A network module-based method for identifying cancer prognostic signatures

Guanming Wu,Lincoln Stein

doi:10.1186/gb-2012-13-12-r112

Guanming Wu, Lincoln Stein

Open Access

https://doi.org/10.1186/gb-2012-13-12-r112

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Abstract

Discovering robust prognostic gene signatures as biomarkers using genomics data can be challenging. We have developed a simple but efficient method for discovering prognostic biomarkers in cancer gene expression data sets using modules derived from a highly reliable gene functional interaction network. When applied to breast cancer, we discover a novel 31-gene signature associated with patient survival. The signature replicates across 5 independent gene expression studies, and outperforms 48 published gene signatures. When applied to ovarian cancer, the algorithm identifies a 75-gene signature associated with patient survival. A Cytoscape plugin implementation of the signature discovery method is available at http://wiki.reactome.org/index.php/Reactome_FI_Cytoscape_Plugin

Highlights

A key goal in the application of genomics to disease is the identification of clinically relevant biomarkers that can distinguish otherwise indistinguishable patient subtypes
We assign the Pearson correlation coefficient (PCC) to the edges of the functional interaction (FI) network, thereby converting an unweighted generic graph into a weighted disease-specific graph
Using the univariate Cox proportional hazards (Cox PH) model [27,28] to measure the correlation of mean expression level of each of eight modules selected by the trained superpc model to patient survival time (Table 3), we found that each module is correlated with patient overall survival at P-values ≤ 0.002

Summary

Introduction

A key goal in the application of genomics to disease is the identification of clinically relevant biomarkers that can distinguish otherwise indistinguishable patient subtypes. Laboratory tests based on these biomarkers can aid clinicians in identifying patients who are at higher risk of developing aggressive disease and would benefit from earlier, more aggressive therapy [1,2]. Biomarker-based tests can guide clinicians in the choice of therapies most likely to benefit distinct patient groups [3,4,5]. Van de Vijver et al [7] have built a classification system for breast cancer based on the gene expression profile of 70 genes, and found that their classifier outperforms standard systems based on clinical and histologic criteria. Pawitan et al [8] have developed a 64-gene signature to predict the response to therapy of patients with breast cancer

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