Big Data-Led Cancer Research, Application, and Insights.
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.
Discipline of Surgery, School of Medicine, The Lambe Institute for Translational Research, National University of Ireland Galway, Galway, Ireland. james.brown@nuigalway.ie.
James A L
JA
Brown
Physiology & Medical Physics Department, Royal College of Surgeons in Ireland, Dublin, Ireland.
Triona
T
Ni Chonghaile
Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom.
Kyle B
KB
Matchett
Trinity Translational Medicine Institute, Department of Surgery, Trinity College Dublin, Dublin, Ireland.
Niamh
N
Lynam-Lennon
Graduate Entry Medical School, University of Limerick, Limerick, Ireland.
Patrick A
PA
Kiely
D035843:Biomedical Research; D063646:Carcinogenesis; D044127:Epigenesis, Genetic; D006801:Humans; D064307:Microbiota; D009369:Neoplasms / Q000628:therapy*; D011379:Prognosis; D015398:Signal Transduction