An empirical Bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer.
BACKGROUND
The nuclear transcription factor estrogen receptor alpha (ER-alpha) is the target of several antiestrogen therapeutic agents for breast cancer. However, many ER-alpha positive patients do not respond to these treatments from the beginning, or stop responding after being treated for a period of time. Because of the association of gene transcription alteration and drug resistance and the emerging evidence on the role of DNA methylation on transcription regulation, understanding of these relationships can facilitate development of approaches to re-sensitize breast cancer cells to treatment by restoring DNA methylation patterns.


METHODS
We constructed a hierarchical empirical Bayes model to investigate the simultaneous change of gene expression and promoter DNA methylation profiles among wild type (WT) and OHT/ICI resistant MCF7 breast cancer cell lines.


RESULTS
We found that compared with the WT cell lines, almost all of the genes in OHT or ICI resistant cell lines either do not show methylation change or hypomethylated. Moreover, the correlations between gene expression and methylation are quite heterogeneous across genes, suggesting the involvement of other factors in regulating transcription. Analysis of our results in combination with H3K4me2 data on OHT resistant cell lines suggests a clear interplay between DNA methylation and H3K4me2 in the regulation of gene expression. For hypomethylated genes with alteration of gene expression, most (~80%) are up-regulated, consistent with current view on the relationship between promoter methylation and gene expression.


CONCLUSIONS
We developed an empirical Bayes model to study the association between DNA methylation in the promoter region and gene expression. Our approach generates both global (across all genes) and local (individual gene) views of the interplay. It provides important insight on future effort to develop therapeutic agent to re-sensitize breast cancer cells to treatment.
Department of Medicine/Division of Biostatistics, Indiana University, Indianapolis, IN, USA.
Jaesik
J
Jeong
Lang
L
Li
Yunlong
Y
Liu
Kenneth P
KP
Nephew
Tim Hui-Ming
TH
Huang
Changyu
C
Shen
D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D045744:Cell Line, Tumor; D019175:DNA Methylation; D019008:Drug Resistance, Neoplasm / Q000235:genetics*; D020847:Estrogen Receptor Modulators / Q000494:pharmacology* / Q000627:therapeutic use; D005189:False Positive Reactions; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic / Q000187:drug effects; D052138:Genes, Neoplasm / Q000235:genetics; D006657:Histones / Q000378:metabolism; D006801:Humans; D015233:Models, Statistical
D020847:Estrogen Receptor Modulators; D006657:Histones