Prediction of epigenetically regulated genes in breast cancer cell lines.
BACKGROUND
Methylation of CpG islands within the DNA promoter regions is one mechanism that leads to aberrant gene expression in cancer. In particular, the abnormal methylation of CpG islands may silence associated genes. Therefore, using high-throughput microarrays to measure CpG island methylation will lead to better understanding of tumor pathobiology and progression, while revealing potentially new biomarkers. We have examined a recently developed high-throughput technology for measuring genome-wide methylation patterns called mTACL. Here, we propose a computational pipeline for integrating gene expression and CpG island methylation profiles to identify epigenetically regulated genes for a panel of 45 breast cancer cell lines, which is widely used in the Integrative Cancer Biology Program (ICBP). The pipeline (i) reduces the dimensionality of the methylation data, (ii) associates the reduced methylation data with gene expression data, and (iii) ranks methylation-expression associations according to their epigenetic regulation. Dimensionality reduction is performed in two steps: (i) methylation sites are grouped across the genome to identify regions of interest, and (ii) methylation profiles are clustered within each region. Associations between the clustered methylation and the gene expression data sets generate candidate matches within a fixed neighborhood around each gene. Finally, the methylation-expression associations are ranked through a logistic regression, and their significance is quantified through permutation analysis.


RESULTS
Our two-step dimensionality reduction compressed 90% of the original data, reducing 137,688 methylation sites to 14,505 clusters. Methylation-expression associations produced 18,312 correspondences, which were used to further analyze epigenetic regulation. Logistic regression was used to identify 58 genes from these correspondences that showed a statistically significant negative correlation between methylation profiles and gene expression in the panel of breast cancer cell lines. Subnetwork enrichment of these genes has identified 35 common regulators with 6 or more predicted markers. In addition to identifying epigenetically regulated genes, we show evidence of differentially expressed methylation patterns between the basal and luminal subtypes.


CONCLUSIONS
Our results indicate that the proposed computational protocol is a viable platform for identifying epigenetically regulated genes. Our protocol has generated a list of predictors including COL1A2, TOP2A, TFF1, and VAV3, genes whose key roles in epigenetic regulation is documented in the literature. Subnetwork enrichment of these predicted markers further suggests that epigenetic regulation of individual genes occurs in a coordinated fashion and through common regulators.
Loss
Leandro A
LA
Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. laloss@lbl.gov
Sadanandam
Anguraj
A
Durinck
Steffen
S
Nautiyal
Shivani
S
Flaucher
Diane
D
Carlton
Victoria E H
VE
Moorhead
Martin
M
Lu
Yontao
Y
Gray
Joe W
JW
Faham
Malek
M
Spellman
Paul
P
Parvin
Bahram
B
D000951:Antigens, Neoplasm / Q000235:genetics; D001943:Breast Neoplasms / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D045744:Cell Line, Tumor; D024042:Collagen Type I / Q000235:genetics; D000090602:Collagen Type I, alpha 1 Chain; D018899:CpG Islands; D019175:DNA Methylation; D004250:DNA Topoisomerases, Type II / Q000235:genetics; D004268:DNA-Binding Proteins / Q000235:genetics; D044127:Epigenesis, Genetic; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D016147:Genes, Tumor Suppressor; D055106:Genome-Wide Association Study; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D000075223:Poly-ADP-Ribose Binding Proteins; D011401:Promoter Regions, Genetic; D051567:Proto-Oncogene Proteins c-vav / Q000235:genetics; D000071163:Trefoil Factor-1; D025521:Tumor Suppressor Proteins / Q000235:genetics
D000951:Antigens, Neoplasm; D024042:Collagen Type I; D000090602:Collagen Type I, alpha 1 Chain; D004268:DNA-Binding Proteins; D000075223:Poly-ADP-Ribose Binding Proteins; D051567:Proto-Oncogene Proteins c-vav; C051838:TFF1 protein, human; D000071163:Trefoil Factor-1; D025521:Tumor Suppressor Proteins; C496621:VAV3 protein, human; D004250:DNA Topoisomerases, Type II; C000618811:TOP2A protein, human