Quantification of transplant-derived circulating cell-free DNA in absence of a donor genotype

Eilon Sharon, Hao Shi, Sandhya Kharbanda, Winston Koh, Lance R Martin, Kiran K Khush, Hannah Valantine, Jonathan K Pritchard, Iwijn De Vlaminck, Eilon Sharon, Hao Shi, Sandhya Kharbanda, Winston Koh, Lance R Martin, Kiran K Khush, Hannah Valantine, Jonathan K Pritchard, Iwijn De Vlaminck

Abstract

Quantification of cell-free DNA (cfDNA) in circulating blood derived from a transplanted organ is a powerful approach to monitoring post-transplant injury. Genome transplant dynamics (GTD) quantifies donor-derived cfDNA (dd-cfDNA) by taking advantage of single-nucleotide polymorphisms (SNPs) distributed across the genome to discriminate donor and recipient DNA molecules. In its current implementation, GTD requires genotyping of both the transplant recipient and donor. However, in practice, donor genotype information is often unavailable. Here, we address this issue by developing an algorithm that estimates dd-cfDNA levels in the absence of a donor genotype. Our algorithm predicts heart and lung allograft rejection with an accuracy that is similar to conventional GTD. We furthermore refined the algorithm to handle closely related recipients and donors, a scenario that is common in bone marrow and kidney transplantation. We show that it is possible to estimate dd-cfDNA in bone marrow transplant patients that are unrelated or that are siblings of the donors, using a hidden Markov model (HMM) of identity-by-descent (IBD) states along the genome. Last, we demonstrate that comparing dd-cfDNA to the proportion of donor DNA in white blood cells can differentiate between relapse and the onset of graft-versus-host disease (GVHD). These methods alleviate some of the barriers to the implementation of GTD, which will further widen its clinical application.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1. Illustration of our approach.
Fig 1. Illustration of our approach.
(a) A blood sample is used to genotype the recipient (cellular fraction, done once) and to sequence the cfDNA (see S1 Fig for details). (b-c) Illustration of the “one-genome” statistical model for inferring the percent of dd-cfDNA (red box with gray background). Black arrows show statistical dependency and text boxes show nuisance parameters (red box with white background), hidden variables (dotted line box) and measured data (blue box). (b) Shows the model which assumes that the donor and the recipient are unrelated. (c) When donor and recipient may be closely related (in this work, in case of a bone marrow transplant) the donor genotype depends on the recipient genotype and the local identity by descent (IBD) state between the recipient and donor genotypes. IBD states are modeled for each block of ~2cM along the genome. Transitions between IBD states depend on the number of meioses that separate each pair of recipient-donor chromosomes given their most recent diploid common ancestor (MRCA 1 and MRCA 2). (d) the inferred percent of dd-cfDNA is used to predict organ rejection.
Fig 2. Comparison of predicted levels of…
Fig 2. Comparison of predicted levels of dd-cfDNA by one- and two-genomes methods in heart and lung transplant recipients.
(a) and (b) Comparison between levels of dd-cfDNA predicted by the two-genomes method (x-axis) and the one-genome method (y-axis). (c) and (d) show a comparison of one- and two-genomes methods predictability of organ rejection. Each bar shows the area under the curve (AUC) of discriminating between two rejection states as measured using biopsies using dd-cfDNA fraction estimates [8,9]. Error bars marks AUC 95% confidence interval. The significance of the difference between corresponding receiver operating characteristic (ROC) of the one-genome and two-genomes was evaluated using the DeLong two-sided test [31,32].
Fig 3. Comparison of predicted levels of…
Fig 3. Comparison of predicted levels of dd-cfDNA by one- and two-genomes methods in bone marrow transplant recipients.
(a) Comparison between levels of dd-cfDNA predicted by the two-genomes method (x-axis) and the one-genome method (y-axis) when learning donor and recipient relatedness (orange) or naively assuming that they are unrelated (blue). The later under-estimates dd-cfDNA levels when the recipient and donor are siblings. Dashed lines show 1:1 and 2:1 ratios. (b) An example of cfDNA level estimates in a single bone marrow transplant recipient that is a sibling of the donor (I6).
Fig 4. Comparing the fraction of cfDNA…
Fig 4. Comparing the fraction of cfDNA that is recipient-derived to the fraction of recipient-derived blood cells may detect GVHD.
A proof of principle in a single bone marrow transplant recipient (patient I8), that differences in the recipient-derived cfDNA and recipient-derived blood cells levels may indicate onset of GVHD. The difference between the two measurements is due to injured tissue-derived cfDNA. In contrast, when relapse occurs both measurements should show an increase in recipient-derived fraction (not shown in this figure). This may help to distinguish between GVHD and relapse in bone-marrow transplanted patients.

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