1
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Xin K, Wei X, Shao J, Chen F, Liu Q, Liu B. Establishment of a novel tumor neoantigen prediction tool for personalized vaccine design. Hum Vaccin Immunother 2024; 20:2300881. [PMID: 38214336 DOI: 10.1080/21645515.2023.2300881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/28/2023] [Indexed: 01/13/2024] Open
Abstract
The personalized neoantigen nanovaccine (PNVAC) platform for patients with gastric cancer we established previously exhibited promising anti-tumor immunoreaction. However, limited by the ability of traditional neoantigen prediction tools, a portion of epitopes failed to induce specific immune response. In order to filter out more neoantigens to optimize our PNVAC platform, we develop a novel neoantigen prediction model, NUCC. This prediction tool trained through a deep learning approach exhibits better neoantigen prediction performance than other prediction tools, not only in two independent epitope datasets, but also in a totally new epitope dataset we construct from scratch, including 25 patients with advance gastric cancer and 150 candidate mutant peptides, 13 of which prove to be neoantigen by immunogenicity test in vitro. Our work lay the foundation for the improvement of our PNVAC platform for gastric cancer in the future.
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Affiliation(s)
- Kai Xin
- Department of Oncology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Xiao Wei
- Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, China
| | - Jie Shao
- Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, China
| | - Fangjun Chen
- Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, China
| | - Qin Liu
- Department of Oncology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
- Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, China
| | - Baorui Liu
- Department of Oncology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
- Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, China
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2
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Yao R, Xie C, Xia X. Recent progress in mRNA cancer vaccines. Hum Vaccin Immunother 2024; 20:2307187. [PMID: 38282471 PMCID: PMC10826636 DOI: 10.1080/21645515.2024.2307187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 01/16/2024] [Indexed: 01/30/2024] Open
Abstract
The research and development of messenger RNA (mRNA) cancer vaccines have gradually overcome numerous challenges through the application of personalized cancer antigens, structural optimization of mRNA, and the development of alternative RNA-based vectors and efficient targeted delivery vectors. Clinical trials are currently underway for various cancer vaccines that encode tumor-associated antigens (TAAs), tumor-specific antigens (TSAs), or immunomodulators. In this paper, we summarize the optimization of mRNA and the emergence of RNA-based expression vectors in cancer vaccines. We begin by reviewing the advancement and utilization of state-of-the-art targeted lipid nanoparticles (LNPs), followed by presenting the primary classifications and clinical applications of mRNA cancer vaccines. Collectively, mRNA vaccines are emerging as a central focus in cancer immunotherapy, offering the potential to address multiple challenges in cancer treatment, either as standalone therapies or in combination with current cancer treatments.
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Affiliation(s)
- Ruhui Yao
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Chunyuan Xie
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xiaojun Xia
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
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3
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Su Z, Wu Y, Cao K, Du J, Cao L, Wu Z, Wu X, Wang X, Song Y, Wang X, Duan H. APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules. Methods 2024; 228:38-47. [PMID: 38772499 DOI: 10.1016/j.ymeth.2024.05.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/28/2024] [Accepted: 05/18/2024] [Indexed: 05/23/2024] Open
Abstract
Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.
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Affiliation(s)
- Zhihao Su
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Yejian Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Kaiqiang Cao
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Jie Du
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Lujing Cao
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Zhipeng Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Xinyi Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Xinqiao Wang
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Ying Song
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Xudong Wang
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.
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4
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Bulashevska A, Nacsa Z, Lang F, Braun M, Machyna M, Diken M, Childs L, König R. Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy. Front Immunol 2024; 15:1394003. [PMID: 38868767 PMCID: PMC11167095 DOI: 10.3389/fimmu.2024.1394003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/13/2024] [Indexed: 06/14/2024] Open
Abstract
Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.
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Affiliation(s)
- Alla Bulashevska
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Zsófia Nacsa
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Franziska Lang
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Markus Braun
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Martin Machyna
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Mustafa Diken
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Liam Childs
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Renate König
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
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5
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Omelchenko AA, Siwek JC, Chhibbar P, Arshad S, Nazarali I, Nazarali K, Rosengart A, Rahimikollu J, Tilstra J, Shlomchik MJ, Koes DR, Joglekar AV, Das J. Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.01.592062. [PMID: 38746274 PMCID: PMC11092674 DOI: 10.1101/2024.05.01.592062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The explosion of sequence data has allowed the rapid growth of protein language models (pLMs). pLMs have now been employed in many frameworks including variant-effect and peptide-specificity prediction. Traditionally, for protein-protein or peptide-protein interactions (PPIs), corresponding sequences are either co-embedded followed by post-hoc integration or the sequences are concatenated prior to embedding. Interestingly, no method utilizes a language representation of the interaction itself. We developed an interaction LM (iLM), which uses a novel language to represent interactions between protein/peptide sequences. Sliding Window Interaction Grammar (SWING) leverages differences in amino acid properties to generate an interaction vocabulary. This vocabulary is the input into a LM followed by a supervised prediction step where the LM's representations are used as features. SWING was first applied to predicting peptide:MHC (pMHC) interactions. SWING was not only successful at generating Class I and Class II models that have comparable prediction to state-of-the-art approaches, but the unique Mixed Class model was also successful at jointly predicting both classes. Further, the SWING model trained only on Class I alleles was predictive for Class II, a complex prediction task not attempted by any existing approach. For de novo data, using only Class I or Class II data, SWING also accurately predicted Class II pMHC interactions in murine models of SLE (MRL/lpr model) and T1D (NOD model), that were validated experimentally. To further evaluate SWING's generalizability, we tested its ability to predict the disruption of specific protein-protein interactions by missense mutations. Although modern methods like AlphaMissense and ESM1b can predict interfaces and variant effects/pathogenicity per mutation, they are unable to predict interaction-specific disruptions. SWING was successful at accurately predicting the impact of both Mendelian mutations and population variants on PPIs. This is the first generalizable approach that can accurately predict interaction-specific disruptions by missense mutations with only sequence information. Overall, SWING is a first-in-class generalizable zero-shot iLM that learns the language of PPIs.
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Affiliation(s)
- Alisa A. Omelchenko
- Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, PA, USA
- The joint CMU-Pitt PhD program in computational biology, School of Medicine, University of Pittsburgh, PA, USA
| | - Jane C. Siwek
- Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, PA, USA
- The joint CMU-Pitt PhD program in computational biology, School of Medicine, University of Pittsburgh, PA, USA
| | - Prabal Chhibbar
- Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Integrative systems biology PhD program, School of Medicine, University of Pittsburgh, PA, USA
| | - Sanya Arshad
- Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Iliyan Nazarali
- Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kiran Nazarali
- Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - AnnaElaine Rosengart
- Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Javad Rahimikollu
- Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, PA, USA
- The joint CMU-Pitt PhD program in computational biology, School of Medicine, University of Pittsburgh, PA, USA
| | - Jeremy Tilstra
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Division of Rheumatology and Clinical Immunology, Department of Medicine, School of Medicine, University of Pittsburgh, PA, USA
| | - Mark J. Shlomchik
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - David R. Koes
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, PA, USA
| | - Alok V. Joglekar
- Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, PA, USA
| | - Jishnu Das
- Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, PA, USA
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6
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Wilson E, Cava JK, Chowell D, Raja R, Mangalaparthi KK, Pandey A, Curtis M, Anderson KS, Singharoy A. The electrostatic landscape of MHC-peptide binding revealed using inception networks. Cell Syst 2024; 15:362-373.e7. [PMID: 38554709 DOI: 10.1016/j.cels.2024.03.001] [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: 04/04/2023] [Revised: 11/24/2023] [Accepted: 03/05/2024] [Indexed: 04/02/2024]
Abstract
Predictive modeling of macromolecular recognition and protein-protein complementarity represents one of the cornerstones of biophysical sciences. However, such models are often hindered by the combinatorial complexity of interactions at the molecular interfaces. Exemplary of this problem is peptide presentation by the highly polymorphic major histocompatibility complex class I (MHC-I) molecule, a principal component of immune recognition. We developed human leukocyte antigen (HLA)-Inception, a deep biophysical convolutional neural network, which integrates molecular electrostatics to capture non-bonded interactions for predicting peptide binding motifs across 5,821 MHC-I alleles. These predictions of generated motifs correlate strongly with experimental peptide binding and presentation data. Beyond molecular interactions, the study demonstrates the application of predicted motifs in analyzing MHC-I allele associations with HIV disease progression and patient response to immune checkpoint inhibitors. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Eric Wilson
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85207, USA; The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - John Kevin Cava
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85207, USA
| | - Diego Chowell
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Remya Raja
- Department of Immunology, Mayo Clinic, Scottsdale, AZ 85259, USA
| | - Kiran K Mangalaparthi
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Akhilesh Pandey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; Center for Individualized Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Marion Curtis
- Department of Immunology, Mayo Clinic, Scottsdale, AZ 85259, USA; College of Medicine and Science, Mayo Clinic, Scottsdale, AZ 85259, USA; Department of Cancer Biology, Mayo Clinic, Scottsdale, AZ 85259, USA
| | - Karen S Anderson
- School of Life Sciences, Arizona State University, Tempe, AZ 85207, USA.
| | - Abhishek Singharoy
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85207, USA.
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7
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Adams C, Laukens K, Bittremieux W, Boonen K. Machine learning-based peptide-spectrum match rescoring opens up the immunopeptidome. Proteomics 2024; 24:e2300336. [PMID: 38009585 DOI: 10.1002/pmic.202300336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/18/2023] [Accepted: 10/23/2023] [Indexed: 11/29/2023]
Abstract
Immunopeptidomics is a key technology in the discovery of targets for immunotherapy and vaccine development. However, identifying immunopeptides remains challenging due to their non-tryptic nature, which results in distinct spectral characteristics. Moreover, the absence of strict digestion rules leads to extensive search spaces, further amplified by the incorporation of somatic mutations, pathogen genomes, unannotated open reading frames, and post-translational modifications. This inflation in search space leads to an increase in random high-scoring matches, resulting in fewer identifications at a given false discovery rate. Peptide-spectrum match rescoring has emerged as a machine learning-based solution to address challenges in mass spectrometry-based immunopeptidomics data analysis. It involves post-processing unfiltered spectrum annotations to better distinguish between correct and incorrect peptide-spectrum matches. Recently, features based on predicted peptidoform properties, including fragment ion intensities, retention time, and collisional cross section, have been used to improve the accuracy and sensitivity of immunopeptide identification. In this review, we describe the diverse bioinformatics pipelines that are currently available for peptide-spectrum match rescoring and discuss how they can be used for the analysis of immunopeptidomics data. Finally, we provide insights into current and future machine learning solutions to boost immunopeptide identification.
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Affiliation(s)
- Charlotte Adams
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
- Laboratory of Protein Science, Proteomics and Epigenetic Signaling (PPES), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Kris Laukens
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Wout Bittremieux
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Kurt Boonen
- Laboratory of Protein Science, Proteomics and Epigenetic Signaling (PPES), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- ImmuneSpec BV, Niel, Belgium
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8
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Wan YTR, Koşaloğlu‐Yalçın Z, Peters B, Nielsen M. A large-scale study of peptide features defining immunogenicity of cancer neo-epitopes. NAR Cancer 2024; 6:zcae002. [PMID: 38288446 PMCID: PMC10823584 DOI: 10.1093/narcan/zcae002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 01/03/2024] [Accepted: 01/12/2024] [Indexed: 01/31/2024] Open
Abstract
Accurate prediction of immunogenicity for neo-epitopes arising from a cancer associated mutation is a crucial step in many bioinformatics pipelines that predict outcome of checkpoint blockade treatments or that aim to design personalised cancer immunotherapies and vaccines. In this study, we performed a comprehensive analysis of peptide features relevant for prediction of immunogenicity using the Cancer Epitope Database and Analysis Resource (CEDAR), a curated database of cancer epitopes with experimentally validated immunogenicity annotations from peer-reviewed publications. The developed model, ICERFIRE (ICore-based Ensemble Random Forest for neo-epitope Immunogenicity pREdiction), extracts the predicted ICORE from the full neo-epitope as input, i.e. the nested peptide with the highest predicted major histocompatibility complex (MHC) binding potential combined with its predicted likelihood of antigen presentation (%Rank). Key additional features integrated into the model include assessment of the BLOSUM mutation score of the neo-epitope, and antigen expression levels of the wild-type counterpart which is often reflecting a neo-epitope's abundance. We demonstrate improved and robust performance of ICERFIRE over existing immunogenicity and epitope prediction models, both in cross-validation and on external validation datasets.
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Affiliation(s)
- Yat-tsai Richie Wan
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, DK 28002, Denmark
| | - Zeynep Koşaloğlu‐Yalçın
- Center for Infectious Disease and Vaccine Research, La Jolla Institute of Immunology, La Jolla, CA 92037, USA
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute of Immunology, La Jolla, CA 92037, USA
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, DK 28002, Denmark
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9
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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: 5] [Impact Index Per Article: 5.0] [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.
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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
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10
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Shahbazy M, Ramarathinam SH, Li C, Illing PT, Faridi P, Croft NP, Purcell AW. MHCpLogics: an interactive machine learning-based tool for unsupervised data visualization and cluster analysis of immunopeptidomes. Brief Bioinform 2024; 25:bbae087. [PMID: 38487848 PMCID: PMC10940831 DOI: 10.1093/bib/bbae087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 12/12/2023] [Accepted: 02/15/2024] [Indexed: 03/18/2024] Open
Abstract
The major histocompatibility complex (MHC) encodes a range of immune response genes, including the human leukocyte antigens (HLAs) in humans. These molecules bind peptide antigens and present them on the cell surface for T cell recognition. The repertoires of peptides presented by HLA molecules are termed immunopeptidomes. The highly polymorphic nature of the genres that encode the HLA molecules confers allotype-specific differences in the sequences of bound ligands. Allotype-specific ligand preferences are often defined by peptide-binding motifs. Individuals express up to six classical class I HLA allotypes, which likely present peptides displaying different binding motifs. Such complex datasets make the deconvolution of immunopeptidomic data into allotype-specific contributions and further dissection of binding-specificities challenging. Herein, we developed MHCpLogics as an interactive machine learning-based tool for mining peptide-binding sequence motifs and visualization of immunopeptidome data across complex datasets. We showcase the functionalities of MHCpLogics by analyzing both in-house and published mono- and multi-allelic immunopeptidomics data. The visualization modalities of MHCpLogics allow users to inspect clustered sequences down to individual peptide components and to examine broader sequence patterns within multiple immunopeptidome datasets. MHCpLogics can deconvolute large immunopeptidome datasets enabling the interrogation of clusters for the segregation of allotype-specific peptide sequence motifs, identification of sub-peptidome motifs, and the exportation of clustered peptide sequence lists. The tool facilitates rapid inspection of immunopeptidomes as a resource for the immunology and vaccine communities. MHCpLogics is a standalone application available via an executable installation at: https://github.com/PurcellLab/MHCpLogics.
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Affiliation(s)
- Mohammad Shahbazy
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Sri H Ramarathinam
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Chen Li
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Patricia T Illing
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Pouya Faridi
- Centre for Cancer Research, Hudson Institute of Medical Research, Clayton, VIC 3168, Australia
- Monash Proteomics and Metabolomics Platform, Department of Medicine, School of Clinical Sciences, Monash University, Clayton, VIC 3800, Australia
| | - Nathan P Croft
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Anthony W Purcell
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
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11
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Bravi B. Development and use of machine learning algorithms in vaccine target selection. NPJ Vaccines 2024; 9:15. [PMID: 38242890 PMCID: PMC10798987 DOI: 10.1038/s41541-023-00795-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 12/07/2023] [Indexed: 01/21/2024] Open
Abstract
Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.
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Affiliation(s)
- Barbara Bravi
- Department of Mathematics, Imperial College London, London, SW7 2AZ, UK.
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12
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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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13
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Ricker CA, Meli K, Van Allen EM. Historical perspective and future directions: computational science in immuno-oncology. J Immunother Cancer 2024; 12:e008306. [PMID: 38191244 PMCID: PMC10826578 DOI: 10.1136/jitc-2023-008306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/07/2023] [Indexed: 01/10/2024] Open
Abstract
Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life.
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Affiliation(s)
- Cora A Ricker
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Kevin Meli
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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14
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Lang F, Sorn P, Schrörs B, Weber D, Kramer S, Sahin U, Löwer M. Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates. iScience 2023; 26:108014. [PMID: 37965155 PMCID: PMC10641489 DOI: 10.1016/j.isci.2023.108014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 10/28/2022] [Accepted: 09/18/2023] [Indexed: 11/16/2023] Open
Abstract
Previous studies showed that the neoantigen candidate load is an imperfect predictor of immune checkpoint blockade (ICB) efficacy. Further studies provided evidence that the response to ICB is also affected by the qualitative properties of a few or even single candidates, limiting the predictive power based on candidate quantity alone. Here, we predict ICB efficacy based on neoantigen candidates and their neoantigen features in the context of the mutation type, using Multiple-Instance Learning via Embedded Instance Selection (MILES). Multiple instance learning is a type of supervised machine learning that classifies labeled bags that are formed by a set of unlabeled instances. MILES performed better compared with neoantigen candidate load alone for low-abundant fusion genes in renal cell carcinoma. Our findings suggest that MILES is an appropriate method to predict the efficacy of ICB therapy based on neoantigen candidates without requiring direct T cell response information.
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Affiliation(s)
- Franziska Lang
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany
| | - Patrick Sorn
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany
| | - Barbara Schrörs
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany
| | - David Weber
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany
| | - Stefan Kramer
- Institute of Computer Science, Johannes Gutenberg University, 55128 Mainz, Germany
| | - Ugur Sahin
- BioNTech SE, 55131 Mainz, Germany
- University Medical Center of the Johannes Gutenberg University, 55131 Mainz, Germany
| | - Martin Löwer
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany
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15
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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: 3] [Impact Index Per Article: 3.0] [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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16
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Kaur A, Surnilla A, Zaitouna AJ, Mumphrey MB, Basrur V, Grigorova I, Cieslik M, Carrington M, Nesvizhskii AI, Raghavan M. Mass Spectrometric Profiling of HLA-B44 Peptidomes Provides Evidence for Tapasin-Mediated Tryptophan Editing. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2023; 211:1298-1307. [PMID: 37737643 PMCID: PMC10592002 DOI: 10.4049/jimmunol.2300232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 08/29/2023] [Indexed: 09/23/2023]
Abstract
The extreme polymorphisms of HLA class I proteins result in structural variations in their peptide binding sites to achieve diversity in Ag presentation. External factors could independently constrict or alter HLA class I peptide repertoires. Such effects of the assembly factor tapasin were assessed for HLA-B*44:05 (Y116) and a close variant, HLA-B*44:02 (D116), which have low and high tapasin dependence, respectively, for their cell surface expression. Analyses of the HLA-B*44:05 peptidomes in the presence and absence of tapasin reveal that peptides with C-terminal tryptophans and higher predicted affinities are preferentially selected by tapasin, coincident with reduced frequencies of peptides with other C-terminal amino acids, including leucine. Comparisons of the HLA-B*44:05 and HLA-B*44:02 peptidomes indicate the expected structure-based alterations near the peptide C termini, but also C-terminal amino acid frequency and predicted affinity changes among the unique and shared peptide groups for B*44:02 and B*44:05. Overall, these findings indicate that the presence of tapasin and the tapasin dependence of assembly alter HLA class I peptide-binding preferences at the peptide C terminus. The particular C-terminal amino acid preferences that are altered by tapasin are expected to be determined by the intrinsic peptide-binding specificities of HLA class I allotypes. Additionally, the findings suggest that tapasin deficiency and reduced tapasin dependence expand the permissive affinities of HLA class I-bound peptides, consistent with prior findings that HLA class I allotypes with low tapasin dependence have increased breadth of CD8+ T cell epitope presentation and are more protective in HIV infections.
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Affiliation(s)
- Amanpreet Kaur
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Avrokin Surnilla
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Anita J. Zaitouna
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Michael B. Mumphrey
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Venkatesha Basrur
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Irina Grigorova
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Marcin Cieslik
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Mary Carrington
- Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA
- Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Alexey I. Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Malini Raghavan
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA
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17
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García-Mulero S, Fornelino R, Punta M, Lise S, Varela M, del Carpio LP, Moreno R, Costa-García M, Rieder D, Trajanoski Z, Gros A, Alemany R, Piulats JM, Sanz-Pamplona R. Driver mutations in GNAQ and GNA11 genes as potential targets for precision immunotherapy in uveal melanoma patients. Oncoimmunology 2023; 12:2261278. [PMID: 38126027 PMCID: PMC10732647 DOI: 10.1080/2162402x.2023.2261278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 09/17/2023] [Indexed: 12/23/2023] Open
Abstract
Uveal melanoma (UM) is the most common ocular malignancy in adults. Nearly 95% of UM patients carry the mutually exclusive mutations in the homologous genes GNAQ (amino acid change Q209L/Q209P) and GNA11 (aminoacid change Q209L). UM is located in an immunosuppressed organ and does not suffer immunoediting. Therefore, we hypothesize that driver mutations in GNAQ/11 genes could be recognized by the immune system. Genomic and transcriptomic data from primary uveal tumors were collected from the TCGA-UM dataset (n = 80) and used to assess the immunogenic potential for GNAQ/GNA11 Q209L/Q209P mutations using a variety of tools and HLA type information. All prediction tools showed stronger GNAQ/11 Q209L binding to HLA than GNAQ/11 Q209P. The immunogenicity analysis revealed that Q209L is likely to be presented by more than 73% of individuals in 1000 G databases whereas Q209P is only predicted to be presented in 24% of individuals. GNAQ/11 Q209L showed a higher likelihood to be presented by HLA-I molecules than almost all driver mutations analyzed. Finally, samples carrying Q209L had a higher immune-reactive phenotype. Regarding cancer risk, seven HLA genotypes with low Q209L affinity show higher frequency in uveal melanoma patients than in the general population. However, no clear association was found between any HLA genotype and survival. Results suggest a high potential immunogenicity of the GNAQ/11 Q209L variant that could allow the generation of novel therapeutic tools to treat UM like neoantigen vaccinations.
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Affiliation(s)
- Sandra García-Mulero
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL) and CIBERESP, Barcelona, Spain
- Anatomy Unit, Department of Pathology and Experimental Therapy, and Department of Clinical Sciences, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Roberto Fornelino
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL) and CIBERESP, Barcelona, Spain
| | - Marco Punta
- Bioinformatics Core, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Stefano Lise
- Bioinformatics Core, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Mar Varela
- Department of Pathology, Bellvitge University Hospital, Barcelona, Spain
| | - Luis P. del Carpio
- Procure Program, Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | - Rafael Moreno
- Procure Program, Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | - Marcel Costa-García
- Procure Program, Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | - Dietmar Rieder
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - Zlatko Trajanoski
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - Alena Gros
- Tumor Immunology and Immunotherapy, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Ramón Alemany
- Procure Program, Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | | | - Rebeca Sanz-Pamplona
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL) and CIBERESP, Barcelona, Spain
- Institute for Health Research Aragon (IISA), ARAID Foundation, Aragon Government, University Hospital Lozano Blesa, Zaragoza, Spain
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18
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Li F, Wang C, Guo X, Akutsu T, Webb GI, Coin LJM, Kurgan L, Song J. ProsperousPlus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction and machine-learning model construction. Brief Bioinform 2023; 24:bbad372. [PMID: 37874948 DOI: 10.1093/bib/bbad372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 08/30/2023] [Accepted: 09/29/2023] [Indexed: 10/26/2023] Open
Abstract
Proteases contribute to a broad spectrum of cellular functions. Given a relatively limited amount of experimental data, developing accurate sequence-based predictors of substrate cleavage sites facilitates a better understanding of protease functions and substrate specificity. While many protease-specific predictors of substrate cleavage sites were developed, these efforts are outpaced by the growth of the protease substrate cleavage data. In particular, since data for 100+ protease types are available and this number continues to grow, it becomes impractical to publish predictors for new protease types, and instead it might be better to provide a computational platform that helps users to quickly and efficiently build predictors that address their specific needs. To this end, we conceptualized, developed, tested and released a versatile bioinformatics platform, ProsperousPlus, that empowers users, even those with no programming or little bioinformatics background, to build fast and accurate predictors of substrate cleavage sites. ProsperousPlus facilitates the use of the rapidly accumulating substrate cleavage data to train, empirically assess and deploy predictive models for user-selected substrate types. Benchmarking tests on test datasets show that our platform produces predictors that on average exceed the predictive performance of current state-of-the-art approaches. ProsperousPlus is available as a webserver and a stand-alone software package at http://prosperousplus.unimelb-biotools.cloud.edu.au/.
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Affiliation(s)
- Fuyi Li
- College of Information Engineering, Northwest A&F University, Shaanxi 712100, China
- South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
- The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, VIC 3000, Australia
| | - Cong Wang
- College of Information Engineering, Northwest A&F University, Shaanxi 712100, China
| | - Xudong Guo
- College of Information Engineering, Northwest A&F University, Shaanxi 712100, China
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan
| | - Geoffrey I Webb
- Monash Data Futures Institute, Monash University, VIC 3800, Australia
| | - Lachlan J M Coin
- The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, VIC 3000, Australia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Jiangning Song
- Monash Data Futures Institute, Monash University, VIC 3800, Australia
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, VIC 3800, Australia
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19
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Pu T, Peddle A, Zhu J, Tejpar S, Verbandt S. Neoantigen identification: Technological advances and challenges. Methods Cell Biol 2023; 183:265-302. [PMID: 38548414 DOI: 10.1016/bs.mcb.2023.06.005] [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
Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.
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Affiliation(s)
- Ting Pu
- Digestive Oncology Unit, KULeuven, Leuven, Belgium
| | | | - Jingjing Zhu
- de Duve Institute, Université catholique de Louvain, Brussels, Belgium
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20
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Fast E, Dhar M, Chen B. TAPIR: a T-cell receptor language model for predicting rare and novel targets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.12.557285. [PMID: 37745475 PMCID: PMC10515850 DOI: 10.1101/2023.09.12.557285] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
T-cell receptors (TCRs) are involved in most human diseases, but linking their sequences with their targets remains an unsolved grand challenge in the field. In this study, we present TAPIR (T-cell receptor and Peptide Interaction Recognizer), a T-cell receptor (TCR) language model that predicts TCR-target interactions, with a focus on novel and rare targets. TAPIR employs deep convolutional neural network (CNN) encoders to process TCR and target sequences across flexible representations (e.g., beta-chain only, unknown MHC allele, etc.) and learns patterns of interactivity via several training tasks. This flexibility allows TAPIR to train on more than 50k either paired (alpha and beta chain) or unpaired TCRs (just alpha or beta chain) from public and proprietary databases against 1933 unique targets. TAPIR demonstrates state-of-the-art performance when predicting TCR interactivity against common benchmark targets and is the first method to demonstrate strong performance when predicting TCR interactivity against novel targets, where no examples are provided in training. TAPIR is also capable of predicting TCR interaction against MHC alleles in the absence of target information. Leveraging these capabilities, we apply TAPIR to cancer patient TCR repertoires and identify and validate a novel and potent anti-cancer T-cell receptor against a shared cancer neoantigen target (PIK3CA H1047L). We further show how TAPIR, when extended with a generative neural network, is capable of directly designing T-cell receptor sequences that interact with a target of interest.
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Affiliation(s)
- Ethan Fast
- Vcreate, Inc., Menlo Park, CA, 94025, USA
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21
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Lee CH, Huh J, Buckley PR, Jang M, Pinho MP, Fernandes RA, Antanaviciute A, Simmons A, Koohy H. A robust deep learning workflow to predict CD8 + T-cell epitopes. Genome Med 2023; 15:70. [PMID: 37705109 PMCID: PMC10498576 DOI: 10.1186/s13073-023-01225-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 08/30/2023] [Indexed: 09/15/2023] Open
Abstract
BACKGROUND T-cells play a crucial role in the adaptive immune system by triggering responses against cancer cells and pathogens, while maintaining tolerance against self-antigens, which has sparked interest in the development of various T-cell-focused immunotherapies. However, the identification of antigens recognised by T-cells is low-throughput and laborious. To overcome some of these limitations, computational methods for predicting CD8 + T-cell epitopes have emerged. Despite recent developments, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8 + T-cell epitopes. METHODS We developed TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning workflow for predicting CD8 + T-cell epitopes from MHC-I presented pathogenic and self-peptides. TRAP uses transfer learning, deep learning architecture and MHC binding information to make context-specific predictions of CD8 + T-cell epitopes. TRAP also detects low-confidence predictions for peptides that differ significantly from those in the training datasets to abstain from making incorrect predictions. To estimate the immunogenicity of pathogenic peptides with low-confidence predictions, we further developed a novel metric, RSAT (relative similarity to autoantigens and tumour-associated antigens), as a complementary to 'dissimilarity to self' from cancer studies. RESULTS TRAP was used to identify epitopes from glioblastoma patients as well as SARS-CoV-2 peptides, and it outperformed other algorithms in both cancer and pathogenic settings. TRAP was especially effective at extracting immunogenicity-associated properties from restricted data of emerging pathogens and translating them onto related species, as well as minimising the loss of likely epitopes in imbalanced datasets. We also demonstrated that the novel metric termed RSAT was able to estimate immunogenic of pathogenic peptides of various lengths and species. TRAP implementation is available at: https://github.com/ChloeHJ/TRAP . CONCLUSIONS This study presents a novel computational workflow for accurately predicting CD8 + T-cell epitopes to foster a better understanding of antigen-specific T-cell response and the development of effective clinical therapeutics.
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Affiliation(s)
- Chloe H Lee
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
| | - Jaesung Huh
- Visual Geometry Group, Department of Engineering Science, University of Oxford, Oxford, OX2 6NN, UK
| | - Paul R Buckley
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
| | - Myeongjun Jang
- Intelligent Systems Lab, Department of Computer Science, University of Oxford, Oxford, OX1 3QG, UK
| | - Mariana Pereira Pinho
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
| | - Ricardo A Fernandes
- Chinese Academy of Medical Sciences (CAMS) Oxford Institute (COI), University of Oxford, Oxford, OX3 7BN, UK
| | - Agne Antanaviciute
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
| | - Alison Simmons
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Hashem Koohy
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK.
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK.
- Alan Turning Fellow in Health and Medicine, The Alan Turing Institute, London, UK.
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22
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Zhou LY, Zou F, Sun W. Prioritizing candidate peptides for cancer vaccines through predicting peptide presentation by HLA-I proteins. Biometrics 2023; 79:2664-2676. [PMID: 35833513 PMCID: PMC10548401 DOI: 10.1111/biom.13717] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 07/01/2022] [Indexed: 11/02/2022]
Abstract
Cancer (treatment) vaccines that are made of neoantigens, or peptides unique to tumor cells due to somatic mutations, have emerged as a promising method to reinvigorate the immune response against cancer. A key step to prioritizing neoantigens for cancer vaccines is computationally predicting which neoantigens are presented on the cell surface by a human leukocyte antigen (HLA). We propose to address this challenge by training a neural network using mass spectrometry (MS) data composed of peptides presented by at least one of several HLAs of a subject. We embed the neural network within a mixture model and train the neural network by maximizing the likelihood of the mixture model. After evaluating our method using data sets where the peptide presentation status was known, we applied it to analyze somatic mutations of 60 melanoma patients and identified a group of neoantigens more immunogenic in tumor cells than in normal cells. Moreover, neoantigen burden estimated by our method was significantly associated with a measurement of the immune system activity, suggesting these neoantigens could induce an immune response.
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Affiliation(s)
- Laura Y. Zhou
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Fei Zou
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Wei Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington
- Department of Biostatistics, University of Washington, Seattle, Washington
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23
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Zhan Y, Ye L, Ouyang Q, Yin J, Cui J, Liu K, Guo C, Zhang H, Zhai J, Zheng C, Guo A, Sun B. The binding profile of SARS-CoV-2 with human leukocyte antigen polymorphisms reveals critical alleles involved in immune evasion. J Med Virol 2023; 95:e29113. [PMID: 37750416 DOI: 10.1002/jmv.29113] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 08/26/2023] [Accepted: 09/11/2023] [Indexed: 09/27/2023]
Abstract
The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), astonished the world and led to millions of deaths. Due to viral new mutations and immune evasion, SARS-CoV-2 ranked first in transmission and influence. The binding affinity of human leukocyte antigen (HLA) polymorphisms to SARS-CoV-2 might be related to immune escape, but the mechanisms remained unclear. In this study, we obtained the binding affinity of SARS-CoV-2 strains with different HLA proteins and identified 31 risk alleles. Subsequent structural predictions identified 10 active binding sites in these HLA proteins that may promote immune evasion. Particularly, we also found that the weak binding ability with HLA class I polymorphisms could contribute to the immune evasion of Omicron. These findings suggest important implications for preventing the immune evasion of SARS-CoV-2 and providing new insights for the vaccine design.
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Affiliation(s)
- Yan Zhan
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Beijing, China
- National Clinical Research Center for Geriatric Disorders, Changsha, China
| | - Ling Ye
- Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Qianying Ouyang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Beijing, China
- National Clinical Research Center for Geriatric Disorders, Changsha, China
| | - Jiye Yin
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Beijing, China
- National Clinical Research Center for Geriatric Disorders, Changsha, China
| | - Jiajia Cui
- Department of Geriatric Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Ke Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Pharmacogenetics, Institute of Clinical Pharmacology, Central South University, Changsha, China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Beijing, China
- National Clinical Research Center for Geriatric Disorders, Changsha, China
| | - Chengxian Guo
- Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha, China
| | | | - Jingbo Zhai
- Key Laboratory of Zoonose Prevention and Control at Universities of Inner Mongolia Autonomous Region, Medical College, Inner Mongolia Minzu University, Tongliao, China
| | - Chunfu Zheng
- Department of Microbiology, Immunology and Infectious Diseases, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Aoxiang Guo
- Department of Pharmacy, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
- Shenzhen Key Laboratory of Chinese Medicine Active substance screening and Translational Research, Shenzhen, China
| | - Bao Sun
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China
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24
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Yang K, Halima A, Chan TA. Antigen presentation in cancer - mechanisms and clinical implications for immunotherapy. Nat Rev Clin Oncol 2023; 20:604-623. [PMID: 37328642 DOI: 10.1038/s41571-023-00789-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/25/2023] [Indexed: 06/18/2023]
Abstract
Over the past decade, the emergence of effective immunotherapies has revolutionized the clinical management of many types of cancers. However, long-term durable tumour control is only achieved in a fraction of patients who receive these therapies. Understanding the mechanisms underlying clinical response and resistance to treatment is therefore essential to expanding the level of clinical benefit obtained from immunotherapies. In this Review, we describe the molecular mechanisms of antigen processing and presentation in tumours and their clinical consequences. We examine how various aspects of the antigen-presentation machinery (APM) shape tumour immunity. In particular, we discuss genomic variants in HLA alleles and other APM components, highlighting their influence on the immunopeptidomes of both malignant cells and immune cells. Understanding the APM, how it is regulated and how it changes in tumour cells is crucial for determining which patients will respond to immunotherapy and why some patients develop resistance. We focus on recently discovered molecular and genomic alterations that drive the clinical outcomes of patients receiving immune-checkpoint inhibitors. An improved understanding of how these variables mediate tumour-immune interactions is expected to guide the more precise administration of immunotherapies and reveal potentially promising directions for the development of new immunotherapeutic approaches.
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Affiliation(s)
- Kailin Yang
- Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH, USA
| | - Ahmed Halima
- Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH, USA
| | - Timothy A Chan
- Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH, USA.
- Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH, USA.
- National Center for Regenerative Medicine, Cleveland, OH, USA.
- Case Comprehensive Cancer Center, Cleveland, OH, USA.
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25
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Hashemi N, Hao B, Ignatov M, Paschalidis IC, Vakili P, Vajda S, Kozakov D. Improved prediction of MHC-peptide binding using protein language models. FRONTIERS IN BIOINFORMATICS 2023; 3:1207380. [PMID: 37663788 PMCID: PMC10469926 DOI: 10.3389/fbinf.2023.1207380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 08/04/2023] [Indexed: 09/05/2023] Open
Abstract
Major histocompatibility complex Class I (MHC-I) molecules bind to peptides derived from intracellular antigens and present them on the surface of cells, allowing the immune system (T cells) to detect them. Elucidating the process of this presentation is essential for regulation and potential manipulation of the cellular immune system. Predicting whether a given peptide binds to an MHC molecule is an important step in the above process and has motivated the introduction of many computational approaches to address this problem. NetMHCPan, a pan-specific model for predicting binding of peptides to any MHC molecule, is one of the most widely used methods which focuses on solving this binary classification problem using shallow neural networks. The recent successful results of Deep Learning (DL) methods, especially Natural Language Processing (NLP-based) pretrained models in various applications, including protein structure determination, motivated us to explore their use in this problem. Specifically, we consider the application of deep learning models pretrained on large datasets of protein sequences to predict MHC Class I-peptide binding. Using the standard performance metrics in this area, and the same training and test sets, we show that our models outperform NetMHCpan4.1, currently considered as the-state-of-the-art.
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Affiliation(s)
- Nasser Hashemi
- Division of Systems Engineering, Boston University, Boston, MA, United States
| | - Boran Hao
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, United States
| | - Mikhail Ignatov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States
| | - Ioannis Ch. Paschalidis
- Division of Systems Engineering, Boston University, Boston, MA, United States
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, United States
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| | - Pirooz Vakili
- Division of Systems Engineering, Boston University, Boston, MA, United States
| | - Sandor Vajda
- Division of Systems Engineering, Boston University, Boston, MA, United States
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
- Department of Chemistry, Boston University, Boston, MA, United States
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
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26
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Han N, Liu Z. Targeting alternative splicing in cancer immunotherapy. Front Cell Dev Biol 2023; 11:1232146. [PMID: 37635865 PMCID: PMC10450511 DOI: 10.3389/fcell.2023.1232146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/01/2023] [Indexed: 08/29/2023] Open
Abstract
Tumor immunotherapy has made great progress in cancer treatment but still faces several challenges, such as a limited number of targetable antigens and varying responses among patients. Alternative splicing (AS) is an essential process for the maturation of nearly all mammalian mRNAs. Recent studies show that AS contributes to expanding cancer-specific antigens and modulating immunogenicity, making it a promising solution to the above challenges. The organoid technology preserves the individual immune microenvironment and reduces the time/economic costs of the experiment model, facilitating the development of splicing-based immunotherapy. Here, we summarize three critical roles of AS in immunotherapy: resources for generating neoantigens, targets for immune-therapeutic modulation, and biomarkers to guide immunotherapy options. Subsequently, we highlight the benefits of adopting organoids to develop AS-based immunotherapies. Finally, we discuss the current challenges in studying AS-based immunotherapy in terms of existing bioinformatics algorithms and biological technologies.
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Affiliation(s)
- Nan Han
- Chinese Academy of Sciences Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhaoqi Liu
- Chinese Academy of Sciences Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
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27
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Albert BA, Yang Y, Shao XM, Singh D, Smit KN, Anagnostou V, Karchin R. Deep neural networks predict class I major histocompatibility complex epitope presentation and transfer learn neoepitope immunogenicity. NAT MACH INTELL 2023; 5:861-872. [PMID: 37829001 PMCID: PMC10569228 DOI: 10.1038/s42256-023-00694-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 06/23/2023] [Indexed: 10/14/2023]
Abstract
Identifying neoepitopes that elicit an adaptive immune response is a major bottleneck to developing personalized cancer vaccines. Experimental validation of candidate neoepitopes is extremely resource intensive and the vast majority of candidates are non-immunogenic, creating a needle-in-a-haystack problem. Here we address this challenge, presenting computational methods for predicting class I major histocompatibility complex (MHC-I) epitopes and identifying immunogenic neoepitopes with improved precision. The BigMHC method comprises an ensemble of seven pan-allelic deep neural networks trained on peptide-MHC eluted ligand data from mass spectrometry assays and transfer learned on data from assays of antigen-specific immune response. Compared with four state-of-the-art classifiers, BigMHC significantly improves the prediction of epitope presentation on a test set of 45,409 MHC ligands among 900,592 random negatives (area under the receiver operating characteristic = 0.9733; area under the precision-recall curve = 0.8779). After transfer learning on immunogenicity data, BigMHC yields significantly higher precision than seven state-of-the-art models in identifying immunogenic neoepitopes, making BigMHC effective in clinical settings.
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Affiliation(s)
- Benjamin Alexander Albert
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Yunxiao Yang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Xiaoshan M. Shao
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Dipika Singh
- The Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
- Bloomberg~Kimmel Institute for Cancer Immunotherapy, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Kellie N. Smit
- The Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
- Bloomberg~Kimmel Institute for Cancer Immunotherapy, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Valsamo Anagnostou
- The Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
- Bloomberg~Kimmel Institute for Cancer Immunotherapy, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Rachel Karchin
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- The Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
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28
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Li X, Pak HS, Huber F, Michaux J, Taillandier-Coindard M, Altimiras ER, Bassani-Sternberg M. A microfluidics-enabled automated workflow of sample preparation for MS-based immunopeptidomics. CELL REPORTS METHODS 2023; 3:100479. [PMID: 37426762 PMCID: PMC10326370 DOI: 10.1016/j.crmeth.2023.100479] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/22/2023] [Accepted: 04/19/2023] [Indexed: 07/11/2023]
Abstract
Mass spectrometry (MS)-based immunopeptidomics is an attractive antigen discovery method with growing clinical implications. However, the current experimental approach to extract HLA-restricted peptides requires a bulky sample source, which remains a challenge for obtaining clinical specimens. We present an innovative workflow that requires a low sample volume, which streamlines the immunoaffinity purification (IP) and C18 peptide cleanup on a single microfluidics platform with automated liquid handling and minimal sample transfers, resulting in higher assay sensitivity. We also demonstrate how the state-of-the-art data-independent acquisition (DIA) method further enhances the depth of tandem MS spectra-based peptide sequencing. Consequently, over 4,000 and 5,000 HLA-I-restricted peptides were identified from as few as 0.2 million RA957 cells and a melanoma tissue of merely 5 mg, respectively. We also identified multiple immunogenic tumor-associated antigens and hundreds of peptides derived from non-canonical protein sources. This workflow represents a powerful tool for identifying the immunopeptidome of sparse samples.
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Affiliation(s)
- Xiaokang Li
- Ludwig Institute for Cancer Research, University of Lausanne, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
- Agora Cancer Research Centre, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
| | - Hui Song Pak
- Ludwig Institute for Cancer Research, University of Lausanne, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
- Agora Cancer Research Centre, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
| | - Florian Huber
- Ludwig Institute for Cancer Research, University of Lausanne, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
- Agora Cancer Research Centre, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
| | - Justine Michaux
- Ludwig Institute for Cancer Research, University of Lausanne, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
- Agora Cancer Research Centre, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
| | - Marie Taillandier-Coindard
- Ludwig Institute for Cancer Research, University of Lausanne, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
- Agora Cancer Research Centre, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
| | - Emma Ricart Altimiras
- Ludwig Institute for Cancer Research, University of Lausanne, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
- Agora Cancer Research Centre, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
- Agora Cancer Research Centre, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
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29
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Schmidt J, Chiffelle J, Perez MAS, Magnin M, Bobisse S, Arnaud M, Genolet R, Cesbron J, Barras D, Navarro Rodrigo B, Benedetti F, Michel A, Queiroz L, Baumgaertner P, Guillaume P, Hebeisen M, Michielin O, Nguyen-Ngoc T, Huber F, Irving M, Tissot-Renaud S, Stevenson BJ, Rusakiewicz S, Dangaj Laniti D, Bassani-Sternberg M, Rufer N, Gfeller D, Kandalaft LE, Speiser DE, Zoete V, Coukos G, Harari A. Neoantigen-specific CD8 T cells with high structural avidity preferentially reside in and eliminate tumors. Nat Commun 2023; 14:3188. [PMID: 37280206 DOI: 10.1038/s41467-023-38946-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 05/23/2023] [Indexed: 06/08/2023] Open
Abstract
The success of cancer immunotherapy depends in part on the strength of antigen recognition by T cells. Here, we characterize the T cell receptor (TCR) functional (antigen sensitivity) and structural (monomeric pMHC-TCR off-rates) avidities of 371 CD8 T cell clones specific for neoantigens, tumor-associated antigens (TAAs) or viral antigens isolated from tumors or blood of patients and healthy donors. T cells from tumors exhibit stronger functional and structural avidity than their blood counterparts. Relative to TAA, neoantigen-specific T cells are of higher structural avidity and, consistently, are preferentially detected in tumors. Effective tumor infiltration in mice models is associated with high structural avidity and CXCR3 expression. Based on TCR biophysicochemical properties, we derive and apply an in silico model predicting TCR structural avidity and validate the enrichment in high avidity T cells in patients' tumors. These observations indicate a direct relationship between neoantigen recognition, T cell functionality and tumor infiltration. These results delineate a rational approach to identify potent T cells for personalized cancer immunotherapy.
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Affiliation(s)
- Julien Schmidt
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Johanna Chiffelle
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Marta A S Perez
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Morgane Magnin
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Sara Bobisse
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Marion Arnaud
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Raphael Genolet
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Julien Cesbron
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - David Barras
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Blanca Navarro Rodrigo
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Fabrizio Benedetti
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Alexandra Michel
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Lise Queiroz
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Petra Baumgaertner
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Philippe Guillaume
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Michael Hebeisen
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
| | - Olivier Michielin
- Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Tu Nguyen-Ngoc
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
| | - Florian Huber
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Melita Irving
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
| | - Stéphanie Tissot-Renaud
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Brian J Stevenson
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Sylvie Rusakiewicz
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Denarda Dangaj Laniti
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Nathalie Rufer
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
| | - David Gfeller
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Lana E Kandalaft
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Daniel E Speiser
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
| | - Vincent Zoete
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - George Coukos
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Alexandre Harari
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland.
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland.
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30
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Bedran G, Gasser HC, Weke K, Wang T, Bedran D, Laird A, Battail C, Zanzotto FM, Pesquita C, Axelson H, Rajan A, Harrison DJ, Palkowski A, Pawlik M, Parys M, O'Neill JR, Brennan PM, Symeonides SN, Goodlett DR, Litchfield K, Fahraeus R, Hupp TR, Kote S, Alfaro JA. The Immunopeptidome from a Genomic Perspective: Establishing the Noncanonical Landscape of MHC Class I-Associated Peptides. Cancer Immunol Res 2023; 11:747-762. [PMID: 36961404 PMCID: PMC10236148 DOI: 10.1158/2326-6066.cir-22-0621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 11/25/2022] [Accepted: 03/16/2023] [Indexed: 03/25/2023]
Abstract
Tumor antigens can emerge through multiple mechanisms, including translation of noncoding genomic regions. This noncanonical category of tumor antigens has recently gained attention; however, our understanding of how they recur within and between cancer types is still in its infancy. Therefore, we developed a proteogenomic pipeline based on deep learning de novo mass spectrometry (MS) to enable the discovery of noncanonical MHC class I-associated peptides (ncMAP) from noncoding regions. Considering that the emergence of tumor antigens can also involve posttranslational modifications (PTM), we included an open search component in our pipeline. Leveraging the wealth of MS-based immunopeptidomics, we analyzed data from 26 MHC class I immunopeptidomic studies across 11 different cancer types. We validated the de novo identified ncMAPs, along with the most abundant PTMs, using spectral matching and controlled their FDR to 1%. The noncanonical presentation appeared to be 5 times enriched for the A03 HLA supertype, with a projected population coverage of 55%. The data reveal an atlas of 8,601 ncMAPs with varying levels of cancer selectivity and suggest 17 cancer-selective ncMAPs as attractive therapeutic targets according to a stringent cutoff. In summary, the combination of the open-source pipeline and the atlas of ncMAPs reported herein could facilitate the identification and screening of ncMAPs as targets for T-cell therapies or vaccine development.
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Affiliation(s)
- Georges Bedran
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
| | | | - Kenneth Weke
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
| | - Tongjie Wang
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Dominika Bedran
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
| | - Alexander Laird
- Urology Department, Western General Hospital, NHS Lothian, Edinburgh, United Kingdom
- Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Christophe Battail
- CEA, Grenoble Alpes University, INSERM, IRIG, Biosciences and Bioengineering for Health Laboratory (BGE) - UA13 INSERM-CEA-UGA, Grenoble, France
| | | | - Catia Pesquita
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Håkan Axelson
- Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Ajitha Rajan
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - David J. Harrison
- School of Medicine, University of St Andrews, St Andrews, United Kingdom
| | - Aleksander Palkowski
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
| | - Maciej Pawlik
- Academic Computer Centre CYFRONET, AGH University of Science and Technology, Cracow, Poland
| | - Maciej Parys
- Royal (Dick) School of Veterinary Studies and The Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - J. Robert O'Neill
- Cambridge Oesophagogastric Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Paul M. Brennan
- Translational Neurosurgery, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Stefan N. Symeonides
- Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - David R. Goodlett
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, Canada
- University of Victoria Genome BC Proteome Centre, Victoria, Canada
| | - Kevin Litchfield
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, United Kingdom
- Tumour Immunogenomics and Immunosurveillance Laboratory, University College London Cancer Institute, London, United Kingdom
| | - Robin Fahraeus
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
- Inserm UMRS1131, Institut de Génétique Moléculaire, Université Paris 7, Paris, France
| | - Ted R. Hupp
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
- Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Sachin Kote
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
| | - Javier A. Alfaro
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, Canada
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31
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Duette G, Lee E, Martins Costa Gomes G, Tungatt K, Doyle C, Stylianou VV, Lee A, Maddocks S, Taylor J, Khanna R, Bull RA, Martinello M, Sandgren KJ, Cunningham AL, Palmer S. Highly Networked SARS-CoV-2 Peptides Elicit T Cell Responses with Enhanced Specificity. Immunohorizons 2023; 7:508-527. [PMID: 37358499 PMCID: PMC10580120 DOI: 10.4049/immunohorizons.2300034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 06/05/2023] [Indexed: 06/27/2023] Open
Abstract
Identifying SARS-CoV-2-specific T cell epitope-derived peptides is critical for the development of effective vaccines and measuring the duration of specific SARS-CoV-2 cellular immunity. In this regard, we previously identified T cell epitope-derived peptides within topologically and structurally essential regions of SARS-CoV-2 spike and nucleocapsid proteins by applying an immunoinformatics pipeline. In this study, we selected 30 spike- and nucleocapsid-derived peptides and assessed whether these peptides induce T cell responses and avoid major mutations found in SARS-CoV-2 variants of concern. Our peptide pool was highly specific, with only a single peptide driving cross-reactivity in people unexposed to SARS-COV-2, and immunogenic, inducing a polyfunctional response in CD4+ and CD8+ T cells from COVID-19 recovered individuals. All peptides were immunogenic and individuals recognized broad and diverse peptide repertoires. Moreover, our peptides avoided most mutations/deletions associated with all four SARS-CoV-2 variants of concern while retaining their physicochemical properties even when genetic changes are introduced. This study contributes to an evolving definition of individual CD4+ and CD8+ T cell epitopes that can be used for specific diagnostic tools for SARS-CoV-2 T cell responses and is relevant to the development of variant-resistant and durable T cell-stimulating vaccines.
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Affiliation(s)
- Gabriel Duette
- Centre for Virus Research, The Westmead Institute for Medical Research, Westmead, New South Wales, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Eunok Lee
- Centre for Virus Research, The Westmead Institute for Medical Research, Westmead, New South Wales, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | | | - Katie Tungatt
- Centre for Virus Research, The Westmead Institute for Medical Research, Westmead, New South Wales, Australia
| | - Chloe Doyle
- Centre for Virus Research, The Westmead Institute for Medical Research, Westmead, New South Wales, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Vicki V. Stylianou
- Centre for Virus Research, The Westmead Institute for Medical Research, Westmead, New South Wales, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Ashley Lee
- Centre for Virus Research, The Westmead Institute for Medical Research, Westmead, New South Wales, Australia
| | - Susan Maddocks
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, Westmead Hospital, Westmead, New South Wales, Australia
| | - Janette Taylor
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, Westmead Hospital, Westmead, New South Wales, Australia
| | - Rajiv Khanna
- QIMR Berghofer Centre for Immunotherapy and Vaccine Development, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Rowena A. Bull
- School of Medical Sciences, Faculty of Medicine, UNSW Australia, Sydney, New South Wales, Australia
- The Kirby Institute, UNSW Australia, Sydney, New South Wales, Australia
| | - Marianne Martinello
- The Kirby Institute, UNSW Australia, Sydney, New South Wales, Australia
- Westmead Hospital, Sydney, New South Wales, Australia
- Blacktown & Mount Druitt Hospital, Blacktown, New South Wales, Australia
| | - Kerrie J. Sandgren
- Centre for Virus Research, The Westmead Institute for Medical Research, Westmead, New South Wales, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Anthony L. Cunningham
- Centre for Virus Research, The Westmead Institute for Medical Research, Westmead, New South Wales, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Sarah Palmer
- Centre for Virus Research, The Westmead Institute for Medical Research, Westmead, New South Wales, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
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32
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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.
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Affiliation(s)
- Arie Admon
- Faculty of Biology, Technion-Israel Institute of Technology, Israel.
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33
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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: 22] [Impact Index Per Article: 22.0] [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.
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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.
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34
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Ye Z, Li S, Mi X, Shao B, Dai Z, Ding B, Feng S, Sun B, Shen Y, Xiao Z. STMHCpan, an accurate Star-Transformer-based extensible framework for predicting MHC I allele binding peptides. Brief Bioinform 2023; 24:7147024. [PMID: 37122066 DOI: 10.1093/bib/bbad164] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 03/22/2023] [Accepted: 04/06/2023] [Indexed: 05/02/2023] Open
Abstract
Peptide-major histocompatibility complex I (MHC I) binding affinity prediction is crucial for vaccine development, but existing methods face limitations such as small datasets, model overfitting due to excessive parameters and suboptimal performance. Here, we present STMHCPan (STAR-MHCPan), an open-source package based on the Star-Transformer model, for MHC I binding peptide prediction. Our approach introduces an attention mechanism to improve the deep learning network architecture and performance in antigen prediction. Compared with classical deep learning algorithms, STMHCPan exhibits improved performance with fewer parameters in receptor affinity training. Furthermore, STMHCPan outperforms existing ligand benchmark datasets identified by mass spectrometry. It can also handle peptides of arbitrary length and is highly scalable for predicting T-cell responses. Our software is freely available for use, training and extension through Github (https://github.com/Luckysoutheast/STMHCPan.git).
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Affiliation(s)
- Zheng Ye
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Shaohao Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Xue Mi
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Baoyi Shao
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Zhu Dai
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Bo Ding
- Department of Obstetrics and Gynecoloty, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, China
| | - Songwei Feng
- Department of Obstetrics and Gynecoloty, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, China
| | - Bo Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Yang Shen
- Department of Obstetrics and Gynecoloty, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, China
| | - Zhongdang Xiao
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, China
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35
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Huang R, Zhao B, Hu S, Zhang Q, Su X, Zhang W. Adoptive neoantigen-reactive T cell therapy: improvement strategies and current clinical researches. Biomark Res 2023; 11:41. [PMID: 37062844 PMCID: PMC10108522 DOI: 10.1186/s40364-023-00478-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/21/2023] [Indexed: 04/18/2023] Open
Abstract
Neoantigens generated by non-synonymous mutations of tumor genes can induce activation of neoantigen-reactive T (NRT) cells which have the ability to resist the growth of tumors expressing specific neoantigens. Immunotherapy based on NRT cells has made preeminent achievements in melanoma and other solid tumors. The process of manufacturing NRT cells includes identification of neoantigens, preparation of neoantigen expression vectors or peptides, induction and activation of NRT cells, and analysis of functions and phenotypes. Numerous improvement strategies have been proposed to enhance the potency of NRT cells by engineering TCR, promoting infiltration of T cells and overcoming immunosuppressive factors in the tumor microenvironment. In this review, we outline the improvement of the preparation and the function assessment of NRT cells, and discuss the current status of clinical trials related to NRT cell immunotherapy.
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Affiliation(s)
- Ruichen Huang
- Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Second Military Medical University, Shanghai, 200433, People's Republic of China
| | - Bi Zhao
- Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Second Military Medical University, Shanghai, 200433, People's Republic of China
| | - Shi Hu
- Department of Biophysics, College of Basic Medical Sciences, Second Military Medical University, 800 Xiangyin Road, Shanghai, 200433, People's Republic of China
| | - Qian Zhang
- National Key Laboratory of Medical Immunology, Institute of Immunology, Second Military Medical University, 800 Xiangyin Road, Shanghai, 200433, People's Republic of China
| | - Xiaoping Su
- School of Basic Medicine, Wenzhou Medical University, Wenzhou, 325000, People's Republic of China.
| | - Wei Zhang
- Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Second Military Medical University, Shanghai, 200433, People's Republic of China.
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36
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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.
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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
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37
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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: 11] [Impact Index Per Article: 11.0] [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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38
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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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39
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Kaur A, Surnilla A, Zaitouna AJ, Basrur V, Mumphrey MB, Grigorova I, Cieslik M, Carrington M, Nesvizhskii AI, Raghavan M. Mass spectrometric profiling of HLA-B44 peptidomes provides evidence for tapasin-mediated tryptophan editing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.26.530125. [PMID: 36909546 PMCID: PMC10002704 DOI: 10.1101/2023.02.26.530125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
Activation of CD8 + T cells against pathogens and cancers involves the recognition of antigenic peptides bound to human leukocyte antigen (HLA) class-I proteins. Peptide binding to HLA class I proteins is coordinated by a multi-protein complex called the peptide loading complex (PLC). Tapasin, a key PLC component, facilitates the binding and optimization of HLA class I peptides. However, different HLA class I allotypes have variable requirements for tapasin for their assembly and surface expression. HLA-B*44:02 and HLA-B*44:05, which differ only at residue 116 of their heavy chain sequences, fall at opposite ends of the tapasin-dependency spectrum. HLA-B*44:02 (D116) is highly tapasin-dependent, whereas HLA-B*44:05 (Y116) is highly tapasinindependent. Mass spectrometric comparisons of HLA-B*4405 and HLA-B*44:02 peptidomes were undertaken to better understand the influences of tapasin upon HLA-B44 peptidome compositions. Analyses of the HLA-B*44:05 peptidomes in the presence and absence of tapasin reveal that peptides with the C-terminal tryptophan residues and those with higher predicted binding affinities are selected in the presence of tapasin. Additionally, when tapasin is present, C-terminal tryptophans are also more highly represented among peptides unique to B*44:02 and those shared between B*44:02 and B*44:05, compared with peptides unique to B*44:05. Overall, our findings demonstrate that tapasin influences the C-terminal composition of HLA class I-bound peptides and favors the binding of higher affinity peptides. For the HLA-B44 family, the presence of tapasin or high tapasin-dependence of an allotype results in better binding of peptides with C-terminal tryptophans, consistent with a role for tapasin in stabilizing an open conformation to accommodate bulky C-terminal residues.
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Affiliation(s)
- Amanpreet Kaur
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Avrokin Surnilla
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Anita J. Zaitouna
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Venkatesha Basrur
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Michael B. Mumphrey
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Irina Grigorova
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Marcin Cieslik
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Mary Carrington
- Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA
- Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Alexey I. Nesvizhskii
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Malini Raghavan
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA
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40
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Kacen A, Javitt A, Kramer MP, Morgenstern D, Tsaban T, Shmueli MD, Teo GC, da Veiga Leprevost F, Barnea E, Yu F, Admon A, Eisenbach L, Samuels Y, Schueler-Furman O, Levin Y, Nesvizhskii AI, Merbl Y. Post-translational modifications reshape the antigenic landscape of the MHC I immunopeptidome in tumors. Nat Biotechnol 2023; 41:239-251. [PMID: 36203013 PMCID: PMC11197725 DOI: 10.1038/s41587-022-01464-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 08/09/2022] [Indexed: 11/08/2022]
Abstract
Post-translational modification (PTM) of antigens provides an additional source of specificities targeted by immune responses to tumors or pathogens, but identifying antigen PTMs and assessing their role in shaping the immunopeptidome is challenging. Here we describe the Protein Modification Integrated Search Engine (PROMISE), an antigen discovery pipeline that enables the analysis of 29 different PTM combinations from multiple clinical cohorts and cell lines. We expanded the antigen landscape, uncovering human leukocyte antigen class I binding motifs defined by specific PTMs with haplotype-specific binding preferences and revealing disease-specific modified targets, including thousands of new cancer-specific antigens that can be shared between patients and across cancer types. Furthermore, we uncovered a subset of modified peptides that are specific to cancer tissue and driven by post-translational changes that occurred in the tumor proteome. Our findings highlight principles of PTM-driven antigenicity, which may have broad implications for T cell-mediated therapies in cancer and beyond.
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Affiliation(s)
- Assaf Kacen
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Aaron Javitt
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Matthias P Kramer
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - David Morgenstern
- De Botton Institute for Protein Profiling, Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot, Israel
| | - Tomer Tsaban
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Merav D Shmueli
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Guo Ci Teo
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | | | - Eilon Barnea
- Faculty of Biology, Technion-Israel Institute of Technology, Haifa, Israel
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Arie Admon
- Faculty of Biology, Technion-Israel Institute of Technology, Haifa, Israel
| | - Lea Eisenbach
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Yardena Samuels
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Yishai Levin
- De Botton Institute for Protein Profiling, Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot, Israel
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Yifat Merbl
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel.
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41
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Kim JY, Cha H, Kim K, Sung C, An J, Bang H, Kim H, Yang JO, Chang S, Shin I, Noh SJ, Shin I, Cho DY, Lee SH, Choi JK. MHC II immunogenicity shapes the neoepitope landscape in human tumors. Nat Genet 2023; 55:221-231. [PMID: 36624345 DOI: 10.1038/s41588-022-01273-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 11/30/2022] [Indexed: 01/11/2023]
Abstract
Despite advances in predicting physical peptide-major histocompatibility complex I (pMHC I) binding, it remains challenging to identify functionally immunogenic neoepitopes, especially for MHC II. By using the results of >36,000 immunogenicity assay, we developed a method to identify pMHC whose structural alignment facilitates T cell reaction. Our method predicted neoepitopes for MHC II and MHC I that were responsive to checkpoint blockade when applied to >1,200 samples of various tumor types. To investigate selection by spontaneous immunity at the single epitope level, we analyzed the frequency spectrum of >25 million mutations in >9,000 treatment-naive tumors with >100 immune phenotypes. MHC II immunogenicity specifically lowered variant frequencies in tumors under high immune pressure, particularly with high TCR clonality and MHC II expression. A similar trend was shown for MHC I neoepitopes, but only in particular tissue types. In summary, we report immune selection imposed by MHC II-restricted natural or therapeutic T cell reactivity.
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Affiliation(s)
- Jeong Yeon Kim
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.,Penta Medix Co., Ltd., Seongnam-si, Republic of Korea
| | - Hongui Cha
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyeonghui Kim
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Changhwan Sung
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.,Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea
| | - Jinhyeon An
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Hyoeun Bang
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.,Penta Medix Co., Ltd., Seongnam-si, Republic of Korea
| | - Hyungjoo Kim
- Penta Medix Co., Ltd., Seongnam-si, Republic of Korea.,Department of Life Science, Hanyang University, Seoul, Republic of Korea
| | - Jin Ok Yang
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.,Korea Bioinformation Center, KRIBB, Daejeon, Republic of Korea
| | - Suhwan Chang
- Department of Biomedical Sciences, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Incheol Shin
- Department of Life Science, Hanyang University, Seoul, Republic of Korea.,Natural Science Institute, Hanyang University, Seoul, Republic of Korea
| | - Seung-Jae Noh
- Penta Medix Co., Ltd., Seongnam-si, Republic of Korea
| | - Inkyung Shin
- Penta Medix Co., Ltd., Seongnam-si, Republic of Korea
| | - Dae-Yeon Cho
- Penta Medix Co., Ltd., Seongnam-si, Republic of Korea.
| | - Se-Hoon Lee
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. .,Department of Health Sciences and Technology, Samsung Advanced Institute of Health Science and Technology, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
| | - Jung Kyoon Choi
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea. .,Penta Medix Co., Ltd., Seongnam-si, Republic of Korea.
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42
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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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43
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Farriol-Duran R, Vallejo-Vallés M, Amengual-Rigo P, Floor M, Guallar V. NetCleave: An Open-Source Algorithm for Predicting C-Terminal Antigen Processing for MHC-I and MHC-II. Methods Mol Biol 2023; 2673:211-226. [PMID: 37258917 DOI: 10.1007/978-1-0716-3239-0_15] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
T cell epitopes presented on the surface of mammalian cells are subjected to a complex network of antigen processing and presentation. Among them, C-terminal antigen processing constitutes one of the main bottlenecks for the generation of epitopes, as it defines the C-terminal end of the final epitope and delimits the peptidome that will be presented downstream. Previously (Amengual-Rigo and Guallar, Sci Rep 111(11):1-8, 2021), we demonstrated that NetCleave stands out as one of the best algorithms for the prediction of C-terminal processing, which in its turn can be crucial to design peptide-based vaccination strategies. In this chapter, we provide a pipeline to exploit the full capabilities of NetCleave, an open-source and retrainable algorithm for predicting the C-terminal antigen processing for the MHC-I and MHC-II pathways.
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Affiliation(s)
| | | | | | - Martin Floor
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Víctor Guallar
- Barcelona Supercomputing Center (BSC), Barcelona, Spain.
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
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44
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Cormican JA, Horokhovskyi Y, Soh WT, Mishto M, Liepe J. inSPIRE: An Open-Source Tool for Increased Mass Spectrometry Identification Rates Using Prosit Spectral Prediction. Mol Cell Proteomics 2022; 21:100432. [PMID: 36280141 PMCID: PMC9720494 DOI: 10.1016/j.mcpro.2022.100432] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 10/17/2022] [Accepted: 10/19/2022] [Indexed: 11/05/2022] Open
Abstract
Rescoring of mass spectrometry (MS) search results using spectral predictors can strongly increase peptide spectrum match (PSM) identification rates. This approach is particularly effective when aiming to search MS data against large databases, for example, when dealing with nonspecific cleavage in immunopeptidomics or inflation of the reference database for noncanonical peptide identification. Here, we present inSPIRE (in silico Spectral Predictor Informed REscoring), a flexible and performant open-source rescoring pipeline built on Prosit MS spectral prediction, which is compatible with common database search engines. inSPIRE allows large-scale rescoring with data from multiple MS search files, increases sensitivity to minor differences in amino acid residue position, and can be applied to various MS sample types, including tryptic proteome digestions and immunopeptidomes. inSPIRE boosts PSM identification rates in immunopeptidomics, leading to better performance than the original Prosit rescoring pipeline, as confirmed by benchmarking of inSPIRE performance on ground truth datasets. The integration of various features in the inSPIRE backbone further boosts the PSM identification in immunopeptidomics, with a potential benefit for the identification of noncanonical peptides.
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Affiliation(s)
- John A Cormican
- Max-Planck-Institute for Multidisciplinary Sciences (MPI-NAT), Göttingen, Germany
| | - Yehor Horokhovskyi
- Max-Planck-Institute for Multidisciplinary Sciences (MPI-NAT), Göttingen, Germany
| | - Wai Tuck Soh
- Max-Planck-Institute for Multidisciplinary Sciences (MPI-NAT), Göttingen, Germany
| | - Michele Mishto
- Centre for Inflammation Biology and Cancer Immunology (CIBCI) & Peter Gorer Department of Immunobiology, King's College London, London, United Kingdom; The Francis Crick Institute, London, United Kingdom.
| | - Juliane Liepe
- Max-Planck-Institute for Multidisciplinary Sciences (MPI-NAT), Göttingen, Germany.
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45
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Solanki A, Riedel M, Cornette J, Udell J, Vasmatzis G. Hydrophobicity identifies false positives and false negatives in peptide-MHC binding. Front Oncol 2022; 12:1034810. [PMID: 36419888 PMCID: PMC9677119 DOI: 10.3389/fonc.2022.1034810] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 10/17/2022] [Indexed: 06/10/2024] Open
Abstract
Major Histocompability Complex (MHC) Class I molecules allow cells to present foreign and endogenous peptides to T-Cells so that cells infected by pathogens can be identified and killed. Neural networks tools such as NetMHC-4.0 and NetMHCpan-4.1 are used to predict whether peptides will bind to variants of MHC molecules. These tools are trained on data gathered from binding affinity and eluted ligand experiments. However, these tools do not track hydrophobicity, a significant biochemical factor relevant to peptide binding, in their predictions. A previous study had concluded that the peptides predicted to bind to HLA-A*0201 by NetMHC-4.0 were much more hydrophobic than expected. This paper expands that study by also focusing on HLA-B*2705 and HLA-B*0801, which prefer binding hydrophilic and balanced peptides respectively. The correlation of hydrophobicity of 9-mer peptides with their predicted binding strengths to these various HLAs was investigated. Two studies were performed, one using the data that the two neural networks were trained on, and the other using a sample of the human proteome. NetMHC-4.0 was found to have a statistically significant bias towards predicting highly hydrophobic peptides as strong binders to HLA-A*0201 and HLA-B*2705 in both studies. Machine Learning metrics were used to identify the causes for this bias: hydrophobic false positives and hydrophilic false negatives. These results suggest that the retraining the neural networks with biochemical attributes such as hydrophobicity and better training data could increase the accuracy of their predictions. This would increase their impact in applications such as vaccine design and neoantigen identification.
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Affiliation(s)
- Arnav Solanki
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Marc Riedel
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States
| | - James Cornette
- Department of Mathematics, Iowa State University, Ames, IA, United States
| | - Julia Udell
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States
- Biomarker Discovery Group, Mayo Clinic, Center for Individualized Medicine, Rochester, MN, United States
| | - George Vasmatzis
- Biomarker Discovery Group, Mayo Clinic, Center for Individualized Medicine, Rochester, MN, United States
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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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Low TY, Chen YJ, Ishihama Y, Chung MCM, Cordwell S, Poon TCW, Kwon HJ. The Second Asia-Oceania Human Proteome Organization (AOHUPO) Online Education Series on the Renaissance of Clinical Proteomics: Biomarkers, Imaging and Therapeutics. Mol Cell Proteomics 2022; 21:100436. [PMID: 36309314 PMCID: PMC9700300 DOI: 10.1016/j.mcpro.2022.100436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 10/23/2022] [Indexed: 11/06/2022] Open
Abstract
In 2021, the Asia-Oceania Human Proteome Organization (AOHUPO) initiated a new endeavor named the AOHUPO Online Education Series with the aim to promote scientific education and collaboration, exchange of ideas and culture among the young scientists in the AO region. Following the warm participation, the AOHUPO organized the second series in 2022, with the theme "The Renaissance of Clinical Proteomics: Biomarkers, Imaging and Therapeutics". This time, the second AOHUPO Online Education Series was hosted by the UKM Medical Molecular Biology Institute (UMBI) affiliated to the National University of Malaysia (UKM) in Kuala Lumpur, Malaysia on three consecutive Fridays (11th, 18th and 25th of March). More than 300 participants coming from 29 countries/regions registered for this 3-days event. This event provided an amalgamation of six prominent speakers and all participants whose interests lay mainly in applying MS-based and non-MS-based proteomics for clinical investigation.
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Affiliation(s)
- Teck Yew Low
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
| | - Yasushi Ishihama
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
| | - Max Ching Ming Chung
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Stuart Cordwell
- School of Life and Environmental Sciences and Sydney Mass Spectrometry, The University of Sydney, Sydney, Australia
| | - Terence Chuen Wai Poon
- Pilot Laboratory, Proteomics Core, Institute of Translational Medicine, Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Ho Jeong Kwon
- Chemical Genomics Leader Research Initiative, Department of Biotechnology, Yonsei University, Seoul, South Korea,For correspondence: Ho Jeong Kwon
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Manolio C, Ragone C, Cavalluzzo B, Mauriello A, Tornesello ML, Buonaguro FM, Salomone Megna A, D'Alessio G, Penta R, Tagliamonte M, Buonaguro L. Antigenic molecular mimicry in viral-mediated protection from cancer: the HIV case. Lab Invest 2022; 20:472. [PMID: 36243758 PMCID: PMC9569184 DOI: 10.1186/s12967-022-03681-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 09/29/2022] [Indexed: 11/17/2022]
Abstract
Background People living with HIV/AIDS (PLWHA) show a reduced incidence for three cancer types, namely breast, prostate and colon cancers. In the present study, we assessed whether a molecular mimicry between HIV epitopes and tumor associated antigens and, consequently, a T cell cross-reactivity could provide an explanation for such an epidemiological evidence. Methods Homology between published TAAs and non-self HIV-derived epitopes have been assessed by BLAST homology. Structural analyses have been performed by bioinformatics tools. Immunological validation of CD8+ T cell cross-reactivity has been evaluated ex vivo by tetramer staining. Findings Sequence homologies between multiple TAAs and HIV epitopes have been found. High structural similarities between the paired TAAs and HIV epitopes as well as comparable patterns of contact with HLA and TCR α and β chains have been observed. Furthermore, cross-reacting CD8+ T cells have been identified. Interpretation This is the first study showing a molecular mimicry between HIV antigens an TAAs identified in breast, prostate and colon cancers. Therefore, it is highly reasonable that memory CD8+ T cells elicited during the HIV infection may play a key role in controlling development and progression of such cancers in the PLWHA lifetime. This represents the first demonstration ever that a viral infection may induce a natural “preventive” anti-cancer memory T cells, with highly relevant implications beyond the HIV infection. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-022-03681-4.
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Affiliation(s)
- Carmen Manolio
- Innovative Immunological Models Unit, Istituto Nazionale Tumori - IRCCS - "Fond G. Pascale", Via Mariano Semmola, 52, Naples, Italy
| | - Concetta Ragone
- Innovative Immunological Models Unit, Istituto Nazionale Tumori - IRCCS - "Fond G. Pascale", Via Mariano Semmola, 52, Naples, Italy
| | - Beatrice Cavalluzzo
- Innovative Immunological Models Unit, Istituto Nazionale Tumori - IRCCS - "Fond G. Pascale", Via Mariano Semmola, 52, Naples, Italy
| | - Angela Mauriello
- Innovative Immunological Models Unit, Istituto Nazionale Tumori - IRCCS - "Fond G. Pascale", Via Mariano Semmola, 52, Naples, Italy
| | - Maria Lina Tornesello
- Molecular Biology and Viral Oncogenesis Unit, Istituto Nazionale Tumori - IRCCS - "Fond G. Pascale", Naples, Italy
| | - Franco M Buonaguro
- Molecular Biology and Viral Oncogenesis Unit, Istituto Nazionale Tumori - IRCCS - "Fond G. Pascale", Naples, Italy
| | | | - Giovanna D'Alessio
- Division of Infectious Diseases, AORN San Pio Hospital, Benevento, Italy
| | - Roberta Penta
- Cellular Manipulation and Immunogenetics, Oncology Dep, Ba.S.C.O. Unit, AORN Santobono-Pausilipon, Naples, Italy
| | - Maria Tagliamonte
- Innovative Immunological Models Unit, Istituto Nazionale Tumori - IRCCS - "Fond G. Pascale", Via Mariano Semmola, 52, Naples, Italy
| | - Luigi Buonaguro
- Innovative Immunological Models Unit, Istituto Nazionale Tumori - IRCCS - "Fond G. Pascale", Via Mariano Semmola, 52, Naples, Italy.
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Sources of Cancer Neoantigens beyond Single-Nucleotide Variants. Int J Mol Sci 2022; 23:ijms231710131. [PMID: 36077528 PMCID: PMC9455963 DOI: 10.3390/ijms231710131] [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: 07/01/2022] [Revised: 09/01/2022] [Accepted: 09/02/2022] [Indexed: 11/17/2022] Open
Abstract
The success of checkpoint blockade therapy against cancer has unequivocally shown that cancer cells can be effectively recognized by the immune system and eliminated. However, the identity of the cancer antigens that elicit protective immunity remains to be fully explored. Over the last decade, most of the focus has been on somatic mutations derived from non-synonymous single-nucleotide variants (SNVs) and small insertion/deletion mutations (indels) that accumulate during cancer progression. Mutated peptides can be presented on MHC molecules and give rise to novel antigens or neoantigens, which have been shown to induce potent anti-tumor immune responses. A limitation with SNV-neoantigens is that they are patient-specific and their accurate prediction is critical for the development of effective immunotherapies. In addition, cancer types with low mutation burden may not display sufficient high-quality [SNV/small indels] neoantigens to alone stimulate effective T cell responses. Accumulating evidence suggests the existence of alternative sources of cancer neoantigens, such as gene fusions, alternative splicing variants, post-translational modifications, and transposable elements, which may be attractive novel targets for immunotherapy. In this review, we describe the recent technological advances in the identification of these novel sources of neoantigens, the experimental evidence for their presentation on MHC molecules and their immunogenicity, as well as the current clinical development stage of immunotherapy targeting these neoantigens.
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Baumgaertner P, Schmidt J, Costa-Nunes CM, Bordry N, Guillaume P, Luescher I, Speiser DE, Rufer N, Hebeisen M. CD8 T cell function and cross-reactivity explored by stepwise increased peptide-HLA versus TCR affinity. Front Immunol 2022; 13:973986. [PMID: 36032094 PMCID: PMC9399405 DOI: 10.3389/fimmu.2022.973986] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 07/22/2022] [Indexed: 12/05/2022] Open
Abstract
Recruitment and activation of CD8 T cells occur through specific triggering of T cell receptor (TCR) by peptide-bound human leucocyte antigen (HLA) ligands. Within the generated trimeric TCR-peptide:HLA complex, the molecular binding affinities between peptide and HLA, and between TCR and peptide:HLA both impact T cell functional outcomes. However, how their individual and combined effects modulate immunogenicity and overall T cell responsiveness has not been investigated systematically. Here, we established two panels of human tumor peptide variants differing in their affinity to HLA. For precise characterization, we developed the “blue peptide assay”, an upgraded cell-based approach to measure the peptide:HLA affinity. These peptide variants were then used to investigate the cross-reactivity of tumor antigen-specific CD8 T cell clonotypes derived from blood of cancer patients after vaccination with either the native or an affinity-optimized Melan-A/MART-1 epitope, or isolated from tumor infiltrated lymph nodes (TILNs). Vaccines containing the native tumor epitope generated T cells with better functionality, and superior cross-reactivity against potential low affinity escape epitopes, as compared to T cells induced by vaccines containing an HLA affinity-optimized epitope. Comparatively, Melan-A/MART-1-specific TILN cells displayed functional and cross-reactive profiles that were heterogeneous and clonotype-dependent. Finally, we took advantage of a collection of T cells expressing affinity-optimized NY-ESO-1-specific TCRs to interrogate the individual and combined impact of peptide:HLA and TCR-pHLA affinities on overall CD8 T cell responses. We found profound and distinct effects of both biophysical parameters, with additive contributions and absence of hierarchical dominance. Altogether, the biological impact of peptide:HLA and TCR-pHLA affinities on T cell responses was carefully dissected in two antigenic systems, frequently targeted in human cancer immunotherapy. Our technology and stepwise comparison open new insights into the rational design and selection of vaccine-associated tumor-specific epitopes and highlight the functional and cross-reactivity profiles that endow T cells with best tumor control capacity.
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Affiliation(s)
- Petra Baumgaertner
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Epalinges, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne, Epalinges, Switzerland
- *Correspondence: Michael Hebeisen, ; Petra Baumgaertner,
| | - Julien Schmidt
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Epalinges, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne, Epalinges, Switzerland
| | - Carla-Marisa Costa-Nunes
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Epalinges, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne, Epalinges, Switzerland
| | - Natacha Bordry
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Epalinges, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne, Epalinges, Switzerland
| | - Philippe Guillaume
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Epalinges, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne, Epalinges, Switzerland
| | - Immanuel Luescher
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Epalinges, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne, Epalinges, Switzerland
| | - Daniel E. Speiser
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Epalinges, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne, Epalinges, Switzerland
| | - Nathalie Rufer
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Epalinges, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne, Epalinges, Switzerland
| | - Michael Hebeisen
- Department of Oncology, Lausanne University Hospital and University of Lausanne, Epalinges, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch - University of Lausanne, Epalinges, Switzerland
- *Correspondence: Michael Hebeisen, ; Petra Baumgaertner,
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