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. 2023 Jan 27:14:1089830.
doi: 10.3389/fgene.2023.1089830. eCollection 2023.

Comparison of methods for donor-derived cell-free DNA quantification in plasma and urine from solid organ transplant recipients

Nicholas Kueng  1   2 , Séverine Arcioni  1   3 , Fanny Sandberg  1   2 , Christian Kuhn  4 , Vanessa Banz  5 , Carlo R Largiadèr  1 , Daniel Sidler  4 , Ursula Amstutz  1
Affiliations

Comparison of methods for donor-derived cell-free DNA quantification in plasma and urine from solid organ transplant recipients

Nicholas Kueng et al. Front Genet. .

Abstract

In allograft monitoring of solid organ transplant recipients, liquid biopsy has emerged as a novel approach using quantification of donor-derived cell-free DNA (dd-cfDNA) in plasma. Despite early clinical implementation and analytical validation of techniques, direct comparisons of dd-cfDNA quantification methods are lacking. Furthermore, data on dd-cfDNA in urine is scarce and high-throughput sequencing-based methods so far have not leveraged unique molecular identifiers (UMIs) for absolute dd-cfDNA quantification. Different dd-cfDNA quantification approaches were compared in urine and plasma of kidney and liver recipients: A) Droplet digital PCR (ddPCR) using allele-specific detection of seven common HLA-DRB1 alleles and the Y chromosome; B) high-throughput sequencing (HTS) using a custom QIAseq DNA panel targeting 121 common polymorphisms; and C) a commercial dd-cfDNA quantification method (AlloSeq® cfDNA, CareDx). Dd-cfDNA was quantified as %dd-cfDNA, and for ddPCR and HTS using UMIs additionally as donor copies. In addition, relative and absolute dd-cfDNA levels in urine and plasma were compared in clinically stable recipients. The HTS method presented here showed a strong correlation of the %dd-cfDNA with ddPCR (R 2 = 0.98) and AlloSeq® cfDNA (R 2 = 0.99) displaying only minimal to no proportional bias. Absolute dd-cfDNA copies also correlated strongly (τ = 0.78) between HTS with UMI and ddPCR albeit with substantial proportional bias (slope: 0.25; 95%-CI: 0.19-0.26). Among 30 stable kidney transplant recipients, the median %dd-cfDNA in urine was 39.5% (interquartile range, IQR: 21.8-58.5%) with 36.6 copies/μmol urinary creatinine (IQR: 18.4-109) and 0.19% (IQR: 0.01-0.43%) with 5.0 copies/ml (IQR: 1.8-12.9) in plasma without any correlation between body fluids. The median %dd-cfDNA in plasma from eight stable liver recipients was 2.2% (IQR: 0.72-4.1%) with 120 copies/ml (IQR: 85.0-138) while the median dd-cfDNA copies/ml was below 0.1 in urine. This first head-to-head comparison of methods for absolute and relative quantification of dd-cfDNA in urine and plasma supports a method-independent %dd-cfDNA cutoff and indicates the suitability of the presented HTS method for absolute dd-cfDNA quantification using UMIs. To evaluate the utility of dd-cfDNA in urine for allograft surveillance, absolute levels instead of relative amounts will most likely be required given the extensive variability of %dd-cfDNA in stable kidney recipients.

Keywords: biomarker; cell-free DNA; dd-cfDNA; ddPCR; high-throughput sequencing; kidney transplant; liver transplant; urine.

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Conflict of interest statement

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.

Figures

FIGURE 1
FIGURE 1
%dd-cfDNA of spike-in solutions measured by HTS alone and both HTS and ddPCR. (A) Scatter plot of the by QIAseq measured %dd-cfDNA versus theoretical %dd-cfDNA (n = 5). (B) Scatter plot of the %dd-cfDNA measured by ddPCR and QIAseq method (n = 6). The blue line represents the regression line calculated with the (A) linear regression method and (B) Passing Bablok regression method. The grey area represents the 95%-CI bounds calculated with the bootstrap (quantile) method.
FIGURE 2
FIGURE 2
Method comparison plot for QIAseq, AlloSeq cfDNA and ddPCR. The plots in the second row visualize an enlargement of the area within the black squares in plots in the first row. Comparisons of the %dd-cfDNA between (A) QIAseq versus ddPCR, (B) QIAseq versus AlloSeq cfDNA and (C) ddPCR versus AlloSeq cfDNA. The blue line represents the regression line calculated with the Passing Bablok regression method. The grey area represents the 95%-CI bounds calculated with the bootstrap (quantile) method.
FIGURE 3
FIGURE 3
Comparison of the dd-cfDNA copy numbers measured by ddPCR and QIAseq method. The plot in the second row represents a zoomed area as outlined by the grey area in between both plots. The blue line represents the regression line calculated with the Passing Bablok regression method and the grey area the 95%-CI bounds calculated with the bootstrap (quantile) method.
FIGURE 4
FIGURE 4
%dd-cfDNA and number of copies in plasma and urine from stable kidney transplant recipients. Boxplots of (A,C) the dd-cfDNA percentage and copies/ml in plasma; (B,D) dd-cfDNA percentage and copies/ml in urine and (E) dd-cfDNA copies normalized by UCr in urine.
FIGURE 5
FIGURE 5
%dd-cfDNA and number of copies in plasma and urine from stable liver transplant recipients. Boxplots of the dd-cfDNA percentage and copies/ml for (A,C) plasma, and (B,D) urine.
FIGURE 6
FIGURE 6
Correlation of the %dd-cfDNA and copy numbers in plasma versus urine in follow-up and post transplantation (postTPL) samples. (A) %dd-cfDNA correlation in plasma versus urine; (B) absolute dd-cfDNA copies/ml or copies/µmol UCr in plasma versus urine.
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