Abstract
BackgroundMany 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.ResultsWe 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.ConclusionsIn 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/.
Highlights
Many large-scale studies analyzed high-throughput genomic data to identify altered pathways essential to the development and progression of specific types of cancer
We observed that more than 70% of the genes that are identified as significantly altered based on copy number alterations are not annotated in current pathway databases (Additional file 1: Figure S1)
Due to this low coverage of gene annotation in significantly altered copy number regions, overrepresentation-based enrichment analyses, and standard pathway-based analyses are omitting some of the most significantly altered genes in their analyses. They provide a limited analysis of pathway activity that is based on the small non-representative fraction of altered genes that are currently annotated in pathway databases
Summary
Many large-scale studies analyzed high-throughput genomic data to identify altered pathways essential to the development and progression of specific types of cancer. This effort is epitomized by The Cancer Genome Atlas [12,13,14] and its umbrella group, the International Cancer Genome Consortium [15] In these studies, enrichment analysis was performed to identify statistically significant overlap between the list of altered genes and pathways or predefined gene sets [16,17,18,19]. Pathway-based methods have been developed to incorporate interactions of member genes in known biological pathways to measure activities of pathways These pathway-based methods were shown to be more accurate at identifying cancer-related pathways compared to overrepresentation-based enrichment analysis [22,28]
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