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Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content

Journal article
Authors Teresia Kling
P. Johansson
José Sánchez
V. D. Marinescu
Rebecka Jörnsten
S. Nelander
Published in Nucleic Acids Research
Volume 43
Issue 15
Pages Article e98
ISSN 0305-1048
Publication year 2015
Published at Sahlgrenska Cancer Center
Department of Mathematical Sciences, Mathematical Statistics
Institute of Medicine, Department of Molecular and Clinical Medicine
Pages Article e98
Language en
Subject categories Bioinformatics (Computational Biology), Cancer and Oncology


Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, based on generalized Sparse Inverse Covariance Selection (SICS). The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer. It is shown to be statistically robust and effective at detecting direct pathway links in data from TCGA. To facilitate interpretation of the results, we introduce a publicly accessible tool (, in which the derived networks are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model for eight TCGA cancers, using data from 3900 patients. The model rediscovered known mechanisms and contained interesting predictions. Possible applications include prediction of regulatory relationships, comparison of network modules across multiple forms of cancer and identification of drug targets. © 2015 The Author(s).

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