DNA methylation epigenotypes in breast cancer molecular subtypes
1 BIOMICs Research Group, Centro de Investigacion y Estudios Avanzados 'Lucio Lascaray', University of the Basque Country UPV/EHU, Miguel de Unamuno 3,1006, Vitoria-Gazteiz, Spain
2 Oral Medicine and Pathology Unit, Faculty of Medicine and Dentistry, University of the Basque Country UPV/EH, Barrio Sarriena s/n. E-48940 Leioa, Vizcaya, Spain
3 Service of Anatomic Pathology, Hospital Txagorritxu, C/Jose Achotegui s/n, E-01009 Vitoria-Gasteiz, Alava, Spain
4 Service of Anatomic Pathology, Hospital Donostia, Paseo Beguiristain 107-115, E-20014 San Sebastian, Guipuzcoa, Spain
5 Service of Anatomic Pathology, Instituto Oncologico, C/Aldakoenea 44, E-20012 San Sebastian, Spain
6 Department of Bioinformatics and Genomics, Centro de Investigación Principe Felipe, Avda. Autopista del Saler, 164, E-6012 Valencia, Spain
7 Molecular Biology and Biochemistry, Faculty of Pharmacy, University of the Basque Country UPV/EHU, Paseo de la Universidad 7, E-01006 Vitoria-Gasteiz, Spain
8 Cancer Epigenetics Laboratory, Instituto Universitario de Oncologia del Principado de Asturias (IUOPA), HUCA, Universidad de Oviedo, E- 33006 Oviedo, Spain
9 Liverpool CR-UK Cancer Research Centre, University of Liverpool Cancer, School of Cancer Studies, 200 London Road, Liverpool L3 9TA, UK
10 Department of Immunology and Oncology, National Center for Biotechnology, CNB-CSIC, Cantoblanco, E-28049 Madrid, Spain
Breast Cancer Research 2010, 12:R77 doi:10.1186/bcr2721Published: 29 September 2010
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. So far, these epigenetic contributions to sporadic breast cancer subtypes have not been well characterized, and only a limited understanding exists of the epigenetic mechanisms affected in those particular breast cancer subtypes. The present study was undertaken to dissect the breast cancer methylome and to deliver specific epigenotypes associated with particular breast cancer subtypes.
By using a microarray approach, we analyzed DNA methylation in regulatory regions of 806 cancer-related genes in 28 breast cancer paired samples. We subsequently performed substantial technical and biologic validation by pyrosequencing, investigating the top qualifying 19 CpG regions in independent cohorts encompassing 47 basal-like, 44 ERBB2+ overexpressing, 48 luminal A, and 48 luminal B paired breast cancer/adjacent tissues. With the all-subset selection method, we identified the most subtype-predictive methylation profiles in multivariable logistic regression analysis.
The approach efficiently recognized 15 individual CpG loci differentially methylated in breast cancer tumor subtypes. We further 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.
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.