源自体细胞 DNA 改变的新抗原的鉴定成为癌症免疫治疗的一种有前景的策略。然而，并非所有体细胞突变都会导致免疫原性，因此需要有效的工具来预测新表位的免疫原性。提出的管道为基于基因组测序数据的新表位鉴定提供了全面的解决方案。该管道由数据预处理步骤和三个机器学习预测步骤组成。预处理步骤分析基因组数据中不同类型的改变，生成所有可能抗原的列表，并确定人类白细胞抗原 (HLA) 类型和 T 细胞受体 (TCR) 库。第一个预测步骤对抗原和新抗原进行分类，选择新抗原以供进一步考虑。下一步预测新抗原与 I 类主要组织相容性复合物 (MHC-I) 之间的结合强度。第三步是预测诱导免疫反应的可能性。满足所有三个预测阶段的新表位被认为是确保免疫原性的有效候选者。预测管道用于两种方案：从患者的测序数据中选择新抗原并生成新的候选新抗原。实施两种不同的技术 - 蒙特卡洛和强化学习 - 来促进生成机制。© 2024 Wiley‐VCH GmbH。

Identification of neoantigens, derived from somatic DNA alterations, emerges as a promising strategy for cancer immunotherapies. However, not all somatic mutations result in immunogenicity, hence, efficient tools to predict the immunogenicity of neoepitopes are needed. A pipeline is presented that provides a comprehensive solution for the identification of neoepitopes based on genomic sequencing data. The pipeline consists of a data pre-processing step and three machine learning predictive steps. The pre-processing step analyzes genomic data for different types of alterations, produces a list of all possible antigens, and determines the human leukocyte antigen (HLA) type and T-cell receptor (TCR) repertoire. The first predictive step performs a classification into antigens and neoantigens, selecting neoantigens for further consideration. The next step predicts the strength of binding between neoantigens and available major histocompatibility complexes of class I (MHC-I). The third step is engaged to predict the likelihood of inducing an immune response. Neoepitopes satisfying all three predictive stages are assumed to be potent candidates to ensure immunogenicity. The predictive pipeline is used in two regimes: selecting neoantigens from patients' sequencing data and generating novel neoantigen candidates. Two different techniques - Monte Carlo and Reinforcement Learning - are implemented to facilitate the generative regime.© 2024 Wiley‐VCH GmbH.