Zhijing Tan1, Jianhui Zhu1, Paul M Stemmer2, Liangliang Sun3, Zhichang Yang3, Kendall Schultz4, Matthew J Gaffrey4, Anthony J Cesnik5, Xinpei Yi6, Xiaohu Hao7, Michael R Shortreed8, Tujin Shi4, David M Lubman1. 1. Department of Surgery, The University of Michigan, Ann Arbor, Michigan 48109, United States. 2. Institute of Environmental Health Sciences, Wayne State University, Detroit, Michigan 48202, United States. 3. Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States. 4. Integrative Omics Group, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States. 5. Department of Genetics, Stanford University, Stanford, California 94305, United States. 6. Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas 77030, United States. 7. Shanghai Institutes for Biological Science, Chinese Academy of Science, Shanghai 200031, China. 8. Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States.
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.
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 n class="CellLine">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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