title: Detection of gene pathways with predictive power for breast cancer prognosis.
abstract: BACKGROUND
  Prognosis is of critical interest in breast cancer research. Biomedical studies suggest that genomic measurements may have independent predictive power for prognosis. Gene profiling studies have been conducted to search for predictive genomic measurements. Genes have the inherent pathway structure, where pathways are composed of multiple genes with coordinated functions. The goal of this study is to identify gene pathways with predictive power for breast cancer prognosis. Since our goal is fundamentally different from that of existing studies, a new pathway analysis method is proposed.
  
  
  RESULTS
  The new method advances beyond existing alternatives along the following aspects. First, it can assess the predictive power of gene pathways, whereas existing methods tend to focus on model fitting accuracy only. Second, it can account for the joint effects of multiple genes in a pathway, whereas existing methods tend to focus on the marginal effects of genes. Third, it can accommodate multiple heterogeneous datasets, whereas existing methods analyze a single dataset only. We analyze four breast cancer prognosis studies and identify 97 pathways with significant predictive power for prognosis. Important pathways missed by alternative methods are identified.
  
  
  CONCLUSIONS
  The proposed method provides a useful alternative to existing pathway analysis methods. Identified pathways can provide further insights into breast cancer prognosis.
authors.affiliation: School of Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu
authors.forename: Shuangge
authors.initials: S
authors.lastname: Ma
authors.forename: Michael R
authors.initials: MR
authors.lastname: Kosorok
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D020022:Genetic Predisposition to Disease; D006801:Humans; D011379:Prognosis; D015398:Signal Transduction / Q000235:genetics*
chemical_list: D014408:Biomarkers, Tumor