Publication:
PLoS ONE 4(2): e4523. doi:10.1371/journal.pone.0004523
February 2009
Human disease studies using DNA microarrays in both clinical/observational and experimental/controlled studies are having increasing impact on our understanding of the complexity of human diseases. A fundamental concept is the use of gene expression as a "common currency" that links the results of in vitro controlled experiments to in vivo observational human studies. Many studies -- in cancer and other diseases -- have shown promise in using in vitro cell manipulations to improve understanding of in vivo biology, but experiments often simply fail to reflect the enormous complexity and phenotypic variation seen in human diseases. We address this with a framework and methods to dissect, enhance and extend the in vivo utility of in vitro derived gene expression signatures. From an experimentally defined gene expression signature we use statistical factor analysis to generate multiple quantitative factors -- or sub-signatures -- in human cancer gene expression data. These sub-signatures retain their relationship to the original, one-dimensional in vitro signature but represent greater complexity of in vivo biology related to the initial signature gene set. In a breast cancer analysis, we shown that sub-signatures can reflect findamentally different biological processes linked to molecular and clinical features of human cancers, and that in combination they can improve predictive of clinical outcomes.