Boolean dynamic modeling of cancer signaling networks: Prognosis, progression, and therapeutics

Abstract

Cancer is a multifactorial disease. Aberrant functioning of the underlying complex signaling network that orchestrates cellular response to external or internal cues governs incidence, progression, and recurrence of cancer. Detailed understanding of cancer's etiology can offer useful insights into arriving at novel therapeutic and disease management strategies. Such an understanding for most cancers is currently limited due to unavailability of a predictive large‐scale, integrated signaling model accounting for all tumor orchestrating factors. We suggest that the potential of Boolean dynamic (BD) modeling approaches, though qualitative, can be harnessed for developing holistic models capturing multi‐scale, multi‐cellular signaling processes involved in cancer incidence and progression. We believe that constraining such an integrated BD model with variety of omics data at different scales from laboratory and clinical settings could offer deeper insights into causal mechanisms governing the disease leading to better prognosis. We review the recent literature employing different BD modeling strategies to model variety of cancer signaling programs leading to identification of cancer‐specific prognostic markers such as SMAD proteins, which may also serve as early predictors of tumor cells hijacking the epithelial‐mesenchymal plasticity program. In silico simulations of BD models of different cancer signaling networks combined with attractor landscape analysis and validated with experimental data predicted the nature of short‐ and long‐term response of standard targeted therapeutic agents such as Nutlin‐3, a small molecule inhibitor for p53‐MDM2 interaction. BD simulations also offered a mechanistic view of emerging resistance to drugs such as Trastuzumab for HER+ breast cancer, analysis of which suggested new combination therapies to circumvent them. We believe future improvements in BD modeling techniques, and tools can lead to development of a comprehensive platform that can drive holistic approaches toward better decision‐making in the clinical settings, and thereby help identify novel therapeutic strategies for improved cancer treatment at personalised levels.

Network Modeling and Control of Dynamic Disease Pathways, Review and Perspectives

“…Boolean modeling can be applied to those systems where the organization of networks is more important than the kinetic details of the individual interactions [123]. Boolean model in cancer signaling networks can provide holistic models capturing multi-scale, multi-cellular signaling processes involved in cancer incidence and progression [124]. Baur et al [125] modeled different components using Boolean networks in the growth factor signaling pathway to study the efficacy of different drugs in cancer therapies.…”

An Integrated View of Virus-Triggered Cellular Plasticity Using Boolean Networks

“…EMT-driven BNs can also be used to interrogate the effect of combinatorial therapies to treat cancer, as shown for hepatoma [87]. A recent review has proposed that cancer signaling-driven BNs will be useful for investigating the prognosis of and therapeutic responses to different interventions, contributing to the improvement of the decision-making process in oncology practice [88].…”

Boolean dynamic modeling of TNFR1 signaling predicts a nested feedback loop regulating the apoptotic response at single-cell level

“…Use of general asynchronous (GA) or random-order asynchronous (ROA) updating scheme permits incorporation of cell-to-cell variability in the BD model of the network while estimating its ability to settle into different phenotypes. 21,25,26 GA updating-based BD model of TNFR1 network unraveled that inhibition of AKT/PKB, 27 RIPK1 28 and FADD 28 are crucial in modulating the steady-state probability of attaining long-term pro-survival responses (attractor). Since GA update scheme could introduce spurious self-loops and bias signal flow paths to prefer a certain entity, the probabilities predicted by these models may not reflect the true extent of the network’s ability to settle into the attractors.…”

“…Boolean dynamic (BD) modeling which assumes every node can be either active (ON) or inactive (OFF) circumvents this limitation. 20,21 Boolean model of TNFR1 signaling network using synchronous updating scheme showed that feedback loops involving caspase3 may control culmination into either pro-survival or apoptotic response. 22 Multivalued discrete dynamical simulations, an extension of BD modeling, predicted that hepatocytes and Jurkat-T cells require smac − mimetics for the TNFR1 signaling mediated apoptotic response.…”