单细胞追踪揭示黑色素瘤 TIL 治疗过程中肿瘤反应性 T 细胞的可塑性
Single-cell tracking reveals tumor-reactive T cell plasticity during melanoma TIL therapy.
TIL(肿瘤浸润淋巴细胞)过继细胞治疗可在转移性黑色素瘤中诱导持久缓解,然而在体外扩增过程中及回输后,调控肿瘤反应性T细胞命运的克隆和转录动态仍知之甚少。
英文原题:Tumor reactivity assessment using clonal expression reveals tumor reactive CD8(+) T cell heterogeneity across solid tumors.
Tumor reactivity assessment using clonal expression reveals tumor reactive CD8(+) T cell heterogeneity across solid tumors.
TRACE是一种肿瘤反应性评分算法,随开放模型权重一同发布,可应用于组织或血液单细胞RNAseq数据集。其应用对于表征TIL中TRT的比例以及建立与免疫治疗临床反应的相关性应具有普遍意义。
TIL(肿瘤浸润淋巴细胞)驱动了一大类免疫疗法的抗肿瘤活性。原位TIL由识别肿瘤抗原的T细胞(肿瘤反应性T细胞,即TRT)以及针对其他抗原具有特异性的旁观者T细胞组成。TRT克隆型与一种独特的、由肿瘤驱动的耗竭转录状态相关,使得基于单细胞RNA测序(scRNA-seq)的预测模型能够利用实验验证的克隆标签来识别TRT。
在本研究中,利用已验证的肿瘤反应性克隆型及相关scRNA-seq数据的聚合数据集构建了一个克隆型水平的CD8+ TRT分类器(TRACE),该数据集来自多篇出版物,克服了在单一数据集、供体或适应症上训练的局限性。TRACE在训练或预测时不需要对数据集进行操作,使其能够轻松应用于新出现的测试数据集。
TRACE在留出的TIL和PBMC克隆上表现出稳健的性能——平均Matthews相关系数为0.84,F1分数为0.85——与其他TRT预测方法相当或更优。我们通过将离体扩增的TIL与自体黑色素瘤肿瘤细胞系共培养,实验证实了TRACE鉴定的TRT克隆的反应性。最后,我们应用TRACE评估了来自多个肿瘤图谱、涵盖肺癌、结直肠癌和胰腺癌的数百份患者样本中TRT的频率。观察到TRACE评分在肿瘤中的耗竭CD8+ T细胞中显著更高,但在正常邻近或非癌症样本中的耗竭细胞中并非如此,表明其对识别肿瘤抗原经历过的T细胞具有特异性。
INTRODUCTION: Tumor infiltrating lymphocytes (TIL) drive the anti-tumor activity of a broad class of immunotherapies. In situ TIL are composed of T cells that recognize tumor antigens (Tumor Reactive T cells, or TRTs) as well as bystander T cells with specificity for other antigens. TRT clonotypes are associated with a unique and tumor-driven exhausted transcriptional state, enabling single-cell RNA sequencing (scRNA-seq)-based predictive models for TRTs using experimentally validated clone labels. METHODS: In this study, a clonotype-level CD8 + TRT classifier (TRACE) was built using an aggregated dataset of validated tumor reactive clonotypes and associated scRNA-seq data from multiple publications that overcomes the limitations of training on a single dataset, donor, or indication. TRACE does not require dataset manipulation for training or prediction, enabling it to be easily applied to new test datasets as they emerge. RESULTS: TRACE exhibited robust performance on held-out TIL and PBMC clones - achieving a mean Matthews correlation coefficient of 0.84 and F1-score of 0.85 - comparable to or outperforming other TRT prediction methods. We experimentally confirmed the reactivity of TRACE-identified TRT clones by co-culturing ex vivo expanded TIL with an autologous melanoma tumor cell line. Finally, we applied TRACE to evaluate the frequency of TRTs across hundreds of patient samples from multiple tumor atlases spanning lung, colorectal, and pancreatic cancer. TRACE scores were observed to be significantly higher in exhausted CD8 + T cells in tumors but not in exhausted cells in normal adjacent or non-cancer samples, suggesting specificity towards identifying tumor-antigen experienced T cells. CONCLUSION: TRACE is a tumor reactivity scoring algorithm released with open model weights that can be applied to tissue or blood single-cell RNAseq datasets. Its application should be of general interest for characterizing the fraction of TRTs in TIL and for establishing correlations with clinical response to immunotherapies.
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