Inferring Pathway Activity toward Precise Disease Classification

By Eunjung Lee, Han-Yu Chuang, Jong-Won Kim, Trey Ideker, Doheon Lee. PLoS Comput. Biol., 2008

594 citations recorded by the source corpus.

The advent of microarray technology has made it possible to classify disease states based on gene expression profiles of patients. Typically, marker genes are selected by measuring the power of their expression profiles to discriminate among patients of different disease states. However, expression-based classification can be challenging in complex diseases due to factors such as cellular heterogeneity within a tissue sample and genetic heterogeneity across patients. A promising technique for coping with these challenges is to incorporate pathway information into the disease classification procedure in order to classify disease based on the activity of entire signaling pathways or protein complexes rather than on the expression levels of individual genes or proteins. We propose a new classification method based on pathway activities inferred for each patient. For each pathway, an activity level is summarized from the gene expression levels of its condition-responsive genes (CORGs), defined as the subset of genes in the pathway whose combined expression delivers optimal discriminative power for the disease phenotype. We show that classifiers using pathway activity achieve better performance than classifiers based on individual gene expression, for both simple and complex case-control studies including differentiation of perturbed from non-perturbed cells and subtyping of several different kinds of cancer. Moreover, the new method outperforms several previous approaches that use a static (i.e., non-conditional) definition of pathways. Within a pathway, the identified CORGs may facilitate the development of better diagnostic markers and the discovery of core alterations in human disease.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a800965f7e80d7296646ca7fc5cab680d5f3a2d9

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Citations in the corpus

Identification of diagnostic subnetwork markers for cancer in human protein-protein interaction network (2010, background): “Lee et al. [23] estimated the pathway activity by computing the average expression level of the condition responsive genes (CORGs) within a pathway.”
Network-based inference of protein activity helps functionalize the genetic landscape of cancer (2016, background): “Several approaches have been proposed to estimate pathway activity 40,41 , co-regulation of gene expression modules 42 , or activity of selected proteins 43 from gene expression signatures.”
Protein Networks as Logic Functions in Development and Cancer (2011, background): “74 (Figures 4A, S5A), which was better by 3–6% than previously reported results for a variety of standard and network/pathway-based classification methods [24,25,27].”
GSVA: gene set variation analysis for microarray and RNA-Seq data (2013, methodology): “Three unsupervised, single sample enrichment methods have been developed, Pathway Level analysis of Gene Expression (PLAGE), single sample GSEA (ssGSEA) and the combined z-score [5,22,35].”
Network-based machine learning and graph theory algorithms for precision oncology (2017, background): “45 In this approach, the gene expression in a pathway is normalized as one feature for the collection of pathways from a molecular signature database.”