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Bioinformatics Advance Access first published online on July 4, 2009
This version published online on July 6, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp416
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© The Author (2009). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Cross-Scale, Cross-Pathway Evaluation using an Agent-Based Non-Small Cell Lung Cancer Model

Zhihui Wang 1, Christina M. Birch 2, Jonathan Sagotsky 1 and Thomas S. Deisboeck 1,*

1Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
2Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

*To whom correspondence should be addressed. Dr. Thomas S. Deisboeck, E-mail: deisboec{at}helix.mgh.harvard.edu


  Abstract

We present a multiscale agent-based non-small cell lung cancer model that consists of a three-dimensional environment with which cancer cells interact while processing phenotypic changes. At the molecular level, transforming growth factor • (TGF•) has been integrated into our previously developed in silico model as a second extrinsic input in addition to epidermal growth factor (EGF). The main aim of this study is to investigate how the effects of individual and combinatorial change in EGF and TGF• concentrations at the molecular level alter tumor growth dynamics on the multi-cellular level, specifically tumor volume and expansion rate. Our simulation results show that separate EGF and TGF• fluctuations trigger competing multi-cellular phenotypes, yet synchronous EGF and TGF• signaling yields a spatially more aggressive tumor that overall exhibits an EGF-driven phenotype. By altering EGF and TGF• concentration levels simultaneously and asynchronously, we discovered a particular region of EGF-TGF• profiles that ensures phenotypic stability of the tumor system. Within this region, concentration changes in EGF and TGF• do not impact the resulting multi-cellular response substantially, while outside these concentration ranges, a change at the molecular level will substantially alter either tumor volume or tumor expansion rate, or both. By evaluating tumor growth dynamics across different scales, we show that, under certain conditions, therapeutic targeting of only one signaling pathway may be insufficient. Potential implications of these in silico results for future clinicopharmacological applications are discussed.

Associate Editor: Dr. Limsoon Wong

Received on March 5, 2009; revised on July 1, 2009; accepted on July 1, 2009

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