Identification of Epigenetic Biomarkers with the use of Gene Expression and DNA Methylation for Breast Cancer Subtypes

Author(s):  
Indrajit Saha ◽  
Somnath Rakshit ◽  
Michal Wlasnowolski ◽  
Dariusz Plewczynski
2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 1041-1041
Author(s):  
Joaquina Martínez-Galan ◽  
Sandra Rios ◽  
Juan Ramon Delgado ◽  
Blanca Torres-Torres ◽  
Jesus Lopez-Peñalver ◽  
...  

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.


2015 ◽  
Vol 138 (1) ◽  
pp. 87-97 ◽  
Author(s):  
Balázs Győrffy ◽  
Giulia Bottai ◽  
Thomas Fleischer ◽  
Gyöngyi Munkácsy ◽  
Jan Budczies ◽  
...  

2015 ◽  
Vol 51 ◽  
pp. S41
Author(s):  
B. Gyorffy ◽  
G. Bottai ◽  
T. Fleischer ◽  
G. Munkacsy ◽  
L. Paladini ◽  
...  

2015 ◽  
Vol 9 (4) ◽  
pp. 861-876 ◽  
Author(s):  
Ivan O. Potapenko ◽  
Torben Lüders ◽  
Hege G. Russnes ◽  
Åslaug Helland ◽  
Therese Sørlie ◽  
...  

2014 ◽  
Vol 9 (3) ◽  
pp. 555-568 ◽  
Author(s):  
Olafur A. Stefansson ◽  
Sebastian Moran ◽  
Antonio Gomez ◽  
Sergi Sayols ◽  
Carlos Arribas-Jorba ◽  
...  

2019 ◽  
Author(s):  
Kyuri Jo ◽  
Beatriz Santos Buitrago ◽  
Minsu Kim ◽  
Sungmin Rhee ◽  
Carolyn Talcott ◽  
...  

AbstractFor breast cancer, clinically important subtypes are well characterised at the molecular level in terms of gene expression profiles. In addition, signaling pathways in breast cancer have been extensively studied as therapeutic targets due to their roles in tumor growth and metastasis. However, it is challenging to put signaling pathways and gene expression profiles together to characterise biological mechanisms of breast cancer subtypes since many signaling events result from post-translational modifications, rather than gene expression differences.We present a logic-based approach to explain the differences in gene expression profiles among breast cancer subtypes using Pathway Logic and transcriptional network information. Pathway Logic is a rewriting-logic-based formal system for modeling biological pathways including post-translational modifications. Proposed method demonstrated its utility by constructing subtype-specific path from key receptors (TNFR, TGFBR1 and EGFR) to key transcription factor (TF) regulators (RELA, ATF2, SMAD3 and ELK1) and identifying potential pathway crosstalk via TFs in basal-specific paths, which could provide a novel insight on aggressive breast cancer subtypes.AvailabilityAnalysis result is available at http://epigenomics.snu.ac.kr/PL/


2007 ◽  
Vol 46 (1) ◽  
pp. 87-97 ◽  
Author(s):  
Kristian Wennmalm ◽  
Stefano Calza ◽  
Alexander Ploner ◽  
Per Hall ◽  
Judith Bjöhle ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document