Network modelling reveals the mechanism underlying colitis-associated colon cancer and identifies novel combinatorial anti-cancer targets

Abstract

The connection between inflammation and tumourigenesis has been well established. However, the detailed molecular mechanism underlying inflammation-associated tumourigenesis remains unknown because this process involves a complex interplay between immune microenvironments and epithelial cells. To obtain a more systematic understanding of inflammation-associated tumourigenesis as well as to identify novel therapeutic approaches, we constructed a knowledge-based network describing the development of colitis-associated colon cancer (CAC) by integrating the extracellular microenvironment and intracellular signalling pathways. Dynamic simulations of the CAC network revealed a core network module, including P53, MDM2, and AKT, that may govern the malignant transformation of colon epithelial cells in a pro-tumor inflammatory microenvironment. Furthermore, in silico mutation studies and experimental validations led to a novel finding that concurrently targeting ceramide and PI3K/AKT pathway by chemical probes or marketed drugs achieves synergistic anti-cancer effects. Overall, our network model can guide further mechanistic studies on CAC and provide new insights into the design of combinatorial cancer therapies in a rational manner.

Systems Biology of Cancer Metastasis

“…Higher-level pathway modeling: Petri nets (Pennisi et al, 2016) and Boolean networks (Lu et al, 2015;Steinway et al, 2014), Bayesian networks (Friedman et al, 2000) Larger systems can be simulated by increasing the level of abstraction where detailed parameters are not available…”

“…At the gene and transcript levels, most computational approaches are informed by large-scale omics studies. Modeling of larger signaling networks may involve abstraction at a higher level, using methods such as Petri nets (Pennisi et al, 2016), Boolean networks (Lu et al, 2015;Steinway et al, 2014), Bayesian networks (Friedman et al, 2000), or systemic perturbation-effect networks (Korkut et al, 2015).…”

Boolean network modeling in systems pharmacology

“…In addition, a few studies have extended pre-existing networks to investigate specific interests. The main goals for developing Boolean networks have been to identify potential therapeutic strategies [106,17,18,107,25,26,28–32,34], characterize cellular differentiation [14,11,13,36], understand differential responses to cancer therapies due to mutational differences [27], understand the impact of patient heterogeneity on the response to drug treatments [21,35], and as an initial framework prior to the development of quantitative models [23]. The networks provided in this table could be potentially extended or repurposed for investigating additional features of interest, as opposed to starting from the ground up.…”

“…The state of each node is governed by the previous states of its regulating nodes through a set of logical functions. Boolean networks have been applied to model signal transduction, gene regulation, and cellular differentiation for several types of physiological and pathophysiological systems, such as the immune system and related diseases [11–23], breast cancer [24–29], gastrointestinal cancers [30–32], hepatic cancer [33,34], lung cancer [35], and several others [36–40]. In oncology, Boolean network modeling can provide a framework for studying system trajectories under pathophysiological conditions and in response to drug treatment.…”

Negative differential resistance effect of blue phosphorene-graphene heterostructure device

“…By applying an external electric field and intentional doping, various Shottky barriers of GSJ can be achieved [7,8]. Furthermore, that GSJ can also integrate to other 2D semiconductors to form different heterojunctions such as graphene-hBN [9,10], graphene-MoS 2 and also graphene-black phosphorus [11][12][13][14].…”