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The Cardiac Liquid Biopsy That Already Works, and What Holds It Up

September 16, 2026

Liquid biopsy in cardiovascular medicine is usually discussed as a future capability. In one corner of transplant medicine, it’s already being used. 

Donor-derived cell-free DNA (dd-cfDNA) testing for rejection surveillance is in routine clinical use for kidney and heart transplant recipients (Khush, 2019). Kidney transplantation is where dd-cfDNA has been most extensively studied (Kang, 2025); heart transplantation is one setting within that broader picture, with both a dd-cfDNA assay and a longer-standing gene-expression test that reads a different signal entirely (Alansari, 2025). Together they make transplant surveillance the field’s clearest working example of a proportional cell-free DNA measurement, and its clearest lesson in what a proportional measurement demands from the specimen that produces it. 

How the measurement works

After a heart transplant, a fraction of the cell-free DNA circulating in the recipient’s blood originates from the donor organ. Under rejection, that fraction rises (Khush, 2019). Published studies in heart and kidney transplantation have evaluated different cut-offs, and the reported thresholds cluster in the low single digits of a percent or below, not at any single number that applies across assays, organs, time since transplant, and clinical context (Khush, 2019). That variability is not a shortcoming of the method. It is the direct consequence of what the measurement actually is: a fraction, not a count. 

Where the fragility sits

A fraction has a denominator, and in donor-derived cell-free DNA testing the denominator is overwhelmingly the recipient’s own cell-free DNA, the majority of which comes from white blood cells breaking down through their normal turnover (Sun, 2015). That dependency is where the measurement is vulnerable. If white blood cells break down further after the blood draw, rather than in the body before it, they release additional recipient DNA into the same pool the donor fraction is measured against. The denominator grows. The donor fraction falls. Nothing has changed in the transplant. The number has simply moved in the direction that reads as reassuring. 

This mechanism has been demonstrated analytically. It has not been demonstrated clinically. There is no published evidence that it has caused a missed rejection in routine care, and the work supporting the dilution effect has largely used specimens constructed to isolate the mechanism rather than patient samples collected under real surveillance conditions. What has been measured is a shift in the ratio under controlled post-draw handling. Extending that finding into a claim about clinical outcomes would be extending it further than the evidence goes. 

How the field has responded

Two adaptations follow directly from that fragility. Collection protocols for dd-cfDNA testing specify tubes designed to stabilize blood cells after the draw, limiting the additional release of recipient DNA that would otherwise dilute the donor signal (Kang, 2025). And researchers are pairing the donor fraction with an absolute quantity of donor DNA rather than relying on the ratio alone, because a ratio inherits every change in the total pool it is measured against. 

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One recent two-threshold approach in heart transplantation tested a sample as positive if it crossed either a donor-fraction cutoff or a separate donor-quantity cutoff and reported meaningfully fewer false positives than the fraction-only cutoff it was compared against, without giving up sensitivity or specificity (Kim, 2025). The result is specific to that algorithm and that validation set. It illustrates the direction the field is moving in rather than a new universal threshold. 

What else is being measured

Gene-expression profiling of the recipient’s own immune cells has a longer regulatory and guideline history in heart transplant than dd-cfDNA does (Alansari, 2025 and Pham, 2010). The two are complementary rather than competing: one reads graft injury through donor DNA released into circulation, the other reads recipient immune activation through gene transcripts. That distinction matters for collection as well as interpretation. A cellular RNA readout carries its own handling requirement, because transcription continues inside a blood tube after the draw the same way cell breakdown does, just through a different mechanism acting on a different analyte. 

What is coming next, and how early it still is

The forward-looking work in this space applies tissue-of-origin methylation to cardiac injury rather than to transplant rejection specifically. Methylation markers have separated heart-derived cell-free DNA from other tissue sources after cardiac intervention, including distinguishing how much of a post-procedure rise in total cell-free DNA actually came from the heart versus from white blood cells (Ren, 2022). Independent groups have measured cardiomyocyte-specific cell-free DNA in myocardial infarction (Feng, 2025) and applied a methylation-based version of that same approach to cardiotoxicity from cancer therapy (Yu, 2023). in addition to the original work establishing cardiomyocyte-specific methylation signatures (Zemmour, 2018). In myocarditis, a single-center study of fewer than 25 patients used methylation patterns to separate cardiomyocyte injury from cardiac fibroblast injury and to detect involvement beyond the heart in the same patients (Zhao, 2025). These are small, largely single-center cohorts, and they should be read that way. The findings are genuine; the evidence base behind them is still early. 

Where it fits alongside established markers

None of this displaces troponin or the other established cardiac injury markers, which are fast, inexpensive, and extensively validated. What tissue-of-origin methylation adds is a different kind of question rather than a faster answer to the same one: which cell type was actually injured, and whether more than one organ was involved. Those are questions troponin was never built to answer. The opportunity for a methylation-based approach lies in resolving those questions alongside the tools already in routine use, instead of replacing them. 

What this means for a collection protocol today

Every application described here, established or emerging, shares the same exposure: a proportional readout, measured against a denominator that keeps changing after the blood leaves the patient. Transplant surveillance has already worked out how consequential that is and built collection protocols around it. Anyone designing a cardiac collection protocol now, for rejection surveillance or for the injury and methylation work still taking shape behind it, is designing for the same problem. What happens to blood cells between the draw and the lab is not a detail to standardize later. It is part of the measurement. 

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Reading List

  1. Khush KK, Patel J, Pinney S, et al. Noninvasive detection of graft injury after heart transplant using donor-derived cell-free DNA: a prospective multicenter study. Am J Transplant. 2019. PMID: 30835940. 
  2. Kang H, Cho SI, Oh EJ. Donor-derived cell-free DNA in solid organ transplantation: analytical considerations, diagnostic performance, and clinical interpretation. Clin Transplant Res. 2025. 
  3.  Alansari H, Gorthi J. Rejection Surveillance: Integrating Cell-Free DNA and Gene Expression Panels to Decrease Invasiveness in Routine Monitoring of Heart Transplant Recipients. Methodist DeBakey Cardiovasc J. 2025. 
  4. Sun K, Jiang P, Chan KCA, et 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(40):E5503-E5512. DOI: 10.1073/pnas.1508736112. 
  5. Kim PJ, Olympios M, Sideris K, et al. A two-threshold algorithm using donor-derived cell-free DNA fraction and quantity to detect acute rejection after heart transplantation. Am J Transplant. 2025;25(9):1895-1905. PMID 40334845.
  6. Pham MX, Teuteberg JJ, Kfoury AG, et al. Gene-expression profiling for rejection surveillance after cardiac transplantation. N Engl J Med. 2010;362(20):1890-1900. 
  7. Ren J, et al. Heart-specific DNA methylation analysis in plasma for the investigation of myocardial damage. J Transl Med. 2022;20:36. DOI: 10.1186/s12967-022-03234-9. PMID: 35062960. 
  8. Feng Y, Zhuo Y, Cheng H. Predictive value of circulating cardiomyocyte-specific cell-free DNA levels for heart failure risk after acute ST-segment elevation myocardial infarction. Adv Interv Cardiol (Postępy Kardiol Interwencyjnej). 2025;21(4):565-576. DOI: 10.5114/aic.2025.156807. PMID: 41743784. 
  9. Yu AF, Moore ZR, Moskowitz CS, et al. Association of circulating cardiomyocyte cell-free DNA with cancer therapy-related cardiac dysfunction in patients undergoing treatment for ERBB2-positive breast cancer. JAMA Cardiol. 2023;8(7). 
  10. Zemmour H, Planer D, Magenheim J, et al. Non-invasive detection of human cardiomyocyte death using methylation patterns of circulating DNA. Nat Commun. 2018;9:1443. 
  11. Zhao Y, et al. cfDNA methylation detection as potential liquid biopsy of multiple organ injury in myocarditis patients. Clin Epigenetics. 2025;17:106. DOI: 10.1186/s13148-025-01914-z. PMID: 40537817.