Dataset Information

Examining the pathogenesis of breast cancer using a novel agent-based model of mammary ductal epithelium dynamics.

ABSTRACT: The study of the pathogenesis of breast cancer is challenged by the long time-course of the disease process and the multi-factorial nature of generating oncogenic insults. The characterization of the longitudinal pathogenesis of malignant transformation from baseline normal breast duct epithelial dynamics may provide vital insight into the cascading systems failure that leads to breast cancer. To this end, extensive information on the baseline behavior of normal mammary epithelium and breast cancer oncogenesis was integrated into a computational model termed the Ductal Epithelium Agent-Based Model (DEABM). The DEABM is composed of computational agents that behave according to rules established from published cellular and molecular mechanisms concerning breast duct epithelial dynamics and oncogenesis. The DEABM implements DNA damage and repair, cell division, genetic inheritance and simulates the local tissue environment with hormone excretion and receptor signaling. Unrepaired DNA damage impacts the integrity of the genome within individual cells, including a set of eight representative oncogenes and tumor suppressors previously implicated in breast cancer, with subsequent consequences on successive generations of cells. The DEABM reproduced cellular population dynamics seen during the menstrual cycle and pregnancy, and demonstrated the oncogenic effect of known genetic factors associated with breast cancer, namely TP53 and Myc, in simulations spanning ?40 years of simulated time. Simulations comparing normal to BRCA1-mutant breast tissue demonstrated rates of invasive cancer development similar to published epidemiologic data with respect to both cumulative incidence over time and estrogen-receptor status. Investigation of the modeling of ER?-positive (ER+) tumorigenesis led to a novel hypothesis implicating the transcription factor and tumor suppressor RUNX3. These data suggest that the DEABM can serve as a potentially valuable framework to augment the traditional investigatory workflow for future hypothesis generation and testing of the mechanisms of breast cancer oncogenesis.

SUBMITTER: Chapa J

PROVIDER: S-EPMC3660364 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Examining the pathogenesis of breast cancer using a novel agent-based model of mammary ductal epithelium dynamics.

Chapa Joaquin J Bourgo Ryan J RJ Greene Geoffrey L GL Kulkarni Swati S An Gary G

PloS one 20130521 5

The study of the pathogenesis of breast cancer is challenged by the long time-course of the disease process and the multi-factorial nature of generating oncogenic insults. The characterization of the longitudinal pathogenesis of malignant transformation from baseline normal breast duct epithelial dynamics may provide vital insight into the cascading systems failure that leads to breast cancer. To this end, extensive information on the baseline behavior of normal mammary epithelium and breast can ...[more]

PMID: 23704974

Similar Datasets

Project description:IntroductionBreast cancer, the product of numerous rare mutational events that occur over an extended time period, presents numerous challenges to investigators interested in studying the transformation from normal breast epithelium to malignancy using traditional laboratory methods, particularly with respect to characterizing transitional and pre-malignant states. Dynamic computational modeling can provide insight into these pathophysiological dynamics, and as such we use a previously validated agent-based computational model of the mammary epithelium (the DEABM) to investigate the probabilistic mechanisms by which normal populations of ductal cells could transform into states replicating features of both pre-malignant breast lesions and a diverse set of breast cancer subtypes.MethodsThe DEABM consists of simulated cellular populations governed by algorithms based on accepted and previously published cellular mechanisms. Cells respond to hormones, undergo mitosis, apoptosis and cellular differentiation. Heritable mutations to 12 genes prominently implicated in breast cancer are acquired via a probabilistic mechanism. 3000 simulations of the 40-year period of menstrual cycling were run in wild-type (WT) and BRCA1-mutated groups. Simulations were analyzed by development of hyperplastic states, incidence of malignancy, hormone receptor and HER-2 status, frequency of mutation to particular genes, and whether mutations were early events in carcinogenesis.ResultsCancer incidence in WT (2.6%) and BRCA1-mutated (45.9%) populations closely matched published epidemiologic rates. Hormone receptor expression profiles in both WT and BRCA groups also closely matched epidemiologic data. Hyperplastic populations carried more mutations than normal populations and mutations were similar to early mutations found in ER+ tumors (telomerase, E-cadherin, TGFB, RUNX3, p < .01). ER- tumors carried significantly more mutations and carried more early mutations in BRCA1, c-MYC and genes associated with epithelial-mesenchymal transition.ConclusionsThe DEABM generates diverse tumors that express tumor markers consistent with epidemiologic data. The DEABM also generates non-invasive, hyperplastic populations, analogous to atypia or ductal carcinoma in situ (DCIS), via mutations to genes known to be present in hyperplastic lesions and as early mutations in breast cancers. The results demonstrate that agent-based models are well-suited to studying tumor evolution through stages of carcinogenesis and have the potential to be used to develop prevention and treatment strategies.

Dataset Information

Examining the pathogenesis of breast cancer using a novel agent-based model of mammary ductal epithelium dynamics.

Publications

Examining the pathogenesis of breast cancer using a novel agent-based model of mammary ductal epithelium dynamics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets