德国海德堡大学E. W. Green等研究人员合作发现，从scRNA-seq数据中预测肿瘤反应性T细胞受体可实现个性化T细胞疗法。相关论文于2024年3月7日在线发表在《自然—生物技术》杂志上。

研究人员表示，鉴定患者来源的肿瘤反应性T细胞受体（TCR）作为个性化转基因T细胞疗法的基础，仍然是一项耗时耗钱的工作。目前鉴定肿瘤反应性TCR的方法是分析肿瘤突变来预测T细胞激活（新）抗原，并利用这些抗原来富集肿瘤浸润淋巴细胞（TIL）培养物或验证用于转基因自体疗法的单个TCR。

研究人员结合高通量TCR克隆和反应性验证来训练了predicTCR，它是一种机器学习分类器，能根据单个TIL RNA测序以抗原抗辨的方式识别单个肿瘤反应性TIL。与以前基于基因组富集的方法相比，predicTCR能更好地识别不同癌症TIL中的肿瘤反应性TCR，将特异性和灵敏度（几何平均数）从0.38提高到0.74。通过在短短几天内预测肿瘤反应性TCR，就能确定TCR克隆型的优先次序，从而加快个性化T细胞疗法的生产。

附：英文原文

Title: Prediction of tumor-reactive T cell receptors from scRNA-seq data for personalized T cell therapy

Author: Tan, C. L., Lindner, K., Boschert, T., Meng, Z., Rodriguez Ehrenfried, A., De Roia, A., Haltenhof, G., Faenza, A., Imperatore, F., Bunse, L., Lindner, J. M., Harbottle, R. P., Ratliff, M., Offringa, R., Poschke, I., Platten, M., Green, E. W.

Issue&Volume: 2024-03-07

Abstract: The identification of patient-derived, tumor-reactive T cell receptors (TCRs) as a basis for personalized transgenic T cell therapies remains a time- and cost-intensive endeavor. Current approaches to identify tumor-reactive TCRs analyze tumor mutations to predict T cell activating (neo)antigens and use these to either enrich tumor infiltrating lymphocyte (TIL) cultures or validate individual TCRs for transgenic autologous therapies. Here we combined high-throughput TCR cloning and reactivity validation to train predicTCR, a machine learning classifier that identifies individual tumor-reactive TILs in an antigen-agnostic manner based on single-TIL RNA sequencing. PredicTCR identifies tumor-reactive TCRs in TILs from diverse cancers better than previous gene set enrichment-based approaches, increasing specificity and sensitivity (geometric mean) from 0.38 to 0.74. By predicting tumor-reactive TCRs in a matter of days, TCR clonotypes can be prioritized to accelerate the manufacture of personalized T cell therapies.

DOI: 10.1038/s41587-024-02161-y

Source: https://www.nature.com/articles/s41587-024-02161-y