Colorectal cancer cell line proteomes are representative of primary tumors and predict drug sensitivity

Proteomics holds promise for individualizing cancer treatment. We analyzed to what extent the proteomic landscape of human colorectal cancer (CRC) is maintained in established CRC cell lines and the utility of proteomics for predicting therapeutic responses. Proteomic and transcriptomic analyses were performed on 44 CRC cell lines, compared against primary CRCs (n=95) and normal tissues (n=60), and integrated with genomic and drug sensitivity data. Cell lines mirrored the proteomic aberrations of primary tumors, in particular for intrinsic programs. Tumor relationships of protein expression with DNA copy number aberrations and signatures of post-transcriptional regulation were recapitulated in cell lines. The 5 proteomic subtypes previously identified in tumors were represented among cell lines. Nonetheless, systematic differences between cell line and tumor proteomes were apparent, attributable to stroma, extrinsic signaling, and growth conditions. Contribution of tumor stroma obscured signatures of DNA mismatch repair identified in cell lines with a hypermutation phenotype. Global proteomic data showed improved utility for predicting both known drug-target relationships and overall drug sensitivity as compared with genomic or transcriptomic measurements. Inhibition of targetable proteins associated with drug responses further identified corresponding synergistic or antagonistic drug combinations. Our data provide evidence for CRC proteomic subtype-specific drug responses. Proteomes of established CRC cell line are representative of primary tumors. Proteomic data tend to exhibit improved prediction of drug sensitivity as compared with genomic and transcriptomic profiles. Our integrative proteogenomic analysis highlights the potential of proteome profiling to inform personalized cancer medicine

Classification of High Content Screening Data by Deep Convolutional Neural Networks

In drug discovery, high content screening (HCS) is an imaging-based method forcell-based screening of large libraries of drug compounds. HCS generates enormous amounts of images that need to be analysed and quantified by automated image analysis. This analysis is typically performed by a variety of algorithms segmenting cells and sub-cellular compartments and quantifying properties such as fluorescence intensities, morphological features, and textural characteristics. These quantified data can then be used to train a classifier to classify the imaged cells according to the phenotypic effects of the compounds. Recent developments in machine learning have enabled a new kind of image analysis in which classifiers based on convolutional neural networks can be trained on the image data directly, by passing the image quantification step. This has been shown to produce highly accurate predictions and simplify the analysis process. In this study, convolutional neural networks (CNNs) were used to classify HCS images of cells treated with a set of different drug compounds. A set of network architectures and hyper-parameters were explored in order to optimise the classification performance. The results were compared with the accuracies achieved with a classical image analysis pipeline in combination with a classifier. With this data set, the best CNN-based classifier achieved an accuracy of 91.3 %, where as classical image analysis combined with a random forest classifier achieved a classification accuracy of 78.8 %. In addition to the large increase in classification accuracy, CNNs have benefits such as being less biased when it comes to image quantification algorithm selection, and require less hands-on time during optimisation

High Content Analysis of Proteins and Protein Interactions by Proximity Ligation

Fundamental to all biological processes is the interplay between biomolecules such as proteins and nucleic acids. Studies of interactions should therefore be more informative than mere detection of expressed proteins. Preferably, such studies should be performed in material that is as biologically and clinically relevant as possible, i.e. in primary cells and tissues. In addition, to be able to take into account the heterogeneity of such samples, the analyses should be performed in situ to retain information on the sub-cellular localization where the interactions occur, enabling determination of the activity status of individual cells and allowing discrimination between e.g. tumor cells and surrounding stroma. This requires assays with an utmost level of sensitivity and selectivity. Taking these issues into consideration, the in situ proximity-ligation assay (in situ PLA) was developed, providing localized detection of proteins, protein-protein interactions and post-translational modifications in fixed cells and tissues. The high sensitivity and selectivity afforded by the assay's requirement for dual target recognition in combination with powerful signal amplification enables visualization of single protein molecules in intact single cells and tissue sections. To further increase the usefulness and application of in situ PLA, the assay was adapted to high content analysis techniques such as flow cytometry and high content screening. The use of in situ PLA in flow cytometry offers the possibility for high-throughput analysis of cells in solution with the unique characteristics offered by the assay. For high content screening, it was demonstrated that in situ PLA can enable cell-based drug screening of compounds affecting post-translational modifications and protein-protein interactions in primary cells, offering superior abilities over current assays. The methods presented in this thesis provide powerful new tools to study proteins in genetically unmodified cells and tissues, and should offer exciting new possibilities for molecular biology, diagnostics and drug discovery.

Parallel Visualization of Multiple Protein Complexes in Individual Cells in Tumor Tissue

Cellular functions are regulated and executed by complex protein interaction networks. Accordingly, it is essential to understand the interplay between proteins in determining the activity status of signaling cascades. New methods are therefore required to provide information on different protein interaction events at the single cell level in heterogeneous cell populations such as in tissue sections. Here, we describe a multiplex proximity ligation assay for simultaneous visualization of multiple protein complexes in situ. The assay is an enhancement of the original proximity ligation assay, and it is based on using proximity probes labeled with unique tag sequences that can be used to read out which probes, from a pool of probes, have bound a certain protein complex. Using this approach, it is possible to gain information on the constituents of different protein complexes, the subcellular location of the complexes, and how the balance between different complex constituents can change between normal and malignant cells, for example. As a proof of concept, we used the assay to simultaneously visualize multiple protein complexes involving EGFR, HER2, and HER3 homo- and heterodimers on a single-cell level in breast cancer tissue sections. The ability to study several protein complex formations concurrently at single cell resolution could be of great potential for a systems understanding, paving the way for improved disease diagnostics and possibilities for drug development

Revista mexicana de análisis político y administración pública : REMAP

Introduction - The normal process of epithelial mesenchymal transition (EMT) is subverted by carcinoma cells to facilitate metastatic spread. Cancer cells rarely undergo a full conversion to the mesenchymal phenotype, and instead adopt positions along the epithelial-mesenchymal axis, a propensity we refer to as epithelial mesenchymal plasticity (EMP). EMP is associated with increased risk of metastasis in breast cancer and consequent poor prognosis. Drivers towards the mesenchymal state in malignant cells include growth factor stimulation or exposure to hypoxic conditions. Methods - We have examined EMP in two cell line models of breast cancer: the PMC42 system (PMC42-ET and PMC42-LA sublines) and MDA-MB-468 cells. Transition to a mesenchymal phenotype was induced across all three cell lines using epidermal growth factor (EGF) stimulation, and in MDA-MB-468 cells by hypoxia. We used RNA sequencing to identify gene expression changes that occur as cells transition to a more-mesenchymal phenotype, and identified the cell signalling pathways regulated across these experimental systems. We then used inhibitors to modulate signalling through these pathways, verifying the conclusions of our transcriptomic analysis. Results - We found that EGF and hypoxia both drive MDA-MB-468 cells to phenotypically similar mesenchymal states. Comparing the transcriptional response to EGF and hypoxia, we have identified differences in the cellular signalling pathways that mediate, and are influenced by, EMT. Significant differences were observed for a number of important cellular signalling components previously implicated in EMT, such as HBEGF and VEGFA. We have shown that EGF- and hypoxia-induced transitions respond differently to treatment with chemical inhibitors (presented individually and in combinations) in these breast cancer cells. Unexpectedly, MDA-MB-468 cells grown under hypoxic growth conditions became even more mesenchymal following exposure to certain kinase inhibitors that prevent growth-factor induced EMT, including the mTOR inhibitor everolimus and the AKT1/2/3 inhibitor AZD5363. Conclusions - While resulting in a common phenotype, EGF and hypoxia induced subtly different signalling systems in breast cancer cells. Our findings have important implications for the use of kinase inhibitor-based therapeutic interventions in breast cancers, where these heterogeneous signalling landscapes will influence the therapeutic response. © 2015 Cursons et al.; licensee BioMed Central