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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, article id 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, article id 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, id: diva2:862695
Funder
Swedish Research CouncilSwedish Cancer SocietySwedish Childhood Cancer Foundation
Available from: 2015-10-23 Created: 2015-10-15 Last updated: 2018-02-04Bibliographically approved
In thesis
1. Large scale integration and interactive exploration of cancer data – with applications to glioblastoma
Open this publication in new window or tab >>Large scale integration and interactive exploration of cancer data – with applications to glioblastoma
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Glioblastoma is the most common malignant brain tumor, with a median survival of approximately 15 months. The standard of care treatment consists of surgical resection followed by radiotherapy and chemotherapy, where chemotherapy only prolongs survival by approximately 3 months. There is therefore an urgent need for new approaches to better understand the molecular vulnerabilities of glioblastoma. To this end, we have conducted four interdisciplinary studies.

In study 1 we develop a method for efficiently constructing and exploring large integrative network models that include multiple cohorts and multiple types of molecular data. We apply this method to 8 cancers from The Cancer Genome Atlas (TCGA) and make the integrative network available for exploration and visualization through a custom web interface.

In study 2 we establish a biobank of 48 patient derived glioblastoma cell cultures called the Human Glioma Cell Culture (HGCC) resource. We show that the HGCC cell cultures represent all transcriptional subtypes, carry genomic aberrations typical of glioblastoma, and initiate tumors in vivo. The HGCC is an open resource for translational glioblastoma research, made available through hgcc.se.

In study 3 we extend the analysis of HGCC cell cultures both in terms of number (to over 100) and in terms of data types (adding mutation, methylation and drug response data). Large-scale drug profiling starting from over 1500 compounds identified two distinct groups of cell cultures defined by vulnerability to proteasome inhibition, p53/p21 activity, stemness and protein turnover. By applying machine learning methods to the combined drug profiling and matched genomics data we construct a first network of predictive biomarkers.

In study 4 we use the methods developed in study 1 applied to the data generated in studies 2 and 3 to construct an integrative network model of HGCC and glioblastoma data from TCGA. We present an interactive method for exploring this network based on searching for network patterns representing specific hypotheses defined by the user.

In conclusion, this thesis combines the development of integrative models with applications to novel data relevant for translational glioblastoma research. This work highlights several potentially therapeutically relevant aspects, and paves a path towards more comprehensive and informative models of glioblastoma.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2018. p. 58
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1426
Keyword
Glioblastoma, data integration, network modeling, interactive exploration, precision medicine
National Category
Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy) Cancer and Oncology Bioinformatics (Computational Biology)
Research subject
Oncology; Molecular Medicine; Statistics
Identifiers
urn:nbn:se:uu:diva-340843 (URN)978-91-513-0231-7 (ISBN)
Public defence
2018-03-23, Rudbecksalen, Dag Hammarskjölds väg 20, Uppsala, 13:00 (English)
Opponent
Supervisors
Available from: 2018-02-26 Created: 2018-02-04 Last updated: 2018-04-03

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Johansson, PatrikMarinescu, Voichita D.Nelander, Sven

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