Understanding focal adhesion kinase networks in cancer cell adhesion and invasion

Understanding how cancer cells survive, invade and migrate is of fundamental importance to the development of approaches to inhibit invasion and metastasis in patients. Multiple mechanisms control cancer cell behaviour in the context of adhesion and invasion, which complicates the analysis of individual molecules or isolated signalling cascades. Instead, analysis of protein complexes and resulting networks implicated in cancer permits a more global assessment of the functional molecular units and interactions mediating cancer cell phenotypes.

This PhD project aims to understand better the networks of proteins associated with key adhesion proteins and how these networks together control the adhesion and invasion of cancer cells. The project will initially focus on focal adhesion kinase (FAK), an adhesion non-receptor tyrosine kinase implicated in tumourigenesis, which the laboratory has studied extensively and for which key reagents and tools (such as knockout cells) are available.

To build the groundwork for FAK-mediated protein–protein interaction networks, the student will isolate FAK-associated protein complexes from squamous cell carcinoma cells using immunoprecipitation. Complexes isolated from cells adherent to different extracellular matrix ligands will provide information on the cell-surface integrin receptors through which specific FAK complexes may be signalling. Plasma membrane proteins will also be isolated, and cells expressing FAK mutants or no FAK will enable the role of FAK on cell-surface expression of adhesion receptors to be dissected. Isolated complexes will be analysed by immunoblotting and by mass spectrometry, which will enable the direct measurement of the molecular composition of FAK complexes and the cell-surface proteome.

To bring additional context to the findings, the resultant datasets will be analysed alongside proteomic datasets from several other adhesion proteins currently under investigation in the laboratory. The student will learn how to analyse and interpret large proteomic datasets using various approaches, including the integration of existing transcriptomic datasets. Key network hubs and functionally relevant groups of proteins differentially regulated by FAK mutation or deletion will be identified and targeted for follow-up biological analysis. The role of identified hits in cancer cell adhesion will be investigated using adhesion and invasion assays following their depletion by RNA interference in cells with or without FAK expression. This will be complemented by immunofluorescence analysis to reveal the subcellular localization of identified hits. Stable cell lines depleted of particularly promising hits will be generated and intravital imaging will be used to assess resultant phenotypes in vivo, including cell proliferation, migration, intravasation, immune system interaction and apoptosis.

The project will be split evenly between “wet” and “dry” experiments, and, as cells and data are already available, these aspects can be undertaken immediately and in parallel to maximize efficiency. The project will provide experience of all stages of a network biology workflow, including sample isolation and preparation, proteomic data acquisition, network analysis, dataset integration and management, hypothesis refinement, high-definition functional biology and mechanistic follow-up. The result of the project will be a refined understanding of the role of adhesion proteins in cancer cell adhesion and invasion, which will provide insights into potential therapeutic strategies and open the way for systems-level modelling of cancer cell adhesion.

Applicants should submit a covering letter stating clearly why you are interested in applying in the project. Applications should also include a full up-to-date C.V. (include vacation address), and names and addresses of two academic referees, sent to: [Email Address Removed]

Academic enquiries specifically related to the project can be made directly to Dr Adam Byron ([Email Address Removed]).