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Tan Z, Zhu J, Stemmer PM, Sun L, Yang Z, Schultz K, Gaffrey MJ, Cesnik AJ, Yi X, Hao X, Shortreed MR, Shi T, Lubman DM. Comprehensive Detection of Single Amino Acid Variants and Evaluation of Their Deleterious Potential in a PANC-1 Cell Line. J Proteome Res 2020; 19:1635-1646. [PMID: 32058723 DOI: 10.1021/acs.jproteome.9b00840] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Identifying single amino acid variants (SAAVs) in cancer is critical for precision oncology. Several advanced algorithms are now available to identify SAAVs, but attempts to combine different algorithms and optimize them on large data sets to achieve a more comprehensive coverage of SAAVs have not been implemented. Herein, we report an expanded detection of SAAVs in the PANC-1 cell line using three different strategies, which results in the identification of 540 SAAVs in the mass spectrometry data. Among the set of 540 SAAVs, 79 are evaluated as deleterious SAAVs based on analysis using the novel AssVar software in which one of the driver mutations found in each protein of KRAS, TP53, and SLC37A4 is further validated using independent selected reaction monitoring (SRM) analysis. Our study represents the most comprehensive discovery of SAAVs to date and the first large-scale detection of deleterious SAAVs in the PANC-1 cell line. This work may serve as the basis for future research in pancreatic cancer and personal immunotherapy and treatment.
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Affiliation(s)
- Zhijing Tan
- Department of Surgery, The University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Jianhui Zhu
- Department of Surgery, The University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Paul M Stemmer
- Institute of Environmental Health Sciences, Wayne State University, Detroit, Michigan 48202, United States
| | - Liangliang Sun
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Zhichang Yang
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kendall Schultz
- Integrative Omics Group, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Matthew J Gaffrey
- Integrative Omics Group, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Anthony J Cesnik
- Department of Genetics, Stanford University, Stanford, California 94305, United States
| | - Xinpei Yi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas 77030, United States
| | - Xiaohu Hao
- Shanghai Institutes for Biological Science, Chinese Academy of Science, Shanghai 200031, China
| | - Michael R Shortreed
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Tujin Shi
- Integrative Omics Group, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - David M Lubman
- Department of Surgery, The University of Michigan, Ann Arbor, Michigan 48109, United States
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Boonen K, Hens K, Menschaert G, Baggerman G, Valkenborg D, Ertaylan G. Beyond Genes: Re-Identifiability of Proteomic Data and Its Implications for Personalized Medicine. Genes (Basel) 2019; 10:E682. [PMID: 31492022 PMCID: PMC6770961 DOI: 10.3390/genes10090682] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 08/30/2019] [Accepted: 09/01/2019] [Indexed: 02/07/2023] Open
Abstract
The increasing availability of high throughput proteomics data provides us with opportunities as well as posing new ethical challenges regarding data privacy and re-identifiability of participants. Moreover, the fact that proteomics represents a level between the genotype and the phenotype further exacerbates the situation, introducing dilemmas related to publicly available data, anonymization, ownership of information and incidental findings. In this paper, we try to differentiate proteomics from genomics data and cover the ethical challenges related to proteomics data sharing. Finally, we give an overview of the proposed solutions and the outlook for future studies.
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Affiliation(s)
- Kurt Boonen
- VITO Health, Boeretang 200, Mol 2400, Belgium.
- Centre for Proteomics, University of Antwerpen, Antwerp 2020, Belgium.
| | - Kristien Hens
- Department of Philosophy, University of Antwerp, Antwerp 2000 & Institute of Philosophy, KU Leuven, Leuven 3000, Belgium.
| | - Gerben Menschaert
- Biobix, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent 9000, Belgium.
| | - Geert Baggerman
- VITO Health, Boeretang 200, Mol 2400, Belgium.
- Centre for Proteomics, University of Antwerpen, Antwerp 2020, Belgium.
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Muth T, Hartkopf F, Vaudel M, Renard BY. A Potential Golden Age to Come-Current Tools, Recent Use Cases, and Future Avenues for De Novo Sequencing in Proteomics. Proteomics 2018; 18:e1700150. [PMID: 29968278 DOI: 10.1002/pmic.201700150] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 05/23/2018] [Indexed: 01/15/2023]
Abstract
In shotgun proteomics, peptide and protein identification is most commonly conducted using database search engines, the method of choice when reference protein sequences are available. Despite its widespread use the database-driven approach is limited, mainly because of its static search space. In contrast, de novo sequencing derives peptide sequence information in an unbiased manner, using only the fragment ion information from the tandem mass spectra. In recent years, with the improvements in MS instrumentation, various new methods have been proposed for de novo sequencing. This review article provides an overview of existing de novo sequencing algorithms and software tools ranging from peptide sequencing to sequence-to-protein mapping. Various use cases are described for which de novo sequencing was successfully applied. Finally, limitations of current methods are highlighted and new directions are discussed for a wider acceptance of de novo sequencing in the community.
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Affiliation(s)
- Thilo Muth
- Bioinformatics Unit (MF 1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, 13353, Berlin, Germany
| | - Felix Hartkopf
- Bioinformatics Unit (MF 1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, 13353, Berlin, Germany
| | - Marc Vaudel
- K.G. Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, 5020, Bergen, Norway.,Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, 5020, Bergen, Norway
| | - Bernhard Y Renard
- Bioinformatics Unit (MF 1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, 13353, Berlin, Germany
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