Combining Donor-derived Cell-free DNA Fraction and Quantity to Detect Kidney Transplant Rejection Using Molecular Diagnoses and Histology as Confirmation.

Background: Donor-derived cell-free DNA (dd-cfDNA) fraction and quantity have both been shown to be associated with allograft rejection. The present study compared the relative predictive power of each of these variables to the combination of the two, and developed an algorithm incorporating both variables to detect active rejection in renal allograft biopsies.
Methods: The first 426 sequential indication biopsy samples collected from the Trifecta study ( ClinicalTrials.gov # NCT04239703) with microarray-derived gene expression and dd-cfDNA results were included. After exclusions to simulate intended clinical use, 367 samples were analyzed. Biopsies were assessed using the molecular microscope diagnostic system and histology (Banff 2019). Logistic regression analysis examined whether combining dd-cfDNA fraction and quantity adds predictive value to either alone. The first 149 sequential samples were used to develop a two-threshold algorithm and the next 218 to validate the algorithm.
Results: In regression, the combination of dd-cfDNA fraction and quantity was found to be significantly more predictive than either variable alone ( P = 0.009 and P < 0.0001). In the test set, the area under the receiver operating characteristic curve of the two-variable system was 0.88, and performance of the two-threshold algorithm showed a sensitivity of 83.1% and specificity of 81.0% for molecular diagnoses and a sensitivity of 73.5% and specificity of 80.8% for histology diagnoses.
Conclusions: This prospective, biopsy-matched, multisite dd-cfDNA study in kidney transplant patients found that the combination of dd-cfDNA fraction and quantity was more powerful than either dd-cfDNA fraction or quantity alone and validated a novel two-threshold algorithm incorporating both variables.
Competing Interests: P.F.H. reports having shares in Transcriptome Sciences Inc, a University of Alberta research company with an interest in molecular diagnostics, and is a consultant to Natera. Inc. K.S.M.-T. is an employee of Transcriptome Sciences Inc. A.P. reports current employment with, and an ownership interest in, Natera, Inc, N.K., E.A., C.C., N.A.H.B., Z.D., N.L., R.K.S., B.G.Z., P.V.H., M.R., H.T., and P.G. are all employees of Natera, Inc, with stocks or options to buy stocks in the company. The other authors declare no conflicts of interest.
(Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.)