Wang Yunzhe, Wengler James, Fang Yuzhu, Zhou Joseph, Ruan Hang, Zhang Zhao, Han Leng
MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China.
Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX 77030, USA.
Genomics Proteomics Bioinformatics. 2025 Jan 20. doi: 10.1093/gpbjnl/qzaf001.
Tumor-specific antigens, also known as neoantigens, have potential utility in anti-cancer immunotherapy, including immune checkpoint blockade (ICB), neoantigen-specific T cell receptor-engineered T (TCR-T), chimeric antigen receptor T (CAR-T), and therapeutic cancer vaccines (TCVs). After recognizing presented neoantigens, the immune system becomes activated and triggers the death of tumor cells. Neoantigens may be derived from multiple origins, including somatic mutations (single nucleotide variants, insertion/deletions, and gene fusions), circular RNAs, alternative splicing, RNA editing, and polymorphic microbiome. An increasing amount of bioinformatics tools and algorithms are being developed to predict tumor neoantigens derived from different sources, which may require inputs from different multi-omics data. In addition, calculating the peptide-major histocompatibility complex (MHC) affinity can aid in selecting putative neoantigens, as high binding affinities facilitate antigen presentation. Based on these approaches and previous experiments, many resources were developed to reveal the landscape of tumor neoantigens across multiple cancer types. Herein, we summarized these tools, algorithms, and resources to provide an overview of computational analysis for neoantigen discovery and prioritization, as well as the future development of potential clinical utilities in this field.
肿瘤特异性抗原，也称为新抗原，在抗癌免疫治疗中具有潜在应用价值，包括免疫检查点阻断（ICB）、新抗原特异性T细胞受体工程化T细胞（TCR-T）、嵌合抗原受体T细胞（CAR-T）和治疗性癌症疫苗（TCV）。识别呈递的新抗原后，免疫系统被激活并触发肿瘤细胞死亡。新抗原可能来源于多种途径，包括体细胞突变（单核苷酸变异、插入/缺失和基因融合）、环状RNA、可变剪接、RNA编辑和多态性微生物群。越来越多的生物信息学工具和算法正在被开发出来，用于预测来自不同来源的肿瘤新抗原，这可能需要不同多组学数据的输入。此外，计算肽-主要组织相容性复合体（MHC）亲和力有助于选择推定的新抗原，因为高结合亲和力有利于抗原呈递。基于这些方法和先前的实验，开发了许多资源来揭示多种癌症类型中肿瘤新抗原的全貌。在此，我们总结了这些工具、算法和资源，以概述新抗原发现和优先级排序的计算分析，以及该领域潜在临床应用的未来发展。
