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Bioinformatics. 2018 Sep 1;34(17):i997-i1004. doi: 10.1093/bioinformatics/bty616.

Comparative Network Reconstruction using mixed integer programming.

Author information

1
Division of Molecular Carcinogenesis, The Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
2
Institute of Pathology, Charité Universitätsmedizin, Berlin, Germany.
3
IRI Life Sciences, Humboldt University of Berlin, Berlin, Germany.
4
Berlin Institute of Health, Berlin, Germany.
5
Faculty of EEMCS, Delft University of Technology, Delft, The Netherlands.

Abstract

Motivation:

Signal-transduction networks are often aberrated in cancer cells, and new anti-cancer drugs that specifically target oncogenes involved in signaling show great clinical promise. However, the effectiveness of such targeted treatments is often hampered by innate or acquired resistance due to feedbacks, crosstalks or network adaptations in response to drug treatment. A quantitative understanding of these signaling networks and how they differ between cells with different oncogenic mutations or between sensitive and resistant cells can help in addressing this problem.

Results:

Here, we present Comparative Network Reconstruction (CNR), a computational method to reconstruct signaling networks based on possibly incomplete perturbation data, and to identify which edges differ quantitatively between two or more signaling networks. Prior knowledge about network topology is not required but can straightforwardly be incorporated. We extensively tested our approach using simulated data and applied it to perturbation data from a BRAF mutant, PTPN11 KO cell line that developed resistance to BRAF inhibition. Comparing the reconstructed networks of sensitive and resistant cells suggests that the resistance mechanism involves re-establishing wild-type MAPK signaling, possibly through an alternative RAF-isoform.

Availability and implementation:

CNR is available as a python module at https://github.com/NKI-CCB/cnr. Additionally, code to reproduce all figures is available at https://github.com/NKI-CCB/CNR-analyses.

Supplementary information:

Supplementary data are available at Bioinformatics online.

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