Johannes Gutenberg University, Mainz, Germany.
Agenus Inc., Lexington, MA, United States.
Prog Mol Biol Transl Sci. 2019;164:25-60. doi: 10.1016/bs.pmbts.2019.06.016. Epub 2019 Jul 18.
Tumor cells accumulate aberrations not present in normal cells, leading to presentation of neoantigens on MHC molecules on their surface. These non-self neoantigens distinguish tumor cells from normal cells to the immune system and are thus targets for cancer immunotherapy. The rapid development of molecular profiling platforms, such as next-generation sequencing, has enabled the generation of large datasets characterizing tumor cells. The simultaneous development of algorithms has enabled rapid and accurate processing of these data. Bioinformatic software tools encoding the algorithms can be strung together in a workflow to identify neoantigens. Here, with a focus on high-throughput sequencing, we review state-of-the art bioinformatic tools along with the steps and challenges involved in neoantigen identification and recognition.
肿瘤细胞积累了正常细胞中不存在的异常，导致其表面 MHC 分子呈现新抗原。这些非自身的新抗原使肿瘤细胞能够与免疫系统区分开来，因此成为癌症免疫治疗的靶点。分子分析平台（如下一代测序）的快速发展使得生成大量的肿瘤细胞数据集成为可能。算法的同时发展使得这些数据的快速准确处理成为可能。编码算法的生物信息学软件工具可以串联在一起形成一个工作流程，以识别新抗原。在这里，我们重点关注高通量测序，同时回顾了新抗原鉴定和识别所涉及的最新生物信息学工具以及步骤和挑战。
