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Sun H, Han X, Du Z, Chen G, Guo T, Xie F, Gu W, Shi Z. Machine learning for the identification of neoantigen-reactive CD8 + T cells in gastrointestinal cancer using single-cell sequencing. Br J Cancer 2024; 131:387-402. [PMID: 38849478 PMCID: PMC11263575 DOI: 10.1038/s41416-024-02737-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 05/18/2024] [Accepted: 05/23/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND It appears that tumour-infiltrating neoantigen-reactive CD8 + T (Neo T) cells are the primary driver of immune responses to gastrointestinal cancer in patients. However, the conventional method is very time-consuming and complex for identifying Neo T cells and their corresponding T cell receptors (TCRs). METHODS By mapping neoantigen-reactive T cells from the single-cell transcriptomes of thousands of tumour-infiltrating lymphocytes, we developed a 26-gene machine learning model for the identification of neoantigen-reactive T cells. RESULTS In both training and validation sets, the model performed admirably. We discovered that the majority of Neo T cells exhibited notable differences in the biological processes of amide-related signal pathways. The analysis of potential cell-to-cell interactions, in conjunction with spatial transcriptomic and multiplex immunohistochemistry data, has revealed that Neo T cells possess potent signalling molecules, including LTA, which can potentially engage with tumour cells within the tumour microenvironment, thereby exerting anti-tumour effects. By sequencing CD8 + T cells in tumour samples of patients undergoing neoadjuvant immunotherapy, we determined that the fraction of Neo T cells was significantly and positively linked with the clinical benefit and overall survival rate of patients. CONCLUSION This method expedites the identification of neoantigen-reactive TCRs and the engineering of neoantigen-reactive T cells for therapy.
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Affiliation(s)
- Hongwei Sun
- Key Laboratory of Laboratory Medicine, Ministry of Education, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiao Han
- KangChen Bio-tech., Ltd, ShangHai, China
| | - Zhengliang Du
- Key Laboratory of Laboratory Medicine, Ministry of Education, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Geer Chen
- Key Laboratory of Laboratory Medicine, Ministry of Education, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Tonglei Guo
- Data and Analysis Center for Genetic Diseases, Beijing Chigene Translational Medicine Research Center Co, Ltd, Tongzhou District, Beijing, China
| | - Fei Xie
- Data and Analysis Center for Genetic Diseases, Beijing Chigene Translational Medicine Research Center Co, Ltd, Tongzhou District, Beijing, China
| | - Weiyue Gu
- Data and Analysis Center for Genetic Diseases, Beijing Chigene Translational Medicine Research Center Co, Ltd, Tongzhou District, Beijing, China
- Chineo Medical Technology Co., Ltd, Beijing, 100101, China
| | - Zhiwen Shi
- Key Laboratory of Laboratory Medicine, Ministry of Education, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
- Data and Analysis Center for Genetic Diseases, Beijing Chigene Translational Medicine Research Center Co, Ltd, Tongzhou District, Beijing, China.
- Chineo Medical Technology Co., Ltd, Beijing, 100101, China.
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2
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Conev A, Fasoulis R, Hall-Swan S, Ferreira R, Kavraki LE. HLAEquity: Examining biases in pan-allele peptide-HLA binding predictors. iScience 2024; 27:108613. [PMID: 38188519 PMCID: PMC10770483 DOI: 10.1016/j.isci.2023.108613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 11/13/2023] [Accepted: 11/29/2023] [Indexed: 01/09/2024] Open
Abstract
Peptide-HLA (pHLA) binding prediction is essential in screening peptide candidates for personalized peptide vaccines. Machine learning (ML) pHLA binding prediction tools are trained on vast amounts of data and are effective in screening peptide candidates. Most ML models report the ability to generalize to HLA alleles unseen during training ("pan-allele" models). However, the use of datasets with imbalanced allele content raises concerns about biased model performance. First, we examine the data bias of two ML-based pan-allele pHLA binding predictors. We find that the pHLA datasets overrepresent alleles from geographic populations of high-income countries. Second, we show that the identified data bias is perpetuated within ML models, leading to algorithmic bias and subpar performance for alleles expressed in low-income geographic populations. We draw attention to the potential therapeutic consequences of this bias, and we challenge the use of the term "pan-allele" to describe models trained with currently available public datasets.
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Affiliation(s)
- Anja Conev
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Romanos Fasoulis
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Sarah Hall-Swan
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Rodrigo Ferreira
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Lydia E. Kavraki
- Department of Computer Science, Rice University, Houston, TX, USA
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3
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Müller M, Huber F, Arnaud M, Kraemer AI, Altimiras ER, Michaux J, Taillandier-Coindard M, Chiffelle J, Murgues B, Gehret T, Auger A, Stevenson BJ, Coukos G, Harari A, Bassani-Sternberg M. Machine learning methods and harmonized datasets improve immunogenic neoantigen prediction. Immunity 2023; 56:2650-2663.e6. [PMID: 37816353 DOI: 10.1016/j.immuni.2023.09.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 06/26/2023] [Accepted: 09/05/2023] [Indexed: 10/12/2023]
Abstract
The accurate selection of neoantigens that bind to class I human leukocyte antigen (HLA) and are recognized by autologous T cells is a crucial step in many cancer immunotherapy pipelines. We reprocessed whole-exome sequencing and RNA sequencing (RNA-seq) data from 120 cancer patients from two external large-scale neoantigen immunogenicity screening assays combined with an in-house dataset of 11 patients and identified 46,017 somatic single-nucleotide variant mutations and 1,781,445 neo-peptides, of which 212 mutations and 178 neo-peptides were immunogenic. Beyond features commonly used for neoantigen prioritization, factors such as the location of neo-peptides within protein HLA presentation hotspots, binding promiscuity, and the role of the mutated gene in oncogenicity were predictive for immunogenicity. The classifiers accurately predicted neoantigen immunogenicity across datasets and improved their ranking by up to 30%. Besides insights into machine learning methods for neoantigen ranking, we have provided homogenized datasets valuable for developing and benchmarking companion algorithms for neoantigen-based immunotherapies.
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Affiliation(s)
- Markus Müller
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, 1015 Lausanne, Switzerland.
| | - Florian Huber
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Marion Arnaud
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Anne I Kraemer
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Emma Ricart Altimiras
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Justine Michaux
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Marie Taillandier-Coindard
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Johanna Chiffelle
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Baptiste Murgues
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Talita Gehret
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Aymeric Auger
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Brian J Stevenson
- Agora Cancer Research Centre, 1011 Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, 1015 Lausanne, Switzerland
| | - George Coukos
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland; Center of Experimental Therapeutics, Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
| | - Alexandre Harari
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland; Center of Experimental Therapeutics, Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland; Center of Experimental Therapeutics, Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland.
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4
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Zhu Y, Li X, Chen T, Wang J, Zhou Y, Mu X, Du Y, Wang J, Tang J, Liu J. Personalised neoantigen-based therapy in colorectal cancer. Clin Transl Med 2023; 13:e1461. [PMID: 37921274 PMCID: PMC10623652 DOI: 10.1002/ctm2.1461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 10/06/2023] [Accepted: 10/13/2023] [Indexed: 11/04/2023] Open
Abstract
Colorectal cancer (CRC) has become one of the most common tumours with high morbidity, mortality and distinctive evolution mechanism. The neoantigens arising from the somatic mutations have become considerable treatment targets in the management of CRC. As cancer-specific aberrant peptides, neoantigens can trigger the robust host immune response and exert anti-tumour effects while minimising the emergence of adverse events commonly associated with alternative therapeutic regimens. In this review, we summarised the mechanism, generation, identification and prognostic significance of neoantigens, as well as therapeutic strategies challenges of neoantigen-based therapy in CRC. The evidence suggests that the establishment of personalised neoantigen-based therapy holds great promise as an effective treatment approach for patients with CRC.
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Affiliation(s)
- Ya‐Juan Zhu
- Department of Biotherapy and Cancer CenterState Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengduChina
| | - Xiong Li
- Department of GastroenterologyThe Second Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Ting‐Ting Chen
- The Second Clinical Medical College of Lanzhou UniversityLanzhouChina
| | - Jia‐Xiang Wang
- Department of Renal Cancer and MelanomaPeking University Cancer Hospital & InstituteBeijingChina
| | - Yi‐Xin Zhou
- Department of Biotherapy and Cancer CenterState Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengduChina
| | - Xiao‐Li Mu
- Department of Biotherapy and Cancer CenterState Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengduChina
| | - Yang Du
- Department of Biotherapy and Cancer CenterState Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengduChina
| | - Jia‐Ling Wang
- Department of Biotherapy and Cancer CenterState Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengduChina
| | - Jie Tang
- Clinical Trial CenterWest China HospitalSichuan UniversityChengduChina
| | - Ji‐Yan Liu
- Department of Biotherapy and Cancer CenterState Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengduChina
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5
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Godazandeh K, Van Olmen L, Van Oudenhove L, Lefever S, Bogaert C, Fant B. Methods behind neoantigen prediction for personalized anticancer vaccines. Methods Cell Biol 2023; 183:161-186. [PMID: 38548411 DOI: 10.1016/bs.mcb.2023.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Next to conventional cancer therapies, immunotherapies such as immune checkpoint inhibitors have broadened the cancer treatment landscape over the past decades. Recent advances in next generation sequencing and bioinformatics technologies have made it possible to identify a patient's own immunogenic neoantigens. These cancer neoantigens serve as important targets for personalized immunotherapy which has the benefit of being more active and effective in targeting cancer cells. This paper is a step-by-step guide discussing the different analyses and challenges encountered during in-silico neoantigen prediction. The protocol describes all the tools and steps required for the identification of immunogenic neoantigens.
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6
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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.
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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.
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7
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Contemplating immunopeptidomes to better predict them. Semin Immunol 2023; 66:101708. [PMID: 36621290 DOI: 10.1016/j.smim.2022.101708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/16/2022] [Accepted: 12/20/2022] [Indexed: 01/09/2023]
Abstract
The identification of T-cell epitopes is key for a complete molecular understanding of immune recognition mechanisms in infectious diseases, autoimmunity and cancer. T-cell epitopes further provide targets for personalized vaccines and T-cell therapy, with several therapeutic applications in cancer immunotherapy and elsewhere. T-cell epitopes consist of short peptides displayed on Major Histocompatibility Complex (MHC) molecules. The recent advances in mass spectrometry (MS) based technologies to profile the ensemble of peptides displayed on MHC molecules - the so-called immunopeptidome - had a major impact on our understanding of antigen presentation and MHC ligands. On the one hand, these techniques enabled researchers to directly identify hundreds of thousands of peptides presented on MHC molecules, including some that elicited T-cell recognition. On the other hand, the data collected in these experiments revealed fundamental properties of antigen presentation pathways and significantly improved our ability to predict naturally presented MHC ligands and T-cell epitopes across the wide spectrum of MHC alleles found in human and other organisms. Here we review recent computational developments to analyze experimentally determined immunopeptidomes and harness these data to improve our understanding of antigen presentation and MHC binding specificities, as well as our ability to predict MHC ligands. We further discuss the strengths and limitations of the latest approaches to move beyond predictions of antigen presentation and tackle the challenges of predicting TCR recognition and immunogenicity.
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8
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Gfeller D, Schmidt J, Croce G, Guillaume P, Bobisse S, Genolet R, Queiroz L, Cesbron J, Racle J, Harari A. Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8 + T-cell epitopes. Cell Syst 2023; 14:72-83.e5. [PMID: 36603583 PMCID: PMC9811684 DOI: 10.1016/j.cels.2022.12.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 10/12/2022] [Accepted: 12/08/2022] [Indexed: 01/06/2023]
Abstract
The recognition of pathogen or cancer-specific epitopes by CD8+ T cells is crucial for the clearance of infections and the response to cancer immunotherapy. This process requires epitopes to be presented on class I human leukocyte antigen (HLA-I) molecules and recognized by the T-cell receptor (TCR). Machine learning models capturing these two aspects of immune recognition are key to improve epitope predictions. Here, we assembled a high-quality dataset of naturally presented HLA-I ligands and experimentally verified neo-epitopes. We then integrated these data in a refined computational framework to predict antigen presentation (MixMHCpred2.2) and TCR recognition (PRIME2.0). The depth of our training data and the algorithmic developments resulted in improved predictions of HLA-I ligands and neo-epitopes. Prospectively applying our tools to SARS-CoV-2 proteins revealed several epitopes. TCR sequencing identified a monoclonal response in effector/memory CD8+ T cells against one of these epitopes and cross-reactivity with the homologous peptides from other coronaviruses.
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Affiliation(s)
- David Gfeller
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Lausanne, Switzerland,Agora Cancer Research Centre, 1011 Lausanne, Switzerland,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland,Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland,Corresponding author
| | - Julien Schmidt
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland,Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Giancarlo Croce
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Lausanne, Switzerland,Agora Cancer Research Centre, 1011 Lausanne, Switzerland,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland,Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Philippe Guillaume
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland,Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Sara Bobisse
- Agora Cancer Research Centre, 1011 Lausanne, Switzerland,Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland,Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Raphael Genolet
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland,Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Lise Queiroz
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland,Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Julien Cesbron
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland,Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Julien Racle
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Lausanne, Switzerland,Agora Cancer Research Centre, 1011 Lausanne, Switzerland,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland,Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Alexandre Harari
- Agora Cancer Research Centre, 1011 Lausanne, Switzerland,Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland,Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
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9
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Frentzen A, Greenbaum JA, Kim H, Peters B, Koşaloğlu-Yalçın Z. Estimating tissue-specific peptide abundance from public RNA-Seq data. Front Genet 2023; 14:1082168. [PMID: 36713080 PMCID: PMC9878344 DOI: 10.3389/fgene.2023.1082168] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/04/2023] [Indexed: 01/15/2023] Open
Abstract
Several novel MHC class I epitope prediction tools additionally incorporate the abundance levels of the peptides' source antigens and have shown improved performance for predicting immunogenicity. Such tools require the user to input the MHC alleles and peptide sequences of interest, as well as the abundance levels of the peptides' source proteins. However, such expression data is often not directly available to users, and retrieving the expression level of a peptide's source antigen from public databases is not trivial. We have developed the Peptide eXpression annotator (pepX), which takes a peptide as input, identifies from which proteins the peptide can be derived, and returns an estimate of the expression level of those source proteins from selected public databases. We have also investigated how the abundance level of a peptide can be best estimated in cases when it can originate from multiple transcripts and proteins and found that summing up transcript-level expression values performs best in distinguishing ligands from decoy peptides.
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Affiliation(s)
- Angela Frentzen
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, San Diego, CA, United States
| | - Jason A. Greenbaum
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, San Diego, CA, United States
| | - Haeuk Kim
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, San Diego, CA, United States
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, San Diego, CA, United States,Department of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Zeynep Koşaloğlu-Yalçın
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, San Diego, CA, United States,*Correspondence: Zeynep Koşaloğlu-Yalçın,
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10
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Lybaert L, Lefever S, Fant B, Smits E, De Geest B, Breckpot K, Dirix L, Feldman SA, van Criekinge W, Thielemans K, van der Burg SH, Ott PA, Bogaert C. Challenges in neoantigen-directed therapeutics. Cancer Cell 2023; 41:15-40. [PMID: 36368320 DOI: 10.1016/j.ccell.2022.10.013] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 08/19/2022] [Accepted: 10/11/2022] [Indexed: 11/11/2022]
Abstract
A fundamental prerequisite for the efficacy of cancer immunotherapy is the presence of functional, antigen-specific T cells within the tumor. Neoantigen-directed therapy is a promising strategy that aims at targeting the host's immune response against tumor-specific antigens, thereby eradicating cancer cells. Initial forays have been made in clinical environments utilizing vaccines and adoptive cell therapy; however, many challenges lie ahead. We provide an in-depth overview of the current state of the field with an emphasis on in silico neoantigen discovery and the clinical aspects that need to be addressed to unlock the full potential of this therapy.
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Affiliation(s)
| | | | | | - Evelien Smits
- Center for Oncological Research, University of Antwerp, 2610 Wilrijk, Belgium
| | - Bruno De Geest
- Department of Pharmaceutics, Ghent University, 9000 Ghent, Belgium
| | - Karine Breckpot
- Laboratory of Molecular and Cellular Therapy, Department of Biomedical Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Luc Dirix
- Translational Cancer Research Unit, Center for Oncological Research, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Steven A Feldman
- Center for Cancer Cell Therapy, Stanford University School of Medicine, Stanford, CA, USA
| | - Wim van Criekinge
- Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Kris Thielemans
- Laboratory of Molecular and Cellular Therapy, Department of Biomedical Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Sjoerd H van der Burg
- Medical Oncology, Oncode Institute, Leiden University Medical Center, Leiden, the Netherlands
| | - Patrick A Ott
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
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11
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Tadros DM, Eggenschwiler S, Racle J, Gfeller D. The MHC Motif Atlas: a database of MHC binding specificities and ligands. Nucleic Acids Res 2022; 51:D428-D437. [PMID: 36318236 PMCID: PMC9825574 DOI: 10.1093/nar/gkac965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 10/07/2022] [Accepted: 10/14/2022] [Indexed: 01/07/2023] Open
Abstract
The highly polymorphic Major Histocompatibility Complex (MHC) genes are responsible for the binding and cell surface presentation of pathogen or cancer specific T-cell epitopes. This process is fundamental for eliciting T-cell recognition of infected or malignant cells. Epitopes displayed on MHC molecules further provide therapeutic targets for personalized cancer vaccines or adoptive T-cell therapy. To help visualizing, analyzing and comparing the different binding specificities of MHC molecules, we developed the MHC Motif Atlas (http://mhcmotifatlas.org/). This database contains information about thousands of class I and class II MHC molecules, including binding motifs, peptide length distributions, motifs of phosphorylated ligands, multiple specificities or links to X-ray crystallography structures. The database further enables users to download curated datasets of MHC ligands. By combining intuitive visualization of the main binding properties of MHC molecules together with access to more than a million ligands, the MHC Motif Atlas provides a central resource to analyze and interpret the binding specificities of MHC molecules.
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Affiliation(s)
| | | | - Julien Racle
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Lausanne, Switzerland,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - David Gfeller
- To whom correspondence should be addressed. Tel: +41 21 692 59 83;
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Zhao LP, Hu JH, Hu D, Wang HJ, Huang CG, Luo RH, Zhou ZH, Huang XY, Xie T, Lou JS. Hyperprogression, a challenge of PD-1/PD-L1 inhibitors treatments: potential mechanisms and coping strategies. Biomed Pharmacother 2022; 150:112949. [PMID: 35447545 DOI: 10.1016/j.biopha.2022.112949] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 04/01/2022] [Accepted: 04/08/2022] [Indexed: 11/29/2022] Open
Abstract
Immunotherapy is now a mainstay in cancer treatments. Programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immune checkpoint inhibitor (ICI) therapies have opened up a new venue of advanced cancer immunotherapy. However, hyperprogressive disease (HPD) induced by PD-1/PD-L1 inhibitors caused a significant decrease in the overall survival (OS) of the patients, which compromise the efficacy of PD-1/PD-L1 inhibitors. Therefore, HPD has become an urgent issue to be addressed in the clinical uses of PD-1/PD-L1 inhibitors. The mechanisms of HPD remain unclear, and possible predictive factors of HPD are not well understood. In this review, we summarized the potential mechanisms of HPD and coping strategies that can effectively reduce the occurrence and development of HPD.
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Affiliation(s)
- Li-Ping Zhao
- School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Zhejiang 311121, China; Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Jun-Hu Hu
- School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Zhejiang 311121, China; Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Die Hu
- School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Zhejiang 311121, China; Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Hao-Jie Wang
- School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Zhejiang 311121, China; Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Chang-Gang Huang
- School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Zhejiang 311121, China; Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Ru-Hua Luo
- School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Zhejiang 311121, China; Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Zhao-Huang Zhou
- School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Zhejiang 311121, China; Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Xin-Yun Huang
- Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, NY 10065, USA.
| | - Tian Xie
- School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Zhejiang 311121, China; Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China.
| | - Jian-Shu Lou
- School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Zhejiang 311121, China; Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China.
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Thibault P, Perreault C. Immunopeptidomics: Reading the Immune Signal That Defines Self From Nonself. Mol Cell Proteomics 2022; 21:100234. [PMID: 35567924 PMCID: PMC9252926 DOI: 10.1016/j.mcpro.2022.100234] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Pierre Thibault
- Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, Quebec, Canada; Department of Chemistry, Université de Montréal, Montreal, Quebec, Canada
| | - Claude Perreault
- Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, Quebec, Canada; Department of Medicine, Université de Montréal, Montreal, Quebec, Canada
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14
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Pyke RM, Mellacheruvu D, Dea S, Abbott CW, McDaniel L, Bhave DP, Zhang SV, Levy E, Bartha G, West J, Snyder MP, Chen RO, Boyle SM. A machine learning algorithm with subclonal sensitivity reveals widespread pan-cancer human leukocyte antigen loss of heterozygosity. Nat Commun 2022; 13:1925. [PMID: 35414054 PMCID: PMC9005524 DOI: 10.1038/s41467-022-29203-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 02/18/2022] [Indexed: 11/09/2022] Open
Abstract
Human leukocyte antigen loss of heterozygosity (HLA LOH) allows cancer cells to escape immune recognition by deleting HLA alleles, causing the suppressed presentation of tumor neoantigens. Despite its importance in immunotherapy response, few methods exist to detect HLA LOH, and their accuracy is not well understood. Here, we develop DASH (Deletion of Allele-Specific HLAs), a machine learning-based algorithm to detect HLA LOH from paired tumor-normal sequencing data. With cell line mixtures, we demonstrate increased sensitivity compared to previously published tools. Moreover, our patient-specific digital PCR validation approach provides a sensitive, robust orthogonal approach that could be used for clinical validation. Using DASH on 610 patients across 15 tumor types, we find that 18% of patients have HLA LOH. Moreover, we show inflated HLA LOH rates compared to genome-wide LOH and correlations between CD274 (encodes PD-L1) expression and microsatellite instability status, suggesting the HLA LOH is a key immune resistance strategy. Human leukocyte antigen loss of heterozygosity (HLA LOH) is an important mechanism of immune escape in patients with cancer. Here the authors design and validate a machine learning algorithm with subclonal sensitivity for the identification of HLA LOH from paired tumor-normal sequencing data.
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Affiliation(s)
| | | | | | | | | | | | | | - Eric Levy
- Personalis, Inc, Menlo Park, CA, USA
| | | | - John West
- Personalis, Inc, Menlo Park, CA, USA
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Koşaloğlu-Yalçın Z, Lee J, Greenbaum J, Schoenberger SP, Miller A, Kim YJ, Sette A, Nielsen M, Peters B. Combined assessment of MHC binding and antigen abundance improves T cell epitope predictions. iScience 2022; 25:103850. [PMID: 35128348 PMCID: PMC8806398 DOI: 10.1016/j.isci.2022.103850] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 10/19/2021] [Accepted: 01/26/2022] [Indexed: 01/16/2023] Open
Abstract
Many steps of the MHC class I antigen processing pathway can be predicted using computational methods. Here we show that epitope predictions can be further improved by considering abundance levels of peptides' source proteins. We utilized biophysical principles and existing MHC binding prediction tools in concert with abundance estimates of source proteins to derive a function that estimates the likelihood of a peptide to be an MHC class I ligand. We found that this combination improved predictions for both naturally eluted ligands and cancer neoantigen epitopes. We compared the use of different measures of antigen abundance, including mRNA expression by RNA-Seq, gene translation by Ribo-Seq, and protein abundance by proteomics on a dataset of SARS-CoV-2 epitopes. Epitope predictions were improved above binding predictions alone in all cases and gave the highest performance when using proteomic data. Our results highlight the value of incorporating antigen abundance levels to improve epitope predictions.
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Affiliation(s)
- Zeynep Koşaloğlu-Yalçın
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Jenny Lee
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Jason Greenbaum
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Stephen P. Schoenberger
- Division of Hematology and Oncology, Center for Personalized Cancer Therapy, San Diego Moore's Cancer Center, University of California, San Diego, San Diego, CA, USA
- Laboratory of Cellular Immunology, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Aaron Miller
- Division of Hematology and Oncology, Center for Personalized Cancer Therapy, San Diego Moore's Cancer Center, University of California, San Diego, San Diego, CA, USA
- Laboratory of Cellular Immunology, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Young J. Kim
- Department of Otolaryngology-Head & Neck Surgery, Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Alessandro Sette
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
- Department of Medicine, University of California, San Diego, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, DK Lyngby, 2800, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, CP San Martín, B1650, Argentina
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
- Department of Medicine, University of California, San Diego, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
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Identification of thermodynamic quantities of the stability of peptide-MHC I complex using nanoscale differential scanning fluorimetry. Mol Immunol 2021; 141:338-339. [PMID: 34895765 DOI: 10.1016/j.molimm.2021.12.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 12/01/2021] [Indexed: 11/23/2022]
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Fotakis G, Trajanoski Z, Rieder D. Computational cancer neoantigen prediction: current status and recent advances. IMMUNO-ONCOLOGY TECHNOLOGY 2021; 12:100052. [PMID: 35755950 PMCID: PMC9216660 DOI: 10.1016/j.iotech.2021.100052] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Over the last few decades, immunotherapy has shown significant therapeutic efficacy in a broad range of cancer types. Antitumor immune responses are contingent on the recognition of tumor-specific antigens, which are termed neoantigens. Tumor neoantigens are ideal targets for immunotherapy since they can be recognized as non-self antigens by the host immune system and thus are able to elicit an antitumor T-cell response. There are an increasing number of studies that highlight the importance of tumor neoantigens in immunoediting and in the sensitivity to immune checkpoint blockade. Therefore, one of the most fundamental tasks in the field of immuno-oncology research is the identification of patient-specific neoantigens. To this end, a plethora of computational approaches have been developed in order to predict tumor-specific aberrant peptides and quantify their likelihood of binding to patients' human leukocyte antigen molecules in order to be recognized by T cells. In this review, we systematically summarize and present the most recent advances in computational neoantigen prediction, and discuss the challenges and novel methods that are being developed to resolve them. Tumors have the ability to acquire immune escape mechanisms. Tumor-specific aberrant peptides (neoantigens) can elicit an immune response by the host immune system. The identification of neoantigens is one of the most fundamental tasks in the field of immuno-oncology research. A plethora of computational approaches have been developed in order to predict patient-specificneoantigens.
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Affiliation(s)
- G Fotakis
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - Z Trajanoski
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - D Rieder
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
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Therapeutic cancer vaccines: reasons to believe. Emerg Top Life Sci 2021; 5:591-595. [PMID: 34495328 DOI: 10.1042/etls20210205] [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: 06/17/2021] [Revised: 08/23/2021] [Accepted: 08/24/2021] [Indexed: 11/17/2022]
Abstract
Our hopes of using the power of the immune system to control tumours have been partially fulfilled with anti-PD1 antibodies and other checkpoint inhibitors and the use of engineered T cells targeting lineage-specific surface markers with chimeric antigen receptors. Can these successes be generalised? Therapeutic cancer vaccines aim to educate or re-educate the immune system to recognise tumour specific or tumour associated antigens. After many false dawns, some positive data for the effectiveness of such an approach is starting to emerge in advanced solid tumours, albeit as combination therapies with checkpoint inhibitors. But is the field targeting the right antigens? Interventions using the most effective vaccine platforms to target certain sets of antigens in patients with low disease burden might bring impressive long-term benefits to patients as single agents.
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