Open AccessPICan: An integromics framework for dynamic cancer biomarker discovery
Author(s) -
McArt Darragh G.,
Blayney Jaine K.,
Boyle David P.,
Irwin Gareth W.,
Moran Michael,
Hutchinson Ryan A.,
Bankhead Peter,
Kieran Declan,
Wang Yinhai,
Dunne Philip D.,
Kennedy Richard D.,
Mullan Paul B.,
Harkin D. Paul,
Catherwood Mark A.,
James Jacqueline A.,
Salto-Tellez Manuel,
Hamilton Peter W.
Publication year - 2015
Publication title -
molecular oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.332
H-Index - 88
eISSN - 1878-0261
pISSN - 1574-7891
DOI - 10.1016/j.molonc.2015.02.002
Subject(s) - biomarker discovery , biomarker , computational biology , clinical significance , cancer , genomics , bioinformatics , medicine , biology , computer science , proteomics , genome , gene , genetics
Modern cancer research on prognostic and predictive biomarkers demands the integration of established and emerging high‐throughput technologies. However, these data are meaningless unless carefully integrated with patient clinical outcome and epidemiological information. Integrated datasets hold the key to discovering new biomarkers and therapeutic targets in cancer. We have developed a novel approach and set of methods for integrating and interrogating phenomic, genomic and clinical data sets to facilitate cancer biomarker discovery and patient stratification. Applied to a known paradigm, the biological and clinical relevance of TP53, PICan was able to recapitulate the known biomarker status and prognostic significance at a DNA, RNA and protein levels.