Big Data-Led Cancer Research, Application, and Insights

Cancer Res. 2016 Nov 1;76(21):6167-6170. doi: 10.1158/0008-5472.CAN-16-0860. Epub 2016 Oct 20.


Insights distilled from integrating multiple big-data or "omic" datasets have revealed functional hierarchies of molecular networks driving tumorigenesis and modifiers of treatment response. Identifying these novel key regulatory and dysregulated elements is now informing personalized medicine. Crucially, although there are many advantages to this approach, there are several key considerations to address. Here, we examine how this big data-led approach is impacting many diverse areas of cancer research, through review of the key presentations given at the Irish Association for Cancer Research Meeting and importantly how the results may be applied to positively affect patient outcomes. Cancer Res; 76(21); 6167-70. ©2016 AACR.

MeSH terms

  • Biomedical Research*
  • Carcinogenesis
  • Epigenesis, Genetic
  • Humans
  • Microbiota
  • Neoplasms / therapy*
  • Prognosis
  • Signal Transduction