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Development of a blood-based gene expression algorithm for assessment of obstructive coronary artery disease in
Michael R Elashoff1, James A Wingrove, Philip Beineke
1CardioDx, Inc., 2500 Faber Place, Palo Alto, CA 94602, USA.
Insights
Researchers developed a gene expression blood test to assess coronary artery disease (CAD) likelihood in non-diabetic individuals. This non-invasive method utilizes gene expression, age, and sex to predict obstructive CAD, offering diagnostic utility.
Area of Science:
- Genomics and Molecular Biology
- Cardiovascular Disease Research
- Biomarker Discovery
Background:
- Gene expression in peripheral blood cells correlates with coronary artery disease (CAD) presence and severity.
- A non-invasive blood test for obstructive CAD assessment would significantly enhance diagnostic capabilities.
Purpose of the Study:
- To develop and validate a gene expression-based classifier for assessing obstructive coronary artery disease (CAD) likelihood.
- To identify key clinical and demographic factors influencing gene expression related to CAD.
Main Methods:
- Microarray analysis of RNA from peripheral blood cells in two patient cohorts (CATHGEN and PREDICT).
- Reverse transcription quantitative polymerase chain reaction (RT-PCR) for targeted gene expression analysis.
- Development of a predictive algorithm using LASSO and Ridge Regression, incorporating gene expression, age, and sex.
Main Results:
- Initial analysis identified thousands of genes associated with CAD; diabetic status, age, and sex were significant factors.
- A refined classifier developed from 23 genes in non-diabetic patients demonstrated a cross-validated AUC of 0.77 for obstructive CAD.
- The final algorithm integrated sex-specific age functions and 6 meta-gene terms.
Conclusions:
- A whole blood classifier integrating gene expression, age, and sex has been successfully developed for obstructive CAD assessment in non-diabetic individuals.
- The classifier leverages data from microarray and RT-PCR analyses of patients undergoing invasive angiography.
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, NCT00500617.
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