1
|
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.
Collapse
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
| |
Collapse
|
2
|
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] [MESH Headings] [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.
Collapse
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.
| |
Collapse
|
3
|
Guasp P, Reiche C, Sethna Z, Balachandran VP. RNA vaccines for cancer: Principles to practice. Cancer Cell 2024; 42:1163-1184. [PMID: 38848720 DOI: 10.1016/j.ccell.2024.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/29/2024] [Accepted: 05/06/2024] [Indexed: 06/09/2024]
Abstract
Vaccines are the most impactful medicines to improve health. Though potent against pathogens, vaccines for cancer remain an unfulfilled promise. However, recent advances in RNA technology coupled with scientific and clinical breakthroughs have spurred rapid discovery and potent delivery of tumor antigens at speed and scale, transforming cancer vaccines into a tantalizing prospect. Yet, despite being at a pivotal juncture, with several randomized clinical trials maturing in upcoming years, several critical questions remain: which antigens, tumors, platforms, and hosts can trigger potent immunity with clinical impact? Here, we address these questions with a principled framework of cancer vaccination from antigen detection to delivery. With this framework, we outline features of emergent RNA technology that enable rapid, robust, real-time vaccination with somatic mutation-derived neoantigens-an emerging "ideal" antigen class-and highlight latent features that have sparked the belief that RNA could realize the enduring vision for vaccines against cancer.
Collapse
Affiliation(s)
- Pablo Guasp
- Immuno-Oncology Service, Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Charlotte Reiche
- Immuno-Oncology Service, Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Zachary Sethna
- Immuno-Oncology Service, Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Vinod P Balachandran
- Immuno-Oncology Service, Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA; David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| |
Collapse
|
4
|
Liang Y, Lv D, Liu K, Yang L, Shu H, Wen L, Lv C, Sun Q, Yin J, Liu H, Xu J, Liu Z, Ding N. MicroProteinDB: A database to provide knowledge on sequences, structures and function of ncRNA-derived microproteins. Comput Biol Med 2024; 177:108660. [PMID: 38820774 DOI: 10.1016/j.compbiomed.2024.108660] [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: 03/20/2024] [Revised: 05/08/2024] [Accepted: 05/26/2024] [Indexed: 06/02/2024]
Abstract
Omics-based technologies have revolutionized our comprehension of microproteins encoded by ncRNAs, revealing their abundant presence and pivotal roles within complex functional landscapes. Here, we developed MicroProteinDB (http://bio-bigdata.hrbmu.edu.cn/MicroProteinDB), which offers and visualizes the extensive knowledge to aid retrieval and analysis of computationally predicted and experimentally validated microproteins originating from various ncRNA types. Employing prediction algorithms grounded in diverse deep learning approaches, MicroProteinDB comprehensively documents the fundamental physicochemical properties, secondary and tertiary structures, interactions with functional proteins, family domains, and inter-species conservation of microproteins. With five major analytical modules, it will serve as a valuable knowledge for investigating ncRNA-derived microproteins.
Collapse
Affiliation(s)
- Yinan Liang
- The First Affiliated Hospital, Harbin Medical University, Harbin, 150001, China
| | - Dezhong Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Kefan Liu
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150081, China
| | - Liting Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Huan Shu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Luan Wen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Chongwen Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Qisen Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Jiaqi Yin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Hui Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| | - Zhigang Liu
- Affiliated Foshan Maternity&Child Healthcare Hospital, Southern Medical University, Guangzhou, 510000, China.
| | - Na Ding
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| |
Collapse
|
5
|
Hao Q, Long Y, Yang Y, Deng Y, Ding Z, Yang L, Shu Y, Xu H. Development and Clinical Applications of Therapeutic Cancer Vaccines with Individualized and Shared Neoantigens. Vaccines (Basel) 2024; 12:717. [PMID: 39066355 PMCID: PMC11281709 DOI: 10.3390/vaccines12070717] [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: 05/29/2024] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 07/28/2024] Open
Abstract
Neoantigens, presented as peptides on the surfaces of cancer cells, have recently been proposed as optimal targets for immunotherapy in clinical practice. The promising outcomes of neoantigen-based cancer vaccines have inspired enthusiasm for their broader clinical applications. However, the individualized tumor-specific antigens (TSA) entail considerable costs and time due to the variable immunogenicity and response rates of these neoantigens-based vaccines, influenced by factors such as neoantigen response, vaccine types, and combination therapy. Given the crucial role of neoantigen efficacy, a number of bioinformatics algorithms and pipelines have been developed to improve the accuracy rate of prediction through considering a series of factors involving in HLA-peptide-TCR complex formation, including peptide presentation, HLA-peptide affinity, and TCR recognition. On the other hand, shared neoantigens, originating from driver mutations at hot mutation spots (e.g., KRASG12D), offer a promising and ideal target for the development of therapeutic cancer vaccines. A series of clinical practices have established the efficacy of these vaccines in patients with distinct HLA haplotypes. Moreover, increasing evidence demonstrated that a combination of tumor associated antigens (TAAs) and neoantigens can also improve the prognosis, thus expand the repertoire of shared neoantigens for cancer vaccines. In this review, we provide an overview of the complex process involved in identifying personalized neoantigens, their clinical applications, advances in vaccine technology, and explore the therapeutic potential of shared neoantigen strategies.
Collapse
Affiliation(s)
- Qing Hao
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
| | - Yuhang Long
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
| | - Yi Yang
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
| | - Yiqi Deng
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
- Colorectal Cancer Center, Department of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Zhenyu Ding
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
| | - Li Yang
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
| | - Yang Shu
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
- Gastric Cancer Center, Department of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
- Institute of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Heng Xu
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
- Institute of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Center of Clinical Laboratory Medicine, Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| |
Collapse
|
6
|
Manav N, Jit BP, Kataria B, Sharma A. Cellular and epigenetic perspective of protein stability and its implications in the biological system. Epigenomics 2024:1-22. [PMID: 38884355 DOI: 10.1080/17501911.2024.2351788] [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: 11/29/2023] [Accepted: 04/30/2024] [Indexed: 06/18/2024] Open
Abstract
Protein stability is a fundamental prerequisite in both experimental and therapeutic applications. Current advancements in high throughput experimental techniques and functional ontology approaches have elucidated that impairment in the structure and stability of proteins is intricately associated with the cause and cure of several diseases. Therefore, it is paramount to deeply understand the physical and molecular confounding factors governing the stability of proteins. In this review article, we comprehensively investigated the evolution of protein stability, examining its emergence over time, its relationship with organizational aspects and the experimental methods used to understand it. Furthermore, we have also emphasized the role of Epigenetics and its interplay with post-translational modifications (PTMs) in regulating the stability of proteins.
Collapse
Affiliation(s)
- Nisha Manav
- Department of Biochemistry, All India Institute of Medical Sciences New Delhi, Ansari Nagar, 110029, India
| | - Bimal Prasad Jit
- Department of Biochemistry, All India Institute of Medical Sciences New Delhi, Ansari Nagar, 110029, India
| | - Babita Kataria
- Department of Medical Oncology, National Cancer Institute, All India Institute of Medical Sciences, Jhajjar, 124105, India
| | - Ashok Sharma
- Department of Biochemistry, All India Institute of Medical Sciences New Delhi, Ansari Nagar, 110029, India
- Department of Biochemistry, National Cancer Institute, All India Institute of Medical Sciences, Jhajjar, 124105, India
| |
Collapse
|
7
|
Koncz B, Balogh GM, Manczinger M. A journey to your self: The vague definition of immune self and its practical implications. Proc Natl Acad Sci U S A 2024; 121:e2309674121. [PMID: 38722806 PMCID: PMC11161755 DOI: 10.1073/pnas.2309674121] [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] [Indexed: 06/10/2024] Open
Abstract
The identification of immunogenic peptides has become essential in an increasing number of fields in immunology, ranging from tumor immunotherapy to vaccine development. The nature of the adaptive immune response is shaped by the similarity between foreign and self-protein sequences, a concept extensively applied in numerous studies. Can we precisely define the degree of similarity to self? Furthermore, do we accurately define immune self? In the current work, we aim to unravel the conceptual and mechanistic vagueness hindering the assessment of self-similarity. Accordingly, we demonstrate the remarkably low consistency among commonly employed measures and highlight potential avenues for future research.
Collapse
Affiliation(s)
- Balázs Koncz
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Hungarian Research Network (HUN-REN) Biological Research Centre, Szeged6726, Hungary
- Hungarian Centre of Excellence for Molecular Medicine - Biological Research Centre (HCEMM-BRC) Systems Immunology Research Group, Szeged6726, Hungary
- Department of Dermatology and Allergology, University of Szeged, Szeged6720, Hungary
| | - Gergő Mihály Balogh
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Hungarian Research Network (HUN-REN) Biological Research Centre, Szeged6726, Hungary
- Hungarian Centre of Excellence for Molecular Medicine - Biological Research Centre (HCEMM-BRC) Systems Immunology Research Group, Szeged6726, Hungary
- Department of Dermatology and Allergology, University of Szeged, Szeged6720, Hungary
| | - Máté Manczinger
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Hungarian Research Network (HUN-REN) Biological Research Centre, Szeged6726, Hungary
- Hungarian Centre of Excellence for Molecular Medicine - Biological Research Centre (HCEMM-BRC) Systems Immunology Research Group, Szeged6726, Hungary
- Department of Dermatology and Allergology, University of Szeged, Szeged6720, Hungary
| |
Collapse
|
8
|
Santamorena MM, Tischer-Zimmermann S, Bonifacius A, Mireisz CNM, Costa B, Khan F, Kulkarni U, Lauruschkat CD, Sampaio KL, Stripecke R, Blasczyk R, Maecker-Kolhoff B, Kraus S, Schlosser A, Cicin-Sain L, Kalinke U, Eiz-Vesper B. Engineered HCMV-infected APCs enable the identification of new immunodominant HLA-restricted epitopes of anti-HCMV T-cell immunity. HLA 2024; 103:e15541. [PMID: 38923358 DOI: 10.1111/tan.15541] [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: 10/27/2023] [Revised: 04/24/2024] [Accepted: 05/07/2024] [Indexed: 06/28/2024]
Abstract
Complications due to HCMV infection or reactivation remain a challenging clinical problem in immunocompromised patients, mainly due to insufficient or absent T-cell functionality. Knowledge of viral targets is crucial to improve monitoring of high-risk patients and optimise antiviral T-cell therapy. To expand the epitope spectrum, genetically-engineered dendritic cells (DCs) and fibroblasts were designed to secrete soluble (s)HLA-A*11:01 and infected with an HCMV mutant lacking immune evasion molecules (US2-6 + 11). More than 700 HLA-A*11:01-restricted epitopes, including more than 50 epitopes derived from a broad range of HCMV open-reading-frames (ORFs) were identified by mass spectrometry and screened for HLA-A*11:01-binding using established prediction tools. The immunogenicity of the 24 highest scoring new candidates was evaluated in vitro in healthy HLA-A*11:01+/HCMV+ donors. Thus, four subdominant epitopes and one immunodominant epitope, derived from the anti-apoptotic protein UL36 and ORFL101C (A11SAL), were identified. Their HLA-A*11:01 complex stability was verified in vitro. In depth analyses revealed highly proliferative and cytotoxic memory T-cell responses against A11SAL, with T-cell responses comparable to the immunodominant HLA-A*02:01-restricted HCMVpp65NLV epitope. A11SAL-specific T cells were also detectable in vivo in immunosuppressed transplant patients and shown to be effective in an in vitro HCMV-infection model, suggesting their crucial role in inhibiting viral replication and improvement of patient's outcome. The developed in vitro pipeline is the first to utilise genetically-engineered DCs to identify naturally presented immunodominant HCMV-derived epitopes. It therefore offers advantages over in silico predictions, is transferable to other HLA alleles, and will significantly expand the repertoire of viral targets to improve therapeutic options.
Collapse
Affiliation(s)
- Maria Michela Santamorena
- Institute of Transfusion Medicine and Transplant Engineering, Hannover Medical School (MHH), Hannover, Germany
| | - Sabine Tischer-Zimmermann
- Institute of Transfusion Medicine and Transplant Engineering, Hannover Medical School (MHH), Hannover, Germany
| | - Agnes Bonifacius
- Institute of Transfusion Medicine and Transplant Engineering, Hannover Medical School (MHH), Hannover, Germany
- German Center for Infection Research (DZIF), Site Hannover-Braunschweig, Hannover, Germany
| | - Chiara Noemi-Marie Mireisz
- Rudolf Virchow Center for Integrative and Translational Bioimaging, University of Wuerzburg, Wuerzburg, Germany
| | - Bibiana Costa
- Institute for Experimental Infection Research, TWINCORE, Centre for Experimental and Clinical Infection Research, Hannover, Germany
| | - Fawad Khan
- Immune Ageing and Chronic Infection, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Upasana Kulkarni
- Immune Ageing and Chronic Infection, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | | | | | - Renata Stripecke
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Düsseldorf, Center for Molecular Medicine Cologne (CMMC), Institute of Translational Immuno-oncology, Cologne, Germany
- German Center for Infections Research (DZIF) Bonn-Cologne, Cologne, Germany
| | - Rainer Blasczyk
- Institute of Transfusion Medicine and Transplant Engineering, Hannover Medical School (MHH), Hannover, Germany
| | - Britta Maecker-Kolhoff
- German Center for Infection Research (DZIF), Site Hannover-Braunschweig, Hannover, Germany
- German Center for Infections Research (DZIF) Bonn-Cologne, Cologne, Germany
- Department of Pediatric Hematology and Oncology, Hannover Medical School, Hannover, Germany
| | - Sabrina Kraus
- Department of Internal Medicine II, University Hospital Wuerzburg, Wuerzburg, Germany
| | - Andreas Schlosser
- Rudolf Virchow Center for Integrative and Translational Bioimaging, University of Wuerzburg, Wuerzburg, Germany
| | - Luka Cicin-Sain
- German Center for Infection Research (DZIF), Site Hannover-Braunschweig, Hannover, Germany
- Immune Ageing and Chronic Infection, Helmholtz Centre for Infection Research, Braunschweig, Germany
- Cluster of Excellence - Resolving Infection Susceptibility (RESIST, EXC 2155), Hannover Medical School, Hannover, Germany
| | - Ulrich Kalinke
- Institute for Experimental Infection Research, TWINCORE, Centre for Experimental and Clinical Infection Research, Hannover, Germany
- Cluster of Excellence - Resolving Infection Susceptibility (RESIST, EXC 2155), Hannover Medical School, Hannover, Germany
| | - Britta Eiz-Vesper
- Institute of Transfusion Medicine and Transplant Engineering, Hannover Medical School (MHH), Hannover, Germany
- German Center for Infection Research (DZIF), Site Hannover-Braunschweig, Hannover, Germany
| |
Collapse
|
9
|
Hesnard L, Thériault C, Cahuzac M, Durette C, Vincent K, Hardy MP, Lanoix J, Lavallée GO, Humeau J, Thibault P, Perreault C. Immunogenicity of Non-Mutated Ovarian Cancer-Specific Antigens. Curr Oncol 2024; 31:3099-3121. [PMID: 38920720 PMCID: PMC11203340 DOI: 10.3390/curroncol31060236] [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: 04/04/2024] [Revised: 05/24/2024] [Accepted: 05/27/2024] [Indexed: 06/27/2024] Open
Abstract
Epithelial ovarian cancer (EOC) has not significantly benefited from advances in immunotherapy, mainly because of the lack of well-defined actionable antigen targets. Using proteogenomic analyses of primary EOC tumors, we previously identified 91 aberrantly expressed tumor-specific antigens (TSAs) originating from unmutated genomic sequences. Most of these TSAs derive from non-exonic regions, and their expression results from cancer-specific epigenetic changes. The present study aimed to evaluate the immunogenicity of 48 TSAs selected according to two criteria: presentation by highly prevalent HLA allotypes and expression in a significant fraction of EOC tumors. Using targeted mass spectrometry analyses, we found that pulsing with synthetic TSA peptides leads to a high-level presentation on dendritic cells. TSA abundance correlated with the predicted binding affinity to the HLA allotype. We stimulated naïve CD8 T cells from healthy blood donors with TSA-pulsed dendritic cells and assessed their expansion with two assays: MHC-peptide tetramer staining and TCR Vβ CDR3 sequencing. We report that these TSAs can expand sizeable populations of CD8 T cells and, therefore, represent attractive targets for EOC immunotherapy.
Collapse
Affiliation(s)
- Leslie Hesnard
- Institute for Research in Immunology and Cancer (IRIC), University of Montreal, Montreal, QC H3T 1J4, Canada; (L.H.); (C.T.); (M.C.); (C.D.); (K.V.); (M.-P.H.); (J.L.); (G.O.L.); (J.H.); (P.T.)
| | - Catherine Thériault
- Institute for Research in Immunology and Cancer (IRIC), University of Montreal, Montreal, QC H3T 1J4, Canada; (L.H.); (C.T.); (M.C.); (C.D.); (K.V.); (M.-P.H.); (J.L.); (G.O.L.); (J.H.); (P.T.)
| | - Maxime Cahuzac
- Institute for Research in Immunology and Cancer (IRIC), University of Montreal, Montreal, QC H3T 1J4, Canada; (L.H.); (C.T.); (M.C.); (C.D.); (K.V.); (M.-P.H.); (J.L.); (G.O.L.); (J.H.); (P.T.)
| | - Chantal Durette
- Institute for Research in Immunology and Cancer (IRIC), University of Montreal, Montreal, QC H3T 1J4, Canada; (L.H.); (C.T.); (M.C.); (C.D.); (K.V.); (M.-P.H.); (J.L.); (G.O.L.); (J.H.); (P.T.)
| | - Krystel Vincent
- Institute for Research in Immunology and Cancer (IRIC), University of Montreal, Montreal, QC H3T 1J4, Canada; (L.H.); (C.T.); (M.C.); (C.D.); (K.V.); (M.-P.H.); (J.L.); (G.O.L.); (J.H.); (P.T.)
| | - Marie-Pierre Hardy
- Institute for Research in Immunology and Cancer (IRIC), University of Montreal, Montreal, QC H3T 1J4, Canada; (L.H.); (C.T.); (M.C.); (C.D.); (K.V.); (M.-P.H.); (J.L.); (G.O.L.); (J.H.); (P.T.)
| | - Joël Lanoix
- Institute for Research in Immunology and Cancer (IRIC), University of Montreal, Montreal, QC H3T 1J4, Canada; (L.H.); (C.T.); (M.C.); (C.D.); (K.V.); (M.-P.H.); (J.L.); (G.O.L.); (J.H.); (P.T.)
| | - Gabriel Ouellet Lavallée
- Institute for Research in Immunology and Cancer (IRIC), University of Montreal, Montreal, QC H3T 1J4, Canada; (L.H.); (C.T.); (M.C.); (C.D.); (K.V.); (M.-P.H.); (J.L.); (G.O.L.); (J.H.); (P.T.)
| | - Juliette Humeau
- Institute for Research in Immunology and Cancer (IRIC), University of Montreal, Montreal, QC H3T 1J4, Canada; (L.H.); (C.T.); (M.C.); (C.D.); (K.V.); (M.-P.H.); (J.L.); (G.O.L.); (J.H.); (P.T.)
| | - Pierre Thibault
- Institute for Research in Immunology and Cancer (IRIC), University of Montreal, Montreal, QC H3T 1J4, Canada; (L.H.); (C.T.); (M.C.); (C.D.); (K.V.); (M.-P.H.); (J.L.); (G.O.L.); (J.H.); (P.T.)
- Department of Chemistry, University of Montreal, Montreal, QC H2V 0B3, Canada
| | - Claude Perreault
- Institute for Research in Immunology and Cancer (IRIC), University of Montreal, Montreal, QC H3T 1J4, Canada; (L.H.); (C.T.); (M.C.); (C.D.); (K.V.); (M.-P.H.); (J.L.); (G.O.L.); (J.H.); (P.T.)
- Department of Medicine, University of Montreal, Montreal, QC H3C 3J7, Canada
| |
Collapse
|
10
|
Bulashevska A, Nacsa Z, Lang F, Braun M, Machyna M, Diken M, Childs L, König R. Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy. Front Immunol 2024; 15:1394003. [PMID: 38868767 PMCID: PMC11167095 DOI: 10.3389/fimmu.2024.1394003] [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: 02/29/2024] [Accepted: 05/13/2024] [Indexed: 06/14/2024] Open
Abstract
Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.
Collapse
Affiliation(s)
- Alla Bulashevska
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Zsófia Nacsa
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Franziska Lang
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Markus Braun
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Martin Machyna
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Mustafa Diken
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Liam Childs
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Renate König
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| |
Collapse
|
11
|
Mösch A, Grazioli F, Machart P, Malone B. NeoAgDT: optimization of personal neoantigen vaccine composition by digital twin simulation of a cancer cell population. Bioinformatics 2024; 40:btae205. [PMID: 38614133 PMCID: PMC11076149 DOI: 10.1093/bioinformatics/btae205] [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: 07/07/2023] [Revised: 03/28/2024] [Accepted: 04/11/2024] [Indexed: 04/15/2024] Open
Abstract
MOTIVATION Neoantigen vaccines make use of tumor-specific mutations to enable the patient's immune system to recognize and eliminate cancer. Selecting vaccine elements, however, is a complex task which needs to take into account not only the underlying antigen presentation pathway but also tumor heterogeneity. RESULTS Here, we present NeoAgDT, a two-step approach consisting of: (i) simulating individual cancer cells to create a digital twin of the patient's tumor cell population and (ii) optimizing the vaccine composition by integer linear programming based on this digital twin. NeoAgDT shows improved selection of experimentally validated neoantigens over ranking-based approaches in a study of seven patients. AVAILABILITY AND IMPLEMENTATION The NeoAgDT code is published on Github: https://github.com/nec-research/neoagdt.
Collapse
Affiliation(s)
- Anja Mösch
- Biomedical AI Group, NEC Laboratories Europe GmbH, Heidelberg 69115, Germany
| | - Filippo Grazioli
- Biomedical AI Group, NEC Laboratories Europe GmbH, Heidelberg 69115, Germany
| | - Pierre Machart
- Biomedical AI Group, NEC Laboratories Europe GmbH, Heidelberg 69115, Germany
| | - Brandon Malone
- Biomedical AI Group, NEC Laboratories Europe GmbH, Heidelberg 69115, Germany
| |
Collapse
|
12
|
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.
Collapse
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.
| |
Collapse
|
13
|
Eskandari A, Leow TC, Rahman MBA, Oslan SN. Advances in Therapeutic Cancer Vaccines, Their Obstacles, and Prospects Toward Tumor Immunotherapy. Mol Biotechnol 2024:10.1007/s12033-024-01144-3. [PMID: 38625508 DOI: 10.1007/s12033-024-01144-3] [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: 01/26/2024] [Accepted: 03/15/2024] [Indexed: 04/17/2024]
Abstract
Over the past few decades, cancer immunotherapy has experienced a significant revolution due to the advancements in immune checkpoint inhibitors (ICIs) and adoptive cell therapies (ACTs), along with their regulatory approvals. In recent times, there has been hope in the effectiveness of cancer vaccines for therapy as they have been able to stimulate de novo T-cell reactions against tumor antigens. These tumor antigens include both tumor-associated antigen (TAA) and tumor-specific antigen (TSA). Nevertheless, the constant quest to fully achieve these abilities persists. Therefore, this review offers a broad perspective on the existing status of cancer immunizations. Cancer vaccine design has been revolutionized due to the advancements made in antigen selection, the development of antigen delivery systems, and a deeper understanding of the strategic intricacies involved in effective antigen presentation. In addition, this review addresses the present condition of clinical tests and deliberates on their approaches, with a particular emphasis on the immunogenicity specific to tumors and the evaluation of effectiveness against tumors. Nevertheless, the ongoing clinical endeavors to create cancer vaccines have failed to produce remarkable clinical results as a result of substantial obstacles, such as the suppression of the tumor immune microenvironment, the identification of suitable candidates, the assessment of immune responses, and the acceleration of vaccine production. Hence, there are possibilities for the industry to overcome challenges and enhance patient results in the coming years. This can be achieved by recognizing the intricate nature of clinical issues and continuously working toward surpassing existing limitations.
Collapse
Affiliation(s)
- Azadeh Eskandari
- Enzyme and Microbial Technology Research Centre, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia.
- Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia.
| | - Thean Chor Leow
- Enzyme and Microbial Technology Research Centre, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
- Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
- Enzyme Technology and X-ray Crystallography Laboratory, VacBio 5, Institute of Bioscience, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
| | | | - Siti Nurbaya Oslan
- Enzyme and Microbial Technology Research Centre, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
- Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
- Enzyme Technology and X-ray Crystallography Laboratory, VacBio 5, Institute of Bioscience, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
| |
Collapse
|
14
|
Borch A, Carri I, Reynisson B, Alvarez HMG, Munk KK, Montemurro A, Kristensen NP, Tvingsholm SA, Holm JS, Heeke C, Moss KH, Hansen UK, Schaap-Johansen AL, Bagger FO, de Lima VAB, Rohrberg KS, Funt SA, Donia M, Svane IM, Lassen U, Barra C, Nielsen M, Hadrup SR. IMPROVE: a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition. Front Immunol 2024; 15:1360281. [PMID: 38633261 PMCID: PMC11021644 DOI: 10.3389/fimmu.2024.1360281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 03/07/2024] [Indexed: 04/19/2024] Open
Abstract
Background Mutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoantigen candidates from sequencing data, but limited data availability has hindered their capacity to predict which of the many neoepitopes will most likely give rise to T cell recognition. Method To address this, we make use of experimentally validated T cell recognition towards 17,500 neoepitope candidates, with 467 being T cell recognized, across 70 cancer patients undergoing immunotherapy. Results We evaluated 27 neoepitope characteristics, and created a random forest model, IMPROVE, to predict neoepitope immunogenicity. The presence of hydrophobic and aromatic residues in the peptide binding core were the most important features for predicting neoepitope immunogenicity. Conclusion Overall, IMPROVE was found to significantly advance the identification of neoepitopes compared to other current methods.
Collapse
Affiliation(s)
- Annie Borch
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Ibel Carri
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Birkir Reynisson
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Heli M. Garcia Alvarez
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Kamilla K. Munk
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | | | | | - Siri A. Tvingsholm
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Jeppe Sejerø Holm
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Christina Heeke
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Keith Henry Moss
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Ulla Kring Hansen
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | | | | | | | | | - Samuel A. Funt
- Department of Medicine, Weill Cornell Medical College, New York, NY, United States
| | - Marco Donia
- National Center for Cancer Immune Therapy, Copenhagen University Hospital, Herlev, Denmark
| | - Inge Marie Svane
- National Center for Cancer Immune Therapy, Copenhagen University Hospital, Herlev, Denmark
| | - Ulrik Lassen
- Department of Oncology, Phase 1 Unit, Rigshospitalet, Copenhagen, Denmark
| | - Carolina Barra
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Sine Reker Hadrup
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| |
Collapse
|
15
|
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.
Collapse
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
| |
Collapse
|
16
|
Wang M, Lei C, Wang J, Li Y, Li M. TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning. Brief Bioinform 2024; 25:bbae154. [PMID: 38600667 PMCID: PMC11006794 DOI: 10.1093/bib/bbae154] [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: 12/26/2023] [Revised: 02/16/2024] [Accepted: 03/13/2024] [Indexed: 04/12/2024] Open
Abstract
Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding motifs. Based on the discovery, we make the preprocessing and coding closer to the natural biological process. Besides, due to the input being based on multiple types of features and the attention module focused on the BiGRU hidden layer, TripHLApan has learned more sequence level binding information. The application of transfer learning strategies ensures the accuracy of prediction results under special lengths (peptides in length 8) and model scalability with the data explosion. Compared with the current optimal models, TripHLApan exhibits strong predictive performance in various prediction environments with different positive and negative sample ratios. In addition, we validate the superiority and scalability of TripHLApan's predictive performance using additional latest data sets, ablation experiments and binding reconstitution ability in the samples of a melanoma patient. The results show that TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines. TripHLApan is publicly available at https://github.com/CSUBioGroup/TripHLApan.git.
Collapse
Affiliation(s)
- Meng Wang
- School of Computer Science and engineering, Central South University, Changsha 410083, China
| | - Chuqi Lei
- School of Computer Science and engineering, Central South University, Changsha 410083, China
| | - Jianxin Wang
- School of Computer Science and engineering, Central South University, Changsha 410083, China
| | - Yaohang Li
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA
| | - Min Li
- School of Computer Science and engineering, Central South University, Changsha 410083, China
| |
Collapse
|
17
|
Xiang N, Zhang K, Zhao Y, Xu C, Zhang X, Meng S. Characterization of antigen presentation capability for neoantigen-based products using targeted LC-MS/MS method. J Pharm Biomed Anal 2024; 240:115886. [PMID: 38184916 DOI: 10.1016/j.jpba.2023.115886] [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: 09/27/2023] [Revised: 11/15/2023] [Accepted: 11/26/2023] [Indexed: 01/09/2024]
Abstract
The generation of an immune response in neoantigen-based products relies on antigen presentation, which is closely analyzed by bioassays for T-cell functions such as tetramer or cytokine release. Mass spectrometry (MS) has the potential to directly assess the antigen-presenting capability of antigen-presenting cells (APCs), offering advantages such as speed, multi-target analysis, robustness, and ease of transferability. However, it has not been used for quality control of these products due to challenges in sensitivity, including the number of cells and peptide diversity. In this study, we describe the development and validation of an improved targeted LC-MS/MS method with high sensitivity for characterizing antigen presentation, which could be applied in the quality control of neoantigen-based products. The parameters for the extraction were carefully optimized by different short peptides. Highly sensitive targeted triple quadrupole mass spectrometry combined with ultra-high performance liquid chromatography (UHPLC) was employed using a selective ion monitoring mode (Multiple Reaction Monitoring, MRM). Besides, we successfully implemented robust quality control peptides to ensure the reliability and consistency of this method, which proved invaluable for different APCs. With reference to the guidelines from ICH Q2 (R2), M10, as well as considering the specific attributes of the product itself, we validated the method for selectivity, specificity, sensitivity, limit of detection (LOD), recovery rate, matrix effect, repeatability, and application in dendritic cells (DCs) associated with neoantigen-based products. The validation process yields satisfactory results. Combining this approach with T cell assays will comprehensively assess cell product quality attributes from physicochemical and biological perspectives.
Collapse
Affiliation(s)
- Nan Xiang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; The Cell Collection and Research Center, National Institutes for Food and Drug Control, No. 31 Huatuo St., Daxing District, Beijing, China
| | - Kehua Zhang
- The Cell Collection and Research Center, National Institutes for Food and Drug Control, No. 31 Huatuo St., Daxing District, Beijing, China
| | - Yinghua Zhao
- SCIEX China, 5F, Building 1, No. 24 Jiuxianqiao Middle Road, Chaoyang District, Beijing, China
| | - Chongfeng Xu
- The Cell Collection and Research Center, National Institutes for Food and Drug Control, No. 31 Huatuo St., Daxing District, Beijing, China
| | - Xiuqing Zhang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Shufang Meng
- The Cell Collection and Research Center, National Institutes for Food and Drug Control, No. 31 Huatuo St., Daxing District, Beijing, China.
| |
Collapse
|
18
|
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.
Collapse
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
| |
Collapse
|
19
|
Katsikis PD, Ishii KJ, Schliehe C. Challenges in developing personalized neoantigen cancer vaccines. Nat Rev Immunol 2024; 24:213-227. [PMID: 37783860 DOI: 10.1038/s41577-023-00937-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/17/2023] [Indexed: 10/04/2023]
Abstract
The recent success of cancer immunotherapies has highlighted the benefit of harnessing the immune system for cancer treatment. Vaccines have a long history of promoting immunity to pathogens and, consequently, vaccines targeting cancer neoantigens have been championed as a tool to direct and amplify immune responses against tumours while sparing healthy tissue. In recent years, extensive preclinical research and more than one hundred clinical trials have tested different strategies of neoantigen discovery and vaccine formulations. However, despite the enthusiasm for neoantigen vaccines, proof of unequivocal efficacy has remained beyond reach for the majority of clinical trials. In this Review, we focus on the key obstacles pertaining to vaccine design and tumour environment that remain to be overcome in order to unleash the true potential of neoantigen vaccines in cancer therapy.
Collapse
Affiliation(s)
- Peter D Katsikis
- Department of Immunology, Erasmus University Medical Center, Rotterdam, Netherlands.
| | - Ken J Ishii
- Division of Vaccine Science, Department of Microbiology and Immunology, The Institute of Medical Science, The University of Tokyo (IMSUT), Tokyo, Japan
- International Vaccine Design Center (vDesC), The Institute of Medical Science, The University of Tokyo (IMSUT), Tokyo, Japan
| | - Christopher Schliehe
- Department of Immunology, Erasmus University Medical Center, Rotterdam, Netherlands
| |
Collapse
|
20
|
Conev A, Fasoulis R, Hall-Swan S, Ferreira R, Kavraki LE. HLAEquity: Examining biases in pan-allele peptide-HLA binding predictors. iScience 2024; 27:108613. [PMID: 38188519 PMCID: PMC10770483 DOI: 10.1016/j.isci.2023.108613] [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: 11/08/2023] [Revised: 11/13/2023] [Accepted: 11/29/2023] [Indexed: 01/09/2024] Open
Abstract
Peptide-HLA (pHLA) binding prediction is essential in screening peptide candidates for personalized peptide vaccines. Machine learning (ML) pHLA binding prediction tools are trained on vast amounts of data and are effective in screening peptide candidates. Most ML models report the ability to generalize to HLA alleles unseen during training ("pan-allele" models). However, the use of datasets with imbalanced allele content raises concerns about biased model performance. First, we examine the data bias of two ML-based pan-allele pHLA binding predictors. We find that the pHLA datasets overrepresent alleles from geographic populations of high-income countries. Second, we show that the identified data bias is perpetuated within ML models, leading to algorithmic bias and subpar performance for alleles expressed in low-income geographic populations. We draw attention to the potential therapeutic consequences of this bias, and we challenge the use of the term "pan-allele" to describe models trained with currently available public datasets.
Collapse
Affiliation(s)
- Anja Conev
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Romanos Fasoulis
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Sarah Hall-Swan
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Rodrigo Ferreira
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Lydia E. Kavraki
- Department of Computer Science, Rice University, Houston, TX, USA
| |
Collapse
|
21
|
Ricker CA, Meli K, Van Allen EM. Historical perspective and future directions: computational science in immuno-oncology. J Immunother Cancer 2024; 12:e008306. [PMID: 38191244 PMCID: PMC10826578 DOI: 10.1136/jitc-2023-008306] [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: 12/07/2023] [Indexed: 01/10/2024] Open
Abstract
Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life.
Collapse
Affiliation(s)
- Cora A Ricker
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Kevin Meli
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | | |
Collapse
|
22
|
Kaushik S, Kumari L, Deepak RK. Humanized mouse model for vaccine evaluation: an overview. Clin Exp Vaccine Res 2024; 13:10-20. [PMID: 38362371 PMCID: PMC10864885 DOI: 10.7774/cevr.2024.13.1.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 11/09/2023] [Accepted: 12/21/2023] [Indexed: 02/17/2024] Open
Abstract
Animal models are essential in medical research for testing drugs and vaccines. These models differ from humans in various respects, so their results are not directly translatable in humans. To address this issue, humanized mice engrafted with functional human cells or tissue can be helpful. We propose using humanized mice that support the engraftment of human hematopoietic stem cells (HSCs) without irradiation to evaluate vaccines that influence patient immunity. For infectious diseases, several types of antigens and adjuvants have been developed and evaluated for vaccination. Peptide vaccines are generally used for their capability to fight cancer and infectious diseases. Evaluation of adjuvants is necessary as they induce inflammation, which is effective for an enhanced immune response but causes adverse effects in some individuals. A trial can be done on humanized mice to check the immunogenicity of a particular adjuvant and peptide combination. Messenger RNA has also emerged as a potential vaccine against viruses. These vaccines need to be tested with human immune cells because they work by producing a particular peptide of the pathogen. Humanized mice with human HSCs that can produce both myeloid and lymphoid cells show a similar immune response that these vaccines will produce in a patient.
Collapse
Affiliation(s)
| | - Lata Kumari
- All India Institute of Medical Sciences, New Delhi, India
| | | |
Collapse
|
23
|
Fan T, Zhang M, Yang J, Zhu Z, Cao W, Dong C. Therapeutic cancer vaccines: advancements, challenges, and prospects. Signal Transduct Target Ther 2023; 8:450. [PMID: 38086815 PMCID: PMC10716479 DOI: 10.1038/s41392-023-01674-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 09/08/2023] [Accepted: 09/19/2023] [Indexed: 12/18/2023] Open
Abstract
With the development and regulatory approval of immune checkpoint inhibitors and adoptive cell therapies, cancer immunotherapy has undergone a profound transformation over the past decades. Recently, therapeutic cancer vaccines have shown promise by eliciting de novo T cell responses targeting tumor antigens, including tumor-associated antigens and tumor-specific antigens. The objective was to amplify and diversify the intrinsic repertoire of tumor-specific T cells. However, the complete realization of these capabilities remains an ongoing pursuit. Therefore, we provide an overview of the current landscape of cancer vaccines in this review. The range of antigen selection, antigen delivery systems development the strategic nuances underlying effective antigen presentation have pioneered cancer vaccine design. Furthermore, this review addresses the current status of clinical trials and discusses their strategies, focusing on tumor-specific immunogenicity and anti-tumor efficacy assessment. However, current clinical attempts toward developing cancer vaccines have not yielded breakthrough clinical outcomes due to significant challenges, including tumor immune microenvironment suppression, optimal candidate identification, immune response evaluation, and vaccine manufacturing acceleration. Therefore, the field is poised to overcome hurdles and improve patient outcomes in the future by acknowledging these clinical complexities and persistently striving to surmount inherent constraints.
Collapse
Affiliation(s)
- Ting Fan
- Department of Oncology, East Hospital Affiliated to Tongji University, Tongji University School of Medicine, Shanghai, China
| | - Mingna Zhang
- Postgraduate Training Base, Shanghai East Hospital, Jinzhou Medical University, Shanghai, 200120, China
| | - Jingxian Yang
- Department of Oncology, East Hospital Affiliated to Tongji University, Tongji University School of Medicine, Shanghai, China
| | - Zhounan Zhu
- Department of Oncology, East Hospital Affiliated to Tongji University, Tongji University School of Medicine, Shanghai, China
| | - Wanlu Cao
- Department of Oncology, East Hospital Affiliated to Tongji University, Tongji University School of Medicine, Shanghai, China.
| | - Chunyan Dong
- Department of Oncology, East Hospital Affiliated to Tongji University, Tongji University School of Medicine, Shanghai, China.
| |
Collapse
|
24
|
Seclì L, Leoni G, Ruzza V, Siani L, Cotugno G, Scarselli E, D’Alise AM. Personalized Cancer Vaccines Go Viral: Viral Vectors in the Era of Personalized Immunotherapy of Cancer. Int J Mol Sci 2023; 24:16591. [PMID: 38068911 PMCID: PMC10706435 DOI: 10.3390/ijms242316591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 11/17/2023] [Accepted: 11/20/2023] [Indexed: 12/18/2023] Open
Abstract
The aim of personalized cancer vaccines is to elicit potent and tumor-specific immune responses against neoantigens specific to each patient and to establish durable immunity, while minimizing the adverse events. Over recent years, there has been a renewed interest in personalized cancer vaccines, primarily due to the advancement of innovative technologies for the identification of neoantigens and novel vaccine delivery platforms. Here, we review the emerging field of personalized cancer vaccination, with a focus on the use of viral vectors as a vaccine platform. The recent advancements in viral vector technology have led to the development of efficient production processes, positioning personalized viral vaccines as one of the preferred technologies. Many clinical trials have shown the feasibility, safety, immunogenicity and, more recently, preliminary evidence of the anti-tumor activity of personalized vaccination, fostering active research in the field, including further clinical trials for different tumor types and in different clinical settings.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Anna Morena D’Alise
- Nouscom, Via di Castel Romano 100, 00128 Rome, Italy; (L.S.); (G.L.); (V.R.); (L.S.); (G.C.); (E.S.)
| |
Collapse
|
25
|
Newey A, Yu L, Barber LJ, Choudhary JS, Bassani-Sternberg M, Gerlinger M. Multifactorial Remodeling of the Cancer Immunopeptidome by IFNγ. CANCER RESEARCH COMMUNICATIONS 2023; 3:2345-2357. [PMID: 37991387 PMCID: PMC10655636 DOI: 10.1158/2767-9764.crc-23-0121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 09/15/2023] [Accepted: 11/02/2023] [Indexed: 11/23/2023]
Abstract
IFNγ alters the immunopeptidome presented on HLA class I (HLA-I), and its activity on cancer cells is known to be important for effective immunotherapy responses. We performed proteomic analyses of untreated and IFNγ-treated colorectal cancer patient-derived organoids and combined this with transcriptomic and HLA-I immunopeptidomics data to dissect mechanisms that lead to remodeling of the immunopeptidome through IFNγ. IFNγ-induced changes in the abundance of source proteins, switching from the constitutive to the immunoproteasome, and differential upregulation of different HLA alleles explained some, but not all, observed peptide abundance changes. By selecting for peptides which increased or decreased the most in abundance, but originated from proteins with limited abundance changes, we discovered that the amino acid composition of presented peptides also influences whether a peptide is upregulated or downregulated on HLA-I through IFNγ. The presence of proline within the peptide core was most strongly associated with peptide downregulation. This was validated in an independent dataset. Proline substitution in relevant core positions did not influence the predicted HLA-I binding affinity or stability, indicating that proline effects on peptide processing may be most relevant. Understanding the multiple factors that influence the abundance of peptides presented on HLA-I in the absence or presence of IFNγ is important to identify the best targets for antigen-specific cancer immunotherapies such as vaccines or T-cell receptor engineered therapeutics. SIGNIFICANCE IFNγ remodels the HLA-I-presented immunopeptidome. We showed that peptide-specific factors influence whether a peptide is upregulated or downregulated and identified a preferential loss or downregulation of those with proline near the peptide center. This will help selecting immunotherapy target antigens which are consistently presented by cancer cells.
Collapse
Affiliation(s)
- Alice Newey
- Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
- The Institute of Cancer Research, London, United Kingdom
| | - Lu Yu
- The Institute of Cancer Research, London, United Kingdom
| | - Louise J. Barber
- Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
- The Institute of Cancer Research, London, United Kingdom
| | - Jyoti S. Choudhary
- The Proteomics Core Facility, The Institute of Cancer Research, London, United Kingdom
| | - Michal Bassani-Sternberg
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Marco Gerlinger
- Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
- St Bartholomew's Hospital Cancer Centre, London, United Kingdom
| |
Collapse
|
26
|
Federico L, Malone B, Tennøe S, Chaban V, Osen JR, Gainullin M, Smorodina E, Kared H, Akbar R, Greiff V, Stratford R, Clancy T, Munthe LA. Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence. Front Immunol 2023; 14:1265044. [PMID: 38045681 PMCID: PMC10691274 DOI: 10.3389/fimmu.2023.1265044] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 11/01/2023] [Indexed: 12/05/2023] Open
Abstract
During the COVID-19 pandemic we utilized an AI-driven T cell epitope prediction tool, the NEC Immune Profiler (NIP) to scrutinize and predict regions of T cell immunogenicity (hotspots) from the entire SARS-CoV-2 viral proteome. These immunogenic regions offer potential for the development of universally protective T cell vaccine candidates. Here, we validated and characterized T cell responses to a set of minimal epitopes from these AI-identified universal hotspots. Utilizing a flow cytometry-based T cell activation-induced marker (AIM) assay, we identified 59 validated screening hits, of which 56% (33 peptides) have not been previously reported. Notably, we found that most of these novel epitopes were derived from the non-spike regions of SARS-CoV-2 (Orf1ab, Orf3a, and E). In addition, ex vivo stimulation with NIP-predicted peptides from the spike protein elicited CD8+ T cell response in PBMC isolated from most vaccinated donors. Our data confirm the predictive accuracy of AI platforms modelling bona fide immunogenicity and provide a novel framework for the evaluation of vaccine-induced T cell responses.
Collapse
Affiliation(s)
- Lorenzo Federico
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | | | - Viktoriia Chaban
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Julie Røkke Osen
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Murat Gainullin
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Eva Smorodina
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Oslo University Hospital, Oslo, Norway
| | - Hassen Kared
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Rahmad Akbar
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Oslo University Hospital, Oslo, Norway
| | - Victor Greiff
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Oslo University Hospital, Oslo, Norway
| | | | | | - Ludvig Andre Munthe
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| |
Collapse
|
27
|
Ye Y, Shen Y, Wang J, Li D, Zhu Y, Zhao Z, Pan Y, Wang Y, Liu X, Wan J. SIGANEO: Similarity network with GAN enhancement for immunogenic neoepitope prediction. Comput Struct Biotechnol J 2023; 21:5538-5543. [PMID: 38034402 PMCID: PMC10681954 DOI: 10.1016/j.csbj.2023.10.050] [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: 07/19/2023] [Revised: 10/26/2023] [Accepted: 10/27/2023] [Indexed: 12/02/2023] Open
Abstract
Target selection of the personalized cancer neoantigen vaccine, which is highly dependent on computational prediction algorithms, is crucial for its clinical efficacy. Due to the limited number of experimentally validated immunogenic neoepitopes as well as the complexity of neoantigens in eliciting T cell response, the accuracy of neoepitope immunogenicity prediction methods requires persistent efforts for improvement. We present a deep learning framework for neoepitope immunogenicity prediction - SIGANEO by integrating GAN-like network with similarity network to address issues of missing values and limited data concerning neoantigen prediction. This framework exhibits superior performance over competing machine-learning-based neoantigen prediction algorithms over an independent test dataset from TESLA consortium. Particularly for the clinical setting of neoantigen vaccine where only the top 10 and 20 predictions are selected for vaccine production, SIGANEO achieves significantly better accuracy for predicting experimentally validated neoepitopes. Our work demonstrates that deep learning techniques can greatly boost the accuracy of target identification for cancer neoantigen vaccine.
Collapse
Affiliation(s)
- Yilin Ye
- Shenzhen Neocura Biotechnology Co. Ltd., Shenzhen 518055, China
| | - Yiming Shen
- Shenzhen Neocura Biotechnology Co. Ltd., Shenzhen 518055, China
| | - Jian Wang
- Shenzhen Neocura Biotechnology Co. Ltd., Shenzhen 518055, China
| | - Dong Li
- Shenzhen Neocura Biotechnology Co. Ltd., Shenzhen 518055, China
| | - Yu Zhu
- Shenzhen Neocura Biotechnology Co. Ltd., Shenzhen 518055, China
| | - Zhao Zhao
- Shenzhen Neocura Biotechnology Co. Ltd., Shenzhen 518055, China
| | - Youdong Pan
- Shenzhen Neocura Biotechnology Co. Ltd., Shenzhen 518055, China
| | - Yi Wang
- Shenzhen Neocura Biotechnology Co. Ltd., Shenzhen 518055, China
| | - Xing Liu
- The Center for Microbes, Development and Health, Key Laboratory of Molecular Virology and Immunology, Institut Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ji Wan
- Shenzhen Neocura Biotechnology Co. Ltd., Shenzhen 518055, China
| |
Collapse
|
28
|
García-Mulero S, Fornelino R, Punta M, Lise S, Varela M, del Carpio LP, Moreno R, Costa-García M, Rieder D, Trajanoski Z, Gros A, Alemany R, Piulats JM, Sanz-Pamplona R. Driver mutations in GNAQ and GNA11 genes as potential targets for precision immunotherapy in uveal melanoma patients. Oncoimmunology 2023; 12:2261278. [PMID: 38126027 PMCID: PMC10732647 DOI: 10.1080/2162402x.2023.2261278] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 09/17/2023] [Indexed: 12/23/2023] Open
Abstract
Uveal melanoma (UM) is the most common ocular malignancy in adults. Nearly 95% of UM patients carry the mutually exclusive mutations in the homologous genes GNAQ (amino acid change Q209L/Q209P) and GNA11 (aminoacid change Q209L). UM is located in an immunosuppressed organ and does not suffer immunoediting. Therefore, we hypothesize that driver mutations in GNAQ/11 genes could be recognized by the immune system. Genomic and transcriptomic data from primary uveal tumors were collected from the TCGA-UM dataset (n = 80) and used to assess the immunogenic potential for GNAQ/GNA11 Q209L/Q209P mutations using a variety of tools and HLA type information. All prediction tools showed stronger GNAQ/11 Q209L binding to HLA than GNAQ/11 Q209P. The immunogenicity analysis revealed that Q209L is likely to be presented by more than 73% of individuals in 1000 G databases whereas Q209P is only predicted to be presented in 24% of individuals. GNAQ/11 Q209L showed a higher likelihood to be presented by HLA-I molecules than almost all driver mutations analyzed. Finally, samples carrying Q209L had a higher immune-reactive phenotype. Regarding cancer risk, seven HLA genotypes with low Q209L affinity show higher frequency in uveal melanoma patients than in the general population. However, no clear association was found between any HLA genotype and survival. Results suggest a high potential immunogenicity of the GNAQ/11 Q209L variant that could allow the generation of novel therapeutic tools to treat UM like neoantigen vaccinations.
Collapse
Affiliation(s)
- Sandra García-Mulero
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL) and CIBERESP, Barcelona, Spain
- Anatomy Unit, Department of Pathology and Experimental Therapy, and Department of Clinical Sciences, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Roberto Fornelino
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL) and CIBERESP, Barcelona, Spain
| | - Marco Punta
- Bioinformatics Core, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Stefano Lise
- Bioinformatics Core, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Mar Varela
- Department of Pathology, Bellvitge University Hospital, Barcelona, Spain
| | - Luis P. del Carpio
- Procure Program, Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | - Rafael Moreno
- Procure Program, Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | - Marcel Costa-García
- Procure Program, Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | - Dietmar Rieder
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - Zlatko Trajanoski
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - Alena Gros
- Tumor Immunology and Immunotherapy, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Ramón Alemany
- Procure Program, Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | | | - Rebeca Sanz-Pamplona
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL) and CIBERESP, Barcelona, Spain
- Institute for Health Research Aragon (IISA), ARAID Foundation, Aragon Government, University Hospital Lozano Blesa, Zaragoza, Spain
| |
Collapse
|
29
|
Gupta Y, Baranwal M, Chudasama B. Immunoinformatics-Based Identification of the Conserved Immunogenic Peptides Targeting of Zika Virus Precursor Membrane Protein. Viral Immunol 2023; 36:503-519. [PMID: 37486711 DOI: 10.1089/vim.2023.0015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/25/2023] Open
Abstract
Zika virus infections lead to neurological complications such as congenital Zika syndrome and Guillain-Barré syndrome. Rising Zika infections in newborns and adults have triggered the need for vaccine development. In the current study, the precursor membrane (prM) protein of the Zika virus is explored for its functional importance and design of epitopes enriched conserved peptides with the usage of different immunoinformatics approach. Phylogenetic and mutational analyses inferred that the prM protein is highly conserved. Three conserved peptides containing multiple T and B cell epitopes were designed by employing different epitope prediction algorithms. IEDB population coverage analysis of selected peptides in six different continents has shown the population coverage of 60-99.8% (class I HLA) and 80-100% (class II HLA). Molecular docking of selected peptides/epitopes was carried out with each of class I and II HLA alleles using HADDOCK. A majority of peptide-HLA complex (pHLA) have HADDOCK scores found to be comparable and more than native-HLA complex representing the good binding interaction of peptides to HLA. Molecular dynamics simulation with best docked pHLA complexes revealed that pHLA complexes are stable with RMSD <5.5Å. Current work highlights the importance of prM as a strong antigenic protein and selected peptides have the potential to elicit humoral and cell-mediated immune responses.
Collapse
Affiliation(s)
- Yogita Gupta
- Department of Biotechnology, Thapar Institute of Engineering and Technology, Patiala, India
| | - Manoj Baranwal
- Department of Biotechnology, Thapar Institute of Engineering and Technology, Patiala, India
| | - Bhupendra Chudasama
- School of Physics & Materials Science, Thapar Institute of Engineering and Technology, Patiala, India
| |
Collapse
|
30
|
Li F, Wang C, Guo X, Akutsu T, Webb GI, Coin LJM, Kurgan L, Song J. ProsperousPlus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction and machine-learning model construction. Brief Bioinform 2023; 24:bbad372. [PMID: 37874948 DOI: 10.1093/bib/bbad372] [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: 07/30/2023] [Revised: 08/30/2023] [Accepted: 09/29/2023] [Indexed: 10/26/2023] Open
Abstract
Proteases contribute to a broad spectrum of cellular functions. Given a relatively limited amount of experimental data, developing accurate sequence-based predictors of substrate cleavage sites facilitates a better understanding of protease functions and substrate specificity. While many protease-specific predictors of substrate cleavage sites were developed, these efforts are outpaced by the growth of the protease substrate cleavage data. In particular, since data for 100+ protease types are available and this number continues to grow, it becomes impractical to publish predictors for new protease types, and instead it might be better to provide a computational platform that helps users to quickly and efficiently build predictors that address their specific needs. To this end, we conceptualized, developed, tested and released a versatile bioinformatics platform, ProsperousPlus, that empowers users, even those with no programming or little bioinformatics background, to build fast and accurate predictors of substrate cleavage sites. ProsperousPlus facilitates the use of the rapidly accumulating substrate cleavage data to train, empirically assess and deploy predictive models for user-selected substrate types. Benchmarking tests on test datasets show that our platform produces predictors that on average exceed the predictive performance of current state-of-the-art approaches. ProsperousPlus is available as a webserver and a stand-alone software package at http://prosperousplus.unimelb-biotools.cloud.edu.au/.
Collapse
Affiliation(s)
- Fuyi Li
- College of Information Engineering, Northwest A&F University, Shaanxi 712100, China
- South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
- The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, VIC 3000, Australia
| | - Cong Wang
- College of Information Engineering, Northwest A&F University, Shaanxi 712100, China
| | - Xudong Guo
- College of Information Engineering, Northwest A&F University, Shaanxi 712100, China
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan
| | - Geoffrey I Webb
- Monash Data Futures Institute, Monash University, VIC 3800, Australia
| | - Lachlan J M Coin
- The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, VIC 3000, Australia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Jiangning Song
- Monash Data Futures Institute, Monash University, VIC 3800, Australia
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, VIC 3800, Australia
| |
Collapse
|
31
|
Boll LM, Perera-Bel J, Rodriguez-Vida A, Arpí O, Rovira A, Juanpere N, Vázquez Montes de Oca S, Hernández-Llodrà S, Lloreta J, Albà MM, Bellmunt J. The impact of mutational clonality in predicting the response to immune checkpoint inhibitors in advanced urothelial cancer. Sci Rep 2023; 13:15287. [PMID: 37714872 PMCID: PMC10504302 DOI: 10.1038/s41598-023-42495-2] [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: 04/05/2023] [Accepted: 09/11/2023] [Indexed: 09/17/2023] Open
Abstract
Immune checkpoint inhibitors (ICI) have revolutionized cancer treatment and can result in complete remissions even at advanced stages of the disease. However, only a small fraction of patients respond to the treatment. To better understand which factors drive clinical benefit, we have generated whole exome and RNA sequencing data from 27 advanced urothelial carcinoma patients treated with anti-PD-(L)1 monoclonal antibodies. We assessed the influence on the response of non-synonymous mutations (tumor mutational burden or TMB), clonal and subclonal mutations, neoantigen load and various gene expression markers. We found that although TMB is significantly associated with response, this effect can be mostly explained by clonal mutations, present in all cancer cells. This trend was validated in an additional cohort. Additionally, we found that responders with few clonal mutations had abnormally high levels of T and B cell immune markers, suggesting that a high immune cell infiltration signature could be a better predictive biomarker for this subset of patients. Our results support the idea that highly clonal cancers are more likely to respond to ICI and suggest that non-additive effects of different signatures should be considered for predictive models.
Collapse
Affiliation(s)
| | | | - Alejo Rodriguez-Vida
- Hospital del Mar Research Institute, Barcelona, Spain
- Medical Oncology Department, Hospital del Mar, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Oncología (CIBERONC-ISCIII), Barcelona, Spain
| | - Oriol Arpí
- Hospital del Mar Research Institute, Barcelona, Spain
| | - Ana Rovira
- Hospital del Mar Research Institute, Barcelona, Spain
- Medical Oncology Department, Hospital del Mar, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Oncología (CIBERONC-ISCIII), Barcelona, Spain
| | | | | | | | - Josep Lloreta
- Hospital del Mar Research Institute, Barcelona, Spain
- Department of Medicine and Life Science, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - M Mar Albà
- Hospital del Mar Research Institute, Barcelona, Spain.
- Catalan Institute for Research and Advanced Studies (ICREA), Barcelona, Spain.
| | - Joaquim Bellmunt
- Hospital del Mar Research Institute, Barcelona, Spain.
- Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
32
|
Godazandeh K, Van Olmen L, Van Oudenhove L, Lefever S, Bogaert C, Fant B. Methods behind neoantigen prediction for personalized anticancer vaccines. Methods Cell Biol 2023; 183:161-186. [PMID: 38548411 DOI: 10.1016/bs.mcb.2023.05.002] [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] [Indexed: 04/02/2024]
Abstract
Next to conventional cancer therapies, immunotherapies such as immune checkpoint inhibitors have broadened the cancer treatment landscape over the past decades. Recent advances in next generation sequencing and bioinformatics technologies have made it possible to identify a patient's own immunogenic neoantigens. These cancer neoantigens serve as important targets for personalized immunotherapy which has the benefit of being more active and effective in targeting cancer cells. This paper is a step-by-step guide discussing the different analyses and challenges encountered during in-silico neoantigen prediction. The protocol describes all the tools and steps required for the identification of immunogenic neoantigens.
Collapse
|
33
|
Tian J, Ma J. The Value of Microbes in Cancer Neoantigen Immunotherapy. Pharmaceutics 2023; 15:2138. [PMID: 37631352 PMCID: PMC10459105 DOI: 10.3390/pharmaceutics15082138] [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: 07/17/2023] [Revised: 08/06/2023] [Accepted: 08/11/2023] [Indexed: 08/27/2023] Open
Abstract
Tumor neoantigens are widely used in cancer immunotherapy, and a growing body of research suggests that microbes play an important role in these neoantigen-based immunotherapeutic processes. The human body and its surrounding environment are filled with a large number of microbes that are in long-term interaction with the organism. The microbiota can modulate our immune system, help activate neoantigen-reactive T cells, and play a great role in the process of targeting tumor neoantigens for therapy. Recent studies have revealed the interconnection between microbes and neoantigens, which can cross-react with each other through molecular mimicry, providing theoretical guidance for more relevant studies. The current applications of microbes in immunotherapy against tumor neoantigens are mainly focused on cancer vaccine development and immunotherapy with immune checkpoint inhibitors. This article summarizes the related fields and suggests the importance of microbes in immunotherapy against neoantigens.
Collapse
Affiliation(s)
- Junrui Tian
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China;
- Cancer Research Institute and School of Basic Medical Science, Central South University, Changsha 410078, China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Changsha 410078, China
| | - Jian Ma
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China;
- Cancer Research Institute and School of Basic Medical Science, Central South University, Changsha 410078, China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Changsha 410078, China
| |
Collapse
|
34
|
Zhuang L, Ye Z, Li L, Yang L, Gong W. Next-Generation TB Vaccines: Progress, Challenges, and Prospects. Vaccines (Basel) 2023; 11:1304. [PMID: 37631874 PMCID: PMC10457792 DOI: 10.3390/vaccines11081304] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 07/28/2023] [Accepted: 07/28/2023] [Indexed: 08/27/2023] Open
Abstract
Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is a prevalent global infectious disease and a leading cause of mortality worldwide. Currently, the only available vaccine for TB prevention is Bacillus Calmette-Guérin (BCG). However, BCG demonstrates limited efficacy, particularly in adults. Efforts to develop effective TB vaccines have been ongoing for nearly a century. In this review, we have examined the current obstacles in TB vaccine research and emphasized the significance of understanding the interaction mechanism between MTB and hosts in order to provide new avenues for research and establish a solid foundation for the development of novel vaccines. We have also assessed various TB vaccine candidates, including inactivated vaccines, attenuated live vaccines, subunit vaccines, viral vector vaccines, DNA vaccines, and the emerging mRNA vaccines as well as virus-like particle (VLP)-based vaccines, which are currently in preclinical stages or clinical trials. Furthermore, we have discussed the challenges and opportunities associated with developing different types of TB vaccines and outlined future directions for TB vaccine research, aiming to expedite the development of effective vaccines. This comprehensive review offers a summary of the progress made in the field of novel TB vaccines.
Collapse
Affiliation(s)
- Li Zhuang
- Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, Eighth Medical Center of Chinese PLA General Hospital, Beijing 100091, China
- Hebei North University, Zhangjiakou 075000, China
| | - Zhaoyang Ye
- Hebei North University, Zhangjiakou 075000, China
| | - Linsheng Li
- Hebei North University, Zhangjiakou 075000, China
| | - Ling Yang
- Hebei North University, Zhangjiakou 075000, China
| | - Wenping Gong
- Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, Eighth Medical Center of Chinese PLA General Hospital, Beijing 100091, China
| |
Collapse
|
35
|
Shi Y, Jing B, Xi R. Comprehensive analysis of neoantigens derived from structural variation across whole genomes from 2528 tumors. Genome Biol 2023; 24:169. [PMID: 37461029 DOI: 10.1186/s13059-023-03005-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 07/02/2023] [Indexed: 07/20/2023] Open
Abstract
BACKGROUND Neoantigens are critical for anti-tumor immunity and have been long-envisioned as promising therapeutic targets. However, current neoantigen analyses mostly focus on single nucleotide variations (SNVs) and indel mutations and seldom consider structural variations (SVs) that are also prevalent in cancer. RESULTS Here, we develop a computational method termed NeoSV, which incorporates SV annotation, protein fragmentation, and MHC binding prediction together, to predict SV-derived neoantigens. Analysis of 2528 whole genomes reveals that SVs significantly contribute to the neoantigen repertoire in both quantity and quality. Whereas most neoantigens are patient-specific, shared neoantigens are identified with high occurrence rates in breast, ovarian, and gastrointestinal cancers. We observe extensive immunoediting on SV-derived neoantigens, especially on clonal events, which suggests their immunogenic potential. We also demonstrate that genomic alteration-related neoantigen burden, which integrates SV-derived neoantigens, depicts the tumor-immune interplay better than tumor neoantigen burden and may improve patient selection for immunotherapy. CONCLUSIONS Our study fills the gap in the current neoantigen repertoire and provides a valuable resource for cancer vaccine development.
Collapse
Affiliation(s)
- Yang Shi
- School of Mathematical Sciences, Peking University, Beijing, China
| | - Biyang Jing
- School of Life Sciences, Peking University, Beijing, China
| | - Ruibin Xi
- School of Mathematical Sciences, Peking University, Beijing, China.
- Center for Statistical Science, Peking University, Beijing, China.
| |
Collapse
|
36
|
Schmidt J, Chiffelle J, Perez MAS, Magnin M, Bobisse S, Arnaud M, Genolet R, Cesbron J, Barras D, Navarro Rodrigo B, Benedetti F, Michel A, Queiroz L, Baumgaertner P, Guillaume P, Hebeisen M, Michielin O, Nguyen-Ngoc T, Huber F, Irving M, Tissot-Renaud S, Stevenson BJ, Rusakiewicz S, Dangaj Laniti D, Bassani-Sternberg M, Rufer N, Gfeller D, Kandalaft LE, Speiser DE, Zoete V, Coukos G, Harari A. Neoantigen-specific CD8 T cells with high structural avidity preferentially reside in and eliminate tumors. Nat Commun 2023; 14:3188. [PMID: 37280206 DOI: 10.1038/s41467-023-38946-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 05/23/2023] [Indexed: 06/08/2023] Open
Abstract
The success of cancer immunotherapy depends in part on the strength of antigen recognition by T cells. Here, we characterize the T cell receptor (TCR) functional (antigen sensitivity) and structural (monomeric pMHC-TCR off-rates) avidities of 371 CD8 T cell clones specific for neoantigens, tumor-associated antigens (TAAs) or viral antigens isolated from tumors or blood of patients and healthy donors. T cells from tumors exhibit stronger functional and structural avidity than their blood counterparts. Relative to TAA, neoantigen-specific T cells are of higher structural avidity and, consistently, are preferentially detected in tumors. Effective tumor infiltration in mice models is associated with high structural avidity and CXCR3 expression. Based on TCR biophysicochemical properties, we derive and apply an in silico model predicting TCR structural avidity and validate the enrichment in high avidity T cells in patients' tumors. These observations indicate a direct relationship between neoantigen recognition, T cell functionality and tumor infiltration. These results delineate a rational approach to identify potent T cells for personalized cancer immunotherapy.
Collapse
Affiliation(s)
- Julien Schmidt
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Johanna Chiffelle
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Marta A S Perez
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Morgane Magnin
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Sara Bobisse
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Marion Arnaud
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Raphael Genolet
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Julien Cesbron
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - David Barras
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Blanca Navarro Rodrigo
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Fabrizio Benedetti
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Alexandra Michel
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Lise Queiroz
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Petra Baumgaertner
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Philippe Guillaume
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Michael Hebeisen
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
| | - Olivier Michielin
- Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Tu Nguyen-Ngoc
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
| | - Florian Huber
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Melita Irving
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
| | - Stéphanie Tissot-Renaud
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Brian J Stevenson
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Sylvie Rusakiewicz
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Denarda Dangaj Laniti
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Nathalie Rufer
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
| | - David Gfeller
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Lana E Kandalaft
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Daniel E Speiser
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
| | - Vincent Zoete
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - George Coukos
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Alexandre Harari
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland.
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland.
| |
Collapse
|
37
|
Kalemati M, Darvishi S, Koohi S. CapsNet-MHC predicts peptide-MHC class I binding based on capsule neural networks. Commun Biol 2023; 6:492. [PMID: 37147498 PMCID: PMC10162658 DOI: 10.1038/s42003-023-04867-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 04/24/2023] [Indexed: 05/07/2023] Open
Abstract
The Major Histocompatibility Complex (MHC) binds to the derived peptides from pathogens to present them to killer T cells on the cell surface. Developing computational methods for accurate, fast, and explainable peptide-MHC binding prediction can facilitate immunotherapies and vaccine development. Various deep learning-based methods rely on separate feature extraction from the peptide and MHC sequences and ignore their pairwise binding information. This paper develops a capsule neural network-based method to efficiently capture the peptide-MHC complex features to predict the peptide-MHC class I binding. Various evaluations confirmed our method outperformance over the alternative methods, while it can provide accurate prediction over less available data. Moreover, for providing precise insights into the results, we explored the essential features that contributed to the prediction. Since the simulation results demonstrated consistency with the experimental studies, we concluded that our method can be utilized for the accurate, rapid, and interpretable peptide-MHC binding prediction to assist biological therapies.
Collapse
Affiliation(s)
- Mahmood Kalemati
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Saeid Darvishi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Somayyeh Koohi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
| |
Collapse
|
38
|
Vensko SP, Olsen K, Bortone D, Smith CC, Chai S, Beckabir W, Fini M, Jadi O, Rubinsteyn A, Vincent BG. LENS: Landscape of Effective Neoantigens Software. Bioinformatics 2023; 39:btad322. [PMID: 37184881 PMCID: PMC10246587 DOI: 10.1093/bioinformatics/btad322] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 04/04/2023] [Accepted: 05/12/2023] [Indexed: 05/16/2023] Open
Abstract
MOTIVATION Elimination of cancer cells by T cells is a critical mechanism of anti-tumor immunity and cancer immunotherapy response. T cells recognize cancer cells by engagement of T cell receptors with peptide epitopes presented by major histocompatibility complex molecules on the cancer cell surface. Peptide epitopes can be derived from antigen proteins coded for by multiple genomic sources. Bioinformatics tools used to identify tumor-specific epitopes via analysis of DNA and RNA-sequencing data have largely focused on epitopes derived from somatic variants, though a smaller number have evaluated potential antigens from other genomic sources. RESULTS We report here an open-source workflow utilizing the Nextflow DSL2 workflow manager, Landscape of Effective Neoantigens Software (LENS), which predicts tumor-specific and tumor-associated antigens from single nucleotide variants, insertions and deletions, fusion events, splice variants, cancer-testis antigens, overexpressed self-antigens, viruses, and endogenous retroviruses. The primary advantage of LENS is that it expands the breadth of genomic sources of discoverable tumor antigens using genomics data. Other advantages include modularity, extensibility, ease of use, and harmonization of relative expression level and immunogenicity prediction across multiple genomic sources. We present an analysis of 115 acute myeloid leukemia samples to demonstrate the utility of LENS. We expect LENS will be a valuable platform and resource for T cell epitope discovery bioinformatics, especially in cancers with few somatic variants where tumor-specific epitopes from alternative genomic sources are an elevated priority. AVAILABILITY AND IMPLEMENTATION More information about LENS, including code, workflow documentation, and instructions, can be found at (https://gitlab.com/landscape-of-effective-neoantigens-software).
Collapse
Affiliation(s)
- Steven P Vensko
- Lineberger Comprehensive Cancer Center, University of North Carolina—Chapel Hill, Chapel Hill, NC, United States
| | - Kelly Olsen
- Department of Microbiology and Immunology, University of North Carolina—Chapel Hill, Chapel Hill, NC, United States
| | - Dante Bortone
- Lineberger Comprehensive Cancer Center, University of North Carolina—Chapel Hill, Chapel Hill, NC, United States
| | - Christof C Smith
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Shengjie Chai
- Uber Technologies, Inc., San Francisco, CA, United States
| | - Wolfgang Beckabir
- Department of Microbiology and Immunology, University of North Carolina—Chapel Hill, Chapel Hill, NC, United States
| | - Misha Fini
- Department of Microbiology and Immunology, University of North Carolina—Chapel Hill, Chapel Hill, NC, United States
| | - Othmane Jadi
- Lineberger Comprehensive Cancer Center, University of North Carolina—Chapel Hill, Chapel Hill, NC, United States
| | - Alex Rubinsteyn
- Department of Genetics, University of North Carolina—Chapel Hill, Chapel Hill, NC, United States
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina—Chapel Hill, Chapel Hill, NC, United States
- Computational Medicine Program, University of North Carolina—Chapel Hill, Chapel Hill, NC, United States
| | - Benjamin G Vincent
- Lineberger Comprehensive Cancer Center, University of North Carolina—Chapel Hill, Chapel Hill, NC, United States
- Department of Microbiology and Immunology, University of North Carolina—Chapel Hill, Chapel Hill, NC, United States
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina—Chapel Hill, Chapel Hill, NC, United States
- Computational Medicine Program, University of North Carolina—Chapel Hill, Chapel Hill, NC, United States
- Division of Hematology, Department of Medicine, University of North Carolina—Chapel Hill, Chapel Hill, NC, United States
| |
Collapse
|
39
|
Qi T, Vincent BG, Cao Y. A multispecies framework for modeling adaptive immunity and immunotherapy in cancer. PLoS Comput Biol 2023; 19:e1010976. [PMID: 37083574 PMCID: PMC10155959 DOI: 10.1371/journal.pcbi.1010976] [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: 07/06/2022] [Revised: 05/03/2023] [Accepted: 02/24/2023] [Indexed: 04/22/2023] Open
Abstract
Predator-prey theory is commonly used to describe tumor growth in the presence of selective pressure from the adaptive immune system. These interactions are mediated by the tumor immunopeptidome (what the tumor "shows" the body) and the T-cell receptor (TCR) repertoire (how well the body "sees" cancer cells). The tumor immunopeptidome comprises neoantigens which can be gained and lost throughout tumorigenesis and treatment. Heterogeneity in the immunopeptidome is predictive of poor response to immunotherapy in some tumor types, suggesting that the TCR repertoire is unable to support a fully polyclonal response against every neoantigen. Importantly, while tumor and T-cell populations are known to compete with each other for intratumoral resources, whether between-lineage competition among peripheral T cells influences the TCR repertoire is unknown and difficult to interrogate experimentally. Computational models may offer a way to investigate these phenomena and deepen our understanding of the tumor-immune axis. Here, we construct a predator-prey-like model and calibrate it to preclinical and clinical data to describe tumor growth and immunopeptidome diversification. Simultaneously, we model the expansion of antigen-specific T-cell lineages and their consumption of both lineage-specific antigenic resources and lineage-agnostic, shared resources. This predator-prey-like framework accurately described clinically observed immunopeptidomes; recapitulated response-associated effects of immunotherapy, including immunoediting; and allowed exploration of treatment of tumors with varying growth and mutation rates.
Collapse
Affiliation(s)
- Timothy Qi
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Benjamin G. Vincent
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Division of Hematology/Oncology, School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Yanguang Cao
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| |
Collapse
|
40
|
Feola S, Chiaro J, Cerullo V. Integrating immunopeptidome analysis for the design and development of cancer vaccines. Semin Immunol 2023; 67:101750. [PMID: 37003057 DOI: 10.1016/j.smim.2023.101750] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/17/2023] [Accepted: 03/17/2023] [Indexed: 04/03/2023]
Abstract
The repertoire of naturally presented peptides within the MHC (major histocompatibility complex) or HLA (human leukocyte antigens) system on the cellular surface of every mammalian cell is referred to as ligandome or immunopeptidome. This later gained momentum upon the discovery of CD8 + T cells able to recognize and kill cancer cells in an MHC-I antigen-restricted manner. Indeed, cancer immune surveillance relies on T cell recognition of MHC-I-restricted peptides, making the identification of those peptides the core for designing T cell-based cancer vaccines. Moreover, the breakthrough of antibodies targeting immune checkpoint molecules has led to a new and strong interest in discovering suitable targets for CD8 +T cells. Therapeutic cancer vaccines are designed for the artificial generation and/or stimulation of CD8 +T cells; thus, their combination with ICIs to unleash the breaks of the immune system comes as a natural consequence to enhance anti-tumor efficacy. In this context, the identification and knowledge of peptide candidates take advantage of the fast technology updates in immunopeptidome and mass spectrometric methodologies, paying the way to the rational design of vaccines for immunotherapeutic approaches. In this review, we discuss mainly the role of immunopeptidome analysis and its application for the generation of therapeutic cancer vaccines with main focus on HLA-I peptides. Here, we review cancer vaccine platforms based on two different preparation methods: pathogens (viruses and bacteria) and not (VLPs, nanoparticles, subunits vaccines) that exploit discoveries in the ligandome field to generate and/or enhance anti-tumor specific response. Finally, we discuss possible drawbacks and future challenges in the field that remain still to be addressed.
Collapse
Affiliation(s)
- Sara Feola
- Drug Research Program (DRP) ImmunoViroTherapy Lab (IVT), Faculty of Pharmacy Helsinki University, Viikinkaari 5E, Finland; Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Fabianinkatu 33, Finland; Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, Haartmaninkatu 8, Finland
| | - Jacopo Chiaro
- Drug Research Program (DRP) ImmunoViroTherapy Lab (IVT), Faculty of Pharmacy Helsinki University, Viikinkaari 5E, Finland; Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Fabianinkatu 33, Finland; Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, Haartmaninkatu 8, Finland
| | - Vincenzo Cerullo
- Drug Research Program (DRP) ImmunoViroTherapy Lab (IVT), Faculty of Pharmacy Helsinki University, Viikinkaari 5E, Finland; Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Fabianinkatu 33, Finland; Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, Haartmaninkatu 8, Finland; Department of Molecular Medicine and Medical Biotechnology, Naples University "Federico II", S. Pansini 5, Italy.
| |
Collapse
|
41
|
Li T, Li Y, Zhu X, He Y, Wu Y, Ying T, Xie Z. Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction. Semin Cancer Biol 2023; 91:50-69. [PMID: 36870459 DOI: 10.1016/j.semcancer.2023.02.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 02/13/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023]
Abstract
Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.
Collapse
Affiliation(s)
- Tong Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yupeng Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiaoyi Zhu
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China
| | - Yao He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yanling Wu
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China
| | - Tianlei Ying
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China.
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China; Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China.
| |
Collapse
|
42
|
Lim CP, Kok BH, Lim HT, Chuah C, Abdul Rahman B, Abdul Majeed AB, Wykes M, Leow CH, Leow CY. Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design. Pathog Glob Health 2023; 117:134-151. [PMID: 35550001 PMCID: PMC9970233 DOI: 10.1080/20477724.2022.2072456] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
The ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has globally devastated public health, the economies of many countries and quality of life universally. The recent emergence of immune-escaped variants and scenario of vaccinated individuals being infected has raised the global concerns about the effectiveness of the current available vaccines in transmission control and disease prevention. Given the high rate mutation of SARS-CoV-2, an efficacious vaccine targeting against multiple variants that contains virus-specific epitopes is desperately needed. An immunoinformatics approach is gaining traction in vaccine design and development due to the significant reduction in time and cost of immunogenicity studies and increasing reliability of the generated results. It can underpin the development of novel therapeutic methods and accelerate the design and production of peptide vaccines for infectious diseases. Structural proteins, particularly spike protein (S), along with other proteins have been studied intensively as promising coronavirus vaccine targets. Numbers of promising online immunological databases, tools and web servers have widely been employed for the design and development of next generation COVID-19 vaccines. This review highlights the role of immunoinformatics in identifying immunogenic peptides as potential vaccine targets, involving databases, and prediction and characterization of epitopes which can be harnessed for designing future coronavirus vaccines.
Collapse
Affiliation(s)
- Chin Peng Lim
- School of Pharmaceutical Sciences, Universiti Sains Malaysia, Gelugor, Malaysia.,Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Gelugor, Malaysia
| | - Boon Hui Kok
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Gelugor, Malaysia
| | - Hui Ting Lim
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Gelugor, Malaysia
| | - Candy Chuah
- Faculty of Health Sciences, Universiti Teknologi MARA, Penang, Malaysia
| | | | | | - Michelle Wykes
- Molecular Immunology Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Chiuan Herng Leow
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Gelugor, Malaysia
| | - Chiuan Yee Leow
- School of Pharmaceutical Sciences, Universiti Sains Malaysia, Gelugor, Malaysia
| |
Collapse
|
43
|
Pandya N, Kumar A. Immunoinformatics analysis for design of multi-epitope subunit vaccine by using heat shock proteins against Schistosoma mansoni. J Biomol Struct Dyn 2023; 41:1859-1878. [PMID: 35040367 DOI: 10.1080/07391102.2021.2025430] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
The development of T cell and B cell that able provide long-term immune response against the schistosomiasisis to the people belongs to the epidemic area. Heat Shock Proteins (HSPs) are up-regulated in schistosomes as their environment changes owing to the developmental cycle, assisting the parasite in living with the adverse circumstances related with its life cycle. Schistosomiasis is still a severe health problem in the people of many countries in worldwide. In this work, to develop a chimeric antigen, we used an advanced and powerful immunoinformatics technique that targeted Schistosoma mansoni (S. mansoni) Heat shock protein (HSPs). Antigenicity, immunogenicity, allergenicity, and physicochemical characteristics were all assessed in silico for the developed subunit vaccine. The 3D structure of the vaccine was constructed and the stability of the vaccine construct was increased by using disulphide engineering. The protein-protein docking and simulation were performed between the vaccine construct and Toll-like receptor-4. The antigenicity probability value obtained for the vaccine construct was 0.93, which indicates that vaccine is non-allergenic and safe for human consumption. Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Nirali Pandya
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India
| | - Amit Kumar
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India
| |
Collapse
|
44
|
Systems Biology Approaches for the Improvement of Oncolytic Virus-Based Immunotherapies. Cancers (Basel) 2023; 15:cancers15041297. [PMID: 36831638 PMCID: PMC9954314 DOI: 10.3390/cancers15041297] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/06/2023] [Accepted: 02/10/2023] [Indexed: 02/22/2023] Open
Abstract
Oncolytic virus (OV)-based immunotherapy is mainly dependent on establishing an efficient cell-mediated antitumor immunity. OV-mediated antitumor immunity elicits a renewed antitumor reactivity, stimulating a T-cell response against tumor-associated antigens (TAAs) and recruiting natural killer cells within the tumor microenvironment (TME). Despite the fact that OVs are unspecific cancer vaccine platforms, to further enhance antitumor immunity, it is crucial to identify the potentially immunogenic T-cell restricted TAAs, the main key orchestrators in evoking a specific and durable cytotoxic T-cell response. Today, innovative approaches derived from systems biology are exploited to improve target discovery in several types of cancer and to identify the MHC-I and II restricted peptide repertoire recognized by T-cells. Using specific computation pipelines, it is possible to select the best tumor peptide candidates that can be efficiently vectorized and delivered by numerous OV-based platforms, in order to reinforce anticancer immune responses. Beyond the identification of TAAs, system biology can also support the engineering of OVs with improved oncotropism to reduce toxicity and maintain a sufficient portion of the wild-type virus virulence. Finally, these technologies can also pave the way towards a more rational design of armed OVs where a transgene of interest can be delivered to TME to develop an intratumoral gene therapy to enhance specific immune stimuli.
Collapse
|
45
|
Vijver SV, Danklmaier S, Pipperger L, Gronauer R, Floriani G, Hackl H, Das K, Wollmann G. Prediction and validation of murine MHC class I epitopes of the recombinant virus VSV-GP. Front Immunol 2023; 13:1100730. [PMID: 36741416 PMCID: PMC9893851 DOI: 10.3389/fimmu.2022.1100730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 12/30/2022] [Indexed: 01/20/2023] Open
Abstract
Oncolytic viruses are currently tested as a novel platform for cancer therapy. These viruses preferentially replicate in and kill malignant cells. Due to their microbial origin, treatment with oncolytic viruses naturally results in anti-viral responses and general immune activation. Consequently, the oncolytic virus treatment also induces anti-viral T cells. Since these can constitute the dominant activated T cell pool, monitoring of the anti-viral T cell response may aid in better understanding of the immune responses post oncolytic virotherapy. This study aimed to identify the anti-viral T cells raised by VSV-GP virotherapy in C57BL/6J mice, one of the most widely used models for preclinical studies. VSV-GP is a novel oncolytic agent that recently entered a clinical phase I study. To identify the VSV-GP epitopes to which mouse anti-viral T cells react, we used a multilevel adapted bioinformatics viral epitope prediction approach based on the tools netMHCpan, MHCflurry and netMHCstabPan, which are commonly used in neoepitope identification. Predicted viral epitopes were ranked based on consensus binding strength categories, predicted stability, and dissimilarity to the mouse proteome. The top ranked epitopes were selected and included in the peptide candidate matrix in order to use a matrix deconvolution approach. Using ELISpot, we showed which viral epitopes presented on C57BL/6J mouse MHC-I alleles H2-Db and H2-Kb trigger IFN-γ secretion due to T cell activation. Furthermore, we validated these findings using an intracellular cytokine staining. Collectively, identification of the VSV-GP T cell epitopes enables monitoring of the full range of anti-viral T cell responses upon VSV-GP virotherapy in future studies with preclinical mouse models to more comprehensively delineate anti-viral from anti-tumor T cell responses. These findings also support the development of novel VSV-GP variants expressing immunomodulatory transgenes and can improve the assessment of anti-viral immunity in preclinical models.
Collapse
Affiliation(s)
- Saskia V. Vijver
- Institute of Virology, Medical University of Innsbruck, Innsbruck, Austria
- Christian Doppler Laboratory for Viral Immunotherapy of Cancer, Medical University of Innsbruck, Innsbruck, Austria
| | - Sarah Danklmaier
- Institute of Virology, Medical University of Innsbruck, Innsbruck, Austria
- Christian Doppler Laboratory for Viral Immunotherapy of Cancer, Medical University of Innsbruck, Innsbruck, Austria
| | - Lisa Pipperger
- Institute of Virology, Medical University of Innsbruck, Innsbruck, Austria
- Christian Doppler Laboratory for Viral Immunotherapy of Cancer, Medical University of Innsbruck, Innsbruck, Austria
| | - Raphael Gronauer
- Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria
| | - Gabriel Floriani
- Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria
| | - Hubert Hackl
- Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria
| | | | - Guido Wollmann
- Institute of Virology, Medical University of Innsbruck, Innsbruck, Austria
- Christian Doppler Laboratory for Viral Immunotherapy of Cancer, Medical University of Innsbruck, Innsbruck, Austria
| |
Collapse
|
46
|
Cireli E, Çavaş L. A Sample Guideline for Reverse Vaccinology Approach for the Development of Subunit Vaccine Using Varicella Zoster as a Model Disease. Methods Mol Biol 2023; 2673:453-474. [PMID: 37258932 DOI: 10.1007/978-1-0716-3239-0_30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
For the development of multi-peptide vaccine, identification of antigenic epitopes is crucial. If it is done using wet lab techniques, the identification process can be time-consuming, laborious, and cost-intensive. In silico tools, on the other hand, enable researchers to predict potential epitopes with little to no cost for further in vivo and in vitro testing. The rapid identification process using in silico tools helps in responding to health emergencies faster. Developing an efficient and high coverage vaccine is one of the ways to reduce morbidity and mortality rates of the diseases and protect the affected populations. In this chapter, we introduce the necessary tools and methodology for the identification and characterization of antigenic epitopes to design a multi-epitope vaccine using varicella-zoster virus as an example vector model.
Collapse
Affiliation(s)
- Elif Cireli
- Department of Life Sciences and Chemistry, Constructor University Bremen, Bremen, Germany
| | - Levent Çavaş
- Dokuz Eylül University, Faculty of Science, Department of Chemistry (Biochemistry Division), İzmir, Turkey.
| |
Collapse
|
47
|
Sei S, Ahadova A, Keskin DB, Bohaumilitzky L, Gebert J, von Knebel Doeberitz M, Lipkin SM, Kloor M. Lynch syndrome cancer vaccines: A roadmap for the development of precision immunoprevention strategies. Front Oncol 2023; 13:1147590. [PMID: 37035178 PMCID: PMC10073468 DOI: 10.3389/fonc.2023.1147590] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 03/09/2023] [Indexed: 04/11/2023] Open
Abstract
Hereditary cancer syndromes (HCS) account for 5~10% of all cancer diagnosis. Lynch syndrome (LS) is one of the most common HCS, caused by germline mutations in the DNA mismatch repair (MMR) genes. Even with prospective cancer surveillance, LS is associated with up to 50% lifetime risk of colorectal, endometrial, and other cancers. While significant progress has been made in the timely identification of germline pathogenic variant carriers and monitoring and early detection of precancerous lesions, cancer-risk reduction strategies are still centered around endoscopic or surgical removal of neoplastic lesions and susceptible organs. Safe and effective cancer prevention strategies are critically needed to improve the life quality and longevity of LS and other HCS carriers. The era of precision oncology driven by recent technological advances in tumor molecular profiling and a better understanding of genetic risk factors has transformed cancer prevention approaches for at-risk individuals, including LS carriers. MMR deficiency leads to the accumulation of insertion and deletion mutations in microsatellites (MS), which are particularly prone to DNA polymerase slippage during DNA replication. Mutations in coding MS give rise to frameshift peptides (FSP) that are recognized by the immune system as neoantigens. Due to clonal evolution, LS tumors share a set of recurrent and predictable FSP neoantigens in the same and in different LS patients. Cancer vaccines composed of commonly recurring FSP neoantigens selected through prediction algorithms have been clinically evaluated in LS carriers and proven safe and immunogenic. Preclinically analogous FSP vaccines have been shown to elicit FSP-directed immune responses and exert tumor-preventive efficacy in murine models of LS. While the immunopreventive efficacy of "off-the-shelf" vaccines consisting of commonly recurring FSP antigens is currently investigated in LS clinical trials, the feasibility and utility of personalized FSP vaccines with individual HLA-restricted epitopes are being explored for more precise targeting. Here, we discuss recent advances in precision cancer immunoprevention approaches, emerging enabling technologies, research gaps, and implementation barriers toward clinical translation of risk-tailored prevention strategies for LS carriers. We will also discuss the feasibility and practicality of next-generation cancer vaccines that are based on personalized immunogenic epitopes for precision cancer immunoprevention.
Collapse
Affiliation(s)
- Shizuko Sei
- Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Rockville, MD, United States
- *Correspondence: Shizuko Sei, ; Steven M. Lipkin, ; Matthias Kloor,
| | - Aysel Ahadova
- Department of Applied Tumor Biology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Applied Tumor Biology, German Cancer Research Center Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
| | - Derin B. Keskin
- Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA, United States
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States
- Broad Institute of The Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, United States
- Department of Computer Science, Metropolitan College, Boston University, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Lena Bohaumilitzky
- Department of Applied Tumor Biology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Applied Tumor Biology, German Cancer Research Center Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
| | - Johannes Gebert
- Department of Applied Tumor Biology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Applied Tumor Biology, German Cancer Research Center Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
| | - Magnus von Knebel Doeberitz
- Department of Applied Tumor Biology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Applied Tumor Biology, German Cancer Research Center Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
| | - Steven M. Lipkin
- Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medical College, New York, NY, United States
- *Correspondence: Shizuko Sei, ; Steven M. Lipkin, ; Matthias Kloor,
| | - Matthias Kloor
- Department of Applied Tumor Biology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Applied Tumor Biology, German Cancer Research Center Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
- *Correspondence: Shizuko Sei, ; Steven M. Lipkin, ; Matthias Kloor,
| |
Collapse
|
48
|
Farriol-Duran R, Vallejo-Vallés M, Amengual-Rigo P, Floor M, Guallar V. NetCleave: An Open-Source Algorithm for Predicting C-Terminal Antigen Processing for MHC-I and MHC-II. Methods Mol Biol 2023; 2673:211-226. [PMID: 37258917 DOI: 10.1007/978-1-0716-3239-0_15] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
T cell epitopes presented on the surface of mammalian cells are subjected to a complex network of antigen processing and presentation. Among them, C-terminal antigen processing constitutes one of the main bottlenecks for the generation of epitopes, as it defines the C-terminal end of the final epitope and delimits the peptidome that will be presented downstream. Previously (Amengual-Rigo and Guallar, Sci Rep 111(11):1-8, 2021), we demonstrated that NetCleave stands out as one of the best algorithms for the prediction of C-terminal processing, which in its turn can be crucial to design peptide-based vaccination strategies. In this chapter, we provide a pipeline to exploit the full capabilities of NetCleave, an open-source and retrainable algorithm for predicting the C-terminal antigen processing for the MHC-I and MHC-II pathways.
Collapse
Affiliation(s)
| | | | | | - Martin Floor
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Víctor Guallar
- Barcelona Supercomputing Center (BSC), Barcelona, Spain.
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
| |
Collapse
|
49
|
Castro A, Kaabinejadian S, Yari H, Hildebrand W, Zanetti M, Carter H. Subcellular location of source proteins improves prediction of neoantigens for immunotherapy. EMBO J 2022; 41:e111071. [PMID: 36314681 PMCID: PMC9753441 DOI: 10.15252/embj.2022111071] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 09/20/2022] [Accepted: 09/26/2022] [Indexed: 12/23/2022] Open
Abstract
Antigen presentation via the major histocompatibility complex (MHC) is essential for anti-tumor immunity. However, the rules that determine which tumor-derived peptides will be immunogenic are still incompletely understood. Here, we investigated whether constraints on peptide accessibility to the MHC due to protein subcellular location are associated with peptide immunogenicity potential. Analyzing over 380,000 peptides from studies of MHC presentation and peptide immunogenicity, we find clear spatial biases in both eluted and immunogenic peptides. We find that including parent protein location improves the prediction of peptide immunogenicity in multiple datasets. In human immunotherapy cohorts, the location was associated with a neoantigen vaccination response, and immune checkpoint blockade responders generally had a higher burden of neopeptides from accessible locations. We conclude that protein subcellular location adds important information for optimizing cancer immunotherapies.
Collapse
Affiliation(s)
- Andrea Castro
- Bioinformatics and Systems Biology ProgramUniversity of California San DiegoLa JollaCAUSA
| | - Saghar Kaabinejadian
- Department of Microbiology and ImmunologyUniversity of Oklahoma Health Sciences CenterOklahoma CityOKUSA
- Pure MHC LLCOklahoma CityOKUSA
| | - Hooman Yari
- Department of Microbiology and ImmunologyUniversity of Oklahoma Health Sciences CenterOklahoma CityOKUSA
| | - William Hildebrand
- Department of Microbiology and ImmunologyUniversity of Oklahoma Health Sciences CenterOklahoma CityOKUSA
| | - Maurizio Zanetti
- The Laboratory of Immunology and Department of MedicineUniversity of California San DiegoLa JollaCAUSA
- Moores Cancer CenterUniversity of California San DiegoLa JollaCAUSA
| | - Hannah Carter
- Moores Cancer CenterUniversity of California San DiegoLa JollaCAUSA
- Department of Medicine, Division of Medical GeneticsUniversity of California San DiegoLa JollaCAUSA
| |
Collapse
|
50
|
Huang X, Zhang G, Tang TY, Gao X, Liang TB. Personalized pancreatic cancer therapy: from the perspective of mRNA vaccine. Mil Med Res 2022; 9:53. [PMID: 36224645 PMCID: PMC9556149 DOI: 10.1186/s40779-022-00416-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 09/14/2022] [Indexed: 11/25/2022] Open
Abstract
Pancreatic cancer is characterized by inter-tumoral and intra-tumoral heterogeneity, especially in genetic alteration and microenvironment. Conventional therapeutic strategies for pancreatic cancer usually suffer resistance, highlighting the necessity for personalized precise treatment. Cancer vaccines have become promising alternatives for pancreatic cancer treatment because of their multifaceted advantages including multiple targeting, minimal nonspecific effects, broad therapeutic window, low toxicity, and induction of persistent immunological memory. Multiple conventional vaccines based on the cells, microorganisms, exosomes, proteins, peptides, or DNA against pancreatic cancer have been developed; however, their overall efficacy remains unsatisfactory. Compared with these vaccine modalities, messager RNA (mRNA)-based vaccines offer technical and conceptional advances in personalized precise treatment, and thus represent a potentially cutting-edge option in novel therapeutic approaches for pancreatic cancer. This review summarizes the current progress on pancreatic cancer vaccines, highlights the superiority of mRNA vaccines over other conventional vaccines, and proposes the viable tactic for designing and applying personalized mRNA vaccines for the precise treatment of pancreatic cancer.
Collapse
Affiliation(s)
- Xing Huang
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China. .,Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China. .,Zhejiang Clinical Research Center of Hepatobiliary and Pancreatic Diseases, Hangzhou, 310003, China. .,The Innovation Center for the Study of Pancreatic Diseases of Zhejiang Province, Hangzhou, 310009, China. .,Cancer Center, Zhejiang University, Hangzhou, 310058, China.
| | - Gang Zhang
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China.,Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China.,Zhejiang Clinical Research Center of Hepatobiliary and Pancreatic Diseases, Hangzhou, 310003, China.,The Innovation Center for the Study of Pancreatic Diseases of Zhejiang Province, Hangzhou, 310009, China.,Cancer Center, Zhejiang University, Hangzhou, 310058, China
| | - Tian-Yu Tang
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China.,Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China.,Zhejiang Clinical Research Center of Hepatobiliary and Pancreatic Diseases, Hangzhou, 310003, China.,The Innovation Center for the Study of Pancreatic Diseases of Zhejiang Province, Hangzhou, 310009, China.,Cancer Center, Zhejiang University, Hangzhou, 310058, China
| | - Xiang Gao
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China.,Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China.,Zhejiang Clinical Research Center of Hepatobiliary and Pancreatic Diseases, Hangzhou, 310003, China.,The Innovation Center for the Study of Pancreatic Diseases of Zhejiang Province, Hangzhou, 310009, China.,Cancer Center, Zhejiang University, Hangzhou, 310058, China
| | - Ting-Bo Liang
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China. .,Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China. .,Zhejiang Clinical Research Center of Hepatobiliary and Pancreatic Diseases, Hangzhou, 310003, China. .,The Innovation Center for the Study of Pancreatic Diseases of Zhejiang Province, Hangzhou, 310009, China. .,Cancer Center, Zhejiang University, Hangzhou, 310058, China.
| |
Collapse
|