# Blood biomarkers of kidney transplant rejection, an endless search?

Lola Jacquemont, Jean-Paul Soulillou, Nicolas Degauque

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Lola Jacquemont, Jean-Paul Soulillou, Nicolas Degauque. Blood biomarkers of kidney transplant rejection, an endless search?. Expert Review of Molecular Diagnostics, 2017, 17 (7), pp.687-697. 10.1080/14737159.2017.1337512. inserm-02155438

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Submitted on 13 Jan 2025

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Distributed under a Creative Commons Attribution 4.0 International License
**Publisher:** Taylor & Francis

**Journal:** *Expert Review of Molecular Diagnostics*

**DOI:** 10.1080/14737159.2017.1337512

Review

**Blood biomarkers of kidney transplant rejection, an endless search?**

Lola Jacquemont<sup>1,2</sup>, Jean-Paul Soulillou<sup>1,2</sup> and Nicolas Degauque<sup>1,2,3</sup>

<sup>1</sup> Centre de Recherche en Transplantation et Immunologie UMR1064, INSERM, Université de Nantes, Nantes, France

<sup>2</sup> Institut de Transplantation Urologie Néphrologie (ITUN), CHU Nantes, Nantes, France

<sup>3</sup> Labex IGO, Nantes, France

Corresponding author

Nicolas Degauque

Pavillon Jean Monnet

Hôtel Dieu - CHU de Nantes

30, Bd Jean Monnet

44093 Nantes cedex 01

France

Phone +33 (0)240087410

Fax +33 (0)240087411

Email nicolas.degauque@univ-nantes.fr
**Abstract**

**Introduction.** The tailoring of immunosuppressive treatment is recognized as a promising strategy to improve long-term kidney graft outcome. To guide the standard care of transplant recipients, physicians need objective biomarkers that can identify an ongoing pathology with the graft or low intensity signals that will be later evolved to accelerated transplant rejection.

The early identification of “high-risk / low-risk” patients enables the adjustment of standard of caring, including managing the frequency of clinical visits and the immunosuppression dosing. Given their ease of availability and the compatibility with a large technical array, blood-based biomarkers have been widely scrutinized for use as potential predictive and diagnostic biomarkers.

**Areas covered.** Here, the authors report on non-invasive biomarkers, such as modification of immune cell subsets and mRNA and miRNA profiles, identified in the blood of kidney transplant recipients collected before or after transplantation.

**Expert Commentary.** Combined with functional tests, the identification of biomarkers will improve our understanding of pathological processes and will contribute to a global improvement in clinical management.

**Keywords**

Biomarker, Kidney transplantation, Lymphocyte, Personalized medicine, Rejection
# 1. Introduction

Kidney transplantation constitutes the treatment of choice for end-stage chronic renal disease, enabling better survival rates [1] and quality of life [2] compared to dialysis. However, although the incidence of rejection in the first year has substantially decreased in the last decades due to the development of more effective immunosuppressive drugs and better HLA matching, rejection episodes remain frequent and are a significant risk factor for chronic rejection [3,4] associated with a poor long-term renal function [5]. Moreover, these advances have not led to a dramatic improvement in long-term graft survival with a similar year loss rate after the first year following surgery [6]. This pattern suggests that the allograft loss rate is a composite effect of non-immune [7] and immune causes mainly related to a limited efficacy of immunosuppressive drugs on antibody-mediated graft injury [8].

Currently, the routine clinical follow-up of the kidney transplant patients includes the measurement of serum creatinine levels and clearance and of proteinuria. These markers which are used as an indicator of kidney graft failure are not specific to rejection and, in the absence of an obvious cause of graft dysfunction, a graft biopsy remains the gold standard to identify an ongoing rejection and document its nature [9,10]. Moreover, the increase of serum creatinine and proteinuria is a delayed response to the rejection process, and advanced tissue injuries could already be at a partially irreversible stage.

In the absence of clinically validated makers, protocol biopsies have been developed to diagnose rejection before the occurrence of graft dysfunction. However, diagnosis of an infra-clinic rejection requires repeated biopsies, and sampling variations can confuse the diagnosis [11]. Moreover, biopsy procedures are invasive, costly and complications such as bleeding may occur [12]. Finally, the ability of biopsy-based techniques to predict immunologic risk still needs to be validated and the identification for non-invasive procedure would represent a major step in patient management, comfort and costs.
The identification of non-invasive biomarkers used as predictive tools or surrogate end-points of organ rejection is a key focus in transplant research. These biomarkers might help clinicians adjust immunosuppressive drugs and thus minimize drug-associated complications. Combine with early preemptive therapeutic intervention, an improvement of kidney graft outcome could be thus achieved. Ideally, these biomarkers would become the cornerstone of personalized medicine. They would allow doctors to reduce immunosuppression in patients with a low risk of rejection and/or high risk of infectious and neoplastic complications and to adapt the immunosuppression protocol in patients with high risk of rejection. Furthermore, by identifying subclasses of the immune process involved in the rejection, the data from the biomarker field may allow the identification of new therapeutic targets.

In this review, restricted to human data, we will focus on blood biomarkers of acute and chronic rejection in kidney graft recipients. While this approach has not yet reached a state of clinical validation, it allows for the search of biomarker combinations with functional tests, which would result in a better understanding of the pathological processes and ultimately will contribute to a global improvement in clinical management. Indeed, the key benefits of using blood-based biomarkers lies in the availability of non-invasive, longitudinal blood samples and their linked medical information which can be used to closely monitor patients, including in anuric patients and finally to be potentially used in patients receiving other types of transplants contrary to urine biomarkers. There are several recent reviews of histological-based biomarkers [13,14] and urinary biomarkers which offer a new and promising way of non-invasive monitoring [15]. Monitoring of anti-donor specific (DSA) antibodies has also been the topic of several excellent reviews [16,17]; however, we will not address these topics in this review.

## 2. Pre and Post-transplantation monitoring of the phenotype and the function of T lymphocyte
T cells are central initiators and mediators of alloimmune responses, playing a key role in allograft rejection [18,19]. Alloreactive T cells can directly or indirectly [20] recognize HLA-mismatched tissue leading to their activation and implementing an effector response that results in organ damage.

## 2.1 Identification of pre-transplant risk factor using T cell phenotype

End stage renal disease (ESRD) is associated with an immunodeficiency characterized by an increased propensity to infections and a decreased response to vaccination. Several studies showed a reduced CD4/CD8 T cell ratio and fewer naïve (CD45RA⁺CCR7⁺) and central memory (CD45RA⁻CCR7⁺) CD4 and CD8 T cells [21–24]. In a retrospective study of 185 patients with ESRD, Betjes confirmed these results by showing a significant decrease in naïve and central memory T cells and an increase of the terminally differentiated CD8 TEMRA (CD45RA⁺CCR7⁻) (25% vs 18%) in the non-rejecting group (n=138) as compared to healthy controls [25]. Moreover, the percentage of TEMRA cells in the non-rejecting group was significantly higher compared to those in the rejecting group (n=47; 25% vs 16%). Those results have been partially confirmed in a prospective study of 222 living donor kidney transplant recipients, with acute rejection during the first three months observed in 30 patients [26]. The rejecting group had significantly lower numbers of CD4⁺CD28null cells and a trend to a lower number of CD8⁺CD28null T cells. The univariate Cox regression analysis of the T cell subsets showed that a higher number of CD4⁺CD28null T cells was associated with a lower risk of acute rejection (HR 0.65, p = 0.025) [26]. These reports suggest that the outcome of kidney graft is inversely associated with the size of naïve T cell pool which could result in a decreased ability to mount primary T cell anti-donor response. The monitoring of CD45 isoforms pre-transplantation has further strengthen this statement. CD45 is a transmembrane protein tyrosine phosphatase which is essential for signal transduction after T cell receptor engagement. The pre-transplant level of CD45RC, mainly expressed on naïve T
cells (CD45RA⁺RO⁻CCR7⁺), has been associated with a higher risk of rejection [27]. In this study, patients with more than 54.7% of CD45RO⁺CD8⁺T cells before transplantation had a six-fold higher risk to reject their kidney transplant than those below this threshold (HR 5.99; p = 0.006).

However, several reports have also shown that patients with preformed memory T cells against HLA or cross-reacting with donor HLA are at higher risk of rejection. For instance, cross-reactive T cells selected against chronic viruses such as HCMV and EBV can also be activated by allogeneic HLA molecules [28] and therefore constitutes a state of pre-formed sensitization against donor HLA antigens. Viral cross-reactivity against allogeneic HLA molecules have been documented in numerous publications [29]. The negative impact of pre-formed anti donor memory T cells on kidney graft outcome is also suggested by the result of costimulation blockers. Targeting CD28 pathway using Belatacept is an effective alternative strategy to improve long-term allograft function [30]. Nevertheless, a sizable portion of patients underwent acute rejection episodes. Patients with co-stimulation blockade-resistant rejection have been shown to exhibit pre-transplantation a high frequency of CD57⁺CD28⁻CD4 T cells [31] and CD28⁻CD8 T cells [32]. Finally, accumulation of T cells with an activated phenotype (CD95 or CD69) before and/or rapidly after transplantation has been also identified as a negative factor for kidney graft outcome [33]. CD95 (Fas receptor) is preferentially expressed on memory CD45RO⁺T cells and is known to enhance survival but also to promote activation induced cell death. Expression of CD69, an antigen member of the C-type lectin superfamily, precedes the proliferation and is commonly used to monitor T cell activation. The monitoring of the expression of CD95 and CD69 on T cells after 72 hours of stimulation with Concanavalin A before and during the first months after transplantation has shown that the rejection time free was significantly shorter in patients with pre-transplantation percentage of CD95⁺ in CD4⁺T cells higher than 70% (AUC 0.807, p=0.05) and in CD8⁺T
cells higher than 65% (AUC 0.787, p=0.008). Lower percentage of CD8⁺CD69⁺ in the first month post transplantation was associated with an absence of rejection, confirming results obtained in cardiac transplantation [34,35].

Collectively, although there is lack of consensus regarding the beneficial impact of the size naïve T cells pool for long-term graft outcome, the presence of an increased pool of differentiated (and particularly donor committed) T cell is a negative marker as for long-term graft outcome.

Some discrepancy in the reports are likely related to the techniques used to monitor T cell population. Phenotypic analysis of T cell subsets can identify naïve or antigen-experienced T cells but cannot identify the TCR specificity and/or T cell function against donor-HLA. In summary, technical progress in cytometry and a more refined understanding in phenotype/function relationship should make more popular pre-graft assessment of immune risk based on T cell characteristics.

## 2.2 Pre-transplant functional assay, the IFN-γ ELISPOT assay approach

The use of functional assays to identify pre-transplant lymphocyte reactivity against donor-specific HLA has also been proposed as a possible biomarker; however, outside of living donor situations, it proves to be too complex a procedure for a clinical usage. Nevertheless, new understanding in the immune response against an allograft and the rapid technical progress in this field may allow clinical validation in the next future. Monitoring the frequency of anti-donor T cells as reflection of cellular immunity has been shown to be feasible using the highly sensitive Interferon-γ (IFN-γ) ELISPOT assay [36]. Highly elevated pre-transplant frequencies of IFN-γ producing T cells against donor cells has been shown to correlate with the post-transplant risk of developing acute rejection episodes [37]. These results have been confirmed in several studies in which a high pre-transplant donor specific T cells frequencies were found in patients with acute rejection vs. non-rejecters [38]–[40]. High
pre-transplant frequencies (>200 spots/300,000 PBMCs) were associated with a risk of severe acute rejection episodes which were independent of other risk pre-sensitization factors such as high panel reactive antibodies, former transplantations, blood transfusions and pregnancies. Moreover, mean ELISPOT frequencies observed early after transplantation correlated inversely with 6 and 12 months glomerular filtration rate [38].

Induction therapy has been shown to be an important confounding factor in the interpretation of donor-specific ELISPOT-based assay. Indeed, some groups have shown no association between pre-transplant anti-donor T-cell alloreactivity and the risk of acute rejection when T cell depletion is used as an induction therapy. In a retrospective analysis of 90 kidney transplanted patients, Crespo and al. have shown a higher incidence of delayed graft function in high pre-transplant anti-donor alloreactive patients, but no difference in terms of rejection was found between T cell alloreactive and non-alloreactive recipients [39]. Neither antibody-mediated rejection nor T cell-mediated rejection were associated with pre-formed donor-specific T-cell alloreactivity. On the other hand, the use of T cell depletion as induction therapy seemed to provide significantly better protection for T cell sensitized patients against developing T cell-mediated rejection when compared anti-IL-2 receptor antibodies or no induction therapy. Clinical Trials in Organ Transplantation-01, a prospective multicenter observational study, failed to identify a correlation between pre-transplant IFN-γ ELISPOT and either post-transplant acute rejection or glomerular fraction rate [40]. However, post-hoc analyses after categorizing subjects based on induction therapy showed that acute rejection occurred more frequently in anti-thymoglobulin treated subjects who were pre-transplant IFN-γ ELISPOT<sup>pos</sup>. Moreover, multivariable linear regression confirmed an association between pre-transplant IFN-γ ELISPOT<sup>pos</sup> status and lower glomerular filtration rate at 6 and 12 months only in patients who received anti-IL-2 receptor or no induction therapy. It was also found to be independent of panel reactive antibodies, number of HLA mismatches, recipient
age and gender and donor source. Although controlled studies are needed to test the
hypothesis that ATG induction is preferentially beneficial to transplant candidates with high
frequencies of donor reactive memory T cells, these are further examples of the complexity of
defining biomarkers which are potentially influenced by the treatment – and also by the
evolution of the clinical management.

2.3 Monitoring post transplantation T cell characteristics to predict and/or diagnose kidney rejection

Comparing drug-free operationally tolerant patients, patients with chronic rejection and healthy individuals, Baeten and al. showed a significant increase in TEMRA (CD45RA⁺CCR7⁻) CD8⁺ lymphocytes in chronic rejection group as compared to the two other groups [41]. Moreover, the expression of CD28 and CD27 on TEMRA CD8 T cells was significantly decreased in patients with chronic rejection and these profiles were stable over time and independent of treatment. The CD8⁺CD28⁻ lymphocytes were less sensitive to apoptosis than their CD8⁺ counterparts and were characterized by high levels of preformed perforin and granzyme A, pointing toward a cytotoxic function. CD8 T cells from stable kidney recipients under conventional maintenance immunosuppression displayed a mixed profile meaning that increase of TEMRA CD8 T cells is strongly, but not exclusively, associated with active chronic rejection. To analyze whether such a chronic rejection-like signature in stable patients may help to identify a poor prognosis and subsequent rejection, a prospective study including 131 patients with a stable graft function has been performed [42].

In this study, we found that the restriction of the TCR Vβ repertoire diversity was associated
with an increase of highly differentiated TEMRA (CD45RA⁺CCR7⁻CD27⁻CD28⁻) CD8 T
cells. These TEMRA CD8 cells are characterized by a high expression of cytotoxic
molecules, perforin and granzyme-B, T-bet and CD57 and they have the ability to secrete
TNF-α and IFN-γ. We also report that stable patients who have an increase in highly
differentiated TEMRA CD8 T cells have a 2-fold higher risk of long-term graft dysfunction
[42].

**2.4 ImmuKnow®, the story of a former “promising” biomarker**

The ImmuKnow® assay (Cylex™) is an approved test by the Food and Drug Administration (FDA) for an assessment of cell mediated immunity in immunocompromised individuals and indicated for guiding immunosuppression dosage after organ transplantation [43]. The test measures intracellular adenosine triphosphate (ATP) synthesis by CD4 T cells in response to nonspecific mitogenic stimulus. ATP levels are used as a surrogate marker of the magnitude of T-cell mediated immune responses.

ImmuKnow® assay was first used at the time of clinical event; very low ATP levels correlated with the occurrence of opportunistic infections [44–46], whereas high ATP levels correlated with acute rejection [44]. However, two more recent retrospective studies failed to demonstrate any relationship between ATP levels and the advent of infections or rejections [47,48]. Finally, when ImmuKnow® was used to predict acute rejection, very contradictory data was reported. For instance, in a prospective study, Myslik et al. found that ATP values post-operatively had poor predictive value regarding rejection or infection [49]. Furthermore, recent meta-analysis evaluating the predictive capacity of this assay failed to demonstrate any benefit to identify patients at higher risk for rejection and infection among solid organ transplantation [50–52]. While ImmuKnow® is technically easy, its clinical application is hampered by its poor predictive capacity, thus making this assay no longer relevant for clinical use in solid organ transplantation.

**2.5 Granzyme B and perforin as markers of cellular immunity**

One of the most potent death-inducing mechanisms used by cytotoxic T lymphocytes is exocytosis of granules that contain perforin (PRF) and Granzyme B (GZMB). Histological studies have shown the abundance of GZMB and PRF in many types of acutely rejecting
allografts [53–56]. For instance, Strehlau et al. have demonstrated that *GZMB* and *PRF* gene expressions in renal allograft biopsies is a reliable tool for acute rejection diagnosis and follow-up [57]. Vasconcellos et al. have shown that *GZMB* and *PRF* gene expression in peripheral blood lymphocytes (PBL) is closely associated with a diagnosis of rejection [58]. *PRF* mRNA predicted acute rejection with a sensitivity of 82% and a specificity of 85% and *GZMB* mRNA predicted with a sensitivity of 55% and a specificity of 85%. The up-regulated expression of the two genes was a diagnostic marker of acute rejection with a positive predictive value of 100%. Recently, several studies have suggested that *GZMB* and *PRF* in PBL are useful markers for acute rejection diagnosis [59–61] and that their expression levels could be a promising non-invasive tool. Moreover, as shown by Simon et al., serial measurements of gene expression in PBL after transplantation could predict acute rejection [62]. In this study, patients were identified as “rejectors” a median of 11 days before clinical diagnosis based on increased gene expression on days 5-7 post transplantation with assay specificity of 68% and 65% and sensitivity of 69% and 75% for *GZMB* and *PRF* respectively. However, Graziotto et al., reported that the gene expressions of *GZMB* and *PRF* in PBL in kidney transplantation was not significantly upregulated during acute rejection [63]. Finally, a recent meta-analysis suggested that neither *GZMB* and *PRF*, if evaluated alone in different types of samples (urines, PBL, graft biopsies), could be a convincing non-invasive diagnosis marker for acute rejection [64]. For these reason, evaluating *GZMB* and *PRF* expressions after kidney transplantation may be a good indicator for a biopsy to be performed in order to assess the risk of allograft rejection.

## 2.6 Regulatory CD4 T cells, a long road toward their use as biomarker

Cell-mediated immunoregulation is one of the main mechanisms for maintaining immunologic tolerance and for controlling T cell homeostasis. The contribution of regulatory T cells in the maintenance of kidney graft survival - or as a cell therapy to control
alloreactivity - is of major interest but their role is less clear in human. The presence of intra-
graft FOXP3⁺ T cells has been associated with a better graft outcome in stable recipients with
subclinical signs of rejection [65]. In fact, presence of FOXP3⁺ Treg distinguished harmless
from injurious infiltrates, as evidenced by predicting better graft function 2 and 3 years after
transplantation. Interestingly, the presence of Treg in the graft was associated with an overall
donor-specific hypo-responsiveness of T cells toward alloantigen [66].

Patients with end stage renal disease have a deficiency of peripheral Treg comparing to healthy volunteers mainly due to the long-term inflammatory processes adversely affecting the peripheral regulatory mechanisms [67]. Moreover, acute rejection is associated with lower levels of pre-transplant peripheral Treg (CD4⁺CD25⁺FOXP3⁺). After transplantation, a similar pattern is observed and ratios between memory CD8 or CD4 T cells and Treg are higher in rejectors than in non-rejectors [67,68]. An increase in circulating memory Treg (CD4⁺CD127lowFOXP3⁺HLA-DR⁺CD45RA⁻) is associated with better outcome in kidney transplant patients [69]. Otherwise, in a study evaluating immunological markers in the blood of patients in who were pre-treated with tacrolimus prior to renal transplantation, two markers were associated with successful reduction of immunosuppression: 1) a low ratio of memory CD4 T cells/Treg prior to the start of immunosuppression reduction and 2) a decrease in the percentage of naive T cells before start of tacrolimus reduction compared to pre-transplant values [68]. Finally, several studies demonstrated that peripheral Treg content was higher in stable group compared with chronic rejection group [70–72].

Conversely, other clinical studies demonstrate that expression of FOXP3 in blood and graft is
not sufficient to distinguish rejecting from non-rejecting patients [73] and that FOXP3⁺ cells
can also be an inherent component of acute allograft rejection [74]. In a multicenter study,
San Segundo et al. showed that natural Treg defined as CD4⁺CD25highCD127low/-
FOXP3⁺ were not able to predict an episode of acute rejection in kidney transplant recipients
[75]. Before transplantation, a significantly increased percentage of activated Treg defined as CD4⁺CD25highCD45RO⁺ was observed in patients at risk to develop an acute rejection episode (11). In chronic antibody mediated rejection, peripheral blood FOXP3 mRNA failed to distinguish between rejection and non-rejection status [73]. Finally, the expression of FOXP3 is not sufficient to identify Treg and the investigation of the epigenetic status of the Foxp3 gene is needed to identify T cell with regulatory functions [76]. By analyzing the specific DNA demethylation of the Treg-specific demethylated region (TSDR) and chromatin modifications of the Foxp3 gene occur in Treg [77], it has been recently shown that operationally tolerant patients have a higher frequency of circulating memory CD4 Treg (mTreg; CD4⁺CD45RA⁻Foxp3hi) compared to kidney transplant recipients with stable graft function [78]. Of interest, mTreg were not able to prevent the proliferation of effector CD4 T cells when mTreg were purified from Stable patients whereas mTreg from operationally tolerant patients were highly suppressive. Collectively, the refinement in the understanding of regulatory T cells function and the identification of markers of effective suppressive function will nurture the use of CD4 Treg as biomarker of kidney graft function and outcome.

### 3. B Cell Lymphocytes, not only an antibody factory

Traditionally, donor specific antibodies (DSA) have been considered as a strong barrier for sensitized recipients to access transplantation due to their ability to induce hyperacute rejection. The role of alloantibodies, especially DSA, in early rejection of kidney transplant recipients, is well documented [79] and has progressively regained attention. Long-term monitoring of DSA has emerged as one of the most important advances of the last two decades. The demonstration of the deleterious effect of DSA and their association with antibody-mediated rejection based on clinical and tissular alteration correlation has been assessed by more codified histopathology [10] or transcriptional profiling [80]. Recent data suggest that in addition to their role in antibody production, B cells participate in antibody
independent responses, potentially leading to allograft rejection or allograft tolerance [81].
The fact that B cells can be involved in antigen presentation during acute cellular rejection
has been shown in animal models [82]. CD20+ B cells infiltrate have been found in kidney
biopsies from patients with acute rejection [83–86]. The absence of correlation between
immunoglobulin or complement deposition and anti-HLA antibodies suggest that B cells have
an antibody-independent pathogenic function. The screening of peripheral B cell phenotype
has gained a growing interest over the last decade. Viklicky and al. showed that the number of
B cells was lower in stable graft recipients than in healthy individuals, and that these B cells
displayed a more differentiated phenotype which is defined by a lower percentage of naïve
CD27 IgD+ at 2 and 5 years post transplantation and a higher percentage of both non switched
CD27-IgD+ and switched CD27-IgD+ memory B cells [87]. Nevertheless, no difference
between stable graft recipients and the patients who experienced kidney graft rejection was
found. More recently, it has been shown that patients with chronic antibody-mediated
rejection display a unique B cell phenotype with a reduced ratio of activated naïve
(IgD+CD38+) to memory B (IgD+CD38+) cells associated with an impaired
immunosuppressive activity [88]. Patients with cABMR displayed a predominance of IgD-
CD38- late memory cells (15.6±2.3% vs. 8.6±1.6% in healthy volunteers, vs. 8.4±1.1% in
stable patients) and a lack of IgD+ CD38+/- cells compared with stable patients and healthy
volunteers. Similar results have been observed in ABO-incompatible renal transplant
recipients [89].

**3.1 Increased Regulatory B cells in optimal kidney transplantation**

B cells have recently been described by their ability to regulate immune responses, a new
concept that may be important in transplantation. There are a limited number of experimental
animal studies that underpin the possibility that Breg may be important for tolerance
induction [90]. In kidney transplantation, we have first shown that patients with drug-free
long-term graft function display increased number of peripheral B cells [91]. Further studies
have shown that these cells exhibited an inhibitory phenotype associated with a lack of CD19⁺CD38⁺CD138⁺CD20⁻ plasma cells compared to stable patients [92]. Later, we found that, in contrast to patients with stable graft function, operationally tolerant patients exhibit a higher frequency of transitional (CD20⁺CD24ʰiCD38ʰi) B cells and naïve (CD20⁺CD24lowCD38low) B cells and a lower frequency of differentiated plasma cells (CD19⁺CD38⁺CD138⁺) [93]. Moreover, data suggest that a balance between B cells producing IL-10 and a deficiency in plasma cells may encourage an environment favorable to the tolerance maintenance. This hypothesis has been reinforced by the study of Shabir et al., who show that an increase of transitional B cells (CD19⁺CD24ʰiCD38ʰi) before and after transplantation could be protective for acute rejection in kidney transplant patients [94]. Finally, in a cohort of ABO compatible and incompatible kidney grafts recipients, another regulatory B cell phenotype (CD24⁺⁺CD27⁺) was decreased during acute rejection [89]. Moreover, there was a significant reduction of B cells expressing IL-10, the major cytokine described for regulatory B cells, during acute allograft rejection. Finally, we have shown that the percentage of GZMB⁺ B cells was increased in operationally tolerant patients as compared to stable recipients [95].

### 4. Natural killer cells

Natural killer (NK) cells effector functions are regulated by activating and inhibitory receptors. The first activating NK receptor identified is CD16 (FcγR-IIIA), a Fc-receptor for immunoglobulin G, responsible for antibody-dependent cellular cytotoxicity (ADCC). Inhibitory receptors are responsible for recognizing self class I major histocompatibility complex antigens. They repress NK cell functions interacting with distinct sets of classical HLA class I molecules and HLA-E [96]. Therefore, NK cells can respond against pathological cells which down-regulate HLA class I expression as well as against normal allogenic cells lacking the appropriate HLA ligands.
During chronic humoral rejection, microvascular damage, such as glomerulitis and peritubular capillaritis, can occur in the absence of complement deposition in kidney graft [97,98]. It has thus been suggested that effector mechanisms other than complement may be activated in the presence of DSA and the activation of NK cells has been proposed to play a role [99–101]. In experimental studies of solid organ transplantation, Hirohashi et al. showed that NK cells induce DSA-associated chronic allograft vasculopathy in heart transplantation [102]. Moreover, patients with DSA and microvascular injury displayed increased expression of a subset of NK-cell related genes and biopsies showed that CD56⁺ cells were increased in ABMR compared with TCMR [99].

Killer-cell immunoglobuline-like (KIR) ligand mismatches, namely NK cell alloreactivity, could influence kidney graft survival. Indeed, NK cells use KIR to discriminate between HLA-A, -B and -C allotypes. NK cells expressing an inhibitory NK receptor binding to self HLA can be activated when confronted with allogenic cells lacking a ligand for that inhibitory receptor. However, there is not a lot of data about peripheral blood NK cell subsets and effect of KIR matching in kidney transplantation. One retrospective study in HLA-A, -B and -DR compatible transplantation shows that KIR ligands mismatches were associated with an approximately 25% reduction in 10-year graft survival [103]. However, these results could not be reproduced by other group [104,105].

Comparing patients without anti-HLA antibodies or with anti-HLA DSA and anti-HLA non-DSA, Crespo et al. showed that patients with DSA displayed a lower proportion of peripheral NK cells and of the major CD56dimCD16⁺ subset, known to be the most differentiated and cytotoxic subset in peripheral blood [106]. Moreover, higher proportions of CD56bright and NKG2A⁺ NK cells, which are precursors of CD56dim NK cells, were detected in DSA and HLA non-DSA groups. The authors suggest that NK subset redistribution could reflect an enhanced antibody-dependent turnover following activation via CD16.
## 5. Gene profiling

Numerous recent studies have aimed to identify signature of kidney graft outcome using transcriptional profiling of kidney biopsies. Transcriptional profiling studies on renal allograft biopsies have demonstrated significant expression changes in many genes that identify patients with acute rejection, as well as other conditions in the differential diagnosis for allograft dysfunction [57,80,83,107]. These changes are mainly related to the composition and activation state of infiltrating cells and the transcriptional changes in renal tissue including endothelial response to injury during chronic rejection.

In peripheral blood, transcriptional profiles have also been shown to correlate with biopsy proven acute rejection and, more importantly, to distinguish acute rejection from other causes of graft dysfunction. Li et al. described a signature of 5 genes of acute rejection that can potentially be used to avoid invasive renal biopsy [108]. More recently, a blood quantitative real time PCR test called Kidney Solid Organ Response Test (kSORT) identified a 17-genes set using a discovery group of 143 samples and a validation group of 124 independent samples collected at the time of the biopsy [109]. kSORT was later used to predict the acute rejection in 191 serial samples and was shown to predict acute rejection up to 3 months prior to detection by biopsy. This test allowed an equal detection of antibody-mediated and cell-mediated acute rejection and was independent of time post-transplantation and of recipient and donor ages. Out of the patients with blood samples 0-3 months prior to an acute rejection-positive biopsy, the 62.9% of the patient samples taken at time of stable graft function had very high prediction score. In contrast, the scores in patients with stable graft function and no acute rejection during patient follow up was significantly lower. ESCAPE study (Evaluation of Sub-Clinical Acute rejection PrEdiction) combined the performance of kSORT and the measurement of circulating anti-donor T cell alloreactivity using the IFN-γ ELISPOT in a prospective study including 75 patients. 22 of the 75 patients showed sub-clinical acute
rejection histological lesions at 6 months [110]. Most of patients with a high-risk kSORT score (64.3%) and positive ELISPOT (71.4%) showed sub-clinical acute rejection at 6 months. When combining the two tests, patients with a double negative test displayed a stable biopsy (96.9%). These studies strongly suggest that these signatures could be a valuable support to the clinician in a near future.

Using DNA micro-arrays, the Transplant Genomics Collaborative Group identified distinct gene expression signatures for both biopsies and peripheral blood lymphocytes that distinguish acute rejection, acute dysfunction without rejection and well-functioning transplants with no rejection history [111,112]. The TruGraf™ test is currently being tested in collaboration with the Mayo Clinic Center for Individualized Medicine to enable a personalized immunosuppression regimen for solid organ transplant recipients.

## 5.1 MicroRNAs

MicroRNAs (miRNAs) are small RNAs that regulate gene expression by translational repression or by messenger RNA degradation. They are involved in several cellular processes, such as development, proliferation, differentiation, apoptosis, metabolism and also in the regulation of innate and adaptive responses. They have been associated with multiple diseases including autoimmune diseases and cancers [113]. Several studies presented miRNAs isolated from biopsy samples, peripheral blood as potential biomarkers for allograft function [114–116]. Anglicheau et al. identified that intragraft miRNA profiles could distinguish patients with acute rejection from patients with normal biopsy [60]. More recently, our group highlighted an increase of miR-142-5p in blood of patients undergoing chronic antibody mediated rejection [117]. Moreover, its expression was not modulated during acute rejection and not influenced by immunosuppression. Finally, Matz et al. described a combination of 5 miRNAs which help to identify severe T-cell mediated vascular rejection after renal transplantation [118].
## 5.2 Donor-derived cell-free DNA

The development of microchimerism after transplantation is well recognized and may improve graft acceptance. Several studies demonstrate that donor-derived cell-free DNA (dd-cfDNA) can be detected in the plasma of organ transplant recipients and that elevated levels of donor DNA can be used as a biomarker of organ rejection [119-123]. In kidney transplant recipients, urinary dd-cfDNA is significantly increased during acute rejection compared to stable allografts and chronic allograft nephropathy. There was no difference in patients with BK virus nephropathy [124]. More recently, Bloom and al. showed that the median level of dd-cfDNA in blood of patients with active rejection was significantly higher than in the comparator group without active rejection (AUC 0.74; 95%CI 0.61-0.86). However, they were not able to distinguish injury associated with the interstitial inflammation and tubulitis caused by BKV from similar degrees of inflammation and tubulitis caused by TCMR [125].

## 6. Conclusion

Numerous non-invasive biomarkers have been identified in recent years with the promise to identify high-risk patients. These biomarkers have been proposed to overcome the need of diagnostic biopsy or to identify patients with a poor prognostic outcome of their kidney graft (Table 1). Candidate biomarkers are numerous and the transition from discovery to validation studies is a main challenge in the transplant community. Here, we have summarized the current findings of diagnostic and predictive blood biomarkers based on immune cell profiling, gene profiling and functional assay. Large multi-centric prospective trials are now greatly needed to accelerate the implementation of biomarkers into the clinical practice. However, the search of one perfect biomarker that accurately assesses the risk of rejection and even the immunological mechanisms of this rejection is probably an ambitious ideal. Currently, various clinical and biological parameters, such as previous transplantations, donor and recipient ages and allo-immunization, are known to be associated with a higher risk of
rejection and a poorer long-term prognosis. One good strategy could be to use clinical data
and a restricted set of biomarkers to design a composite predictive score.In our lab, we have
reported a one-year scoring system predictive of long term graft failure called Kidney
Transplant Failure Score (KTFS) based on eight risk factors of graft loss (creatinine values at
3 and 12 months, proteinuria value at 3 months, recipient age and gender, number of previous
transplantations, donor creatinine value and rejection episodes) [126] The KTFS is associated
with an area under the time-dependent ROC curve (AUC) of 0.78 (CI95% = [0.71, 0.86]) for
a prognostic up to eight years post transplantation. Its usefulness to drive the patient follow-
up decisions is under study in a randomized health-economics clinical trial [127]. It is
important to stress that the clinical variables used within the KTFS are already available and
thus, do not represent any additional costs toward identifying patients with a high or low risk
profiles to lose their graft within a 8 year period. We acknowledge that the underlying
mechanisms of graft rejection are highly heterogeneous ranging from non-immune related
causes to ongoing immune insult to the graft. Therefore, we propose that a major usefulness
of biomarkers is to identify patients that will lose their graft for immune-related mechanisms.
For instance, in order to improve the prognostic capacities of the KTFS, we are currently
investigating whether the monitoring of CD8 T cell subsets could improve the prognostic
capacities of the KTFS. Given that an increase in TEMRA CD8 T cells is associated with a
worse kidney graft outcome [42], this refined version of the KTFS could enable the
identification of patients at high immunological risk of graft loss. The early identification of
patients at high-risk should offer a therapeutic window during which innovative therapeutic
strategies could be tested to improve the kidney graft survival.

7. Expert Commentary

The search for markers associated with or predictive of allograft rejection has fed the
scientific literature and meetings devoted to transplantation for decades. To date, the only
validated and accepted marker by the community (and also popular as a non-invasive procedure) is the de novo appearance of DSA. Cellular correlates, particularly T/B cell function and phenotype, and more recently transcriptional profiling have only indirectly impacted the management of transplant recipients by providing an important body of information on the mechanisms of the development of the immune response against an allograft in the moving clinical context of modern immunosuppression. Despite the success of the DSA monitoring, the well documented need for innate and adaptive (and thus of alloreactive T cells) immune response to mount an anti-DSA response suggests that monitoring of T/B cells markers should gain a clinical notoriety in the future. Research into T/B cell markers faces two major difficulties. The first one is related by a strong “Red Queen effect”. Indeed, all biomarkers study are inevitably temporary because of the continuous progress in immunosuppression which strongly affects the T/B cell function. Particularly, this effect is likely to ruin the search for predictive markers because the validation procedures require long survey of patients, often making the biomarkers obsolete prior to the conclusion of the study. Nevertheless, there is a continuous indirect benefit in the endless “monitoring” approach: a better knowledge of the mechanisms of the rejection.

The second difficulty lies in the level of sophistication – and thus difficulty in validation- of
the techniques of T/B cells analysis. Furthermore, these studies often utilize frozen cells
which are less efficient to those found in a fresh blood draw. However, the recent progress in
phenotype/function relationship and in cell fluorometry analysis makes it likely that some T/B
markers emerged in the near future.

8. 5 years view

In order to project the role of biomarkers in transplantation in the near future (which is a five
year prospective), we need to consider the achievements of the last decades in the field of
biomarkers in general and of blood lymphocytes in particular. However, one must keep in
mind that extreme caution is required to draw firm conclusion in the field. There are currently no “validated” diagnostic biomarkers (i-e recognized as such as by regulatory agencies or health insurance organization) on which clinician can universally rely on. The validation process is even more challenging for predictive biomarkers, which could anticipate the outcome of an allograft and guide patient care. The reasons for these difficulties are continuous evolving techniques and clinical management strategies which not only curve the magnitude of the immune response but also its nature. Multicenter studies and workshops aimed at standardizing the methods across different centers (which can be difficult) have also contributed to a depreciation of monitoring, particularly when based on the exploration of the recipient lymphocyte response.

However, this standing point does not fully consider the actual major impact of biomarker monitoring in the daily clinical management of our patients. For instance, even if there is no definitive consensus and if reiterated calls for caution are published regarding biomarker usage, biomarker monitoring has become a solid basis to identify the immunological risk before kidney transplantation and an reliable indicator of the need for histological analysis prior to allograft dysfunction.

The technical challenges to monitor lymphocytes before and after transplantation are numerous (compatibility with frozen samples, time from sampling to result delivery, benefit/cost balance) but they can be all be overcome. Technical advances will ensure the reliability of the measurement, their ease of delivery and the drastic reduction between blood collection and biomarker characterization. The advantage of lymphocyte monitoring lays in its predictive ability and the well-documented mechanisms of immune function and allograft injury. For instance, the production of DSA is under the control of T helper cells and monitoring of circulating follicular T cells may allow to detect an active anti-donor response before the rise of de novo DSA. The spectacular progress in the field of flow cytometry
enables now to obtain a complex picture of T/B cell population which gathers an in-depth characterization of their phenotype (including transcriptional factor) with the detection of cytokine and other soluble factors. This approach allows for the gap between phenotypic approach and functional test to be closed. Finally, the ability to detect mRNA in a heterogeneous population using fluorescent-coupled primers allows for a transcriptomic approach to identify the cell subsets that are responsible for the rise of candidate biomarkers.

These technical improvements will progressively shift the attention from function based tests (ADCC, ELISPOT, transcriptional studies and analysis of supernatant from activated cells), which are difficult to conceive in a daily clinical care, to multicolor complex phenotypes.

Therefore, our expectation is that the assessment of blood mononucleated markers before transplantation as well as serially after surgery, in a manner similar to that used for DSA monitoring, will gain in usefulness and popularity. Last but not least, beside the generation potential biomarkers, the monitoring of blood mononuclear cell characteristics will remain a driving force of our understanding of the mechanism of rejection and tolerance to allograft and will continue to shape the global understanding of alloreactivity to the transplant physician community.

## Key issues

* Pre-transplantation, the presence of an increased pool of differentiated (and particularly donor committed) T cell is a negative marker for long-term graft outcome.

* Highly elevated pre-transplant frequencies of IFN-γ producing T cells against donor cells is correlated with the post-transplant risk of developing acute rejection episodes but the induction therapy must be take into account for the interpretation of FN-γ ELISPOT assay

* Increased frequency of highly differentiated CD8 T cell post-transplantation is associated with a worse kidney graft outcome.
* Granzyme B and perforin are predictive biomarkers of acute cellular rejection

* Chronic antibody mediated rejection display a unique B cell phenotype with a reduced ratio of activated naive (IgD+CD38+) to memory B (IgD-CD38+/-) cells

* The need to validate candidate biomarkers on multicenter prospective controlled trials and to integrate clinical parameters with biomarkers to design composite score of kidney graft outcome

## Acknowledgments

The authors would like to thank Dr Michelle Yap for fruitful discussion and scientific editing.

## Funding

This work was funded by a grant from the Fondation Centaure and was realized in the context of the IHU-Cesti project, which received French government financial support managed by the Agence Nationale de la Recherche via the ‘‘Investment Into The Future’’ program ANR-10-IBHU-005. The IHU-Cesti project is also supported by Nantes Metropole and the Pays de la Loire Region. This work was also supported by the FP7 VISICORT project that has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement 602470. This work was realized in the context of the LabEX IGO program supported by the National Research Agency via the investment of the future program ANR-11-LABX-0016-01.

## Declaration of Interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials
discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

## References

### Reference annotations

* *Of interest*

** *Of considerable interest*

[1] Oniscu GC. Impact of Cadaveric Renal Transplantation on Survival in Patients Listed for Transplantation. J. Am. Soc. Nephrol. 2005;16:1859–1865.

[2] Ogutmen B, Yildirim A, Sever MS, et al. Health-Related Quality of Life After Kidney Transplantation in Comparison Intermittent Hemodialysis, Peritoneal Dialysis, and Normal Controls. Transplant. Proc. 2006;38:419–421.

[3] Humar A, Kerr S, Hassoun A, et al. The association between acute rejection and chronic rejection in kidney transplantation. Transplant. Proc. 1999;31:1302–1303.

[4] Meier-Kriesche HU, Ojo AO, Hanson JA, et al. Increased impact of acute rejection on chronic allograft failure in recent era. Transplantation. 2000;70:1098–1100.

[5] Berczi C, Asztalos L, Kincses Z, et al. Effect of acute rejection episodes on long-term renal graft survival. Transplant. Proc. 1998;30:1775.
[6] Meier-Kriesche H-U, Schold JD, Srinivas TR, et al. Lack of improvement in renal allograft survival despite a marked decrease in acute rejection rates over the most recent era. Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2004;4:378-383.

[7] Giral M, Foucher Y, Karam G, et al. Kidney and recipient weight incompatibility reduces long-term graft survival. J. Am. Soc. Nephrol. JASN. 2010;21:1022-1029.

[8] El-Zoghby ZM, Stegall MD, Lager DJ, et al. Identifying Specific Causes of Kidney Allograft Loss. Am. J. Transplant. 2009;9:527-535.

[9] Williams WW, Taheri D, Tolkoff-Rubin N, et al. Clinical role of the renal transplant biopsy. Nat. Rev. Nephrol. 2012;8:110-121.

**[10] Loupy A, Haas M, Solez K, et al. The Banff 2015 Kidney Meeting Report: Current Challenges in Rejection Classification and Prospects for Adopting Molecular Pathology. Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2017;17:28-41. The implementation of molecular classifiers to the Banff Classification is now recognized as a key step to improve the stratification of kidney transplant biopsies.

[11] Sorof JM, Vartanian RK, Olson JL, et al. Histopathological concordance of paired renal allograft biopsy cores. Effect on the diagnosis and management of acute rejection. Transplantation. 1995;60:1215-1219.

[12] Fereira LC, Karras A, Martinez F, et al. Complications of protocol renal biopsy. Transplantation. 2004;77:1475-1476.

[13] Chong AS, Perkins DL. Transplantation: molecular phenotyping of T-cell-mediated rejection. Nat. Rev. Nephrol. 2014;10:678-680

*[14] Halloran PF, Famulski KS, Reeve J. Molecular assessment of disease states in kidney transplant biopsy samples. Nat. Rev. Nephrol. 2016;12:534-548.
This review summarizes the contribution of the Halloran's group in the identification of
molecular changes associated with kidney injury. Molecular assessment improves the
understanding of the pathological processes leading to adverse kidney graft outcome.

[15] Danger R, Sawitzki B, Brouard S. Immune monitoring in renal transplantation: The search for biomarkers. Eur. J. Immunol. 2016;46:2695-2704.

[16] Hourmant M, Cesbron-Gautier A, Terasaki PI, et al. Frequency and clinical implications of development of donor-specific and non-donor-specific HLA antibodies after kidney transplantation. J. Am. Soc. Nephrol. JASN. 2005;16:2804-2812.

[17] Loupy A, Hill GS, Jordan SC. The impact of donor-specific anti-HLA antibodies on late kidney allograft failure. Nat. Rev. Nephrol. 2012;8:348-357.

[18] Heeger PS. T-cell allorecognition and transplant rejection: a summary and update. Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2003;3:525-533.

[19] Issa F, Schiopu A, Wood KJ. Role of T cells in graft rejection and transplantation tolerance. Expert Rev. Clin. Immunol. 2010;6:155-169.

[20] Marino J, Paster J, Benichou G. Allorecognition by T Lymphocytes and Allograft Rejection. Front. Immunol. 2016;7:582.

[21] Girndt M, Sester U, Sester M, et al. Impaired cellular immune function in patients with end-stage renal failure. Nephrol. Dial. Transplant. Off. Publ. Eur. Dial. Transpl. Assoc. - Eur. Ren. Assoc. 1999;14:2807-2810.

[22] Meier P, von Fliedner V, Markert M, et al. One-year immunological evaluation of chronic hemodialysis in end-stage renal disease patients. Blood Purif. 2000;18:128-137.

[23] Yoon J-W, Gollapudi S, Pahl MV, et al. Naïve and central memory T-cell lymphopenia in end-stage renal disease. Kidney Int. 2006;70:371-376.
[24] Litjens NHR, Huisman M, van den Dorpel M, et al. Impaired Immune Responses and Antigen-Specific Memory CD4+ T Cells in Hemodialysis Patients. J. Am. Soc. Nephrol. 2008;19:1483-1490.

[25] Betjes MGH, Meijers RWJ, de Wit EA, et al. Terminally Differentiated CD8+ Temra Cells Are Associated With the Risk for Acute Kidney Allograft Rejection: Transplant. J. 2012;94:63-69.

[26] Dedeoglu B, Meijers RWJ, Klepper M, et al. Loss of CD28 on Peripheral T Cells Decreases the Risk for Early Acute Rejection after Kidney Transplantation. Gregson A, editor. PLoS ONE. 2016;11:e0150826.

[27] Ordonez L, Bernard I, Chabod M, et al. A higher risk of acute rejection of human kidney allografts can be predicted from the level of CD45RC expressed by the recipients' CD8 T cells. PloS One. 2013;8:e69791.

[28] Degauque N, Brouard S, Soulillou J-P. Cross-Reactivity of TCR Repertoire: Current Concepts, Challenges, and Implication for Allotransplantation. Front. Immunol. 2016;7:89.

[29] Smith C, Miles JJ, Khanna R. Advances in direct T-cell alloreactivity: function, avidity, biophysics and structure. Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2012;12:15-26.

[30] Vincenti F. Belatacept and Long-Term Outcomes in Kidney Transplantation. N. Engl. J. Med. 2016;374:2600-2601.

[31] Espinosa J, Herr F, Tharp G, et al. CD57(+) CD4 T Cells Underlie Belatacept-Resistant Allograft Rejection. Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2016;16:1102-1112.

[32] Xu H, Perez SD, Cheeseman J, et al. The allo- and viral-specific immunosuppressive effect of belatacept, but not tacrolimus, attenuates with progressive T cell maturation. Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2014;14:319-332.
[33] Mancebo E, Castro MJ, Allende LM, et al. High proportion of CD95+ and CD38+ in cultured CD8+ T cells predicts acute rejection and infection, respectively, in kidney recipients. Transpl. Immunol. 2016;34:33-41.

[34] Schowengerdt KO, Fricker FJ, Bahjat KS, et al. Increased expression of the lymphocyte early activation marker CD69 in peripheral blood correlates with histologic evidence of cardiac allograft rejection. Transplantation. 2000;69:2102-2107.

[35] Creemers P, Brink J, Wainwright H, et al. Evaluation of peripheral blood CD4 and CD8 lymphocyte subsets, CD69 expression and histologic rejection grade as diagnostic markers for the presence of cardiac allograft rejection. Transpl. Immunol. 2002;10:285-292.

[36] Tary-Lehmann M, Hricik DE, Justice AC, et al. Enzyme-linked immunosorbent assay spot detection of interferon-gamma and interleukin 5-producing cells as a predictive marker for renal allograft failure. Transplantation. 1998;66:219-224.

[37] Heeger PS, Greenspan NS, Kuhlenschmidt S, et al. Pretransplant frequency of donor-specific, IFN-gamma-producing lymphocytes is a manifestation of immunologic memory and correlates with the risk of posttransplant rejection episodes. J. Immunol. Baltim. Md 1950. 1999;163:2267-2275.

[38] Nickel P, Presber F, Bold G, et al. Enzyme-linked immunosorbent spot assay for donor-reactive interferon-gamma-producing cells identifies T-cell presensitization and correlates with graft function at 6 and 12 months in renal-transplant recipients. Transplantation. 2004;78:1640-1646.

[39] Crespo E, Lucia M, Cruzado JM, et al. Pre-transplant donor-specific T-cell alloreactivity is strongly associated with early acute cellular rejection in kidney transplant recipients not receiving T-cell depleting induction therapy. PloS One. 2015;10:e0117618.

**[40] 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: ELISPOT and Kidney Transplantation. Am. J. Transplant. 2015;15:3166-
3173. This study is one of the first attempt to validate a candidate biomarker in routine clinical
practice using a large multicentric clinical trial. The design of this study is a perfect example of
what should be done in the field of biomarker.

*[41] Baeten D. Phenotypically and Functionally Distinct CD8+ Lymphocyte Populations in Long-Term Drug-Free Tolerance and Chronic Rejection in Human Kidney Graft Recipients. J. Am. Soc. Nephrol. 2005;17:294-304. This study highlights the modification of the CD8 T cell compartment after kidney transplantation and underlines the relationship between the frequency of CD8 T cell subsets and opposite kidney graft outcome.

[42] Yap M, Boeffard F, Clave E, et al. Expansion of highly differentiated cytotoxic terminally differentiated effector memory CD8+ T cells in a subset of clinically stable kidney transplant recipients: a potential marker for late graft dysfunction. J. Am. Soc. Nephrol. JASN. 2014;25:1856-1868.

[43] Kowalski R, Post D, Schneider MC, et al. Immune cell function testing: an adjunct to therapeutic drug monitoring in transplant patient management. Clin. Transplant. 2003;17:77-88.

[44] 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.

[45] Millán O, Sánchez-Fueyo A, Rimola A, et al. Is the intracellular ATP concentration of CD4+ T-Cells a predictive biomarker of immune status in stable transplant recipients? Transplantation. 2009;88:S78-S84.

[46] Sánchez-Velasco P, Rodrigo E, Valero R, et al. Intracellular ATP concentrations of CD4 cells in kidney transplant patients with and without infection. Clin. Transplant. 2008;22:55-60.
[47] 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. CJASN. 2011;6:423-429.

[48] Ryan CM, Chaudhuri A, Concepcion W, et al. Immune cell function assay does not identify biopsy-proven pediatric renal allograft rejection or infection. Pediatr. Transplant. 2014;18:446-452.

[49] Myslik F, House AA, Yanko D, et al. Preoperative Cylex assay predicts rejection risk in patients with kidney transplant. Clin. Transplant. 2014;28:606-610.

[50] 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.

[51] Rodrigo E, López-Hoyos M, Corral M, et al. ImmuKnow as a diagnostic tool for predicting infection and acute rejection in adult liver transplant recipients: a systematic review and meta-analysis. Liver Transplant. Off. Publ. Am. Assoc. Study Liver Dis. Int. Liver Transplant. Soc. 2012;18:1245-1253.

[52] 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.

[53] Hameed A, Truong LD, Price V, et al. Immunohistochemical localization of granzyme B antigen in cytotoxic cells in human tissues. Am. J. Pathol. 1991;138:1069-1075.

[54] Griffiths GM, Namikawa R, Mueller C, et al. Granzyme A and perforin as markers for rejection in cardiac transplantation. Eur. J. Immunol. 1991;21:687-693.

[55] Lipman ML, Stevens AC, Strom TB. Heightened intragraft CTL gene expression in acutely rejecting renal allografts. J. Immunol. Baltim. Md 1950. 1994;152:5120-5127.
[56] Kummer JA, Wever PC, Kamp AM, et al. Expression of granzyme A and B proteins by cytotoxic lymphocytes involved in acute renal allograft rejection. Kidney Int. 1995;47:70-77.

[57] 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. U. S. A. 1997;94:695-700.

[58] 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.

[59] Netto MVP, Fonseca B a. L, 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.

*[60] Anglicheau D, Suthanthiran M. Noninvasive prediction of organ graft rejection and outcome using gene expression patterns. Transplantation. 2008;86:192-199.

This study is the first to identify a miRNA based signature of acute rejection and that is predictive of kidney graft function.

[61] Alachkar N. Serum and urinary biomarkers in acute kidney transplant rejection. Nephrol. Ther. 2012;8:13-19.

[62] 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. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2003;3:1121-1127.

[63] Graziotto R, Del Prete D, Rigotti P, et al. Perforin, Granzyme B, and fas ligand for molecular diagnosis of acute renal-allograft rejection: analyses on serial biopsies suggest methodological issues. Transplantation. 2006;81:1125-1132.
[64] 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.

[65] Bestard O, Cruzado JM, Rama I, et al. Presence of FoxP3+ Regulatory T Cells Predicts Outcome of Subclinical Rejection of Renal Allografts. J. Am. Soc. Nephrol. 2008;19:2020-2026.

[66] Bestard O, Cruzado JM, Mestre M, et al. Achieving donor-specific hyporesponsiveness is associated with FOXP3+ regulatory T cell recruitment in human renal allograft infiltrates. J. Immunol. Baltim. Md 1950. 2007;179:4901-4909.

[67] Karczewski M, Karczewski J, Kostrzewa A, et al. The Role of Foxp3+ Regulatory T Cells in Kidney Transplantation. Transplant. Proc. 2009;41:1527-1529.

[68] Kreijveld E, Koenen HJPM, van Cranenbroek B, et al. Immunological Monitoring of Renal Transplant Recipients to Predict Acute Allograft Rejection Following the Discontinuation of Tacrolimus. Zoccali C, editor. PLoS ONE. 2008;3:e2711.

[69] Schaier M, Seissler N, Schmitt E, et al. DR(high+)CD45RA(-)-Tregs potentially affect the suppressive activity of the total Treg pool in renal transplant patients. PloS One. 2012;7:e34208.

[70] Braudeau C, Ashton-Chess J, Giral M, et al. Contrasted Blood and Intragraft Toll-Like Receptor 4 mRNA Profiles in Operational Tolerance Versus Chronic Rejection in Kidney Transplant Recipients: Transplantation. 2008;86:130-136.

[71] Alvarez CM, Opelz G, Garcia LF, et al. Expression of regulatory T-cell-related molecule genes and clinical outcome in kidney transplant recipients. Transplantation. 2009;87:857-863.
[72] Iwase H, Kobayashi T, Kodera Y, et al. Clinical significance of regulatory T-cell-related gene expression in peripheral blood after renal transplantation. Transplantation. 2011;91:191-198.

*[73] 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.

This study highlights the benefits of the analysis of targeted gene expression in the graft biopsies and in peripheral blood compartment to understand the contribution between regulatory and effector immune responses.

[74] Veronese F, Rotman S, Smith RN, et al. Pathological and clinical correlates of FOXP3+ cells in renal allografts during acute rejection. Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2007;7:914-922.

[75] San Segundo D, Millán O, Muñoz-Cacho P, et al. High Proportion of Pretransplantation Activated Regulatory T cells (CD4+CD25highCD62L+CD45RO+) Predicts Acute Rejection in Kidney Transplantation: Results of a Multicenter Study. Transplantation. 2014;98:1213-1218.

[76] Miyara M, Yoshioka Y, Kitoh A, et al. Functional delineation and differentiation dynamics of human CD4+ T cells expressing the FoxP3 transcription factor. Immunity. 2009;30:899-911.

[77] Polansky JK, Kretschmer K, Freyer J, et al. DNA methylation controls Foxp3 gene expression. Eur. J. Immunol. 2008;38:1654-1663.

[78] Braza F, Dugast E, Panov I, et al. Central Role of CD45RA- Foxp3hi Memory Regulatory T Cells in Clinical Kidney Transplantation Tolerance. J. Am. Soc. Nephrol. JASN. 2015.
[79] Soulillou JP, Peyrat MA, Guenel J. Association between treatment-resistant kidney-allograft rejection and post-transplant appearance of antibodies to donor B-lymphocyte alloantigens. Lancet Lond. Engl. 1978;1:354-356.

[80] Halloran PF, Famulski K, Reeve J. The molecular phenotypes of rejection in kidney transplant biopsies. Curr. Opin. Organ Transplant. 2015;20:359-367.

[81] 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.

[82] Ng Y-H, Oberbarnscheidt MH, Chandramoorthy HCK, et al. B Cells Help Alloreactive T Cells Differentiate Into Memory T Cells: B Cells Help Development of Alloreactive Memory T Cells. Am. J. Transplant. 2010;10:1970-1980.

[83] Sarwal M, Chua M-S, Kambham N, et al. Molecular Heterogeneity in Acute Renal Allograft Rejection Identified by DNA Microarray Profiling. N. Engl. J. Med. 2003;349:125-138.

[84] Doria C, di Francesco F, Ramirez CB, et al. The presence of B-cell nodules does not necessarily portend a less favorable outcome to therapy in patients with acute cellular rejection of a renal allograft. Transplant. Proc. 2006;38:3441-3444.

[85] Bagnasco SM, Tsai W, Rahman MH, et al. CD20-Positive Infiltrates in Renal Allograft Biopsies with Acute Cellular Rejection Are Not Associated with Worse Graft Survival. Am. J. Transplant. 2007;7:1968-1973.

[86] Tsai EW, Ettenger RB. B cells and rejection: is there a link? Meaning and management of rejection characterized by steroid-resistance and B-cell infiltrates. Pediatr. Transplant. 2009;13:150-154.

[87] Viklicky O, Hribova P, Volk H-D, et al. Molecular Phenotypes of Acute Rejection Predict Kidney Graft Prognosis. J. Am. Soc. Nephrol. 2010;21:173-180.
[88] Nouël A, Ségalen I, Jamin C, et al. B cells display an abnormal distribution and an impaired suppressive function in patients with chronic antibody-mediated rejection. Kidney Int. 2014;85:590-599.

[89] Schlößer HA, Thelen M, Dieplinger G, et al. Prospective Analyses of Circulating B Cell Subsets in ABO-Compatible and ABO-Incompatible Kidney Transplant Recipients. Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2017;17:542-550.

[90] Yeung MY, Ding Q, Brooks CR, et al. TIM-1 Signaling Is Required for Maintenance and Induction of Regulatory B Cells: Breg Induction Requires TIM-1 Signaling. Am. J. Transplant. 2015;15:942-953.

[91] Louis S, Braveau C, Giral M, et al. Contrasting CD25hiCD4+T cells/FOXP3 patterns in chronic rejection and operational drug-free tolerance. Transplantation. 2006;81:398-407.

[92] Pallier A, Hillion S, Danger R, et al. Patients with drug-free long-term graft function display increased numbers of peripheral B cells with a memory and inhibitory phenotype. Kidney Int. 2010;78:503-513.

[93] Chesneau M, Pallier A, Braza F, et al. Unique B Cell Differentiation Profile in Tolerant Kidney Transplant Patients: B Cell Differentiation in Tolerance. Am. J. Transplant. 2014;14:144-155.

[94] Shabir S, Girdlestone J, Briggs D, et al. Transitional B Lymphocytes Are Associated With Protection From Kidney Allograft Rejection: A Prospective Study: Transitional B Cells and Rejection. Am. J. Transplant. 2015;15:1384-1391.

[95] Chesneau M, Michel L, Dugast E, et al. Tolerant Kidney Transplant Patients Produce B Cells with Regulatory Properties. J. Am. Soc. Nephrol. JASN. 2015;26:2588-2598.

[96] Lanier LL. Up on the tightrope: natural killer cell activation and inhibition. Nat. Immunol. 2008;9:495-502.
[97] Sis B, Jhangri GS, Riopel J, et al. A new diagnostic algorithm for antibody-mediated microcirculation inflammation in kidney transplants. Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2012;12:1168–1179.

[98] Haas M, Sis B, Racusen LC, et al. Banff 2013 meeting report: inclusion of c4d-negative antibody-mediated rejection and antibody-associated arterial lesions. Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2014;14:272–283.

[99] Hidalgo LG, Sis B, Sellares J, et al. NK cell transcripts and NK cells in kidney biopsies from patients with donor-specific antibodies: evidence for NK cell involvement in antibody-mediated rejection. Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2010;10:1812–1822.

[100] Benichou G, Yamada Y, Aoyama A, et al. Natural killer cells in rejection and tolerance of solid organ allografts. Curr. Opin. Organ Transplant. 2011;16:47–53.

[101] Hidalgo LG, Sellares J, Sis B, et al. Interpreting NK cell transcripts versus T cell transcripts in renal transplant biopsies. Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2012;12:1180–1191.

[102] Hirohashi T, Chase CM, Della Pelle P, et al. A novel pathway of chronic allograft rejection mediated by NK cells and alloantibody. Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2012;12:313–321.

[103] van Bergen J, Haasnoot GW, Roodnat JI, et al. KIR-Ligand Mismatches Are Associated With Reduced Long-Term Graft Survival in HLA-Compatible Kidney Transplantation: KIR-Mediated Kidney Graft Loss. Am. J. Transplant. 2011;11:1959–1964.

[104] Kreijveld E, van der Meer A, Tijssen HJ, et al. KIR gene and KIR ligand analysis to predict graft rejection after renal transplantation. Transplantation. 2007;84:1045–1051.
[105] Tran TH, Unterrainer C, Fiedler G, et al. No impact of KIR-ligand mismatch on allograft outcome in HLA-compatible kidney transplantation. Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2013;13:1063–1068.

[106] Crespo M, Yelamos J, Redondo D, et al. Circulating NK-cell subsets in renal allograft recipients with anti-HLA donor-specific antibodies. Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2015;15:806–814.

[107] Hoffmann SC, Hale DA, Kleiner DE, et al. Functionally significant renal allograft rejection is defined by transcriptional criteria. Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2005;5:573–581.

[108] Li L, Khatri P, Sigdel TK, et al. A peripheral blood diagnostic test for acute rejection in renal transplantation. Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2012;12:2710–2718.

[109] 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.

[110] 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. 2016.

[111] Flechner SM, Kurian SM, Head SR, et al. Kidney transplant rejection and tissue injury by gene profiling of biopsies and peripheral blood lymphocytes. Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2004;4:1475–1489.

[112] 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. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2014;14:1164–1172.
[113] Peng Y, Croce CM. The role of MicroRNAs in human cancer. Signal Transduct. Target. Ther. 2016;1:15004.

[114] 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. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg. 2011;11:2110-2122.

[115] Wilflingseder J, Regele H, Perco P, et al. miRNA profiling discriminates types of rejection and injury in human renal allografts. Transplantation. 2013;95:835-841.

[116] Betts G, Shankar S, Sherston S, et al. Examination of serum miRNA levels in kidney transplant recipients with acute rejection. Transplantation. 2014;97:e28-e30.

[117] 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.

[118] 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.

[119] Lo YM, Tein MS, Pang CC, et al. Presence of donor-specific DNA in plasma of kidney and liver-transplant recipients. Lancet Lond. Engl. 1998;351:1329-1330.

[120] Snyder TM, Khush KK, Valantine HA, et al. Universal noninvasive detection of solid organ transplant rejection. Proc. Natl. Acad. Sci. 2011;108:6229-6234.

[121] Beck J, Bierau S, Balzer S, et al. Digital droplet PCR for rapid quantification of donor DNA in the circulation of transplant recipients as a potential universal biomarker of graft injury. Clin. Chem. 2013;59:1732-1741.

[122] De Vlaminck I, Valantine HA, Snyder TM, et al. Circulating Cell-Free DNA Enables Noninvasive Diagnosis of Heart Transplant Rejection. Sci. Transl. Med. 2014;6:241ra77-ra241ra77.
[123] De Vlaminck I, Martin L, Kertesz M, et al. Noninvasive monitoring of infection and rejection after lung transplantation. Proc. Natl. Acad. Sci. 2015;112:13336-13341.

[124] Sigdel TK, Vitalone MJ, Tran TQ, et al. A rapid noninvasive assay for the detection of renal transplant injury. Transplantation. 2013;96:97-101.

[125] Bloom RD, Bromberg JS, Poggio ED, et al. Cell-Free DNA and Active Rejection in Kidney Allografts. J. Am. Soc. Nephrol. JASN. 2017;

[126] Foucher Y, Daguin P, Akl A, et al. A clinical scoring system highly predictive of long-term kidney graft survival. Kidney Int. 2010;78:1288-1294.

[127] Foucher Y, Meurette A, Daguin P, et al. A personalized follow-up of kidney transplant recipients using video conferencing based on a 1-year scoring system predictive of long term graft failure (TELEGRAFT study): protocol for a randomized controlled trial. BMC Nephrol. 2015;16:6.
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<table><caption>Table 1. Summary of potential biomarkers in renal transplantation</caption><thead><tr><td>Biomarker</td><td>Measure</td><td>Timing</td><td>Clinical context</td><td>References</td></tr></thead><tbody><tr><td>TEMRA CD8+ T cells</td><td>Cell frequency</td><td>Pre / Post-transplantation</td><td>Acute rejection / Kidney graft failure</td><td>25, 26, 42</td></tr><tr><td>IFN-γ Elispot</td><td>Cell frequency</td><td>Pre / Post-transplantation</td><td>Acute rejection / Kidney graft function</td><td>37-40</td></tr><tr><td>Immunoknow</td><td>Intracellular ATP concentration</td><td>Post-transplantation</td><td>Acute rejection</td><td>45, 47-49, 52</td></tr><tr><td>Perforin</td><td>mRNA</td><td>Post-transplantation</td><td>Acute rejection</td><td>58-60, 62-64</td></tr><tr><td>Granzyme B</td><td>mRNA</td><td>Post-transplantation</td><td>Acute rejection</td><td>58-60, 62-64</td></tr><tr><td>FoxP3</td><td>Cell frequency, mRNA</td><td>Post-transplantation</td><td>Acute rejection / Chronic rejection</td><td>67-75</td></tr><tr><td>TSDR of FoxP3 gene</td><td>% of demethylation</td><td>Post-transplantation</td><td>Operational tolerance</td><td>78</td></tr><tr><td>B cell phenotype</td><td>Cell frequency</td><td>Post-transplantation</td><td>Acute rejection / CAMR</td><td>88, 89, 94</td></tr><tr><td>NK subset redistribution</td><td>Cell frequency</td><td>Post-transplantation</td><td>Anti-HLA immunization</td><td>107</td></tr><tr><td>kSORT</td><td>mRNA</td><td>Post-transplantation</td><td>Acute rejection / Sub-clinical rejection</td><td>110, 111</td></tr><tr><td>TruGraf™</td><td>mRNA</td><td>Post-transplantation</td><td>Acute rejection</td><td>112-113</td></tr><tr><td>miRNA profiles</td><td>miRNA</td><td>Post-transplantation</td><td>CAMR / TCMR</td><td>118, 119</td></tr><tr><td>Donor-derived cell-free DNA</td><td>Cell-free DNA</td><td>Post-transplantation</td><td>Acute rejection</td><td>125, 126</td></tr></tbody></table>