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In the past decade, cancer research has seen an increasing trend towards high-throughput techniques and translational approaches. The increasing availability of assays that utilise smaller quantities of source material and produce higher volumes of data output have resulted in the necessity for data storage solutions beyond those previously used. Multifactorial data, both large in sample size and heterogeneous in context, needs to be integrated in a standardised, cost-effective and secure manner. This requires technical solutions and administrative support not normally financially accounted for in small- to moderate-sized research groups. In this review, we highlight the Big Data challenges faced by translational research groups in the precision medicine era; an era in which the genomes of over 75 000 patients will be sequenced by the National Health Service over the next 3 years to advance healthcare. In particular, we have looked at three main themes of data management in relation to cancer research, namely (1) cancer ontology management, (2) IT infrastructures that have been developed to support data management and (3) the unique ethical challenges introduced by utilising Big Data in research.

We show for the first time to our knowledge that socioeconomic status affects early-phase cancer trial referrals. The least-deprived patients are almost twice as likely to be referred compared with the most deprived. This may be because more-deprived patients are less suitable for a trial-as a result of comorbidities, for example-or because of inequalities that could be addressed by patient or referrer education. Once reviewed at the unit, enrollment onto a trial is not affected by deprivation.