Hyungtai Sim, Geun-Ho Park, Woong-Yang Park, Se-Hoon Lee, Murim Choi
{"title":"Systemic CD8+ T cell effector signature predicts prognosis of lung cancer immunotherapy","authors":"Hyungtai Sim, Geun-Ho Park, Woong-Yang Park, Se-Hoon Lee, Murim Choi","doi":"10.1101/2024.09.16.613381","DOIUrl":null,"url":null,"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.","PeriodicalId":501161,"journal":{"name":"bioRxiv - Genomics","volume":"40 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"bioRxiv - Genomics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.09.16.613381","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.