Many types of brain cancer remain incurable. For example, glioblastoma (GBM), the most common and most deadly form of adult brain cancer, results in more years of life loss than any other form of the disease, with a survival rate and treatment options that have not improved for over 20 years.
Why is this? We encountered a global pandemic and managed to vaccinate against it in a matter of months, so why does a cure for GBM remain so elusive? The answer lies in its origins. COVID is a disease caused by a virus; a foreign species that invades our bodies and sets the immune alarm bells, which we scientists have developed extensive knowledge of, ringing.
Cancer is ‘when human cells go bad’. Tumours don’t result from an external enemy, but an internal cellular transformation that is more insidious because it is hidden from our body’s defences, triggering uncontrolled cell division where it does not belong.
To fully understand, and combat, cancer we need to know the normal cells and how they function as part of the tissue in which they reside. Then we have to know the cancerous cells and cancerous tissue in the same level of detail so we can identify the changes, no matter how subtle, that cause tumours to be malignant and potentially fatal.
For many cancers, this research approach has yielded the information we need to drug tumour-specific characteristics and halt, or at least impede, the disease – but sadly not for most brain cancers.
The brain is the most complex organ of the human body and the one we know least about. It follows that full understanding of, and comparison with, the cancerous brain, in ways that can be therapeutically exploited, also lags behind.
However, new biomedical technologies are advancing just as quickly as the phones in our pockets. We are using these to shed new light on the human brain by characterising cell types and behaviours in unprecedented detail. This new understanding and the approaches that led to it can then be applied to brain tumours, but only if we have the required brain tumour tissue collected, stored and ready to be examined.
Brain tumours are thankfully rare, but when it comes to research there is strength in numbers: the more samples we can inspect, the more power we have to find common therapeutic vulnerabilities shared across cohorts of brain tumours. Therein lies the path to a cure.
Tissue banking is not ‘sexy’ science, it is not the idea that will cause a breakthrough, but it is the fundamental foundation of future Eureka moments. It is a means to an end; and that end is a cure. Even brain tumour research done in petri dishes and conical flasks must validate its results back in human tissue.
Tissue banking itself is not exciting and for that reason it is tough to find funding. However, the potential it can unlock when done correctly to preserve tissue, acquire as much associated clinical data as possible and be accessible to all researchers, is invaluable.
The Leeds Neuro Research Tissue Bank facilitates a huge range of brain cancer research in Leeds, and beyond, helping us advance towards finding a cure. As a brain cancer researcher and UKRI Future Leaders Fellow, I believe we must bank brain tumour tissue if we want to cure brain cancer.
Associate Professor Lucy Stead is a brain tumour researcher from The University of Leeds. She leads the Leeds Neuropathology Research Tissue Bank, which is generously funded by Yorkshire’s Brain Tumour Charity and OSCAR’s Paediatric Brain Tumour Charity.
For more information, or to apply for use of tissue in your own research, see https://braincancer.leeds.ac.uk/neurortb/ or contact [email protected]
Areas of expertise: Neuro-Oncology; Cancer genomics; Cancer transcriptomics; Computational biology; Next generation sequencing; Tumour heterogeneity.
Research: I am interested in investigating intratumour heterogeneity in GBM; specifically I wish to test whether treatment-resistant subclones emerge in recurrent tumours, and characterise them in clinically relevant ways in multiple patients. I am a trained computational biologist with expertise in next-generation sequencing data analysis.
