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

Philip F Halloran, Jeff Reeve, Katelynn S Madill-Thomsen, Navchetan Kaur, Ebad Ahmed, Carlos Cantos, Nour Al Haj Baddar, Zachary Demko, Nathan Liang, Ryan K Swenerton, Bernhard G Zimmermann, Paul Van Hummelen, Adam Prewett, Matthew Rabinowitz, Hossein Tabriziani, Phil Gauthier, Paul Billings, Trifecta Investigators*, Justyna Fryc, Beata Naumnik, Jonathan Bromberg, Matt Weir, Nadiesda Costa, Daniel Brennan, Sam Kant, Vignesh Viswanathan, Milagros Samaniego-Picota, Iman Francis, Anita Patel, Alicja Dębska-Ślizień, Joanna Konopa, Andrzej Chamienia, Andrzej Więcek, Grzegorz Piecha, Željka Veceric-Haler, Miha Arnol, Nika Kojc, Maciej Glyda, Katarzyna Smykal-Jankowiak, Ondrej Viklicky, Petra Hruba, Silvie Rajnochová Bloudíčkova, Janka Slatinská, Marius Miglinas, Marek Myślak, Joanna Mazurkiewicz, Marta Gryczman, Leszek Domański, Mahmoud Kamel, Agnieszka Perkowska-Ptasińska, Dominika Dęborska-Materkowska, Michal Ciszek, Magdalena Durlik, Leszek Pączek, Ryszard Grenda, Miroslaw Banasik, Mladen Knotek, Ksenija Vucur, Zeljka Jurekovic, Thomas Müller, Thomas Schachtner, Andrew Malone, Tarek Alhamad, Arksarapuk Jittirat, Emilio Poggio, Richard Fatica, Ziad Zaky, Kevin Chow, Peter Hughes, Sanjiv Anand, Gaurav Gupta, Layla Kamal, Dhiren Kumar, Irfan Moinuddin, Sindhura Bobba, Philip F Halloran, Jeff Reeve, Katelynn S Madill-Thomsen, Navchetan Kaur, Ebad Ahmed, Carlos Cantos, Nour Al Haj Baddar, Zachary Demko, Nathan Liang, Ryan K Swenerton, Bernhard G Zimmermann, Paul Van Hummelen, Adam Prewett, Matthew Rabinowitz, Hossein Tabriziani, Phil Gauthier, Paul Billings, Trifecta Investigators*, Justyna Fryc, Beata Naumnik, Jonathan Bromberg, Matt Weir, Nadiesda Costa, Daniel Brennan, Sam Kant, Vignesh Viswanathan, Milagros Samaniego-Picota, Iman Francis, Anita Patel, Alicja Dębska-Ślizień, Joanna Konopa, Andrzej Chamienia, Andrzej Więcek, Grzegorz Piecha, Željka Veceric-Haler, Miha Arnol, Nika Kojc, Maciej Glyda, Katarzyna Smykal-Jankowiak, Ondrej Viklicky, Petra Hruba, Silvie Rajnochová Bloudíčkova, Janka Slatinská, Marius Miglinas, Marek Myślak, Joanna Mazurkiewicz, Marta Gryczman, Leszek Domański, Mahmoud Kamel, Agnieszka Perkowska-Ptasińska, Dominika Dęborska-Materkowska, Michal Ciszek, Magdalena Durlik, Leszek Pączek, Ryszard Grenda, Miroslaw Banasik, Mladen Knotek, Ksenija Vucur, Zeljka Jurekovic, Thomas Müller, Thomas Schachtner, Andrew Malone, Tarek Alhamad, Arksarapuk Jittirat, Emilio Poggio, Richard Fatica, Ziad Zaky, Kevin Chow, Peter Hughes, Sanjiv Anand, Gaurav Gupta, Layla Kamal, Dhiren Kumar, Irfan Moinuddin, Sindhura Bobba

Abstract

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.

Conflict of interest statement

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.

Figures

Graphical abstract
Graphical abstract
FIGURE 1.
FIGURE 1.
Study flowchart. BK, polyoma virus.
FIGURE 2.
FIGURE 2.
receiver operating characteristic curves predicting molecular and histological rejection in the test set, based on test set predictions from the logistic regression model built using dd-cfDNA fraction and quantity in the training set. MMDx shows better performance than traditional histology in rejection diagnosis of KTx. AUC, area under the receiver operating characteristic curve; dd-cfDNA, donor-derived cell-free DNA; KTx, kidney transplant; MMDx, molecular microscope diagnostic system.
FIGURE 3.
FIGURE 3.
Plot of dd-cfDNA fraction (%) and quantity (cp/mL) for the full cohort (n = 367). The blue dashed horizontal and vertical lines indicate the dd-cfDNA quantity (78 cp/mL) and fraction (1%) thresholds, respectively. Patients with biopsy-proven rejection: AMR, TCMR, and Mixed, as adjudicated by MMDx, are depicted as red, green and yellow dots, respectively. Patients with biopsies that show nonrejection are represented by gray dots. The two-threshold algorithm considers samples in the lower-left quadrant as low risk for rejection, and samples in the remaining 3 quadrants, those with either dd-cfDNA quantity or fraction above the relevant thresholds, as high risk for rejection. AMR, antibody-mediated rejection; dd-cfDNA, donor-derived cell-free DNA; MMDx, molecular microscope diagnostic system; TCMR, T-cell-mediated rejection.

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