The application discloses a gp96 tumor neoantigen prediction method and application thereof, and relates to the technical field of tumor neoantigen prediction. The method comprises the following steps: S1, isolation and purification of gp96-tumor polypeptide complexes; S2, dissociation and purification of polypeptides; S3,
mass spectrometric identification and
sequence analysis of
mutant peptides; S4, MHC affinity prediction; and S5,
immunogenicity verification. The application takes gp96 as a natural biological
concentrator, the captured polypeptides include products in a natural
antigen processing path of
tumor cells, and
antigen peptide segments with potential immunological significance are enriched. In combination with MHC affinity prediction and
immunogenicity function
verification, the
true positive rate of neoantigen screening is significantly improved. In a
verification experiment on colon
cancer samples, among 7 candidate neoantigens obtained through prediction, 4 can significantly activate
autologous T cells of a patient to secrete IFN-gamma, and show a high
immunogenicity verification positive rate.