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DysRegNet: Patient-specific and confounder-aware dysregulated network inference

Olga Lazareva, View ORCID ProfileZakaria Louadi, Johannes Kersting, View ORCID ProfileJan Baumbach, View ORCID ProfileDavid B. Blumenthal, View ORCID ProfileMarkus List
doi: https://doi.org/10.1101/2022.04.29.490015
Olga Lazareva
1Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
5Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
6European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
6European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
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Zakaria Louadi
1Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
2Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
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Johannes Kersting
1Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
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Jan Baumbach
2Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
3Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
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David B. Blumenthal
4Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
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Markus List
1Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
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  • For correspondence: markus.list@tum.de
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Abstract

Gene regulation is frequently altered in diseases in unique and often patient-specific ways. Hence, personalized strategies have been proposed to infer patient-specific gene-regulatory networks. However, existing methods do not focus on disease-specific dysregulation or lack assessments of statistical significance. Moreover, they do not account for clinically important confounders such as age, sex or treatment history.

To overcome these shortcomings, we present DysRegNet, a novel method for inferring patient-specific regulatory alterations (dysregulations) from gene expression profiles. We compared DysRegNet to state-of-the-art methods and demonstrated that DysRegNet produces more interpretable and biologically meaningful networks. Independent information on promoter methylation and single nucleotide variants further corroborate our results. We apply DysRegNet to eleven TCGA cancer types and illustrate how the inferred networks can be used for down-stream analysis. We show that unique as well as cancer-type-specific dysregulation patterns exist and highlight immune-related mechanisms that are not obvious when focusing on individual genes rather than their interactions.

DysRegNet is available as a Python package (https://github.com/biomedbigdata/DysRegNet_package) and analysis results for eleven TCGA cancer types are further available through an interactive web interface (https://exbio.wzw.tum.de/dysregnet).

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 01, 2022.
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DysRegNet: Patient-specific and confounder-aware dysregulated network inference
Olga Lazareva, Zakaria Louadi, Johannes Kersting, Jan Baumbach, David B. Blumenthal, Markus List
bioRxiv 2022.04.29.490015; doi: https://doi.org/10.1101/2022.04.29.490015
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DysRegNet: Patient-specific and confounder-aware dysregulated network inference
Olga Lazareva, Zakaria Louadi, Johannes Kersting, Jan Baumbach, David B. Blumenthal, Markus List
bioRxiv 2022.04.29.490015; doi: https://doi.org/10.1101/2022.04.29.490015

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