Biomarkers in the age of omics: time for a systems biology approach.
Journal - Omics : a journal of integrative biology (United States )
Limitations to biomarker discovery are not only technical or bioinformatic but conceptual as well. In our attempt to offer a solution, we are highlighting three issues that we think are limiting progress in biomarkers discovery. First, the confusion stemming from the imposition of a pathology-type immunohistochemical marker (IHCM) concept on omics data without fully understanding the characteristics and limitations of IHCMs as applied in clinical pathology. Second, the lack of serious consideration for the scope of disease heterogeneity. Third, the refusal of the biomedical community to borrow from other biological disciplines their well established methods for dealing with heterogeneity. Therefore, real progress in biomarker discovery will be attained when we recognize that an omics biomarker cannot be assigned and validated without a priori data modeling and subtyping of the disease itself to reveal the extent of its heterogeneity, and its omics' clonal aberrations (drivers) underlying its subtypes and pathways' diversity. To further support our viewpoints, we are contributing a novel a systems biology method such as parsimony phylogenetic approach for disease modeling prior to biomarker circumscription. As an analytical approach that has been successfully used for a half of a century in other biological disciplines, parsimony phylogenetics simultaneously achieves several objectives: it provides disease modeling in a hierarchical phylogenetic classification, identifies biomarkers as the shared derived expressions or mutations--synapomorphies, constructs the omics profiles of specimens based on the most parsimonious arrangement of their heterogeneous data, and permits network profiling of affected signaling pathways as the biosignature of disease classes.
|ISSN : ||1557-8100|
|Mesh Heading : ||Biological Markers Genomics Humans Immunohistochemistry Proteomics|
|Mesh Heading Relevant : ||Systems Biology analysis|