Heritable clustering and pathway discovery in breast cancer integrating epigenetic and phenotypic data
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Date
2007-02-01
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BioMed Central
Abstract
Background: 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.
Results: 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.
Conclusion: 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.
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Citation
Zailong Wang et al, "Heritable clustering and pathway discovery in breast cancer integrating epigenetic and phenotypic data," BMC Bioinformatics 8 (2007), doi:10.1186/1471-2105-8-38, http://www.biomedcentral.com/1471-2105/8/38