Immune Monitoring of Kidney Allografts

In Translation Immune Monitoring of Kidney Allografts Julie Ho, MD,1,2 Chris Wiebe, MD,1 Ian W. Gibson, MD,3,4 David N. Rush, MD,1 and Peter W. Nickerson, MD1,2,4 Current strategies for posttransplant monitoring of kidney transplants consist of measuring serial serum creatinine levels, clinical follow-up, and in some programs, protocol biopsies. These strategies may be insufficient to predict acute rejection in kidney transplants, which remains the major factor affecting long-term transplant outcomes. Immune monitoring may conceptually be divided into strategies for detecting humoral rejection (eg, donor-specific antibody) or cellular rejection. Cellular rejection markers may be separated further into those related to cytotoxic T lymphocytes (granzyme A/B, perforin, Fas ligand, and serpin B9), regulatory T cells (FOXP3), and CD4 T cells (the chemokines CXCL9, CXCL10, CXCL11, CCL2, and fractalkine, as well as TIM-3). Finally, transcriptomic changes and renal tubular injury markers also may be useful for detecting early inflammatory changes post– kidney transplant. Ultimately, novel strategies for monitoring the immune status of the kidney transplant may lead to early therapeutic intervention and improved kidney transplant outcomes. Am J Kidney Dis. 60(4):629-640. © 2012 by the National Kidney Foundation, Inc. INDEX WORDS: Biomarker; rejection; kidney transplant; donor-specific antibody; chemokines.

BACKGROUND Toll of Transplant Loss Transplant is the preferred treatment for most patients with end-stage kidney disease. In the era of modern immunosuppression, rates of acute rejection have decreased significantly, thereby improving shortterm transplant outcomes. Unfortunately, long-term transplant survival rates beyond 5 years are largely unchanged,1,2 and kidney transplant failure is one of the 4 most common causes of end-stage kidney disease.3 The return to dialysis therapy after transplant loss is associated with lower quality of life, increased costs, immunologic sensitization that may impede retransplant, and increased risk of death.4,5 US Renal Data System data show that adjusted patient survival after transplant loss is ⬍40% at 10 years compared with ⬎75% with a kidney transplant.4 Canadian Organ Replacement Registry data also show that transplant loss is an independent predictor of mortality, with a 3-fold increased risk of death compared with patients who maintain transplant function.5 What Are the Causes of Transplant Loss? Our understanding of the natural history of transplant loss has evolved significantly. In a seminal protocol biopsy study, Nankivell et al6 described the natural history of “chronic allograft nephropathy” in patients on primarily cyclosporine-based immunosuppression therapy and showed significant interstitial fibrosis/tubular atrophy (IFTA) at 5 years. A recent protocol biopsy study of patients with modern immunosuppression (mycophenolate mofetil/prednisone/ tacrolimus or sirolimus) showed that only mild IFTA was present at 1 year, and there was less progression and severity of IFTA at 5 years posttransplant.7 This Am J Kidney Dis. 2012;60(4):629-640

group also showed that the underlying causes of transplant failure are largely identifiable, primarily immune mediated, and potentially amenable to intervention.8 Therefore, noninvasive markers of immune function and early detection of immune-mediated injury may be the key to improving long-term transplant outcomes.9-11 Inflammation in the Kidney Transplant Acute rejection accounts for up to 12% of all transplant losses and recurrent rejection accounts for almost a third of patients with IFTA, a leading cause of late transplant loss.8 Depending on baseline immunosuppression, the prevalence of subclinical rejection, defined as Banff grade I acute rejection in patients with stable transplant function, ranges from 4.6%-30%.12-14 Subclinical rejection has been found to correlate with the development of IFTA at 1-5 years posttransplant.15,16 Treatment of subclinical rejection in the cyclosporine era has been shown to improve histologic (IFTA) and functional outcomes.13,17 Although clinically significant, the incidence of subcliniFrom the 1Section of Nephrology, 2Manitoba Centre for Proteomics and Systems Biology, and 3Department of Pathology, University of Manitoba; and 4Diagnostic Services of Manitoba, Winnipeg, MB, Canada. Received November 4, 2011. Accepted in revised form January 24, 2012. Originally published online April 27, 2012. Address correspondence to Julie Ho, MD, FRCPC, Sections of Nephrology & Biomedical Proteomics, Health Sciences Centre, GE421C, 820 Sherbrook St, Winnipeg, MB R3A 1R9, Canada. E-mail: [email protected] © 2012 by the National Kidney Foundation, Inc. 0272-6386/$36.00 http://dx.doi.org/10.1053/j.ajkd.2012.01.028 629

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cal rejection has decreased for patients on modern immunosuppression therapy.14 Emerging data have shown that chronic inflammation in areas of IFTA also is clinically significant despite the lack of inclusion in the current Banff schema.18 It is important to distinguish between IFTA and IFTA with inflammation because the latter is associated with both decreased function of the kidney transplant and transplant loss.19-21 Although there is no evidence to date that treating IFTA with inflammation improves transplant outcomes, it is an area that warrants further investigation. Therefore, it is important to develop a noninvasive means of monitoring the kidney transplant to institute timely intervention for clinical and subclinical rejection and potentially IFTA with inflammation.

CASE VIGNETTE A patient developed end-stage kidney disease secondary to type 1 diabetes and underwent a single-haplotype–match living related kidney transplant with immediate transplant function. She was crossmatch negative for class I and class II HLA antigen donorspecific antibodies (DSAs) by flow cytometry pretransplant. Initial treatment consisted of basiliximab, mycophenolate mofetil, cyclosporine, and prednisone. She had normal transplant function and underwent protocol biopsies at 1 and 2 months, which showed minimal inflammation (Banff scores g0 i0 t1 v0 ptc0) with no long-term changes (Fig 1A and B). At 3 months, she still had normal transplant function (serum creatinine level, 1.03 mg/dL [91 ␮mol/L], corresponding to estimated glomerular filtration rate of 60 mL/min/1.73 m2 by the MDRD [Modification of Diet in Renal Disease] Study equation, and no proteinuria), but her 3-month protocol biopsy showed Banff IIa acute rejection (g0 i2 t3 v1, ptc2focal, C4d negative), which was treated with pulse steroids (Fig 1C and D). At 6 months, she had maintained normal transplant function (serum creatinine level, 0.99 mg/dL [88 ␮mol/L], corresponding to estimated glomerular filtration rate of 63 mL/min/1.73 m2, and no proteinuria), but her 6-month protocol biopsy showed Banff Ib acute rejection (g0 i3 t3 v0, ptc3- focal, C4d negative; Fig 1E). At this point, she also had developed significant IFTA (cg0 ci2 ct2 cv0; Fig 1F). Despite her low immunologic risk and in the absence of changes to serum creatinine values, she showed recurrent subclinical rejection and significant long-term changes on subsequent biopsies. This calls into question whether current strategies for detecting acute rejection are sufficient for posttransplant monitoring of kidney transplants.

PATHOGENESIS Acute rejection may be classified broadly into humoral and cellular rejection, and the pathophysiology recently has been reviewed in detail.22 Briefly, antibody-mediated rejection is characterized by infiltration of the kidney transplant by complement-fixing antibodies that target HLA antigens on the peritubular and glomerular capillary endothelium. This results in complement activation and release of cytokines, chemokines, and adhesion molecules that lead to platelet aggregation and leukocyte infiltration, which ultimately causes the acute pathologic lesions of glo630

merulitis, peritubular capillaritis, microthrombi, and vessel necrosis.23 Cellular rejection develops when donor alloantigens are presented by donor (direct) or recipient (indirect) antigen-presenting cells through class I HLA (endogenous or intracellular antigens) or class II HLA antigens (exogenous or extracellular antigens), which, with the aid of costimulatory molecules, results in activation of naive T cells. T Cells mature and differentiate into T-cell subsets including cytotoxic T helper type 1 (TH1) and TH2 cells or cytoprotective immunoregulatory T cells (eg, Treg).11 Although there are areas of overlap, CD4 and CD8 T cells infiltrate the kidney transplant, release cytokines and chemokines, and cause cell death directly or indirectly.22 Although new molecular diagnostic techniques demonstrate areas of overlap between humoral and cellular rejection,24 immune monitoring may be divided broadly into detecting mediators of the alloimmune response for humoral and cellular rejection, as well as nonspecific markers of renal tubular injury (Fig 2). Novel potential biomarkers for noninvasive monitoring of the kidney transplant have been identified using genomics, transcriptomics, proteomics, and metabolomics, and these methods have variable capacity for translation into broadly applicable clinical assays (Box 1; Table 1).

RECENT ADVANCES Biomarkers for Risk of Humoral Rejection Monitoring for donor-specific anti-HLA antigen antibodies (DSAs) before transplant has been the standard of care in kidney transplantation since 1969, when Patel and Terasaki25 showed that a positive complement-dependent cytotoxicity crossmatch was highly predictive of hyperacute rejection. This important observation showed how monitoring for antibodies could help predict and, by excluding complementdependent cytotoxicity–positive patients, avoid hyperacute rejection. With the advent of more sensitive solid-phase assays (eg, flow-based detection of single-antigen beads) a new era of highly sensitive antibody detection began.26 Studies have since shown that DSAs detected pretransplant by solid-phase assays are associated with increased rates of subclinical and clinical antibody-mediated rejection in the early posttransplant period.27-31 By comparing DSA screening tests with differing sensitivities, Gloor et al30 showed that baseline DSA levels correlate with risk of early and late adverse transplant outcomes. Others have extended this idea by evaluating the mean fluorescence intensity of DSA detected by single-antigen beads pretransplant as a surrogate of DSA titer, but have found variable results.28,29,31-33 The sensitivity Am J Kidney Dis. 2012;60(4):629-640

Biomarkers of Kidney Allograft Rejection

Figure 1. (A, B) One-month protocol biopsy. Normal transplant function. Occasional focus of mild tubulitis (arrows), no significant interstitial inflammation (Banff scores g0, i0, t1,v0, ptc0). (C, D) Threemonth protocol biopsy. Normal transplant function, serum creatinine level of 1.03 mg/dL (91 ␮mol/L; estimated glomerular filtration rate [eGFR], 60 mL/min/1.73 m2). Banff IIa acute rejection with severe interstitial inflammation and tubulitis (C) and focus of intimal arteritis (D, arrow). Banff scores g0, i2, t3, v1, ptc2, C4d negative. Treated with pulse steroids. (E, F) Sixmonth protocol biopsy. Normal transplant function, serum creatinine level of 0.99 mg/dL (88 ␮mol/L; eGFR, 63 mL/min/ 1.73 m2). Banff Ib acute rejection with severe interstitial inflammation and tubulitis (E), patchy (30%) interstitial fibrosis/ tubular atrophy (F). Banff scores: g0, i3, t3, v0, ptc3, cg0, mm0, ci2, ct2, cv0, C4d negative. Treated with pulse steroids.

and specificity of DSA for predicting antibodymediated rejection will vary depending on the type of assay used; however, a positive cytotoxic crossmatch had the highest specificity (97%) and peak mean fluorescence intensity had the highest sensitivity (91%) in a study by Lefaucheur et al.32 Combining these 2 Am J Kidney Dis. 2012;60(4):629-640

tests resulted in a positive predictive value of 72% for antibody-mediated rejection posttransplant. The development of de novo DSA posttransplant also has been associated with higher transplant failure rates.34-44 Furthermore, multiple reports have shown that de novo DSA develops prior to transplant failure, 631

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Figure 2. Strategies for noninvasive monitoring of renal transplants. Abbreviations: CXCL10, chemokine (C-X-C motif) ligand 10 or interferon-␥–induced protein 10 (IP10); CXCL9, chemokine (C-X-C motif) ligand 9 or monokine induced by interferon-␥ 9 (Mig); CXCL11, chemokine (C-X-C motif) ligand 11 or interferon-inducible T-cell ␣ chemoattractant (ITAC); FOXP3, forkhead box P3; TIM-3, T-cell immunoglobulin mucin-3; NGAL, neutrophil gelatinase-associated lipocalin.

and as Wiebe et al44 have shown, before the onset of proteinuria or increase in creatinine levels in most cases. This highlights a potential window of opportunity for interventional studies.35,44,45 Everly et al43 performed serial DSA measurements during treatment of acute rejection in 16 patients and found that patients who experienced a ⬎50% reduction in solidphase mean fluorescence intensity within 14 days of Box 1. Biomarker Discovery Requirements Biomarker development would ideally include: ● ● ●

●

Independent discovery and validation cohorts, with external validation by independent investigators Sufficient power, especially if multiple biomarkers are being assessed Histology available for all patients, including healthy kidney transplant controls, to prevent misclassification of subclinical rejection Inclusion of inflammatory states other than rejection (eg, BKV, CMV, acute/recurrent GN, UTI) to determine the specificity of the biomarker

Once developed, the ideal biomarker(s) would: ● ● ● ● ●

Be highly sensitive and specific and follow Good Laboratory Practice Guidelines Detect inflammation before loss of kidney function or development of IFTA Correlate with response to therapy Correlate with long-term outcomes in a prospective longitudinal cohort Have the capacity for high-throughput inexpensive reproducible assays, performed on readily accessible laboratory equipment

Abbreviations: BKV, BK virus; CMV, cytomegalovirus; GN, glomerulonephritis; IFTA, interstitial fibrosis/tubular atrophy; UTI, urinary tract infection. 632

treatment onset had improved transplant survival at 21 months. Thus, monitoring response to therapy with serial DSA titers eventually may lead to a goaldirected approach for antibody-mediated rejection treatment. Solid-phase DSA evaluation is already widely available in most transplant centers, is reproducible, and currently is the only available monitoring strategy that is donor specific. Future study is needed to confirm the utility of serial DSA monitoring during acute rejection and define an appropriate cost-effective monitoring schedule for the detection of de novo DSA in stable patients. Furthermore, no studies to date have shown that treating patients with a stable functioning transplant who have persisting or develop de novo DSA will prevent clinical antibody-mediated rejection or progression to transplant failure. Biomarkers for Risk of Cellular Rejection An overview of the diagnostic performance of the biomarkers discussed here is provided in Table 2. Transcript Based

DNA microarrays offer a powerful tool for in-depth analysis of kidney tissue transcriptomic changes occurring during acute rejection. Sarwal et al24 showed that B-cell transcripts were significantly upregulated in a subset of patients with acute rejection that subsequently correlated with increased CD20-positive B-cell infiltrates on immunohistochemistry and clinically with steroid-resistant rejection and transplant loss. Mueller et al46 were able to identify increased cytotoxic T lymphocytes (CTLs) and interBackground.

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Biomarker

Sample

Assay

Detects Subclinical Rejection

Cohort(s)

Elevated Before BiopsyProven AR

Suitable for Therapy Monitoring

Yes

Yes

Prognostic

Independent Validation

Humoral Rejection DSA

Serum

Cytotoxicity, solid phase

Retro, prosp

Yes

Yes

Yes

— — — Predictive of transplant function 6 mo after AR Predictive of rejection reversal — — Level at mo 1 predictive of transplant function at 6 mo — Predictive of steroid responsiveness

Yes — Yes Yes

Cellular Rejection Perforin Granzyme A Granzyme B Serpin B9/PI9

Urine, PBL Urine Urine, PBL Urine

RT-PCR RT-PCR RT-PCR RT-PCR

Cross Cross Cross Cross

Yes Yes Noa Noa

Yes Yes Yes —

— — — —

FOXP3

Urine

RT-PCR

Cross

—

—

Yes

TIM-3 CXCL9 CXCL10b

Urine, PBL Urine Urine

RT-PCR ELISA, multiplex beads ELISA, multiplex beads, RT-PCR

Cross Cross, prosp obs Cross, prosp obs

— Yes Yes

— Yes Yes

Yes Yes Yes

CXCL11 Fractalkine

Urine Urine

ELISA, multiplex beads ELISA

Cross Cross

Noa —

— —

— Yes

Cleaved B2M A1M NGAL

Urine Urine Urine

SELDI-TOF MS Immunonephelometry ELISA

Cross Cross Cross

— — —

— — —

— — Yes Yesc

— —

Renal Tubular Injury Markers Noa Noa Noa

— — —

Yes — —

Abbreviations: A1M, ␣1 microglobulin; AR, acute rejection; B2M, ␤2 microglobulin; DSA, donor-specific antibody; ELISA, enzyme-linked immunosorbent assay; NGAL, neutrophil gelatinase-associated lipocalin; PBL, peripheral-blood leukocytes; RT-PCR, reverse transcription–polymerase chain reaction; prosp, prospective; obs, observational; retro, retrospective; cross, cross-sectional; SELDI-TOF MS, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. a Biomarker level is not elevated in subclinical rejection. b Urinary CXCL10 to creatinine ratio is the only biomarker to have its diagnostic performance independently validated for posttransplant monitoring for detection of subclinical tubulitis. c For both subclinical rejection and AR.

Biomarkers of Kidney Allograft Rejection

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Table 1. Biomarker Characteristics for Clinical and Subclinical Rejection

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634 Table 2. Biomarker Performance for Clinical and Subclinical Rejection Acute Rejectiona Biomarker

Sensitivity (%)

Subclinical Rejectiona

Specificity (%)

Ref

Sensitivity (%)

Specificity (%)

Prognostic Ref

Sensitivity (%)

Specificity (%)

Ref

Cellular Rejection Perforin Granzyme A Granzyme B Serpin B9/PI9 FOXP3 TIM-3 CXCL9 CXCL10b

CXCL11 Fractalkine

55-100 80 60-88 76-92 100

79-95 100 77-92 64-90 100

50, 52, 54, 74 53 50, 52, 54, 74 52-54 52

80 80 — — —

90 100 — — —

53 53 — — —

— — — — Rejection reversal: 90 ATN vs ATN & AR: 84-100 ATN vs ATN & AR: 96-100 58 — — — — 79.5-93 80-93.5 65-66, 68, 70, 74 SC vs N & B: 86 SC vs N & B: 64 64 — Protein: 65-89; mRNA: Protein: 72-97; mRNA: 78; 63, 65-67, 70, 74 SC vs N & B: 68; SC vs N & B: 90; 64, 69 Level at mo 1 for 100; protein 2-3 d before protein 2-3 d before N vs B & SC: 73 N vs B & SC: 73 prediction of biopsy for AR: 71 biopsy for AR: 95 GFR at 6 mo: 58 NR NR 66 SC vs N & B: 45 SC vs N & B: 87 64 — 82 76.5 74 — — — Steroid-sensitive vs -resistant AR: 74; reversible AR vs transplant loss: 100

— — — — Rejection reversal: 73 — — Level at mo 1 for prediction of GFR at 6 mo: 75 — Steroid-sensitive vs -resistant AR: 75; reversible AR vs transplant loss: 68.3

— — — — 51 — — 67

— 74

Renal Tubular Injury Markers NR NR NS (AUROC⫽0.68)

79-81 81 64

Note: In terms of humoral rejection, although part of the Banff diagnostic criteria for antibody-mediated rejection include positive DSA, there currently are no data for the diagnostic performance of screening de novo DSA for detecting acute antibody-mediated rejection posttransplant. Abbreviations: A1M, ␣1-microglobulin; AR, acute rejection; ATN, acute tubular necrosis, delayed graft function; ATN & AR, acute tubular necrosis and acute rejection, delayed graft function; AUROC, area under the receiver operating characteristic curve; B, borderline tubulitis; B2M, ␤2-microglobulin; GFR, glomerular filtration rate; mRNA, messenger RNA; N, normal; NGAL, neutrophil gelatinase-associated lipocalin; NR, not reported; NS, not significant; ref, reference; SC, subclinical rejection. a Unless otherwise indicated, sensitivity and specificity values given are versus normal. b Urinary CXCL10 to creatinine ratio is the only biomarker to have its diagnostic performance independently validated for posttransplant monitoring for detection of subclinical tubulitis.

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Cleaved B2M NR A1M NR NGAL NS (AUROC⫽0.68)

Biomarkers of Kidney Allograft Rejection

feron ␥–related transcripts in kidney transplant biopsies and highlighted potential discrepancies of the Banff criteria in classifying patients at the cusp of borderline versus higher grades of rejection.47 The transcriptomic changes that were identified were continuous in nature and showed overlap in antibody- and T-cell–mediated rejection.24,46 Flechner et al48 showed distinct gene expression profiles in peripheral-blood lymphocytes and biopsy tissue of patients with acute rejection. Although DNA microarrays are powerful tools for elucidating the underlying pathophysiology of transplant rejection, their clinical utility will be limited by cost until specific transcripts can be identified for monitoring (eg, by reverse transcription– polymerase chain reaction [RT-PCR]) in a noninvasive biological fluid (eg, blood and urine). CD8 T cells. CTLs cause cell death through various mechanisms. Specifically, CTLs can release perforin, which perforates cell membranes, causing direct cell death during rejection.22 CTLs also release granzymes A and B, which cause cell death through caspase-dependent and independent apoptosis.49 Li et al50 demonstrated with a quantitative RT-PCR approach that perforin and granzyme B messenger RNA (mRNA) levels are elevated significantly in urinary cells of patients with acute rejection, and this observation has since been replicated in independent cohorts.51,52 These findings were extended by van Ham et al53 to show that perforin and granzyme B levels also are significantly elevated in subclinical rejection, but that urinary granzyme A mRNA showed a stronger diagnostic performance (sensitivity, 80%; specificity, 100%) for clinical and subclinical rejection. Urinary granzyme A level was unable to distinguish tubulitis from cytomegalovirus infection.53 There was no clear dose-response relationship with perforin or granzyme A/B to the degree of inflammation present.50,53 Interestingly, the highest levels of perforin and granzyme B transcripts were present in both subclinical and Banff Ia/b rejection, but decreased with Banff II/III rejection and were undetectable with granzyme A, which is a limitation.50,53 Serine proteinase inhibitor 9 (serpin B9) is highly expressed in CTLs and is a natural antagonist to granzyme B that blocks CTL killing. Muthukumar et al54 found that serpin B9 mRNA levels in urine were significantly elevated in patients with acute rejection and that serpin B9 level correlated with subsequent transplant function. Although this finding was confirmed independently by other groups, they also reported that serpin B9 did not distinguish acute rejection as well as granzyme A or FOXP3 (forkhead box P3).52,53 CTLs also express Fas ligand, which binds to and activates Fas receptors to induce caspase-dependent apoptosis during acute rejection.22 However, Fas Am J Kidney Dis. 2012;60(4):629-640

ligand mRNA level in urine was not found to be significantly elevated in acute rejection versus healthy controls.52 Treg cells. FOXP3 is a transcription factor that is expressed by Treg cells, which have been shown to be important in animal models of immune tolerance in transplant.22 Muthukumar et al51 showed with quantitative RT-PCR that urinary FOXP3 mRNA levels are significantly higher in patients with acute rejection (sensitivity, 90%; specificity, 73%). FOXP3 is also a predictor of steroid responsiveness and transplant failure 6 months after rejection51 and has improved diagnostic performance compared with perforin, granzyme B, serpin B9, and Fas ligand.51,52 However, because these studies used apparently healthy, IFTA, and acute tubular necrosis controls, it was not possible to determine whether urinary FOXP3 mRNA is specific to rejection versus other inflammatory states. The performance of FOXP3 for detecting acute rejection is improved by the addition of costimulatory molecules OX40 (CD134), OX40L (CD134 ligand), and PD-1 (programmed cell death 1 molecule), leading to an area under the receiver operating characteristic curve (AUROC) of 0.98,55 indicating that a panel of biomarkers may be very useful. CD4 T cells. TIM-3 is a T-cell immunoglobulin domain, type I membrane protein that is preferentially expressed on terminally differentiated TH1 cells.56 Urinary TIM-3 mRNA level has been shown to be significantly elevated in patients with acute rejection versus healthy controls.57 It is able to distinguish acute rejection versus acute tubular necrosis in the setting of delayed transplant function, as well as from chronic pathologic states (eg, IFTA).58 Urinary TIM-3 mRNA level decreases after treatment for acute rejection and is a promising biomarker that requires further validation.58 Protein Based

CXCR3 is a chemokine receptor that is expressed by activated T cells and natural killer cells and binds to CXCL9 (monokine-induced by interferon ␥), CXCL10 (interferon ␥–induced protein of 10 kDa), and CXCL11 (interferon-inducible T-cell ␣ chemoattractant).59 CXCL9 and CXCL10 can be secreted by infiltrating inflammatory cells and renal tubular and mesangial cells and are involved in leukocyte recruitment to rejecting transplants, as well as mediating the TH1 helper response.60,61 In acute kidney transplant rejection, CXCL9 and CXCL10 are highly expressed in infiltrating leukocytes and renal tubules, whereas CXCL9 expression is increased in the glomerulus.62,63 Due to their role in the propagation of alloimmune-mediated inflammation, these interferon ␥–de635

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pendent chemokines have been evaluated as urinary markers of acute rejection. There is a significant body of evidence showing that urinary CXCL10 is a sensitive marker for inflammation, and a number of groups have shown that urinary CXCL10 is associated with acute rejection.62-70 Furthermore, the increase in urinary CXCL10 expression has been shown to precede the increase in serum creatinine level.67,68 In addition, urinary CXCL10 level is sufficiently sensitive to detect underlying inflammation associated with both borderline and subclinical tubulitis in addition to clinical rejection64,67,69,70 and decreases after treatment of rejection.62,66-68 Finally, elevated urinary CXCL10 expression, if untreated, has been associated with the early development of IFTA71 and decreased kidney transplant function at 6 months.67 Elevated levels of pretransplant serum CXCL10 are associated with decreased kidney transplant survival.72,73 Urinary CXCL9 expression is significantly elevated in acute rejection, and although there is a consistent and robust response that has been independently confirmed by different groups, the diagnostic performance has not been as strong as with urinary CXCL10.62,64,65,68 Although CXCL11 levels are elevated in acute rejection, CXCL11 was not as discriminatory as either CXCL10 or CXCL9 and therefore is not as effective as a potential biomarker.64,66 Fractalkine is a chemokine that interacts with its cognate receptor, CX3C chemokine receptor 1 (CX3CR1). It acts as a chemotactic agent and adhesion molecule, thereby promoting the migration and trafficking of T cells, monocytes, and natural killer cells.74 Peng et al74 showed that urinary fractalkine level is significantly elevated in acute rejection (AUROC, 0.83) and that higher levels are associated with steroid-resistant rejection and transplant loss. However, both CXCL9 and CXCL10 performed the same as or better than fractalkine for detecting acute rejection in this cohort (CXCL9 AUROC, 0.90; CXCL10 AUROC, 0.81).74 Immune monitoring with chemokines also may be informative of long-term outcomes in addition to acute transplant rejection.67,71 CCL2 (monocyte chemotactic protein 1; also known as MCP-1), a ligand of chemokine (C-C motif) receptor 2 (CCR2), is a chemoattractant protein for T cells, monocytes/macrophages, and natural killer cells and is produced by a number of cell lineages, including local tubular and glomerular epithelial cells and infiltrating monocytes/macrophages and lymphocytes.75,76 CCL2 also has a key role in the generation of memory CD8 T cells.77 In a prospective longitudinal cohort, we showed that urinary CCL2 level at 6 months was predictive for the eventual development of IFTA at 636

24 months in kidney transplant patients; however, these results require confirmation in an independent cohort.71 Metabolite Based

Different genomic, transcriptomic, and proteomic techniques offer powerful tools for unbiased biomarker discovery. However, evaluating up- or downregulation of genes and proteins in response to injury may be insufficient because their function may be determined by factors other than the quantity of transcript or protein (eg, posttranslational modifications). Metabolomics offers a potential solution to this by analyzing downstream metabolites. Rush et al78 were able to use nuclear magnetic resonance spectroscopy in a multicenter prospective study evaluating longterm transplant outcomes to accurately differentiate IFTA from IFTA with inflammation in this cohort. Renal Tubular Injury Markers Tubular injury markers may be sensitive indicators of kidney transplant distress. Although they do not directly monitor the immune status of the transplant, they can provide a noninvasive means of identifying tubular injury associated with rejection. In an elegant series of experiments, Schaub et al79-81 used proteomic techniques to identify cleaved ␤2-microglobulin in urine of patients with acute tubulitis. However, urinary cleaved ␤2-microglobulin has since been identified in kidney ischemia-reperfusion injury and is not specific to acute rejection.82 Schaub et al81 evaluated other markers of renal tubular injury (retinol binding protein, ␣1-microglobulin, and NGAL [neutrophil gelatinase-associated lipocalin]) and found that although their levels are elevated in acute rejection, none of these renal tubular injury markers was able to distinguish subclinical tubulitis from normal histology. Limitations Although there are many promising noninvasive markers for monitoring kidney transplants, there are limitations that need to be considered. Many biomarker studies are limited to single centers with highly selected patient populations. They frequently have a cross-sectional study design that assesses concurrent urine and biopsy data and very few have longitudinal data. Furthermore, there often is a lack of normal histology controls and nonrejection inflammatory controls, making it difficult to determine true specificity. Therefore, before a potential biomarker can be considered for clinical use, there are several key questions that should be answered, as detailed in the following paragraphs. Am J Kidney Dis. 2012;60(4):629-640

Biomarkers of Kidney Allograft Rejection

1. Does the biomarker detect subclinical injury? Although urinary granzyme A mRNA level is elevated with subclinical injury,52 urinary CXCL9 and CXCL10 have shown the best performance for detecting subclinical tubulitis associated with cellular rejection.63,66,68,70 Pretransplant DSA level has been shown to correlate with the risk of developing subclinical antibody-mediated rejection, but there had been no studies evaluating the efficacy of serial DSA monitoring for detection of subclinical antibodymediated rejection until Wiebe et al.27-29,44 2. Is the biomarker detectable before the onset of decreased transplant function? Ideally, kinetic studies are valuable to determine whether a biomarker is sensitive enough to be detectable before biopsy. Unfortunately, very few studies have serial samples available for analysis before kidney biopsy. These data were available in 2 studies evaluating urinary granzyme A, granzyme B, and perforin mRNA, but are limited by their small numbers.49,52 Matz et al67 reported serial urinary CXCL10 levels and showed that they increased in a sequential fashion within 1 week prebiopsy and in the absence of changes to serum creatinine level. Although DSA has been clearly shown to precede transplant failure,35,45 it was reported only recently in a systematic fashion with serial histology and DSA screening by Wiebe et al,44 in which DSA detection was shown to increase before the development of subclinical and clinical antibody-mediated rejection. 3. Does the biomarker correlate with response to treatment? Although many biomarkers report an association with steroid-responsive rejection or the eventual development of transplant dysfunction/failure,24,51,54,67,74 it is helpful to know whether a biomarker will have clinical utility for monitoring response to therapy. Urinary fractalkine, granzyme B, perforin, and TIM-3 mRNA levels decrease after treatment for cellular rejection.58,74 The most robust evidence for cellular rejection is the CXCR3 chemokines urinary CXCL966,68 and CXCL10,62,66,67 levels of which decrease after therapy. In antibodymediated rejection, several groups have shown that DSA titers are decreased after treatment43,83-85 and further showed that failure to decrease DSA titer is associated with increased transplant loss,43,83,84 which leads us to our final question. 4. Does use of the biomarker affect clinical outcomes? This is a question that remains largely unanswered, but it is a critical one that must be Am J Kidney Dis. 2012;60(4):629-640

addressed before clinical implementation. To date, the only evidence for influencing transplant outcomes is related to monitoring and treating DSA for antibody-mediated rejection,43,84,85 but this requires further validation. There has not been a prospective evaluation of intervention based on serial DSA monitoring or therapy directed toward prespecified DSA targets.

SUMMARY Currently, the best evidence for immune monitoring is with serial DSA and urinary chemokines. It is critical to know whether biomarker screening in a multicenter prospective study with an unselected patient population will be a successful strategy to improve kidney transplant outcomes. The National Institute of Allergy and Infectious Diseases–sponsored Clinical Trials in Organ Transplantation (CTOT) studies are evaluating the utility of serial screening for urinary chemokines and DSA as part of randomized controlled trials. Data from these studies likely will be very informative for the broader applicability of urinary chemokine and DSA screening. Until that time and further studies, the clinical role for these biomarkers is largely undefined and protocol biopsy remains the gold standard for detecting subclinical inflammation. Unbiased biomarker studies (eg, genomics and proteomics) have provided novel insights into the underlying pathophysiology of rejection, and although these can only be associations in observational studies, they nevertheless are important and may further complement mechanistic studies. Conversely, deeper understanding of the pathophysiology of acute rejection may help guide the development of novel biomarkers targeting specific injury/repair pathways. Serial biomarker screening of our patient with urinary chemokines may have detected the subclinical transplant inflammation before the 3-month protocol biopsy. Furthermore, serial chemokine screening could have been used to monitor response and effectiveness of treatment for subclinical inflammation and thereby prevent progression to IFTA, which was detected at the 6-month protocol biopsy. Ultimately, noninvasive biomarkers may change the way in which patients are monitored after kidney transplant, and this may improve long-term transplant outcomes. ACKNOWLEDGEMENTS Support: Dr Ho is funded by the KRESCENT Young Investigator Program and the Norman S. Coplon Satellite Healthcare Extramural Grant Program. Dr Wiebe is supported by the Manitoba Medical Services Foundation/R. Samuel McLaughlin Fellowship, the Manitoba Health Research Council, and the University of Manitoba, Department of Internal Medicine. Dr Nickerson holds 637

Ho et al the Flynn Family Chair in Renal Transplantation at the University of Manitoba. Drs Rush, Gibson, and Nickerson are supported by operating grants from the Canadian Institutes of Health Research and the National Institutes of Health (National Institute of Allergy and Infectious Diseases) CTOT. Financial Disclosure: The authors declare that they have no relevant financial interests.

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