Toward a quantitative understanding of cancer cell signaling: mathematical models, computational tools, applications, and beyond
Seager, Robert John
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Tumor development and progression are dictated by more than the activities of cancer cells alone and are in large part determined by interactions with the immune system and the surrounding stroma. Therefore, a robust understanding of cancer in the context of this dynamic interplay is required to truly understand the progression toward and past malignancy. The work included herein attempts to develop such an understanding of three particular facets of cancer biology at a quantitative level. The first facet of this work modeled the effects of the mechanical properties and cytokine composition of the tumor microenvironment on tumor-associated macrophage migration. Using an integrated in silico and three-dimensional in vitro approach, we studied how interstitial flow, in concert with other environmental factors, affects macrophage migration and its potential contribution to cancer invasion. The model suggested interleukin-8 (IL-8), chemokine (C-C motif) ligand 2 (CCL2), and β-integrin as key pathways that commonly regulate various Rho GTPases, and, in agreement with the model, in vitro macrophage migration remained elevated when exposed to a saturating concentration of recombinant IL-8 or CCL2, or to the co-addition of a sub-optimal concentration of both cytokines. Next, we sought to quantitatively analyze the effect of tumor-localized macrophage cytokine signaling on the migration behaviors of cancer cells. Using an integrated experimental and computational approach, we analyzed the signaling networks associated with two cytokines secreted by tumor-associated macrophages (TAMs), transforming growth factor beta 1 (TGF-β1) and tumor necrosis factor alpha (TNFα), with the results suggesting that migratory behavior is driven by a nonlinear signaling network characterized by extensive crosstalk between the downstream intracellular signaling pathways activated by these cytokines, where migration persistence is controlled by the synergistic integration of TGF-β1 and TNFα signals and migration speed is more directly regulated by TGF-β1 signals alone. Furthermore, computational analysis of this network suggested that signaling kinase TAK1 and inhibitor Smad7 are key nodes in the signaling network structure underlying this synergistic signal integration. Finally, we developed a quantitative model of TGF-β signaling and associated gene expression in cancer to analyze the effects of the mechanical properties and cytokine composition of the tumor microenvironment on TGF-β signaling, which can switch between acting as a tumor promoter and tumor suppressor through unclear mechanisms. Sensitivity analyses of the model suggested that signals originating in the mechanical tumor microenvironment, in particular extracellular matrix-induced signaling, move TGF-β signaling toward a tumor-promoting expression profile, and furthermore that the most influential reactions on this expression regulation occur at or near the transcriptional level. We then used the model to conduct a simulated drug screen to demonstrate potential applications for models of this type in the development of therapeutic tools targeting mechanically induced TGF-β signaling in cancer. Taken together, the results of these efforts all support the hypothesis that environmental cues not only bear influence on the outcomes of the processes regulated by these dynamic systems, but can, depending on the nature of the integration of these environmental signals at the intracellular level, be responsible for the biological decision-making between fundamentally different behaviors and outcomes.