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.

识别源自患者的肿瘤反应性 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且成本密集的工作。目前识别肿瘤反应性 TCR 的方法是通过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) 培养物或验证个体 TCR 用于转基因自体疗法。在这里，我们结合高通量 TCR 克隆和反应性验证来训练 predicTCR，这是一种机器学习分类器，可基于单 TIL RNA 测序以与抗原无关的方式识别个体肿瘤反应性 TIL。PredicTCR 比以前基于基因集富集的方法更好地识别来自不同癌症的 TIL 中的肿瘤反应性 TCR，将特异性和灵敏度（几何平均值）从 0.38 提高到 0.74。通过在几天内预测肿瘤反应性 TCR，可以优先考虑 TCR 克隆型，以加速个性化 T 细胞疗法的制造。