Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Front Immunol. 2024 May 21;15:1394593. doi: 10.3389/fimmu.2024.1394593. eCollection 2024.
Microsatellite instability (MSI) secondary to mismatch repair (MMR) deficiency is characterized by insertions and deletions (indels) in short DNA sequences across the genome. These indels can generate neoantigens, which are ideal targets for precision immune interception. However, current neoantigen databases lack information on neoantigens arising from coding microsatellites. To address this gap, we introduce The MicrOsatellite Neoantigen Discovery Tool (MONET).
MONET identifies potential mutated tumor-specific neoantigens (neoAgs) by predicting frameshift mutations in coding microsatellite sequences of the human genome. Then MONET annotates these neoAgs with key features such as binding affinity, stability, expression, frequency, and potential pathogenicity using established algorithms, tools, and public databases. A user-friendly web interface (https://monet.mdanderson.org/) facilitates access to these predictions.
MONET predicts over 4 million and 15 million Class I and Class II potential frameshift neoAgs, respectively. Compared to existing databases, MONET demonstrates superior coverage (>85% vs. <25%) using a set of experimentally validated neoAgs.
MONET is a freely available, user-friendly web tool that leverages publicly available resources to identify neoAgs derived from microsatellite loci. This systems biology approach empowers researchers in the field of precision immune interception.
由于错配修复 (MMR) 缺陷导致的微卫星不稳定性 (MSI) 的特征是基因组中短 DNA 序列的插入和缺失 (indels)。这些 indels 可以产生新抗原，这是精确免疫拦截的理想靶点。然而，当前的新抗原数据库缺乏关于源自编码微卫星的新抗原的信息。为了解决这一差距，我们引入了微卫星新抗原发现工具 (MONET)。
MONET 通过预测人类基因组中编码微卫星序列中的移码突变来识别潜在的突变肿瘤特异性新抗原 (neoAg)。然后，MONET 使用既定的算法、工具和公共数据库，对这些 neoAg 进行结合亲和力、稳定性、表达、频率和潜在致病性等关键特征进行注释。用户友好的网络界面 (https://monet.mdanderson.org/) 方便了这些预测的访问。
MONET 分别预测了超过 400 万个和 1500 万个 Class I 和 Class II 潜在移码 neoAg。与现有数据库相比，MONET 使用一组经过实验验证的 neoAg 具有更高的覆盖率 (>85% 对 <25%)。
MONET 是一个免费的、用户友好的网络工具，利用公共资源来识别源自微卫星位点的 neoAg。这种系统生物学方法为精确免疫拦截领域的研究人员提供了支持。
