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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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Emilius L, Bremm F, Binder AK, Schaft N, Dörrie J. Tumor Antigens beyond the Human Exome. Int J Mol Sci 2024; 25:4673. [PMID: 38731892 PMCID: PMC11083240 DOI: 10.3390/ijms25094673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 04/18/2024] [Accepted: 04/22/2024] [Indexed: 05/13/2024] Open
Abstract
With the advent of immunotherapeutics, a new era in the combat against cancer has begun. Particularly promising are neo-epitope-targeted therapies as the expression of neo-antigens is tumor-specific. In turn, this allows the selective targeting and killing of cancer cells whilst healthy cells remain largely unaffected. So far, many advances have been made in the development of treatment options which are tailored to the individual neo-epitope repertoire. The next big step is the achievement of efficacious "off-the-shelf" immunotherapies. For this, shared neo-epitopes propose an optimal target. Given the tremendous potential, a thorough understanding of the underlying mechanisms which lead to the formation of neo-antigens is of fundamental importance. Here, we review the various processes which result in the formation of neo-epitopes. Broadly, the origin of neo-epitopes can be categorized into three groups: canonical, noncanonical, and viral neo-epitopes. For the canonical neo-antigens that arise in direct consequence of somatic mutations, we summarize past and recent findings. Beyond that, our main focus is put on the discussion of noncanonical and viral neo-epitopes as we believe that targeting those provides an encouraging perspective to shape the future of cancer immunotherapeutics.
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Affiliation(s)
- Lisabeth Emilius
- Department of Dermatology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; (L.E.); (F.B.); (A.K.B.); (J.D.)
- Comprehensive Cancer Center Erlangen European Metropolitan Area of Nuremberg (CCC ER-EMN), 91054 Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
- Bavarian Cancer Research Center (BZKF), 91054 Erlangen, Germany
| | - Franziska Bremm
- Department of Dermatology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; (L.E.); (F.B.); (A.K.B.); (J.D.)
- Comprehensive Cancer Center Erlangen European Metropolitan Area of Nuremberg (CCC ER-EMN), 91054 Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
- Bavarian Cancer Research Center (BZKF), 91054 Erlangen, Germany
| | - Amanda Katharina Binder
- Department of Dermatology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; (L.E.); (F.B.); (A.K.B.); (J.D.)
- Comprehensive Cancer Center Erlangen European Metropolitan Area of Nuremberg (CCC ER-EMN), 91054 Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
- Bavarian Cancer Research Center (BZKF), 91054 Erlangen, Germany
| | - Niels Schaft
- Department of Dermatology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; (L.E.); (F.B.); (A.K.B.); (J.D.)
- Comprehensive Cancer Center Erlangen European Metropolitan Area of Nuremberg (CCC ER-EMN), 91054 Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
- Bavarian Cancer Research Center (BZKF), 91054 Erlangen, Germany
| | - Jan Dörrie
- Department of Dermatology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; (L.E.); (F.B.); (A.K.B.); (J.D.)
- Comprehensive Cancer Center Erlangen European Metropolitan Area of Nuremberg (CCC ER-EMN), 91054 Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
- Bavarian Cancer Research Center (BZKF), 91054 Erlangen, Germany
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Gao Y, Zhou L, Su Q, Li Q. Identification of Lung Adenocarcinoma Subtypes Based on MHC-II Gene Expression Profile and Immunological Analysis. Int Arch Allergy Immunol 2024:1-16. [PMID: 38636483 DOI: 10.1159/000538056] [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: 12/04/2023] [Accepted: 02/26/2024] [Indexed: 04/20/2024] Open
Abstract
INTRODUCTION Major histocompatibility complex class II molecule (MHC-II) is pivotal in anti-tumor immunity, and targeting MHC-II in tumors may help improve patient survival. But function of MHC-II in the immunotherapy and prognosis of lung adenocarcinoma (LUAD) patients has not been thoroughly studied and reported. METHODS We selected LUAD-related MHC-II genes from public databases based on previous literature reports. We identified different subtypes according to expression differences of these genes in different LUAD samples through cluster analysis. We used R package to conduct a series of analyses on different subtypes, exploring their survival differences, gene expression differences, pathway enrichment differences, and differences in immune characteristics and immune therapy. Finally, we screened potential drugs from the cMAP database. RESULTS We identified two MHC-II-related LUAD subtypes. Our analyses presented that patients with cluster2 subtype showed better prognosis, higher immune scores, higher levels of immune cell infiltration and immune function activation. In addition, patients with this subtype had higher immunophenoscore, lower TIDE scores, and DEPTH scores. We also identified 10 small molecule drugs, such as lenalidomide, VX-745, and tyrphostin-AG-1295. CONCLUSION Overall, MHC-II is not only a potential biomarker for accurately distinguishing LUAD subtypes but also a predictive factor for their survival. Our study offers novel insights into understanding of impact of MHC-II in LUAD and offers a new perspective for improving the accurate classification of LUAD patients and enhancing drug treatment.
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Affiliation(s)
- Yongcai Gao
- Department of Respiratory Medicine, Suizhou Hospital, Hubei University of Medicine, Suizhou, China
| | - Lingli Zhou
- Department of Respiratory Medicine, Suizhou Hospital, Hubei University of Medicine, Suizhou, China
| | - Qiong Su
- Department of Respiratory Medicine, Suizhou Hospital, Hubei University of Medicine, Suizhou, China
| | - Qiang Li
- Department of Neurosurgery Medicine, Suizhou Hospital, Hubei University of Medicine, Suizhou, China
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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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Ghimire P, Kinnersley B, Karami G, Arumugam P, Houlston R, Ashkan K, Modat M, Booth TC. Radiogenomic biomarkers for immunotherapy in glioblastoma: A systematic review of magnetic resonance imaging studies. Neurooncol Adv 2024; 6:vdae055. [PMID: 38680991 PMCID: PMC11046988 DOI: 10.1093/noajnl/vdae055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/01/2024] Open
Abstract
Background Immunotherapy is an effective "precision medicine" treatment for several cancers. Imaging signatures of the underlying genome (radiogenomics) in glioblastoma patients may serve as preoperative biomarkers of the tumor-host immune apparatus. Validated biomarkers would have the potential to stratify patients during immunotherapy clinical trials, and if trials are beneficial, facilitate personalized neo-adjuvant treatment. The increased use of whole genome sequencing data, and the advances in bioinformatics and machine learning make such developments plausible. We performed a systematic review to determine the extent of development and validation of immune-related radiogenomic biomarkers for glioblastoma. Methods A systematic review was performed following PRISMA guidelines using the PubMed, Medline, and Embase databases. Qualitative analysis was performed by incorporating the QUADAS 2 tool and CLAIM checklist. PROSPERO registered: CRD42022340968. Extracted data were insufficiently homogenous to perform a meta-analysis. Results Nine studies, all retrospective, were included. Biomarkers extracted from magnetic resonance imaging volumes of interest included apparent diffusion coefficient values, relative cerebral blood volume values, and image-derived features. These biomarkers correlated with genomic markers from tumor cells or immune cells or with patient survival. The majority of studies had a high risk of bias and applicability concerns regarding the index test performed. Conclusions Radiogenomic immune biomarkers have the potential to provide early treatment options to patients with glioblastoma. Targeted immunotherapy, stratified by these biomarkers, has the potential to allow individualized neo-adjuvant precision treatment options in clinical trials. However, there are no prospective studies validating these biomarkers, and interpretation is limited due to study bias with little evidence of generalizability.
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Affiliation(s)
- Prajwal Ghimire
- Department of Neurosurgery, Kings College Hospital NHS Foundation Trust, London, UK
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Ben Kinnersley
- Department of Oncology, University College London, London, UK
| | | | | | - Richard Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, UK
| | - Keyoumars Ashkan
- Department of Neurosurgery, Kings College Hospital NHS Foundation Trust, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
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Kim K, Kim H, Shin I, Noh SJ, Kim JY, Suh KJ, Kim YN, Lee JY, Cho DY, Kim SH, Kim JH, Lee SH, Choi JK. Genomic hypomethylation in cell-free DNA predicts responses to checkpoint blockade in lung and breast cancer. Sci Rep 2023; 13:22482. [PMID: 38110532 PMCID: PMC10728099 DOI: 10.1038/s41598-023-49639-4] [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: 08/08/2023] [Accepted: 12/10/2023] [Indexed: 12/20/2023] Open
Abstract
Genomic hypomethylation has recently been identified as a determinant of therapeutic responses to immune checkpoint blockade (ICB). However, it remains unclear whether this approach can be applied to cell-free DNA (cfDNA) and whether it can address the issue of low tumor purity encountered in tissue-based methylation profiling. In this study, we developed an assay named iMethyl, designed to estimate the genomic hypomethylation status from cfDNA. This was achieved through deep targeted sequencing of young LINE-1 elements with > 400,000 reads per sample. iMethyl was applied to a total of 653 ICB samples encompassing lung cancer (cfDNA n = 167; tissue n = 137; cfDNA early during treatment n = 40), breast cancer (cfDNA n = 91; tissue n = 50; PBMC n = 50; cfDNA at progression n = 44), and ovarian cancer (tissue n = 74). iMethyl-liquid predicted ICB responses accurately regardless of the tumor purity of tissue samples. iMethyl-liquid was also able to monitor therapeutic responses early during treatment (3 or 6 weeks after initiation of ICB) and detect progressive hypomethylation accompanying tumor progression. iMethyl-tissue had better predictive power than tumor mutation burden and PD-L1 expression. In conclusion, our iMethyl-liquid method allows for reliable noninvasive prediction, early evaluation, and monitoring of clinical responses to ICB therapy.
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Affiliation(s)
- Kyeonghui Kim
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Hyemin Kim
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Inkyung Shin
- Penta Medix Co., Ltd, Seongnam-si, Gyeongi-do, Republic of Korea
| | - Seung-Jae Noh
- Penta Medix Co., Ltd, Seongnam-si, Gyeongi-do, Republic of Korea
| | - Jeong Yeon Kim
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Koung Jin Suh
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeongi-do, Republic of Korea
| | - Yoo-Na Kim
- Department of Obstetrics and Gynecology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jung-Yun Lee
- Department of Obstetrics and Gynecology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dae-Yeon Cho
- Penta Medix Co., Ltd, Seongnam-si, Gyeongi-do, Republic of Korea
| | - Se Hyun Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeongi-do, Republic of Korea.
| | - Jee Hyun Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeongi-do, Republic of Korea.
| | - Se-Hoon Lee
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Seoul, Republic of Korea.
- Department of Health Sciences and Technology, Samsung Advanced Institute of Health Science and Technology, Sungkyunkwan University, Seoul, Republic of Korea.
| | - Jung Kyoon Choi
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.
- Penta Medix Co., Ltd, Seongnam-si, Gyeongi-do, Republic of Korea.
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