TY - JOUR
T1 - Large-scale integrative network-based analysis identifies common pathways disrupted by copy number alterations across cancers
AU - Hwang, Tae Hyun
AU - Atluri, Gowtham
AU - Kuang, Rui
AU - Kumar, Vipin
AU - Starr, Timothy
AU - Silverstein, Kevin A.T.
AU - Haverty, Peter M.
AU - Zhang, Zemin
AU - Liu, Jinfeng
N1 - Funding Information:
We would like to thank to Minnesota Supercomputing Institute for allowing us to use their computational resources TKS is supported by grants from the Randy Shaver Community Cancer Research and Community Fund, the Masonic Cancer Center, and the NIH-NCI Grant R00CA151672. GW, and VK are supported by a grant from the National Science Foundation (NSF IIS-0916439). RK is supported by grants from the National Science Foundation (NSF III-1117153, IIS 1149697).
PY - 2013/1/1
Y1 - 2013/1/1
N2 - 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/.
AB - 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/.
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U2 - 10.1186/1471-2164-14-440
DO - 10.1186/1471-2164-14-440
M3 - Article
AN - SCOPUS:84879802605
SN - 1471-2164
VL - 14
JO - BMC genomics
JF - BMC genomics
IS - 1
M1 - 440
ER -