TY - JOUR
T1 - Simulating brain tumor heterogeneity with a multiscale agent-based model
T2 - Linking molecular signatures, phenotypes and expansion rate
AU - Zhang, Le
AU - Strouthos, Costas G.
AU - Wang, Zhihui
AU - Deisboeck, Thomas S.
N1 - Funding Information:
This work has been supported in part by NIH grants CA 085139 and CA 113004 (The Center for the Development of a Virtual Tumor, CViT at http://www.cvit.org ) and by the Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging and the Department of Radiology at Massachusetts General Hospital.
PY - 2009/1/1
Y1 - 2009/1/1
N2 - We have extended our previously developed 3D multi-scale agent-based brain tumor model to simulate cancer heterogeneity and to analyze its impact across the scales of interest. While our algorithm continues to employ an epidermal growth factor receptor (EGFR) gene-protein interaction network to determine the cells' phenotype, it now adds an implicit treatment of tumor cell adhesion related to the model's biochemical microenvironment. We simulate a simplified tumor progression pathway that leads to the emergence of five distinct glioma cell clones with different EGFR density and cell 'search precisions'. The in silico results show that microscopic tumor heterogeneity can impact the tumor system's multicellular growth patterns. Our findings further confirm that EGFR density results in the more aggressive clonal populations switching earlier from proliferation-dominated to a more migratory phenotype. Moreover, analyzing the dynamic molecular profile that triggers the phenotypic switch between proliferation and migration, our in silico oncogenomics data display spatial and temporal diversity in documenting the regional impact of tumorigenesis, and thus support the added value of multi-site and repeated assessments in vitro and in vivo. Potential implications from this in silico work for experimental and computational studies are discussed.
AB - We have extended our previously developed 3D multi-scale agent-based brain tumor model to simulate cancer heterogeneity and to analyze its impact across the scales of interest. While our algorithm continues to employ an epidermal growth factor receptor (EGFR) gene-protein interaction network to determine the cells' phenotype, it now adds an implicit treatment of tumor cell adhesion related to the model's biochemical microenvironment. We simulate a simplified tumor progression pathway that leads to the emergence of five distinct glioma cell clones with different EGFR density and cell 'search precisions'. The in silico results show that microscopic tumor heterogeneity can impact the tumor system's multicellular growth patterns. Our findings further confirm that EGFR density results in the more aggressive clonal populations switching earlier from proliferation-dominated to a more migratory phenotype. Moreover, analyzing the dynamic molecular profile that triggers the phenotypic switch between proliferation and migration, our in silico oncogenomics data display spatial and temporal diversity in documenting the regional impact of tumorigenesis, and thus support the added value of multi-site and repeated assessments in vitro and in vivo. Potential implications from this in silico work for experimental and computational studies are discussed.
KW - Agent-based model
KW - Cancer heterogeneity
KW - Epidermal growth factor receptor
KW - Glioma
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U2 - 10.1016/j.mcm.2008.05.011
DO - 10.1016/j.mcm.2008.05.011
M3 - Article
AN - SCOPUS:55549113888
VL - 49
SP - 307
EP - 319
JO - Mathematical and Computer Modelling
JF - Mathematical and Computer Modelling
SN - 0895-7177
IS - 1-2
ER -