News | Cardiac Diagnostics | January 19, 2016

Preliminary Results Published of Largest-Ever Study Linking Biomarkers with Coronary Artery Disease

Study demonstrates proof of concept of causal machine learning approach with identification of known coronary disease-associated pathway

January 19, 2016 — Global Genomics Group (G3) and GNS Healthcare (GNS), announced preliminary results of the GLOBAL Clinical Study designed to identify biomarkers associated with coronary artery disease (CAD). The study provided proof of concept of the approach by prospectively identifying a biochemical pathway known to be associated with CAD.

G3 is a life sciences company that leverages large-scale genomic and molecular disease profiles to develop new diagnostics and therapeutics. GNS is a precision medicine company that applies causal machine learning technology to match health interventions to individual patients and discover new target intervention pathways.

The GLOBAL Clinical Study is the first large-scale study that leverages pan-omic measurements — including whole genome sequencing, whole genome methylation, whole transcriptome sequencing, unbiased proteomics, metabolomics, lipidomics and lipoprotein proteomics — in a specific clinically-relevant patient population – patients referred for coronary computed tomography (CT) angiography for assessment of suspected CAD – with matched normal controls. Subjects from both groups were phenotyped by G3’s advanced, proprietary imaging methods.

The companies utilized the patented GNS REFS (Reverse Engineering and Forward Simulation) platform, which applies advanced machine learning Bayesian reconstruction algorithms, a form of artificial intelligence that extracts cause and effect mechanisms, to rapidly analyze comprehensive pan-omic measurements in order to learn the causal genetic, molecular and phenotypic networks driving atherosclerotic CAD. The approach is designed to enable rapid insights and discoveries that otherwise would not have been possible with current technology. The preliminary results reported here are based on trillions of data points analyzed from G3’s G3LOBAL Database.

Output from the machine learning technology consists of associations of biomarkers with CAD. GNS’s causal machine learning technology provided validation of the approach by identifying the low-density lipoprotein (LDL) disease pathway as associated with CAD, including mechanisms linked to new treatments, such as those targeting the PCSK9 gene. This pathway, identified by more traditional biochemical and cell biology approaches, is well known to cardiologists and a number of drugs that interact with it are approved for lowering the risk of CAD.

The causal, mechanistic computer models may greatly accelerate the pace of discovery. The recapitulation of known LDL biology and discovery of new biology announced, for instance, were achieved by G3 and GNS in less than a year. In contrast, the original discovery of LDL biology can be traced to a series of discoveries, beginning as early as 1938, accelerating in the 1970s, and earning the Nobel Prize in Physiology or Medicine in 1985 for the identification of the LDL receptor.

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