Abstract B134: Computational analysis of the signaling pathways in the tumor microenvironment

Abstract

Abstract Tumor microenvironment and angiogenesis are two important factors contributing to the cancer development and drug resistance, which presents a challenge to the cancer treatment. Recent cancer studies found the bidirectional interaction between cancer cells and stellate cells is of importance in the tumor microenvironment, which can stimulate the synthesis and secretion of growth factors and cytokines, promote the growth of surrounding cancer cells through paracrine pathways. Since the pathways implicated in the tumor microenvironment are complicated and highly interconnected, it is not realistic to use traditional computational techniques, for example, ordinary differential equation and stochastic simulation algorithm, to analyze such a large network in a fast and effective way. Given a large crosstalk model of signaling pathways, one of systems biologist's interests is to discover and identify the key cellular components and signal transduction sequences that will drive the system to a pre-specified state (e.g., apoptosis, proliferation or angiogenesis) at a specific time point. To systematically investigate the interaction in the tumor microenvironment, we have constructed an in silico discrete value model of multicellular signaling pathways to describe the expression levels of different signaling components and dynamics of different signaling pathways without introducing any unknown parameters in the biochemical reactions. The frequently mutated pathways in our tumor microenvironment model include the pathways of Hedgehog, Wnt, Rb-E2F, P53, RAS, PI3K, VEGF, etc. Model Checking is a powerful formal verification technique, which has been successfully applied for the verification of hardware system and digital circuits. The Model Checking algorithm can automatically and exhaustively search the state transition system (up to 10 to the power of 100 possible states) to determine, whether or not, a given model M satisfies a desired temporal logic formula Φ. We, then, applied the Symbolic Model Checking, to automatically analyze the cell's proliferation, angiogenesis and apoptosis in the proposed model. Our studies predicted several important temporal logic properties and dynamic behaviors (oscillation phenomenon) in the tumor microenvironment that can be checked in the future experiments. The verification technique identified several signaling components, including the RAS, AKT, DVL, PTEN, etc, whose mutation or loss of function can promote cell growth and inhibit apoptosis (programmed cell death), some of which have been confirmed by existing experiments. With the help of Model Checking, our work provides a comprehensive understanding of the signaling networks and their crosstalk in the tumor microenvironment which might help cancer researchers to develop effective multi-gene targeted therapies for the cancer patients. Citation Format: Haijun Gong. Computational analysis of the signaling pathways in the tumor microenvironment. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research: Genetics, Biology, and Clinical Applications; Oct 3-6, 2013; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2013;11(10 Suppl):Abstract nr B134.