Interdisciplinary Tumor Complexity Modeling

Ohio State University, Columbus OH

Investigators

Linked publications & trials

Abstract

DESCRIPTION (provided by applicant): "Interdisciplinary Tumor Complexity Modeling" (2nd RESUBMISSION). In spite of aggressive l therapies, the outcome for patients suffering from highly malignant brain tumors remains uniformly fatal. Responsible for this grim outcome are rapid tumor growth, clonal heterogeneity, acquired treatment resistance and extensive tumor invasion, rendering cytoreductive therapy ineffective. We believe that malignant tumors behave as complex dynamic, adaptive and self-organizing biosystems rather than as unorganized cell masses. If this is true, such malignant tumors also have to be investigated and ultimately targeted as complex: systems. Our work is therefore motivated by the following three hypotheses: (1) malignant brain tumors behave as complex dynamic biosystems; (2) these tumors systems invade according to the principle of "least resistance, most permission and highest attraction "; (3) their spatio-temporal behavior can be studied, simulated and predicted using an interdisciplinary approach combining in vitro and in vivo experiments, human imaging data and computational modeling. To investigate these hypotheses, our specific aims are as follows. Specific AIM 1: We will develop a novel 3D in vitro assay system, suitable of displaying several key-features of multicellular tumor spheroids (MTS) in parallel over a prolonged period of time. The experimental studies using these devices include the microstructural analysis of the extracellular matrix gel-medium as well as the structural, genetics and functional analysis of the spatio-temporal expansion of the micro-tumor system (i.e., on site proliferation and invasive cell network). We will also study tumor growth, invasion and physiology (blood flow and blood volume) in vivo, using MR-imaging of an orthotopic xenogeneic brain tumor model in athymic rats. Studies follow, which investigate invasive tumor cell dynamics in vivo with and without specifically implanted "attractor" sites. Both, in vitro and in vivo results will generate dynamic, multiscaled multi-modality data sets, which will then be incorporated into the computational models. Specific AIM 2: We will develop a set of related, innovative computational models to simulate brain tumor proliferation, genetic and epigenetic heterogeneity, angiogenesis and most importantly, tissue invasion. Discrete and continuum approaches include a variety of techniques such as cellular automata, Kinetic Monte Carlo (KMC) simulations, agent based modeling, gene-regulatory net modeling, fractal analysis and coupled reaction-diffusion equations. Once developed, the computational models will drive the experiments and vice versa. Finally, the merged models will be used to predict the course of brain tumor expansion using real human imaging data (retrospective study) and will be further developed into powerful virtual reality platforms for treatment planning and surgical training tools (feasibility study). Based on our convincing preliminary studies paradigm-shifting insights into brain tumor growth, heterogeneity, invasion and angiogenesis can be expected. The presented work is highly innovative and profoundly interdisciplinary as it combines many seemingly disparate disciplines such as cancer research, statistical physics and mechanics, materials science, biomedical engineering and -imaging, computational visualization, mathematical biology, computational and complex systems science. This Bioengineering Research Partnership investigates groundbreaking tumor biology concepts. This work can therefore very well build the basis for the development of novel diagnostic tools, innovative patient specific treatment planning devices and thus, may ultimately lead to more successful therapeutic strategies, capable of changing the grim outcome of the many patients suffering from this devastating disease.