The MMDx® diagnostic system: A critical re-appraisal of its knowledge gaps and a call for rigorous validation studies

Molecular diagnosis of antibody-mediated rejection: Evaluating biopsy-based transcript diagnostics in the presence of donor-specific antibodies but without microvascular inflammation, a single-center descriptive analysis

Biopsy-based transcript diagnostics may identify molecular antibody-mediated rejection (AMR) when microvascular inflammation (MVI) is absent. In this single-center cohort, biopsy-based transcript diagnostics were validated in 326 kidney allograft biopsies. A total of 71 histological AMR and 35 T cell-mediated rejection (TCMR) cases were identified as molecular AMR and TCMR in 55% and 63%, respectively. Among 121 cases without MVI (glomerulitis + peritubular capillaritis = 0), 45 (37%) donor-specific antibody (DSA)-positive and 76 (63%) DSA-negative cases were analyzed. Twenty-one out of the 121 (17%) cases showed borderline changes, or TCMR, while BK nephropathy was excluded. None of the 45 DSA-positive patients showed molecular AMR. Among 76 DSA-negative patients, 2 had mixed molecular AMR/TCMR. All-AMR phenotype scores (sum of R4-R6) exhibited median values of 0.13 and 0.12 for DSA-positive and DSA-negative patients, respectively (P = .84). A total of 13% (6/45) DSA-positive and 11% (8/76) DSA-negative patients showed an all-AMR phenotype score > 0.30 (P = .77). Patients with a higher all-AMR phenotype score showed 33% more histologic TCMR (P = .005). The median all-AMR phenotype scores of glomerular basement membrane double contours = 0 and glomerular basement membrane double contours > 0 biopsies were 0.12 and 0.10, respectively (P = .35). Biopsy-based transcript diagnostics did not identify molecular AMR in cases without MVI. Follow-up biopsies and outcome data should evaluate the clinical relevance of subthreshold molecular alterations.

Caveats in Interpretation of Molecular Diagnostics in Heart Allografts

Histologic separation of injury, T cell-mediated rejection, or antibody-mediated rejection in allograft heart biopsies is difficult. A critical review of publications was performed to evaluate the caveats of using molecular diagnostics (MDX) to distinguish between these entities. Typically, only 1 to 2 fragments of unknown histologic appearance are evaluated. Archetype and molecular classifier analyses use gene lists derived from histologic labels and associated reproducibility issues influence the accuracy of the derived MDX classes. Archetypes A1, A2, and A3 archetypes created by bioinformatics were renamed no rejection, T cell-mediated rejection, and antibody-mediated rejection despite as little as 40% concordance with histologic diagnoses and overlapping archetype scores. Additional archetypes S4 and minor injury were created using arbitrary cutoffs based on visual examination of principal component analysis plots. Therapeutic implications of the numerous discrepancies with histology remain unexplored. Many MDX-derived observations are ambiguous and open to alternate logical explanations. Better molecular methods and more rigorous validation studies are needed to advance the field. Ideally, these methods should analyze all available biopsy fragments to minimize sampling issues. It is also desirable to incorporate spatial transcriptomics into the workflow, so that gene expression data can be directly compared with the underlying histology lesions.

T-cell Mediated Rejection Associated Microvascular Inflammation in the Allograft Kidney: RNAseq Analysis Using the Banff Human Organ Transplant Gene Panel

BACKGROUND

Microvascular inflammation (MVI) can occur in biopsies showing T-cell mediated rejection (TCMR), but it is not well established that T-cells can directly mediate microvascular injury (TCMR-MVI).

METHODS

This was a cross sectional RNAseq based Banff Human Organ Transplant (BHOT) gene expression (GE) analysis. The objective of this study was to probe the molecular signature of TCMR-MVI in comparison with C4d+, DSA+ antibody mediated rejection (ABMR), stable renal function (STA), and TCMR without MVI. Transcriptome analysis utilized CLC genomic workbench and R-studio software.

RESULTS

No gene set was specific for any diagnostic category, and all were expressed at low levels in STA biopsies. BHOT gene set scores could differentiate ABMR from TCMR and TCMR-MVI, but not TCMR from TCMR-MVI. TCMR-MVI underexpressed several genes associated with ABMR including DSATs, ENDAT, immunoglobulin genes, ADAMDEC1, PECAM1 and NK cell transcripts (MYBL1, GNLY), but overexpressed C3, NKBBIZ, and LTF. On the other hand, there was no significant difference in the expression of these genes in TCMR-MVI versus TCMR. This indicates that the GE profile of TCMR MVI aligns more closely with TCMR than ABMR. The limitations of classifying biopsies using the binary ABMR-TCMR algorithm, and the occurrence of common pathogenesis mechanisms amongst different rejection phenotype was highlighted by the frequent presence of molecular mixed rejection.

CONCLUSIONS

T-cell mediated mechanisms play a significant role in the pathogenesis of MVI. GE was broadly different between rejection phenotypes, but molecular scores varied substantially between biopsies with the same Banff grade. It was not always possible to achieve precise molecular score-based diagnostic categorization of individual patients.

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.

Prediction of Acute Cardiac Rejection Based on Gene Expression Profiles

Acute cardiac rejection remains a significant challenge in the post-transplant period, necessitating meticulous monitoring and timely intervention to prevent graft failure. Thus, the goal of the present study was to identify novel biomarkers involved in acute cardiac rejection, paving the way for personalized diagnostic, preventive, and treatment strategies. A total of 809 differentially expressed genes were identified in the GSE150059 dataset. We intersected genes selected by analysis of variance, recursive feature elimination, least absolute shrinkage and selection operator, and random forest classifier to identify the most relevant genes involved in acute cardiac rejection. Thus, HCP5, KLRD1, GZMB, PLA1A, GNLY, and KLRB1 were used to train eight machine learning models: random forest, logistic regression, decision trees, support vector machines, gradient boosting machines, K-nearest neighbors, XGBoost, and neural networks. Models were trained, tested, and validated on the GSE150059 dataset (MMDx-based diagnosis of rejection). Eight algorithms achieved great performance in predicting acute cardiac rejection. However, all machine learning models demonstrated poor performance in two external validation sets that had rejection diagnosis based on histology: merged GSE2596 and GSE4470 dataset and GSE9377 dataset, thus highlighting differences between these two methods. According to SHAP and LIME, KLRD1 and HCP5 were the most impactful genes.

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.

Diagnosis of T-cell-mediated kidney rejection by biopsy-based proteomic biomarkers and machine learning

Background

Biopsy-based diagnosis is essential for maintaining kidney allograft longevity by ensuring prompt treatment for graft complications. Although histologic assessment remains the gold standard, it carries significant limitations such as subjective interpretation, suboptimal reproducibility, and imprecise quantitation of disease burden. It is hoped that molecular diagnostics could enhance the efficiency, accuracy, and reproducibility of traditional histologic methods.

Methods

Quantitative label-free mass spectrometry analysis was performed on a set of formalin-fixed, paraffin-embedded (FFPE) biopsies from kidney transplant patients, including five samples each with diagnosis of T-cell-mediated rejection (TCMR), polyomavirus BK nephropathy (BKPyVN), and stable (STA) kidney function control tissue. Using the differential protein expression result as a classifier, three different machine learning algorithms were tested to build a molecular diagnostic model for TCMR.

Results

The label-free proteomics method yielded 800-1350 proteins that could be quantified with high confidence per sample by single-shot measurements. Among these candidate proteins, 329 and 467 proteins were defined as differentially expressed proteins (DEPs) for TCMR in comparison with STA and BKPyVN, respectively. Comparing the FFPE quantitative proteomics data set obtained in this study using label-free method with a data set we previously reported using isobaric labeling technology, a classifier pool comprised of features from DEPs commonly quantified in both data sets, was generated for TCMR prediction. Leave-one-out cross-validation result demonstrated that the random forest (RF)-based model achieved the best predictive power. In a follow-up blind test using an independent sample set, the RF-based model yields 80% accuracy for TCMR and 100% for STA. When applying the established RF-based model to two public transcriptome datasets, 78.1%-82.9% sensitivity and 58.7%-64.4% specificity was achieved respectively.

Conclusions

This proof-of-principle study demonstrates the clinical feasibility of proteomics profiling for FFPE biopsies using an accurate, efficient, and cost-effective platform integrated of quantitative label-free mass spectrometry analysis with a machine learning-based diagnostic model. It costs less than 10 dollars per test.