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.

Cell-to-cell variability during Tumor Necrosis Factor Receptor 1 (TNFR1) signaling triggered by the pleiotropic cytokine TNFα can lead to pro-survival and apoptotic phenotypic responses at single-cell level. Harnessing the ability to modulate the signal flow responsible for the balance between these two phenotypes and make cells favour apoptosis have been considered in cancer therapies. We show that a 6-node nested feedback loop facilitates the crucial crosstalk regulation modulating the signal flow between these two responses. We identify this by systematically analysing the partial state transition graph (pSTG) underlying a Boolean dynamic model of the TNFR1 signaling network that accounts for signal flow path variability. We demonstrate a novel approach “Boolean Modeling based Prediction of Steady-state probability of Phenotype Reachability (BM-ProSPR)” that enables constructing a reliable pSTG in a computationally efficient manner and predicting accurately the network’s ability to settle into different phenotypes. We deduce that knocking-off Comp1 – IKK* complex tweaks the signal flow paths leading to a 62% increase in the steady state probability of TNFR1 signaling culminating in apoptosis and thereby favours phenotype switching from pro-survival to apoptosis. Priming cancerous cells with inhibitors targeting the interaction involving Comp1 and IKK* prior to TNFα exposure could be a potential therapeutic strategy.