Cytokine-based immune monitoring

    Cytokine-based immune monitoring O.Mill´an, M. Brunet PII: DOI: Reference:

S0009-9120(16)00006-0 doi: 10.1016/j.clinbiochem.2016.01.004 CLB 9207

To appear in:

Clinical Biochemistry

Received date: Revised date: Accepted date:

2 April 2015 4 January 2016 5 January 2016

Please cite this article as: O.Mill´ an, Brunet M, Cytokine-based immune monitoring, Clinical Biochemistry (2016), doi: 10.1016/j.clinbiochem.2016.01.004

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Cytokine-based immune monitoring

T

O.Millán and M. Brunet

IP

Pharmacology and Toxicology Laboratory, Biomedical Diagnostic Center (CDB),

NU

SC R

University of Barcelona, IDIBAPS, CIBERehd, Hospital Clínico, Barcelona, Spain

Corresponding author:

MA

Dr. M. Brunet

Professor of Barcelona University

D

Head of Pharmacology and Toxicology

TE

Biomedical Diagnostic Center

CE P

Hospital Clinic of Barcelona, IDIBAPS Barcelona

AC

Spain

E-mail: [email protected]

1

ACCEPTED MANUSCRIPT Abstract ...................................................................... 3 1. Introduction ...................................................................................... 4 Cytokine-based immune-monitoring strategies to assess the risk of rejection........ 5

1.2.

Cytokine-based immune-monitoring strategies for predicting infection risk ........... 6

SC R

IP

T

1.1.

2. Cytokine-based Immune monitoring of Teff cell responses in

2.1.

NU

Transplantation. ...................................................................................... 7 IFN-γ as predictive biomarker of the risk of rejection in clinical transplantation ..... 7

MA

2.1.1. Antigen-specific assays ............................................... 7

D

2.1.2. Non-antigen-specific assays .......................................... 8 IL-2 as predictive biomarker of the risk of rejection in clinical transplantation ..... 11

2.3.

IL-17 as a predictive biomarker of the risk of rejection and clinical graft outcome in

TE

2.2.

CE P

transplantation. ................................................................................................................... 13

3. Cytokine-based immune monitoring: the risk of infection .............. 14

3.2.

Non-virus-specific monitoring ................................................................................. 15

AC

3.1.

Virus-specific monitoring ........................................................................................ 16 3.2.1. CMV-specific immune response ..................................... 16 3.2.2. BKV-specific immune response ..................................... 17

Limitations and conclusions............................................. 18 REFERENCES ............................................................... 20

2

ACCEPTED MANUSCRIPT ABSTRACT Several studies conducted during the last decade have shown that some promising biomarkers and surrogate markers may be useful in implementing

T

personalized immunomodulatory therapies and improving graft and recipient care in

IP

solid organ transplantation.

SC R

The complexity of the immune system response against the implanted graft can change remarkably in the long-term follow-up, and the dynamic balance between T-effector/T-regulatory cell populations determines the outcome of the anti-donor response, risk of rejection, and immunosuppression requirements. For this

NU

reason, at any time before and after transplantation, monitoring the T-effector cell activity, associated with an increase in pro-inflammatory cytokine production and

MA

release, can be considered as a surrogate marker of the risk of rejection and immunosuppression requirements. Furthermore, infections remain a cause of major complications following transplantation, highlighting the need for developing new

D

approaches aimed at identifying the risk of infection in solid organ recipients.

TE

Another main aspect to be considered is that immunosuppressive agents may immunomodulate each treated patient differently. Immunosuppressive drugs show pharmacokinetic

and

CE P

high

pharmacodynamic

inter-patient

variability.

Some

pharmacodynamic biomarkers such as measurement of the inhibition of target activity can reflect the individual’s susceptibility to the treatment.

AC

Monitoring a panel of valid biomarkers may provide patient stratification and better immunosuppression treatment selection. After transplantation, therapy should be

adjusted

based

on

the

prediction

of

rejection

episodes

(maintained

alloreactivity), prognosis of allograft damage progression, and personal drug response. This review focuses on current knowledge, indicating that monitoring T-cell changes in the production of cytokines such as interferon gamma (IFN-γ) and interleukin (IL)-2, and also the expression of IL-17 by central and effector memory T cells, could be used to predict the risk of rejection and infection, thereby guiding immunosuppressive therapy in transplant recipients.

3

ACCEPTED MANUSCRIPT 1. Introduction The major challenge to successful solid organ transplantation (SOT) is the control of the cellular and humoral immune response, which is critical in preventing

T

graft rejection and chronic allograft loss while achieving individual effective

IP

protection against opportunistic infections. The level of these risks varies widely, depending on several donor–recipient genetic and immunologic interactions,

SC R

nonimmunologic factors, and individual response to pharmacological treatment. Thus, immunosuppressive therapy should be chosen according to the patient’s characteristics and be adequately adjusted to prevent rejection, chronic allograft

NU

injury, and the over-immunosuppression associated with opportunistic infections, comorbidities, and toxicity.

MA

Information on the individual immune status of patients before and after transplantation may help in assessing the risk of rejection and treatment stratification,1 and also in identifying patients at a risk of infection,2 thus enabling

D

tailored immunosuppressive therapy.

TE

During the last decade, the general strategy to prevent drug–related adverse events was to minimize (sometimes withdraw) immunosuppression with either

CE P

corticosteroids or calcineurin inhibitors. Unfortunately, these new approaches are associated with reactivation of the alloreactive immune response, which occasionally leads to rejection and graft injury.3,4

AC

Given that several factors may influence the individual net state of the immune system response, monitoring the transplant recipient's dynamic immune response is essential to determining the optimal degree of immunosuppression required. Measurement of selected immune biomarkers could identify patients at a high risk of rejection, candidates for immunosuppression minimization, and those at a high risk of posttransplant infection. In addition, a holistic approach to immunosuppressive

drug

monitoring,

based

on

pharmacokinetic

and

pharmacodynamic variables, is required to assess individual drug response, which may help in stratifying patient treatment.

4

ACCEPTED MANUSCRIPT 1.1.

Cytokine-based immune-monitoring strategies to assess the risk of rejection

T lymphocytes are critical mediators of allograft rejection. Recent studies

T

have challenged the traditional concept of dividing CD4+ T cells into reciprocal T-

IP

effector (Teff) and T-regulatory (Treg) populations, demonstrating plasticity in switching from one type to the other; in addition, these studies have shown that

SC R

multiple cytokines can mediate effector and regulatory effects on the immune system response.3,5,6 Both CD4+- and CD8+-activated T cells actively participate in acute rejection (AR) via the production and release of different pro-inflammatory

NU

cytokines.7

Cytokines modulate the immune response and play an essential role during T-

MA

cell differentiation, thus determining their biological function. Many cytokines also seem to have paradoxical functions and to respond differently, depending on the specific subset of T cells synthesizing these cytokines, their concentrations, and the

D

microenvironments in which they are released.8–10

TE

Cytokine production and secretion can be modified by immunosuppressive drugs and by the rejection process.11–13 Various T-cell subtypes may play a distinct

CE P

role in the alloreactive immune response. Different methodologies are used to monitor the production of cytokines. In several studies, the capacity of different Tcell subsets (CD3+CD4+ and CD3+CD8+) for intracellular cytokine production has been tested as a tool for immune monitoring by flow cytometry.14–16 Stadler M et al.16

AC

showed that the production of intracellular cytokines (interleukin (IL)-2, interferon gamma (IFN-γ), and tumor necrosis factor (TNF)-α) of T cells was significantly lower in kidney transplant recipients treated with cyclosporine and mycophenolate mofetil than in healthy control volunteers. Other studies in kidney transplant recipients have demonstrated the advantages of the ELISPOT technique (enzyme-linked immunospot) in the assessment of alloreactive memory Teff cells during rejection episodes (antidonor IFN-γ secretion).17,18 Other analytical methods have been proposed for monitoring circulating or soluble cytokines; some are based on singleplex immunoassays, for example, enzyme-linked immunosorbent assay (ELISA), while others use multiplex bead arrays, for example, Luminex®, or Meso Scale Discovery platforms. Pro-inflammatory cytokines such as TNF-α and IL-17 have been associated with increased rates of tissue injury and graft loss in SOT.19,20 De Menezes Neves et 5

ACCEPTED MANUSCRIPT al.21 observed significantly increased levels of both IL-17 and TNF-α in the same kidney compartments, suggesting the contribution of these pro-inflammatory cytokines to AR. In addition, the anti-inflammatory cytokine transforming growth factor beta (TGF- has been identified as a fibrogenic factor; increased expression

T

of this cytokine has been related to chronic dysfunction of the renal allograft.22 It

IP

should be noted that the residual gene expression of nuclear factor of activated T

SC R

cell (NFAT)-regulated genes (measured by quantitative analysis) in the peripheral blood of transplant recipients, including IL-2, IFN-γ, and granulocyte macrophage colony-stimulating factor (GM-CSF),has been also established as a specific biomarker

1.2.

NU

for calcineurin inhibitors.23–25

Cytokine-based immune-monitoring strategies for predicting infection

MA

risk

In the context of transplantation, infection remains one of the leading causes of morbidity and mortality in recipients. The immune system response to pathogens

D

can have a marked impact on the regulatory and effector activity of T cells. In this

TE

respect, virus–induced responses include the direct activation of T-cell clones, which cross-react with alloantigens and give rise to memory-phenotype allospecific cross-

CE P

reactive T-cell populations.

The biomarker used to predict the risk of infection in SOT may be of a simply quantitative nature (e.g., serum immunoglobulin concentrations), but other biomarkers, such as adenosine triphosphate (ATP) production in lymphocytes under

AC

the influence of a nonspecific mitogen, allow functional assessment. Pathogenspecific responses can now be monitored using various methods based on cytokinerelease assays (e.g., ELISPOT; QuantiFERON®).2 In this respect, the specific T-cellmediated immune response based on IFN-γ release is currently being assessed using the ELISPOT and QuantiFERON® methods for cytomegalovirus (CMV, the most common viral infection associated with posttransplant morbidity and mortality) and polyoma BK virus (BKV, which is common in kidney transplant recipients and has been associated with nephropathy).26 However, implementation of these tests in routine clinical practice requires standardization, selection of the most appropriate antigen(s), and establishment of criteria to define cutoff values for positive responses. Nevertheless, the combination of immune and virological monitoring is being increasingly considered a helpful tool

6

ACCEPTED MANUSCRIPT to identify transplant recipients at a high risk of infection, and also to assess treatment efficacy. This review focuses on cytokines considered useful for monitoring the immune

T

response, that is, the molecules that have been previously identified as the most

IP

robust candidates for surrogate markers of the risk of rejection and infection before and after transplantation. In this respect, changes in IFN-γ and IL-2 production could

SC R

be used to determine the risk of rejection; IFN-γ could also be useful in predicting some infections. In addition, ongoing studies investigating the immunomodulatory effects of therapies against inflammation and rejection as well as immunosuppressive

NU

therapy have begun to identify promising new biomarkers, such as IL-17 expression by central and effector memory T cells, which may be useful in developing tools to

2. Cytokine-based

immune

of

Teff

cell

responses

in

IFN-γ as predictive biomarker of the risk of rejection in clinical

TE

2.1.

monitoring

D

transplantation

MA

predict and prevent chronic allograft injury in clinical transplantation.27,28

transplantation

CE P

IFN-γis a cytokine mostly produced by activated T and natural killer (NK) cells that plays a central and dual role in the immune system to establish Th-1 responses, commonly associated with their pro-inflammatory effects, and anti-

AC

inflammatory activities, which indicate its pleiotropic functions.29,30 Thus, IFN-γ has paradoxical functions, eliciting inflammatory Th-1-driven immune responses in some physiological environments and inducing Treg cell activity in others.6 The molecular mechanisms driving different pathways still need to be clarified, instead of immunomodulation, but some studies suggest that in contrast to the pro-inflammatory effects of IFN-γ at relatively high concentrations, a low dose of IFN-γ is likely to exert suppressive effects on T-cell activation and trafficking.29,31 2.1.1. Antigen-specific assays When measured by the ELISPOT assay, and when donor-specific stimulation is used, IFN-γ production can be used to assess donor-reactive memory/effector T cells. By the ELISPOT assay, the frequencies of cytokine-secreting naive or effector memory recipient T cells are quantified after in vitro donor-specific stimulation.

7

ACCEPTED MANUSCRIPT The IFN-γ ELISPOT assay may help predict the indirect alloreactivity and identify patients at a risk of chronic rejection.32 Hricik et al.18 demonstrated that the ELISPOT assay for IFN-γ showed an independent correlation between early cellular alloreactivity and long-term renal function, and an early increase in IFN-γ levels may

T

serve as a surrogate marker of chronic allograft dysfunction in kidney transplant

IP

recipients. Furthermore, IFN-γELISPOT has been used to evaluate both the pre-

SC R

transplantation and early posttransplantation frequency of donor-specific IFN-γproducing T cells and their impact on the posttransplantation clinical outcome. Several findings in kidney transplant recipients have shown that high frequencies of donor-reactive memory T cells (cellular alloreactivity) are associated with increased poorer first-year graft function.33–35

NU

IFN-γ production, a high risk of AR in the early posttransplantation period, and

MA

More recently, evidence of the long-term persistence of the anti-donor cellular response in some patients after renal transplantation, which may lead to graft dysfunction, suggests the advantages of monitoring IFN-γ as a biomarker of this

D

maintained T-cell donor-specific alloreactivity, with the aim of preventing chronic

TE

graft damage.36 In the maintenance period, the probability of ongoing cellular alloreactivity or the reactivity of this Teff cell response during immunosuppression

CE P

minimization (at present, an empirical process not based on evidence from valid biomarkers) are considered important factors that have a significant clinical impact on graft rejection, dysfunction, and loss.

AC

2.1.2. Non-antigen-specific assays 2.1.2.1

Intracellular

T-cell

IFN-γ

Measurement

by

Flow

Cytometry

Currently, there has been growing interest in determining the types of cell subpopulations that synthesize specific cytokines, because the cell subset can determine the type of immune response (effector vs. Treg cell response). Flow cytometry allows the analysis of selected panels of cytokines to monitor alloreactivity and the immunosuppressive effect. Recently, several studies have evaluated IFN-γ as a biomarker of the risk of rejection in liver transplantation. Okanami et al.37 used flow cytometry to evaluate staining for IFN-γ to detect alloreactive T cells; they found that the postoperative–

8

ACCEPTED MANUSCRIPT preoperative ratio of the donor-specific CD8+ T cells producing IFN-was a possible indicator of the risk of rejection in liver transplant recipients. A study conducted in stable liver transplant recipients being weaned from

T

immunosuppressive therapy found that the percentage of CD4+ IFN-γ and CD8+ IFN-γ

IP

could be used as surrogate markers of the risk of rejection.15 The objectives of the study were to assess whether the immune response recovers after withdrawal of

SC R

long-term immunosuppressive treatment in these patients, and to identify specific biomarkers that might reflect immune response reactivity and predict rejection. To that end, IFN-γexpression in CD3+CD4+CD69+ lymphocytes and CD3+CD8+CD69+

NU

lymphocytes (mediated by the Teff cell response) was analyzed during the weaning process. The percentage of CD8+IFN-γ+ and CD4+IFN-γ+ was significantly higher in patients who experienced rejection than in those who did not (p = 0.016 and

MA

p = 0.024, respectively). A significant finding was that intracellular IFN-γ levels in CD8− T cells before starting the weaning protocol appeared to be a useful surrogate marker for identifying patients at a high risk of rejection and failure in the process.

TE

D

These promising data were later corroborated in de novo liver transplant recipients.38 These findings were consistent with those from a multicenter prospective study in 79 kidney and 63 liver transplant recipients; these results suggested that the

CE P

pre- and posttransplantation percentages of cells with intracellular IFN-γ in CD4+CD69+ and CD8+CD69+ T-cell subsets might help in identifying liver and kidney transplant recipients at a high risk of AR.39 The study found that liver and kidney

AC

patients presenting with AR before transplantation had a significantly higher percentage of IFN-γ cells in the CD4+CD69+ T-cell subset (liver: 29.14 ± 7.5 vs. 14.5 ± 14.4, p = 0.011; kidney: 33.3 ± 3.99 vs. 13.3 ± 9.4, p < 0.001) and in the CD8+CD69+ T-cell subset (liver: 46.5 ± 10.9 vs. 29.4 ± 19.9, p = 0.001; kidney: 62.8 ± 22.1 vs. 16.03 ± 4.57, p = 0.005). These authors additionally established cutoff values for %IFN-γin these T-cell subsets for predicting AR, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) curves, which accurately discriminated between the rejector and non-rejector groups: In liver recipients, the cutoff was 24.07% (sensitivity = 71.8 and specificity = 100) in the case of CD3+CD4+CD69+IFN-γ+and 40.13% (sensitivity = 71.8 and specificity = 100) for CD3+CD8+CD69+IFN-γ+ cells. In kidney recipients, these cutoff values were 28.66% (sensitivity = 93.9

and

specificity = 100)

and

32.68%

(sensitivity = 72.7

and

specificity = 100), respectively. The AUC for IFN-γ was 0.86 in liver patients and >0.9 in kidney patients (p < 0.001). All patients with organ rejection showed pre9

ACCEPTED MANUSCRIPT transplantation levels of %IFN-γ+ in these T-cell populations above the established cutoff point. Therefore, and very importantly, the specificity value for the prediction of AR was 100%, and the sensitivity was consistently >70%. The last step in this study was the construction of a logistic regression model to identify the combination of

T

pre-transplantation biomarkers most suited to discriminate patients at a high risk of

NFAT-regulated gene expression

SC R

2.1.2.2

IP

rejection (Table 1).39

The functional effects of calcineurin inhibition, specifically inhibition of NFAT-regulated gene transcription in the peripheral blood, can be monitored via the

NU

quantitative analysis of gene expression of IFN-γ and IL-2 in whole blood samples by real-time polymerase chain reaction.23,40 An inverse correlation between the

MA

expression of these two mediators and blood cyclosporine A (CsA) or tacrolimus concentration has been found in patients treated with these drugs.23,41 Limited data are currently available on NFAT monitoring in relation to the risk of ARs. NFAT-

D

regulated gene expression is strongly inhibited in renal allograft recipients treated with CsA during the early posttransplantation period. Moreover, high residual

TE

expression of NFAT-regulated genes has been related to the onset of AR episodes, and low residual expression with infectious complications. In a biopsy-controlled

CE P

study, reduced CsA dose and increased residual NFAT-regulated gene expression had no adverse effects, that is, ARs, as long as the residual gene expression was below 30%.25 With respect to tacrolimus, the first study on NFAT-regulated gene expression

AC

in kidney transplant recipients showed that, with similar trough tacrolimus levels in all patients, the residual expression was significantly higher in those who presented with an AR episode.41 Although this biomarker has always been monitored after transplantation, these findings suggest that its measurement before transplant could be potentially useful in predicting the risk of rejection and in determining immunosuppression requirements. However, currently, no evidence is available to support these data. In short, several prospective studies have found that some parameters measured before transplantation might be considered good biomarkers of the risk of rejection, patient risk stratification, and prediction of clinical outcome. Following transplantation, monitoring of IFN-γproduction by ELISPOT, intracellular flow cytometry, or residual NFAT gene expression may be useful in assessing the patient's risk of rejection and personal response, and in predicting graft outcome, which

10

ACCEPTED MANUSCRIPT facilitates tailored immunosuppressive therapy in kidney and liver transplant recipients. Monitoring IFN-γ production by ELISPOT with donor-specific stimulation can

T

help identify patients with an increased immune response to a defined donor

IP

antigen. This analysis can be performed in 48 h at a cost of approximately 70€. However, the ELISPOT assay has two clear disadvantages: first, donor-specific cells

SC R

are generally not available for biomarker monitoring in routine clinical practice; second, it is impossible to simultaneously analyze different lymphocyte subsets and/or cytokines, which are donor nonspecific immune parameters that are also

NU

correlated with graft outcome. Multiparameter flow cytometry has the advantage of allowing simultaneous analysis of multiple cell phenotypic markers and intracellular pro-inflammatory/regulatory cytokine production; it may also provide information on

MA

intracellular signaling pathways. Non-alloantigen-specific assays provide information on the net state of recipient immunosuppression (general immune competence),23 thus possibly

predicting not

only

graft immunological outcomes but

also

D

complications related to over-immunosuppression. Flow cytometry analysis can be

TE

conducted in 24 h at a cost of approximately 60€. Furthermore, multicenter clinical trials are currently under way, using

CE P

standardized and validated methods.42,43 These studies aim to evaluate the real predictive value of IFN-γ measurement before transplantation as a valid biomarker of alloreactivity and the risk of graft rejection, which is beneficial for patient

AC

stratification and immunosuppression election, as well as after transplantation to monitor the maintained alloreactivity and better select candidate recipients for immunosuppression minimization. Thus, late rejection episodes are avoided.

2.2.

IL-2 as predictive biomarker of the risk of rejection in clinical transplantation

Initially, IL-2 was considered a cytokine secreted by activated T cells, playing an important role in the proliferation of lymphocytes, macrophages, and NK cells during inflammation.44,45 In addition, it is well known that IL-2 promotes T-cell survival and differentiation into effector and memory T cells.46–48 However, recent observations indicate that IL-2-dependent signals derived from Teff cells are crucial in the peripheral generation and maintenance of Treg cells.49 Expression of IL-2 has 11

ACCEPTED MANUSCRIPT been repeatedly proven necessary for the survival of activated cells and the successful generation of effector and memory responses. This cytokine is critical for the survival and proliferation of CD4+CD25+FoxP3+ T cells (Treg cells), which mediate the

maintenance

of

both

natural

and

induced

tolerance.50,51

However,

T

CD4+CD25−FoxP3− T cells (T helper (Th) cells) are the main source of IL-2 in vivo;

IP

thus, Treg cells are dependent on the IL-2 produced by helper cells for proliferation

SC R

and survival.52,53 In relation to the dual role of IL-2, the use of IL-2 monitoring to predict the risk of rejection has been explored, and some studies have also assessed whether the inhibition of IL-2 may serve as a biomarker of individual response to CsA or tacrolimus.36,54 Boleslawski et al.54 proposed monitoring the percentage of

NU

CD3+CD8+IL-2+ cells as a surrogate marker to identify de novo liver transplant recipients at a high risk of AR. In this study, intracellular IL-2 production in CD8+ T cells before transplantation was closely associated with the onset of AR, particularly

MA

in patients treated with tacrolimus and prednisone. Similarly, Akoglu et al.55 found that IL-2 production in CD8+ T cells correlated with the Banff score in adult liver transplant recipients during a rejection episode (Spearman’s ρ = 0.81; p = 0.027);

D

that is, the incidence of biopsy-proven AR was significantly related to a reduction in

TE

the %CD8+IL-2+ and %CD8+IFN-γ+ during the early posttransplantation period. It is worth noting that this was not related to tacrolimus exposure. Thus, these results

CE P

strongly concur with those of the previously cited study in stable liver transplant recipients,15 in which the percentage of CD8+IL-2+ cells was significantly higher in patients with rejection (p = 0.013), during immunosuppression weaning.

AC

The role of intracellular IFN-γ and IL-2 expression before and after transplantation as early predictive biomarkers of AR and individual response to immunosuppressive therapy was recently explored in de novo adult liver recipients.38 The percentage of CD8+IL-2+ cells was significantly higher in patients presenting with rejection than in those who did not. However, if only CD4+ T cells were considered in the analysis, significant differences in the median frequency of CD3+CD4+ T cells producing IL-2 would be found only 15 days after surgery. It is worth noting that patients with a percentage of inhibition of CD8+IL-2+ and CD8+IFN-γ+ cells lower than 40% during the first week after transplant (compared to pre-transplant values) experienced biopsy-proven AR.38 Furthermore, in a multicenter study of kidney and transplant recipients,39 a significantly higher expression of IL-2 in CD8+CD69+ T cells was found before transplant in all recipients who later presented with AR (liver: 28.1 ± 10.6 vs. 13.9 ± 7.8, p = 0.001; kidney: 17.8 ± 10.6 vs. 7.14 ± 8.7, p = 0.005), while this significant difference was not found in the case of CD4+CD69+ T cells (liver: 12

ACCEPTED MANUSCRIPT 27.9 ± 5.37 vs. 30.5 ± 8.7; kidney: 23.35 ± 7.3 vs. 20.4 ± 14.4). These measurements may therefore help in identifying liver and kidney transplant recipients at a high risk of AR.36 Based on the AUC of the ROC curves, the cutoff values for predicting AR with the percentage of pre-transplant IL-2positivity in CD8+CD69+ cells in liver and kidney were

16.93%

(sensitivity = 85.71

and

specificity = 75)

T

patients

and

20.05%

IP

(sensitivity = 46 and specificity = 93.1), respectively. The AUC for IL-2 was 0.753

SC R

(acceptable; p < 0.001).39

In line with these findings, a recent study in 407 kidney transplant recipients showed that the intracellular level of IL-2 in CD8+ T cells is higher not only during

NU

acute organ rejection but also after the episode, compared to recipients with no history of rejection.56

MA

In summary, published studies indicate that the intracellular production of IL2 in T lymphocytes is associated with alloreactivity, suggesting its potential use as a surrogate marker of the risk of rejection. In addition, the percentage of inhibition of

D

IL-2 production in CD8+ T cells may also reflect individual susceptibility to tacrolimus

TE

or cyclosporine.38

Nevertheless, most of these promising results are of single-center pilot

CE P

studies. Multicenter clinical trials, with harmonized guidelines and standard operating procedures, are still needed in order to obtain conclusive results on the clinical utility of %CD8+IL-2+ monitoring and its implementation in tailoring immunosuppressive therapy (tacrolimus or cyclosporine dosage requirement) and

AC

better prevention of graft rejection. 2.3.

IL-17 as a predictive biomarker of the risk of rejection and clinical graft outcome in transplantation

There is limited clinical research on the changes in peripheral lymphocyte subsets during the early postoperative period after SOT. The conventional two-subset Th cell model (Th1 and Th2) provides the conceptual framework of how CD4+ T-cell clones produce and release several cytokines with different functions in the immune system response.57 Currently, it is well known that AR is a T-cell-dependent process that may be driven by different types of Th cells. As a result, significant improvements have been made to this model, which now includes Th9, Th17, Th22, and follicular T helper (Tfh), as well as specific subsets of Treg cells.27 The results from recent studies suggest that, in addition to Th1 and Th2 cell differentiation and 13

ACCEPTED MANUSCRIPT cytokine production, naive human CD4 T cells can also differentiate into Th17 cells in the presence of IL-6 and TGF-. Th17 cells may play a role in the development of rejection and chronic graft injury by particularly secreting the pro-inflammatory cytokines IL-17 and IL-22. Moreover, the balance between Th17 and Treg cells

T

appears to be critical in allograft rejection and immunological tolerance.58 Some

IP

previous experimental studies have proven the major role of IL-17 in human allograft

SC R

rejection and have suggested methods of improving graft outcome through Th-17– targeted immunosuppression in combination with other drugs.59 Millan et al.39 proposed different pre- and posttransplantation risk prediction

NU

models for AR based on composite biomarker panels. Posttransplantation risk prediction models for AR in liver and kidney transplant recipients involved the measurement of soluble IL-17 combined with intracellular% IFN-γin CD4+CD69+,

MA

%IFN-γin CD8+CD69+, and %IL-2in CD8+CD69+, whereas the pre-transplantation soluble IL-17 levels were useful in identifying only the liver recipients at high risk. These algorithms can help physicians select and adjust immunosuppressive therapy

TE

D

more accurately.

More recently, van Besouw et al.60 demonstrated that, particularly early after heart transplantation, IL-17-producing CD4+ T cells home to the graft, which

CE P

contributes to the AR process. In kidney recipients, IL-17 was found to be associated with rejection: elevated levels of this cytokine were detected 2 days after transplantation in mononuclear cell infiltrates.61 Some authors have also shown that

AC

the increased expression of IL-17 and TNF-α, associated with low expression of Treg cells (FoxP3+), may lead to a predominance of Th17 lymphocytes.62 These single-center experiences suggest that IL-17 is an active participant in early AR after transplantation. Nevertheless, monitoring IL-17 production as a predictive biomarker of the risk of AR and immunological status in SOT requires further investigation.

3. Cytokine-based immune monitoring: the risk of infection Infections remain a major complication after transplantation. In recent years, there has been growing interest in the development of immune-monitoring approaches aimed at identifying the risk of infection and, eventually, the modulation of immunosuppressive strategies in SOT recipients. Immune monitoring after transplantation

can

be

performed

using

non-virus-specific

or

virus-specific 14

ACCEPTED MANUSCRIPT approaches.63 In this respect, CMV and BKV are of particular interest. CMV is a major cause of morbidity in these patients, but a preventable cause of mortality.64 Emerging data on CMV infection suggest that the risk of late-onset CMV disease may be predicted with immune monitoring. BKV reactivation is common, often leading to

T

distinctive pathological entities according to patient groups; BKV is associated with

IP

nephropathy and ureteral stenosis in kidney recipients. The development of mechanisms that BKV uses to replicate. 3.1.

Non-virus-specific monitoring

SC R

malignancies has also been associated with the several potentially oncogenic

NU

Strategies proposed for nonspecific immune monitoring are remarkably heterogeneous in terms of their complexity, technical requirements, and capacity for



MA

functional assessment. These include the following strategies: The humoral response can be monitored by analyzing the serum immunoglobulin levels.

Immunophenotyping of peripheral blood lymphocyte subsets can be conducted via

D



TE

flow cytometry. The kinetics of certain T-cell subsets has been analyzed for this purpose. Calarota et al.65 reported that patients who develop CMV infection 

CE P

present with lower CD4+ and CD8+ T-cell counts than those who do not. Intracellular ATP concentration in stimulated CD4+ T cells can be determined (ImmuknowTM assay; Viracor-IBT Laboratories). The predictive value of iATP for entities such as CMV and BKV infections has been examined.66 However, a

AC

common limitation in these studies is the indication for testing; this test is generally not requested as a monitoring strategy, but it is only performed after clinical suspicion of complications. Thus, optimal application in clinical practice is yet to be determined. 

The cytokines and chemokines can be quantified. ELISA is the most widely used method, but it measures one analyte at a time in a given sample. Simultaneous quantification of multiple cytokines/chemokines is currently possible (e.g., Luminex platform), and some groups have analyzed pro-inflammatory cytokines such as IL-1, IL-6, IL-8, or TNF-α67 and chemokines such as CXCL9 and CXCL-1068 to establish a correlation with infections such as CMV or BKV. However, multicenter studies are still required.



The residual expression of NFAT-regulated genes can be quantified. The association between this gene expression (IL-2, IFN-γ, and GM-CSF) and the 15

ACCEPTED MANUSCRIPT incidence of CMV infection69,70 has been examined. The results suggest that the residual gene expression of IFN-γmight be a particularly suitable marker for assessing the risk of CMV infection in liver transplant recipients. Virus-specific monitoring

T

3.2.

IP

Measurement of antigen-specific T-cell immunity has been proven useful in

SC R

assessing a given patient’s risk of developing CMV and BKV infections. 3.2.1. CMV-specific immune response

NU

Various assays for the quantification and monitoring of CMV-specific T-cell responses, aimed at predicting infection and/or disease in SOT recipients, have been evaluated.26,71,72 CMV-specific IFN-γ-producing CD8+ T cells play a crucial role in

MA

limiting viremia during the acute phase of primary infection, whereas CD4+ T-cell subsets have a greater role to play in establishing long-term immune control. Although several assays can be used, only QuantiFERON®-CMV and IFN-γ ELISPOT

D

appear to be reliable methods in clinical practice. The ELISPOT assay quantifies the

TE

production of IFN-γ by assessing the number of spot-forming units in a given number of peripheral-blood mononuclear cells. QuantiFERON®-CMV (Celletis Ltd., Melbourne,

CE P

Australia), commercially available and the only standardized assay to date, determines the concentration of IFN-γ per milliliter of whole blood. Lower cutoff thresholds for defining a positive QuantiFERON®-CMV response (currently set at >0.2 IU/mL by the manufacturer) may eventually be more appropriate, but the

AC

standardization and cutoff values for positive ELISPOT are yet to be established. The advantages of monitoring CMV-specific cell-mediated immunity (using the QuantiFERON®-CMV technique) to predict CMV disease after discontinuation of prophylaxis were recently assessed in an international cohort of CMV-negative recipients who received organs from CMV-positive donors.26The findings indicated that this assay could be useful in predicting low, intermediate, or high risk of developing CMV disease after prophylaxis. In a study analyzing the benefits of ELISPOT, Bestard O et al.73 performed this assay with samples from 137 kidney recipients before transplantation. Patients who developed CMV infection had a significantly lower anti-IE-1 T-cell response before transplant than those who remained free of infection. The sensitivity and negative predictive values were good (over 0.80 and 0.90, respectively) for cutoff values of seven to eight spot-forming units ×103 peripheral-blood mononuclear cells. 16

ACCEPTED MANUSCRIPT Finally, a recent study compared the performance of the two assays investigating IFN-γ release, which were found to be similarly capable of predicting CMV infection. However, the areas under the ROC curves were only modest (0.66 and

T

0.62, respectively).74

IP

3.2.2. BKV-specific immune response

SC R

As mentioned previously, BKV reactivation may lead to nephropathy in renal transplant recipients, associated with impaired BKV-specific immunity.75 Most studies that have analyzed BKV-specific T-cell response have used peptide-based ELISPOT assays (large T or small T or VP1–VP3 antigens).76,77 Schachtner et al.78 found that

NU

patients who rapidly mounted BKV-specific T-cell responses after diagnosis of viral reactivation (with no reduction in immunosuppression) presented with self-limited

MA

BKV reactivation. By contrast, patients with BKV-associated nephropathy developed BKV-specific T-cell response only after reduction of immunosuppressive therapy. In addition, a detectable IFN-γ ELISPOT response to all BKV antigens may be indicative

AC

CE P

TE

D

of recent or ongoing recovery from BKV infection in renal transplant recipients.76

17

ACCEPTED MANUSCRIPT

LIMITATIONS AND CONCLUSIONS

T

The validated IFN-γELISPOT assay and the standard operating procedures

IP

for flow cytometry %CD4+IFN-γ%CD8+IFN-γand %CD8+IL-2 techniques are sensitive and reproducible quantitative analyses of these intracellular cytokines. The residual

SC R

NFAT-regulated gene expression has always been monitored after transplantation, but two whole-blood extraction times are necessary: in basal conditions (pre-dose) and 1 h after the dose. The implementation of these biomarkers in clinical transplantation is hindered by the complexity of these methodologies and the

NU

requirement of fresh samples for intracellular cytokine monitoring. Assays using cell isolation and ex vivo stimulation are more time consuming and more difficult to

MA

standardize. The type of biological matrix is strongly associated with the characteristics of the biomarker, the clinical event associated with the biomarker,

D

and the type of organ (Table 2).

Related to confounding clinical factors, the recent data suggest that bacterial

TE

infections stimulate innate immunity, and they may qualitatively and quantitatively alter the level of the alloreactive immune response. Thus, the production and

CE P

release of some cytokines may also be modified by specific bacterial and viral infections.12 Furthermore, pathogen–host immune interactions may drive the dynamic balance between T-cell regulatory activity and T-cell alloreactivity, with an impact

AC

on graft clinical outcome. Thus, although the expression of most pro-inflammatory cytokines (IL-1, IL-6, IL-8, and TNF-α) is increased during AR, some of these mediators are unable to differentiate between AR episodes and infections, which limits their usefulness in clinical practice. Given the clinical complexity of the immune system response, the evaluation of clinical outcome of the graft, and the pharmacodynamic diversity of immunosuppressive drugs, it is expected that a single biomarker will not be adequate to predict individual response, the risk of rejection, and graft dysfunction. In the literature, it is accepted that a panel of selected biomarkers including the most relevant markers of drug exposure/effect, lymphocyte alloreactive/regulatory activity, and graft damage (including drug-related toxicity) may provide valuable information for physicians for patient stratification and better treatment selection and adjustment. The optimal time point(s) and frequency for monitoring these cytokines as predictive biomarkers of risk assessment have yet to be established. The 18

ACCEPTED MANUSCRIPT sampling time is often empirically adopted, and there is no consensus on this parameter. However, it seems clear that the frequency of cytokine monitoring should depend on the clinical event to be evaluated and the type of implanted graft. For instance, when assessing AR risk, renal transplantation (<10%) requires closer and

T

more continuous monitoring than liver transplantation does (<25%). Furthermore, the

IP

analytical methods should be cost-effective with a reasonable turnaround time to

SC R

allow timely adjustment of immunosuppression.

%CD4+IFN-γ and %CD8+IFN-γmay be a robust marker for predicting the risk of rejection that can be combined with other biomarkers – with different limitations –

NU

and drug exposure to achieve the most appropriate level of immunosuppression in each treated patient.

MA

%CD8+ IL-2may be a useful predictive biomarker of the risk of rejection and may reflect individual susceptibility to tacrolimus or cyclosporine. When combined with other biomarkers and drug exposure, this marker can be monitored to improve

D

conventional therapeutic drug monitoring in transplant recipients receiving

TE

calcineurin inhibitors.

Some ongoing randomized, controlled, multicenter European studies are

CE P

evaluating the clinical utility of the IFN-γ and IL-2 ELISPOT assay, with donor-specific stimulation (Biodrim; HEALTH-F4-2012-305147). These were the first to offer controlled biomarker-driven perioperative stratification of kidney transplant patients into low/high respondents and CNI-free IS in low respondents beginning after flow

AC

transplantation. These studies also measured the levels of intracellular cytokines by cytometry,

with

mitogenic

stimulation,

in

the

improvement

of

immunosuppression minimization in kidney transplant recipients. The monitoring of IL-17 production has only just been considered a predictive biomarker of the risk of AR in SOT, and it requires further investigation. The cutoff values (positive vs. negative) as well as algorithms based on these biomarkers to predict the risk of rejection must be validated in independent populations and at the interlaboratory level in multicenter clinical trials. Finally, it is worth noting that the immune response throughout the months following transplantation is not a static process, nor is the risk of infection. Therefore, dynamic assessments through scheduled testing at different time points (early, intermediate, and late posttransplantation periods) should be encouraged. 19

ACCEPTED MANUSCRIPT Unfortunately, studies using non-virus-specific strategies are limited by small sample sizes, heterogeneity in baseline risk profiles of patients, and the lack of precise assessment of the different infectious diseases and causative agents. Moreover, studies using virus-specific approaches have focused mainly on CMV infection. Thus,

T

prospective studies are urgently needed to clarify the benefits of these new tools in

IP

transplant settings.

SC R

The development of noninvasive, accurate, and reliable assays to monitor the immune response should make immunosuppression safer, more tolerable, and more cost-effective. In the past decade, many assays have been developed to

NU

noninvasively quantify the level of T-cell response in transplantation, for example, cytokine-based immune monitoring (Table 3). Although some assays have been prospectively validated, formal studies are required to determine whether they can

MA

be used as an alternative to or in combination with conventional approaches (e.g., allograft biopsy) to improve transplant outcomes and to reduce morbidity and mortality associated with over- and under-immunosuppression. Future randomized

D

clinical trials are required to validate the role of cytokine-based immune monitoring

TE

to predict the risk of rejection, infection, and graft injury. These trials should assess whether titrating immunosuppression therapy using a combination of these assays

CE P

results in better clinical outcomes. In conclusion, the task of translating immune-monitoring assays into routine clinical practice is challenging, but it represents the key to future individualized

AC

immunosuppression.

REFERENCES [1]

Mehrotra A, Leventhal J, Purroy C, Cravedi P. Monitoring T cell alloreactivity.

Transplant Rev (Orlando). 2015;29:53-9. [2]

Fernandez-Ruiz M, Kumar D, Humar A. Clinical immune-monitoring strategies

for predicting infection risk in solid organ transplantation. Clin Transl Immunology. 2014;3:e12. [3]

Mitchell P, Afzali B, Lombardi G, Lechler RI. The T helper 17-regulatory T cell

axis in transplant rejection and tolerance. Curr Opin Organ Transplant. 2009;14:32631.

20

ACCEPTED MANUSCRIPT [4]

Wood KJ, Zaitsu M, Goto R. Cell mediated rejection. In: Zachary AA, Leffell

MS, Eds. Transplantation immunology: methods and protocols. 2nd ed. New York: Humana Press: Springer; 2013. p. 71-83. Benseler V, McCaughan GW, Schlitt HJ, Bishop GA, Bowen DG, Bertolino P.

T

[5]

IP

The liver: a special case in transplantation tolerance. Semin Liver Dis. 2007;27:194-

[6]

SC R

213.

Wood KJ, Sawitzki B. Interferon gamma: a crucial role in the function of

induced regulatory T cells in vivo. Trends Immunol. 2006;27:183-7. Strom TB, Tilney NL, Carpenter CB, Busch GJ. Identity and cytotoxic capacity

NU

[7]

of cells infiltrating renal allografts. N Engl J Med. 1975;292:1257-63. Benitez F, Najafian N. Novel noninvasive assays to predict transplantation

MA

[8]

rejection and tolerance: enumeration of cytokine-producing alloreactive T cells. Clin

[9]

D

Lab Med. 2008;28:365-73.

Cunnusamy K, Niederkorn JY. IFN-gamma blocks CD4+CD25+ Tregs and

TE

abolishes immune privilege of minor histocompatibility mismatched corneal

[10]

CE P

allografts. Am J Transplant. 2013;13:3076-84. Garcia-Martinez K, Leon K. Modeling the role of IL2 in the interplay between

CD4+ helper and regulatory T cells: studying the impact of IL2 modulation therapies.

[11]

AC

Int Immunol. 2012;24:427-46. Heeger PS, Greenspan NS, Kuhlenschmidt S, Dejelo C, Hricik DE, Schulak JA,

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. 1999;163:2267-75. [12]

McCarthy MK, Weinberg JB. The immunoproteasome and viral infection: a

complex regulator of inflammation. Front Microbiol. 2015;6:21. [13]

Millán O, Brunet M, Campistol JM, Faura A, Rojo I, Vidal E, et al.

Pharmacodynamic approach to immunosuppressive therapies using calcineurin inhibitors and mycophenolate mofetil. Clin Chem. 2003;49:1891-9. [14]

Barten MJ, Rahmel A, Bocsi J, Boldt A, Garbade J, Dhein S, et al. Cytokine

analysis to predict immunosuppression. Cytometry A. 2006;69:155-7. 21

ACCEPTED MANUSCRIPT [15]

Millán O, Benítez C, Guillén D, López A, Rimola A, Sánchez-Fueyo A, et al.

Biomarkers of immunoregulatory status in stable liver transplant recipients undergoing weaning of immunosuppressive therapy. Clin Immunol. 2010;137:337-46. Stalder M, Birsan T, Holm B, Haririfar M, Scandling J, Morris RE. Quantification

T

[16]

IP

of immunosuppression by flow cytometry in stable renal transplant recipients. Ther

[17]

SC R

Drug Monit. 2003;25:22-7.

Bestard O, Cruzado JM, Lucia M, Crespo E, Casis L, Sawitzki B, et al.

Prospective assessment of antidonor cellular alloreactivity is a tool for guidance of

[18]

NU

immunosuppression in kidney transplantation. Kidney Int. 2013;84:1226-36. Hricik DE, Rodriguez V, Riley J, Bryan K, Tary-Lehmann M, Greenspan N, et al.

Enzyme

linked

immunosorbent

spot

(ELISPOT)

assay

for

interferon-gamma

MA

independently predicts renal function in kidney transplant recipients. Am J Transplant. 2003;3:878-84.

Fehervari Z, Sakaguchi S. CD4+ Tregs and immune control. J Clin Invest.

D

[19]

[20]

TE

2004;114:1209-17.

Loong CC, Hsieh HG, Lui WY, Chen A, Lin CY. Evidence for the early

CE P

involvement of interleukin 17 in human and experimental renal allograft rejection. J Pathol. 2002;197:322-32. [21]

de Menezes Neves PD, Machado JR, dos Reis MA, Faleiros AC, de Lima Pereira

AC

SA, Rodrigues DB. Distinct expression of interleukin 17, tumor necrosis factor alpha, transforming growth factor beta, and forkhead box P3 in acute rejection after kidney transplantation. Ann Diagn Pathol. 2013;17:75-9. [22]

Pribylova-Hribova P, Kotsch K, Lodererova A, Viklicky O, Vitko S, Volk HD, et

al. TGF-beta1 mRNA upregulation influences chronic renal allograft dysfunction. Kidney Int. 2006;69:1872-9. [23]

Giese T, Zeier M, Schemmer P, Uhl W, Schoels M, Dengler T, et al. Monitoring

of NFAT-regulated gene expression in the peripheral blood of allograft recipients: a novel perspective toward individually optimized drug doses of cyclosporine A. Transplantation. 2004;77:339-44. [24]

Herden U, Kromminga A, Hagel C, Hartleb J, Nashan B, Sterneck M, et al.

Monitoring of nuclear factor of activated T-cell-regulated gene expression in de novo 22

ACCEPTED MANUSCRIPT and long-term liver transplant recipients treated with cyclosporine a. Ther Drug Monit. 2011;33:185-91. [25]

Sommerer C, Giese T, Schmidt J, Meuer S, Zeier M. Ciclosporin A tapering

T

monitored by NFAT-regulated gene expression: a new concept of individual

Manuel O, Husain S, Kumar D, Zayas C, Mawhorter S, Levi ME, et al.

SC R

[26]

IP

immunosuppression. Transplantation. 2008;85:15-21.

Assessment of cytomegalovirus-specific cell-mediated immunity for the prediction of cytomegalovirus disease in high-risk solid-organ transplant recipients: a multicenter

[27]

NU

cohort study. Clin Infect Dis. 2013;56:817-24.

Askar M. T helper subsets & regulatory T cells: rethinking the paradigm in the

[28]

MA

clinical context of solid organ transplantation. Int J Immunogenet. 2014;41:185-94. Kim HY, Cho ML, Jhun JY, Byun JK, Kim EK, Yim YB, et al. The imbalance of T

helper 17/regulatory T cells and memory B cells during the early post-transplantation

D

period in peripheral blood of living donor liver transplantation recipients under

[29]

TE

calcineurin inhibitor-based immunosuppression. Immunology. 2013;138:124-33. Evaristo C, Alegre ML. IFN-gamma: The Dr. Jekyll and Mr. Hyde of

[30]

CE P

immunology? Am J Transplant. 2013;13:3057-8. Shachar I, Karin N. The dual roles of inflammatory cytokines and chemokines

in the regulation of autoimmune diseases and their clinical implications. J Leukoc

[31]

AC

Biol. 2013;93:51-61.

Flaishon L, Topilski I, Shoseyov D, Hershkoviz R, Fireman E, Levo Y, et al.

Cutting edge: anti-inflammatory properties of low levels of IFN-gamma. J Immunol. 2002;168:3707-11. [32]

Najafian N, Salama AD, Fedoseyeva EV, Benichou G, Sayegh MH. Enzyme-

linked immunosorbent spot assay analysis of peripheral blood lymphocyte reactivity to donor HLA-DR peptides: potential novel assay for prediction of outcomes for renal transplant recipients. J Am Soc Nephrol. 2002;13:252-9. [33]

Kim SH, Oh EJ, Kim MJ, Park YJ, Han K, Yang HJ, et al. Pretransplant donor-

specific interferon-gamma ELISPOT assay predicts acute rejection episodes in renal transplant recipients. Transplant Proc. 2007;39:3057-60.

23

ACCEPTED MANUSCRIPT [34]

Nather BJ, Nickel P, Bold G, Presber F, Schonemann C, Pratschke J, et al.

Modified ELISPOT technique--highly significant inverse correlation of post-Tx donorreactive IFNgamma-producing cell frequencies with 6 and 12 months graft function in

Nickel P, Presber F, Bold G, Biti D, Schönemann C, Tullius SG, et al. Enzyme-

IP

[35]

T

kidney transplant recipients. Transpl Immunol. 2006;16:232-7.

linked immunosorbent spot assay for donor-reactive interferon-gamma-producing

SC R

cells identifies T-cell presensitization and correlates with graft function at 6 and 12 months in renal-transplant recipients. Transplantation. 2004;78:1640-6. [36]

Bestard O, Nickel P, Cruzado JM, Schoenemann C, Boenisch O, Sefrin A, et al.

NU

Circulating alloreactive T cells correlate with graft function in longstanding renal transplant recipients. J Am Soc Nephrol. 2008;19:1419-29. Okanami Y, Tsujimura K, Mizuno S, Tabata M, Isaji S, Akatsuka Y, et al.

MA

[37]

Intracellular interferon-gamma staining analysis of donor-specific T-cell responses in

Millán O, Rafael-Valdivia L, Torrademé E, López A, Fortuna V, Sánchez-Cabus

TE

[38]

D

liver transplant recipients. Transplant Proc. 2012;44:548-54.

S, et al. Intracellular IFN-gamma and IL-2 expression monitoring as surrogate markers

CE P

of the risk of acute rejection and personal drug response in de novo liver transplant recipients. Cytokine. 2013;61:556-64. [39]

Millán O, Rafael-Valdivia L, San Segundo D, Boix F, Castro-Panete MJ, López-

AC

Hoyos M, et al. Should IFN-gamma, IL-17 and IL-2 be considered predictive biomarkers of acute rejection in liver and kidney transplant? Results of a multicentric study. Clin Immunol. 2014;154:141-54. [40]

Sommerer C, Meuer S, Zeier M, Giese T. Calcineurin inhibitors and NFAT-

regulated gene expression. Clin Chim Acta. 2012;413:1379-86. [41]

Sommerer C, Zeier M, Meuer S, Giese T. Individualized monitoring of nuclear

factor of activated T cells-regulated gene expression in FK506-treated kidney transplant recipients. Transplantation. 2010;89:1417-23. [42]

Ashoor I, Najafian N, Korin Y, Reed EF, Mohanakumar T, Ikle D, et al.

Standardization and cross validation of alloreactive IFNgamma ELISPOT assays within the

clinical

trials

in organ

transplantation

consortium.

Am J Transplant.

2013;13:1871-9. 24

ACCEPTED MANUSCRIPT [43]

Bestard O, Crespo E, Stein M, Lucía M, Roelen DL, de Vaal YJ, et al. Cross-

validation of IFN-gamma Elispot assay for measuring alloreactive memory/effector T cell responses in renal transplant recipients. Am J Transplant. 2013;13:1880-90. Ho IC, Kim JI, Szabo SJ, Glimcher LH. Tissue-specific regulation of cytokine

T

[44]

Liao W, Lin JX, Leonard WJ. IL-2 family cytokines: new insights into the

SC R

[45]

IP

gene expression. Cold Spring Harb Symp Quant Biol. 1999;64:573-84.

complex roles of IL-2 as a broad regulator of T helper cell differentiation. Curr Opin Immunol. 2011;23:598-604.

Dooms H, Kahn E, Knoechel B, Abbas AK. IL-2 induces a competitive survival

NU

[46]

advantage in T lymphocytes. J Immunol. 2004;172:5973-9. Hoyer KK, Dooms H, Barron L, Abbas AK. Interleukin-2 in the development and

MA

[47]

control of inflammatory disease. Immunol Rev. 2008;226:19-28. Williams MA, Tyznik AJ, Bevan MJ. Interleukin-2 signals during priming are

D

[48]

[49]

TE

required for secondary expansion of CD8+ memory T cells. Nature. 2006;441:890-3. Malek TR, Bayer AL. Tolerance, not immunity, crucially depends on IL-2. Nat

[50]

CE P

Rev Immunol. 2004;4:665-74.

Almeida AR, Legrand N, Papiernik M, Freitas AA. Homeostasis of peripheral

CD4+ T cells: IL-2R alpha and IL-2 shape a population of regulatory cells that controls

[51]

AC

CD4+ T cell numbers. J Immunol. 2002;169:4850-60. de la Rosa M, Rutz S, Dorninger H, Scheffold A. Interleukin-2 is essential for

CD4+CD25+ regulatory T cell function. Eur J Immunol. 2004;34:2480-8. [52]

Almeida AR, Zaragoza B, Freitas AA. Indexation as a novel mechanism of

lymphocyte homeostasis: the number of CD4+CD25+ regulatory T cells is indexed to the number of IL-2-producing cells. J Immunol. 2006;177:192-200. [53]

Barthlott T, Moncrieffe H, Veldhoen M, Atkins CJ, Christensen J, O'Garra A, et

al. CD25+ CD4+ T cells compete with naive CD4+ T cells for IL-2 and exploit it for the induction of IL-10 production. Int Immunol. 2005;17:279-88.

25

ACCEPTED MANUSCRIPT [54]

Boleslawski E, Conti F, Sanquer S, Podevin P, Chouzenoux S, Batteux F, et al.

Defective inhibition of peripheral CD8+ T cell IL-2 production by anti-calcineurin drugs during acute liver allograft rejection. Transplantation. 2004;77:1815-20. Akoglu B, Kriener S, Martens S, Herrmann E, Hofmann WP, Milovic V, et al.

T

[55]

[56]

SC R

liver transplant recipients. Clin Exp Med. 2009;9:259-62.

IP

Interleukin-2 in CD8+ T cells correlates with Banff score during organ rejection in

Akoglu B, Lafferton B, Kalb S, Yosuf SE, Herrmann E, Zeuzem S, et al.

Rejection quantity in kidney transplant recipients is associated with increasing

[57]

NU

intracellular interleukin-2 in CD8+ T-cells. Transpl Immunol. 2014;31:17-21. Mosmann TR, Cherwinski H, Bond MW, Giedlin MA, Coffman RL. Two types of

murine helper T cell clone. I. Definition according to profiles of lymphokine activities

[58]

MA

and secreted proteins. J Immunol. 1986;136:2348-57. Marwaha AK, Leung NJ, McMurchy AN, Levings MK. TH17 Cells in Autoimmunity

Sullivan JA, Adams AB, Burlingham WJ. The emerging role of TH17 cells in

TE

[59]

D

and Immunodeficiency: Protective or Pathogenic? Front Immunol. 2012;3:129.

[60]

CE P

organ transplantation. Transplantation. 2014;97:483-9. van Besouw NM, Caliskan K, Peeters AM, Klepper M, Dieterich M, Maat LP, et

al. Interleukin-17-producing CD4(+) cells home to the graft early after human heart

[61]

AC

transplantation. J Heart Lung Transplant. 2015;34:933-40. Crispim JC, Grespan R, Martelli-Palomino G, Rassi DM, Costa RS, Saber LT, et

al. Interleukin-17 and kidney allograft outcome. Transplant Proc. 2009;41:1562-4. [62]

Harrington LE, Hatton RD, Mangan PR, Turner H, Murphy TL, Murphy KM, et al.

Interleukin 17-producing CD4+ effector T cells develop via a lineage distinct from the T helper type 1 and 2 lineages. Nat Immunol. 2005;6:1123-32. [63]

Calarota SA, Aberle JH, Puchhammer-Stockl E, Baldanti F. Approaches for

monitoring of non virus-specific and virus-specific T-cell response in solid organ transplantation and their clinical applications. J Clin Virol. 2015;70:109-19. [64]

Razonable RR. Epidemiology of cytomegalovirus disease in solid organ and

hematopoietic stem cell transplant recipients. Am J Health Syst Pharm. 2005;62:S713. 26

ACCEPTED MANUSCRIPT [65]

Calarota SA, Zelini P, De Silvestri A, Chiesa A, Comolli G, Sarchi E, et al.

Kinetics of T-lymphocyte subsets and posttransplant opportunistic infections in heart and kidney transplant recipients. Transplantation. 2012;93:112-9. Ling X, Xiong J, Liang W, Schroder PM, Wu L, Ju W, et al. Can immune cell

T

[66]

IP

function assay identify patients at risk of infection or rejection? A meta-analysis.

[67]

SC R

Transplantation. 2012;93:737-43.

Patella M, Anile M, Del Porto P, Diso D, Pecoraro Y, Onorati I, et al. Role of

cytokine profile in the differential diagnosis between acute lung rejection and pulmonary

infections

after

lung

transplantation.

[68]

J

Cardiothorac

Surg.

NU

2015;47:1031-6.

Eur

Rollag H, Ueland T, Asberg A, Hartmann A, Jardine AG, Humar A, et al.

MA

Characterization of cytomegalovirus disease in solid organ transplant recipients by markers of inflammation in plasma. PLoS One. 2013;8:e60767. Steinebrunner N, Sandig C, Sommerer C, Hinz U, Giese T, Stremmel W, et al.

D

[69] genes

correlates

TE

Reduced residual gene expression of nuclear factor of activated T cells-regulated with

the

risk

of

cytomegalovirus

infection

after

liver

[70]

CE P

transplantation. Transpl Infect Dis. 2014;16:379-86. Sommerer C, Zeier M, Czock D, Schnitzler P, Meuer S, Giese T.

Pharmacodynamic disparities in tacrolimus-treated patients developing cytomegalus

[71]

AC

virus viremia. Ther Drug Monit. 2011;33:373-9. Egli A, Silva M, Jr., O'Shea D, Wilson LE, Baluch A, Lisboa LF, et al. An analysis

of regulatory T-cell and Th-17 cell dynamics during cytomegalovirus replication in solid organ transplant recipients. PLoS One. 2012;7:e43937. [72]

Manuel O. Clinical Experience with Immune Monitoring for Cytomegalovirus in

Solid-Organ Transplant Recipients. Curr Infect Dis Rep. 2013;15:491-6. [73]

Bestard O, Lucia M, Crespo E, Van Liempt B, Palacio D, Melilli E, et al.

Pretransplant immediately early-1-specific T cell responses provide protection for CMV infection after kidney transplantation. Am J Transplant. 2013;13:1793-805. [74]

Abate D, Saldan A, Mengoli C, Fiscon M, Silvestre C, Fallico L, et al.

Comparison of cytomegalovirus (CMV) enzyme-linked immunosorbent spot and CMV

27

ACCEPTED MANUSCRIPT quantiferon gamma interferon-releasing assays in assessing risk of CMV infection in kidney transplant recipients. J Clin Microbiol. 2013;51:2501-7. [75]

Comoli P, Cioni M, Basso S, Gagliardone C, Potenza L, Verrina E, et al.

T

Immunity to Polyomavirus BK Infection: Immune Monitoring to Regulate the Balance

IP

between Risk of BKV Nephropathy and Induction of Alloimmunity. Clin Dev Immunol.

[76]

SC R

2013;2013:256923.

Chakera A, Bennett S, Lawrence S, Morteau O, Mason PD, O'Callaghan CA, et

al. Antigen-specific T cell responses to BK polyomavirus antigens identify functional anti-viral immunity and may help to guide immunosuppression following renal

[77]

NU

transplantation. Clin Exp Immunol. 2011;165:401-9.

Costa C, Mantovani S, Piceghello A, Di Nauta A, Sinesi F, Sidoti F, et al.

MA

Evaluation of polyomavirus BK cellular immune response by an ELISpot assay and relation to viral replication in kidney transplant recipients. New Microbiol.

Schachtner T, Muller K, Stein M, Diezemann C, Sefrin A, Babel N, et al. BK

TE

[78]

D

2014;37:219-23.

virus-specific immunity kinetics: a predictor of recovery from polyomavirus BK-

AC

CE P

associated nephropathy. Am J Transplant. 2011;11:2443-52.

28

ACCEPTED MANUSCRIPT Table 1. Pre-transplantation risk prediction models for AR in liver and

T

kidney transplant recipients.

Lo

Biomarker

Re gression Coefficient

%IFN-in +

0.0

+

NU

LIVER

CD4 CD69 %IFNinCD8+

905

CD69+

020

222

MA

0.0

TE

D

057

Odd

s Ratio (OR)

0.

969

129

Confidence Interval (CI)

0.947 –

1.00

1.266 0.905 –

2 0.

95%

1.09 5

0.

0.0

IL17

Constant

p

SC R

Organ

IP

gistic

1.109 1.00

6

0.998 – 1.013

4.9545

KIDNEY

CE P

Logit AR = -4.9545 + (CD4CD69IFN- x 0.0905) + (CD8CD69IFN- x 0.0020) + (IL17 x 0.0057) %IFN-in

+

0.0

+

807

inCD8+CD69+

176

AC

CD4 CD69 %IFN-

+

%IL2+

CD8 CD69

+

Constant

0. 194

0.1

in

127

430

1.12

1.224 0.967 -

3 0.

129

0.960 -

4 0.

0.1

1.08

1.308 1.15

4

0.959 – 1.388

13.5178

Logit AR = -13.5178 + (CD4CD69IFN- x 0.0807)+ (CD8CD69IFN- x 0.1176) + (CD8CD69IL2 x 0.1430)

29

ACCEPTED MANUSCRIPT Logistic regression analysis to construct a prediction model for acute rejection (AR) based on combinations of these biomarkers was performed using the modelbuilding strategy proposed by Hosmer and Lemeshow. Logit AR can be used to predict the probability of AR with the formula elogit

AR

/ 1 + elogit

AR

. This model included the

T

Pre transplant value profile of %CD4+CD69+IFN-, %CD8+CD69+IFN-; %CD8+CD69+IL-2 admission

for

transplantation

in

kidney

patients

before

starting

SC R

hospital

IP

and IL-17 within one month before transplantation in liver patients and at the time of immunosuppressive treatment, and at least one month after any event capable of substantially modifying the immune status of patients (39).

A

ines/Chemo

ssay

kines

prinici

S ample

ampl

Stimul

ple

ation

tabili ty

CE P

D

d

AC

tion of

W es,

blood

donor (

e

ng cells by

Y

hole

cytokin

secreti

S

T

L

urn-

evel

otenti

eferenc

Aro

of

al

es

undS Tim

anal

Clinic

ytica

al

e*

l

Utilit

valid

y

2

ation

1

7

P

R

0°C

E

numera

e

TE

equire

arkers

OT (IFN-)

x vivo r

Biom

ELISP

E

MA

Cytok

NU

Table 2: Cytokines Biomarkers: methodological aspects

PBMCl isolatio

6h

2h

a

A

A

dvanc

ssess

mJ

ed

ment

Transpla

specific

for AR

nt. 2013

stimula

and

Jul;13(7)

tion

Infecti

:1871-9

on

n)

enzym

A

e linked

mJ

immun

Transpla

ospot

nt. 2013

immun

Jul;13(7)

oassay

:1880-90

30

ACCEPTED MANUSCRIPT

W

Y

hole

es,

ation of

blood

pool

6h

IFN-

immun

per ml

ogenic

of

viral

whole

peptide

blood

s

enzym e linked

ospot immun

nes

TE

F

)

blood

Mitoge

dvanc

ssess

ranspl

ed

ment

Infect

for

Dis.

infecti

2007

on

Jun;9(2): 165-70.

1 6h

5 h

a

(

No cell

A

C

dvanc

ssess

lin

ed

ment

Immunol

for AR

2010;

nic

ytometr

stimula

137:

tion

337-46.

isolatio

AC

(IL-

b

hole

c

y

2,IL-17, IFN)

W

CE P

cytoki

T

D

oassay

low

A

MA

immun

ellular

4h

a

NU

by

Intrac

2

T

oncentr

1

IP

FERON

C

SC R

Quanti

n) C ytokine 2013; 61: 55664

Solubl e cytokines

S

a

olid

)

phase

EDTA

a ) No

0 ,5h

24h

< imited

l

A

A

ssess

nn Clin

ment

Lab Sci.

(IL31

ACCEPTED MANUSCRIPT 2,IL-17, IFN-

immun

)

plasma

for AR

2013l;43

oassay

and

(4):389-

(single

Infecti

94

plex

on

ELISA,

T

o

multiple b ) Cell

x and

culture

Meso

supern

Sclae

atants

b

2

ver

imited

Transpl.

2

SC R

Lumine

)

4h

Mitoge

4h-

Li

l

2012

72h

nic

Feb;18(2 ):166-76

NU

x

IP

r

stimula

Discov no cell isolatio

J Immunol Methods.

D

n)

tion

MA

(

ery)

TE

2011 Mar

CE P

7;366(12):119-

AC

22

J Immunol Methods. 2014 Jun;408: 13-23

Cytoki

q

W

D

ne mRNA

RT-

hole

ependi

expression

PCR,

blood

ng on

microar

,

the

rays

urine

assay

< 1h

2 4-

72h > 1h if

imited

l

A

T

ssess

ransplant

ment

Proc.

for AR

2012 Jan;44(1

stabil

32

ACCEPTED MANUSCRIPT (

ized

):236-40

Cell isolat ion

T

depe

IP

nding

SC R

on the assa

NFAT-

q

W

Y

RT-

hole

es,

gene

PCR

blood

expression

mRNA

(IL-2, IFN-)

expres

4h

a

A

T

dvanc

ssess

ransplant

Mitoge

ed

ment

ation

nic

with

for AR

2008;

stimula

CsA,

and

85: 15-

tion

limite

Infecti

21

d with

on

D

T AC

CE P

IFNγ

4h

2

TE

I L-2,

2

MA

regulated

sion of

NU

y)

and

GM-

AC

CSF

Chem okines

LISA

E

S erum

o

/ (CXC L9, CXCL10)

N

E

2 4h

a

A

T

dvanc

ssess

ranspl Int

ed

ment

2010;

for AR

23: 46575

DTA plas

C

ma/

lin Chim Acta. u

rine

2012 Sep 8;413(17 33

ACCEPTED MANUSCRIPT 18):1364

T ransplant ation. 2014 Sep 12

AC

CE P

TE

D

MA

NU

SC R

IP

T

-73

34

ACCEPTED MANUSCRIPT Table 3: Assessment of cytokine-based immune monitoring

rgan

N

Ass

umbe

ay

O utcome

r of

nts

dney

5

ELI

5

SPOT

A

R

NU

cik DE,

Ki

2003

2

Pre and

18

Post TX

AR

35

34

D

kel P,

eferenc es

MA

4

R

correlation with

(IFN-)

Nic

s

SC R

patie

Hri

Result

T

thor/Year

O

IP

Au

TE

2004

2

Nät her BJ,

33

4

36

5

AC

2006

CE P

3

Ki m SH,

3

2007

4

Be stard O,2008 Mill an O,

Li ver

2 4

FC: Intracellular

A R

Liver

15

patients undergoing 35

ACCEPTED MANUSCRIPT 2010

cyto

weaning for

kines

immunosuppre ssive therapy

(IL-

IP

T

2, IFN-)

Pre and

SC R

Post TX

correlation with

ver

4

A

NU

Li

AR

7

38

R

Mill

Pre and

FC:

an O,

Post TX

MA

Intracellular

2013

correlation with

cyto

AR

CE P

Li

TE

D

kines

ver/Kidn

(IL39

2, IFN-)

6

A

3/79

R

Post TX

ey

an O, 2014

correlation with AR

AC

Mill

Pre and

FC: Intracellular Post TX

cyto

correlation with

kines

AR

55

(IL2 Li ver

1

2, IFN-); ELISA Soluble IL17

Bol

A R

Post TX correlation with AR

eslawski

36

ACCEPTED MANUSCRIPT E, 2004

56 FC: Intracellular A

IL-2 in

6

CD8+

R

IP

ver

6

T

Li

SC R

Ak oglu B,

Li

4 07

FC:

Intracellular

A

R

MA

ver

NU

2009

57

IL-2 in CD8+

D

Ak oglu B,

CE P

TE

2014

FC:

Intracellular IL-2 in

AC

CD8+

Gie se T, 2004

Ki

2

NF

C

Mainten

dney;

5; 26;

AT-

yclospori

ance period:

Heart;

14

regulated

ne

Biologic

gene

treatment

effectiveness

liver

expression

23

of CsA therapy

(IL-2, IFN) Respon se to

37

ACCEPTED MANUSCRIPT Tacrolimus 45 Ta

dney

T

mmerer C,

Ki

crolimus 2

2010

treatment NF AT-

SC R

62

IP

So

Mainten

ance period

regulated

NU

gene

correlation with AR

25

expression (IL-2, IFN-

So

)

mmerer C,

MA

Ki dney

2008

2

R

CE P

TE

D

0

A

ATregulated gene

AC Cri spim JC,

NF

expression (IL-2, IFN) Ki

dney

1 9

IL17 ELISA

A R

Post TX

61

correlation with

2009

AR

Post TX correlation with H

6

IL-

A

60

AR

va 38

ACCEPTED MANUSCRIPT n Besouw

eart

7

17 mRNA

R

2014

ung

2 0

20015

ELI

C

SA TNF-α,

MV

IL1β, IL6,

Infection

ver

2

NF

0

AT-

N, 2014

regulated

C

Post TX

MV

correlation with

Infection

CMV

MA

gene

CMV

SC R

inebrunner

Li

NU

Ste

67

correlation with

IL8, MIP-1α ,IL-10.

Post TX

T

ella M

L

IP

Pat

69

expression (IL-2, IFN-

Respon

)

dney

7

3

CE P

2011

AT-

AC

regulated

and risk of

crolimus

developing

treatment

CMV

and CMV infection Post TX

gene

correlation with

expression

CMV

(IL-2, IFN-

72

)

nuel O, 2013

Ta

NF

Ma Ki dney,

70

Tacrolimus,

TE

Ki

mmerer C,

se to

D

So

6 C

7,26 MV

Liver

Infection

Pre TX correlation with CMV

73 Qua ntiFERON39

ACCEPTED MANUSCRIPT Be

1

stard O,

IFN-

37 Ki

2013

C

dney

MV

ELI SPOT

Ch akera A,

Ki dney

2 6

ELI

B KV

correlation with

(IFN-)

Infection

BKV

TE CE P Ki

dney

B

ELI

sta C,

SPOT

KV

2014

(IFN-)

Infection

Post TX correlation with BKV

Ki dney Sc

77

BKV

1

49

76

Post TX correlation with

AC

Co

Post TX

SPOT

D

2011

MA

NU

(IFN-)

SC R

IP

T

Infection

78

1 8 B

ELI

hachtner

SPOT

KV

T, 2011

(IFN-)

Infection

40

ACCEPTED MANUSCRIPT AR: Acute Rejection; FC: Flow Cytometry; CMV: Cytomegalovirus; BKV:

AC

CE P

TE

D

MA

NU

SC R

IP

T

Polyoma BK virus; TX: Transplantation

41

ACCEPTED MANUSCRIPT

AC

CE P

TE

D

MA

NU

SC R

IP

T

Highlights not required.

42