Heritable clustering and pathway discovery in breast cancer integrating epigenetic and phenotypic data

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 conf...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Zailong Wang, Pearlly Yan, Dustin P. Potter, Charis Eng, Tim Hui-Ming Huang, Shili Lin
• Keywords: BMCBI 2007, Breast Cancer, Heritable Clustering, Tumor Progression