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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 diag...

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Detalles Bibliográficos
Autores principales: Wang, Zailong, Yan, Pearlly, Potter, Dustin, Eng, Charis, Huang, Tim H-M, Lin, Shili
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1800873/
https://www.ncbi.nlm.nih.gov/pubmed/17270052
http://dx.doi.org/10.1186/1471-2105-8-38
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author Wang, Zailong
Yan, Pearlly
Potter, Dustin
Eng, Charis
Huang, Tim H-M
Lin, Shili
author_facet Wang, Zailong
Yan, Pearlly
Potter, Dustin
Eng, Charis
Huang, Tim H-M
Lin, Shili
author_sort Wang, Zailong
collection PubMed
description 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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spelling pubmed-18008732007-02-23 Heritable clustering and pathway discovery in breast cancer integrating epigenetic and phenotypic data Wang, Zailong Yan, Pearlly Potter, Dustin Eng, Charis Huang, Tim H-M Lin, Shili BMC Bioinformatics Research Article 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. BioMed Central 2007-02-01 /pmc/articles/PMC1800873/ /pubmed/17270052 http://dx.doi.org/10.1186/1471-2105-8-38 Text en Copyright © 2007 Wang et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Zailong
Yan, Pearlly
Potter, Dustin
Eng, Charis
Huang, Tim H-M
Lin, Shili
Heritable clustering and pathway discovery in breast cancer integrating epigenetic and phenotypic data
title Heritable clustering and pathway discovery in breast cancer integrating epigenetic and phenotypic data
title_full Heritable clustering and pathway discovery in breast cancer integrating epigenetic and phenotypic data
title_fullStr Heritable clustering and pathway discovery in breast cancer integrating epigenetic and phenotypic data
title_full_unstemmed Heritable clustering and pathway discovery in breast cancer integrating epigenetic and phenotypic data
title_short Heritable clustering and pathway discovery in breast cancer integrating epigenetic and phenotypic data
title_sort heritable clustering and pathway discovery in breast cancer integrating epigenetic and phenotypic data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1800873/
https://www.ncbi.nlm.nih.gov/pubmed/17270052
http://dx.doi.org/10.1186/1471-2105-8-38
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