Detection of Cardiac Allograft Rejection and Response to Immunosuppressive Therapy With Peripheral Blood Gene Expression

Department of Internal Medicine, University of Iowa, Iowa City, Iowa, United States
Circulation (Impact Factor: 14.43). 01/2005; 110(25):3815-21. DOI: 10.1161/01.CIR.0000150539.72783.BF
Source: PubMed


Assessment of gene expression in peripheral blood may provide a noninvasive screening test for allograft rejection. We hypothesized that changes in peripheral blood expression profiles would correlate with biopsy-proven rejection and would resolve after treatment of rejection episodes.
We performed a case-control study nested within a cohort of 189 cardiac transplant patients who had blood samples obtained during endomyocardial biopsy (EMB). Using Affymetrix HU133A microarrays, we analyzed whole-blood expression profiles from 3 groups: (1) control samples with negative EMB (n=7); (2) samples obtained during rejection (at least International Society for Heart and Lung Transplantation grade 3A; n=7); and (3) samples obtained after rejection, after treatment and normalization of the EMB (n=7). We identified 91 transcripts differentially expressed in rejection compared with control (false discovery rate <0.10). In postrejection samples, 98% of transcripts returned toward control levels, displaying an intermediate expression profile for patients with treated rejection (P<0.0001). Cluster analysis of the 40 transcripts with >25% change in expression levels during rejection demonstrated good discrimination between control and rejection samples and verified the intermediate expression profile of postrejection samples. Quantitative real-time polymerase chain reaction confirmed significant differential expression for the predictive markers CFLAR and SOD2 (UniGene ID No. 355724 and No. 384944).
These data demonstrate that peripheral blood expression profiles correlate with biopsy-proven allograft rejection. Intermediate expression profiles of treated rejection suggest persistent immune activation despite normalization of the EMB. If validated in larger studies, expression profiling may prove to be a more sensitive screening test for allograft rejection than EMB.

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Available from: Emily J Tsai, Oct 03, 2015
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    • "However, the sensitivity and specificity of these approaches have been limited. In other fields, high-dimensional gene expression data offer a higher level of precision in the diagnosis of acute and chronic diseases [4,5]. Infectious disease lends itself well to these analyses since the host response to pathogens entails a cascade of cellular events and expressed molecules that can be readily measured on a genome scale. "
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    ABSTRACT: Capturing the host response by using genomic technologies such as transcriptional profiling provides a new paradigm for classifying and diagnosing infectious disease and for potentially distinguishing infection from other causes of serious respiratory illness. This strategy has been used to define a blood-based RNA signature as a classifier for pandemic H1N1 influenza infection that is distinct from bacterial pneumonia and other inflammatory causes of respiratory disease. To realize the full potential of this approach as a diagnostic test will require additional independent validation of the results and studies to examine the specificity of this signature for viral versus bacterial infection or co-infection.
    Critical care (London, England) 11/2012; 16(6):168. DOI:10.1186/cc11685 · 4.48 Impact Factor
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    • "Peripheral blood monitoring of AR in heart transplant patients has also been evaluated. Horwitz et al. identified a gene expression profile that identified AR from control samples [34]. Interestingly, an analysis of expression profiles following treatment for AR showed an intermediate level of expression for most genes suggesting the persistence of low-level inflammation/immunity. "
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    ABSTRACT: The diagnosis of rejection in kidney transplant patients is based on histologic classification of a graft biopsy. The current "gold standard" is the Banff 97 criteria; however, there are several limitations in classifying rejection based on biopsy samples. First, a biopsy involves an invasive procedure. Second, there is significant variance among blinded pathologists in the interpretation of a biopsy. And third, there is also variance between the histology and the molecular profiles of a biopsy. To increase the positive predictive value of classifiers of rejection, a Banff committee is developing criteria that integrate histologic and molecular data into a unified classifier that could diagnose and prognose rejection. To develop the most appropriate molecular criteria, there have been studies by multiple groups applying omics technologies in attempts to identify biomarkers of rejection. In this review, we discuss studies using genome-wide data sets of the transcriptome and proteome to investigate acute rejection, chronic allograft dysfunction, and tolerance. We also discuss studies which focus on genetic biomarkers in urine and peripheral blood, which will provide clinicians with minimally invasive methods for monitoring transplant patients. We also discuss emerging technologies, including whole-exome sequencing and RNA-Seq and new bioinformatic and systems biology approaches, which should increase the ability to develop both biomarkers and mechanistic understanding of the rejection process.
    Seminars in Immunopathology 02/2011; 33(2):211-8. DOI:10.1007/s00281-011-0243-2 · 7.75 Impact Factor
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    • "A simple blood test that predicts the extent of coronary artery disease could provide an additional useful tool for screening for coronary artery disease in at-risk populations. A similar approach has been successfully used for detection of cardiac allograft rejection and the response to immunosuppressive therapy [23]. "
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    ABSTRACT: Systemic and local inflammation plays a prominent role in the pathogenesis of atherosclerotic coronary artery disease, but the relationship of whole blood gene expression changes with coronary disease remains unclear. We have investigated whether gene expression patterns in peripheral blood correlate with the severity of coronary disease and whether these patterns correlate with the extent of atherosclerosis in the vascular wall. Patients were selected according to their coronary artery disease index (CADi), a validated angiographical measure of the extent of coronary atherosclerosis that correlates with outcome. RNA was extracted from blood of 120 patients with at least a stenosis greater than 50% (CADi≥23) and from 121 controls without evidence of coronary stenosis (CADi = 0). 160 individual genes were found to correlate with CADi (rho>0.2, P<0.003). Prominent differential expression was observed especially in genes involved in cell growth, apoptosis and inflammation. Using these 160 genes, a partial least squares multivariate regression model resulted in a highly predictive model (r2 = 0.776, P<0.0001). The expression pattern of these 160 genes in aortic tissue also predicted the severity of atherosclerosis in human aortas, showing that peripheral blood gene expression associated with coronary atherosclerosis mirrors gene expression changes in atherosclerotic arteries. In conclusion, the simultaneous expression pattern of 160 genes in whole blood correlates with the severity of coronary artery disease and mirrors expression changes in the atherosclerotic vascular wall.
    PLoS ONE 09/2009; 4(9):e7037. DOI:10.1371/journal.pone.0007037 · 3.23 Impact Factor
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