A Decentralized Kidney Transplant Biopsy Classifier for Transplant Rejection Developed Using Genes of the Banff-Human Organ Transplant Panel

The Use of Machine Learning in the Diagnosis of Kidney Allograft Rejection: Current Knowledge and Applications

Kidney allograft rejection is one of the main limitations to long-term kidney transplant survival. The diagnostic gold standard for detecting rejection is a kidney biopsy, an invasive procedure that can often give imprecise results due to complex diagnostic criteria and high interobserver variability. In recent years, several additional diagnostic approaches to rejection have been investigated, some of them with the aid of machine learning (ML). In this review, we addressed studies that investigated the detection of kidney allograft rejection over the last decade using various ML algorithms. Various ML techniques were used in three main categories: (a) histopathologic assessment of kidney tissue with the aim to improve the diagnostic accuracy of a kidney biopsy, (b) assessment of gene expression in rejected kidney tissue or peripheral blood and the development of diagnostic classifiers based on these data, (c) radiologic assessment of kidney tissue using diffusion-weighted magnetic resonance imaging and the construction of a computer-aided diagnostic system. In histopathology, ML algorithms could serve as a support to the pathologist to avoid misclassifications and overcome interobserver variability. Diagnostic platforms based on biopsy-based transcripts serve as a supplement to a kidney biopsy, especially in cases where histopathologic diagnosis is inconclusive. ML models based on radiologic evaluation or gene signature in peripheral blood may be useful in cases where kidney biopsy is contraindicated in addition to other non-invasive biomarkers. The implementation of ML-based diagnostic methods is usually slow and undertaken with caution considering ethical and legal issues. In summary, the approach to the diagnosis of rejection should be individualized and based on all available diagnostic tools (including ML-based), leaving the responsibility for over- and under-treatment in the hands of the clinician.

Transcript analysis of uterus transplant cervical biopsies using the Banff Human Organ Transplant panel

Uterus transplantation is being more widely implemented in clinical practice. Monitoring of rejection is routinely done for cervical biopsies and is dependent on histopathological assessment, as rejections are clinically silent and nonhistological biomarkers are missing. Until this gap is filled, it is important to corroborate the histopathological diagnosis of rejection through independent methods such as gene expression analysis. In this study, we compared our previously published scoring system for grading rejection in uterus transplant cervical biopsies to the gene expression profile in the same biopsy. For this, we used the Banff Human Organ Transplant gene panel to analyze the expression of 788 genes in 75 paraffin-embedded transplant cervical biopsies with a spectrum of histologic findings, as well as in 24 cervical biopsies from healthy controls. We found that gene expression in borderline changes did not differ from normal transplants, whereas the genes with increased expression in mild rejections overlapped with previously published rejection-associated transcripts. Moderate/severe rejection samples showed a gene expression pattern characterized by a mixture of rejection-associated and tissue injury-associated genes and a decrease in epithelial transcripts. In summary, our findings support our proposed scoring system for rejection but argue against the treatment of borderline changes.

Systematic Biopsy-Based Transcriptomics and Diagnosis of Antibody-Mediated Kidney Transplant Rejection in Clinical Practice

Key Points

Background

To diagnose kidney transplant antibody-mediated rejection (AMR), biopsy-based transcriptomics can substitute for some histological criteria according to the Banff classification. However, clinical accessibility of these assays is still limited. Here, we aimed to evaluate the impact of integrating a routine-compatible molecular assay for the diagnosis of AMR in clinical practice.

Methods

All biopsies performed in our center between 2013 and 2017 were retrospectively included. These biopsies were classified into three groups: AMR biopsies which displayed the full Banff criteria of AMR independently of biopsy-based transcriptomics; undetermined for AMR biopsies which did not meet AMR histological criteria, but would have been considered as AMR if biopsy-based transcriptomics had been positive; and control biopsies which showed no features of rejection.

Results

Within the inclusion period, 342 biopsies had a complete Banff scoring. Thirty-six of the biopsies already met AMR criteria, and 43 of 306 (14%) were considered as undetermined for AMR. Among these biopsies, 24 of 43 (56%) had a molecular signature of AMR, reclassifying them into the AMR category. Five-year death-censored survival of these biopsies was unfavorable and statistically equivalent to that of the AMR category (P = 0.22), with 15 of 24 (63%) graft loss.

Conclusions

A significant proportion of biopsies could benefit from a biopsy-based transcriptomics for AMR diagnosis according to the Banff classification. Using a routine-compatible molecular tool, more than the half of these biopsies were reclassified as AMR and associated with poor allograft survival.

Antibody Mediated Rejection and T-cell Mediated Rejection Molecular Signatures Using Next-Generation Sequencing in Kidney Transplant Biopsies

Recently, interest in transcriptomic assessment of kidney biopsies has been growing. This study investigates the use of NGS to identify gene expression changes and analyse the pathways involved in rejection. An Illumina bulk RNA sequencing on the polyadenylated RNA of 770 kidney biopsies was conducted. Differentially-expressed genes (DEGs) were determined for AMR and TCMR using DESeq2. Genes were segregated according to their previous descriptions in known panels (microarray or the Banff Human Organ Transplant (B-HOT) panel) to obtain NGS-specific genes. Pathway enrichment analysis was performed using the Reactome and Kyoto Encyclopaedia of Genes and Genomes (KEGG) public repositories. The differential gene expression using NGS analysis identified 6,141 and 8,478 transcripts associated with AMR and TCMR. While most of the genes identified were included in the microarray and the B-HOT panels, NGS analysis identified 603 (9.8%) and 1,186 (14%) new specific genes. Pathways analysis showed that the B-HOT panel was associated with the main immunological processes involved during AMR and TCMR. The microarrays specifically integrated metabolic functions and cell cycle progression processes. Novel NGS-specific based transcripts associated with AMR and TCMR were discovered, which might represent a novel source of targets for drug designing and repurposing.

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.

The transformative potential of artificial intelligence in solid organ transplantation

Solid organ transplantation confronts numerous challenges ranging from donor organ shortage to post-transplant complications. Here, we provide an overview of the latest attempts to address some of these challenges using artificial intelligence (AI). We delve into the application of machine learning in pretransplant evaluation, predicting transplant rejection, and post-operative patient outcomes. By providing a comprehensive overview of AI's current impact, this review aims to inform clinicians, researchers, and policy-makers about the transformative power of AI in enhancing solid organ transplantation and facilitating personalized medicine in transplant care.

The Banff 2022 Kidney Meeting Work Plan: Data-driven refinement of the Banff Classification for renal allografts

The XVIth Banff Meeting for Allograft Pathology was held in Banff, Alberta, Canada, from September 19 to 23, 2022, as a joint meeting with the Canadian Society of Transplantation. In addition to a key focus on the impact of microvascular inflammation and biopsy-based transcript analysis on the Banff Classification, further sessions were devoted to other aspects of kidney transplant pathology, in particular T cell-mediated rejection, activity and chronicity indices, digital pathology, xenotransplantation, clinical trials, and surrogate endpoints. Although the output of these sessions has not led to any changes in the classification, the key role of Banff Working Groups in phrasing unanswered questions, and coordinating and disseminating results of investigations addressing these unanswered questions was emphasized. This paper summarizes the key Banff Meeting 2022 sessions not covered in the Banff Kidney Meeting 2022 Report paper and also provides an update on other Banff Working Group activities relevant to kidney allografts.

Development and Validation of a Multiclass Model Defining Molecular Archetypes of Kidney Transplant Rejection: A Large Cohort Study of the Banff Human Organ Transplant Gene Expression Panel

Gene expression profiling from formalin-fixed paraffin-embedded (FFPE) renal allograft biopsies is a promising approach for feasibly providing a molecular diagnosis of rejection. However, large-scale studies evaluating the performance of models using NanoString platform data to define molecular archetypes of rejection are lacking. We tested a diverse retrospective cohort of over 1400 FFPE biopsy specimens, rescored according to Banff 2019 criteria and representing 10 of 11 United Network of Organ Sharing regions, using the Banff Human Organ Transplant panel from NanoString and developed a multiclass model from the gene expression data to assign relative probabilities of 4 molecular archetypes: No Rejection, Antibody-Mediated Rejection, T Cell-Mediated Rejection, and Mixed Rejection. Using Least Absolute Shrinkage and Selection Operator regularized regression with 10-fold cross-validation fitted to 1050 biopsies in the discovery cohort and technically validated on an additional 345 biopsies, our model achieved overall accuracy of 85% in the discovery cohort and 80% in the validation cohort, with ≥75% positive predictive value for each class, except for the Mixed Rejection class in the validation cohort (positive predictive value, 53%). This study represents the technical validation of the first model built from a large and diverse sample of diagnostic FFPE biopsy specimens to define and classify molecular archetypes of histologically defined diagnoses as derived from Banff Human Organ Transplant panel gene expression profiling data.

The Banff 2022 Kidney Meeting Report: Re-Appraisal of Microvascular Inflammation and the Role of Biopsy-Based Transcript Diagnostics

The XVI-th Banff Meeting for Allograft Pathology was held in Banff, Alberta, Canada, from 19th-23rd September 2022, as a joint meeting with the Canadian Society of Transplantation. To mark the 30th anniversary of the first Banff Classification, pre-meeting discussions were held on the past, present, and future of the Banff Classification. This report is a summary of the meeting highlights that were most important in terms of their effect on the Classification, including discussions around microvascular inflammation and biopsy-based transcript analysis for diagnosis. In a post-meeting survey, agreement was reached on the delineation of the following phenotypes: (1) "Probable antibody-mediated rejection (AMR)", which represents DSA-positive cases with some histological features of AMR but below current thresholds for a definitive AMR diagnosis; and (2) "Microvascular inflammation (MVI), DSA-negative and C4d-negative", a phenotype of unclear cause requiring further study, which represents cases with MVI not explained by DSA. Although biopsy-based transcript diagnostics are considered promising and remain an integral part of the Banff Classification (limited to diagnosis of AMR), further work needs to be done to agree on the exact classifiers, thresholds, and clinical context of use.

Histologic and molecular features of antibody-mediated rejection

PURPOSE OF REVIEW

This review aims to summarize the highlights from recent research that involved pathological and molecular analysis of kidney allografts.

RECENT FINDINGS

As the research on antibody-mediated rejection (AMR) continues to evolve, studies are focused on identification through transcript studies of pathogenetic pathways involved in the development of AMR as well as refinement of diagnostic methods either by correlating Banff pathologic lesions with clinical and molecular data or by machine learning. Of note, the past year has generated high impact research that underscore the importance of pathologic and molecular correlations and detection of transcripts or gene sets that would aid prognostication. The studies involving refinement of pathologic criteria also highlight the continuous efforts to achieve diagnostic accuracy and standardization.

SUMMARY

Research involving histologic and molecular characteristics that define AMR are central to identification and understanding of pathogenetic pathways and remain critical in the development of diagnostic criteria.

The Histological Spectrum and Clinical Significance of T Cell-mediated Rejection of Kidney Allografts

T cell-mediated rejection (TCMR) remains a significant cause of long-term kidney allograft loss, either indirectly through induction of donor-specific anti-HLA alloantibodies or directly through chronic active TCMR. Whether found by indication or protocol biopsy, Banff defined acute TCMR should be treated with antirejection therapy and maximized maintenance immunosuppression. Neither isolated interstitial inflammation in the absence of tubulitis nor isolated tubulitis in the absence of interstitial inflammation results in adverse outcomes, and neither requires antirejection treatment. RNA gene expression analysis of biopsy material may supplement conventional histology, especially in ambiguous cases. Lesser degrees of tubular and interstitial inflammation (Banff borderline) may portend adverse outcomes and should be treated when found on an indication biopsy. Borderline lesions on protocol biopsies may resolve spontaneously but require close follow-up if untreated. Following antirejection therapy of acute TCMR, surveillance protocol biopsies should be considered. Minimally invasive blood-borne assays (donor-derived cell-free DNA and gene expression profiling) are being increasingly studied as a means of following stable patients in lieu of biopsy. The clinical benefit and cost-effectiveness require confirmation in randomized controlled trials. Treatment of acute TCMR is not standardized but involves bolus corticosteroids with lymphocyte depleting antibodies for severe, refractory, or relapsing cases. Arteritis may be found with acute TCMR, active antibody-mediated rejection, or mixed rejections and should be treated accordingly. The optimal treatment ofchronic active TCMR is uncertain. Randomized controlled trials are necessary to optimally define therapy.

Feasibility and Potential of Transcriptomic Analysis Using the NanoString nCounter Technology to Aid the Classification of Rejection in Kidney Transplant Biopsies

BACKGROUND

Transcriptome analysis could be an additional diagnostic parameter in diagnosing kidney transplant (KTx) rejection. Here, we assessed feasibility and potential of NanoString nCounter analysis of KTx biopsies to aid the classification of rejection in clinical practice using both the Banff-Human Organ Transplant (B-HOT) panel and a customized antibody-mediated rejection (AMR)-specific NanoString nCounter Elements (Elements) panel. Additionally, we explored the potential for the classification of KTx rejection building and testing a classifier within our dataset.

METHODS

Ninety-six formalin-fixed paraffin-embedded KTx biopsies were retrieved from the archives of the ErasmusMC Rotterdam and the University Hospital Cologne. Biopsies with AMR, borderline or T cell-mediated rejections (BLorTCMR), and no rejection were compared using the B-HOT and Elements panels.

RESULTS

High correlation between gene expression levels was found when comparing the 2 chemistries pairwise (r = 0.76-0.88). Differential gene expression (false discovery rate; P  < 0.05) was identified in biopsies diagnosed with AMR (B-HOT: 294; Elements: 76) and BLorTCMR (B-HOT: 353; Elements: 57) compared with no rejection. Using the most predictive genes from the B-HOT analysis and the Element analysis, 2 least absolute shrinkage and selection operators-based regression models to classify biopsies as AMR versus no AMR (BLorTCMR or no rejection) were developed achieving an receiver-operating-characteristic curve of 0.994 and 0.894, sensitivity of 0.821 and 0.480, and specificity of 1.00 and 0.979, respectively, during cross-validation.

CONCLUSIONS

Transcriptomic analysis is feasible on KTx biopsies previously used for diagnostic purposes. The B-HOT panel has the potential to differentiate AMR from BLorTCMR or no rejection and could prove valuable in aiding kidney transplant rejection classification.