Mammalian Genome

, Volume 22, Issue 3–4, pp 249–259 | Cite as

DNA methylation changes in murine breast adenocarcinomas allow the identification of candidate genes for human breast carcinogenesis

  • Deanna Acosta
  • Masako Suzuki
  • Diana Connolly
  • Reid F. Thompson
  • Melissa J. Fazzari
  • John M. Greally
  • Cristina MontagnaEmail author


Epigenetic inactivation due to aberrant promoter methylation is a key process in breast tumorigenesis. Murine models for human breast cancer have been established for nearly every important human oncogene or tumor suppressor gene. Mouse-to-human comparative gene expression and cytogenetic profiling have been widely investigated for these models; however, little is known about the conservation of epigenetic alterations during tumorigenesis. To determine if this key process in human breast tumorigenesis is also mirrored in a murine breast cancer model, we mapped cytosine methylation changes in primary adenocarcinomas and paired lung metastases derived from the polyomavirus middle T antigen mouse model. Global changes in methylcytosine levels were observed in all tumors when compared to the normal mammary gland. Aberrant methylation and associated gene silencing was observed for Hoxa7, a gene that is differentially methylated in human breast tumors, and Gata2, a novel candidate gene. Analysis of HOXA7 and GATA2 expression in a bank of human primary tumors confirms that the expression of these genes is also reduced in human breast cancer. In addition, HOXA7 hypermethylation is observed in breast cancer tissues when compared to adjacent tumor-free tissue. Based on these studies, we present a model in which comparative epigenetic techniques can be used to identify novel candidate genes important for human breast tumorigenesis, in both primary and metastatic tumors.


Methylation Change Normal Mammary Gland Gata2 Expression Human Primary Tumor Cytogenetic Profile 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank members of the Greally’s lab for constructive discussion and Zhixia Yang for her assistance with sample preparation. We thank the Shared Resources at Albert Einstein College of Medicine: GIF (Genome Imaging Facility) for help with the SKY; Dr. Shahina Maqbool and the Center for Epigenomics for assistance with the epigenomic studies; Brent Calder and the Computational Genomic Core for help with the data processing. We are grateful to Dr. Thomas Ried for providing the mouse SKY kits, to Dr. Jeffrey Pollard for providing the PyMT mice, and to Dr. Maria Figueroa for help with the R scripts.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Deanna Acosta
    • 1
  • Masako Suzuki
    • 1
  • Diana Connolly
    • 1
  • Reid F. Thompson
    • 1
  • Melissa J. Fazzari
    • 1
    • 2
  • John M. Greally
    • 1
  • Cristina Montagna
    • 1
    • 3
    • 4
    Email author
  1. 1.Department of GeneticsAlbert Einstein College of Medicine of Yeshiva UniversityBronxUSA
  2. 2.Department of Epidemiology and Population HealthAlbert Einstein College of Medicine of Yeshiva UniversityBronxUSA
  3. 3.Department of PathologyAlbert Einstein College of Medicine of Yeshiva UniversityBronxUSA
  4. 4.Albert Einstein College of MedicineBronxUSA

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