基于新抗原的肿瘤免疫治疗,抗原呈递的准确预测是筛选T细胞特异性表位的关键步骤。质谱鉴定的表位数据对建立抗原呈递预测模型具有重要价值。尽管近年来质谱数据的积累持续增加,但是大部分人类白细胞抗原(human leukocyte antigen, HLA)分型所对应的多肽数量相对较少,无法建立可靠的预测模型。为此,本研究尝试利用迁移学习的方法,先利用混合分型的表位数据建立模型以识别抗原表位的共同特征,在此预训练模型的基础上再利用分型特异性数据建立抗原呈递预测模型Pluto。在相同的验证集上,Pluto的平均0.1%阳性预测值(positive predictive value, PPV)比从头训练的模型高0.078。在外部的质谱数据独立评估上,Pluto的平均0.1% PPV为0.4255,高于从头训练模型(0.3824)和其他主流工具,包括MixMHCpred (0.3369)、NetMHCpan4.0-EL (0.4000)、NetMHCpan4.0-BA (0.3188)和MHCflurry (0.3002)。此外,在免疫原性预测评估上,Pluto相对于其他工具也能找到更多的新抗原。Pluto开源网址：https://github.com/weipenegHU/Pluto。

Accurate epitope presentation prediction is a key procedure in tumour immunotherapies based on neoantigen for targeting T cell specific epitopes. Epitopes identified by mass spectrometry (MS) is valuable to train an epitope presentation prediction model. In spite of the accelerating accumulation of MS data, the number of epitopes that match most of human leukocyte antigens (HLAs) is relatively small, which makes it difficult to build a reliable prediction model. Therefore, this research attempted to use the transfer learning method to train a model to learn common features among the mixed allele specific epitopes. Then based on this pre-trained model, we used the allele-specific epitopes to train the final epitope presentation prediction model, termed Pluto. The average 0.1% positive predictive value (PPV) of Pluto outperformed the prediction model without pretraining with a margin of 0.078 on the same validation dataset. When evaluating Pluto on external HLA eluted ligand datasets, Pluto achieved an averaged 0.1% PPV of 0.4255, which is better than the prediction model without pretraining (0.3824) and other popular methods, including MixMHCpred (0.3369), NetMHCpan4.0-EL (0.4000), NetMHCpan4.0-BA (0.3188) and MHCflurry (0.3002). Moreover, when it comes to the evaluation of predicting immunogenicity, Pluto can identify more neoantigens than other tools. Pluto is publicly available at https://github.com/weipenegHU/Pluto.