1
|
Rocha LGDN, Guimarães PAS, Carvalho MGR, Ruiz JC. Tumor Neoepitope-Based Vaccines: A Scoping Review on Current Predictive Computational Strategies. Vaccines (Basel) 2024; 12:836. [PMID: 39203962 PMCID: PMC11360805 DOI: 10.3390/vaccines12080836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 07/09/2024] [Accepted: 07/11/2024] [Indexed: 09/03/2024] Open
Abstract
Therapeutic cancer vaccines have been considered in recent decades as important immunotherapeutic strategies capable of leading to tumor regression. In the development of these vaccines, the identification of neoepitopes plays a critical role, and different computational methods have been proposed and employed to direct and accelerate this process. In this context, this review identified and systematically analyzed the most recent studies published in the literature on the computational prediction of epitopes for the development of therapeutic vaccines, outlining critical steps, along with the associated program's strengths and limitations. A scoping review was conducted following the PRISMA extension (PRISMA-ScR). Searches were performed in databases (Scopus, PubMed, Web of Science, Science Direct) using the keywords: neoepitope, epitope, vaccine, prediction, algorithm, cancer, and tumor. Forty-nine articles published from 2012 to 2024 were synthesized and analyzed. Most of the identified studies focus on the prediction of epitopes with an affinity for MHC I molecules in solid tumors, such as lung carcinoma. Predicting epitopes with class II MHC affinity has been relatively underexplored. Besides neoepitope prediction from high-throughput sequencing data, additional steps were identified, such as the prioritization of neoepitopes and validation. Mutect2 is the most used tool for variant calling, while NetMHCpan is favored for neoepitope prediction. Artificial/convolutional neural networks are the preferred methods for neoepitope prediction. For prioritizing immunogenic epitopes, the random forest algorithm is the most used for classification. The performance values related to the computational models for the prediction and prioritization of neoepitopes are high; however, a large part of the studies still use microbiome databases for training. The in vitro/in vivo validations of the predicted neoepitopes were verified in 55% of the analyzed studies. Clinical trials that led to successful tumor remission were identified, highlighting that this immunotherapeutic approach can benefit these patients. Integrating high-throughput sequencing, sophisticated bioinformatics tools, and rigorous validation methods through in vitro/in vivo assays as well as clinical trials, the tumor neoepitope-based vaccine approach holds promise for developing personalized therapeutic vaccines that target specific tumor cancers.
Collapse
Affiliation(s)
- Luiz Gustavo do Nascimento Rocha
- Biologia Computacional e Sistemas (BCS), Instituto Oswaldo Cruz (IOC), Fundação Oswaldo Cruz, Rio de Janeiro 21040-900, Brazil; (L.G.d.N.R.); (P.A.S.G.)
- Grupo Informática de Biossistemas e Genômica, Instituto René Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-002, Brazil
| | - Paul Anderson Souza Guimarães
- Biologia Computacional e Sistemas (BCS), Instituto Oswaldo Cruz (IOC), Fundação Oswaldo Cruz, Rio de Janeiro 21040-900, Brazil; (L.G.d.N.R.); (P.A.S.G.)
- Grupo Informática de Biossistemas e Genômica, Instituto René Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-002, Brazil
| | - Maria Gabriela Reis Carvalho
- Biologia Computacional e Sistemas (BCS), Instituto Oswaldo Cruz (IOC), Fundação Oswaldo Cruz, Rio de Janeiro 21040-900, Brazil; (L.G.d.N.R.); (P.A.S.G.)
- Grupo Informática de Biossistemas e Genômica, Instituto René Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-002, Brazil
| | - Jeronimo Conceição Ruiz
- Biologia Computacional e Sistemas (BCS), Instituto Oswaldo Cruz (IOC), Fundação Oswaldo Cruz, Rio de Janeiro 21040-900, Brazil; (L.G.d.N.R.); (P.A.S.G.)
- Grupo Informática de Biossistemas e Genômica, Instituto René Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-002, Brazil
| |
Collapse
|
2
|
Liu R, Hu YF, Huang JD, Fan X. A Bayesian approach to estimate MHC-peptide binding threshold. Brief Bioinform 2023; 24:bbad208. [PMID: 37279464 DOI: 10.1093/bib/bbad208] [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: 03/22/2023] [Revised: 05/08/2023] [Accepted: 05/16/2023] [Indexed: 06/08/2023] Open
Abstract
Major histocompatibility complex (MHC)-peptide binding is a critical step in enabling a peptide to serve as an antigen for T-cell recognition. Accurate prediction of this binding can facilitate various applications in immunotherapy. While many existing methods offer good predictive power for the binding affinity of a peptide to a specific MHC, few models attempt to infer the binding threshold that distinguishes binding sequences. These models often rely on experience-based ad hoc criteria, such as 500 or 1000nM. However, different MHCs may have different binding thresholds. As such, there is a need for an automatic, data-driven method to determine an accurate binding threshold. In this study, we proposed a Bayesian model that jointly infers core locations (binding sites), the binding affinity and the binding threshold. Our model provided the posterior distribution of the binding threshold, enabling accurate determination of an appropriate threshold for each MHC. To evaluate the performance of our method under different scenarios, we conducted simulation studies with varying dominant levels of motif distributions and proportions of random sequences. These simulation studies showed desirable estimation accuracy and robustness of our model. Additionally, when applied to real data, our results outperformed commonly used thresholds.
Collapse
Affiliation(s)
- Ran Liu
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Ye-Fan Hu
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 3/F, Laboratory Block, 21 Sassoon Road, Hong Kong SAR, China
- Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 4/F Professional Block, Queen Mary Hospital, 102 Pokfulam Road, Hong Kong SAR, China
- BayVax Biotech Limited, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China
| | - Jian-Dong Huang
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 3/F, Laboratory Block, 21 Sassoon Road, Hong Kong SAR, China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Clinical Oncology Center, Shenzhen Key Laboratory for Cancer Metastasis and Personalized Therapy, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, China
- Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen University, Guangzhou 510120, China
- State Key Laboratory of Cognitive and Brain Research, The University of Hong Kong, Hong Kong SAR, China
| | - Xiaodan Fan
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China
| |
Collapse
|
3
|
Basir HRG, Majzoobi MM, Ebrahimi S, Noroozbeygi M, Hashemi SH, Keramat F, Mamani M, Eini P, Alizadeh S, Solgi G, Di D. Susceptibility and Severity of COVID-19 Are Both Associated With Lower Overall Viral-Peptide Binding Repertoire of HLA Class I Molecules, Especially in Younger People. Front Immunol 2022; 13:891816. [PMID: 35911710 PMCID: PMC9331187 DOI: 10.3389/fimmu.2022.891816] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 06/10/2022] [Indexed: 12/24/2022] Open
Abstract
An important number of studies have been conducted on the potential association between human leukocyte antigen (HLA) genes and COVID-19 susceptibility and severity since the beginning of the pandemic. However, case-control and peptide-binding prediction methods tended to provide inconsistent conclusions on risk and protective HLA alleles, whereas some researchers suggested the importance of considering the overall capacity of an individual's HLA Class I molecules to present SARS-CoV-2-derived peptides. To close the gap between these approaches, we explored the distributions of HLA-A, -B, -C, and -DRB1 1st-field alleles in 142 Iranian patients with COVID-19 and 143 ethnically matched healthy controls, and applied in silico predictions of bound viral peptides for each individual's HLA molecules. Frequency comparison revealed the possible predisposing roles of HLA-A*03, B*35, and DRB1*16 alleles and the protective effect of HLA-A*32, B*58, B*55, and DRB1*14 alleles in the viral infection. None of these results remained significant after multiple testing corrections, except HLA-A*03, and no allele was associated with severity, either. Compared to peptide repertoires of individual HLA molecules that are more likely population-specific, the overall coverage of virus-derived peptides by one's HLA Class I molecules seemed to be a more prominent factor associated with both COVID-19 susceptibility and severity, which was independent of affinity index and threshold chosen, especially for people under 60 years old. Our results highlight the effect of the binding capacity of different HLA Class I molecules as a whole, and the more essential role of HLA-A compared to HLA-B and -C genes in immune responses against SARS-CoV-2 infection.
Collapse
Affiliation(s)
- Hamid Reza Ghasemi Basir
- Department of Pathology, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | | | - Samaneh Ebrahimi
- Department of Immunology, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Mina Noroozbeygi
- Department of Immunology, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Seyed Hamid Hashemi
- Brucellosis Research Centre, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Fariba Keramat
- Brucellosis Research Centre, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Mojgan Mamani
- Brucellosis Research Centre, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Peyman Eini
- Brucellosis Research Centre, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Saeed Alizadeh
- Department of Radiology, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Ghasem Solgi
- Department of Immunology, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Da Di
- Anthropology Unit, Department of Genetics and Evolution, University of Geneva, Geneva, Switzerland
| |
Collapse
|
4
|
Thibault P, Perreault C. Immunopeptidomics: Reading the Immune Signal That Defines Self From Nonself. Mol Cell Proteomics 2022; 21:100234. [PMID: 35567924 PMCID: PMC9252926 DOI: 10.1016/j.mcpro.2022.100234] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Pierre Thibault
- Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, Quebec, Canada; Department of Chemistry, Université de Montréal, Montreal, Quebec, Canada
| | - Claude Perreault
- Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, Quebec, Canada; Department of Medicine, Université de Montréal, Montreal, Quebec, Canada
| |
Collapse
|