Modelling how the brain microenvironment influences therapy response and metastasis of the most common childhood brain tumour

Medulloblastoma is the most common type of malignant paediatric brain tumour, accounting for 10% of cancer deaths in children. While survival rates have improved in recent years, metastatic spread is almost universally fatal. Current therapies also cause irreparable damage to the surrounding brain leading to decreased quality of life in surviving patients, consequently there is a need for new targeted therapy options. The degree of metastatic spread and resistance to current therapies is influenced by the underlying molecular subtype of the tumour. Development of novel therapies therefore requires reliable models which reflect subgroup specific behaviour (resistance and migration capability).
Medulloblastoma research has traditionally relied on either basic two-dimensional (2D) culture or orthotopic mouse models, neither of which accurately recapitulates a child's brain. We have recently developed a 3D tumour microenvironment model (Linke et al 2020), which recapitulates resistance and migration patterns observed in patients. In addition to being more representative, these studies take a fraction of the time and are far cheaper than animal-intensive projects.
Our in depth molecular (single cell analyses) and metabolic (3D OrbiSIMs) analyses (Linke et al in preparation) have given us further insights into how medulloblastoma cells interact with their microenvironment within these models. The student will therefore use these findings to further tailor our model, making it even more relevant to patients. This will be done by:-
1. Our data indicates that specific extracellular matrix (ECM) factors differentially influence migration of medulloblastoma subgroups. This will be investigated by either using CRISPR Cas9 to knockout expression in tumour cells (verified by quantitative PCR, western blotting and sequencing assays) or adding specific ECM factors to the model. Migration will then be imaged using time-lapse microscopy on quantified in migration assays.
2. The brain microenvironment consists of several unique cells types, including microglial immune cells which our findings indicate influence outcome in a subgroup dependent manner. This will be investigated by adding immune cells to our model and analysing their effects on migration (imaged using time-lapse microscopy on quantified in migration assays) and therapy response (cell viability and cell death assays).
3. Findings will be correlated with patient data allowing verification of key biomarkers/pathways which can then be targeted in our model system. Targeting efficacy will be assessed using cell viability/death and migration assays.
4. To date our model has only been used to grow established cell lines. In order to make it relevant to personalised medicine approaches, it will be necessary to optimise conditions for growth of primary cells. Cell viability assays will be used to measure proliferation and growth will be determined using microscopy.
The overall aim being to gain an in-depth understanding of how the tumour interacts with surrounding normal cells and tissue to produce a microenvironment that is conducive to tumour cell migration and resistance. These findings will help to guide future clinical trials in children with medulloblastoma and have the potential to support studies of other paediatric brain tumours.