Heralding an era in which diagnosis is pre-emptive, driven by risk profiling, and where treatments are personalised, maximising efficacy and minimising side effects

Data; the new hope for cancer control?


Cancer remains one of the great challenges of our time. It is the second leading cause of death globally and kills about nine million people every year. Te number of new cases is expected to rise by about 70% over the next two decades. A highlight from the NCRI conference held in Glasgow last autumn was a session which asked the question: are data-driv- en approaches the way forward in tackling these challenges? Te task for the session’s three

presenters (Andrew Morris, Richard Martin and Eva Morris) wasn’t insignificant: every single cancer patient can generate nearly one terabyte of biomedical data. Data come from a vast array of sources – including patient his- tory, hospital and primary care and diagnostic imaging. Recently, the gathering of detailed genetic information on both patients and their tumours has become far more commonplace – as has the use of techniques such as ma- chine learning with the capacity to mine the growing mountains of data at our disposal. Te hope is that somewhere within these huge datasets lie clues to better diagnosis and treatment of cancer. Te session explored the

cancer data landscape from three

fascinating perspectives. Richard Martin, from Bristol University, tackled the issue of genomic information – almost every day, it seems, scientists discover an as- sociation between ‘snips’ of genes (SNPs) and various cancer traits. Genome-wide-association studies (GWAS’s) are slowly uncovering these associations, but we face a problem – how do we make sense of all these data, and how can we tease out the individual contribu- tions of genes, the environment and lifestyle? Richard reported on the ‘MR-

Base’ database – it contains 45.6 billion SNP-trait associations from >5200 GWAS studies, on >4 million individuals. It supports ‘Mendelian randomisation’ stud- ies which can examine important associations between genes and cancer traits, and explore genetic influences on treatment effi- cacy and side effects – all within weeks rather than the years it takes for traditional epidemio- logical studies. It’s an important step forward in genetic data utilisation, with the potential to develop personalised cancer risk and treatment models with far greater efficiency.

Eva Morris, of Leeds University, demonstrated what can be achieved when datasets rel-


evant to single cancer (in this case bowel cancer) are brought together and interrogated. ‘Big data’ come in many shapes and forms; patients generate data when they visit GPs with symp- toms, undertake screening tests, have investigations and undergo treatment. Vital in big data is linkage – for example, linking bowel screening data with cancer registries and other databases can provide the intelligence needed to optimise many aspects of this national screening programme. Other examples include analy- ses of colonoscopy effectiveness and outcomes, and monitor- ing of treatment practices such as radiotherapy and adjuvant chemotherapy. Recognition of the importance of this kind of cancer intelligence is growing; we are awash with routinely-collected cancer data, and Eva illustrated how the potential of these data can be unleashed to improve patient outcomes.

So, how should the UK respond to the challenge of data-driven innovation? Tere’s recognition that much of the data we collect is under-utilised – it’s potential to improve patient outcomes can only be realised through wide- scale collective effort. But health data are collected by multiple

agencies which don’t necessar- ily link up. It varies in quality, it isn’t standardised, and there are complex issues of data owner- ship and patient confidentiality. To help us through this conun- drum, Andrew Morris described the establishment of Health Data Research UK (HDRUK), which he leads. Andrew reminded us of the prize in cancer control; a new era in which diagnosis is pre-emptive, driven by risk profiling, where treatments are personalised, maximising efficacy and minimising side effects. He described the ‘new social con- tract’ needed to underpin these changes; where data are shared efficiently and safely, respecting patient autonomy. For data to be useful clini-

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