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Zhou M, Ko M, Hoge AC, Luu K, Liu Y, Russell ML, Hannon WW, Zhang Z, Carrot-Zhang J, Beroukhim R, Van Allen EM, Choudhury AD, Nelson PS, Freedman M, Taplin ME, Meyerson M, Viswanathan SR, Ha G. Patterns of structural variation define prostate cancer across disease states. JCI Insight 2022; 7:161370. [PMID: 35943799 PMCID: PMC9536266 DOI: 10.1172/jci.insight.161370] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 08/04/2022] [Indexed: 11/19/2022] Open
Abstract
The complex genomic landscape of prostate cancer evolves across disease states under therapeutic pressure directed toward inhibiting androgen receptor (AR) signaling. While significantly altered genes in prostate cancer have been extensively defined, there have been fewer systematic analyses of how structural variation shapes the genomic landscape of this disease across disease states. We uniformly characterized structural alterations across 531 localized and 143 metastatic prostate cancers profiled by whole genome sequencing, 125 metastatic samples of which were also profiled via whole transcriptome sequencing. We observed distinct significantly recurrent breakpoints in localized and metastatic castration-resistant prostate cancers (mCRPC), with pervasive alterations in noncoding regions flanking the AR, MYC, FOXA1, and LSAMP genes enriched in mCRPC and TMPRSS2-ERG rearrangements enriched in localized prostate cancer. We defined 9 subclasses of mCRPC based on signatures of structural variation, each associated with distinct genetic features and clinical outcomes. Our results comprehensively define patterns of structural variation in prostate cancer and identify clinically actionable subgroups based on whole genome profiling.
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Affiliation(s)
- Meng Zhou
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, United States of America
| | - Minjeong Ko
- Public Health Sciences and Human Biology Divisions, Fred Hutchinson Cancer Center, Seattle, United States of America
| | - Anna Ch Hoge
- Public Health Sciences and Human Biology Divisions, Fred Hutchinson Cancer Center, Seattle, United States of America
| | - Kelsey Luu
- Public Health Sciences and Human Biology Divisions, Fred Hutchinson Cancer Center, Seattle, United States of America
| | - Yuzhen Liu
- Public Health Sciences and Human Biology Divisions, Fred Hutchinson Cancer Center, Seattle, United States of America
| | - Magdalena L Russell
- Public Health Sciences and Human Biology Divisions, Fred Hutchinson Cancer Center, Seattle, United States of America
| | - William W Hannon
- Public Health Sciences and Human Biology Divisions, Fred Hutchinson Cancer Center, Seattle, United States of America
| | - Zhenwei Zhang
- Department of Pathology, University of Massachusetts Medical School, Worchester, United States of America
| | - Jian Carrot-Zhang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, United States of America
| | - Rameen Beroukhim
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, United States of America
| | - Eliezer M Van Allen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, United States of America
| | - Atish D Choudhury
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, United States of America
| | - Peter S Nelson
- Public Health Sciences and Human Biology Divisions, Fred Hutchinson Cancer Center, Seattle, United States of America
| | - Matthew Freedman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, United States of America
| | - Mary-Ellen Taplin
- Department of Medical Oncology, Dana Faber Cancer Institute, Boston, United States of America
| | - Matthew Meyerson
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, United States of America
| | - Srinivas R Viswanathan
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, United States of America
| | - Gavin Ha
- Public Health Sciences and Human Biology Divisions, Fred Hutchinson Cancer Center, Seattle, United States of America
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Zhang Y, Chen F, Creighton CJ. SVExpress: identifying gene features altered recurrently in expression with nearby structural variant breakpoints. BMC Bioinformatics 2021; 22:135. [PMID: 33743584 PMCID: PMC7981925 DOI: 10.1186/s12859-021-04072-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 03/15/2021] [Indexed: 12/29/2022] Open
Abstract
Background Combined whole-genome sequencing (WGS) and RNA sequencing of cancers offer the opportunity to identify genes with altered expression due to genomic rearrangements. Somatic structural variants (SVs), as identified by WGS, can involve altered gene cis-regulation, gene fusions, copy number alterations, or gene disruption. The absence of computational tools to streamline integrative analysis steps may represent a barrier in identifying genes recurrently altered by genomic rearrangement. Results Here, we introduce SVExpress, a set of tools for carrying out integrative analysis of SV and gene expression data. SVExpress enables systematic cataloging of genes that consistently show increased or decreased expression in conjunction with the presence of nearby SV breakpoints. SVExpress can evaluate breakpoints in proximity to genes for potential enhancer translocation events or disruption of topologically associated domains, two mechanisms by which SVs may deregulate genes. The output from any commonly used SV calling algorithm may be easily adapted for use with SVExpress. SVExpress can readily analyze genomic datasets involving hundreds of cancer sample profiles. Here, we used SVExpress to analyze SV and expression data across 327 cancer cell lines with combined SV and expression data in the Cancer Cell Line Encyclopedia (CCLE). In the CCLE dataset, hundreds of genes showed altered gene expression in relation to nearby SV breakpoints. Altered genes involved TAD disruption, enhancer hijacking, and gene fusions. When comparing the top set of SV-altered genes from cancer cell lines with the top SV-altered genes previously reported for human tumors from The Cancer Genome Atlas and the Pan-Cancer Analysis of Whole Genomes datasets, a significant number of genes overlapped in the same direction for both cell lines and tumors, while some genes were significant for cell lines but not for human tumors and vice versa. Conclusion Our SVExpress tools allow computational biologists with a working knowledge of R to integrate gene expression with SV breakpoint data to identify recurrently altered genes. SVExpress is freely available for academic or commercial use at https://github.com/chadcreighton/SVExpress. SVExpress is implemented as a set of Excel macros and R code. All source code (R and Visual Basic for Applications) is available. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04072-0.
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Affiliation(s)
- Yiqun Zhang
- Dan L. Duncan Comprehensive Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Fengju Chen
- Dan L. Duncan Comprehensive Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Chad J Creighton
- Dan L. Duncan Comprehensive Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX, 77030, USA. .,Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA. .,Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA. .,Department of Medicine, Baylor College of Medicine, Houston, TX, 77030, USA.
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