James A. Wingrove

Correlation of peripheral-blood gene expression with the extent of coronary artery stenosis.

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. Methods and Results—From an initial coronary catheterization cohort we identified 41 patients, comprising 27 cases with angiographically significant CAD and 14 controls without coronary stenosis. Whole-genome microarray analysis performed on peripheral-blood mononuclear cells yielded 526 genes with >1.3-fold differential expression (P<0.05) between cases and controls. Real-time polymerase chain reaction on 106 genes (the 50 most significant microarray genes and 56 additional literature genes) in an independent subset of 95 patients (63 cases, 32 controls) from the same cohort yielded 14 genes (P<0.05) that independently discriminated CAD state in a multivariable analysis that included clinical and demographic factors. From an independent second catheterization cohort, 215 patients were selected for real-time polymerase chain reaction–based replication. A case:control subset of 107 patients (86 cases, 21 controls) replicated 11 of the 14 multivariably significant genes from the first cohort. An analysis of the 14 genes in the entire set of 215 patients demonstrated that gene expression was proportional to maximal coronary artery stenosis (P<0.001 by ANOVA). Conclusions—Gene expression in peripheral-blood cells reflects the presence and extent of CAD in patients undergoing angiography.

Multicenter Validation of the Diagnostic Accuracy of a Blood-Based Gene Expression Test for Assessing Obstructive Coronary Artery Disease in Nondiabetic Patients

BACKGROUND Diagnosing obstructive coronary artery disease (CAD) in at-risk patients can be challenging and typically requires both noninvasive imaging methods and coronary angiography, the gold standard. Previous studies have suggested that peripheral blood gene expression can indicate the presence of CAD. OBJECTIVE To validate a previously developed 23-gene, expression-based classification test for diagnosis of obstructive CAD in nondiabetic patients. DESIGN Multicenter prospective trial with blood samples obtained before coronary angiography. (ClinicalTrials.gov registration number: NCT00500617) SETTING: 39 centers in the United States. PATIENTS An independent validation cohort of 526 nondiabetic patients with a clinical indication for coronary angiography. MEASUREMENTS Receiver-operating characteristic (ROC) analysis of classifier score measured by real-time polymerase chain reaction, additivity to clinical factors, and reclassification of patient disease likelihood versus disease status defined by quantitative coronary angiography. Obstructive CAD was defined as 50% or greater stenosis in 1 or more major coronary arteries by quantitative coronary angiography. RESULTS The area under the ROC curve (AUC) was 0.70 ± 0.02 (P < 0.001); the test added to clinical variables (Diamond-Forrester method) (AUC, 0.72 with the test vs. 0.66 without; P = 0.003) and added somewhat to an expanded clinical model (AUC, 0.745 with the test vs. 0.732 without; P = 0.089). The test improved net reclassification over both the Diamond-Forrester method and the expanded clinical model (P < 0.001). At a score threshold that corresponded to a 20% likelihood of obstructive CAD (14.75), the sensitivity and specificity were 85% and 43% (yielding a negative predictive value of 83% and a positive predictive value of 46%), with 33% of patient scores below this threshold. LIMITATION Patients with chronic inflammatory disorders, elevated levels of leukocytes or cardiac protein markers, or diabetes were excluded. CONCLUSION A noninvasive whole-blood test based on gene expression and demographic characteristics may be useful for assessing obstructive CAD in nondiabetic patients without known CAD. PRIMARY FUNDING SOURCE CardioDx.

Development of a blood-based gene expression algorithm for assessment of obstructive coronary artery disease in non-diabetic patients.

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

A whole blood gene expression-based signature for smoking status

BackgroundSmoking is the leading cause of preventable death worldwide and has been shown to increase the risk of multiple diseases including coronary artery disease (CAD). We sought to identify genes whose levels of expression in whole blood correlate with self-reported smoking status.MethodsMicroarrays were used to identify gene expression changes in whole blood which correlated with self-reported smoking status; a set of significant genes from the microarray analysis were validated by qRT-PCR in an independent set of subjects. Stepwise forward logistic regression was performed using the qRT-PCR data to create a predictive model whose performance was validated in an independent set of subjects and compared to cotinine, a nicotine metabolite.ResultsMicroarray analysis of whole blood RNA from 209 PREDICT subjects (41 current smokers, 4 quit ≤ 2 months, 64 quit > 2 months, 100 never smoked; NCT00500617) identified 4214 genes significantly correlated with self-reported smoking status. qRT-PCR was performed on 1,071 PREDICT subjects across 256 microarray genes significantly correlated with smoking or CAD. A five gene (CLDND1, LRRN3, MUC1, GOPC, LEF1) predictive model, derived from the qRT-PCR data using stepwise forward logistic regression, had a cross-validated mean AUC of 0.93 (sensitivity=0.78; specificity=0.95), and was validated using 180 independent PREDICT subjects (AUC=0.82, CI 0.69-0.94; sensitivity=0.63; specificity=0.94). Plasma from the 180 validation subjects was used to assess levels of cotinine; a model using a threshold of 10 ng/ml cotinine resulted in an AUC of 0.89 (CI 0.81-0.97; sensitivity=0.81; specificity=0.97; kappa with expression model = 0.53).ConclusionWe have constructed and validated a whole blood gene expression score for the evaluation of smoking status, demonstrating that clinical and environmental factors contributing to cardiovascular disease risk can be assessed by gene expression.

Identification of Factors Contributing to Variability in a Blood-Based Gene Expression Test

Background Corus CAD is a clinically validated test based on age, sex, and expression levels of 23 genes in whole blood that provides a score (1–40 points) proportional to the likelihood of obstructive coronary disease. Clinical laboratory process variability was examined using whole blood controls across a 24 month period: Intra-batch variability was assessed using sample replicates; inter-batch variability examined as a function of laboratory personnel, equipment, and reagent lots. Methods/Results To assess intra-batch variability, five batches of 132 whole blood controls were processed; inter-batch variability was estimated using 895 whole blood control samples. ANOVA was used to examine inter-batch variability at 4 process steps: RNA extraction, cDNA synthesis, cDNA addition to assay plates, and qRT-PCR. Operator, machine, and reagent lots were assessed as variables for all stages if possible, for a total of 11 variables. Intra- and inter-batch variations were estimated to be 0.092 and 0.059 Cp units respectively (SD); total laboratory variation was estimated to be 0.11 Cp units (SD). In a regression model including all 11 laboratory variables, assay plate lot and cDNA kit lot contributed the most to variability (p = 0.045; 0.009 respectively). Overall, reagent lots for RNA extraction, cDNA synthesis, and qRT-PCR contributed the most to inter-batch variance (52.3%), followed by operators and machines (18.9% and 9.2% respectively), leaving 19.6% of the variance unexplained. Conclusion Intra-batch variability inherent to the PCR process contributed the most to the overall variability in the study while reagent lot showed the largest contribution to inter-batch variability.

A peripheral blood gene expression score is associated with atherosclerotic Plaque Burden and Stenosis by cardiovascular CT-angiography : Results from the PREDICT and COMPASS studies

OBJECTIVE We previously validated a gene expression score (GES) based on age, sex and peripheral blood cell expression levels of 23 genes measured by quantitative real-time PCR (qRT-PCR) for diagnosis of obstructive coronary artery disease (CAD) (≥ 50% luminal diameter stenosis). In this study we sought to determine the association between the GES and coronary arterial Plaque Burden and Stenosis by CT-angiography. METHODS A total of 610 patients (mean age: 57 ± 11; 50% male) from the PREDICT and COMPASS studies from 59 centers were analyzed. Coronary artery calcium (CAC) scoring, CT angiography (CTA)-based plaque and stenosis and GES measurements were performed. CAC was expressed as Agatston score and CTA evaluated for stenosis severity: 0. None; 1. Minimal, 2. Mild, 3. Moderate, 4. Severe and 5. Occluded. Correlation analysis, one-way analysis of variance (ANOVA) and receiver operating characteristics (ROC) analyses were performed. RESULTS GES was significantly associated with plaque burden by CAC (r = 0.50; p < 0.001) and CTA (segment involvement score index: r = 0.37, p < 0.001); a low score (≤ 15) had sensitivity of 0.71 and a high score (≥ 28) a specificity of 0.97 for the prediction of zero vs. non-zero CAC. Increasing GES was associated with a greater degree of categorical stenosis by ANOVA (p < 0.001); GES significantly correlated with maximum luminal stenosis (r = 0.41; p < 0.01) and segment stenosis score index (r = 0.38; p < 0.01). A low score had sensitivity of 0.90 and a high score a specificity of 0.87 for ≥ 70% stenosis. CONCLUSIONS A previously validated GES is significantly associated with Plaque Burden and Stenosis by CT. CLINICAL TRIAL REGISTRATION (PREDICT [NCT00500617] and COMPASS [NCT01117506]), www.clinicaltrials.gov.

Coronary Atherosclerotic Plaque Detected by Computed Tomographic Angiography in Subjects with Diabetes Compared to Those without Diabetes

Objectives Little data are available regarding coronary plaque composition and semi-quantitative scores in individuals with diabetes; the extent to which diabetes may affect the presence and extent of Coronary Artery Calcium (CAC) needs more evaluation. Considering that this information may be of great value in formulating preventive interventions in this population, we compared these findings in individuals with diabetes to those without. Methods Multi-Detector Computed Tomographic (MDCT) images of 861 consecutive patients with diabetes who were referred to Los Angeles Biomedical Research Institute from January 2000 to September 2012, were evaluated using a 15–coronary segment model. All 861 patients underwent calcium scoring and from these; 389 had coronary CT angiography (CTA). CAC score was compared to 861 age, sex and ethnicity matched controls without diabetes after adjustment for Body Mass Index (BMI), family history of coronary artery disease, hyperlipidemia, hypertension and smoking. Segment Involvement Score (SIS; the total number of segments with any plaque), Segment Stenosis Score (SSS; the sum of maximal stenosis score per segment), Total Plaque Score (TPS; the sum of the plaque amount per segment) and plaque compositionwere compared to 389 age, sex and ethnicity matched controls without diabetes after adjustment for BMI, family history of coronary artery disease, hyperlipidemia, hypertension and smoking. Results Diabetes was positively correlated to the presence and extent of CAC (P<0.0001 for both). SIS, SSS and TPS were significantly higher in those with diabetes (P<0.0001). Number of mixed and calcified plaques were significantly higher in those with diabetes (P = 0.018 and P<0.001 respectively) but there was no significant difference in the number of non-calcified plaques between the two groups (P = 0.398). Conclusions Patients with diabetes have higher CAC and semi-quantitative coronary plaque scores compared to the age, gender and ethnicity matched controls without diabetes after adjustment for cardiovascular risk factors. Since mixed plaque is associated with worse long-term clinical outcomes, these findings support more aggressive preventive measures in this population.

An age- and sex-specific gene expression score is associated with revascularization and coronary artery disease: Insights from the Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE) trial

Background Identifying predictors of coronary artery disease (CAD)‐related procedures and events remains a priority. Methods We measured an age‐ and sex‐specific gene expression score (ASGES) previously validated to detect obstructive CAD (oCAD) in symptomatic nondiabetic patients in the PROMISE trial. The outcomes were oCAD (≥70% stenosis in ≥1 vessel or ≥50% left main stenosis on CT angiography [CTA]) and a composite endpoint of death, myocardial infarction, revascularization, or unstable angina. Results The ASGES was determined in 2370 nondiabetic participants (47.5% male, median age 59.5 years, median follow‐up 25 months), including 1137 with CTA data. An ASGES >15 was associated with oCAD (odds ratio 2.5 [95% CI 1.6‐3.8], P < .001) and the composite endpoint (hazard ratio [HR] 2.6 [95% CI 1.8‐3.9], P < .001) in unadjusted analyses. After adjustment for Framingham risk, an ASGES >15 remained associated with the composite endpoint (P = .02); the only component that was associated was revascularization (adjusted HR 2.69 [95% CI 1.52‐4.79], P < .001). Compared to noninvasive testing, the ASGES improved prediction for the composite (increase in c‐statistic = 0.036; continuous net reclassification index = 43.2%). Patients with an ASGES ≤15 had a composite endpoint rate no different from those with negative noninvasive test results (3.2% vs. 2.6%, P = .29). Conclusions A blood‐based genomic test for detecting oCAD significantly predicts near‐term revascularization procedures, but not non‐revascularization events. Larger studies will be needed to clarify the risk with non‐revascularization events.

A VALIDATED, PERIPHERAL BLOOD GENE EXPRESSION PATTERN CORRELATES WITH CORONARY ARTERIAL PLAQUE BURDEN BY QUANTITATIVE CORONARY ANGIOGRAPHY AND CORONARY ARTERY CALCIUM SCORING

Category: 48. Genetics and Clinical Outcomes Presentation Number: 924-4 Citation: J. Am. Coll. Cardiol. 2011;57;E1157 Authors: Szilard Voros, Michael R. Elashoff, Amy J. Sehnert, Hsiao D. Lieu, James A. Wingrove, Susan E. Daniels, Steven Rosenberg, Alexandra Lansky, Robert S. Schwartz, William E. Kraus, Eric J. Topol, Piedmont Heart Institute, Atlanta, GA, CardioDx Inc, Palo Alto, CA Abstract: Background: Coronary plaque burden can be measured indirectly by quantitative coronary angiography (QCA) and by coronary artery calcium (CAC) scoring by CT. We previously validated a real-time-PCR-based 23-geneexpression test, Corus CAD, to predict obstructive CAD using QCA (≥50% stenosis). Background: Coronary plaque burden can be measured indirectly by quantitative coronary angiography (QCA) and by coronary artery calcium (CAC) scoring by CT. We previously validated a real-time-PCR-based 23-geneexpression test, Corus CAD, to predict obstructive CAD using QCA (≥50% stenosis). Methods: In the validation cohort of the PREDICT study (NCT 00500617), 526 patients had gene expression score and QCA-derived plaque burden available and in 79 patients (49 cases, 30 controls), CAC score also. Plaque burden was defined as the sum of occluded luminal volumes for all QCA lesions; occluded volume is calculated from lesion length and reference and minimal vessel luminal diameter, assuming a symmetrical hourglass shape. Linear regression analysis was used to evaluate relationships. Results: In the 526 patients, there was significant correlation between gene expression score and QCA-derived plaque burden (p=1.4x10-8, R=0.38). In the CT subset, gene expression and CAC scores (range 0-3189; median=270) were correlated with R=0.63 (p=3x10-10). ROC analysis for the prediction of non-zero CAC by gene expression score showed an AUC 0.92. Both scores were significantly associated with disease burden by QCA (R=0.57 and 0.51,

Novel Approaches to Cardiovascular Diagnostics: Focus on Coronary Artery Disease and Myocardial Infarction

Abstract There is a significant unmet need in the diagnosis and prediction of coronary artery disease (CAD) and subsequent major adverse cardiovascular events including myocardial infarction. Recently, a number of genomic approaches have been taken to address these problems, including genetic, transcriptomic, and proteomic methodologies. Most of these efforts focused on the discovery of putative markers and initial construction of multimarker classifiers for these clinical endpoints and were limited by incomplete clinical validation and comparison to clinical factor models. However, one such classifier for obstructive CAD has been validated in independent multicenter validation cohorts. Initial forays into combining different types of classifiers (genetic, proteomic, etc.) using so-called systems biology approaches is in its infancy but holds considerable promise to further improve the outlook for patients at risk for these serious and prevalent conditions.