Potential impact of microarray diagnosis of T cell-mediated rejection in kidney transplants: The INTERCOM study

Limitations of biopsy-based transcript diagnostics to detect T-cell-mediated allograft rejection

BACKGROUND

Isolated tubulitis, borderline changes and isolated arteritis suspicious for histologic T-cell-mediated rejection (hTCMR) remain findings of uncertain significance. Although the Molecular Microscope Diagnostics System (MMDx) has not been trained on those lesions, it was suggested that MMDx might reclassify a subgroup to molecular TCMR (mTCMR).

METHODS

In this single-center cohort of 326 consecutive, unselected kidney allograft biopsies assessed by histology and MMDx, we analyzed 249 cases with isolated tubulitis (i0, t1-3, v0; n = 101), borderline changes (according to Banff 2022, v0; n = 9), isolated arteritis (no borderline, v1; n = 37), no inflammation (i0, t0, v0; n = 67) and a positive control cohort (hTCMR, n = 27; mixed histologic rejection, n = 8; both according to Banff 2022; total n = 35). The first three groups were summarized as TCMR-suspicion (n = 147). Subcategorization included the presence and absence of microvascular inflammation (MVI); g+ptc ptc ≥2. Molecular rejection rates and differentiation were investigated.

RESULTS

Molecular rejection rates were 37/147 cases (25.2%; 32 with MVI) in TCMR-suspicion, 6/67 (9%; 4 with MVI) in no inflammation and 30/35 (85.7%; 19 with MVI) in the positive control cohort. Molecular antibody-mediated rejection (mAMR) was present in 39/73 (53.4%) of cases. The presence of donor-specific antibodies at the time of the biopsy was high (127/249, 51%). Only 3 mAMR/TCMR and 0 pure mTCMR cases were detected in TCMR-suspicion and no inflammation, compared with 12 mAMR/TCMR and 10 mTCMR cases in the positive control cohort (P < .001). Even though the TCMR-specific molecular (Classifier) score differentiated between TCMR-suspicion and no inflammation (P = 0.005), rejection phenotype scores (R2 and R3) did not (P = .157 and .121).

CONCLUSIONS

MMDx did not identify pure mTCMR among isolated tubulitis, borderline changes or isolated arteritis, likely due to low sensitivity for TCMR lesions. However, it identified mAMR or mAMR/TCMR, especially in cases with MVI. Subthreshold findings remain to be further studied.

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.

Identification of RNA-binding protein genes associated with renal rejection and graft survival

Rejection is one of the major factors affecting the long-term prognosis of kidney transplantation, and timely recognition and aggressive treatment of rejection is essential to prevent disease progression. RBPs are proteins that bind to RNA to form ribonucleoprotein complexes, thereby affecting RNA stability, processing, splicing, localization, transport, and translation, which play a key role in post-transcriptional gene regulation. However, their role in renal transplant rejection and long-term graft survival is unclear. The aim of this study was to comprehensively analyze the expression of RPBs in renal rejection and use it to construct a robust prediction strategy for long-term graft survival. The microarray expression profiles used in this study were obtained from GEO database. In this study, a total of eight hub RBPs were identified, all of which were upregulated in renal rejection samples. Based on these RBPs, the renal rejection samples could be categorized into two different clusters (cluster A and cluster B). Inflammatory activation in cluster B and functional enrichment analysis showed a strong association with rejection-related pathways. The diagnostic prediction model had a high diagnostic accuracy for T cell mediated rejection (TCMR) in renal grafts (area under the curve = 0.86). The prognostic prediction model effectively predicts the prognosis and survival of renal grafts (p < .001) and applies to both rejection and non-rejection situations. Finally, we validated the expression of hub genes, and patient prognosis in clinical samples, respectively, and the results were consistent with the above analysis.

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.

Older age is associated with a distinct and marked reduction of functionality of both alloreactive CD4+ and CD8+ T cells

Introduction

Older recipient age is associated with a significant decreased risk for rejection after kidney transplantation which is incompletely understood.

Methods

In a longitudinal study, circulating alloreactive T cells were assessed of young (≤45 years) and older (≥55 years) stable kidney transplant recipients. Alloreactive T-cells were identified by CD137-expression and phenotype, cytokine producing and proliferative capacity, were evaluated using multiparameter flowcytometry.

Results

The results show that before transplantation frequencies of alloreactive CD4+ and CD8+ T-cells in older KT-recipients are significantly higher and shifted towards an effector memory-phenotype. However, the frequency of polyfunctional (≥2 pro-inflammatory cytokines) CD4+ T-cells was significantly lower and less IL2 was produced. The frequency of polyfunctional alloreactive CD4+ T-cells and proliferation of alloreactive T-cells donor-specifically declined after transplantation reaching a nadir at 12 months after transplantation, irrespective of age. A striking difference was observed for the proliferative response of alloreactive CD8+ T-cells. This was not only lower in older compared to younger recipients but could also not be restored by exogenous IL2 or IL15 in the majority of older recipients while the response to polyclonal stimulation was unaffected.

Conclusion

In conclusion, older age is associated with a distinct and marked reduction of functionality of both alloreactive CD4+ and CD8+ T-cells.

Recent Insights in Noninvasive Diagnostic for the Assessment of Kidney and Cardiovascular Outcome in Kidney Transplant Recipients

Kidney transplantation improves quality of life and prolongs survival of patients with end-stage kidney disease. However, kidney transplant recipients present a higher risk for cardiovascular events compared to the general population. Risk assessment for graft failure as well as cardiovascular events is still based on invasive procedures. Biomarkers in blood and urine, but also new diagnostic approaches like genetic or molecular testing, can be useful tools to monitor graft function and to identify patients of high cardiovascular risk. Many biomarkers have been introduced, whereas most of these biomarkers have not been implemented in clinical routine. Here, we discuss recent developments in biomarkers and diagnostic models in kidney transplant recipients. Because many factors impact graft function and cardiovascular risk, it is most likely that no biomarker will meet the highest demands and standards. We advocate to shift focus to the identification of patients benefitting from molecular and genetic testing as well as from analysis of more specific biomarkers instead of finding one biomarker fitting to all patients.

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.

The utilization of molecular microscope in management of heart transplant recipients in the era of noninvasive monitoring

INTRODUCTION

Monitoring for graft rejection is a fundamental tenet of post-transplant follow-up. In heart transplantation (HT) in particular, rejection has been traditionally assessed with endomyocardial biopsy (EMB). EMB has potential complications and noted limitations, including interobserver variability in interpretation. Additional tests, such as basic cardiac biomarkers, cardiac imaging, gene expression profiling (GEP) scores, donor-derived cell-free DNA (dd-cfDNA) and the novel molecular microscope diagnostic system (MMDx) have become critical tools in rejection surveillance beyond standard EMB.

METHODS

This paper describes an illustrative case followed by a review of MMDx within the context of other noninvasive screening modalities for rejection.

CONCLUSIONS

We suggest MMDx be used to assist with early detection of rejection in cases of discordance between EMB and other noninvasive studies.

Evolution of human kidney allograft pathology diagnostics through 30 years of the Banff classification process

The second half of the previous century witnessed a tremendous rise in the number of clinical kidney transplants worldwide. This activity was, however, accompanied by many issues and challenges. An accurate diagnosis and appropriate management of causes of graft dysfunction were and still are, a big challenge. Kidney allograft biopsy played a vital role in addressing the above challenge. However, its interpretation was not standardized for many years until, in 1991, the Banff process was started to fill this void. Thereafter, regular Banff meetings took place every 2 years for the past 30 years. Marked changes have taken place in the interpretation of kidney allograft biopsies, diagnosis, and classification of rejection and other non-rejection pathologies from the original Banff 93 classification. This review attempts to summarize those changes for increasing the awareness and understanding of kidney allograft pathology through the eyes of the Banff process. It will interest the transplant surgeons, physicians, pathologists, and allied professionals associated with the care of kidney transplant patients.

Rejection markers in kidney transplantation: do new technologies help children?

Recent insights in allorecognition and graft rejection mechanisms revealed a more complex picture than originally considered, involving multiple pathways of both adaptive and innate immune response, supplied by efficient inflammatory synergies. Current pillars of transplant monitoring are serum creatinine, proteinuria, and drug blood levels, which are considered as traditional markers, due to consolidated experience, low cost, and widespread availability. The most diffuse immunological biomarkers are donor-specific antibodies, which are included in routine post-transplant monitoring in many centers, although with some reproducibility issues and interpretation difficulties. Confirmed abnormalities in these traditional biomarkers raise the suspicion for rejection and guide the indication for graft biopsy, which is still considered the gold standard for rejection monitoring. Rapidly evolving new "omic" technologies have led to the identification of several novel biomarkers, which may change the landscape of transplant monitoring should their potential be confirmed. Among them, urinary chemokines and measurement of cell-free DNA of donor origin are perhaps the most promising. However, at the moment, these approaches remain highly expensive and cost-prohibitive in most settings, with limited clinical applicability; approachable costs upon technology investments would speed their integration. In addition, transcriptomics, metabolomics, proteomics, and the study of blood and urinary extracellular vesicles have the potential for early identification of subclinical rejection with high sensitivity and specificity, good reproducibility, and for gaining predictive value in an affordable cost setting. In the near future, information derived from these new biomarkers is expected to integrate traditional tools in routine use, allowing identification of rejection prior to clinical manifestations and timely therapeutic intervention. This review will discuss traditional, novel, and invasive and non-invasive biomarkers, underlining their strengths, limitations, and present or future applications in children.

Molecular immune monitoring in kidney transplant rejection: a state-of-the-art review

Although current regimens of immunosuppressive drugs are effective in renal transplant recipients, long-term renal allograft outcomes remain suboptimal. For many years, the diagnosis of renal allograft rejection and of several causes of renal allograft dysfunction, such as chronic subclinical inflammation and infection, was mostly based on renal allograft biopsy, which is not only invasive but also possibly performed too late for proper management. In addition, certain allograft dysfunctions are difficult to differentiate from renal histology due to their similar pathogenesis and immune responses. As such, non-invasive assays and biomarkers may be more beneficial than conventional renal biopsy for enhancing graft survival and optimizing immunosuppressive drug regimens during long-term care. This paper discusses recent biomarker candidates, including donor-derived cell-free DNA, transcriptomics, microRNAs, exosomes (or other extracellular vesicles), urine chemokines, and nucleosomes, that show high potential for clinical use in determining the prognosis of long-term outcomes of kidney transplantation, along with their limitations.

Allograft tissue under the microscope: only the beginning

PURPOSE OF REVIEW

To review novel modalities for interrogating a kidney allograft biopsy to complement the current Banff schema.

RECENT FINDINGS

Newer approaches of Artificial Intelligence (AI), Machine Learning (ML), digital pathology including Ex Vivo Microscopy, evaluation of the biopsy gene expression using bulk, single cell, and spatial transcriptomics and spatial proteomics are now available for tissue interrogation.

SUMMARY

Banff Schema of classification of allograft histology has standardized reporting of tissue pathology internationally greatly impacting clinical care and research. Inherent sampling error of biopsies, and lack of automated morphometric analysis with ordinal outputs limit its performance in prognostication of allograft health. Over the last decade, there has been an explosion of newer methods of evaluation of allograft tissue under the microscope. Digital pathology along with the application of AI and ML algorithms could revolutionize histopathological analyses. Novel molecular diagnostics such as spatially resolved single cell transcriptomics are identifying newer mechanisms underlying the pathologic diagnosis to delineate pathways of immunological activation, tissue injury, repair, and regeneration in allograft tissues. While these techniques are the future of tissue analysis, costs and complex logistics currently limit their clinical use.

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

Background

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

Methods

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

Results

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

Conclusions

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

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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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 T Cell-Mediated Rejection and Tubulointerstitial Inflammation as Clinical Trial Endpoints in Kidney Transplantation

The diagnosis of acute T cell-mediated rejection (aTCMR) after kidney transplantation has considerable relevance for research purposes. Its definition is primarily based on tubulointerstitial inflammation and has changed little over time; aTCMR is therefore a suitable parameter for longitudinal data comparisons. In addition, because aTCMR is managed with antirejection therapies that carry additional risks, anxieties, and costs, it is a clinically meaningful endpoint for studies. This paper reviews the history and classifications of TCMR and characterizes its potential role in clinical trials: a role that largely depends on the nature of the biopsy taken (indication vs protocol), the level of inflammation observed (e.g., borderline changes vs full TCMR), concomitant chronic lesions (chronic active TCMR), and the therapeutic intervention planned. There is ongoing variability-and ambiguity-in clinical monitoring and management of TCMR. More research, to investigate the clinical relevance of borderline changes (especially in protocol biopsies) and effective therapeutic strategies that improve graft survival rates with minimal patient morbidity, is urgently required. The present paper was developed from documentation produced by the European Society for Organ Transplantation (ESOT) as part of a Broad Scientific Advice request that ESOT submitted to the European Medicines Agency for discussion in 2020. This paper proposes to move toward refined definitions of aTCMR and borderline changes to be included as primary endpoints in clinical trials of kidney transplantation.

Preliminary Investigation of the Biomarkers of Acute Renal Transplant Rejection Using Integrated Proteomics Studies, Gene Expression Omnibus Datasets, and RNA Sequencing

A kidney transplant is often the best treatment for end-stage renal disease. Although immunosuppressive therapy sharply reduces the occurrence of acute allograft rejection (AR), it remains the main cause of allograft dysfunction. We aimed to identify effective biomarkers for AR instead of invasive kidney transplant biopsy. We integrated the results of several proteomics studies related to AR and utilized public data sources. Gene ontology (GO) and pathway analyses were used to identify important biological processes and pathways. The performance of the identified proteins was validated using several public gene expression omnibus (GEO) datasets. Samples that performed well were selected for further validation through RNA sequencing of peripheral blood mononuclear cells of patients with AR (n = 16) and non-rejection (n = 19) from our medical center. A total of 25 differentially expressed proteins (DEPs) overlapped in proteomic studies of urine and blood samples. GO analysis showed that the DEPs were mainly involved in the immune system and blood coagulation. Pathway analysis showed that the complement and coagulation cascade pathways were well enriched. We found that immunoglobulin heavy constant alpha 1 (IGHA1) and immunoglobulin κ constant (IGKC) showed good performance in distinguishing AR from non-rejection groups validated with several GEO datasets. Through RNA sequencing, the combination of IGHA1, IGKC, glomerular filtration rate, and donor age showed good performance in the diagnosis of AR with ROC AUC 91.4% (95% CI: 82–100%). Our findings may contribute to the discovery of potential biomarkers for AR monitoring.

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.

The negative impact of T cell-mediated rejection on renal allograft survival in the modern era

The prevalence and long-term impact of T cell-mediated rejection (TCMR) is poorly defined in the modern era of tacrolimus/mycophenolate-based maintenance therapy. This observational study evaluated 775 kidney transplant recipients with serial histology and correlated TCMR events with the risk of graft loss. After a ~30% incidence of a first Banff Borderline or greater TCMR detected on for-cause (17%) or surveillance (13%) biopsies, persistent (37.4%) or subsequent (26.3%) TCMR occurred in 64% of recipients on follow-up biopsies. Alloimmune risk categories based on the HLA-DR/DQ single molecule eplet molecular mismatch correlated with the number of TCMR events (p = .002) and Banff TCMR grade (p = .007). Both a first and second TCMR event correlated with death-censored and all-cause graft loss when adjusted for baseline covariates and other significant time-dependent covariates such as DGF and ABMR. Therefore, a substantial portion of kidney transplant recipients, especially those with intermediate and high HLA-DR/DQ molecular mismatch scores, remain under-immunosuppressed, which in turn identifies the need for novel agents that can more effectively prevent or treat TCMR.

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.

Elevated number of IL-21+ TFH and CD86+CD38+ B cells in blood of renal transplant recipients with AMR under conventional immuno-suppression

The objective of this study is to detect the number of different subsets of TFH and B cells in renal transplant recipients (RTR) with antibody-mediated acute rejection (AMR), acute rejection (AR), chronic rejection (CR), or transplant stable (TS). The present study was a prospective study. The numbers of ICOS +, PD-1+ and IL-21+ TFH, CD86+, CD38+, CD27+, and IgD- B cells in 21 patients with end-stage renal disease (ESRD) and post-transplant times were measured by flow cytometry. The level of serum IL-21 was detected by ELISA. The numbers of circulating CD4+CXCR5+, CD4+CXCR5+ICOS+, CD4+CXCR5+PD-1+, CD4+CXCR5+IL-21+ TFH, CD19+CD86+, and CD19 +CD86+CD38+ B cells as well as the level of serum IL-21 in the AMR, AR, and CR groups at post-transplantation were significantly higher than those at pre-transplantation. In contrast, the number of circulating CD19+CD27+IgD B cells was significantly increased in the TS groups in respect to the other groups. Moreover, the numbers of circulating CD4+CXCR5+IL-21+ TFH cells, CD19+CD86+CD38+ B cells as well as the level of serum IL-21 were positive related to the level of serum Cr while showing negative correlated with the values of eGFR in the AMR groups at post-transplantation for 4 and 12 weeks. Circulating TFH cells may be a biomarker in RTR with AMR, which can promote the differentiation of B cells into plasma cells by activating B cells, thereby promoting disease progression.

US payer budget impact of a microarray assay with machine learning to evaluate kidney transplant rejection in for-cause biopsies

AIM

This study evaluates the economic impact to US commercial payers of MMDx-Kidney used in conjunction with histologic evaluation of for-cause kidney transplant biopsies.

MATERIALS AND METHODS

An Excel-based model was developed to assess the cost impact of histology plus MMDx-Kidney versus histology alone for the evaluation of potential rejection in kidney transplant patients who receive a for-cause biopsy. Different model time periods were assessed, ranging from 1 to 5 years post-biopsy. A targeted literature review was used to identify parameter estimates, validated by two external clinicians with expertise in managing kidney transplant rejection. A sensitivity analysis was conducted to evaluate the relative impact of key clinical and cost parameters. In particular, the model identified the magnitude of MMDx-Kidney's impact on graft failure from rejection that would be required for MMDx-Kidney to be cost-neutral.

RESULTS

By more accurately characterizing rejection, MMDx-Kidney is estimated to increase antirejection treatment costs by $1,126 per test. Nevertheless, a break-even analysis shows that the costs of MMDx-Kidney and anti-rejection medication, as well as the costs associated with an increase in the number of patients with functioning transplants, may be offset by reductions in costs associated with graft failure (i.e. costs of hospitalizations, dialysis, and repeat transplants) over 5 years, assuming MMDx-Kidney reduces annual graft failure from rejection by at least 5%. For the base case, with a 25% relative reduction in annual rate of graft failures from rejection, MMDx-Kidney increases overall costs incurred in the first year of the model but starts generating savings by the second year of the model.

CONCLUSIONS

Compared with histologic evaluation of for-cause kidney transplant biopsies alone, the use of MMDx-Kidney in conjunction with histologic evaluation improves the diagnoses of graft dysfunction and may have the potential to generate overall savings from reductions in rejection-related graft failure.

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.

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

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

A 2-fold Approach to Polyoma Virus (BK) Nephropathy in Kidney Transplants: Distinguishing Direct Virus Effects From Cognate T Cell-mediated Inflammation

BACKGROUND

BK nephropathy (BKN) in kidney transplants diagnosed by histology is challenging because it involves damage from both virus activity and cognate T cell-mediated inflammation, directed against alloantigens (rejection) or viral antigens. The present study of indication biopsies from the Integrated Diagnostic System in the International Collaborative Microarray Study Extension study measured major capsid viral protein 2 (VP2) mRNA to assess virus activity and a T cell-mediated rejection (TCMR) classifier to assess cognate T cell-mediated inflammation.

METHODS

Biopsies were assessed by local standard-of-care histology and by genome-wide microarrays and Molecular Microscope Diagnostic System (MMDx) algorithms to detect rejection and injury. In a subset of 102 biopsies (50 BKN and 52 BKN-negative biopsies with various abnormalities), we measured VP2 transcripts by real-time polymerase chain reaction.

RESULTS

BKN was diagnosed in 55 of 1679 biopsies; 30 had cognate T cell-mediated activity assessed by by MMDx and TCMR lesions, but only 3 of 30 were histologically diagnosed as TCMR. We developed a BKN probability classifier that predicted histologic BKN (area under the curve = 0.82). Virus activity (VP2 expression) was highly selective for BKN (area under the curve = 0.94) and correlated with acute injury, atrophy-fibrosis, macrophage activation, and the BKN classifier, but not with the TCMR classifier. BKN with molecular TCMR had more tubulitis and inflammation than BKN without molecular TCMR. In 5 BKN cases with second biopsies, VP2 mRNA decreased in second biopsies, whereas in 4 of 5 TCMR classifiers, scores increased. Genes and pathways associated with BKN and VP2 mRNA were similar, reflecting injury, inflammation, and macrophage activation but none was selective for BKN.

CONCLUSIONS

Risk-benefit decisions in BKN may be assisted by quantitative assessment of the 2 major pathologic processes, virus activity and cognate T cell-mediated inflammation.

Noninvasive Assessment of the Alloimmune Response in Kidney Transplantation

Transplantation remains the optimal mode of kidney replacement therapy, but unfortunately long-term graft survival after 1 year remains suboptimal. The main mechanism of chronic allograft injury is alloimmune, and current clinical monitoring of kidney transplants includes measuring serum creatinine, proteinuria, and immunosuppressive drug levels. The most important biomarker routinely monitored is human leukocyte antigen (HLA) donor-specific antibodies (DSAs) with the frequency based on underlying immunologic risk. HLA-DSA should be measured if there is graft dysfunction, immunosuppression minimization, or nonadherence. Antibody strength is semiquantitatively estimated as mean fluorescence intensity, with titration studies for equivocal cases and for following response to treatment. Determination of in vitro C1q or C3d positivity or HLA-DSA IgG subclass analysis remains of uncertain significance, but we do not recommend these for routine use. Current evidence does not support routine monitoring of non-HLA antibodies except anti-angiotensin II type 1 receptor antibodies when the phenotype is appropriate. The monitoring of both donor-derived cell-free DNA in blood or gene expression profiling of serum and/or urine may detect subclinical rejection, although mainly as a supplement and not as a replacement for biopsy. The optimal frequency and cost-effectiveness of using these noninvasive assays remain to be determined. We review the available literature and make recommendations.

Pure T-cell mediated rejection following kidney transplant according to response to treatment

The focus of studies on kidney transplantation (KT) has largely shifted from T-cell mediated rejection (TCMR) to antibody-mediated rejection (ABMR). However, there are still cases of pure acute TCMR in histological reports, even after a long time following transplant. We thus evaluated the impact of pure TCMR on graft survival (GS) according to treatment response. We also performed molecular diagnosis using a molecular microscope diagnostic system on a separate group of 23 patients. A total of 63 patients were divided into non-responders (N = 22) and responders (N = 44). Non-response to rejection treatment was significantly associated with the following factors: glomerular filtration rate (GFR) at biopsy, ΔGFR, TCMR within one year, t score, and IF/TA score. We also found that non-responder vs. responder (OR = 3.31; P = 0.036) and lower GFR at biopsy (OR = 0.56; P = 0.026) were independent risk factors of graft failure. The responders had a significantly superior overall GS rate compared with the non-responders (P = 0.004). Molecular assessment showed a good correlation with histologic diagnosis in ABMR, but not in TCMR. Solitary TCMR was a significant risk factor of graft failure in patients who did not respond to rejection treatment.

Advances in single-cell sequencing: insights from organ transplantation

Single-cell RNA sequencing (scRNA-seq) is a comprehensive technical tool to analyze intracellular and intercellular interaction data by whole transcriptional profile analysis. Here, we describe the application in biomedical research, focusing on the immune system during organ transplantation and rejection. Unlike conventional transcriptome analysis, this method provides a full map of multiple cell populations in one specific tissue and presents a dynamic and transient unbiased method to explore the progression of allograft dysfunction, starting from the stress response to final graft failure. This promising sequencing technology remarkably improves individualized organ rejection treatment by identifying decisive cellular subgroups and cell-specific interactions.

Challenges of Diagnosing Antibody-Mediated Rejection: The Role of Invasive and Non-Invasive Biomarkers

Kidney transplantation is the best treatment modality for end-stage kidney disease, leading to improvement in a patient’s quality and quantity of life. With significant improvements in short-term outcomes, prolonging long-term allograft and patient survival remain ongoing challenges. The ability to monitor allograft function, immune tolerance and predict rejection accurately would enable personalization and better prognostication during post-transplant care. Though kidney biopsy remains the backbone of transplant diagnostics, emerging biomarkers can help detecting kidney allograft injury early enough to prevent permanent damage and detect injury before it is clinically apparent. In this review, we summarize the recent biomarkers that have shown promise in the prediction of acute rejection with a focus on antibody-mediated rejection in kidney transplantation.

Histologic Antibody-Mediated Kidney Allograft Rejection in the Absence of Donor Specific HLA Antibodies

Histologic antibody-mediated rejection (hAMR) is defined as a kidney allograft biopsy satisfying the first 2 Banff criteria for diagnosing antibody-mediated rejection (AMR): tissue injury and evidence of current/recent antibody interaction with the endothelium. In approximately one-half of such cases, circulating HLA donor specific antibodies (DSA) are not detectable by current methodology at the time of biopsy. Some studies indicated a better prognosis for HLA-DSA-negative cases of hAMR compared to those with detectable HLA-DSA, whereas others found equally poor survival compared to hAMR-negative cases. We reviewed the literature regarding the pathophysiology of HLA-DSA-negative hAMR. We find 3 nonmutually exclusive possibilities: 1) HLA-DSA are involved, but just not detected; 2) non-HLA DSA (allo- or autoantibodies) are pathogenically involved; and/or 3) antibody-independent NK cell activation is mediating the process through "missing self" or other activating mechanisms. These possibilities are discussed in detail. Recommendations regarding the approach to such patients are made. Clearly, more research is necessary regarding the measurement of non-HLA antibodies, recipient/donor NK cell genotyping, and the use of antibody reduction therapy or other immunosuppression in any subset of patients with HLA-DSA-negative hAMR.

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.

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.

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.

CXCR4 inhibition in human pancreatic and colorectal cancers induces an integrated immune response

Inhibition of the chemokine receptor CXCR4 in combination with blockade of the PD-1/PD-L1 T cell checkpoint induces T cell infiltration and anticancer responses in murine and human pancreatic cancer. Here we elucidate the mechanism by which CXCR4 inhibition affects the tumor immune microenvironment. In human immune cell-based chemotaxis assays, we find that CXCL12-stimulated CXCR4 inhibits the directed migration mediated by CXCR1, CXCR3, CXCR5, CXCR6, and CCR2, respectively, chemokine receptors expressed by all of the immune cell types that participate in an integrated immune response. Inhibiting CXCR4 in an experimental cancer medicine study by 1-wk continuous infusion of the small-molecule inhibitor AMD3100 (plerixafor) induces an integrated immune response that is detected by transcriptional analysis of paired biopsies of metastases from patients with microsatellite stable colorectal and pancreatic cancer. This integrated immune response occurs in three other examples of immune-mediated damage to noninfected tissues: Rejecting renal allografts, melanomas clinically responding to anti-PD1 antibody therapy, and microsatellite instable colorectal cancers. Thus, signaling by CXCR4 causes immune suppression in human pancreatic ductal adenocarcinoma and colorectal cancer by impairing the function of the chemokine receptors that mediate the intratumoral accumulation of immune cells.

A Rejection Gene Expression Score in Indication and Surveillance Biopsies Is Associated with Graft Outcome

Rejection-associated gene expression has been characterized in renal allograft biopsies for cause. The aim is to evaluate rejection gene expression in subclinical rejection and in biopsies with borderline changes or interstitial fibrosis and tubular atrophy (IFTA). We included 96 biopsies. Most differentially expressed genes between normal surveillance biopsies (n = 17) and clinical rejection (n = 12) were obtained. A rejection-associated gene (RAG) score was defined as its geometric mean. The following groups were considered: (a) subclinical rejection (REJ-S, n = 6); (b) borderline changes in biopsies for cause (BL-C, n = 13); (c) borderline changes in surveillance biopsies (BL-S, n = 12); (d) IFTA in biopsies for cause (IFTA-C, n = 20); and (e) IFTA in surveillance biopsies (IFTA-S, n = 16). The outcome variable was death-censored graft loss or glomerular filtration rate decline ≥ 30 % at 2 years. A RAG score containing 109 genes derived from normal and clinical rejection (area under the curve, AUC = 1) was employed to classify the study groups. A positive RAG score was observed in 83% REJ-S, 38% BL-C, 17% BL-S, 25% IFTA-C, and 5% IFTA-S. A positive RAG score was an independent predictor of graft outcome from histological diagnosis (hazard ratio: 3.5 and 95% confidence interval: 1.1–10.9; p = 0.031). A positive RAG score predicts graft outcome in surveillance and for cause biopsies with a less severe phenotype than clinical rejection.

The impact of belatacept on the phenotypic heterogeneity of renal T cell-mediated alloimmune response: The critical role of maintenance treatment and inflammatory load

Belatacept offers superior long-term outcome relative to calcineurin inhibitor (CNI)-based immunosuppression. However, the higher frequency of early T cell-mediated rejection (TCMR) in belatacept-treated patients hampered the widespread adoption of costimulation blockade. Here, we applied gene expression analysis and whole-slide inflammatory cell quantification to assess the impact of belatacept on intragraft immune signature. We studied formalin-fixed, paraffin-embedded renal biopsies from 92 patients stratified by histopathologic diagnosis (TCMR, borderline changes, or normal) and immunosuppression regimen (belatacept, CNI). An interaction model was built to explore maintenance treatment-dependent expression level changes of immune response-related genes across diagnostic categories of normal, borderline changes, and TCMR. Ninety-one percent of genes overexpressed in TCMR showed significant correlation with whole section inflammatory load. There were 27 genes that had a positive association with belatacept treatment. These were mostly related to myeloid cells and innate immunity. Genes negatively associated with costimulation blockade (n = 14) could be linked to B-cell differentiation and proliferation. We concluded that expression levels of genes characteristic of TCMR are strongly interconnected with quantitative changes of the biopsy inflammatory load. Our results might suggest differential involvement of the innate immune system, and an altered B-cell engagement during TCMR in belatacept-treated patients relative to CNI-treated referents.

Banff 2019 Meeting Report: Molecular diagnostics in solid organ transplantation-Consensus for the Banff Human Organ Transplant (B-HOT) gene panel and open source multicenter validation

This meeting report from the XV Banff conference describes the creation of a multiorgan transplant gene panel by the Banff Molecular Diagnostics Working Group (MDWG). This Banff Human Organ Transplant (B-HOT) panel is the culmination of previous work by the MDWG to identify a broadly useful gene panel based on whole transcriptome technology. A data-driven process distilled a gene list from peer-reviewed comprehensive microarray studies that discovered and validated their use in kidney, liver, heart, and lung transplant biopsies. These were supplemented by genes that define relevant cellular pathways and cell types plus 12 reference genes used for normalization. The 770 gene B-HOT panel includes the most pertinent genes related to rejection, tolerance, viral infections, and innate and adaptive immune responses. This commercially available panel uses the NanoString platform, which can quantitate transcripts from formalin-fixed paraffin-embedded samples. The B-HOT panel will facilitate multicenter collaborative clinical research using archival samples and permit the development of an open source large database of standardized analyses, thereby expediting clinical validation studies. The MDWG believes that a pathogenesis and pathway based molecular approach will be valuable for investigators and promote therapeutic decision-making and clinical trials.

Key driver genes as potential therapeutic targets in renal allograft rejection

Acute rejection (AR) in renal transplantation is an established risk factor for reduced allograft survival. Molecules with regulatory control among immune pathways of AR that are inadequately suppressed, despite standard-of-care immunosuppression, could serve as important targets for therapeutic manipulation to prevent rejection. Here, an integrative, network-based computational strategy incorporating gene expression and genotype data of human renal allograft biopsy tissue was applied, to identify the master regulators - the key driver genes (KDGs) - within dysregulated AR pathways. A 982-meta-gene signature with differential expression in AR versus non-AR was identified from a meta-analysis of microarray data from 735 human kidney allograft biopsy samples across 7 data sets. Fourteen KDGs were derived from this signature. Interrogation of 2 publicly available databases identified compounds with predicted efficacy against individual KDGs or a key driver-based gene set, respectively, which could be repurposed for AR prevention. Minocycline, a tetracycline antibiotic, was chosen for experimental validation in a murine cardiac allograft model of AR. Minocycline attenuated the inflammatory profile of AR compared with controls and when coadministered with immunosuppression prolonged graft survival. This study demonstrates that a network-based strategy, using expression and genotype data to predict KDGs, assists target prioritization for therapeutics in renal allograft rejection.

Treatment of borderline infiltrates with minimal inflammation in kidney transplant recipients has no effect on allograft or patient outcomes

In 2005, the Banff committee expanded the "borderline changes" category to include lesions with minimal (<10%) inflammation: "i0" borderline infiltrates. Clinical significance and optimal treatment of i0 borderline infiltrates are not known. Data suggest that i0 borderline infiltrates may have a more favorable prognosis than borderline infiltrates with higher grades of interstitial inflammation. In this single-center, retrospective, observational study, we assessed 90 renal transplant recipients with i0 borderline infiltrates on biopsies indicated for graft dysfunction. We studied the impact of treatment with corticosteroids on allograft function, allograft survival, and patient survival. We found no differences between treated and untreated groups with respect to eGFR at 4 weeks and 6 months after biopsy. Follow-up biopsies, available in 67% of patients, were negative for rejection in almost half of all cases, regardless of treatment status. The frequencies of persistent borderline infiltrates (38%) and higher-grade T cell-mediated rejection (1A or greater, 14%) on follow-up biopsies were similar between the two groups. There were no differences in rejection-free allograft survival, death-censored graft failure, or patient mortality among treated vs non-treated i0 borderline patients. Our findings suggest that the natural history of i0 borderline infiltrates, in relatively low immunologic risk patients, is not affected by corticosteroid treatment.

The Use of Donor-Derived Cell-Free DNA for Assessment of Allograft Rejection and Injury Status

Patient monitoring after kidney transplantation (KT) for early detection of allograft rejection remains key in preventing allograft loss. Serum creatinine has poor predictive value to detect ongoing active rejection as its increase is not sensitive, nor specific for acute renal allograft rejection. Diagnosis of acute rejection requires allograft biopsy and histological assessment, which can be logistically challenging in some cases and carries inherent risk for complications related to procedure. Donor-derived cell-free DNA (dd-cfDNA), DNA of donor origin in the blood of KT recipient arising from cells undergoing injury and death, has been examined as a potential surrogate marker for allograft rejection. A rise in dd-cfDNA levels precedes changes in serum creatinine allows early detections and use as a screening tool for allograft rejection. In addition, when used in conjunction with donor-specific antibodies (DSA), it increases the pre-biopsy probability of antibody-mediated rejection (ABMR) aiding the decision-making process. Advancements in noninvasive biomarker assays such as dd-cfDNA may offer the opportunity to improve and expand the spectrum of available diagnostic tools to monitor and detect risk for rejection and positively impact outcomes for KT recipients. In this this article, we discussed the evolution of dd-cfDNA assays and recent evidence of assessment of allograft rejection and injury status of KT by the use of dd-cfDNA.

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.

Molecular Assessment of Kidney Allografts: Are We Closer to a Daily Routine?

Kidney allograft pathology assessment has been traditionally based on clinical and histological criteria. Despite improvements in Banff histological classification, the diagnostics in particular cases is problematic reflecting a complex pathogenesis of graft injuries. With the advent of molecular techniques, polymerase-chain reaction, oligo- and microarray technologies allowed to study molecular phenotypes of graft injuries, especially acute and chronic rejections. Moreover, development of the molecular microscope diagnostic system (MMDx) to assess kidney graft biopsies, represents the first clinical application of a microarray-based method in transplantation. Whether MMDx may replace conventional pathology is the subject of ongoing research, however this platform is particularly useful in complex histological findings and may help clinicians to guide the therapy.

Generating automated kidney transplant biopsy reports combining molecular measurements with ensembles of machine learning classifiers

We previously reported a system for assessing rejection in kidney transplant biopsies using microarray-based gene expression data, the Molecular Microscope^® Diagnostic System (MMDx). The present study was designed to optimize the accuracy and stability of MMDx diagnoses by replacing single machine learning classifiers with ensembles of diverse classifier methods. We also examined the use of automated report sign-outs and the agreement between multiple human interpreters of the molecular results. Ensembles generated diagnoses that were both more accurate than the best individual classifiers, and nearly as stable as the best, consistent with expectations from the machine learning literature. Human experts had ≈93% agreement (balanced accuracy) signing out the reports, and random forest-based automated sign-outs showed similar levels of agreement with the human experts (92% and 94% for predicting the expert MMDx sign-outs for T cell-mediated (TCMR) and antibody-mediated rejection (ABMR), respectively). In most cases disagreements, whether between experts or between experts and automated sign-outs, were in biopsies near diagnostic thresholds. Considerable disagreement with histology persisted. The balanced accuracies of MMDx sign-outs for histology diagnoses of TCMR and ABMR were 73% and 78%, respectively. Disagreement with histology is largely due to the known noise in histology assessments (ClinicalTrials.gov NCT01299168).

Defining housekeeping genes suitable for RNA-seq analysis of the human allograft kidney biopsy tissue

BACKGROUND

RNA-seq is poised to play a major role in the management of kidney transplant patients. Rigorous definition of housekeeping genes (HKG) is essential for further progress in this field. Using single genes or a limited set HKG is inherently problematic since their expression might be altered by specific diseases in the patients being studied.

METHODS

To generate a HKG set specific for kidney transplantation, we performed RNA-sequencing from renal allograft biopsies collected in a variety of clinical settings. Various normalization methods were applied to identify transcripts that had a coefficient of variation of expression that was below the 2nd percentile across all samples, and the corresponding genes were designated as housekeeping genes. Comparison with transcriptomic data from the Gene Expression Omnibus (GEO) database, pathway analysis and molecular biological functions were utilized to validate the housekeeping genes set.

RESULTS

We have developed a bioinformatics solution to this problem by using nine different normalization methods to derive large HKG gene sets from a RNA-seq data set of 47,611 transcripts derived from 30 biopsies. These biopsies were collected in a variety of clinical settings, including normal function, acute rejection, interstitial nephritis, interstitial fibrosis/tubular atrophy and polyomavirus nephropathy. Transcripts with coefficient of variation below the 2nd percentile were designated as HKG, and validated by showing their virtual absence in diseased allograft derived transcriptomic data sets available in the GEO. Pathway analysis indicated a role for these genes in maintenance of cell morphology, pyrimidine metabolism, and intracellular protein signaling.

CONCLUSIONS

Utilization of these objectively defined HKG data sets will guard against errors resulting from focusing on individual genes like 18S RNA, actin & tubulin, which do not maintain constant expression across the known spectrum of renal allograft pathology.

Molecular phenotype of kidney transplant indication biopsies with inflammation in scarred areas

In kidney transplant biopsies, inflammation in areas of atrophy-fibrosis (i-IFTA) is associated with increased risk of failure, presumably because inflammation is evoked by recent parenchymal injury from rejection or other insults, but some cases also have rejection. The present study explored the frequency of rejection in i-IFTA, by using histology Banff 2015 and a microarray-based molecular diagnostic system (MMDx). In unselected indication biopsies (108 i-IFTA, 73 uninflamed IFTA [i0-IFTA], and 53 no IFTA), i-IFTA biopsies occurred later, showed more scarring, and had more antibody-mediated rejection (ABMR) based on histology (28%) and MMDx (45%). T cell-mediated rejection (TCMR) was infrequent in i-IFTA based on histology (8%) and MMDx (16%). Twelve i-IFTA biopsies (11%) had molecular TCMR not diagnosed by histology, although 6 were called borderline and almost all had histologic TCMR lesions. The prominent feature of i-IFTA biopsies was molecular injury (eg, acute kidney injury [AKI] transcripts). In multivariate analysis of biopsies >1 year posttransplant, the strongest associations with graft loss were AKI transcripts and histologic atrophy-scarring; i-IFTA was not significant when molecular AKI was included. We conclude that i-IFTA in indication biopsies reflects recent/ongoing parenchymal injury, often with concomitant ABMR but few with TCMR. Thus, the application of Banff i-IFTA in the population of late biopsies needs to be reconsidered.

Predictability of human differential gene expression

The identification of genes that are differentially expressed provides a molecular foothold onto biological questions of interest. Whether some genes are more likely to be differentially expressed than others, and to what degree, has never been assessed on a global scale. Here, we reanalyze more than 600 studies and find that knowledge of a gene’s prior probability of differential expression (DE) allows for accurate prediction of DE hit lists, regardless of the biological question. This result suggests redundancy in transcriptomics experiments that both informs gene set interpretation and highlights room for growth within the field.

Differential expression (DE) is commonly used to explore molecular mechanisms of biological conditions. While many studies report significant results between their groups of interest, the degree to which results are specific to the question at hand is not generally assessed, potentially leading to inaccurate interpretation. This could be particularly problematic for metaanalysis where replicability across datasets is taken as strong evidence for the existence of a specific, biologically relevant signal, but which instead may arise from recurrence of generic processes. To address this, we developed an approach to predict DE based on an analysis of over 600 studies. A predictor based on empirical prior probability of DE performs very well at this task (mean area under the receiver operating characteristic curve, ∼0.8), indicating that a large fraction of DE hit lists are nonspecific. In contrast, predictors based on attributes such as gene function, mutation rates, or network features perform poorly. Genes associated with sex, the extracellular matrix, the immune system, and stress responses are prominent within the “DE prior.” In a series of control studies, we show that these patterns reflect shared biology rather than technical artifacts or ascertainment biases. Finally, we demonstrate the application of the DE prior to data interpretation in three use cases: (i) breast cancer subtyping, (ii) single-cell genomics of pancreatic islet cells, and (iii) metaanalysis of lung adenocarcinoma and renal transplant rejection transcriptomics. In all cases, we find hallmarks of generic DE, highlighting the need for nuanced interpretation of gene phenotypic associations.