Gene set-based module discovery in the breast cancer transcriptome.
BACKGROUND
Although microarray-based studies have revealed global view of gene expression in cancer cells, we still have little knowledge about regulatory mechanisms underlying the transcriptome. Several computational methods applied to yeast data have recently succeeded in identifying expression modules, which is defined as co-expressed gene sets under common regulatory mechanisms. However, such module discovery methods are not applied cancer transcriptome data.


RESULTS
In order to decode oncogenic regulatory programs in cancer cells, we developed a novel module discovery method termed EEM by extending a previously reported module discovery method, and applied it to breast cancer expression data. Starting from seed gene sets prepared based on cis-regulatory elements, ChIP-chip data, and gene locus information, EEM identified 10 principal expression modules in breast cancer based on their expression coherence. Moreover, EEM depicted their activity profiles, which predict regulatory programs in each subtypes of breast tumors. For example, our analysis revealed that the expression module regulated by the Polycomb repressive complex 2 (PRC2) is downregulated in triple negative breast cancers, suggesting similarity of transcriptional programs between stem cells and aggressive breast cancer cells. We also found that the activity of the PRC2 expression module is negatively correlated to the expression of EZH2, a component of PRC2 which belongs to the E2F expression module. E2F-driven EZH2 overexpression may be responsible for the repression of the PRC2 expression modules in triple negative tumors. Furthermore, our network analysis predicts regulatory circuits in breast cancer cells.


CONCLUSIONS
These results demonstrate that the gene set-based module discovery approach is a powerful tool to decode regulatory programs in cancer cells.
Niida
Atsushi
A
Laboratory of Molecular and Genetic Information, Institute of Molecular and Cellular Biosciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, Japan. aniida@ims.u-tokyo.ac.jp
Smith
Andrew D
AD
Imoto
Seiya
S
Aburatani
Hiroyuki
H
Zhang
Michael Q
MQ
Akiyama
Tetsu
T
D000465:Algorithms; D000818:Animals; D001943:Breast Neoplasms / Q000235:genetics* / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D050684:E2F Transcription Factors / Q000235:genetics / Q000378:metabolism; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D063146:Polycomb-Group Proteins; D012097:Repressor Proteins / Q000235:genetics / Q000378:metabolism
D050684:E2F Transcription Factors; D063146:Polycomb-Group Proteins; D012097:Repressor Proteins