Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes

Subthreshold rejection activity in many kidney transplants currently classified as having no rejection

Most kidney transplant patients who undergo biopsies are classified as having no rejection based on consensus thresholds. However, we hypothesized that because these patients have normal adaptive immune systems, T cell-mediated rejection (TCMR) and antibody-mediated rejection (ABMR) may exist as subthreshold activity in some transplants currently classified as no rejection. To examine this question, we studied genome-wide microarray results from 5086 kidney transplant biopsies (from 4170 patients). An updated molecular archetypal analysis designated 56% of biopsies as no rejection. Subthreshold molecular TCMR and/or ABMR activity molecular activity was detectable as elevated classifier scores in many biopsies classified as no rejection, with ABMR activity in many TCMR biopsies and TCMR activity in many ABMR biopsies. In biopsies classified as no rejection histologically and molecularly, molecular TCMR classifier scores correlated with increases in histologic TCMR features and molecular injury, lower estimated glomerular filtration rate, and higher risk of graft loss, and molecular ABMR activity correlated with increased glomerulitis and donor-specific antibody. No rejection biopsies with high subthreshold TCMR or ABMR activity had a higher probability of having TCMR or ABMR, respectively, diagnosed in a future biopsy. We conclude that many kidney transplant recipients have unrecognized subthreshold TCMR or ABMR activity, with significant implications for future problems.

Transcriptomic Signatures of Antibody-mediated Rejection in Early Biopsies With Negative Histology in HLA-incompatible Kidney Transplantation

Background

Presensitized patients with circulating donor-specific antibodies (DSAs) before transplantation are at risk for antibody-mediated rejection (AMR). Peritransplant desensitization mitigates but does not eliminate the alloimmune response. We examined the possibility that subthreshold AMR activity undetected by histology could be operating in some early biopsies.

Methods

Transcriptome of kidney allograft biopsies performed within the first month in presensitized patients (DSA+) who had received desensitization and did not develop active/probable AMR by histology (R-) was compared with biopsies showing active/probable AMR (R+/DSA+). As negative controls, biopsies without rejection by histology in patients without DSA at transplantation were used (R-/DSA-). RNA sequencing from biopsies selected from the biobank was used in cohort 1 (n = 32) and microarray, including the molecular microscope (Molecular Microscope Diagnostic System [MMDx]) algorithm, in recent cohort 2 (n = 30).

Results

The transcriptome of R-/DSA+ was similar to R+/DSA+ as these groups differed in 14 transcripts only. Contrarily, large differences were found between both DSA+ groups and negative controls. Fast gene set enrichment analyses showed upregulation of the immune system in both DSA+ groups (gene ontology terms: adaptive immune response, humoral immune response, antigen receptor-mediated signaling, and B-cell receptor signaling or complement activation) when compared with negative controls. MMDx assessment in cohort 2 classified 50% of R-/DSA+ samples as AMR and found no differences in AMR molecular scores between R+ and R- DSA+ groups. In imlifidase desensitization, MMDx series showed a gradual increase in AMR scores over time.

Conclusions

Presensitized kidney transplant recipients exhibited frequent molecular calls of AMR in biopsy-based transcript diagnostics despite desensitization therapy and negative histology.

Evaluation of a Decentralized Donor-Derived Cell-Free DNA Assay for Kidney Allograft Rejection Monitoring

Donor-derived cell-free DNA (dd-cfDNA) is an emerging non-invasive biomarker for allograft injury detection. This study aimed to evaluate a new, decentralized dd-cfDNA testing kit against a centralized dd-cfDNA testing service broadly utilized in the United States. Kidney transplant recipients with decentralized and centralized dd-cfDNA measurements and concomitant kidney allograft biopsies were included in the study. 580 kidney allograft recipients from 3 referral centers were included for 603 total evaluations. Correlation between assays was evaluated using r-squared (r 2) and Spearman's rank correlation test, and associations with rejection using logistic regression analyses and discrimination using area under the curve. Mean dd-cfDNA levels from decentralized and centralized tests were 0.51% ± 0.81% and 0.43% ± 0.78%, respectively. The assays were highly correlated, with r 2 = 0.95 and Spearman's rank correlation 0.88 (p < 0.0001). Both tests showed significant association with allograft rejection (p < 0.0001) and good and similar discriminations to predict rejection (AUC: 0.758 for the decentralized and AUC: 0.760 for the centralized dd-cfDNA; p = 0.8466). Consistency between the assays was also confirmed across clinical scenarios including post-transplant timepoint, allograft stability, and allograft rejection subcategories. This decentralized dd-cfDNA assessment demonstrates high accuracy and value to non-invasively monitor kidney recipients.

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.

Archetypal Analysis of Kidney Allograft Biopsies Using Next-generation Sequencing Technology

BACKGROUND

In kidney transplantation, molecular diagnostics may be a valuable approach to improve the precision of the diagnosis. Using next-generation sequencing (NGS), we aimed to identify clinically relevant archetypes.

METHODS

We conducted an Illumina bulk RNA sequencing on 770 kidney biopsies (540 kidney recipients) collected between 2006 and 2021 from 11 European centers. Differentially expressed genes were determined for 11 Banff lesions. An ElasticNet model was used for feature selection, and 4 machine learning classifiers were trained to predict the probability of presence of the lesions. NGS-based classifiers were used in an unsupervised archetypal analysis to different archetypes. The association of the archetypes with allograft survival was assessed using the iBox risk prediction score.

RESULTS

The ElasticNet feature selection reduced the number of the genes from a range of 859-10 830 to a range of 52-867 genes. NGS-based classifiers demonstrated robust performances (precision-recall area under the curves 0.708-0.980) in predicting the Banff lesions. Archetypal analysis revealed 8 distinct phenotypes, each characterized by distinct clinical, immunological, and histological features. Although the archetypes confirmed the well-defined Banff rejection phenotypes for T cell-mediated rejection and antibody-mediated rejection, equivocal histologic antibody-mediated rejection, and borderline diagnoses were reclassified into different archetypes based on their molecular signatures. The 8 NGS-based archetypes displayed distinct allograft survival profiles with incremental graft loss rates between archetypes, ranging from 90% to 56% rates 7 y after evaluation (P < 0.0001).

CONCLUSIONS

Using molecular phenotyping, 8 archetypes were identified. These NGS-based archetypes might improve disease characterization, reclassify ambiguous Banff diagnoses, and enable patient-specific risk stratification.

Present and Future Applications of Artificial Intelligence in Kidney Transplantation

Artificial intelligence (AI) has a wide and increasing range of applications across various sectors. In medicine, AI has already made an impact in numerous fields, rapidly transforming healthcare delivery through its growing applications in diagnosis, treatment and overall patient care. Equally, AI is swiftly and essentially transforming the landscape of kidney transplantation (KT), offering innovative solutions for longstanding problems that have eluded resolution through traditional approaches outside its spectrum. The purpose of this review is to explore the present and future applications of artificial intelligence in KT, with a focus on pre-transplant evaluation, surgical assistance, outcomes and post-transplant care. We discuss its great potential and the inevitable limitations that accompany these technologies. We conclude that by fostering collaboration between AI technologies and medical practitioners, we can pave the way for a future where advanced, personalised care becomes the standard in KT and beyond.

Application of graft-derived cell-free DNA for solid organ transplantation

Monitoring the status of grafts and the occurrence of postoperative complications, such as rejection, is crucial for ensuring the success and long-term survival of organ transplants. Traditional histopathological examination, though effective, is an invasive procedure and poses risks of complications, making frequent use impractical. In recent years, graft-derived cell-free DNA (gd-cfDNA) has emerged as a promising non-invasive biomarker. It not only provides early warnings of rejection and other types of graft injury but also offers important information about the effectiveness of immunosuppressive therapy and prognosis. gd-cfDNA shows potential in the monitoring of organ transplants. The early, real-time information on graft injury provided by gd-cfDNA facilitates timely individualized treatment and improves patient outcomes. However, the progress of research on gd-cfDNA varies across different organs. Therefore, this article will comprehensively review the application and findings of gd-cfDNA in monitoring various solid organs, discussing the advantages, limitations, and some future research directions to aid in its clinical application.

Comparing Plasma Donor-derived Cell-free DNA to Gene Expression in Endomyocardial Biopsies in the Trifecta-Heart Study

BACKGROUND

Plasma donor-derived cell-free DNA (dd-cfDNA) is used to screen for rejection in heart transplants. We launched the Trifecta-Heart study ( ClinicalTrials.gov No. NCT04707872), an investigator-initiated, prospective trial, to examine the correlations between genome-wide molecular changes in endomyocardial biopsies (EMBs) and plasma dd-cfDNA. The present report analyzes the correlation of plasma dd-cfDNA with gene expression in EMBs from 4 vanguard centers and compared these correlations with those in 604 kidney transplant biopsies in the Trifecta-Kidney study ( ClinicalTrials.gov No. NCT04239703).

METHODS

We analyzed 137 consecutive dd-cfDNA-EMB pairs from 70 patients. Plasma %dd-cfDNA was measured by the Prospera test (Natera Inc), and gene expression in EMBs was assessed by Molecular Microscope Diagnostic System using machine-learning algorithms to interpret rejection and injury states.

RESULTS

Top transcripts correlating with dd-cfDNA were related to genes increased in rejection such as interferon gamma-inducible genes (eg, HLA-DMA ) but also with genes induced by injury and expressed in macrophages (eg, SERPINA1 and HMOX1 ). In gene enrichment analysis, the top dd-cfDNA-correlated genes reflected inflammation and rejection pathways. Dd-cfDNA correlations with rejection genes in EMB were similar to those seen in kidney transplant biopsies, with somewhat stronger correlations for TCMR genes in hearts and ABMR genes in kidneys. However, the correlations with parenchymal injury-induced genes and macrophage genes were much stronger in hearts.

CONCLUSIONS

In this first analysis of Trifecta-Heart study, dd-cfDNA correlates significantly with molecular rejection but also with injury and macrophage infiltration, reflecting the proinflammatory properties of injured cardiomyocytes. The relationship supports the utility of dd-cfDNA in clinical management of heart transplant recipients.

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.

Perspective for Donor-Derived Cell-Free DNA in Antibody-Mediated Rejection After Kidney Transplantation: Defining Context of Use and Clinical Implications

Antibody-mediated rejection (AMR) is a major cause of graft failure limiting long-term graft survival after kidney transplantation. Current diagnostic strategy to detect AMR is suboptimal and requires further improvement. Previously suggested treatment regimens for AMR could not demonstrate efficacy, however novel therapeutic agents are currently under investigation. Donor-derived cell-free DNA (dd-cfDNA) is a novel non-invasive biomarker for allograft injury, that has been mainly studied in the context of rejection. Its short-half-life in circulation and injury-dependent release are its key advantages that contribute to its superior diagnostic accuracy, compared to traditional biomarkers. Moreover, previous studies showed that dd-cfDNA-release is well-linked to histological and molecular features of AMR, and thus able to reflect real-time injury. Further observations suggest that dd-cfDNA can be used as a suitable screening tool for early detection of AMR in patients with donor-specific-anti-HLA-antibodies (DSA), as well as for monitoring AMR activity after anti-rejection treatment. The weight of evidence suggests that the integration of dd-cfDNA in the graft surveillance of patients with AMR, or those suspicious of AMR (e.g., due to the presence of donor-specific anti-HLA-antibodies) has an added value and might have a positive impact on outcomes in this specific cohort.

Redefining the molecular rejection states in 3230 heart transplant biopsies: Relationships to parenchymal injury and graft survival

The first-generation Molecular Microscope (MMDx) system for heart transplant endomyocardial biopsies used expression of rejection-associated transcripts (RATs) to diagnose not only T cell-mediated rejection (TCMR) and antibody-mediated rejection (ABMR) but also acute injury. However, the ideal system should detect rejection without being influenced by injury, to permit analysis of the relationship between rejection and parenchymal injury. To achieve this, we developed a new rejection classification in an expanded cohort of 3230 biopsies: 1641 from INTERHEART (ClinicalTrials.gov NCT02670408), plus 1589 service biopsies added to improve the power of the machine learning algorithms. The new system used 6 rejection classifiers instead of RATs and generated 7 rejection archetypes: No rejection, 48%; Minor, 24%; TCMR1, 2.3%; TCMR2, 2.7%; TCMR/mixed, 2.7%; early-stage ABMR, 3.9%; and fully developed ABMR, 16%. Using rejection classifiers eliminated cross-reactions with acute injury, permitting separate assessment of rejection and injury. TCMR was associated with severe-recent injury and late atrophy-fibrosis and rarely had normal parenchyma. ABMR was better tolerated, seldom producing severe injury, but in later biopsies was often associated with atrophy-fibrosis, indicating long-term risk. Graft survival and left ventricular ejection fraction were reduced not only in hearts with TCMR but also in hearts with severe-recent injury and atrophy-fibrosis, even without rejection.

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.

Unveiling Spatial Immune Cell Profile in Kidney Allograft Rejections Using 36-plex Immunofluorescence Imaging

BACKGROUND

Kidney allograft rejections are orchestrated by a variety of immune cells. Because of the complex histopathologic features, accurate pathological diagnosis poses challenges even for expert pathologists. The objective of this study was to unveil novel spatial indices associated with transplant rejection by using a spatial bioinformatic approach using 36-plex immunofluorescence image data.

METHODS

The image obtained from 11 T cell-mediated rejection (TCMR) and 12 antibody-mediated rejection (AMR) samples were segmented into 753 737 single cells using DeepCell's Mesmer algorithm. These cells were categorized into 13 distinct cell types through unsupervised clustering based on their biomarker expression profiles. Cell neighborhood analysis allowed us to stratify kidney tissue into 8 distinct neighborhood components consisting of unique cell type enrichment profiles.

RESULTS

In contrast to TCMR samples, AMR samples exhibited a higher frequency of neighborhood components that were characterized by an enrichment of CD31+ endothelial cells. Although the overall frequency of CD68+ macrophages in AMR samples was not significantly high, CD68+ macrophages within endothelial cell-rich lesions exhibited a significantly higher frequency in AMR samples than TCMR samples. Furthermore, the frequency of interactions between CD31+ cells and CD68+ cells was significantly increased in AMR samples, implying the pivotal role of macrophages in AMR pathogenesis. Importantly, patients demonstrating a high frequency of CD31:CD68 interactions experienced significantly poorer outcomes in terms of chronic AMR progression.

CONCLUSIONS

Collectively, these data indicate the potential of spatial bioinformatic as a valuable tool for aiding in pathological diagnosis and for uncovering new insights into the mechanisms underlying transplant rejection.

Precision diagnostics in transplanted organs using microarray-assessed gene expression: concepts and technical methods of the Molecular Microscope® Diagnostic System (MMDx)

There is a major unmet need for improved accuracy and precision in the assessment of transplant rejection and tissue injury. Diagnoses relying on histologic and visual assessments demonstrate significant variation between expert observers (as represented by low kappa values) and have limited ability to assess many biological processes that produce little histologic changes, for example, acute injury. Consensus rules and guidelines for histologic diagnosis are useful but may have errors. Risks of over- or under-treatment can be serious: many therapies for transplant rejection or primary diseases are expensive and carry risk for significant adverse effects. Improved diagnostic methods could alleviate healthcare costs by reducing treatment errors, increase treatment efficacy, and serve as useful endpoints for clinical trials of new agents that can improve outcomes. Molecular diagnostic assessments using microarrays combined with machine learning algorithms for interpretation have shown promise for increasing diagnostic precision via probabilistic assessments, recalibrating standard of care diagnostic methods, clarifying ambiguous cases, and identifying potentially missed cases of rejection. This review describes the development and application of the Molecular Microscope® Diagnostic System (MMDx), and discusses the history and reasoning behind many common methods, statistical practices, and computational decisions employed to ensure that MMDx scores are as accurate and precise as possible. MMDx provides insights on disease processes and highly reproducible results from a comparatively small amount of tissue and constitutes a general approach that is useful in many areas of medicine, including kidney, heart, lung, and liver transplants, with the possibility of extrapolating lessons for understanding native organ disease states.

Identification of liver transplant biopsy phenotypes associated with distinct liver biological markers and allograft survival

The intricate association between histologic lesions and circulating antihuman leucocyte antigen donor-specific antibodies (DSA) in liver transplantation (LT) requires further clarification. We conducted a probabilistic, unsupervised approach in a comprehensively well-annotated LT cohort to identify clinically relevant archetypes. We evaluated 490 pairs of LT biopsies with DSA testing from 325 recipients transplanted between 2010 and 2020 across 3 French centers and an external cohort of 202 biopsies from 128 recipients. Unsupervised archetypal analysis integrated all clinico-immuno-histologic parameters of each biopsy to identify biopsy archetypes. The median time after LT was 1.17 (interquartile range, 0.38-2.38) years. We identified 7 archetypes distinguished by clinico-immuno-histologic parameters: archetype #1: severe T cell-mediated rejection (15.9%); #2: chronic rejection with ductopenia (1.8%); #3: architectural and microvascular damages (3.5%); #4: (sub)normal (55.9%); #5: mild T cell-mediated rejection (4.9%); #6: acute antibody-mediated rejection (6.5%); and #7: chronic rejection with DSA (11.4%). Cell infiltrates vary in the archetype. These archetypes were associated with distinct liver biological markers and allograft outcomes. These findings remained consistent when stratified using the patient's age or indications for LT, with good performance in the external cohort (mean highest probability assignment = 0.58, standard deviation ± 0.17). In conclusion, we have identified clinically meaningful archetypes, providing valuable insights into the intricate DSA-histology association, which may help standardize liver allograft pathology classification.

Design, cohort profile and comparison of the KTD-Innov study: a prospective multidimensional biomarker validation study in kidney allograft rejection

There is an unmet need for robust and clinically validated biomarkers of kidney allograft rejection. Here we present the KTD-Innov study (ClinicalTrials.gov, NCT03582436), an unselected deeply phenotyped cohort of kidney transplant recipients with a holistic approach to validate the clinical utility of precision diagnostic biomarkers. In 2018-2019, we prospectively enrolled consecutive adult patients who received a kidney allograft at seven French centers and followed them for a year. We performed multimodal phenotyping at follow-up visits, by collecting clinical, biological, immunological, and histological parameters, and analyzing a panel of 147 blood, urinary and kidney tissue biomarkers. The primary outcome was allograft rejection, assessed at each visit according to the international Banff 2019 classification. We evaluated the representativeness of participants by comparing them with patients from French, European, and American transplant programs transplanted during the same period. A total of 733 kidney transplant recipients (64.1% male and 35.9% female) were included during the study. The median follow-up after transplantation was 12.3 months (interquartile range, 11.9-13.1 months). The cumulative incidence of rejection was 9.7% at one year post-transplant. We developed a distributed and secured data repository in compliance with the general data protection regulation. We established a multimodal biomarker biobank of 16,736 samples, including 9331 blood, 4425 urinary and 2980 kidney tissue samples, managed and secured in a collaborative network involving 7 clinical centers, 4 analytical platforms and 2 industrial partners. Patients' characteristics, immune profiles and treatments closely resembled those of 41,238 French, European and American kidney transplant recipients. The KTD-Innov study is a unique holistic and multidimensional biomarker validation cohort of kidney transplant recipients representative of the real-world transplant population. Future findings from this cohort are likely to be robust and generalizable.

Multi-centre analytical performance verification of an IVD assay to quantify donor-derived cell-free DNA in solid organ transplant recipients

Donor-derived cell-free DNA (dd-cfDNA) has been widely studied as biomarker for non-invasive allograft rejection monitoring. Earlier rejection detection enables more prompt diagnosis and intervention, ultimately improving patient treatment and outcomes. This multi-centre study aims to verify analytical performance of a next-generation sequencing-based dd-cfDNA assay at end-user environments. Three independent laboratories received the same experimental design and 16 blinded samples to perform cfDNA extraction and the dd-cfDNA assay workflow. dd-cfDNA results were compared between sites and against manufacturer validation to evaluate concordance, reproducibility, repeatability and verify analytical performance. A total of 247 sample libraries were generated across 18 runs, with completion time of <24 h. A 96.0% first pass rate highlighted minimal failures. Overall observed versus expected dd-cfDNA results demonstrated good concordance and a strong positive correlation with linear least squares regression r2 = 0.9989, and high repeatability and reproducibility within and between sites, respectively (p > 0.05). Manufacturer validation established limit of blank 0.18%, limit of detection 0.23% and limit of quantification 0.23%, and results from independent sites verified those limits. Parallel analyses illustrated no significant difference (p = 0.951) between dd-cfDNA results with or without recipient genotype. The dd-cfDNA assay evaluated here has been verified as a reliable method for efficient, reproducible dd-cfDNA quantification in plasma from solid organ transplant recipients without requiring genotyping. Implementation of onsite dd-cfDNA testing at clinical laboratories could facilitate earlier detection of allograft injury, bearing great potential for patient care.

Histopathology, mRNA expression profile, and donor-derived cell-free DNA for assessment of rejection in pediatric heart transplantation

BACKGROUND

The relationship between histopathologic and molecular ("MMDx"®) assessments of endomyocardial biopsy (EMB) and serum donor-derived cell-free DNA (ddcfDNA) in acute rejection (AR) assessment following pediatric heart transplantation (HT) is unknown.

METHODS

EMB sent for MMDx and histopathology from November 2021 to September 2022 were reviewed. MMDx and histopathology results were compared. DdcfDNA obtained ≤1 week prior to EMB were compared with histopathology and MMDx results. The discrimination of ddcfDNA for AR was assessed using receiver-operating curves.

FINDINGS

In this study, 177 EMBs were obtained for histopathology and MMDx, 101 had time-matched ddcfDNA values. MMDx and Histopathology displayed moderate agreement for T-cell-mediated rejection (TCMR, Kappa = 0.52, p < .001) and antibody-mediated rejection (ABMR, Kappa = 0.41, p < .001). Discordant results occurred in 24% of cases, most often with ABMR. Compared with no AR, ddcfDNA values were elevated in cases of AR diagnosed by both histopathology and MMDx (p < .01 for all). Additionally, ddcfDNA values were elevated in injury patterns on MMDx, even when AR was not present (p = .01). DdcfDNA displayed excellent discrimination (AUC 0.83) for AR by MMDx and/or histopathology. Using a threshold of ≥0.135%, ddcfDNA had a sensitivity of 90%, specificity of 63%, PPV of 52%, and NPV of 94%.

CONCLUSIONS

Histopathology and MMDx displayed moderate agreement in diagnosing AR following pediatric HT, with most discrepancies noted in the presence of ABMR. DdcfDNA is elevated with AR, with excellent discrimination and high NPV particularly when utilizing MMDx. A combination of all three tests may be necessary in some cases.

Distinct Molecular Processes Mediate Donor-derived Cell-free DNA Release From Kidney Transplants in Different Disease States

BACKGROUND

Among all biopsies in the Trifecta-Kidney Study ( ClinicalTrials.gov NCT04239703), elevated plasma donor-derived cell-free DNA (dd-cfDNA) correlated most strongly with molecular antibody-mediated rejection (AMR) but was also elevated in other states: T cell-mediated rejection (TCMR), acute kidney injury (AKI), and some apparently normal biopsies. The present study aimed to define the molecular correlates of plasma dd-cfDNA within specific states.

METHODS

Dd-cfDNA was measured by the Prospera test. Molecular rejection and injury states were defined using the Molecular Microscope system. We studied the correlation between dd-cfDNA and the expression of genes, transcript sets, and classifier scores within specific disease states, and compared AMR, TCMR, and AKI to biopsies classified as normal and no injury (NRNI).

RESULTS

In all 604 biopsies, dd-cfDNA was elevated in AMR, TCMR, and AKI. Within AMR biopsies, dd-cfDNA correlated with AMR activity and stage. Within AKI, the correlations reflected acute parenchymal injury, including cell cycling. Within biopsies classified as MMDx Normal and archetypal No injury (NRNI), dd-cfDNA still correlated significantly with rejection- and injury-related genes. TCMR activity (eg, the TCMR Prob classifier) correlated with dd-cfDNA, but within TCMR biopsies, top gene correlations were complex and not the top TCMR-selective genes.

CONCLUSIONS

In kidney transplants, elevated plasma dd-cfDNA is associated with 3 distinct molecular states in the donor tissue: AMR, recent parenchymal injury (including cell cycling), and TCMR, potentially complicated by parenchymal disruption. Moreover, subtle rejection- and injury-related changes in the donor tissue can contribute to dd-cfDNA elevations in transplants considered to have no rejection or injury.

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.

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.

Interferon-γ and its response are determinants of antibody-mediated rejection and clinical outcomes in patients after renal transplantation

Interferon-γ (IFN-γ) is an important cytokine in tissue homeostasis and immune response, while studies about it in antibody-mediated rejection (ABMR) are very limited. This study aims to comprehensively elucidate the role of IFN-γ in ABMR after renal transplantation. In six renal transplantation cohorts, the IFN-γ responses (IFNGR) biological process was consistently top up-regulated in ABMR compared to stable renal function or even T cell-mediated rejection in both allografts and peripheral blood. According to single-cell analysis, IFNGR levels were found to be broadly elevated in most cell types in allografts and peripheral blood with ABMR. In allografts with ABMR, M1 macrophages had the highest IFNGR levels and were heavily infiltrated, while kidney resident M2 macrophages were nearly absent. In peripheral blood, CD14+ monocytes had the top IFNGR level and were significantly increased in ABMR. Immunofluorescence assay showed that levels of IFN-γ and M1 macrophages were sharply elevated in allografts with ABMR than non-rejection. Importantly, the IFNGR level in allografts was identified as a strong risk factor for long-term renal graft survival. Together, this study systematically analyzed multi-omics from thirteen independent cohorts and identified IFN-γ and IFNGR as determinants of ABMR and clinical outcomes in patients after renal transplantation.

Using Regression Equations to Enhance Interpretation of Histology Lesions of Kidney Transplant Rejection

BACKGROUND

The Banff system for histologic diagnosis of rejection in kidney transplant biopsies uses guidelines to assess designated features-lesions, donor-specific antibody (DSA), and C4d staining. We explored whether using regression equations to interpret the features as well as current guidelines could establish the relative importance of each feature and improve histologic interpretation.

METHODS

We developed logistic regression equations using the designated features to predict antibody-mediated rejection (AMR/mixed) and T-cell-mediated rejection (TCMR/mixed) in 1679 indication biopsies from the INTERCOMEX study ( ClinicalTrials.gov NCT01299168). Equations were trained on molecular diagnoses independent of the designated features.

RESULTS

In regression and random forests, the important features predicting molecular rejection were as follows: for AMR, ptc and g, followed by cg; for TCMR, t > i. V-lesions were relatively unimportant. C4d and DSA were also relatively unimportant for predicting AMR: by AUC, the model excluding them (0.853) was nearly as good as the model including them (0.860). Including time posttransplant slightly but significantly improved all models. By AUC, regression predicted molecular AMR and TCMR better than Banff histologic diagnoses. More importantly, in biopsies called "no rejection" by Banff guidelines, regression equations based on histology features identified histologic and molecular rejection-related changes in some biopsies and improved survival predictions. Thus, regression can screen for missed rejection.

CONCLUSIONS

Using lesion-based regression equations in addition to Banff histology guidelines defines the relative important of histology features for identifying rejection, allows screening for potential missed diagnoses, and permits early estimates of AMR when C4d and DSA are not available.

[Kidney transplantation]

Every patient with kidney failure requiring dialysis in Germany has the right to at least be evaluated for a transplantation. When an affected person can be considered for a transplantation, it must be clarified which allocation program is the right one for the person and whether a living organ donor can be considered. It should also be individually discussed with patients which type of donor organ should be accepted. Following a transplantation an individualized immunosuppression is relevant not only for the long-term survival of the transplant but also for the adherence of the patient.

Challenges and Opportunities for the Clinical Translation of Spatial Transcriptomics Technologies

Background

The first spatially resolved transcriptomics platforms, GeoMx (Nanostring) and Visium (10x Genomics) were launched in 2019 and were recognized as the method of the year by Nature Methods in 2020. The subsequent refinement and expansion of these and other technologies to increase -plex, work with formalin-fixed paraffin-embedded tissue, and analyze protein in addition to gene expression have only added to their significance and impact on the biomedical sciences. In this perspective, we focus on two platforms for spatial transcriptomics, GeoMx and Visium, and how these platforms have been used to provide novel insight into kidney disease. The choice of platform will depend largely on experimental questions and design. The application of these technologies to clinically sourced biopsies presents the opportunity to identify specific tissue biomarkers that help define disease etiology and more precisely target therapeutic interventions in the future.

Summary

In this review, we provide a description of the existing and emerging technologies that can be used to capture spatially resolved gene and protein expression data from tissue. These technologies have provided new insight into the spatial heterogeneity of diseases, how reactions to disease are distributed within a tissue, which cells are affected, and molecular pathways that predict disease and response to therapy.

Key Message

The upcoming years will see intense use of spatial transcriptomics technologies to better define the pathophysiology of kidney diseases and develop novel diagnostic tests to guide personalized treatments for patients.

Morphologic and Molecular Features of Antibody-Mediated Transplant Rejection: Pivotal Role of Molecular Injury as an Independent Predictor of Renal Allograft Functional Decline

Current knowledge about the factors correlating with functional decline and subsequent failure of kidney allografts in antibody-mediated rejection (ABMR) is limited. We conducted a cohort study involving 75 renal allograft recipients diagnosed with late ABMR occurring at least 6 months after transplantation. The study aimed to examine the correlation of molecular and histologic features with estimated glomerular filtration rate (eGFR) trajectories and death-censored graft survival. We focused on sum scores reflecting histologic ABMR activity versus chronicity and molecular scores of ABMR probability (ABMRProb), injury-repair response (IRRAT) and fibrosis (ciprob). In multivariable Cox analysis, a Banff lesion-based chronicity index (ci+ct+cg[x2]; hazard ratio per interquartile range [IQR]: 1.97 [95% confidence interval: 0.97 to 3.99]) and IRRAT (1.93 [0.96 to 3.89]) showed the strongest associations with graft failure. Among biopsy variables, IRRAT exhibited the highest relative variable importance and emerged as the sole independent predictor of eGFR slope (change per IQR: -4.2 [-7.8 to -0.6] mL/min/1.73 m2/year). In contrast, morphologic chronicity associated with baseline eGFR only. We conclude that the extent of molecular injury is a robust predictor of renal function decline. Transcriptome analysis has the potential to improve outcome prediction and possibly identify modifiable injury, guiding targeted therapeutic interventions.

Defining a natural killer cell-enriched molecular rejection-like state in lung transplant transbronchial biopsies

In lung transplantation, antibody-mediated rejection (AMR) diagnosed using the International Society for Heart and Lung Transplantation criteria is uncommon compared with other organs, and previous studies failed to find molecular AMR (ABMR) in lung biopsies. However, understanding of ABMR has changed with the recognition that ABMR in kidney transplants is often donor-specific antibody (DSA)-negative and associated with natural killer (NK) cell transcripts. We therefore searched for a similar molecular ABMR-like state in transbronchial biopsies using gene expression microarray results from the INTERLUNG study (#NCT02812290). After optimizing rejection-selective transcript sets in a training set (N = 488), the resulting algorithms separated an NK cell-enriched molecular rejection-like state (NKRL) from T cell-mediated rejection (TCMR)/Mixed in a test set (N = 488). Applying this approach to all 896 transbronchial biopsies distinguished 3 groups: no rejection, TCMR/Mixed, and NKRL. Like TCMR/Mixed, NKRL had increased expression of all-rejection transcripts, but NKRL had increased expression of NK cell transcripts, whereas TCMR/Mixed had increased effector T cell and activated macrophage transcripts. NKRL was usually DSA-negative and not recognized as AMR clinically. TCMR/Mixed was associated with chronic lung allograft dysfunction, reduced one-second forced expiratory volume at the time of biopsy, and short-term graft failure, but NKRL was not. Thus, some lung transplants manifest a molecular state similar to DSA-negative ABMR in kidney and heart transplants, but its clinical significance must be established.

Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms

Background: Interstitial fibrosis and tubular atrophy (IFTA) are the histopathological manifestations of chronic kidney disease (CKD) and one of the causes of long-term renal loss in transplanted kidneys. Necroptosis as a type of programmed death plays an important role in the development of IFTA, and in the late functional decline and even loss of grafts. In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes. Methods: We screened all 162 "kidney transplant"-related cohorts in the GEO database and obtained five data sets (training sets: GSE98320 and GSE76882, validation sets: GSE22459 and GSE53605, and survival set: GSE21374). The training set was constructed after removing batch effects of GSE98320 and GSE76882 by using the SVA package. The differentially expressed gene (DEG) analysis was used to identify necroptosis-related DEGs. A total of 13 machine learning algorithms-LASSO, Ridge, Enet, Stepglm, SVM, glmboost, LDA, plsRglm, random forest, GBM, XGBoost, Naive Bayes, and ANNs-were used to construct 114 IFTA diagnostic models, and the optimal models were screened by the AUC values. Post-transplantation patients were then grouped using consensus clustering, and the different subgroups were further explored using PCA, Kaplan-Meier (KM) survival analysis, functional enrichment analysis, CIBERSOFT, and single-sample Gene Set Enrichment Analysis. Results: A total of 55 necroptosis-related DEGs were identified by taking the intersection of the DEGs and necroptosis-related gene sets. Stepglm[both]+RF is the optimal model with an average AUC of 0.822. A total of four molecular subgroups of renal transplantation patients were obtained by clustering, and significant upregulation of fibrosis-related pathways and upregulation of immune response-related pathways were found in the C4 group, which had poor prognosis. Conclusion: Based on the combination of the 13 machine learning algorithms, we developed 114 IFTA classification models. Furthermore, we tested the top model using two independent data sets from GEO.

Novel transcriptomic signatures associated with premature kidney allograft failure

BACKGROUND

The power to predict kidney allograft outcomes based on non-invasive assays is limited. Assessment of operational tolerance (OT) patients allows us to identify transcriptomic signatures of true non-responders for construction of predictive models.

METHODS

In this observational retrospective study, RNA sequencing of peripheral blood was used in a derivation cohort to identify a protective set of transcripts by comparing 15 OT patients (40% females), from the TOMOGRAM Study (NCT05124444), 14 chronic active antibody-mediated rejection (CABMR) and 23 stable graft function patients ≥15 years (STA). The selected differentially expressed transcripts between OT and CABMR were used in a validation cohort (n = 396) to predict 3-year kidney allograft loss at 3 time-points using RT-qPCR.

FINDINGS

Archetypal analysis and classifier performance of RNA sequencing data showed that OT is clearly distinguishable from CABMR, but similar to STA. Based on significant transcripts from the validation cohort in univariable analysis, 2 multivariable Cox models were created. A 3-transcript (ADGRG3, ATG2A, and GNLY) model from POD 7 predicted graft loss with C-statistics (C) 0.727 (95% CI, 0.638-0.820). Another 3-transcript (IGHM, CD5, GNLY) model from M3 predicted graft loss with C 0.786 (95% CI, 0.785-0.865). Combining 3-transcripts models with eGFR at POD 7 and M3 improved C-statistics to 0.860 (95% CI, 0.778-0.944) and 0.868 (95% CI, 0.790-0.944), respectively.

INTERPRETATION

Identification of transcripts distinguishing OT from CABMR allowed us to construct models predicting premature graft loss. Identified transcripts reflect mechanisms of injury/repair and alloimmune response when assessed at day 7 or with a loss of protective phenotype when assessed at month 3.

FUNDING

Supported by the Ministry of Health of the Czech Republic under grant NV19-06-00031.

Relating Molecular T Cell-mediated Rejection Activity in Kidney Transplant Biopsies to Time and to Histologic Tubulitis and Atrophy-fibrosis

BACKGROUND

We studied the variation in molecular T cell-mediated rejection (TCMR) activity in kidney transplant indication biopsies and its relationship with histologic lesions (particularly tubulitis and atrophy-fibrosis) and time posttransplant.

METHODS

We examined 175 kidney transplant biopsies with molecular TCMR as defined by archetypal analysis in the INTERCOMEX study ( ClinicalTrials.gov #NCT01299168). TCMR activity was defined by a molecular classifier.

RESULTS

Archetypal analysis identified 2 TCMR classes, TCMR1 and TCMR2: TCMR1 had higher TCMR activity and more antibody-mediated rejection ("mixed") activity and arteritis but little hyalinosis, whereas TCMR2 had less TCMR activity but more atrophy-fibrosis. TCMR1 and TCMR2 had similar levels of molecular injury and tubulitis. Both TCMR1 and TCMR2 biopsies were uncommon after 2 y posttransplant and were rare after 10 y, particularly TCMR1. Within late TCMR biopsies, TCMR classifier activity and activity molecules such as IFNG fell progressively with time, but tubulitis and molecular injury were sustained. Atrophy-fibrosis was increased in TCMR biopsies, even in the first year posttransplant, and rose with time posttransplant. TCMR1 and TCMR2 both reduced graft survival, but in random forests, the strongest determinant of survival after biopsies with TCMR was molecular injury, not TCMR activity.

CONCLUSIONS

TCMR varies in intensity but is always strongly related to molecular injury and atrophy-fibrosis, which ultimately explains its effect on survival. We hypothesize, based on the reciprocal relationship with hyalinosis, that the TCMR1-TCMR2 gradient reflects calcineurin inhibitor drug underexposure, whereas the time-dependent decline in TCMR activity and frequency after the first year reflects T-cell exhaustion.

Utility of Banff Human Organ Transplant Gene Panel in Human Kidney Transplant Biopsies

BACKGROUND

Microarray transcript analysis of human renal transplantation biopsies has successfully identified the many patterns of graft rejection. To evaluate an alternative, this report tests whether gene expression from the Banff Human Organ Transplant (B-HOT) probe set panel, derived from validated microarrays, can identify the relevant allograft diagnoses directly from archival human renal transplant formalin-fixed paraffin-embedded biopsies. To test this hypothesis, principal components (PCs) of gene expressions were used to identify allograft diagnoses, to classify diagnoses, and to determine whether the PC data were rich enough to identify diagnostic subtypes by clustering, which are all needed if the B-HOT panel can substitute for microarrays.

METHODS

RNA was isolated from routine, archival formalin-fixed paraffin-embedded tissue renal biopsy cores with both rejection and nonrejection diagnoses. The B-HOT panel expression of 770 genes was analyzed by PCs, which were then tested to determine their ability to identify diagnoses.

RESULTS

PCs of microarray gene sets identified the Banff categories of renal allograft diagnoses, modeled well the aggregate diagnoses, showing a similar correspondence with the pathologic diagnoses as microarrays. Clustering of the PCs identified diagnostic subtypes including non-chronic antibody-mediated rejection with high endothelial expression. PCs of cell types and pathways identified new mechanistic patterns including differential expression of B and plasma cells.

CONCLUSIONS

Using PCs of gene expression from the B-Hot panel confirms the utility of the B-HOT panel to identify allograft diagnoses and is similar to microarrays. The B-HOT panel will accelerate and expand transcript analysis and will be useful for longitudinal and outcome studies.

On a Long and Winding Road: Alloantibodies in Organ Transplantation

Today we know that both the humoral and the cellular arm of the immune system are engaged in severe immunological challenges. A close interaction between B and T cells can be observed in most "natural" challenges, including infections, malignancies, and autoimmune diseases. The importance and power of humoral immunity are impressively demonstrated by the current coronavirus disease 2019 pandemic. Organ transplant rejection is a normal immune response to a completely "artificial" challenge. It took a long time before the multifaceted action of different immunological forces was recognized and a unified, generally accepted opinion could be formed. Here, we address prominent paradigms and paradigm shifts in the field of transplantation immunology. We identify several instances in which the transplant community missed a timely paradigm shift because essential, available knowledge was ignored. Moreover, we discuss key findings that critically contributed to our understanding of transplant immunology but sometimes developed with delay and in a roundabout way, as was the case with antibody-mediated rejection-a main focus of this article. These include the discovery of the molecular principles of histocompatibility, the recognition of the microcirculation as a key interface of immune damage, the refinement of alloantibody detection, the description of C4d as a footmark of endothelium-bound antibody, and last but not least, the developments in biopsy-based diagnostics beyond conventional morphology, which only now give us a glimpse of the enormous complexity and pathogenetic diversity of rejection.

Antibody-mediated Rejection Without Detectable Donor-specific Antibody Releases Donor-derived Cell-free DNA: Results From the Trifecta Study

BACKGROUND

Trifecta (ClinicalTrials.gov #NCT04239703) is a prospective trial defining relationships between donor-derived cell-free DNA (dd-cfDNA), donor-specific antibody (DSA), and molecular findings in kidney transplant biopsies. Previous analyses of double results showed dd-cfDNA was strongly associated with rejection-associated molecules in the biopsy. The present study analyzed the triple results in 280 biopsies, focusing on the question of dd-cfDNA levels in DSA-negative antibody-mediated rejection (AMR).

METHODS

Molecular Microscope Diagnostic System biopsy testing was performed at Alberta Transplant Applied Genomics Centre, dd-cfDNA testing at Natera, Inc, and central HLA antibody testing at One Lambda Inc. Local DSA and histologic diagnoses were assigned per center standard-of-care.

RESULTS

DSA was frequently negative in both molecular (56%) and histologic (51%) AMR. DSA-negative AMR had slightly less molecular AMR activity and histologic peritubular capillaritis than DSA-positive AMR. However, all AMRs-DSA-positive or -negative-showed elevated %dd-cfDNA. There was no association between dd-cfDNA and DSA in biopsies without rejection. In AMR, %dd-cfDNA ≥1.0 was more frequent (75%) than DSA positivity (44%). In logistic regression, dd-cfDNA percent (area under the curve [AUC] 0.85) or quantity (AUC 0.86) predicted molecular AMR better than DSA (AUC 0.66). However, the best predictions incorporated both dd-cfDNA and DSA, plus time posttransplant (AUC 0.88).

CONCLUSIONS

DSA-negative AMR has moderately decreased mean molecular and histologic AMR-associated features compared with DSA-positive AMR, though similarly elevated dd-cfDNA levels. In predicting AMR at the time of indication biopsies in this population, dd-cfDNA is superior to DSA, reflecting the prevalence of DSA-negative AMR, but the optimal predictions incorporated both dd-cfDNA and DSA.

The Molecular Diagnosis Might Be Clinically Useful in Discrepant Kidney Allograft Biopsy Findings: An Analysis of Clinical Outcomes

BACKGROUND

The Molecular Microscope Diagnostic System (MMDx) may overcome histology shortcomings. Previous studies have simply examined discrepant findings but have not attempted to determine clinical endpoints. To measure performance, clinical outcomes are strongly required.

METHODS

This single-center cohort study described discrepancies between MMDx and histology from 51 kidney transplant recipients (KTRs) and analyzed 72 indication biopsies, including 21 follow-up biopsies. Clinical performance was assessed by a combined endpoint of graft failure, rejection on follow-up biopsy, de novo donor-specific antibody, and improvement of kidney allograft function upon antirejection treatment.

RESULTS

MMDx agreed in 33 (65%) and differed in 18 (35%) of 51 KTRs. Most discrepancies occurred in biopsies called no rejection by MMDx and rejection by histology (15/24, 63%). In contrast, in biopsies called rejection by MMDx, 3 were classified as no rejection by histology (3/27, 11%). Discrepant findings between MMDx and histology occurred following delayed graft function and MMDx from biopsies with a low percentage of cortex. Among 15 biopsies classified as no rejection by MMDx but rejection by histology, the clinical course suggested no rejection in 9 cases. Six KTRs reached the endpoint, showing predominant t ≥ 2 lesions.

CONCLUSIONS

The most often occurring discrepancy is rejection by histology but no rejection by MMDx. As more KTRs do not meet the combined endpoint for rejection, MMDx might be clinically useful in these discrepant cases. Although strong histological findings have priority in indicating the treatment, clinical implementation of MMDx could strengthen treatment strategies.

The Molecular Microscope Diagnostic System: Assessment of Rejection and Injury in Heart Transplant Biopsies

This review describes the development of the Molecular Microscope Diagnostic System (MMDx) for heart transplant endomyocardial biopsies (EMBs). MMDx-Heart uses microarrays to measure biopsy-based gene expression and ensembles of machine learning algorithms to interpret the results and compare each new biopsy to a large reference set of earlier biopsies. MMDx assesses T cell-mediated rejection (TCMR), antibody-mediated rejection (AMR), recent parenchymal injury, and atrophy-fibrosis, continually "learning" from new biopsies. Rejection-associated transcripts mapped in kidney transplants and experimental systems were used to identify TCMR, AMR, and recent injury-induced inflammation. Rejection and injury emerged as gradients of intensity, rather than binary classes. AMR was one-third donor-specific antibody (DSA)-negative, and many EMBs first considered to have no rejection displayed minor AMR-like changes, with increased probability of DSA positivity and subtle inflammation. Rejection-associated transcript-based algorithms now classify EMBs as "Normal," "Minor AMR changes," "AMR," "possible AMR," "TCMR," "possible TCMR," and "recent injury." Additionally, MMDx uses injury-associated transcript sets to assess the degree of parenchymal injury and atrophy-fibrosis in every biopsy and study the effect of rejection on the parenchyma. TCMR directly injures the parenchyma whereas AMR usually induces microcirculation stress but relatively little initial parenchymal damage, although slowly inducing parenchymal atrophy-fibrosis. Function (left ventricular ejection fraction) and short-term risk of failure are strongly determined by parenchymal injury. These discoveries can guide molecular diagnostic applications, either as a central MMDx system or adapted to other platforms. MMDx can also help calibrate noninvasive blood-based biomarkers to avoid unnecessary biopsies and monitor response to therapy.

Revisiting transplant immunology through the lens of single-cell technologies

Solid organ transplantation (SOT) is the standard of care for end-stage organ disease. The most frequent complication of SOT involves allograft rejection, which may occur via T cell- and/or antibody-mediated mechanisms. Diagnosis of rejection in the clinical setting requires an invasive biopsy as there are currently no reliable biomarkers to detect rejection episodes. Likewise, it is virtually impossible to identify patients who exhibit operational tolerance and may be candidates for reduced or complete withdrawal of immunosuppression. Emerging single-cell technologies, including cytometry by time-of-flight (CyTOF), imaging mass cytometry, and single-cell RNA sequencing, represent a new opportunity for deep characterization of pathogenic immune populations involved in both allograft rejection and tolerance in clinical samples. These techniques enable examination of both individual cellular phenotypes and cell-to-cell interactions, ultimately providing new insights into the complex pathophysiology of allograft rejection. However, working with these large, highly dimensional datasets requires expertise in advanced data processing and analysis using computational biology techniques. Machine learning algorithms represent an optimal strategy to analyze and create predictive models using these complex datasets and will likely be essential for future clinical application of patient level results based on single-cell data. Herein, we review the existing literature on single-cell techniques in the context of SOT.

Omics and Artificial Intelligence in Kidney Diseases

Omics applications in nephrology may have relevance in the future to improve clinical care of kidney disease patients. In a short term, patients will benefit from specific measurement and computational analyses around biomarkers identified at various omics-levels. In mid term and long term, these approaches will need to be integrated into a holistic representation of the kidney and all its influencing factors for individualized patient care. Research demonstrates robust data to justify the application of omics for better understanding, risk stratification, and individualized treatment of kidney disease patients. Despite these advances in the research setting, there is still a lack of evidence showing the combination of omics technologies with artificial intelligence and its application in clinical diagnostics and care of patients with kidney disease.

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.

The molecular features of chronic lung allograft dysfunction in lung transplant airway mucosa

BACKGROUND

Many lung transplants fail due to chronic lung allograft dysfunction (CLAD). We recently showed that transbronchial biopsies (TBBs) from CLAD patients manifest severe parenchymal injury and dedifferentiation, distinct from time-dependent changes. The present study explored time-selective and CLAD-selective transcripts in mucosal biopsies from the third bronchial bifurcation (3BMBs), compared to those in TBBs.

METHODS

We used genome-wide microarray measurements in 324 3BMBs to identify CLAD-selective changes as well as time-dependent changes and develop a CLAD classifier. CLAD-selective transcripts were identified with linear models for microarray data (limma) and were used to build an ensemble of 12 classifiers to predict CLAD. Hazard models and random forests were then used to predict the risk of graft loss using the CLAD classifier, transcript sets associated with rejection, injury, and time.

RESULTS

T cell-mediated rejection and donor-specific antibody were increased in CLAD 3BMBs but most had no rejection. Like TBBs, 3BMBs showed a time-dependent increase in transcripts expressed in inflammatory cells that was not associated with CLAD or survival. Also like TBBs, the CLAD-selective transcripts in 3BMBs reflected severe parenchymal injury and dedifferentiation, not inflammation or rejection. While 3BMBs and TBBs did not overlap in their top 20 CLAD-selective transcripts, many CLAD-selective transcripts were significantly increased in both for example LOXL1, an enzyme controlling matrix remodeling. In Cox models for one-year survival, the 3BMB CLAD-selective transcripts and CLAD classifier predicted graft loss and correlated with CLAD stage. Many 3BMB CLAD-selective transcripts were also increased by injury in kidney transplants and correlated with decreased kidney survival, including LOXL1.

CONCLUSIONS

Mucosal and transbronchial biopsies from CLAD patients reveal a diffuse molecular injury and dedifferentiation state that impacts prognosis and correlates with the physiologic disturbances. CLAD state in lung transplants shares features with failing kidney transplants, indicating elements shared by the injury responses of distressed organs.

N6-methyladenosine regulators-related immune genes enable predict graft loss and discriminate T-cell mediate rejection in kidney transplantation biopsies for cause

Objective

The role of m6A modification in kidney transplant-associated immunity, especially in alloimmunity, still remains unknown. This study aims to explore the potential value of m6A-related immune genes in predicting graft loss and diagnosing T cell mediated rejection (TCMR), as well as the possible role they play in renal graft dysfunction.

Methods

Renal transplant-related cohorts and transcript expression data were obtained from the GEO database. First, we conducted correlation analysis in the discovery cohort to identify the m6A-related immune genes. Then, lasso regression and random forest were used respectively to build prediction models in the prognosis and diagnosis cohort, to predict graft loss and discriminate TCMR in dysfunctional renal grafts. Connectivity map (CMap) analysis was applied to identify potential therapeutic compounds for TCMR.

Results

The prognostic prediction model effectively predicts the prognosis and survival of renal grafts with clinical indications (P< 0.001) and applies to both rejection and non-rejection situations. The diagnostic prediction model discriminates TCMR in dysfunctional renal grafts with high accuracy (area under curve = 0.891). Meanwhile, the classifier score of the diagnostic model, as a continuity index, is positively correlated with the severity of main pathological injuries of TCMR. Furthermore, it is found that METTL3, FTO, WATP, and RBM15 are likely to play a pivotal part in the regulation of immune response in TCMR. By CMap analysis, several small molecular compounds are found to be able to reverse TCMR including fenoldopam, dextromethorphan, and so on.

Conclusions

Together, our findings explore the value of m6A-related immune genes in predicting the prognosis of renal grafts and diagnosis of TCMR.

CARARIME: Interactive web server for comprehensive analysis of renal allograft rejection in immune microenvironment

Background

Renal transplantation is a very effective treatment for renal failure patients following kidney transplant. However, the clinical benefit is restricted by the high incidence of organ rejection. Therefore, there exists a wealth of literature regarding the mechanism of renal transplant rejection, including a large library of expression data. In recent years, research has shown the immune microenvironment to play an important role in renal transplant rejection. Nephrology web analysis tools currently exist to address chronic nephropathy, renal tumors and children’s kidneys, but no such tool exists that analyses the impact of immune microenvironment in renal transplantation rejection.

Methods

To fill this gap, we have developed a web page analysis tool called Comprehensive Analysis of Renal Allograft Rerejction in Immune Microenvironment (CARARIME).

Results

CARARIME analyzes the gene expression and immune microenvironment of published renal transplant rejection cohorts, including differential analysis (gene expression and immune cells), prognosis analysis (logistics regression, Univariable Cox Regression and Kaplan Meier), correlation analysis, enrichment analysis (GSEA and ssGSEA), and ROC analysis.

Conclusions

Using this tool, researchers can easily analyze the immune microenvironment in the context of renal transplant rejection by clicking on the available options, helping to further the development of approaches to renal transplant rejection in the immune microenvironment field. CARARIME can be found in http://www.cararime.com.

Anti-interleukin-6 Antibody Clazakizumab in Antibody-mediated Kidney Transplant Rejection: Effect on Donor-derived Cell-free DNA and C-X-C Motif Chemokine Ligand 10

Targeting interleukin-6 (IL-6) was shown to counteract donor-specific antibody production and antibody-mediated rejection (AMR) activity. It is not known whether, or to what extent, IL-6 antagonism modulates biomarkers indicative of tissue damage (donor-derived cell-free DNA [dd-cfDNA]) and parenchymal inflammation (C-X-C motif chemokine ligand [CXCL] 10).

METHODS

We report a secondary endpoint analysis of a phase 2 trial of anti-IL-6 antibody clazakizumab in late AMR (ClinicalTrials.gov, NCT03444103). Twenty kidney transplant recipients were randomized to treatment with clazakizumab or placebo over 12 wk (part A), followed by an extension in which all recipients received clazakizumab through week 52 (part B). Biomarkers were evaluated at day 0 and after 12 and 52 wk, respectively.

RESULTS

Fractional dd-cfDNA (dd-cfDNA[%]) did not significantly change under clazakizumab, with no differences between study arms (clazakizumab versus placebo) at week 12 (1.65% [median; interquartile range: 0.91%-2.78%] versus 0.97% [0.56%-2.30%]; P = 0.25) and no significant decrease from weeks 12 to 52 (1.15% [0.70%-2.38%] versus 1.0% [0.61%-1.70%]; P = 0.25). Similarly, urine CXCL10 was not different between groups at week 12 (55.7 [41.0-91.4] versus 60.2 [48.8-208.7.0] pg/mg creatinine; P = 0.44) and did not change over part B (CXCL10 [pg/mg creatinine]: from 58 [46.3-93.1] to 67.4 [41.5-132.0] pg/mL creatinine; P = 0.95). Similar results were obtained for serum CXCL10. There was no association between biomarker levels and resolution of molecular and morphologic AMR activity.

CONCLUSIONS

Our results suggest that IL-6 blockade does not significantly affect levels of dd-cfDNA[%] and CXCL10. Subtle responses to this therapeutic principle may be overlooked by early biomarker surveillance.

Assessing the Relationship Between Molecular Rejection and Parenchymal Injury in Heart Transplant Biopsies

BACKGROUND

The INTERHEART study (ClinicalTrials.gov #NCT02670408) used genome-wide microarrays to detect rejection in endomyocardial biopsies; however, many heart transplants with no rejection have late dysfunction and impaired survival. We used the microarray measurements to develop a molecular classification of parenchymal injury.

METHODS

In 1320 endomyocardial biopsies from 645 patients previously studied for rejection-associated transcripts, we measured the expression of 10 injury-induced transcript sets: 5 induced by recent injury; 2 reflecting macrophage infiltration; 2 normal heart transcript sets; and immunoglobulin transcripts, which correlate with time. We used archetypal clustering to assign injury groups.

RESULTS

Injury transcript sets correlated with impaired function. Archetypal clustering based on the expression of injury transcript sets assigned each biopsy to 1 of 5 injury groups: 87 Severe-injury, 221 Late-injury, and 3 with lesser degrees of injury, 376 No-injury, 526 Mild-injury, and 110 Moderate-injury. Severe-injury had extensive loss of normal transcripts (dedifferentiation) and increase in macrophage and injury-induced transcripts. Late-injury was characterized by high immunoglobulin transcript expression. In Severe- and Late-injury, function was depressed, and short-term graft failure was increased, even in hearts with no rejection. T cell-mediated rejection almost always had parenchymal injury, and 85% had Severe- or Late-injury. In contrast, early antibody-mediated rejection (ABMR) had little injury, but late ABMR often had the Late-injury state.

CONCLUSION

Characterizing heart transplants for their injury state provides new understanding of dysfunction and outcomes and demonstrates the differential impact of T cell-mediated rejection versus ABMR on the parenchyma. Slow deterioration from ABMR emerges as a major contributor to late dysfunction.

Molecular diagnosis of ABMR with or without donor-specific antibody in kidney transplant biopsies: Differences in timing and intensity but similar mechanisms and outcomes

We studied the clinical, histologic, and molecular features distinguishing DSA-negative from DSA-positive molecularly defined antibody-mediated rejection (mABMR). We analyzed mABMR biopsies with available DSA assessments from the INTERCOMEX study: 148 DSA-negative versus 248 DSA-positive, compared with 864 no rejection (excluding TCMR and Mixed). DSA-positivity varied with mABMR stage: early-stage (EABMR) 56%; fully developed (FABMR) 70%; and late-stage (LABMR) 58%. DSA-negative patients with mABMR were usually sensitized, 60% being HLA antibody-positive. Compared with DSA-positive mABMR, DSA-negative mABMR was more often C4d-negative; earlier by 1.5 years (average 2.4 vs. 3.9 years); and had lower ABMR activity and earlier stage in molecular and histology features. However, the top ABMR-associated transcripts were identical in DSA-negative versus DSA-positive mABMR, for example, NK-associated (e.g., KLRD1 and GZMB) and IFNG-inducible (e.g., PLA1A). Genome-wide class comparison between DSA-negative and DSA-positive mABMR showed no significant differences in transcript expression except those related to lower intensity and earlier time of DSA-negative ABMR. Three-year graft loss in DSA-negative mABMR was the same as DSA-positive mABMR, even after adjusting for ABMR stage. Thus, compared with DSA-positive mABMR, DSA-negative mABMR is on average earlier, less active, and more often C4d-negative but has similar graft loss, and genome-wide analysis suggests that it involves the same mechanisms. SUMMARY SENTENCE: In 398 kidney transplant biopsies with molecular antibody-mediated rejection, the 150 DSA-negative cases are earlier, less intense, and mostly C4d-negative, but use identical molecular mechanisms and have the same risk of graft loss as the 248 DSA-positive cases.

Identifying RBBP7 as a Promising Diagnostic Biomarker for BK Virus-Associated Nephropathy

BK virus-associated nephropathy (BKVN) remains a major infectious complication due to powerful immunosuppression in kidney transplant recipients, and its histologic appearance can mimic rejection, leading to diagnostic and treatment dilemmas thus molecular diagnostic methods would be beneficial. We collected gene expression profiles of 169 kidney biopsies taken from BKVN, rejection, and stable functioning allografts, based on single sample gene set enrichment analysis and random forest algorithm, and three hallmark activities associated with DNA damage and proliferation were found to be relatively specific in BKVN. Subsequently, weighted gene co-expression network analysis and support vector machines (SVM) algorithm identified RBBP7 as a robust and promising biomarker with high accuracy in both training and validation cohorts (AUC =0.938, 0.977, respectively). Besides, potential drugs for BKVN treatment such as mepacrine were discovered, which may contribute to targeted antiviral therapy and effective patient management rather than simply reducing the doses of immunosuppressive agents in clinical practice. RBBP7 (retinoblastoma binding protein 7) serves as component of serval complexes that regulate chromatin metabolism and functions in affecting DNA replication and controlling cell proliferation. In this research, upregulation of RBBP7 was found to be associated with the higher infiltration of CD8 naïve T, iTreg, and neutrophil cells and the lower amounts of Th1, central memory T, NKT, CD8 T, and dendritic cells. Moreover, the infiltration of Th1, Th17, and NKT cells was steadily different between BKVN and rejection allografts through immune cell assessment. In conclusion, we identified and verified RBBP7 as a molecular biomarker for BKVN diagnosis, which demonstrated great distinguishing ability with allograft rejection and would support clinical decision-making.

The promise of artificial intelligence for kidney pathophysiology

PURPOSE OF REVIEW

We seek to determine recent advances in kidney pathophysiology that have been enabled or enhanced by artificial intelligence. We describe some of the challenges in the field as well as future directions.

RECENT FINDINGS

We first provide an overview of artificial intelligence terminologies and methodologies. We then describe the use of artificial intelligence in kidney diseases to discover risk factors from clinical data for disease progression, annotate whole slide imaging and decipher multiomics data. We delineate key examples of risk stratification and prognostication in acute kidney injury (AKI) and chronic kidney disease (CKD). We contextualize these applications in kidney disease oncology, one of the subfields to benefit demonstrably from artificial intelligence using all if these approaches. We conclude by elucidating technical challenges and ethical considerations and briefly considering future directions.

SUMMARY

The integration of clinical data, patient derived data, histology and proteomics and genomics can enhance the work of clinicians in providing more accurate diagnoses and elevating understanding of disease progression. Implementation research needs to be performed to translate these algorithms to the clinical setting.

T Cells With Activated STAT4 Drive the High-Risk Rejection State to Renal Allograft Failure After Kidney Transplantation

In kidney transplantation, deteriorated progression of rejection is considered to be a leading course of postoperative mortality. However, the conventional histologic diagnosis is limited in reading the rejection status at the molecular level, thereby triggering mismatched pathogenesis with clinical phenotypes. Here, by applying uniform manifold approximation and projection and Leiden algorithms to 2,611 publicly available microarray datasets of renal transplantation, we uncovered six rejection states with corresponding signature genes and revealed a high-risk (HR) state that was essential in promoting allograft loss. By identifying cell populations from single-cell RNA sequencing data that were associated with the six rejection states, we identified a T-cell population to be the pathogenesis-triggering cells associated with the HR rejection state. Additionally, by constructing gene regulatory networks, we identified that activated STAT4, as a core transcription factor that was regulated by PTPN6 in T cells, was closely linked to poor allograft function and prognosis. Taken together, our study provides a novel strategy to help with the precise diagnosis of kidney allograft rejection progression, which is powerful in investigating the underlying molecular pathogenesis, and therefore, for further clinical intervention.

Proposed Definitions of Antibody-Mediated Rejection for Use as a Clinical Trial Endpoint in Kidney Transplantation

Antibody-mediated rejection (AMR) is caused by antibodies that recognize donor human leukocyte antigen (HLA) or other targets. As knowledge of AMR pathophysiology has increased, a combination of factors is necessary to confirm the diagnosis and phenotype. However, frequent modifications to the AMR definition have made it difficult to compare data and evaluate associations between AMR and graft outcome. The present paper was developed following a Broad Scientific Advice request from the European Society for Organ Transplantation (ESOT) to the European Medicines Agency (EMA), which explored whether updating guidelines on clinical trial endpoints would encourage innovations in kidney transplantation research. ESOT considers that an AMR diagnosis must be based on a combination of histopathological factors and presence of donor-specific HLA antibodies in the recipient. Evidence for associations between individual features of AMR and impaired graft outcome is noted for microvascular inflammation scores ≥2 and glomerular basement membrane splitting of >10% of the entire tuft in the most severely affected glomerulus. Together, these should form the basis for AMR-related endpoints in clinical trials of kidney transplantation, although modifications and restrictions to the Banff diagnostic definition of AMR are proposed for this purpose. The EMA provided recommendations based on this Broad Scientific Advice request in December 2020; further discussion, and consensus on the restricted definition of the AMR endpoint, is required.