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Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content
Univ Gothenburg, Sahlgrenska Canc Ctr, SE-40530 Gothenburg, Sweden.;Univ Gothenburg, Dept Mol & Clin Med, SE-40530 Gothenburg, Sweden..
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Uppsala University, Science for Life Laboratory, SciLifeLab.
Univ Gothenburg, Math Sci, SE-41296 Gothenburg, Sweden.;Chalmers, SE-41296 Gothenburg, Sweden..
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology. Uppsala University, Science for Life Laboratory, SciLifeLab.
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2015 (English)In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 43, no 15, e98Article in journal (Refereed) Published
Abstract [en]

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 ( ext-link-type="uri" xlink:href="http://cancerlandscapes.org/">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.

Place, publisher, year, edition, pages
2015. Vol. 43, no 15, e98
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:uu:diva-264636DOI: 10.1093/nar/gkv413ISI: 000361303300003PubMedID: 25953855OAI: oai:DiVA.org:uu-264636DiVA: diva2:862695
Funder
Swedish Research CouncilSwedish Cancer SocietySwedish Childhood Cancer Foundation
Available from: 2015-10-23 Created: 2015-10-15 Last updated: 2017-12-01Bibliographically approved

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Johansson, PatrikMarinescu, Voichita D.Nelander, Sven

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