A genome-wide microRNA (miRNome) screen coupled with high-throughput monitoring of protein levels reveals complex, modular miRNA regulation of the EGFR-driven cell-cycle network, and identifies new miRNAs that can suppress breast cancer cell proliferation.

The advancement of efficient technologies to comply with the needs of systems biology and drug discovery has so far not received adequate attention. A substantial bottleneck for the time-resolved quantitative description of signaling networks is the limited throughput and the inadequate sensitivity of currently established methods. Here, we present an improved protein microarray-based approach towards the sensitive detection of proteins in the fg-range which is based on signal detection in the near-infrared range. The high sensitivity of the assay permits the specific quantification of proteins derived from as little as only 20,000 cells with an error rate of only 5%. The capacity is limited to the analysis of up to 500 different samples per microarray. Protein abundance is determined qualitatively, and quantitatively, if recombinant protein is available. This novel approach was called IPAQ (infrared-based protein arrays with quantitative readout). IPAQ offers a highly sensitive experimental approach superior to the established standard protein quantification technologies, and is suitable for quantitative proteomics. Employing the IPAQ approach, a detailed analysis of activated signaling networks in biopsy samples and of crosstalk between signaling modules as required in drug discovery strategies can easily be performed.

Increasing the efficacy of targeted cancer therapies requires the identification of robust biomarkers suitable for patient stratification. This study focused on the identification of molecular mechanisms causing resistance against the anti-ERBB2-directed therapeutic antibodies trastuzumab and pertuzumab presently used to treat patients with ERBB2-amplified breast cancer. Immunohistochemistry and clinical data were evaluated and yielded evidence for the existence of ERBB2-amplified breast cancer with high-level epidermal growth-factor receptor (EGFR) expression as a separate tumor entity. Because the proto-oncogene EGFR tightly interacts with ERBB2 on the protein level, the hypothesis that high-level EGFR expression might contribute to resistance against ERBB2-directed therapies was experimentally validated. SKBR3 and HCC1954 cells were chosen as model systems of EGFR-high/ERBB2-amplified breast cancer and exposed to trastuzumab, pertuzumab and erlotinib, respectively, and in combination. Drug impact was quantified in cell viability assays and on the proteomic level using reverse-phase protein arrays. Phosphoprotein dynamics revealed a significant downregulation of AKT signaling after exposure to trastuzumab, pertuzumab or a coapplication of both antibodies in SKBR3 cells but no concomitant impact on ERK1/2, RB or RPS6 phosphorylation. On the other hand, signaling was fully downregulated in SKBR3 cells after coinhibition of EGFR and ERBB2. Inhibitory effects in HCC1954 cells were driven by erlotinib alone, and a significant upregulation of RPS6 and RB phosphorylation was observed after coincubation with pertuzumab and trastuzumab. In summary, proteomic data suggest that high-level expression of EGFR in ERBB2-amplified breast cancer cells attenuates the effect of anti-ERBB2-directed antibodies. In conclusion, EGFR expression may serve as diagnostic and predictive biomarker to advance personalized treatment concepts of patients with ERBB2-amplified breast cancer.

Osteoarthritis (OA) is the most common rheumatic disease and one of the most disabling pathologies worldwide. To date, the diagnostic methods of OA are very limited, and there are no available medications capable of halting its characteristic cartilage degeneration. Therefore, there is a significant interest in new biomarkers useful for the early diagnosis, prognosis, and therapeutic monitoring. In the recent years, protein microarrays have emerged as a powerful proteomic tool to search for new biomarkers. In this study, we have used two concepts for generating protein arrays, antigen microarrays, and NAPPA (nucleic acid programmable protein arrays), to characterize differential autoantibody profiles in a set of 62 samples from OA, rheumatoid arthritis (RA), and healthy controls. An untargeted screen was performed on 3840 protein fragments spotted on planar antigen arrays, and 373 antigens were selected for validation on bead-based arrays. In the NAPPA approach, a targeted screening was performed on 80 preselected proteins. The autoantibody targeting CHST14 was validated by ELISA in the same set of patients. Altogether, nine and seven disease related autoantibody target candidates were identified, and this work demonstrates a combination of these two array concepts for biomarker discovery and their usefulness for characterizing disease-specific autoantibody profiles.

The RPPAanalyzer is open source and freely available as an R-Package on the CRAN platform http://cran.r-project.org/.

Motivation: Network modelling in systems biology has become an important tool to study molecular interactions in cancer research, because understanding the interplay of proteins is necessary for developing novel drugs and therapies. De novo reconstruction of signalling pathways from data allows to unravel interactions between proteins and make qualitative statements on possible aberrations of the cellular regulatory program. We present a new method for reconstructing signalling networks from time course experiments after external perturbation and show an application of the method to data measuring abundance of phosphorylated proteins in a human breast cancer cell line, generated on reverse phase protein arrays.Results: Signalling dynamics is modelled using active and passive states for each protein at each timepoint. A fixed signal propagation scheme generates a set of possible state transitions on a discrete timescale for a given network hypothesis, reducing the number of theoretically reachable states. A likelihood score is proposed, describing the probability of measurements given the states of the proteins over time. The optimal sequence of state transitions is found via a hidden Markov model and network structure search is performed using a genetic algorithm that optimizes the overall likelihood of a population of candidate networks. Our method shows increased performance compared with two different dynamical Bayesian network approaches. For our real data, we were able to find several known signalling cascades from the ERBB signalling pathway.Availability: Dynamic deterministic effects propagation networks is implemented in the R programming language and available at http://www.dkfz.de/mga2/ddepn/Contact: c.bender@dkfz.de

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.

The quantitative analysis of signaling networks requires highly sensitive methods for the time-resolved determination of protein phosphorylation. For this reason, we developed a quantitative protein microarray that monitors the activation of multiple signaling pathways in parallel, and at high temporal resolution. A label-free sandwich approach was combined with near infrared detection, thus permitting the accurate quantification of low-level phosphoproteins in limited biological samples corresponding to less than 50,000 cells, and with a very low standard deviation of approximately 5%. The identification of suitable antibody pairs was facilitated by determining their accuracy and dynamic range using our customized software package Quantpro. Thus, we are providing an important tool to generate quantitative data for systems biology approaches, and to drive innovative diagnostic applications.