Biopsy transcriptome expression profiling to identify kidney transplants at risk of chronic injury: a multicentre, prospective study

Abstract

Background: Chronic injury in kidney transplants remains a major cause of allograft loss. The aim of this study was to identify a gene set capable of predicting renal allografts at risk of progressive injury due to fibrosis.

Methods: This Genomics of Chronic Allograft Rejection (GoCAR) study is a prospective, multicentre study. We prospectively collected biopsies from renal allograft recipients (n=204) with stable renal function 3 months after transplantation. We used microarray analysis to investigate gene expression in 159 of these tissue samples. We aimed to identify genes that correlated with the Chronic Allograft Damage Index (CADI) score at 12 months, but not fibrosis at the time of the biopsy. We applied a penalised regression model in combination with permutation-based approach to derive an optimal gene set to predict allograft fibrosis. The GoCAR study is registered with ClinicalTrials.gov, number NCT00611702.

Findings: We identified a set of 13 genes that was independently predictive for the development of fibrosis at 1 year (ie, CADI-12 ≥2). The gene set had high predictive capacity (area under the curve [AUC] 0·967), which was superior to that of baseline clinical variables (AUC 0·706) and clinical and pathological variables (AUC 0·806). Furthermore routine pathological variables were unable to identify which histologically normal allografts would progress to fibrosis (AUC 0·754), whereas the predictive gene set accurately discriminated between transplants at high and low risk of progression (AUC 0·916). The 13 genes also accurately predicted early allograft loss (AUC 0·842 at 2 years and 0·844 at 3 years). We validated the predictive value of this gene set in an independent cohort from the GoCAR study (n=45, AUC 0·866) and two independent, publically available expression datasets (n=282, AUC 0·831 and n=24, AUC 0·972).

Interpretation: Our results suggest that this set of 13 genes could be used to identify kidney transplant recipients at risk of allograft loss before the development of irreversible damage, thus allowing therapy to be modified to prevent progression to fibrosis.

Funding: National Institutes of Health.

Copyright © 2016 Elsevier Ltd. All rights reserved.

Figures

Figure 1. Patient eligibility

*Expression microarray was done on RNA extracted from the first 159 patients based on date of enrolment. †The 101 corresponding protocol biopsies at 12 months post-transplantion were used to identify the optimal gene set based on CADI score at 12 months. ‡These patients were included in analysis of progression versus non-progression of CADI scores, where progression was defined as an increase in CADI score of at least 2 points. §This cohort was used for independent qPCR validation of the 13 gene set. CADI=Chronic Allograft Damage Index. qPCR=quantitative PCR.

Figure 2. Prediction of high and low CADI scores and progression or non-progression at 12 months post-transplantation

(A) Prediction of high or low CADI-12 scores with the 13-gene set and clinical and pathological variables. (B) Internal validation of the ability of the set of 13 genes to predict high or low CADI-12 was done with qPCR of biopsies collected at 3 months post-transplantation in an independent cohort of 45 patients within the GoCAR study. (C) Prediction of fibrosis progression versus non-progression at 12 and 24 months with the 13-gene set and clinical and pathological variables. (D) ROC curves for external validation of the 13-gene set in two publically available biopsy microarray datasets., CADI-12 scores of 2 or more were deemed high and scores of less than 2 were deemed low. Progression was shown with CADI-3 of 3 or less and an increase in CADI-12 score of at least 2 points. AUC=area under the curve. CADI-12=Chronic Allograft Damage Index at 12 months. ROC=receiver operating characteristic.

Figure 3. Survival analysis of time to allograft loss

(A) Kaplan-Meier plot of time to allograft loss for patients stratified into high-risk and low-risk groups according to the gene set risk score, which was calculated by the linear combination of eigenvalues of significant principle components multiplied by their coefficiencies in a Cox proportional hazard model. Hazard ratio of graft loss was estimated from the coefficiency of the gene risk score in the Cox proportional model. (B) ROC curves for prediction of allograft loss within 2 years or 3 years after the 3-month biopsy. (C) Kaplan-Meier plot of time to allograft loss for patients from a publically available dataset (GSE21374) who were stratified into high-risk and low-risk groups according to the gene set risk score. (D) ROC curves for prediction of time to allograft loss by 1 year and 2 years post-biopsy through application of the gene set risk score to the publically available dataset. AUC=area under the curve. ROC=receiver operating characteristic. HR=hazard ratio.