Remember me
Despite advances in kidney transplant immunology, kidney transplant immune rejection remains a leading cause of allograft dysfunction [1]. Noninvasive diagnostic tests for rejection, such as urine chemokines and donor-derived cell-free DNA, do not yet substitute for a biopsy, which determines the type of rejection and its activity, providing the diagnostic and prognostic information necessary for patient management decisions [2].
The Banff Classification for Allograft Pathology recognizes two main types of rejection. In T-cell mediated rejection (TCMR), activated T cells infiltrate the graft endothelium (intimal arteritis lesion “v”), interstitium (interstitial inflammation “i”), and tubules (tubulitis “t”) [3,4▪]. In antibody-mediated rejection (AMR), donor-specific antibodies (DSA) target human leukocyte antigen (HLA) molecules on graft endothelium, recruit intravascular immune cells (microvascular inflammation, “MVI” corresponding to peritubular capillaritis “ptc” and glomerulitis “g”), with or without complement-dependant effects evidenced by C4d deposition, and cause endothelial cell injury [5].
Analysis of gene expression in a homogenised piece of kidney biopsy (“bulk” biopsy-based transcriptomics, BBT) has yielded considerable insights into the pathophysiology of rejection and shown consistent gene expression profiles in TCMR and AMR. Both types of rejection are characterized by expression of interferon-gamma inducible transcripts, with TCMR also expressing signals from activated effector T cells, activated macrophages and dendritic cells, and AMR-expressing transcripts indicative of endothelial activation and natural killer (NK) cells and/or monocytes [6]. Although single-cell and spatial transcriptomics are important new areas of discovery research [7], this review will focus on current progress in the clinical application of bulk BBT, which are more immediately amenable to diagnostic use.
Box 1: no caption available
STANDARD HISTOLOGY AND BIOPSY-BASED TRANSCRIPTOMIC WORKFLOWS FOR THE DIAGNOSIS OF REJECTIONTable 1 provides a comparison of key aspects of histology and BBT with regards to the diagnostic workflow. These are important to consider, in view of the potential “competition” for precious biopsy tissue that using both techniques entails. Histology, the current “gold standard”, is highly accessible and provides a wider range of diagnostic information than BBT, but has key weaknesses that BBT may be able to redress. Histology appears to be more vulnerable to limited tissue sampling than BBT for a diagnosis of rejection [8,9]. It also suffers from interobserver variability, partly because its semi-quantitative scoring is vulnerable at threshold boundaries [10], whereas data from BBT may better reflect the gradual nature of pathophysiological processes. Many histological features lack specificity, for example tubulitis “t” and interstitial inflammation “i” are seen in TCMR but also in polyoma virus nephropathy (PVN); MVI is typical for AMR but is also seen in patients without a DSA; isolated “cg”, “g”, “v” or thrombotic microangiopathy (TMA) can be seen in AMR, but also in glomerulonephritis, TCMR or ischaemic injury [11]. We review below whether BBT can help identify rejection in these circumstances.
Table 1 - Comparison of histology and biopsy-based transcripts Histology Biopsy-based transcripts Accessibility Laboratory equipment - Usual equipment - Limited availability in standard pathology laboartoriesFFPE, formalin-fixed paraffin-embedded, PTLD, posttransplant lymphoproliferative disorder.
Rejection-associated molecular signatures have been noted with BBT using reverse transcriptase quantitative PCR (qPCR) [12–16], microarrays [4▪,17], RNA sequencing [6,18,19▪,20], and probe-based techniques such as Nanostring [9,21▪▪,22▪▪–24▪▪,25] or multiplex ligand-dependant probe-based assay (MLPA) [26▪▪,27]. In general, differential gene expression patterns first discovered using microarrays have been validated by qPCR and probe-based techniques, which provides evidence of robust gene expression changes in both AMR and TCMR.
Table 2 provides an overview of the key aspects of each of the gene expression analysis technique. An important consideration with respect to biopsy sample workflow, is whether the technique generally requires a good quantity of high-quality RNA extracted from a separate piece of tissue handled to preserve RNA (snap frozen or in an RNA preservative), or can be performed on the limited amount of fragmented RNA that can be extracted from formalin-fixed and paraffin-embedded (FFPE) tissue left over after standard histological diagnosis is complete. Probe-based techniques are designed to detect these degraded RNA species, by using multiple short probes [28].
Table 2 - Comparison of gene expression analysis techniques used for biopsy-based transcriptomics Technique Type of tissue sample Maximum number of transcripts Shortest realistic turnaround time Cost Advantages Disadvantages Validated for diagnostic use in transplantation RT-qPCR Usually requires RNA-later or Snap Frozen Tissue Limited; depends on quantity if RNA <8 h Low per sample;FFPE, formalin-fixed paraffin-embedded.
The Molecular Microscope Diagnostic System (MMDx; Thermo Fisher Scientific, Waltham, MA, USA) is based on a DNA chip assessing 19 462-genes on fresh or RNA-later stored tissue [4▪]. Classifiers were derived from molecular phenotyping of large cohorts and prospective evaluation of real-life feasibility investigations [29]. It has a validated accuracy for the diagnosis of AMR and a weaker relationship with TCMR. Technical validity is demonstrated, although statistical measures of variability have not yet been reported [30]. The MMDx Kidney platform is licensed for commercial use as a send out test in a CLIA-approved laboratory as a laboratory developed test not requiring FDA approval for use in the USA. In Europe, the software is IVD-CE certified as a medical device. In terms of availability, it is based on a central assessment in Portland (Oregon) or Prague (Czechia) and provides results in 48 h after the sample is received. A report is generated with a probability of rejection (TCMR, AMR, or no-rejection) along with other molecular data such as acute tubular injury or atrophy-fibrosis scores. Potential use cases are identified, for example, “subpathological” AMR, DSA-negative MVI, polyomavirus nephropathy (PVN), isolated v-lesions, and so on. Some centres have started reporting on the clinical use of the MMDx platform [31,32,33▪▪].
Nanostring analysis of transplant biopsies using a consensus “Banff Human Organ Transplant” (B-HOT) panel or subsets of this panel have indicated ability to classify AMR and TCMR, both in silico and in retrospective cohort analyses [21▪▪,22▪▪,24▪▪,34▪,35▪,36]. The use of material left in the paraffin block after histological diagnosis is complete is a key benefit of this technology. The Nanostring platform is approved by FDA (USA) and IVD-CE (EU) certified; however, consensus for normalization across runs, platforms, and centres is needed to enable comparison of classifiers and prospective multicentre clinical trials testing clinical utility.
qPCR and MLPA are simpler, less expensive techniques that have also been investigated for use in transplant diagnostics. The latter can be performed using FFPE samples. Both use a subset of the genes in the signatures of rejection identified using microarray or NanoString technology. Both techniques yield gene expression results that correlate with results using Nanostring, but also require further validation in prospective clinical trials.
In our view, diagnostic gene expression panels for rejection with either a limited number of genes or the full transcriptome are both likely to yield sufficient accuracy to reach a molecular diagnosis of rejection and its subtypes. However, a restricted gene panel may also limit the number of questions that can be answered in any one given assay (e.g. diagnosis, prognosis, and so on). A diagnostic test might use additive or weighted scores of a handful of genes, or more complex machine learning-derived algorithms. Some institutions may be set up to validate cost-effective laboratory developed tests, whereas others might prefer to send samples away. Availability of a range of validated technologies for local clinical teams to choose from, depending on local expertise, equipment, and funding will enable widespread clinical implementation. However, it will be important to agree on acceptable performance metrics and determine if results of different molecular assays for the same indications are comparable.
TRANSLATION TO CLINICAL PRACTICE: WHICH INDICATIONS?The existence of molecular signatures for different types of rejection is insufficient on its own to justify clinical use of BBT. BBT must also show clinical validity (add diagnostic or prognostic information to current practice) and clinical utility (cost-benefit analysis) [11,37].
The Banff Classification currently supports use of BBT only as an adjunct technique in the diagnosis of AMR, where transcripts “if thoroughly validated as substitute for MVI and available” can substitute for C4d positivity or MVI above threshold.
Potential future applications range from comprehensive use of BBT in all biopsies alongside standard histological assessment, to use only in a defined set of circumstances where BBT adds information to histology (Fig. 1). It is important to note that the assumption that BBT will be redundant in cases with obvious histological rejection, and useful in cases where histology is ambiguous, is incorrect, as outlined below and in Table 3. Discrepancies between histological and molecular rejection occur in around 35% of cases, more in TCMR than AMR [31]. Histology may be better than molecular for some indications (e.g. diagnosis of infection or glomerulonephritis) whereas molecular may ultimately be proven better for others [33▪▪].
FIGURE 1: Potential clinical workflows integrating biopsy-based diagnostics. Current kidney transplant biopsy assessment is based on histological assessment including immunochemistry and electron microscopy (a). This open view approach enables the description of multiple parameters, including unexpected lesions, which are included in the pathology report. Future kidney transplant biopsy assessment may integrate biopsy-based transcriptomics systematically or in defined circumstances (b). We note that digital pathology and machine learning are likely to become part of the pathology workflow. CNI, calcineurin inhibitors; EM, electron microscopy; FFPE, formalin-fixed paraffin embedded; ML, machine learning; PTLD, posttransplant lymphoproliferative disorders.
Table 3 - Potential clinicopathological scenarios where biopsy-based transcriptomics might help with diagnosis Banff Classification Category Banff Classification sub-category Histological challenge How would biopsy-based transcriptomics help? References Normal biopsy or nonspecific changes Banff lesion scores below threshold for rejection (TCMR or AMR) Is there molecular TCMR?AMR, antibody-mediated rejection; DSA, donor specific antibody; g, glomerulitis; MVI, microvascular inflammation; ptc, peritubular capillaritis; TCMR, T-cell mediated rejection; TMA, thrombotic microangiopathy; v, intimal arteritis.
The histological diagnosis of AMR requires integration of several histological lesion scores (g, ptc, v, TMA, cg, PTCML), additional diagnostic parameters (e.g. “in the absence of glomerulonephritis”), C4d, and DSA data. Many biopsies can show some but not all of these features, leading to “incomplete” AMR phenotypes, the significance of which is not yet fully understood, but which were clearly defined in the most recent Banff Report.
The assumption that biopsies with complete histological AMR will have increased AMR BBT is untrue. A proportion of cases with histological AMR are negative for AMR BBT and a proportion of cases without rejection are positive for AMR BBT, with overall discrepancies reported around 20% [24▪▪,31,38▪▪]. The lack of good treatment options for AMR makes it hard to compare histological and molecular definitions of disease for their ability to best stratify patients for treatment, or to potentially integrate both modalities for best diagnostic ability. Clinical trials that include molecular analysis are limited, and more are needed [39–44]. Molecular scores of AMR probability or of injury-repair response (IRRAT) in patients with histological AMR predict future eGFR or graft loss, providing evidence for prognostic (if not diagnostic) superiority of molecular analysis in this context [45▪]. Many potential explanations have been put forward for discrepancies between “histological AMR” and “molecular AMR”, for example sampling of the biopsy tissue, poor application of Banff rules by pathologists [46], and inadequacy of current Banff rules. Interesting recent work suggests semi-supervised clustering of Banff lesion scores or logistic regression equations trained on molecular diagnoses of rejection might identify improved rules for histological diagnosis of rejection using Banff lesion scores [47▪▪,48,49▪▪].
BBT have also been investigated in incomplete AMR phenotypes, including cases that are “MVI-positive, C4d-negative, DSA-negative”, and cases with histological lesions of AMR below threshold, either with a DSA (“probable AMR”) or without a DSA (isolated v, g, cg, and TMA).
MVI-positive, C4d-negative, DSA-negative cases have been variably attributed to inability to detect circulating anti-HLA DSA, non-HLA DSA, other causes of NK-mediated rejection (including “missing self”), or ischemia-reperfusion. The hope is that BBT might enable classification of this phenotype according to pathophysiology, potentially facilitating treatment. In reality, studies examining differences in transcript expression between MVI-positive biopsies with and without detectable DSA have not shown any differences [5,17,50].
We used a 9-gene molecular AMR classifier to separate 50 biopsies with incomplete histological features of AMR into six biopsies with molecular AMR and 44 biopsies without molecular AMR. The six biopsies with molecular AMR had MVI of 1 or more and a worse outcome, similar to that of AMR. Cases with isolated v or TMA were negative for molecular AMR in our study. Other studies with isolated v-lesions have not consistently identified a molecular signature associated with TCMR or ABMR, and data are generally lacking for cases with TMA as the main diagnostic feature [24▪▪,51,52]. These data suggest that molecular AMR is mainly driven by MVI, which is not surprising, as it is the most frequent histological finding in AMR.
Finally, some patients with a DSA have no histological features of rejection, and BBT might provide an opportunity for earlier diagnosis in such patients with high immunological risk. Indeed, a proportion of these biopsies may have molecular AMR [53], but it remains unclear whether these patients are more at risk of developing histological rejection at a later stage, or if treatment of rejection would alter outcomes.
Biopsy-based transcriptomics for T-cell mediated rejection phenotypesTCMR comprises active TCMR and chronic active TCMR (caTCMR) in the Banff Classification. As for AMR, discrepancies have been found between histological TCMR and molecular TCMR, with little evidence for which is superior to the other. Ideally, BBT might help in circumstances where histology is problematic, namely borderline for TCMR, PVN versus TCMR and caTCMR.
caTCMR refers to cases that where the interstitial inflammation component mainly affects the scarred areas of the biopsy. Whilst inflammation in areas of atrophy is associated with allograft loss, it can be due to TCMR or to other pathological processes such as infections [37]. BBT show that such biopsies show a variety of molecular signatures, and in fact have molecular AMR in 45% of cases and molecular TCMR in 16% of cases according to the MMDx system [54].
Similarly, biopsies that are borderline for TCMR have shown a range of molecular features. In a study using MMDx, 74% of biopsies borderline for TCMR had a no-rejection gene signature, 13% had a molecular signature of AMR, and only 9% had a molecular signature of TCMR [31]. In another study, a molecular tubulitis score in an early borderline biopsy predicted patients at risk for molecular rejection in a later follow-up biopsy [55]. Data (in particular, clinical trial data) are lacking on whether different molecular signatures correlate with different response to different treatments.
PVN and TCMR both show tubulitis and interstitial inflammation and are distinguished by looking for evidence for polyomavirus replication in urine, blood, or within the kidney tissue (nuclear inclusions, SV40 immunostaining). It has also been shown that BBT can identify polyomavirus-specific transcripts, supporting PVN diagnosis in contentious cases [56]. However, as TCMR and PVN share a common pathophysiological pathway of antigen-driven T cell activation, assessment of the co-occurrence of these two pathological processes using BBT is limited. Moreover, more data are needed to better understand the evolution of the molecular signal of polyomavirus in order not to misidentify a de-novo TCMR following PVN resolution [57].
CONCLUSIONExtensive investigations using bulk transcriptomics over the last few decades have enabled a better understanding of rejection, and future use of more powerful platforms for single cell and spatial transcriptomics will no doubt increase that output. Translational research has established that BBT can detect rejection with good accuracy compared to a histological “gold standard”. Frequent discrepancies between histology and transcriptomics suggest potential for synergy between the two techniques, although to date, the indications where BBT adds value are unclear. Only a few centres use biopsy-based transcriptomics [58], and key barriers to wider use include comparability of data and models across centres and platforms; defined and tested cost-effective contexts of use, and guidelines for integrating information from standard histology and BBT. It is important to stress that there are many tasks for which BBT cannot replace histology, and this has implications for how we select portions of precious biopsy samples for best diagnostic yield. Digitization of standard histology enhanced by machine learning is likely to be synergistic with BBT, but has potential to supplant BBT as a diagnostic tool, as has already been noted in the field of cancer [59].
Panels and tools for molecular diagnosis have been defined and tested for AMR, although prospective clinical trials that investigate their clinical utility when used in addition to standard histology are needed. As effective AMR treatments are currently limited [60], BBT may not have that much impact on allograft survival. Nevertheless, BBT has potential to help define specific molecular pathways as therapeutic targets.
AcknowledgementsNone.
This study was not funded.
Financial support and sponsorshipDr Roufosse is supported by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Imperial College Healthcare NHS Trust and Imperial College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. Dr Roufosse's research activity is made possible with generous support from Sidharth and Indira Burman.
Conflicts of interestThere are no conflicts of interest.
REFERENCES AND RECOMMENDED READINGPapers of particular interest, published within the annual period of review, have been highlighted as:
▪ of special interest
▪▪ of outstanding interest
REFERENCES 1. Mayrdorfer M, Liefeldt L, Wu K, et al. Exploring the complexity of death-censored kidney allograft failure. J Am Soc Nephrol 2021; 32:1513–1526. 2. Huang E, Mengel M, Clahsen-van Groningen MC, Jackson AM. Diagnostic potential of minimally invasive biomarkers: a biopsy-centered viewpoint from the Banff Minimally Invasive Diagnostics Working Group. Transplantation 2023; 107:45–52. 3. Halloran PF, Famulski KS, Reeve J. Molecular assessment of disease states in kidney transplant biopsy samples. Nat Rev Nephrol 2016; 12:534–548. 4▪. Halloran PF, Madill-Thomsen KS, Reeve J. The molecular phenotype of kidney transplants: insights from the MMDx Project. Transplantation 2023; 108:45–71. 5. Callemeyn J, Lamarthée B, Koenig A, et al. Allorecognition and the spectrum of kidney transplant rejection. Kidney Int 2022; 101:692–710. 6. Lamarthée B, Callemeyn J, Van Herck Y, et al. Transcriptional and spatial profiling of the kidney allograft unravels a central role for FcyRIII+ innate immune cells in rejection. Nat Commun 2023; 14:4359. 7. Salem F, Perin L, Sedrakyan S, et al. The spatially resolved transcriptional profile of acute T cell-mediated rejection in a kidney allograft. Kidney Int 2022; 101:131–136. 8. Madill-Thomsen KS, Wiggins RC, Eskandary F, et al. The effect of cortex/medulla proportions on molecular diagnoses in kidney transplant biopsies: rejection and injury can be assessed in medulla. Am J Transplant 2017; 17:2117–2128. 9. Toulza F, Dominy K, Cook T, et al. Technical considerations when designing a gene expression panel for renal transplant diagnosis. Sci Rep 2020; 10:17909. 10. Furness PN, Taub N. Convergence of European Renal Transplant Pathology Assessment Procedures (CERTPAP) Project: international variation in the interpretation of renal transplant biopsies: report of the CERTPAP Project. Kidney Int 2001; 60:1998–2012. 11. Naesens M, Roufosse C, Haas M, et al. The Banff 2022 Kidney Meeting Report: reappraisal of microvascular inflammation and the role of biopsy-based transcript diagnostics. Am J Transplant 2023; Article in press. 12. Sellarés J, Reeve J, Loupy A, et al. Molecular diagnosis of antibody-mediated rejection in human kidney transplants. Am J Transplant 2013; 13:971–983. 13. Dominy KM, Willicombe M, Al Johani T, et al. Molecular assessment of C4d-positive renal transplant biopsies without evidence of rejection. Kidney Int Rep 2018; 4:148–158. 14. Christakoudi S, Runglall M, Mobillo P, et al. Development of a multivariable gene-expression signature targeting T-cell-mediated rejection in peripheral blood of kidney transplant recipients validated in cross-sectional and longitudinal samples. EBioMedicine 2019; 41:571–583. 15. Hruba P, Madill-Thomsen K, Mackova M, et al. Molecular patterns of isolated tubulitis differ from tubulitis with interstitial inflammation in early indication biopsies of kidney allografts. Sci Rep 2020; 10:22220. 16. Toulza F, Dominy K, Willicombe M, et al. Diagnostic application of transcripts associated with antibody-mediated rejection in kidney transplant biopsies. Nephrol Dial Transplant 2021; 37:1576–1584. 17. Callemeyn J, Lerut E, de Loor H, et al. Transcriptional changes in kidney allografts with histology of antibody-mediated rejection without anti-HLA donor-specific antibodies. J Am Soc Nephrol 2020; 31:2168–2183. 18. Malone AF, Wu H, Fronick C, et al. Harnessing expressed single nucleotide variation and single cell RNA sequencing to define immune cell chimerism in the rejecting kidney transplant. J Am Soc Nephrol 2020; 31:1977–1986. 19▪. Van Loon E, Lamarthée B, Barba T, et al. Circulating donor-specific anti-HLA antibodies associate with immune activation independent of kidney transplant histopathological findings. Front Immunol 2022; 13:818569. 20. Wen N, Wu J, Li H, et al. Immune landscape in rejection of renal transplantation revealed by high-throughput single-cell RNA sequencing. Front Cell Dev Biol 2023; 11:1208566. 21▪▪. Zhang H, Haun RS, Collin F, et al. Development and validation of a multi-class model defining molecular archetypes of kidney transplant rejection: a large cohort study of the Banff Human Organ Transplant Gene Expression Panel. Lab Invest 2023. 22▪▪. Rosales IA, Mahowald GK, Tomaszewski K, et al. Banff human organ transplant transcripts correlate with renal allograft pathology and outcome: importance of capillaritis and subpathologic rejection. J Am Soc Nephrol 2022; 33:2306–2319. 23▪▪. Cristoferi I, Varol H, van Baardwijk M, et al. Multiomic profiling of transplant glomerulopathy reveals a novel T-cell dominant subclass. Kidney Int 2023. 24▪▪. Beadle J, Papadaki A, Toulza F, et al. Application of the Banff Human Organ Transplant Panel to kidney transplant biopsies with features suspicious for antibody-mediated rejection. Kidney Int 2023; 104:526–541. 25. Danger R, Le Berre L, Cadoux M, et al. Subclinical rejection-free diagnostic after kidney transplantation using blood gene expression. Kidney Int 2023; 103:1167–1179. 26▪▪. de Nattes T, Beadle J, Toulza F, et al. A simple molecular tool for the assessment of kidney transplant biopsies. Clin J Am Soc Nephrol 2023; 18:499–509. 27. de Nattes T, Kaveri R, Farce F, et al. Daratumumab for antibody-mediated rejection: is it time to target the real culprit? Am J Transplant 2023; 23:1990–1994. 28. Mengel M, Loupy A, Haas M, et al. 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. Am J Transplant 2020. 29. Halloran PF, Reeve J, Akalin E, et al. Real time central assessment of kidney transplant indication biopsies by microarrays: the INTERCOMEX Study. Am J Transplant 2017; 17:2851–2862. 30. Randhawa P. The MMDx® diagnostic system: a critical re-appraisal of its knowledge gaps and a call for rigorous validation studies. Clin Transplant 2022; 36:e14747. 31. Madill-Thomsen K, Perkowska-Ptasińska A, Böhmig GA, et al. MMDx-Kidney Study Group: discrepancy analysis comparing molecular and histology diagnoses in kidney transplant biopsies. Am J Transplant 2020; 20:1341–1350. 32. Alam A, Van Zyl J, Paul Milligan G, et al. Evolving the surveillance and workup of heart transplant rejection: a real-world analysis of the Molecular Microscope Diagnostic System. Am J Transplant 2022; 22:2443–2450. 33▪▪. Suo L, Murillo MC, Gallay B, Hod-Dvorai R. Discrepancy analysis between histology and molecular diagnoses in kidney allograft biopsies: a single-center experience. Int J Mol Sci 2023; 24:13817. 34▪. van Baardwijk M, Cristoferi I, Ju J, et al. A decentralized kidney transplant biopsy classifier for transplant rejection developed using genes of the Banff-Human Organ Transplant Panel. Front Immunol 2022; 13:841519. 35▪. Smith RN, Rosales IA, Tomaszewski KT, et al. Utility of Banff human organ transplant gene panel in human kidney transplant biopsies. Transplantation 2023; 107:1188–1199. 36. Varol H, Ernst A, Cristoferi I, et al. Feasibility and potential of transcriptomic analysis using the NanoString nCounter technology to aid the classification of rejection in kidney transplant biopsies. Transplantation 2023; 107:903–912. 37. Roufosse C, Naesens M, Haas M, et al. The Banff 2022 Kidney Meeting Work Plan: data-driven refinement of the Banff Classification for renal allografts. Am J Transplant 2023. 38▪▪. Schachtner T, von Moos S, Kokkonen SM, et al. The molecular diagnosis might be clinically useful in discrepant kidney allograft biopsy findings: an analysis of clinical outcomes. Transplantation 2023; 107:485–494. 39. Borski A, Eskandary F, Haindl S, et al. Antiinterleukin-6 antibody clazakizumab in antibody-mediated renal allograft rejection: accumulation of antibody-neutralized interleukin-6 without signs of proinflammatory rebound phenomena. Transplantation 2023; 107:495–503. 40. Kumar D, Raynaud M, Chang J, et al. Impact of belatacept conversion on renal function, histology, and gene expression in kidney transplant patients with chronic active antibody-mediated rejection. Transplantation 2021; 105:660–667. 41. Kumar D, Yakubu I, Safavi F, et al. Lack of histological and molecular signature response to tocilizumab in kidney transplants with chronic active antibody mediated rejection: a case series. Kidney360 2020; 1:663–670. 42. Doberer K, Duerr M, Halloran PF, et al. A randomized clinical trial of anti-IL-6 antibody clazakizumab in late antibody-mediated kidney transplant rejection. J Am Soc Nephrol 2021; 32:708–722. 43. Doberer K, Kläger J, Gualdoni GA, et al. CD38 antibody daratumumab for the treatment of chronic active antibody-mediated kidney allograft rejection. Transplantation 2021; 105:451–457. 44. Eskandary F, Regele H, Baumann L, et al. A randomized trial of bortezomib in late antibody-mediated kidney transplant rejection. J Am Soc Nephrol 2018; 29:591–605. 45▪. Herz CT, Diebold M, Kainz A, et al. Morphologic and molecular features of antibody-mediated transplant rejection: pivotal role of molecular injury as an independent predictor of renal allograft functional decline. Transpl Int 2023; 36:12135. 46. Yoo D, Goutaudier V, Divard G, et al. An automated histological classification system for precision diagnostics of kidney allografts. Nat Med 2023; 29:1211–1220. 47▪▪. Vaulet T, Divard G, Thaunat O, et al. Data-driven chronic allograft phenotypes: a novel and validated complement for histologic assessment of kidney transplant biopsies. J Am Soc Nephrol 2022; 33:2026–2039.
Comments (0)