and later lines of therapy. In their cohort, the most frequent
identified alterations included TP53 (35%), VHL
(23%), EGFR (17%), NF1 (16%), and ARID1A (12%). This
cohort of patients remains the largest assessment of
ctDNA sequencing in metastatic RCC to date. Variations
seen across first-line and refractory settings suggests underlying
mechanisms for therapeutic resistance (Figure
3, e.g. TP53 mutations), as well as identification of alterations
which may prompt non-conventional therapy selection
for certain patients.
As noted above, the excitement for ctDNA to guide
targeted therapy in RCC has started to gain traction. For
instance, inhibition of the MET pathway remains an active
area of investigation, and evidence for MET alteration
identification across solid tumors is increasing. To
investigate this further, Ikeda et al.26 performed ctDNA
digital sequencing (using a 54-70 gene panel) in a pancancer
cohort of 438 patients, 263 of whom had tissue
sequencing for comparison. MET alterations were seen
in 7.1% of patients which correlated with presence of
bone metastases; TP53 and PTEN abnormalities were also
found to be correlated as well. Importantly, MET alterations
were detected at a lower frequency in tissue
(1.14%) compared to ctDNA (7.1%), again highlighting
that ctDNA analyses complement standard tissue sequencing.
To further characterize the complexities of applying
ctDNA as a biomarker for metastatic RCC, we performed
a large cohort analysis incorporating a comparative genomics
approach with matched primary tissues at Memorial
Sloan Kettering Cancer Center.27 In our cohort,
110 metastatic ccRCC patients underwent a single-time
point collection for ctDNA, and the median time between
ctDNA collection and previously collected tissue
used for comparison was 24 months. Although the mutational
profiles were similar between these two tissue
platforms – with VHL and PBRM1 alterations recovered
with the highest frequency in both blood and tissue,
there remained discordance between the total number
of alterations recovered. For instance, the majority of
VHL and PBRM1 alterations were only identified in primary
tissue and not in ctDNA. Alterations of these genes
found in ctDNA, though, were always found in the
matched primary tissue. In sum, investigating other
methodologies which use an enriched RCC specific geneset
panel or higher sequencing depth may improve and
enhance ctDNA detection and concordance in this patient
population.
With the focus on ctDNA undergoing closer scrutiny,
application of this tool in varied disease stages has been
explored and presented at scientific symposia. A report
by Correa et al.28 of a cohort of 42 patients with stage IIV
RCC who underwent complete surgical resection
demonstrated the impact of ctDNA on prognosis. At
baseline, for example, ctDNA was detected in 41% off patients
and was significantly associated with increased
tumor size, advanced tumor stage, and poorly differentiated
tumors. Postoperatively, 8 of 8 ctDNA-positive patients
relapsed while only 16 of 33 ctDNA-negative
patients relapsed. This report concludes that ctDNA values
have the potential to be used as a prognostic marker
across multiple disease settings.
28 Kidney Cancer Journal
Future Directions
Looking ahead, future studies need to address a wide
range of issues to determine the translational impact of
ctDNA in RCC. A few notable areas of exploration include:
1. Robust ctDNA testing with matched tissues NGS data
to provide reliable sensitivity, specificity and posi-
tive/negative predictive metrics.
2. Studies to “benchmark” each assay, delineating how
each of these platforms work and how they can be
used in clinical practice.
3. An improved understanding of which relevant alter-
ations need to be identified and their relationship to
a disease stage (e.g. prognostic or predictive power,
understanding genomic changes and their relation-
ship to therapeutic resistance).
4. Correlation of clinical variables like disease sites or
treatment effects with ctDNA variables like ctDNA
velocity or load to improve upon clinical significance
during assay development.
5. Discovery of disease states like “minimal residual
disease” after curative intent surgeries, or respond-
ing/progressive disease states for systemic therapy
monitoring.
Conclusion
Cell free and circulating tumor DNA assessments are
non-invasive tools which can provide pertinent and serial
genomic tumor assessments. Although the experience
of ctDNA has not advanced to the stage where it
can be considered an actionable routine part of clinical
practice for RCC disease management, all signs point toward
it becoming integrated as a complementary tool to
current tissue sequencing efforts. As new technology
emerges on the forefront – including integration of
epigenomics or analyses of other circulating substances
like exosome-derived DNA, ensuring that these assays are
benchmarked and robustly tested in the RCC population
remains crucial. Studies such as these can propel the use
of these innovative tools and usher in a new era of precision
testing for patients with kidney cancers.
References
1. Schwarzenbach H, Hoon DS, Pantel K. Cell-free nucleic acids as biomarkers
in cancer patients. Nat Rev Cancer. 2011;11:426-437.
2. Hauser S, Zahalka T, Ellinger J, Fechner G, Heukamp LC, VON
Ruecker A, Müller SC, Bastian PJ. Cell-free circulating DNA: Diagnostic
value in patients with renal cell cancer. Anticancer Res. 2010; 30:2785–
2789.
3. Lu H, Busch J, Jung M, Rabenhorst S, Ralla B, Kilic E, Mergemeier S,
Budach N, Fendler A, Jung K. Diagnostic and prognostic potential of
circulating cell-free genomic and mitochondrial DNA fragments in clear cell
renal cell carcinoma patients. Clin Chim Acta. 2016; 452:109–119.
4. de Martino M, Klatte T, Haitel A, Marberger M. Serum cell-free DNA
in renal cell carcinoma: a diagnostic and prognostic marker. Cancer.
2012; 118:82–90.
5. Skrypkina I, Tsyba L, Onyshchenko K, Morderer D, Kashparova O,
Nikolaienko O, Panasenko G, Vozianov S, Romanenko A, Rynditch A.
Concentration and Methylation of Cell-Free DNA from Blood Plasma
as Diagnostic Markers of Renal Cancer. Dis Markers. 2016; 2016:
3693096.
6. Feng G, Ye X, Fang F, Pu C, Huang H, Li G. Quantification of plasma
cell-free DNA in predicting therapeutic efficacy of sorafenib on metastatic
clear cell renal cell carcinoma. Dis Markers. 2013; 34:105–111.
7. 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; 91:273–278.
8. Hauser S, Zahalka T, Fechner G, Müller SC, Ellinger J. Serum DNA