scholarly journals Deconvolution of DNA methylation identifies differentially methylated gene regions on 1p36 across breast cancer subtypes

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Alexander J. Titus ◽  
Gregory P. Way ◽  
Kevin C. Johnson ◽  
Brock C. Christensen
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Breast Cancer Subtypes ◽  
Cancer Subtypes ◽  
Differentially Methylated Gene

Epigenetic aspects of triple-negative in patients with breast cancer.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 1041-1041
Author(s):  
Joaquina Martínez-Galan ◽  
Sandra Rios ◽  
Juan Ramon Delgado ◽  
Blanca Torres-Torres ◽  
Jesus Lopez-Peñalver ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Triple Negative ◽  
Breast Cancer Subtype ◽  
Regulation Of Gene Expression ◽  
Breast Cancer Subtypes ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Luminal B

1041 Background: Identification of gene expression-based breast cancer subtypes is considered a critical means of prognostication. Genetic mutations along with epigenetic alterations contribute to gene-expression changes occurring in breast cancer. However, the reproducibility of differential DNA methylation discoveries for cancer and the relationship between DNA methylation and aberrant gene expression have not been systematically analysed. The present study was undertaken to dissect the breast cancer methylome and to deliver specific epigenotypes associated with particular breast cancer subtypes. Methods: By using Real Time QMSPCR SYBR green we analyzed DNA methylation in regulatory regions of 107 pts with breast cancer and analyzed association with prognostics factor in triple negative breast cancer and methylation promoter ESR1, APC, E-Cadherin, Rar B and 14-3-3 sigma. Results: We identified novel subtype-specific epigenotypes that clearly demonstrate the differences in the methylation profiles of basal-like and human epidermal growth factor 2 (HER2)-overexpressing tumors. Of the cases, 37pts (40%) were Luminal A (LA), 32pts (33%) Luminal B (LB), 14pts (15%) Triple-negative (TN), and 9pts (10%) HER2+. DNA hypermethylation was highly inversely correlated with the down-regulation of gene expression. Methylation of this panel of promoter was found more frequently in triple negative and HER2 phenotype. ESR1 was preferably associated with TN(80%) and HER2+(60%) subtype. With a median follow up of 6 years, we found worse overall survival (OS) with more frequent ESR1 methylation gene(p>0.05), Luminal A;ESR1 Methylation OS at 5 years 81% vs 93% when was ESR1 Unmethylation. Luminal B;ESR1 Methylation 86% SG at 5 years vs 92% in Unmethylation ESR1. Triple negative;ESR1 Methylation SG at 5 years 75% vs 80% in unmethylation ESR1. HER2;ESR1 Methylation SG at 5 years was 66.7% vs 75% in unmethylation ESR1. Conclusions: Our results provide evidence that well-defined DNA methylation profiles enable breast cancer subtype prediction and support the utilization of this biomarker for prognostication and therapeutic stratification of patients with breast cancer.

scholarly journals A DNA methylation-based definition of biologically distinct breast cancer subtypes

2014 ◽  
Vol 9 (3) ◽  
pp. 555-568 ◽  
Author(s):  
Olafur A. Stefansson ◽  
Sebastian Moran ◽  
Antonio Gomez ◽  
Sergi Sayols ◽  
Carlos Arribas-Jorba ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Breast Cancer Subtypes ◽  
Cancer Subtypes ◽  
Definition Of

scholarly journals Aberrant DNA methylation impacts gene expression and prognosis in breast cancer subtypes

2015 ◽  
Vol 138 (1) ◽  
pp. 87-97 ◽  
Author(s):  
Balázs Győrffy ◽  
Giulia Bottai ◽  
Thomas Fleischer ◽  
Gyöngyi Munkácsy ◽  
Jan Budczies ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Breast Cancer Subtypes ◽  
Cancer Subtypes ◽  
Aberrant Dna Methylation

243 Aberrant DNA methylation impacts gene expression and prognosis in breast cancer subtypes

2015 ◽  
Vol 51 ◽  
pp. S41
Author(s):  
B. Gyorffy ◽  
G. Bottai ◽  
T. Fleischer ◽  
G. Munkacsy ◽  
L. Paladini ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Breast Cancer Subtypes ◽  
Cancer Subtypes ◽  
Aberrant Dna Methylation

scholarly journals Deconvolution of DNA methylation signatures identifies common differentially methylated gene regions on 1p36 across breast cancer subtypes

2017 ◽  
Author(s):  
Alexander J. Titus ◽  
Gregory P. Way ◽  
Kevin C. Johnson ◽  
Brock C. Christensen
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Complex Disease ◽  
Molecular Subtype ◽  
Early Stage ◽  
The Cancer Genome Atlas ◽  
Data Set ◽  
Disease Heterogeneity ◽  
Cancer Subtypes ◽  
Differentially Methylated Gene

ABSTRACTBreast cancer is a complex disease and studying DNA methylation (DNAm) in tumors is complicated by disease heterogeneity. We compared DNAm in breast tumors with normal-adjacent breast samples from The Cancer Genome Atlas (TCGA). We constructed models stratified by tumor stage and PAM50 molecular subtype and performed cell-type reference-free deconvolution on each model. We identified nineteen differentially methylated gene regions (DMGRs) in early stage tumors across eleven genes (AGRN, C1orf170, FAM41C, FLJ39609, HES4, ISG15, KLHL17, NOC2L, PLEKHN1, SAMD11, WASH5P). These regions were consistently differentially methylated in every subtype and all implicated genes are localized on chromosome 1p36.3. We also validated seventeen DMGRs in an independent data set. Identification and validation of shared DNAm alterations across tumor subtypes in early stage tumors advances our understanding of common biology underlying breast carcinogenesis and may contribute to biomarker development. We also provide evidence on the importance and potential function of 1p36 in cancer.

scholarly journals Epigenetic clock detected a breast cancer mitosis subtype with improved immunotherapy

2021 ◽  
Author(s):  
Shipeng Shang ◽  
Xin Li ◽  
Yue Gao ◽  
Shuang Guo ◽  
Hanxiao Zhou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Breast Tissue ◽  
Pearson Correlation ◽  
Breast Cancer Subtypes ◽  
Chronological Age ◽  
Epigenetic Clock ◽  
Methylation Data ◽  
Cancer Subtypes ◽  
Epigenetic Age

Abstract Background Epigenetic clock based on DNA methylation can estimate the epigenetic age of tissue and cell that can describe the process of aging. However, the exploration of diseases by the epigenetic clock is still an uncharted territory. Our objective was to assess the role of the epigenetic clock in breast cancer. Methods In this study, DNA methylation data of breast tissue sample was download from TCGA and GEO database. DNA methylation level of CpG sites and age of samples was calculated by using pearson correlation test. Differentially expressed genes were identified using the limma package and Kruskal-Wallis test was used for the difference between cancer subtypes. Results We developed a workflow to construct the Breast Epigenetic Clock (BEpiC) that could accurately predict the chronological age of normal breast tissue. Furthermore, the BEpiC was applied to breast cancer to identify three breast cancer subtypes (including development, homeostasis, and mitosis) by using the deviation between epigenetic age and chronological age. Interestingly, the prognosis of the three breast cancer subtypes is significantly different. In addition, the three breast cancer subtypes had distinct differences in multiple immune cells and the mitosis subtype had the highest tumor mutation burden that was used to estimate response to checkpoint inhibitors. Conclusion Our model highlights that epigenetic age of breast cancer samples had an important impact on immunotherapy. We constructed a BEpiC web server (http://bio-bigdata.hrbmu.edu.cn/BEpiC/) where users submit DNA methylation data and age information to predict the epigenetic age of breast tissue and breast cancer subtypes. Trial registration Not applicable

Identification of new therapeutic leads for triple negative breast cancer subtypes

Planta Medica ◽  
2015 ◽  
Vol 81 (11) ◽  
Author(s):  
AJ Robles ◽  
L Du ◽  
S Cai ◽  
RH Cichewicz ◽  
SL Mooberry
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Breast Cancer Subtypes ◽  
Cancer Subtypes ◽  
Therapeutic Leads
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