Systems Biology Approach to Biomarker Identification

Ideally, a systematic approach to biomarker identification will involve multiple “-omic” technologies to investigate a disease process at all levels, including whole genome association studies to identify causative mutations or polymorphisms, as well as expression profiling, proteomics and metabolomics to identify expression signatures and protein and small-molecule profiles that are either specific to the disease process or provide mechanistic insights into disease pathology. Table 1.6 summarizes the use of various “omics” technologies - genomics, epigenomics/ epigenetics, proteomics, glycomics and metabolomics - for discovery of biomarkers. Many of these biomarkers are interrelated in some way. Genomics is used to identify relevant disease genes, aberrant cellular signaling pathways and expression signatures correlated with disease. Proteomics is used to identify aberrant protein expression, post-translational modification, protein interactions and protein profiles that are specific to a particular disorder. Finally, metabolomics is implemented to identify the presence of abnormal levels of small molecule metabolites that are specific to and indicative of an underlying disease process.

The qualitative nature of pathology’s immunohistochemical biomarkers, however, cannot be extrapolated to the realm of “omics” biomarkers, and the latter should be defined within their own paradigm preferably through a systems biology approach (Abu-Asab et al. 2011). The authors have proposed that only shared derived mutations/expressions (also known as clonal aberrations or synapomor- phies) in relation to normal conditions are the potential omics biomarkers. Within the evolutionary paradigm, they demonstrated how a parsimony phylogenetic analysis models a disease onto a tree-like diagram — the cladogram that maps heterogeneous multigene expression profiles and at the same time shows the major shared clonal expressions at various levels of the hierarchical classification. Shared clonal expressions are the potential omics biomarkers that can be translated to a clinical setting in order to provide specimen characterization for early detection, diagnosis, prognosis, and posttreatment assessment.

Table 1.6 Various “omics” technologies for discovery of biomarkers


Sample sources




Nucleated cells

Positional cloning

Mapping of disease loci

Nucleated cells

SNP genotyping

Identification of

disease gene

Nucleated cells


Mapping of disease loci

Pathologically affected cells

Expression arrays

Identification of dysregulated genes

Pathologically affected cells

Comparative genomic hybridization arrays

Detection of gene amplification and loss of heterozygosity



Affected tissues

Analysis of DNA methylation Exome sequencing, bioinformatics

Biomarkers and molecular diagnostics, e.g. cancer


Affected tissues Body fluids: urine, blood, saliva

2D gel electrophoresis, liquid chromatography- mass spectrometry (LC-MS), ICAT-MS

Identification of protein biomarkers


Body fluids: urine, blood, saliva

Nuclear magnetic resonance (NMR) MS

Identification of small molecules


Body fluids: urine, blood, saliva


Oligosaccharide arrays

Identification of carbohydrates Identification of glycoproteins

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