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ReviewOpen AccessOpen Access license

Cell-free DNA as a solid-organ transplant biomarker: technologies and approaches

    Rebecca L Edwards

    Department of Pathology & Laboratory Medicine, Children's Hospital Los Angeles, Los Angeles, CA 90027, USA

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    ,
    Jondavid Menteer

    Keck School of Medicine, University of Southern California, Los Angeles, CA 90027, USA

    Division of Cardiology, Children's Hospital Los Angeles, Los Angeles, CA 90027, USA

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    ,
    Rachel M Lestz

    Keck School of Medicine, University of Southern California, Los Angeles, CA 90027, USA

    Division of Nephrology, Children's Hospital Los Angeles, Los Angeles, CA 90027, USA

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    &
    Lee Ann Baxter-Lowe

    *Author for correspondence:

    E-mail Address: lbaxterlowe@chla.usc.edu

    Department of Pathology & Laboratory Medicine, Children's Hospital Los Angeles, Los Angeles, CA 90027, USA

    Keck School of Medicine, University of Southern California, Los Angeles, CA 90027, USA

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    Published Online:23 Feb 2022https://doi.org/10.2217/bmm-2021-0968

    Abstract

    High-quality biomarkers that detect emergent graft damage and/or rejection after solid-organ transplantation offer new opportunities to improve post-transplant monitoring, allow early therapeutic intervention and facilitate personalized patient management. Donor-derived cell-free DNA (DD-cfDNA) is a particularly exciting minimally invasive biomarker because it has the potential to be quantitative, time-sensitive and cost-effective. Increased DD-cfDNA has been associated with graft damage and rejection episodes. Efforts are underway to further improve sensitivity and specificity. This review summarizes the procedures used to process and detect DD-cfDNA, measurement of DD-cfDNA in clinical transplantation, approaches for improving sensitivity and specificity and long-term prospects as a transplant biomarker to supplement traditional organ monitoring and invasive biopsies.

    Clinical perspective

    Approximately 40,000 people in the USA receive a life-saving organ transplant annually, but long-term survival can be substantially improved [1]. One-year survival rates are above 80% for most organs but the typical half-life of transplanted organs is less than 15 years, with the shortest survival for single-lung transplants, which have a half-life of only 6 years [2–4]. Immunosuppression is a key factor influencing graft and patient survival; inadequate immunosuppression increases rejection and graft loss while excess immunosuppression can lead to opportunistic infections, cancer and other toxicities. Improved biomarkers offer the potential for enhanced personalized management of immunosuppression, optimized post-transplant monitoring and early therapeutic intervention at the onset of graft damage.

    After transplantation of solid organs, functional parameters are used to monitor graft dysfunction and biopsies are used to detect pathologic organ changes. In kidney and liver transplantation, graft function is monitored using serum creatinine or aminotransferases, respectively [5–7]. In heart transplantation, natriuretic peptide levels, echocardiography and magnetic resonance imaging are frequently used [8,9]. In lung transplantation, pulmonary function tests and 6-minute-walk tests are used [10]. Biopsies are not desirable for routine monitoring because they are invasive, expensive and risk complications; nonetheless, they are the gold standard for diagnosing rejection and graft damage.

    Biomarkers that provide sensitive and cost-effective monitoring of graft damage and rejection offer an exciting opportunity to improve therapeutic decisions that could contribute to improved patient and graft survival through less invasive testing. Circulating cell-free DNA (cf-DNA), particularly donor-derived cf-DNA (DD-cfDNA), is one of the most promising new biomarkers for monitoring graft damage and providing an early indication of rejection in organ transplants [11,12]. DD-cfDNA has been measured in blood and urine; this review is limited to DD-cfDNA in blood because it has been most widely studied.

    Cell-free DNA characteristics

    In 1948, the presence of cf-DNA was described by Mandela and Metais and subsequent research has shown that levels of cf-DNA vary and can change due to a variety of factors [13]. In healthy individuals, levels of circulating cf-DNA range 0–100 ng/ml. There are three main types of cf-DNA found in bodily fluids: free DNA, DNA coupled to proteins and DNA encapsulated in or connected to extracellular vesicles [14]. The half-life of cf-DNA is only 16 min to 2.5 h because it is rapidly degraded by nucleases and is eliminated by the liver and kidneys [15,16]. The majority of cf-DNA fragments are 150–180 base pairs (bp) in length, nuclear derived and released into the bloodstream as a result of apoptotic cell death [17]. However, circulating DNA fragments of up to 21,000 bp have been observed; these fragments are associated with tissue necrosis [18,19]. Additionally, cf-DNA can also be derived from mitochondria. This cf-DNA is usually smaller, typically ranging 30–80 bp [20].

    The levels and characteristics of cf-DNA are frequently altered in disease states and this information has been clinically useful. In chronic inflammation, malignancies and injury processes, cf-DNA can be increased. In certain circumstances characterizing cf-DNA from specific cells (e.g., a fetus, a tumor or a transplanted organ) can be clinically useful [18,21–25]. Additional factors contribute to the attractiveness of cf-DNA as a biomarker including minimally invasive collection, several options for accurate and cost-effective detection and informative characteristics such as size and sequence [26].

    Specimen handling & isolation methods

    Collection and processing of specimens are important because cf-DNA is unstable and often present in low abundance. Plasma is preferable to serum because cf-DNA is released from blood cells during clotting [27,28]. The collection tubes are also important. EDTA tubes can be used, but these need to be processed within approximately 4 h to limit the release of DNA from leukocytes [29]. Specialized cf-DNA stabilization tubes including Cell-Free DNA BCT (Streck, NE, USA), Cell-Free DNA Collection Tube (Roche Diagnostics, Basel, Switzerland) and PAXgene Blood ccfDNA Tube, (Qiagen, MD, USA) can extend the time before processing.

    Independent of the collection conditions, under certain circumstances, cf-DNA can be altered or degraded before isolation [29–32]. To minimize events that would alter cf-DNA levels or quality, it is preferable to process samples on-site. When shipping of blood samples is necessary, it is recommended that tubes be transported in the upright position and handled gently to prevent hemolysis (e.g., vibration and shaking) [30]. To obtain reliable results, temperature extremes (less than 10°C or more than 40°C) during shipping should be avoided [31]. Even if these precautions are taken, inconsistent DD-cfDNA percentage has been reported in shipped whole-blood samples. This problem can be mitigated by separating plasma prior to shipment on dry ice [33]. However, most publications describe the analysis of DD-cfDNA from blood specimens shipped at ambient temperature.

    The centrifugation procedure to prepare plasma is important to maximize the yield and quality of cf-DNA while preventing contamination from genomic DNA [34]. A two-step process (an initial low speed followed by higher speed centrifugation to remove cellular debris) is widely used. There are several different protocols to isolate and extract cf-DNA and the extraction method can affect cf-DNA yield from healthy individuals and patients [35,36]. For example, the Maxwell RSC ccfDNA plasma kits provide rapid automatic sample processing and the Qiagen circulating nucleic acid kit provides particularly high cf-DNA yields (1936 genome equivalents/ml of plasma) of which almost 90% are fragments of short size. For solid-organ transplant recipients, most publications describe using a spin column-based method (Tables 1 & 2). Commercial cf-DNA isolation kits include the QIAamp circulating nucleic acid kit (Qiagen, MD, USA), Maxwell RSC ccfDNA Plasma kit (Promega, WI, USA) and MagMAX Cell-free DNA isolation kit (Life Technologies, CA, USA).

    Table 1. Detection cell-free DNA in solid-organ transplant recipients.
    Approaches for detecting cell-free DNA in solid-organ transplant recipients
    ApproachStudyYearOrgan(s)Recipients (n)TargetsTechnology platformBlood collection tubescf-DNA isolation methodRef.
    Detecting total cf-DNAGarcia Moreira et al.2009Kidney100HBB, TSPY1 genesqPCREDTAQIAamp DNA Blood Mini Kit[48]
    Detecting Y chromosomeLui et al.2003Heart, kidney, liver31HBB, SRY genesqPCREDTAQIAamp DNA Blood Kit[60]
    Macher et al.2014Liver10HBB, SRY genesqPCRxMagna Pure Compact Nucleic Acid Isolation Kit I[61]
    Detecting HLA typesZou et al.2017Lung188 HLA-DR allelesddPCRxQIAamp Circulating Nucleic Acid Kit[62]
    Gadi et al.2006Pancreas-kidney42HLA specific targetsqPCRxDNA Mini Kit[63]
    Multitarget NGS assaysSnyder et al.2011Heart44SNPsNGSxNucleospin Plasma F kit[59]
    De Vlaminck et al.2015Lung51SNPsNGSxQIAamp Circulating Nucleic Acid Kit[42]
    De Vlaminck et al.2014Heart65SNPsNGSxQIAamp Circulating Nucleic Acid Kit[41]
    Gielis et al.2020Kidney1071027 SNPsNGSStreckQIAamp Circulating Nucleic Acid Kit[79]
    Zhang et al.2020Kidney3756,049 SNPsNGSxQIAamp Circulating Nucleic Acid Kit[80]
    Zhao et al.2021Liver496200 SNPsNGSStreckQIAamp Circulating Nucleic Acid Kit[43]
    Non-NGS multitarget assaysHidestrand et al.2014Heart3294 SNPsqPCRxQIAamp Circulating Nucleic Acid Kit[66]
    Ragalie et al.2018Heart8894 SNPsqPCRxQIAamp Circulating Nucleic Acid Kit[82]
    Richmond et al.2020Heart17494 SNPsqPCRxReliaPrep HT Circulating Nucleic Acid Kit[83]
    North et al.2020Heart7694 SNPsqPCRStreck/EDTAProprietary extraction protocol[33]
    Dauber et al.2020Kidney2934 INDELsqPCREDTAQIAamp Circulating Nucleic Acid Kit[47]
    Beck et al.2013Heart, kidney, liver3441 SNPsddPCRStreck/EDTAHigh Pure viral nucleic acid extraction kit[45]
    Schutz et al.2017Liver10740 SNPsddPCRStreckHigh Pure viral nucleic acid extraction kit[84]
    Knuttgen et al.2021Heart8740 SNPsddPCRStreckHigh Pure viral nucleic acid extraction kit[86]
    Absolute amount of DD-cfDNAWhitlam et al.2019Kidney5538 genetic variantsddPCRStreckQIAamp Circulating Nucleic Acid Kit[89]
    Oellerich et al.2019Kidney18941 SNPsddPCREDTAHigh Pure viral nucleic acid extraction LV kit[90]

    cf-DNA: Cell-free DNA; DNA: Deoxyribonucleic acid; ddPCR: Droplet digital polymerase chain reaction; DD-cfDNA: Donor-derived cell-free DNA; EDTA: Ethylenediaminetetraacetic acid; HLA: Human Leukocyte antigen; INDELs: Insertion and deletion mutations; NGS: Next-generation sequencing; qPCR: Quantitative polymerase chain reaction; SNPs: Single-nucleotide polymorphisms; Streck: Streck Cell-Free DNA BCT tubes (Streck, NE, USA).

    Table 2. Commercial testing for cell-free DNA in solid-organ transplant recipients.
    Commercial providerStudyYearOrgan(s)Recipients (n)TargetsTechnology platformBlood collection tubescf-DNA isolation methodRef.
    CareDxBloom et al.2017Kidney102266 SNPsNGSStreckQIAamp Circulating Nucleic Acid Kit[24]
    Huang et al.2019Kidney63266 SNPsNGSStreckQIAamp Circulating Nucleic Acid Kit[68]
    Khush et al.2019Heart740266 SNPsNGSStreckQIAamp Circulating Nucleic Acid Kit[69]
    Khush et al.2021Lung38266 SNPsNGSStreckQIAamp Circulating Nucleic Acid Kit[70]
    Puliyanda et al.2020Kidney67266 SNPsNGSStreckQIAamp Circulating Nucleic Acid Kit[71]
    Sayah et al.2020Lung69266 SNPsNGSStreckQIAamp Circulating Nucleic Acid Kit[72]
    Levine et al.2020Lung48266 SNPsNGSStreckQIAamp Circulating Nucleic Acid Kit[73]
    NateraSigdel et al.2018Kidney17813,392 SNPsNGSxQIAamp Circulating Nucleic Acid Kit[25]
    Altug et al.2019Kidney613,392 SNPsNGSStreckIn-house extraction protocol/QIAamp Circulating Nucleic Acid Kit[75]

    cf-DNA isolation kit manufacturers DNA mini kit: QIAamp Circulating Nucleic acid kit, QIAamp DNA Blood kit, QIAamp DNA Blood mini kit, (Qiagen, MD, USA); High pure viral nucleic acid (LV) extraction KIT, Manga Pure Compact Nucleic acid Isolation Kit I, (Roche, Basel, Switzerland); Nucleospin Plasma F kit, (E&K Scientific Products, CA, USA) and ReliaPrep HT Circulating Nucleic Acid Kit, (Promega, WI, USA).

    Detecting donor-derived cell-free DNA after solid-organ transplantation

    In 1998, Lo et al. reported the presence of DD-cfDNA in plasma from kidney and liver transplant recipients [12]. Even then, it was hypothesized that this DNA originated from the transplanted organ and could be a biomarker for rejection. In the years that followed, many investigators generated evidence supporting their hypothesis and pursued the development of diagnostic tests that detect DD-cfDNA (Figure 1, Table 1 & 2).

    Figure 1. Articles related to cell-free DNA and solid-organ transplantation identified in PubMed from 2010–2020.

    PubMed was searched using the terms: ‘cell-free DNA’ and ‘transplantation’. Articles not related to solid-organ transplantation were manually removed (accessed on: 19/10/2021).

    DD-cfDNA in the blood is usually highest immediately after solid-organ transplantation and generally decreases thereafter. Soon after kidney transplantation, more than 5% of the cf-DNA in the blood can be derived from the graft and this declines at varying rates. The percentage of DD-cfDNA often decreases to approximately 0.5% within 10 days [37–39]. Gielis et al. report 35.8% of kidney recipients did not reach baseline levels of DD-cfDNA 10 days after transplantation [39]. A recent study of 125 kidney recipients with no evidence of active rejection reported a median DD-cfDNA level of 0.32% 1-month post-transplantation [40]. In a single study one day after heart transplantation, the average fraction of DD-cfDNA was 3.8%. After 7 days the level was less than 1% [41]. After lung and liver transplantation, levels of cf-DNA were much higher immediately after transplant. In a study of lung transplants one day after lung transplantation, DD-cfDNA averaged 26%. It is was then reduced to 1–3% within the first week [42]. After liver transplantation, plasma DD-cfDNA fractions have been reported to approach 90% of total cf-DNA. By day 10, these donor fractions usually dropped to 15% [43]. It has been suggested that the kinetics in the reduction of cf-DNA after transplantation could have potential prognostic value and may predict graft dysfunction [42,44].

    Several technologies including quantitative polymerase chain reaction (qPCR), next-generation sequencing (NGS) and droplet digital PCR (dd-PCR) have been explored to measure the amount of circulating cf-DNA. Each technology offers different advantages. NGS-based approaches [24,25,41] can simultaneously sequence thousands of targets with the potential to obtain additional information (e.g., length of sequence). However, NGS methods can have a relatively long turnaround time (often at least 3 days), with variable sensitivity and high cost. Digital PCR and qPCR offer faster and less expensive detection options, but practical limitations (e.g., the number of targets that can be detected) may affect performance characteristics [45–48]. Potential advantages of digital PCR include absolute quantitation and high sensitivity [49].

    This review describes different approaches and technology platforms that have been utilized to develop minimally invasive assays for measuring DD-cfDNA in transplantation (Table 1 & 2). Most publications use rejection as an end point for evaluating the performance of DD-cfDNA as a biomarker. Variability in positive predictive value (PPV) and negative predictive value (NPV) suggests this biomarker is more useful as a measure of nonspecific graft damage. As a result, this test shows promise as a screening tool but has limited usefulness for the diagnosis of rejection.

    Assays detecting total cell-free DNA

    Total cf-DNA has been investigated as a potential biomarker for rejection, but the results show that total cf-DNA lacks specificity. For example, Garcia Moreira et al. [48] investigated whether total cf-DNA and/or DD-cfDNA could be effective markers for acute rejection. They used qPCR to determine total cf-DNA and DD-cfDNA levels of HBB and TSPY1 genes in plasma and urine samples from 100 renal transplant recipients. In this study, a total cf-DNA concentration cutoff of 12,000 genome equivalents/ml plasma distinguished acute rejection episodes from other events in 86% of post-transplantation complications (sensitivity: 89%; specificity: 85%). The authors noted that similar increases in total cf-DNA were observed after infection. Other studies have shown higher levels of total cf-DNA in patients with graft damage compared with those with stable graft function. A major limitation of using total cf-DNA as a biomarker is that levels can be influenced by age, BMI, exercise, circadian rhythm, sepsis, cancer, chemotherapy, hemodialysis, myocardial infarction, sample processing, shipping and storage methods (Figure 2) [31–33,50–59].

    Figure 2. Factors influencing cell-free DNA levels.

    Assays detecting Y chromosome

    Detection of the Y chromosome can be informative in sex-mismatched donor-recipient pairs. Lui et al. [60] employed real-time PCR detecting the SRY gene in plasma to determine the percentage of male cf-DNA in heart, liver and kidney sex-mismatched transplant pairs. A similar technique was employed by Macher et al., who measured the SRY gene levels in plasma of ten female recipients of male liver grafts [61]. SRY gene levels increased immediately after transplantation, then fell during postoperative hospitalization. If the donor organ was injured, SRY gene levels increased. A major limitation of this approach is that is only useful in sex-mismatched pairs.

    Assays detecting human leukocyte antigen types

    Human leukocyte antigen (HLA) differences can be used to distinguish donor and recipient DNA without the necessity for donor-recipient sex-mismatched pairs. Zou et al. used a dd-PCR method to determine the percentage of DD-cfDNA in 18 lung transplant recipients [62]. They used probes for eight common HLA-DR alleles to differentiate donor and recipient DNA. DD-cfDNA levels of <2% were observed in stable patients while patients with any acute rejection had considerably higher amounts of DD-cfDNA, with some DD-cfDNA values exceeding 10%. Gadi et al. used qPCR with a panel of probes for several HLA-specific targets to determine DD-cfDNA in the sera of 42 pancreas-kidney transplant recipients [63]. Median DD-cfDNA concentrations of 10.4 gEq/ml were measured in patients with rejection compared with 0.9 gEq/ml in nonrejection patients. While multiple HLA mismatches are present in most pancreas-kidney transplants, the method may not be suited to detecting DD-cfDNA in cases where donor recipients are HLA-DR matched. This limitation could be mitigated by detecting other informative targets.

    Assays detecting multiple targets

    Limitations of single-target approaches have been addressed by taking advantage of the 4–5 million single-nucleotide polymorphisms (SNPs) in an individual's genome to distinguish donor and recipient DNA [64]. NGS has been used to interrogate thousands of potentially informative loci [24,65]. Preferably, the recipient and donor are homozygous for different SNPs. Other technologies including qPCR and digital PCR have been used to detect specific targets such as SNPs [45,66].

    Multitarget next-generation sequencing assays

    An early retrospective study used NGS to interrogate pretransplant samples from heart recipients and their donors to identify optimal SNPs for detecting DD-cfDNA in plasma collected after transplantation [59]. This study, in addition to prospective heart and lung transplantation studies, revealed that unrelated donor-receipt pairs have approximately 50,000 useful SNP markers [41,42]. In a retrospective study of 44 heart recipients, DD-cfDNA was correlated with rejection when the positive threshold was 1.7% (PPV: 83%). Prospective studies also showed a correlation between DD-cfDNA levels and biopsy-proven rejection. In 65 heart transplant recipients, DD-cfDNA above 0.25% was reported to be a marker of rejection (area under the curve [AUC]: 0.83; sensitivity: 58%; specificity: 93%) [41]. In 51 lung transplant recipients, a DD-cfDNA threshold of 1% was reported to be associated with rejection (AUC: 0.9; sensitivity: 100%; specificity: 73%) [42]. A limitation of this approach is that genomic DNA from the donor and recipient is needed to determine informative SNPs before the cf-DNA data can be analyzed.

    Commercially available next-generation sequencing tests

    The need for donor genotyping was eliminated by targeted NGS tests developed to measure DD-cfDNA [67]. One of the commercial tests, CareDX Allosure (CA, USA) has been described in several publications [24,68-73] A panel of 266 SNPs located on 22 somatic chromosomes was used to study 102 kidney recipients including 27 with any biopsy-proven rejection [24]. The test was reported to have 85% specificity, 59% sensitivity, an AUC of 0.74, 61% PPV, and 84% NPV using a 1% cutoff for the fraction of DD-cfDNA to distinguish the presence or absence of active rejection. Table 3 summarizes the results of subsequent CareDX investigations using this test in kidney, heart and lung recipients [24,68–73]. These data illustrate organ-specific differences in DD-cfDNA levels that correlated with rejection (from 0.2% in heart to 1% in kidney). It is difficult to evaluate the efficacy of this test because each dataset used a different cutoff for the positive threshold.

    Table 3. Results of donor-derived cell-free DNA test from a single commercial provider.
    StudyYearOrganPatientsCutoffAUCPPV %NPV %Sensitivity %Specificity %Ref.
    Bloom et al.2017Kidney1021.000.74618459.085.0[24]
    Huang et al.2019Kidney630.740.7177.17579.472.4[68]
    Puliyanda et al.2020Kidney671.000.996NRNR86.0100.0[71]
    Khush et al.2019Heart7400.200.648.997.144.080.0[69]
    Sayah et al.2020Lung690.870.7234.185.573.152.9[72]
    Levine et al.2020Lung480.510.98NRNR81100[73]
    Khush et al.2021Lung380.850.6743.383.655.675.8[70]

    AUC: Area under the curve; NPV: Negative predictive value; NR: Not reported; PPV: Positive predictive value.

    Natera (CA, USA) took advantage of their experience using cf-DNA for minimally invasive prenatal detection of fetal chromosome abnormalities to develop a DD-cfDNA test (Prospera) [22,25,74,75]. This approach targets 13,392 SNPs located on four chromosomes (2, 13, 18, 21) to discriminate recipient and DD-cfDNA [67]. A retrospective study of 178 kidney transplant recipients diagnosed with acute T-cell-mediated rejection (TCMR) and/or antibody-mediated rejection (AMR), showed that greater than 1% DD-cfDNA correlated with graft rejection (specificity: 73%; sensitivity: 89%; AUC: 0.87; PPV: 52%; NPV: 95%) [25]. One limitation of this report is that conditions for blood collection and storage, which can affect results, were not described.

    Several other companies have developed DD-cfDNA tests, but these have produced few or no peer-reviewed publications. For example, Eurofins Viracor Transplant Rejection Allograft Check (TRAC; MO, USA) has a DD-cfDNA assay that uses NGS and genome-wide recipient genotype data to determine the percentage of cf-DNA derived from donor organs. According to a document from the eurofins-viracor.com website (mm-0997-rev2-1219-trac-dd-cf-dna-kidney-lab-bulletin.pdf), when cf-DNA was determined for 77 kidney transplant recipients, increased DD-cfDNA was associated with rejection using a 0.69% cutoff for a positive result (sensitivity: 58%; specificity: 85%; PPV: 55%; NPV: 86%; AUC: 0.85).

    One of the drawbacks of these commercial NGS tests is that while they are otherwise suited for routine graft surveillance, their cost is prohibitively high for use as a screening test. Puttarajappa et al. conducted an economic analysis for using commercial DD-cfDNA tests to screen for subclinical rejection in kidney transplantation [76]. They concluded that screening for subclinical rejection more than once in the first year after transplantation was not cost-effective. They estimated that if cf-DNA tests were used two to three times annually they would only be cost-effective at less than US$700 per test. However, they reported costs of commercial NGS-based cf-DNA tests as US$2200–US$2800 [77]. A cf-DNA test employing an alternate technology platform, dd-PCR, has a reported cost of about US$400 per test, but there are only a few small studies based on this technology [78]. An additional limitation of commercial tests that are performed by a central laboratory is that the requirement for shipping can delay testing for days and expose specimens to conditions that can alter the cf-DNA results [33].

    Other multitarget next-generation sequencing assays

    Another targeted NGS-based approach used 1027 SNPs to determine the percentage of DD-cfDNA in 107 kidney recipients [79]. A threshold value of 0.88% was associated with an acute rejection episode or other allograft injury (AUC: 0.64; sensitivity: 38%; specificity: 85%). This assay did not perform better than serum creatinine (AUC: 0.64). The authors report DD-cfDNA as a fraction of total circulating cf-DNA rather than absolute quantification of DD-cfDNA levels. The authors conclude the usefulness of reporting % of DD-cfDNA is limited by various nonrejection-related increases that occur in kidney transplant recipients.

    An NGS assay developed by Zhang et al. used 56,049 SNPs to evaluate the performance of DD-cfDNA in detecting AMR in 37 kidney transplant recipients [80]. The authors reported that the median DD-cfDNA was significantly higher in patients with AMR (2.4%) compared to those without (0.65%). A limitation of this study is that it only discriminates AMR from no rejection; TCMR or other types were not included. Under these conditions with a 1% DD-cfDNA threshold the assay had an AUC of 0.90, 89% sensitivity, 74% specificity, 76% PPV and 88% NPV. These results have not been confirmed in a larger patient population.

    Another recent study using NGS with a panel of 6200 SNPs reported that DD-cfDNA levels were elevated in 49 pediatric liver recipients with any biopsy-confirmed rejection [43]. The AUC for DD-cfDNA% was 0.88, which was higher than the AUC for traditional liver function tests. A DD-cfDNA threshold of 28.7% correlated with rejection (sensitivity: 73%; specificity: 95%; PPV: 80%; NPV: 92%). The linear range of the assay was not reported. Prior work with reference materials validated assay precision for donor DNA fractions of 0.6–21% [38]. These NGS assays have predominantly been used to screen for graft damage in kidney, heart and lung recipients where cutoff values are reported at 1% DD-cfDNA or less with a linear quantifiable range validated between 0.15–16% [81].

    The NGS-based approaches described here demonstrate that DD-cfDNA detection has some clinical usefulness but there have been many inconstancies in data interpretation and results. Differing assay performance characteristics and variability in positive thresholds even when using the same tests on samples from patients with the same graft type makes it difficult to evaluate assay robustness.

    Non-next-generation sequencing multitarget assays

    DD-cfDNA has also been measured using qPCR and digital PCR, which offer several potential advantages including lower cost and faster turnaround time. A current limitation is that most published studies have been relatively small scale (8–115 transplant recipients). In 2014, Hidestrand et al. used qPCR with a panel of 94 SNPs to quantify DD-cfDNA in 32 heart transplant recipients. With the cutoff for rejection set at 1%, the assay identified all rejection episodes (sensitivity: 100%; specificity: 84%) [66]. Follow-up studies in heart transplant recipients described other DD-cfDNA% thresholds for detection: Hidestrand et al. 1%, Ragalie et al. 0.87%, Richmond et al. 0.20% and North et al. 0.32% [33,66,82,83]. The low diagnostic cutoffs for DD-cfDNA% in heart transplant recipients of 0.20–0.25% align with other published studies employing NGS-based methods [41,69].

    A qPCR approach targeting 34 insertion/deletion polymorphisms (INDELs) was used to measure DD-cfDNA in plasma samples from 29 kidney transplant recipients [47]. Receiver operating characteristic (ROC) analysis revealed an AUC for discriminating biopsy-proven rejection and nonrejection of 0.84. At a cutoff value of 2.7% DD-cfDNA, the assay had a sensitivity of 0.88 and a specificity of 0.81 with 64% PPV and 94% NPV. Dd-PCR has been explored because it offers a rapid assay with high accuracy and sensitivity [84,85]. In 2013, Beck et al. detected 41 SNPs with high minor allele frequencies in plasma from stable transplant recipients and observed DD-cfDNA fractions below 6.8% in liver, 2.5% in kidney and 3.4% in heart recipients [45]. Schutz et al. measured DD-cfDNA in 115 liver transplant recipients using a panel of 40 SNPs and dd-PCR in reactions that achieved a minimum of 10,000 droplets [46]. At a DD-cfDNA threshold of 10%, the authors reported 90% sensitivity and 93% specificity. The AUC was higher for DD-cfDNA (AUC: 0.97) than for same-day liver function tests including aspartate aminotransferase (AUC: 0.96) and alanine aminotransferase (AUC: 0.95) [84]. A study using the same dd-PCR method followed 87 heart recipients for 12 months post-transplant reported 76% sensitivity and 83% specificity of DD-cfDNA for rejection, at a cutoff of 0.35%. The authors observed a significant increase in DD-cfDNA (0.36%) 9–30 days prior to biopsy-proven rejection [86]. Dd-PCR has some drawbacks. Limitations in droplet-to-droplet volume uniformity can impact quantification accuracy and reproducibility. The dd-PCR system used in these studies routinely generated only 11,000–16,000 analyzable droplets per sample out of a possible 20,000. Consequently, only 55–80% of the samples that are loaded are analyzed [87]. Fluidics-based digital PCR may offer an opportunity to overcome this limitation [88].

    Measuring the absolute amount of donor-derived cell-free DNA

    An alternative approach to determining the percentage of cf-DNA is to measure the absolute quantity of DD-cfDNA. This could mitigate contributions from increases in total cf-DNA levels caused by nonrejection events or sample processing [89,90]. There are only a few published reports using this approach and outcomes were variable. Whitlam et al. used dd-PCR to investigate the absolute copy number of donor DNA using 38 genetic variants in 55 kidney recipients. Copy number variations not present within a recipient's genome but present within the extracted cf-DNA were presumed to represent DD-cfDNA [89]. DD-cfDNA was significantly elevated in patients with acute AMR compared with those with negative biopsies (30 vs 7 copies/ml). For acute AMR, using a threshold of 21 copies/ml, the diagnostic odds ratio was 69 with 90% sensitivity, 88% specificity, 0.60 PPV and 0.98 NPV. However, results showed the assay did not correlate with the diagnosis of borderline or cellular-mediated rejection. The authors reported a sharp increase in total cf-DNA levels in patients with E. coli bacteremia, hemoptysis, fevers and foot inflammation (17,341–20,542 copies/ml). This suggests that their are nongraft-related effects on total cf-DNA levels. Therefore measuring total cf-DNA may not be a suitable approach for monitoring graft damage.

     Oellerich et al. [90] used dd-PCR with a panel of 41 informative SNPs to compare measuring absolute levels of DD-cfDNA with %DD-cfDNA in 189 kidney transplant recipients. ROC analysis showed superior performance of measuring absolute DD-cfDNA (AUC: 0.83) compared with %DD-cfDNA (AUC: 0.73) with diagnostic odds ratios of 7.31 for DD-cfDNA levels at a threshold of 52 cp/ml and 6.02 for %DD-cfDNA at a threshold of 0.43%. This study also reported that the absolute amount of DD-cfDNA was significantly higher in patients with lower tacrolimus levels (<8 μg/l) in comparison with those with higher tacrolimus levels (p = 0.0036). This suggests that absolute DD-cfDNA levels also have the potential to detect allograft injury resulting from inadequate immunosuppression [90].

    Conclusion

    In solid-organ transplantation, numerous studies provide evidence that elevated DD-cfDNA levels correlate with clinically relevant end points. However, the low and variable diagnostic thresholds suggest there are fundamental aspects of utilizing cf-DNA as a biomarker that warrant further investigation. For example, it is unclear whether it is best to measure the absolute amount of DD-cfDNA, % of DD-cfDNA or a combination of both measurements. Confounding variables such as the effects of sample processing and shipping should be clarified and diminished to the extent possible. Nongraft-related effects on total cf-DNA levels due to other biological variables (e.g., inflammation, infection and exercise) should be considered when interpreting results. Large increases in recipient cf-DNA levels could alter %DD-cfDNA, potentially obscuring graft damage. Reporting absolute DD-cfDNA levels rather than % of DD-cfDNA is a possible solution. Finally, assays will need to be standardized to be most effective in routine practice.

     For commercial tests that require shipping it is important to remain cautious about utility and efficacy. Limitations to the implementation of commercial tests for routine transplant surveillance include high cost, relatively long turnaround time and potential for inconsistent results caused by shipping whole-blood samples for cf-DNA analysis at a central laboratory [33]. It may be optimal for these tests to be performed within individual transplantation centers because cf-DNA is unstable and test results can potentially be influenced by shipping and delayed sample processing. Testing on-site could facilitate communication between lab and clinical teams to personalize the interpretation of data.

     There is strong demand for DD-cfDNA to be a specific marker for rejection, however, this may not be feasible due to the many confounding variables that affect cf-DNA. Two recently published meta-analyses concluded DD-cfDNA could be a useful marker for AMR among kidney transplant recipients with renal dysfunction. Still, the authors concluded that it is not currently a reliable marker for detecting cellular rejection [91,92].

    Future perspective

    In the transplant field, there is a high level of enthusiasm for minimally invasive DD-cfDNA testing for routine monitoring of graft damage to supplement relatively insensitive organ function tests and invasive biopsies (Figure 3). Additionally, improved biomarkers provide an opportunity to advance personalized management of transplant patients such as optimizing levels of immunosuppressive medications to prevent rejection and minimize drug toxicities [90]. There is also evidence that cf-DNA could reveal emergent graft damage before functional parameters drift outside of the normal range. This could allow early therapeutic interventions to prevent clinically significant graft damage. The expectation is that DD-cfDNA testing will contribute to prolonged graft and patient survival. Pediatric patients may benefit most from this testing, especially if coupled with a reduced need for biopsy (with procedure-related complications and age-related need for anesthesia and related costs) and longer post-transplant life-years [3]. Ongoing investigations offer promise to understand better the role of DD-cfDNA as a transplant biomarker and new technology that will improve DD-cfDNA measurement. As technology platforms suited to measuring cf-DNA continue to evolve, additional improvements in accuracy, sensitivity and potentially assay cost should follow.

    Figure 3. Donor-derived cell-free DNA as a transplant biomarker.

    (A) Increases in DNA derived from the donor organ were observed following organ damage. Increases are also observed in subclinical disease. (B) Measuring DD-cfDNA levels in transplant recipients longitudinally to screen for: (1) subclinical disease, (2) clinical disease, (3) response to treatment and (4) graft failure.

    Continued improvement of DD-cfDNA measurement along with the development of complementary biomarkers shows strong promise to revolutionize the management of transplant patients in the near future. For example, in kidney transplant recipients combining a positive DD-cfDNA result (1% cutoff) with positive donor-specific antibodies (DSA), a mean fluorescence intensity >1000 increases accuracy in identifying active AMR [93]. For samples with ≥1% DD-cfDNA that were also DSA+, the PPV was 81%. The PPV for DSA+ only samples were 48% and for DD-cfDNA+ only samples, 44% [24,93]. Characterizing graft-derived extracellular vesicles shows promise as a complementary biomarker that could identify the cause of graft damage [94].

    Executive summary

    Clinical perspective

    • Monitoring of solid-organ transplants typically involves invasive biopsy procedures and insensitive functional tests.

    • The ability to measure cell-free DNA (cf-DNA), and specifically donor-derived cfDNA (DD-cfDNA), allows minimally invasive screening for organ damage and rejection after solid-organ transplantation.

    • A sensitive and cost-effective screening assay offers a new opportunity to personalize patient management, improve quality of life and prolong survival.

    • Current approaches for detecting DD-cfDNA have some pitfalls and limitations that are important to address.

    Cell-free DNA characteristics

    • Circulating cf-DNA is composed of short fragments of DNA which can be detected in blood and other bodily fluids.

    • In healthy individuals, cf-DNA is present at low levels. Changes in cf-DNA can be utilized as biomarkers.

    • Cf-DNA has a short half-life, can be accurately quantified and its size and sequence can be informative.

    Specimen handling & isolation methods

    • Sample collection, handling, shipping, processing and storage can influence cf-DNA levels and characteristics.

    • Standardization of these factors may improve the tests' usefulness.

    Detecting donor-derived cell-free DNA after solid-organ transplantation

    • Since the discovery of cf-DNA almost a quarter-century ago, the measurement of DD-cfDNA as a biomarker for transplant organ damage has developed substantially.

    • Several methods have been used to measure DD-cfDNA levels post-transplantation including next-generation sequencing (NGS), quantitative polymerase chain reaction (qPCR) and droplet digital PCR (dd-PCR).

    • The clinical use of DD-cfDNA levels relies on the sensitivity of the assays. While elevated levels are not specific for organ rejection, very low levels can have excellent negative predictive value.

    • Most publications report DD-cfDNA as a percentage of total cf-DNA. Absolute DD-cfDNA quantification may prove to be valuable, but rigorous studies are needed.

    Conclusion & future perspective

    • Circulating cf-DNA, specifically DD-cfDNA, is one of the most exciting new biomarkers for monitoring graft damage and providing an early indication of rejection in organ transplants.

    • Ongoing investigations and technological improvements will likely substantially enhance performance characteristics including accuracy, sensitivity, specificity and turnaround time.

    • Standardized clinical interpretation, accessibility to individual centers and improved cost-effectiveness are anticipated.

    • Combining DD-cfDNA with other informative biomarkers and infectious disease detection will improve diagnostic potential.

    • The use of DD-cfDNA as a sensitive screening tool has high potential to provide information to guide personalized transplant monitoring and immunosuppression.

    Author contributions

    RL Edwards and LA Baxter-Lowe prepared the manuscript. J Menteer and RM Lestz critically reviewed the manuscript. All authors read and approved the final manuscript.

    Acknowledgments

    The authors acknowledge Carl M. Grushkin, MD for his support of this work.

    Financial & competing interests disclosure

    This work was generously supported by the GOFARR Fund (Project 8031000-000013350). Authors have collaborated with Luminex, A Diasorin Company (TX, USA.) in scientific pursuits regarding the subject matter. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

    No writing assistance was utilized in the production of this manuscript.

    Open access

    This work is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/

    Papers of special note have been highlighted as: • of interest; •• of considerable interest

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