Orthogonal Comparison of Molecular Signatures of Kidney Transplants With Subclinical and Clinical Acute Rejection: Equivalent Performance Is Agnostic to Both Technology and Platform

Abstract

We performed orthogonal technology comparisons of concurrent peripheral blood and biopsy tissue samples from 69 kidney transplant recipients who underwent comprehensive algorithm-driven clinical phenotyping. The sample cohort included patients with normal protocol biopsies and stable transplant (sTx) function (n = 25), subclinical acute rejection (subAR, n = 23), and clinical acute rejection (cAR, n = 21). Comparisons between microarray and RNA sequencing (RNA-seq) signatures were performed and demonstrated a strong correlation between the blood and tissue compartments for both technology platforms. A number of shared differentially expressed genes and pathways between subAR and cAR in both platforms strongly suggest that these two clinical phenotypes form a continuum of alloimmune activation. SubAR is associated with fewer or less expressed genes than cAR in blood, whereas in biopsy tissues, this clinical phenotype demonstrates a more robust molecular signature for both platforms. The discovery work done in this study confirms a clear ability to detect gene expression profiles for sTx, subAR, and cAR in both blood and biopsy tissue, yielding equivalent predictive performance that is agnostic to both technology and platform. Our data also provide strong biological insights into the molecular mechanisms underlying these signatures, underscoring their logistical potential as molecular diagnostics to improve clinical outcomes following kidney transplantation.

Keywords: clinical research/practice; diagnostic techniques and imaging; genomics; kidney (allograft) function/dysfunction; kidney transplantation/nephrology; microarray/gene array; rejection: acute; translational research/science.

Conflict of interest statement

Disclosure

The authors of this manuscript have conflicts of interest to disclose as described by the American Journal of Transplantation. DRS, SMK, JJF and MMA are founding scientists and have ownership stock in TGI. RMF and SR are full-time employees at TGI. The other authors have no conflicts of interest to disclose.

© 2017 The American Society of Transplantation and the American Society of Transplant Surgeons.

Figures

Figure 1

Correlation between the blood findings and the biopsy findings comparing the two analytical methodologies (microarrays and RNA-seq). The similarity between the technologies was also reflected in the consistently higher number of differentially expressed genes in the biopsy compared to the blood. The M (log ratios) and A (average) scale (MA) plots for all comparisons are shown in Figures 1a for RNAseq (NGS) and b for the microarrays.

Figure 1

Correlation between the blood findings and the biopsy findings comparing the two analytical methodologies (microarrays and RNA-seq). The similarity between the technologies was also reflected in the consistently higher number of differentially expressed genes in the biopsy compared to the blood. The M (log ratios) and A (average) scale (MA) plots for all comparisons are shown in Figures 1a for RNAseq (NGS) and b for the microarrays.

Figure 2

Supervised Principal Components Analysis (PCA) plots for the two tissues and technologies showing the separation of the phenotypes using the maximum set classifiers.

Figure 3

Scatter plots of the fold changes for cAR vs TX (x-axes) and subAR vs. TX (y-axes) to demonstrate that that cAR fold changes are of greater magnitude than subAR changes. 3a, Microarrays - Blood 3b, RNA-seq - Blood 3c, Microarray - Biopsies 3d RNA-seq - Biopsies. Green dots denote greater cAR vs TX fold changes and blue dots denote greater subAR vs. TX fold changes. The table shows the number of genes in each comparison and the overlapping genes that were plotted to create figures 3a-d.