Research article
Development of a blood-based gene expression algorithm for assessment of obstructive coronary artery disease in non-diabetic patients
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* Corresponding author: Steven Rosenberg srosenberg@cardiodx.com
1 CardioDx, Inc., 2500 Faber Place, Palo Alto, CA 94602 USA
2 Division of Cardiology, University of California, San Francisco, CA 94143 USA
3 Fuqua Heart Center, Piedmont Heart Institute, Atlanta, GA 30309 USA
4 Department of Cardiology and Center for Genomic Medicine, Duke University School of Medicine, Durham, NC 27710 USA
5 Minneapolis Heart Institute and Foundation, Minneapolis, MN 55407 USA
6 Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, OH 44195 USA
7 Oklahoma Cardiovascular Research Group, Oklahoma City, OK 73109 USA
8 Cardiovascular Research Institute, Medstar Research Institute, Washington, DC 20010 USA
9 Department of Medicine, Vanderbilt Heart and Vascular Institute, Nashville, TN 37232 USA
10 Department of Medicine, Yale University Medical Center, New Haven, CT 06520 USA
11 Scripps Translational Science Institute, La Jolla, CA 92037 USA
BMC Medical Genomics 2011, 4:26 doi:10.1186/1755-8794-4-26
Published: 28 March 2011Abstract
Background
Alterations 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.
Results
Microarray 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.
Conclusions
We 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 information
PREDICT, Personalized Risk Evaluation and Diagnosis in the Coronary Tree, http://www.clinicaltrials.gov webcite, NCT00500617