Epigenetic signatures on plasma cell-free DNA to detect kidney allograft rejection in a non-invasive way: development of a 10-plex digital PCR assay.

Abstract

The standard of care for the follow-up of kidney allograft recipients combines non-invasive but non-specific biomarkers and kidney biopsies for the gold standard histology-based diagnosis, limited by the sampling bias, haemorrhagic risk, and low cost-effectiveness. We hypothesized that a targeted epigenetic analysis of cell-free DNA (cfDNA) would combine non-invasiveness and specificity for the diagnosis of kidney allograft rejection. We developed an in silico pipeline to identify 9 specific methylation signatures of epithelial or endothelial cell types in glomerular and tubular kidney compartments. Methylation-specific digital Polymerase Chain Reaction (dPCR) were designed and validated for these markers and combined in a 10-plex dPCR. In a retrospective cohort of 170 plasma cfDNA from adult kidney transplant recipients, we evaluated the diagnostic properties of our biomarkers for predicting rejection, evaluated on solid biopsy according to Banff 2022 classification. Combining the dedicated biomarkers with standard-of-care blood tests (donor-specific antibody (DSA), estimated glomerular filtration rate (eGFR)) produced a prediction model with an Area under the Curve (AUC) for biopsy-proven kidney transplant rejection vs. no rejection greater than with DSA and eGFR alone (AUC = of 0.884 vs. 0.776, p = 0.0005). In an alternative model for the prediction of any graft lesion of Banff classification vs. pristine biopsies (all Banff score = 0) epigenetic kidney biomarkers outperformed DSA (AUC = 0.754 vs. 0.596, p = 0.004). Thus, epigenetic signatures derived from the combination of kidney cell type specific methylation marker of cfDNA constitute a promising non-invasive diagnostic and theragnostic tool for kidney transplant patients.