Evolutionary medicine: A meaningful connection between omics, disease, and treatment

Abstract

The evolutionary nature of diseases requires that their omics be analyzed by evolution-compatible analytical tools such as parsimony phylogenetics in order to reveal common mutations and pathways' modifications. Since the heterogeneity of the omics data renders some analytical tools such as phenetic clustering and Bayesian likelihood inefficient, a parsimony phylogenetic paradigm seems to connect between the omics and medicine. It offers a seamless, dynamic, predictive, and multidimensional analytical approach that reveals biological classes, and disease ontogenies; its analysis can be translated into practice for early detection, diagnosis, biomarker identification, prognosis, and assessment of treatment. Parsimony phylogenetics identifies classes of specimens, the clades, by their shared derived expressions, the synapomorphies, which are also the potential biomarkers for the classes that they delimit. Synapomorphies are determined through polarity assessment (ancestral vs. derived) of m/z or gene-expression values and parsimony analysis; this process also permits intra and interplatform comparability and produces higher concordance between platforms. Furthermore, major trends in the data are also interpreted from the graphical representation of the data as a tree diagram termed cladogram; it depicts directionality of change, identifies the transitional patterns from healthy to diseased, and can be developed into a predictive tool for early detection.

Cancer heterogeneity: origins and implications for genetic association studies

“…In breast cancer for instance, hierarchical clustering analysis used for microarray-based class discovery appears subjective [58,59], and single sample predictors used to classify patients into subtypes seem to work well only for basal-like tumors [60]. Alternatives to mRNA-based classifications are progressively being explored and include modeling of gene-expression microarray data [61–63] and classifications based on microRNAs [64] and on epigenetic profiling such as DNA methylation patterns [65]. …”

A Systems Biology Interpretation of Array Comparative Genomic Hybridization (aCGH) Data through Phylogenetics

“…Stratifying patients into clades of a cladogram opens new opportunities of study in functional analysis for the impact of chromosomal aberrations in driving disease progression in its various subtypes (Abu-Asab et al, 2011). The directionality of the cladogram offers insight into colorectal cancer progression with accumulating shared chromosomal aberrations (Abu-Asab et al, 2008a).…”

Analyzing Heterogeneous Complexity in Complementary and Alternative Medicine Research: A Systems Biology Solution via Parsimony Phylogenetics

“…Phylogenetics, also termed cladistics, is an analytical paradigm based on the principles of evolution [27]. Its current codes known as phylogenetic systematics were laid down in the mid-1950s by the German systematist Willi Hennig [28].…”