Multiomic profiling of transplant glomerulopathy reveals a novel T-cell dominant subclass.

Kidney transplant (KTx) biopsies showing transplant glomerulopathy (TG) (glomerular basement membrane double contours (cg) > 0) and microvascular inflammation (MVI) in the absence of C4d staining and donor-specific antibodies (DSAs) do not fulfill the criteria for chronic active antibody-mediated rejection (CA-AMR) diagnosis and do not fit into any other Banff category. To investigate this, we initiated a multicenter intercontinental study encompassing 36 cases, comparing the immunomic and transcriptomic profiles of 14 KTx biopsies classified as cg+MVI DSA-/C4d- with 22 classified as CA-AMR DSA+/C4d+ through novel transcriptomic analysis using the NanoString Banff-Human Organ Transplant (B-HOT) panel and subsequent orthogonal subset analysis using two innovative 5-marker multiplex immunofluorescent panels. Nineteen genes were differentially expressed between the two study groups. Samples diagnosed with CA-AMR DSA+/C4d+ showed a higher glomerular abundance of natural killer cells and higher transcriptomic cell type scores for macrophages in an environment characterized by increased expression of complement-related genes (i.e., C5AR1) and higher activity of angiogenesis, interstitial fibrosis tubular atrophy, CA-AMR, and DSA-related pathways when compared to samples diagnosed with cg+MVI DSA-/C4d-. Samples diagnosed with cg+MVI DSA-/C4d- displayed a higher glomerular abundance and activity of T cells (CD3+, CD3+CD8+, and CD3+CD8-). Thus, we show that using novel multiomic techniques, KTx biopsies with cg+MVI DSA-/C4d- have a prominent T-cell presence and activity, putting forward the possibility that these represent a more T-cell dominant phenotype.

Mast Cells in Kidney Transplant Biopsies With Borderline T Cell-mediated Rejection and Their Relation to Chronicity.

Mast cells are potential contributors to chronic changes in kidney transplants (KTx). Here, the role of mast cells (MCs) in KTx is investigated in patients with minimal inflammatory lesions. Methods: Fourty-seven KTx biopsies (2009-2018) with borderline pathological evidence for T cell-mediated rejection according to the Banff'17 Update were retrospectively included and corresponding clinical data was collected. Immunohistochemistry for tryptase was performed on formalin-fixed paraffin-embedded sections. Cortical MCs were counted and corrected for area (MC/mm²). Interstitial fibrosis was assessed by Sirius Red staining and quantified using digital image analysis (QuPath). Results: Increased MC number was correlated to donor age (spearman's r = 0.35, P = 0.022), deceased donor kidneys (mean difference = 0.74, t [32.5] = 2.21, P = 0.035), and delayed graft function (MD = 0.78, t [33.9] = 2.43, P = 0.020). Increased MC number was also correlated to the amount of interstitial fibrosis (r = 0.42, P = 0.003) but did not correlate with transplant function over time (r = -0.14, P = 0.36). Additionally, transplant survival 2 y post-biopsy was not correlated to MC number (mean difference = -0.02, t [15.36] = -0.06, P = 0.96). Conclusions: MC number in suspicious (borderline) for acute T cell-mediated rejection is correlated to interstitial fibrosis and time post-transplantation, suggesting MCs to be a marker for cumulative burden of tissue injury. There was no association between MCs and transplant function over time or transplant survival 2 y post-biopsy. It remains unclear whether MCs are just a bystander or have pro-inflammatory or anti-inflammatory effects in the KTx with minimal lesions.

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.

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

Introduction: A decentralized and multi-platform-compatible molecular diagnostic tool for kidney transplant biopsies could improve the dissemination and exploitation of this technology, increasing its clinical impact. As a first step towards this molecular diagnostic tool, we developed and validated a classifier using the genes of the Banff-Human Organ Transplant (B-HOT) panel extracted from a historical Molecular Microscope® Diagnostic system microarray dataset. Furthermore, we evaluated the discriminative power of the B-HOT panel in a clinical scenario. Materials and Methods: Gene expression data from 1,181 kidney transplant biopsies were used as training data for three random forest models to predict kidney transplant biopsy Banff categories, including non-rejection (NR), antibody-mediated rejection (ABMR), and T-cell-mediated rejection (TCMR). Performance was evaluated using nested cross-validation. The three models used different sets of input features: the first model (B-HOT Model) was trained on only the genes included in the B-HOT panel, the second model (Feature Selection Model) was based on sequential forward feature selection from all available genes, and the third model (B-HOT+ Model) was based on the combination of the two models, i.e. B-HOT panel genes plus highly predictive genes from the sequential forward feature selection. After performance assessment on cross-validation, the best-performing model was validated on an external independent dataset based on a different microarray version. Results: The best performances were achieved by the B-HOT+ Model, a multilabel random forest model trained on B-HOT panel genes with the addition of the 6 most predictive genes of the Feature Selection Model (ST7, KLRC4-KLRK1, TRBC1, TRBV6-5, TRBV19, and ZFX), with a mean accuracy of 92.1% during cross-validation. On the validation set, the same model achieved Area Under the ROC Curve (AUC) of 0.965 and 0.982 for NR and ABMR respectively. Discussion: This kidney transplant biopsy classifier is one step closer to the development of a decentralized kidney transplant biopsy classifier that is effective on data derived from different gene expression platforms. The B-HOT panel proved to be a reliable highly-predictive panel for kidney transplant rejection classification. Furthermore, we propose to include the aforementioned 6 genes in the B-HOT panel for further optimization of this commercially available panel.

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.

The effect of early broad-spectrum versus delayed narrow-spectrum antibiotic therapy on the primary cure rate of acute infection after osteosynthesis.

PURPOSE: Infection near metal implants is a problem that presents challenging treatment dilemmas for physicians. The aim of this study was to analyse the efficacy of two treatment protocols for acute fracture-related infections. METHODS: Seventy-one patients in two level-1 trauma centres in the Netherlands were retrospectively included in this study. These trauma centres had different standardised protocols for acute infection after osteosynthesis: 39 patients were selected from protocol A and 32 from protocol B. Both protocols involve immediate surgical debridement and soft tissue coverage, but differ in antibiotic approach: (A) immediate empirical combination antibiotic therapy with rifampicin, or (B) postponed (1-5 days) targeted antibiotic therapy. The primary outcome of these protocols was success, defined as a fracture healing in the absence of infection. The secondary outcome was antibiotic resistance patterns. Logistic regression was conducted on patients and treatment-related factors in association with primary success. RESULTS: Primary success was achieved in 72% of protocol A patients, in 47% of those in protocol B (P = 0.033), and with prolongation of treatment success was achieved in 90% and 78% of patients, respectively. Protocol A exhibited a better primary success rate (adjusted OR 3.45, CI 1.13-10.52) when adjusted for age and soft tissue injury. There was no significant difference in antibiotic resistance between the two protocols. CONCLUSION: Both protocols yielded high overall success rates. Immediate empirical antibiotics can be used safely without additional bacterial resistance and may contribute to increased success rates.