An agent-based modeling framework linking inflammation and cancer using evolutionary principles: description of a generative hierarchy for the hallmarks of cancer and developing a bridge between mechanism and epidemiological data.
Inflammation plays a critical role in the development and progression of cancer, evident in multiple patient populations manifesting increased, non-resolving inflammation, such as inflammatory bowel disease, viral hepatitis and obesity. Given the complexity of both the inflammatory response and the process of oncogenesis, we utilize principles from the field of Translational Systems Biology to bridge the gap between basic mechanistic knowledge and clinical/epidemiologic data by integrating inflammation and oncogenesis within an agent-based model, the Inflammation and Cancer Agent-based Model (ICABM). The ICABM utilizes two previously published and clinically/epidemiologically validated mechanistic models to demonstrate the role of an increased inflammatory milieu on oncogenesis. Development of the ICABM required the creation of a generative hierarchy of the basic hallmarks of cancer to provide a foundation to ground the plethora of molecular and pathway components currently being studied. The ordering schema emphasizes the essential role of a fitness/selection frame shift to sub-organismal evolution as a basic property of cancer, where the generation of genetic instability as a negative effect for multicellular eukaryotic organisms represents the restoration of genetic plasticity used as an adaptive strategy by colonies of prokaryotic unicellular organisms. Simulations with the ICABM demonstrate that inflammation provides a functional environmental context that drives the shift to sub-organismal evolution, where increasingly inflammatory environments led to increasingly damaged genomes in microtumors (tumors below clinical detection size) and cancers. The flexibility of this platform readily facilitates tailoring the ICABM to specific cancers, their associated mechanisms and available epidemiological data. One clinical example of an epidemiological finding that could be investigated with this platform is the increased incidence of triple negative breast cancers in the premenopausal African-American population, which has been identified as having up-regulated of markers of inflammation. The fundamental nature of the ICABM suggests its usefulness as a base platform upon which additional molecular detail could be added as needed.
['An|Gary|G|', 'Kulkarni|Swati|S|']
D001741:African Americans D005075:Biological Evolution D001943:Breast Neoplasms D063646:Carcinogenesis D005260:Female D006801:Humans D007249:Inflammation D009369:Neoplasms D017697:Premenopause D049490:Systems Biology
Agent-based modeling Inflammation Cancer Systems biology Evolution Computational biology