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Predictive Models of Liver Cancer
Citation:
SHAH, I. A., J. JACK, J. F. WAMBAUGH, AND C. CORTON. Predictive Models of Liver Cancer. Presented at Society of Toxicology Annual Meeting, Salt Lake City, UT, March 07 - 12, 2010.
Impact/Purpose:
Presentation
Description:
Predictive models of chemical-induced liver cancer face the challenge of bridging causative molecular mechanisms to adverse clinical outcomes. The latent sequence of intervening events from chemical insult to toxicity are poorly understood because they span multiple levels of biological organization and timescales. The availability of high-throughput molecular assays provide a global view of epigenetic, transcriptional and pathway level changes that can shed much needed light on the regulatory networks perturbed by xenobiotic stressors. Nevertheless, it is important to resolve the role of these networks in the normal homeostatic response of cells as opposed to irreversible alterations due to persistent stress that could be more predictive of toxicity. We believe it is necessary to model the altered cellular phenotypes in linking molecular mechanisms to neoplastic lesions. This talk will outline our implementation of this approach in the US EPA Virtual Liver (v-LiverTM) – a cellular systems model of hepatic tissues aimed at predicting chemical-induced histopathologic effects through simulation. The v-LiverTM is part of a broader community effort to develop in silico models of the cellular fabric of living tissues, called Virtual Tissues (VTs). As a proof of concept we are using molecular and cellular data on 20 nuclear receptor (NR) activating hepatocarcinogens from the EPA ToxCastTM Program and short-term in vivo studies. The first part of this talk will discuss the integration of mechanistic knowledge with high-throughput screening and -omic data to model the effects of NR-mediated networks on hepatocyte injury, death and proliferation. The second part of the talk will present the integration of pharmacokinetic (PK) and cell-level data to simulate dose-dependent hepatic effects.
URLs/Downloads:
Modeling Nuclear Receptor Mediated Pathways in Liver Cancer (slides) (PDF, NA pp, 2840 KB, about PDF)Predictive Models of Liver Cancer (abs) (PDF, NA pp, 8 KB, about PDF)