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Ou MY, Xiao Q, Ju XC, Zeng PM, Huang J, Sheng AL, Luo ZG. The CTNNBIP1-CLSTN1 fusion transcript regulates human neocortical development. Cell Rep 2021; 35:109290. [PMID: 34192541 DOI: 10.1016/j.celrep.2021.109290] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 02/17/2021] [Accepted: 06/02/2021] [Indexed: 12/21/2022] Open
Abstract
Fusion transcripts or RNAs have been found in both disordered and healthy human tissues and cells; however, their physiological functions in the brain development remain unknown. In the analysis of deposited RNA-sequence libraries covering early to middle embryonic stages, we identify 1,055 fusion transcripts present in the developing neocortex. Interestingly, 98 fusion transcripts exhibit distinct expression patterns in various neural progenitors (NPs) or neurons. We focus on CTNNBIP1-CLSTN1 (CTCL), which is enriched in outer radial glial cells that contribute to cortex expansion during human evolution. Intriguingly, downregulation of CTCL in cultured human cerebral organoids causes marked reduction in NPs and precocious neuronal differentiation, leading to impairment of organoid growth. Furthermore, the expression of CTCL fine-tunes Wnt/β-catenin signaling that controls cortex patterning. Together, this work provides evidence indicating important roles of fusion transcript in human brain development and evolution.
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Affiliation(s)
- Min-Yi Ou
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qi Xiao
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiang-Chun Ju
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
| | - Peng-Ming Zeng
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Jing Huang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Ai-Li Sheng
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhen-Ge Luo
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.
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52
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Apostolides M, Jiang Y, Husić M, Siddaway R, Hawkins C, Turinsky AL, Brudno M, Ramani AK. MetaFusion: A high-confidence metacaller for filtering and prioritizing RNA-seq gene fusion candidates. Bioinformatics 2021; 37:3144-3151. [PMID: 33944895 DOI: 10.1093/bioinformatics/btab249] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 03/04/2021] [Accepted: 05/03/2021] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Current fusion detection tools use diverse calling approaches and provide varying results, making selection of the appropriate tool challenging. Ensemble fusion calling techniques appear promising; however, current options have limited accessibility and function. RESULTS MetaFusion is a flexible meta-calling tool that amalgamates outputs from any number of fusion callers. Individual caller results are standardized by conversion into the new file type Common Fusion Format (CFF). Calls are annotated, merged using graph clustering, filtered, and ranked to provide a final output of high confidence candidates. MetaFusion consistently achieves higher precision and recall than individual callers on real and simulated datasets, and reaches up to 100% precision, indicating that ensemble calling is imperative for high confidence results. MetaFusion uses FusionAnnotator to annotate calls with information from cancer fusion databases, and is provided with a benchmarking toolkit to calibrate new callers. AVAILABILITY MetaFusion is freely available at https://github.com/ccmbioinfo/MetaFusion. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Michael Apostolides
- Centre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada
| | - Yue Jiang
- Centre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada
| | - Mia Husić
- Centre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada
| | - Robert Siddaway
- The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON, Canada
| | - Cynthia Hawkins
- The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON, Canada.,Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada.,Division of Pathology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Andrei L Turinsky
- Centre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada
| | - Michael Brudno
- Centre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada.,Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, Canada.,Department of Computer Science, University of Toronto, Toronto, ON, Canada.,University Health Network, Toronto, ON, Canada
| | - Arun K Ramani
- Centre for Computational Medicine, The Hospital For Sick Children, Toronto, ON, Canada
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53
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Quistgaard EM. BAP31: Physiological functions and roles in disease. Biochimie 2021; 186:105-129. [PMID: 33930507 DOI: 10.1016/j.biochi.2021.04.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 04/20/2021] [Accepted: 04/21/2021] [Indexed: 12/22/2022]
Abstract
B-cell receptor-associated protein 31 (BAP31 or BCAP31) is a ubiquitously expressed transmembrane protein found mainly in the endoplasmic reticulum (ER), including in mitochondria-associated membranes (MAMs). It acts as a broad-specificity membrane protein chaperone and quality control factor, which can promote different fates for its clients, including ER retention, ER export, ER-associated degradation (ERAD), or evasion of degradation, and it also acts as a MAM tetherer and regulatory protein. It is involved in several cellular processes - it supports ER and mitochondrial homeostasis, promotes proliferation and migration, plays several roles in metabolism and the immune system, and regulates autophagy and apoptosis. Full-length BAP31 can be anti-apoptotic, but can also mediate activation of caspase-8, and itself be cleaved by caspase-8 into p20-BAP31, which promotes apoptosis by mobilizing ER calcium stores at MAMs. BAP31 loss-of-function mutations is the cause of 'deafness, dystonia, and central hypomyelination' (DDCH) syndrome, characterized by severe neurological symptoms and early death. BAP31 is furthermore implicated in a growing number of cancers and other diseases, and several viruses have been found to target it to promote their survival or life cycle progression. The purpose of this review is to provide an overview and examination of the basic properties, functions, mechanisms, and roles in disease of BAP31.
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Affiliation(s)
- Esben M Quistgaard
- Department of Molecular Biology and Genetics - DANDRITE, Aarhus University, Gustav Wieds Vej 10, DK-8000 Aarhus C, Denmark.
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54
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Wang LB, Karpova A, Gritsenko MA, Kyle JE, Cao S, Li Y, Rykunov D, Colaprico A, Rothstein JH, Hong R, Stathias V, Cornwell M, Petralia F, Wu Y, Reva B, Krug K, Pugliese P, Kawaler E, Olsen LK, Liang WW, Song X, Dou Y, Wendl MC, Caravan W, Liu W, Cui Zhou D, Ji J, Tsai CF, Petyuk VA, Moon J, Ma W, Chu RK, Weitz KK, Moore RJ, Monroe ME, Zhao R, Yang X, Yoo S, Krek A, Demopoulos A, Zhu H, Wyczalkowski MA, McMichael JF, Henderson BL, Lindgren CM, Boekweg H, Lu S, Baral J, Yao L, Stratton KG, Bramer LM, Zink E, Couvillion SP, Bloodsworth KJ, Satpathy S, Sieh W, Boca SM, Schürer S, Chen F, Wiznerowicz M, Ketchum KA, Boja ES, Kinsinger CR, Robles AI, Hiltke T, Thiagarajan M, Nesvizhskii AI, Zhang B, Mani DR, Ceccarelli M, Chen XS, Cottingham SL, Li QK, Kim AH, Fenyö D, Ruggles KV, Rodriguez H, Mesri M, Payne SH, Resnick AC, Wang P, Smith RD, Iavarone A, Chheda MG, Barnholtz-Sloan JS, Rodland KD, Liu T, Ding L. Proteogenomic and metabolomic characterization of human glioblastoma. Cancer Cell 2021; 39:509-528.e20. [PMID: 33577785 PMCID: PMC8044053 DOI: 10.1016/j.ccell.2021.01.006] [Citation(s) in RCA: 310] [Impact Index Per Article: 103.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 06/02/2020] [Accepted: 01/11/2021] [Indexed: 02/07/2023]
Abstract
Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology. We identify key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation, as well as potential targets for EGFR-, TP53-, and RB1-altered tumors. Immune subtypes with distinct immune cell types are discovered using bulk omics methodologies, validated by snRNA-seq, and correlated with specific expression and histone acetylation patterns. Histone H2B acetylation in classical-like and immune-low GBM is driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identify specific lipid distributions across subtypes and distinct global metabolic changes in IDH-mutated tumors. This work highlights biological relationships that could contribute to stratification of GBM patients for more effective treatment.
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Affiliation(s)
- Liang-Bo Wang
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Alla Karpova
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Marina A Gritsenko
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Jennifer E Kyle
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Song Cao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Dmitry Rykunov
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Antonio Colaprico
- Sylvester Comprehensive Cancer Center, University of Miami, FL 33136, USA; Division of Biostatistics, Department of Public Health Science, University of Miami, FL 33136, USA
| | - Joseph H Rothstein
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Runyu Hong
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Vasileios Stathias
- Sylvester Comprehensive Cancer Center, University of Miami, FL 33136, USA; Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; BD2K-LINCS Data Coordination and Integration Center, Miami, FL 33136, USA
| | - MacIntosh Cornwell
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Francesca Petralia
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yige Wu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Boris Reva
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Karsten Krug
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Pietro Pugliese
- Department of Science and Technology, University of Sannio, 82100, Benevento, Italy
| | - Emily Kawaler
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Lindsey K Olsen
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Wen-Wei Liang
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Xiaoyu Song
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yongchao Dou
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Michael C Wendl
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Genetics, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Mathematics, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Wagma Caravan
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Wenke Liu
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Daniel Cui Zhou
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Jiayi Ji
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Chia-Feng Tsai
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Vladislav A Petyuk
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Jamie Moon
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Weiping Ma
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Rosalie K Chu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Karl K Weitz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Matthew E Monroe
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Rui Zhao
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Xiaolu Yang
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; Poznań University of Medical Sciences, 61-701 Poznań, Poland
| | - Seungyeul Yoo
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Azra Krek
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Alexis Demopoulos
- Department of Neurology, Northwell Health System, Lake Success, NY 11042 USA
| | - Houxiang Zhu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Matthew A Wyczalkowski
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Joshua F McMichael
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | | | - Caleb M Lindgren
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Hannah Boekweg
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Shuangjia Lu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Jessika Baral
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Lijun Yao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Kelly G Stratton
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Lisa M Bramer
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Erika Zink
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Sneha P Couvillion
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Kent J Bloodsworth
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Shankha Satpathy
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Weiva Sieh
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Simina M Boca
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC 20007, USA
| | - Stephan Schürer
- Sylvester Comprehensive Cancer Center, University of Miami, FL 33136, USA; Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; BD2K-LINCS Data Coordination and Integration Center, Miami, FL 33136, USA; Institute for Data Science & Computing, University of Miami, FL 33136, USA
| | - Feng Chen
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Cell Biology and Physiology, Washington University in St. Louis, St. Louis, MO 63130, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Maciej Wiznerowicz
- International Institute for Molecular Oncology, 60-203 Poznań, Poland; Poznań University of Medical Sciences, 61-701 Poznań, Poland
| | | | - Emily S Boja
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Christopher R Kinsinger
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Tara Hiltke
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | | | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - D R Mani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Michele Ceccarelli
- Department of Electrical Engineering and Information Technology, University of Naples "Federico II", 80128, Naples, Italy; BIOGEM, 83031 Ariano Irpino, Italy
| | - Xi S Chen
- Sylvester Comprehensive Cancer Center, University of Miami, FL 33136, USA; Division of Biostatistics, Department of Public Health Science, University of Miami, FL 33136, USA
| | - Sandra L Cottingham
- Department of Pathology, Spectrum Health and Helen DeVos Children's Hospital, Grand Rapids, MI 49503, USA
| | - Qing Kay Li
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Albert H Kim
- Department of Neurological Surgery, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Kelly V Ruggles
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Mehdi Mesri
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Samuel H Payne
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Adam C Resnick
- Center for Data Driven Discovery in Biomedicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Neurosurgery, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Pei Wang
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Antonio Iavarone
- Institute for Cancer Genetics, Columbia University Medical Center, New York, NY 10032, USA; Department of Neurology, Columbia University Medical Center, New York, NY 10032, USA; Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY 10032, USA
| | - Milan G Chheda
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Neurology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Jill S Barnholtz-Sloan
- Case Comprehensive Cancer Center and Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA; Research and Education, University Hospitals Health System, Cleveland, OH 44106, USA
| | - Karin D Rodland
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; Department of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, OR 97221, USA.
| | - Tao Liu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA.
| | - Li Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Genetics, Washington University in St. Louis, St. Louis, MO 63130, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA.
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55
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Pan Y, Kadash-Edmondson KE, Wang R, Phillips J, Liu S, Ribas A, Aplenc R, Witte ON, Xing Y. RNA Dysregulation: An Expanding Source of Cancer Immunotherapy Targets. Trends Pharmacol Sci 2021; 42:268-282. [PMID: 33711255 PMCID: PMC8761020 DOI: 10.1016/j.tips.2021.01.006] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 01/18/2021] [Accepted: 01/25/2021] [Indexed: 12/14/2022]
Abstract
Cancer transcriptomes frequently exhibit RNA dysregulation. As the resulting aberrant transcripts may be translated into cancer-specific proteins, there is growing interest in exploiting RNA dysregulation as a source of tumor antigens (TAs) and thus novel immunotherapy targets. Recent advances in high-throughput technologies and rapid accumulation of multiomic cancer profiling data in public repositories have provided opportunities to systematically characterize RNA dysregulation in cancer and identify antigen targets for immunotherapy. However, given the complexity of cancer transcriptomes and proteomes, important conceptual and technological challenges exist. Here, we highlight the expanding repertoire of TAs arising from RNA dysregulation and introduce multiomic and big data strategies for identifying optimal immunotherapy targets. We discuss extant barriers for translating these targets into effective therapies as well as the implications for future research.
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Affiliation(s)
- Yang Pan
- Bioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Kathryn E Kadash-Edmondson
- Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Robert Wang
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - John Phillips
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Song Liu
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14263, USA
| | - Antoni Ribas
- Department of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Surgery, University of California, Los Angeles, Los Angeles, CA 90095, USA; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Richard Aplenc
- Division of Oncology, Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Owen N Witte
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, CA 90095, USA; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, USA; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yi Xing
- Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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56
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Wang L, Xiong X, Yao Z, Zhu J, Lin Y, Lin W, Li K, Xu X, Guo Y, Chen Y, Pan Y, Zhou F, Fan J, Chen Y, Gao S, Jim Yeung SC, Zhang H. Chimeric RNA ASTN2-PAPPA as aggravates tumor progression and metastasis in human esophageal cancer. Cancer Lett 2021; 501:1-11. [PMID: 33388371 DOI: 10.1016/j.canlet.2020.10.052] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 10/26/2020] [Accepted: 10/29/2020] [Indexed: 02/05/2023]
Abstract
Transcription-induced chimeric RNAs are an emerging area of research into molecular signatures for disease biomarker and therapeutic target development. Despite their importance, little is known for chimeric RNAs-relevant roles and the underlying mechanisms for cancer pathogenesis and progression. Here we describe a unique ASTN2-PAPPAantisense chimeric RNA (A-PaschiRNA) that could be the first reported chimeric RNA derived from the splicing of exons and intron antisense of two neighboring genes, respectively. Aberrant A-PaschiRNA level in ESCC tissues was associated with tumor progression and patients' outcome. In vitro and in vivo studies demonstrated that A-PaschiRNA aggravated ESCC metastasis and enhanced stemness through modulating OCT4. Mechanistic studies demonstrated that ERK5-mediated non-canonical PAF1 activity was required for A-PaschiRNA-induced cancer malignancy. The study defined an undocumented function of chimeric RNAs in aggravating cancer stemness and metastasis.
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Affiliation(s)
- Lu Wang
- Department of General Surgery, The First Affiliated Hospital of Jinan University, Institute of Precision Cancer Medicine and Pathology, School of Medicine, Jinan University, Guangzhou, Guangdong, China
| | - Xiao Xiong
- Department of General Surgery, The First Affiliated Hospital of Jinan University, Institute of Precision Cancer Medicine and Pathology, School of Medicine, Jinan University, Guangzhou, Guangdong, China
| | - Zhimeng Yao
- Department of General Surgery, The First Affiliated Hospital of Jinan University, Institute of Precision Cancer Medicine and Pathology, School of Medicine, Jinan University, Guangzhou, Guangdong, China
| | - Jianlin Zhu
- Department of General Surgery, The First Affiliated Hospital of Jinan University, Institute of Precision Cancer Medicine and Pathology, School of Medicine, Jinan University, Guangzhou, Guangdong, China
| | - Yusheng Lin
- Department of General Surgery, The First Affiliated Hospital of Jinan University, Institute of Precision Cancer Medicine and Pathology, School of Medicine, Jinan University, Guangzhou, Guangdong, China; Department of Hematology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Wan Lin
- Cancer Research Center, Shantou University Medical College, Shantou, Guangdong, 515041, China
| | - Kai Li
- Department of General Surgery, The First Affiliated Hospital of Jinan University, Institute of Precision Cancer Medicine and Pathology, School of Medicine, Jinan University, Guangzhou, Guangdong, China
| | - Xiaozheng Xu
- Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA
| | - Yi Guo
- Endoscopy Center, Affiliated Cancer Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Yuping Chen
- Department of Thoracic Surgery, Affiliated Cancer Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Yunlong Pan
- Department of General Surgery, The First Affiliated Hospital of Jinan University, Institute of Precision Cancer Medicine and Pathology, School of Medicine, Jinan University, Guangzhou, Guangdong, China
| | - Fuyou Zhou
- The Fourth Affiliated Hospital of Henan University of Science and Technology, Anyang, Henan, 455001, China; Department of Thoracic Surgery, Anyang Tumor Hospital, Anyang, Henan, 455001, China
| | - Jun Fan
- Department of Medical Biochemistry and Molecular Biology, School of Medicine, Jinan University, Guangzhou, 510632, China
| | - Yan Chen
- Department of Medical Biochemistry and Molecular Biology, School of Medicine, Jinan University, Guangzhou, 510632, China
| | - Shegan Gao
- College of Clinical Medicine, The First Affiliated Hospital of Henan University of Science and Technology, Henan Key Laboratory of Cancer Epigenetics, Luoyang, 471003, China.
| | - Sai-Ching Jim Yeung
- Department of Emergency Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Endocrine Neoplasia and Hormonal Disorders, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Hao Zhang
- Department of General Surgery, The First Affiliated Hospital of Jinan University, Institute of Precision Cancer Medicine and Pathology, School of Medicine, Jinan University, Guangzhou, Guangdong, China.
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57
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Landscape of Chimeric RNAs in Non-Cancerous Cells. Genes (Basel) 2021; 12:genes12040466. [PMID: 33805149 PMCID: PMC8064075 DOI: 10.3390/genes12040466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/18/2021] [Accepted: 03/19/2021] [Indexed: 11/21/2022] Open
Abstract
Gene fusions and their products (RNA and protein) have been traditionally recognized as unique features of cancer cells and are used as ideal biomarkers and drug targets for multiple cancer types. However, recent studies have demonstrated that chimeric RNAs generated by intergenic alternative splicing can also be found in normal cells and tissues. In this study, we aim to identify chimeric RNAs in different non-neoplastic cell lines and investigate the landscape and expression of these novel candidate chimeric RNAs. To do so, we used HEK-293T, HUVEC, and LO2 cell lines as models, performed paired-end RNA sequencing, and conducted analyses for chimeric RNA profiles. Several filtering criteria were applied, and the landscape of chimeric RNAs was characterized at multiple levels and from various angles. Further, we experimentally validated 17 chimeric RNAs from different classifications. Finally, we examined a number of validated chimeric RNAs in different cancer and non-cancer cells, including blood from healthy donors, and demonstrated their ubiquitous expression pattern.
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58
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Shi Y, Yuan J, Rraklli V, Maxymovitz E, Cipullo M, Liu M, Li S, Westerlund I, Bedoya-Reina OC, Bullova P, Rorbach J, Juhlin CC, Stenman A, Larsson C, Kogner P, O’Sullivan MJ, Schlisio S, Holmberg J. Aberrant splicing in neuroblastoma generates RNA-fusion transcripts and provides vulnerability to spliceosome inhibitors. Nucleic Acids Res 2021; 49:2509-2521. [PMID: 33555349 PMCID: PMC7969022 DOI: 10.1093/nar/gkab054] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 01/14/2021] [Accepted: 01/21/2021] [Indexed: 11/12/2022] Open
Abstract
The paucity of recurrent mutations has hampered efforts to understand and treat neuroblastoma. Alternative splicing and splicing-dependent RNA-fusions represent mechanisms able to increase the gene product repertoire but their role in neuroblastoma remains largely unexplored. Here we investigate the presence and possible roles of aberrant splicing and splicing-dependent RNA-fusion transcripts in neuroblastoma. In addition, we attend to establish whether the spliceosome can be targeted to treat neuroblastoma. Through analysis of RNA-sequenced neuroblastoma we show that elevated expression of splicing factors is a strong predictor of poor clinical outcome. Furthermore, we identified >900 primarily intrachromosomal fusions containing canonical splicing sites. Fusions included transcripts from well-known oncogenes, were enriched for proximal genes and in chromosomal regions commonly gained or lost in neuroblastoma. As a proof-of-principle that these fusions can generate altered gene products, we characterized a ZNF451-BAG2 fusion, producing a truncated BAG2-protein which inhibited retinoic acid induced differentiation. Spliceosome inhibition impeded neuroblastoma fusion expression, induced apoptosis and inhibited xenograft tumor growth. Our findings elucidate a splicing-dependent mechanism generating altered gene products in neuroblastoma and show that the spliceosome is a potential target for clinical intervention.
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Affiliation(s)
- Yao Shi
- Department of Cell and Molecular Biology, Karolinska Institutet, Solnavägen 9, SE-171 65 Stockholm, Sweden
| | - Juan Yuan
- Department of Cell and Molecular Biology, Karolinska Institutet, Solnavägen 9, SE-171 65 Stockholm, Sweden
| | - Vilma Rraklli
- Department of Cell and Molecular Biology, Karolinska Institutet, Solnavägen 9, SE-171 65 Stockholm, Sweden
| | - Eva Maxymovitz
- Department of Cell and Molecular Biology, Karolinska Institutet, Solnavägen 9, SE-171 65 Stockholm, Sweden
| | - Miriam Cipullo
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Solnavägen 9, SE-171-65 Solna, Sweden
| | - Mingzhi Liu
- Department of Cell and Molecular Biology, Karolinska Institutet, Solnavägen 9, SE-171 65 Stockholm, Sweden
| | - Shuijie Li
- Department of Microbiology, Tumor- and Cellbiology, Karolinska Institutet, Solnavägen 9, SE-171 65 Solna, Sweden
| | - Isabelle Westerlund
- Department of Cell and Molecular Biology, Karolinska Institutet, Solnavägen 9, SE-171 65 Stockholm, Sweden
| | - Oscar C Bedoya-Reina
- Department of Microbiology, Tumor- and Cellbiology, Karolinska Institutet, Solnavägen 9, SE-171 65 Solna, Sweden
| | - Petra Bullova
- Department of Microbiology, Tumor- and Cellbiology, Karolinska Institutet, Solnavägen 9, SE-171 65 Solna, Sweden
| | - Joanna Rorbach
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Solnavägen 9, SE-171-65 Solna, Sweden
| | - C Christofer Juhlin
- Department of Oncology-Pathology, Karolinska Institutet, Cancer Center Karolinska (CCK), Karolinska University Hospital, SE-171 76 Stockholm, Sweden
| | - Adam Stenman
- Department of Oncology-Pathology, Karolinska Institutet, Cancer Center Karolinska (CCK), Karolinska University Hospital, SE-171 76 Stockholm, Sweden
| | - Catharina Larsson
- Department of Oncology-Pathology, Karolinska Institutet, Cancer Center Karolinska (CCK), Karolinska University Hospital, SE-171 76 Stockholm, Sweden
| | - Per Kogner
- Department of Women's and Children's Health, Karolinska Institutet, Karolinska University Hospital, SE-171 76 Stockholm, Sweden
| | - Maureen J O’Sullivan
- Department of Histopathology, Our Lady's Children's Hospital, Dublin, Ireland
- Trinity Translational Medicine Institute, Trinity College, Dublin, Ireland
| | - Susanne Schlisio
- Department of Microbiology, Tumor- and Cellbiology, Karolinska Institutet, Solnavägen 9, SE-171 65 Solna, Sweden
| | - Johan Holmberg
- Department of Cell and Molecular Biology, Karolinska Institutet, Solnavägen 9, SE-171 65 Stockholm, Sweden
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59
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Huang C, Chen L, Savage SR, Eguez RV, Dou Y, Li Y, da Veiga Leprevost F, Jaehnig EJ, Lei JT, Wen B, Schnaubelt M, Krug K, Song X, Cieślik M, Chang HY, Wyczalkowski MA, Li K, Colaprico A, Li QK, Clark DJ, Hu Y, Cao L, Pan J, Wang Y, Cho KC, Shi Z, Liao Y, Jiang W, Anurag M, Ji J, Yoo S, Zhou DC, Liang WW, Wendl M, Vats P, Carr SA, Mani DR, Zhang Z, Qian J, Chen XS, Pico AR, Wang P, Chinnaiyan AM, Ketchum KA, Kinsinger CR, Robles AI, An E, Hiltke T, Mesri M, Thiagarajan M, Weaver AM, Sikora AG, Lubiński J, Wierzbicka M, Wiznerowicz M, Satpathy S, Gillette MA, Miles G, Ellis MJ, Omenn GS, Rodriguez H, Boja ES, Dhanasekaran SM, Ding L, Nesvizhskii AI, El-Naggar AK, Chan DW, Zhang H, Zhang B. Proteogenomic insights into the biology and treatment of HPV-negative head and neck squamous cell carcinoma. Cancer Cell 2021; 39:361-379.e16. [PMID: 33417831 PMCID: PMC7946781 DOI: 10.1016/j.ccell.2020.12.007] [Citation(s) in RCA: 178] [Impact Index Per Article: 59.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 09/13/2020] [Accepted: 12/07/2020] [Indexed: 02/08/2023]
Abstract
We present a proteogenomic study of 108 human papilloma virus (HPV)-negative head and neck squamous cell carcinomas (HNSCCs). Proteomic analysis systematically catalogs HNSCC-associated proteins and phosphosites, prioritizes copy number drivers, and highlights an oncogenic role for RNA processing genes. Proteomic investigation of mutual exclusivity between FAT1 truncating mutations and 11q13.3 amplifications reveals dysregulated actin dynamics as a common functional consequence. Phosphoproteomics characterizes two modes of EGFR activation, suggesting a new strategy to stratify HNSCCs based on EGFR ligand abundance for effective treatment with inhibitory EGFR monoclonal antibodies. Widespread deletion of immune modulatory genes accounts for low immune infiltration in immune-cold tumors, whereas concordant upregulation of multiple immune checkpoint proteins may underlie resistance to anti-programmed cell death protein 1 monotherapy in immune-hot tumors. Multi-omic analysis identifies three molecular subtypes with high potential for treatment with CDK inhibitors, anti-EGFR antibody therapy, and immunotherapy, respectively. Altogether, proteogenomics provides a systematic framework to inform HNSCC biology and treatment.
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Affiliation(s)
- Chen Huang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Lijun Chen
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Sara R Savage
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Rodrigo Vargas Eguez
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Yongchao Dou
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | | | - Eric J Jaehnig
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jonathan T Lei
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Bo Wen
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Michael Schnaubelt
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Karsten Krug
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Xiaoyu Song
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Marcin Cieślik
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hui-Yin Chang
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Matthew A Wyczalkowski
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Kai Li
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Antonio Colaprico
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Division of Biostatistics, Department of Public Health Science, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Qing Kay Li
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - David J Clark
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Yingwei Hu
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Liwei Cao
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Jianbo Pan
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA; Department of Ophthalmology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Yuefan Wang
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Kyung-Cho Cho
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Zhiao Shi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yuxing Liao
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Wen Jiang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Meenakshi Anurag
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jiayi Ji
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Seungyeul Yoo
- Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Daniel Cui Zhou
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Wen-Wei Liang
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Michael Wendl
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Pankaj Vats
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Steven A Carr
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - D R Mani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Zhen Zhang
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Jiang Qian
- Department of Ophthalmology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Xi S Chen
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Division of Biostatistics, Department of Public Health Science, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Alexander R Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Pei Wang
- Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Arul M Chinnaiyan
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Christopher R Kinsinger
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Eunkyung An
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Tara Hiltke
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Mehdi Mesri
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Mathangi Thiagarajan
- Leidos Biomedical Research Inc., Frederick NaVonal Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Alissa M Weaver
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Andrew G Sikora
- Department of Head and Neck Surgery, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
| | - Jan Lubiński
- Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, 71-252 Szczecin, Poland; International Institute for Molecular Oncology, 60-203 Poznań, Poland
| | - Małgorzata Wierzbicka
- Poznań University of Medical Sciences, 61-701 Poznań, Poland; Institute of Human Genetics Polish Academy of Sciences, 60-479 Poznań, Poland
| | - Maciej Wiznerowicz
- International Institute for Molecular Oncology, 60-203 Poznań, Poland; Poznań University of Medical Sciences, 61-701 Poznań, Poland
| | - Shankha Satpathy
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Michael A Gillette
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - George Miles
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Matthew J Ellis
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Emily S Boja
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Saravana M Dhanasekaran
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Li Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Adel K El-Naggar
- Department of Pathology, Division of Pathology and Laboratory Medicine, MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Daniel W Chan
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA.
| | - Hui Zhang
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA.
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
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Simoneau J, Gosselin R, Scott MS. Factorial study of the RNA-seq computational workflow identifies biases as technical gene signatures. NAR Genom Bioinform 2021; 2:lqaa043. [PMID: 33575596 PMCID: PMC7671328 DOI: 10.1093/nargab/lqaa043] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 05/15/2020] [Accepted: 06/05/2020] [Indexed: 12/12/2022] Open
Abstract
RNA-seq is a modular experimental and computational approach aiming in identifying and quantifying RNA molecules. The modularity of the RNA-seq technology enables adaptation of the protocol to develop new ways to explore RNA biology, but this modularity also brings forth the importance of methodological thoroughness. Liberty of approach comes with the responsibility of choices, and such choices must be informed. Here, we present an approach that identifies gene group-specific quantification biases in current RNA-seq software and references by processing datasets using diverse RNA-seq computational pipelines, and by decomposing these expression datasets with an independent component analysis matrix factorization method. By exploring the RNA-seq pipeline using this systemic approach, we identify genome annotations as a design choice that affects to the same extent quantification results as does the choice of aligners and quantifiers. We also show that the different choices in RNA-seq methodology are not independent, identifying interactions between genome annotations and quantification software. Genes were mainly affected by differences in their sequence, by overlapping genes and genes with similar sequence. Our approach offers an explanation for the observed biases by identifying the common features used differently by the software and references, therefore providing leads for the betterment of RNA-seq methodology.
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Affiliation(s)
- Joël Simoneau
- Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Québec, J1K 2R1, Canada
| | - Ryan Gosselin
- Department of Chemical & Biotechnological Engineering, Faculty of Engineering, Université de Sherbrooke, Sherbrooke, Québec, J1K 2R1, Canada
| | - Michelle S Scott
- Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Québec, J1K 2R1, Canada
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61
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Taniue K, Akimitsu N. Fusion Genes and RNAs in Cancer Development. Noncoding RNA 2021; 7:10. [PMID: 33557176 PMCID: PMC7931065 DOI: 10.3390/ncrna7010010] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/02/2021] [Accepted: 02/03/2021] [Indexed: 02/07/2023] Open
Abstract
Fusion RNAs are a hallmark of some cancers. They result either from chromosomal rearrangements or from splicing mechanisms that are non-chromosomal rearrangements. Chromosomal rearrangements that result in gene fusions are particularly prevalent in sarcomas and hematopoietic malignancies; they are also common in solid tumors. The splicing process can also give rise to more complex RNA patterns in cells. Gene fusions frequently affect tyrosine kinases, chromatin regulators, or transcription factors, and can cause constitutive activation, enhancement of downstream signaling, and tumor development, as major drivers of oncogenesis. In addition, some fusion RNAs have been shown to function as noncoding RNAs and to affect cancer progression. Fusion genes and RNAs will therefore become increasingly important as diagnostic and therapeutic targets for cancer development. Here, we discuss the function, biogenesis, detection, clinical relevance, and therapeutic implications of oncogenic fusion genes and RNAs in cancer development. Further understanding the molecular mechanisms that regulate how fusion RNAs form in cancers is critical to the development of therapeutic strategies against tumorigenesis.
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Affiliation(s)
- Kenzui Taniue
- Isotope Science Center, The University of Tokyo, 2-11-16, Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan
- Cancer Genomics and Precision Medicine, Division of Gastroenterology and Hematology/Oncology, Department of Medicine, Asahikawa Medical University, 2-1 Midorigaoka Higashi, Asahikawa, Hokkaido 078-8510, Japan
| | - Nobuyoshi Akimitsu
- Isotope Science Center, The University of Tokyo, 2-11-16, Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan
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62
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Performances of Targeted RNA Sequencing for the Analysis of Fusion Transcripts, Gene Mutation, and Expression in Hematological Malignancies. Hemasphere 2021; 5:e522. [PMID: 33880432 PMCID: PMC8051993 DOI: 10.1097/hs9.0000000000000522] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 11/13/2020] [Indexed: 11/26/2022] Open
Abstract
RNA sequencing holds great promise to improve the diagnostic of hematological malignancies, because this technique enables to detect fusion transcripts, to look for somatic mutations in oncogenes, and to capture transcriptomic signatures of nosological entities. However, the analytical performances of targeted RNA sequencing have not been extensively described in diagnostic samples. Using a targeted panel of 1385 cancer-related genes in a series of 100 diagnosis samples and 8 controls, we detected all the already known fusion transcripts and also discovered unknown and/or unsuspected fusion transcripts in 12 samples. Regarding the analysis of transcriptomic profiles, we show that targeted RNA sequencing is performant to discriminate acute lymphoblastic leukemia entities driven by different oncogenic translocations. Additionally, we show that 86% of the mutations identified at the DNA level are also detectable at the messenger RNA (mRNA) level, except for nonsense mutations that are subjected to mRNA decay. We conclude that targeted RNA sequencing might improve the diagnosis of hematological malignancies. Standardization of the preanalytical steps and further refinements of the panel design and of the bioinformatical pipelines will be an important step towards its use in standard diagnostic procedures.
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63
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Chen W, Cui W, Qiu Y, Cui D. Research Progress of Chimeric RNA and Health. Health (London) 2021. [DOI: 10.4236/health.2021.134036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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64
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Minati R, Perreault C, Thibault P. A Roadmap Toward the Definition of Actionable Tumor-Specific Antigens. Front Immunol 2020; 11:583287. [PMID: 33424836 PMCID: PMC7793940 DOI: 10.3389/fimmu.2020.583287] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 10/30/2020] [Indexed: 12/15/2022] Open
Abstract
The search for tumor-specific antigens (TSAs) has considerably accelerated during the past decade due to the improvement of proteogenomic detection methods. This provides new opportunities for the development of novel antitumoral immunotherapies to mount an efficient T cell response against one or multiple types of tumors. While the identification of mutated antigens originating from coding exons has provided relatively few TSA candidates, the possibility of enlarging the repertoire of targetable TSAs by looking at antigens arising from non-canonical open reading frames opens up interesting avenues for cancer immunotherapy. In this review, we outline the potential sources of TSAs and the mechanisms responsible for their expression strictly in cancer cells. In line with the heterogeneity of cancer, we propose that discrete families of TSAs may be enriched in specific cancer types.
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Affiliation(s)
- Robin Minati
- École Normale Supérieure de Lyon, Université Claude Bernard Lyon I, Université de Lyon, Lyon, France
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, QC, Canada
| | - Claude Perreault
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, QC, Canada
- Department of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Pierre Thibault
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, QC, Canada
- Department of Chemistry, Université de Montréal, Montréal, QC, Canada
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65
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Fusion genes as biomarkers in pediatric cancers: A review of the current state and applicability in diagnostics and personalized therapy. Cancer Lett 2020; 499:24-38. [PMID: 33248210 DOI: 10.1016/j.canlet.2020.11.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 11/09/2020] [Accepted: 11/11/2020] [Indexed: 12/13/2022]
Abstract
The incidence of pediatric cancers is rising steadily across the world, along with the challenges in understanding the molecular mechanisms and devising effective therapeutic strategies. Pediatric cancers are presented with diverse molecular characteristics and more distinct subtypes when compared to adult cancers. Recent studies on the genomic landscape of pediatric cancers using next-generation sequencing (NGS) approaches have redefined this field by providing better subtype characterization and novel actionable targets. Since early identification and personalized treatment strategies influence therapeutic outcomes, survival, and quality of life in pediatric cancer patients, the quest for actionable biomarkers is of great value in this field. Fusion genes that are prevalent and recurrent in several pediatric cancers are ideally suited in this context due to their disease-specific occurrence. In this review, we explore the current status of fusion genes in pediatric cancer subtypes and their use as biomarkers for diagnosis and personalized therapy. We discuss the technological advancements made in recent years in NGS sequencing and their impact on fusion detection algorithms that have revolutionized this field. Finally, we also discuss the advantages of pairing liquid biopsy protocols for fusion detection and their eventual use in diagnosis and treatment monitoring.
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66
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Lovino M, Ciaburri MS, Urgese G, Di Cataldo S, Ficarra E. DEEPrior: a deep learning tool for the prioritization of gene fusions. Bioinformatics 2020; 36:3248-3250. [PMID: 32016382 PMCID: PMC7214024 DOI: 10.1093/bioinformatics/btaa069] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 01/20/2020] [Accepted: 01/28/2020] [Indexed: 12/21/2022] Open
Abstract
SUMMARY In the last decade, increasing attention has been paid to the study of gene fusions. However, the problem of determining whether a gene fusion is a cancer driver or just a passenger mutation is still an open issue. Here we present DEEPrior, an inherently flexible deep learning tool with two modes (Inference and Retraining). Inference mode predicts the probability of a gene fusion being involved in an oncogenic process, by directly exploiting the amino acid sequence of the fused protein. Retraining mode allows to obtain a custom prediction model including new data provided by the user. AVAILABILITY AND IMPLEMENTATION Both DEEPrior and the protein fusions dataset are freely available from GitHub at (https://github.com/bioinformatics-polito/DEEPrior). The tool was designed to operate in Python 3.7, with minimal additional libraries. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Gianvito Urgese
- Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, Torino 10129, Italy
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67
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Elfman J, Pham LP, Li H. The relationship between chimeric RNAs and gene fusions: Potential implications of reciprocity in cancer. J Genet Genomics 2020; 47:341-348. [PMID: 33008771 DOI: 10.1016/j.jgg.2020.04.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 04/09/2020] [Accepted: 04/20/2020] [Indexed: 12/12/2022]
Affiliation(s)
- Justin Elfman
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA, 22904 USA
| | - Lam-Phong Pham
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, 22904 USA
| | - Hui Li
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA, 22904 USA; Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, 22904 USA.
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68
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Evolution and structure of clinically relevant gene fusions in multiple myeloma. Nat Commun 2020; 11:2666. [PMID: 32471990 PMCID: PMC7260243 DOI: 10.1038/s41467-020-16434-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 04/27/2020] [Indexed: 12/19/2022] Open
Abstract
Multiple myeloma is a plasma cell blood cancer with frequent chromosomal translocations leading to gene fusions. To determine the clinical relevance of fusion events, we detect gene fusions from a cohort of 742 patients from the Multiple Myeloma Research Foundation CoMMpass Study. Patients with multiple clinic visits enable us to track tumor and fusion evolution, and cases with matching peripheral blood and bone marrow samples allow us to evaluate the concordance of fusion calls in patients with high tumor burden. We examine the joint upregulation of WHSC1 and FGFR3 in samples with t(4;14)-related fusions, and we illustrate a method for detecting fusions from single cell RNA-seq. We report fusions at MYC and a neighboring gene, PVT1, which are related to MYC translocations and associated with divergent progression-free survival patterns. Finally, we find that 4% of patients may be eligible for targeted fusion therapies, including three with an NTRK1 fusion. Multiple myeloma is characterised by frequent gene fusions. Here, the authors use data from the Multiple Myeloma Research Foundation CoMMpass Study to further investigate fusion genes in this disease and their clinical relevance.
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69
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Abstract
Chimeric RNAs are hybrid transcripts containing exons from two separate genes. Chimeric RNAs are traditionally considered to be transcribed from fusion genes caused by chromosomal rearrangement. These canonical chimeric RNAs are well characterized to be expressed in a cancer-unique pattern and/or act as oncogene products. However, benefited by the development of advanced deep sequencing technologies, novel types of non-canonical chimeric RNAs have been discovered to be generated from intergenic splicing without genomic aberrations. They can be formed through trans-splicing or cis-splicing between adjacent genes (cis-SAGe) mechanisms. Non-canonical chimeric RNAs are widely detected in normal physiology, although several have been shown to have a cancer-specific expression pattern. Further studies have indicated that some of them play fundamental roles in controlling cell growth and motility, and may have functions independent of the parental genes. These discoveries are unveiling a new layer of the functional transcriptome and are also raising the possibility of utilizing non-canonical chimeric RNAs as cancer diagnostic markers and therapeutic targets. In this chapter, we will overview different categories of chimeric RNAs and their expression in various types of cancerous and normal samples. Acknowledging that chimeric RNAs are not unique to cancer, we will discuss both bioinformatic and biological methods to identify credible cancer-specific chimeric RNAs. Furthermore, we will describe downstream methods to explore their molecular processing mechanisms and potential functions. A better understanding of the biogenesis mechanisms and functional products of cancer-specific chimeric RNAs will pave ways for the development of novel cancer biomarkers and therapeutic targets.
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Affiliation(s)
- Xinrui Shi
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Sandeep Singh
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Emily Lin
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Hui Li
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA, United States; Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, United States.
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70
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Wang L, Yekula A, Muralidharan K, Small JL, Rosh ZS, Kang KM, Carter BS, Balaj L. Novel Gene Fusions in Glioblastoma Tumor Tissue and Matched Patient Plasma. Cancers (Basel) 2020; 12:cancers12051219. [PMID: 32414213 PMCID: PMC7281415 DOI: 10.3390/cancers12051219] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 04/27/2020] [Accepted: 05/07/2020] [Indexed: 11/30/2022] Open
Abstract
Sequencing studies have provided novel insights into the heterogeneous molecular landscape of glioblastoma (GBM), unveiling a subset of patients with gene fusions. Tissue biopsy is highly invasive, limited by sampling frequency and incompletely representative of intra-tumor heterogeneity. Extracellular vesicle-based liquid biopsy provides a minimally invasive alternative to diagnose and monitor tumor-specific molecular aberrations in patient biofluids. Here, we used targeted RNA sequencing to screen GBM tissue and the matched plasma of patients (n = 9) for RNA fusion transcripts. We identified two novel fusion transcripts in GBM tissue and five novel fusions in the matched plasma of GBM patients. The fusion transcripts FGFR3-TACC3 and VTI1A-TCF7L2 were detected in both tissue and matched plasma. A longitudinal follow-up of a GBM patient with a FGFR3-TACC3 positive glioma revealed the potential of monitoring RNA fusions in plasma. In summary, we report a sensitive RNA-seq-based liquid biopsy strategy to detect RNA level fusion status in the plasma of GBM patients.
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Affiliation(s)
- Lan Wang
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA; (L.W.); (A.Y.); (K.M.); (J.L.S.); (Z.S.R.); (K.M.K.)
| | - Anudeep Yekula
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA; (L.W.); (A.Y.); (K.M.); (J.L.S.); (Z.S.R.); (K.M.K.)
| | - Koushik Muralidharan
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA; (L.W.); (A.Y.); (K.M.); (J.L.S.); (Z.S.R.); (K.M.K.)
| | - Julia L. Small
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA; (L.W.); (A.Y.); (K.M.); (J.L.S.); (Z.S.R.); (K.M.K.)
| | - Zachary S. Rosh
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA; (L.W.); (A.Y.); (K.M.); (J.L.S.); (Z.S.R.); (K.M.K.)
| | - Keiko M. Kang
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA; (L.W.); (A.Y.); (K.M.); (J.L.S.); (Z.S.R.); (K.M.K.)
- School of Medicine, University of California San Diego, San Diego, CA 92092, USA
| | - Bob S. Carter
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA; (L.W.); (A.Y.); (K.M.); (J.L.S.); (Z.S.R.); (K.M.K.)
- Correspondence: (B.S.C.); (L.B.)
| | - Leonora Balaj
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA; (L.W.); (A.Y.); (K.M.); (J.L.S.); (Z.S.R.); (K.M.K.)
- Correspondence: (B.S.C.); (L.B.)
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71
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Singh S, Qin F, Kumar S, Elfman J, Lin E, Pham LP, Yang A, Li H. The landscape of chimeric RNAs in non-diseased tissues and cells. Nucleic Acids Res 2020; 48:1764-1778. [PMID: 31965184 PMCID: PMC7038929 DOI: 10.1093/nar/gkz1223] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 12/13/2019] [Accepted: 01/20/2020] [Indexed: 12/17/2022] Open
Abstract
Chimeric RNAs and their encoded proteins have been traditionally viewed as unique features of neoplasia, and have been used as biomarkers and therapeutic targets for multiple cancers. Recent studies have demonstrated that chimeric RNAs also exist in non-cancerous cells and tissues, although large-scale, genome-wide studies of chimeric RNAs in non-diseased tissues have been scarce. Here, we explored the landscape of chimeric RNAs in 9495 non-diseased human tissue samples of 53 different tissues from the GTEx project. Further, we established means for classifying chimeric RNAs, and observed enrichment for particular classifications as more stringent filters are applied. We experimentally validated a subset of chimeric RNAs from each classification and demonstrated functional relevance of two chimeric RNAs in non-cancerous cells. Importantly, our list of chimeric RNAs in non-diseased tissues overlaps with some entries in several cancer fusion databases, raising concerns for some annotations. The data from this study provides a large repository of chimeric RNAs present in non-diseased tissues, which can be used as a control dataset to facilitate the identification of true cancer-specific chimeras.
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Affiliation(s)
- Sandeep Singh
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Fujun Qin
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Shailesh Kumar
- National Institute of Plant Genome Research (NIPGR), New Delhi 110067, India
| | - Justin Elfman
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA.,Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Emily Lin
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Lam-Phong Pham
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Amy Yang
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Hui Li
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA.,Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
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72
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GOLT1A-KISS1 fusion is associated with metastasis in adenoid cystic carcinomas. Biochem Biophys Res Commun 2020; 526:70-77. [PMID: 32192769 DOI: 10.1016/j.bbrc.2020.03.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 03/01/2020] [Indexed: 11/21/2022]
Abstract
Genetic alterations can drive carcinogenesis. Numerous studies have shown that gene fusion is associated with cancer progression and could provide valuable biomarkers for clinical diagnosis or targets for cancer therapy. Adenoid cystic carcinoma (ACC) is a rare form of adenocarcinoma, characterized by frequent local recurrence and high rates of distant metastasis, ultimately resulting in low survival rates. Owing to the lack of effective therapeutic targets and limited biomarkers for diagnosis, a deeper understanding of the molecular basis of ACC is urgently needed. Here, we show that gene fusion is associated with ACC metastasis. We identified a metastasis suppressor KISS1 fused with a close-by gene, GOLT1A, in highly metastatic ACC cell lines and human specimens. Such fusion blocks KISS1 translation, but not transcription, by introducing 5' upstream open reading frames (uORFs) in the GOLT1A-KISS1 fusion transcript. Deletion of these uORFs rescued KISS1 expression and reduced invasion and migration of metastatic ACC cells. We also detected GOLT1A-KISS1 fusion transcripts in other types of highly metastatic cancer cell lines. Taken together, our results highlight the significance of this novel GOLT1A-KISS1 gene fusion in tumor metastasis and provide a valuable biomarker for clinical diagnosis and future therapeutic targeting of ACC.
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73
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Vellichirammal NN, Albahrani A, Banwait JK, Mishra NK, Li Y, Roychoudhury S, Kling MJ, Mirza S, Bhakat KK, Band V, Joshi SS, Guda C. Pan-Cancer Analysis Reveals the Diverse Landscape of Novel Sense and Antisense Fusion Transcripts. MOLECULAR THERAPY. NUCLEIC ACIDS 2020; 19:1379-1398. [PMID: 32160708 PMCID: PMC7044684 DOI: 10.1016/j.omtn.2020.01.023] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 01/03/2020] [Accepted: 01/14/2020] [Indexed: 01/26/2023]
Abstract
Gene fusions that contribute to oncogenicity can be explored for identifying cancer biomarkers and potential drug targets. To investigate the nature and distribution of fusion transcripts in cancer, we examined the transcriptome data of about 9,000 primary tumors from 33 different cancers in TCGA (The Cancer Genome Atlas) along with cell line data from CCLE (Cancer Cell Line Encyclopedia) using ChimeRScope, a novel fusion detection algorithm. We identified several fusions with sense (canonical, 39%) or antisense (non-canonical, 61%) transcripts recurrent across cancers. The majority of the recurrent non-canonical fusions found in our study are novel, unexplored, and exhibited highly variable profiles across cancers, with breast cancer and glioblastoma having the highest and lowest rates, respectively. Overall, 4,344 recurrent fusions were identified from TCGA in this study, of which 70% were novel. Additional analysis of 802 tumor-derived cell line transcriptome data across 20 cancers revealed significant variability in recurrent fusion profiles between primary tumors and corresponding cell lines. A subset of canonical and non-canonical fusions was validated by examining the structural variation evidence in whole-genome sequencing (WGS) data or by Sanger sequencing of fusion junctions. Several recurrent fusion genes identified in our study show promise for drug repurposing in basket trials and present opportunities for mechanistic studies.
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Affiliation(s)
| | - Abrar Albahrani
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Jasjit K Banwait
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA; Bioinformatics and Systems Biology Core. University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Nitish K Mishra
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - You Li
- HitGen, South Keyuan Road 88, Chengdu, China
| | - Shrabasti Roychoudhury
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Mathew J Kling
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Sameer Mirza
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Kishor K Bhakat
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Vimla Band
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Shantaram S Joshi
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Chittibabu Guda
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA; Bioinformatics and Systems Biology Core. University of Nebraska Medical Center, Omaha, NE 68198, USA.
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74
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Persson H, Søkilde R, Häkkinen J, Vallon-Christersson J, Mitelman F, Borg Å, Höglund M, Rovira C. Analysis of fusion transcripts indicates widespread deregulation of snoRNAs and their host genes in breast cancer. Int J Cancer 2020; 146:3343-3353. [PMID: 32067223 DOI: 10.1002/ijc.32927] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 01/23/2020] [Accepted: 01/30/2020] [Indexed: 12/20/2022]
Abstract
Genomic rearrangements in cancer can join the sequences of two separate genes. Studies of such gene fusion events have mainly focused on identification of fusion proteins from the chimeric transcripts. We have previously investigated how fusions instead can affect the expression of intronic microRNA (miRNA) genes that are encoded within fusion gene partners. Here, we extend our analysis to small nucleolar RNAs (snoRNAs) that also are embedded within protein-coding or noncoding host genes. We found that snoRNA hosts are selectively enriched in fusion transcripts, like miRNA host genes, and that this enrichment is associated with all snoRNA classes. These structural changes may have functional consequences for the cell; proteins involved in the protein translation machinery are overrepresented among snoRNA host genes, a gene architecture assumed to be needed for closely coordinated expression of snoRNAs and host proteins. Our data indicate that this structure is frequently disrupted in cancer. We furthermore observed that snoRNA genes involved in fusions tend to associate with stronger promoters than the natural host, suggesting a mechanism that selects for snoRNA overexpression. In summary, we highlight a previously unexplored frequent structural change in cancer that affects important components of cellular physiology.
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Affiliation(s)
- Helena Persson
- Department of Clinical Sciences Lund, Oncology, Lund University Cancer Center, Lund, Sweden
| | - Rolf Søkilde
- Department of Clinical Sciences Lund, Oncology, Lund University Cancer Center, Lund, Sweden.,BioCARE, Strategic Cancer Research Program, Lund, Sweden
| | - Jari Häkkinen
- Department of Clinical Sciences Lund, Oncology, Lund University Cancer Center, Lund, Sweden
| | | | - Felix Mitelman
- Department of Laboratory Medicine, Clinical Genetics, Lund University, Skåne University Hospital, Lund, Sweden
| | - Åke Borg
- Department of Clinical Sciences Lund, Oncology, Lund University Cancer Center, Lund, Sweden.,BioCARE, Strategic Cancer Research Program, Lund, Sweden.,CREATE Health, Strategic Centre for Translational Cancer Research, Lund, Sweden
| | - Mattias Höglund
- Department of Clinical Sciences Lund, Oncology, Lund University Cancer Center, Lund, Sweden
| | - Carlos Rovira
- Department of Clinical Sciences Lund, Oncology, Lund University Cancer Center, Lund, Sweden.,BioCARE, Strategic Cancer Research Program, Lund, Sweden
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75
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Oliver GR, Jenkinson G, Klee EW. Computational Detection of Known Pathogenic Gene Fusions in a Normal Tissue Database and Implications for Genetic Disease Research. Front Genet 2020; 11:173. [PMID: 32180803 PMCID: PMC7059617 DOI: 10.3389/fgene.2020.00173] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 02/13/2020] [Indexed: 11/13/2022] Open
Abstract
Several recent studies have demonstrated the utility of RNA-Seq in the diagnosis of rare inherited disease. Diagnostic rates 35% higher than those previously achievable with DNA-Seq alone have been attained. These studies have primarily profiled gene expression and splicing defects, however, some have also shown that fusion transcripts are diagnostic or phenotypically relevant in patients with constitutional disorders. Fusion transcripts have traditionally been studied as oncogenic phenomena, with relevance only to cancer testing. Consequently, fusion detection algorithms were biased toward the detection of well-known oncogenic fusions, hindering their application to rare Mendelian genetic disease studies. A recent methodology published by the authors successfully tailored a traditional algorithm to the detection of pathogenic fusion events in inherited disease. A key mechanism of decreasing false positive or biologically benign events was comparison to a database of events detected in normal tissues. This approach is akin to population frequency-based filtering of genetic variants. It is predicated on the idea that pathogenic fusion transcripts are absent from normal tissue. We report on an analysis of RNA-Seq data from the genotype-tissue expression (GTEx) project in which known pathogenic fusions are computationally detected at low levels in normal tissues unassociated with the disease phenotype. Examples include archetypal cancer fusion transcripts, as well as fusions responsible for rare inherited disease. We consider potential explanations for the detectability of such transcripts and discuss the bearing such results have on the future profiling of genetic disease patients for pathogenic gene fusions.
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Affiliation(s)
- Gavin Robert Oliver
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, United States.,Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Garrett Jenkinson
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, United States.,Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Eric W Klee
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, United States.,Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
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76
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Crane E, Naumann W, Tait D, Higgins R, Herzog T, Brown J. Molecular variations in uterine carcinosarcomas identify therapeutic opportunities. Int J Gynecol Cancer 2020; 30:480-484. [PMID: 32114514 DOI: 10.1136/ijgc-2019-000920] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 02/04/2020] [Accepted: 02/11/2020] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVE To perform comprehensive genomic profiling on a large cohort of patients with uterine carcinosarcomas to identify potential therapeutic targets. METHODS Molecular profiling was conducted on 168 retrospectively de-identified patients with uterine carcinosarcomas using the Caris Life Sciences platform. Specimens were evaluated for aberrations in protein expression by immunohistochemistry, DNA sequence mutation using a 592-gene next generation sequencing panel, copy number amplification using next generation sequencing or in situ hybridization, and fusion events using NextGen RNA sequencing. Tumor mutational load and microsatellite instability were also evaluated. RESULTS We identified 168 patients with uterine carcinosarcoma; median age of the cohort was 67 years. The most common mutations were observed in the following genes: TP53 (86%), PIK3CA (34%), FBXW7 (23%), PTEN (18%), KRAS (16%), PPP2R1A (10%). Tumor mutational load was low to moderate in most cases (50% and 45%, respectively). HER2/neu (ERBB2) was amplified in 9% of tumors. Immunohistochemistry protein expression was elevated in TOP2A (95%), TS (80%), PTEN (76%), and TUBB3 (66%). Mismatch repair deficiency was rare (4%). CONCLUSIONS Multiple somatic mutations and copy number alterations in genes that are therapeutic targets were identified in half of cases. Uterine carcinosarcomas represent an aggressive histology with limited treatment options and poor outcomes, and clinical trials are needed to validate new therapeutic targets.
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Affiliation(s)
- Erin Crane
- Levine Cancer Institution, Charlotte, North Carolina, USA
| | - Wendel Naumann
- Levine Cancer Institution, Charlotte, North Carolina, USA
| | - David Tait
- Levine Cancer Institution, Charlotte, North Carolina, USA
| | - Robert Higgins
- Levine Cancer Institution, Charlotte, North Carolina, USA
| | | | - Jubilee Brown
- Levine Cancer Institution, Charlotte, North Carolina, USA
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Dou Y, Kawaler EA, Cui Zhou D, Gritsenko MA, Huang C, Blumenberg L, Karpova A, Petyuk VA, Savage SR, Satpathy S, Liu W, Wu Y, Tsai CF, Wen B, Li Z, Cao S, Moon J, Shi Z, Cornwell M, Wyczalkowski MA, Chu RK, Vasaikar S, Zhou H, Gao Q, Moore RJ, Li K, Sethuraman S, Monroe ME, Zhao R, Heiman D, Krug K, Clauser K, Kothadia R, Maruvka Y, Pico AR, Oliphant AE, Hoskins EL, Pugh SL, Beecroft SJI, Adams DW, Jarman JC, Kong A, Chang HY, Reva B, Liao Y, Rykunov D, Colaprico A, Chen XS, Czekański A, Jędryka M, Matkowski R, Wiznerowicz M, Hiltke T, Boja E, Kinsinger CR, Mesri M, Robles AI, Rodriguez H, Mutch D, Fuh K, Ellis MJ, DeLair D, Thiagarajan M, Mani DR, Getz G, Noble M, Nesvizhskii AI, Wang P, Anderson ML, Levine DA, Smith RD, Payne SH, Ruggles KV, Rodland KD, Ding L, Zhang B, Liu T, Fenyö D. Proteogenomic Characterization of Endometrial Carcinoma. Cell 2020; 180:729-748.e26. [PMID: 32059776 PMCID: PMC7233456 DOI: 10.1016/j.cell.2020.01.026] [Citation(s) in RCA: 264] [Impact Index Per Article: 66.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 11/11/2019] [Accepted: 01/16/2020] [Indexed: 02/07/2023]
Abstract
We undertook a comprehensive proteogenomic characterization of 95 prospectively collected endometrial carcinomas, comprising 83 endometrioid and 12 serous tumors. This analysis revealed possible new consequences of perturbations to the p53 and Wnt/β-catenin pathways, identified a potential role for circRNAs in the epithelial-mesenchymal transition, and provided new information about proteomic markers of clinical and genomic tumor subgroups, including relationships to known druggable pathways. An extensive genome-wide acetylation survey yielded insights into regulatory mechanisms linking Wnt signaling and histone acetylation. We also characterized aspects of the tumor immune landscape, including immunogenic alterations, neoantigens, common cancer/testis antigens, and the immune microenvironment, all of which can inform immunotherapy decisions. Collectively, our multi-omic analyses provide a valuable resource for researchers and clinicians, identify new molecular associations of potential mechanistic significance in the development of endometrial cancers, and suggest novel approaches for identifying potential therapeutic targets.
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Affiliation(s)
- Yongchao Dou
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Emily A Kawaler
- Institute for Systems Genetics, NYU School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York, NY 10016, USA
| | - Daniel Cui Zhou
- Department of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Marina A Gritsenko
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Chen Huang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Lili Blumenberg
- Department of Medicine, NYU School of Medicine, New York, NY 10016, USA
| | - Alla Karpova
- Department of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Vladislav A Petyuk
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Sara R Savage
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Shankha Satpathy
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Wenke Liu
- Institute for Systems Genetics, NYU School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York, NY 10016, USA
| | - Yige Wu
- Department of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Chia-Feng Tsai
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Bo Wen
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Zhi Li
- Institute for Systems Genetics, NYU School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York, NY 10016, USA
| | - Song Cao
- Department of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Jamie Moon
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Zhiao Shi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - MacIntosh Cornwell
- Institute for Systems Genetics, NYU School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York, NY 10016, USA
| | - Matthew A Wyczalkowski
- Department of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Rosalie K Chu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Suhas Vasaikar
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Hua Zhou
- Institute for Systems Genetics, NYU School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York, NY 10016, USA
| | - Qingsong Gao
- Department of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Kai Li
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sunantha Sethuraman
- Department of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Matthew E Monroe
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Rui Zhao
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - David Heiman
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Karsten Krug
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Karl Clauser
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ramani Kothadia
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Yosef Maruvka
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alexander R Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Amanda E Oliphant
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Emily L Hoskins
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Samuel L Pugh
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Sean J I Beecroft
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - David W Adams
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Jonathan C Jarman
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Andy Kong
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hui-Yin Chang
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Boris Reva
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yuxing Liao
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Dmitry Rykunov
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Antonio Colaprico
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Division of Biostatistics, Department of Public Health Science, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Xi Steven Chen
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Division of Biostatistics, Department of Public Health Science, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Andrzej Czekański
- Department of Oncology, Wroclaw Medical University, 50-367 Wrocław, Poland; Wroclaw Comprehensive Cancer Center, 53-413 Wrocław, Poland
| | - Marcin Jędryka
- Department of Oncology, Wroclaw Medical University, 50-367 Wrocław, Poland; Wroclaw Comprehensive Cancer Center, 53-413 Wrocław, Poland
| | - Rafał Matkowski
- Department of Oncology, Wroclaw Medical University, 50-367 Wrocław, Poland; Wroclaw Comprehensive Cancer Center, 53-413 Wrocław, Poland
| | - Maciej Wiznerowicz
- Poznan University of Medical Sciences, 61-701 Poznań, Poland; University Hospital of Lord's Transfiguration, 60-569 Poznań, Poland; International Institute for Molecular Oncology, 60-203 Poznań, Poland
| | - Tara Hiltke
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Emily Boja
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Christopher R Kinsinger
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Mehdi Mesri
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - David Mutch
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Katherine Fuh
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Matthew J Ellis
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Deborah DeLair
- Department of Pathology, NYU Langone Health, New York, NY 10016, USA
| | - Mathangi Thiagarajan
- Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - D R Mani
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Gad Getz
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Michael Noble
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Pei Wang
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Matthew L Anderson
- College of Medicine Obstetrics & Gynecology, University of South Florida Health, Tampa, FL 33620, USA
| | - Douglas A Levine
- Gynecologic Oncology, Laura and Isaac Perlmutter Cancer Center, NYU Langone Health, New York, NY 10016, USA
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Samuel H Payne
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Kelly V Ruggles
- Department of Medicine, NYU School of Medicine, New York, NY 10016, USA
| | - Karin D Rodland
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; Department of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, OR 97221, USA.
| | - Li Ding
- Department of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA.
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Tao Liu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA.
| | - David Fenyö
- Institute for Systems Genetics, NYU School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York, NY 10016, USA.
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Fusion transcripts in normal human cortex increase with age and show distinct genomic features for single cells and tissues. Sci Rep 2020; 10:1368. [PMID: 31992760 PMCID: PMC6987184 DOI: 10.1038/s41598-020-58165-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 12/24/2019] [Indexed: 01/24/2023] Open
Abstract
Fusion transcripts can contribute to diversity of molecular networks in the human cortex. In this study, we explored the occurrence of fusion transcripts in normal human cortex along with single neurons and astrocytes. We identified 1305 non-redundant fusion events from 388 transcriptomes representing 59 human cortices and 329 single cells. Our results indicate while the majority of fusion transcripts in human cortex are intra-chromosomal (85%), events found in single neurons and astrocytes were primarily inter-chromosomal (80%). The number of fusions in single neurons was significantly higher than that in single astrocytes (p < 0.05), indicating fusion as a possible contributor towards transcriptome diversity in neuronal cells. The identified fusions were largely private and 4 specific recurring events were found both in cortex and in single neurons but not in astrocytes. We found a significant increase in the number of fusion transcripts in human brain with increasing age both in single cells and whole cortex (p < 0.0005 and < 0.005, respectively). This is likely one of the many possible contributors for the inherent plasticity of the adult brain. The fusion transcripts in fetal brain were enriched for genes for long-term depression; while those in adult brain involved genes enriched for long-term potentiation pathways. Our findings demonstrate fusion transcripts are naturally occurring phenomenon spanning across the health-disease continuum, and likely contribute to the diverse molecular network of human brain.
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Lee SJ, Hong JY, Kim K, Kim KM, Kang SY, Lee T, Kim ST, Park SH, Park YS, Lim HY, Kang WK, Lee J, Park JO. Detection of Fusion Genes Using a Targeted RNA Sequencing Panel in Gastrointestinal and Rare Cancers. JOURNAL OF ONCOLOGY 2020; 2020:4659062. [PMID: 32411236 PMCID: PMC7204148 DOI: 10.1155/2020/4659062] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 12/09/2019] [Accepted: 12/19/2019] [Indexed: 12/22/2022]
Abstract
Successful identification and targeting of oncogenic gene fusion is a major breakthrough in cancer treatment. Here, we investigate the therapeutic implications and feasibility of using a targeted RNA sequencing panel to identify fusion genes in gastrointestinal and rare cancers. From February through December 2017, patients with gastrointestinal, hepatobiliary, gynecologic, sarcoma, or rare cancers were recruited for a clinical sequencing project at Samsung Medical Center (NCT #02593578). The median age of the patients was 58 years (range, 31-81 years), and the male-to-female ratio was 1.3 : 1. A total of 118 patients passed the quality control process for a next-generation sequencing- (NGS-) based targeted sequencing assay. The NGS-based targeted sequencing assay was performed to detect gene fusions in 36-53 cancer-implicated genes. The following cancer types were included in this study: 28 colorectal cancers, 27 biliary tract cancers, 25 gastric cancers, 18 soft tissue sarcomas, 9 pancreatic cancers, 6 ovarian cancers, and 9 other rare cancers. Strong fusion was detected in 25 samples (21.2%). We found that 5.9% (7/118) of patients had known targetable fusion genes involving NTRK1 (n=3), FGFR (n=3), and RET (n=1), and 10.2% (12/118) of patients had potentially targetable fusion genes involving RAF1 (n=4), BRAF (n=2), ALK (n=2), ROS1 (n=1), EGFR (n=1), and CLDN18 (n=2). Thus, we successfully identified a substantial proportion of patients harboring fusion genes by RNA panel sequencing of gastrointestinal/rare cancers. Targetable and potentially targetable involved fusion genes were NTRK1, RET, FGFR3, FGFR2, BRAF, RAF1, ALK, ROS1, and CLDN18. Detection of fusion genes by RNA panel sequencing may be beneficial in refractory patients with gastrointestinal/rare cancers.
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Affiliation(s)
- Su Jin Lee
- Division of Hematology-Oncology, Department of Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Division of Hematology-Oncology, Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul, Republic of Korea
| | - Jung Yong Hong
- Division of Hematology-Oncology, Department of Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyung Kim
- Division of Hematology-Oncology, Department of Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyoung-Mee Kim
- Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - So Young Kang
- Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Taeyang Lee
- Division of Hematology-Oncology, Department of Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Seung Tae Kim
- Division of Hematology-Oncology, Department of Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Se Hoon Park
- Division of Hematology-Oncology, Department of Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Young Suk Park
- Division of Hematology-Oncology, Department of Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Ho Yeong Lim
- Division of Hematology-Oncology, Department of Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Won Ki Kang
- Division of Hematology-Oncology, Department of Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jeeyun Lee
- Division of Hematology-Oncology, Department of Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Joon Oh Park
- Division of Hematology-Oncology, Department of Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
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Balamurali D, Gorohovski A, Detroja R, Palande V, Raviv-Shay D, Frenkel-Morgenstern M. ChiTaRS 5.0: the comprehensive database of chimeric transcripts matched with druggable fusions and 3D chromatin maps. Nucleic Acids Res 2020; 48:D825-D834. [PMID: 31747015 PMCID: PMC7145514 DOI: 10.1093/nar/gkz1025] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/18/2019] [Accepted: 10/26/2019] [Indexed: 12/11/2022] Open
Abstract
Chimeric RNA transcripts are formed when exons from two genes fuse together, often due to chromosomal translocations, transcriptional errors or trans-splicing effect. While these chimeric RNAs produce functional proteins only in certain cases, they play a significant role in disease phenotyping and progression. ChiTaRS 5.0 (http://chitars.md.biu.ac.il/) is the latest and most comprehensive chimeric transcript repository, with 111 582 annotated entries from eight species, including 23 167 known human cancer breakpoints. The database includes unique information correlating chimeric breakpoints with 3D chromatin contact maps, generated from public datasets of chromosome conformation capture techniques (Hi-C). In this update, we have added curated information on druggable fusion targets matched with chimeric breakpoints, which are applicable to precision medicine in cancers. The introduction of a new section that lists chimeric RNAs in various cell-lines is another salient feature. Finally, using text-mining techniques, novel chimeras in Alzheimer's disease, schizophrenia, dyslexia and other diseases were collected in ChiTaRS. Thus, this improved version is an extensive catalogue of chimeras from multiple species. It extends our understanding of the evolution of chimeric transcripts in eukaryotes and contributes to the analysis of 3D genome conformational changes and the functional role of chimeras in the etiopathogenesis of cancers and other complex diseases.
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Affiliation(s)
- Deepak Balamurali
- Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, The Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Alessandro Gorohovski
- Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, The Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Rajesh Detroja
- Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, The Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Vikrant Palande
- Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, The Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Dorith Raviv-Shay
- Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, The Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Milana Frenkel-Morgenstern
- Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, The Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
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Abstract
Knockdown assays are widely used to study the functions of a gene of interest. RNA interference (RNAi) describes a set of well-known methods used to reduce the expression of a target gene by degrading its mRNA with short hairpin RNAs (shRNAs) or short interfering RNAs (siRNAs). Knockdown of chimeric RNAs present different challenges than standard RNAi targeting for regular genes. Most specifically, sequence homology restricts the targeting region to the chimeric junction and can result in off-target effects on the parental genes. In this chapter, we provide guidelines and procedures for RNAi design of chimeric RNAs, knockdown of chimeric RNAs, downstream experiments for chimeric RNA functional studies and necessary controls to accompany each set of experiments.
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Affiliation(s)
- Fujun Qin
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Xinrui Shi
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Hui Li
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, USA.
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82
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Abstract
Chimeric RNAs as well as their fused protein products have therapeutic applications ranging from diagnostics to being used as therapeutic target. Many algorithms have been developed to identify chimeric RNAs, however, identification and validation of fused protein product of the chimeric RNA is still an emerging field. These chimeric proteins can be validated by searching and identifying them in publicly available proteomics datasets. Here we describe the detailed steps for (1) downloading and processing publicly available proteomics datasets, (2) developing fusion peptide database by performing in silico tryptic digestion of chimeric proteins, and (3) software used to identify chimeric peptides in the proteomics data.
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Affiliation(s)
- Sandeep Singh
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Hui Li
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, USA.
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Abstract
Traditional gene fusions are involved in the development of various neoplasias. DUS4L-BCAP29, a chimeric fusion RNA, has been reported to be a cancer-related fusion in prostate and gastric cancers. This chimeric RNA is believed to play a tumorigenic role. Here, we showed that the DUS4L-BCAP29 fusion transcript exists in a variety of normal tissues. It is also present in noncancerous epithelial and fibroblast cell lines. Quantitatively, the fusion transcript has a similar expression level in noncancerous gastric and prostate cell lines and tissues to its expression in cancerous cell lines and tissues. Previously, a loss-of-function approach was used to report a probable functionality for this fusion. However, this approach is not sufficient to prove such functionality. Alternatively, a gain-of-function approach showed that overexpression of DUS4L-BCAP29 promotes cell growth and motility, even in noncancerous cell lines. Finally, we provide further evidence that the fusion transcript is a product of cis-splicing between adjacent genes. In summary, we believe that in contrast to traditional gene fusions, DUS4L-BCAP29 cannot be used as a cancer biomarker. Instead, it is a fusion transcript that exists in normal physiology and its progrowth effect is not unique to cancer situations.
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Abstract
Chimeric RNAs can be formed by trans-splicing from different transcripts or cis-splicing of adjacent genes (cis-SAGe). Cis-SAGe results from read-through transcription of two neighbor genes. To investigate the mechanisms underlying intergenic splicing of adjacent genes, it is important to develop an assay to detect transcriptional read-through. Here, we describe a general RT-PCR based method to confirm the process for cis-SAGe candidates. In this method, we use PCR to amplify cDNA that is reverse transcribed from the read-through precursor mRNA. The result provides a foundation for further downstream mechanistic studies.
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85
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Barresi V, Cosentini I, Scuderi C, Napoli S, Di Bella V, Spampinato G, Condorelli DF. Fusion Transcripts of Adjacent Genes: New Insights into the World of Human Complex Transcripts in Cancer. Int J Mol Sci 2019; 20:ijms20215252. [PMID: 31652751 PMCID: PMC6862657 DOI: 10.3390/ijms20215252] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 10/18/2019] [Accepted: 10/20/2019] [Indexed: 12/12/2022] Open
Abstract
The awareness of genome complexity brought a radical approach to the study of transcriptome, opening eyes to single RNAs generated from two or more adjacent genes according to the present consensus. This kind of transcript was thought to originate only from chromosomal rearrangements, but the discovery of readthrough transcription opens the doors to a new world of fusion RNAs. In the last years many possible intergenic cis-splicing mechanisms have been proposed, unveiling the origins of transcripts that contain some exons of both the upstream and downstream genes. In some cases, alternative mechanisms, such as trans-splicing and transcriptional slippage, have been proposed. Five databases, containing validated and predicted Fusion Transcripts of Adjacent Genes (FuTAGs), are available for the scientific community. A comparative analysis revealed that two of them contain the majority of the results. A complete analysis of the more widely characterized FuTAGs is provided in this review, including their expression pattern in normal tissues and in cancer. Gene structure, intergenic splicing patterns and exon junction sequences have been determined and here reported for well-characterized FuTAGs. The available functional data and the possible roles in cancer progression are discussed.
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Affiliation(s)
- Vincenza Barresi
- Department of Biomedical and Biotechnological Sciences, Section of Medical Biochemistry, University of Catania, 95123 Catania, Italy.
| | - Ilaria Cosentini
- Department of Biomedical and Biotechnological Sciences, Section of Medical Biochemistry, University of Catania, 95123 Catania, Italy.
| | - Chiara Scuderi
- Department of Biomedical and Biotechnological Sciences, Section of Medical Biochemistry, University of Catania, 95123 Catania, Italy.
| | - Salvatore Napoli
- Department of Biomedical and Biotechnological Sciences, Section of Medical Biochemistry, University of Catania, 95123 Catania, Italy.
| | - Virginia Di Bella
- Department of Biomedical and Biotechnological Sciences, Section of Medical Biochemistry, University of Catania, 95123 Catania, Italy.
| | - Giorgia Spampinato
- Department of Biomedical and Biotechnological Sciences, Section of Medical Biochemistry, University of Catania, 95123 Catania, Italy.
| | - Daniele Filippo Condorelli
- Department of Biomedical and Biotechnological Sciences, Section of Medical Biochemistry, University of Catania, 95123 Catania, Italy.
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86
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Haas BJ, Dobin A, Li B, Stransky N, Pochet N, Regev A. Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods. Genome Biol 2019; 20:213. [PMID: 31639029 PMCID: PMC6802306 DOI: 10.1186/s13059-019-1842-9] [Citation(s) in RCA: 315] [Impact Index Per Article: 63.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Accepted: 09/28/2019] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Accurate fusion transcript detection is essential for comprehensive characterization of cancer transcriptomes. Over the last decade, multiple bioinformatic tools have been developed to predict fusions from RNA-seq, based on either read mapping or de novo fusion transcript assembly. RESULTS We benchmark 23 different methods including applications we develop, STAR-Fusion and TrinityFusion, leveraging both simulated and real RNA-seq. Overall, STAR-Fusion, Arriba, and STAR-SEQR are the most accurate and fastest for fusion detection on cancer transcriptomes. CONCLUSION The lower accuracy of de novo assembly-based methods notwithstanding, they are useful for reconstructing fusion isoforms and tumor viruses, both of which are important in cancer research.
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Affiliation(s)
- Brian J. Haas
- Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA
| | - Alexander Dobin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724 USA
| | - Bo Li
- Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA
- Center for Immunology and Inflammatory Diseases, Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02129 USA
| | | | - Nathalie Pochet
- Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA
- Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 USA
| | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA
- Howard Hughes Medical Institute, and Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02140 USA
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87
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Oliver GR, Tang X, Schultz-Rogers LE, Vidal-Folch N, Jenkinson WG, Schwab TL, Gaonkar K, Cousin MA, Nair A, Basu S, Chanana P, Oglesbee D, Klee EW. A tailored approach to fusion transcript identification increases diagnosis of rare inherited disease. PLoS One 2019; 14:e0223337. [PMID: 31577830 PMCID: PMC6774566 DOI: 10.1371/journal.pone.0223337] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 09/18/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND RNA sequencing has been proposed as a means of increasing diagnostic rates in studies of undiagnosed rare inherited disease. Recent studies have reported diagnostic improvements in the range of 7.5-35% by profiling splicing, gene expression quantification and allele specific expression. To-date however, no study has systematically assessed the presence of gene-fusion transcripts in cases of germline disease. Fusion transcripts are routinely identified in cancer studies and are increasingly recognized as having diagnostic, prognostic or therapeutic relevance. Isolated reports exist of fusion transcripts being detected in cases of developmental and neurological phenotypes, and thus, systematic application of fusion detection to germline conditions may further increase diagnostic rates. However, current fusion detection methods are unsuited to the investigation of germline disease due to performance biases arising from their development using tumor, cell-line or in-silico data. METHODS We describe a tailored approach to fusion candidate identification and prioritization in a cohort of 47 undiagnosed, suspected inherited disease patients. We modify an existing fusion transcript detection algorithm by eliminating its cell line-derived filtering steps, and instead, prioritize candidates using a custom workflow that integrates genomic and transcriptomic sequence alignment, biological and technical annotations, customized categorization logic, and phenotypic prioritization. RESULTS We demonstrate that our approach to fusion transcript identification and prioritization detects genuine fusion events excluded by standard analyses and efficiently removes phenotypically unimportant candidates and false positive events, resulting in a reduced candidate list enriched for events with potential phenotypic relevance. We describe the successful genetic resolution of two previously undiagnosed disease cases through the detection of pathogenic fusion transcripts. Furthermore, we report the experimental validation of five additional cases of fusion transcripts with potential phenotypic relevance. CONCLUSIONS The approach we describe can be implemented to enable the detection of phenotypically relevant fusion transcripts in studies of rare inherited disease. Fusion transcript detection has the potential to increase diagnostic rates in rare inherited disease and should be included in RNA-based analytical pipelines aimed at genetic diagnosis.
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Affiliation(s)
- Gavin R. Oliver
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Xiaojia Tang
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Laura E. Schultz-Rogers
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Noemi Vidal-Folch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - W. Garrett Jenkinson
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Tanya L. Schwab
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Krutika Gaonkar
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Margot A. Cousin
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Asha Nair
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Shubham Basu
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Pritha Chanana
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Devin Oglesbee
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, United States of America
- Department of Medical Genetics, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Eric W. Klee
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, United States of America
- Department of Clinical Genomics, Mayo Clinic, Rochester, Minnesota, United States of America
- * E-mail:
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88
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Wu H, Singh S, Shi X, Xie Z, Lin E, Li X, Li H. Functional heritage: the evolution of chimeric RNA into a gene. RNA Biol 2019; 17:125-134. [PMID: 31566065 DOI: 10.1080/15476286.2019.1670038] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Once believed to be unique features of neoplasia, chimeric RNAs are now being discovered in normal physiology. We speculated that some chimeric RNAs may be functional precursors of genes, and that forming chimeric RNA at the transcriptional level may be a 'trial' mechanism before the functional element is fixed into the genome. Supporting this idea, we identified a chimeric RNA, HNRNPA1L2-SUGT1 (H-S), whose sequence is highly similar to that of a 'pseudogene' MRPS31P5. Sequence analysis revealed that MRPS31P5 transcript is more similar to H-S chimeric RNA than its 'parent' gene, MRPS31. Evolutionarily, H-S precedes MRPS31P5, as it can be detected bioinformatically and experimentally in marmosets, which do not yet possess MRPS31P5 in their genome. Conversely, H-S is minimally expressed in humans, while instead, MRPS31P5 is abundantly expressed. Silencing H-S in marmoset cells resulted in similar phenotype as silencing MRPS31P5 in human cells. In addition, whole transcriptome analysis and candidate downstream target validation revealed common signalling pathways shared by the two transcripts. Interestingly, H-S failed to rescue the phenotype caused by silencing MPRS31P5 in human and rhesus cells, whereas MRPS31P5 can at least partially rescue the phenotype caused by silencing H-S in marmoset cells, suggesting that MRPS31P5 may have further evolved into a distinct entity. Thus, multiple lines of evidence support that MRPS31P5 is not truly a pseudogene of MRPS31, but a likely functional descendent of H-S chimera. Instead being a gene fusion product, H-S is a product of cis-splicing between adjacent genes, while MRPS31P5 is likely produced by genome rearrangement.
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Affiliation(s)
- Hao Wu
- Department of Gastrointestinal Surgery, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China.,Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Sandeep Singh
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Xinrui Shi
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Zhongqiu Xie
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Emily Lin
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Xiaorong Li
- Department of Gastrointestinal Surgery, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Hui Li
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, USA.,Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA, USA
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89
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Driver Fusions and Their Implications in the Development and Treatment of Human Cancers. Cell Rep 2019; 23:227-238.e3. [PMID: 29617662 PMCID: PMC5916809 DOI: 10.1016/j.celrep.2018.03.050] [Citation(s) in RCA: 352] [Impact Index Per Article: 70.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 02/25/2018] [Accepted: 03/13/2018] [Indexed: 12/11/2022] Open
Abstract
Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy.
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90
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Wu H, Li X, Li H. Gene fusions and chimeric RNAs, and their implications in cancer. Genes Dis 2019; 6:385-390. [PMID: 31832518 PMCID: PMC6889028 DOI: 10.1016/j.gendis.2019.08.002] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Revised: 08/03/2019] [Accepted: 08/21/2019] [Indexed: 01/26/2023] Open
Abstract
Gene fusions are appreciated as ideal cancer biomarkers and therapeutic targets. Chimeric RNAs are traditionally thought to be products of gene fusions, and thus, also cancer-specific. Recent research has demonstrated that chimeric RNAs can be generated by intergenic splicing in the absence of gene fusion, and such chimeric RNAs are also found in normal physiology. These new findings challenge the traditional theory of chimeric RNAs exclusivity to cancer, and complicates use of chimeric RNAs in cancer detection. Here, we provide an overview of gene fusions and chimeric RNAs, and emphasize their differences. We note that gene fusions are able to generate chimeric RNAs in accordance with the central dogma of biology, and that chimeric RNAs may also be able to influence the generation of the gene fusions per the “horse before the cart” hypothesis. We further expand upon the “horse before the cart” hypothesis, summarizing current evidence in support of the theory and exploring its potential impact on the field.
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Affiliation(s)
- Hao Wu
- Department of Gastrointestinal Surgery, The Third Xiangya Hospital of Central South University, Changsha, Hunan, 410013, China
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, 22908, USA
| | - Xiaorong Li
- Department of Gastrointestinal Surgery, The Third Xiangya Hospital of Central South University, Changsha, Hunan, 410013, China
| | - Hui Li
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, 22908, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA, 22908, USA
- Corresponding author. Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, 22908, USA. Fax: +1 434 2437244. http://lilab.medicine.virginia.edu
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91
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Chwalenia K, Qin F, Singh S, Li H. A cell-based splicing reporter system to identify regulators of cis-splicing between adjacent genes. Nucleic Acids Res 2019; 47:e24. [PMID: 30590765 PMCID: PMC6393300 DOI: 10.1093/nar/gky1288] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 11/14/2018] [Accepted: 12/14/2018] [Indexed: 12/14/2022] Open
Abstract
Chimeric RNAs generated by cis-splicing between adjacent genes (cis-SAGe) are increasingly recognized as a widespread phenomenon. These chimeric messenger RNAs are present in normal human cells, and are also detected in various cancers. The mechanisms for how this group of chimeras is formed are not yet clear, in part due to the lack of a tractable system for their experimental investigation. Here we developed a fast, easy and versatile cell-based reporter system to identify regulators of cis-SAGe. The reporter, consisting of four main cassettes, simultaneously measures the effects of a candidate regulator on cis-SAGe and canonical splicing. Using this cell-based assay, we screened 102 candidate factors involved in RNA pol II cleavage and termination, elongation, splicing, alternative splicing and R-loop formation. We discovered that two factors, SRRM1 and SF3B1, affect not only cis-SAGe chimeras, but also other types of chimeric RNAs in a genome-wide fashion. This system can be used for studying trans-acting factors and cis-acting sequence elements and factors, as well as for screening small molecule inhibitors.
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Affiliation(s)
- Katarzyna Chwalenia
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Fujun Qin
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Sandeep Singh
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Hui Li
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA.,Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA.,School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
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92
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Weinberg BA, Xiu J, Lindberg MR, Shields AF, Hwang JJ, Poorman K, Salem ME, Pishvaian MJ, Holcombe RF, Marshall JL, Morse MA. Molecular profiling of biliary cancers reveals distinct molecular alterations and potential therapeutic targets. J Gastrointest Oncol 2019; 10:652-662. [PMID: 31392046 DOI: 10.21037/jgo.2018.08.18] [Citation(s) in RCA: 100] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Background Biliary tract cancers (BTCs) are a heterogeneous group of aggressive, rare malignancies with limited standard chemotherapeutic options for advanced disease. Recent studies have demonstrated potential novel biliary cancer targets and a possible role for immunotherapy in the treatment of patients with this disease. Intrahepatic cholangiocarcinoma (IHCC), extrahepatic cholangiocarcinoma (EHCC), and gallbladder carcinoma (GBC) are frequently grouped together in clinical trials despite differences in tumor biology. Methods To further investigate tumor biology differences, we profiled 1,502 BTCs using next-generation sequencing (NGS), immunohistochemistry, in situ hybridization, and RNA sequencing. Results IHCCs had higher rates of IDH1, BAP1, and PBRM1 mutations and FGFR2 fusions; EHCCs had higher rates of KRAS, CDKN2A, and BRCA1 mutations; and GBCs had higher rates of homologous recombination repair deficiency and Her2/neu overexpression and amplification. IHCCs and GBCs had higher rates of potential positive predictive biomarkers for immune checkpoint inhibition (PD-L1 expression, high microsatellite instability, and high tumor mutational burden) than EHCCs. Conclusions These findings support clinical molecular profiling of BTCs to inform potential therapeutic selection and clinical trial design based on the primary tumor's site of origin within the biliary tree.
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Affiliation(s)
- Benjamin A Weinberg
- Ruesch Center for the Cure of Gastrointestinal Cancers, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | | | - Michael R Lindberg
- Ruesch Center for the Cure of Gastrointestinal Cancers, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Anthony F Shields
- Department of Oncology, Karmanos Cancer Institute, Wayne State University, Detroit, MI, USA
| | - Jimmy J Hwang
- Levine Cancer Institute, Carolinas HealthCare System, Charlotte, NC, USA
| | | | - Mohamed E Salem
- Levine Cancer Institute, Carolinas HealthCare System, Charlotte, NC, USA
| | - Michael J Pishvaian
- Ruesch Center for the Cure of Gastrointestinal Cancers, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | | | - John L Marshall
- Ruesch Center for the Cure of Gastrointestinal Cancers, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Michael A Morse
- Division of Medical Oncology, Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA
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93
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Not Only Mutations Matter: Molecular Picture of Acute Myeloid Leukemia Emerging from Transcriptome Studies. JOURNAL OF ONCOLOGY 2019; 2019:7239206. [PMID: 31467542 PMCID: PMC6699387 DOI: 10.1155/2019/7239206] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 06/12/2019] [Indexed: 01/08/2023]
Abstract
The last two decades of genome-scale research revealed a complex molecular picture of acute myeloid leukemia (AML). On the one hand, a number of mutations were discovered and associated with AML diagnosis and prognosis; some of them were introduced into diagnostic tests. On the other hand, transcriptome studies, which preceded AML exome and genome sequencing, remained poorly translated into clinics. Nevertheless, gene expression studies significantly contributed to the elucidation of AML pathogenesis and indicated potential therapeutic directions. The power of transcriptomic approach lies in its comprehensiveness; we can observe how genome manifests its function in a particular type of cells and follow many genes in one test. Moreover, gene expression measurement can be combined with mutation detection, as high-impact mutations are often present in transcripts. This review sums up 20 years of transcriptome research devoted to AML. Gene expression profiling (GEP) revealed signatures distinctive for selected AML subtypes and uncovered the additional within-subtype heterogeneity. The results were particularly valuable in the case of AML with normal karyotype which concerns up to 50% of AML cases. With the use of GEP, new classes of the disease were identified and prognostic predictors were proposed. A plenty of genes were detected as overexpressed in AML when compared to healthy control, including KIT, BAALC, ERG, MN1, CDX2, WT1, PRAME, and HOX genes. High expression of these genes constitutes usually an unfavorable prognostic factor. Upregulation of FLT3 and NPM1 genes, independent on their mutation status, was also reported in AML and correlated with poor outcome. However, transcriptome is not limited to the protein-coding genes; other types of RNA molecules exist in a cell and regulate genome function. It was shown that microRNA (miRNA) profiles differentiated AML groups and predicted outcome not worse than protein-coding gene profiles. For example, upregulation of miR-10a, miR-10b, and miR-196b and downregulation of miR-192 were found as typical of AML with NPM1 mutation whereas overexpression of miR-155 was associated with FLT3-internal tandem duplication (FLT3-ITD). Development of high-throughput technologies and microarray replacement by next generation sequencing (RNA-seq) enabled uncovering a real variety of leukemic cell transcriptomes, reflected by gene fusions, chimeric RNAs, alternatively spliced transcripts, miRNAs, piRNAs, long noncoding RNAs (lncRNAs), and their special type, circular RNAs. Many of them can be considered as AML biomarkers and potential therapeutic targets. The relations between particular RNA puzzles and other components of leukemic cells and their microenvironment, such as exosomes, are now under investigation. Hopefully, the results of this research will shed the light on these aspects of AML pathogenesis which are still not completely understood.
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94
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Doulazmi M, Cros C, Dusart I, Trembleau A, Dubacq C. Alternative polyadenylation produces multiple 3' untranslated regions of odorant receptor mRNAs in mouse olfactory sensory neurons. BMC Genomics 2019; 20:577. [PMID: 31299892 PMCID: PMC6624953 DOI: 10.1186/s12864-019-5927-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Accepted: 06/23/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Odorant receptor genes constitute the largest gene family in mammalian genomes and this family has been extensively studied in several species, but to date far less attention has been paid to the characterization of their mRNA 3' untranslated regions (3'UTRs). Given the increasing importance of UTRs in the understanding of RNA metabolism, and the growing interest in alternative polyadenylation especially in the nervous system, we aimed at identifying the alternative isoforms of odorant receptor mRNAs generated through 3'UTR variation. RESULTS We implemented a dedicated pipeline using IsoSCM instead of Cufflinks to analyze RNA-Seq data from whole olfactory mucosa of adult mice and obtained an extensive description of the 3'UTR isoforms of odorant receptor mRNAs. To validate our bioinformatics approach, we exhaustively analyzed the 3'UTR isoforms produced from 2 pilot genes, using molecular approaches including northern blot and RNA ligation mediated polyadenylation test. Comparison between datasets further validated the pipeline and confirmed the alternative polyadenylation patterns of odorant receptors. Qualitative and quantitative analyses of the annotated 3' regions demonstrate that 1) Odorant receptor 3'UTRs are longer than previously described in the literature; 2) More than 77% of odorant receptor mRNAs are subject to alternative polyadenylation, hence generating at least 2 detectable 3'UTR isoforms; 3) Splicing events in 3'UTRs are restricted to a limited subset of odorant receptor genes; and 4) Comparison between male and female data shows no sex-specific differences in odorant receptor 3'UTR isoforms. CONCLUSIONS We demonstrated for the first time that odorant receptor genes are extensively subject to alternative polyadenylation. This ground-breaking change to the landscape of 3'UTR isoforms of Olfr mRNAs opens new avenues for investigating their respective functions, especially during the differentiation of olfactory sensory neurons.
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Affiliation(s)
- Mohamed Doulazmi
- CNRS, Institut de Biologie Paris Seine, Biological adaptation and ageing, B2A, Sorbonne Université, F-75005 Paris, France
| | - Cyril Cros
- CNRS, INSERM, Institut de Biologie Paris Seine, Neuroscience Paris Seine, NPS, Sorbonne Université, F-75005 Paris, France
- Present Address: Columbia University, New York, NY 10027 USA
| | - Isabelle Dusart
- CNRS, INSERM, Institut de Biologie Paris Seine, Neuroscience Paris Seine, NPS, Sorbonne Université, F-75005 Paris, France
| | - Alain Trembleau
- CNRS, INSERM, Institut de Biologie Paris Seine, Neuroscience Paris Seine, NPS, Sorbonne Université, F-75005 Paris, France
| | - Caroline Dubacq
- CNRS, INSERM, Institut de Biologie Paris Seine, Neuroscience Paris Seine, NPS, Sorbonne Université, F-75005 Paris, France
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95
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Ferguson SD, Zhou S, Huse JT, de Groot JF, Xiu J, Subramaniam DS, Mehta S, Gatalica Z, Swensen J, Sanai N, Spetzler D, Heimberger AB. Targetable Gene Fusions Associate With the IDH Wild-Type Astrocytic Lineage in Adult Gliomas. J Neuropathol Exp Neurol 2019; 77:437-442. [PMID: 29718398 PMCID: PMC5961205 DOI: 10.1093/jnen/nly022] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Gene fusions involving oncogenes have been reported in gliomas and may serve as novel therapeutic targets. Using RNA-sequencing, we interrogated a large cohort of gliomas to assess for the incidence of targetable genetic fusions. Gliomas (n = 390) were profiled using the ArcherDx FusionPlex Assay. Fifty-two gene targets were analyzed and fusions with preserved kinase domains were investigated. Overall, 36 gliomas (9%) harbored a total of 37 potentially targetable fusions, the majority of which were found in astrocytomas (n = 34). Within this lineage 11% (25/235) of glioblastomas, 12% (5/42) of anaplastic astrocytomas, 8% (2/25) of grade II astrocytomas, and 33% (2/6) of pilocytic astrocytoma harbored targetable fusions. Fusions were significantly more frequent in IDH wild-type tumors (12%, n = 31/261) relative to IDH mutants (4%; n = 4/109) (p = 0.011). No fusions were seen in oligodendrogliomas. The most frequently observed therapeutically targetable fusions were in FGFR (n = 12), MET (n = 11), and NTRK (n = 8). Several additional novel fusions that have not been previously described in gliomas were identified including EGFR:VWC2 and FGFR3:NBR1. In summary, targetable gene fusions are enriched in IDH wild-type high-grade astrocytic tumors, which will influence enrollment in and interpretation of clinical trials of glioma patients.
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Affiliation(s)
- Sherise D Ferguson
- Neurosurgery, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Shouhao Zhou
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jason T Huse
- Department of Neuropathology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - John F de Groot
- Department of Neuro-Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | | | - Deepa S Subramaniam
- Lombardi Comprehensive Cancer Center, Georgetown University, Washington, District of Columbia
| | - Shwetal Mehta
- Barrow Neurological Institute and Barrow Neurosurgical Associates, Phoenix, Arizona
| | | | | | - Nader Sanai
- Barrow Neurological Institute and Barrow Neurosurgical Associates, Phoenix, Arizona
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96
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Grioni A, Fazio G, Rigamonti S, Bystry V, Daniele G, Dostalova Z, Quadri M, Saitta C, Silvestri D, Songia S, Storlazzi CT, Biondi A, Darzentas N, Cazzaniga G. A Simple RNA Target Capture NGS Strategy for Fusion Genes Assessment in the Diagnostics of Pediatric B-cell Acute Lymphoblastic Leukemia. Hemasphere 2019; 3:e250. [PMID: 31723839 PMCID: PMC6746019 DOI: 10.1097/hs9.0000000000000250] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2018] [Revised: 04/04/2019] [Accepted: 04/04/2019] [Indexed: 02/07/2023] Open
Abstract
Supplemental Digital Content is available in the text Acute lymphoblastic leukemia (ALL) is the most frequent pediatric cancer. Fusion genes are hallmarks of ALL, and they are used as biomarkers for risk stratification as well as targets for precision medicine. Hence, clinical diagnostics pursues broad and comprehensive strategies for accurate discovery of fusion genes. Currently, the gold standard methodologies for fusion gene detection are fluorescence in situ hybridization and polymerase chain reaction; these, however, lack sensitivity for the identification of new fusion genes and breakpoints. In this study, we implemented a simple operating procedure (OP) for detecting fusion genes. The OP employs RNA CaptureSeq, a versatile and effortless next-generation sequencing assay, and an in-house as well as a purpose-built bioinformatics pipeline for the subsequent data analysis. The OP was evaluated on a cohort of 89 B-cell precursor ALL (BCP-ALL) pediatric samples annotated as negative for fusion genes by the standard techniques. The OP confirmed 51 samples as negative for fusion genes, and, more importantly, it identified known (KMT2A rearrangements) as well as new fusion events (JAK2 rearrangements) in the remaining 38 investigated samples, of which 16 fusion genes had prognostic significance. Herein, we describe the OP and its deployment into routine ALL diagnostics, which will allow substantial improvements in both patient risk stratification and precision medicine.
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Affiliation(s)
- Andrea Grioni
- Centro Ricerca Tettamanti, Clinica Pediatrica, Università degli Studi di Milano-Bicocca, Fondazione MBBM/Ospedale S. Gerardo, Monza, Italy.,Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Grazia Fazio
- Centro Ricerca Tettamanti, Clinica Pediatrica, Università degli Studi di Milano-Bicocca, Fondazione MBBM/Ospedale S. Gerardo, Monza, Italy
| | - Silvia Rigamonti
- Centro Ricerca Tettamanti, Clinica Pediatrica, Università degli Studi di Milano-Bicocca, Fondazione MBBM/Ospedale S. Gerardo, Monza, Italy
| | - Vojtech Bystry
- Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Giulia Daniele
- Department of Biology, University of Bari "Aldo Moro", Bari, Italy
| | - Zuzana Dostalova
- Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Manuel Quadri
- Centro Ricerca Tettamanti, Clinica Pediatrica, Università degli Studi di Milano-Bicocca, Fondazione MBBM/Ospedale S. Gerardo, Monza, Italy
| | - Claudia Saitta
- Centro Ricerca Tettamanti, Clinica Pediatrica, Università degli Studi di Milano-Bicocca, Fondazione MBBM/Ospedale S. Gerardo, Monza, Italy.,Cancer Center, Humanitas Research Hospital, Humanitas University, Rozzano, Milan, Italy
| | - Daniela Silvestri
- Center of Biostatistics for Clinical Epidemiology, Department of Health Science, University of Milano-Bicocca, Milan, Italy.,Pediatric Hematology-Oncology Unit, Department of Pediatrics, University of Milano-Bicocca, MBBM Foundation/ASST Monza, Monza, Italy
| | - Simona Songia
- Centro Ricerca Tettamanti, Clinica Pediatrica, Università degli Studi di Milano-Bicocca, Fondazione MBBM/Ospedale S. Gerardo, Monza, Italy
| | | | - Andrea Biondi
- Centro Ricerca Tettamanti, Clinica Pediatrica, Università degli Studi di Milano-Bicocca, Fondazione MBBM/Ospedale S. Gerardo, Monza, Italy.,Clinica Pediatrica, Università degli Studi di Milano-Bicocca, Fondazione MBBM/Ospedale S. Gerardo, Monza, Italy
| | - Nikos Darzentas
- Central European Institute of Technology, Masaryk University, Brno, Czech Republic.,Department of Hematology, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Giovanni Cazzaniga
- Centro Ricerca Tettamanti, Clinica Pediatrica, Università degli Studi di Milano-Bicocca, Fondazione MBBM/Ospedale S. Gerardo, Monza, Italy
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97
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Tang Y, Ma S, Wang X, Xing Q, Huang T, Liu H, Li Q, Zhang Y, Zhang K, Yao M, Yang GL, Li H, Zang X, Yang B, Guan F. Identification of chimeric RNAs in human infant brains and their implications in neural differentiation. Int J Biochem Cell Biol 2019; 111:19-26. [DOI: 10.1016/j.biocel.2019.03.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 03/06/2019] [Accepted: 03/30/2019] [Indexed: 02/07/2023]
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98
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A Deep Learning Approach to the Screening of Oncogenic Gene Fusions in Humans. Int J Mol Sci 2019; 20:ijms20071645. [PMID: 30987060 PMCID: PMC6480333 DOI: 10.3390/ijms20071645] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2019] [Revised: 03/21/2019] [Accepted: 03/29/2019] [Indexed: 12/28/2022] Open
Abstract
Gene fusions have a very important role in the study of cancer development. In this regard, predicting the probability of protein fusion transcripts of developing into a cancer is a very challenging and yet not fully explored research problem. To this date, all the available approaches in literature try to explain the oncogenic potential of gene fusions based on protein domain analysis, that is cancer-specific and not easy to adapt to newly developed information. In our work, we choose the raw protein sequences as the input baseline, and propose the use of deep learning, and more specifically Convolutional Neural Networks, to infer the oncogenity probability score of gene fusion transcripts and to group them into a number of categories (e.g., oncogenic/not oncogenic). This is an inherently flexible methodology that, unlike previous approaches, can be re-trained with very less efforts on newly available data (for example, from a different cancer). Based on experimental results on a large dataset of pre-annotated gene fusions, our method is able to predict the oncogenity potential of gene fusion transcripts with accuracy of about 72%, which increases to 86% if we consider the only instances that are classified with a high confidence level.
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99
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Interfering Expression of Chimeric Transcript SEPT7P2-PSPH Promotes Cell Proliferation in Patients with Nasopharyngeal Carcinoma. JOURNAL OF ONCOLOGY 2019; 2019:1654724. [PMID: 31057610 PMCID: PMC6463592 DOI: 10.1155/2019/1654724] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Revised: 01/09/2019] [Accepted: 02/03/2019] [Indexed: 01/09/2023]
Abstract
Introduction Nasopharyngeal carcinoma (NPC) is a distinct type of head and neck cancer which is mostly prevalent in southern China. The development of NPC involves accumulation of multiple genetic changes. Chromosomal translocation is always thought to be accompanied with the fusion chimeric products. To data, the role of the fusion chimeric transcript remains obscure. Materials and Methods We performed RNA sequencing to detect the fusion genes in ten NPC tissues. Sanger sequencing and quantitative RT-PCR were used to measure the level of the fusion chimeric transcript in NPC tissues and cell lines. The functional experiments such as CCK8 assay, colony formation, and migration/invasion were conducted to analyze the role of this transcript in NPC in vitro. Results We demonstrated that the chimeric transcript SEPT7P2-PSPH was formed by trans-splicing of adjacent genes in the absence of chromosomal rearrangement and observed in both NPC patients and cell lines in parallel. Low-expression of the SEPT7P2-PSPH chimeric transcript induced the protein expression of PSPH and promoted cell proliferation, metastasis/invasion, and transforming ability in vitro. Conclusions Our findings indicate that the chimeric transcript SEPT7P2-PSPH is a product of trans-splicing of two adjacent genes and might be a tumor suppressor gene, potentially having the role of anticancer activity.
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100
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Heyer EE, Deveson IW, Wooi D, Selinger CI, Lyons RJ, Hayes VM, O'Toole SA, Ballinger ML, Gill D, Thomas DM, Mercer TR, Blackburn J. Diagnosis of fusion genes using targeted RNA sequencing. Nat Commun 2019; 10:1388. [PMID: 30918253 PMCID: PMC6437215 DOI: 10.1038/s41467-019-09374-9] [Citation(s) in RCA: 107] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Accepted: 02/22/2019] [Indexed: 01/05/2023] Open
Abstract
Fusion genes are a major cause of cancer. Their rapid and accurate diagnosis can inform clinical action, but current molecular diagnostic assays are restricted in resolution and throughput. Here, we show that targeted RNA sequencing (RNAseq) can overcome these limitations. First, we establish that fusion gene detection with targeted RNAseq is both sensitive and quantitative by optimising laboratory and bioinformatic variables using spike-in standards and cell lines. Next, we analyse a clinical patient cohort and improve the overall fusion gene diagnostic rate from 63% with conventional approaches to 76% with targeted RNAseq while demonstrating high concordance for patient samples with previous diagnoses. Finally, we show that targeted RNAseq offers additional advantages by simultaneously measuring gene expression levels and profiling the immune-receptor repertoire. We anticipate that targeted RNAseq will improve clinical fusion gene detection, and its increasing use will provide a deeper understanding of fusion gene biology. Rapid and accurate detection of fusion genes is important in cancer diagnostics. Here, the authors demonstrate that targeted RNA sequencing provides fast, sensitive and quantitative gene fusion detection and overcomes the limitations of approaches currently in clinical use.
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Affiliation(s)
- Erin E Heyer
- Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia
| | - Ira W Deveson
- Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia.,St. Vincent's Clinical School, UNSW Australia, Sydney, 2031, NSW, Australia
| | - Danson Wooi
- Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia.,St. Vincent's Clinical School, UNSW Australia, Sydney, 2031, NSW, Australia
| | - Christina I Selinger
- Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital, Sydney, 2050, NSW, Australia
| | - Ruth J Lyons
- Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia
| | - Vanessa M Hayes
- Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia.,St. Vincent's Clinical School, UNSW Australia, Sydney, 2031, NSW, Australia.,Faculty of Health Sciences, University of Limpopo, Turfloop Campus, Mankweng, 0727, South Africa.,School of Health Systems and Public Health, University of Pretoria, Pretoria, 0002, South Africa.,Central Clinical School, University of Sydney, Sydney, 2006, NSW, Australia
| | - Sandra A O'Toole
- St. Vincent's Clinical School, UNSW Australia, Sydney, 2031, NSW, Australia.,Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital, Sydney, 2050, NSW, Australia.,Central Clinical School, University of Sydney, Sydney, 2006, NSW, Australia.,The Kinghorn Cancer Centre and Cancer Division, Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia.,Australian Clinical Labs, Sydney, 2010, NSW, Australia
| | - Mandy L Ballinger
- The Kinghorn Cancer Centre and Cancer Division, Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia
| | - Devinder Gill
- Department of Haematology, Princess Alexandra Hospital, Brisbane, 4102, QLD, Australia
| | - David M Thomas
- The Kinghorn Cancer Centre and Cancer Division, Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia
| | - Tim R Mercer
- Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia. .,St. Vincent's Clinical School, UNSW Australia, Sydney, 2031, NSW, Australia. .,Altius Institute for Biomedical Sciences, Seattle, 98121, WA, USA.
| | - James Blackburn
- Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, 2010, NSW, Australia. .,St. Vincent's Clinical School, UNSW Australia, Sydney, 2031, NSW, Australia.
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