Journal Logo

Editorials and Perspectives: Overview

Peripheral Blood Sampling for the Detection of Allograft Rejection: Biomarker Identification and Validation

Heidt, Sebastiaan1; San Segundo, David1; Shankar, Sushma1; Mittal, Shruti1; Muthusamy, Anand S.R.2; Friend, Peter J.2; Fuggle, Susan V.2; Wood, Kathryn J.1,3

Author Information
doi: 10.1097/TP.0b013e318218e978
  • Free

Abstract

Organ transplantation is the treatment of choice for patients with end-stage organ failure. Despite excellent short-term outcomes, mainly as a result of cocktails of immunosuppressive drugs, acute and chronic rejection episodes still occur, potentially affecting long-term graft function. Currently, especially in the case of kidney transplants, rejection is suspected when graft function deteriorates and is usually confirmed by biopsy. At this stage, organ damage has long been established, and consequently, antirejection treatment starts relatively late in terms of initiation of the rejection process. In the future, noninvasive monitoring of graft-specific immune activation would allow for early diagnosis and ultimately, prediction and preemptive management of acute or chronic rejection (CR). In addition, biomarkers for assessing the patient's pretransplant immune status alongside the pretransplant crossmatch (XM) may be used to tailor immunosuppression, such that the balance between the risk of rejection and side effects of immunosuppression can be minimized.

The peripheral blood is an easily accessible source of patient material for all types of transplants on which a variety of assays can be performed to define biomarkers that accurately reflect, detect, or predict detrimental immune responses to the graft, regardless of the organ transplanted (Fig. 1). There is an ongoing debate as to whether peripheral blood can give an accurate indication of the events that are taking place within the graft. Although analysis of gene, microRNA (miRNA), or protein expression in a transplant biopsy will undoubtedly give more direct information, sequential analysis, a prerequisite for accurate diagnosis, is unlikely to be possible even for kidney transplants. For kidney transplant recipients, urine is an easily accessible source of material that is proving to be informative. However, urine biomarkers will not be applicable for patients receiving other types of transplants. Therefore, we have focused this review on peripheral blood biomarkers that are currently under investigation for determining the pretransplant immune status and for posttransplantation monitoring to predict or detect acute and chronic allograft dysfunction.

F1-1
FIGURE 1.:
Assays to detect biomarkers in the peripheral blood of transplant recipients. Serum or plasma can be routinely assayed for anti-human leukocyte antigen (HLA) antibodies by several techniques, antiendothelial cell antibodies by the XM-ONE assay, and a variety of proteins by using ELISA. Cellular assays on peripheral blood mononuclear cells include functional assays such as the Immuknow assay for determining general immune function and interferon (IFN)-γ ELISPOT for quantifying alloimmune reactivity. The cellular component can be further characterized by using flow cytometry or mRNA/microRNA (miRNA) profiling. EC, endothelial cell; sCD30, soluble CD30; RT-PCR, reverse-transcriptase polymerase chain reaction.

ANTIBODY DETECTION

Multiple assays to identify and quantitate human leukocyte antigen (HLA) antibodies are available, the majority of which are well established (1), and are therefore not covered here. In contrast, the detection and quantitation of non-HLA antibodies, particularly antiendothelial cell antibodies (AECA), a novel parameter of donor-reactive immunity, is not yet a routine practice in the majority of centers, but there is increasing evidence to suggest that pretransplant detection of AECA might aid in pretransplant risk stratification.

In transplant immunology, the notion has arisen that other antigenic systems in addition to HLA play a role in graft outcome (2). In HLA-identical sibling kidney transplantation, pretransplant panel reactive antibody (PRA) positivity resulted in lower 10-year graft survival rates (3), suggesting a role for other antibody specificities such as the angiotensin II type 1 receptor, to which antibodies were present in HLA antibody–negative kidney transplant recipients with refractory vascular rejection (4).

By using primary arterial endothelial cell (EC) cultures in flow cytometry (FCM), it was shown that AECA were present in a substantial proportion of pretransplant sera, including sera from HLA nonsensitized patients (5). Recently, an endothelial cell cross-match (ECXM) technique has been developed based on precursor ECs isolated from whole blood of the organ donor (6). In a multicenter cohort of 147 kidney transplant recipients, 24% had donor-specific AECA and experienced more rejection episodes than patients without AECA (7). However, because a significant proportion of ECXM-positive patients had no increased risk of rejection, further studies are needed to refine which AECA will be of clinical relevance.

sCD30

The membrane glycoprotein CD30 is expressed on a variety of cells including CD4+ and CD8+ lymphocytes producing Th2 cytokines, B cells, and natural killer cells (8). On stimulation of CD30+ T cells, CD30 is enzymatically cleaved, leaving soluble CD30 (sCD30) that can be detected in the plasma or serum as a measure for immune cell activation (9).

Although healthy individuals have low serum sCD30 levels, patients with end-stage renal disease have increased levels (10, 11). The first study correlating pretransplant sCD30 levels with graft loss showed decreased 5-year graft survival rates for patients with high sCD30 levels (10). Subsequently, pretransplant sCD30 levels have been suggested to be predictive for the grade of kidney rejection (12), whereas in lung transplantation, increased pretransplant sCD30 levels were associated with decreased early graft function (13). After transplantation, sCD30 levels generally decrease, but remain relatively high, or increase in the event of rejection as shown in one cohort of kidney transplant recipients (specificity 100% and sensitivity 88%) (14) and confirmed in an independent cohort (15). Because Th2 responses are linked to antibody production by B cells, it has also been suggested that sCD30 levels may predict antibody-mediated rejection (AMR) (12). However, data presented to date fail to show an association between posttransplant sCD30 levels and HLA antibodies (15, 16) or AMR (17).

Although the association of high pretransplant sCD30 with graft failure is unlikely to be affected by the type of maintenance immunosuppression (18, 19), for reasons that are not clear, the association between pretransplant sCD30 and acute rejection (AR) was less evident in patients receiving induction therapy (20, 21). Our own preliminary data in kidney transplant recipients receiving alemtuzumab or basiliximab induction also failed to show a correlation between pretransplant sCD30 and AR (Fig. 2).

F2-1
FIGURE 2.:
Pretransplant soluble CD30 (sCD30) levels fail to predict acute rejection (AR) in a cohort of kidney transplant recipients receiving alemtuzumab or basiliximab induction therapy. Pretransplant serum was assessed for the level of sCD30 by ELISA. Patients with an AR episode within the first year after transplantation (n=12) had similar pretransplant sCD30 levels compared with stable patients (n=52). Healthy controls (n=13) had low levels of sCD30.

A positive association between pretransplant sCD30 levels and AR or graft outcome has not been found in all studies reported (15, 18, 22, 23). Alternative explanations for these discrepant results, such as the biologic variability of sCD30 levels with time (24), may need to be taken in account. Further data are required to validate the prognostic value of pretransplant sCD30 levels for AR or graft outcome for individual patients (25, 26).

CXCR3-BINDING CHEMOKINES

The chemokines CXC chemokine ligand 9 (CXCL9; Mig) and CXC chemokine ligand 10 (CXCL10; interferon gamma-inducible protein-10) are downstream of interferon (IFN)-γ signaling and attract CXC chemokine receptor 3 (CXCR3+) T cells into the graft. Romagnani and coworkers (27, 28) suggested that high pretransplant serum CXCL10 levels were predictive of renal graft loss and early AR of cardiac allografts (29). By analogy, high pretransplant CXCL9 levels in kidney transplant recipients were predictive for AR and 5-year graft survival (specificity 83% and sensitivity 75%) (30). However, the clinical usefulness for these assays seems to be limited because pretransplant levels of CXCL9 and CXCL10 in patients receiving alemtuzumab or basiliximab induction therapy did not predict for AR, likely due to depletion or inactivation of CXCR3+ responder cells (31).

There exist few data on the relation of posttransplant serum CXCR3-binding chemokine levels and graft outcome. In lung transplantation, it has recently been shown that posttransplant CXCL10 levels, but not CXCL9 levels, were predictive for early fibrosis recurrence (32). More research is needed to establish whether posttransplant serum CXCR3-binding chemokines are predictive for graft outcome.

POLYCLONAL LYMPHOCYTE STIMULATION ASSAYS

The overall function of lymphocytes can be determined by their ability to proliferate (33, 34) or by their intracellular adenosine triphosphate (iATP) content after phytohemagglutinin (PHA) stimulation, the latter reflecting the “energy” levels available within the cell. Both assays have the potential to provide an indication of the level of immunosuppression, that is, lower proliferation or ATP, less ability to respond to immune stimuli. A Food and Drug Administration–approved iATP assay is available commercially, the ImmuKnow assay (Cylex, Columbia, MD).

One of the early studies using this approach reported that high CD4+ T-cell iATP levels were associated with a risk of AR in recipients of kidney, liver, heart, and small bowel transplants, whereas low levels were a risk factor for infections (35). These data have been confirmed for kidney, liver, and heart transplantation in separate studies (36–38) and were interpreted as indicating that the quantification of iATP levels may be a tool to determine the immune status of a patient. Not all studies have found correlation with AR but have found a correlation between iATP levels and infection with varying levels of specificity and sensitivity (39–42). In addition, it has been hypothesized that high pretransplant iATP levels correlating with both AR and poor early function in kidney transplant recipients might be predictive for transplant outcome (43), but thus far there are no published data confirming this hypothesis. This type of assay has also been shown to reflect the level of immunosuppression in kidney transplant recipients with long-term surviving allografts; recipients with squamous cell carcinoma showed impaired responses toward phytohemagglutinin compared with patients without malignancy (44).

Overall, the main utility of assays based on mitogenic stimulation of lymphocytes is for detection of overimmunosuppression. A potential drawback of these techniques is that they determine a patient's general immune status but not the T-cell reactivity directed toward the allograft. The published data are inconsistent and suggest that results should be interpreted with caution when trying to relate them to rejection, infection, or long-term graft outcome.

IFN-γ ELISPOT

Determining donor-reactive memory T-cell reactivity is of great interest for risk stratification in transplantation, because memory T cells respond more rapidly to stimulation by alloantigen (45) and are more difficult to suppress compared with their naïve counterparts (46, 47). Thus far, the IFN-γ ELISPOT is one of the few functional assays available for determining donor-specific memory T-cell reactivity.

The IFN-γ ELISPOT was originally described for the quantification of pretransplant alloreactive memory T cells in kidney transplant recipients (48). In subsequent studies, increased levels of donor-reactive IFN-γ–producing T cells prekidney and postkidney transplantation were shown to be a risk factor for AR and a predictor of graft function, independent of PRA level (49–51). Moreover, the IFN-γ ELISPOT detected alloreactive T cells in lymphopenic patients (specificity 80% and sensitivity 83%), making this a broadly applicable assay (52).

Analogous to PRA detection, panels of HLA-typed target cells have been used to determine pretransplant T-cell alloreactivity (53, 54). Similarly, but independent of PRA, a high level of panel reactive T cells has been associated with immune-mediated graft injury in a study of 30 kidney transplant recipients (specificity 78% and sensitivity 86%) (55). On balance, current data suggest that the IFN-γ ELISPOT is a potentially powerful technique to determine the level of donor-specific and general alloimmunization. However, not all studies have found a correlation between a positive IFN-γ ELISPOT and transplant outcome (43), emphasizing the critically important steps of optimization, standardization, and validation in developing any biomarker assay. The European RISET consortium (www.risetfp6.org) has implemented a rigorous approach to validate the IFN-γ ELISPOT, enabling it to be used by multiple laboratories (Volk and coworkers, unpublished results, 2010) (56).

FLOW CYTOMETRY

A huge advantage of FCM is the capacity to quantify many immune cell subsets with great flexibility. Alterations in a variety of subsets have been described in patients experiencing AR or in patients with long-term surviving grafts. Although standard multiparameter FCM does not provide information about cell function, there are now strategies for determining the antigen specificity of T and B cells, signaling pathway activity, transcription factor expression, and cytokine production that are being investigated in the setting of transplantation.

As mentioned earlier, memory T cells are relatively resistant to immunosuppression (57), a finding confirmed by our own clinical data (58). Furthermore, we and others have shown that effector memory T cells were associated with AR in kidney transplant recipients (57–59) and in heart transplant recipients (60). Memory T cells can be detected by FCM because of an altered cell surface phenotype compared with naïve T cells, including CD45RA, CCR7, and CD62L. FCM analysis showed that the percentage of activated CD8+CD69+ T cells was increased during both acute renal (specificity 85% and sensitivity 86%) (61) and cardiac allograft rejection (62). Likewise, frequencies of CD28- and CD38-expressing T cells were higher in liver transplant recipients with AR compared with patients with stable function or infection (63). Lun et al. (64) found that the percentage of CD25+ T cells was increased in liver transplant recipients during AR (CD4+CD25+: specificity 91% and sensitivity 63%; CD8+CD25+: specificity 90% and sensitivity 56%).

Despite data suggesting that general activation markers may be useful for detecting AR, the discriminatory potential of such assays is likely to be poor as the same activation markers will be increased on any T cell as it responds to antigen. Our own preliminary data showed that the frequencies of CD25+, CD38+, and HLA-DR+ CD8+ T cells were increased not only during AR but also during infection in kidney transplant recipients (Heidt et al., unpublished data, 2010). These data are consistent with the demonstration that cytomegalovirus (CMV) infection in renal transplant patients coincided with increased levels of CD8+CD38+ T cells and the up-regulation of CD69 on CD8+ T cells correlating with CMV viremia but not with acute renal allograft rejection (65–67).

Regulatory T cells (Tregs) have been shown to prevent allograft rejection in many animal models of transplantation (68). Therefore, monitoring of Treg in the peripheral blood may detect unresponsiveness to the graft. We and others have shown lower percentages of Treg in the peripheral blood of renal transplant recipients with chronic graft dysfunction compared with stable or operationally tolerant patients (69–71). A proportion of liver transplant recipients who are electively weaned off immunosuppressive drugs exhibit operational tolerance to donor alloantigens, making these patients valuable for defining biomarkers of tolerance (72). In adult and pediatric liver transplant patients who were successfully weaned off immunosuppressive drugs, increased numbers of CD4+CD25+ cells with regulatory activity were present in the peripheral blood compared with the levels present in patients remaining on immunosuppression or healthy controls (73).

Although increased numbers of Treg have been found in the peripheral blood of immunosuppression-free liver transplant recipients, this has not been the case for immunosuppression-free kidney transplant recipients (74, 75). Thus, findings for a defined clinical outcome for one organ type cannot be extrapolated to another.

Detection of T cells with the phenotype characteristic of Treg by FCM using CD25 as a marker in humans is well known to be difficult and prone to investigator bias. The addition of other markers to the panel, including CD127 (a chain of the interleukin [IL] 7 receptor) or CD45 (leukocyte common antigen) can reduce this. The activation marker CD127 facilitates clear-cut gating, with CD127low T cells being regulatory and CD127high T cells being activated (76, 77). Human CD4+CD25+CD127lowFOXP3+ cells have been shown to have higher regulatory activity compared with CD4+CD25highFOXP3+ cells in a humanized mouse model of transplant vasculopathy, supporting the hypothesis that addition of CD127 as a marker facilitates in defining Treg (78). Including one of the isoforms of CD45, the leukocyte common antigen has also been reported to be beneficial. CD4+CD25+CD45RO+CD127low Treg levels were low in liver and kidney transplant recipients compared with healthy subjects, whereas concomitantly, levels of CD4+CD25+CD45RO+CD127high activated T cells were increased with the highest levels found in patients with chronic AMR (79). Alonso-Arias et al. have confirmed the increased percentages of CD4+CD25+CD45RO+CD127high activated T cells in kidney transplant recipients. In addition, relatively high levels of CD127low cells without regulatory properties correlated with renal failure (80).

It is well recognized that immunosuppressive drugs have differential impact on the frequency of Treg in transplant patients (81, 82). Surprisingly, immunosuppressive drugs can also obscure FCM detection of Treg. Our laboratory found highly variable FOXP3 expression with time after venesection only in samples from patients on calcineurin inhibitor-based immunosuppression (Carroll and Wood, unpublished observations, 2010), emphasizing the importance of standardizing and validating workflow, such that time between sample collection and processing is kept to an absolute minimum for biomarker detection.

B cells may also have a role in regulating immune responses. Increased numbers of B cells with a regulatory phenotype, defined as CD1d+CD5+, have been found in the peripheral blood of immunosuppression-free kidney transplant recipients compared with the number present in patients with stable graft function remaining on immunosuppression. However, no differences between stable patients and those undergoing CR were found (83). These findings have been confirmed in the larger cohorts of the Reprogramming the Immune System for Establishment of Tolerance (RISET) and Immune Tolerance Network (ITN) networks (74, 75). Similar observations have been made in pediatric liver transplant patients, where the percentage of B cells was increased in stable drug-free patients compared with patients under immunosuppression and healthy controls (73). Data on the contribution of B cells in clinical tolerance and rejection are exciting and warrant more in-depth analysis of B-cell subsets in transplant patients in the near future.

Overall, FCM is a useful monitoring technique providing a wealth of data. However, because flow cytometer configuration and setup, and data acquisition and analysis, will be different among centers, standardization to enable multiparameter phenotyping to be used in biomarker studies is crucial (84).

GENE EXPRESSION PROFILING

The use of molecular biology in the search for biomarkers allows the screening of vast numbers of genes by microarray techniques, whereas the more sensitive polymerase chain reaction–based techniques are better suited for hypothesis-driven research (85). This section will mainly discuss the hypothesis-driven studies.

Perforin, granzyme B, and FasL are molecules involved in cytotoxicity that have been studied extensively in relation to rejection (Table 1). Up-regulation of two or more of these genes in the peripheral blood correlated with AR in kidney transplant recipients (minimum specificity 60% and sensitivity 100%) (86), a finding that has been confirmed and extended in other studies. For example, granzyme B and HLA-DR expression combined yielded high specificity (100%) and poor sensitivity (50%) to diagnose AR (87), whereas in other studies, altered expression of perforin, granzyme B, or FasL correlated with AR (88, 89) or CR (90, 91)with varying specificity and sensitivity. In addition, some studies have shown by serial measurements that AR can be detected by increased granzyme B and perforin expression approximately a week before diagnosis with specificity up to 97% and sensitivity up to 73%, depending on timing and patient cohort (92, 93), but this has not been replicated in all studies (94).

T1-1
TABLE 1:
Studies describing perforin, granzyme B, and FasL mRNA levels in peripheral blood of kidney transplant recipients

As with other potential biomarkers already discussed, not all studies have produced consistent findings. For example, Shin and coworkers (94) found an association between acute kidney rejection and perforin expression levels, but they observed no association with granzyme B or FasL. In addition, Graziotto et al. (95) failed to find an increase in granzyme B, perforin, or FasL expression in the peripheral blood during kidney rejection, whereas perforin and FasL expression was increased in biopsies, raising the question whether peripheral blood mRNA levels for these molecules are universally applicable to different patient cohorts.

The studies referred earlier compare patients experiencing rejection with those who have stable graft function or patients with nonimmunologic pathology (Table 1). We assayed serial samples from kidney transplant recipients with AR and patients with confirmed BK virus or CMV infection and found that increased mRNA expression levels of both granzyme B and perforin in the peripheral blood often coincide with AR and with infection (Fig. 3). These preliminary results are consistent with data from islet allograft recipients, in whom expression of cytotoxic molecules often correlated with infection (96), and from kidney transplant recipients, in whom mRNA levels of urinary cytotoxic molecules were up-regulated during viral infections (97). This may not be surprising because any form of immune stimulation, not least viral infection, may lead to detectable changes of cytotoxic cell function.

F3-1
FIGURE 3.:
Peripheral blood perforin and granzyme B mRNA levels are regularly increased on infection and acute rejection. Example of a kidney transplant recipient who was treated with basiliximab induction, tacrolimus, azathioprine, and steroids and experienced acute rejection (AR) at 39 days posttransplantation, followed by a cytomegalovirus (CMV) infection at day 225. Both perforin and granzyme B mRNA levels, as determined by reverse-transcriptase polymerase chain reaction, showed a peak at time of rejection and at time of infection. HPRT, hypoxanthine phosphoribosyltransferase.

The changes in gene expression of general markers of immune activation, such as costimulatory molecules and cytokines, have also been correlated with rejection, such as CD154 (98, 99). In addition, expression levels of cytokines such as IL-4, IL-5, IL-6, and IFN-γ in mitogen-stimulated peripheral blood mononuclear cells have been found to correlate with AR of renal allografts (100, 101). Using daily sampling of peripheral blood, IL-4 and tumor necrosis factor-α (TNF-α) mRNA expression in peripheral blood mononuclear cell were shown to be predictive for AR in kidney transplantation 2 days before clinical diagnosis (101).

Other potential biomarkers include the expression of genes linked to the presence of Treg as discussed earlier. Aquino-Dias et al. (89) showed that an increased FOXP3 mRNA expression was associated with AR of kidney allografts (specificity 95% and sensitivity 94%), whereas in CR, decreased FOXP3 expression levels have been described (90). However, in long-term surviving renal transplant recipients, FOXP3 expression did not correlate with rejection (91). In addition, Dijke et al. (102) have shown that peripheral blood FOXP3 expression did not correlate with cardiac allograft rejection, whereas FOXP3 levels in biopsies did. The markers tolerance associated gene-1 (TOAG-1) and α-1,2-mannosidase were initially discovered in experimental studies (103) and have been translated to clinical transplantation. The ratio of FOXP3:α-1,2-mannosidase mRNA expression was shown to be substantially lower in kidney transplant patients with CR compared with stable patients and tolerant drug-free patients (74). These potential biomarkers require further validation to determine, again, whether there are specific clinical situations where they are uninformative as the lower ratio of FOXP3:α-1,2-mannosidase expression has not been confirmed in a separate study, in which expression levels of CTLA4 and transforming growth factor-β were also unaltered (90).

In the large multicenter study by the RISET and ITN consortia, 30 genes discovered by microarray analysis and confirmed by polymerase chain reaction were associated with operational tolerance in immunosuppression-free kidney transplant recipients, many of which were B cell related (75). It will be interesting to see whether these genes are predictive for specific unresponsiveness to donor alloantigens. Interestingly, the microarray-derived gene signatures for tolerant kidney and liver transplant recipients are distinct (74, 75, 104, 105).

miRNA PROFILING

A novel area of biomarker research aims at detecting miRNAs, small noncoding single-stranded RNA molecules involved in posttranslational regulation of gene expression (106). Two studies on miRNA expression in renal allograft biopsies during AR so far have identified specific, but distinct patterns of miRNA expression correlating with AR in small patient groups (107, 108). It will be interesting to see whether quantifying miRNA levels in the peripheral blood will provide relevant biomarkers.

DISCUSSION

The past 2 decades have seen much progress in defining the immunologic mechanisms involved in allograft rejection. By taking this knowledge to generate hypotheses as to which genes and molecules are predictive of rejection and by using a vast array of techniques to identify new molecules and pathways that could play a role, many laboratories have identified potential biomarkers for rejection and tolerance in different sources of patient material. Peripheral blood biomarkers are of particular interest, because, at least theoretically, they can be applied irrespective of the type of transplant and can be used both in the short and long term after transplantation to profile the immune and metabolic status of transplant recipients.

Studies on biomarkers for rejection are promising, but, as the field stands today, not without caveats. Most studies describe patients with defined conditions, such as (acute) rejection or stable function, sometimes ignoring other causes for immune activation such as infection. For any biomarker to be clinically applicable for predicting rejection, its capacity to differentiate between immune activation toward the allograft and infectious agents must be established. Because many parameters of immune activation will not be specific to alloimmunity, we hypothesize that a combination of markers will be needed to provide sufficient specificity and sensitivity. For immune tolerance, it has already been shown that a combination of markers provided the highest specificity for defining operationally tolerant patients (74). Obviously, disadvantages of using multiple parameters in a clinical setting are increased analysis time and data complexity, which has to be taken into account when translating biomarker as a diagnostic entity into the clinical setting.

Validation of findings from single-center analyses in multicenter studies is a critical step in defining biomarkers that can be used to predict rejection in the future (109). Currently, striking discrepancies in the biomarker data exist, as reported by various laboratories investigating the same parameter. This is likely due to differences in recipient and donor populations, clinical management, and the actual laboratory techniques used to perform the assays. Therefore, it is of great importance that the assays used to detect biomarkers are optimized, validated, and standardized, and subsequently, candidate biomarkers are investigated in large patient cohorts from different transplant centers using the same, validated, assays (110). International research networks such as the RISET, ITN, and Genome Canada networks have already begun this process and are invaluable for discovering and validating biomarkers to enable them to be introduced into clinical practice (111).

The establishment of noninvasive monitoring techniques will greatly improve patient care, facilitating the development of personalized medicine for transplant recipients. Biomarkers will likely enable the detection of rejection episodes at an earlier stage than is currently possible, a identification of drug toxicity and signs of specific unresponsiveness to donor alloantigens to modify the immunosuppressive regimen. Recent advances in the identification of peripheral blood biomarkers are encouraging even if further work is required to validate and implement these new approaches. Nevertheless, we anticipate that it is only a matter of time before noninvasive monitoring is implemented as a part of routine clinical practice in transplantation.

ACKNOWLEDGMENTS

The authors thank Sally Ruse for patient enrollment and Ian Toogood-Johnson for technical assistance with the data reported from the authors own laboratory.

REFERENCES

1. Bray RA, Gebel HM. Strategies for human leukocyte antigen antibody detection. Curr Opin Organ Transplant 2009; 14: 392.
2. Glotz D, Lucchiari N, Pegaz-Fiornet B, et al. Endothelial cells as targets of allograft rejection. Transplantation 2006; 82(1 suppl): S19.
3. Opelz G. Non-HLA transplantation immunity revealed by lymphocytotoxic antibodies. Lancet 2005; 365: 1570.
4. Dragun D, Muller DN, Brasen JH, et al. Angiotensin II type 1-receptor activating antibodies in renal-allograft rejection. N Engl J Med 2005; 352: 558.
5. Le Bas-Bernardet S, Hourmant M, Coupel S, et al. Non-HLA-type endothelial cell reactive alloantibodies in pre-transplant sera of kidney recipients trigger apoptosis. Am J Transplant 2003; 3: 167.
6. Vermehren D, Sumitran-Holgersson S. Isolation of precursor endothelial cells from peripheral blood for donor-specific crossmatching before organ transplantation. Transplantation 2002; 74: 1479.
7. Breimer ME, Rydberg L, Jackson AM, et al. Multicenter evaluation of a novel endothelial cell crossmatch test in kidney transplantation. Transplantation 2009; 87: 549.
8. Tarkowski M. Expression and a role of CD30 in regulation of T-cell activity. Curr Opin Hematol 2003; 10: 267.
9. Wang D, Wu WZ, Chen JH, et al. Pre-transplant soluble CD30 level as a predictor of not only acute rejection and graft loss but pneumonia in renal transplant recipients. Transpl Immunol 2010; 22: 115.
10. Süsal C, Pelzl S, Dohler B, et al. Identification of highly responsive kidney transplant recipients using pretransplant soluble CD30. J Am Soc Nephrol 2002; 13: 1650.
11. Nakao K, Nagake Y, Okamoto A, et al. Serum levels of soluble CD26 and CD30 in patients on hemodialysis. Nephron 2002; 91: 215.
12. Rajakariar R, Jivanji N, Varagunam M, et al. High pre-transplant soluble CD30 levels are predictive of the grade of rejection. Am J Transplant 2005; 5: 1922.
13. Shah AS, Leffell MS, Lucas D, et al. Elevated pretransplantation soluble CD30 is associated with decreased early allograft function after human lung transplantation. Hum Immunol 2009; 70: 101.
14. Pelzl S, Opelz G, Daniel V, et al. Evaluation of posttransplantation soluble CD30 for diagnosis of acute renal allograft rejection. Transplantation 2003; 75: 421.
15. Weimer R, Susal C, Yildiz S, et al. Post-transplant sCD30 and neopterin as predictors of chronic allograft nephropathy: Impact of different immunosuppressive regimens. Am J Transplant 2006; 6: 1865.
16. Hamer R, Roche L, Smillie D, et al. Soluble CD30 and Cd27 levels in patients undergoing HLA antibody-incompatible renal transplantation. Transpl Immunol 2010; 23: 161.
17. Slavcev A, Honsova E, Lodererova A, et al. Soluble CD30 in patients with antibody-mediated rejection of the kidney allograft. Transpl Immunol 2007; 18: 22.
18. Heinemann FM, Rebmann V, Witzke O, et al. Association of elevated pretransplant sCD30 levels with graft loss in 206 patients treated with modern immunosuppressive therapies after renal transplantation. Transplantation 2007; 83: 706.
19. Sengul S, Keven K, Gormez U, et al. Identification of patients at risk of acute rejection by pretransplantation and posttransplantation monitoring of soluble CD30 levels in kidney transplantation. Transplantation 2006; 81: 1216.
20. Pelzl S, Opelz G, Wiesel M, et al. Soluble CD30 as a predictor of kidney graft outcome. Transplantation 2002; 73: 3.
21. Kovac J, Arnol M, Vidan Jeras B, et al. Pretransplant soluble CD30 serum concentration does not affect kidney graft outcomes 3 years after transplantation. Transplant Proc 2010; 42: 4043.
22. Matinlauri IH, Kyllonen LE, Salmela KT, et al. Serum sCD30 in monitoring of alloresponse in well HLA-matched cadaveric kidney transplantations. Transplantation 2005; 80: 1809.
23. Spiridon C, Nikaein A, Lerman M, et al. CD30, a marker to detect the high-risk kidney transplant recipients. Clin Transplant 2008; 22: 765.
24. Altermann W, Schlaf G, Rothhoff A, et al. High variation of individual soluble serum CD30 levels of pre-transplantation patients: sCD30 a feasible marker for prediction of kidney allograft rejection? Nephrol Dial Transplant 2007; 22: 2795.
25. Vaidya S, Partlow D, Barnes T, et al. Pretransplant soluble CD30 is a better predictor of posttransplant development of donor-specific antibodies and acute vascular rejection than panel reactive antibodies. Transplantation 2006; 82: 1606.
26. Platt RE, Wu KS, Poole K, et al. Soluble CD30 as a prognostic factor for outcome following renal transplantation. J Clin Pathol 2009; 62: 662.
27. Rotondi M, Rosati A, Buonamano A, et al. High pretransplant serum levels of CXCL10/IP-10 are related to increased risk of renal allograft failure. Am J Transplant 2004; 4: 1466.
28. Lazzeri E, Rotondi M, Mazzinghi B, et al. High CXCL10 expression in rejected kidneys and predictive role of pretransplant serum CXCL10 for acute rejection and chronic allograft nephropathy. Transplantation 2005; 79: 1215.
29. Crescioli C, Buonamano A, Scolletta S, et al. Predictive role of pretransplant serum CXCL10 for cardiac acute rejection. Transplantation 2009; 87: 249.
30. Rotondi M, Netti GS, Lazzeri E, et al. High pretransplant serum levels of CXCL9 are associated with increased risk of acute rejection and graft failure in kidney graft recipients. Transpl Int 2010; 23: 465.
31. Heidt S, Shankar S, Muthusamy ASR, et al. Pretransplant serum CXCL9 and CXCL10 levels fail to predict acute rejection in kidney transplant recipients receiving induction therapy. Transplantation 2011; 91: e59.
32. Berres ML, Trautwein C, Schmeding M, et al. Serum chemokine CXC ligand 10 (CXCL10) predicts fibrosis progression after liver transplantation for hepatitis C infection. Hepatology 2011; 53: 596.
33. Hutchinson P, Jose M, Atkins RC, et al. Ex vivo lymphocyte proliferative function is severely inhibited in renal transplant patients on mycophenolate mofetil treatment. Transpl Immunol 2004; 13: 55.
34. Blazik M, Hutchinson P, Jose MD, et al. Leukocyte phenotype and function predicts infection risk in renal transplant recipients. Nephrol Dial Transplant 2005; 20: 2226.
35. Kowalski RJ, Post DR, Mannon RB, et al. Assessing relative risks of infection and rejection: A meta-analysis using an immune function assay. Transplantation 2006; 82: 663.
36. Israeli M, Yussim A, Mor E, et al. Preceeding the rejection: In search for a comprehensive post-transplant immune monitoring platform. Transpl Immunol 2007; 18: 7.
37. Cabrera R, Ararat M, Soldevila-Pico C, et al. Using an immune functional assay to differentiate acute cellular rejection from recurrent hepatitis C in liver transplant patients. Liver Transpl 2009; 15: 216.
38. Israeli M, Ben-Gal T, Yaari V, et al. Individualized immune monitoring of cardiac transplant recipients by noninvasive longitudinal cellular immunity tests. Transplantation 2010; 89: 968.
39. Millan O, Sanchez-Fueyo A, Rimola A, et al. Is the intracellular ATP concentration of CD4+ T-cells a predictive biomarker of immune status in stable transplant recipients? Transplantation 2009; 88(3 suppl): S78.
40. Kobashigawa JA, Kiyosaki KK, Patel JK, et al. Benefit of immune monitoring in heart transplant patients using ATP production in activated lymphocytes. J Heart Lung Transplant 2010; 29: 504.
41. Serban G, Whittaker V, Fan J, et al. Significance of immune cell function monitoring in renal transplantation after thymoglobulin induction therapy. Hum Immunol 2009; 70: 882.
42. Huskey J, Gralla J, Wiseman AC. Single time point immune function assay (ImmuKnowTM) testing does not aid in the prediction of future opportunistic infections or acute rejection. Clin J Am Soc Nephrol 2011; 6: 423.
43. Reinsmoen NL, Cornett KM, Kloehn R, et al. Pretransplant donor-specific and non-specific immune parameters associated with early acute rejection. Transplantation 2008; 85: 462.
44. Carroll RP, Segundo DS, Hollowood K, et al. Immune phenotype predicts risk for posttransplantation squamous cell carcinoma. J Am Soc Nephrol 2010; 21: 713.
45. Brook MO, Wood KJ, Jones ND. The impact of memory T cells on rejection and the induction of tolerance. Transplantation 2006; 82: 1.
46. Korn T, Reddy J, Gao W, et al. Myelin-specific regulatory T cells accumulate in the CNS but fail to control autoimmune inflammation. Nat Med 2007; 13: 423.
47. Yang J, Brook MO, Carvalho-Gaspar M, et al. Allograft rejection mediated by memory T cells is resistant to regulation. Proc Natl Acad Sci USA 2007; 104: 19954.
48. Heeger PS, Greenspan NS, Kuhlenschmidt S, et al. Pretransplant frequency of donor-specific, IFN-gamma-producing lymphocytes is a manifestation of immunologic memory and correlates with the risk of posttransplant rejection episodes. J Immunol 1999; 163: 2267.
49. Hricik DE, Rodriguez V, Riley J, et al. Enzyme linked immunosorbent spot (ELISPOT) assay for interferon-gamma independently predicts renal function in kidney transplant recipients. Am J Transplant 2003; 3: 878.
50. Nickel P, Presber F, Bold G, et al. Enzyme-linked immunosorbent spot assay for donor-reactive interferon-gamma-producing cells identifies T-cell presensitization and correlates with graft function at 6 and 12 months in renal-transplant recipients. Transplantation 2004; 78: 1640.
51. Augustine JJ, Siu DS, Clemente MJ, et al. Pre-transplant IFN-gamma ELISPOTs are associated with post-transplant renal function in African American renal transplant recipients. Am J Transplant 2005; 5: 1971.
52. Nather BJ, Nickel P, Bold G, et al. Modified ELISPOT technique—Highly significant inverse correlation of post-Tx donor-reactive IFNgamma-producing cell frequencies with 6 and 12 months graft function in kidney transplant recipients. Transpl Immunol 2006; 16: 232.
53. Andree H, Nickel P, Nasiadko C, et al. Identification of dialysis patients with panel-reactive memory T cells before kidney transplantation using an allogeneic cell bank. J Am Soc Nephrol 2006; 17: 573.
54. Poggio ED, Clemente M, Hricik DE, et al. Panel of reactive T cells as a measurement of primed cellular alloimmunity in kidney transplant candidates. J Am Soc Nephrol 2006; 17: 564.
55. Poggio ED, Augustine JJ, Clemente M, et al. Pretransplant cellular alloimmunity as assessed by a panel of reactive T cells assay correlates with acute renal graft rejection. Transplantation 2007; 83: 847.
56. Goldman M, Wood K. Transplantation research: Will we ever reach the holy grail? Transplantation 2009; 87(9 suppl): S99.
57. Pearl JP, Parris J, Hale DA, et al. Immunocompetent T-cells with a memory-like phenotype are the dominant cell type following antibody-mediated T-cell depletion. Am J Transplant 2005; 5: 465.
58. Trzonkowski P, Zilvetti M, Friend P, et al. Recipient memory-like lymphocytes remain unresponsive to graft antigens after CAMPATH-1H induction with reduced maintenance immunosuppression. Transplantation 2006; 82: 1342.
59. Trzonkowski P, Zilvetti M, Chapman S, et al. Homeostatic repopulation by CD28CD8+ T cells in alemtuzumab-depleted kidney transplant recipients treated with reduced immunosuppression. Am J Transplant 2008; 8: 338.
60. Lanio N, Sarmiento E, Gallego A, et al. The potential role of T-cell memory distribution as predisposing factor for rejection in heart transplant recipients. Transplant Proc 2009; 41: 2480.
61. Posselt AM, Vincenti F, Bedolli M, et al. CD69 expression on peripheral CD8 T cells correlates with acute rejection in renal transplant recipients. Transplantation 2003; 76: 190.
62. Creemers P, Brink J, Wainwright H, et al. Evaluation of peripheral blood CD4 and CD8 lymphocyte subsets, CD69 expression and histologic rejection grade as diagnostic markers for the presence of cardiac allograft rejection. Transpl Immunol 2002; 10: 285.
63. Boleslawski E, BenOthman S, Grabar S, et al. CD25, CD28 and CD38 expression in peripheral blood lymphocytes as a tool to predict acute rejection after liver transplantation. Clin Transplant 2008; 22: 494.
64. Lun A, Cho MY, Muller C, et al. Diagnostic value of peripheral blood T-cell activation and soluble IL-2 receptor for acute rejection in liver transplantation. Clin Chim Acta 2002; 320: 69.
65. Belles-Isles M, Houde I, Lachance JG, et al. Monitoring of cytomegalovirus infections by the CD8+CD38+ T-cell subset in kidney transplant recipients. Transplantation 1998; 65: 279.
66. Ticha O, Stouracova M, Kuman M, et al. Monitoring of CD38(high) expression in peripheral blood CD8+ lymphocytes in patients after kidney transplantation as a marker of cytomegalovirus infection. Transpl Immunol 2010; 24: 50.
67. Karpinski M, Rush D, Jeffery J, et al. Heightened peripheral blood lymphocyte CD69 expression is neither sensitive nor specific as a noninvasive diagnostic test for renal allograft rejection. J Am Soc Nephrol 2003; 14: 226.
68. Wood KJ, Sakaguchi S. Regulatory T cells in transplantation tolerance. Nat Rev Immunol 2003; 3: 199.
69. Akl A, Jones ND, Rogers N, et al. An investigation to assess the potential of CD25highCD4+ T cells to regulate responses to donor alloantigens in clinically stable renal transplant recipients. Transpl Int 2008; 21: 65.
70. Louis S, Braudeau C, Giral M, et al. Contrasting CD25hiCD4+T cells/FOXP3 patterns in chronic rejection and operational drug-free tolerance. Transplantation 2006; 81: 398.
71. Braudeau C, Racape M, Giral M, et al. Variation in numbers of CD4+CD25highFOXP3+ T cells with normal immuno-regulatory properties in long-term graft outcome. Transpl Int 2007; 20: 845.
72. Lerut J, Sanchez-Fueyo A. An appraisal of tolerance in liver transplantation. Am J Transplant 2006; 6: 1774.
73. Li Y, Koshiba T, Yoshizawa A, et al. Analyses of peripheral blood mononuclear cells in operational tolerance after pediatric living donor liver transplantation. Am J Transplant 2004; 4: 2118.
74. Sagoo P, Perucha E, Sawitzki B, et al. Development of a cross-platform biomarker signature to detect renal transplant tolerance in humans. J Clin Invest 2010; 120: 1848.
75. Newell KA, Asare A, Kirk AD, et al. Identification of a B cell signature associated with renal transplant tolerance in humans. J Clin Invest 2010; 120: 1836.
76. Seddiki N, Santner-Nanan B, Martinson J, et al. Expression of interleukin (IL)-2 and IL-7 receptors discriminates between human regulatory and activated T cells. J Exp Med 2006; 203: 1693.
77. Liu W, Putnam AL, Xu-Yu Z, et al. CD127 expression inversely correlates with FoxP3 and suppressive function of human CD4+ T reg cells. J Exp Med 2006; 203: 1701.
78. Nadig SN, Wieckiewicz J, Wu DC, et al. In vivo prevention of transplant arteriosclerosis by ex vivo-expanded human regulatory T cells. Nat Med 2010; 16: 809.
79. Codarri L, Vallotton L, Ciuffreda D, et al. Expansion and tissue infiltration of an allospecific CD4+CD25+CD45RO+IL-7Ralphahigh cell population in solid organ transplant recipients. J Exp Med 2007; 204: 1533.
80. Alonso-Arias R, Suarez-Alvarez B, Lopez-Vazquez A, et al. CD127(low) expression in CD4+CD25(high) T cells as immune biomarker of renal function in transplant patients. Transplantation 2009; 88(3 suppl): S85.
81. Segundo DS, Ruiz JC, Izquierdo M, et al. Calcineurin inhibitors, but not rapamycin, reduce percentages of CD4+CD25+FOXP3+ regulatory T cells in renal transplant recipients. Transplantation 2006; 82: 550.
82. Hendrikx TK, Velthuis JH, Klepper M, et al. Monotherapy rapamycin allows an increase of CD4 CD25 FoxP3 T cells in renal recipients. Transpl Int 2009; 22: 884.
83. Pallier A, Hillion S, Danger R, et al. Patients with drug-free long-term graft function display increased numbers of peripheral B cells with a memory and inhibitory phenotype. Kidney Int 2010; 78: 503.
84. Maecker HT, McCoy JP Jr, Amos M, et al. A model for harmonizing flow cytometry in clinical trials. Nat Immunol 2010; 11: 975.
85. Anglicheau D, Suthanthiran M. Noninvasive prediction of organ graft rejection and outcome using gene expression patterns. Transplantation 2008; 86: 192.
86. Vasconcellos LM, Schachter AD, Zheng XX, et al. Cytotoxic lymphocyte gene expression in peripheral blood leukocytes correlates with rejecting renal allografts. Transplantation 1998; 66: 562.
87. Sabek O, Dorak MT, Kotb M, et al. Quantitative detection of T-cell activation markers by real-time PCR in renal transplant rejection and correlation with histopathologic evaluation. Transplantation 2002; 74: 701.
88. Netto MV, Fonseca BA, Dantas M, et al. Granzyme B, FAS-ligand and perforin expression during acute cellular rejection episodes after kidney transplantation: Comparison between blood and renal aspirates. Transplant Proc 2002; 34: 476.
89. Aquino-Dias EC, Joelsons G, da Silva DM, et al. Non-invasive diagnosis of acute rejection in kidney transplants with delayed graft function. Kidney Int 2008; 73: 877.
90. Iwase H, Kobayashi T, Kodera Y, et al. Clinical significance of regulatory T-cell-related gene expression in peripheral blood after renal transplantation. Transplantation 2011; 91: 191.
91. Ashton-Chess J, Dugast E, Colvin RB, et al. Regulatory, effector, and cytotoxic T cell profiles in long-term kidney transplant patients. J Am Soc Nephrol 2009; 20: 1113.
92. Simon T, Opelz G, Wiesel M, et al. Serial peripheral blood perforin and granzyme B gene expression measurements for prediction of acute rejection in kidney graft recipients. Am J Transplant 2003; 3: 1121.
93. Veale JL, Liang LW, Zhang Q, et al. Noninvasive diagnosis of cellular and antibody-mediated rejection by perforin and granzyme B in renal allografts. Hum Immunol 2006; 67: 777.
94. Shin GT, Kim SJ, Lee TS, et al. Gene expression of perforin by peripheral blood lymphocytes as a marker of acute rejection. Nephron Clin Pract 2005; 100: c63.
95. Graziotto R, Del Prete D, Rigotti P, et al. Perforin, granzyme B, and fas ligand for molecular diagnosis of acute renal-allograft rejection: Analyses on serial biopsies suggest methodological issues. Transplantation 2006; 81: 1125.
96. Han D, Xu X, Baidal D, et al. Assessment of cytotoxic lymphocyte gene expression in the peripheral blood of human islet allograft recipients: Elevation precedes clinical evidence of rejection. Diabetes 2004; 53: 2281.
97. Yannaraki M, Rebibou JM, Ducloux D, et al. Urinary cytotoxic molecular markers for a noninvasive diagnosis in acute renal transplant rejection. Transpl Int 2006; 19: 759.
98. Shoker A, George D, Yang H, et al. Heightened CD40 ligand gene expression in peripheral CD4+ T cells from patients with kidney allograft rejection. Transplantation 2000; 70: 497.
99. Alakulppi NS, Kyllonen LE, Partanen J, et al. Diagnosis of acute renal allograft rejection by analyzing whole blood mRNA expression of lymphocyte marker molecules. Transplantation 2007; 83: 791.
100. Dugre FJ, Gaudreau S, Belles-Isles M, et al. Cytokine and cytotoxic molecule gene expression determined in peripheral blood mononuclear cells in the diagnosis of acute renal rejection. Transplantation 2000; 70: 1074.
101. Tan L, Howell WM, Smith JL, et al. Sequential monitoring of peripheral T-lymphocyte cytokine gene expression in the early post renal allograft period. Transplantation 2001; 71: 751.
102. Dijke IE, Caliskan K, Korevaar SS, et al. FOXP3 mRNA expression analysis in the peripheral blood and allograft of heart transplant patients. Transpl Immunol 2008; 18: 250.
103. Sawitzki B, Bushell A, Steger U, et al. Identification of gene markers for the prediction of allograft rejection or permanent acceptance. Am J Transplant 2007; 7: 1091.
104. Brouard S, Mansfield E, Braud C, et al. Identification of a peripheral blood transcriptional biomarker panel associated with operational renal allograft tolerance. Proc Natl Acad Sci USA 2007; 104: 15448.
105. Martinez-Llordella M, Lozano JJ, Puig-Pey I, et al. Using transcriptional profiling to develop a diagnostic test of operational tolerance in liver transplant recipients. J Clin Invest 2008; 118: 2845.
106. Anglicheau D, Muthukumar T, Suthanthiran M. MicroRNAs: Small RNAs with big effects. Transplantation 2010; 90: 105.
107. Sui W, Dai Y, Huang Y, et al. Microarray analysis of microRNA expression in acute rejection after renal transplantation. Transpl Immunol 2008; 19: 81.
108. Anglicheau D, Sharma VK, Ding R, et al. MicroRNA expression profiles predictive of human renal allograft status. Proc Natl Acad Sci USA 2009; 106: 5330.
109. Sawitzki B, Pascher A, Babel N, et al. Can we use biomarkers and functional assays to implement personalized therapies in transplantation? Transplantation 2009; 87: 1595.
110. Poste G. Bring on the biomarkers. Nature 2011; 469: 156.
111. Sarwal MM, Benjamin J, Butte AJ, et al. Transplantomics and biomarkers in organ transplantation—A report from the 1st International Conference. Transplantation 2011; 91: 379.
Keywords:

Immune monitoring; Transplantation; Biomarkers; Allograft rejection; Chronic allograft dysfunction

© 2011 Lippincott Williams & Wilkins, Inc.