Microvascular Inflammation of Kidney Allografts and Clinical Outcomes.

BACKGROUND: The heterogeneous clinical presentation of graft microvascular inflammation poses a major challenge to successful kidney transplantation. The effect of microvascular inflammation on allograft outcomes is unclear. METHODS: We conducted a cohort study that included kidney-transplant recipients from more than 30 transplantation centers in Europe and North America who had undergone allograft biopsy between 2004 and 2023. We integrated clinical and pathological data to classify biopsy specimens according to the 2022 Banff Classification of Renal Allograft Pathology, which includes two new diagnostic categories: probable antibody-mediated rejection and microvascular inflammation without evidence of an antibody-mediated response. We then assessed the association between the newly recognized microvascular inflammation phenotypes and allograft survival and disease progression. RESULTS: A total of 16,293 kidney-transplant biopsy specimens from 6798 patients were assessed. We identified the newly recognized microvascular inflammation phenotypes in 788 specimens, of which 641 were previously categorized as specimens with no evidence of rejection. As compared with patients without rejection, the hazard ratio for graft loss was 2.1 (95% confidence interval [CI], 1.5 to 3.1) among patients with microvascular inflammation without evidence of an antibody-mediated response and 2.7 (95% CI, 2.2 to 3.3) among patients with antibody-mediated rejection. Patients with a diagnosis of probable antibody-mediated rejection had a higher risk of graft failure beyond year 5 after biopsy than those without rejection (hazard ratio, 1.7; 95% CI, 0.8 to 3.5). Patients with a diagnosis of either newly recognized microvascular inflammation phenotype had a higher risk of progression of transplant glomerulopathy during follow-up than patients without microvascular inflammation. CONCLUSIONS: Microvascular inflammation in kidney allografts includes distinct phenotypes, with various disease progression and allograft outcomes. Our findings support the clinical use of additional rejection phenotypes to standardize diagnostics for kidney allografts. (Funded by OrganX. ClinicalTrials.gov number, NCT06496269.).

Prognostic Biomarkers in Kidney Transplantation: A Systematic Review and Critical Appraisal.

SIGNIFICANCE STATEMENT: Why are there so few biomarkers accepted by health authorities and implemented in clinical practice, despite the high and growing number of biomaker studies in medical research ? In this meta-epidemiological study, including 804 studies that were critically appraised by expert reviewers, the authors have identified all prognostic kidney transplant biomarkers and showed overall suboptimal study designs, methods, results, interpretation, reproducible research standards, and transparency. The authors also demonstrated for the first time that the limited number of studies challenged the added value of their candidate biomarkers against standard-of-care routine patient monitoring parameters. Most biomarker studies tended to be single-center, retrospective studies with a small number of patients and clinical events. Less than 5% of the studies performed an external validation. The authors also showed the poor transparency reporting and identified a data beautification phenomenon. These findings suggest that there is much wasted research effort in transplant biomarker medical research and highlight the need to produce more rigorous studies so that more biomarkers may be validated and successfully implemented in clinical practice. BACKGROUND: Despite the increasing number of biomarker studies published in the transplant literature over the past 20 years, demonstrations of their clinical benefit and their implementation in routine clinical practice are lacking. We hypothesized that suboptimal design, data, methodology, and reporting might contribute to this phenomenon. METHODS: We formed a consortium of experts in systematic reviews, nephrologists, methodologists, and epidemiologists. A systematic literature search was performed in PubMed, Embase, Scopus, Web of Science, and Cochrane Library between January 1, 2005, and November 12, 2022 (PROSPERO ID: CRD42020154747). All English language, original studies investigating the association between a biomarker and kidney allograft outcome were included. The final set of publications was assessed by expert reviewers. After data collection, two independent reviewers randomly evaluated the inconsistencies for 30% of the references for each reviewer. If more than 5% of inconsistencies were observed for one given reviewer, a re-evaluation was conducted for all the references of the reviewer. The biomarkers were categorized according to their type and the biological milieu from which they were measured. The study characteristics related to the design, methods, results, and their interpretation were assessed, as well as reproducible research practices and transparency indicators. RESULTS: A total of 7372 publications were screened and 804 studies met the inclusion criteria. A total of 1143 biomarkers were assessed among the included studies from blood ( n =821, 71.8%), intragraft ( n =169, 14.8%), or urine ( n =81, 7.1%) compartments. The number of studies significantly increased, with a median, yearly number of 31.5 studies (interquartile range [IQR], 23.8-35.5) between 2005 and 2012 and 57.5 (IQR, 53.3-59.8) between 2013 and 2022 ( P < 0.001). A total of 655 studies (81.5%) were retrospective, while 595 (74.0%) used data from a single center. The median number of patients included was 232 (IQR, 96-629) with a median follow-up post-transplant of 4.8 years (IQR, 3.0-6.2). Only 4.7% of studies were externally validated. A total of 346 studies (43.0%) did not adjust their biomarker for key prognostic factors, while only 3.1% of studies adjusted the biomarker for standard-of-care patient monitoring factors. Data sharing, code sharing, and registration occurred in 8.8%, 1.1%, and 4.6% of studies, respectively. A total of 158 studies (20.0%) emphasized the clinical relevance of the biomarker, despite the reported nonsignificant association of the biomarker with the outcome measure. A total of 288 studies assessed rejection as an outcome. We showed that these rejection studies shared the same characteristics as other studies. CONCLUSIONS: Biomarker studies in kidney transplantation lack validation, rigorous design and methodology, accurate interpretation, and transparency. Higher standards are needed in biomarker research to prove the clinical utility and support clinical use.

Competing and Noncompeting Risk Models for Predicting Kidney Allograft Failure.

BACKGROUND: Prognostic models are becoming increasingly relevant in clinical trials as potential surrogate endpoints, and for patient management as clinical decision support tools. However, the impact of competing risks on model performance remains poorly investigated. We aimed to carefully assess the performance of competing risk and noncompeting risk models in the context of kidney transplantation, where allograft failure and death with a functioning graft are two competing outcomes. METHODS: We included 11,046 kidney transplant recipients enrolled in 10 countries. We developed prediction models for long-term kidney graft failure prediction, without accounting (i.e., censoring) and accounting for the competing risk of death with a functioning graft, using Cox, Fine-Gray, and cause-specific Cox regression models. To this aim, we followed a detailed and transparent analytical framework for competing and noncompeting risk modelling, and carefully assessed the models' development, stability, discrimination, calibration, overall fit, clinical utility, and generalizability in external validation cohorts and subpopulations. More than 15 metrics were used to provide an exhaustive assessment of model performance. RESULTS: Among 11,046 recipients in the derivation and validation cohorts, 1,497 (14%) lost their graft and 1,003 (9%) died with a functioning graft after a median follow-up post-risk evaluation of 4.7 years (IQR 2.7-7.0). The cumulative incidence of graft loss was similarly estimated by Kaplan-Meier and Aalen-Johansen methods (17% versus 16% in the derivation cohort). Cox and competing risk models showed similar and stable risk estimates for predicting long-term graft failure (average mean absolute prediction error of 0.0140, 0.0138 and 0.0135 for Cox, Fine-Gray, and cause-specific Cox models, respectively). Discrimination and overall fit were comparable in the validation cohorts, with concordance index ranging from 0.76 to 0.87. Across various subpopulations and clinical scenarios, the models performed well and similarly, although in some high-risk groups (such as donors over 65 years old), the findings suggest a trend towards moderately improved calibration when using a competing risk approach. CONCLUSIONS: Competing and noncompeting risk models performed similarly in predicting long-term kidney graft failure.

Evaluation of non-invasive biomarkers of kidney allograft rejection in a prospective multicenter unselected cohort study (EU-TRAIN).

Non-invasive biomarkers are promising tools for improving kidney allograft rejection monitoring, but their clinical adoption requires more evidence in specifically designed studies. To address this unmet need, we designed the EU-TRAIN study, a large prospective multicentric unselected cohort funded by the European Commission. Here, we included consecutive adult patients who received a kidney allograft in nine European transplant centers between November 2018 and June 2020. We prospectively assessed gene expression levels of 19 blood messenger RNAs, four antibodies targeting non-human leukocyte antigen (HLA) endothelial antigens, together with circulating anti-HLA donor-specific antibodies (DSA). The primary outcome was allograft rejection (antibody-mediated, T cell-mediated, or mixed) in the first year post-transplantation. Overall, 412 patients were included, with 812 biopsies paired with a blood sample. CD4 gene expression was significantly associated with rejection, while circulating anti-HLA DSA had a significant association with allograft rejection and a strong association with antibody-mediated rejection. All other tested biomarkers, including AKR1C3, CD3E, CD40, CD8A, CD9, CTLA4, ENTPD1, FOXP3, GZMB, ID3, IL7R, MS4A1, MZB1, POU2AF1, POU2F1, TCL1A, TLR4, and TRIB1, as well as antibodies against angiotensin II type 1 receptor, endothelin 1 type A receptor, C3a and C5a receptors, did not show significant associations with allograft rejection. The blood messenger RNAs and non-HLA antibodies did not show an additional value beyond standard of care monitoring parameters and circulating anti-HLA DSA to predict allograft rejection in the first year post-transplantation. Thus, our results open avenues for specifically designed studies to demonstrate the clinical relevance and implementation of other candidate non-invasive biomarkers in kidney transplantation practice.

De novo thrombotic microangiopathy after kidney transplantation in adults: Interplay between complement genetics and multiple endothelial injury.

De novo thrombotic microangiopathy (dnTMA), after renal transplantation may significantly alter graft outcomes. However, its pathogenesis and the role of complement alternative pathway dysregulation remain elusive. We studied all consecutive adult patients with a kidney allograft biopsy performed between January 2004 and March 2016 displaying dnTMA. Ninety-two patients were included. The median time of occurrence was 166 (IQR 25-811) days. The majority (82.6 %) had TMA localized only in the graft. Calcineurin inhibitor toxicity and antibody-mediated rejection (ABMR) were the 2 most frequent causes (54.3% and 37.0%, respectively). However, etiological factors were multiple in 37% patients. Interestingly, pathogenic variants in the genes of complement alternative pathway were significantly more frequent in the 42 tested patients than in healthy controls (16.7% vs 3.7% respectively, P < .008). The overall graft survival after biopsy was 66.0% at 5 years and 23.4% at 10 years, significantly worse than a matched cohort without TMA. Moreover, graft survival of patients with TMA and ABMR was worse than a matched cohort with ABMR without TMA. The 2 main prognostic factors were a positive C4d staining and a lower estimated glomerular filtration rate at diagnosis. DnTMA is a severe and multifactorial disease, induced by 1 or several endothelium-insulting conditions, mostly calcineurin inhibitor toxicity and ABMR.

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

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

The Banff 2022 Kidney Meeting Report: Reappraisal of microvascular inflammation and the role of biopsy-based transcript diagnostics.

The XVI-th Banff Meeting for Allograft Pathology was held at Banff, Alberta, Canada, from 19th to 23rd September 2022, as a joint meeting with the Canadian Society of Transplantation. To mark the 30th anniversary of the first Banff Classification, premeeting discussions were held on the past, present, and future of the Banff Classification. This report is a summary of the meeting highlights that were most important in terms of their effect on the Classification, including discussions around microvascular inflammation and biopsy-based transcript analysis for diagnosis. In a postmeeting survey, agreement was reached on the delineation of the following phenotypes: (1) "Probable antibody-mediated rejection (AMR)," which represents donor-specific antibodies (DSA)-positive cases with some histologic features of AMR but below current thresholds for a definitive AMR diagnosis; and (2) "Microvascular inflammation, DSA-negative and C4d-negative," a phenotype of unclear cause requiring further study, which represents cases with microvascular inflammation not explained by DSA. Although biopsy-based transcript diagnostics are considered promising and remain an integral part of the Banff Classification (limited to diagnosis of AMR), further work needs to be done to agree on the exact classifiers, thresholds, and clinical context of use.

The Banff 2022 Kidney Meeting Work Plan: Data-driven refinement of the Banff Classification for renal allografts.

The XVIth Banff Meeting for Allograft Pathology was held in Banff, Alberta, Canada, from September 19 to 23, 2022, as a joint meeting with the Canadian Society of Transplantation. In addition to a key focus on the impact of microvascular inflammation and biopsy-based transcript analysis on the Banff Classification, further sessions were devoted to other aspects of kidney transplant pathology, in particular T cell-mediated rejection, activity and chronicity indices, digital pathology, xenotransplantation, clinical trials, and surrogate endpoints. Although the output of these sessions has not led to any changes in the classification, the key role of Banff Working Groups in phrasing unanswered questions, and coordinating and disseminating results of investigations addressing these unanswered questions was emphasized. This paper summarizes the key Banff Meeting 2022 sessions not covered in the Banff Kidney Meeting 2022 Report paper and also provides an update on other Banff Working Group activities relevant to kidney allografts.

Antibody Mediated Rejection and T-cell Mediated Rejection Molecular Signatures Using Next-Generation Sequencing in Kidney Transplant Biopsies.

Recently, interest in transcriptomic assessment of kidney biopsies has been growing. This study investigates the use of NGS to identify gene expression changes and analyse the pathways involved in rejection. An Illumina bulk RNA sequencing on the polyadenylated RNA of 770 kidney biopsies was conducted. Differentially-expressed genes (DEGs) were determined for AMR and TCMR using DESeq2. Genes were segregated according to their previous descriptions in known panels (microarray or the Banff Human Organ Transplant (B-HOT) panel) to obtain NGS-specific genes. Pathway enrichment analysis was performed using the Reactome and Kyoto Encyclopaedia of Genes and Genomes (KEGG) public repositories. The differential gene expression using NGS analysis identified 6,141 and 8,478 transcripts associated with AMR and TCMR. While most of the genes identified were included in the microarray and the B-HOT panels, NGS analysis identified 603 (9.8%) and 1,186 (14%) new specific genes. Pathways analysis showed that the B-HOT panel was associated with the main immunological processes involved during AMR and TCMR. The microarrays specifically integrated metabolic functions and cell cycle progression processes. Novel NGS-specific based transcripts associated with AMR and TCMR were discovered, which might represent a novel source of targets for drug designing and repurposing.

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

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

Machine learning does not outperform traditional statistical modelling for kidney allograft failure prediction.

Machine learning (ML) models have recently shown potential for predicting kidney allograft outcomes. However, their ability to outperform traditional approaches remains poorly investigated. Therefore, using large cohorts of kidney transplant recipients from 14 centers worldwide, we developed ML-based prediction models for kidney allograft survival and compared their prediction performances to those achieved by a validated Cox-Based Prognostication System (CBPS). In a French derivation cohort of 4000 patients, candidate determinants of allograft failure including donor, recipient and transplant-related parameters were used as predictors to develop tree-based models (RSF, RSF-ERT, CIF), Support Vector Machine models (LK-SVM, AK-SVM) and a gradient boosting model (XGBoost). Models were externally validated with cohorts of 2214 patients from Europe, 1537 from North America, and 671 from South America. Among these 8422 kidney transplant recipients, 1081 (12.84%) lost their grafts after a median post-transplant follow-up time of 6.25 years (Inter Quartile Range 4.33-8.73). At seven years post-risk evaluation, the ML models achieved a C-index of 0.788 (95% bootstrap percentile confidence interval 0.736-0.833), 0.779 (0.724-0.825), 0.786 (0.735-0.832), 0.527 (0.456-0.602), 0.704 (0.648-0.759) and 0.767 (0.711-0.815) for RSF, RSF-ERT, CIF, LK-SVM, AK-SVM and XGBoost respectively, compared with 0.808 (0.792-0.829) for the CBPS. In validation cohorts, ML models' discrimination performances were in a similar range of those of the CBPS. Calibrations of the ML models were similar or less accurate than those of the CBPS. Thus, when using a transparent methodological pipeline in validated international cohorts, ML models, despite overall good performances, do not outperform a traditional CBPS in predicting kidney allograft failure. Hence, our current study supports the continued use of traditional statistical approaches for kidney graft prognostication.

Qualifying a novel clinical trial endpoint (iBOX) predictive of long-term kidney transplant outcomes.

New immunosuppressive therapies that improve long-term graft survival are needed in kidney transplant. Critical Path Institute's Transplant Therapeutics Consortium received a qualification opinion for the iBOX Scoring System as a novel secondary efficacy endpoint for kidney transplant clinical trials through European Medicines Agency's qualification of novel methodologies for drug development. This is the first qualified endpoint for any transplant indication and is now available for use in kidney transplant clinical trials. Although the current efficacy failure endpoint has typically shown the noninferiority of therapeutic regimens, the iBOX Scoring System can be used to demonstrate the superiority of a new immunosuppressive therapy compared to the standard of care from 6 months to 24 months posttransplant in pivotal or exploratory drug therapeutic studies.

Validation of a prediction system for risk of kidney allograft failure in pediatric kidney transplant recipients: An international observational study.

Predicting long-term kidney allograft failure is an unmet need for clinical care and clinical trial optimization in children. We aimed to validate a kidney allograft failure risk prediction system in a large international cohort of pediatric kidney transplant recipients. Patients from 20 centers in Europe and the United States, transplanted between 2004 and 2017, were included. Allograft assessment included estimated glomerular filtration rate, urine protein-to-creatinine ratio, circulating antihuman leukocyte antigen donor-specific antibody, and kidney allograft histology. Individual predictions of allograft failure were calculated using the integrative box (iBox) system. Prediction performances were assessed using discrimination and calibration. The allograft evaluations were performed in 706 kidney transplant recipients at a median time of 9.1 (interquartile range, 3.3-19.2) months posttransplant; mean estimated glomerular filtration rate was 68.7 ± 28.1 mL/min/1.73 m2, and median urine protein-to-creatinine ratio was 0.1 (0.0-0.4) g/g, and 134 (19.0%) patients had antihuman leukocyte antigen donor-specific antibodies. The iBox exhibited accurate calibration and discrimination for predicting the outcomes up to 10 years after evaluation, with a C-index of 0.81 (95% confidence interval, 0.75-0.87). This study confirms the generalizability of the iBox to predict long-term kidney allograft failure in children, with performances similar to those reported in adults. These results support the use of the iBox to improve patient monitoring and facilitate clinical trials in children.

Monoclonal gammopathy of unknown significance in kidney transplanted patients: novel insights into long-term outcomes.

BACKGROUND: Because of increased access to kidney transplantation in elderly subjects, the prevalence of monoclonal gammopathies of unknown significance (MGUS) in kidney transplantation (KT) is growing. However, little is known about the consequences of MGUS on long-term outcomes. METHODS: We identified 70 recipients with MGUS present at transplantation (KTMG) and 114 patients with MGUS occurring after KT (DNMG), among 3059 patients who underwent a KT in two French kidney transplantation centers. We compared outcomes of KTMG with those of matched controls. RESULTS: Baseline characteristics were similar except for an older age in KTMG compared with the DNMG group (62 vs 57 years, P = .03). Transient MGUS occurred more frequently in DNMG patients (45% vs 24%, P = .007). When compared with matched controls without MGUS, KTMG patients showed higher frequency and earlier post-transplant solid cancers (15% vs 5%, P = .04) and a trend for more bacterial infections (63% vs 48%, P = .08), without difference regarding patient and graft survival, rejection episodes or hematological complications. KTMG patients with an abnormal kappa/lambda ratio and/or severe hypogammaglobulinemia at the time of KT experienced shorter overall survival. CONCLUSIONS: MGUS detection at the time of KT is neither associated with a higher occurrence of graft rejection, nor adversely affects graft or overall survival. MGUS should not contraindicate KT. However, MGUS at the time of KT may be associated with higher risk of early neoplastic and infectious complications and warrants prolonged surveillance. Measurement of serum free light chain should be performed before transplant to refine the risk evaluation of KTMG patients and propose personalized follow-up and immunosuppression.

Sudden cardiac death after heart transplantation: a population-based study.

AIMS: The epidemiology of sudden cardiac death (SCD) after heart transplantation (HTx) remains imprecisely described. We aimed to assess the incidence and determinants of SCD in a large cohort of HTx recipients, compared with the general population. METHODS AND RESULTS: Consecutive HTx recipients (n = 1246, 2 centres) transplanted between 2004 and 2016 were included. We prospectively assessed clinical, biological, pathologic, and functional parameters. SCD was centrally adjudicated. We compared the SCD incidence beyond the first year post-transplant in this cohort with that observed in the general population of the same geographic area (registry carried out by the same group of investigators; n = 19 706 SCD). We performed a competing risk multivariate Cox model to identify variables associated with SCD. The annual incidence of SCD was 12.5 per 1,000 person-years [95% confidence interval (CI), 9.7-15.9] in the HTx recipients cohort compared with 0.54 per 1,000 person-years (95% CI, 0.53-0.55) in the general population (P < 0.001). The risk of SCD was markedly elevated among the youngest HTx recipients with standardized mortality ratios for SCD up to 837 for recipients ≤30 years. Beyond the first year, SCD was the leading cause of death. Five variables were independently associated with SCD: older donor age (P = 0.003), younger recipient age (P = 0.001) and ethnicity (P = 0.034), pre-existing donor-specific antibodies (P = 0.009), and last left ventricular ejection fraction (P = 0.048). CONCLUSION: HTx recipients, particularly the youngest, were at very high risk of SCD compared with the general population. The consideration of specific risk factors may help identify high-risk subgroups.

Banff Human Organ Transplant Consensus Gene Panel for the Detection of Antibody Mediated Rejection in Heart Allograft Biopsies.

The molecular refinement of the diagnosis of heart allograft rejection based on whole-transcriptome analyses faces several hurdles that greatly limit its widespread clinical application. The targeted Banff Human Organ Transplant gene panel (B-HOT, including 770 genes of interest) has been developed to facilitate reproducible and cost-effective gene expression analysis of solid organ allografts. We aimed to determine in silico the ability of this targeted panel to capture the antibody-mediated rejection (AMR) molecular profile using whole-transcriptome data from 137 heart allograft biopsies (71 biopsies reflecting the entire landscape of histologic AMR, 66 non-AMR control biopsies including cellular rejection and non-rejection cases). Differential gene expression, pathway and network analyses demonstrated that the B-HOT panel captured biologically and clinically relevant genes (IFNG-inducible, NK-cells, injury, monocytes-macrophage, B-cell-related genes), pathways (interleukin and interferon signaling, neutrophil degranulation, immunoregulatory interactions, endothelial activation) and networks reflecting the pathophysiological mechanisms underlying the AMR process previously identified in whole-transcriptome analysis. Our findings support the potential clinical use of the B-HOT-gene panel as a reliable proxy to whole-transcriptome analysis for the gene expression profiling of cardiac allograft rejection.

Qualifying a Novel Clinical Trial Endpoint (iBOX) Predictive of Long-Term Kidney Transplant Outcomes.

New immunosuppressive therapies that improve long-term graft survival are needed in kidney transplant. Critical Path Institute's Transplant Therapeutics Consortium received a qualification opinion for the iBOX Scoring System as a novel secondary efficacy endpoint for kidney transplant clinical trials through European Medicines Agency's qualification of novel methodologies for drug development. This is the first qualified endpoint for any transplant indication and is now available for use in kidney transplant clinical trials. Although the current efficacy failure endpoint has typically shown the noninferiority of therapeutic regimens, the iBOX Scoring System can be used to demonstrate the superiority of a new immunosuppressive therapy compared to the standard of care from 6 months to 24 months posttransplant in pivotal or exploratory drug therapeutic studies.

Data-Driven Chronic Allograft Phenotypes: A Novel and Validated Complement for Histologic Assessment of Kidney Transplant Biopsies.

BACKGROUND: No validated system currently exists to realistically characterize the chronic pathology of kidney transplants that represents the dynamic disease process and spectrum of disease severity. We sought to develop and validate a tool to describe chronicity and severity of renal allograft disease and integrate it with the evaluation of disease activity. METHODS: The training cohort included 3549 kidney transplant biopsies from an observational cohort of 937 recipients. We reweighted the chronic histologic lesions according to their time-dependent association with graft failure, and performed consensus k-means clustering analysis. Total chronicity was calculated as the sum of the weighted chronic lesion scores, scaled to the unit interval. RESULTS: We identified four chronic clusters associated with graft outcome, based on the proportion of ambiguous clustering. The two clusters with the worst survival outcome were determined by interstitial fibrosis and tubular atrophy (IFTA) and by transplant glomerulopathy. The chronic clusters partially overlapped with the existing Banff IFTA classification (adjusted Rand index, 0.35) and were distributed independently of the acute lesions. Total chronicity strongly associated with graft failure (hazard ratio [HR], 8.33; 95% confidence interval [CI], 5.94 to 10.88; P<0.001), independent of the total activity scores (HR, 5.01; 95% CI, 2.83 to 7.00; P<0.001). These results were validated on an external cohort of 4031 biopsies from 2054 kidney transplant recipients. CONCLUSIONS: The evaluation of total chronicity provides information on kidney transplant pathology that complements the estimation of disease activity from acute lesion scores. Use of the data-driven algorithm used in this study, called RejectClass, may provide a holistic and quantitative assessment of kidney transplant injury phenotypes and severity.

Data-driven Derivation and Validation of Novel Phenotypes for Acute Kidney Transplant Rejection using Semi-supervised Clustering.

BACKGROUND: Over the past decades, an international group of experts iteratively developed a consensus classification of kidney transplant rejection phenotypes, known as the Banff classification. Data-driven clustering of kidney transplant histologic data could simplify the complex and discretionary rules of the Banff classification, while improving the association with graft failure. METHODS: The data consisted of a training set of 3510 kidney-transplant biopsies from an observational cohort of 936 recipients. Independent validation of the results was performed on an external set of 3835 biopsies from 1989 patients. On the basis of acute histologic lesion scores and the presence of donor-specific HLA antibodies, stable clustering was achieved on the basis of a consensus of 400 different clustering partitions. Additional information on kidney-transplant failure was introduced with a weighted Euclidean distance. RESULTS: Based on the proportion of ambiguous clustering, six clinically meaningful cluster phenotypes were identified. There was significant overlap with the existing Banff classification (adjusted rand index, 0.48). However, the data-driven approach eliminated intermediate and mixed phenotypes and created acute rejection clusters that are each significantly associated with graft failure. Finally, a novel visualization tool presents disease phenotypes and severity in a continuous manner, as a complement to the discrete clusters. CONCLUSIONS: A semisupervised clustering approach for the identification of clinically meaningful novel phenotypes of kidney transplant rejection has been developed and validated. The approach has the potential to offer a more quantitative evaluation of rejection subtypes and severity, especially in situations in which the current histologic categorization is ambiguous.

Assessment of the Utility of Kidney Histology as a Basis for Discarding Organs in the United States: A Comparison of International Transplant Practices and Outcomes.

BACKGROUND: Many kidneys donated for transplant in the United States are discarded because of abnormal histology. Whether histology adds incremental value beyond usual donor attributes in assessing allograft quality is unknown. METHODS: This population-based study included patients who received a deceased donor kidney that had been biopsied before implantation according to a prespecified protocol in France and Belgium, where preimplantation biopsy findings are generally not used for decision making in the allocation process. We also studied kidneys that had been acquired from deceased United States donors for transplantation that were biopsied during allocation and discarded because of low organ quality. Using donor and recipient characteristics, we fit multivariable Cox models for death-censored graft failure and examined whether predictive accuracy (C index) improved after adding donor histology. We matched the discarded United States kidneys to similar kidneys transplanted in Europe and calculated predicted allograft survival. RESULTS: In the development cohort of 1629 kidney recipients at two French centers, adding donor histology to the model did not significantly improve prediction of long-term allograft failure. Analyses using an external validation cohort from two Belgian centers confirmed the lack of improved accuracy from adding histology. About 45% of 1103 United States kidneys discarded because of histologic findings could be accurately matched to very similar kidneys that had been transplanted in France; these discarded kidneys would be expected to have allograft survival of 93.1% at 1 year, 80.7% at 5 years, and 68.9% at 10 years. CONCLUSIONS: In this multicenter study, donor kidney histology assessment during allocation did not provide substantial incremental value in ascertaining organ quality. Many kidneys discarded on the basis of biopsy findings would likely benefit United States patients who are wait listed.