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

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 d...

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Autores principales: Halloran, Philip F., Reeve, Jeff, Madill-Thomsen, Katelynn S., Kaur, Navchetan, Ahmed, Ebad, Cantos, Carlos, Al Haj Baddar, Nour, Demko, Zachary, Liang, Nathan, Swenerton, Ryan K., Zimmermann, Bernhard G., Van Hummelen, Paul, Prewett, Adam, Rabinowitz, Matthew, Tabriziani, Hossein, Gauthier, Phil, Billings, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9698190/
https://www.ncbi.nlm.nih.gov/pubmed/35765145
http://dx.doi.org/10.1097/TP.0000000000004212
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author Halloran, Philip F.
Reeve, Jeff
Madill-Thomsen, Katelynn S.
Kaur, Navchetan
Ahmed, Ebad
Cantos, Carlos
Al Haj Baddar, Nour
Demko, Zachary
Liang, Nathan
Swenerton, Ryan K.
Zimmermann, Bernhard G.
Van Hummelen, Paul
Prewett, Adam
Rabinowitz, Matthew
Tabriziani, Hossein
Gauthier, Phil
Billings, Paul
author_facet Halloran, Philip F.
Reeve, Jeff
Madill-Thomsen, Katelynn S.
Kaur, Navchetan
Ahmed, Ebad
Cantos, Carlos
Al Haj Baddar, Nour
Demko, Zachary
Liang, Nathan
Swenerton, Ryan K.
Zimmermann, Bernhard G.
Van Hummelen, Paul
Prewett, Adam
Rabinowitz, Matthew
Tabriziani, Hossein
Gauthier, Phil
Billings, Paul
author_sort Halloran, Philip F.
collection PubMed
description 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.
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spelling pubmed-96981902022-11-28 Combining Donor-derived Cell-free DNA Fraction and Quantity to Detect Kidney Transplant Rejection Using Molecular Diagnoses and Histology as Confirmation Halloran, Philip F. Reeve, Jeff Madill-Thomsen, Katelynn S. Kaur, Navchetan Ahmed, Ebad Cantos, Carlos Al Haj Baddar, Nour Demko, Zachary Liang, Nathan Swenerton, Ryan K. Zimmermann, Bernhard G. Van Hummelen, Paul Prewett, Adam Rabinowitz, Matthew Tabriziani, Hossein Gauthier, Phil Billings, Paul Transplantation Original Clinical Science—General 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. Lippincott Williams & Wilkins 2022-11-22 2022-12 /pmc/articles/PMC9698190/ /pubmed/35765145 http://dx.doi.org/10.1097/TP.0000000000004212 Text en Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
spellingShingle Original Clinical Science—General
Halloran, Philip F.
Reeve, Jeff
Madill-Thomsen, Katelynn S.
Kaur, Navchetan
Ahmed, Ebad
Cantos, Carlos
Al Haj Baddar, Nour
Demko, Zachary
Liang, Nathan
Swenerton, Ryan K.
Zimmermann, Bernhard G.
Van Hummelen, Paul
Prewett, Adam
Rabinowitz, Matthew
Tabriziani, Hossein
Gauthier, Phil
Billings, Paul
Combining Donor-derived Cell-free DNA Fraction and Quantity to Detect Kidney Transplant Rejection Using Molecular Diagnoses and Histology as Confirmation
title Combining Donor-derived Cell-free DNA Fraction and Quantity to Detect Kidney Transplant Rejection Using Molecular Diagnoses and Histology as Confirmation
title_full Combining Donor-derived Cell-free DNA Fraction and Quantity to Detect Kidney Transplant Rejection Using Molecular Diagnoses and Histology as Confirmation
title_fullStr Combining Donor-derived Cell-free DNA Fraction and Quantity to Detect Kidney Transplant Rejection Using Molecular Diagnoses and Histology as Confirmation
title_full_unstemmed Combining Donor-derived Cell-free DNA Fraction and Quantity to Detect Kidney Transplant Rejection Using Molecular Diagnoses and Histology as Confirmation
title_short Combining Donor-derived Cell-free DNA Fraction and Quantity to Detect Kidney Transplant Rejection Using Molecular Diagnoses and Histology as Confirmation
title_sort combining donor-derived cell-free dna fraction and quantity to detect kidney transplant rejection using molecular diagnoses and histology as confirmation
topic Original Clinical Science—General
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9698190/
https://www.ncbi.nlm.nih.gov/pubmed/35765145
http://dx.doi.org/10.1097/TP.0000000000004212
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