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Title: Prediction of epigenetically regulated genes in breast cancer cell lines

Abstract

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 profles 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 profles are clustered within each region. Associations between the clustered methylation and the gene expression data sets generate candidate matches within a fxed neighborhood around each gene. Finally, the methylation-expression associations are ranked through a logisticmore » regression, and their significance is quantified through permutation analysis. 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 signifcant negative correlation between methylation profles and gene expression in the panel of breast cancer cell lines. Subnetwork enrichment of these genes has identifed 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. 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.« less

Authors:
; ; ; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
Life Sciences Division
OSTI Identifier:
986315
Report Number(s):
LBNL-3814E
Journal ID: 1471-2105; TRN: US201017%%199
DOE Contract Number:  
DE-AC02-05CH11231; P50 CA 58207, U54 CA 112970
Resource Type:
Journal Article
Resource Relation:
Journal Name: Bioinformatics; Journal Volume: 11; Journal Issue: 1
Country of Publication:
United States
Language:
English
Subject:
59; BIOLOGY; BIOLOGICAL MARKERS; DNA; GENES; MAMMARY GLANDS; METHYLATION; NEOPLASMS; PROMOTERS

Citation Formats

Loss, Leandro A, Sadanandam, Anguraj, Durinck, Steffen, Nautiyal, Shivani, Flaucher, Diane, Carlton, Victoria EH, Moorhead, Martin, Lu, Yontao, Gray, Joe W, Faham, Malek, Spellman, Paul, and Parvin, Bahram. Prediction of epigenetically regulated genes in breast cancer cell lines. United States: N. p., 2010. Web. doi:10.1186/1471-2105-11-305.
Loss, Leandro A, Sadanandam, Anguraj, Durinck, Steffen, Nautiyal, Shivani, Flaucher, Diane, Carlton, Victoria EH, Moorhead, Martin, Lu, Yontao, Gray, Joe W, Faham, Malek, Spellman, Paul, & Parvin, Bahram. Prediction of epigenetically regulated genes in breast cancer cell lines. United States. doi:10.1186/1471-2105-11-305.
Loss, Leandro A, Sadanandam, Anguraj, Durinck, Steffen, Nautiyal, Shivani, Flaucher, Diane, Carlton, Victoria EH, Moorhead, Martin, Lu, Yontao, Gray, Joe W, Faham, Malek, Spellman, Paul, and Parvin, Bahram. Tue . "Prediction of epigenetically regulated genes in breast cancer cell lines". United States. doi:10.1186/1471-2105-11-305. https://www.osti.gov/servlets/purl/986315.
@article{osti_986315,
title = {Prediction of epigenetically regulated genes in breast cancer cell lines},
author = {Loss, Leandro A and Sadanandam, Anguraj and Durinck, Steffen and Nautiyal, Shivani and Flaucher, Diane and Carlton, Victoria EH and Moorhead, Martin and Lu, Yontao and Gray, Joe W and Faham, Malek and Spellman, Paul and Parvin, Bahram},
abstractNote = {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 profles 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 profles are clustered within each region. Associations between the clustered methylation and the gene expression data sets generate candidate matches within a fxed neighborhood around each gene. Finally, the methylation-expression associations are ranked through a logistic regression, and their significance is quantified through permutation analysis. 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 signifcant negative correlation between methylation profles and gene expression in the panel of breast cancer cell lines. Subnetwork enrichment of these genes has identifed 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. 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.},
doi = {10.1186/1471-2105-11-305},
journal = {Bioinformatics},
number = 1,
volume = 11,
place = {United States},
year = {Tue May 04 00:00:00 EDT 2010},
month = {Tue May 04 00:00:00 EDT 2010}
}