Shao Yan, Gao Yang, Wu Ling-Yu, Ge Shu-Guang, Wen Peng-Bo
School of Medical Infand Engineering, Xuzhou Medical University, No. 209, Tongshan Road, Yunlong District, Xuzhou, Jiangsu 221004, China.
Department of Histology and Embryology, Shantou University Medical College, No. 243, Daxue Road, Shantou, Guangdong 515063, China.
Database (Oxford). 2025 Feb 13;2025. doi: 10.1093/database/baaf010.
With the continuous advancements in cancer immunotherapy, neoantigen-based therapies have demonstrated remarkable clinical efficacy. However, accurately predicting the immunogenicity of neoantigens remains a significant challenge. This is mainly due to two core factors: the scarcity of high-quality neoantigen datasets and the limited prediction accuracy of existing immunogenicity prediction tools. This study addressed these issues through several key steps. First, it collected and organized immunogenic neoantigen peptide data from publicly available literature and neoantigen databases. Second, it analyzed the data to identify key features influencing neoantigen immunogenicity prediction. Finally, it integrated existing prediction tools to create TumorAgDB1.0, a comprehensive tumor neoantigen database. TumorAgDB1.0 offers a user-friendly platform. Users can efficiently search for neoantigen data using parameters like amino acid sequence and peptide length. The platform also offers detailed information on the characteristics of neoantigens and tools for predicting tumor neoantigen immunogenicity. Additionally, the database includes a data download function, allowing researchers to easily access high-quality data to support the development and improvement of neoantigen immunogenicity prediction tools. In summary, TumorAgDB1.0 is a powerful tool for neoantigen screening and validation in tumor immunotherapy. It offers strong support to researchers. Database URL: https://tumoragdb.com.cn.
随着癌症免疫疗法的不断进步，基于新抗原的疗法已展现出显著的临床疗效。然而，准确预测新抗原的免疫原性仍然是一项重大挑战。这主要归因于两个核心因素：高质量新抗原数据集的匮乏以及现有免疫原性预测工具的预测准确性有限。本研究通过几个关键步骤解决了这些问题。首先，它从公开文献和新抗原数据库中收集并整理了免疫原性新抗原肽数据。其次，对数据进行分析以识别影响新抗原免疫原性预测的关键特征。最后，整合现有预测工具创建了TumorAgDB1.0，一个全面的肿瘤新抗原数据库。TumorAgDB1.0提供了一个用户友好的平台。用户可以使用氨基酸序列和肽长度等参数高效搜索新抗原数据。该平台还提供有关新抗原特征的详细信息以及预测肿瘤新抗原免疫原性的工具。此外，该数据库具有数据下载功能，使研究人员能够轻松获取高质量数据，以支持新抗原免疫原性预测工具的开发和改进。总之，TumorAgDB1.0是肿瘤免疫治疗中用于新抗原筛选和验证的强大工具。它为研究人员提供了有力支持。数据库网址：https://tumoragdb.com.cn。
