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Aparicio B, Theunissen P, Hervas-Stubbs S, Fortes P, Sarobe P. Relevance of mutation-derived neoantigens and non-classical antigens for anticancer therapies. Hum Vaccin Immunother 2024; 20:2303799. [PMID: 38346926 PMCID: PMC10863374 DOI: 10.1080/21645515.2024.2303799] [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: 09/29/2023] [Accepted: 01/06/2024] [Indexed: 02/15/2024] Open
Abstract
Efficacy of cancer immunotherapies relies on correct recognition of tumor antigens by lymphocytes, eliciting thus functional responses capable of eliminating tumor cells. Therefore, important efforts have been carried out in antigen identification, with the aim of understanding mechanisms of response to immunotherapy and to design safer and more efficient strategies. In addition to classical tumor-associated antigens identified during the last decades, implementation of next-generation sequencing methodologies is enabling the identification of neoantigens (neoAgs) arising from mutations, leading to the development of new neoAg-directed therapies. Moreover, there are numerous non-classical tumor antigens originated from other sources and identified by new methodologies. Here, we review the relevance of neoAgs in different immunotherapies and the results obtained by applying neoAg-based strategies. In addition, the different types of non-classical tumor antigens and the best approaches for their identification are described. This will help to increase the spectrum of targetable molecules useful in cancer immunotherapies.
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Affiliation(s)
- Belen Aparicio
- Program of Immunology and Immunotherapy, Center for Applied Medical Research (CIMA) University of Navarra, Pamplona, Spain
- Cancer Center Clinica Universidad de Navarra (CCUN), Pamplona, Spain
- Navarra Institute for Health Research (IDISNA), Pamplona, Spain
- CIBERehd, Pamplona, Spain
| | - Patrick Theunissen
- Cancer Center Clinica Universidad de Navarra (CCUN), Pamplona, Spain
- Navarra Institute for Health Research (IDISNA), Pamplona, Spain
- CIBERehd, Pamplona, Spain
- DNA and RNA Medicine Division, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
| | - Sandra Hervas-Stubbs
- Program of Immunology and Immunotherapy, Center for Applied Medical Research (CIMA) University of Navarra, Pamplona, Spain
- Cancer Center Clinica Universidad de Navarra (CCUN), Pamplona, Spain
- Navarra Institute for Health Research (IDISNA), Pamplona, Spain
- CIBERehd, Pamplona, Spain
| | - Puri Fortes
- Cancer Center Clinica Universidad de Navarra (CCUN), Pamplona, Spain
- Navarra Institute for Health Research (IDISNA), Pamplona, Spain
- CIBERehd, Pamplona, Spain
- DNA and RNA Medicine Division, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
- Spanish Network for Advanced Therapies (TERAV ISCIII), Spain
| | - Pablo Sarobe
- Program of Immunology and Immunotherapy, Center for Applied Medical Research (CIMA) University of Navarra, Pamplona, Spain
- Cancer Center Clinica Universidad de Navarra (CCUN), Pamplona, Spain
- Navarra Institute for Health Research (IDISNA), Pamplona, Spain
- CIBERehd, Pamplona, Spain
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2
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Jahanafrooz Z, Oroojalian F, Mokhtarzadeh A, Rahdar A, Díez-Pascual AM. Nanovaccines: Immunogenic tumor antigens, targeted delivery, and combination therapy to enhance cancer immunotherapy. Drug Dev Res 2024; 85:e22244. [PMID: 39138855 DOI: 10.1002/ddr.22244] [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: 01/19/2024] [Revised: 04/16/2024] [Accepted: 07/29/2024] [Indexed: 08/15/2024]
Abstract
Nanovaccines have been designed to overcome the limitations associated with conventional vaccines. Effective delivery methods such as engineered carriers or smart nanoparticles (NPs) are critical requisites for inducing self-tolerance and optimizing vaccine immunogenicity with minimum side effects. NPs can be used as adjuvants, immunogens, or nanocarriers to develop nanovaccines for efficient antigen delivery. Multiloaded nanovaccines carrying multiple tumor antigens along with immunostimulants can effectively increase immunity against tumor cells. They can be biologically engineered to boost interactions with dendritic cells and to allow a gradual and constant antigen release. Modifying NPs surface properties, using high-density lipoprotein-mimicking nanodiscs, and developing nano-based artificial antigen-presenting cells such as dendritic cell-derived-exosomes are amongst the new developed technologies to enhance antigen-presentation and immune reactions against tumor cells. The present review provides an overview on the different perspectives, improvements, and barriers of successful clinical application of current cancer therapeutic and vaccination options. The immunomodulatory effects of different types of nanovaccines and the nanoparticles incorporated into their structure are described. The advantages of using nanovaccines to prevent and treat common illnesses such as AIDS, malaria, cancer and tuberculosis are discussed. Further, potential paths to develop optimal cancer vaccines are described. Given the immunosuppressive characteristics of both cancer cells and the tumor microenvironment, applying immunomodulators and immune checkpoint inhibitors in combination with other conventional anticancer therapies are necessary to boost the effectiveness of the immune response.
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Affiliation(s)
- Zohreh Jahanafrooz
- Department of Biology, Faculty of Sciences, University of Maragheh, Maragheh, Iran
| | - Fatemeh Oroojalian
- Natural Products & Medicinal Plants Research Center, North Khorasan University of Medical Sciences Bojnurd, Bojnurd, Iran
- Department of Medical Nanotechnology, School of Medicine, North Khorasan University of Medical Sciences, Bojnurd, Iran
| | - Ahad Mokhtarzadeh
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Abbas Rahdar
- Department of Physics, Faculty of Sciences, University of Zabol, Zabol, Iran
| | - Ana M Díez-Pascual
- Universidad de Alcalá, Facultad de Ciencias, Departamento de Química Analítica, Química Física e Ingenieria Química, Alcalá de Henares, Spain
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3
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Brosda S, Aoude LG, Bonazzi VF, Patel K, Lonie JM, Belle CJ, Newell F, Koufariotis LT, Addala V, Naeini MM, Pearson JV, Krause L, Waddell N, Barbour AP. Spatial intra-tumour heterogeneity and treatment-induced genomic evolution in oesophageal adenocarcinoma: implications for prognosis and therapy. Genome Med 2024; 16:90. [PMID: 39020404 PMCID: PMC11253399 DOI: 10.1186/s13073-024-01362-z] [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: 12/12/2023] [Accepted: 07/09/2024] [Indexed: 07/19/2024] Open
Abstract
BACKGROUND Oesophageal adenocarcinoma (OAC) is a highly heterogeneous cancer with poor survival. Standard curative treatment is chemotherapy with or without radiotherapy followed by oesophagectomy. Genomic heterogeneity is a feature of OAC and has been linked to treatment resistance. METHODS Whole-genome sequencing data from 59 treatment-naïve and 18 post-treatment samples from 29 OAC patients was analysed. Twenty-seven of these were enrolled in the DOCTOR trial, sponsored by the Australasian Gastro-Intestinal Trials Group. Two biopsies from each treatment-naïve tumour were assessed to define 'shared' (between both samples) and 'private' (present in one sample) mutations. RESULTS Mutational signatures SBS2/13 (APOBEC) and SBS3 (BRCA) were almost exclusively detected in private mutation populations of treatment-naïve tumours. Patients presenting these signatures had significantly worse disease specific survival. Furthermore, mutational signatures associated with platinum-based chemotherapy treatment as well as high platinum enrichment scores were only detected in post-treatment samples. Additionally, clones with high putative neoantigen binding scores were detected in some treatment-naïve samples suggesting immunoediting of clones. CONCLUSIONS This study demonstrates the high intra-tumour heterogeneity in OAC, as well as indicators for treatment-induced changes during tumour evolution. Intra-tumour heterogeneity remains a problem for successful treatment strategies in OAC.
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Affiliation(s)
- Sandra Brosda
- Frazer Institute, The University of Queensland, 37 Kent Street, Woolloongabba, QLD, 4102, Australia.
| | - Lauren G Aoude
- Frazer Institute, The University of Queensland, 37 Kent Street, Woolloongabba, QLD, 4102, Australia
| | - Vanessa F Bonazzi
- Frazer Institute, The University of Queensland, 37 Kent Street, Woolloongabba, QLD, 4102, Australia
| | - Kalpana Patel
- Frazer Institute, The University of Queensland, 37 Kent Street, Woolloongabba, QLD, 4102, Australia
| | - James M Lonie
- Frazer Institute, The University of Queensland, 37 Kent Street, Woolloongabba, QLD, 4102, Australia
| | - Clemence J Belle
- Frazer Institute, The University of Queensland, 37 Kent Street, Woolloongabba, QLD, 4102, Australia
| | - Felicity Newell
- QIMR Berghofer Medical Research Institute, Herston, QLD, 4006, Australia
| | | | - Venkateswar Addala
- QIMR Berghofer Medical Research Institute, Herston, QLD, 4006, Australia
| | - Marjan M Naeini
- QIMR Berghofer Medical Research Institute, Herston, QLD, 4006, Australia
- Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia
- Faculty of Medicine, St Vincent's Clinical School, University of New South Wales, Sydney, NSW, 2052, Australia
| | - John V Pearson
- QIMR Berghofer Medical Research Institute, Herston, QLD, 4006, Australia
| | - Lutz Krause
- Frazer Institute, The University of Queensland, 37 Kent Street, Woolloongabba, QLD, 4102, Australia
- Microba Life Sciences, Brisbane, QLD, 4000, Australia
| | - Nicola Waddell
- QIMR Berghofer Medical Research Institute, Herston, QLD, 4006, Australia
| | - Andrew P Barbour
- Frazer Institute, The University of Queensland, 37 Kent Street, Woolloongabba, QLD, 4102, Australia
- Princess Alexandra Hospital, Woolloongabba, QLD, 4102, Australia
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4
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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.
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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.
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5
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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.
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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
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6
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Ma J, Ayres CM, Brambley CA, Chandran SS, Rosales TJ, Corcelli SA, Kovrigin EL, Klebanoff CA, Baker BM. Dynamic allostery in the peptide/MHC complex enables TCR neoantigen selectivity. RESEARCH SQUARE 2024:rs.3.rs-4457195. [PMID: 38854019 PMCID: PMC11160895 DOI: 10.21203/rs.3.rs-4457195/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
The inherent cross-reactivity of the T cell receptor (TCR) is balanced by high specificity, which often manifests in confounding ways not easily interpretable from static structures. We show here that TCR discrimination between an HLA-A*03:01 (HLA-A3)-restricted public neoantigen derived from mutant PIK3CA and its wild-type (WT) counterpart emerges from motions within the HLA binding groove that vary with the identity of the peptide's first primary anchor. The motions form a dynamic gate that in the complex with the WT peptide impedes a large conformational change required for TCR binding. The more rigid neoantigen is insusceptible to this limiting dynamic, and with the gate open, is able to transit its central tryptophan residue underneath the peptide backbone to the contralateral side of the HLA-A3 peptide binding groove, facilitating TCR binding. Our findings reveal a novel mechanism driving TCR specificity for a cancer neoantigen that is rooted in the dynamic and allosteric nature of peptide/MHC-I complexes, with implications for resolving long-standing and often confounding questions about the determinants of T cell specificity.
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Affiliation(s)
- Jiaqi Ma
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, USA
- Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, USA
| | - Cory M Ayres
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, USA
- Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, USA
| | - Chad A Brambley
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, USA
- Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, USA
| | - Smita S Chandran
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center (MSKCC), New York, NY, USA
- Center for Cell Engineering, MSKCC, New York, NY, USA
| | - Tatiana J Rosales
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, USA
- Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, USA
| | - Steven A Corcelli
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, USA
| | - Evgenii L Kovrigin
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, USA
| | - Christopher A Klebanoff
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center (MSKCC), New York, NY, USA
- Center for Cell Engineering, MSKCC, New York, NY, USA
- Weill Cornell Medical College, New York, NY, USA
- Parker Institute for Cancer Immunotherapy, New York, NY, USA
| | - Brian M Baker
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, USA
- Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, USA
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7
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Emilius L, Bremm F, Binder AK, Schaft N, Dörrie J. Tumor Antigens beyond the Human Exome. Int J Mol Sci 2024; 25:4673. [PMID: 38731892 PMCID: PMC11083240 DOI: 10.3390/ijms25094673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 04/18/2024] [Accepted: 04/22/2024] [Indexed: 05/13/2024] Open
Abstract
With the advent of immunotherapeutics, a new era in the combat against cancer has begun. Particularly promising are neo-epitope-targeted therapies as the expression of neo-antigens is tumor-specific. In turn, this allows the selective targeting and killing of cancer cells whilst healthy cells remain largely unaffected. So far, many advances have been made in the development of treatment options which are tailored to the individual neo-epitope repertoire. The next big step is the achievement of efficacious "off-the-shelf" immunotherapies. For this, shared neo-epitopes propose an optimal target. Given the tremendous potential, a thorough understanding of the underlying mechanisms which lead to the formation of neo-antigens is of fundamental importance. Here, we review the various processes which result in the formation of neo-epitopes. Broadly, the origin of neo-epitopes can be categorized into three groups: canonical, noncanonical, and viral neo-epitopes. For the canonical neo-antigens that arise in direct consequence of somatic mutations, we summarize past and recent findings. Beyond that, our main focus is put on the discussion of noncanonical and viral neo-epitopes as we believe that targeting those provides an encouraging perspective to shape the future of cancer immunotherapeutics.
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Affiliation(s)
- Lisabeth Emilius
- Department of Dermatology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; (L.E.); (F.B.); (A.K.B.); (J.D.)
- Comprehensive Cancer Center Erlangen European Metropolitan Area of Nuremberg (CCC ER-EMN), 91054 Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
- Bavarian Cancer Research Center (BZKF), 91054 Erlangen, Germany
| | - Franziska Bremm
- Department of Dermatology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; (L.E.); (F.B.); (A.K.B.); (J.D.)
- Comprehensive Cancer Center Erlangen European Metropolitan Area of Nuremberg (CCC ER-EMN), 91054 Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
- Bavarian Cancer Research Center (BZKF), 91054 Erlangen, Germany
| | - Amanda Katharina Binder
- Department of Dermatology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; (L.E.); (F.B.); (A.K.B.); (J.D.)
- Comprehensive Cancer Center Erlangen European Metropolitan Area of Nuremberg (CCC ER-EMN), 91054 Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
- Bavarian Cancer Research Center (BZKF), 91054 Erlangen, Germany
| | - Niels Schaft
- Department of Dermatology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; (L.E.); (F.B.); (A.K.B.); (J.D.)
- Comprehensive Cancer Center Erlangen European Metropolitan Area of Nuremberg (CCC ER-EMN), 91054 Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
- Bavarian Cancer Research Center (BZKF), 91054 Erlangen, Germany
| | - Jan Dörrie
- Department of Dermatology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; (L.E.); (F.B.); (A.K.B.); (J.D.)
- Comprehensive Cancer Center Erlangen European Metropolitan Area of Nuremberg (CCC ER-EMN), 91054 Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
- Bavarian Cancer Research Center (BZKF), 91054 Erlangen, Germany
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8
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Ma W, Zhang J, Yao H. NeoMUST: an accurate and efficient multi-task learning model for neoantigen presentation. Life Sci Alliance 2024; 7:e202302255. [PMID: 38290755 PMCID: PMC10828515 DOI: 10.26508/lsa.202302255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/01/2024] Open
Abstract
Accurate identification of neoantigens is important for advancing cancer immunotherapies. This study introduces Neoantigen MUlti-taSk Tower (NeoMUST), a model employing multi-task learning to effectively capture task-specific information across related tasks. Our results show that NeoMUST rivals existing algorithms in predicting the presentation of neoantigens via MHC-I molecules, while demonstrating a significantly shorter training time for enhanced computational efficiency. The use of multi-task learning enables NeoMUST to leverage shared knowledge and task dependencies, leading to improved performance metrics and a significant reduction in the training time. NeoMUST, implemented in Python, is freely accessible at the GitHub repository. Our model will facilitate neoantigen prediction and empower the development of effective cancer immunotherapeutic approaches.
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Affiliation(s)
- Wang Ma
- Fresh Wind Biotechnologies Inc. (Tianjin), Tianjin, China
| | - Jiawei Zhang
- Fresh Wind Biotechnologies Inc. (Tianjin), Tianjin, China
| | - Hui Yao
- Fresh Wind Biotechnologies USA Inc., Houston, TX, USA
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9
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Chen M, Zhang X, Ming Z, Lingyu, Feng X, Han Z, An HX. Characterizing and forecasting neoantigens-resulting from MUC mutations in COAD. J Transl Med 2024; 22:315. [PMID: 38539235 PMCID: PMC10967086 DOI: 10.1186/s12967-024-05103-z] [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: 01/05/2024] [Accepted: 03/15/2024] [Indexed: 08/09/2024] Open
Abstract
BACKGROUND The treatment for colon adenocarcinoma (COAD) faces challenges in terms of immunotherapy effectiveness due to multiple factors. Because of the high tumor specificity and immunogenicity, neoantigen has been considered a pivotal target for cancer immunotherapy. Therefore, this study aims to identify and predict the potential tumor antigens of MUC somatic mutations (MUCmut) in COAD. METHODS Three databases of TCGA, TIMER2.0, and cBioPortal were used for a detailed evaluation of the association between MUCmut and multi-factors like tumor mutation burden (TMB), microsatellite instability (MSI), prognosis, and the tumor microenvironment within the context of total 2242 COAD patients. Next, TSNAdb and the differential agretopicity index (DAI) were utilized to predict high-confidence neopeptides for MUCmut based on 531 COAD patients' genomic information. DAI was calculated by subtraction of its predicted HLA binding affinity of the MUCmut peptide from the corresponding wild-type peptide. RESULTS The top six mutation frequencies (14 to 2.9%) were from MUC16, MUC17, MUC5B, MUC2, MUC4 and MUC6. COAD patients with MUC16 and MUC4 mutations had longer DFS and PFS. However, patients with MUC13 and MUC20 mutations had shorter OS. Patients with the mutation of MUC16, MUC5B, MUC2, MUC4, and MUC6 exhibited higher TMB and MSI. Moreover, these mutations from the MUC family were associated with the infiltration of diverse lymphocyte cells and the expression of immune checkpoint genes. Through TSNAdb 1.0/NetMHCpan v2.8, 452 single nucleotide variants (SNVs) of MUCmut peptides were identified. Moreover, through TSNAdb2.0/NetMHCpan v4.0, 57 SNVs, 1 Q-frame shift (TS), and 157 short insertions/deletions (INDELs) of MUCmut were identified. Finally, 10 high-confidence neopeptides of MUCmut were predicted by DAI. CONCLUSIONS Together, our findings establish the immunogenicity and therapeutic potential of mutant MUC family-derived neoantigens. Through combining the tools of TSNAdb and DAI, a group of novel MUCmut neoantigens were identified as potential targets for immunotherapy.
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Affiliation(s)
- Min Chen
- Clinical Central Research Core, Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, Shanxi, China.
| | - Xin Zhang
- The Center Laboratory, Shanghai Medical College, Zhongshan Hospital (Xiamen Affiliated) of Fudan University, Fudan University, Xiamen, China
| | - Zihe Ming
- Cancer Center and Department of Breast and Thyroid Surgery, School of Medicine, Xiang'an Hospital of Xiamen University, Xiamen University, Xiamen, China
| | - Lingyu
- Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Xiaorong Feng
- Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Chemistry and Chemical Engineering Guangdong Laboratory, Shantou University, Guangdong, China
| | - Zhenguo Han
- Department of Colorectal and Anal Surgery, Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Han-Xiang An
- Clinical Central Research Core, Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, Shanxi, China.
- The Cancer Center, Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, Shanxi, China.
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10
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Fasoulis R, Rigo MM, Antunes DA, Paliouras G, Kavraki LE. Transfer learning improves pMHC kinetic stability and immunogenicity predictions. IMMUNOINFORMATICS (AMSTERDAM, NETHERLANDS) 2024; 13:100030. [PMID: 38577265 PMCID: PMC10994007 DOI: 10.1016/j.immuno.2023.100030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
The cellular immune response comprises several processes, with the most notable ones being the binding of the peptide to the Major Histocompability Complex (MHC), the peptide-MHC (pMHC) presentation to the surface of the cell, and the recognition of the pMHC by the T-Cell Receptor. Identifying the most potent peptide targets for MHC binding, presentation and T-cell recognition is vital for developing peptide-based vaccines and T-cell-based immunotherapies. Data-driven tools that predict each of these steps have been developed, and the availability of mass spectrometry (MS) datasets has facilitated the development of accurate Machine Learning (ML) methods for class-I pMHC binding prediction. However, the accuracy of ML-based tools for pMHC kinetic stability prediction and peptide immunogenicity prediction is uncertain, as stability and immunogenicity datasets are not abundant. Here, we use transfer learning techniques to improve stability and immunogenicity predictions, by taking advantage of a large number of binding affinity and MS datasets. The resulting models, TLStab and TLImm, exhibit comparable or better performance than state-of-the-art approaches on different stability and immunogenicity test sets respectively. Our approach demonstrates the promise of learning from the task of peptide binding to improve predictions on downstream tasks. The source code of TLStab and TLImm is publicly available at https://github.com/KavrakiLab/TL-MHC.
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Affiliation(s)
- Romanos Fasoulis
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, United States
| | - Mauricio Menegatti Rigo
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, United States
| | - Dinler Amaral Antunes
- Department of Biology and Biochemistry, University of Houston, 4800 Calhoun Rd, Houston, 77004, TX, United States
| | - Georgios Paliouras
- Institute of Informatics and Telecommunications, NCSR Demokritos, Patr. Gregoriou E and 27 Neapoleos St, Athens, 15341, Greece
| | - Lydia E. Kavraki
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, United States
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11
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Wan YTR, Koşaloğlu‐Yalçın Z, Peters B, Nielsen M. A large-scale study of peptide features defining immunogenicity of cancer neo-epitopes. NAR Cancer 2024; 6:zcae002. [PMID: 38288446 PMCID: PMC10823584 DOI: 10.1093/narcan/zcae002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 01/03/2024] [Accepted: 01/12/2024] [Indexed: 01/31/2024] Open
Abstract
Accurate prediction of immunogenicity for neo-epitopes arising from a cancer associated mutation is a crucial step in many bioinformatics pipelines that predict outcome of checkpoint blockade treatments or that aim to design personalised cancer immunotherapies and vaccines. In this study, we performed a comprehensive analysis of peptide features relevant for prediction of immunogenicity using the Cancer Epitope Database and Analysis Resource (CEDAR), a curated database of cancer epitopes with experimentally validated immunogenicity annotations from peer-reviewed publications. The developed model, ICERFIRE (ICore-based Ensemble Random Forest for neo-epitope Immunogenicity pREdiction), extracts the predicted ICORE from the full neo-epitope as input, i.e. the nested peptide with the highest predicted major histocompatibility complex (MHC) binding potential combined with its predicted likelihood of antigen presentation (%Rank). Key additional features integrated into the model include assessment of the BLOSUM mutation score of the neo-epitope, and antigen expression levels of the wild-type counterpart which is often reflecting a neo-epitope's abundance. We demonstrate improved and robust performance of ICERFIRE over existing immunogenicity and epitope prediction models, both in cross-validation and on external validation datasets.
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Affiliation(s)
- Yat-tsai Richie Wan
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, DK 28002, Denmark
| | - Zeynep Koşaloğlu‐Yalçın
- Center for Infectious Disease and Vaccine Research, La Jolla Institute of Immunology, La Jolla, CA 92037, USA
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute of Immunology, La Jolla, CA 92037, USA
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, DK 28002, Denmark
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12
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Srivastava PK. Cancer neoepitopes viewed through negative selection and peripheral tolerance: a new path to cancer vaccines. J Clin Invest 2024; 134:e176740. [PMID: 38426497 PMCID: PMC10904052 DOI: 10.1172/jci176740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024] Open
Abstract
A proportion of somatic mutations in tumors create neoepitopes that can prime T cell responses that target the MHC I-neoepitope complexes on tumor cells, mediating tumor control or rejection. Despite the compelling centrality of neoepitopes to cancer immunity, we know remarkably little about what constitutes a neoepitope that can mediate tumor control in vivo and what distinguishes such a neoepitope from the vast majority of similar candidate neoepitopes that are inefficacious in vivo. Studies in mice as well as clinical trials have begun to reveal the unexpected paradoxes in this area. Because cancer neoepitopes straddle that ambiguous ground between self and non-self, some rules that are fundamental to immunology of frankly non-self antigens, such as viral or model antigens, do not appear to apply to neoepitopes. Because neoepitopes are so similar to self-epitopes, with only small changes that render them non-self, immune response to them is regulated at least partially the way immune response to self is regulated. Therefore, neoepitopes are viewed and understood here through the clarifying lens of negative thymic selection. Here, the emergent questions in the biology and clinical applications of neoepitopes are discussed critically and a mechanistic and testable framework that explains the complexity and translational potential of these wonderful antigens is proposed.
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13
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Adams AC, Macy AM, Borden ES, Herrmann LM, Brambley CA, Ma T, Li X, Hughes A, Roe DJ, Mangold AR, Buetow KH, Wilson MA, Baker BM, Hastings KT. Distinct sets of molecular characteristics define tumor-rejecting neoantigens. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.13.579546. [PMID: 38405868 PMCID: PMC10888839 DOI: 10.1101/2024.02.13.579546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Challenges in identifying tumor-rejecting neoantigens limit the efficacy of neoantigen vaccines to treat cancers, including cutaneous squamous cell carcinoma (cSCC). A minority of human cSCC tumors shared neoantigens, supporting the need for personalized vaccines. Using a UV-induced mouse cSCC model which recapitulated the mutational signature and driver mutations found in human disease, we found that CD8 T cells constrain cSCC. Two MHC class I neoantigens were identified that constrained cSCC growth. Compared to the wild-type peptides, one tumor-rejecting neoantigen exhibited improved MHC binding and the other had increased solvent accessibility of the mutated residue. Across known neoantigens that do not impact MHC binding, structural modeling of the peptide/MHC complexes indicated that increased solvent accessibility, which will facilitate TCR recognition of the neoantigen, distinguished tumor-rejecting from non-immunogenic neoantigens. This work reveals characteristics of tumor-rejecting neoantigens that may be of considerable importance in identifying optimal vaccine candidates in cSCC and other cancers.
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14
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Shahjahan, Dey JK, Dey SK. Translational bioinformatics approach to combat cardiovascular disease and cancers. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:221-261. [PMID: 38448136 DOI: 10.1016/bs.apcsb.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.
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Affiliation(s)
- Shahjahan
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India
| | - Joy Kumar Dey
- Central Council for Research in Homoeopathy, Ministry of Ayush, Govt. of India, New Delhi, Delhi, India
| | - Sanjay Kumar Dey
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India.
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15
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Sueangoen N, Grove H, Chuangchot N, Prasopsiri J, Rungrotmongkol T, Sanachai K, Darai N, Thongchot S, Suriyaphol P, Sa-Nguanraksa D, Thuwajit P, Yenchitsomanus PT, Thuwajit C. Stimulating T cell responses against patient-derived breast cancer cells with neoantigen peptide-loaded peripheral blood mononuclear cells. Cancer Immunol Immunother 2024; 73:43. [PMID: 38349410 PMCID: PMC10864427 DOI: 10.1007/s00262-024-03627-3] [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: 10/20/2023] [Accepted: 01/06/2024] [Indexed: 02/15/2024]
Abstract
Breast cancer stands as a formidable global health challenge for women. While neoantigens exhibit efficacy in activating T cells specific to cancer and instigating anti-tumor immune responses, the accuracy of neoantigen prediction remains suboptimal. In this study, we identified neoantigens from the patient-derived breast cancer cells, PC-B-142CA and PC-B-148CA cells, utilizing whole-genome and RNA sequencing. The pVAC-Seq pipeline was employed, with minor modification incorporating criteria (1) binding affinity of mutant (MT) peptide with HLA (IC50 MT) ≤ 500 nm in 3 of 5 algorithms and (2) IC50 wild type (WT)/MT > 1. Sequencing results unveiled 2513 and 3490 somatic mutations, and 646 and 652 non-synonymous mutations in PC-B-142CA and PC-B-148CA, respectively. We selected the top 3 neoantigens to perform molecular dynamic simulation and synthesized 9-12 amino acid neoantigen peptides, which were then pulsed onto healthy donor peripheral blood mononuclear cells (PBMCs). Results demonstrated that T cells activated by ADGRL1E274K, PARP1E619K, and SEC14L2R43Q peptides identified from PC-B-142CA exhibited significantly increased production of interferon-gamma (IFN-γ), while PARP1E619K and SEC14L2R43Q peptides induced the expression of CD107a on T cells. The % tumor cell lysis was notably enhanced by T cells activated with MT peptides across all three healthy donors. Moreover, ALKBH6V83M and GAAI823T peptides from PC-B-148CA remarkably stimulated IFN-γ- and CD107a-positive T cells, displaying high cell-killing activity against target cancer cells. In summary, our findings underscore the successful identification of neoantigens with anti-tumor T cell functions and highlight the potential of personalized neoantigens as a promising avenue for breast cancer treatment.
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Grants
- R016341038 The Research and Innovation Grant, the National Research Council of Thailand, Ministry of Higher Education, Science, Research and Innovation
- R016341038 The Research and Innovation Grant, the National Research Council of Thailand, Ministry of Higher Education, Science, Research and Innovation
- R016334002 Siriraj Research Grant, Faculty of Medicine Siriraj Hospital, Mahidol University
- R016334002 Siriraj Research Grant, Faculty of Medicine Siriraj Hospital, Mahidol University
- Mahidol University
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Affiliation(s)
- Natthaporn Sueangoen
- Graduate Program in Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Harald Grove
- Division of Bioinformatics and Data Management for Research, Research Group and Research Network Division, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Nisa Chuangchot
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
- Siriraj Center of Research Excellence for Cancer Immunotherapy (SiCORE-CIT), Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Jaturawitt Prasopsiri
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Thanyada Rungrotmongkol
- Center of Excellence in Biocatalyst and Sustainable Biotechnology, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand
| | - Kamonpan Sanachai
- Department of Biochemistry, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand
| | - Nitchakan Darai
- ASEAN Institute for Health Development, Mahidol University, Nakon Pathom, Thailand
| | - Suyanee Thongchot
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
- Siriraj Center of Research Excellence for Cancer Immunotherapy (SiCORE-CIT), Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Prapat Suriyaphol
- Division of Bioinformatics and Data Management for Research, Research Group and Research Network Division, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Doonyapat Sa-Nguanraksa
- Division of Head Neck and Breast Surgery, Department of Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Peti Thuwajit
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Pa-Thai Yenchitsomanus
- Siriraj Center of Research Excellence for Cancer Immunotherapy (SiCORE-CIT), Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Chanitra Thuwajit
- Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
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16
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Banerjee A, Pattinson DJ, Wincek CL, Bunk P, Chapin SR, Navlakha S, Meyer HV. BATMAN: Improved T cell receptor cross-reactivity prediction benchmarked on a comprehensive mutational scan database. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.22.576714. [PMID: 38370810 PMCID: PMC10871174 DOI: 10.1101/2024.01.22.576714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Predicting T cell receptor (TCR) activation is challenging due to the lack of both unbiased benchmarking datasets and computational methods that are sensitive to small mutations to a peptide. To address these challenges, we curated a comprehensive database encompassing complete single amino acid mutational assays of 10,750 TCR-peptide pairs, centered around 14 immunogenic peptides against 66 TCRs. We then present an interpretable Bayesian model, called BATMAN, that can predict the set of peptides that activates a TCR. When validated on our database, BATMAN outperforms existing methods by 20% and reveals important biochemical predictors of TCR-peptide interactions.
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Affiliation(s)
- Amitava Banerjee
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - David J Pattinson
- Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI 53711, USA
| | - Cornelia L. Wincek
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
- Heidelberg University, 69117 Heidelberg, Germany
| | - Paul Bunk
- School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Sarah R. Chapin
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Saket Navlakha
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Hannah V. Meyer
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
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17
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Chuwdhury GS, Guo Y, Chiang CL, Lam KO, Kam NW, Liu Z, Dai W. ImmuneMirror: A machine learning-based integrative pipeline and web server for neoantigen prediction. Brief Bioinform 2024; 25:bbae024. [PMID: 38343325 PMCID: PMC10859690 DOI: 10.1093/bib/bbae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 12/05/2023] [Accepted: 01/16/2024] [Indexed: 02/15/2024] Open
Abstract
Neoantigens are derived from somatic mutations in the tumors but are absent in normal tissues. Emerging evidence suggests that neoantigens can stimulate tumor-specific T-cell-mediated antitumor immune responses, and therefore are potential immunotherapeutic targets. We developed ImmuneMirror as a stand-alone open-source pipeline and a web server incorporating a balanced random forest model for neoantigen prediction and prioritization. The prediction model was trained and tested using known immunogenic neopeptides collected from 19 published studies. The area under the curve of our trained model was 0.87 based on the testing data. We applied ImmuneMirror to the whole-exome sequencing and RNA sequencing data obtained from gastrointestinal tract cancers including 805 tumors from colorectal cancer (CRC), esophageal squamous cell carcinoma (ESCC) and hepatocellular carcinoma patients. We discovered a subgroup of microsatellite instability-high (MSI-H) CRC patients with a low neoantigen load but a high tumor mutation burden (> 10 mutations per Mbp). Although the efficacy of PD-1 blockade has been demonstrated in advanced MSI-H patients, almost half of such patients do not respond well. Our study identified a subset of MSI-H patients who may not benefit from this treatment with lower neoantigen load for major histocompatibility complex I (P < 0.0001) and II (P = 0.0008) molecules, respectively. Additionally, the neopeptide YMCNSSCMGV-TP53G245V, derived from a hotspot mutation restricted by HLA-A02, was identified as a potential actionable target in ESCC. This is so far the largest study to comprehensively evaluate neoantigen prediction models using experimentally validated neopeptides. Our results demonstrate the reliability and effectiveness of ImmuneMirror for neoantigen prediction.
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Affiliation(s)
- Gulam Sarwar Chuwdhury
- Department of Clinical Oncology, Center of Cancer Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong (SAR), P. R. China
| | - Yunshan Guo
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
| | - Chi-Leung Chiang
- Department of Clinical Oncology, Center of Cancer Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong (SAR), P. R. China
| | - Ka-On Lam
- Department of Clinical Oncology, Center of Cancer Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong (SAR), P. R. China
| | - Ngar-Woon Kam
- Department of Clinical Oncology, Center of Cancer Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong (SAR), P. R. China
- Laboratory for Synthetic Chemistry and Chemical Biology Limited, Hong Kong Science Park, Shatin, Hong Kong
| | - Zhonghua Liu
- Department of Biostatistics, Columbia University, New York, NY, USA
| | - Wei Dai
- Department of Clinical Oncology, Center of Cancer Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong (SAR), P. R. China
- University of Hong Kong-Shenzhen Hospital, Shenzhen, P. R. China
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18
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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.
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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
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19
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Zdrenka M, Kowalewski A, Ahmadi N, Sadiqi RU, Chmura Ł, Borowczak J, Maniewski M, Szylberg Ł. Refining PD-1/PD-L1 assessment for biomarker-guided immunotherapy: A review. BIOMOLECULES & BIOMEDICINE 2024; 24:14-29. [PMID: 37877810 PMCID: PMC10787614 DOI: 10.17305/bb.2023.9265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/23/2023] [Accepted: 06/23/2023] [Indexed: 10/26/2023]
Abstract
Anti-programmed cell death ligand 1 (anti-PD-L1) immunotherapy is an increasingly crucial in cancer treatment. To date, the Federal Drug Administration (FDA) has approved four PD-L1 immunohistochemistry (IHC) staining protocols, commercially available in the form of "kits", facilitating testing for PD-L1 expression. These kits comprise four PD-L1 antibodies on two separate IHC platforms, each utilizing distinct, non-interchangeable scoring systems. Several factors, including tumor heterogeneity and the size of the tissue specimens assessed, can lead to PD-L1 status misclassification, potentially hindering the initiation of therapy. Therefore, the development of more accurate predictive biomarkers to distinguish between responders and non-responders prior to anti-PD-1/PD-L1 therapy warrants further research. Achieving this goal necessitates refining sampling criteria, enhancing current methods of PD-L1 detection, and deepening our understanding of the impact of additional biomarkers. In this article, we review potential solutions to improve the predictive accuracy of PD-L1 assessment in order to more precisely anticipate patients' responses to anti-PD-1/PD-L1 therapy, monitor disease progression and predict clinical outcomes.
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Affiliation(s)
- Marek Zdrenka
- Department of Tumor Pathology and Pathomorphology, Oncology Centre-Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Adam Kowalewski
- Department of Tumor Pathology and Pathomorphology, Oncology Centre-Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Navid Ahmadi
- Department of Cardiothoracic Surgery, Royal Papworth Hospital, Cambridge, UK
| | | | - Łukasz Chmura
- Department of Pathomorphology, Jagiellonian University Medical College, Kraków, Poland
| | - Jędrzej Borowczak
- Department of Obstetrics, Gynaecology and Oncology, Chair of Pathomorphology and Clinical Placentology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland
| | - Mateusz Maniewski
- Department of Obstetrics, Gynaecology and Oncology, Chair of Pathomorphology and Clinical Placentology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland
| | - Łukasz Szylberg
- Department of Tumor Pathology and Pathomorphology, Oncology Centre-Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
- Department of Obstetrics, Gynaecology and Oncology, Chair of Pathomorphology and Clinical Placentology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland
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20
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Custodio JM, Ayres CM, Rosales TJ, Brambley CA, Arbuiso AG, Landau LM, Keller GLJ, Srivastava PK, Baker BM. Structural and physical features that distinguish tumor-controlling from inactive cancer neoepitopes. Proc Natl Acad Sci U S A 2023; 120:e2312057120. [PMID: 38085776 PMCID: PMC10742377 DOI: 10.1073/pnas.2312057120] [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: 07/17/2023] [Accepted: 10/23/2023] [Indexed: 12/18/2023] Open
Abstract
Neoepitopes arising from amino acid substitutions due to single nucleotide polymorphisms are targets of T cell immune responses to cancer and are of significant interest in the development of cancer vaccines. However, understanding the characteristics of rare protective neoepitopes that truly control tumor growth has been a challenge, due to their scarcity as well as the challenge of verifying true, neoepitope-dependent tumor control in humans. Taking advantage of recent work in mouse models that circumvented these challenges, here, we compared the structural and physical properties of neoepitopes that range from fully protective to immunologically inactive. As neoepitopes are derived from self-peptides that can induce immune tolerance, we studied not only how the various neoepitopes differ from each other but also from their wild-type counterparts. We identified multiple features associated with protection, including features that describe how neoepitopes differ from self as well as features associated with recognition by diverse T cell receptor repertoires. We demonstrate both the promise and limitations of neoepitope structural analysis and predictive modeling and illustrate important aspects that can be incorporated into neoepitope prediction pipelines.
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Affiliation(s)
- Jean M. Custodio
- Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN46556
| | - Cory M. Ayres
- Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN46556
| | - Tatiana J. Rosales
- Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN46556
| | - Chad A. Brambley
- Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN46556
| | - Alyssa G. Arbuiso
- Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN46556
| | - Lauren M. Landau
- Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN46556
| | - Grant L. J. Keller
- Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN46556
| | - Pramod K. Srivastava
- Department of Immunology, and Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, CT06030
| | - Brian M. Baker
- Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN46556
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21
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Gillig MA, Brennick CA, George MM, Balsbaugh JL, Shcheglova TV, Mandoiu II, Rosales T, Baker BM, Srivastava PK, Karandikar SH. CD8+ T Cell-Dependent Antitumor Activity In Vivo of a Mass Spectrometry-Identified Neoepitope despite Undetectable CD8+ Immunogenicity In Vitro. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2023; 211:1783-1791. [PMID: 37966257 PMCID: PMC10694033 DOI: 10.4049/jimmunol.2300356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 10/15/2023] [Indexed: 11/16/2023]
Abstract
Identification of neoepitopes that can control tumor growth in vivo remains a challenge even 10 y after the first genomics-defined cancer neoepitopes were identified. In this study, we identify a neoepitope, resulting from a mutation in the junction plakoglobin (Jup) gene (chromosome 11), from the mouse colon cancer line MC38-FABF (C57BL/6). This neoepitope, Jup mutant (JupMUT), was detected during mass spectrometry of MHC class I-eluted peptides from the tumor. JupMUT has a predicted binding affinity of 564 nM for the Kb molecule and a higher predicted affinity of 82 nM for Db. However, whereas structural modeling of JupMUT and its unmutated counterpart Jup wild-type indicates that there are little conformational differences between the two epitopes bound to Db, large structural divergences are predicted between the two epitopes bound to Kb. Together with in vitro binding data with RMA-S cells, these data suggest that Kb rather than Db is the relevant MHC class I molecule of JupMUT. Immunization of naive C57BL/6 mice with JupMUT elicits CD8-dependent tumor control of a MC38-FABF challenge. Despite the CD8 dependence of JupMUT-mediated tumor control in vivo, CD8+ T cells from JupMUT-immunized mice do not produce higher levels of IFN-γ than do naive mice. The structural and immunological characteristics of JupMUT are substantially different from those of many other neoepitopes that have been shown to mediate tumor control.
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Affiliation(s)
- Marc A. Gillig
- Department of Immunology, Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, CT
| | - Cory A. Brennick
- Department of Immunology, Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, CT
| | - Mariam M. George
- Department of Immunology, Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, CT
| | - Jeremy L. Balsbaugh
- Proteomics and Metabolomics Facility, Center for Open Research Resources and Equipment, University of Connecticut, Storrs, CT
| | - Tatiana V. Shcheglova
- Department of Immunology, Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, CT
| | - Ion I. Mandoiu
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT
| | - Tatiana Rosales
- Department of Chemistry and Biochemistry, Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN
| | - Brian M. Baker
- Department of Chemistry and Biochemistry, Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN
| | - Pramod K. Srivastava
- Department of Immunology, Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, CT
| | - Sukrut H. Karandikar
- Department of Immunology, Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, CT
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22
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Mariuzza RA, Wu D, Pierce BG. Structural basis for T cell recognition of cancer neoantigens and implications for predicting neoepitope immunogenicity. Front Immunol 2023; 14:1303304. [PMID: 38045695 PMCID: PMC10693334 DOI: 10.3389/fimmu.2023.1303304] [Citation(s) in RCA: 1] [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: 09/27/2023] [Accepted: 11/03/2023] [Indexed: 12/05/2023] Open
Abstract
Adoptive cell therapy (ACT) with tumor-specific T cells has been shown to mediate durable cancer regression. Tumor-specific T cells are also the basis of other therapies, notably cancer vaccines. The main target of tumor-specific T cells are neoantigens resulting from mutations in self-antigens over the course of malignant transformation. The detection of neoantigens presents a major challenge to T cells because of their high structural similarity to self-antigens, and the need to avoid autoimmunity. How different a neoantigen must be from its wild-type parent for it to induce a T cell response is poorly understood. Here we review recent structural and biophysical studies of T cell receptor (TCR) recognition of shared cancer neoantigens derived from oncogenes, including p53R175H, KRASG12D, KRASG12V, HHATp8F, and PIK3CAH1047L. These studies have revealed that, in some cases, the oncogenic mutation improves antigen presentation by strengthening peptide-MHC binding. In other cases, the mutation is detected by direct interactions with TCR, or by energetically driven or other indirect strategies not requiring direct TCR contacts with the mutation. We also review antibodies designed to recognize peptide-MHC on cell surfaces (TCR-mimic antibodies) as an alternative to TCRs for targeting cancer neoantigens. Finally, we review recent computational advances in this area, including efforts to predict neoepitope immunogenicity and how these efforts may be advanced by structural information on peptide-MHC binding and peptide-MHC recognition by TCRs.
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Affiliation(s)
- Roy A. Mariuzza
- W.M. Keck Laboratory for Structural Biology, University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, United States
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, United States
| | - Daichao Wu
- Laboratory of Structural Immunology, Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Brian G. Pierce
- W.M. Keck Laboratory for Structural Biology, University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, United States
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, United States
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23
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Lang F, Sorn P, Schrörs B, Weber D, Kramer S, Sahin U, Löwer M. Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates. iScience 2023; 26:108014. [PMID: 37965155 PMCID: PMC10641489 DOI: 10.1016/j.isci.2023.108014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 10/28/2022] [Accepted: 09/18/2023] [Indexed: 11/16/2023] Open
Abstract
Previous studies showed that the neoantigen candidate load is an imperfect predictor of immune checkpoint blockade (ICB) efficacy. Further studies provided evidence that the response to ICB is also affected by the qualitative properties of a few or even single candidates, limiting the predictive power based on candidate quantity alone. Here, we predict ICB efficacy based on neoantigen candidates and their neoantigen features in the context of the mutation type, using Multiple-Instance Learning via Embedded Instance Selection (MILES). Multiple instance learning is a type of supervised machine learning that classifies labeled bags that are formed by a set of unlabeled instances. MILES performed better compared with neoantigen candidate load alone for low-abundant fusion genes in renal cell carcinoma. Our findings suggest that MILES is an appropriate method to predict the efficacy of ICB therapy based on neoantigen candidates without requiring direct T cell response information.
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Affiliation(s)
- Franziska Lang
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany
| | - Patrick Sorn
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany
| | - Barbara Schrörs
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany
| | - David Weber
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany
| | - Stefan Kramer
- Institute of Computer Science, Johannes Gutenberg University, 55128 Mainz, Germany
| | - Ugur Sahin
- BioNTech SE, 55131 Mainz, Germany
- University Medical Center of the Johannes Gutenberg University, 55131 Mainz, Germany
| | - Martin Löwer
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany
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24
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Müller M, Huber F, Arnaud M, Kraemer AI, Altimiras ER, Michaux J, Taillandier-Coindard M, Chiffelle J, Murgues B, Gehret T, Auger A, Stevenson BJ, Coukos G, Harari A, Bassani-Sternberg M. Machine learning methods and harmonized datasets improve immunogenic neoantigen prediction. Immunity 2023; 56:2650-2663.e6. [PMID: 37816353 DOI: 10.1016/j.immuni.2023.09.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 06/26/2023] [Accepted: 09/05/2023] [Indexed: 10/12/2023]
Abstract
The accurate selection of neoantigens that bind to class I human leukocyte antigen (HLA) and are recognized by autologous T cells is a crucial step in many cancer immunotherapy pipelines. We reprocessed whole-exome sequencing and RNA sequencing (RNA-seq) data from 120 cancer patients from two external large-scale neoantigen immunogenicity screening assays combined with an in-house dataset of 11 patients and identified 46,017 somatic single-nucleotide variant mutations and 1,781,445 neo-peptides, of which 212 mutations and 178 neo-peptides were immunogenic. Beyond features commonly used for neoantigen prioritization, factors such as the location of neo-peptides within protein HLA presentation hotspots, binding promiscuity, and the role of the mutated gene in oncogenicity were predictive for immunogenicity. The classifiers accurately predicted neoantigen immunogenicity across datasets and improved their ranking by up to 30%. Besides insights into machine learning methods for neoantigen ranking, we have provided homogenized datasets valuable for developing and benchmarking companion algorithms for neoantigen-based immunotherapies.
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Affiliation(s)
- Markus Müller
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, 1015 Lausanne, Switzerland.
| | - Florian Huber
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Marion Arnaud
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Anne I Kraemer
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Emma Ricart Altimiras
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Justine Michaux
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Marie Taillandier-Coindard
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Johanna Chiffelle
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Baptiste Murgues
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Talita Gehret
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Aymeric Auger
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Brian J Stevenson
- Agora Cancer Research Centre, 1011 Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, 1015 Lausanne, Switzerland
| | - George Coukos
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland; Center of Experimental Therapeutics, Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
| | - Alexandre Harari
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland; Center of Experimental Therapeutics, Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland; Center of Experimental Therapeutics, Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland.
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25
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Ghorani E, Swanton C, Quezada SA. Cancer cell-intrinsic mechanisms driving acquired immune tolerance. Immunity 2023; 56:2270-2295. [PMID: 37820584 DOI: 10.1016/j.immuni.2023.09.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 09/11/2023] [Accepted: 09/11/2023] [Indexed: 10/13/2023]
Abstract
Immune evasion is a hallmark of cancer, enabling tumors to survive contact with the host immune system and evade the cycle of immune recognition and destruction. Here, we review the current understanding of the cancer cell-intrinsic factors driving immune evasion. We focus on T cells as key effectors of anti-cancer immunity and argue that cancer cells evade immune destruction by gaining control over pathways that usually serve to maintain physiological tolerance to self. Using this framework, we place recent mechanistic advances in the understanding of cancer immune evasion into broad categories of control over T cell localization, antigen recognition, and acquisition of optimal effector function. We discuss the redundancy in the pathways involved and identify knowledge gaps that must be overcome to better target immune evasion, including the need for better, routinely available tools that incorporate the growing understanding of evasion mechanisms to stratify patients for therapy and trials.
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Affiliation(s)
- Ehsan Ghorani
- Cancer Immunology and Immunotherapy Unit, Department of Surgery and Cancer, Imperial College London, London, UK; Department of Medical Oncology, Imperial College London Hospitals, London, UK.
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK; Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK; Department of Oncology, University College London Hospitals, London, UK
| | - Sergio A Quezada
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK; Cancer Immunology Unit, Research Department of Hematology, University College London Cancer Institute, London, UK.
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26
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Forsdyke DR. Aggregation-prone peptides from within a non-self-protein homoaggregate are preferred for MHC association: Historical overview. Scand J Immunol 2023; 98:e13306. [PMID: 38441340 DOI: 10.1111/sji.13306] [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: 03/18/2023] [Revised: 06/02/2023] [Accepted: 06/12/2023] [Indexed: 03/07/2024]
Abstract
New technologies assist re-evaluation of hypotheses on generation of immune cell repertoires and distinctions of self from non-self. Findings include positive correlations between peptide propensities to aggregate and their binding to major histocompatibility complex (MHC) proteins. This recalls the hypothesis that foreign proteins may homoaggregate in host cytosols prior to releasing their peptides (p) to form pMHC complexes. Clues to this included aggregation-related phenomena associated with infections (rouleaux formation, pyrexia, certain brain diseases). By virtue of 'promiscuous' gene expression by thymic presenting cells - perhaps adapted from earlier evolving gonadal mechanisms - developing T cells monitor surface pMHC clusterings. This evaluates intracellular concentrations of the corresponding proteins, and hence, following Burnet's two signal principle, degrees of self-reactivity. After positive selection in the thymic cortex for reactivity with 'near-self', high-level pMHC clustering suffices in the medulla for negatively selection. Following Burnet's principle, in the periphery low-level clustering suffices for T cell stimulation and high-level clustering again provokes negative selection (immunological tolerance). For evolving intracellular pathogens, fine-tuned polymorphisms of their host species have limited to 'near-self' some mimicking adaptations. It is proposed that while entire pathogen proteins may have evolved to minimize their aggregability, the greater aggregability of their peptides remains partially hidden within. Two-step proofreading mechanisms in prospective hosts select proteins containing aggregable peptide for the generation of pMHC clusters at the surface of presenting cells. Through mutations, some proteins of pathogens and cancer cells tend to converge towards the host 'near-self' that its T cells have auditioned to address.
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Affiliation(s)
- Donald R Forsdyke
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Ontario, Canada
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27
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Giannakopoulou E, Lehander M, Virding Culleton S, Yang W, Li Y, Karpanen T, Yoshizato T, Rustad EH, Nielsen MM, Bollineni RC, Tran TT, Delic-Sarac M, Gjerdingen TJ, Douvlataniotis K, Laos M, Ali M, Hillen A, Mazzi S, Chin DWL, Mehta A, Holm JS, Bentzen AK, Bill M, Griffioen M, Gedde-Dahl T, Lehmann S, Jacobsen SEW, Woll PS, Olweus J. A T cell receptor targeting a recurrent driver mutation in FLT3 mediates elimination of primary human acute myeloid leukemia in vivo. NATURE CANCER 2023; 4:1474-1490. [PMID: 37783807 PMCID: PMC10597840 DOI: 10.1038/s43018-023-00642-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 08/28/2023] [Indexed: 10/04/2023]
Abstract
Acute myeloid leukemia (AML), the most frequent leukemia in adults, is driven by recurrent somatically acquired genetic lesions in a restricted number of genes. Treatment with tyrosine kinase inhibitors has demonstrated that targeting of prevalent FMS-related receptor tyrosine kinase 3 (FLT3) gain-of-function mutations can provide significant survival benefits for patients, although the efficacy of FLT3 inhibitors in eliminating FLT3-mutated clones is variable. We identified a T cell receptor (TCR) reactive to the recurrent D835Y driver mutation in the FLT3 tyrosine kinase domain (TCRFLT3D/Y). TCRFLT3D/Y-redirected T cells selectively eliminated primary human AML cells harboring the FLT3D835Y mutation in vitro and in vivo. TCRFLT3D/Y cells rejected both CD34+ and CD34- AML in mice engrafted with primary leukemia from patients, reaching minimal residual disease-negative levels, and eliminated primary CD34+ AML leukemia-propagating cells in vivo. Thus, T cells targeting a single shared mutation can provide efficient immunotherapy toward selective elimination of clonally involved primary AML cells in vivo.
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Grants
- G0801073 Medical Research Council
- MC_UU_00016/5 Medical Research Council
- MC_UU_12009/5 Medical Research Council
- South-Eastern Regional Health Authority Norway, the Research Council of Norway, the Norwegian Cancer Society, the Norwegian Childhood Cancer Foundation, Stiftelsen Kristian Gerhard Jebsen, European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 865805), the University of Oslo and Oslo University Hospital and Novo Nordisk Foundation.
- Knut and Alice Wallenberg Foundation, The Swedish Research Council, Tobias Foundation, Torsten Söderberg Foundation, Center for Innovative Medicine (CIMED) at Karolinska Institutet, and The UK Medical Research Council
- Technical University of Denmark (DTU)
- Aarhus University Hospital
- Leiden University Medical Center
- Oslo University Hospital
- Karolinska University Hospital
- University of Oslo and Oslo University Hospital
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Affiliation(s)
- Eirini Giannakopoulou
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Madeleine Lehander
- Department of Medicine Huddinge, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Stina Virding Culleton
- Department of Medicine Huddinge, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Weiwen Yang
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Yingqian Li
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Terhi Karpanen
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Genomics Group, Faculty of Biosciences and Aquaculture, Nord University, Bodø, Norway
| | - Tetsuichi Yoshizato
- Department of Medicine Huddinge, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Even H Rustad
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Morten Milek Nielsen
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ravi Chand Bollineni
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Trung T Tran
- Department of Immunology, Oslo University Hospital, Oslo, Norway
| | - Marina Delic-Sarac
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Thea Johanne Gjerdingen
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Karolos Douvlataniotis
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Maarja Laos
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Muhammad Ali
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Amy Hillen
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Stefania Mazzi
- Department of Medicine Huddinge, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Desmond Wai Loon Chin
- Department of Medicine Huddinge, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Adi Mehta
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Jeppe Sejerø Holm
- Section for Experimental and Translational Immunology, Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Amalie Kai Bentzen
- Section for Experimental and Translational Immunology, Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Marie Bill
- Department of Hematology, Aarhus University Hospital, Aarhus, Denmark
| | - Marieke Griffioen
- Department of Hematology, Leiden University Medical Center, Leiden, the Netherlands
| | - Tobias Gedde-Dahl
- Hematology Department, Section for Stem Cell Transplantation, Oslo University Hospital, Rikshospitalet, Clinic for Cancer Medicine, Oslo, Norway
| | - Sören Lehmann
- Department of Medicine Huddinge, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
- Karolinska University Hospital, Stockholm, Sweden
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Sten Eirik W Jacobsen
- Department of Medicine Huddinge, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden.
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.
- Karolinska University Hospital, Stockholm, Sweden.
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
| | - Petter S Woll
- Department of Medicine Huddinge, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden.
| | - Johanna Olweus
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway.
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
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28
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Jalil AT, Abdulhadi MA, Al-Ameer LR, Taher WM, Abdulameer SJ, Abosaooda M, Fadhil AA. Peptide-Based Therapeutics in Cancer Therapy. Mol Biotechnol 2023:10.1007/s12033-023-00873-1. [PMID: 37768503 DOI: 10.1007/s12033-023-00873-1] [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: 02/24/2023] [Accepted: 08/16/2023] [Indexed: 09/29/2023]
Abstract
A monster called cancer is still one of the most challenging human problems and one of the leading causes of death in the world. Different types of treatment methods are used for cancer therapy; however, there are challenges such as high cost and harmful side effects in using these methods. Recent years have witnessed a surge in the development of therapeutic peptides for a wide range of diseases, notably cancer. Peptides are preferred over antibiotics, radiation therapy, and chemotherapy in the treatment of cancer due to a number of aspects, including flexibility, easy modification, low immunogenicity, and inexpensive cost of production. The use of therapeutic peptides in cancer treatment is a novel and intriguing strategy. These peptides provide excellent prospects for targeted drug delivery because of their high selectivity, specificity, small dimensions, good biocompatibility, and simplicity of modification. Target specificity and minimal toxicity are benefits of therapeutic peptides. Additionally, peptides can be used to design antigens or adjuvants for vaccine development. Here, types of therapeutic peptides for cancer therapy will be discussed, such as peptide-based cancer vaccines and tumor-targeting peptides (TTP) and cell-penetrating peptides (CPP).
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Affiliation(s)
- Abduladheem Turki Jalil
- Department of Medical Laboratories Techniques, Al-Mustaqbal University College, Hilla, Babylon, 51001, Iraq.
| | - Mohanad Ali Abdulhadi
- Department of Medical Laboratory Techniques, Al-Maarif University College, Al-Anbar, Iraq
| | - Lubna R Al-Ameer
- College of Pharmacy, Al-Zahraa University for Women, Karbala, Iraq
| | | | - Sada Jasim Abdulameer
- Biology Department, College of Education for Pure Science, Wasit University, Kut, Wasit, Iraq
| | | | - Ali A Fadhil
- Medical Technical College, Al-Farahidi University, Baghdad, Iraq
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Téllez T, Martin-García D, Redondo M, García-Aranda M. Clusterin Expression in Colorectal Carcinomas. Int J Mol Sci 2023; 24:14641. [PMID: 37834086 PMCID: PMC10572822 DOI: 10.3390/ijms241914641] [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] [Received: 08/16/2023] [Revised: 09/25/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023] Open
Abstract
Colorectal cancer is the third most diagnosed cancer, behind only breast and lung cancer. In terms of overall mortality, it ranks second due to, among other factors, problems with screening programs, which means that one of the factors that directly impacts survival and treatment success is early detection of the disease. Clusterin (CLU) is a molecular chaperone that has been linked to tumorigenesis, cancer progression and resistance to anticancer treatments, which has made it a promising drug target. However, it is still necessary to continue this line of research and to adjust the situations in which its use is more favorable. The aim of this paper is to review the current genetic knowledge on the role of CLU in tumorigenesis and cancer progression in general, and discuss its possible use as a therapeutic target in colorectal cancer.
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Affiliation(s)
- Teresa Téllez
- Surgical Specialties, Biochemistry and Immunology Department, Faculty of Medicine, University of Málaga, 29010 Malaga, Spain; (T.T.); (D.M.-G.)
- Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Red de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud (RICAPPS), Instituto de Investigación Biomédica de Málaga (IBIMA), 29590 Malaga, Spain;
- Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina—IBIMA Plataforma BIONAND, 29590 Malaga, Spain
| | - Desirée Martin-García
- Surgical Specialties, Biochemistry and Immunology Department, Faculty of Medicine, University of Málaga, 29010 Malaga, Spain; (T.T.); (D.M.-G.)
- Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Red de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud (RICAPPS), Instituto de Investigación Biomédica de Málaga (IBIMA), 29590 Malaga, Spain;
- Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina—IBIMA Plataforma BIONAND, 29590 Malaga, Spain
- Research and Innovation Unit, Hospital Costa del Sol, 29602 Marbella, Spain
| | - Maximino Redondo
- Surgical Specialties, Biochemistry and Immunology Department, Faculty of Medicine, University of Málaga, 29010 Malaga, Spain; (T.T.); (D.M.-G.)
- Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Red de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud (RICAPPS), Instituto de Investigación Biomédica de Málaga (IBIMA), 29590 Malaga, Spain;
- Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina—IBIMA Plataforma BIONAND, 29590 Malaga, Spain
- Research and Innovation Unit, Hospital Costa del Sol, 29602 Marbella, Spain
| | - Marilina García-Aranda
- Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Red de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud (RICAPPS), Instituto de Investigación Biomédica de Málaga (IBIMA), 29590 Malaga, Spain;
- Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina—IBIMA Plataforma BIONAND, 29590 Malaga, Spain
- Research and Innovation Unit, Hospital Costa del Sol, 29602 Marbella, Spain
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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.
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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.
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31
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Lee CH, Huh J, Buckley PR, Jang M, Pinho MP, Fernandes RA, Antanaviciute A, Simmons A, Koohy H. A robust deep learning workflow to predict CD8 + T-cell epitopes. Genome Med 2023; 15:70. [PMID: 37705109 PMCID: PMC10498576 DOI: 10.1186/s13073-023-01225-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 08/30/2023] [Indexed: 09/15/2023] Open
Abstract
BACKGROUND T-cells play a crucial role in the adaptive immune system by triggering responses against cancer cells and pathogens, while maintaining tolerance against self-antigens, which has sparked interest in the development of various T-cell-focused immunotherapies. However, the identification of antigens recognised by T-cells is low-throughput and laborious. To overcome some of these limitations, computational methods for predicting CD8 + T-cell epitopes have emerged. Despite recent developments, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8 + T-cell epitopes. METHODS We developed TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning workflow for predicting CD8 + T-cell epitopes from MHC-I presented pathogenic and self-peptides. TRAP uses transfer learning, deep learning architecture and MHC binding information to make context-specific predictions of CD8 + T-cell epitopes. TRAP also detects low-confidence predictions for peptides that differ significantly from those in the training datasets to abstain from making incorrect predictions. To estimate the immunogenicity of pathogenic peptides with low-confidence predictions, we further developed a novel metric, RSAT (relative similarity to autoantigens and tumour-associated antigens), as a complementary to 'dissimilarity to self' from cancer studies. RESULTS TRAP was used to identify epitopes from glioblastoma patients as well as SARS-CoV-2 peptides, and it outperformed other algorithms in both cancer and pathogenic settings. TRAP was especially effective at extracting immunogenicity-associated properties from restricted data of emerging pathogens and translating them onto related species, as well as minimising the loss of likely epitopes in imbalanced datasets. We also demonstrated that the novel metric termed RSAT was able to estimate immunogenic of pathogenic peptides of various lengths and species. TRAP implementation is available at: https://github.com/ChloeHJ/TRAP . CONCLUSIONS This study presents a novel computational workflow for accurately predicting CD8 + T-cell epitopes to foster a better understanding of antigen-specific T-cell response and the development of effective clinical therapeutics.
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Affiliation(s)
- Chloe H Lee
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
| | - Jaesung Huh
- Visual Geometry Group, Department of Engineering Science, University of Oxford, Oxford, OX2 6NN, UK
| | - Paul R Buckley
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
| | - Myeongjun Jang
- Intelligent Systems Lab, Department of Computer Science, University of Oxford, Oxford, OX1 3QG, UK
| | - Mariana Pereira Pinho
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
| | - Ricardo A Fernandes
- Chinese Academy of Medical Sciences (CAMS) Oxford Institute (COI), University of Oxford, Oxford, OX3 7BN, UK
| | - Agne Antanaviciute
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
| | - Alison Simmons
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Hashem Koohy
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK.
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK.
- Alan Turning Fellow in Health and Medicine, The Alan Turing Institute, London, UK.
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32
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Witney MJ, Tscharke DC. BMX-A and BMX-S: Accessible cell-free methods to estimate peptide-MHC-I affinity and stability. Mol Immunol 2023; 161:1-10. [PMID: 37478775 DOI: 10.1016/j.molimm.2023.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 06/12/2023] [Accepted: 07/11/2023] [Indexed: 07/23/2023]
Abstract
The affinity and stability of peptide binding to Major Histocompatibility Complex Class I (MHC-I) molecules are fundamental parameters that underpin the specificity and magnitude of CD8+ T cell responses. These parameters can be estimated in some cases by computational tools, but experimental validation remains valuable, especially for stability. Methods to measure peptide binding can be broadly categorised into either cell-based assays using TAP-deficient cell lines such as RMA/S, or cell-free strategies, such as peptide competition-binding assays and surface plasmon resonance. Cell-based assays are subject to confounding biological activity, including peptide trimming by peptidases and dilution of peptide-loaded MHC-I on the surface of cells through cell division. Current cell-free methods require in-house production and purification of MHC-I. In this study, we present the development of new cell-free assays to estimate the relative affinity and dissociation kinetics of peptide binding to MHC-I. These assays, which we have called BMX-A (relative affinity) and BMX-S (kinetic stability), are reliable, scalable and accessible, in that they use off-the-shelf commercial reagents and standard flow cytometry techniques.
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Affiliation(s)
- Matthew J Witney
- John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia
| | - David C Tscharke
- John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia.
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Nguyen KB, Roerden M, Copeland CJ, Backlund CM, Klop-Packel NG, Remba T, Kim B, Singh NK, Birnbaum ME, Irvine DJ, Spranger S. Decoupled neoantigen cross-presentation by dendritic cells limits anti-tumor immunity against tumors with heterogeneous neoantigen expression. eLife 2023; 12:e85263. [PMID: 37548358 PMCID: PMC10425174 DOI: 10.7554/elife.85263] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 08/06/2023] [Indexed: 08/08/2023] Open
Abstract
Cancer immunotherapies, in particular checkpoint blockade immunotherapy (CBT), can induce control of cancer growth, with a fraction of patients experiencing durable responses. However, the majority of patients currently do not respond to CBT and the molecular determinants of resistance have not been fully elucidated. Mounting clinical evidence suggests that the clonal status of neoantigens (NeoAg) impacts the anti-tumor T cell response. High intratumor heterogeneity (ITH), where the majority of NeoAgs are expressed subclonally, is correlated with poor clinical response to CBT and poor infiltration with tumor-reactive T cells. However, the mechanism by which ITH blunts tumor-reactive T cells is unclear. We developed a transplantable murine lung cancer model to characterize the immune response against a defined set of NeoAgs expressed either clonally or subclonally to model low or high ITH, respectively. Here we show that clonal expression of a weakly immunogenic NeoAg with a relatively strong NeoAg increased the immunogenicity of tumors with low but not high ITH. Mechanistically we determined that clonal NeoAg expression allowed cross-presenting dendritic cells to acquire and present both NeoAgs. Dual NeoAg presentation by dendritic cells was associated with a more mature DC phenotype and a higher stimulatory capacity. These data suggest that clonal NeoAg expression can induce more potent anti-tumor responses due to more stimulatory dendritic cell:T cell interactions. Therapeutic vaccination targeting subclonally expressed NeoAgs could be used to boost anti-tumor T cell responses.
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Affiliation(s)
- Kim Bich Nguyen
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of TechnologyCambridgeUnited States
- Department of Biology, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Malte Roerden
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | | | - Coralie M Backlund
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of TechnologyCambridgeUnited States
- Department of Biological Engineering, MITCambridgeUnited States
| | - Nory G Klop-Packel
- Department of Biology, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Tanaka Remba
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Byungji Kim
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Nishant K Singh
- Department of Biology, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Michael E Birnbaum
- Department of Biology, Massachusetts Institute of TechnologyCambridgeUnited States
- Department of Biological Engineering, MITCambridgeUnited States
- Ragon Institute of MGH, MIT and HarvardCambridgeUnited States
| | - Darrell J Irvine
- Department of Biology, Massachusetts Institute of TechnologyCambridgeUnited States
- Department of Biological Engineering, MITCambridgeUnited States
- Ragon Institute of MGH, MIT and HarvardCambridgeUnited States
| | - Stefani Spranger
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of TechnologyCambridgeUnited States
- Department of Biology, Massachusetts Institute of TechnologyCambridgeUnited States
- Ragon Institute of MGH, MIT and HarvardCambridgeUnited States
- Ludwig Center at MIT’s Koch Institute for Integrative Cancer ResearchCambridgeUnited States
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34
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Nibeyro G, Baronetto V, Folco JI, Pastore P, Girotti MR, Prato L, Morón G, Luján HD, Fernández EA. Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis. Front Immunol 2023; 14:1094236. [PMID: 37564650 PMCID: PMC10411733 DOI: 10.3389/fimmu.2023.1094236] [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: 11/09/2022] [Accepted: 07/10/2023] [Indexed: 08/12/2023] Open
Abstract
Introduction Identification of tumor specific neoantigen (TSN) immunogenicity is crucial to develop peptide/mRNA based anti-tumoral vaccines and/or adoptive T-cell immunotherapies; thus, accurate in-silico classification/prioritization proves critical for cost-effective clinical applications. Several methods were proposed as TSNs immunogenicity predictors; however, comprehensive performance comparison is still lacking due to the absence of well documented and adequate TSN databases. Methods Here, by developing a new curated database having 199 TSNs with experimentally-validated MHC-I presentation and positive/negative immune response (ITSNdb), sixteen metrics were evaluated as immunogenicity predictors. In addition, by using a dataset emulating patient derived TSNs and immunotherapy cohorts containing predicted TSNs for tumor neoantigen burden (TNB) with outcome association, the metrics were evaluated as TSNs prioritizers and as immunotherapy response biomarkers. Results Our results show high performance variability among methods, highlighting the need for substantial improvement. Deep learning predictors were top ranked on ITSNdb but show discrepancy on validation databases. In overall, current predicted TNB did not outperform existing biomarkers. Conclusion Recommendations for their clinical application and the ITSNdb are presented to promote development and comparison of computational TSNs immunogenicity predictors.
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Affiliation(s)
- Guadalupe Nibeyro
- Centro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas (CIDIE), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)/Universidad Católica de Córdoba (UCC) & Fundación para el Progreso de la Medicina, Córdoba, Argentina
| | - Veronica Baronetto
- Centro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas (CIDIE), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)/Universidad Católica de Córdoba (UCC) & Fundación para el Progreso de la Medicina, Córdoba, Argentina
| | - Juan I. Folco
- Facultad de Ingeniería, Universidad Católica de Córdoba (UCC), Córdoba, Argentina
| | - Pablo Pastore
- Facultad de Ingeniería, Universidad Católica de Córdoba (UCC), Córdoba, Argentina
| | - Maria Romina Girotti
- Universidad Argentina de la Empresa (UADE), Instituto de Tecnología (INTEC), Buenos Aires, Argentina
| | - Laura Prato
- Instituto Académico Pedagógico de Ciencias Básicas y Aplicadas, Universidad Nacional de Villa María, Villa María, Córdoba, Argentina
| | - Gabriel Morón
- Departamento de Bioquímica Clínica, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba (UNC), Córdoba, Argentina
- Centro de Investigaciones en Bioquímica Clínica e Inmunología (CIBICI), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Córdoba, Argentina
| | - Hugo D. Luján
- Centro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas (CIDIE), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)/Universidad Católica de Córdoba (UCC) & Fundación para el Progreso de la Medicina, Córdoba, Argentina
- Facultad de Ciencias de la Salud, Universidad Católica de Córdoba (UCC), Córdoba, Argentina
| | - Elmer A. Fernández
- Centro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas (CIDIE), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)/Universidad Católica de Córdoba (UCC) & Fundación para el Progreso de la Medicina, Córdoba, Argentina
- Facultad de Ingeniería, Universidad Católica de Córdoba (UCC), Córdoba, Argentina
- Facultad de Ciencias Exactas, Físicas y Naturales (FCEFyN), Universidad Nacional de Córdoba (UNC), Córdoba, Argentina
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Osei-Hwedieh DO, Sedlacek AL, Hernandez LM, Yamoah AA, Iyer SG, Weiss KR, Binder RJ. Immunosurveillance shapes the emergence of neo-epitope landscapes of sarcomas, revealing prime targets for immunotherapy. JCI Insight 2023; 8:e170324. [PMID: 37427594 PMCID: PMC10371341 DOI: 10.1172/jci.insight.170324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 05/25/2023] [Indexed: 07/11/2023] Open
Abstract
T cells recognize tumor-derived mutated peptides presented on MHC by tumors. The recognition of these neo-epitopes leads to rejection of tumors, an event that is critical for successful cancer immunosurveillance. Determination of tumor-rejecting neo-epitopes in human tumors has proved difficult, though recently developed systems approaches are becoming increasingly useful at evaluating their immunogenicity. We have used the differential aggretope index to determine the neo-epitope burden of sarcomas and observed a conspicuously titrated antigenic landscape, ranging from the highly antigenic osteosarcomas to the low antigenic leiomyosarcomas and liposarcomas. We showed that the antigenic landscape of the tumors inversely reflected the historical T cell responses in the tumor-bearing patients. We predicted that highly antigenic tumors with poor antitumor T cell responses, such as osteosarcomas, would be responsive to T cell-based immunotherapy regimens and demonstrated this in a murine osteosarcoma model. Our study presents a potentially novel pipeline for determining antigenicity of human tumors, provides an accurate predictor of potential neo-epitopes, and will be an important indicator of which cancers to target with T cell-enhancing immunotherapy.
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Affiliation(s)
| | | | | | | | | | - Kurt R. Weiss
- Department of Orthopaedic Surgery, UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania, USA
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Peri A, Salomon N, Wolf Y, Kreiter S, Diken M, Samuels Y. The landscape of T cell antigens for cancer immunotherapy. NATURE CANCER 2023:10.1038/s43018-023-00588-x. [PMID: 37415076 DOI: 10.1038/s43018-023-00588-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 05/18/2023] [Indexed: 07/08/2023]
Abstract
The remarkable capacity of immunotherapies to induce durable regression in some patients with metastatic cancer relies heavily on T cell recognition of tumor-presented antigens. As checkpoint-blockade therapy has limited efficacy, tumor antigens have the potential to be exploited for complementary treatments, many of which are already in clinical trials. The surge of interest in this topic has led to the expansion of the tumor antigen landscape with the emergence of new antigen categories. Nonetheless, how different antigens compare in their ability to elicit efficient and safe clinical responses remains largely unknown. Here, we review known cancer peptide antigens, their attributes and the relevant clinical data and discuss future directions.
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Affiliation(s)
- Aviyah Peri
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Nadja Salomon
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University Mainz gGmbH, Mainz, Germany
| | - Yochai Wolf
- Ella Lemelbaum Institute for Immuno-oncology and Skin Cancer, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel.
- Department of Pathology, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Sebastian Kreiter
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University Mainz gGmbH, Mainz, Germany.
| | - Mustafa Diken
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University Mainz gGmbH, Mainz, Germany.
| | - Yardena Samuels
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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37
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Amaya-Ramirez D, Martinez-Enriquez LC, Parra-López C. Usefulness of Docking and Molecular Dynamics in Selecting Tumor Neoantigens to Design Personalized Cancer Vaccines: A Proof of Concept. Vaccines (Basel) 2023; 11:1174. [PMID: 37514989 PMCID: PMC10386133 DOI: 10.3390/vaccines11071174] [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: 03/17/2023] [Revised: 04/24/2023] [Accepted: 04/27/2023] [Indexed: 07/30/2023] Open
Abstract
Personalized cancer vaccines based on neoantigens are a new and promising treatment for cancer; however, there are still multiple unresolved challenges to using this type of immunotherapy. Among these, the effective identification of immunogenic neoantigens stands out, since the in silico tools used generate a significant portion of false positives. Inclusion of molecular simulation techniques can refine the results these tools produce. In this work, we explored docking and molecular dynamics to study the association between the stability of peptide-HLA complexes and their immunogenicity, using as a proof of concept two HLA-A2-restricted neoantigens that were already evaluated in vitro. The results obtained were in accordance with the in vitro immunogenicity, since the immunogenic neoantigen ASTN1 remained bound at both ends to the HLA-A2 molecule. Additionally, molecular dynamic simulation suggests that position 1 of the peptide has a more relevant role in stabilizing the N-terminus than previously proposed. Likewise, the mutations may have a "delocalized" effect on the peptide-HLA interaction, which means that the mutated amino acid influences the intensity of the interactions of distant amino acids of the peptide with the HLA. These findings allow us to propose the inclusion of molecular simulation techniques to improve the identification of neoantigens for cancer vaccines.
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Affiliation(s)
| | - Laura Camila Martinez-Enriquez
- Grupo de Inmunología y Medicina Traslacional, Departamento de Microbiología, Facultad de Medicina, Universidad Nacional de Colombia, Bogotá 111321, Colombia
| | - Carlos Parra-López
- Grupo de Inmunología y Medicina Traslacional, Departamento de Microbiología, Facultad de Medicina, Universidad Nacional de Colombia, Bogotá 111321, Colombia
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38
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Viborg N, Pavlidis MA, Barrio-Calvo M, Friis S, Trolle T, Sørensen AB, Thygesen CB, Kofoed SV, Kleine-Kohlbrecher D, Hadrup SR, Rønø B. DNA based neoepitope vaccination induces tumor control in syngeneic mouse models. NPJ Vaccines 2023; 8:77. [PMID: 37244905 DOI: 10.1038/s41541-023-00671-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 05/10/2023] [Indexed: 05/29/2023] Open
Abstract
Recent findings have positioned tumor mutation-derived neoepitopes as attractive targets for cancer immunotherapy. Cancer vaccines that deliver neoepitopes via various vaccine formulations have demonstrated promising preliminary results in patients and animal models. In the presented work, we assessed the ability of plasmid DNA to confer neoepitope immunogenicity and anti-tumor effect in two murine syngeneic cancer models. We demonstrated that neoepitope DNA vaccination led to anti-tumor immunity in the CT26 and B16F10 tumor models, with the long-lasting presence of neoepitope-specific T-cell responses in blood, spleen, and tumors after immunization. We further observed that engagement of both the CD4+ and CD8+ T cell compartments was essential to hamper tumor growth. Additionally, combination therapy with immune checkpoint inhibition provided an additive effect, superior to either monotherapy. DNA vaccination offers a versatile platform that allows the encoding of multiple neoepitopes in a single formulation and is thus a feasible strategy for personalized immunotherapy via neoepitope vaccination.
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Affiliation(s)
- Nadia Viborg
- Evaxion Biotech, Hørsholm, Denmark
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | | | | | | | | | | | | | | | | | - Sine Reker Hadrup
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
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Homan EJ, Bremel RD. Determinants of tumor immune evasion: the role of T cell exposed motif frequency and mutant amino acid exposure. Front Immunol 2023; 14:1155679. [PMID: 37215122 PMCID: PMC10196236 DOI: 10.3389/fimmu.2023.1155679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 04/17/2023] [Indexed: 05/24/2023] Open
Abstract
Few neoepitopes detected in tumor biopsies are immunogenic. Tumor-specific T cell responses require both the presentation of an epitope that differs from wildtype and the presence of T cells with neoepitope-cognate receptors. We show that mutations detected in tumor biopsies result in an increased frequency of rare amino acid combinations compared to the human proteome and gastrointestinal microorganisms. Mutations in a large data set of oncogene and tumor suppressor gene products were compared to wildtype, and to the count of corresponding amino acid motifs in the human proteome and gastrointestinal microbiome. Mutant amino acids in T cell exposed positions of potential neoepitopes consistently generated amino acid motifs that are less common in both proteome reference datasets. Approximately 10% of the mutant amino acid motifs are absent from the human proteome. Motif frequency does not change when mutants were positioned in the MHC anchor positions hidden from T cell receptors. Analysis of neoepitopes in GBM and LUSC cases showed less common T cell exposed motifs, and HLA binding preferentially placing mutant amino acids in an anchor position for both MHC I and MHC II. Cross-presentation of mutant exposed neoepitopes by MHC I and MHC II was particularly uncommon. Review of a tumor mutation dataset known to generate T cell responses showed immunogenic epitopes were those with mutant amino acids exposed to the T cell receptor and with exposed pentamer motifs present in the human and microbiome reference databases. The study illustrates a previously unrecognized mechanism of tumor immune evasion, as rare T cell exposed motifs produced by mutation are less likely to have cognate T cells in the T cell repertoire. The complex interactions of HLA genotype, binding positions, and mutation specific changes in T cell exposed motif underscore the necessity of evaluating potential neoepitopes in each individual patient.
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Huang R, Zhao B, Hu S, Zhang Q, Su X, Zhang W. Adoptive neoantigen-reactive T cell therapy: improvement strategies and current clinical researches. Biomark Res 2023; 11:41. [PMID: 37062844 PMCID: PMC10108522 DOI: 10.1186/s40364-023-00478-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/21/2023] [Indexed: 04/18/2023] Open
Abstract
Neoantigens generated by non-synonymous mutations of tumor genes can induce activation of neoantigen-reactive T (NRT) cells which have the ability to resist the growth of tumors expressing specific neoantigens. Immunotherapy based on NRT cells has made preeminent achievements in melanoma and other solid tumors. The process of manufacturing NRT cells includes identification of neoantigens, preparation of neoantigen expression vectors or peptides, induction and activation of NRT cells, and analysis of functions and phenotypes. Numerous improvement strategies have been proposed to enhance the potency of NRT cells by engineering TCR, promoting infiltration of T cells and overcoming immunosuppressive factors in the tumor microenvironment. In this review, we outline the improvement of the preparation and the function assessment of NRT cells, and discuss the current status of clinical trials related to NRT cell immunotherapy.
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Affiliation(s)
- Ruichen Huang
- Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Second Military Medical University, Shanghai, 200433, People's Republic of China
| | - Bi Zhao
- Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Second Military Medical University, Shanghai, 200433, People's Republic of China
| | - Shi Hu
- Department of Biophysics, College of Basic Medical Sciences, Second Military Medical University, 800 Xiangyin Road, Shanghai, 200433, People's Republic of China
| | - Qian Zhang
- National Key Laboratory of Medical Immunology, Institute of Immunology, Second Military Medical University, 800 Xiangyin Road, Shanghai, 200433, People's Republic of China
| | - Xiaoping Su
- School of Basic Medicine, Wenzhou Medical University, Wenzhou, 325000, People's Republic of China.
| | - Wei Zhang
- Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Second Military Medical University, Shanghai, 200433, People's Republic of China.
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Xia H, McMichael J, Becker-Hapak M, Onyeador OC, Buchli R, McClain E, Pence P, Supabphol S, Richters MM, Basu A, Ramirez CA, Puig-Saus C, Cotto KC, Freshour SL, Hundal J, Kiwala S, Goedegebuure SP, Johanns TM, Dunn GP, Ribas A, Miller CA, Gillanders WE, Fehniger TA, Griffith OL, Griffith M. Computational prediction of MHC anchor locations guides neoantigen identification and prioritization. Sci Immunol 2023; 8:eabg2200. [PMID: 37027480 PMCID: PMC10450883 DOI: 10.1126/sciimmunol.abg2200] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 03/16/2023] [Indexed: 04/09/2023]
Abstract
Neoantigens are tumor-specific peptide sequences resulting from sources such as somatic DNA mutations. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Accurate neoantigen identification is thus critical for both designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly predicting whether the presenting peptide sequence can successfully induce an immune response. Because most somatic mutations are single-nucleotide variants, changes between wild-type and mutated peptides are typically subtle and require cautious interpretation. A potentially underappreciated variable in neoantigen prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient's specific MHC molecules. Whereas a subset of peptide positions are presented to the T cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T cell responses. We computationally predicted anchor positions for different peptide lengths for 328 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 6 to 38% of neoantigen candidates are potentially misclassified and can be rescued using allele-specific knowledge of anchor positions. A subset of anchor results were orthogonally validated using protein crystallography structures. Representative anchor trends were experimentally validated using peptide-MHC stability assays and competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, we hope to formalize, streamline, and improve the identification process for relevant clinical studies.
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Affiliation(s)
- Huiming Xia
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Joshua McMichael
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Michelle Becker-Hapak
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Onyinyechi C. Onyeador
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Rico Buchli
- Pure Protein LLC, Oklahoma City, OK 73104, USA
| | - Ethan McClain
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Patrick Pence
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Suangson Supabphol
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- The Center of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Megan M. Richters
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Anamika Basu
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Cody A. Ramirez
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Cristina Puig-Saus
- Division of Hematology/Oncology, Department of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, Los Angeles, CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
| | - Kelsy C. Cotto
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Sharon L. Freshour
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Jasreet Hundal
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Susanna Kiwala
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - S. Peter Goedegebuure
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Tanner M. Johanns
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Gavin P. Dunn
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Antoni Ribas
- Division of Hematology/Oncology, Department of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, Los Angeles, CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
| | - Christopher A. Miller
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
| | - William E. Gillanders
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Todd A. Fehniger
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Obi L. Griffith
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Malachi Griffith
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
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Tippalagama R, Chihab LY, Kearns K, Lewis S, Panda S, Willemsen L, Burel JG, Lindestam Arlehamn CS. Antigen-specificity measurements are the key to understanding T cell responses. Front Immunol 2023; 14:1127470. [PMID: 37122719 PMCID: PMC10140422 DOI: 10.3389/fimmu.2023.1127470] [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: 12/19/2022] [Accepted: 03/30/2023] [Indexed: 05/02/2023] Open
Abstract
Antigen-specific T cells play a central role in the adaptive immune response and come in a wide range of phenotypes. T cell receptors (TCRs) mediate the antigen-specificities found in T cells. Importantly, high-throughput TCR sequencing provides a fingerprint which allows tracking of specific T cells and their clonal expansion in response to particular antigens. As a result, many studies have leveraged TCR sequencing in an attempt to elucidate the role of antigen-specific T cells in various contexts. Here, we discuss the published approaches to studying antigen-specific T cells and their specific TCR repertoire. Further, we discuss how these methods have been applied to study the TCR repertoire in various diseases in order to characterize the antigen-specific T cells involved in the immune control of disease.
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Mayoh C, Gifford AJ, Terry R, Lau LMS, Wong M, Rao P, Shai-Hee T, Saletta F, Khuong-Quang DA, Qin V, Mateos MK, Meyran D, Miller KE, Yuksel A, Mould EVA, Bowen-James R, Govender D, Senapati A, Zhukova N, Omer N, Dholaria H, Alvaro F, Tapp H, Diamond Y, Pozza LD, Moore AS, Nicholls W, Gottardo NG, McCowage G, Hansford JR, Khaw SL, Wood PJ, Catchpoole D, Cottrell CE, Mardis ER, Marshall GM, Tyrrell V, Haber M, Ziegler DS, Vittorio O, Trapani JA, Cowley MJ, Neeson PJ, Ekert PG. A novel transcriptional signature identifies T-cell infiltration in high-risk paediatric cancer. Genome Med 2023; 15:20. [PMID: 37013636 PMCID: PMC10071693 DOI: 10.1186/s13073-023-01170-x] [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: 10/13/2022] [Accepted: 03/08/2023] [Indexed: 04/05/2023] Open
Abstract
BACKGROUND Molecular profiling of the tumour immune microenvironment (TIME) has enabled the rational choice of immunotherapies in some adult cancers. In contrast, the TIME of paediatric cancers is relatively unexplored. We speculated that a more refined appreciation of the TIME in childhood cancers, rather than a reliance on commonly used biomarkers such as tumour mutation burden (TMB), neoantigen load and PD-L1 expression, is an essential prerequisite for improved immunotherapies in childhood solid cancers. METHODS We combined immunohistochemistry (IHC) with RNA sequencing and whole-genome sequencing across a diverse spectrum of high-risk paediatric cancers to develop an alternative, expression-based signature associated with CD8+ T-cell infiltration of the TIME. Furthermore, we explored transcriptional features of immune archetypes and T-cell receptor sequencing diversity, assessed the relationship between CD8+ and CD4+ abundance by IHC and deconvolution predictions and assessed the common adult biomarkers such as neoantigen load and TMB. RESULTS A novel 15-gene immune signature, Immune Paediatric Signature Score (IPASS), was identified. Using this signature, we estimate up to 31% of high-risk cancers harbour infiltrating T-cells. In addition, we showed that PD-L1 protein expression is poorly correlated with PD-L1 RNA expression and TMB and neoantigen load are not predictive of T-cell infiltration in paediatrics. Furthermore, deconvolution algorithms are only weakly correlated with IHC measurements of T-cells. CONCLUSIONS Our data provides new insights into the variable immune-suppressive mechanisms dampening responses in paediatric solid cancers. Effective immune-based interventions in high-risk paediatric cancer will require individualised analysis of the TIME.
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Affiliation(s)
- Chelsea Mayoh
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
- University of New South Wales Centre for Childhood Cancer Research, UNSW, Kensington, NSW, Australia
| | - Andrew J Gifford
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
- Anatomical Pathology, NSW Health Pathology, Prince of Wales Hospital, Randwick, NSW, Australia
| | - Rachael Terry
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
| | - Loretta M S Lau
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
- Kids Cancer Centre, Sydney Children's Hospital, Randwick, NSW, Australia
| | - Marie Wong
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
| | - Padmashree Rao
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
| | - Tyler Shai-Hee
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
| | - Federica Saletta
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
| | - Dong-Anh Khuong-Quang
- Children's Cancer Centre, Royal Children's Hospital, Parkville, VIC, Australia
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC, Australia
| | - Vicky Qin
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Parkville, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, Australia
| | - Marion K Mateos
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
- Kids Cancer Centre, Sydney Children's Hospital, Randwick, NSW, Australia
| | - Deborah Meyran
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Parkville, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, Australia
| | - Katherine E Miller
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Aysen Yuksel
- Tumour Bank, Children's Hospital Westmead, Westmead, NSW, Australia
| | - Emily V A Mould
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
| | - Rachel Bowen-James
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Computer Science and Engineering, UNSW Sydney, Kensington, NSW, Australia
- School of Biomedical Engineering, UNSW Sydney, Kensington, NSW, Australia
| | - Dinisha Govender
- Cancer Centre for Children, Children's Hospital Westmead, Westmead, NSW, Australia
| | - Akanksha Senapati
- Kids Cancer Centre, Sydney Children's Hospital, Randwick, NSW, Australia
| | - Nataliya Zhukova
- Monash Children's Hospital, Melbourne, VIC, Australia
- Centre for Cancer Research, Hudson Institute of Medical Research, Clayton, VIC, Australia
- Department of Paediatrics, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Natacha Omer
- Oncology Service, Children's Health Queensland Hospital & Health Service, Brisbane, QLD, Australia
- The University of Queensland Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Hetal Dholaria
- Department of Paediatric and Adolescent Oncology and Haematology, Perth Children's Hospital, Nedlands, WA, Australia
- Brain Tumour Research Program, Telethon Kids Institute, Nedlands, WA, Australia
| | - Frank Alvaro
- John Hunter Children's Hospital, New Lambton Heights, NSW, Australia
| | - Heather Tapp
- Michael Rice Cancer Centre, Women's and Children's Hospital, South Australia Health and Medical Research Institute, Adelaide, SA, Australia
| | - Yonatan Diamond
- Kids Cancer Centre, Sydney Children's Hospital, Randwick, NSW, Australia
- Children's Cancer Centre, Royal Children's Hospital, Parkville, VIC, Australia
| | - Luciano Dalla Pozza
- Cancer Centre for Children, Children's Hospital Westmead, Westmead, NSW, Australia
| | - Andrew S Moore
- Oncology Service, Children's Health Queensland Hospital & Health Service, Brisbane, QLD, Australia
- Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Wayne Nicholls
- Oncology Service, Children's Health Queensland Hospital & Health Service, Brisbane, QLD, Australia
- School of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Nicholas G Gottardo
- Department of Paediatric and Adolescent Oncology and Haematology, Perth Children's Hospital, Nedlands, WA, Australia
- Brain Tumour Research Program, Telethon Kids Institute, Nedlands, WA, Australia
| | - Geoffrey McCowage
- Cancer Centre for Children, Children's Hospital Westmead, Westmead, NSW, Australia
| | - Jordan R Hansford
- Michael Rice Cancer Centre, Women's and Children's Hospital, South Australia Health and Medical Research Institute, Adelaide, SA, Australia
- South Australia ImmunoGENomics Cancer Institute, University of Adelaide, Adelaide, SA, Australia
| | - Seong-Lin Khaw
- Children's Cancer Centre, Royal Children's Hospital, Parkville, VIC, Australia
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC, Australia
| | - Paul J Wood
- Monash Children's Hospital, Melbourne, VIC, Australia
- Centre for Cancer Research, Hudson Institute of Medical Research, Clayton, VIC, Australia
- Department of Paediatrics, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | | | - Catherine E Cottrell
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
- Department of Pathology, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Elaine R Mardis
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
- Department of Neurosurgery, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Glenn M Marshall
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
- Kids Cancer Centre, Sydney Children's Hospital, Randwick, NSW, Australia
| | - Vanessa Tyrrell
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
| | - Michelle Haber
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
| | - David S Ziegler
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
- Kids Cancer Centre, Sydney Children's Hospital, Randwick, NSW, Australia
| | - Orazio Vittorio
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
| | - Joseph A Trapani
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Parkville, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, Australia
| | - Mark J Cowley
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
| | - Paul J Neeson
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Parkville, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, Australia
| | - Paul G Ekert
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia.
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia.
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC, Australia.
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Parkville, VIC, Australia.
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, Australia.
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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.
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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.
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45
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Contemplating immunopeptidomes to better predict them. Semin Immunol 2023; 66:101708. [PMID: 36621290 DOI: 10.1016/j.smim.2022.101708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/16/2022] [Accepted: 12/20/2022] [Indexed: 01/09/2023]
Abstract
The identification of T-cell epitopes is key for a complete molecular understanding of immune recognition mechanisms in infectious diseases, autoimmunity and cancer. T-cell epitopes further provide targets for personalized vaccines and T-cell therapy, with several therapeutic applications in cancer immunotherapy and elsewhere. T-cell epitopes consist of short peptides displayed on Major Histocompatibility Complex (MHC) molecules. The recent advances in mass spectrometry (MS) based technologies to profile the ensemble of peptides displayed on MHC molecules - the so-called immunopeptidome - had a major impact on our understanding of antigen presentation and MHC ligands. On the one hand, these techniques enabled researchers to directly identify hundreds of thousands of peptides presented on MHC molecules, including some that elicited T-cell recognition. On the other hand, the data collected in these experiments revealed fundamental properties of antigen presentation pathways and significantly improved our ability to predict naturally presented MHC ligands and T-cell epitopes across the wide spectrum of MHC alleles found in human and other organisms. Here we review recent computational developments to analyze experimentally determined immunopeptidomes and harness these data to improve our understanding of antigen presentation and MHC binding specificities, as well as our ability to predict MHC ligands. We further discuss the strengths and limitations of the latest approaches to move beyond predictions of antigen presentation and tackle the challenges of predicting TCR recognition and immunogenicity.
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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.
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47
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Biri-Kovács B, Bánóczi Z, Tummalapally A, Szabó I. Peptide Vaccines in Melanoma: Chemical Approaches towards Improved Immunotherapeutic Efficacy. Pharmaceutics 2023; 15:pharmaceutics15020452. [PMID: 36839774 PMCID: PMC9963291 DOI: 10.3390/pharmaceutics15020452] [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: 12/12/2022] [Revised: 01/22/2023] [Accepted: 01/25/2023] [Indexed: 01/31/2023] Open
Abstract
Cancer of the skin is by far the most common of all cancers. Although the incidence of melanoma is relatively low among skin cancers, it can account for a high number of skin cancer deaths. Since the start of deeper insight into the mechanisms of melanoma tumorigenesis and their strong interaction with the immune system, the development of new therapeutical strategies has been continuously rising. The high number of melanoma cell mutations provides a diverse set of antigens that the immune system can recognize and use to distinguish tumor cells from normal cells. Peptide-based synthetic anti-tumor vaccines are based on tumor antigens that elicit an immune response due to antigen-presenting cells (APCs). Although targeting APCs with peptide antigens is the most important assumption for vaccine development, peptide antigens alone are poorly immunogenic. The immunogenicity of peptide antigens can be improved not only by synthetic modifications but also by the assistance of adjuvants and/or delivery systems. The current review summarizes the different chemical approaches for the development of effective peptide-based vaccines for the immunotherapeutic treatment of advanced melanoma.
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Affiliation(s)
- Beáta Biri-Kovács
- ELKH-ELTE Research Group of Peptide Chemistry, 1117 Budapest, Hungary
| | - Zoltán Bánóczi
- ELKH-ELTE Research Group of Peptide Chemistry, 1117 Budapest, Hungary
- Institute of Chemistry, Eötvös Loránd University, 1117 Budapest, Hungary
| | | | - Ildikó Szabó
- ELKH-ELTE Research Group of Peptide Chemistry, 1117 Budapest, Hungary
- Institute of Chemistry, Eötvös Loránd University, 1117 Budapest, Hungary
- MTA-TTK Lendület “Momentum” Peptide-Based Vaccines Research Group, Institute of Materials and Environmental Chemistry, Research Centre for Natural Sciences, 1117 Budapest, Hungary
- Correspondence: ; Tel.: +36-13722500
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Gfeller D, Schmidt J, Croce G, Guillaume P, Bobisse S, Genolet R, Queiroz L, Cesbron J, Racle J, Harari A. Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8 + T-cell epitopes. Cell Syst 2023; 14:72-83.e5. [PMID: 36603583 PMCID: PMC9811684 DOI: 10.1016/j.cels.2022.12.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 10/12/2022] [Accepted: 12/08/2022] [Indexed: 01/06/2023]
Abstract
The recognition of pathogen or cancer-specific epitopes by CD8+ T cells is crucial for the clearance of infections and the response to cancer immunotherapy. This process requires epitopes to be presented on class I human leukocyte antigen (HLA-I) molecules and recognized by the T-cell receptor (TCR). Machine learning models capturing these two aspects of immune recognition are key to improve epitope predictions. Here, we assembled a high-quality dataset of naturally presented HLA-I ligands and experimentally verified neo-epitopes. We then integrated these data in a refined computational framework to predict antigen presentation (MixMHCpred2.2) and TCR recognition (PRIME2.0). The depth of our training data and the algorithmic developments resulted in improved predictions of HLA-I ligands and neo-epitopes. Prospectively applying our tools to SARS-CoV-2 proteins revealed several epitopes. TCR sequencing identified a monoclonal response in effector/memory CD8+ T cells against one of these epitopes and cross-reactivity with the homologous peptides from other coronaviruses.
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Affiliation(s)
- David Gfeller
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Lausanne, Switzerland,Agora Cancer Research Centre, 1011 Lausanne, Switzerland,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland,Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland,Corresponding author
| | - Julien Schmidt
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland,Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Giancarlo Croce
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Lausanne, Switzerland,Agora Cancer Research Centre, 1011 Lausanne, Switzerland,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland,Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Philippe Guillaume
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland,Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Sara Bobisse
- Agora Cancer Research Centre, 1011 Lausanne, Switzerland,Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland,Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Raphael Genolet
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland,Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Lise Queiroz
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland,Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Julien Cesbron
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland,Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Julien Racle
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Lausanne, Switzerland,Agora Cancer Research Centre, 1011 Lausanne, Switzerland,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland,Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Alexandre Harari
- Agora Cancer Research Centre, 1011 Lausanne, Switzerland,Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland,Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
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Cai Y, Chen R, Gao S, Li W, Liu Y, Su G, Song M, Jiang M, Jiang C, Zhang X. Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy. Front Oncol 2023; 12:1054231. [PMID: 36698417 PMCID: PMC9868469 DOI: 10.3389/fonc.2022.1054231] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 12/16/2022] [Indexed: 01/10/2023] Open
Abstract
The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.
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Affiliation(s)
- Yu Cai
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Rui Chen
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Shenghan Gao
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Wenqing Li
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Yuru Liu
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Guodong Su
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Mingming Song
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Mengju Jiang
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Chao Jiang
- Department of Neurology, The Second Affiliated Hospital of Xi’an Medical University, Xi’an, Shaanxi, China,*Correspondence: Chao Jiang, ; Xi Zhang,
| | - Xi Zhang
- School of Medicine, Northwest University, Xi’an, Shaanxi, China,*Correspondence: Chao Jiang, ; Xi Zhang,
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Sun B, Zhang J, Li Z, Xie M, Luo C, Wang Y, Chen L, Wang Y, Jiang D, Yang K. Integration: Gospel for immune bioinformatician on epitope-based therapy. Front Immunol 2023; 14:1075419. [PMID: 36798136 PMCID: PMC9927647 DOI: 10.3389/fimmu.2023.1075419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 01/17/2023] [Indexed: 02/04/2023] Open
Affiliation(s)
- Baozeng Sun
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China
| | - Junqi Zhang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China
| | - Zhikui Li
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China
| | - Mingyang Xie
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China
| | - Cheng Luo
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China
| | - Yongkai Wang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China
| | - Longyu Chen
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China
| | - Yueyue Wang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China
| | - Dongbo Jiang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China.,The Key Laboratory of Bio-hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China.,Department of Microbiology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China
| | - Kun Yang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China.,The Key Laboratory of Bio-hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China.,Department of Rheumatology, Tangdu Hospital, Air-Force Medical University (the Fourth Military Medical University), Xi'an, Shaanxi, China
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