Steven Rosenberg

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.

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.