Cross-scale, cross-pathway evaluation using an agent-based non-small cell lung cancer model

Zhihui Wang, Christina M. Birch, Jonathan Sagotsky, Thomas S. Deisboeck

Research output: Contribution to journalArticle

45 Scopus citations

Abstract

We present a multiscale agent-based non-small cell lung cancer model that consists of a 3D environment with which cancer cells interact while processing phenotypic changes. At the molecular level, transforming growth factor β (TGFβ) 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 TGFβ 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 TGFβ fluctuations trigger competing multi-cellular phenotypes, yet synchronous EGF and TGFβ signaling yields a spatially more aggressive tumor that overall exhibits an EGF-driven phenotype. By altering EGF and TGFβ concentration levels simultaneously and asynchronously, we discovered a particular region of EGF-TGFβ profiles that ensures phenotypic stability of the tumor system. Within this region, concentration changes in EGF and TGFβ 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 clinico-pharmacological applications are discussed.

Original languageEnglish (US)
Pages (from-to)2389-2396
Number of pages8
JournalBioinformatics
Volume25
Issue number18
DOIs
StatePublished - Sep 1 2009

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computational Mathematics
  • Statistics and Probability

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