Biological networks provide a natural representation of complex biological systems and thus have been used in a variety of applications, from gene function prediction to identifying disease genes. In particular, complex diseases such as cancer can be better understood from the perspective of dysregulated pathways, rather than as a disease resulting from alterations of individual genes. One of the most popular types of biological networks used in disease studies is a gene interaction network. In a gene interaction network, genes are represented as nodes, and edges connect pairs of genes that are physically interacting or functionally related. Physical interaction networks can be constructed based on physical interactions, such as protein-protein interactions, protein-DNA interactions, and phosphorylation [1–3]. Functional interaction networks connect genes with similar or related functions and are typically inferred from multiple sources, including physical interactions, co-expression, Gene Ontology (GO) terms, etc. [4,5]. Other types of networks that are also considered in disease studies are patient similarity networks [6–8], disease-phenotype networks [9–13], and drug target networks [14].#R##N##R##N#Interaction networks can be generic or condition-dependent. Generic networks, such as protein-protein interaction networks, summarize existing knowledge of the system that is typically not state or tissue-specific. Such static interaction maps between genes provide a scaffold for disease studies. Combined with additional disease related features, they can guide the identification of genes and pathways affected by a disease. For example, gene expression data have been shown to be useful for identifying network markers of a disease in the form of connected subnetworks that have significantly differential gene expression in two disease phenotypes [15–20]. Importantly, it has been demonstrated that such network markers can lead to a more robust disease classification relative to classification based on individual genes [15]. Subsequently, many different algorithms have been developed to identify gene modules that are either differentially expressed between different disease states or show similar expression patterns. Network-based algorithms have also been used to explain expression Quantitative Trait Loci (eQTL) relationships and infer causal pathways through which genetic alterations affect gene expression changes [21,22].#R##N##R##N#One of the critical and challenging problems in disease studies is the identification and explanation of genotype-phenotype relationships. In cancer datasets, the relationships between genetic alterations and phenotypes are typically not one-to-one. Different genetic aberrations in different cancer patients can lead to the same disease phenotype, which makes it difficult to uncover genotype-phenotype relationships. One explanation of the heterogeneity between cancer cases is that different genetic alterations can dysregulate the same pathways, resulting in similar disease phenotypes. A network-centric view of diseases helps overcome the challenges posed by the complex genotype-phenotype relationships and facilitates finding genotypic causes of diseases [15,20,21]. It is worth noting that in addition to intertumor heterogeneity, variability between cells within individual tumors has been observed. Such intratumor heterogeneity has typically been studied using different approaches such as phylogeny-based methods.#R##N##R##N#Another challenge in cancer studies relates to differentiating cancer-driving mutations from passenger mutations. It is generally assumed that while somatic cancer cells typically contain many genomic aberrations, only a few of them are driver mutations, i.e., mutations that provide a growth advantage for tumor cells and thus are causal with respect to cancer progression. In contrast, the mutations that do not confer growth advantages are referred to as passenger mutations. For example, linkage disequilibrium accompanying some types of genomic aberrations, such as copy number variations, results in many passenger mutations. In addition, cancer cell divisions accumulate random mutations—most of them passengers. A faulty gene in the DNA repair mechanism or environmental factors such as UVC exposure can result in both passenger and driver mutations. Identification of drivers among such mutations is fundamental to cancer studies. While a high mutation rate in cancer samples is an indicator of possible driver activity, information on mutation rates alone does not suffice to correctly identify all cancer drivers. It is believed that there are many cancer driving mutations that occur at relatively low frequency, making it difficult to distinguish them from background noise. By placing the mutated genes in the context of other genes and identifying closely connected clusters of mutated genes in interaction networks, network approaches have been used to find defective gene modules in complex diseases such as cancer, autism, and autoimmune diseases [23–27].#R##N##R##N#Several large-scale, cancer-related datasets, such as the data generated by TCGA (The Cancer Genome Atlas, http://cancergenome.nih.gov) and ICGC (International Cancer Genome Consortium, https://icgc.org), are currently available. These publicly available datasets offer comprehensive genomic profiles, including gene expression, somatic mutations, copy number variations, and DNA methylation, for an unprecedented number of patients. Integration of such datasets together with interaction networks can offer great opportunities to advance our understanding of the cause of diseases, classification of disease subtypes, and identifying drug targets [15–22].#R##N##R##N#In this review, we outline the basic strategies of network-based approaches in the analysis of cancer datasets, mostly focusing on methods that deal with intertumor heterogeneity. In the first few sections, we discuss how gene interactions can be used to overcome difficulties due to cancer heterogeneity and find cancer driving genes (Network Propagation to Uncover Cancer Driving Pathways and Heterogeneity of Cancer and Personalized Networks). The mutual exclusivity relationships that emerge because of such heterogeneity and the methods utilizing such relationships are reviewed in Taking Advantage of Cancer Heterogeneity: Utilizing Mutual Exclusivity of Genomic Alterations. In the following sections, we discuss other types of disease-related networks, such as patient similarity networks (Patient Similarity Networks) and disease phenotype networks (Disease Similarity Networks).
['Yoo-Ah Kim', 'Dong-Yeon Cho', 'Teresa M. Przytycka']
Understanding Genotype-Phenotype Effects in Cancer via Network Approaches