Funded project summary

Award amount: £71,349 Principal Investigator: Dr Anke Richard-Bruning
Institution: Bioprocess and Biochemical Engineering group (BioProChem), Department of Chemical and Process Engineering
Project title: Bioprinting 3D Migratory Microenvironments for Brain Tumour Drug Screening

Project Summary: Glioblastomas are extremely aggressive types of brain cancer that are highly capable of spreading and recurring. One strategy in preventing tumour recurrence uses drugs that inhibit invasion; however, cancer drug screening is often conducted on 2D cell layers and is poorly representative of the 3D arrangement of cells within the body. 3D-bioprinting can be used to create 3D tumour models that better resemble the complex environments within cancerous tissue, yet the materials that are typically used for model fabrication are often low in viscosity and do not retain their shape once deposited. It is also challenging to integrate multiple materials within a single model – which is crucial when trying to replicate gradients in stiffness and cell type as found in cancerous tissue.

We have recently developed a technique for creating multi-layered soft tissue models that uses a supporting gel bed during model printing called Suspended Layer Additive Manufacturing (SLAM). It allows the careful placement of cells and other tumour components during model formation so that the low-viscosity printing solutions will keep their shape once deposited in a way that is not possible with other 3D-bioprinting methods. Printing in this manner will ensure chemical and mechanical properties of the models will reflect those of real tumours once solidified and extracted. The effects of potential cancer-therapeutic drugs on tumour models will then more realistically represent the effects on tumours in the body.

Recently, we found that certain members of a gene family called ARHGAPs are key regulators of cell migration in brain tumours. Now, we propose that by using the newly developed bioprinting technology SLAM, we have the potential to create larger, more intricate and biorelevant glioblastoma models with higher precision. This will enable us to investigate exactly how ARHGAPs affect cancer cell migration and importantly, assess candidate inhibitory drugs.