Molecular Diversity of Clinically Stable Human Kidney Allografts

Question Can immunologic heterogeneity be identified in histologically stable kidney allografts? Findings This prognostic study used 28 public kidney transplant data sets with 2273 kidney tissue to develop and validate an unbiased, 6-gene and 5-cell-type transcriptional Instability Score to provide histology-independent reclassification of human transplant samples. Using this score, 46% of histologically stable samples were found to have molecular evidence of rejection, which was validated by an independent cohort that showed undiagnosed graft rejection and poor projected graft function survival. Meaning These findings suggest that the Instability Score could provide an important adjunct for comprehensive and highly quantitative phenotyping of protocol kidney transplant biopsy samples and could be integrated into clinical care for accurate estimation of subsequent patient clinical outcomes. This prognostic study uses microarray gene expression data from the National Center for Biotechnology Information Gene Expression Omnibus to assess whether immunologic heterogeneity can be identified in histologically stable kidney allografts. Importance Clinical decision and immunosuppression dosing in kidney transplantation rely on transplant biopsy tissue histology even though histology has low specificity, sensitivity, and reproducibility for rejection diagnosis. The inclusion of stable allografts in mechanistic and clinical studies is vital to provide a normal, noninjured comparative group for all interrogative studies on understanding allograft injury. Objective To refine the definition of a stable allograft as one that is clinically, histologically, and molecularly quiescent using publicly available transcriptomics data. Design, Setting, and Participants In this prognostic study, the National Center for Biotechnology Information Gene Expression Omnibus was used to search for microarray gene expression data from kidney transplant tissues, resulting in 38 studies from January 1, 2017, to December 31, 2018. The diagnostic annotations included 510 acute rejection (AR) samples, 1154 histologically stable (hSTA) samples, and 609 normal samples. Raw fluorescence intensity data were downloaded and preprocessed followed by data set merging and batch correction. Main Outcomes and Measures The primary measure was area under the receiver operating characteristics curve from a set of feature selected genes and cell types for distinguishing AR from normal kidney tissue. Results Within the 28 data sets, the feature selection procedure identified a set of 6 genes (KLF4, CENPJ, KLF2, PPP1R15A, FOSB, TNFAIP3) (area under the curve [AUC], 0.98) and 5 immune cell types (CD4(+) T-cell central memory [Tcm], CD4(+) T-cell effector memory [Tem], CD8(+) Tem, natural killer [NK] cells, and Type 1 T helper [T(H)1] cells) (AUC, 0.92) that were combined into 1 composite Instability Score (InstaScore) (AUC, 0.99). The InstaScore was applied to the hSTA samples: 626 of 1154 (54%) were found to be immune quiescent and redefined as histologically and molecularly stable (hSTA/mSTA); 528 of 1154 (46%) were found to have molecular evidence of rejection (hSTA/mAR) and should not have been classified as stable allografts. The validation on an independent cohort of 6 months of protocol biopsy samples in December 2019 showed that hSTA/mAR samples had a significant change in graft function (r = 0.52, P < .001) and graft loss at 5-year follow-up (r = 0.17). A drop by 10 mL/min/1.73m(2) in estimated glomerular filtration rate was estimated as a threshold in allograft transitioning from hSTA/mSTA to hSTA/mAR. Conclusions and Relevance The results of this prognostic study suggest that the InstaScore could provide an important adjunct for comprehensive and highly quantitative phenotyping of protocol kidney transplant biopsy samples and could be integrated into clinical care for accurate estimation of subsequent patient clinical outcomes.

Automated summary

This prognostic study utilized public kidney transplant data to develop an Instability Score based on gene expression data, which identified immune heterogeneity in histologically stable kidney allografts. The findings suggest that integrating the Instability Score into clinical care could improve the accuracy of estimating patient clinical outcomes.