Dataset Information

DNA Methylation Patterns Can Estimate Nonequivalent Outcomes of Breast Cancer with the Same Receptor Subtypes.

ABSTRACT: Breast cancer has various molecular subtypes and displays high heterogeneity. Aberrant DNA methylation is involved in tumor origin, development and progression. Moreover, distinct DNA methylation patterns are associated with specific breast cancer subtypes. We explored DNA methylation patterns in association with gene expression to assess their impact on the prognosis of breast cancer based on Infinium 450K arrays (training set) from The Cancer Genome Atlas (TCGA). The DNA methylation patterns of 12 featured genes that had a high correlation with gene expression were identified through univariate and multivariable Cox proportional hazards models and used to define the methylation risk score (MRS). An improved ability to distinguish the power of the DNA methylation pattern from the 12 featured genes (p = 0.00103) was observed compared with the average methylation levels (p = 0.956) or gene expression (p = 0.909). Furthermore, MRS provided a good prognostic value for breast cancers even when the patients had the same receptor status. We found that ER-, PR- or Her2- samples with high-MRS had the worst 5-year survival rate and overall survival time. An independent test set including 28 patients with death as an outcome was used to test the validity of the MRS of the 12 featured genes; this analysis obtained a prognostic value equivalent to the training set. The predict power was validated through two independent datasets from the GEO database. The DNA methylation pattern is a powerful predictor of breast cancer survival, and can predict outcomes of the same breast cancer molecular subtypes.

SUBMITTER: Zhang M

PROVIDER: S-EPMC4638352 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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DNA Methylation Patterns Can Estimate Nonequivalent Outcomes of Breast Cancer with the Same Receptor Subtypes.

Zhang Min M Zhang Shaojun S Wen Yanhua Y Wang Yihan Y Wei Yanjun Y Liu Hongbo H Zhang Dongwei D Su Jianzhong J Wang Fang F Zhang Yan Y

PloS one 20151109 11

Breast cancer has various molecular subtypes and displays high heterogeneity. Aberrant DNA methylation is involved in tumor origin, development and progression. Moreover, distinct DNA methylation patterns are associated with specific breast cancer subtypes. We explored DNA methylation patterns in association with gene expression to assess their impact on the prognosis of breast cancer based on Infinium 450K arrays (training set) from The Cancer Genome Atlas (TCGA). The DNA methylation patterns o ...[more]

PMID: 26550991

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Project description:Introduction: Five different molecular subtypes of breast cancer have been identified through gene expression profiling. Each subtype has a characteristic expression pattern suggested to partly depend on cellular origin. We aimed to investigate whether the molecular subtypes also display distinct methylation profiles. Methods: We analysed methylation status of 807 cancer-related genes in 189 fresh frozen primary breast tumours and four normal breast tissue samples using an array-based methylation assay. Results: Unsupervised analysis revealed three groups of breast cancer with characteristic methylation patterns. The three groups were associated with the luminal A, luminal B and basal-like molecular subtypes of breast cancer, respectively, whereas cancers of the HER2-enriched and normal-like subtypes were distributed among the three groups. The methylation frequencies were significantly different between subtypes, with luminal B and basal-like tumours being most and least frequently methylated, respectively. Moreover, targets of the polycomb repressor complex in breast cancer and embryonic stem cells were more methylated in luminal B tumours than in other tumours. BRCA2-mutated tumours had a particularly high degree of methylation. Finally, by utilizing gene expression data, we observed that a large fraction of genes reported as having subtype-specific expression patterns might be regulated through methylation. Conclusions: We have found that breast cancers of the basal-like, luminal A and luminal B molecular subtypes harbour specific methylation profiles. Our results suggest that methylation may play an important role in the development of breast cancers. DNA methylation profiling of breast cancer samples and normal breast tissue samples. The Illumina GoldenGate Methylation Cancer Panel I was used to obtain DNA methylation profiles across approximately 1500 CpGs. Samples included 189 breast cancer samples and 4 normal breast tissue samples. Bisulphite converted DNA from the samples were hybridised to the Illumina GoldenGate Methylation Cancer Panel I

Project description:Aberrant DNA methylation is frequently observed in breast cancer. However, the relationship between methylation patterns and the heterogeneity of breast cancer has not been comprehensively characterized.Whole-genome DNA methylation analysis using Illumina Infinium HumanMethylation450 BeadChip arrays was performed on 188 human breast tumors. Unsupervised bootstrap consensus clustering was performed to identify DNA methylation epigenetic subgroups (epitypes). The Cancer Genome Atlas data, including methylation profiles of 669 human breast tumors, was used for validation. The identified epitypes were characterized by integration with publicly available genome-wide data, including gene expression levels, DNA copy numbers, whole-exome sequencing data, and chromatin states.We identified seven breast cancer epitypes. One epitype was distinctly associated with basal-like tumors and with BRCA1 mutations, one epitype contained a subset of ERBB2-amplified tumors characterized by multiple additional amplifications and the most complex genomes, and one epitype displayed a methylation profile similar to normal epithelial cells. Luminal tumors were stratified into the remaining four epitypes, with differences in promoter hypermethylation, global hypomethylation, proliferative rates, and genomic instability. Specific hyper- and hypomethylation across the basal-like epitype was rare. However, we observed that the candidate genomic instability drivers BRCA1 and HORMAD1 displayed aberrant methylation linked to gene expression levels in some basal-like tumors. Hypomethylation in luminal tumors was associated with DNA repeats and subtelomeric regions. We observed two dominant patterns of aberrant methylation in breast cancer. One pattern, constitutively methylated in both basal-like and luminal breast cancer, was linked to genes with promoters in a Polycomb-repressed state in normal epithelial cells and displayed no correlation with gene expression levels. The second pattern correlated with gene expression levels and was associated with methylation in luminal tumors and genes with active promoters in normal epithelial cells.Our results suggest that hypermethylation patterns across basal-like breast cancer may have limited influence on tumor progression and instead reflect the repressed chromatin state of the tissue of origin. On the contrary, hypermethylation patterns specific to luminal breast cancer influence gene expression, may contribute to tumor progression, and may present an actionable epigenetic alteration in a subset of luminal breast cancers.

Project description:We examined whether differences in tumor DNA methylation were associated with more aggressive hormone receptor-negative breast cancer in an ethnically diverse group of patients in the Breast Cancer Care in Chicago (BCCC) study and using data from The Cancer Genome Atlas (TCGA).DNA was extracted from formalin-fixed, paraffin-embedded samples on 75 patients (21 White, 31 African-American, and 23 Hispanic) (training dataset) enrolled in the BCCC. Hormone receptor status was defined as negative if tumors were negative for both estrogen and progesterone (ER/PR) receptors (N?=?22/75). DNA methylation was analyzed at 1505 CpG sites within 807 gene promoters using the Illumina GoldenGate assay. Differential DNA methylation as a predictor of hormone receptor status was tested while controlling for false discovery rate and assigned to the gene closest to the respective CpG site. Next, those genes that predicted ER/PR status were validated using TCGA data with respect to DNA methylation (validation dataset), and correlations between CpG methylation and gene expression were examined. In the training dataset, 5.7 % of promoter mean methylation values (46/807) were associated with receptor status at P?<?0.05; for 88 % of these (38/46), hypermethylation was associated with receptor-positive disease. Hypermethylation for FZD9, MME, BCAP31, HDAC9, PAX6, SCGB3A1, PDGFRA, IGFBP3, and PTGS2 genes most strongly predicted receptor-positive disease. Twenty-one of 24 predictor genes from the training dataset were confirmed in the validation dataset. The level of DNA methylation at 19 out 22 genes, for which gene expression data were available, was associated with gene activity.Higher levels of promoter methylation strongly correlate with hormone receptor positive status of breast tumors. For most of the genes identified in our training dataset as ER/PR receptor status predictors, DNA methylation correlated with stable gene expression level. The predictors performed well when evaluated on independent set of samples, with different racioethnic distribution, thus providing evidence that this set of DNA methylation biomarkers will likely generalize to prospective patient samples.

Dataset Information

DNA Methylation Patterns Can Estimate Nonequivalent Outcomes of Breast Cancer with the Same Receptor Subtypes.

Publications

DNA Methylation Patterns Can Estimate Nonequivalent Outcomes of Breast Cancer with the Same Receptor Subtypes.

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