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

Abstract

Motivation: Recent breakthroughs in cancer research have come via the up-and-coming field of pathway analysis. By applying statistical methods to prior known gene and protein regulatory information, pathway analysis provides a meaningful way to interpret genomic data. While many gene/protein regulatory relationships have been studied, never before has such a significant amount data been made available in organized forms of gene/protein regulatory networks and pathways. However, pathway analysis research is still in its infancy, especially when applying it to solve practical problems. Results: In this paper we propose a new method of studying biological pathways, one that cross analyzes mutation information, transcriptome and proteomics data. Using this outcome, we identify routes of aberrant pathways potentially responsible for the etiology of disease. 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 encoded in the pathways. Far more complex interactions, such as phosphorylation and methylation, among others, in the pathways can be modeled using this approach. The effectiveness of our model is demonstrated through its ability to distinguish real pathways from decoys on TCGA mRNA-seq, mutation, Copy Number Variation and phosphorylation data for both Breast cancer and Ovarian cancer study. The majority of pathways distinguished can be confirmed by biological literature. Moreover, the proportion of correctly indentified pathways is % higher than previous work where only mRNA-seq mutation data is incorporated for breast cancer patients. Consequently, such an in-depth pathway analysis incorporating more diverse data can give rise to the accuracy of perturbed pathway detection.