AI & future brain tumour treatment: Development of a panel of predictive immunotherapy biomarkers using MRI-based radiogenomic analysis of glioblastomas

Lead Supervisor
Dr Thomas Booth
Senior Lecturer in Neuroimaging
Biomedical Engineering & Imaging Sciences and King’s College Hospital
King’s College London
thomas.booth@kcl.ac.uk

Co-supervisor
Dr Marc Modat, Dr Igor Vivanco, Prof Keyoumars Ashkan, Prof Richard Houlston

Project Details

Research question 

We hypothesise that in patients with glioblastoma, a phenotype from imaging data can be correlated with the underlying genotype, allowing these radiogenomic biomarkers to guide more precise immunotherapy treatment options based on each patient’s molecular profiles.

Background 

Glioblastoma is the most common primary brain cancer. It is an aggressive tumour and significantly influences the survival of patients despite surgery/biopsy and chemoradiotherapy treatments. Standard-of-care glioblastoma treatment has not resulted in satisfactory improvements in patients’ survival outcomes. Therefore novel therapies, including immunotherapy, have been investigated in randomized settings. However, not all glioblastomas respond to these immunotherapies with significant variability in patients’ clinical responses.

Currently, most potential biomarkers for predicting immunotherapy response are genetic and molecular biomarkers, for which tissue sampling is needed. However, repeat tissue sampling has a high risk of procedure-​related morbidity in glioblastoma patients, unlike other systemic cancers. Besides, biopsy has a potential drawback of sampling bias. In contrast, radiogenomic models, which are non-invasive and can include the entire tissue volume, use machine learning to associate quantitative medical imaging data to the underlying genome.

Aims 

To develop and validate a panel of radiogenomic biomarkers that will serve, ultimately, as immunotherapy biomarkers during trials. If proven these will guide individualised treatment decisions and plausibly improve clinical outcome. 

Methods 

To achieve this, we will use imaging-based and genomic data during the study, facilitated by a broader supervisory team with expertise in bioinformatics, cancer genomes and pharmacology. Please contact the supervisors for more information.

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