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

Teresia Kling ; P. Johansson ; José Sánchez (Institutionen för matematiska vetenskaper, matematisk statistik) ; V. D. Marinescu ; Rebecka Jörnsten (Institutionen för matematiska vetenskaper, matematisk statistik) ; S. Nelander
Nucleic Acids Research (0305-1048). Vol. 43 (2015), 15, p. Article e98.
[Artikel, refereegranskad vetenskaplig]

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).

Denna post skapades 2015-09-29. Senast ändrad 2016-11-07.
CPL Pubid: 223321


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Institutioner (Chalmers)

Institutionen för medicin, avdelningen för molekylär och klinisk medicin (GU)
Sahlgrenska Cancer Center (GU)
Institutionen för matematiska vetenskaper, matematisk statistik (2005-2016)


Bioinformatik (beräkningsbiologi)
Cancer och onkologi

Chalmers infrastruktur