Individualized identification of disease-associated pathways with disrupted coordination of gene expression

• Published in: Briefings in bioinformatics Vol. 17; no. 1; pp. 78 - 87
• Main Authors: Wang, Hongwei, Cai, Hao, Ao, Lu, Yan, Haidan, Zhao, Wenyuan, Qi, Lishuang, Gu, Yunyan, Guo, Zheng
• Format: Journal Article
• Language: English
• Published: England Oxford Publishing Limited (England) 01.01.2016
• Subjects: Adenocarcinoma - genetics; Adenocarcinoma - mortality; Adenocarcinoma of Lung; Antineoplastic Agents, Hormonal - therapeutic use; Bioinformatics; Breast Neoplasms - drug therapy; Breast Neoplasms - genetics; Breast Neoplasms - mortality; Cancer; Computational Biology - methods; Databases, Genetic - statistics & numerical data; Disease-Free Survival; Female; Gene expression; Gene Expression Profiling - statistics & numerical data; Gene Expression Regulation, Neoplastic; Genetic Predisposition to Disease; Humans; Kaplan-Meier Estimate; Lung Neoplasms - genetics; Lung Neoplasms - mortality; Male; Medical treatment; Middle Aged; Neoplasms, Hormone-Dependent - drug therapy; Neoplasms, Hormone-Dependent - genetics; Neoplasms, Hormone-Dependent - mortality; Oligonucleotide Array Sequence Analysis - statistics & numerical data; Precision Medicine - methods; Precision Medicine - statistics & numerical data; Prognosis; Tamoxifen - therapeutic use; relative expression orderings; stable/reversal gene pair; individualized pathway
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbv030

Current pathway analysis approaches are primarily dedicated to capturing deregulated pathways at the population level and cannot provide patient-specific pathway deregulation information. In this article, the authors present a simple approach, called individPath, to detect pathways with significantly disrupted intra-pathway relative expression orderings for each disease sample compared with the stable, normal intra-pathway relative expression orderings pre-determined in previously accumulated normal samples. Through the analysis of multiple microarray data sets for lung and breast cancer, the authors demonstrate individPath's effectiveness for detecting cancer-associated pathways with disrupted relative expression orderings at the individual level and dissecting the heterogeneity of pathway deregulation among different patients. The portable use of this simple approach in clinical contexts is exemplified by the identification of prognostic intra-pathway gene pair signatures to predict overall survival of resected early-stage lung adenocarcinoma patients and signatures to predict relapse-free survival of estrogen receptor-positive breast cancer patients after tamoxifen treatment.