. PMID: 30429608.

22. Nuzzo PV, Berchuck JE, Korthauer K, Spisak S, Nassar AH, Abou Alaiwi 
S, et al. Detection of renal cell carcinoma using plasma and urine cell-
free DNA methylomes. Nat Med.2020;26(7):1041–1043. doi: 10.1038/
s41591-020-0933-1. PMID: 32572266; PMCID: PMC8288043. [Author 
correction published 07 September 2020.]

23. Kim YJ, Kang Y, Kim JS, Sung HH, Jeon HG, Jeong BC, et al. Potential 
of circulating tumor DNA as a predictor of therapeutic responses to 
immune checkpoint blockades in metastatic renal cell carcinoma. Sci 
Rep.2021;11(1):5600. doi: 10.1038/s41598-021-85099-4. PMID: 
33692449; PMCID: PMC7970914.

24. Wan J, Zhu L, Jiang Z, Cheng K. Monitoring of plasma cell-free 
DNA in predicting postoperative recurrence of clear cell renal cell 
carcinoma. Urol Int.2013;91(3):273–278. doi: 10.1159/000351409. 
PMID: 23860338. 

25. Pal SK, Sonpavde G, Agarwal N, Vogelzang NJ, Srinivas S, Haas 
NB, et al. Evolution of circulating tumor DNA profile from first-
line to subsequent therapy in metastatic renal cell carcinoma. Eur 
Urol.2017;72(4):557–564. doi: 10.1016/j.eururo.2017.03.046. PMID: 
28413127. 

26. Hsieh JJ, Chen D, Wang PI, Marker M, Redzematovic A, Chen 
YB, et al. Genomic biomarkers of a randomized trial comparing 
first-line everolimus and sunitinib in patients with metastatic 
renal cell carcinoma. Eur Urol.2017;71(3):405–414. doi: 10.1016/j.
eururo.2016.10.007. PMID: 27751729; PMCID: PMC5431298.

27. Maia MC, Bergerot PG, Dizman N, Hsu J, Jones J, Lanman RB, et 
al. Association of circulating tumor DNA (ctDNA) detection in 
metastatic renal cell carcinoma (mRCC) with tumor burden. Kidney 
Cancer.2017;1(1):65–70. doi: 10.3233/KCA-170007. PMID: 30334006; 
PMCID: PMC6179113.

28. Tsui DWY, Cheng ML, Shady M, Yang JL, Stephens D, Won H, et al. 
Tumor fraction-guided cell-free DNA profiling in metastatic solid tumor 
patients. Genome Med.2021;13(1):96. doi: 10.1186/s13073-021-00898-
8. PMID: 34059130; PMCID: PMC8165771.

29. Zehir A, Benayed R, Shah RH, Syed A, Middha S, Kim HR, et al. 
Mutational landscape of metastatic cancer revealed from prospective 
clinical sequencing of 10,000 patients. Nat Med.2017;23(6):703–713. 
doi: 10.1038/nm.4333. PMID: 28481359; PMCID: PMC5461196. 
[Erratum:  Nat Med.2017 Aug 4;23(8):1004.]

30. Kotecha RR, Gedvilaite E, Ptashkin R, Knezevic A, Murray S, Johnson 
I, et al. Matched molecular profiling of cell-free DNA and tumor tissue 
in patients with advanced clear cell renal cell carcinoma. JCO Precis 
Oncol.2022;6:e2200012. doi: 10.1200/PO.22.00012. PMID: 35797508; 
PMCID: PMC9489165 (available on 2023-07-07).

31. Zengin ZB, Weipert C, Salgia NJ, Dizman N, Hsu J, Meza L, et al. 
Complementary role of circulating tumor DNA assessment and tissue 
genomic profiling in metastatic renal cell carcinoma. Clin Cancer 
Res.2021;27(17):4807–4813. doi: 10.1158/1078-0432.CCR-21-0572. 
PMID: 34130999.

32. Yamamoto Y, Uemura M, Fujita M, Maejima K, Koh Y, Matsushita 
M, et al. Clinical significance of the mutational landscape and 
fragmentation of circulating tumor DNA in renal cell carcinoma. Cancer 
Sci.2019;110(2):617–628. doi: 10.1111/cas.13906. PMID: 30536551; 
PMCID: PMC6361573.

33. Smith CG, Moser T, Mouliere F, Field-Rayner J, Eldridge M, Riediger AL, 
et al. Comprehensive characterization of cell-free tumor DNA in plasma 
and urine of patients with renal tumors. Genome Med.2020;12(1):23.  
doi: 10.1186/s13073- 020 - 00723-8. PMID: 32111235; PMCID: 
PMC7048087.

34. Vano YA, Elaidi R, Bennamoun M, Chevreau C, Borchiellini D, 
Pannier D, et al. Nivolumab, nivolumab-ipilimumab, and VEGFR-
tyrosine kinase inhibitors as first-line treatment for metastatic 
clear-cell renal cell carcinoma (BIONIKK): a biomarker-driven, 
open-label, non-comparative, randomised, phase 2 trial.  Lancet 
Oncol.2022;23(5):612–624. doi: 10.1016/S1470-2045(22)00128-0. 
PMID: 35390339. 

35. Masini C, Iotti C, De Giorgi U, Bellia RS, Buti S, Salaroli F, et al. 
Nivolumab in combination with stereotactic body radiotherapy in 
pretreated patients with metastatic renal cell carcinoma. Results of 
the phase II NIVES Study. Eur Urol.2022;81(3):274–282. doi: 10.1016/j.
eururo.2021.09.016. PMID: 34602312.

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