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Lowdon RF, Wang T. Epigenomic annotation of noncoding mutations identifies mutated pathways in primary liver cancer. PLoS One 2017; 12:e0174032. [PMID: 28333948 PMCID: PMC5363827 DOI: 10.1371/journal.pone.0174032] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Accepted: 03/02/2017] [Indexed: 11/19/2022] Open
Abstract
Evidence that noncoding mutation can result in cancer driver events is mounting. However, it is more difficult to assign molecular biological consequences to noncoding mutations than to coding mutations, and a typical cancer genome contains many more noncoding mutations than protein-coding mutations. Accordingly, parsing functional noncoding mutation signal from noise remains an important challenge. Here we use an empirical approach to identify putatively functional noncoding somatic single nucleotide variants (SNVs) from liver cancer genomes. Annotation of candidate variants by publicly available epigenome datasets finds that 40.5% of SNVs fall in regulatory elements. When assigned to specific regulatory elements, we find that the distribution of regulatory element mutation mirrors that of nonsynonymous coding mutation, where few regulatory elements are recurrently mutated in a patient population but many are singly mutated. We find potential gain-of-binding site events among candidate SNVs, suggesting a mechanism of action for these variants. When aggregating noncoding somatic mutation in promoters, we find that genes in the ERBB signaling and MAPK signaling pathways are significantly enriched for promoter mutations. Altogether, our results suggest that functional somatic SNVs in cancer are sporadic, but occasionally occur in regulatory elements and may affect phenotype by creating binding sites for transcriptional regulators. Accordingly, we propose that noncoding mutation should be formally accounted for when determining gene- and pathway-mutation burden in cancer.
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Affiliation(s)
- Rebecca F. Lowdon
- Center for Genome Sciences and Systems Biology, Department of Genetics, Washington University in St. Louis, Saint Louis, Missouri, United States of America
| | - Ting Wang
- Center for Genome Sciences and Systems Biology, Department of Genetics, Washington University in St. Louis, Saint Louis, Missouri, United States of America
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Zhang H, Deng Y, Zhang Y, Ping Y, Zhao H, Pang L, Zhang X, Wang L, Xu C, Xiao Y, Li X. Cooperative genomic alteration network reveals molecular classification across 12 major cancer types. Nucleic Acids Res 2016; 45:567-582. [PMID: 27899621 PMCID: PMC5314758 DOI: 10.1093/nar/gkw1087] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Revised: 10/18/2016] [Accepted: 10/27/2016] [Indexed: 11/22/2022] Open
Abstract
The accumulation of somatic genomic alterations that enables cells to gradually acquire growth advantage contributes to tumor development. This has the important implication of the widespread existence of cooperative genomic alterations in the accumulation process. Here, we proposed a computational method HCOC that simultaneously consider genetic context and downstream functional effects on cancer hallmarks to uncover somatic cooperative events in human cancers. Applying our method to 12 TCGA cancer types, we totally identified 1199 cooperative events with high heterogeneity across human cancers, and then constructed a pan-cancer cooperative alteration network. These cooperative events are associated with genomic alterations of some high-confident cancer drivers, and can trigger the dysfunction of hallmark associated pathways in a co-defect way rather than single alterations. We found that these cooperative events can be used to produce a prognostic classification that can provide complementary information with tissue-of-origin. In a further case study of glioblastoma, using 23 cooperative events identified, we stratified patients into molecularly relevant subtypes with a prognostic significance independent of the Glioma-CpG Island Methylator Phenotype (GCIMP). In summary, our method can be effectively used to discover cancer-driving cooperative events that can be valuable clinical markers for patient stratification.
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Affiliation(s)
- Hongyi Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yulan Deng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yanyan Ping
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Hongying Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Lin Pang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Xinxin Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Li Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Chaohan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
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