Liquid Biopsy Technology and Implications for Pancreatic Cancer: History
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Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Alexander G. Raufi
,
Michael S. May
,
Matthew J. Hadfield
,
Attila A. Seyhan
,
Wafik S. El-Deiry

Pancreatic cancer is a highly aggressive malignancy with a climbing incidence. The majority of cases are detected late, with incurable locally advanced or metastatic disease. Even in individuals who undergo resection, recurrence is unfortunately very common. There is no universally accepted screening modality for the general population and diagnosis, evaluation of treatment response, and detection of recurrence relies primarily on the use of imaging. Identification of minimally invasive techniques to help diagnose, prognosticate, predict response or resistance to therapy, and detect recurrence are desperately needed. Liquid biopsies represent an emerging group of technologies which allow for non-invasive serial sampling of tumor material. 

  • liquid biopsy
  • pancreatic ductal adenocarcinoma
  • ctDNAs

1. ctDNA

The term cell free DNA (cfDNA) is used to describe extracellular DNA isolated from blood or other bodily fluids. cfDNA arising from malignant cells is more specifically referred to as circulating tumor DNA (ctDNA) and contains cancer-specific genetic alterations. cfDNA and ctDNA are released from normal and malignant cells either through cell death processes, such as apoptosis or necrosis, or through excretion [1]. Both can be isolated from plasma or serum, but plasma generally yields better sample quality due to decreased contamination from leukocyte DNA [2]. While cfDNA fragments are on average 166 base pairs (bps) in length, ctDNA is generally more fragmented, with an average length of about 140 bps [3] The relative amount of cfDNA that is ctDNA, referred to as the variant allele frequency (VAF), and can vary greatly. For example, in early stages of cancer the VAF is often less than 1%, whereas in the metastatic setting this is often much higher, with reports ranging from 5–80% depending on extent and location of disease [4][5]. Notably, many studies including patients with metastatic disease will report cfDNA levels as a surrogate for ctDNA when prognosticating or evaluating response to treatment [6].
Genomic profiling has revealed a high frequencies of a limited number of mutations occurring in Kirsten rat sarcoma virus (KRAS), tumor protein 53 (TP53), cyclin-dependent kinase inhibitor 2A (CDKN2A), and SMAD4. This makes PDAC an ideal disease to use ctDNA for screening, monitoring for treatment response or recurrence, and to guiding tumor-specific treatment [7][8]. The vast majority of PDAC tumors carry a mutation in or amplification of KRAS or inactivation of TP53 (90% and 73% of cases, respectively) [8]. The next two commonly mutated genes are CDKN2A and SMAD4 (35% and 31%, respectively) [8]. In addition, 10–20% of patients with PDAC will carry a germline mutation in a gene encoding a DNA damage response protein such as ATM, BRCA1/2, or PALB2 [8]. Lastly, tumor mutational burden (TMB) and microsatellite instability (MSI) can also be determined using ctDNA, thus serving as a predictive tool for therapies targeting immune checkpoints [9][10]. Until recently, ctDNA sequencing methods lacked the required sensitivity and specificity for use in PDAC, however, this is beginning to change with recent technological advancements.

1.1. Methods for Detecting and Analyzing ctDNA

Techniques for ctDNA detection vary widely in regards to the types of genetic anomalies that can be identified, the VAF required, and cost. Most established methods use gene amplification to overcome the paucity of ctDNA in patients with PDAC, particularly in early-stage disease. Examples include real-time quantitative polymerase chain reaction (qPCR), digital PCR (dPCR), droplet digital PCR (ddPCR), and next-generation sequencing (NGS). With the exception of NGS, all of these techniques are limited by requiring predefined gene mutations of interest, which are amplified using prespecified sets of primers.
Unlike conventional PCR, qPCR monitors DNA amplification in real-time, with improved speed, reproducibility, and quantitation. However, qPCR is limited by low sensitivity and generally requires a VAF of 10% to successfully identify tumor-derived gene mutations. dPCR overcomes some of these limitations by separating a sample of DNA into thousands of compartments with zero, one, or multiple DNA strands [11]. Compartments are then amplified using parallel PCR reactions and from this the number of initial DNA strands is determined. Focusing on compartments containing a single parent DNA strand reduces the background noise associated with traditional PCR methods and enables the detection of tumor DNA at VAFs as low as 0.1% [12]. ddPCR is a method of dPCR which uses water-oil emulsion droplets to further fractionate a DNA sample into tens of thousands of droplets. PCR amplification is then performed independently in each droplet which further decreases background noise and allows for the detection of tumor DNA at VAFs as low as 0.01% [13].
Although more costly, NGS platforms have several advantages, including the ability to screen for unknown mutations, as well as structural and copy-number variations, which cannot be detected by PCR-based methods [14]. High-throughput analysis and whole-genome sequencing are also possible. Newer NGS technologies may even permit detection of malignant gene mutations at similar VAFs as those detectable with ddPCR [15][16][17]. One notable drawback of NGS is that it is currently more expensive than the aforementioned methods, typically costing thousands of dollars per sample. This cost, however, is dramatically decreasing. In addition, the limited mutational load in PDAC may reduce the need of NGS platforms [7]. NGS-based RNA sequencing of both tumor and peripheral blood using whole transcriptome sequencing platforms have also become commercially available, allowing for the identification of differentially expressed genes as well as identification of fusions, variant transcripts, and point mutations.
While dPCR, ddPCR, and NGS improve upon the many limitations of conventional PCR, none is able to detect epigenetic changes. Some of these changes, for example, methylation of CpGs clusters in promotor regions of tumor suppressor genes, have been implicated in tumorigenesis but are undetectable [18]. Recent advances in high-throughput quantitative methylation assays can now provide rapid and accurate identification of tumor DNA methylation using peripheral blood samples [19]. Furthermore, DNA methylation profiling has demonstrated reliability in predicting tumor of origin in patients with cancer of unknown primary [20]. More recently, epigenome and ATAC-sequencing have been used to simultaneously profile gene expression and open chromatin regions, and genome-scale DNA methylation (using reduced representation bisulfite sequencing; RBBS) [21][22]. In addition, isolating cell-free methylated-DNA using immunoprecipitation can be performed and coupled with NGS and PCR-based sequencing techniques, to improve specificity and reduce background noise [23]. In pancreatic cancer, differential hydroxymethylation of genes related to pancreas development or function (GATA4, GATA6, PROX1, ONECUT1, MEIS2), and cancer pathogenesis (YAP1, TEAD1, PROX1, IGF1) have also been shown to reliably identify pancreatic cancer from peripheral blood samples. As with DNA sequencing methods, the sensitivity and specificity of this method improves with more advanced cancer [24].
Lastly, several commercial liquid biopsy platforms capable of detecting ctDNA are now being used to guide clinical decisions for individuals with solid tumors. Examples include GuardantTM (breast, colon, and lung cancers and multi-cancer detection) [25], FoundationOne® (multi-cancer detection) [26], SignateraTM (colorectal cancer) [27], Galleri® (multi-cancer detection) [28], CancerSEEK (multi-cancer detection) [29] and TempusTM (multi-cancer detection) [30]. Additionally, Caris® now provides bioinformatics testing of both circulating DNA and RNA [31].

1.2. ctDNA as a Screening Tool

Since surgery represents the only modality through which PDAC can be cured, detection of early-stage disease is paramount. Currently, no cost-effective screening tool exists for the general population; however, ctDNA detection platforms, which can be used longitudinally with frequent sampling requiring relative low blood volumes, may soon fill this void. Multiple studies using various platforms have investigated ctDNA as a potential screening tool. A 2017 study compared ctDNA (quantified with ddPCR) to CA 19-9 and endoscopic ultrasound (EUS)/biopsy in 52 patients with PDAC, 10 patients with benign pancreatic tumors, and 6 patients with non-PDAC pancreatic malignancies. The investigators found that ctDNA had a sensitivity and specificity for PDAC of 65% and 75%, compared to 79% and 93%, for CA 19-9, and 73% and 88% for EUS/biopsy, respectively [32]. The relatively low sensitivity and specificity of ctDNA for detection of PDAC was thought to be due to a low VAF of ctDNA. Other groups have also reported correlations between KRAS VAF strongly and PDAC clinical stage [33]. This may be in part due to decreased numbers of cells undergoing apoptosis and necrosis in early-stage disease. Further complicating matters is the fact that ctDNA is rapidly cleared from the circulation by both endo- and exonuclease action and urinary excretion [34]. In fact the half-life of ctDNA ranges from as low as several minutes to two hours [35].
Combining ctDNA with established PDAC biomarkers as “composite or combination biomarkers,” may help to overcome these limitations. For example, one study defining positivity as having two of three of the following biomarkers: ctDNA, CA19-9, and CTCs, reported a sensitivity and specificity of 78% and 91%, respectively [32]. Another study found that combining ctDNA detection with optimized cutoffs of four tumor markers (CA19-9, CEA, hepatocyte growth factor (HGF), and osteopontin (OPN)) increased sensitivity for PDAC detection sensitivity from 30% to 64% with 99.5% specificity [36].

1.3. cctDNA to Guide Treatment and Monitor for Recurrence

Using ctDNA to guide treatment, predict, and detect tumor recurrence is of great interest and the number of studies incorporating ctDNA in these settings have dramatically increased. Like somatic and germline sequencing, ctDNA can also be used to identify potentially actionable mutations. For example, ctDNA can be used to detect mutations in DNA damage response genes that predict benefit to PARP inhibitors or platinum chemotherapy. Using ctDNA, one can also identify fusions in NTRK and potentially actionable mutations in HER2, AKT1, AKT2 and CDK4, facilitating prompt referral to relevant clinical trials [37]. Importantly, there is a high concordance in mutations detected in ctDNA and those found within primary tumors (66/66 in one study) [36].
As in resected colon cancer, detection of ctDNA following surgery has been shown to predict worse outcomes [38][39]. For example, a study using ddPCR demonstrated that persistence of ctDNA postoperatively predicted a median DFS and median OS of 8 months and 17 months, respectively, compared with 19 months and >30 months, respectively, in patients with undetectable ctDNA postoperatively [1][39][40]. Persistence of ctDNA following surgery is likely due to either residual local disease or occult micrometastatic disease and may support the use of additional chemoradiotherapy or chemotherapy. Several studies have also shown that ctDNA predicts a shorter disease-free survival (DFS) when detected prior to surgical resection of localized tumors [32][38][41][42]. Similarly, detection of ctDNA following completion of neoadjuvant chemotherapy has also been shown to predict recurrence following surgery [40].
Currently, monitoring for disease progression or recurrence is limited to imaging and tumor markers (e.g., CA19-9), both of which have limitations. Even for the most experienced radiologists, distinguishing between local recurrence and post-surgical or treatment-related inflammatory changes can be exceedingly difficult. Additionally, tumor markers are not expressed in many cases and lack specificity, occasionally increasing due to inflammation or radiation. As ctDNA technology continues to improve, it may soon be incorporated into routine use and eventually replace the use of imaging and tumor marker surveillance. Strikingly, a study from 2015 demonstrated that ctDNA could predict recurrence 6.5 months in advance of computed tomography [37]. Recent work by Sugimori et al., showed that fluctuations in KRAS VAF in patients with advanced pancreatic cancer undergoing treatment consistently correlated with increased risk of tumor progression and survival [43]. In locally advanced and metastatic PDAC, of the 13 patients with detectable ctDNA at baseline treated with chemotherapy, 9 had disappearance of detectable ctDNA with treatment and all were found to have detectable ctDNA prior to or near the time of tumor recurrence by imaging [43]. Among PDAC patients with liver metastases, ctDNA trends have been successfully used to predict partial response, stable disease and disease progression, with ctDNA levels correlating with the number and size of metastases [43][44]. Combining ctDNA and with tumor markers may further increase these prognostic stratification [32][36][45]. In a 2020 study of 61 patients with metastatic PDAC, the use of the combination of CA19-9 with VAF, cfDNA concentration and cfDNA fragmentation improved prognostication of PDAC patients into high, medium and low risk groups for recurrence and death [45]. Improvements in these ctDNA technology may also reduce treatment-related morbidity, as early recognition of treatment resistance could spare patients unnecessary toxicity and facilitate more rapid changes in treatment plans.

2. Exosomes

2.1. Methods for Capturing Exosomes

Several technologies have been developed to capture and isolate circulating exosomes, each with varying degrees of sensitivity and specificity. Three primary methods, size-based, density-based, and affinity-based, have been used to isolate exosomes. For example, tunable resistive pulse sensor (TRPS) technology separates exosomes using different sized pores [46][47]. This technology also relies on differential centrifugation, a potential drawback as it may damage the exosomal membranes thus altering both the quantitative and qualitative nature of its cargo [48][49]. The utilization of centrifugation also limits this technique with regard to its widespread adaptability. To overcome this, several newer technologies have been developed. Exosomal total isolation chip (ExoTIC) is an example of an emerging platform for detecting circulation exosomes without centrifugation [50]. This technology utilizes a porous membrane that can both separate and isolate exosomes based on size. Its modular design and reproducibility have made it an attractive option for clinical application. Affinity-based methods have the ability to isolate exosomes with high purity using either antibody-coated magnetic beads targeting exosome surface proteins. Tetraspanins, a family of proteins with over 30 members (e.g., CD9, CD63, and CD81) characterized by four transmembrane domains, are commonly expressed in exosomes and are often targeted for exosome capture [51][52]. These techniques have higher specificity with regard to sorting of exomes but are associated with lower quantitative values and overall yield. Further development these techniques is ongoing and necessary.

2.2. Exosomes as a Screening Tool

Similar to ctDNA, exosomes have emerged as promising biomarkers for early detection of pancreatic cancer. The use of exosomes for PDAC screening has several possible advantages over ctDNA. First, many pancreatic cells are exocrine cells, and as such, continuously release exosomes into the blood. Second, exosomes may have a longer half-life than ctDNA. Third, given that exosomes express various surface proteins, discussed in detail below, differentiating the exosomal cell-of-origin is also possible. Several groups have compared exosome and ctDNA capture methods on samples obtained from patients with PDAC and demonstrated increased sensitivity using exosomes. For example, Allenson et al., demonstrated that in patients with localized, locally advanced, and metastatic PDAC, KRAS mutations were detected at higher percentages in peripheral blood exosomal DNA compared to ctDNA (66.7%, 80%, and 85% vs. 45.5%, 30.8%, and 57.9%, respectively) [53].
Similar to the cells from which they originate, exosomes express a wide array of proteins that can aid development of screening assays. Glypican-1 (GPC1), a cell surface proteoglycan with high expression in exosomes derived from prostate cancer cells, is one of the better studied markers that has also shown promise in PDAC. A study by Melo et al., used mass spectroscopy identify GPC1 and flow cytometry to isolate GPC1+ exosomes in murine pancreatic cancer models, healthy human subjects, and patients with either benign pancreatic disease as well early to late-stage PDAC. They reported near 100% sensitivity and specificity [54]. Buscail et al., performed a comprehensive study evaluating the combined diagnostic performance of CTCs and exosomes using samples obtained from patients on a prospective translational clinical trial [55]. Using both peripheral and portal blood obtained from patients with resectable PDAC, they demonstrated feasibility of capturing CD63 bead-coupled Glypican-1 (GPC1)-positive exosomes which were then combined with CRISPR/Cas9-improved KRAS quantification by ddPCR. They reported 64% of patients having GPC1+ exosomes in peripheral and/or portal blood. When combined, CTC and GPC1-positive-exosome detection showed 100% sensitivity, 80% of specificity, and a negative predictive value of 100% [55].
Although promising, data surrounding the use of GPC1 as a biomarker for PDAC-derived exosomes is conflicting. For example, Lai et al., reported that GPC1 was not diagnostic for PDAC but they did identify 6 exosomal miRNAs that correlated with PDAC presence and were able to show subsequent decline following resection [56]. CD44v6, Tspan8, EpCAM, MET, and CD104 are other examples of cell surface proteins typically expressed in exosomes isolated from patients with PDAC and not healthy individuals [57]. Madhaven et al., demonstrated that the combination of flow cytometry coupled with RT-PCR examining microRNA expression patterns on pancreatic cancer cell exosomes could increase the screening sensitivity to 100% with a specificity of 80% [58]. Further validation in large prospective studies is needed before using exosomes for screening of PDAC can be adopted for general use.

2.3. Exosomes to Guide Treatment and Monitor for Recurrence

To date, few studies have explored the use of exosomes to guide treatment decisions or determine risk of recurrence in individuals who have undergone PDAC resection. Takahasi et al., isolated exosomes from patients with stage II PDAC and utilized a microarray-based miRNAs expression profiling platform to identify potential miRNA biomarkers [59]. They discovered a significant correlation between patients with elevated levels of exosomal microRNA-451a (miR-451a) and recurrence. Kawamura et al., also investigated microRNA (miR-451a, miR-4525, and miR-21) in exosomes sampled from peripheral blood and the portal vein during pancreatectomy and found that not only were levels of these miRNAs higher in portal venous blood but also that high expression was an independent prognostic factor for overall survival and disease-free survival [60]. In 2022, Bunduc et al., published a systematic review and meta-analysis on the prognostic potential of exosomes in PDAC [61]. In total, eleven studies comprising a total of 634 patients with all stages of disease were compiled. Detectable exosome miRNAs at any stage predicted increased mortality and progression and also correlated with increased mortality when identified preoperatively. The authors highlighted that the variability of study platforms likely resulted in data heterogeneity, a fundamental problem with liquid biopsy studies today. Although significant strides are being made in exosomal capture further validation and ultimately uniformization is required before these platforms are adopted into standard of care practice.

3. Circulating Tumor Cells

3.1. Methods for Capturing CTCs

Multiple technologies to capture and enrich for CTCs have recently been developed. These involve immunoaffinity methods, targeting specific antigens on the surface of tumor cells, microfluidic capture devices, and sized-based separation techniques. Immunoaffinity methods typically involve both positive enrichment for epithelial cell markers (e.g., Epithelial cellular adhesion molecule (EpCAM) or cytokeratin (CK)) and negative enrichment with CD45 to remove leukocytes. CellSearch® is the only FDA-approved CTC isolation method and relies on magnetic beads coated with antibodies to EpCAM, CK, and CD45 [62]. Other similar platforms using magnetic beads include MACS® and Dynabeads®. Tumor antigen-independent microfluidic CTC-chip technology represents another immunocapture platform that utilizes two-stage magnetophoresis and depletion antibodies against leukocytes to isolate CTCs [63]. This platform is appealing and had garnered wide-scale adoption given that pre-labeling and processing of samples prior to testing is not required. Using this technology, Nagrath et al., reported successful identification of CTCs in 115 of 116 (99%) peripheral blood samples obtained from patients with metastatic PDAC as well as lung, prostate, breast, and colon cancer [63]. They also reported a range of 5–1281 CTCs per mL and approximately 50% purity. Similar to the CTC-chip, the Herringbone-chip, passes peripheral blood through channels with micro vortices to increase the CTC exposure to EpCAM coated chip surfaces. A small study in prostate cancer described the identification of CTCs in 93% of patients with metastatic disease using this technology [64]. Lastly, several size-based separation techniques using membrane microfilters have been developed to isolate CTCs. These include isolation by size of epithelial tumor cells (ISET), ScreenCell, and ApoStream [65][66][67]. All of these methods can be used in conjunction with other DNA or RNA detection platforms. While CTCs may represent a promising biomarker assay, they likely do not fully represent the heterogenous cell population within a tumor, especially as only those cells that have undergone epithelial-mesenchymal transition will be captured.

3.2. CTCs as a Screening Tool

Using CTCs for early detection of PDAC remains controversial. Depending on the stage and method used, CTCs can be detected in patients with PDAC but at lower rates compared to other solid tumors [68]. Reported ranges in sensitivity are wide, while specificity typically approaches 90–100% in most studies [69]. Perhaps unsurprisingly, patients in whom CTCs are discovered have a worse prognosis as compared to those who do not have detectable CTCs [70]. This was highlighted in a meta-analysis by Han et al., which revealed a worse overall survival in PDAC patients with CTC-positive disease compared with those without detectable CTCs (HR = 1.23, 95% CI = 0.88–2.08, p < 0.001) [71]. There are also data suggesting concordance between the number of CTCs in peripheral blood and the stage of disease [72]. As one might also expect, the detection rate of CTCs is quite low in those with early-stage disease. For example, one study including patients with locally advanced PDAC reported a detection rate of only 11% [73]. Interestingly, other studies have reported that 33% to 62% of patients with premalignant lesions, such as intraductal papillary mucinous neoplasms, have detectable CTCs [74][75][76][77].
Unfortunately, comparisons to other liquid biopsy methods are limited, given that most studies have not attempted to simultaneously isolate ctDNA, exosomes, and CTCs. A large meta-analysis by Zhu et al., evaluated the diagnostic value of these three liquid biopsy methods by reviewing 19 studies involving a total of 1872 patients [78]. They reported the sensitivity, specificity and AUC for the diagnosis of PDAC using ctDNA (0.64, 0.92, and 0.94) exosomes (0.93, 0.92, and 0.98), and CTCs (0.74, 0.83, and 0.81). They argue that the lower-than-expected sensitivity for CTCs may be explained by deceased blood flow through pancreatic tissues or perhaps because CTCs are trapped in the liver. It is also possible that many tumors do not shed CTCs at early stages. Several studies have reported greater CTC yields in portal venous blood as compared to peripheral blood.

3.3. CTCs to Guide Treatment and Monitor for Recurrence

CTCs may have a future role in predicting and determining treatment response. Several studies have reported that higher levels of CTCs prior to the initiation of chemotherapy predict a less robust response to treatment and reduced DFS and OS [79]. Okubo et al., conducted a prospective study including 65 PDAC patients who underwent CellSearch for isolation of CTCs and showed not only that CTC positivity was significantly greater in patients with liver metastases, but also that the presence or absence of CTCs could serve an independent prognostic factor [80]. In addition, CTC positivity 3 months after beginning therapy was 45.4% and 24.1% in those with progressive disease versus those with either stable disease or partial response, respectively. Overall survival was also significantly lower in patients with detectable CTCs after treatment (p = 0.045). A study by Ren et al., explored the effects of 5-fluorouracil on CTCs in an effort to identify early changes that may predict response treatment [81]. They found that Apoptotic CTCs were not only detectable but may predict response to chemotherapy. Furthermore, greater than 70% of patients with detectable CTCs prior to chemotherapy had none detected after 7 days.
The expression of cell surface proteins on CTCs has also been explored as potential predictive or prognostic biomarkers for PDAC. For example, one study evaluating 50 PDAC patients found that those with tumors expressing human mucin 1 (MUC-1) on CTCs had inferior OS compared to patients with non-MUC-1 expressing CTCs [82]. The role of cell surface proteins on CTCs has also been explored as potential marker for disease recurrence. Vimentin is an epithelial cell surface protein that has been studied by Wei et al. They demonstrated in a study of 100 patients with PDAC that increased vimentin+ CTCs correlated with increased disease burden in patients undergoing resection and could be used as a reliable biomarker in PDAC [83]. The utilization of CTCs as a potential risk stratification tool has been evaluated in smaller studies. The CLUSTER study, for example, prospectively measured CTCs in patients with PDAC and showed that preoperative CTC levels correlated with disease recurrence at one year in patients undergoing resection [84].
CTCs may also provide insights into mechanisms of drug resistance. Viable CTCs collected serially from patients receiving various treatments can and have been used for elucidating mechanisms of response or resistance to therapies in tissue culture. These cells can also be used for generating organoids and PDX models. For example, one study explored interactions between portal vein CTCs and immune populations and showed that CTCs could recruit immune cells and increase fibroblast differentiation [85].
Like cfDNA and exosomes, CTCs may also represent a minimally invasive method to monitor for disease recurrence. The characterization of CTCs by phenotype has been explored as a potential way of stratifying by risk of disease recurrence and OS. Poruk et al., explored aldehyde dehydrogenase (ALDH), CD133, and CD44 as markers of CTCs with a tumor-initiating cell (TIC) phenotype in patients with PDAC undergoing surgical resection [86]. The authors found that ALDH-positive CTCs and triple-positive CTCs were associated with decreased survival (p ≤ 0.01) and tumor recurrence. Although these data are promising, larger prospective trials are warranted to better characterize the role of CTCs in PDAC.

This entry is adapted from the peer-reviewed paper 10.3390/ijms24044238

References

  1. Lee, J.S.; Park, S.S.; Lee, Y.K.; Norton, J.A.; Jeffrey, S.S. Liquid biopsy in pancreatic ductal adenocarcinoma: Current status of circulating tumor cells and circulating tumor DNA. Mol. Oncol. 2019, 13, 1623–1650.
  2. Parpart-Li, S.; Bartlett, B.; Popoli, M.; Adleff, V.; Tucker, L.; Steinberg, R.; Georgiadis, A.; Phallen, J.; Brahmer, J.; Azad, N.; et al. he Effect of Preservative and Temperature on the Analysis of Circulating Tumor DNA. Clin. Cancer Res. 2017, 23, 2471–2477.
  3. Underhill, H.R.; Kitzman, J.O.; Hellwig, S.; Welker, N.C.; Daza, R.; Baker, D.N.; Gligorich, K.M.; Rostomily, R.C.; Bronner, M.P.; Shendure, J. Fragment length of circulating tumor DNA. PLoS Genet. 2016, 12, e1006162.
  4. Diaz, L.A.; Bardelli, A. Liquid biopsies: Genotyping circulating tumor DNA. J. Clin. Oncol. 2014, 32, 579–586.
  5. Bernard, V.; Kim, D.U.; San Lucas, F.A.; 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.
  6. Huang, C.J.; Huang, W.Y.; Chen, C.Y.; Chao, Y.J.; Chiang, N.J.; Shan, Y.S. Cancer-cell-derived cell-free DNA can predict distant metastasis earlier in pancreatic cancer: A prospective cohort study. Ther. Adv. Med. Oncol. 2022, 14, 17588359221106558.
  7. Pietri, E.; Balsano, R.; Coriano, M.; Gelsomino, F.; Leonardi, F.; Bui, S.; Gnetti, L.; Dalla Valle, R.; Garajová, I. The implication of liquid biopsies to predict chemoresistance in pancreatic cancer. Cancer Drug Resist. 2021, 4, 559–572.
  8. Waddell, N.; Pajic, M.; Patch, A.M.; Chang, D.K.; Kassahn, K.S.; Bailey, P.; Johns, A.L.; Miller, D.; Nones, K.; Quek, K.; et al. Whole genomes redefine the mutational landscape of pancreatic cancer. Nature 2015, 518, 495–501.
  9. Dong, J.; Li, B.; Lin, D.; Zhou, Q.; Huang, D. Advances in targeted therapy and immunotherapy for non-small cell lung cancer based on accurate molecular typing. Front. Pharmacol. 2019, 10, 230.
  10. Wang, Z.; Duan, J.; Cai, S.; Han, M.; Dong, H.; Zhao, J.; Zhu, B.; Wang, S.; Zhuo, M.; Sun, J.; et al. Assessment of blood tumor mutational burden as a potential biomarker for immunotherapy in patients with non–small cell lung cancer with use of a next-generation sequencing cancer gene panel. JAMA Oncol. 2019, 5, 696–702.
  11. Mao, X.; Liu, C.; Tong, H.; Chen, Y.; Liu, K. Principles of digital PCR and its applications in current obstetrical and gynecological diseases. Am. J. Transl. Res. 2019, 11, 7209–7222.
  12. 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.
  13. Heredia-Soto, V.; Rodríguez-Salas, N.; Feliu, J. Liquid biopsy in pancreatic cancer: Are we ready to apply it in the clinical practice? Cancers 2021, 13, 1986.
  14. Zill, O.A.; Greene, C.; Sebisanovic, D.; Siew, L.M.; Leng, J.; Vu, M.; Hendifar, A.E.; Wang, Z.; Atreya, C.E.; Kelley, R.K.; et al. Cell-Free DNA Next-Generation Sequencing in Pancreatobiliary Carcinomas. Cancer Discov. 2015, 5, 1040–1048.
  15. Newman, A.M.; Bratman, S.V.; To, J.; Wynne, J.F.; Eclov, N.C.; Modlin, L.A.; Liu, C.L.; Neal, J.W.; Wakelee, H.A.; Merritt, R.E.; et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat. Med. 2014, 20, 548–554.
  16. 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 improved detection of circulating tumor DNA. Nat. Biotechnol. 2016, 34, 547–555.
  17. Pécuchet, N.; Rozenholc, Y.; Zonta, E.; Pietrasz, D.; Didelot, A.; Combe, P.; Gibault, L.; Bachet, J.B.; Taly, V.; Fabre, E.; et al. Analysis of base-position error rate of next-generation sequencing to detect tumor mutations in circulating DNA. Clin. Chem. 2016, 62, 1492–1503.
  18. Grønbaek, K.; Hother, C.; Jones, P.A. Epigenetic changes in cancer. Apmis 2007, 115, 1039–1059.
  19. Eads, C.A.; Danenberg, K.D.; Kawakami, K.; Saltz, L.B.; Blake, C.; Shibata, D.; Danenberg, P.V.; Laird, P.W. MethyLight: A high-throughput assay to measure DNA methylation. Nucleic Acids Res. 2000, 28, e32.
  20. Moran, S.; Martínez-Cardús, A.; Sayols, S.; Musulén, E.; Balañá, C.; Estival-Gonzalez, A.; Moutinho, C.; Heyn, H.; Diaz-Lagares, A.; de Moura, M.C.; et al. Epigenetic profiling to classify cancer of unknown primary: A multicentre, retrospective analysis. Lancet Oncol. 2016, 17, 1386–1395.
  21. Xu, W.; Wen, Y.; Liang, Y.; Xu, Q.; Wang, X.; Jin, W.; Chen, X. A plate-based single-cell ATAC-seq workflow for fast and robust profiling of chromatin accessibility. Nat. Protoc. 2021, 16, 4084–4107.
  22. Swanson, E.; Lord, C.; Reading, J.; Heubeck, A.T.; Genge, P.C.; Thomson, Z.; Weiss, M.D.; Li, X.J.; Savage, A.K.; Green, R.R.; et al. Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq. Elife 2021, 10, e63632.
  23. Shen, S.Y.; Singhania, R.; Fehringer, G.; Chakravarthy, A.; Roehrl, M.H.; 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. Nature 2018, 563, 579–583.
  24. Guler, G.D.; Ning, Y.; Ku, C.J.; Phillips, T.; McCarthy, E.; Ellison, C.K.; Bergamaschi, A.; Collin, F.; Lloyd, P.; Scott, A.; et al. Detection of early stage pancreatic cancer using 5-hydroxymethylcytosine signatures in circulating cell free DNA. Nat. Commun. 2020, 11, 5270.
  25. Guardant Health Presents New Data Showing Blood Test Highly Accurate in Detecting Multiple Cancers at the American Association for Cancer Research Annual Meeting. Available online: https://investors.guardanthealth.com/press-releases/press-releases/2022/Guardant-Health-Presents-New-Data-Showing-Blood-Test-Highly-Accurate-in-Detecting-Multiple-Cancers-at-the-American-Association-for-Cancer-Research-Annual-Meeting/default.aspx (accessed on 28 December 2022).
  26. FoundationOne Liquid CDx. Available online: https://www.foundationmedicine.com/test/foundationone-liquid-cdx (accessed on 28 December 2022).
  27. Signatera Clinician Information. Available online: https://www.natera.com/oncology/signatera-advanced-cancer-detection/clinicians/#pg-menu-tabs (accessed on 28 December 2022).
  28. Galleri. Available online: https://www.galleri.com (accessed on 28 December 2022).
  29. Single Blood Test Screens for Eight Cancer Types. Available online: https://www.hopkinsmedicine.org/news/newsroom/news-releases/single-blood-test-screens-for-eight-cancer-types (accessed on 28 December 2022).
  30. Validation of a Liquid Biopsy Assay With Molecular and Clinical Profiling of Circulating Tumor DNA. Available online: https://www.tempus.com/publications/validation-of-a-liquid-biopsy-assay-with-molecular-and-clinical-profiling-of-circulating-tumor-dna/ (accessed on 28 December 2022).
  31. Caris Assure Blood Based Profiling. Available online: https://www.carislifesciences.com/products-and-services/molecular-profiling/blood-profiling/ (accessed on 28 December 2022).
  32. Sefrioui, D.; Blanchard, F.; Toure, E.; Basile, P.; Beaussire, L.; Dolfus, C.; Perdrix, A.; Paresy, M.; Antonietti, M.; Iwanicki-Caron, I.; et al. Diagnostic value of CA19.9, circulating tumour DNA and circulating tumour cells in patients with solid pancreatic tumours. Br. J. Cancer 2017, 117, 1017–1025.
  33. Wang, Z.Y.; Ding, X.Q.; Zhu, H.; Wang, R.X.; Pan, X.R.; Tong, J.H. KRAS mutant allele fraction in circulating cell-free DNA correlates with clinical stage in pancreatic cancer patients. Front. Oncol. 2019, 9, 1295.
  34. Nagai, M.; Sho, M.; Akahori, T.; Nakagawa, K.; Nakamura, K. Application of liquid biopsy for surgical management of pancreatic cancer. Ann. Gastroenterol. Surg. 2020, 4, 216–223.
  35. Kustanovich, A.; Schwartz, R.; Peretz, T.; Grinshpun, A. Life and death of circulating cell-free DNA. Cancer Biol. Ther. 2019, 20, 1057–1067.
  36. 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.
  37. Sausen, M.; Phallen, J.; Adleff, V.; Jones, S.; Leary, R.J.; Barrett, M.T.; Anagnostou, V.; Parpart-Li, S.; Murphy, D.; Kay Li, Q.; et al. Clinical implications of genomic alterations in the tumour and circulation of pancreatic cancer patients. Nat. Commun. 2015, 6, 7686.
  38. Lee, B.; Lipton, L.; Cohen, J.; Tie, J.; Javed, A.A.; Li, L.; Goldstein, D.; Burge, M.; Cooray, P.; Nagrial, A.; et al. Circulating tumor DNA as a potential marker of adjuvant chemotherapy benefit following surgery for localized pancreatic cancer. Ann. Oncol. 2019, 30, 1472–1478.
  39. Jiang, J.; Ye, S.; Xu, Y.; Chang, L.; Hu, X.; Ru, G.; Guo, Y.; Yi, X.; Yang, L.; Huang, D. Circulating Tumor DNA as a Potential Marker to Detect Minimal Residual Disease and Predict Recurrence in Pancreatic Cancer. Front. Oncol. 2020, 10, 1220.
  40. Groot, V.P.; Mosier, S.; Javed, A.A.; Teinor, J.A.; Gemenetzis, G.; Ding, D.; Haley, L.M.; Yu, J.; Burkhart, R.A.; Hasanain, A.; et al. Circulating Tumor DNA as a Clinical Test in Resected Pancreatic Cancer. Clin. Cancer Res. 2019, 25, 4973–4984.
  41. Pietrasz, D.; Pecuchet, 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.
  42. Hadano, N.; Murakami, Y.; Uemura, K.; Hashimoto, Y.; Kondo, N.; Nakagawa, N.; Sueda, T.; Hiyama, E. Prognostic value of circulating tumour DNA in patients undergoing curative resection for pancreatic cancer. Br. J. Cancer 2016, 115, 59–65.
  43. Sugimori, M.; Sugimori, K.; Tsuchiya, H.; Suzuki, Y.; Tsuyuki, S.; Kaneta, Y.; Hirotani, A.; Sanga, K.; Tozuka, Y.; Komiyama, S.; et al. Quantitative monitoring of circulating tumor DNA in patients with advanced pancreatic cancer undergoing chemotherapy. Cancer Sci. 2020, 111, 266–278.
  44. Uesato, Y.; Sasahira, N.; Ozaka, M.; Sasaki, T.; Takatsuki, M.; Zembutsu, H. Evaluation of circulating tumor DNA as a biomarker in pancreatic cancer with liver metastasis. PLoS ONE 2020, 15, e0235623.
  45. Toledano-Fonseca, M.; Cano, M.T.; Inga, E.; Rodriguez-Alonso, R.; Gomez-Espana, M.A.; Guil-Luna, S.; Mena-Osuna, R.; de la Haba-Rodriguez, J.R.; Rodriguez-Ariza, A.; Aranda, E. Circulating Cell-Free DNA-Based Liquid Biopsy Markers for the Non-Invasive Prognosis and Monitoring of Metastatic Pancreatic Cancer. Cancers 2020, 12, 1754.
  46. Coumans, F.A.W.; van der Pol, E.; Böing, A.N.; Hajji, N.; Sturk, G.; van Leeuwen, T.G.; Nieuwland, R. Reproducible extracellular vesicle size and concentration determination with tunable resistive pulse sensing. J. Extracell. Vesicles 2014, 3, 25922.
  47. Woo, H.K.; Sunkara, V.; Park, J.; Kim, T.H.; Han, J.R.; Kim, C.J.; Choi, H.I.; Kim, Y.K.; Cho, Y.K. Exodisc for Rapid, Size-Selective, and Efficient Isolation and Analysis of Nanoscale Extracellular Vesicles from Biological Samples. ACS Nano 2017, 11, 1360–1370.
  48. Yu, L.L.; Zhu, J.; Liu, J.X.; Jiang, F.; Ni, W.K.; Qu, L.S.; Ni, R.Z.; Lu, C.H.; Xiao, M.B. A Comparison of Traditional and Novel Methods for the Separation of Exosomes from Human Samples. Biomed. Res. Int. 2018, 2018, 3634563.
  49. Livshits, M.A.; Khomyakova, E.; Evtushenko, E.G.; Lazarev, V.N.; Kulemin, N.A.; Semina, S.E.; Generozov, E.V.; Govorun, V.M. Corrigendum: Isolation of exosomes by differential centrifugation: Theoretical analysis of a commonly used protocol. Sci. Rep. 2016, 6, 21447.
  50. Liu, F.; Vermesh, O.; Mani, V.; Ge, T.J.; Madsen, S.J.; Sabour, A.; Hsu, E.C.; Gowrishankar, G.; Kanada, M.; Jokerst, J.V.; et al. The Exosome Total Isolation Chip. ACS Nano 2017, 11, 10712–10723.
  51. Buscail, E.; Maulat, C.; Muscari, F.; Chiche, L.; Cordelier, P.; Dabernat, S.; Alix-Panabières, C.; Buscail, L. Liquid Biopsy Approach for Pancreatic Ductal Adenocarcinoma. Cancers 2019, 11, 852.
  52. Jankovičová, J.; Sečová, P.; Michalková, K.; Antalíková, J. Tetraspanins, More than Markers of Extracellular Vesicles in Reproduction. Int. J. Mol. Sci. 2020, 21, 7568.
  53. Allenson, K.; Castillo, J.; San Lucas, F.A.; Scelo, G.; Kim, D.U.; Bernard, V.; Davis, G.; Kumar, T.; Katz, M.; Overman, M.J.; et al. High prevalence of mutant KRAS in circulating exosome-derived DNA from early-stage pancreatic cancer patients. Ann. Oncol. 2017, 28, 741–747.
  54. Melo, S.A.; Luecke, L.B.; Kahlert, C.; Fernandez, A.F.; Gammon, S.T.; Kaye, J.; LeBleu, V.S.; Mittendorf, E.A.; Weitz, J.; Rahbari, N.; et al. Glypican-1 identifies cancer exosomes and detects early pancreatic cancer. Nature 2015, 523, 177–182.
  55. Buscail, E.; Alix-Panabières, C.; Quincy, P.; Cauvin, T.; Chauvet, A.; Degrandi, O.; Caumont, C.; Verdon, S.; Lamrissi, I.; Moranvillier, I.; et al. High Clinical Value of Liquid Biopsy to Detect Circulating Tumor Cells and Tumor Exosomes in Pancreatic Ductal Adenocarcinoma Patients Eligible for Up-Front Surgery. Cancers 2019, 11, 1656.
  56. Lai, X.; Wang, M.; McElyea, S.D.; Sherman, S.; House, M.; Korc, M. A microRNA signature in circulating exosomes is superior to exosomal glypican-1 levels for diagnosing pancreatic cancer. Cancer Lett. 2017, 393, 86–93.
  57. Zhang, H.; Xing, J.; Dai, Z.; Wang, D.; Tang, D. Exosomes: The key of sophisticated cell-cell communication and targeted metastasis in pancreatic cancer. Cell Commun. Signal. 2022, 20, 9.
  58. Madhavan, B.; Yue, S.; Galli, U.; Rana, S.; Gross, W.; Müller, M.; Giese, N.A.; Kalthoff, H.; Becker, T.; Büchler, M.W.; et al. Combined evaluation of a panel of protein and miRNA serum-exosome biomarkers for pancreatic cancer diagnosis increases sensitivity and specificity. Int. J. Cancer 2015, 136, 2616–2627.
  59. Takahasi, K.; Iinuma, H.; Wada, K.; Minezaki, S.; Kawamura, S.; Kainuma, M.; Ikeda, Y.; Shibuya, M.; Miura, F.; Sano, K. Usefulness of exosome-encapsulated microRNA-451a as a minimally invasive biomarker for prediction of recurrence and prognosis in pancreatic ductal adenocarcinoma. J. Hepatobiliary Pancreat. Sci. 2018, 25, 155–161.
  60. Kawamura, S.; Iinuma, H.; Wada, K.; Takahashi, K.; Minezaki, S.; Kainuma, M.; Shibuya, M.; Miura, F.; Sano, K. Exosome-encapsulated microRNA-4525, microRNA-451a and microRNA-21 in portal vein blood is a high-sensitive liquid biomarker for the selection of high-risk pancreatic ductal adenocarcinoma patients. J. Hepatobiliary Pancreat. Sci. 2019, 26, 63–72.
  61. Bunduc, S.; Gede, N.; Váncsa, S.; Lillik, V.; Kiss, S.; Juhász, M.F.; Erőss, B.; Szakács, Z.; Gheorghe, C.; Mikó, A.; et al. Exosomes as prognostic biomarkers in pancreatic ductal adenocarcinoma-a systematic review and meta-analysis. Transl. Res. 2022, 244, 126–136.
  62. Allard, W.J.; Matera, J.; Miller, M.C.; Repollet, M.; Connelly, M.C.; Rao, C.; Tibbe, A.G.; Uhr, J.W.; Terstappen, L.W. Tumor cells circulate in the peripheral blood of all major carcinomas but not in healthy subjects or patients with nonmalignant diseases. Clin. Cancer Res. 2004, 10, 6897–6904.
  63. Nagrath, S.; Sequist, L.V.; Maheswaran, S.; Bell, D.W.; Irimia, D.; Ulkus, L.; Smith, M.R.; Kwak, E.L.; Digumarthy, S.; Muzikansky, A.; et al. Isolation of rare circulating tumour cells in cancer patients by microchip technology. Nature 2007, 450, 1235–1239.
  64. Stott, S.L.; Hsu, C.-H.; Tsukrov, D.I.; Yu, M.; Miyamoto, D.T.; Waltman, B.A.; Rothenberg, S.M.; Shah, A.M.; Smas, M.E.; Korir, G.K.; et al. Isolation of circulating tumor cells using a microvortex-generating herringbone-chip. Proc. Natl. Acad. Sci. USA 2010, 107, 18392–18397.
  65. Gupta, V.; Jafferji, I.; Garza, M.; Melnikova, V.O.; Hasegawa, D.K.; Pethig, R.; Davis, D.W. ApoStream(), a new dielectrophoretic device for antibody independent isolation and recovery of viable cancer cells from blood. Biomicrofluidics 2012, 6, 24133.
  66. Farace, F.; Massard, C.; Vimond, N.; Drusch, F.; Jacques, N.; Billiot, F.; Laplanche, A.; Chauchereau, A.; Lacroix, L.; Planchard, D.; et al. A direct comparison of CellSearch and ISET for circulating tumour-cell detection in patients with metastatic carcinomas. Br. J. Cancer 2011, 105, 847–853.
  67. Lin, H.K.; Zheng, S.; Williams, A.J.; Balic, M.; Groshen, S.; Scher, H.I.; Fleisher, M.; Stadler, W.; Datar, R.H.; Tai, Y.C.; et al. Portable filter-based microdevice for detection and characterization of circulating tumor cells. Clin. Cancer Res. 2010, 16, 5011–5018.
  68. Kaczor-Urbanowicz, K.E.; Cheng, J.; King, J.C.; Sedarat, A.; Pandol, S.J.; Farrell, J.J.; Wong, D.T.W.; Kim, Y. Reviews on Current Liquid Biopsy for Detection and Management of Pancreatic Cancers. Pancreas 2020, 49, 1141–1152.
  69. Zhang, Y.; Wang, F.; Ning, N.; Chen, Q.; Yang, Z.; Guo, Y.; Xu, D.; Zhang, D.; Zhan, T.; Cui, W. Patterns of circulating tumor cells identified by CEP8, CK and CD45 in pancreatic cancer. Int. J. Cancer 2015, 136, 1228–1233.
  70. Earl, J.; Garcia-Nieto, S.; Martinez-Avila, J.C.; Montans, J.; Sanjuanbenito, A.; Rodríguez-Garrote, M.; Lisa, E.; Mendía, E.; Lobo, E.; Malats, N.; et al. Circulating tumor cells (Ctc) and kras mutant circulating free Dna (cfdna) detection in peripheral blood as biomarkers in patients diagnosed with exocrine pancreatic cancer. BMC Cancer 2015, 15, 797.
  71. Han, L.; Chen, W.; Zhao, Q. Prognostic value of circulating tumor cells in patients with pancreatic cancer: A meta-analysis. Tumour Biol. 2014, 35, 2473–2480.
  72. Ankeny, J.S.; Court, C.M.; Hou, S.; Li, Q.; Song, M.; Wu, D.; Chen, J.F.; Lee, T.; Lin, M.; Sho, S.; et al. Circulating tumour cells as a biomarker for diagnosis and staging in pancreatic cancer. Br. J. Cancer 2016, 114, 1367–1375.
  73. Bidard, F.C.; Huguet, F.; Louvet, C.; Mineur, L.; Bouché, O.; Chibaudel, B.; Artru, P.; Desseigne, F.; Bachet, J.B.; Mathiot, C.; et al. Circulating tumor cells in locally advanced pancreatic adenocarcinoma: The ancillary CirCe 07 study to the LAP 07 trial. Ann. Oncol. 2013, 24, 2057–2061.
  74. Rhim, A.D.; Thege, F.I.; Santana, S.M.; Lannin, T.B.; Saha, T.N.; Tsai, S.; Maggs, L.R.; Kochman, M.L.; Ginsberg, G.G.; Lieb, J.G.; et al. Detection of circulating pancreas epithelial cells in patients with pancreatic cystic lesions. Gastroenterology 2014, 146, 647–651.
  75. Cauley, C.E.; Pitman, M.B.; Zhou, J.; Perkins, J.; Kuleman, B.; Liss, A.S.; Fernandez-Del Castillo, C.; Warshaw, A.L.; Lillemoe, K.D.; Thayer, S.P. Circulating Epithelial Cells in Patients with Pancreatic Lesions: Clinical and Pathologic Findings. J. Am. Coll. Surg. 2015, 221, 699–707.
  76. Dopico, P.J.; Le, M.N.; Burgess, B.; Yang, Z.; Zhao, Y.; Wang, Y.; George, T.J.; Fan, Z.H. Longitudinal Study of Circulating Biomarkers in Patients with Resectable Pancreatic Ductal Adenocarcinoma. Biosensors 2022, 12, 206.
  77. Chen, J.; Wang, H.; Zhou, L.; Liu, Z.; Tan, X. A combination of circulating tumor cells and CA199 improves the diagnosis of pancreatic cancer. J. Clin. Lab. Anal. 2022, 36, e24341.
  78. Zhu, Y.; Zhang, H.; Chen, N.; Hao, J.; Jin, H.; Ma, X. Diagnostic value of various liquid biopsy methods for pancreatic cancer: A systematic review and meta-analysis. Medicine 2020, 99, e18581.
  79. Amantini, C.; Morelli, M.B.; Nabissi, M.; Piva, F.; Marinelli, O.; Maggi, F.; Bianchi, F.; Bittoni, A.; Berardi, R.; Giampieri, R.; et al. Expression Profiling of Circulating Tumor Cells in Pancreatic Ductal Adenocarcinoma Patients: Biomarkers Predicting Overall Survival. Front. Oncol. 2019, 9, 874.
  80. Okubo, K.; Uenosono, Y.; Arigami, T.; Mataki, Y.; Matsushita, D.; Yanagita, S.; Kurahara, H.; Sakoda, M.; Kijima, Y.; Maemura, K.; et al. Clinical impact of circulating tumor cells and therapy response in pancreatic cancer. Eur. J. Surg. Oncol. 2017, 43, 1050–1055.
  81. Ren, C.; Han, C.; Zhang, J.; He, P.; Wang, D.; Wang, B.; Zhao, P.; Zhao, X. Detection of apoptotic circulating tumor cells in advanced pancreatic cancer following 5-fluorouracil chemotherapy. Cancer Biol. Ther. 2011, 12, 700–706.
  82. Dotan, E.; Alpaugh, R.K.; Ruth, K.; Negin, B.P.; Denlinger, C.S.; Hall, M.J.; Astsaturov, I.; McAleer, C.; Fittipaldi, P.; Thrash-Bingham, C.; et al. Prognostic Significance of MUC-1 in Circulating Tumor Cells in Patients With Metastatic Pancreatic Adenocarcinoma. Pancreas 2016, 45, 1131–1135.
  83. Wei, T.; Zhang, X.; Zhang, Q.; Yang, J.; Chen, Q.; Wang, J.; Li, X.; Chen, J.; Ma, T.; Li, G.; et al. Vimentin-positive circulating tumor cells as a biomarker for diagnosis and treatment monitoring in patients with pancreatic cancer. Cancer Lett. 2019, 452, 237–243.
  84. Gemenetzis, G.; Groot, V.P.; Yu, J.; Ding, D.; Teinor, J.A.; Javed, A.A.; Wood, L.D.; Burkhart, R.A.; Cameron, J.L.; Makary, M.A.; et al. Circulating Tumor Cells Dynamics in Pancreatic Adenocarcinoma Correlate With Disease Status: Results of the Prospective CLUSTER Study. Ann. Surg. 2018, 268, 408–420.
  85. Arnoletti, J.P.; Fanaian, N.; Reza, J.; Sause, R.; Almodovar, A.J.; Srivastava, M.; Patel, S.; Veldhuis, P.P.; Griffith, E.; Shao, Y.P.; et al. Pancreatic and bile duct cancer circulating tumor cells (CTC) form immune-resistant multi-cell type clusters in the portal venous circulation. Cancer Biol. Ther. 2018, 19, 887–897.
  86. Poruk, K.E.; Blackford, A.L.; Weiss, M.J.; Cameron, J.L.; He, J.; Goggins, M.; Rasheed, Z.A.; Wolfgang, C.L.; Wood, L.D. Circulating Tumor Cells Expressing Markers of Tumor-Initiating Cells Predict Poor Survival and Cancer Recurrence in Patients with Pancreatic Ductal Adenocarcinoma. Clin. Cancer Res. 2017, 23, 2681–2690.
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