Personalising the treatment of brain tumours through advanced graphical modelling

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Cancer of the brain - the 9th commonest form in the UK - is a major cause of death and disability that remains stubbornly resistant to treatment. Despite intense research, survival has remained essentially unchanged over the past thirty years. Only one in twenty patients with glioblastoma - a common, fast-growing brain tumour - can expect to be alive five years after diagnosis, and often with severely impaired quality of life. Other kinds of cancer have seen striking improvements in treatment outcomes over recent decades: why is the brain different? One possible answer is the unusual complexity of brain tumours. The disease mechanisms that cause the uncontrolled, disorganised cell division - the hallmark of all cancer - appear to be both many and diverse, and vary greatly from one patient to another. Treatment here must be closely personalised if it is to be effective, yet the diversity makes determining what treatment works very difficult. We are caught in a Catch 22 situation: to determine what is best for each patient we need to be guided by the study of many patients together, yet individual outcomes are so diverse that the average of a large group tells us little about each individual. How do we break out of this? As with all human diversity, to understand this biological diversity we must first describe it in sufficient detail to render each individual distinct and recognizable. Such a rich description or phenotype can be thought of as a characteristic "fingerprint" pattern unique to each individual yet enabling comparison with others. Machines able to make sense of complex patterns - such as the artificial intelligence systems now revolutionising the world - can then be deployed to the task of identifying the right treatment for the right patient, helping deliver care that is both personalised and founded on robust evidence. Crucially, there is already a wealth of detailed information collected during routine care across all NHS hospitals dealing with brain cancer - brain scans, tissue examinations, genetic analyses - from which such patterns may be extracted, allowing us to adopt this approach without disturbing established pathways of care. But no framework to deliver it across the NHS currently exists: our task is to create its foundations, establish its feasibility, and pilot its application. Our task has two parts. First, we must create complex computer algorithms that make sense of characteristic tumour patterns extracted from brain scans, tissue samples, and genetics, placing them into meaningful relation to each other so that their connection with the patient's disease course and treatment can be established. Second, we must assess the accuracy of the algorithms with copious data from many, diverse patients, from both the past and the future, to validate findings and ensure generalisability across both different patient groups and hospital sites. At the heart of our proposed approach lies the idea of making sense of biological patterns as networks. By way of analogy, the path of a cell from normal to cancerous may be seen as a journey through the London Underground, where the final destination of malignancy is reached via a set of genetic stops defining a characteristic path. Capturing the set of all possible paths - a map of the "tumour underground" - then gives us a means of understanding what is going on, and how the disease process varies from one patient to another. Success will provide a framework that will enable better individual prediction of patient outcomes, more accurate, personalised prescription of treatments, and a powerful means of illuminating disease mechanisms on which future treatment innovation depends.