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

Abstract

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.

Differential expression analysis at the individual level reveals a lncRNA prognostic signature for lung adenocarcinoma

“…However, these methods are arbitrary in setting a threshold for prognostic signature detection and are difficult to apply to clinical experiments [11, 34]. Our study reveals a robust 2-lncRNA signature for LUAD patients, which was validated in independent datasets and also by the GO enrichment analysis.…”

Personalised pathway analysis reveals association between DNA repair pathway dysregulation and chromosomal instability in sporadic breast cancer

“…Numerous pathway analysis tools have been developed; however, most of them are designed for providing pathway dysregulation information at population level instead of tumour level. Among the recently proposed methods for personalised pathways analysis (Ahn et al., 2014; Drier et al., 2013; Vaske et al., 2010; Wang et al., 2015a,b), Pathifier (Drier et al., 2013) has proven to be particularly robust. It has been successfully applied to provide a pathway‐based classification of breast cancer (Livshits et al., 2015), and when combined with Cox regression and L1 penalised estimation, has achieved better prognosis prediction compared with gene‐based models (Huang et al., 2014).…”

Identifying disease-associated pathways in one-phenotype data based on reversal gene expression orderings

“…For such one-sided data, current functional enrichment analysis tools which focus on quantitative expression differences between two phenotypes have difficulty in finding phenotype-related functional pathways. The within-sample REOs have been found robust against systematic batch effects and transferable among independent datasets which enables the reuse of accumulated samples 19, 21, 38 . In the present work, we proposed an REO-based algorithm DRFunc , which could robustly identify the underlying disturbed pathways from such one-sided dataset by integrating control samples of the same tissue measured by other independent experiments.…”

“…We previously developed a tool, individPath , to identify patient-specific dysregulated pathways based on reversal REOs in an individual sample compared with the highly stable REOs identified from a large cohort of normal samples which were accumulated previously from various sources 19 . Compared with the algorithms based on the quantitative expression values, the REO-based algorithms have some unique advantages, including insensitive to batch effects, free of between-sample data normalization, reproducible across independent data 17, 20 and reuse of accumulated data 21, 22 .…”