The biophysics of cell division in tumours

1) Brief description of the context of the research including potential impact

We are developing methods for imaging the microstructure of human tissue at high resolution and in three dimensions, across large tissue samples. Cells are often densely packed within tumours, and form complex three-dimensional patterns that interact with blood vessel networks. Moreover, alongside the extracellular matrix, the composition of cells can cause the mechanical properties of tumours to differ significantly from those of the normal surrounding tissue.

This effect is utilized in the clinical identification of tumours - for example the appearance of a hard lump in breast tissue - but its cause is incompletely understood. To investigate the genetic and molecular origins of increased mechanical stiffness in tumours, we are combining novel imaging techniques (such as high-resolution episcopic microscopy (HREM), magnetic resonance imaging (MRI) and atomic force microscopy (AFM)) with mathematical modelling to provide a clearer picture of the biophysics and biomechanics of cancer.

2) Aims and Objectives -The specific objectives are to:

The main aim of this project is to optimise the imaging of mitotic cells within tumours using High-Resolution Episcopic Microscopy (HREM). This currently has a resolution of up to ~0.8microns in x-y and in z (sections are typically 1micron in depth). Importantly, using HREM, we also expect to be able to use a range of fluorescent markers to image mitotic events in their native 3D environment deep in tumours.

For this analysis, the student will: i) de-paraffinise tumours that are positive/negative for oncogenic Ras-ERK signalling; ii) stain tumours for markers of the DNA, cortex/membrane, spindle and ECM, iii) dehydrate tumours and embed them in resin, iv) mount and image the tissue using HREM. This procedure is complex and will require optimisation for imaging individual cells, although we have preliminary data showing individual nuclei can be discerned in tissue samples.

Once we have imaged mitotic events inside tumours, we will use microfabrication methods to recapitulate these environments in the lab to determine how the physical conditions found in real tumours likely influence mitosis. Parameters we will replicate will include: i) crowding, ii) ECM organisation, iii) interphase cell shape, iv) adhesive contact area.

Finally, we aim to be able to process tumours for imaging prior to embedding. This will enable us to use new methods (e.g. optical coherence elastography) to measure the mechanical properties of the tissue, and to compare this mechanical profile with cell shape information and, ultimately, with local genetic information. These data will also be used to parameterise mathematical models of tumour biomechanics.

3) Novelty of Research Methodology This PhD studentship is part of the International Alliance for Cancer Early Detection (ACED). 4) Alignment to EPSRC's strategies and research areas

The project is mainly linked to the theme Artificial intelligence and robotics since we will mainly use machine learning (we aim to find imaging-based biomarkers) to conduct our research. It also involves healthcare technologies as we plan to improve cancer early detection. This early cancer detection approach is critical as it will increase patient survival and reduce the suffering of patients.

5) Any companies or collaborators involved N/A