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In this cross-sectional and longitudinal analysis of mapping the T-cell repertoire in kidney transplant recipients, we have investigated and validated T-cell clonality, immune repertoire chronology at rejection, and contemporaneous allograft biopsy quantitative tissue injury, to better understand the pathobiology of acute T-cell fraction, T-cell repertoire and antibody-mediated kidney transplant rejection. To follow the dynamic evolution of T-cell repertoire changes before and after engraftment and during biopsy-confirmed acute rejection, we sequenced 323 peripheral blood samples from 200 unique kidney transplant recipients, with (n=100) and without (n=100) biopsy-confirmed acute rejection. We report that patients who develop acute allograft rejection, have lower (p=0.01) T-cell fraction even before transplantation, followed by its rise after transplantation and at the time of acute rejection accompanied by high TCR repertoire turnover (p=0.004). Acute rejection episodes occurring after the first 6 months post-transplantation, and those with a component of antibody-mediated rejection, had the highest turnover; p=0.0016) of their T-cell repertoire. In conclusion, we validated that detecting repertoire changes in kidney transplantation correlates with post-transplant rejection episodes suggesting that T-cell receptor sequencing may provide recipient pre-transplant and post-transplant predictors of rejection risk.

IntroductionPeripheral blood (PB) molecular patterns characterizing the different effector immune pathways driving distinct kidney rejection types remain to be fully elucidated. We hypothesized that transcriptome analysis using RNA sequencing (RNAseq) in samples of kidney transplant patients would enable the identification of unique protein-coding and noncoding genes that may be able to segregate different rejection phenotypes.MethodsWe evaluated 37 biopsy-paired PB samples from the discovery cohort, with stable (STA), antibody-mediated rejection (AMR), and T cell-mediated rejection (TCMR) by RNAseq. Advanced machine learning tools were used to perform 3-way differential gene expression analysis to identify gene signatures associated with rejection. We then performed functional in silico analysis and validation by Fluidigm (San Francisco, CA) in 62 samples from 2 independent kidney transplant cohorts.ResultsWe found 102 genes (63 coding genes and 39 noncoding genes) associated with AMR (54 upregulated), TCMR (23 upregulated), and STA (25 upregulated) perfectly clustered with each rejection phenotype and highly correlated with main histologic lesions (ρ = 0.91). For the genes associated with AMR, we found enrichment in regulation of endoplasmic reticulum stress, adaptive immunity, and Ig class-switching. In the validation, we found that the SIGLEC17P pseudogene and 9 SIGLEC17P-related coding genes were highly expressed among AMR but not in TCMR and STA samples.ConclusionsThis analysis identifies a critical gene signature in PB in kidney transplant patients undergoing AMR, sufficient to differentiate them from patients with TCMR and immunologically quiescent kidney allografts. Our findings provide the basis for new studies dissecting the role of noncoding genes in the pathophysiology of kidney allograft rejection and their potential value as noninvasive biomarkers of the rejection process.

IntroductionPeripheral blood (PB) molecular patterns characterizing the different effector immune pathways driving distinct kidney rejection types remain to be fully elucidated. We hypothesized that transcriptome analysis using RNA sequencing (RNAseq) in samples of kidney transplant patients would enable the identification of unique protein-coding and noncoding genes that may be able to segregate different rejection phenotypes.MethodsWe evaluated 37 biopsy-paired PB samples from the discovery cohort, with stable (STA), antibody-mediated rejection (AMR), and T cell-mediated rejection (TCMR) by RNAseq. Advanced machine learning tools were used to perform 3-way differential gene expression analysis to identify gene signatures associated with rejection. We then performed functional in silico analysis and validation by Fluidigm (San Francisco, CA) in 62 samples from 2 independent kidney transplant cohorts.ResultsWe found 102 genes (63 coding genes and 39 noncoding genes) associated with AMR (54 upregulated), TCMR (23 upregulated), and STA (25 upregulated) perfectly clustered with each rejection phenotype and highly correlated with main histologic lesions (ρ = 0.91). For the genes associated with AMR, we found enrichment in regulation of endoplasmic reticulum stress, adaptive immunity, and Ig class-switching. In the validation, we found that the SIGLEC17P pseudogene and 9 SIGLEC17P-related coding genes were highly expressed among AMR but not in TCMR and STA samples.ConclusionsThis analysis identifies a critical gene signature in PB in kidney transplant patients undergoing AMR, sufficient to differentiate them from patients with TCMR and immunologically quiescent kidney allografts. Our findings provide the basis for new studies dissecting the role of noncoding genes in the pathophysiology of kidney allograft rejection and their potential value as noninvasive biomarkers of the rejection process.

In this clinical validation study, we developed and validated a urinary Q-Score generated from the quantitative test QSant, formerly known as QiSant, for the detection of biopsy-confirmed acute rejection in kidney transplants. Using a cohort of 223 distinct urine samples collected from three independent sites and from both adult and pediatric renal transplant patients, we examined the diagnostic utility of the urinary Q-Score for detection of acute rejection in renal allografts. Statistical models based upon the measurements of the six QSant biomarkers (cell-free DNA, methylated-cell-free DNA, clusterin, CXCL10, creatinine, and total protein) generated a renal transplant Q-Score that reliably differentiated stable allografts from acute rejections in both adult and pediatric renal transplant patients. The composite Q-Score was able to detect both T cell-mediated rejection and antibody-mediated rejection patients and differentiate them from stable non-rejecting patients with a receiver-operator characteristic curve area under the curve of 99.8% and an accuracy of 98.2%. Q-Scores < 32 indicated the absence of active rejection and Q-Scores ≥ 32 indicated an increased risk of active rejection. At the Q-Score cutoff of 32, the overall sensitivity was 95.8% and specificity was 99.3%. At a prevalence of 25%, positive and negative predictive values for active rejection were 98.0% and 98.6%, respectively. The Q-Score also detected subclinical rejection in patients without an elevated serum creatinine level but identified by a protocol biopsy. This study confirms that QSant is an accurate and quantitative measurement suitable for routine monitoring of renal allograft status.

In this clinical validation study, we developed and validated a urinary Q-Score generated from the quantitative test QSant, formerly known as QiSant, for the detection of biopsy-confirmed acute rejection in kidney transplants. Using a cohort of 223 distinct urine samples collected from three independent sites and from both adult and pediatric renal transplant patients, we examined the diagnostic utility of the urinary Q-Score for detection of acute rejection in renal allografts. Statistical models based upon the measurements of the six QSant biomarkers (cell-free DNA, methylated-cell-free DNA, clusterin, CXCL10, creatinine, and total protein) generated a renal transplant Q-Score that reliably differentiated stable allografts from acute rejections in both adult and pediatric renal transplant patients. The composite Q-Score was able to detect both T cell-mediated rejection and antibody-mediated rejection patients and differentiate them from stable non-rejecting patients with a receiver-operator characteristic curve area under the curve of 99.8% and an accuracy of 98.2%. Q-Scores < 32 indicated the absence of active rejection and Q-Scores ≥ 32 indicated an increased risk of active rejection. At the Q-Score cutoff of 32, the overall sensitivity was 95.8% and specificity was 99.3%. At a prevalence of 25%, positive and negative predictive values for active rejection were 98.0% and 98.6%, respectively. The Q-Score also detected subclinical rejection in patients without an elevated serum creatinine level but identified by a protocol biopsy. This study confirms that QSant is an accurate and quantitative measurement suitable for routine monitoring of renal allograft status.