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Establishing Biomarkers in Transplant Medicine: A Critical Review of Current Approaches

Anglicheau, Dany MD, PhD; Naesens, Maarten MD, PhD; Essig, Marie MD, PhD; Gwinner, Wilfried MD, PhD; Marquet, Pierre MD, PhD

doi: 10.1097/TP.0000000000001321
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Although the management of kidney transplant recipients has greatly improved over recent decades, the assessment of individual risks remains highly imperfect. Individualized strategies are necessary to recognize and prevent immune complications early and to fine-tune immunosuppression, with the overall goal to improve patient and graft outcomes. This review discusses current biomarkers and their limitations, and recent advancements in the field of noninvasive biomarker discovery. A wealth of noninvasive monitoring tools has been suggested that use easily accessible biological fluids such as urine and blood, allowing frequent and sequential assessments of recipient's immune status. This includes functional cell-based assays and the evaluation of molecular expression on a wide spectrum of platforms. Nevertheless, the translation and validation of exploratory findings and their implementation into standard clinical practice remain challenging. This requires dedicated prospective interventional trials demonstrating that the use of these biomarkers avoids invasive procedures and improves patient or transplant outcomes.

This comprehensive review discusses current biomarkers and their limitations, and the recent advancements in the field of noninvasive biomarker discovery to make diagnoses and improve care for renal transplant recipients.

1 Necker-Enfants Malades Institute, French National Institute of Health and Medical Research, Paris, France.

2 Paris Descartes, Sorbonne Paris Cité University, Paris, France.

3 Department of Nephrology and Kidney Transplantation, Necker Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France.

4 KU Leuven-University of Leuven, Department of Microbiology and Immunology, Laboratory of Nephrology; University Hospitals Leuven, Department of Nephrology and Renal Transplantation, Leuven, Belgium.

5 CHU Limoges, Department of Nephrology, Dialysis and Transplantation, Limoges, France.

6 U850 INSERM, Univ. Limoges, CHU Limoges, Limoges, France.

7 Department of Nephrology, Hannover Medical School, Hannover, Germany.

Received 26 February 2016. Revision received 26 April 2016.

Accepted 27 April 2016.

This work is part of the BIOMARGIN European research network (Collaborative Project) supported by the European Commission under the Health Cooperation Work Programme of the 7th Framework Programme (grant 305499).

The authors declare no conflicts of interest.

D.A., M.N., M.E., W.G., and P.M. wrote the article.

Correspondence: Dany Anglicheau, MD, PhD, Service de Néphrologie et Transplantation Adulte, Hôpital Necker-Enfants Malades, 149, rue de Sèvres, 75015 Paris, France. (

More than 50 years after the first successful renal transplantation, routine kidney posttransplant patient care still relies on the same approaches and procedures. Monitoring of the allograft includes the surveillance of serum creatinine levels, the glomerular filtration rate, and proteinuria. These markers are nonspecific, and diagnosis requires an invasive allograft biopsy, which is the current criterion for graft status evaluation. Moreover, given the low sensitivity of these markers for injury processes, this approach only recognizes the pathological processes at a relatively advanced stage of tissue injury and fails to detect subclinical changes. Protocol biopsies have been proposed in recipients of kidney allografts to detect changes before graft dysfunction is apparent. However, the diagnosis of subclinical changes requires multiple biopsies. Given that biopsy procedures are invasive, complications may occur; furthermore, sampling errors may jeopardize their diagnostic usefulness. The costs of these procedures must also be considered.

These shortcomings have stimulated research in the field of noninvasive biomarkers for transplant pathologies. Developments in high-throughput molecular techniques have further advanced the identification of new biomarkers that might guide clinicians in the adjustment of immunosuppression and predict complications and graft outcome. Ideally, these new tools would translate into a personalized therapy that could (1) reduce over-immunosuppression in patients with a low risk of rejection but with a high risk of infectious complications and (2) elicit an early intervention in patients with a high risk of rejection. In the end, such precision medicine, with tailored immunosuppressive regimens at the individual level, could reduce patient morbidity and increase long-term graft survival. Although the literature is full of innovative approaches with fairly good diagnostic and/or prognostic performances, very few, if any, have reached the clinic. There is no proof that using these newly identified biomarkers improves patient outcomes.1

In this review, we discuss the performance and important characteristics of clinically used biomarkers, including statistical issues that must be considered before using them.

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Allograft Biopsy as a Gold Standard

For many years, histological examination of kidney graft biopsies has been the criterion to identify rejection and other lesions in transplanted patients.2,3 From a biomarker perspective, the use of allograft biopsies should draw attention to the following considerations. Assessment of the sensitivity, specificity, and predictive values of a test requires knowledge of the true diagnosis. Because that is the criterion, it is impossible to assess the sensitivity and specificity of allograft biopsies. Hence, the problem of sampling error, or rather the limitation of random sampling, comes into play. Not observing a histological lesion is obviously not a proof of the absence of this lesion in the kidney allograft because it may be false negative. However, no other tool will provide the true state of the allograft; therefore, the real sensitivity of the biopsy remains unknown.

Molecular technologies have been developed during the past decade as a refinement of the histological evaluation of the graft biopsy. The emerging transcriptomic profiling of the allograft biopsy has been advocated to circumvent some of the limitations of the conventional histological assessment of biopsy samples. In this regard, the molecular microscope strategy particularly developed by the Halloran group has undoubtedly provided novel insights into disease mechanisms with great expectations of development of more accurate diagnostic, prognostic, and theranostic biomarkers.4,5 However, routine adoption of these molecular approaches has not yet been achieved and prospective multicenter validation studies would strengthen the clinical significance of this new diagnostic tool.6 In addition, even with the new approach of transcriptomic profiling of the allograft, the random sampling limitation remains unsolved.

In a more generalized view, any evaluation of noninvasive biomarkers against the criterion allograft biopsy result cannot establish markers with a better diagnostic performance than the criterion. Thus, in our view, noninvasive markers that reasonably match the biopsy result may be used and should be further refined by evaluating them with additional end points, such as graft function and failure, in dedicated clinical trials.

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Allograft Function

Early graft function is a major determinant of long-term graft outcome.7 An increase in serum creatinine has a very low specificity for the prediction of acute rejection (AR). In a recent study of 281 consecutive indication biopsies, only one third displayed signs of rejection, thus demonstrating a 30% specificity of allograft dysfunction as a noninvasive marker of AR,8 if one considers that biopsy itself is able to detect AR with 100% sensitivity (see above). Nevertheless, acute allograft dysfunction can indicate other important lesions such as acute tubular injury (ATI), toxic effects of the medication or infection-associated nephropathy. Conversely, the demonstration of subclinical rejection by protocol biopsies illustrates that serum creatinine measurements are not sensitive enough to detect all grades of ongoing rejection. On the other hand, given the finding that subclinical rejection is not always associated with impaired graft survival, subclinical intragraft inflammation in itself is not necessarily a specific predictor of graft dysfunction.9 However, the early identification of subclinical rejection would be of great value. For example, subclinical T cell–mediated rejection (TCMR) has been associated with subsequent interstitial fibrosis and tubular atrophy (IFTA),10 de novo donor-specific antibodies (dnDSAs),11 antibody-mediated rejection (AMR), and worse graft function at 24 months,12 all of which are associated with graft loss.

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Worldwide, proteinuria is routinely measured after kidney transplantation13 and is included in guidelines on the standard of care for kidney recipients.14 Surprisingly, the association between proteinuria and pathological entities of the renal allograft has not yet been described in great detail.15,16 However, the clinical importance of albuminuria and proteinuria after renal transplantation is not clear; their thresholds are not well established, nor are their diagnostic or predictive performances after renal transplantation.17-19 Until recently, the sensitivity and specificity of proteinuria measurements for graft injury and transplant failure had not been evaluated. Amer et al20 suggested that albuminuria and proteinuria are nonspecific signs of allograft injury. However, more recently, the diagnostic and prognostic performance of proteinuria for intragraft pathology was evaluated in greater detail, and it was demonstrated that greater than 1.0 g/24 hours of proteinuria after transplantation is highly specific for potentially treatable disease processes (transplant glomerulopathy, microcirculation inflammation, and de novo or recurrent glomerular disease).21 The specificity of greater than 1.0 g/24 h of proteinuria for intragraft pathology in for-cause biopsies was far greater than anticipated (85-91%). However, despite excellent specificity, the diagnostic performance of proteinuria for graft pathologies in for-cause biopsies remains low because of its low sensitivity (21-32%). By measuring proteinuria, many cases with ongoing and potentially treatable injury processes are missed. In addition, proteinuria is an important predictor of graft failure, independent of these underlying specific diagnoses. Novel noninvasive biomarkers that are much more sensitive for these treatable injury processes are therefore necessary. Nevertheless, the diagnostic and prognostic performance of proteinuria after renal transplantation remains the benchmark against which novel biomarkers are to be compared.

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DSA Monitoring

At the time of transplantation, a high-level of donor-specific antibody (DSA) is generally considered a contraindication for successful transplantation, and at a lower level, the presence of DSA displays a wide spectrum of risks that must be considered at the individual level. With the increased sensitivity of screening methods that can now detect very low level of DSAs, the specificity and positive predictive value (PPV) for the risk of subsequent AMR has decreased. For example, a positive remote complement-dependent cytotoxicity crossmatch had a high PPV for a subsequent AMR of 54.2% and a high specificity of 97% but a low sensitivity of 40.6%. Conversely, with the Luminex technique, an historical peak serum result was highly sensitive for subsequent AMR (sensitivity of 90.6%), but it had a low PPV of 34.9%.22

Further, there is accumulating evidence that patients with dnDSAs detected on solid phase platforms have reduced graft survival. Strikingly, despite the widespread use of this posttransplant monitoring, no study has yet clearly evaluated the predictive performance of this strategy to predict AMR or the long-term graft function. In a recent study of 281 indication biopsies in 244 kidney transplant recipients, the presence of DSA (both preformed and de novo) at the time of biopsy was indeed associated with the diagnosis of AMR. However, a moderate area under the curve (AUC) value of 0.75 and the fact that many patients with DSA did not have AMR suggested that DSA should be rather considered as a risk factor of AMR, instead of being a true diagnostic tool.8 In their comprehensive analysis of dnDSAs, Wiebe and colleagues11 demonstrated an association of peritubular capillaritis and C4d deposition with dnDSAs, but the percentage of biopsies meeting the criteria for AMR was not provided.

Finally, Tambur et al23 recently explored different strategies to assess the strength of DSA. This study highlighted some of the pitfalls of the Luminex technique, including the low sensitivity of the C1q-binding assessment and the issue of the prozone effect, which severely increase the false negative rate.

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The Need for Novel Noninvasive Biomarkers

The currently used markers of graft dysfunction and injury processes are highly imperfect; their predictive values remain relatively low with regard to posttransplant graft outcome. These limitations highlight the need for robust, noninvasive methods to predict and diagnose acute and chronic graft lesions early and more accurately. Table 1 outlines how biomarkers might change the paradigm of kidney transplant care in clinical practice. Not all biomarkers must be both highly sensitive and specific to be clinically useful. The value of a biomarker is highly dependent on its anticipated clinical use. For example, a near perfect diagnostic biomarker (ie, high PPV and high negative predictive value [NPV]) would be required to diagnose AR and decide treatment based on the biomarker level. Excluding AR at time of acute graft dysfunction would require a biomarker with a perfect NPV for AR.



Before adopting such alternative models of patient care, clinical validation of candidate biomarkers is the key issue in designing and translating them into clinical applications. Here, one should be cautious not to over-interpret significant associations of biomarkers with clinical conditions, histological lesions or other outcome parameters. This is illustrated in Figure 1. Significant associations between a novel test and an outcome parameter of interest must be interpreted as reflecting their diagnostic or prognostic performance, which is most commonly quantified using receiver-operating characteristic curves and in the PPV and NPV of the test.9 The latter depend importantly on the disease or outcome parameter prevalence (Figure 2), as was clearly illustrated in a recent review by Lo and Kirk.9 By this, it is clear that the diagnostic and predictive values must be tested in real-life settings with a representative prevalence of the disease or outcome. If not, for example, in case-control study designs, the diagnostic, prognostic and predictive performances of a novel test remain meaningless, as is often the case in biomarker research articles (Figure 3).







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The explosive evolution in the fields of genetics and molecular biology during the past decades and advances of chemistry and engineering have resulted in affordable, fast, and accurate tools for the interrogation of complete sets of data (eg, genome, transcriptome, proteome, peptidome, metabolome, antibodyome, and so on). The development of these tools has stimulated very active research in the field of transplantation.24 These omics approaches have not only accelerated biomarker discovery but have also improved our understanding of multifactorial biological processes and diseases. Previous review articles have already discussed the pipeline of candidate biomarkers of transplantation medicine in detail.9,24-27 In the following chapter, we discuss some of the recently published and attractive approaches in the field of noninvasive kidney transplant monitoring (Table 2).



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Functional Cell-Based Immune Monitoring

Evaluation of circulating antidonor T cell alloreactivity using the IFN-γ enzyme-linked immunosorbent spot assay (ELISPOT) is an attractive way to evaluate antidonor immune responsiveness in vitro. Before transplantation, the high NPV of this test suggests that low percentages of circulating antidonor alloreactive memory/effector T cells might reliably rule out transplant recipients with a high risk of TCMR.28-31 Hricik et al32 recently reported an association between pretransplant ELISPOT reactivity and renal allograft function at 6 and 12 months, but this was limited to patients who did not receive Thymoglobulin induction. This result raised the possibility that pretransplant IFN-γ ELISPOT results could be used to identify patients who would benefit from Thymoglobulin induction, as previously discussed by Augustine et al.28 After transplantation, Bestard et al reported that donor-specific responsiveness at 6 months was associated with subclinical rejection (AUC: 0.75) and 1-year graft function.29

In addition to providing candidates for the prediction of alloimmune injuries, cell-based immune monitoring has been evaluated for its ability to detect over-immunosuppression. The ImmuKnow assay (Cylex, Columbia, MD) was one of the first tests that claimed to have the potential to detect overimmunosuppression. A recent meta-analysis of the numerous studies performed on transplant recipients provided disappointing results, with a sensitivity of 58% and a specificity of 69% for predicting the risk of infection.33 A recently published study evaluated the ability of 2 functional assays (panel of reactive T cell interferon-γ ELISPOT—IFN-γ PRT ELISPOT—and natural killer-mediated lysis of the K562 leukemia cell line) to identify overimmunosuppressed kidney transplant recipients.34 Low IFN-γ PRT ELISPOT and low natural killer cell function both predicted the risk of reaching a combined end point of metastatic cancer, cancer-related death, or infectious-related death.

Even if these functional cell-based immune monitoring assays offer a proxy for the degree of function of the T cell-mediated immune response, with a potential predictive value in regards to both underimmunosuppression or overimmunosuppression, their laborious, time-consuming, and impractical use could restrict their wide application in clinical practice.

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Molecular Blood Biomarkers

Peripheral blood is a very accessible matrix for biomarker research in kidney transplant recipients, and the search for noninvasive markers in peripheral blood has long been the main focus of several groups in the field of kidney transplantation.35-41

Recently, 2 important advancements were published in the field of peripheral blood messenger (m)RNA expression analysis of peripheral blood. First, in a multicenter study, Kurian et al42 used sophisticated 3-way classifier tools in a test set (N = 75) and validation set (N = 73) approach, and they identified 200 probe sets that discriminated AR from normal samples with stable graft function and samples with other diagnoses at the time of graft dysfunction. Sensitivity, specificity, PPV, NPV, and the area under the receiver-operating characteristic (ROC) curve were calculated for the validation cohort and appeared acceptable for clinical practice. However, this was only a selected patient cohort that did not reflect real-life disease prevalence, which possibly led to the inflation of these performance metrics. Moreover, in this study, AMR could not be tested independently because no cases with pure AMR were included. The lack of prospective validation and the absence of data for AMR represent the main limitations of this 200-gene panel. Nevertheless, based on these results, an application (TruGraf) has been further developed by Transplant Genomics, and the platform is being validated in a prospective trial involving 300 kidney transplant patients from whom serial protocol biopsies are performed. These data are necessary to evaluate the true diagnostic, predictive, and perhaps prognostic value of the proposed peripheral blood test for kidney allograft rejection. Whether the use of this platform improves patient care will require additional studies.

A second peripheral blood gene expression test was developed in the Assessment of Acute Rejection in Renal Transplantation study.43 In this larger multicenter study including 558 samples from 436 adult and pediatric kidney allograft recipients, a 17-gene set (the Kidney Solid Organ Response Test [kSORT]) was selected and independently validated. The samples included in this study were a heterogeneous mixture of prospectively collected samples at the time of protocol biopsies in pediatric recipients and samples obtained at time of a for-cause biopsy, although some adult blood samples collected at time of a 6-month protocol biopsy were also included. The samples were classified as AR (N = 188; including some cases with AMR) versus no AR (N = 370; including other disease processes such as polyomavirus nephropathy), without discrimination between AMR and TCMR. The selection criteria for these samples, and their representativeness, remain unclear. Based on the results of prior research,44 a selected panel of 43 genes was evaluated and tested using quantitative polymerase chain reaction, including internal cross-validation steps. This yielded a 17-gene panel (kSORT) that was locked and used for independent validation in 100 samples. After the elimination of “indeterminate” results (15% of samples), the AUC under the ROC curve for the noninvasive diagnosis of AR (vs no rejection) reached 92%. The excellent performance of this 17-gene panel in this validation case-control study is certainly promising, but the case-control design obviates evaluating real-life diagnostic performance. Another exciting finding is that the kSORT panel is able to predict future rejection, with apparent high accuracy. Extensive validation in a real-life setting, with prospectively collected, unselected samples, is warranted for the further development of this 17-gene test to evaluate the actual positive and negative predictive values. The kSORT test is not approved by health authorities, but is further developed by Immucor, Inc.

In addition to providing innovative tools for the diagnosis of AR, transcriptomic profiling of peripheral blood mononuclear cells has been shown to identify operational tolerance in kidney transplant recipients and 5 studies reported an increased expression of B cell–related genes in tolerant patients compared with stable patients.45-49 More recently, a meta-analysis identified a specific gene signature and a selection of the top-20 biomarkers accurately discriminated tolerant from stable patients, providing proof of principle that tolerance can be noninvasively identified among kidney transplanted recipients.50,51

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Urine Nucleic Acids

After a decade of single-center studies that demonstrated the potential of urinary cell messenger RNA profiling as a noninvasive approach to diagnose TCMR,25,52 the Clinical Trials in Organ Transplantation 04 (CTOT4) study recently tested the most promising biomarkers in a large multicenter prospective cohort study and validated a molecular signature of CD3ε mRNA, IP-10 (also known as CXCL10) mRNA, and 18S rRNA levels in urinary cells that appeared to be diagnostic and prognostic of acute TCMR in kidney allografts.53 The ROC curve of the 3-gene signature that discriminated between specimens showing acute TCMR and those showing no rejection had an AUC of 0.85 in the primary data set and 0.74 in an external validation data set, which corresponded to sensitivities of 72% to 79% and specificities of 71% to 78% for the diagnosis of TCMR at the time of allograft biopsy (mainly indication biopsies). In addition, the rise of the average trajectory of the 3-gene signature in repeated urine samples during the weeks before the biopsy showing rejection suggested that this approach may also be predictive of TCMR and predate graft dysfunction. From a technical point of view, urine samples are primarily characterized by extensive degradation of RNAs, thus complicating the widespread use of the PCR-based method developed by the Cornell group. However, a multicenter evaluation of this standardized protocol for noninvasive gene expression profiling has been recently performed and demonstrated a reasonably good concordance between laboratories,54 which is a prerequisite for the potential generalization of this tool if its use in real-life conditions proves to be useful. Finally, the CTOT4 study highlighted that only 83% of urine samples passed quality control, revealing an intrinsic technical limitation of this strategy.53

Emerging data also suggest that micro (mi)RNAs may be monitored in the urine samples of kidney transplant recipients.55 Lorenzen et al56 profiled urinary miRNAs of stable renal transplant recipients and transplant patients with acute TCMR and reported that miR-10a, miR-10b, and miR-210 were strongly deregulated in the urine of patients with AR. In addition, low miR-210 levels were associated with a higher decline in glomerular filtration rate at 1 year posttransplantation. Scian et al57 evaluated miRNA signatures in allograft tissue and in the urine of patients with chronic allograft dysfunction (CAD) and IFTA. Fifty-six miRNAs were identified in biopsy samples with IFTA. Furthermore, differential expression of selected miRNAs between patient groups was also observed in the urine. A characteristic miRNA signature for IFTA that was correlated with paired urine samples was further identified. By combining biopsy and urine sample profiling of kidney transplant patients with CAD and interstitial fibrosis of their renal allograft and patients with stable and adequate graft function, Maluf et al58 identified a subset of urine miRNAs that serve as potential biomarkers for monitoring graft function and anticipating progression to CAD in kidney recipients. Finally, Matz and colleagues39 recently reported that a 5-miRNA blood signature was highly associated with the diagnosis of vascular TCMR compared with stable kidney recipients. Together, these emerging data results support the potential use of miRNAs as noninvasive markers of rejection or IFTA and for use in monitoring graft function.

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Urine Target Proteins

Biomarkers of Kidney Allograft Rejection

Alternative approaches of quantifying immune-related proteins as biomarkers of acute graft rejection have been evaluated (extensively reviewed in Hirt-Minkowski et al59). Among these approaches, increased urinary levels of CXCL9 and CXCL10 have been consistently associated with TCMR, whereas increased urinary CXCL10 was more recently associated with AMR.8 Urinary CXCL9 and CXCL10 essentially appear to have a very high NPV. For example, the results of the CTOT1 study showed that the CXCL9 protein had an NPV of 92% for diagnosing Banff ≥1A TCMR, and Rabant et al reported that the CXCL10 protein had an NPV of 87% for diagnosing AMR and that the CXCL9 protein had an NPV of 99% for diagnosing TCMR.

However, another study showed that CXCL9 and CXCL10 in urine were similarly elevated in BK virus infection and AR compared with healthy controls, stable allograft recipients or recipients with CNI toxicity or IFTA.60 Because AR and BK virus nephropathy (BKVN) require diametrically opposite treatment approaches, this finding is not in favor of the clinical utility of CXCL9 and CXCL10 as specific AR biomarkers, but instead suggests that they may be markers of inflammation.

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Biomarkers of BK Virus Nephropathy

After kidney transplantation, risk assessment for potential BKVN includes quantitative PCR assays for BK virus loads in blood or urine or quantitation of urinary decoy cells in urine cytology specimens. These assays indicate viral activation and an increased risk for BKVN, but they cannot reliably predict the actual presence of intrarenal disease. Singh and colleagues described in 2009 a new noninvasive biomarker—the PV Haufen test—to accurately predict BKVN.61 Haufen bodies are 3-dimensional cast-like densely arranged polyomavirus aggregates observed by electron microscopy. In a first study, they demonstrated that the presence of urinary Haufen bodies marked BKVN with positive and negative predictive values exceeding 95%.61 They also showed that quantitative assessment of Haufen bodies reflected the severity of intrarenal disease.62 A recent study from the same team also suggested that the polyomavirus Haufen test might predict BKVN in children after hematopoietic cell transplantation.63 Prospective and independent studies are required to address the clinical significance of this new biomarker.

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Biomarkers of ATI

Cystatin C, KIM-1, IL-18, albumin, α1-antitrypsin, α1-microglobulin, Insulin-like growth factor-binding protein 7, β2-microglobulin and neutrophil gelatinase-associated lipocalin are promising biomarkers of acute kidney injury, both in plasma and in urine.64,65 Although a few individual studies found that NGAL was a very weak predictor of delayed graft function, confounded by female sex and urinary tract infection,66 a meta-analysis showed that it predicted delayed graft function with a good diagnostic performance67; however, it mixed studies in blood or urine and cutoffs ranging from 20 to 560 ng/mL. Another study showed that patients with BK viremia or BK nephropathy had NGAL plasma levels similar to controls,68 which indicates that AKI diagnosis using NGAL cannot be confounded by BKVN.

In the context of critical care medicine, the promising results of AKI biomarkers for the identification and prediction of AKI might be dependent of the population in which they are applied. In critically ill children, most AKI markers have been validated in the cardiopulmonary bypass setting in homogenous populations of patients. In noncardiac pediatric intensive care unit patients, these biomarkers had reduced predictive value, suggesting that demographic heterogeneity likely contributes to the inconsistent discriminatory performance of these biomarkers. This heterogeneity prompted the development of the renal angina index, a clinical methodology to stratify patients for the development of AKI.69 A prospective cohort study demonstrated that incorporation of urinary biomarker in the renal angina index early after intensive care unit admission optimized AKI prediction in critically ill children,70 suggesting that combination of early clinical parameters and biomarkers may improve prediction of AKI.

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Biomarkers of Chronic Allograft Nephropathy, IFTA or Long-Term Graft Outcome

Interstitial fibrosis and tubular atrophy shares common biomarker candidates with other renal graft lesions, such as β2-microglobulin with AR60 or urine cystatin C with AKI.71 Still, a biomarker tree associating elevated urine levels of β2-microglobulin (as the first node), NGAL, clusterin, and KIM-1 was proposed to distinguish patients with IFTA from transplant patients with normal renal function.60,72 Although the smart combination of unspecific with more specific biomarkers might be synergistic, the small patient groups studied and the absence of any independent validation set cast doubts on the actual diagnostic performance of this combination of simple, readily available tests.

Urinary chemokines might also predict subsequent events. Ho et al73 investigated urinary CCL2, CXCL9, CXCL10, and α1-microglobulin in a prospective cohort of patients and showed that CCL2 levels at 6 months (but not at 1 and 3 months) were associated with both IFTA and graft dysfunction at 24 months. The same group further reported that the CCL2:Cr ratio at 6 months was associated with graft survival.74 However, these results have not been confirmed in an independent population.

Rabant et al75 recently demonstrated that urinary CXCL10 levels at 1 and 3 months are highly predictive of immunological quiescence during the first 400 days after transplantation, with an NPV of 97% for predicting subsequent clinical AR. These high NPVs suggest that urinary chemokine levels could be used to identify patients at a very low risk of subsequent rejection, that is, the optimal candidates for immunosuppressive drug weaning; however, in a substudy of the multicenter CTOT9 trial, Hricik and colleagues found in a cohort of low-immunological risk patients who were randomized at 6 months posttransplant to tacrolimus withdrawal (prematurely stopped for higher rates of AR or DSA) that in all randomized patients, CXCL9 was low at 3 and 6 months, suggesting that this strategy cannot predict which recipients could be successfully withdrawn from tacrolimus. However, longitudinal monitoring also suggested that increased CXCL9 levels might be associated with immunological events after changes in immunosuppression.1,76

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Urine Proteomics/Peptidomics

High-throughput peptidomic and proteomic approaches have been applied to characterize the molecular patterns of graft injuries and to define sets of biomarkers for the noninvasive diagnosis of clinically defined conditions.

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Biomarkers of Rejection

Most studies have focused on AR, namely, TCMR, using urine as the diagnostic matrix and to a lesser extent blood, as discussed in detail in a recent review.77 Collectively, these studies suggest that proteomic or peptidomic biomarkers have the potential to predict AR with high sensitivity and specificity. Usability of the biomarker sets for the diagnosis of rejection was confirmed in some studies, using independent patient cohorts for validation. Some common biomarkers have been identified including fragments of collagens, β2-microglobulin, α-1-antichymotrypsin, and uromodulin. Moreover, pathway analysis has revealed that many of the identified molecules belong to distinct pathophysiologic processes, such as major histocompatibility complex antigen presentation, interferon γ and integrin signaling, complement activation, platelet functions, keratin sulfate, glycosaminoglycan, and collagen metabolism.

Despite these promising results, several caveats and open questions remain. Stringent histologic criteria in accordance with the rejection types of the Banff classification were not consistently applied in all of the studies. Absence of rejection in the comparator group was not confirmed by biopsy in many studies, leaving the issue of subclinical rejection in clinically stable patients open. Few studies specifically addressed subclinical rejection,78-80 AMR,81-85 and the emerging entity of mixed rejection cases,86 that is, combined TCMR and AMR. Additionally, there is a lack of studies on chronic rejection phenotypes, which have only recently been more specifically defined in the Banff classification.82,83,87 Because many of the studies on rejection were small, there is a paucity of data concerning potential confounding factors, such as urinary tract infection, cytomegalovirus infection, or BK virus nephropathy. Additionally, pathologies that can be often found in biopsies concomitantly with rejection, such as ATI,88 may have distinct proteomic signatures as discussed below. Because ATI is a nonspecific finding and can also be found in nonrejection grafts, biomarker sets for rejection should be established and tested against appropriate controls with this confounder. From a technical perspective, different proteomic platforms and procedures for sample preparation and analysis make comparisons of the large numbers of heterogeneous peptides and proteins identified for the diagnosis of rejection difficult. Before implementing proteomic markers into widespread clinical application, standardization of these procedures or development of simplified test systems is necessary.

Another important aspect of implementing these test systems and biomarkers into clinical use relates to their applicability to the general kidney transplant population. Given the small- to midsize patient cohorts studied and the highly artificial, nonrandom sample selection in most of the published studies, true in-place validation in nonselected, consecutively recruited patients is required. To this end, a few prospective studies are underway or are nearly completed that will address this point.77 In addition to noninvasive diagnosis of rejection, other clinically important issues that are worth studying in greater detail in the future are proteomic markers that can indicate upcoming rejection in advance and markers that can indicate the response to antirejection treatment.

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Biomarkers of BK Virus Nephropathy

The first untargeted proteomic studies of BKVN used the now-obsolete Surface-Enhanced Laser Desorption/Ionization Time-of-Flight mass spectrometry technology and showed significantly different proteomic profiles in patients with BKVN versus AR and stable transplant groups. However, the proteins concerned could not be identified.87 A more recent study using the comparative Isobaric tags for Relative and Absolute Quantification proteomic approach identified 10 urinary proteins upregulated in BKVN and showed that 2 of the candidate biomarkers of AR, fibrinogen β and fibrinogen γ, best discriminated AR from BKVN.84 The ability to distinguish BKVN from tubulointerstitial rejection is important because both conditions present with tubulitis. Nevertheless, the small number of BKVN samples examined so far precludes firm conclusions regarding the clinical utility of the proposed markers.

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Biomarkers of ATI

Acute tubular injury and, in its severest form, acute tubular necrosis, reflect miscellaneous causes of tubular damage, such as ischemia-reperfusion, drug toxicity, tubulointerstitial rejection, sepsis, and impaired renal perfusion. Several untargeted discovery studies based on small patient groups and SELDI-TOF reported MS peak clusters differentiating acute tubular necrosis from AR.85,89 These studies sometimes displayed perfect sensitivity and specificity,85 but SELDI-TOF could not identify the compounds detected as mass peaks. In the setting of cardiac surgery, further analysis showed that the associated peptides belong to established tubular injury markers, such as albumin, α1-acid glycoprotein, α1-microglobulin, β2-microglobulin, and NGAL.65,90

Using capillary electrophoresis coupled to time-of-flight mass spectrometry, Metzger et al91 identified 20 peptides that were significantly associated with AKI in patients from an intensive care unit. These urinary peptides were produced by the natural degradation of albumin, α1-antitrypsin, β2-microglobulin, fibrinogen α, and collagens 1 α(I) and 1 α(III). This proteomic marker pattern was able to detect AKI up to 5 days before a rise in serum creatinine in an independent patient group with kidney damage in an intensive care unit setting and in allogeneic hematopoietic stem cell transplantation.

Using liquid chromatography coupled to high-resolution tandem mass spectrometry for the untargeted proteomics analysis of urinary exosomes, Pisitkun et al86 identified a large number of proteins uniquely present in patients with tubular injury (n = 353 proteins), TCMR (n = 322), and AMR (n = 165). Through a systems biology analysis of these group-specific proteins, they proposed candidate biomarkers “to be tested in validation trials.” Again, similar to biomarkers for rejection, the level of evidence of the different biomarker candidates for AKI is still low, either because there is a lack of prospective testing or because of unconfirmed diagnostic performance.

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Biomarkers of Chronic Allograft Nephropathy or IFTA

The search for biomarkers of chronic allograft nephropathy (CAN) has been rendered difficult because of the terminology and breadth of this condition that has evolved from 2 phenotypes of supposedly known mechanisms, namely, chronic nephrotoxicity and chronic rejection, to the current, more histologically based interstitial fibrosis/tubular atrophy (IFTA), after going through the clinical definitions of CAN and CAD. In fact, IFTA is the sequel of different injuries, either nonspecific or without identified cause, or as a result of rejection, CNI toxicity, BKV nephritis, or nonrecovering ATI.

Using a rather complex sample work-up, Banon-Maneus et al92 identified 19 urine proteins with differential concentrations between patients with IFTA grades 0, I, and II/III. However, no control group with other types of graft lesions was included; thus, the actual specificity of these biomarker candidates with regard to IFTA is unknown.

The urinary proteome of 75 renal transplant recipients and 20 healthy volunteers was analyzed using SELDI-TOF MS. Mass spectrometry patterns were able to classify patients into subgroups with normal histology and Banff CAN grades 2 to 3 with a sensitivity of 86% and a specificity of 92%. Several urinary proteins associated with advanced CAN were identified, including α1-microglobulin, β2-microglobulin, prealbumin, and endorepellin Increased tissue expression of the endorepellin/perlecan ratio was confirmed by an immunofluorescence analysis of renal biopsies.93 In a study comparing the proteome of peripheral blood lymphocytes between 2 rather small groups of patients with mild or moderate/severe CAN, Kurian et al37 found unique proteins with a 100% class prediction value. However, no mention was made of corrections for multiple testing nor were control groups with other types of graft lesions or independent validation sets included.

A recent review article summarized results on biomarkers of CNI nephrotoxicity.94 The molecular signatures included the activation of proinflammatory responses, oxidative stress, ER stress, and the unfolded protein response; however, these results were mostly based on cultured cell lines and animal studies. Thus, reliable peptide patterns in patients with well-specified histological features of chronic CNI nephrotoxicity are still missing.

In summary, establishment of proteomics biomarkers of IFTA is even less advanced than those of AR because they have been less frequently studied. Moreover, a major limitation is the absence of a unique definition of the pathophysiological entity over the years. In addition, IFTA can result from different causes and can either be stable (because of self-contained or limited injury) or progressive, which adds to the complexity of identifying specific biomarkers.

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Role of Noninvasive Biomarkers Derived From Native Kidney Diseases

In addition to the many biomarkers that have been assessed in kidney transplant recipients, a number of noninvasive biomarkers have also been evaluated in CKD patients. Although a systematic review of this literature is beyond the scope of this review, a number of these markers might be relevant in transplantation medicine. For example, several biomarkers have been evaluated in glomerular diseases. For instance, urine pellet podocyte-associated mRNAs might be useful for monitoring the risk of progression and response to treatment.95,96 In the context of preeclampsia, podocyturia was demonstrated at time of diagnosis but, assessed at the end of the second trimester, may also allow identifying pregnant women at risk of subsequent preeclampsia.97 In the context of inflammatory renal diseases including lupus nephritis and renal vasculitis, a number of noninvasive biomarkers have been associated with the disease activity, such as urinary soluble CD163,98 urinary osteoprotegerin,99 urinary CD72,100 or more complex proteomic urinary signatures.101 Whether these biomarkers may be informative in kidney transplant recipients deserves specific studies.

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Applying Imaging Methods to Biomarker Development

In addition to the abovementioned biological markers, several medical imaging methods have also been used to noninvasively diagnose graft dysfunction. For example, proton magnetic resonance spectroscopy has been used to detect metabolic changes in morphologically normal organs and has been used to demonstrate the improved cerebral metabolic functions in type 1 diabetes patients after kidney-pancreas transplantation compared to kidney alone transplantation.102 High-energy phosphates metabolism assessed using 31P-magnetic resonance spectroscopy has been used to evaluate the renal allograft status. In a study of type 1 diabetes patients, it was shown that kidney-pancreas transplantation was associated with better high-energy phosphates than kidney alone transplantation, suggesting that restoration of β-cell function positively affects kidney graft metabolism.103 Very recently, it was reported that fluorodeoxyglucose F18 positron emission tomography coupled with computed tomography may noninvasively distinguish nonrejection from AR in kidney transplant recipients.104

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The major shortcomings of most studies are that they: (1) often involve small numbers of samples/patients and do not take into account clinical or therapeutic confounders, (2) often lack specifics on sample selection, (3) do not uniformly relate biomarker results to rigorously classified biopsy findings, (4) are not always confirmed in independent and representative patient cohorts, and (5) involve too many biomarkers or use techniques that are too sophisticated for implementation in clinical practice. Our view is that noninvasive biomarker translation into the clinic requires 3 mandatory steps: (i) identification and thorough validation of candidate biomarkers for clearly defined disease entities; (ii) demonstration of their clinical utility in clearly defined target populations, including description of thresholds and confounders; and (iii) establishment of short turnaround time and validated assays run on easily accessible and reliable technical platforms.

The first prerequisite implies a multistage program from biomarker discovery to validation and qualification applying the highest possible quality controls at all clinical and analytical steps (Figure 2). In this step, multicenter investigations in large observational cohort studies should further evaluate the predictive, diagnostic and prognostic performance of biomarkers, identify relevant confounding factors, and most importantly, estimate the generalizability of the proposed biomarker models.

The demonstration that an innovative candidate biomarker or set of biomarkers is clinically useful is the second major step. The ultimate goals are to facilitate individualization/optimization of the immunosuppressive drug therapy, including weaning or reintroduction of therapy, avoidance, or diminished use of biopsies and improved patient treatment, quality of life and long-term graft survival. Pragmatically, this requires proof of the utility of the candidate biomarker at an individual patient level through both large observational cohort studies and prospective interventional trials. To the best of our knowledge, no study has addressed this point to date. In a personalized medicine strategy, noninvasive biomarkers also have the potential to help titrate immunosuppressive therapy in an individual, but again this can only be demonstrated through appropriately designed clinical trials.

At the end of this long maturation process, robust, quick, validated, and easily transferable techniques are necessary to enable rapid and reliable diagnosis in the setting of local transplant centers. Along with these test systems, simple interpretation and decision algorithms should be provided to transform sophisticated molecular information into a clinical, individualized application.

We designed the European FP7 Research Program “Biomarkers of Renal Graft Injuries in kidney allograft recipients” (BIOMARGIN) following these considerations and requirements. A practical objective of BIOMARGIN is to discover, select and validate: (1) blood and/or urine biomarkers at different omics levels (transcriptomics, proteomics, metabolomics) of renal allograft lesions, assessed through biopsy histological analysis by a committee of experts (“gold standard”); and (2) early predictors of chronic graft dysfunction and ultimately graft loss, less invasive than graft biopsy and with improved predictive values of long-term outcomes. Another goal is to provide clinicians with tools (robust, validated, straightforward, and easily transferable reference techniques for the quantitative determination of the selected biomarkers and combinations thereof; interpretation algorithms) to obtain such information in a timely manner. The BIOMARGIN work plan has been designed to be able to transfer as the most pertinent biomarkers to the clinics (Figure 4). This strategy explains a multistage work plan (4 clinical studies, see Figure 3) that is rendered possible by the existence of large biobanks gathered in similar conditions in 4 European transplantation centers. However, with the time (4 years) and funds allotted for the project, it will not be possible to evaluate the clinical utility of the selected biomarker signatures through an interventional clinical trial, which will likely take another 4 to 5 years to complete. This demonstrates the complexity of developing clinically usable and useful biomarkers in renal transplantation.



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1. Matas AJ, Gaston RS. Moving beyond minimization trials in kidney transplantation. J Am Soc Nephrol. 2015;26:2898–2901.
2. Henderson LK, Nankivell BJ, Chapman JR. Surveillance protocol kidney transplant biopsies: their evolving role in clinical practice. Am J Transplant. 2011;11:1570–1575.
3. Naesens M. Zero-Time Renal Transplant Biopsies: A Comprehensive Review. Transplantation. 2016;100:1425–1439.
4. Halloran PF, Famulski K, Reeve J. The molecular phenotypes of rejection in kidney transplant biopsies. Curr Opin Organ Transplant. 2015;20:359–367.
5. Halloran PF, Reeve JP, Pereira AB, et al. Antibody-mediated rejection, T cell-mediated rejection, and the injury-repair response: new insights from the Genome Canada studies of kidney transplant biopsies. Kidney Int. 2014;85:258–264.
6. Adam B, Mengel M. Transplant biopsy beyond light microscopy. BMC Nephrol. 2015;16:132.
7. Hariharan S, McBride MA, Cherikh WS, et al. Post-transplant renal function in the first year predicts long-term kidney transplant survival. Kidney Int. 2002;62:311–318.
8. Rabant M, Amrouche L, Lebreton X, et al. Urinary C-X-C motif chemokine 10 independently improves the noninvasive diagnosis of antibody-mediated kidney allograft rejection. J Am Soc Nephrol. 2015;26:2840–2851.
9. Lo DJ, Kaplan B, Kirk AD. Biomarkers for kidney transplant rejection. Nat Rev Nephrol. 2014;10:215–225.
10. Heilman RL, Devarapalli Y, Chakkera HA, et al. Impact of subclinical inflammation on the development of interstitial fibrosis and tubular atrophy in kidney transplant recipients. Am J Transplant. 2010;10:563–570.
11. Wiebe C, Gibson IW, Blydt-Hansen TD, et al. Evolution and clinical pathologic correlations of de novo donor-specific HLA antibody post kidney transplant. Am J Transplant. 2012;12:1157–1167.
12. Mengel M, Gwinner W, Schwarz A, et al. Infiltrates in protocol biopsies from renal allografts. Am J Transplant. 2007;7:356–365.
13. Nankivell BJ, Kuypers DR. Diagnosis and prevention of chronic kidney allograft loss. Lancet. 2011;378:1428–1437.
14. Kidney Disease: Improving Global Outcomes (KDIGO) Transplant Work Group. KDIGO clinical practice guideline for the care of kidney transplant recipients. Am J Transplant. 2009;(9 Suppl 3):S1–S155.
15. Legendre C, Anglicheau D. Transplantation: proteinuria in kidney transplantation: an ongoing story. Nat Rev Nephrol. 2013;9:251–252.
16. Tsampalieros A, Knoll GA. Evaluation and management of proteinuria after kidney transplantation. Transplantation. 2015;99:2049–2060.
17. Amer H, Fidler ME, Myslak M, et al. Proteinuria after kidney transplantation, relationship to allograft histology and survival. Am J Transplant. 2007;7:2748–2756.
18. Cherukuri A, Welberry-Smith MP, Tattersall JE, et al. The clinical significance of early proteinuria after renal transplantation. Transplantation. 2010;89:200–207.
19. Halimi JM, Buchler M, Al-Najjar A, et al. Urinary albumin excretion and the risk of graft loss and death in proteinuric and non-proteinuric renal transplant recipients. Am J Transplant. 2007;7:618–625.
20. Amer H, Lieske JC, Rule AD, et al. Urine high and low molecular weight proteins one-year post-kidney transplant: relationship to histology and graft survival. Am J Transplant. 2013;13:676–684.
21. Naesens M, Lerut E, Emonds MP, et al. Proteinuria as a noninvasive marker for renal allograft histology and failure: an observational cohort study. J Am Soc Nephrol. 2016;27:281–292.
22. Lefaucheur C, Loupy A, Hill GS, et al. Preexisting donor-specific HLA antibodies predict outcome in kidney transplantation. J Am Soc Nephrol. 2010;21:1398–1406.
23. Tambur AR, Herrera ND, Haarberg KM, et al. Assessing antibody strength: comparison of MFI, C1q, and titer information. Am J Transplant. 2015;15:2421–2430.
24. Naesens M, Sarwal MM. Molecular diagnostics in transplantation. Nat Rev Nephrol. 2010;6:614–628.
25. Anglicheau D, Suthanthiran M. Noninvasive prediction of organ graft rejection and outcome using gene expression patterns. Transplantation. 2008;86:192–199.
26. Perkins D, Verma M, Park KJ. Advances of genomic science and systems biology in renal transplantation: a review. Semin Immunopathol. 2011;33:211–218.
27. Traitanon O, Poggio ED, Fairchild RL. Molecular monitoring of alloimmune-mediated injury in kidney transplant patients. Curr Opin Nephrol Hypertens. 2014;23:625–630.
28. Augustine JJ, Siu DS, Clemente MJ, et al. Pre-transplant IFN-gamma ELISPOTs are associated with post-transplant renal function in African American renal transplant recipients. Am J Transplant. 2005;5:1971–1975.
29. Bestard O, Cruzado JM, Lucia M, et al. Prospective assessment of antidonor cellular alloreactivity is a tool for guidance of immunosuppression in kidney transplantation. Kidney Int. 2013;84:1226–1236.
30. Mehrotra A, Leventhal J, Purroy C, et al. Monitoring T cell alloreactivity. Transplant Rev (Orlando). 2015;29:53–59.
31. Nickel P, Bestard O, Volk HD, et al. Diagnostic value of T-cell monitoring assays in kidney transplantation. Curr Opin Organ Transplant. 2009;14:426–431.
32. Hricik DE, Augustine J, Nickerson P, et al. Interferon gamma ELISPOT testing as a risk-stratifying biomarker for kidney transplant injury: results from the CTOT-01 multicenter study. Am J Transplant. 2015;15:3166–3173.
33. Ling X, Xiong J, Liang W, et al. Can immune cell function assay identify patients at risk of infection or rejection? A meta-analysis. Transplantation. 2012;93:737–743.
34. Hope CM, Troelnikov A, Hanf W, et al. Peripheral natural killer cell and allo-stimulated T-cell function in kidney transplant recipients associate with cancer risk and immunosuppression-related complications. Kidney Int. 2015;88:1374–1382.
35. Aquino-Dias EC, Joelsons G, da Silva DM, et al. Non-invasive diagnosis of acute rejection in kidney transplants with delayed graft function. Kidney Int. 2008;73:877–884.
36. Gunther OP, Balshaw RF, Scherer A, et al. Functional genomic analysis of peripheral blood during early acute renal allograft rejection. Transplantation. 2009;88:942–951.
37. Kurian SM, Heilman R, Mondala TS, et al. Biomarkers for early and late stage chronic allograft nephropathy by proteogenomic profiling of peripheral blood. PLoS One. 2009;4:e6212.
38. Li L, Ying L, Naesens M, et al. Interference of globin genes with biomarker discovery for allograft rejection in peripheral blood samples. Physiol Genomics. 2008;32:190–197.
39. Matz M, Fabritius K, Lorkowski C, et al. Identification of T cell-mediated vascular rejection after kidney transplantation by the combined measurement of 5 specific MicroRNAs in blood. Transplantation. 2016;100:898–907.
40. Rascio F, Pontrelli P, Accetturo M, et al. A type I interferon signature characterizes chronic antibody-mediated rejection in kidney transplantation. J Pathol. 2015;237:72–84.
41. Simon T, Opelz G, Wiesel M, et al. Serial peripheral blood perforin and granzyme B gene expression measurements for prediction of acute rejection in kidney graft recipients. Am J Transplant. 2003;3:1121–1127.
42. Kurian SM, Williams AN, Gelbart T, et al. Molecular classifiers for acute kidney transplant rejection in peripheral blood by whole genome gene expression profiling. Am J Transplant. 2014;14:1164–1172.
43. Roedder S, Sigdel T, Salomonis N, et al. The kSORT assay to detect renal transplant patients at high risk for acute rejection: results of the multicenter AART study. PLoS Med. 2014;11:e1001759.
44. Li L, Khatri P, Sigdel TK, et al. A peripheral blood diagnostic test for acute rejection in renal transplantation. Am J Transplant. 2012;12:2710–2718.
45. Braud C, Baeten D, Giral M, et al. Immunosuppressive drug-free operational immune tolerance in human kidney transplant recipients: Part I. Blood gene expression statistical analysis. J Cell Biochem. 2008;103:1681–1692.
46. Brouard S, Mansfield E, Braud C, et al. Identification of a peripheral blood transcriptional biomarker panel associated with operational renal allograft tolerance. Proc Natl Acad Sci U S A. 2007;104:15448–15453.
47. Lozano JJ, Pallier A, Martinez-Llordella M, et al. Comparison of transcriptional and blood cell-phenotypic markers between operationally tolerant liver and kidney recipients. Am J Transplant. 2011;11:1916–1926.
48. Newell KA, Asare A, Kirk AD, et al. Identification of a B cell signature associated with renal transplant tolerance in humans. J Clin Invest. 2010;120:1836–1847.
49. Sagoo P, Perucha E, Sawitzki B, et al. Development of a cross-platform biomarker signature to detect renal transplant tolerance in humans. J Clin Invest. 2010;120:1848–1861.
50. Baron D, Giral M, Brouard S. Reconsidering the detection of tolerance to individualize immunosuppression minimization and to improve long-term kidney graft outcomes. Transpl Int. 2015;28:938–959.
51. Baron D, Ramstein G, Chesneau M, et al. A common gene signature across multiple studies relate biomarkers and functional regulation in tolerance to renal allograft. Kidney Int. 2015;87:984–995.
52. Lee JR, Muthukumar T, Dadhania D, et al. Urinary cell mRNA profiles predictive of human kidney allograft status. Immunol Rev. 2014;258:218–240.
53. Suthanthiran M, Schwartz JE, Ding R, et al. Urinary-cell mRNA profile and acute cellular rejection in kidney allografts. N Engl J Med. 2013;369:20–31.
54. Keslar KS, Lin M, Zmijewska AA, et al. Multicenter evaluation of a standardized protocol for noninvasive gene expression profiling. Am J Transplant. 2013;13:1891–1897.
55. Amrouche L, Rabant M, Anglicheau D. MicroRNAs as biomarkers of graft outcome. Transplant Rev (Orlando). 2014;28:111–118.
56. Lorenzen JM, Volkmann I, Fiedler J, et al. Urinary miR-210 as a mediator of acute T-cell mediated rejection in renal allograft recipients. Am J Transplant. 2011;11:2221–2227.
57. Scian MJ, Maluf DG, David KG, et al. MicroRNA profiles in allograft tissues and paired urines associate with chronic allograft dysfunction with IF/TA. Am J Transplant. 2011;11:2110–2122.
58. Maluf DG, Dumur CI, Suh JL, et al. The urine microRNA profile may help monitor post-transplant renal graft function. Kidney Int. 2014;85:439–449.
59. Hirt-Minkowski P, De Serres SA, Ho J. Developing renal allograft surveillance strategies—urinary biomarkers of cellular rejection. Can J Kidney Health Dis. 2015;2:28.
60. Johnston O, Cassidy H, O'Connell S, et al. Identification of β2-microglobulin as a urinary biomarker for chronic allograft nephropathy using proteomic methods. Proteomics Clin Appl. 2011;5:422–431.
61. Singh HK, Andreoni KA, Madden V, et al. Presence of urinary Haufen accurately predicts polyomavirus nephropathy. J Am Soc Nephrol. 2009;20:416–427.
62. Singh HK, Reisner H, Derebail VK, et al. Polyomavirus nephropathy: quantitative urinary polyomavirus-Haufen testing accurately predicts the degree of intrarenal viral disease. Transplantation. 2015;99:609–615.
63. Laskin BL, Singh HK, Beier UH, et al. The noninvasive urinary polyomavirus Haufen test predicts BK virus nephropathy in children after hematopoietic cell transplantation: a pilot study. [published online ahead of print February 18th, 2016]. Transplantation. 2016 doi: 10.1097/TP.0000000000001085.
64. Clerico A, Galli C, Fortunato A, et al. Neutrophil gelatinase-associated lipocalin (NGAL) as biomarker of acute kidney injury: a review of the laboratory characteristics and clinical evidences. Clin Chem Lab Med. 2012;50:1505–1517.
65. Devarajan P. Genomic and proteomic characterization of acute kidney injury. Nephron. 2015;131:85–91.
66. Kaufeld JK, Gwinner W, Scheffner I, et al. Urinary NGAL ratio is not a sensitive biomarker for monitoring acute tubular injury in kidney transplant patients: NGAL and ATI in renal transplant patients. J Transplant. 2012;2012:563404.
67. Haase-Fielitz A, Haase M, Devarajan P. Neutrophil gelatinase-associated lipocalin as a biomarker of acute kidney injury: a critical evaluation of current status. Ann Clin Biochem. 2014;51(Pt 3):335–351.
68. Rau S, Schonermarck U, Jager G, et al. BK virus-associated nephropathy: neutrophil gelatinase-associated lipocalin as a new diagnostic tool? Clin Transplant. 2013;27:E184–E191.
69. Goldstein SL, Chawla LS. Renal angina. Clin J Am Soc Nephrol. 2010;5:943–949.
70. Menon S, Goldstein SL, Mottes T, et al. Urinary biomarker incorporation into the renal angina index early in intensive care unit admission optimizes acute kidney injury prediction in critically ill children: a prospective cohort study. Nephrol Dial Transplant. 2016;31:586–594.
71. Mendes Mde F, Salgado JV, de Ribamar Lima J, et al. Increased urinary cystatin C level is associated with interstitial fibrosis and tubular atrophy in kidney allograft recipients. Clin Biochem. 2015;48:546–549.
72. Cassidy H, Slyne J, O'Kelly P, et al. Urinary biomarkers of chronic allograft nephropathy. Proteomics Clin Appl. 2015;9:574–585.
73. Ho J, Rush DN, Gibson IW, et al. Early urinary CCL2 is associated with the later development of interstitial fibrosis and tubular atrophy in renal allografts. Transplantation. 2010;90:394–400.
74. Ho J, Wiebe C, Gibson IW, et al. Elevated urinary CCL2: Cr at 6 months is associated with renal allograft interstitial fibrosis and inflammation at 24 months. Transplantation. 2014;98:39–46.
75. Rabant M, Amrouche L, Morin L, et al. Early low urinary CXCL9 and CXCL10 might predict immunological quiescence in clinically and histologically stable kidney recipients. Am J Transplant. 2015.
76. Hricik DE, Formica RN, Nickerson P, et al. Adverse outcomes of tacrolimus withdrawal in immune-quiescent kidney transplant recipients. J Am Soc Nephrol. 2015;26:3114–3122.
77. Gwinner W, Metzger J, Husi H, et al. Proteomics for rejection diagnosis in renal transplant patients: where are we now? World J Transplant. 2016;6:28–41.
78. Mao Y, Yu J, Chen J, et al. Diagnosis of renal allograft subclinical rejection by urine protein fingerprint analysis. Transpl Immunol. 2008;18:255–259.
79. Metzger J, Chatzikyrkou C, Broecker V, et al. Diagnosis of subclinical and clinical acute T-cell-mediated rejection in renal transplant patients by urinary proteome analysis. Proteomics Clin Appl. 2011;5:322–333.
80. Wittke S, Haubitz M, Walden M, et al. Detection of acute tubulointerstitial rejection by proteomic analysis of urinary samples in renal transplant recipients. Am J Transplant. 2005;5:2479–2488.
81. Akkina SK, Zhang Y, Nelsestuen GL, et al. Temporal stability of the urinary proteome after kidney transplant: more sensitive than protein composition? J Proteome Res. 2009;8:94–103.
82. Quintana LF, Campistol JM, Alcolea MP, et al. Application of label-free quantitative peptidomics for the identification of urinary biomarkers of kidney chronic allograft dysfunction. Mol Cell Proteomics. 2009;8:1658–1673.
83. Quintana LF, Sole-Gonzalez A, Kalko SG, et al. Urine proteomics to detect biomarkers for chronic allograft dysfunction. J Am Soc Nephrol. 2009;20:428–435.
84. Sigdel TK, Salomonis N, Nicora CD, et al. The identification of novel potential injury mechanisms and candidate biomarkers in renal allograft rejection by quantitative proteomics. Mol Cell Proteomics. 2014;13:621–631.
85. Yang H, Mao Y, Yu J, et al. Diagnosis of c4d + renal allograft acute humoral rejection by urine protein fingerprint analysis. J Int Med Res. 2010;38:176–186.
86. Pisitkun T, Gandolfo MT, Das S, et al. Application of systems biology principles to protein biomarker discovery: urinary exosomal proteome in renal transplantation. Proteomics Clin Appl. 2012;6:268–278.
87. Jahnukainen T, Malehorn D, Sun M, et al. Proteomic analysis of urine in kidney transplant patients with BK virus nephropathy. J Am Soc Nephrol. 2006;17:3248–3256.
88. Gwinner W, Hinzmann K, Erdbruegger U, et al. Acute tubular injury in protocol biopsies of renal grafts: prevalence, associated factors and effect on long-term function. Am J Transplant. 2008;8:1684–1693.
89. Wang M, Jin Q, Tu H, et al. Detection of renal allograft dysfunction with characteristic protein fingerprint by serum proteomic analysis. Int Urol Nephrol. 2011;43:1009–1017.
90. Ho J, Lucy M, Krokhin O, et al. Mass spectrometry-based proteomic analysis of urine in acute kidney injury following cardiopulmonary bypass: a nested case-control study. Am J Kidney Dis. 2009;53:584–595.
91. Metzger J, Kirsch T, Schiffer E, et al. Urinary excretion of twenty peptides forms an early and accurate diagnostic pattern of acute kidney injury. Kidney Int. 2010;78:1252–1262.
92. Bañón-Maneus E, Diekmann F, Carrascal M, et al. Two-dimensional difference gel electrophoresis urinary proteomic profile in the search of nonimmune chronic allograft dysfunction biomarkers. Transplantation. 2010;89:548–558.
93. O'Riordan E, Orlova TN, Mendelev N, et al. Urinary proteomic analysis of chronic allograft nephropathy. Proteomics Clin Appl. 2008;2:1025–1035.
94. Fernando M, Peake PW, Endre ZH. Biomarkers of calcineurin inhibitor nephrotoxicity in transplantation. Biomark Med. 2014;8:1247–1262.
95. Wickman L, Afshinnia F, Wang SQ, et al. Urine podocyte mRNAs, proteinuria, and progression in human glomerular diseases. J Am Soc Nephrol. 2013;24:2081–2095.
96. Sato Y, Wharram BL, Lee SK, et al. Urine podocyte mRNAs mark progression of renal disease. J Am Soc Nephrol. 2009;20:1041–1052.
97. Craici IM, Wagner SJ, Bailey KR, et al. Podocyturia predates proteinuria and clinical features of preeclampsia: longitudinal prospective study. Hypertension. 2013;61:1289–1296.
98. O'Reilly VP, Wong L, Kennedy C, et al. Urinary soluble CD163 in active renal vasculitis. J Am Soc Nephrol. 2016.
99. Gupta R, Aggarwal A, Sinha S, et al. Urinary osteoprotegerin: a potential biomarker of lupus nephritis disease activity. Lupus. 2016.
100. Vadasz Z, Goldeberg Y, Halasz K, et al. Increased soluble CD72 in systemic lupus erythematosus is in association with disease activity and lupus nephritis. Clin Immunol. 2016;164:114–118.
101. Brunner HI, Bennett MR, Mina R, et al. Association of noninvasively measured renal protein biomarkers with histologic features of lupus nephritis. Arthritis Rheum. 2012;64:2687–2697.
102. Fiorina P, Vezzulli P, Bassi R, et al. Near normalization of metabolic and functional features of the central nervous system in type 1 diabetic patients with end-stage renal disease after kidney-pancreas transplantation. Diabetes Care. 2012;35:367–374.
103. Fiorina P, Perseghin G, De Cobelli F, et al. Altered kidney graft high-energy phosphate metabolism in kidney-transplanted end-stage renal disease type 1 diabetic patients: a cross-sectional analysis of the effect of kidney alone and kidney-pancreas transplantation. Diabetes Care. 2007;30:597–603.
104. Lovinfosse P, Weekers L, Bonvoisin C, et al. Fluorodeoxyglucose F(18) positron emission tomography coupled with computed tomography in suspected acute renal allograft rejection. Am J Transplant. 2016;16:310–316.
105. Li B, Hartono C, Ding R, et al. Noninvasive diagnosis of renal-allograft rejection by measurement of messenger RNA for perforin and granzyme B in urine. N Engl J Med. 2001;344:947–954.
    106. Muthukumar T, Ding R, Dadhania D, et al. Serine proteinase inhibitor-9, an endogenous blocker of granzyme B/perforin lytic pathway, is hyperexpressed during acute rejection of renal allografts. Transplantation. 2003;75:1565–1570.
      107. Dadhania D, Muthukumar T, Ding R, et al. Molecular signatures of urinary cells distinguish acute rejection of renal allografts from urinary tract infection. Transplantation. 2003;75:1752–1754.
        108. Ding R, Li B, Muthukumar T, et al. CD103 mRNA levels in urinary cells predict acute rejection of renal allografts. Transplantation. 2003;75:1307–1312.
          109. Kotsch K, Mashreghi MF, Bold G, et al. Enhanced granulysin mRNA expression in urinary sediment in early and delayed acute renal allograft rejection. Transplantation. 2004;77:1866–1875.
            110. Tatapudi RR, Muthukumar T, Dadhania D, et al. Noninvasive detection of renal allograft inflammation by measurements of mRNA for IP-10 and CXCR3 in urine. Kidney Int. 2004;65:2390–2397.
              111. Muthukumar T, Dadhania D, Ding R, et al. Messenger RNA for FOXP3 in the urine of renal-allograft recipients. N Engl J Med. 2005;353:2342–2351.
                112. Matz M, Beyer J, Wunsch D, et al. Early post-transplant urinary IP-10 expression after kidney transplantation is predictive of short- and long-term graft function. Kidney Int. 2006;69:1683–1690.
                  113. Yannaraki M, Rebibou JM, Ducloux D, et al. Urinary cytotoxic molecular markers for a noninvasive diagnosis in acute renal transplant rejection. Transpl Int. 2006;19:759–768.
                    114. Seiler M, Brabcova I, Viklicky O, et al. Heightened expression of the cytotoxicity receptor NKG2D correlates with acute and chronic nephropathy after kidney transplantation. Am J Transplant. 2007;7:423–433.
                      115. Renesto PG, Ponciano VC, Cenedeze MA, et al. High expression of Tim-3 mRNA in urinary cells from kidney transplant recipients with acute rejection. Am J Transplant. 2007;7:1661–1665.
                        116. Manfro RC, Aquino-Dias EC, Joelsons G, et al. Noninvasive Tim-3 messenger RNA evaluation in renal transplant recipients with graft dysfunction. Transplantation. 2008;86:1869–1874.
                          117. Ozbay A, Torring C, Olsen R, et al. Transcriptional profiles in urine during acute rejection, bacteriuria, CMV infection and stable graft function after renal transplantation. Scand J Immunol. 2009;69:357–365.
                            118. Afaneh C, Muthukumar T, Lubetzky M, et al. Urinary cell levels of mRNA for OX40, OX40L, PD-1, PD-L1, or PD-L2 and acute rejection of human renal allografts. Transplantation. 2010;90:1381–1387.
                              119. van Ham SM, Heutinck KM, Jorritsma T, et al. Urinary granzyme A mRNA is a biomarker to diagnose subclinical and acute cellular rejection in kidney transplant recipients. Kidney Int. 2010;78:1033–1040.
                                120. Matignon M, Ding R, Dadhania DM, et al. Urinary cell mRNA profiles and differential diagnosis of acute kidney graft dysfunction. J Am Soc Nephrol. 2014;25:1586–1597.
                                  121. Hricik DE, Nickerson P, Formica RN, et al. Multicenter validation of urinary CXCL9 as a risk-stratifying biomarker for kidney transplant injury. Am J Transplant. 2013;13:2634–2644.
                                    122. Hu H, Aizenstein BD, Puchalski A, et al. Elevation of CXCR3-binding chemokines in urine indicates acute renal-allograft dysfunction. Am J Transplant. 2004;4:432–437.
                                      123. Hauser IA, Spiegler S, Kiss E, et al. Prediction of acute renal allograft rejection by urinary monokine induced by IFN-gamma (MIG). J Am Soc Nephrol. 2005;16:1849–1858.
                                        124. Peng W, Chen J, Jiang Y, et al. Urinary fractalkine is a marker of acute rejection. Kidney Int. 2008;74:1454–1460.
                                          125. Schaub S, Nickerson P, Rush D, et al. Urinary CXCL9 and CXCL10 levels correlate with the extent of subclinical tubulitis. Am J Transplant. 2009;9:1347–1353.
                                            126. Hu H, Kwun J, Aizenstein BD, et al. Noninvasive detection of acute and chronic injuries in human renal transplant by elevation of multiple cytokines/chemokines in urine. Transplantation. 2009;87:1814–1820.
                                              127. Jackson JA, Kim EJ, Begley B, et al. Urinary chemokines CXCL9 and CXCL10 are noninvasive markers of renal allograft rejection and BK viral infection. Am J Transplant. 2011;11:2228–2234.
                                                128. Ho J, Rush DN, Karpinski M, et al. Validation of urinary CXCL10 as a marker of borderline, subclinical, and clinical tubulitis. Transplantation. 2011;92:878–882.
                                                  129. Hirt-Minkowski P, Amico P, Ho J, et al. Detection of clinical and subclinical tubulo-interstitial inflammation by the urinary CXCL10 chemokine in a real-life setting. Am J Transplant. 2012;12:1811–1823.
                                                    130. Blydt-Hansen TD, Gibson IW, Gao A, et al. Elevated urinary CXCL10-to-creatinine ratio is associated with subclinical and clinical rejection in pediatric renal transplantation. Transplantation. 2015;99:797–804.
                                                      131. Vasconcellos LM, Schachter AD, Zheng XX, et al. Cytotoxic lymphocyte gene expression in peripheral blood leukocytes correlates with rejecting renal allografts. Transplantation. 1998;66:562–566.
                                                        132. Shoker A, George D, Yang H, et al. Heightened CD40 ligand gene expression in peripheral CD4+ T cells from patients with kidney allograft rejection. Transplantation. 2000;70:497–505.
                                                          133. Dugré FJ, Gaudreau S, Belles-Isles M, et al. Cytokine and cytotoxic molecule gene expression determined in peripheral blood mononuclear cells in the diagnosis of acute renal rejection. Transplantation. 2000;70:1074–1080.
                                                            134. Sabek O, Dorak MT, Kotb M, et al. Quantitative detection of T-cell activation markers by real-time PCR in renal transplant rejection and correlation with histopathologic evaluation. Transplantation. 2002;74:701–707.
                                                              135. Netto MV, Fonseca BA, Dantas M, et al. Granzyme B, FAS-ligand and perforin expression during acute cellular rejection episodes after kidney transplantation: comparison between blood and renal aspirates. Transplant Proc. 2002;34:476–478.
                                                                136. Shin GT, Kim SJ, Lee TS, et al. Gene expression of perforin by peripheral blood lymphocytes as a marker of acute rejection. Nephron Clin Pract. 2005;100:c63–c70.
                                                                  137. Veale JL, Liang LW, Zhang Q, et al. Noninvasive diagnosis of cellular and antibody-mediated rejection by perforin and granzyme B in renal allografts. Hum Immunol. 2006;67:777–786.
                                                                    138. Danger R, Paul C, Giral M, et al. Expression of miR-142-5p in peripheral blood mononuclear cells from renal transplant patients with chronic antibody-mediated rejection. PLoS One. 2013;8:e60702.
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