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 individPaths 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. 