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Erschienen in: Journal of Cancer Research and Clinical Oncology 7/2020

Open Access 27.04.2020 | Review – Cancer Research

Impact of circulating tumor DNA in hepatocellular and pancreatic carcinomas

verfasst von: Sameer A. Dhayat, Zixuan Yang

Erschienen in: Journal of Cancer Research and Clinical Oncology | Ausgabe 7/2020

Abstract

Hepatocellular carcinoma (HCC) and pancreatic cancer (PC) belong to the most lethal malignancies worldwide. Despite advances in surgical techniques and perioperative multidisciplinary management, the prognosis of both carcinoma entities remains poor mainly because of rapid tumor progression and early dissemination with diagnosis in advanced tumor stages with poor sensitivity to current therapy regimens. Both highly heterogeneous visceral carcinomas exhibit unique somatic alterations, but share common driver genes and mutations as well. Recently, circulating tumor DNA (ctDNA) could be identified as a liquid biopsy tool with huge potential as non-invasive biomarker in early diagnosis and prognosis. CtDNA released from necrotic or apoptotic cells of primary tumors, metastasis, and circulating tumor cells can reveal genetic and epigenetic alterations with tumor-specific and individual mutation and methylation profiles. In this article, we focus on clinical impact of ctDNA as potential biomarker in patients with HCC and PC.
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Abkürzungen
ADAMTS1
A disintegrin and metalloproteinase with thrombospondin motifs 1
AFP
Alpha-fetoprotein
APC
Adenomatous polyposis coli
ARID1A
AT-rich interaction domain 1A
BNC1
Basonuclin-1
BRCA
Breast cancer gene
CA
Carbohydrate antigen
CCND1
Cyclin D1
CDKN2A
Cyclin-dependent kinase inhibitor 2A
CEA
Carcinoembryonic antigen
Cf
Cell-free
Ct
Circulating tumor
CTNNB1
Catenin Beta 1
ddPCR
Digital droplet PCR
DFS
Disease-free survival
EGFR
Epidermal growth factor receptor
5-FU
5-Fluorouracil
GSTP1
Glutathione S-transferase P 1
HBV
Hepatitis B virus
HCC
Hepatocellular carcinoma
HCV
Hepatitis C virus
KRAS
Kirsten rat sarcoma viral oncogene homolog
LINE-1
Long interspersed nucleotide element 1
MSP
Methylation-specific PCR
NGS
Next-generation sequencing
PC
Pancreatic cancer
PDGFR
Platelet-derived growth factor receptor
qRT
Quantitative real time
RASSF1A
Ras association domain family protein 1A
RFS
Recurrence-free survival
SFRP1
Secreted frizzled-related protein 1
TERT
Telomerase reverse transcriptase
TGF
Transforming growth factor
VEGFR
Vascular endothelial growth factor receptor
WNT
Wingless-type

Background

Hepatocellular carcinoma (HCC) and pancreatic cancer (PC) represent two of the most challenging visceral malignancies in oncology with rising incidence and lack of reliable biomarkers for early diagnosis, prognosis, and therapy response. PC and HCC are estimated to become the second and third respective leading causes of cancer-related death in western countries by 2030 (Rahib et al. 2014; Siegel et al. 2019). As both carcinoma entities share some common risk factors, either environmental or genetic, combined analyses may provide useful information. Hepatocellular carcinogenesis is a multistep process occurring in one-third of patients with liver cirrhosis, on the background of chronic infection with hepatitis B or C virus (HBV, HCV), alcoholic or non-alcoholic steatohepatitis, and obesity (Villanueva 2019). PC occurs with increased frequency among individuals with tobacco smoking, type 2 diabetes, obesity, chronic pancreatitis or hereditary risk factors (Ryan et al. 2014). In both carcinoma entities, the majority of patients are diagnosed at advanced stages with a low 5-year survival rate of less than 10–20% and a high 5-year recurrence rate of 70–80%, even following oncological tumor resection (Siegel et al. 2019). Therefore, early diagnosis at surgically manageable stages and early recurrence detection would have a tremendous impact on survival of patients with HCC or PC. However, current screening of proteomic serum markers, such as alpha-fetoprotein (AFP) in HCC or carcinoembryonic antigen (CEA) and carbohydrate antigen (CA) 19.9 in PC have not shown to be effective due to their reduced predictive values (Bolondi et al. 2013; Gamil et al. 2018; Poruk et al. 2013). In addition, imaging techniques failed to detect early lesions or to distinguish between benign and malignant lesions, so far. In addition to the risk of neoplastic needle tract seeding, minimal invasive solid biopsies by endoscopic-ultrasound-guided fine needle aspiration in PC or percutaneous needle biopsy in HCC cannot accurately track dynamic changes due to high tumor heterogeneity (Stigliano and Burroughs 2005; Yoshida et al. 2019). This molecular heterogeneity is the reason for the large variation in individual patient`s prognosis and response to chemotherapy.
Cytotoxic chemotherapy agents continue to form the backbone for the treatment of advanced PC limited to the pyrimidine antimetabolites gemcitabine and 5-fluorouracil (5-FU), Topoisomerase I inhibition by irinotecan, the DNA crosslinking agents oxaliplatin and cisplatin, and the tubulin inhibitor paclitaxel (Burris et al. 1997; Reni et al. 2005; Stathopoulos et al. 2006). However, the median survival remains 6–11 months. Since 2007, gemcitabine-based combination chemotherapies with the selective epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor erlotinib could improve the overall survival in locally advanced, unresectable, or metastatic PC (Moore et al. 2007). 5-FU-based FOLFIRINOX therapy including calcium folinate, irinotecan, and oxaliplatin since 2011 as well as the addition of nanoparticle albumin-bound paclitaxel to gemcitabine since 2013 significantly prolonged the overall survival and progression-free survival compared with mono-gemcitabine therapy in locally advanced and metastatic PC (Conroy et al. 2011; Von Hoff et al. 2013). Due to its greater toxicity, FOLFIRINOX was recommended for PC patients in good physical condition. High resistance to current chemotherapy regimens still affects the treatment of PC and HCC (Chin et al. 2018). However, better therapy response with extended survival time of more than 2 years in small subgroups of patients with advanced PC seems to be connected with exceptionally favorable prognostic factors and molecular characteristics (Collisson et al. 2011; Cui et al. 2012).
Despite the identification of many frequently mutated genes as potential therapeutic targets in HCC, the multikinase inhibitor Sorafenib that inhibits vascular endothelial growth factor receptor (VEGFR) 1–3, platelet-derived growth factor receptor (PDGFR) beta, c-KIT, and RAF/mitogen-activated protein/MEK was the sole drug approved for the treatment of advanced HCC between 2007 and 2016 with a response rate of less than 5% and an extended median overall survival of 2.5 months (Llovet et al. 2008). Currently, two first-line alternatives to Sorafenib are approved. The first Lenvatinib is a multikinase inhibitor as well, that inhibits VEGFR 1–3, PDGFR alpha, fibroblast growth factor receptor 1–4, c-Kit proto-oncogene receptor tyrosine kinase, and RET proto-oncogene with an improved median overall survival of 13.6 months versus 12.3 months. The other one is the monoclonal antibody Nivolumab, that inhibits the immune checkpoint molecule programmed cell death protein 1 and showed a response rate of 23% in Sorafenib naïve HCC patients (Forner et al. 2018). Therefore, the most effective treatment of heterogeneous cancers like HCC and PC might be a tailored combination of drugs targeting specific genomic and epigenomic alterations. Routine molecular testing is still performed in clinical diagnostic for targeted therapy and prognostic stratification in cancer entities, such as breast cancer, melanoma and leukemia (El-Deiry et al. 2019). In patients with diagnosed colorectal carcinoma, the decision for an anti-EGFR antibody therapy is based on routine mutation analysis of kirsten rat sarcoma viral oncogene homolog (KRAS), neuroblastoma RAS viral oncogene homolog, and B-Raf proto-oncogene serine/threonine kinase gene resulting in a valine-to-glutamate change at the residue 600 (Karapetis et al. 2008). In addition to environmental factors, recent genome-wide association studies revealed that genetic and epigenetic abnormalities might be significant determinants of HCC or PC susceptibility with important influence on the individual predisposition to disease progression, e.g. chronic inflammation, and resulting carcinogenesis. Moreover, several recent studies have shown that identification of molecular biomarkers and real-time monitoring of disease and therapy efficacy in PC and HCC could be achieved by liquid biopsies (Tables 1 and 2). In this review, we discuss recent studies focusing on detection and clinical impact of circulating DNA mutation and methylation as potential biomarker for early diagnosis, prognosis, and therapy response in HCC and PC.
Table 1
Circulating Tumor DNA in Hepatocellular Carcinoma
References
Origin
HCC (n)
Technique
Circulating markers
ctDNA mutation (%)
tiDNA mutation (%)
Mutation in liquid and tumor tissue
Carcinoma vs. healthy
Biomarker
Sensitivity (%)
Specificity (%)
Concordance ctDNA/tiDNA (%)
Sensitivity (%)
Specificity (%)
Huang et al. (2016)
Plasma
48
ddPCR Sanger seq
TERT
CTNNB1
TP53
24.4
12.2
14.6
19.5
0
4.9
62.5
NA
50.0
84.9
87.8
87.2
80.5
87.8
85.4
NA
NA
NA
Liao et al. (2016)
Plasma
41
NGS
TERT
CTNNB1
TP53
4.9
9.8
4.9
70.7
26.8
65.9
6.9
27.3
2.3
95.1
96.7
100.0
34.1
78.0
39.0
NA
NA
Poor RFS
An et al. (2019)
Plasma
26
NGS
354 genes
(TP53)
96.2
50
NA
30.8
NA
NA
88.5
69.2
NA
NA
DFS (HR = 7.66; p < 0.001)
Szymanska et al. (2004)
Plasma
17
PCR–RFLP
SOMA
TP53
41
34.5
71.4
61.5
76.7
NA
NA
NA
Lin et al. (2011)
Urine
17
LNA PCR
TP53
52.9
42.9
66.7
0.0
28.6
53.0
75.0
NA
Kimbi et al. (2005)
Serum
158
PCR and Seq
TP53
17.7
NA
NA
NA
NA
18
83.3
NA
Marchio et al. (2018)
Plasma
149
ddPCR
TP53
24.8
NA
NA
NA
NA
NA
NA
NA
Ikeda et al. (2018b)
Plasma
14
NGS
68 genes (TP53)
79
57
NA
NA
NA
NA
NA
NA
NA
Ikeda et al. (2018a)
Blood
26
NGS
70 genes (TP53)
88.5
61.5
NA
NA
NA
NA
NA
NA
NA
Riviere et al. (2018)
Plasma
31
NGS
68 genes (TP53)
74
61.2
NA
NA
NA
NA
NA
NA
NA
He et al. (2019)
Plasma
29
NGS
35 genes
(TP53)
96.4
50
NA
75
NA
NA
75
33
NA
NA
NA
Kaseb et al. (2019)
Plasma
219
NGS
70 genes
87.8
NA
NA
NA
NA
NA
NA
NA
Xiong et al. (2019)
Plasma
37
NGS
TP53
64
NA
NA
NA
NA
65
100
NA
Xu et al. (2017)
Plasma
1098
MSP
target panel
NA
NA
NA
NA
NA
85.7
94.3
OS (HR = 2.41; p < 0.001)
Iyer et al. (2010)
Plasma
28
MSP
mp15
mp16
mAPC
mFHIT
10.7
46.4
53.5
67.8
14.2
71.4
64.2
75
50
60
77.8
94.7
95.8
87.5
90
66.7
89.2
67.9
82.1
85.7
NA
NA
NA
Wong et al. (1999)
Plasma
22
MSP
mp16
59.1
72.7
NA
NA
81
NA
NA
NA
Wong et al. (2000)
Plasma
25
MSP
mp15 / mp16
87
92
NA
NA
74
NA
NA
NA
Wong et al. (2003)
Plasma
29
MSP
mp16INK4a
79.3
66.7
NA
NA
46.7
NA
NA
NA
Huang et al. (2014)
Serum
66
MSP
mINK4A
65.3
NA
NA
NA
NA
65.3
87.2
NA
Huang et al. (2011)
Plasma
72
MSP
mAPC, mGSTP1, mRASSF1A, and mSFRP1
84.7
NA
NA
NA
NA
92.7
81.9
OS (HR = 3.26; p = 0.003)
Chan et al. (2008)
Serum
63
MSP
mRASSF1A
93
NA
NA
NA
NA
77
89
poor DFS
Yeo et al. (2005)
Plasma
40
MSP
mRASSF1A
42.5
92.5
45.9
100.0
50.0
NA
NA
NA
Hu et al. (2010)
Serum
35
MSP
mRASSF1A
40
88.6
45.2
100
51.4
70–100
52–100
NA
Mohamed et al. (2012)
Serum
40
MSP
mRASSF1A
90
NA
NA
NA
NA
75
80
NA
Mansour et al. (2017)
Serum
45
MSP
mRASSF1A
87.7
NA
NA
NA
NA
86.7
72.5
correlation with tumor size (r = 0.728; p < 0.001)
Liu et al. (2017)
Serum
105
MSP
mRASSF1A
hypomLINE-1
73.3
66.7
NA
NA
NA
NA
NA
NA
poor DFS and OS
Tangkijvanich et al. (2007)
Serum
85
MSP
hypomLINE-1
70.4
NA
NA
NA
NA
NA
NA
increased HCC risk (OR = 1.74), poor OS
Oussalah et al. (2018)
Plasma
51
MSP
mSEPT9
83.0
NA
NA
NA
NA
94.1
84.4
NA
Sun et al. (2013)
Serum
43
MSP
mTFPI2
46.5
NA
NA
NA
NA
80.8
80
NA
Wu et al. (2017)
Plasma
237
MSP
mTBX2
75.5
NA
NA
NA
NA
75.5
41.2
Increased HCC risk (OR = 2.39)
Iizuka et al. (2011)
Serum
108
MSP
mBASP1
mCCND2
mAPC
mCFTR
mRASSF1A
NA
NA
NA
NA
NA
62.0
64.8
17.6
56.5
83.3
78.6
42.9
78.6
83.9
58.9
NA
Zhang et al. (2013)
Serum
31
MSP
mDBX2
mTHY1
NA
NA
NA
NA
NA
88.9
85.2
87.1
80.7
NA
Ji et al. (2014)
Serum
121
MSP
mMT1M
mMT1G
mMT1M/G
48.8
70.2
NA
NA
NA
NA
48.8
70.2
90.9
93.5
87.1
83.9
Correlation with tumor size (r = 0.321; p < 0.001)
Han et al. (2014)
Serum
160
MSP
mTGR5
mTGR5 and mAFP
48.1
NA
NA
NA
NA
NA
NA
65.0
NA
85.2
NA
Wang et al. (2006)
Serum
32
MSP
GSTP1
50
88.5
60.9
100
65.4
50.0
62.5
NA
Wen et al. (2015)
Plasma
36
MSP
mRGS10, mST8SIA6, mVIM, and mRUNX2
85.6
NA
NA
NA
NA
94.0
89.0
NA
ctDNA: circulating tumor DNA, ddPCR: droplet digital polymerase chain reaction, DFS: disease free survival, HCC: hepatocellular carcinoma, HR: hazard ratio, LNA: Locked Nucleic Acid Clam, MSP: methylation specific PCR, n: number of patients, NGS: next-generation sequencing, OR: odds ratio, OS: overall survival, r: Pearson’s coefficient of correlation, RFLP: restriction fragment length polymorphism, RFS: recurrence free survival, seq: sequencing, SOMA: short oligonucleotide mass spectrometry analysis, tiDNA: tissue-related DNA
Table 2
Circulating Tumor DNA in Pancreatic Carcinoma
Reference
Origin
PC (n)
Technique
Circulating markers
ctDNA mutation (%)
tiDNA mutation (%)
Mutation in liquid and tumor tissue
Carcinoma vs. healthy
Biomarker
Sensitivity (%)
Specificity (%)
Concordance ctDNA/tiDNA (%)
Sensitivity (%)
Specificity (%)
Riviere et al. (2018)
Plasma
25
NGS
KRAS
MYC
EGFR
NA
NA
NA
NA
96
94
91
NA
NA
NA
Jiao et al. (2007)
Plasma
83
PCR and Seq
MSP
KRAS
mppENK, mp16
62.7
NA
NA
NA
NA
NA
NA
NA
Patel et al. (2019)
Blood
112
NGS
KRAS
TP53
43.8
45.8
90.9
77.3
NA
NA
52
61
NA
NA
ctDNA: OS
(HR = 4.35; p = 0.001)
Watanabe et al. (2019)
Plasma
39
ddPCR
KRAS
30.8
88.1
NA
NA
NA
NA
NA
OS
(HR = 54.5; p < 0.001)
Takai et al. (2015)
Plasma
259
ddPCR
KRAS
32
NA
NA
NA
NA
NA
NA
NA
Sugimori et al. (2020)
Plasma
45
ddPCR
KRAS
51.1
95.7
NA
NA
NA
NA
NA
Worse PFS
Groot et al. (2019)
Preop plasma
59
ddPCR
KRAS
49
NA
90
88
NA
NA
NA
RFS (HR = 2.67; p = 0.011); OS (HR = 2.37; p = 0.048)
Tjensvoll et al. (2016)
Plasma
14
PNA-clamp PCR
KRAS
Pre-CTX
71.4
NA
NA
NA
NA
NA
NA
RFS (HR = 1.29; p = 0.014); OS (HR = 1.43; p = 0.01)
Chen et al. (2017)
Plasma
189
NGS
KRAS
93.7
NA
NA
NA
NA
NA
NA
TTP (HR = 1.45; p = 0.002);
OS (HR = 1.45; p = 0.001)
Kinugasa et al. (2015)
Serum
66
ddPCR, PCR-PHFA
KRAS
62.6
74.7
76.8
78.9
77.3
NA
NA
OS (HR = 3.24; p = 0.001)
Adamo et al. (2017)
Plasma
26
NGS
KRAS
27
78
NA
NA
NA
NA
NA
OS (HR = 2.89; p = 0.018)
Maire et al. (2002)
Serum
47
PCR
KRAS
47
NA
NA
NA
NA
47
87
NA
Dianxu et al. (2002)
Plasma
41
PCR–RFLP
Seq
KRAS
70.7
91.7
75.8
100.0
77.8
NA
100
NA
Pratt et al. (2019)
Plasma
17
ddPCR
KRAS
86
NA
86
70
NA
NA
NA
NA
Earl et al. (2015)
Plasma
31
ddPCR
KRAS
25.8
58.3
42.9
60
50
NA
NA
OS (HR = 12.2; p < 0.001)
Uemura et al. (2004)
Plasma
28
Mismatch ligation assay
KRAS
32.1
92.9
34.6
100.0
39.2
NA
100
NA
Marchese et al. (2006)
Plasma
30
PCR and Seq
KRAS
0
70
NA
NA
NA
NA
NA
NA
Park et al. (2018)
Plasma
17
Targeted-NGS and ddPCR
KRAS
58.8
76.5
NA
NA
NA
NA
NA
NA
Del Re et al. (2017)
Plasma follow-up during palliative CTX
27
ddPCR
KRAS
70.4
NA
NA
NA
NA
NA
NA
PFS (2.5 vs. 7.5 months; p = 0.03);
OS (6.5 vs. 11.5 months; p = 0.009)
Perets et al. (2018)
Plasma
17
NGS
KRAS
29.4
NA
NA
NA
NA
NA
NA
OS (r = -0.76; p = 0.03)
Kim et al. (2018)
Plasma
106
ddPCR
KRAS
80.5
96.1
78.4
33.3
NA
NA
NA
PFS (HR = 2.08; p = 0.009)
OS (HR = 1.97; p = 0.034)
Lin et al. (2018)
Plasma
65
ddPCR
KRAS
80
100
NA
NA
100
NA
NA
OS (HR = 3.1; p < 0.001)
Chen et al. (2010)
Plasma
91
PCR
KRAS
33
NA
NA
NA
NA
NA
NA
ctDNA: OS (HR = 7.39; p < 0.001)
Cohen et al. (2017)
Plasma
221
NGS
KRAS
KRAS and four proteins
30
NA
NA
NA
NA
NA
NA
NA
100
NA
NA
64
NA
99.5
NA
Nakano et al. (2018)
Serum
45
PNA clamp PCR
KRAS
24.4 (pre-op)
44.4 (post-op)
83.3
NA
NA
NA
NA
NA
RFS (HR = 2.92; p = 0.027)
Kruger et al. (2018)
Plasma
54
BEAMing
KRAS
67
58
75
100
79
83
100
Early CTX response prediction
Hadano et al. (2016)
Preop plasma
105
ddPCR
KRAS
31
82
NA
NA
100
NA
NA
OS (HR = 3.2; p < 0.001)
Dabritz et al. (2009)
Plasma
56
PNA clamp PCR
KRAS
36
100
NA
NA
NA
NA
NA
NA
Wu et al. (2014)
Plasma
36
COLD-PCR
Sanger sec
KRAS
72.2
NA
80.6
87.5
NA
NA
NA
NA
Semrad et al. (2015)
Plasma
27
PCR
KRAS
37
78
NA
NA
NA
NA
NA
DFS (1.8 vs. 4.6 months; p = 0.014)
OS (3.0 vs. 10.5 months; p = 0.003)
Sausen et al. (2015)
Plasma
51
ddPCR
KRAS
43
88
NA
99.9
NA
NA
NA
NA
Ako et al. (2017)
Serum, plasma
40
ddPCR
KRAS
48
93
NA
NA
NA
NA
NA
Poor prognosis
Van Laethem et al. (2017)
Plasma
60
BEAMing
KRAS
65
NA
NA
NA
NA
NA
NA
PFS (HR = 0.32; p = 0.002)
OS (HR = 0.27; p = 0.001)
Wei et al. (2019)
Plasma
38
NGS
KRAS
T53
84
60
NA
NA
NA
NA
NA
NA
Correlation with tumor burden post CTX
Pietrasz et al. (2017)
Plasma
135
NGS and ddPCR
KRAS
TP53
SMAD4
41.3
22.1
7.7
NA
NA
NA
NA
NA
NA
ctDNA: OS (HR = 1.99; p = 0.016)
Zill et al. (2015)
Plasma
17
NGS
KRAS
58.8
64.7
100
100
100
NA
NA
NA
TP53
52.9
58.8
90
100
94
APC
11.8
11.8
100
100
100
SMAD4
5.9
11.8
100
100
94
FBXW7
11.8
5.9
50
100
100
KRAS, TP53, APC, SMAD, and FBXW7
82.3
88.2
92.3
100
97.7
Pishvaian et al. (2017)
Blood
23
NGS
KRAS
T53
SMAD4
CDKN2A
29
NA
0
0
87
NA
26.1
47.8
NA
NA
39
26.1
0
0
NA
NA
NA
Yu et al. (2017)
panc juice
115
NGS
TP53 and SMAD4
64.7
NA
NA
NA
NA
64.7
100
NA
Kanda et al. (2013)
panc juice
43
PCR and Sanger sec
TP53
67.4
NA
NA
NA
NA
67.4
100
NA
Cheng et al. (2017)
Plasma
188
ddPCR
NGS
BRCA2
KDR
EGFR
ERBB2
KRAS
11.7
13.8
13.3
13.3
72.3
NA
NA
NA
NA
NA
NA
ERBB2: OS (HR = 1.61; p = 0.035)
KRAS: OS (HR = 1.45; p = 0.019)
Berger et al. (2018)
Plasma
20
NGS and ddPCR
KRAS and TP53
80
81.8
NA
NA
NA
80
NA
PFS (r = – 0.86; p = 0.01)
Henriksen et al. (2016)
Plasma
95
MSP
10 genes
NA
NA
NA
NA
NA
76
83
UICC stages I-II vs. III–IV (AUC = 0.82)
Liggett et al. (2010)
Plasma
30
microarray- mediated methylation
17 marker panel
NA
NA
NA
NA
NA
91.2
90.8
NA
Park et al. (2012)
Plasma
106
MSP
mNPTX2
80
NA
NA
NA
NA
80
76
NA
Melnikov et al. (2009)
Plasma
30
multiplexed array- methylation
mCCND2, mPLAU
mSOCS1, mVHL, mTHBS1
NA
NA
NA
NA
NA
76
59
NA
Melson et al. (2014)
Plasma
30
MSP
mVHL, mMYF3, mTMS, mGPC3, mSRBC
NA
NA
NA
NA
NA
81
67
NA
Eissa et al. (2019)
Plasma
39
MSP
mBNC1
65.1
NA
NA
NA
NA
64.1
93.7
NA
mADAMTS1
87.2
87.2
95.8
mBNC1 and mADAMTS1
97.4
97.4
91.6
Yi et al. (2013)
Serum
42
MOB and MSP
mBNC1
92
NA
NA
NA
NA
79
89
NA
mADAMTS1
68
48
92
mBNC1 and mADAMTS1
NA
81
85
AUC: area under the curve, BEAMing: beads, emulsion, amplification, magnetics PCR, COLD-PCR: co-amplification-at-lower denaturation-temperature PCR, ctDNA: circulating tumor DNA, CTX: chemotherapy, ddPCR: droplet digital polymerase chain reaction, DFS: disease free survival, LNA: Locked Nucleic Acid, MSP: methylation specific PCR, MOB: methylation on beads, n: number of patients, NGS: next-generation sequencing, OS: overall survival, PC: pancreatic cancer, PNA: Peptide Nucleic Acid, panc: pancreatic, PFS: progression free survival, PHFA: preferential homoduplex formation assay, preop: preoperative, r: Pearson’s coefficient of correlation, RFLP: restriction fragment length polymorphism, RFS: recurrence free survival, seq: sequencing, SOMA: short oligonucleotide mass spectrometry analysis; TTP: time to progression, tiDNA: tissue-related DNA, UICC: Union for International Cancer Control

Cell-free and circulating tumor DNA

Cell-free (cf) DNA originates from normal cells exported by exosomes as well as from apoptotic and necrotic cells with highly fragmented, double-stranded DNA of approximately 150–180 base pair fragments in size being released into the bloodstream. In 1948, Mandel and Metais first reported the presence of cfDNA in human circulation followed by detection in urine, saliva, and other body fluids (Botezatu et al. 2000; Liao et al. 2000; Mandel and Metais 1948; Mao et al. 1994). A recent study showed that most of cfDNA derive from bone-marrow and liver in healthy individuals (Sun et al. 2015). Examples for clinical applications of cfDNA are the non-invasive prenatal testing for chromosomal aneuploidies by fetal cfDNA in the plasma of pregnant women or the monitoring of graft rejection following organ transplantation by donor-derived cfDNA in the plasma of the recipients (Chiu et al. 2008; Lo et al. 1997, 1998; Snyder et al. 2011). In 1977, Leon et al. demonstrated that cancer patients had a relative higher level of cfDNA than healthy controls with increased levels after radiation therapy (Leon et al. 1977). It was postulated that cancer patients have higher levels of cfDNA than healthy individuals (Shapiro et al. 1983). However, cfDNA levels have also been linked to outcomes in patients with a variety of other physiological and pathological conditions, including exercise, inflammation, circadian rhythm, exposure to smoking, sepsis, and trauma (Aucamp et al. 2018). In fact, cfDNA is composed of both coding and non-coding genomic DNA that can be used to examine mutations and polymorphisms, microsatellite instability, and epigenetic methylation (Bruhn et al. 2000; Downward 2003; Grutzmann et al. 2008; Jahr et al. 2001; Schwarzenbach et al. 2011). Epigenetic changes are considered as an early event in carcinogenesis and might, therefore, be a suitable early diagnostic tumor marker.
Since the extraction of plasma DNA with the same genetic changes as the primary tumor in 1989 and identification of mutated KRAS sequences in the plasma or serum from patients with PC in 1994, circulating tumor (ct) DNA is becoming a research hotspot with high potential as liquid biopsy marker in cancer medicine (Sorenson et al. 1994; Stroun et al. 1989). CtDNAs as a part of circulating cfDNA are mutant DNA fragments released to the circulation by tumor cells of different cancer entities. CtDNA is considered to be released from an increasing proportion of necrotic and apoptotic cells in primary tumors, secondary deposits and circulating tumor cells, corresponding to an increase in ctDNA. The half-lives of ctDNAs range from 15 min to a few hours which enables ctDNA analysis to be considered as a ‘real-time’ snapshot of disease burden with supposed clearance through nuclease activity, renal excretion, and uptake by the liver and spleen (Diehl et al. 2008; Minchin et al. 2001; Tamkovich et al. 2006; Yu et al. 2013). CtDNA can be detected by tumor-specific mutants and are less impacted by intratumor heterogeneity than a single specimen of tumor tissue (Bettegowda et al. 2014; Diaz and Bardelli 2014; Fleischhacker and Schmidt 2007; Melo et al. 2015; Sausen et al. 2014). It is supposed, that ctDNA harbor the same (epi-) genetic alterations as the originating primary tumor cells. Recently, Heitzer et al. demonstrated the impact of ctDNA in early detection, surveillance, and personalized treatment in different cancer entities like colorectal, breast, and non-small cell lung cancer (Heitzer et al. 2015). In addition, previous studies have indicated a positive correlation between ctDNA levels and tumor burden in various cancer types with increasing copy numbers of ctDNA per mL plasma in advanced and metastatic tumors, as well as different methylation status of ctDNA to normal cfDNA and blood leukocytes (Bettegowda et al. 2014; Guo et al. 2017; Madic et al. 2012; Xu et al. 2017). Furthermore, it is hypothesized that ctDNA could be a signaling trigger for cancer progression by horizontal DNA transfer affecting the biology of host cells (Bergsmedh et al. 2001; Gahan and Stroun 2010; Garcia-Olmo et al. 1999). Thus, ctDNA levels may serve as an early biomarker for diagnostic and therapy monitoring, prior to clinically or radiographically measureable changes of tumor burden in patients. However, ctDNAs represent a variable fraction of cfDNAs ranging from 0.01% to more than 50% in cancer patients (Diaz and Bardelli 2014; Diehl et al. 2008). Deoxyribonuclease activity and the release of cfDNA by normal cells in peripheral circulation reduces ctDNA concentrations. Therefore, the detection of ctDNA is challenging and necessitates the standardization of extraction procedures to be able to distinguish ctDNA from the large amount of cfDNA.

Next-generation sequencing and digital droplet PCR

In the last 2 decades, growing knowledge on non-coding genome functionality and genome-wide sequence variation has improved personalized medicine and molecular oncology. With technological advances broadly categorized into PCR-based and genomic sequencing-based techniques, sensitivity and specificity of biomolecular techniques for ctDNA detection have been highly improved. Currently, high-throughput next-generation sequencing (NGS) and digital droplet PCR (ddPCR) are the most promising methods for the detection of mutations in liquid biopsies. In general, plasma samples are used in preference over serum because of lower concentrations of wild-type DNA (Jung et al. 2003). NGS techniques allow the simultaneous assessment and detection of multiple genetic aberrations and copy number changes, including targeted techniques such as enhanced tagged amplicon deep sequencing, or whole-exome sequencing. The aim of NGS is to generate extensive information about the mutation landscape, then to screen the genome and discover new genomic aberrations, e.g. those that confer resistance to a specific targeted therapy (Murtaza et al. 2013). The whole-genome and whole-exome sequencing could provide more comprehensive information regarding the mutant status of ctDNA. However, the targeted region deep sequencing, which focuses on a fewer gene loci with ultra-deep sequencing, has gained popularity, because the sequencing region can be customized according to cancer types, sequencing purpose, costs, and turnaround time (Leary et al. 2010; Martinez et al. 2013). Among PCR-based techniques, digital PCR including droplet PCR and BEAMing (beads, emulsion, amplification, magnetics) PCR appears as the most promising approach for detection of highly recurrent hotspot mutations at frequencies as low as 0.01%. Compared to quantitative real-time (qRT) PCR, samples are processed with a water–oil emulsion to allow for individual droplets to be assessed as a discrete PCR sample and does not rely on external calibrant. DNA templates are distributed into thousands of droplets each containing only one DNA fragment. Digital PCR has a higher tolerance for enzyme-inhibiting substances thereby improving sensitivity and specificity of mutant DNA detection (Hindson et al. 2011; Zhang et al. 2015). In combination with circulating nucleic acids, ddPCR has gained wide applicability in liquid biopsy diagnostic of cancer, especially in the assessment of methylation status to identify epigenetic dysregulation during carcinogenesis and measurement of changes in gene expression to early diagnosis of cancer, the absolute quantification of copy number variations to predict disease progression, or the detection of rare mutations within ctDNA to guide targeted therapy (Huggett et al. 2015).

Genomic alterations of ctDNA in HCC

NGS profiling of surgically resected HCC revealed a highly heterogeneous cancer caused by the accumulation of genomic and epigenomic alterations (Ozen et al. 2013). Recently, integrative genomic characterization by whole exome sequencing and analyses of DNA copy number, DNA methylation, RNA, microRNA, and proteomic expression defined two major molecular subtypes of HCC. One subtype is defined as a proliferation class associated with higher HBV prevalence and poor clinical outcome characterized by activation of proliferative signaling pathways such as Phosphatidylinositide-3-kinase/AKT/mammalian target of rapamycin and RAS- Mitogen-Activated Protein Kinase pathways, Wingless-type (WNT)/Transforming Growth Factor (TGF) beta signaling, amplification of mitogenic Fibroblast Growth Factor family members and of the cell cycle regulatory subunit Cyclin D1 (CCND1) and inactivation of the tumor suppressor TP53. The second subtype contains a heterogenous non-proliferation class associated with higher HCV prevalence or alcohol abuse and a better clinical outcome characterized by WNT–beta–catenin pathway activation via Catenin Beta 1 (CTNNB1) mutation, Telomerase Reverse Transcriptase (TERT) promotor mutation and silencing of the tumor suppressor Cyclin Dependent Kinase Inhibitor 2A (CDKN2A) by mutation and DNA methylation (Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma 2017). This comprehensive and integrative characterization of molecular profiling in HCC tissues followed by the identification and quantification of corresponding ctDNA in the plasma may provide powerful data for targeted therapies and monitoring of therapy response.
Several genomic and epigenomic alterations within ctDNA as molecular targets in the context of aberrant signaling pathways were detected by NGS, ddPCR and methylation-specific PCR (MSP) in HCC patients (Table 1). TERT promotor mutations were found to be the most common point mutations in several carcinoma entities with reactivation of telomerase enabling limitless cell proliferation driven by oncogenes (Bell et al. 2016). In HCC, TERT promotor mutations were found in dysplastic nodules and early stages with a reported frequency of 59–90% correlating with poor survival (Nault and Villanueva 2015). DdPCR by Huang et al. and NGS by Liao et al. of TERT mutation (c.1-124C > T) in plasma ctDNA of HCC patients revealed a frequency of 23% and 5% with a high specificity of 85% and 95%, respectively (Huang et al. 2016; Liao et al. 2016). Aberrant amplification of TERT was reported to be significantly associated with CTNNB1 mutations in HCC, indicating that the interaction between upregulation of TERT mutations and dysregulation of the WNT–beta–catenin pathway could promote hepatocellular carcinogenesis (Nault et al. 2013). Gain of function mutations in the CTNNB1 gene encoding beta catenin allow the accumulation of beta catenin within the cell nucleus through WNT pathway und promote tumor progression in about 30% of cases in HCC. CtDNA analysis on CTNNB1 mutations (c.121A > G, c.122C > T, c.133T > C, c134C > T) resulted in a high specific frequency of about 10% in two small HCC collectives screened by ddPCR and NGS (Huang et al. 2016; Liao et al. 2016). Another well known driver gene of HCC is TP53 with a high mutation frequency of more than 30%. Mainly missense mutations in the DNA-binding domain of TP53 are generally thought to abrogate the tumor suppressor function of p53 as the guardian of the genome. Loss of p53 function with consecutive dysregulation of apoptosis, cell cycle arrest, DNA repair and metabolic regulation is a prerequisite for tumor initiation and progression in a multitude of human cancers. However, mutant p53 not only lose tumor suppressive functions of wild-type p53 but also gain new oncogenic properties promoting tumor cell proliferation, angiogenesis, and metastasis. The mechanism for the accumulation of mutant p53 and its mutational gain of function in tumors is not yet well understood. The frequency of mutant TP53 in blood and urine ctDNA of HCC studies ranged between 5 and 60% (An et al. 2019; He et al. 2019; Huang et al. 2016; Ikeda et al. 2018a, b; Kaseb et al. 2019; Kimbi et al. 2005; Liao et al. 2016; Lin et al. 2011; Marchio et al. 2018; Riviere et al. 2018; Szymanska et al. 2004; Xiong et al. 2019). Liao et al. could reveal that the median recurrence-free survival (RFS) of patients with the presence of TERT, CTNNB1 and TP53 mutations detected in ctDNA post surgical treatment was significantly decreased with 3 versus 12 months (Liao et al. 2016). Similarly, postoperative detection of ctDNA related mutations by An et al. with TP53 as the most common mutant gene correlated significantly with worse disease-free survival (DFS) of 6.7 versus 17.5 months (An et al. 2019).

Methylation alterations of ctDNA in HCC

Besides genetic alterations with change of DNA sequence, epigenetic silencing of tumor suppressor genes by promotor hypermethylation has been proven to be present in precursor lesions of HCC. Aberrant DNA hypermethylation consists of the addition of a methyl residue on cytosines preceding guanosines leading to a condensed chromatin structure without transcriptional activity. High concordance of DNA methylation in plasma and tumor DNA could be shown for a number of tumor suppressor genes in HCC (Iyer et al. 2010). Aberrant methylation of the cyclin-dependent kinase inhibitor genes p15 (CDKN2B or p15INK4b) and p16 (CDKN2A or p16INK4a) on chromosome 9p21 in the peripheral circulation of HCC patients using MSP is one of first detected epigenetic changes associated with hepatocellular tumorigenesis. Wong et al. detected concurrent p15 and p16 methylation in 74% of ctDNA of 23 blood samples from 92% of HCC patients with tumor p15/p16 methylation (Wong et al. 1999, 2000). High incidence of p16INK4a promoter hypermethylation in ctDNA with significant decrease in postoperative blood samples was shown to be a useful marker in the detection and monitoring of HCC (Wong et al. 2003). Correspondingly, Huang et al. demonstrated higher levels of methylated p16INK4a in circulating cfDNA of 66 HCC serum samples versus 43 benign chronic liver diseases (Huang et al. 2014). Further common tumor suppressing and cell cycle regulation-related genes with promoter hypermethylation are the adenomatous polyposis coli (APC) on chromosome 5q21 and the Ras association domain family protein 1A (RASSF1A) genes on chromosome 3p21.3 with high frequency of promoter hypermethylation in tumor and blood samples of HCC patients (Hu et al. 2010; Huang et al. 2011; Yeo et al. 2005). Mohamed et al. as well as Mansour et al. could demonstrate that serum levels of methylated RASSF1A could well discriminate HCC patients from healthy volunteers and from chronic HCV infection with an incidence of about 90% in HCC serum samples (Mansour et al. 2017; Mohamed et al. 2012). Similar frequencies of methylated RASSF1A in serum of HCC patients at diagnosis or 1 year after tumor resection versus low concentrations in HBV carriers correlated with poorer disease-free survival (DFS) in a study of Chan et al. (Chan et al. 2008). Moreover, elevated plasma methylation levels of APC or RASSF1A correlated with poorer overall survival reaching significance for RASSF1A in multivariate analysis (Huang et al. 2011). Coevaluation of RASSF1A and Long Interspersed Nucleotide Element 1 (LINE-1) as one of the major repetitive DNA sequence of the human genome and most active mediator of retrotransposition revealed LINE-1 hypomethylation in 66.7% and RASSF1A promoter hypermethylation in 73.3% only in HCC serum DNA samples correlating with early recurrence and poor survival after curative resection (Liu et al. 2017). Tangkijvanich et al. described significantly increased serum LINE-1 hypomethylation in HCC as independent prognostic factor of overall survival (Tangkijvanich et al. 2007). Combined detection of ctDNA methylation markers was performed by several studies to improve the efficiency in early HCC diagnostic. Plasma methylation analysis of the four genes panel with APC, glutathione S-transferase P 1 (GSTP1), RASSF1A, and secreted frizzled-related protein 1 (SFRP1) resulted in an increased accuracy of 93% to differentiate between HCC and healthy controls (Huang et al. 2011). Xu et al. constructed a diagnostic prediction model using a cfDNA methylation marker panel that predicted HCC survival and could effectively discriminate patients with HCC from individuals with HBV/HCV infection, fatty liver disease as well as healthy controls superior to AFP (Xu et al. 2017). Although a multitude of aberrant methylated genes could be identified as prognostic target in HCC, there is no recognized biomarker confirmed in multiple centers (Han et al. 2014; Iizuka et al. 2011; Ji et al. 2014; Oussalah et al. 2018; Sun et al. 2013; Wang et al. 2006; Wen et al. 2015; Wu et al. 2017; Zhang et al. 2013).

Genomic alterations of ctDNA in PC

In the last decade, comprehensive genomic analysis allowed important advances in the understanding of the molecular pathogenesis of PC with reclassification in different specific subtypes (Bailey et al. 2016; Biankin et al. 2012; Collisson et al. 2011; Jones et al. 2008; Moffitt et al. 2015; Waddell et al. 2015; Witkiewicz et al. 2015). Several studies using different techniques could reveal that reproducible molecular subgroups with consistent alterations in genes and signaling pathways are emerging in PC. Evaluating 456 specimens of resected PC by a combination of whole-genome sequencing and deep-exome sequencing, Bailey et al. identified genetic mutations particularly of KRAS in 92%, cell cycle checkpoint mutations as TP53 and CDKN2A in 78%, aberrations in TGF beta signaling as SMAD4, TGFBR1, and Activin A receptor 1B in 47%, mutations leading to histone modification in 24%, mutations in the Breast Cancer Gene (BRCA) pathway in 17%, and mutations in the ATP-dependent chromatin remodeling complex as AT-rich interaction domain 1A (ARID1A) in 14% (Bailey et al. 2016). However, there is still a lack of consensus in the clinical applicability of current PC subtyping approaches. Recently, plasma cfDNA profiling in 38 patients with advanced PC receiving first-line FOLFIRINOX chemotherapy could demonstrate that 65.8% of patients had at least one common driver gene alteration in KRAS, TP53, SMAD4, or CDKN2A in high concordance with corresponding tumor tissue (Table 2) (Wei et al. 2019). Interestingly, the dynamics of total cfDNA concentration correlated positively with tumor burden following chemotherapy and might be a promising tool for early response prediction and therapy surveillance in patients with advanced PC. Among these key genes, KRAS is the best characterized tumor-related gene in PC with the highest frequency of KRAS point mutations located in codon 12 and with appearance even at early stages of PC carcinogenesis (Almoguera et al. 1988; Rhim et al. 2014; Uemura et al. 2003). Therefore, KRAS mutant ctDNA represents a promising biomarker and therapeutic target of PC. Using ddPCR and targeted NGS, different KRAS mutations were detected in up to 80% of PC serum and plasma samples and were associated with decreased survival (Adamo et al. 2017; Ako et al. 2017; Chen et al. 2010; Cohen et al. 2017; Dabritz et al. 2009; Del Re et al. 2017; Dianxu et al. 2002; Earl et al. 2015; Hadano et al. 2016; Jiao et al. 2007; Kim et al. 2018; Kinugasa et al. 2015; Kruger et al. 2018; Lin et al. 2018; Maire et al. 2002; Marchese et al. 2006; Nakano et al. 2018; Park et al. 2018; Patel et al. 2019; Perets et al. 2018; Pratt et al. 2019; Riviere et al. 2018; Sausen et al. 2015; Semrad et al. 2015; Sugimori et al. 2020; Takai et al. 2015; Tjensvoll et al. 2016; Uemura et al. 2004; Van Laethem et al. 2017; Watanabe et al. 2019; Wu et al. 2014). Chen et al. published a KRAS mutant ctDNA detection rate of 93.7% which correlates with time to progression and overall survival of 189 patients with unresectable PC (Chen et al. 2017). In metastatic PC absence of KRAS mutant ctDNA was significantly associated with survival benefit of 37.5 versus 8 months (p < 0.004) (Perets et al. 2018). Correspondingly, PC patients with KRAS mutant ctDNA were more likely to relapse after curative surgery than those without KRAS mutant ctDNA with desease-free survival of 6.1 versus 16.1 months and overall survival of 13.6 versus 27.6 months (p < 0.001) (Groot et al. 2019; Hadano et al. 2016; Sausen et al. 2015). Serial plasma testing of KRAS mutant ctDNA in advanced PC patients receiving chemotherapy allowed the monitoring of rapid changes of KRAS mutant ctDNA levels superior to CA19-9 and CEA kinetics (Kruger et al. 2018). Targeting KRAS pathway is a promising effort to make therapeutic progress in PC (Krantz and O’Reilly 2018). Although mutant KRAS is often identified in plasma as a ctDNA benchmark for PC, advances in sequencing of the whole PC genomic landscape have expanded the panel of key mutations (Kanda et al. 2013; Pietrasz et al. 2017; Pishvaian et al. 2017; Yu et al. 2017; Zill et al. 2015). CtDNA whole-exome sequencing of 60 hotspot genes in metastatic PC identified KRAS, BRCA2, KDR, EGFR, and ERBB2 as candidate genetic mutations. DdPCR could validate ctDNA mutations at ERBB2 exon17 and KRAS G12V that were significantly correlated with worse overall survival (Cheng et al. 2017). Berger et al. performed NGS and ddPCR to dynamically monitor the most frequently ctDNA mutated genes in PC. TP53 and KRAS mutation levels were significantly decreased during treatment, and on the other hand significantly increased during tumor progression correlating with progression-free survival (Berger et al. 2018). However, current biomolecular technologies confirm the genetic heterogeneity of PC with a large number of low-frequently mutated loci and a large fraction of patients who does not harbor mutations in KRAS or TP53 (Martinez et al. 2013).

Methylation alterations of ctDNA in PC

Methylation analyses of ctDNA that reveal epigenetic alterations with more or less diagnostic and prognostic impact reflect the remarkable heterogeneity in PC patients. CfDNA promotor hypermethylation in plasma or serum could be detected in all stages of PC (Henriksen et al. 2017b). Henriksen et al. developed a survival prediction model based on plasma-derived cfDNA hypermethylation of a large gene panel that enables the stratification of patients into risk groups (Henriksen et al. 2017a). Further methylation analyses of ctDNA were able to differentiate PC from chronic pancreatitis and healthy controls (Henriksen et al. 2016; Liggett et al. 2010; Melnikov et al. 2009; Melson et al. 2014; Park et al. 2012). Confirming study results of Yi et al., Eissa et al. could show that a two-gene promotor methylation panel of Zinc finger protein basonuclin-1 (BNC1) and A disintegrin and metalloproteinase with thrombospondin motifs 1 (ADAMTS1) increased sensitivity to 97.4% and specificity to 91.6% in plasma cfDNA of early PC stages (Eissa et al. 2019; Yi et al. 2013). So far, in relatively, few PC patients no single ctDNA promotor hypermethylation with adequate sensitivity and specificity has been found. Furthermore, serial ctDNA studies following the methylation profile of PC patients in accordance with treatment and tumor recurrence are lacking.

Challenges in clinical utility of circulating DNA

CtDNA has gained considerable attention as novel liquid biopsy marker for cancer detection in asymptomatic individuals and of residual disease. Indeed, ctDNA has huge clinical potential for prognostication and response monitoring of patients with HCC and PC characterized by high tumor heterogeneity and dismal prognosis. However, although literature regarding ctDNA assays and molecular profiling is rapidly growing, its translation into clinical applicability is highly complex. Limited data are available regarding the blood draw procedure and pre-analytical variables that increase degradation of cfDNA or contamination by cellular genomic DNA derived from leukocyte lysis (Lee et al. 2001). Indeed, varying cfDNA purification methods and various protocol modifications may affect cfDNA yield and purity. There is consensus that cfDNA analysis requires special processing and handling by using cell-stabilization tubes and avoiding repeated freeze–thaw cycles. Furthermore, patient-related factors as medical treatment, smoking, exercise, age-related clonal hematopoiesis, inflammation or cardio-pulmonary disorders may contribute to the release of cfDNA. Interestingly, several studies could demonstrate that false-positive plasma genotyping is due to clonal hematopoiesis with non-malignant mutations harbored by hematopoietic cells with increasing frequency in 10% of patients over the age of 65 years, but only in 1% of patients under the age of 50 years (Hu et al. 2018; Jaiswal et al. 2014). The proportion of ctDNA as a fraction of cfDNA varies substantially between different patients, and different subclonal variants might be identified. Therefore, allele fractions of variants in ctDNA need to be interpreted with great caution.
In the last decade, numerous platforms for genotyping of cfDNA have been developed. However, the test characteristics of each platform as ddPCR and NGS vary and were validated in different patient populations with different lower limits of detection. Therefore, direct comparison of these platforms with reported high diagnostic specificity, but modest sensitivity is challenging and requires rigorous cross-assay comparisons. The low diagnostic sensitivity of ctDNA tests in carcinomas could be a major reason of discordant tissue and ctDNA genotyping results. Although clinical utility of ctDNA assays is mainly based on retrospective analyses, first FDA-approved application in 2018 for cfDNA assay in routine clinical practice could demonstrate high concordance between plasma and tumor tissue genotyping for early detection of specific EGFR mutation (T790M) and therapy stratification in advanced non-small cell lung cancer patients (Zhang et al. 2018). The concept of plasma genotyping is highly promising, although its application in the clinical routine to identify and treat patients with HCC or PC requires ongoing evaluation.

Conclusion

Overall, ctDNA mapping of somatic driver mutations and specific epigenetic alterations has great potential in early detection and dynamic monitoring of hepatic or pancreatic carcinomas to achieve a significant decrease of mortality. However, low sensitivity of current ctDNA assays rest a major challenge.
So far, ctDNA analysis in PC and HCC could reveal high frequency of common key mutations as TP53 and CDKN2A. CTNNB1 and TERT mutations and aberrant methylation of RASSF1A and CDKN2A were detected in ctDNA of HCC patients, whereas high frequency of KRAS mutations was characteristic for PC. Prospective trial data based on sufficient sample size and defined entry criteria regarding the blood draw procedure and pre-analytical variables as well as standardization of experimental techniques that demonstrate the clinical utility of ctDNA assays in PC and HCC are required.

Acknowledgements

Open Access funding provided by Projekt DEAL.

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Conflict of interest

The authors declare that they have has no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
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Metadaten
Titel
Impact of circulating tumor DNA in hepatocellular and pancreatic carcinomas
verfasst von
Sameer A. Dhayat
Zixuan Yang
Publikationsdatum
27.04.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal of Cancer Research and Clinical Oncology / Ausgabe 7/2020
Print ISSN: 0171-5216
Elektronische ISSN: 1432-1335
DOI
https://doi.org/10.1007/s00432-020-03219-5

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