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用健康的捐赠者衍生的癌症特异性T细胞对表位细胞-TCR预测模型进行培训
Donovan Flumens1, Sofie Gielis2, Esther Bartholomeus3
1Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.
Methods in cell biology
|March 28, 2024
概括
本研究提出了一种训练预测模型的方法,用于使用TCRex网络工具识别向癌症的T细胞受体 (TCR). 这种方法尽量减少了癌症研究中需要大量患者样本的需求.
科学领域:
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 发现用于癌症治疗的表位特异性T细胞受体 (TCR) 是资源密集型的,通常需要大量的患者细胞.
- 形预测模型正在成为一种有效识别TCR的策略,保护宝贵的生物样本.
- 威尔姆斯瘤1 (WT1) 抗原是一种临床相关的,瘤相关的抗原,在各种恶性瘤中过度表达.
研究的目的:
- 提供一个训练预测模型的协议,以识别WT1特定的TCR.
- 展示使用TCRex网络工具进行TCR目录分析的方法.
- 建立一种方法来生成训练数据集,当已知的表位特异性TCR稀缺时.
主要方法:
- 为训练TCR预测模型开发一个逐步的协议.
- 使用TCRex网络工具对TCR目录进行形分析.
- 组装一个训练数据集,使用来自健康捐赠者的WT137-45-反应性CD8 T细胞克隆的TCR序列.
主要成果:
- 建立了一个训练表位特异性TCR预测模型的协议.
- 使用TCRex网络工具来分析WT1特定的TCR目录.
- 描述了一个从有限的T细胞克隆中创建训练数据集的工作流程.
结论:
- 提出的协议可以有效地培训用于癌症研究的TCR预测模型.
- 这种方法减少了对大量患者样本体积的依赖.
- 这种方法有助于识别TCRs对WT1.1.等瘤相关抗原的鉴定.
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