22nd Annual NASA Space Radiation Investigators' Workshop (2011)
7117.pdf
NASA Specialized Center of Research
The contribution of non-targeted effects in HZE cancer risk: in silico model
Jonathan Tang1, Chen-Yi Chen1, David Nguyen1, M.H. Barcellos-Hoff2, J.H. Mao1, S.V. Costes1,*
1
Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, MS:977, Berkeley, CA 94720, 2Depts of Radiation
Oncology and Cell Biology, New York University School of Medicine, 566 First Ave, New York, NY10016
The objective of this NSCOR is to define the efficiency and physiological context in which high LET radiation
increases epithelial cancer risk. The impact of radiation non-targeted effects (NTE) in promoting epithelial cells
initiated by targeted events to cancer is poorly understood. Project 3 of this NSCOR addresses this issue using
systems modeling of the experimental data acquired in Project 1. Mammary tumor data, microarray data, MaSC
enrichment data and cytokine measurements are being collected in Project 1 (see Illa Bochaca et al. NSCOR
abstract). We developed an agent-based computational biology model of the mouse mammary gland. Agents are
autonomous software objects that have defined rules or mathematical equations that determine how they behave and
interact with each other and their local environment. We previously used this model to analyze the complex
interplay between apoptosis, proliferation, and polarization of mammary epithelial cells that leads to alveolar
morphogenesis and homeostasis in vitro [1].
We have now extended this model to represent a mammary epithelium by including morphogenesis, cell
lineage, and stem cell self-renewal capacity. Our in silico mammary gland consists of interacting agents simulating
mammary epithelial cells, including mammary stem cells (MaSC) to fully differentiated luminal cells. MaSC self
renewal probability and branching probability were obtained by generating simulations with branching patterns that
matched measurements by microscopy in whole mounts of mammary gland (1 MaSC/progenitors for every 2000
agents, 450 branches with sizes distributed about 250±200 µm, leading to a total of half million agents and
approximately 250 MaSC/progenitors in one full gland). We used gene expression microarray data analysis of mice
irradiated with either low LET γ-radiation (100 cGy), or one of two fluences of 350 MeV/amu Si (11.10 cGy and
30.28 cGy) to identify gene differentially expressed after exposure to ionizing radiation. Using the Pavlidis template
matching method (p<0.005 and fold change > 1.25) and Conceptgen, we found highly significant enrichment for a
MaSC signature identified by Lim et al [2] one week after low or high LET radiation exposure. At week 4, the
MaSC signature was still significant but weaker, and at week 12, the MaSC signature was not enriched. These
results confirm recent data showing the mammary lineage hierarchy is affected by low LET ionizing radiation [3].
We benchmarked the model to data from Project 1, which uses a Tp53 null transplanted mammary chimera.
Low LET irradiation (10-100 cGy) of the host prior to transplantation significantly accelerates tumor development
[3]. Tp53 null cells are genomically unstable, have reduced apoptosis, and have enhanced stem cell self-renewal
capacity. The current goal of modeling is to decouple these factors and assess the relative contribution of each to
carcinogenesis, and to evaluate how ionizing radiation influences these factors or the microenvironmental context to
further drive carcinogenesis. Based on previous publications [4], we assumed a 5 fold increase in mutation rate for
Tp53 null mouse cells compared to wild type. Genetic instability induced mutations were thus modeled by allowing
each epithelial agent undergoing division to produce mutant progeny in a stochastic manner. We defined a set of
functional characteristics of the agents that could be altered by mutation: essential housekeeping functions, nonessential functions, and functions that dictate cellular phenotype (genomic stability, growth factor dependence,
apoptosis integrity, anti-growth signal sensitivity, extracellular matrix regulation, and polarity). We tuned the Tp53
null transplant model so that in silico tumor incidence (i.e. duct diameters > 2 mm) and latency matched
experimental data (i.e. 65% tumor incidence 1 year after transplant). Ionizing radiation also induces a persistent
stromal activation of transforming growth factor β (TGFβ), which can induce apoptosis of genomically unstable
cells in vitro [5]. Thus we modeled the impact of ionizing radiation in silico by transiently increasing MaSC selfrenewal probability and by increasing the rate of "spontaneous death" due to TGFβ in the agent population. This led
to an accelerated tumor growth observed in low LET irradiated hosts [3]. Our agent-based modeling of
carcinogenesis shows that NTE, via increased genomic surveillance and MaSC self-renewal, can account for tumor
acceleration in irradiated hosts.
The agent-based model developed in this NSCOR serves as a system biology platform to integrate diverse,
multi-scale information in order to predict the consequences of specific radiation effects on cancer progression. By
measuring and extrapolating identified variables across dose and LET, the agent-based model will allow us to
predict tumor incidence in a mechanistic manner across a large spectrum of radiation qualities and fluences.
1. Tang, J. et al. (2011) Integr Biol (Camb) 3 408-21. 2. Lim, E. et al. (2010) Breast Cancer Res 12 R21.
3. Nguyen, D.H. et al. (2011) Cancer Cell 19 640-51. 4. Havre, P.A. et al. (1995) Cancer Research 55 4420-4.
5. Maxwell, C.A. et al. (2008) Cancer Research 68 8304-11.