Boolean ErbB network reconstructions and perturbation simulations reveal individual drug response in different breast cancer cell lines

BMC Systems Biology

Tim Beißbarth²  Ulrike Korf³  Johanna Sonntag³  Frauke Henjes⁴  Christian Bender¹  Silvia Von der Heyde² 

[1] TRON - Translational Oncology at the University Medical Center Mainz, Langenbeckstraße 1, 55131 Mainz, Germany;Statistical Bioinformatics, Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, 37073 Göttingen, Germany;Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120, Heidelberg, Germany;Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Box 1031, 17121 Solna, Sweden

关键词: Drug resistance;    Breast cancer cell line;    Boolean model;    Network reconstruction;    RPPA;    ErbB;   

Others  :  864923
DOI  :  10.1186/1752-0509-8-75

 received in 2013-12-20, accepted in 2014-06-10,  发布年份 2014

【 摘 要 】

Background

Despite promising progress in targeted breast cancer therapy, drug resistance remains challenging. The monoclonal antibody drugs trastuzumab and pertuzumab as well as the small molecule inhibitor erlotinib were designed to prevent ErbB-2 and ErbB-1 receptor induced deregulated protein signalling, contributing to tumour progression. The oncogenic potential of ErbB receptors unfolds in case of overexpression or mutations. Dimerisation with other receptors allows to bypass pathway blockades. Our intention is to reconstruct the ErbB network to reveal resistance mechanisms. We used longitudinal proteomic data of ErbB receptors and downstream targets in the ErbB-2 amplified breast cancer cell lines BT474, SKBR3 and HCC1954 treated with erlotinib, trastuzumab or pertuzumab, alone or combined, up to 60 minutes and 30 hours, respectively. In a Boolean modelling approach, signalling networks were reconstructed based on these data in a cell line and time course specific manner, including prior literature knowledge. Finally, we simulated network response to inhibitor combinations to detect signalling nodes reflecting growth inhibition.

Results

The networks pointed to cell line specific activation patterns of the MAPK and PI3K pathway. In BT474, the PI3K signal route was favoured, while in SKBR3, novel edges highlighted MAPK signalling. In HCC1954, the inferred edges stimulated both pathways. For example, we uncovered feedback loops amplifying PI3K signalling, in line with the known trastuzumab resistance of this cell line. In the perturbation simulations on the short-term networks, we analysed ERK1/2, AKT and p70S6K. The results indicated a pathway specific drug response, driven by the type of growth factor stimulus. HCC1954 revealed an edgetic type of PIK3CA-mutation, contributing to trastuzumab inefficacy. Drug impact on the AKT and ERK1/2 signalling axes is mirrored by effects on RB and RPS6, relating to phenotypic events like cell growth or proliferation. Therefore, we additionally analysed RB and RPS6 in the long-term networks.

Conclusions

We derived protein interaction models for three breast cancer cell lines. Changes compared to the common reference network hint towards individual characteristics and potential drug resistance mechanisms. Simulation of perturbations were consistent with the experimental data, confirming our combined reverse and forward engineering approach as valuable for drug discovery and personalised medicine.

【 授权许可】

   
2014 von der Heyde et al.; licensee BioMed Central Ltd.

【 图 表 】

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