Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content
Artikel i vetenskaplig tidskrift, 2015

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 (cancerlandscapes.org), 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).

Författare

Teresia Kling

Göteborgs universitet

P. Johansson

Uppsala universitet

José Sánchez

Göteborgs universitet

Chalmers, Matematiska vetenskaper, matematisk statistik

V. D. Marinescu

Uppsala universitet

Rebecka Jörnsten

Göteborgs universitet

Chalmers, Matematiska vetenskaper, matematisk statistik

S. Nelander

Uppsala universitet

Nucleic Acids Research

0305-1048 (ISSN) 1362-4962 (eISSN)

Vol. 43 15 Article e98- e98

Ämneskategorier

Bioinformatik (beräkningsbiologi)

Cancer och onkologi

DOI

10.1093/nar/gkv413

Mer information

Senast uppdaterat

2018-06-07