DNA Methylation Alterations in Breast Cancer

2002 ◽  
Author(s):  
Fumiichiro Yamamoto
Keyword(s):  
Breast Cancer ◽  
Dna Methylation

scholarly journals VTRNA2-1: Genetic Variation, Heritable Methylation and Disease Association

2021 ◽  
Vol 22 (5) ◽  
pp. 2535
Author(s):  
Pierre-Antoine Dugué ◽  
Chenglong Yu ◽  
Timothy McKay ◽  
Ee Ming Wong ◽  
Jihoon Eric Joo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Genetic Variation ◽  
Genetic Factors ◽  
Disease Association ◽  
Country Of Birth ◽  
Cpg Site ◽  
Multiple Case ◽  
Metastable Epiallele ◽  
Methylation Quantitative Trait Loci

VTRNA2-1 is a metastable epiallele with accumulating evidence that methylation at this region is heritable, modifiable and associated with disease including risk and progression of cancer. This study investigated the influence of genetic variation and other factors such as age and adult lifestyle on blood DNA methylation in this region. We first sequenced the VTRNA2-1 gene region in multiple-case breast cancer families in which VTRNA2-1 methylation was identified as heritable and associated with breast cancer risk. Methylation quantitative trait loci (mQTL) were investigated using a prospective cohort study (4500 participants with genotyping and methylation data). The cis-mQTL analysis (334 variants ± 50 kb of the most heritable CpG site) identified 43 variants associated with VTRNA2-1 methylation (p < 1.5 × 10−4); however, these explained little of the methylation variation (R2 < 0.5% for each of these variants). No genetic variants elsewhere in the genome were found to strongly influence VTRNA2-1 methylation. SNP-based heritability estimates were consistent with the mQTL findings (h2 = 0, 95%CI: −0.14 to 0.14). We found no evidence that age, sex, country of birth, smoking, body mass index, alcohol consumption or diet influenced blood DNA methylation at VTRNA2-1. Genetic factors and adult lifestyle play a minimal role in explaining methylation variability at the heritable VTRNA2-1 cluster.

scholarly journals DNA Methylation Based Molecular Subtypes Predict Prognosis in Breast Cancer Patients

2021 ◽  
Vol 28 ◽  
pp. 107327482098851
Author(s):  
Zeng-Hong Wu ◽  
Yun Tang ◽  
Yan Zhou
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Molecular Subtypes ◽  
Methylation Status ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Consensus Clustering ◽  
Breast Cancer Patients ◽  
Cancer Subtypes ◽  
Novel Biomarkers

Background: Epigenetic changes are tightly linked to tumorigenesis development and malignant transformation’ However, DNA methylation occurs earlier and is constant during tumorigenesis. It plays an important role in controlling gene expression in cancer cells. Methods: In this study, we determining the prognostic value of molecular subtypes based on DNA methylation status in breast cancer samples obtained from The Cancer Genome Atlas database (TCGA). Results: Seven clusters and 204 corresponding promoter genes were identified based on consensus clustering using 166 CpG sites that significantly influenced survival outcomes. The overall survival (OS) analysis showed a significant prognostic difference among the 7 groups (p<0.05). Finally, a prognostic model was used to estimate the results of patients on the testing set based on the classification findings of a training dataset DNA methylation subgroups. Conclusions: The model was found to be important in the identification of novel biomarkers and could be of help to patients with different breast cancer subtypes when predicting prognosis, clinical diagnosis and management.

scholarly journals PITX2 DNA-Methylation: Predictive versus Prognostic Value for Anthracycline-Based Chemotherapy in Triple-Negative Breast Cancer Patients

Breast Care ◽  
2020 ◽  
pp. 1-9
Author(s):  
Rudolf Napieralski ◽  
Gabriele Schricker ◽  
Gert Auer ◽  
Michaela Aubele ◽  
Jonathan Perkins ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Cancer Patients ◽  
Triple Negative Breast Cancer ◽  
Predictive Value ◽  
Untreated Patient ◽  
Triple Negative ◽  
Statistical Significance ◽  
Breast Cancer Patients ◽  
The Impact

<b><i>Background:</i></b> PITX2 DNA methylation has been shown to predict outcomes in high-risk breast cancer patients after anthracycline-based chemotherapy. To determine its prognostic versus predictive value, the impact of PITX2 DNA methylation on outcomes was studied in an untreated cohort vs. an anthracycline-treated triple-negative breast cancer (TNBC) cohort. <b><i>Material and Methods:</i></b> The percent DNA methylation ratio (PMR) of paired-like homeodomain transcription factor 2 (PITX2) was determined by a validated methylation-specific real-time PCR test. Patient samples of routinely collected archived formalin-fixed paraffin-embedded (FFPE) tissue and clinical data from 144 TNBC patients of 2 independent cohorts (i.e., 66 untreated patients and 78 patients treated with anthracycline-based chemotherapy) were analyzed. <b><i>Results:</i></b> The risk of 5- and 10-year overall survival (OS) increased continuously with rising PITX2 DNA methylation in the anthracycline-treated population, but it increased only slightly during 10-year follow-up time in the untreated patient population. PITX2 DNA methylation with a PMR cutoff of 2 did not show significance for poor vs. good outcomes (OS) in the untreated patient cohort (HR = 1.55; <i>p</i> = 0.259). In contrast, the PITX2 PMR cutoff of 2 identified patients with poor (PMR &#x3e;2) vs. good (PMR ≤2) outcomes (OS) with statistical significance in the anthracycline-treated cohort (HR = 3.96; <i>p</i> = 0.011). The results in the subgroup of patients who did receive anthracyclines only (no taxanes) confirmed this finding (HR = 5.71; <i>p</i> = 0.014). <b><i>Conclusion:</i></b> In this hypothesis-generating study PITX2 DNA methylation demonstrated predominantly predictive value in anthracycline treatment in TNBC patients. The risk of poor outcome (OS) correlates with increasing PITX2 DNA methylation.

scholarly journals The Roles of DNA Demethylases in Triple-Negative Breast Cancer

2021 ◽  
Vol 14 (7) ◽  
pp. 628
Author(s):  
Shoghag Panjarian ◽  
Jean-Pierre J. Issa
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Tumor Suppressor Genes ◽  
Hormone Receptors ◽  
Triple Negative ◽  
Breast Cancers ◽  
Therapeutic Implications ◽  
High Propensity ◽  
Dna Methylation Profile

Triple-negative breast cancers (TNBCs) are very heterogenous, molecularly diverse, and are characterized by a high propensity to relapse or metastasize. Clinically, TNBC remains a diagnosis of exclusion by the lack of hormone receptors (Estrogen Receptor (ER) and Progesterone Receptor (PR)) as well as the absence of overexpression and/or amplification of HER2. DNA methylation plays an important role in breast cancer carcinogenesis and TNBCs have a distinct DNA methylation profile characterized by marked hypomethylation and lower gains of methylations compared to all other subtypes. DNA methylation is regulated by the balance of DNA methylases (DNMTs) and DNA demethylases (TETs). Here, we review the roles of TETs as context-dependent tumor-suppressor genes and/or oncogenes in solid tumors, and we discuss the current understandings of the oncogenic role of TET1 and its therapeutic implications in TNBCs.

scholarly journals Accelerated aging in normal breast tissue of women with breast cancer

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Shoghag Panjarian ◽  
Jozef Madzo ◽  
Kelsey Keith ◽  
Carolyn M. Slater ◽  
Carmen Sapienza ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Anomaly Detection ◽  
Breast Tissue ◽  
Preventive Intervention ◽  
Normal Breast Tissue ◽  
Accelerated Aging ◽  
Normal Breast ◽  
Age Related ◽  
Unsupervised Anomaly Detection

Abstract Background DNA methylation alterations have similar patterns in normal aging tissue and in cancer. In this study, we investigated breast tissue-specific age-related DNA methylation alterations and used those methylation sites to identify individuals with outlier phenotypes. Outlier phenotype is identified by unsupervised anomaly detection algorithms and is defined by individuals who have normal tissue age-dependent DNA methylation levels that vary dramatically from the population mean. Methods We generated whole-genome DNA methylation profiles (GSE160233) on purified epithelial cells and used publicly available Infinium HumanMethylation 450K array datasets (TCGA, GSE88883, GSE69914, GSE101961, and GSE74214) for discovery and validation. Results We found that hypermethylation in normal breast tissue is the best predictor of hypermethylation in cancer. Using unsupervised anomaly detection approaches, we found that about 10% of the individuals (39/427) were outliers for DNA methylation from 6 DNA methylation datasets. We also found that there were significantly more outlier samples in normal-adjacent to cancer (24/139, 17.3%) than in normal samples (15/228, 5.2%). Additionally, we found significant differences between the predicted ages based on DNA methylation and the chronological ages among outliers and not-outliers. Additionally, we found that accelerated outliers (older predicted age) were more frequent in normal-adjacent to cancer (14/17, 82%) compared to normal samples from individuals without cancer (3/17, 18%). Furthermore, in matched samples, we found that the epigenome of the outliers in the pre-malignant tissue was as severely altered as in cancer. Conclusions A subset of patients with breast cancer has severely altered epigenomes which are characterized by accelerated aging in their normal-appearing tissue. In the future, these DNA methylation sites should be studied further such as in cell-free DNA to determine their potential use as biomarkers for early detection of malignant transformation and preventive intervention in breast cancer.

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