Original Article
Molecular Heterogeneity in Acute Renal Allograft Rejection Identified by DNA Microarray Profiling
N Engl J Med 2003; 349:125-138July 10, 2003
- Abstract
Background
The causes and clinical course of acute rejection vary, and it is not possible to predict graft outcome reliably on the basis of available clinical, pathological, and genetic markers. We hypothesized that previously unrecognized molecular heterogeneity might underlie some of the variability in the clinical course of acute renal allograft rejection and in its response to treatment.
Methods
We used DNA microarrays in a systematic study of gene-expression patterns in biopsy samples from normal and dysfunctional renal allografts. A combination of exploratory and supervised bioinformatic methods was used to analyze these profiles.
Results
We found consistent differences among the gene-expression patterns associated with acute rejection, nephrotoxic effects of drugs, chronic allograft nephropathy, and normal kidneys. The gene-expression patterns associated with acute rejection suggested at least three possible distinct subtypes of acute rejection that, although indistinguishable by light microscopy, were marked by differences in immune activation and cellular proliferation. Since the gene-expression patterns pointed to substantial variation in the composition of immune infiltrates, we used immunohistochemical staining to define these subtypes further. This analysis revealed a striking association between dense CD20+ B-cell infiltrates and both clinical glucocorticoid resistance (P=0.01) and graft loss (P<0.001).
Conclusions
Systematic analysis of gene-expression patterns provides a window on the biology and pathogenesis of renal allograft rejection. Biopsy samples from patients with acute rejection that are indistinguishable on conventional histologic analysis reveal extensive differences in gene expression, which are associated with differences in immunologic and cellular features and clinical course. The presence of dense clusters of B cells in a biopsy sample was strongly associated with severe graft rejection, suggesting a pivotal role of infiltrating B cells in acute rejection.
Media in This Article
Figure 1
Hierarchical Clustering of 1340 Transcripts in 67 Biopsy Samples on the Basis of Similarity in Gene-Expression Patterns (Panel A) and a Dendrogram Showing the Degree of Relatedness of Samples (Panel B).Figure 2
Expanded View of the Gene Clusters, Showing Specific Features of the Gene-Expression Patterns within the Signatures in the Various Subtypes of Acute Rejection.Article Activity
- Article
Acute rejection is a complex process of injury to the allograft caused by infiltrating cells of the host immune system. It leads to multiple responses within the graft and is a major risk factor for chronic rejection and loss of the graft.1-3 Acute rejection typically develops soon after transplantation and is thought to be secondary to cell-mediated immune responses involving delayed mechanisms of hypersensitivity and cytotoxicity. Despite efforts at systematization,4,5 clinical and pathological diagnosis and classification of acute rejection remain unreliable in predicting responses to therapy and graft outcomes.6-10 Thus, there is a great need to improve risk stratification and modes of early treatment. We investigated the possibility that variations in gene-expression patterns in allograft-biopsy samples from patients with acute rejection and related disorders would permit the identification of molecularly distinct subtypes of acute rejection that may be related to differences in clinical behavior.
Methods
Patient Information
We analyzed 67 allograft-biopsy samples from 50 patients (1.4 to 22 years of age). Immunosuppressive therapy consisted of glucocorticoids, a calcineurin inhibitor (tacrolimus or cyclosporine), an antimetabolite (azathioprine or mycophenolate mofetil), and induction therapy with daclizumab. Nine graft losses occurred between 1.5 and 8.0 years after transplantation, a mean of 10 months after a biopsy was performed because of acute rejection. Written informed consent was obtained from all study patients, and the study was approved by the institutional review board of Stanford University.
Biopsy Samples
A total of 52 biopsy samples were obtained between 1 month and 10 years after transplantation during acute allograft dysfunction (defined by an increase of more than 10 percent in the serum creatinine concentration from base line) or chronic allograft dysfunction (defined by a glomerular filtration rate11 of 50 ml per minute per 1.73 m2 of body-surface area); 8 biopsy samples were obtained at the time of engraftment; and 7 samples were obtained at times when graft function was stable (as defined by a glomerular filtration rate of more than 80 ml per minute per 1.73 m2). All biopsy samples were snap-frozen. All but five biopsy samples were obtained before the intensification of treatment for rejection. Microscopical analyses were performed by investigators who were unaware of the clinical outcomes.5,12,13 No biopsy sample contained evidence of post-transplantation lymphoproliferative disorder or viral inclusions.
Microarray Hybridization and Data Analysis
Each microarray was a lymphochip14 gridded at Stanford University, and each contained 28,032 DNA spots representing approximately 12,440 human genes. Total RNA was isolated from frozen biopsy samples (TRI Reagent, Molecular Research Center). A common reference pool of RNA15 was used as an internal standard. Sample or reference RNA was subjected to two successive rounds of amplification16 before undergoing hybridization to microarrays.
All 67 biopsy samples were used for initial unsupervised, hierarchical clustering (i.e., analysis without prior knowledge of sample identity).17 For subsequent supervised analyses (i.e., comparative analyses between defined sample groups), with the use of significance analysis of microarrays,18 the five samples from patients with partially treated acute rejection were excluded in order to eliminate possible bias due to the effects of drugs. The enrichment of specific functional groups of genes was assessed in our data set on the basis of the hypergeometric distribution,19 with the use of 86 T-cell–specific genes,20 2610 T-cell–inducible transcripts,21 and 874 cell-cycle–related genes.22 Kaplan–Meier survival analyses, based on the Cox log-rank method, were used to determine the relation between graft survival or recovery of graft function (defined as the return of the serum creatinine concentration to the base-line level one month after the treatment of acute rejection) and the density of CD20+ cells.
Immunohistochemistry
Immunohistochemical staining for CD20, CD4, CD8, and proliferating-cell nuclear antigen (PCNA) was performed on samples from patients with untreated acute rejection. In addition, an independent set of 31 archived biopsy samples from patients with acute rejection was also analyzed by CD20 staining.
Entire cores were scanned in a blinded fashion by a single observer to determine the density of CD20+, CD4+, and CD8+ cells. Cell density per high-power field and the number of high-power fields counted per core were documented. For each specimen, the single high-power field with the highest CD20+ cell count was identified, and cell counts of more than 275 and less than 100 were chosen arbitrarily as definitions of CD20+ and CD20 – status, so that the high threshold was more than 2.5 times the low threshold.
Supplemental Information
Additional information on methods, immunohistochemical images, and analytic methods are available as supplementary appendixes at http://genome-www.stanford.edu/rejection/ or from the National Auxiliary Publications Service (NAPS). Data are available at the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo).
Results
Clustering of Samples
The gene-expression profiles of 67 allograft-biopsy samples were compared by the hierarchical clustering of samples according to the correlation in their patterns of expression in 1340 selected complementary DNA (cDNA) fragments, representing approximately 912 genes (Figure 1Figure 1
Hierarchical Clustering of 1340 Transcripts in 67 Biopsy Samples on the Basis of Similarity in Gene-Expression Patterns (Panel A) and a Dendrogram Showing the Degree of Relatedness of Samples (Panel B). and supplementary appendixes). In general, biopsy samples from patients with similar clinical diagnoses clustered together on the basis of corresponding similarities in gene expression, irrespective of the immunosuppressive regimen the patient was receiving. The possibility that differential sampling of the medullary and cortical regions might account for the observed molecular variation was addressed through the comparison of these data with those obtained from an examination of variation in gene expression in distinct regions of the kidney. Patterns of gene expression in normal cortex and medulla were characterized in samples obtained from the gross dissection of normal kidneys (supplementary appendixes). The exclusion of data from genes whose expression was highly correlated with the depth of the biopsy did not change the composition of the clusters of samples (supplementary appendixes).Clustering of Allograft Biopsies According to Gene-Expression Profile
We identified four clusters of expression patterns in the biopsy samples, which generally corresponded closely with clinicopathological categories (Figure 1 and Table 1Table 1
Pathological and Clinical Characteristics of 52 Biopsy Samples from Dysfunctional Kidneys.). Biopsy samples from patients with acute rejection were observed to have relative molecular heterogeneity. Unlike the samples from normal kidney or those from patients with chronic allograft nephropathy, toxic drug effects, or infection, the samples from patients with acute rejection were dispersed among three of the four major clusters. Although one distinct cluster (cluster A) consisted only of samples from patients with acute rejection, the remaining 14 of the 26 samples from such patients were dispersed in clusters B and C.All samples from patients with a clinicopathological diagnosis of toxic drug effects or infection were grouped in cluster B. All samples from patients with chronic allograft nephropathy were grouped in cluster C, and all the samples in this cluster showed clinicopathological evidence of chronic allograft nephropathy (in some cases, with accompanying acute rejection). All biopsy samples from normal kidneys were grouped in cluster D, and only normal samples were found in this cluster. The division of the samples into these four clusters reflects only a fraction of the molecular variations among them: within each of these clusters, extensive residual variation in gene expression was observed (the gene-expression signatures of each cluster are available at http://genome-www.stanford.edu/rejection/ or from the National Auxiliary Publications Service (NAPS). We focused our detailed analysis on the molecular characterization of the samples from patients with acute rejection.
Molecular Heterogeneity of Acute Rejection
We examined the characteristic gene-expression patterns that distinguished the 26 biopsy samples from patients with acute rejection. There were significant differences between these samples and those from normal kidney in the expression of 586 genes, representing 64 percent of all 912 unique genes analyzed; the median false discovery rate was 12 percent, or 68 genes (the false discovery rate, or the percentage of genes identified by chance, is calculated as the median number [or 90th percentile] of falsely identified genes divided by the number of genes achieving significance levels for differential expression18). The varying levels of expression of many of these genes suggests a varying abundance of distinctive cell populations, such as T and B lymphocytes, natural killer cells, macrophages, and endothelial cells.
At least three different groups of biopsy samples from patients with acute rejection, which were not differentiated by light microscopy, could be defined by unsupervised hierarchical clustering on the basis of pervasive differences in their gene-expression profiles: acute rejection type I, which we designated as AR-I (cluster A, accounting for 12 biopsy samples, with one repeated experiment), acute rejection type II (designated as AR-II, cluster B, accounting for 9 biopsy samples, 5 of which were from patients who had been partially treated at the time of biopsy), and acute rejection type III (designated as AR-III, cluster C, accounting for 5 biopsy samples). These differences in patterns of expression may reflect distinct mechanisms of molecular pathogenesis of rejection (Figure 1).
Defining Subtypes of Acute Renal Allograft Rejection
A total of 385 genes (42 percent of all unique genes analyzed) were differentially expressed in the biopsy samples in the AR-I group and the other samples from patients with acute rejection (median false discovery rate, 24 percent, or 94 genes). The functional theme reflected in these genes suggests that there is greater apoptosis as well as infiltration and activation of lymphocytes, driven by NF-κB and interferon-γ in AR-I than in the other subtypes of acute rejection (Figure 2AFigure 2
Expanded View of the Gene Clusters, Showing Specific Features of the Gene-Expression Patterns within the Signatures in the Various Subtypes of Acute Rejection. and supplementary appendixes). Also prominent in this subtype are increased transcripts from T cells (interleukin-2–receptor chains and T-cell–receptor chains), natural killer cells (natural-killer–cell transcript 4), and macrophages (matrix metalloproteinase-7 and macrophage receptor).Responses occurring downstream of T-cell activation may be enhanced, as suggested by increased expression of cytotoxic T-lymphocyte–effector genes (granzyme A and RANTES [regulated upon activation normal T-cell expressed and secreted], which are important effectors of acute rejection23-25), adhesion molecules, cytokines, cytokine receptors, and growth factors (Figure 2A). In support of this hypothesis, we found an enrichment19 of two classes of T-cell transcripts within the gene cluster characteristic of AR-I: 15 of 23 T-cell–specific transcripts20 (P<0.001 for the enrichment of T-cell–specific genes in AR-I as compared with the rest of the gene cluster) (supplementary appendixes), and 43 of 145 T-cell–inducible transcripts21 (P<0.001 for the enrichment of T-cell–inducible genes in AR-I as compared with the rest of the gene cluster) (supplementary appendixes). Furthermore, all eight of eight genes that were noted to be both T-cell–inducible and T-cell–specific were characteristic of the AR-I signature (P<0.001, supporting increased T-cell infiltration and activation in AR-I as compared with the rest of the gene cluster). Unexpectedly, an overriding signature for B cells (CD20, CD74, immunoglobulin heavy and light chains, and other molecules associated with B-cell receptors) was found in AR-I, as compared with the other subtypes (Figure 2 and supplementary appendixes).
Nine samples from patients with acute rejection (AR-II) shared features with biopsy samples from grafts with clinicopathological evidence of toxic drug effects or infection. Some similarities to the gene-expression profiles of the AR-I samples (Figure 1 and Figure 2) may reflect common features of immune activation by pathogens and alloantigens. Many features of the expression program of the innate immune response were prominent in these samples. Genes of the annexin family — specifically, annexin V, a potential marker of acute rejection26 — were expressed at a particularly high level in this group of biopsy samples. Expression of transforming growth factor β (induced by calcineurin-inhibitor drugs)27 was relatively elevated, supporting the clustering of samples from patients with toxic drug effects in this group of biopsy samples.
Five AR-III samples clustered with samples from patients with chronic allograft nephropathy in cluster C, despite the fact that they met the Banff histologic criteria for acute rejection. Perhaps the most striking feature of these samples was the expression of genes involved in cellular proliferation and cell cycling (Figure 2), suggesting active tissue repair and regeneration. Sixty of the 1340 transcripts in our data set were related to cell-cycle functions,22 and 14 of these 60 genes were among the genes whose expression was significantly elevated in AR-III, representing a statistically significant enrichment19 (P<0.001, for the enrichment of cell-cycle genes in AR-III as compared with the rest of the gene cluster). The molecular features of lymphocyte infiltration and activation were minimal in this subtype (Figure 2 and supplementary appendixes), suggesting a relatively quiescent rejection process and ongoing recovery from previous or chronic tubulointerstitial inflammation or tubular necrosis.
Immunohistochemical Features of Samples from Patients with Acute Rejection
Because we observed not only a robust T-cell signature, but also a B-cell signature in the AR-I group, we used immunohistochemical analysis to investigate whether variation in the cellular composition of infiltrating lymphocytes in the 20 unique biopsy samples from patients with untreated acute rejection might account for some of the differences among groups in the observed gene-expression patterns; one sample in AR-I was examined twice by microarray analysis (Table 1). We were particularly interested in further study of B cells, since B cells have not historically been reported to be key players in acute rejection.28 We chose CD20, a marker for B cells that is present in AR-I, to corroborate the observation of B-cell enrichment independently by immunohistochemical analysis.
On staining, we found that there was a greater abundance of CD8+ T lymphocytes than of CD4+ T lymphocytes in biopsy samples from patients with acute rejection. There were no overall quantitative differences in these patterns among the subtypes of acute rejection, although two biopsy samples with glucocorticoid resistance from patients in the AR-I group had a higher density of CD8+ cells (Figure 3AFigure 3
Immunohistochemical Staining of Tissues. and supplementary appendixes). The apparent absence of major differences in the density of CD4+ cells and CD8+ cells among the subtypes of acute rejection suggests that the relatively prominent T-cell signature in AR-I is largely attributable to an activated T-cell phenotype (evidenced by markers of early and late T-cell activation) rather than to increased numbers of infiltrating T cells and that, conversely, infiltrating T cells in AR-III are relatively quiescent.CD20 staining revealed unexpected large aggregates of B cells without formation of follicles (Figure 3A and supplementary appendixes) in 9 of 20 biopsy samples from patients with acute rejection: 7 of 11 in the AR-I group, 1 of 4 in the AR-II group, and 1 of 5 in the AR-III group (Table 1). This finding contrasts with a previous report of few B cells in samples from patients with acute rejection.28 CD20 staining of 31 archived biopsy samples from patients with acute rejection that were not examined by microarray also revealed a similar proportion of CD20+ lymphocyte aggregates in 9 biopsy samples (supplementary appendixes). Immunofluorescence staining of biopsy samples for immunoglobulin and complement deposition was negative, despite the presence of the B-cell aggregates; in situ hybridization for Epstein–Barr virus and simian virus 40 was negative, ruling out an association between B-cell infiltrates and viral infection or post-transplantation lymphoproliferative disorder (data not shown).
The presence of proliferating-cell nuclear antigen, a marker of cell proliferation, was confirmed in all 5 AR-III samples but not in any of the samples from patients with chronic allograft nephropathy (Figure 3B), distinguishing these groups of biopsy samples with otherwise similar expression profiles.
Clinical Correlates of the Subtype of Acute Rejection and CD20+ Cell Density
Analysis of the recovery of graft function over time revealed that grafts that were clustered in the AR-I group had significantly poorer functional recovery than those classified as either AR-II or AR-III (P=0.02) (Table 2Table 2
Correlations between Acute Rejection (AR) Subtype or CD20 Status and Graft Outcome. and supplementary appendixes). When data from the five samples from partially treated patients in the AR-II group were removed from the data set, a trend toward a correlation remained, despite reduced numbers of samples (P=0.06) (Table 2 and supplementary appendixes). In addition, four of five samples from patients with glucocorticoid-resistant acute rejection (defined by the absence of a clinical response to glucocorticoid pulse treatment) clustered in the AR-I group.A strong association between the density of CD20+ cells on immunostaining and the clinical phenotype of glucocorticoid resistance was observed among patients in the AR-I group: all four biopsy samples from patients in this group who had glucocorticoid-resistant acute rejection had a high density of CD20+ cells (one patient required antibody therapy with muromonab-CD3 [OKT3] at the outset for presumed vascular rejection, and the others required such therapy after the failure of glucocorticoid pulse therapy) (Table 1). The density of CD20+ cells was strongly correlated with graft loss when all samples from patients with acute rejection were considered together (P<0.001) (Table 2 and supplementary appendixes). To provide an independent test of the significance of this result, we examined the clinical correlates of the retrospective series of 31 biopsy samples from patients with acute rejection and confirmed that dense aggregates of CD20+ cells at the time of biopsy were strongly associated with glucocorticoid resistance (P<0.001) and poor graft outcomes (Table 3Table 3
Clinical Correlates of CD20 Status in Renal-Biopsy Samples from Patients with Acute Rejection.).None of the other variables we studied correlated with either the density of CD20+ lymphocytes or the assignment of a subtype of acute rejection defined according to the pattern of gene expression. These variables were the weight of the donor or the recipient, the age of the recipient, the number of HLA mismatches, the use of a transplant from a living or cadaveric donor, whether or not there were repeated transplantations, the presence or absence of panel-reactive antibody before transplantation, the occurrence or nonoccurrence of delayed graft function, the interval since transplantation, the type of immunosuppression, the presence or absence of hypertension, the presence or absence of anemia, the type of immunosuppressive therapy, or the presence or absence of humoral rejection as determined by complement C4d staining.
Discussion
We examined the global transcript profiles of kidney-biopsy samples to help us to understand and classify acute allograft rejection. Using DNA microarrays, we identified molecular variation suggesting the existence of distinct molecular and prognostic variants of acute rejection, which could not previously be clearly defined on the basis of clinical or pathological criteria. Many of the observed differences in gene-expression patterns among samples from patients with acute rejection appear to reflect differences in the composition and activation of infiltrating lymphocytes. Confounding influences of time may be involved in the ostensible disparities in gene expression that we report here, since there was residual heterogeneity within the subtypes of acute rejection that we defined by cluster analysis. A prospective and extensive longitudinal study of more samples by a variety of methods is needed to refine the classification of acute rejection, with clearer connections between patterns of gene expression, pathophysiology, and clinical course. Since our study is based largely on pediatric patients, similar analyses should be conducted in adult renal-transplant recipients.
The molecular and immunohistochemical evidence of B-cell infiltration in a subgroup of samples from patients with acute rejection was the most unexpected and important finding in this study. Dense CD20 staining was observed in approximately one third of the 52 biopsy samples from patients with acute rejection that underwent immunohistochemical analysis and was significantly associated with glucocorticoid resistance and eventual graft failure. Staining for CD20 may make possible a rapid clinical test that will permit the definition of a high-risk group of patients with acute rejection who may warrant more aggressive and specific treatment. The association between CD20+ lymphocyte infiltration and graft loss was unexpected. We speculate that in patients who have such an infiltration, early treatment with a monoclonal antibody against CD20 (rituximab) may be beneficial.29 The association of CD20 staining with glucocorticoid resistance does not suggest that these cases are necessarily humorally mediated despite the presence of C1s, C1r, and C4b in some biopsy samples: staining for complement C4d, which was used as a putative marker of humoral rejection in the biopsy samples from patients with acute rejection in our study, showed poor correlation with CD20 staining (P=1.00) (data not shown).
A preponderance of CD8+ T cells and plasma-cell infiltrates has been associated with glucocorticoid resistance and poor outcomes,30-33 whereas B cells have been reported to be infrequent or absent in acute rejection. The pathophysiological role of B-cell infiltrates in this study requires further investigation; their presence does not seem to result in direct allograft injury, since immunofluorescence staining of biopsy samples for immunoglobulin and complement deposition was negative in out study (data not shown). As pharmacologic suppression of T cells has improved over the past decade (with the introduction of tacrolimus, mycophenolate mofetil, sirolimus, and monoclonal antibodies against the interleukin-2 receptor), B cells may have evolved as efficient antigen-presenting cells for indirect allorecognition, and their continued presence in the graft, unaffected by current immunosuppressive management, may be resulting in a large fraction of episodes of refractory rejection.34,35
In conclusion, molecular profiling of transplants in patients with acute rejection identified new subtypes of acute rejection and a correlation between CD20+ lymphoid aggregates and poorer graft outcomes; these findings may point toward improvements in the individualization of therapy. Gene-expression profiling thus opens a new door for the study of acute rejection and may provide a means to a better understanding of other categories of graft dysfunction.
Supported by grants from the National Institutes of Health (NIH5P3-05 and NIH3P3-05S1, to Dr. Sarwal), the Clinical Center for Immunological Studies at Stanford University (to Dr. Sarwal), the Packard Foundation, Roche Pharmaceuticals, and the Howard Hughes Medical Institute.
NAPS See NAPS document no. 05611 for 56 pages of supplementary material. To order, contact NAPS, c/o Microfiche Publications, 248 Hempstead Tpke., West Hempstead, NY 11552.
We are indebted to Dr. Patrick O. Brown for scientific direction and support, without which this work would not have been possible; to Drs. Xin Chen, Ash A. Alizadeh, and Maximilian Diehn for invaluable scientific assistance; to Addie Whitney for printing of the DNA microarrays and for helpful advice; to Stella Chang for assistance with RNA amplification; to Dr. John Higgins for the cortex and medulla samples; to the Pediatric Nephrology team for their assistance with sample collection; to Dr. Alan Krensky for his support; to Wijan Prapong for assistance with the supplementary material; to Dr. Robert Colvin at Harvard University for conducting C4d staining; to Jennifer Boldrick for helpful advice, discussions, and review of the manuscript; and to the staff at the Stanford Microarray Database for their support and assistance with Web-site maintenance.
Source Information
From the Departments of Pediatrics (M.S., M.-S.C., S.-C.H., T.S., O.S.), Pathology (N.K., M.M.), and Surgery (O.S.), Stanford University, Stanford, Calif.
Address reprint requests to Dr. Sarwal at the Department of Pediatrics, G320, 300 Pasteur Dr., Stanford, CA 94305, or at msarwal@stanford.edu.
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