Network-based Survival Analysis Reveals Subnetwork Signatures for Predicting Outcomes of Ovarian Cancer Treatment

Abstract

Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox's proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by or . This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are initially sensitive to chemotherapy. Net-Cox toolbox is available at http://compbio.cs.umn.edu/Net-Cox/.

The Spike-and-Slab Lasso Generalized Linear Models for Prediction and Associated Genes Detection

“…With the L 1 -penalty, the penalized likelihood can be solved by extremely fast optimization algorithms, for example, the lars and the cyclic coordinate descent algorithms (Efron et al 2004;Friedman et al 2010), making them very popular in high-dimensional data analysis. Recently, these penalization approaches have been applied widely for prediction and prognosis (Rapaport et al 2007;Barillot et al 2012;Sohn and Sung 2013;Zhang et al 2013;Yuan et al 2014;Zhao et al 2014).…”

The `dnet¿ approach promotes emerging research on cancer patient survival

“…In the literature, a growing number of methods have been reported for network-based survival analysis [31][32][33][34][35][36]. Some of them have been implemented as tools, including Net-Cox [31], Reactome FI [32], HyperModules [36] and HotNet [35].…”

“…Some of them have been implemented as tools, including Net-Cox [31], Reactome FI [32], HyperModules [36] and HotNet [35]. Among these, all but Reactome FI are conceptually similar to dnet in using survival data to guide survival network discovery, while HyperModules currently does not support the Cox regression required in this application.…”

Survival analysis tools in genomics research

“…Cox regression-based method together with time-dependent receiver operating characteristic (ROC) curve analysis was also reported [7]. NetCox is a method based on Cox regression modeling using the information of co-regulated multiple genes, which was reported to improve replication of the prognostic model [8]. survcomp is an R-based Bioconductor [9] package for survival risk model comparison based on time-dependent ROC curve and c index [10].…”