Analysis of ERBB signalling and the impact of targeted therapeutics using protein microarrays

This work was focused on the quantitative analysis of time-resolved in vitro measurements of ligand-induced ERBB signalling in breast cancer cell lines, as well as the development of experimental methods suitable for the large-scale analysis of signalling networks. First, an automated protocol for the highly reproducible stimulation of cell lines with growth factors was developed. In parallel, protein microarray technologies were advanced to the quantification of phosphoproteins and resulted in two different assay formats: microspot immunoassays and reverse phase protein arrays. In collaboration with the bioinformatics group, data analysis tools were developed for both platforms. Experiments in ERBB2 overexpressing cell lines, HCC1954 and SKBR3, demonstrated that both ERBB2 targeting monoclonal antibodies, trastuzumab and pertuzumab, did not efficiently prevent ligand-induced signalling in vitro. Moreover, the combination of both antibody therapeutics did not result in improved efficacy. However, combining a single therapeutic antibody with the EGFR inhibiting small molecule erlotinib significantly downregulated ligand-induced signalling. Furthermore, treatment of proliferating cells with the combination of trastuzumab and erlotinib resulted in a dephosphorylation of the ribosomal protein S6 and the cell cycle regulator protein RB resulting in cell cycle arrest. Thus, the combination of erlotinib with trastuzumab could be postulated as potential therapy for the treatment of ERBB2-positive breast cancer patients. A comparative analysis of ERBB signalling in four cell lines, MCF7, BT474, HCC1954, and SKBR3, revealed that the phosphorylation of the ribosomal protein S6 is a strong predictor to analyse the activation status of signalling networks since the S6 protein integrates signals from the MAPK as well as the PI3K pathway, the two major pathways downstream of ERBB receptors. Due to differential ERBB receptor expression or additional oncogenic mutations, therapeutics affected ERK1/2 and AKT signalling to different extents in the four cell lines whereas the S6 phosphorylation reflected reliably the cellular response on exogenous perturbations

Discovery of circulating proteins associated to knee radiographic osteoarthritis

[Abstract] Currently there are no sufficiently sensitive biomarkers able to reflect changes in joint remodelling during osteoarthritis (OA). In this work, we took an affinity proteomic approach to profile serum samples for proteins that could serve as indicators for the diagnosis of radiographic knee OA. Antibody suspension bead arrays were applied to analyze serum samples from patients with OA (n = 273), control subjects (n = 76) and patients with rheumatoid arthritis (RA, n = 244). For verification, a focused bead array was built and applied to an independent set of serum samples from patients with OA (n = 188), control individuals (n = 83) and RA (n = 168) patients. A linear regression analysis adjusting for sex, age and body mass index (BMI) revealed that three proteins were significantly elevated (P < 0.05) in serum from OA patients compared to controls: C3, ITIH1 and S100A6. A panel consisting of these three proteins had an area under the curve of 0.82 for the classification of OA and control samples. Moreover, C3 and ITIH1 levels were also found to be significantly elevated (P < 0.05) in OA patients compared to RA patients. Upon validation in additional study sets, the alterations of these three candidate serum biomarker proteins could support the diagnosis of radiographic knee OA.Instituto de Salud Carlos III; PI-16/02124Instituto de Salud Carlos III; PI-14/01707Instituto de Salud Carlos III; PI-12/00329Instituto de Salud Carlos III; CIBER-CB06/01/0040Instituto de Salud Carlos III; RETIC-RIER-RD12/0009/001

Inferring signalling networks from longitudinal data using sampling based approaches in the R-package 'ddepn'

<p>Abstract</p> <p>Background</p> <p>Network inference from high-throughput data has become an important means of current analysis of biological systems. For instance, in cancer research, the functional relationships of cancer related proteins, summarised into signalling networks are of central interest for the identification of pathways that influence tumour development. Cancer cell lines can be used as model systems to study the cellular response to drug treatments in a time-resolved way. Based on these kind of data, modelling approaches for the signalling relationships are needed, that allow to generate hypotheses on potential interference points in the networks.</p> <p>Results</p> <p>We present the R-package 'ddepn' that implements our recent approach on network reconstruction from longitudinal data generated after external perturbation of network components. We extend our approach by two novel methods: a Markov Chain Monte Carlo method for sampling network structures with two edge types (activation and inhibition) and an extension of a prior model that penalises deviances from a given reference network while incorporating these two types of edges. Further, as alternative prior we include a model that learns signalling networks with the scale-free property.</p> <p>Conclusions</p> <p>The package 'ddepn' is freely available on R-Forge and CRAN <url>http://ddepn.r-forge.r-project.org</url>, <url>http://cran.r-project.org</url>. It allows to conveniently perform network inference from longitudinal high-throughput data using two different sampling based network structure search algorithms.</p

Identification of a Novel Autoimmune Peptide Epitope of Prostein in Prostate Cancer

There is a demand for novel targets and approaches to diagnose and treat prostate cancer (PCA). In this context, serum and plasma samples from a total of 609 individuals from two independent patient cohorts were screened for IgG reactivity against a sum of 3833 human protein fragments. Starting from planar protein arrays with 3786 protein fragments to screen 80 patients with and without PCA diagnosis, 161 fragments (4%) were chosen for further analysis based on their reactivity profiles. Adding 71 antigens from literature, the selection of antigens was corroborated for their reactivity in a set of 550 samples using suspension bead arrays. The antigens prostein (SLC45A3), TATA-box binding protein (TBP), and insulin-like growth factor 2 mRNA binding protein 2 (IGF2BP2) showed higher reactivity in PCA patients with late disease compared with early disease. Because of its prostate tissue specificity, we focused on prostein and continued with mapping epitopes of the 66-mer protein fragment using patient samples. Using bead-based assays and 15-mer peptides, a minimal peptide epitope was identified and refined by alanine scanning to the KPxAPFP. Further sequence alignment of this motif revealed homology to transmembrane protein 79 (TMEM79) and TGF-beta-induced factor 2 (TGIF2), thus providing a reasoning for cross-reactivity found in females. A comprehensive workflow to discover and validate IgG reactivity against prostein and homologous targets in human serum and plasma was applied. This study provides useful information when searching for novel biomarkers or drug targets that are guided by the reactivity of the immune system against autoantigens

Analysis of Autoantibody Profiles in Osteoarthritis Using Comprehensive Protein Array Concepts

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