Takayuki Iwamoto    Department   Kawasaki Medical School  Kawasaki Medical School, Department of Breast and Thyroid Surgery,    Position   Assistant Professor
Article types	原著
Language	English
Peer review	Peer reviewed
Title	Uncovering the molecular secrets of inflammatory breast cancer biology: an integrated analysis of three distinct affymetrix gene expression datasets.
Journal	Formal name：Clinical cancer research : an official journal of the American Association for Cancer Research Abbreviation：Clin Cancer Res ISSN code：15573265/10780432
Domestic / Foregin	Foregin
Volume, Issue, Page	19(17),pp.4685-96
Author and coauthor	Steven J Van Laere, Naoto T Ueno, Pascal Finetti, Peter Vermeulen, Anthony Lucci, Fredika M Robertson, Melike Marsan, Takayuki Iwamoto, Savitri Krishnamurthy, Hiroko Masuda, Peter van Dam, Wendy A Woodward, Patrice Viens, Massimo Cristofanilli, Daniel Birnbaum, Luc Dirix, James M Reuben, François Bertucci
Publication date	2013/09
Summary	BACKGROUND:Inflammatory breast cancer (IBC) is a poorly characterized form of breast cancer. So far, the results of expression profiling in IBC are inconclusive due to various reasons including limited sample size. Here, we present the integration of three Affymetrix expression datasets collected through the World IBC Consortium allowing us to interrogate the molecular profile of IBC using the largest series of IBC samples ever reported.EXPERIMENTAL DESIGN:Affymetrix profiles (HGU133-series) from 137 patients with IBC and 252 patients with non-IBC (nIBC) were analyzed using unsupervised and supervised techniques. Samples were classified according to the molecular subtypes using the PAM50-algorithm. Regression models were used to delineate IBC-specific and molecular subtype-independent changes in gene expression, pathway, and transcription factor activation.RESULTS:Four robust IBC-sample clusters were identified, associated with the different molecular subtypes (P<0.001), all of which were identified in IBC with a similar prevalence as in nIBC, except for the luminal A subtype (19% vs. 42%; P<0.001) and the HER2-enriched subtype (22% vs. 9%; P<0.001). Supervised analysis identified and validated an IBC-specific, molecular subtype-independent 79-gene signature, which held independent prognostic value in a series of 871 nIBCs. Functional analysis revealed attenuated TGF-β signaling in IBC.CONCLUSION:We show that IBC is transcriptionally heterogeneous and that all molecular subtypes described in nIBC are detectable in IBC, albeit with a different frequency. The molecular profile of IBC, bearing molecular traits of aggressive breast tumor biology, shows attenuation of TGF-β signaling, potentially explaining the metastatic potential of IBC tumor cells in an unexpected manner.
DOI	10.1158/1078-0432.CCR-12-2549
PMID	23396049