基于TCGA数据预测肿瘤代表性新抗原的一种生物信息学新方案

A new bioinformatics approach for prediction of potential tumor neoantigens based on the cancer genome atlas dataset

摘要:

肿瘤的特异性基因突变是肿瘤免疫疗法的理想靶标，突变的基因在健康组织中缺乏表达，而且具有高度免疫原性，容易被免疫系统识别。肿瘤患者突变基因组的高度特异性使得个体化免疫治疗存在极大挑战，而每一种肿瘤都具有区别于其他肿瘤的代表性的基因突变特征，基于这些突变特征，有可能开发出特定肿瘤适用的免疫治疗策略。文中提出一个兼顾抗原胞内呈递和与胞外MHC分子结合能力的肿瘤新抗原预测策略，整体设计更为合理；相对于常规方法，能够大幅缩小实验验证的范围。基于该策略，利用TCGA数据库中多种肿瘤的基因突变数据进行肿瘤新抗原预测并预测到大量潜在的肿瘤新抗原。肿瘤新抗原的预测结果显示出肿瘤类型的特异性，并且在特定肿瘤数据集中能够覆盖20%–70%不等比例的肿瘤患者。文中提出的肿瘤新抗原预测方案在未来的肿瘤临床治疗上具有潜在的应用价值。

Abstract:

Tumor-specific gene mutations might generate suitable neoepitopes for cancer immunotherapy that are highly immunogenic and absent in normal tissues. The high heterogeneity of the tumor genome poses a big challenge for precision cancer immunotherapy. Mutations characteristic of each tumor can help to distinguish it from other tumors. Based on these mutations’ characteristic, it is possible to develop immunotherapeutic strategies for specific tumors. In this study, a tumor neoantigen prediction scheme was proposed, in which both the intracellular antigen presentation process and the ability to bind with extracellular MHC molecule were taken into consideration. The overall design is meritorious and may help reduce the cost for validation experiments compared with conventional methods. This strategy was tested with several cancer genome datasets in the TCGA database, and a number of potential tumor neoantigens were predicted for each dataset. These predicted neoantigens showed tumor type specificity and were found in 20% to 70% of cancer patients. This scheme might prove useful clinically in future.