Activated Oncogenic Pathway Modifies Iron Network in Breast Epithelial Cells: A Dynamic Modeling Perspective.
Document Type
Article
Publication Date
2-6-2017
JAX Source
PLoS Comput Biol 2017 Feb 6; 13(2):e1005352
Volume
13
Issue
2
First Page
1005352
Last Page
1005352
ISSN
1553-7358
PMID
28166223
Abstract
Dysregulation of iron metabolism in cancer is well documented and it has been suggested that there is interdependence between excess iron and increased cancer incidence and progression. In an effort to better understand the linkages between iron metabolism and breast cancer, a predictive mathematical model of an expanded iron homeostasis pathway was constructed that includes species involved in iron utilization, oxidative stress response and oncogenic pathways. The model leads to three predictions. The first is that overexpression of iron regulatory protein 2 (IRP2) recapitulates many aspects of the alterations in free iron and iron-related proteins in cancer cells without affecting the oxidative stress response or the oncogenic pathways included in the model. This prediction was validated by experimentation. The second prediction is that iron-related proteins are dramatically affected by mitochondrial ferritin overexpression. This prediction was validated by results in the pertinent literature not used for model construction. The third prediction is that oncogenic Ras pathways contribute to altered iron homeostasis in cancer cells. This prediction was validated by a combination of simulation experiments of Ras overexpression and catalase knockout in conjunction with the literature. The model successfully captures key aspects of iron metabolism in breast cancer cells and provides a framework upon which more detailed models can be built. PLoS Comput Biol 2017 Feb 6; 13(2):e1005352.
Recommended Citation
Chifman J,
Arat S,
Deng Z,
Lemler E,
Pino J,
Harris L,
Kochen M,
Lopez C,
Akman S,
Torti F,
Torti S,
Laubenbacher R.
Activated Oncogenic Pathway Modifies Iron Network in Breast Epithelial Cells: A Dynamic Modeling Perspective. PLoS Comput Biol 2017 Feb 6; 13(2):e1005352