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Cotto KC, Feng YY, Ramu A, Richters M, Freshour SL, Skidmore ZL, Xia H, McMichael JF, Kunisaki J, Campbell KM, Chen THP, Rozycki EB, Adkins D, Devarakonda S, Sankararaman S, Lin Y, Chapman WC, Maher CA, Arora V, Dunn GP, Uppaluri R, Govindan R, Griffith OL, Griffith M. Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer. Nat Commun 2023; 14:1589. [PMID: 36949070 PMCID: PMC10033906 DOI: 10.1038/s41467-023-37266-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 03/08/2023] [Indexed: 03/24/2023] Open
Abstract
Somatic mutations within non-coding regions and even exons may have unidentified regulatory consequences that are often overlooked in analysis workflows. Here we present RegTools ( www.regtools.org ), a computationally efficient, free, and open-source software package designed to integrate somatic variants from genomic data with splice junctions from bulk or single cell transcriptomic data to identify variants that may cause aberrant splicing. We apply RegTools to over 9000 tumor samples with both tumor DNA and RNA sequence data. RegTools discovers 235,778 events where a splice-associated variant significantly increases the splicing of a particular junction, across 158,200 unique variants and 131,212 unique junctions. To characterize these somatic variants and their associated splice isoforms, we annotate them with the Variant Effect Predictor, SpliceAI, and Genotype-Tissue Expression junction counts and compare our results to other tools that integrate genomic and transcriptomic data. While many events are corroborated by the aforementioned tools, the flexibility of RegTools also allows us to identify splice-associated variants in known cancer drivers, such as TP53, CDKN2A, and B2M, and other genes.
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Affiliation(s)
- Kelsy C Cotto
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Yang-Yang Feng
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Avinash Ramu
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Megan Richters
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Sharon L Freshour
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Zachary L Skidmore
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Huiming Xia
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Joshua F McMichael
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Jason Kunisaki
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Katie M Campbell
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Timothy Hung-Po Chen
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Emily B Rozycki
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Douglas Adkins
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Siddhartha Devarakonda
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Sumithra Sankararaman
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Yiing Lin
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - William C Chapman
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Christopher A Maher
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Vivek Arora
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Gavin P Dunn
- Department of Neurosurgery, Mass General Hospital, Boston, MA, USA
- Center for Brain Tumor Immunology and Immunotherapy, Mass General Hospital, Boston, MA, USA
| | - Ravindra Uppaluri
- Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ramaswamy Govindan
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Obi L Griffith
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA.
| | - Malachi Griffith
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA.
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Pan YJ, Liu BW, Pei DS. The Role of Alternative Splicing in Cancer: Regulatory Mechanism, Therapeutic Strategy, and Bioinformatics Application. DNA Cell Biol 2022; 41:790-809. [PMID: 35947859 DOI: 10.1089/dna.2022.0322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
[Formula: see text] Alternative splicing (AS) can generate distinct transcripts and subsequent isoforms that play differential functions from the same pre-mRNA. Recently, increasing numbers of studies have emerged, unmasking the association between AS and cancer. In this review, we arranged AS events that are closely related to cancer progression and presented promising treatments based on AS for cancer therapy. Obtaining proliferative capacity, acquiring invasive properties, gaining angiogenic features, shifting metabolic ability, and getting immune escape inclination are all splicing events involved in biological processes. Spliceosome-targeted and antisense oligonucleotide technologies are two novel strategies that are hopeful in tumor therapy. In addition, bioinformatics applications based on AS were summarized for better prediction and elucidation of regulatory routines mingled in. Together, we aimed to provide a better understanding of complicated AS events associated with cancer biology and reveal AS a promising target of cancer treatment in the future.
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Affiliation(s)
- Yao-Jie Pan
- Department of Pathology, Laboratory of Clinical and Experimental Pathology, Xuzhou Medical University, Xuzhou, China
| | - Bo-Wen Liu
- Department of General Surgery, Xuzhou Medical University, Xuzhou, China
| | - Dong-Sheng Pei
- Department of Pathology, Laboratory of Clinical and Experimental Pathology, Xuzhou Medical University, Xuzhou, China
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Rao S, Pitel B, Wagner AH, Boca SM, McCoy M, King I, Gupta S, Park BH, Warner JL, Chen J, Rogan PK, Chakravarty D, Griffith M, Griffith OL, Madhavan S. Collaborative, Multidisciplinary Evaluation of Cancer Variants Through Virtual Molecular Tumor Boards Informs Local Clinical Practices. JCO Clin Cancer Inform 2020; 4:602-613. [PMID: 32644817 PMCID: PMC7397775 DOI: 10.1200/cci.19.00169] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/18/2020] [Indexed: 12/17/2022] Open
Abstract
PURPOSE The cancer research community is constantly evolving to better understand tumor biology, disease etiology, risk stratification, and pathways to novel treatments. Yet the clinical cancer genomics field has been hindered by redundant efforts to meaningfully collect and interpret disparate data types from multiple high-throughput modalities and integrate into clinical care processes. Bespoke data models, knowledgebases, and one-off customized resources for data analysis often lack adequate governance and quality control needed for these resources to be clinical grade. Many informatics efforts focused on genomic interpretation resources for neoplasms are underway to support data collection, deposition, curation, harmonization, integration, and analytics to support case review and treatment planning. METHODS In this review, we evaluate and summarize the landscape of available tools, resources, and evidence used in the evaluation of somatic and germline tumor variants within the context of molecular tumor boards. RESULTS Molecular tumor boards (MTBs) are collaborative efforts of multidisciplinary cancer experts equipped with genomic interpretation resources to aid in the delivery of accurate and timely clinical interpretations of complex genomic results for each patient, within an institution or hospital network. Virtual MTBs (VMTBs) provide an online forum for collaborative governance, provenance, and information sharing between experts outside a given hospital network with the potential to enhance MTB discussions. Knowledge sharing in VMTBs and communication with guideline-developing organizations can lead to progress evidenced by data harmonization across resources, crowd-sourced and expert-curated genomic assertions, and a more informed and explainable usage of artificial intelligence. CONCLUSION Advances in cancer genomics interpretation aid in better patient and disease classification, more streamlined identification of relevant literature, and a more thorough review of available treatments and predicted patient outcomes.
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Affiliation(s)
- Shruti Rao
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC
| | - Beth Pitel
- Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Alex H. Wagner
- McDonnell Genome Institute and Department of Medicine, Washington University School of Medicine, St Louis, MO
| | - Simina M. Boca
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC
| | - Matthew McCoy
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC
| | - Ian King
- Laboratory Medicine Program, University Health Network and Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Samir Gupta
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC
| | - Ben Ho Park
- Division of Hematology and Oncology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN
| | - Jeremy L. Warner
- Departments of Medicine and Biomedical Informatics, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN
| | - James Chen
- Division of Medical Oncology, Department of Biomedical Informatics, The Ohio State University, Columbus, OH
| | - Peter K. Rogan
- Departments of Biochemistry and Oncology, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Debyani Chakravarty
- Kravis Center of Molecular Oncology, Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Malachi Griffith
- McDonnell Genome Institute and Department of Medicine, Washington University School of Medicine, St Louis, MO
| | - Obi L. Griffith
- McDonnell Genome Institute and Department of Medicine, Washington University School of Medicine, St Louis, MO
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC
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Tanimoto K, Muramatsu T, Inazawa J. Massive computational identification of somatic variants in exonic splicing enhancers using The Cancer Genome Atlas. Cancer Med 2019; 8:7372-7384. [PMID: 31631560 PMCID: PMC6885893 DOI: 10.1002/cam4.2619] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 10/01/2019] [Accepted: 10/02/2019] [Indexed: 12/26/2022] Open
Abstract
Owing to the development of next-generation sequencing (NGS) technologies, a large number of somatic variants have been identified in various types of cancer. However, the functional significance of most somatic variants remains unknown. Somatic variants that occur in exonic splicing enhancer (ESE) regions are thought to prevent serine and arginine-rich (SR) proteins from binding to ESE sequence motifs, which leads to exon skipping. We computationally identified somatic variants in ESEs by compiling numerous open-access datasets from The Cancer Genome Atlas (TCGA). Using somatic variants and RNA-seq data from 9635 patients across 32 TCGA projects, we identified 646 ESE-disrupting variants. The false positive rate of our method, estimated using a permutation test, was approximately 1%. Of these ESE-disrupting variants, approximately 71% were located in the binding motifs of four classical SR proteins. ESE-disrupting variants occurred in proportion to the number of somatic variants, but not necessarily in the specific genes associated with the biological processes of cancer. Existing bioinformatics tools could not predict the pathogenicity of ESE-disrupting variants identified in this study, although these variants could cause exon skipping. We demonstrated that ESE-disrupting nonsense variants tended to escape nonsense-mediated decay surveillance. Using integrated analyses of open access data, we could specifically identify ESE-disrupting variants. We have generated a powerful tool, which can handle datasets without normal samples or raw data, and thus contribute to reducing variants of uncertain significance because our statistical approach only uses the exon-junction read counts from the tumor samples.
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Affiliation(s)
- Kousuke Tanimoto
- Genome Laboratory, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, Japan.,Genomics Research Support Unit, Research Core, Tokyo Medical and Dental University (TMDU), Japan, Tokyo, Japan
| | - Tomoki Muramatsu
- Department of Molecular Cytogenetics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Johji Inazawa
- Department of Molecular Cytogenetics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, Japan.,Bioresource Research Center, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
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