Computational prediction of MHC anchor locations guides neoantigen identification and prioritization.
Sci Immunol
; 8(82): eabg2200, 2023 04 14.
Article
em En
| MEDLINE
| ID: mdl-37027480
ABSTRACT
Neoantigens are tumor-specific peptide sequences resulting from sources such as somatic DNA mutations. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Accurate neoantigen identification is thus critical for both designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly predicting whether the presenting peptide sequence can successfully induce an immune response. Because most somatic mutations are single-nucleotide variants, changes between wild-type and mutated peptides are typically subtle and require cautious interpretation. A potentially underappreciated variable in neoantigen prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient's specific MHC molecules. Whereas a subset of peptide positions are presented to the T cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T cell responses. We computationally predicted anchor positions for different peptide lengths for 328 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 6 to 38% of neoantigen candidates are potentially misclassified and can be rescued using allele-specific knowledge of anchor positions. A subset of anchor results were orthogonally validated using protein crystallography structures. Representative anchor trends were experimentally validated using peptide-MHC stability assays and competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, we hope to formalize, streamline, and improve the identification process for relevant clinical studies.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Antígenos de Neoplasias
/
Neoplasias
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Sci Immunol
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Estados Unidos