Immune Monitoring in Kidney Transplantation

C H A P T E R

28 Immune Monitoring in Kidney Transplantation Mark Nguyen1, Anna Geraedts2 and Minnie Sarwal1 1

University of California, San Francisco, San Francisco, CA, United States 2Maastricht University, Maastricht, The Netherlands

28.1 INTRODUCTION In the modern age of immunosuppression, 1-year allograft survival has improved, but overall graft and patient survival have remained largely unchanged.1,2 With the recently revised kidney transplant allocation system, larger numbers of sensitized patients will be receiving transplants.3 Previously, patients with high levels of human leukocyte antigen (HLA) antibodies remained on the waitlist due low likelihood of negative crossmatching. Those patients are now given increased priority when a matching organ becomes available. Likewise, advances in desensitization and induction therapies have allowed for recipients with ABO incompatibility and high panel reactive antibody (PRA) levels to receive organs.4 These sensitized patients are at much higher risk for immune mediated graft failure; hence, it is essential to monitor the global immune response to prevent or diagnose graft injury. The etiology of long-term graft loss is multifactorial in nature but a large percentage is inherently related to immunosuppression.5 Calcineurin inhibitor (CNI) toxicity and BK virus nephropathy, both classic cases of overimmunosuppression, are serious posttransplant complications and associated with progression to dialysis dependence.6,7 On the contrary, alloimmunity, an issue with under-immunosuppression, has been implicated as a major contributor to graft failure in the setting of cellular rejection, acute and chronic antibody-mediated rejection (AMR), and transplant glomerulopathy.8 Distinguishing between the types of rejection is clinically difficult and generally requires histopathology (i.e., biopsy). Finding the right balance of immunosuppression may be the key to improved allograft outcomes. Therefore, immune monitoring is critical in preventing rejection and improvement may make invasive biopsies avoidable in the future. Successful monitoring for rejection requires a deep understanding of the immune response and the development of tools that take advantage of that knowledge. Immunity, whether innate or adaptive, is the culmination of complicated cellular and molecular processes resulting in protection from “foreign objects” or antigens. Ongoing investigation of this complex network of interactions has elucidated several important pathways and markers that provide insight into the current status of immunosuppression.8 11 In order to provide reliable information, assays that monitor these processes need to be sensitive, specific, reproducible, cost-effective, and readily available. Furthermore, results from these monitoring tools need to have quick turnaround and be easily interpretable so appropriate measures can be taken prior to graft injury or failure. Here we review the current offerings of post renal transplant monitoring, their shortcomings, and emerging assays that are shaping the future immune monitoring.

28.2 CURRENT STANDARDS OF IMMUNE MONITORING The universally accepted standards for monitoring renal function include routine clinic follow-up, serial serum creatinine measurements, and urine protein quantification; however, these current modalities lack sensitivity and Kidney Transplantation, Bioengineering, and Regeneration. DOI: http://dx.doi.org/10.1016/B978-0-12-801734-0.00028-X

403

© 2017 Elsevier Inc. All rights reserved.

404

28. IMMUNE MONITORING IN KIDNEY TRANSPLANTATION

Markers predicting higher risk of graft injury Normal Allograft

Gene profile (blood and/or urine) indicating allograft injury Transcriptomic alteration

Histologic Alteration

Clinical Allograft

Graft Loss

Evidence of subclinical rejection or interstitial fibrosis/tubular atrophy on protocol biopsies

Loss of GFR Proteinuria Overt rejection

Dialysis dependence Retransplantation

FIGURE 28.1

Progression of allograft injury. Patients are thought to have genetic signatures that indicate a higher risk kidney injury prior to histologic or clinical evidence being apparent. Interventions prior to histologic or clinical alterations may prevent progression to allograft failure.

specificity with detection of injury in clinically stable patients (i.e., subclinical rejection (SCR)). Drug level monitoring and protocol biopsies are additional monitoring tools unique to kidney transplant patients. These modalities provide critical information needed to guide management and improve long-term outcomes. Despite clear clinical benefit, there are limitations which have led to the development of noninvasive assays with the aim of detecting subclinical injury prior to significant graft dysfunction (Fig. 28.1).

28.2.1 Drug Level Monitoring—Calcineurin Inhibitor Levels CNIs are currently the standard of care for immunosuppression in most kidney transplant recipients. Cyclosporine (CsA) and tacrolimus have dramatically improved short-term allograft outcomes since their adoption into clinical use. Under-dosing has been associated with higher rates of acute rejection and over-dosing increases the risk of electrolyte disturbances, metabolic derangements, and nephrotoxicity. Given the narrow therapeutic index, drug monitoring is vital with CNIs. The pharmacokinetics of CNI can be inconsistent and dependent on a number of variables such as presence of meals, variability of gastrointestinal motility (i.e., diarrhea), concurrent usage of medications affecting CYP450 3A4 activity, and decreased renal function.12 15 Due to inter- and intrapatient variability, drug monitoring allows for individualization of drug dosing in order to ensure efficacy and limit toxicity. Although there have not been randomized control trials comparing the outcomes for monitoring and not monitoring, it is generally accepted that drug monitoring is considered favorable.

28.2.2 Cyclosporine Monitoring Cyclosporine is a cyclic polypeptide that is highly insoluble in water, requiring suspension in emulsions for administration. Sandimmune, the first formulation of cyclosporine, had unpredictable bioavailability resulting in the development of Neoral.12,16 Neoral has been shown to be comparatively safe and tolerable in renal transplant patients, the majority of CsA dosing is based on pharmacokinetic trials using the improved formulation.17,18 Drug exposure of cyclosporine when measured after 4 hours (AUC0 4) has been shown to correlate well with the AUC over the entire 12-hour dosing interval.19 This is consistent with the thought that the highest variability in exposure occurs immediately after administration during the absorptive phase.20 Lower levels of AUC0 4 in

I. KIDNEY TRANSPLANTATION

405

28.2 CURRENT STANDARDS OF IMMUNE MONITORING

TABLE 28.1

Target Cyclosporine Levels

Time (months)

Target C0 (ng/mL)

Target C2 (ng/mL)

0 3

200 300

1150 1550

3 6

125 250

800 1200

6 12

125 175

750 900

. 12

100 175

. 600

Target levels in the lower end of the range with use of thymoglobulin induction. C0, trough level; C2, level at 2 h after administration.

TABLE 28.2 Differences in Toxicity Profile Between Cyclosporine and Tacrolimus Adverse effect

Cyclosporine

Tacrolimus

New onset diabetes mellitus

m

mm

Dyslipidemia

m

Hypertension

mm

Osteopenia

m

Renal insufficiency

m

m

Neurotoxicity

m

mm

m

m

Diarrhea, nausea/vomiting Hyperkalemia

m

m

Hypomagnesemia

m

m

Hyperuricemia, gout

m

Gingival hyperplasia

m

Hirsutism

m

Malignancy

m

m

renal transplant patients have been shown to increase the risk of acute rejection. Conversely, the same study reported higher levels that are associated with nephrotoxicity and metabolic derangements. Compared to trough levels (C0), levels at 2 hours after drug administration (C2) correlate more closely to the AUC0 4 with a correlation (r2) of 0.80 versus 0.13 for C0.21 As such, it has been recommended that C2 be used for drug monitoring; however, most centers tend to use C0 due to ease of implementation in the outpatient setting. Furthermore, it has not been shown that using C2 or AUC0 4 over C0 offers a benefit in allograft outcomes. The risk of rejection is highest during the first 3 months following transplantation. Attaining goal cyclosporine levels immediately posttransplant has been shown to remarkably attenuate this risk, as demonstrated in a number of studies.22,23 As time passes, the risk of rejection decreases, so CNI target levels and doses should be lowered accordingly. The use of antibody therapy with induction agents have allowed for lower CNI goal levels reducing the risk of CNI toxicity.24,25 Goal cyclosporine levels are shown in Table 28.1.

28.2.3 Tacrolimus Monitoring Tacrolimus was first approved for use in transplant patients in 1994. Like cyclosporine, it is insoluble in water and has similar immunosuppressive benefits and monitoring characteristics. Although their mechanism of action affects the same pathway, their toxicity profiles are slightly different (Table 28.2). Trough levels are generally followed for the same reasons as those for cyclosporine—although C0 levels has been shown to better correlate with the AUC, with a correlation of up to 0.86.26 Higher levels should be targeted early after transplantation and similarly be lowered as time passes. As with cyclosporine, induction agents have also allowed for lower target goals.27 Goal tacrolimus trough levels are shown in Table 28.3.

I. KIDNEY TRANSPLANTATION

406

28. IMMUNE MONITORING IN KIDNEY TRANSPLANTATION

TABLE 28.3 Target Tacrolimus Levels Time (months)

Target C0 (ng/mL)

0 3

8 12

3 12

6 10

. 12

4 8

Target levels in the lower end of the range with use of thymoglobulin induction. C0 trough level.

Tacrolimus has compared favorably to cyclosporine in clinical outcomes leading to its preferential use by the majority of transplant centers.28 30 A randomized trial comprising of renal transplant patients receiving either tacrolimus or cyclosporine revealed a significantly higher occurrence (42.8% vs 25.9%; P , .001) of a composite endpoint consisting of biopsy-proven acute rejection, graft loss, and patient death in a 24-month period.31 The Efficacy Limiting Toxicity Elimination (ELITE-Symphony) study found, at 1 year, that patients receiving low dose tacrolimus compared to standard dose cyclosporine, low dose cyclosporine and sirolimus, had better eGFR, lower rates of biopsy-proven acute rejection, superior graft survival, and higher rates of posttransplant diabetes mellitus.32

28.3 PROTOCOL BIOPSIES Following serial serum creatinine measurements is the most widely implemented method of monitoring allograft function; however, it has limited sensitivity in detecting early rejection or other pathologic processes occurring in the allograft. Histologic evidence of rejection based on Banff criteria in kidneys with stable serum creatinine has been seen with the implementation of protocol biopsies.33,34 The main purpose of performing surveillance biopsies is to detect the presence of early rejection or chronic allograft nephropathy (CAN), two processes mediated by immune response. It is thought that early detection of these entities will allow for more timely therapies and improve allograft outcomes. The incidence of SCR in the literature has been reported in 3% 43% of allografts.35 39 Variability has been attributed to a number of factors including induction agent, quality of donor organ, delayed graft function (DGF) and immunosuppression regimen. This detection has proved critical, as SCR is predictive of poorer early and late outcomes. SCR at 3 months has been shown to significantly increase the incidence of CAN at 12 months, and early treatment improves rates of acute rejection episodes, eGFR decline, and interstitial fibrosis.37 Untreated SCR was found to correlate with higher degrees of interstitial fibrosis and tubular atrophy in subsequent biopsies. Likewise, evidence of CAN on protocol biopsies has been predictive of decreased renal function and allograft loss.40 43 High-risk patients may benefit the most from protocol biopsies. For instance, sensitized patients are at much greater risk of rejection than unsensitized patients. A study reviewing 116 surveillance biopsies performed at 1, 3, 6, and 12 months from 50 positive crossmatch patients revealed that 39.7% of biopsies performed at 1 month had subclinical cellular rejection.44 Biopsies at every time interval had a disconcerting 20% 30% positive staining for C4d which is concerning for AMR. DGF is another strong predictor of poor allograft function and the clinical diagnosis of rejection in this scenario is extremely challenging. In a study of 83 patients, 33 had DGF, of whom 18% had SCR on protocol biopsy (7 days posttransplant) compared to 4% of early functioning allografts.45 A large percentage of allograft failure in patients with DGF has been attributed to acute rejection.46 Transplant allograft biopsies are generally considered safe, and numerous studies have reported that major complications (i.e., hemorrhage, peritonitis, and graft loss) occur less than 1% of the time. Most of these procedures are done in an outpatient setting with high compliance from patients.47,48

28.4 DISADVANTAGES OF CURRENT STANDARDS The monitoring of serum creatinine and proteinuria lacks specificity and sensitivity. Once abnormalities are noted, there has already been significant injury, and treatment has been delayed. Immune monitoring using drug levels as a surrogate is challenging as plasma levels do not necessarily correlate well with AUC and, given CNI’s

I. KIDNEY TRANSPLANTATION

28.5 NONINVASIVE MONITORING

407

wide distribution in body, it may not accurately reflect pharmacodynamics.49 Moreover, pharmacokinetic monitoring does not take into account genetic variations or polymorphisms that can have a drastic influence on pharmacodynamics.50 The pharmacodynamic approach of monitoring of nuclear factor of activated T cell gene expression with CNI administration has shown promising results but may be difficult to interpret in the setting infection and has not been validated.51 High variability of SCR and CAN incidence on protocol biopsies in the literature has been attributed to differences in methodology. The dissimilarities in induction agents, immunosuppression regimen, HLA mismatches, and DGF incidence in between studies make generalizability and comparison difficult. More recent studies with modern immunosuppression have shown much-improved rates of SCR.39 The diagnosis of SCR and CAN is challenging to make, as the Banff criteria were originally intended for use in situations with high suspicion for rejection and clearer histologic findings.52 Frequently there are borderline changes that make distinguishing between SCR, CAN, or normal variation problematic. Certain histologic features of CAN are also seen with increasing age, so potentially many donor derived changes could be misclassified as CAN. Biopsies are ultimately operator dependent and subject to sampling error, leading to additional misdiagnosis. This variability can lead to both under- and over-treatment. Although biopsies are generally considered safe, they still expose patients, the majority of whom do not have rejection, to invasive procedures, discomfort, and unwarranted anxiety while adding to the costs of insurance payers.

28.5 NONINVASIVE MONITORING The limitations in current standards of immune monitoring have prompted the transplant community to develop new methods with higher sensitivity, specificity, and reproducibility. Monitoring has classically been separated into humoral and cellular (Fig. 28.2). Improved understanding of the immunity coupled with the advancement and increasing availability of molecular techniques have provided for novel methods of immune monitoring.

28.5.1 Humoral Immunity AMR, caused by circulating anti-HLA antibodies, can be categorized as hyperacute, acute, or chronic. Rates of hyperacute rejection have dramatically improved due to crossmatching, whereas acute and chronic rejections remain an obstacle to long-term allograft survival. Monitoring of donor-specific antibodies (DSA) remains the cornerstone of humoral immunity assessment; however novel assays are emerging with the promise to better characterize and understand B cells. Allograft Rejection Monitoring

Humoral

- DSA - B cell ELISPOT - C4d, C3d, C1q

Cellular - Urine proteomics - T cell cytokine assay - ImmuKnow - IFN-γ (T cell) ELISPOT - CRM - KSORT - CD8+ TEMRA

FIGURE 28.2 Strategies for noninvasive monitoring. DSA, donor-specific antibodies; ELISPOT, enzyme-linked immunospot; IFN-γ, interferon-gamma; CRM, common response module; KSORT, kidney solid organ response test.

I. KIDNEY TRANSPLANTATION

408

28. IMMUNE MONITORING IN KIDNEY TRANSPLANTATION

28.5.1.1 Anti-HLA: Donor Specific Antibodies The existence of antibodies to donor HLA antigens plays an important role in rejection and allograft failure. Preexisting antibodies have been known to result in poor outcomes since the 1960s when cytotoxic crossmatch positivity was first shown to result in immediate allograft failure.53 The crossmatch has since been a large determinant of organ allocation. Over the years the crossmatch has been refined with flow cytometry and ELISA for the detection of anti-HLA antibodies and more specifically, DSA.54 57 More recently, the Luminex assay has become the recognized standard of detecting DSA.58 60 It has become ubiquitous in transplant allocation, rejection risk assessment, AMR diagnosis, and immune monitoring. Preexisting DSA results in significantly poorer outcomes. A large observational study of 402 deceased donor kidney recipients revealed that 8-year graft survival was significantly worse in patients with preexisting DSA (61%) compared to nonsensitized (84%) and sensitized patients without DSA (93%). The peak DSA strength measured in mean fluorescence intensity correlated strongly with antibody-mediated rejection.61 Additional studies have reaffirmed preexisting DSA results in poorer allograft outcomes and higher rates of AMR.62 64 Better characterization of anti-HLA antibodies and understanding of AMR have led to the development of desensitization protocols. Those protocols aim to overcome the barrier of preexisting DSA and have been gaining wider acceptance. Desensitized patients have a survival benefit when compared to matched patients on remaining on the transplant waitlist.65 Despite acceptable short-term graft survival rates, AMR and SR occur more frequently in desensitized patients.66,67 Formation of de novo DSA following transplant is not uncommon, occurring in up to 20% in the first 5 years after transplant. Like preexisting DSA, de novo DSA portends higher rates of AMR and allograft failure.68,69 Additionally, DSA with the ability to activate the complement cascade is independently associated with graft failure. C4d binding in AMR has been well described but emerging evidence has shown complement split products, C1q and C3d, are predictive of AMR, graft loss, and transplant glomerulopathy.70 74 The presence of C1qpositive de novo DSA was associated with transplant glomerulopathy (sensitivity 81%, specificity 95%) and graft loss (sensitivity 79%, specificity 95%).73 Likewise, C3d-binding DSA at the time of AMR diagnosis was independently associated with allograft loss (hazard ratio 2.8; P 5 .03).74 It is clear complement activation plays a critical role in AMR and future studies are warranted to better refine immune profiling before and after transplantation. Current recommendations of DSA protocol monitoring are listed below: 1. High-risk patients (i.e., desensitized or DSA positive/XM negative) should be monitored by measurement of DSA and protocol biopsies in the first 3 months after transplantation. 2. Intermediate-risk patients (i.e., history of DSA but currently negative) should be monitored for DSA within the first month. If DSA is present, a biopsy should be performed. 3. Low-risk patients (i.e., nonsensitized first transplantation) should be screened for DSA at least once 3 12 months after transplantation. DSA monitoring should be considered with graft dysfunction, immunosuppression change or nonadherence, or suspicion of AMR. If DSA is detected, a biopsy should be performed and subsequent treatment should be administered based on the biopsy results. Early detection and reduction of DSA has led to improved outcomes so therapy should not be delayed.75 Additional work will be needed to understand the factors that contribute to de novo formation of DSA and long-term outcomes with treatment of DSA positivity without signs of rejection. 28.5.1.2 B Cell Anti-HLA Enzyme-Linked ImmunoSpot (ELISPOT) It is suggested an anamnestic response by memory B cells to antigens can drive acute rejection.76 Circulating levels of HLA antibodies in sensitized patients could be lower than the threshold of detection, resulting in a negative pretransplant screen. Upon reexposure to donor-derived antigens after transplant, memory B cells have the capability of producing antibodies, resulting in accelerated AMR.77 Memory B cells can also promote T cell mediated rejection by secretion of inflammatory cytokines and functioning as antigen presenting cells. Detection of HLA specific B cells from peripheral blood has been achieved with the implementation of HLA tetramer staining.78,79 ELISPOT has recently emerged as a modality to detect and quantify HLA specific B cells. Early assays have encouraging results for detecting both class I and II HLA specific B cells.80,81 Both class I and II donor specific memory B cells have been identified in sensitized patients and elevated donor-specific memory B cell responses were observed during AMR and prior to transplantation, independent of DSA levels. Higher levels were seen in more severe cases of AMR.82 These assays have not been implemented in the clinical setting but offer utility in improved risk stratification, understanding tolerance, and guidance of immunosuppression.

I. KIDNEY TRANSPLANTATION

409

28.5 NONINVASIVE MONITORING

28.5.2 Cellular Immunity Cellular rejection involves the alloactivation of antigen specific T cell lymphocytes. These helper T cells initiate a cascade of events resulting in the activation of cytotoxic T cells and, eventually, graft injury. Numerous modalities of cellular monitoring have resulted in improved detection of acute rejection. 28.5.2.1 Urine Transcriptomics and Proteomics Urine offers several advantages in clinical transcriptomics and proteomics. It can be easily obtained in large quantities without any invasive procedures. Proteins and peptides are also relatively stable in urine making processing and storage more reliable. A large body of literature has accumulated utilizing mass spectrometry, ELISA, multiplex beads and RT-PCR for the detection of numerous novel urinary biomarkers for allograft rejection (Table 28.4). The various biomarkers have been reviewed thoroughly elsewhere.100 Here we will focus on the recently reported studies from the Clinical Trials in Organ Transplantation (CTOT) consortium. CTOT-01 protocol was a prospective, multicenter observational study of noninvasive biomarkers in primary kidney allograft recipients.101 The study’s goal was to validate and determine the clinical utility of a panel of biomarkers in a cohort of 280 adult and pediatric patients. Mass spectrometry and ELISA were used to evaluate urinary markers that have been previously reported to be elevated in acute rejection (CCR1, CCR5, CXCR3, CCL5, CXCL9, CXCL10, IL-8, perforin and granzyme B). The major findings were urine granzyme B and CXCL9 were significantly elevated in patients with biopsy-proven rejection; however, their positive predictive values of 61% 67% were disappointing. In contrast, low levels of CXCL9 had a negative predictive value of 92% which may potentially be useful to rule out rejection. A low level of CXCL9 at 6 months was also associated with stable renal function and lower rates of AR. CTOT-17 will report on the much-anticipated 5-year outcomes of the CTOT-01 cohort. CTOT-04 protocol was a prospective, observational study of urine mRNA as potential markers for acute rejection in a cohort of 485 adult and pediatric patients.102 The urine mRNA of interest were CD3ε, perforin, TABLE 28.4

Urine Biomarkers Characteristics for Subclinical and Clinical Rejection Subclinical rejection

Biomarker 83 86

Perforin

Assay

Sensitivity (%)

Specificity (%)

Sensitivity (%)

Specificity (%)

Prognostic role

RT-PCR

80

90

55 100

79 95

87

RT-PCR

80

100

80

100

83 86

RT-PCR

60 88

77 92

RT-PCR

76 92

64 90

Predictive of graft function 6 months after AR

RT-PCR

100

100

Predictive of rejection reversal

RT-PCR

84 100

96 100

79.5 93

80 93.5

65 89

72 97

Level at 1 month predictive of graft function at 6 months

82

76.5

Predictive of steroid responsiveness

Granzyme A Granzyme B Serpin B9

Acute rejection

84,85,87

FOXP384,88 89,90

TIM-3

86,91 94

ELISA, multiplex beads

CXCL9

CXCL1092,93,95

97

86

64

ELISA, multiplex beads

CXCL1193

ELISA, multiplex beads

Fractalkine86

ELISA

SRM urine panel (35 peptides)98

SRM

uCRM score (11 gene panel)99

RT-PCR

45

87

AUC of 93% for acute rejection 88

94.1

RT-PCR, reverse transcriptase polymerase chain reaction; ELISA, enzyme-linked immunosorbent assay; SRM, Selective reaction monitoring; uCRM, urine common rejection module. Source: Adapted from Ho J., Wiebe C., Gibson I.W., Rush D.N., Nickerson P.W. Immune monitoring of kidney allografts. Am J Kidney Dis. 2012;60(4):629 640.

I. KIDNEY TRANSPLANTATION

410

28. IMMUNE MONITORING IN KIDNEY TRANSPLANTATION

granzyme B, proteinase inhibitor 9, CD103, CXCL10, CXCR3, TGF-β mRNA, and 18S rRNA. From the analysis, urinary CD3ε, CXCL10, perforin, and granzyme B mRNA were found to be elevated in acute cellular rejection (ACR). A signature three-gene set consisting of CD3ε mRNA, CXCL10 mRNA, and 18S rRNA was able to distinguish ACR with AUC 0.85 (P , .001). The gene set was externally validated (AUC 0.5; P , .001) in a cohort of 64 patients originally in the CTOT-01 study. It was also able to differentiate ACR from acute AMR and borderline rejection (AUC 0.78; P , .001). Both studies were limited by the yield of samples passing quality control. This is an inherent problem with extracting RNA from urine and has been an obstacle to clinical adoption. Prospective, interventional trials are needed to determine whether urinary proteomics monitoring protocols will lead to improved long-term allograft outcomes. 28.5.2.2 T Cell Activation Cytokine Assay Alloactivated T cells mediate cellular rejection with release of cytokines. In an observational, cross-sectional study of 64 transplant patients, blood was assayed for a panel of 21 cytokines secreted from peripheral blood monocytes cells.103 From a training cohort of acute rejecters, a panel of 6 cytokines (IL-1β, IL-6, TNF-α, IL-4, GM-CSF, and MCP-1) was found to differentiate AR. IL-6 was able to distinguish patients with acute rejection or borderline changes from patients with no rejection, with a sensitivity of 92%, and a specificity of 63%. A separate prospective study revealed elevated IL-10 and IFN-γ were elevated in acute rejection.104 IL-4, IL-6, and IL-10 were elevated in patients with chronic rejection, suggesting Th2 response may initiate or maintain chronic injury. Elevated pretransplant levels of IL-10 and IFN-γ have also been strongly associated with AR.105 The balance of proinflammatory and regulatory cytokines provides insight into global immunity and the understanding that complex interplay will be critically important in improving graft outcomes. 28.5.2.3 ImmuKnow Approved by the United States Food and Drug Administration in 2002, the ImmuKnow assay was designed to assess the global cellular immune function of transplant recipients.106 The assay measures intracellular ATP levels in stimulated CD41 T cells. Higher levels of ATP are thought to be a surrogate for CD41 activity and, thereby, net cellular immunity. The ImmuKnow assay was examined in a meta-analysis of 504 solid organ transplant recipients (kidney, heart, kidney-pancreas, liver, and small bowel) from 10 different centers.107 Data from the patients were pooled based on the clinical condition at the time of blood draw and up to 1 month prior to a clinical event. A total of 1833 ImmuKnow assays were performed. Clinical events consisted of stable (routine clinic visits), rejection (biopsyproven or medically treated due to clinical suspicion), or infection (positive culture, elevated PCR in the blood). Longitudinal samples from each patient were averaged during periods of clinical stability, whereas a sample taken during an adverse event was analyzed separately. Reduced ATP values (,225 ng/mL) were found to be associated with an increased risk of infections, and high values ( . 525 ng/mL) were found to be associated with an increased risk of acute rejection episodes. Similar results were found in a retrospective study of 42 kidney transplant patients and 25 healthy controls. A prospective, randomized in liver transplant recipients demonstrated outcomes were improved when ImmuKnow was used to guide immunosuppression.108 Most studies have used the Immuknow assay in the setting of known infection or rejection, limiting its application as a predictive tool. A single center study retrospectively analyzed 1330 assays from 583 patients.109 Assays were performed at protocol based screening at 0, 1, 6, and 12 months posttransplant, in the setting of clinical suspicion (rise in serum creatinine or infection), and testing of stable patients in the outpatient setting. Assays drawn within 90 days before a clinical event were selected for analysis. There was no association found between a single Immuknow assay and subsequent opportunistic infection or rejection within 90 days. In conjunction with other tests, the ImmuKnow assay could be useful marker for global immune status; however, given the variability of current studies, there is currently no clear consensus to its optimal utility in the clinical setting. 28.5.2.4 Interferon-Gamma ELISPOT Cytokines, as previously discussed, are major regulators of the immune response, and Interferon-Gamma (IFN-γ) has been a major focus of interest. The IFN-γ ELISPOT assay has been a powerful tool in detecting the frequency of alloantigen specific, activated or memory T cells, a marker for cellular immunity. A study of 23 kidney transplant recipients revealed that mean posttransplant IFN-γ frequencies were inversely correlated to eGFR at 6 and 12 months after transplant with a P-value of .007 and .033, respectively.110 Additional multivariate

I. KIDNEY TRANSPLANTATION

28.5 NONINVASIVE MONITORING

411

analysis revealed that low activity of donor-specific T cell alloreactivity was the most significant correlate to preserved eGFR at long-term follow-up (24 114 months).111 The use of pretransplant IFN-γ ELISPOT positivity has been shown to correlate with acute rejection and decreased renal function after transplant in various studies.112 115 In a cohort of 100 patients, positive pretransplant ELISPOT was associated with acute rejection (odds ratio 4.6, P 5 .009) independent of DGF, donor characteristics, HLA matching, African American ethnicity and dialysis vintage.116 Interestingly, the CTOT-01 study revealed patients with positive IFN-γ ELISPOT and received antithymoglobulin induction did not have reduced eGFR at 6 and 12 months following transplant.117 Similar to PRA, a panel reactive T cell (PRT) test has been developed as a screening tool. Frequency of IFN-γ spots against a panel of allogeneic cells provides a measure of patient sensitization. In a study of 30 subjects, subjects with a pretransplant positive IFN-γ ELISPOT to .75% of cell lines (i.e., PRT .75%) had a 54% incidence of acute rejection compared to an incidence of 5% with a lesser PRT score.118 In addition to acute rejection, positive IFN-γ ELISPOT ( . 25/300,000) cells are statistically higher in patients with CAN compared to control. In a study of 45 renal allograft recipients, 50% of subjects with CAN had a positive ELISPOT compared to 28.6% in control subjects—suggesting T cell mediated injury plays a role in CAN.119 There has been some conflicting data with ELISPOT suggesting it may be dependent on center specific protocols (i.e., induction agent, HLA matching, etc.). Regardless, larger trials will be needed to determine a reference range for positive results, validate the PRT, and, ultimately, guide immunosuppression management. 28.5.2.5 Common Rejection Module A microarray meta-analysis of eight independent transplants datasets, with 236 graft biopsy samples from four organs, identified a common immune response module of 11 genes (BASP1, CD6, CXCL10, CXCL9, INPP5D, ISG20, LCK, NKG7, PSMB9, RUNX3, TAP1) that distinguished acute rejection across different engrafted tissues.120 The common rejection module (CRM) genes correlated with degree of graft injury and predicted graft injury in two independent cohorts. Treatment of mouse HLA-mismatched cardiac transplants with atorvastatin and dasatinib via drug repositioning decreased CRM gene expression and cellular infiltrate during AR. A follow-up study evaluated tissue qPCR expression of CRM in 146 independent renal allografts from 122 unique patients with and without AR.121 A tissue CRM (tCRM) score was calculated by using the geometric mean of the fold changes of the respective genes. A tCRM score threshold of .2.24 was able to accurately distinguish AR from no-AR with high sensitivity (82.7%) and specificity (82.5%). When applied to an independent cohort, a PPV of 82.4% was obtained with the same threshold. A modified tCRM score (modeled 7 of 11 genes) at 6 months was able to predict interstitial fibrosis and tubular atrophy at 24 months (P 5 .037). In the urine, the CRM score (uCRM) was robust in distinguishing acute rejection (AUC 5 0.966; 95% CI 0.911 1), and was further validated in 87 independent serial samples (AUC 5 0.95; 95% CI 0.914 1).99 The tCRM and uCRM scores may provide a companion diagnostic for differentiating patients into high and low immune risk, for stratification into different investigative treatment arms, with an increased margin for patient and graft safety; however, validation will be needed against larger cohorts. These genes were derived from biopsy tissues but have the potential to be applied to serum making it noninvasive. 28.5.2.6 Kidney Solid Organ Response Test The Assessment of Acute Rejection in Renal Transplantation study was conducted across eight transplant centers and included both pediatric and adult subjects on diverse immunosuppression.122 Gene expression was measured by quantitative real-time PCR on peripheral blood samples to develop this noninvasive assay. From a training cohort of 143 samples, the Kidney Solid Organ Response Test (KSORT)—a 17-gene set—was established as discriminatory markers for acute rejection. KSORT was validated in 124 independent samples with an AUC of 0.95 and was able to robustly detect acute rejection in peripheral blood samples accurately (AUC 0.93) independent of age and time posttransplantation. KSORT was also able to predict AR in greater than 60% of samples up to 3 months prior to transplant. KSORT is limited by its reliance on histologic readings as the reference for its performance and inability to delineate T-cell and AMR. The study design was limited by the heterogeneity of the samples and lack of serially collected samples with validating biopsies. Despite these limitations, KSORT offers a promising method to detect and predict acute rejection and is being studied in a prospective, randomized, double blinded clinical trial.

I. KIDNEY TRANSPLANTATION

412 TABLE 28.5

28. IMMUNE MONITORING IN KIDNEY TRANSPLANTATION

Summary of Noninvasive Immune Monitoring Tests for Acute Rejection

Test

Analyte

DSA Monitoring

Donor specific anti-HLA antibodies

Sample source Blood

Assay

Type of rejection detected

Flow cytometry, ELISA, Luminex

Humoral Circulating DSA promote antibody-mediated rejection. Higher titers increase risk of rejection.

Biological basis of test

Performance at acute rejection detection Peak ELISA HLA-DSA PPV 41.3 Sensitivity 59.4% Specificity 92.7% Peak Luminex HLA-DSA PPV 34.9 Sensitivity 90.6% Specificity 85.4%

Current Luminex HLADSA PPV 31.6 Sensitivity 75.0% Specificity 86.2% Humoral Amnestic response by donor Donor specific memory B specific memory T cells when cell frequencies of .0.35 reexposed to donor derived predicted the presence of antigens. endarteritis in patients with AMRR (AUC 0.89)

B cell ElISPOT

Donor specific memory B cells

Blood

ELISPOT

T cell Alloactivation cytokine assay

Cytokines (IL-1β, IL-6, TNFα, IL-4, GM-CSF, and monocyte chemoattractant protein-1)

Blood

Flow cytometry, Luminex

Cellular

Cytokines are released from alloactivated T cells and mediate an inflammatory response.

tCRM (common rejection module)

11 Genes (BASP1, CD6, CXCL10, CXCL9, INPP5D, ISG20, LCK, NKG7, PSMB9, RUNX3, and TAP1)

Biopsy tissue, potential use in blood

qPCR

Cellular

Genes that were found to be significantly overexpressed (P , .0005) during acute rejection, irrespective of the transplanted organ.

Immuknow

ATP levels in CD4 T cells

Blood

ELISPOT

Cellular

Higher concentrations of ATP signify greater CD4 T cell activity and an overall increased global immune response.

IFN-γ (T cell) ELISPOT

IFN-γ

Blood

ELISPOT

Cellular

kSORT

Blood 17 Genes (FLAR, DUSP1, IFNGR1, ITGAX, MAPK9, NAMPT, NKTR, PSEN1, RNF130, RYBP, CEACAM4, EPOR, GZMK, RARA, RHEB, RXRA, SLC25A37)

qPCR

Cellular

Terminally differentiated CD81 TEMRA cells

CD81 TEMRA cells

Whole blood staining

Cellular

Blood

IL-6—AUC 0.79 TNF-α—AUC 0.86 MCP-1—AUC 0.73 IL-1β—AUC 0.81 GM-CSF—AUC 0.73 IL-4—AUC 0.76 Sensitivity—82.7% Specificity—82.5% PPV—82.4% (tCRM threshold of 2.24)

Detection limit of ATP 1 ng/mL For concentration .525 ng/mL Sensitivity—23.4% Specificity—80.9% IFN-γ is a cytokine marker Pretrasnplant cutoff level for T-cell activity. It activates of 12 spots per 200,000 macrophages and class II PBLs major histocompatibility Sensitivity—81.8% complex molecule expression. Specificity—64.7% NPV—89% PPV—46% Combination of significant Sensitivity—83% 92.3% genes in acute rejection Specificity—90.6% 99% identified by whole genome microarray analysis of biopsy and paired blood samples. Higher frequency of CD81 TEMRA cells decreases the diversity of T cells yielding lower frequencies of alloreactive T cells. TEMRA cells may also have immunosuppressive role.

Hazard ratio 0.96 for every percent increase in CD81 TEMRA cells TEMRA Hazard ratio 0.66 for every increase in tertile of CD81 TEMRA cells

ELISPOT, enzyme-linked immunospot; ELISA, enzyme-linked immunosorbent assay; qPCR, quantitative polymerase chain reaction; PPV, positive predictive value; NPV, negative predictive value.

I. KIDNEY TRANSPLANTATION

REFERENCES

413

28.5.2.7 Terminally Differentiated CD81 TEMRA Cells Various types of T cells have been shown to contribute to transplant tolerance. These include the CD41CD251 regulatory T cells (Tregs) that express the transcription factor FOXP3, IL-10-producing Tr1 cells, CD81282 T cells, and anergic T cells. Progressive loss of renal function is associated with profound dysregulation of the T-cell system which may result in overall depressed T cell immunity and therefore, decreased risk of acute rejection.123 Patients with acute rejection have more naı¨ve T cells and lower levels of T cell dysregulation and differentiation.124 The percentage of CD81 TEMRA cells was shown to have a hazard ratio of 0.96 (P 5 .006) for rejection with every percent increase in CD81 TEMRA cells. It is hypothesized that a larger number of CD81 TEMRA cells decreases the diversity of T cells yielding lower frequencies of alloreactive T cells.125 CD81 TEMRA cells have also been implicated with suppressive functions as their presence may play a role in immunosenescence and decreased vaccine response.126 In contrast to short-term benefits, the accumulation of highly differentiate T cells has been implicated as a risk factor for long-term graft dysfunction.127 Larger prospective studies will be required to elucidate the role of TEMRA cells in allograft function. 28.5.2.8 Kidney Spontaneous Operational Tolerance Test Operation tolerance sporadically occurs in transplant patients allowing for total withdrawal of immunosuppression without any harm to the graft. Transcriptional profiling has allowed for better understanding of this unique state of immune quiescence. In a single study of 571 unique peripheral blood samples from 348 HLAmismatched renal transplant recipients and 101 nontransplant controls, microarray analysis was used to discover 141 genes differently expressed in operationally tolerant patients. Among those peripheral blood genes, a minimal set of 21 was able to accurately discriminate between tolerance and chronic rejection.128 High-throughput microfluidic qPCR for the 21 genes in a second independent sample revealed a set of three genes (KLF6, BNC2, and CYP1B1) was able to classify the tolerant samples with 84.6% sensitivity, 90.2% specificity, and an AUC of 0.95 (95% CI 0.97 0.92). The study design was limited due to small sample volumes, but Kidney Spontaneous Operational Tolerance Test (KSPOT) offers a potential means to monitor for graft accommodation and limit morbidity from immunosuppression.

28.6 CONCLUSION Current standards of immune monitoring have improved short-term outcomes, but rejection remains a significant barrier to long-term allograft survival. Therapeutic drug monitoring, serial serum creatinine measurements, and protocol biopsies lack the refinement and practicality needed to risk-stratify patients, guide immunosuppression therapy, and follow treatment responses. Novel modalities in immune monitoring offer the possibility of noninvasive prediction and detection of rejection, allograft survival, and tolerance. These new assays offer promising results thus far but need prospective, longitudinal studies in order to make the final transition from bench to bedside (Table 28.5).

References 1. Meier-Kriesche HU, Schold JD, Srinivas TR, Kaplan B. Lack of improvement in renal allograft survival despite a marked decrease in acute rejection rates over the most recent era. Am J Transplant 2004;4(3):378 83. 2. Lamb KE, Lodhi S, Meier-Kriesche HU. Long-term renal allograft survival in the United States: a critical reappraisal. Am J Transplant 2011;11(3):450 62. 3. Israni AK, Salkowski N, Gustafson S, et al. New national allocation policy for deceased donor kidneys in the United States and possible effect on patient outcomes. J Am Soc Nephrol 2014;25(8):1842 8. 4. Marfo K, Lu A, Ling M, Akalin E. Desensitization protocols and their outcome. Clin J Am Soc Nephrol 2011;6(4):922 36. 5. El-Zoghby ZM, Stegall MD, Lager DJ, et al. Identifying specific causes of kidney allograft loss. Am J Transplant 2009;9(3):527 35. 6. Naesens M, Kuypers DR, Sarwal M. Calcineurin inhibitor nephrotoxicity. Clin J Am Soc Nephrol 2009;4(2):481 508. 7. Vasudev B, Hariharan S, Hussain SA, Zhu YR, Bresnahan BA, Cohen EP. BK virus nephritis: risk factors, timing, and outcome in renal transplant recipients. Kidney Int 2005;68(4):1834 9. 8. Colvin RB. Antibody-mediated renal allograft rejection: diagnosis and pathogenesis. J Am Soc Nephrol 2007;18(4):1046 56. 9. Halloran PF, Reeve JP, Pereira AB, Hidalgo LG, Famulski KS. Antibody-mediated rejection, T cell-mediated rejection, and the injuryrepair response: new insights from the Genome Canada studies of kidney transplant biopsies. Kidney Int 2014;85(2):258 64. 10. Sarwal M, Chua MS, Kambham N, et al. Molecular heterogeneity in acute renal allograft rejection identified by DNA microarray profiling. N Engl J Med 2003;349(2):125 38.

I. KIDNEY TRANSPLANTATION

414

28. IMMUNE MONITORING IN KIDNEY TRANSPLANTATION

11. Karczewski M, Karczewski J, Poniedzialek B, Wiktorowicz K, Smietanska M, Glyda M. Distinct cytokine patterns in different states of kidney allograft function. Transplant Proc 2009;41(10):4147 9. 12. Kahan BD, Welsh M, Schoenberg L, et al. Variable oral absorption of cyclosporine. A biopharmaceutical risk factor for chronic renal allograft rejection. Transplantation 1996;62(5):599 606. 13. Bekersky I, Dressler D, Mekki Q. Effect of time of meal consumption on bioavailability of a single oral 5 mg tacrolimus dose. J Clin Pharmacol 2004;41(3):289 97. 14. Kelly PA, Wang H, Napoli KL, Kahan BD, Strobel HW. Metabolism of cyclosporine by cytochromes P450 and 3A4. Eur J Drug Metab Pharmacokinet 1999;24(4):321 8. 15. Hu RH, Lee PH, Tsai MK. Clinical influencing factors for daily dose, trough level, and relative clearance of tacrolimus in renal transplant recipients. Transplant Proc 2000;32(7):1689 92. 16. Kovarik JM, Mueller EA, van Bree JB, et al. Cyclosporine pharmacokinetics and variability from a microemulsion formulation: a multicenter investigation in kidney transplant patients. Transplantation 1994;58(6):658 63. 17. Keown P, Niese D. Cyclosporine microemulsion increases drug exposure and reduces acute rejection without incremental toxicity in de novo renal transplantation. International Sandimmune Neoral Study Group. Kidney Int 1998;54(3):938 44. 18. Keown P, Landsberg D, Halloran P, et al. A randomized, prospective multicenter pharmacoepidemiologic study of cyclosporine microemulsion in stable renal graft recipients. Report of the Canadian Neoral Renal Transplantation Study Group. Transplantation 1996;62:1744 52. 19. Barone G, Chang CT, Choc MG, et al. The pharmacokinetics of a microemulsion formulation of cyclosporine in primary renal allograft recipients. The Neoral Study Group. Transplantation 1996;61:875 80. 20. Mahalati K, Belitsky P, Sketris I, West K, Panek R. Neoral monitoring by simplified sparse sampling area under the concentration-time curve: its relationship to acute rejection and cyclosporine nephrotoxicity early after kidney transplantation. Transplantation 1999;68:55 62. 21. International Neoral Renal Transplantation Study Group. Cyclosporine microemulsion (Neoral) absorption profiling and sparse-sample predictors during the first 3 months after renal transplantation. Am J Transplant 2002;2:148 56. 22. Clase CM, Mahalati K, Kiberd BA, et al. Adequate early cyclosporin exposure is critical to prevent renal allograft rejection: patients monitored by absorption profiling. Am J Transplant 2002;2:789 95. 23. Mahalati K, Belitsky P, West K, et al. Approaching the therapeutic window for cyclosporine in kidney transplantation: a prospective study. J Am Soc Nephrol 2001;12(4):828 33. 24. Balbontin F, Kiberd B, Fraser A, Kiberd M, Lawen J. Basiliximab lowers the cyclosporine therapeutic threshold in the early post-kidney transplant period. Clin Transplant 2005;19:225 9. 25. Hardinger KL, Schnitzler MA, Koch MJ, et al. Cyclosporine minimization and cost reductionin renal transplant recipients receiving a C2monitored, cyclosporine-based quadruple immunosuppressive regimen. Transplantation 2004;78:1198 203. 26. Belitsky P, Lawen J. Tacrolimus monitoring by simplified sparse sampling under the concentration time curve. Transplant Proc 2003;35:2445 8. 27. Ekberg H, Bernasconi C, Tedesco-Silva H, et al. Calcineurin inhibitor minimization in the Symphony study: observational results 3 years after transplantation. Am J Transplant 2009;9(8):1876 85. 28. Margreiter R. Efficacy and safety of tacrolimus compared with ciclosporin microemulsion in renal transplantation: a randomised multicentre study. Lancet 2002;359:741 6. 29. OPTN/SRTR 2013 Annual Data Report: Kidney. Am J Transplant 2015; (Suppl 2):1 34. 30. Webster AC, Woodroffe RC, Taylor RS, Chapman JR, Craig JC. Tacrolimus versus ciclosporin as primary immunosuppression for kidney transplant recipients: meta-analysis and meta-regression of randomised trial data. BMJ 2005;331:810. 31. Kramer BK, Montagnino G, Del Castillo D, et al. Efficacy and safety of tacrolimus compared with cyclosporin A microemulsion in renal transplantation: 2 year follow-up results. Nephrol Dial Transplant 2005;20:968 73. 32. Ekberg H, Tedesco-Silva H, Demirbas A, et al. Reduced exposure to calcineurin inhibitors in renal transplantation. N Engl J Med 2007;357 (25):2562. 33. Jain S, Curwood V, Kazi J, White SA, Furness PN, Nicholson ML. Acute rejection in protocol renal transplant biopsies: institutional variations. Transplant Proc 2000;32:616. 34. Roberts ISD, Reddy S, Russell C, et al. Subclinical rejection and borderline changes in early protocol biopsy specimens after renal transplantation. Transplantation 2004;77:1194 8. 35. Rush D, Nickerson P, Gough J, et al. Beneficial effects of early subclinical rejection: a randomized study. J Am Soc Nephrol 1998;9:2129 34. 36. Jain S, Curwood V, Kazi J, White SA, Furness PN, Nicholson ML. Acute rejection in protocol renal transplant biopsies: institutional variations. Transplant Proc 2000;32:616. 37. Nankivell BJ, Fenton-Lee CA, Kuypers DRJ, et al. Effect of histological damage on long-term kidney transplant outcome. Transplantation 2001;71:515 23. 38. Roberts ISD, Reddy S, Russell C, et al. Subclinical rejection and borderline changes in early protocol biopsy specimens after renal transplantation. Transplantation 2004;77:1194 8. 39. Choi BS, Shin MJ, Shin SJ, et al. Clinical significance of an early protocol biopsy in living-donor renal transplantation: ten-year experience at a single center. Am J Transplant 2005;5:1354 60. 40. Seron D, Moreso F, Bover J, et al. Early protocol renal allograft biopsies and graft outcome. Kidney Int 1997;51:310 16. 41. Isoniemi H, Taskinen E, Hayry P. Histological chronic allograft damage index accurately predicts chronic renal allograft rejection. Transplantation 1994;58:1195 8. 42. Legendre C, Thervet E, Skhiri H, et al. Histologic features of chronic allograft nephropathy by protocol biopsies in kidney transplant recipients. Transplantation 1998;65:1506 9. 43. Fujisawa M, Ono H, Isotani S, et al. Significance of chronic transplant nephrology on early protocol biopsies for graft outcome in pediatric renal transplantation. Transplant Proc 1999;31:1687 90.

I. KIDNEY TRANSPLANTATION

REFERENCES

415

44. Kraus ES, Parekh RS, Oberai P, et al. Subclinical rejection in stable positive crossmatch kidney transplant patients: incidence and correlations. Am J Transplant 2009;9(8):1826 34. 45. Jain S, Curwood V, White SA, Furness PN, Nicholson ML. Sub-clinical acute rejection detected using protocol biopsies in patients with delayed graft function. Transpl Int 2000;13(Suppl. 1):52 5. 46. Qureshi F, Rabb H, Kasiske BL. Silent acute rejection during prolonged delayed graft function reduces allograft survival. Transplantation 2002;74:1400 4. 47. Rush D. Protocol biopsies should be part of the routine management of kidney transplant recipients. Am J Kidney Dis 2002;40:671 3. 48. Furness PN, Philpott CM, Chorbadjian MT, et al. Protocol biopsy of the stable renal transplant: a multicenter study of methods and complication rates. Transplantation 2003;76:969 73. 49. Akhlaghi F, Trull AK. Distribution of cyclosporine in organ transplant recipients. Clin Pharmacokinet 2002;41:15 637. 50. Crettol S, Venetz JP, Fontana M, et al. Influence of ABCB1 genetic polymorphisms on cyclosporine intracellular concentration in transplant recipients. Pharmacogenet Genomics 2008;18(4):307 15. 51. Steinebrunner N, Sandig C, Sommerer C, et al. Pharmacodynamic monitoring of nuclear factor of activated T cell-regulated gene expression in liver allograft recipients on immunosuppressive therapy with calcineurin inhibitors in the course of time and correlation with acute rejection episodes--a prospective study. Ann Transplant 2014;19:32 40. 52. Racusen LC, Solez K, Colvin RB, et al. The Banff 97 working classification of renal allograft pathology. Kidney Int 1999;55:713 23. 53. Patel R, Terasaki PI. Significance of the positive crossmatch test in kidney transplantation. N Engl J Med 1969;280(14):735 9. 54. Talbot D, Givan AL, Shenton BK, et al. Value of the flow cytometric crossmatch in renal transplantation. Transplant Proc 1987;19:4315 16. 55. Mu¨ller-Steinhardt M, Fricke L, Kirchner H, Hoyer J, Klu¨ter H. Monitoring of anti-HLA class I and II antibodies by flow cytometry in patients after first cadaveric kidney transplantation. Clin Transplant 2000;14(1):85 9. 56. Moore SB, Ploeger NA, DeGoey SR. HLA antibody screening: comparison of a solid phase enzyme-linked immunoassay with antiglobulin-augmented lymphocytotoxicity. Transplantation 1997;64(11):1617 20. 57. Martin L, Guignier F, Bocrie O, et al. Detection of anti-HLA antibodies with flow cytometry in needle core biopsies of renal transplants recipients with chronic allograft nephropathy. Transplantation 2005;79(10):1459 61. 58. Pei R, Lee JH, Shih NJ, Chen M, Terasaki PI. Single human leukocyte antigen flow cytometry beads for accurate identification of human leukocyte antigen antibody specificities. Transplantation 2003;75(1):43 9. 59. Yang CW, Oh EJ, Lee SB, et al. Detection of donor-specific anti-HLA class I and II antibodies using antibody monitoring system. Transplant Proc 2006;38(9):2803 6. 60. Tait BD, Su¨sal C, Gebel HM, et al. Consensus guidelines on the testing and clinical management issues associated with HLA and nonHLA antibodies in transplantation. Transplantation 2013;95(1):19 47. 61. Lefaucheur C, Loupy A, Hill GS, et al. Preexisting donor-specific HLA antibodies predict outcome in kidney transplantation. J Am Soc Nephrol 2010;21(8):1398 406. 62. Amico P, Ho¨nger G, Mayr M, Steiger J, Hopfer H, Schaub S. Clinical relevance of pretransplant donor-specific HLA antibodies detected by single-antigen flow-beads. Transplantation 2009;87(11):1681 8. 63. Caro-Oleas JL, Gonza´lez-Escribano MF, Gonza´lez-Roncero FM, et al. Clinical relevance of HLA donor-specific antibodies detected by single antigen assay in kidney transplantation. Nephrol Dial Transplant 2012;27(3):1231 8. 64. Gloor JM, Winters JL, Cornell LD, et al. Baseline donor-specific antibody levels and outcomes in positive crossmatch kidney transplantation. Am J Transplant 2010;10:582 9. 65. Montgomery RA, Lonze BE, King KE, et al. Desensitization in HLA-incompatible kidney recipients and survival. N Engl J Med 2011;365 (4):318 26. 66. Marfo K, Lu A, Ling M, Akalin E. Desensitization protocols and their outcome. Clin J Am Soc Nephrol 2011;6(4):922 36. 67. Gloor J, Stegall MD. Sensitized renal transplant recipients: current protocols and future directions. Nat Rev Nephrol 2010;6(5):297 306. 68. Wiebe C, Gibson IW, Blydt-Hansen TD, et al. Evolution and clinical pathologic correlations of de novo donor-specific HLA antibody post kidney transplant. Am J Transplant 2012;12(5):1157 67. 69. Everly MJ, Rebellato LM, Haisch CE, et al. Incidence and impact of de novo donor-specific alloantibody in primary renal allografts. Transplantation 2013;95(3):410 17. 70. Loupy A, Lefaucheur C, Vernerey D, et al. Complement-binding anti-HLA antibodies and kidney-allograft survival. N Engl J Med 2013;369(13):1215 26. 71. Yell M, Muth BL, Kaufman DB, Djamali A. Ellis Tm. C1q binding activity of de novo donor-specific HLA antibodies in renal transplant recipients with and without antibody-mediated rejection. Transplantation 2015;99(6):1151 5. 72. Thammanichanond D, Mongkolsuk T, Rattanasiri S, et al. Significance of C1q-fixing donor-specific antibodies after kidney transplantation. Transplant Proc 2014;46(2):368 71. 73. Yabu JM, Higgins JP, Chen G, Sequeira F, Busque S, Tyan DB. C1q-fixing human leukocyte antigen antibodies are specific for predicting transplant glomerulopathy and late graft failure after kidney transplantation. Transplantation 2011;91(3):342 7. 74. Sicard A, Ducreux S, Rabeyrin M, et al. Detection of C3d-binding donor-specific anti-HLA antibodies at diagnosis of humoral rejection predicts renal graft loss. J Am Soc Nephrol 2015;26(2):457 67. 75. Everly MJ, Everly JJ, Arend LJ, et al. Reducing de novo donor-specific antibody levels during acute rejection diminishes renal allograft loss. Am J Transplant 2009;9(5):1063 71. 76. Zachary AA, Lucas DP, Montgomery RA, Leffell MS. Rituximab prevents an anamnestic response in patients with cryptic sensitization to HLA. Transplantation 2013;95(5):701 4. 77. Kurosaki T, Kometani K, Ise W. Memory B cells. Nature Rev Immunol 2015;15:149 59. 78. Mulder A, Eijsink C, Kardol MJ, et al. Identification, isolation, and culture of HLA-A2-specific B lymphocytes using MHC class I tetramers. J Immunol 2003;171(12):6599 603. 79. Zachary AA, Kopchaliiska D, Montgomery RA, Leffell MS. HLA-specific B cells: I. A method for their detection, quantification, and isolation using HLA tetramers. Transplantation 2007;83(7):982 8.

I. KIDNEY TRANSPLANTATION

416

28. IMMUNE MONITORING IN KIDNEY TRANSPLANTATION

80. Heidt S, Roelen DL, de Vaal YJ, et al. A novel ELISPOT assay to quantify HLA-specific B cells in HLA-immunized individuals. Am J Transplant 2012;12(6):1469 78. 81. Karahan GE, de Vaal YJ, Roelen DL, et al. Quantification of HLA class II-specific memory B cells in HLA-sensitized individuals. Hum Immunol 2015;76(2-3):129 36. 82. Lu´cia M, Luque S, Crespo E, et al. Preformed circulating HLA-specific memory B cells predict high risk of humoral rejection in kidney transplantation. Kidney Int 2015;88(4):874 87. 83. Li B, Hartono C, Ding R, et al. Noninvasive diagnosis of renal-allograft rejection by measurement of messenger RNA for perforin and granzyme B in urine. N Engl J Med 2001;344(13):947 54. 84. Aquino-Dias EC, Joelsons G, da Silva DM, et al. Non-invasive diagnosis of acute rejection in kidney transplants with delayed graft function. Kidney Int 2008;73(7):877 84. 85. Muthukumar T, Ding R, Dadhania D, et al. Serine proteinase inhibitor-9, an endogenous blocker of granzyme B/perforin lytic pathway, is hyperexpressed during acute rejection of renal allografts. Transplantation 2003;75(9):1565 70. 86. Peng W, Chen J, Jiang Y, et al. Urinary fractalkine is a marker of acute rejection. Kidney Int 2008;74(11):1454 60. 87. van Ham SM, Heutinck KM, Jorritsma T, et al. Urinary granzyme A mRNA is a biomarker to diagnose subclinical and acute cellular rejection in kidney transplant recipients. Kidney Int 2010;78(10):1033 40. 88. Muthukumar T, Dadhania D, Ding R, et al. Messenger RNA for FOXP3 in the urine of renal-allograft recipients. N Engl J Med 2005;353 (22):2342 51. 89. Renesto PG, Ponciano VC, Cenedeze MA, Saraiva Camara NO, Pacheco-Silva A. High expression of Tim-3 mRNAin urinary cells from kidney transplant recipients with acute rejection. Am J Transplant 2007;7(6):1661 5. 90. Manfro RC, Aquino-Dias EC, Joelsons G, Nogare AL, Carpio VN, Goncalves LF. Noninvasive Tim-3 messenger RNA evaluation in renal transplant recipients with graft dysfunction. Transplantation 2008;86(12):1869 74. 91. Schaub S, Nickerson P, Rush D, et al. Urinary CXCL9 and CXCL10 levels correlate with the extent of subclinical tubulitis. Am J Transplant 2009;9(6):1347 53. 92. Hu H, Kwun J, Aizenstein BD, Knechtle SJ. Noninvasive detection of acute and chronic injuries in human renal transplant by elevation of multiple cytokines/chemokines in urine. Transplantation 2009;87(12):1814 20. 93. Hu H, Aizenstein BD, Puchalski A, Burmania JA, Hamawy MM, Knechtle SJ. Elevation of CXCR3-binding chemokines in urine indicates acute renal-allograft dysfunction. Am J Transplant 2004;4(3):432 7. 94. Jackson JA, Kim EJ, Begley B, et al. Urinary chemokines CXCL9 and CXCL10 are noninvasive markers of renal allograft rejection and BK viral infection. Am J Transplant 2011;11(10):2228 34. 95. Tatapudi RR, Muthukumar T, Dadhania D, et al. Noninvasive detection of renal allograft inflammation by measurements of mRNA for IP-10 and CXCR3 in urine. Kidney Int 2004;65(6):2390 7. 96. Matz M, Beyer J, Wunsch D, et al. Early post-transplant urinary IP-10 expression after kidney transplantation is predictive of short- and long-term graft function. Kidney Int 2006;69(9):1683 90. 97. Ho J, Rush DN, Karpinski M, et al. Validation of urinary CXCL10 as a marker of borderline, subclinical and clinical tubulitis. Transplantation 2011;92(8):878 82. 98. Sigdel TK, Gao Y, Jintang H, et al. Mining the Human Urine Proteome for Monitoring Renal Transplant Injury. Manuscript submitted for publication. Kidney Int 2015;89:1244 52. 99. Sigdel TK et al. (2015). A non-invasive Urinary Common Rejection Module (uCRM) gene expression score enables accurate discrimination of Acute Rejection in kidney Transplant patients. Unpublished data (in submission, not yet submitted). 100. Ho J, Wiebe C, Gibson IW, Rush DN, Nickerson PW. Immune monitoring of kidney allografts. Am J Kidney Dis 2012;60(4):629 40. 101. Hricik DE, Nickerson P, Formica RN, et al. CTOT-01 consortium. Multicenter validation of urinary CXCL9 as a risk-stratifying biomarker for kidney transplant injury. Am J Transplant 2013;13(10):2634 44. 102. Suthanthiran M, Schwartz JE, Ding R, et al. Clinical trials in organ transplantation 04 (CTOT-04) study investigators. Urinary-cell mRNA profile and acute cellular rejection in kidney allografts. N Engl J Med 2013;369(1):20 31. 103. De Serres SA, Mfarrej BG, Grafals M, et al. Derivation and validation of a cytokine-based assay to screen for acute rejection in renal transplant recipients. Clin J Am Soc Nephrol 2012;7(6):1018 25. 104. Karczewski M, Karczewski J, Poniedzialek B, Wiktorowicz K, Smietanska M, Glyda M. Distinct cytokine patterns in different states of kidney allograft function. Transplant Proc 2009;41(10):4147 9. 105. Karczewski J, Karczewski M, Glyda M, Wiktorowicz K. Role of TH1/TH2 cytokines in kidney allograft rejection. Transplant Proc 2008;40 (10):3390 2. 106. 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(2):77 88. 107. 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(5):663 8. 108. Ravaioli M, Neri F, Lazzarotto T, Bertuzzo VR, et al. Immunosuppression modifications based on an immune response assay: results of a randomized, controlled trial. Transplantation 2015;99(8):1625 32. 109. Huskey J, Gralla J, Wiseman AC. Single time point immune function assay (ImmuKnowt) testing does not aid in the prediction of future opportunistic infections or acute rejection. Clin J Am Soc Nephrol 2011;6(2):423 9. 110. Na¨ther BJ, Nickel P, Bold G, Presber F, 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 kidney transplant recipients. Transpl Immunol 2006;16(3-4):232 7. 111. Bestard O, Nickel P, Cruzado JM, et al. Circulating alloreactive T cells correlate with graft function in longstanding renal transplant recipients. J Am Soc Nephrol 2008;19:1419 29.

I. KIDNEY TRANSPLANTATION

REFERENCES

417

112. Heeger PS, Greenspan NS, Kuhlenschmidt S, Dejelo C, Hricik DE, Schulak JA. Pretransplant frequency of donor-specific, IFN-gammaproducing lymphocytes is a manifestation of immunologic memory and correlates with the risk of posttransplant rejection episodes. J Immunol 1999;163:2267 75. 113. Tary-Lehmann M, Hricik DE, Justice AC, Potter NS, Heeger PS. Enzyme linked immunosorbent assay spot detection of interferongamma and interleukin 5-producing cells as a predictive marker for allograft failure. Transplantation 1998;66:219 24. 114. Augustine JJ, Siu DS, Clemente MJ, Schulak JA, Heeger PS, Hricik DE. Pre-transplant IFN-gamma ELISPOTs are associated with posttransplant renal function in African American renal transplant recipients. Am J Transplant 2005;5:1971 5. 115. Kim SH, Oh EJ, Kim MJ, Park YJ, Han K, Yang HJ. Pretransplant donor-specific interferon-gamma ELISPOT assay predicts acute rejection episodes in renal transplant recipients. Transplant Proc 2007;39:3057 60. 116. Augustine JJ, Poggio ED, Clemente M, et al. Hemodialysis vintage, black ethnicity and pretransplantation antidonor cellular immunity in kidney transplant recipients. J Am Soc Nephrol 2007;18:1602 6. 117. Hricik DE, Augustine J, Nickerson P, et al. CTOT-01 consortium. Interferon gamma ELISPOT testing as a risk-stratifying biomarker for kidney transplant injury: results from the CTOT-01 multicenter study. Am J Transplant 2015. Available from: http://dx.doi.org/10.1111/ ajt.13401. 118. Poggio ED, Augustine JJ, Clemente M, et al. Pretransplant cellular alloimmunity as assessed by a panel of reactive T cells assay correlates with acute renal graft rejection. Transplantation 2007;83:847 52. 119. Poggio ED, Clemente M, Riley J, et al. Alloreactivity in renal transplant recipients with and without chronic allograft nephropathy. J Am Soc Nephrol 2004;15:1952 60. 120. Khatri P, Roedder S, Kimura N, et al. A common rejection module (CRM) for acute rejection across multiple organs identifies novel therapeutics for organ transplantation. J Exp Med 2013;210(11):2205 21. 121. Sigdel TK, Bestard O, Tran TQ, Hsieh SC, Roedder S, Damm I, et al. A computational gene expression score for predicting immune injury in renal allografts. PLoS One 2015;10(9):e0138133. 122. 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(11):e1001759. 123. Betjes MG, Huisman M, Weimar W, Litjens NH. Expansion of cytolytic CD4 1 CD28- T cells in end-stage renal disease. Kidney Int 2008;74(6):760 7. 124. Betjes MG, Meijers RW, de Wit EA, Weimar W, Litjens NH. Terminally differentiated CD8 1 Temra cells are associated with the risk for acute kidney allograft rejection. Transplantation 2012;94(1):63 9. 125. Hadrup SR, Strindhall J, Køllgaard T, et al. Longitudinal studies of clonally expanded CD8 T cells reveal a repertoire shrinkage predicting mortality and an increased number of dysfunctional cytomegalovirus-specific T cells in the very elderly. J Immunol 2006;176 (4):2645 53. 126. Effros RB. Role of T lymphocyte replicative senescence in vaccine efficacy. Vaccine 2007;25(4):599 604. 127. Yap M, Boeffard F, Clave E, et al. Expansion of highly differentiated cytotoxic terminally differentiated effector memory CD8 1 T cells in a subset of clinically stable kidney transplant recipients: a potential marker for late graft dysfunction. J Am Soc Nephrol 2014;25 (8):1856 68. 128. Roedder S, Li L, Alonso MN, et al. A three-gene assay for monitoring immune quiescence in kidney transplantation. J Am Soc Nephrol 2015;26(8):2042 53.

I. KIDNEY TRANSPLANTATION