Role of Donor-derived Cell-free DNA In Predicting Short-term Allograft Health In Liver Transplant Recipients.

Background/aims: Predicting allograft dysfunction prior to clinical or biochemical evidence remains one of the challenges in transplantation, and a preclinical detection and early management of its cause allows for improved post-transplant outcomes. Donor-derived cell-free DNA (ddcfDNA) has been proposed as an important biomarker of allograft injury and has shown to predict dysfunction prior to any biochemical derangements. We aimed to investigate the diagnostic performance of ddcfDNA in detecting and differentiating the causes of early pre-biochemical detection of graft injury and in predicting the short-term outcomes of graft health using a patented protocol and proprietary set of single-nucleotide polymorphisms.

Methods: Blood samples were collected on defined postoperative days (1, 3, 7, and at 3 months) and were analysed through relatively economical patented protocol (Trunome™). Biopsy, biochemical tests, and clinical criteria were analysed between various subgroups.

Results: Of a total 50 patients, percentage ddcfDNA (%ddcfDNA) levels were significantly elevated in the rejection group (n = 8) as compared to that in the non-rejection group (n = 42; median elevation: 12.8% vs 4.3%, respectively), with a significant correlation (r = 0.92, < 0.0001). Area under the receiver operating characteristic curve (AUC-ROC) analysis revealed that the %ddcfDNA levels can predict graft health more precisely than the conventional liver function tests (AUC for %ddcfDNA: 0.86; < 0.001; AUC for aspartate transaminase 0.65,  = 0.08; AUC for alanine transaminase: 0.75, < 0.01). Moreover, %ddcfDNA levels (with a threshold of >10.2%) on post-operative day 7 accurately predicted short-term (3 months) health status of the graft with 93.33% sensitivity, 94.44% specificity, 87.50% positive predictive value, 97.14% negative predictive value, and 94.12% accuracy.

Conclusion: A single-timepoint ddcfDNA on postoperative day 7 accurately predicts graft health and improves risk stratification in the short-term.