Title: Quantitative assessment of DNA hypermethylation in the inflammatory and non-inflammatory breast cancer phenotypes
Authors: Van der Auwera, Ilse ×
Bovie, Catherine
Svensson, Cecilia
Limame, Ridha
Trinh, Xuan B
van Dam, Peter
Van Laere, Steven
Van Marck, Eric
Vermeulen, Peter B
Dirix, Luc Y #
Issue Date: Dec-2009
Publisher: Landes Bioscience
Series Title: Cancer Biology & Therapy vol:8 issue:23 pages:2252-2259
Abstract: In this study, a comparative quantitative methylation profiling of inflammatory breast cancer (IBC) and non-IBC was set up for the identification of tumor-specific methylation patterns. Methylation ratios of six genes (DAPK, TWIST, HIN-1, RASSF1A, RARbeta2 and APC) were measured in benign breast tissues (n = 9) and in tumor samples from non-IBC (n = 81) and IBC (n = 19) patients using quantitative methylation-specific PCR. Median methylation ratios observed in breast cancer (n = 100) were significantly higher than those observed in benign breast tissues for five of six genes (TWIST, HIN-1, RASSF1A, RARbeta2 and APC). Only one of the individual genes studied, RARbeta2, showed differential methylation ratios in IBC and non-IBC (p = 0.016). Using the maximal methylation ratio observed in benign breast tissue as a threshold, the methylation frequency of two genes, RARbeta2 and APC, was significantly increased in IBC (n = 19) when compared to non-IBC (n = 81): 53 vs. 23% for RARbeta2 (p = 0.012) and 84 vs. 54% for APC (p = 0.017). Using hierarchical clustering, methylation patterns could not classify breast cancers according to their phenotype. The finding of differential frequencies of methylation in IBC and non-IBC for two out of six genes suggests that gene-specific patterns of methylation could provide a basis for molecular classification of IBC. Testing for additional genes could help to define the IBC phenotype based on patterns of aberrant gene promoter methylation.
ISSN: 1538-4047
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Non-KU Leuven Association publications
× corresponding author
# (joint) last author

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