Heritable clustering and pathway discovery in breast cancer integrating epigenetic and phenotypic data
1 Mathematical Biosciences Institute, The Ohio State University, 231 W. 18th Avenue, Columbus, OH 43210, USA
2 Department of Molecular Virology, Immunology, and Medical Genetics, Columbus, OH 43210, USA
3 Human Cancer Genetics Program, Comprehensive Cancer Center, The Ohio State University, 420 W. 12th Avenue, Columbus, OH 43210, USA
4 Department of Statistics, The Ohio State University, 1598 Neil Avenue, Columbus, OH 43210, USA
5 Cleveland Clinic Genomic Medicine Institute, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH 44195, USA
BMC Bioinformatics 2007, 8:38 doi:10.1186/1471-2105-8-38Published: 1 February 2007
In order to recapitulate tumor progression pathways using epigenetic data, we developed novel clustering and pathway reconstruction algorithms, collectively referred to as heritable clustering. This approach generates a progression model of altered DNA methylation from tumor tissues diagnosed at different developmental stages. The samples act as surrogates for natural progression in breast cancer and allow the algorithm to uncover distinct epigenotypes that describe the molecular events underlying this process. Furthermore, our likelihood-based clustering algorithm has great flexibility, allowing for incomplete epigenotype or clinical phenotype data and also permitting dependencies among variables.
Using this heritable clustering approach, we analyzed methylation data obtained from 86 primary breast cancers to recapitulate pathways of breast tumor progression. Detailed annotation and interpretation are provided to the optimal pathway recapitulated. The result confirms the previous observation that aggressive tumors tend to exhibit higher levels of promoter hypermethylation.
Our results indicate that the proposed heritable clustering algorithms are a useful tool for stratifying both methylation and clinical variables of breast cancer. The application to the breast tumor data illustrates that this approach can select meaningful progression models which may aid the interpretation of pathways having biological and clinical significance. Furthermore, the framework allows for other types of biological data, such as microarray gene expression or array CGH data, to be integrated.