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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 prioritization relies on correctly inferring whether the presenting peptide sequence can successfully induce an immune response. An important, yet 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 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. However, a systematic method for determining anchor locations and application of these to evaluate neoantigens has not been reported. Here, we calculated high probability anchor positions for 328 HLA alleles using a reference dataset generated from clinical and TCGA patient samples. Results showed clusters of different anchor trends among the HLA alleles analyzed. A subset of anchor results were orthogonally validated using protein crystallography structures. Analysis of 923 tumor samples showed that 7-41% of neoantigen candidates were potentially misclassified in the neoantigen selection process and can be rescued using allele-specific knowledge of anchor positions. Representative anchor trends have been 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 and streamline the identification process for relevant clinical studies. [ABSTRACT FROM AUTHOR]