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Gull N, Jones MR, Peng PC, Coetzee SG, Silva TC, Plummer JT, Reyes ALP, Davis BD, Chen SS, Lawrenson K, Lester J, Walsh C, Rimel BJ, Li AJ, Cass I, Berg Y, Govindavari JPB, Rutgers JKL, Berman BP, Karlan BY, Gayther SA. DNA methylation and transcriptomic features are preserved throughout disease recurrence and chemoresistance in high grade serous ovarian cancers. J Exp Clin Cancer Res 2022; 41:232. [PMID: 35883104 PMCID: PMC9327231 DOI: 10.1186/s13046-022-02440-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 07/13/2022] [Indexed: 11/18/2022] Open
Abstract
Background Little is known about the role of global DNA methylation in recurrence and chemoresistance of high grade serous ovarian cancer (HGSOC). Methods We performed whole genome bisulfite sequencing and transcriptome sequencing in 62 primary and recurrent tumors from 28 patients with stage III/IV HGSOC, of which 11 patients carried germline, pathogenic BRCA1 and/or BRCA2 mutations. Results Landscapes of genome-wide methylation (on average 24.2 million CpGs per tumor) and transcriptomes in primary and recurrent tumors showed extensive heterogeneity between patients but were highly preserved in tumors from the same patient. We identified significant differences in the burden of differentially methylated regions (DMRs) in tumors from BRCA1/2 compared to non-BRCA1/2 carriers (mean 659 DMRs and 388 DMRs in paired comparisons respectively). We identified overexpression of immune pathways in BRCA1/2 carriers compared to non-carriers, implicating an increased immune response in improved survival (P = 0.006) in these BRCA1/2 carriers. Conclusion These findings indicate methylome and gene expression programs established in the primary tumor are conserved throughout disease progression, even after extensive chemotherapy treatment, and that changes in methylation and gene expression are unlikely to serve as drivers for chemoresistance in HGSOC. Supplementary Information The online version contains supplementary material available at 10.1186/s13046-022-02440-z.
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Jones MR, Peng PC, Coetzee SG, Tyrer J, Reyes ALP, Corona RI, Davis B, Chen S, Dezem F, Seo JH, Kar S, Dareng E, Berman BP, Freedman ML, Plummer JT, Lawrenson K, Pharoah P, Hazelett DJ, Gayther SA. Ovarian Cancer Risk Variants Are Enriched in Histotype-Specific Enhancers and Disrupt Transcription Factor Binding Sites. Am J Hum Genet 2020; 107:622-635. [PMID: 32946763 PMCID: PMC7536645 DOI: 10.1016/j.ajhg.2020.08.021] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 08/15/2020] [Indexed: 12/14/2022] Open
Abstract
Quantifying the functional effects of complex disease risk variants can provide insights into mechanisms underlying disease biology. Genome-wide association studies have identified 39 regions associated with risk of epithelial ovarian cancer (EOC). The vast majority of these variants lie in the non-coding genome, where they likely function through interaction with gene regulatory elements. In this study we first estimated the heritability explained by known common low penetrance risk alleles for EOC. The narrow sense heritability (hg2) of EOC overall and high-grade serous ovarian cancer (HGSOCs) were estimated to be 5%-6%. Partitioned SNP heritability across broad functional categories indicated a significant contribution of regulatory elements to EOC heritability. We collated epigenomic profiling data for 77 cell and tissue types from Roadmap Epigenomics and ENCODE, and from H3K27Ac ChIP-seq data generated in 26 ovarian cancer and precursor-related cell and tissue types. We identified significant enrichment of risk single-nucleotide polymorphisms (SNPs) in active regulatory elements marked by H3K27Ac in HGSOCs. To further investigate how risk SNPs in active regulatory elements influence predisposition to ovarian cancer, we used motifbreakR to predict the disruption of transcription factor binding sites. We identified 469 candidate causal risk variants in H3K27Ac peaks that are predicted to significantly break transcription factor (TF) motifs. The most frequently broken motif was REST (p value = 0.0028), which has been reported as both a tumor suppressor and an oncogene. Overall, these systematic functional annotations with epigenomic data improve interpretation of EOC risk variants and shed light on likely cells of origin.
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Affiliation(s)
- Michelle R Jones
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Pei-Chen Peng
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Simon G Coetzee
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Jonathan Tyrer
- CR-UK Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
| | - Alberto Luiz P Reyes
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Rosario I Corona
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Women's Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Brian Davis
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Stephanie Chen
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Felipe Dezem
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Women's Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Ji-Heui Seo
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Siddartha Kar
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
| | - Eileen Dareng
- CR-UK Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
| | - Benjamin P Berman
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Hebrew University-Hadassah Medical School, Jerusalem 9112102, Israel
| | - Matthew L Freedman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Jasmine T Plummer
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Kate Lawrenson
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Women's Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Paul Pharoah
- CR-UK Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
| | - Dennis J Hazelett
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Simon A Gayther
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
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Reyes ALP, Silva TC, Coetzee SG, Plummer JT, Davis BD, Chen S, Hazelett DJ, Lawrenson K, Berman BP, Gayther SA, Jones MR. GENAVi: a shiny web application for gene expression normalization, analysis and visualization. BMC Genomics 2019; 20:745. [PMID: 31619158 PMCID: PMC6796420 DOI: 10.1186/s12864-019-6073-7] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Accepted: 08/29/2019] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND The development of next generation sequencing (NGS) methods led to a rapid rise in the generation of large genomic datasets, but the development of user-friendly tools to analyze and visualize these datasets has not developed at the same pace. This presents a two-fold challenge to biologists; the expertise to select an appropriate data analysis pipeline, and the need for bioinformatics or programming skills to apply this pipeline. The development of graphical user interface (GUI) applications hosted on web-based servers such as Shiny can make complex workflows accessible across operating systems and internet browsers to those without programming knowledge. RESULTS We have developed GENAVi (Gene Expression Normalization Analysis and Visualization) to provide a user-friendly interface for normalization and differential expression analysis (DEA) of human or mouse feature count level RNA-Seq data. GENAVi is a GUI based tool that combines Bioconductor packages in a format for scientists without bioinformatics expertise. We provide a panel of 20 cell lines commonly used for the study of breast and ovarian cancer within GENAVi as a foundation for users to bring their own data to the application. Users can visualize expression across samples, cluster samples based on gene expression or correlation, calculate and plot the results of principal components analysis, perform DEA and gene set enrichment and produce plots for each of these analyses. To allow scalability for large datasets we have provided local install via three methods. We improve on available tools by offering a range of normalization methods and a simple to use interface that provides clear and complete session reporting and for reproducible analysis. CONCLUSION The development of tools using a GUI makes them practical and accessible to scientists without bioinformatics expertise, or access to a data analyst with relevant skills. While several GUI based tools are currently available for RNA-Seq analysis we improve on these existing tools. This user-friendly application provides a convenient platform for the normalization, analysis and visualization of gene expression data for scientists without bioinformatics expertise.
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Affiliation(s)
- Alberto Luiz P Reyes
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Tiago C Silva
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Simon G Coetzee
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Jasmine T Plummer
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Brian D Davis
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Stephanie Chen
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Dennis J Hazelett
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Kate Lawrenson
- Women's Cancer Program, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Benjamin P Berman
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Simon A Gayther
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Michelle R Jones
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.
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