Association of Breast Cancer DNA Methylation Profiles with Hormone Receptor Status and Response to Tamoxifen

Widschwendter, Martin; Siegmund, Kimberly D.; Müller, Hannes M.; Fiegl, Heidi; Marth, Christian; Müller‐Holzner, Elisabeth; Jones, Peter A.; Laird, Peter W.

doi:10.1158/0008-5472.can-03-3852

Cited by 300 publications

(300 citation statements)

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“…Previous studies have reported the association of DNA methylation patterns with molecular subtypes of breast cancer through hormone receptor and HER2/neu status. [14][15][16][17]35 In an early study by Sunami et al, 14 double-negative (ER-negative, HER2/neunegative) breast cancers, corresponding to the basal-like subtype, were reported to have lower frequencies of RASSF1A, GSTP1, and APC methylation, and this was confirmed in the present study. Suijkerbuijk et al 36 examined the methylation of 11 genes involved in breast carcinogenesis (RARB, RASSF1, TWIST, CCND2, ESR1, SCGB3A1, BRCA1, BRCA2, CDKN2A, APC, CDH1) by quantitative multiplex methylation-specific PCR in BRAC1-associated and sporadic breast cancers, and showed that the median values for RASSF1, TWIST, SCGB3A1, APC, and the cumulative methylation index for the 11 genes, were significantly lower in BRAC1-associated hereditary breast cancers than in sporadic ones.…”

Section: Discussionsupporting

confidence: 85%

“…7 There are reports that promoter CpG island hypermethylation is associated with various histopathologic characteristics of breast cancers, including histologic type, 10,11 tumor grade, 12,13 hormone receptor, and HER2/neu status. [14][15][16][17] Array-based comprehensive DNA methylation profiling has shown that breast cancer molecular subtypes have their own methylation profiles. [18][19][20][21] Moreover, these different methylation profiles were evident throughout the CpG islands of the genome, not limited to functional genes.…”

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confidence: 99%

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Distinct patterns of promoter CpG island methylation of breast cancer subtypes are associated with stem cell phenotypes

et al. 2012

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Although DNA methylation profiles in breast cancer have been connected to breast cancer molecular subtype, there have been no studies of the association of DNA methylation with stem cell phenotype. This study was designed to evaluate the promoter CpG island methylation of 15 genes in relation to breast cancer subtype, and to investigate whether the patterns of CpG island methylation in each subtype are associated with their cancer stem cell phenotype represented by CD44 þ /CD24À and ALDH1 expression. We performed MethyLight analysis of the methylation status of 15 promoter CpG island loci involved in breast cancer progression (APC, DLEC1, GRIN2B, GSTP1, HOXA1, HOXA10, IGF2, MT1G, RARB, RASSF1A, RUNX3, SCGB3A1, SFRP1, SFRP4, and TMEFF2) and determined cancer stem cell phenotype by CD44/CD24 and ALDH1 immunohistochemistry in 36 luminal A, 33 luminal B, 30 luminal-HER2, 40 HER2 enriched, and 40 basal-like subtypes of breast cancer. The number of CpG island loci methylated differed significantly between subtypes, and was highest in the luminal-HER2 subtype and lowest in the basal-like subtype. Methylation frequencies and levels in 12 of the 15 genes differed significantly between subtypes, and the basal-like subtype had significantly lower methylation frequencies and levels in nine of the genes than the other subtypes. CD44 þ /CD24À and ALDH1 þ putative stem cell populations were most enriched in the basal-like subtype. Methylation of promoter CpG islands was significantly lower in CD44 þ /CD24-cell ( þ ) tumors than in CD44 þ /CD24-cell (À) tumors, even within the basallike subtype. ALDH1 ( þ ) tumors were also less methylated than ALDH1 (À) tumors. Our findings showed that promoter CpG island methylation was different in relation to breast cancer subtype and stem cell phenotype of tumor, suggesting that breast cancers have distinct patterns of CpG island methylation according to molecular subtypes and these are associated with different stem cell phenotypes of the tumor.

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Section: Discussionsupporting

confidence: 85%

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confidence: 99%

Distinct patterns of promoter CpG island methylation of breast cancer subtypes are associated with stem cell phenotypes

et al. 2012

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“…DIRAS3 is often imprinted and LOH in breast and ovarian tumors most often affect the non-imprinted allele [30]. Furthermore, methylation of the DIRAS3 promoter has been demonstrated to predict poor survival in breast cancer [31]. This illustrates that different mechanisms, in this case allelic loss and methylation, can deactivate the same gene and support the viability of our method to detect these genes.…”

Section: Candidate Genesmentioning

confidence: 53%

Gene expression meta-analysis identifies chromosomal regions and candidate genes involved in breast cancer metastasis

Thomassen

Tan

Kruse

2008

Breast Cancer Res Treat

118

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. Abstract Breast cancer cells exhibit complex karyotypic alterations causing deregulation of numerous genes. Some of these genes are probably causal for cancer formation and local growth whereas others are causal for the various steps of metastasis. In a fraction of tumors deregulation of the same genes might be caused by epigenetic modulations, point mutations or the influence of other genes. We have investigated the relation of gene expression and chromosomal position, using eight datasets including more than 1200 breast tumors, to identify chromosomal regions and candidate genes possibly causal for breast cancer metastasis. By use of ''Gene Set Enrichment Analysis'' we have ranked chromosomal regions according to their relation to metastasis. Overrepresentation analysis identified regions with increased expression for chromosome 1q41-42, 8q24, 12q14, 16q22, 16q24, 17q12-21.2, 17q21-23, 17q25, 20q11, and 20q13 among metastasizing tumors and reduced gene expression at 1p31-21, 8p22-21, and 14q24. By analysis of genes with extremely imbalanced expression in these regions we identified DIRAS3 at 1p31, PSD3, LPL, EPHX2 at 8p21-22, and FOS at 14q24 as candidate metastasis suppressor genes. Potential metastasis promoting genes includes RECQL4 at 8q24, PRMT7 at 16q22, GINS2 at 16q24, and AURKA at 20q13.

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“…13 All primers and probes have been published elsewhere. 21 The differences in amounts of input genomic DNA were normalized by the collagen type II, alpha 1 gene (COL2A1). The percentage of methylated reference was calculated as follows: the methylated MGMT/COL2A1 ratio for each sample was divided by the same ratio obtained for a SssItreated genomic DNA used as standard, and values were multiplied by 100.…”

Section: Methylightmentioning

confidence: 99%

Comparative assessment of 5 methods (methylation‐specific polymerase chain reaction, methylight, pyrosequencing, methylation‐sensitive high‐resolution melting, and immunohistochemistry) to analyze O6‐methylguanine‐DNA‐methyltranferase in a series of 100 glioblastoma patients

Quillien

Lavenu

Karayan‐Tapon

et al. 2012

Cancer

187

165

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BACKGROUND: There is a strong need to determine the best technique for O 6 -methylguanine-DNA-methyltranferase (MGMT) analysis, because MGMT status is currently used in clinical trials and occasionally in routine clinical practice for glioblastoma patients. METHODS:The authors compared analytical performances and predictive values of 5 techniques in a series of 100 glioblastoma patients who received standard of care treatment (Stupp protocol). RESULTS: MGMT promoter was considered methylated in 33%, 33%, 42%, and 60% of patients by methylation-sensitive high-resolution melting, MethyLight, pyrosequencing (with an optimal risk cutoff at 8% for the average percentage of the 5 CpGs tested), and methylation-specific polymerase chain reaction (MS-PCR), respectively. Fifty-nine percent of the samples had <23% (the optimal risk cutoff) of MGMT-positive tumor cells. The best predictive values for overall survival (OS), after adjustment for age and performance status, were obtained by pyrosequencing (hazard ratio [HR], 0.32; P < .0001), MS-PCR (HR, 0.37; P < .0001), and immunohistochemistry (HR, 0.43; P ¼ .0005) as compared with methylation-sensitive high-resolution melting (HR, 0.52 P ¼ .02) and MethyLight (HR, 0.6; P ¼ .05). For progression-free survival (PFS), the best predictive values were obtained with pyrosequencing (HR, 0.35; P < .0001), methylation-sensitive high-resolution melting (HR, 0.46; P ¼ .002), and MS-PCR (HR, 0.49; P ¼ .002). Combining pyrosequencing and immunohistochemistry slightly improved predictive power for OS, but not for PFS. Poor reproducibility and interobserver variability were, however, observed for immunohistochemistry. CONCLUSIONS: Good prediction of survival in addition to high reproducibility and sensitivity made pyrosequencing the best among the 5 techniques tested in this study. Cancer 2012;118:4201-11.

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Association of Breast Cancer DNA Methylation Profiles with Hormone Receptor Status and Response to Tamoxifen

Cited by 300 publications

References 35 publications

Distinct patterns of promoter CpG island methylation of breast cancer subtypes are associated with stem cell phenotypes

Distinct patterns of promoter CpG island methylation of breast cancer subtypes are associated with stem cell phenotypes

Gene expression meta-analysis identifies chromosomal regions and candidate genes involved in breast cancer metastasis

Comparative assessment of 5 methods (methylation‐specific polymerase chain reaction, methylight, pyrosequencing, methylation‐sensitive high‐resolution melting, and immunohistochemistry) to analyze O6‐methylguanine‐DNA‐methyltranferase in a series of 100 glioblastoma patients

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