Technical considerations when designing a gene expression panel for renal transplant diagnosis

Molecular diagnosis of kidney allograft rejection based on the Banff Human Organ Transplant (B-HOT) gene panel: a multicenter international study

Transcriptomic analysis of kidney biopsies has demonstrated potential to improve diagnosis of allograft rejection. Here, we developed a molecular assessment of antibody-mediated rejection (AMR) and T-cell-mediated rejection (TCMR) based on the Banff-Human-Organ-Transplant (B-HOT) consensus gene panel. Expression assays of formalin-fixed paraffin-embedded kidney biopsies from well-phenotyped cohorts were used to develop prediction models for AMR and TCMR and an automated report of gene expression-based diagnosis. The study population consisted of 950 kidney allograft biopsies from 10 transplantation centers in Europe and North America. The development cohort included 664 renal allograft biopsies split into a training (n=537) and test set (n=127), and two external validation cohorts (n=286). We performed gene selection using regularized regression and developed several different base models based on B-HOT expression data, which were combined into a single ensemble model for each rejection diagnosis. Model performance was assessed in the test set and the two external validation cohorts, showing good discriminative abilities (respective PR-AUC AMR=0.811, 0.891, 0.832 and TCMR=0.736, 0.810, 0.782). We identified challenging biopsies with histology below diagnostic thresholds for which gene expression-based probability can refine rejection diagnosis. This automated molecular diagnostic system shows potential for improving kidney allograft rejection diagnosis in routine practice and clinical trials.

Banff 2022 Kidney Commentary: Reflections and Future Directions

In September 2022, in Banff, Alberta, Canada, the XVIth Banff meeting, corresponding to the 30th anniversary of the Banff classification, was held, leading to 2 recent publications. Discussions at the Banff meeting focused on proposing improvements to the Banff process as a whole. In line with this, a unique opportunity was offered to a selected group of 16 representatives from the pathology and transplant nephrology community, experts in the field of kidney transplantation, to review these 2 Banff manuscripts. The aim was to provide an insightful commentary, to gauge any prospective influence the proposed changes may have, and to identify any potential areas for future enhancement within the Banff classification. The group expressed its satisfaction with the incorporation of 2 new entities, namely "microvascular inflammation/injury donor-specific antibodies-negative and C4d negative" and "probable antibody-mediated rejection," into category 2. These changes expand the classification, facilitating the capture of more biopsies and providing an opportunity to explore the clinical implications of these lesions further. However, we found that the Banff classification remains complex, potentially hindering its widespread utilization, even if a degree of complexity may be unavoidable given the intricate pathophysiology of kidney allograft pathology. Addressing the histomorphologic diagnosis of chronic active T cell-mediated rejection (CA TCMR), potentially reconsidering a diagnostic-agnostic approach, as for category 2, to inflammation in interstitial fibrosis and tubular atrophy and chronic active T cell-mediated rejection was also an important objective. Furthermore, we felt a need for more evidence before molecular diagnostics could be routinely integrated and emphasized the need for clinical and histologic context determination and the substantiation of its clinical impact through rigorous clinical trials. Finally, our discussions stressed the ongoing necessity for multidisciplinary decision-making regarding patient care.

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.

Role of Complement-dependent Cytotoxicity Crossmatch and HLA Typing in Solid Organ Transplant

BACKGROUND

Solid organ transplantation is a life-saving medical operation that has progressed greatly because of developments in diagnostic tools and histocompatibility tests. Crossmatching for complement-dependent cytotoxicity (CDC) and human leukocyte antigen (HLA) typing are two important methods for checking graft compatibility and reducing the risk of graft rejection. HLA typing and CDC crossmatching are critical in kidney, heart, lung, liver, pancreas, intestine, and multi-organ transplantation.

METHODS

A systematic literature search was conducted on the internet, using PubMed, Scopus, and Google Scholar databases, to identify peer-reviewed publications about solid organ transplants, HLA typing, and CDC crossmatching.

CONCLUSION

Recent advances in HLA typing have allowed for high-resolution evaluation, epitope matching, and personalized therapy methods. Genomic profiling, next-generation sequencing, and artificial intelligence have improved HLA typing precision, resulting in better patient outcomes. Artificial intelligence (AI) driven virtual crossmatching and predictive algorithms have eliminated the requirement for physical crossmatching in the context of CDC crossmatching, boosting organ allocation and transplant efficiency. This review elaborates on the importance of HLA typing and CDC crossmatching in solid organ transplantation.

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.

Advanced Genomics-Based Approaches for Defining Allograft Rejection With Single Cell Resolution

Solid organ transplant recipients require long-term immunosuppression for prevention of rejection. Calcineurin inhibitor (CNI)-based immunosuppressive regimens have remained the primary means for immunosuppression for four decades now, yet little is known about their effects on graft resident and infiltrating immune cell populations. Similarly, the understanding of rejection biology under specific types of immunosuppression remains to be defined. Furthermore, development of innovative, rationally designed targeted therapeutics for mitigating or preventing rejection requires a fundamental understanding of the immunobiology that underlies the rejection process. The established use of microarray technologies in transplantation has provided great insight into gene transcripts associated with allograft rejection but does not characterize rejection on a single cell level. Therefore, the development of novel genomics tools, such as single cell sequencing techniques, combined with powerful bioinformatics approaches, has enabled characterization of immune processes at the single cell level. This can provide profound insights into the rejection process, including identification of resident and infiltrating cell transcriptomes, cell-cell interactions, and T cell receptor α/β repertoires. In this review, we discuss genomic analysis techniques, including microarray, bulk RNAseq (bulkSeq), single-cell RNAseq (scRNAseq), and spatial transcriptomic (ST) techniques, including considerations of their benefits and limitations. Further, other techniques, such as chromatin analysis via assay for transposase-accessible chromatin sequencing (ATACseq), bioinformatic regulatory network analyses, and protein-based approaches are also examined. Application of these tools will play a crucial role in redefining transplant rejection with single cell resolution and likely aid in the development of future immunomodulatory therapies in solid organ transplantation.

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.