MRC Precision Medicine DTP: Quantitative proteomic analysis of dysregulated adhesion protein localisation in patient-derived cancer cells

Summary
This project seeks to use proteomic data and computational approaches to identify signatures of cell adhesion proteins that are mislocalised in cells from brain cancer patients. Such protein localisation signatures will pave the way to understanding mechanisms of brain cancer cell behaviour and could represent new therapeutic targets.

Background
Understanding how cancer cells survive, invade and migrate is of fundamental importance to the development of approaches to inhibit invasion and metastasis in patients. There is an urgent need for better understanding of the biology of brain cancer, for which prognosis remains dismal. Although whole-genome sequencing has revealed the complexity of the mutational landscape, it is yet to yield tractable precision medicine hypotheses that impact clinical treatment. We therefore need new knowledge of the sets of proteins that are dysregulated in cancer cells and that can be targeted therapeutically.
Cell adhesion proteins play central roles in controlling cancer cell behaviour. We and others have found that several of these cell adhesion proteins can localise to unexpected parts of cancer cells to influence tumour growth (Schoenherr et al. 2018). Analysis of protein network signatures implicated in cancer permits a global assessment of the protein interactions mediating cancer cell phenotypes and could identify improved therapeutic strategies (Byron & Frame 2016).

Aims
This interdisciplinary PhD project aims to identify signatures of adhesion proteins that are mislocalised in brain cancer cells. We will use cells derived from patients with glioblastoma, the most aggressive form of glioma, for which clinical progress has been particularly poor. We have access to new, disease-relevant cellular models of glioblastoma, from which proteins at different subcellular locations will be isolated biochemically using established protocols in the laboratory.
Using quantitative mass spectrometry, the project aims to characterise the subcellular proteomes of brain cancer (and normal equivalent) cells. We will use an advanced informatics-based workflow to generate accurate, deep-coverage, quantitative mass spectrometric data (Zhang et al. 2016). Computational analysis of the large-scale proteomic data and network analysis approaches will characterise the subcellular localisation of, and interrogate the potential connections between, adhesion proteins. We know that several adhesion proteins are important for regulating intracellular trafficking networks (Schoenherr et al. 2018). A key goal will be to discover the extent of dysregulation of adhesion protein localisation across the entire ‘adhesome’ (Horton, Byron et al. 2015).

Training outcomes
The student will obtain training in cancer cell biology, proteomic data analysis, bioinformatics and network analysis, gaining experience in both laboratory and computational techniques to identify potential therapeutic targets in brain cancer. The student will generate large-scale biomedical datasets by mass spectrometry–based proteomics and learn advanced informatics-based analysis of quantitative mass spectrometry data at the Karolinska Institute.
Dysregulated adhesion protein signatures will be bioinformatically extracted from the data and integrated with molecular annotation and patient data available for the brain cancer cells. Key network hubs predicted in silico to have or mediate dysregulated intracellular transport will be validated experimentally using super-resolution imaging. The functional relevance of selected identified proteins will be assessed using relevant cell biological assays, including for cancer cell proliferation, gene expression, migration and invasion. Thus, the identification of potential adhesion protein signatures may provide candidates for future investigation as drug targets in glioblastoma.
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This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location:

http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

Please note, you must apply to one of the projects and you should contact the primary supervisor prior to making your application. Additional information on the application process if available from the link above.

For more information about Precision Medicine visit:

http://www.ed.ac.uk/usher/precision-medicine