REVIEW article

Front. Immunol., 23 September 2024

Sec. Alloimmunity and Transplantation

Volume 15 - 2024 | https://doi.org/10.3389/fimmu.2024.1461480

Application of graft-derived cell-free DNA for solid organ transplantation

  • Department of Urology II, The First Hospital of Jilin University, Changchun, China

Abstract

Monitoring the status of grafts and the occurrence of postoperative complications, such as rejection, is crucial for ensuring the success and long-term survival of organ transplants. Traditional histopathological examination, though effective, is an invasive procedure and poses risks of complications, making frequent use impractical. In recent years, graft-derived cell-free DNA (gd-cfDNA) has emerged as a promising non-invasive biomarker. It not only provides early warnings of rejection and other types of graft injury but also offers important information about the effectiveness of immunosuppressive therapy and prognosis. gd-cfDNA shows potential in the monitoring of organ transplants. The early, real-time information on graft injury provided by gd-cfDNA facilitates timely individualized treatment and improves patient outcomes. However, the progress of research on gd-cfDNA varies across different organs. Therefore, this article will comprehensively review the application and findings of gd-cfDNA in monitoring various solid organs, discussing the advantages, limitations, and some future research directions to aid in its clinical application.

1 Introduction

Solid organ transplantation is a crucial treatment for end-stage organ failure. However, post-transplantation, there is a risk of occurrence of transplant-related complications (such as rejection) and graft function decline, which may lead to graft loss and reduced patient survival rates (). Therefore, monitoring the status of transplanted organs is vital for the long-term survival of recipients. Only more comprehensive monitoring of the grafts can reduce the happening of adverse outcomes and improve patient survival rates. The current gold standard for assessing graft health remains histopathological biopsy. However, due to its invasive nature, high cost, and associated risk of complications, its clinical application is limited, making it unsuitable for frequent routine monitoring (, ). Other indicators for monitoring organ function, such as serum creatinine for kidneys and transaminases for the liver, have low sensitivity to graft injury and exhibit a lag in response (). Thus, there is currently a lack of reliable, low-risk, and suitable methods for repeated monitoring. In recent years, graft-derived cell-free DNA (gd-cfDNA), released by damaged graft cells, has emerged as a novel non-invasive biomarker for monitoring post-transplantation rejection and other types of graft injury, providing a new option for continuous, minimally invasive monitoring of organ transplants (). However, the application of gd-cfDNA is still in the early exploratory stages and has not yet reached a consensus. This article will comprehensively review the current research developments across various types of transplanted organs.

2 Production of gd-cfDNA

In the cell nucleus, DNA wraps around histones to form nucleosomes. During cell damage, apoptosis, or necrosis, chromosomal DNA undergoes degradation and release. Initially, DNA is cleaved into large fragments (50–300 kb), followed by further degradation, resulting in the release of smaller DNA fragments (180–200 bp) and nucleosomes into the blood. These DNA fragments are eventually cleared in the liver and kidneys (–). The half-life of these DNA fragments ranges from approximately 15 to 90 minutes (). These double-stranded DNA fragments circulating in the plasma are called cfDNA, also known as circulating free DNA or extracellular DNA. The discovery of cfDNA dates back to 1948 (). As a reliable “liquid biopsy” method, cfDNA has been widely used in fields such as prenatal diagnosis and cancer monitoring (, ).

gd-cfDNA is a specific subcategory of cfDNA and refers to the cfDNA derived from graft cells circulating in the recipient’s plasma after transplantation. The majority of cfDNA in the recipient’s blood (over 95%) originates from the recipient’s apoptotic hematopoietic cells (). Compared to hematopoietic-derived DNA (with a peak length of approximately 166 bp), non-hematopoietic-derived DNA is shorter, with gd-cfDNA primarily ranging from 105 to 145 bp (). The level of graft-derived cell-free DNA (gd-cfDNA) is typically quantified in two ways: relative quantification as gd-cfDNA(%) and absolute quantification as gd-cfDNA(cp/mL), which refers to the percentage of gd-cfDNA relative to total circulating cfDNA and the number of gd-cfDNA copies per milliliter of serum, respectively (). When the graft is stable, the amount of gd-cfDNA released from apoptotic graft cells constitutes only a tiny fraction of the total cfDNA in the recipient’s blood, and it is shorter in length than recipient-derived cfDNA (, , ). When the graft experiences ischemia-reperfusion injury, rejection, or infection, resulting in graft cell necrosis, varying amounts of gd-cfDNA are released into the recipient’s plasma. (Figure 1). The extent of gd-cfDNA elevation correlates with the type and severity of graft injury, making Changes in gd-cfDNA levels a dynamic indicator for monitoring graft injury (). Since gd-cfDNA has an overall negative charge, it cannot be filtered by the glomerulus (); it is typically measured in blood. But unlike other organ transplants, in kidney transplantation, if there is tubulitis or interstitial inflammation, gd-cfDNA can also be excreted through the kidneys, leading to elevated gd-cfDNA levels in urine (, ). Thus, urinary gd-cfDNA levels can also reflect the status of the transplanted kidney. Overall, by detecting gd-cfDNA in blood or urine samples, gd-cfDNA can dynamically and early reflect the graft status, providing clinicians with a basis for decision-making ().

Figure 1

3 Detection methods of gd-cfDNA

In 1998, Lo et al. () identified Y chromosome-specific genes from donors in female recipients using polymerase chain reaction (PCR). However, at that time, the detection method was limited to specific cases where the donor and recipient were of different sexes. Currently, gd-cfDNA detection methods can be categorized into targeted and random approaches (Table 1). The quantitative detection of gd-cfDNA primarily relies on genetic markers, typically single nucleotide polymorphisms (SNPs), to differentiate between donor and recipient alleles (). The development of quantitative techniques has provided diverse options for clinical practice. Real-time quantitative PCR (qPCR), as an early commonly used technique, is characterized by its simplicity, low cost, and rapid processing time, though it has relatively lower sensitivity and specificity (, ). Droplet digital PCR (ddPCR) has high sensitivity and accuracy compared to qPCR, but it may require more expensive reagents and equipment, and data analysis and interpretation may take longer (, ). Next-generation sequencing (NGS) technology, with its ability to sequence thousands of targets simultaneously, provides high sensitivity and accuracy for detecting complex genetic variations and unknown sequences (). This capability makes NGS particularly advantageous in multiple transplants, multi-organ transplants, and monitoring rejection and infection (, , ). Despite the challenges of cost, technical complexity, and analysis time associated with NGS (, , ), its capability in gd-cfDNA monitoring cannot be overlooked. The turnaround time for ddPCR is shorter, with results typically available in one working day, whereas NGS requires 2–3 working days (). These methods can provide relative quantification in relation to a calibrator, but only ddPCR can achieve absolute quantification ().

Table 1

ApproachMethodTechnologyMeasurementGenotyping required
TargetedTargeted approaches in ddPCR or targeted NGS use preselected SNPs with high minor allele frequency, increasing the chance of donor-recipient allele differences. If the recipient is homozygous for an SNP, any alternative allele fragment is from the graft, so only a few SNPs are needed, and donor genotyping isn’t required (, ).PCR-based with ddPCR
read-out		TheraSure ()Recipient
PCR-based with NGS read-outAlloSure () or Prospera ()Not required
RandomRandom approaches use adapter ligation and NGS to sequence dsDNA fragments with SNP markers directly from plasma. In this method, the donor and recipient are genotyped, often with SNP-chip technology, to assign sequencing reads. Alternatively, the gd-cfDNA fraction can be estimated using a statistical model that considers population allele frequency, donor-recipient kinship, and sequencing/genotyping error rates, eliminating the need for donor genotyping (, ).Ligation-basedTransplant Rejection Assessment using gd-cfDNA (TRAC) ()Recipient or Donor and Recipient

Comparison of targeted approach and random approach.

4 Comparison of gd-cfDNA with common monitoring indicators

Histopathological biopsy remains the current gold standard for organ transplant monitoring and diagnosis, but it requires operation by specialized medical personnel (, ). Moreover, approximately 25% of kidney biopsy samples lack sufficient glomeruli, especially when using finer biopsy needles, which may increase the likelihood of requiring repeated punctures and thus elevate unnecessary complications risk (). Additionally, biopsy sampling captures only a few glomeruli and a limited number of renal tubules from a localized area, leading to sampling errors and an incomplete understanding of the overall condition of the kidney. Studies have shown that 43% of clinically indicated biopsies and 65% of protocol biopsies are unnecessary (). Some biomarkers used to monitor graft function, such as plasma creatinine, can measure glomerular function but are not sensitive indicators of graft injury. By the time complications like rejection lead to a significant rise in plasma creatinine, substantial tissue damage may have already occurred in the transplanted kidney (). Similarly, other commonly used indicators for monitoring organ function also have some limitations. (Table 2).

Table 2

OrgansIndicatorLimitationsFindings of gd-cfDNA
LiverBiopsyRisk of bleeding, infection, sepsis, and bile leak ()Early predictive value: peaked 1–2 weeks before liver biopsy diagnosis of AR ().
Liver function testsInability to accurately assess rejection and infection (, );
Liver enzyme half-lives are long, with aspartate aminotransferase (AST) at 17 hours and alanine aminotransferase (ALT) at 47 hours, resulting in slow clearance and lag ()		Short half-life and increased 4–6 days before aminotransferase elevation (, ).
The ROC-AUC for diagnosing acute rejection (AR) was 0.99, outperforming alanine aminotransferase (0.86), alkaline phosphatase (0.66), γ-glutamyl transferase (0.80), and bilirubin (0.35) ().
KidneyBiopsyHigh incidence of complications such as hematuria, perirenal hematoma, and arteriovenous fistula ()Provides more comprehensive graft information when combined with Banff scoring ().
Increased 30 days before histological changes ().
Serum creatinine, proteinuria, and eGFRLow specificity and lag in distinguishing rejection from infection ()ROC-AUC for rejections was 0.80, significantly higher than serum creatinine (0.50) and eGFR (0.74) (, ).
LungBiopsyRisk of bleeding, pneumothorax, and mediastinal emphysema ()Increased an average of 2.8 months before biopsy and lung function tests showed abnormalities ().
BronchoscopyDue to complications such as accidental bleeding and injury, radiological diagnosis of graft injury is often relied upon ()High sensitivity and specificity for detecting clinically asymptomatic graft injury ().
HeartBiopsyRisk of cardiac perforation, valvular damage, and nerve injury ()Detected graft injury 0.5–3.2 months before biopsy-confirmed rejection ().
Left ventricular ejection fractionTypically occurs at a late stage, with low sensitivity for monitoring rejection ()Detects all rejection types before clinical dysfunction occurs ().
Troponin, B-type Natriuretic Peptide, C-Reactive Protein, etc.Limited monitoring value, no longer used for rejection screening (–)High sensitivity and ability to distinguish rejection types ().
PancreasBiopsyRisk of infection, elevated blood amylase, and pancreatic fluid leakage ()It can differentiate between pancreatic rejection and pancreatitis ().
Secretory function indicators, such as amylase, HbA1c, C-peptide, etc.Low specificity; pancreatic exocrine function may be normal, and elevation may not accurately reflect graft status ()ROC-AUC for diagnosing acute pancreatic rejection (0.89) significantly outperformed lipase (0.74) and amylase (0.46) ().

Limitations of common monitoring indicators and findings of gd-cfDNA research.

ROC-AUC, Receiver Operating Characteristic - Area Under the Curve; DSA, Donor-specific antibody.

Donor-specific antibody (DSA) is a crucial biomarker for predicting the development of antibody-mediated rejection (AMR) and assessing graft survival (). However, limitations in DSA detection—such as variability in detection methods, sensitivity, and clinical significance standards—restrict its role in rejection (, ). Although increasing sensitivity of DSA detection assays, a significant proportion of patients with the same histologic picture of ABMR, do not have detectable circulating DSA (, ). A recent kidney transplant study indicated that gd-cfDNA might be a better predictor of AMR than DSA. In that study, DSA was frequently negative in both molecular (56%) and histologic (51%) AMR. In AMR, gd-cfDNA(%)≥1.0 was more frequent (75%) than DSA positivity (44%). Moreover, all AMR patients, DSA-positive or DSA-negative, showed elevated gd-cfDNA levels (mean 1.88% vs. 0.32% in the non-rejection group) (). Some studies have demonstrated a correlation between elevated gd-cfDNA levels and the occurrence of DSA, allowing for the prediction of DSA development. In heart transplantation, gd-cfDNA levels were significantly higher in patients who experienced De novo donor-specific antibodies (dnDSA) compared to those who did not (0.34% vs. 0.06%) and were elevated a median of 20 days before dnDSA was detected (). In kidney transplant recipients, 40% (17/42) of patients with gd-cfDNA ≥0.5% developed dnDSA, compared to only 2.7% (1/37) of patients with gd-cfDNA <0.5%, showing a significant difference (). Kidney transplant recipients with gd-cfDNA levels of 0.5% or higher have nearly a threefold increased risk of developing dnDSA, with levels rising a median of 91 days before the identification of DSA (interquartile range, 30–125 days) (). Similarly, in heart transplantation, among 613 DSA samples paired with gd-cfDNA levels, gd-cfDNA greater than 0.15% was associated with a fourfold increase in the incidence of dnDSA in the first year post-transplantation (). However, research on the diagnostic performance of gd-cfDNA and DSA in rejection remains limited, and further studies are needed to confirm their roles.

gd-cfDNA is directly derived from the graft and has a short half-life, providing high sensitivity to tissue damage. The minimally invasive nature of blood or urine sampling for gd-cfDNA testing further adds to its advantages (, ). From an economic perspective, gd-cfDNA monitoring also offers substantial benefits. The estimated total cost of a kidney biopsy is approximately $3,931 (), and the average cost of an endomyocardial biopsy (EMB) after heart transplantation is as high as $7,918 (). If biopsy-related complications occur (such as hospitalization and hematoma evacuation), additional costs can increase by an average of $10,743 (). In comparison, the cost of ddPCR for the first year is about $4,012 (including testing on days 7 and 14 post-transplant, monthly testing for the first 6 months, and quarterly testing thereafter). In subsequent years, quarterly testing with ddPCR costs around $1,604 (). The cost difference and risk considerations make gd-cfDNA a valuable non-invasive monitoring method in managing organ transplant recipients.

It is important to note that gd-cfDNA testing cannot yet fully replace pathological biopsy as the gold standard for diagnosing rejection; it serves as a complementary test or routine monitoring tool. Compared to creatinine, gd-cfDNA more effectively guides the timing and necessity of clinical biopsies, significantly reducing the number of biopsies and associated risks ().

5 Baseline levels of gd-cfDNA

The baseline level of gd-cfDNA is the stable value of gd-cfDNA observed in the recipient’s blood in the early post-transplant period after the concentration of gd-cfDNA has declined from the initial peak caused by surgical trauma and ischaemia-reperfusion injury. It is critical for monitoring the health of the graft, as deviations from this baseline may be indicative of graft injury, such as rejection (, ). The baseline gd-cfDNA levels are influenced by several factors, including the type of transplanted organ, the recipient’s immune response, and the extent of surgical trauma during transplantation. The baseline levels vary by organ type. The speed at which elevated gd-cfDNA levels return to baseline also varies among individuals and types of transplanted organs. Table 3 summarizes the baseline levels of gd-cfDNA found in current studies after various organ transplants and the recovery Times to baseline levels (, , ). Pancreas transplantation has higher risks for undesirable immune response and complications compared with other types of organ transplantation, which may contribute to the relatively slow return to gd-cfDNA baseline levels after pancreas transplantation, but more research is needed to discover the exact reasons for this (, ). Furthermore, baseline levels of gd-cfDNA in liver and lung transplants are slightly higher than in kidney and heart transplants, possibly due to difference in the number of cells in the graft tissue (). In recipients of stable bilateral lung transplants, the median gd-cfDNA level is higher than those of single lung transplants (0.46% vs. 0.15%) ().

Table 3

OrgansBaseline levelsRecovery times
Liver3.3%-5.0%1-2 weeks
Kidney0.3%-1.2%1-2 weeks
Lung1.0%-3.0%2-3 weeks
Heart0.1%-0.5%1-2 weeks
Pancreas0.1%-1.0%1 month

Baseline levels of gd-cfDNA and recovery times for various organs.

6 The role of gd-cfDNA in organ transplantation

6.1 gd-cfDNA and rejection

Rejection remains a severe complication affecting graft function and recipient survival post-transplantation. Timely diagnosis and treatment are vital to ensuring successful transplantation and long-term survival (). During graft rejection, gd-cfDNA (%) levels increase significantly (), and this rise occurs earlier than other indicators, such as serum creatinine and transaminases ().

Studies indicate that for every 1% increase in gd-cfDNA levels, the risk of rejection increases 3.3-fold, with an overall rejection risk ratio of 1.89 (). gd-cfDNA levels are significantly positively correlated with the type and severity of graft injury and Banff rejection scorings (, –). For instance, in kidney transplantation, the median gd-cfDNA in the non-rejection group is 0.3%, while in AMR, it is 2.9%, and in T-cell-mediated rejection (TCMR), it is 1.2%, with the highest levels observed in AMR (). This may be due to AMR being the most severe and destructive form of immune-mediated graft injury, leading to more cell necrosis and the release of gd-cfDNA (). In reflecting the severity of rejection, gd-cfDNA levels post-liver transplantation are 9.1%, 12.1%, and 28.6% in mild, moderate, and severe acute rejection (AR), respectively, significantly higher than the 0.16% in the non-rejection group and differences in gd-cfDNA levels exist among AR patients of varying severity (). gd-cfDNA as a non-invasive marker for allograft rejection monitoring has the potential to be a valuable non-invasive marker for allograft rejection monitoring and has already been utilized by the International Society for Heart and Lung Transplantation (ISHLT) for graft rejection monitoring (–94).

To better use gd-cfDNA for monitoring and identifying rejection post-organ transplantation, different studies have set varying gd-cfDNA thresholds to analyze diagnostic efficacy for graft injury (Table 4). In patients with stable graft status during the first year after liver transplantation, a 10% threshold demonstrated higher diagnostic efficacy for rejection than traditional liver function tests (LFTs) conducted on the same day (). In kidney transplantation, 0.5%–1.0% thresholds showed good diagnostic performance for rejection, outperforming serum creatinine and eGFR (, , ). After lung transplantation, gd-cfDNA levels below 1.0% help exclude AR (102). In heart transplantation, when gd-cfDNA does not exceed 0.25%, there is a high confidence that rejection has not occurred, and this threshold is widely applied clinically (). There is a lack of research on gd-cfDNA monitoring after pancreas-only transplantation. However, after simultaneous pancreas-kidney transplantation (SPK), a gd-cfDNA threshold of 70 cp/mL has been effectively used to detect early pancreatic graft rejection ().

Table 4

OrgansFirst author, YearReferenceSample typesInjury typesThresholdsROC-AUCSensitivitySpecificityPPVNPV
KidneySigdel, 2013(95)UrineAR3 cp/μg creatinine0.8081.00%75.00%NANA
Oellerich, 2019()PlasmaRejection0.43%0.7373.00%69.00%12.00%98.00%
Bu, 2022()PlasmaRejection0.50%0.8078.00%71.00%50.00%90.00%
Huang, 2018(96)PlasmaRejection0.74%0.7179.40%72.40%77.10%75.00%
Huang, 2018(96)PlasmaAMR0.74%0.82100.00%71.80%68.60%100.00%
Bloom, 2017()PlasmaAR1.00%0.7459.00%85.00%61.00%84.00%
Bloom, 2017()PlasmaAMR1.00%0.8781.00%83.00%44.00%96.00%
Sigdel, 2018()PlasmaAR1.00%0.8788.70%72.60%51.90%95.10%
Graver, 2023()PlasmaAR0.5%-1%NA50%-100%69%-96%12%-77%75%-98%
Oellerich, 2021()PlasmaRejection0.74%-1.0%0.7480.00%76.00%56.00%90.00%
Whitlam, 2019(97)PlasmaAMR21 cp/mL0.9290%88%60%98%
Oellerich, 2019()PlasmaRejection52 cp/mL0.8373.00%73.00%13.00%98.00%
LiverLevitsky, 2022()PlasmaAR5.30%0.9587.00%NANA100.00%
Schütz, 2017()PlasmaAR10.00%0.9790.30%92.90%NANA
Fernández-Galán, 2022()PlasmaAR13.80%0.7785.70%63.30%35.30%95.50%
Levitsky, 2022()PlasmaAR20.40%0.7166.70%NANA87.80%
Zhao, 2021(98)PlasmaPediatric Rejection28.70%0.8872.70%94.70%80.00%92.30%
Zhao, 2021(98)PlasmaPediatric Rejection2076 cp/mL0.8481.80%81.90%56.20%93.90%
Cox, 2022()PlasmaTCMR33.50%0.73NA97.00%NA86.10%
Goh, 2019()PlasmaAR898 cp/mL0.9983.30%100.00%100.00%87.70%
LungAgbor-Enoh, 2018()PlasmaRejection0.50%0.8078.00%71.00%50.00%90.00%
Jang, 2021()PlasmaAR0.50%0.8995.00%65.00%85.63%92.92%
Khush, 2021(99)PlasmaAR0.85%0.6755.60%74.80%70.00%63.04%
Sayah, 2020(100)PlasmaAR0.87%0.7273.10%52.90%60.78%65.71%
DeVlaminck, 2015(101)PlasmaAR1.00%0.90100.00%73.00%83.80%88.10%
Keller, 2022(102)PlasmaAR1.00%0.8273.90%87.70%43.40%96.50%
Keller, 2022(102)PlasmaAR1.10%0.8678.00%83.00%81.88%79.14%
Sorbini, 2022(103)PlasmaAR1.25%0.8780.70%73.00%75.47%80.00%
Pedini, 2023()PlasmaAR1.72%0.80NANA75.00%91.40%
Trindade, 2023(104)PlasmaALAD0.85%-1%0.8787.00%78.00%74.00%89.00%
HeartKhush, 2021(105)PlasmaAR0.15%NA55.90%71.50%7.80%97.40%
Kim, 2022(106)PlasmaAR0.15%0.8678.50%76.90%97.30%25.10%
Khush, 2021(105)PlasmaAR0.20%0.6444.10%80.40%8.90%97.10%
Agbor-Enoh, 2021()PlasmaAR0.25%0.92NANANA99.00%
Kittleson, 2021()PlasmaAR0.25%0.9281.00%NANA99.00%
Holzhauser, 2023()PlasmaAR0.25%0.9281.00%85.00%NA99.20%
Borkowski, 2024(107)PlasmaAR0.25%NANANANA99.00%

Diagnostic efficacy of different gd-cfDNA thresholds in graft injury.

AR, Acute Rejection; AMR, Antibody Mediated Rejection; TCMR, T-cell-mediated rejection; ALAD, Acute lung allograft dysfunction; NA, not available; ROC-AUC, Receiver Operating Characteristic - Area Under the Curve; PPV, Positive predictive value; NPV, Negative predictive value.

It is important to note that plasma gd-cfDNA levels are relatively low in early Banff 1A and borderline TCMR in kidney transplants. The median gd-cfDNA level in TCMR patients is 0.7%, but it is only 0.20% in borderline TCMR patients (Banff t1/i1), even lower than the 0.23% in the non-rejection group, leading to poor diagnostic accuracy (). This may be because borderline and Type I (IA and IB) TCMR primarily manifest as tubulitis and interstitial inflammation, damaging tubular epithelial cells, which increases urinary gd-cfDNA levels but has a smaller effect on plasma gd-cfDNA levels (, 108). Therefore, it is recommended to simultaneously test plasma and urinary gd-cfDNA in kidney transplant patients for a comprehensive assessment.

In lung transplant recipients, gd-cfDNA (%) median levels significantly increase in patients with AR (12.0%), higher than in stable patients (1.1%). After the resolution of rejection, gd-cfDNA levels decrease to the level in the stable group (). Other study has also shown that after early aggressive treatment of AR, gd-cfDNA levels gradually decline and return to lower levels (<0.5%), providing crucial information for monitoring the efficacy of rejection treatment (109).

6.2 gd-cfDNA and other types of graft injury

gd-cfDNA is not a specific marker for rejection and is also associated with various types of graft injury, though the degree of elevation varies, which can help in differentiation (110) (Table 5). Furthermore, not all types of infections result in elevated gd-cfDNA levels. For instance, cytomegalovirus infections (e.g., cytomegalovirus-related hepatitis) do not cause direct graft injury and therefore do not lead to an increase in gd-cfDNA levels ().

Table 5

OrgansInjury typesFindings
LiverHepatitis C virus infectionCompared to 88 recipients with stable graft function (median gd-cfDNA of 3.3%), 17 recipients infected with hepatitis C virus (5.9%) showed a slight increase, which was lower than the 17 patients in the AR group (29.6%), with statistically significant differences between each group ().
Graft-versus-host disease (GVHD)GVHD lacks sensitive and specific biomarkers for assessing the immune status of solid organ recipients; 97% of granulocytes in the blood were observed to be of donor origin in a patient who developed GVHD, with a higher proportion of T cells and B cells, and significantly elevated gd-cfDNA levels (39.8%). After aggressive treatment, the levels of gd-cfDNA (%) and donor-derived immune cells decreased, indicating that gd-cfDNA might be a promising biomarker for monitoring GVHD and treatment efficacy (111).
Infection and drug-induced liver injuryAll 49 recipients in the study developed complications. Among the 11 patients with rejection, the median gd-cfDNA was 41.7%, 16.6% in the 10 patients with EBV infection, 11.2% in the 22 patients with CMV infection, and 7.8% in the 6 patients with drug-induced liver injury. There was a significant difference between the rejection group and the other groups, but no statistical difference among the other three groups; gd-cfDNA can distinguish infection and drug-induced liver injury from rejection (98).
KidneyFibrosis/tubular atrophy and acute tubular necrosisAmong 189 kidney transplant patients, the median gd-cfDNA was 0.29% in 83 stable patients, 0.57% in 15 patients with rejection, 0.46% in 24 patients with interstitial fibrosis/tubular atrophy (IF/TA), and 0.46% in 29 patients with acute tubular necrosis (ATN). There were significant differences between the three groups with complications and the stable group; IF/TA also showed significant differences compared to the other two groups. Therefore, elevated gd-cfDNA is associated not only with rejection but also with IF/TA and ATN ().
Acute tubular necrosis and acute pyelonephritisIncreases in gd-cfDNA (%) (>0.88%) were significantly associated with acute tubular necrosis and acute pyelonephritis; however, there was no correlation with BKV or CMV infection, symptomatic lower urinary tract infections, fluid retention, prerenal acute kidney injury, or serum creatinine elevation due to calcineurin inhibitor use (112).
BK polyomavirus-associated nephropathy (BKPyVAN)The urinary gd-cfDNA concentration had a true positive rate of 95.2% for diagnosing BKPyVAN, significantly higher than the 71.4% and 33.3% for urinary and plasma BKV-DNA loads, respectively; suggesting that urinary gd-cfDNA is better than urinary/plasma BKV viral load for diagnosing BKPyVAN (110).
LungPrimary graft dysfunction (PGD)There is an association between gd-cfDNA and the occurrence and severity of PGD: on post-transplant day 3, gd-cfDNA (%) in PGD patients was significantly higher than in non-PGD patients (median: 12.2% vs 8.5%) (113).
Pulmonary infection and reduced oxygenationThe median gd-cfDNA (12.0%) in the acute rejection group showed a significant difference compared to the infection group (4.2%) and the stable group (1.1%), which helps distinguish rejection from infection, but the difference between the infection group and the stable group was not statistically significant; the gd-cfDNA level was negatively correlated with oxygenation levels (PaO2/FiO2 ratio) ()
Respiratory viral infection (RVI)An increase in gd-cfDNA (≥1%) in 7 days after RVI was closely associated with the development of CLAD, decline in lung function, and increased risk of graft failure (114).
Chronic lung allograft dysfunction (CLAD)Early post-transplant levels of gd-cfDNA in PGD patients were higher, and those who developed CLAD had gd-cfDNA levels about twice as high as those who did not develop CLAD (median: 22.4% vs 9.9%), with the difference being statistically significant (113).
HeartCardiac allograft vasculopathy (CAV)Compared to samples without CAV, recipients with CAV had significantly elevated gd-cfDNA levels (mean 0.47% vs 0.09%), with a statistically significant difference (115).
PancreasPancreatitisIn a study of 46 pancreatic transplants, the median gd-cfDNA was 2.25% in 13 patients with rejection, 0.36% in 2 patients with pancreatitis, and 0.18% in the stable group; there was a significant difference in gd-cfDNA levels between the rejection/pancreatitis group and the stable group ().

Relationship between different types of graft injury and gd-cfDNA.

gd-cfCDNA, graft-derived cell-free DNA; AR, Acute rejection; GVHD, Graft-versus-host disease; CMV, Cytomegalovirus; EBV, Epstein-Barr Virus; BKPyVAN, BK polyomavirus-associated nephropathy; IF/TA, Interstitial fibrosis/tubular atrophy; ATN, Acute tubular necrosis; PGD, Primary graft dysfunction; RVI, Respiratory viral infection; CLAD, Chronic lung allograft dysfunction; CAV, Cardiac allograft vasculopathy.

BK polyomavirus-associated nephropathy (BKPyVAN) is also a severe complication that can lead to graft dysfunction and loss in kidney transplantation. BKPyVAN is the most severe stage of BK virus infection, causing the death of graft kidney cells and releasing high levels of gd-cfDNA (116). However, BKPyVAN shares features with TCMR, especially Banff IA and IB TCMR, both characterized by tubulointerstitial inflammation and graft dysfunction (, 117). The standard diagnosis of BKPyVAN is through histopathological detection of Simian virus 40 (SV40) in kidney biopsy tissue (118). However, due to the focal nature of BKPyVAN lesions, there is a high false-negative rate in biopsies (119), making it difficult to distinguish between the two when SV40 staining is negative. Since the treatment principles for BKPyVAN and TCMR are entirely different, there is an intense need for more precise diagnostic techniques. Fortunately, elevated urinary gd-cfDNA levels are more significantly associated with BKPyVAN, aiding in differentiation (110, 120). Although BKPyVAN and Type I TCMR have similar histological features, biopsy-confirmed BKPyVAN recipients have significantly higher urinary gd-cfDNA (%) and concentrations than those with Type I TCMR (median 68.4% vs. 55.3% and 10.4 ng/mL vs. 6.1 ng/mL, respectively) (117). This might be because BKPyVAN primarily infects tubular epithelial cells, leading to the kidney cell lysis and the release of gd-cfDNA into the tubular lumen, which is then excreted in the urine (121, 122). Whereas the TCMR mainly results in inflammatory cell infiltration rather than cell lysis (). Shen et al. (117) identified a urinary gd-cfDNA concentration threshold of 7.81 ng/mL, which may effectively distinguish confirmed BKPyVAN from Type I TCMR.

6.3 Monitoring immunosuppressive therapy with gd-cfDNA

Organ transplant recipients require long-term immunosuppression to prevent immune rejection (). The application of gd-cfDNA in monitoring the effectiveness of immunosuppressive therapy is gaining increasing attention, particularly in detecting rejection due to inadequate immunosuppression, where gd-cfDNA outperforms traditional therapeutic drug monitoring (). Given the high variability in individual sensitivity to immunosuppressive drugs, maintaining an immunosuppressive therapeutic window through drug concentration monitoring may not be suitable for every patient. Moreover, there is a significant difference in drug concentrations between blood and lymphocytes for every patient, meaning that immunosuppressant levels may not accurately reflect the drug’s impact on immune cells (123–125). gd-cfDNA levels can predict damage before the onset of severe AR symptoms; if immunosuppressant drug levels are below the therapeutic range at this time, it indicates a need to increase the dosage ().

Oellerich et al. (126) simultaneously measured gd-cfDNA levels and tacrolimus trough concentrations in liver transplant patients, finding that when drug concentrations were in the therapeutic range and gd-cfDNA relative quantification was below 10%, graft function remained stable. Furthermore, when gd-cfDNA relative quantification was below 10% post-liver transplantation, the minimum effective tacrolimus trough concentration was 6.8 ng/mL (originally used 8 ng/mL), suggesting that combined monitoring of gd-cfDNA relative quantification could help reduce the drug dosage. Another study conducted a 180-day follow-up on a liver transplant recipient with hepatorenal syndrome. Due to the severe renal insufficiency and worsening symptoms experienced by this recipient, tacrolimus was switched to everolimus on postoperative day 84. When tacrolimus blood levels dropped to the lower end of the therapeutic range, gd-cfDNA (%) rapidly increased to 66%. Nevertheless, as everolimus blood levels reached the therapeutic range, gd-cfDNA (%) quickly decreased and remained below 5%, indicating that gd-cfDNA (%) correlates with the concentration of immunosuppressants in the appropriate therapeutic window ().

In a kidney transplant study, 6 months of gd-cfDNA monitoring was performed after adjusting the immunosuppressant mycophenolic acid (MPA). Among 17 recipients in the low-risk group (gd-cfDNA <1%), no rejection occurred after MPA dose reduction; however, in 4 patients in the high-risk group (gd-cfDNA ≥1%) whose MPA dosage remained unchanged, 2 developed graft dysfunction, and 1 experienced graft loss (127). Continuous monitoring and dynamic changes in gd-cfDNA can guide clinicians in making more precise decisions when adjusting immunosuppressant doses, contributing to personalized treatment in immunosuppressive therapy and demonstrating significant potential in improving the management of organ transplant recipients (, ).

6.4 Prognostic prediction with gd-cfDNA

Early dynamic changes in gd-cfDNA post-transplantation can reflect the recovery of organ function and provide information related to long-term prognosis. In kidney transplantation, gd-cfDNA levels below 0.5% on postoperative day 7 are considered normal recovery, whereas patients with levels equal to or greater than 0.5% had a median dialysis time of 13.50 days post-transplantation. The median gd-cfDNA level in patients with delayed graft function (fDGF) in 24 hours reached 7.20%, while it was 2.70% in those without functional DGF. Patients whose gd-cfDNA decreased to below 0.5% in 7 days postoperatively had a higher 7-year expected graft survival rate compared to those whose gd-cfDNA levels remained at or above 0.5% after 7 days (79.5% ± 16.8% vs. 67.7% ± 24.1%) (128). Patients with gd-cfDNA ≥0.5% had a doubling increased risk of a 25% decline in eGFR in three years post-transplantation ().

Compared to patients with low and moderate gd-cfDNA levels (low, medium, and high gd-cfDNA levels were 0.7%, 1.6%, and 3.6%), those with high gd-cfDNA levels (%) after lung transplantation had a 6.6-fold increased risk of graft failure, with progression to CLAD or death (). Additionally, plasma gd-cfDNA levels negatively correlated with oxygenation levels (PaO2/FiO2 ratio) immediately after and 72 hours post-lung transplantation, suggesting that increased gd-cfDNA levels may indicate declining oxygenation capacity of the transplanted lung ().

Approximately 50% of heart transplant recipients develop cardiac allograft vasculopathy (CAV) 10 years post-transplantation, a major obstacle to long-term survival typically detected via selective coronary angiography, which carries significant risk (129). An increase in gd-cfDNA levels to ≥0.12% two years post-transplantation is significantly associated with the occurrence of CAV (130). Early evaluation of such patients helps in personalizing the understanding of CAV risk. Furthermore, higher gd-cfDNA levels post-heart transplantation are associated with a composite endpoint of death, re-transplantation, hemodynamic compromise, or graft dysfunction in three years (131). Recipients with left ventricular ejection fraction (LVEF) below 40% had significantly higher gd-cfDNA (%) than those with LVEF of 40% or above (0.46% vs. 0.04%) (106).

gd-cfDNA monitoring can dynamically reflect graft status, helping doctors identify high-risk patients for potential graft dysfunction or early decline, allowing timely intervention or treatment. gd-cfDNA shows potential and application value in prognosis prediction post-organ transplantation, though further research is needed to verify its clinical accuracy and reliability.

7 Advantages

The main advantages of gd-cfDNA can be summarized as follows:

  • Non-invasive Monitoring: gd-cfDNA offers a more frequent and less invasive monitoring method than biopsy, reducing patient discomfort and risk.

  • Early Diagnosis, High Sensitivity, and Dynamic Monitoring: With a shorter half-life, gd-cfDNA demonstrates higher sensitivity. gd-cfDNA can provide early warnings before graft injury, such as rejection, enabling early diagnosis and timely treatment. The more severe the injury, the higher the gd-cfDNA levels (132).

  • Immunosuppressive Efficacy Allows for Personalized Treatment: Due to its short half-life, gd-cfDNA allows for longitudinal monitoring of disease progression, providing near real-time indications of organ injury (133). Dynamic monitoring of gd-cfDNA levels enables safer and more effective use of immunosuppressants, dose adjustments, and personalized treatment, improving the long-term survival quality of organ transplant recipients ().

  • Predicting Transplant Outcomes: Elevated gd-cfDNA levels post-transplantation are associated with poor graft function recovery, graft injury, and inadequate immunosuppression, helping to predict long-term transplant outcomes (134).

8 Limitations

  • Limited Specificity for Injury Types: gd-cfDNA is highly sensitive in reflecting graft cell damage and death; however, its ability to distinguish the types of injuries is currently limited. It is not a specific marker for any type of injury but is observed in various graft injuries. Though studies have indicated that gd-cfDNA levels are higher in AR recipients compared to other pathological states caused by non-rejection factors (). At present, gd-cfDNA may serve as a reliable marker for AMR, but its role in addressing Acute Cellular Rejection (ACR), chronic rejection, and other types of graft injuries has not yet been fully proven (135).

  • Uncertain sample stability: Most commercial assays require blood samples to be sent to external laboratories for analysis, potentially using different detection techniques, which may lead to inconsistencies in results and pricing. Additionally, the instability of cfDNA necessitates rapid and precise handling during sample transfer, transportation, and processing to avoid variations in results ().

  • Uncertainty in Thresholds and Undefined Monitoring Frequency: There is variability in the diagnostic thresholds and monitoring frequency of gd-cfDNA across different studies, and no standardized guidelines currently exist. Further research is needed to determine the optimal monitoring frequency and thresholds, especially in the early post-transplant period when the risk of AR and infection is highest ().

  • Global Disparities in Medical Resources: As an advanced medical technology, the accessibility of gd-cfDNA testing varies significantly across different countries and regions (136, 137).

  • Monitoring in Multi-Organ Transplants and Long-Term Monitoring: Due to the unique nature of multi-organ transplants, there is currently limited research on gd-cfDNA monitoring in these cases. Moreover, there is a lack of reports on routine graft monitoring beyond five years in clinically stable organ transplant recipients ().

9 Discussion

As an emerging non-invasive biomarker, gd-cfDNA shows great potential in monitoring and prognostic assessment following organ transplantation. This is particularly significant for thoracic organs. Given the stringent size-matching requirements for thoracic organ transplants, the unique immunological characteristics post-lung transplantation, and the lack of extracorporeal life support options, long-term survival rates remain lower than other solid organ transplants (138, 139). Additionally, the incidence of AR in the first year post-lung transplantation is 26.6%, significantly higher than that of other organ transplants (140). Chronic lung allograft dysfunction (CLAD) occurs in approximately 50% of lung transplant recipients in five years, posing a major obstacle to long-term survival. Currently, there are no reliable predictive markers or effective preventive or therapeutic methods for CLAD (141), highlighting the urgent need for reliable graft monitoring tools. Currently, Primary graft dysfunction (PGD) and AR have been identified as strong independent risk factors for the development of CLAD (142, 143). The detection of gd-cfDNA enables the early identification of patients at risk for AR, PGD, and CLAD, facilitating timely therapeutic interventions that enhance the management of long-term complications and improve the prognosis of transplant recipients (141, 144).

Currently, research progress on gd-cfDNA varies across different organ fields. Some studies explored differences between graft injuries based on time trends, the extent of gd-cfDNA elevation, fragment length, and genomic composition. For example, in liver transplant research, the distribution and proportion of gd-cfDNA fragment sizes in plasma have been found to monitor post-transplant graft function (145). In lung transplantation, the combined approach of gd-cfDNA (%) and cfDNA fragment size is used to determine the occurrence and potential source of infection (). Heart transplant studies have found that the genomic composition of DNA fragments differs between AMR and ACR, with AMR exhibiting a higher percentage of short fragments and higher guanine-cytosine content than ACR (, ). Transcriptome analysis of biopsy tissues has revealed that gd-cfDNA-associated gene expression patterns may be related to the type of rejection and response to treatment (146). These findings may help distinguish different types of injuries to better understand graft status monitoring.

Moreover, there is currently no consensus in clinical practice on whether to use gd-cfDNA (%) or gd-cfDNA (cp/mL) to optimize monitoring (147). The release of recipient cfDNA is influenced by factors such as immunosuppressants, age, and increased BMI (148). For instance, in two liver transplant studies, the median gd-cfDNA (%) in the AR group was 21.8% () and 41.9% (98), respectively. The former study population consisted of adult liver transplant recipients (median age 53.7 years), while the latter involved pediatric liver transplant recipients (median age 19.4 months). Schütz et al. (149) found that as the dose of immunosuppressants was reduced, the rate of leukocyte apoptosis decreased, leading to a decline in total cfDNA levels over time, while gd-cfDNA (%) correspondingly increased. However, the absolute quantification of gd-cfDNA remained stable during this period, indicating that the absolute quantification of gd-cfDNA may perform better than gd-cfDNA (%) in monitoring (, 97). Nonetheless, the gd-cfDNA (%) assay was less sensitive to preanalytical variables and more advantageous in comparison among studies (150). In the field of lung transplantation, the concept of relative change value (RCV) in gd-cfDNA (%) has been proposed, suggesting that an increase of more than 73% from baseline may indicate pathological changes, thereby enhancing diagnostic performance (104).

gd-cfDNA can provide early warnings for rejection and various types of graft injuries with high sensitivity. However, gd-cfDNA monitoring cannot entirely replace biopsies; instead, it aids clinicians in more accurately identifying patients who genuinely need biopsies, reducing unnecessary invasive procedures and associated risks (151). As a single non-invasive test, gd-cfDNA has lower specificity for injury types. However, it can provide some information about the injury, which can be used to better determine the specific type of injury by combining it with other biomarkers or artificial intelligence techniques. For example, biopsy remains the gold standard for graft assessment, but relying solely on histological predictions has limitations; combining gd-cfDNA with Banff biopsy scores offers a more comprehensive assessment of graft injury and prognosis than biopsy features alone (). Studies have shown that combining gd-cfDNA with DSA improves diagnostic accuracy for AMR compared to using either marker alone, and it significantly outperforms traditional graft function indicators (, , 152). For instance, when predicting AMR, the AUC for gd-cfDNA (%) and DSA alone was 0.85 and 0.66, respectively, while the combined AUC was 0.88 (). Some experienced heart transplant centers transition monitoring to a strategy that combines gene expression profiling (GEP) with gd-cfDNA in patients with stable grafts after 8 weeks post-transplant. GEP can assess the quiescent or activated state of the immune system, while gd-cfDNA monitoring serves as a specific marker for graft injury (151). Infection markers, such as procalcitonin (PCT), combined with gd-cfDNA, can enhance the ability to differentiate between AR and infection; during severe infections post-transplant, PCT is similarly elevated above the threshold when gd-cfDNA levels rise significantly, but PCT levels do not exceed threshold values in AR patients (153). gd-cfDNA monitoring combined with artificial intelligence leads to powerful predictive models, guiding clinical decisions and potentially better identifying high-risk patients (, 98, 107). Additionally, combining gd-cfDNA with other testing methods, such as organ function tests, pathogen detection, and mRNA transcripts in the graft, may provide more comprehensive information on graft function, improve diagnostic efficiency, enable personalized post-transplant management, and ultimately enhance the quality of life and survival rates of transplant recipients ().

In addressing the challenges of variable thresholds, undefined monitoring frequency, and global disparities in medical resources in the clinical application of gd-cfDNA, future research should focus on establishing standardized monitoring protocols, narrowing global healthcare gaps, and exploring new possibilities for gd-cfDNA. Through these efforts, gd-cfDNA has the potential to become a revolutionary monitoring tool in organ transplantation, providing more personalized and precise diagnostic information for transplant recipients, improving their quality of life, and increasing long-term survival rates.

Statements

Author contributions

WZ: Writing – original draft, Writing – review & editing. BL: Data curation, Writing – review & editing. DJ: Investigation, Writing – review & editing. RW: Resources, Writing – review & editing. HC: Investigation, Writing – review & editing. HW: Formal analysis, Writing – review & editing. ZY: Writing – review & editing, Resources. BG: Writing – review & editing.

Funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Abbreviations

gd-cfDNA, graft-derived cell-free DNA; cfDNA, cell-free DNA; ddPCR, Droplet digital PCR; NGS, Next-generation sequencing; qPCR, quantitative PCR; AR, Acute rejection; EMB, endomyocardial biopsy; ROC-AUC, Receiver Operating Characteristic - Area Under the Curve; AMR, antibody-mediated rejection; ACR, Acute Cellular Rejection; TCMR, T-cellmediated rejection; SPK, pancreas-kidney transplantation; DSA, Donorspecific antibodies; dnDSA, De novo donor-specific antibodies; PGD, Primary graft dysfunction; CAV, Cardiac allograft vasculopathy; CLAD, Chronic lung allograft dysfunction; RVI, Respiratory viral infection; BKPyVAN, BK polyomavirus-associated nephropathy; IF/TA, Interstitial fibrosis/ tubular atrophy; ATN, Acute tubular necrosis; GVHD, Graft-versus-host disease; PCT, procalcitonin.

References

  • 1

    ZhouQLiTWangKZhangQGengZDengSet al. Current status of xenotransplantation research and the strategies for preventing xenograft rejection. Front Immunol. (2022) 13:928173. doi: 10.3389/fimmu.2022.928173

  • 2

    ReeveJBöhmigGAEskandaryFEineckeGLefaucheurCLoupyAet al. Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes. JCI Insight. (2017) 2. doi: 10.1172/jci.insight.94197

  • 3

    HolzhauserLDeFilippisEMNikolovaABykuMContrerasJPDe MarcoTet al. The end of endomyocardial biopsy? A practical guide for noninvasive heart transplant rejection surveillance. JACC Heart Fail. (2023) 11:263–76. doi: 10.1016/j.jchf.2022.11.002

  • 4

    KatariaAKumarDGuptaG. Donor-derived cell-free DNA in solid-organ transplant diagnostics: indications, limitations, and future directions. Transplantation. (2021) 105:1203–11. doi: 10.1097/TP.0000000000003651

  • 5

    BloomRDBrombergJSPoggioEDBunnapradistSLangoneAJSoodPet al. Cell-free DNA and active rejection in kidney allografts. J Am Soc Nephrol. (2017) 28:2221–32. doi: 10.1681/ASN.2016091034

  • 6

    NagataSNagaseHKawaneKMukaeNFukuyamaH. Degradation of chromosomal DNA during apoptosis. Cell Death Differ. (2003) 10:108–16. doi: 10.1038/sj.cdd.4401161

  • 7

    HanDSCNiMChanRWYChanVWHLuiKOChiuRWKet al. The biology of cell-free DNA fragmentation and the roles of DNASE1, DNASE1L3, and DFFB. Am J Hum Genet. (2020) 106:202–14. doi: 10.1016/j.ajhg.2020.01.008

  • 8

    CelecPVlkováBLaukováLBábíčkováJBoorP. Cell-free DNA: the role in pathophysiology and as a biomarker in kidney diseases. Expert Rev Mol Med. (2018) 20:e1. doi: 10.1017/erm.2017.12

  • 9

    MartuszewskiAPaluszkiewiczPKrólMBanasikMKepinskaM. Donor-derived cell-free DNA in kidney transplantation as a potential rejection biomarker: A systematic literature review. J Clin Med. (2021) 10. doi: 10.3390/jcm10020193

  • 10

    MandelPMetaisP. Nuclear acids in human blood plasma. C R Seances Soc Biol Fil. (1948) 142:241–3.

  • 11

    HuangCCDuMWangL. Bioinformatics analysis for circulating cell-free DNA in cancer. Cancers (Basel). (2019) 11. doi: 10.3390/cancers11060805

  • 12

    EverettTRChittyLS. Cell-free fetal DNA: the new tool in fetal medicine. Ultrasound Obstet Gynecol. (2015) 45:499–507. doi: 10.1002/uog.14746

  • 13

    SunKJiangPChanKCWongJChengYKLiangRHet al. Plasma DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments. Proc Natl Acad Sci U.S.A. (2015) 112:E5503–12. doi: 10.1073/pnas.1508736112

  • 14

    ZhengYWChanKCSunHJiangPSuXChenEZet al. Nonhematopoietically derived DNA is shorter than hematopoietically derived DNA in plasma: a transplantation model. Clin Chem. (2012) 58:549–58. doi: 10.1373/clinchem.2011.169318

  • 15

    BeckJSchmitzJKanzowPKollmarOOellerichMSchützE. Absolute quantification of graft derived cell-free DNA (GcfDNA) early after liver transplantation (LTx) using droplet digital PCR. Clin Chem. (2014) 60:S194–5.

  • 16

    van der VaartMPretoriusPJ. The origin of circulating free DNA. Clin Chem. (2007) 53:2215. doi: 10.1373/clinchem.2007.092734

  • 17

    ChittyLSLoYM. Noninvasive prenatal screening for genetic diseases using massively parallel sequencing of maternal plasma DNA. Cold Spring Harb Perspect Med. (2015) 5:a023085. doi: 10.1101/cshperspect.a023085

  • 18

    GauthierVJTylerLNMannikM. Blood clearance kinetics and liver uptake of mononucleosomes in mice. J Immunol. (1996) 156:1151–6. doi: 10.4049/jimmunol.156.3.1151

  • 19

    ZhouQLiuFGuoLChenRYuanXLiCet al. A novel urine cell-free DNA preservation solution and its application in kidney transplantation. Nephrol (Carlton). (2021) 26:684–91. doi: 10.1111/nep.13884

  • 20

    KanzowPKollmarOSchützEOellerichMSchmitzJBeckJet al. Graft-derived cell-free DNA as an early organ integrity biomarker after transplantation of a marginal HELLP syndrome donor liver. Transplantation. (2014) 98:e43–5. doi: 10.1097/TP.0000000000000303

  • 21

    LoYMTeinMSPangCCYeungCKTongKLHjelmNM. Presence of donor-specific DNA in plasma of kidney and liver-transplant recipients. Lancet. (1998) 351:1329–30. doi: 10.1016/S0140-6736(05)79055-3

  • 22

    BeckJBierauSBalzerSAndagRKanzowPSchmitzJet al. Digital droplet PCR for rapid quantification of donor DNA in the circulation of transplant recipients as a potential universal biomarker of graft injury. Clin Chem. (2013) 59:1732–41. doi: 10.1373/clinchem.2013.210328

  • 23

    SharonEShiHKharbandaSKohWMartinLRKhushKKet al. Quantification of transplant-derived circulating cell-free DNA in absence of a donor genotype. PloS Comput Biol. (2017) 13:e1005629. doi: 10.1371/journal.pcbi.1005629

  • 24

    GrskovicMHillerDJEubankLASninskyJJChristophersonCCollinsJPet al. Validation of a clinical-grade assay to measure donor-derived cell-free DNA in solid organ transplant recipients. J Mol Diagn. (2016) 18:890–902. doi: 10.1016/j.jmoldx.2016.07.003

  • 25

    SigdelTKArchilaFAConstantinTPrinsSALibertoJDammIet al. Optimizing detection of kidney transplant injury by assessment of donor-derived cell-free DNA via massively multiplex PCR. J Clin Med. (2018) 8. doi: 10.3390/jcm8010019

  • 26

    SnyderTMKhushKKValantineHAQuakeSR. Universal noninvasive detection of solid organ transplant rejection. Proc Natl Acad Sci U.S.A. (2011) 108:6229–34. doi: 10.1073/pnas.1013924108

  • 27

    GraverASLeeDPowerDAWhitlamJB. Understanding donor-derived cell-free DNA in kidney transplantation: an overview and case-based guide for clinicians. Transplantation. (2023) 107:1675–86. doi: 10.1097/TP.0000000000004482

  • 28

    CasasSTangprasertchaiNSOikonomakiKMathersSSolletZCSamaraSet al. Multi-centre analytical performance verification of an IVD assay to quantify donor-derived cell-free DNA in solid organ transplant recipients. Hla. (2024) 103:e15518. doi: 10.1111/tan.15518

  • 29

    DauberEMKollmannDKozakowskiNRasoul-RockenschaubSSolimanTBerlakovichGAet al. 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. (2020) 33:298–309. doi: 10.1016/j.jmoldx.2016.07.003

  • 30

    HuggettJFCowenSFoyCA. Considerations for digital PCR as an accurate molecular diagnostic tool. Clin Chem. (2015) 61:79–88. doi: 10.1373/clinchem.2014.221366

  • 31

    Agbor-EnohSWangYTuncIJangMKDavisADe VlaminckIet al. Donor-derived cell-free DNA predicts allograft failure and mortality after lung transplantation. EBioMedicine. (2019) 40:541–53. doi: 10.1016/j.ebiom.2018.12.029

  • 32

    PetterssonLFrischknechtLWesterlingSRamezanaliHWeidmannLLopezKCet al. Detection of donor-derived cell-free DNA in the setting of multiple kidney transplantations. Front Immunol. (2024) 15:1282521. doi: 10.3389/fimmu.2024.1282521

  • 33

    LiJTanJChenGLiQZhouZBinYet al. Sequencing of cell-free DNA to monitor cytomegalovirus infection after liver transplant. Exp Clin Transplant. (2021) 19:331–8. doi: 10.6002/ect

  • 34

    GordonPMKhanASajidUChangNSureshVDimnikLet al. An algorithm measuring donor cell-free DNA in plasma of cellular and solid organ transplant recipients that does not require donor or recipient genotyping. Front Cardiovasc Med. (2016) 3:33. doi: 10.3389/fcvm.2016.00033

  • 35

    HidestrandMTomita-MitchellAHidestrandPMOliphantAGoetschMStammKet al. Highly sensitive noninvasive cardiac transplant rejection monitoring using targeted quantification of donor-specific cell-free deoxyribonucleic acid. J Am Coll Cardiol. (2014) 63:1224–6. doi: 10.1016/j.jacc.2013.09.029

  • 36

    OellerichMChristensonRHBeckJSchützESherwoodKPriceCPet al. Donor-derived cell-free DNA testing in solid organ transplantation: A value proposition. J Appl Lab Med. (2020) 5:993–1004. doi: 10.1093/jalm/jfaa062

  • 37

    OellerichMShipkovaMAsendorfTWalsonPDSchauerteVMettenmeyerNet 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. (2019) 19:3087–99. doi: 10.1111/ajt.15416

  • 38

    BoydACainOChauhanAWebbGJ. Medical liver biopsy: background, indications, procedure and histopathology. Frontline Gastroenterol. (2020) 11:40–7. doi: 10.1136/flgastro-2018-101139

  • 39

    LiuSWangJ. Current and future perspectives of cell-free DNA in liquid biopsy. Curr Issues Mol Biol. (2022) 44:2695–709. doi: 10.3390/cimb44060184

  • 40

    RushDArlenDBoucherABusqueSCockfieldSMGirardinCet al. Lack of benefit of early protocol biopsies in renal transplant patients receiving TAC and MMF: a randomized study. Am J Transplant. (2007) 7:2538–45. doi: 10.1111/j.1600-6143.2007.01979.x

  • 41

    YangJYCSarwalRDSigdelTKDammIRosenbaumBLibertoJMet al. A urine score for noninvasive accurate diagnosis and prediction of kidney transplant rejection. Sci Transl Med. (2020) 12. doi: 10.1126/scitranslmed.aba2501

  • 42

    American society of nephrology renal research report. J Am Soc Nephrol. (2005) 16:1886–903. doi: 10.1681/ASN.2005030285

  • 43

    BoyumJHAtwellTDSchmitGDPoteruchaJJSchleckCDHarmsenWSet al. Incidence and risk factors for adverse events related to image-guided liver biopsy. Mayo Clin Proc. (2016) 91:329–35. doi: 10.1016/j.mayocp.2015.11.015

  • 44

    Fernández-GalánEBadenasCFondevilaCJiménezWNavasaMPuig-ButilléJAet al. 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 Transpl. (2022) 28:257–68. doi: 10.1016/j.transproceed.2023.08.016

  • 45

    Rodríguez-PerálvarezMGermaniGPapastergiouVTsochatzisEThalassinosELuongTVet al. Early tacrolimus exposure after liver transplantation: relationship with moderate/severe acute rejection and long-term outcome. J Hepatol. (2013) 58:262–70. doi: 10.1016/j.jhep.2012.09.019

  • 46

    Rodríguez-PerálvarezMGermaniGDariusTLerutJTsochatzisEBurroughsAK. Tacrolimus trough levels, rejection and renal impairment in liver transplantation: a systematic review and meta-analysis. Am J Transplant. (2012) 12:2797–814. doi: 10.1111/j.1600-6143.2012.04140.x

  • 47

    DashtiSHKasraianfardAEbrahimiANassiri-ToosiMPakshirMSRahimiMet al. Hemodynamic changes and early recovery of liver graft function after liver transplantation. Int J Organ Transplant Med. (2020) 11:1–7. doi: 10.1126/scitranslmed.3007803

  • 48

    SchützEFischerABeckJHardenMKochMWuenschTet al. a noninvasive early rejection and graft damage marker in liver transplantation: A prospective, observational, multicenter cohort study. PloS Med. (2017) 14:e1002286. doi: 10.1126/scitranslmed.aba2501

  • 49

    BeckJBierauSBalzerSAndagRKanzowPHenneckeJet al. Rapid and cost effective measurement of circulating cell free graft DNA for the early detection of liver transplant rejection. Clin Chem. (2013) 59:A27. doi: 10.1038/s41598-020-80845-6

  • 50

    GohSKDoHTestroAPavlovicJVagoALokanJet al. The measurement of donor-specific cell-free DNA identifies recipients with biopsy-proven acute rejection requiring treatment after liver transplantation. Transplant Direct. (2019) 5:e462. doi: 10.1097/TXD.0000000000000902

  • 51

    SchwarzAGwinnerWHissMRadermacherJMengelMHallerH. Safety and adequacy of renal transplant protocol biopsies. Am J Transplant. (2005) 5:1992–6. doi: 10.1111/j.1600-6143.2005.00988.x

  • 52

    HuangEJordanSC. Donor-derived cell-free DNA in kidney transplantation: evolving concepts and potential limitations. Kidney Int. (2022) 101:676–7. doi: 10.1016/j.kint.2022.01.012

  • 53

    OellerichMSherwoodKKeownPSchützEBeckJStegbauerJet al. Liquid biopsies: donor-derived cell-free DNA for the detection of kidney allograft injury. Nat Rev Nephrol. (2021) 17:591–603. doi: 10.1038/s41581-021-00428-0

  • 54

    BuLGuptaGPaiAAnandSStitesEMoinuddinIet al. Clinical outcomes from the Assessing Donor-derived cell-free DNA Monitoring Insights of kidney Allografts with Longitudinal surveillance (ADMIRAL) study. Kidney Int. (2022) 101:793–803. doi: 10.1016/j.kint.2021.11.034

  • 55

    ZhouGRenYLiJYangTSuNZhaoLet al. Safety and diagnostic efficacy of cone beam computed tomography-guided transbronchial cryobiopsy for interstitial lung disease: a cohort study. Eur Respir J. (2020) 56. doi: 10.1183/13993003.00724-2020

  • 56

    Agbor-EnohSJacksonAMTuncIBerryGJCochraneAGrimmDet al. Late manifestation of alloantibody-associated injury and clinical pulmonary antibody-mediated rejection: Evidence from cell-free DNA analysis. J Heart Lung Transplant. (2018) 37:925–32. doi: 10.1016/j.healun.2018.01.1305

  • 57

    TanakaSSugimotoSKurosakiTMiyoshiKOtaniSSuzawaKet al. Donor-derived cell-free DNA is associated with acute rejection and decreased oxygenation in primary graft dysfunction after living donor-lobar lung transplantation. Sci Rep. (2018) 8:15366. doi: 10.1038/s41598-018-33848-3

  • 58

    Agbor-EnohSShahPTuncIHsuSRussellSFellerEet al. Cell-free DNA to detect heart allograft acute rejection. Circulation. (2021) 143:1184–97. doi: 10.1161/CIRCULATIONAHA.120.049098

  • 59

    De VlaminckIValantineHASnyderTMStrehlCCohenGLuikartHet al. Circulating cell-free DNA enables noninvasive diagnosis of heart transplant rejection. Sci Transl Med. (2014) 6:241ra77. doi: 10.1126/scitranslmed.3007803

  • 60

    CostanzoMRDipchandAStarlingRAndersonAChanMDesaiSet al. The International Society of Heart and Lung Transplantation Guidelines for the care of heart transplant recipients. J Heart Lung Transplant. (2010) 29:914–56. doi: 10.1016/j.healun.2010.05.034

  • 61

    DenglerTJZimmermannRBraunKMüller-BardorffMZeheleinJSackFUet al. Elevated serum concentrations of cardiac troponin T in acute allograft rejection after human heart transplantation. J Am Coll Cardiol. (1998) 32:405–12. doi: 10.1016/S0735-1097(98)00257-5

  • 62

    AroraSGullestadLWergelandRSimonsenSHolmTHognestadAet al. Probrain natriuretic peptide and C-reactive protein as markers of acute rejection, allograft vasculopathy, and mortality in heart transplantation. Transplantation. (2007) 83:1308–15. doi: 10.1097/01.tp.0000263338.39555.21

  • 63

    KittlesonMMGargS. Solid gold, or liquid gold?: towards a new diagnostic standard for heart transplant rejection. Circulation. (2021) 143:1198–201. doi: 10.1161/CIRCULATIONAHA.120.052925

  • 64

    AkiravEMLebastchiJGalvanEMHenegariuOAkiravMAblamunitsVet al. Detection of β cell death in diabetes using differentially methylated circulating DNA. Proc Natl Acad Sci U.S.A. (2011) 108:19018–23. doi: 10.1038/s41598-018-33848-3

  • 65

    WilliamsMDFeiMSChaddeEHollingerEFChanEYOlaitanO. Early experience using donor-derived cell-free DNA for surveillance of rejection following simultaneous pancreas and kidney transplantation. Transplant Direct. (2022) 8:e1321. doi: 10.1097/TXD.0000000000001321

  • 66

    Ventura-AguiarPRamirez-BajoMJRoviraJBañón-ManeusEHierroNLazoMet al. Donor-derived cell-free DNA shows high sensitivity for the diagnosis of pancreas graft rejection in simultaneous pancreas-kidney transplantation. Transplantation. (2022) 106:1690–7. doi: 10.1097/TP.0000000000004088

  • 67

    ZhangR. Donor-specific antibodies in kidney transplant recipients. Clin J Am Soc Nephrol. (2018) 13:182–92. doi: 10.2215/CJN.00700117

  • 68

    HaasMLoupyALefaucheurCRoufosseCGlotzDSeronDet al. The Banff 2017 Kidney Meeting Report: Revised diagnostic criteria for chronic active T cell-mediated rejection, antibody-mediated rejection, and prospects for integrative endpoints for next-generation clinical trials. Am J Transplant. (2018) 18:293–307. doi: 10.1111/ajt.14625

  • 69

    JordanSCBunnapradistSBrombergJSLangoneAJHillerDYeeJPet al. Donor-derived cell-free DNA identifies antibody-mediated rejection in donor specific antibody positive kidney transplant recipients. Transplant Direct. (2018) 4:e379. doi: 10.1097/TXD.0000000000000821

  • 70

    SenevACoemansMLerutEVan SandtVDaniëlsLKuypersDet al. Histological picture of antibody-mediated rejection without donor-specific anti-HLA antibodies: Clinical presentation and implications for outcome. Am J Transplant. (2019) 19:763–80. doi: 10.1111/ajt.15074

  • 71

    LuqueSLúciaMMelilliELefaucheurCCrespoMLoupyAet al. Value of monitoring circulating donor-reactive memory B cells to characterize antibody-mediated rejection after kidney transplantation. Am J Transplant. (2019) 19:368–80. doi: 10.1111/ajt.15055

  • 72

    HalloranPFReeveJMadill-ThomsenKSDemkoZPrewettAGauthierPet al. Antibody-mediated rejection without detectable donor-specific antibody releases donor-derived cell-free DNA: results from the trifecta study. Transplantation. (2023) 107:709–19. doi: 10.1097/TP.0000000000004324

  • 73

    DePasqualeEKobashigawaJHallSWolf-DotyTTeutebergJKhushK. Donor derived cell free DNA as a risk factor for initiating de-novo donor specific antibodies in heart transplantation. J Heart Lung Transplant. (2021) 40:S217–S8. doi: 10.1016/j.healun.2021.01.628

  • 74

    StitesEKumarDOlaitanOJohn SwansonSLecaNWeirMet al. High levels of dd-cfDNA identify patients with TCMR 1A and borderline allograft rejection at elevated risk of graft injury. Am J Transplant. (2020) 20:2491–8. doi: 10.1111/ajt.15822

  • 75

    TeutebergJKobashigawaJShahPGhoshSRossDDePasqualeEet al. Donor-derived cell-free DNA predicts de novo DSA after heart transplantation. J Heart Lung Transplant. (2021) 40:S30. doi: 10.1016/j.healun.2021.01.1810

  • 76

    RoyFMLeeDLewisPRoseS. An economic analysis of the cost effectiveness of blood gene expression profiling in kidney transplant recipients. iMedPub. (2017) 1). doi: 10.1111/ajt.17039

  • 77

    VilaltaA. Cost and use trends of endomyocardial biopsy in heart transplant patients: A 4-year claims data analysis. Transplant Proc. (2023) 55:2186–90. doi: 10.1016/j.transproceed.2023.08.016

  • 78

    PuttarajappaCMMehtaRBRobertsMSSmithKJHariharanS. Economic analysis of screening for subclinical rejection in kidney transplantation using protocol biopsies and noninvasive biomarkers. Am J Transplant. (2021) 21:186–97. doi: 10.1111/ajt.16150

  • 79

    JangMKTuncIBerryGJMarboeCKongHKellerMBet al. Donor-derived cell-free DNA accurately detects acute rejection in lung transplant patients, a multicenter cohort study. J Heart Lung Transplant. (2021) 40:822–30. doi: 10.1016/j.healun.2021.04.009

  • 80

    GielisEMBeirnaertCDendoovenAMeysmanPLaukensKDe SchrijverJet al. Plasma donor-derived cell-free DNA kinetics after kidney transplantation using a single tube multiplex PCR assay. PloS One. (2018) 13:e0208207. doi: 10.1371/journal.pone.0208207

  • 81

    YooARiedelAQianIBartosicASoltaniRKibriaGet al. An initial analysis of the baseline levels of dd-cfDNA after pancreas transplantation: A prospective study from high-volume centers in the United States. Transplant Direct. (2023) 9:e1459. doi: 10.1097/TXD.0000000000001459

  • 82

    KellerMBMedaRFuSYuKJangMKCharyaAet al. Comparison of donor-derived cell-free DNA between single versus double lung transplant recipients. Am J Transplant. (2022) 22:2451–7. doi: 10.1111/ajt.17039

  • 83

    Costanzo-NordinMR. Cardiac allograft vasculopathy: relationship with acute cellular rejection and histocompatibility. J Heart Lung Transplant. (1992) 11:S90–103. doi: 10.1016/j.healun.2018.01.178

  • 84

    PediniPCoiffardBCherouatNCasasSFinaFBoutonnetAet al. Clinical relevance of cell-free DNA quantification and qualification during the first month after lung transplantation. Front Immunol. (2023) 14:1183949. doi: 10.3389/fimmu.2023.1183949

  • 85

    LevitskyJKandpalMGuoKKleiboekerSSinhaRAbecassisM. Donor-derived cell-free DNA levels predict graft injury in liver transplant recipients. Am J Transplant. (2022) 22:532–40. doi: 10.1111/ajt.16835

  • 86

    KantSKumarDMoinuddinIAlhamadTMuradHFBettinottiMet al. Utility of donor-derived cell-free DNA in detecting ABMR in patients with AT1R antibodies. Kidney Int Rep. (2021) 6:2706–8. doi: 10.1016/j.ekir.2021.07.026

  • 87

    LiYLiangB. Circulating donor-derived cell-free DNA as a marker for rejection after lung transplantation. Front Immunol. (2023) 14:1263389. doi: 10.3389/fimmu.2023.1263389

  • 88

    Agbor-EnohSTuncIDe VlaminckIFideliUDavisACuttinKet al. Applying rigor and reproducibility standards to assay donor-derived cell-free DNA as a non-invasive method for detection of acute rejection and graft injury after heart transplantation. J Heart Lung Transplant. (2017) 36:1004–12. doi: 10.1016/j.healun.2017.05.026

  • 89

    ValantineHShahPShahKHsuSFellerERodrigoMet al. (176)–validation of donor-derived cell-free DNA to detect heart-transplant rejection. J Heart Lung Transplant. (2018) 37:S78–S9. doi: 10.1016/j.healun.2018.01.178

  • 90

    LoupyALefaucheurC. Antibody-mediated rejection of solid-organ allografts. N Engl J Med. (2018) 379:1150–60. doi: 10.1056/NEJMra1802677

  • 91

    CoxDRALowNGohSKLeeEVagoAJackettLet al. Low levels of hepatocyte-specific methylation in cell-free DNA are a strong negative predictor for acute T cell-mediated rejection requiring treatment following liver transplantation. Liver Transpl. (2022) 28:1024–38. doi: 10.1002/lt.26388

  • 92

    VellecaAShulloMADhitalKAzekaEColvinMDePasqualeEet al. The International Society for Heart and Lung Transplantation (ISHLT) guidelines for the care of heart transplant recipients. J Heart Lung Transplant. (2023) 42:e1–e141. doi: 10.1016/j.healun.2022.10.015

  • 93

    RodgersNGerdingBCusiVVaidaFTadaYMorrisGPet al. Comparison of two donor-derived cell-free DNA tests and a blood gene-expression profile test in heart transplantation. Clin Transplant. (2023) 37:e14984. doi: 10.1111/ctr.14984

  • 94

    OellerichMBuddeKOsmanodjaBBornemann-KolatzkiKBeckJSchützEet al. Donor-derived cell-free DNA as a diagnostic tool in transplantation. Front Genet. (2022) 13:1031894. doi: 10.3389/fgene.2022.1031894

  • 95

    SigdelTKVitaloneMJTranTQDaiHHsiehSCSalvatierraOet al. A rapid noninvasive assay for the detection of renal transplant injury. Transplantation. (2013) 96:97–101. doi: 10.1097/TP.0b013e318295ee5a

  • 96

    HuangESethiSPengANajjarRMirochaJHaasMet al. Early clinical experience using donor-derived cell-free DNA to detect rejection in kidney transplant recipients. Am J Transplant. (2019) 19:1663–70. doi: 10.1111/ajt.15289

  • 97

    WhitlamJBLingLSkeneAKanellisJIerinoFLSlaterHRet al. Diagnostic application of kidney allograft-derived absolute cell-free DNA levels during transplant dysfunction. Am J Transplant. (2019) 19:1037–49. doi: 10.1111/ajt.15142

  • 98

    ZhaoDZhouTLuoYWuCXuDZhongCet al. Preliminary clinical experience applying donor-derived cell-free DNA to discern rejection in pediatric liver transplant recipients. Sci Rep. (2021) 11:1138. doi: 10.1038/s41598-020-80845-6

  • 99

    KhushKKDe VlaminckILuikartHRossDJNicollsMR. Donor-derived, cell-free DNA levels by next-generation targeted sequencing are elevated in allograft rejection after lung transplantation. ERJ Open Res. (2021) 7. doi: 10.1183/23120541.00462-2020

  • 100

    SayahDWeigtSSRamseyAArdehaliAGoldenJRossDJ. Plasma donor-derived cell-free DNA levels are increased during acute cellular rejection after lung transplant: pilot data. Transplant Direct. (2020) 6:e608. doi: 10.1097/TXD.0000000000001063

  • 101

    De VlaminckIMartinLKerteszMPatelKKowarskyMStrehlCet al. Noninvasive monitoring of infection and rejection after lung transplantation. Proc Natl Acad Sci U S A. (2015) 112:13336–41. doi: 10.1073/pnas.1517494112

  • 102

    KellerMSunJMutebiCShahPLevineDAryalSet al. Donor-derived cell-free DNA as a composite marker of acute lung allograft dysfunction in clinical care. J Heart Lung Transplant. (2022) 41:458–66. doi: 10.1016/j.healun.2021.12.009

  • 103

    SorbiniMTogliattoGMioliFSimonatoEMarroMCappuccioMet al. Validation of a simple, rapid, and cost-effective method for acute rejection monitoring in lung transplant recipients. Transpl Int. (2022) 35:10546. doi: 10.3389/ti.2022.10546

  • 104

    TrindadeAJChapinKCGrayJNFuruyaYMullicanAHoyHet al. Relative change in donor-derived cell-free DNA is superior to absolute values for diagnosis of acute lung allograft dysfunction. Transplant Direct. (2023) 9:e1487. doi: 10.1097/TXD.0000000000001487

  • 105

    KhushKK. Clinical utility of donor-derived cell-free DNA testing in cardiac transplantation. J Heart Lung Transplant. (2021) 40:397–404. doi: 10.1016/j.healun.2021.01.1564

  • 106

    KimPJOlymbiosMSiuAWever PinzonOAdlerELiangNet al. A novel donor-derived cell-free DNA assay for the detection of acute rejection in heart transplantation. J Heart Lung Transplant. (2022) 41:919–27. doi: 10.1016/j.healun.2022.04.002

  • 107

    BorkowskiPSinghNBorkowskaN. Advancements in heart transplantation: donor-derived cell-free DNA as next-generation biomarker. Cureus. (2024) 16:e54018. doi: 10.7759/cureus.54018

  • 108

    BloomRD. Using (cell-free) DNA to incriminate rejection as the cause of kidney allograft dysfunction: Do we have a verdict? Am J Transplant. (2019) 19:1609–10. doi: 10.1111/ajt.15338

  • 109

    GrskovicMHillerDJEubankLASninskyJJChristophersonCCollinsJPet al. Plasma levels of donor-derived cell-free DNA increase with rejection and often decrease after treatment in organ transplant recipients. Am J Of Transplant. (2015) 18(6):890–902. doi: 10.1093/clinchem/hvac053

  • 110

    ChenXTQiuJWuZXZhangHChenTYangSCet al. Using both plasma and urine donor-derived cell-free DNA to identify various renal allograft injuries. Clin Chem. (2022) 68:814–25. doi: 10.1093/clinchem/hvac053

  • 111

    LewisDGlehn-PonsirenasRGulbahceNHooeyLJChaffinJMMilesJet al. High levels of donor-derived cell-free DNA in a case of graft-versus-host-disease following liver transplantation. Am J Transplant. (2022) 22:973–6. doi: 10.1111/ajt.16894

  • 112

    GielisEMLedeganckKJDendoovenAMeysmanPBeirnaertCLaukensKet al. The use of plasma donor-derived, cell-free DNA to monitor acute rejection after kidney transplantation. Nephrol Dial Transplant. (2020) 35:714–21. doi: 10.1093/ndt/gfz091

  • 113

    KellerMBushEDiamondJMShahPMatthewJBrownAWet al. Use of donor-derived-cell-free DNA as a marker of early allograft injury in primary graft dysfunction (PGD) to predict the risk of chronic lung allograft dysfunction (CLAD). J Heart Lung Transplant. (2021) 40:488–93. doi: 10.1016/j.healun.2021.02.008

  • 114

    BazemoreKPermpalungNMathewJLemmaMHaileBAveryRet al. Elevated cell-free DNA in respiratory viral infection and associated lung allograft dysfunction. Am J Transplant. (2022) 22:2560–70. doi: 10.1111/ajt.17125

  • 115

    RagalieWSStammKMahnkeDLiangHLSimpsonPKatzRet al. Noninvasive assay for donor fraction of cell-free DNA in pediatric heart transplant recipients. J Am Coll Cardiol. (2018) 71:2982–3. doi: 10.1016/j.jacc.2018.04.026

  • 116

    HirschHHRandhawaPS. BK polyomavirus in solid organ transplantation-Guidelines from the American Society of Transplantation Infectious Diseases Community of Practice. Clin Transplant. (2019) 33:e13528. doi: 10.1111/ctr.13528

  • 117

    ShenJGuoLLeiWLiuSYanPLiuHet al. Urinary donor-derived cell-free DNA as a non-invasive biomarker for BK polyomavirus-associated nephropathy. J Zhejiang Univ Sci B. (2021) 22:917–28. doi: 10.1631/jzus.B2100131

  • 118

    KnowlesWAPipkinPAndrewsNVyseAMinorPBrownDWet al. Population-based study of antibody to the human polyomaviruses BKV and JCV and the simian polyomavirus SV40. J Med Virol. (2003) 71:115–23. doi: 10.1002/jmv.10450

  • 119

    DrachenbergCBPapadimitriouJCHirschHHWaliRCrowderCNogueiraJet al. Histological patterns of polyomavirus nephropathy: correlation with graft outcome and viral load. Am J Transplant. (2004) 4:2082–92. doi: 10.1046/j.1600-6143.2004.00603.x

  • 120

    ChenXTChenWFLiJDengRHHuangYYangSCet al. Urine donor-derived cell-free DNA helps discriminate BK polyomavirus-associated nephropathy in kidney transplant recipients with BK polyomavirus infection. Front Immunol. (2020) 11:1763. doi: 10.3389/fimmu.2020.01763

  • 121

    SawinskiDGoralS. BK virus infection: an update on diagnosis and treatment. Nephrol Dial Transplant. (2015) 30:209–17. doi: 10.1093/ndt/gfu023

  • 122

    ArthurRRShahKVBaustSJSantosGWSaralR. Association of BK viruria with hemorrhagic cystitis in recipients of bone marrow transplants. N Engl J Med. (1986) 315:230–4. doi: 10.1056/NEJM198607243150405

  • 123

    CrettolSVenetzJPFontanaMAubertJDAnsermotNFathiMet al. Influence of ABCB1 genetic polymorphisms on cyclosporine intracellular concentration in transplant recipients. Pharmacogenet Genomics. (2008) 18:307–15. doi: 10.1097/FPC.0b013e3282f7046f

  • 124

    FalckPAsbergAGuldsethHBremerSAkhlaghiFReubsaetJLet al. Declining intracellular T-lymphocyte concentration of cyclosporine a precedes acute rejection in kidney transplant recipients. Transplantation. (2008) 85:179–84. doi: 10.1097/TP.0b013e31815feede

  • 125

    RianthavornPEttengerRBMalekzadehMMarikJLStruberM. Noncompliance with immunosuppressive medications in pediatric and adolescent patients receiving solid-organ transplants. Transplantation. (2004) 77:778–82. doi: 10.1097/01.TP.0000110410.11524.7B

  • 126

    OellerichMSchützEKanzowPSchmitzJBeckJKollmarOet al. Use of graft-derived cell-free DNA as an organ integrity biomarker to reexamine effective tacrolimus trough concentrations after liver transplantation. Ther Drug Monit. (2014) 36:136–40. doi: 10.1097/FTD.0000000000000044

  • 127

    OsuchukwuGTrevinoAMcCormickSKaurNPrigmoreBAl Haj BaddarNet al. Use of donor-derived cell-free DNA to inform tapering of immunosuppression therapy in kidney transplant recipients: an observational study. Transplant Direct. (2024) 10:e1610. doi: 10.1097/TXD.0000000000001610

  • 128

    CucchiariDCuadrado-PayanEGonzalez-RocaERevueltaIArgudoMRamirez-BajoMJet al. Early kinetics of donor-derived cell-free DNA after transplantation predicts renal graft recovery and long-term function. Nephrol Dial Transplant. (2023) 39:114–21. doi: 10.1093/ndt/gfad120

  • 129

    Prada-DelgadoOEstévez-LoureiroRPaniagua-MartínMJLópez-SainzACrespo-LeiroMG. Prevalence and prognostic value of cardiac allograft vasculopathy 1 year after heart transplantation according to the ISHLT recommended nomenclature. J Heart Lung Transplant. (2012) 31:332–3. doi: 10.1016/j.healun.2011.12.006

  • 130

    HolzhauserLClerkinKJFujinoTAlenghatFJRaikhelkarJKimGet al. Donor-derived cell-free DNA is associated with cardiac allograft vasculopathy. Clin Transplant. (2021) 35:e14206. doi: 10.1111/ctr.14206

  • 131

    Crespo-LeiroMHillerDWoodwardRGrskovicMMarchisCSongMet al. Analysis of donor-derived cell-free DNA with 3-year outcomes in heart transplant recipients. J Heart Lung Transplant. (2017) 36:S69–70. doi: 10.1016/j.healun.2017.01.172

  • 132

    Jiménez-CollVEl Kaaoui El BandJLlorenteSGonzález-LópezRFernández-GonzálezMMartínez-BanaclochaHet al. All that glitters in cfDNA analysis is not gold or its utility is completely established due to graft damage: A critical review in the field of transplantation. Diagnostics (Basel). (2023) 13. doi: 10.3390/diagnostics13121982

  • 133

    Lehmann-WermanRNeimanDZemmourHMossJMagenheimJVaknin-DembinskyAet al. Identification of tissue-specific cell death using methylation patterns of circulating DNA. Proc Natl Acad Sci U.S.A. (2016) 113:E1826–34. doi: 10.1073/pnas.1519286113

  • 134

    ShahPAgbor-EnohSLeeSAndargieTESinhaSSKongHet al. Racial differences in donor-derived cell-free DNA and mitochondrial DNA after heart transplantation, on behalf of the GRAfT investigators. Circ Heart Fail. (2024) 17:e011160. doi: 10.1161/CIRCHEARTFAILURE.123.011160

  • 135

    XiaoHGaoFPangQXiaQZengXPengJet al. Diagnostic accuracy of donor-derived cell-free DNA in renal-allograft rejection: A meta-analysis. Transplantation. (2021) 105:1303–10. doi: 10.1097/TP.0000000000003443

  • 136

    KumarNTandonARanaRRanaDSBhallaAKGuptaAet al. Donor-derived cell-free DNA as a non-invasive biomarker for graft rejection in kidney transplant recipients: A prospective study among the Indian population. Diagnostics (Basel). (2023) 13. doi: 10.3390/diagnostics13233540

  • 137

    HallMOlopadeOI. Confronting genetic testing disparities: knowledge is power. Jama. (2005) 293:1783–5. doi: 10.1001/jama.293.14.1783

  • 138

    VerledenGMGlanvilleARLeaseEDFisherAJCalabreseFCorrisPAet al. Chronic lung allograft dysfunction: Definition, diagnostic criteria, and approaches to treatment-A consensus report from the Pulmonary Council of the ISHLT. J Heart Lung Transplant. (2019) 38:493–503. doi: 10.1016/j.healun.2019.03.009

  • 139

    BosSVosRVan RaemdonckDEVerledenGM. Survival in adult lung transplantation: where are we in 2020? Curr Opin Organ Transplant. (2020) 25:268–73. doi: 10.1016/j.healun.2019.08.001

  • 140

    ChambersDCCherikhWSHarhayMOHayesDJr.HsichEKhushKKet al. The International Thoracic Organ Transplant Registry of the International Society for Heart and Lung Transplantation: Thirty-sixth adult lung and heart-lung transplantation Report-2019; Focus theme: Donor and recipient size match. J Heart Lung Transplant. (2019) 38:1042–55. doi: 10.1016/j.healun.2019.08.001

  • 141

    BansalSFlemingTMohanakumarT. The detection of donor-derived cell-free DNA may serve as a biomarker for the early detection of chronic lung allograft dysfunction. EBioMedicine. (2019) 40:13–4. doi: 10.1016/j.ebiom.2019.01.044

  • 142

    SharplesLDMcNeilKStewartSWallworkJ. Risk factors for bronchiolitis obliterans: a systematic review of recent publications. J Heart Lung Transplant. (2002) 21:271–81. doi: 10.1016/S1053-2498(01)00360-6

  • 143

    StewartKCPattersonGA. Current trends in lung transplantation. Am J Transplant. (2001) 1:204–10. doi: 10.1034/j.1600-6143.2001.001003204.x

  • 144

    LuoJLiuLChenLXuXWangYWeiBet al. Over-shedding of donor-derived cell-free DNA at immune-related regions into plasma of lung transplant recipient. Clin Transl Med. (2022) 12:e622. doi: 10.1002/ctm2.622

  • 145

    NgHIZhuXXuanLLongYMaoYShiYet al. Analysis of fragment size distribution of cell-free DNA: A potential non-invasive marker to monitor graft damage in living-related liver transplantation for inborn errors of metabolism. Mol Genet Metab. (2019) 127:45–50. doi: 10.1016/j.ymgme.2019.03.004

  • 146

    KurianSFriedewaldJ. Comparing plasma donor-derived cell-free DNA to indication kidney biopsy tissue gene expression: toward understanding the molecular equivalents of non-invasive tests. J Am Soc Nephrol. (2022) 33:256–8. doi: 10.1681/ASN.2021121595

  • 147

    Jimenez-CollVLlorenteSBoixFAlfaroRGaliánJAMartinez-BanaclochaHet al. Monitoring of serological, cellular and genomic biomarkers in transplantation, computational prediction models and role of cell-free DNA in transplant outcome. Int J Mol Sci. (2023) 24. doi: 10.3390/ijms24043908

  • 148

    SureshkumarKKAramadaHRChopraB. Impact of body mass index and recipient age on baseline donor-derived cell free DNA (dd-cfDNA) in kidney transplant recipients. Clin Transplant. (2020) 34:e14101. doi: 10.1111/ctr.14101

  • 149

    SchützEAsendorfTBeckJSchauerteVMettenmeyerNShipkovaMet al. Time-dependent apparent increase in dd-cfDNA percentage in clinically stable patients between one and five years following kidney transplantation. Clin Chem. (2020) 66:1290–9. doi: 10.1093/clinchem/hvaa175

  • 150

    AbecasisGRAutonABrooksLDDePristoMADurbinRMHandsakerREet al. An integrated map of genetic variation from 1,092 human genomes. Nature. (2012) 491:56–65. doi: 10.1038/nature11632

  • 151

    KhushKKPatelJPinneySKaoAAlharethiRDePasqualeEet al. Noninvasive detection of graft injury after heart transplant using donor-derived cell-free DNA: A prospective multicenter study. Am J Transplant. (2019) 19:2889–99. doi: 10.1111/ajt.15339

  • 152

    ObrişcăBButiuMSibuleskyLBakthavatsalamRSmithKDGimferrerIet al. Combining donor-derived cell-free DNA and donor specific antibody testing as non-invasive biomarkers for rejection in kidney transplantation. Sci Rep. (2022) 12:15061. doi: 10.1038/s41598-022-19017-7

  • 153

    García MoreiraVPrieto GarcíaBBaltar MartínJMOrtega SuárezFAlvarezFV. Cell-free DNA as a noninvasive acute rejection marker in renal transplantation. Clin Chem. (2009) 55:1958–66. doi: 10.1373/clinchem.2009.129072

Summary

Keywords

graft-derived cell-free DNA, gd-cfDNA, organ transplantation, graft injury, rejection, non-invasive biomarker

Citation

Zhang W, Liu B, Jia D, Wang R, Cao H, Wu H, Ye Z and Gao B (2024) Application of graft-derived cell-free DNA for solid organ transplantation. Front. Immunol. 15:1461480. doi: 10.3389/fimmu.2024.1461480

Received

08 July 2024

Accepted

05 September 2024

Published

23 September 2024

Volume

15 - 2024

Edited by

Sarah Julia Reiling, McGill University Health Centre, Canada

Reviewed by

Harold Cliff Sullivan, Emory University, United States

Valia Bravo-Egana, Augusta University, United States

Updates

Copyright

© 2024 Zhang, Liu, Jia, Wang, Cao, Wu, Ye and Gao.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Baoshan Gao,

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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