Glioblastoma tumours can be sorted into two distinct groups—"Up" and "Down" responders—based on how their cancer cells adapt to survive chemotherapy and radiation, and this researcher wants to find out exactly which cells drive those adaptations. This matters because glioblastoma is the deadliest adult brain cancer. Standard treatment buys patients only 6–9 months before the tumour regrows, and complete surgical removal is impossible because cancerous cells invade healthy brain tissue. Previous work showed that the tumour microenvironment—the mix of non-cancerous cells surrounding the tumour—is crucial for driving the adaptive response, but it is not clear whether the pathological signals come from the cancer cells themselves or from those supporting cells. If this research succeeds, it will produce a ranked list of therapeutic targets specific to each responder subtype. That would allow clinicians to match patients to treatments based on their tumour’s adaptive strategy, rather than giving everyone the same one-size-fits-all therapy. The work is fundamental science—it uses single-cell analysis of RNA and chromatin accessibility to map how the cellular landscape changes from initial diagnosis to recurrence—but it directly feeds into the design of experimental models and the prioritisation of drug targets for personalised medicine.
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Glioblastoma (GBM) is the most common and most deadly form of adult brain cancer. GBM patients receive a standard treatment of surgery, radiation and chemotherapy but tumours fatally recur 6-9 months later, causing this cancer to result in more years of life loss, per patient, than any other. This is because complete surgical removal of GBM is not possible; cancerous cells invade the healthy brain and cannot be removed. Research focused on characterising the remaining cells to understanding why some of them survive chemoradiation, has revealed that these cells can adapt their characteristics to survive and seed tumour regrowth. My own research in this area has identified the likely mechanism that underpins this adaption, and discovered that tumours can be stratified into two subgroups based on how their tumours adapt. These subgroups, termed Up and Down responders, employ different mechanisms to resist treatment, eliciting great promise that patient stratification and personalised medicine may lead to more effective treatment of this deadly disease. However, my work has also shown that the GBM tumour microenvironment (TME) is crucial for driving the responder subtype and this has raised two challenges that must be overcome to facilitate patient impact: 1) which cells (cancerous or TME) are expressing the pathological signatures and drivers of adaptive responses? and 2) which experimental model(s) can be used to further explore the drivers of adaption and targets for personalised medicine? The former is the focus of my fellowship renewal as it directly impacts the prioritisation of therapeutic targets for follow on study in, as well as feeding into the identification of, the correct experimental models. My overarching goal for my renewal is to identify the most clinically relevant personalised medicine targets for GBM via single cell analysis of responder subtypes. To achieve this, I have the following aims and objectives: Aim 1: Identify changes in GBM cellular landscape through treatment in the responder subtypes Obj.1.1. Redefine cell types in GBM by consolidation of RNA and chromatin accessibility data (Establishment and maintenance of cell type); Obj.1.2. Characterise and compare the cellular composition and landscape of primary and matched recurrent GBMs and how this differs between responder subtypes; Aim 2: Identify the subtype-specific drivers of plasticity within neoplastic GBM cells Obj.2.1. Characterise spatiotemporal changes in chromatin accessibility between primary and matched recurrent GBM and how this differs between responder subtypes; Obj.2.2. Characterise and compare regulatory networks driving changes in gene expression from primary to matched recurrence and how this differs between responder subtypes; Obj.2.3. Characterise the cell-cell interactions associated with tumour adaption in both responder subtypes. Aim 3: Create a list of prioritised targets for downstream work Obj.3.1. Integrate single cell and bulk datasets to identify subtype-specific therapeutic vulnerabilities and prioritise targets based on translational criteria
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