Network Modeling of TGFβ Signaling in Hepatocellular Carcinoma Epithelial-to-Mesenchymal Transition Reveals Joint Sonic Hedgehog and Wnt Pathway Activation

Steinway, Steven N.; Zañudo, Jorge Gómez Tejeda; Ding, Wei; Rountree, C. Bart; Feith, David J.; Albert, Réka

doi:10.1158/0008-5472.can-14-0225

Cited by 226 publications

(293 citation statements)

References 48 publications

(66 reference statements)

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“…This is particularly true when dealing with diseases such as cancer, which involve the deregulation of multiple and intricate pathways (7). Numbers of models have already proved useful in elucidating questions related to cancer biology (8)(9)(10)(11)(12)(13).…”

Section: Assessing Phenotypesmentioning

confidence: 99%

A Modeling Approach to Explain Mutually Exclusive and Co-Occurring Genetic Alterations in Bladder Tumorigenesis

et al. 2015

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Relationships between genetic alterations, such as co-occurrence or mutual exclusivity, are often observed in cancer, where their understanding may provide new insights into etiology and clinical management. In this study, we combined statistical analyses and computational modeling to explain patterns of genetic alterations seen in 178 patients with bladder tumors (either muscle-invasive or non-muscle-invasive). A statistical analysis on frequently altered genes identified pair associations, including co-occurrence or mutual exclusivity. Focusing on genetic alterations of protein-coding genes involved in growth factor receptor signaling, cell cycle, and apoptosis entry, we complemented this analysis with a literature search to focus on nine pairs of genetic alterations of our dataset, with subsequent verification in three other datasets available publicly. To understand the reasons and contexts of these patterns of associations while accounting for the dynamics of associated signaling pathways, we built a logical model. This model was validated first on published mutant mice data, then used to study patterns and to draw conclusions on counter-intuitive observations, allowing one to formulate predictions about conditions where combining genetic alterations benefits tumorigenesis. For example, while CDKN2A homozygous deletions occur in a context of FGFR3-activating mutations, our model suggests that additional PIK3CA mutation or p21CIP deletion would greatly favor invasiveness. Furthermore, the model sheds light on the temporal orders of gene alterations, for example, showing how mutual exclusivity of FGFR3 and TP53 mutations is interpretable if FGFR3 is mutated first. Overall, our work shows how to predict combinations of the major gene alterations leading to invasiveness through two main progression pathways in bladder cancer.

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Section: Assessing Phenotypesmentioning

confidence: 99%

A Modeling Approach to Explain Mutually Exclusive and Co-Occurring Genetic Alterations in Bladder Tumorigenesis

et al. 2015

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“…In silico modeling can be integrated with knowledge of tumor kinase network signaling to provide valuable tools for assessing potential combinations and resistance mechanisms. Predictive, mathematical models are used to predict tumor growth and metastasis (66,67). Studies using computational modeling used known activities of PI3K/AKT and MAPK/ERK on tumor cell proliferation and death to design synergistic drug combinations in HER2 þ breast cancer (67).…”

Section: Methods Of Detecting Mechanisms Of Response and Resistancementioning

confidence: 99%

“…Studies using computational modeling used known activities of PI3K/AKT and MAPK/ERK on tumor cell proliferation and death to design synergistic drug combinations in HER2 þ breast cancer (67). Complex modeling of phenotype switching (e.g., epithelial-mesenchymal transition) have been performed to identify critical signaling nodes and bypass mechanisms (66). Taken together, the identification of innate sensitivity and resistance in vitro has benefited from better characterized tumor cell lines, more precise validation tools, and more predictive model systems.…”

Section: Methods Of Detecting Mechanisms Of Response and Resistancementioning

confidence: 99%

Durability of Kinase-Directed Therapies—A Network Perspective on Response and Resistance

Murray

Miller

2015

Molecular Cancer Therapeutics

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Protein kinase-directed cancer therapies yield impressive initial clinical responses, but the benefits are typically transient. Enhancing the durability of clinical response is dependent upon patient selection, using drugs with more effective pharmacology, anticipating mechanisms of drug resistance, and applying concerted drug combinations. Achieving these tenets requires an understanding of the targeted kinase's role in signaling networks, how the network responds to drug perturbation, and patient-topatient network variations. Protein kinases create sophisticated, malleable signaling networks with fidelity coded into the processes that regulate their presence and function. Robust and reliable signaling is facilitated through network processes (e.g., feedback regulation, and compensatory signaling). The routine use of kinase-directed therapies and advancements in both genomic analysis and tumor cell biology are illuminating the complexity of tumor network biology and its capacity to respond to perturbations. Drug efficacy is attenuated by alterations of the drug target (e.g., steric interference, compensatory activity, and conformational changes), compensatory signaling (bypass mechanisms and phenotype switching), and engagement of other oncogenic capabilities (polygenic disease). Factors influencing anticancer drug response and resistance are examined to define the behavior of kinases in network signaling, mechanisms of drug resistance, drug combinations necessary for durable clinical responses, and strategies to identify mechanisms of drug resistance.

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“…8). This type of network reduction has been shown to have no effect on the permitted dynamics and enables us to fully explore the state space [47]. In the reduced network, the adhesion factor E-cadherin is the sink node and its OFF state will indicate the transition to a mesenchymal state.…”

Section: Biological Examplesmentioning

confidence: 99%

Compensatory interactions to stabilize multiple steady states or mitigate the effects of multiple deregulations in biological networks

2016

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Complex diseases can be modeled as damage to intra-cellular networks that results in abnormal cell behaviors. Network-based dynamic models such as Boolean models have been employed to model a variety of biological systems including those corresponding to disease. Previous work designed compensatory interactions to stabilize an attractor of a Boolean network after single node damage. We generalize this method to a multi-node damage scenario and to the simultaneous stabilization of multiple steady state attractors. We classify the emergent situations, with a special focus on combinatorial effects, and characterize each class through simulation. We explore how the structural and functional properties of the network affect its resilience and its possible repair scenarios. We demonstrate the method ′ s applicability to two intra-cellular network models relevant to cancer. This work has implications in designing prevention strategies for complex disease.

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Network Modeling of TGFβ Signaling in Hepatocellular Carcinoma Epithelial-to-Mesenchymal Transition Reveals Joint Sonic Hedgehog and Wnt Pathway Activation

Cited by 226 publications

References 48 publications

A Modeling Approach to Explain Mutually Exclusive and Co-Occurring Genetic Alterations in Bladder Tumorigenesis

A Modeling Approach to Explain Mutually Exclusive and Co-Occurring Genetic Alterations in Bladder Tumorigenesis

Durability of Kinase-Directed Therapies—A Network Perspective on Response and Resistance

Compensatory interactions to stabilize multiple steady states or mitigate the effects of multiple deregulations in biological networks

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