Biomarkers to detect rejection after kidney ...

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Apr 6, 2017 - plantation, and is known as AlloMap, which is now FDA- approved [24, 25] ... Combination of T-cell reactivity and gene expression panel testing.
Pediatr Nephrol DOI 10.1007/s00467-017-3712-6

REVIEW

Biomarkers to detect rejection after kidney transplantation Vikas R. Dharnidharka 1

&

Andrew Malone 2

Received: 6 April 2017 / Revised: 20 May 2017 / Accepted: 25 May 2017 # IPNA 2017

Abstract Detecting acute rejection in kidney transplantation has been traditionally done using histological analysis of invasive allograft biopsies, but this method carries a risk and is not perfect. Transplant professionals have been working to develop more accurate or less invasive biomarkers that can predict acute rejection or subsequent worse allograft survival. These biomarkers can use tissue, blood or urine as a source. They can comprise individual molecules or panels, singly or in combination, across different components or pathways of the immune system. This review highlights the most recent evidence for biomarker efficacy, especially from multicenter trials. Keywords Kidney . Transplant . Pediatrics . Rejection . Biomarkers . Allograft loss

Introduction The need for biomarkers in kidney transplantation stems from several observations. First, both early allograft and early patient survival have improved dramatically over the past 40 years, each currently above 90% at 1-year post-transplant,

in adults and in children [1]. Yet, the longer term outcomes have not improved nearly as much [2]. For clinical trials of newer agents, powering a study on the longer-term outcomes is prohibitively expensive, whereas powering a study on shorter-term outcomes necessitates the use of the noninferiority of outcomes, a less desirable concept than the superiority. Shorter term studies may also involve surrogate outcomes, which may not reflect rigorous long-term outcomes. Second, we know from previous studies that molecular and cellular events of acute rejection (AR) occur before the rise in the clinical biomarker, serum creatinine. When patients have elevated serum creatinine from chronic allograft dysfunction and underlying fibrosis, differentiating a superimposed AR from chronic injury progression becomes very difficult. Serum creatinine is itself an imperfect functional marker of glomerular filtration, and even less effective as an injury marker. In this respect, the discipline of nephrology lags behind the discipline of cardiology, where functional cardiac pump markers are now discrete from increasingly specific injury markers (serum AST > CK-MB > troponins). For the specific injury of AR, a kidney biopsy remains the existing gold standard, although it is by no means perfect. Serial kidney biopsies are impractical to perform at the same frequency as blood or urine testing. Therefore, based on the above-mentioned reasons, a great need exists for better markers of AR, either from tissue or from less invasively obtained body fluids.

* Vikas R. Dharnidharka [email protected]

Types of biomarkers in transplantation 1

2

Division of Pediatric Nephrology, Hypertension and Pheresis, Washington University in St Louis & St. Louis Children’s Hospital, St Louis, MO, USA Division of Nephrology, Washington University School of Medicine, St Louis, MO, USA

Biomarkers studied in the transplantation field have focused on disparate outcomes, such as AR, immune allograft-specific tolerance, subclinical allograft injury, calcineurin nephrotoxicity, over-immunosuppression (e.g., BK virus nephropathy) or

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chronic fibrosis/rejection. This review focuses only on the biomarkers that have been studied for AR (cellular or antibodymediated) outcomes or progression to chronic rejection and graft loss. Although much of the data stems from adult kidney transplants, where available, pediatric data are highlighted.

Characteristics of an ideal biomarker for AR When specifically discussing AR or progression to chronic rejection, an ideal biomarker would possess the following characteristics: 1. 2. 3. 4. 5.

6.

7. 8.

Easy to obtain Quick turnaround Inexpensive Changes before elevation in serum creatinine and can predict an upcoming AR High area under the curve (AUC), positive predictive value (PPV; few false-negatives) and negative predictive values (NPV; few false-positives) High specificity, i.e., does not elevate with confounding conditions of delayed graft function, acute ischemic injury, bacterial or viral kidney infection Independent of transplant recipient age and gender Independent of time since transplantation

The most commonly used test of biomarker discriminatory accuracy is the receiver-operating characteristic (ROC), where sensitivity (or true-positive rate) is plotted against 1-specificity (false-positive rate) and an AUC is generated. A test with perfect accuracy has an AUC of 1.0, whereas a test with an AUC of 0.50 is no better than a coin flip and therefore worthless. The first section of this review focuses on biomarkers obtained from less invasive body fluids such as blood and urine. The second section focuses on biomarkers obtained from kidney allograft tissue.

Less invasive biomarkers Table 1 lists the several different less invasive biomarkers. For the purposes of this review, we discuss them under the headings of tests of immune humoral reactivity, T cell reactivity, peripheral blood or urine gene expression panels, chemokines, miRNAs, cell-free DNA and other tests of immune reactivity. Antibody levels to human leucocyte antigens One of the earliest and key discoveries in transplant immunology, by Patel and Terasaki, was that pre-existing antibodies to

donor-expressed HLA antigens were associated with hyperacute rejection and nonfunction [3]. That discovery, and the technology used, became the basis for the standard of care pretransplant antibody testing. Along the same lines, the use of cross-match testing identified those donor T cells to which the recipient serum reacted, and that had low sensitivity but high specificity in predicting AR. Recent advances in technology now allow for much lower levels of antibody to be detected, leading to an increase in sensitivity, but a decrease in specificity [4]. The presence of these low-level antibodies was responsible for the recognition of a chronic form of antibodymediated rejection. De novo post-transplant development antibodies directed against donor HLA have been shown in several studies to predict either AR or worse graft survival [5–7]. Some centers, therefore, now perform serial donor-specific antibody (DSA) monitoring. However, this testing is not yet routine, in part because: 1. A positive DSA testing shows only moderately high AUC to predict rejection—a significant proportion of DSApositive patients do not go on to develop antibodymediated rejection 2. Not all studies show an association to worse graft survival 3. It remains unclear as to whether all DSAs are detrimental, or only class II DSAs, or those DSAs that bind complement, shown by C1q binding [7] or are associated with C4d deposition [8] 4. Assays are not standardized between laboratories [9] and the use of mean fluorescent intensities (MFIs) as a quantitative index remains unproven [10] 5. What to do about positive DSAs and normal serum creatinine remains unclear [11] 6. Some DSAs may resolve on their own over time Further, antibodies to donor antigens that are outside the HLA system, or antibodies that are not specific to the donor, may also be detrimental [12]. Non-HLA-directed antibodies, such as those directed against angiotensin 2 type 1 receptor, may be present in patients with refractory vascular rejection [13]. In the pediatric SNS01 study, a randomized multicenter trial of steroid avoidance, de novo antibodies developed in 24% of the overall cohort by 3 years post-transplant [14], which appears to be a higher proportion than that seen in adults at the same time point [15]. Most antibodies in the SNS01 study were directed against HLA and associated with AR and worse allograft survival, as shown by other smaller pediatric studies [16], though not all [17]. Another study in children by Fichtner et al. showed that only the C1q-binding DSAs and not other DSA types were associated with subsequent antibody-mediated rejection and subsequent worse allograft survival [18].

Pediatr Nephrol Table 1

Less invasive biomarkers

Monitoring type

Antibody assays

T cell reactivity assays Gene expression arrays

Chemokines

miRNAs Donor-derived cell-free DNA Other tests of Immune activity

Sample type

Results – (AUC)

Anti-HLA Ab/DSA

Peripheral blood

C1q binding assay

Peripheral blood

ELISPOT interferon-γ

Peripheral blood

(0.75)

kSORT

Peripheral blood

(0.92)

Quest renal transplant monitoring panel

Urine

(0.74–0.83)

TruGraf

Peripheral blood

(0.82–0.97)

CXCL9 (aka MIG), CXCL10 (aka IP10), CCR1, CCR5, CXCR3, CCL5 (RANTES) miR-10a, miR-10b, miR-210

Urine

(0.75–0.90)

Urine Peripheral blood

NR (0.74–0.87)

CD4-ATP (Immuknow)

Peripheral blood

Indoleamine 2,3 dioxygenase Fractalkine sCD30 CD103

Peripheral blood Urine Peripheral blood Urine

(0.73)

Comments

High sensitivity for chronic ABMR; relatively easy to obtain, quick turnaround In some studies, improves DSA specificity, not as easy to obtain Pre-transplant use has high NPV for later T cellmediated rejection; difficult to standardize 17-gene panel, better for ABMR; commercially available, turnaround time NR 4-gene panel, derived from studies by [33–35]; commercially available; turnaround time NR Exact genes not delineated; uses ∼200 probe sets; not yet commercially available, but in an Easy Access program to four centers Protein results show higher AUCs than mRNA results; not yet easy to obtain Early results are promising Better for ABMR than TCMR; commercially available, 72-h turnaround Low efficacy for acute rejection, slightly better for infection; easy to obtain, quick turnaround

(0.60–0.70) (0.83) (0.73) (0.60)

ABMR antibody-mediated acute rejection, TCMR T cell-mediated rejection, kSORT kidney solid organ response test, NR not reported, IP10 interferongamma-inducible protein of 10Kd, FOXP3 X-linked forkhead/winged helix transcription factor, MIG monokine induced by interferon gamma, RANTES regulated on activation, normal T cell expressed and secreted

Assessment of recipient T-cell reactivity to donor antigens The pre-transplant crossmatch studies use donor cells and assess recipient humoral reactivity. The pre- and post-transplant DSAs also represent the humoral component of recipient immune reactivity. The mixed lymphocyte reaction test uses recipient lymphocytes to test the cell-mediated component, but has limited predictive value [19]. Until recently, the risk of early cell-mediated AR, primarily T cell-mediated rejection (TCMR) was hard to predict. Heeger and colleagues developed a modified ELISA test, known as ELISPOT, for this purpose [20]. The IFN-γ ELISPOT measures production of the cytokine interferon-γ by recipient alloreactive memory or effector T cells in response to various donor-expressed antigen spots in individual ELISA plate wells. A higher number of positive spots would suggest a higher risk of TCMR in the early post-transplant period. Indeed, a higher pre-transplant ELISPOT number was associated with higher TCMR risk and identified patients who would benefit from induction therapy [21]. Standardization of this assay was recently achieved [22]. However, within the subset of 176 subjects with available IFN-γ ELISPOT results in the large NIH-funded prospective multicenter CTOT-01 study, pre-transplant IFN-γ

ELISPOT positivity surprisingly did not correlate with either the incidence of AR or the estimated glomerular filtration rate (eGFR) at 6 or 12 months [23]. Peripheral blood gene expression panels With the advent of technologies that allow assessment of the differential expression of genes, and the development of technologies that allow assays of thousands of genes at once, a natural progression was the development of gene expression microarrays and studies to associate these transcriptomes with disease states or outcomes of interest. In the transplantation world, several groups have developed such gene expression profiles to predict either AR or graft outcomes, using either peripheral blood or kidney allograft tissue. The first such panel to predict TCMR was actually developed for heart transplantation, and is known as AlloMap, which is now FDAapproved [24, 25]. Subsequent work with other panels has been done in kidney transplantation. Table 2 lists and compares some of the more frequently studied panels. In peripheral blood, Sarwal and colleagues, using a pediatric kidney transplant cohort from their center and then in the NIHfunded multicenter SNS01 study, initially developed and then

Pediatr Nephrol Table 2 Genes represented in different array panels

AlloMap (11 genes; heart transplant; in peripheral blood; to predict T cellmediated rejection)

kSORT (17 genes, kidney transplant; in peripheral blood; to predict acute rejection)

Quest renal transplant monitoring panel (four genes; in urine; to predict acute rejection)

GoCAR (13 genes; in kidney transplant tissue; to predict fibrosis)

SEMA7A RHOU

CFLAR DUSP1 (also called DUSP12) IFNGR1 ITGAX

FOXP3 GZMB

CHCHD10 KLHL13

PRF1 CXCL10 (also called IP-10)

FJX1 MET (also called SLTM or HGFR) SERINC5 RNF149 SPRY4 TGIF1 KAAG1 ST5 WNT9A ASB15 RXRA

IL1R2 ITGAM FLT3 MARCH8 WDR40A (now DCAF12) PF4 C6orf25 (now MPIG6B) PDCD1 ITGA4

MAPK9 NAMPT NKTR PSEN1 RNF130 RYBP CEACAM4 EPOR GZMK RARA RHEB RXRA SLC25A37

A comparison of HUGO Gene Nomenclature Committee (HGNC) ID numbers for each of the above genes on www.genenames.org (accessed 31 March 2017) showed no overlap in HGNC ID numbers

validated a five-gene signature that showed an AUC in the 0.93–0.95 range to predict AR [26, 27]. Further work by the same group, in a larger cohort called the Assessment of Acute Rejection in Renal Transplantation (AART) study, which includes adult renal transplant patients, led to the development of a larger 17-gene panel set (now called kSORT), including the original five genes, which showed an AUC of 0.92 [28]. Kurian et al. used a different approach, initially identifying 200 gene probe sets in adult kidney transplant recipients, that discriminated AR by way of three-way classifier tools [29]. The number of genes in these probe sets is very large. The initial results showed AUCs within the range of 0.82–0.97. The test is now referred to as TruGraf, and is under further prospective study. Combination of T-cell reactivity and gene expression panel testing In the recent Evaluation of Subclinical Acute Rejection Prediction (ESCAPE) study, the combination of kSORT and ELISPOT, in 75 kidney transplant recipients, showed greater accuracy together than either one alone [30]. The ELISPOTalone had an AUC of 0.705 for subclinical AR, of 0.765 for subclinical T-cell-mediated AR (sc-TCMR), but only 0.386 AUC for scABMR. KSORT alone had an AUC of 0.732 for subclinical AR, of 0.672 for subclinical T-cell-mediated AR, and 0.786 AUC for sc-ABMR. Therefore, as expected, ELISPOTwas more strongly associated with TCMR and kSORT with ABMR. Combined, they showed higher AUCs of 0.916 for subclinical AR, of 0.856 for sc-TCMR, and of 0.964 for sc-ABMR.

Urine gene signature panels The Strom laboratory had pioneered the concept of using elevations in CD8 T cell effector molecule gene expression (perforin, granzyme B, Fas ligand) in either biopsy tissue [31] or peripheral blood [32] to detect AR. The Suthanthiran laboratory at Cornell University then pioneered the concept of looking at urinary mRNA transcripts of key genes involved in either CD8 T cell effector activity (perforin, granzyme B) or T regulatory cell activity (FOXP3) as markers of AR. In their earliest paper [33], levels of perforin and granzyme B were significantly elevated in their discovery group (sensitivity and specificity 83% for perforin; AUC not mentioned). In a small validation group of patients early post-transplant, elevation of these urinary markers identified those in whom AR developed. In a later study [34], the same group showed that urinary mRNA levels of the signature T-regulatory cell gene FOXP3, expressed as a ratio to 18S ribosomal RNA, were elevated in patients with AR, as were ratios of urinary CD25, CD3ε, and perforin (granzyme B was not mentioned). Higher absolute levels of FOXP3 mRNA also differentiated those patients who responded to AR treatment (sensitivity of 90% and a specificity of 73%), whereas levels of CD25, CD3ε or perforin did not. Most recently, using the large prospective multicenter Clinical Trials in Organ Transplantation (CTOT) consortium, the CTOT-04 study showed that a three-gene signature of 18S ribosomal (rRNA)-normalized measures of CD3ε mRNA, IP10 mRNA, and 18S rRNA was able to differentiate biopsyproven acute cellular rejection (AUC 0.83 in the discovery

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group and 0.74 in the validation group) from biopsies with other conditions or no AR [35]. This three-gene signature became elevated before the detection of clinical rejection. Urinary tract infection did not affect the signature, but BK virus infection did lead to elevation in the panel. In this study, the mRNA levels of CD3ε, perforin, granzyme B, and IP-10 were all elevated before AR (P < 0.001 for all comparisons), but not the levels of CXCR3 (P = 0.06), CD103 (P = 0.13), TGF-β1 (P = 0.11), and proteinase inhibitor 9 (P = 0.38). The best discriminating panel removed perforin and granzyme B. CD25 and FOXP3 were not mentioned. IP-10 had previously been studied by multiple groups, e.g., Hu et al. [36] and Lazzeri et al. [37], and had shown significant elevations in AR, but also after acute ischemic injury and BK virus infection. All of these studies have been performed in adult kidney transplant recipients. In the commercially available Quest Laboratories Renal Transplant Monitoring panel, the following four gene mRNA transcripts are measured in urine: FOXP3, granzyme B, perforin, and IP-10. Urinary chemokines Many of the targets mentioned above bind to chemokine ligands. Chemokines have been implicated as necessary intermediates in AR. Thus, many groups have studied different chemokines as possible biomarkers for AR. These chemokines include CCR1, CCR5, CXCR3, CCL5 (aka RANTES), CXCL9 (aka monokine induced by IFN-γ; MIG), and CXCL10 (aka IP-10). Many of these biomarkers showed promise in single-center studies. For example, Tatapudi et al. found an AUC of 0.90 for urine CXCL10 mRNA and 0.76 for urine CXCR3 mRNA, but in this study, 18S ribosomal RNA was not associated with rejection [38]. Matz et al. found that urine CXCL10 mRNA showed an AUC of 0.85 for AR and urine CXCL10 protein showed an AUC of 0.77 [39]. Peng et al. [40] found a slightly better AUC for urine CXCL9 protein (0.90) versus urine CXCL10 (0.81). Soluble CD103 mRNA in urine had an AUC of 0.73 for AR [41]. In pediatric patients, Blydt-Hansen et al. showed that urine CXCL10 protein had an AUC of 0.88 for clinical TCMR and an AUC of 0.81 for subclinical TCMR [42]. These various chemokines (mRNA and protein) were simultaneously studied in the large prospective multicenter CTOT-01 study [43]. Urinary CXCL9 mRNA (AUC 0.79) and CXCL9 protein (AUC 0.86) were the best discriminators of AR. Multivariate models incorporating other chemokines did not increase the discrimination. This study included some pediatric patients from one center (40 out of 280 = 14.3%), but the data for pediatric patients were not presented separately. Interestingly, CXCL10 is also known as IP-10, which was included in the best performing panel in the CTOT-04 study cited previously, but dropped out of the CTOT-01 study, as it did not appear to discriminate between AR and other

conditions, similar to CCR1 and CXC3. Granzyme B differentiated AR, but had a lower AUC of 0.730 and did not add further value to CXCL9 alone. Perforin and granzyme B were co-linear; thus, only granzyme B was further evaluated. Several other studies have shown higher AUCs for urine proteins versus urine mRNAs, as reviewed by Anglicheau et al. [4], highlighting the difficulties of urine as a matrix for RNA, in part because of a high RNA decomposition rate in urine. MicroRNAs MicroRNAs (miRNAs) are small noncoding RNAs that function as important regulators of gene and protein expression by RNA interference. They may also serve as biomarkers in transplantation. Lorenzen et al. found that urinary miR-10b and miR-210 were downregulated and miR-10a upregulated in patients with AR compared with controls. Only miR-210 differed between patients with acute cellular rejection compared with stable transplant patients with urinary tract infection or transplant patients before/after rejection [44]. In another study, a five-panel microRNA signature in blood (miRNA15B, miRNA-16, miRNA-103A, miRNA-106A, and miRNA-107) was associated with T cell-mediated vascular rejection [45]. Donor-derived cell-free DNA After allograft cell injury, donor-derived cell-free DNA (ddcfDNA) is released by the injured cell into the recipient circulation. By identifying genetic differences between the donor and recipient, dd-cfDNA can be quantified as a fraction of the total cfDNA in blood or urine [46]. After promising results in heart transplantation [47] and in small studies of kidney transplantation [46], dd-cfDNA was recently evaluated in the large prospective multicenter DART study of kidney transplant recipients. This biomarker showed an AUC of 0.74 for any rejection, but performed better when distinguishing antibody-mediated rejection (AUC 0.87). The test was unable to discriminate Banff 1A rejection when using the suggested cutoff of 1% dd-cfDNA. Other tests of alloimmune reactivity Measures of indoleamine 2,3 dioxygenase (IDO) enzyme activity, by assays of product kynurenine (kyn) to substrate tryptophan (trp) in peripheral blood, have shown elevations in blood and urine before early AR in the first 20 days [48]. Similar elevations in serum kyn were seen by Lahdou et al. in the first 6 months, where the AUC was 0.73 [49]. In pediatric kidney transplant recipients, longitudinal measurement of serum kyn/trp in the first year post-transplant showed a significant elevation within the same patient when AR occurred in the next 30 days, with an AUC of 0.67 [50].

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The FDA-approved Immuknow test measures the metabolic activity of CD4+ T cells [51]. Higher ATP levels from these cells were initially presented as a biomarker for AR. Later studies suggested a stronger association of low ATP levels with infection [52], although this was not shown in other studies with a longer gap between testing and rejection event, in both adults [53] and children [54] after kidney transplant. Longitudinal measures in pediatric kidney transplant showed a stronger association between a drop of ∼100 points in ATP levels and infection risk [50] in the next 30 days, but were not present in a study of adult recipients when AR was assessed in the next 90 days [55]. Longitudinal panels that simultaneously assess rejection and infection risk may be especially useful in children [56]. These results highlight how the statistical adjustment for multiple measures per subject has not been common in most transplant studies. Rather, each sample from a time point has been treated as independent even if from the same patient, which may or may not be true. Anglicheau et al. also highlight how slight changes in AUC and a lower prevalence of a condition can dramatically alter the PPV and NPV of a test [4]. Many other single-molecule pathways have been tested. Soluble CD30 from peripheral blood was initially promising as an AR biomarker [57, 58], but a meta-analysis of 12 studies showed poor overall accuracy of pre-transplant sCD30 levels, with AUC of 0.60 [59]. In one study of pediatric kidney transplant recipients, elevated post-transplant sCD30 levels were associated with AR [60]. Urinary fractalkine, another biomarker molecule, was found to have an AUC of 0.83 for TCMR.

Invasive biomarkers Biomarkers of allograft rejection are a laudable goal and the ideal with regard to patient safety and surveillance efficiency, but the reference gold standard of diagnosis remains poor, making comparisons difficult. That is to say, the histological diagnosis of for-cause biopsy tissue is hugely variable and diagnostic algorithms are arbitrary. Studies have shown that the diagnosis of rejection subtypes has large inter-observer error rates. A study of interobserver reproducibility from 14 countries in Europe revealed poor agreement, with Kappa values 0.195 to 0.378 [61]. For example, t1 vs t2 differs by 4 vs 5 lymphocytes in one tubular cross-section anywhere in the cortex, giving a Kappa value of 0.17. Furthermore, there is also controversy over the significance and relevance of the vlesion in transplant biopsies. Is it an antibody-mediated lesion, a cellular rejection-related lesion or is it contextdependent? To resolve these issues, several groups have used microarray technology to study the transcription profiles of biopsy tissue in an effort to more accurately

diagnose rejection, fibrosis risk, and even prognosticate future function and transplant survival [62–68]. MMx: the molecular microscope The molecular microscope is a method developed by the University of Alberta group. This method was developed initially by studying the gene transcripts expressed in animal models of rejection (TCMR), cell culture, and later in human biopsy studies. Pathogenesis-based transcript sets were developed to group gene transcripts into disease-related sets. The molecular phenotypes of the biopsies studied were determined using machine learning techniques to develop classifiers that can predict disease groups. To determine the molecular signatures associated with each disease group, a comparison was made against all other biopsy diagnoses to reduce the noise produced by gene sets common to multiple disease groups, for example, comparing TCMR biopsies with all other biopsies in the cohort, including normal, ABMR, and AKI-only tissue. Pathway analysis of the most differentially expressed genes in histological TCMR included T-cell receptor signaling and CTLA-4 co-stimulation. In histological ABMR transcripts, pathway analysis revealed angiogenesis, leucocyte–endothelial interactions, and NK-cell CD16a Fc receptor signaling. Transcripts common to all rejection were IFN-γ-induced, such as CXC11, CXCL9, CXCL10, and CCL4 expressed from CD8 T-cells, NK cells, and macrophages. Molecular classifiers were developed for each disease group (such as TCMR) using a linear discriminant analysis approach and repeated k-fold cross-validation on a 403-strong for-cause biopsy cohort. A central pathology laboratory adjudicated histological diagnoses. This set of biopsies was used as the discovery set and included pediatric patients (aged 5 to 81), with some pediatric patients in the ABMR subset (ages 13 to 61), but no pediatric patients in the TCMR subset [65, 66]. The INTERCOM study cohort consisted of 300 for-cause adult biopsies from international centers. This cohort was used as the validation set, relied on local histological diagnoses, and was used to determine the clinical applicability of the molecular microscope. The molecular TCMR score ability to predict a local histological diagnosis of TCMR or mixed rejection was good, with an AUC of 0.84 [69]. The molecular score reclassified 26% of the biopsies. Interestingly, 6 of the 13 clinical/histological polyoma virus biopsies were molecularly TCMR and 7 were molecularly negative. Thus, the molecular microscope cannot yet differentiate BK virus nephropathy (BKN) from TCMR and resolve current controversies regarding the possible relationship between BKN and TCMR. The TCMR molecular score (like histological diagnosis) was not associated with graft loss in the INTERCOM study. The ABMR molecular score, when applied to the INTERCOM biopsy cohort, also demonstrated a high degree of accuracy with an AUC of 0.85 [70]. The ABMR molecular score was

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high in biopsies in which a local diagnosis or suspicion of histological ABMR was reported, regardless of C4d positivity. The ABMR score also correlated with positive DSAs at the time of biopsy. Finally, multivariate analysis of a positive ABMR molecular score, but not histological ABMR (in biopsies >1 year post-transplant) was associated with graft loss. To assess the feasibility of the molecular microscope having an impact on clinical care, the INTERCOMEX study was performed [71]. This was an international study that included 451 biopsies with associated feedback from clinicians. Clinicians reported that the molecular approach agreed with clinical judgment more frequently than did histology (p = 0.0042). These results had a turnaround time of less than 48 h and potentially had an impact on clinical care. This test will be commercially available soon, but is not yet FDA-approved for clinical use. GoCAR The Genomics of Chronic Allograft Rejection (GoCAR) study was a multicentered, international, and prospective study that used the same microarray technology to define the transcript profiles from early post-transplant biopsies that might predict progressive injury, GFR decline, and fibrosis at 12 months [68]. This group used the chronic allograft disease index (CADI) at 3 months and 12 months post-transplant. The CADI score is a composite score of: 1. 2. 3. 4. 5. 6.

Diffuse or focal inflammation Fibrosis in the interstitium Mesangial matrix increase Sclerosis in glomeruli Intimal proliferation of vessels Tubular atrophy

Each individual parameter is scored from 0 to 3 [72]. Microarray analysis was performed on 159 biopsies performed per protocol at 3 months and a further 45 biopsies at 3 months were used as a validation set. A penalized regression model and permutation-based approach was used to define a gene set that optimally predicted high CADI-12 (12 months) score, CADI-3 score, and increasing CADI score from 3 months to 12 months. This resulting set of 13 genes was validated using qPCR in the 45-biopsy validation set and two external data sets consisting of a further 282 biopsies [73, 74]. This gene set was much better at predicting high fibrosis scores (high CADI score) at 12 months (AUC 0.967) than clinical or pathological data alone. Furthermore, the ability to predict remained even after 3-month biopsies with i + t > 2 were removed from the analysis (AUC 0.975). This gene set could also predict low Modification of Diet in Renal Disease (MDRD) eGFR (with a cutoff of 30 ml/min) at 12 and 24 months post-transplant, with AUCs of 0.872 and 0.928

respectively. The gene set also performed well in the external cohorts, with AUCs of 0.831 and 0.972. Table 2 compares the genes in the different gene array panels, either from peripheral blood or from tissue. Transplant genomics gene co-expression networks A third group sought to analyze the transcription profiles of biopsy tissue with interstitial fibrosis and tubular atrophy (IFTA) [67]. They used the same technology as the previous two groups to interrogate 234 for-cause and indication biopsies. They sought to compare the transcript profiles from IFTA biopsies with clinical and histological AF biopsies. They used kidney tissue with no AR and normal function as controls. AR was defined as acute cellular rejection with no evidence of BK, CMV, glomerulonephritis or infection. The group analyzed co-expressed genes to develop gene coexpression networks (GCN) and this was done in an unbiased manner. They used AR and IFTA samples to examine the biological processes that define these phenotypes using this approach (GCN). The take-home message from this work is that the gene profiles from all biopsies with IFTA were highly similar to those with AR alone, and that IFTA is a marker of ongoing immunemediated damage. Gene profiling can more sensitively detect these processes. Furthermore, molecular profiles of all biopsies with IFTA correlated with worse graft survival compared with normal biopsies. This was true even for biopsies with IFTA and no inflammation. This study did not define a Btest^ for AR as such; rather, it sought to highlight the pathological mechanisms common to AR and IFTA. Common rejection module Khatri et al. used a meta-analysis strategy to identify, from 8 transplant datasets (236 samples), common genes that were upregulated in rejecting tissue from all solid organ transplants [75]. A set of 11 genes was identified: BASP1, CD6, CD7, CXCL10, CXCL9, INPP5D, ISG20, LCK, NKG7, PSMB9, RUNX3, and TAP1. These genes were then found to be upregulated in a validation cohort of three additional datasets (796 samples). To our knowledge, this common rejection module is not yet commercially available.

Summary Many new and exciting technologies have led to potential new biomarkers for AF. Some are further ahead in the evaluation process than others. The AUCs of these tests may be greater when used in combination, to assess different components of immune reactivity or infection risk. Currently, none of these tests is used in regular clinical nephrology practice. Only a few of these tests are FDA-approved. Some tests are commercially

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available, such as DSA testing, CD4-ATP, and kSORT. However, this field is dynamic and moving quickly; thus, we expect some of these biomarker tests to become available and in routine clinical use in the near future. Compliance with ethical standards Disclosures No relevant disclosures.

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