Personalized characterization of diseases using sample-specific networks

Abstract

A complex disease generally results not from malfunction of individual molecules but from dysfunction of the relevant system or network, which dynamically changes with time and conditions. Thus, estimating a condition-specific network from a single sample is crucial to elucidating the molecular mechanisms of complex diseases at the system level. However, there is currently no effective way to construct such an individual-specific network by expression profiling of a single sample because of the requirement of multiple samples for computing correlations. We developed here with a statistical method, i.e. a sample-specific network (SSN) method, which allows us to construct individual-specific networks based on molecular expressions of a single sample. Using this method, we can characterize various human diseases at a network level. In particular, such SSNs can lead to the identification of individual-specific disease modules as well as driver genes, even without gene sequencing information. Extensive analysis by using the Cancer Genome Atlas data not only demonstrated the effectiveness of the method, but also found new individual-specific driver genes and network patterns for various types of cancer. Biological experiments on drug resistance further validated one important advantage of our method over the traditional methods, i.e. we can even identify such drug resistance genes that actually have no clear differential expression between samples with and without the resistance, due to the additional network information.

Network perturbation by recurrent regulatory variants in cancer

“…For example, a personalized characterization of regulatory variants can be conducted by using sample-specific networks [35]. This approach will be useful when one is interested in a specific driver gene and would like to know which particular genes are affected by the variants of this driver gene.…”

Quantifying critical states of complex diseases using single-sample dynamic network biomarkers

“…Hence, the significance of sPCC(x , y) for any two genes (x, y) can be evaluated by the p -value of the “Z” score from Eq (5) as follows: where PCC n (x , y) is the Pearson correlation coefficient between two genes (x, y) in the reference samples, n is the sample size of the reference data, sPCC(x , y) is the differential PCC between PCC n+1 and PCC n for the two genes (x, y) in Eq (4), Z(x, y) is the “Z” score of the Z-test for the two genes (x, y), and the p- value can be calculated as the standard normal cumulative distribution function [9]. Note that we can directly evaluate the significance of Z based on the volcano distribution without approximation [7]. Also we can directly use Z(x,y) as the normalized differential PCC for the single sample without the statistical test.…”

Modeling cytokine regulatory network dynamics driving neuroinflammation in central nervous system disorders

“…These approaches typically utilize statistical and clustering analyses to identify patterns in network dynamics and attempt to relate the patterns of disease dynamics to distinct underlying parameters, providing new biomarker and drug target candidates. These models can differentiate disease subtypes and the patient-specific drug responses (Liu et al, 2016). Considering the intrinsic stochasticity and extent of uncertainty in the neuroinflammation network structures and parameters, such model-based large-scale exploration is crucial to evaluate the very many possibilities in which disease dynamics may unfold.…”