Computer simulation of glioma growth and morphology.

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion.

Computer simulation of glioma growth and morphology.

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion.

Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth.

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

Integrin signaling in the regulation of lymphocyte morphology

In normal lymphocytes an “inside-out” signal up-regulating integrin adhesion is followed by a ligand mediated “outside-in” signal for cell spreading. Although PKC mediates both events, distinct roles were found for different PLCs. The inhibition of phosphatidylinositol specific PLC decreased both cell adhesion and spreading on fibronectin in T cell receptor/CD28 activated peripheral blood T cells. However, inhibition of phosphatidylcholine specific PLC only blocked cell spreading and did not affect adhesion, indicating that “inside-out” signaling for the integrin α4β1 proceeds through phosphatidylinositol specific PLC and PKC, while the “outside-in” signal utilizes phosphatidylcholine specific PLC and PKC. Furthermore, β1 integrin chain mediated morphological changes in the T lymphocytic cell line HPB-ALL directly paralleled PKA activation, treatment of these cells with an inhibitory anti-β1 antibody blocked PKA activation and cell spreading, and this inhibition could be overcome by activating adenylate cyclase. Furthermore, inhibition of PKA was found to decrease the overall strength of cell adhesion or cellular avidity without affecting individual receptor affinity for soluble ligand. ^ When HPB-ALL cells interact with immobilized FN, two separate morphological phenotypes can be induced. Some cells flattened their cell body into a triangular shape and begin to migrate, while others extended a pseudopod from their stationary cell body. This second morphology recapitulates the shape changes observed during transendothelial migration. During these morphological changes, α4β1 integrins are internalized into endocytic vesicles that ultimately accumulate at the juncture between the cell body and an extending pseudopod. From this juncture, they are rapidly transported down the length of the pseudopod to its most distal end. ^ In addition to an accumulation of integrin containing vesicles, the pseudopod base was found to have increased amounts of the small GTPase RhoA and active PKA. The inhibition of PKA or RhoA resulted in lymphocytes with similar aberrant stellate morphologies. Furthermore, inhibition of PKA blocked the α4β1 mediated phosphorylation of RhoA. The co-localization of active PKA, RhoA and integrin containing endocytic vesicles indicates that integrin triggering can cause the rapid redistribution and activation of key signaling intermediates and raises the possibility that regulation of lymphocyte morphology by PKA and RhoA is through adhesion receptor recycling. ^

Roles of Alix in regulating cell morphology

Mammalian Alix (ALG2-interacting protein X&barbelow;) is a conserved adaptor protein that is involved in endosomal trafficking, apoptosis and growth factor receptor turnover. Accumulating evidence also indicates that Alix plays roles in promoting/maintaining spread and aligned fibroblast morphology in monolayer culture. Since cell morphology is determined by the structure and dynamics of an integrin-mediated transmembrane protein network that links extracellular matrix to intracellular cytoskeleton, we hypothesized that Alix plays direct or indirect roles in regulating certain components or steps in this transmembrane protein network. To test this hypothesis, we first examined the subcellular localization of Alix and discovered that, as a predominantly cytoplasmic protein, Alix is also present on the substratum/cell surface and in the conditioned medium of fibroblast cultures. Further, precoating of culture surfaces with recombinant Alix promotes spreading and fibronectin assembly to NIH/3T3 cells, and siRNA-mediated Alix knockdown in W138 cells has the opposite effects. These findings indicate the extracellular functions of Alix in regulating cell spreading and extracellular matrix assembly. In a separate study, we analyzed Alix immunocomplexes from normal fibroblast W138 cells by mass spectrometry and identified actin as a major partner protein of Alix. Follow-up studies demonstrated that Alix preferentially binds filamentous actin (F-actin) in vitro and is required for maintaining normal F-actin content and proper actin cytoskeleton assembly in W138 cells. These findings establish direct and essential roles of Alix in regulating actin cytoskeleton. Finally, we investigated the effects of Alix knockdown on the activation and subcellular localization of FAK and Pyk2, the focal adhesion kinases required for cell spreading/migration by promoting turnover of integrin-mediated cell adhesions. We discovered that Alix knockdown inhibits FAK and Pyk2 localizations to focal adhesions or plasma membrane, in association with characteristics of reduced turnover of focal adhesions. These findings reveal a positive role of Alix in focal adhesion turnover. Based on these results, we conclude that Alix targets both intracellularly and extracellularly components to regulate extracellular matrix remodeling, actin cytoskeleton assembly and focal adhesion turnover. A combination of these three functions of Alix explains its crucial role in regulating spread and aligned fibroblast morphology. ^