Large-scale integrative network-based analysis identifies common pathways disrupted by copy number alterations across cancers
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- 作者：Tae Hyun Hwang (13) (17) (18) (19)
Gowtham Atluri (14)
Rui Kuang (14)
Vipin Kumar (14)
Timothy Starr (13) (16)
Kevin AT Silverstein (13)
Peter M Haverty (15)
Zemin Zhang (15)
Jinfeng Liu (15) - 刊名：BMC Genomics
- 出版年：2013
- 出版时间：December 2013
- 年：2013
- 卷：14
- 期：1
- 全文大小：1167KB
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Gowtham Atluri (14)
Rui Kuang (14)
Vipin Kumar (14)
Timothy Starr (13) (16)
Kevin AT Silverstein (13)
Peter M Haverty (15)
Zemin Zhang (15)
Jinfeng Liu (15)
13. Masonic Cancer Center, University of Minnesota 鈥?Twin Cities, Minneapolis, MN, USA
17. Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
18. Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
19. Simmons Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
14. Department of Computer Science and Engineering, University of Minnesota 鈥?Twin Cities, Minneapolis, MN, USA
16. Department of Obstetrics, Gynecology & Women鈥檚 Health, University of Minnesota, Minneapolis, MN, USA
15. Department of Bioinformatics and Computational Biology, Genentech Inc, South San Francisco, CA, USA
文摘
Background Many large-scale studies analyzed high-throughput genomic data to identify altered pathways essential to the development and progression of specific types of cancer. However, no previous study has been extended to provide a comprehensive analysis of pathways disrupted by copy number alterations across different human cancers. Towards this goal, we propose a network-based method to integrate copy number alteration data with human protein-protein interaction networks and pathway databases to identify pathways that are commonly disrupted in many different types of cancer. Results We applied our approach to a data set of 2,172 cancer patients across 16 different types of cancers, and discovered a set of commonly disrupted pathways, which are likely essential for tumor formation in majority of the cancers. We also identified pathways that are only disrupted in specific cancer types, providing molecular markers for different human cancers. Analysis with independent microarray gene expression datasets confirms that the commonly disrupted pathways can be used to identify patient subgroups with significantly different survival outcomes. We also provide a network view of disrupted pathways to explain how copy number alterations affect pathways that regulate cell growth, cycle, and differentiation for tumorigenesis. Conclusions In this work, we demonstrated that the network-based integrative analysis can help to identify pathways disrupted by copy number alterations across 16 types of human cancers, which are not readily identifiable by conventional overrepresentation-based and other pathway-based methods. All the results and source code are available at http://compbio.cs.umn.edu/NetPathID/.