Szilard Voros, M.D.

Full-spectrum omics will outshine the clashing hues of complex diseases.

Technological advances such as high-throughput sequencing are transforming medicine from symptom-based diagnosis and treatment to personalized medicine as scientists employ novel rapid genomic methodologies to gain a broader comprehension of disease and disease progression. As next-generation sequencing becomes more rapid, researchers are turning toward large-scale panomics, the collective use of all omics such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and lipoprotein proteomics to better understand, identify, and treat complex disease.

Genomics has been a cornerstone in understanding disease, and the sequencing of the human genome has led to the identification of numerous disease biomarkers through genome-wide association studies (GWAS).1 It was the goal of these studies that these biomarkers would serve to predict individual disease risk, enable early detection of disease, help make treatment decisions, and identify new therapeutic targets. In reality, however, only a few have gone on to become established in clinical practice.1,2 For example in human GWAS studies for heart failure at least 35 biomarkers have been identified but only natriuretic peptides have moved into clinical practice, where they are limited primarily for use as a diagnostic tool.2

The limited success of genomics alone to provide a broader understanding of disease has resulted in Pharma and Biotech realizing that more comprehensive disease analysis using systems biology is required to understand the multidimensional biological networks involved in complex disease. For example, atherosclerosis, the underlying cause of coronary artery disease, is a complex disease with both heritable and environmental factors that involves multiple cell types and interactions of many different molecular pathways. In human GWAS studies of coronary artery disease (CAD), out of the 3 billion base pairs and about 20,000 to 25,000 genes only 46 genomic loci have reached genome-wide significance.3 It has been recognized that the core of many of the problems with GWAS and underlying much of the missing heritability is the issue of phenotype resolution.4

Consequently, researchers have begun to utilize systems biology-based approaches that integrate many multiscale types of biological information including genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and lipoprotein proteomics. This data is then used to develop predictive and actionable models of the biology and pathobiology underlying complex diseases such as atherosclerosis, cancer, and neurodegenerative disease. Importantly, the chance for success with clinical candidates for therapeutics can be improved with a deeper understanding of the complex biology of the disease.

In moving forward with these systems biology approaches, however, there are several important parameters that should be considered. The ability of an integrative approach to generate actionable data is largely dependent on three study components, precise patient classification through next-generation phenotyping, comprehensive panomic analysis, and systems biology driven bioinformatics.

The data from multiple omics in combination can be used synergistically to identify pathways and biomarkers that will allow a more precise characterization of the disease and are likely to have higher clinical utility in diagnostics, guiding targeted treatment and identification of new therapeutic targets. Recent advances in high-throughput sequencing now make it possible to analyze multiple omics rapidly, enabling these complex disease studies to be performed. Several studies have taken an expanded approach to understanding disease through these methods including the PREDICT study which examined the response of tumor-derived tissue to different anticancer agents utilizing genomics and transcriptomics.5 More recently, the GLOBAL study has undertaken the most ambitious panomics study to-date and has completed enrollment of 7,500 patients to study the underlying biology and disease progression of atherosclerosis.

One of the biggest challenges of a large-scale integrative approach to understanding disease is precisely defining the population of interest. In order to generate accurate data it is essential that the patients are precisely phenotyped, in this case, separating those with atherosclerosis, the primary underlying cause of cardiovascular disease, from healthy individuals. Atherosclerotic lesions that can rupture are the cause for the majority of acute coronary syndromes and have different morphologic characteristics when compared to stable lesions. Imprecise evaluation of the disease status prior to the study could therefore lead to an inaccurate separation of case- and control-cohorts, which would not generate consistent data that would enable the identification of relevant disease-related pathways. The advances of cardiovascular computed tomography (CT), an advanced imaging technique, have been previously shown in the ATLANTA project. Cardiovascular CT permits heart disease patients to be precisely separated using noninvasive imaging techniques.6 This not only allows accuracy in enrolling trial patients, but also enables the safe and quick screening of patients for large-scale studies, such as the GLOBAL study. 

In the GLOBAL study, the precisely characterized case- and control-cohorts will undergo a panomic analysis, which consists of a combination of whole-genome sequencing, whole-genome epigenomics, transcriptomics, whole-transcriptome sequencing from peripheral blood, and unbiased, mass spectrometry-based proteomics, metabolomics, lipidomics, and lipoprotein proteomics.

When used in combination, these newer techniques can be used to generate disease “signatures” through the simultaneous analysis of RNAs, proteins, and metabolites with high-throughput methods. For example, it has been demonstrated that complex diseases frequently can have a high number of misregulated genes; however, the number of proteins elevated can be much lower.7 Therefore, the data from multiple omics in combination (in this case genomics and proteomics) can synergize to identify patterns and biomarkers that are likely to have higher clinical utility in guiding diagnostic and therapeutic choices than a single genomics approach.

Finally, systems-driven bioinformatics is required to analyze the huge amounts of data generated from these panomic studies. This is illustrated by the GLOBAL study, which will generate over 22 trillion data points that will be analyzed using a specially designed bioinformatics platform to evaluate the data in an unbiased manner. Only through careful and systematic evaluation of these large datasets will patterns and pathways emerge to provide a more precise understanding of atherosclerosis and potential targets for therapeutic development.  

The rapid advancement of sequencing techniques, diagnostic imaging, and bioinformatics has propelled the use of panomic analysis in large-scale studies from concept to reality. The use of panomics in understanding complex diseases will provide an in-depth understanding of disease allowing for diagnosis, early disease screening and intervention, development of novel therapeutics, and targeted patient treatment.

Szilard Voros, M.D. ([email protected]), is co-founder and CEO of G3 (Global Genomics Group).

1 Simon, R. EMBO Mol Med 2011; 3, 1–7.
2 Kalogeropoulos, A. Prog Cardiovasc Dis. 2012; 55(1):3–13.
3 CARDIoGRAMplusC4D Consortium et al. Nature Genetics. 2013; 45, 25–33.
4 McRae et al. Circ Cardiovasc Genet 2011; 4;334–336.
5 Swanton et al. Genome Med. 2010; 2:53.
6 Voros, S. Am J Cardiol. 2014 Jan 1;113: 23–9.
7 Brooks, J. Genome Res 2012 Feb;22(2): 183–7.

This article was originally published in the June 12 issue of Clinical OMICs. For more content like this and details on how to get a free subscription to this new digital publication, go to

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