Heritable clustering and pathway discovery in breast cancer integrating epigenetic and phenotypic data |
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| Authors: | Zailong Wang Pearlly Yan Dustin Potter Charis Eng Tim H-M Huang Shili Lin |
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| Institution: | (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 |
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| 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. |
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