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 realtime 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 biopsydefined 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.

Background Diagnosis of significant coronary artery disease (CAD) in at risk patients can be challenging, typically including non-invasive imaging modalities and ultimately the gold standard of coronary angiography. Previous studies suggested that peripheral blood gene expression can reflect the presence of CAD. Objective To validate a previously developed 23-gene expression-based classifier for diagnosis of obstructive CAD in non-diabetic patients. Design Multi-center prospective trial with blood samples drawn prior to coronary angiography. Setting Thirty-nine US centers. Patients An independent validation cohort of 526 non-diabetic patients clinically-indicated for coronary angiography Intervention None. Measurements Receiver-operator characteristics (ROC) analysis of classifier score measured by real-time polymerase chain reaction (RT-PCR), additivity to clinical factors, and reclassification of patient disease likelihood vs disease status defined by quantitative coronary angiography (QCA). Obstructive CAD defined as ≥50% stenosis in ≥1 major coronary artery by QCA. Results The overall ROC curve area (AUC) was 0.70 ±0.02, (p<0.001); the classifier added to clinical variables (Diamond-Forrester method) (AUC 0.72 with classifier vs 0.66 without, p = 0.003). Net reclassification was improved by the classifier over Diamond-Forrester and an expanded clinical model (both p<0.001). At a score threshold corresponding to 20% obstructive CAD likelihood (14.75), the sensitivity and specificity were 85% and 43%, yielding NPV of 83% and PPV 46%, with 33% of patient scores below this threshold. Limitations The study excluded patients with chronic inflammatory disorders, elevated white blood counts or cardiac protein markers, and diabetes. Conclusions This non-invasive whole blood test, based on gene expression and demographics, may be useful for assessment of obstructive CAD in non-diabetic patients without known CAD. Primary Funding Source CardioDx, Inc.

Background-The molecular pathophysiology of coronary artery disease (CAD) includes cytokine release and a localized inflammatory response within the vessel wall. The extent to which CAD and its severity is reflected by gene expression in circulating cells is unknown. Key Words: gene expression Ⅲ coronary disease Ⅲ blood, peripheral Ⅲ atherosclerosis Ⅲ leukocytes Ⅲ polymerase chain reaction Ⅲ stenosis C oronary artery disease (CAD) and sequelae of atherosclerotic disease such as stroke and myocardial infarction are the largest source of morbidity and mortality in the developed world. The risk of developing CAD events over time can be estimated with clinical factors and family history, as in the Framingham Risk Score. 1 For patients with suspicious clinical histories, extant coronary disease may be diagnosed by indirect methods, including nuclear perfusion imaging and computed tomography angiography, but coronary angiography remains the "gold standard." These tests have drawbacks, including radiation exposure, contrast agent allergy, nephrotoxicity, and, in the case of coronary angiography, invasiveness of the procedure. Therefore, the development of a blood test that reliably identified patients with CAD would have diagnostic utility. Methods and Results-From Editorial see p 7 Clinical Perspective see p 38The cellular and molecular basis of atherosclerotic plaque development has a systemic inflammatory component involving primarily monocytes/macrophages and CD4 ϩ T cells. 2,3 Oxidized lipids initiate the process with subsequent responses by endothelial, vascular smooth muscle cells, and circulating cells. Peripheral-blood studies have identified gene expression signatures that are correlated with the presence of systemic inflammatory and immune-mediated disorders, as well as cardiovascular diseases, 4 suggesting that such an approach might be useful for CAD. However, although investigators have identified profiles for atherosclerosis directly from arterial wall samples in murine models and human atheroma samples, 5-9 it is unclear whether such localized processes are detectable or their severity reflected in the peripheral circulation. As a first step, we sought to identify genes for which expression levels distinguished patients with and without significant coronary artery stenosis. We approached this problem by microarray analysis on an angiographically defined patient cohort to identify a set of discriminatory genes. We then replicated these results using real-time polymerase chain reaction (RT-PCR) on 2 addi-

BackgroundAlterations in gene expression in peripheral blood cells have been shown to be sensitive to the presence and extent of coronary artery disease (CAD). A non-invasive blood test that could reliably assess obstructive CAD likelihood would have diagnostic utility.ResultsMicroarray analysis of RNA samples from a 195 patient Duke CATHGEN registry case:control cohort yielded 2,438 genes with significant CAD association (p < 0.05), and identified the clinical/demographic factors with the largest effects on gene expression as age, sex, and diabetic status. RT-PCR analysis of 88 CAD classifier genes confirmed that diabetic status was the largest clinical factor affecting CAD associated gene expression changes. A second microarray cohort analysis limited to non-diabetics from the multi-center PREDICT study (198 patients; 99 case: control pairs matched for age and sex) evaluated gene expression, clinical, and cell population predictors of CAD and yielded 5,935 CAD genes (p < 0.05) with an intersection of 655 genes with the CATHGEN results. Biological pathway (gene ontology and literature) and statistical analyses (hierarchical clustering and logistic regression) were used in combination to select 113 genes for RT-PCR analysis including CAD classifiers, cell-type specific markers, and normalization genes.RT-PCR analysis of these 113 genes in a PREDICT cohort of 640 non-diabetic subject samples was used for algorithm development. Gene expression correlations identified clusters of CAD classifier genes which were reduced to meta-genes using LASSO. The final classifier for assessment of obstructive CAD was derived by Ridge Regression and contained sex-specific age functions and 6 meta-gene terms, comprising 23 genes. This algorithm showed a cross-validated estimated AUC = 0.77 (95% CI 0.73-0.81) in ROC analysis.ConclusionsWe have developed a whole blood classifier based on gene expression, age and sex for the assessment of obstructive CAD in non-diabetic patients from a combination of microarray and RT-PCR data derived from studies of patients clinically indicated for invasive angiography.Clinical trial registration informationPREDICT, Personalized Risk Evaluation and Diagnosis in the Coronary Tree, http://www.clinicaltrials.gov, NCT00500617