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Circulating Cell-Free Tumour DNA for Early Detection of Pancreatic Cancer

MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK
Department of Medical Oncology, Christie NHS Foundation Trust, Manchester M20 4BX, UK
Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK
Department of Oncology, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, UK
Department of Medical Oncology, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 7LE, UK
Author to whom correspondence should be addressed.
Cancers 2020, 12(12), 3704;
Received: 23 November 2020 / Accepted: 4 December 2020 / Published: 9 December 2020



Simple Summary

Pancreatic ductal adenocarcinoma (PDAC) has a poor 5-year survival rate and is the 7th leading cause of cancer-related deaths in the world. The high mortality for this disease is partly due to late presentation rendering therapeutics ineffective. Since a majority of patients are diagnosed at advanced stages due to the lack of specific symptoms, and the prognosis is linked to the stage of detection, there is a need for robust early detection methods for PDAC. Here, we review the potential use of circulating tumour DNA (ctDNA) as a non-invasive biomarker in the early detection of pancreatic cancer. In brief ctDNA levels in blood correlate with disease progression but its widespread application for early PDAC detection requires further investigation and potentially, a combination of ctDNA sequence and methylome analysis with other, protein-based biomarkers.


Pancreatic cancer is a lethal disease, with mortality rates negatively associated with the stage at which the disease is detected. Early detection is therefore critical to improving survival outcomes. A recent focus of research for early detection is the use of circulating cell-free tumour DNA (ctDNA). The detection of ctDNA offers potential as a relatively non-invasive method of diagnosing pancreatic cancer by using genetic sequencing technology to detect tumour-specific mutational signatures in blood samples before symptoms manifest. These technologies are limited by a number of factors that lower sensitivity and specificity, including low levels of detectable ctDNA in early stage disease and contamination with non-cancer circulating cell-free DNA. However, genetic and epigenetic analysis of ctDNA in combination with other standard diagnostic tests may improve early detection rates. In this review, we evaluate the genetic and epigenetic methods under investigation in diagnosing pancreatic cancer and provide a perspective for future developments.

1. Introduction

Pancreatic ductal adenocarcinoma (PDAC) is the 14th most prevalent cancer and the seventh most common cause of cancer-related death worldwide [1]. Its incidence is expected to double by 2030, becoming the second-leading cause of cancer-related death in the United States [2,3]. Pancreatic ductal adenocarcinoma also has the lowest five-year survival rate of any malignancy, which is around 5–10% [4,5]. This is attributed to the disease frequently presenting and/or being detected in an advanced stage, in which a cure is highly unlikely [6]. Indeed, a potential cure for PDAC involves surgical resection with adjuvant chemotherapy [7]. Alternatively, borderline resectable and locally advanced PDAC cases may benefit from neoadjuvant therapy, which can be safer than surgery. A number of studies have compared the two approaches [8,9,10,11,12,13], but the broad clinical utility of neoadjuvant therapy still requires further investigation in a phase 3 clinical trial setting [14]. Altogether, approximately 85% of patients diagnosed with PDAC are not suitable for potentially curative therapy due to locally advanced or metastatic spread. Early detection of PDAC is thus critical to allow access to surgery and therefore, for the improvement in survival outcomes for patients. One area of current research is using liquid biopsies to detect the disease at an early stage.
The development of next-generation DNA sequencing methods, together with technical advances in molecular biology, has sparked interest in liquid biopsy as a tool for the early detection of cancer [15]. Currently, the only blood-based marker used in the diagnosis of pancreatic cancer is serum CA19.9 [16]; however, its relatively low sensitivity (79%) and specificity (82%) limits its validity as a stand-alone test [17,18]. Newly uncovered circulating biomarkers raise hopes for more sensitive and specific diagnostic tests that can analyse a myriad of body fluids e.g., saliva, blood or urine [19,20,21]. Such methods include the detection of exosomes, DNA, RNA or circulating tumour cells (CTCs) (Figure 1). The numbers of detectable CTCs are often low in many tumour types, particularly in early stage disease, and there are no validated biomarkers for cell selection, making this approach somewhat limited [22]. In contrast, circulating nucleic acids are more prevalent in early stage disease, offering a potential biological marker for detection. In this review, we discuss the application of circulating tumour DNA (ctDNA) in the early detection of pancreatic cancer.

2. Liquid Biopsies for ctDNA

A liquid biopsy is a non-invasive investigation that holds great promise in detecting early stage cancer. It involves sampling blood and/or other body fluids for the presence of ctDNA or CTCs. Initial research has focused on advanced cancers, where ctDNA levels are highest and therefore isolation and sequencing of tumour-specific nucleic acids are more feasible [23,24]. Indeed, in advanced stage pancreatic cancer, the presence of ctDNA in the blood has been associated with both relapse and residual disease following surgery [25,26], and could be used to tailor therapeutic approaches [27].
Liquid biopsies are more accessible than tumour biopsies, and can provide a new diagnostic tool, particularly in tumour types where invasive biopsies are comparatively more unsafe and difficult. In addition, while single-site tumour biopsies may not reflect tumour heterogeneity, particularly in metastatic disease, ctDNA analysis offers the potential to provide a method to detect inter- and intra-tumour heterogeneity across metastatic sites [28]. Therefore, ctDNA analysis offers the potential to detect early stage PDAC and reduce the number of potentially harmful procedures, as well as limiting costly and unnecessary therapies that may be associated with toxicity [29].
Circulating cell-free DNA in blood was first described by Mandel and Metais in 1948 [30]. Subsequent investigations led to the observation in 1977 by Leon et al. [31] that circulating cell-free DNA (cfDNA) levels are increased in cancer. Further studies have since indicated that cfDNA levels are also elevated in other pathophysiological processes, such as following physical trauma [32], cerebral infarction [33], physical exercise [34] and solid organ transplantation [35]. Another major breakthrough came from the discovery of the presence of foetal Y-chromosomal cfDNA in maternal blood [36], which led to the development of non-invasive prenatal testing [37]; a screening method now used to detect congenital chromosomal abnormalities [38,39,40]. In the context of cancer medicine, cfDNA studies have shown that the fraction of the cfDNA produced directly from cancer cells (i.e., ctDNA) can also be detected, but has a shorter half-life than non-cancerous cfDNA [41] and therefore requires almost immediate sample processing.
Circulating cell-free DNA is fragmented, typically double-stranded and approximately 150–350 base pairs in length [42]. It can be released from a variety of healthy cells, but the majority originates from haematopoietic cells [43,44]. In patients diagnosed with cancer, cfDNA is also released from cancer cells, though the mechanism of ctDNA release remains unclear—either active secretion and/or release in cell death, supported by studies showing that ctDNA levels increase rapidly after treatment is administered in a range of cancers [45,46,47]. The mechanism of DNA release might affect cancer detection, as actively secreted ctDNA may be biased in certain chemotherapy-sensitive metastatic sites. ctDNA constitutes between approximately 0.1% and >90% of all cfDNA depending on a number of factors, in particular cancer stage [24,48]. Moreover, Bettegowda et al. showed that early stage cancers have a lower percentage of ctDNA then late stage malignancies [24]. For pancreatic cancer, ctDNA was detected in 48% of patients with localised tumours, but in more than 80% of advanced cancers. This shows a potential limitation of ctDNA as an early detection tool for pancreatic cancer.

3. Detection of ctDNA

An important property of ctDNA related to early cancer detection is its low stability; different studies estimate ctDNA half-life to be between 16 and 150 min [48,49,50], thus necessitating longitudinal analysis of tumour responses to therapy and/or disease progression. This short half-life also brings a technical challenge: the instability of cfDNA combined with the propensity for blood cells to lyse once outside the body, resulting in contamination, necessitates cfDNA separation by centrifugation within no more than 4 h of sample collection in standard phlebotomy tubes [15,51,52], which creates logistic burden and does not allow for central processing in specialised centres, therefore limiting widespread clinical implementation. In an attempt to address this issue, recent developments allow for the ability to fix and stabilise cfDNA for 7–14 days at room temperature [53]. Specifically, Cell-Free DNA BCT® (Streck, La Vista, NV, USA) tubes are estimated to preserve cfDNA in blood for 7 days due to an improved vacuum and moisture retention system [54,55,56] and have been used in a number of recent diagnostic studies of different tumour types [57,58].
Another significant challenge in the diagnostic utility of ctDNA is its low concentration in plasma, complicating detection and analysis. There is approximately 10–15 ng of cfDNA per millilitre and ctDNA constitutes a small fraction of cfDNA in most early stage of cancers [48]. These factors highlight the need for ultrasensitive detection techniques. In clinical trials, current sequencing methods involve targeted or genome-wide approaches to genetic sequencing. The former is more prevalent and only detects hotspot mutations in specific genes. This technology could be applicable to somatic KRAS mutations ubiquitously found in PDAC. Conversely, genome-wide sequencing assays are an unrestricted method that is more expensive and technically challenging, but identifies many more mutations across the genome as well as structural variants and mutational signatures. Currently, the most sensitive methods are polymerase chain reaction (PCR)–based approaches, including single-molecule PCR BEAMing [59], TAm-Seq [60], digital PCR [61] and droplet digital PCR [62]. Next-generation sequencing techniques are also widely used, with the biggest limiting factor being the low fidelity of DNA polymerase [60,63,64]. However, the incorporation of deep sequencing, molecular barcoding and digital error suppression can increase sensitivity and specificity of the approach [65,66].

4. ctDNA in Pancreatic Cancer

Initial insights into the utility of ctDNA in the diagnosis of pancreatic cancer were elucidated by Shapiro et al. [67], who showed that ctDNA can be found in patients diagnosed with pancreatic cancer, but is absent in healthy individuals. Almost 10 years after these initial findings, Sorenson et al. [68] were able to detect somatic KRAS mutations in the plasma of patients diagnosed with pancreatic cancer. These mutations matched those found in the tumour, sparking interest in ctDNA as an early detection marker for pancreatic cancer. Indeed, KRAS is a candidate gene for early detection because it is considered a clonal oncogenic driver of pancreatic cancer and is present in ~95% of cases [69,70]. A description of the most relevant studies of ctDNA application in PDAC detection and monitoring is provided in Table 1.

5. The Use of ctDNA to Detect Early Stage Pancreatic Cancer

Despite the promising results emerging from ctDNA use, the clinical applicability of liquid biopsy remains debatable across tumour types. A number of studies have shown low sensitivity and specificity of ctDNA application, particularly in breast [85] and lung [86] early stage cancers. Similarly, in a large case–control study, Calvez-Kelm et al. [76] reported that ctDNA is a less accurate method to detect early stage PDAC than CA19–9, and a combination of the two tests did not increase sensitivity. This finding was supported by a more recent study by Fiala et al. [87], which showed that the current sequencing technology was unable to detect ctDNA of pancreatic cancer less than 10 mm in diameter with detection efficiency increasing with cancer progression.
As a result, ctDNA analysis for early detection is currently unable to be used as a stand-alone method to detect early stage cancer [24]. Therefore, various approaches of combining ctDNA with other molecular biomarkers are being evaluated. Several studies have reported the applicability of such approaches in PDAC. Indeed, CA19–9 was shown to be a less sensitive marker than when combined with TIMP-1 protein testing [88]. In another study, Capello et al. showed that the sensitivity of PDAC detection is even higher when the two factors are combined with LRG-1 protein testing [89]. In an attempt to integrate protein biomarkers and ctDNA in PDAC detection, Cohen et al. [78] presented a study incorporating the data from KRAS ctDNA sequencing and four additional protein markers (CA19–9, CEA, HGF and OPN) and reported an increased specificity and sensitivity in early stage pancreatic cancer detection.
A similar method was exploited by Cohen et al. [80] in 2018. This group used CancerSEEK technology, based on ultra-deep PCR-based sequencing, to analyse 1933 distinct genomic loci combined with eight protein biomarkers. Based on data from liquid biopsies taken from 1005 patients with early stage, resectable cancer and 812 healthy individuals, the sensitivity rate of detection varied from 43% in stage I, 72% in stage II to 78% in stage III with specificity exceeding 99% (7 out of 812 controls were identified as cancer-positive). The sensitivity also varied based on tumour type e.g., 33% in breast cancer, 98% in ovarian cancer and 72% in pancreatic cancer. However, the accuracy of the approach has been questioned. In particular, Young et al. [90] criticised the use of case–control data where subject selection and the choice of a robust control are challenging. Specifically, no advanced stage cancers were included, and the healthy controls were likely to be biased and not represent the potential screening population (e.g., individuals with gastrointestinal tract symptoms or those characterised by genetic predispositions). In addition, the cases analysed included eight different cancer types of various stages, and it may therefore not be possible to draw robust conclusions about the utility of this approach in a single tumour type. For these reasons, the sensitivity and specificity reported could be biased and an additional study is required to validate the method.
One challenge related to ctDNA biology is a lack of consistency in the study design. As investigated tumour stages vary between studies, which include both case–control and cohort studies, a robust comparison of different methods is challenging. Moreover, PDAC should be confirmed using established techniques (e.g., histology), with a follow-up period for the detection of false-negative early stage cancers. In addition, controls should consist of individuals in high-risk groups, likely to be frequently screened, as well as healthy volunteers. If possible, calculations of sensitivity and specificity should be applied to each tumour type and stage separately to enable reliable comparison between studies.
Efficient, early diagnosis of PDAC also requires the identification of a screening population at risk of developing PDAC. There is a number of risk factors associated with PDAC, with the most relevant being new-onset diabetes mellitus [91], chronic pancreatitis [92] or genetic predisposition [93]. Ben et al. showed that, while long-lasting diabetes mellitus increases the risk of developing PDAC by 1.5-fold, recent detection (within 1–3 years from the disease onset) increases this ratio to 5–8-fold [94]. Similarly, the risk of developing PDAC increases eightfold over the five-year period following chronic pancreatitis diagnosis [92]. Finally, genetic predispositions are estimated to contribute to 5–10% of all PDAC cases [93], with mutations in the six most significant genes being reported in 5.5% of patients [95]. Altogether, the currently available data point at certain risk factors, which should determine the screening population for PDAC using ctDNA-based liquid biopsy.

6. Methylation Analysis in Cancer Diagnosis Based on Liquid Biopsy

Genome-wide epigenetic changes are common in cancer. Somatic methylations can be cancer-driving events and may precede genetic mutations during carcinogenesis [96,97,98]. As a result, epigenetics has attracted considerable research attention over the recent years. Introducing this property into ctDNA testing brings hope to increase the clinical utility of liquid biopsies [29].
Methylations of CpG islands provide a method to regulate gene expression by modulating transcription. The differences in methylation occupancy underlie many cancer types, with tumour suppressor genes being most frequently affected [99]. Certain methylations are tumour specific and therefore could be used to detect cancer as well as its tissue of origin based on ctDNA analysis. For example, HOXA9 is methylated in 95% of high-grade ovarian serous carcinomas and together with EN1 methylation levels can be used to distinguish between benign and malignant ovarian tumours with up to 98.8% sensitivity and 91.7% specificity [100].
Methylation assays for cfDNA have also been shown to be useful for PDAC detection and progression-tracking. Henriksen et al. [101] demonstrated that cancer-specific promoter methylation can be a marker for early PDAC detection. Moreover, Ligget et al. [102] showed that methylation analysis of 17 gene promoters in ctDNA was able to distinguish between patients diagnosed with chronic pancreatitis and PDAC with a sensitivity of 91.2% and specificity of 90.8%. More recently, Eissa et al. [103] identified the methylation of ADAMTS1 and BNC1 in ctDNA as a diagnostic biomarker in PDAC. Indeed, for localised PDAC, the sensitivity of this two-gene panel ctDNA analysis was 94.8%, and the specificity was 91.6%. These findings show promise in the early detection of PDAC and may enable differentiation from chronic pancreatitis.
A larger and more complex study was performed by Liu et al. [58], who investigated ctDNA application in early cancer detection in 15,254 subjects with or without over 50 types of cancer. The most relevant methylation regions were identified using whole-genome methylation assays followed by a wide-range analysis of the cancer detection properties. Subsequently, a targeted methylation assay was tested on a separate group of patients of all cancer stages. Specifically, stage I PDAC was detected with 63% sensitivity and 99% specificity, rising to 83% and 99%, respectively, in stage II. Taken together, these data provide further evidence that targeted ctDNA methylation analysis could be used in the early detection of PDAC.
Despite the improvements in sequencing technology, the low ctDNA concentration in patients with early stage disease is still one of the most significant challenges to the introduction of methylation analysis of ctDNA at the initial stage of cancer. So far, various attempts to unravel the methylome of ctDNA for cancer detection or monitoring have been done with whole-genome bisulphite sequencing [44,104]. However, this method is limited by input DNA degradation during bisulfide conversion [105], particularly in early cancer detection when a large enough blood sample would be required (with standard intake of up to 80 mL of whole blood) and ctDNA can constitute less than 0.1% of the cfDNA fraction [28]. Moreover, the methylome reflects both normal tissue and cancer heterogeneity and its analysis is thus complex, requiring a substantial amount of comparative data. Therefore, new methods are being developed to increase the efficiency of this technique. In 2018, Shen et al. [106] demonstrated a novel approach based on targeted amplification of cancer-specific methylation regions using Enterobacter phage lambda DNA to increase the initial DNA amount to at least 100 ng, increasing detection efficiency. Although this method demonstrates improved ctDNA methylome detection in a cost-effective manner, there is little knowledge of the cancer-specific DNA methylome, so a genome-wide methylation analysis appears to be more suitable.

7. Technical Advances Facilitate the Detection and Analysis of ctDNA and Also Unravel Its Properties

The discovery of ctDNA being typically shorter than cfDNA could be used for ctDNA isolation by size. The methylation pattern could also be exploited to detect cancer, instead of screening for specific methylations. Cancer progression is linked to genome-wide methylation loss, along with an accumulation of methyl groups in the regulatory regions of certain genes [107]. This property has been exploited by Sina et al. [108] who developed a promising method for cancer detection based on Methylscape analysis, leading to the increased aggregation of ctDNA on gold beads. Despite the low specificity observed and small sample size, the study presented a novel approach to ctDNA analysis. A description of the most relevant methylation studies of ctDNA application in the early PDAC detection is provided in Table 2.

8. Future Directions

Increasing understanding of cancer pathology and the introduction of early detection as well as targeted and immuno-therapies have revolutionised cancer care. Despite these advances, cancer is still the third leading cause of death in the world. Improving the detection of early stage disease is a promising strategy to advance survival outcomes [109]. ctDNA is a promising diagnostic biomarker, but does not yet represent a validated method for cancer detection. Newer approaches incorporate additional properties of ctDNA beyond sequencing, such as the length of fragments or methylation signatures, combined with cancer-associated protein biomarkers. This integrative approach is continuously revised and improved following technological advancement. Hence, ctDNA analysis has the potential to be introduced as a screening tool to detect early stage cancer, particularly in high-risk groups.

9. Conclusions

Circulating cell-free DNA analysis has a potential role in a number of clinical scenarios, including early cancer detection. However, studies on the applicability of ctDNA testing in clinical practice have had mixed outcomes and few ctDNA assays have so far demonstrated promise in the diagnosis of pancreatic cancer. The incorporation of additional markers and methylome analysis has been shown to improve the specificity and sensitivity of ctDNA analysis. Further evaluation is required before the introduction of ctDNA into pancreatic cancer detection and monitoring in clinics.

Author Contributions

J.J.J. and S.S. performed the literature search, designed the draft of the review and drew all figures and tables; J.J.J., R.D.M. and S.S. contributed to the editing and critical reading of the manuscript and provided the final version of the review. All authors have read and agreed to the published version of the manuscript.


This work was supported by a PhD Fellowship from the Boehringer Ingelheim Fonds to J.J., but the research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Bray, F.; Ferlay, J.; Soerjomataram, I.; Siegel, R.L.; Torre, L.A.; Jemal, A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2018, 68, 394–424. [Google Scholar] [CrossRef] [PubMed][Green Version]
  2. Rahib, L.; Smith, B.D.; Aizenberg, R.; Rosenzweig, A.B.; Fleshman, J.M.; Matrisian, L.M. Projecting cancer incidence and deaths to 2030: The unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 2014, 74, 2913–2921. [Google Scholar] [CrossRef] [PubMed][Green Version]
  3. Font-Burgada, J.; Sun, B.; Karin, M. Obesity and Cancer: The Oil that Feeds the Flame. Cell Metab. 2016, 23, 48–62. [Google Scholar] [CrossRef] [PubMed][Green Version]
  4. Cancer Statistics. Available online: (accessed on 19 November 2020).
  5. Cancer Research UK. Available online: (accessed on 19 November 2020).
  6. Werner, J.; Combs, S.E.; Springfeld, C.; Hartwig, W.; Hackert, T.; Büchler, M.W. Advanced-stage pancreatic cancer: Therapy options. Nat. Rev. Clin. Oncol. 2013, 10, 323–333. [Google Scholar] [CrossRef]
  7. Conroy, T.; Hammel, P.; Hebbar, M.; Ben Abdelghani, M.; Wei, A.C.; Raoul, J.-L.; Choné, L.; Francois, E.; Artru, P.; Biagi, J.J.; et al. FOLFIRINOX or Gemcitabine as Adjuvant Therapy for Pancreatic Cancer. N. Engl. J. Med. 2018, 379, 2395–2406. [Google Scholar] [CrossRef]
  8. Ghaneh, P.; Palmer, D.H.; Cicconi, S.; Halloran, C.; Psarelli, E.E.; Rawcliffe, C.L.; Sripadam, R.; Mukherjee, S.; Wadsley, J.; Al-Mukhtar, A.; et al. ESPAC-5F: Four-arm, prospective, multicenter, international randomized phase II trial of immediate surgery compared with neoadjuvant gemcitabine plus capecitabine (GEMCAP) or FOLFIRINOX or chemoradiotherapy (CRT) in patients with borderline resectable pancreatic cancer. J. Clin. Oncol. 2020, 38, 4505. [Google Scholar] [CrossRef]
  9. Sohal, D.; Lew, D.L.; Ahmad, S.A.; Gandhi, N.; Beg, M.S.; Wang-Gillam, A.; Wade, J.L.; Guthrie, K.A.; Lowy, A.M.; Philip, P.A.; et al. SWOG S1505: Initial findings on eligibility and neoadjuvant chemotherapy experience with mFOLFIRINOX versus gemcitabine/nab-paclitaxel for resectable pancreatic adenocarcinoma. J. Clin. Oncol. 2019, 37, 4137. [Google Scholar] [CrossRef]
  10. Unno, M.; Motoi, F.; Matsuyama, Y.; Satoi, S.; Matsumoto, I.; Aosasa, S.; Shirakawa, H.; Wada, K.; Fujii, T.; Yoshitomi, H.; et al. Randomized phase II/III trial of neoadjuvant chemotherapy with gemcitabine and S-1 versus upfront surgery for resectable pancreatic cancer (Prep-02/JSAP-05). J. Clin. Oncol. 2019, 37, 189. [Google Scholar] [CrossRef]
  11. O’Reilly, E.M.; Perelshteyn, A.; Jarnagin, W.R.; Schattner, M.; Gerdes, H.; Capanu, M.; Tang, L.H.; LaValle, J.; Winston, C.; DeMatteo, R.P.; et al. A Single-Arm, Nonrandomized Phase II Trial of Neoadjuvant Gemcitabine and Oxaliplatin in Patients with Resectable Pancreas Adenocarcinoma. Ann. Surg. 2014, 260, 142–148. [Google Scholar] [CrossRef][Green Version]
  12. Tajima, H.; Ohta, T.; Kitagawa, H.; Okamoto, K.; Sakai, S.; Makino, I.; Kinoshita, J.; Furukawa, H.; Nakamura, K.; Hayashi, H.; et al. Pilot study of neoadjuvant chemotherapy with gemcitabine and oral S-1 for resectable pancreatic cancer. Exp. Ther. Med. 2012, 3, 787–792. [Google Scholar] [CrossRef]
  13. Heinrich, S.; Pestalozzi, B.C.; Schäfer, M.; Weber, A.; Bauerfeind, P.; Knuth, A.; Clavien, P.-A. Prospective Phase II Trial of Neoadjuvant Chemotherapy with Gemcitabine and Cisplatin for Resectable Adenocarcinoma of the Pancreatic Head. J. Clin. Oncol. 2008, 26, 2526–2531. [Google Scholar] [CrossRef] [PubMed]
  14. Oba, A.; Ho, F.; Bao, Q.R.; Al-Musawi, M.H.; Schulick, R.D.; del Chiaro, M. Neoadjuvant Treatment in Pancreatic Cancer. Front. Oncol. 2020, 10, 245. [Google Scholar] [CrossRef] [PubMed]
  15. Corcoran, R.B.; Chabner, B.A. Application of Cell-free DNA Analysis to Cancer Treatment. N. Engl. J. Med. 2018, 379, 1754–1765. [Google Scholar] [CrossRef] [PubMed][Green Version]
  16. Kim, J.-E.; Lee, K.T.; Lee, J.K.; Paik, S.W.; Rhee, J.C.; Choi, K.W. Clinical usefulness of carbohydrate antigen 19-9 as a screening test for pancreatic cancer in an asymptomatic population. J. Gastroenterol. Hepatol. 2004, 19, 182–186. [Google Scholar] [CrossRef] [PubMed]
  17. Huang, Z.; Liu, F. Diagnostic value of serum carbohydrate antigen 19-9 in pancreatic cancer: A meta-analysis. Tumor Biol. 2014, 35, 7459–7465. [Google Scholar] [CrossRef] [PubMed]
  18. Goonetilleke, K.; Siriwardena, A.K. Systematic review of carbohydrate antigen (CA 19-9) as a biochemical marker in the diagnosis of pancreatic cancer. Eur. J. Surg. Oncol. (EJSO) 2007, 33, 266–270. [Google Scholar] [CrossRef] [PubMed]
  19. Frohlich, J.J. Serum Tumor Markers in Breast Cancer: Are They of Clinical Value? Clin. Chem. 2006, 52, 345–351. [Google Scholar] [CrossRef][Green Version]
  20. Singh, N.; Gupta, S.; Pandey, R.M.; Chauhan, S.S.; Saraya, A. High Levels of Cell-Free Circulating Nucleic Acids in Pancreatic Cancer are Associated With Vascular Encasement, Metastasis and Poor Survival. Cancer Investig. 2015, 33, 78–85. [Google Scholar] [CrossRef]
  21. Fazel, R.; Krumholz, H.M.; Wang, Y.; Ross, J.S.; Chen, J.; Ting, H.H.; Shah, N.D.; Nasir, K.; Einstein, A.J.; Nallamothu, B.K. Exposure to Low-Dose Ionizing Radiation from Medical Imaging Procedures. N. Engl. J. Med. 2009, 361, 849–857. [Google Scholar] [CrossRef][Green Version]
  22. Martini, V.; Timme, S.; Fichtner-Feigl, S.; Hoeppner, J.; Kulemann, B. Circulating Tumor Cells in Pancreatic Cancer: Current Perspectives. Cancers 2019, 11, 1659. [Google Scholar] [CrossRef][Green Version]
  23. Dawson, S.-J.; Tsui, D.W.; Murtaza, M.; Biggs, H.; Rueda, O.M.; Chin, S.-F.; Dunning, M.J.; Gale, D.; Forshew, T.; Mahler-Araujo, B.; et al. Analysis of Circulating Tumor DNA to Monitor Metastatic Breast Cancer. N. Engl. J. Med. 2013, 368, 1199–1209. [Google Scholar] [CrossRef] [PubMed][Green Version]
  24. Bettegowda, C.; Sausen, M.; Leary, R.J.; Kinde, I.; Wang, Y.; Agrawal, N.; Bartlett, B.R.; Wang, H.; Luber, B.; Alani, R.M.; et al. Detection of Circulating Tumor DNA in Early- and Late-Stage Human Malignancies. Sci. Transl. Med. 2014, 6, 224ra24. [Google Scholar] [CrossRef] [PubMed][Green Version]
  25. Sausen, M.; Phallen, J.; Adleff, V.; Jones, S.; Leary, R.J.; Barrett, M.T.; Anagnostou, V.; Parpart-Li, S.; Murphy, D.; Li, Q.K.; et al. Clinical implications of genomic alterations in the tumour and circulation of pancreatic cancer patients. Nat. Commun. 2015, 6, 7686. [Google Scholar] [CrossRef]
  26. Pietrasz, D.; Pécuchet, N.; Garlan, F.; Didelot, A.; Dubreuil, O.; Doat, S.; Imbert-Bismut, F.; Karoui, M.; Vaillant, J.-C.; Taly, V.; et al. Plasma Circulating Tumor DNA in Pancreatic Cancer Patients Is a Prognostic Marker. Clin. Cancer Res. 2017, 23, 116–123. [Google Scholar] [CrossRef] [PubMed][Green Version]
  27. Yi, X.; Ma, J.; Guan, Y.; Chen, R.; Yang, L.; Xia, X. The feasibility of using mutation detection in ctDNA to assess tumor dynamics. Int. J. Cancer 2017, 140, 2642–2647. [Google Scholar] [CrossRef]
  28. Abbosh, C.; The TRACERx Consortium; Birkbak, N.J.; Wilson, G.A.; Jamal-Hanjani, M.; Constantin, T.; Salari, R.; Le Quesne, J.; Moore, D.A.; Veeriah, S.; et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nat. Cell Biol. 2017, 545, 446–451. [Google Scholar] [CrossRef]
  29. Warton, K.; Mahon, K.L.; Samimi, G. Methylated circulating tumor DNA in blood: Power in cancer prognosis and response. Endocr. Relat. Cancer 2016, 23, R157–R171. [Google Scholar] [CrossRef][Green Version]
  30. Mandel, P.; Metais, P. Les acides nucléiques du plasma sanguin chez l’homme. Compt. Ren. Sean. Soc. Biol. 1948, 142, 241–243. [Google Scholar]
  31. Leon, S.A.; Shapiro, B.; Sklaroff, D.M.; Yaros, M.J. Free DNA in the serum of cancer patients and the effect of therapy. Cancer Res. 1977, 37, 646–650. [Google Scholar]
  32. Filho, E.M.R.; Simon, D.; Ikuta, N.; Klovan, C.; Dannebrock, F.A.; De Oliveira, C.O.; Regner, A. Elevated Cell-Free Plasma DNA Level as an Independent Predictor of Mortality in Patients with Severe Traumatic Brain Injury. J. Neurotrauma 2014, 31, 1639–1646. [Google Scholar] [CrossRef][Green Version]
  33. Tsai, N.-W.; Lin, T.-K.; Chen, S.-D.; Chang, W.-N.; Wang, H.-C.; Yang, T.-M.; Lin, Y.-J.; Jan, C.-R.; Huang, C.-R.; Liou, C.-W.; et al. The value of serial plasma nuclear and mitochondrial DNA levels in patients with acute ischemic stroke. Clin. Chim. Acta 2011, 412, 476–479. [Google Scholar] [CrossRef] [PubMed]
  34. Breitbach, S.; Sterzing, B.; Magallanes, C.; Tug, S.; Simon, P. Direct measurement of cell-free DNA from serially collected capillary plasma during incremental exercise. J. Appl. Physiol. 2014, 117, 119–130. [Google Scholar] [CrossRef] [PubMed][Green Version]
  35. De Vlaminck, I.; Valantine, H.A.; Snyder, T.M.; Strehl, C.; Cohen, G.; Luikart, H.; Neff, N.F.; Okamoto, J.; Bernstein, D.; Weisshaar, D.; et al. Circulating Cell-Free DNA Enables Noninvasive Diagnosis of Heart Transplant Rejection. Sci. Transl. Med. 2014, 6, 241ra77. [Google Scholar] [CrossRef] [PubMed][Green Version]
  36. Lo, Y.M.D.; Corbetta, N.; Chamberlain, P.F.; Rai, V.; Sargent, I.L.; Redman, C.W.; Wainscoat, J.S. Presence of fetal DNA in maternal plasma and serum. Lancet 1997, 350, 485–487. [Google Scholar] [CrossRef]
  37. Lo, Y.M.D.; Lun, F.M.F.; Chan, K.C.A.; Tsui, N.B.Y.; Chong, K.C.; Lau, T.K.; Leung, T.Y.; Zee, B.C.Y.; Cantor, C.R.; Chiu, R.W.K. Digital PCR for the molecular detection of fetal chromosomal aneuploidy. Proc. Natl. Acad. Sci. USA 2007, 104, 13116–13121. [Google Scholar] [CrossRef][Green Version]
  38. Allyse, M.; Minear, M.A.; Rote, M.; Hung, A.; Chandrasekharan, S.; Berson, E.; Sridhar, S. Non-invasive prenatal testing: A review of international implementation and challenges. Int. J. Women’s Heal. 2015, 7, 113–126. [Google Scholar] [CrossRef][Green Version]
  39. Hill, M.; Wright, D.; Daley, R.; Lewis, C.; McKay, F.; Mason, S.; Lench, N.J.; Howarth, A.; Boustred, C.; Lo, K.; et al. Evaluation of non-invasive prenatal testing (NIPT) for aneuploidy in an NHS setting: A reliable accurate prenatal non-invasive diagnosis (RAPID) protocol. BMC Pregnancy Childbirth 2014, 14, 229. [Google Scholar] [CrossRef][Green Version]
  40. Hyett, J.A.; Gardener, G.; Stojilkovic-Mikic, T.; Finning, K.M.; Martin, P.G.; Rodeck, C.H.; Chitty, L.S. Reduction in diagnostic and therapeutic interventions by non-invasive determination of fetal sex in early pregnancy. Prenat. Diagn. 2005, 25, 1111–1116. [Google Scholar] [CrossRef]
  41. Stroun, M.; Anker, P.; Maurice, P.; Lyautey, J.; Lederrey, C.; Beljanski, M. Neoplastic Characteristics of the DNA Found in the Plasma of Cancer Patients. Oncology 1989, 46, 318–322. [Google Scholar] [CrossRef]
  42. Fan, H.C.; Blumenfeld, Y.J.; Chitkara, U.; Hudgins, L.; Quake, S.R. Analysis of the Size Distributions of Fetal and Maternal Cell-Free DNA by Paired-End Sequencing. Clin. Chem. 2010, 56, 1279–1286. [Google Scholar] [CrossRef][Green Version]
  43. Snyder, M.W.; Kircher, M.; Hill, A.J.; Daza, R.M.; Shendure, J. Cell-free DNA Comprises an In Vivo Nucleosome Footprint that Informs Its Tissues-Of-Origin. Cell 2016, 164, 57–68. [Google Scholar] [CrossRef] [PubMed][Green Version]
  44. Sun, K.; Jiang, P.; Chan, K.C.A.; Wong, J.; Cheng, Y.K.Y.; Liang, R.H.S.; Chan, W.-K.; Ma, E.S.K.; Chan, S.L.; Cheng, S.H.; et al. Plasma DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments. Proc. Natl. Acad. Sci. USA 2015, 112, E5503–E5512. [Google Scholar] [CrossRef] [PubMed][Green Version]
  45. Tie, J.; Kinde, I.; Wang, Y.; Wong, H.L.; Roebert, J.; Christie, M.; Tacey, M.; Wong, R.; Singh, M.; Karapetis, C.S.; et al. Circulating tumor DNA as an early marker of therapeutic response in patients with metastatic colorectal cancer. Ann. Oncol. 2015, 26, 1715–1722. [Google Scholar] [CrossRef] [PubMed]
  46. Xi, L.; Pham, T.H.-T.; Payabyab, E.C.; Sherry, R.M.; Rosenberg, S.A.; Raffeld, M. Circulating Tumor DNA as an Early Indicator of Response to T-cell Transfer Immunotherapy in Metastatic Melanoma. Clin. Cancer Res. 2016, 22, 5480–5486. [Google Scholar] [CrossRef][Green Version]
  47. Riediger, A.L.; Dietz, S.; Schirmer, U.; Meister, M.; Heinzmann-Groth, I.; Schneider, M.; Muley, T.; Thomas, M.; Sültmann, H. Mutation analysis of circulating plasma DNA to determine response to EGFR tyrosine kinase inhibitor therapy of lung adenocarcinoma patients. Sci. Rep. 2016, 6, 33505. [Google Scholar] [CrossRef][Green Version]
  48. Diehl, F.; Schmidt, K.; Choti, M.A.; Romans, K.; Goodman, S.; Li, M.; Thornton, K.; Agrawal, N.; Sokoll, L.; Szabo, S.A.; et al. Circulating mutant DNA to assess tumor dynamics. Nat. Med. 2008, 14, 985–990. [Google Scholar] [CrossRef]
  49. Tug, S.; Helmig, S.; Deichmann, E.R.; Schmeier-Jürchott, A.; Wagner, E.; Zimmermann, T.; Radsak, M.; Giacca, M.; Simon, P. Exercise-induced increases in cell free DNA in human plasma originate predominantly from cells of the haematopoietic lineage. Exerc. Immunol. Rev. 2015, 21, 164–173. [Google Scholar]
  50. Lo, Y.M.D.; Zhang, J.; Leung, T.N.; Lau, T.K.; Chang, A.M.; Hjelm, N.M. Rapid Clearance of Fetal DNA from Maternal Plasma. Am. J. Hum. Genet. 1999, 64, 218–224. [Google Scholar] [CrossRef][Green Version]
  51. El Messaoudi, S.; Rolet, F.; Mouliere, F.; Thierry, A.R. Circulating cell free DNA: Preanalytical considerations. Clin. Chim. Acta 2013, 424, 222–230. [Google Scholar] [CrossRef]
  52. Swinkels, D.W.; Wiegerinck, E.; Steegers, E.A.; De Kok, J.B. Effects of Blood-Processing Protocols on Cell-free DNA Quantification in Plasma. Clin. Chem. 2003, 49, 525–526. [Google Scholar] [CrossRef][Green Version]
  53. Alidousty, C.; Brandes, D.; Heydt, C.; Wagener, S.; Wittersheim, M.; Schäfer, S.C.; Holz, B.; Merkelbach-Bruse, S.; Büttner, R.; Fassunke, J.; et al. Comparison of Blood Collection Tubes from Three Different Manufacturers for the Collection of Cell-Free DNA for Liquid Biopsy Mutation Testing. J. Mol. Diagn. 2017, 19, 801–804. [Google Scholar] [CrossRef] [PubMed][Green Version]
  54. Risberg, B.; Tsui, D.W.Y.; Biggs, H.; De Almagro, A.R.-V.M.; Dawson, S.-J.; Hodgkin, C.; Jones, L.; Parkinson, C.; Piskorz, A.; Marass, F.; et al. Effects of Collection and Processing Procedures on Plasma Circulating Cell-Free DNA from Cancer Patients. J. Mol. Diagn. 2018, 20, 883–892. [Google Scholar] [CrossRef] [PubMed][Green Version]
  55. Warton, K.; Yuwono, N.; Cowley, M.J.; McCabe, M.J.; So, A.; Ford, C.E. Evaluation of Streck BCT and PAXgene Stabilised Blood Collection Tubes for Cell-Free Circulating DNA Studies in Plasma. Mol. Diagn. Ther. 2017, 21, 563–570. [Google Scholar] [CrossRef] [PubMed]
  56. Diaz, I.M.; Nocon, A.; Mehnert, D.H.; Fredebohm, J.; Diehl, F.; Holtrup, F. Performance of Streck cfDNA Blood Collection Tubes for Liquid Biopsy Testing. PLoS ONE 2016, 11, e0166354. [Google Scholar] [CrossRef]
  57. Razavi, P.; Li, B.T.; Brown, D.N.; Jung, B.; Hubbell, E.; Shen, R.; Abida, W.; Juluru, K.; De Bruijn, I.; Hou, C.; et al. High-intensity sequencing reveals the sources of plasma circulating cell-free DNA variants. Nat. Med. 2019, 25, 1928–1937. [Google Scholar] [CrossRef]
  58. Liu, M.; Oxnard, G.; Klein, E.; Swanton, C.; Seiden, M.; Cummings, S.R.; Absalan, F.; Alexander, G.; Allen, B.; Amini, H.; et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 2020, 31, 745–759. [Google Scholar] [CrossRef]
  59. Diehl, F.; Li, M.; He, Y.; Kinzler, K.W.; Vogelstein, B.; Dressman, D. BEAMing: Single-molecule PCR on microparticles in water-in-oil emulsions. Nat. Methods 2006, 3, 551–559. [Google Scholar] [CrossRef]
  60. Forshew, T.; Murtaza, M.; Parkinson, C.; Gale, D.; Tsui, D.W.Y.; Kaper, F.; Dawson, S.-J.; Piskorz, A.M.; Jimenez-Linan, M.; Bentley, D.; et al. Noninvasive Identification and Monitoring of Cancer Mutations by Targeted Deep Sequencing of Plasma DNA. Sci. Transl. Med. 2012, 4, 136ra68. [Google Scholar] [CrossRef]
  61. Taly, V.; Pekin, D.; Benhaim, L.; Kotsopoulos, S.K.; Le Corre, D.; Li, X.; Atochin, I.; Link, D.R.; Griffiths, A.D.; Pallier, K.; et al. Multiplex Picodroplet Digital PCR to Detect KRAS Mutations in Circulating DNA from the Plasma of Colorectal Cancer Patients. Clin. Chem. 2013, 59, 1722–1731. [Google Scholar] [CrossRef]
  62. Hindson, B.J.; Ness, K.D.; Masquelier, D.A.; Belgrader, P.; Heredia, N.J.; Makarewicz, A.J.; Bright, I.J.; Lucero, M.Y.; Hiddessen, A.L.; Legler, T.C.; et al. High-Throughput Droplet Digital PCR System for Absolute Quantitation of DNA Copy Number. Anal. Chem. 2011, 83, 8604–8610. [Google Scholar] [CrossRef]
  63. Adalsteinsson, V.A.; Ha, G.; Freeman, S.S.; Choudhury, A.D.; Stover, D.; Parsons, H.A.; Gydush, G.; Reed, S.C.; Rotem, D.; Rhoades, J.; et al. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. Nat. Commun. 2017, 8, 1–13. [Google Scholar] [CrossRef] [PubMed][Green Version]
  64. Murtaza, M.; Dawson, S.-J.; Tsui, D.W.Y.; Gale, D.; Forshew, T.; Piskorz, A.M.; Parkinson, C.; Chin, S.-F.; Kingsbury, Z.; Wong, A.S.C.; et al. Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nat. Cell Biol. 2013, 497, 108–112. [Google Scholar] [CrossRef] [PubMed]
  65. Kinde, I.; Wu, J.; Papadopoulos, N.; Kinzler, K.W.; Vogelstein, B. Detection and quantification of rare mutations with massively parallel sequencing. Proc. Natl. Acad. Sci. USA 2011, 108, 9530–9535. [Google Scholar] [CrossRef] [PubMed][Green Version]
  66. Newman, A.M.; Lovejoy, A.F.; Klass, D.M.; Kurtz, D.M.; Chabon, J.J.; Scherer, F.; Stehr, H.; Liu, C.L.; Bratman, S.V.; Say, C.; et al. Integrated digital error suppression for noninvasive detection of circulating tumor DNA in NSCLC. J. Clin. Oncol. 2016, 34, e20500. [Google Scholar] [CrossRef]
  67. Shapiro, B.; Chakrabarty, M.; Cohn, E.M.; Leon, S.A. Determination of circulating DNA levels in patients with benign or malignant gastrointestinal disease. Cancer 1983, 51, 2116–2120. [Google Scholar] [CrossRef]
  68. Sorenson, G.D.; Pribish, D.M.; Valone, F.H.; Memoli, V.A.; Bzik, D.J.; Yao, S.L. Soluble normal and mutated DNA sequences from single-copy genes in human blood. Cancer Epidemiology Biomarkers Prev. 1994, 3, 67–71. [Google Scholar]
  69. Kim, J.; Reber, H.A.; Dry, S.M.; Elashoff, D.; Chen, S.L.; Umetani, N.; Kitago, M.; Hines, O.J.; Kazanjian, K.K.; Hiramatsu, S.; et al. Unfavourable prognosis associated with K-ras gene mutation in pancreatic cancer surgical margins. Gut 2006, 55, 1598–1605. [Google Scholar] [CrossRef][Green Version]
  70. Talar-Wojnarowska, R.; Gasiorowska, A.; Smolarz, B.; Romanowicz-Makowska, H.; Strzelczyk, J.; Janiak, A.; Kulig, A.; Malecka-Panas, E. Usefulness of p16 and K-ras mutation in pancreatic adenocarcinoma and chronic pancreatitis differential diagnosis. J. Physiol. Pharmacol. Off. J. Pol. Physiol. Soc. 2004, 55, 129–138. [Google Scholar]
  71. Maire, F.; Micard, S.; Hammel, P.; Voitot, H.; Levy, P.E.; Cugnenc, P.-H.; Ruszniewski, P.; Puig, P.L. Differential diagnosis between chronic pancreatitis and pancreatic cancer: Value of the detection of KRAS2 mutations in circulating DNA. Br. J. Cancer 2002, 87, 551–554. [Google Scholar] [CrossRef][Green Version]
  72. Feng, D.-X.; Shengdao, Z.; Tianquan, H.; Yu, J.; Ruoqing, L.; Zurong, Y.; Xuezhi, W. A Prospective Study of Detection of Pancreatic Carcinoma by Combined Plasma K -ras Mutations and Serum CA19-9 Analysis. Pancreas 2002, 25, 336–341. [Google Scholar] [CrossRef]
  73. Tjensvoll, K.; Lapin, M.; Buhl, T.; Oltedal, S.; Berry, K.S.-O.; Gilje, B.; Søreide, J.A.; Javle, M.; Nordgård, O.; Smaaland, R. Clinical relevance of circulating KRAS mutated DNA in plasma from patients with advanced pancreatic cancer. Mol. Oncol. 2015, 10, 635–643. [Google Scholar] [CrossRef] [PubMed][Green Version]
  74. Berger, A.W.; Schwerdel, D.; Costa, I.G.; Hackert, T.; Strobel, O.; Lam, S.; Barth, T.F.; Schröppel, B.; Meining, A.; Büchler, M.W.; et al. Detection of Hot-Spot Mutations in Circulating Cell-Free DNA From Patients With Intraductal Papillary Mucinous Neoplasms of the Pancreas. Gastroenterology 2016, 151, 267–270. [Google Scholar] [CrossRef] [PubMed]
  75. Brychta, N.; Krahn, T.; Von Ahsen, O. Detection of KRAS Mutations in Circulating Tumor DNA by Digital PCR in Early Stages of Pancreatic Cancer. Clin. Chem. 2016, 62, 1482–1491. [Google Scholar] [CrossRef] [PubMed][Green Version]
  76. Calvez-Kelm, F.; Foll, M.; Wozniak, M.B.; Delhomme, T.M.; Durand, G.; Chopard, P.; Pertesi, M.; Fabianova, E.; Adamcakova, Z.; Holcatova, I.; et al. KRAS mutations in blood circulating cell-free DNA: A pancreatic cancer case-control. Oncotarget 2016, 7, 78827–78840. [Google Scholar] [CrossRef] [PubMed][Green Version]
  77. Takai, E.; Totoki, Y.; Nakamura, H.; Kato, M.; Shibata, T.; Yachida, S. Clinical Utility of Circulating Tumor DNA for Molecular Assessment and Precision Medicine in Pancreatic Cancer. Adv. Exp. Med. Biol. 2016, 924, 13–17. [Google Scholar] [CrossRef] [PubMed]
  78. Cohen, J.D.; Javed, A.A.; Thoburn, C.; Wong, F.; Tie, J.; Gibbs, P.; Schmidt, C.M.; Yip-Schneider, M.T.; Allen, P.J.; Schattner, M.; et al. Combined circulating tumor DNA and protein biomarker-based liquid biopsy for the earlier detection of pancreatic cancers. Proc. Natl. Acad. Sci. USA 2017, 114, 10202–10207. [Google Scholar] [CrossRef] [PubMed][Green Version]
  79. Cheng, H.; Liu, C.; Jiang, J.; Luo, G.; Lu, Y.; Jin, K.; Guo, M.; Zhang, Z.; Xu, J.; Liu, L.; et al. Analysis of ctDNA to predict prognosis and monitor treatment responses in metastatic pancreatic cancer patients. Int. J. Cancer 2017, 140, 2344–2350. [Google Scholar] [CrossRef]
  80. Cohen, J.D.; Li, L.; Wang, Y.; Thoburn, C.; Afsari, B.; Danilova, L.; Douville, C.; Javed, A.A.; Wong, F.; Mattox, A.; et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 2018, 359, 926–930. [Google Scholar] [CrossRef][Green Version]
  81. Chen, L.; Zhang, Y.; Cheng, Y.; Zhang, D.; Zhu, S.; Ma, X. Prognostic value of circulating cell-free DNA in patients with pancreatic cancer: A systemic review and meta-analysis. Gene 2018, 679, 328–334. [Google Scholar] [CrossRef]
  82. Bernard, V.; Kim, D.U.; Lucas, F.A.S.; Castillo, J.; Allenson, K.; Mulu, F.C.; Stephens, B.M.; Huang, J.; Semaan, A.; Guerrero, P.A.; et al. Circulating Nucleic Acids Are Associated With Outcomes of Patients With Pancreatic Cancer. Gastroenterology 2019, 156, 108–118.e4. [Google Scholar] [CrossRef][Green Version]
  83. Berger, A.W.; Schwerdel, D.; Reinacher-Schick, A.; Uhl, W.; Algül, H.; Friess, H.; Janssen, K.-P.; König, A.; Ghadimi, M.; Gallmeier, E.; et al. A Blood-Based Multi Marker Assay Supports the Differential Diagnosis of Early-Stage Pancreatic Cancer. Theranostics 2019, 9, 1280–1287. [Google Scholar] [CrossRef] [PubMed]
  84. Liu, X.; Liu, L.; Ji, Y.; Li, C.; Wei, T.; Yang, X.; Zhang, Y.; Cai, X.; Gao, Y.; Xu, W.; et al. Enrichment of short mutant cell-free DNA fragments enhanced detection of pancreatic cancer. EBioMedicine 2019, 41, 345–356. [Google Scholar] [CrossRef] [PubMed][Green Version]
  85. Liu, M.C.; Maddala, T.; Aravanis, A.; Hubbell, E.; Beausang, J.F.; Filippova, D.; Gross, S.; Jamshidi, A.; Kurtzman, K.; Shen, L.; et al. Breast cancer cell-free DNA (cfDNA) profiles reflect underlying tumor biology: The Circulating Cell-Free Genome Atlas (CCGA) study. J. Clin. Oncol. 2018, 36, 536. [Google Scholar] [CrossRef][Green Version]
  86. Fernandez-Cuesta, L.; Perdomo, S.; Avogbe, P.H.; Leblay, N.; Delhomme, T.M.; Gaborieau, V.; Abedi-Ardekani, B.; Chanudet, E.; Olivier, M.; Zaridze, D.; et al. Identification of Circulating Tumor DNA for the Early Detection of Small-cell Lung Cancer. EBioMedicine 2016, 10, 117–123. [Google Scholar] [CrossRef][Green Version]
  87. Fiala, C.; Diamandis, E. Utility of circulating tumor DNA in cancer diagnostics with emphasis on early detection. BMC Med. 2018, 16, 1–10. [Google Scholar] [CrossRef][Green Version]
  88. Zhou, W.; Sokoll, L.J.; Bruzek, D.J.; Zhang, L.; Velculescu, V.E.; Goldin, S.B.; Hruban, R.H.; Kern, S.E.; Hamilton, S.R.; Chan, D.W.; et al. Identifying markers for pancreatic cancer by gene expression analysis. Cancer Epidemiology Biomarkers Prev. 1998, 7, 109–112. [Google Scholar]
  89. Capello, M.; Bantis, L.E.; Scelo, G.; Zhao, Y.; Li, P.; Dhillon, D.S.; Patel, N.J.; Kundnani, D.L.; Wang, H.; Abbruzzese, J.L.; et al. Sequential Validation of Blood-Based Protein Biomarker Candidates for Early-Stage Pancreatic Cancer. J. Natl. Cancer Inst. 2017, 109. [Google Scholar] [CrossRef]
  90. Young, R.P.; Christmas, T.; Hopkins, R.J. Multi-analyte assays and early detection of common cancers. J. Thorac. Dis. 2018, 10, S2165–S2167. [Google Scholar] [CrossRef]
  91. Pannala, R.; Leirness, J.B.; Bamlet, W.R.; Basu, A.; Petersen, G.M.; Chari, S.T. Prevalence and Clinical Profile of Pancreatic Cancer–Associated Diabetes Mellitus. Gastroenterology 2008, 134, 981–987. [Google Scholar] [CrossRef][Green Version]
  92. Kirkegård, J.; Mortensen, F.V.; Cronin-Fenton, D. Chronic Pancreatitis and Pancreatic Cancer Risk: A Systematic Review and Meta-analysis. Am. J. Gastroenterol. 2017, 112, 1366–1372. [Google Scholar] [CrossRef][Green Version]
  93. Bamlet, W.R.; Chaffee, K.G.; Olswold, C.; De Andrade, M.; Petersen, G.M. Risk of malignancy in first-degree relatives of patients with pancreatic carcinoma. Cancer 2005, 104, 388–394. [Google Scholar] [CrossRef]
  94. Ben, Q.; Xu, M.; Ning, X.; Liu, J.; Hong, S.; Huang, W.; Zhang, H.; Li, Z. Diabetes mellitus and risk of pancreatic cancer: A meta-analysis of cohort studies. Eur. J. Cancer 2011, 47, 1928–1937. [Google Scholar] [CrossRef] [PubMed]
  95. Hu, C.; Hart, S.N.; Polley, E.C.; Gnanaolivu, R.; Shimelis, H.; Lee, K.Y.; Lilyquist, J.; Na, J.; Moore, R.M.; Antwi, S.O.; et al. Association Between Inherited Germline Mutations in Cancer Predisposition Genes and Risk of Pancreatic Cancer. JAMA 2018, 319, 2401–2409. [Google Scholar] [CrossRef] [PubMed]
  96. Kanwal, R.; Gupta, S. Epigenetic modifications in cancer. Clin. Genet. 2012, 81, 303–311. [Google Scholar] [CrossRef][Green Version]
  97. Grønbæk, K.; Hother, C.; Jones, P.A. Epigenetic changes in cancer. APMIS 2007, 115, 1039–1059. [Google Scholar] [CrossRef]
  98. Sharma, S.; Kelly, T.K.; Jones, P.A. Epigenetics in cancer. Carcinogenesis 2009, 31, 27–36. [Google Scholar] [CrossRef]
  99. Baylin, S.B.; Jones, P.A. A decade of exploring the cancer epigenome—Biological and translational implications. Nat. Rev. Cancer 2011, 11, 726–734. [Google Scholar] [CrossRef]
  100. Montavon, C.; Gloss, B.S.; Warton, K.; Barton, C.A.; Statham, A.L.; Scurry, J.P.; Tabor, B.; Nguyen, T.V.; Qu, W.; Samimi, G.; et al. Prognostic and diagnostic significance of DNA methylation patterns in high grade serous ovarian cancer. Gynecol. Oncol. 2012, 124, 582–588. [Google Scholar] [CrossRef]
  101. Henriksen, S.D.; Madsen, P.H.; Larsen, A.C.; Johansen, M.B.; Drewes, A.M.; Pedersen, I.S.; Krarup, H.B.; Thorlacius-Ussing, O. Cell-free DNA promoter hypermethylation in plasma as a diagnostic marker for pancreatic adenocarcinoma. Clin. Epigenetics 2016, 8, 1–12. [Google Scholar] [CrossRef][Green Version]
  102. Liggett, T.; Melnikov, A.; Yi, Q.-L.; Replogle, C.; Brand, R.; Kaul, K.; Talamonti, M.; Abrams, R.A.; Levenson, V. Differential methylation of cell-free circulating DNA among patients with pancreatic cancer versus chronic pancreatitis. Cancer 2010, 116, 1674–1680. [Google Scholar] [CrossRef]
  103. Eissa, M.A.L.; Lerner, L.; Abdelfatah, E.; Shankar, N.; Canner, J.K.; Hasan, N.M.; Yaghoobi, V.; Huang, B.; Kerner, Z.; Takaesu, F.; et al. Promoter methylation of ADAMTS1 and BNC1 as potential biomarkers for early detection of pancreatic cancer in blood. Clin. Epigenetics 2019, 11, 1–10. [Google Scholar] [CrossRef] [PubMed]
  104. Chan, K.C.A.; Jiang, P.; Chan, C.W.M.; Sun, K.; Wong, J.; Hui, E.P.; Chan, S.L.; Chan, W.C.; Hui, D.S.C.; Ng, S.S.M.; et al. Noninvasive detection of cancer-associated genome-wide hypomethylation and copy number aberrations by plasma DNA bisulfite sequencing. Proc. Natl. Acad. Sci. USA 2013, 110, 18761–18768. [Google Scholar] [CrossRef] [PubMed][Green Version]
  105. Grunau, C. Bisulfite genomic sequencing: Systematic investigation of critical experimental parameters. Nucleic Acids Res. 2001, 29, e65. [Google Scholar] [CrossRef] [PubMed]
  106. Shen, S.Y.; Singhania, R.; Fehringer, G.; Chakravarthy, A.; Roehrl, M.H.A.; Chadwick, D.; Zuzarte, P.C.; Borgida, A.; Wang, T.T.; Li, T.; et al. Sensitive tumour detection and classification using plasma cell-free DNA methylomes. Nat. Cell Biol. 2018, 563, 579–583. [Google Scholar] [CrossRef]
  107. Suzuki, M.M.; Bird, A. DNA methylation landscapes: Provocative insights from epigenomics. Nat. Rev. Genet. 2008, 9, 465–476. [Google Scholar] [CrossRef]
  108. Ibn Sina, A.A.; Carrascosa, L.G.; Liang, Z.; Grewal, Y.S.; Wardiana, A.; Shiddiky, M.J.A.; Gardiner, R.A.; Samaratunga, H.; Gandhi, M.K.; Scott, R.J.; et al. Epigenetically reprogrammed methylation landscape drives the DNA self-assembly and serves as a universal cancer biomarker. Nat. Commun. 2018, 9, 1–13. [Google Scholar] [CrossRef][Green Version]
  109. Cho, H.; Mariotto, A.B.; Schwartz, L.M.; Luo, J.; Woloshin, S. When Do Changes in Cancer Survival Mean Progress? The Insight from Population Incidence and Mortality. J. Natl. Cancer Inst. Monogr. 2014, 2014, 187–197. [Google Scholar] [CrossRef][Green Version]
Figure 1. Potential cancer biomarkers present in the blood and detectable using liquid biopsies. Key biomarkers that are currently used in an attempt to detect early stage cancer are shown.
Figure 1. Potential cancer biomarkers present in the blood and detectable using liquid biopsies. Key biomarkers that are currently used in an attempt to detect early stage cancer are shown.
Cancers 12 03704 g001
Table 1. Clinical studies investigating ctDNA in pancreatic cancer.
Table 1. Clinical studies investigating ctDNA in pancreatic cancer.
AuthorCancer TypeDetection MethodInterpretationNumber of Patients (n)SensitivitySpecificityDiagnostic Targets
Sorenson et al., 1994 [68]Different stages of PDACPCRKRAS mutation found in blood of patients with PC, the mutations were identical with those in tumour biopsyN/AN/AN/AN/A
Maire et al., 2002 [71]Late stages of PCPCRKRAS2 mutation detected in 47% patients with PC, and in 13% of chronic pancreatitis; analysis of KRAS2 + CA19.9 increased sensitivity to 98% and specificity to 77%47 PC, 31 ctrl47%/98% with CA19–913% in ctrl (specificity of 77% with CA19–9)KRAS2
Dianxu et al., 2002 [72]Early stages of PCPCR-RFLPKRAS mutation found in 70.7% of patients with PC but not in 21 controls. Combined with CA19–9, proportion increased to 90.2%58 + 21 ctrl70.7%/90.2% with CA19–9N/AKRAS/KRAS + CA19–9
Singh et al., 2015 [20]All stages of PCPCR-RFLPctDNA exceeding 62 ng/mL linked to lower OS and metastasisN/AN/AN/AN/A
Sausen et al., 2015 [25]Stage II of PCNGS, ddPCRctDNA detected in 43% of patients, ctDNA linked to an adverse prognosis and predicted relapse 6,5 months before CT detectionwhole-exome analyses of 24 tumours, targeted genomic analyses of 77N/AN/AN/A
Tjensvoll et al., 2016 [73]All stages of PCPNA-clamp PCRKRAS mutation detected in 71% patients, ctDNA corresponded to CT results and CA19–9 levels14 patients with several blood samples71%N/AKRAS mutations
Berger et al., 2016 [74]Metastatic PCddPCRKRAS mutation in 41.7% of patients, 0% in control population21 IPMN patients; 38 controls; 24 metastatic PDAC, 26 resected SCA; 16 borderline IPMN41.7%84.2%KRASG12.D and KRASG12.V
Brychta et al., 2016 * [75]Early stage pancreatic cancer (mostly stage I & II)ChIP-based digital PCRDetection rates varied between 0% and 50% for specific mutations. KRAS mutation not detected in healthy patients50 (82% of stage I & II)72% (based on both liquid and standard biopsy)N/AKRASG12.D, KRASG12.V, and KRASG12.C mutations in blood and tumour samples
Le Calvez-Kelm et al., 2016 * [76]All stages of PCNGSKRAS cfDNA mutations detected in 21.1% of cancers. No improvement over CA19–9437 PC cases, 141 chronic pancreatitis subjects, 394 healthy controls21.1%96.3%KRAS, CA 19–9
Takai et al., 2016 * [77]All stages of PCddPCR and NGSddPCR detected KRAS mutation in 58.9% of non-resectable PC25958.8% in inoperable tumoursN/AKRAS mutations
Cohen et al., 2017 * [78]Resectable PDACPCR-based test and protein biomarkersKRAS mutation detected in 30% of PC patients (66/221), in 66% when combined with protein biomarkers221 with resectable PC, 182 controls30% (only KRAS), 66%: KRAS + four protein biomarkersN/AKRAS mutations and protein biomarkers
Cheng et al., 2017 [79]Metastatic PCddPCR, NGS72.3% of PC patients presented with ctDNA-detected KRAS mutation10: exome seq, 188 ddPCR,76.9%N/A60 genes screened
Pietrasz et al., 2017 [26]All stages of PCNGS, ddPCRctDNA found in 48% of patients with PC, the presence of ctDNA was a predictor of an adverse prognosis135, 31 resectable48%N/AN/A
Cohen et al., 2018 * [80]I–III stages of PCNGSHighly efficient multi-analyte test used1005 patients of 8 cancer types, stage I–III76% (any stage)99%CancerSEEK tested for 8 cancer types; 8 protein biomarkers and mutations in 1933 distinct genomic positions
Chen et al., 2018 [81]All stages of PCMeta-analysis of literature; significant in predicting OS and PFSThe presence of ctDNA or elevated cfDNA linked to poor prognosis1243 from 18 articlesN/AN/AN/A
Bernard et al., 2019 [82]Localised or metastatic PCddPCR from ctDNA and exosomesctDNA showed no correlation with outcomes, as opposed to exosome levels. However, detection of ctDNA post-resection correlated with lower PFS and OS194 (overall receiving treatment, also with metastasis), 34 with resectable PCN/AN/AN/A
Berger et al., 2019 * [83]Resectable PCFluorimetry (HS Assay for cfDNA quantification)CA19–9, THBS2 and cfDNA levels in combination were a better PC biomarker (c-statistics 0.90) than any of those separately5290%N/Athrombospondin-2 (THBS2), CA19–9
Liu et al., 2019 * [84]Mostly stage I and IIhybrid-capture-based cfDNA sequencing (SLHC-seq)ctDNA fragmentation pattern may affect the detection of early PC; cancer-specific mutations found in 88% patients, KRAS hotspots in 70%11288%8 mutations detected in 28 heathy controls791 cancer-specific mutations
* Early PDAC detection study. CA19–9, carbohydrate antigen 19–9; cfDNA, cell-free DNA; ctDNA, circulating tumour DNA; ctrl, control; ddPCR, digital droplet PCR; IPMN, intraductal papillary mucinous neoplasms; NGS, new-generation sequencing; OS, overall survival; PC, pancreatic cancer; PCR, polymerase chain reaction; PCR-RFLP, PCR Restriction Fragment Length Polymorphism; PDAC, pancreatic ductal adenocarcinoma; PFS, progression-free survival; SCA, serous cystadenoma.
Table 2. Selected methylation studies of ctDNA in pancreatic cancer.
Table 2. Selected methylation studies of ctDNA in pancreatic cancer.
AuthorCancer TypeDetection MethodInterpretationNumber of Patients (n)SensitivitySpecificityDiagnostic Targets
Liggett et al., 2010 [102]All stages of PC, chronic pancreatitisMethylation-specific PCR17 gene promoters were indicated as informative to differentiate between chronic pancreatitis and PC30 overall91.2%90.8%56 fragments in each sample (MethDet56)
Henriksen et al., 2016 [101]All stages of PCMethylation-specific PCRThe number of tested methylated genes was significantly (p < 0.001) higher in cancer patients than in control97 PDAC76%83%28 gene panel
Eissa et al., 2019 [103]All stages of PCMethylation on beadsMethylation of ADAMTS1 and BNC1 is a reliable marker for early detection of PC3997.3%91.6%ADAMTS1 and BNC1
Liu et al., 2020 [58]All stages, data shown here for stage IBisulfite sequencingctDNA is a good diagnosis method for early stage PDAC and for identification of cancer origin2063%99%panel of > 100,000 methylation regions
ctDNA, circulating tumour DNA; PC, pancreatic cancer; PDAC, pancreatic ductal adenocarcinoma.
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Jaworski, J.J.; Morgan, R.D.; Sivakumar, S. Circulating Cell-Free Tumour DNA for Early Detection of Pancreatic Cancer. Cancers 2020, 12, 3704.

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Jaworski JJ, Morgan RD, Sivakumar S. Circulating Cell-Free Tumour DNA for Early Detection of Pancreatic Cancer. Cancers. 2020; 12(12):3704.

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Jaworski, Jedrzej J., Robert D. Morgan, and Shivan Sivakumar. 2020. "Circulating Cell-Free Tumour DNA for Early Detection of Pancreatic Cancer" Cancers 12, no. 12: 3704.

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