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• W2008161741 abstract "Rejection diagnosis by endomyocardial biopsy (EMB) is invasive, expensive and variable. We investigated gene expression profiling of peripheral blood mononuclear cells (PBMC) to discriminate ISHLT grade 0 rejection (quiescence) from moderate/severe rejection (ISHLT ≥3A). Patients were followed prospectively with blood sampling at post-transplant visits. Biopsies were graded by ISHLT criteria locally and by three independent pathologists blinded to clinical data. Known alloimmune pathways and leukocyte microarrays identified 252 candidate genes for which real-time PCR assays were developed. An 11 gene real-time PCR test was derived from a training set (n = 145 samples, 107 patients) using linear discriminant analysis (LDA), converted into a score (0–40), and validated prospectively in an independent set (n = 63 samples, 63 patients). The test distinguished biopsy-defined moderate/severe rejection from quiescence (p = 0.0018) in the validation set, and had agreement of 84% (95% CI 66% C94%) with grade ISHLT ≥3A rejection. Patients >1 year post-transplant with scores below 30 (approximately 68% of the study population) are very unlikely to have grade ≥3A rejection (NPV = 99.6%). Gene expression testing can detect absence of moderate/severe rejection, thus avoiding biopsy in certain clinical settings. Additional clinical experience is needed to establish the role of molecular testing for clinical event prediction and immunosuppression management. Rejection diagnosis by endomyocardial biopsy (EMB) is invasive, expensive and variable. We investigated gene expression profiling of peripheral blood mononuclear cells (PBMC) to discriminate ISHLT grade 0 rejection (quiescence) from moderate/severe rejection (ISHLT ≥3A). Patients were followed prospectively with blood sampling at post-transplant visits. Biopsies were graded by ISHLT criteria locally and by three independent pathologists blinded to clinical data. Known alloimmune pathways and leukocyte microarrays identified 252 candidate genes for which real-time PCR assays were developed. An 11 gene real-time PCR test was derived from a training set (n = 145 samples, 107 patients) using linear discriminant analysis (LDA), converted into a score (0–40), and validated prospectively in an independent set (n = 63 samples, 63 patients). The test distinguished biopsy-defined moderate/severe rejection from quiescence (p = 0.0018) in the validation set, and had agreement of 84% (95% CI 66% C94%) with grade ISHLT ≥3A rejection. Patients >1 year post-transplant with scores below 30 (approximately 68% of the study population) are very unlikely to have grade ≥3A rejection (NPV = 99.6%). Gene expression testing can detect absence of moderate/severe rejection, thus avoiding biopsy in certain clinical settings. Additional clinical experience is needed to establish the role of molecular testing for clinical event prediction and immunosuppression management. The goal of care after cardiac transplantation is to prevent allograft rejection while minimizing immunosuppressive side effects (1Eisen HJ Tuzcu EM Dorent R et al.Everolimus for the prevention of allograft rejection and vasculopathy in cardiac-transplant recipients..N Engl J Med. 2003; 349: 847-858Crossref PubMed Scopus (1025) Google Scholar,2Kobashigawa J Miller L Renlund D et al.A randomized active-controlled trial of mycophenolate mofetil in heart transplant recipients. Mycophenolate Mofetil Investigators..Transplantation. 1998; 66: 507-515Crossref PubMed Scopus (525) Google Scholar). The gold standard of rejection surveillance in cardiac transplantation is endomyocardial biopsy (EMB). However, EMB is invasive, expensive, subject to sampling error, inter-observer variability (3Nielsen H Sorensen FB Nielsen B Bagger JP Thayssen P Baandrup U. Reproducibility of the acute rejection diagnosis in human cardiac allografts. The Stanford Classification and the International Grading System..J Heart Lung Transplant. 1993; 12: 239-243PubMed Google Scholar, 4Winters GL McManus B. Consistencies and controversies in the application of the International Society for Heart and Lung Transplantation working formulation for heart transplant biopsy specimens. Rapamycin Cardiac Rejection Treatment Trial Pathologists..J Heart Lung Transplant. 1996; 15: 728-735PubMed Google Scholar, 5Winters GL Marboe CC Billingham M. The International Society for Heart and Lung Transplantation grading system for heart transplant biopsy specimen: clarification and commentary..J Heart Lung Transplant. 1998; 17: 754-760PubMed Google Scholar), and causes morbidity (0.5–1.5%). Although noninvasive alternatives to EMB are clearly needed, methods such as echocardiography, ultrasonic myocardial back-scatter, radionuclide imaging, magnetic resonance imaging, intra-myocardial electrograms and multiparametric immune monitoring have been difficult to validate and implement (6Angermann CE Nassau K Stempfle HU et al.Recognition of acute cardiac allograft rejection from serial integrated backscatter analyses in human orthotopic heart transplant recipients. Comparison with conventional echocardiography..Circulation. 1997; 95: 140-150Crossref PubMed Scopus (58) Google Scholar, 7Bourge R Eisen H Hershberger R et al.Noninvasive rejection monitoring of cardiac transplants using high resolution intramyocardial electrograms: initial US multicenter experience..PACE. 1998; 21: 2338-2344Crossref Scopus (41) Google Scholar, 8Deng MC Erren M Roeder N et al.T-cell and monocyte subsets, inflammatory molecules, rejection and hemodynamics early after cardiac transplantation..Transplantation. 1998; 65: 1255-1261Crossref PubMed Scopus (36) Google Scholar, 9Eisen HJ Eisenberg S Saffitz JE Bolman RM Sobel BE Bergmann S. Noninvasive diagnosis of cardiac transplant rejection using labeled lymphocytes..Circulation. 1987; 75: 868Crossref PubMed Scopus (33) Google Scholar, 10Frist W Yasuda T Segall G et al.Noninvasive detection of human cardiac transplant rejection with indium-111 antimyosin (Fab) imaging..Circulation. 1987; 76: 81-85PubMed Google Scholar, 11Hammer C Klanke D Lersch C et al.Cyoimmunologic monitoring (CIM) for differentiation between cardiac rejection and viral, bacterial, or fungal infection: its specificity and sensitivity..Transplant Proc. 1989; 21: 3631-3633PubMed Google Scholar, 12Hummel M Dandel M Knollmann F et al.Long-term surveillance of heart-transplanted patients: noninvasive monitoring of acute rejection episodes and transplant vasculopathy..Transplant Proc. 2001; 33: 3539-3542Crossref PubMed Scopus (22) Google Scholar, 13Kimball P Radovancevic B Isom T Frazier B Kerman R. Cytokine panel predicts early rejection and therapeutic response after cardiac transplantation..Transplant Proc. 1995; 27: 1286-1287PubMed Google Scholar, 14Knosalla C Grauhan O Muller J et al.Intramyocardial electrogram recordings (IMEG) for diagnosis of cellular and humoral mediated cardiac allograft rejection..Ann Thorac Cardiovasc Surg. 2000; 6: 89-94PubMed Google Scholar, 15Masuyama T Valantine HA Gibbons R Schnittger I Popp R. Serial measurement of integrated ultrasonic backscatter in human cardiac allografts forthe recognition of acute rejection..Circulation. 1990; 81: 829-839Crossref PubMed Scopus (79) Google Scholar, 16Melter M Exeni A Reinders ME et al.Expression of the chemokine receptor CXCR3 and its ligand IP-10 during human cardiac allograft rejection..Circulation. 2001; 104: 2558-2564Crossref PubMed Scopus (183) Google Scholar, 17Park JW Warnecke H Deng M Schueler S Heinrich KW Hetzer R. Early diastolic left ventricular function as a marker of acute cardiac rejection: a prospective serial echocardiographic study..Int J Cardiol. 1992; 37: 351-359Abstract Full Text PDF PubMed Scopus (17) Google Scholar, 18Pfitzmann R Muller J Grauhan O Hetzer R. Intramyocardial impedance measurements for diagnosis of acute cardiac allograft rejection..Ann Thorac Surg. 2000; 70: 527-532Abstract Full Text Full Text PDF PubMed Scopus (25) Google Scholar, 19Smart FW Young JB Weilbaecher D Kleiman NS Wendt 3rd, RE Johnston D. Magnetic resonance imaging for assessment of tissue rejection after heterotopic heart transplantation..J Heart Lung Transplant. 1993; 12: 403-410PubMed Google Scholar, 20Warnecke H Muller J Cohnert T et al.Clinical heart transplantation without routine endomyocardial biopsy..J Heart Lung Transplant. 1992; 11: 1093-1102PubMed Google Scholar). As recirculating peripheral blood mononuclear cells (PBMC) may reflect earlier host responses than those at local sites, measurement of PBMC gene expression might provide useful diagnostic information and reduce the need for EMB in patients who are asymptomatic. Recent studies using microarray analysis (21Horwitz PA Tsai EJ Putt ME et al.Detection of cardiac allograft rejection and response to immunosuppressive therapy with peripheral blood gene expression..Circulation. 2004; 110: 3815-3821Crossref PubMed Scopus (149) Google Scholar) or real-time PCR analysis of cytokine genes (22Schoels M Dengler TJ Richter R Meuer SC Giese T. Detection of cardiac allograft rejection by real-time PCR analysis of circulating mononuclear cells..Clin Transplant. 2004; 18: 513-517Crossref PubMed Scopus (44) Google Scholar) have suggested that gene expression measurements in PBMC may be correlated with cardiac allograft rejection. However, these single center studies are limited by the absence of methodology to recognize the imperfect ‘gold standard’ nature of EMB, which creates significant challenges for diagnostic development and validation study design and analysis (23Walter SD Irwig L Glasziou P. Meta-analysis of diagnostic tests with imperfect reference standards..J Clin Epidemiol. 1999; 52: 943-951Abstract Full Text Full Text PDF PubMed Scopus (91) Google Scholar,24Zhou X Obuchowski N McClish D. Statistical Methods in Diagnostic Medicine. John Wiley & Sons, New York2002Crossref Google Scholar). In addition, the absence of multicenter independent validation sets in both studies suggests the need for more extensive investigation. Based on the assumption that a gene expression signature of immune activation and leukocyte trafficking would be detectable in recipient PBMC and reflect the rejection status of the donor allograft, we tested the hypothesis that a gene expression test could discriminate ISHLT grade 0 rejection (quiescence) from moderate/severe (ISHLT grade ≥3A) rejection (nonquiescence). After approval by local Institutional Reviews Boards at eight centers, all patients undergoing heart transplantation and providing informed consent were eligible for the Cardiac Allograft Rejection Gene Expression Observational (CARGO) study beginning in September 2001. Enrolled patients were followed at each clinical encounter with data collection including EMB, hemodynamics and/or echocardiography, immunosuppression, laboratory data and complications, which were captured in electronic clinical report forms. EMB slides were obtained from centers for interpretation by a panel of pathologists blinded to the clinical data. The study was conducted in three phases (Figure 1A): (1Eisen HJ Tuzcu EM Dorent R et al.Everolimus for the prevention of allograft rejection and vasculopathy in cardiac-transplant recipients..N Engl J Med. 2003; 349: 847-858Crossref PubMed Scopus (1025) Google Scholar) candidate gene discovery using a combination of focused genomic and knowledge-base approaches; (2Kobashigawa J Miller L Renlund D et al.A randomized active-controlled trial of mycophenolate mofetil in heart transplant recipients. Mycophenolate Mofetil Investigators..Transplantation. 1998; 66: 507-515Crossref PubMed Scopus (525) Google Scholar) diagnostic development using PCR assays and rigorous statistical methods and (3Nielsen H Sorensen FB Nielsen B Bagger JP Thayssen P Baandrup U. Reproducibility of the acute rejection diagnosis in human cardiac allografts. The Stanford Classification and the International Grading System..J Heart Lung Transplant. 1993; 12: 239-243PubMed Google Scholar) validation in a prospective and blinded study. Samples were selected and divided into a training set, used for candidate gene discovery and diagnostic development, and a set used for validation of the gene expression signature described below. Data from an additional set of representative samples not used in any of the three phases were evaluated after the validation studies to estimate the negative predictive value (NPV) and positive predictive value (PPV) in the CARGO population. Biopsies performed by standard techniques were graded by local pathologists. A subset of biopsy samples, including all local grades 1B, 2, 3A and 3B and a representative set of grades 0 and 1A samples, were also graded by three independent (‘central’) pathologists blinded to clinical information. After an evaluation of the concordance of these biopsy grades by the four pathologists, criteria for selecting acute cellular rejection and quiescent samples were defined prior to developing and validating the classifier. PBMC were isolated from eight mL of venous blood using density gradient centrifugation (CPT, Becton-Dickinson). Samples were frozen in lysis buffer (RLT, Qiagen) within 2 h of phlebotomy. Total RNA was isolated from each sample (RNeasy, Qiagen). The effects of processing time on gene assays were tested in PBMC isolated from six venous blood samples from each of nine donors. Samples were treated identically except the interval between blood draw and first centrifugation step was varied from 1 to 8 h. Any gene assays showing significant systematic variations across this time period were eliminated from the development process. A custom microarray was designed using RNA sequences expressed in stimulated and resting human leukocytes (PCR Select, Clontech) and from publicly available sequence databases. A total of 7370 genes were represented by 50-mer oligonucleotides (Sigma) on a spotted custom microarray (Telechem). To increase the power and quality of results, a large number of clinical samples (285) were used. Microarray data are available at GEO (25GEOhttp://www.ncbi.nlm.nih.gov/geo/Google Scholar) with accession number GSE2445. The experimental methods are described in detail in the Supplement Section. Our leukocyte-focused genomic microarray approach was complemented with (1Eisen HJ Tuzcu EM Dorent R et al.Everolimus for the prevention of allograft rejection and vasculopathy in cardiac-transplant recipients..N Engl J Med. 2003; 349: 847-858Crossref PubMed Scopus (1025) Google Scholar) a review of the literature on pathways involved in immune activation, recruitment and mobilization, in general, and solid organ transplant rejection, in particular; and (2Kobashigawa J Miller L Renlund D et al.A randomized active-controlled trial of mycophenolate mofetil in heart transplant recipients. Mycophenolate Mofetil Investigators..Transplantation. 1998; 66: 507-515Crossref PubMed Scopus (525) Google Scholar) genes related to genes suggested to be significant by microarrays (by pathways and families). PCR primers and probes were designed using PRIMER3 (version 0.9, Whitehead Research Institute). Assays were designed on the full-length mRNA after masking to avoid problematic sequences. Assays were qualified for inclusion in the training set by specificity, linear dynamic range and efficiency using both human PBMC cDNA and synthetic oligonucleotide templates. For each gene, triplicate 10 μL real-time PCR reactions were performed on the ABI 7900HT system using FAM-TAMRA probes and standard Taqman protocols (Applied Biosystems) on cDNA from 0.5 ng total RNA. Normalization genes were empirically selected using PCR data from the training samples. Genes which did not discriminate between rejection and quiescence samples with small standard deviations across all samples were considered as normalization genes. Six such genes spanning different expression levels were chosen. Three additional assays were included as controls: two to detect genomic DNA contamination by the difference between a transcribed and nontranscribed region of the Gus-B gene and the third, a spiked-in control template for an Arabidopsis gene to determine if the PCR reaction was successful. RT-PCR data on 252 genes for the training set of 36 rejection and 109 quiescent CARGO samples were generated to derive a panel of candidate genes for classifier development and to validate microarray results. Gene expression results were analyzed with Student’s t-test, median ratios, hierarchical clustering by TreeView and an expert assessment of biological relevance. Metagenes, defined as transcripts behaving in a concordant manner (26Brunet JP Tamayo P Golub TR Mesirov J. Metagenes and molecular pattern discovery using matrix factorization..Proc Natl Acad Sci U S A. 2004; 101: 4164-4169Crossref PubMed Scopus (1341) Google Scholar), were constructed by averaging gene expression levels that were correlated across training samples with correlation coefficients of at least 0.7. Genes significantly distinguishing rejection from quiescence in the PCR training set by t-test (p ≤ 0.01), by median ratio differences of <0.75 or >1.25 or by correlation with significant genes were used for metagene construction and classifier development. The methods for analyzing gene expression data included principal components analysis, linear discriminant analysis (LDA, StatSoft, Inc.), logistic regression (SAS Institute, Inc.), prediction analysis of microarrays (PAM) (27Tibshirani R Hastie T Narasimhan B Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression..Proc Natl Acad Sci U S A. 2002; 99: 6567-6572Crossref PubMed Scopus (2183) Google Scholar), voting, classification and regression trees (TreeNet, Salford Systems), Random Forests, nearest shrunken centroids and k-nearest neighbors. We sought to develop a classifier that quantitatively distinguished current moderate/severe acute cellular rejection (ISHLT grade ≥3A) from quiescence (ISHLT grade 0) using gene and metagene expression levels as the variables. The final classifier was developed using LDA as implemented in the ‘discriminant function analysis’ module of Statistica (StatSoft, Inc.). LDA constructs a linear classifier by automatically selecting genes and/or metagenes that, in combination, optimally separate rejection and quiescent samples in the training set. The robustness of selected genes and the appropriate number of genes in the classifier were both evaluated by cross-validation. An independent cohort of CARGO patients was selected to validate the effectiveness of the LDA classifier defined in the diagnostic development phase using a prospective and blinded study protocol. The primary objective of the validation study was to test the pre-specified hypothesis that the diagnostic score distinguishes quiescence, defined as ISHLT grade 0, from moderate/severe biopsy-proven acute rejection, defined as ISHLT grade ≥3A, both grades determined from local and centralized cardio-pathological examination. This was assessed using a 2-tailed Student’s t-test for comparing score distributions for rejection and quiescent samples. Secondary and exploratory objectives included documentation of diagnostic performance across thresholds and description of correlations to clinical variables. Results for the validation study are reported for unique samples from patients not used for training (primary validation study), as well as for a larger set of samples not used for training (secondary validation study). These latter samples may provide improved power but may be biased to the extent that a longitudinal set of samples from an individual patient are not completely independent with respect to gene expression. A representative set of samples, across all local biopsy grades and ≥1 year post-transplant were evaluated to assess the discriminant equation performance on a stable patient population. From these samples, PPV (fraction of samples with scores at or above the threshold expected to have concurrent biopsy grade ≥3A) and NPV (fraction of samples with scores below the threshold expected to be free from biopsy grade ≥3A) were estimated at multiple test thresholds. Given the risk associated with undetected acute cellular rejection, and the clinical use of EMB, we sought a threshold that maximized the NPV at the expense of the PPV. Plasma was tested for quantitative CMV viral load using the COBAS protocol (Roche). These samples were selected from the CARGO study and represented known or suspected CMV infection and matched controls. Patients and samples used in these studies were selected from the CARGO database, with donor and recipient characteristics similar to those reported by the United Network for Organ Sharing (UNOS) for 2003 (28http://www.unos.org>http://www.unos.org>http://www.unos.orgGoogle Scholar) (Table 1). The relationships between the samples and patients used in the three phases are shown in Figure 1B.Table 1Clinical characteristics of study patients and samplesUNOS 2003CARGO (N = 629 patients, 4917 samples)Microarray discoveryDiagnostic development & trainingPCR 1 validation (unique patients)PCR 2 validation (unique samples)Rejection (N = 28 patients, 38 samples)No rejection (N = 94 patients, 247 samples)p-va lueRejection (N = 28 patients, 36 samples)No rejection (N = 86 patients, 109 samples)p-va lueRejection (N = 31 patients, 31 samples)No rejection (N = 32 patients, 32 samples)p-valueRejection (N = 50 patients, 62 samples)No rejection (N = 83 patients, 122 samples)p-valueRecipient ageUnder 1814.0%6.4%0.0%0.0%0.0%1.8%6.4%12.5%3.2%1.6%18-349.4%9.9%15.8%13.0%13.9%5.5%12.9%9.4%14.5%13.9%35-4921.1%17.7%23.7%12.6%NS13.9%21.1%NS3.2%18.7%0.00617.7%18.0%NS50-6447.2%53.1%57.9%65.6%69.4%56.9%67.7%31.2%53.2%54.9%65+8.5%12.9%2.6%8.9%2.8%14.7%9.7%28.1%11.3%11.5%Recipient raceWhite71.1%72.3%73.7%74.1%72.2%78.9%70.9%65.6%67.7%66.4%Black16.0%17.3%21.1%15.4%19.4%10.1%16.1%15.6%24.2%18.9%Hispanic8.8%6.2%5.3%7.7%NS8.3%6.4%NS6.4%3.1%NS6.5%9.0%NSAsian1.9%1.2%0.0%0.0%0.0%1.8%3.2%3.1%0.0%2.5%Other2.1%3.1%0.0%2.8%0.0%2.8%0.0%3.1%1.6%3.3%Recipient sexMale73.6%74.6%92.1%77.3%NS86.1%73.4%NS74.2%78.1%NS80.6%81.1%NSFemale26.4%25.4%7.9%22.7%13.9%26.6%22.6%12.5%19.4%18.9%Donor ageUnder 1821.6%17.9%18.4%11.4%17.1%17.5%16.1%21.8%20.0%17.1%18-3444.2%46.5%47.4%49.4%51.4%46.6%45.2%46.8%38.2%51.3%35-4925.7%24.9%26.3%28.2%NS22.9%24.3%NS6.45%15.6%NS32.7%19.7%NS50-648.2%10.5%7.9%11.0%8.6%11.7%16.9%9.3%9.1%11.1%65+0.2%0.2%0.0%0.0%0.0%0.0%0.0%3.1%0.0%0.9%Donor raceWhite69.6%71.4%73.7%66.9%75.0%62.4%86.6%67.8%83.9%66.4%Black12.0%14.2%5.3%14.6%5.6%12.8%10%14.3%8.1%13.9%Hispanic15.7%11.4%21.1%16.3%NS19.4%18.3%NS3.3%14.3%NS8.1%13.1%NSAsian1.6%0.7%0.0%2.1%0.0%0.9%0.0%0.0%0.0%0.8%Other1.2%2.3%0.0%0.0%0.0%5.5%0.0%3.5%0.0%5.7%Donor sexMale68.4%63.7%71.1%66.1%NS61.1%68.5%NS53.3%75%NS58.1%74.2%NSFemale31.6%36.3%28.9%33.9%38.9%31.5%46.7%25%41.9%25.8%Primary diagnosisCoronary artery disease42.1%23.8%15.8%21.9%22.2%32.1%29.1%34.4%16.1%30.3%Cardiomyopathy47.0%70.0%81.6%69.2%77.8%58.7%61.3%53.1%79.0%64.8%Congenital heart disease8.5%2.3%0.0%0.4%NS0.0%1.8%NS0.0%3.1%NS0.0%2.5%0.025Retransplant3.3%0.5%2.6%5.3%0.0%1.8%0.0%0.0%3.2%0.8%Valvular disease1.9%2.4%0.0%1.2%0.0%1.8%0.0%0.0%0.0%0.8%Other0.5%1.0%0.0%2.0%0.0%3.7%6.4%0.0%1.6%0.8%Immunosuppression*From UNOS 2001 data. This percentage represents the number of transplants in which a particular drug was used for maintenance at any point in the year after transplant divided by the number of transplants in 2001, and only accounts for patients with immunosuppressive information.Cyclosporine64.9%50.4%71.1%71.7%52.8%44.0%58.1%34.4%72.6%53.3%FK-50642.9%36.6%26.3%25.5%47.2%54.1%38.7%50%25.8%38.5%Mycophenolate80.5%72.0%81.6%87.4%72.2%78.0%74.2%75%80.6%83.6%Rapamycin7.5%9.8%5.3%2.4%NS22.2%14.7%NS12.9%6.3%0.04812.9%8.2%NSAzathioprine14.7%1.4%0.0%0.4%0.0%1.8%0.0%3.1%0.0%1.6%Corticosteroids91.1%82.2%97.4%94.3%94.4%91.7%77.4%75%88.7%82.8%Zenapax2.5%9.4%2.6%4.5%0.0%7.3%3.2%3.1%6.5%4.1%Days post-TxAverage days post-TxNA2418362254206149147205265NS = Not significant (p > 0.05).Comparison of clinical parameters of patients and samples used in the microarray, diagnostic development and validation studies.CARGO = Cardiac Allograft Rejection Gene expression Observation study.UNOS = United Network for Organ Sharing.* From UNOS 2001 data. This percentage represents the number of transplants in which a particular drug was used for maintenance at any point in the year after transplant divided by the number of transplants in 2001, and only accounts for patients with immunosuppressive information. Open table in a new tab NS = Not significant (p > 0.05).Comparison of clinical parameters of patients and samples used in the microarray, diagnostic development and validation studies.CARGO = Cardiac Allograft Rejection Gene expression Observation study.UNOS = United Network for Organ Sharing. In the gene discovery phase, 285 rejection and quiescent samples from 98 patients were hybridized to the leukocyte microarrays covering 7370 genes. Ninety-seven genes were selected as candidates for PCR assay development from these microarray studies based on false detection rates from SAM <20% (29Tusher VG Tibshirani R Chu G. Significance analysis of microarrays applied to the ionizing radiation response..Proc Natl Acad Sci U S A. 2001; 98: 5116-5121Crossref PubMed Scopus (9801) Google Scholar), p-values in nonparametric analysis <0.05 or clustering with genes involved in rejection. This gene set was expanded to include related genes identified by correlated expression or related functions, as well as genes from the literature involved in transplant rejection, yielding an additional 155 gene candidates. In the diagnostic development phase, 252 real-time PCR assays were developed to assess and confirm the discriminatory ability of the candidate genes from the gene discovery phase. These PCR assays were performed on 145 samples including 36 rejections (from 28 patients) and 109 quiescent samples (from 86 patients). Centralized pathology reading was used to identify these samples, where at least two of four pathologists were required to classify a sample as grade ≥3A for rejection, and three of four pathologists were required to classify a sample as grade 0 for quiescence. These criteria were set prospectively based upon centralized reading of over 800 CARGO samples and were used in the diagnostic development and validation PCR studies (30Marboe CC Billingham M Eisen H et al.Nodular endocardial infiltrates (Quilty Lesions) cause significant variability in the diagnosis of ISHLT rejection grades 2 and 3A in endomyocardial biopsies from cardiac allograft recipients..J Heart Lung Transplant. 2005; 24: S219-S226Abstract Full Text Full Text PDF PubMed Scopus (119) Google Scholar). Analysis of this set of PCR data (see PCR-heatmap Figure 2A) yielded 68 genes that distinguished rejection samples from quiescent samples by t-test (p < 0.01), median ratio of (>1.25 or <0.75), or by correlation to discriminatory genes (Table 2). By hierarchical clustering (31Eisen MB Spellman PT Brown PO Botstein D. Cluster analysis and display of genome-wide expression patterns..Proc Natl Acad Sci U S A. 1998; 95: 14863-14868Crossref PubMed Scopus (13267) Google Scholar) (Figure 2B), the predominant genes showing increased expression with rejection were T-cell/NK and CD8+ T-cell activation markers (perforin, granulysin) and erythropoiesis markers (ALAS2, WDR40A, MIR). Six genes (CXCR4, hIAN7, HBG, CXCR3, ADM and TNFSF6) were eliminated due to significant variation in gene expression with sample processing time (32Baechler EC Batliwalla FM Karypis G et al.Expression levels for many genes in human peripheral blood cells are highly sensitive to ex vivo incubation..Genes Immunology. 2004; 5: 347-353Crossref PubMed Scopus (130) Google Scholar) yielding 62 genes for discriminatory signature development.Table 2Genes that discriminate between quiescence and rejectionGeneGenBankSourceDiscovery PCR trainingt-testRatioProgrammed Cell death 1PDCD1iterature.6E-05.46Semaphorin 7ASEMA7AayE-05.29Interleukin-1 receptor-soluble formIL1R2Array.4E-05.48Importin alpha7KPNA6Array.1E-04.13Chemokine (C-X-C motif) receptor 3CXCR3iterature.1E-04.32IkarosZNFN1A1iterature.5E-04.15Integrin beta 7ITGB7iterature.7E-04.21Integrin alpha-MITGAMiterature.6E-04.85Chemokine (C-X-C motif) receptor 4CXCR4Array.6E-04.60Matrix metalloproteinase 9MMP9iterature.1E-04.30Vanin-2VNN2Array.4E-04.70FLT3 ligandFLT3LGiterature.0E-04.27CD160 NK cell receptorBY55rray.6E-04.21Integrin alpha4ITGA4iterature.0011.18Lymphocyte specific kinaseLCKiterature.3E-03.27T-cell receptor betaTCRBiterature.4E-03.30Adenosine deaminaseADAiterature.0015.24AdrenomedullinADMArray.0016.70Fms-like tyrosine kinase 3FLT3iterature.0020.60Inositol polyphosphate-5-phosphataseINPP5AArray.0022.89Lymphocyte activation gene 3LAG3iterature.0026.30Fas LigandTNFSF6iterature.0027.59Signal regulatory protein beta-1SIRPB1iterature.0029.76Carboxypeptidase MCPMiterature.0030.79DAP12 associating lectin 1CLECSF5iterature.0030.72Platelet factor 4PF4iterature.0032.74Immune associated nucleotide receptor 7hIAN7Array.0032.26Calgranulin AS100A8Array.0048.69Guanine nucleotide exchange factorVAV1iterature.0057.08Thrombopoietin receptorMPLiterature.0061.84G6b Inhibitory receptorG6biterature.0068.67Ras homolog gene family, member UARHUArray.0068.20Notch homolog 1NOTCH1iterature.0073.11Cas-Br-M (murine) ecotropic retroviral transforming sequenceCBLBiterature.0081.15T-cell transcription factorGATA3iterature.0095.15T-cell R alphaTCRAiterature.0096.11Calgranulin BS100A9Array*Correlated to significant Array gene..0098.80IL18IL18iterature.01.84CD8A antigenCD8Aiterature.01.13Bruton’s tyrosine kinaseBTKArray.01.93GranulysinGNLYiterature.02.29CD28 antigenCD28iterature.02.13Immunoglobulin J polypeptideIGJArray*Correlated to significant Array gene..02.49erythrocyte" @default.
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