Department of Endocrinology and Metabolism, Shanghai Tenth People's Hospital; Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200009, China.
School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Aging (Albany NY). 2020 Jul 22;12(14):14633-14648. doi: 10.18632/aging.103516.
Cancer neoantigens have shown great potential in immunotherapy, while current software focuses on identifying neoantigens which are derived from SNVs, indels or gene fusions. Alternative splicing widely occurs in tumor samples and it has been proven to contribute to the generation of candidate neoantigens. Here we present , which is an integrated computational pipeline for the identification of personalized Alternative Splicing based NEOantigens with RNA-seq. Our analyses showed that could identify neopeptides which are presented by MHC I complex through mass spectrometry data validation. When was applied to two immunotherapy-treated cohorts, we found that alternative splicing based neopeptides generally have a higher immune score than that of somatic neopeptides and alternative splicing based neopeptides could be a marker to predict patient survival pattern. Our identification of alternative splicing derived neopeptides would contribute to a more complete understanding of the tumor immune landscape. Prediction of patient-specific alternative splicing neopeptides has the potential to contribute to the development of personalized cancer vaccines.
癌症新生抗原在免疫治疗中显示出巨大的潜力，而当前的软件主要专注于识别源自 SNVs、indels 或基因融合的新生抗原。选择性剪接在肿瘤样本中广泛发生，并已被证明有助于产生候选新生抗原。在这里，我们提出了一种基于 RNA-seq 的个性化选择性剪接新抗原的综合计算管道，该管道被命名为 。我们的分析表明， 可以通过质谱数据验证来识别 MHC I 复合物呈现的新肽。当 将其应用于两个免疫治疗队列时，我们发现基于选择性剪接的新肽通常比体细胞新肽具有更高的免疫评分，并且基于选择性剪接的新肽可以作为预测患者生存模式的标志物。我们对选择性剪接衍生新肽的鉴定将有助于更全面地了解肿瘤免疫景观。预测患者特异性选择性剪接新肽有可能有助于开发个性化癌症疫苗。
