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Wang S, Yang Y, Li L, Ma P, Jiang Y, Ge M, Yu Y, Huang H, Fang Y, Jiang N, Miao H, Guo H, Yan L, Ren Y, Sun L, Zha Y, Li N. Identification of Tumor Antigens and Immune Subtypes of Malignant Mesothelioma for mRNA Vaccine Development. Vaccines (Basel) 2022; 10:1168. [PMID: 35893817 PMCID: PMC9331978 DOI: 10.3390/vaccines10081168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/07/2022] [Accepted: 07/13/2022] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND mRNA-based cancer vaccines have been considered a promising anticancer therapeutic approach against various cancers, yet their efficacy for malignant mesothelioma (MESO) is still not clear. The present study is designed to identify MESO antigens that have the potential for mRNA vaccine development, and to determine the immune subtypes for the selection of suitable patients. METHODS A total of 87 MESO datasets were used for the retrieval of RNA sequencing and clinical data from The Cancer Genome Atlas (TCGA) databases. The possible antigens were identified by a survival and a genome analysis. The samples were divided into two immune subtypes by the application of a consensus clustering algorithm. The functional annotation was also carried out by using the DAVID program. Furthermore, the characterization of each immune subtype related to the immune microenvironment was integrated by an immunogenomic analysis. A protein-protein interaction network was established to categorize the hub genes. RESULTS The five tumor antigens were identified in MESO. FAM134B, ALDH3A2, SAV1, and RORC were correlated with superior prognoses and the infiltration of antigen-presenting cells (APCs), while FN1 was associated with poor survival and the infiltration of APCs. Two immune subtypes were identified; TM2 exhibited significantly improved survival and was more likely to benefit from vaccination compared with TM1. TM1 was associated with a relatively quiet microenvironment, high tumor mutation burden, and enriched DNA damage repair pathways. The immune checkpoints and immunogenic cell death modulators were also differentially expressed between two subtypes. Finally, FN1 was identified to be the hub gene. CONCLUSIONS FAM134B, ALDH3A2, SAV1, RORC, and FN1 are considered as possible and effective mRNA anti-MESO antigens for the development of an mRNA vaccine, and TM2 patients are the most suitable for vaccination.
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Affiliation(s)
- Shuhang Wang
- Clinical Cancer Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China; (S.W.); (P.M.); (Y.J.); (Y.Y.); (H.H.); (Y.F.); (N.J.); (H.M.)
| | - Yuqi Yang
- NHC Key Laboratory of Pulmonary Immunological Diseases, Guizhou Provincial People’s Hospital, Guiyang 550002, China;
| | - Lu Li
- State Key Laboratory of Translational Medicine and Innovative Drug Development, Jiangsu Simcere Diagnostics Co., Ltd., Nanjing 210018, China; (L.L.); (M.G.); (H.G.); (L.Y.); (Y.R.)
| | - Peiwen Ma
- Clinical Cancer Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China; (S.W.); (P.M.); (Y.J.); (Y.Y.); (H.H.); (Y.F.); (N.J.); (H.M.)
| | - Yale Jiang
- Clinical Cancer Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China; (S.W.); (P.M.); (Y.J.); (Y.Y.); (H.H.); (Y.F.); (N.J.); (H.M.)
| | - Minghui Ge
- State Key Laboratory of Translational Medicine and Innovative Drug Development, Jiangsu Simcere Diagnostics Co., Ltd., Nanjing 210018, China; (L.L.); (M.G.); (H.G.); (L.Y.); (Y.R.)
| | - Yue Yu
- Clinical Cancer Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China; (S.W.); (P.M.); (Y.J.); (Y.Y.); (H.H.); (Y.F.); (N.J.); (H.M.)
| | - Huiyao Huang
- Clinical Cancer Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China; (S.W.); (P.M.); (Y.J.); (Y.Y.); (H.H.); (Y.F.); (N.J.); (H.M.)
| | - Yuan Fang
- Clinical Cancer Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China; (S.W.); (P.M.); (Y.J.); (Y.Y.); (H.H.); (Y.F.); (N.J.); (H.M.)
| | - Ning Jiang
- Clinical Cancer Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China; (S.W.); (P.M.); (Y.J.); (Y.Y.); (H.H.); (Y.F.); (N.J.); (H.M.)
| | - Huilei Miao
- Clinical Cancer Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China; (S.W.); (P.M.); (Y.J.); (Y.Y.); (H.H.); (Y.F.); (N.J.); (H.M.)
| | - Hao Guo
- State Key Laboratory of Translational Medicine and Innovative Drug Development, Jiangsu Simcere Diagnostics Co., Ltd., Nanjing 210018, China; (L.L.); (M.G.); (H.G.); (L.Y.); (Y.R.)
| | - Linlin Yan
- State Key Laboratory of Translational Medicine and Innovative Drug Development, Jiangsu Simcere Diagnostics Co., Ltd., Nanjing 210018, China; (L.L.); (M.G.); (H.G.); (L.Y.); (Y.R.)
| | - Yong Ren
- State Key Laboratory of Translational Medicine and Innovative Drug Development, Jiangsu Simcere Diagnostics Co., Ltd., Nanjing 210018, China; (L.L.); (M.G.); (H.G.); (L.Y.); (Y.R.)
| | - Lichao Sun
- State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yan Zha
- NHC Key Laboratory of Pulmonary Immunological Diseases, Guizhou Provincial People’s Hospital, Guiyang 550002, China;
| | - Ning Li
- Clinical Cancer Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China; (S.W.); (P.M.); (Y.J.); (Y.Y.); (H.H.); (Y.F.); (N.J.); (H.M.)
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Lin H, Wang K, Xiong Y, Zhou L, Yang Y, Chen S, Xu P, Zhou Y, Mao R, Lv G, Wang P, Zhou D. Identification of Tumor Antigens and Immune Subtypes of Glioblastoma for mRNA Vaccine Development. Front Immunol 2022; 13:773264. [PMID: 35185876 PMCID: PMC8847306 DOI: 10.3389/fimmu.2022.773264] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 01/14/2022] [Indexed: 02/05/2023] Open
Abstract
The use of vaccines for cancer therapy is a promising immunotherapeutic strategy that has been shown to be effective against various cancers. Vaccines directly target tumors but their efficacy against glioblastoma multiforme (GBM) remains unclear. Immunotyping that classifies tumor samples is considered to be a biomarker for immunotherapy. This study aimed to identify potential GBM antigens suitable for vaccine development and develop a tool to predict the response of GBM patients to vaccination based on the immunotype. Gene Expression Profiling Interactive Analysis (GEPIA) was applied to evaluate the expression profile of GBM antigens and their influence on clinical prognosis, while the cBioPortal program was utilized to integrate and analyze genetic alterations. The correlation between antigens and antigen processing cells was assessed using TIMER. RNA-seq data of GBM samples and their corresponding clinical data were downloaded from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) for further clustering analysis. Six overexpressed and mutated tumor antigens (ARHGAP9, ARHGAP30, CLEC7A, MAN2B1, ARPC1B and PLB1) were highly correlated with the survival rate of GBM patients and the infiltration of antigen presenting cells in GBMs. With distinct cellular and molecular characteristics, three immune subtypes (IS1-IS3) of GBMs were identified and GBMs from IS3 subtype were more likely to benefit from vaccination. Through graph learning-based dimensional reduction, immune landscape was depicted and revealed the existence of heterogeneity among individual GBM patients. Finally, WGCNA can identify potential vaccination biomarkers by clustering immune related genes. In summary, the six tumor antigens are potential targets for developing anti-GBMs mRNA vaccine, and the immunotypes can be used for evaluating vaccination response.
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Affiliation(s)
- Han Lin
- Department of Neurosurgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Department of Head and Neck Surgery, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Kun Wang
- Department of Neurosurgery, The First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Yuxin Xiong
- Division of Vascular Intervention Radiology, The Third Affiliated Hospital of Sun Yet-Sen University, Guangzhou, China
| | - Liting Zhou
- International Department, Affiliated High School of South China Normal University, Guangzhou, China
| | - Yong Yang
- Department of Neurosurgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Shanwei Chen
- Department of Neurosurgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Shantou University Medical College, Shantou, China
| | - Peihong Xu
- Department of Neurosurgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Shantou University Medical College, Shantou, China
| | - Yujun Zhou
- Department of Neurosurgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Southern Medical University, Guangzhou, China
| | - Rui Mao
- Department of Neurosurgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Guangzhao Lv
- Department of Neurosurgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Shantou University Medical College, Shantou, China
| | - Peng Wang
- Department of Neurosurgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Dong Zhou
- Department of Neurosurgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
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Chen B, Khodadoust MS, Olsson N, Wagar LE, Fast E, Liu CL, Muftuoglu Y, Sworder BJ, Diehn M, Levy R, Davis MM, Elias JE, Altman RB, Alizadeh AA. Predicting HLA class II antigen presentation through integrated deep learning. Nat Biotechnol 2019; 37:1332-1343. [PMID: 31611695 PMCID: PMC7075463 DOI: 10.1038/s41587-019-0280-2] [Citation(s) in RCA: 197] [Impact Index Per Article: 39.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 09/09/2019] [Indexed: 12/21/2022]
Abstract
Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules would be valuable for vaccine development and cancer immunotherapies. Current computational methods trained on in vitro binding data are limited by insufficient training data and algorithmic constraints. Here we describe MARIA (major histocompatibility complex analysis with recurrent integrated architecture; https://maria.stanford.edu/ ), a multimodal recurrent neural network for predicting the likelihood of antigen presentation from a gene of interest in the context of specific HLA class II alleles. In addition to in vitro binding measurements, MARIA is trained on peptide HLA ligand sequences identified by mass spectrometry, expression levels of antigen genes and protease cleavage signatures. Because it leverages these diverse training data and our improved machine learning framework, MARIA (area under the curve = 0.89-0.92) outperformed existing methods in validation datasets. Across independent cancer neoantigen studies, peptides with high MARIA scores are more likely to elicit strong CD4+ T cell responses. MARIA allows identification of immunogenic epitopes in diverse cancers and autoimmune disease.
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Affiliation(s)
- Binbin Chen
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Michael S Khodadoust
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Niclas Olsson
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA
| | - Lisa E Wagar
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - Ethan Fast
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Nash, Vaduz, Liechtenstein
| | - Chih Long Liu
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Yagmur Muftuoglu
- Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Brian J Sworder
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Maximilian Diehn
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
- Stem Cell Biology & Regenerative Medicine, Stanford University, Stanford, CA, USA
| | - Ronald Levy
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Mark M Davis
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - Joshua E Elias
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA
- Chan Zuckerberg Biohub, Stanford University, Stanford, CA, USA
| | - Russ B Altman
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Ash A Alizadeh
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
- Stem Cell Biology & Regenerative Medicine, Stanford University, Stanford, CA, USA.
- Center for Cancer Systems Biology, Stanford University, Stanford, CA, USA.
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Weiss E, Sarnovsky R, Ho M, Arons E, Kreitman R, Angelus E, Antignani A, FitzGerald D. Generation of antibody-based therapeutics targeting the Idiotype of B-cell Malignancies. Antib Ther 2019; 2:1-10. [PMID: 30801054 PMCID: PMC6383771 DOI: 10.1093/abt/tby012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 12/07/2018] [Accepted: 12/14/2018] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND A feature of many B-cell tumors is a surface-expressed immunoglobulin (sIg). The complementarity determiningregions (CDRs)of the sIg, termed the 'idiotype', are unique to each tumor. We report on a phage selection strategy to generate anti-idiotype therapeutics that react with sIg CDR3 sequences: the MEC1 B-cell tumor line was used as proof of concept. METHODS To create a mimetic of the MEC1 idiotype, CDR3 sequences from heavy and light chains of the sIg were grafted into a scFv framework scaffold. Using the Tomlinson I phage library of human scFvs, we enriched for binders to MEC1 CDR3 sequences over unrelated CDR3 sequences. RESULTS By ELISA we identified 10 binder phage. Of these, five were sequenced, found to be unique and characterized further. By flow cytometry each of the five phage bound to MEC1 cells, albeit with different patterns of reactivity. To establish specificity of binding and utility, the scFv sequences from two of these binders (phage 1, and 7) were converted into antibody-toxin fusion proteins (immunotoxins) and also cloned into a human IgG1 expression vector. Binder-1 and -7 immunotoxins exhibited specific killing of MEC1 cells with little toxicity for non-target B-cell lines. The full-length antibody recreated from the binder-1 scFv so exhibited specific binding. CONCLUSION Our results establish the utility of using engrafted CDR3 sequences for selecting phage that recognize the idiotype of B-cell tumors.
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Affiliation(s)
- Emily Weiss
- Biotherapy Section, Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, 37/5124 Bethesda, MD 20892, USA
| | - Robert Sarnovsky
- Biotherapy Section, Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, 37/5124 Bethesda, MD 20892, USA
| | - Mitchell Ho
- Biotherapy Section, Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, 37/5124 Bethesda, MD 20892, USA
| | - Evgeny Arons
- Biotherapy Section, Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, 37/5124 Bethesda, MD 20892, USA
| | - Robert Kreitman
- Biotherapy Section, Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, 37/5124 Bethesda, MD 20892, USA
| | - Evan Angelus
- Biotherapy Section, Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, 37/5124 Bethesda, MD 20892, USA
| | - Antonella Antignani
- Biotherapy Section, Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, 37/5124 Bethesda, MD 20892, USA
| | - David FitzGerald
- Biotherapy Section, Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, 37/5124 Bethesda, MD 20892, USA
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Khodadoust M, Olsson N, Wagar L, Haabeth O, Chen B, Swaminathan K, Rawson K, Liu C, Steiner D, Lund P, Rao S, Zhang L, Marceau C, Stehr H, Newman A, Czerwinski DK, Carlton V, Moorhead M, Faham M, Kohrt H, Carette J, Green M, Davis M, Levy R, Elias JE, Alizadeh A. Antigen presentation profiling reveals recognition of lymphoma immunoglobulin neoantigens. Nature 2017; 543:723-727. [PMID: 28329770 PMCID: PMC5808925 DOI: 10.1038/nature21433] [Citation(s) in RCA: 197] [Impact Index Per Article: 28.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Accepted: 01/30/2017] [Indexed: 02/07/2023]
Abstract
Cancer somatic mutations can generate neoantigens that distinguish malignant from normal cells. However, the personalized identification and validation of neoantigens remains a major challenge. Here we discover neoantigens in human mantle-cell lymphomas by using an integrated genomic and proteomic strategy that interrogates tumour antigen peptides presented by major histocompatibility complex (MHC) class I and class II molecules. We applied this approach to systematically characterize MHC ligands from 17 patients. Remarkably, all discovered neoantigenic peptides were exclusively derived from the lymphoma immunoglobulin heavy- or light-chain variable regions. Although we identified MHC presentation of private polymorphic germline alleles, no mutated peptides were recovered from non-immunoglobulin somatically mutated genes. Somatic mutations within the immunoglobulin variable region were almost exclusively presented by MHC class II. We isolated circulating CD4+ T cells specific for immunoglobulin-derived neoantigens and found these cells could mediate killing of autologous lymphoma cells. These results demonstrate that an integrative approach combining MHC isolation, peptide identification, and exome sequencing is an effective platform to uncover tumour neoantigens. Application of this strategy to human lymphoma implicates immunoglobulin neoantigens as targets for lymphoma immunotherapy.
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Affiliation(s)
- M.S. Khodadoust
- Department of Medicine, Division of Oncology, Stanford University, Stanford, California, USA
| | - N. Olsson
- Department of Chemical & Systems Biology, Stanford University, Stanford, California, USA
| | - L.E. Wagar
- Department of Microbiology & Immunology, Stanford University, Stanford, California, USA
| | - O.A.W. Haabeth
- Department of Medicine, Division of Oncology, Stanford University, Stanford, California, USA
| | - B. Chen
- Department of Medicine, Division of Oncology, Stanford University, Stanford, California, USA
- Department of Genetics, Stanford University, Stanford, California, USA
| | - K. Swaminathan
- Department of Chemical & Systems Biology, Stanford University, Stanford, California, USA
| | - K. Rawson
- Department of Chemical & Systems Biology, Stanford University, Stanford, California, USA
| | - C.L. Liu
- Department of Medicine, Division of Oncology, Stanford University, Stanford, California, USA
| | - D. Steiner
- Department of Pathology, Stanford University, Stanford, California, USA
| | - P. Lund
- Department of Microbiology & Immunology, Stanford University, Stanford, California, USA
| | - S. Rao
- Department of Chemical & Systems Biology, Stanford University, Stanford, California, USA
| | - L. Zhang
- Department of Chemical & Systems Biology, Stanford University, Stanford, California, USA
| | - C. Marceau
- Department of Microbiology & Immunology, Stanford University, Stanford, California, USA
| | - H. Stehr
- Department of Medicine, Division of Oncology, Stanford University, Stanford, California, USA
| | - A.M. Newman
- Department of Medicine, Division of Oncology, Stanford University, Stanford, California, USA
- Center for Cancer Systems Biology, Stanford University, Stanford, California, USA
| | - D. K. Czerwinski
- Department of Medicine, Division of Oncology, Stanford University, Stanford, California, USA
| | | | - M. Moorhead
- Adaptive Biotechnologies, Seattle, Washington, USA
| | - M. Faham
- Adaptive Biotechnologies, Seattle, Washington, USA
| | - H.E. Kohrt
- Department of Medicine, Division of Oncology, Stanford University, Stanford, California, USA
| | - J. Carette
- Department of Microbiology & Immunology, Stanford University, Stanford, California, USA
| | - M.R. Green
- Department of Medicine, Division of Oncology, Stanford University, Stanford, California, USA
| | - M.M. Davis
- Department of Microbiology & Immunology, Stanford University, Stanford, California, USA
- Howard Hughes Medical Institute, Stanford University, Stanford, California, USA
| | - R. Levy
- Department of Medicine, Division of Oncology, Stanford University, Stanford, California, USA
| | - J. E. Elias
- Department of Chemical & Systems Biology, Stanford University, Stanford, California, USA
| | - A.A. Alizadeh
- Department of Medicine, Division of Oncology, Stanford University, Stanford, California, USA
- Howard Hughes Medical Institute, Stanford University, Stanford, California, USA
- Institute for Stem Cell Biology & Regenerative Medicine, Stanford University, Stanford, California, USA
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Cherryholmes GA, Stanton SE, Disis ML. Current methods of epitope identification for cancer vaccine design. Vaccine 2015; 33:7408-7414. [PMID: 26238725 DOI: 10.1016/j.vaccine.2015.06.116] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2015] [Accepted: 06/23/2015] [Indexed: 01/05/2023]
Abstract
The importance of the immune system in tumor development and progression has been emerging in many cancers. Previous cancer vaccines have not shown long-term clinical benefit possibly because were not designed to avoid eliciting regulatory T-cell responses that inhibit the anti-tumor immune response. This review will examine different methods of identifying epitopes derived from tumor associated antigens suitable for immunization and the steps used to design and validate peptide epitopes to improve efficacy of anti-tumor peptide-based vaccines. Focusing on in silico prediction algorithms, we survey the advantages and disadvantages of current cancer vaccine prediction tools.
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Affiliation(s)
- Gregory A Cherryholmes
- Tumor Vaccine Group, Center for Translational Medicine in Women's Health, University of Washington, 850 Republican Street, Seattle, WA 98109, United States.
| | - Sasha E Stanton
- Tumor Vaccine Group, Center for Translational Medicine in Women's Health, University of Washington, 850 Republican Street, Seattle, WA 98109, United States.
| | - Mary L Disis
- Tumor Vaccine Group, Center for Translational Medicine in Women's Health, University of Washington, 850 Republican Street, Seattle, WA 98109, United States.
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