CAERUS: Predicting CAncER oUtcomeS Using Relationship between Protein Structural Information, Protein Networks, Gene Expression Data, and Mutation Data

Abstract

Carcinogenesis is a complex process with multiple genetic and environmental factors contributing to the development of one or more tumors. Understanding the underlying mechanism of this process and identifying related markers to assess the outcome of this process would lead to more directed treatment and thus significantly reduce the mortality rate of cancers. Recently, molecular diagnostics and prognostics based on the identification of patterns within gene expression profiles in the context of protein interaction networks were reported. However, the predictive performances of these approaches were limited. In this study we propose a novel integrated approach, named CAERUS, for the identification of gene signatures to predict cancer outcomes based on the domain interaction network in human proteome. We first developed a model to score each protein by quantifying the domain connections to its interacting partners and the somatic mutations present in the domain. We then defined proteins as gene signatures if their scores were above a preset threshold. Next, for each gene signature, we quantified the correlation of the expression levels between this gene signature and its neighboring proteins. The results of the quantification in each patient were then used to predict cancer outcome by a modified naïve Bayes classifier. In this study we achieved a favorable accuracy of 88.3%, sensitivity of 87.2%, and specificity of 88.9% on a set of well-documented gene expression profiles of 253 consecutive breast cancer patients with different outcomes. We also compiled a list of cancer-associated gene signatures and domains, which provided testable hypotheses for further experimental investigation. Our approach proved successful on different independent breast cancer data sets as well as an ovarian cancer data set. This study constitutes the first predictive method to classify cancer outcomes based on the relationship between the domain organization and protein network.

Integrative approaches for finding modular structure in biological networks

“…For example, active modules showing characteristic patterns of gene expression correlated with specific disease phenotypes can yield valuable biomarkers for disease classification 62,95,96 . Module-based biomarkers achieve greater predictive power and reproducibility over single gene markers, as demonstrated for the classification of numerous human disorders including Alzheimer’s 97 , diabetes 36,98-100 and several forms of cancers including breast cancers 45,55,62,99,101,102 , ovarian cancer 73,103,104 , glioblastomas 67,70,73,74 , and others 39,72,95,105,106 . Because active modules can reveal pathway-centric insights reinforced by multiple lines of evidence, they naturally provide mechanistic explanations for complex traits and multi-genic diseases like cancer.…”

An Iron Regulatory Gene Signature in Breast Cancer: More than a Prognostic Genetic Profile?

“…In another study, a novel integrated approach, named CAERUS, for the identification of gene signatures to predict cancer outcomes based on the domain-interaction network in the human proteome has been proposed. An accuracy of 88.3%, sensitivity of 87.2% and specificity of 88.9% was achieved [7]. In the first of two further reports [8], using high-throughput RNAi screening of a series of pharmacologically tractable genes comprehensive, functional viability profiles for a wide panel of commonly used breast tumor cell models have been generated.…”

Integration of proteomics into systems biology of cancer

“…An overview of publicly available databases relevant to proteomics studies in cancer research has been presented recently 43. The review covers general use protein databases, gene/protein expression databases, gene mutation and single nucleotide polymorphism databases, tumor antigen databases, protein–protein interaction, and biological pathway databases.…”