Genome-wide methylation and expression profiling identifies promoter characteristics affecting demethylation-induced gene up-regulation in melanoma.
BACKGROUND
Abberant DNA methylation at CpG dinucleotides represents a common mechanism of transcriptional silencing in cancer. Since CpG methylation is a reversible event, tumor supressor genes that have undergone silencing through this mechanism represent promising targets for epigenetically active anti-cancer therapy. The cytosine analog 5-aza-2'-deoxycytidine (decitabine) induces genomic hypomethylation by inhibiting DNA methyltransferase, and is an example of an epigenetic agent that is thought to act by up-regulating silenced genes.


METHODS
It is unclear why decitabine causes some silenced loci to re-express, while others remain inactive. By applying data-mining techniques to large-scale datasets, we attempted to elucidate the qualities of promoter regions that define susceptibility to the drug's action. Our experimental data, derived from melanoma cell strains, consist of genome-wide gene expression data before and after treatment with decitabine, as well as genome-wide data on un-treated promoter methylation status, and validation of specific genes by bisulfite sequencing.


RESULTS
We show that the combination of promoter CpG content and methylation level informs the ability of decitabine treatment to up-regulate gene expression. Promoters with high methylation levels and intermediate CpG content appear most susceptible to up-regulation by decitabine, whereas few of those highly methylated promoters with high CpG content are up-regulated. For promoters with low methylation levels, those with high CpG content are more likely to be up-regulated, whereas those with low CpG content are underrepresented among up-regulated genes.


CONCLUSIONS
Clinically, elucidating the patterns of action of decitabine could aid in predicting the likelihood of up-regulating epigenetically silenced tumor suppressor genes and others from pathways involved with tumor biology. As a first step toward an eventual translational application, we build a classifier to predict gene up-regulation based on promoter methylation and CpG content, which achieves a performance of 0.77 AUC.
Rubinstein
Jill C
JC
Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT 06520, USA.
Tran
Nam
N
Ma
Shuangge
S
Halaban
Ruth
R
Krauthammer
Michael
M
D000368:Aged; D000964:Antimetabolites, Antineoplastic / Q000494:pharmacology*; D001374:Azacitidine / Q000031:analogs & derivatives* / Q000494:pharmacology; D045744:Cell Line, Tumor; D018899:CpG Islands / Q000187:drug effects; D019175:DNA Methylation / Q000187:drug effects*; D015254:DNA Modification Methylases / Q000037:antagonists & inhibitors; D000077209:Decitabine; D004791:Enzyme Inhibitors / Q000494:pharmacology*; D044127:Epigenesis, Genetic; D005260:Female; D020869:Gene Expression Profiling; D015894:Genome, Human; D006801:Humans; D008297:Male; D008545:Melanoma / Q000235:genetics*; D008875:Middle Aged; D011401:Promoter Regions, Genetic; D025521:Tumor Suppressor Proteins / Q000235:genetics / Q000378:metabolism; D015854:Up-Regulation
D000964:Antimetabolites, Antineoplastic; D004791:Enzyme Inhibitors; D025521:Tumor Suppressor Proteins; D000077209:Decitabine; D015254:DNA Modification Methylases; D001374:Azacitidine