Neoantigen, a peptide fragment formed by genetic mutation, gives immunologist a new target for cancer therapy. Development of biotechnology has opened a new era for discovering high-frequency neoantigens. The aim of our study was to identify breast cancer neoantigens for tumor immunotherapy using an efficient way. Here, we established a computational pipeline to identify neoantigens associated with breast cancer using data from database and evaluated the immunogenicity of neoantigens using the peripheral blood of healthy donators in vitro. We identified 39,401 missense mutation sites from 285,283 single nucleotide variations (SNVs) obtained from database, and confirmed candidate epitopes by analyzing the binding affinity of mutant epitopes and human leukocyte antigen (HLA) using 6 algorithms. Peptide-binding assay was used as a complement for affinity testing. The immunogenicity of candidate peptides with high affinity were assessed through enzyme-linked immunospot (ELISPOT) assay and Cytotoxicity assay. In our study, we identified 10 candidate peptides with high binding affinity of HLA-A*0201 alleles, and seven of ten peptides showed the ability of inducing specific cytotoxic lymphocytes(CTLs) ex vivo, in healthy HLA-A2+ donors. We found that the peptide derived from TWISTNB have the highest immunogenicity and cytotoxicity among those candidate peptides. Furthermore, it can trigger the immune response of specific-CTLs to destroy target cells expressing this neoantigen in vitro, and without cross-reactivity with wild-type peptides. We conclude that the effective pipeline will provide potential possibilities to rapidly identify abundant high-frequency neoantigens and create neoantigen library for immunotherapy of breast cancer and even other tumors.