Big data in basic and translational cancer research

Historically, the primary focus of cancer research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen the rapid accumulation of large-scale cancer omics data catalysed by breakthroughs in high-throughput technologies. This fast data growth has given rise to an evolving concept of 'big data' in cancer, whose analysis demands large computational resources and can potentially bring novel insights into essential questions. Indeed, the combination of big data, bioinformatics and artificial intelligence has led to notable advances in our basic understanding of cancer biology and to translational advancements. Further advances will require a concerted effort among data scientists, clinicians, biologists and policymakers. Here, we review the current state of the art and future challenges for harnessing big data to advance cancer research and treatment. The increasing size of cancer datasets requires new ways of thinking for analysing and integrating these data. In this Review, Jiang et al. discuss considerations and strategies for wielding 'big data' ? large, information-rich datasets ? in basic research and for translational applications such as identifying biomarkers, informing clinical trials and developing new assays and treatments.

Automated summary

Historically, cancer research has focused on a few essential pathways and genes, but recent advances in high-throughput technologies have led to the rapid accumulation of large-scale cancer omics data. The analysis of this "big data" requires significant computational resources and has the potential to bring new insights to cancer research. The combination of big data, bioinformatics, and artificial intelligence has already made notable advances in our understanding of cancer biology and translational research. Future progress will require collaboration among data scientists, clinicians, biologists, and policymakers.