RNA expression profiling of nonhuman primate renal allograft rejection identifies tolerance

Abstract

Tolerance induction to prevent allograft rejection is a long-standing clinical goal. However, convincing and dependable tolerance identification remains elusive. Hypothesizing that intragraft RNA expression is informative in both rejection and tolerance, we profile intrarenal allograft RNA expression in a mixed chimerism renal allograft model of cynomolgus monkeys and identify biologically significant tolerance. Analysis of 67 genes identified 3 dominant factors, each with a different pattern of gene expressions, relating to T cell-mediated rejection (TCMR), chronic antibody-mediated rejection (CAMR), or Tolerance. Clustering these 3 factors created 9 groups. One of the 9 clustered groups, the Tolerance cluster, showed the lowest probability of terminal rejection, the longest duration of allograft survival, and the lowest relative risk of terminal rejection. The Tolerance factor consists of a novel set of gene expressions including cytokine and immunoregulatory genes adding mechanistic insights into tolerance. The Tolerance factor could not be identified within current pathologic diagnostic categories. The TCMR and CAMR factors are dominant to the Tolerance factor, causing rejection even if the Tolerance factor is present. These 3 factors determine the probability of terminal rejection or tolerance. This novel a posteriori approach permits identification of pathways of rejection, including tolerance.

Banff 2019 Meeting Report: Molecular diagnostics in solid organ transplantation–Consensus for the Banff Human Organ Transplant (B-HOT) gene panel and open source multicenter validation

“…Recently, more practical technologies based on FFPE biopsy analysis are now available, in particular the NanoString nCounter system (NanoString Technologies, Seattle, WA). Several NanoString publications using FFPE transplant specimens identify similar transcript associations with the molecular and histologic phenotypes as those reported in microarray studies 3,4,13‐18,27‐29,29‐33 . Among the advantages of NanoString are (1) a separate core processed at the time of biopsy is not required; (2) transcripts are assessed in the same sample analyzed by light microscopy; and (3) large retrospective and longitudinal analyses of archived samples can be readily performed in the setting of multicenter studies, which will enable retrospective randomization with long‐term survival end points available (Table 1).…”

“…After redundant and duplicate genes were removed, the list contained 1749 genes. Then the MDWG members identified overlap between these genes and genes described in the peer‐reviewed literature 2,8,12,29,32,33,38‐50,9,51,52,10,53‐56,11,57‐64,65 as being strongly associated with relevant clinical phenotypes and identified 1050 genes to be considered for inclusion. In the next step, a list including all genes with consensus expert opinion were selected and for which all Hugo duplicates were then combined, leaving 670 unique genes.…”

Validation of the Nanostring of the Banff Human Organ Transplant Gene Panel in Archival Data from Human Kidney Transplants and its Modelling Complexity

“…Using principal components of cell types and pathways is conceptually easier to understand immunologically. Creating unsupervised principal components is the easiest for feature selection and has an advantage that a latent variable or pathway may be present, which is not readily identi ed by the rst two gene selection methods [18,19]. These three methods, including just nding the highest genes by t-tests, will likely vary between independently derived data sets because results are very dependent on the sample size of the data set, the balance of the classes employed, and the purity of the annotated class diagnosis.…”

“…Nevertheless, creating more homogeneous groups of samples may identify clinically important groups of samples. Clustering expression rejection data can identify novel subgroups, not appreciated in the annotated classes [19,27]. This is most important in the NO REJECTION diagnosis, which is the most heterogeneous by gene expression (Table 2) and the most frequent diagnosis.…”

“…Allograft gene expression better de nes endpoints and risk in renal allografts [10,11], subtypes of ABMR [12], dysfunction [4,13,14], de ne a new diagnosis of C4d-negative ABMR [15,16], and identify NK cells in ABMR [17]. In addition, in non-human primates, unsupervised principal components from Nanostring gene expression identi ed gene expression patterns that predicted outcomes including tolerance [18,19].…”