An Extension of Deep Pathway Analysis: A Pathway Route Analysis Framework Incorporating Multi-dimensional Cancer Genomics Data

摘要

Recent breakthroughs in cancer research have happened via the up-and-coming field of pathway analysis. By applying statistical methods to previously known gene and protein regulatory information, pathway analysis provides a meaningful way to interpret genomic data. In this paper we propose systematic methodology framework for studying biological pathways; one that cross-analyzes mutation information, transcriptome and proteomics data. Each pathway route is encoded as a bayesian network which is initialized with a sequence of conditional probabilities specifically designed to encode directionality of regulatory relationships defined by the pathways. Proteomics regulations, such as phosphorylation, is modeled by dynamically generated bayesian network through combining certain type of proteomics data to the regulated target. The entire pipeline is automated in R. The effectiveness of our model is demonstrated through its ability to distinguish real pathways from decoy pathways on TCGA mRNA-seq, mutation, copy number variation and phosphorylation data for both breast cancer and ovarian cancer study.