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

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

Introduction

A decentralized and multi-platform-compatible molecular diagnostic tool for kidney transplant biopsies could improve the dissemination and exploitation of this technology, increasing its clinical impact. As a first step towards this molecular diagnostic tool, we developed and validated a classifier using the genes of the Banff-Human Organ Transplant (B-HOT) panel extracted from a historical Molecular Microscope^® Diagnostic system microarray dataset. Furthermore, we evaluated the discriminative power of the B-HOT panel in a clinical scenario.

Materials and Methods

Gene expression data from 1,181 kidney transplant biopsies were used as training data for three random forest models to predict kidney transplant biopsy Banff categories, including non-rejection (NR), antibody-mediated rejection (ABMR), and T-cell-mediated rejection (TCMR). Performance was evaluated using nested cross-validation. The three models used different sets of input features: the first model (B-HOT Model) was trained on only the genes included in the B-HOT panel, the second model (Feature Selection Model) was based on sequential forward feature selection from all available genes, and the third model (B-HOT+ Model) was based on the combination of the two models, i.e. B-HOT panel genes plus highly predictive genes from the sequential forward feature selection. After performance assessment on cross-validation, the best-performing model was validated on an external independent dataset based on a different microarray version.

Results

The best performances were achieved by the B-HOT+ Model, a multilabel random forest model trained on B-HOT panel genes with the addition of the 6 most predictive genes of the Feature Selection Model (ST7, KLRC4-KLRK1, TRBC1, TRBV6-5, TRBV19, and ZFX), with a mean accuracy of 92.1% during cross-validation. On the validation set, the same model achieved Area Under the ROC Curve (AUC) of 0.965 and 0.982 for NR and ABMR respectively.

Discussion

This kidney transplant biopsy classifier is one step closer to the development of a decentralized kidney transplant biopsy classifier that is effective on data derived from different gene expression platforms. The B-HOT panel proved to be a reliable highly-predictive panel for kidney transplant rejection classification. Furthermore, we propose to include the aforementioned 6 genes in the B-HOT panel for further optimization of this commercially available panel.

Biological Characteristics and Predictive Model of Biopsy-Proven Acute Rejection (BPAR) After Kidney Transplantation: Evidences of Multi-Omics Analysis

Objectives: Early diagnosis and detection of acute rejection following kidney transplantation are of great significance for guiding the treatment and improving the prognosis of renal transplant recipients. In this study, we are aimed to explore the biological characteristics of biopsy-proven acute rejection (BPAR) and establish a predictive model.

Methods: Gene expression matrix of the renal allograft samples in the GEO database were screened and included, using Limma R package to identify differentially expressed transcripts between BPAR and No-BPAR groups. Then a predictive model of BPAR was established based on logistic regression of which key transcripts involved in the predictive model were further explored using functional enrichment analyses including Gene Ontology analysis (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and Gene Set Enrichment Analysis (GSEA).

Results: A total of four studies (GSE129166, GSE48581, GSE36059, and GSE98320) were included for extensive analysis of differential expression. 32 differential expressed transcripts were observed to be significant between two groups after the pooled analysis. Afterward, a predictive model containing the five most significant transcripts (IDO1, CXCL10, IFNG, GBP1, PMAIP1) showed good predictive efficacy for BPAR after kidney transplantation (AUC = 0.919, 95%CI = 0.902–0.939). Results of functional enrichment analysis showed that The functions of differential genes are mainly manifested in chemokine receptor binding, chemokine activity, G protein-coupled receptor binding, etc. while the immune infiltration analysis indicated that immune cells mainly related to acute rejection include Macrophages. M1, T cells gamma delta, T cells CD4 memory activated, eosinophils, etc.

Conclusion: We have identified a total of 32 differential expressed transcripts and based on that, a predictive model with five significant transcripts was established, which was suggested as a highly recommended tool for the prediction of BPAR after kidney transplantation. However, an extensive study should be performed for the evaluation of the predictive model and mechanism involved.

Archetypal Analysis of Injury in Kidney Transplant Biopsies Identifies Two Classes of Early AKI

All transplanted kidneys are subjected to some degree of injury as a result of the donation-implantation process and various post-transplant stresses such as rejection. Because transplants are frequently biopsied, they present an opportunity to explore the full spectrum of kidney response-to-wounding from all causes. Defining parenchymal damage in transplanted organs is important for clinical management because it determines function and survival. In this study, we classified the scenarios associated with parenchymal injury in genome-wide microarray results from 1,526 kidney transplant indication biopsies collected during the INTERCOMEX study. We defined injury groups by using archetypal analysis (AA) of scores for gene sets and classifiers previously identified in various injury states. Six groups and their characteristics were defined in this population: No injury, minor injury, two classes of acute kidney injury ("AKI," AKI1, and AKI2), chronic kidney disease (CKD), and CKD combined with AKI. We compared the two classes of AKI, namely, AKI1 and AKI2. AKI1 had a poor function and increased parenchymal dedifferentiation but minimal response-to-injury and inflammation, instead having increased expression of PARD3, a gene previously characterized as being related to epithelial polarity and adherens junctions. In contrast, AKI2 had a poor function and increased response-to-injury, significant inflammation, and increased macrophage activity. In random forest analysis, the most important predictors of function (estimated glomerular filtration rate) and graft loss were injury-based molecular scores, not rejection scores. AKI1 and AKI2 differed in 3-year graft survival, with better survival in the AKI2 group. Thus, injury archetype analysis of injury-induced gene expression shows new heterogeneity in kidney response-to-wounding, revealing AKI1, a class of early transplants with a poor function but minimal inflammation or response to injury, a deviant response characterized as PC3, and an increased risk of failure. Given the relationship between parenchymal injury and kidney survival, further characterization of the injury phenotypes in kidney transplants will be important for an improved understanding that could have implications for understanding native kidney diseases (ClinicalTrials.gov #NCT01299168).

Safety, tolerability, and efficacy of monoclonal CD38 antibody felzartamab in late antibody-mediated renal allograft rejection: study protocol for a phase 2 trial

BACKGROUND

Antibody-mediated rejection (ABMR) is a cardinal cause of renal allograft loss. This rejection type, which may occur at any time after transplantation, commonly presents as a continuum of microvascular inflammation (MVI) culminating in chronic tissue injury. While the clinical relevance of ABMR is well recognized, its treatment, particularly a long time after transplantation, has remained a big challenge. A promising strategy to counteract ABMR may be the use of CD38-directed treatment to deplete alloantibody-producing plasma cells (PC) and natural killer (NK) cells.

METHODS

This investigator-initiated trial is planned as a randomized, placebo-controlled, double-blind, parallel-group, multi-center phase 2 trial designed to assess the safety and tolerability (primary endpoint), pharmacokinetics, immunogenicity, and efficacy of the fully human CD38 monoclonal antibody felzartamab (MOR202) in late ABMR. The trial will include 20 anti-HLA donor-specific antibody (DSA)-positive renal allograft recipients diagnosed with active or chronic active ABMR ≥ 180 days post-transplantation. Subjects will be randomized 1:1 to receive felzartamab (16 mg/kg per infusion) or placebo for a period of 6 months (intravenous administration on day 0, and after 1, 2, 3, 4, 8, 12, 16, and 20 weeks). Two follow-up allograft biopsies will be performed at weeks 24 and 52. Secondary endpoints (preliminary assessment) will include morphologic and molecular rejection activity in renal biopsies, immunologic biomarkers in the blood and urine, and surrogate parameters predicting the progression to allograft failure (slope of renal function; iBOX prediction score).

DISCUSSION

Based on the hypothesis that felzartamab is able to halt the progression of ABMR via targeting antibody-producing PC and NK cells, we believe that our trial could potentially provide the first proof of concept of a new treatment in ABMR based on a prospective randomized clinical trial.

TRIAL REGISTRATION

EU Clinical Trials Register (EudraCT) 2021-000545-40 . Registered on 23 June 2021.

CLINICALTRIALS

gov NCT05021484 . Registered on 25 August 2021.

Transcripts associated with chronic lung allograft dysfunction in transbronchial biopsies of lung transplants

Transplanted lungs suffer worse outcomes than other organ transplants with many developing chronic lung allograft dysfunction (CLAD), diagnosed by physiologic changes. Histology of transbronchial biopsies (TBB) yields little insight, and the molecular basis of CLAD is not defined. We hypothesized that gene expression in TBBs would reveal the nature of CLAD and distinguish CLAD from changes due simply to time posttransplant. Whole-genome mRNA profiling was performed with microarrays in 498 prospectively collected TBBs from the INTERLUNG study, 90 diagnosed as CLAD. Time was associated with increased expression of inflammation genes, for example, CD1E and immunoglobulins. After correcting for time, CLAD manifested not as inflammation but as parenchymal response-to-wounding, with increased expression of genes such as HIF1A, SERPINE2, and IGF1 that are increased in many injury and disease states and cancers, associated with development, angiogenesis, and epithelial response-to-wounding in pathway analysis. Fibrillar collagen genes were increased in CLAD, indicating matrix changes, and normal transcripts were decreased-dedifferentiation. Gene-based classifiers predicted CLAD with AUC 0.70 (no time-correction) and 0.87 (time-corrected). CLAD related gene sets and classifiers were strongly prognostic for graft failure and correlated with CLAD stage. Thus, in TBBs, molecular changes indicate that CLAD primarily reflects severe parenchymal injury-induced changes and dedifferentiation.

Diagnostic Biomarkers and Immune Infiltration in Patients With T Cell-Mediated Rejection After Kidney Transplantation

T cell-mediated rejection (TCMR) is an important rejection type in kidney transplantation, characterized by T cells and macrophages infiltration. The application of bioinformatic analysis in genomic research has been widely used. In the present study, Microarray data was analyzed to identify the potential diagnostic markers of TCMR in kidney transplantation. Cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) was performed to determine the distribution of immune cell infiltration in the pathology. Totally 129 upregulated differently expressed genes (DEGs) and 378 downregulated DEGs were identified. The GO and KEGG results demonstrated that DEGs were mainly associated with pathways and diseases involved in immune response. The intersection of the two algorithms (PPI network and LASSO) contains three overlapping genes (CXCR6, CXCL13 and FCGR1A). After verification in GSE69677, only CXCR6 and CXCL13 were selected. Immune cells Infiltration analysis demonstrated that CXCR6 and CXCL13 were positively correlated with gamma delta T cells (p < 0.001), CD4⁺ memory activated T cells (p < 0.001), CD8⁺ T cells (p < 0.001) and M1 macrophages (p = 0.006), and negatively correlated with M2 macrophages (p < 0.001) and regulatory T cells (p < 0.001). Immunohistochemical staining and image analysis confirmed the overexpression of CXCR6 and CXCL13 in human allograft TCMR samples. CXCR6 and CXCL13 could be diagnostic biomarkers of TCMR and potential targets for immunotherapy in patients with TCMR.

The molecular phenotypes of injury, steatohepatitis, and fibrosis in liver transplant biopsies in the INTERLIVER study

To extend previous molecular analyses of rejection in liver transplant biopsies in the INTERLIVER study (ClinicalTrials.gov #NCT03193151), the present study aimed to define the gene expression selective for parenchymal injury, fibrosis, and steatohepatitis. We analyzed genome-wide microarray measurements from 337 liver transplant biopsies from 13 centers. We examined expression of genes previously annotated as increased in injury and fibrosis using principal component analysis (PCA). PC1 reflected parenchymal injury and related inflammation in the early posttransplant period, slowly regressing over many months. PC2 separated early injury from late fibrosis. Positive PC3 identified a distinct mildly inflamed state correlating with histologic steatohepatitis. Injury PCs correlated with liver function and histologic abnormalities. A classifier trained on histologic steatohepatitis predicted histologic steatohepatitis with cross-validated AUC = 0.83, and was associated with pathways reflecting metabolic abnormalities distinct from fibrosis. PC2 predicted histologic fibrosis (AUC = 0.80), as did a molecular fibrosis classifier (AUC = 0.74). The fibrosis classifier correlated with matrix remodeling pathways with minimal overlap with those selective for steatohepatitis, although some biopsies had both. Genome-wide assessment of liver transplant biopsies can not only detect molecular changes induced by rejection but also those correlating with parenchymal injury, steatohepatitis, and fibrosis, offering potential insights into disease mechanisms for primary diseases.

The Trifecta Study: Comparing Plasma Levels of Donor-derived Cell-Free DNA with the Molecular Phenotype of Kidney Transplant Biopsies

BACKGROUND

The relationship between the donor-derived cell-free DNA fraction (dd-cfDNA[%]) in plasma in kidney transplant recipients at time of indication biopsy and gene expression in the biopsied allograft has not been defined.

METHODS

In the prospective, multicenter Trifecta study, we collected tissue from 300 biopsies from 289 kidney transplant recipients to compare genome-wide gene expression in biopsies with dd-cfDNA(%) in corresponding plasma samples drawn just before biopsy. Rejection was assessed with the microarray-based Molecular Microscope Diagnostic System using automatically assigned rejection archetypes and molecular report sign-outs, and histology assessments that followed Banff guidelines.

RESULTS

The median time of biopsy post-transplantation was 455 days (5 days to 32 years), with a case mix similar to that of previous studies: 180 (60%) no rejection, 89 (30%) antibody-mediated rejection (ABMR), and 31 (10%) T cell-mediated rejection (TCMR) and mixed. In genome-wide mRNA measurements, all 20 top probe sets correlating with dd-cfDNA(%) were previously annotated for association with ABMR and all types of rejection, either natural killer (NK) cell-expressed (e.g., GNLY, CCL4, TRDC, and S1PR5) or IFN-γ-inducible (e.g., PLA1A, IDO1, CXCL11, and WARS). Among gene set and classifier scores, dd-cfDNA(%) correlated very strongly with ABMR and all types of rejection, reasonably strongly with active TCMR, and weakly with inactive TCMR, kidney injury, and atrophy fibrosis. Active ABMR, mixed, and active TCMR had the highest dd-cfDNA(%), whereas dd-cfDNA(%) was lower in late-stage ABMR and less-active TCMR. By multivariate random forests and logistic regression, molecular rejection variables predicted dd-cfDNA(%) better than histologic variables.

CONCLUSIONS

The dd-cfDNA(%) at time of indication biopsy strongly correlates with active molecular rejection and has the potential to reduce unnecessary biopsies.

CLINICAL TRIAL REGISTRATION NUMBER

NCT04239703.

The Biology and Molecular Basis of Organ Transplant Rejection

Allograft rejection is defined as tissue injury in a transplanted allogeneic organ produced by the effector mechanisms of the adaptive alloimmune response. Effector T lymphocytes and IgG alloantibodies cause two different types of rejection that can occur either individually or simultaneously: T cell-mediated rejection (TCMR) and antibody-mediated rejection (ABMR). In TCMR, cognate effector T cells infiltrate the graft and orchestrate an interstitial inflammatory response in the kidney interstitium in which effector T cells engage antigen-presenting myeloid cells, activating the T cells, antigen-presenting cells, and macrophages. The result is intense expression of IFNG and IFNG-induced molecules, expression of effector T cell molecules and macrophage molecules and checkpoints, and deterioration of parenchymal function. The diagnostic lesions of TCMR follow, i.e. interstitial inflammation, parenchymal deterioration, and intimal arteritis. In ABMR, HLA IgG alloantibodies produced by plasma cells bind to the donor antigens on graft microcirculation, leading to complement activation, margination, and activation of NK cells and neutrophils and monocytes, and endothelial injury, sometimes with intimal arteritis. TCMR becomes infrequent after 5-10 years post-transplant, probably reflecting adaptive mechanisms such as checkpoints, but ABMR can present even decades post-transplant. Some rejection is triggered by inadequate immunosuppression and non-adherence, challenging the clinician to target effective immunosuppression even decades post-transplant.

Allorecognition and the spectrum of kidney transplant rejection

Detection of mismatched human leukocyte antigens by adaptive immune cells is considered as the main cause of transplant rejection, leading to either T-cell mediated rejection or antibody-mediated rejection. This canonical view guided the successful development of immunosuppressive therapies and shaped the diagnostic Banff classification for kidney transplant rejection that is used in clinics worldwide. However, several observations have recently emerged that question this dichotomization between T-cell mediated rejection and antibody-mediated rejection, related to heterogeneity in the serology, histology, and prognosis of the rejection phenotypes. In parallel, novel insights were obtained concerning the dynamics of donor-specific anti-human leukocyte antigen antibodies, the immunogenicity of donor-recipient non-human leukocyte antigen mismatches, and the autoreactivity against self-antigens. Moreover, the potential of innate allorecognition was uncovered, as exemplified by natural killer cell-mediated microvascular inflammation through missing self, and by the emerging evidence on monocyte-driven allorecognition. In this review, we highlight the gaps in the current classification of rejection, provide an overview of the expanding insights into the mechanisms of allorecognition, and critically appraise how these could improve our understanding and clinical approach to kidney transplant rejection. We argue that consideration of the complex interplay of various allorecognition mechanisms can foster a more integrated view of kidney transplant rejection and can lead to improved risk stratification, targeted therapies, and better outcome after kidney transplantation.

Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study

BACKGROUND

Histopathological assessment of transplant biopsies is currently the standard method to diagnose allograft rejection and can help guide patient management, but it is one of the most challenging areas of pathology, requiring considerable expertise, time, and effort. We aimed to analyse the utility of deep learning to preclassify histology of kidney allograft biopsies into three main broad categories (ie, normal, rejection, and other diseases) as a potential biopsy triage system focusing on transplant rejection.

METHODS

We performed a retrospective, multicentre, proof-of-concept study using 5844 digital whole slide images of kidney allograft biopsies from 1948 patients. Kidney allograft biopsy samples were identified by a database search in the Departments of Pathology of the Amsterdam UMC, Amsterdam, Netherlands (1130 patients) and the University Medical Center Utrecht, Utrecht, Netherlands (717 patients). 101 consecutive kidney transplant biopsies were identified in the archive of the Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany. Convolutional neural networks (CNNs) were trained to classify allograft biopsies as normal, rejection, or other diseases. Three times cross-validation (1847 patients) and deployment on an external real-world cohort (101 patients) were used for validation. Area under the receiver operating characteristic curve (AUROC) was used as the main performance metric (the primary endpoint to assess CNN performance).

FINDINGS

Serial CNNs, first classifying kidney allograft biopsies as normal (AUROC 0·87 [ten times bootstrapped CI 0·85-0·88]) and disease (0·87 [0·86-0·88]), followed by a second CNN classifying biopsies classified as disease into rejection (0·75 [0·73-0·76]) and other diseases (0·75 [0·72-0·77]), showed similar AUROC in cross-validation and deployment on independent real-world data (first CNN normal AUROC 0·83 [0·80-0·85], disease 0·83 [0·73-0·91]; second CNN rejection 0·61 [0·51-0·70], other diseases 0·61 [0·50-0·74]). A single CNN classifying biopsies as normal, rejection, or other diseases showed similar performance in cross-validation (normal AUROC 0·80 [0·73-0·84], rejection 0·76 [0·66-0·80], other diseases 0·50 [0·36-0·57]) and generalised well for normal and rejection classes in the real-world data. Visualisation techniques highlighted rejection-relevant areas of biopsies in the tubulointerstitium.

INTERPRETATION

This study showed that deep learning-based classification of transplant biopsies could support pathological diagnostics of kidney allograft rejection.

FUNDING

European Research Council; German Research Foundation; German Federal Ministries of Education and Research, Health, and Economic Affairs and Energy; Dutch Kidney Foundation; Human(e) AI Research Priority Area of the University of Amsterdam; and Max-Eder Programme of German Cancer Aid.

Incorporation of Donor-derived Cell-free DNA Into Clinical Practice for Renal Allograft Management

BACKGROUND

Donor-derived cell-free DNA (dd-cfDNA) in plasma is an established noninvasive biomarker for allograft injury and rejection. A single-nucleotide polymorphism (SNP)-based massively multiplexed polymerase chain reaction methodology can be used to quantify dd-cfDNA in kidney transplant recipients. In this study we describe our clinical experience in using a SNP-based dd-cfDNA assay for the management of active rejection in renal transplant recipients.

METHODS

To assess the clinical utility of a clinically available SNP-based massively multiplexed polymerase chain reaction dd-cfDNA assay, we analyzed biopsy data contemporaneous to dd-cfDNA results at 33 participating clinics and calculated the rate of rejection in dd-cfDNA-matched biopsy results.

RESULTS

A total of 1347 dd-cfDNA test samples from 879 patients were accessioned from October 3, 2019, to November 2, 2020. The dd-cfDNA testing classified 25.2% (340/1347) of samples as high-risk (dd-cfDNA fraction ≥ 1%). Clinical follow-up was available for 32.1% (109/340) of the high-risk results, which included samples from 28 patients with definitive biopsy results within 2 weeks of dd-cfDNA testing. Pathology reports indicated a 64% (18/28) rate of active rejection in biopsy result-matched samples. Total cfDNA measurements indicated a skewed distribution and a correlation with dd-cfDNA-derived patient risk classification.

CONCLUSIONS

This is the first report showing the impact of dd-cfDNA on patient management in a multicenter real-world clinical cohort. The data indicate that incorporating dd-cfDNA testing into practice may improve physician decision making regarding renal allograft recipients.

Donor-Specific Antibody Is Associated with Increased Expression of Rejection Transcripts in Renal Transplant Biopsies Classified as No Rejection

BACKGROUND

Donor -specific HLA antibody (DSA) is present in many kidney transplant patients whose biopsies are classified as no rejection (NR). We explored whether in some NR kidneys DSA has subtle effects not currently being recognized.

METHODS

We used microarrays to examine the relationship between standard-of-care DSA and rejection-related transcript increases in 1679 kidney transplant indication biopsies in the INTERCOMEX study (ClinicalTrials.gov NCT01299168), focusing on biopsies classified as NR by automatically assigned archetypal clustering. DSA testing results were available for 835 NR biopsies and were positive in 271 (32%).

RESULTS

DSA positivity in NR biopsies was associated with mildly increased expression of antibody-mediated rejection (ABMR)-related transcripts, particularly IFNG-inducible and NK cell transcripts. We developed a machine learning DSA probability (DSA_Prob) classifier based on transcript expression in biopsies from DSA-positive versus DSA-negative patients, assigning scores using 10-fold cross-validation. This DSA_Prob classifier was very similar to a previously described "ABMR probability" classifier trained on histologic ABMR in transcript associations and prediction of molecular or histologic ABMR. Plotting the biopsies using Uniform Manifold Approximation and Projection revealed a gradient of increasing molecular ABMR-like transcript expression in NR biopsies, associated with increased DSA (P<2 × 10^-16). In biopsies with no molecular or histologic rejection, increased DSA_Prob or ABMR probability scores were associated with increased risk of kidney failure over 3 years.

CONCLUSIONS

Many biopsies currently considered to have no molecular or histologic rejection have mild increases in expression of ABMR-related transcripts, associated with increasing frequency of DSA. Thus, mild molecular ABMR-related pathology is more common than previously realized.

Machine Learning Applications in Solid Organ Transplantation and Related Complications

The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning promises to inform clinical decision making by deciphering prodigious amounts of available data. This paper reviews current research describing how algorithms have the potential to augment clinical practice in solid organ transplantation. We provide a general introduction to different machine learning techniques, describing their strengths, limitations, and barriers to clinical implementation. We summarize emerging evidence that recent advances that allow machine learning algorithms to predict acute post-surgical and long-term outcomes, classify biopsy and radiographic data, augment pharmacologic decision making, and accurately represent the complexity of host immune response. Yet, many of these applications exist in pre-clinical form only, supported primarily by evidence of single-center, retrospective studies. Prospective investigation of these technologies has the potential to unlock the potential of machine learning to augment solid organ transplantation clinical care and health care delivery systems.

Diagnostic value of donor-derived cell-free DNA to predict antibody-mediated rejection in donor-specific antibody-positive renal allograft recipients

Circulating donor‐specific antibodies (DSA) do not necessarily indicate antibody‐mediated rejection (ABMR). Here, we evaluated the diagnostic value of donor‐derived cell‐free DNA (dd‐cfDNA) as an add‐on to DSA detection. The study included two independent cohorts of DSA⁺ kidney allograft recipients, 45 subclinical cases identified by cross‐sectional antibody screening (cohort 1), and 30 recipients subjected to indication biopsies (cohort 2). About 50% of the DSA⁺ recipients had ABMR and displayed higher dd‐cfDNA levels than DSA⁺ABMR⁻ recipients (cohort 1: 1.90% [median; IQR: 0.78–3.90%] vs. 0.52% [0.35–0.72%]; P < 0.001); (cohort 2: 1.20% [0.82–2.50%] vs. 0.59% [0.28–2.05%]; P = 0.086). Receiver operating characteristic (ROC) analysis revealed an area under the curve (AUC) of 0.89 and 0.69 for dd‐cfDNA, and 0.88 and 0.77 for DSA mean fluorescence intensity (MFI), respectively. In combined models, adding dd‐cfDNA to DSA‐MFI or vice versa significantly improved the diagnostic accuracy. Limited diagnostic performance of dd‐cfDNA in cohort 2 was related to the frequent finding of other types of graft injury among ABMR⁻ recipients, like T cell‐mediated rejection or glomerulonephritis. For dd‐cfDNA in relation to injury of any cause an AUC of 0.97 was calculated. Monitoring of dd‐cfDNA in DSA⁺ patients may be a useful tool to detect ABMR and other types of injury.

Many heart transplant biopsies currently diagnosed as no rejection have mild molecular antibody-mediated rejection-related changes

BACKGROUND

The Molecular Microscope (MMDx) system classifies heart transplant endomyocardial biopsies as No-rejection (NR), Early-injury, T cell-mediated (TCMR), antibody-mediated (ABMR), mixed, and possible rejection (possible TCMR, possible ABMR). Rejection-like gene expression patterns in NR biopsies have not been described. We extended the MMDx methodology, using a larger data set, to define a new "Minor" category characterized by low-level inflammation in non-rejecting biopsies.

METHODS

Using MMDx criteria from a previous study, molecular rejection was assessed in 1,320 biopsies (645 patients) using microarray expression of rejection-associated transcripts (RATs). Of these biopsies, 819 were NR. A new archetypal analysis model in the 1,320 data set split the NRs into NR-Normal (N = 462) and NR-Minor (N = 359).

RESULTS

Compared to NR-Normal, NR-Minor were more often histologic TCMR1R, with a higher prevalence of donor-specific antibody (DSA). DSA positivity increased in a gradient: NR-Normal 24%; NR-Minor 34%; possible ABMR 42%; ABMR 66%. The top 20 transcripts distinguishing NR-Minor from NR-Normal were all ABMR-related and/or IFNG-inducible, and also exhibited a gradient of increasing expression from NR-Normal through ABMR. In random forest analysis, TCMR and Early-injury were associated with reduced LVEF and increased graft loss, but NR-Minor and ABMR scores were not. Surprisingly, hearts with MMDx ABMR showed comparatively little graft loss.

CONCLUSIONS

Many heart transplants currently diagnosed as NR by histologic or molecular assessment have minor increases in ABMR-related and IFNG-inducible transcripts, associated with DSA positivity and mild histologic inflammation. These results suggest that low-level ABMR-related molecular stress may be operating in many more hearts than previously estimated. (ClinicalTrials.gov #NCT02670408).

Data-Driven Kidney Transplant Phenotyping as a Histology-Independent Framework for Biomarker Discovery

BACKGROUND

In transplant medicine, clinical decision making largely relies on histology of biopsy specimens. However, histology suffers from low specificity, sensitivity, and reproducibility, leading to suboptimal stratification of patients. We developed a histology-independent immune framework of kidney graft homeostasis and rejection.

METHODS

We applied tailored RNA deconvolution for leukocyte enumeration and coregulated gene network analysis to published bulk human kidney transplant RNA transcriptomes as input for unsupervised, high-dimensional phenotype clustering. We used framework-based graft survival analysis to identify a biomarker that was subsequently characterized in independent transplant biopsy specimens.

RESULTS

We found seven immune phenotypes that confirm known rejection types and uncovered novel signatures. The molecular phenotypes allow for improved graft survival analysis compared with histology, and identify a high-risk group in nonrejecting transplants. Two fibrosis-related phenotypes with distinct immune features emerged with reduced graft survival. We identified lysyl oxidase-like 2 (LOXL2)-expressing peritubular CD68+ macrophages as a framework-derived biomarker of impaired allograft function. These cells precede graft fibrosis, as demonstrated in longitudinal biopsy specimens, and may be clinically useful as a biomarker for early fibrogenesis.

CONCLUSIONS

This study provides a comprehensive, data-driven atlas of human kidney transplant phenotypes and demonstrates its utility to identify novel clinical biomarkers.

The Future Role of Machine Learning in Clinical Transplantation

The use of artificial intelligence and machine learning (ML) has revolutionized our daily lives and will soon be instrumental in healthcare delivery. The rise of ML is due to multiple factors: increasing access to massive datasets, exponential increases in processing power, and key algorithmic developments that allow ML models to tackle increasingly challenging questions. Progressively more transplantation research is exploring the potential utility of ML models throughout the patient journey, although this has not yet widely transitioned into the clinical domain. In this review, we explore common approaches used in ML in solid organ clinical transplantation and consider opportunities for ML to help clinicians and patients. We discuss ways in which ML can aid leverage of large complex datasets, generate cutting-edge prediction models, perform clinical image analysis, discover novel markers in molecular data, and fuse datasets to generate novel insights in modern transplantation practice. We focus on key areas in transplantation in which ML is driving progress, explore the future potential roles of ML, and discuss the challenges and limitations of these powerful tools.

Change in Estimated GFR and Risk of Allograft Failure in Patients Diagnosed With Late Active Antibody-mediated Rejection Following Kidney Transplantation

BACKGROUND

There are challenges in designing adequate, well-controlled studies of patients with active antibody-mediated rejection (AMR) after kidney transplantation (KTx).

METHODS

We assessed the functional relationship between change in estimated glomerular filtration rate (eGFR) following the diagnosis of AMR and the risk of subsequent death-censored graft failure using the joint modeling framework. We included recipients of solitary KTx between 1995 and 2013 at 4 transplant centers diagnosed with biopsy-proven active AMR at least 1 year post-KTx, who had a minimum of 3-year follow-up.

RESULTS

A total of 91 patients across participating centers were included in the analysis. Of the 91 patients, n = 54 patients (59%) met the death-censored graft failure endpoint and n = 62 patients (68%) met the all-cause graft failure composite endpoint. Kaplan-Meier death-censored graft survival rates at 12, 36, and 60 months postdiagnosis of AMR pooled across centers were 88.9%, 58.9%, and 36.4%, respectively. Spaghetti plots indicated a linear trend in the change in eGFR, especially in the first 12 months postdiagnosis of active AMR. A significant change in eGFR was observed within the first 12 months postdiagnosis of active AMR, getting worse by a factor of -0.757 mL/min/1.73 m2 per month during the 12-month analysis period (a delta of -9.084 mL/min/1.73 m2 at 1 y). Notably, an extrapolated 30% improvement in the slope of eGFR in the first 12 months was associated with a 10% improvement in death-censored graft failure at 5 years.

CONCLUSIONS

If prospectively validated, this study may inform the design of pivotal clinical trials for therapies for late AMR.

Artificial intelligence and kidney transplantation

Artificial intelligence and its primary subfield, machine learning, have started to gain widespread use in medicine, including the field of kidney transplantation. We made a review of the literature that used artificial intelligence techniques in kidney transplantation. We located six main areas of kidney transplantation that artificial intelligence studies are focused on: Radiological evaluation of the allograft, pathological evaluation including molecular evaluation of the tissue, prediction of graft survival, optimizing the dose of immunosuppression, diagnosis of rejection, and prediction of early graft function. Machine learning techniques provide increased automation leading to faster evaluation and standardization, and show better performance compared to traditional statistical analysis. Artificial intelligence leads to improved computer-aided diagnostics and quantifiable personalized predictions that will improve personalized patient care.

Discovering novel injury features in kidney transplant biopsies associated with TCMR and donor aging

We previously characterized the molecular changes in acute kidney injury (AKI) and chronic kidney disease (CKD) in kidney transplant biopsies, but parenchymal changes selective for specific types of injury could be missed by such analyses. The present study searched for injury changes beyond AKI and CKD related to specific scenarios, including correlations with donor age. We defined injury using previously defined gene sets and classifiers and used principal component analysis to discover new injury dimensions. As expected, Dimension 1 distinguished normal vs. injury, and Dimension 2 separated early AKI from late CKD, correlating with time posttransplant. However, Dimension 3 was novel, distinguishing a set of genes related to epithelial polarity (e.g., PARD3) that were increased in early AKI and decreased in T cell-mediated rejection (TCMR) but not in antibody-mediated rejection. Dimension 3 was increased in kidneys from older donors and was particularly important in survival of early kidneys. Thus high Dimension 3 scores emerge as a previously unknown element in the kidney response-to-injury that affects epithelial polarity genes and is increased in AKI but depressed in TCMR, indicating that in addition to general injury elements, certain injury elements are selective for specific pathologic mechanisms. (ClinicalTrials.gov NCT01299168).

How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?

IgA nephropathy (IgAN) is the most common glomerulonephritis. It is characterized by the deposition of immune complexes containing immunoglobulin A (IgA) in the kidney’s glomeruli, triggering an inflammatory process. In many patients, the disease has a progressive course, eventually leading to end-stage kidney disease. The current understanding of IgAN’s pathophysiology is incomplete, with the involvement of several potential players, including the mucosal immune system, the complement system, and the microbiome. Dissecting this complex pathophysiology requires an integrated analysis across molecular, cellular, and organ scales. Such data can be obtained by employing emerging technologies, including single-cell sequencing, next-generation sequencing, proteomics, and complex imaging approaches. These techniques generate complex “big data,” requiring advanced computational methods for their analyses and interpretation. Here, we introduce such methods, focusing on the broad areas of bioinformatics and artificial intelligence and discuss how they can advance our understanding of IgAN and ultimately improve patient care. The close integration of advanced experimental and computational technologies with medical and clinical expertise is essential to improve our understanding of human diseases. We argue that IgAN is a paradigmatic disease to demonstrate the value of such a multidisciplinary approach.

Factors associated with kidney graft survival in pure antibody-mediated rejection at the time of indication biopsy: Importance of parenchymal injury but not disease activity

We studied the relative association of clinical, histologic, and molecular variables with risk of kidney transplant failure after an indication biopsy, both in all kidneys and in kidneys with pure antibody-mediated rejection (ABMR). From a prospective study of 1679 biopsies with histologic and molecular testing, we selected one random biopsy per patient (N = 1120), including 321 with pure molecular ABMR. Diagnoses were associated with actuarial survival differences but not good predictions. Therefore we concentrated on clinical (estimated GFR [eGFR], proteinuria, time posttransplant, donor-specific antibody [DSA]) and molecular and histologic features reflecting injury (acute kidney injury [AKI] and atrophy-fibrosis [chronic kidney disease (CKD)] and rejection. For all biopsies, univariate analysis found that failure was strongly associated with low eGFR, AKI, CKD, and glomerular deterioration, but not with rejection activity. In molecular ABMR, the findings were similar: Molecular and histologic activity and DSA were not important compared with injury. Survival in DSA-negative and DSA-positive molecular ABMR was similar. Multivariate survival analysis confirmed the dominance of molecular AKI, CKD, and eGFR. Thus, at indication biopsy, the dominant predictors of failure, both in all kidneys and in ABMR, were related to molecular AKI and CKD and to eGFR, not rejection activity, presumably because rejection confers risk via injury.

In-silico performance, validation, and modeling of the Nanostring Banff Human Organ transplant gene panel using archival data from human kidney transplants

BACKGROUND

RNA gene expression of renal transplantation biopsies is commonly used to identify the immunological patterns of graft rejection. Mostly done with microarrays, seminal findings defined the patterns of gene sets associated with rejection and non-rejection kidney allograft diagnoses. To make gene expression more accessible, the Molecular Diagnostics Working Group of the Banff Foundation for Allograft Pathology and NanoString Technologies partnered to create the Banff Human Organ Transplant Panel (BHOT), a gene panel set of 770 genes as a surrogate for microarrays (~ 50,000 genes). The advantage of this platform is that gene expressions are quantifiable on formalin fixed and paraffin embedded archival tissue samples, making gene expression analyses more accessible. The purpose of this report is to test in silico the utility of the BHOT panel as a surrogate for microarrays on archival microarray data and test the performance of the modelled BHOT data.

METHODS

BHOT genes as a subset of genes from downloaded archival public microarray data on human renal allograft gene expression were analyzed and modelled by a variety of statistical methods.

RESULTS

Three methods of parsing genes verify that the BHOT panel readily identifies renal rejection and non-rejection diagnoses using in silico statistical analyses of seminal archival databases. Multiple modelling algorithms show a highly variable pattern of misclassifications per sample, either between differently constructed principal components or between modelling algorithms. The misclassifications are related to the gene expression heterogeneity within a given diagnosis because clustering the data into 9 groups modelled with fewer misclassifications.

CONCLUSION

This report supports using the Banff Human Organ Transplant Panel for gene expression of human renal allografts as a surrogate for microarrays on archival tissue. The data modelled satisfactorily with aggregate diagnoses although with limited per sample accuracy and, thereby, reflects and confirms the modelling complexity and the challenges of modelling gene expression as previously reported.

Three-month course of intragraft transcriptional changes in kidney allografts with early histological minimal injury - a cohort study

The tubulitis with/without interstitial inflammation not meeting criteria for T-cell-mediated rejection (minimal allograft injury) is the most frequent histological findings in early transplant biopsies. The course of transcriptional changes in sequential kidney graft biopsies has not been studied yet. Molecular phenotypes were analyzed using the Molecular Microscope^® Diagnostic System (MMDx) in 46 indication biopsies (median 13 postoperative days) diagnosed as minimal allograft injury and in corresponding follow-up biopsies at 3 months. All 46 patients with minimal injury in early biopsy received steroid pulses. MMDx interpreted indication biopsies as no-rejection in 34/46 (74%), T-cell-mediated rejection (TCMR) in 4/46 (9%), antibody-mediated rejection in 6/46 (13%), and mixed rejection in 2/46 (4%) cases. Follow-up biopsies were interpreted by MMDx in 37/46 (80%) cases as no-rejection, in 4/46 (9%) as TCMR, and in 5/46 (11%) as mixed rejection. Follow-up biopsies showed a decrease in MMDx-assessed acute kidney injury (P = 0.001) and an increase of atrophy-fibrosis (P = 0.002). The most significant predictor of MMDx rejection scores in follow-up biopsies was the tubulitis classifier score in initial biopsies (AUC = 0.84, P = 0.002), confirmed in multivariate binary regression (OR = 16, P = 0.016). Molecular tubulitis score at initial biopsy has the potential to discriminate patients at risk for molecular rejection score at follow-up biopsy.

A Randomized Clinical Trial of Anti-IL-6 Antibody Clazakizumab in Late Antibody-Mediated Kidney Transplant Rejection

BACKGROUND

Late antibody-mediated rejection (ABMR) is a leading cause of transplant failure. Blocking IL-6 has been proposed as a promising therapeutic strategy.

METHODS

We performed a phase 2 randomized pilot trial to evaluate the safety (primary endpoint) and efficacy (secondary endpoint analysis) of the anti-IL-6 antibody clazakizumab in late ABMR. The trial included 20 kidney transplant recipients with donor-specific, antibody-positive ABMR ≥365 days post-transplantation. Patients were randomized 1:1 to receive 25 mg clazakizumab or placebo (4-weekly subcutaneous injections) for 12 weeks (part A), followed by a 40-week open-label extension (part B), during which time all participants received clazakizumab.

RESULTS

Five (25%) patients under active treatment developed serious infectious events, and two (10%) developed diverticular disease complications, leading to trial withdrawal. Those receiving clazakizumab displayed significantly decreased donor-specific antibodies and, on prolonged treatment, modulated rejection-related gene-expression patterns. In 18 patients, allograft biopsies after 51 weeks revealed a negative molecular ABMR score in seven (38.9%), disappearance of capillary C4d deposits in five (27.8%), and resolution of morphologic ABMR activity in four (22.2%). Although proteinuria remained stable, the mean eGFR decline during part A was slower with clazakizumab compared with placebo (-0.96; 95% confidence interval [95% CI], -1.96 to 0.03 versus -2.43; 95% CI, -3.40 to -1.46 ml/min per 1.73 m2 per month, respectively, P=0.04). During part B, the slope of eGFR decline for patients who were switched from placebo to clazakizumab improved and no longer differed significantly from patients initially allocated to clazakizumab.

CONCLUSIONS

Although safety data indicate the need for careful patient selection and monitoring, our preliminary efficacy results suggest a potentially beneficial effect of clazakizumab on ABMR activity and progression.

The Immune Cell Landscape in Renal Allografts

Immune cell infiltration plays an important role in the pathophysiology of kidney grafts, but the composition of immune cells is ill-defined. Here, we aimed at evaluating the levels and composition of infiltrating immune cells in kidney grafts. We used CIBERSORT, an established algorithm, to estimate the proportions of 22 immune cell types based on gene expression profiles. We found that non-rejecting kidney grafts were characteristic with high rates of M2 macrophages and resting mast cells. The proportion of M1 macrophages and activated NK cells were increased in antibody-mediated rejection (ABMR). In T cell-mediated rejection (TCMR), a significant increase in CD8 T cell and γδT cell infiltration was observed. CD8 positive T cells were dramatically increased in mixed-ABMR/TCMR. Then, the function of ABMR and TCMR prognostic molecular biomarkers were identified. Finally, we described the gene expression of molecular markers for ABMR diagnosis was elevated and related to the ratio of monocytes and M1 macrophages in ABMR biopsies, while the expression of TCMR diagnosis markers was increased too and positively correlated with γδT cells and activated CD4 memory T cells in TCMR biopsies. Our data suggest that CIBERSORT’s deconvolution analysis of gene expression data provides valuable information on the composition of immune cells in renal allografts.

CD38 Antibody Daratumumab for the Treatment of Chronic Active Antibody-mediated Kidney Allograft Rejection

BACKGROUND

Late antibody-mediated rejection (AMR) is a major cause of transplant failure. Potential therapeutic targets are plasma cells and natural killer (NK) cells, both expressing high levels of CD38.

METHODS

Here, we report the use of CD38 monoclonal antibody daratumumab (9-mo course) in a kidney allograft recipient diagnosed with smoldering myeloma and anti-HLA class II donor-specific antibody-positive chronic active AMR 13 years after transplantation. Patient monitoring included serial HLA single-antigen testing, peripheral blood immune cell phenotyping, as well as follow-up allograft and bone marrow biopsies at 3 and 9 months, including analyses of rejection-related gene expression patterns.

RESULTS

Daratumumab led to persistent CD138+ cell depletion in the bone marrow and blood and substantially decreased NK cells counts in blood and graft tissue. At the same time, donor-specific antibody in serum disappeared, and in vitro alloantibody production by CD138+ cells enriched from bone marrow aspirates was abrogated. A 3-month follow-up biopsy revealed a complete resolution of microcirculation inflammation (g+ptc: 3 to 0) and molecular AMR activity (AMR score: 0.79 to <0.2). The same biopsy showed (subclinical) tubulointerstitial inflammation, which prompted steroid treatment. Over an observation period of 12 months, graft function stabilized.

CONCLUSIONS

Targeting CD38 for plasma cell and NK cell depletion may be an effective strategy to counteract AMR. Our results may encourage the design of future trials to clarify the role of this innovative treatment concept in organ transplantation.

Molecular Analysis of Renal Allograft Biopsies: Where Do We Stand and Where Are We Going?

A renal core biopsy for histological evaluation is the gold standard for diagnosing renal transplant pathology. However, renal biopsy interpretation is subjective and can render insufficient precision, making it difficult to apply a targeted therapeutic regimen for the individual patient. This warrants a need for additional methods assessing disease state in the renal transplant. Significant research activity has been focused on the role of molecular analysis in the diagnosis of renal allograft rejection. The identification of specific molecular expression patterns in allograft biopsies related to different types of allograft injury could provide valuable information about the processes underlying renal transplant dysfunction and can be used for the development of molecular classifier scores, which could improve our diagnostic and prognostic ability and could guide treatment. Molecular profiling has the potential to be more precise and objective than histological evaluation and may identify injury even before it becomes visible on histology, making it possible to start treatment at the earliest time possible. Combining conventional diagnostics (histology, serology, and clinical data) and molecular evaluation will most likely offer the best diagnostic approach. We believe that the use of state-of-the-art molecular analysis will have a significant impact in diagnostics after renal transplantation. In this review, we elaborate on the molecular phenotype of both acute and chronic T cell-mediated rejection and antibody-mediated rejection and discuss the additive value of molecular profiling in the setting of diagnosing renal allograft rejection and how this will improve transplant patient care.

New concepts in chronic antibody-mediated kidney allograft rejection: prevention and treatment

PURPOSE OF REVIEW

Chronic antibody-mediated rejection (AMR) is a cardinal cause of transplant failure, with currently no proven effective prevention or treatment. The present review will focus on new therapeutic concepts currently under clinical evaluation.

RECENT FINDINGS

One interesting treatment approach may be interference with interleukin-6 (IL-6) signaling to modulate B-cell immunity and donor-specific antibody (DSA) production. Currently, a large phase III randomized controlled trial is underway to clarify the safety and efficacy of clazakizumab, a high-affinity anti-IL-6 antibody, in chronic AMR. A prevention/treatment strategy may be costimulation blockade using belatacept to interfere with germinal center responses and DSA formation. In a recent uncontrolled study, belatacept conversion was shown to stabilize renal function and dampen AMR activity. Moreover, preliminary clinical results suggest efficacy of CD38 antibodies to deplete plasma and natural killer cells to treat AMR, with anecdotal reports demonstrating at least transient resolution of active rejection.

SUMMARY

There are promising concepts on the horizon for the prevention and treatment of chronic AMR. The design of adequately powered placebo-controlled trials to clarify the safety and efficacy of such new therapies, however, remains a big challenge, and will rely on the definition of precise surrogate endpoints predicting long-term allograft survival. Mapping the natural history of AMR would greatly help the understanding of who would derive benefits from treatment.

Molecular Diversity of Clinically Stable Human Kidney Allografts

IMPORTANCE

Clinical decision and immunosuppression dosing in kidney transplantation rely on transplant biopsy tissue histology even though histology has low specificity, sensitivity, and reproducibility for rejection diagnosis. The inclusion of stable allografts in mechanistic and clinical studies is vital to provide a normal, noninjured comparative group for all interrogative studies on understanding allograft injury.

OBJECTIVE

To refine the definition of a stable allograft as one that is clinically, histologically, and molecularly quiescent using publicly available transcriptomics data.

DESIGN, SETTING, AND PARTICIPANTS

In this prognostic study, the National Center for Biotechnology Information Gene Expression Omnibus was used to search for microarray gene expression data from kidney transplant tissues, resulting in 38 studies from January 1, 2017, to December 31, 2018. The diagnostic annotations included 510 acute rejection (AR) samples, 1154 histologically stable (hSTA) samples, and 609 normal samples. Raw fluorescence intensity data were downloaded and preprocessed followed by data set merging and batch correction.

MAIN OUTCOMES AND MEASURES

The primary measure was area under the receiver operating characteristics curve from a set of feature selected genes and cell types for distinguishing AR from normal kidney tissue.

RESULTS

Within the 28 data sets, the feature selection procedure identified a set of 6 genes (KLF4, CENPJ, KLF2, PPP1R15A, FOSB, TNFAIP3) (area under the curve [AUC], 0.98) and 5 immune cell types (CD4+ T-cell central memory [Tcm], CD4+ T-cell effector memory [Tem], CD8+ Tem, natural killer [NK] cells, and Type 1 T helper [TH1] cells) (AUC, 0.92) that were combined into 1 composite Instability Score (InstaScore) (AUC, 0.99). The InstaScore was applied to the hSTA samples: 626 of 1154 (54%) were found to be immune quiescent and redefined as histologically and molecularly stable (hSTA/mSTA); 528 of 1154 (46%) were found to have molecular evidence of rejection (hSTA/mAR) and should not have been classified as stable allografts. The validation on an independent cohort of 6 months of protocol biopsy samples in December 2019 showed that hSTA/mAR samples had a significant change in graft function (r = 0.52, P < .001) and graft loss at 5-year follow-up (r = 0.17). A drop by 10 mL/min/1.73m2 in estimated glomerular filtration rate was estimated as a threshold in allograft transitioning from hSTA/mSTA to hSTA/mAR.

CONCLUSIONS AND RELEVANCE

The results of this prognostic study suggest that the InstaScore could provide an important adjunct for comprehensive and highly quantitative phenotyping of protocol kidney transplant biopsy samples and could be integrated into clinical care for accurate estimation of subsequent patient clinical outcomes.

Molecular patterns of isolated tubulitis differ from tubulitis with interstitial inflammation in early indication biopsies of kidney allografts

The Banff 2019 kidney allograft pathology update excluded isolated tubulitis without interstitial inflammation (ISO-T) from the category of borderline (suspicious) for acute T cell-mediated rejection due to its proposed benign clinical outcome. In this study, we explored the molecular assessment of ISO-T. ISO-T or interstitial inflammation with tubulitis (I + T) was diagnosed in indication biopsies within the first 14 postoperative days. The molecular phenotype of ISO-T was compared to I + T either by using RNA sequencing (n = 16) or by Molecular Microscope Diagnostic System (MMDx, n = 51). RNA sequencing showed lower expression of genes related to interferon-y (p = 1.5 *10^-16), cytokine signaling (p = 2.1 *10^-20) and inflammatory response (p = 1.0*10^-13) in the ISO-T group than in I + T group. Transcripts with increased expression in the I + T group overlapped significantly with previously described pathogenesis-based transcript sets associated with cytotoxic and effector T cell transcripts, and with T cell-mediated rejection (TCMR). MMDx classified 25/32 (78%) ISO-T biopsies and 12/19 (63%) I + T biopsies as no-rejection. ISO-T had significantly lower MMDx scores for interstitial inflammation (p = 0.014), tubulitis (p = 0.035) and TCMR (p = 0.016) compared to I + T. Fewer molecular signals of inflammation in isolated tubulitis suggest that this is also a benign phenotype on a molecular level.

Molecular T-cell‒mediated rejection in transbronchial and mucosal lung transplant biopsies is associated with future risk of graft loss

BACKGROUND

We previously developed molecular assessment systems for lung transplant transbronchial biopsies (TBBs) with high surfactant and bronchial mucosal biopsies, identifying T-cell‒mediated rejection (TCMR) on the basis of the expression of rejection-associated transcripts, but the relationship of rejection to graft loss is unknown. This study aimed to develop molecular assessments for TBBs and mucosal biopsies and to establish the impact of molecular TCMR on graft survival.

METHODS

We used microarrays and machine learning to assign TCMR scores to an expanded cohort of 457 TBBs (367 high surfactant plus 90 low surfactant) and 314 mucosal biopsies. We tested the score agreement between TBB-TBB, mucosal-mucosal, and TBB-mucosal biopsy pairs in the same patient. We also assessed the association of molecular TCMR scores with graft loss (death or retransplantation) and compared it with the prognostic associations for histology and donor-specific antibodies.

RESULTS

The molecular TCMR scores assigned in all the TBBs performed similarly to those in high-surfactant TBBs, indicating that variation in alveolation in TBBs does not prevent the detection of TCMR. Mucosal biopsy pieces showed less piece-to-piece variation than TBBs. TCMR scores in TBBs agreed with those in mucosal biopsies. In both TBBs and mucosal biopsies, molecular TCMR was associated with graft loss, whereas histologic rejection and donor-specific antibodies were not.

CONCLUSIONS

Molecular TCMR can be detected in TBBs regardless of surfactant and in mucosal biopsies, which show less variability in the sampled tissue than TBBs. On the basis of these findings, molecular TCMR appears to be an important predictor of the risk of future graft failure.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02812290.

The molecular diagnosis of rejection in liver transplant biopsies: First results of the INTERLIVER study

Molecular diagnosis of rejection is emerging in kidney, heart, and lung transplant biopsies and could offer insights for liver transplant biopsies. We measured gene expression by microarrays in 235 liver transplant biopsies from 10 centers. Unsupervised archetypal analysis based on expression of previously annotated rejection-related transcripts identified 4 groups: normal "R1_normal " (N = 129), T cell-mediated rejection (TCMR) "R2_TCMR " (N = 37), early injury "R3_injury " (N = 61), and fibrosis "R4_late " (N = 8). Groups differed in median time posttransplant, for example, R3_injury 99 days vs R4_late 3117 days. R2_TCMR biopsies expressed typical TCMR-related transcripts, for example, intense IFNG-induced effects. R3_injury displayed increased expression of parenchymal injury transcripts (eg, hypoxia-inducible factor EGLN1). R4_late biopsies showed immunoglobulin transcripts and injury-related transcripts. R2_TCMR correlated with histologic rejection although with many discrepancies, and R4_late with fibrosis. R2_TCMR , R3_injury , and R4_late correlated with liver function abnormalities. Supervised classifiers trained on histologic rejection showed less agreement with histology than unsupervised R2_TCMR scores. No confirmed cases of clinical antibody-mediated rejection (ABMR) were present in the population, and strategies that previously revealed ABMR in kidney and heart transplants failed to reveal a liver ABMR phenotype. In conclusion, molecular analysis of liver transplant biopsies detects rejection, has the potential to resolve ambiguities, and could assist with immunosuppressive management.

Lack of Histological and Molecular Signature Response to Tocilizumab in Kidney Transplants with Chronic Active Antibody Mediated Rejection: A Case Series

Background

Traditional therapies for caAbMR have unclear efficacy with significant side effects in recipients of kidney transplants (KTs). A recent single-center case series suggested tocilizumab (TCZ) could stabilize renal function and improve microvascular inflammation. Here we report our findings of the use of TCZ in patients with caAbMR.

Methods

Ten adult recipients of KTs with biopsy-proven caAbMR were treated with TCZ at 8 mg/kg per month. Patients were monitored for adverse events, and therapy was interrupted in the setting of serious infections. Six patients (60%) underwent post-treatment biopsies.

Results

Patients (mean age of 43 years) were initiated on TCZ at a median of 36 months post-KT. A majority of patients were black (70%), underwent regrafts (40%), and were sensitized (mean cPRA=41%). Patients received a median of six doses of TCZ (range=3-10). At a median follow-up of 12 months (range=8-24 months), renal function did not show improvement (mean eGFR, 42±18 ml/min per 1.73 m² to 37±24 ml/min per 1.73 m²; P=0.27). The slope of decline in eGFR remained unchanged (-0.14±0.9 to -0.33±1.1; P=0.25). There was no improvement in mean MVI (g+ptc) (4.8±1.4 to 4.2±2.0; P=0.39) scores or Molecular Microscope Diagnostic System (MMDx) AbMR scores (0.79±0.17 to 0.78±0.26; P=0.86). There was a numeric worsening of chronicity (ci+ct) scores (2.5±0.8 to 3.3±1.7; P=0.38) and MMDx atrophy fibrosis scores (0.36±0.24 to 0.58±0.15; P=0.21). Patient survival was 90%, with one patient death due to complications from a hip infection. Overall death-censored graft survival was 80%, with two graft losses in patients who had recurrent infections requiring hospitalization.

Conclusions

In this early experience, we report a lack of efficacy and toxicity with the use of TCZ for caAbMR. Prospective clinical trials are needed to clarify the role of IL-6 blockade and the possibility of increased incidence of infections in patients with caAbMR who are treated with TCZ.

Targeted Urine Metabolomics for Monitoring Renal Allograft Injury and Immunosuppression in Pediatric Patients

Despite new advancements in surgical tools and therapies, exposure to immunosuppressive drugs related to non-immune and immune injuries can cause slow deterioration and premature failure of organ transplants. Diagnosis of these injuries by non-invasive urine monitoring would be a significant clinical advancement for patient management, especially in pediatric cohorts. We investigated the metabolomic profiles of biopsy matched urine samples from 310 unique kidney transplant recipients using gas chromatography-mass spectrometry (GC-MS). Focused metabolite panels were identified that could detect biopsy confirmed acute rejection with 92.9% sensitivity and 96.3% specificity (11 metabolites) and could differentiate BK viral nephritis (BKVN) from acute rejection with 88.9% sensitivity and 94.8% specificity (4 metabolites). Overall, targeted metabolomic analyses of biopsy-matched urine samples enabled the generation of refined metabolite panels that non-invasively detect graft injury phenotypes with high confidence. These urine biomarkers can be rapidly assessed for non-invasive diagnosis of specific transplant injuries, opening the window for precision transplant medicine.

An age-independent gene signature for monitoring acute rejection in kidney transplantation

Acute rejection (AR) remains a significant problem that negatively impacts long-term renal allograft survival. Numerous therapies are used to prevent AR that differ by center and recipient age. This variability confounds diagnostic methods. Methods: To develop an age-independent gene signature for AR effective across a broad array of immunosuppressive regimens, we compiled kidney transplant biopsy (n=1091) and peripheral blood (n=392) gene expression profiles from 12 independent public datasets. After removing genes differentially expressed in pediatric and adult patients, we compared gene expression profiles from biopsy and peripheral blood samples of patients with AR to those who were stable (STA), using Mann-Whitney U Tests with validation in independent testing datasets. We confirmed this signature in pediatric and adult patients (42 AR and 47 STA) from our institutional biorepository. Results: We identified a novel age-independent gene network that identified AR from both kidney and blood samples. We developed a 90-probe set signature targeting 76 genes that differentiated AR from STA and found an 8 gene subset (DIP2C, ENOSF1, FBXO21, KCTD6, PDXDC1, REXO2, HLA-E, and RAB31) that was associated with AR. Conclusion: We used publicly available datasets to create a gene signature of AR that identified AR irrespective of immunosuppression regimen or recipient age. This study highlights a novel model to screen and validate biomarkers across multiple treatment regimens.

Discrepancy analysis comparing molecular and histology diagnoses in kidney transplant biopsies

Discrepancy analysis comparing two diagnostic platforms offers potential insights into both without assuming either is always correct. Having optimized the Molecular Microscope Diagnostic System (MMDx) in renal transplant biopsies, we studied discrepancies within MMDx (reports and sign-out comments) and between MMDx and histology. Interpathologist discrepancies have been documented previously and were not assessed. Discrepancy cases were classified as "clear" (eg, antibody-mediated rejection [ABMR] vs T cell-mediated rejection [TCMR]), "boundary" (eg, ABMR vs possible ABMR), or "mixed" (eg, Mixed vs ABMR). MMDx report scores showed 99% correlations; sign-out interpretations showed 7% variation between observers, all located around boundaries. Histology disagreed with MMDx in 37% of biopsies, including 315 clear discrepancies, all with implications for therapy. Discrepancies were distributed widely in all histology diagnoses but increased in some scenarios; for example, histology TCMR contained 14% MMDx ABMR and 20% MMDx no rejection. MMDx usually gave unambiguous diagnoses in cases with ambiguous histology, for example, borderline and transplant glomerulopathy. Histology lesions or features associated with more frequent discrepancies (eg, tubulitis, arteritis, and polyomavirus nephropathy) were not associated with increased MMDx uncertainty, indicating that MMDx can clarify biopsies with histologic ambiguity. The patterns of histology-MMDx discrepancies highlight specific histology diagnoses in which MMDx assessment should be considered for guiding therapy.

Non-invasive Chemokine Detection: Improved Prediction of Antibody-Mediated Rejection in Donor-Specific Antibody-Positive Renal Allograft Recipients

Background: Screening for donor-specific antibodies (DSA) has limited diagnostic value in patients with late antibody-mediated rejection (ABMR). Here, we evaluated whether biomarkers reflecting microcirculation inflammation or tissue injury-as an adjunct to DSA detection-are able to improve non-invasive ABMR monitoring. Methods: Upon prospective cross-sectional antibody screening of 741 long-term kidney transplant recipients with a silent clinical course, 86 DSA-positive patients were identified and biopsied. Serum and urine levels of E-selectin/CD62E, vascular cell adhesion molecule 1 (VCAM-1), granzyme B, hepatocyte growth factor (HGF), C-C motif chemokine ligand (CCL)3, CCL4, C-X-C motif chemokine ligand (CXCL)9, CXCL10, and CXCL11 in DSA-positive recipients were investigated applying multiplexed bead-based immunoassays. Results: Diagnosis of ABMR (50 patients) was associated with significantly higher levels of CXCL9 and CXCL10 in blood and urine and of HGF in blood. Overall, urinary CXCL9 had the highest diagnostic accuracy for ABMR (area under the receiver operating characteristic curve: 0.77; accuracy: 80%) and its combined evaluation with the mean fluorescence intensity of the immunodominant DSA (DSAmax MFI) revealed a net reclassification improvement of 73% compared to DSAmax MFI alone. Conclusions: Our results suggest urinary CXCL9 testing, combined with DSA analysis, as a valuable non-invasive tool to uncover clinically silent ABMR late after transplantation.

Applying single-cell technologies to clinical pathology: progress in nephropathology

Cells represent the basic building blocks of living organisms. Accurate characterisation of cellular phenotype, intercellular signalling networks, and the spatial organisation of cells within organs is crucial to deliver a better understanding of the processes underpinning physiology, and the perturbations that lead to disease. Single-cell methodologies have increased rapidly in scale and scope in recent years and are set to generate important insights into human disease. Here, we review current practices in nephropathology, which are dominated by relatively simple morphological descriptions of tissue biopsies based on their appearance using light microscopy. Bulk transcriptomics have more recently been used to explore glomerular and tubulointerstitial kidney disease, renal cancer, and the responses to injury and alloimmunity in kidney transplantation, generating novel disease insights and prognostic biomarkers. These studies set the stage for single-cell transcriptomic approaches that reveal cell-type-specific gene expression patterns in health and disease. These technologies allow genome-wide disease susceptibility genes to be interpreted with the knowledge of the specific cell populations within organs that express them, identifying candidate cell types for further study. Single-cell technologies are also moving beyond assaying individual cellular transcriptomes, to measuring the epigenetic landscape of single cells. Single-cell antigen-receptor gene sequencing also enables specific T- and B-cell clones to be tracked in different tissues and disease states. In the coming years these rich 'multi-omic' descriptions of kidney disease will enable histopathological descriptions to be comprehensively integrated with molecular phenotypes, enabling better disease classification and prognostication and the application of personalised treatment strategies. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Molecular phenotyping of rejection-related changes in mucosal biopsies from lung transplants

Diagnosing lung transplant rejection currently depends on histologic assessment of transbronchial biopsies (TBB) with limited reproducibility and considerable risk of complications. Mucosal biopsies are safer but not histologically interpretable. Microarray-based diagnostic systems for TBBs and other transplants suggest such systems could assess mucosal biopsies as well. We studied 243 mucosal biopsies from the third bronchial bifurcation (3BMBs) collected from seven centers and classified them using unsupervised machine learning algorithms. Using the expression of a set of rejection-associated transcripts annotated in kidneys and validated in hearts and lung transplant TBBs, the algorithms identified and scored major rejection and injury-related phenotypes in 3BMBs without need for labeled training data. No rejection or injury, rejection, late inflammation, and recent injury phenotypes were thus scored in new 3BMBs. The rejection phenotype correlated with IFNG-inducible transcripts, the hallmarks of rejection. Progressive atrophy-related changes reflected by the late inflammation phenotype in 3BMBs suggest widespread time-dependent airway deterioration, which was especially pronounced after two years posttransplant. Thus molecular assessment of 3BMBs can detect rejection in a previously unusable biopsy format with potential utility in patients with severe lung dysfunction where TBB is not possible and provide unique insights into airway deterioration. ClinicalTrials.gov NCT02812290.

Diagnosis of renal transplant rejection: Banff classification and beyond

Diagnosis of renal transplant rejection is dependent on interpretation of renal allograft biopsies. The Banff Classification of Allograft Pathology, which was developed as a standardized working classification system in 1991, has contributed to the standardization of definitions for histologic injuries resulting from renal allograft rejections and provided a universal grading system for assessing these injuries. It has also helped to provide insight into the underlying pathogenic mechanisms that contribute to transplant rejection. In addition to histological and immunologic parameters, molecular tools are now being used to facilitate the diagnosis of rejection. In this review, I will discuss morphologic features of renal transplant rejections as well as major revisions and pitfalls of the Banff classification system, and provide future perspectives.

Transcriptome Analysis in Renal Transplant Biopsies Not Fulfilling Rejection Criteria

The clinical significance of renal transplant biopsies displaying borderline changes suspicious for T-cell mediated rejection (TCMR) or interstitial fibrosis and tubular atrophy (IFTA) with interstitial inflammation has not been well defined. Molecular profiling to evaluate renal transplant biopsies using microarrays has been shown to be an objective measurement that adds precision to conventional histology. We review the contribution of transcriptomic analysis in surveillance and indication biopsies with borderline changes and IFTA associated with variable degrees of inflammation. Transcriptome analysis applied to biopsies with borderline changes allows to distinguish patients with rejection from those in whom mild inflammation mainly represents a response to injury. Biopsies with IFTA and inflammation occurring in unscarred tissue display a molecular pattern similar to TCMR while biopsies with IFTA and inflammation in scarred tissue, apart from T-cell activation, also express B cell, immunoglobulin and mast cell-related genes. Additionally, patients at risk for IFTA progression can be identified by genes mainly reflecting fibroblast dysregulation and immune activation. At present, it is not well established whether the expression of rejection gene transcripts in patients with fibrosis and inflammation is the consequence of an alloimmune response, tissue damage or a combination of both.

Quantitative Proteomics for Monitoring Renal Transplant Injury

PURPOSE

This study is aimed at developing a molecular diagnostics platform to enhance the interpretation of renal allograft biopsies using quantitative proteomic profiling of formalin-fixed and paraffin-embedded (FFPE) specimens.

EXPERIMENTAL DESIGN

A quantitative proteomics platform composed of 1) an optimized FFPE protein sample preparation method, 2) a tandem mass tag TMT10-plex-based proteomic workflow, and 3) a systematic statistical analysis pipeline to reveal differentially expressed proteins has been developed. This platform is then tested on a small sample set (five samples per phenotype) to reveal proteomic signatures that can differentiate T-cell mediated rejection (TCMR) and polyomavirus BK nephropathy (BKPyVN) from healthy functionally stable kidney tissue (STA).

RESULTS

Among 2798 quantified proteins, the expression levels of 740 BKPyVN and 638 TCMR associated proteins are significantly changed compared to STA specimens. Principal component analysis demonstrated good segregation of all three phenotypes investigated. Protein detection and quantitation are highly reproducible: replicate comparative analyses demonstrated 71-84% overlap of detected proteins, and the coefficient of variation for protein measurements is <15% in triplicate liquid chromatography-tandem mass spectrometry runs.

CONCLUSIONS AND CLINICAL RELEVANCE

Quantitative proteomics can be applied to archived FFPE specimens to differentiate different causes of renal allograft injury.

Bioinformatics analyses on the immune status of renal transplant patients, a systemic research of renal transplantation

BACKGROUND

Kidney transplantation is the most effective treatment for end-stage renal disease. Allograft rejections severely affect survivals of allograft kidneys and recipients.

METHODS

Using bioinformatics approaches, the present study was designed to investigate immune status in renal transplant recipients. Fifteen datasets from Gene Expression Omnibus (GEO) were collected and analysed. Analysis of gene enrichment and protein-protein interactions were also used.

RESULTS

There were 40 differentially expressed genes (DEGs) identified in chronic rejection group when compared with stable recipients, which were enriched in allograft rejection module. There were 135 DEGs identified in acute rejection patients, compared with stable recipients, in which most genes were enriched in allograft rejection and immune deficiency. There were 288 DEGs identified in stable recipients when compared to healthy subjects. Most genes were related to chemokine signalling pathway. In integrated comparisons, expressions of MHC molecules and immunoglobulins were increased in both acute and chronic rejection; expressions of LILRB and MAP 4 K1 were increased in acute rejection patients, but not in stable recipients. There were no overlapping DEGs in blood samples of transplant recipients.

CONCLUSION

By performing bioinformatics analysis on the immune status of kidney transplant patients, the present study reports several DEGs in the renal biopsy of transplant recipients, which are requested to be validated in clinical practice.

Diagnostic, Prognostic, and Therapeutic Value of Non-Coding RNA Expression Profiles in Renal Transplantation

End-stage renal disease is a public health problem responsible for millions of deaths worldwide each year. Although transplantation is the preferred treatment for patients in need of renal replacement therapy, long-term allograft survival remains challenging. Advances in high-throughput methods for large-scale molecular data generation and computational analysis are promising to overcome the current limitations posed by conventional diagnostic and disease classifications post-transplantation. Non-coding RNAs (ncRNAs) are RNA molecules that, despite lacking protein-coding potential, are essential in the regulation of epigenetic, transcriptional, and post-translational mechanisms involved in both health and disease. A large body of evidence suggests that ncRNAs can act as biomarkers of renal injury and graft loss after transplantation. Hence, the focus of this review is to discuss the existing molecular signatures of non-coding transcripts and their value to improve diagnosis, predict the risk of rejection, and guide therapeutic choices post-transplantation.