Detecting aberrant signal transduction pathways from high-throughput data using GIST algorithm

Abstract

It is biologically important to integrate high-throughput data to identify aberrant signal transduction pathways in cancer research. The high-throughput data acquired from The Cancer Genome Atlas (TCGA) Project offer a comprehensive picture of the genomic and transcriptional changes across hundreds of tumor samples. In this paper we propose a novel method, namely Gibbs sampler to Infer Signal Transduction pathways (GIST), to detect aberrant pathways that are highly associated with biological phenotypes or clinical information. GIST endeavors to estimate the edge probability by using a Markov Chain Monte Carlo (MCMC) method (i.e., a Gibbs sampling strategy). Through the sampling process, GIST is able to infer the correct signal transduction direction because the sampled edge probabilities are jointly determined by gene expression data and network topology. We first tested the efficacy of the GIST algorithm on yeast data and successfully uncovered several biologically meaningful signaling pathways. A case study on TCGA ovarian cancer data was further designed, aiming to unravel diverse signaling pathways associated with the development of ovarian cancer. The experimental results demonstrated the feasibility of applying GIST to identify and prioritize important signaling pathways in ovarian cancer for further biological validation.