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

Towards whole-organ modelling of tumour growth

Multiscale approaches to modelling biological phenomena are growing rapidly. We present here some recent results on the formulation of a theoretical framework which can be developed into a fully integrative model for cancer growth. The model takes account of vascular adaptation and cell-cycle dynamics. We explore the effects of spatial inhomogeneity induced by the blood flow through the vascular network and of the possible effects of p27 on the cell cycle. We show how the model may be used to investigate the efficiency of drug-delivery protocols

In silico modelling of tumour margin diffusion and infiltration: Review of current status

Extent: 16p.As a result of advanced treatment techniques, requiring precise target definitions, a need for more accurate delineation of the Clinical Target Volume (CTV) has arisen. Mathematical modelling is found to be a powerful tool to provide fairly accurate predictions for the Microscopic Extension (ME) of a tumour to be incorporated in a CTV. In general terms, biomathematical models based on a sequence of observations or development of a hypothesis assume some links between biological mechanisms involved in cancer development and progression to provide quantitative or qualitative measures of tumour behaviour as well as tumour response to treatment. Generally, two approaches are taken: deterministic and stochastic modelling. In this paper, recent mathematical models, including deterministic and stochastic methods, are reviewed and critically compared. It is concluded that stochastic models are more promising to provide a realistic description of cancer tumour behaviour due to being intrinsically probabilistic as well as discrete, which enables incorporation of patient-specific biomedical data such as tumour heterogeneity and anatomical boundaries.Fatemeh Leyla Moghaddasi, Eva Bezak, and Loredana Marc

The Forces behind Directed Cell Migration

Directed cell migration is an essential building block of life, present when an embryo develops, a dendritic cell migrates toward a lymphatic vessel, or a fibrotic organ fails to restore its normal parenchyma. Directed cell migration is often guided by spatial gradients in a physicochemical property of the cell microenvironment, such as a gradient in chemical factors dissolved in the medium or a gradient in the mechanical properties of the substrate. Single cells and tissues sense these gradients, establish a back-to-front polarity, and coordinate the migration machinery accordingly. Central to these steps we find physical forces. In some cases, these forces are integrated into the gradient sensing mechanism. Other times, they transmit information through cells and tissues to coordinate a collective response. At any time, they participate in the cellular migratory system. In this review, we explore the role of physical forces in gradient sensing, polarization, and coordinating movement from single cells to multicellular collectives. We use the framework proposed by the molecular clutch model and explore to what extent asymmetries in the different elements of the clutch can lead to directional migration

Multi-scale Models of Tumor Growth and Invasion

Cancer is a complex, multi-scale disease marked by unchecked cellular growth and proliferation. As a tumor grows, it is known to lose its capacity to maintain a compact structure. This stage of development, known as invasion, is marked by the disaggregation and dispersion of peripheral cells, and the formation of finger-like margins. This thesis provides an overview of three multi-scale models of tumor growth and invasion. The hybrid discrete-continuum (HDC) model couples a cellular automaton approach, which is used to direct the behavior and interactions of individual cells, with a system of reaction-diffusion-chemotaxis equations that describe the micro-environment. The evolutionary hybrid cellular automaton (EHCA) model maintains the core of the HDC approach, but employs an artificial response network to describe cellular dynamics. In contrast to these two, the immersed boundary (IBCell) model describes cells as fully deformable, viscoelastic entities that interact with each other using membrane bound receptors. As part of this thesis, the HDC model has been modified to examine the role of the ECM as a barrier to cellular expansion. The results of these simulations will be presented and discussed in the context of tumor progression

LKB1 loss in melanoma disrupts directional migration toward extracellular matrix cues

Somatic inactivation of the serine/threonine kinase gene STK11/LKB1/PAR-4 occurs in a variety of cancers, including ∼10% of melanoma. However, how the loss of LKB1 activity facilitates melanoma invasion and metastasis remains poorly understood. In LKB1-null cells derived from an autochthonous murine model of melanoma with activated Kras and Lkb1 loss and matched reconstituted controls, we have investigated the mechanism by which LKB1 loss increases melanoma invasive motility. Using a microfluidic gradient chamber system and time-lapse microscopy, in this paper, we uncover a new function for LKB1 as a directional migration sensor of gradients of extracellular matrix (haptotaxis) but not soluble growth factor cues (chemotaxis). Systematic perturbation of known LKB1 effectors demonstrated that this response does not require canonical adenosine monophosphate–activated protein kinase (AMPK) activity but instead requires the activity of the AMPK-related microtubule affinity-regulating kinase (MARK)/PAR-1 family kinases. Inhibition of the LKB1–MARK pathway facilitated invasive motility, suggesting that loss of the ability to sense inhibitory matrix cues may promote melanoma invasion