A Peripheral Blood Gene Expression Signature to Diagnose Subclinical Acute Rejection

Abstract

Background: In kidney transplant recipients, surveillance biopsies can reveal, despite stable graft function, histologic features of acute rejection and borderline changes that are associated with undesirable graft outcomes. Noninvasive biomarkers of subclinical acute rejection are needed to avoid the risks and costs associated with repeated biopsies.

Methods: We examined subclinical histologic and functional changes in kidney transplant recipients from the prospective Genomics of Chronic Allograft Rejection (GoCAR) study who underwent surveillance biopsies over 2 years, identifying those with subclinical or borderline acute cellular rejection (ACR) at 3 months (ACR-3) post-transplant. We performed RNA sequencing on whole blood collected from 88 individuals at the time of 3-month surveillance biopsy to identify transcripts associated with ACR-3, developed a novel sequencing-based targeted expression assay, and validated this gene signature in an independent cohort.

Results: Study participants with ACR-3 had significantly higher risk than those without ACR-3 of subsequent clinical acute rejection at 12 and 24 months, faster decline in graft function, and decreased graft survival in adjusted Cox analysis. We identified a 17-gene signature in peripheral blood that accurately diagnosed ACR-3, and validated it using microarray expression profiles of blood samples from 65 transplant recipients in the GoCAR cohort and three public microarray datasets. In an independent cohort of 110 transplant recipients, tests of the targeted expression assay on the basis of the 17-gene set showed that it identified individuals at higher risk of ongoing acute rejection and future graft loss.

Conclusions: Our targeted expression assay enabled noninvasive diagnosis of subclinical acute rejection and inflammation in the graft and may represent a useful tool to risk-stratify kidney transplant recipients.

Keywords: acute rejection; gene expression; renal transplantation; whole blood.

Graphical abstract

Figure 1.

GoCAR (n=191) and Belgian (n=46) cohorts were used in this study. Of 191 patients, 129 were randomly selected for transcriptomic analysis using RNA sequencing (n=88, discovery set) and microarray (n=65, validation) for identification of a peripheral blood gene signature to diagnose subclinical acute rejection. Of note, 26 patients were overlapped between the RNAseq and the microarray cohorts for correlation analysis of gene expression between the two technologies. The sequencing-based targeted expression (TREx) assay was developed on the gene set identified from transcriptomic analysis. In TREx assay, 113 of 127 patients from the transcriptomic analysis cohort were used for the training set to build the penalized logistic regression statistical model which was validated on an independent testing cohort of 110 patients (64 GoCAR patients and 46 patients from the Belgian cohort).

Figure 2.

ACR-3/BACR-3 is associated with adverse outcomes compared with NACR-3. Line graphs compare CADI (A and B) Ci+Ct scores between ACR-3 (bold red line), BACR-3 (BACR at 3 months, dotted red line), and NACR-3 (no-ACR at 3 months, green line) on serial 3-, 12-, and 24-month surveillance biopsy specimens (line through median, whiskers=EM). Bar graphs compare ACR prevalence on (C) 12- and (D) 24-month biopsy specimens in ACR-3 and NACR-3 groups. These increases in ACR and CADI at 12 or 24 months are subclinical observations. (E) Kaplan–Meier curves compare adjusted death-censored survival of ACR-3 (green) and NACR-3 (blue) groups in the GoCAR cohort (see Supplemental Table 1B). *P<0.05; ***P<0.001.

Figure 3.

Whole blood transcriptomic signatures of the patients at 3 months post-transplant are associated with subclinical acute rejection (ACR-3). (A) The volcano plot of DEGs between the recipients who developed or did not develop ACR-3. The x axis depicts the log₂ ratio of gene expression and the y axis depicts the −log₁₀ of LIMMA P test. The top up- or downregulated genes are labeled with boxes. (B) The bar chart of significant gene ontology function groups by enrichment analysis on DEGs. The bars represent −log₁₀ P value of enrichment significance of gene pathways by Fisher exact test; the lengths of red and green bars represent the percentages of up- and downregulated genes, respectively. (C) Immune cell enrichment analysis of DEGs associated with ACR-3. The heatmap shows expression of DEGs that were significantly enriched for immune cell types in the ImmGene dataset. (D) The heatmap of enrichment P value (−log₁₀ P) of immune cell–specific signatures in DEGs between ACR and non-ACR in GoCAR RNAseq, microarray, and three public datasets (GSE14346, GSE15296, and GSE50084). AR, acute rejection; gd, gama delta; NK, natural killer.

Figure 4.

The 17-gene set for diagnosis of ACR-3 was identified from GoCAR and validated in internal and external datasets. (A) The receiver operating characteristic (ROC) curve for diagnosis of ACR-3 with 17-gene set in GoCAR RNAseq discovery set (n=88; AUC=0.980, shown by black curve; leave-one-out cAUC=0.833, shown by blue curve). (B) The ROC curve for diagnosis of ACR-3 with 17-gene set in GoCAR microarray validation set (n=65, AUC/cAUC=1.000/0.802). (C) The ROC curve for diagnosis of ACR with 17-gene set in a public dataset (GSE14346: AUC/cAUC=0.959/0.818). (D) The ROC curve for diagnosis of ACR with 17-gene set in a public dataset (GSE15962: AUC/cAUC=0.988/0.832). (E) The ROC curve for diagnosis of ACR with 17-gene set in a public dataset set (GSE50084: AUC/cAUC=1.000/0.979).

Figure 5.

ACR-3 diagnosis with 17-gene set was validated by TREx assay. (A) The heatmap of expression of 17-gene set in TREx training set (n=113); ACR-3 or NACR-3 cases ordered by risk scores are on the left or right of the vertical yellow line, respectively. The up- or downregulated genes in ACR-3 are above or below the horizontal yellow line, respectively. (B) The ROC curve for diagnosis of ACR-3 with 17-gene set in the training set (n=113, AUC=0.830). (C) The dot plot of the probability risk scores for the patients in the training set (n=113, PPV=0.79, NPV=0.98 at tertile cutoffs). (D) The dot plot of the probability risk scores for the patients in the testing set (n=110, PPV=0.73, NPV=0.89 at tertile cutoffs defined from the training set). (E) Kaplan–Meier curve of graft loss for the kidney transplant recipients stratified into two groups (high/intermediate and low probability risks) in TREx (n=223). (F) The Kaplan–Meier curve of graft loss with the kidney transplant recipients without ACR (NACR-3) stratified by intermediate or low probability risks in TREx cohort. Cum, cumulative.