Reference : Elucidating the altered transcriptional programs in breast cancer using independent c...
Scientific journals : Article
Life sciences : Biochemistry, biophysics & molecular biology
Elucidating the altered transcriptional programs in breast cancer using independent component analysis
Teschendorff, A. E. [> > > >]
Journee, Michel [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Absil, P.-A. [> > > >]
Sepulchre, Rodolphe mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Caldas, C. [> > > >]
PLoS Computational Biology
Public Library Science
Yes (verified by ORBi)
San Francisco
[en] The quantity of mRNA transcripts in a cell is determined by a complex interplay of cooperative and counteracting biological processes. Independent Component Analysis ( ICA) is one of a few number of unsupervised algorithms that have been applied to microarray gene expression data in an attempt to understand phenotype differences in terms of changes in the activation/ inhibition patterns of biological pathways. While the ICA model has been shown to outperform other linear representations of the data such as Principal Components Analysis ( PCA), a validation using explicit pathway and regulatory element information has not yet been performed. We apply a range of popular ICA algorithms to six of the largest microarray cancer datasets and use pathway- knowledge and regulatory- element databases for validation. We show that ICA outperforms PCA and clustering- based methods in that ICA components map closer to known cancer- related pathways, regulatory modules, and cancer phenotypes. Furthermore, we identify cancer signalling and oncogenic pathways and regulatory modules that play a prominent role in breast cancer and relate the differential activation patterns of these to breast cancer phenotypes. Importantly, we find novel associations linking immune response and epithelial - mesenchymal transition pathways with estrogen receptor status and histological grade, respectively. In addition, we find associations linking the activity levels of biological pathways and transcription factors ( NF1 and NFAT) with clinical outcome in breast cancer. ICA provides a framework for a more biologically relevant interpretation of genomewide transcriptomic data. Adopting ICA as the analysis tool of choice will help understand the phenotype - pathway relationship and thus help elucidate the molecular taxonomy of heterogeneous cancers and of other complex genetic diseases.

File(s) associated to this reference

Fulltext file(s):

Open access
journal.pcbi.0030161.pdfNo commentaryPublisher postprint918.8 kBView/Open

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.