A urinary Common Rejection Module (uCRM) score for non-invasive kidney transplant monitoring

Abstract

A Common Rejection Module (CRM) consisting of 11 genes expressed in allograft biopsies was previously reported to serve as a biomarker for acute rejection (AR), correlate with the extent of graft injury, and predict future allograft damage. We investigated the use of this gene panel on the urine cell pellet of kidney transplant patients. Urinary cell sediments collected from patients with biopsy-confirmed acute rejection, borderline AR (bAR), BK virus nephropathy (BKVN), and stable kidney grafts with normal protocol biopsies (STA) were analyzed for expression of these 11 genes using quantitative polymerase chain reaction (qPCR). We assessed these 11 CRM genes for their abundance, autocorrelation, and individual expression levels. Expression of 10/11 genes were elevated in AR when compared to STA. Psmb9 and Cxcl10could classify AR versus STA as accurately as the 11-gene model (sensitivity = 93.6%, specificity = 97.6%). A uCRM score, based on the geometric mean of the expression levels, could distinguish AR from STA with high accuracy (AUC = 0.9886) and correlated specifically with histologic measures of tubulitis and interstitial inflammation rather than tubular atrophy, glomerulosclerosis, intimal proliferation, tubular vacuolization or acute glomerulitis. This urine gene expression-based score may enable the non-invasive and quantitative monitoring of AR.

Fig 1. Sample selection and study schematic of the study.

1,760 urine samples were collected between 2000 and 2016, of which 643 had matching biopsy data. 178 of these 643 had well-defined phenotypes of AR, bAR, BKVN, or STA. After RNA extraction, cDNA synthesis, and qPCR quantification, 28 samples did not pass QA/QC, leaving 150 samples for statistical analysis and modeling.

Fig 2. Relative abundance and correlation of abundance of CRM genes in the urine and expression of CRM genes across different clinical phenotypes of kidney transplantation.

A. Pearson correlation matrix demonstrating correlation among 11 CRM genes in their expression in urinary cell sediments. The size of the square serves as a visual indicator of the strength of the correlation. B. Heatmap with supervised clustering by phenotype demonstrating relative expression of CRM genes in AR, bAR, BKVN, and STA. C. Violin plots depicting the distribution of the CRM genes in AR, bAR, BKVN, and STA urine cell pellet. * indicates that AR vs STA was significant after multiple comparisons. ^# indicates that bAR vs STA was significant after multiple comparisons. Additional statistics are available in Table 2.

Fig 3. Performance evaluation of uCRM genes in phenotype discrimination.

A. Unsupervised clustering of AR and STA gene expression data using T-distributed Stochastic Neighbor Embedding (t-SNE) with a perplexity value of 78. B. Use of Variable Selection Using Random Forests (VSURF) to identify genes that have high importance in AR detection. C. Distribution of transplant outcomes in terms of AR, bAR, and STA on the two VSURF-selected genes Cxcl10 and Psmb9.

Fig 4. uCRM score classification performance and threshold development by decision tree.

A. A decision tree based on uCRM score classification performance and threshold that could correctly classify AR, bAR, and STA with 96.6% accuracy (85/88). B. Scatter dot plot of the uCRM score for AR, bAR, and STA phenotypes. Significance was determined by nonparametric Kruskal-Wallis test with Dunn’s multiple comparisons correction. C. ROC curve of the uCRM score in discriminating between AR and STA phenotypes (AUC = 0.9886, P < 0.0001). * < 0.05. *** P < 0.001. **** P < 0.0001.

Fig 5. The uCRM score correlates with the extent of AR lesions.

The uCRM scores were correlated with the extent of the acute allograft lesions. A. Correlation between the tubular (t) scores and the uCRM scores (R = 0.5479, P < 0.0001) and B. Correlation between the interstitial inflammation (ii) scores and the uCRM scores (R = 0.4420, P < 0.0001). Dotted lines depict the 95% confidence interval of the regression line.