Abstract LB-248: Towards a global picture of breast cancer: transcriptional networks, pathways, regulators, and validation

Abstract

Abstract The unprecedented volumes of large-scale genomic and genetic data being generated today, combined with the poor understanding of the genetics underlying complex biological systems, demand a systems biology approach to identify a global landscape of interactomes that contribute to a variety of clinical endpoints such as tumor progression. Along this direction, two kinds of gene network analyses, association (gene coexpression network) and causality inference (Bayesian network), have been almost independently developed in the past decade. In spite of the significant success of the methods, it is still far away from generating a clear picture for even a single pathway involved in complex human disease like cancer due to insufficient information and various limitations of the methods. Here we present a framework to combine the two types of network analyses with an aim of integrating many high-throughput gene expression datasets on multiple platforms. By applying the integrative network approach to four large scale microarray datasets in breast cancer, we first systematically uncover gene modules/pathways involved in breast cancer progression through a weighted gene coexpression network analysis, then use these gene modules to partition a complex Bayesian network constructed from the same datasets, and finally identify key regulators of the modules predictive of outcomes. A significant portion of the predicted novel gene targets are validated through a large number of siRNA experiments in breast cancer cell lines. This allows us to globally validate breast cancer gene networks. The integrative approach and the findings will have a significant impact on breast cancer research and drug development. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr LB-248.