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Volume: 18 Issue: 1 January 2020 - Supplement - 1

FULL TEXT

New Markers for Transplant Rejection

Monitoring allograft function after kidney transplant has routinely relied on the use of nonspecific markers, such as serum creatinine, glomerular filtration rate, proteinuria, and donor-specific antibodies. These traditional markers have low sensitivity and fail to detect subclinical changes. Diagnosis of renal allograft dysfunction still requires an allograft biopsy, as it remains the criterion standard for assessment of graft status. However, renal biopsy is an invasive procedure, and sampling errors may result in misdiagnosis, perhaps causing graft failure. New biomarkers have been developed to monitor allograft function, although many are not yet routinely used. Other shortcomings, such as lack of standardization and high cost, should be solved before their widespread application in the clinic. A recipient's immune status could be monitored by use of urine or blood samples. These include functional cell-based assays and the evaluation of molecular expression at the messenger RNA or protein levels. Molecular technologies, including molecular microscope diagnostic systems, have been recently developed to improve the yield of histologic evaluation of the allograft biopsy. Prospective, interventional trials are required to demonstrate whether these new biomarkers improve patient or transplant outcomes. Implementation of these technologies into standard clinical practice remains challenging until their generalizability, cost, ease of interpretation, and the identification of patients who may benefit from more than standard-of-care surveillance can be determined. These biomarkers could allow immunosuppressive therapy to be individualized for patients.


Key words : Blood biomarkers, Messenger RNA, Molecular microscope diagnosis, Urine biomarkers

Introduction

Although solid-organ transplant is the modality of choice for end-stage kidney, heart, liver, lung, pancreas, and bowel disease, the duration of allograft survival does not match the life expectancy of patients. Therefore, early diagnosis and effective treatments, especially those targeted for rejection, are needed to extend the survival of transplanted solid organs and the patients who receive them.1

Clinical parameters for diagnosis of allograft rejections are needed, including diminished ejection fraction in the case of cardiac transplant, diminished forced expiratory volume in 1 second in the case of lung transplant, and decreased glomerular filtration rate in the case of renal transplant. These clinical findings are usually seen at the last stage of allograft failure; therefore, identification of an ideal biomarker that predicts patients at risk of solid allograft rejection remains important for transplant specialists.

The National Institute of Health (NIH) defined biomarkers as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or phar­macologic responses to a therapeutic intervention.”2 An ideal biomarker should be able to diagnose or identify patients affected by a disease or an abnormal condition, should be able to stage the severity or extent of disease, should estimate the prognosis of a disease, and should predict and monitor clinical responses to interventions.

For diagnosis of renal allograft rejection, although not ideal, routinely used traditional biomarkers include increased creatinine and proteinuria (Table 1). These biomarkers can indicate the need for renal biopsy for diagnosis of rejection, with rejection then classified based on the Banff classification. However, biopsy, which is the criterion standard for diagnosis of acute and chronic renal transplant rejection, has its own restrictions, including inconvenience, invasive­ness, expense, poor concordance between patholo­gists, and changing Banff classification. Therefore, other biomarkers are needed to identify incipient allograft injuries, to discern the type of injuries, and, preferably, to predict allograft outcome. These nontraditional biomarkers should be readily available, accurate, inexpensive, and noninvasive.

New biomarkers should go through a stringent set of 7 stages before application in routine clinical prac­tice, as shown by Menon and associates.3 Presently, we are at stages 5 and 6; that is, these biomarkers are being examined for clinical trials before they are ready to be standardized and commercialized.3

New and Old Biomarkers in Serum, Urine, and Tissue
Efforts to identify clinically relevant transplant biomarkers have focussed on alloimmune pathways of antigen recognition, gene transcription, gene translation, posttranscription regulation of gene expression, antigen expression, cell activation, cytokine secretion, and antibody secretion.4

Antigen recognition biomarkers
A positive microcytotoxicity crossmatch assay predicates alloreactive antibodies in the recipient circulation and therefore predicts the likelihood of antibody-mediated rejection (ABMR). Although T- and B-cell flow cytometry crossmatches are more sensitive assays than complement-dependent cytotoxicity assays, they are insufficient to result in a positive microcytotoxicity assay. Over recent decades, T- and B-cell flow cytometry crossmatch has been increasingly used as the most sensitive crossmatch modality among crossmatch-negative kidney transplants. Negative T- and B-cell flow cytometry crossmatches have been associated with an approximate 15% reduction in the relative risk of acute rejection. Among older recipients and recipients of deceased-donor transplants, 5-year graft survival has improved.5 Therefore, use of flow cytometry crossmatch, particularly in high-risk groups, may improve transplant outcomes. However, although crossmatch remains the cornerstone of clinical transplant, it has several limitations, including the need for donor cells, fresh recipient serum to capture recent sensitizing events, and high false-positive rates, particularly with B-cell flow crossmatch.4

T-cell alloreactivity assays
Memory T-cell activity can be determined by measurement of interferon-gamma (IFN-γ) secretion by recipient T cells when exposed to donor antigens.

The IFN-γ enzyme-linked immunosorbent spot (ELISPOT) assay can quantify donor-reactive memory T cells before and after kidney transplant, with early studies showing that higher levels of cellular alloreactivity are associated with acute rejection, delayed graft function, and chronic allograft dysfunction. Although serial monitoring of T-cell alloreactivity with this assay may be helpful, the need for donor cells and the labor-intensive nature of ELISPOT makes this a less than an ideal biomarker for diagnosis of rejection (Figure 1). More recently, the FluoroSpot assay, which utilizes fluorochrome-conjugated detection antibodies, has been used. This assay allows simultaneous detection of multiple distinct cytokines and subsequent T-cell subpo­pulation analysis.6

There are 2 ways to stimulate T cells: by a nonspecific stimulus (such as phytohemagglutinin, concanavalin A, or phorbol 12-myristate 13-acetate/ionomycin) or by a donor-specific stimulus (splenocytes or B lymphocytes from the donor). Although a specific stimulus is preferable to determine the specific response against the donor, obtaining this source of donor cells can sometimes be difficult. In addition, when this type of activation is used, only some T-cell clones will expand. This phenomenon is a disadvantage when testing, for example, patient sensitivity against a variety of drugs (in pre­transplant situations). Moreover, anergic patients show a low response to alloantigens. The type of nonspecific stimulus used to activate T cells depends on the signaling pathway blocked by the immuno­suppressive drug of interest. Different stimuli could be used to activate distinct pathways. Phorbol 12-myristate 13-acetate/ionomycin is an appropriate stimulus to activate T cells, whereas concanavalin A is suitable to evaluate lymphocyte proliferation.7

Adenosine triphosphate (ATP) secretion is thought to be one of the earliest steps to follow antigen recognition. In this assay, named “Immuknow,” CD4-positive T cells are magnetically isolated from mitogen-stimulated peripheral blood mononuclear cells and then lysed to facilitate measurement of ATP release. Low ATP levels are associated with infections, whereas high levels are associated with increased likelihood of rejection. A large prospective study is necessary to confirm these associations over the long term (Figure 2).

Expression of T-Cell Genetic Biomarkers
CD8 biomarkers
CD8-positive T cells induce target cell apoptosis by producing perforin and granzyme B (GRB), which could lead to tubular epithelial cell damage associated with kidney rejection. The mechanisms responsible for CD8-positive regulatory T-cell (Treg) suppression include immunosuppressive cytokines and the killing of targeted T cells via the perforin/GRB and Fas ligand (FasL)/Fas pathways, as well as the downregulation of CD80/CD86.8

To assess the noninvasive diagnostic performance of GRB and perforin for acute rejection, a systematic analyses of 16 studies (which included 680 patients) showed that the probability of developing acute rejection in kidney transplant recipients increased from 15% to 73% when both GRB and perforin tests were positive; this probability was reduced to 2% if these tests were negative.9 However, the study concluded that neither GRB nor perforin, if evaluated alone, could be a convincing noninvasive diagnostic marker for acute rejection. Their combined use after kidney transplant may be a better choice for evaluation of acute rejection, allowing better evaluation of need for allograft biopsy and earlier therapeutic intervention. Stability of mRNA in decaying cells may limit the precision of these assays, which are yet to be validated in large trials.

CD4 biomarkers
FOXP3 Tregs are mainly CD4-positive T cells that exert suppressive action in an activated T-cell milieu. Their activity is mediated by cytokine deprivation, direct inhibition, or even cytotoxicity of neighboring activated T cells. Increased FOXP3 mRNA in excreted urinary cells has been associated with reversibility of allograft rejection when studied in kidney transplant recipients with or without recovery from acute rejection.

A multistage systematic review of peripheral blood immune cell phenotypes encompassing all regulatory cells in kidney transplant recipients suggested new markers of acute rejection-associated acute allograft dysfunction events in kidney transplant recipients treated with mammalian target of rapamycin inhibitors alone or combined with belatacept.10 Patients with increased Tregs presented with low frequency of acute rejection-associated acute allograft dysfunction events compared with those in which the number of Tregs remained unchanged or even diminished (odds ratio = 95%). Nevertheless, trials to consider Tregs in the circulation as a predictive biomarker are scarce.10

Although peripheral blood FOXP3 transcripts are significantly lower in kidney transplant recipients with chronic rejection than in stable recipients, intragraft FOXP3 mRNA levels have been shown to be correlated with severity and progressive cellular rejection.11

Genome-Wide Association Study for Allograft Rejection
Despite recent acceptance of whole genome sequencing, genome-wide association studies using arrays of single-nucleotide polymorphism remain a powerful approach to identify novel genes or loci by analyzing millions of genetic variants, with no a priori hypothesis on gene function, allowing for the discovery of previously unrealized pathways. Studying genetic susceptibility of acute rejection is particularly complex, as acute rejection is not an isolated disease but a complication resulting from alloreactivity that is modulated by recipient, donor, and immunosuppressive therapy factors. Finding the gene(s) variant(s) would be an important milestone for predicting rejection.12

The use of peripheral blood for gene expression profiles as a minimally invasive means to circumvent the need for biopsy has been extensively studied. Several products are commercially available or soon to be commercially available, including TruGraf, kidney solid-organ response test (kSORT), and Allosure. TruGraf measures expression levels and is intended to be used to monitor stable grafts and to provide early detection of grafts that may develop rejection. The kSORT uses quantitative real-time polymerase chain reaction to measure expression of 17 genes to provide a risk score for acute rejection, whereas Allosure uses donor-derived cell-free DNA levels to discriminate between rejection and no rejection. However, because of limitations with urine and blood genetic biomarkers, kidney transplant biopsy remains the procedure of choice for diagnosis of rejection. In this regard allograft biopsy for genetic study by microarray technology allows more objec­tive, quantitative, and biology-based measurements than conventional histopathology-based diagnoses. In addition, it promises a more clinically relevant diagnostic depiction of the rejection process, providing clinicians more confidence in their clinical management decisions.13

Molecular Microscope Diagnostic System
The molecular microscope diagnostic system (MMDS) is a microarray-based system that evaluates gene expression in kidney allograft biopsies to predict T-cell mediated or ABMR, even before changes appear with standard pathology methods. The strategy of this system to improve risk stratification in early kidney allograft ABMR was examined in 939 kidney transplant recipients at Necker Hospital (2004–2010; principal cohort) and 321 kidney recipients at Saint Louis Hospital (2006–2010; validation cohort) through assessment of ABMR during year 1 posttransplant.14 Patients with ABMR and who have similar histopathologies may show different levels of molecular signals, reflecting distinct activity and disease state. The study concluded that MMDS provided insight beyond the classic, histology-based approach and allowed for risk stratification that could guide clinical management and clinical trials in transplant medicine. After the MMDS has interpreted the allograft biopsy, microarray testing with a specific transcript set named “pathogenesis-based transcript sets” could be used to differentiate among T-cell-mediated rejection, ABMR, interstitial fibrosis/tubular atrophy, and acute kidney injury phenotypes.

In addition to diagnosis, MMDS could assist with prognostic stratification. It has also other advantages, including smaller biopsy sample required and the reproducibility of results.

Kidney Solid-Organ Response Test
The kidney solid-organ response test is a microarray-based assay that was developed to detect patients with high risk for acute rejection. This test uses quantitative polymerase chain reaction to measure the relative mRNA expression levels of 17 genes known to be associated with acute rejection with 93% sensitivity and specificity. The kSORT is a less invasive microarray-based evaluation of peripheral blood for identification of a set of 17 genes involved in leukocyte trafficking, activation, adhesion, and cytolysis.15 Gene expression data in 558 blood samples from 436 renal transplant patients collected across 8 transplant centers in the United States, Mexico, and Spain between 5 February 2005 and 15 December 2012 in the Assessment of Acute Rejection in Renal Transplantation reported that kSORT predicted acute rejection up to 3 months before detection via allograft biopsy. The test detected acute rejection in blood independent of age, time posttransplant, and sample source without additional data normalization (area under the concentration curve = 0.93; 95% confidence interval, 0.86–0.99). The test could predict rejection with high sensitivity and specificity but could not distinguish between T-cell rejection and ABMR.15 Combination of kSORT with other tests such as the antidonor IFN-γ ELISPOT assay may help distinguish the rejection phenotype even more precisely.

Donor-Derived Cell-Free DNA Analysis
During allograft rejection, large amounts of donor-derived cell-free DNA are released from the injured allograft into the bloodstream. After transplant, cell-free DNA derived from the donor organ can be detected in the recipient’s circulation. Its release is representative of cell damage during transplant; thus increased donor-derived cell-free DNA may be a good indicator of graft injury, shown by a sudden increase of donor-derived cell-free DNA in the recipient’s circulation followed by a decrease within 1 week after transplant. Plasma donor-derived cell-free DNA fractions were shown to decrease exponentially within 10 days after transplant to a donor-derived cell-free DNA threshold value of 0.88% or less.16 In solid-organ transplant patients, increased plasma donor-derived cell-free DNA was observed during rejection compared with that shown in recipients with stable graft function, thus suggesting a role for donor-derived cell-free DNA measurement as a good biomarker for rejection. In a systematic review17 of 47 studies (18 kidney, 7 liver, 11 heart, 1 kidney-pancreas, 5 lung, and 5 multiorgan), donor-derived cell-free DNA dropped rapidly within 2 weeks posttransplant, with baseline levels varying by organ type. Levels were elevated in the presence of allograft injury, including acute rejection and infection, and returned to baseline after successful treatment. Elevation of cell-free DNA levels was seen before clinically apparent organ injury. Discriminatory power was greatest for higher grades of T-cell-mediated and acute ABMR, with high negative predictive values. The study concluded that cell-free DNA is a promising biomarker for monitoring patients after solid-organ transplant. Future studies are needed to define how it can be used in routine clinical practice and to determine clinical benefits with routine prospective monitoring.

Urine Biomarkers
The most easily and noninvasive sampling for evaluation of allograft function is urine after transplant. The transplanted kidney may act as an “in vivo” flow cytometer, sorting cells involved in rejection into the urine. The most familiar groups of urine biomarkers specified for diagnosis of rejection include urine mRNA, urine chemokines, urine micro-RNA (miRNA), and urine proteomics.

Urine mRNA
Although several potential mRNAs (eg, perforin, GRB, IFN-inducible protein-10, CD3, and FOXP3) could distinguish or even predict the diagnosis of acute T-cell-mediated rejection more than 75%, the potential for extensive degradation of mRNAs in urine may be 1 important limitation of this assay.

Apoptosis affected by cytotoxic T lymphocytes, thought to play a major role in renal allograft rejection, is mediated by 2 major pathways: the Fas/FasL lytic pathway and the perforin/GRB degranulation pathway. The mechanism of cell death by perforin is the induction of holes in the target-cell membranes; cell death by GRB is induction of DNA fragmentation. Urinary mRNA levels of perforin and GRB are highly accurate in predicting acute rejection (perforin mRNA sensitivity of 83% and specificity of 83%; GRB mRNA sensitivity of 79% and specificity of 77% compared with stable controls).18 Although perforin, GRB, and FasL gene expression are each up-regulated in clinical settings other than due to acute rejection, combining these urinary biomarkers may yield a higher test performance.

Urinary FOXP3 mRNA, which is a marker of Tregs, inhibits autoreactive immune response acti­vation. It is identified as a CD4-positive T-cell subpopulation that expresses CD25 and cytotoxic T-lymphocyte antigen 4 on their cell surfaces and releases suppressor cytokines interleukin 10 and interleukin 35, suggesting a suppressor role for these cells. Urinary FOXP3 mRNA levels may offer a noninvasive test to help predict acute rejection and improve outcomes for renal transplant patients. Significant inverse relationships were shown between FOXP3 mRNA levels and serum creatinine measured during an episode of acute rejection and between urinary FOXP3 levels and time from kidney transplant to development of acute rejection. In addition, urinary FOXP3 mRNA levels were significantly higher in patients with successful reversal of rejection than in those without reversal. Combined FOXP3 transcripts and serum creatinine levels were thus shown to better predict reversal of rejection (90% sensitivity and 96% specificity) than either FOXP3 transcripts or serum creatinine alone (90% sensitivity and 73% specificity and 85% sensitivity and 90% specificity, respectively). In addition, patients with acute rejection and high levels of urinary FOXP3 responded better to steroid treatment and had significantly lower risk of graft failure.19 Reversal of acute rejection can be predicted with 90% sensitivity and 73% specificity with use of the optimal identified cutoff for FOXP3 mRNA of 3.46 (P = .001). FOXP3 mRNA levels identified patients at risk for graft failure within 6 months after an episode of acute rejection.

Perforin, GZB, and FOXP3 mRNA analyses may be also be used in the setting of acute rejection and delayed graft function.

Urine chemokines
A number of chemokines are produced during an episode of acute rejection, suggesting their possible use as urinary biomarkers. CXCR3-binding chemokines CXCL9 (monokine induced by IFN-γ), CXCL10 (IFN-γ-inducible protein 10, IP-10), and CXCL11 (IFN-γ-inducible protein 9) are important signaling molecules for recruiting alloantigen-primed T cells to the site of inflammation and for enhancing proinflammatory cytokine production. These chemokines are secreted by leukocytes in the transplanted kidney and by tubular epithelial cells. They induce, maintain, and amplify inflammatory and immune reactions. Urinary CXCR3 and CXCL10 mRNA levels were higher in patients with acute rejection than in those without acute rejection.20 Measurement of mRNA encoding IP-10 or the chemokine receptor CXCR3 in urinary cells offers a noninvasive means of elucidating cellular traffic causing acute rejection of human renal allografts. Receiver operating characteristic curve analyses demonstrated that acute rejection can be predicted with a sensitivity of 100% and a specificity of 78% using the (log-transformed) cutoff value of 9.11 copies of IP-10 and with a sensitivity of 63% and a specificity of 83% using the cutoff value of 11.59 copies of CXCR3. Immunohistologic analyses of allograft biopsies showed exuberant expression of IP-10 and CXCR3 during acute rejection, whereas both were absent in grafts with stable function. Measurement of CXCR3 mRNA had a lower sensitivity (63%) for acute rejection but a higher specificity (83% vs 78%) than a CXCL10 assay that used a cutoff value of 11.59 copies.

The CXCL10 chemokine may be an interesting candidate to uncover ongoing immune processes within the graft. As shown by histology and long-term graft function assessed by the glomerular filtration rate 6 months posttransplant, IP-10 levels are correlated with incidence of acute rejection episodes. Expression of IP-10 in urine of patients with ongoing acute rejection episodes several days before biopsy was indicated by rising serum creatinine levels. Most importantly, elevated levels of urinary IP-10 protein within the first 4 weeks posttransplant were predictive of graft function at 6 months even in the absence of acute rejection. These data reveal a correlation between elevated IP-10 expression in urine at early time points posttransplant and intragraft immune activation that leads to acute rejection and compromised long-term graft function.20

The urinary CXCL10-to-creatinine ratio can distinguish borderline, subclinical, and clinical tubulitis from normal histology and interstitial fibrosis and tubular atrophy. With cutoff value of 2.87 ng CXCL10/mmol creatinine, the ratio had 81.8% sensitivity and 86.4% specificity to differentiate normal transplant from subclinical and clinical tubulitis. CXCL10 has been validated as a specific marker of active inflammation and confirmed CXCL10 as a noninvasive, sensitive, and specific marker for tubulitis. These data confirmed that urine chemokine monitoring can identify patients with renal allograft inflammation. The assay is not a specific diagnostic test for rejection, but it may be useful as noninvasive tool to distinguish among allograft recipients who require closer observation versus those with a benign clinical course. Urinary CXCL9 protein will be better than urinary CXCL9 mRNA; combining CXCL9 protein and CXCL9 mRNA provides the best positive (71.4%) and negative (92.5%) predictive values for diagnosing or ruling out acute rejection. CXCL9 can be a marker for excluding acute rejection episodes with low CXCL9, which could indicate low immunologic risk that may predict stable long-term allograft function. In addition to proven efficacy, testing for urinary CXCL9 and CXCL10 is available on an enzyme-linked immunosorbent assay or Luminex-based platform and is simple, noninvasive, and cost-effective and therefore ideally suited to longitudinal monitoring.

Urine micro-RNA
Research on the role of noncoding RNAs (miRNAs) has recently substantially increased. Micro-RNAs are endogenous, single-stranded molecules made up of around 22 noncoding nucleotides. They act as key regulators of B- and T-cell differentiation, maturation, and proliferation and play a role in Treg function and antigen signaling. They are characteristically stable in urine samples and in formalin-fixed tissues and highly resistant to freeze-thaw cycles.21 Their role in regu­lation of pathologic processes, their relative tissue specificity, and their presence in biological fluids have triggered translational research into the potential utility of miRNAs as noninvasive biomarkers.

Urinary miRNA not only shows potential as a novel marker to detect acute rejection but may also help predict outcomes in renal transplant patients with acute rejection. Urinary miRNA of patients with acute rejection, stable patients without rejection, patients before and after rejection, and patients with urinary tract infections showed that miR-10a, miR-10b, and mi-R210 were downregulated in urine samples collected during acute rejection. After successful treatment for rejection, miR-210 expression increased to stable levels. Furthermore, low levels of urinary miR-210 were significantly associated with a decline in glomerular filtration rate at 1 year posttransplant. Consequently, urinary miR-210 may serve as a novel biomarker for acute rejection and in predicting allograft outcome.

Urine proteomics
Development of "omics" methods in the field of transplant has paved the way for the development of several candidate biomarkers. Omics research includes genomics (studying the genome to estimate the risk for an individual to develop a disease), transcriptomics (studying expression patterns of all gene transcripts, such as mRNA), proteomics (systematic analysis of proteins for their identity, quantity, and function), and metabolomics (quantitative analysis of all metabolites of a specific biologic sample). Mass spectrometry offers a nonbiased high-throughput approach to identify 1 or more markers of rejection. A number of urinary immune-related proteins have been identified as biomarkers of acute rejection of the renal allograft. These include the protein products of the specific genomes, such as elevated urinary levels of chemokine (C-X-C motif) ligands 9 and 10 (CXCL9 and CXCL10). Monocyte chemoattractant protein 1 at 6 months posttransplant predicted severe interstitial fibrosis and tubular atrophy and graft dysfunction at 2 years posttransplant. However, low urinary CXCL10 levels were associated with improved rejection-free allograft survival at 1 year posttransplant. A low level of urinary CXCL10 showed a 97% negative predictive value for T-cell rejection; therefore, this could be a candidate to test optimal immunosuppressive drug weaning. However, increased urinary CXCL9 and CXCL10 levels have also been detected in patients with BK virus infection; hence, the positive predictive value of the test is lower than optimum. In one study, urine samples were studied of 108 patients and an independent validation set of 154 patients, which comprised the clinical biopsy-confirmed phenotypes of acute rejection, stable graft, chronic allograft injury, BK virus nephritis, nephrotic syndrome, and healthy, normal control.22 Increased levels of fibrinogen beta, fibrinogen gamma, and HLA-DRB1 were validated by enzyme-linked immunosorbent assay in acute rejection using an independent sample set. The fibrinogen proteins further segregated acute rejection from BK virus nephritis. This finding thus supported the utility of monitoring these urinary proteins for the specific and sensitive noninvasive diagnosis of acute renal allograft rejection.

Alloantibody Detection as Rejection Biomarkers
Monitoring of the donor-specific antibodies (DSAs) is based on pre- or posttransplant levels. Preexisting or de novo donor-specific anti-HLA and non-HLA antibodies often precede the development of proteinuria, rapid decline in allograft function, and allograft failure. Although techniques for alloantibody detection have recently progressed, complement-dependent cytotoxicity crossmatch remains in widespread use. Increased sensitivity of this assay has been provided by flow cytometry crossmatching and the advent of solid-phase assays (ie, Luminex), approved by the Food and Drug Administration for qualitative use, although, in practice, often interpreted per their quantitative strength. They are used in generating calculated panel reactive antibody titers and virtual crossmatch testing and for longitudinal screening. The high sensitivity of the Luminex assay results in significant false-positive results (Table 2). Attempts to address this pitfall were made by measuring the complement binding ability of Luminex-detected donor-specific anti-HLA antibodies.

In an observational study,23 preexisting donor-specific HLA antibodies (HLA-DSA) and incidence of acute ABMR were studied versus survival of patients and grafts among 402 consecutive deceased-donor kidney transplant recipients. The use of Luminex single-antigen assays on the peak reactive and current serum samples showed that all patients had a negative lymphocytotoxic crossmatch test on the day of transplant. Eight-year graft survival was significantly worse (61%) in patients with preexisting HLA-DSA than in sensitized patients without HLA-DSA (93%) and nonsensitized patients (84%). Peak HLA-DSA Luminex mean fluorescence intensity (MFI) predicted ABMR better than current HLA-DSA MFI (P = .028). As MFI of the highest-ranked HLA-DSA detected on peak serum increased, graft survival decreased and the relative risk for ABMR increased: Patients with MFI > 6000 had > 100-fold higher risk for ABMR than patients with MFI < 465 (relative risk 113; 95% confidence interval, 31-414). The presence of HLA-DSA was not associated with patient survival, and risk of ABMR and graft loss was directly correlated with peak HLA-DSA strength. Thus, quantification of HLA antibodies allows stratification of immunologic risk, which should help guide selection of acceptable grafts for sensitized patients.

Conclusions

Implementation of the new biomarkers into standard clinical practice remains challenging. Most new biomarkers have high negative predictive value for rejection but not enough positive predictive value to allow a treatment decision for patients before renal biopsy. Dedicated, prospective, interventional trials are required to demonstrate that the use of these biomarkers improves patient or transplant outcomes. Significant limitations should be solved before regular use in clinical practice; these include generalizability, cost, ease of interpretation, and identification of patient populations who may benefit from more than standard-of-care surveillance. A definitive diagnosis of renal allograft dysfunction still requires an allograft biopsy, which remains the criterion standard to assess graft status. Array-based and/or targeted assays that aim to predict rejection, alloantibody formation, infection, fibrosis, or allograft loss by utilizing kidney tissue, peripheral blood, and urine are under investigation.


References:

  1. Valenzuela NM, Reed EF. Antibody-mediated rejection across solid organ transplants: manifestations, mechanisms, and therapies. J Clin Invest. 2017;127(7):2492-2504.
    CrossRef - PubMed
  2. Salvadori M, Tsalouchos A. Biomarkers in renal transplantation: An updated review. World J Transplant. 2017;7(3):161-178.
    CrossRef - PubMed
  3. Menon MC, Murphy B, Heeger PS. Moving biomarkers toward clinical implementation in kidney transplantation. J Am Soc Nephrol. 2017;28(3):735-747.
    CrossRef - PubMed
  4. Safa K, Magee CN, Azzi J. A critical review of biomarkers in kidney transplantation. Curr Opin Nephrol Hypertens. 2017;26(6):509-515.
    CrossRef - PubMed
  5. Salvalaggio PR, Graff RJ, Pinsky B, et al. Crossmatch testing in kidney transplantation: patterns of practice and associations with rejection and graft survival. Saudi J Kidney Dis Transpl. 2009;20(4):577-589.
    CrossRef - PubMed
  6. Korber N, Behrends U, Hapfelmeier A, Protzer U, Bauer T. Validation of an IFNgamma/IL2 FluoroSpot assay for clinical trial monitoring. J Transl Med. 2016;14(1):175.
    CrossRef - PubMed
  7. Millan O, Urtasun N, Brunet M. Biomarkers of the immunomodulatory effect of immunosuppressive drugs in transplant recipients. Transplant Rev (Orlando). 2009;23(2):120-128.
    CrossRef - PubMed
  8. Su J, Xie Q, Xu Y, Li XC, Dai Z. Role of CD8(+) regulatory T cells in organ transplantation. Burns Trauma. 2014;2(1):18-23.
    CrossRef - PubMed
  9. Heng B, Li Y, Shi L, et al. A Meta-analysis of the significance of granzyme B and perforin in noninvasive diagnosis of acute rejection after kidney transplantation. Transplantation. 2015;99(7):1477-1486.
    CrossRef - PubMed
  10. Herrera-Gomez F, Del Aguila W, Tejero-Pedregosa A, et al. The number of FoxP3 regulatory T cells in the circulation may be a predictive biomarker for kidney transplant recipients: A multistage systematic review. Int Immunopharmacol. 2018;65:483-492.
    CrossRef - PubMed
  11. Veronese F, Rotman S, Smith RN, et al. Pathological and clinical correlates of FOXP3+ cells in renal allografts during acute rejection. Am J Transplant. 2007;7(4):914-922.
    CrossRef - PubMed
  12. Ghisdal L, Baron C, Lebranchu Y, et al. Genome-wide association study of acute renal graft rejection. Am J Transplant. 2017;17(1):201-209.
    CrossRef - PubMed
  13. Barner M, DeKoning J, Kashi Z, Halloran P. Recent advancements in the assessment of renal transplant dysfunction with an emphasis on microarray molecular diagnostics. Clin Lab Med. 2018;38(4):623-635.
    CrossRef - PubMed
  14. Loupy A, Lefaucheur C, Vernerey D, et al. Molecular microscope strategy to improve risk stratification in early antibody-mediated kidney allograft rejection. J Am Soc Nephrol. 2014;25(10):2267-2277.
    CrossRef - PubMed
  15. 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.
    CrossRef - PubMed
  16. Gielis EM, Beirnaert C, Dendooven A, et al. Plasma donor-derived cell-free DNA kinetics after kidney transplantation using a single tube multiplex PCR assay. PLoS One. 2018;13(12):e0208207.
    CrossRef - PubMed
  17. Knight SR, Thorne A, Lo Faro ML. Donor-specific cell-free DNA as a biomarker in solid organ transplantation. A systematic review. Transplantation. 2019;103(2):273-283.
    CrossRef - PubMed
  18. 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):947-954.
    CrossRef - PubMed
  19. 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):2342-2351.
    CrossRef - PubMed
  20. 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):1683-1690.
    CrossRef - PubMed
  21. Merhi B, Bayliss G, Gohh RY. Role for urinary biomarkers in diagnosis of acute rejection in the transplanted kidney. World J Transplant. 2015;5(4):251-260.
    CrossRef - PubMed
  22. Sigdel TK, Salomonis N, Nicora CD, et al. The identification of novel potential injury mechanisms and candidate biomarkers in renal allograft rejection by quantitative proteomics. Mol Cell Proteomics. 2014;13(2):621-631.
    CrossRef - PubMed
  23. 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):1398-1406.
    CrossRef - PubMed


Volume : 18
Issue : 1
Pages : 1 - 9
DOI : 10.6002/ect.TOND-TDTD2019.L6


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From the Urology and Nephrology Research Center, Shahidbeheshti University of Medical Sciences, Tehran, Iran
Acknowledgements: The author has no sources of funding for this study and no conflicts of interest to declare.
Corresponding author: Hassan Argani, Urology and Nephrology Research Center, Shahidbeheshti University of Medical Sciences, Tehran, Iran
E-mail: hassanargani@gmail.com