Tumour-specific neoantigens play a major role for developing personal vaccines in cancer immunotherapy. We propose a personalized de novo peptide sequencing workflow to identify HLA-I and HLA-II neoantigens directly and solely from mass spectrometry data. Our workflow trains a personal deep learning model on the immunopeptidome of an individual patient and then uses it to predict mutated neoantigens of that patient. This personalized learning and mass spectrometry-based approach enables comprehensive and accurate identification of neoantigens. We applied the workflow to datasets of five patients with melanoma and expanded their predicted immunopeptidomes by 5–15%. Subsequently, we discovered neoantigens of both HLA-I and HLA-II, including those with validated T-cell responses and those that had not been reported in previous studies.

肿瘤特异性新抗原在癌症免疫疗法中开发个人疫苗方面起着重要作用。我们提出了个性化的从头肽测序工作流程，以直接且仅从质谱数据中直接鉴定HLA-I和HLA-II新抗原。我们的工作流程在单个患者的免疫肽组上训练了个人深度学习模型，然后将其用于预测该患者的突变新抗原。这种基于个性化学习和质谱的方法可以全面，准确地鉴定新抗原。我们将工作流程应用于五名黑色素瘤患者的数据集，并将他们的预测免疫肽组扩大了5-15％。随后，我们发现了HLA-I和HLA-II的新抗原，包括具有经过验证的T细胞反应的抗原和以前研究中未报道的抗原。