Systemic CD8+ T cell effector signature predicts prognosis of lung cancer immunotherapy

Hyungtai Sim, Geun-Ho Park, Woong-Yang Park, Se-Hoon Lee, Murim Choi
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Abstract

Background: While immune checkpoint inhibitors (ICIs) are adopted as standard therapy in non-small cell lung cancer (NSCLC) patients, factors that influence variable prognosis still remain elusive. Therefore, a deeper understanding is needed of how germline variants regulate the transcriptomes of circulating immune cells in metastasis, and ultimately influence immunotherapy outcomes. Methods: We collected peripheral blood mononuclear cells (PBMCs) from 73 ICI-treated NSCLC patients, conducted single-cell RNA sequencing, and called germline variants via SNP microarray. Determination of expression quantitative trait loci (eQTL) allows elucidating genetic interactions between germline variants and gene expression. Utilizing aggregation-based eQTL mapping and network analysis across eight blood cell types, we sought cell-type-specific and ICI-prognosis-dependent gene regulatory signatures. Results: Our sc-eQTL analysis identified 3,616 blood- and 702 lung-cancer-specific eGenes across eight major clusters and treatment conditions, highlighting involvement of immune-related pathways. Network analysis revealed TBX21-EOMES regulons activity in CD8+ T cells and the enrichment of eQTLs in higher-centrality genes as predictive factors of ICI response. Conclusions: Our findings suggest that in the circulating immune cells of NSCLC patients, transcriptomic regulation differs in a cell type- and treatment-specific manner. They further highlight the role of eQTL loci as broad controllers of ICI-prognosis-predicting gene networks. The predictive networks and identification of eQTL contributions can lead to deeper understanding and personalized ICI therapy response prediction based on germline variants.
全身 CD8+ T 细胞效应物特征可预测肺癌免疫疗法的预后
背景:虽然免疫检查点抑制剂(ICIs)已成为非小细胞肺癌(NSCLC)患者的标准疗法,但影响不同预后的因素仍然难以捉摸。因此,需要更深入地了解种系变异如何调控转移过程中循环免疫细胞的转录组,并最终影响免疫疗法的结果。研究方法我们收集了73名接受过ICI治疗的NSCLC患者的外周血单核细胞(PBMC),进行了单细胞RNA测序,并通过SNP芯片调用了种系变异。通过确定表达量性状位点(eQTL),可以阐明种系变异与基因表达之间的遗传相互作用。利用基于聚集的 eQTL 图谱和八种血细胞类型的网络分析,我们找到了细胞类型特异性和依赖于 ICI 预后的基因调控特征。结果:我们的 sc-eQTL 分析在八个主要群组和治疗条件中发现了 3,616 个血液特异性 eGenes 和 702 个肺癌特异性 eGenes,突显了免疫相关通路的参与。网络分析显示,CD8+ T细胞中的TBX21-EOMES调控子活性和高中心性基因中eQTL的富集是ICI反应的预测因素。结论我们的研究结果表明,在 NSCLC 患者的循环免疫细胞中,转录组调控以细胞类型和治疗特异性的方式存在差异。这些发现进一步凸显了eQTL位点作为ICI-预后预测基因网络的广泛控制者的作用。预测网络和 eQTL 贡献的鉴定可以加深对种系变异的理解,并基于种系变异进行个性化 ICI 治疗反应预测。
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