1
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Zhu L, Cui X, Yan Z, Tao Y, Shi L, Zhang X, Yao Y, Shi L. Design and evaluation of a multi-epitope DNA vaccine against HPV16. Hum Vaccin Immunother 2024; 20:2352908. [PMID: 38780076 PMCID: PMC11123455 DOI: 10.1080/21645515.2024.2352908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/05/2024] [Indexed: 05/25/2024] Open
Abstract
Cervical cancer, among the deadliest cancers affecting women globally, primarily arises from persistent infection with high-risk human papillomavirus (HPV). To effectively combat persistent infection and prevent the progression of precancerous lesions into malignancy, a therapeutic HPV vaccine is under development. This study utilized an immunoinformatics approach to predict epitopes of cytotoxic T lymphocytes (CTLs) and helper T lymphocytes (HTLs) using the E6 and E7 oncoproteins of the HPV16 strain as target antigens. Subsequently, through meticulous selection of T-cell epitopes and other necessary elements, a multi-epitope vaccine was constructed, exhibiting good immunogenic, physicochemical, and structural characteristics. Furthermore, in silico simulations showed that the vaccine not only interacted well with toll-like receptors (TLR2/TLR3/TLR4), but also induced a strong innate and adaptive immune response characterized by elevated Th1-type cytokines, such as interferon-gamma (IFN-γ) and interleukin-2 (IL2). Additionally, our study investigated the effects of different immunization intervals on immune responses, aiming to optimize a time-efficient immunization program. In animal model experiments, the vaccine exhibited robust immunogenic, therapeutic, and prophylactic effects. Administered thrice, it consistently induced the expansion of specific CD4 and CD8 T cells, resulting in substantial cytokines release and increased proliferation of memory T cell subsets in splenic cells. Overall, our findings support the potential of this multi-epitope vaccine in combating HPV16 infection and signify its candidacy for future HPV vaccine development.
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Affiliation(s)
- Lanfang Zhu
- Department of Immunogenetics, Institute of Medical Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Kunming, China
| | - Xiangjie Cui
- Department of Immunogenetics, Institute of Medical Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Kunming, China
| | - Zhiling Yan
- Department of Gynaecologic Oncology, The No. 3 Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yufen Tao
- Department of Immunogenetics, Institute of Medical Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Kunming, China
| | - Lei Shi
- Department of Immunogenetics, Institute of Medical Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Kunming, China
| | - Xinwen Zhang
- Department of Immunogenetics, Institute of Medical Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Kunming, China
| | - Yufeng Yao
- Yunnan Key Laboratory of Vaccine Research & Development on Severe Infectious Disease, Institute of Medical Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Kunming, China
| | - Li Shi
- Department of Immunogenetics, Institute of Medical Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Kunming, China
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2
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Xin K, Wei X, Shao J, Chen F, Liu Q, Liu B. Establishment of a novel tumor neoantigen prediction tool for personalized vaccine design. Hum Vaccin Immunother 2024; 20:2300881. [PMID: 38214336 DOI: 10.1080/21645515.2023.2300881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/28/2023] [Indexed: 01/13/2024] Open
Abstract
The personalized neoantigen nanovaccine (PNVAC) platform for patients with gastric cancer we established previously exhibited promising anti-tumor immunoreaction. However, limited by the ability of traditional neoantigen prediction tools, a portion of epitopes failed to induce specific immune response. In order to filter out more neoantigens to optimize our PNVAC platform, we develop a novel neoantigen prediction model, NUCC. This prediction tool trained through a deep learning approach exhibits better neoantigen prediction performance than other prediction tools, not only in two independent epitope datasets, but also in a totally new epitope dataset we construct from scratch, including 25 patients with advance gastric cancer and 150 candidate mutant peptides, 13 of which prove to be neoantigen by immunogenicity test in vitro. Our work lay the foundation for the improvement of our PNVAC platform for gastric cancer in the future.
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Affiliation(s)
- Kai Xin
- Department of Oncology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Xiao Wei
- Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, China
| | - Jie Shao
- Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, China
| | - Fangjun Chen
- Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, China
| | - Qin Liu
- Department of Oncology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
- Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, China
| | - Baorui Liu
- Department of Oncology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
- Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, China
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3
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Liu X, Jin W, Bao D, He T, Wang W, Li Z, Yang X, Tong Y, Shu M, Wang Y, Yuan J, Yang Y. DIPAN: Detecting personalized intronic polyadenylation derived neoantigens from RNA sequencing data. Comput Struct Biotechnol J 2024; 23:2057-2066. [PMID: 38783901 PMCID: PMC11112131 DOI: 10.1016/j.csbj.2024.05.008] [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: 12/06/2023] [Revised: 05/05/2024] [Accepted: 05/06/2024] [Indexed: 05/25/2024] Open
Abstract
Intronic polyadenylation (IPA) refers to a particular type of alternative polyadenylation where a gene makes use of a polyadenylation site located within its introns. Aberrant IPA events have been observed in various types of cancer. IPA can produce noncoding transcripts or truncated protein-coding transcripts with altered coding sequences in the resulting protein product. Therefore, IPA events hold the potential to act as a reservoir of tumor neoantigens. Here, we developed a computational method termed DIPAN, which incorporates IPA detection, protein fragmentation, and MHC binding prediction to predict IPA-derived neoantigens. Utilizing RNA-seq from breast cancer cell lines and ovarian cancer clinical samples, we demonstrated the significant contribution of IPA events to the neoantigen repertoire. Through mass spectrometry immunopeptidome analysis, we further illustrated the processing and presentation of IPA-derived neoantigens on the surface of cancer cells. While most IPA-derived neoantigens are sample-specific, shared neoantigens were identified in both cancer cell lines and clinical samples. Furthermore, we demonstrated an association between IPA-derived neoantigen burden and overall survival in cancer patients.
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Affiliation(s)
- Xiaochuan Liu
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Geriatrics Institute, Tianjin Medical University General Hospital, The Second Hospital of Tianjin Medical University, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Wen Jin
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Geriatrics Institute, Tianjin Medical University General Hospital, The Second Hospital of Tianjin Medical University, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
- Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Dengyi Bao
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Geriatrics Institute, Tianjin Medical University General Hospital, The Second Hospital of Tianjin Medical University, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Tongxin He
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Geriatrics Institute, Tianjin Medical University General Hospital, The Second Hospital of Tianjin Medical University, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Wenhui Wang
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Geriatrics Institute, Tianjin Medical University General Hospital, The Second Hospital of Tianjin Medical University, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
- Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Zekun Li
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Geriatrics Institute, Tianjin Medical University General Hospital, The Second Hospital of Tianjin Medical University, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xiaoxiao Yang
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Geriatrics Institute, Tianjin Medical University General Hospital, The Second Hospital of Tianjin Medical University, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
- Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Yang Tong
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Geriatrics Institute, Tianjin Medical University General Hospital, The Second Hospital of Tianjin Medical University, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Meng Shu
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Geriatrics Institute, Tianjin Medical University General Hospital, The Second Hospital of Tianjin Medical University, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Yuting Wang
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Geriatrics Institute, Tianjin Medical University General Hospital, The Second Hospital of Tianjin Medical University, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Jiapei Yuan
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Yang Yang
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammatory Biology, Tianjin Geriatrics Institute, Tianjin Medical University General Hospital, The Second Hospital of Tianjin Medical University, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
- Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
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4
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Hamza H, Ghosh M, Löffler MW, Rammensee HG, Planz O. Identification and relative abundance of naturally presented and cross-reactive influenza A virus MHC class I-restricted T cell epitopes. Emerg Microbes Infect 2024; 13:2306959. [PMID: 38240239 PMCID: PMC10854457 DOI: 10.1080/22221751.2024.2306959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 01/14/2024] [Indexed: 02/10/2024]
Abstract
Cytotoxic T lymphocytes are key for controlling viral infection. Unravelling CD8+ T cell-mediated immunity to distinct influenza virus strains and subtypes across prominent HLA types is relevant for combating seasonal infections and emerging new variants. Using an immunopeptidomics approach, naturally presented influenza A virus-derived ligands restricted to HLA-A*24:02, HLA-A*68:01, HLA-B*07:02, and HLA-B*51:01 molecules were identified. Functional characterization revealed multifunctional memory CD8+ T cell responses for nine out of sixteen peptides. Peptide presentation kinetics was optimal around 12 h post infection and presentation of immunodominant epitopes shortly after infection was not always persistent. Assessment of immunogenic epitopes revealed that they are highly conserved across the major zoonotic reservoirs and may contain a single substitution in the vicinity of the anchor residues. These findings demonstrate how the identified epitopes promote T cell pools, possibly cross-protective in individuals and can be potential targets for vaccination.
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Affiliation(s)
- Hazem Hamza
- Institute for Immunology, University of Tübingen, Tübingen, Germany
- Virology Laboratory, Environmental Research Division, National Research Centre, Giza, Egypt
| | - Michael Ghosh
- Institute for Immunology, University of Tübingen, Tübingen, Germany
| | - Markus W Löffler
- Institute for Immunology, University of Tübingen, Tübingen, Germany
- Institute for Clinical and Experimental Transfusion Medicine, Medical Faculty of Tübingen, Tübingen, Germany
- Centre for Clinical Transfusion Medicine, University Hospital Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), partner site Tübingen, Tübingen, Germany
| | - Hans-Georg Rammensee
- Institute for Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), partner site Tübingen, Tübingen, Germany
- Cluster of Excellence CMFI (EXC2124) "Controlling Microbes to Fight Infections", University of Tübingen, Tübingen, Germany
| | - Oliver Planz
- Institute for Immunology, University of Tübingen, Tübingen, Germany
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5
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Strum S, Andersen MH, Svane IM, Siu LL, Weber JS. State-Of-The-Art Advancements on Cancer Vaccines and Biomarkers. Am Soc Clin Oncol Educ Book 2024; 44:e438592. [PMID: 38669611 DOI: 10.1200/edbk_438592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
Abstract
The origins of cancer vaccines date back to the 1800s. Since then, there have been significant efforts to generate vaccines against solid and hematologic malignancies using a variety of platforms. To date, these efforts have generally been met with minimal success. However, in the era of improved methods and technological advancements, supported by compelling preclinical and clinical data, a wave of renewed interest in the field offers the promise of discovering field-changing paradigms in the management of established and resected disease using cancer vaccines. These include novel approaches to personalized neoantigen vaccine development, as well as innovative immune-modulatory vaccines (IMVs) that facilitate activation of antiregulatory T cells to limit immunosuppression caused by regulatory immune cells. This article will introduce some of the limitations that have affected cancer vaccine development over the past several decades, followed by an introduction to the latest advancements in neoantigen vaccine and IMV therapy, and then conclude with a discussion of some of the newest technologies and progress that are occurring across the cancer vaccine space. Cancer vaccines are among the most promising frontiers for breakthrough innovations and strategies poised to make a measurable impact in the ongoing fight against cancer.
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Affiliation(s)
- Scott Strum
- Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Canada
| | - Mads Hald Andersen
- National Center for Cancer Immune Therapy, Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte, Herlev, Denmark
| | - Inge Marie Svane
- National Center for Cancer Immune Therapy, Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte, Herlev, Denmark
| | - Lillian L Siu
- Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Canada
| | - Jeffrey S Weber
- Laura and Isaac Perlmutter Cancer Center, NYU Langone Health, New York, NY
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6
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Park R, Li J, Slebos RJC, Chaudhary R, Poole MI, Ferraris C, Farinhas J, Hernandez-Prera J, Kirtane K, Teer JK, Song X, Hall MS, Tasoulas J, Amelio AL, Chung CH. Phase Ib trial of IRX-2 plus durvalumab in patients with recurrent and/or metastatic head and neck squamous cell carcinoma. Oral Oncol 2024; 154:106866. [PMID: 38820888 DOI: 10.1016/j.oraloncology.2024.106866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 04/28/2024] [Accepted: 05/19/2024] [Indexed: 06/02/2024]
Abstract
OBJECTIVES IRX-2 is a multi-cytokine immune-activating agent with anti-tumor activity in non-metastatic head and neck squamous cell carcinoma (HNSCC). Here, we evaluated combined IRX-2 and durvalumab in patients with recurrent and/or metastatic HNSCC. MATERIALS AND METHODS This was a phase Ib trial consisting of dose escalation and expansion. Primary endpoints were safety and biomarkers to assess the immune response in the tumor microenvironment including significant increases in PD-L1 expression and CD8 + tumor infiltrating lymphocytes (TIL) comparing pre- and on-treatment tumor biopsies. Secondary endpoints were objective response rates (ORR) and survival outcomes. RESULTS Sixteen patients were evaluable for response, and nine patients were evaluable for biomarkers. Thirteen patients (68 %) had exposure to prior anti-PD-1 therapy. No dose-limiting or grade ≥ 3 treatment-related adverse events were observed. On-treatment biopsies showed significantly increased PD-L1 (p = 0.005), CD3+ (p = 0.020), CD4+ (p = 0.022), and CD8 + T cells (p = 0.017) compared to pre-treatment. Median overall survival and progression-free survival (PFS) were 6.18 months (95 % CI, 2.66-8.61) and 2.53 months (95 % CI, 1.81-4.04), respectively. One patient had an objective response (ORR, 5.3 %) with an ongoing PFS of > 25 months. Disease control rate was 42 %. The responder harbored an ARID1A variant of unknown significance (VUS) that was predicted to bind her HLA-I alleles with a higher affinity than the reference peptide. CONCLUSIONS IRX-2 and durvalumab were safe and elicited the evidence of immune activation in the tumor microenvironment determined by increased PD-L1 expression and CD8+ TILs. CLINICAL TRIAL REGISTRATION NUMBER NCT03381183.
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Affiliation(s)
- Robin Park
- Department of Head and Neck-Endocrine Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Jiannong Li
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Robbert J C Slebos
- Department of Head and Neck-Endocrine Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Ritu Chaudhary
- Department of Head and Neck-Endocrine Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Maria I Poole
- Department of Head and Neck-Endocrine Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Carina Ferraris
- Department of Head and Neck-Endocrine Oncology, Moffitt Cancer Center, Tampa, FL, USA; Nova Southeastern University Medical School, Fort Lauderdale, FL, USA
| | - Joaquim Farinhas
- Department of Head and Neck-Endocrine Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Kedar Kirtane
- Department of Head and Neck-Endocrine Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Jamie K Teer
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Xiaofei Song
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - MacLean S Hall
- Department of Immunology, Moffitt Cancer Center, Tampa, FL, USA
| | - Jason Tasoulas
- Otolaryngology/Head and Neck Surgery, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Antonio L Amelio
- Department of Head and Neck-Endocrine Oncology, Moffitt Cancer Center, Tampa, FL, USA; Otolaryngology/Head and Neck Surgery, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Tumor Biology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Christine H Chung
- Department of Head and Neck-Endocrine Oncology, Moffitt Cancer Center, Tampa, FL, USA.
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7
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Levin N, Kim SP, Marquardt CA, Vale NR, Yu Z, Sindiri S, Gartner JJ, Parkhurst M, Krishna S, Lowery FJ, Zacharakis N, Levy L, Prickett TD, Benzine T, Ray S, Masi RV, Gasmi B, Li Y, Islam R, Bera A, Goff SL, Robbins PF, Rosenberg SA. Neoantigen-specific stimulation of tumor-infiltrating lymphocytes enables effective TCR isolation and expansion while preserving stem-like memory phenotypes. J Immunother Cancer 2024; 12:e008645. [PMID: 38816232 DOI: 10.1136/jitc-2023-008645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/22/2024] [Indexed: 06/01/2024] Open
Abstract
BACKGROUND Tumor-infiltrating lymphocytes (TILs) targeting neoantigens can effectively treat a selected set of metastatic solid cancers. However, harnessing TILs for cancer treatments remains challenging because neoantigen-reactive T cells are often rare and exhausted, and ex vivo expansion can further reduce their frequencies. This complicates the identification of neoantigen-reactive T-cell receptors (TCRs) and the development of TIL products with high reactivity for patient treatment. METHODS We tested whether TILs could be in vitro stimulated against neoantigens to achieve selective expansion of neoantigen-reactive TILs. Given their prevalence, mutant p53 or RAS were studied as models of human neoantigens. An in vitro stimulation method, termed "NeoExpand", was developed to provide neoantigen-specific stimulation to TILs. 25 consecutive patient TILs from tumors harboring p53 or RAS mutations were subjected to NeoExpand. RESULTS We show that neoantigenic stimulation achieved selective expansion of neoantigen-reactive TILs and broadened the neoantigen-reactive CD4+ and CD8+ TIL clonal repertoire. This allowed the effective isolation of novel neoantigen-reactive TCRs. Out of the 25 consecutive TIL samples, neoantigenic stimulation enabled the identification of 16 unique reactivities and 42 TCRs, while conventional TIL expansion identified 9 reactivities and 14 TCRs. Single-cell transcriptome analysis revealed that neoantigenic stimulation increased neoantigen-reactive TILs with stem-like memory phenotypes expressing IL-7R, CD62L, and KLF2. Furthermore, neoantigenic stimulation improved the in vivo antitumor efficacy of TILs relative to the conventional OKT3-induced rapid TIL expansion in p53-mutated or KRAS-mutated xenograft mouse models. CONCLUSIONS Taken together, neoantigenic stimulation of TILs selectively expands neoantigen-reactive TILs by frequencies and by their clonal repertoire. NeoExpand led to improved phenotypes and functions of neoantigen-reactive TILs. Our data warrant its clinical evaluation. TRIAL REGISTRATION NUMBER NCT00068003, NCT01174121, and NCT03412877.
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Affiliation(s)
- Noam Levin
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Sanghyun P Kim
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Charles A Marquardt
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Nolan R Vale
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Zhiya Yu
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Sivasish Sindiri
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Jared J Gartner
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Maria Parkhurst
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Sri Krishna
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Frank J Lowery
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Nikolaos Zacharakis
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Lior Levy
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Todd D Prickett
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Tiffany Benzine
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Satyajit Ray
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Robert V Masi
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Billel Gasmi
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Yong Li
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Rafiqul Islam
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Alakesh Bera
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Stephanie L Goff
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Paul F Robbins
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Steven A Rosenberg
- Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
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8
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Ingels J, De Cock L, Stevens D, Mayer RL, Théry F, Sanchez GS, Vermijlen D, Weening K, De Smet S, Lootens N, Brusseel M, Verstraete T, Buyle J, Van Houtte E, Devreker P, Heyns K, De Munter S, Van Lint S, Goetgeluk G, Bonte S, Billiet L, Pille M, Jansen H, Pascal E, Deseins L, Vantomme L, Verdonckt M, Roelandt R, Eekhout T, Vandamme N, Leclercq G, Taghon T, Kerre T, Vanommeslaeghe F, Dhondt A, Ferdinande L, Van Dorpe J, Desender L, De Ryck F, Vermassen F, Surmont V, Impens F, Menten B, Vermaelen K, Vandekerckhove B. Neoantigen-targeted dendritic cell vaccination in lung cancer patients induces long-lived T cells exhibiting the full differentiation spectrum. Cell Rep Med 2024; 5:101516. [PMID: 38626769 DOI: 10.1016/j.xcrm.2024.101516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 02/09/2024] [Accepted: 03/25/2024] [Indexed: 05/24/2024]
Abstract
Non-small cell lung cancer (NSCLC) is known for high relapse rates despite resection in early stages. Here, we present the results of a phase I clinical trial in which a dendritic cell (DC) vaccine targeting patient-individual neoantigens is evaluated in patients with resected NSCLC. Vaccine manufacturing is feasible in six of 10 enrolled patients. Toxicity is limited to grade 1-2 adverse events. Systemic T cell responses are observed in five out of six vaccinated patients, with T cell responses remaining detectable up to 19 months post vaccination. Single-cell analysis indicates that the responsive T cell population is polyclonal and exhibits the near-entire spectrum of T cell differentiation states, including a naive-like state, but excluding exhausted cell states. Three of six vaccinated patients experience disease recurrence during the follow-up period of 2 years. Collectively, these data support the feasibility, safety, and immunogenicity of this treatment in resected NSCLC.
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Affiliation(s)
- Joline Ingels
- Department of Diagnostic Sciences, Ghent University, 9000 Ghent, East-Flanders, Belgium; Cancer Research Institute Ghent (CRIG), 9000 Ghent, Easy-Flanders, Belgium
| | - Laurenz De Cock
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Easy-Flanders, Belgium; Department of Biomolecular Medicine, Ghent University, 9000 Ghent, East-Flanders, Belgium
| | - Dieter Stevens
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Easy-Flanders, Belgium; Respiratory Medicine, Ghent University Hospital, 9000 Ghent, East-Flanders, Belgium
| | - Rupert L Mayer
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Easy-Flanders, Belgium; Department of Biomolecular Medicine, Ghent University, 9000 Ghent, East-Flanders, Belgium; VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, East-Flanders, Belgium
| | - Fabien Théry
- Department of Biomolecular Medicine, Ghent University, 9000 Ghent, East-Flanders, Belgium; VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, East-Flanders, Belgium
| | - Guillem Sanchez Sanchez
- Department of Pharmacotherapy and Pharmaceutics, Université Libre de Bruxelles, 1050 Brussels, Brussels, Belgium; Institute for Medical Immunology, Université Libre de Bruxelles, 1050 Brussels, Brussels, Belgium; Université Libre de Bruxelles Center for Research in Immunology, Université Libre de Bruxelles, 1050 Brussels, Brussels, Belgium; WELBIO Department, WEL Research Institute, 1300 Wavre, Walloon Brabant, Belgium
| | - David Vermijlen
- Department of Pharmacotherapy and Pharmaceutics, Université Libre de Bruxelles, 1050 Brussels, Brussels, Belgium; Institute for Medical Immunology, Université Libre de Bruxelles, 1050 Brussels, Brussels, Belgium; Université Libre de Bruxelles Center for Research in Immunology, Université Libre de Bruxelles, 1050 Brussels, Brussels, Belgium; WELBIO Department, WEL Research Institute, 1300 Wavre, Walloon Brabant, Belgium
| | - Karin Weening
- Department of Diagnostic Sciences, Ghent University, 9000 Ghent, East-Flanders, Belgium
| | - Saskia De Smet
- GMP Unit Cell Therapy, Ghent University Hospital, 9000 Ghent, East-Flanders, Belgium
| | - Nele Lootens
- GMP Unit Cell Therapy, Ghent University Hospital, 9000 Ghent, East-Flanders, Belgium
| | - Marieke Brusseel
- GMP Unit Cell Therapy, Ghent University Hospital, 9000 Ghent, East-Flanders, Belgium
| | - Tasja Verstraete
- Respiratory Medicine, Ghent University Hospital, 9000 Ghent, East-Flanders, Belgium
| | - Jolien Buyle
- Respiratory Medicine, Ghent University Hospital, 9000 Ghent, East-Flanders, Belgium
| | - Eva Van Houtte
- GMP Unit Cell Therapy, Ghent University Hospital, 9000 Ghent, East-Flanders, Belgium
| | - Pam Devreker
- GMP Unit Cell Therapy, Ghent University Hospital, 9000 Ghent, East-Flanders, Belgium
| | - Kelly Heyns
- GMP Unit Cell Therapy, Ghent University Hospital, 9000 Ghent, East-Flanders, Belgium
| | - Stijn De Munter
- Department of Diagnostic Sciences, Ghent University, 9000 Ghent, East-Flanders, Belgium; Cancer Research Institute Ghent (CRIG), 9000 Ghent, Easy-Flanders, Belgium
| | - Sandra Van Lint
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Easy-Flanders, Belgium; Respiratory Medicine, Ghent University Hospital, 9000 Ghent, East-Flanders, Belgium
| | - Glenn Goetgeluk
- Department of Diagnostic Sciences, Ghent University, 9000 Ghent, East-Flanders, Belgium
| | - Sarah Bonte
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Easy-Flanders, Belgium; VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, East-Flanders, Belgium
| | - Lore Billiet
- Department of Diagnostic Sciences, Ghent University, 9000 Ghent, East-Flanders, Belgium; Cancer Research Institute Ghent (CRIG), 9000 Ghent, Easy-Flanders, Belgium
| | - Melissa Pille
- Department of Diagnostic Sciences, Ghent University, 9000 Ghent, East-Flanders, Belgium
| | - Hanne Jansen
- Department of Diagnostic Sciences, Ghent University, 9000 Ghent, East-Flanders, Belgium
| | - Eva Pascal
- Department of Diagnostic Sciences, Ghent University, 9000 Ghent, East-Flanders, Belgium; Cancer Research Institute Ghent (CRIG), 9000 Ghent, Easy-Flanders, Belgium
| | - Lucas Deseins
- Department of Diagnostic Sciences, Ghent University, 9000 Ghent, East-Flanders, Belgium; Cancer Research Institute Ghent (CRIG), 9000 Ghent, Easy-Flanders, Belgium
| | - Lies Vantomme
- Department of Biomolecular Medicine, Ghent University, 9000 Ghent, East-Flanders, Belgium
| | - Maarten Verdonckt
- Department of Diagnostic Sciences, Ghent University, 9000 Ghent, East-Flanders, Belgium
| | - Ria Roelandt
- VIB Single Cell Core, VIB, 9000/3000 Ghent/Leuven, East-Flanders/Flemish Brabant, Belgium
| | - Thomas Eekhout
- VIB Single Cell Core, VIB, 9000/3000 Ghent/Leuven, East-Flanders/Flemish Brabant, Belgium
| | - Niels Vandamme
- VIB Single Cell Core, VIB, 9000/3000 Ghent/Leuven, East-Flanders/Flemish Brabant, Belgium
| | - Georges Leclercq
- Department of Diagnostic Sciences, Ghent University, 9000 Ghent, East-Flanders, Belgium
| | - Tom Taghon
- Department of Diagnostic Sciences, Ghent University, 9000 Ghent, East-Flanders, Belgium
| | - Tessa Kerre
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Easy-Flanders, Belgium; VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, East-Flanders, Belgium; Hematology, Ghent University Hospital, 9000 Ghent, East-Flanders, Belgium
| | - Floris Vanommeslaeghe
- Nephrology, Ghent University Hospital, Ghent University, 9000 Ghent, East-Flanders, Belgium
| | - Annemieke Dhondt
- Nephrology, Ghent University Hospital, Ghent University, 9000 Ghent, East-Flanders, Belgium
| | - Liesbeth Ferdinande
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Easy-Flanders, Belgium; Pathology, Ghent University Hospital, 9000 Ghent, East-Flanders, Belgium
| | - Jo Van Dorpe
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Easy-Flanders, Belgium; Pathology, Ghent University Hospital, 9000 Ghent, East-Flanders, Belgium
| | - Liesbeth Desender
- Thoracic and Vascular Surgery, Ghent University Hospital, 9000 Ghent, East-Flanders, Belgium
| | - Frederic De Ryck
- Thoracic and Vascular Surgery, Ghent University Hospital, 9000 Ghent, East-Flanders, Belgium
| | - Frank Vermassen
- Thoracic and Vascular Surgery, Ghent University Hospital, 9000 Ghent, East-Flanders, Belgium
| | - Veerle Surmont
- Respiratory Medicine, Ghent University Hospital, 9000 Ghent, East-Flanders, Belgium
| | - Francis Impens
- Department of Biomolecular Medicine, Ghent University, 9000 Ghent, East-Flanders, Belgium; VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, East-Flanders, Belgium
| | - Björn Menten
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Easy-Flanders, Belgium; Department of Biomolecular Medicine, Ghent University, 9000 Ghent, East-Flanders, Belgium
| | - Karim Vermaelen
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Easy-Flanders, Belgium; Respiratory Medicine, Ghent University Hospital, 9000 Ghent, East-Flanders, Belgium.
| | - Bart Vandekerckhove
- Department of Diagnostic Sciences, Ghent University, 9000 Ghent, East-Flanders, Belgium; Cancer Research Institute Ghent (CRIG), 9000 Ghent, Easy-Flanders, Belgium; GMP Unit Cell Therapy, Ghent University Hospital, 9000 Ghent, East-Flanders, Belgium.
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9
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Eralp B, Sefer E. Reference-free inferring of transcriptomic events in cancer cells on single-cell data. BMC Cancer 2024; 24:607. [PMID: 38769480 PMCID: PMC11107047 DOI: 10.1186/s12885-024-12331-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 05/02/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND Cancerous cells' identity is determined via a mixture of multiple factors such as genomic variations, epigenetics, and the regulatory variations that are involved in transcription. The differences in transcriptome expression as well as abnormal structures in peptides determine phenotypical differences. Thus, bulk RNA-seq and more recent single-cell RNA-seq data (scRNA-seq) are important to identify pathogenic differences. In this case, we rely on k-mer decomposition of sequences to identify pathogenic variations in detail which does not need a reference, so it outperforms more traditional Next-Generation Sequencing (NGS) analysis techniques depending on the alignment of the sequences to a reference. RESULTS Via our alignment-free analysis, over esophageal and glioblastoma cancer patients, high-frequency variations over multiple different locations (repeats, intergenic regions, exons, introns) as well as multiple different forms (fusion, polyadenylation, splicing, etc.) could be discovered. Additionally, we have analyzed the importance of less-focused events systematically in a classic transcriptome analysis pipeline where these events are considered as indicators for tumor prognosis, tumor prediction, tumor neoantigen inference, as well as their connection with respect to the immune microenvironment. CONCLUSIONS Our results suggest that esophageal cancer (ESCA) and glioblastoma processes can be explained via pathogenic microbial RNA, repeated sequences, novel splicing variants, and long intergenic non-coding RNAs (lincRNAs). We expect our application of reference-free process and analysis to be helpful in tumor and normal samples differential scRNA-seq analysis, which in turn offers a more comprehensive scheme for major cancer-associated events.
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Affiliation(s)
- Batuhan Eralp
- Department of Computer Science, Ozyegin University, Istanbul, Turkey
| | - Emre Sefer
- Department of Computer Science, Ozyegin University, Istanbul, Turkey.
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10
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Su Z, Wu Y, Cao K, Du J, Cao L, Wu Z, Wu X, Wang X, Song Y, Wang X, Duan H. APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules. Methods 2024; 228:38-47. [PMID: 38772499 DOI: 10.1016/j.ymeth.2024.05.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/28/2024] [Accepted: 05/18/2024] [Indexed: 05/23/2024] Open
Abstract
Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.
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Affiliation(s)
- Zhihao Su
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Yejian Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Kaiqiang Cao
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Jie Du
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Lujing Cao
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Zhipeng Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Xinyi Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Xinqiao Wang
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Ying Song
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Xudong Wang
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.
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11
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Jiang M, Yu Z, Lan X. VitTCR: A deep learning method for peptide recognition prediction. iScience 2024; 27:109770. [PMID: 38711451 PMCID: PMC11070698 DOI: 10.1016/j.isci.2024.109770] [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: 06/23/2023] [Revised: 01/21/2024] [Accepted: 04/15/2024] [Indexed: 05/08/2024] Open
Abstract
This study introduces VitTCR, a predictive model based on the vision transformer (ViT) architecture, aimed at identifying interactions between T cell receptors (TCRs) and peptides, crucial for developing cancer immunotherapies and vaccines. VitTCR converts TCR-peptide interactions into numerical AtchleyMaps using Atchley factors for prediction, achieving AUROC (0.6485) and AUPR (0.6295) values. Benchmark analysis indicates VitTCR's performance is comparable to other models, with further comparative studies suggested to understand its effectiveness in varied contexts. Additionally, integrating a positional bias weight matrix (PBWM), derived from amino acid contact probabilities in structurally resolved pMHC-TCR complexes, slightly improves VitTCR's accuracy. The model's predictions show weak yet statistically significant correlations with immunological factors like T cell clonal expansion and activation percentages, underscoring the biological relevance of VitTCR's predictive capabilities. VitTCR emerges as a valuable computational tool for predicting TCR-peptide interactions, offering insights for immunotherapy and vaccine development.
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Affiliation(s)
- Mengnan Jiang
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Zilan Yu
- School of Medicine, Tsinghua University, Beijing 100084, China
- Centre for Life Sciences, Tsinghua University, Beijing 100084, China
| | - Xun Lan
- School of Medicine, Tsinghua University, Beijing 100084, China
- Centre for Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, MOE Key Laboratory of Tsinghua University, Beijing, China
- MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China
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12
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Gao Y, Dong K, Gao Y, Jin X, Yang J, Yan G, Liu Q. Unified cross-modality integration and analysis of T cell receptors and T cell transcriptomes by low-resource-aware representation learning. CELL GENOMICS 2024; 4:100553. [PMID: 38688285 PMCID: PMC11099349 DOI: 10.1016/j.xgen.2024.100553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/09/2024] [Accepted: 04/06/2024] [Indexed: 05/02/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) are pivotal for investigating T cell heterogeneity. Integrating these modalities, which is expected to uncover profound insights in immunology that might otherwise go unnoticed with a single modality, faces computational challenges due to the low-resource characteristics of the multimodal data. Herein, we present UniTCR, a novel low-resource-aware multimodal representation learning framework designed for the unified cross-modality integration, enabling comprehensive T cell analysis. By designing a dual-modality contrastive learning module and a single-modality preservation module to effectively embed each modality into a common latent space, UniTCR demonstrates versatility in connecting TCR sequences with T cell transcriptomes across various tasks, including single-modality analysis, modality gap analysis, epitope-TCR binding prediction, and TCR profile cross-modality generation, in a low-resource-aware way. Extensive evaluations conducted on multiple scRNA-seq/TCR-seq paired datasets showed the superior performance of UniTCR, exhibiting the ability of exploring the complexity of immune system.
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Affiliation(s)
- Yicheng Gao
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Kejing Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Yuli Gao
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Xuan Jin
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Jingya Yang
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China
| | - Gang Yan
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China; Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311121, China.
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13
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Paterson RL, La Manna MP, Arena De Souza V, Walker A, Gibbs-Howe D, Kulkarni R, Fergusson JR, Mulakkal NC, Monteiro M, Bunjobpol W, Dembek M, Martin-Urdiroz M, Grant T, Barber C, Garay-Baquero DJ, Tezera LB, Lowne D, Britton-Rivet C, Pengelly R, Chepisiuk N, Singh PK, Woon AP, Powlesland AS, McCully ML, Caccamo N, Salio M, Badami GD, Dorrell L, Knox A, Robinson R, Elkington P, Dieli F, Lepore M, Leonard S, Godinho LF. An HLA-E-targeted TCR bispecific molecule redirects T cell immunity against Mycobacterium tuberculosis. Proc Natl Acad Sci U S A 2024; 121:e2318003121. [PMID: 38691588 PMCID: PMC11087797 DOI: 10.1073/pnas.2318003121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 03/08/2024] [Indexed: 05/03/2024] Open
Abstract
Peptides presented by HLA-E, a molecule with very limited polymorphism, represent attractive targets for T cell receptor (TCR)-based immunotherapies to circumvent the limitations imposed by the high polymorphism of classical HLA genes in the human population. Here, we describe a TCR-based bispecific molecule that potently and selectively binds HLA-E in complex with a peptide encoded by the inhA gene of Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis in humans. We reveal the biophysical and structural bases underpinning the potency and specificity of this molecule and demonstrate its ability to redirect polyclonal T cells to target HLA-E-expressing cells transduced with mycobacterial inhA as well as primary cells infected with virulent Mtb. Additionally, we demonstrate elimination of Mtb-infected cells and reduction of intracellular Mtb growth. Our study suggests an approach to enhance host T cell immunity against Mtb and provides proof of principle for an innovative TCR-based therapeutic strategy overcoming HLA polymorphism and therefore applicable to a broader patient population.
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Affiliation(s)
| | - Marco P. La Manna
- Department of Biomedicine, Neurosciences and Advanced Diagnostic, University of Palermo, Palermo90127, Italy
- Central Laboratory of Advanced Diagnosis and Biomedical Research, Azienda Ospedaliera Universitaria Policlinico Paolo Giaccone, University of Palermo, Palermo90127, Italy
| | | | - Andrew Walker
- Immunocore Ltd., Abingdon, OxfordshireOX14 4RY, United Kingdom
| | - Dawn Gibbs-Howe
- Immunocore Ltd., Abingdon, OxfordshireOX14 4RY, United Kingdom
| | - Rakesh Kulkarni
- Immunocore Ltd., Abingdon, OxfordshireOX14 4RY, United Kingdom
| | | | | | - Mauro Monteiro
- Immunocore Ltd., Abingdon, OxfordshireOX14 4RY, United Kingdom
| | | | - Marcin Dembek
- Immunocore Ltd., Abingdon, OxfordshireOX14 4RY, United Kingdom
| | | | - Tressan Grant
- Immunocore Ltd., Abingdon, OxfordshireOX14 4RY, United Kingdom
| | - Claire Barber
- Immunocore Ltd., Abingdon, OxfordshireOX14 4RY, United Kingdom
| | - Diana J. Garay-Baquero
- National Institute for Health and Care Research, Biomedical Research Centre and Institute for Life Sciences, Faculty of Medicine, University of Southampton, SouthamptonSO16 6YD, United Kingdom
| | - Liku Bekele Tezera
- Department of Biomedicine, Neurosciences and Advanced Diagnostic, University of Palermo, Palermo90127, Italy
| | - David Lowne
- Immunocore Ltd., Abingdon, OxfordshireOX14 4RY, United Kingdom
| | | | - Robert Pengelly
- Immunocore Ltd., Abingdon, OxfordshireOX14 4RY, United Kingdom
| | | | | | - Amanda P. Woon
- Immunocore Ltd., Abingdon, OxfordshireOX14 4RY, United Kingdom
| | | | | | - Nadia Caccamo
- Department of Biomedicine, Neurosciences and Advanced Diagnostic, University of Palermo, Palermo90127, Italy
- Central Laboratory of Advanced Diagnosis and Biomedical Research, Azienda Ospedaliera Universitaria Policlinico Paolo Giaccone, University of Palermo, Palermo90127, Italy
| | - Mariolina Salio
- Immunocore Ltd., Abingdon, OxfordshireOX14 4RY, United Kingdom
| | - Giusto Davide Badami
- Department of Biomedicine, Neurosciences and Advanced Diagnostic, University of Palermo, Palermo90127, Italy
- Central Laboratory of Advanced Diagnosis and Biomedical Research, Azienda Ospedaliera Universitaria Policlinico Paolo Giaccone, University of Palermo, Palermo90127, Italy
| | - Lucy Dorrell
- Immunocore Ltd., Abingdon, OxfordshireOX14 4RY, United Kingdom
| | - Andrew Knox
- Immunocore Ltd., Abingdon, OxfordshireOX14 4RY, United Kingdom
| | - Ross Robinson
- Immunocore Ltd., Abingdon, OxfordshireOX14 4RY, United Kingdom
| | - Paul Elkington
- National Institute for Health and Care Research, Biomedical Research Centre and Institute for Life Sciences, Faculty of Medicine, University of Southampton, SouthamptonSO16 6YD, United Kingdom
| | - Francesco Dieli
- Department of Biomedicine, Neurosciences and Advanced Diagnostic, University of Palermo, Palermo90127, Italy
- Central Laboratory of Advanced Diagnosis and Biomedical Research, Azienda Ospedaliera Universitaria Policlinico Paolo Giaccone, University of Palermo, Palermo90127, Italy
| | - Marco Lepore
- Immunocore Ltd., Abingdon, OxfordshireOX14 4RY, United Kingdom
| | - Sarah Leonard
- Immunocore Ltd., Abingdon, OxfordshireOX14 4RY, United Kingdom
| | - Luis F. Godinho
- Immunocore Ltd., Abingdon, OxfordshireOX14 4RY, United Kingdom
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14
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Omelchenko AA, Siwek JC, Chhibbar P, Arshad S, Nazarali I, Nazarali K, Rosengart A, Rahimikollu J, Tilstra J, Shlomchik MJ, Koes DR, Joglekar AV, Das J. Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.01.592062. [PMID: 38746274 PMCID: PMC11092674 DOI: 10.1101/2024.05.01.592062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The explosion of sequence data has allowed the rapid growth of protein language models (pLMs). pLMs have now been employed in many frameworks including variant-effect and peptide-specificity prediction. Traditionally, for protein-protein or peptide-protein interactions (PPIs), corresponding sequences are either co-embedded followed by post-hoc integration or the sequences are concatenated prior to embedding. Interestingly, no method utilizes a language representation of the interaction itself. We developed an interaction LM (iLM), which uses a novel language to represent interactions between protein/peptide sequences. Sliding Window Interaction Grammar (SWING) leverages differences in amino acid properties to generate an interaction vocabulary. This vocabulary is the input into a LM followed by a supervised prediction step where the LM's representations are used as features. SWING was first applied to predicting peptide:MHC (pMHC) interactions. SWING was not only successful at generating Class I and Class II models that have comparable prediction to state-of-the-art approaches, but the unique Mixed Class model was also successful at jointly predicting both classes. Further, the SWING model trained only on Class I alleles was predictive for Class II, a complex prediction task not attempted by any existing approach. For de novo data, using only Class I or Class II data, SWING also accurately predicted Class II pMHC interactions in murine models of SLE (MRL/lpr model) and T1D (NOD model), that were validated experimentally. To further evaluate SWING's generalizability, we tested its ability to predict the disruption of specific protein-protein interactions by missense mutations. Although modern methods like AlphaMissense and ESM1b can predict interfaces and variant effects/pathogenicity per mutation, they are unable to predict interaction-specific disruptions. SWING was successful at accurately predicting the impact of both Mendelian mutations and population variants on PPIs. This is the first generalizable approach that can accurately predict interaction-specific disruptions by missense mutations with only sequence information. Overall, SWING is a first-in-class generalizable zero-shot iLM that learns the language of PPIs.
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Affiliation(s)
- Alisa A. Omelchenko
- Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, PA, USA
- The joint CMU-Pitt PhD program in computational biology, School of Medicine, University of Pittsburgh, PA, USA
| | - Jane C. Siwek
- Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, PA, USA
- The joint CMU-Pitt PhD program in computational biology, School of Medicine, University of Pittsburgh, PA, USA
| | - Prabal Chhibbar
- Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Integrative systems biology PhD program, School of Medicine, University of Pittsburgh, PA, USA
| | - Sanya Arshad
- Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Iliyan Nazarali
- Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kiran Nazarali
- Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - AnnaElaine Rosengart
- Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Javad Rahimikollu
- Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, PA, USA
- The joint CMU-Pitt PhD program in computational biology, School of Medicine, University of Pittsburgh, PA, USA
| | - Jeremy Tilstra
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Division of Rheumatology and Clinical Immunology, Department of Medicine, School of Medicine, University of Pittsburgh, PA, USA
| | - Mark J. Shlomchik
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - David R. Koes
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, PA, USA
| | - Alok V. Joglekar
- Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, PA, USA
| | - Jishnu Das
- Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, PA, USA
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15
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Sarvmeili J, Baghban Kohnehrouz B, Gholizadeh A, Shanehbandi D, Ofoghi H. Immunoinformatics design of a structural proteins driven multi-epitope candidate vaccine against different SARS-CoV-2 variants based on fynomer. Sci Rep 2024; 14:10297. [PMID: 38704475 PMCID: PMC11069592 DOI: 10.1038/s41598-024-61025-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 04/30/2024] [Indexed: 05/06/2024] Open
Abstract
The ideal vaccines for combating diseases that may emerge in the future require more than simply inactivating a few pathogenic strains. This study aims to provide a peptide-based multi-epitope vaccine effective against various severe acute respiratory syndrome coronavirus 2 strains. To design the vaccine, a library of peptides from the spike, nucleocapsid, membrane, and envelope structural proteins of various strains was prepared. Then, the final vaccine structure was optimized using the fully protected epitopes and the fynomer scaffold. Using bioinformatics tools, the antigenicity, allergenicity, toxicity, physicochemical properties, population coverage, and secondary and three-dimensional structures of the vaccine candidate were evaluated. The bioinformatic analyses confirmed the high quality of the vaccine. According to further investigations, this structure is similar to native protein and there is a stable and strong interaction between vaccine and receptors. Based on molecular dynamics simulation, structural compactness and stability in binding were also observed. In addition, the immune simulation showed that the vaccine can stimulate immune responses similar to real conditions. Finally, codon optimization and in silico cloning confirmed efficient expression in Escherichia coli. In conclusion, the fynomer-based vaccine can be considered as a new style in designing and updating vaccines to protect against coronavirus disease.
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Affiliation(s)
- Javad Sarvmeili
- Department of Plant Breeding and Biotechnology, University of Tabriz, Tabriz, 51666, Iran
| | | | - Ashraf Gholizadeh
- Department of Animal Biology, University of Tabriz, Tabriz, 51666, Iran
| | - Dariush Shanehbandi
- Department of Immunology, Tabriz University of Medical Sciences, Tabriz, 51666, Iran
| | - Hamideh Ofoghi
- Department of Biotechnology, Iranian Research Organization for Science and Technology, Tehran, 33131, Iran
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16
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Fuchs KJ, van de Meent M, Honders MW, Khatri I, Kester MGD, Koster EAS, Koutsoumpli G, de Ru AH, van Bergen CAM, van Veelen PA, ’t Hoen PAC, van Balen P, van den Akker EB, Veelken JH, Halkes CJM, Falkenburg JHF, Griffioen M. Expanding the repertoire reveals recurrent, cryptic, and hematopoietic HLA class I minor histocompatibility antigens. Blood 2024; 143:1856-1872. [PMID: 38427583 PMCID: PMC11076866 DOI: 10.1182/blood.2023022343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 02/15/2024] [Accepted: 02/15/2024] [Indexed: 03/03/2024] Open
Abstract
ABSTRACT Allogeneic stem cell transplantation (alloSCT) is a curative treatment for hematological malignancies. After HLA-matched alloSCT, antitumor immunity is caused by donor T cells recognizing polymorphic peptides, designated minor histocompatibility antigens (MiHAs), that are presented by HLA on malignant patient cells. However, T cells often target MiHAs on healthy nonhematopoietic tissues of patients, thereby inducing side effects known as graft-versus-host disease. Here, we aimed to identify the dominant repertoire of HLA-I-restricted MiHAs to enable strategies to predict, monitor or modulate immune responses after alloSCT. To systematically identify novel MiHAs by genome-wide association screening, T-cell clones were isolated from 39 transplanted patients and tested for reactivity against 191 Epstein-Barr virus transformed B cell lines of the 1000 Genomes Project. By discovering 81 new MiHAs, we more than doubled the antigen repertoire to 159 MiHAs and demonstrated that, despite many genetic differences between patients and donors, often the same MiHAs are targeted in multiple patients. Furthermore, we showed that one quarter of the antigens are cryptic, that is translated from unconventional open reading frames, for example long noncoding RNAs, showing that these antigen types are relevant targets in natural immune responses. Finally, using single cell RNA-seq data, we analyzed tissue expression of MiHA-encoding genes to explore their potential role in clinical outcome, and characterized 11 new hematopoietic-restricted MiHAs as potential targets for immunotherapy. In conclusion, we expanded the repertoire of HLA-I-restricted MiHAs and identified recurrent, cryptic and hematopoietic-restricted antigens, which are fundamental to predict, follow or manipulate immune responses to improve clinical outcome after alloSCT.
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Affiliation(s)
- Kyra J. Fuchs
- Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands
| | - Marian van de Meent
- Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands
| | - M. Willy Honders
- Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands
| | - Indu Khatri
- Department of Immunology, Leiden University Medical Center, Leiden, The Netherlands
| | - Michel G. D. Kester
- Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands
| | - Eva A. S. Koster
- Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands
| | - Georgia Koutsoumpli
- Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands
| | - Arnoud H. de Ru
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Peter A. van Veelen
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - Peter A. C. ’t Hoen
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Peter van Balen
- Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands
| | - Erik B. van den Akker
- Center for Computational Biology, Leiden University Medical Center, Leiden, The Netherlands
| | - J. Hendrik Veelken
- Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands
| | | | | | - Marieke Griffioen
- Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands
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17
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Bolivar AM, Duzagac F, Deng N, Reyes-Uribe L, Chang K, Wu W, Bowen CM, Taggart MW, Thirumurthi S, Lynch PM, You YN, Rodriguez-Pascual J, Lipkin SM, Kopetz S, Scheet P, Lizee GA, Reuben A, Sinha KM, Vilar E. Genomic Landscape of Lynch Syndrome Colorectal Neoplasia Identifies Shared Mutated Neoantigens for Immunoprevention. Gastroenterology 2024; 166:787-801.e11. [PMID: 38244726 PMCID: PMC11034773 DOI: 10.1053/j.gastro.2024.01.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 12/18/2023] [Accepted: 01/07/2024] [Indexed: 01/22/2024]
Abstract
BACKGROUND & AIMS Lynch syndrome (LS) carriers develop mismatch repair-deficient neoplasia with high neoantigen (neoAg) rates. No detailed information on targetable neoAgs from LS precancers exists, which is crucial for vaccine development and immune-interception strategies. We report a focused somatic mutation and frameshift-neoAg landscape of microsatellite loci from colorectal polyps without malignant potential (PWOMP), precancers, and early-stage cancers in LS carriers. METHODS We generated paired whole-exome and transcriptomic sequencing data from 8 colorectal PWOMP, 41 precancers, 8 advanced precancers, and 12 early-stage cancers of 43 LS carriers. A computational pipeline was developed to predict, rank, and prioritize the top 100 detected mutated neoAgs that were validated in vitro using ELISpot and tetramer assays. RESULTS Mutation calling revealed >10 mut/Mb in 83% of cancers, 63% of advanced precancers, and 20% of precancers. Cancers displayed an average of 616 MHC-I neoAgs/sample, 294 in advanced precancers, and 107 in precancers. No neoAgs were detected in PWOMP. A total of 65% of our top 100 predicted neoAgs were immunogenic in vitro, and were present in 92% of cancers, 50% of advanced precancers, and 29% of precancers. We observed increased levels of naïve CD8+ and memory CD4+ T cells in mismatch repair-deficient cancers and precancers via transcriptomics analysis. CONCLUSIONS Shared frameshift-neoAgs are generated within unstable microsatellite loci at initial stages of LS carcinogenesis and can induce T-cell responses, generating opportunities for vaccine development, targeting LS precancers and early-stage cancers.
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Affiliation(s)
- Ana M Bolivar
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Fahriye Duzagac
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Nan Deng
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Laura Reyes-Uribe
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kyle Chang
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Wenhui Wu
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Charles M Bowen
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Melissa W Taggart
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Selvi Thirumurthi
- Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas; Clinical Cancer Genetics Program, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Patrick M Lynch
- Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas; Clinical Cancer Genetics Program, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Y Nancy You
- Clinical Cancer Genetics Program, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Colorectal Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | | | - Steven M Lipkin
- Division of Gastroenterology and Hepatology, Weill Cornell University, New York, New York
| | - Scott Kopetz
- Department of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Paul Scheet
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Gregory A Lizee
- Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Alexandre Reuben
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Krishna M Sinha
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Eduardo Vilar
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas; Clinical Cancer Genetics Program, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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18
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Zhang M, Xu W, Luo L, Guan F, Wang X, Zhu P, Zhang J, Zhou X, Wang F, Ye S. Identification and affinity enhancement of T-cell receptor targeting a KRAS G12V cancer neoantigen. Commun Biol 2024; 7:512. [PMID: 38684865 PMCID: PMC11058820 DOI: 10.1038/s42003-024-06209-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 04/17/2024] [Indexed: 05/02/2024] Open
Abstract
Neoantigens derived from somatic mutations in Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS), the most frequently mutated oncogene, represent promising targets for cancer immunotherapy. Recent research highlights the potential role of human leukocyte antigen (HLA) allele A*11:01 in presenting these altered KRAS variants to the immune system. In this study, we successfully generate and identify murine T-cell receptors (TCRs) that specifically recognize KRAS8-16G12V from three predicted high affinity peptides. By determining the structure of the tumor-specific 4TCR2 bound to KRASG12V-HLA-A*11:01, we conduct structure-based design to create and evaluate TCR variants with markedly enhanced affinity, up to 15.8-fold. This high-affinity TCR mutant, which involved only two amino acid substitutions, display minimal conformational alterations while maintaining a high degree of specificity for the KRASG12V peptide. Our research unveils the molecular mechanisms governing TCR recognition towards KRASG12V neoantigen and yields a range of affinity-enhanced TCR mutants with significant potential for immunotherapy strategies targeting tumors harboring the KRASG12V mutation.
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Affiliation(s)
- Mengyu Zhang
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin Key Laboratory of Function and Application of Biological Macromolecular Structures, School of Life Sciences, Tianjin University, 92 Weijin Road, Nankai District, Tianjin, 300072, China
| | - Wei Xu
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, 100101, China
- Department of Savaid Medical School, University of Chinese Academy of Sciences (CAS), Beijing, 100049, China
| | - Lingjie Luo
- School of Medicine, Shanghai University, Shanghai, 200444, China
| | - Fenghui Guan
- The Cancer Hospital of the University of Chinese Academy of Sciences, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China
| | - Xiangyao Wang
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin Key Laboratory of Function and Application of Biological Macromolecular Structures, School of Life Sciences, Tianjin University, 92 Weijin Road, Nankai District, Tianjin, 300072, China
| | - Pei Zhu
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin Key Laboratory of Function and Application of Biological Macromolecular Structures, School of Life Sciences, Tianjin University, 92 Weijin Road, Nankai District, Tianjin, 300072, China
| | - Jianhua Zhang
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, 100101, China
- Department of Savaid Medical School, University of Chinese Academy of Sciences (CAS), Beijing, 100049, China
| | - Xuyu Zhou
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences (CAS), Beijing, 100101, China.
- Department of Savaid Medical School, University of Chinese Academy of Sciences (CAS), Beijing, 100049, China.
| | - Feng Wang
- State Key Laboratory of Oncogenes and Related Genes, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Sheng Ye
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin Key Laboratory of Function and Application of Biological Macromolecular Structures, School of Life Sciences, Tianjin University, 92 Weijin Road, Nankai District, Tianjin, 300072, China.
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19
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Marx OM, Mankarious MM, Koltun WA, Yochum GS. Identification of differentially expressed genes and splicing events in early-onset colorectal cancer. Front Oncol 2024; 14:1365762. [PMID: 38680862 PMCID: PMC11047122 DOI: 10.3389/fonc.2024.1365762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 03/27/2024] [Indexed: 05/01/2024] Open
Abstract
Background The incidence of colorectal cancer (CRC) has been steadily increasing in younger individuals over the past several decades for reasons that are incompletely defined. Identifying differences in gene expression profiles, or transcriptomes, in early-onset colorectal cancer (EOCRC, < 50 years old) patients versus later-onset colorectal cancer (LOCRC, > 50 years old) patients is one approach to understanding molecular and genetic features that distinguish EOCRC. Methods We performed RNA-sequencing (RNA-seq) to characterize the transcriptomes of patient-matched tumors and adjacent, uninvolved (normal) colonic segments from EOCRC (n=21) and LOCRC (n=22) patients. The EOCRC and LOCRC cohorts were matched for demographic and clinical characteristics. We used The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) database for validation. We used a series of computational and bioinformatic tools to identify EOCRC-specific differentially expressed genes, molecular pathways, predicted cell populations, differential gene splicing events, and predicted neoantigens. Results We identified an eight-gene signature in EOCRC comprised of ALDOB, FBXL16, IL1RN, MSLN, RAC3, SLC38A11, WBSCR27 and WNT11, from which we developed a score predictive of overall CRC patient survival. On the entire set of genes identified in normal tissues and tumors, cell type deconvolution analysis predicted a differential abundance of immune and non-immune populations in EOCRC versus LOCRC. Gene set enrichment analysis identified increased expression of splicing machinery in EOCRC. We further found differences in alternative splicing (AS) events, including one within the long non-coding RNA, HOTAIRM1. Additional analysis of AS found seven events specific to EOCRC that encode potential neoantigens. Conclusion Our transcriptome analyses identified genetic and molecular features specific to EOCRC which may inform future screening, development of prognostic indicators, and novel drug targets.
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Affiliation(s)
- Olivia M. Marx
- Koltun and Yochum Laboratory, Department of Surgery, Division of Colon & Rectal Surgery, Pennsylvania State University College of Medicine, Hershey, PA, United States
- Department of Biochemistry & Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Marc M. Mankarious
- Koltun and Yochum Laboratory, Department of Surgery, Division of Colon & Rectal Surgery, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Walter A. Koltun
- Koltun and Yochum Laboratory, Department of Surgery, Division of Colon & Rectal Surgery, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Gregory S. Yochum
- Koltun and Yochum Laboratory, Department of Surgery, Division of Colon & Rectal Surgery, Pennsylvania State University College of Medicine, Hershey, PA, United States
- Department of Biochemistry & Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA, United States
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20
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Ma W, Zhang J, Yao H. NeoMUST: an accurate and efficient multi-task learning model for neoantigen presentation. Life Sci Alliance 2024; 7:e202302255. [PMID: 38290755 PMCID: PMC10828515 DOI: 10.26508/lsa.202302255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/01/2024] Open
Abstract
Accurate identification of neoantigens is important for advancing cancer immunotherapies. This study introduces Neoantigen MUlti-taSk Tower (NeoMUST), a model employing multi-task learning to effectively capture task-specific information across related tasks. Our results show that NeoMUST rivals existing algorithms in predicting the presentation of neoantigens via MHC-I molecules, while demonstrating a significantly shorter training time for enhanced computational efficiency. The use of multi-task learning enables NeoMUST to leverage shared knowledge and task dependencies, leading to improved performance metrics and a significant reduction in the training time. NeoMUST, implemented in Python, is freely accessible at the GitHub repository. Our model will facilitate neoantigen prediction and empower the development of effective cancer immunotherapeutic approaches.
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Affiliation(s)
- Wang Ma
- Fresh Wind Biotechnologies Inc. (Tianjin), Tianjin, China
| | - Jiawei Zhang
- Fresh Wind Biotechnologies Inc. (Tianjin), Tianjin, China
| | - Hui Yao
- Fresh Wind Biotechnologies USA Inc., Houston, TX, USA
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21
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Giziński S, Preibisch G, Kucharski P, Tyrolski M, Rembalski M, Grzegorczyk P, Gambin A. Enhancing antigenic peptide discovery: Improved MHC-I binding prediction and methodology. Methods 2024; 224:1-9. [PMID: 38295891 DOI: 10.1016/j.ymeth.2024.01.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 12/30/2023] [Accepted: 01/16/2024] [Indexed: 02/05/2024] Open
Abstract
The Major Histocompatibility Complex (MHC) is a critical element of the vertebrate cellular immune system, responsible for presenting peptides derived from intracellular proteins. MHC-I presentation is pivotal in the immune response and holds considerable potential in the realms of vaccine development and cancer immunotherapy. This study delves into the limitations of current methods and benchmarks for MHC-I presentation. We introduce a novel benchmark designed to assess generalization properties and the reliability of models on unseen MHC molecules and peptides, with a focus on the Human Leukocyte Antigen (HLA)-a specific subset of MHC genes present in humans. Finally, we introduce HLABERT, a pretrained language model that outperforms previous methods significantly on our benchmark and establishes a new state-of-the-art on existing benchmarks.
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Affiliation(s)
| | - Grzegorz Preibisch
- Deepflare, Warsaw, Poland; University of Warsaw, Department of Mathematics Informatics and Mechanics, Warsaw, Poland.
| | | | | | | | | | - Anna Gambin
- University of Warsaw, Department of Mathematics Informatics and Mechanics, Warsaw, Poland.
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22
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Yasmin S, Ansari MY, Pandey K, Dikhit MR. Identification of potential vaccine targets for elicitation of host immune cells against SARS-CoV-2 by reverse vaccinology approach. Int J Biol Macromol 2024; 265:130754. [PMID: 38508555 DOI: 10.1016/j.ijbiomac.2024.130754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 03/06/2024] [Accepted: 03/07/2024] [Indexed: 03/22/2024]
Abstract
The COVID-19 pandemic has emerged as a critical global health crisis, demanding urgent and effective strategies for containment. While some knowledge exists about epitope sequences recognized by human immune cells and their activation of CD8+ T cells within the HLA context, comprehensive information remains limited. This study employs reverse vaccinology to explore antigenic HLA-restricted T-cell epitopes capable of eliciting durable immunity. Screening reveals 187 consensus epitopes, with 23 offering broad population coverage worldwide, spanning over 5000 HLA alleles. Sequence alignment analysis highlights the genetic distinctiveness of these peptides from Homo sapiens and their intermediate to high TAP binding efficiency. Notably, these epitopes share 100 % sequence identity across strains from nine countries, indicating potential for a uniform protective immune response among diverse ethnic populations. Docking simulations further confirm their binding capacity with the HLA allele, validating them as promising targets for SARS-CoV-2 immune recognition. The anticipated epitopes are connected with suitable linkers and adjuvant, and then assessed for its translational efficacy within a bacterial expression vector through computational cloning. Through docking, it is observed that the chimeric vaccine construct forms lasting hydrogen bonds with Toll-like receptor (TLR4), while immune simulation illustrates an increased cytotoxic response aimed at CD8+ T cells. This comprehensive computational analysis suggests the chimeric vaccine construct's potential to provoke a robust immune response against SARS-CoV-2. By delineating these antigenic fragments, our study offers valuable insights into effective vaccine and immunotherapy development against COVID-19, contributing significantly to global efforts in combating this infectious threat.
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Affiliation(s)
- Sabina Yasmin
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Khalid University (KKU), Abha 62529, Saudi Arabia
| | - Mohammad Yousuf Ansari
- Department of Pharmaceutical Chemistry, M.M. College of Pharmacy, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala 133207, India.
| | - Krishna Pandey
- Department of Clinical Medicine, ICMR-Rajendra Memorial Research Institute of Medical Sciences, Agamkuan, Patna 800007, India
| | - Manas Ranjan Dikhit
- Department of Bioinformatics, ICMR-Rajendra Memorial Research Institute of Medical Sciences, Agamkuan, Patna 800007, India.
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23
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Minegishi Y, Haga Y, Ueda K. Emerging potential of immunopeptidomics by mass spectrometry in cancer immunotherapy. Cancer Sci 2024; 115:1048-1059. [PMID: 38382459 PMCID: PMC11007014 DOI: 10.1111/cas.16118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/02/2024] [Accepted: 02/07/2024] [Indexed: 02/23/2024] Open
Abstract
With significant advances in analytical technologies, research in the field of cancer immunotherapy, such as adoptive T cell therapy, cancer vaccine, and immune checkpoint blockade (ICB), is currently gaining tremendous momentum. Since the efficacy of cancer immunotherapy is recognized only by a minority of patients, more potent tumor-specific antigens (TSAs, also known as neoantigens) and predictive markers for treatment response are of great interest. In cancer immunity, immunopeptides, presented by human leukocyte antigen (HLA) class I, play a role as initiating mediators of immunogenicity. The latest advancement in the interdisciplinary multiomics approach has rapidly enlightened us about the identity of the "dark matter" of cancer and the associated immunopeptides. In this field, mass spectrometry (MS) is a viable option to select because of the naturally processed and actually presented TSA candidates in order to grasp the whole picture of the immunopeptidome. In the past few years the search space has been enlarged by the multiomics approach, the sensitivity of mass spectrometers has been improved, and deep/machine-learning-supported peptide search algorithms have taken immunopeptidomics to the next level. In this review, along with the introduction of key technical advancements in immunopeptidomics, the potential and further directions of immunopeptidomics will be reviewed from the perspective of cancer immunotherapy.
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Affiliation(s)
- Yuriko Minegishi
- Cancer Proteomics Group, Cancer Precision Medicine CenterJapanese Foundation for Cancer ResearchTokyoJapan
| | - Yoshimi Haga
- Cancer Proteomics Group, Cancer Precision Medicine CenterJapanese Foundation for Cancer ResearchTokyoJapan
| | - Koji Ueda
- Cancer Proteomics Group, Cancer Precision Medicine CenterJapanese Foundation for Cancer ResearchTokyoJapan
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24
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Yazdani Z, Rafiei A, Ghoreyshi M, Abediankenari S. In Silico Analysis of a Candidate Multi-epitope Peptide Vaccine Against Human Brucellosis. Mol Biotechnol 2024; 66:769-783. [PMID: 36940016 PMCID: PMC10026239 DOI: 10.1007/s12033-023-00698-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 02/13/2023] [Indexed: 03/21/2023]
Abstract
Brucellosis is one of the neglected endemic zoonoses in the world. Vaccination appears to be a promising health strategy to prevent it. This study used advanced computational techniques to develop a potent multi-epitope vaccine for human brucellosis. Seven epitopes from four main brucella species that infect humans were selected. They had significant potential to induce cellular and humoral responses. They showed high antigenic ability without the allergenic characteristic. In order to improve its immunogenicity, suitable adjuvants were also added to the structure of the vaccine. The physicochemical and immunological properties of the vaccine were evaluated. Then its two and three-dimensional structure was predicted. The vaccine was docked with toll-like receptor4 to assess its ability to stimulate innate immune responses. For successful expression of the vaccine protein in Escherichia coli, in silico cloning, codon optimization, and mRNA stability were evaluated. The immune simulation was performed to reveal the immune response profile of the vaccine after injection. The designed vaccine showed the high ability to induce immune response, especially cellular responses to human brucellosis. It showed the appropriate physicochemical properties, a high-quality structure, and a high potential for expression in a prokaryotic system.
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Affiliation(s)
- Zahra Yazdani
- Department of Immunology, Molecular and Cell Biology Research Center, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
- Students Research Committee, Mazandaran University of Medical Sciences, Sari, Iran
| | - Alireza Rafiei
- Department of Immunology, Molecular and Cell Biology Research Center, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran.
| | - Mehrafarin Ghoreyshi
- Students Research Committee, Mazandaran University of Medical Sciences, Sari, Iran
| | - Saeid Abediankenari
- Immunogenetics Research Center, Department of Immunology, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
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Passaro A, Al Bakir M, Hamilton EG, Diehn M, André F, Roy-Chowdhuri S, Mountzios G, Wistuba II, Swanton C, Peters S. Cancer biomarkers: Emerging trends and clinical implications for personalized treatment. Cell 2024; 187:1617-1635. [PMID: 38552610 PMCID: PMC7616034 DOI: 10.1016/j.cell.2024.02.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 02/21/2024] [Accepted: 02/28/2024] [Indexed: 04/02/2024]
Abstract
The integration of cancer biomarkers into oncology has revolutionized cancer treatment, yielding remarkable advancements in cancer therapeutics and the prognosis of cancer patients. The development of personalized medicine represents a turning point and a new paradigm in cancer management, as biomarkers enable oncologists to tailor treatments based on the unique molecular profile of each patient's tumor. In this review, we discuss the scientific milestones of cancer biomarkers and explore future possibilities to improve the management of patients with solid tumors. This progress is primarily attributed to the biological characterization of cancers, advancements in testing methodologies, elucidation of the immune microenvironment, and the ability to profile circulating tumor fractions. Integrating these insights promises to continually advance the precision oncology field, fostering better patient outcomes.
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Affiliation(s)
- Antonio Passaro
- Division of Thoracic Oncology, European Institute of Oncology IRCCS, Milan, Italy
| | - Maise Al Bakir
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Emily G Hamilton
- Department of Radiation Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Maximilian Diehn
- Department of Radiation Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Fabrice André
- Gustave-Roussy Cancer Center, Paris Saclay University, Villejuif, France
| | - Sinchita Roy-Chowdhuri
- Department of Anatomic Pathology and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Giannis Mountzios
- Fourth Department of Medical Oncology and Clinical Trials Unit, Henry Dunant Hospital Center, Athens, Greece
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Charles Swanton
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK; Department of Oncology, University College London Hospitals, London, UK
| | - Solange Peters
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland.
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Zhang L, Song W, Zhu T, Liu Y, Chen W, Cao Y. ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model. Brief Bioinform 2024; 25:bbae133. [PMID: 38561979 PMCID: PMC10985285 DOI: 10.1093/bib/bbae133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 02/11/2024] [Accepted: 03/02/2024] [Indexed: 04/04/2024] Open
Abstract
Peptide binding to major histocompatibility complex (MHC) proteins plays a critical role in T-cell recognition and the specificity of the immune response. Experimental validation such peptides is extremely resource-intensive. As a result, accurate computational prediction of binding peptides is highly important, particularly in the context of cancer immunotherapy applications, such as the identification of neoantigens. In recent years, there is a significant need to continually improve the existing prediction methods to meet the demands of this field. We developed ConvNeXt-MHC, a method for predicting MHC-I-peptide binding affinity. It introduces a degenerate encoding approach to enhance well-established panspecific methods and integrates transfer learning and semi-supervised learning methods into the cutting-edge deep learning framework ConvNeXt. Comprehensive benchmark results demonstrate that ConvNeXt-MHC outperforms state-of-the-art methods in terms of accuracy. We expect that ConvNeXt-MHC will help us foster new discoveries in the field of immunoinformatics in the distant future. We constructed a user-friendly website at http://www.combio-lezhang.online/predict/, where users can access our data and application.
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Affiliation(s)
- Le Zhang
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Wenkai Song
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Tinghao Zhu
- College of Computer Science, Sichuan University, Chengdu 610065, China
- Nuclear Power Institute of China, Chengdu 610213, China
| | - Yang Liu
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, No. 29 Wangjiang Road, Chengdu 610065, China
| | - Wei Chen
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Yang Cao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, No. 29 Wangjiang Road, Chengdu 610065, China
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Zhang X, Wu J, Luo Y, Wang Y, Wu Y, Xu X, Zhang Y, Kong R, Chi Y, Sun Y, Chen S, He Q, Zhu F, Zhou Z. CovEpiAb: a comprehensive database and analysis resource for immune epitopes and antibodies of human coronaviruses. Brief Bioinform 2024; 25:bbae183. [PMID: 38653491 PMCID: PMC11036340 DOI: 10.1093/bib/bbae183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 02/24/2024] [Accepted: 04/08/2024] [Indexed: 04/25/2024] Open
Abstract
Coronaviruses have threatened humans repeatedly, especially COVID-19 caused by SARS-CoV-2, which has posed a substantial threat to global public health. SARS-CoV-2 continuously evolves through random mutation, resulting in a significant decrease in the efficacy of existing vaccines and neutralizing antibody drugs. It is critical to assess immune escape caused by viral mutations and develop broad-spectrum vaccines and neutralizing antibodies targeting conserved epitopes. Thus, we constructed CovEpiAb, a comprehensive database and analysis resource of human coronavirus (HCoVs) immune epitopes and antibodies. CovEpiAb contains information on over 60 000 experimentally validated epitopes and over 12 000 antibodies for HCoVs and SARS-CoV-2 variants. The database is unique in (1) classifying and annotating cross-reactive epitopes from different viruses and variants; (2) providing molecular and experimental interaction profiles of antibodies, including structure-based binding sites and around 70 000 data on binding affinity and neutralizing activity; (3) providing virological characteristics of current and past circulating SARS-CoV-2 variants and in vitro activity of various therapeutics; and (4) offering site-level annotations of key functional features, including antibody binding, immunological epitopes, SARS-CoV-2 mutations and conservation across HCoVs. In addition, we developed an integrated pipeline for epitope prediction named COVEP, which is available from the webpage of CovEpiAb. CovEpiAb is freely accessible at https://pgx.zju.edu.cn/covepiab/.
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Affiliation(s)
- Xue Zhang
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - JingCheng Wu
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yuanyuan Luo
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yilin Wang
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yujie Wu
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xiaobin Xu
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yufang Zhang
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ruiying Kong
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ying Chi
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 310058, China
- ZJU-UoE Institute, Zhejiang University, Haining 314400, China
| | - Yisheng Sun
- Key Lab of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310015, China
| | - Shuqing Chen
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Qiaojun He
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Zhejiang University Innovation Institute for Artificial Intelligence in Medicine, Engineering Research Center of Innovative Anticancer Drugs, Ministry of Education, Hangzhou 310018, China
| | - Feng Zhu
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Zhejiang University Innovation Institute for Artificial Intelligence in Medicine, Engineering Research Center of Innovative Anticancer Drugs, Ministry of Education, Hangzhou 310018, China
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 310058, China
| | - Zhan Zhou
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Zhejiang University Innovation Institute for Artificial Intelligence in Medicine, Engineering Research Center of Innovative Anticancer Drugs, Ministry of Education, Hangzhou 310018, China
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 310058, China
- The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu 322000, China
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Li Y, Wu X, Fang D, Luo Y. Informing immunotherapy with multi-omics driven machine learning. NPJ Digit Med 2024; 7:67. [PMID: 38486092 PMCID: PMC10940614 DOI: 10.1038/s41746-024-01043-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 02/14/2024] [Indexed: 03/18/2024] Open
Abstract
Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.
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Affiliation(s)
- Yawei Li
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Xin Wu
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, 60612, USA
| | - Deyu Fang
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA.
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA.
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29
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Nguyen TL, Kim H. Immunoinformatics and computational approaches driven designing a novel vaccine candidate against Powassan virus. Sci Rep 2024; 14:5999. [PMID: 38472237 PMCID: PMC10933373 DOI: 10.1038/s41598-024-56554-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 03/07/2024] [Indexed: 03/14/2024] Open
Abstract
Powassan virus (POWV) is an arthropod-borne virus (arbovirus) capable of causing severe illness in humans for severe neurological complications, and its incidence has been on the rise in recent years due to climate change, posing a growing public health concern. Currently, no vaccines to prevent or medicines to treat POWV disease, emphasizing the urgent need for effective countermeasures. In this study, we utilize bioinformatics approaches to target proteins of POWV, including the capsid, envelope, and membrane proteins, to predict diverse B-cell and T-cell epitopes. These epitopes underwent screening for critical properties such as antigenicity, allergenicity, toxicity, and cytokine induction potential. Eight selected epitopes were then conjugated with adjuvants using various linkers, resulting in designing of a potentially stable and immunogenic vaccine candidate against POWV. Moreover, molecular docking, molecular dynamics simulations, and immune simulations revealed a stable interaction pattern with the immune receptor, suggesting the vaccine's potential to induce robust immune responses. In conclusion, our study provided a set of derived epitopes from POWV's proteins, demonstrating the potential for a novel vaccine candidate against POWV. Further in vitro and in vivo studies are warranted to advance our efforts and move closer to the goal of combatting POWV and related arbovirus infections.
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Affiliation(s)
- Truc Ly Nguyen
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Heebal Kim
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, 08826, Republic of Korea.
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Republic of Korea.
- eGnome, Inc., Seoul, 05836, Republic of Korea.
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30
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Wallace Z, Heunis T, Paterson RL, Suckling RJ, Grant T, Dembek M, Donoso J, Brener J, Long J, Bunjobpol W, Gibbs-Howe D, Kay DP, Leneghan DB, Godinho LF, Walker A, Singh PK, Knox A, Leonard S, Dorrell L. Instability of the HLA-E peptidome of HIV presents a major barrier to therapeutic targeting. Mol Ther 2024; 32:678-688. [PMID: 38219014 PMCID: PMC10928138 DOI: 10.1016/j.ymthe.2024.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 11/14/2023] [Accepted: 01/08/2024] [Indexed: 01/15/2024] Open
Abstract
Naturally occurring T cells that recognize microbial peptides via HLA-E, a nonpolymorphic HLA class Ib molecule, could provide the foundation for new universal immunotherapeutics. However, confidence in the biological relevance of putative ligands is crucial, given that the mechanisms by which pathogen-derived peptides can access the HLA-E presentation pathway are poorly understood. We systematically interrogated the HIV proteome using immunopeptidomic and bioinformatic approaches, coupled with biochemical and cellular assays. No HIV HLA-E peptides were identified by tandem mass spectrometry analysis of HIV-infected cells. In addition, all bioinformatically predicted HIV peptide ligands (>80) were characterized by poor complex stability. Furthermore, infected cell elimination assays using an affinity-enhanced T cell receptor bispecific targeted to a previously reported HIV Gag HLA-E epitope demonstrated inconsistent presentation of the peptide, despite normal HLA-E expression on HIV-infected cells. This work highlights the instability of the HIV HLA-E peptidome as a major challenge for drug development.
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Affiliation(s)
- Zoë Wallace
- Immunocore Ltd., Abingdon, Oxfordshire OX14 4RY, UK.
| | - Tiaan Heunis
- Immunocore Ltd., Abingdon, Oxfordshire OX14 4RY, UK
| | | | | | | | | | - Jose Donoso
- Immunocore Ltd., Abingdon, Oxfordshire OX14 4RY, UK
| | | | - Joshua Long
- Immunocore Ltd., Abingdon, Oxfordshire OX14 4RY, UK
| | | | | | - Daniel P Kay
- Immunocore Ltd., Abingdon, Oxfordshire OX14 4RY, UK
| | | | | | | | | | - Andrew Knox
- Immunocore Ltd., Abingdon, Oxfordshire OX14 4RY, UK
| | | | - Lucy Dorrell
- Immunocore Ltd., Abingdon, Oxfordshire OX14 4RY, UK
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31
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Khalid S, Guo J, Muhammad SA, Bai B. Designing, cloning and simulation studies of cancer/testis antigens based multi-epitope vaccine candidates against cutaneous melanoma: An immunoinformatics approach. Biochem Biophys Rep 2024; 37:101651. [PMID: 38371523 PMCID: PMC10873875 DOI: 10.1016/j.bbrep.2024.101651] [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: 09/05/2023] [Revised: 01/18/2024] [Accepted: 01/18/2024] [Indexed: 02/20/2024] Open
Abstract
Background Melanoma is the most fatal kind of skin cancer. Among its various types, cutaneous melanoma is the most prevalent one. Melanoma cells are thought to be highly immunogenic due to the presence of distinct tumor-associated antigens (TAAs), which includes carcinoembryonic antigen (CEA), cancer/testis antigens (CTAs) and neo-antigens. The CTA family is a group of antigens that are only expressed in malignancies and testicular germ cells. Methods We used integrative framework and systems-level analysis to predict potential vaccine candidates for cutaneous melanoma involving epitopes prediction, molecular modeling and molecular docking to cross-validate the binding affinity and interaction between potential vaccine agents and major histocompatibility molecules (MHCs) followed by molecular dynamics simulation, immune simulation and in silico cloning. Results In this study, three cancer/testis antigens were targeted for immunotherapy of cutaneous melanoma. Among many CTAs that were studied for their expression in primary and malignant melanoma, NY-ESO-1, MAGE1 and SSX2 antigens are most prevalent in cutaneous melanoma. Cytotoxic and Helper epitopes were predicted, and the finest epitopes were shortlisted based on binding score. The vaccine construct was composed of the four epitope-rich domains of antigenic proteins, an appropriate adjuvant, His tag and linkers. This potential multi-epitope vaccine was further evaluated in terms of antigenicity, allergencity, toxicity and other physicochemical properties. Molecular interaction estimated through protein-protein docking unveiled good interactions characterized by favorable binding energies. Molecular dynamics simulation ensured the stability of docked complex and the predicted immune response through immune simulation revealed elevated levels of antibodies titer, cytokines, interleukins and immune cells (NK, DC and MA) population. Conclusion The findings indicate that the potential vaccine candidates could be effective immunotherapeutic agents that modify the treatment strategies of cutaneous melanoma.
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Affiliation(s)
- Sana Khalid
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University Multan, Pakistan
| | - Jinlei Guo
- School of Intelligent Medical Engineering, Sanquan College of Xinxiang Medical University, Xinxiang, China
| | - Syed Aun Muhammad
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University Multan, Pakistan
| | - Baogang Bai
- School of Information and Technology, Wenzhou Business College, Wenzhou, China
- Zhejiang Province Engineering Research Center of Intelligent Medicine, Wenzhou, China
- The 1st School of Medical, School of Information and Engineering, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Fasoulis R, Rigo MM, Antunes DA, Paliouras G, Kavraki LE. Transfer learning improves pMHC kinetic stability and immunogenicity predictions. IMMUNOINFORMATICS (AMSTERDAM, NETHERLANDS) 2024; 13:100030. [PMID: 38577265 PMCID: PMC10994007 DOI: 10.1016/j.immuno.2023.100030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
The cellular immune response comprises several processes, with the most notable ones being the binding of the peptide to the Major Histocompability Complex (MHC), the peptide-MHC (pMHC) presentation to the surface of the cell, and the recognition of the pMHC by the T-Cell Receptor. Identifying the most potent peptide targets for MHC binding, presentation and T-cell recognition is vital for developing peptide-based vaccines and T-cell-based immunotherapies. Data-driven tools that predict each of these steps have been developed, and the availability of mass spectrometry (MS) datasets has facilitated the development of accurate Machine Learning (ML) methods for class-I pMHC binding prediction. However, the accuracy of ML-based tools for pMHC kinetic stability prediction and peptide immunogenicity prediction is uncertain, as stability and immunogenicity datasets are not abundant. Here, we use transfer learning techniques to improve stability and immunogenicity predictions, by taking advantage of a large number of binding affinity and MS datasets. The resulting models, TLStab and TLImm, exhibit comparable or better performance than state-of-the-art approaches on different stability and immunogenicity test sets respectively. Our approach demonstrates the promise of learning from the task of peptide binding to improve predictions on downstream tasks. The source code of TLStab and TLImm is publicly available at https://github.com/KavrakiLab/TL-MHC.
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Affiliation(s)
- Romanos Fasoulis
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, United States
| | - Mauricio Menegatti Rigo
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, United States
| | - Dinler Amaral Antunes
- Department of Biology and Biochemistry, University of Houston, 4800 Calhoun Rd, Houston, 77004, TX, United States
| | - Georgios Paliouras
- Institute of Informatics and Telecommunications, NCSR Demokritos, Patr. Gregoriou E and 27 Neapoleos St, Athens, 15341, Greece
| | - Lydia E. Kavraki
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, United States
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Wu J, Sun W, Zhang Y, Mao L, Ding T, Huang X, Lin D. Impact of platinum-based chemotherapy on the tumor mutational burden and immune microenvironment in non-small cell lung cancer with postoperative recurrence. Clin Transl Oncol 2024:10.1007/s12094-024-03397-5. [PMID: 38421562 DOI: 10.1007/s12094-024-03397-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 01/22/2024] [Indexed: 03/02/2024]
Abstract
PURPOSE To investigate the impact of platinum-based adjuvant chemotherapy on the immunotherapeutic biomarkers of postoperative recurrent tumors in non-small cell lung cancer (NSCLC). METHODS This study involved twenty-two cases of NSCLC, all of which underwent postoperative platinum-based chemotherapy, with matched surgical samples obtained from both their primary tumors (PTs) and recurrent tumors (RTs). Multiplex immunofluorescence was performed to assess the tumor proportion score (TPS) and immune cells (IC) on whole sections. Whole exon sequencing (WES) was conducted to investigate the tumor mutational burden (TMB) and tumor neoantigen burden (TNB). RESULTS Compared to paired PTs, RTs exhibited higher PD-L1 expression, along with a slightly elevated density of intratumoral PD-L1+ cells (p = 0.082) and an increased tumor proportion score (mean TPS: 40.51% vs. 28.56%, p = 0.046). Regarding IC infiltration, RTs generally demonstrated significantly lower CD8+ cytotoxic T lymphocyte (CTL) density (p = 0.011) and lower CD68+ macrophage density (p = 0.005), with a loss of tertiary lymphoid structure (TLS). The comparison between RTs and PTs revealed no significant differences in TMB (p = 0.795), whereas the count of TNB in RTs was notably increased compared to PTs (p = 0.033). Prognosis analysis indicated that a higher density of CD8+ CTLs in RTs was positively correlated with improved overall survival (OS). CONCLUSIONS In NSCLC patients with a history of postoperative platinum-based chemotherapy, the RTs demonstrated a trend towards increased PD-L1 expression and TMB/TNB, but a state of immunosuppression characterized by decreased ICs and loss of TLS, which may potentially impact the therapeutic benefits of immunotherapy.
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Affiliation(s)
- Jianghua Wu
- Department of Pathology, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), No. 52, Fu-Cheng Road, Beijing, 100142, China
| | - Wei Sun
- Department of Pathology, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), No. 52, Fu-Cheng Road, Beijing, 100142, China
| | - Yanhui Zhang
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center of Cancer, Tianjin, China
| | - Luning Mao
- Department of Pathology, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), No. 52, Fu-Cheng Road, Beijing, 100142, China
| | - Tingting Ding
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center of Cancer, Tianjin, China
| | - Xiaozheng Huang
- Department of Pathology, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), No. 52, Fu-Cheng Road, Beijing, 100142, China
| | - Dongmei Lin
- Department of Pathology, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), No. 52, Fu-Cheng Road, Beijing, 100142, China.
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Ashique S, Mishra N, Mohanto S, Garg A, Taghizadeh-Hesary F, Gowda BJ, Chellappan DK. Application of artificial intelligence (AI) to control COVID-19 pandemic: Current status and future prospects. Heliyon 2024; 10:e25754. [PMID: 38370192 PMCID: PMC10869876 DOI: 10.1016/j.heliyon.2024.e25754] [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: 08/12/2023] [Revised: 01/25/2024] [Accepted: 02/01/2024] [Indexed: 02/20/2024] Open
Abstract
The impact of the coronavirus disease 2019 (COVID-19) pandemic on the everyday livelihood of people has been monumental and unparalleled. Although the pandemic has vastly affected the global healthcare system, it has also been a platform to promote and develop pioneering applications based on autonomic artificial intelligence (AI) technology with therapeutic significance in combating the pandemic. Artificial intelligence has successfully demonstrated that it can reduce the probability of human-to-human infectivity of the virus through evaluation, analysis, and triangulation of existing data on the infectivity and spread of the virus. This review talks about the applications and significance of modern robotic and automated systems that may assist in spreading a pandemic. In addition, this study discusses intelligent wearable devices and how they could be helpful throughout the COVID-19 pandemic.
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Affiliation(s)
- Sumel Ashique
- Department of Pharmaceutical Sciences, Bengal College of Pharmaceutical Sciences & Research, Durgapur, 713212, West Bengal, India
| | - Neeraj Mishra
- Department of Pharmaceutics, Amity Institute of Pharmacy, Amity University, Gwalior, 474005, Madhya Pradesh, India
| | - Sourav Mohanto
- Department of Pharmaceutics, Yenepoya Pharmacy College & Research Centre, Yenepoya (Deemed to be University), Mangalore, Karnataka, 575018, India
| | - Ashish Garg
- Guru Ramdas Khalsa Institute of Science and Technology, Pharmacy, Jabalpur, M.P, 483001, India
| | - Farzad Taghizadeh-Hesary
- ENT and Head and Neck Research Center and Department, The Five Senses Health Institute, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
- Clinical Oncology Department, Iran University of Medical Sciences, Tehran, Iran
| | - B.H. Jaswanth Gowda
- Department of Pharmaceutics, Yenepoya Pharmacy College & Research Centre, Yenepoya (Deemed to be University), Mangalore, Karnataka, 575018, India
- School of Pharmacy, Queen's University Belfast, Medical Biology Centre, Belfast, BT9 7BL, UK
| | - Dinesh Kumar Chellappan
- Department of Life Sciences, School of Pharmacy, International Medical University, Bukit Jalil, Kuala Lumpur, 57000, Malaysia
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Miller AM, Koşaloğlu-Yalçın Z, Westernberg L, Montero L, Bahmanof M, Frentzen A, Lanka M, Logandha Ramamoorthy Premlal A, Seumois G, Greenbaum J, Brightman SE, Soria Zavala K, Thota RR, Naradikian MS, Makani SS, Lippman SM, Sette A, Cohen EEW, Peters B, Schoenberger SP. A functional identification platform reveals frequent, spontaneous neoantigen-specific T cell responses in patients with cancer. Sci Transl Med 2024; 16:eabj9905. [PMID: 38416845 DOI: 10.1126/scitranslmed.abj9905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 01/29/2024] [Indexed: 03/01/2024]
Abstract
The clinical impact of tumor-specific neoantigens as both immunotherapeutic targets and biomarkers has been impeded by the lack of efficient methods for their identification and validation from routine samples. We have developed a platform that combines bioinformatic analysis of tumor exomes and transcriptional data with functional testing of autologous peripheral blood mononuclear cells (PBMCs) to simultaneously identify and validate neoantigens recognized by naturally primed CD4+ and CD8+ T cell responses across a range of tumor types and mutational burdens. The method features a human leukocyte antigen (HLA)-agnostic bioinformatic algorithm that prioritizes mutations recognized by patient PBMCs at a greater than 40% positive predictive value followed by a short-term in vitro functional assay, which allows interrogation of 50 to 75 expressed mutations from a single 50-ml blood sample. Neoantigens validated by this method include both driver and passenger mutations, and this method identified neoantigens that would not have been otherwise detected using an in silico prediction approach. These findings reveal an efficient approach to systematically validate clinically actionable neoantigens and the T cell receptors that recognize them and demonstrate that patients across a variety of human cancers have a diverse repertoire of neoantigen-specific T cells.
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Affiliation(s)
- Aaron M Miller
- Center for Cancer Immunotherapy, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
- Division of Hematology and Oncology, UCSD Moores Cancer Center, 3855 Health Sciences Drive, La Jolla, CA 92093, USA
| | - Zeynep Koşaloğlu-Yalçın
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Luise Westernberg
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Leslie Montero
- Center for Cancer Immunotherapy, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Milad Bahmanof
- Center for Cancer Immunotherapy, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Angela Frentzen
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Manasa Lanka
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | | | - Gregory Seumois
- Center for Cancer Immunotherapy, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Jason Greenbaum
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Spencer E Brightman
- Center for Cancer Immunotherapy, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Karla Soria Zavala
- Center for Cancer Immunotherapy, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Rukman R Thota
- Center for Cancer Immunotherapy, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Martin S Naradikian
- Center for Cancer Immunotherapy, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Samir S Makani
- Division of Hematology and Oncology, UCSD Moores Cancer Center, 3855 Health Sciences Drive, La Jolla, CA 92093, USA
| | - Scott M Lippman
- Division of Hematology and Oncology, UCSD Moores Cancer Center, 3855 Health Sciences Drive, La Jolla, CA 92093, USA
| | - Alessandro Sette
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Ezra E W Cohen
- Division of Hematology and Oncology, UCSD Moores Cancer Center, 3855 Health Sciences Drive, La Jolla, CA 92093, USA
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
- Department of Medicine, University of California, San Diego (UCSD), La Jolla, CA 92037, USA
| | - Stephen P Schoenberger
- Center for Cancer Immunotherapy, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
- Division of Hematology and Oncology, UCSD Moores Cancer Center, 3855 Health Sciences Drive, La Jolla, CA 92093, USA
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Blanco-Heredia J, Souza CA, Trincado JL, Gonzalez-Cao M, Gonçalves-Ribeiro S, Gil SR, Pravdyvets D, Cedeño S, Callari M, Marra A, Gazzo AM, Weigelt B, Pareja F, Vougiouklakis T, Jungbluth AA, Rosell R, Brander C, Tresserra F, Reis-Filho JS, Tiezzi DG, de la Iglesia N, Heyn H, De Mattos-Arruda L. Converging and evolving immuno-genomic routes toward immune escape in breast cancer. Nat Commun 2024; 15:1302. [PMID: 38383522 PMCID: PMC10882008 DOI: 10.1038/s41467-024-45292-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 01/19/2024] [Indexed: 02/23/2024] Open
Abstract
The interactions between tumor and immune cells along the course of breast cancer progression remain largely unknown. Here, we extensively characterize multiple sequential and parallel multiregion tumor and blood specimens of an index patient and a cohort of metastatic triple-negative breast cancers. We demonstrate that a continuous increase in tumor genomic heterogeneity and distinct molecular clocks correlated with resistance to treatment, eventually allowing tumors to escape from immune control. TCR repertoire loses diversity over time, leading to convergent evolution as breast cancer progresses. Although mixed populations of effector memory and cytotoxic single T cells coexist in the peripheral blood, defects in the antigen presentation machinery coupled with subdued T cell recruitment into metastases are observed, indicating a potent immune avoidance microenvironment not compatible with an effective antitumor response in lethal metastatic disease. Our results demonstrate that the immune responses against cancer are not static, but rather follow dynamic processes that match cancer genomic progression, illustrating the complex nature of tumor and immune cell interactions.
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Affiliation(s)
- Juan Blanco-Heredia
- IrsiCaixa, Germans Trias i Pujol University Hospital, Badalona, Spain
- Germans Trias i Pujol Research Institute (IGTP), Badalona, Spain
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Carla Anjos Souza
- IrsiCaixa, Germans Trias i Pujol University Hospital, Badalona, Spain
- Germans Trias i Pujol Research Institute (IGTP), Badalona, Spain
| | - Juan L Trincado
- Centro Nacional de Análisis Genómico (CNAG), Barcelona, Spain
- Josep Carreras Leukemia Research Institute, Barcelona, Spain
| | | | | | - Sara Ruiz Gil
- Centro Nacional de Análisis Genómico (CNAG), Barcelona, Spain
| | | | - Samandhy Cedeño
- IrsiCaixa, Germans Trias i Pujol University Hospital, Badalona, Spain
| | - Maurizio Callari
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Antonio Marra
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrea M Gazzo
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Britta Weigelt
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Fresia Pareja
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Theodore Vougiouklakis
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Achim A Jungbluth
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rafael Rosell
- Dexeus Institute of Oncology, Quironsalud Group, Barcelona, Spain
| | - Christian Brander
- IrsiCaixa, Germans Trias i Pujol University Hospital, Badalona, Spain
- ICREA, Passeig de Lluís Companys, 23, Barcelona, Spain
- Universitat de Vic-Universitat Central de Catalunya, Catalunya, Spain
| | | | - Jorge S Reis-Filho
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Daniel Guimarães Tiezzi
- Department of Gynecology and Obstetrics - Breast Disease Division and Laboratory for Translational Data Science, Ribeirao Preto Medical School, University of Sao Paulo, Ribeirao Preto, Brazil
- Advanced Research Center in Medicine (CEPAM), Union of the Colleges of the Great Lakes (UNILAGO), São José do Rio Preto, Brazil
| | | | - Holger Heyn
- Centro Nacional de Análisis Genómico (CNAG), Barcelona, Spain
- Omniscope, Barcelona, Spain
| | - Leticia De Mattos-Arruda
- IrsiCaixa, Germans Trias i Pujol University Hospital, Badalona, Spain.
- Germans Trias i Pujol Research Institute (IGTP), Badalona, Spain.
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37
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Pulliam T, Jani S, Jing L, Ryu H, Jojic A, Shasha C, Zhang J, Kulikauskas R, Church C, Garnett-Benson C, Gooley T, Chapuis A, Paulson K, Smith KN, Pardoll DM, Newell EW, Koelle DM, Topalian SL, Nghiem P. Circulating cancer-specific CD8 T cell frequency is associated with response to PD-1 blockade in Merkel cell carcinoma. Cell Rep Med 2024; 5:101412. [PMID: 38340723 PMCID: PMC10897614 DOI: 10.1016/j.xcrm.2024.101412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 12/01/2023] [Accepted: 01/12/2024] [Indexed: 02/12/2024]
Abstract
Understanding cancer immunobiology has been hampered by difficulty identifying cancer-specific T cells. Merkel cell polyomavirus (MCPyV) causes most Merkel cell carcinomas (MCCs). All patients with virus-driven MCC express MCPyV oncoproteins, facilitating identification of virus (cancer)-specific T cells. We studied MCPyV-specific T cells from 27 patients with MCC using MCPyV peptide-HLA-I multimers, 26-color flow cytometry, single-cell transcriptomics, and T cell receptor (TCR) sequencing. In a prospective clinical trial, higher circulating MCPyV-specific CD8 T cell frequency before anti-PD-1 treatment was strongly associated with 2-year recurrence-free survival (75% if detectable, 0% if undetectable, p = 0.0018; ClinicalTrial.gov: NCT02488759). Intratumorally, such T cells were typically present, but their frequency did not significantly associate with response. Circulating MCPyV-specific CD8 T cells had increased stem/memory and decreased exhaustion signatures relative to their intratumoral counterparts. These results suggest that cancer-specific CD8 T cells in the blood may play a role in anti-PD-1 responses. Thus, strategies that augment their number or mobilize them into tumors could improve outcomes.
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Affiliation(s)
- Thomas Pulliam
- Division of Dermatology, Department of Medicine, University of Washington, Seattle, WA 98109, USA
| | - Saumya Jani
- Division of Dermatology, Department of Medicine, University of Washington, Seattle, WA 98109, USA; Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98109, USA
| | - Lichen Jing
- Department of Medicine, University of Washington, Seattle, WA 98109, USA
| | - Heeju Ryu
- Vaccine and Infectious Disease Department, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Ana Jojic
- Vaccine and Infectious Disease Department, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Carolyn Shasha
- Vaccine and Infectious Disease Department, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Jiajia Zhang
- Department of Oncology, Johns Hopkins University, Baltimore, MD 21827, USA; The Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Rima Kulikauskas
- Division of Dermatology, Department of Medicine, University of Washington, Seattle, WA 98109, USA
| | - Candice Church
- Division of Dermatology, Department of Medicine, University of Washington, Seattle, WA 98109, USA
| | | | - Ted Gooley
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Aude Chapuis
- Department of Medicine, University of Washington, Seattle, WA 98109, USA; Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Kelly Paulson
- Paul G. Allen Research Center, Providence-Swedish Cancer Institute, Seattle, WA 98104, USA; Elson S. Floyd College of Medicine, Washington State University, Spokane, WA 99202, USA
| | - Kellie N Smith
- Department of Oncology, Johns Hopkins University, Baltimore, MD 21827, USA; The Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Drew M Pardoll
- Department of Oncology, Johns Hopkins University, Baltimore, MD 21827, USA; The Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Evan W Newell
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98109, USA; Vaccine and Infectious Disease Department, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - David M Koelle
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98109, USA; Department of Medicine, University of Washington, Seattle, WA 98109, USA; Vaccine and Infectious Disease Department, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; Department of Global Health, University of Washington, Seattle, WA 98109, USA; Benaroya Research Institute, Seattle, WA 98101, USA
| | - Suzanne L Topalian
- The Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University, Baltimore, MD 21287, USA; Department of Surgery, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Paul Nghiem
- Division of Dermatology, Department of Medicine, University of Washington, Seattle, WA 98109, USA.
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38
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Ryu H, Bi TM, Pulliam TH, Sarkar K, Church CD, Kumar N, Mayer-Blackwell K, Jani S, Ramchurren N, Hansen UK, Hadrup SR, Fling SP, Koelle DM, Nghiem P, Newell EW. Merkel cell polyomavirus-specific and CD39 +CLA + CD8 T cells as blood-based predictive biomarkers for PD-1 blockade in Merkel cell carcinoma. Cell Rep Med 2024; 5:101390. [PMID: 38340724 PMCID: PMC10897544 DOI: 10.1016/j.xcrm.2023.101390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 11/29/2023] [Accepted: 12/21/2023] [Indexed: 02/12/2024]
Abstract
Merkel cell carcinoma is a skin cancer often driven by Merkel cell polyomavirus (MCPyV) with high rates of response to anti-PD-1 therapy despite low mutational burden. MCPyV-specific CD8 T cells are implicated in anti-PD-1-associated immune responses and provide a means to directly study tumor-specific T cell responses to treatment. Using mass cytometry and combinatorial tetramer staining, we find that baseline frequencies of blood MCPyV-specific cells correlated with response and survival. Frequencies of these cells decrease markedly during response to therapy. Phenotypes of MCPyV-specific CD8 T cells have distinct expression patterns of CD39, cutaneous lymphocyte-associated antigen (CLA), and CD103. Correspondingly, overall bulk CD39+CLA+ CD8 T cell frequencies in blood correlate with MCPyV-specific cell frequencies and similarly predicted favorable clinical outcomes. Conversely, frequencies of CD39+CD103+ CD8 T cells are associated with tumor burden and worse outcomes. These cell subsets can be useful as biomarkers and to isolate blood-derived tumor-specific T cells.
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Affiliation(s)
- Heeju Ryu
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Timothy M Bi
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Thomas H Pulliam
- Department of Medicine, Division of Dermatology, University of Washington, Seattle, WA, USA
| | - Korok Sarkar
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Candice D Church
- Department of Medicine, Division of Dermatology, University of Washington, Seattle, WA, USA
| | - Nandita Kumar
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Lab Medicine and Pathology, University of Washington, Seattle, WA, USA
| | | | - Saumya Jani
- Department of Medicine, Division of Dermatology, University of Washington, Seattle, WA, USA; Department of Lab Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Nirasha Ramchurren
- Cancer Immunotherapy Trails Network, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Ulla K Hansen
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Sine R Hadrup
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Steven P Fling
- Cancer Immunotherapy Trails Network, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - David M Koelle
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Lab Medicine and Pathology, University of Washington, Seattle, WA, USA; Department of Medicine, Division of Allergy and Infectious Diseases, University of Washington, Seattle, WA, USA; Department of Global Health, University of Washington, Seattle, WA, USA; Benaroya Research Institute, Seattle, WA, USA
| | - Paul Nghiem
- Department of Medicine, Division of Dermatology, University of Washington, Seattle, WA, USA
| | - Evan W Newell
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Lab Medicine and Pathology, University of Washington, Seattle, WA, USA.
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Zhou M, Zhao F, Yu L, Liu J, Wang J, Zhang JZH. An Efficient Approach to the Accurate Prediction of Mutational Effects in Antigen Binding to the MHC1. Molecules 2024; 29:881. [PMID: 38398632 PMCID: PMC10892774 DOI: 10.3390/molecules29040881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 02/07/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
The major histocompatibility complex (MHC) can recognize and bind to external peptides to generate effective immune responses by presenting the peptides to T cells. Therefore, understanding the binding modes of peptide-MHC complexes (pMHC) and predicting the binding affinity of pMHCs play a crucial role in the rational design of peptide vaccines. In this study, we employed molecular dynamics (MD) simulations and free energy calculations with an Alanine Scanning with Generalized Born and Interaction Entropy (ASGBIE) method to investigate the protein-peptide interaction between HLA-A*02:01 and the G9209 peptide derived from the melanoma antigen gp100. The energy contribution of individual residue was calculated using alanine scanning, and hotspots on both the MHC and the peptides were identified. Our study shows that the pMHC binding is dominated by the van der Waals interactions. Furthermore, we optimized the ASGBIE method, achieving a Pearson correlation coefficient of 0.91 between predicted and experimental binding affinity for mutated antigens. This represents a significant improvement over the conventional MM/GBSA method, which yields a Pearson correlation coefficient of 0.22. The computational protocol developed in this study can be applied to the computational screening of antigens for the MHC1 as well as other protein-peptide binding systems.
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Affiliation(s)
- Mengchen Zhou
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China;
| | - Fanyu Zhao
- NYU-ECNU Center for Computational Chemistry and Shanghai Frontiers Science Center of AI and DL, NYU Shanghai, 567 West Yangsi Road, Shanghai 200126, China;
| | - Lan Yu
- Department of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Jinfeng Liu
- Department of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Jian Wang
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
| | - John Z. H. Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China;
- NYU-ECNU Center for Computational Chemistry and Shanghai Frontiers Science Center of AI and DL, NYU Shanghai, 567 West Yangsi Road, Shanghai 200126, China;
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
- Department of Chemistry, New York University, New York, NY 10003, USA
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan 030006, China
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Adams AC, Macy AM, Borden ES, Herrmann LM, Brambley CA, Ma T, Li X, Hughes A, Roe DJ, Mangold AR, Buetow KH, Wilson MA, Baker BM, Hastings KT. Distinct sets of molecular characteristics define tumor-rejecting neoantigens. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.13.579546. [PMID: 38405868 PMCID: PMC10888839 DOI: 10.1101/2024.02.13.579546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Challenges in identifying tumor-rejecting neoantigens limit the efficacy of neoantigen vaccines to treat cancers, including cutaneous squamous cell carcinoma (cSCC). A minority of human cSCC tumors shared neoantigens, supporting the need for personalized vaccines. Using a UV-induced mouse cSCC model which recapitulated the mutational signature and driver mutations found in human disease, we found that CD8 T cells constrain cSCC. Two MHC class I neoantigens were identified that constrained cSCC growth. Compared to the wild-type peptides, one tumor-rejecting neoantigen exhibited improved MHC binding and the other had increased solvent accessibility of the mutated residue. Across known neoantigens that do not impact MHC binding, structural modeling of the peptide/MHC complexes indicated that increased solvent accessibility, which will facilitate TCR recognition of the neoantigen, distinguished tumor-rejecting from non-immunogenic neoantigens. This work reveals characteristics of tumor-rejecting neoantigens that may be of considerable importance in identifying optimal vaccine candidates in cSCC and other cancers.
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Shahjahan, Dey JK, Dey SK. Translational bioinformatics approach to combat cardiovascular disease and cancers. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:221-261. [PMID: 38448136 DOI: 10.1016/bs.apcsb.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.
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Affiliation(s)
- Shahjahan
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India
| | - Joy Kumar Dey
- Central Council for Research in Homoeopathy, Ministry of Ayush, Govt. of India, New Delhi, Delhi, India
| | - Sanjay Kumar Dey
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India.
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42
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Wang F, Zhang Z, Mao M, Yang Y, Xu P, Lu S. COSMIC-based mutation database enhances identification efficiency of HLA-I immunopeptidome. J Transl Med 2024; 22:144. [PMID: 38336780 PMCID: PMC10858511 DOI: 10.1186/s12967-023-04821-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 12/20/2023] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Neoantigens have emerged as a promising area of focus in tumor immunotherapy, with several established strategies aiming to enhance their identification. Human leukocyte antigen class I molecules (HLA-I), which present intracellular immunopeptides to T cells, provide an ideal source for identifying neoantigens. However, solely relying on a mutation database generated through commonly used whole exome sequencing (WES) for the identification of HLA-I immunopeptides, may result in potential neoantigens being missed due to limitations in sequencing depth and sample quality. METHOD In this study, we constructed and evaluated an extended database for neoantigen identification, based on COSMIC mutation database. This study utilized mass spectrometry-based proteogenomic profiling to identify the HLA-I immunopeptidome enriched from HepG2 cell. HepG2 WES-based and the COSMIC-based mutation database were generated and utilized to identify HepG2-specific mutant immunopeptides. RESULT The results demonstrated that COSMIC-based database identified 5 immunopeptides compared to only 1 mutant peptide identified by HepG2 WES-based database, indicating its effectiveness in identifying mutant immunopeptides. Furthermore, HLA-I affinity of the mutant immunopeptides was evaluated through NetMHCpan and peptide-docking modeling to validate their binding to HLA-I molecules, demonstrating the potential of mutant peptides identified by the COSMIC-based database as neoantigens. CONCLUSION Utilizing the COSMIC-based mutation database is a more efficient strategy for identifying mutant peptides from HLA-I immunopeptidome without significantly increasing the false positive rate. HepG2 specific WES-based database may exclude certain mutant peptides due to WES sequencing depth or sample heterogeneity. The COSMIC-based database can effectively uncover potential neoantigens within the HLA-I immunopeptidomes.
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Affiliation(s)
- Fangzhou Wang
- Medical School of Chinese People's Liberation Army (PLA), Faculty of Hepato-Pancreato-Biliary Surgery, Chinese PLA General Hospital, Institute of Hepatobiliary Surgery of Chinese PLA, Key Laboratory of Digital Hepatobiliary Surgery PLA, 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Zhenpeng Zhang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics and Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, 38 Life Science Park Road, Changping District, Beijing, 102206, China
| | - Mingsong Mao
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics and Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, 38 Life Science Park Road, Changping District, Beijing, 102206, China
- School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Yudai Yang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics and Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, 38 Life Science Park Road, Changping District, Beijing, 102206, China
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Ping Xu
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics and Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, 38 Life Science Park Road, Changping District, Beijing, 102206, China.
- School of Basic Medical Sciences, Anhui Medical University, Hefei, China.
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
- School of Medicine, Guizhou University, Guiyang, China.
| | - Shichun Lu
- Medical School of Chinese People's Liberation Army (PLA), Faculty of Hepato-Pancreato-Biliary Surgery, Chinese PLA General Hospital, Institute of Hepatobiliary Surgery of Chinese PLA, Key Laboratory of Digital Hepatobiliary Surgery PLA, 28 Fuxing Road, Haidian District, Beijing, 100853, China.
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Han J, Dong Y, Zhu X, Reuben A, Zhang J, Xu J, Bai H, Duan J, Wan R, Zhao J, Bai J, Xia X, Yi X, Cheng C, Wang J, Wang Z. Assessment of human leukocyte antigen-based neoantigen presentation to determine pan-cancer response to immunotherapy. Nat Commun 2024; 15:1199. [PMID: 38331912 PMCID: PMC10853168 DOI: 10.1038/s41467-024-45361-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 01/19/2024] [Indexed: 02/10/2024] Open
Abstract
Despite the central role of human leukocyte antigen class I (HLA-I) in tumor neoantigen presentation, quantitative determination of presentation capacity remains elusive. Based on a pooled pan-cancer genomic dataset of 885 patients treated with immune checkpoint inhibitors (ICIs), we developed a score integrating the binding affinity of neoantigens to HLA-I, as well as HLA-I allele divergence, termed the HLA tumor-Antigen Presentation Score (HAPS). Patients with a high HAPS were more likely to experience survival benefit following ICI treatment. Analysis of the tumor microenvironment indicated that the antigen presentation pathway was enriched in patients with a high HAPS. Finally, we built a neural network incorporating factors associated with neoantigen production, presentation, and recognition, which exhibited potential for differentiating cancer patients likely to benefit from ICIs. Our findings highlight the clinical utility of evaluating HLA-I tumor antigen presentation capacity and describe how ICI response may depend on HLA-mediated immunity.
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Affiliation(s)
- Jiefei Han
- State Key Laboratory of Molecular Oncology, CAMS Key Laboratory of Translational Research on Lung Cancer, Department of Medical 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
- Department of Neuro-oncology, Neurosurgery Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yiting Dong
- State Key Laboratory of Molecular Oncology, CAMS Key Laboratory of Translational Research on Lung Cancer, Department of Medical 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
| | - Xiuli Zhu
- Geneplus-Beijing Institute, Beijing, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Alexandre Reuben
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, USA
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, USA
| | - Jiachen Xu
- State Key Laboratory of Molecular Oncology, CAMS Key Laboratory of Translational Research on Lung Cancer, Department of Medical 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
| | - Hua Bai
- State Key Laboratory of Molecular Oncology, CAMS Key Laboratory of Translational Research on Lung Cancer, Department of Medical 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
| | - Jianchun Duan
- State Key Laboratory of Molecular Oncology, CAMS Key Laboratory of Translational Research on Lung Cancer, Department of Medical 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
| | - Rui Wan
- State Key Laboratory of Molecular Oncology, CAMS Key Laboratory of Translational Research on Lung Cancer, Department of Medical 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
| | - Jie Zhao
- State Key Laboratory of Molecular Oncology, CAMS Key Laboratory of Translational Research on Lung Cancer, Department of Medical 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
| | - Jing Bai
- Geneplus-Beijing Institute, Beijing, China
| | | | - Xin Yi
- Geneplus-Beijing Institute, Beijing, China
| | - Chao Cheng
- Department of Medicine, Dan L Duncan Comprehensive Cancer Center, Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas, USA.
| | - Jie Wang
- State Key Laboratory of Molecular Oncology, CAMS Key Laboratory of Translational Research on Lung Cancer, Department of Medical 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.
| | - Zhijie Wang
- State Key Laboratory of Molecular Oncology, CAMS Key Laboratory of Translational Research on Lung Cancer, Department of Medical 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.
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Robertson IB, Mulvaney R, Dieckmann N, Vantellini A, Canestraro M, Amicarella F, O'Dwyer R, Cole DK, Harper S, Dushek O, Kirk P. Tuning the potency and selectivity of ImmTAC molecules by affinity modulation. Clin Exp Immunol 2024; 215:105-119. [PMID: 37930865 PMCID: PMC10847821 DOI: 10.1093/cei/uxad120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 09/08/2023] [Accepted: 10/31/2023] [Indexed: 11/08/2023] Open
Abstract
T-cell-engaging bispecifics have great clinical potential for the treatment of cancer and infectious diseases. The binding affinity and kinetics of a bispecific molecule for both target and T-cell CD3 have substantial effects on potency and specificity, but the rules governing these relationships are not fully understood. Using immune mobilizing monoclonal TCRs against cancer (ImmTAC) molecules as a model, we explored the impact of altering affinity for target and CD3 on the potency and specificity of the redirected T-cell response. This class of bispecifics binds specific target peptides presented by human leukocyte antigen on the cell surface via an affinity-enhanced T-cell receptor and can redirect T-cell activation with an anti-CD3 effector moiety. The data reveal that combining a strong affinity TCR with an intermediate affinity anti-CD3 results in optimal T-cell activation, while strong affinity of both targeting and effector domains significantly reduces maximum cytokine release. Moreover, by optimizing the affinity of both parts of the molecule, it is possible to improve the selectivity. These results could be effectively modelled based on kinetic proofreading with limited signalling. This model explained the experimental observation that strong binding at both ends of the molecules leads to reduced activity, through very stable target-bispecific-effector complexes leading to CD3 entering a non-signalling dark state. These findings have important implications for the design of anti-CD3-based bispecifics with optimal biophysical parameters for both activity and specificity.
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Affiliation(s)
- Ian B Robertson
- Immunocore Limited, Drug Discovery and Protein Engineering, Abingdon, Oxon, UK
| | - Rachel Mulvaney
- Immunocore Limited, Drug Discovery and Protein Engineering, Abingdon, Oxon, UK
| | - Nele Dieckmann
- Immunocore Limited, Drug Discovery and Protein Engineering, Abingdon, Oxon, UK
| | - Alessio Vantellini
- Immunocore Limited, Drug Discovery and Protein Engineering, Abingdon, Oxon, UK
| | - Martina Canestraro
- Immunocore Limited, Drug Discovery and Protein Engineering, Abingdon, Oxon, UK
| | | | - Ronan O'Dwyer
- Immunocore Limited, Drug Discovery and Protein Engineering, Abingdon, Oxon, UK
| | - David K Cole
- Immunocore Limited, Drug Discovery and Protein Engineering, Abingdon, Oxon, UK
| | - Stephen Harper
- Immunocore Limited, Drug Discovery and Protein Engineering, Abingdon, Oxon, UK
| | - Omer Dushek
- Sir William Dunn School of Pathology, University of Oxford, Oxford, UK
| | - Peter Kirk
- Immunocore Limited, Drug Discovery and Protein Engineering, Abingdon, Oxon, UK
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45
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Wu J, Mao L, Lei W, Sun W, Yang X, Zhang Y, Huang X, Lin D. Genomic discordances and heterogeneous mutational burden, PD-L1 expression and immune infiltrates of non-small cell lung cancer metastasis. J Clin Pathol 2024:jcp-2023-209328. [PMID: 38307721 DOI: 10.1136/jcp-2023-209328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 01/21/2024] [Indexed: 02/04/2024]
Abstract
AIMS To investigate the genomic discordances and heterogeneous mutational burden, PD-L1 expression and immune cell (IC) infiltrates of non-small cell lung cancer (NSCLC) metastasis. METHODS Surgical samples from 41 cases of NSCLC with metastatic tumours (MTs) and paired primary tumours (PTs) were collected. PD-L1 expression and ICs were quantified using image-based immunohistochemistry profiling. Whole exome sequencing was employed to explore discrepancies in genomic characteristics, tumour mutational burden (TMB) and tumour neoantigen burden (TNB) in 28 cases. RESULTS Non-synonymous mutations in MTs were slightly more than in PTs, with only 42.34% of mutations shared between paired PTs and MTs. The heterogeneity of TMB showed no significant difference (p=0.785) between MTs and PTs, while TNB significantly increased in MTs (p=0.013). MTs generally exhibited a higher density of PD-L1+ cells and a higher tumour proportion score with a lower density of IC infiltrates. Subgroup analysis considering clinicopathological factors revealed that the heterogeneity of immune biomarkers was closely associated with the histology of lung adenocarcinoma, metastatic sites of extrapulmonary, time intervals and treatment history. Prognosis analysis indicated that a high density of CD8+ T cells was a low-risk factor, whereas a high density of PD-L1+ cells in MTs was a high-risk factor for cancer-related death in metastatic NSCLC. CONCLUSIONS The mutational burden, PD-L1 expression and IC infiltrates undergo changes during NSCLC metastasis, which may impact the immunotherapeutic benefits in patients with NSCLC with metastatic progression and should be monitored according to clinical scenarios.
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Affiliation(s)
- Jianghua Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Pathology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Luning Mao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Pathology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Wanjun Lei
- Tianjin Medical Laboratory, BGI-Tianjin, BGI-Shenzhen, Tianjin, China
| | - Wei Sun
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Pathology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Xin Yang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Pathology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Yanhui Zhang
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center of Cancer; Key Laboratory of Cancer Prevention and Therapy; Tianjin's Clinical Research Center of Cancer, Tianjin, China
| | - Xiaozheng Huang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Pathology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Dongmei Lin
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Pathology, Peking University Cancer Hospital & Institute, Beijing, China
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Hansen UK, Church CD, Carnaz Simões AM, Frej MS, Bentzen AK, Tvingsholm SA, Becker JC, Fling SP, Ramchurren N, Topalian SL, Nghiem PT, Hadrup SR. T antigen-specific CD8+ T cells associate with PD-1 blockade response in virus-positive Merkel cell carcinoma. J Clin Invest 2024; 134:e177082. [PMID: 38618958 PMCID: PMC11014655 DOI: 10.1172/jci177082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 01/23/2024] [Indexed: 04/16/2024] Open
Abstract
Merkel cell carcinoma (MCC) is a highly immunogenic skin cancer primarily induced by Merkel cell polyomavirus, which is driven by the expression of the oncogenic T antigens (T-Ags). Blockade of the programmed cell death protein-1 (PD-1) pathway has shown remarkable response rates, but evidence for therapy-associated T-Ag-specific immune response and therapeutic strategies for the nonresponding fraction are both limited. We tracked T-Ag-reactive CD8+ T cells in peripheral blood of 26 MCC patients under anti-PD1 therapy, using DNA-barcoded pMHC multimers, displaying all peptides from the predicted HLA ligandome of the oncoproteins, covering 33 class I haplotypes. We observed a broad T cell recognition of T-Ags, including identification of 20 T-Ag-derived epitopes we believe to be novel. Broadening of the T-Ag recognition profile and increased T cell frequencies during therapy were strongly associated with clinical response and prolonged progression-free survival. T-Ag-specific T cells could be further boosted and expanded directly from peripheral blood using artificial antigen-presenting scaffolds, even in patients with no detectable T-Ag-specific T cells. These T cells provided strong tumor-rejection capacity while retaining a favorable phenotype for adoptive cell transfer. These findings demonstrate that T-Ag-specific T cells are associated with the clinical outcome to PD-1 blockade and that Ag-presenting scaffolds can be used to boost such responses.
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Affiliation(s)
- Ulla Kring Hansen
- Section of Experimental and Translational Immunology, Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
- PokeAcell Aps, BioInnovation Institute, Copenhagen, Denmark
| | - Candice D. Church
- Department of Dermatology, Department of Medicine, University of Washington, Seattle, Washington, USA
| | | | - Marcus Svensson Frej
- Section of Experimental and Translational Immunology, Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
- PokeAcell Aps, BioInnovation Institute, Copenhagen, Denmark
| | - Amalie Kai Bentzen
- Section of Experimental and Translational Immunology, Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Siri A. Tvingsholm
- Section of Experimental and Translational Immunology, Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Jürgen C. Becker
- Department of Translational Skin Cancer Research, University Hospital Essen and German Cancer Consortium (DKTK), Essen, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Dermatology, University Hospital Essen, Essen, Germany
| | | | | | - Suzanne L. Topalian
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, USA
| | - Paul T. Nghiem
- Department of Dermatology, Department of Medicine, University of Washington, Seattle, Washington, USA
- Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Sine Reker Hadrup
- Section of Experimental and Translational Immunology, Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
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Yankilevich P, Nazerai L, Willis SC, Schmiegelow K, De Zio D, Nielsen M. An analysis pipeline for understanding 6-thioguanine effects on a mouse tumour genome. Cancer Immunol Immunother 2024; 73:22. [PMID: 38279992 PMCID: PMC10821971 DOI: 10.1007/s00262-023-03610-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 12/07/2023] [Indexed: 01/29/2024]
Abstract
Mouse tumour models are extensively used as a pre-clinical research tool in the field of oncology, playing an important role in anticancer drugs discovery. Accordingly, in cancer genomics research, the demand for next-generation sequencing (NGS) is increasing, and consequently, the need for data analysis pipelines is likewise growing. Most NGS data analysis solutions to date do not support mouse data or require highly specific configuration for their use. Here, we present a genome analysis pipeline for mouse tumour NGS data including the whole-genome sequence (WGS) data analysis flow for somatic variant discovery, and the RNA-seq data flow for differential expression, functional analysis and neoantigen prediction. The pipeline is based on standards and best practices and integrates mouse genome references and annotations. In a recent study, the pipeline was applied to demonstrate the efficacy of low dose 6-thioguanine (6TG) treatment on low-mutation melanoma in a pre-clinical mouse model. Here, we further this study and describe in detail the pipeline and the results obtained in terms of tumour mutational burden (TMB) and number of predicted neoantigens, and correlate these with 6TG effects on tumour volume. Our pipeline was expanded to include a neoantigen analysis, resulting in neopeptide prediction and MHC class I antigen presentation evaluation. We observed that the number of predicted neoepitopes were more accurate indicators of tumour immune control than TMB. In conclusion, this study demonstrates the usability of the proposed pipeline, and suggests it could be an essential robust genome analysis platform for future mouse genomic analysis.
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Affiliation(s)
- Patricio Yankilevich
- Bioinformatics Core Facility, Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA) - CONICET - Partner Institute of the Max Planck Society, Buenos Aires, Argentina.
| | - Loulieta Nazerai
- Melanoma Research Team, Danish Cancer Institute, Copenhagen, Denmark
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Shona Caroline Willis
- Melanoma Research Team, Danish Cancer Institute, Copenhagen, Denmark
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Kjeld Schmiegelow
- Department of Pediatrics and Adolescent Medicine, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Daniela De Zio
- Melanoma Research Team, Danish Cancer Institute, Copenhagen, Denmark
- Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark
| | - Morten Nielsen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, Lyngby, Denmark.
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Chuwdhury GS, Guo Y, Chiang CL, Lam KO, Kam NW, Liu Z, Dai W. ImmuneMirror: A machine learning-based integrative pipeline and web server for neoantigen prediction. Brief Bioinform 2024; 25:bbae024. [PMID: 38343325 PMCID: PMC10859690 DOI: 10.1093/bib/bbae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 12/05/2023] [Accepted: 01/16/2024] [Indexed: 02/15/2024] Open
Abstract
Neoantigens are derived from somatic mutations in the tumors but are absent in normal tissues. Emerging evidence suggests that neoantigens can stimulate tumor-specific T-cell-mediated antitumor immune responses, and therefore are potential immunotherapeutic targets. We developed ImmuneMirror as a stand-alone open-source pipeline and a web server incorporating a balanced random forest model for neoantigen prediction and prioritization. The prediction model was trained and tested using known immunogenic neopeptides collected from 19 published studies. The area under the curve of our trained model was 0.87 based on the testing data. We applied ImmuneMirror to the whole-exome sequencing and RNA sequencing data obtained from gastrointestinal tract cancers including 805 tumors from colorectal cancer (CRC), esophageal squamous cell carcinoma (ESCC) and hepatocellular carcinoma patients. We discovered a subgroup of microsatellite instability-high (MSI-H) CRC patients with a low neoantigen load but a high tumor mutation burden (> 10 mutations per Mbp). Although the efficacy of PD-1 blockade has been demonstrated in advanced MSI-H patients, almost half of such patients do not respond well. Our study identified a subset of MSI-H patients who may not benefit from this treatment with lower neoantigen load for major histocompatibility complex I (P < 0.0001) and II (P = 0.0008) molecules, respectively. Additionally, the neopeptide YMCNSSCMGV-TP53G245V, derived from a hotspot mutation restricted by HLA-A02, was identified as a potential actionable target in ESCC. This is so far the largest study to comprehensively evaluate neoantigen prediction models using experimentally validated neopeptides. Our results demonstrate the reliability and effectiveness of ImmuneMirror for neoantigen prediction.
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Affiliation(s)
- Gulam Sarwar Chuwdhury
- Department of Clinical Oncology, Center of Cancer Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong (SAR), P. R. China
| | - Yunshan Guo
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
| | - Chi-Leung Chiang
- Department of Clinical Oncology, Center of Cancer Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong (SAR), P. R. China
| | - Ka-On Lam
- Department of Clinical Oncology, Center of Cancer Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong (SAR), P. R. China
| | - Ngar-Woon Kam
- Department of Clinical Oncology, Center of Cancer Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong (SAR), P. R. China
- Laboratory for Synthetic Chemistry and Chemical Biology Limited, Hong Kong Science Park, Shatin, Hong Kong
| | - Zhonghua Liu
- Department of Biostatistics, Columbia University, New York, NY, USA
| | - Wei Dai
- Department of Clinical Oncology, Center of Cancer Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong (SAR), P. R. China
- University of Hong Kong-Shenzhen Hospital, Shenzhen, P. R. China
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Shahbazy M, Ramarathinam SH, Li C, Illing PT, Faridi P, Croft NP, Purcell AW. MHCpLogics: an interactive machine learning-based tool for unsupervised data visualization and cluster analysis of immunopeptidomes. Brief Bioinform 2024; 25:bbae087. [PMID: 38487848 PMCID: PMC10940831 DOI: 10.1093/bib/bbae087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 12/12/2023] [Accepted: 02/15/2024] [Indexed: 03/18/2024] Open
Abstract
The major histocompatibility complex (MHC) encodes a range of immune response genes, including the human leukocyte antigens (HLAs) in humans. These molecules bind peptide antigens and present them on the cell surface for T cell recognition. The repertoires of peptides presented by HLA molecules are termed immunopeptidomes. The highly polymorphic nature of the genres that encode the HLA molecules confers allotype-specific differences in the sequences of bound ligands. Allotype-specific ligand preferences are often defined by peptide-binding motifs. Individuals express up to six classical class I HLA allotypes, which likely present peptides displaying different binding motifs. Such complex datasets make the deconvolution of immunopeptidomic data into allotype-specific contributions and further dissection of binding-specificities challenging. Herein, we developed MHCpLogics as an interactive machine learning-based tool for mining peptide-binding sequence motifs and visualization of immunopeptidome data across complex datasets. We showcase the functionalities of MHCpLogics by analyzing both in-house and published mono- and multi-allelic immunopeptidomics data. The visualization modalities of MHCpLogics allow users to inspect clustered sequences down to individual peptide components and to examine broader sequence patterns within multiple immunopeptidome datasets. MHCpLogics can deconvolute large immunopeptidome datasets enabling the interrogation of clusters for the segregation of allotype-specific peptide sequence motifs, identification of sub-peptidome motifs, and the exportation of clustered peptide sequence lists. The tool facilitates rapid inspection of immunopeptidomes as a resource for the immunology and vaccine communities. MHCpLogics is a standalone application available via an executable installation at: https://github.com/PurcellLab/MHCpLogics.
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Affiliation(s)
- Mohammad Shahbazy
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Sri H Ramarathinam
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Chen Li
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Patricia T Illing
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Pouya Faridi
- Centre for Cancer Research, Hudson Institute of Medical Research, Clayton, VIC 3168, Australia
- Monash Proteomics and Metabolomics Platform, Department of Medicine, School of Clinical Sciences, Monash University, Clayton, VIC 3800, Australia
| | - Nathan P Croft
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Anthony W Purcell
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
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50
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Bravi B. Development and use of machine learning algorithms in vaccine target selection. NPJ Vaccines 2024; 9:15. [PMID: 38242890 PMCID: PMC10798987 DOI: 10.1038/s41541-023-00795-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 12/07/2023] [Indexed: 01/21/2024] Open
Abstract
Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.
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Affiliation(s)
- Barbara Bravi
- Department of Mathematics, Imperial College London, London, SW7 2AZ, UK.
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