Prediction of Liver Transplant Rejection With a Biologically Relevant Gene Expression Signature

Josh Levitsky, Manoj Kandpal, Kexin Guo, Lihui Zhao, Sunil Kurian, Thomas Whisenant, Michael Abecassis, Josh Levitsky, Manoj Kandpal, Kexin Guo, Lihui Zhao, Sunil Kurian, Thomas Whisenant, Michael Abecassis

Abstract

Background: Noninvasive biomarkers distinguishing early immune activation before acute rejection (AR) could more objectively inform immunosuppression management in liver transplant recipients (LTRs). We previously reported a genomic profile distinguishing LTR with AR versus stable graft function. This current study includes key phenotypes with other causes of graft dysfunction and uses a novel random forest approach to augment the specificity of predicting and diagnosing AR.

Methods: Gene expression results in LTRs with AR versus non-AR (combination of other causes of graft dysfunction and normal function) were analyzed from single and multicenter cohorts. A 70:30 approach (61 ARs; 162 non-ARs) was used for training and testing sets. Microarray data were normalized using a LT-specific vector.

Results: Random forest modeling on the training set generated a 59-probe classifier distinguishing AR versus non-AR (area under the curve 0.83; accuracy 0.78, sensitivity 0.70, specificity 0.81, positive predictive value 0.54, negative predictive value [NPV] 0.89; F-score 0.61). Using a locked threshold, the classifier performed well on the testing set (accuracy 0.72, sensitivity 0.67, specificity 0.73, positive predictive value 0.48, NPV 0.86; F-score 0.56). Probability scores increased in samples preceding AR versus non-AR, when liver function tests were normal, and decreased following AR treatment (P < 0.001). Ingenuity pathway analysis of the genes revealed a high percentage related to immune responses and liver injury.

Conclusions: We have developed a blood-based biologically relevant biomarker that can be detected before AR-associated graft injury distinct from LTR never developing AR. Given its high NPV ("rule out AR"), the biomarker has the potential to inform precision-guided immunosuppression minimization in LTRs.

Conflict of interest statement

J.L. and S.K. provided consultancy to Transplant Genomics Incorporated (Eurofins/Viracor). M.A. is a cofounder of Transplant Genomics Incorporated (Eurofins/Viracor). J.L. received research funding from Novartis. The other authors declare no conflicts of interest.

Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.

Figures

FIGURE 1.
FIGURE 1.
The ROCs—AR vs non-AR. The AUC is displayed as well as the performance characteristics (25% AR prevalence adjustment) at the 0.34 threshold. AR, acute rejection; AUC, area under the curve; ROC, receiver operating curve.
FIGURE 2.
FIGURE 2.
Serial changes in AR vs non-AR gene expression scores using line slopes. A, Pre-AR vs pre–non-AR (P = 0.0002). B, Pre-AR vs post-AR (P = 0.0001). AR, acute rejection.
FIGURE 3.
FIGURE 3.
Three-dimensional principal component analysis score plots for sample clustering, using 59 probes from the random forest classifier between AR and non-AR. The left plot is for training (n = 156) and the right plot is for testing (67) sample sets. AR, acute rejection.

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Source: PubMed

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