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A critical review of biomarkers in kidney transplantation

Safa, Kassema; Magee, Ciara N.b; Azzi, Jamilc

Current Opinion in Nephrology and Hypertension: November 2017 - Volume 26 - Issue 6 - p 509–515
doi: 10.1097/MNH.0000000000000361
DIALYSIS AND TRANSPLANTATION: Edited by J. Kevin Tucker and Anil Chandraker

Purpose of review Improved long-term kidney allograft survival remains a critical goal in transplantation; the achievement of this, however, is highly dependent on the identification of biomarkers that can either predict or allow advance detection of patients at risk of allograft injury. The present review outlines the commonly used biomarkers in kidney transplantation, while also highlighting those currently under investigation, discussing their advantages and limitations.

Recent findings Most of the approved biomarkers currently used in kidney transplantation capture antigen recognition or alloantibody production. However, tremendous progress has recently been made in the development of markers of other signaling pathways pertinent to the alloimmune response. Microarray gene sets that predict rejection or poor prognostic phenotypes have been identified in kidney biopsies (the ‘molecular microscope diagnostic system’ and the ‘genomics of chronic allograft rejection’ scores), peripheral blood (the ‘kidney solid organ response test’), and urine (the ‘3-genes signature’). Strategies targeting serial measurements of urinary chemokines such as CXCL9 and CXCL10 also appear promising.

Summary Although the range of biomarkers in current use is limited, there are many assays in the development and validation pipeline that appear promising but that have yet to reach mainstream clinical transplantation. The ‘ideal biomarker’ may eventually transpire to be the combination of several assays.

aDivision of Nephrology and Transplant Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA

bDepartment of Renal Medicine and Transplantation, UCL Center for Nephrology, Royal Free Hospital, London, UK

cSchuster Family Transplantation Research Center, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA

Correspondence to Kassem Safa, MD, 165 Cambridge st, Suite 302, Boston, MA 02114, USA. Tel: +617 643 5257; fax: +617 643 6722; e-mail:; Jamil Azzi, MD, 221 Longwood Avenue, Suite 300, Boston, MA 02115, USA. Tel: +617 732 6383; fax: +617 975 0840; e-mail:

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Kidney transplantation is the best therapeutic option for end-stage kidney disease and offers the best survival, quality of life, and cost-effectiveness compared with dialysis. The waiting list for kidney transplantation continues to grow, however, with more than 100 000 patients currently listed for a kidney transplant in the United States, but only about 17 000 transplants performed annually [1]; extending the lifespan of an individual transplant is therefore of paramount importance. Although short-term allograft outcomes have improved with potent immunosuppressive strategies, long-term allograft failure rates remain unacceptably high, predominantly because of chronic antibody-mediated rejection, infections, and/or toxicity of immunosuppressive regimens [2]. These complications often result from our inability to accurately gauge the alloimmune response, leading to the infamous ‘over’ or ‘under’ immunosuppression. The limited panel of clinical testing currently available includes qualitative or semiquantitative measurement of alloantibodies, in addition to drug-level monitoring and markers of allograft injuries, but clearly, this repertoire requires expansion. In this review, we will highlight the commonly used biomarkers in kidney transplantation alongside those under investigation, outlining their advantages and limitations.

Box 1

Box 1

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Skin transplants were perhaps the earliest biomarker in kidney transplantation: weeks before the first kidney transplant performed by Joseph Murray at the Peter Bent Brigham in Boston in 1954, full thickness skin grafts were exchanged between the donor–recipient twin brothers. One month later, when the potential recipient was hospitalized for uremia and was supported by ‘the artificial kidney’, there was no evidence of rejection of the skin grafts, providing proof of tissue compatibility [3]. Once transplanted, the kidney allograft functioned immediately and had a prolonged survival. A later but equally pivotal milestone in the identification of kidney transplantation biomarkers was the development of the microcytotoxicity assay by Paul Terasaki in Los Angeles: donor cells were mixed with recipient serum to detect cytotoxicity, which predicted donor–recipient pairs at risk for ‘immediate failure’ [4]. Almost half a century later, the quest for ideal biomarkers in kidney transplantation continues, reflecting the complexity of the immune response.

According to the NIH Biomarkers Definition Working Group in 2001 [5], a biomarker is a biological marker with ‘a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention’. An ideal biomarker is one that can be tested cheaply and rapidly using easily obtained samples, with high positive and negative predictive values. The identification of biomarkers is a multiphase process that starts with a scientific discovery based on experimentation. The candidate biomarker is then verified within a small sample, followed typically by internal single-center then external multicenter validation phases. Once validity is established, the standardization phase refines the technique, before reaching the commercialization phase. These development phases are often intertwined, each providing feedback and optimization potential [6]. This is an arduous path, however, and there are few biomarkers that have successfully navigated the stringent requirements to reach commercialization.

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Efforts to identify clinically relevant transplant biomarkers have extended to focus on the alloimmune pathways of antigen recognition, gene transcription and translation, posttranscription regulation of gene expression, antigen expression, cell activation, cytokine secretion, and antibody secretion (Fig. 1).



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Antigen recognition biomarkers

The Crossmatch

As mentioned above, a positive microcytoxicity assay heralds the presence of circulating alloreactive antibodies, and, consequently, the likelihood of hyperacute or acute rejection. T and B cell flow cytometry crossmatches are more sensitive assays, and can detect circulating donor-specific antibodies that are alloreactive but insufficient to result in a positive microcytotoxic assay. Nevertheless, a positive T-cell flow crossmatch is associated with early graft loss, subclinical rejection, and antibody-mediated rejection [7]. Although the crossmatch remains the cornerstone of clinical transplantation, it has several limitations, including the need for donor cells, fresh recipient serum to capture recent sensitizing events, and high false-positive rates, particularly with the B-cell flow crossmatch.

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T-cell alloreactivity assays

Although antidonor memory T-cell activity is not commonly assessed before transplantation, it can be determined by measurement of interferon-γ (IFN-γ) secretion by recipient T cells when exposed to donor antigens. The IFN-γ enzyme-linked immunosorbent spot (ELISPOT) assay can quantify donor-reactive memory T cells before and after kidney transplantation; early studies showed that higher levels of cellular alloreactivity are associated with acute rejection, delayed graft function, and chronic allograft dysfunction [8]. However, in the multicenter Clinical Trials in Organ Transplantation-01 (CTOT-01), a similar pretransplant correlation was not seen universally, thought because of the effects of variable use of depleting induction immunotherapy [9▪]. Although serial monitoring of T-cell alloreactivity with this assay may be very helpful, the need for donor cells and the labor-intensive nature of ELISPOT makes this a less than an ideal biomarker.

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Adenosine triphosphate secretion

Increased adenosine triphosphate (ATP) production is thought to be one of the earliest steps that follow antigen recognition. The Immuknow assay, whereby CD4+ T cells are magnetically isolated from mitogen-stimulated peripheral blood mononuclear cells and then lyzed to facilitate measurement of ATP release, received regulatory approval following a multicenter trial that analyzed the immune response by measuring ATP release in clinically stable transplant patients, compared to those with confirmed rejection episodes or documented infections [10]. Low ATP levels were associated with infections, whereas high levels were associated with increased likelihood to develop rejection. A subsequent large retrospective study failed to show a similar association at 90 days after one measurement [11], whereas a meta-analysis showed poor predictive value of this assay for infection or rejection after kidney transplantation [12]. The widespread clinical application of this test has been stymied by the need for a short interval between collection and sample storage, the ‘time after transplant’ effect on ATP values [13], and a limited ability to interpret single results.

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Gene expression biomarkers

Targeted approach

T-cell cytotoxicity: perforin/granzyme B

Activated cytotoxic T lymphocytes (CD8+) induce target cell apoptosis by producing perforin (PERF) and granyme B (GrzB) and lead to tubular epithelial cell injury in kidney transplant rejection. Indeed, mRNA levels of PRF and GrzB in urinary cells have been shown to correlate with acute rejection [14]. Furthermore, in several studies, intra-graft GrzB transcripts showed significant accumulation in acute rejection compared to nonrejection [15–17]. A meta-analysis encompassing 680 patients from studies looking at quantitative detection of GrzB and PRF mRNA showed that combined elevation of these markers was associated with higher probability of acute rejection [18▪]. Stability of mRNA in decaying cells may limit the precision of these assays, which are yet to be validated in large trials.

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Regulatory molecules: FOXP3

Regulatory T cells are mainly CD4+ T cells that are CD25 and FOXP3 positive, and which exert suppressive action in an activated T-cell milieu. Their activity is mediated by cytokine deprivation, direct inhibition, or even cytotoxicity of neighboring activated T cells. Increased FOXP3 mRNA in urinary cells has been associated with reversibility of allograft rejection when studied in kidney transplant recipients with or without recovery from acute rejection [19]. Furthermore, peripheral blood FOXP3 transcripts were significantly lower in kidney transplant recipients with chronic rejection compared to ‘clinically tolerant recipients’ [20]. Conversely, intragraft FOXP3 mRNA levels correlated with the severity of cellular rejection [21]; indeed, a higher density of intragraft FOXP3+ T cells as detected by immunohistochemistry was noted in patients with acute cellular rejection and was associated with worse graft survival [22]. Among patients with delayed graft function, levels of FOXP3 mRNA in kidney tissue, urinary cells, and peripheral leucocytes were also higher in those patients who transpired to have acute rejection compared to those with acute tubular injury or no rejection [23]. The type of induction agent used, the temporal relation of FOXP3 mRNA measurement to the rejection episode, and the lack of dynamic measurements could explain these differences, particularly when FOXP3 mRNA levels are interpreted in isolation.

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Array-based approach

Microarray analysis

The ‘molecular microscope diagnostic system’ is a microarray-based system that evaluates gene expression on kidney allograft biopsies to predict T-cell-mediated or antibody-mediated rejection before changes are visible by standard pathological methods [24]. Using microarray testing with ‘pathogenesis-based transcript sets’ (PBTs) that associate with T-cell-mediated rejection, antibody-mediated rejection, interstitial fibrosis/tubular atrophy, or acute kidney injury phenotypes, in addition to machine learning, a biopsy result is plotted in relation to a reference set of samples with established diagnoses [25▪]. This allows diagnostic and prognostic stratification in comparison to the reference set. A recent large international multicenter study validated this method, showing that with a 29-h workflow, microarray analysis of allograft biopsy correlated better with clinical judgment than did histology [26▪▪]. Advantages of this assay include the smaller sample requirement, reproducibility of the results, and the probabilistic readout of the result; the main disadvantage is the need for a kidney biopsy to obtain a sample for analysis.

The ‘kidney solid organ response test’ (kSORT) is a less invasive microarray-based test, performed using peripheral blood, that identifies a set of 17 genes involved in leukocyte trafficking, activation, adhesion, and cytolysis [27]. The test could predict rejection with high sensitivity and specificity, but could not distinguish between T-cell and antibody-mediated rejection. A subsequent study by the same group further validated this assay, and showed that combining it with the antidonor IFN-γ ELISPOT assay may help distinguish the rejection phenotype [28▪▪]. Advantages of the kSORT assay include the high accuracy for predicting rejection using peripheral blood; its main limitation is the inability to discern the nature of the rejection when used alone.

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Genomics of chronic allograft rejection score

The prospective multicentre study, genomics of chronic allograft rejection (GoCAR), aimed to generate a gene set that could predict chronic failure due to fibrosis rather than focus on predicting allograft rejection. The study identified and externally validated a set of 13 genes that was superior to standard clinical and histological variables in predicting progression to fibrosis and early graft loss after transplantation [29▪]. The genes were involved in cell growth pathways, tumor growth and suppression, and membrane repair. However, the gene signature was obtained 3 months after transplantation, which is likely to reflect a very different alloimmune milieu than that present long after kidney transplant and is associated with the development of de-novo donor-specific antibodies and shorter allograft survival, and is again limited by the requirement for an invasive biopsy.

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Urine mRNA

Based on the single center work at Cornell to predict allograft rejection via a gene signature in urinary cells [30], a multicenter clinical trials in organ transplantation (CTOT-04) took place, investigating thousands of samples collected from hundreds of patients at varying time-points after kidney transplantation, wherein measured mRNA levels were correlated with the occurrence of acute allograft rejection. A 3-gene signature of CD3ε and interferon-inducible protein 10 (IP-10) mRNAs, in addition to18S rRNA, could predict the occurrence of rejection, and could distinguish acute cellular rejection from acute antibody-mediated rejection. Furthermore, neither urinary tract infection nor the type of induction immunosuppression used affected the diagnostic capacity of the gene signature [31]. The main advantage of this assay is the noninvasive nature of the sample required (Fig. 2), yet, processing urine for mRNA extraction could be technically challenging especially in peripheral laboratories.



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Gene translation and protein synthesis biomarkers

Urine chemokines

The CXCR3 chemokines CXCL9 [monokine induced by interferon-γ (MIG)] and CXCL10 [interferon-induced protein-10 (IP-10)] have been of significant interest in the search for reliable transplant biomarkers, and studies to date indicate they are among the most promising candidates [32,33]. In the multicenter CTOT-01 trial, urinary levels of CXCL9 mRNA and protein were shown to robustly diagnose acute rejection, with levels rising up to 30 days prior to clinical detection [32]. Furthermore, low levels of urinary CXCL9 protein at 6 months were associated with low likelihood of future development of acute rejection or reduction in GFR up to 2 years posttransplant [32]. A further study showed that, compared to stable individuals and those with calcineurin inhibitor toxicity or interstitial fibrosis/tubular atrophy, levels of CXCL9 and CXCL10 were elevated in both acute rejection and polyoma (BK-virus) virus infection, although were unable to distinguish between these entities [33]. CXCL10 has also been shown to sensitively detect acute rejection, with levels rising prior to any clinical changes, and in particular, in a cohort of patients with both indication and protocol biopsies, has been shown to be capable of detecting borderline and subclinical tubulitis, and overt acute rejection [34]. More recent work has shown that the combination of urinary CXCL10, normalized to urinary creatinine levels, and donor-specific antibody levels, improved the diagnosis of antibody-mediated rejection compared to donor-specific antibodies alone; furthermore, the urinary CXCL10:Cr ratio at the time of antibody-mediated rejection was independently associated with graft loss [35▪]. In addition to proven efficacy, testing for urinary CXCL9 and CXCL10 is available on an ELISA or Luminex-based platform, is simple, noninvasive and cost-effective, and is therefore ideally suited to longitudinal monitoring.

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Humoral response biomarkers

Alloantibody detection

Accumulating data support that preexisting or de-novo donor-specific anti-HLA and non-HLA antibodies often precede the development of proteinuria, rapid decline in allograft function, and allograft failure [36–39]. Techniques for alloantibody detection have evolved over the last five decades, and although the complement-dependent cytotoxicity crossmatch remains in widespread use, a significant increase in the sensitivity of this assay has been provided by the flow cytometry crossmatch and the advent of solid phase assays (i.e. Luminex), which are FDA-approved for qualitative use, but in practice, are often interpreted per their quantitative strength. They are used in generating calculated panel reactive antibody titers and virtual crossmatch testing, and for longitudinal screening. The high sensitivity of the Luminex assay results in significant inter and intra-laboratory variability [40] which may hamper interpretation; furthermore, the lack of a cellular element in this platform compared to the crossmatch assays poses a challenge in establishing the relevance of the detected antibody when clinical allograft injury is absent. Attempts to address this pitfall were made by measuring the complement binding ability of Luminex-detected donor-specific anti-HLA antibodies; such ability was found to be a predictor of aggressive antibody-mediated disorder including allograft loss [41]. A subsequent report confirmed this observation but also highlighted a comparable negative effect of noncomplement binding donor-specific antibodies [42▪▪].

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Cellular antigen expression profiling

Among the unique features of lymphocytes is their ability to undergo somatic rearrangement that allows them to express T-cell and B-cell receptors that respond to specific antigens, conferring the immune system with immense diversity. This diversity is made possible by the random somatic recombination of DNA from genes coding for the variable, diversity, and joining segments of the receptors, and endows each T-cell or B-cell with the ability to detect one or very few antigens [43]. Immunosequencing allows the profiling of each T-cell and B-cell clone by multiplex PCR combined with high-throughput bioinformatics, generating a repertoire of specific lymphocyte clones that can be followed longitudinally, the expansion of which may correlate with rejection, infection, or tolerance after kidney transplantation [44▪▪,45,46]. This approach is only in the very early stages of biomarkers discovery but may prove to be a promising tool to measure the alloimmune response.

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Proteomics and mass spectrometry

Mass spectrometry offers a nonbiased high throughput approach to identify one or more markers of rejection. Although rejection is a dynamic heterogeneous process, the proteome can theoretically mirror these changes and offers an ideal platform to identify biomarkers for rejection [47]. Most of the published studies have focused on the urine proteome, with identification of multiple molecules, although only a few have been reproduced by different groups, including α1-antichymotrypsin, uromodulin, β2-microglobulin, and fragments of collagen [48]. It is important to note that differing MS methods may influence the proteome signature and markers identified which may complicate its translation to the clinic.

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Because of the complex and redundant nature of the immune response, the path towards the identification of an ideal biomarker in transplantation remains long. Array-based and/or targeted assays that aim to predict rejection, alloantibody formation, infection, fibrosis, or allograft loss by utilizing kidney tissue, peripheral blood or urine are under investigation. Thus far, very few tests have gained regulatory approval for commercial clinical use in kidney transplantation, but many are being validated in the hope that they will facilitate improved longitudinal monitoring, allowing more tailored immunosuppression, and, in turn, improved long-term allograft survival.

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Financial support and sponsorship


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Conflicts of interest

There are no conflicts of interest.

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Papers of particular interest, published within the annual period of review, have been highlighted as:

  • ▪ of special interest
  • ▪▪ of outstanding interest
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1. Hart A, Smith JM, Skeans MA, et al. OPTN/SRTR 2015 Annual Data Report: Kidney. Am J Transplant 2017; 17 (Suppl 1):21–116.
2. Meier-Kriesche HU, Schold JD, Srinivas TR, Kaplan B. Lack of improvement in renal allograft survival despite a marked decrease in acute rejection rates over the most recent era. Am J Transplant 2004; 4:378–383.
3. Merrill JP, Murray JE, Harrison JH, Guild WR. Successful homotransplantation of the human kidney between identical twins. JAMA 1956; 160:277–282.
4. Patel R, Terasaki PI. Significance of the positive crossmatch test in kidney transplantation. New Engl J Med 1969; 280:735–739.
5. Biomarkers Definitions Working G. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Therap 2001; 69:89–95.
6. Menon MC, Murphy B, Heeger PS. Moving Biomarkers toward clinical implementation in kidney transplantation. J Am Soc Nephrol 2017; 28:735–747.
7. Karpinski M, Rush D, Jeffery J, et al. Flow cytometric crossmatching in primary renal transplant recipients with a negative antihuman globulin enhanced cytotoxicity crossmatch. J Am Soc Nephrol 2001; 12:2807–2814.
8. Hricik DE, Rodriguez V, Riley J, et al. Enzyme linked immunosorbent spot (ELISPOT) assay for interferon-gamma independently predicts renal function in kidney transplant recipients. Am J Transplant 2003; 3:878–884.
9▪. 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.

Positive INF-γ ELISPOT is associated with lower GFR in patients who did not receive induction immunosuppression.

10. Kowalski RJ, Post DR, Mannon RB, et al. Assessing relative risks of infection and rejection: a meta-analysis using an immune function assay. Transplantation 2006; 82:663–668.
11. Huskey J, Gralla J, Wiseman AC. Single time point immune function assay (ImmuKnow) testing does not aid in the prediction of future opportunistic infections or acute rejection. Clin J Am Soc Nephrol 2011; 6:423–429.
12. Wang Z, Liu X, Lu P, et al. Performance of the ImmuKnow assay in differentiating infection and acute rejection after kidney transplantation: a meta-analysis. Transplant Proc 2014; 46:3343–3351.
13. Moon HH, Kim TS, Lee S, et al. Serial ImmuKnow assay in stable kidney transplant recipients. Central-Europ J Immunol 2014; 39:96–99.
14. 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. New Engl J Med 2001; 344:947–954.
15. Ashton-Chess J, Dugast E, Colvin RB, et al. Regulatory, effector, and cytotoxic T cell profiles in long-term kidney transplant patients. J Am Soc Nephrol 2009; 20:1113–1122.
16. Strehlau J, Pavlakis M, Lipman M, et al. Quantitative detection of immune activation transcripts as a diagnostic tool in kidney transplantation. Proc Natl Acad Sci USA 1997; 94:695–700.
17. Lipman ML, Stevens AC, Strom TB. Heightened intragraft CTL gene expression in acutely rejecting renal allografts. J Immunol 1994; 152:5120–5127.
18▪. Heng B, Li Y, Shi L, et al. A meta-analysis of the significance of granzyme B and perforin in noninvasive diagnosis of acute rejection after kidney transplantation. Transplantation 2015; 99:1477–1486.

Meta-analysis showing an advantage in combining the use of both granzyme B and perforin assays to predict rejection rather than each of the assays alone.

19. 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.
20. Louis S, Braudeau C, Giral M, et al. Contrasting CD25hiCD4+T cells/FOXP3 patterns in chronic rejection and operational drug-free tolerance. Transplantation 2006; 81:398–407.
21. Naka EL, Ponciano VC, Rangel EB, et al. FOXP3-positive regulatory cells inside the allograft and the correlation with rejection. Transplant Proc 2006; 38:3202–3204.
22. Veronese F, Rotman S, Smith RN, et al. Pathological and clinical correlates of FOXP3+ cells in renal allografts during acute rejection. Am J Transplant 2007; 7:914–922.
23. Aquino-Dias EC, Joelsons G, da Silva DM, et al. Noninvasive diagnosis of acute rejection in kidney transplants with delayed graft function. Kidney Int 2008; 73:877–884.
24. 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.
25▪. Halloran PF, Famulski KS, Reeve J. Molecular assessment of disease states in kidney transplant biopsy samples. Nat Rev Nephrol 2016; 12:534–548.

Figure 6 of this review displays the readout of the molecular diagnostic system.

26▪▪. 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; [Epub ahead of print].

Multicenter validation and feasibility trial showing that molecular diagnosis on kidney allograft tissue was more congruent with clinical judgment compared with histology.

27. 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.
28▪▪. Crespo E, Roedder S, Sigdel T, et al. Molecular and functional noninvasive immune monitoring in the ESCAPE study for prediction of subclinical renal allograft rejection. Transplantation 2017; 101:1400–1409.

Elegant trial showing that combining an array-based assay (kSORT) with targeted based one (INFg ELISPOT) helps distinguising subclinical rejection phenotypes.

29▪. O’Connell PJ, Zhang W, Menon MC, et al. Biopsy transcriptome expression profiling to identify kidney transplants at risk of chronic injury: a multicentre, prospective study. Lancet 2016; 388:983–993.

GoCAR trial (multi center, NIH funded) identified a set of 13 genes on kidney biopsy that could predict the progression to fibrosis.

30. Anglicheau D, Suthanthiran M. Noninvasive prediction of organ graft rejection and outcome using gene expression patterns. Transplantation 2008; 86:192–199.
31. Suthanthiran M, Schwartz JE, Ding R, et al. Urinary-cell mRNA profile and acute cellular rejection in kidney allografts. New Engl J Med 2013; 369:20–31.
32. 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.
33. 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.
34. 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.
35▪. 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.

Correcting CXCL10 to creatinine and combining it with donor-specific antibody monitoring could noninvasively diagnose antibody-mediated rejection.

36. 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.
37. 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.
38. Zou Y, Stastny P, Susal C, et al. Antibodies against MICA antigens and kidney-transplant rejection. New Engl J Med 2007; 357:1293–1300.
39. Mao Q, Terasaki PI, Cai J, et al. Extremely high association between appearance of HLA antibodies and failure of kidney grafts in a five-year longitudinal study. Am J Transplant 2007; 7:864–871.
40. Zachary AA, Vega RM, Lucas DP, Leffell MS. HLA antibody detection and characterization by solid phase immunoassays: methods and pitfalls. Methods Mol Biol 2012; 882:289–308.
41. Loupy A, Lefaucheur C, Vernerey D, et al. Complement-binding anti-HLA antibodies and kidney-allograft survival. New Engl J Med 2013; 369:1215–1226.
42▪▪. Guidicelli G, Guerville F, Lepreux S, et al. Non-complement-binding de novo donor-specific anti-HLA antibodies and kidney allograft survival. J Am Soc Nephrol 2016; 27:615–625.

Trial evaluating the long-term effect of C1q binding of donor-specific antibodies and the ability to predict allograft loss.

43. Nemazee D. Receptor selection in B and T lymphocytes. Ann Rev Immunol 2000; 18:19–51.
44▪▪. Alachkar H, Mutonga M, Kato T, et al. Quantitative characterization of T-cell repertoire and biomarkers in kidney transplant rejection. BMC Nephrol 2016; 17:181.

Study shows that peripheral blood TCR repertoire changes with rejection, suggesting that monitoring of the TCR clonal expansion may predict rejection.

45. Lai L, Wang L, Chen H, et al. T cell repertoire following kidney transplantation revealed by high-throughput sequencing. Transplant Immunol 2016; 39:34–45.
46. Dziubianau M, Hecht J, Kuchenbecker L, et al. TCR repertoire analysis by next generation sequencing allows complex differential diagnosis of T cell-related pathology. Am J Transplant 2013; 13:2842–2854.
47. Hanash S, Celis JE. The Human Proteome Organization: a mission to advance proteome knowledge. Mol Cell Proteomics 2002; 1:413–414.
48. Gwinner W, Metzger J, Husi H, Marx D. Proteomics for rejection diagnosis in renal transplant patients: where are we now? World J Transplant 2016; 6:28–41.

biomarkers; kidney transplant; rejection

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