Biomarkers of Kidney Injury and Rejection

Definition of Biomarker and Surrogate Endpoint

Biomarker is one of the most misused terms in transplantation. A PubMed search of “biomarker and kidney transplantation” revealed 5857 articles. Yet most of these potential biomarkers have not undergone the rigorous validation required for clinical use. The definition of biomarker and SEPs are outlined in Table 26.1 . According to the Biomarkers Definitions Working Group a biomarker is: “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.” Predictive biomarkers (i.e., those that forecast the likely response to a specific treatment) require extensive external validation, ideally in external randomized clinical trials and meta-analysis. Biomarkers can take on many forms, from cellular assays, to imaging or serum markers. However, as transplant immunosuppression becomes more target specific, such as with newer agents like belatacept and Janus kinase (JAK) inhibitors, molecular and genetic biomarkers will become increasingly important. If transplant immunosuppression is to become more personalized then biomarker discovery is an essential prerequisite. Without validated biomarkers it will not be possible to rationally stratify patients to different treatments based on risk and expected response to therapy. Therefore the first step is a sound understanding of the underlying pathophysiology of what we are trying to prevent (e.g., acute cellular rejection [ACR]). The more biologically plausible the biomarker is, the more likely it is to be adopted for clinical use.

Prognostic biomarkers do not necessarily have to predict the effect of treatment on a particular outcome but they still require robust statistical validation to be clinically useful ( Fig. 26.1 ). First, there must be a demonstration that a correlation exists between the biomarker and the outcome of interest (e.g., graft loss). Second, there must be an independent statistical validation of the relationship. Depending on the robustness of the clinical data used to show an association between the biomarker and the endpoint further studies will be required. If the association was demonstrated on retrospectively collected samples or tissues, then it needs to be validated prospectively and then by multicenter validation. Ultimately its clinical usefulness will require evaluation in randomized clinical trials where it is not clear what the most appropriate treatment is for an individual patient based on the best clinical indicators available. For instance, patients at low risk of rejection or graft loss may benefit from minimizing immunosuppression over time, whereas patients at high risk of graft loss would require a completely different strategy. Recent trials have shown that we are not good at predicting this early after transplant, and a prognostic biomarker would be useful if it could reliably stratify patients to different treatment options.

Just as biomarkers are an essential component for stratifying patients for clinical trials, SEPs will be an essential component of trial design. SEPs are measurements that provide early and accurate prediction of both the clinical endpoint (e.g., graft loss) and the effects of treatment on this endpoint. For surrogates to be fully accepted they need to be validated in clinical trials. Whereas observational studies and meta-analysis can demonstrate that a SEP is prognostic for disease outcome, it requires randomized clinical trials to show that the effect of the intervention on the SEP correlates sufficiently with the effect on the true endpoint. This concept is more advanced in oncology than it is in transplantation. Yet even in oncology there are few SEP for solid tumors, whereas the most accepted SEP is in nonsolid hematologic tumors where clinical remission has long been regarded as a marker of time to disease progression and overall survival.

There are several candidates for SEP in transplantation. The most important SEP is one that predicts patient death and/or graft loss. In transplantation, death and graft loss are relatively late events and therefore are impractical endpoints for clinical trials in transplantation. Therefore trials focused on events in the first 1 to 3 years tend to give little insight into important outcomes over the long term. People have proposed several SEPs that are predictive of death and graft failure including: 1-year serum creatinine, doubling of serum creatinine, acute rejection, spot albumin:creatinine ratios, and delta estimated glomerular filtration rate (eGFR). Although none of these is perfect, delta eGFR seems the most promising at this point. A good SEP needs to be both prevalent and predictive. When evaluated in patients enrolled in the ANZDATA registry a decline in eGFR of ≥30% from year 1 to 3 posttransplant not only was found to be reasonably common, occurring in 10% of patients but also was associated with a 2.2-fold risk of death and a 5-fold increase in death-censored graft failure. Other SEPs such as 1-year serum creatinine were less predictive than a delta measurement, whereas a doubling of serum creatinine was more predictive than a ≥30% change in GFR but also less prevalent, occurring in only 1% of recipients. Spot urine albumin:creatinine ratio might be predictive of hard endpoints but is difficult to standardize and lacks specificity. Other more recent predictors of graft loss include inflammation in areas of interstitial fibrosis, inflammation within areas of interstitial fibrosis and tubular atrophy (iIFTA), and de novo donor-specific antibody (DSA). More information is required to determine how predictive iIFTA is of graft loss using modern immunosuppression, but it does require a biopsy to derive the SEP. De novo DSA will be discussed in detail in the later part of this chapter.

The problem with all these measurements is that most have been validated from registry studies, which are retrospective populations recruited over a long timeframe and covering many different eras of immunosuppression. For instance, patients on cyclosporine and azathioprine may have more episodes of rejections than those on other immunosuppressive regimens, often with greater severity, and this may skew the assessments of prediction. What is required now is to validate these tests in prospectively recruited patient cohorts on current standard of care immunosuppression to confirm these original observations. If these observations are confirmed, the next step is to use the SEP in a randomized clinical trial to test its clinical utility. For a SEP to be useful as an endpoint in a clinical trial the effect of any trial therapy on the SEP must be equivalent to the effect on the hard endpoint (e.g., death-censored graft loss).

In essence, the greater the association of the SEP with the clinical endpoint and the greater the statistical validation, the more likely it is to be accepted for interventional clinical trials. Ideally SEP should adhere to the Prentice criteria ( Table 26.2 ). The surrogate should predict the clinical endpoint, treatment or intervention should have a significant effect on both the surrogate and the clinical endpoint, and the treatment effect on the surrogate should mirror the treatment effect on the clinical endpoint. This latter point is ideal but has been proven to be unrealistic. An alternative definition is that the surrogate must be prognostic of transplant outcome and the effect of treatment on the SEP must correlate sufficiently with the effect on the true outcome. Either way the correlation must be validated in clinical trials or meta-analysis. In addition, the SEP should be biologically plausible.

New Technologies for Biomarker Assays

The advent of the new “omic” technologies has transformed the field of biomarker development. They provide new tools to accurately identify the underlying pathology associated with graft injury and acute rejection. As with all new technologies they need robust validation in the clinical setting, and initial exploratory findings must be developed into easy-to-use, reproducible, and accessible clinical assays. Older genomic techniques confined the study of disease phenomena to one or a few candidate genes at a given time. These genes were selected based on biologic plausibility or animal experimentation, and the studies were hypothesis-driven. High-throughput technologies, such as microarray and RNA-sequencing, are able to identify the differential expression patterns of thousands of genes associated with a particular outcome or disease state. By combining these with computational analysis it is possible to reveal the cumulative signaling pathways operational in a pathologic state. With this information, it is possible to develop gene-based biomarkers that can be used clinically to stratify patients according to risk.

Important characteristics of commonly employed strategies to examine genomic information in research settings in transplantation are summarized in Table 26.3 . Microarrays work on the principle of DNA complementarity (see review). The NanoString nCounter system is a new technology for molecular profiling that uses a novel color-coded barcode technology, with each barcode representing a single target molecule. It enables digital counting of individual molecules without the need for amplification. Up to 800 transcripts can be profiled in a single reaction, and it has been applied to samples of blood, raw cells, and urine. Its role in transcriptome profiling in other biomedical fields, such as oncology, has been prominent in recent years, but only recently has it been used in the setting of renal transplantation.

Proteomics is the large-scale study of proteins. Previously, profiling of the proteome was limited by its complexity, but has gained significant momentum with technologic advancements, particularly in relation to mass spectrometry (MS). Urine has been frequently used in both nontargeted and targeted proteomic studies of the renal allograft, as has peripheral blood. Earlier studies using MS-based technology in urine proteomic profiling identified protein signals that were present in subjects with acute rejection, but did not further characterize the protein associated with these signals. In a proof of principle study, surface-enhanced laser desorption/ionization—time of flight—mass spectrometry (SELDI-TOF-MS) was used to discover urine protein peaks associated with ACR. A follow-up study revealed that these were protein derivatives of nontryptic cleaved forms of β-2 microglobulin. However, a multicenter study did not identify a statistically significant correlation of intact or cleaved β-2 microglobulin in urine in the presence of acute rejection. This prospective targeted observational study conducted through the Clinical Trials in Organ Transplantation-01 (CTOT-1) protocol is described in more detail later. Other studies have applied a variety of proteomic techniques to identify protein expression in an unbiased high-throughput manner in allograft rejection, before using targeted enzyme-linked immunosorbent assay (ELISA) for validation of biomarker panels. However, as with the majority of promising proteomic studies to date, multicenter prospective trials are required to validate these candidate markers before meaningful conclusions can be made.

Transcriptomics is the study of the RNA transcripts encoded by the genome. Although much of the work in the past decade has been in mRNA evaluation, studies in microRNA (miRNA) expression profiling in the setting of renal transplantation has increased exponentially in recent years. Small, noncoding RNAs, miRNAs regulate gene expression of target mRNAs. Similar to genome and mRNA profiling, large-scale sequencing of miRNAs has been dominated by microarray applications. One of the advantages of this technology is that there are far fewer miRNA than genes and hence the computational analysis is simpler, and there remains the possibility that a more robust marker could be identified as a predictor of either acute rejection or IFTA. Scian and Mas have elegantly summarized miRNA profiling studies of the renal allograft in their reviews. To date no conclusive patterns of miRNA expression have been associated with acute rejection or IFTA.

Metabolomics relates to the global profiling of small molecule (<1500 Da) metabolites. Because the genome, transcriptome, and proteome can undergo further changes after translation, the metabolome reflects the downstream products of these processes. There have been advances in separation techniques, such as capillary electrophoresis and ultrahigh-pressure liquid chromatography (UPLC) systems, and improvements in analytic approaches, being MS-based methods and high-resolution nuclear magnetic resonance (NMR) spectroscopy. This has enabled the simultaneous study of dozens of small molecule metabolites in tissue and biologic fluids. Whereas the use of nontargeted metabolomic approaches in the study of renal diseases and drug metabolism have been well documented, studies demonstrating its potential role in monitoring kidney transplants are limited. It has been used to identify borderline tubulitis and acute T cell mediated rejection in pediatric renal transplant recipients. Smaller studies have also been published evaluating the differential urinary and serum metabolomic profile in adult allograft recipients with acute rejection.

Biomarkers of Acute Kidney Injury

Being able to differentiate AKI from acute rejection would be a useful tool in the management of transplant recipients especially in the first few weeks after transplantation. In addition, a test undertaken in the donor or within the donor allograft that predicted DGF or ultimate graft performance would help in donor graft allocation and the management of patients postoperatively. In nephrology, neutrophil gelatinase associated lipocalin (NGAL), kidney injury molecule-1 (Kim-1), and N-acetyl-b-D-glucosaminidase (NAG) have been identified as biomarkers of AKI. NGAL is released from tubules, tends to rise before serum creatinine, and is not affected by volume depletion. Kim-1 was overexpressed in kidney biopsy specimens in patients with acute tubular injury, and urine excretion of Kim-1 has been shown to distinguish acute tubular injury from other forms of AKI. In kidney transplant recipients, high urinary excretion of Kim-1 was found to be an independent predictor of graft loss within a 4-year follow-up with hazard ratios between 3.6 and 5.1. However, its sensitivity and specificity as a test were not evaluated.

The advantage of these biomarkers is that they are measured in the urine and do not require renal tissue for analysis. However, evidence of the utility of these biomarkers in transplantation has been sparse. When measured in deceased donor urine, NGAL, in combination with liver fatty acid-binding protein (LFABP), were predictors of kidney injury and were associated with worse eGFR at 6 months, whereas expression of YKL-40, a marker of kidney repair, was associated with improved function after DGF. Other studies have shown an association between elevated urinary levels of NGAL and interleukin (IL)-18 in deceased donors and the onset of DGF in the recipient. However, in both cases the association was modest and, as yet, have not been introduced into clinical practice. In a longitudinal study of 182 transplant patients, elevated urinary NGAL levels were somewhat predictive of AKI from all causes other than rejection (specificity 0.86 and sensitivity 0.86), whereas if very high levels of NGAL were present it was highly predictive of acute rejection (sensitivity 1.00 and specificity 0.93). Some of these molecules may ultimately be useful in clinical settings, but further prospective evaluation in clinical trials is required. Other urinary molecules of AKI have been evaluated in the transplant setting. Cystatin C showed an association with DGF but with a receiver operating characteristic (ROC) area under the curve of 0.54 to 0.74, the accuracy in the transplant setting was low.

The problem with many of these assays in the transplant setting is that AKI is often seen in association with acute rejection. Hence, developing an assay that can reliably separate nonimmunologic causes of AKI and rejection-associated AKI has been difficult. More recently, genomics has been used to develop a molecular signature of AKI. Paradoxically it has been difficult to develop such a signature in studies of renal disease, because AKI is self-limiting and renal biopsies are not often performed. Kidney transplants experience AKI and are often biopsied in these circumstances, and are thus ideally positioned to study the gene expression patterns of AKI. cDNA microarrays were used to study the gene expression patterns of 14 living donor kidneys, 15 deceased donor kidneys with immediate function, and 14 deceased donor kidneys with AKI (defined by dialysis in first week). The gene expression patterns of living donor kidneys were observed to cluster separately from deceased donor kidneys (132 genes). In kidney transplants with AKI immediately after transplant there were 48 transcripts related to cell cycle regulation, cell growth/metabolism, and signal transduction that were significantly differentially regulated compared with those with immediate function. In a separate study, the same authors compared gene expression patterns of AKI allografts with carefully selected, pristine 6-week protocol biopsies, and identified 394 transcripts that were differentially expressed in AKI. Using the geometric mean of the fold increase in the top 30 differentially regulated genes (compared with control nephrectomies), they devised an IRRAT (injury-repair-response-associated-transcript) score that correlated inversely with renal function at the time, and directly with eGFR improvement thereafter. The IRRAT score was significantly associated with DGF, deceased donor status and interstitial inflammation, but not histology. IRRAT scores have since been observed to be elevated in both ACR and AMR, suggesting the nonspecific nature of this injury response signature. Of note, AKI-related gene expression patterns identified by different groups in transplantation have shown significant overlap with both mouse orthologs identified in ischemia-reperfusion models, and with each other, suggesting molecular homogeneity in this injury response. Currently, genetic markers of AKI have relied on analysis of biopsy material. From a clinical utility perspective, this makes it no more beneficial than histologic evaluation, which is the current gold standard. For biomarker development IRRAT scores or other genetic markers of AKI would need prospective validation in a clinical trial and ideally would use serum or urine for analysis, thereby avoiding the requirement for a biopsy.