Dataset Information

Array-based DNA methylation profiling for breast cancer subtype discrimination.

ABSTRACT:

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.

SUBMITTER: Van der Auwera I

PROVIDER: S-EPMC2935385 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature

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Publications

Array-based DNA methylation profiling for breast cancer subtype discrimination.

Van der Auwera Ilse I Yu Wayne W Suo Liping L Van Neste Leander L van Dam Peter P Van Marck Eric A EA Pauwels Patrick P Vermeulen Peter B PB Dirix Luc Y LY Van Laere Steven J SJ

PloS one 20100907 9

<h4>Background</h4>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.<h4>Methodology/principal findin ...[more]

PMID: 20830311

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Project description:Undifferentiated solid tumors with small blue round cell histology and expression of CD99 mostly resemble Ewing sarcoma. However, they also may include other tumors such as mesenchymal chondrosarcoma, synovial sarcoma, or small cell osteosarcoma. Definitive classification usually requires detection of entity-specific mutations. While this approach identifies the majority of Ewing sarcomas, a subset of lesions remains unclassified and, therefore, has been termed "Ewing-like sarcomas" or small blue round cell tumors not otherwise specified. We developed an approach for further characterization of small blue round cell tumors not otherwise specified using an array-based DNA-methylation profiling approach. Data were analyzed by unsupervised clustering and t-distributed stochastic neighbor embedding analysis and compared with a reference methylation data set of 460 well-characterized prototypical sarcomas encompassing 18 subtypes. Verification was performed by additional FISH analyses, RNA sequencing from formalin-fixed paraffin-embedded material or immunohistochemical marker analyses. In a cohort of more than 1,000 tumors assumed to represent Ewing sarcomas, 30 failed to exhibit the typical EWS translocation. These tumors were subjected to methylation profiling and could be assigned to Ewing sarcoma in 14 (47%), to small blue round cell tumors with CIC alteration in 6 (20%), to small blue round cell tumors with BCOR alteration in 4 (13%), to synovial sarcoma and to malignant rhabdoid tumor in 2 cases each. One single case each was allotted to mesenchymal chondrosarcoma and adamantinoma. 12/14 tumors classified as Ewing sarcoma could be verified by demonstrating either a canonical EWS translocation evading initial testing, by identifying rare breakpoints or fusion partners. The methylation-based assignment of the remaining small blue round cell tumors not otherwise specified also could be verified by entity-specific molecular alterations in 13/16 cases. In conclusion, array-based DNA-methylation analysis of undifferentiated tumors with small blue round cell histology is a powerful tool for precisely classifying this diagnostically challenging tumor group.

Dataset Information

Array-based DNA methylation profiling for breast cancer subtype discrimination.

Background

Methodology/principal findings

Conclusions/significance

Publications

Array-based DNA methylation profiling for breast cancer subtype discrimination.

Similar Datasets

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