Percolation transition of cooperative mutational effects in colorectal tumorigenesis

Abstract

Cancer is caused by the accumulation of multiple genetic mutations, but their cooperative effects are poorly understood. Using a genome-wide analysis of all the somatic mutations in colorectal cancer patients in a large-scale molecular interaction network, here we find that a giant cluster of mutation-propagating modules in the network undergoes a percolation transition, a sudden critical transition from scattered small modules to a large connected cluster, during colorectal tumorigenesis. Such a large cluster ultimately results in a giant percolated cluster, which is accompanied by phenotypic changes corresponding to cancer hallmarks. Moreover, we find that the most commonly observed sequence of driver mutations in colorectal cancer has been optimized to maximize the giant percolated cluster. Our network-level percolation study shows that the cooperative effect rather than any single dominance of multiple somatic mutations is crucial in colorectal tumorigenesis.

The Hidden Control Architecture of Complex Brain Networks

“…A variety of biological processes are determined by the underlying regulatory networks (Assmus et al., 2006, Dubitzky et al., 2013, Eshaghi et al., 2010, Kim and Cho, 2006, Kim et al., 2011, Kwon and Cho, 2007, Lee et al., 2018, Murray et al., 2010, Park et al., 2006, Shin et al., 2006, Sreenath et al., 2008) and disruption of the networks can lead to diverse biological disorders (Shin et al., 2014, Shin et al., 2017, Yeo et al., 2018). Hence, control of a biological network has become an important issue to systematically regulate or modulate biological processes at a network level (Kim et al., 2013, Wolkenhauer et al., 2004).…”

Topological estimation of signal flow in complex signaling networks

“…Arakelyan and his colleagues proposed an algorithm to estimate signal flow in pathways 29 , 30 , and elucidated molecular mechanisms underlying malignant and chronic lung diseases by integrating the algorithm with gene expression data 31 . A set of algorithms based on Gaussian smoothing 32 was proposed for unsigned 33 , 34 or undirected 21 networks to predict gene functions 33 or mutational effects 35 , 36 .…”

Systems Biology of Cancer Metastasis

“…The large number of passenger mutations in metastatic tumors and relatively limited number of patient samples pose a challenge, but heuristics applied to interactome network can allow prioritization of genes for further investigation. Network approaches have been applied to protein interaction, regulatory, and gene co-expression networks, as well as on mutation co-occurrence, to provide systems molecular interaction snapshots of metastasis (Shin et al, 2017). In addition, systems network analysis can be used to generate falsifiable hypotheses linking disease to holistic systems properties, as has been shown for metastasis as a progression toward network entropy (Teschendorff and Severini, 2010;West et al, 2012).…”