Computational prediction of MHC anchor locations guide neoantigen identification and prioritization

Abstract

Neoantigens are novel peptide sequences resulting from somatic mutations in tumors that upon loading onto major histocompatibility complex (MHC) molecules allow recognition by T cells. Accurate neoantigen identification is thus critical for 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. As the majority of somatic mutations are SNVs, changes between wildtype and mutant peptide are subtle and require cautious interpretation. An important yet potentially underappreciated variable in neoantigen-prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient’s specific HLA alleles. While a subset of peptide positions is 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 high probability anchor positions for different peptide lengths for over 300 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 7-41% of neoantigen candidates are potentially misclassified and can be rescued using allele-specific knowledge of anchor positions.

Identification of HLA class I-restricted immunogenic neoantigens in triple negative breast cancer

“…In both groups, we have been able to identify immunogenic peptides with capacity to induce T cells discriminating between MUT and WT peptides, indicating that not only the binding capacity, but also the position of the mutation may help to identify neoAgs. Indeed, it has been recently shown the relevance of the position of the mutated amino acid in neoAg selection ( 36 , 37 ).…”