Biopsy-based transcriptomics in the diagnosis of kidney transplant rejection.

PURPOSE OF REVIEW: The last year has seen considerable progress in translational research exploring the clinical utility of biopsy-based transcriptomics of kidney transplant biopsies to enhance the diagnosis of rejection. This review will summarize recent findings with a focus on different platforms, potential clinical applications, and barriers to clinical adoption. RECENT FINDINGS: Recent literature has focussed on using biopsy-based transcriptomics to improve diagnosis of rejection, in particular antibody-mediated rejection. Different techniques of gene expression analysis (reverse transcriptase quantitative PCR, microarrays, probe-based techniques) have been used either on separate samples with ideally preserved RNA, or on left over tissue from routine biopsy processing. Despite remarkable consistency in overall patterns of gene expression, there is no consensus on acceptable indications, or whether biopsy-based transcriptomics adds significant value at reasonable cost to current diagnostic practice. SUMMARY: Access to biopsy-based transcriptomics will widen as regulatory approvals for platforms and gene expression models develop. Clinicians need more evidence and guidance to inform decisions on how to use precious biopsy samples for biopsy-based transcriptomics, and how to integrate results with standard histology-based diagnosis.

Application of the Banff Human Organ Transplant Panel to kidney transplant biopsies with features suspicious for antibody-mediated rejection.

The Banff Classification for Allograft Pathology includes the use of gene expression in the diagnosis of antibody-mediated rejection (AMR) of kidney transplants, but a predictive set of genes for classifying biopsies with 'incomplete' phenotypes has not yet been studied. Here, we developed and assessed a gene score that, when applied to biopsies with features of AMR, would identify cases with a higher risk of allograft loss. To do this, RNA was extracted from a continuous retrospective cohort of 349 biopsies randomized 2:1 to include 220 biopsies in a discovery cohort and 129 biopsies in a validation cohort. The biopsies were divided into three groups: 31 that fulfilled the 2019 Banff Criteria for active AMR, 50 with histological features of AMR but not meeting the full criteria (Suspicious-AMR), and 269 with no features of active AMR (No-AMR). Gene expression analysis using the 770 gene Banff Human Organ Transplant NanoString panel was carried out with LASSO Regression performed to identify a parsimonious set of genes predictive of AMR. We identified a nine gene score that was highly predictive of active AMR (accuracy 0.92 in the validation cohort) and was strongly correlated with histological features of AMR. In biopsies suspicious for AMR, our gene score was strongly associated with risk of allograft loss and independently associated with allograft loss in multivariable analysis. Thus, we show that a gene expression signature in kidney allograft biopsy samples can help classify biopsies with incomplete AMR phenotypes into groups that correlate strongly with histological features and outcomes.

Diagnostic application of transcripts associated with antibody-mediated rejection in kidney transplant biopsies.

BACKGROUND: The diagnosis of antibody-mediated rejection (AMR) is reached using the Banff Classification for Allograft Pathology, which now includes gene expression analysis. In this study, we investigate the application of 'increased expression of thoroughly validated gene transcripts/classifiers strongly associated with AMR' as diagnostic criteria. METHOD: We used quantitative real-time polymerase chain reaction for 10 genes associated with AMR in a retrospective cohort of 297 transplant biopsies, including biopsies that met the full diagnostic criteria for AMR, even without molecular data (AMR, n = 27), biopsies that showed features of AMR, but that would only meet criteria for AMR with increased transcripts [suspicious for AMR (AMRsusp), n = 49] and biopsies that would never meet criteria for AMR (No-AMR, n = 221). RESULTS: A 10-gene AMR score trained by a receiver-operating characteristic to identify AMR found 16 cases with a high score among the AMRsusp cases (AMRsusp-high) that had significantly worse graft survival than those with a low score (AMRsusp-low; n = 33). In both univariate and multivariate Cox regression analysis, the AMR 10-gene score was significantly associated with an increased hazard ratio (HR) for graft loss (GL) in the AMRsusp group (HR = 1.109, P = 0.004 and HR = 1.138, P = 0.012, respectively), but not in the whole cohort. Net reclassification index and integrated discrimination improvement analyses demonstrated improved risk classification and superior discrimination, respectively, for GL when considering the gene score in addition to histological and serological data, but only in the AMRsusp group, not the whole cohort. CONCLUSIONS: This study provides evidence that a gene score strongly associated with AMR helps identify cases at higher risk of GL in biopsies that are suspicious for AMR but do not meet full criteria.

A Simple Molecular Tool for the Assessment of Kidney Transplant Biopsies.

BACKGROUND: The Banff Classification for Allograft Pathology recommendations for the diagnosis of kidney transplant rejection includes molecular assessment of the transplant biopsy. However, implementation of molecular tools in clinical practice is still limited, partly due to the required expertise and financial investment. The reverse transcriptase multiplex ligation-dependent probe amplification (RT-MLPA) assay is a simple, rapid, and inexpensive assay that permits simultaneous evaluation of a restricted gene panel using paraffin-embedded tissue blocks. The aim of this study was to develop and validate a RT-MLPA assay for diagnosis and classification of rejection. METHODS: A retrospective cohort of 220 kidney transplant biopsies from two centers, which included 52 antibody-mediated rejection, 51 T-cell-mediated rejection, and 117 no-rejection controls, was assessed. A 17-gene panel was identified on the basis of relevant pathophysiological pathways. A support vector machine classifier was developed. A subset of 109 biopsies was also assessed using the Nanostring Banff Human Organ Transplant panel to compare the two assays. RESULTS: The support vector machine classifier train and test accuracy scores were 0.84 and 0.83, respectively. In the test cohort, the F1 score for antibody-mediated rejection, T-cell-mediated rejection, and control were 0.88, 0.86, and 0.69, respectively. Using receiver-operating characteristic curves, the area under the curve for class predictions was 0.96, 0.89, and 0.91, respectively, with a weighted average at 0.94. Classifiers' performances were highest for antibody-mediated rejection diagnosis with 94% correct predictions, compared with 88% correct predictions for control biopsies and 60% for T-cell-mediated rejection biopsies. Gene expression levels assessed by RT-MLPA and Nanostring were correlated: r = 0.68, P < 0.001. Equivalent gene expression profiles were obtained with both assays in 81% of the samples. CONCLUSIONS: The 17-gene panel RT-MLPA assay, developed here for formalin-fixed paraffin-embedded kidney transplant biopsies, classified kidney transplant rejection with an overall accurate prediction ratio of 0.83. PODCAST: This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_04_10_CJN10100822.mp3.