Next Article in Journal
Mitochondrial DNA and Inflammation Are Associated with Cerebral Vessel Remodeling and Early Diabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus
Previous Article in Journal
Mutations in Glycosyltransferases and Glycosidases: Implications for Associated Diseases
Previous Article in Special Issue
Categorizing Extrachromosomal Circular DNA as Biomarkers in Serum of Cancer
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Technical Advances in Circulating Cell-Free DNA Detection and Analysis for Personalized Medicine in Patients’ Care

by
Monica Sorbini
1,*,
Tullia Carradori
1,
Gabriele Maria Togliatto
2,
Tiziana Vaisitti
1,2 and
Silvia Deaglio
1,2
1
Department of Medical Sciences, University of Turin, 10126 Turin, Italy
2
Immunogenetics and Transplant Biology Service, Città della Salute e della Scienza, 10126 Turin, Italy
*
Author to whom correspondence should be addressed.
Biomolecules 2024, 14(4), 498; https://doi.org/10.3390/biom14040498
Submission received: 24 March 2024 / Revised: 13 April 2024 / Accepted: 17 April 2024 / Published: 19 April 2024
(This article belongs to the Special Issue Recent Developments in the Biology of Extracellular or Cell-Free DNA)

Abstract

:
Circulating cell-free DNA (cfDNA) refers to small fragments of DNA molecules released after programmed cell death and necrosis in several body fluids such as blood, saliva, urine, and cerebrospinal fluid. The discovery of cfDNA has revolutionized the field of non-invasive diagnostics in the oncologic field, in prenatal testing, and in organ transplantation. Despite the potential of cfDNA and the solid results published in the recent literature, several challenges remain, represented by a low abundance, a need for highly sensitive assays, and analytical issues. In this review, the main technical advances in cfDNA analysis are presented and discussed, with a comprehensive examination of the current available methodologies applied in each field. Considering the potential advantages of cfDNA, this biomarker is increasing its consensus among clinicians, as it allows us to monitor patients’ conditions in an easy and non-invasive way, offering a more personalized care. Nevertheless, cfDNA analysis is still considered a diagnostic marker to be further validated, and very few centers are implementing its analysis in routine diagnostics. As technical improvements are enhancing the performances of cfDNA analysis, its application will transversally improve patients’ quality of life.

1. Introduction

In recent decades, the field of molecular biology has experienced a significant step forward with the emergence of circulating cell-free DNA (cfDNA) as a versatile biomarker with relevant clinical implications. CfDNA, consisting of double-stranded DNA fragments released into the bloodstream following cellular apoptosis and necrosis, represents a significant advance in the detection and monitoring of various physiological and pathological conditions. These conditions range from cancer to post-transplant monitoring and prenatal diagnosis, making cfDNA analysis a crucial tool in modern medicine.
This manuscript aims to explore the significance of cfDNA analysis, highlighting its new insights and contributions to the existing literature. By providing a comprehensive overview of its diverse origins, clinical applications, and technical challenges, it aims to serve as a contribution in the understanding and use of cfDNA as a biomarker.
At its core, cfDNA analysis represents a non-invasive and innovative approach, often referred to as “liquid biopsy”. This term underscores its ability to provide diagnostic and prognostic information by analyzing genetic material obtained from bodily fluids, primarily blood plasma or serum. This revolutionary technique holds significant promise in various fields, including oncology, prenatal screening, and transplantation medicine.
Through a detailed examination of recent advancements, this review aims to clarify the crucial roles of cfDNA in disease detection, treatment monitoring, and patient management.
Through a structured analysis of key themes and methodologies, this review aims to serve as a valuable resource for researchers, clinicians, and healthcare professionals engaged in cfDNA analysis.
By providing insights into the evolving landscape of cfDNA research and its potential implications for personalized medicine, this manuscript is a valuable resource for researchers, clinicians, and healthcare professionals engaged in cfDNA analysis. Ultimately, by advancing our understanding of cfDNA and its clinical applications, this manuscript contributes to the ongoing dialogue on this revolutionary biomarker, paving the way for better diagnostic and therapeutic strategies in modern medicine.

2. Circulating Cell-Free DNA

CfDNA is represented by double-stranded extracellular DNA fragments released into the bloodstream after the apoptosis and necrosis processes in physiological and pathological situations. It was first described in 1948 [1] when Mandel and Matais detected the presence of DNA in plasma samples from healthy and affected individuals. CfDNA originates from many sources within the body and can be isolated from various body fluids such as blood, urine, effusions, and cerebrospinal fluid [2]. In healthy conditions, it derives mainly from blood cells [3,4], but it can arise from inflammatory cells, tumor cells, fetal cells crossing the placenta during pregnancy, or can be released from graft cells after solid organ transplantation [5]. Human plasma DNA consists of a mixture of DNA fragments of different sizes, mostly ranging between 100 and 200 base pairs [6,7], with a peak at 166 bases; this peculiar length was related to the nucleosomal structure [8,9], as during the cell death process, proteins associated with DNA seem to protect short fragments from degradation. However, smaller (<100 bases) or larger fragments of several kilobases have also been reported [10,11,12] and associated, respectively, to mitochondrial and necrotic origin [9,13]. The CfDNA concentration in blood widely ranges between undetectable and a high concentration (up to 100 ng/mL) in healthy subjects [9,13], but it is known that its levels can be affected by many individual conditions, such as age, BMI, circadian rhythm [13], exercise [4], inflammation [5], infections [14,15,16], and pharmacologic treatment [5], that tend to increase cfDNA presence.

3. cfDNA Applications in Clinical Care

Since the discovery of cfDNA, its potential applications in various fields have been continuously explored. The application of cfDNA analysis, which is defined as “liquid biopsy”, is used to monitor pathological conditions in oncologic, prenatal, and transplantation fields in a non-invasive and revolutionary method [9].

3.1. Oncologic Applications

In oncology, the presence of the circulating tumor cfDNA (ctDNA) and the analysis of its genetic alterations allows the detection of cancer disease, the monitoring of treatment response, and the detection of minimal residual disease, enabling personalized treatment strategies [17,18]. Currently, the most common use of ctDNA analysis is therapy selection and stratification of patients based on the likelihood of response to targeted therapies [19,20,21,22] by searching for specific mutation markers for resistance or sensitivity, such as tyrosine kinase inhibitors, programmed death inhibitors-1 [23], programmed death ligand-1 [24], and cytotoxic T lymphocyte-associated protein 4 [25]. Through ctDNA analysis, it is, therefore, possible to differentiate and predict immune checkpoint blockade response patterns [26,27], characterize the tumor heterogeneity [28], and detect resistance for targeted therapy and chemotherapy early [29,30,31,32].
Another important and recent use of ctDNA is the approximation of tumor burden [33,34] enabled as the ctDNA quantity is directly associated with the number of tumor cells present in the body.
Methylation markers have also been proposed for the detection of early cancer, with the advantage of discriminating the tissue of origin of the cfDNA based on the tissue-specific methylation pattern [35,36].

3.2. Prenatal Screening

In prenatal testing, the analysis of fetal cfDNA in maternal blood has revolutionized the field, allowing non-invasive prenatal testing (NIPT) that can investigate chromosomal abnormalities and fetal aneuploidies as an alternative to more invasive methods such as karyotyping and FISH on fetal blood, chorionic villus sampling, or amniocentesis [37]. Non-invasive prenatal screening can be performed from 5 to 7 weeks [38], looking for pathological variations with a targeted or genome-wide approach. In addition, it is possible to noninvasively determine fetal sex, genotype fetal blood group D antigen, and detect variants involved in paternally inherited or de novo disorders [39].
To date, a low fetal fraction is the most important cause of negative results in cfDNA screening and is reported as a cause of test failure in up to 6.1% of tests performed [40,41]. This poor fetal cfDNA concentration may result from an increased maternal fraction due to the conditions of the mother [4,13], both physiological, such as intense physical exercise, age, BMI, or the circadian rhythm, and pathological, such as in the case of concomitant inflammation [5] or infection [14,15,16]. All these conditions tend to cause a stronger release of background DNA from the mother, resulting in an apparent failure to detect the fetal DNA fraction and in the test’s failure.

3.3. Transplantation

Clinical studies have highlighted the potential of detecting and quantifying the fraction of donor-derived cell-free DNA (dd-cfDNA), i.e., the portion of cfDNA derived from the transplanted organ, to monitor transplant status and detect rejection earlier and with greater sensitivity than traditional methods, such as graft biopsy, allowing early intervention and improved transplantation outcomes [42,43,44]. Dd-cfDNA has been shown to be a potential biomarker of acute rejection, well correlating with biopsy-proven rejection, and more generally, it is a signal of graft damage, post-transplant complications, and infection. Differences in the percentage of dd-cfDNA between graft types have been observed, reflecting the effective size and the organ-specific cell turnover [45], similar to results reported for ctDNA changes associated with tumor burden.
Dd-cfDNA is discriminated from recipient cfDNA by exploiting widespread genetic polymorphisms in the genome. The first published approaches to detect dd-cfDNA relied on a panel of short-tandem repeats (STRs), variable-number tandem repeats (VNTRs), single nucleotide (SNPs), or insertion–deletion polymorphisms (INDELs) chosen as polymorphic enough to distinguish all possible donor–recipient pairs and therefore were defined as “targeted approaches” [46,47] as they target pre-selected sequences in the genome. A particular method to discriminate the portion of dd-cfDNA present in the bloodstream is based on the donor and recipient Human Leukocyte Antigen (HLA) typing [48,49]. Since transplant centers generally check the HLA loci to identify the best match for transplantation, this information is therefore available and can be used to discriminate donor cfDNA from that of the recipient.
More recent NGS techniques do not require genotyping and are commonly called “random approaches” since after the sequencing phase, specific donor, and recipient polymorphisms are selected based on the genomic profile of both subjects [50,51].
The identification of cfDNA tissue source may represent a valid alternative for graft versus host disease (GVHD) non-invasive detection. Acute GVHD remains an important complication after allogeneic hematopoietic cell transplantation (HCT) [52]. Currently, there are no validated non-invasive biomarkers that are used in routine clinical applications for acute GVHD. Candidate molecules were cytokines and peptides involved in the systemic inflammation and pathophysiology of GVHD, but their performance resulted in limited and poorly specific [52]. As the liver, skin, and intestine are the most involved organs in the disease, a significant increase in cfDNA deriving from these tissues can be informative of the development of the pathology [53].
Each tissue is characterized by an epigenetic signature that allows for the identification of the DNA origin through the analysis of its methylation profile [54]. Advanced molecular analyses as whole-genome bisulfite sequencing allow for the correct identification and quantification of the cfDNA source, enabling the non-invasive monitoring of GVHD [55]. This approach has been tested by Pellan Cheng and colleagues [56], who analyzed a pilot cohort of HCT recipients, and the result of their proof-of-principle study showed the potential of cfDNA to assist in personalizing care after HCT.

4. Technical Issues for High-Quality cfDNA Analysis

4.1. The Relevance of Correct Sampling

Performing a liquid biopsy means, in practice, the retrieving of cfDNA from a body fluid, mostly peripheral blood. However, the rapid turnover and short half-life of cfDNA [9,13] require proper sampling, considering the relatively low concentration of this marker. Most studies were performed using EDTA BD vacutainer [57,58], which does not preserve blood cells from apoptosis and release genomic DNA, affecting the quantity and quality of cfDNA itself [16,59] if the plasma is not rapidly separated from the corpuscular part [13]. To prevent cfDNA degradation and its dilution into genomic DNA, ad hoc collection tubes are available from different companies (Qiagen, Hilden, Germany, Roche, Basel, Switzerland, and Streck, La Vista, NE, USA), which were successfully used in some studies [60,61]. Their main advantage is that tubes keep cfDNA stable and free from genomic contamination for up to 14 days, improving the performance of the following research studies, drug discovery, and assay development.
To improve the purity of cfDNA, it is essential to effectively separate plasma from other blood fractions containing cells which can potentially contaminate the sample. A two-step centrifugation procedure is commonly employed when working with cfDNA, as it allows for the removal of cellular debris still present in the plasma specimen after the initial centrifugation [13]. However, the recommended centrifugation protocol is typically provided in the tube datasheet.
Additionally, the selection of the appropriate extraction method is crucial for ensuring high-quality cfDNA. The majority of commercial kits for cfDNA analysis recommend the use of validated options to achieve optimal results in terms of both quantity and purity of the cfDNA. Among the plethora of available extraction kits, a significant proportion involve capturing cfDNA fragments using magnetic beads to separate them from genomic DNA contaminants, followed by repeated washing steps to effectively isolate nucleotides from proteins and lipids.
Nevertheless, prior to conducting molecular tests, it is highly advisable to precisely quantify the samples using a fluorometric instrument and verify the expected fragment size using automated electrophoresis systems. These systems can readily detect genomic contamination by analyzing a small volume of cfDNA.

4.2. Technical Comparison of cfDNA Analysis Methods

Advancements in technology, particularly the advent of quantitative PCR (qPCR) and next-generation sequencing (NGS), significantly enhanced the detection sensitivity and precision of cfDNA analysis. Methods for cfDNA analysis are generally divided into NGS and non-NGS approaches (Figure 1).

4.2.1. NGS-Based Methods

NGS-based approaches have the potential to simultaneously sequence thousands of targets. Considering the Illumina technology, its high accuracy and flexibility made it the most spread platform for cfDNA analysis compared to competitors, such as Ion Torrent, Oxford Nanopore, and Pacific Biosciences, which are still limited by their technical features that do not apply properly with short cfDNA fragments [85,86].
In the NGS workflow, DNA samples are amplified targeting hundreds or thousands of single nucleotide polymorphisms (SNPs) [33,46,87,88] selected depending on the application field, then DNA fragments are tagged by adaptors and indexed before being sequenced with an elevated depth that permits sensitive results after bioinformatics analyses. Assay types can vary according to the aim of the analysis, moving from tagged-amplicon deep sequencing (TAm-Seq), if the target sequence has been previously characterized [62,63], to personalized profiling by deep sequencing, such as CAPP-Seq applied in oncology [62,64], to whole genome bisulfite sequencing (WGBS-Seq) for DNA methylation analysis [65,66], and to whole exome (WES) or genome sequencing (WGS), which provide a comprehensive evaluation of tumor mutations, identifying potential oncogenes and tumor suppressor genes, deleterious alterations, and variants of unknown significance [62,67]. However, WES and WGS are limited by low sensitivity, excessive time and cost, and difficulties in the interpretation of results [2].
For accurate detection of low-abundance targets, such as in the case of liquid biopsy in which the fraction of target DNA within a cfDNA sample is potentially poorly represented, deep sequencing is necessary to provide the required sensitivity [89]. Recent improvements in sequencing instrumentation offer options with extremely high coverage depth for large portions of the entire genome in a single sample [90]. Although the cost of performing NGS has decreased considerably [91], this method can have a relatively consistent cost with a long turnaround time (often at least 3 days) and with variable sensitivity. Indeed, when assays are designed to cover several genetic targets, the comprehensive nature of NGS can provide value in efficiency and cost reduction, while NGS is more expensive and time-consuming when analyzing a small number of variants or samples [92]. Moreover, NGS does not always provide an absolute quantification of cfDNA meant as the total number of DNA copies [42,43,44,50,93,94,95,96,97].

4.2.2. Non-NGS Methods

Real-time or qPCR, microarrays, and digital PCR (dPCR) are included in non-NGS methods and offer a faster and less expensive detection option compared to NGS. These methods are generally used to detect and quantify the presence of known specific mutations or polymorphisms in cfDNA samples [14,68,69,70,71]. However, to enhance assay sensitivity, improved PCR approaches were developed. To identify single base changes or short deletion, the amplification refractory mutation system (ARMS-PCR) exploits sequence-specific PCR primers that allow amplification of DNA only when the target is contained within the sample, thus lowering the limit of detection in comparison with conventional PCR [71,72]. The same results can be obtained by peptide nucleic acid (PNA) clamp PCR, which prevents the nucleic acid amplification of wild-type DNA, increasing the amplification of the mutant DNA [73,74]. Another alternative is the co-amplification at lower denaturation temperature-based PCR (COLD-PCR), which results in the enhancement of both known and unknown minority alleles during PCR, irrespective of the mutation type and position. This method is based on the exploitation of the critical temperature at which mutation-containing DNA is preferentially melted over the wild type [71].
To increase the number of targets that can be examined simultaneously, PCR can be coupled with mass spectrometry. After amplification, PCR products are analyzed with mass spectrometry, searching for dozens of target mutations in a single reaction with great sensitivity [75].
Besides encouraging results, qPCR efficiency may be affected by variations in amplification. Furthermore, qPCR measures the fluorescence accumulation of the amplified product and requires normalization to a standard curve or to a reference, resulting in a relative quantification. The main difference between qPCR and dPCR is that, unlike conventional amplification, the reaction in dPCR is partitioned into thousands of sub-reactions, allowing absolute quantitation and high sensitivity. DPCR was first described in 1992 by Sykes et al., who changed standard amplification with the integration of limiting dilution, end-point PCR, and Poisson statistics [98]. While partitioning the samples in thousands of independent amplification reactions, dPCR reach higher accuracy and an absolute quantification of the target, which is determined by Poisson statistics. The evolution of the Sykes method was achieved by Vogelstein and Kinzler who added the detection of the target through fluorescent probes to the partitioning of the sample [99]. Current dPCR technology uses reagents and workflows similar to those used for most standard TaqMan probe-based assays with a smaller sample requirement, reducing cost and preserving precious samples. The methods described by Sykes, Vogelstein, and Kinzler have been improved and are commercially available as different platforms. dPCR amplification can be performed on a microfluidic chip [100], microarrays [101], or spinning microfluidic discs [102] or can be based on oil–water emulsions [76]. Moreover, dPCR technology enables high-throughput analysis with a reduced cost compared with other methods while maintaining great sensitivity and accuracy. Moreover, because cfDNA is poorly concentrated in plasma, repeated testing on different sample aliquots may not be possible. DPCR can overcome this limit, since it allows for an accurate detection and quantitation without separate calibration reactions [103], resulting in a reagent and sample saving. Compared with commercial qPCR assays [94], dPCR assays achieve a better limit of detection as well as a more accurate result.
However, dPCR shows practical drawbacks. The number of targets that can be detected is significantly lower compared to NGS-based methods due to the possibility of a multiplex from two to a maximum of six fluorophores using the most innovative instruments. Moreover, limitations in droplet-to-droplet volume uniformity can influence the quantification accuracy and reproducibility, but fluidics-based dPCR may offer an opportunity to overcome this limitation [77,104]. Then, PCR efficiency can vary due to different amplicon lengths [105], as longer amplicons are amplified less efficiently, which might result in underestimation of the true cfDNA value [78]. Similarly, Dauber et al. demonstrated that the cfDNA concentration was five times higher when using smaller amplicons compared with larger amplicons [68]. Therefore, the use of short amplicons is recommended for the accurate quantification of cfDNA to avoid underestimation of the target.
NGS and dPCR techniques were demonstrated to produce similar results in different application fields. The comparison on kidney transplant recipient samples highlighted no significant differences in the detection of cfDNA, with a significant association between the measurements obtained with both methods [106]. Moreover, lower limits of quantification were similar and in line with what is already reported in the literature [107], even though NGS method resulted more sensitive in the lower range than the dPCR method [106]. The quantification of mixed chimerism after hematopoietic stem cell transplantation appeared to be feasible with both methodologies conserving high performances in terms of sensitivity, reproducibility, and linearity [108]. Conversely, dPCR performed better in the detection of KRAS mutation in the oncologic field, with high sensitivity and specificity [79], and a limit of quantification 10-fold lower compared to NGS [80].
A great advantage of dPCR is the possibility to obtain the absolute concentration of the target, expressed as copies/µL or copies/mL, which is not influenced by fluctuations in the background cfDNA, derived from the patient. Indeed, NGS results can be expressed only in a cfDNA percentage that can be biased and underestimated as a consequence of physiological or pathological conditions of the subject (e.g., concomitant infections, BMI, exercise, etc.) [4,5,13,14,15,16]. The use of cfDNA as a concentration has also been shown to be superior to the ratio as a biomarker for allograft rejection [81].
In contrast with amplification-based methods, an imaging single-DNA-molecules method for high-precision cfDNA detection was developed. In the VANADIS assay (PerkinElmer, Waltham, MA), DNA fragments are labeled with fluorescent oligonucleotides specific for precise genetic targets, then circularized and copied multiple times before being placed on a 96-well nanofilter microplate and analyzed by imaging [82]. This assay is now applied to prenatal screening with high accuracy [83,84]. Since this method does not require DNA amplification and sequencing, it is easily implemented in any laboratory, scalable, and fully automated.

5. Conclusions

Given the potential applications of cfDNA, this biomarker is increasing the general agreement among clinicians in the oncology, prenatal, and transplantation fields. Despite the encouraging results, however, the cfDNA analysis is not a reality as it is exploited in a relatively small number of centers, and it is still considered a research marker to be further validated. Challenges persist in cfDNA analysis, highlighted by the scarcity of biomarkers, the necessity for developing highly sensitive methodologies, and the analytical complexities linked to processing and interpretation. Nevertheless, ongoing research endeavors strive to overcome these obstacles, potentially enhancing the clinical utility of cfDNA and facilitating its integration into routine diagnostic practices.
Novel and more powerful technologies are improving the sensitivity and the performances of cfDNA analysis, making its application easy, feasible, and attracting. NGS and dPCR, which are the main players in liquid biopsy, serve distinct purposes. NGS is a powerful tool for large-scale sequencing and genomics studies, while dPCR excels in quantifying specific cfDNA targets with exceptional precision and sensitivity. Considering the costs, NGS can be cost-effective for high-throughput sequencing projects but may be expensive for small-scale studies, while dPCR is generally more cost-effective for targeted, low-throughput applications. Therefore, the choice between these techniques should be based on the specific research goals and the scale of the project.
It is true that currently it is not entirely clear whether cfDNA can be considered a reliable diagnostic tool compared to standard methods. Regulations and regulatory agencies may vary significantly from country to country, which can influence the adoption and use of cfDNA in clinical practice [109,110].
In addition, the lack of standardized guidelines for the validation and reporting of cfDNA methods is a significant challenge in the field. While some progress has been made in prenatal diagnosis, there remains a need for comprehensive guidelines across various applications of cfDNA analysis [111]. Establishing such guidelines would not only enhance the reliability and reproducibility of cfDNA-based tests but also facilitate their wider adoption in clinical practice.
However, there is growing evidence suggesting the potential of cfDNA as a diagnostic marker in various clinical contexts, such as oncology, prenatal screening, and transplantation medicine. While further research and validation of cfDNA as a diagnostic tool are needed, advances in technology and understanding of cfDNA biology are contributing to making it increasingly promising as an integral part of disease diagnosis and monitoring. With additional studies and international collaborations, we may be able to better clarify the role and efficacy of cfDNA in different clinical settings and harmonize regulations to promote uniform adoption of this innovative technology.
In conclusion, the introduction of liquid biopsy offers new insights into disease detection and treatment response monitoring in the evolving field of precision medicine. In the future, cfDNA could be applied transversely to achieve a more personalized medicine, improving patients’ quality of life.

Funding

This work was supported by PRIN: Projects of National Relevant Interest Research—2022 359 PNRR Call Prot. P2022ZRF5H.

Conflicts of Interest

M. Sorbini, T. Vaisitti, and S. Deaglio are inventors of a patent (P2021.10-P022840IT-01) owned by the University of Turin for the development of a ddPCR kit for the quantification of dd-cfDNA, with royalty fees paid.

References

  1. Mandel, P.; Metais, P. Nuclear Acids In Human Blood Plasma. C. R. Seances Soc. Biol. Fil. 1948, 142, 241–243. [Google Scholar] [PubMed]
  2. Nikanjam, M.; Kato, S.; Kurzrock, R. Liquid biopsy: Current technology and clinical applications. J. Hematol. Oncol. 2022, 15, 131. [Google Scholar] [CrossRef] [PubMed]
  3. Lo, Y.M.D.; Han, D.S.C.; Jiang, P.; Chiu, R.W.K. Epigenetics, fragmentomics, and topology of cell-free DNA in liquid biopsies. Science 2021, 372, eaaw3616. [Google Scholar] [CrossRef] [PubMed]
  4. 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] [PubMed]
  5. Aucamp, J.; Bronkhorst, A.J.; Badenhorst, C.P.S.; Pretorius, P.J. The diverse origins of circulating cell-free DNA in the human body: A critical re-evaluation of the literature: The diverse origins of circulating cell-free DNA. Biol. Rev. 2018, 93, 1649–1683. [Google Scholar] [CrossRef] [PubMed]
  6. Grabuschnig, S.; Bronkhorst, A.J.; Holdenrieder, S.; Rosales Rodriguez, I.; Schliep, K.P.; Schwendenwein, D.; Ungerer, V.; Sensen, C.W. Putative Origins of Cell-Free DNA in Humans: A Review of Active and Passive Nucleic Acid Release Mechanisms. Int. J. Mol. Sci. 2020, 21, 8062. [Google Scholar] [CrossRef] [PubMed]
  7. McCoubrey-Hoyer, A.; Okarma, T.B.; Holman, H.R. Partial purification and characterization of plasma DNA and its relation to disease activity in systemic lupus erythematosus. Am. J. Med. 1984, 77, 23–34. [Google Scholar] [CrossRef] [PubMed]
  8. Lo, Y.M.D.; Chan, K.C.A.; Sun, H.; Chen, E.Z.; Jiang, P.; Lun, F.M.F.; Zheng, Y.W.; Leung, T.Y.; Lau, T.K.; Cantor, C.R.; et al. Maternal plasma DNA sequencing reveals the genome-wide genetic and mutational profile of the fetus. Sci. Transl. Med. 2010, 2, 61ra91. [Google Scholar] [CrossRef] [PubMed]
  9. Kustanovich, A.; Schwartz, R.; Peretz, T.; Grinshpun, A. Life and death of circulating cell-free DNA. Cancer Biol. Ther. 2019, 20, 1057–1067. [Google Scholar] [CrossRef]
  10. Rumore, P.M.; Steinman, C.R. Endogenous circulating DNA in systemic lupus erythematosus. Occurrence as multimeric complexes bound to histone. J. Clin. Investig. 1990, 86, 69–74. [Google Scholar] [CrossRef]
  11. Giacona, M.B.; Ruben, G.C.; Iczkowski, K.A.; Roos, T.B.; Porter, D.M.; Sorenson, G.D. Cell-free DNA in human blood plasma: Length measurements in patients with pancreatic cancer and healthy controls. Pancreas 1998, 17, 89–97. [Google Scholar] [CrossRef] [PubMed]
  12. Thierry, A.R.; El Messaoudi, S.; Gahan, P.B.; Anker, P.; Stroun, M. Origins, structures, and functions of circulating DNA in oncology. Cancer Metastasis Rev. 2016, 35, 347–376. [Google Scholar] [CrossRef]
  13. Edwards, R.L.; Menteer, J.; Lestz, R.M.; Baxter-Lowe, L.A. Cell-free DNA as a solid-organ transplant biomarker: Technologies and approaches. Biomark. Med. 2022, 16, 401–415. [Google Scholar] [CrossRef] [PubMed]
  14. Moreira, V.G.; Prieto, B.; Rodríguez, J.S.M.; Alvarez, F.V. Usefulness of cell-free plasma DNA, procalcitonin and C-reactive protein as markers of infection in febrile patients. Ann. Clin. Biochem. 2010, 47 Pt 3, 253–258. [Google Scholar] [CrossRef] [PubMed]
  15. Burnham, P.; Dadhania, D.; Heyang, M.; Chen, F.; Westblade, L.F.; Suthanthiran, M.; Lee, J.R.; De Vlaminck, I. Urinary cell-free DNA is a versatile analyte for monitoring infections of the urinary tract. Nat. Commun. 2018, 9, 2412. [Google Scholar] [CrossRef] [PubMed]
  16. Siljan, W.W.; Holter, J.C.; Nymo, S.H.; Schjalm, C.; Müller, F.; Jenum, P.A.; Husebye, E.; Ueland, T.; Aukrust, P.; Mollnes, T.E.; et al. Circulating cell-free DNA is elevated in community-acquired bacterial pneumonia and predicts short-term outcome. J. Infect. 2016, 73, 383–386. [Google Scholar] [CrossRef]
  17. Cisneros-Villanueva, M.; Hidalgo-Pérez, L.; Rios-Romero, M.; Cedro-Tanda, A.; Ruiz-Villavicencio, C.A.; Page, K.; Hastings, R.; Fernandez-Garcia, D.; Allsopp, R.; Fonseca-Montaño, M.A.; et al. Cell-free DNA analysis in current cancer clinical trials: A review. Br. J. Cancer 2022, 126, 391–400. [Google Scholar] [CrossRef] [PubMed]
  18. O’Sullivan, H.M.; Feber, A.; Popat, S. Minimal Residual Disease Monitoring in Radically Treated Non-Small Cell Lung Cancer: Challenges and Future Directions. OncoTargets Ther. 2023, 16, 249–259. [Google Scholar] [CrossRef]
  19. Spigel, D.R.; Ervin, T.J.; Ramlau, R.; Daniel, D.B.; Goldschmidt, J.H.; Blumenschein, G.R.; Krzakowski, M.J.; Robinet, G.; Clement-Duchene, C.; Barlesi, F.; et al. Final efficacy results from OAM4558g, a randomized phase II study evaluating MetMAb or placebo in combination with erlotinib in advanced NSCLC. J. Clin. Oncol. 2011, 29 (Suppl. S15), 7505. [Google Scholar] [CrossRef]
  20. Zhou, C.; Wu, Y.-L.; Chen, G.; Feng, J.; Liu, X.-Q.; Wang, C.; Zhang, S.; Wang, J.; Zhou, S.; Ren, S.; et al. Erlotinib versus chemotherapy as first-line treatment for patients with advanced EGFR mutation-positive non-small-cell lung cancer (OPTIMAL, CTONG-0802): A multicentre, open-label, randomised, phase 3 study. Lancet Oncol. 2011, 12, 735–742. [Google Scholar] [CrossRef]
  21. Gatzemeier, U.; Pluzanska, A.; Szczesna, A.; Kaukel, E.; Roubec, J.; Brennscheidt, U.; De Rosa, F.; Mueller, B.; Von Pawel, J. Results of a phase III trial of erlotinib (OSI-774) combined with cisplatin and gemcitabine (GC) chemotherapy in advanced non-small cell lung cancer (NSCLC). J. Clin. Oncol. 2004, 22 (Suppl. S14), 7010. [Google Scholar] [CrossRef]
  22. Oxnard, G.R.; Thress, K.S.; Alden, R.S.; Lawrance, R.; Paweletz, C.P.; Cantarini, M.; Yang, J.C.-H.; Barrett, J.C.; Jänne, P.A. Association Between Plasma Genotyping and Outcomes of Treatment with Osimertinib (AZD9291) in Advanced Non–Small-Cell Lung Cancer. J. Clin. Oncol. 2016, 34, 3375–3382. [Google Scholar] [CrossRef]
  23. Paz-Ares, L.; Luft, A.; Vicente, D.; Tafreshi, A.; Gümüş, M.; Mazières, J.; Hermes, B.; Çay Şenler, F.; Csőszi, T.; Fülöp, A.; et al. Pembrolizumab plus Chemotherapy for Squamous Non-Small-Cell Lung Cancer. N. Engl. J. Med. 2018, 379, 2040–2051. [Google Scholar] [CrossRef]
  24. Fabrizio, D.; Lieber, D.; Malboeuf, C.; Silterra, J.; White, E.; Coyne, M.; Brennan, T.; Ma, J.; Kennedy, M.; Schleifman, E.; et al. Abstract 5706: A blood-based next-generation sequencing assay to determine tumor mutational burden (bTMB) is associated with benefit to an anti-PD-L1 inhibitor, atezolizumab. Cancer Res. 2018, 78, 5706. [Google Scholar] [CrossRef]
  25. Hellmann, M.D.; Nathanson, T.; Rizvi, H.; Creelan, B.C.; Sanchez-Vega, F.; Ahuja, A.; Ni, A.; Novik, J.B.; Mangarin, L.M.B.; Abu-Akeel, M.; et al. Genomic Features of Response to Combination Immunotherapy in Patients with Advanced Non-Small-Cell Lung Cancer. Cancer Cell 2018, 33, 843–852.e4. [Google Scholar] [CrossRef] [PubMed]
  26. Kato, S.; Li, B.; Adashek, J.J.; Cha, S.W.; Bianchi-Frias, D.; Qian, D.; Kim, L.; So, T.W.; Mitchell, M.; Kamei, N.; et al. Serial changes in liquid biopsy-derived variant allele frequency predict immune checkpoint inhibitor responsiveness in the pan-cancer setting. Oncoimmunology 2022, 11, 2052410. [Google Scholar] [CrossRef]
  27. Zhang, Q.; Luo, J.; Wu, S.; Si, H.; Gao, C.; Xu, W.; Abdullah, S.E.; Higgs, B.W.; Dennis, P.A.; van der Heijden, M.S.; et al. Prognostic and Predictive Impact of Circulating Tumor DNA in Patients with Advanced Cancers Treated with Immune Checkpoint Blockade. Cancer Discov. 2020, 10, 1842–1853. [Google Scholar] [CrossRef]
  28. Bardelli, A.; Pantel, K. Liquid Biopsies, What We Do Not Know (Yet). Cancer Cell 2017, 31, 172–179. [Google Scholar] [CrossRef]
  29. 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. Off. J. Eur. Soc. Med. Oncol. 2015, 26, 1715–1722. [Google Scholar] [CrossRef]
  30. Magbanua, M.J.M.; Swigart, L.B.; Wu, H.-T.; Hirst, G.L.; Yau, C.; Wolf, D.M.; Tin, A.; Salari, R.; Shchegrova, S.; Pawar, H.; et al. Circulating tumor DNA in neoadjuvant-treated breast cancer reflects response and survival. Ann. Oncol. 2021, 32, 229–239. [Google Scholar] [CrossRef]
  31. Cao, H.; Liu, X.; Chen, Y.; Yang, P.; Huang, T.; Song, L.; Xu, R. Circulating Tumor DNA Is Capable of Monitoring the Therapeutic Response and Resistance in Advanced Colorectal Cancer Patients Undergoing Combined Target and Chemotherapy. Front. Oncol. 2020, 10, 466. [Google Scholar] [CrossRef]
  32. Ma, F.; Zhu, W.; Guan, Y.; Yang, L.; Xia, X.; Chen, S.; Li, Q.; Guan, X.; Yi, Z.; Qian, H.; et al. ctDNA dynamics: A novel indicator to track resistance in metastatic breast cancer treated with anti-HER2 therapy. Oncotarget 2016, 7, 66020–66031. [Google Scholar] [CrossRef]
  33. Dawson, S.-J.; Tsui, D.W.Y.; 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]
  34. Gandara, D.R.; Paul, S.M.; Kowanetz, M.; Schleifman, E.; Zou, W.; Li, Y.; Rittmeyer, A.; Fehrenbacher, L.; Otto, G.; Malboeuf, C.; et al. Blood-based tumor mutational burden as a predictor of clinical benefit in non-small-cell lung cancer patients treated with atezolizumab. Nat. Med. 2018, 24, 1441–1448. [Google Scholar] [CrossRef]
  35. Lehmann-Werman, R.; Neiman, D.; Zemmour, H.; Moss, J.; Magenheim, J.; Vaknin-Dembinsky, A.; Rubertsson, S.; Nellgård, B.; Blennow, K.; Zetterberg, H.; et al. Identification of tissue-specific cell death using methylation patterns of circulating DNA. Proc. Natl. Acad. Sci. USA 2016, 113, E1826–E1834. [Google Scholar] [CrossRef] [PubMed]
  36. Jamshidi, A.; Liu, M.C.; Klein, E.A.; Venn, O.; Hubbell, E.; Beausang, J.F.; Gross, S.; Melton, C.; Fields, A.P.; Liu, Q.; et al. Evaluation of cell-free DNA approaches for multi-cancer early detection. Cancer Cell 2022, 40, 1537–1549.e12. [Google Scholar] [CrossRef] [PubMed]
  37. Rather, R.A.; Saha, S.C. Reappraisal of evolving methods in non-invasive prenatal screening: Discovery, biology and clinical utility. Heliyon 2023, 9, e13923. [Google Scholar] [CrossRef]
  38. Wright, C.F.; Burton, H. The use of cell-free fetal nucleic acids in maternal blood for non-invasive prenatal diagnosis. Hum. Reprod. Update 2009, 15, 139–151. [Google Scholar] [CrossRef] [PubMed]
  39. Nectoux, J. Current, Emerging, and Future Applications of Digital PCR in Non-Invasive Prenatal Diagnosis. Mol. Diagn. Ther. 2018, 22, 139–148. [Google Scholar] [CrossRef]
  40. Hui, L.; Bianchi, D.W. Fetal fraction and noninvasive prenatal testing: What clinicians need to know. Prenat. Diagn. 2020, 40, 155–163. [Google Scholar] [CrossRef]
  41. Gil, M.M.; Accurti, V.; Santacruz, B.; Plana, M.N.; Nicolaides, K.H. Analysis of cell-free DNA in maternal blood in screening for aneuploidies: Updated meta-analysis. Ultrasound Obstet. Gynecol. Off. J. Int. Soc. Ultrasound Obstet. Gynecol. 2017, 50, 302–314. [Google Scholar] [CrossRef] [PubMed]
  42. 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]
  43. De Vlaminck, I.; Martin, L.; Kertesz, M.; Patel, K.; Kowarsky, M.; Strehl, C.; Cohen, G.; Luikart, H.; Neff, N.F.; Okamoto, J.; et al. Noninvasive monitoring of infection and rejection after lung transplantation. Proc. Natl. Acad. Sci. USA 2015, 112, 13336–13341. [Google Scholar] [CrossRef] [PubMed]
  44. Gielis, E.M.; Ledeganck, K.J.; Dendooven, A.; Meysman, P.; Beirnaert, C.; Laukens, K.; De Schrijver, J.; Van Laecke, S.; Van Biesen, W.; Emonds, M.-P.; et al. The use of plasma donor-derived, cell-free DNA to monitor acute rejection after kidney transplantation. Nephrol. Dial. Transplant. Off. Publ. Eur. Dial. Transpl. Assoc.-Eur. Ren. Assoc. 2020, 35, 714–721. [Google Scholar] [CrossRef]
  45. Oellerich, M.; Budde, K.; Osmanodja, B.; Bornemann-Kolatzki, K.; Beck, J.; Schütz, E.; Walson, P.D. Donor-derived cell-free DNA as a diagnostic tool in transplantation. Front. Genet. 2022, 13, 1031894. [Google Scholar] [CrossRef] [PubMed]
  46. Grskovic, M.; Hiller, D.J.; Eubank, L.A.; Sninsky, J.J.; Christopherson, C.; Collins, J.P.; Thompson, K.; Song, M.; Wang, Y.S.; Ross, D.; et al. Validation of a Clinical-Grade Assay to Measure Donor-Derived Cell-Free DNA in Solid Organ Transplant Recipients. J. Mol. Diagn. JMD 2016, 18, 890–902. [Google Scholar] [CrossRef] [PubMed]
  47. Sigdel, T.K.; Archila, F.A.; Constantin, T.; Prins, S.A.; Liberto, J.; Damm, I.; Towfighi, P.; Navarro, S.; Kirkizlar, E.; Demko, Z.P.; et al. Optimizing Detection of Kidney Transplant Injury by Assessment of Donor-Derived Cell-Free DNA via Massively Multiplex PCR. J. Clin. Med. 2018, 8, 19. [Google Scholar] [CrossRef]
  48. Sorbini, M.; Togliatto, G.M.; Simonato, E.; Boffini, M.; Cappuccio, M.; Gambella, A.; Arruga, F.; Mora, N.; Marro, M.; Caorsi, C.; et al. HLA-DRB1 mismatch-based identification of donor-derived cell free DNA (dd-cfDNA) as a marker of rejection in heart transplant recipients: A single-institution pilot study. J. Heart Lung Transplant. Off. Publ. Int. Soc. Heart Transplant. 2021, 40, 794–804. [Google Scholar] [CrossRef] [PubMed]
  49. Sorbini, M.; Togliatto, G.; Mioli, F.; Simonato, E.; Marro, M.; Cappuccio, M.; Arruga, F.; Caorsi, C.; Mansouri, M.; Magistroni, P.; et al. Validation of a Simple, Rapid, and Cost-Effective Method for Acute Rejection Monitoring in Lung Transplant Recipients. Transpl. Int. Off. J. Eur. Soc. Organ Transplant. 2022, 35, 10546. [Google Scholar] [CrossRef]
  50. Snyder, T.M.; Khush, K.K.; Valantine, H.A.; Quake, S.R. Universal noninvasive detection of solid organ transplant rejection. Proc. Natl. Acad. Sci. USA 2011, 108, 6229–6234. [Google Scholar] [CrossRef]
  51. Sharon, E.; Shi, H.; Kharbanda, S.; Koh, W.; Martin, L.R.; Khush, K.K.; Valantine, H.; Pritchard, J.K.; De Vlaminck, I. Quantification of transplant-derived circulating cell-free DNA in absence of a donor genotype. PLoS Comput. Biol. 2017, 13, e1005629. [Google Scholar] [CrossRef] [PubMed]
  52. Chen, Y.-B.; Cutler, C.S. Biomarkers for acute GVHD: Can we predict the unpredictable? Bone Marrow Transplant. 2013, 48, 755–760. [Google Scholar] [CrossRef] [PubMed]
  53. Waterhouse, M.; Pennisi, S.; Pfeifer, D.; Deuter, M.; von Bubnoff, N.; Scherer, F.; Strüssmann, T.; Wehr, C.; Duyster, J.; Bertz, H.; et al. Colon and liver tissue damage detection using methylated SESN3 and PTK2B genes in circulating cell-free DNA in patients with acute graft-versus-host disease. Bone Marrow Transplant. 2021, 56, 327–333. [Google Scholar] [CrossRef] [PubMed]
  54. Moss, J.; Magenheim, J.; Neiman, D.; Zemmour, H.; Loyfer, N.; Korach, A.; Samet, Y.; Maoz, M.; Druid, H.; Arner, P.; et al. Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nat. Commun. 2018, 9, 5068. [Google Scholar] [CrossRef] [PubMed]
  55. Sun, K.; Jiang, P.; Chan, K.C.A.; Wong, J.; Cheng, Y.K.Y.; Liang, R.H.S.; Chan, W.; 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]
  56. Cheng, A.P.; Cheng, M.P.; Loy, C.J.; Lenz, J.S.; Chen, K.; Smalling, S.; Burnham, P.; Timblin, K.M.; Orejas, J.L.; Silverman, E.; et al. Cell-free DNA profiling informs all major complications of hematopoietic cell transplantation. Proc. Natl. Acad. Sci. USA 2022, 119, e2113476118. [Google Scholar] [CrossRef]
  57. Levitsky, J.; Kandpal, M.; Guo, K.; Kleiboeker, S.; Sinha, R.; Abecassis, M. Donor-derived cell-free DNA levels predict graft injury in liver transplant recipients. Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2022, 22, 532–540. [Google Scholar] [CrossRef] [PubMed]
  58. Verhoeven, J.G.H.P.; Boer, K.; Peeters, A.M.A.; Clahsen-van Groningen, M.C.; Roodnat, J.I.; van de Wetering, J.; Nieboer, D.; Bost, D.A.; Baan, C.C.; Hesselink, D.A. A Novel High-throughput Droplet Digital PCR-based Indel Quantification Method for the Detection of Circulating Donor-derived Cell-free DNA After Kidney Transplantation. Transplantation 2022, 106, 1777–1786. [Google Scholar] [CrossRef]
  59. Norton, S.E.; Lechner, J.M.; Williams, T.; Fernando, M.R. A stabilizing reagent prevents cell-free DNA contamination by cellular DNA in plasma during blood sample storage and shipping as determined by digital PCR. Clin. Biochem. 2013, 46, 1561–1565. [Google Scholar] [CrossRef]
  60. Knüttgen, F.; Beck, J.; Dittrich, M.; Oellerich, M.; Zittermann, A.; Schulz, U.; Fuchs, U.; Knabbe, C.; Schütz, E.; Gummert, J.; et al. Graft-derived Cell-free DNA as a Noninvasive Biomarker of Cardiac Allograft Rejection: A Cohort Study on Clinical Validity and Confounding Factors. Transplantation 2022, 106, 615–622. [Google Scholar] [CrossRef]
  61. Clausen, F.B.; Jørgensen, K.M.C.L.; Wardil, L.W.; Nielsen, L.K.; Krog, G.R. Droplet digital PCR-based testing for donor-derived cell-free DNA in transplanted patients as noninvasive marker of allograft health: Methodological aspects. PLoS ONE 2023, 18, e0282332. [Google Scholar] [CrossRef] [PubMed]
  62. Li, H.; Jing, C.; Wu, J.; Ni, J.; Sha, H.; Xu, X.; Du, Y.; Lou, R.; Dong, S.; Feng, J. Circulating tumor DNA detection: A potential tool for colorectal cancer management. Oncol. Lett. 2019, 17, 1409–1416. [Google Scholar] [CrossRef] [PubMed]
  63. 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]
  64. Newman, A.M.; Bratman, S.V.; To, J.; Wynne, J.F.; Eclov, N.C.W.; 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. [Google Scholar] [CrossRef]
  65. Wardenaar, R.; Liu, H.; Colot, V.; Colomé-Tatché, M.; Johannes, F. Evaluation of MeDIP-chip in the context of whole-genome bisulfite sequencing (WGBS-seq) in Arabidopsis. Methods Mol. Biol. Clifton NJ 2013, 1067, 203–224. [Google Scholar] [CrossRef]
  66. Hon, G.C.; Hawkins, R.D.; Caballero, O.L.; Lo, C.; Lister, R.; Pelizzola, M.; Valsesia, A.; Ye, Z.; Kuan, S.; Edsall, L.E.; et al. Global DNA hypomethylation coupled to repressive chromatin domain formation and gene silencing in breast cancer. Genome Res. 2012, 22, 246–258. [Google Scholar] [CrossRef] [PubMed]
  67. Imperial, R.; Nazer, M.; Ahmed, Z.; Kam, A.E.; Pluard, T.J.; Bahaj, W.; Levy, M.; Kuzel, T.M.; Hayden, D.M.; Pappas, S.G.; et al. Matched Whole-Genome Sequencing (WGS) and Whole-Exome Sequencing (WES) of Tumor Tissue with Circulating Tumor DNA (ctDNA) Analysis: Complementary Modalities in Clinical Practice. Cancers 2019, 11, 1399. [Google Scholar] [CrossRef]
  68. Dauber, E.-M.; Kollmann, D.; Kozakowski, N.; Rasoul-Rockenschaub, S.; Soliman, T.; Berlakovich, G.A.; Mayr, W.R. Quantitative PCR of INDELs to measure donor-derived cell-free DNA-a potential method to detect acute rejection in kidney transplantation: A pilot study. Transpl. Int. Off. J. Eur. Soc. Organ Transplant. 2020, 33, 298–309. [Google Scholar] [CrossRef]
  69. García-Fernández, N.; Macher, H.C.; Suárez-Artacho, G.; Gómez-Bravo, M.Á.; Molinero, P.; Guerrero, J.M.; Porras-López, M.; Rubio, A. Donor-Specific Cell-Free DNA qPCR Quantification as a Noninvasive Accurate Biomarker for Early Rejection Detection in Liver Transplantation. J. Clin. Med. 2022, 12, 36. [Google Scholar] [CrossRef]
  70. Fernández-Galán, E.; Badenas, C.; Fondevila, C.; Jiménez, W.; Navasa, M.; Puig-Butillé, J.A.; Brunet, M. Monitoring of Donor-Derived Cell-Free DNA by Short Tandem Repeats: Concentration of Total Cell-Free DNA and Fragment Size for Acute Rejection Risk Assessment in Liver Transplantation. Liver Transplant. Off. Publ. Am. Assoc. Study Liver Dis. Int. Liver Transplant. Soc. 2022, 28, 257–268. [Google Scholar] [CrossRef]
  71. Galbiati, S.; Damin, F.; Burgio, V.; Brisci, A.; Soriani, N.; Belcastro, B.; Di Resta, C.; Gianni, L.; Chiari, M.; Ronzoni, M.; et al. Evaluation of three advanced methodologies, COLD-PCR, microarray and ddPCR, for identifying the mutational status by liquid biopsies in metastatic colorectal cancer patients. Clin. Chim. Acta 2019, 489, 136–143. [Google Scholar] [CrossRef] [PubMed]
  72. Zhang, X.; Chang, N.; Yang, G.; Zhang, Y.; Ye, M.; Cao, J.; Xiong, J.; Han, Z.; Wu, S.; Shang, L.; et al. A comparison of ARMS-Plus and droplet digital PCR for detecting EGFR activating mutations in plasma. Oncotarget 2017, 8, 112014–112023. [Google Scholar] [CrossRef] [PubMed]
  73. Simarro, J.; Pérez-Simó, G.; Mancheño, N.; Ansotegui, E.; Muñoz-Núñez, C.F.; Gómez-Codina, J.; Juan, Ó.; Palanca, S. Technical Validation and Clinical Implications of Ultrasensitive PCR Approaches for EGFR-Thr790Met Mutation Detection in Pretreatment FFPE Samples and in Liquid Biopsies from Non-Small Cell Lung Cancer Patients. Int. J. Mol. Sci. 2022, 23, 8526. [Google Scholar] [CrossRef]
  74. Watanabe, K.; Fukuhara, T.; Tsukita, Y.; Morita, M.; Suzuki, A.; Tanaka, N.; Terasaki, H.; Nukiwa, T.; Maemondo, M. EGFR Mutation Analysis of Circulating Tumor DNA Using an Improved PNA-LNA PCR Clamp Method. Can. Respir. J. 2016, 2016, e5297329. [Google Scholar] [CrossRef] [PubMed]
  75. Lamy, P.-J.; van der Leest, P.; Lozano, N.; Becht, C.; Duboeuf, F.; Groen, H.J.M.; Hilgers, W.; Pourel, N.; Rifaela, N.; Schuuring, E.; et al. Mass Spectrometry as a Highly Sensitive Method for Specific Circulating Tumor DNA Analysis in NSCLC: A Comparison Study. Cancers 2020, 12, 3002. [Google Scholar] [CrossRef] [PubMed]
  76. O’Leary, B.; Hrebien, S.; Beaney, M.; Fribbens, C.; Garcia-Murillas, I.; Jiang, J.; Li, Y.; Huang Bartlett, C.; André, F.; Loibl, S.; et al. Comparison of BEAMing and Droplet Digital PCR for Circulating Tumor DNA Analysis. Clin. Chem. 2019, 65, 1405–1413. [Google Scholar] [CrossRef] [PubMed]
  77. Dueck, M.E.; Lin, R.; Zayac, A.; Gallagher, S.; Chao, A.K.; Jiang, L.; Datwani, S.S.; Hung, P.; Stieglitz, E. Precision cancer monitoring using a novel, fully integrated, microfluidic array partitioning digital PCR platform. Sci. Rep. 2019, 9, 19606. [Google Scholar] [CrossRef]
  78. Sikora, A.; Zimmermann, B.G.; Rusterholz, C.; Birri, D.; Kolla, V.; Lapaire, O.; Hoesli, I.; Kiefer, V.; Jackson, L.; Hahn, S. Detection of increased amounts of cell-free fetal DNA with short PCR amplicons. Clin. Chem. 2010, 56, 136–138. [Google Scholar] [CrossRef]
  79. Ye, P.; Cai, P.; Xie, J.; Wei, Y. The diagnostic accuracy of digital PCR, ARMS and NGS for detecting KRAS mutation in cell-free DNA of patients with colorectal cancer: A systematic review and meta-analysis. PLoS ONE 2021, 16, e0248775. [Google Scholar] [CrossRef]
  80. Dong, L.; Wang, S.; Fu, B.; Wang, J. Evaluation of droplet digital PCR and next generation sequencing for characterizing DNA reference material for KRAS mutation detection. Sci. Rep. 2018, 8, 9650. [Google Scholar] [CrossRef]
  81. Oellerich, M.; Shipkova, M.; Asendorf, T.; Walson, P.D.; Schauerte, V.; Mettenmeyer, N.; Kabakchiev, M.; Hasche, G.; Gröne, H.-J.; Friede, T.; et al. Absolute quantification of donor-derived cell-free DNA as a marker of rejection and graft injury in kidney transplantation: Results from a prospective observational study. Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2019, 19, 3087–3099. [Google Scholar] [CrossRef] [PubMed]
  82. Dahl, F.; Ericsson, O.; Karlberg, O.; Karlsson, F.; Howell, M.; Persson, F.; Roos, F.; Stenberg, J.; Ahola, T.; Alftrén, I.; et al. Imaging Single DNA Molecules for High Precision NIPT. Sci. Rep. 2018, 8, 4549. [Google Scholar] [CrossRef] [PubMed]
  83. Pooh, R.K.; Masuda, C.; Matsushika, R.; Machida, M.; Nakamura, T.; Takeda, M.; Ohashi, H.; Kumagai, M.; Uenishi, K.; Roos, F.; et al. Clinical Validation of Fetal cfDNA Analysis Using Rolling-Circle-Replication and Imaging Technology in Osaka (CRITO Study). Diagnostics 2021, 11, 1837. [Google Scholar] [CrossRef] [PubMed]
  84. Saidel, M.L.; Ananth, U.; Rose, D.; Farrell, C. Non-Invasive prenatal testing with rolling circle amplification: Real-world clinical experience in a non-molecular laboratory. J. Clin. Lab. Anal. 2023, 37, e24870. [Google Scholar] [CrossRef] [PubMed]
  85. Laver, T.; Harrison, J.; O’Neill, P.A.; Moore, K.; Farbos, A.; Paszkiewicz, K.; Studholme, D.J. Assessing the performance of the Oxford Nanopore Technologies MinION. Biomol. Detect. Quantif. 2015, 3, 1–8. [Google Scholar] [CrossRef] [PubMed]
  86. Carneiro, M.O.; Russ, C.; Ross, M.G.; Gabriel, S.B.; Nusbaum, C.; DePristo, M.A. Pacific biosciences sequencing technology for genotyping and variation discovery in human data. BMC Genom. 2012, 13, 375. [Google Scholar] [CrossRef] [PubMed]
  87. Halloran, P.F.; Reeve, J.; Madill-Thomsen, K.S.; Demko, Z.; Prewett, A.; Gauthier, P.; Billings, P.; Lawrence, C.; Lowe, D.; Hidalgo, L.G.; et al. Antibody-mediated Rejection without Detectable Donor-specific Antibody Releases Donor-derived Cell-free DNA: Results From the Trifecta Study. Transplantation 2023, 107, 709–719. [Google Scholar] [CrossRef] [PubMed]
  88. 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]
  89. Lin, C.; Liu, X.; Zheng, B.; Ke, R.; Tzeng, C.-M. Liquid Biopsy, ctDNA Diagnosis through NGS. Life 2021, 11, 890. [Google Scholar] [CrossRef]
  90. Reuter, J.A.; Spacek, D.V.; Snyder, M.P. High-throughput sequencing technologies. Mol. Cell 2015, 58, 586–597. [Google Scholar] [CrossRef]
  91. Levy, S.E.; Boone, B.E. Next-Generation Sequencing Strategies. Cold Spring Harb. Perspect. Med. 2019, 9, a025791. [Google Scholar] [CrossRef] [PubMed]
  92. Schwarz, U.I.; Gulilat, M.; Kim, R.B. The Role of Next-Generation Sequencing in Pharmacogenetics and Pharmacogenomics. Cold Spring Harb. Perspect. Med. 2019, 9, a033027. [Google Scholar] [CrossRef] [PubMed]
  93. Dengu, F. Next-generation sequencing methods to detect donor-derived cell-free DNA after transplantation. Transplant. Rev. 2020, 34, 100542. [Google Scholar] [CrossRef]
  94. Zhao, Y.; Xia, Q.; Yin, Y.; Wang, Z. Comparison of Droplet Digital PCR and Quantitative PCR Assays for Quantitative Detection of Xanthomonas citri Subsp. citri. PLoS ONE 2016, 11, e0159004. [Google Scholar] [CrossRef] [PubMed]
  95. Feingold, B.; Rose-Felker, K.; West, S.C.; Zinn, M.D.; Berman, P.; Moninger, A.; Huston, A.; Stinner, B.; Xu, Q.; Zeevi, A.; et al. Early findings after integration of donor-derived cell-free DNA into clinical care following pediatric heart transplantation. Pediatr. Transplant. 2022, 26, e14124. [Google Scholar] [CrossRef] [PubMed]
  96. Amadio, J.M.; Rodenas-Alesina, E.; Superina, S.; Kozuszko, S.; Tsang, K.; Simard, A.; Aleksova, N.; Kobulnik, J.; Fan, C.-P.S.; Wijeysundera, H.C.; et al. Sparing the Prod: Providing an Alternative to Endomyocardial Biopsies with Noninvasive Surveillance After Heart Transplantation During COVID-19. CJC Open 2022, 4, 479–487. [Google Scholar] [CrossRef] [PubMed]
  97. Kamath, M.; Shekhtman, G.; Grogan, T.; Hickey, M.J.; Silacheva, I.; Shah, K.S.; Shah, K.S.; Hairapetian, A.; Gonzalez, D.; Godoy, G.; et al. Variability in Donor-Derived Cell-Free DNA Scores to Predict Mortality in Heart Transplant Recipients—A Proof-of-Concept Study. Front. Immunol. 2022, 13, 825108. [Google Scholar] [CrossRef]
  98. Sykes, P.J.; Neoh, S.H.; Brisco, M.J.; Hughes, E.; Condon, J.; Morley, A.A. Quantitation of targets for PCR by use of limiting dilution. BioTechniques 1992, 13, 444–449. [Google Scholar]
  99. Vogelstein, B.; Kinzler, K.W. Digital PCR. Proc. Natl. Acad. Sci. USA 1999, 96, 9236–9241. [Google Scholar] [CrossRef]
  100. Ottesen, E.A.; Hong, J.W.; Quake, S.R.; Leadbetter, J.R. Microfluidic digital PCR enables multigene analysis of individual environmental bacteria. Science 2006, 314, 1464–1467. [Google Scholar] [CrossRef]
  101. Morrison, T.; Hurley, J.; Garcia, J.; Yoder, K.; Katz, A.; Roberts, D.; Cho, J.; Kanigan, T.; Ilyin, S.E.; Horowitz, D.; et al. Nanoliter high throughput quantitative PCR. Nucleic Acids Res. 2006, 34, e123. [Google Scholar] [CrossRef] [PubMed]
  102. Sundberg, S.O.; Wittwer, C.T.; Gao, C.; Gale, B.K. Spinning disk platform for microfluidic digital polymerase chain reaction. Anal. Chem. 2010, 82, 1546–1550. [Google Scholar] [CrossRef] [PubMed]
  103. 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] [PubMed]
  104. Quan, P.-L.; Sauzade, M.; Brouzes, E. dPCR: A Technology Review. Sensors 2018, 18, 1271. [Google Scholar] [CrossRef] [PubMed]
  105. Verhoeven, J.G.H.P.; Peeters, A.M.A.; Hesselink, D.A.; Boer, K. Pitfalls in the Detection of Donor-Derived Cell-Free DNA in Transplant Recipients. Clin. Chem. 2021, 67, 1030–1032. [Google Scholar] [CrossRef] [PubMed]
  106. Verhoeven, J.G.H.P.; Baan, C.C.; Peeters, A.M.A.; Nieboer, D.; Hesselink, D.A.; Boer, K. A comparison of two different analytical methods for donor-derived cell-free DNA quantification. Clin. Biochem. 2021, 96, 82–84. [Google Scholar] [CrossRef] [PubMed]
  107. Oellerich, M.; Christenson, R.H.; Beck, J.; Schütz, E.; Sherwood, K.; Price, C.P.; Keown, P.A.; Walson, P.D. Donor-Derived Cell-Free DNA Testing in Solid Organ Transplantation: A Value Proposition. J. Appl. Lab. Med. 2020, 5, 993–1004. [Google Scholar] [CrossRef] [PubMed]
  108. Picard, C.; Frassati, C.; Cherouat, N.; Maioli, S.; Moskovtchenko, P.; Cherel, M.; Chiaroni, J.; Pedini, P. New methods for the quantification of mixed chimerism in transplantation. Front. Immunol. 2023, 14, 1023116. [Google Scholar] [CrossRef] [PubMed]
  109. Horgan, D.; Čufer, T.; Gatto, F.; Lugowska, I.; Verbanac, D.; Carvalho, Â.; Lal, J.A.; Kozaric, M.; Toomey, S.; Ivanov, H.Y.; et al. Accelerating the Development and Validation of Liquid Biopsy for Early Cancer Screening and Treatment Tailoring. Healthcare 2022, 10, 1714. [Google Scholar] [CrossRef]
  110. Lockwood, C.M.; Borsu, L.; Cankovic, M.; Earle, J.S.L.; Gocke, C.D.; Hameed, M.; Jordan, D.; Lopategui, J.R.; Pullambhatla, M.; Reuther, J.; et al. Recommendations for Cell-Free DNA Assay Validations: A Joint Consensus Recommendation of the Association for Molecular Pathology and College of American Pathologists. J. Mol. Diagn. 2023, 25, 876–897. [Google Scholar] [CrossRef]
  111. Abedalthagafi, M.; Bawazeer, S.; Fawaz, R.I.; Heritage, A.M.; Alajaji, N.M.; Faqeih, E. Non-invasive prenatal testing: A revolutionary journey in prenatal testing. Front. Med. 2023, 10, 1265090. [Google Scholar] [CrossRef] [PubMed]
Figure 1. List of the NGS-based and non-NGS methods for cfDNA analysis described in the review. The different methodologies are divided according to their technological approaches. The main methods are highlighted in blue, while derived methods are indicated by arrows. References are listed by application field [27,29,33,34,35,39,46,47,51,56,57,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84]. NGS: Next-generation Sequencing; TAm-Seq: Tagged-amplicon Deep Sequencing; CAPP-Seq: Cancer Personalized Profiling by Deep Sequencing; WGBS-Seq: Whole Genome Bisulfite Sequencing; WES: Whole Exome Sequencing; WGS: Whole Genome Sequencing; qPCR: quantitative PCR; ARMS-PCR: Amplification Refractory Mutation System PCR; PNA Clamp PCR: Peptide Nucleic Acid Clamp PCR; COLD-PCR: Co-amplification at Lower Denaturation Temperature-based PCR; dPCR: digital PCR; BEAMing: Beads, Emulsion, Amplification, Magnetics PCR.
Figure 1. List of the NGS-based and non-NGS methods for cfDNA analysis described in the review. The different methodologies are divided according to their technological approaches. The main methods are highlighted in blue, while derived methods are indicated by arrows. References are listed by application field [27,29,33,34,35,39,46,47,51,56,57,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84]. NGS: Next-generation Sequencing; TAm-Seq: Tagged-amplicon Deep Sequencing; CAPP-Seq: Cancer Personalized Profiling by Deep Sequencing; WGBS-Seq: Whole Genome Bisulfite Sequencing; WES: Whole Exome Sequencing; WGS: Whole Genome Sequencing; qPCR: quantitative PCR; ARMS-PCR: Amplification Refractory Mutation System PCR; PNA Clamp PCR: Peptide Nucleic Acid Clamp PCR; COLD-PCR: Co-amplification at Lower Denaturation Temperature-based PCR; dPCR: digital PCR; BEAMing: Beads, Emulsion, Amplification, Magnetics PCR.
Biomolecules 14 00498 g001
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Sorbini, M.; Carradori, T.; Togliatto, G.M.; Vaisitti, T.; Deaglio, S. Technical Advances in Circulating Cell-Free DNA Detection and Analysis for Personalized Medicine in Patients’ Care. Biomolecules 2024, 14, 498. https://doi.org/10.3390/biom14040498

AMA Style

Sorbini M, Carradori T, Togliatto GM, Vaisitti T, Deaglio S. Technical Advances in Circulating Cell-Free DNA Detection and Analysis for Personalized Medicine in Patients’ Care. Biomolecules. 2024; 14(4):498. https://doi.org/10.3390/biom14040498

Chicago/Turabian Style

Sorbini, Monica, Tullia Carradori, Gabriele Maria Togliatto, Tiziana Vaisitti, and Silvia Deaglio. 2024. "Technical Advances in Circulating Cell-Free DNA Detection and Analysis for Personalized Medicine in Patients’ Care" Biomolecules 14, no. 4: 498. https://doi.org/10.3390/biom14040498

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop