Yu Jijun, Wang Luoxuan, Kong Xiangya, Cao Yang, Zhang Mengmeng, Sun Zhaolin, Liu Yang, Wang Jing, Shen Beifen, Bo Xiaochen, Feng Jiannan
State Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology and Toxicology, Beijing, China.
Beijing Key Laboratory of Therapeutic Gene Engineering Antibody, Beijing, China.
Front Bioeng Biotechnol. 2022 May 12;10:819583. doi: 10.3389/fbioe.2022.819583. eCollection 2022.
Cancer vaccines have gradually attracted attention for their tremendous preclinical and clinical performance. With the development of next-generation sequencing technologies and related algorithms, pipelines based on sequencing and machine learning methods have become mainstream in cancer antigen prediction; of particular focus are neoantigens, mutation peptides that only exist in tumor cells that lack central tolerance and have fewer side effects. The rapid prediction and filtering of neoantigen peptides are crucial to the development of neoantigen-based cancer vaccines. However, due to the lack of verified neoantigen datasets and insufficient research on the properties of neoantigens, neoantigen prediction algorithms still need to be improved. Here, we recruited verified cancer antigen peptides and collected as much relevant peptide information as possible. Then, we discussed the role of each dataset for algorithm improvement in cancer antigen research, especially neoantigen prediction. A platform, Cancer Antigens Database (CAD, http://cad.bio-it.cn/), was designed to facilitate users to perform a complete exploration of cancer antigens online.
癌症疫苗因其出色的临床前和临床性能逐渐受到关注。随着下一代测序技术和相关算法的发展，基于测序和机器学习方法的流程已成为癌症抗原预测的主流；特别受关注的是新抗原，即仅存在于缺乏中枢耐受且副作用较少的肿瘤细胞中的突变肽。新抗原肽的快速预测和筛选对于基于新抗原的癌症疫苗的开发至关重要。然而，由于缺乏经过验证的新抗原数据集以及对新抗原特性的研究不足，新抗原预测算法仍需改进。在此，我们招募了经过验证的癌症抗原肽，并收集了尽可能多的相关肽信息。然后，我们讨论了每个数据集在癌症抗原研究（尤其是新抗原预测）算法改进中的作用。设计了一个平台——癌症抗原数据库（CAD，http://cad.bio-it.cn/），以方便用户在线对癌症抗原进行全面探索。
