1
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de Graaf JF, Pesic T, Spitzer FS, Oosterhuis K, Camps MG, Zoutendijk I, Teunisse B, Zhu W, Arakelian T, Zondag GC, Arens R, van Bergen J, Ossendorp F. Neoantigen-specific T cell help outperforms non-specific help in multi-antigen DNA vaccination against cancer. MOLECULAR THERAPY. ONCOLOGY 2024; 32:200835. [PMID: 39040850 PMCID: PMC11261851 DOI: 10.1016/j.omton.2024.200835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 05/22/2024] [Accepted: 06/13/2024] [Indexed: 07/24/2024]
Abstract
CD4+ T helper antigens are essential components of cancer vaccines, but the relevance of the source of these MHC class II-restricted antigens remains underexplored. To compare the effectiveness of tumor-specific versus tumor-unrelated helper antigens, we designed three DNA vaccines for the murine MC-38 colon carcinoma, encoding CD8+ T cell neoantigens alone (noHELP) or in combination with either "universal" helper antigens (uniHELP) or helper neoantigens (neoHELP). Both types of helped vaccines increased the frequency of vaccine-induced CD8+ T cells, and particularly uniHELP increased the fraction of KLRG1+ and PD-1low effector cells. However, when mice were subsequently injected with MC-38 cells, only neoHELP vaccination resulted in significantly better tumor control than noHELP. In contrast to uniHELP, neoHELP-induced tumor control was dependent on the presence of CD4+ T cells, while both vaccines relied on CD8+ T cells. In line with this, neoHELP variants containing wild-type counterparts of the CD4+ or CD8+ T cell neoantigens displayed reduced tumor control. These data indicate that optimal personalized cancer vaccines should include MHC class II-restricted neoantigens to elicit tumor-specific CD4+ T cell help.
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Affiliation(s)
| | - Tamara Pesic
- Department of Immunology, Leiden University Medical Center, 2300 RC Leiden, the Netherlands
| | - Felicia S. Spitzer
- Department of Immunology, Leiden University Medical Center, 2300 RC Leiden, the Netherlands
| | | | - Marcel G.M. Camps
- Department of Immunology, Leiden University Medical Center, 2300 RC Leiden, the Netherlands
| | | | | | - Wahwah Zhu
- Synvolux BV, 2333 CH Leiden, the Netherlands
| | - Tsolere Arakelian
- Department of Immunology, Leiden University Medical Center, 2300 RC Leiden, the Netherlands
| | - Gerben C. Zondag
- Immunetune BV, 2333 CH Leiden, the Netherlands
- Synvolux BV, 2333 CH Leiden, the Netherlands
| | - Ramon Arens
- Department of Immunology, Leiden University Medical Center, 2300 RC Leiden, the Netherlands
| | | | - Ferry Ossendorp
- Department of Immunology, Leiden University Medical Center, 2300 RC Leiden, the Netherlands
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2
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Zhou Z, Wang J, Wang J, Yang S, Wang R, Zhang G, Li Z, Shi R, Wang Z, Lu Q. Deciphering the tumor immune microenvironment from a multidimensional omics perspective: insight into next-generation CAR-T cell immunotherapy and beyond. Mol Cancer 2024; 23:131. [PMID: 38918817 PMCID: PMC11201788 DOI: 10.1186/s12943-024-02047-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 06/17/2024] [Indexed: 06/27/2024] Open
Abstract
Tumor immune microenvironment (TIME) consists of intra-tumor immunological components and plays a significant role in tumor initiation, progression, metastasis, and response to therapy. Chimeric antigen receptor (CAR)-T cell immunotherapy has revolutionized the cancer treatment paradigm. Although CAR-T cell immunotherapy has emerged as a successful treatment for hematologic malignancies, it remains a conundrum for solid tumors. The heterogeneity of TIME is responsible for poor outcomes in CAR-T cell immunotherapy against solid tumors. The advancement of highly sophisticated technology enhances our exploration in TIME from a multi-omics perspective. In the era of machine learning, multi-omics studies could reveal the characteristics of TIME and its immune resistance mechanism. Therefore, the clinical efficacy of CAR-T cell immunotherapy in solid tumors could be further improved with strategies that target unfavorable conditions in TIME. Herein, this review seeks to investigate the factors influencing TIME formation and propose strategies for improving the effectiveness of CAR-T cell immunotherapy through a multi-omics perspective, with the ultimate goal of developing personalized therapeutic approaches.
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Affiliation(s)
- Zhaokai Zhou
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Jiahui Wang
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
- Department of Nephrology, Union Medical College Hospital, Chinese Academy of Medical Sciences, PekingBeijing, 100730, China
| | - Jiaojiao Wang
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
| | - Shuai Yang
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Ruizhi Wang
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Ge Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Zhengrui Li
- Department of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Run Shi
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zhan Wang
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Qiong Lu
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China.
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3
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Koncz B, Balogh GM, Manczinger M. A journey to your self: The vague definition of immune self and its practical implications. Proc Natl Acad Sci U S A 2024; 121:e2309674121. [PMID: 38722806 PMCID: PMC11161755 DOI: 10.1073/pnas.2309674121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2024] Open
Abstract
The identification of immunogenic peptides has become essential in an increasing number of fields in immunology, ranging from tumor immunotherapy to vaccine development. The nature of the adaptive immune response is shaped by the similarity between foreign and self-protein sequences, a concept extensively applied in numerous studies. Can we precisely define the degree of similarity to self? Furthermore, do we accurately define immune self? In the current work, we aim to unravel the conceptual and mechanistic vagueness hindering the assessment of self-similarity. Accordingly, we demonstrate the remarkably low consistency among commonly employed measures and highlight potential avenues for future research.
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Affiliation(s)
- Balázs Koncz
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Hungarian Research Network (HUN-REN) Biological Research Centre, Szeged6726, Hungary
- Hungarian Centre of Excellence for Molecular Medicine - Biological Research Centre (HCEMM-BRC) Systems Immunology Research Group, Szeged6726, Hungary
- Department of Dermatology and Allergology, University of Szeged, Szeged6720, Hungary
| | - Gergő Mihály Balogh
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Hungarian Research Network (HUN-REN) Biological Research Centre, Szeged6726, Hungary
- Hungarian Centre of Excellence for Molecular Medicine - Biological Research Centre (HCEMM-BRC) Systems Immunology Research Group, Szeged6726, Hungary
- Department of Dermatology and Allergology, University of Szeged, Szeged6720, Hungary
| | - Máté Manczinger
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Hungarian Research Network (HUN-REN) Biological Research Centre, Szeged6726, Hungary
- Hungarian Centre of Excellence for Molecular Medicine - Biological Research Centre (HCEMM-BRC) Systems Immunology Research Group, Szeged6726, Hungary
- Department of Dermatology and Allergology, University of Szeged, Szeged6720, Hungary
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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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Yang Q, Xu L, Dong W, Li X, Wang K, Dong S, Zhang X, Yang T, Jiang F, Zhang B, Luo G, Gao X, Wang G. HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses. Brief Bioinform 2024; 25:bbae302. [PMID: 38920343 PMCID: PMC11200192 DOI: 10.1093/bib/bbae302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/20/2024] [Accepted: 06/11/2024] [Indexed: 06/27/2024] Open
Abstract
While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen presentation by human leukocyte antigen (HLA) class II molecules remains a challenge. This identification is critical for developing vaccines and cancer immunotherapies. Current prediction methods are limited, primarily due to a lack of high-quality training epitope datasets and algorithmic constraints. To predict the exogenous HLA class II-restricted peptides across most of the human population, we utilized the mass spectrometry data to profile >223 000 eluted ligands over HLA-DR, -DQ, and -DP alleles. Here, by integrating these data with peptide processing and gene expression, we introduce HLAIImaster, an attention-based deep learning framework with adaptive domain knowledge for predicting neoepitope immunogenicity. Leveraging diverse biological characteristics and our enhanced deep learning framework, HLAIImaster is significantly improved against existing tools in terms of positive predictive value across various neoantigen studies. Robust domain knowledge learning accurately identifies neoepitope immunogenicity, bridging the gap between neoantigen biology and the clinical setting and paving the way for future neoantigen-based therapies to provide greater clinical benefit. In summary, we present a comprehensive exploitation of the immunogenic neoepitope repertoire of cancers, facilitating the effective development of "just-in-time" personalized vaccines.
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Affiliation(s)
- Qiang Yang
- School of Medicine and Health, Harbin Institute of Technology, Yikuang Street, Harbin 150000, China
| | - Long Xu
- School of Computer Science and Technology, Harbin Institute of Technology, West Dazhi Street, Harbin 150001, China
| | - Weihe Dong
- College of Computer and Control Engineering, Northeast Forestry University, Hexing Road, Harbin 150004, China
| | - Xiaokun Li
- School of Computer Science and Technology, Harbin Institute of Technology, West Dazhi Street, Harbin 150001, China
- School of Computer Science and Technology, Heilongjiang University, Xuefu Road, Harbin 150080, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Xuefu Road, Harbin 150090, China
- Shandong Hengxun Technology Co., Ltd., Miaoling Road, Qingdao 266100, China
| | - Kuanquan Wang
- School of Computer Science and Technology, Harbin Institute of Technology, West Dazhi Street, Harbin 150001, China
| | - Suyu Dong
- College of Computer and Control Engineering, Northeast Forestry University, Hexing Road, Harbin 150004, China
| | - Xianyu Zhang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Haping Road, Harbin 150081, China
| | - Tiansong Yang
- Department of Rehabilitation, The First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine, and Traditional Chinese Medicine Informatics Key Laboratory of Heilongjiang Province, Heping Road, Harbin 150040, China
| | - Feng Jiang
- School of Medicine and Health, Harbin Institute of Technology, Yikuang Street, Harbin 150000, China
| | - Bin Zhang
- Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal 23955, Saudi Arabia
| | - Gongning Luo
- Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal 23955, Saudi Arabia
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal 23955, Saudi Arabia
| | - Guohua Wang
- College of Computer and Control Engineering, Northeast Forestry University, Hexing Road, Harbin 150004, China
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Sugiyama N, Terry FE, Gutierrez AH, Hirano T, Hoshi M, Mizuno Y, Martin W, Yasunaga S, Niiro H, Fujio K, De Groot AS. Individual and population-level variability in HLA-DR associated immunogenicity risk of biologics used for the treatment of rheumatoid arthritis. Front Immunol 2024; 15:1377911. [PMID: 38812524 PMCID: PMC11134572 DOI: 10.3389/fimmu.2024.1377911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 04/24/2024] [Indexed: 05/31/2024] Open
Abstract
Hypothesis While conventional in silico immunogenicity risk assessments focus on measuring immunogenicity based on the potential of therapeutic proteins to be processed and presented by a global population-wide set of human leukocyte antigen (HLA) alleles to T cells, future refinements might adjust for HLA allele frequencies in different geographic regions or populations, as well for as individuals in those populations. Adjustment by HLA allele distribution may reveal risk patterns that are specific to population groups or individuals, which current methods that rely on global-population HLA prevalence may obscure. Key findings This analysis uses HLA frequency-weighted binding predictions to define immunogenicity risk for global and sub-global populations. A comparison of assessments tuned for North American/European versus Japanese/Asian populations suggests that the potential for anti-therapeutic responses (anti-therapeutic antibodies or ATA) for several commonly prescribed Rheumatoid Arthritis (RA) therapeutic biologics may differ, significantly, between the Caucasian and Japanese populations. This appears to align with reports of differing product-related immunogenicity that is observed in different populations. Relevance to clinical practice Further definition of population-level (regional) and individual patient-specific immunogenic risk profiles may enable prescription of the RA therapeutic with the highest probability of success to each patient, depending on their population of origin and/or their individual HLA background. Furthermore, HLA-specific immunogenicity outcomes data are limited, thus there is a need to expand HLA-association studies that examine the relationship between HLA haplotype and ATA in the clinic.
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Affiliation(s)
- Naonobu Sugiyama
- Rheumatology, Inflammation and Immunology Medical Affairs, Pfizer Japan Inc., Tokyo, Japan
| | | | | | - Toshitaka Hirano
- Rheumatology, Inflammation and Immunology Medical Affairs, Pfizer Japan Inc., Tokyo, Japan
| | - Masato Hoshi
- Rheumatology, Inflammation and Immunology Medical Affairs, Pfizer Japan Inc., Tokyo, Japan
| | - Yasushi Mizuno
- Rheumatology, Inflammation and Immunology Medical Affairs, Pfizer Japan Inc., Tokyo, Japan
| | | | - Shin’ichiro Yasunaga
- Department of Biochemistry, Faculty of Medicine, Fukuoka University, Fukuoka, Japan
| | - Hiroaki Niiro
- Department of Medical Education, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan
| | - Keishi Fujio
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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7
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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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8
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Sammut SJ, Galson JD, Minter R, Sun B, Chin SF, De Mattos-Arruda L, Finch DK, Schätzle S, Dias J, Rueda OM, Seoane J, Osbourn J, Caldas C, Bashford-Rogers RJM. Predictability of B cell clonal persistence and immunosurveillance in breast cancer. Nat Immunol 2024; 25:916-924. [PMID: 38698238 PMCID: PMC11065701 DOI: 10.1038/s41590-024-01821-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 03/15/2024] [Indexed: 05/05/2024]
Abstract
B cells and T cells are important components of the adaptive immune system and mediate anticancer immunity. The T cell landscape in cancer is well characterized, but the contribution of B cells to anticancer immunosurveillance is less well explored. Here we show an integrative analysis of the B cell and T cell receptor repertoire from individuals with metastatic breast cancer and individuals with early breast cancer during neoadjuvant therapy. Using immune receptor, RNA and whole-exome sequencing, we show that both B cell and T cell responses seem to coevolve with the metastatic cancer genomes and mirror tumor mutational and neoantigen architecture. B cell clones associated with metastatic immunosurveillance and temporal persistence were more expanded and distinct from site-specific clones. B cell clonal immunosurveillance and temporal persistence are predictable from the clonal structure, with higher-centrality B cell antigen receptors more likely to be detected across multiple metastases or across time. This predictability was generalizable across other immune-mediated disorders. This work lays a foundation for prioritizing antibody sequences for therapeutic targeting in cancer.
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MESH Headings
- Humans
- Female
- Breast Neoplasms/immunology
- B-Lymphocytes/immunology
- Immunologic Surveillance
- Receptors, Antigen, T-Cell/genetics
- Receptors, Antigen, T-Cell/immunology
- Receptors, Antigen, T-Cell/metabolism
- Receptors, Antigen, B-Cell/metabolism
- Receptors, Antigen, B-Cell/genetics
- Receptors, Antigen, B-Cell/immunology
- T-Lymphocytes/immunology
- Monitoring, Immunologic
- Exome Sequencing
- Antigens, Neoplasm/immunology
- Neoplasm Metastasis
- Clone Cells
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Affiliation(s)
- Stephen-John Sammut
- Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK.
- The Royal Marsden Hospital NHS Foundation Trust, London, UK.
| | | | | | - Bo Sun
- Wellcome Centre for Human Genetics, Oxford, UK
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
| | - Suet-Feung Chin
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Leticia De Mattos-Arruda
- IrsiCaixa, Germans Trias i Pujol University Hospital, Badalona, Spain
- Germans Trias i Pujol Research Institute (IGTP), Badalona, Spain
| | | | | | | | - Oscar M Rueda
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Joan Seoane
- Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron University Hospital, Institució Catalana de Recerca i Estudis Avançats (ICREA), Universitat Autònoma de Barcelona (UAB), CIBERONC, Barcelona, Spain
| | | | - Carlos Caldas
- School of Clinical Medicine, University of Cambridge, Cambridge, UK.
| | - Rachael J M Bashford-Rogers
- Wellcome Centre for Human Genetics, Oxford, UK.
- Department of Biochemistry, University of Oxford, Oxford, UK.
- Oxford Cancer Centre, Oxford, UK.
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9
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Zhang X, Wu J, Luo Y, Wang Y, Wu Y, Xu X, Zhang Y, Kong R, Chi Y, Sun Y, Chen S, He Q, Zhu F, Zhou Z. CovEpiAb: a comprehensive database and analysis resource for immune epitopes and antibodies of human coronaviruses. Brief Bioinform 2024; 25:bbae183. [PMID: 38653491 PMCID: PMC11036340 DOI: 10.1093/bib/bbae183] [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: 01/03/2024] [Revised: 02/24/2024] [Accepted: 04/08/2024] [Indexed: 04/25/2024] Open
Abstract
Coronaviruses have threatened humans repeatedly, especially COVID-19 caused by SARS-CoV-2, which has posed a substantial threat to global public health. SARS-CoV-2 continuously evolves through random mutation, resulting in a significant decrease in the efficacy of existing vaccines and neutralizing antibody drugs. It is critical to assess immune escape caused by viral mutations and develop broad-spectrum vaccines and neutralizing antibodies targeting conserved epitopes. Thus, we constructed CovEpiAb, a comprehensive database and analysis resource of human coronavirus (HCoVs) immune epitopes and antibodies. CovEpiAb contains information on over 60 000 experimentally validated epitopes and over 12 000 antibodies for HCoVs and SARS-CoV-2 variants. The database is unique in (1) classifying and annotating cross-reactive epitopes from different viruses and variants; (2) providing molecular and experimental interaction profiles of antibodies, including structure-based binding sites and around 70 000 data on binding affinity and neutralizing activity; (3) providing virological characteristics of current and past circulating SARS-CoV-2 variants and in vitro activity of various therapeutics; and (4) offering site-level annotations of key functional features, including antibody binding, immunological epitopes, SARS-CoV-2 mutations and conservation across HCoVs. In addition, we developed an integrated pipeline for epitope prediction named COVEP, which is available from the webpage of CovEpiAb. CovEpiAb is freely accessible at https://pgx.zju.edu.cn/covepiab/.
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Affiliation(s)
- Xue Zhang
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - JingCheng Wu
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yuanyuan Luo
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yilin Wang
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yujie Wu
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xiaobin Xu
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yufang Zhang
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ruiying Kong
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ying Chi
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 310058, China
- ZJU-UoE Institute, Zhejiang University, Haining 314400, China
| | - Yisheng Sun
- Key Lab of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310015, China
| | - Shuqing Chen
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Qiaojun He
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Zhejiang University Innovation Institute for Artificial Intelligence in Medicine, Engineering Research Center of Innovative Anticancer Drugs, Ministry of Education, Hangzhou 310018, China
| | - Feng Zhu
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Zhejiang University Innovation Institute for Artificial Intelligence in Medicine, Engineering Research Center of Innovative Anticancer Drugs, Ministry of Education, Hangzhou 310018, China
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 310058, China
| | - Zhan Zhou
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Zhejiang University Innovation Institute for Artificial Intelligence in Medicine, Engineering Research Center of Innovative Anticancer Drugs, Ministry of Education, Hangzhou 310018, China
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 310058, China
- The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu 322000, China
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10
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Wang M, Lei C, Wang J, Li Y, Li M. TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning. Brief Bioinform 2024; 25:bbae154. [PMID: 38600667 PMCID: PMC11006794 DOI: 10.1093/bib/bbae154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 02/16/2024] [Accepted: 03/13/2024] [Indexed: 04/12/2024] Open
Abstract
Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding motifs. Based on the discovery, we make the preprocessing and coding closer to the natural biological process. Besides, due to the input being based on multiple types of features and the attention module focused on the BiGRU hidden layer, TripHLApan has learned more sequence level binding information. The application of transfer learning strategies ensures the accuracy of prediction results under special lengths (peptides in length 8) and model scalability with the data explosion. Compared with the current optimal models, TripHLApan exhibits strong predictive performance in various prediction environments with different positive and negative sample ratios. In addition, we validate the superiority and scalability of TripHLApan's predictive performance using additional latest data sets, ablation experiments and binding reconstitution ability in the samples of a melanoma patient. The results show that TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines. TripHLApan is publicly available at https://github.com/CSUBioGroup/TripHLApan.git.
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Affiliation(s)
- Meng Wang
- School of Computer Science and engineering, Central South University, Changsha 410083, China
| | - Chuqi Lei
- School of Computer Science and engineering, Central South University, Changsha 410083, China
| | - Jianxin Wang
- School of Computer Science and engineering, Central South University, Changsha 410083, China
| | - Yaohang Li
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA
| | - Min Li
- School of Computer Science and engineering, Central South University, Changsha 410083, China
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11
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Li Y, Wu X, Fang D, Luo Y. Informing immunotherapy with multi-omics driven machine learning. NPJ Digit Med 2024; 7:67. [PMID: 38486092 PMCID: PMC10940614 DOI: 10.1038/s41746-024-01043-6] [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: 10/14/2023] [Accepted: 02/14/2024] [Indexed: 03/18/2024] Open
Abstract
Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.
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Affiliation(s)
- Yawei Li
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Xin Wu
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, 60612, USA
| | - Deyu Fang
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA.
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA.
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12
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Yang Y, Wei Z, Cia G, Song X, Pucci F, Rooman M, Xue F, Hou Q. MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods. Front Immunol 2024; 15:1293706. [PMID: 38646540 PMCID: PMC11027168 DOI: 10.3389/fimmu.2024.1293706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 02/19/2024] [Indexed: 04/23/2024] Open
Abstract
Major histocompatibility complex Class II (MHCII) proteins initiate and regulate immune responses by presentation of antigenic peptides to CD4+ T-cells and self-restriction. The interactions between MHCII and peptides determine the specificity of the immune response and are crucial in immunotherapy and cancer vaccine design. With the ever-increasing amount of MHCII-peptide binding data available, many computational approaches have been developed for MHCII-peptide interaction prediction over the last decade. There is thus an urgent need to provide an up-to-date overview and assessment of these newly developed computational methods. To benchmark the prediction performance of these methods, we constructed an independent dataset containing binding and non-binding peptides to 20 human MHCII protein allotypes from the Immune Epitope Database, covering DP, DR and DQ alleles. After collecting 11 known predictors up to January 2022, we evaluated those available through a webserver or standalone packages on this independent dataset. The benchmarking results show that MixMHC2pred and NetMHCIIpan-4.1 achieve the best performance among all predictors. In general, newly developed methods perform better than older ones due to the rapid expansion of data on which they are trained and the development of deep learning algorithms. Our manuscript not only draws a full picture of the state-of-art of MHCII-peptide binding prediction, but also guides researchers in the choice among the different predictors. More importantly, it will inspire biomedical researchers in both academia and industry for the future developments in this field.
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Affiliation(s)
- Yaqing Yang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Zhonghui Wei
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Gabriel Cia
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Brussels, Belgium
| | - Xixi Song
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Brussels, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Brussels, Belgium
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Qingzhen Hou
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
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13
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Harris CT, Cohen S. Reducing Immunogenicity by Design: Approaches to Minimize Immunogenicity of Monoclonal Antibodies. BioDrugs 2024; 38:205-226. [PMID: 38261155 PMCID: PMC10912315 DOI: 10.1007/s40259-023-00641-2] [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] [Accepted: 12/13/2023] [Indexed: 01/24/2024]
Abstract
Monoclonal antibodies (mAbs) have transformed therapeutic strategies for various diseases. Their high specificity to target antigens makes them ideal therapeutic agents for certain diseases. However, a challenge to their application in clinical practice is their potential risk to induce unwanted immune response, termed immunogenicity. This challenge drives the continued efforts to deimmunize these protein therapeutics while maintaining their pharmacokinetic properties and therapeutic efficacy. Because mAbs hold a central position in therapeutic strategies against an array of diseases, the importance of conducting comprehensive immunogenicity risk assessment during the drug development process cannot be overstated. Such assessment necessitates the employment of in silico, in vitro, and in vivo strategies to evaluate the immunogenicity risk of mAbs. Understanding the intricacies of the mechanisms that drive mAb immunogenicity is crucial to improving their therapeutic efficacy and safety and developing the most effective strategies to determine and mitigate their immunogenic risk. This review highlights recent advances in immunogenicity prediction strategies, with a focus on protein engineering strategies used throughout development to reduce immunogenicity.
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Affiliation(s)
- Chantal T Harris
- Department of BioAnalytical Sciences, Genentech Inc., South San Francisco, CA, 94080-4990, USA
| | - Sivan Cohen
- Department of BioAnalytical Sciences, Genentech Inc., South San Francisco, CA, 94080-4990, USA.
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14
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Srivastava PK. Cancer neoepitopes viewed through negative selection and peripheral tolerance: a new path to cancer vaccines. J Clin Invest 2024; 134:e176740. [PMID: 38426497 PMCID: PMC10904052 DOI: 10.1172/jci176740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024] Open
Abstract
A proportion of somatic mutations in tumors create neoepitopes that can prime T cell responses that target the MHC I-neoepitope complexes on tumor cells, mediating tumor control or rejection. Despite the compelling centrality of neoepitopes to cancer immunity, we know remarkably little about what constitutes a neoepitope that can mediate tumor control in vivo and what distinguishes such a neoepitope from the vast majority of similar candidate neoepitopes that are inefficacious in vivo. Studies in mice as well as clinical trials have begun to reveal the unexpected paradoxes in this area. Because cancer neoepitopes straddle that ambiguous ground between self and non-self, some rules that are fundamental to immunology of frankly non-self antigens, such as viral or model antigens, do not appear to apply to neoepitopes. Because neoepitopes are so similar to self-epitopes, with only small changes that render them non-self, immune response to them is regulated at least partially the way immune response to self is regulated. Therefore, neoepitopes are viewed and understood here through the clarifying lens of negative thymic selection. Here, the emergent questions in the biology and clinical applications of neoepitopes are discussed critically and a mechanistic and testable framework that explains the complexity and translational potential of these wonderful antigens is proposed.
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15
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Notin P, Rollins N, Gal Y, Sander C, Marks D. Machine learning for functional protein design. Nat Biotechnol 2024; 42:216-228. [PMID: 38361074 DOI: 10.1038/s41587-024-02127-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 01/05/2024] [Indexed: 02/17/2024]
Abstract
Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation.
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Affiliation(s)
- Pascal Notin
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Computer Science, University of Oxford, Oxford, UK.
| | | | - Yarin Gal
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Chris Sander
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Debora Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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16
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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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17
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Wallace RP, Refvik KC, Antane JT, Brünggel K, Tremain AC, Raczy MR, Alpar AT, Nguyen M, Solanki A, Slezak AJ, Watkins EA, Lauterbach AL, Cao S, Wilson DS, Hubbell JA. Synthetically mannosylated antigens induce antigen-specific humoral tolerance and reduce anti-drug antibody responses to immunogenic biologics. Cell Rep Med 2024; 5:101345. [PMID: 38128533 PMCID: PMC10829756 DOI: 10.1016/j.xcrm.2023.101345] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 09/21/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023]
Abstract
Immunogenic biologics trigger an anti-drug antibody (ADA) response in patients that reduces efficacy and increases adverse reactions. Our laboratory has shown that targeting protein antigen to the liver microenvironment can reduce antigen-specific T cell responses; herein, we present a strategy to increase delivery of otherwise immunogenic biologics to the liver via conjugation to a synthetic mannose polymer, p(Man). This delivery leads to reduced antigen-specific T follicular helper cell and B cell responses resulting in diminished ADA production, which is maintained throughout subsequent administrations of the native biologic. We find that p(Man)-antigen treatment impairs the ADA response against recombinant uricase, a highly immunogenic biologic, without a dependence on hapten immunodominance or control by T regulatory cells. We identify increased T cell receptor signaling and increased apoptosis and exhaustion in T cells as effects of p(Man)-antigen treatment via transcriptomic analyses. This modular platform may enhance tolerance to biologics, enabling long-term solutions for an ever-increasing healthcare problem.
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Affiliation(s)
- Rachel P Wallace
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Kirsten C Refvik
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Jennifer T Antane
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Kym Brünggel
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Andrew C Tremain
- Committee on Immunology, University of Chicago, Chicago, IL 60637, USA
| | - Michal R Raczy
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Aaron T Alpar
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Mindy Nguyen
- Animal Resources Center, University of Chicago, Chicago, IL 60637, USA
| | - Ani Solanki
- Animal Resources Center, University of Chicago, Chicago, IL 60637, USA
| | - Anna J Slezak
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Elyse A Watkins
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Abigail L Lauterbach
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Shijie Cao
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - D Scott Wilson
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA; Biomedical Engineering Department, Johns Hopkins University, Baltimore, MD 21211, USA.
| | - Jeffrey A Hubbell
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA; Committee on Immunology, University of Chicago, Chicago, IL 60637, USA; Committee on Cancer Biology, University of Chicago, Chicago, IL 60637, USA.
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18
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Ricker CA, Meli K, Van Allen EM. Historical perspective and future directions: computational science in immuno-oncology. J Immunother Cancer 2024; 12:e008306. [PMID: 38191244 PMCID: PMC10826578 DOI: 10.1136/jitc-2023-008306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/07/2023] [Indexed: 01/10/2024] Open
Abstract
Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life.
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Affiliation(s)
- Cora A Ricker
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Kevin Meli
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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19
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Huang X, Gan Z, Cui H, Lan T, Liu Y, Caron E, Shao W. The SysteMHC Atlas v2.0, an updated resource for mass spectrometry-based immunopeptidomics. Nucleic Acids Res 2024; 52:D1062-D1071. [PMID: 38000392 PMCID: PMC10767952 DOI: 10.1093/nar/gkad1068] [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: 09/15/2023] [Revised: 10/16/2023] [Accepted: 10/25/2023] [Indexed: 11/26/2023] Open
Abstract
The SysteMHC Atlas v1.0 was the first public repository dedicated to mass spectrometry-based immunopeptidomics. Here we introduce a newly released version of the SysteMHC Atlas v2.0 (https://systemhc.sjtu.edu.cn), a comprehensive collection of 7190 MS files from 303 allotypes. We extended and optimized a computational pipeline that allows the identification of MHC-bound peptides carrying on unexpected post-translational modifications (PTMs), thereby resulting in 471K modified peptides identified over 60 distinct PTM types. In total, we identified approximately 1.0 million and 1.1 million unique peptides for MHC class I and class II immunopeptidomes, respectively, indicating a 6.8-fold increase and a 28-fold increase to those in v1.0. The SysteMHC Atlas v2.0 introduces several new features, including the inclusion of non-UniProt peptides, and the incorporation of several novel computational tools for FDR estimation, binding affinity prediction and motif deconvolution. Additionally, we enhanced the user interface, upgraded website framework, and provided external links to other resources related. Finally, we built and provided various spectral libraries as community resources for data mining and future immunopeptidomic and proteomic analysis. We believe that the SysteMHC Atlas v2.0 is a unique resource to provide key insights to the immunology and proteomics community and will accelerate the development of vaccines and immunotherapies.
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Affiliation(s)
- Xiaoxiang Huang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ziao Gan
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Haowei Cui
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Tian Lan
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yansheng Liu
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Etienne Caron
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Wenguang Shao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
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20
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Racle J, Gfeller D. How to Predict Binding Specificity and Ligands for New MHC-II Alleles with MixMHC2pred. Methods Mol Biol 2024; 2809:215-235. [PMID: 38907900 DOI: 10.1007/978-1-0716-3874-3_14] [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: 06/24/2024]
Abstract
MHC-II molecules are key mediators of antigen presentation in vertebrate species and bind to their ligands with high specificity. The very high polymorphism of MHC-II genes within species and the fast-evolving nature of these genes across species has resulted in tens of thousands of different alleles, with hundreds of new alleles being discovered yearly through large sequencing projects in different species. Here we describe how to use MixMHC2pred to predict the binding specificity of any MHC-II allele directly from its amino acid sequence. We then show how both MHC-II ligands and CD4+ T cell epitopes can be predicted in different species with our approach. MixMHC2pred is available at http://mixmhc2pred.gfellerlab.org/ .
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Affiliation(s)
- Julien Racle
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
- Agora Cancer Research Centre, Lausanne, Switzerland.
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
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21
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Barra C, Nilsson JB, Saksager A, Carri I, Deleuran S, Garcia Alvarez HM, Høie MH, Li Y, Clifford JN, Wan YTR, Moreta LS, Nielsen M. In Silico Tools for Predicting Novel Epitopes. Methods Mol Biol 2024; 2813:245-280. [PMID: 38888783 DOI: 10.1007/978-1-0716-3890-3_17] [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: 06/20/2024]
Abstract
Identifying antigens within a pathogen is a critical task to develop effective vaccines and diagnostic methods, as well as understanding the evolution and adaptation to host immune responses. Historically, antigenicity was studied with experiments that evaluate the immune response against selected fragments of pathogens. Using this approach, the scientific community has gathered abundant information regarding which pathogenic fragments are immunogenic. The systematic collection of this data has enabled unraveling many of the fundamental rules underlying the properties defining epitopes and immunogenicity, and has resulted in the creation of a large panel of immunologically relevant predictive (in silico) tools. The development and application of such tools have proven to accelerate the identification of novel epitopes within biomedical applications reducing experimental costs. This chapter introduces some basic concepts about MHC presentation, T cell and B cell epitopes, the experimental efforts to determine those, and focuses on state-of-the-art methods for epitope prediction, highlighting their strengths and limitations, and catering instructions for their rational use.
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Affiliation(s)
- Carolina Barra
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark.
| | | | - Astrid Saksager
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Ibel Carri
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Martín, Argentina
| | - Sebastian Deleuran
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Heli M Garcia Alvarez
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Martín, Argentina
| | - Magnus Haraldson Høie
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Yuchen Li
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | | | - Yat-Tsai Richie Wan
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Lys Sanz Moreta
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Morten Nielsen
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Martín, Argentina
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22
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Sidiropoulos DN, Ho WJ, Jaffee EM, Kagohara LT, Fertig EJ. Systems immunology spanning tumors, lymph nodes, and periphery. CELL REPORTS METHODS 2023; 3:100670. [PMID: 38086385 PMCID: PMC10753389 DOI: 10.1016/j.crmeth.2023.100670] [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: 05/06/2023] [Revised: 10/20/2023] [Accepted: 11/17/2023] [Indexed: 12/21/2023]
Abstract
The immune system defines a complex network of tissues and cell types that orchestrate responses across the body in a dynamic manner. The local and systemic interactions between immune and cancer cells contribute to disease progression. Lymphocytes are activated in lymph nodes, traffic through the periphery, and impact cancer progression through their interactions with tumor cells. As a result, therapeutic response and resistance are mediated across tissues, and a comprehensive understanding of lymphocyte dynamics requires a systems-level approach. In this review, we highlight experimental and computational methods that can leverage the study of leukocyte trafficking through an immunomics lens and reveal how adaptive immunity shapes cancer.
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Affiliation(s)
- Dimitrios N Sidiropoulos
- Johns Hopkins University School of Medicine, Baltimore, MD, USA; Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Won Jin Ho
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Elizabeth M Jaffee
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Luciane T Kagohara
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA.
| | - Elana J Fertig
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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23
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Huff AL, Longway G, Mitchell JT, Andaloori L, Davis-Marcisak E, Chen F, Lyman MR, Wang R, Mathew J, Barrett B, Rahman S, Leatherman J, Yarchoan M, Azad NS, Yegnasubramanian S, Kagohara LT, Fertig EJ, Jaffee EM, Armstrong TD, Zaidi N. CD4 T cell-activating neoantigens enhance personalized cancer vaccine efficacy. JCI Insight 2023; 8:e174027. [PMID: 38063199 PMCID: PMC10795827 DOI: 10.1172/jci.insight.174027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 10/17/2023] [Indexed: 12/18/2023] Open
Abstract
Personalized cancer vaccines aim to activate and expand cytotoxic antitumor CD8+ T cells to recognize and kill tumor cells. However, the role of CD4+ T cell activation in the clinical benefit of these vaccines is not well defined. We previously established a personalized neoantigen vaccine (PancVAX) for the pancreatic cancer cell line Panc02, which activates tumor-specific CD8+ T cells but required combinatorial checkpoint modulators to achieve therapeutic efficacy. To determine the effects of neoantigen-specific CD4+ T cell activation, we generated a vaccine (PancVAX2) targeting both major histocompatibility complex class I- (MHCI-) and MHCII-specific neoantigens. Tumor-bearing mice vaccinated with PancVAX2 had significantly improved control of tumor growth and long-term survival benefit without concurrent administration of checkpoint inhibitors. PancVAX2 significantly enhanced priming and recruitment of neoantigen-specific CD8+ T cells into the tumor with lower PD-1 expression after reactivation compared with the CD8+ vaccine alone. Vaccine-induced neoantigen-specific Th1 CD4+ T cells in the tumor were associated with decreased Tregs. Consistent with this, PancVAX2 was associated with more proimmune myeloid-derived suppressor cells and M1-like macrophages in the tumor, demonstrating a less immunosuppressive tumor microenvironment. This study demonstrates the biological importance of prioritizing and including CD4+ T cell-specific neoantigens for personalized cancer vaccine modalities.
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Affiliation(s)
- Amanda L. Huff
- Johns Hopkins Convergence Institute and
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Gabriella Longway
- Johns Hopkins Convergence Institute and
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jacob T. Mitchell
- Johns Hopkins Convergence Institute and
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Human Genetics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Lalitya Andaloori
- Johns Hopkins Convergence Institute and
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Emily Davis-Marcisak
- Johns Hopkins Convergence Institute and
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Human Genetics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Fangluo Chen
- Johns Hopkins Convergence Institute and
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Melissa R. Lyman
- Johns Hopkins Convergence Institute and
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Rulin Wang
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jocelyn Mathew
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Benjamin Barrett
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Sabahat Rahman
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - James Leatherman
- Johns Hopkins Convergence Institute and
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Mark Yarchoan
- Johns Hopkins Convergence Institute and
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Nilofer S. Azad
- Johns Hopkins Convergence Institute and
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Srinivasan Yegnasubramanian
- Johns Hopkins Convergence Institute and
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
- inHealth Precision Medicine Program
| | - Luciane T. Kagohara
- Johns Hopkins Convergence Institute and
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Human Genetics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Applied Mathematics and Statistics, and
| | - Elana J. Fertig
- Johns Hopkins Convergence Institute and
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Applied Mathematics and Statistics, and
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Elizabeth M. Jaffee
- Johns Hopkins Convergence Institute and
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Todd D. Armstrong
- Johns Hopkins Convergence Institute and
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Neeha Zaidi
- Johns Hopkins Convergence Institute and
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
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24
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Parizi FM, Marzella DF, Ramakrishnan G, ‘t Hoen PAC, Karimi-Jafari MH, Xue LC. PANDORA v2.0: Benchmarking peptide-MHC II models and software improvements. Front Immunol 2023; 14:1285899. [PMID: 38143769 PMCID: PMC10739464 DOI: 10.3389/fimmu.2023.1285899] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 11/17/2023] [Indexed: 12/26/2023] Open
Abstract
T-cell specificity to differentiate between self and non-self relies on T-cell receptor (TCR) recognition of peptides presented by the Major Histocompatibility Complex (MHC). Investigations into the three-dimensional (3D) structures of peptide:MHC (pMHC) complexes have provided valuable insights of MHC functions. Given the limited availability of experimental pMHC structures and considerable diversity of peptides and MHC alleles, it calls for the development of efficient and reliable computational approaches for modeling pMHC structures. Here we present an update of PANDORA and the systematic evaluation of its performance in modelling 3D structures of pMHC class II complexes (pMHC-II), which play a key role in the cancer immune response. PANDORA is a modelling software that can build low-energy models in a few minutes by restraining peptide residues inside the MHC-II binding groove. We benchmarked PANDORA on 136 experimentally determined pMHC-II structures covering 44 unique αβ chain pairs. Our pipeline achieves a median backbone Ligand-Root Mean Squared Deviation (L-RMSD) of 0.42 Å on the binding core and 0.88 Å on the whole peptide for the benchmark dataset. We incorporated software improvements to make PANDORA a pan-allele framework and improved the user interface and software quality. Its computational efficiency allows enriching the wealth of pMHC binding affinity and mass spectrometry data with 3D models. These models can be used as a starting point for molecular dynamics simulations or structure-boosted deep learning algorithms to identify MHC-binding peptides. PANDORA is available as a Python package through Conda or as a source installation at https://github.com/X-lab-3D/PANDORA.
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Affiliation(s)
- Farzaneh M. Parizi
- Medical BioSciences Department, Radboud University Medical Center, Nijmegen, Netherlands
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Dario F. Marzella
- Medical BioSciences Department, Radboud University Medical Center, Nijmegen, Netherlands
| | - Gayatri Ramakrishnan
- Medical BioSciences Department, Radboud University Medical Center, Nijmegen, Netherlands
| | - Peter A. C. ‘t Hoen
- Medical BioSciences Department, Radboud University Medical Center, Nijmegen, Netherlands
| | | | - Li C. Xue
- Medical BioSciences Department, Radboud University Medical Center, Nijmegen, Netherlands
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25
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Nilsson JB, Kaabinejadian S, Yari H, Kester MG, van Balen P, Hildebrand WH, Nielsen M. Accurate prediction of HLA class II antigen presentation across all loci using tailored data acquisition and refined machine learning. SCIENCE ADVANCES 2023; 9:eadj6367. [PMID: 38000035 PMCID: PMC10672173 DOI: 10.1126/sciadv.adj6367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/25/2023] [Indexed: 11/26/2023]
Abstract
Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules is crucial for rational development of immunotherapies and vaccines targeting CD4+ T cell activation. So far, most prediction methods for HLA class II antigen presentation have focused on HLA-DR because of limited availability of immunopeptidomics data for HLA-DQ and HLA-DP while not taking into account alternative peptide binding modes. We present an update to the NetMHCIIpan prediction method, which closes the performance gap between all three HLA class II loci. We accomplish this by first integrating large immunopeptidomics datasets describing the HLA class II specificity space across all loci using a refined machine learning framework that accommodates inverted peptide binders. Next, we apply targeted immunopeptidomics assays to generate data that covers additional HLA-DP specificities. The final method, NetMHCIIpan-4.3, achieves high accuracy and molecular coverage across all HLA class II allotypes.
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Affiliation(s)
- Jonas B. Nilsson
- Department of Health Technology, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Saghar Kaabinejadian
- Pure MHC LLC, Oklahoma City, OK, USA
- Department of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Hooman Yari
- Department of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Michel G. D. Kester
- Department of Hematology, Leiden University Medical Center, Leiden, Netherlands
| | - Peter van Balen
- Department of Hematology, Leiden University Medical Center, Leiden, Netherlands
| | - William H. Hildebrand
- Department of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, DK-2800 Lyngby, Denmark
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26
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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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27
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Federico L, Malone B, Tennøe S, Chaban V, Osen JR, Gainullin M, Smorodina E, Kared H, Akbar R, Greiff V, Stratford R, Clancy T, Munthe LA. Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence. Front Immunol 2023; 14:1265044. [PMID: 38045681 PMCID: PMC10691274 DOI: 10.3389/fimmu.2023.1265044] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 11/01/2023] [Indexed: 12/05/2023] Open
Abstract
During the COVID-19 pandemic we utilized an AI-driven T cell epitope prediction tool, the NEC Immune Profiler (NIP) to scrutinize and predict regions of T cell immunogenicity (hotspots) from the entire SARS-CoV-2 viral proteome. These immunogenic regions offer potential for the development of universally protective T cell vaccine candidates. Here, we validated and characterized T cell responses to a set of minimal epitopes from these AI-identified universal hotspots. Utilizing a flow cytometry-based T cell activation-induced marker (AIM) assay, we identified 59 validated screening hits, of which 56% (33 peptides) have not been previously reported. Notably, we found that most of these novel epitopes were derived from the non-spike regions of SARS-CoV-2 (Orf1ab, Orf3a, and E). In addition, ex vivo stimulation with NIP-predicted peptides from the spike protein elicited CD8+ T cell response in PBMC isolated from most vaccinated donors. Our data confirm the predictive accuracy of AI platforms modelling bona fide immunogenicity and provide a novel framework for the evaluation of vaccine-induced T cell responses.
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Affiliation(s)
- Lorenzo Federico
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | | | - Viktoriia Chaban
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Julie Røkke Osen
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Murat Gainullin
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Eva Smorodina
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Oslo University Hospital, Oslo, Norway
| | - Hassen Kared
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Rahmad Akbar
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Oslo University Hospital, Oslo, Norway
| | - Victor Greiff
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Oslo University Hospital, Oslo, Norway
| | | | | | - Ludvig Andre Munthe
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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28
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Meng W, Schreiber RD, Lichti CF. Recent advances in immunopeptidomic-based tumor neoantigen discovery. Adv Immunol 2023; 160:1-36. [PMID: 38042584 DOI: 10.1016/bs.ai.2023.10.001] [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] [Indexed: 12/04/2023]
Abstract
The role of aberrantly expressed proteins in tumors in driving immune-mediated control of cancer has been well documented for more than five decades. Today, we know that both aberrantly expressed normal proteins as well as mutant proteins (neoantigens) can function as tumor antigens in both humans and mice. Next-generation sequencing (NGS) and high-resolution mass spectrometry (MS) technologies have made significant advances since the early 2010s, enabling detection of rare but clinically relevant neoantigens recognized by T cells. MS profiling of tumor-specific immunopeptidomes remains the most direct method to identify mutant peptides bound to cellular MHC. However, the need for use of large numbers of cells or significant amounts of tumor tissue to achieve neoantigen detection has historically limited the application of MS. Newer, more sensitive MS technologies have recently demonstrated the capacities to detect neoantigens from fewer cells. Here, we highlight recent advancements in immunopeptidomics-based characterization of tumor-specific neoantigens. Various tumor antigen categories and neoantigen identification approaches are also discussed. Furthermore, we summarize recent reports that achieved successful tumor neoantigen detection by MS using a variety of starting materials, MS acquisition modes, and novel ion mobility devices.
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Affiliation(s)
- Wei Meng
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, United States; The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, Saint Louis, MO, United States
| | - Robert D Schreiber
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, United States; The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, Saint Louis, MO, United States.
| | - Cheryl F Lichti
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, United States; The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, Saint Louis, MO, United States.
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29
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Capponi S, Daniels KG. Harnessing the power of artificial intelligence to advance cell therapy. Immunol Rev 2023; 320:147-165. [PMID: 37415280 DOI: 10.1111/imr.13236] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 06/17/2023] [Indexed: 07/08/2023]
Abstract
Cell therapies are powerful technologies in which human cells are reprogrammed for therapeutic applications such as killing cancer cells or replacing defective cells. The technologies underlying cell therapies are increasing in effectiveness and complexity, making rational engineering of cell therapies more difficult. Creating the next generation of cell therapies will require improved experimental approaches and predictive models. Artificial intelligence (AI) and machine learning (ML) methods have revolutionized several fields in biology including genome annotation, protein structure prediction, and enzyme design. In this review, we discuss the potential of combining experimental library screens and AI to build predictive models for the development of modular cell therapy technologies. Advances in DNA synthesis and high-throughput screening techniques enable the construction and screening of libraries of modular cell therapy constructs. AI and ML models trained on this screening data can accelerate the development of cell therapies by generating predictive models, design rules, and improved designs.
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Affiliation(s)
- Sara Capponi
- Department of Functional Genomics and Cellular Engineering, IBM Almaden Research Center, San Jose, California, USA
- Center for Cellular Construction, San Francisco, California, USA
| | - Kyle G Daniels
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
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30
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Hartman K, Steiner G, Siegel M, Looney CM, Hickling TP, Bray-French K, Springer S, Marban-Doran C, Ducret A. Expanding the MAPPs Assay to Accommodate MHC-II Pan Receptors for Improved Predictability of Potential T Cell Epitopes. BIOLOGY 2023; 12:1265. [PMID: 37759665 PMCID: PMC10525474 DOI: 10.3390/biology12091265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 09/08/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023]
Abstract
A critical step in the immunogenicity cascade is attributed to human leukocyte antigen (HLA) II presentation triggering T cell immune responses. The liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based major histocompatibility complex (MHC) II-associated peptide proteomics (MAPPs) assay is implemented during preclinical risk assessments to identify biotherapeutic-derived T cell epitopes. Although studies indicate that HLA-DP and HLA-DQ alleles are linked to immunogenicity, most MAPPs studies are restricted to using HLA-DR as the dominant HLA II genotype due to the lack of well-characterized immunoprecipitating antibodies. Here, we address this issue by testing various commercially available clones of MHC-II pan (CR3/43, WR18, and Tü39), HLA-DP (B7/21), and HLA-DQ (SPV-L3 and 1a3) antibodies in the MAPPs assay, and characterizing identified peptides according to binding specificity. Our results reveal that HLA II receptor-precipitating reagents with similar reported specificities differ based on clonality and that MHC-II pan antibodies do not entirely exhibit pan-specific tendencies. Since no individual antibody clone is able to recover the complete HLA II peptide repertoire, we recommend a mixed strategy of clones L243, WR18, and SPV-L3 in a single immunoprecipitation step for more robust compound-specific peptide detection. Ultimately, our optimized MAPPs strategy improves the predictability and additional identification of T cell epitopes in immunogenicity risk assessments.
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Affiliation(s)
- Katharina Hartman
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Guido Steiner
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Michel Siegel
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Cary M. Looney
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Timothy P. Hickling
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Katharine Bray-French
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Sebastian Springer
- School of Science, Department of Biochemistry and Cell Biology, Constructor University, Campus Ring 1, 28759 Bremen, Germany
| | - Céline Marban-Doran
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Axel Ducret
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
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Wallace RP, Refvik KC, Antane JT, Brünggel K, Tremain AC, Raczy MR, Alpar AT, Nguyen M, Solanki A, Slezak AJ, Watkins EA, Lauterbach AL, Cao S, Wilson DS, Hubbell JA. Synthetically mannosylated antigens induce antigen-specific humoral tolerance and reduce anti-drug antibody responses to immunogenic biologics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.07.534593. [PMID: 37066302 PMCID: PMC10104138 DOI: 10.1101/2023.04.07.534593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Immunogenic biologics trigger an anti-drug antibody (ADA) response in patients, which reduces efficacy and increases adverse reactions. Our laboratory has previously shown that targeting protein antigen to the liver microenvironment can reduce antigen-specific T cell responses; herein, we present a strategy to increase delivery of otherwise immunogenic biologics to the liver via conjugation to a synthetic mannose polymer (p(Man)). This delivery leads to reduced antigen-specific T follicular helper cell and B cell responses resulting in diminished ADA production, which is maintained throughout subsequent administrations of the native biologic. We found that p(Man)-antigen treatment impairs the ADA response against recombinant uricase, a highly immunogenic biologic, without a dependence on hapten immunodominance or control by Tregs. We identify increased TCR signaling and increased apoptosis and exhaustion in T cells as effects of p(Man)-antigen treatment via transcriptomic analyses. This modular platform may enhance tolerance to biologics, enabling long-term solutions for an ever-increasing healthcare problem.
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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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Sajulga R, Bolon YT, Maiers MJ, Petersdorf EW. Assessment of HLA-DPB1 genetic variation using an HLA-DP tool and its implications in clinical transplantation. Blood Adv 2023; 7:4809-4821. [PMID: 37126658 PMCID: PMC10469530 DOI: 10.1182/bloodadvances.2022009554] [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: 12/15/2022] [Revised: 04/06/2023] [Accepted: 04/23/2023] [Indexed: 05/03/2023] Open
Abstract
HLA-DP is a classic transplantation antigen that mediates alloreactivity through T-cell epitope (TCE) diversity and expression levels. A current challenge is to integrate these functional features into the prospective selection of unrelated donor candidates for transplantation. Genetically, HLA-DPB1 exon 2 defines the permissive and nonpermissive TCE groups, and exons 2 and 3 (in linkage with rs9277534) indicate low- and high-expression allotypes. In this study, we analyzed 356 272 exon 2-exon 3-phased sequences from individuals across 5 self-identified race and ethnicity categories: White, Hispanic, Asian or Pacific Islander, Black or African American, and American Indian or Alaskan Native. This sequence data set revealed the complex relationship between TCE and expression models and the importance of exon 3 sequence data. We also studied archived donor search lists for 2545 patients who underwent transplantation from an HLA-11/12 unrelated donor mismatched for a single HLA-DPB1 allele. Depending on the order in which the TCE and expression criteria were considered, some patients had different TCE- and expression-favorable donors. In addition, this data set revealed that many expression-favorable alternatives existed in the search lists. To improve the selection of candidate donors, we provide, disseminate, and automate our findings through our multifaceted tool called Expression of HLA-DP Assessment Tool, consisting of a public web application, Python package, and analysis pipeline.
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Affiliation(s)
- Ray Sajulga
- Center for International Blood and Marrow Transplant Research, National Marrow Donor Program/Be The Match, Minneapolis, MN
| | - Yung-Tsi Bolon
- Center for International Blood and Marrow Transplant Research, National Marrow Donor Program/Be The Match, Minneapolis, MN
| | - Martin J. Maiers
- Center for International Blood and Marrow Transplant Research, National Marrow Donor Program/Be The Match, Minneapolis, MN
| | - Effie W. Petersdorf
- Division of Clinical Research, Fred Hutchinson Cancer Center, Seattle, WA
- Department of Medicine, University of Washington, Seattle, WA
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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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35
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Prensner JR, Abelin JG, Kok LW, Clauser KR, Mudge JM, Ruiz-Orera J, Bassani-Sternberg M, Moritz RL, Deutsch EW, van Heesch S. What Can Ribo-Seq, Immunopeptidomics, and Proteomics Tell Us About the Noncanonical Proteome? Mol Cell Proteomics 2023; 22:100631. [PMID: 37572790 PMCID: PMC10506109 DOI: 10.1016/j.mcpro.2023.100631] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 07/21/2023] [Accepted: 08/08/2023] [Indexed: 08/14/2023] Open
Abstract
Ribosome profiling (Ribo-Seq) has proven transformative for our understanding of the human genome and proteome by illuminating thousands of noncanonical sites of ribosome translation outside the currently annotated coding sequences (CDSs). A conservative estimate suggests that at least 7000 noncanonical ORFs are translated, which, at first glance, has the potential to expand the number of human protein CDSs by 30%, from ∼19,500 annotated CDSs to over 26,000 annotated CDSs. Yet, additional scrutiny of these ORFs has raised numerous questions about what fraction of them truly produce a protein product and what fraction of those can be understood as proteins according to conventional understanding of the term. Adding further complication is the fact that published estimates of noncanonical ORFs vary widely by around 30-fold, from several thousand to several hundred thousand. The summation of this research has left the genomics and proteomics communities both excited by the prospect of new coding regions in the human genome but searching for guidance on how to proceed. Here, we discuss the current state of noncanonical ORF research, databases, and interpretation, focusing on how to assess whether a given ORF can be said to be "protein coding."
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Affiliation(s)
- John R Prensner
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan Medical School, Ann Arbor, Michigan, USA; Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, Michigan, USA.
| | | | - Leron W Kok
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Karl R Clauser
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Jonathan M Mudge
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
| | - Jorge Ruiz-Orera
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, Agora Center Bugnon 25A, University of Lausanne, Lausanne, Switzerland; Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Agora Cancer Research Centre, Lausanne, Switzerland
| | - Robert L Moritz
- Institute for Systems Biology (ISB), Seattle, Washington, USA
| | - Eric W Deutsch
- Institute for Systems Biology (ISB), Seattle, Washington, USA
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Hartout P, Počuča B, Méndez-García C, Schleberger C. Investigating the human and nonobese diabetic mouse MHC class II immunopeptidome using protein language modeling. Bioinformatics 2023; 39:btad469. [PMID: 37527005 PMCID: PMC10421966 DOI: 10.1093/bioinformatics/btad469] [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: 11/22/2022] [Revised: 06/17/2023] [Accepted: 07/31/2023] [Indexed: 08/03/2023] Open
Abstract
MOTIVATION Identifying peptides associated with the major histocompability complex class II (MHCII) is a central task in the evaluation of the immunoregulatory function of therapeutics and drug prototypes. MHCII-peptide presentation prediction has multiple biopharmaceutical applications, including the safety assessment of biologics and engineered derivatives in silico, or the fast progression of antigen-specific immunomodulatory drug discovery programs in immune disease and cancer. This has resulted in the collection of large-scale datasets on adaptive immune receptor antigenic responses and MHC-associated peptide proteomics. In parallel, recent deep learning algorithmic advances in protein language modeling have shown potential in leveraging large collections of sequence data and improve MHC presentation prediction. RESULTS Here, we train a compact transformer model (AEGIS) on human and mouse MHCII immunopeptidome data, including a preclinical murine model, and evaluate its performance on the peptide presentation prediction task. We show that the transformer performs on par with existing deep learning algorithms and that combining datasets from multiple organisms increases model performance. We trained variants of the model with and without MHCII information. In both alternatives, the inclusion of peptides presented by the I-Ag7 MHC class II molecule expressed by nonobese diabetic mice enabled for the first time the accurate in silico prediction of presented peptides in a preclinical type 1 diabetes model organism, which has promising therapeutic applications. AVAILABILITY AND IMPLEMENTATION The source code is available at https://github.com/Novartis/AEGIS.
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Affiliation(s)
- Philip Hartout
- Discovery Sciences, Novartis Institutes for Biomedical Research, Basel 4056, Switzerland
| | - Bojana Počuča
- NIBR Research Informatics, Novartis Institutes for Biomedical Research, Basel 4056, Switzerland
| | - Celia Méndez-García
- Discovery Sciences, Novartis Institutes for Biomedical Research, Basel 4056, Switzerland
| | - Christian Schleberger
- Discovery Sciences, Novartis Institutes for Biomedical Research, Basel 4056, Switzerland
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Stražar M, Park J, Abelin JG, Taylor HB, Pedersen TK, Plichta DR, Brown EM, Eraslan B, Hung YM, Ortiz K, Clauser KR, Carr SA, Xavier RJ, Graham DB. HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery. Immunity 2023; 56:1681-1698.e13. [PMID: 37301199 PMCID: PMC10519123 DOI: 10.1016/j.immuni.2023.05.009] [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: 08/25/2022] [Revised: 02/08/2023] [Accepted: 05/11/2023] [Indexed: 06/12/2023]
Abstract
CD4+ T cell responses are exquisitely antigen specific and directed toward peptide epitopes displayed by human leukocyte antigen class II (HLA-II) on antigen-presenting cells. Underrepresentation of diverse alleles in ligand databases and an incomplete understanding of factors affecting antigen presentation in vivo have limited progress in defining principles of peptide immunogenicity. Here, we employed monoallelic immunopeptidomics to identify 358,024 HLA-II binders, with a particular focus on HLA-DQ and HLA-DP. We uncovered peptide-binding patterns across a spectrum of binding affinities and enrichment of structural antigen features. These aspects underpinned the development of context-aware predictor of T cell antigens (CAPTAn), a deep learning model that predicts peptide antigens based on their affinity to HLA-II and full sequence of their source proteins. CAPTAn was instrumental in discovering prevalent T cell epitopes from bacteria in the human microbiome and a pan-variant epitope from SARS-CoV-2. Together CAPTAn and associated datasets present a resource for antigen discovery and the unraveling genetic associations of HLA alleles with immunopathologies.
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Affiliation(s)
- Martin Stražar
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jihye Park
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Hannah B Taylor
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Thomas K Pedersen
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Eric M Brown
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Basak Eraslan
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Yuan-Mao Hung
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Kayla Ortiz
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Karl R Clauser
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Steven A Carr
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ramnik J Xavier
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Daniel B Graham
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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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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Bedran G, Polasky DA, Hsiao Y, Yu F, da Veiga Leprevost F, Alfaro JA, Cieslik M, Nesvizhskii AI. Unraveling the glycosylated immunopeptidome with HLA-Glyco. Nat Commun 2023; 14:3461. [PMID: 37308510 PMCID: PMC10258777 DOI: 10.1038/s41467-023-39270-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 06/02/2023] [Indexed: 06/14/2023] Open
Abstract
Recent interest in targeted therapies has been sparked by the study of MHC-associated peptides (MAPs) that undergo post-translational modifications (PTMs), particularly glycosylation. In this study, we introduce a fast computational workflow that merges the MSFragger-Glyco search algorithm with a false discovery rate control for glycopeptide analysis from mass spectrometry-based immunopeptidome data. By analyzing eight large-scale publicly available studies, we find that glycosylated MAPs are predominantly presented by MHC class II. Here, we present HLA-Glyco, a comprehensive resource containing over 3,400 human leukocyte antigen (HLA) class II N-glycopeptides from 1,049 distinct protein glycosylation sites. This resource provides valuable insights, including high levels of truncated glycans, conserved HLA-binding cores, and differences in glycosylation positional specificity between HLA allele groups. We integrate the workflow within the FragPipe computational platform and provide HLA-Glyco as a free web resource. Overall, our work provides a valuable tool and resource to aid the nascent field of glyco-immunopeptidomics.
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Affiliation(s)
- Georges Bedran
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Daniel A Polasky
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Yi Hsiao
- Department of Computational Medicine and Bioinformatics, Ann Arbor, MI, USA
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | | | - Javier A Alfaro
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, Canada
- School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Marcin Cieslik
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, Ann Arbor, MI, USA
- Michigan Center for Translational Pathology, University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
- Department of Computational Medicine and Bioinformatics, Ann Arbor, MI, USA.
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Kreidieh FY, Tawbi HA, Alexaki A, Borghaei H, Kandalaft LE. Novel Immunotherapeutics: Perspectives on Checkpoints, Bispecifics, and Vaccines in Development. Am Soc Clin Oncol Educ Book 2023; 43:e391278. [PMID: 37364224 DOI: 10.1200/edbk_391278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2023]
Abstract
Over the past decade, the advent of molecular techniques and deeper understanding of the tumor microenvironment (TME) have enabled the development of a multitude of immunotherapy targets and approaches. Despite the revolutionary advancement in immunotherapy, treatment resistance remains a challenge leading to decreased response rate in a significant proportion of patients. As such, there has recently been an evolving focus to enhance efficacy, durability, and toxicity profiles of immunotherapy. Although immune checkpoint inhibitors have revolutionized cancer treatment with many already-approved antibodies and several others in the pipeline, bispecific antibodies build on their success in an attempt to deliver an even more potent immune response against tumor cells. On the other hand, vaccines comprise the oldest and most versatile form of immunotherapy. Peptide and nucleic acid vaccines are relatively simple to manufacture compared with oncolytic virus-based vaccines, whereas the dendritic cell vaccines are the most complex, requiring autologous cell culture. Nevertheless, a crucial question in the development of cancer vaccines is the choice of antigen whereby shared and patient-private antigen approaches are currently being pursued. There is hope that cancer vaccines will join the repertoire of successful novel immunotherapeutics in the market. Better insights into the impact of immunotherapy on effector T cells and other immune cell populations in the TME shall be a major priority across the immune-oncology discipline and can help identify predictive biomarkers to evaluate response to treatment and identify patients who would most likely benefit from immunotherapy.
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Affiliation(s)
- Firas Y Kreidieh
- Department of Melanoma Medical Oncology, Division of Cancer Medicine, University of Texas M.D. Anderson Cancer Center, Houston, TX
| | - Hussein A Tawbi
- Department of Melanoma Medical Oncology, Division of Cancer Medicine, University of Texas M.D. Anderson Cancer Center, Houston, TX
| | - Aikaterini Alexaki
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | | | - Lana E Kandalaft
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, and Department of Oncology, University Hospital of Lausanne (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, Switzerland
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41
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Huff AL, Zaidi N. Vaccine boosts T cells that target pancreatic tumours. Nature 2023; 618:37-38. [PMID: 37165219 PMCID: PMC10548444 DOI: 10.1038/d41586-023-01526-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Efforts to tackle pancreatic cancer by harnessing immune cells have had limited success. A clinical trial reports promising results from testing a personalized approach to boosting immune responses to such tumours. See p.144
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42
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Prensner JR, Abelin JG, Kok LW, Clauser KR, Mudge JM, Ruiz-Orera J, Bassani-Sternberg M, Deutsch EW, van Heesch S. What can Ribo-seq and proteomics tell us about the non-canonical proteome? BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.16.541049. [PMID: 37292611 PMCID: PMC10245706 DOI: 10.1101/2023.05.16.541049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Ribosome profiling (Ribo-seq) has proven transformative for our understanding of the human genome and proteome by illuminating thousands of non-canonical sites of ribosome translation outside of the currently annotated coding sequences (CDSs). A conservative estimate suggests that at least 7,000 non-canonical open reading frames (ORFs) are translated, which, at first glance, has the potential to expand the number of human protein-coding sequences by 30%, from ∼19,500 annotated CDSs to over 26,000. Yet, additional scrutiny of these ORFs has raised numerous questions about what fraction of them truly produce a protein product and what fraction of those can be understood as proteins according to conventional understanding of the term. Adding further complication is the fact that published estimates of non-canonical ORFs vary widely by around 30-fold, from several thousand to several hundred thousand. The summation of this research has left the genomics and proteomics communities both excited by the prospect of new coding regions in the human genome, but searching for guidance on how to proceed. Here, we discuss the current state of non-canonical ORF research, databases, and interpretation, focusing on how to assess whether a given ORF can be said to be "protein-coding". In brief The human genome encodes thousands of non-canonical open reading frames (ORFs) in addition to protein-coding genes. As a nascent field, many questions remain regarding non-canonical ORFs. How many exist? Do they encode proteins? What level of evidence is needed for their verification? Central to these debates has been the advent of ribosome profiling (Ribo-seq) as a method to discern genome-wide ribosome occupancy, and immunopeptidomics as a method to detect peptides that are processed and presented by MHC molecules and not observed in traditional proteomics experiments. This article provides a synthesis of the current state of non-canonical ORF research and proposes standards for their future investigation and reporting. Highlights Combined use of Ribo-seq and proteomics-based methods enables optimal confidence in detecting non-canonical ORFs and their protein products.Ribo-seq can provide more sensitive detection of non-canonical ORFs, but data quality and analytical pipelines will impact results.Non-canonical ORF catalogs are diverse and span both high-stringency and low-stringency ORF nominations.A framework for standardized non-canonical ORF evidence will advance the research field.
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Affiliation(s)
- John R. Prensner
- Department of Pediatrics, Division of Pediatric Hematology/Oncology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | | | - Leron W. Kok
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS, Utrecht, the Netherlands
| | - Karl R. Clauser
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Jonathan M. Mudge
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Jorge Ruiz-Orera
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
| | - 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
| | - Eric W. Deutsch
- Institute for Systems Biology (ISB), Seattle, Washington 98109, USA
| | - Sebastiaan van Heesch
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS, Utrecht, the Netherlands
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43
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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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44
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Zhang Z, Wei X. Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy. Semin Cancer Biol 2023; 90:57-72. [PMID: 36796530 DOI: 10.1016/j.semcancer.2023.02.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/12/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023]
Abstract
The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.
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Affiliation(s)
- Zhe Zhang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu 610041, PR China
| | - Xiawei Wei
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China.
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45
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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: 24] [Impact Index Per Article: 24.0] [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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46
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Nilsson JB, Kaabinejadian S, Yari H, Peters B, Barra C, Gragert L, Hildebrand W, Nielsen M. Machine learning reveals limited contribution of trans-only encoded variants to the HLA-DQ immunopeptidome. Commun Biol 2023; 6:442. [PMID: 37085710 PMCID: PMC10121683 DOI: 10.1038/s42003-023-04749-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 03/23/2023] [Indexed: 04/23/2023] Open
Abstract
Human leukocyte antigen (HLA) class II antigen presentation is key for controlling and triggering T cell immune responses. HLA-DQ molecules, which are believed to play a major role in autoimmune diseases, are heterodimers that can be formed as both cis and trans variants depending on whether the α- and β-chains are encoded on the same (cis) or opposite (trans) chromosomes. So far, limited progress has been made for predicting HLA-DQ antigen presentation. In addition, the contribution of trans-only variants (i.e. variants not observed in the population as cis) in shaping the HLA-DQ immunopeptidome remains largely unresolved. Here, we seek to address these issues by integrating state-of-the-art immunoinformatics data mining models with large volumes of high-quality HLA-DQ specific mass spectrometry immunopeptidomics data. The analysis demonstrates highly improved predictive power and molecular coverage for models trained including these novel HLA-DQ data. More importantly, investigating the role of trans-only HLA-DQ variants reveals a limited to no contribution to the overall HLA-DQ immunopeptidome. In conclusion, this study furthers our understanding of HLA-DQ specificities and casts light on the relative role of cis versus trans-only HLA-DQ variants in the HLA class II antigen presentation space. The developed method, NetMHCIIpan-4.2, is available at https://services.healthtech.dtu.dk/services/NetMHCIIpan-4.2 .
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Affiliation(s)
| | - Saghar Kaabinejadian
- Pure MHC, LLC, Oklahoma City, OK, USA
- Department of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Hooman Yari
- Department of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, 92037, California, USA
| | - Carolina Barra
- Department of Health Technology, Technical University of Denmark, DK-2800, Lyngby, Denmark
| | - Loren Gragert
- Department of Pathology and Laboratory Medicine, Tulane University School of Medicine, New Orleans, LA, 70112, USA
| | - William Hildebrand
- Department of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, DK-2800, Lyngby, Denmark.
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47
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Omenn GS, Lane L, Overall CM, Pineau C, Packer NH, Cristea IM, Lindskog C, Weintraub ST, Orchard S, Roehrl MH, Nice E, Liu S, Bandeira N, Chen YJ, Guo T, Aebersold R, Moritz RL, Deutsch EW. The 2022 Report on the Human Proteome from the HUPO Human Proteome Project. J Proteome Res 2023; 22:1024-1042. [PMID: 36318223 PMCID: PMC10081950 DOI: 10.1021/acs.jproteome.2c00498] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".
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Affiliation(s)
- Gilbert S. Omenn
- University of Michigan, Ann Arbor, Michigan 48109, United States
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics and University of Geneva, 1015 Lausanne, Switzerland
| | | | - Charles Pineau
- French Institute of Health and Medical Research, 35042 RENNES Cedex, France
| | - Nicolle H. Packer
- Macquarie University, Sydney, NSW 2109, Australia
- Griffith University’s Institute for Glycomics, Sydney, NSW 2109, Australia
| | | | | | - Susan T. Weintraub
- University of Texas Health Science Center-San Antonio, San Antonio, Texas 78229-3900, United States
| | - Sandra Orchard
- EMBL-EBI, Hinxton, Cambridgeshire, CB10 1SD, United Kingdom
| | - Michael H.A. Roehrl
- Memorial Sloan Kettering Cancer Center, New York, New York, 10065, United States
| | | | - Siqi Liu
- BGI Group, Shenzhen 518083, China
| | - Nuno Bandeira
- University of California, San Diego, La Jolla, California 92093, United States
| | - Yu-Ju Chen
- National Taiwan University, Academia Sinica, Nankang, Taipei 11529, Taiwan
| | - Tiannan Guo
- Westlake University Guomics Laboratory of Big Proteomic Data, Hangzhou 310024, Zhejiang Province, China
| | - Ruedi Aebersold
- Institute of Molecular Systems Biology in ETH Zurich, 8092 Zurich, Switzerland
| | - Robert L. Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Eric W. Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
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48
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Abelin JG, Bergstrom EJ, Rivera KD, Taylor HB, Klaeger S, Xu C, Verzani EK, Jackson White C, Woldemichael HB, Virshup M, Olive ME, Maynard M, Vartany SA, Allen JD, Phulphagar K, Harry Kane M, Rachimi S, Mani DR, Gillette MA, Satpathy S, Clauser KR, Udeshi ND, Carr SA. Workflow enabling deepscale immunopeptidome, proteome, ubiquitylome, phosphoproteome, and acetylome analyses of sample-limited tissues. Nat Commun 2023; 14:1851. [PMID: 37012232 PMCID: PMC10070353 DOI: 10.1038/s41467-023-37547-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 03/20/2023] [Indexed: 04/05/2023] Open
Abstract
Serial multi-omic analysis of proteome, phosphoproteome, and acetylome provides insights into changes in protein expression, cell signaling, cross-talk and epigenetic pathways involved in disease pathology and treatment. However, ubiquitylome and HLA peptidome data collection used to understand protein degradation and antigen presentation have not together been serialized, and instead require separate samples for parallel processing using distinct protocols. Here we present MONTE, a highly sensitive multi-omic native tissue enrichment workflow, that enables serial, deep-scale analysis of HLA-I and HLA-II immunopeptidome, ubiquitylome, proteome, phosphoproteome, and acetylome from the same tissue sample. We demonstrate that the depth of coverage and quantitative precision of each 'ome is not compromised by serialization, and the addition of HLA immunopeptidomics enables the identification of peptides derived from cancer/testis antigens and patient specific neoantigens. We evaluate the technical feasibility of the MONTE workflow using a small cohort of patient lung adenocarcinoma tumors.
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Affiliation(s)
- Jennifer G Abelin
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA.
| | - Erik J Bergstrom
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Keith D Rivera
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Hannah B Taylor
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Susan Klaeger
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Charles Xu
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Eva K Verzani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - C Jackson White
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Hilina B Woldemichael
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Maya Virshup
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Meagan E Olive
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Myranda Maynard
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Stephanie A Vartany
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Joseph D Allen
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Kshiti Phulphagar
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - M Harry Kane
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Suzanna Rachimi
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - D R Mani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Michael A Gillette
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
- Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Shankha Satpathy
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Karl R Clauser
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Namrata D Udeshi
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA.
| | - Steven A Carr
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA.
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49
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Racle J, Guillaume P, Schmidt J, Michaux J, Larabi A, Lau K, Perez MAS, Croce G, Genolet R, Coukos G, Zoete V, Pojer F, Bassani-Sternberg M, Harari A, Gfeller D. Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes. Immunity 2023:S1074-7613(23)00129-2. [PMID: 37023751 DOI: 10.1016/j.immuni.2023.03.009] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 11/09/2022] [Accepted: 03/15/2023] [Indexed: 04/08/2023]
Abstract
CD4+ T cells orchestrate the adaptive immune response against pathogens and cancer by recognizing epitopes presented on class II major histocompatibility complex (MHC-II) molecules. The high polymorphism of MHC-II genes represents an important hurdle toward accurate prediction and identification of CD4+ T cell epitopes. Here we collected and curated a dataset of 627,013 unique MHC-II ligands identified by mass spectrometry. This enabled us to precisely determine the binding motifs of 88 MHC-II alleles across humans, mice, cattle, and chickens. Analysis of these binding specificities combined with X-ray crystallography refined our understanding of the molecular determinants of MHC-II motifs and revealed a widespread reverse-binding mode in HLA-DP ligands. We then developed a machine-learning framework to accurately predict binding specificities and ligands of any MHC-II allele. This tool improves and expands predictions of CD4+ T cell epitopes and enables us to discover viral and bacterial epitopes following the aforementioned reverse-binding mode.
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Affiliation(s)
- Julien Racle
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland; Agora Cancer Research Centre, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
| | - Philippe Guillaume
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland; Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - Julien Schmidt
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland; Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - Justine Michaux
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Agora Cancer Research Centre, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland; Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland; Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Amédé Larabi
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Kelvin Lau
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Marta A S Perez
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Giancarlo Croce
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland; Agora Cancer Research Centre, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Raphaël Genolet
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland; Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - George Coukos
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Agora Cancer Research Centre, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland; Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - Vincent Zoete
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Florence Pojer
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Agora Cancer Research Centre, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland; Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland; Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Alexandre Harari
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Agora Cancer Research Centre, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland; Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland; Agora Cancer Research Centre, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
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50
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Sandmann CL, Schulz JF, Ruiz-Orera J, Kirchner M, Ziehm M, Adami E, Marczenke M, Christ A, Liebe N, Greiner J, Schoenenberger A, Muecke MB, Liang N, Moritz RL, Sun Z, Deutsch EW, Gotthardt M, Mudge JM, Prensner JR, Willnow TE, Mertins P, van Heesch S, Hubner N. Evolutionary origins and interactomes of human, young microproteins and small peptides translated from short open reading frames. Mol Cell 2023; 83:994-1011.e18. [PMID: 36806354 PMCID: PMC10032668 DOI: 10.1016/j.molcel.2023.01.023] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 12/12/2022] [Accepted: 01/25/2023] [Indexed: 02/19/2023]
Abstract
All species continuously evolve short open reading frames (sORFs) that can be templated for protein synthesis and may provide raw materials for evolutionary adaptation. We analyzed the evolutionary origins of 7,264 recently cataloged human sORFs and found that most were evolutionarily young and had emerged de novo. We additionally identified 221 previously missed sORFs potentially translated into peptides of up to 15 amino acids-all of which are smaller than the smallest human microprotein annotated to date. To investigate the bioactivity of sORF-encoded small peptides and young microproteins, we subjected 266 candidates to a mass-spectrometry-based interactome screen with motif resolution. Based on these interactomes and additional cellular assays, we can associate several candidates with mRNA splicing, translational regulation, and endocytosis. Our work provides insights into the evolutionary origins and interaction potential of young and small proteins, thereby helping to elucidate this underexplored territory of the human proteome.
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Affiliation(s)
- Clara-L Sandmann
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany; DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, 13347 Berlin, Germany
| | - Jana F Schulz
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany; DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, 13347 Berlin, Germany
| | - Jorge Ruiz-Orera
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
| | - Marieluise Kirchner
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Core Facility Proteomics, 10117 Berlin, Germany
| | - Matthias Ziehm
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Core Facility Proteomics, 10117 Berlin, Germany
| | - Eleonora Adami
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
| | - Maike Marczenke
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
| | - Annabel Christ
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
| | - Nina Liebe
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
| | - Johannes Greiner
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
| | - Aaron Schoenenberger
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
| | - Michael B Muecke
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany; DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, 13347 Berlin, Germany; Charité-Universitätsmedizin, 10117 Berlin, Germany
| | - Ning Liang
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
| | | | - Zhi Sun
- Institute for Systems Biology, Seattle, WA 98109, USA
| | | | - Michael Gotthardt
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany; DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, 13347 Berlin, Germany; Charité-Universitätsmedizin, 10117 Berlin, Germany
| | - Jonathan M Mudge
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - John R Prensner
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Division of Pediatric Hematology/Oncology, Boston Children's Hospital, Boston, MA 02115, USA
| | - Thomas E Willnow
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany; Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark
| | - Philipp Mertins
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Core Facility Proteomics, 10117 Berlin, Germany
| | | | - Norbert Hubner
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany; DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, 13347 Berlin, Germany; Charité-Universitätsmedizin, 10117 Berlin, Germany.
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