Designing of neoepitopes based vaccine against breast cancer using integrated immuno and bioinformatics approach.
PMID 37584493 | DOI 10.1080/07391102.2023.2247081 · Journal of biomolecular structure & dynamics · 2024 · View on PubMed ↗
Cancer is characterized by genetic instability due to accumulation of somatic mutations in the genes which generate neoepitopes (mutated epitopes) for targeting by Cytotoxic T lymphocytes (CTL). Breast cancer has a high transformation rate with unique composition of mutational burden and neoepitopes load that open a platform to designing a neoepitopes-based vaccine. Neoepitopes-based therapeutic cancer vaccines designed by neoantigens have shown to be feasible, nontoxic, and immunogenic in cancer patients. Stimulation of CTL by neoepitope-based vaccine of self-antigenic proteins plays a key role in distinguishing cancer cells from normal cells and selectively targets only malignant cells. A neoepitopes-based vaccine to combat breast cancer was designed by combining immunology and bioinformatics approaches. The vaccine construct was assembled by the fusion of CTL neoepitopes, helper sequences (used for better separation of the epitopes), and adjuvant together with linkers. The neoepitopes were identified from somatic mutations in the MUC16, TP53, RYR2, F5, DNAH17, ASPM, and ABCA13 self-antigenic proteins. The vaccine construct was undertaken to study the immune simulations (IS), physiochemical characteristics (PP), molecular docking (MD) and simulations, and cloning in appropriate vector. Together, these parameters establish safety, stability, and a strong binding affinity against class I MHC molecules capable of inducing a complete immune response against breast cancer cells.Communicated by Ramaswamy H. Sarma.
Validated evidence
| Type | Entity | Source evidence | Confidence | Extractor |
|---|---|---|---|---|
| gene | MUC16 | “The neoepitopes were identified from somatic mutations in the MUC16, TP53, RYR2, F5, DNAH17, ASPM, and ABCA13 self-antigenic proteins.” | 0.98 | hgnc_dict_v1 |
| gene | TP53 | “The neoepitopes were identified from somatic mutations in the MUC16, TP53, RYR2, F5, DNAH17, ASPM, and ABCA13 self-antigenic proteins.” | 0.98 | hgnc_dict_v1 |
| gene | RYR2 | “The neoepitopes were identified from somatic mutations in the MUC16, TP53, RYR2, F5, DNAH17, ASPM, and ABCA13 self-antigenic proteins.” | 0.98 | hgnc_dict_v1 |
| gene | F5 | “The neoepitopes were identified from somatic mutations in the MUC16, TP53, RYR2, F5, DNAH17, ASPM, and ABCA13 self-antigenic proteins.” | 0.98 | hgnc_dict_v1 |
| gene | DNAH17 | “The neoepitopes were identified from somatic mutations in the MUC16, TP53, RYR2, F5, DNAH17, ASPM, and ABCA13 self-antigenic proteins.” | 0.98 | hgnc_dict_v1 |
| gene | ASPM | “The neoepitopes were identified from somatic mutations in the MUC16, TP53, RYR2, F5, DNAH17, ASPM, and ABCA13 self-antigenic proteins.” | 0.98 | hgnc_dict_v1 |
| gene | ABCA13 | “The neoepitopes were identified from somatic mutations in the MUC16, TP53, RYR2, F5, DNAH17, ASPM, and ABCA13 self-antigenic proteins.” | 0.98 | hgnc_dict_v1 |
| phenotype | breast cancer | “Designing of neoepitopes based vaccine against breast cancer using integrated immuno and bioinformatics approach.” | 0.98 | phenotype_alias_lexicon_v2 |