Data Science

Images are routinely collected in the clinic (radiology, endoscopy and pathology) and laboratory (atoms, cells and tissues). Developing novel approaches to image acquisition, processing, reconstruction, analysis and interpretation are the subject of intense study in Oxford. Applying the right technique in the correct setting can be critical in predicting and understanding tumour behaviour, as well as detecting and treating cancer patients.

Drawing on skills in biology, computer science, information engineering and statistics, researchers in this field specialise in developing new tools and algorithms to interpret high-throughput-high-content genome-wide or single-cell experiments. This seeks to address how tumours evolve and react to environmental changes. As the costs of these investigations continue to fall with advancing technology, the volume of available data grows, driving the need for standardised and efficient analytical platforms. By introducing AI-based approaches, researchers are able to both guide clinical decision making and lead the discovery of fundamental molecular processes in cancer biology through the integration and statistical analysis of new and publicly available data.

Mechanistic and multiscale modelling allow us to simulate biological process and interactions (such as hypoxia, immune cell infiltration, DNA dynamics), as well as functions that vary within space and time (tumour evolution, cell signalling). In doing so, we can make predictions based on hypothetical mechanisms in the context of experimental observations, and novel hypotheses can be derived for further testing. In addition, novel methods for analysing biological data are being generated (such as topological data analysis), which can be tailored to the noisy and often incomplete datasets generated during laboratory analysis and clinical research.

Health economics is a branch of economics which addresses issues related to efficiency, effectiveness, value and behaviour in the production and consumption of health and healthcare. Ensuring that cancer interventions, such as new diagnostic tools, drugs or surgical approaches, are cost-effective and are likely to improve patient health outcomes is central to Oxford health economics research. This is undertaken by working on clinical trials and observational data with clinical and laboratory colleagues. Oxford researchers also carry out interviews and surveys with patients, clinical staff, policy makers and the general public to ascertain their views on cancer care and funding options. A particular area of interest and expertise for health economists in Oxford is the evaluation of novel genomic technologies in cancer. 

The majority of cancer patients are diagnosed in primary care, and the majority of their interactions are with general practitioners. Ensuring primary care is optimised from both a health-outcomes and patient-experience perspective is the objective of numerous researchers in this field. This is being done by integrating statisticians, digi-trials, infectious disease, clinical informatics, behavioural science and sociology.

Electronic health records collected as part of standard clinical care, national biobanks and targeted cohorts contain detailed information about a patient’s clinical history. Analysis of these on a large-scale enables us to identify the individuals with the highest risk of disease, or those likely to respond positively to specific treatments. This research requires a range of expertise such as the ability to ethically and securely collect, store and curate the data, link it with other datasets and analyse it using advanced statistical or machine learning methods. This needs to be combined with on-the-ground clinical knowledge, both in primary and secondary care, to direct the research questions and to evaluate how best to implement the findings for patient benefit.

Examples of the unique resources Oxford cancer researchers support include very large cohort datasets (e.g. The Million Women Study, Our Future Health), major biobanks (UK Biobank and China Kadoorie) and groups drawing together electronic health data (NCIMI, QResearch, Oxford-Royal College of General Practitioners Research & Surveillance Centre, CORECT-R, NIHR Health Informatics Collaborative, Clinical Trial Service Unit & Cancer Epidemiology Unit, the Turing Institute, Health Data Research UK and NHS Digital.)