PMID- 27333083
OWN - NLM
STAT- MEDLINE
DCOM- 20170525
LR  - 20180102
IS  - 1938-5404 (Electronic)
IS  - 0033-7587 (Linking)
VI  - 186
IP  - 1
DP  - 2016 Jul
TI  - Simulating Space Radiation-Induced Breast Tumor Incidence Using Automata.
PG  - 27-38
LID - 10.1667/RR14338.1 [doi]
AB  - Estimating cancer risk from space radiation has been an ongoing challenge for 
      decades primarily because most of the reported epidemiological data on 
      radiation-induced risks are derived from studies of atomic bomb survivors who 
      were exposed to an acute dose of gamma rays instead of chronic high-LET cosmic 
      radiation. In this study, we introduce a formalism using cellular automata to 
      model the long-term effects of ionizing radiation in human breast for different 
      radiation qualities. We first validated and tuned parameters for an 
      automata-based two-stage clonal expansion model simulating the age dependence of 
      spontaneous breast cancer incidence in an unexposed U.S. POPULATION: We then 
      tested the impact of radiation perturbation in the model by modifying parameters 
      to reflect both targeted and nontargeted radiation effects. Targeted effects (TE) 
      reflect the immediate impact of radiation on a cell's DNA with classic end points 
      being gene mutations and cell death. They are well known and are directly derived 
      from experimental data. In contrast, nontargeted effects (NTE) are persistent and 
      affect both damaged and undamaged cells, are nonlinear with dose and are not well 
      characterized in the literature. In this study, we introduced TE in our model and 
      compared predictions against epidemiologic data of the atomic bomb survivor 
      cohort. TE alone are not sufficient for inducing enough cancer. NTE independent 
      of dose and lasting  approximately 100 days postirradiation need to be added to accurately 
      predict dose dependence of breast cancer induced by gamma rays. Finally, by 
      integrating experimental relative biological effectiveness (RBE) for TE and 
      keeping NTE (i.e., radiation-induced genomic instability) constant with dose and 
      LET, the model predicts that RBE for breast cancer induced by cosmic radiation 
      would be maximum at 220 keV/mum. This approach lays the groundwork for further 
      investigation into the impact of chronic low-dose exposure, inter-individual 
      variation and more complex space radiation scenarios.
FAU - Heuskin, A C
AU  - Heuskin AC
AD  - a   Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, 
      California.
AD  - c   NAmur Research Institute for Life Sciences (NARILIS), Research Center for the 
      Physics of Matter and Radiation (PMR), University of Namur, Namur, Belgium.
FAU - Osseiran, A I
AU  - Osseiran AI
AD  - a   Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, 
      California.
FAU - Tang, J
AU  - Tang J
AD  - b   Exogen Biotechnology Inc., Berkeley, California.
FAU - Costes, S V
AU  - Costes SV
AD  - a   Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, 
      California.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20160622
PL  - United States
TA  - Radiat Res
JT  - Radiation research
JID - 0401245
SB  - IM
MH  - Breast Neoplasms/*etiology/genetics/*pathology
MH  - Cosmic Radiation/adverse effects
MH  - *Extraterrestrial Environment
MH  - Humans
MH  - Incidence
MH  - Linear Energy Transfer
MH  - *Models, Biological
MH  - Mutation Rate
MH  - Neoplasms, Radiation-Induced/*etiology/genetics/*pathology
MH  - Relative Biological Effectiveness
MH  - Risk Assessment
EDAT- 2016/06/23 06:00
MHDA- 2017/05/26 06:00
CRDT- 2016/06/23 06:00
PHST- 2016/06/23 06:00 [entrez]
PHST- 2016/06/23 06:00 [pubmed]
PHST- 2017/05/26 06:00 [medline]
AID - 10.1667/RR14338.1 [doi]
PST - ppublish
SO  - Radiat Res. 2016 Jul;186(1):27-38. doi: 10.1667/RR14338.1. Epub 2016 Jun 22.