title: Stratified pathway analysis to identify gene sets associated with oral contraceptive use and breast cancer.
abstract: Cancer biomarker discovery can facilitate drug development, improve staging of patients, and predict patient prognosis. Because cancer is the result of many interacting genes, analysis based on a set of genes with related biological functions or pathways may be more informative than single gene-based analysis for cancer biomarker discovery. The relevant pathways thus identified may help characterize different aspects of molecular phenotypes related to the tumor. Although it is well known that cancer patients may respond to the same treatment differently because of clinical variables and variation of molecular phenotypes, this patient heterogeneity has not been explicitly considered in pathway analysis in the literature. We hypothesize that combining pathway and patient clinical information can more effectively identify relevant pathways pertinent to specific patient subgroups, leading to better diagnosis and treatment. In this article, we propose to perform stratified pathway analysis based on clinical information from patients. In contrast to analysis using all the patients, this more focused analysis has the potential to reveal subgroup-specific pathways that may lead to more biological insights into disease etiology and treatment response. As an illustration, the power of our approach is demonstrated through its application to a breast cancer dataset in which the patients are stratified according to their oral contraceptive use.
authors.affiliation: Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA. ; School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
authors.forename: Herbert
authors.initials: H
authors.lastname: Pang
authors.affiliation: Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
keywords: cancer; pathways; progesterone receptor; random forests