| Literature DB >> 31443694 |
Miao Peng1,2,3, Yongzhen Mo2, Yian Wang2, Pan Wu2, Yijie Zhang2, Fang Xiong2, Can Guo2, Xu Wu1,2, Yong Li4, Xiaoling Li2, Guiyuan Li1,2,3, Wei Xiong1,2,3, Zhaoyang Zeng5,6,7.
Abstract
Genetic instability of tumor cells often leads to the occurrence of a large number of mutations, and expression of non-synonymous mutations can produce tumor-specific antigens called neoantigens. Neoantigens are highly immunogenic as they are not expressed in normal tissues. They can activate CD4+ and CD8+ T cells to generate immune response and have the potential to become new targets of tumor immunotherapy. The development of bioinformatics technology has accelerated the identification of neoantigens. The combination of different algorithms to identify and predict the affinity of neoantigens to major histocompatibility complexes (MHCs) or the immunogenicity of neoantigens is mainly based on the whole-exome sequencing technology. Tumor vaccines targeting neoantigens mainly include nucleic acid, dendritic cell (DC)-based, tumor cell, and synthetic long peptide (SLP) vaccines. The combination with immune checkpoint inhibition therapy or radiotherapy and chemotherapy might achieve better therapeutic effects. Currently, several clinical trials have demonstrated the safety and efficacy of these vaccines. Further development of sequencing technologies and bioinformatics algorithms, as well as an improvement in our understanding of the mechanisms underlying tumor development, will expand the application of neoantigen vaccines in the future.Entities:
Keywords: Immunotherapy; Malignancy; Neoantigen; Tumor; Vaccine
Mesh:
Substances:
Year: 2019 PMID: 31443694 PMCID: PMC6708248 DOI: 10.1186/s12943-019-1055-6
Source DB: PubMed Journal: Mol Cancer ISSN: 1476-4598 Impact factor: 27.401
The summary of neoantigen prediction software
| Software | Principle | Year |
|---|---|---|
| HLAminer [ | Based on the shotgun sequencing database from Illumina platform, the HLA type was predicted by orienting the assembly of shotgun sequence data and comparing it with the reference allele sequence database | 2012 |
| VariantEffect Predictor Tool [ | Automate annotations in a standard way to reduce manual review time, annotate and prioritize variants | 2016 |
| NetMHCpan [ | Sequence comparison method based on artificial neural network, and predict the affinity of peptide-MHC-I type molecular | 2016 |
| UCSC browser [ | Based on sequence search, the fusion of multiple databases can provide fast and accurate access to any gene segment | 2002 |
| CloudNeo pipeline [ | Docker container was used to complete the tasks in the workflow. After the mutant VCF file and bam file representing HLA typing were input respectively, the HLA affinity prediction of all mutant peptides was obtained | 2017 |
| OptiType [ | The HLA typing algorithm based on integer linear programming provides sequencing databases including RNA, exome and whole genome | 2014 |
| ATHLATES [ | Assembly, allele recognition and allele pair inference were applied to short sequences, and the HLA genotyping at allele level was achieved by exon sequencing | 2013 |
| pVAC-Seq [ | To integrate tumor mutation and expression data and identify personalized mutagens by tumor sequencing | 2016 |
| MuPeXI [ | The extraction and induction of mutant peptides can roughly identify tumor-specific peptides, predict their immunogenicity, and evaluate their potential for new epitopes | 2017 |
| Strelka [ | Based on a new Bayesian model, the matching tumor-normal sample sequencing data was used to analyze and predict somatic cell variation, with high accuracy and sensitivity | 2012 |
| Strelka2 [ | Based on the mixed model, the error parameters of each sample insertion or deletion were estimated, and the liquid tumor analysis was improved | 2018 |
| VarScan2 [ | Somatic and copy number mutations in tumor-normal exome data were detected by heuristic statistical algorithm | 2012 |
| Somaticseq [ | Based on a randomized enhancement algorithm, more than 70 individual genome and sequencing features were extracted for each candidate site to accurately detect somatic mutations | 2015 |
| SMMPMBEC [ | Using matrix as a Bayesian prior, based on the optimal neural network predicting peptide with MHC-I type molecules | 2009 |
| NeoPredPipe [ | Based on a pipeline connecting commonly used bioinformatic software via custom python scripts to provide neoantigen burden, tumor heterogeneity, immune stimulation potential and HLA haplotype of patients | 2019 |
Fig. 1Mutations in tumor tissue produce neoantigens. Clonal neoantigens can be expressed by a large number of proliferating tumor cells. Various software packages were used to compare the sequence differences between tumor cells and normal cells, and to predict and prioritize the immunogenicity of antigens for screening the optimal tumor neoantigens
Fig. 2Major types of neoantigen vaccine. In vivo, neoantigens are eventually presented to CD4+ T cells and CD8+ T cells to induce specific immune responses and achieve anti-tumor effects
Fig. 3Combination of neoantigen vaccines with other therapies. Combination of neoantigen vaccines with the checkpoint inhibition therapy can relieve the tumor cell-mediated inhibition of effector T cells. Radiotherapy and chemotherapy can assist vaccines play a better effect. Drugs targeting the immunosuppressive factors in the tumor microenvironment were administered to circumvent the inactivation of T cells by various molecules and cells in the tumor microenvironment. In combination with CAR-T therapy, T cells specifically targeting neoantigens were cultured in vitro and then injected into the body to generate effector T cells and memory T cells, thereby enhancing the anti-tumor effect