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Okada M, Shimizu K, Nakazato H, Yamasaki S, Fujii SI. Detection of mutant antigen-specific T cell receptors against multiple myeloma for T cell engineering. Mol Ther Methods Clin Dev 2023; 29:541-555. [PMID: 37359417 PMCID: PMC10285226 DOI: 10.1016/j.omtm.2023.05.014] [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: 03/21/2023] [Accepted: 05/12/2023] [Indexed: 06/28/2023]
Abstract
Multiple myeloma (MM) remains an incurable hematological neoplasm. Neoantigen-specific T cell receptor (TCR)-engineered T (TCR-T) cell therapy is a potential alternative treatment. Particularly, TCRs derived from a third-party donor may cover broad ranges of neoantigens, whereas TCRs in patients suffering from immune disorders are limited. However, the efficacy and feasibility of treating MM have not been evaluated thoroughly. In this study, we established a system for identifying immunogenic mutant antigens on MM cells and their corresponding TCRs using healthy donor-derived peripheral blood mononuclear cells (PBMCs). Initially, the immune responses to 35 candidate peptides predicted by the immunogenomic analysis were investigated. Peptide-reactive T lymphocytes were enriched, and subsequently, TCR repertoires were determined by single-cell TCR sequencing. Eleven reconstituted TCRs showed mutation-specific responses against 4 peptides. Particularly, we verified the HLA-A∗24:02-binding QYSPVQATF peptide derived from COASY S55Y as the naturally processed epitope across MM cells, making it a promising immune target. Corresponding TCRs specifically recognized COASY S55Y+HLA-A∗24:02+ MM cells and augmented tumoricidal activity. Finally, adoptive cell transfer of TCR-T cells showed objective responses in the xenograft model. We initiatively proposed the utility of tumor mutated antigen-specific TCR genes to suppress MM. Our unique strategy will facilitate further identification of neoantigen-specific TCRs.
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Affiliation(s)
- Masahiro Okada
- Laboratory for Immunotherapy, RIKEN Center for Integrative Medical Sciences, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Kanako Shimizu
- Laboratory for Immunotherapy, RIKEN Center for Integrative Medical Sciences, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Hiroshi Nakazato
- Laboratory for Immunotherapy, RIKEN Center for Integrative Medical Sciences, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Satoru Yamasaki
- Laboratory for Immunotherapy, RIKEN Center for Integrative Medical Sciences, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Shin-ichiro Fujii
- Laboratory for Immunotherapy, RIKEN Center for Integrative Medical Sciences, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- RIKEN Program for Drug Discovery and Medical Technology Platforms, RIKEN, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
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Vilov S, Heinig M. DeepSom: a CNN-based approach to somatic variant calling in WGS samples without a matched normal. Bioinformatics 2023; 39:6986966. [PMID: 36637201 PMCID: PMC9843587 DOI: 10.1093/bioinformatics/btac828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 12/19/2022] [Accepted: 01/12/2023] [Indexed: 01/14/2023] Open
Abstract
MOTIVATION Somatic mutations are usually called by analyzing the DNA sequence of a tumor sample in conjunction with a matched normal. However, a matched normal is not always available, for instance, in retrospective analysis or diagnostic settings. For such cases, tumor-only somatic variant calling tools need to be designed. Previously proposed approaches demonstrate inferior performance on whole-genome sequencing (WGS) samples. RESULTS We present the convolutional neural network-based approach called DeepSom for detecting somatic single nucleotide polymorphism and short insertion and deletion variants in tumor WGS samples without a matched normal. We validate DeepSom by reporting its performance on five different cancer datasets. We also demonstrate that on WGS samples DeepSom outperforms previously proposed methods for tumor-only somatic variant calling. AVAILABILITY AND IMPLEMENTATION DeepSom is available as a GitHub repository at https://github.com/heiniglab/DeepSom. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sergey Vilov
- Institute of Computational Biology, Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), 85764 Neuherberg, Germany
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Sun D, Xu M, Pan C, Tang H, Wang P, Wu D, Luo H. Systematic assessment and optimizing algorithm of tumor mutational burden calculation and their implications in clinical decision-making. Front Oncol 2022; 12:972972. [PMID: 36425562 PMCID: PMC9679647 DOI: 10.3389/fonc.2022.972972] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 10/03/2022] [Indexed: 04/18/2024] Open
Abstract
Tumor mutation burden (TMB) has been validated as a biomarker to predict the response of immune checkpoint inhibitors (ICIs) treatment in various cancers. However, the effects of different sequencing platforms, cancer types, and calculation algorithms on TMB as well as its cut-off value for predicting immunotherapy efficacy in the East Asian population still need to be further investigated. In this study, the data of 4126 samples generated by targeted panel sequencing or whole-exome sequencing (WES) in different platforms and public sequencing data from 3680 samples that contained targeted panel sequencing, WES and whole-genome sequencing (WGS) were obtained. The impact of different sequencing platforms and methods on TMB calculation was assessed. No significant bias was found in TMB calculated by different platforms. However, TMB calculated from WGS was significantly lower than those calculated from targeted panel sequencing and WES. The distribution of TMB at different sequencing depths and tumor purity were analyzed. There was no significant difference in the distribution of TMB when the sequencing depth was greater than 500, the tumor purity estimated by hematoxylin-eosin (HE) staining was between 0.1-1.0 or estimated by next-generation sequencing (NGS) was greater than 0.4. In addition, the somatic-germline-zygosity (SGZ) algorithm was optimized to calculate TMB from tumor-only sequencing samples in the East Asian population. The correlation coefficient of TMB calculated with the optimized SGZ algorithm and paired normal-tumor sequencing is 0.951. Furthermore, the optimal cut-off value of TMB in East Asian lung cancer patients treated with ICIs was determined to be 7 mut/Mb instead of 10 mut/Mb through the ROC curve and Log-rank analysis in the training cohort and validated in the test cohort. Patients with TMB ≥ 7 mut/Mb had better outcomes than patients with TMB<7 mut/Mb. In conclusion, this study systematically analyzed the factors that influenced the TMB calculation and optimized the SGZ algorithm to calculate TMB from tumor-only sequencing samples in the East Asian population. More importantly, the cut-off value of TMB for predicting immunotherapy efficacy was determined to be 7 mut/Mb instead of 10 mut/Mb in East Asian lung cancer patients, which can help in clinical decision-making.
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Affiliation(s)
- Daqiang Sun
- Department of Thoracic Surgery, Tianjin Chest Hospital, Affiliated Chest Hospital of Tianjin University, Tianjin, China
| | - Meilin Xu
- Pathology Department, Tianjin Chest Hospital, Affiliated Chest Hospital of Tianjin University, Tianjin, China
| | - Chaohu Pan
- The First Affiliated Hospital, Jinan University, Guangzhou, China
- Department of Medicine, YuceBio Technology Co., Ltd, Shenzhen, China
| | - Hongzhen Tang
- Department of Medicine, YuceBio Technology Co., Ltd, Shenzhen, China
| | - Peng Wang
- Department of Medicine, YuceBio Technology Co., Ltd, Shenzhen, China
| | - Dongfang Wu
- Department of Medicine, YuceBio Technology Co., Ltd, Shenzhen, China
| | - Haitao Luo
- Department of Medicine, YuceBio Technology Co., Ltd, Shenzhen, China
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Borden ES, Buetow KH, Wilson MA, Hastings KT. Cancer Neoantigens: Challenges and Future Directions for Prediction, Prioritization, and Validation. Front Oncol 2022; 12:836821. [PMID: 35311072 PMCID: PMC8929516 DOI: 10.3389/fonc.2022.836821] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 02/07/2022] [Indexed: 12/16/2022] Open
Abstract
Prioritization of immunogenic neoantigens is key to enhancing cancer immunotherapy through the development of personalized vaccines, adoptive T cell therapy, and the prediction of response to immune checkpoint inhibition. Neoantigens are tumor-specific proteins that allow the immune system to recognize and destroy a tumor. Cancer immunotherapies, such as personalized cancer vaccines, adoptive T cell therapy, and immune checkpoint inhibition, rely on an understanding of the patient-specific neoantigen profile in order to guide personalized therapeutic strategies. Genomic approaches to predicting and prioritizing immunogenic neoantigens are rapidly expanding, raising new opportunities to advance these tools and enhance their clinical relevance. Predicting neoantigens requires acquisition of high-quality samples and sequencing data, followed by variant calling and variant annotation. Subsequently, prioritizing which of these neoantigens may elicit a tumor-specific immune response requires application and integration of tools to predict the expression, processing, binding, and recognition potentials of the neoantigen. Finally, improvement of the computational tools is held in constant tension with the availability of datasets with validated immunogenic neoantigens. The goal of this review article is to summarize the current knowledge and limitations in neoantigen prediction, prioritization, and validation and propose future directions that will improve personalized cancer treatment.
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Affiliation(s)
- Elizabeth S Borden
- Department of Basic Medical Sciences, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, United States.,Department of Research and Internal Medicine (Dermatology), Phoenix Veterans Affairs Health Care System, Phoenix, AZ, United States
| | - Kenneth H Buetow
- School of Life Sciences, Arizona State University, Tempe, AZ, United States.,Center for Evolution and Medicine, Arizona State University, Tempe, AZ, United States
| | - Melissa A Wilson
- School of Life Sciences, Arizona State University, Tempe, AZ, United States.,Center for Evolution and Medicine, Arizona State University, Tempe, AZ, United States
| | - Karen Taraszka Hastings
- Department of Basic Medical Sciences, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, United States.,Department of Research and Internal Medicine (Dermatology), Phoenix Veterans Affairs Health Care System, Phoenix, AZ, United States
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