1
|
Huber F, Arnaud M, Stevenson BJ, Michaux J, Benedetti F, Thevenet J, Bobisse S, Chiffelle J, Gehert T, Müller M, Pak H, Krämer AI, Altimiras ER, Racle J, Taillandier-Coindard M, Muehlethaler K, Auger A, Saugy D, Murgues B, Benyagoub A, Gfeller D, Laniti DD, Kandalaft L, Rodrigo BN, Bouchaab H, Tissot S, Coukos G, Harari A, Bassani-Sternberg M. A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy. Nat Biotechnol 2024:10.1038/s41587-024-02420-y. [PMID: 39394480 DOI: 10.1038/s41587-024-02420-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 09/04/2024] [Indexed: 10/13/2024]
Abstract
The accurate identification and prioritization of antigenic peptides is crucial for the development of personalized cancer immunotherapies. Publicly available pipelines to predict clinical neoantigens do not allow direct integration of mass spectrometry immunopeptidomics data, which can uncover antigenic peptides derived from various canonical and noncanonical sources. To address this, we present an end-to-end clinical proteogenomic pipeline, called NeoDisc, that combines state-of-the-art publicly available and in-house software for immunopeptidomics, genomics and transcriptomics with in silico tools for the identification, prediction and prioritization of tumor-specific and immunogenic antigens from multiple sources, including neoantigens, viral antigens, high-confidence tumor-specific antigens and tumor-specific noncanonical antigens. We demonstrate the superiority of NeoDisc in accurately prioritizing immunogenic neoantigens over recent prioritization pipelines. We showcase the various features offered by NeoDisc that enable both rule-based and machine-learning approaches for personalized antigen discovery and neoantigen cancer vaccine design. Additionally, we demonstrate how NeoDisc's multiomics integration identifies defects in the cellular antigen presentation machinery, which influence the heterogeneous tumor antigenic landscape.
Collapse
Affiliation(s)
- Florian Huber
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Marion Arnaud
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
| | - Brian J Stevenson
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, Lausanne, Switzerland
| | - Justine Michaux
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Fabrizio Benedetti
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
| | - Jonathan Thevenet
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Sara Bobisse
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
| | - Johanna Chiffelle
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
| | - Talita Gehert
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
| | - Markus Müller
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, Lausanne, Switzerland
| | - HuiSong Pak
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Anne I Krämer
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Emma Ricart Altimiras
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Julien Racle
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, Lausanne, Switzerland
| | - Marie Taillandier-Coindard
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Katja Muehlethaler
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Aymeric Auger
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
| | - Damien Saugy
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
| | - Baptiste Murgues
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
| | - Abdelkader Benyagoub
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, Lausanne, Switzerland
| | - Denarda Dangaj Laniti
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
| | - Lana Kandalaft
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Blanca Navarro Rodrigo
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Hasna Bouchaab
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Department of Medical Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Stephanie Tissot
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - George Coukos
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
| | - Alexandre Harari
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland.
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland.
- AGORA Cancer Research Center, Lausanne, Switzerland.
- Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland.
| |
Collapse
|
2
|
Kina E, Larouche JD, Thibault P, Perreault C. The cryptic immunopeptidome in health and disease. Trends Genet 2024:S0168-9525(24)00210-5. [PMID: 39389870 DOI: 10.1016/j.tig.2024.09.003] [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: 05/23/2024] [Revised: 08/01/2024] [Accepted: 09/17/2024] [Indexed: 10/12/2024]
Abstract
Peptides presented by MHC proteins regulate all aspects of T cell biology. These MHC-associated peptides (MAPs) form what is known as the immunopeptidome and their comprehensive analysis has catalyzed the burgeoning field of immunopeptidomics. Advances in mass spectrometry (MS) and next-generation sequencing have facilitated significant breakthroughs in this area, some of which are highlighted in this article on the cryptic immunopeptidome. Here, 'cryptic' refers to peptides and proteins encoded by noncanonical open reading frames (ORFs). Cryptic MAPs derive mainly from short unstable proteins found in normal, infected, and neoplastic cells. Cryptic MAPs show minimal overlap with cryptic proteins found in whole-cell extracts. In many cancer types, most cancer-specific MAPs are cryptic.
Collapse
Affiliation(s)
- Eralda Kina
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Québec, Canada
| | - Jean-David Larouche
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Québec, Canada
| | - Pierre Thibault
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Québec, Canada
| | - Claude Perreault
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Québec, Canada.
| |
Collapse
|
3
|
Bulashevska A, Nacsa Z, Lang F, Braun M, Machyna M, Diken M, Childs L, König R. Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy. Front Immunol 2024; 15:1394003. [PMID: 38868767 PMCID: PMC11167095 DOI: 10.3389/fimmu.2024.1394003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/13/2024] [Indexed: 06/14/2024] Open
Abstract
Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.
Collapse
Affiliation(s)
- Alla Bulashevska
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Zsófia Nacsa
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Franziska Lang
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Markus Braun
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Martin Machyna
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Mustafa Diken
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Liam Childs
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Renate König
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| |
Collapse
|
4
|
Minegishi Y, Haga Y, Ueda K. Emerging potential of immunopeptidomics by mass spectrometry in cancer immunotherapy. Cancer Sci 2024; 115:1048-1059. [PMID: 38382459 PMCID: PMC11007014 DOI: 10.1111/cas.16118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/02/2024] [Accepted: 02/07/2024] [Indexed: 02/23/2024] Open
Abstract
With significant advances in analytical technologies, research in the field of cancer immunotherapy, such as adoptive T cell therapy, cancer vaccine, and immune checkpoint blockade (ICB), is currently gaining tremendous momentum. Since the efficacy of cancer immunotherapy is recognized only by a minority of patients, more potent tumor-specific antigens (TSAs, also known as neoantigens) and predictive markers for treatment response are of great interest. In cancer immunity, immunopeptides, presented by human leukocyte antigen (HLA) class I, play a role as initiating mediators of immunogenicity. The latest advancement in the interdisciplinary multiomics approach has rapidly enlightened us about the identity of the "dark matter" of cancer and the associated immunopeptides. In this field, mass spectrometry (MS) is a viable option to select because of the naturally processed and actually presented TSA candidates in order to grasp the whole picture of the immunopeptidome. In the past few years the search space has been enlarged by the multiomics approach, the sensitivity of mass spectrometers has been improved, and deep/machine-learning-supported peptide search algorithms have taken immunopeptidomics to the next level. In this review, along with the introduction of key technical advancements in immunopeptidomics, the potential and further directions of immunopeptidomics will be reviewed from the perspective of cancer immunotherapy.
Collapse
Affiliation(s)
- Yuriko Minegishi
- Cancer Proteomics Group, Cancer Precision Medicine CenterJapanese Foundation for Cancer ResearchTokyoJapan
| | - Yoshimi Haga
- Cancer Proteomics Group, Cancer Precision Medicine CenterJapanese Foundation for Cancer ResearchTokyoJapan
| | - Koji Ueda
- Cancer Proteomics Group, Cancer Precision Medicine CenterJapanese Foundation for Cancer ResearchTokyoJapan
| |
Collapse
|
5
|
Katsikis PD, Ishii KJ, Schliehe C. Challenges in developing personalized neoantigen cancer vaccines. Nat Rev Immunol 2024; 24:213-227. [PMID: 37783860 DOI: 10.1038/s41577-023-00937-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/17/2023] [Indexed: 10/04/2023]
Abstract
The recent success of cancer immunotherapies has highlighted the benefit of harnessing the immune system for cancer treatment. Vaccines have a long history of promoting immunity to pathogens and, consequently, vaccines targeting cancer neoantigens have been championed as a tool to direct and amplify immune responses against tumours while sparing healthy tissue. In recent years, extensive preclinical research and more than one hundred clinical trials have tested different strategies of neoantigen discovery and vaccine formulations. However, despite the enthusiasm for neoantigen vaccines, proof of unequivocal efficacy has remained beyond reach for the majority of clinical trials. In this Review, we focus on the key obstacles pertaining to vaccine design and tumour environment that remain to be overcome in order to unleash the true potential of neoantigen vaccines in cancer therapy.
Collapse
Affiliation(s)
- Peter D Katsikis
- Department of Immunology, Erasmus University Medical Center, Rotterdam, Netherlands.
| | - Ken J Ishii
- Division of Vaccine Science, Department of Microbiology and Immunology, The Institute of Medical Science, The University of Tokyo (IMSUT), Tokyo, Japan
- International Vaccine Design Center (vDesC), The Institute of Medical Science, The University of Tokyo (IMSUT), Tokyo, Japan
| | - Christopher Schliehe
- Department of Immunology, Erasmus University Medical Center, Rotterdam, Netherlands
| |
Collapse
|
6
|
Kina E, Laverdure JP, Durette C, Lanoix J, Courcelles M, Zhao Q, Apavaloaei A, Larouche JD, Hardy MP, Vincent K, Gendron P, Hesnard L, Thériault C, Ruiz Cuevas MV, Ehx G, Thibault P, Perreault C. Breast cancer immunopeptidomes contain numerous shared tumor antigens. J Clin Invest 2024; 134:e166740. [PMID: 37906288 PMCID: PMC10760959 DOI: 10.1172/jci166740] [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: 10/31/2022] [Accepted: 10/23/2023] [Indexed: 11/02/2023] Open
Abstract
Hormone receptor-positive breast cancer (HR+) is immunologically cold and has not benefited from advances in immunotherapy. In contrast, subsets of triple-negative breast cancer (TNBC) display high leukocytic infiltration and respond to checkpoint blockade. CD8+ T cells, the main effectors of anticancer responses, recognize MHC I-associated peptides (MAPs). Our work aimed to characterize the repertoire of MAPs presented by HR+ and TNBC tumors. Using mass spectrometry, we identified 57,094 unique MAPs in 26 primary breast cancer samples. MAP source genes highly overlapped between both subtypes. We identified 25 tumor-specific antigens (TSAs) mainly deriving from aberrantly expressed regions. TSAs were most frequently identified in TNBC samples and were more shared among The Cancer Genome Atlas (TCGA) database TNBC than HR+ samples. In the TNBC cohort, the predicted number of TSAs positively correlated with leukocytic infiltration and overall survival, supporting their immunogenicity in vivo. We detected 49 tumor-associated antigens (TAAs), some of which derived from cancer-associated fibroblasts. Functional expansion of specific T cell assays confirmed the in vitro immunogenicity of several TSAs and TAAs. Our study identified attractive targets for cancer immunotherapy in both breast cancer subtypes. The higher prevalence of TSAs in TNBC tumors provides a rationale for their responsiveness to checkpoint blockade.
Collapse
Affiliation(s)
- Eralda Kina
- Institute for Research in Immunology and Cancer (IRIC), and
- Department of Medicine, University of Montreal, Montreal, Quebec, Canada
| | | | | | - Joël Lanoix
- Institute for Research in Immunology and Cancer (IRIC), and
| | | | - Qingchuan Zhao
- Institute for Research in Immunology and Cancer (IRIC), and
- Department of Medicine, University of Montreal, Montreal, Quebec, Canada
| | - Anca Apavaloaei
- Institute for Research in Immunology and Cancer (IRIC), and
- Department of Medicine, University of Montreal, Montreal, Quebec, Canada
| | - Jean-David Larouche
- Institute for Research in Immunology and Cancer (IRIC), and
- Department of Medicine, University of Montreal, Montreal, Quebec, Canada
| | | | | | | | - Leslie Hesnard
- Institute for Research in Immunology and Cancer (IRIC), and
| | | | - Maria Virginia Ruiz Cuevas
- Institute for Research in Immunology and Cancer (IRIC), and
- Department of Medicine, University of Montreal, Montreal, Quebec, Canada
| | - Grégory Ehx
- Laboratory of Hematology, GIGA-I3, University of Liege and CHU of Liège, Liege, Belgium
| | - Pierre Thibault
- Institute for Research in Immunology and Cancer (IRIC), and
- Department of Chemistry, University of Montreal, Montreal, Quebec, Canada
| | - Claude Perreault
- Institute for Research in Immunology and Cancer (IRIC), and
- Department of Medicine, University of Montreal, Montreal, Quebec, Canada
| |
Collapse
|
7
|
Pan S, Fan R, Han B, Tong A, Guo G. The potential of mRNA vaccines in cancer nanomedicine and immunotherapy. Trends Immunol 2024; 45:20-31. [PMID: 38142147 DOI: 10.1016/j.it.2023.11.003] [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: 10/12/2023] [Revised: 11/21/2023] [Accepted: 11/22/2023] [Indexed: 12/25/2023]
Abstract
Owing to their outstanding performance against COVID-19, mRNA vaccines have brought great hope for combating various incurable diseases, including cancer. Differences in the encoded proteins result in different molecular and cellular mechanisms of mRNA vaccines. With the rapid development of nanotechnology and molecular medicine, personalized antigen-encoding mRNA vaccines that enhance antigen presentation can trigger effective immune responses and prevent off-target toxicities. Herein, we review new insights into the influence of encoded antigens, cytokines, and other functional proteins on the mechanisms of mRNA vaccines. We also highlight the importance of delivery systems and chemical modifications for mRNA translation efficiency, stability, and targeting, and we discuss the potential problems and application prospects of mRNA vaccines as versatile tools for combating cancer.
Collapse
Affiliation(s)
- Shulin Pan
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Rangrang Fan
- Department of Neurosurgery and Institute of Neurosurgery, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Bo Han
- School of Pharmacy, Shihezi University, and Key Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education, Shihezi, 832002, China
| | - Aiping Tong
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Gang Guo
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China.
| |
Collapse
|
8
|
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.
Collapse
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.
| |
Collapse
|
9
|
Nelde A, Schuster H, Heitmann JS, Bauer J, Maringer Y, Zwick M, Volkmer JP, Chen JY, Stanger AMP, Lehmann A, Appiah B, Märklin M, Rücker-Braun E, Salih HR, Roerden M, Schroeder SM, Häring MF, Schlosser A, Schetelig J, Schmitz M, Boerries M, Köhler N, Lengerke C, Majeti R, Weissman IL, Rammensee HG, Walz JS. Immune Surveillance of Acute Myeloid Leukemia Is Mediated by HLA-Presented Antigens on Leukemia Progenitor Cells. Blood Cancer Discov 2023; 4:468-489. [PMID: 37847741 PMCID: PMC10618727 DOI: 10.1158/2643-3230.bcd-23-0020] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 06/13/2023] [Accepted: 09/28/2023] [Indexed: 10/19/2023] Open
Abstract
Therapy-resistant leukemia stem and progenitor cells (LSC) are a main cause of acute myeloid leukemia (AML) relapse. LSC-targeting therapies may thus improve outcome of patients with AML. Here we demonstrate that LSCs present HLA-restricted antigens that induce T-cell responses allowing for immune surveillance of AML. Using a mass spectrometry-based immunopeptidomics approach, we characterized the antigenic landscape of patient LSCs and identified AML- and AML/LSC-associated HLA-presented antigens absent from normal tissues comprising nonmutated peptides, cryptic neoepitopes, and neoepitopes of common AML driver mutations of NPM1 and IDH2. Functional relevance of shared AML/LSC antigens is illustrated by presence of their cognizant memory T cells in patients. Antigen-specific T-cell recognition and HLA class II immunopeptidome diversity correlated with clinical outcome. Together, these antigens shared among AML and LSCs represent prime targets for T cell-based therapies with potential of eliminating residual LSCs in patients with AML. SIGNIFICANCE The elimination of therapy-resistant leukemia stem and progenitor cells (LSC) remains a major challenge in the treatment of AML. This study identifies and functionally validates LSC-associated HLA class I and HLA class II-presented antigens, paving the way to the development of LSC-directed T cell-based immunotherapeutic approaches for patients with AML. See related commentary by Ritz, p. 430 . This article is featured in Selected Articles from This Issue, p. 419.
Collapse
Affiliation(s)
- Annika Nelde
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
| | - Heiko Schuster
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
| | - Jonas S. Heitmann
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Jens Bauer
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
| | - Yacine Maringer
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
| | - Melissa Zwick
- Department of Medicine I, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jens-Peter Volkmer
- Institute for Stem Cell Biology and Regenerative Medicine and the Ludwig Cancer Center, Stanford University School of Medicine, Stanford, California
| | - James Y. Chen
- Institute for Stem Cell Biology and Regenerative Medicine and the Ludwig Cancer Center, Stanford University School of Medicine, Stanford, California
| | - Anna M. Paczulla Stanger
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Tübingen, Germany
- Department of Biomedicine, University of Basel and University Hospital Basel, Basel, Switzerland
| | - Ariane Lehmann
- Faculty of Medicine, Medical Center, Institute of Medical Bioinformatics and Systems Medicine (IBSM), University of Freiburg, Germany
| | - Bismark Appiah
- Faculty of Medicine, Medical Center, Institute of Medical Bioinformatics and Systems Medicine (IBSM), University of Freiburg, Germany
| | - Melanie Märklin
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Elke Rücker-Braun
- Department of Medicine I, University Hospital of Dresden, Dresden, Germany
- Center for Regenerative Therapies Dresden, Technische Universität Dresden, Dresden, Germany
| | - Helmut R. Salih
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Malte Roerden
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Tübingen, Germany
| | - Sarah M. Schroeder
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Department of Otorhinolaryngology, Head and Neck Surgery, University of Tübingen, Tübingen, Germany
| | - Max-Felix Häring
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Tübingen, Germany
| | | | - Johannes Schetelig
- Department of Medicine I, University Hospital of Dresden, Dresden, Germany
- German Bone Marrow Donor Center (DKMS), Clinical Trials Unit, Dresden, Germany
| | - Marc Schmitz
- Institute of Immunology, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- German Cancer Consortium (DKTK), partner site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Melanie Boerries
- Faculty of Medicine, Medical Center, Institute of Medical Bioinformatics and Systems Medicine (IBSM), University of Freiburg, Germany
- Comprehensive Cancer Center Freiburg (CCCF), Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK), Partner Site, Freiburg, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Natalie Köhler
- Department of Medicine I, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Centre for Integrative Biological Signalling Studies (CIBSS), University of Freiburg, Freiburg, Germany
| | - Claudia Lengerke
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Tübingen, Germany
- Department of Biomedicine, University of Basel and University Hospital Basel, Basel, Switzerland
- Clinic for Hematology, University of Basel and University Hospital Basel, Basel, Switzerland
- German Cancer Consortium (DKTK), DKFZ partner site Tübingen, Germany
| | - Ravindra Majeti
- Institute for Stem Cell Biology and Regenerative Medicine and the Ludwig Cancer Center, Stanford University School of Medicine, Stanford, California
- Division of Hematology, Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - Irving L. Weissman
- Institute for Stem Cell Biology and Regenerative Medicine and the Ludwig Cancer Center, Stanford University School of Medicine, Stanford, California
| | - Hans-Georg Rammensee
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK), DKFZ partner site Tübingen, Germany
| | - Juliane S. Walz
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
| |
Collapse
|
10
|
Ternette N, Adamopoulou E, Purcell AW. How mass spectrometric interrogation of MHC class I ligandomes has advanced our understanding of immune responses to viruses. Semin Immunol 2023; 68:101780. [PMID: 37276649 DOI: 10.1016/j.smim.2023.101780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 05/19/2023] [Accepted: 05/19/2023] [Indexed: 06/07/2023]
Affiliation(s)
- Nicola Ternette
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford OX37BN, UK.
| | - Eleni Adamopoulou
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford OX37BN, UK
| | - Anthony W Purcell
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC 3800, Australia
| |
Collapse
|
11
|
Admon A. The biogenesis of the immunopeptidome. Semin Immunol 2023; 67:101766. [PMID: 37141766 DOI: 10.1016/j.smim.2023.101766] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/26/2023] [Accepted: 04/26/2023] [Indexed: 05/06/2023]
Abstract
The immunopeptidome is the repertoire of peptides bound and presented by the MHC class I, class II, and non-classical molecules. The peptides are produced by the degradation of most cellular proteins, and in some cases, peptides are produced from extracellular proteins taken up by the cells. This review attempts to first describe some of its known and well-accepted concepts, and next, raise some questions about a few of the established dogmas in this field: The production of novel peptides by splicing is questioned, suggesting here that spliced peptides are extremely rare, if existent at all. The degree of the contribution to the immunopeptidome by degradation of cellular protein by the proteasome is doubted, therefore this review attempts to explain why it is likely that this contribution to the immunopeptidome is possibly overstated. The contribution of defective ribosome products (DRiPs) and non-canonical peptides to the immunopeptidome is noted and methods are suggested to quantify them. In addition, the common misconception that the MHC class II peptidome is mostly derived from extracellular proteins is noted, and corrected. It is stressed that the confirmation of sequence assignments of non-canonical and spliced peptides should rely on targeted mass spectrometry using spiking-in of heavy isotope-labeled peptides. Finally, the new methodologies and modern instrumentation currently available for high throughput kinetics and quantitative immunopeptidomics are described. These advanced methods open up new possibilities for utilizing the big data generated and taking a fresh look at the established dogmas and reevaluating them critically.
Collapse
Affiliation(s)
- Arie Admon
- Faculty of Biology, Technion-Israel Institute of Technology, Israel.
| |
Collapse
|
12
|
Kraemer AI, Chong C, Huber F, Pak H, Stevenson BJ, Müller M, Michaux J, Altimiras ER, Rusakiewicz S, Simó-Riudalbas L, Planet E, Wiznerowicz M, Dagher J, Trono D, Coukos G, Tissot S, Bassani-Sternberg M. The immunopeptidome landscape associated with T cell infiltration, inflammation and immune editing in lung cancer. NATURE CANCER 2023; 4:608-628. [PMID: 37127787 DOI: 10.1038/s43018-023-00548-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 03/24/2023] [Indexed: 05/03/2023]
Abstract
One key barrier to improving efficacy of personalized cancer immunotherapies that are dependent on the tumor antigenic landscape remains patient stratification. Although patients with CD3+CD8+ T cell-inflamed tumors typically show better response to immune checkpoint inhibitors, it is still unknown whether the immunopeptidome repertoire presented in highly inflamed and noninflamed tumors is substantially different. We surveyed 61 tumor regions and adjacent nonmalignant lung tissues from 8 patients with lung cancer and performed deep antigen discovery combining immunopeptidomics, genomics, bulk and spatial transcriptomics, and explored the heterogeneous expression and presentation of tumor (neo)antigens. In the present study, we associated diverse immune cell populations with the immunopeptidome and found a relatively higher frequency of predicted neoantigens located within HLA-I presentation hotspots in CD3+CD8+ T cell-excluded tumors. We associated such neoantigens with immune recognition, supporting their involvement in immune editing. This could have implications for the choice of combination therapies tailored to the patient's mutanome and immune microenvironment.
Collapse
Affiliation(s)
- Anne I Kraemer
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Chloe Chong
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Florian Huber
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - HuiSong Pak
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Brian J Stevenson
- Agora Cancer Research Centre, Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland
| | - Markus Müller
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland
| | - Justine Michaux
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Emma Ricart Altimiras
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Sylvie Rusakiewicz
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Laia Simó-Riudalbas
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Evarist Planet
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Maciej Wiznerowicz
- International Institute for Molecular Oncology, Poznań, Poland
- Poznań University of Medical Sciences, Poznań, Poland
| | - Julien Dagher
- Department of Pathology, Centre hospitalier universitaire vaudois, Lausanne, Switzerland
| | - Didier Trono
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - George Coukos
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Stephanie Tissot
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois, Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.
- Department of Oncology, Centre hospitalier universitaire vaudois, Lausanne, Switzerland.
- Agora Cancer Research Centre, Lausanne, Switzerland.
- Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.
| |
Collapse
|
13
|
Pyke RM, Mellacheruvu D, Dea S, Abbott C, Zhang SV, Phillips NA, Harris J, Bartha G, Desai S, McClory R, West J, Snyder MP, Chen R, Boyle SM. Precision Neoantigen Discovery Using Large-Scale Immunopeptidomes and Composite Modeling of MHC Peptide Presentation. Mol Cell Proteomics 2023; 22:100506. [PMID: 36796642 PMCID: PMC10114598 DOI: 10.1016/j.mcpro.2023.100506] [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: 12/30/2022] [Accepted: 01/24/2023] [Indexed: 02/16/2023] Open
Abstract
Major histocompatibility complex (MHC)-bound peptides that originate from tumor-specific genetic alterations, known as neoantigens, are an important class of anticancer therapeutic targets. Accurately predicting peptide presentation by MHC complexes is a key aspect of discovering therapeutically relevant neoantigens. Technological improvements in mass spectrometry-based immunopeptidomics and advanced modeling techniques have vastly improved MHC presentation prediction over the past 2 decades. However, improvement in the accuracy of prediction algorithms is needed for clinical applications like the development of personalized cancer vaccines, the discovery of biomarkers for response to immunotherapies, and the quantification of autoimmune risk in gene therapies. Toward this end, we generated allele-specific immunopeptidomics data using 25 monoallelic cell lines and created Systematic Human Leukocyte Antigen (HLA) Epitope Ranking Pan Algorithm (SHERPA), a pan-allelic MHC-peptide algorithm for predicting MHC-peptide binding and presentation. In contrast to previously published large-scale monoallelic data, we used an HLA-null K562 parental cell line and a stable transfection of HLA allele to better emulate native presentation. Our dataset includes five previously unprofiled alleles that expand MHC diversity in the training data and extend allelic coverage in underprofiled populations. To improve generalizability, SHERPA systematically integrates 128 monoallelic and 384 multiallelic samples with publicly available immunoproteomics data and binding assay data. Using this dataset, we developed two features that empirically estimate the propensities of genes and specific regions within gene bodies to engender immunopeptides to represent antigen processing. Using a composite model constructed with gradient boosting decision trees, multiallelic deconvolution, and 2.15 million peptides encompassing 167 alleles, we achieved a 1.44-fold improvement of positive predictive value compared with existing tools when evaluated on independent monoallelic datasets and a 1.17-fold improvement when evaluating on tumor samples. With a high degree of accuracy, SHERPA has the potential to enable precision neoantigen discovery for future clinical applications.
Collapse
Affiliation(s)
| | | | - Steven Dea
- Personalis, Inc, Menlo Park, California, USA
| | | | | | | | | | | | - Sejal Desai
- Personalis, Inc, Menlo Park, California, USA
| | | | - John West
- Personalis, Inc, Menlo Park, California, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University, Palo Alto, California, USA
| | | | | |
Collapse
|
14
|
Shapiro IE, Bassani-Sternberg M. The impact of immunopeptidomics: From basic research to clinical implementation. Semin Immunol 2023; 66:101727. [PMID: 36764021 DOI: 10.1016/j.smim.2023.101727] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/30/2023] [Accepted: 02/01/2023] [Indexed: 02/10/2023]
Abstract
The immunopeptidome is the set of peptides presented by the major histocompatibility complex (MHC) molecules, in humans also known as the human leukocyte antigen (HLA), on the surface of cells that mediate T-cell immunosurveillance. The immunopeptidome is a sampling of the cellular proteome and hence it contains information about the health state of cells. The peptide repertoire is influenced by intra- and extra-cellular perturbations - such as in the case of drug exposure, infection, or oncogenic transformation. Immunopeptidomics is the bioanalytical method by which the presented peptides are extracted from biological samples and analyzed by high-performance liquid chromatography coupled to tandem mass spectrometry (MS), resulting in a deep qualitative and quantitative snapshot of the immunopeptidome. In this review, we discuss published immunopeptidomics studies from recent years, grouped into three main domains: i) basic, ii) pre-clinical and iii) clinical research and applications. We review selected fundamental immunopeptidomics studies on the antigen processing and presentation machinery, on HLA restriction and studies that advanced our understanding of various diseases, and how exploration of the antigenic landscape allowed immune targeting at the pre-clinical stage, paving the way to pioneering exploratory clinical trials where immunopeptidomics is directly implemented in the conception of innovative treatments for cancer patients.
Collapse
Affiliation(s)
- Ilja E Shapiro
- Ludwig Institute for Cancer Research, University of Lausanne, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland; Center of Experimental Therapeutics, Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), 1005 Lausanne, Switzerland.
| |
Collapse
|
15
|
Tan X, Xu L, Jian X, Ouyang J, Hu B, Yang X, Wang T, Xie L. PGNneo: A Proteogenomics-Based Neoantigen Prediction Pipeline in Noncoding Regions. Cells 2023; 12:cells12050782. [PMID: 36899918 PMCID: PMC10000440 DOI: 10.3390/cells12050782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/26/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open
Abstract
The development of a neoantigen-based personalized vaccine has promise in the hunt for cancer immunotherapy. The challenge in neoantigen vaccine design is the need to rapidly and accurately identify, in patients, those neoantigens with vaccine potential. Evidence shows that neoantigens can be derived from noncoding sequences, but there are few specific tools for identifying neoantigens in noncoding regions. In this work, we describe a proteogenomics-based pipeline, namely PGNneo, for use in discovering neoantigens derived from the noncoding region of the human genome with reliability. In PGNneo, four modules are included: (1) noncoding somatic variant calling and HLA typing; (2) peptide extraction and customized database construction; (3) variant peptide identification; (4) neoantigen prediction and selection. We have demonstrated the effectiveness of PGNneo and applied and validated our methodology in two real-world hepatocellular carcinoma (HCC) cohorts. TP53, WWP1, ATM, KMT2C, and NFE2L2, which are frequently mutating genes associated with HCC, were identified in two cohorts and corresponded to 107 neoantigens from non-coding regions. In addition, we applied PGNneo to a colorectal cancer (CRC) cohort, demonstrating that the tool can be extended and verified in other tumor types. In summary, PGNneo can specifically detect neoantigens generated by noncoding regions in tumors, providing additional immune targets for cancer types with a low tumor mutational burden (TMB) in coding regions. PGNneo, together with our previous tool, can identify coding and noncoding region-derived neoantigens and, thus, will contribute to a complete understanding of the tumor immune target landscape. PGNneo source code and documentation are available at Github. To facilitate the installation and use of PGNneo, we provide a Docker container and a GUI.
Collapse
Affiliation(s)
- Xiaoxiu Tan
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Shanghai-MOST Key Laboratory of Health and Disease Genomics & Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Linfeng Xu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics & Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Xingxing Jian
- Shanghai-MOST Key Laboratory of Health and Disease Genomics & Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Jian Ouyang
- Shanghai-MOST Key Laboratory of Health and Disease Genomics & Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Bo Hu
- Liver Cancer Institute, Fudan University, Shanghai 200032, China
| | - Xinrong Yang
- Liver Cancer Institute, Fudan University, Shanghai 200032, China
| | - Tao Wang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Correspondence: (T.W.); (L.X.)
| | - Lu Xie
- Shanghai-MOST Key Laboratory of Health and Disease Genomics & Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
- Correspondence: (T.W.); (L.X.)
| |
Collapse
|
16
|
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: 24] [Impact Index Per Article: 24.0] [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.
Collapse
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
| |
Collapse
|
17
|
Karnaukhov V, Paes W, Woodhouse IB, Partridge T, Nicastri A, Brackenridge S, Shcherbinin D, Chudakov DM, Zvyagin IV, Ternette N, Koohy H, Borrow P, Shugay M. HLA variants have different preferences to present proteins with specific molecular functions which are complemented in frequent haplotypes. Front Immunol 2022; 13:1067463. [PMID: 36605212 PMCID: PMC9808399 DOI: 10.3389/fimmu.2022.1067463] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 11/21/2022] [Indexed: 12/24/2022] Open
Abstract
Human leukocyte antigen (HLA) genes are the most polymorphic loci in the human genome and code for proteins that play a key role in guiding adaptive immune responses by presenting foreign and self peptides (ligands) to T cells. Each person carries up to 6 HLA class I variants (maternal and paternal copies of HLA-A, HLA-B and HLA-C genes) and also multiple HLA class II variants, which cumulatively define the landscape of peptides presented to T cells. Each HLA variant has its own repertoire of presented peptides with a certain sequence motif which is mainly defined by peptide anchor residues (typically the second and the last positions for HLA class I ligands) forming key interactions with the peptide-binding groove of HLA. In this study, we aimed to characterize HLA binding preferences in terms of molecular functions of presented proteins. To focus on the ligand presentation bias introduced specifically by HLA-peptide interaction we performed large-scale in silico predictions of binding of all peptides from human proteome for a wide range of HLA variants and established which functions are characteristic for proteins that are more or less preferentially presented by different HLA variants using statistical calculations and gene ontology (GO) analysis. We demonstrated marked distinctions between HLA variants in molecular functions of preferentially presented proteins (e.g. some HLA variants preferentially present membrane and receptor proteins, while others - ribosomal and DNA-binding proteins) and reduced presentation of extracellular matrix and collagen proteins by the majority of HLA variants. To explain these observations we demonstrated that HLA preferentially presents proteins enriched in amino acids which are required as anchor residues for the particular HLA variant. Our observations can be extrapolated to explain the protective effect of certain HLA alleles in infectious diseases, and we hypothesize that they can also explain susceptibility to certain autoimmune diseases and cancers. We demonstrate that these differences lead to differential presentation of HIV, influenza virus, SARS-CoV-1 and SARS-CoV-2 proteins by various HLA alleles. Taking into consideration that HLA alleles are inherited in haplotypes, we hypothesized that haplotypes composed of a combination of HLA variants with different presentation preferences should be more advantageous as they allow presenting a larger repertoire of peptides and avoiding holes in immunopeptidome. Indeed, we demonstrated that HLA-A/HLA-B and HLA-A/HLA-C haplotypes which have a high frequency in the human population are comprised of HLA variants that are more distinct in terms of functions of preferentially presented proteins than the control pairs.
Collapse
Affiliation(s)
- Vadim Karnaukhov
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Science, Moscow, Russia
| | - Wayne Paes
- Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
| | - Isaac B. Woodhouse
- Medical Research Council (MRC) Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM) Centre for Computational Biology, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), University of Oxford, Oxford, United Kingdom
| | - Thomas Partridge
- Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
| | - Annalisa Nicastri
- The Jenner Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Simon Brackenridge
- Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
| | - Dmitrii Shcherbinin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Science, Moscow, Russia
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Dmitry M. Chudakov
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Science, Moscow, Russia
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Ivan V. Zvyagin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Science, Moscow, Russia
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Nicola Ternette
- The Jenner Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Hashem Koohy
- Medical Research Council (MRC) Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM) Centre for Computational Biology, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), University of Oxford, Oxford, United Kingdom
| | - Persephone Borrow
- Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
| | - Mikhail Shugay
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Science, Moscow, Russia
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
| |
Collapse
|
18
|
Fujiwara K, Shao Y, Niu N, Zhang T, Herbst B, Henderson M, Muth S, Zhang P, Zheng L. Direct identification of HLA class I and class II-restricted T cell epitopes in pancreatic cancer tissues by mass spectrometry. J Hematol Oncol 2022; 15:154. [PMID: 36284347 PMCID: PMC9597957 DOI: 10.1186/s13045-022-01373-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 10/11/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Identifying T cell epitopes on pancreatic ductal adenocarcinoma (PDAC) associated antigens or neoantigens has been a challenge. In this study, we attempted to identify PDAC T cell epitopes by mass spectrometry (MS). METHODS We isolated HLA class I (HLA-I) and HLA class II (HLA-II)-restricted peptides, respectively, from tissues of human PDAC by using the pan-HLA-I or pan-HLA-II affinity purification column and identified T cell epitopes by peptidome analysis with MS. RESULTS Through peptidome analysis, we identified T cell epitopes shared by multiple patients with different HLA types and those containing sequences of both anti-HLA-I and HLA-II antibodies-affinity purified peptides. The identified epitopes bound non-matched HLA molecules and induced T cell response in peripheral T cells from both HLA-type matched and non-matched patients. Peptides containing both HLA class I and class II epitopes were able to induce polyfunctional cytokine responses in peripheral T cells. CONCLUSIONS T cell epitopes in PDAC can be discovered by the MS approach and can be designed into vaccine and TCR-T cell therapies for both HLA-type matched and non-matched patients.
Collapse
Affiliation(s)
- Kenji Fujiwara
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.,The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.,The Pancreatic Cancer Precision Medicine Center of Excellence Program, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.,Department of Surgery, Kimura Hospital, Fukuoka, Japan
| | - Yingkuan Shao
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.,The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.,The Pancreatic Cancer Precision Medicine Center of Excellence Program, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.,The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Nan Niu
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.,The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.,The Pancreatic Cancer Precision Medicine Center of Excellence Program, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Tengyi Zhang
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.,The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.,The Pancreatic Cancer Precision Medicine Center of Excellence Program, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Brian Herbst
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.,The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.,The Pancreatic Cancer Precision Medicine Center of Excellence Program, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.,The Cellular and Molecular Medicine Graduate Program, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Mackenzie Henderson
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.,The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.,The Pancreatic Cancer Precision Medicine Center of Excellence Program, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Stephen Muth
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.,The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.,The Pancreatic Cancer Precision Medicine Center of Excellence Program, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Pingbo Zhang
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.
| | - Lei Zheng
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA. .,The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA. .,The Pancreatic Cancer Precision Medicine Center of Excellence Program, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA. .,The Cellular and Molecular Medicine Graduate Program, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA. .,Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.
| |
Collapse
|
19
|
Liu Z, Lv J, Dang Q, Liu L, Weng S, Wang L, Zhou Z, Kong Y, Li H, Han Y, Han X. Engineering neoantigen vaccines to improve cancer personalized immunotherapy. Int J Biol Sci 2022; 18:5607-5623. [PMID: 36263174 PMCID: PMC9576504 DOI: 10.7150/ijbs.76281] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 08/25/2022] [Indexed: 01/12/2023] Open
Abstract
Immunotherapy treatments harnessing the immune system herald a new era of personalized medicine, offering considerable benefits for cancer patients. Over the past years, tumor neoantigens emerged as a rising star in immunotherapy. Neoantigens are tumor-specific antigens arising from somatic mutations, which are proceeded and presented by the major histocompatibility complex on the cell surface. With the advancement of sequencing technology and bioinformatics engineering, the recognition of neoantigens has accelerated and is expected to be incorporated into the clinical routine. Currently, tumor vaccines against neoantigens mainly encompass peptides, DNA, RNA, and dendritic cells, which are extremely specific to individual patients. Due to the high immunogenicity of neoantigens, tumor vaccines could activate and expand antigen-specific CD4+ and CD8+ T cells to intensify anti-tumor immunity. Herein, we introduce the origin and prediction of neoantigens and compare the advantages and disadvantages of multiple types of neoantigen vaccines. Besides, we review the immunizations and the current clinical research status in neoantigen vaccines, and outline strategies for enhancing the efficacy of neoantigen vaccines. Finally, we present the challenges facing the application of neoantigens.
Collapse
Affiliation(s)
- Zaoqu Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China.,Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China.,Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China
| | - Jinxiang Lv
- Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Qin Dang
- Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Long Liu
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Siyuan Weng
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Libo Wang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Zhaokai Zhou
- Department of Pediatric Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 40052, China
| | - Ying Kong
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Huanyun Li
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Yilin Han
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Xinwei Han
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China.,Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China.,Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China.,✉ Corresponding author: Xinwei Han.
| |
Collapse
|
20
|
Arnaud M, Chiffelle J, Genolet R, Navarro Rodrigo B, Perez MAS, Huber F, Magnin M, Nguyen-Ngoc T, Guillaume P, Baumgaertner P, Chong C, Stevenson BJ, Gfeller D, Irving M, Speiser DE, Schmidt J, Zoete V, Kandalaft LE, Bassani-Sternberg M, Bobisse S, Coukos G, Harari A. Sensitive identification of neoantigens and cognate TCRs in human solid tumors. Nat Biotechnol 2022; 40:656-660. [PMID: 34782741 PMCID: PMC9110298 DOI: 10.1038/s41587-021-01072-6] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 08/20/2021] [Indexed: 12/18/2022]
Abstract
The identification of patient-specific tumor antigens is complicated by the low frequency of T cells specific for each tumor antigen. Here we describe NeoScreen, a method that enables the sensitive identification of rare tumor (neo)antigens and of cognate T cell receptors (TCRs) expressed by tumor-infiltrating lymphocytes. T cells transduced with tumor antigen-specific TCRs identified by NeoScreen mediate regression of established tumors in patient-derived xenograft mice.
Collapse
Affiliation(s)
- Marion Arnaud
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland
- Centre des Thérapies Expérimentales (CTE), Department of Oncology - Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- Department of Oncology - University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Johanna Chiffelle
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland
- Centre des Thérapies Expérimentales (CTE), Department of Oncology - Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- Department of Oncology - University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Raphael Genolet
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland
- Centre des Thérapies Expérimentales (CTE), Department of Oncology - Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- Department of Oncology - University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Blanca Navarro Rodrigo
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland
- Centre des Thérapies Expérimentales (CTE), Department of Oncology - Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- Department of Oncology - University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Marta A S Perez
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Florian Huber
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland
- Centre des Thérapies Expérimentales (CTE), Department of Oncology - Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- Department of Oncology - University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Morgane Magnin
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland
- Centre des Thérapies Expérimentales (CTE), Department of Oncology - Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- Department of Oncology - University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Tu Nguyen-Ngoc
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology - University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Philippe Guillaume
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland
- Centre des Thérapies Expérimentales (CTE), Department of Oncology - Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- Department of Oncology - University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Petra Baumgaertner
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland
- Centre des Thérapies Expérimentales (CTE), Department of Oncology - Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- Department of Oncology - University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Chloe Chong
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland
- Centre des Thérapies Expérimentales (CTE), Department of Oncology - Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- Department of Oncology - University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Brian J Stevenson
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - David Gfeller
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology - University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Melita Irving
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology - University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Daniel E Speiser
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology - University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Julien Schmidt
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland
- Centre des Thérapies Expérimentales (CTE), Department of Oncology - Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- Department of Oncology - University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Vincent Zoete
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Oncology - University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Lana E Kandalaft
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland
- Centre des Thérapies Expérimentales (CTE), Department of Oncology - Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- Department of Oncology - University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland
- Centre des Thérapies Expérimentales (CTE), Department of Oncology - Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- Department of Oncology - University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Sara Bobisse
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland
- Centre des Thérapies Expérimentales (CTE), Department of Oncology - Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- Department of Oncology - University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - George Coukos
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland.
- Centre des Thérapies Expérimentales (CTE), Department of Oncology - Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland.
- Department of Oncology - University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland.
| | - Alexandre Harari
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne (UNIL), Lausanne, Switzerland.
- Centre des Thérapies Expérimentales (CTE), Department of Oncology - Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland.
- Department of Oncology - University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland.
| |
Collapse
|
21
|
Becker JP, Riemer AB. The Importance of Being Presented: Target Validation by Immunopeptidomics for Epitope-Specific Immunotherapies. Front Immunol 2022; 13:883989. [PMID: 35464395 PMCID: PMC9018990 DOI: 10.3389/fimmu.2022.883989] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 03/16/2022] [Indexed: 11/26/2022] Open
Abstract
Presentation of tumor-specific or tumor-associated peptides by HLA class I molecules to CD8+ T cells is the foundation of epitope-centric cancer immunotherapies. While often in silico HLA binding predictions or in vitro immunogenicity assays are utilized to select candidates, mass spectrometry-based immunopeptidomics is currently the only method providing a direct proof of actual cell surface presentation. Despite much progress in the last decade, identification of such HLA-presented peptides remains challenging. Here we review typical workflows and current developments in the field of immunopeptidomics, highlight the challenges which remain to be solved and emphasize the importance of direct target validation for clinical immunotherapy development.
Collapse
Affiliation(s)
- Jonas P. Becker
- Immunotherapy and Immunoprevention, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Angelika B. Riemer
- Immunotherapy and Immunoprevention, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Molecular Vaccine Design, German Center for Infection Research (DZIF), Partner Site Heidelberg, Heidelberg, Germany
| |
Collapse
|
22
|
A transformer-based model to predict peptide–HLA class I binding and optimize mutated peptides for vaccine design. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00459-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
|
23
|
Kubiniok P, Marcu A, Bichmann L, Kuchenbecker L, Schuster H, Hamelin DJ, Duquette JD, Kovalchik KA, Wessling L, Kohlbacher O, Rammensee HG, Neidert MC, Sirois I, Caron E. Understanding the constitutive presentation of MHC class I immunopeptidomes in primary tissues. iScience 2022; 25:103768. [PMID: 35141507 PMCID: PMC8810409 DOI: 10.1016/j.isci.2022.103768] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 06/15/2021] [Accepted: 01/11/2022] [Indexed: 12/20/2022] Open
Abstract
Understanding the molecular principles that govern the composition of the MHC-I immunopeptidome across different primary tissues is fundamentally important to predict how T cells respond in different contexts in vivo. Here, we performed a global analysis of the MHC-I immunopeptidome from 29 to 19 primary human and mouse tissues, respectively. First, we observed that different HLA-A, HLA-B, and HLA-C allotypes do not contribute evenly to the global composition of the MHC-I immunopeptidome across multiple human tissues. Second, we found that tissue-specific and housekeeping MHC-I peptides share very distinct properties. Third, we discovered that proteins that are evolutionarily hyperconserved represent the primary source of the MHC-I immunopeptidome at the organism-wide scale. Fourth, we uncovered new components of the antigen processing and presentation network, including the carboxypeptidases CPE, CNDP1/2, and CPVL. Together, this study opens up new avenues toward a system-wide understanding of antigen presentation in vivo across mammalian species. Tissue-specific and housekeeping MHC class I peptides share distinct properties HLA-A, HLA-B, and HLA-C allotypes contribute very unevenly to the pool of class I peptides MHC-I immunopeptidomes are represented by evolutionarily conserved proteins An extended antigen processing and presentation pathway is uncovered
Collapse
Affiliation(s)
- Peter Kubiniok
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Ana Marcu
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, 72076 Tübingen, Baden-Württemberg, Germany
- Cluster of Excellence iFIT (EXC 2180), “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, 72076 Tübingen, Baden-Württemberg, Germany
| | - Leon Bichmann
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, 72076 Tübingen, Baden-Württemberg, Germany
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, 72074 Tübingen, Baden-Württemberg, Germany
| | - Leon Kuchenbecker
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, 72074 Tübingen, Baden-Württemberg, Germany
| | - Heiko Schuster
- Immatics Biotechnologies GmbH, 72076 Tübingen, Baden-Württemberg, Germany
| | - David J. Hamelin
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | | | | | - Laura Wessling
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Oliver Kohlbacher
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, 72074 Tübingen, Baden-Württemberg, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, 72076 Tübingen, Baden-Württemberg, Germany
- Biomolecular Interactions, Max Planck Institute for Developmental Biology, 72076 Tübingen, Baden-Württemberg, Germany
- Cluster of Excellence Machine Learning in the Sciences (EXC 2064), University of Tübingen, 72074 Tübingen, Baden-Württemberg, Germany
- Translational Bioinformatics, University Hospital Tübingen, 72076 Tübingen, Baden-Württemberg, Germany
| | - Hans-Georg Rammensee
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, 72076 Tübingen, Baden-Württemberg, Germany
- Cluster of Excellence iFIT (EXC 2180), “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, 72076 Tübingen, Baden-Württemberg, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), 72076 Tübingen, Baden-Württemberg, Germany
| | - Marian C. Neidert
- Clinical Neuroscience Center and Department of Neurosurgery, University Hospital and University of Zürich, 8057&8091 Zürich, Switzerland
| | - Isabelle Sirois
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Etienne Caron
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
- Department of Pathology and Cellular Biology, Faculty of Medicine, Université de Montréal, QC H3T 1J4, Canada
- Corresponding author
| |
Collapse
|
24
|
Neoantigen Cancer Vaccines: Generation, Optimization, and Therapeutic Targeting Strategies. Vaccines (Basel) 2022; 10:vaccines10020196. [PMID: 35214655 PMCID: PMC8877108 DOI: 10.3390/vaccines10020196] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/21/2022] [Accepted: 01/23/2022] [Indexed: 12/30/2022] Open
Abstract
Alternatives to conventional cancer treatments are highly sought after for high-risk malignancies that have a poor response to established treatment modalities. With research advancing rapidly in the past decade, neoantigen-based immunotherapeutic approaches represent an effective and highly tolerable therapeutic option. Neoantigens are tumor-specific antigens that are not expressed in normal cells and possess significant immunogenic potential. Several recent studies have described the conceptual framework and methodologies to generate neoantigen-based vaccines as well as the formulation of appropriate clinical trials to advance this approach for patient care. This review aims to describe some of the key studies in the recent literature in this rapidly evolving field and summarize the current advances in neoantigen identification and selection, vaccine generation and delivery, and the optimization of neoantigen-based therapeutic strategies, including the early data from pivotal clinical studies.
Collapse
|
25
|
Mohsen MO, Speiser DE, Michaux J, Pak H, Stevenson BJ, Vogel M, Inchakalody VP, de Brot S, Dermime S, Coukos G, Bassani-Sternberg M, Bachmann MF. Bedside formulation of a personalized multi-neoantigen vaccine against mammary carcinoma. J Immunother Cancer 2022; 10:jitc-2021-002927. [PMID: 35017147 PMCID: PMC8753436 DOI: 10.1136/jitc-2021-002927] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/29/2021] [Indexed: 12/28/2022] Open
Abstract
Background Harnessing the immune system to purposely recognize and destroy tumors represents a significant breakthrough in clinical oncology. Non-synonymous mutations (neoantigenic peptides) were identified as powerful cancer targets. This knowledge can be exploited for further improvements of active immunotherapies, including cancer vaccines, as T cells specific for neoantigens are not attenuated by immune tolerance mechanism and do not harm healthy tissues. The current study aimed at developing an optimized multitarget vaccine using short or long neoantigenic peptides utilizing virus-like particles (VLPs) as an efficient vaccine platform. Methods Mutations of murine mammary carcinoma cells were identified by integrating mass spectrometry-based immunopeptidomics and whole exome sequencing. Neoantigenic peptides were synthesized and covalently linked to virus-like nanoparticles using a Cu-free click chemistry method for easy preparation of vaccines against mouse mammary carcinoma. Results As compared with short peptides, vaccination with long peptides was superior in the generation of neoantigen-specific CD4+ and CD8+ T cells, which readily produced interferon gamma (IFN-γ) and tumor-necrosis factor α (TNF-α). The resulting anti-tumor effect was associated with favorable immune re-polarization in the tumor microenvironment through reduction of myeloid-derived suppressor cells. Vaccination with long neoantigenic peptides also decreased post-surgical tumor recurrence and metastases, and prolonged mouse survival, despite the tumor’s low mutational burden. Conclusion Integrating mass spectrometry-based immunopeptidomics and whole exome sequencing is an efficient approach for identifying neoantigenic peptides. Our multitarget VLP-based vaccine shows a promising anti-tumor effect in an aggressive murine mammary carcinoma model. Future clinical application using this strategy is readily feasible and practical, as click chemistry coupling of personalized synthetic peptides to the nanoparticles can be done at the bedside directly before injection.
Collapse
Affiliation(s)
- Mona O Mohsen
- Department of Medical Oncology, Hamad Medical Corporation, Doha, Qatar .,Department of BioMedical Research, University of Bern, Bern, Switzerland
| | - Daniel E Speiser
- Department of Oncology UNIL CHUV, University of Lausanne, Epalinges, Switzerland
| | - Justine Michaux
- Department of Oncology, University Hospital of Lausanne, Lausanne, Switzerland.,Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
| | - HuiSong Pak
- Department of Oncology, University Hospital of Lausanne, Lausanne, Switzerland.,Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
| | | | - Monique Vogel
- Department of BioMedical Research, University of Bern, Bern, Switzerland
| | | | | | - Said Dermime
- Department of Medical Oncology, National Center for Cancer Care and Research, Doha, Qatar
| | - Georges Coukos
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland .,University of Lausanne, Lausanne, Switzerland
| | - Martin F Bachmann
- Department of BioMedical Research, University of Bern, Bern, Switzerland.,Nuffield Department of Medicine, University of Oxford, Oxford, UK
| |
Collapse
|
26
|
Identification of tumor antigens with immunopeptidomics. Nat Biotechnol 2021; 40:175-188. [PMID: 34635837 DOI: 10.1038/s41587-021-01038-8] [Citation(s) in RCA: 100] [Impact Index Per Article: 33.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 07/29/2021] [Indexed: 12/18/2022]
Abstract
The identification of actionable tumor antigens is indispensable for the development of several cancer immunotherapies, including T cell receptor-transduced T cells and patient-specific mRNA or peptide vaccines. Most known tumor antigens have been identified through extensive molecular characterization and are considered canonical if they derive from protein-coding regions of the genome. By eluting human leukocyte antigen-bound peptides from tumors and subjecting these to mass spectrometry analysis, the peptides can be identified by matching the resulting spectra against reference databases. Recently, mass-spectrometry-based immunopeptidomics has enabled the discovery of noncanonical antigens-antigens derived from sequences outside protein-coding regions or generated by noncanonical antigen-processing mechanisms. Coupled with transcriptomics and ribosome profiling, this method enables the identification of thousands of noncanonical peptides, of which a substantial fraction may be detected exclusively in tumors. Spectral matching against the immense noncanonical reference may generate false positives. However, sensitive mass spectrometry, analytical validation and advanced bioinformatics solutions are expected to uncover the full landscape of presented antigens and clinically relevant targets.
Collapse
|
27
|
Wang Q. Building Personalized Cancer Therapeutics through Multi-Omics Assays and Bacteriophage-Eukaryotic Cell Interactions. Int J Mol Sci 2021; 22:ijms22189712. [PMID: 34575870 PMCID: PMC8468737 DOI: 10.3390/ijms22189712] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 08/31/2021] [Accepted: 09/01/2021] [Indexed: 12/11/2022] Open
Abstract
Bacteriophage-eukaryotic cell interaction provides the biological foundation of Phage Display technology, which has been widely adopted in studies involving protein-protein and protein-peptide interactions, and it provides a direct link between the proteins and the DNA encoding them. Phage display has also facilitated the development of new therapeutic agents targeting personalized cancer mutations. Proteins encoded by mutant genes in cancers can be processed and presented on the tumor cell surface by human leukocyte antigen (HLA) molecules, and such mutant peptides are called Neoantigens. Neoantigens are naturally existing tumor markers presented on the cell surface. In clinical settings, the T-cell recognition of neoantigens is the foundation of cancer immunotherapeutics. This year, we utilized phage display to successfully develop the 1st antibody-based neoantigen targeting approach for next-generation personalized cancer therapeutics. In this article, we discussed the strategies for identifying neoantigens, followed by using phage display to create personalized cancer therapeutics-a complete pipeline for personalized cancer treatment.
Collapse
Affiliation(s)
- Qing Wang
- Complete Omics Inc., 1448 S. Rolling Rd, Baltimore, MD 21227, USA
| |
Collapse
|
28
|
Zitvogel L, Perreault C, Finn OJ, Kroemer G. Beneficial autoimmunity improves cancer prognosis. Nat Rev Clin Oncol 2021; 18:591-602. [PMID: 33976418 DOI: 10.1038/s41571-021-00508-x] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/26/2021] [Indexed: 02/06/2023]
Abstract
Many tumour antigens that do not arise from cancer cell-specific mutations are targets of humoral and cellular immunity despite their expression on non-malignant cells. Thus, in addition to the expected ability to detect mutations and stress-associated shifts in the immunoproteome and immunopeptidome (the sum of MHC class I-bound peptides) unique to malignant cells, the immune system also recognizes antigens expressed in non-malignant cells, which can result in autoimmune reactions against non-malignant cells from the tissue of origin. These autoimmune manifestations include, among others, vitiligo, thyroiditis and paraneoplastic syndromes, concurrent with melanoma, thyroid cancer and non-small-cell lung cancer, respectively. Importantly, despite the undesirable effects of these symptoms, such events can have prognostic value and correlate with favourable disease outcomes, suggesting 'beneficial autoimmunity'. Similarly, the occurrence of dermal and endocrine autoimmune adverse events in patients receiving immune-checkpoint inhibitors can have a positive predictive value for therapeutic outcomes. Neoplasias derived from stem cells deemed 'not essential' for survival (such as melanocytes, thyroid cells and most cells in sex-specific organs) have a particularly good prognosis, perhaps because the host can tolerate autoimmune reactions that destroy tumour cells at some cost to non-malignant tissues. In this Perspective, we discuss examples of spontaneous as well as therapy-induced autoimmunity that correlate with favourable disease outcomes and make a strong case in favour of this 'beneficial autoimmunity' being important not only in patients with advanced-stage disease but also in cancer immunosurveillance.
Collapse
Affiliation(s)
- Laurence Zitvogel
- Gustave Roussy Comprehensive Cancer Institute, Villejuif, France. .,Université Paris Saclay, Faculty of Medicine, Le Kremlin-Bicêtre, France. .,INSERM U1015, Gustave Roussy, Villejuif, France. .,Equipe labellisée par la Ligue contre le cancer, Villejuif, France. .,Center of Clinical Investigations in Biotherapies of Cancer (CICBT) BIOTHERIS, Villejuif, France. .,Suzhou Institute for Systems Medicine, Chinese Academy of Medical Sciences, Suzhou, China.
| | - Claude Perreault
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, QC, Canada
| | - Olivera J Finn
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Guido Kroemer
- Gustave Roussy Comprehensive Cancer Institute, Villejuif, France. .,Suzhou Institute for Systems Medicine, Chinese Academy of Medical Sciences, Suzhou, China. .,Equipe labellisée par la Ligue contre le cancer, Université de Paris, Sorbonne Université, INSERM U1138, Centre de Recherche des Cordeliers, Institut Universitaire de France, Paris, France. .,Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Villejuif, France. .,Pôle de Biologie, Hôpital Européen Georges Pompidou, AP-HP, Paris, France. .,Karolinska Institute, Department of Women's and Children's Health, Karolinska University Hospital, Stockholm, Sweden.
| |
Collapse
|
29
|
Koşaloğlu-Yalçın Z, Blazeska N, Carter H, Nielsen M, Cohen E, Kufe D, Conejo-Garcia J, Robbins P, Schoenberger SP, Peters B, Sette A. The Cancer Epitope Database and Analysis Resource: A Blueprint for the Establishment of a New Bioinformatics Resource for Use by the Cancer Immunology Community. Front Immunol 2021; 12:735609. [PMID: 34504503 PMCID: PMC8421848 DOI: 10.3389/fimmu.2021.735609] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/09/2021] [Indexed: 12/17/2022] Open
Abstract
Recent years have witnessed a dramatic rise in interest towards cancer epitopes in general and particularly neoepitopes, antigens that are encoded by somatic mutations that arise as a consequence of tumorigenesis. There is also an interest in the specific T cell and B cell receptors recognizing these epitopes, as they have therapeutic applications. They can also aid in basic studies to infer the specificity of T cells or B cells characterized in bulk and single-cell sequencing data. The resurgence of interest in T cell and B cell epitopes emphasizes the need to catalog all cancer epitope-related data linked to the biological, immunological, and clinical contexts, and most importantly, making this information freely available to the scientific community in a user-friendly format. In parallel, there is also a need to develop resources for epitope prediction and analysis tools that provide researchers access to predictive strategies and provide objective evaluations of their performance. For example, such tools should enable researchers to identify epitopes that can be effectively used for immunotherapy or in defining biomarkers to predict the outcome of checkpoint blockade therapies. We present here a detailed vision, blueprint, and work plan for the development of a new resource, the Cancer Epitope Database and Analysis Resource (CEDAR). CEDAR will provide a freely accessible, comprehensive collection of cancer epitope and receptor data curated from the literature and provide easily accessible epitope and T cell/B cell target prediction and analysis tools. The curated cancer epitope data will provide a transparent benchmark dataset that can be used to assess how well prediction tools perform and to develop new prediction tools relevant to the cancer research community.
Collapse
MESH Headings
- Antigens, Neoplasm/genetics
- Antigens, Neoplasm/immunology
- Computational Biology
- Databases, Genetic
- Epitopes, B-Lymphocyte
- Epitopes, T-Lymphocyte
- Humans
- Immunotherapy
- Mutation
- Neoplasms/genetics
- Neoplasms/immunology
- Neoplasms/therapy
- Receptors, Antigen, B-Cell/genetics
- Receptors, Antigen, B-Cell/immunology
- Receptors, Antigen, T-Cell/genetics
- Receptors, Antigen, T-Cell/immunology
- Tumor Microenvironment
Collapse
Affiliation(s)
- Zeynep Koşaloğlu-Yalçın
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Nina Blazeska
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Hannah Carter
- Department of Medicine, University of California San Diego, La Jolla, CA, United States
- Moore’s Cancer Center, University of California San Diego, La Jolla, CA, United States
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Argentina
| | - Ezra Cohen
- Moore’s Cancer Center, University of California San Diego, La Jolla, CA, United States
| | - Donald Kufe
- Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Jose Conejo-Garcia
- Department of Gynecologic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - Paul Robbins
- National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Stephen P. Schoenberger
- Laboratory of Cellular Immunology, La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United States
- Department of Medicine, University of California San Diego, La Jolla, CA, United States
| | - Alessandro Sette
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United States
- Department of Medicine, University of California San Diego, La Jolla, CA, United States
| |
Collapse
|
30
|
Joyce S, Ternette N. Know thy immune self and non-self: Proteomics informs on the expanse of self and non-self, and how and where they arise. Proteomics 2021; 21:e2000143. [PMID: 34310018 PMCID: PMC8865197 DOI: 10.1002/pmic.202000143] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 06/30/2021] [Accepted: 07/19/2021] [Indexed: 12/30/2022]
Abstract
T cells play an important role in the adaptive immune response to a variety of infections and cancers. Initiation of a T cell mediated immune response requires antigen recognition in a process termed MHC (major histocompatibility complex) restri ction. A T cell antigen is a composite structure made up of a peptide fragment bound within the antigen‐binding groove of an MHC‐encoded class I or class II molecule. Insight into the precise composition and biology of self and non‐self immunopeptidomes is essential to harness T cell mediated immunity to prevent, treat, or cure infectious diseases and cancers. T cell antigen discovery is an arduous task! The pioneering work in the early 1990s has made large‐scale T cell antigen discovery possible. Thus, advancements in mass spectrometry coupled with proteomics and genomics technologies make possible T cell antigen discovery with ease, accuracy, and sensitivity. Yet we have only begun to understand the breadth and the depth of self and non‐self immunopeptidomes because the molecular biology of the cell continues to surprise us with new secrets directly related to the source, and the processing and presentation of MHC ligands. Focused on MHC class I molecules, this review, therefore, provides a brief historic account of T cell antigen discovery and, against a backdrop of key advances in molecular cell biologic processes, elaborates on how proteogenomics approaches have revolutionised the field.
Collapse
Affiliation(s)
- Sebastian Joyce
- Department of Veterans Affairs, Tennessee Valley Healthcare System and the Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Nicola Ternette
- Centre for Cellular and Molecular Physiology, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| |
Collapse
|
31
|
de Sousa E, Lérias JR, Beltran A, Paraschoudi G, Condeço C, Kamiki J, António PA, Figueiredo N, Carvalho C, Castillo-Martin M, Wang Z, Ligeiro D, Rao M, Maeurer M. Targeting Neoepitopes to Treat Solid Malignancies: Immunosurgery. Front Immunol 2021; 12:592031. [PMID: 34335558 PMCID: PMC8320363 DOI: 10.3389/fimmu.2021.592031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 05/07/2021] [Indexed: 12/26/2022] Open
Abstract
Successful outcome of immune checkpoint blockade in patients with solid cancers is in part associated with a high tumor mutational burden (TMB) and the recognition of private neoantigens by T-cells. The quality and quantity of target recognition is determined by the repertoire of ‘neoepitope’-specific T-cell receptors (TCRs) in tumor-infiltrating lymphocytes (TIL), or peripheral T-cells. Interferon gamma (IFN-γ), produced by T-cells and other immune cells, is essential for controlling proliferation of transformed cells, induction of apoptosis and enhancing human leukocyte antigen (HLA) expression, thereby increasing immunogenicity of cancer cells. TCR αβ-dependent therapies should account for tumor heterogeneity and availability of the TCR repertoire capable of reacting to neoepitopes and functional HLA pathways. Immunogenic epitopes in the tumor-stroma may also be targeted to achieve tumor-containment by changing the immune-contexture in the tumor microenvironment (TME). Non protein-coding regions of the tumor-cell genome may also contain many aberrantly expressed, non-mutated tumor-associated antigens (TAAs) capable of eliciting productive anti-tumor immune responses. Whole-exome sequencing (WES) and/or RNA sequencing (RNA-Seq) of cancer tissue, combined with several layers of bioinformatic analysis is commonly used to predict possible neoepitopes present in clinical samples. At the ImmunoSurgery Unit of the Champalimaud Centre for the Unknown (CCU), a pipeline combining several tools is used for predicting private mutations from WES and RNA-Seq data followed by the construction of synthetic peptides tailored for immunological response assessment reflecting the patient’s tumor mutations, guided by MHC typing. Subsequent immunoassays allow the detection of differential IFN-γ production patterns associated with (intra-tumoral) spatiotemporal differences in TIL or peripheral T-cells versus TIL. These bioinformatics tools, in addition to histopathological assessment, immunological readouts from functional bioassays and deep T-cell ‘adaptome’ analyses, are expected to advance discovery and development of next-generation personalized precision medicine strategies to improve clinical outcomes in cancer in the context of i) anti-tumor vaccination strategies, ii) gauging mutation-reactive T-cell responses in biological therapies and iii) expansion of tumor-reactive T-cells for the cellular treatment of patients with cancer.
Collapse
Affiliation(s)
- Eric de Sousa
- ImmunoSurgery Unit, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Joana R Lérias
- ImmunoSurgery Unit, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Antonio Beltran
- Department of Pathology, Champalimaud Clinical Centre, Lisbon, Portugal
| | | | - Carolina Condeço
- ImmunoSurgery Unit, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Jéssica Kamiki
- ImmunoSurgery Unit, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | | | - Nuno Figueiredo
- Digestive Unit, Champalimaud Clinical Centre, Lisbon, Portugal
| | - Carlos Carvalho
- Digestive Unit, Champalimaud Clinical Centre, Lisbon, Portugal
| | | | - Zhe Wang
- Jiangsu Industrial Technology Research Institute (JITRI), Applied Adaptome Immunology Institute, Nanjing, China
| | - Dário Ligeiro
- Lisbon Centre for Blood and Transplantation, Instituto Português do Sangue e Transplantação (IPST), Lisbon, Portugal
| | - Martin Rao
- ImmunoSurgery Unit, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Markus Maeurer
- ImmunoSurgery Unit, Champalimaud Centre for the Unknown, Lisbon, Portugal.,I Medical Clinic, Johannes Gutenberg University of Mainz, Mainz, Germany
| |
Collapse
|
32
|
Pyke RM, Mellacheruvu D, Dea S, Abbott CW, Zhang SV, Phillips NA, Harris J, Bartha G, Desai S, McClory R, West J, Snyder MP, Chen R, Boyle SM. Withdrawn: Precision Neoantigen Discovery Using Large-scale Immunopeptidomes and Composite Modeling of MHC Peptide Presentation. Mol Cell Proteomics 2021; 20:100111. [PMID: 34126241 PMCID: PMC8318994 DOI: 10.1016/j.mcpro.2021.100111] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 05/07/2021] [Accepted: 06/02/2021] [Indexed: 02/07/2023] Open
Abstract
This article has been withdrawn by the authors. A publication of the manuscript with the correct figures and tables has been approved and the authors state the conclusions of the manuscript remain unaffected. Specifically, errors are in Figure 6A, Supplementary Figure 10B, Supplementary Figure 10C, and Supplementary Table 5. The details of the errors are as follows: the HLA types for one sample were incorrectly assigned because of a tumor/normal mislabeling from the biobank vendor. Due to the differing HLA types between the tumor and normal sample, the sequence analysis established that the HLA alleles for this patient had been deleted (HLA LOH). The authors conclude that this was an artifact caused by the normal sample mislabeling. The corrected version can be accessed (Pyke, R.M., Mellacheruvu, D., Dea, S., Abbott, C.W., Zhang, S.V., Philips, N.A., Harris, J., Bartha, G., Desai, S., McClory, R., West, J., Snyder, M,P., Chen, R., Boyle, S.M. (2022) Precision Neoantigen Discovery Using Large-Scale Immunopeptidomics and Composite Modeling of MHC Peptide Presentation. Mol. Cell. Proteomics 22, 100506
Collapse
Affiliation(s)
| | | | - Steven Dea
- Personalis, Inc, Menlo Park, California, USA
| | | | | | | | | | | | - Sejal Desai
- Personalis, Inc, Menlo Park, California, USA
| | | | - John West
- Personalis, Inc, Menlo Park, California, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University, Palo Alto, California, USA
| | | | | |
Collapse
|
33
|
Pitfalls in HLA Ligandomics-How to Catch a Li(e)gand. Mol Cell Proteomics 2021; 20:100110. [PMID: 34129939 PMCID: PMC8313844 DOI: 10.1016/j.mcpro.2021.100110] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 05/10/2021] [Accepted: 06/02/2021] [Indexed: 11/21/2022] Open
Abstract
Knowledge about the peptide repertoire presented by human leukocyte antigens (HLA) holds the key to unlock target-specific cancer immunotherapies such as adoptive cell therapies or bispecific T cell engaging receptors. Therefore, comprehensive and accurate characterization of HLA peptidomes by mass spectrometry (immunopeptidomics) across tissues and disease states is essential. With growing numbers of immunopeptidomics datasets and the scope of peptide identification strategies reaching beyond the canonical proteome, the likelihood for erroneous peptide identification as well as false annotation of HLA-independent peptides as HLA ligands is increasing. Such “fake ligands” can lead to selection of nonexistent targets for immunotherapeutic development and need to be recognized as such as early as possible in the preclinical pipeline. Here we present computational and experimental methods that enable the identification of “fake ligands” that might be introduced at different steps of the immunopeptidomics workflow. The statistics presented herein allow discrimination of true HLA ligands from coisolated HLA-independent proteolytic fragments. In addition, we describe necessary steps to ensure system suitability of the chromatographic system. Furthermore, we illustrate an algorithm for detection of source fragmentation events that are introduced by electrospray ionization during mass spectrometry. For confirmation of peptide sequences, we present an experimental pipeline that enables high-throughput sequence verification through similarity of fragmentation pattern and coelution of synthetic isotope-labeled internal standards. Based on these methods, we show the overall high quality of existing datasets but point out limitations and pitfalls critical for individual peptides and how they can be uncovered in order to identify true ligands. Best practices to identify true HLA ligands as targets for cancer immunotherapies. Quality control in mass spectrometry to improve neoantigen/crypto-target discovery. Computational methods to assess fragment contamination in immunopeptidomics. Experimental methods for LC system suitability testing and HT sequence verification.
Collapse
|
34
|
Chen I, Chen MY, Goedegebuure SP, Gillanders WE. Challenges targeting cancer neoantigens in 2021: a systematic literature review. Expert Rev Vaccines 2021; 20:827-837. [PMID: 34047245 DOI: 10.1080/14760584.2021.1935248] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Introduction: Cancer neoantigens represent important targets of cancer immunotherapy. The goal of cancer neoantigen vaccines is to induce neoantigen-specific immune responses and antitumor immunity while minimizing the potential for autoimmune toxicity. Advances in sequencing technologies, neoantigen prediction algorithms, and other technologies have dramatically improved the ability to identify and prioritize cancer neoantigens. Unfortunately, results from preclinical studies and early phase clinical trials highlight important challenges to the successful clinical translation of neoantigen cancer vaccines.Areas covered: In this review, we provide an overview of current strategies for the identification and prioritization of cancer neoantigens with a particular emphasis on the two most common strategies used for neoantigen identification: (1) direct identification of peptide ligands eluted from peptide-MHC complexes, and (2) next-generation sequencing combined with neoantigen prediction algorithms. We highlight the limitations of current neoantigen prediction pipelines, and discuss broader challenges associated with cancer neoantigen vaccines including tumor purity/heterogeneity and the immunosuppressive tumor microenvironment.Expert opinion: Despite current limitations, neoantigen prediction is likely to improve rapidly based on advances in sequencing, machine learning, and information sharing. The successful development of robust cancer neoantigen prediction strategies is likely to have a significant impact, with the potential to facilitate cancer neoantigen vaccine design.
Collapse
Affiliation(s)
- Ina Chen
- Department of Surgery, Washington University and Siteman Cancer Center in St. Louis, St Louis, Missouri, USA
| | - Michael Y Chen
- Department of Surgery, Washington University and Siteman Cancer Center in St. Louis, St Louis, Missouri, USA
| | - S Peter Goedegebuure
- Department of Surgery, Washington University and Siteman Cancer Center in St. Louis, St Louis, Missouri, USA.,The Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine, St Louis, MO, USA
| | - William E Gillanders
- Department of Surgery, Washington University and Siteman Cancer Center in St. Louis, St Louis, Missouri, USA.,The Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine, St Louis, MO, USA
| |
Collapse
|
35
|
Marcu A, Bichmann L, Kuchenbecker L, Kowalewski DJ, Freudenmann LK, Backert L, Mühlenbruch L, Szolek A, Lübke M, Wagner P, Engler T, Matovina S, Wang J, Hauri-Hohl M, Martin R, Kapolou K, Walz JS, Velz J, Moch H, Regli L, Silginer M, Weller M, Löffler MW, Erhard F, Schlosser A, Kohlbacher O, Stevanović S, Rammensee HG, Neidert MC. HLA Ligand Atlas: a benign reference of HLA-presented peptides to improve T-cell-based cancer immunotherapy. J Immunother Cancer 2021; 9:e002071. [PMID: 33858848 PMCID: PMC8054196 DOI: 10.1136/jitc-2020-002071] [Citation(s) in RCA: 106] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/25/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND The human leucocyte antigen (HLA) complex controls adaptive immunity by presenting defined fractions of the intracellular and extracellular protein content to immune cells. Understanding the benign HLA ligand repertoire is a prerequisite to define safe T-cell-based immunotherapies against cancer. Due to the poor availability of benign tissues, if available, normal tissue adjacent to the tumor has been used as a benign surrogate when defining tumor-associated antigens. However, this comparison has proven to be insufficient and even resulted in lethal outcomes. In order to match the tumor immunopeptidome with an equivalent counterpart, we created the HLA Ligand Atlas, the first extensive collection of paired HLA-I and HLA-II immunopeptidomes from 227 benign human tissue samples. This dataset facilitates a balanced comparison between tumor and benign tissues on HLA ligand level. METHODS Human tissue samples were obtained from 16 subjects at autopsy, five thymus samples and two ovary samples originating from living donors. HLA ligands were isolated via immunoaffinity purification and analyzed in over 1200 liquid chromatography mass spectrometry runs. Experimentally and computationally reproducible protocols were employed for data acquisition and processing. RESULTS The initial release covers 51 HLA-I and 86 HLA-II allotypes presenting 90,428 HLA-I- and 142,625 HLA-II ligands. The HLA allotypes are representative for the world population. We observe that immunopeptidomes differ considerably between tissues and individuals on source protein and HLA-ligand level. Moreover, we discover 1407 HLA-I ligands from non-canonical genomic regions. Such peptides were previously described in tumors, peripheral blood mononuclear cells (PBMCs), healthy lung tissues and cell lines. In a case study in glioblastoma, we show that potential on-target off-tumor adverse events in immunotherapy can be avoided by comparing tumor immunopeptidomes to the provided multi-tissue reference. CONCLUSION Given that T-cell-based immunotherapies, such as CAR-T cells, affinity-enhanced T cell transfer, cancer vaccines and immune checkpoint inhibition, have significant side effects, the HLA Ligand Atlas is the first step toward defining tumor-associated targets with an improved safety profile. The resource provides insights into basic and applied immune-associated questions in the context of cancer immunotherapy, infection, transplantation, allergy and autoimmunity. It is publicly available and can be browsed in an easy-to-use web interface at https://hla-ligand-atlas.org .
Collapse
Affiliation(s)
- Ana Marcu
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
| | - Leon Bichmann
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Leon Kuchenbecker
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Daniel Johannes Kowalewski
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
| | - Lena Katharina Freudenmann
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
| | - Linus Backert
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Lena Mühlenbruch
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
| | - András Szolek
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Maren Lübke
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
| | - Philipp Wagner
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Department of Obstetrics and Gynecology, University Hospital of Tübingen, Tübingen, Germany
| | - Tobias Engler
- Department of Obstetrics and Gynecology, University Hospital of Tübingen, Tübingen, Germany
| | - Sabine Matovina
- Department of Obstetrics and Gynecology, University Hospital of Tübingen, Tübingen, Germany
| | - Jian Wang
- Neuroimmunology and MS Research, Neurology Clinic, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Mathias Hauri-Hohl
- Pediatric Stem Cell Transplantation, University Children's Hospital Zurich, Zurich, Switzerland
| | - Roland Martin
- Neuroimmunology and MS Research, Neurology Clinic, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Konstantina Kapolou
- Clinical Neuroscience Center and Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
| | - Juliane Sarah Walz
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), University Hospital of Tübingen, Tübingen, Germany
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology (IKP) and Robert Bosch Center for Tumor Diseases (RBCT), Stuttgart, Germany
| | - Julia Velz
- Clinical Neuroscience Center and Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
| | - Holger Moch
- Department of Pathology and Molecular Pathology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Luca Regli
- Clinical Neuroscience Center and Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
| | - Manuela Silginer
- Clinical Neuroscience Center and Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Clinical Neuroscience Center and Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Markus W Löffler
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
- Department of General, Visceral and Transplant Surgery, University Hospital of Tübingen, Tübingen, Germany
- Department of Clinical Pharmacology, University of Hospital Tübingen, Tübingen, Germany
| | - Florian Erhard
- Institute for Virology and Immunobiology, Julius-Maximilians-University Würzburg, Würzburg, Bayern, Germany
| | - Andreas Schlosser
- Rudolf Virchow Center - Center for Integrative and Translational Bioimaging, Julius-Maximilians-University Würzburg, Würzburg, Germany
| | - Oliver Kohlbacher
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
- Quantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany
- Biomolecular Interactions, Max Planck Institute for Developmental Biology, Tübingen, Germany
- Cluster of Excellence Machine Learning in the Sciences (EXC 2064), University of Tübingen, Tübingen, Germany
- Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen, Germany
| | - Stefan Stevanović
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
| | - Hans-Georg Rammensee
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
| | - Marian Christoph Neidert
- Clinical Neuroscience Center and Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
- Department of Neurosurgery, Cantonal Hospital St.Gallen, St.Gallen, Switzerland
- Neuroscience Center Zurich (ZNZ), University of Zurich and ETH Zurich, Zurich, Switzerland
| |
Collapse
|
36
|
Tanyi JL, Chiang CLL, Chiffelle J, Thierry AC, Baumgartener P, Huber F, Goepfert C, Tarussio D, Tissot S, Torigian DA, Nisenbaum HL, Stevenson BJ, Guiren HF, Ahmed R, Huguenin-Bergenat AL, Zsiros E, Bassani-Sternberg M, Mick R, Powell DJ, Coukos G, Harari A, Kandalaft LE. Personalized cancer vaccine strategy elicits polyfunctional T cells and demonstrates clinical benefits in ovarian cancer. NPJ Vaccines 2021; 6:36. [PMID: 33723260 PMCID: PMC7960755 DOI: 10.1038/s41541-021-00297-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 02/17/2021] [Indexed: 01/31/2023] Open
Abstract
T cells are important for controlling ovarian cancer (OC). We previously demonstrated that combinatorial use of a personalized whole-tumor lysate-pulsed dendritic cell vaccine (OCDC), bevacizumab (Bev), and cyclophosphamide (Cy) elicited neoantigen-specific T cells and prolonged OC survival. Here, we hypothesize that adding acetylsalicylic acid (ASA) and low-dose interleukin (IL)-2 would increase the vaccine efficacy in a recurrent advanced OC phase I trial (NCT01132014). By adding ASA and low-dose IL-2 to the OCDC-Bev-Cy combinatorial regimen, we elicited vaccine-specific T-cell responses that positively correlated with patients' prolonged time-to-progression and overall survival. In the ID8 ovarian model, animals receiving the same regimen showed prolonged survival, increased tumor-infiltrating perforin-producing T cells, increased neoantigen-specific CD8+ T cells, and reduced endothelial Fas ligand expression and tumor-infiltrating T-regulatory cells. This combinatorial strategy was efficacious and also highlighted the predictive value of the ID8 model for future ovarian trial development.
Collapse
Affiliation(s)
- Janos L. Tanyi
- grid.25879.310000 0004 1936 8972Ovarian Cancer Research Center, Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Cheryl L.-L. Chiang
- grid.9851.50000 0001 2165 4204Department of Oncology, Lausanne University Hospital (CHUV), Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Johanna Chiffelle
- grid.9851.50000 0001 2165 4204Department of Oncology, Lausanne University Hospital (CHUV), Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Anne-Christine Thierry
- grid.8515.90000 0001 0423 4662Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Petra Baumgartener
- grid.8515.90000 0001 0423 4662Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Florian Huber
- grid.9851.50000 0001 2165 4204Department of Oncology, Lausanne University Hospital (CHUV), Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Christine Goepfert
- grid.5734.50000 0001 0726 5157Institute of Animal Pathology, COMPATH, Vetsuisse Faculty, University of Bern, Bern, Switzerland ,grid.5333.60000000121839049School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - David Tarussio
- grid.8515.90000 0001 0423 4662Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Stephanie Tissot
- grid.8515.90000 0001 0423 4662Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Drew A. Torigian
- grid.411115.10000 0004 0435 0884Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA USA
| | - Harvey L. Nisenbaum
- grid.411115.10000 0004 0435 0884Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA USA
| | - Brian J. Stevenson
- grid.9851.50000 0001 2165 4204Department of Oncology, Lausanne University Hospital (CHUV), Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Hajer Fritah Guiren
- grid.9851.50000 0001 2165 4204Department of Oncology, Lausanne University Hospital (CHUV), Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Ritaparna Ahmed
- grid.9851.50000 0001 2165 4204Department of Oncology, Lausanne University Hospital (CHUV), Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Anne-Laure Huguenin-Bergenat
- grid.9851.50000 0001 2165 4204Department of Oncology, Lausanne University Hospital (CHUV), Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Emese Zsiros
- grid.25879.310000 0004 1936 8972Ovarian Cancer Research Center, Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Michal Bassani-Sternberg
- grid.9851.50000 0001 2165 4204Department of Oncology, Lausanne University Hospital (CHUV), Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Rosemarie Mick
- grid.25879.310000 0004 1936 8972Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Daniel J. Powell
- grid.25879.310000 0004 1936 8972Ovarian Cancer Research Center, Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - George Coukos
- grid.9851.50000 0001 2165 4204Department of Oncology, Lausanne University Hospital (CHUV), Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Alexandre Harari
- grid.9851.50000 0001 2165 4204Department of Oncology, Lausanne University Hospital (CHUV), Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland ,grid.8515.90000 0001 0423 4662Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Lana E. Kandalaft
- grid.9851.50000 0001 2165 4204Department of Oncology, Lausanne University Hospital (CHUV), Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland ,grid.8515.90000 0001 0423 4662Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| |
Collapse
|
37
|
Wang Y, Shi T, Song X, Liu B, Wei J. Gene fusion neoantigens: Emerging targets for cancer immunotherapy. Cancer Lett 2021; 506:45-54. [PMID: 33675984 DOI: 10.1016/j.canlet.2021.02.023] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 02/22/2021] [Accepted: 02/24/2021] [Indexed: 12/22/2022]
Abstract
Tumor neoantigens play an important role in current cancer immunotherapies. The most commonly studied class of tumor neoantigens contains those derived from single-nucleotide variants (SNVs) and insertions or deletions (Indels). However, gene fusions are also ideal sources of tumor neoantigens, as they can form new open reading frames (ORFs). Compared with SNV and Indel (SNV&Indel) neoantigens, fusion gene neoantigens tend to be more immunogenic, have more targets per mutation, and are more broadly shared across different cancer types. As a result, they are an important class of tumor neoantigens and emerging targets for cancer immunotherapies, with uses as prognostic biomarkers of immune checkpoint blockade (ICB) and in the development of tumor vaccines, adoptive cell therapies and tumor immune microenvironment modulation. In this review, we introduce the chromosomal basis and characteristics of gene fusions. Then, we summarize the predictive tools, mutation burden and immunogenicity of gene fusion neoantigens. Further, we discuss applications and future improvements of gene fusion neoantigens with respect to current cancer immunotherapies and novel developments in cancer treatment.
Collapse
Affiliation(s)
- Yue Wang
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School & Clinical Cancer Institute of Nanjing University, Nanjing, 210008, China
| | - Tao Shi
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School & Clinical Cancer Institute of Nanjing University, Nanjing, 210008, China
| | - Xueru Song
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School & Clinical Cancer Institute of Nanjing University, Nanjing, 210008, China
| | - Baorui Liu
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School & Clinical Cancer Institute of Nanjing University, Nanjing, 210008, China
| | - Jia Wei
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School & Clinical Cancer Institute of Nanjing University, Nanjing, 210008, China.
| |
Collapse
|
38
|
Karnaukhov V, Paes W, Woodhouse IB, Partridge T, Nicastri A, Brackenridge S, Scherbinin D, Chudakov DM, Zvyagin IV, Ternette N, Koohy H, Borrow P, Shugay M. HLA binding of self-peptides is biased towards proteins with specific molecular functions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.02.16.431395. [PMID: 33619495 PMCID: PMC7899460 DOI: 10.1101/2021.02.16.431395] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Human leukocyte antigen (HLA) is highly polymorphic and plays a key role in guiding adaptive immune responses by presenting foreign and self peptides to T cells. Each HLA variant selects a minor fraction of peptides that match a certain motif required for optimal interaction with the peptide-binding groove. These restriction rules define the landscape of peptides presented to T cells. Given these limitations, one might suggest that the choice of peptides presented by HLA is non-random and there is preferential presentation of an array of peptides that is optimal for distinguishing self and foreign proteins. In this study we explore these preferences with a comparative analysis of self peptides enriched and depleted in HLA ligands. We show that HLAs exhibit preferences towards presenting peptides from certain proteins while disfavoring others with specific functions, and highlight differences between various HLA genes and alleles in those preferences. We link those differences to HLA anchor residue propensities and amino acid composition of preferentially presented proteins. The set of proteins that peptides presented by a given HLA are most likely to be derived from can be used to distinguish between class I and class II HLAs and HLA alleles. Our observations can be extrapolated to explain the protective effect of certain HLA alleles in infectious diseases, and we hypothesize that they can also explain susceptibility to certain autoimmune diseases and cancers. We demonstrate that these differences lead to differential presentation of HIV, influenza virus, SARS-CoV-1 and SARS-CoV-2 proteins by various HLA alleles. Finally, we show that the reported self peptidome preferences of distinct HLA variants can be compensated by combinations of HLA-A/HLA-B and HLA-A/HLA-C alleles in frequent haplotypes.
Collapse
Affiliation(s)
- Vadim Karnaukhov
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Wayne Paes
- Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Isaac B. Woodhouse
- Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, UK
| | - Thomas Partridge
- Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Annalisa Nicastri
- The Jenner Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Simon Brackenridge
- Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Dmitrii Scherbinin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Science, Moscow, Russia
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Dmitry M. Chudakov
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Science, Moscow, Russia
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Ivan V. Zvyagin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Science, Moscow, Russia
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Nicola Ternette
- The Jenner Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Hashem Koohy
- Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, UK
| | - Persephone Borrow
- Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Mikhail Shugay
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Science, Moscow, Russia
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
| |
Collapse
|
39
|
Nelde A, Rammensee HG, Walz JS. The Peptide Vaccine of the Future. Mol Cell Proteomics 2021; 20:100022. [PMID: 33583769 PMCID: PMC7950068 DOI: 10.1074/mcp.r120.002309] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 11/27/2020] [Accepted: 12/07/2020] [Indexed: 12/14/2022] Open
Abstract
The approach of peptide-based anticancer vaccination has proven the ability to induce cancer-specific immune responses in multiple studies for various cancer entities. However, clinical responses remain so far limited to single patients and broad clinical applicability was not achieved. Therefore, further efforts are required to improve peptide vaccination in order to integrate this low-side-effect therapy into the clinical routine of cancer therapy. To design clinically effective peptide vaccines in the future, different issues have to be addressed and optimized comprising antigen target selection as well as choice of optimal adjuvants and vaccination schedules. Furthermore, the combination of peptide-based vaccines with other immuno- and molecular targeted therapies as well as the development of predictive biomarkers could further improve efficacy. In this review, current approaches in the development of peptide-based vaccines and critical implications for optimal vaccine design are discussed.
Collapse
Affiliation(s)
- Annika Nelde
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), University Hospital Tübingen, Tübingen, Germany; Department of Immunology, Institute for Cell Biology, University of Tübingen, Tübingen, Germany; Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
| | - Hans-Georg Rammensee
- Department of Immunology, Institute for Cell Biology, University of Tübingen, Tübingen, Germany; Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany; German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Partner Site Tübingen, Tübingen, Germany
| | - Juliane S Walz
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), University Hospital Tübingen, Tübingen, Germany; Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany.
| |
Collapse
|
40
|
Schmidt J, Smith AR, Magnin M, Racle J, Devlin JR, Bobisse S, Cesbron J, Bonnet V, Carmona SJ, Huber F, Ciriello G, Speiser DE, Bassani-Sternberg M, Coukos G, Baker BM, Harari A, Gfeller D. Prediction of neo-epitope immunogenicity reveals TCR recognition determinants and provides insight into immunoediting. CELL REPORTS MEDICINE 2021; 2:100194. [PMID: 33665637 PMCID: PMC7897774 DOI: 10.1016/j.xcrm.2021.100194] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 12/11/2020] [Accepted: 01/11/2021] [Indexed: 02/07/2023]
Abstract
CD8+ T cell recognition of peptide epitopes plays a central role in immune responses against pathogens and tumors. However, the rules that govern which peptides are truly recognized by existing T cell receptors (TCRs) remain poorly understood, precluding accurate predictions of neo-epitopes for cancer immunotherapy. Here, we capitalize on recent (neo-)epitope data to train a predictor of immunogenic epitopes (PRIME), which captures molecular properties of both antigen presentation and TCR recognition. PRIME not only improves prioritization of neo-epitopes but also correlates with T cell potency and unravels biophysical determinants of TCR recognition that we experimentally validate. Analysis of cancer genomics data reveals that recurrent mutations tend to be less frequent in patients where they are predicted to be immunogenic, providing further evidence for immunoediting in human cancer. PRIME will facilitate identification of pathogen epitopes in infectious diseases and neo-epitopes in cancer immunotherapy.
Collapse
Affiliation(s)
- Julien Schmidt
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland
| | - Angela R Smith
- Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, USA
| | - Morgane Magnin
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland
| | - Julien Racle
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Jason R Devlin
- Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, USA
| | - Sara Bobisse
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland
| | - Julien Cesbron
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland
| | | | - Santiago J Carmona
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Florian Huber
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland
| | - Giovanni Ciriello
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Daniel E Speiser
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland
| | - George Coukos
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland
| | - Brian M Baker
- Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, USA
| | - Alexandre Harari
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland.,Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| |
Collapse
|
41
|
Mei S, Li F, Xiang D, Ayala R, Faridi P, Webb GI, Illing PT, Rossjohn J, Akutsu T, Croft NP, Purcell AW, Song J. Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules. Brief Bioinform 2021; 22:6102669. [PMID: 33454737 DOI: 10.1093/bib/bbaa415] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 11/29/2020] [Accepted: 12/16/2020] [Indexed: 12/17/2022] Open
Abstract
Neopeptide-based immunotherapy has been recognised as a promising approach for the treatment of cancers. For neopeptides to be recognised by CD8+ T cells and induce an immune response, their binding to human leukocyte antigen class I (HLA-I) molecules is a necessary first step. Most epitope prediction tools thus rely on the prediction of such binding. With the use of mass spectrometry, the scale of naturally presented HLA ligands that could be used to develop such predictors has been expanded. However, there are rarely efforts that focus on the integration of these experimental data with computational algorithms to efficiently develop up-to-date predictors. Here, we present Anthem for accurate HLA-I binding prediction. In particular, we have developed a user-friendly framework to support the development of customisable HLA-I binding prediction models to meet challenges associated with the rapidly increasing availability of large amounts of immunopeptidomic data. Our extensive evaluation, using both independent and experimental datasets shows that Anthem achieves an overall similar or higher area under curve value compared with other contemporary tools. It is anticipated that Anthem will provide a unique opportunity for the non-expert user to analyse and interpret their own in-house or publicly deposited datasets.
Collapse
Affiliation(s)
- Shutao Mei
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Australia
| | - Fuyi Li
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Australia
| | - Dongxu Xiang
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Australia
| | - Rochelle Ayala
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Australia
| | - Pouya Faridi
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Australia
| | | | - Patricia T Illing
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Australia
| | - Jamie Rossjohn
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Australia
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Japan
| | - Nathan P Croft
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Australia
| | - Anthony W Purcell
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Australia
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Biochemistry and Molecular Biology, Monash University, Australia
| |
Collapse
|
42
|
Forlani G, Michaux J, Pak H, Huber F, Marie Joseph EL, Ramia E, Stevenson BJ, Linnebacher M, Accolla RS, Bassani-Sternberg M. CIITA-Transduced Glioblastoma Cells Uncover a Rich Repertoire of Clinically Relevant Tumor-Associated HLA-II Antigens. Mol Cell Proteomics 2021; 20:100032. [PMID: 33592498 PMCID: PMC8724627 DOI: 10.1074/mcp.ra120.002201] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 09/17/2020] [Accepted: 09/22/2020] [Indexed: 12/30/2022] Open
Abstract
CD4+ T cell responses are crucial for inducing and maintaining effective anticancer immunity, and the identification of human leukocyte antigen class II (HLA-II) cancer-specific epitopes is key to the development of potent cancer immunotherapies. In many tumor types, and especially in glioblastoma (GBM), HLA-II complexes are hardly ever naturally expressed. Hence, little is known about immunogenic HLA-II epitopes in GBM. With stable expression of the class II major histocompatibility complex transactivator (CIITA) coupled to a detailed and sensitive mass spectrometry-based immunopeptidomics analysis, we here uncovered a remarkable breadth of the HLA-ligandome in HROG02, HROG17, and RA GBM cell lines. The effect of CIITA expression on the induction of the HLA-II presentation machinery was striking in each of the three cell lines, and it was significantly higher compared with interferon gamma (IFNɣ) treatment. In total, we identified 16,123 unique HLA-I peptides and 32,690 unique HLA-II peptides. In order to genuinely define the identified peptides as true HLA ligands, we carefully characterized their association with the different HLA allotypes. In addition, we identified 138 and 279 HLA-I and HLA-II ligands, respectively, most of which are novel in GBM, derived from known GBM-associated tumor antigens that have been used as source proteins for a variety of GBM vaccines. Our data further indicate that CIITA-expressing GBM cells acquired an antigen presenting cell-like phenotype as we found that they directly present external proteins as HLA-II ligands. Not only that CIITA-expressing GBM cells are attractive models for antigen discovery endeavors, but also such engineered cells have great therapeutic potential through massive presentation of a diverse antigenic repertoire.
Collapse
Affiliation(s)
- Greta Forlani
- Laboratories of General Pathology and Immunology "Giovanna Tosi", Department of Medicine and Surgery, School of Medicine, University of Insubria, Varese, Italy
| | - Justine Michaux
- Ludwig Cancer Research Center, University of Lausanne, Lausanne, Switzerland; Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - HuiSong Pak
- Ludwig Cancer Research Center, University of Lausanne, Lausanne, Switzerland; Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Florian Huber
- Ludwig Cancer Research Center, University of Lausanne, Lausanne, Switzerland; Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Elodie Lauret Marie Joseph
- Ludwig Cancer Research Center, University of Lausanne, Lausanne, Switzerland; Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Elise Ramia
- Laboratories of General Pathology and Immunology "Giovanna Tosi", Department of Medicine and Surgery, School of Medicine, University of Insubria, Varese, Italy
| | | | - Michael Linnebacher
- Department of General Surgery, Molecular Oncology and Immunotherapy, University Medical Center Rostock, Rostock, Germany
| | - Roberto S Accolla
- Laboratories of General Pathology and Immunology "Giovanna Tosi", Department of Medicine and Surgery, School of Medicine, University of Insubria, Varese, Italy
| | - Michal Bassani-Sternberg
- Ludwig Cancer Research Center, University of Lausanne, Lausanne, Switzerland; Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland.
| |
Collapse
|
43
|
Stryhn A, Kongsgaard M, Rasmussen M, Harndahl MN, Østerbye T, Bassi MR, Thybo S, Gabriel M, Hansen MB, Nielsen M, Christensen JP, Randrup Thomsen A, Buus S. A Systematic, Unbiased Mapping of CD8 + and CD4 + T Cell Epitopes in Yellow Fever Vaccinees. Front Immunol 2020; 11:1836. [PMID: 32983097 PMCID: PMC7489334 DOI: 10.3389/fimmu.2020.01836] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 07/08/2020] [Indexed: 12/30/2022] Open
Abstract
Examining CD8+ and CD4+ T cell responses after primary Yellow Fever vaccination in a cohort of 210 volunteers, we have identified and tetramer-validated 92 CD8+ and 50 CD4+ T cell epitopes, many inducing strong and prevalent (i.e., immunodominant) T cell responses. Restricted by 40 and 14 HLA-class I and II allotypes, respectively, these responses have wide population coverage and might be of considerable academic, diagnostic and therapeutic interest. The broad coverage of epitopes and HLA overcame the otherwise confounding effects of HLA diversity and non-HLA background providing the first evidence of T cell immunodomination in humans. Also, double-staining of CD4+ T cells with tetramers representing the same HLA-binding core, albeit with different flanking regions, demonstrated an extensive diversification of the specificities of many CD4+ T cell responses. We suggest that this could reduce the risk of pathogen escape, and that multi-tetramer staining is required to reveal the true magnitude and diversity of CD4+ T cell responses. Our T cell epitope discovery approach uses a combination of (1) overlapping peptides representing the entire Yellow Fever virus proteome to search for peptides containing CD4+ and/or CD8+ T cell epitopes, (2) predictors of peptide-HLA binding to suggest epitopes and their restricting HLA allotypes, (3) generation of peptide-HLA tetramers to identify T cell epitopes, and (4) analysis of ex vivo T cell responses to validate the same. This approach is systematic, exhaustive, and can be done in any individual of any HLA haplotype. It is all-inclusive in the sense that it includes all protein antigens and peptide epitopes, and encompasses both CD4+ and CD8+ T cell epitopes. It is efficient and, importantly, reduces the false discovery rate. The unbiased nature of the T cell epitope discovery approach presented here should support the refinement of future peptide-HLA class I and II predictors and tetramer technologies, which eventually should cover all HLA class I and II isotypes. We believe that future investigations of emerging pathogens (e.g., SARS-CoV-2) should include population-wide T cell epitope discovery using blood samples from patients, convalescents and/or long-term survivors, who might all hold important information on T cell epitopes and responses.
Collapse
Affiliation(s)
- Anette Stryhn
- Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael Kongsgaard
- Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael Rasmussen
- Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mikkel Nors Harndahl
- Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Østerbye
- Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Maria Rosaria Bassi
- Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Søren Thybo
- Department of Infectious Diseases, Copenhagen University Hospital, Copenhagen, Denmark
| | | | - Morten Bagge Hansen
- Department of Clinical Immunology, Copenhagen University Hospital, Copenhagen, Denmark
| | - Morten Nielsen
- Department of Health Technology, The Technical University of Denmark, Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Jan Pravsgaard Christensen
- Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Allan Randrup Thomsen
- Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Soren Buus
- Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
44
|
Marino F, Semilietof A, Michaux J, Pak HS, Coukos G, Müller M, Bassani-Sternberg M. Biogenesis of HLA Ligand Presentation in Immune Cells Upon Activation Reveals Changes in Peptide Length Preference. Front Immunol 2020; 11:1981. [PMID: 32983136 PMCID: PMC7485268 DOI: 10.3389/fimmu.2020.01981] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 07/22/2020] [Indexed: 02/05/2023] Open
Abstract
Induction of an effective tumor immunity is a complex process that includes the appropriate presentation of the tumor antigens, activation of specific T cells, and the elimination of malignant cells. Potent and efficient T cell activation is dependent on multiple factors, such as timely expression of co-stimulatory molecules, the differentiation state of professional antigen presenting cells (e.g., dendritic cells; DCs), the functionality of the antigen processing and presentation machinery (APPM), and the repertoire of HLA class I and II-bound peptides (termed immunopeptidome) presented to T cells. So far, how molecular perturbations underlying DCs maturation and differentiation affect the in vivo cross-presented HLA class I and II immunopeptidomes is largely unknown. Yet, this knowledge is crucial for further development of DC-based immunotherapy approaches. We applied a state-of-the-art sensitive MS-based immunopeptidomics approach to characterize the naturally presented HLA-I and -II immunopeptidomes eluted from autologous immune cells having distinct functional and biological states including CD14+ monocytes, immature DC (ImmDC) and mature DC (MaDC) monocyte-derived DCs and naive or activated T and B cells. We revealed a presentation of significantly longer HLA peptides upon activation that is HLA allotype specific. This was apparent in the self-peptidome upon cell activation and in the context of presentation of exogenously loaded antigens, suggesting that peptide length is an important feature with potential implications on the rational design of anti-cancer vaccines.
Collapse
Affiliation(s)
- Fabio Marino
- Agora Center, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.,Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Aikaterini Semilietof
- Agora Center, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.,Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Justine Michaux
- Agora Center, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.,Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Hui-Song Pak
- Agora Center, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.,Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - George Coukos
- Agora Center, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.,Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Markus Müller
- Vital IT, Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Agora Center, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.,Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| |
Collapse
|
45
|
Xu P, Luo H, Kong Y, Lai WF, Cui L, Zhu X. Cancer neoantigen: Boosting immunotherapy. Biomed Pharmacother 2020; 131:110640. [PMID: 32836075 DOI: 10.1016/j.biopha.2020.110640] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 08/13/2020] [Accepted: 08/16/2020] [Indexed: 12/21/2022] Open
Abstract
Tumor neoantigen has a high degree of immunogenicity. As one of the emerging methods of tumor immunotherapy, the vaccine developed against it has served to clinical trials of various solid tumors, especially in the treatment of melanoma. Currently, a variety of immunotherapy methods have been applied to the treatment of the tumor. However, other therapeutic methods have the disadvantages of low specificity and prominent side effects. Treatments require tumor antigen with higher immunogenicity as the target of immune attack. This review will recommend the identification of neoantigen, the influencing factors of neoantigen, and the application of personalized vaccines for neoantigen in metastatic tumors such as malignant melanoma.
Collapse
Affiliation(s)
- Peijia Xu
- Guangdong Key Laboratory for Research and Development of Natural Drugs, The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, 524023, China; The Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang, 524023, China; The Key Lab of Zhanjiang for R&D Marine Microbial Resources in the Beibu Gulf Rim, Guangdong Medical University, Zhanjiang, 524023, China
| | - Haiqing Luo
- Cancer Center, Affiliated Hospital, Guangdong Medical University, Zhanjiang, 524023, China
| | - Ying Kong
- Department of Clinical Laboratory, Hubei No. 3 People's Hospital of Jianghan University, Wuhan, 430033, China
| | - Wing-Fu Lai
- Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hong Kong, China; School of Life and Health Sciences, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China.
| | - Liao Cui
- Guangdong Key Laboratory for Research and Development of Natural Drugs, The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, 524023, China; The Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang, 524023, China.
| | - Xiao Zhu
- Guangdong Key Laboratory for Research and Development of Natural Drugs, The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, 524023, China; The Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang, 524023, China; The Key Lab of Zhanjiang for R&D Marine Microbial Resources in the Beibu Gulf Rim, Guangdong Medical University, Zhanjiang, 524023, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang, 524023, China.
| |
Collapse
|
46
|
Harari A, Graciotti M, Bassani-Sternberg M, Kandalaft LE. Antitumour dendritic cell vaccination in a priming and boosting approach. Nat Rev Drug Discov 2020; 19:635-652. [PMID: 32764681 DOI: 10.1038/s41573-020-0074-8] [Citation(s) in RCA: 153] [Impact Index Per Article: 38.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/19/2020] [Indexed: 02/06/2023]
Abstract
Mobilizing antitumour immunity through vaccination potentially constitutes a powerful anticancer strategy but has not yet provided robust clinical benefits in large patient populations. Although major hurdles still exist, we believe that currently available strategies for vaccines that target dendritic cells or use them to present antitumour antigens could be integrated into existing clinical practice using prime-boost approaches. In the priming phase, these approaches capitalize on either standard treatment modalities to trigger in situ vaccination and release tumour antigens or vaccination with dendritic cells loaded with tumour lysates or patient-specific neoantigens. In a second boost phase, personalized synthetic vaccines specifically boost T cells that were triggered during the priming phase. This immunotherapy approach has been enabled by the substantial recent improvements in dendritic cell vaccines. In this Perspective, we discuss these improvements, highlight how the prime-boost approach can be translated into clinical practice and provide solutions for various anticipated hurdles.
Collapse
Affiliation(s)
- Alexandre Harari
- Center of Experimental Therapeutics, Department of Oncology, University Hospital of Lausanne, Lausanne, Switzerland.,Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Michele Graciotti
- Center of Experimental Therapeutics, Department of Oncology, University Hospital of Lausanne, Lausanne, Switzerland.,Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Center of Experimental Therapeutics, Department of Oncology, University Hospital of Lausanne, Lausanne, Switzerland.,Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Lana E Kandalaft
- Center of Experimental Therapeutics, Department of Oncology, University Hospital of Lausanne, Lausanne, Switzerland. .,Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.
| |
Collapse
|
47
|
Wang G, Wan H, Jian X, Li Y, Ouyang J, Tan X, Zhao Y, Lin Y, Xie L. INeo-Epp: A Novel T-Cell HLA Class-I Immunogenicity or Neoantigenic Epitope Prediction Method Based on Sequence-Related Amino Acid Features. BIOMED RESEARCH INTERNATIONAL 2020; 2020:5798356. [PMID: 32626747 PMCID: PMC7315274 DOI: 10.1155/2020/5798356] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 05/23/2020] [Indexed: 12/30/2022]
Abstract
In silico T-cell epitope prediction plays an important role in immunization experimental design and vaccine preparation. Currently, most epitope prediction research focuses on peptide processing and presentation, e.g., proteasomal cleavage, transporter associated with antigen processing (TAP), and major histocompatibility complex (MHC) combination. To date, however, the mechanism for the immunogenicity of epitopes remains unclear. It is generally agreed upon that T-cell immunogenicity may be influenced by the foreignness, accessibility, molecular weight, molecular structure, molecular conformation, chemical properties, and physical properties of target peptides to different degrees. In this work, we tried to combine these factors. Firstly, we collected significant experimental HLA-I T-cell immunogenic peptide data, as well as the potential immunogenic amino acid properties. Several characteristics were extracted, including the amino acid physicochemical property of the epitope sequence, peptide entropy, eluted ligand likelihood percentile rank (EL rank(%)) score, and frequency score for an immunogenic peptide. Subsequently, a random forest classifier for T-cell immunogenic HLA-I presenting antigen epitopes and neoantigens was constructed. The classification results for the antigen epitopes outperformed the previous research (the optimal AUC = 0.81, external validation data set AUC = 0.77). As mutational epitopes generated by the coding region contain only the alterations of one or two amino acids, we assume that these characteristics might also be applied to the classification of the endogenic mutational neoepitopes also called "neoantigens." Based on mutation information and sequence-related amino acid characteristics, a prediction model of a neoantigen was established as well (the optimal AUC = 0.78). Further, an easy-to-use web-based tool "INeo-Epp" was developed for the prediction of human immunogenic antigen epitopes and neoantigen epitopes.
Collapse
Affiliation(s)
- Guangzhi Wang
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China
| | - Huihui Wan
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Xingxing Jian
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China
- Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education and Key Laboratory of Carcinogenesis, National Health and Family Planning Commission, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Yuyu Li
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Jian Ouyang
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China
| | - Xiaoxiu Tan
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yong Zhao
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Yong Lin
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Lu Xie
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China
| |
Collapse
|
48
|
Leng Q, Tarbe M, Long Q, Wang F. Pre-existing heterologous T-cell immunity and neoantigen immunogenicity. Clin Transl Immunology 2020; 9:e01111. [PMID: 32211191 PMCID: PMC7085466 DOI: 10.1002/cti2.1111] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 01/13/2020] [Accepted: 01/30/2020] [Indexed: 12/22/2022] Open
Abstract
Neoantigens are tumor‐specific mutated proteins that are exempt from central tolerance and are therefore capable of efficiently eliciting effective T‐cell responses. The identification of immunogenic neoantigens in tumor‐specific mutated proteins has promising clinical implications for cancer immunotherapy. However, the factors that may contribute to neoantigen immunogenicity are not yet fully understood. Through molecular mimicry of antigens arising during cancer progression, the gut microbiota and previously encountered pathogens potentially have profound impacts on T‐cell responses to previously unencountered tumor neoantigens. Here, we review the characteristics of immunogenic neoantigens and how host exposure to microbes may affect T‐cell responses to neoantigens. We address the hypothesis that pre‐existing heterologous memory T‐cell immunity is a major factor that influences neoantigen immunogenicity in individual cancer patients. Accumulating data suggest that differences in individual histories of microbial exposure should be taken into account during the optimisation of algorithms that predict neoantigen immunogenicity.
Collapse
Affiliation(s)
- Qibin Leng
- Affiliated Cancer Hospital & Institute of Guangzhou Medical University State Key Laboratory of Respiratory Diseases, Guangzhou Medical University Guangzhou China.,The Joint Center for Infection and Immunity Guangzhou Women and Children's Medical Center Guangzhou Institute of Pediatrics Guangzhou Medical University Guangzhou China.,Institute Pasteur of Shanghai Chinese Academy of Science Shanghai China
| | - Marion Tarbe
- The Joint Center for Infection and Immunity Guangzhou Women and Children's Medical Center Guangzhou Institute of Pediatrics Guangzhou Medical University Guangzhou China.,Institute Pasteur of Shanghai Chinese Academy of Science Shanghai China
| | - Qi Long
- Department of Biostatistics, Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA
| | - Feng Wang
- Department of Immunology and Microbiology Center for Microbiota and Immunological Diseases Shanghai General Hospital Shanghai Institute of Immunology Shanghai Jiao Tong University School of Medicine Shanghai China.,Research Center of Translational Medicine Shanghai Children's Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
| |
Collapse
|
49
|
Chong C, Müller M, Pak H, Harnett D, Huber F, Grun D, Leleu M, Auger A, Arnaud M, Stevenson BJ, Michaux J, Bilic I, Hirsekorn A, Calviello L, Simó-Riudalbas L, Planet E, Lubiński J, Bryśkiewicz M, Wiznerowicz M, Xenarios I, Zhang L, Trono D, Harari A, Ohler U, Coukos G, Bassani-Sternberg M. Integrated proteogenomic deep sequencing and analytics accurately identify non-canonical peptides in tumor immunopeptidomes. Nat Commun 2020; 11:1293. [PMID: 32157095 PMCID: PMC7064602 DOI: 10.1038/s41467-020-14968-9] [Citation(s) in RCA: 186] [Impact Index Per Article: 46.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 02/12/2020] [Indexed: 12/20/2022] Open
Abstract
Efforts to precisely identify tumor human leukocyte antigen (HLA) bound peptides capable of mediating T cell-based tumor rejection still face important challenges. Recent studies suggest that non-canonical tumor-specific HLA peptides derived from annotated non-coding regions could elicit anti-tumor immune responses. However, sensitive and accurate mass spectrometry (MS)-based proteogenomics approaches are required to robustly identify these non-canonical peptides. We present an MS-based analytical approach that characterizes the non-canonical tumor HLA peptide repertoire, by incorporating whole exome sequencing, bulk and single-cell transcriptomics, ribosome profiling, and two MS/MS search tools in combination. This approach results in the accurate identification of hundreds of shared and tumor-specific non-canonical HLA peptides, including an immunogenic peptide derived from an open reading frame downstream of the melanoma stem cell marker gene ABCB5. These findings hold great promise for the discovery of previously unknown tumor antigens for cancer immunotherapy.
Collapse
Affiliation(s)
- Chloe Chong
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Markus Müller
- Vital IT, Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, 1015, Lausanne, Switzerland
| | - HuiSong Pak
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Dermot Harnett
- Max Delbrück Centre for Molecular Medicine in the Helmholtz Association, Institute for Medical Systems Biology, Hannoversche Straße 28, 10115, Berlin, Germany
| | - Florian Huber
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Delphine Grun
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, 1015, Lausanne, Switzerland
| | - Marion Leleu
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, 1015, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, 1015, Lausanne, Switzerland
| | - Aymeric Auger
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Marion Arnaud
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Brian J Stevenson
- Vital IT, Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, 1015, Lausanne, Switzerland
| | - Justine Michaux
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Ilija Bilic
- Max Delbrück Centre for Molecular Medicine in the Helmholtz Association, Institute for Medical Systems Biology, Hannoversche Straße 28, 10115, Berlin, Germany
| | - Antje Hirsekorn
- Max Delbrück Centre for Molecular Medicine in the Helmholtz Association, Institute for Medical Systems Biology, Hannoversche Straße 28, 10115, Berlin, Germany
| | - Lorenzo Calviello
- Max Delbrück Centre for Molecular Medicine in the Helmholtz Association, Institute for Medical Systems Biology, Hannoversche Straße 28, 10115, Berlin, Germany
| | - Laia Simó-Riudalbas
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, 1015, Lausanne, Switzerland
| | - Evarist Planet
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, 1015, Lausanne, Switzerland
| | - Jan Lubiński
- Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, ul. Rybacka 1, 70-204, Szczecin, Poland
- International Institute for Molecular Oncology, Jakuba Krauthofera 23, 60-203, Poznań, Poland
| | - Marta Bryśkiewicz
- Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, ul. Rybacka 1, 70-204, Szczecin, Poland
- International Institute for Molecular Oncology, Jakuba Krauthofera 23, 60-203, Poznań, Poland
| | - Maciej Wiznerowicz
- International Institute for Molecular Oncology, Jakuba Krauthofera 23, 60-203, Poznań, Poland
- Poznan University of Medical Sciences, Fredry 10, 61-701, Poznań, Poland
| | - Ioannis Xenarios
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland
- Genome Center Health 2030, Chemin de Mines 9, 1202, Genève, Switzerland
- Department of Training and Research, CHUV/UNIL Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland
| | - Lin Zhang
- Center for Research on Reproduction and Women's Health, University of Pennsylvania, 421 Curie Boulevard, Philadelphia, PA, 19104, USA
- Department of Obstetrics and Gynecology, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA
| | - Didier Trono
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, 1015, Lausanne, Switzerland
| | - Alexandre Harari
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Uwe Ohler
- Max Delbrück Centre for Molecular Medicine in the Helmholtz Association, Institute for Medical Systems Biology, Hannoversche Straße 28, 10115, Berlin, Germany
- Departments of Biology and Computer Science, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany
| | - George Coukos
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland.
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland.
| |
Collapse
|
50
|
Perez MAS, Bassani-Sternberg M, Coukos G, Gfeller D, Zoete V. Analysis of Secondary Structure Biases in Naturally Presented HLA-I Ligands. Front Immunol 2019; 10:2731. [PMID: 31824508 PMCID: PMC6883762 DOI: 10.3389/fimmu.2019.02731] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Accepted: 11/07/2019] [Indexed: 12/31/2022] Open
Abstract
Recent clinical developments in antitumor immunotherapy involving T-cell related therapeutics have led to a renewed interest for human leukocyte antigen class I (HLA-I) binding peptides, given their potential use as peptide vaccines. Databases of HLA-I binding peptides hold therefore information on therapeutic targets essential for understanding immunity. In this work, we use in depth and accurate HLA-I peptidomics datasets determined by mass-spectrometry (MS) and analyze properties of the HLA-I binding peptides with structure-based computational approaches. HLA-I binding peptides are studied grouping all alleles together or in allotype-specific contexts. We capitalize on the increasing number of structurally determined proteins to (1) map the 3D structure of HLA-I binding peptides into the source proteins for analyzing their secondary structure and solvent accessibility in the protein context, and (2) search for potential differences between these properties in HLA-I binding peptides and in a reference dataset of HLA-I motif-like peptides. This is performed by an in-house developed heuristic search that considers peptides across all the human proteome and converges to a collection of peptides that exhibit exactly the same motif as the HLA-I peptides. Our results, based on 9-mers matched to protein 3D structures, clearly show enriched sampling for HLA-I presentation of helical fragments in the source proteins. This enrichment is significant, as compared to 9-mer HLA-I motif-like peptides, and is not entirely explained by the helical propensity of the preferred residues in the HLA-I motifs. We give possible hypothesis for the secondary structure biases observed in HLA-I peptides. This contribution is of potential interest for researchers working in the field of antigen presentation and proteolysis. This knowledge refines the understanding of the rules governing antigen presentation and could be added to the parameters of the current peptide-MHC class I binding predictors to increase their antigen predictive ability.
Collapse
Affiliation(s)
- Marta A S Perez
- Computer-Aided Molecular Engineering, Department of Oncology, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Human Integrated Tumor Immunology Discovery Engine, Department of Oncology, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - George Coukos
- Human Integrated Tumor Immunology Discovery Engine, Department of Oncology, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - David Gfeller
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Computational Cancer Biology, Department of Oncology, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Vincent Zoete
- Computer-Aided Molecular Engineering, Department of Oncology, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| |
Collapse
|