Personalized characterization of diseases using sample-specific networks

• Published in: Nucleic acids research Vol. 44; no. 22; p. e164
• Main Authors: Liu, Xiaoping, Wang, Yuetong, Ji, Hongbin, Aihara, Kazuyuki, Chen, Luonan
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 15.12.2016
• Subjects: Algorithms; Cell Line, Tumor; Gene Expression Profiling; Gene Regulatory Networks; Genes, Neoplasm; Humans; Methods Online; Models, Genetic; Mutation; Neoplasms - genetics; Neoplasms - metabolism; Phenotype; Precision Medicine; Transcriptome
• ISSN: 0305-1048; 1362-4962
• DOI: 10.1093/nar/gkw772

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.