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

Abstract

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.

Genetics paired with CT angiography in the setting of atherosclerosis

“…In this study [49], the investigators integrate multivariable including age, gender, and gene expression to develop a novel gene expression evaluation system, which was named gene expression score (GES). GES ranges from 1 to 40 that may align the possibility of the presence of obstructive CAD [50]. Based on super high sensitivity (89%) and negative predictive value (96%) plus patients with a low GES (≤15) presenting a very low incidence of revascularization and adverse cardiac events over 1 year, the authors conclude that first-line use Corus® CAD may avoid unnecessary invasive cardiac tests for obstructive CAD identification compared to the traditional algorithm of stress MPI.…”

“…The PREDICT is a prospective, multicenter, observational study aimed to establish a diagnostic blood assay by real-time PCR to quantify the genes' expression of atherosclerotic CAD and stenosis measured by CTA [50,53]. Unlike other similar trails, in this study, Alexandra Lansky et al pooled high-throughput gene expression data to validate a previously developed novel gene expression evaluation system-GES [54] to predict the likelihood of obstructive CAD.…”

“…Numerous validation studies include the association between GES and atherosclerotic plaque burden, as well as coronary luminal stenosis status (blockages or narrowing) also be performed in nearly the same period. In these validation studies, a representative cross-sectional epidemiological study from Voros et al used clinical trial data from both PREDICT and COMPASS [55] to validate if the GES, a novel gene expression evaluation system, predicts plaque burden as measured by quantitative cardiac CTA [50]. They got an excellent dose-response relationship between GES and plaque burden and luminal stenosis.…”

The limitations of opportunistic epidemiology, pseudopod epidemiology

“…The GWAS and subsequent approaches to whole genome analysis and epigenetics changed the focus of many longitudinal studies to the host (genetics) and then molecular biology, proteomics, metabolomics, etc. [8][9][10][11][12][13][14][15][16][17].…”

Transcriptional Signatures, Imaging, and Coronary Artery Disease Diagnosis

“…Kraus and coworkers describe the establishment, characteristics, and track record of CATHGEN, one of the first and largest biorepositories for CAD that collected suitable samples for DNA, cellular and extracellular RNA, proteomic, and metabolomics analyses in a population referred for cardiac catheterization. Wingrove and Rhees take us through examples of methods required to discover, develop, and validate whole blood RNA signatures, utilizing in part, samples from the CATHGEN biorepository described by Kraus et al, and leading to the commercialization of a CAD diagnostic classifier comprised of sex-specific age-dependent risk functions and expression levels of 23 genes [14,15]. Friede and colleagues describe how genomic-based RNA classifiers could be used in clinical practice, including those for diagnosis and prognosis of CAD, as well as novel whole blood gene expression classifiers for aspirin responsiveness and cardiovascular risk factors such as smoking.…”