Although transplantation may represent the only available treatment for most types of organ failure, it is not a cure and comes with many complications, including rejection and infections. Traditional biomarkers, including serum creatinine for kidneys or transaminase concentrations for livers, do not distinguish rejection from other causes of transplant injury and tend to rise relatively late in the disease process. Moreover, in the absence of an “immunostat,” drug concentrations are used as a proxy for the net state of immunosuppression. Infections are often diagnosed with relatively insensitive and time-consuming assays such as serology and culture. Thus, there is a clear unmet need for an effective biomarker enabling early detection and reliable discrimination of the many different complications arising after transplantation.
The potential of measuring circulating cell-free DNA (cfDNA) in this respect has recently been demonstrated.1,2 Cheng et al3 recently showed that cfDNA is a potential marker for allograft rejection and can also be used to monitor the 4 most important complications of hematopoietic stem cell transplantation, including graft versus host disease, infection, graft failure, and recurrence of hematologic malignancy.
In a proof of principle study that included 27 recipients of a hematopoietic stem cell transplant followed prospectively, the authors used a single assay that assessed genome-wide methylation of circulating cfDNA. Positioning of methylation on DNA is an essential component of cell type–specific gene expression regulation and represents a hallmark of cellular identity. Based on methylation profiles, determined with whole-genome bisulfite sequencing followed by bioinformatics analyses, the origin of circulating cfDNA in these 27 patients was determined.3 Microbial cfDNA was distinguished from human cfDNA, which was then identified as originating from either tumor, donor, or recipient. The origin of recipient cfDNA informed on tissue-specific injury, distinguishing graft versus host disease. Levels of donor-derived cfDNA (in case of a donor–recipient chromosome X–Y mismatch) provided information on successful engraftment or transplant failure, whereas levels of tumor-derived cfDNA (extensive genotyping of the hematologic malignancy was performed before transplantation) diagnosed the recurrence of hematologic malignancy. Finally, BK virus–derived cfDNA could be detected and turned out to be more sensitive than a clinically used BK virus–specific polymerase chain reaction assay for the detection of BK viremia.3
The work by Cheng et al3 is an elegant example of the potential diagnostic value of circulating cfDNA and will likely improve posttransplant care of hematopoietic stem cell recipients. The cfDNA assay introduced by the authors enables early detection of several major complications after hematopoietic stem cell transplantation and requires only a small volume of blood (0.5–1.9 mL of plasma). Although the number of included patients was relatively small and the cfDNA profiling required advanced sequencing and complex bioinformatics analysis, we do believe that this approach illustrates how transplantation diagnostics may evolve in the near future.
What are the implications of this work for solid organ transplantation? The study confirms the potential of cfDNA to serve as a noninvasive biomarker providing a timely assessment of allograft rejection. Several commercial and in-house cfDNA assays detecting donor-specific cfDNA are now available and the findings of Cheng et al3 will likely fuel a more widespread use of such cfDNA assays.2,4 Moving forward, cfDNA measurements may also help in establishing effective and personalized immunosuppressive therapies.5
Still, there remain challenges and unanswered questions about the value of circulating cfDNA (reviewed by Kataria et al).1 Several studies have demonstrated that cfDNA levels are elevated early after transplantation (particularly during postoperative d 0–10),1,2 most likely because of ischemia-reperfusion injury. Furthermore, cfDNA assays may not be able to reliably distinguish between different types of rejection and other causes of transplant injury. In general, antibody-mediated rejection is accompanied with higher cfDNA levels compared with T cell–mediated rejection, although those findings have not been equivocal.1,2,4 cf DNA can also increase as a result of infections and data on recurrent diseases affecting cfDNA levels remain limited.1 Of additional relevance, a “normal” cfDNA concentration does not rule out rejection and an elevated cfDNA concentration does not provide proof.1 The discriminative power of cfDNA may be improved in combination with other biomarkers, including gene expression profiles in blood, urine, or tissue samples.6,7 Determining the tissue origin of cfDNA as demonstrated by Cheng et al3 may represent a helpful addition in this context. Liver allograft rejection has been diagnosed using hepatocyte-specific DNA methylation profiles.8 Tracing the origin of cfDNA in more detail and potentially narrowed down to a specific cell type may open exciting and novel possibilities for the discrimination of different causes of allograft injury.
An additional and clinically most relevant application of circulating cfDNA as shown by Cheng et al3 is the potential to diagnose infection. cfDNA allows the detection of numerous different pathogens with particular relevance for the challenging diagnosis of posttransplant infections, including tuberculosis and endocarditis.9 Finally, the study by Cheng et al3 has shown the potential to use cfDNA for the diagnosis of malignancies with relevance for and beyond recurrent hepatocellular carcinoma after liver transplantation.10
Although unresolved issues remain, the seminal work by Cheng et al3 illustrates the broad possibilities of cfDNA analysis for clinical application in transplantation medicine. It is time to broaden the scope of cfDNA assays beyond rejection diagnosis and start using the full potential of this approach.
1. Kataria A, Kumar D, Gupta G. Donor-derived cell-free DNA in solid-organ transplant diagnostics: indications, limitations, and future directions. Transplantation. 2021;105:1203–1211.
2. Verhoeven JGHP, Boer K, Peeters AMA, et al. A novel high-throughput droplet digital PCR-based indel quantification method for the detection of circulating donor-derived cell-free DNA after kidney transplantation. Transplantation. [Epub ahead of print. March 10, 2022]. doi:10.1097/TP.0000000000004078
3. Cheng AP, Cheng MP, Loy CJ, et al. Cell-free DNA profiling informs all major complications of hematopoietic cell transplantation. Proc Natl Acad Sci U S A. 2022;119:e2113476118.
4. Bu L, Gupta G, Pai A, et 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.
5. Oellerich M, Schütz E, Kanzow P, et 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;36:136–140.
6. Park S, Guo K, Heilman RL, et al. Combining blood gene expression and cellfree DNA to diagnose subclinical rejection in kidney transplant recipients. Clin J Am Soc Nephrol. 2021;16:1539–1551.
7. Halloran PF, Reeve J, Madill-Thomsen KS, et al.; Trifecta Investigators. The Trifecta study: comparing plasma levels of donor-derived cell-free DNA with the molecular phenotype of kidney transplant biopsies. J Am Soc Nephrol. 2022;33:387–400.
8. Lehmann-Werman R, Magenheim J, Moss J, et al. Monitoring liver damage using hepatocyte-specific methylation markers in cell-free circulating DNA. JCI Insight. 2018;3:120687.
9. Blauwkamp TA, Thair S, Rosen MJ, et al. Analytical and clinical validation of a microbial cell-free DNA sequencing test for infectious disease. Nat Microbiol. 2019;4:663–674.
10. Jiang P, Sun K, Tong YK, et al. Preferred end coordinates and somatic variants as signatures of circulating tumor DNA associated with hepatocellular carcinoma. Proc Natl Acad Sci U S A. 2018;115:E10925–E10933.