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Conference Paper: An integrative bioinformatic approach for identifying subtypes and subtype-specific drivers in cancer

TitleAn integrative bioinformatic approach for identifying subtypes and subtype-specific drivers in cancer
Authors
Issue Date2012
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001142
Citation
The 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB’12), San Diego, CA., 9-12 May 2012. In IEEE CIBCB Proceedings, 2012, p. 169-176 How to Cite?
AbstractCancer is a complex disease and within a cancer, subtypes of patients with distinct behaviors often exist. The subtypes might have been caused by different hits, such as copy number aberrations (CNAs) and point mutations, on different pathways/cells-of-origin in a common tissue/organ. Identifying the subtypes with subtype-specific drivers, i.e., hits, is key to the understanding of cancer and development of novel treatments. Here, we report the development of an integrative method to identify the subtypes of cancer. Specifically, we consider CNAs and their impact on gene expressions. Based on these relations, we propose an iterative approach that alternates between kernel based gene expression clustering and gene signature selection. We applied the method to datasets of the pediatric cancer medulloblastoma (MB). The consensus number of clusters quickly converges to three; and for each of these three subtypes, the signature detection also converges to a consistent set of a few hundred highly functionally related genes. For each of the subtypes, we correlate its signature with the set of within-subtype recurrent CNA-affected genes for identifying drivers. The top-ranked driver candidates are found to be enriched with known pathways in certain subtypes of MB as well as containing novel genes that might reveal new understandings for other subtypes.
Persistent Identifierhttp://hdl.handle.net/10722/165157
ISBN

 

DC FieldValueLanguage
dc.contributor.authorChen, Pen_US
dc.contributor.authorHung, YSen_US
dc.contributor.authorFan, Yen_US
dc.contributor.authorWong, STCen_US
dc.date.accessioned2012-09-20T08:15:55Z-
dc.date.available2012-09-20T08:15:55Z-
dc.date.issued2012en_US
dc.identifier.citationThe 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB’12), San Diego, CA., 9-12 May 2012. In IEEE CIBCB Proceedings, 2012, p. 169-176en_US
dc.identifier.isbn978-1-4673-1191-5-
dc.identifier.urihttp://hdl.handle.net/10722/165157-
dc.description.abstractCancer is a complex disease and within a cancer, subtypes of patients with distinct behaviors often exist. The subtypes might have been caused by different hits, such as copy number aberrations (CNAs) and point mutations, on different pathways/cells-of-origin in a common tissue/organ. Identifying the subtypes with subtype-specific drivers, i.e., hits, is key to the understanding of cancer and development of novel treatments. Here, we report the development of an integrative method to identify the subtypes of cancer. Specifically, we consider CNAs and their impact on gene expressions. Based on these relations, we propose an iterative approach that alternates between kernel based gene expression clustering and gene signature selection. We applied the method to datasets of the pediatric cancer medulloblastoma (MB). The consensus number of clusters quickly converges to three; and for each of these three subtypes, the signature detection also converges to a consistent set of a few hundred highly functionally related genes. For each of the subtypes, we correlate its signature with the set of within-subtype recurrent CNA-affected genes for identifying drivers. The top-ranked driver candidates are found to be enriched with known pathways in certain subtypes of MB as well as containing novel genes that might reveal new understandings for other subtypes.-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001142-
dc.relation.ispartofIEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology Proceedingsen_US
dc.rightsIEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology Proceedings. Copyright © IEEE.-
dc.rights©2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleAn integrative bioinformatic approach for identifying subtypes and subtype-specific drivers in canceren_US
dc.typeConference_Paperen_US
dc.identifier.emailChen, P: h0795456@hku.hken_US
dc.identifier.emailHung, YS: yshung@hkucc.hku.hk-
dc.identifier.emailFan, Y: yfan@tmhs.org-
dc.identifier.emailWong, STC: stwong@tmhs.org-
dc.identifier.authorityHung, YS=rp00220en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/CIBCB.2012.6217227-
dc.identifier.scopuseid_2-s2.0-84864033158-
dc.identifier.hkuros206404en_US
dc.identifier.spage169-
dc.identifier.epage176-
dc.publisher.placeUnited States-
dc.description.otherThe 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB’12), San Diego, CA., 9-12 May 2012. In IEEE CIBCB Proceedings, 2012, p. 169-176-

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