Cargando…

Array-Based DNA Methylation Profiling for Breast Cancer Subtype Discrimination

BACKGROUND: Abnormal DNA methylation is well established for breast cancer and contributes to its progression by silencing tumor suppressor genes. DNA methylation profiling platforms might provide an alternative approach to expression microarrays for accurate breast tumor subtyping. We sought to det...

Descripción completa

Detalles Bibliográficos
Autores principales: Van der Auwera, Ilse, Yu, Wayne, Suo, Liping, Van Neste, Leander, van Dam, Peter, Van Marck, Eric A., Pauwels, Patrick, Vermeulen, Peter B., Dirix, Luc Y., Van Laere, Steven J.
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2935385/
https://www.ncbi.nlm.nih.gov/pubmed/20830311
http://dx.doi.org/10.1371/journal.pone.0012616
_version_ 1782186390865313792
author Van der Auwera, Ilse
Yu, Wayne
Suo, Liping
Van Neste, Leander
van Dam, Peter
Van Marck, Eric A.
Pauwels, Patrick
Vermeulen, Peter B.
Dirix, Luc Y.
Van Laere, Steven J.
author_facet Van der Auwera, Ilse
Yu, Wayne
Suo, Liping
Van Neste, Leander
van Dam, Peter
Van Marck, Eric A.
Pauwels, Patrick
Vermeulen, Peter B.
Dirix, Luc Y.
Van Laere, Steven J.
author_sort Van der Auwera, Ilse
collection PubMed
description BACKGROUND: Abnormal DNA methylation is well established for breast cancer and contributes to its progression by silencing tumor suppressor genes. DNA methylation profiling platforms might provide an alternative approach to expression microarrays for accurate breast tumor subtyping. We sought to determine whether the distinction of the inflammatory breast cancer (IBC) phenotype from the non-IBC phenotype by transcriptomics could be sustained by methylomics. METHODOLOGY/PRINCIPAL FINDINGS: We performed methylation profiling on a cohort of IBC (N = 19) and non-IBC (N = 43) samples using the Illumina Infinium Methylation Assay. These results were correlated with gene expression profiles. Methylation values allowed separation of breast tumor samples into high and low methylation groups. This separation was significantly related to DNMT3B mRNA levels. The high methylation group was enriched for breast tumor samples from patients with distant metastasis and poor prognosis, as predicted by the 70-gene prognostic signature. Furthermore, this tumor group tended to be enriched for IBC samples (54% vs. 24%) and samples with a high genomic grade index (67% vs. 38%). A set of 16 CpG loci (14 genes) correctly classified 97% of samples into the low or high methylation group. Differentially methylated genes appeared to be mainly related to focal adhesion, cytokine-cytokine receptor interactions, Wnt signaling pathway, chemokine signaling pathways and metabolic processes. Comparison of IBC with non-IBC led to the identification of only four differentially methylated genes (TJP3, MOGAT2, NTSR2 and AGT). A significant correlation between methylation values and gene expression was shown for 4,981 of 6,605 (75%) genes. CONCLUSIONS/SIGNIFICANCE: A subset of clinical samples of breast cancer was characterized by high methylation levels, which coincided with increased DNMT3B expression. Furthermore, an association was observed with molecular signatures indicative of poor patient prognosis. The results of the current study also suggest that aberrant DNA methylation is not the main force driving the molecular biology of IBC.
format Text
id pubmed-2935385
institution National Center for Biotechnology Information
language English
publishDate 2010
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-29353852010-09-09 Array-Based DNA Methylation Profiling for Breast Cancer Subtype Discrimination Van der Auwera, Ilse Yu, Wayne Suo, Liping Van Neste, Leander van Dam, Peter Van Marck, Eric A. Pauwels, Patrick Vermeulen, Peter B. Dirix, Luc Y. Van Laere, Steven J. PLoS One Research Article BACKGROUND: Abnormal DNA methylation is well established for breast cancer and contributes to its progression by silencing tumor suppressor genes. DNA methylation profiling platforms might provide an alternative approach to expression microarrays for accurate breast tumor subtyping. We sought to determine whether the distinction of the inflammatory breast cancer (IBC) phenotype from the non-IBC phenotype by transcriptomics could be sustained by methylomics. METHODOLOGY/PRINCIPAL FINDINGS: We performed methylation profiling on a cohort of IBC (N = 19) and non-IBC (N = 43) samples using the Illumina Infinium Methylation Assay. These results were correlated with gene expression profiles. Methylation values allowed separation of breast tumor samples into high and low methylation groups. This separation was significantly related to DNMT3B mRNA levels. The high methylation group was enriched for breast tumor samples from patients with distant metastasis and poor prognosis, as predicted by the 70-gene prognostic signature. Furthermore, this tumor group tended to be enriched for IBC samples (54% vs. 24%) and samples with a high genomic grade index (67% vs. 38%). A set of 16 CpG loci (14 genes) correctly classified 97% of samples into the low or high methylation group. Differentially methylated genes appeared to be mainly related to focal adhesion, cytokine-cytokine receptor interactions, Wnt signaling pathway, chemokine signaling pathways and metabolic processes. Comparison of IBC with non-IBC led to the identification of only four differentially methylated genes (TJP3, MOGAT2, NTSR2 and AGT). A significant correlation between methylation values and gene expression was shown for 4,981 of 6,605 (75%) genes. CONCLUSIONS/SIGNIFICANCE: A subset of clinical samples of breast cancer was characterized by high methylation levels, which coincided with increased DNMT3B expression. Furthermore, an association was observed with molecular signatures indicative of poor patient prognosis. The results of the current study also suggest that aberrant DNA methylation is not the main force driving the molecular biology of IBC. Public Library of Science 2010-09-07 /pmc/articles/PMC2935385/ /pubmed/20830311 http://dx.doi.org/10.1371/journal.pone.0012616 Text en Van der Auwera et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Van der Auwera, Ilse
Yu, Wayne
Suo, Liping
Van Neste, Leander
van Dam, Peter
Van Marck, Eric A.
Pauwels, Patrick
Vermeulen, Peter B.
Dirix, Luc Y.
Van Laere, Steven J.
Array-Based DNA Methylation Profiling for Breast Cancer Subtype Discrimination
title Array-Based DNA Methylation Profiling for Breast Cancer Subtype Discrimination
title_full Array-Based DNA Methylation Profiling for Breast Cancer Subtype Discrimination
title_fullStr Array-Based DNA Methylation Profiling for Breast Cancer Subtype Discrimination
title_full_unstemmed Array-Based DNA Methylation Profiling for Breast Cancer Subtype Discrimination
title_short Array-Based DNA Methylation Profiling for Breast Cancer Subtype Discrimination
title_sort array-based dna methylation profiling for breast cancer subtype discrimination
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2935385/
https://www.ncbi.nlm.nih.gov/pubmed/20830311
http://dx.doi.org/10.1371/journal.pone.0012616
work_keys_str_mv AT vanderauwerailse arraybaseddnamethylationprofilingforbreastcancersubtypediscrimination
AT yuwayne arraybaseddnamethylationprofilingforbreastcancersubtypediscrimination
AT suoliping arraybaseddnamethylationprofilingforbreastcancersubtypediscrimination
AT vannesteleander arraybaseddnamethylationprofilingforbreastcancersubtypediscrimination
AT vandampeter arraybaseddnamethylationprofilingforbreastcancersubtypediscrimination
AT vanmarckerica arraybaseddnamethylationprofilingforbreastcancersubtypediscrimination
AT pauwelspatrick arraybaseddnamethylationprofilingforbreastcancersubtypediscrimination
AT vermeulenpeterb arraybaseddnamethylationprofilingforbreastcancersubtypediscrimination
AT dirixlucy arraybaseddnamethylationprofilingforbreastcancersubtypediscrimination
AT vanlaerestevenj arraybaseddnamethylationprofilingforbreastcancersubtypediscrimination