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

Abstract

BackgroundDespite 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.ResultsThe 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.ConclusionsWe 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.

An in silico target identification using Boolean network attractors: Avoiding pathological phenotypes

“…The goal of this work is to propose a computational methodology implemented in an algorithm for in silico therapeutic target discovery using Boolean network attractors. It assumes that Boolean network attractors correspond to phenotypes produced by the modeled biological network, an assumption successfully applied in several works [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57]. Assuming that a phenotype is an observable state, and thus relatively stable, of a biological system and assuming that the state of a biological system results from its dynamics, a phenotype is likely to correspond to an attractor.…”

Boolean network modeling in systems pharmacology

“…In addition, a few studies have extended pre-existing networks to investigate specific interests. The main goals for developing Boolean networks have been to identify potential therapeutic strategies [106,17,18,107,25,26,28–32,34], characterize cellular differentiation [14,11,13,36], understand differential responses to cancer therapies due to mutational differences [27], understand the impact of patient heterogeneity on the response to drug treatments [21,35], and as an initial framework prior to the development of quantitative models [23]. The networks provided in this table could be potentially extended or repurposed for investigating additional features of interest, as opposed to starting from the ground up.…”

Logic Modeling in Quantitative Systems Pharmacology

“…For example, discrete logical modeling has been used to describe the DNA damage response signal transduction network in human epithelial cells and predict candidate target proteins for sensitization of carcinomas to DNA‐damaging agents 39. Also, the ErbB network was explored with a model built from prior knowledge and protein phosphorylation data to understand the drug resistance mechanisms of breast cancer cell lines 40. Logic modeling was applied to bladder cancer to investigate the effects of gene alterations leading to invasiveness 41.…”

“…39 Also, the ErbB network was explored with a model built from prior knowledge and protein phosphorylation data to understand the drug resistance mechanisms of breast cancer cell lines. 40 Logic modeling was applied to bladder cancer to investigate the effects of gene alterations leading to invasiveness. 41 However, the use of logic modeling tools is not limited to cancer applications, and research in other diseases also benefit from these techniques.…”